| #!/usr/bin/env python3 |
| # Copyright 2021 The IREE Authors |
| # |
| # Licensed under the Apache License v2.0 with LLVM Exceptions. |
| # See https://llvm.org/LICENSE.txt for license information. |
| # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| """iree_generated_trace_runner_test generator for e2e matmul tests. |
| """ |
| |
| import argparse |
| import os |
| import yaml |
| import re |
| import enum |
| import dataclasses |
| import typing |
| import itertools |
| |
| |
| # Data type of matrix entries. The string values must match MLIR data types. |
| # This is a superset of the values accepted for the --lhs_rhs_types= flag, |
| # as this also includes accumulator-specific types like i32. |
| @enum.unique |
| class MatrixElemTypeId(enum.Enum): |
| I8 = "i8" |
| I32 = "i32" |
| F32 = "f32" |
| F16 = "f16" |
| |
| |
| # Enumerates of the collections of shapes that we can generate tests for. |
| # The values are the accepted values for the --shapes= flag. |
| @enum.unique |
| class ShapesId(enum.Enum): |
| SMALL = "small" |
| LARGE = "large" |
| GPU_LARGE = "gpu_large" |
| GPU_LARGE_ALIGNED = "gpu_large_aligned" |
| |
| |
| # Enumerates of the collections of compilation info that we can generate tests |
| # for. The values are the accepted values for the --compilation_info= flag. |
| @enum.unique |
| class CompilationInfoId(enum.Enum): |
| NONE = "" |
| LLVMGPUMatmulSimt = "LLVMGPUMatmulSimt" |
| LLVMGPUMatmulTensorCore = "LLVMGPUMatmulTensorCore" |
| LLVMGPUMatmulTensorCoreMmaSync = "LLVMGPUMatmulTensorCoreMmaSync" |
| SPIRVVectorizeMali = "SPIRVVectorizeMali" |
| SPIRVVectorizeNVIDIA = "SPIRVVectorizeNVIDIA" |
| |
| |
| # Enumerates ways to construct MLIR tensor types. |
| @enum.unique |
| class Dynamicity(enum.Enum): |
| DYNAMIC = "dynamic" # Use '?' everywhere. Example: tensor<?x?xf32>. |
| STATIC = "static" # Use fixed values everywhere. Example: tensor<4x6xf32>. |
| MIXED = "mixed" # Randomly mix '?' and values. Example: tensor<?x4xf32>. |
| |
| |
| # Enumerates ways to initialize matrix buffer contents. |
| @enum.unique |
| class MatrixGenerator(enum.Enum): |
| ZERO = "zero" # Fill with zeros |
| RANDOM = "random" # Fill with (deterministic) pseudorandom values. |
| |
| |
| # Describes the shape of a matrix multiplication in the usual convention: |
| # the LHS is {m}x{k}, the RHS is {k}x{n}, the accumulator/result is {m}x{n}. |
| # The extra `accumulate` boolean tells whether the matmul is accumulating into |
| # an existing accumulator (C += A * B) or just overwriting the result |
| # (C = A * B). |
| @dataclasses.dataclass |
| class TestShape: |
| m: int |
| k: int |
| n: int |
| accumulate: bool |
| |
| |
| # Describes how to construct compilation info for the testcase. |
| @dataclasses.dataclass |
| class CompilationInfo: |
| # Lowering Config |
| tile_sizes: typing.List[typing.List[int]] |
| # Translation Info |
| dispatch_lowering_pass_pipeline: str |
| workload_per_wg: typing.List[int] |
| software_pipeline_depth: int |
| # Compilation info |
| workgroup_size: typing.List[int] |
| |
| # Prints the workgroup size as 'index' types |
| def workgroup_size_str(self): |
| return "[" + ", ".join([f"{size} : index" for size in self.workgroup_size |
| ]) + "]" |
| |
| |
| # Returns the list of TestShape's to use for the collection of shapes |
| # identified by shapes_id. |
| def get_test_shapes(shapes_id: ShapesId): |
| # Notes: |
| # 1. Be conservative in adding more shapes, as that can increase both the |
| # build and execution latency of tests. The build latency is nearly the |
| # same for all shapes, while execution latency grows cubicly i.e. |
| # linearly with m*k*n. |
| # 2. Some shapes are commented out: they used to be tested but have been |
| # disabled to improve the trade-off between test coverage and build |
| # latency. |
| if shapes_id == ShapesId.SMALL: |
| return [ |
| # square matrices. Start by the simplest case of 1x1x1. |
| TestShape(m=1, k=1, n=1, accumulate=True), |
| TestShape(m=1, k=1, n=1, accumulate=False), |
| # test 9x9x9 because as many kernel M0/K0/N0 dims are equal to 8, |
| # this will often be the smallest value that exercises something above |
| # the kernel's size. |
| TestShape(m=9, k=9, n=9, accumulate=True), |
| # rectangular matrices. |
| # >= 2x differences between M/N/K dims may exercise tiling corner cases |
| # not exercised by nearly-square matrices. |
| TestShape(m=6, k=13, n=3, accumulate=True), |
| TestShape(m=15, k=37, n=7, accumulate=False), |
| TestShape(m=81, k=19, n=41, accumulate=True), |
| # shapes involving vectors (i.e. most rectangular cases) |
| # This is particularly relevant because we have dedicated kernels for |
| # the matrix*vector / vector*matrix case. |
| TestShape(m=1, k=10, n=10, accumulate=True), # vector*matrix |
| TestShape(m=1, k=10, n=10, accumulate=False), # vector*matrix |
| TestShape(m=10, k=1, n=10, accumulate=True), # outer-product |
| TestShape(m=10, k=10, n=1, accumulate=True), # matrix*vector |
| TestShape(m=10, k=10, n=1, accumulate=False), # matrix*vector |
| ] |
| if shapes_id == ShapesId.LARGE: |
| return [ |
| # some random large sizes |
| TestShape(m=123, k=456, n=789, accumulate=True), |
| TestShape(m=654, k=321, n=234, accumulate=False), |
| # shapes involving vectors (i.e. most rectangular cases) |
| TestShape(m=1, k=1000, n=1000, accumulate=True), # large vector*matrix |
| TestShape(m=1000, k=1000, n=1, accumulate=True), # large matrix*vector |
| TestShape(m=1000, k=1000, n=1, accumulate=False), # large matrix*vector |
| # Be conservative in adding larger shapes. They can result in |
| # high latency tests. If you have to, consider splitting them |
| # out in a way that constrains the latency impact, e.g. by |
| # running on fewer backends/drivers or with fewer generators |
| # (see get_test_generators). |
| ] |
| if shapes_id == ShapesId.GPU_LARGE_ALIGNED: |
| return [ |
| TestShape(m=256, k=128, n=512, accumulate=True), |
| TestShape(m=256, k=128, n=512, accumulate=False), |
| ] |
| if shapes_id == ShapesId.GPU_LARGE: |
| return [ |
| # unaligned cases. |
| TestShape(m=457, k=330, n=512, accumulate=False), |
| TestShape(m=457, k=330, n=514, accumulate=False), |
| TestShape(m=438, k=330, n=514, accumulate=False), |
| TestShape(m=540, k=332, n=516, accumulate=False), |
| TestShape(m=1000, k=4, n=512, accumulate=False), |
| TestShape(m=4, k=1000, n=512, accumulate=False), |
| TestShape(m=512, k=1000, n=4, accumulate=False), |
| ] |
| |
| raise ValueError(shapes_id) |
| |
| |
| # Returns the list of Dynamicity's to use for the collection of shapes |
| # identified by shapes_id. |
| def get_dynamicities(shapes_id: ShapesId): |
| if shapes_id == ShapesId.GPU_LARGE or shapes_id == ShapesId.GPU_LARGE_ALIGNED: |
| return [ |
| Dynamicity.STATIC, |
| ] |
| else: |
| return [ |
| Dynamicity.DYNAMIC, |
| Dynamicity.STATIC, |
| ] |
| raise ValueError(shapes_id) |
| |
| |
| @dataclasses.dataclass |
| class TileWorkgroupSizePair: |
| tile_size: typing.List[typing.List[int]] |
| workgroup_size: typing.List[int] |
| |
| |
| # Constructs a TileWorkgroupSizePair for SPIRV Targets enforcing the constraints between |
| # the workgroup_size and tile size |
| def get_spirv_tile_workgroup_size_pair(workgroup_size, |
| t_tile_k, |
| t_tile_m=4, |
| t_tile_n=4): |
| x, y, z = workgroup_size |
| wg_tile_m = y * t_tile_m |
| wg_tile_n = x * t_tile_n |
| return TileWorkgroupSizePair( |
| [[wg_tile_m, wg_tile_n], [t_tile_m, t_tile_n], [0, 0, t_tile_k]], |
| workgroup_size) |
| |
| |
| # Returns all the TileWorkgroupSizePairs for a given SPIRV Target |
| def get_all_spirv_tile_workgroup_size_pairs(t_tile_k): |
| tile_workgroup_size_pairs = [ |
| get_spirv_tile_workgroup_size_pair([32, 8, 1], t_tile_k), |
| get_spirv_tile_workgroup_size_pair([16, 8, 1], t_tile_k), |
| get_spirv_tile_workgroup_size_pair([64, 2, 1], t_tile_k), |
| get_spirv_tile_workgroup_size_pair([8, 8, 1], t_tile_k), |
| get_spirv_tile_workgroup_size_pair([32, 1, 1], t_tile_k), |
| get_spirv_tile_workgroup_size_pair([16, 2, 1], t_tile_k), |
| get_spirv_tile_workgroup_size_pair([32, 1, 1], t_tile_k), |
| ] |
| return tile_workgroup_size_pairs |
| |
| |
| # Returns the list of CompilationInfo's to use for the CompilationInfoId. |
| def get_test_compilation_infos( |
| compilation_info_id: CompilationInfoId, lhs_rhs_type: MatrixElemTypeId |
| ) -> typing.List[typing.Optional[CompilationInfo]]: |
| if compilation_info_id == CompilationInfoId.NONE: |
| return [None] |
| if compilation_info_id == CompilationInfoId.LLVMGPUMatmulSimt: |
| tile_workgroup_size_pairs = [ |
| TileWorkgroupSizePair([[32, 128, 32]], [32, 8, 1]), |
| TileWorkgroupSizePair([[128, 64, 8]], [16, 8, 1]), |
| TileWorkgroupSizePair([[16, 256, 32]], [64, 2, 1]), |
| TileWorkgroupSizePair([[8, 32, 32]], [8, 8, 1]), |
| TileWorkgroupSizePair([[8, 128, 4]], [32, 1, 1]), |
| TileWorkgroupSizePair([[16, 64, 4]], [16, 2, 1]), |
| TileWorkgroupSizePair([[1, 128, 8]], [32, 1, 1]), |
| ] |
| elif compilation_info_id == CompilationInfoId.SPIRVVectorizeNVIDIA: |
| tile_workgroup_size_pairs = get_all_spirv_tile_workgroup_size_pairs(32) |
| elif compilation_info_id == CompilationInfoId.SPIRVVectorizeMali: |
| tile_workgroup_size_pairs = get_all_spirv_tile_workgroup_size_pairs(4) |
| elif compilation_info_id == CompilationInfoId.LLVMGPUMatmulTensorCore or compilation_info_id == CompilationInfoId.LLVMGPUMatmulTensorCoreMmaSync: |
| tile_workgroup_size_pairs = [] |
| ## WarpShape = 2x2 |
| tile_workgroup_size_pairs.append( |
| TileWorkgroupSizePair([[32, 32, 16]], [64, 2, 1])) |
| tile_workgroup_size_pairs.append( |
| TileWorkgroupSizePair([[64, 64, 64]], [64, 2, 1])) |
| |
| ## WarpShape = 4x1 |
| tile_workgroup_size_pairs.append( |
| TileWorkgroupSizePair([[32, 32, 32]], [64, 1, 1])) |
| |
| ## WarpShape = 2x2 with large tiles using larger Shared Memory capacity. |
| if lhs_rhs_type == MatrixElemTypeId.F16: |
| tile_workgroup_size_pairs.append( |
| TileWorkgroupSizePair([[128, 128, 64]], [64, 2, 1])) |
| elif lhs_rhs_type == MatrixElemTypeId.F32: |
| tile_workgroup_size_pairs.append( |
| TileWorkgroupSizePair([[128, 128, 16]], [64, 2, 1])) |
| |
| compilation_infos = [] |
| for tile_workgroup_size_pair in tile_workgroup_size_pairs: |
| compilation_infos.append( |
| CompilationInfo( |
| tile_sizes=tile_workgroup_size_pair.tile_size, |
| dispatch_lowering_pass_pipeline=compilation_info_id.value, |
| workload_per_wg=[ |
| a for a in reversed(tile_workgroup_size_pair.tile_size[0:2]) |
| ], |
| workgroup_size=tile_workgroup_size_pair.workgroup_size, |
| software_pipeline_depth=3)) |
| return compilation_infos |
| |
| |
| # Intentionally fixed seed! We want full reproducibility here, both across runs |
| # and across machines. |
| # Intentionally not shared with pseudorandom_generator_seed to limit the ways |
| # in which shuffling testcases changes which random values are generated. |
| local_pseudorandom_state = 1 |
| |
| |
| # A shape dimension value, i.e. a size value that could appear in a MLIR type |
| # such as 'tensor<?x4xf32>'. None means a dynamic size, similar to '?' in MLIR. |
| @dataclasses.dataclass |
| class DimSize: |
| value: typing.Optional[int] |
| |
| |
| # Generates a compile-time MLIR size value, i.e. either a fixed positive integer |
| # or None (which maps to MLIR '?') depending on dynamicity. |
| def shape_dim(x: int, dynamicity: Dynamicity): |
| if dynamicity == Dynamicity.DYNAMIC: |
| return DimSize(None) |
| elif dynamicity == Dynamicity.STATIC: |
| return DimSize(x) |
| else: |
| raise ValueError(dynamicity) |
| |
| |
| # Stringification used for generating MLIR types, e.g. tensor<?x?xf32>. |
| def int_or_question_mark(s: DimSize): |
| return s.value or "?" |
| |
| |
| # Stringification used for generating alphanumeric identifiers, e.g. |
| # func.func @somefunction_DYNxDYNxf32, where we can't use "?" characters. |
| def int_or_DYN(s: DimSize): |
| return s.value or "DYN" |
| |
| |
| # Describes the fully resolved shape dimensions of all 3 input matrices, |
| # LHS, RHS, and Accumulator, in a testcase. |
| # Each value is a string, which may either represent a positive integer such as "123", |
| # or a "?" string, meaning a dynamic dimension as in MLIR. |
| # These string values are used to generate MLIR function names and tensor shapes. |
| @dataclasses.dataclass |
| class TestInputMatricesShapes: |
| lhs_rows: DimSize |
| lhs_cols: DimSize |
| rhs_rows: DimSize |
| rhs_cols: DimSize |
| acc_rows: DimSize |
| acc_cols: DimSize |
| |
| |
| # Helper for generate_function. Generates TestInputMatricesShapes, i.e. |
| # converts from the runtime shape dimensions in TestShape and given dynamicity to |
| # the set of shapes to be used in a test function's input tensors. |
| def generate_shapes(shape: TestShape, dynamicity: Dynamicity): |
| shapes = TestInputMatricesShapes( |
| lhs_rows=shape_dim(shape.m, dynamicity), |
| lhs_cols=shape_dim(shape.k, dynamicity), |
| rhs_rows=shape_dim(shape.k, dynamicity), |
| rhs_cols=shape_dim(shape.n, dynamicity), |
| acc_rows=shape_dim(shape.m, dynamicity), |
| acc_cols=shape_dim(shape.n, dynamicity), |
| ) |
| return shapes |
| |
| |
| # Helper for generate_function. |
| # Generates a name for a test function in the generated MLIR code. |
| def generate_function_name( |
| lhs_rhs_type: MatrixElemTypeId, |
| acc_type: MatrixElemTypeId, |
| shapes: TestInputMatricesShapes, |
| accumulate: bool, |
| compilation_info: typing.Optional[CompilationInfo] = None): |
| input_t = lhs_rhs_type.value |
| acc_t = acc_type.value |
| lhs_m = int_or_DYN(shapes.lhs_rows) |
| lhs_k = int_or_DYN(shapes.lhs_cols) |
| rhs_k = int_or_DYN(shapes.rhs_rows) |
| rhs_n = int_or_DYN(shapes.rhs_cols) |
| acc_m = int_or_DYN(shapes.acc_rows) |
| acc_n = int_or_DYN(shapes.acc_cols) |
| |
| info = "" |
| if compilation_info: |
| tile_sizes = list(itertools.chain(*compilation_info.tile_sizes)) |
| tile_workgroup_key = "_".join([ |
| str(a) for a in tile_sizes |
| ]) + "_" + "_".join([str(a) for a in compilation_info.workgroup_size]) |
| info = f"_for_{compilation_info.dispatch_lowering_pass_pipeline}_{tile_workgroup_key}" |
| |
| matmul_kind = "matmul_accumulate" if accumulate else "matmul" |
| return f"{matmul_kind}_{lhs_m}x{lhs_k}x{input_t}_times_{rhs_k}x{rhs_n}x{input_t}_into_{acc_m}x{acc_n}x{acc_t}{info}" |
| |
| |
| # Represents a generated test function. |
| @dataclasses.dataclass |
| class MLIRFunction: |
| name: str |
| definition: str |
| |
| |
| # Generates a test function in the generated MLIR code. |
| # The generated function will take the same arguments as linalg.matmul and |
| # will just call linalg.matmul with them, returning its result. |
| def generate_function( |
| lhs_rhs_type: MatrixElemTypeId, |
| acc_type: MatrixElemTypeId, |
| shape: TestShape, |
| dynamicity: Dynamicity, |
| compilation_info: typing.Optional[CompilationInfo] = None): |
| shapes = generate_shapes(shape, dynamicity) |
| func_name = generate_function_name(lhs_rhs_type, acc_type, shapes, |
| shape.accumulate, compilation_info) |
| lhs_m = int_or_question_mark(shapes.lhs_rows) |
| lhs_k = int_or_question_mark(shapes.lhs_cols) |
| rhs_k = int_or_question_mark(shapes.rhs_rows) |
| rhs_n = int_or_question_mark(shapes.rhs_cols) |
| acc_m = int_or_question_mark(shapes.acc_rows) |
| acc_n = int_or_question_mark(shapes.acc_cols) |
| lhs_tensor_type = f"tensor<{lhs_m}x{lhs_k}x{lhs_rhs_type.value}>" |
| rhs_tensor_type = f"tensor<{rhs_k}x{rhs_n}x{lhs_rhs_type.value}>" |
| acc_tensor_type = f"tensor<{acc_m}x{acc_n}x{acc_type.value}>" |
| |
| # Compilation info is optional; prints empty string by default. |
| func_definition = "" |
| compilation_info_attr = "" |
| if compilation_info: |
| if "SPIRV" in compilation_info.dispatch_lowering_pass_pipeline == "SPIRVVectorizeMali": |
| dispatch_lowering_pass_pipeline = "SPIRVBaseVectorize" |
| elif compilation_info.dispatch_lowering_pass_pipeline == "SPIRVVectorizeNVIDIA": |
| # TODO: change to test SPIRVMatmulPromoteVectorize too |
| dispatch_lowering_pass_pipeline = "SPIRVBaseVectorize" |
| else: |
| dispatch_lowering_pass_pipeline = compilation_info.dispatch_lowering_pass_pipeline |
| compilation_info_string = ( |
| f"#compilation{generate_function.compilation_index} = #iree_codegen.compilation_info<\n" |
| f" lowering_config = <tile_sizes = {compilation_info.tile_sizes}>,\n" |
| f" translation_info = <{dispatch_lowering_pass_pipeline}\n" |
| f" pipeline_depth = {compilation_info.software_pipeline_depth}>,\n" |
| f" workgroup_size = {compilation_info.workgroup_size_str()}>\n") |
| compilation_info_attr = f"{{compilation_info = #compilation{generate_function.compilation_index}}} " |
| func_definition = func_definition + compilation_info_string |
| generate_function.compilation_index += 1 |
| |
| if shape.accumulate: |
| func_definition = func_definition + ( |
| f"func.func @{func_name}(%lhs: {lhs_tensor_type}, %rhs: {rhs_tensor_type}, %acc: {acc_tensor_type}) -> {acc_tensor_type} {{\n" |
| f" %result = linalg.matmul {compilation_info_attr}ins(%lhs, %rhs: {lhs_tensor_type}, {rhs_tensor_type}) outs(%acc: {acc_tensor_type}) -> {acc_tensor_type}\n" |
| f" return %result: {acc_tensor_type}\n" |
| f"}}\n") |
| else: |
| literal_zero_for_acc_type = "0.0" if "f" in acc_type.value else "0" |
| acc_dyn_sizes = [] |
| if acc_m == "?": |
| func_definition = func_definition + ( |
| f"func.func @{func_name}(%lhs: {lhs_tensor_type}, %rhs: {rhs_tensor_type}) -> {acc_tensor_type} {{\n" |
| f" %c0 = arith.constant 0 : index\n" |
| f" %c1 = arith.constant 1 : index\n" |
| f" %acc_dim0 = tensor.dim %lhs, %c0 : {lhs_tensor_type}\n" |
| f" %acc_dim1 = tensor.dim %rhs, %c1 : {rhs_tensor_type}\n" |
| f" %init_acc = tensor.empty(%acc_dim0, %acc_dim1) : {acc_tensor_type}\n" |
| f" %c0_acc_type = arith.constant {literal_zero_for_acc_type}: {acc_type.value}\n" |
| f" %acc = linalg.fill ins(%c0_acc_type : {acc_type.value}) outs(%init_acc : {acc_tensor_type}) -> {acc_tensor_type}\n" |
| f" %result = linalg.matmul {compilation_info_attr}ins(%lhs, %rhs: {lhs_tensor_type}, {rhs_tensor_type}) outs(%acc: {acc_tensor_type}) -> {acc_tensor_type}\n" |
| f" return %result: {acc_tensor_type}\n" |
| f"}}\n") |
| else: |
| func_definition = func_definition + ( |
| f"func.func @{func_name}(%lhs: {lhs_tensor_type}, %rhs: {rhs_tensor_type}) -> {acc_tensor_type} {{\n" |
| f" %init_acc = tensor.empty() : {acc_tensor_type}\n" |
| f" %c0_acc_type = arith.constant {literal_zero_for_acc_type}: {acc_type.value}\n" |
| f" %acc = linalg.fill ins(%c0_acc_type : {acc_type.value}) outs(%init_acc : {acc_tensor_type}) -> {acc_tensor_type}\n" |
| f" %result = linalg.matmul {compilation_info_attr}ins(%lhs, %rhs: {lhs_tensor_type}, {rhs_tensor_type}) outs(%acc: {acc_tensor_type}) -> {acc_tensor_type}\n" |
| f" return %result: {acc_tensor_type}\n" |
| f"}}\n") |
| return MLIRFunction( |
| name=func_name, |
| definition=func_definition, |
| ) |
| |
| |
| # Counter for producing unique compilation info attrs |
| generate_function.compilation_index = 0 |
| |
| # Intentionally fixed seed! We want full reproducibility here, both across runs |
| # and across machines. |
| # Intentionally not shared with local_pseudorandom_state to limit the ways |
| # in which shuffling testcases changes which random values are generated. |
| pseudorandom_generator_seed = 1 |
| |
| |
| def contents_generator_tag(generator: MatrixGenerator): |
| if generator == MatrixGenerator.ZERO: |
| return "" |
| elif generator == MatrixGenerator.RANDOM: |
| global pseudorandom_generator_seed |
| pseudorandom_generator_seed = pseudorandom_generator_seed + 1 |
| return f"!tag:iree:fully_specified_pseudorandom {pseudorandom_generator_seed}" |
| else: |
| raise ValueError(generator) |
| |
| |
| # Generate a matrix function argument in the output trace, as a dictionary |
| # to be passed to yaml.dump. |
| def generate_trace_matrix_arg(matrix_shape: list, |
| element_type: MatrixElemTypeId, |
| generator: MatrixGenerator): |
| result = { |
| "type": "hal.buffer_view", |
| "shape": matrix_shape, |
| "element_type": element_type.value, |
| } |
| generator_tag = contents_generator_tag(generator) |
| if generator_tag: |
| result["contents_generator"] = generator_tag |
| return result |
| |
| |
| # Generates the output trace for a testcase i.e. a single test function call, |
| # as a dictionary to be passed to yaml.dump. |
| def generate_trace(func_name: str, lhs_rhs_type: MatrixElemTypeId, |
| acc_type: MatrixElemTypeId, shape: TestShape): |
| args = [ |
| generate_trace_matrix_arg([shape.m, shape.k], lhs_rhs_type, |
| MatrixGenerator.RANDOM), |
| generate_trace_matrix_arg([shape.k, shape.n], lhs_rhs_type, |
| MatrixGenerator.RANDOM), |
| ] |
| if shape.accumulate: |
| args.append( |
| generate_trace_matrix_arg([shape.m, shape.n], acc_type, |
| MatrixGenerator.RANDOM)) |
| |
| result = generate_trace_matrix_arg([shape.m, shape.n], acc_type, |
| MatrixGenerator.ZERO) |
| return { |
| "type": "call", |
| "function": "module." + func_name, |
| "args": args, |
| "results": [result], |
| } |
| |
| |
| # Generates all output files' contents as strings. |
| def generate(lhs_rhs_type: MatrixElemTypeId, acc_type: MatrixElemTypeId, |
| shapes_id: ShapesId, compilation_info_id: CompilationInfoId): |
| function_definitions = {} |
| traces = [] |
| |
| for compilation_info in get_test_compilation_infos(compilation_info_id, |
| lhs_rhs_type): |
| for shape in get_test_shapes(shapes_id): |
| for dynamicity in get_dynamicities(shapes_id): |
| function = generate_function(lhs_rhs_type, acc_type, shape, dynamicity, |
| compilation_info) |
| # Different testcases may differ only by runtime parameters but |
| # share the same code. For example, dynamic-shapes testcases |
| # share the same code involing tensor<?x?xf32> even though the runtime |
| # value in the trace are different. That's why we append conditionally |
| # to traces, but unconditionally to function_definitions. |
| if function.name not in function_definitions: |
| function_definitions[function.name] = function.definition |
| traces.append( |
| generate_trace(function.name, lhs_rhs_type, acc_type, shape)) |
| |
| return (function_definitions, traces) |
| |
| |
| def parse_arguments(): |
| parser = argparse.ArgumentParser(description="Generator of e2e matmul tests") |
| parser.add_argument("--output_code", |
| type=str, |
| help="Path of output .mlir file", |
| required=True) |
| parser.add_argument("--output_trace", |
| type=str, |
| help="Path of output .yaml trace file", |
| required=True) |
| parser.add_argument("--lhs_rhs_type", |
| type=str, |
| choices=["i8", "f32", "f16"], |
| help="Numeric type of input matrices", |
| required=True) |
| parser.add_argument("--shapes", |
| type=str, |
| choices=[s.value for s in ShapesId], |
| help="Collection of matrix shapes to test", |
| required=True) |
| parser.add_argument("--compilation_info", |
| type=str, |
| choices=[i.value for i in CompilationInfoId], |
| help="Collection of compilation info setups to test", |
| default="", |
| required=False) |
| |
| parser.add_argument( |
| "--module_path", |
| type=str, |
| help= |
| "Module path (typically .vmfb) to be referenced in the output trace. Should match the output path of the iree-compile command generating the module.", |
| required=True) |
| parser.add_argument( |
| "--requirements", |
| type=str, |
| help= |
| "Target requirements for this module. Comma-separated. As in -iree-llvmcpu-target-cpu-features. If the target device does not meet all of the requirements, the test will be skipped.", |
| required=False) |
| return parser.parse_args() |
| |
| |
| def write_code_file(function_definitions, filename): |
| with open(filename, "w") as file: |
| for funcname in function_definitions: |
| file.write(function_definitions[funcname] + "\n") |
| |
| |
| def write_trace_file(traces, filename, module_path, requirements): |
| yaml_documents = [ |
| { |
| "type": "context_load", |
| }, |
| { |
| "type": "module_load", |
| "module": { |
| "name": "hal", |
| "type": "builtin", |
| } |
| }, |
| { |
| "type": "module_load", |
| "module": { |
| "name": "module", |
| "type": "bytecode", |
| "path": os.path.relpath(module_path, os.path.dirname(filename)) |
| } |
| }, |
| ] |
| if requirements: |
| yaml_documents.append({ |
| "type": "requirements", |
| "target_features": [req.lstrip("+") for req in requirements.split(",")], |
| }) |
| |
| for trace in traces: |
| yaml_documents.append(trace) |
| |
| dumped_yaml = yaml.dump_all(yaml_documents) |
| |
| # TODO: This regex substitution is a hack as I couldn't figure how to have |
| # PyYAML dump our custom contents_generator into the desired format, e.g. |
| # contents_generator: !tag:iree:fully_specified_pseudorandom 368 |
| # Someone with better knowledge of YAML is welcome to fix this, possibly by |
| # changing that format if that's appropriate! So long as the e2e_matmul tests |
| # pass. |
| processed_yaml = re.sub(r"'(![^']*)'", "\\1", dumped_yaml) |
| |
| with open(filename, "w") as file: |
| file.write(processed_yaml) |
| |
| |
| # For now, the accumulator type can always be inferred from the input LHS/RHS |
| # type, so we do that. That is temporary: eventually there will be cases |
| # where the same input types are used with different accumulator types, e.g. |
| # f16 inputs with both f16 and f32 accumulator. |
| def infer_acc_type(lhs_rhs_type: MatrixElemTypeId): |
| if lhs_rhs_type == MatrixElemTypeId.I8: |
| return MatrixElemTypeId.I32 |
| else: |
| return lhs_rhs_type |
| |
| |
| def main(args): |
| lhs_rhs_type = MatrixElemTypeId(args.lhs_rhs_type) |
| acc_type = infer_acc_type(lhs_rhs_type) |
| shapes_id = ShapesId(args.shapes) |
| compilation_info_id = CompilationInfoId(args.compilation_info) |
| (function_definitions, traces) = generate(lhs_rhs_type, acc_type, shapes_id, |
| compilation_info_id) |
| |
| write_code_file(function_definitions, args.output_code) |
| write_trace_file(traces, args.output_trace, args.module_path, |
| args.requirements) |
| |
| |
| if __name__ == "__main__": |
| main(parse_arguments()) |
