ExpandCopy.cpp

/*
 * 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/expand_copy/expand_copy_wgsl.h>

#include <webgpu/webgpu.h>

#include <stdexcept>

namespace executorch::backends::webgpu {

namespace {

// out coord -> in coord; size-1 in-dims broadcast via clamp (mirrors mul).
void expand_copy_impl(WebGPUGraph& graph, const std::vector<int>& args) {
  const int in_id = args.at(0);
  const int out_id = args.at(args.size() - 1);

  if (graph.get_value_type(in_id) != WebGPUGraph::ValueType::Tensor ||
      graph.get_value_type(out_id) != WebGPUGraph::ValueType::Tensor) {
    throw std::runtime_error("expand_copy: in/out arg is not a tensor");
  }

  WGPUDevice device = graph.device();
  const auto& in_tensor = graph.get_tensor(in_id);
  const auto& out_tensor = graph.get_tensor(out_id);

  TensorMeta out_meta;
  TensorMeta in_meta;
  fill_tensor_meta(out_tensor, &out_meta);
  fill_tensor_meta_broadcast(in_tensor, out_meta.ndim, &in_meta);
  if (out_tensor.nbytes !=
          static_cast<size_t>(out_meta.numel) * sizeof(float) ||
      in_tensor.nbytes != static_cast<size_t>(in_meta.numel) * sizeof(float)) {
    throw std::runtime_error(
        "expand_copy: non-fp32 operand (nbytes != numel*4)");
  }

  uint32_t wg_size =
      utils::clamp_workgroup_size(device, kExpandCopyWorkgroupSizeX);
  uint32_t workgroup_count = utils::compute_1d_workgroup_count(
      device, out_meta.numel, wg_size, "expand_copy");

  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));
  graph.add_uniform_buffer_bytes(2 * sizeof(TensorMeta));

  WGPUShaderSourceWGSL wgsl_desc = {};
  wgsl_desc.chain.sType = WGPUSType_ShaderSourceWGSL;
  wgsl_desc.code = {kExpandCopyWGSL, WGPU_STRLEN};
  WGPUShaderModuleDescriptor shader_desc = {};
  shader_desc.nextInChain = &wgsl_desc.chain;
  WGPUShaderModule shader = wgpuDeviceCreateShaderModule(device, &shader_desc);

  WGPUBindGroupLayoutEntry entries[4] = {};
  entries[0].binding = 0;
  entries[0].visibility = WGPUShaderStage_Compute;
  entries[0].buffer.type = WGPUBufferBindingType_ReadOnlyStorage;
  entries[1].binding = 1;
  entries[1].visibility = WGPUShaderStage_Compute;
  entries[1].buffer.type = WGPUBufferBindingType_Storage;
  entries[2].binding = 2;
  entries[2].visibility = WGPUShaderStage_Compute;
  entries[2].buffer.type = WGPUBufferBindingType_Uniform;
  entries[3].binding = 3;
  entries[3].visibility = WGPUShaderStage_Compute;
  entries[3].buffer.type = WGPUBufferBindingType_Uniform;

  WGPUBindGroupLayoutDescriptor bgl_desc = {};
  bgl_desc.entryCount = 4;
  bgl_desc.entries = entries;
  WGPUBindGroupLayout bgl = wgpuDeviceCreateBindGroupLayout(device, &bgl_desc);

  WGPUPipelineLayoutDescriptor pl_desc = {};
  pl_desc.bindGroupLayoutCount = 1;
  pl_desc.bindGroupLayouts = &bgl;
  WGPUPipelineLayout pipeline_layout =
      wgpuDeviceCreatePipelineLayout(device, &pl_desc);

  WGPUComputePipelineDescriptor pipeline_desc = {};
  pipeline_desc.layout = pipeline_layout;
  pipeline_desc.compute.module = shader;
  pipeline_desc.compute.entryPoint = {"main", WGPU_STRLEN};
  pipeline_desc.compute.constantCount = 1;
  pipeline_desc.compute.constants = &wg_size_constant;
  WGPUComputePipeline pipeline =
      wgpuDeviceCreateComputePipeline(device, &pipeline_desc);

  WGPUBindGroupEntry bg_entries[4] = {};
  bg_entries[0].binding = 0;
  bg_entries[0].buffer = in_tensor.buffer;
  bg_entries[0].size = in_tensor.nbytes;
  bg_entries[1].binding = 1;
  bg_entries[1].buffer = out_tensor.buffer;
  bg_entries[1].size = out_tensor.nbytes;
  bg_entries[2].binding = 2;
  bg_entries[2].buffer = out_meta_buf;
  bg_entries[2].size = sizeof(TensorMeta);
  bg_entries[3].binding = 3;
  bg_entries[3].buffer = in_meta_buf;
  bg_entries[3].size = sizeof(TensorMeta);

  WGPUBindGroupDescriptor bg_desc = {};
  bg_desc.layout = bgl;
  bg_desc.entryCount = 4;
  bg_desc.entries = bg_entries;
  WGPUBindGroup bind_group = wgpuDeviceCreateBindGroup(device, &bg_desc);

  graph.add_dispatch({pipeline, bind_group, workgroup_count});

  wgpuShaderModuleRelease(shader);
  wgpuBindGroupLayoutRelease(bgl);
  wgpuPipelineLayoutRelease(pipeline_layout);
  wgpuBufferRelease(out_meta_buf);
  wgpuBufferRelease(in_meta_buf);
}

} // namespace

WEBGPU_REGISTER_OPERATORS {
  WEBGPU_REGISTER_OP(aten.expand_copy.default, expand_copy_impl);
}

} // namespace executorch::backends::webgpu