Rename dimensions to shape

js/web/lib/wasm/jsep/init.ts

@@ -275,8 +275,8 @@ export const init = async (
       // jsepReleaseTensorId,
       (tensorId: number) => backend.releaseTensorId(tensorId),
       // jsepEnsureTensor
-      async (tensorId: number, onnxDataType: number, dimensions: number[], copyOld) =>
-        backend.ensureTensor(tensorId, onnxDataType, dimensions, copyOld),
+      async (tensorId: number, onnxDataType: number, shape: number[], copyOld) =>
+        backend.ensureTensor(tensorId, onnxDataType, shape, copyOld),
       // jsepUploadTensor
       (tensorId: number, data: Uint8Array) => {
         backend.uploadTensor(tensorId, data);

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

@@ -29,7 +29,7 @@ export interface TensorManager {
   ensureTensor(
     tensorId: TensorId,
     dataType: MLOperandDataType,
-    dimensions: readonly number[],
+    shape: readonly number[],
     copyOld: boolean,
   ): Promise<MLTensor>;
   /**
@@ -48,7 +48,7 @@ export interface TensorManager {
   /**
    * Register an externally created MLTensor with a given MLContext and return a TensorId.
    */
-  registerTensor(mlContext: MLContext, mlTensor: MLTensor, dataType: MLOperandDataType, dimensions: number[]): TensorId;
+  registerTensor(mlContext: MLContext, mlTensor: MLTensor, dataType: MLOperandDataType, shape: number[]): TensorId;
 }
 
 let tensorGuid = 1;
@@ -60,8 +60,8 @@ export type MLTensorEntry = [MLTensor, MLOperandDataType, readonly number[]];
  * TensorTracker tracks the MLTensor and pending upload data.
  *
  * We need to track the MLTensor and pending upload data because we delay the creation of MLTensor until
- * we know the data type and dimensions. This is because future implementations of WebNN will only support creating
- * MLTensors with dataTypes and dimensions.
+ * we know the data type and shape. This is because future implementations of WebNN will only support creating
+ * MLTensors with dataTypes and shape.
  */
 class TensorTracker {
   private tensorEntry?: MLTensorEntry;
@@ -103,12 +103,12 @@ class TensorTracker {
   }
 
   public trySelectTensor(context: MLContext, tryMLTensor: MLTensor): boolean {
-    for (const [mlTensor, dataType, dimensions] of this.tensorCache) {
+    for (const [mlTensor, dataType, shape] of this.tensorCache) {
       if (tryMLTensor === mlTensor) {
         if (this.context !== context) {
           throw new Error('MLTensor cannot be registered with a different MLContext.');
         }
-        this.tensorEntry = [mlTensor, dataType, dimensions];
+        this.tensorEntry = [mlTensor, dataType, shape];
         return true;
       }
     }
@@ -117,39 +117,39 @@ class TensorTracker {
 
   public async ensureTensor(
     dataType: MLOperandDataType,
-    dimensions: readonly number[],
+    shape: readonly number[],
     copyOld: boolean,
   ): Promise<MLTensor> {
     if (this.tensorEntry) {
-      const [mlTensor, existingDataType, existingDimensions] = this.tensorEntry;
-      if (existingDataType === dataType && existingDimensions.every((v, i) => v === dimensions[i])) {
+      const [mlTensor, existingDataType, existingShape] = this.tensorEntry;
+      if (existingDataType === dataType && existingShape.every((v, i) => v === shape[i])) {
         return mlTensor;
       }
     }
 
-    for (const [mlTensor, existingDataType, existingDimensions] of this.tensorCache) {
-      if (existingDataType === dataType && existingDimensions.every((v, i) => v === dimensions[i])) {
+    for (const [mlTensor, existingDataType, existingShape] of this.tensorCache) {
+      if (existingDataType === dataType && existingShape.every((v, i) => v === shape[i])) {
         if (copyOld && this.tensorEntry) {
           // WebNN does not support copyTensorToTensor, so we need to read and write the tensors.
           LOG_DEBUG(
             'verbose',
             () =>
               `[WebNN] Slowdown may occur, having to copy existing tensor {dataType: ${
                 dataType
-              }, dimensions: ${dimensions}}`,
+              }, shape: ${shape}}`,
           );
           const data = await this.context.readTensor(this.tensorEntry[0]);
           this.context.writeTensor(mlTensor, data);
         }
-        this.tensorEntry = [mlTensor, existingDataType, existingDimensions];
+        this.tensorEntry = [mlTensor, existingDataType, existingShape];
         return mlTensor;
       }
     }
-    LOG_DEBUG('verbose', () => `[WebNN] MLContext.createTensor {dataType: ${dataType}, dimensions: ${dimensions}}`);
+    LOG_DEBUG('verbose', () => `[WebNN] MLContext.createTensor {dataType: ${dataType}, shape: ${shape}}`);
     // eslint-disable-next-line no-bitwise
     const usage = MLTensorUsage.READ | MLTensorUsage.WRITE;
-    const tensor = await this.context.createTensor({ dataType, dimensions, usage });
-    this.tensorEntry = [tensor, dataType, dimensions];
+    const tensor = await this.context.createTensor({ dataType, shape, usage });
+    this.tensorEntry = [tensor, dataType, shape];
     this.tensorCache.push(this.tensorEntry);
 
     if (this.activeUpload) {
@@ -225,15 +225,15 @@ class TensorManagerImpl implements TensorManager {
   public async ensureTensor(
     tensorId: TensorId,
     dataType: MLOperandDataType,
-    dimensions: number[],
+    shape: number[],
     copyOld: boolean,
   ): Promise<MLTensor> {
     LOG_DEBUG(
       'verbose',
       () =>
         `[WebNN] TensorManager.ensureTensor {tensorId: ${tensorId}, dataType: ${
           dataType
-        }, dimensions: ${dimensions}, copyOld: ${copyOld}}`,
+        }, shape: ${shape}, copyOld: ${copyOld}}`,
     );
     const tensor = this.tensorsById.get(tensorId);
     if (!tensor) {
@@ -244,7 +244,7 @@ class TensorManagerImpl implements TensorManager {
       this.tensorIdsByContext.set(this.backend.currentContext, new Set());
     }
     this.tensorIdsByContext.get(this.backend.currentContext)?.add(tensorId);
-    return tensor.ensureTensor(dataType, dimensions, copyOld);
+    return tensor.ensureTensor(dataType, shape, copyOld);
   }
 
   public upload(tensorId: TensorId, data: Uint8Array): void {
@@ -277,15 +277,15 @@ class TensorManagerImpl implements TensorManager {
     mlContext: MLContext,
     mlTensor: MLTensor,
     dataType: MLOperandDataType,
-    dimensions: readonly number[],
+    shape: readonly number[],
   ): TensorId {
     for (const [tensorId, tensorTracker] of this.tensorsById) {
       if (tensorTracker.trySelectTensor(mlContext, mlTensor)) {
         return tensorId;
       }
     }
     const tensorId = createNewTensorId();
-    this.tensorsById.set(tensorId, new TensorTracker(mlContext, [mlTensor, dataType, dimensions]));
+    this.tensorsById.set(tensorId, new TensorTracker(mlContext, [mlTensor, dataType, shape]));
     let tensors = this.tensorIdsByContext.get(mlContext);
     if (!tensors) {
       tensors = new Set();

js/web/lib/wasm/jsep/webnn/webnn.d.ts

@@ -32,7 +32,7 @@ type MLInputOperandLayout = 'nchw'|'nhwc';
 type MLOperandDataType = 'float32'|'float16'|'int32'|'uint32'|'int64'|'uint64'|'int8'|'uint8';
 interface MLOperandDescriptor {
   dataType: MLOperandDataType;
-  dimensions?: readonly number[];
+  shape?: readonly number[];
 }
 interface MLOperand {
   dataType(): MLOperandDataType;
@@ -405,7 +405,7 @@ interface MLContext {
   createTensor(descriptor: MLTensorDescriptor): Promise<MLTensor>;
   writeTensor(
       destinationTensor: MLTensor, sourceData: ArrayBufferView|ArrayBuffer, sourceElementOffset?: number,
-      srcElementSize?: number): void;
+      sourceElementSize?: number): void;
   readTensor(sourceTensor: MLTensor): Promise<ArrayBuffer>;
   readTensor(sourceTensor: MLTensor, destinationData: ArrayBufferView|ArrayBuffer): Promise<undefined>;
   dispatch(graph: MLGraph, inputs: MLNamedTensor, outputs: MLNamedTensor): void;

js/web/lib/wasm/wasm-types.ts

@@ -33,7 +33,7 @@ export declare namespace JSEP {
   type EnsureTensorFunction = (
     tensorId: number,
     dataType: DataType,
-    dimensions: readonly number[],
+    shape: readonly number[],
     copyOld: boolean,
   ) => Promise<MLTensor>;
   type UploadTensorFunction = (tensorId: number, data: Uint8Array) => void;
@@ -183,21 +183,20 @@ export declare namespace JSEP {
      * [exported from pre-jsep.js] Ensure that an MLTensor of a given type and shape exists for a MLTensor ID.
      * @param tensorId - specify the MLTensor ID.
      * @param onnxDataType - specify the data type.
-     * @param dimensions - specify the dimensions.
+     * @param shape - specify the dimensions (WebNN shape) of the tensor.
      * @param copyOld - specify whether to copy the old tensor if a new tensor was created.
      * @returns the MLTensor associated with the tensor ID.
      */
     jsepEnsureTensor: (
       tensorId: number,
       dataType: DataType,
-      dimensions: number[],
+      shape: number[],
       copyOld: boolean,
     ) => Promise<MLTensor>;
     /**
      * [exported from pre-jsep.js] Upload data to an MLTensor.
      * @param tensorId - specify the MLTensor ID.
      * @param data - specify the data to upload. It can be a TensorProto::data_type or a WebNN MLOperandDataType.
-     * @param dimensions - specify the dimensions.
      * @returns
      */
     jsepUploadTensor: (tensorId: number, data: Uint8Array) => void;

js/web/test/test-runner.ts

@@ -663,7 +663,7 @@ async function createMLTensorForOutput(mlContext: MLContext, type: ort.Tensor.Ty
 
   const mlTensor = await mlContext.createTensor({
     dataType,
-    dimensions: dims as number[],
+    shape: dims as number[],
     usage: MLTensorUsage.READ,
   });
 
@@ -685,7 +685,7 @@ async function createMLTensorForInput(mlContext: MLContext, cpuTensor: ort.Tenso
   const dataType = cpuTensor.type === 'bool' ? 'uint8' : cpuTensor.type;
   const mlTensor = await mlContext.createTensor({
     dataType,
-    dimensions: cpuTensor.dims as number[],
+    shape: cpuTensor.dims as number[],
     usage: MLTensorUsage.WRITE,
   });
   mlContext.writeTensor(mlTensor, cpuTensor.data);

onnxruntime/wasm/pre-jsep.js

@@ -232,8 +232,8 @@ Module['jsepInit'] = (name, params) => {
     Module['jsepCreateMLTensorDownloader'] = (tensorId, type) => {
       return backend['createMLTensorDownloader'](tensorId, type);
     }
-    Module['jsepRegisterMLTensor'] = (tensor, dataType, dimensions) => {
-      return backend['registerMLTensor'](tensor, dataType, dimensions);
+    Module['jsepRegisterMLTensor'] = (tensor, dataType, shape) => {
+      return backend['registerMLTensor'](tensor, dataType, shape);
     }
   }
 };