[ExecuTorch][WebGPU] Add split_with_sizes_copy op

backends/webgpu/CMakeLists.txt

@@ -67,6 +67,7 @@ set(WEBGPU_SRCS
     runtime/ops/leaky_relu/LeakyRelu.cpp
     runtime/ops/upsample_bilinear2d/UpsampleBilinear2d.cpp
     runtime/ops/batch_norm/BatchNorm.cpp
+    runtime/ops/split_with_sizes/SplitWithSizes.cpp
 )
 
 add_library(webgpu_backend ${WEBGPU_SRCS})

backends/webgpu/runtime/ops/split_with_sizes/SplitWithSizes.cpp

@@ -0,0 +1,131 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <executorch/backends/webgpu/runtime/WebGPUGraph.h>
+#include <executorch/backends/webgpu/runtime/WebGPUUtils.h>
+#include <executorch/backends/webgpu/runtime/ops/OperatorRegistry.h>
+#include <executorch/backends/webgpu/runtime/ops/TensorMeta.h>
+#include <executorch/backends/webgpu/runtime/ops/slice/slice_wgsl.h>
+
+#include <webgpu/webgpu.h>
+
+#include <cstdint>
+#include <stdexcept>
+#include <vector>
+
+namespace executorch::backends::webgpu {
+
+namespace {
+
+struct SliceParams {
+  uint32_t dim;
+  uint32_t start;
+  uint32_t step;
+  uint32_t _pad;
+};
+
+// aten.split_with_sizes_copy: N contiguous chunks via per-output slice gather.
+void split_with_sizes_impl(WebGPUGraph& graph, const std::vector<int>& args) {
+  if (args.size() < 4) {
+    throw std::runtime_error("WebGPU split_with_sizes: expected >=4 args");
+  }
+  const int in_id = args.at(0);
+  const std::vector<int64_t>& sizes = graph.get_int_list(args.at(1));
+  int64_t dim = graph.get_int(args.at(2));
+  const std::vector<int>& outs = graph.get_value_list(args.at(args.size() - 1));
+
+  WGPUDevice device = graph.device();
+  const auto& in_tensor = graph.get_tensor(in_id);
+  const int in_ndim = static_cast<int>(in_tensor.dims.size());
+  if (dim < 0) {
+    dim += in_ndim;
+  }
+  if (dim < 0 || dim >= in_ndim) {
+    throw std::runtime_error("split_with_sizes: dim out of range");
+  }
+  if (outs.size() != sizes.size()) {
+    throw std::runtime_error("split_with_sizes: outputs != sizes count");
+  }
+
+  TensorMeta in_meta;
+  fill_tensor_meta(in_tensor, &in_meta);
+  if (in_tensor.nbytes != static_cast<size_t>(in_meta.numel) * sizeof(float)) {
+    throw std::runtime_error("split_with_sizes: non-fp32 input");
+  }
+
+  uint32_t start = 0;
+  for (size_t i = 0; i < outs.size(); i++) {
+    const auto& out_tensor = graph.get_tensor(outs[i]);
+    TensorMeta out_meta;
+    fill_tensor_meta(out_tensor, &out_meta);
+    if (out_tensor.nbytes !=
+        static_cast<size_t>(out_meta.numel) * sizeof(float)) {
+      throw std::runtime_error("split_with_sizes: non-fp32 output");
+    }
+
+    SliceParams params = {};
+    params.dim = static_cast<uint32_t>(dim);
+    params.start = start;
+    params.step = 1u;
+    start += static_cast<uint32_t>(sizes[i]);
+
+    uint32_t wg_size =
+        utils::clamp_workgroup_size(device, kSliceWorkgroupSizeX);
+    uint32_t workgroup_count = utils::compute_1d_workgroup_count(
+        device, out_meta.numel, wg_size, "split_with_sizes");
+
+    WGPUConstantEntry wg_size_constant = {};
+    wg_size_constant.key = {"wg_size", WGPU_STRLEN};
+    wg_size_constant.value = static_cast<double>(wg_size);
+
+    WGPUBuffer out_meta_buf =
+        utils::make_uniform(device, &out_meta, sizeof(TensorMeta));
+    WGPUBuffer in_meta_buf =
+        utils::make_uniform(device, &in_meta, sizeof(TensorMeta));
+    WGPUBuffer params_buf =
+        utils::make_uniform(device, &params, sizeof(SliceParams));
+    graph.add_uniform_buffer_bytes(
+        2 * sizeof(TensorMeta) + sizeof(SliceParams));
+
+    utils::ComputePipelineBundle bundle = utils::make_compute_pipeline(
+        device,
+        kSliceWGSL,
+        {
+            {0,
+             WGPUBufferBindingType_ReadOnlyStorage,
+             in_tensor.buffer,
+             in_tensor.nbytes},
+            {1,
+             WGPUBufferBindingType_Storage,
+             out_tensor.buffer,
+             out_tensor.nbytes},
+            {2,
+             WGPUBufferBindingType_Uniform,
+             out_meta_buf,
+             sizeof(TensorMeta)},
+            {3, WGPUBufferBindingType_Uniform, in_meta_buf, sizeof(TensorMeta)},
+            {4, WGPUBufferBindingType_Uniform, params_buf, sizeof(SliceParams)},
+        },
+        &wg_size_constant,
+        1);
+
+    graph.add_dispatch({bundle.pipeline, bundle.bind_group, workgroup_count});
+
+    graph.own_uniform_buffer(out_meta_buf);
+    graph.own_uniform_buffer(in_meta_buf);
+    graph.own_uniform_buffer(params_buf);
+  }
+}
+
+} // namespace
+
+WEBGPU_REGISTER_OPERATORS {
+  WEBGPU_REGISTER_OP(aten.split_with_sizes_copy.default, split_with_sizes_impl);
+}
+
+} // namespace executorch::backends::webgpu