chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -0,0 +1,216 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# pylint: disable=import-outside-toplevel, invalid-name
+# ruff: noqa: E501
+"""Instantiate a C++ source for profiling CUTLASS kernels."""
+
+from .library import DataTypeTag
+
+
+class Conv2dProfilerEmitter:
+    """Emit a C++ source for profiling CUTLASS kernels."""
+
+    def __init__(self):
+        from jinja2 import Template
+
+        self.reduction = """
+      ReductionDevice reduction_op;
+      static cutlass::conv::Operator const kConvolutionalOperator = ImplicitGemm::kConvolutionalOperator;
+      typename ReductionDevice::Arguments reduction_args(
+        cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, problem_size).mn(),
+        problem_size.split_k_slices,
+        cutlass::conv::implicit_gemm_tensor_c_size(kConvolutionalOperator, problem_size),
+        {
+           reinterpret_cast<ImplicitGemm::ElementC*> (workspace.get()),
+             ReductionStrideIndex(tensor_c.stride()[ImplicitGemm::UnderlyingKernel::kTensorCStrideIdx])
+             },
+        {
+           tensor_d.device_data(),
+             ReductionStrideIndex(tensor_d.stride()[ImplicitGemm::UnderlyingKernel::kTensorCStrideIdx])
+             },
+        {
+           tensor_c.device_data(),
+             ReductionStrideIndex(tensor_c.stride()[ImplicitGemm::UnderlyingKernel::kTensorCStrideIdx])
+             },
+        {ElementComputeEpilogue(1), ElementComputeEpilogue(0)}
+        );
+
+      reduction_op.initialize(reduction_args, nullptr);
+      reduction_op();
+"""
+
+        self.template = Template(
+            """
+#include <iostream>
+#include "cutlass/cutlass.h"
+#include "cutlass/conv/kernel/default_conv2d_fprop.h"
+#include "cutlass/conv/kernel/default_conv2d_wgrad.h"
+#include "cutlass/conv/kernel/default_conv2d_dgrad.h"
+#include "cutlass/conv/device/implicit_gemm_convolution.h"
+#include "cutlass/util/command_line.h"
+#include "cutlass/util/host_tensor.h"
+#include "cutlass/util/reference/host/tensor_fill.h"
+#include "cutlass/reduction/device/reduce_split_k.h"
+#include "cutlass/reduction/thread/reduction_operators.h"
+
+#define CUTLASS_CHECK(status)                                                                    \
+  {                                                                                              \
+    cutlass::Status error = status;                                                              \
+    if (error != cutlass::Status::kSuccess) {                                                    \
+      std::cerr << "Got cutlass error: " << cutlassGetStatusString(error) << " at: " << __LINE__ \
+                << std::endl;                                                                    \
+      exit(EXIT_FAILURE);                                                                        \
+    }                                                                                            \
+  }
+
+{{OperatorDef}}
+using ImplicitGemm = cutlass::conv::device::ImplicitGemmConvolution<{{OperatorName}}>;
+
+struct Options {
+  cutlass::Tensor4DCoord input_size;
+  cutlass::Tensor4DCoord filter_size;
+  cutlass::Tensor4DCoord padding;
+  cutlass::MatrixCoord conv_stride;
+  cutlass::MatrixCoord dilation;
+
+  void parse(int argc, char const **args) {
+    cutlass::CommandLine cmd(argc, args);
+    cmd.get_cmd_line_argument("n", input_size.n());
+    cmd.get_cmd_line_argument("h", input_size.h());
+    cmd.get_cmd_line_argument("w", input_size.w());
+    cmd.get_cmd_line_argument("c", input_size.c());
+    cmd.get_cmd_line_argument("k", filter_size.n());
+    cmd.get_cmd_line_argument("r", filter_size.h());
+    cmd.get_cmd_line_argument("s", filter_size.w());
+    int pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w;
+    cmd.get_cmd_line_argument("pad_h", pad_h);
+    cmd.get_cmd_line_argument("pad_w", pad_w);
+    cmd.get_cmd_line_argument("stride_h", stride_h);
+    cmd.get_cmd_line_argument("stride_w", stride_w);
+    cmd.get_cmd_line_argument("dilation_h", dilation_h);
+    cmd.get_cmd_line_argument("dilation_w", dilation_w);
+    filter_size.c() = input_size.c();
+    padding = {pad_h, pad_h, pad_w, pad_w};
+    conv_stride = {stride_h, stride_w};
+    dilation = {dilation_h, dilation_w};
+  }
+
+  cutlass::Tensor4DCoord output_size() const {
+    auto dilated_h = (filter_size.h() - 1) * dilation.row() + 1;
+    auto dilated_w = (filter_size.w() - 1) * dilation.column() + 1;
+    auto h = (input_size.h() + padding.n() + padding.h() - dilated_h) / conv_stride.row() + 1;
+    auto w = (input_size.w() + padding.w() + padding.c() - dilated_w) / conv_stride.column() + 1;
+    return cutlass::Tensor4DCoord(input_size.n(), h, w, filter_size.n());
+  }
+};
+
+double profile_convolution(Options const &options) {
+  using ElementOutput = {{ElementOutput}};
+  using ElementInputA = typename ImplicitGemm::ElementA;
+  using ElementInputB = typename ImplicitGemm::ElementB;
+
+  int split_k_slices = {{SplitK}};
+  cutlass::conv::Conv2dProblemSize problem_size(
+                        options.input_size,
+                        options.filter_size,
+                        options.padding,
+                        options.conv_stride,
+                        options.dilation,
+                        options.output_size(),
+                        cutlass::conv::Mode::kCrossCorrelation,
+                        split_k_slices
+                        );
+
+  auto conv_kind = ImplicitGemm::kConvolutionalOperator;
+  auto a_extent = implicit_gemm_tensor_a_extent(conv_kind, problem_size);
+  auto b_extent = implicit_gemm_tensor_b_extent(conv_kind, problem_size);
+  auto c_extent = implicit_gemm_tensor_c_extent(conv_kind, problem_size);
+
+  using LayoutC = typename ImplicitGemm::LayoutC;
+  cutlass::HostTensor<ElementInputA, typename ImplicitGemm::LayoutA> tensor_a(a_extent);
+  cutlass::HostTensor<ElementInputB, typename ImplicitGemm::LayoutB> tensor_b(b_extent);
+  cutlass::HostTensor<ElementOutput, typename ImplicitGemm::LayoutC> tensor_c(c_extent);
+  cutlass::HostTensor<ElementOutput, LayoutC> tensor_d(c_extent);
+  cutlass::HostTensor<ImplicitGemm::ElementC, LayoutC> tensor_c_gemm(c_extent);
+
+  using ElementComputeEpilogue = typename ImplicitGemm::ElementCompute;
+
+  cutlass::conv::SplitKMode const split_k_mode = split_k_slices > 1 ?
+      cutlass::conv::SplitKMode::kParallel : cutlass::conv::SplitKMode::kSerial;
+
+  typename ImplicitGemm::Arguments arguments{
+    problem_size,
+    tensor_a.device_ref(),
+    tensor_b.device_ref(),
+    tensor_c_gemm.device_ref(),
+    tensor_c_gemm.device_ref(),
+    {ElementComputeEpilogue(1), ElementComputeEpilogue(0)},
+    split_k_mode,
+  };
+
+  ImplicitGemm implicit_gemm_op;
+  size_t workspace_size = implicit_gemm_op.get_workspace_size(arguments);
+  cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
+  auto status = implicit_gemm_op.can_implement(arguments);
+  CUTLASS_CHECK(status);
+
+  status = implicit_gemm_op.initialize(arguments, workspace.get());
+  CUTLASS_CHECK(status);
+  status = implicit_gemm_op();
+  CUTLASS_CHECK(status);
+
+  cudaEvent_t events[2];
+  for (auto & event : events) {
+    cudaEventCreate(&event);
+  }
+  cudaEventRecord(events[0]);
+
+  for (int iteration = 0; iteration < 100; ++iteration) {
+    auto status = implicit_gemm_op();
+    CUTLASS_CHECK(status);
+    {{Reduction}}
+  }
+
+  cudaEventRecord(events[1]);
+  cudaEventSynchronize(events[1]);
+  float runtime_ms = 0;
+  cudaEventElapsedTime(&runtime_ms, events[0], events[1]);
+
+  for (auto event : events) {
+    (void)cudaEventDestroy(event);
+  }
+  return double(runtime_ms) / 100.0;
+}
+
+int main(int argc, char const **args) {
+  Options options;
+  options.parse(argc, args);
+  std::cout << profile_convolution(options) << std::endl;
+  return 0;
+}
+"""
+        )
+
+    def emit(self, op_def, op_name, element_output, split_k_slices=1):
+        src = self.template.render(
+            OperatorDef=op_def,
+            OperatorName=op_name,
+            ElementOutput=DataTypeTag[element_output],
+            SplitK=split_k_slices,
+            Reduction=self.reduction if split_k_slices > 1 else "",
+        )
+        return src