66d9cddc83
* New updates. * Minor profiler updates Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
474 lines
17 KiB
C++
474 lines
17 KiB
C++
/***************************************************************************************************
|
|
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
* SPDX-License-Identifier: BSD-3-Clause
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions are met:
|
|
*
|
|
* 1. Redistributions of source code must retain the above copyright notice, this
|
|
* list of conditions and the following disclaimer.
|
|
*
|
|
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
|
* this list of conditions and the following disclaimer in the documentation
|
|
* and/or other materials provided with the distribution.
|
|
*
|
|
* 3. Neither the name of the copyright holder nor the names of its
|
|
* contributors may be used to endorse or promote products derived from
|
|
* this software without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
|
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
|
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
|
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
|
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
|
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*
|
|
**************************************************************************************************/
|
|
/*! \file
|
|
\brief Depthwise Direct Conv testbed
|
|
*/
|
|
#pragma once
|
|
|
|
#include <fstream>
|
|
|
|
#include "../../common/cutlass_unit_test.h"
|
|
#include "cache_testbed_output.h"
|
|
#include "conv2d_problems.h"
|
|
#include "cutlass/conv/device/direct_convolution.h"
|
|
|
|
#include "cutlass/core_io.h"
|
|
#include "cutlass/cutlass.h"
|
|
#include "cutlass/util/host_tensor.h"
|
|
#include "cutlass/util/reference/device/convolution.h"
|
|
#include "cutlass/util/reference/device/tensor_compare.h"
|
|
#include "cutlass/util/reference/host/convolution.h"
|
|
#include "cutlass/util/reference/host/tensor_compare.h"
|
|
#include "cutlass/util/reference/host/tensor_fill.h"
|
|
#include "cutlass/util/tensor_view_io.h"
|
|
|
|
namespace test {
|
|
namespace conv {
|
|
namespace device {
|
|
|
|
template <typename Conv2d>
|
|
class TestbedDepthwiseDirectConv2d {
|
|
public:
|
|
|
|
using ElementA = typename Conv2d::ElementA;
|
|
using LayoutA = typename Conv2d::LayoutA;
|
|
using ElementB = typename Conv2d::ElementB;
|
|
using LayoutB = typename Conv2d::LayoutB;
|
|
using ElementC = typename Conv2d::ElementC;
|
|
using LayoutC = typename Conv2d::LayoutC;
|
|
using ElementAccumulator = typename Conv2d::ElementAccumulator;
|
|
using ElementCompute = typename Conv2d::ElementCompute;
|
|
using EpilogueOutputOp = typename Conv2d::EpilogueOutputOp;
|
|
|
|
static cutlass::conv::Operator const kConvolutionalOperator = Conv2d::kConvolutionalOperator;
|
|
|
|
public:
|
|
/// Initialization
|
|
cutlass::Distribution::Kind init_A;
|
|
cutlass::Distribution::Kind init_B;
|
|
cutlass::Distribution::Kind init_C;
|
|
uint64_t seed;
|
|
|
|
cutlass::HostTensor<ElementA, LayoutA> tensor_A;
|
|
cutlass::HostTensor<ElementB, LayoutB> tensor_B;
|
|
cutlass::HostTensor<ElementB, LayoutB> tensor_reordered_B;
|
|
cutlass::HostTensor<ElementC, LayoutC> tensor_C;
|
|
cutlass::HostTensor<ElementC, LayoutC> tensor_D_computed;
|
|
cutlass::HostTensor<ElementC, LayoutC> tensor_D_reference;
|
|
|
|
int tested_problem_count;
|
|
|
|
public:
|
|
TestbedDepthwiseDirectConv2d(cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
|
|
cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
|
|
cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
|
|
uint64_t seed_ = 2080)
|
|
: init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_), tested_problem_count(0) {}
|
|
|
|
/// Helper to initialize a tensor view
|
|
template <typename Element, typename Layout>
|
|
void initialize_tensor(cutlass::TensorView<Element, Layout> view,
|
|
cutlass::Distribution::Kind dist_kind,
|
|
uint64_t seed) {
|
|
if (dist_kind == cutlass::Distribution::Uniform) {
|
|
int scope;
|
|
int bits = cutlass::sizeof_bits<Element>::value;
|
|
|
|
if (bits <= 8) {
|
|
scope = 2;
|
|
} else if (bits == 16) {
|
|
if (cutlass::sizeof_bits<ElementAccumulator>::value <= 16) {
|
|
scope = 3;
|
|
} else {
|
|
scope = 5;
|
|
}
|
|
} else {
|
|
scope = 8;
|
|
}
|
|
cutlass::reference::host::TensorFillRandomUniform(view, seed, scope, -scope, 0);
|
|
} else if (dist_kind == cutlass::Distribution::Identity) {
|
|
cutlass::reference::host::TensorFillIdentity(view);
|
|
|
|
} else if (dist_kind == cutlass::Distribution::Gaussian) {
|
|
cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
|
|
} else if (dist_kind == cutlass::Distribution::Sequential) {
|
|
cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
|
|
} else {
|
|
}
|
|
}
|
|
|
|
void initialize(cutlass::conv::Conv2dProblemSize const &problem_size, uint64_t seed = 2019) {
|
|
tensor_A.resize(implicit_gemm_tensor_a_extent(kConvolutionalOperator, problem_size));
|
|
tensor_B.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size));
|
|
tensor_reordered_B.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size));
|
|
tensor_C.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size));
|
|
tensor_D_computed.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size));
|
|
tensor_D_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size));
|
|
|
|
initialize_tensor(tensor_A.host_view(), init_A, seed);
|
|
initialize_tensor(tensor_B.host_view(), init_B, seed * 17);
|
|
initialize_tensor(tensor_reordered_B.host_view(), init_B, seed * 17);
|
|
initialize_tensor(tensor_C.host_view(), init_C, seed * 39);
|
|
|
|
tensor_A.sync_device();
|
|
tensor_B.sync_device();
|
|
tensor_reordered_B.sync_device();
|
|
tensor_C.sync_device();
|
|
tensor_D_computed.sync_device();
|
|
tensor_D_reference.sync_device();
|
|
}
|
|
|
|
bool sufficient(int smem_size) const {
|
|
//
|
|
// Determine SMEM requirements and waive if not satisfied
|
|
//
|
|
|
|
cudaDeviceProp properties;
|
|
int device_idx;
|
|
cudaError_t result = cudaGetDevice(&device_idx);
|
|
|
|
if (result != cudaSuccess) {
|
|
throw std::runtime_error("cudaGetDevice() API call failed.");
|
|
}
|
|
|
|
result = cudaGetDeviceProperties(&properties, device_idx);
|
|
|
|
if (result != cudaSuccess) {
|
|
throw std::runtime_error("cudaGetDeviceProperties() failed");
|
|
}
|
|
|
|
if (properties.sharedMemPerBlockOptin < smem_size) {
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/// Executes one test
|
|
bool run(cutlass::conv::Conv2dProblemSize const &problem_size,
|
|
cutlass::conv::SplitKMode const &split_k_mode = cutlass::conv::SplitKMode::kSerial,
|
|
ElementCompute alpha = ElementCompute(1.5),
|
|
ElementCompute beta = ElementCompute(1)) {
|
|
// increment tested problem count run by the testbed
|
|
tested_problem_count++;
|
|
|
|
#if 0 // display conv2d problem size for debugging
|
|
std::cout << problem_size << std::endl
|
|
<< "alpha, beta: (" << alpha << ", " << beta << ")" << std::endl
|
|
<< "split_k_mode: "
|
|
<< ((split_k_mode == cutlass::conv::SplitKMode::kSerial) ? "(serial)" : "(parallel)")
|
|
<< std::endl
|
|
<< std::endl;
|
|
#endif
|
|
|
|
initialize(problem_size);
|
|
|
|
// configure the operator
|
|
Conv2d conv2d_op;
|
|
|
|
typename Conv2d::Arguments conv2d_args(problem_size,
|
|
tensor_A.device_ref(),
|
|
tensor_B.device_ref(),
|
|
tensor_C.device_ref(),
|
|
tensor_D_computed.device_ref(),
|
|
{alpha, beta},
|
|
tensor_reordered_B.device_ref(),
|
|
split_k_mode);
|
|
|
|
// find workspace requirement for parallel split-k reduction
|
|
size_t workspace_size = Conv2d::get_workspace_size(conv2d_args);
|
|
|
|
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
|
|
|
|
cutlass::Status status = conv2d_op.can_implement(problem_size);
|
|
|
|
if (status != cutlass::Status::kSuccess) {
|
|
cudaError_t error = cudaGetLastError();
|
|
std::cerr << "This test is not supported: " << cudaGetErrorString(error) << "\n";
|
|
return true;
|
|
}
|
|
|
|
status = conv2d_op.initialize(conv2d_args, workspace.get());
|
|
|
|
if (status != cutlass::Status::kSuccess) {
|
|
cudaError_t error = cudaGetLastError();
|
|
std::cerr << "This test is not supported: " << cudaGetErrorString(error) << "\n";
|
|
return true;
|
|
}
|
|
|
|
if (!sufficient(conv2d_op.get_smem_size())) {
|
|
if (CUTLASS_TEST_UNIT_ENABLE_WARNINGS) {
|
|
std::cerr << "Test waived due to insufficient CUDA device." << std::endl;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
// run conv2d operator
|
|
status = conv2d_op();
|
|
|
|
EXPECT_TRUE(status == cutlass::Status::kSuccess);
|
|
if (status != cutlass::Status::kSuccess) {
|
|
std::cerr << "Failed to run." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
bool passed = false;
|
|
|
|
cudaError_t result = cudaDeviceSynchronize();
|
|
EXPECT_EQ(result, cudaSuccess) << " device reference error: " << cudaGetErrorString(result);
|
|
|
|
tensor_D_computed.sync_host();
|
|
|
|
//
|
|
// Reference check - support caching results
|
|
//
|
|
|
|
CachedTestKey cached_test_key =
|
|
CreateCachedConv2dTestKey<ElementA,
|
|
LayoutA,
|
|
ElementB,
|
|
LayoutB,
|
|
ElementC,
|
|
LayoutC,
|
|
ElementAccumulator,
|
|
ElementCompute>(kConvolutionalOperator,
|
|
problem_size,
|
|
alpha,
|
|
beta,
|
|
tensor_A.host_view(),
|
|
tensor_B.host_view(),
|
|
tensor_C.host_view());
|
|
|
|
//
|
|
// Look for the cached key
|
|
//
|
|
|
|
bool cached_result_loaded = false;
|
|
CachedTestResult cached_test_result;
|
|
|
|
std::string conv2d_result_cache_name =
|
|
std::string("cached_results_") + CUTLASS_TARGET_NAME + ".txt";
|
|
|
|
if (CUTLASS_TEST_ENABLE_CACHED_RESULTS) {
|
|
|
|
CachedTestResultListing cached_results(conv2d_result_cache_name);
|
|
|
|
auto cached = cached_results.find(cached_test_key);
|
|
|
|
cached_result_loaded = cached.first;
|
|
if (cached_result_loaded) {
|
|
cached_test_result = cached.second;
|
|
}
|
|
}
|
|
|
|
if (!cached_result_loaded) {
|
|
#if CUTLASS_CONV_TEST_UNIT_REFERENCE_DEVICE_ENABLED
|
|
|
|
cutlass::reference::device::Conv2d<ElementA,
|
|
LayoutA,
|
|
ElementB,
|
|
LayoutB,
|
|
ElementC,
|
|
LayoutC,
|
|
ElementCompute,
|
|
ElementAccumulator>(kConvolutionalOperator,
|
|
problem_size,
|
|
tensor_A.device_ref(),
|
|
tensor_B.device_ref(),
|
|
tensor_C.device_ref(),
|
|
tensor_D_reference.device_ref(),
|
|
alpha,
|
|
beta);
|
|
|
|
// sync host (copy device data to host) for dumping error output in case of mismatches
|
|
tensor_D_reference.sync_host();
|
|
|
|
#else
|
|
|
|
cutlass::reference::host::Conv2d<ElementA,
|
|
LayoutA,
|
|
ElementB,
|
|
LayoutB,
|
|
ElementC,
|
|
LayoutC,
|
|
ElementCompute,
|
|
ElementAccumulator>(kConvolutionalOperator,
|
|
problem_size,
|
|
tensor_A.host_ref(),
|
|
tensor_B.host_ref(),
|
|
tensor_C.host_ref(),
|
|
tensor_D_reference.host_ref(),
|
|
alpha,
|
|
beta);
|
|
|
|
#endif
|
|
|
|
if (CUTLASS_TEST_ENABLE_CACHED_RESULTS) {
|
|
|
|
cached_test_result.D = TensorHash(tensor_D_reference.host_view());
|
|
|
|
CachedTestResultListing cached_results(conv2d_result_cache_name);
|
|
|
|
cached_results.append(cached_test_key, cached_test_result);
|
|
cached_results.write(conv2d_result_cache_name);
|
|
}
|
|
} // if (!cached_result_loaded)
|
|
|
|
uint32_t tensor_D_hash = TensorHash(tensor_D_computed.host_view());
|
|
|
|
if (CUTLASS_TEST_ENABLE_CACHED_RESULTS) {
|
|
passed = (tensor_D_hash == cached_test_result.D);
|
|
|
|
EXPECT_EQ(tensor_D_hash, cached_test_result.D)
|
|
<< "Hash-based comparison failed for key:" << "\n" << cached_test_key << "\n";
|
|
}
|
|
else {
|
|
|
|
passed = cutlass::reference::host::TensorEquals(
|
|
tensor_D_computed.host_view(),
|
|
tensor_D_reference.host_view());
|
|
}
|
|
|
|
EXPECT_TRUE(passed);
|
|
|
|
std::stringstream ss_problem_size_text;
|
|
ss_problem_size_text << "nhwc_"
|
|
<< problem_size.N << "x"
|
|
<< problem_size.H << "x"
|
|
<< problem_size.W << "x"
|
|
<< problem_size.C
|
|
<< "_krsc_"
|
|
<< problem_size.K << "x"
|
|
<< problem_size.R << "x"
|
|
<< problem_size.S << "x"
|
|
<< problem_size.C
|
|
<< "_padding_"
|
|
<< problem_size.pad_h << "x"
|
|
<< problem_size.pad_w
|
|
<< "_stride_"
|
|
<< problem_size.stride_h << "x"
|
|
<< problem_size.stride_w
|
|
<< "_dilation_"
|
|
<< problem_size.dilation_h << "x"
|
|
<< problem_size.dilation_w << "_"
|
|
<< (problem_size.mode == cutlass::conv::Mode::kCrossCorrelation ? "xcorr_" : "conv_");
|
|
|
|
if (!passed) {
|
|
std::stringstream fname;
|
|
|
|
fname << "error_Conv2d_DirectConv_device_"
|
|
<< (split_k_mode == cutlass::conv::SplitKMode::kSerial ? "serial_reduction_" : "parallel_reduction_")
|
|
<< (Conv2d::kConvolutionalOperator == cutlass::conv::Operator::kFprop ? "fprop_" :
|
|
(Conv2d::kConvolutionalOperator == cutlass::conv::Operator::kDgrad ? "dgrad_" : "wgrad_"))
|
|
<< ss_problem_size_text.str()
|
|
<< Conv2d::ThreadblockShape::kM << "x"
|
|
<< Conv2d::ThreadblockShape::kN << "x"
|
|
<< Conv2d::ThreadblockShape::kK << "_"
|
|
<< Conv2d::WarpShape::kM << "x"
|
|
<< Conv2d::WarpShape::kN << "x"
|
|
<< Conv2d::WarpShape::kK << ".txt";
|
|
|
|
std::cout << fname.str() << std::endl;
|
|
|
|
std::ofstream results(fname.str());
|
|
|
|
results << problem_size << std::endl;
|
|
|
|
results
|
|
<< "\nA:\n" << tensor_A.host_view() << "\n"
|
|
<< "\nB:\n" << tensor_B.host_view() << "\n"
|
|
<< "\nC:\n" << tensor_C.host_view() << "\n";
|
|
|
|
results << "\nD reference (hash: " << cached_test_result.D << ")\n";
|
|
|
|
if (!cached_result_loaded) {
|
|
results
|
|
<< tensor_D_reference.host_view() << "\n";
|
|
}
|
|
|
|
results
|
|
<< "\nD computed (hash: " << tensor_D_hash << ")\n"
|
|
<< tensor_D_computed.host_view() << "\n";
|
|
|
|
}
|
|
|
|
return passed;
|
|
}
|
|
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename DirectConv>
|
|
bool TestSpecificDepthwiseDirectConv2d(const Conv2dProblemVector &problem_sizes) {
|
|
bool passed = true;
|
|
|
|
//
|
|
// Testbed object
|
|
//
|
|
TestbedDepthwiseDirectConv2d<DirectConv> testbed;
|
|
|
|
// Sweep conv2d problem sizes (split-k-mode=kSerial, split-k-slice=1, alpha=1.0, beta=0.0)
|
|
for (auto conv_problem : problem_sizes) {
|
|
//
|
|
// Test
|
|
//
|
|
|
|
// test mode = xcross
|
|
passed = testbed.run(
|
|
conv_problem,
|
|
cutlass::conv::SplitKMode::kSerial);
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
|
|
// test mode = convolution
|
|
passed = testbed.run(
|
|
conv_problem.reset_mode(cutlass::conv::Mode::kConvolution),
|
|
cutlass::conv::SplitKMode::kSerial);
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace device
|
|
} // namespace conv
|
|
} // namespace test
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|