Change `EncodingRole` to `IntegerAttr` (#17708)
In the future it is possible to encode types other than a matmul, so
changing the `EncodingRole` to be the index of the operand. Instead of
using integers, use enum to retain the role's symbolic meaning in the
code.
Note that: It is one's responsibility to make sure the operand index is
correct. The index follows the convention that in MLIR output operands
are after input operands and are at the end of the operand list.
Signed-off-by: Alan Li <me@alanli.org>
diff --git a/compiler/src/iree/compiler/Codegen/Common/CPU/CPULowerToUKernels.cpp b/compiler/src/iree/compiler/Codegen/Common/CPU/CPULowerToUKernels.cpp
index 13fbce9..7e09db4 100644
--- a/compiler/src/iree/compiler/Codegen/Common/CPU/CPULowerToUKernels.cpp
+++ b/compiler/src/iree/compiler/Codegen/Common/CPU/CPULowerToUKernels.cpp
@@ -517,13 +517,13 @@
}
}
-static uint32_t getFlagForRole(IREE::Encoding::EncodingRole role) {
- switch (role) {
- case IREE::Encoding::EncodingRole::LHS:
+static uint32_t getFlagForIndex(int64_t operandIndex) {
+ switch (operandIndex) {
+ case IREE::Encoding::MATMUL_LHS:
return IREE_UK_FLAG_QUERY_TILE_SIZES_OPERAND_ROLE_LHS;
- case IREE::Encoding::EncodingRole::RHS:
+ case IREE::Encoding::MATMUL_RHS:
return IREE_UK_FLAG_QUERY_TILE_SIZES_OPERAND_ROLE_RHS;
- case IREE::Encoding::EncodingRole::RESULT:
+ case IREE::Encoding::MATMUL_RESULT:
return IREE_UK_FLAG_QUERY_TILE_SIZES_OPERAND_ROLE_RESULT;
default:
return IREE_UK_FLAG_QUERY_TILE_SIZES_OPERAND_ROLE_NONE;
@@ -559,12 +559,13 @@
}
uint32_t flagForUserAndOperandTypes = getFlagForUserAndOperandTypes(
encoding, encoding.getElementTypes().getValue());
- uint32_t flagForRole = getFlagForRole(encoding.getRole().getValue());
- if (!flagForUserAndOperandTypes || !flagForRole) {
+ uint32_t flagForIndex =
+ getFlagForIndex(encoding.getOperandIndex().getValue().getZExtValue());
+ if (!flagForUserAndOperandTypes || !flagForIndex) {
return rewriter.notifyMatchFailure(op, "unhandled encoding");
}
inputValues.push_back(rewriter.create<arith::ConstantIntOp>(
- loc, flagForUserAndOperandTypes | flagForRole, 32));
+ loc, flagForUserAndOperandTypes | flagForIndex, 32));
auto fn = getFnNameAndDefAttrs(ukernelName, rewriter, targetAttr);
auto genericMicroKernelOp = rewriter.create<IREE::Codegen::UKernelGenericOp>(
loc, resultTypes, fn.name, inputValues, /*outs=*/ValueRange{},
diff --git a/compiler/src/iree/compiler/Codegen/Common/CPU/CPUMaterializeEncodingPass.cpp b/compiler/src/iree/compiler/Codegen/Common/CPU/CPUMaterializeEncodingPass.cpp
index 9596ca9..e81ed85 100644
--- a/compiler/src/iree/compiler/Codegen/Common/CPU/CPUMaterializeEncodingPass.cpp
+++ b/compiler/src/iree/compiler/Codegen/Common/CPU/CPUMaterializeEncodingPass.cpp
@@ -458,7 +458,7 @@
encoding.getRoundDimsToArray());
// Map the matmul TileMxNxK to an actual tile shape for the tensor at hand,
- // based on its role in the matmul.
+ // based on its operand index in the matmul.
auto rank = tensorType.getRank();
return getEncodingInfoForMatmul(encoding, rank, chosenTileMxNxK);
}
diff --git a/compiler/src/iree/compiler/Codegen/Common/CPU/test/llvmcpu_materialize_encoding.mlir b/compiler/src/iree/compiler/Codegen/Common/CPU/test/llvmcpu_materialize_encoding.mlir
index c962c7c..821c123 100644
--- a/compiler/src/iree/compiler/Codegen/Common/CPU/test/llvmcpu_materialize_encoding.mlir
+++ b/compiler/src/iree/compiler/Codegen/Common/CPU/test/llvmcpu_materialize_encoding.mlir
@@ -6,10 +6,10 @@
}{
%c0 = arith.constant 0 : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<1x1000xbf16>>
- %1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<1x1000xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<1x1000xbf16>, matmul_narrow_M = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], round_dims_to = array<i64: 16, 16, 16>>>>
+ %1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<1x1000xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], original_type = tensor<1x1000xbf16>, matmul_narrow_M = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], round_dims_to = array<i64: 16, 16, 16>>>>
%2 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [1, 1000], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<1x1000xbf16>> -> tensor<1x1000xbf16>
- %3 = iree_encoding.set_encoding %2 : tensor<1x1000xbf16> -> tensor<1x1000xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<1x1000xbf16>, matmul_narrow_M = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], round_dims_to = array<i64: 16, 16, 16>>>
- flow.dispatch.tensor.store %3, %1, offsets = [0, 0], sizes = [1, 1000], strides = [1, 1] : tensor<1x1000xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<1x1000xbf16>, matmul_narrow_M = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], round_dims_to = array<i64: 16, 16, 16>>> -> !flow.dispatch.tensor<writeonly:tensor<1x1000xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<1x1000xbf16>, matmul_narrow_M = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], round_dims_to = array<i64: 16, 16, 16>>>>
+ %3 = iree_encoding.set_encoding %2 : tensor<1x1000xbf16> -> tensor<1x1000xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], original_type = tensor<1x1000xbf16>, matmul_narrow_M = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], round_dims_to = array<i64: 16, 16, 16>>>
+ flow.dispatch.tensor.store %3, %1, offsets = [0, 0], sizes = [1, 1000], strides = [1, 1] : tensor<1x1000xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], original_type = tensor<1x1000xbf16>, matmul_narrow_M = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], round_dims_to = array<i64: 16, 16, 16>>> -> !flow.dispatch.tensor<writeonly:tensor<1x1000xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], original_type = tensor<1x1000xbf16>, matmul_narrow_M = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], round_dims_to = array<i64: 16, 16, 16>>>>
return
}
// This tests that
@@ -50,7 +50,7 @@
%8 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<7x7xf32>>
%9 = flow.dispatch.workload.ordinal %6, 2 : index
%10 = flow.dispatch.workload.ordinal %7, 3 : index
- %11 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<7x7xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%9, %10}
+ %11 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<7x7xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%9, %10}
%12 = flow.dispatch.workload.ordinal %4, 0 : index
%13 = flow.dispatch.workload.ordinal %5, 1 : index
%14 = flow.dispatch.tensor.load %8, offsets = [0, 0], sizes = [7, 7], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<7x7xf32>> -> tensor<7x7xf32>
@@ -60,8 +60,8 @@
^bb0(%arg0: index, %arg1: index):
tensor.yield %cst : f32
} : tensor<7x7xf32> to tensor<?x?xf32>
- %17 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<7x7xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- flow.dispatch.tensor.store %17, %11, offsets = [0, 0], sizes = [%9, %10], strides = [1, 1] : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<7x7xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<7x7xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%9, %10}
+ %17 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<7x7xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ flow.dispatch.tensor.store %17, %11, offsets = [0, 0], sizes = [%9, %10], strides = [1, 1] : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<7x7xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<7x7xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%9, %10}
return
}
// CHECK-LABEL: func @set_encoding_7x7x7_matmul_LHS(
@@ -94,7 +94,7 @@
%8 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<128x80x32xf32>>
%9 = flow.dispatch.workload.ordinal %6, 2 : index
%10 = flow.dispatch.workload.ordinal %7, 3 : index
- %11 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<128x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<128x80x32xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%9, %10}
+ %11 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<128x80x32xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%9, %10}
%12 = flow.dispatch.workload.ordinal %4, 0 : index
%13 = flow.dispatch.workload.ordinal %5, 1 : index
%14 = flow.dispatch.tensor.load %8, offsets = [0, 0, 0], sizes = [128, 80, 32], strides = [1, 1, 1] : !flow.dispatch.tensor<readonly:tensor<128x80x32xf32>> -> tensor<128x80x32xf32>
@@ -104,10 +104,10 @@
^bb0(%arg0: index, %arg1: index, %arg2: index):
tensor.yield %cst : f32
} : tensor<128x80x32xf32> to tensor<128x?x?xf32>
- %17 = iree_encoding.set_encoding %padded : tensor<128x?x?xf32> -> tensor<128x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<128x80x32xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %17 = iree_encoding.set_encoding %padded : tensor<128x?x?xf32> -> tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<128x80x32xf32>, user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %17, %11, offsets = [0, 0, 0], sizes = [128, %9, %10], strides = [1, 1, 1]
- : tensor<128x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<128x80x32xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<writeonly:tensor<128x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<128x80x32xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%9, %10}
+ : tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<128x80x32xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<writeonly:tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<128x80x32xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%9, %10}
return
}
// CHECK-LABEL: func @set_encoding_128x80x32_batch_matmul_LHS(
@@ -141,7 +141,7 @@
%10 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<128x32x320xf32>>
%11 = flow.dispatch.workload.ordinal %8, 2 : index
%12 = flow.dispatch.workload.ordinal %9, 3 : index
- %13 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%5) : !flow.dispatch.tensor<writeonly:tensor<128x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<128x32x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%11, %12}
+ %13 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%5) : !flow.dispatch.tensor<writeonly:tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<128x32x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%11, %12}
%14 = flow.dispatch.workload.ordinal %6, 0 : index
%15 = flow.dispatch.workload.ordinal %7, 1 : index
%16 = flow.dispatch.tensor.load %10, offsets = [0, 0, 0], sizes = [128, 32, 320], strides = [1, 1, 1] : !flow.dispatch.tensor<readonly:tensor<128x32x320xf32>> -> tensor<128x32x320xf32>
@@ -151,10 +151,10 @@
^bb0(%arg0: index, %arg1: index, %arg2: index):
tensor.yield %cst : f32
} : tensor<128x32x320xf32> to tensor<128x?x?xf32>
- %19 = iree_encoding.set_encoding %padded : tensor<128x?x?xf32> -> tensor<128x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<128x32x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %19 = iree_encoding.set_encoding %padded : tensor<128x?x?xf32> -> tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<128x32x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %19, %13, offsets = [0, 0, 0], sizes = [128, %11, %12], strides = [1, 1, 1]
- : tensor<128x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<128x32x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<writeonly:tensor<128x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<128x32x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%11, %12}
+ : tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<128x32x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<writeonly:tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<128x32x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%11, %12}
return
}
// CHECK-LABEL: func @set_encoding_128x32x320_batch_matmul_RHS(
@@ -183,11 +183,11 @@
%6 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<128x80x320xf32>>
%7 = flow.dispatch.workload.ordinal %4, 0 : index
%8 = flow.dispatch.workload.ordinal %5, 1 : index
- %9 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%3) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<128x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x80x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%7, %8}
+ %9 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%3) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x80x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%7, %8}
%10 = flow.dispatch.tensor.load %9, offsets = [0, 0, 0], sizes = [128, %7, %8], strides = [1, 1, 1]
- : !flow.dispatch.tensor<readonly:tensor<128x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x80x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%7, %8}
- -> tensor<128x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x80x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %11 = iree_encoding.unset_encoding %10 : tensor<128x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x80x320xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<128x?x?xf32>
+ : !flow.dispatch.tensor<readonly:tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x80x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>>{%7, %8}
+ -> tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x80x320xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %11 = iree_encoding.unset_encoding %10 : tensor<128x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x80x320xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<128x?x?xf32>
%extracted_slice = tensor.extract_slice %11[0, 0, 0] [128, 80, 320] [1, 1, 1] : tensor<128x?x?xf32> to tensor<128x80x320xf32>
flow.dispatch.tensor.store %extracted_slice, %6, offsets = [0, 0, 0], sizes = [128, 80, 320], strides = [1, 1, 1] : tensor<128x80x320xf32> -> !flow.dispatch.tensor<writeonly:tensor<128x80x320xf32>>
return
@@ -220,14 +220,14 @@
%cst = arith.constant 0.0 : f32
%d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
- %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %1 = iree_encoding.set_encoding %arg1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %2 = tensor.empty(%d0, %d1) : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %3 = linalg.fill ins(%cst : f32) outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %4 = linalg.matmul ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%3 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %5 = iree_encoding.unset_encoding %4 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %1 = iree_encoding.set_encoding %arg1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %2 = tensor.empty(%d0, %d1) : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %3 = linalg.fill ins(%cst : f32) outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %4 = linalg.matmul ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %5 = iree_encoding.unset_encoding %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
return %5 : tensor<?x?xf32>
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -269,19 +269,19 @@
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<16x16xf32> to tensor<?x?xf32>
- %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<16x16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<16x16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
%padded_0 = tensor.pad %1 low[0, 0] high[%c0, %c0] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<16x1xf32> to tensor<?x?xf32>
- %4 = iree_encoding.set_encoding %padded_0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ %4 = iree_encoding.set_encoding %padded_0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
%padded_1 = tensor.pad %2 low[0, 0] high[%c0, %c0] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<16x1xf32> to tensor<?x?xf32>
- %5 = iree_encoding.set_encoding %padded_1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
- %6 = linalg.matmul ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<16x16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>, tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>) outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>) -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
- %7 = iree_encoding.unset_encoding %6 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>> -> tensor<?x?xf32>
+ %5 = iree_encoding.set_encoding %padded_1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ %6 = linalg.matmul ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<16x16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>, tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>) outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ %7 = iree_encoding.unset_encoding %6 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<16x1xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>> -> tensor<?x?xf32>
%extracted_slice = tensor.extract_slice %7[0, 0] [16, 1] [1, 1] : tensor<?x?xf32> to tensor<16x1xf32>
%8 = hal.tensor.export %extracted_slice "output0" : tensor<16x1xf32> -> !hal.buffer_view
func.return %8 : !hal.buffer_view
@@ -304,28 +304,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -371,19 +371,19 @@
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<16x16xf32> to tensor<?x?xf32>
- %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<16x16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>
+ %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<16x16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>
%padded_0 = tensor.pad %1 low[0] high[%c0] {
^bb0(%arg3: index):
tensor.yield %cst : f32
} : tensor<16xf32> to tensor<?xf32>
- %4 = iree_encoding.set_encoding %padded_0 : tensor<?xf32> -> tensor<?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>
+ %4 = iree_encoding.set_encoding %padded_0 : tensor<?xf32> -> tensor<?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>
%padded_1 = tensor.pad %2 low[0] high[%c0] {
^bb0(%arg3: index):
tensor.yield %cst : f32
} : tensor<16xf32> to tensor<?xf32>
- %5 = iree_encoding.set_encoding %padded_1 : tensor<?xf32> -> tensor<?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>
- %6 = linalg.matvec ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<16x16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>, tensor<?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>) outs(%5 : tensor<?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>) -> tensor<?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>
- %7 = iree_encoding.unset_encoding %6 : tensor<?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>> -> tensor<?xf32>
+ %5 = iree_encoding.set_encoding %padded_1 : tensor<?xf32> -> tensor<?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>
+ %6 = linalg.matvec ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<16x16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>, tensor<?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>) outs(%5 : tensor<?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>) -> tensor<?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>>
+ %7 = iree_encoding.unset_encoding %6 : tensor<?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<16xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0)>]>> -> tensor<?xf32>
%extracted_slice = tensor.extract_slice %7[0] [16] [1] : tensor<?xf32> to tensor<16xf32>
%8 = hal.tensor.export %extracted_slice "output0" : tensor<16xf32> -> !hal.buffer_view
func.return %8 : !hal.buffer_view
@@ -403,28 +403,28 @@
} {
%c0 = arith.constant 0 : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<16x16xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
+ : !flow.dispatch.tensor<readonly:tensor<16x16xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<16x1xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
+ : !flow.dispatch.tensor<readonly:tensor<16x1xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<16x1xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
+ : !flow.dispatch.tensor<readwrite:tensor<16x1xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [16, 16], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<16x16xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
- -> tensor<16x16xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<16x16xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
+ -> tensor<16x16xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [16, 1], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<16x1xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
- -> tensor<16x1xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<16x1xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
+ -> tensor<16x1xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [16, 1], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<16x1xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
- -> tensor<16x1xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<16x1xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
+ -> tensor<16x1xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<16x16xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<16x1xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<16x1xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<16x1xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<16x16xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<16x1xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<16x1xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<16x1xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [16, 1], strides = [1, 1]
- : tensor<16x1xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<16x1xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
+ : tensor<16x1xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<16x1xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>>
return
}
// CHECK-LABEL: func @matvec_lowering_f32f32f32_aarch64()
@@ -460,28 +460,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -523,28 +523,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -587,28 +587,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -650,28 +650,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
@@ -713,28 +713,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
@@ -776,28 +776,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f16, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
@@ -839,28 +839,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
@@ -902,28 +902,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
@@ -965,28 +965,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
@@ -1030,28 +1030,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2, element_types = [bf16, bf16, bf16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
@@ -1095,37 +1095,37 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%lhs_f32 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%rhs = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%dest = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
- %empty = tensor.empty(%M, %K) : tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ %empty = tensor.empty(%M, %K) : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%lhs_f16 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]}
- ins(%lhs_f32 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%empty : tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>) {
+ ins(%lhs_f32 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%empty : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: f32, %out: f16):
%17 = arith.truncf %in : f32 to f16
linalg.yield %17 : f16
- } -> tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ } -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%lhs_f16, %rhs : tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%dest : tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%lhs_f16, %rhs : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%dest : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP_CEILDIV_8:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -1160,37 +1160,37 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%lhs_f32 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%rhs = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%dest = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
- %empty = tensor.empty(%M, %K) : tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ %empty = tensor.empty(%M, %K) : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%lhs_f16 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]}
- ins(%lhs_f32 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%empty : tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>) {
+ ins(%lhs_f32 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%empty : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: f32, %out: f16):
%17 = arith.truncf %in : f32 to f16
linalg.yield %17 : f16
- } -> tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ } -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%lhs_f16, %rhs : tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%dest : tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%lhs_f16, %rhs : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%dest : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f16, f16], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
@@ -1226,28 +1226,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-LABEL: func @matmul_lowering_i8i8i32_aarch64()
@@ -1286,28 +1286,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -1351,28 +1351,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -1415,28 +1415,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 4)>
@@ -1481,28 +1481,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -1545,28 +1545,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 4)>
@@ -1606,15 +1606,15 @@
func.func @matmul_lowering_f32f32f32_aarch64_sve(%lhs : tensor<?x?xf32>, %rhs: tensor<?x?xf32>, %acc: tensor<?x?xf32>) -> tensor<?x?xf32> attributes {
hal.executable.target = #hal.executable.target<"xyz", "xyz", {cpu_features = "+sve", target_triple="aarch64-xyz-xyz"}>
} {
- %0 = iree_encoding.set_encoding %lhs : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %1 = iree_encoding.set_encoding %rhs : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %2 = iree_encoding.set_encoding %acc : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %0 = iree_encoding.set_encoding %lhs : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %1 = iree_encoding.set_encoding %rhs : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %2 = iree_encoding.set_encoding %acc : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%3 = linalg.matmul
- ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
+ ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
return %4 : tensor<?x?xf32>
}
@@ -1631,15 +1631,15 @@
func.func @matmul_lowering_f32f32f32_riscv(%lhs : tensor<?x?xf32>, %rhs: tensor<?x?xf32>, %acc: tensor<?x?xf32>) -> tensor<?x?xf32> attributes {
hal.executable.target = #hal.executable.target<"xyz", "xyz", {target_triple="riscv32-xyz-xyz"}>
} {
- %0 = iree_encoding.set_encoding %lhs : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %1 = iree_encoding.set_encoding %rhs : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %2 = iree_encoding.set_encoding %acc : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %0 = iree_encoding.set_encoding %lhs : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %1 = iree_encoding.set_encoding %rhs : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %2 = iree_encoding.set_encoding %acc : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%3 = linalg.matmul
- ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
+ ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
return %4 : tensor<?x?xf32>
}
// RISC-V targets does not implement data-tiling yet.
@@ -1660,28 +1660,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -1725,28 +1725,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -1790,28 +1790,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
@@ -1855,28 +1855,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
@@ -1926,28 +1926,28 @@
^bb0(%arg2: index, %arg3: index, %arg4: index):
tensor.yield %c0_i8 : i8
} : tensor<32x1x128xi8> to tensor<?x?x?xi8>
- %4 = iree_encoding.set_encoding %padded : tensor<?x?x?xi8> -> tensor<?x?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %5 = tensor.empty(%c32, %c1, %c128) : tensor<?x?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %6 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>], iterator_types = ["parallel", "parallel", "parallel"]} ins(%4 : tensor<?x?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?x?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %4 = iree_encoding.set_encoding %padded : tensor<?x?x?xi8> -> tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %5 = tensor.empty(%c32, %c1, %c128) : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %6 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>], iterator_types = ["parallel", "parallel", "parallel"]} ins(%4 : tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?x?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ } -> tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
%padded_0 = tensor.pad %arg1 low[0, 0, 0] high[%c0, %c0, %c0] {
^bb0(%arg2: index, %arg3: index, %arg4: index):
tensor.yield %c0_i8 : i8
} : tensor<32x128x11008xi8> to tensor<?x?x?xi8>
- %7 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xi8> -> tensor<?x?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %8 = tensor.empty(%c32, %c128, %c11008) : tensor<?x?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %9 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>], iterator_types = ["parallel", "parallel", "parallel"]} ins(%7 : tensor<?x?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?x?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %7 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xi8> -> tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %8 = tensor.empty(%c32, %c128, %c11008) : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %9 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>], iterator_types = ["parallel", "parallel", "parallel"]} ins(%7 : tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?x?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %10 = tensor.empty(%c32, %c1, %c11008) : tensor<?x?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?x?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %12 = linalg.batch_matmul ins(%6, %9 : tensor<?x?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?x?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %13 = iree_encoding.unset_encoding %12 : tensor<?x?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xi32>
+ } -> tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %10 = tensor.empty(%c32, %c1, %c11008) : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %12 = linalg.batch_matmul ins(%6, %9 : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %13 = iree_encoding.unset_encoding %12 : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x1x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xi32>
%extracted_slice = tensor.extract_slice %13[0, 0, 0] [32, 1, 11008] [1, 1, 1] : tensor<?x?x?xi32> to tensor<32x1x11008xi32>
return %extracted_slice : tensor<32x1x11008xi32>
}
@@ -1990,28 +1990,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<role = RHS, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<operand_index = 1, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<role = RHS, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi16, #iree_encoding.encoding<role = RHS, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<operand_index = 1, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi16, #iree_encoding.encoding<operand_index = 1, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi16, #iree_encoding.encoding<role = RHS, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi16, #iree_encoding.encoding<operand_index = 1, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, i16, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -2055,35 +2055,35 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%lhs_binding = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%rhs_binding = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%out_binding = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%lhs = flow.dispatch.tensor.load %lhs_binding, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%rhs_i4 = flow.dispatch.tensor.load %rhs_binding, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
- %empty = tensor.empty(%K, %N) : tensor<?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ %empty = tensor.empty(%K, %N) : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%rhs_i32 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]}
- ins(%rhs_i4 : tensor<?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>) outs(%empty : tensor<?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>) {
+ ins(%rhs_i4 : tensor<?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>) outs(%empty : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i4, %out: i32):
%17 = arith.extui %in : i4 to i32
linalg.yield %17 : i32
- } -> tensor<?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ } -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%out = flow.dispatch.tensor.load %out_binding, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
%result = linalg.matmul
- ins(%lhs, %rhs_i32 : tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%out : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%lhs, %rhs_i32 : tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%out : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %result, %out_binding, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
@@ -2125,28 +2125,28 @@
^bb0(%arg2: index):
tensor.yield %c0_i8 : i8
} : tensor<128xi8> to tensor<?xi8>
- %4 = iree_encoding.set_encoding %padded : tensor<?xi8> -> tensor<?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %5 = tensor.empty(%c128) : tensor<?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %6 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>], iterator_types = ["parallel"]} ins(%4 : tensor<?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %4 = iree_encoding.set_encoding %padded : tensor<?xi8> -> tensor<?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %5 = tensor.empty(%c128) : tensor<?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %6 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>], iterator_types = ["parallel"]} ins(%4 : tensor<?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ } -> tensor<?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
%padded_0 = tensor.pad %arg1 low[0, 0] high[%c0, %c0] {
^bb0(%arg2: index, %arg3: index):
tensor.yield %c0_i8 : i8
} : tensor<128x11008xi8> to tensor<?x?xi8>
- %7 = iree_encoding.set_encoding %padded_0 : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %8 = tensor.empty(%c128, %c11008) : tensor<?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %9 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} ins(%7 : tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %7 = iree_encoding.set_encoding %padded_0 : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %8 = tensor.empty(%c128, %c11008) : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %9 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} ins(%7 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %10 = tensor.empty(%c11008) : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %12 = linalg.vecmat ins(%6, %9 : tensor<?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %13 = iree_encoding.unset_encoding %12 : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?xi32>
+ } -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %10 = tensor.empty(%c11008) : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %12 = linalg.vecmat ins(%6, %9 : tensor<?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %13 = iree_encoding.unset_encoding %12 : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?xi32>
%extracted_slice = tensor.extract_slice %13[0] [11008] [1] : tensor<?xi32> to tensor<11008xi32>
return %extracted_slice : tensor<11008xi32>
}
@@ -2198,28 +2198,28 @@
^bb0(%arg2: index, %arg3: index):
tensor.yield %c0_i8 : i8
} : tensor<11008x128xi8> to tensor<?x?xi8>
- %4 = iree_encoding.set_encoding %padded : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %5 = tensor.empty(%c11008, %c128) : tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %6 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} ins(%4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %4 = iree_encoding.set_encoding %padded : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %5 = tensor.empty(%c11008, %c128) : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %6 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} ins(%4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ } -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
%padded_0 = tensor.pad %arg1 low[0] high[%c0] {
^bb0(%arg2: index):
tensor.yield %c0_i8 : i8
} : tensor<128xi8> to tensor<?xi8>
- %7 = iree_encoding.set_encoding %padded_0 : tensor<?xi8> -> tensor<?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %8 = tensor.empty(%c128) : tensor<?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %9 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>], iterator_types = ["parallel"]} ins(%7 : tensor<?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %7 = iree_encoding.set_encoding %padded_0 : tensor<?xi8> -> tensor<?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %8 = tensor.empty(%c128) : tensor<?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %9 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>], iterator_types = ["parallel"]} ins(%7 : tensor<?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %10 = tensor.empty(%c11008) : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %12 = linalg.matvec ins(%6, %9 : tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %13 = iree_encoding.unset_encoding %12 : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?xi32>
+ } -> tensor<?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %10 = tensor.empty(%c11008) : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %12 = linalg.matvec ins(%6, %9 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %13 = iree_encoding.unset_encoding %12 : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_N = 1 : index, original_type = tensor<11008xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?xi32>
%extracted_slice = tensor.extract_slice %13[0] [11008] [1] : tensor<?xi32> to tensor<11008xi32>
return %extracted_slice : tensor<11008xi32>
}
@@ -2271,28 +2271,28 @@
^bb0(%arg2: index, %arg3: index):
tensor.yield %c0_i8 : i8
} : tensor<15x128xi8> to tensor<?x?xi8>
- %4 = iree_encoding.set_encoding %padded : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %5 = tensor.empty(%c15, %c128) : tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %6 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} ins(%4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %4 = iree_encoding.set_encoding %padded : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %5 = tensor.empty(%c15, %c128) : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %6 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} ins(%4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ } -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
%padded_0 = tensor.pad %arg1 low[0] high[%c0] {
^bb0(%arg2: index):
tensor.yield %c0_i8 : i8
} : tensor<128xi8> to tensor<?xi8>
- %7 = iree_encoding.set_encoding %padded_0 : tensor<?xi8> -> tensor<?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %8 = tensor.empty(%c128) : tensor<?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %9 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>], iterator_types = ["parallel"]} ins(%7 : tensor<?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %7 = iree_encoding.set_encoding %padded_0 : tensor<?xi8> -> tensor<?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %8 = tensor.empty(%c128) : tensor<?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %9 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>], iterator_types = ["parallel"]} ins(%7 : tensor<?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %10 = tensor.empty(%c15) : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %12 = linalg.matvec ins(%6, %9 : tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %13 = iree_encoding.unset_encoding %12 : tensor<?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?xi32>
+ } -> tensor<?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %10 = tensor.empty(%c15) : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %12 = linalg.matvec ins(%6, %9 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %13 = iree_encoding.unset_encoding %12 : tensor<?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 15 : index, matmul_narrow_N = 1 : index, original_type = tensor<15xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?xi32>
%extracted_slice = tensor.extract_slice %13[0] [15] [1] : tensor<?xi32> to tensor<15xi32>
return %extracted_slice : tensor<15xi32>
}
@@ -2346,28 +2346,28 @@
^bb0(%arg2: index, %arg3: index):
tensor.yield %c0_i8 : i8
} : tensor<32x128xi8> to tensor<?x?xi8>
- %4 = iree_encoding.set_encoding %padded : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %5 = tensor.empty(%c32, %c128) : tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %6 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} ins(%4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %4 = iree_encoding.set_encoding %padded : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %5 = tensor.empty(%c32, %c128) : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %6 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} ins(%4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ } -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>
%padded_0 = tensor.pad %arg1 low[0, 0, 0] high[%c0, %c0, %c0] {
^bb0(%arg2: index, %arg3: index, %arg4: index):
tensor.yield %c0_i8 : i8
} : tensor<32x128x11008xi8> to tensor<?x?x?xi8>
- %7 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xi8> -> tensor<?x?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %8 = tensor.empty(%c32, %c128, %c11008) : tensor<?x?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %9 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>], iterator_types = ["parallel", "parallel", "parallel"]} ins(%7 : tensor<?x?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?x?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %7 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xi8> -> tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %8 = tensor.empty(%c32, %c128, %c11008) : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %9 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>], iterator_types = ["parallel", "parallel", "parallel"]} ins(%7 : tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%8 : tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i8, %out: i32):
%17 = arith.extsi %in : i8 to i32
linalg.yield %17 : i32
- } -> tensor<?x?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
- %10 = tensor.empty(%c32, %c11008) : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %12 = linalg.batch_vecmat ins(%6, %9 : tensor<?x?xi32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xi32, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
- %13 = iree_encoding.unset_encoding %12 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xi32>
+ } -> tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>
+ %10 = tensor.empty(%c32, %c11008) : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %11 = linalg.fill ins(%c0_i32 : i32) outs(%10 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %12 = linalg.batch_vecmat ins(%6, %9 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128xi8>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x128x11008xi8>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%11 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %13 = iree_encoding.unset_encoding %12 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], matmul_narrow_M = 1 : index, original_type = tensor<32x11008xi32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xi32>
%extracted_slice = tensor.extract_slice %13[0, 0] [32, 11008] [1, 1] : tensor<?x?xi32> to tensor<32x11008xi32>
return %extracted_slice : tensor<32x11008xi32>
}
@@ -2416,19 +2416,19 @@
^bb0(%arg3: index, %arg4: index, %arg5: index):
tensor.yield %c0_i8 : i8
} : tensor<32x11008x128xi8> to tensor<?x?x?xi8>
- %3 = iree_encoding.set_encoding %padded : tensor<?x?x?xi8> -> tensor<?x?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], original_type = tensor<32x11008x128xi8>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ %3 = iree_encoding.set_encoding %padded : tensor<?x?x?xi8> -> tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], original_type = tensor<32x11008x128xi8>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
%padded_0 = tensor.pad %1 low[0, 0] high[%c0, %c0] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %c0_i8 : i8
} : tensor<32x128xi8> to tensor<?x?xi8>
- %4 = iree_encoding.set_encoding %padded_0 : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], original_type = tensor<32x128xi8>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ %4 = iree_encoding.set_encoding %padded_0 : tensor<?x?xi8> -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], original_type = tensor<32x128xi8>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
%padded_1 = tensor.pad %2 low[0, 0] high[%c0, %c0] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %c0_i32 : i32
} : tensor<32x11008xi32> to tensor<?x?xi32>
- %5 = iree_encoding.set_encoding %padded_1 : tensor<?x?xi32> -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], original_type = tensor<32x11008xi32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
- %6 = linalg.batch_matvec ins(%3, %4 : tensor<?x?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], original_type = tensor<32x11008x128xi8>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>, tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], original_type = tensor<32x128xi8>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>) outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], original_type = tensor<32x11008xi32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>) -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], original_type = tensor<32x11008xi32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
- %7 = iree_encoding.unset_encoding %6 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], original_type = tensor<32x11008xi32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>> -> tensor<?x?xi32>
+ %5 = iree_encoding.set_encoding %padded_1 : tensor<?x?xi32> -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], original_type = tensor<32x11008xi32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ %6 = linalg.batch_matvec ins(%3, %4 : tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], original_type = tensor<32x11008x128xi8>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>, tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], original_type = tensor<32x128xi8>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>) outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], original_type = tensor<32x11008xi32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>) -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], original_type = tensor<32x11008xi32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ %7 = iree_encoding.unset_encoding %6 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], original_type = tensor<32x11008xi32>, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>> -> tensor<?x?xi32>
%extracted_slice = tensor.extract_slice %7[0, 0] [32, 11008] [1, 1] : tensor<?x?xi32> to tensor<32x11008xi32>
%8 = hal.tensor.export %extracted_slice "output0" : tensor<32x11008xi32> -> !hal.buffer_view
func.return %8 : !hal.buffer_view
@@ -2449,32 +2449,32 @@
%c128 = arith.constant 128 : index
%cst = arith.constant 0.000000e+00 : f32
%c512 = arith.constant 512 : index
- %3:2 = iree_encoding.upper_bound_tile_size tensor<256x128xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %3:2 = iree_encoding.upper_bound_tile_size tensor<256x128xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%4 = affine.apply #map3()[%3#0, %c256]
%5 = affine.apply #map3()[%3#1, %c128]
%padded = tensor.pad %arg0 low[0, 0] high[%4, %5] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<256x128xf32> to tensor<?x?xf32>
- %6 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<256x128xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %7:2 = iree_encoding.upper_bound_tile_size tensor<256x512xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %6 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<256x128xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %7:2 = iree_encoding.upper_bound_tile_size tensor<256x512xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%8 = affine.apply #map3()[%7#0, %c256]
%9 = affine.apply #map3()[%7#1, %c512]
%padded_0 = tensor.pad %arg1 low[0, 0] high[%8, %9] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<256x512xf32> to tensor<?x?xf32>
- %10 = iree_encoding.set_encoding %padded_0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<256x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %11:2 = iree_encoding.upper_bound_tile_size tensor<128x512xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %10 = iree_encoding.set_encoding %padded_0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<256x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %11:2 = iree_encoding.upper_bound_tile_size tensor<128x512xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%12 = affine.apply #map3()[%11#0, %c128]
%13 = affine.apply #map3()[%11#1, %c512]
%padded_1 = tensor.pad %arg2 low[0, 0] high[%12, %13] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<128x512xf32> to tensor<?x?xf32>
- %14 = iree_encoding.set_encoding %padded_1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %15 = linalg.matmul_transpose_a ins(%6, %10 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<256x128xf32>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<256x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%14 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %16 = iree_encoding.unset_encoding %15 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
+ %14 = iree_encoding.set_encoding %padded_1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %15 = linalg.matmul_transpose_a ins(%6, %10 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<256x128xf32>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<256x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%14 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %16 = iree_encoding.unset_encoding %15 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
%extracted_slice = tensor.extract_slice %16[0, 0] [128, 512] [1, 1] : tensor<?x?xf32> to tensor<128x512xf32>
return %extracted_slice : tensor<128x512xf32>
}
@@ -2507,32 +2507,32 @@
%c256 = arith.constant 256 : index
%cst = arith.constant 0.000000e+00 : f32
%c512 = arith.constant 512 : index
- %3:2 = iree_encoding.upper_bound_tile_size tensor<128x256xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %3:2 = iree_encoding.upper_bound_tile_size tensor<128x256xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%4 = affine.apply #map3()[%3#0, %c128]
%5 = affine.apply #map3()[%3#1, %c256]
%padded = tensor.pad %arg0 low[0, 0] high[%4, %5] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<128x256xf32> to tensor<?x?xf32>
- %6 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<128x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %7:2 = iree_encoding.upper_bound_tile_size tensor<512x256xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %6 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<128x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %7:2 = iree_encoding.upper_bound_tile_size tensor<512x256xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%8 = affine.apply #map3()[%7#0, %c512]
%9 = affine.apply #map3()[%7#1, %c256]
%padded_0 = tensor.pad %arg1 low[0, 0] high[%8, %9] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<512x256xf32> to tensor<?x?xf32>
- %10 = iree_encoding.set_encoding %padded_0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<512x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %11:2 = iree_encoding.upper_bound_tile_size tensor<128x512xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %10 = iree_encoding.set_encoding %padded_0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<512x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %11:2 = iree_encoding.upper_bound_tile_size tensor<128x512xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%12 = affine.apply #map3()[%11#0, %c128]
%13 = affine.apply #map3()[%11#1, %c512]
%padded_1 = tensor.pad %arg2 low[0, 0] high[%12, %13] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %cst : f32
} : tensor<128x512xf32> to tensor<?x?xf32>
- %14 = iree_encoding.set_encoding %padded_1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %15 = linalg.matmul_transpose_b ins(%6, %10 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<128x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<512x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%14 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %16 = iree_encoding.unset_encoding %15 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
+ %14 = iree_encoding.set_encoding %padded_1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %15 = linalg.matmul_transpose_b ins(%6, %10 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<128x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<512x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%14 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %16 = iree_encoding.unset_encoding %15 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
%extracted_slice = tensor.extract_slice %16[0, 0] [128, 512] [1, 1] : tensor<?x?xf32> to tensor<128x512xf32>
return %extracted_slice : tensor<128x512xf32>
}
@@ -2565,7 +2565,7 @@
%c128 = arith.constant 128 : index
%cst = arith.constant 0.000000e+00 : f32
%c512 = arith.constant 512 : index
- %3:3 = iree_encoding.upper_bound_tile_size tensor<2x256x128xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
+ %3:3 = iree_encoding.upper_bound_tile_size tensor<2x256x128xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
%4 = affine.apply #map3()[%3#0, %c2]
%5 = affine.apply #map3()[%3#1, %c256]
%6 = affine.apply #map3()[%3#2, %c128]
@@ -2573,8 +2573,8 @@
^bb0(%arg3: index, %arg4: index, %arg5: index):
tensor.yield %cst : f32
} : tensor<2x256x128xf32> to tensor<?x?x?xf32>
- %7 = iree_encoding.set_encoding %padded : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<2x256x128xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %8:3 = iree_encoding.upper_bound_tile_size tensor<2x256x512xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
+ %7 = iree_encoding.set_encoding %padded : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<2x256x128xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %8:3 = iree_encoding.upper_bound_tile_size tensor<2x256x512xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
%9 = affine.apply #map3()[%8#0, %c2]
%10 = affine.apply #map3()[%8#1, %c256]
%11 = affine.apply #map3()[%8#2, %c512]
@@ -2582,8 +2582,8 @@
^bb0(%arg3: index, %arg4: index, %arg5: index):
tensor.yield %cst : f32
} : tensor<2x256x512xf32> to tensor<?x?x?xf32>
- %12 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x256x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %13:3 = iree_encoding.upper_bound_tile_size tensor<2x128x512xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
+ %12 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<2x256x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %13:3 = iree_encoding.upper_bound_tile_size tensor<2x128x512xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
%14 = affine.apply #map3()[%13#0, %c2]
%15 = affine.apply #map3()[%13#1, %c128]
%16 = affine.apply #map3()[%13#2, %c512]
@@ -2591,9 +2591,9 @@
^bb0(%arg3: index, %arg4: index, %arg5: index):
tensor.yield %cst : f32
} : tensor<2x128x512xf32> to tensor<?x?x?xf32>
- %17 = iree_encoding.set_encoding %padded_1 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %18 = linalg.batch_matmul_transpose_a ins(%7, %12 : tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<2x256x128xf32>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x256x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%17 : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %19 = iree_encoding.unset_encoding %18 : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xf32>
+ %17 = iree_encoding.set_encoding %padded_1 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %18 = linalg.batch_matmul_transpose_a ins(%7, %12 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<2x256x128xf32>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<2x256x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%17 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %19 = iree_encoding.unset_encoding %18 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xf32>
%extracted_slice = tensor.extract_slice %19[0, 0, 0] [2, 128, 512] [1, 1, 1] : tensor<?x?x?xf32> to tensor<2x128x512xf32>
return %extracted_slice : tensor<2x128x512xf32>
}
@@ -2626,7 +2626,7 @@
%c256 = arith.constant 256 : index
%cst = arith.constant 0.000000e+00 : f32
%c512 = arith.constant 512 : index
- %3:3 = iree_encoding.upper_bound_tile_size tensor<2x128x256xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
+ %3:3 = iree_encoding.upper_bound_tile_size tensor<2x128x256xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
%4 = affine.apply #map3()[%3#0, %c2]
%5 = affine.apply #map3()[%3#1, %c128]
%6 = affine.apply #map3()[%3#2, %c256]
@@ -2634,8 +2634,8 @@
^bb0(%arg3: index, %arg4: index, %arg5: index):
tensor.yield %cst : f32
} : tensor<2x128x256xf32> to tensor<?x?x?xf32>
- %7 = iree_encoding.set_encoding %padded : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<2x128x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %8:3 = iree_encoding.upper_bound_tile_size tensor<2x512x256xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
+ %7 = iree_encoding.set_encoding %padded : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<2x128x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %8:3 = iree_encoding.upper_bound_tile_size tensor<2x512x256xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
%9 = affine.apply #map3()[%8#0, %c2]
%10 = affine.apply #map3()[%8#1, %c512]
%11 = affine.apply #map3()[%8#2, %c256]
@@ -2643,8 +2643,8 @@
^bb0(%arg3: index, %arg4: index, %arg5: index):
tensor.yield %cst : f32
} : tensor<2x512x256xf32> to tensor<?x?x?xf32>
- %12 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x512x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %13:3 = iree_encoding.upper_bound_tile_size tensor<2x128x512xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
+ %12 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<2x512x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %13:3 = iree_encoding.upper_bound_tile_size tensor<2x128x512xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
%14 = affine.apply #map3()[%13#0, %c2]
%15 = affine.apply #map3()[%13#1, %c128]
%16 = affine.apply #map3()[%13#2, %c512]
@@ -2652,9 +2652,9 @@
^bb0(%arg3: index, %arg4: index, %arg5: index):
tensor.yield %cst : f32
} : tensor<2x128x512xf32> to tensor<?x?x?xf32>
- %17 = iree_encoding.set_encoding %padded_1 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %18 = linalg.batch_matmul_transpose_b ins(%7, %12 : tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<2x128x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x512x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%17 : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
- %19 = iree_encoding.unset_encoding %18 : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xf32>
+ %17 = iree_encoding.set_encoding %padded_1 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %18 = linalg.batch_matmul_transpose_b ins(%7, %12 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<2x128x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<2x512x256xf32>, user_indexing_maps = [#map, #map1, #map2]>>) outs(%17 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>>
+ %19 = iree_encoding.unset_encoding %18 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<2x128x512xf32>, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xf32>
%extracted_slice = tensor.extract_slice %19[0, 0, 0] [2, 128, 512] [1, 1, 1] : tensor<?x?x?xf32> to tensor<2x128x512xf32>
return %extracted_slice : tensor<2x128x512xf32>
}
@@ -2688,15 +2688,15 @@
%c0_i16 = arith.constant 0 : i16
%c128 = arith.constant 128 : index
%c32 = arith.constant 32 : index
- %0:2 = iree_encoding.upper_bound_tile_size tensor<32x128xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %0:2 = iree_encoding.upper_bound_tile_size tensor<32x128xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%1 = affine.apply #map3()[%0#0, %c32]
%2 = affine.apply #map3()[%0#1, %c128]
%padded = tensor.pad %arg0 low[0, 0] high[%1, %2] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %c0_i16 : i16
} : tensor<32x128xi16> to tensor<?x?xi16>
- %3 = iree_encoding.set_encoding %padded : tensor<?x?xi16> -> tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, ui4, i32], original_type = tensor<32x128xi16>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
- %4:3 = iree_encoding.upper_bound_tile_size tensor<4096x32x128xi4, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
+ %3 = iree_encoding.set_encoding %padded : tensor<?x?xi16> -> tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, ui4, i32], original_type = tensor<32x128xi16>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
+ %4:3 = iree_encoding.upper_bound_tile_size tensor<4096x32x128xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>> -> index, index, index
%5 = affine.apply #map3()[%4#0, %c4096]
%6 = affine.apply #map3()[%4#1, %c32]
%7 = affine.apply #map3()[%4#2, %c128]
@@ -2704,24 +2704,24 @@
^bb0(%arg3: index, %arg4: index, %arg5: index):
tensor.yield %c0_i4 : i4
} : tensor<4096x32x128xi4> to tensor<?x?x?xi4>
- %8 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xi4> -> tensor<?x?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], original_type = tensor<4096x32x128xi4>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
- %9:2 = iree_encoding.upper_bound_tile_size tensor<4096x32xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %8 = iree_encoding.set_encoding %padded_0 : tensor<?x?x?xi4> -> tensor<?x?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], original_type = tensor<4096x32x128xi4>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
+ %9:2 = iree_encoding.upper_bound_tile_size tensor<4096x32xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%10 = affine.apply #map3()[%9#0, %c4096]
%11 = affine.apply #map3()[%9#1, %c32]
%padded_1 = tensor.pad %arg2 low[0, 0] high[%10, %11] {
^bb0(%arg3: index, %arg4: index):
tensor.yield %c0_i32 : i32
} : tensor<4096x32xi32> to tensor<?x?xi32>
- %12 = iree_encoding.set_encoding %padded_1 : tensor<?x?xi32> -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
- %13 = linalg.generic {indexing_maps = [#map, #map1, #map2], iterator_types = ["parallel", "parallel", "reduction"]} ins(%3, %8 : tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, ui4, i32], original_type = tensor<32x128xi16>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], original_type = tensor<4096x32x128xi4>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>) outs(%12 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>) {
+ %12 = iree_encoding.set_encoding %padded_1 : tensor<?x?xi32> -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
+ %13 = linalg.generic {indexing_maps = [#map, #map1, #map2], iterator_types = ["parallel", "parallel", "reduction"]} ins(%3, %8 : tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0, element_types = [i16, ui4, i32], original_type = tensor<32x128xi16>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xi4, #iree_encoding.encoding<operand_index = 1, element_types = [i16, ui4, i32], original_type = tensor<4096x32x128xi4>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>) outs(%12 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>) {
^bb0(%in: i16, %in_2: i4, %out: i32):
%15 = arith.extsi %in : i16 to i32
%16 = arith.extui %in_2 : i4 to i32
%17 = arith.muli %15, %16 : i32
%18 = arith.addi %17, %out : i32
linalg.yield %18 : i32
- } -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
- %14 = iree_encoding.unset_encoding %13 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xi32>
+ } -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>>
+ %14 = iree_encoding.unset_encoding %13 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xi32>
%extracted_slice = tensor.extract_slice %14[0, 0] [4096, 32] [1, 1] : tensor<?x?xi32> to tensor<4096x32xi32>
return %extracted_slice : tensor<4096x32xi32>
}
diff --git a/compiler/src/iree/compiler/Codegen/Common/CPU/test/lower_to_ukernel_ops.mlir b/compiler/src/iree/compiler/Codegen/Common/CPU/test/lower_to_ukernel_ops.mlir
index d44f44d..d7f30d4 100644
--- a/compiler/src/iree/compiler/Codegen/Common/CPU/test/lower_to_ukernel_ops.mlir
+++ b/compiler/src/iree/compiler/Codegen/Common/CPU/test/lower_to_ukernel_ops.mlir
@@ -511,7 +511,7 @@
func.func @query_tile_sizes_2d() -> (index, index) attributes {
hal.executable.target = #hal.executable.target<"vmvx", "vmvx-bytecode-fb", {ukernels = "all"}>
} {
- %result:2 = iree_codegen.query_tile_sizes tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %result:2 = iree_codegen.query_tile_sizes tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
return %result#0, %result#1 : index, index
}
diff --git a/compiler/src/iree/compiler/Codegen/Common/CPU/test/vmvx_materialize_encoding.mlir b/compiler/src/iree/compiler/Codegen/Common/CPU/test/vmvx_materialize_encoding.mlir
index 47e102c..e39b0fc 100644
--- a/compiler/src/iree/compiler/Codegen/Common/CPU/test/vmvx_materialize_encoding.mlir
+++ b/compiler/src/iree/compiler/Codegen/Common/CPU/test/vmvx_materialize_encoding.mlir
@@ -11,28 +11,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
@@ -42,17 +42,17 @@
// CHECK-DAG: %[[M:.+]] = hal.interface.constant.load[0]
// CHECK-DAG: %[[N:.+]] = hal.interface.constant.load[1]
// CHECK-DAG: %[[K:.+]] = hal.interface.constant.load[2]
-// CHECK: %[[LHS_TILE_SIZES:.+]]:2 = iree_codegen.query_tile_sizes tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZES:.+]]:2 = iree_codegen.query_tile_sizes tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
// CHECK-DAG: %[[LHS_OUTER_SIZE0:.+]] = affine.apply #[[MAP_CEILDIV]]()[%[[M]], %[[LHS_TILE_SIZES]]#0]
// CHECK-DAG: %[[LHS_OUTER_SIZE1:.+]] = affine.apply #[[MAP_CEILDIV]]()[%[[K]], %[[LHS_TILE_SIZES]]#1]
// CHECK: %[[LHS_BINDING:.+]] = hal.interface.binding.subspan set(0) binding(0)
// CHECK-SAME: !flow.dispatch.tensor<readonly:tensor<?x?x?x?xi8>>{%[[LHS_OUTER_SIZE0]], %[[LHS_OUTER_SIZE1]], %[[LHS_TILE_SIZES]]#0, %[[LHS_TILE_SIZES]]#1}
-// CHECK: %[[RHS_TILE_SIZES:.+]]:2 = iree_codegen.query_tile_sizes tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+// CHECK: %[[RHS_TILE_SIZES:.+]]:2 = iree_codegen.query_tile_sizes tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
// CHECK-DAG: %[[RHS_OUTER_SIZE0:.+]] = affine.apply #[[MAP_CEILDIV]]()[%[[N]], %[[RHS_TILE_SIZES]]#0]
// CHECK-DAG: %[[RHS_OUTER_SIZE1:.+]] = affine.apply #[[MAP_CEILDIV]]()[%[[K]], %[[RHS_TILE_SIZES]]#1]
// CHECK: %[[RHS_BINDING:.+]] = hal.interface.binding.subspan set(0) binding(1)
// CHECK-SAME: !flow.dispatch.tensor<readonly:tensor<?x?x?x?xi8>>{%[[RHS_OUTER_SIZE0]], %[[RHS_OUTER_SIZE1]], %[[RHS_TILE_SIZES]]#0, %[[RHS_TILE_SIZES]]#1}
-// CHECK: %[[RESULT_TILE_SIZES:.+]]:2 = iree_codegen.query_tile_sizes tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+// CHECK: %[[RESULT_TILE_SIZES:.+]]:2 = iree_codegen.query_tile_sizes tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2 : i64, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
// CHECK-DAG: %[[RESULT_OUTER_SIZE0:.+]] = affine.apply #[[MAP_CEILDIV]]()[%[[M]], %[[RESULT_TILE_SIZES]]#0]
// CHECK-DAG: %[[RESULT_OUTER_SIZE1:.+]] = affine.apply #[[MAP_CEILDIV]]()[%[[N]], %[[RESULT_TILE_SIZES]]#1]
// CHECK: %[[OUTS_BINDING:.+]] = hal.interface.binding.subspan set(0) binding(2)
@@ -82,17 +82,17 @@
%c32_i64 = arith.constant 32 : i64
%cst = arith.constant 0.000000e+00 : f32
%c0 = arith.constant 0 : index
- %0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1x2xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg0, %arg1}
- %1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg2, %arg3}
- %2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg4, %arg5}
- %3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%arg0, %arg1], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1x2xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg0, %arg1} -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1x2xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
- %4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%arg2, %arg3], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg2, %arg3} -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
+ %0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1x2xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg0, %arg1}
+ %1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<2x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg2, %arg3}
+ %2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg4, %arg5}
+ %3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%arg0, %arg1], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1x2xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg0, %arg1} -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1x2xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
+ %4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%arg2, %arg3], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<2x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg2, %arg3} -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<2x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
%5 = affine.apply #map()[%arg6]
%6 = affine.apply #map1()[%arg7]
- %7 = tensor.empty(%6, %5) : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
- %8 = linalg.fill ins(%cst : f32) outs(%7 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>) -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
- %9 = linalg.matmul ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1x2xf32>, user_indexing_maps = [#map2, #map3, #map4]>>, tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>) outs(%8 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>) -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
- flow.dispatch.tensor.store %9, %2, offsets = [0, 0], sizes = [%arg4, %arg5], strides = [1, 1] : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg4, %arg5}
+ %7 = tensor.empty(%6, %5) : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
+ %8 = linalg.fill ins(%cst : f32) outs(%7 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
+ %9 = linalg.matmul ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1x2xf32>, user_indexing_maps = [#map2, #map3, #map4]>>, tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<2x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>) outs(%8 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>
+ flow.dispatch.tensor.store %9, %2, offsets = [0, 0], sizes = [%arg4, %arg5], strides = [1, 1] : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<1x3xf32>, user_indexing_maps = [#map2, #map3, #map4]>>>{%arg4, %arg5}
return
}
// CHECK: func.func @fill_matmul
@@ -134,7 +134,7 @@
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
: !flow.dispatch.tensor<readonly:tensor<?x?xf32>>{%d0, %d1}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%outd0, %outd1}
+ : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%outd0, %outd1}
%2 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%d0, %d1], strides = [1, 1]
: !flow.dispatch.tensor<readonly:tensor<?x?xf32>>{%d0, %d1} -> tensor<?x?xf32>
%p0 = affine.apply affine_map<()[s0, s1] -> (-s0 + s1)>()[%d0, %outd0]
@@ -143,10 +143,10 @@
^bb0(%arg0: index, %arg1: index):
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<?x?xf32>
- %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %3, %1, offsets = [0, 0], sizes = [%outd0, %outd1], strides = [1, 1]
- : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%outd0, %outd1}
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%outd0, %outd1}
return
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
@@ -186,14 +186,14 @@
%outd0 = hal.interface.constant.load [2] : index
%outd1 = hal.interface.constant.load [3] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%d0, %d1}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%d0, %d1}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
: !flow.dispatch.tensor<writeonly:tensor<?x?xf32>>{%outd0, %outd1}
%2 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%d0, %d1], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%d0, %d1}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%d0, %d1}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%3 = iree_encoding.unset_encoding %2
- : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
%4 = tensor.extract_slice %3[0, 0] [%outd0, %outd1] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
flow.dispatch.tensor.store %4, %1, offsets = [0, 0], sizes = [%outd0, %outd1], strides = [1, 1]
: tensor<?x?xf32> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32>>{%outd0, %outd1}
@@ -232,28 +232,28 @@
%N = hal.interface.constant.load[1] : index
%K = hal.interface.constant.load[2] : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
%1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
%2 = hal.interface.binding.subspan set(0) binding(2) type(storage_buffer) alignment(64) offset(%c0)
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
%3 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%M, %K], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %K}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%4 = flow.dispatch.tensor.load %1, offsets = [0, 0], sizes = [%K, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%K, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%5 = flow.dispatch.tensor.load %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ : !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%6 = linalg.matmul
- ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
- tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ ins(%3, %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>,
+ tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%5 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
flow.dispatch.tensor.store %6, %2, offsets = [0, 0], sizes = [%M, %N], strides = [1, 1]
- : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ -> !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>>{%M, %N}
return
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 8)>
diff --git a/compiler/src/iree/compiler/Codegen/Common/EncodingUtils.cpp b/compiler/src/iree/compiler/Codegen/Common/EncodingUtils.cpp
index 08fc157..7eb438b 100644
--- a/compiler/src/iree/compiler/Codegen/Common/EncodingUtils.cpp
+++ b/compiler/src/iree/compiler/Codegen/Common/EncodingUtils.cpp
@@ -8,13 +8,13 @@
#include "mlir/Dialect/Linalg/IR/LinalgInterfaces.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
+#include "mlir/IR/BuiltinAttributes.h"
#include <numeric>
namespace mlir::iree_compiler {
using IREE::Encoding::EncodingAttr;
-using IREE::Encoding::EncodingRole;
using IREE::Encoding::getEncodingAttr;
using IREE::Encoding::getEncodingContractionDims;
@@ -29,7 +29,7 @@
if (!isNarrowNResult(encoding)) {
return tensorType;
}
- auto newRole = encoding.getRole().getValue();
+ auto newIndex = encoding.getOperandIndex();
TypeAttr originalTypeAttr = encoding.getOriginalType();
RankedTensorType originalType = tensorType;
if (originalTypeAttr) {
@@ -52,8 +52,8 @@
int m = cDims->m[0];
int n = cDims->n[0];
std::swap(permIndices[m], permIndices[n]);
- int mDim = encoding.mapDimToRoleIndex(m);
- int nDim = encoding.mapDimToRoleIndex(n);
+ int mDim = encoding.mapDimToOperandIndex(m);
+ int nDim = encoding.mapDimToOperandIndex(n);
std::swap(newShape[mDim], newShape[nDim]);
std::swap(newOriginalShape[mDim], newOriginalShape[nDim]);
}
@@ -84,8 +84,7 @@
OpBuilder builder(context);
auto newEncoding = IREE::Encoding::EncodingAttr::get(
- context, IREE::Encoding::EncodingRoleAttr::get(context, newRole),
- encoding.getElementTypes(),
+ context, newIndex, encoding.getElementTypes(),
TypeAttr::get(RankedTensorType::get(newOriginalShape, elemType)),
encoding.getMatmulNarrow_N(), encoding.getMatmulNarrow_M(),
newIndexingMaps, DenseI64ArrayAttr::get(context, newRoundDimsTo));
@@ -169,7 +168,7 @@
MaterializeEncodingInfo getEncodingInfoForMatmul(EncodingAttr encoding,
int64_t rank,
TileMxNxK tileMxNxK) {
- EncodingRole role = encoding.getRole().getValue();
+ auto index = encoding.getOperandIndex().getValue();
MaterializeEncodingInfo encodingInfo;
auto cDims = getEncodingContractionDims(encoding);
// The following expects M, N, K, and Batch sizes of at most 1 for now
@@ -178,34 +177,34 @@
"Expected at most one M, N, K, and Batch dimension");
if (!cDims->batch.empty()) {
encodingInfo.outerDimsPerm.push_back(
- encoding.mapDimToRoleIndex(cDims->batch[0]));
+ encoding.mapDimToOperandIndex(cDims->batch[0]));
}
- if (role != EncodingRole::RHS && !cDims->m.empty()) {
+ if (index != IREE::Encoding::MATMUL_RHS && !cDims->m.empty()) {
encodingInfo.outerDimsPerm.push_back(
- encoding.mapDimToRoleIndex(cDims->m[0]));
+ encoding.mapDimToOperandIndex(cDims->m[0]));
encodingInfo.innerDimsPos.push_back(
- encoding.mapDimToRoleIndex(cDims->m[0]));
+ encoding.mapDimToOperandIndex(cDims->m[0]));
encodingInfo.innerTileSizes.push_back(tileMxNxK.M);
}
- if (role != EncodingRole::LHS && !cDims->n.empty()) {
+ if (index != IREE::Encoding::MATMUL_LHS && !cDims->n.empty()) {
encodingInfo.outerDimsPerm.push_back(
- encoding.mapDimToRoleIndex(cDims->n[0]));
+ encoding.mapDimToOperandIndex(cDims->n[0]));
encodingInfo.innerDimsPos.push_back(
- encoding.mapDimToRoleIndex(cDims->n[0]));
+ encoding.mapDimToOperandIndex(cDims->n[0]));
encodingInfo.innerTileSizes.push_back(tileMxNxK.N);
}
- if (role != EncodingRole::RESULT) {
+ if (index != IREE::Encoding::MATMUL_RESULT) {
encodingInfo.outerDimsPerm.push_back(
- encoding.mapDimToRoleIndex(cDims->k[0]));
+ encoding.mapDimToOperandIndex(cDims->k[0]));
encodingInfo.innerDimsPos.push_back(
- encoding.mapDimToRoleIndex(cDims->k[0]));
+ encoding.mapDimToOperandIndex(cDims->k[0]));
encodingInfo.innerTileSizes.push_back(tileMxNxK.K);
}
return encodingInfo;
}
bool isNarrowNResult(EncodingAttr encoding) {
- if (encoding.getRole().getValue() != EncodingRole::RESULT) {
+ if (encoding.getOperandIndex().getValue() != IREE::Encoding::MATMUL_RESULT) {
return false;
}
IntegerAttr narrowM = encoding.getMatmulNarrow_M();
diff --git a/compiler/src/iree/compiler/Codegen/Common/MaterializeEncodingIntoPackUnPack.cpp b/compiler/src/iree/compiler/Codegen/Common/MaterializeEncodingIntoPackUnPack.cpp
index 689e88c..c585614 100644
--- a/compiler/src/iree/compiler/Codegen/Common/MaterializeEncodingIntoPackUnPack.cpp
+++ b/compiler/src/iree/compiler/Codegen/Common/MaterializeEncodingIntoPackUnPack.cpp
@@ -347,12 +347,9 @@
return failure();
}
- if (lhsEncoding.getRole().getValue() !=
- mlir::iree_compiler::IREE::Encoding::EncodingRole::LHS ||
- rhsEncoding.getRole().getValue() !=
- mlir::iree_compiler::IREE::Encoding::EncodingRole::RHS ||
- resultEncoding.getRole().getValue() !=
- mlir::iree_compiler::IREE::Encoding::EncodingRole::RESULT) {
+ if (lhsEncoding.getOperandIndex().getValue() != MATMUL_LHS ||
+ rhsEncoding.getOperandIndex().getValue() != MATMUL_RHS ||
+ resultEncoding.getOperandIndex().getValue() != MATMUL_RESULT) {
return failure();
}
diff --git a/compiler/src/iree/compiler/Codegen/Common/test/convert_to_destination_passing_style.mlir b/compiler/src/iree/compiler/Codegen/Common/test/convert_to_destination_passing_style.mlir
index 540c2d5..eb7e978 100644
--- a/compiler/src/iree/compiler/Codegen/Common/test/convert_to_destination_passing_style.mlir
+++ b/compiler/src/iree/compiler/Codegen/Common/test/convert_to_destination_passing_style.mlir
@@ -754,7 +754,7 @@
%c512 = arith.constant 512 : index
%c0 = arith.constant 0 : index
%c16 = arith.constant 16 : index
- %0:2 = iree_codegen.query_tile_sizes tensor<16x16xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %0:2 = iree_codegen.query_tile_sizes tensor<16x16xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%1 = affine.apply affine_map<()[s0] -> (16 ceildiv s0)>()[%0#0]
%2 = affine.apply affine_map<()[s0] -> (16 ceildiv s0)>()[%0#1]
%3 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c512) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<?x?x?x?xi32>>{%1, %2, %0#0, %0#1}
diff --git a/compiler/src/iree/compiler/Codegen/Common/test/materialize_encoding_into_nop.mlir b/compiler/src/iree/compiler/Codegen/Common/test/materialize_encoding_into_nop.mlir
index b98b25c..a527fd1 100644
--- a/compiler/src/iree/compiler/Codegen/Common/test/materialize_encoding_into_nop.mlir
+++ b/compiler/src/iree/compiler/Codegen/Common/test/materialize_encoding_into_nop.mlir
@@ -4,8 +4,8 @@
#map1 = affine_map<(d0, d1, d2) -> (d2, d1)>
#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>
func.func @pack_unpack_gemm_lhs(%arg0 : tensor<?x?xf32>) -> tensor<?x?xf32> {
- %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %1 = iree_encoding.unset_encoding %0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %1 = iree_encoding.unset_encoding %0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}
// CHECK: func @pack_unpack_gemm_lhs(
@@ -23,20 +23,20 @@
^bb0(%b0: index, %b1 : index):
tensor.yield %pad_value : f32
} : tensor<100x250xf32> to tensor<104x252xf32>
- %lhs = iree_encoding.set_encoding %pad_lhs : tensor<104x252xf32> -> tensor<104x252xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %lhs = iree_encoding.set_encoding %pad_lhs : tensor<104x252xf32> -> tensor<104x252xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%pad_rhs = tensor.pad %arg1 low[0, 0] high[2, 4] {
^bb0(%b0: index, %b1 : index):
tensor.yield %pad_value : f32
} : tensor<250x500xf32> to tensor<252x504xf32>
- %rhs = iree_encoding.set_encoding %pad_rhs : tensor<252x504xf32> -> tensor<252x504xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %rhs = iree_encoding.set_encoding %pad_rhs : tensor<252x504xf32> -> tensor<252x504xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
%pad_output = tensor.pad %arg2 low[0, 0] high[4, 4] {
^bb0(%b0: index, %b1 : index):
tensor.yield %pad_value : f32
} : tensor<100x500xf32> to tensor<104x504xf32>
- %output = iree_encoding.set_encoding %pad_output : tensor<104x504xf32> -> tensor<104x504xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %gemm_packed = linalg.matmul ins(%lhs, %rhs : tensor<104x252xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<252x504xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%output : tensor<104x504xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<104x504xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %gemm = iree_encoding.unset_encoding %gemm_packed : tensor<104x504xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<104x504xf32>
+ %output = iree_encoding.set_encoding %pad_output : tensor<104x504xf32> -> tensor<104x504xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %gemm_packed = linalg.matmul ins(%lhs, %rhs : tensor<104x252xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<252x504xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%output : tensor<104x504xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<104x504xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %gemm = iree_encoding.unset_encoding %gemm_packed : tensor<104x504xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<104x504xf32>
%result = tensor.extract_slice %gemm[0, 0] [100, 500] [1, 1] : tensor<104x504xf32> to tensor<100x500xf32>
return %result : tensor<100x500xf32>
}
@@ -60,12 +60,12 @@
#map1 = affine_map<(d0, d1, d2) -> (d2, d1)>
#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>
func.func @gemm_dynamic(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>, %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32> {
- %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %1 = iree_encoding.set_encoding %arg1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %2 = iree_encoding.set_encoding %arg2 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %3 = linalg.matmul ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %1 = iree_encoding.set_encoding %arg1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %2 = iree_encoding.set_encoding %arg2 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %3 = linalg.matmul ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
return %4 : tensor<?x?xf32>
}
// CHECK: func @gemm_dynamic(
@@ -88,14 +88,14 @@
%cst = arith.constant 0.0 : f32
%d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
- %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %1 = iree_encoding.set_encoding %arg1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %2 = tensor.empty(%d0, %d1) : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %3 = linalg.fill ins(%cst : f32) outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %4 = linalg.matmul ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%3 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %5 = iree_encoding.unset_encoding %4 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %1 = iree_encoding.set_encoding %arg1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %2 = tensor.empty(%d0, %d1) : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %3 = linalg.fill ins(%cst : f32) outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %4 = linalg.matmul ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %5 = iree_encoding.unset_encoding %4 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?xf32>
return %5 : tensor<?x?xf32>
}
// CHECK: func @gemm_fill_dynamic(
@@ -119,12 +119,12 @@
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>
#map2 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
func.func @batch_matmul(%arg0 : tensor<128x80x32xf32>, %arg1 : tensor<128x32x320xf32>, %arg2 : tensor<128x80x320xf32>) -> tensor<128x80x320xf32> {
- %0 = iree_encoding.set_encoding %arg0 : tensor<128x80x32xf32> -> tensor<128x80x32xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %1 = iree_encoding.set_encoding %arg1 : tensor<128x32x320xf32> -> tensor<128x32x320xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %2 = iree_encoding.set_encoding %arg2 : tensor<128x80x320xf32> -> tensor<128x80x320xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %3 = linalg.batch_matmul ins(%0, %1 : tensor<128x80x32xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<128x32x320xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%2 : tensor<128x80x320xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<128x80x320xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %4 = iree_encoding.unset_encoding %3 : tensor<128x80x320xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<128x80x320xf32>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<128x80x32xf32> -> tensor<128x80x32xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %1 = iree_encoding.set_encoding %arg1 : tensor<128x32x320xf32> -> tensor<128x32x320xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %2 = iree_encoding.set_encoding %arg2 : tensor<128x80x320xf32> -> tensor<128x80x320xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %3 = linalg.batch_matmul ins(%0, %1 : tensor<128x80x32xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<128x32x320xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%2 : tensor<128x80x320xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<128x80x320xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %4 = iree_encoding.unset_encoding %3 : tensor<128x80x320xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<128x80x320xf32>
return %4 : tensor<128x80x320xf32>
}
// CHECK: func @batch_matmul(
@@ -142,12 +142,12 @@
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>
#map2 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
func.func @batch_matmul_dynamic(%arg0 : tensor<?x?x?xf32>, %arg1 : tensor<?x?x?xf32>, %arg2 : tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
- %0 = iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %1 = iree_encoding.set_encoding %arg1 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %2 = iree_encoding.set_encoding %arg2 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %3 = linalg.batch_matmul ins(%0, %1 : tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%2 : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %4 = iree_encoding.unset_encoding %3 : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xf32>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %1 = iree_encoding.set_encoding %arg1 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %2 = iree_encoding.set_encoding %arg2 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %3 = linalg.batch_matmul ins(%0, %1 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%2 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %4 = iree_encoding.unset_encoding %3 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xf32>
return %4 : tensor<?x?x?xf32>
}
// CHECK: func @batch_matmul_dynamic(
@@ -172,14 +172,14 @@
%d0 = tensor.dim %arg0, %c0 : tensor<?x?x?xf32>
%d1 = tensor.dim %arg0, %c1 : tensor<?x?x?xf32>
%d2 = tensor.dim %arg1, %c2 : tensor<?x?x?xf32>
- %0 = iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %1 = iree_encoding.set_encoding %arg1 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %2 = tensor.empty(%d0, %d1, %d2) : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %3 = linalg.fill ins(%cst : f32) outs(%2 : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %4 = linalg.batch_matmul ins(%0, %1 : tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
- outs(%3 : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
- %5 = iree_encoding.unset_encoding %4 : tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xf32>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %1 = iree_encoding.set_encoding %arg1 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %2 = tensor.empty(%d0, %d1, %d2) : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %3 = linalg.fill ins(%cst : f32) outs(%2 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %4 = linalg.batch_matmul ins(%0, %1 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>, tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>)
+ outs(%3 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>) -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>>
+ %5 = iree_encoding.unset_encoding %4 : tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], user_indexing_maps = [#map, #map1, #map2]>> -> tensor<?x?x?xf32>
return %5 : tensor<?x?x?xf32>
}
// CHECK: func @batch_matmul_fill_dynamic(
@@ -204,10 +204,10 @@
func.func @drop_encoding_for_hal_flow_ops_static() {
%c0 = arith.constant 0 : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<1x1xf32>>
- %1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<1x1xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1x1xf32>, matmul_narrow_M = 1 : index, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>>
+ %1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<1x1xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1x1xf32>, matmul_narrow_M = 1 : index, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>>
%2 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [1, 1], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<1x1xf32>> -> tensor<1x1xf32>
- %3 = iree_encoding.set_encoding %2 : tensor<1x1xf32> -> tensor<1x1xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1x1xf32>, matmul_narrow_M = 1 : index, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
- flow.dispatch.tensor.store %3, %1, offsets = [0, 0], sizes = [1, 1], strides = [1, 1] : tensor<1x1xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1x1xf32>, matmul_narrow_M = 1 : index, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>> -> !flow.dispatch.tensor<writeonly:tensor<1x1xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1x1xf32>, matmul_narrow_M = 1 : index, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>>
+ %3 = iree_encoding.set_encoding %2 : tensor<1x1xf32> -> tensor<1x1xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1x1xf32>, matmul_narrow_M = 1 : index, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ flow.dispatch.tensor.store %3, %1, offsets = [0, 0], sizes = [1, 1], strides = [1, 1] : tensor<1x1xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1x1xf32>, matmul_narrow_M = 1 : index, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>> -> !flow.dispatch.tensor<writeonly:tensor<1x1xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1x1xf32>, matmul_narrow_M = 1 : index, matmul_narrow_N = 1 : index, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>>
return
}
// CHECK-LABEL: func.func @drop_encoding_for_hal_flow_ops_static
@@ -238,10 +238,10 @@
%14 = flow.dispatch.workload.ordinal %8, 0 : index
%15 = flow.dispatch.workload.ordinal %13, 1 : index
%16 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<?x?xbf16>>{%14, %15}
- %17 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<?x?xbf16>, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>>{%14, %15}
+ %17 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], original_type = tensor<?x?xbf16>, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>>{%14, %15}
%18 = flow.dispatch.tensor.load %16, offsets = [0, 0], sizes = [%14, %15], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<?x?xbf16>>{%14, %15} -> tensor<?x?xbf16>
- %19 = iree_encoding.set_encoding %18 : tensor<?x?xbf16> -> tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<?x?xbf16>, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
- flow.dispatch.tensor.store %19, %17, offsets = [0, 0], sizes = [%14, %15], strides = [1, 1] : tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<?x?xbf16>, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<?x?xbf16>, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>>{%14, %15}
+ %19 = iree_encoding.set_encoding %18 : tensor<?x?xbf16> -> tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], original_type = tensor<?x?xbf16>, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>
+ flow.dispatch.tensor.store %19, %17, offsets = [0, 0], sizes = [%14, %15], strides = [1, 1] : tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], original_type = tensor<?x?xbf16>, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16], original_type = tensor<?x?xbf16>, user_indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>]>>>{%14, %15}
return
}
// CHECK-LABEL: func.func @drop_encoding_for_hal_flow_ops_dynamic
diff --git a/compiler/src/iree/compiler/Codegen/Common/test/tile_and_distribute_to_workgroups.mlir b/compiler/src/iree/compiler/Codegen/Common/test/tile_and_distribute_to_workgroups.mlir
index fe1463a..c9c7d86 100644
--- a/compiler/src/iree/compiler/Codegen/Common/test/tile_and_distribute_to_workgroups.mlir
+++ b/compiler/src/iree/compiler/Codegen/Common/test/tile_and_distribute_to_workgroups.mlir
@@ -2232,7 +2232,7 @@
%cst = arith.constant dense<[-918, -4433, 87, -234, -21393, 7738, 529, -8835, -16817, -375, -199, 572, 5082, 15569, -186, 4955]> : tensor<16xi32>
%c12544 = arith.constant 12544 : index
%c16 = arith.constant 16 : index
- %0:2 = iree_codegen.query_tile_sizes tensor<12544x16xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %0:2 = iree_codegen.query_tile_sizes tensor<12544x16xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%1 = affine.apply affine_map<()[s0] -> (12544 ceildiv s0)>()[%0#0]
%2 = affine.apply affine_map<()[s0] -> (16 ceildiv s0)>()[%0#1]
%3 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c200960) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<?x?x?x?xi32>>{%1, %2, %0#0, %0#1}
diff --git a/compiler/src/iree/compiler/Codegen/VMVX/test/pipeline.mlir b/compiler/src/iree/compiler/Codegen/VMVX/test/pipeline.mlir
index 510d4ba..ea0b5c0 100644
--- a/compiler/src/iree/compiler/Codegen/VMVX/test/pipeline.mlir
+++ b/compiler/src/iree/compiler/Codegen/VMVX/test/pipeline.mlir
@@ -10,15 +10,15 @@
%c256 = arith.constant 256 : index
%c512 = arith.constant 512 : index
%c16 = arith.constant 16 : index
- %0:2 = iree_codegen.query_tile_sizes tensor<16x16xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %0:2 = iree_codegen.query_tile_sizes tensor<16x16xi8, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%1 = affine.apply #map3()[%0#0]
%2 = affine.apply #map3()[%0#1]
%3 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<?x?x?x?xi8>>{%1, %2, %0#0, %0#1}
- %4:2 = iree_codegen.query_tile_sizes tensor<16x16xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %4:2 = iree_codegen.query_tile_sizes tensor<16x16xi8, #iree_encoding.encoding<operand_index = 1, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%5 = affine.apply #map3()[%4#0]
%6 = affine.apply #map3()[%4#1]
%7 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c256) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<?x?x?x?xi8>>{%5, %6, %4#0, %4#1}
- %8:2 = iree_codegen.query_tile_sizes tensor<16x16xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
+ %8:2 = iree_codegen.query_tile_sizes tensor<16x16xi32, #iree_encoding.encoding<operand_index = 2, element_types = [i8, i8, i32], user_indexing_maps = [#map, #map1, #map2]>> -> index, index
%9 = affine.apply #map3()[%8#0]
%10 = affine.apply #map3()[%8#1]
%11 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c512) : !flow.dispatch.tensor<readwrite:tensor<?x?x?x?xi32>>{%9, %10, %8#0, %8#1}
diff --git a/compiler/src/iree/compiler/Codegen/VMVX/test/select_lowering_strategy.mlir b/compiler/src/iree/compiler/Codegen/VMVX/test/select_lowering_strategy.mlir
index 94564eb..c8eebf4 100644
--- a/compiler/src/iree/compiler/Codegen/VMVX/test/select_lowering_strategy.mlir
+++ b/compiler/src/iree/compiler/Codegen/VMVX/test/select_lowering_strategy.mlir
@@ -168,7 +168,7 @@
%cst = arith.constant 0.000000e+00 : f32
%c0 = arith.constant 0 : index
%0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<1024x2048xf32>>
- %1:2 = iree_codegen.query_tile_sizes tensor<1024x2048xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1024x2048xf32>>> -> index, index
+ %1:2 = iree_codegen.query_tile_sizes tensor<1024x2048xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1024x2048xf32>>> -> index, index
%2 = affine.apply #map()[%1#0]
%3 = affine.apply #map1()[%1#1]
%4 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<?x?x?x?xf32>>{%2, %3, %1#0, %1#1}
@@ -179,12 +179,12 @@
%15 = arith.addf %in, %in : f32
linalg.yield %15 : f32
} -> tensor<1024x2048xf32>
- %8:2 = iree_codegen.query_tile_sizes tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1024x2048xf32>>> -> index, index
+ %8:2 = iree_codegen.query_tile_sizes tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1024x2048xf32>>> -> index, index
%9 = affine.apply #map()[%8#0]
%10 = affine.apply #map1()[%8#1]
%11 = tensor.empty(%9, %10, %8#0, %8#1) : tensor<?x?x?x?xf32>
%pack = tensor.pack %7 padding_value(%cst : f32) inner_dims_pos = [0, 1] inner_tiles = [%8#0, %8#1] into %11 : tensor<1024x2048xf32> -> tensor<?x?x?x?xf32>
- %12:2 = iree_codegen.query_tile_sizes tensor<1024x2048xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<1024x2048xf32>>> -> index, index
+ %12:2 = iree_codegen.query_tile_sizes tensor<1024x2048xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<1024x2048xf32>>> -> index, index
%13 = affine.apply #map()[%12#0]
%14 = affine.apply #map1()[%12#1]
flow.dispatch.tensor.store %pack, %4, offsets = [0, 0, 0, 0], sizes = [%13, %14, %12#0, %12#1], strides = [1, 1, 1, 1] : tensor<?x?x?x?xf32> -> !flow.dispatch.tensor<writeonly:tensor<?x?x?x?xf32>>{%13, %14, %12#0, %12#1}
diff --git a/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingBase.td b/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingBase.td
index cb3930c..9039d08 100644
--- a/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingBase.td
+++ b/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingBase.td
@@ -44,20 +44,6 @@
class IREEEncoding_EnumAttr<EnumAttrInfo enumInfo, string name = "">
: EnumAttr<IREEEncoding_Dialect, enumInfo, name>;
-def LHS : I32EnumAttrCase<"LHS", 0>;
-def RHS : I32EnumAttrCase<"RHS", 1>;
-def RESULT : I32EnumAttrCase<"RESULT", 2>;
-
-def EncodingRole : IREEEncoding_I32EnumAttr<"EncodingRole",
- "Describes the role of the tensor as an operand or a result of an operation.", [
- LHS,
- RHS,
- RESULT,
- ]>;
-
-def EncodingRoleAttr :
- IREEEncoding_EnumAttr<EncodingRole, "role">;
-
def EncodingAttr :
IREEEncoding_Attr<"Encoding"> {
let mnemonic = "encoding";
@@ -76,7 +62,7 @@
let assemblyFormat = "`<` struct(params) `>`";
let parameters = (ins
- AttrParameter<"EncodingRoleAttr", "role of this tensor as an operand">:$role,
+ AttrParameter<"IntegerAttr", "this tensor operand's index in the parameter list">:$operand_index,
AttrParameter<"ArrayAttr", "element types of the user's operands">:$element_types,
OptionalParameter<"TypeAttr", "type of the original tensor type before padding">:$original_type,
// TODO(#15466): generalize matmul_narrow_{M,N} into a list?
@@ -88,7 +74,7 @@
);
let builders = [
- AttrBuilder<(ins "EncodingRole":$role,
+ AttrBuilder<(ins "int64_t":$operandIndex,
"ArrayRef<Type>":$elemTypes, "Type":$origType,
CArg<"std::optional<int64_t>", "{}">:$matmulNarrowM,
CArg<"std::optional<int64_t>", "{}">:$matmulNarrowN,
@@ -97,12 +83,12 @@
];
let extraClassDeclaration = [{
- /// Returns the indexing map used by the role in the encoding.
- AffineMap getMapForRole();
+ /// Returns the indexing map used by the operand index in the encoding.
+ AffineMap getMapForOperandIndex();
/// Given the dim position of the encoding `user_indexing_maps`, returns the
/// matching index of the given encoding's tensor.
- unsigned mapDimToRoleIndex(int64_t dimPos);
+ unsigned mapDimToOperandIndex(int64_t dimPos);
/// Returns an integer array with values in `round_dims_to`.
ArrayRef<int64_t> getRoundDimsToArray();
diff --git a/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingOps.cpp b/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingOps.cpp
index 77700c0..936c850 100644
--- a/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingOps.cpp
+++ b/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingOps.cpp
@@ -96,7 +96,7 @@
// encoding.encoding
//===----------------------------------------------------------------------===//
-EncodingAttr EncodingAttr::get(MLIRContext *ctx, EncodingRole role,
+EncodingAttr EncodingAttr::get(MLIRContext *ctx, int64_t operandIndex,
ArrayRef<Type> elemTypes, Type origType,
std::optional<int64_t> matmulNarrowM,
std::optional<int64_t> matmulNarrowN,
@@ -106,32 +106,31 @@
auto optionalToAttr = [&](std::optional<int64_t> x) {
return x ? b.getIndexAttr(*x) : IntegerAttr();
};
- auto roleAttr = EncodingRoleAttr::get(ctx, role);
auto origTypeAttr = origType ? TypeAttr::get(origType) : TypeAttr();
auto roundDimsToAttr = roundDimsTo.empty()
? DenseI64ArrayAttr()
: b.getDenseI64ArrayAttr(roundDimsTo);
- return get(ctx, roleAttr, b.getTypeArrayAttr(elemTypes), origTypeAttr,
- optionalToAttr(matmulNarrowM), optionalToAttr(matmulNarrowN),
- b.getAffineMapArrayAttr(maps), roundDimsToAttr);
+ return get(ctx, b.getIndexAttr(operandIndex), b.getTypeArrayAttr(elemTypes),
+ origTypeAttr, optionalToAttr(matmulNarrowM),
+ optionalToAttr(matmulNarrowN), b.getAffineMapArrayAttr(maps),
+ roundDimsToAttr);
}
-AffineMap EncodingAttr::getMapForRole() {
- EncodingRole role = getRole().getValue();
- switch (role) {
- case EncodingRole::LHS:
- return llvm::cast<AffineMapAttr>(getUserIndexingMaps()[0]).getAffineMap();
- case EncodingRole::RHS:
- return llvm::cast<AffineMapAttr>(getUserIndexingMaps()[1]).getAffineMap();
- case EncodingRole::RESULT:
- return llvm::cast<AffineMapAttr>(getUserIndexingMaps()[2]).getAffineMap();
+AffineMap EncodingAttr::getMapForOperandIndex() {
+ auto index = getOperandIndex().getValue().getZExtValue();
+ switch (index) {
+ case MATMUL_LHS:
+ case MATMUL_RHS:
+ case MATMUL_RESULT:
+ return llvm::cast<AffineMapAttr>(getUserIndexingMaps()[index])
+ .getAffineMap();
default:
return AffineMap();
}
}
-unsigned EncodingAttr::mapDimToRoleIndex(int64_t dimPos) {
- AffineMap map = getMapForRole();
+unsigned EncodingAttr::mapDimToOperandIndex(int64_t dimPos) {
+ AffineMap map = getMapForOperandIndex();
auto idx = map.getResultPosition(getAffineDimExpr(dimPos, getContext()));
assert(idx.has_value());
return idx.value();
@@ -163,6 +162,21 @@
return linalg::inferContractionDims(indexingMaps);
}
+std::string stringifyOperandIndex(IntegerAttr valueAttr) {
+ auto value = valueAttr.getValue().getZExtValue();
+ switch (value) {
+ case MATMUL_LHS:
+ return "LHS";
+ case MATMUL_RHS:
+ return "RHS";
+ case MATMUL_RESULT:
+ return "RESULT";
+ default:
+ assert(false && "invalid index");
+ return "";
+ }
+}
+
} // namespace mlir::iree_compiler::IREE::Encoding
// clang-format off
diff --git a/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingOps.h b/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingOps.h
index 96e5dfc..b97562d 100644
--- a/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingOps.h
+++ b/compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingOps.h
@@ -42,6 +42,14 @@
/// Returns the ContractionDimensions for the encoding user_indexing_maps.
FailureOr<linalg::ContractionDimensions>
getEncodingContractionDims(EncodingAttr encoding);
+
+// Assign a name to operand indices for clarity
+const int64_t MATMUL_LHS = 0;
+const int64_t MATMUL_RHS = 1;
+const int64_t MATMUL_RESULT = 2;
+/// Convert operand index to strings for printing
+std::string stringifyOperandIndex(IntegerAttr);
+
} // namespace mlir::iree_compiler::IREE::Encoding
#endif // IREE_COMPILER_DIALECT_ENCODING_IR_ENCODINGOPS_H_
diff --git a/compiler/src/iree/compiler/Dialect/Encoding/IR/test/invalid.mlir b/compiler/src/iree/compiler/Dialect/Encoding/IR/test/invalid.mlir
index 13b111b..b129fb6 100644
--- a/compiler/src/iree/compiler/Dialect/Encoding/IR/test/invalid.mlir
+++ b/compiler/src/iree/compiler/Dialect/Encoding/IR/test/invalid.mlir
@@ -8,9 +8,9 @@
// -----
-func.func @illegal_set_encoding_op_with_source_encoding(%arg0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>) -> tensor<?x?xf32> {
+func.func @illegal_set_encoding_op_with_source_encoding(%arg0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>) -> tensor<?x?xf32> {
// expected-error @+1 {{source of set_encoding op cannot have a tensor encoding}}
- %0 = iree_encoding.set_encoding %arg0: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
+ %0 = iree_encoding.set_encoding %arg0: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
@@ -24,18 +24,18 @@
// -----
-func.func @illegal_set_encoding_op_with_rank_change(%arg0 : tensor<?x?xf32>) -> tensor<?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+func.func @illegal_set_encoding_op_with_rank_change(%arg0 : tensor<?x?xf32>) -> tensor<?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
// expected-error @+1 {{cannot change the rank of the tensor}}
- %0 = iree_encoding.set_encoding %arg0: tensor<?x?xf32> -> tensor<?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- return %0 : tensor<?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ %0 = iree_encoding.set_encoding %arg0: tensor<?x?xf32> -> tensor<?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ return %0 : tensor<?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// -----
-func.func @illegal_set_encoding_op_with_shape_change(%arg0 : tensor<10x20xf32>) -> tensor<20x30xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+func.func @illegal_set_encoding_op_with_shape_change(%arg0 : tensor<10x20xf32>) -> tensor<20x30xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
// expected-error @+1 {{expected to preserve the logical shape of the tensor}}
- %0 = iree_encoding.set_encoding %arg0: tensor<10x20xf32> -> tensor<20x30xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- return %0 : tensor<20x30xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ %0 = iree_encoding.set_encoding %arg0: tensor<10x20xf32> -> tensor<20x30xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ return %0 : tensor<20x30xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// -----
@@ -48,10 +48,10 @@
// -----
-func.func @illegal_unset_encoding_op_with_result_encoding(%arg0 : tensor<?x?xf32>) -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+func.func @illegal_unset_encoding_op_with_result_encoding(%arg0 : tensor<?x?xf32>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
// expected-error @+1 {{result of unset_encoding op cannot have a tensor encoding}}
- %0 = iree_encoding.unset_encoding %arg0: tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- return %0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ %0 = iree_encoding.unset_encoding %arg0: tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ return %0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// -----
@@ -64,16 +64,16 @@
// -----
-func.func @illegal_unset_encoding_op_with_rank_change(%arg0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>) -> tensor<?xf32> {
+func.func @illegal_unset_encoding_op_with_rank_change(%arg0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>) -> tensor<?xf32> {
// expected-error @+1 {{cannot change the rank of the tensor}}
- %0 = iree_encoding.unset_encoding %arg0: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<?xf32>
+ %0 = iree_encoding.unset_encoding %arg0: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> -> tensor<?xf32>
return %0 : tensor<?xf32>
}
// -----
-func.func @illegal_unset_encoding_op_with_shape_change(%arg0 : tensor<20x30xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>) -> tensor<10x20xf32> {
+func.func @illegal_unset_encoding_op_with_shape_change(%arg0 : tensor<20x30xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>) -> tensor<10x20xf32> {
// expected-error @+1 {{expected to preserve the logical shape of the tensor}}
- %0 = iree_encoding.unset_encoding %arg0: tensor<20x30xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<10x20xf32>
+ %0 = iree_encoding.unset_encoding %arg0: tensor<20x30xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> -> tensor<10x20xf32>
return %0 : tensor<10x20xf32>
}
diff --git a/compiler/src/iree/compiler/Dialect/Encoding/IR/test/roundtrip.mlir b/compiler/src/iree/compiler/Dialect/Encoding/IR/test/roundtrip.mlir
index 7b81690..cd6048d 100644
--- a/compiler/src/iree/compiler/Dialect/Encoding/IR/test/roundtrip.mlir
+++ b/compiler/src/iree/compiler/Dialect/Encoding/IR/test/roundtrip.mlir
@@ -1,55 +1,55 @@
// RUN: iree-opt --split-input-file %s | FileCheck %s
// CHECK: @set_encoding_ops(%[[ARG0:.+]]: tensor<?x?xf32>)
-func.func @set_encoding_ops(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
- // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- return %0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+func.func @set_encoding_ops(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
+ // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ return %0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// -----
// CHECK: @set_encoding_ops_mixed_dynamic_static(%[[ARG0:.+]]: tensor<?x10xf32>)
-func.func @set_encoding_ops_mixed_dynamic_static(%arg0: tensor<?x10xf32>) -> tensor<20x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
- // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x10xf32> -> tensor<20x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- %0 = iree_encoding.set_encoding %arg0 : tensor<?x10xf32> -> tensor<20x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- return %0 : tensor<20x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+func.func @set_encoding_ops_mixed_dynamic_static(%arg0: tensor<?x10xf32>) -> tensor<20x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
+ // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x10xf32> -> tensor<20x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<?x10xf32> -> tensor<20x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ return %0 : tensor<20x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// -----
// CHECK: @set_encoding_with_batch_matmul_user(%[[ARG0:.+]]: tensor<?x?x?xf32>)
func.func @set_encoding_with_batch_matmul_user(%arg0: tensor<?x?x?xf32>) {
- // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32]>>
- iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32]>>
- // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32]>>
- iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32]>>
- // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16]>>
- iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16]>>
- // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32]>>
- iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32]>>
- // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16]>>
- iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16]>>
+ // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
+ iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [i8, i8, i32]>>
+ iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [i8, i8, i32]>>
+ // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f16, f16, f32]>>
+ iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f32]>>
+ // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f16, f16, f16]>>
+ iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f16, f16, f16]>>
+ // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [bf16, bf16, f32]>>
+ iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, f32]>>
+ // CHECK: iree_encoding.set_encoding %[[ARG0]] : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [bf16, bf16, bf16]>>
+ iree_encoding.set_encoding %arg0 : tensor<?x?x?xf32> -> tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [bf16, bf16, bf16]>>
return
}
// -----
-// CHECK: @unset_encoding_ops(%[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>)
-func.func @unset_encoding_ops(%arg0: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>) -> tensor<?x?xf32> {
- // CHECK: iree_encoding.unset_encoding %[[ARG0]] : tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
- %0 = iree_encoding.unset_encoding %arg0 : tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
+// CHECK: @unset_encoding_ops(%[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [f32, f32, f32]>>)
+func.func @unset_encoding_ops(%arg0: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>>) -> tensor<?x?xf32> {
+ // CHECK: iree_encoding.unset_encoding %[[ARG0]] : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
+ %0 = iree_encoding.unset_encoding %arg0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
// -----
-// CHECK: @unset_encoding_ops_mixed_dynamic_static(%[[ARG0:.+]]: tensor<10x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>)
-func.func @unset_encoding_ops_mixed_dynamic_static(%arg0: tensor<10x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>) -> tensor<?x20xf32> {
- // CHECK: iree_encoding.unset_encoding %[[ARG0]] : tensor<10x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>
- %0 = iree_encoding.unset_encoding %arg0 : tensor<10x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>> -> tensor<?x20xf32>
+// CHECK: @unset_encoding_ops_mixed_dynamic_static(%[[ARG0:.+]]: tensor<10x?xf32, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [f32, f32, f32]>>)
+func.func @unset_encoding_ops_mixed_dynamic_static(%arg0: tensor<10x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>>) -> tensor<?x20xf32> {
+ // CHECK: iree_encoding.unset_encoding %[[ARG0]] : tensor<10x?xf32, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [f32, f32, f32]>>
+ %0 = iree_encoding.unset_encoding %arg0 : tensor<10x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>> -> tensor<?x20xf32>
return %0 : tensor<?x20xf32>
}
@@ -57,14 +57,14 @@
func.func @encoding_tensors_with_ops(%arg0 : tensor<?x?xf32>,
%arg1 : tensor<?x?xf32>, %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32> {
- %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- %1 = iree_encoding.set_encoding %arg1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>
- %2 = iree_encoding.set_encoding %arg2 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
+ %0 = iree_encoding.set_encoding %arg0 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ %1 = iree_encoding.set_encoding %arg1 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>>
+ %2 = iree_encoding.set_encoding %arg2 : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
%3 = linalg.matmul
- ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>, tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>)
- outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
- %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
+ ins(%0, %1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>, tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>>)
+ outs(%2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
+ %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
return %4 : tensor<?x?xf32>
}
// CHECK-LABEL: func.func @encoding_tensors_with_ops
@@ -72,11 +72,11 @@
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[ARG0]]
-// CHECK-SAME: tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+// CHECK-SAME: tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[ARG1]]
-// CHECK-SAME: tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>
+// CHECK-SAME: tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [f32, f32, f32]>>
// CHECK: %[[OUT:.+]] = iree_encoding.set_encoding %[[ARG2]]
-// CHECK-SAME: tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
+// CHECK-SAME: tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : i64, element_types = [f32, f32, f32]>>
// CHECK: %[[GEMM:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUT]] :
diff --git a/compiler/src/iree/compiler/Dialect/Flow/Transforms/AnnotateDispatches.cpp b/compiler/src/iree/compiler/Dialect/Flow/Transforms/AnnotateDispatches.cpp
index e18a71a..aef1bf5 100644
--- a/compiler/src/iree/compiler/Dialect/Flow/Transforms/AnnotateDispatches.cpp
+++ b/compiler/src/iree/compiler/Dialect/Flow/Transforms/AnnotateDispatches.cpp
@@ -387,20 +387,20 @@
auto opName = getOpNameWithoutDialectName(op);
auto encoding = cast<IREE::Encoding::EncodingAttr>(
op.getResultType().getEncoding());
- auto role = stringifyEnum(encoding.getRole().getValue());
+ auto index =
+ IREE::Encoding::stringifyOperandIndex(encoding.getOperandIndex());
ArrayRef<int64_t> shape = op.getSourceType().getShape();
- bestSummary =
- opName + "_" + role.str() + "_" + loopRangesToString(shape);
+ bestSummary = opName + "_" + index + "_" + loopRangesToString(shape);
;
})
.Case<IREE::Encoding::UnsetEncodingOp>([&](auto op) {
auto opName = getOpNameWithoutDialectName(op);
auto encoding = cast<IREE::Encoding::EncodingAttr>(
op.getSourceType().getEncoding());
- auto role = stringifyEnum(encoding.getRole().getValue());
+ auto index =
+ IREE::Encoding::stringifyOperandIndex(encoding.getOperandIndex());
ArrayRef<int64_t> shape = op.getResultType().getShape();
- bestSummary =
- opName + "_" + role.str() + "_" + loopRangesToString(shape);
+ bestSummary = opName + "_" + index + "_" + loopRangesToString(shape);
})
.Case<IREE::LinalgExt::LinalgExtOp>(
[&](auto op) { bestSummary = summarizeLinalgExtOp(op); })
diff --git a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/annotate_dispatches.mlir b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/annotate_dispatches.mlir
index c891c04..ee3efb7 100644
--- a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/annotate_dispatches.mlir
+++ b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/annotate_dispatches.mlir
@@ -115,19 +115,19 @@
// CHECK: flow.executable.export public @dispatch0_map_DxD_f32
flow.executable.export public @dispatch0
builtin.module {
- func.func @dispatch0(%arg0: !flow.dispatch.tensor<readonly:tensor<?x?xf32>>, %arg1: !flow.dispatch.tensor<readonly:tensor<?x?xf32>>, %arg2: index, %arg3: index, %arg4: index, %arg5: index, %arg6: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>) {
+ func.func @dispatch0(%arg0: !flow.dispatch.tensor<readonly:tensor<?x?xf32>>, %arg1: !flow.dispatch.tensor<readonly:tensor<?x?xf32>>, %arg2: index, %arg3: index, %arg4: index, %arg5: index, %arg6: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>>) {
%0 = flow.dispatch.workload.ordinal %arg2, 0 : index
%1 = flow.dispatch.workload.ordinal %arg3, 1 : index
%2 = flow.dispatch.workload.ordinal %arg4, 2 : index
%3 = flow.dispatch.workload.ordinal %arg5, 3 : index
%4 = flow.dispatch.tie_shape %arg0 : !flow.dispatch.tensor<readonly:tensor<?x?xf32>>{%0, %1}
%5 = flow.dispatch.tie_shape %arg1 : !flow.dispatch.tensor<readonly:tensor<?x?xf32>>{%2, %3}
- %6 = flow.dispatch.tie_shape %arg6 : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>{%2, %3}
+ %6 = flow.dispatch.tie_shape %arg6 : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>>{%2, %3}
%7 = flow.dispatch.tensor.load %4, offsets = [0, 0], sizes = [%0, %1], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<?x?xf32>>{%0, %1} -> tensor<?x?xf32>
%8 = flow.dispatch.tensor.load %5, offsets = [0, 0], sizes = [%2, %3], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<?x?xf32>>{%2, %3} -> tensor<?x?xf32>
%mapped = linalg.map { math.absf } ins(%7 : tensor<?x?xf32>) outs(%8 : tensor<?x?xf32>)
- %9 = iree_encoding.set_encoding %mapped : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- flow.dispatch.tensor.store %9, %6, offsets = [0, 0], sizes = [%2, %3], strides = [1, 1] : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>{%arg4, %arg5}
+ %9 = iree_encoding.set_encoding %mapped : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ flow.dispatch.tensor.store %9, %6, offsets = [0, 0], sizes = [%2, %3], strides = [1, 1] : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>>{%arg4, %arg5}
return
}
}
@@ -136,11 +136,11 @@
// CHECK: flow.executable.export public @dispatch1_unset_encoding_LHS_DxD
flow.executable.export public @dispatch1
builtin.module {
- func.func @dispatch1(%arg0: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>, %arg1: index, %arg2: index, %arg3: !flow.dispatch.tensor<writeonly:tensor<?x?xf32>>) {
- %0 = flow.dispatch.tie_shape %arg0 : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>{%arg1, %arg2}
+ func.func @dispatch1(%arg0: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>>, %arg1: index, %arg2: index, %arg3: !flow.dispatch.tensor<writeonly:tensor<?x?xf32>>) {
+ %0 = flow.dispatch.tie_shape %arg0 : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>>{%arg1, %arg2}
%1 = flow.dispatch.tie_shape %arg3 : !flow.dispatch.tensor<writeonly:tensor<?x?xf32>>{%arg1, %arg2}
- %2 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%arg1, %arg2], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>{%arg1, %arg2} -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- %3 = iree_encoding.unset_encoding %2 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
+ %2 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [%arg1, %arg2], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>>{%arg1, %arg2} -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ %3 = iree_encoding.unset_encoding %2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
flow.dispatch.tensor.store %3, %1, offsets = [0, 0], sizes = [%arg1, %arg2], strides = [1, 1] : tensor<?x?xf32> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32>>{%arg1, %arg2}
return
}
diff --git a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors.mlir b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors.mlir
index 7ec08c9..2741edb 100644
--- a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors.mlir
+++ b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors.mlir
@@ -1875,10 +1875,10 @@
// -----
util.func public @set_encoding_op(%arg0 : tensor<?x?xf32>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
%0 = iree_encoding.set_encoding %arg0
- : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- util.return %0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ util.return %0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// CHECK: util.func public @set_encoding_op
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>
@@ -1890,7 +1890,7 @@
// CHECK-NEXT: %[[INARG:.+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32>>
// CHECK-SAME: %[[INDEXARG0:[a-zA-Z0-9]+]]: index
// CHECK-SAME: %[[INDEXARG1:[a-zA-Z0-9]+]]: index
-// CHECK-SAME: %[[OUTARG:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>
+// CHECK-SAME: %[[OUTARG:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>
// CHECK-DAG: %[[W0:.+]] = flow.dispatch.workload.ordinal %[[INDEXARG0]], 0
// CHECK-DAG: %[[W1:.+]] = flow.dispatch.workload.ordinal %[[INDEXARG1]], 1
// CHECK: %[[LOAD:.+]] = flow.dispatch.tensor.load %[[INARG]]
@@ -1898,7 +1898,7 @@
// CHECK: %[[ENCODING:.+]] = iree_encoding.set_encoding %[[LOAD]]
// CHECK: flow.dispatch.tensor.store %[[ENCODING]], %[[OUTARG]]
// CHECK-SAME: sizes = [%[[W0]], %[[W1]]]
-// CHECK-SAME: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>{%[[W0]], %[[W1]]}
+// CHECK-SAME: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>{%[[W0]], %[[W1]]}
// CHECK: flow.return
// CHECK: count(%[[WL0:[a-zA-Z0-9]+]]: index, %[[WL1:[a-zA-Z0-9]+]]: index)
// CHECK: %[[X:[a-zA-Z0-9]+]], %[[Y:[a-zA-Z0-9]+]], %[[Z:.+]] = flow.dispatch.workgroup_count_from_slice %[[WL0]], %[[WL1]]
@@ -1907,20 +1907,20 @@
// -----
-util.func public @unset_encoding_op(%arg0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>)
+util.func public @unset_encoding_op(%arg0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>)
-> tensor<?x?xf32> {
%0 = iree_encoding.unset_encoding %arg0
- : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
util.return %0 : tensor<?x?xf32>
}
// CHECK: util.func public @unset_encoding_op
-// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK-DAG: %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK: %[[DISPATCH:.+]] = flow.dispatch.workgroups[%[[D0]], %[[D1]]](%[[ARG0]], %[[D0]], %[[D1]])
-// CHECK-NEXT: %[[INARG:.+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>
+// CHECK-NEXT: %[[INARG:.+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>
// CHECK-SAME: %[[INDEXARG0:[a-zA-Z0-9]+]]: index
// CHECK-SAME: %[[INDEXARG1:[a-zA-Z0-9]+]]: index
// CHECK-SAME: %[[OUTARG:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<writeonly:tensor<?x?xf32>>
@@ -1928,7 +1928,7 @@
// CHECK-DAG: %[[D1_W:.+]] = flow.dispatch.workload.ordinal %[[INDEXARG1]], 1
// CHECK: %[[LOAD:.+]] = flow.dispatch.tensor.load %[[INARG]]
// CHECK-SAME: sizes = [%[[D0_W]], %[[D1_W]]]
-// CHECK-SAME: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>{%[[D0_W]], %[[D1_W]]}
+// CHECK-SAME: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>{%[[D0_W]], %[[D1_W]]}
// CHECK: %[[ENCODING:.+]] = iree_encoding.unset_encoding %[[LOAD]]
// CHECK: flow.dispatch.tensor.store %[[ENCODING]], %[[OUTARG]]
// CHECK-SAME: !flow.dispatch.tensor<writeonly:tensor<?x?xf32>>{%[[D0_W]], %[[D1_W]]}
@@ -1942,7 +1942,7 @@
#map = affine_map<()[s0] -> (-s0 + (s0 ceildiv 16) * 16)>
util.func public @pad_and_set_encoding_op(%arg0 : tensor<?x?xf32>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%cst = arith.constant 0.0 : f32
@@ -1955,8 +1955,8 @@
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<?x?xf32>
%encoding = iree_encoding.set_encoding %pad
- : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- util.return %encoding : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ util.return %encoding : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<()[s0] -> ((s0 ceildiv 16) * 16)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<()[s0] -> (-s0 + (s0 ceildiv 16) * 16)>
@@ -1974,7 +1974,7 @@
// CHECK-SAME: %[[INARG:.+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32>>
// CHECK-SAME: %[[PADDED_D0:[a-zA-Z0-9]+]]: index
// CHECK-SAME: %[[PADDED_D1:[a-zA-Z0-9]+]]: index
-// CHECK-SAME: %[[OUTARG:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>
+// CHECK-SAME: %[[OUTARG:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>
// CHECK-DAG: %[[D1_W:.+]] = flow.dispatch.workload.ordinal %[[INDEXARG1]], 0
// CHECK-DAG: %[[D0_W:.+]] = flow.dispatch.workload.ordinal %[[INDEXARG0]], 1
// CHECK-DAG: %[[PADDED_D0_W:.+]] = flow.dispatch.workload.ordinal %[[PADDED_D0]], 2
@@ -1987,7 +1987,7 @@
// CHECK: %[[SET_ENCODING:.+]] = iree_encoding.set_encoding %[[PADDED]]
// CHECK: flow.dispatch.tensor.store %[[SET_ENCODING]], %[[OUTARG]]
// CHECK-SAME: sizes = [%[[PADDED_D0_W]], %[[PADDED_D1_W]]]
-// CHECK-SAME: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>{%[[PADDED_D0_W]], %[[PADDED_D1_W]]}
+// CHECK-SAME: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>{%[[PADDED_D0_W]], %[[PADDED_D1_W]]}
// CHECK: flow.return
// CHECK: count(%[[WL0:[a-zA-Z0-9]+]]: index, %[[WL1:[a-zA-Z0-9]+]]: index,
// CHECK-SAME: %[[WL2:[a-zA-Z0-9]+]]: index, %[[WL3:[a-zA-Z0-9]+]]: index)
@@ -1998,16 +1998,16 @@
// -----
util.func public @unset_encoding_and_slice(
- %arg0: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>,
+ %arg0: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>,
%arg1 : index, %arg2 : index) -> tensor<?x?xf32> {
%0 = iree_encoding.unset_encoding %arg0
- : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
%1 = tensor.extract_slice %0[0, 0] [%arg1, %arg2] [1, 1]
: tensor<?x?xf32> to tensor<?x?xf32>
util.return %1 : tensor<?x?xf32>
}
// CHECK: util.func public @unset_encoding_and_slice
-// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
@@ -2015,7 +2015,7 @@
// CHECK-DAG: %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK-DAG: %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK: %[[DISPATCH:.+]] = flow.dispatch.workgroups[%[[D0]], %[[D1]], %[[ARG1]], %[[ARG2]]](%[[ARG0]], %[[D0]], %[[D1]], %[[ARG1]], %[[ARG2]])
-// CHECK-NEXT: %[[INARG:.+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>
+// CHECK-NEXT: %[[INARG:.+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>
// CHECK-SAME: %[[INDEXARG0:[a-zA-Z0-9]+]]: index
// CHECK-SAME: %[[INDEXARG1:[a-zA-Z0-9]+]]: index
// CHECK-SAME: %[[INDEXARG2:[a-zA-Z0-9]+]]: index
@@ -2027,7 +2027,7 @@
// CHECK-DAG: %[[ARG1_W:.+]] = flow.dispatch.workload.ordinal %[[INDEXARG3]], 3
// CHECK: %[[LOAD:.+]] = flow.dispatch.tensor.load %[[INARG]]
// CHECK-SAME: sizes = [%[[D0_W]], %[[D1_W]]]
-// CHECK-SAME: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>{%[[D0_W]], %[[D1_W]]}
+// CHECK-SAME: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>{%[[D0_W]], %[[D1_W]]}
// CHECK: %[[ENCODING:.+]] = iree_encoding.unset_encoding %[[LOAD]]
// CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ENCODING]][0, 0] [%[[ARG0_W]], %[[ARG1_W]]]
// CHECK: flow.dispatch.tensor.store %[[SLICE]], %[[OUTARG]]
@@ -2040,9 +2040,9 @@
#map = affine_map<(d0, d1) -> (d1)>
#map1 = affine_map<(d0, d1) -> (d0, d1)>
util.func public @root_on_unset_encoding(
- %arg0: tensor<784x96xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>,
+ %arg0: tensor<784x96xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>,
%arg1: tensor<96xf32>) -> tensor<784x96xf32> {
- %0 = iree_encoding.unset_encoding %arg0 : tensor<784x96xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<784x96xf32>
+ %0 = iree_encoding.unset_encoding %arg0 : tensor<784x96xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> -> tensor<784x96xf32>
%1 = tensor.empty() : tensor<784x96xf32>
%cst = arith.constant 0.000000e+00 : f32
%2 = linalg.fill ins(%cst : f32) outs(%1 : tensor<784x96xf32>) -> tensor<784x96xf32>
@@ -2061,18 +2061,18 @@
// CHECL: #[[MAP0:.+]] = affine_map<(d0, d1) -> (d1)>
// CHECK: #[[MAP1:.+]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK: util.func public @root_on_unset_encoding
-// CHECK-SAME: %[[ARG0:.+]]: tensor<784x96xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG0:.+]]: tensor<784x96xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
// CHECK-SAME: %[[ARG1:.+]]: tensor<96xf32>
// CHECK: %[[DISPATCH:.+]] = flow.dispatch.workgroups(%[[ARG0]], %[[ARG1]])
-// CHECK-NEXT: %[[INARG0:.+]]: !flow.dispatch.tensor<readonly:tensor<784x96xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>
+// CHECK-NEXT: %[[INARG0:.+]]: !flow.dispatch.tensor<readonly:tensor<784x96xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>
// CHECK-SAME: %[[INARG1:.+]]: !flow.dispatch.tensor<readonly:tensor<96xf32>>
// CHECK-SAME: %[[OUTARG:.+]]: !flow.dispatch.tensor<writeonly:tensor<784x96xf32>>
// CHECK: %[[LOAD0:.+]] = flow.dispatch.tensor.load %[[INARG0]], offsets = [0, 0], sizes = [784, 96], strides = [1, 1]
-// CHECK-SAME: : !flow.dispatch.tensor<readonly:tensor<784x96xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>
+// CHECK-SAME: : !flow.dispatch.tensor<readonly:tensor<784x96xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>
// CHECK: %[[LOAD1:.+]] = flow.dispatch.tensor.load %[[INARG1]], offsets = [0], sizes = [96], strides = [1]
// CHECK-SAME: : !flow.dispatch.tensor<readonly:tensor<96xf32>>
// CHECK: %[[OUT:.+]] = tensor.empty() : tensor<784x96xf32>
-// CHECK: %[[UNSET:.+]] = iree_encoding.unset_encoding %[[LOAD0]] : tensor<784x96xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<784x96xf32>
+// CHECK: %[[UNSET:.+]] = iree_encoding.unset_encoding %[[LOAD0]] : tensor<784x96xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>> -> tensor<784x96xf32>
// CHECK: %[[GENERIC0:.+]] = linalg.generic {indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP1]]],
// CHECK-SAME: iterator_types = ["parallel", "parallel"]} ins(%[[LOAD1]], %[[UNSET]]
// CHECK-SAME: : tensor<96xf32>, tensor<784x96xf32>) outs(%[[OUT]] : tensor<784x96xf32>)
@@ -2085,26 +2085,26 @@
// -----
util.func public @gemm_encoded(
- %arg0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>,
- %arg1 : tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>,
- %arg2 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>> {
+ %arg0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>,
+ %arg1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>>,
+ %arg2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>> {
%0 = linalg.matmul
ins(%arg0, %arg1
- : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>,
- tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>)
- outs(%arg2 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
- util.return %0 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>,
+ tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>>)
+ outs(%arg2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
+ util.return %0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
}
// CHECK: util.func public @gemm_encoded
-// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
-// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>
-// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : i64, element_types = [f32, f32, f32]>>
// CHECK: %[[DISPATCH:.+]] = flow.dispatch.workgroups
-// CHECK-NEXT: %[[LHS_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>
-// CHECK-SAME: %[[RHS_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>>
-// CHECK-SAME: %[[INIT_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>>
+// CHECK-NEXT: %[[LHS_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>
+// CHECK-SAME: %[[RHS_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [f32, f32, f32]>>>
+// CHECK-SAME: %[[INIT_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readwrite:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : i64, element_types = [f32, f32, f32]>>>
// CHECK-DAG: %[[LHS:.+]] = flow.dispatch.tensor.load %[[LHS_IN]]
// CHECK-DAG: %[[RHS:.+]] = flow.dispatch.tensor.load %[[RHS_IN]]
// CHECK-DAG: %[[INIT:.+]] = flow.dispatch.tensor.load %[[INIT_IN]]
@@ -2116,32 +2116,32 @@
// -----
util.func public @gemm_fill_encoded(
- %arg0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>,
- %arg1 : tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>> {
+ %arg0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>,
+ %arg1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%cst = arith.constant 0.0 : f32
- %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- %d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>
- %empty = tensor.empty(%d0, %d1) : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
- %fill = linalg.fill ins(%cst : f32) outs(%empty : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
+ %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ %d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>>
+ %empty = tensor.empty(%d0, %d1) : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
+ %fill = linalg.fill ins(%cst : f32) outs(%empty : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
%0 = linalg.matmul
ins(%arg0, %arg1
- : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>,
- tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>)
- outs(%fill : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
- util.return %0 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>,
+ tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32]>>)
+ outs(%fill : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>)
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
+ util.return %0 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
}
// CHECK: util.func public @gemm_fill_encoded
-// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
-// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [f32, f32, f32]>>
// CHECK: %[[DISPATCH:.+]] = flow.dispatch.workgroups
-// CHECK-NEXT: %[[LHS_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>>
-// CHECK-SAME: %[[RHS_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32]>>>
-// CHECK-SAME: %[[RESULT:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>>
+// CHECK-NEXT: %[[LHS_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>>
+// CHECK-SAME: %[[RHS_IN:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<readonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : i64, element_types = [f32, f32, f32]>>>
+// CHECK-SAME: %[[RESULT:[a-zA-Z0-9]+]]: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : i64, element_types = [f32, f32, f32]>>>
// CHECK-DAG: %[[LHS:.+]] = flow.dispatch.tensor.load %[[LHS_IN]]
// CHECK-DAG: %[[RHS:.+]] = flow.dispatch.tensor.load %[[RHS_IN]]
// CHECK: %[[EMPTY:.+]] = tensor.empty
diff --git a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors_default.mlir b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors_default.mlir
index 9ded6b9..90aceda 100644
--- a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors_default.mlir
+++ b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/dispatch_linalg_on_tensors_default.mlir
@@ -33,7 +33,7 @@
#map = affine_map<(d0, d1) -> (d1)>
#map1 = affine_map<(d0, d1) -> (d0, d1)>
util.func public @elem_set_encoding(%arg0: tensor<512xf32>, %arg1: tensor<384x512xf32>,
- %arg2: tensor<384x512xf32>) -> tensor<384x512xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+ %arg2: tensor<384x512xf32>) -> tensor<384x512xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
%0 = tensor.empty() : tensor<384x512xf32>
%1 = linalg.generic {indexing_maps = [#map, #map1, #map1, #map1],
iterator_types = ["parallel", "parallel"]}
@@ -44,8 +44,8 @@
%4 = arith.addf %3, %in_1 : f32
linalg.yield %4 : f32
} -> tensor<384x512xf32>
- %2 = iree_encoding.set_encoding %1 : tensor<384x512xf32> -> tensor<384x512xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- util.return %2 : tensor<384x512xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ %2 = iree_encoding.set_encoding %1 : tensor<384x512xf32> -> tensor<384x512xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ util.return %2 : tensor<384x512xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// CHECK-LABEL: util.func public @elem_set_encoding
// CHECK: flow.dispatch.workgroups
diff --git a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/form_dispatch_regions.mlir b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/form_dispatch_regions.mlir
index 130159b..1b1f152 100644
--- a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/form_dispatch_regions.mlir
+++ b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/form_dispatch_regions.mlir
@@ -138,7 +138,7 @@
// -----
util.func public @set_encoding_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,
- %arg2 : index, %arg3 : index) -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+ %arg2 : index, %arg3 : index) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
%cst = arith.constant 0.0 : f32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
@@ -168,8 +168,8 @@
linalg.yield %6 : f32
} -> tensor<?x?xf32>
%6 = iree_encoding.set_encoding %5
- : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- util.return %6 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ util.return %6 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// CHECK-LABEL: util.func public @set_encoding_fusion(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
@@ -188,15 +188,15 @@
// -----
util.func public @set_encoding_pad_fusion(%arg0 : tensor<?x?xf32>,
- %arg1 : index, %arg2 : index) -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+ %arg1 : index, %arg2 : index) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
%cst = arith.constant 0.0 : f32
%0 = tensor.pad %arg0 low[0, 0] high[%arg1, %arg2] {
^bb0(%b0: index, %b1 : index):
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<?x?xf32>
%1 = iree_encoding.set_encoding %0
- : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- util.return %1 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ util.return %1 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// CHECK-LABEL: util.func public @set_encoding_pad_fusion(
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>
@@ -209,7 +209,7 @@
// -----
util.func public @set_encoding_pad_elementwise_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,
- %arg2 : index, %arg3 : index) -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+ %arg2 : index, %arg3 : index) -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
%cst = arith.constant 0.0 : f32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
@@ -243,8 +243,8 @@
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<?x?xf32>
%7 = iree_encoding.set_encoding %6
- : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- util.return %7 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ util.return %7 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// CHECK-LABEL: util.func public @set_encoding_pad_elementwise_fusion(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
@@ -264,12 +264,12 @@
// -----
util.func public @unset_encoding_elementwise_fusion(
- %arg0: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>,
+ %arg0: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>,
%arg1: tensor<?xf32>) -> tensor<?x?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%0 = iree_encoding.unset_encoding %arg0
- : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
%1 = tensor.dim %0, %c0 : tensor<?x?xf32>
%2 = tensor.dim %0, %c1 : tensor<?x?xf32>
%3 = tensor.empty(%1, %2) : tensor<?x?xf32>
@@ -287,7 +287,7 @@
util.return %4 : tensor<?x?xf32>
}
// CHECK-LABEL: util.func public @unset_encoding_elementwise_fusion(
-// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
// CHECK-SAME: %[[ARG1:.+]]: tensor<?xf32>)
// CHECK: %[[RESULT:.+]] = flow.dispatch.region
// CHECK: %[[UNSET_ENCODING:.+]] = iree_encoding.unset_encoding %[[ARG0]]
@@ -299,12 +299,12 @@
// -----
util.func public @unset_encoding_slice_elementwise_fusion(
- %arg0: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>,
+ %arg0: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>,
%arg1: tensor<?xf32>, %arg2 : index, %arg3 : index) -> tensor<?x?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%0 = iree_encoding.unset_encoding %arg0
- : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
+ : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>> -> tensor<?x?xf32>
%1 = tensor.extract_slice %0[0, 0] [%arg2, %arg3] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
%2 = tensor.dim %1, %c0 : tensor<?x?xf32>
%3 = tensor.dim %1, %c1 : tensor<?x?xf32>
@@ -323,7 +323,7 @@
util.return %5 : tensor<?x?xf32>
}
// CHECK-LABEL: util.func public @unset_encoding_slice_elementwise_fusion(
-// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
// CHECK-SAME: %[[ARG1:.+]]: tensor<?xf32>
// CHECK: %[[RESULT0:.+]] = flow.dispatch.region
// CHECK: %[[UNSET_ENCODING:.+]] = iree_encoding.unset_encoding %[[ARG0]]
diff --git a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/tensor_pad_to_tensor_insert_slice.mlir b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/tensor_pad_to_tensor_insert_slice.mlir
index 1a976da..c9e4d5d 100644
--- a/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/tensor_pad_to_tensor_insert_slice.mlir
+++ b/compiler/src/iree/compiler/Dialect/Flow/Transforms/test/tensor_pad_to_tensor_insert_slice.mlir
@@ -82,11 +82,11 @@
util.func public @dispatch_dispatch_0_generic_512x1024_f32(
%arg0: !flow.dispatch.tensor<readonly:tensor<512x1024xf32>>,
%arg1: index, %arg2: index, %arg3: index, %arg4: index,
- %arg5: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>>>) {
+ %arg5: !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>>>) {
%cst = arith.constant 0.000000e+00 : f32
%0 = flow.dispatch.workload.ordinal %arg3, 2 : index
%1 = flow.dispatch.workload.ordinal %arg4, 3 : index
- %2 = flow.dispatch.tie_shape %arg5 : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>>>{%0, %1}
+ %2 = flow.dispatch.tie_shape %arg5 : !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>>>{%0, %1}
%3 = flow.dispatch.workload.ordinal %arg1, 0 : index
%4 = flow.dispatch.workload.ordinal %arg2, 1 : index
%5 = flow.dispatch.tensor.load %arg0, offsets = [0, 0], sizes = [512, 1024], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<512x1024xf32>> -> tensor<512x1024xf32>
@@ -94,8 +94,8 @@
^bb0(%arg6: index, %arg7: index):
tensor.yield %cst : f32
} : tensor<512x1024xf32> to tensor<?x?xf32>
- %11 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>>
- flow.dispatch.tensor.store %11, %2, offsets = [0, 0], sizes = [%0, %1], strides = [1, 1] : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>>>{%0, %1}
+ %11 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>>
+ flow.dispatch.tensor.store %11, %2, offsets = [0, 0], sizes = [%0, %1], strides = [1, 1] : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>> -> !flow.dispatch.tensor<writeonly:tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<512x1024xf32>>>>{%0, %1}
util.return
}
diff --git a/compiler/src/iree/compiler/Dialect/Stream/Transforms/test/encode_host_tensors.mlir b/compiler/src/iree/compiler/Dialect/Stream/Transforms/test/encode_host_tensors.mlir
index dfbaf0b..87ae134 100644
--- a/compiler/src/iree/compiler/Dialect/Stream/Transforms/test/encode_host_tensors.mlir
+++ b/compiler/src/iree/compiler/Dialect/Stream/Transforms/test/encode_host_tensors.mlir
@@ -25,7 +25,7 @@
#map1 = affine_map<(d0, d1, d2) -> (d2, d1)>
#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>
util.func public @sizeof_lhs_encoding_dynamic(%arg0: index, %arg1: index) -> index {
- %0 = stream.tensor.sizeof tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<?x?xf32>, user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 4, 8, 16>>>{%arg0, %arg1} : index
+ %0 = stream.tensor.sizeof tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], original_type = tensor<?x?xf32>, user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 4, 8, 16>>>{%arg0, %arg1} : index
util.return %0 : index
}
// CHECK-LABEL: @sizeof_lhs_encoding_dynamic
@@ -45,7 +45,7 @@
#map1 = affine_map<(d0, d1, d2) -> (d2, d1)>
#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>
util.func public @sizeof_rhs_encoding_dynamic(%arg0: index, %arg1: index) -> index {
- %0 = stream.tensor.sizeof tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<?x?xf32>, user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 4, 8, 16>>>{%arg0, %arg1} : index
+ %0 = stream.tensor.sizeof tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1, element_types = [f32, f32, f32], original_type = tensor<?x?xf32>, user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 4, 8, 16>>>{%arg0, %arg1} : index
util.return %0 : index
}
// CHECK-LABEL: @sizeof_rhs_encoding_dynamic
@@ -66,7 +66,7 @@
#map1 = affine_map<(d0, d1, d2) -> (d2, d1)>
#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>
util.func public @sizeof_result_encoding_dynamic(%arg0: index, %arg1: index) -> index {
- %0 = stream.tensor.sizeof tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<?x?xf32>, user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 4, 8, 16>>>{%arg0, %arg1} : index
+ %0 = stream.tensor.sizeof tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32], original_type = tensor<?x?xf32>, user_indexing_maps = [#map, #map1, #map2], round_dims_to = array<i64: 4, 8, 16>>>{%arg0, %arg1} : index
util.return %0 : index
}
// CHECK-LABEL: @sizeof_result_encoding_dynamic
diff --git a/compiler/src/iree/compiler/GlobalOptimization/SetEncoding.cpp b/compiler/src/iree/compiler/GlobalOptimization/SetEncoding.cpp
index 920679a..43114cd 100644
--- a/compiler/src/iree/compiler/GlobalOptimization/SetEncoding.cpp
+++ b/compiler/src/iree/compiler/GlobalOptimization/SetEncoding.cpp
@@ -9,6 +9,7 @@
// operations in tiled layouts.
//===---------------------------------------------------------------------===//
+#include "iree/compiler/Codegen/Common/EncodingUtils.h"
#include "iree/compiler/Codegen/Dialect/Codegen/IR/IREECodegenAttrs.h"
#include "iree/compiler/Dialect/Encoding/IR/EncodingDialect.h"
#include "iree/compiler/Dialect/Encoding/IR/EncodingOps.h"
@@ -44,7 +45,6 @@
namespace mlir::iree_compiler::GlobalOptimization {
using IREE::Encoding::EncodingAttr;
-using IREE::Encoding::EncodingRole;
//===---------------------------------------------------------------------===//
// Utility functions
@@ -142,7 +142,7 @@
}
static Value padAndSetEncoding(OpBuilder &builder, Location loc, Value source,
- EncodingRole role,
+ int64_t operandIndex,
ArrayRef<Type> operandElemTypes,
MatmulNarrowSizes narrow,
ArrayRef<AffineMap> indexingMaps) {
@@ -150,7 +150,7 @@
// No need to specify original_type in the encoding poadded to pad(), because
// the operand there is the `source` tensor, so it will default to reading its
// original shape.
- auto encodingForPad = EncodingAttr::get(ctx, role, operandElemTypes,
+ auto encodingForPad = EncodingAttr::get(ctx, operandIndex, operandElemTypes,
/*originalType=*/Type{}, narrow.M,
narrow.N, indexingMaps);
Value padded = pad(builder, loc, source, encodingForPad);
@@ -163,7 +163,7 @@
auto encodingForSetEncoding = encodingForPad;
if (padded.getType() != source.getType()) {
encodingForSetEncoding = EncodingAttr::get(
- ctx, role, operandElemTypes,
+ ctx, operandIndex, operandElemTypes,
/*originalType=*/source.getType(), narrow.M, narrow.N, indexingMaps);
}
return setEncoding(builder, loc, padded, encodingForSetEncoding);
@@ -338,23 +338,26 @@
Value encodedLhs, encodedRhs, encodedOut;
if (!padFactor) {
- encodedLhs = padAndSetEncoding(rewriter, loc, lhs, EncodingRole::LHS,
- elemTypes, narrowSizes, maps);
- encodedRhs = padAndSetEncoding(rewriter, loc, rhs, EncodingRole::RHS,
- elemTypes, narrowSizes, maps);
- encodedOut = padAndSetEncoding(rewriter, loc, out, EncodingRole::RESULT,
- elemTypes, narrowSizes, maps);
+ encodedLhs =
+ padAndSetEncoding(rewriter, loc, lhs, IREE::Encoding::MATMUL_LHS,
+ elemTypes, narrowSizes, maps);
+ encodedRhs =
+ padAndSetEncoding(rewriter, loc, rhs, IREE::Encoding::MATMUL_RHS,
+ elemTypes, narrowSizes, maps);
+ encodedOut =
+ padAndSetEncoding(rewriter, loc, out, IREE::Encoding::MATMUL_RESULT,
+ elemTypes, narrowSizes, maps);
} else {
- auto setEncodingWrapper = [&](Value src, EncodingRole role) -> Value {
+ auto setEncodingWrapper = [&](Value src, int64_t operandIndex) -> Value {
SmallVector<int64_t> roundDimsTo(linalgOp.getNumLoops(), padFactor);
auto encoding = EncodingAttr::get(
- linalgOp.getContext(), role, elemTypes, src.getType(),
+ linalgOp.getContext(), operandIndex, elemTypes, src.getType(),
narrowSizes.M, narrowSizes.N, maps, roundDimsTo);
return setEncoding(rewriter, loc, src, encoding);
};
- encodedLhs = setEncodingWrapper(lhs, EncodingRole::LHS);
- encodedRhs = setEncodingWrapper(rhs, EncodingRole::RHS);
- encodedOut = setEncodingWrapper(out, EncodingRole::RESULT);
+ encodedLhs = setEncodingWrapper(lhs, IREE::Encoding::MATMUL_LHS);
+ encodedRhs = setEncodingWrapper(rhs, IREE::Encoding::MATMUL_RHS);
+ encodedOut = setEncodingWrapper(out, IREE::Encoding::MATMUL_RESULT);
}
Value opTiled = clone(rewriter, linalgOp, encodedOut.getType(),
ValueRange{encodedLhs, encodedRhs, encodedOut})
diff --git a/compiler/src/iree/compiler/GlobalOptimization/test/materialize_homogeneous_encodings.mlir b/compiler/src/iree/compiler/GlobalOptimization/test/materialize_homogeneous_encodings.mlir
index 55e9b9f..c1f4314 100644
--- a/compiler/src/iree/compiler/GlobalOptimization/test/materialize_homogeneous_encodings.mlir
+++ b/compiler/src/iree/compiler/GlobalOptimization/test/materialize_homogeneous_encodings.mlir
@@ -13,15 +13,15 @@
%c1 = arith.constant 1 : index
%dim = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%dim_0 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
- %0:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> index, index
+ %0:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> index, index
%1 = affine.apply #map()[%0#0, %dim]
%2 = affine.apply #map()[%0#1, %dim_0]
%padded = tensor.pad %arg0 low[0, 0] high[%1, %2] {
^bb0(%arg1: index, %arg2: index):
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<?x?xf32>
- %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>>
- %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> tensor<?x?xf32>
+ %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>>
+ %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> tensor<?x?xf32>
util.return %4 : tensor<?x?xf32>
}
}
@@ -44,15 +44,15 @@
%c1 = arith.constant 1 : index
%dim = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%dim_0 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
- %0:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> index, index
+ %0:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> index, index
%1 = affine.apply #map()[%0#0, %dim]
%2 = affine.apply #map()[%0#1, %dim_0]
%padded = tensor.pad %arg0 low[0, 0] high[%1, %2] {
^bb0(%arg1: index, %arg2: index):
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<?x?xf32>
- %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>>
- %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> tensor<?x?xf32>
+ %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>>
+ %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> tensor<?x?xf32>
util.return %4 : tensor<?x?xf32>
}
}
@@ -79,15 +79,15 @@
%c1 = arith.constant 1 : index
%dim = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%dim_0 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
- %0:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> index, index
+ %0:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> index, index
%1 = affine.apply #map()[%0#0, %dim]
%2 = affine.apply #map()[%0#1, %dim_0]
%padded = tensor.pad %arg0 low[0, 0] high[%1, %2] {
^bb0(%arg1: index, %arg2: index):
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<?x?xf32>
- %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>>
- %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> tensor<?x?xf32>
+ %3 = iree_encoding.set_encoding %padded : tensor<?x?xf32> -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>>
+ %4 = iree_encoding.unset_encoding %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32], user_indexing_maps = [#map1, #map2, #map3]>> -> tensor<?x?xf32>
util.return %4 : tensor<?x?xf32>
}
}
diff --git a/compiler/src/iree/compiler/GlobalOptimization/test/set_encoding.mlir b/compiler/src/iree/compiler/GlobalOptimization/test/set_encoding.mlir
index a63bc09..042357e 100644
--- a/compiler/src/iree/compiler/GlobalOptimization/test/set_encoding.mlir
+++ b/compiler/src/iree/compiler/GlobalOptimization/test/set_encoding.mlir
@@ -18,27 +18,27 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]]]
// CHECK: tensor<100x250xf32> to tensor<?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]]]
// CHECK: tensor<250x500xf32> to tensor<?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]]]
// CHECK: tensor<100x500xf32> to tensor<?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[MATMUL:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -55,11 +55,11 @@
// PAD-WITHIN-ENCODING-SAME: %[[ARG1:[a-zA-Z0-9]+]]
// PAD-WITHIN-ENCODING-SAME: %[[ARG2:[a-zA-Z0-9]+]]
// PAD-WITHIN-ENCODING: %[[LHS:.+]] = iree_encoding.set_encoding %[[ARG0]]
-// PAD-WITHIN-ENCODING-SAME: tensor<100x250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]], round_dims_to = array<i64: 16, 16, 16>>>
+// PAD-WITHIN-ENCODING-SAME: tensor<100x250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]], round_dims_to = array<i64: 16, 16, 16>>>
// PAD-WITHIN-ENCODING: %[[RHS:.+]] = iree_encoding.set_encoding %[[ARG1]]
-// PAD-WITHIN-ENCODING-SAME: tensor<250x500xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]], round_dims_to = array<i64: 16, 16, 16>>>
+// PAD-WITHIN-ENCODING-SAME: tensor<250x500xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]], round_dims_to = array<i64: 16, 16, 16>>>
// PAD-WITHIN-ENCODING: %[[LHS:.+]] = iree_encoding.set_encoding %[[ARG2]]
-// PAD-WITHIN-ENCODING-SAME: tensor<100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]], round_dims_to = array<i64: 16, 16, 16>>>
+// PAD-WITHIN-ENCODING-SAME: tensor<100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]], round_dims_to = array<i64: 16, 16, 16>>>
// -----
@@ -77,7 +77,7 @@
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>, %[[ARG2:.+]]: tensor<?x?xf32>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[LHS_DIM0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[LHS_DIM0]]]
// CHECK: %[[LHS_DIM1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
@@ -85,8 +85,8 @@
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]]]
// CHECK: tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[RHS_DIM0:.+]] = tensor.dim %[[ARG1]], %[[C0]]
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[RHS_DIM0]]]
// CHECK: %[[RHS_DIM1:.+]] = tensor.dim %[[ARG1]], %[[C1]]
@@ -94,8 +94,8 @@
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]]]
// CHECK: tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[OUTS_DIM0:.+]] = tensor.dim %[[ARG2]], %[[C0]]
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[OUTS_DIM0]]]
// CHECK: %[[OUTS_DIM1:.+]] = tensor.dim %[[ARG2]], %[[C1]]
@@ -103,7 +103,7 @@
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]]]
// CHECK: tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[MATMUL:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -126,21 +126,21 @@
// CHECK-SAME: %[[ARG0:.+]]: tensor<100x250xi8>
// CHECK-SAME: %[[ARG1:.+]]: tensor<250x500xi8>
// CHECK-SAME: %[[ARG2:.+]]: tensor<100x500xi32>
-// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xi8, #iree_encoding.encoding<operand_index = 0 : index, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high
// CHECK: tensor<100x250xi8> to tensor<?x?xi8>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], original_type = tensor<100x250xi8>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xi8, #iree_encoding.encoding<operand_index = 0 : index, element_types = [i8, i8, i32], original_type = tensor<100x250xi8>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xi8, #iree_encoding.encoding<operand_index = 1 : index, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high
// CHECK: tensor<250x500xi8> to tensor<?x?xi8>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], original_type = tensor<250x500xi8>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xi8, #iree_encoding.encoding<operand_index = 1 : index, element_types = [i8, i8, i32], original_type = tensor<250x500xi8>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xi32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high
// CHECK: tensor<100x500xi32> to tensor<?x?xi32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], original_type = tensor<100x500xi32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK-SAME: tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [i8, i8, i32], original_type = tensor<100x500xi32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
// CHECK: %[[MATMUL:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -163,21 +163,21 @@
// CHECK-SAME: %[[ARG0:.+]]: tensor<100x250xf16>
// CHECK-SAME: %[[ARG1:.+]]: tensor<250x500xf16>
// CHECK-SAME: %[[ARG2:.+]]: tensor<100x500xf32>
-// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high
// CHECK: tensor<100x250xf16> to tensor<?x?xf16>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32], original_type = tensor<100x250xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f16, f16, f32], original_type = tensor<100x250xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high
// CHECK: tensor<250x500xf16> to tensor<?x?xf16>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f32], original_type = tensor<250x500xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f16, f16, f32], original_type = tensor<250x500xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high
// CHECK: tensor<100x500xf32> to tensor<?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f16, f16, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
// CHECK: %[[MATMUL:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -200,21 +200,21 @@
// CHECK-SAME: %[[ARG0:.+]]: tensor<100x250xf16>
// CHECK-SAME: %[[ARG1:.+]]: tensor<250x500xf16>
// CHECK-SAME: %[[ARG2:.+]]: tensor<100x500xf16>
-// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high
// CHECK: tensor<100x250xf16> to tensor<?x?xf16>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], original_type = tensor<100x250xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f16, f16, f16], original_type = tensor<100x250xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high
// CHECK: tensor<250x500xf16> to tensor<?x?xf16>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], original_type = tensor<250x500xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f16, f16, f16], original_type = tensor<250x500xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf16, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high
// CHECK: tensor<100x500xf16> to tensor<?x?xf16>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], original_type = tensor<100x500xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK-SAME: tensor<?x?xf16, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f16, f16, f16], original_type = tensor<100x500xf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
// CHECK: %[[MATMUL:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -237,21 +237,21 @@
// CHECK-SAME: %[[ARG0:.+]]: tensor<100x250xbf16>
// CHECK-SAME: %[[ARG1:.+]]: tensor<250x500xbf16>
// CHECK-SAME: %[[ARG2:.+]]: tensor<100x500xf32>
-// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xbf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high
// CHECK: tensor<100x250xbf16> to tensor<?x?xbf16>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], original_type = tensor<100x250xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [bf16, bf16, f32], original_type = tensor<100x250xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xbf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high
// CHECK: tensor<250x500xbf16> to tensor<?x?xbf16>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], original_type = tensor<250x500xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [bf16, bf16, f32], original_type = tensor<250x500xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high
// CHECK: tensor<100x500xf32> to tensor<?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [bf16, bf16, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
// CHECK: %[[MATMUL:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -274,21 +274,21 @@
// CHECK-SAME: %[[ARG0:.+]]: tensor<100x250xbf16>
// CHECK-SAME: %[[ARG1:.+]]: tensor<250x500xbf16>
// CHECK-SAME: %[[ARG2:.+]]: tensor<100x500xbf16>
-// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xbf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high
// CHECK: tensor<100x250xbf16> to tensor<?x?xbf16>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<100x250xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [bf16, bf16, bf16], original_type = tensor<100x250xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[RHS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xbf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high
// CHECK: tensor<250x500xbf16> to tensor<?x?xbf16>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], original_type = tensor<250x500xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [bf16, bf16, bf16], original_type = tensor<250x500xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: %[[OUTS_TILE_SIZE]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xbf16, #iree_encoding.encoding<operand_index = 2 : index, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>> -> index, index
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high
// CHECK: tensor<100x500xbf16> to tensor<?x?xbf16>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], original_type = tensor<100x500xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK-SAME: tensor<?x?xbf16, #iree_encoding.encoding<operand_index = 2 : index, element_types = [bf16, bf16, bf16], original_type = tensor<100x500xbf16>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
// CHECK: %[[MATMUL:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -316,30 +316,30 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#2, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x250xf32> to tensor<?x?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<64x100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<64x100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<64x250x500xf32> to tensor<?x?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<64x250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<64x250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]], %[[OUTS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x500xf32> to tensor<?x?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<64x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<64x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATMUL:.+]] = linalg.batch_matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -364,7 +364,7 @@
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[LHS_DIM0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[LHS_DIM0]]]
// CHECK: %[[LHS_DIM1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
@@ -374,8 +374,8 @@
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[RHS_DIM0:.+]] = tensor.dim %[[ARG1]], %[[C0]]
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[RHS_DIM0]]]
// CHECK: %[[RHS_DIM1:.+]] = tensor.dim %[[ARG1]], %[[C1]]
@@ -385,8 +385,8 @@
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[OUTS_DIM0:.+]] = tensor.dim %[[ARG2]], %[[C0]]
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[OUTS_DIM0]]]
// CHECK: %[[OUTS_DIM1:.+]] = tensor.dim %[[ARG2]], %[[C1]]
@@ -396,7 +396,7 @@
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]], %[[OUTS_PADDING_SIZE2]]]
// CHECK: tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATMUL:.+]] = linalg.batch_matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -424,30 +424,30 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#2, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x250xf16> to tensor<?x?x?xf16>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f16], original_type = tensor<64x100x250xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f16, f16, f16], original_type = tensor<64x100x250xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<64x250x500xf16> to tensor<?x?x?xf16>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f16], original_type = tensor<64x250x500xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f16, f16, f16], original_type = tensor<64x250x500xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xf16, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f16, f16, f16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]], %[[OUTS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x500xf16> to tensor<?x?x?xf16>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f16], original_type = tensor<64x100x500xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f16, f16, f16], original_type = tensor<64x100x500xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATMUL:.+]] = linalg.batch_matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -475,30 +475,30 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#2, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x250xf16> to tensor<?x?x?xf16>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<role = LHS, element_types = [f16, f16, f32], original_type = tensor<64x100x250xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f16, f16, f32], original_type = tensor<64x100x250xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<64x250x500xf16> to tensor<?x?x?xf16>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<role = RHS, element_types = [f16, f16, f32], original_type = tensor<64x250x500xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f16, f16, f32], original_type = tensor<64x250x500xf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f16, f16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]], %[[OUTS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x500xf32> to tensor<?x?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f16, f16, f32], original_type = tensor<64x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f16, f16, f32], original_type = tensor<64x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATMUL:.+]] = linalg.batch_matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -526,30 +526,30 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xbf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#2, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x250xbf16> to tensor<?x?x?xbf16>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, bf16], original_type = tensor<64x100x250xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [bf16, bf16, bf16], original_type = tensor<64x100x250xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xbf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<64x250x500xbf16> to tensor<?x?x?xbf16>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, bf16], original_type = tensor<64x250x500xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [bf16, bf16, bf16], original_type = tensor<64x250x500xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xbf16, #iree_encoding.encoding<operand_index = 2 : index, element_types = [bf16, bf16, bf16], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]], %[[OUTS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x500xbf16> to tensor<?x?x?xbf16>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, bf16], original_type = tensor<64x100x500xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<operand_index = 2 : index, element_types = [bf16, bf16, bf16], original_type = tensor<64x100x500xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATMUL:.+]] = linalg.batch_matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -577,30 +577,30 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xbf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#2, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x250xbf16> to tensor<?x?x?xbf16>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<role = LHS, element_types = [bf16, bf16, f32], original_type = tensor<64x100x250xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [bf16, bf16, f32], original_type = tensor<64x100x250xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xbf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<64x250x500xbf16> to tensor<?x?x?xbf16>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<role = RHS, element_types = [bf16, bf16, f32], original_type = tensor<64x250x500xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xbf16, #iree_encoding.encoding<operand_index = 1 : index, element_types = [bf16, bf16, f32], original_type = tensor<64x250x500xbf16>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [bf16, bf16, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]], %[[OUTS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x500xf32> to tensor<?x?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [bf16, bf16, f32], original_type = tensor<64x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [bf16, bf16, f32], original_type = tensor<64x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATMUL:.+]] = linalg.batch_matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -628,30 +628,30 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x250xi8, #iree_encoding.encoding<operand_index = 0 : index, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#2, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x250xi8> to tensor<?x?x?xi8>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xi8, #iree_encoding.encoding<role = LHS, element_types = [i8, i8, i32], original_type = tensor<64x100x250xi8>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 0 : index, element_types = [i8, i8, i32], original_type = tensor<64x100x250xi8>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x250x500xi8, #iree_encoding.encoding<operand_index = 1 : index, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<64x250x500xi8> to tensor<?x?x?xi8>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xi8, #iree_encoding.encoding<role = RHS, element_types = [i8, i8, i32], original_type = tensor<64x250x500xi8>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?x?xi8, #iree_encoding.encoding<operand_index = 1 : index, element_types = [i8, i8, i32], original_type = tensor<64x250x500xi8>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<64x100x500xi32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [i8, i8, i32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C64]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]], %[[OUTS_PADDING_SIZE2]]]
// CHECK: tensor<64x100x500xi32> to tensor<?x?x?xi32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i8, i8, i32], original_type = tensor<64x100x500xi32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?x?xi32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [i8, i8, i32], original_type = tensor<64x100x500xi32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATMUL:.+]] = linalg.batch_matmul
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -677,25 +677,25 @@
// CHECK-SAME: %[[ARG2:.+]]: tensor<500xf32>
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]] = iree_encoding.upper_bound_tile_size tensor<250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index
+// CHECK: %[[LHS_TILE_SIZE:.+]] = iree_encoding.upper_bound_tile_size tensor<250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index
// CHECK: %[[LHS_PADDING_SIZE:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]], %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0] high[%[[LHS_PADDING_SIZE]]]
// CHECK: tensor<250xf32> to tensor<?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<250xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<250xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]]]
// CHECK: tensor<250x500xf32> to tensor<?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<250x500xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]] = iree_encoding.upper_bound_tile_size tensor<500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<250x500xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]] = iree_encoding.upper_bound_tile_size tensor<500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index
// CHECK: %[[OUTS_PADDING_SIZE:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]], %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0] high[%[[OUTS_PADDING_SIZE]]]
// CHECK: tensor<500xf32> to tensor<?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<500xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<500xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[VECMAT:.+]] = linalg.vecmat
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -721,25 +721,25 @@
// CHECK-SAME: %[[ARG2:.+]]: tensor<100xf32>
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]]]
// CHECK: tensor<100x250xf32> to tensor<?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<100x250xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]] = iree_encoding.upper_bound_tile_size tensor<250xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<100x250xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]] = iree_encoding.upper_bound_tile_size tensor<250xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index
// CHECK: %[[RHS_PADDING_SIZE:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]], %[[C250]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0] high[%[[RHS_PADDING_SIZE]]]
// CHECK: tensor<250xf32> to tensor<?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<250xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]] = iree_encoding.upper_bound_tile_size tensor<100xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index
+// CHECK-SAME: tensor<?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<250xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]] = iree_encoding.upper_bound_tile_size tensor<100xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index
// CHECK: %[[OUTS_PADDING_SIZE:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]], %[[C100]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0] high[%[[OUTS_PADDING_SIZE]]]
// CHECK: tensor<100xf32> to tensor<?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<100xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<100xf32>, matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[MATVEC:.+]] = linalg.matvec
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -766,28 +766,28 @@
// CHECK-DAG: %[[C3:.+]] = arith.constant 3 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<3x250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<3x250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C3]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]]]
// CHECK: tensor<3x250xf32> to tensor<?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<3x250xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<3x250x500xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<3x250xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<3x250x500xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C3]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<3x250x500xf32> to tensor<?x?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<3x250x500xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<3x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<3x250x500xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<3x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C3]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]]]
// CHECK: tensor<3x500xf32> to tensor<?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<3x500xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<3x500xf32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[VECMAT:.+]] = linalg.batch_vecmat
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -812,7 +812,7 @@
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[LHS_DIM0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[LHS_DIM0]]]
// CHECK: %[[LHS_DIM1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
@@ -822,8 +822,8 @@
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[RHS_DIM0:.+]] = tensor.dim %[[ARG1]], %[[C0]]
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[RHS_DIM0]]]
// CHECK: %[[RHS_DIM1:.+]] = tensor.dim %[[ARG1]], %[[C1]]
@@ -831,8 +831,8 @@
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]]]
// CHECK: tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[OUTS_DIM0:.+]] = tensor.dim %[[ARG2]], %[[C0]]
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[OUTS_DIM0]]]
// CHECK: %[[OUTS_DIM1:.+]] = tensor.dim %[[ARG2]], %[[C1]]
@@ -840,7 +840,7 @@
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]]]
// CHECK: tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], matmul_narrow_N = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATVEC:.+]] = linalg.batch_matvec
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -851,24 +851,24 @@
// -----
util.func public @fold_fill_with_set_encoding(%arg0 : index, %arg1 : index)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>> {
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>> {
%cst = arith.constant 0.0 : f32
%0 = tensor.empty(%arg0, %arg1) : tensor<?x?xf32>
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
%2 = iree_encoding.set_encoding %1 : tensor<?x?xf32>
- -> tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
- util.return %2 : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
+ util.return %2 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0, element_types = [f32, f32, f32]>>
}
// CHECK: util.func public @fold_fill_with_set_encoding(
-// CHECK: %[[EMPTY:.+]] = tensor.empty(%{{.+}}, %{{.+}}) : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>
+// CHECK: %[[EMPTY:.+]] = tensor.empty(%{{.+}}, %{{.+}}) : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>
// CHECK: %[[FILL:.+]] = linalg.fill
-// CHECK-SAME: outs(%[[EMPTY]] : tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32]>>)
+// CHECK-SAME: outs(%[[EMPTY]] : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : i64, element_types = [f32, f32, f32]>>)
// CHECK: util.return %[[FILL]]
// -----
util.func public @fold_fill_with_tensor_pad(%arg0 : index, %arg1 : index, %arg2 : index, %arg3 : index)
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>> {
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>> {
%cst = arith.constant 0.0 : f32
%0 = tensor.empty(%arg0, %arg1) : tensor<?x?xf32>
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
@@ -877,12 +877,12 @@
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<?x?xf32>
%3 = iree_encoding.set_encoding %2 : tensor<?x?xf32>
- -> tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
- util.return %3 : tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
+ -> tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
+ util.return %3 : tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2, element_types = [f32, f32, f32]>>
}
// CHECK: util.func public @fold_fill_with_tensor_pad(
// CHECK: %[[EMPTY:.+]] = tensor.empty(
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32]>>
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : i64, element_types = [f32, f32, f32]>>
// CHECK: %[[FILL:.+]] = linalg.fill
// CHECK-SAME: outs(%[[EMPTY]] :
// CHECK: util.return %[[FILL]]
@@ -960,9 +960,9 @@
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK: util.func public @matmul_casted_from_i1_f32f32f32
-// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<64x256xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<256x128xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<64x128xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<64x256xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<256x128xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<64x128xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
// -----
@@ -988,9 +988,9 @@
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK: util.func public @matmul_generic_casted_from_i1_f32f32f32
-// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<64x256xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<256x128xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<64x128xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<64x256xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<256x128xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: set_encoding {{.+}} tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<64x128xf32>, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
// -----
@@ -1004,9 +1004,9 @@
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK: util.func public @matmul_f32f32f32_narrow_M(
-// CHECK: iree_encoding.upper_bound_tile_size tensor<2x250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_M = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: iree_encoding.upper_bound_tile_size tensor<250x500xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_M = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: iree_encoding.upper_bound_tile_size tensor<2x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_M = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: iree_encoding.upper_bound_tile_size tensor<2x250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], matmul_narrow_M = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: iree_encoding.upper_bound_tile_size tensor<250x500xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], matmul_narrow_M = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: iree_encoding.upper_bound_tile_size tensor<2x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], matmul_narrow_M = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
// CHECK: linalg.matmul
// -----
@@ -1021,9 +1021,9 @@
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
// CHECK: util.func public @batch_matmul_f32f32f32_narrow_MN(
-// CHECK: iree_encoding.upper_bound_tile_size tensor<64x4x250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], matmul_narrow_N = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: iree_encoding.upper_bound_tile_size tensor<64x250x2xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], matmul_narrow_N = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
-// CHECK: iree_encoding.upper_bound_tile_size tensor<64x4x2xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], matmul_narrow_N = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: iree_encoding.upper_bound_tile_size tensor<64x4x250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], matmul_narrow_N = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: iree_encoding.upper_bound_tile_size tensor<64x250x2xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], matmul_narrow_N = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
+// CHECK: iree_encoding.upper_bound_tile_size tensor<64x4x2xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], matmul_narrow_N = 2 : index, user_indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP2]]]>>
// CHECK: linalg.batch_matmul
// -----
@@ -1046,27 +1046,27 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<250x100xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<250x100xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C250]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]]]
// CHECK: tensor<250x100xf32> to tensor<?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<250x100xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<250x100xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<250x500xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]]]
// CHECK: tensor<250x500xf32> to tensor<?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]]]
// CHECK: tensor<100x500xf32> to tensor<?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[MATMUL:.+]] = linalg.matmul_transpose_a
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -1093,27 +1093,27 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]]]
// CHECK: tensor<100x250xf32> to tensor<?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<500x250xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<500x250xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C500]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]]]
// CHECK: tensor<500x250xf32> to tensor<?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<500x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<500x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]]]
// CHECK: tensor<100x500xf32> to tensor<?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[MATMUL:.+]] = linalg.matmul_transpose_b
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -1141,30 +1141,30 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x250x100xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x250x100xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C2]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[LHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#2, %[[C100]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<2x250x100xf32> to tensor<?x?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<2x250x100xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x250x500xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<2x250x100xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x250x500xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C2]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C250]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<2x250x500xf32> to tensor<?x?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<2x250x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C2]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]], %[[OUTS_PADDING_SIZE2]]]
// CHECK: tensor<2x100x500xf32> to tensor<?x?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<2x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATMUL:.+]] = linalg.batch_matmul_transpose_a
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -1192,30 +1192,30 @@
// CHECK-DAG: %[[C100:.+]] = arith.constant 100 : index
// CHECK-DAG: %[[C250:.+]] = arith.constant 250 : index
// CHECK-DAG: %[[C500:.+]] = arith.constant 500 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x100x250xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x100x250xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C2]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[LHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#2, %[[C250]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]], %[[LHS_PADDING_SIZE2]]]
// CHECK: tensor<2x100x250xf32> to tensor<?x?x?xf32>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = LHS, element_types = [f32, f32, f32], original_type = tensor<2x100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x500x250xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 0 : index, element_types = [f32, f32, f32], original_type = tensor<2x100x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x500x250xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C2]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C500]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C250]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<2x500x250xf32> to tensor<?x?x?xf32>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RHS, element_types = [f32, f32, f32], original_type = tensor<2x500x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x100x500xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 1 : index, element_types = [f32, f32, f32], original_type = tensor<2x500x250xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<2x100x500xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C2]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C100]]]
// CHECK: %[[OUTS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#2, %[[C500]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]], %[[OUTS_PADDING_SIZE2]]]
// CHECK: tensor<2x100x500xf32> to tensor<?x?x?xf32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<role = RESULT, element_types = [f32, f32, f32], original_type = tensor<2x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?x?xf32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [f32, f32, f32], original_type = tensor<2x100x500xf32>, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[BATCH_MATMUL:.+]] = linalg.batch_matmul_transpose_b
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[OUTS]] :
@@ -1249,28 +1249,28 @@
// CHECK-DAG: %[[C4096:.+]] = arith.constant 4096 : index
// CHECK-DAG: %[[C128:.+]] = arith.constant 128 : index
// CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index
-// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<32x128xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK: %[[LHS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<32x128xi16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[LHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#0, %[[C32]]]
// CHECK: %[[LHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[LHS_TILE_SIZE]]#1, %[[C128]]]
// CHECK: %[[LHS_PAD:.+]] = tensor.pad %[[ARG0]] low[0, 0] high[%[[LHS_PADDING_SIZE0]], %[[LHS_PADDING_SIZE1]]]
// CHECK: tensor<32x128xi16> to tensor<?x?xi16>
// CHECK: %[[LHS:.+]] = iree_encoding.set_encoding %[[LHS_PAD]]
-// CHECK-SAME: tensor<?x?xi16, #iree_encoding.encoding<role = LHS, element_types = [i16, ui4, i32], original_type = tensor<32x128xi16>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<4096x32x128xi4, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
+// CHECK-SAME: tensor<?x?xi16, #iree_encoding.encoding<operand_index = 0 : index, element_types = [i16, ui4, i32], original_type = tensor<32x128xi16>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[RHS_TILE_SIZE:.+]]:3 = iree_encoding.upper_bound_tile_size tensor<4096x32x128xi4, #iree_encoding.encoding<operand_index = 1 : index, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index, index
// CHECK: %[[RHS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#0, %[[C4096]]]
// CHECK: %[[RHS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#1, %[[C32]]]
// CHECK: %[[RHS_PADDING_SIZE2:.+]] = affine.apply #[[MAP]]()[%[[RHS_TILE_SIZE]]#2, %[[C128]]]
// CHECK: %[[RHS_PAD:.+]] = tensor.pad %[[ARG1]] low[0, 0, 0] high[%[[RHS_PADDING_SIZE0]], %[[RHS_PADDING_SIZE1]], %[[RHS_PADDING_SIZE2]]]
// CHECK: tensor<4096x32x128xi4> to tensor<?x?x?xi4>
// CHECK: %[[RHS:.+]] = iree_encoding.set_encoding %[[RHS_PAD]]
-// CHECK-SAME: tensor<?x?x?xi4, #iree_encoding.encoding<role = RHS, element_types = [i16, ui4, i32], original_type = tensor<4096x32x128xi4>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
-// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<4096x32xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
+// CHECK-SAME: tensor<?x?x?xi4, #iree_encoding.encoding<operand_index = 1 : index, element_types = [i16, ui4, i32], original_type = tensor<4096x32x128xi4>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK: %[[OUTS_TILE_SIZE:.+]]:2 = iree_encoding.upper_bound_tile_size tensor<4096x32xi32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [i16, ui4, i32], matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>> -> index, index
// CHECK: %[[OUTS_PADDING_SIZE0:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#0, %[[C4096]]]
// CHECK: %[[OUTS_PADDING_SIZE1:.+]] = affine.apply #[[MAP]]()[%[[OUTS_TILE_SIZE]]#1, %[[C32]]]
// CHECK: %[[OUTS_PAD:.+]] = tensor.pad %[[ARG2]] low[0, 0] high[%[[OUTS_PADDING_SIZE0]], %[[OUTS_PADDING_SIZE1]]]
// CHECK: tensor<4096x32xi32> to tensor<?x?xi32>
// CHECK: %[[OUTS:.+]] = iree_encoding.set_encoding %[[OUTS_PAD]]
-// CHECK-SAME: tensor<?x?xi32> -> tensor<?x?xi32, #iree_encoding.encoding<role = RESULT, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
+// CHECK-SAME: tensor<?x?xi32> -> tensor<?x?xi32, #iree_encoding.encoding<operand_index = 2 : index, element_types = [i16, ui4, i32], original_type = tensor<4096x32xi32>, matmul_narrow_M = 1 : index, user_indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]>>
// CHECK: %[[GENERIC:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP1]], #[[MAP2]], #[[MAP3]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]
diff --git a/compiler/src/iree/compiler/Utils/ElementPackingUtils.cpp b/compiler/src/iree/compiler/Utils/ElementPackingUtils.cpp
index eb30045..6a01bac 100644
--- a/compiler/src/iree/compiler/Utils/ElementPackingUtils.cpp
+++ b/compiler/src/iree/compiler/Utils/ElementPackingUtils.cpp
@@ -71,7 +71,7 @@
auto roundDimsTo = encoding.getRoundDimsToArray();
FailureOr<linalg::ContractionDimensions> cDims =
IREE::Encoding::getEncodingContractionDims(encoding);
- auto indexingMap = encoding.getMapForRole();
+ auto indexingMap = encoding.getMapForOperandIndex();
auto pad = [&](int dim, int value) {
std::optional<unsigned> maybeMappedDim =
indexingMap.getResultPosition(builder.getAffineDimExpr(dim));
diff --git a/runtime/src/iree/builtins/ukernel/exported_bits.h b/runtime/src/iree/builtins/ukernel/exported_bits.h
index f2ccfa0..aa25b0d 100644
--- a/runtime/src/iree/builtins/ukernel/exported_bits.h
+++ b/runtime/src/iree/builtins/ukernel/exported_bits.h
@@ -86,7 +86,7 @@
// query_tile_sizes
//===----------------------------------------------------------------------===//
-// OPERAND_ROLE describes the role that a tensor plays in an
+// OPERAND_INDEX describes the index that a tensor plays in an
// operation, e.g. "left-hand-size operand" (e.g. in a matmul).
#define IREE_UK_FLAG_QUERY_TILE_SIZES_OPERAND_ROLE_MASK 0xFF
#define IREE_UK_FLAG_QUERY_TILE_SIZES_OPERAND_ROLE_NONE 0x00
