Merge google -> main (#2333)
* Control timeline semaphore emulation at runtime, remove extension layer
* Add shape constraints to XLA CHLO-LHLO lowering
* bazel_to_cmake fixes
* LLVM integrates
diff --git a/.github/workflows/update_submodules.yml b/.github/workflows/update_tf.yml
similarity index 95%
rename from .github/workflows/update_submodules.yml
rename to .github/workflows/update_tf.yml
index 68fd9c9..057918b 100644
--- a/.github/workflows/update_submodules.yml
+++ b/.github/workflows/update_tf.yml
@@ -15,7 +15,7 @@
# Creates a PR to update the TF submodule to HEAD & copy the latest
# version of the LLVM Bazel BUILD files from TF.
-name: Update Submodules
+name: Update TensorFlow
on:
schedule:
@@ -23,13 +23,15 @@
- cron: '0 */6 * * *'
jobs:
- update:
+ update_tf:
# Don't run this in everyone's forks.
if: github.repository == 'google/iree'
runs-on: ubuntu-18.04
steps:
- name: Checking out repository
uses: actions/checkout@v2
+ with:
+ - ref: "google"
- name: Initializing submodules
run: ./scripts/git/submodule_versions.py init
- name: Updating submodules
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 117179d..d4dddf8 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -24,8 +24,10 @@
consistently across the project. Most of the code style is derived from the
[Google Style Guides](http://google.github.io/styleguide/) for the appropriate
language and is generally not something we accept changes on (as clang-format
-and clang-tidy handle that for us). Improvements to code structure and clarity
-are welcome but please file issues to track such work first.
+and clang-tidy handle that for us). The compiler portion of the project follows
+[MLIR style](https://mlir.llvm.org/getting_started/DeveloperGuide/#style-guide).
+Improvements to code structure and clarity are welcome but please file issues to
+track such work first.
## Code reviews
@@ -39,25 +41,26 @@
Several of our presubmit builds will only run automatically if you are a project
collaborator. Otherwise a collaborator must label the PR with "kokoro:run". If
you are sending code changes to the project, please ask to be added as a
-collaborator, so that these can run automatically.
+collaborator, so that these can run automatically. It is generally expected that
+PRs will only be merged when all presubmit checks are passing. In some cases,
+pre-existing failures may be ignored.
+
+## Merging
+
+After review and presubmit checks, PRs should be merged with a "squash and
+merge". The squashed commit summary should match the PR title and the commit
+description should match the PR body. Accordingly, please write these as you
+would a helpful commit message. Please also keep PRs small (focused on a single
+issue) to streamline review and ease later culprit-finding. It is assumed that
+the PR author will authors merge their change unless they ask someone else to
+merge it for them (e.g. because they don't have write access).
## Peculiarities
-We use a GitHub integration to import PRs into our upstream (Google internal)
-source code management. Once it is approved internally, each PR will be merged
-into the master branch as a single commit by the same tooling. The description
-will match the PR title followed by the PR description. Accordingly, please
-write these as you would a helpful commit message. Please also keep PRs small
-(focused on a single issue) to streamline review and ease later culprit-finding.
-
-As part of a migration to make the project GitHub-first, our default branch is
-currently called `google` and all PRs should be directed there. This is an
-intermediate state. See
-https://groups.google.com/d/msg/iree-discuss/F07vsG9Ah4o/uAIusKO-BQAJ
-
Our documentation on
[repository management](https://github.com/google/iree/blob/master/docs/repository_management.md)
has more information on some of the oddities in our repository setup and
+workflows. For the most part, these should be transparent to normal developer
workflows.
## Community Guidelines
diff --git a/README.md b/README.md
index 84eccc6..06f88b8 100644
--- a/README.md
+++ b/README.md
@@ -110,12 +110,12 @@
## Build Status
-CI System | Build System | Platform | Component | Status
-:-------: | :----------: | :------: | :-------------: | :----:
-Kokoro | Bazel | Linux | Core | [](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/core/google_result.html)
-Kokoro | Bazel | Linux | Bindings | [](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/bindings/google_result.html)
-Kokoro | Bazel | Linux | Integrations | [](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/integrations/google_result.html)
-Kokoro | CMake | Linux | Core + Bindings | [](https://storage.googleapis.com/iree-oss-build-badges/linux/cmake/google_result.html)
+CI System | Platform | Build System | Component | Status
+:-------: | :------: | :----------: | :-------------: | :----:
+Kokoro | Linux | Bazel | Core | [](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/core/main_result.html)
+Kokoro | Linux | Bazel | Bindings | [](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/bindings/main_result.html)
+Kokoro | Linux | Bazel | Integrations | [](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/integrations/main_result.html)
+Kokoro | Linux | CMake | Core + Bindings | [](https://storage.googleapis.com/iree-oss-build-badges/linux/cmake/main_result.html)
## License
diff --git a/colab/mnist_tensorflow.ipynb b/colab/mnist_tensorflow.ipynb
index df7c055..b791a3b 100644
--- a/colab/mnist_tensorflow.ipynb
+++ b/colab/mnist_tensorflow.ipynb
@@ -51,7 +51,7 @@
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
],
- "execution_count": 0,
+ "execution_count": 1,
"outputs": []
},
{
@@ -95,7 +95,7 @@
"base_uri": "https://localhost:8080/",
"height": 51
},
- "outputId": "d471e3cd-ac1d-42f0-c344-4490a51f3a54"
+ "outputId": "fe0d703a-2ad7-4d14-9aef-c69b4c342a16"
},
"source": [
"import os\n",
@@ -119,8 +119,8 @@
{
"output_type": "stream",
"text": [
- "TensorFlow version: 2.1.0-dev20191126\n",
- "Numpy version: 1.17.4\n"
+ "TensorFlow version: 2.5.0-dev20200626\n",
+ "Numpy version: 1.18.4\n"
],
"name": "stdout"
}
@@ -132,11 +132,11 @@
"id": "43BH_9YcsGs8",
"colab_type": "code",
"cellView": "form",
- "outputId": "fc788105-8739-45d6-8df2-e245d5ff6cb1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
- }
+ },
+ "outputId": "46a560cb-5947-4cfa-f71e-9065bf2c07ab"
},
"source": [
"#@title Notebook settings { run: \"auto\" }\n",
@@ -170,7 +170,7 @@
{
"output_type": "stream",
"text": [
- "Using IREE compiler backend 'vulkan-spirv' and runtime driver 'vulkan'\n"
+ "Using IREE compiler backend 'vulkan-spirv' and runtime driver 'vulkan'\r\n"
],
"name": "stdout"
}
@@ -194,11 +194,11 @@
"id": "GXZIrReTbTHN",
"colab_type": "code",
"cellView": "form",
- "outputId": "5faf02eb-aab4-4e4c-9df9-e7665ce5a810",
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 506
- }
+ "height": 486
+ },
+ "outputId": "9c01fab1-f8cb-4a63-fff1-6b82a9b6b49d"
},
"source": [
"#@title Load MNIST dataset, setup training and evaluation\n",
@@ -258,8 +258,8 @@
{
"output_type": "stream",
"text": [
- "Loaded MNIST dataset!\n",
- "INFO:tensorflow:ParameterServerStrategy (CentralStorageStrategy if you are using a single machine) with compute_devices = ('/device:CPU:0',), variable_device = '/device:CPU:0'\n",
+ "Loaded MNIST dataset!\r\n",
+ "INFO:tensorflow:ParameterServerStrategy (CentralStorageStrategy if you are using a single machine) with compute_devices = ['/job:localhost/replica:0/task:0/device:CPU:0'], variable_device = '/job:localhost/replica:0/task:0/device:CPU:0'\n",
"Configured data for training and evaluation!\n",
" sample shape: (28, 28, 1)\n",
" training samples: 60000\n",
@@ -278,7 +278,7 @@
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
- "image/png": "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\n"
+ "image/png": "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\n"
},
"metadata": {
"tags": [],
@@ -289,7 +289,7 @@
"output_type": "stream",
"text": [
"\n",
- "Ground truth labels: [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n"
+ "Ground truth labels: [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\r\n"
],
"name": "stdout"
}
@@ -318,7 +318,7 @@
" model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
" return model"
],
- "execution_count": 0,
+ "execution_count": 5,
"outputs": []
},
{
@@ -327,11 +327,11 @@
"id": "7Gdxh7qWcPSO",
"colab_type": "code",
"cellView": "form",
- "outputId": "9886da4f-1916-4540-9f53-cc82f8ab113d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 374
- }
+ },
+ "outputId": "50b0aede-9a8f-4ce5-b340-783be5fbfc06"
},
"source": [
"#@title Train the Keras model\n",
@@ -364,7 +364,7 @@
" training_loss.update_state(loss)\n",
" training_accuracy.update_state(labels, logits)\n",
"\n",
- " strategy.experimental_run_v2(step_fn, args=(next(iterator),))\n",
+ " strategy.run(step_fn, args=(next(iterator),))\n",
"\n",
" @tf.function\n",
" def test_step(iterator):\n",
@@ -378,7 +378,7 @@
" test_loss.update_state(loss)\n",
" test_accuracy.update_state(labels, logits)\n",
"\n",
- " strategy.experimental_run_v2(step_fn, args=(next(iterator),))\n",
+ " strategy.run(step_fn, args=(next(iterator),))\n",
"\n",
" for epoch in range(0, num_epochs):\n",
" tf.print(\"Running epoch #%s\" % (epoch + 1))\n",
@@ -413,24 +413,24 @@
"text": [
"Constructed Keras MNIST model, training...\n",
"Running epoch #1\n",
- " Training loss: 0.733241, accuracy: 81.463333\n",
- " Test loss : 0.388786, accuracy: 89.590004\n",
+ " Training loss: 0.732439, accuracy: 81.403336\n",
+ " Test loss : 0.390855, accuracy: 89.490005\n",
"Running epoch #2\n",
- " Training loss: 0.362705, accuracy: 89.995003\n",
- " Test loss : 0.313008, accuracy: 91.320000\n",
+ " Training loss: 0.365308, accuracy: 89.811668\n",
+ " Test loss : 0.315630, accuracy: 91.119995\n",
"Running epoch #3\n",
- " Training loss: 0.308992, accuracy: 91.294998\n",
- " Test loss : 0.279223, accuracy: 92.150002\n",
+ " Training loss: 0.312111, accuracy: 91.129997\n",
+ " Test loss : 0.281829, accuracy: 92.040001\n",
"Running epoch #4\n",
- " Training loss: 0.278085, accuracy: 92.198334\n",
- " Test loss : 0.256378, accuracy: 92.629997\n",
+ " Training loss: 0.281028, accuracy: 92.038330\n",
+ " Test loss : 0.258432, accuracy: 92.629997\n",
"Running epoch #5\n",
- " Training loss: 0.255360, accuracy: 92.818329\n",
- " Test loss : 0.238477, accuracy: 93.099998\n",
+ " Training loss: 0.257909, accuracy: 92.753334\n",
+ " Test loss : 0.240058, accuracy: 93.229996\n",
"Completed training!\n",
"\n",
"Sample prediction:\n",
- "[1.24276197 0.00645936839 94.5438538 1.92194772 5.14127169e-06 1.25558567 0.77046895 1.55102152e-05 0.25887084 2.61111491e-05]\n",
+ "[0.243134052 0.00337268948 95.5214081 0.925373673 2.25061958e-05 0.992091119 2.20864391 3.87712953e-06 0.105901182 4.44369543e-05]\n",
"\n"
],
"name": "stdout"
@@ -442,11 +442,11 @@
"metadata": {
"id": "DmespEaFcSEL",
"colab_type": "code",
- "outputId": "34ecdcca-5ceb-43f0-c793-e209e25dafce",
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 122
- }
+ "height": 153
+ },
+ "outputId": "3c8579db-7b3c-4164-ff91-394346595107"
},
"source": [
"#@title Export the trained model as a SavedModel, with IREE-compatible settings\n",
@@ -472,11 +472,14 @@
{
"output_type": "stream",
"text": [
- "Exporting SavedModel to /tmp/mnist.sm\n",
- "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1788: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+ "Exporting SavedModel to /tmp/mnist.sm\r\n",
+ "WARNING:tensorflow:From c:\\users\\scott\\scoop\\apps\\python\\current\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
- "If using Keras pass *_constraint arguments to layers.\n",
- "INFO:tensorflow:Assets written to: /tmp/mnist.sm/assets\n"
+ "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
+ "WARNING:tensorflow:From c:\\users\\scott\\scoop\\apps\\python\\current\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
+ "INFO:tensorflow:Assets written to: /tmp/mnist.sm\\assets\n"
],
"name": "stdout"
}
@@ -497,11 +500,11 @@
"metadata": {
"id": "rqwIx4j4gS1a",
"colab_type": "code",
- "outputId": "28c7516c-903c-4579-c4bc-8299712a9edd",
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 751
- }
+ "height": 836
+ },
+ "outputId": "24cded90-c436-47ce-b4f4-7a5da46ea38a"
},
"source": [
"#@title Load the SavedModel into IREE's compiler as MLIR xla_hlo\n",
@@ -524,16 +527,16 @@
"Imported MLIR:\n",
" \n",
"\n",
- "module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 175 : i32}} {\n",
- " flow.variable @\"__iree_flow___sm_node15__model.layer-2.kernel\" opaque<\"\", \"0xDEADBEEF\"> : tensor<784x128xf32>\n",
- " flow.variable @\"__iree_flow___sm_node16__model.layer-2.bias\" opaque<\"\", \"0xDEADBEEF\"> : tensor<128xf32>\n",
- " flow.variable @\"__iree_flow___sm_node21__model.layer-3.kernel\" opaque<\"\", \"0xDEADBEEF\"> : tensor<128x10xf32>\n",
- " flow.variable @\"__iree_flow___sm_node22__model.layer-3.bias\" dense<[-0.0719004869, 0.1290171, 0.0102811698, -0.106104381, 0.0260288324, 0.166622087, -8.5693755E-4, 0.070880115, -0.222566754, -0.00140074058]> : tensor<10xf32>\n",
- " func @predict(%arg0: tensor<1x28x28x1xf32>) -> tensor<1x10xf32> attributes {iree.module.export, iree.reflection = {abi = \"sip\", abiv = 1 : i32, sip = \"I8!S5!k0_0R3!_0\"}, tf._input_shapes = [\"tfshape$dim { size: 1 } dim { size: 28 } dim { size: 28 } dim { size: 1 }\", \"tfshape$unknown_rank: true\", \"tfshape$unknown_rank: true\", \"tfshape$unknown_rank: true\", \"tfshape$unknown_rank: true\"], tf.signature.is_stateful} {\n",
- " %0 = flow.variable.address @\"__iree_flow___sm_node15__model.layer-2.kernel\" : !iree.ptr<tensor<784x128xf32>>\n",
- " %1 = flow.variable.address @\"__iree_flow___sm_node16__model.layer-2.bias\" : !iree.ptr<tensor<128xf32>>\n",
- " %2 = flow.variable.address @\"__iree_flow___sm_node21__model.layer-3.kernel\" : !iree.ptr<tensor<128x10xf32>>\n",
- " %3 = flow.variable.address @\"__iree_flow___sm_node22__model.layer-3.bias\" : !iree.ptr<tensor<10xf32>>\n",
+ "module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 443 : i32}} {\n",
+ " flow.variable @\"__iree_flow___sm_node14__model.layer-1.kernel\" opaque<\"\", \"0xDEADBEEF\"> : tensor<784x128xf32> attributes {sym_visibility = \"private\"}\n",
+ " flow.variable @\"__iree_flow___sm_node15__model.layer-1.bias\" opaque<\"\", \"0xDEADBEEF\"> : tensor<128xf32> attributes {sym_visibility = \"private\"}\n",
+ " flow.variable @\"__iree_flow___sm_node20__model.layer-2.kernel\" opaque<\"\", \"0xDEADBEEF\"> : tensor<128x10xf32> attributes {sym_visibility = \"private\"}\n",
+ " flow.variable @\"__iree_flow___sm_node21__model.layer-2.bias\" dense<[-0.114143081, 0.0953421518, 4.84912744E-5, -0.0384164825, 0.0063888072, 0.218958765, 0.0256200824, 0.0551806651, -0.22108613, -0.0278935507]> : tensor<10xf32> attributes {sym_visibility = \"private\"}\n",
+ " func @predict(%arg0: tensor<1x28x28x1xf32> {tf._user_specified_name = \"x\"}) -> tensor<1x10xf32> attributes {iree.module.export, iree.reflection = {abi = \"sip\", abiv = 1 : i32, sip = \"I8!S5!k0_0R3!_0\"}, tf._input_shapes = [#tf.shape<1x28x28x1>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>, #tf.shape<*>], tf.signature.is_stateful} {\n",
+ " %0 = flow.variable.address @\"__iree_flow___sm_node14__model.layer-1.kernel\" : !iree.ptr<tensor<784x128xf32>>\n",
+ " %1 = flow.variable.address @\"__iree_flow___sm_node15__model.layer-1.bias\" : !iree.ptr<tensor<128xf32>>\n",
+ " %2 = flow.variable.address @\"__iree_flow___sm_node20__model.layer-2.kernel\" : !iree.ptr<tensor<128x10xf32>>\n",
+ " %3 = flow.variable.address @\"__iree_flow___sm_node21__model.layer-2.bias\" : !iree.ptr<tensor<10xf32>>\n",
" %4 = xla_hlo.constant dense<0xFF800000> : tensor<f32>\n",
" %5 = xla_hlo.constant dense<0.000000e+00> : tensor<f32>\n",
" %6 = flow.variable.load.indirect %3 : !iree.ptr<tensor<10xf32>> -> tensor<10xf32>\n",
@@ -542,27 +545,32 @@
" %9 = flow.variable.load.indirect %0 : !iree.ptr<tensor<784x128xf32>> -> tensor<784x128xf32>\n",
" %10 = \"xla_hlo.reshape\"(%arg0) : (tensor<1x28x28x1xf32>) -> tensor<1x784xf32>\n",
" %11 = \"xla_hlo.dot\"(%10, %9) : (tensor<1x784xf32>, tensor<784x128xf32>) -> tensor<1x128xf32>\n",
- " %12 = \"xla_hlo.add\"(%11, %8) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<1x128xf32>, tensor<128xf32>) -> tensor<1x128xf32>\n",
- " %13 = \"xla_hlo.maximum\"(%12, %5) {broadcast_dimensions = dense<[]> : tensor<0xi64>} : (tensor<1x128xf32>, tensor<f32>) -> tensor<1x128xf32>\n",
- " %14 = \"xla_hlo.dot\"(%13, %7) : (tensor<1x128xf32>, tensor<128x10xf32>) -> tensor<1x10xf32>\n",
- " %15 = \"xla_hlo.add\"(%14, %6) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<1x10xf32>, tensor<10xf32>) -> tensor<1x10xf32>\n",
- " %16 = \"xla_hlo.reduce\"(%15, %4) ( {\n",
- " ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):\t// no predecessors\n",
- " %21 = xla_hlo.maximum %arg1, %arg2 : tensor<f32>\n",
- " \"xla_hlo.return\"(%21) : (tensor<f32>) -> ()\n",
+ " %12 = \"xla_hlo.broadcast_in_dim\"(%8) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x128xf32>\n",
+ " %13 = xla_hlo.add %11, %12 : tensor<1x128xf32>\n",
+ " %14 = \"xla_hlo.broadcast_in_dim\"(%5) {broadcast_dimensions = dense<[]> : tensor<0xi64>} : (tensor<f32>) -> tensor<1x128xf32>\n",
+ " %15 = xla_hlo.maximum %14, %13 : tensor<1x128xf32>\n",
+ " %16 = \"xla_hlo.dot\"(%15, %7) : (tensor<1x128xf32>, tensor<128x10xf32>) -> tensor<1x10xf32>\n",
+ " %17 = \"xla_hlo.broadcast_in_dim\"(%6) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<10xf32>) -> tensor<1x10xf32>\n",
+ " %18 = xla_hlo.add %16, %17 : tensor<1x10xf32>\n",
+ " %19 = \"xla_hlo.reduce\"(%18, %4) ( {\n",
+ " ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors\n",
+ " %26 = xla_hlo.maximum %arg1, %arg2 : tensor<f32>\n",
+ " \"xla_hlo.return\"(%26) : (tensor<f32>) -> ()\n",
" }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x10xf32>, tensor<f32>) -> tensor<1xf32>\n",
- " %17 = \"xla_hlo.subtract\"(%15, %16) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1x10xf32>, tensor<1xf32>) -> tensor<1x10xf32>\n",
- " %18 = \"xla_hlo.exponential\"(%17) : (tensor<1x10xf32>) -> tensor<1x10xf32>\n",
- " %19 = \"xla_hlo.reduce\"(%18, %5) ( {\n",
- " ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):\t// no predecessors\n",
- " %21 = xla_hlo.add %arg1, %arg2 : tensor<f32>\n",
- " \"xla_hlo.return\"(%21) : (tensor<f32>) -> ()\n",
+ " %20 = \"xla_hlo.broadcast_in_dim\"(%19) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1xf32>) -> tensor<1x10xf32>\n",
+ " %21 = xla_hlo.subtract %18, %20 : tensor<1x10xf32>\n",
+ " %22 = \"xla_hlo.exponential\"(%21) : (tensor<1x10xf32>) -> tensor<1x10xf32>\n",
+ " %23 = \"xla_hlo.reduce\"(%22, %5) ( {\n",
+ " ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors\n",
+ " %26 = xla_hlo.add %arg1, %arg2 : tensor<f32>\n",
+ " \"xla_hlo.return\"(%26) : (tensor<f32>) -> ()\n",
" }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x10xf32>, tensor<f32>) -> tensor<1xf32>\n",
- " %20 = \"xla_hlo.divide\"(%18, %19) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1x10xf32>, tensor<1xf32>) -> tensor<1x10xf32>\n",
- " return %20 : tensor<1x10xf32>\n",
+ " %24 = \"xla_hlo.broadcast_in_dim\"(%23) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1xf32>) -> tensor<1x10xf32>\n",
+ " %25 = xla_hlo.divide %22, %24 : tensor<1x10xf32>\n",
+ " return %25 : tensor<1x10xf32>\n",
" }\n",
- "}\n",
- "Wrote MLIR to path '/usr/local/google/home/scotttodd/saved_models/mnist.mlir'\n"
+ "}\r\n",
+ "Wrote MLIR to path 'C:\\Users\\Scott\\saved_models\\mnist.mlir'\n"
],
"name": "stdout"
}
@@ -573,11 +581,11 @@
"metadata": {
"id": "IDHI7h3khJr9",
"colab_type": "code",
- "outputId": "f2abfd0c-5ada-4533-ab2f-a47f77ccd260",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
- }
+ },
+ "outputId": "b8958b7f-c7bb-4fbd-b800-e58c46134086"
},
"source": [
"#@title Compile the xla_hlo MLIR and prepare a context to execute it\n",
@@ -596,8 +604,8 @@
{
"output_type": "stream",
"text": [
- "Created IREE driver vulkan: <pyiree.rt.binding.HalDriver object at 0x7f58bc3d27f0>\n",
- "SystemContext driver=<pyiree.rt.binding.HalDriver object at 0x7f58bc3d27f0>\n"
+ "Created IREE driver vulkan: <pyiree.rt.binding.HalDriver object at 0x000001DC44C47370>\n",
+ "SystemContext driver=<pyiree.rt.binding.HalDriver object at 0x000001DC44C47370>\n"
],
"name": "stderr"
}
@@ -608,11 +616,11 @@
"metadata": {
"id": "SKflpnLtkLYE",
"colab_type": "code",
- "outputId": "59c52840-5cc6-4038-c330-707bd9313f4b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
- }
+ },
+ "outputId": "337ddf79-6746-4685-89f5-65787280c0af"
},
"source": [
"#@title Execute the compiled module and compare the results with TensorFlow\n",
@@ -632,10 +640,10 @@
"output_type": "stream",
"text": [
"IREE prediction ('vulkan-spirv' backend, 'vulkan' driver):\n",
- "[1.24276221 0.00645937538 94.5438538 1.92194688 5.14127305e-06 1.25558555 0.770468712 1.55102171e-05 0.25887078 2.61111945e-05]\n",
+ "[0.243133873 0.00337268622 95.5214233 0.92537272 2.25061631e-05 0.992090821 2.20864058 3.87712225e-06 0.105901062 4.44369434e-05]\n",
"\n",
"TensorFlow prediction:\n",
- "[1.24276197 0.00645936839 94.5438538 1.92194772 5.14127169e-06 1.25558567 0.77046895 1.55102152e-05 0.25887084 2.61111491e-05]\n"
+ "[0.243134052 0.00337268948 95.5214081 0.925373673 2.25061958e-05 0.992091119 2.20864391 3.87712953e-06 0.105901182 4.44369543e-05]\n"
],
"name": "stdout"
}
diff --git a/docs/repository_management.md b/docs/repository_management.md
index 1d6a342..567b947 100644
--- a/docs/repository_management.md
+++ b/docs/repository_management.md
@@ -7,12 +7,14 @@
transparency. If any of these things are particularly troublesome or painful for
your workflow, please reach out to us so we can prioritize a fix.
-NOTE: We are currently in the process of migrating our repository to be
-GitHub-first and hide the merging complexity in a separate `google` feature
-branch so that standard development workflows don't have to bear the cost for
-every contribution. During this part of the migration period, please direct PRs
-to the `google` branch (which will be marked as the default branch). See
-https://groups.google.com/d/msg/iree-discuss/F07vsG9Ah4o/uAIusKO-BQAJ.
+## Branches
+
+The default branch is called `main`. PRs should be sent there. We also have a
+`google` branch that is synced from the Google internal source repository. This
+branch is merged from and to `main` frequently to upstream any changes coming
+from `google` and as part of updating the internal source repository. For the
+most part this integration with `google` should have minimal effect on normal
+developer workflows.
## Dependencies
@@ -46,6 +48,11 @@
### The special relationship with LLVM and TensorFlow
+TL;DR: Dependency management with TensorFlow and LLVM are managed as part of the
+integration with Google's internal source repository and updates will be picked
+up by the `main` branch at an approximately daily cadence when merging in the
+`google` branch.
+
Currently, the two most challenging projects to manage as dependencies are
TensorFlow and LLVM. Both are typically pinned to specific versions that are
integrated in the Google source repository at a cadence up to many times per
@@ -67,15 +74,14 @@
source of truth prior to accepting code that depends on it.
LLVM is integrated into Google's source repository at a cadence up to several
-times a day. When this necessitates updating IREE to changing LLVM and MLIR
-APIs, these updates are performed atomically as part of the integration.
-Previously this did not trigger a change to the LLVM submodule version in open
-source, which would result in breakages due to version skew. We are
-transitioning to make each integration of LLVM update IREE's submodule in OSS.
-This means that API updates will have some chance of not breaking the build.
-However, it can introduce breakages as well, especially in the Bazel build where
-we still have to wait to update the BUILD files where necessary. We are
-continuing to work on improving this situation.
+times a day. The LLVM submodule version used by IREE is also updated as part of
+this integration. When this necessitates updating IREE to changing LLVM and MLIR
+APIs, these updates are performed atomically as part of the integration. However
+the changes performed here only guarantee that the internal build is not broken,
+which means that such integrations may very well break the OSS build on the
+`google` branch (in particular, it does not update teh LLVM BUILD files). The
+IREE build cop will fix the breakage on the `google` branch. We are continuing
+to work on improving this situation.
### Tasks:
@@ -147,31 +153,3 @@
Any CI systems or other things that retrieve an arbitrary commit should invoke
this prior to running just to make sure that their git view of the submodule
state is consistent.
-
-#### Updating TensorFlow and LLVM versions
-
-WARNING: These scripts have not been updated to reflect the new tooling to
-automatically update the LLVM hash directly from Google's source repository
-rather than through TensorFlow.
-
-```shell
-# By default, updates third_party/tensorflow to the remote head and
-# third_party/llvm-project to the commit that tensorflow references.
-# Also updates the IREE mirrors of key build files. All changes will
-# be staged for commit.
-./scripts/git/update_tf_llvm_submodules.py
-
-# You can also select various other options:
-# Don't update the tensorflow commit, just bring LLVM to the correct
-# linked commit.
-./scripts/git/update_tf_llvm_submodules.py --tensorflow_commit=KEEP
-
-# Don't update the LLVM commit.
-./scripts/git/update_tf_llvm_submodules.py --llvm_commit=KEEP
-
-# Update to a specific TensorFlow commit (and corresponding LLVM).
-./scripts/git/update_tf_llvm_submodules.py --tensorflow_commit=<somehash>
-
-# Update to a specific LLVM commit.
-./scripts/git/update_tf_llvm_submodules.py --llvm_commit=<somehash>
-```
diff --git a/integrations/tensorflow/e2e/BUILD b/integrations/tensorflow/e2e/BUILD
index 3500d99..5c3a16c 100644
--- a/integrations/tensorflow/e2e/BUILD
+++ b/integrations/tensorflow/e2e/BUILD
@@ -73,9 +73,7 @@
"fill_test.py",
"mandelbrot_test.py",
"matrix_ops_test.py",
- "resource_ops_test.py",
"ring_buffer_test.py",
- "sliding_window_test.py",
"strings_test.py",
]
@@ -88,9 +86,7 @@
"fill_test.py",
"mandelbrot_test.py",
"matrix_ops_test.py",
- "resource_ops_test.py",
"ring_buffer_test.py",
- "sliding_window_test.py",
"strings_test.py",
"tensorlist_test.py",
]
