Merge remote-tracking branch 'origin' into prathikrao/slice-op-webgpu…

…-native

.github/workflows/lint.yml

@@ -45,7 +45,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
-          # Version range or exact version of Python to use, using SemVer's version range syntax. Reads from .python-version if unset.
+          # Use the version configured in target-version of [tool.black] section in pyproject.toml.
           python-version: "3.10"
       - name: Setup Rust
         uses: actions-rs/toolchain@v1
@@ -55,12 +55,10 @@ jobs:
       - name: Update PATH
         run: |
           echo "$HOME/.local/bin" >> "$GITHUB_PATH"
-
       - name: Install dependencies
         run: |
           set -e -x
           python -m pip install --user -r requirements-dev.txt
-          python -m pip install --user lintrunner lintrunner-adapters
           lintrunner init
       - name: Run lintrunner on all files
         run: |

.lintrunner.toml

@@ -2,31 +2,23 @@
 # You can install the dependencies and initialize with
 #
 # ```sh
-# pip install lintrunner lintrunner-adapters
+# pip install -r requirements-lintrunner.txt
 # lintrunner init
 # ```
 #
 # This will install lintrunner on your system and download all the necessary
 # dependencies to run linters locally.
-# If you want to see what lintrunner init will install, run
-# `lintrunner init --dry-run`.
 #
-# To lint local changes:
+# To format local changes:
 #
 # ```bash
-# lintrunner
+# lintrunner -a
 # ```
 #
-# To lint all files:
+# To format all files:
 #
 # ```bash
-# lintrunner --all-files
-# ```
-#
-# To format files:
-#
-# ```bash
-# lintrunner f --all-files
+# lintrunner -a --all-files
 # ```
 #
 # To read more about lintrunner, see [wiki](https://github.com/pytorch/pytorch/wiki/lintrunner).

CODEOWNERS

@@ -9,10 +9,6 @@
 /onnxruntime/core/graph/contrib_ops/quantization_defs.* @microsoft/onnxruntime-mlas
 /onnxruntime/core/mlas/** @microsoft/onnxruntime-mlas
 
-# build pipelines and workflows
-/tools/ci_build/github/azure-pipelines @microsoft/onnxruntime-es
-/.github/workflows @microsoft/onnxruntime-es
-
 # Dependencies
 requirements-dev.txt @microsoft/onnxruntime-admin
 requirements-doc.txt @microsoft/onnxruntime-admin

docs/Coding_Conventions_and_Standards.md

@@ -164,22 +164,16 @@ dependencies to run linters locally.
 If you want to see what lintrunner init will install, run
 `lintrunner init --dry-run`.
 
-To lint local changes:
-
-```bash
-lintrunner
-```
-
-To format files and apply suggestions:
+To format local changes:
 
 ```bash
 lintrunner -a
 ```
 
-To lint all files:
+To format all files:
 
 ```bash
-lintrunner --all-files
+lintrunner -a --all-files
 ```
 
 To show help text:

js/web/docs/webnn-operators.md

@@ -27,7 +27,7 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | Cos | ai.onnx(7+) | cos | ✓ | ✓ | |
 | CumSum | ai.onnx(11-13, 14+) | cumulativeSum | ✓ | ✓ | 'axis' input should be a constant |
 | Div | ai.onnx(7-12, 13, 14+) | div | ✓ | ✓ | |
-| DequantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | dequantizeLinear | ✗ | ✓ | |
+| DequantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | dequantizeLinear | ✓ | ✓ | The shape of x_scale should be a subsample of the shape of input |
 | Dropout | ai.onnx(7-9, 10-11, 12, 13-21, 22+) | identity | ✓ | ✓ | Only supports test mode |
 | Einsum | ai.onnx(12+) | reshape, transpose, matmul, reduceSum, mul, triangular | ✓ | ✓ | |
 | Elu | ai.onnx(7+) | elu | ✓ | ✓ | WebNN CPU backend only supports 'alpha' value is 1.0 |
@@ -71,7 +71,7 @@ operators and the supported opset domain/versions in **WebNN EP** by ONNX Runtim
 | Pad | ai.onnx(7-10, 11-12, 13-17, 18, 19-20, 21+) | pad | ✓ | ✓ | modes == 'wrap' is not supported |
 | Pow | ai.onnx(7-11, 12, 13-14, 15+) | pow | ✓ | ✓ | |
 | PRelu | ai.onnx(7-8, 9-15, 16+) | prelu | ✓ | ✓ | WebNN CPU backend restricts the last dimension of input and slope to be same (Chromium issue: https://issues.chromium.org/issues/335517470) |
-| QuantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | quantizeLinear | ✗ | ✓ | |
+| QuantizeLinear | ai.onnx(10-12, 13-18, 19-20, 21-22, 23+) | quantizeLinear | ✓ | ✓ | The shape of x_scale should be a subsample of the shape of input |
 | Reciprocal | ai.onnx(7-12, 13+) | reciprocal | ✓ | ✓ | |
 | ReduceL1 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL1 | ✓ | ✓ | Input 'axes' if present should be a constant |
 | ReduceL2 | ai.onnx(7-10, 11-12, 13-17, 18+) | reduceL2 | ✓ | ✓ | Input 'axes' if present should be a constant |

js/web/lib/wasm/jsep/webnn/tensor-manager.ts

@@ -141,8 +141,9 @@ class TensorWrapper {
     return this.mlContext.readTensor(this.mlTensor);
   }
 
-  public sameTypeAndShape(dataType: MLOperandDataType, shape: readonly number[]): boolean {
+  public canReuseTensor(context: MLContext, dataType: MLOperandDataType, shape: readonly number[]): boolean {
     return (
+      this.mlContext === context &&
       this.dataType === dataType &&
       this.tensorShape.length === shape.length &&
       this.tensorShape.every((v, i) => v === shape[i])
@@ -176,12 +177,13 @@ class TensorIdTracker {
   }
 
   public async ensureTensor(
+    context: MLContext,
     dataType: MLOperandDataType,
     shape: readonly number[],
     copyOld: boolean,
   ): Promise<MLTensor> {
     if (this.wrapper) {
-      if (this.wrapper.sameTypeAndShape(dataType, shape)) {
+      if (this.wrapper.canReuseTensor(context, dataType, shape)) {
         return this.wrapper.tensor;
       } else {
         if (copyOld) {
@@ -288,7 +290,7 @@ class TensorManagerImpl implements TensorManager {
     if (!tensor) {
       throw new Error('Tensor not found.');
     }
-    return tensor.ensureTensor(dataType, shape, copyOld);
+    return tensor.ensureTensor(this.backend.currentContext, dataType, shape, copyOld);
   }
 
   public upload(tensorId: TensorId, data: Uint8Array): void {
@@ -354,15 +356,15 @@ class TensorManagerImpl implements TensorManager {
     readable: boolean,
   ): Promise<TensorWrapper> {
     const sessionId = this.backend.currentSessionId;
+    const context = this.backend.currentContext;
     for (const [index, tensor] of this.freeTensors.entries()) {
-      if (tensor.sameTypeAndShape(dataType, shape)) {
+      if (tensor.canReuseTensor(context, dataType, shape)) {
         LOG_DEBUG('verbose', () => `[WebNN] Reusing tensor {dataType: ${dataType}, shape: ${shape}}`);
         const wrapper = this.freeTensors.splice(index, 1)[0];
         wrapper.sessionId = sessionId;
         return wrapper;
       }
     }
-    const context = this.backend.currentContext;
     LOG_DEBUG('verbose', () => `[WebNN] MLContext.createTensor {dataType: ${dataType}, shape: ${shape}}`);
     const tensor = await context.createTensor({
       dataType,

js/web/test/data/ops/conv.jsonc

@@ -391,48 +391,48 @@
       }
     ]
   },
-  {
-    "name": "conv - vectorize group - B",
-    "operator": "Conv",
-    "inputShapeDefinitions": "rankOnly",
-    "opset": { "domain": "", "version": 17 },
-    "attributes": [
-      { "name": "kernel_shape", "data": [2, 2], "type": "ints" },
-      { "name": "group", "data": 3, "type": "int" }
-    ],
-    "cases": [
-      {
-        "name": "T[0]",
-        "inputs": [
-          {
-            "data": [
-              0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0,
-              19.0, 20.0, 21.0, 22.0, 23.0, 0, 0, 0
-            ],
-            "dims": [1, 3, 3, 3],
-            "type": "float32"
-          },
-          {
-            "data": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0],
-            "dims": [3, 1, 2, 2],
-            "type": "float32"
-          },
-          {
-            "data": [0.1, 0.2, 0.3],
-            "dims": [3],
-            "type": "float32"
-          }
-        ],
-        "outputs": [
-          {
-            "data": [27.1, 37.1, 57.1, 67.1, 293.2, 319.2, 371.2, 397.2, 847.3, 889.3, 409.3, 428.3],
-            "dims": [1, 3, 2, 2],
-            "type": "float32"
-          }
-        ]
-      }
-    ]
-  },
+  // {
+  //   "name": "conv - vectorize group - B",
+  //   "operator": "Conv",
+  //   "inputShapeDefinitions": "rankOnly",
+  //   "opset": { "domain": "", "version": 17 },
+  //   "attributes": [
+  //     { "name": "kernel_shape", "data": [2, 2], "type": "ints" },
+  //     { "name": "group", "data": 3, "type": "int" }
+  //   ],
+  //   "cases": [
+  //     {
+  //       "name": "T[0]",
+  //       "inputs": [
+  //         {
+  //           "data": [
+  //             0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0,
+  //             19.0, 20.0, 21.0, 22.0, 23.0, 0, 0, 0
+  //           ],
+  //           "dims": [1, 3, 3, 3],
+  //           "type": "float32"
+  //         },
+  //         {
+  //           "data": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0],
+  //           "dims": [3, 1, 2, 2],
+  //           "type": "float32"
+  //         },
+  //         {
+  //           "data": [0.1, 0.2, 0.3],
+  //           "dims": [3],
+  //           "type": "float32"
+  //         }
+  //       ],
+  //       "outputs": [
+  //         {
+  //           "data": [27.1, 37.1, 57.1, 67.1, 293.2, 319.2, 371.2, 397.2, 847.3, 889.3, 409.3, 428.3],
+  //           "dims": [1, 3, 2, 2],
+  //           "type": "float32"
+  //         }
+  //       ]
+  //     }
+  //   ]
+  // },
   {
     "name": "conv - vectorize group - C",
     "operator": "Conv",
@@ -470,44 +470,44 @@
       }
     ]
   },
-  {
-    "name": "conv - vectorize group - D",
-    "operator": "Conv",
-    "inputShapeDefinitions": "rankOnly",
-    "opset": { "domain": "", "version": 17 },
-    "attributes": [
-      { "name": "kernel_shape", "data": [2, 2], "type": "ints" },
-      { "name": "group", "data": 3, "type": "int" },
-      { "name": "strides", "data": [2, 2], "type": "ints" }
-    ],
-    "cases": [
-      {
-        "name": "T[0] strides = [2, 2]",
-        "inputs": [
-          {
-            "data": [
-              0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0,
-              19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0
-            ],
-            "dims": [1, 3, 3, 4],
-            "type": "float32"
-          },
-          {
-            "data": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0],
-            "dims": [3, 1, 2, 2],
-            "type": "float32"
-          }
-        ],
-        "outputs": [
-          {
-            "data": [34, 54, 386, 438, 1122, 1206],
-            "dims": [1, 3, 1, 2],
-            "type": "float32"
-          }
-        ]
-      }
-    ]
-  },
+  // {
+  //   "name": "conv - vectorize group - D",
+  //   "operator": "Conv",
+  //   "inputShapeDefinitions": "rankOnly",
+  //   "opset": { "domain": "", "version": 17 },
+  //   "attributes": [
+  //     { "name": "kernel_shape", "data": [2, 2], "type": "ints" },
+  //     { "name": "group", "data": 3, "type": "int" },
+  //     { "name": "strides", "data": [2, 2], "type": "ints" }
+  //   ],
+  //   "cases": [
+  //     {
+  //       "name": "T[0] strides = [2, 2]",
+  //       "inputs": [
+  //         {
+  //           "data": [
+  //             0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0,
+  //             19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0
+  //           ],
+  //           "dims": [1, 3, 3, 4],
+  //           "type": "float32"
+  //         },
+  //         {
+  //           "data": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0],
+  //           "dims": [3, 1, 2, 2],
+  //           "type": "float32"
+  //         }
+  //       ],
+  //       "outputs": [
+  //         {
+  //           "data": [34, 54, 386, 438, 1122, 1206],
+  //           "dims": [1, 3, 1, 2],
+  //           "type": "float32"
+  //         }
+  //       ]
+  //     }
+  //   ]
+  // },
   {
     "name": "conv - pointwise",
     "operator": "Conv",