Merge branch 'master' of https://github.com/tensorflow/tfjs into jax2tfjs

Author: marcvanzee

tfjs-backend-webgpu/src/from_pixels_webgpu_test.ts

@@ -16,6 +16,7 @@
  */
 
 import * as tf from '@tensorflow/tfjs-core';
+import {test_util} from '@tensorflow/tfjs-core';
 import {WebGPUBackend} from './backend_webgpu';
 import {describeWebGPU} from './test_util';
 
@@ -37,26 +38,15 @@ describeWebGPU('fromPixels', () => {
        const textureManager = backend.textureManager;
        textureManager.dispose();
 
-       const video = document.createElement('video');
        const source = document.createElement('source');
        source.src =
            // tslint:disable-next-line:max-line-length
            'data:video/mp4;base64,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';
        source.type = 'video/mp4';
-       video.appendChild(source);
-       document.body.appendChild(video);
-
-       video.autoplay = true;
-       video.loop = true;
-       video.muted = true;
-       video.preload = 'auto';
-       await video.play();
 
-       // ensure video element to be loaded
-       if ('requestVideoFrameCallback' in video) {
-         // tslint:disable-next-line:no-any
-         await new Promise(go => (video as any).requestVideoFrameCallback(go));
-       }
+       const video = await test_util.createVideoElement(source);
+       document.body.appendChild(video);
+       await test_util.play(video);
 
        {
          tf.env().set('WEBGPU_IMPORT_EXTERNAL_TEXTURE', true);

tfjs-backend-webgpu/src/kernels/ScatterNd.ts

@@ -18,10 +18,10 @@
 import {backend_util, KernelConfig, KernelFunc, ScatterNd, ScatterNdAttrs, ScatterNdInputs, TensorInfo, util} from '@tensorflow/tfjs-core';
 
 import {WebGPUBackend} from '../backend_webgpu';
+import {ScatterProgram} from '../scatter_webgpu';
 
 import {fill} from './Fill';
 import {reshape} from './Reshape';
-import {ScatterOptimizedProgram} from '../scatter_optimized_webgpu';
 
 export function scatterNd(args: {
   inputs: ScatterNdInputs,
@@ -54,7 +54,7 @@ export function scatterNd(args: {
     {type: 'int32', data: [sliceRank]}, {type: 'int32', data: strides},
     {type: 'int32', data: [size]}
   ];
-  const program = new ScatterOptimizedProgram(
+  const program = new ScatterProgram(
       flattenX.shape, sliceRank, flattenIndices.shape.length,
       flattenX.shape.length, strides, flattenShape, type);
   const res = backend.runWebGPUProgram(

tfjs-backend-webgpu/src/kernels/SparseToDense.ts

@@ -19,7 +19,7 @@ import {backend_util, KernelConfig, KernelFunc, Rank, SparseToDense, SparseToDen
 
 import {WebGPUBackend} from '../backend_webgpu';
 import {scatterImplCPU} from '../kernel_utils/shared';
-import {ScatterOptimizedProgram} from '../scatter_optimized_webgpu';
+import {ScatterProgram} from '../scatter_webgpu';
 
 import {identity} from './Identity';
 import {reshape} from './Reshape';
@@ -89,7 +89,7 @@ export function sparseToDense(args: {
       break;
     case 1:
       if (true) {
-        const program = new ScatterOptimizedProgram(
+        const program = new ScatterProgram(
             [numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length,
             $sparseValues.shape.length, strides, flattenShape, type,
             sumDupeIndices);
@@ -101,15 +101,15 @@ export function sparseToDense(args: {
     default:
       if (true) {
         // First replace the default value with 0 at indices.
-        const program = new ScatterOptimizedProgram(
+        const program = new ScatterProgram(
             [numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length,
             zero.shape.length, strides, flattenShape, type, sumDupeIndices);
         backend.runWebGPUProgram(
             program, [zero, $sparseIndices], type, uniformData, $denseValues);
       }
       {
         // Then replace 0 with the (sum of) sparse value(s) at indices.
-        const program = new ScatterOptimizedProgram(
+        const program = new ScatterProgram(
             [numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length,
             $sparseValues.shape.length, strides, flattenShape, type);
         backend.runWebGPUProgram(

tfjs-backend-webgpu/src/matmul_packed_webgpu.ts

@@ -160,7 +160,7 @@ const calculateResultSnippet =
 
 export function makeMatMulPackedVec4Source(
     workPerThread: number[], workGroupSize: [number, number, number],
-    transposeA = false, tileInner = 32, splitK = false,
+    transposeA = false, tileInner = 32, splitK = false, splitedDimInner = 32,
     isVectorA = false): string {
   const tileAOuter = workGroupSize[1] * workPerThread[1];
   const tileBOuter = workGroupSize[0] * workPerThread[0];
@@ -209,8 +209,10 @@ export function makeMatMulPackedVec4Source(
     let batch = ${splitK ? '0' : 'i32(globalId.z)'};
     let globalRowStart = i32(workgroupId.y) * ${tileAOuter};
 
-    let numTiles = ${splitK ? '1' : '(uniforms.dimInner - 1) / TileInner + 1'};
-    var kStart = ${splitK ? 'i32(globalId.z) * TileInner' : '0'};
+    let numTiles = ${
+      splitK ? `${Math.ceil(splitedDimInner / tileInner)}` :
+               '(uniforms.dimInner - 1) / TileInner + 1'};
+    var kStart = ${splitK ? `i32(globalId.z) * ${splitedDimInner}` : '0'};
 
     var acc: array<vec4<f32>, RowPerThread>;
 
@@ -281,7 +283,8 @@ const readDataFromSubASnippet = (transposeA: boolean) => {
 
 export function makeMatMulPackedSource(
     workPerThread: number[], workGroupSize: [number, number, number],
-    transposeA = false, tileInner = 32, splitK = false): string {
+    transposeA = false, tileInner = 32, splitK = false,
+    splitedDimInner = 32): string {
   const tileAOuter = workPerThread[1] * workGroupSize[1];
   const tileBOuter = workPerThread[0] * workGroupSize[0];
   const tileAWidth = transposeA ? tileAOuter : tileInner;
@@ -323,8 +326,9 @@ export function makeMatMulPackedSource(
       let globalRowStart = i32(workgroupId.y) * ${tileAOuter};
 
       let numTiles = ${
-      splitK ? '1' : '(uniforms.dimInner - 1) / TileInner + 1'};
-      var kStart = ${splitK ? 'i32(globalId.z) * TileInner' : '0'};
+      splitK ? `${Math.ceil(splitedDimInner / tileInner)}` :
+               '(uniforms.dimInner - 1) / TileInner + 1'};
+      var kStart = ${splitK ? `i32(globalId.z) * ${splitedDimInner}` : '0'};
 
       var acc : array<array<f32, ColPerThread>, RowPerThread>;
 
@@ -565,7 +569,7 @@ export class MatMulPackedProgram implements WebGPUProgram {
         this.isVec4 ?
             makeMatMulPackedVec4Source(
                 this.elementsPerThread, this.workGroupSize, this.transposeA,
-                this.tileInner, false, this.isVectorA) :
+                this.tileInner, false, null, this.isVectorA) :
             (this.isVectorA ? makeVectorMatrixProductSource(
                                   this.workGroupSize, this.transposeA) :
                               makeMatMulPackedSource(

tfjs-backend-webgpu/src/matmul_splitK_webgpu.ts

@@ -37,7 +37,7 @@ export class MatMulSplitKProgram implements WebGPUProgram {
   batchAEqualOne: boolean;
   batchBEqualOne: boolean;
   isVec4 = false;
-  tileInner = 32;
+  splitedDimInner = 128;
 
   constructor(
       outputShape: [number, number, number], dimInner: number,
@@ -51,7 +51,8 @@ export class MatMulSplitKProgram implements WebGPUProgram {
     this.isVec4 = (transposeA && this.outputShape[1] % 4 === 0 ||
                    !transposeA && dimInner % 4 === 0) &&
         this.outputShape[2] % 4 === 0;
-    this.elementsPerThread = [4, 4, this.tileInner];
+    this.elementsPerThread = [4, 4, this.splitedDimInner];
+
     if (!this.isVec4) {
       if (this.outputShape[1] < 16) {
         this.elementsPerThread[1] = 1;
@@ -119,10 +120,10 @@ export class MatMulSplitKProgram implements WebGPUProgram {
       ${
         this.isVec4 ? makeMatMulPackedVec4Source(
                           this.elementsPerThread, this.workGroupSize,
-                          this.transposeA, this.tileInner, true) :
+                          this.transposeA, 32, true, this.splitedDimInner) :
                       makeMatMulPackedSource(
                           this.elementsPerThread, this.workGroupSize,
-                          this.transposeA, this.tileInner, true)}
+                          this.transposeA, 32, true, this.splitedDimInner)}
     `;
     return userCode;
   }