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https://github.com/ROCm/composable_kernel.git
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f91579aab6
* Convolution ND * Code unification across dimensions for generating tensor descriptors. * Example * Instances * Move convnd f32 instance file to comply with repo structure. * Conv 1D tensor layouts. * Formatting and use ReferenceConv * Reference ConvFwd supporting 1D and 2D convolution. * Debug printing TensorLayout name. * Conv fwd 1D instance f32 * Refactor conv ND example. Needed to support various conv dimensio. Needed to support various conv dimensions * Rename conv nd example director to prevent conflicts. * Refactor some common utility to single file. Plus some tests. * Refactor GetHostTensorDescriptor + UT. * Add 1D test case. * Test reference convolution 1d/2d * Remove some leftovers. * Fix convolution example error for 1D * Refactor test check errors utility function. * Test Conv2D Fwd XDL * More UT for 1D case. * Parameterize input & weight initializers. * Rename example to prevent conflicts. * Split convnd instance into separate files for 1d/2d * Address review comments. * Fix data type for flops/gbytes calculations. * Assign example number 11. * 3D cases for convolution utility functions. * 3D reference convolution. * Add support for 3D convolution. * Check for inputs bigger than 2GB. * Formatting * Support for bf16/f16/f32/i8 - conv instances + UT. * Use check_err from test_util.hpp. * Split convnd test into separate files for each dim. * Fix data generation and use proper instances. * Formatting * Skip tensor initialization if not necessary. * Fix CMakefiles. * Remove redundant conv2d_fwd test. * Lower problem size for conv3D UT. * 3D case for convnd example. * Remove leftovers after merge. * Add Conv Specialization string to GetTypeString * Skip instance causing numerical errors. * Small fixes. * Remove redundant includes. * Fix namespace name error. * Script for automatic testing and logging convolution fwd UTs * Comment out numactl cmd. * Refine weights initalization and relax rtol for fp16 * Fix weights initialization for int8. * Add type_convert when store output in ref conv 1D. * Get back old conv2d_fwd_xdl operation. * Silence conv debug print. * format * clean * clean * Fix merge. * Fix namespace for check_err Co-authored-by: Adam Osewski <aosewski@amd.com> Co-authored-by: Chao Liu <chao.liu2@amd.com>
148 lines
5.9 KiB
C++
148 lines
5.9 KiB
C++
#include <half.hpp>
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#include <iostream>
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#include <stdexcept>
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#include <tuple>
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#include <vector>
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#include "data_type.hpp"
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#include "element_wise_operation.hpp"
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#include "conv_test_util.hpp"
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#include "host_tensor.hpp"
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#include "tensor_layout.hpp"
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#include "test_util.hpp"
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// Forward declarations for conv instances.
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using DeviceConvFwdNoOpPtr =
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ck::tensor_operation::device::DeviceConvFwdPtr<ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough>;
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace device_conv2d_fwd_instance {
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void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(std::vector<DeviceConvFwdNoOpPtr>&);
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void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances(std::vector<DeviceConvFwdNoOpPtr>&);
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void add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances(
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std::vector<DeviceConvFwdNoOpPtr>&);
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void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances(std::vector<DeviceConvFwdNoOpPtr>&);
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void add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances(std::vector<DeviceConvFwdNoOpPtr>&);
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} // namespace device_conv2d_fwd_instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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namespace {
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bool TestConv2DNHWC()
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{
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bool res{true};
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ck::conv_util::ConvParams params;
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params.N = 2;
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params.K = 16;
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params.C = 4;
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params.input_spatial_lengths = std::vector<ck::index_t>{16, 16};
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params.conv_filter_strides = std::vector<ck::index_t>{1, 1};
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auto host_tensors = test::conv::GetHostTensors(params);
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const Tensor<float>& input = std::get<0>(host_tensors);
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const Tensor<float>& weights = std::get<1>(host_tensors);
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Tensor<float>& host_output = std::get<2>(host_tensors);
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Tensor<float>& device_output = std::get<3>(host_tensors);
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test::conv::RunReferenceConv<2>(params, input, weights, host_output);
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test::conv::RunConv<2>(params, input, weights, device_output);
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res = res &&
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test::check_err(
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device_output.mData, host_output.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
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return res;
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}
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template <typename T>
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bool TestConv2DNHWCInstances(const std::vector<DeviceConvFwdNoOpPtr>& conv_ptrs)
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{
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ck::conv_util::ConvParams params;
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params.num_dim_spatial = 2;
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params.filter_spatial_lengths = std::vector<ck::index_t>{3, 3};
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params.input_spatial_lengths = std::vector<ck::index_t>{71, 71};
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params.conv_filter_strides = std::vector<ck::index_t>{2, 2};
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params.conv_filter_dilations = std::vector<ck::index_t>{1, 1};
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params.input_left_pads = std::vector<ck::index_t>{1, 1};
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params.input_right_pads = std::vector<ck::index_t>{1, 1};
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auto host_tensors = test::conv::GetHostTensors<T,
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T,
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T,
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ck::tensor_layout::convolution::NHWC,
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ck::tensor_layout::convolution::KYXC,
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ck::tensor_layout::convolution::NHWK>(params);
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const Tensor<T>& input = std::get<0>(host_tensors);
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const Tensor<T>& weights = std::get<1>(host_tensors);
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Tensor<T>& host_output = std::get<2>(host_tensors);
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Tensor<T>& device_output = std::get<3>(host_tensors);
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test::conv::RunReferenceConv<2>(params, input, weights, host_output);
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return test::conv::RunConvInstances<2>(
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params, conv_ptrs, input, weights, device_output, host_output);
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}
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bool TestConv2DNHWCBF16Instances()
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{
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std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
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ck::tensor_operation::device::device_conv2d_fwd_instance::
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add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances(conv_ptrs);
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return TestConv2DNHWCInstances<ck::bhalf_t>(conv_ptrs);
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}
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bool TestConv2DNHWCF16Instances()
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{
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std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
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ck::tensor_operation::device::device_conv2d_fwd_instance::
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add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
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ck::tensor_operation::device::device_conv2d_fwd_instance::
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add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
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return TestConv2DNHWCInstances<ck::half_t>(conv_ptrs);
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}
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bool TestConv2DNHWCF32Instances()
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{
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std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
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ck::tensor_operation::device::device_conv2d_fwd_instance::
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add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances(conv_ptrs);
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return TestConv2DNHWCInstances<float>(conv_ptrs);
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}
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bool TestConv2DNHWCInt8Instances()
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{
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std::vector<DeviceConvFwdNoOpPtr> conv_ptrs;
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ck::tensor_operation::device::device_conv2d_fwd_instance::
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add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances(conv_ptrs);
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return TestConv2DNHWCInstances<int8_t>(conv_ptrs);
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}
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} // anonymous namespace
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int main()
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{
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bool res{true};
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res = TestConv2DNHWC();
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std::cout << "TestConv2DNHWC ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
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res = TestConv2DNHWCBF16Instances();
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std::cout << "\nTestConv2DNHWCBF16Instances ..... " << (res ? "SUCCESS" : "FAILURE")
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<< std::endl;
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res = TestConv2DNHWCF16Instances();
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std::cout << "\nTestConv2DNHWCF16Instances ....." << (res ? "SUCCESS" : "FAILURE") << std::endl;
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res = TestConv2DNHWCF32Instances();
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std::cout << "\nTestConv2DNHWCF32Instances ..... " << (res ? "SUCCESS" : "FAILURE")
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<< std::endl;
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res = TestConv2DNHWCInt8Instances();
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std::cout << "\nTestConv2DNHWCInt8Instances ..... " << (res ? "SUCCESS" : "FAILURE")
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<< std::endl;
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return 0;
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}
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