[Native WebGPU] Add Matmul

onnxruntime/core/providers/webgpu/math/matmul.cc

@@ -0,0 +1,230 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "core/providers/webgpu/math/matmul.h"
+#include "core/common/inlined_containers.h"
+#include "core/providers/cpu/tensor/utils.h"
+#include "core/providers/webgpu/shader_helper.h"
+#include "core/providers/webgpu/webgpu_supported_types.h"
+
+#include "core/providers/webgpu/data_transfer.h"
+namespace onnxruntime {
+namespace webgpu {
+
+ONNX_OPERATOR_VERSIONED_KERNEL_EX(
+    MatMul,
+    kOnnxDomain,
+    1, 12,
+    kWebGpuExecutionProvider,
+    (*KernelDefBuilder::Create())
+        .TypeConstraint("T", WebGpuSupportedNumberTypes()),
+    MatMul);
+
+ONNX_OPERATOR_KERNEL_EX(
+    MatMul,
+    kOnnxDomain,
+    13,
+    kWebGpuExecutionProvider,
+    (*KernelDefBuilder::Create())
+        .TypeConstraint("T", WebGpuSupportedNumberTypes()),
+    MatMul);
+
+static std::string CalcResult(int64_t components, int64_t a_components, int64_t output_number) {
+  std::ostringstream oss;
+  oss << "var a_data: a_value_t;\n";
+  for (int i = 0; i < a_components; ++i) {
+    oss << "let b_data" << i << " = b[(b_offset + (k + " << i << ") * uniforms.N + col) / " << components << "];\n";
+  }
+  for (int i = 0; i < output_number; ++i) {
+    oss << "a_data = a[(a_offset + (row + " << i << ") * uniforms.K + k) / " << a_components << "];\n";
+
+    for (int j = 0; j < a_components; j++) {
+      oss << "values[" << i << "] = fma(b_value_t(a_data" << (a_components == 1 ? "" : "[" + std::to_string(j) + "]") << "), b_data" << j << ", values[" << i << "]);\n";
+    }
+  }
+  return oss.str();
+}
+
+Status MatMulNaiveProgram::GenerateShaderCode(ShaderHelper& shader) const {
+  const auto& a = shader.AddInput("a", ShaderUsage::UseUniform | ShaderUsage::UseIndicesTypeAlias |
+                                           ShaderUsage::UseValueTypeAlias | ShaderUsage::UseElementTypeAlias);
+  const auto& b = shader.AddInput("b", ShaderUsage::UseUniform | ShaderUsage::UseIndicesTypeAlias |
+                                           ShaderUsage::UseValueTypeAlias | ShaderUsage::UseElementTypeAlias);
+
+  std::string process_bias;
+  if (has_bias_) {
+    shader.AddInput("bias", ShaderUsage::UseUniform);
+    process_bias = "value += output_value_t(bias[row + i]);";
+  }
+
+  const auto& output = shader.AddOutput("output", ShaderUsage::UseUniform |
+                                                      ShaderUsage::UseIndicesTypeAlias | ShaderUsage::UseValueTypeAlias);
+  const auto& batch_dims = shader.AddIndices("batch_dims");
+
+  int a_components = a.NumComponents();
+  int components = b.NumComponents();  // components of N
+
+  shader.MainFunctionBody() << shader.GuardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")
+                            << "let col = (global_idx % (uniforms.N / " << components << ")) * " << components << ";\n"
+                            << "var index1 = global_idx / (uniforms.N / " << components << ");\n"
+                            << "let stride1 = uniforms.M / " << output_number_ << ";\n"
+                            << "let row = (index1 % stride1) * " << output_number_ << ";\n"
+                            << "let batch = index1 / stride1;\n";
+  if (output_size_ != 2) {
+    shader.MainFunctionBody() << "let batch_indices = " << batch_dims.OffsetToIndices("batch") << ";\n";
+  }
+  shader.MainFunctionBody() << "var a_indices: a_indices_t;\n"
+                            << ConvertOutputBatchIndicesToInputBatchIndices("a", a, a.Rank() - 2, batch_dims.Rank(), "batch_indices")
+                            << a.IndicesSet("a_indices", a.Rank() - 2, 0) << "\n"
+                            << a.IndicesSet("a_indices", a.Rank() - 1, 0) << "\n"
+                            << "let a_offset = " << a.IndicesToOffset("a_indices") << "*" << a_components << ";\n"
+                            << "var b_indices: b_indices_t;\n"
+                            << ConvertOutputBatchIndicesToInputBatchIndices("b", b, b.Rank() - 2, batch_dims.Rank(), "batch_indices")
+                            << b.IndicesSet("b_indices", b.Rank() - 2, 0) << "\n"
+                            << b.IndicesSet("b_indices", b.Rank() - 1, 0) << "\n"
+                            << "let b_offset = " << b.IndicesToOffset("b_indices") << " * " << components << ";\n"
+                            << "var values: array<output_value_t, " << output_number_ << ">;\n"
+                            << "for (var k: u32 = 0u; k < uniforms.K; k = k + " << a_components << ") {\n"
+                            << CalcResult(components, a_components, output_number_) << "\n"
+                            << "}\n"
+                            << "for (var i = 0u; i < " << output_number_ << "u; i++) {\n"
+                            << "  var value = values[i];\n"
+                            << process_bias << "\n"
+                            << "  let cur_indices = output_indices_t(batch, row + i, col/ " << components << ");\n"
+                            << "  let offset = " << output.IndicesToOffset("cur_indices") << ";\n"
+                            << output.SetByOffset("offset", "value")
+                            << "}\n";
+
+  return Status::OK();
+}
+
+Status MatMul::ComputeInternal(ComputeContext& context) const {
+  // calculate output shape
+  MatMulComputeHelper helper;
+  const auto* a = context.Input(0);
+  const auto* b = context.Input(1);
+
+  ORT_RETURN_IF_ERROR(helper.Compute(a->Shape(), b->Shape()));
+  auto* output_tensor = context.Output(0, helper.OutputShape());
+
+  const uint32_t m = narrow<uint32_t>(helper.M());
+  const uint32_t n = narrow<uint32_t>(helper.N());
+  const uint32_t k = narrow<uint32_t>(helper.K());
+
+  bool has_bias = context.InputCount() > 2;
+
+  if (n < 8 && k < 8) {  // call MatMulNaiveProgram
+    const auto components = GetMaxComponents(n);
+    const auto a_components = GetMaxComponents(k);
+
+    const auto output_number = GetMaxComponents(m);
+    uint32_t output_size = static_cast<uint32_t>(helper.OutputShape().Size() / components / output_number);
+
+    const size_t output_rank = helper.OutputShape().NumDimensions();
+    TensorShape outer_dims = output_rank > 2 ? helper.OutputShape().Slice(0, output_rank - 2) : TensorShape({});
+    const int64_t batch_size = outer_dims.Size();
+
+    const int64_t m_val = a->Shape().NumDimensions() > 2
+                              ? a->Shape()[a->Shape().NumDimensions() - 2]
+                              : helper.M();
+    TensorShape output_shape_shader({batch_size, m_val, helper.N() / components});
+
+    MatMulNaiveProgram program{output_size, output_number, has_bias};
+
+    program
+        .CacheHint(std::to_string(components), std::to_string(a_components), std::to_string(output_number))
+        .AddInputs({{a, ProgramTensorMetadataDependency::TypeAndRank, static_cast<int>(a_components)},
+                    {b, ProgramTensorMetadataDependency::TypeAndRank, static_cast<int>(components)}});
+
+    if (has_bias) {
+      const auto* bias = context.Input(2);
+      program.AddInput({bias, ProgramTensorMetadataDependency::Rank, 1});
+    }
+    program
+        .AddOutputs({{output_tensor, ProgramTensorMetadataDependency::None, output_shape_shader, static_cast<int>(components)}})
+        .SetDispatchGroupSize((output_size + 63) / 64)  // Integer ceiling division
+        .AddIndices(outer_dims)
+        .AddUniformVariables({{output_size}, {m}, {n}, {k}});
+
+    return context.RunProgram(program);
+  }
+
+  int64_t batchA = a->Shape().SizeToDimension(a->Shape().NumDimensions() - 2);
+  int64_t batchB = b->Shape().SizeToDimension(b->Shape().NumDimensions() - 2);
+
+  TensorShape a_shape = a->Shape();
+  TensorShape b_shape = b->Shape();
+  TensorShape output_shape = helper.OutputShape();
+
+  const int64_t m_value = output_shape[output_shape.NumDimensions() - 2];
+  // check if A is  batch of vector (bach is not 1, M is 1) and B is a matrix (batch is 1)
+  if (batchA != 1 && m_value == 1 && batchB == 1) {
+    // optimization for batched vector matrix multiplication
+    // dimensions of A: [1,`batchA`,K]
+    TensorShapeVector dims_a = {1, batchA, helper.K()};
+    // dimensions of B: [1,K,N]
+    TensorShapeVector dims_b = {1, helper.K(), helper.N()};
+
+    a_shape = TensorShape(dims_a);
+    b_shape = TensorShape(dims_b);
+    output_shape = {1, batchA, helper.N()};
+  }
+
+  // helpful dimension variables
+  TensorShape outer_dims_a = a_shape.NumDimensions() > 2
+                                 ? a_shape.Slice(0, a_shape.NumDimensions() - 2)
+                                 : TensorShape({});
+
+  TensorShape outer_dims_b = b_shape.NumDimensions() > 2
+                                 ? b_shape.Slice(0, b_shape.NumDimensions() - 2)
+                                 : TensorShape({});
+
+  TensorShape outer_dims = output_shape.NumDimensions() > 2
+                               ? output_shape.Slice(0, output_shape.NumDimensions() - 2)
+                               : TensorShape({});
+
+  const int64_t batch_size = outer_dims.Size();
+
+  // Get dimensions for matrix multiplication from TensorShape
+  const int32_t dim_a_outer = static_cast<int32_t>(a_shape[a_shape.NumDimensions() - 2]);  // M dimension
+  const int32_t dim_inner = static_cast<int32_t>(a_shape[a_shape.NumDimensions() - 1]);    // K dimension
+  const int32_t dim_b_outer = static_cast<int32_t>(b_shape[b_shape.NumDimensions() - 1]);  // N dimension
+
+  const bool is_vec4 = dim_inner % 4 == 0 && dim_b_outer % 4 == 0;
+
+  InlinedVector<int64_t> elements_per_thread = dim_a_outer <= 8
+                                                   ? InlinedVector<int64_t>({4, 1, 1})
+                                                   : InlinedVector<int64_t>({4, 4, 1});
+
+  const uint32_t dispatch_x = static_cast<uint32_t>((dim_b_outer + MATMUL_PACKED_WORKGROUP_SIZE_X * elements_per_thread[0] - 1) /
+                                                    (MATMUL_PACKED_WORKGROUP_SIZE_X * elements_per_thread[0]));
+  const uint32_t dispatch_y = static_cast<uint32_t>((dim_a_outer + MATMUL_PACKED_WORKGROUP_SIZE_Y * elements_per_thread[1] - 1) /
+                                                    (MATMUL_PACKED_WORKGROUP_SIZE_Y * elements_per_thread[1]));
+  const uint32_t dispatch_z = static_cast<uint32_t>((static_cast<uint32_t>(batch_size) + MATMUL_PACKED_WORKGROUP_SIZE_Z * elements_per_thread[2] - 1) /
+                                                    (MATMUL_PACKED_WORKGROUP_SIZE_Z * elements_per_thread[2]));
+
+  const int components = is_vec4 ? 4 : 1;
+  const TensorShape a_shape_temp = CreateMatMulIntermediateShape(outer_dims_a, dim_a_outer, dim_inner, components);
+  const TensorShape b_shape_temp = CreateMatMulIntermediateShape(outer_dims_b, dim_inner, dim_b_outer, components);
+  const TensorShape output_shape_temp = TensorShape({batch_size, dim_a_outer, dim_b_outer / components});
+
+  MatMulProgram program{has_bias, is_vec4, elements_per_thread};
+  program
+      .CacheHint(absl::StrJoin(elements_per_thread, "-"), std::to_string(is_vec4))
+      .AddInputs({{a, ProgramTensorMetadataDependency::TypeAndRank, a_shape_temp, components},
+                  {b, ProgramTensorMetadataDependency::TypeAndRank, b_shape_temp, components}})
+      .AddOutputs({{output_tensor, ProgramTensorMetadataDependency::Rank, output_shape_temp, components}})
+      .AddUniformVariables({{dim_a_outer}, {dim_b_outer}, {dim_inner}})
+      .AddIndices(outer_dims)
+      .SetDispatchGroupSize(dispatch_x, dispatch_y, dispatch_z)
+      .SetWorkgroupSize(MATMUL_PACKED_WORKGROUP_SIZE_X, MATMUL_PACKED_WORKGROUP_SIZE_Y, MATMUL_PACKED_WORKGROUP_SIZE_Z);
+
+  if (has_bias) {
+    const auto* bias = context.Input(2);
+    program.AddInput({bias, ProgramTensorMetadataDependency::Rank, 1});
+  }
+  return context.RunProgram(program);
+}
+
+}  // namespace webgpu
+}  // namespace onnxruntime

onnxruntime/core/providers/webgpu/math/matmul.h

@@ -0,0 +1,47 @@
+// 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 "core/providers/cpu/math/matmul_helper.h"
+#include "core/providers/webgpu/math/matmul_utils.h"
+#include "core/providers/webgpu/math/matmul_packed.h"
+#include "core/providers/webgpu/webgpu_utils.h"
+
+namespace onnxruntime {
+namespace webgpu {
+
+class MatMul final : public WebGpuKernel {
+ public:
+  MatMul(const OpKernelInfo& info) : WebGpuKernel{info} {}
+
+  Status ComputeInternal(ComputeContext& context) const override;
+
+  constexpr static uint32_t MATMUL_PACKED_WORKGROUP_SIZE_X = 8;
+  constexpr static uint32_t MATMUL_PACKED_WORKGROUP_SIZE_Y = 8;
+  constexpr static uint32_t MATMUL_PACKED_WORKGROUP_SIZE_Z = 1;
+};
+
+class MatMulNaiveProgram final : public Program<MatMulNaiveProgram> {
+ public:
+  MatMulNaiveProgram(const int64_t output_size, int64_t output_number, bool has_bias)
+      : Program{"MatMulNaive"}, output_size_(output_size), output_number_(output_number), has_bias_{has_bias} {
+  }
+
+  Status GenerateShaderCode(ShaderHelper& sh) const override;
+
+  WEBGPU_PROGRAM_DEFINE_UNIFORM_VARIABLES({"output_size", ProgramUniformVariableDataType::Uint32},
+                                          {"M", ProgramUniformVariableDataType::Uint32},
+                                          {"N", ProgramUniformVariableDataType::Uint32},
+                                          {"K", ProgramUniformVariableDataType::Uint32});
+
+ private:
+  const int64_t output_size_;
+  const int64_t output_number_;
+  const bool has_bias_;
+};
+
+}  // namespace webgpu
+}  // namespace onnxruntime