webgpu: support BroadcastArgs kernel

tfjs-backend-webgpu/src/broadcast_args_webgpu.ts

@@ -0,0 +1,69 @@
+/**
+ * @license
+ * Copyright 2023 Google LLC.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+import {getMainHeaderString as main, WebGPUProgram} from './webgpu_program';
+import {computeDispatch, flatDispatchLayout} from './webgpu_util';
+
+export class BroadcastArgsProgram implements WebGPUProgram {
+  outputShape: number[] = [];
+  shaderKey: string;
+  dispatchLayout: {x: number[]};
+  dispatch: [number, number, number];
+  variableNames = ['s0', 's1'];
+  uniforms = 's0Size : i32, s1Size : i32, ';
+  workgroupSize: [number, number, number] = [64, 1, 1];
+  size = true;
+
+  constructor(shape: number) {
+    this.outputShape = [shape];
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(
+        this.dispatchLayout, this.outputShape, this.workgroupSize);
+
+    this.shaderKey = 'broadcastArgs';
+  }
+
+  getUserCode(): string {
+    const userCode = `
+  ${main('index')} {
+    if (index < uniforms.size) {
+      var s0 = 1.0;
+      var s1 = 1.0;
+      let indexS0 = index - uniforms.size + uniforms.s0Size;
+      let indexS1 = index - uniforms.size + uniforms.s1Size;
+      if (indexS0 >= 0) {
+        s0 = getS0(indexS0);
+      }
+      if (indexS1 >= 0) {
+        s1 = getS1(indexS1);
+      }
+
+      if (s0 == 1.0) {
+        setOutputAtIndex(index, s1);
+      } else if (s1 == 1.0) {
+        setOutputAtIndex(index, s0);
+      } else if (s0 != s1) {
+        setOutputAtIndex(index, uniforms.NAN);
+      } else {
+        setOutputAtIndex(index, s0);
+      }
+    }
+  }
+  `;
+    return userCode;
+  }
+}

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

@@ -0,0 +1,55 @@
+/**
+ * @license
+ * Copyright 2023 Google LLC.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+import {backend_util, BroadcastArgs, BroadcastArgsInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
+
+import {WebGPUBackend} from '../backend_webgpu';
+import {BroadcastArgsProgram} from '../broadcast_args_webgpu';
+
+export function broadcastArgs(args: {
+  inputs: BroadcastArgsInputs,
+  backend: WebGPUBackend,
+}): TensorInfo {
+  const {inputs, backend} = args;
+  const {s0, s1} = inputs;
+
+  if (backend.shouldExecuteOnCPU([s0, s1])) {
+    const s0TensorInfo = backend.tensorMap.get(s0.dataId);
+    const s1TensorInfo = backend.tensorMap.get(s1.dataId);
+    const s0Vals = s0TensorInfo.values as TypedArray;
+    const s1Vals = s1TensorInfo.values as TypedArray;
+    const broadcastShape = backend_util.assertAndGetBroadcastShape(
+        Array.from(s0Vals), Array.from(s1Vals));
+    return backend.makeTensorInfo(
+        [broadcastShape.length], 'int32', Int32Array.from(broadcastShape));
+  }
+
+  const s0Size = util.sizeFromShape(s0.shape);
+  const s1Size = util.sizeFromShape(s1.shape);
+  const outputSize = Math.max(s0Size, s1Size);
+
+  const program = new BroadcastArgsProgram(outputSize);
+  const uniformData =
+      [{type: 'int32', data: [s0Size]}, {type: 'int32', data: [s1Size]}];
+  return backend.runWebGPUProgram(program, [s0, s1], 'int32', uniformData);
+}
+
+export const broadcastArgsConfig: KernelConfig = {
+  kernelName: BroadcastArgs,
+  backendName: 'webgpu',
+  kernelFunc: broadcastArgs as unknown as KernelFunc
+};

tfjs-backend-webgpu/src/register_all_kernels.ts

@@ -36,6 +36,7 @@ import {avgPoolGradConfig} from './kernels/AvgPoolGrad';
 import {batchMatMulConfig} from './kernels/BatchMatMul';
 import {batchToSpaceNDConfig} from './kernels/BatchToSpaceND';
 import {bincountConfig} from './kernels/Bincount';
+import {broadcastArgsConfig} from './kernels/BroadcastArgs';
 import {castConfig} from './kernels/Cast';
 import {ceilConfig} from './kernels/Ceil';
 import {clipByValueConfig} from './kernels/ClipByValue';
@@ -183,6 +184,7 @@ const kernelConfigs: KernelConfig[] = [
   batchMatMulConfig,
   batchToSpaceNDConfig,
   bincountConfig,
+  broadcastArgsConfig,
   castConfig,
   ceilConfig,
   clipByValueConfig,

tfjs-backend-webgpu/src/setup_test.ts

@@ -240,7 +240,6 @@ const TEST_FILTERS: TestFilter[] = [
       // Not implemented kernel list.
       'avgPool3d ',
       'avgPool3dBackprop ',
-      'broadcastArgs ',
       'conv2DBackpropFilter ',
       'gradient with clones, input=2x2x1,d2=1,f=1,s=1,d=1,p=same',  // Conv2DBackpropFilter
       'conv1d gradients',  // Conv2DBackpropFilter