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Commit 07180cb7 authored by aska-0096's avatar aska-0096
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workable

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example/30_grouped_conv_fwd_multiple_d/CMakeLists.txt
View file @ 07180cb7
...@@ -16,6 +16,7 @@ if(USE_BITINT_EXTENSION_INT4) ...@@ -16,6 +16,7 @@ if(USE_BITINT_EXTENSION_INT4)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_int4) add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_int4)
endif() # USE_BITINT_EXTENSION_INT4 endif() # USE_BITINT_EXTENSION_INT4
add_example_executable(example_grouped_conv_fwd_bias_relu_add_wmma_fp16 grouped_conv_fwd_bias_relu_add_wmma_fp16.cpp)
add_example_executable(example_grouped_conv_fwd_xdl_fp16 grouped_conv_fwd_xdl_fp16.cpp) add_example_executable(example_grouped_conv_fwd_xdl_fp16 grouped_conv_fwd_xdl_fp16.cpp)
......
example/30_grouped_conv_fwd_multiple_d/common_wmma.hpp 0 → 100644
View file @ 07180cb7
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <algorithm>
#include <array>
#include <iostream>
#include <string>
#include <type_traits>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_wmma_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/convolution_parameter.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
using BF16 = ck::bhalf_t;
using FP16 = ck::half_t;
using FP32 = float;
#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
using I4 = ck::int4_t;
#endif
using I8 = std::int8_t;
using I32 = std::int32_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvSpec =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
template <typename InputLay, typename WeightLay, typename OutputLay>
struct CommonLayoutSetting
{
using InputLayout = InputLay;
using WeightLayout = WeightLay;
using OutputLayout = OutputLay;
};
template <ck::index_t NDimSpatial>
struct CommonLayoutSettingSelector;
namespace ctl = ck::tensor_layout::convolution;
template <>
struct CommonLayoutSettingSelector<1> final
: CommonLayoutSetting<ctl::G_NW_C, ctl::G_K_X_C, ctl::G_NW_K>
{
};
template <>
struct CommonLayoutSettingSelector<2> final
: CommonLayoutSetting<ctl::G_NHW_C, ctl::G_K_YX_C, ctl::G_NHW_K>
{
};
template <>
struct CommonLayoutSettingSelector<3> final
: CommonLayoutSetting<ctl::G_NDHW_C, ctl::G_K_ZYX_C, ctl::G_NDHW_K>
{
};
template <ck::index_t NDimSpatial>
using InputLayout = typename CommonLayoutSettingSelector<NDimSpatial>::InputLayout;
template <ck::index_t NDimSpatial>
using WeightLayout = typename CommonLayoutSettingSelector<NDimSpatial>::WeightLayout;
template <ck::index_t NDimSpatial>
using OutputLayout = typename CommonLayoutSettingSelector<NDimSpatial>::OutputLayout;
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = true;
};
#define DefaultConvParam \
ck::utils::conv::ConvParam \
{ \
2, 32, 2, 256, 192, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, { 1, 1 } \
}
inline void print_help_msg()
{
std::cerr << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=no, 1=yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
}
inline bool parse_cmd_args(int argc,
char* argv[],
ExecutionConfig& config,
ck::utils::conv::ConvParam& conv_param)
{
constexpr int num_execution_config_args =
3; // arguments for do_verification, init_method, time_kernel
constexpr int num_conv_param_leading_args = 5; // arguments for num_dim_spatial_, G_, N_, K_, C_
constexpr int threshold_to_catch_partial_args = 1 + num_execution_config_args;
constexpr int threshold_to_catch_all_args =
threshold_to_catch_partial_args + num_conv_param_leading_args;
if(argc == 1)
{
// use default
}
// catch only ExecutionConfig arguments
else if(argc == threshold_to_catch_partial_args)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
}
// catch both ExecutionConfig & ConvParam arguments
else if(threshold_to_catch_all_args < argc && ((argc - threshold_to_catch_all_args) % 3 == 0))
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
conv_param = ck::utils::conv::parse_conv_param(
num_dim_spatial, threshold_to_catch_partial_args, argv);
}
else
{
print_help_msg();
return false;
}
return true;
}
inline HostTensorDescriptor make_input_descriptor(const ck::utils::conv::ConvParam& conv_param)
{
switch(conv_param.num_dim_spatial_)
{
case 1:
return HostTensorDescriptor(
{conv_param.G_, conv_param.N_, conv_param.C_, conv_param.input_spatial_lengths_[0]},
{
conv_param.C_, // g
conv_param.input_spatial_lengths_[0] * conv_param.G_ * conv_param.C_, // n
1, // c
conv_param.G_ * conv_param.C_ // wi
});
case 2:
return HostTensorDescriptor(
{conv_param.G_,
conv_param.N_,
conv_param.C_,
conv_param.input_spatial_lengths_[0],
conv_param.input_spatial_lengths_[1]},
{
conv_param.C_, // g
conv_param.input_spatial_lengths_[0] * conv_param.input_spatial_lengths_[1] *
conv_param.G_ * conv_param.C_, // n
1, // c
conv_param.input_spatial_lengths_[1] * conv_param.G_ * conv_param.C_, // hi
conv_param.G_ * conv_param.C_ // wi
});
case 3:
return HostTensorDescriptor(
{conv_param.G_,
conv_param.N_,
conv_param.C_,
conv_param.input_spatial_lengths_[0],
conv_param.input_spatial_lengths_[1],
conv_param.input_spatial_lengths_[2]},
{
conv_param.C_, // g
conv_param.input_spatial_lengths_[0] * conv_param.input_spatial_lengths_[1] *
conv_param.input_spatial_lengths_[2] * conv_param.G_ * conv_param.C_, // n
1, // c
conv_param.input_spatial_lengths_[1] * conv_param.input_spatial_lengths_[2] *
conv_param.G_ * conv_param.C_, // di
conv_param.input_spatial_lengths_[2] * conv_param.G_ * conv_param.C_, // hi
conv_param.G_ * conv_param.C_ // wi
});
}
throw std::runtime_error("unsuppored # dim spatial");
}
inline HostTensorDescriptor make_weight_descriptor(const ck::utils::conv::ConvParam& conv_param)
{
switch(conv_param.num_dim_spatial_)
{
case 1:
return HostTensorDescriptor(
{conv_param.G_, conv_param.K_, conv_param.C_, conv_param.filter_spatial_lengths_[0]},
{
conv_param.K_ * conv_param.filter_spatial_lengths_[0] * conv_param.C_, // g
conv_param.filter_spatial_lengths_[0] * conv_param.C_, // k
1, // c
conv_param.C_ // x
});
case 2:
return HostTensorDescriptor(
{conv_param.G_,
conv_param.K_,
conv_param.C_,
conv_param.filter_spatial_lengths_[0],
conv_param.filter_spatial_lengths_[1]},
{
conv_param.K_ * conv_param.filter_spatial_lengths_[0] *
conv_param.filter_spatial_lengths_[1] * conv_param.C_, // g
conv_param.filter_spatial_lengths_[0] * conv_param.filter_spatial_lengths_[1] *
conv_param.C_, // k
1, // c
conv_param.filter_spatial_lengths_[1] * conv_param.C_, // y
conv_param.C_ // x
});
case 3:
return HostTensorDescriptor(
{conv_param.G_,
conv_param.K_,
conv_param.C_,
conv_param.filter_spatial_lengths_[0],
conv_param.filter_spatial_lengths_[1],
conv_param.filter_spatial_lengths_[2]},
{
conv_param.K_ * conv_param.filter_spatial_lengths_[0] *
conv_param.filter_spatial_lengths_[1] * conv_param.filter_spatial_lengths_[2] *
conv_param.C_, // g
conv_param.filter_spatial_lengths_[0] * conv_param.filter_spatial_lengths_[1] *
conv_param.filter_spatial_lengths_[2] * conv_param.C_, // k
1, // c
conv_param.filter_spatial_lengths_[1] * conv_param.filter_spatial_lengths_[2] *
conv_param.C_, // z
conv_param.filter_spatial_lengths_[2] * conv_param.C_, // y
conv_param.C_ // x
});
}
throw std::runtime_error("unsuppored # dim spatial");
}
inline HostTensorDescriptor make_bias_descriptor(const ck::utils::conv::ConvParam& conv_param)
{
switch(conv_param.num_dim_spatial_)
{
case 1:
return HostTensorDescriptor(
{conv_param.G_, conv_param.N_, conv_param.K_, conv_param.output_spatial_lengths_[0]},
{
conv_param.K_, // g
0, // k
1, // c
0 // x
});
case 2:
return HostTensorDescriptor({conv_param.G_,
conv_param.N_,
conv_param.K_,
conv_param.output_spatial_lengths_[0],
conv_param.output_spatial_lengths_[1]},
{
conv_param.K_, // g
0, // n
1, // k
0, // ho
0 // wo
});
case 3:
return HostTensorDescriptor({conv_param.G_,
conv_param.N_,
conv_param.K_,
conv_param.output_spatial_lengths_[0],
conv_param.output_spatial_lengths_[1],
conv_param.output_spatial_lengths_[2]},
{
conv_param.K_, // g
0, // n
1, // k
0, // z
0, // y
0 // x
});
}
throw std::runtime_error("unsuppored # dim spatial");
}
inline HostTensorDescriptor make_output_descriptor(const ck::utils::conv::ConvParam& conv_param)
{
switch(conv_param.num_dim_spatial_)
{
case 1:
return HostTensorDescriptor(
{conv_param.G_, conv_param.N_, conv_param.K_, conv_param.output_spatial_lengths_[0]},
{
conv_param.K_, // g
conv_param.output_spatial_lengths_[0] * conv_param.G_ * conv_param.K_, // n
1, // k
conv_param.G_ * conv_param.K_ // wo
});
case 2:
return HostTensorDescriptor(
{conv_param.G_,
conv_param.N_,
conv_param.K_,
conv_param.output_spatial_lengths_[0],
conv_param.output_spatial_lengths_[1]},
{
conv_param.K_, // g
conv_param.output_spatial_lengths_[0] * conv_param.output_spatial_lengths_[1] *
conv_param.G_ * conv_param.K_, // n
1, // k
conv_param.output_spatial_lengths_[1] * conv_param.G_ * conv_param.K_, // ho
conv_param.G_ * conv_param.K_ // wo
});
case 3:
return HostTensorDescriptor(
{conv_param.G_,
conv_param.N_,
conv_param.K_,
conv_param.output_spatial_lengths_[0],
conv_param.output_spatial_lengths_[1],
conv_param.output_spatial_lengths_[2]},
{
conv_param.K_, // g
conv_param.output_spatial_lengths_[0] * conv_param.output_spatial_lengths_[1] *
conv_param.output_spatial_lengths_[2] * conv_param.G_ * conv_param.K_, // n
1, // k
conv_param.output_spatial_lengths_[1] * conv_param.output_spatial_lengths_[2] *
conv_param.G_ * conv_param.K_, // do
conv_param.output_spatial_lengths_[2] * conv_param.G_ * conv_param.K_, // ho
conv_param.G_ * conv_param.K_ // wo
});
}
throw std::runtime_error("unsuppored # dim spatial");
}
example/30_grouped_conv_fwd_multiple_d/grouped_conv_fwd_bias_relu_add_wmma_fp16.cpp 0 → 100644
View file @ 07180cb7
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common_wmma.hpp"
// kernel data types
using InKernelDataType = FP16;
using WeiKernelDataType = FP16;
using AccDataType = FP32;
using CShuffleDataType = FP16;
using BiasKernelDataType = FP16;
using ResidualKernelDataType = FP16;
using OutKernelDataType = FP16;
// tensor data types
using InUserDataType = InKernelDataType;
using WeiUserDataType = WeiKernelDataType;
using OutUserDataType = OutKernelDataType;
using InElementOp = PassThrough;
using WeiElementOp = PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::AddReluAdd;
#include "run_grouped_conv_fwd_bias_relu_add_wmma_example.inc"
int main(int argc, char* argv[]) { return !run_grouped_conv_fwd_bias_relu_add_example(argc, argv); }
example/30_grouped_conv_fwd_multiple_d/run_grouped_conv_fwd_bias_relu_add_wmma_example.inc 0 → 100644
View file @ 07180cb7
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
template <typename BiasLay, typename ResidualLay>
struct LayoutSetting
{
using BiasLayout = BiasLay;
using ResidualLayout = ResidualLay;
};
template <ck::index_t NDimSpatial>
struct LayoutSettingSelector;
template <>
struct LayoutSettingSelector<1> final : LayoutSetting<ctl::G_K, ctl::G_NW_K>
{
};
template <>
struct LayoutSettingSelector<2> final : LayoutSetting<ctl::G_K, ctl::G_NHW_K>
{
};
template <>
struct LayoutSettingSelector<3> final : LayoutSetting<ctl::G_K, ctl::G_NDHW_K>
{
};
template <ck::index_t NDimSpatial>
using BiasLayout = typename LayoutSettingSelector<NDimSpatial>::BiasLayout;
template <ck::index_t NDimSpatial>
using ResidualLayout = typename LayoutSettingSelector<NDimSpatial>::ResidualLayout;
template <ck::index_t NDimSpatial>
using DeviceConvFwdInstance =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD_Wmma_CShuffle<
NDimSpatial,
InputLayout<NDimSpatial>,
WeightLayout<NDimSpatial>,
ck::Tuple<BiasLayout<NDimSpatial>, ResidualLayout<NDimSpatial>>,
OutputLayout<NDimSpatial>,
InKernelDataType,
WeiKernelDataType,
ck::Tuple<BiasKernelDataType, ResidualKernelDataType>,
OutKernelDataType,
AccDataType,
CShuffleDataType,
InElementOp,
WeiElementOp,
OutElementOp,
ConvSpec, // ConvForwardSpecialization
GemmSpec, // GemmSpecialization
256, // BlockSize
128, // MPerBlock
256, // NPerBlock
8, // K0PerBlock
8, // K1
16, // MPerWMMA
16, // NPerWMMA
4, // MRepeat
4, // NRepeat
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_AK1
true, // ABlockLdsExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_BK1
true, // BBlockLdsExtraN
1,
1,
S<1, 32, 1, 8>,
8>;
template <ck::index_t NDimSpatial>
using HostConvFwdInstance = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
InUserDataType,
WeiUserDataType,
CShuffleDataType,
InElementOp,
WeiElementOp,
PassThrough>;
template <ck::index_t NDimSpatial>
bool run_grouped_conv_fwd_bias_relu_add(const ExecutionConfig& config,
const ck::utils::conv::ConvParam& conv_param)
{
static_assert(1 <= NDimSpatial && NDimSpatial <= 3, "Unsupported NDimSpatial");
const auto in_g_n_c_wis_desc = make_input_descriptor(conv_param);
const auto wei_g_k_c_xs_desc = make_weight_descriptor(conv_param);
const auto bias_g_n_k_wos_desc = make_bias_descriptor(conv_param);
const auto out_g_n_k_wos_desc = make_output_descriptor(conv_param);
Tensor<InUserDataType> in(in_g_n_c_wis_desc);
Tensor<WeiUserDataType> wei(wei_g_k_c_xs_desc);
Tensor<OutUserDataType> bias(bias_g_n_k_wos_desc);
Tensor<OutUserDataType> residual(bias_g_n_k_wos_desc);
Tensor<OutUserDataType> out_host(out_g_n_k_wos_desc);
Tensor<OutKernelDataType> out_device(out_g_n_k_wos_desc);
std::cout << "in: " << in.mDesc << std::endl;
std::cout << "wei: " << wei.mDesc << std::endl;
std::cout << "bias: " << bias.mDesc << std::endl;
std::cout << "residual: " << residual.mDesc << std::endl;
std::cout << "out: " << out_host.mDesc << std::endl;
switch(config.init_method)
{
case 0: break;
case 1:
in.GenerateTensorValue(GeneratorTensor_2<InUserDataType>{-5, 5});
wei.GenerateTensorValue(GeneratorTensor_2<WeiUserDataType>{-5, 5});
bias.GenerateTensorValue(GeneratorTensor_2<OutUserDataType>{-5, 5});
break;
default:
in.GenerateTensorValue(GeneratorTensor_3<InUserDataType>{0.0, 1.0});
wei.GenerateTensorValue(GeneratorTensor_3<WeiUserDataType>{-0.5, 0.5});
bias.GenerateTensorValue(GeneratorTensor_3<OutUserDataType>{-0.5, 0.5});
}
DeviceMem in_device_buf(sizeof(InKernelDataType) * in.mDesc.GetElementSpaceSize());
DeviceMem wei_device_buf(sizeof(WeiKernelDataType) * wei.mDesc.GetElementSpaceSize());
DeviceMem bias_device_buf(sizeof(OutKernelDataType) * bias.mDesc.GetElementSpaceSize());
DeviceMem residual_device_buf(sizeof(OutKernelDataType) * residual.mDesc.GetElementSpaceSize());
DeviceMem out_device_buf(sizeof(OutKernelDataType) * out_device.mDesc.GetElementSpaceSize());
#ifdef BUILD_INT4_EXAMPLE
const Tensor<InKernelDataType> in_converted(in);
const Tensor<WeiKernelDataType> wei_converted(wei);
const Tensor<OutKernelDataType> bias_converted(bias);
const Tensor<OutKernelDataType> residual_converted(residual);
in_device_buf.ToDevice(in_converted.mData.data());
wei_device_buf.ToDevice(wei_converted.mData.data());
bias_device_buf.ToDevice(bias_converted.mData.data());
residual_device_buf.ToDevice(residual_converted.mData.data());
#else
in_device_buf.ToDevice(in.mData.data());
wei_device_buf.ToDevice(wei.mData.data());
bias_device_buf.ToDevice(bias.mData.data());
residual_device_buf.ToDevice(residual.mData.data());
#endif
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
std::array<ck::index_t, NDimSpatial + 3> d0_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> d0_g_n_k_wos_strides{};
std::array<ck::index_t, NDimSpatial + 3> d1_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> d1_g_n_k_wos_strides{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
std::array<ck::index_t, NDimSpatial> input_right_pads{};
auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
copy(bias_g_n_k_wos_desc.GetLengths(), d0_g_n_k_wos_lengths);
copy(bias_g_n_k_wos_desc.GetStrides(), d0_g_n_k_wos_strides);
copy(bias_g_n_k_wos_desc.GetLengths(), d1_g_n_k_wos_lengths);
copy(bias_g_n_k_wos_desc.GetStrides(), d1_g_n_k_wos_strides);
copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
copy(conv_param.conv_filter_strides_, conv_filter_strides);
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
copy(conv_param.input_left_pads_, input_left_pads);
copy(conv_param.input_right_pads_, input_right_pads);
// do Conv
auto conv = DeviceConvFwdInstance<NDimSpatial>{};
auto invoker = conv.MakeInvoker();
auto argument =
conv.MakeArgument(in_device_buf.GetDeviceBuffer(),
wei_device_buf.GetDeviceBuffer(),
std::array<const void*, 2>{bias_device_buf.GetDeviceBuffer(),
residual_device_buf.GetDeviceBuffer()},
out_device_buf.GetDeviceBuffer(),
a_g_n_c_wis_lengths,
a_g_n_c_wis_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
std::array<std::array<ck::index_t, NDimSpatial + 3>, 2>{
{d0_g_n_k_wos_lengths, d1_g_n_k_wos_lengths}},
std::array<std::array<ck::index_t, NDimSpatial + 3>, 2>{
{d0_g_n_k_wos_strides, d1_g_n_k_wos_strides}},
e_g_n_k_wos_lengths,
e_g_n_k_wos_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
if(!conv.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_conv with the specified compilation parameters does "
"not support this Conv problem");
}
float avg_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = conv_param.GetFlops();
std::size_t num_btype = conv_param.GetByte<InUserDataType, WeiUserDataType, OutUserDataType>();
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_btype / 1.E6 / avg_time;
std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< conv.GetTypeString() << std::endl;
if(config.do_verification)
{
Tensor<CShuffleDataType> c_host(out_g_n_k_wos_desc);
auto ref_conv = HostConvFwdInstance<NDimSpatial>{};
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(in,
wei,
c_host,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_,
InElementOp{},
WeiElementOp{},
PassThrough{});
ref_invoker.Run(ref_argument);
// TODO: implement elementwise operation for host
out_host.ForEach([&](auto&, auto idx) {
OutElementOp{}(out_host(idx), c_host(idx), bias(idx), residual(idx));
});
out_device_buf.FromDevice(out_device.mData.data());
#ifdef BUILD_INT4_EXAMPLE
const Tensor<OutUserDataType> out_device_converted(out_device);
return ck::utils::check_err(
out_device_converted, out_host, "Error: incorrect results!", 1e-5f, 1e-4f);
#else
return ck::utils::check_err(
out_device, out_host, "Error: incorrect results!", 1e-5f, 1e-4f);
#endif
}
return true;
}
bool run_grouped_conv_fwd_bias_relu_add_example(int argc, char* argv[])
{
ExecutionConfig config;
ck::utils::conv::ConvParam conv_param = DefaultConvParam;
if(!parse_cmd_args(argc, argv, config, conv_param))
{
return false;
}
switch(conv_param.num_dim_spatial_)
{
case 1: return run_grouped_conv_fwd_bias_relu_add<1>(config, conv_param);
case 2: return run_grouped_conv_fwd_bias_relu_add<2>(config, conv_param);
case 3: return run_grouped_conv_fwd_bias_relu_add<3>(config, conv_param);
}
return false;
}
include/ck/tensor_operation/gpu/device/impl/device_batched_contraction_multiple_d_wmma_cshuffle.hpp
View file @ 07180cb7
...@@ -723,7 +723,7 @@ struct DeviceBatchedContractionMultipleD_Wmma_CShuffle ...@@ -723,7 +723,7 @@ struct DeviceBatchedContractionMultipleD_Wmma_CShuffle
arg.block_2_ctile_map_)) arg.block_2_ctile_map_))
{ {
throw std::runtime_error( throw std::runtime_error(
"wrong! GridwiseGemmMultipleD_xdl_cshuffle has invalid setting"); "wrong! GridwiseGemmMultipleD_wmma_cshuffle has invalid setting");
} }
const index_t G = arg.e_grid_desc_g_m_n_.GetLength(I0); const index_t G = arg.e_grid_desc_g_m_n_.GetLength(I0);
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_wmma_cshuffle.hpp
View file @ 07180cb7
...@@ -17,6 +17,99 @@ ...@@ -17,6 +17,99 @@
namespace ck { namespace ck {
template <typename GridwiseOp,
typename ADataType,
typename BDataType,
typename DsPointer,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
typename AGridDesc_AK0_M_AK1,
typename BGridDesc_BK0_N_BK1,
typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename Block2CTileMap,
typename ComputePtrOffsetOfBatch,
bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_grouped_conv_fwd_multiple_d_wmma_cshuffle(
const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
DsPointer p_ds_grid,
EDataType* __restrict__ p_e_grid,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CDEElementwiseOperation cde_element_op,
const index_t batch_count,
const AGridDesc_AK0_M_AK1 a_grid_desc_k0_m_k1,
const BGridDesc_BK0_N_BK1 b_grid_desc_k0_n_k1,
const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
ds_grid_desc_mblock_mperblock_nblock_nperblock,
const EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
e_grid_desc_mblock_mperblock_nblock_nperblock_,
const Block2CTileMap block_2_ctile_map,
const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__))
// offset base pointer for each work-group
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t e_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
__shared__ char p_shared[GridwiseOp::GetSharedMemoryNumberOfByte()];
DsPointer p_ds_grid_grp;
static constexpr index_t NumDTensor =
DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock::Size();
static_for<0, NumDTensor, 1>{}(
[&](auto i) { p_ds_grid_grp(i) = p_ds_grid[i] + ds_batch_offset[i]; });
GridwiseOp::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
p_b_grid + b_batch_offset,
p_ds_grid_grp,
p_e_grid + e_batch_offset,
p_shared,
a_grid_desc_k0_m_k1,
b_grid_desc_k0_n_k1,
ds_grid_desc_mblock_mperblock_nblock_nperblock,
e_grid_desc_mblock_mperblock_nblock_nperblock_,
a_element_op,
b_element_op,
cde_element_op,
block_2_ctile_map);
#else
ignore = p_a_grid;
ignore = p_b_grid;
ignore = p_ds_grid;
ignore = p_e_grid;
ignore = batch_count;
ignore = a_grid_desc_k0_m_k1;
ignore = b_grid_desc_k0_n_k1;
ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
ignore = e_grid_desc_mblock_mperblock_nblock_nperblock_;
ignore = a_element_op;
ignore = b_element_op;
ignore = cde_element_op;
ignore = compute_ptr_offset_of_batch;
ignore = block_2_ctile_map;
#endif
}
template <typename GridwiseOp, template <typename GridwiseOp,
typename ADataType, typename ADataType,
typename BDataType, typename BDataType,
...@@ -406,8 +499,9 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle ...@@ -406,8 +499,9 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
} }
// E desc for destination in blockwise copy // E desc for destination in blockwise copy
template <typename EGridDesc_M_N_>
__host__ __device__ static constexpr auto __host__ __device__ static constexpr auto
MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const EGridDesc_M_N& e_grid_desc_m_n) MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const EGridDesc_M_N_& e_grid_desc_m_n)
{ {
const auto M = e_grid_desc_m_n.GetLength(I0); const auto M = e_grid_desc_m_n.GetLength(I0);
const auto N = e_grid_desc_m_n.GetLength(I1); const auto N = e_grid_desc_m_n.GetLength(I1);
...@@ -426,8 +520,9 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle ...@@ -426,8 +520,9 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
} }
// Ds desc for source in blockwise copy // Ds desc for source in blockwise copy
template <typename DsGridDesc_M_N_>
__host__ __device__ static constexpr auto __host__ __device__ static constexpr auto
MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const DsGridDesc_M_N& ds_grid_desc_m_n) MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const DsGridDesc_M_N_& ds_grid_desc_m_n)
{ {
return generate_tuple( return generate_tuple(
[&](auto i) { [&](auto i) {
......
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