slice impl

onnxruntime/core/providers/webgpu/tensor/slice.cc

@@ -0,0 +1,262 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "core/common/inlined_containers.h"
+#include "core/providers/webgpu/tensor/slice.h"
+#include "core/providers/cpu/tensor/utils.h"
+#include "core/providers/webgpu/shader_helper.h"
+#include "core/providers/webgpu/webgpu_supported_types.h"
+
+namespace onnxruntime {
+namespace webgpu {
+
+ONNX_OPERATOR_VERSIONED_KERNEL_EX(
+    Slice,
+    kOnnxDomain,
+    1, 9,
+    kWebGpuExecutionProvider,
+    (*KernelDefBuilder::Create()).TypeConstraint("T", WebGpuSupportedFloatTypes()),
+    Slice);
+
+ONNX_OPERATOR_VERSIONED_KERNEL_EX(
+    Slice,
+    kOnnxDomain,
+    10, 10,
+    kWebGpuExecutionProvider,
+    (*KernelDefBuilder::Create()).TypeConstraint("T", WebGpuSupportedFloatTypes()).InputMemoryType(OrtMemTypeCPU, 1).InputMemoryType(OrtMemTypeCPU, 2).InputMemoryType(OrtMemTypeCPU, 3).InputMemoryType(OrtMemTypeCPU, 4),
+    Slice);
+
+ONNX_OPERATOR_VERSIONED_KERNEL_EX(
+    Slice,
+    kOnnxDomain,
+    11, 12,
+    kWebGpuExecutionProvider,
+    (*KernelDefBuilder::Create()).TypeConstraint("T", WebGpuSupportedFloatTypes()).InputMemoryType(OrtMemTypeCPU, 1).InputMemoryType(OrtMemTypeCPU, 2).InputMemoryType(OrtMemTypeCPU, 3).InputMemoryType(OrtMemTypeCPU, 4),
+    Slice);
+
+ONNX_OPERATOR_KERNEL_EX(
+    Slice,
+    kOnnxDomain,
+    13,
+    kWebGpuExecutionProvider,
+    (*KernelDefBuilder::Create()).TypeConstraint("T", WebGpuSupportedFloatTypes()).InputMemoryType(OrtMemTypeCPU, 1).InputMemoryType(OrtMemTypeCPU, 2).InputMemoryType(OrtMemTypeCPU, 3).InputMemoryType(OrtMemTypeCPU, 4),
+    Slice);
+
+Status SliceProgram::GenerateShaderCode(ShaderHelper& shader) const {
+  const ShaderVariableHelper& input = shader.AddInput("input", ShaderUsage::UseUniform | ShaderUsage::UseIndicesTypeAlias);
+  const ShaderVariableHelper& output = shader.AddOutput("output", ShaderUsage::UseUniform | ShaderUsage::UseIndicesTypeAlias);
+
+  shader.MainFunctionBody() << shader.GuardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")
+                            << "let output_indices = " << output.OffsetToIndices("global_idx") << ";\n"
+                            << "var input_indices: input_indices_t;\n"
+                            << "var carry = 0u;\n";
+
+  for (auto i = input.Rank() - 1; i >= 0; i--) {
+    std::string input_shape_i = absl::StrCat("input_shape_", i);
+    std::string steps_i = absl::StrCat("steps_", i);
+    std::string starts_i = absl::StrCat("starts_", i);
+    std::string output_index_i = absl::StrCat("output_index_", i);
+    std::string input_index_i = absl::StrCat("input_index_", i);
+
+    shader.MainFunctionBody() << "let " << input_shape_i << " = " << input.IndicesGet("uniforms.input_shape", i) << ";\n"
+                              << "let " << steps_i << " = " << input.IndicesGet("uniforms.steps", i) << ";\n"
+                              << "let " << starts_i << " = " << input.IndicesGet("uniforms.starts", i) << ";\n"
+                              << "var " << output_index_i << " = " << output.IndicesGet("output_indices", i) << ";\n"
+                              << "var " << input_index_i << " = " << output_index_i << " * " << steps_i << " + " << starts_i << " + carry;\n"
+                              << "carry = " << input_index_i << " / " << input_shape_i << ";\n"
+                              << input_index_i << " = " << input_index_i << " % " << input_shape_i << ";\n"
+                              << "if (" << input.IndicesGet("uniforms.signs", i) << " < 0) {\n"
+                              << "  " << input_index_i << " = " << input_shape_i << " - " << input_index_i << " - 1u + " << starts_i << ";\n"
+                              << "}\n"
+                              << input.IndicesSet("input_indices", i, input_index_i) << ";\n";
+  }
+
+  shader.MainFunctionBody() << output.SetByOffset("global_idx", input.GetByIndices("input_indices"));
+
+  return Status::OK();
+}
+
+Status Slice::ComputeInternal(ComputeContext& context) const {
+  // READ INPUTS
+  const Tensor* input_tensor = context.Input(0);
+  const TensorShape& input_shape = input_tensor->Shape();
+  int64_t input_rank = static_cast<int64_t>(input_shape.NumDimensions());
+
+  auto starts_raw = hasStartsAttr ? gsl::make_span(attr_starts_) : context.Input(1)->DataAsSpan<int64_t>();
+  auto ends_raw = hasEndsAttr ? gsl::make_span(attr_ends_) : context.Input(2)->DataAsSpan<int64_t>();
+
+  ORT_ENFORCE(starts_raw.size() == ends_raw.size(), "starts and ends must have the same size");
+
+  int input_count = context.InputCount();
+
+  const Tensor* axes_tensor = nullptr;
+  const Tensor* steps_tensor = nullptr;
+
+  if (input_count >= 4) {
+    // axes provided as input
+    axes_tensor = context.Input(3);
+  }
+
+  if (input_count == 5) {
+    // steps provided as input
+    steps_tensor = context.Input(4);
+  }
+
+  // Inject defaults if axes or steps not provided
+  std::vector<int64_t> axes_default;
+  if (axes_tensor == nullptr) {
+    // if axes not provided, set to [0, ..., len(starts)-1]
+    for (size_t i = 0; i < starts_raw.size(); i++) {
+      axes_default.push_back(i);
+    }
+  }
+  auto axes_raw = hasAxesAttr ? gsl::make_span(attr_axes_) : (axes_tensor == nullptr ? gsl::make_span(axes_default) : axes_tensor->DataAsSpan<int64_t>());
+
+  std::vector<int64_t> steps_default;
+  if (steps_tensor == nullptr) {
+    // if steps not provided, set to [1, ..., 1] of len(starts)
+    for (size_t i = 0; i < starts_raw.size(); i++) {
+      steps_default.push_back(1);
+    }
+  }
+  auto steps_raw = steps_tensor == nullptr ? gsl::make_span(steps_default) : steps_tensor->DataAsSpan<int64_t>();
+
+  // PROCESS INPUTS
+  std::vector<uint32_t> axes;
+  for (unsigned int i = 0; i < axes_raw.size(); i++) {
+    int64_t val = axes_raw[i];
+    if (val < 0) {
+      val += input_rank;
+    }
+    axes.push_back(static_cast<int32_t>(val));
+  }
+
+  std::vector<uint32_t> starts;
+  for (unsigned int i = 0; i < starts_raw.size(); i++) {
+    int64_t val = starts_raw[i];
+    if (val < 0) {
+      val += input_shape[axes[i]];
+    }
+
+    if (steps_raw[i] < 0) {
+      val = std::max(static_cast<int64_t>(0), std::min(val, static_cast<int64_t>(input_shape[axes[i]] - 1)));
+    } else {
+      val = std::max(static_cast<int64_t>(0), std::min(val, static_cast<int64_t>(input_shape[axes[i]])));
+    }
+    starts.push_back(static_cast<uint32_t>(val));
+  }
+
+  std::vector<uint32_t> ends;
+  for (unsigned int i = 0; i < ends_raw.size(); i++) {
+    int64_t val = ends_raw[i];
+    if (val < 0) {
+      val += input_shape[axes[i]];
+    }
+    if (steps_raw[i] < 0) {
+      val = std::max(static_cast<int64_t>(0), std::min(val, static_cast<int64_t>(input_shape[axes[i]] - 1)));
+    } else {
+      val = std::max(static_cast<int64_t>(0), std::min(val, static_cast<int64_t>(input_shape[axes[i]])));
+    }
+    ends.push_back(static_cast<uint32_t>(val));
+  }
+
+  // temporary steps vector to handle negative steps
+  std::vector<int32_t> steps_tmp;
+  for (unsigned int i = 0; i < steps_raw.size(); i++) {
+    if (steps_raw[i] >= std::numeric_limits<int32_t>::max()) {
+      steps_tmp.push_back(std::numeric_limits<int32_t>::max());
+    } else {
+      steps_tmp.push_back(static_cast<int32_t>(steps_raw[i]));
+    }
+  }
+
+  // Insert missing dimensions
+  if (static_cast<int64_t>(axes.size()) != input_rank) {
+    for (uint32_t i = 0; i < input_rank; i++) {
+      int idx = -1;
+      for (unsigned int j = 0; j < axes_raw.size(); j++) {
+        if (axes_raw[j] == i) {
+          idx = j;
+          break;
+        }
+      }
+      if (idx == -1) {
+        axes.insert(axes.begin() + i, i);
+        starts.insert(starts.begin() + i, 0);
+        ends.insert(ends.begin() + i, static_cast<uint32_t>(input_shape[i]));
+        steps_tmp.insert(steps_tmp.begin() + i, 1);
+      }
+    }
+  }
+
+  // retain the sign of the steps
+  std::vector<int32_t> signs;
+  for (unsigned int i = 0; i < steps_tmp.size(); i++) {
+    signs.push_back(steps_tmp[i] < 0 ? -1 : (steps_tmp[i] > 0 ? 1 : 0));
+  }
+
+  // Convert negative steps to positive steps and reverse starts and ends
+  for (unsigned int i = 0; i < steps_tmp.size(); i++) {
+    if (steps_tmp[i] < 0) {
+      float numSteps = static_cast<float>((static_cast<float>(ends[i]) - static_cast<float>(starts[i])) / static_cast<float>(steps_tmp[i]));
+      float newEnd = static_cast<float>(starts[i]);
+      float newStart = newEnd + numSteps * static_cast<float>(steps_tmp[i]);
+
+      starts[i] = static_cast<uint32_t>(newStart);
+      ends[i] = static_cast<uint32_t>(newEnd);
+      steps_tmp[i] = static_cast<int32_t>(-steps_tmp[i]);
+    }
+  }
+
+  // final steps vector of type unsigned int
+  std::vector<uint32_t> steps;
+  for (unsigned int i = 0; i < steps_tmp.size(); i++) {
+    steps.push_back(static_cast<uint32_t>(steps_tmp[i]));
+  }
+
+  // Reorder inputs in order of axis
+  std::vector<int32_t> signs_reordered;
+  std::vector<uint32_t> steps_reordered, starts_reordered;
+  for (unsigned int i = 0; i < axes.size(); i++) {
+    signs_reordered.push_back(0);
+    steps_reordered.push_back(0);
+    starts_reordered.push_back(0);
+  }
+  for (unsigned int i = 0; i < axes.size(); i++) {
+    int32_t dim = axes[i];
+    signs_reordered[dim] = signs[i];
+    steps_reordered[dim] = steps[i];
+    starts_reordered[dim] = starts[i];
+  }
+
+  // calculate output dims
+  std::vector<int64_t> output_dims;
+  for (unsigned int i = 0; i < axes.size(); i++) {
+    int32_t dim = axes[i];
+    float tmp = ceil((static_cast<float>(ends[dim]) - static_cast<float>(starts[dim])) / static_cast<float>(steps[dim]));
+    if (tmp < 0)
+      output_dims.push_back(0);
+    else
+      output_dims.push_back(static_cast<int64_t>(tmp));
+  }
+
+  TensorShape output_shape(output_dims);
+
+  auto* output_tensor = context.Output(0, output_shape);
+  uint32_t output_size = static_cast<uint32_t>(output_shape.Size());
+
+  if (output_size == 0) {
+    return Status::OK();
+  }
+
+  SliceProgram program{};
+  program
+      .AddInputs({{input_tensor, ProgramTensorMetadataDependency::TypeAndRank}})
+      .AddOutputs({output_tensor})
+      .SetDispatchGroupSize((output_size + WORKGROUP_SIZE - 1) / WORKGROUP_SIZE)
+      .AddUniformVariables({{output_size}, {starts_reordered}, {steps_reordered}, {signs_reordered}});
+  return context.RunProgram(program);
+}
+
+}  // namespace webgpu
+}  // namespace onnxruntime

onnxruntime/core/providers/webgpu/tensor/slice.h

@@ -0,0 +1,41 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+
+#include "core/providers/webgpu/webgpu_kernel.h"
+#include "core/providers/webgpu/program.h"
+#include <iostream>
+
+namespace onnxruntime {
+namespace webgpu {
+
+class SliceProgram final : public Program<SliceProgram> {
+ public:
+  SliceProgram() : Program{"Slice"} {}
+
+  Status GenerateShaderCode(ShaderHelper& sh) const override;
+
+  WEBGPU_PROGRAM_DEFINE_UNIFORM_VARIABLES({"output_size", ProgramUniformVariableDataType::Uint32},
+                                          {"starts", ProgramUniformVariableDataType::Uint32},
+                                          {"steps", ProgramUniformVariableDataType::Uint32},
+                                          {"signs", ProgramUniformVariableDataType::Int32});
+};
+
+class Slice final : public WebGpuKernel {
+ public:
+  Slice(const OpKernelInfo& info) : WebGpuKernel(info) {
+    hasStartsAttr = info.GetAttrs("starts", attr_starts_).IsOK();
+    hasEndsAttr = info.GetAttrs("ends", attr_ends_).IsOK();
+    hasAxesAttr = info.GetAttrs("axes", attr_axes_).IsOK();
+  }
+
+  Status ComputeInternal(ComputeContext& context) const override;
+
+ private:
+  std::vector<int64_t> attr_starts_, attr_ends_, attr_axes_;
+  bool hasStartsAttr, hasEndsAttr, hasAxesAttr;
+};
+
+}  // namespace webgpu
+}  // namespace onnxruntime

onnxruntime/core/providers/webgpu/webgpu_execution_provider.cc

@@ -663,10 +663,10 @@ std::unique_ptr<KernelRegistry> RegisterKernels() {
       // BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kMSInternalNHWCDomain, 18, 18, Resize)>,
       // BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kMSInternalNHWCDomain, 19, Resize)>,
 
-      // BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 1, 9, Slice)>,
-      // BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 10, 10, Slice)>,
-      // BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 11, 12, Slice)>,
-      // BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 13, Slice)>,
+      BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 1, 9, Slice)>,
+      BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 10, 10, Slice)>,
+      BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 11, 12, Slice)>,
+      BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 13, Slice)>,
 
       BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 1, 8, Flatten)>,
       BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kWebGpuExecutionProvider, kOnnxDomain, 9, 10, Flatten)>,