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Commit 05ee41c3 authored by Rosty Geyyer's avatar Rosty Geyyer
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Merge branch 'develop' into lwpck-471

parents 37116c98 ad541ad6
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library/src/tensor_operation_instance/gpu/softmax/device_softmax_f32_f32_instance_rank4_reduce4.cpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_f32_f32_instance_rank4_reduce4.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_f32_f32_instance_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr index_t RANK = 4;
void add_device_softmax_f32_f32_rank4_reduce4_instances(
std::vector<DeviceSoftmaxPtr<F32, F32, F32, PassThrough, PassThrough, RANK>>& instances)
{
add_device_operation_instances(instances, device_softmax_f32_f32_instances<RANK, 4>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance.cpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank3_reduce1.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank3_reduce2.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank3_reduce3.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce1.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce2.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce3.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce4.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_softmax_i8_i8_rank3_instances(
std::vector<DeviceSoftmaxPtr<I8, F32, I8, PassThrough, PassThrough, 3>>& instances)
{
add_device_softmax_i8_i8_rank3_reduce1_instances(instances);
add_device_softmax_i8_i8_rank3_reduce2_instances(instances);
add_device_softmax_i8_i8_rank3_reduce3_instances(instances);
}
void add_device_softmax_i8_i8_rank4_instances(
std::vector<DeviceSoftmaxPtr<I8, F32, I8, PassThrough, PassThrough, 4>>& instances)
{
add_device_softmax_i8_i8_rank4_reduce1_instances(instances);
add_device_softmax_i8_i8_rank4_reduce2_instances(instances);
add_device_softmax_i8_i8_rank4_reduce3_instances(instances);
add_device_softmax_i8_i8_rank4_reduce4_instances(instances);
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank3_reduce1.cpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank3_reduce1.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr index_t RANK = 3;
void add_device_softmax_i8_i8_rank3_reduce1_instances(
std::vector<DeviceSoftmaxPtr<I8, F32, I8, PassThrough, PassThrough, RANK>>& instances)
{
add_device_operation_instances(instances, device_softmax_i8_i8_instances<RANK, 1>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank3_reduce2.cpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank3_reduce2.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr index_t RANK = 3;
void add_device_softmax_i8_i8_rank3_reduce2_instances(
std::vector<DeviceSoftmaxPtr<I8, F32, I8, PassThrough, PassThrough, RANK>>& instances)
{
add_device_operation_instances(instances, device_softmax_i8_i8_instances<RANK, 2>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank3_reduce3.cpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank3_reduce3.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr index_t RANK = 3;
void add_device_softmax_i8_i8_rank3_reduce3_instances(
std::vector<DeviceSoftmaxPtr<I8, F32, I8, PassThrough, PassThrough, RANK>>& instances)
{
add_device_operation_instances(instances, device_softmax_i8_i8_instances<RANK, 3>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce1.cpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce1.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr index_t RANK = 4;
void add_device_softmax_i8_i8_rank4_reduce1_instances(
std::vector<DeviceSoftmaxPtr<I8, F32, I8, PassThrough, PassThrough, RANK>>& instances)
{
add_device_operation_instances(instances, device_softmax_i8_i8_instances<RANK, 1>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce2.cpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce2.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr index_t RANK = 4;
void add_device_softmax_i8_i8_rank4_reduce2_instances(
std::vector<DeviceSoftmaxPtr<I8, F32, I8, PassThrough, PassThrough, RANK>>& instances)
{
add_device_operation_instances(instances, device_softmax_i8_i8_instances<RANK, 2>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce3.cpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce3.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr index_t RANK = 4;
void add_device_softmax_i8_i8_rank4_reduce3_instances(
std::vector<DeviceSoftmaxPtr<I8, F32, I8, PassThrough, PassThrough, RANK>>& instances)
{
add_device_operation_instances(instances, device_softmax_i8_i8_instances<RANK, 3>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce4.cpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <vector>
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_rank4_reduce4.hpp"
#include "ck/library/tensor_operation_instance/gpu/softmax/device_softmax_i8_i8_instance_type.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr index_t RANK = 4;
void add_device_softmax_i8_i8_rank4_reduce4_instances(
std::vector<DeviceSoftmaxPtr<I8, F32, I8, PassThrough, PassThrough, RANK>>& instances)
{
add_device_operation_instances(instances, device_softmax_i8_i8_instances<RANK, 4>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/utility/convolution_parameter.cpp
View file @ 05ee41c3
......@@ -72,14 +72,10 @@ std::size_t ConvParam::GetFlops() const
{
// 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product>
return static_cast<std::size_t>(2) * G_ * N_ * K_ * C_ *
std::accumulate(std::begin(output_spatial_lengths_),
std::begin(output_spatial_lengths_) + num_dim_spatial_,
static_cast<std::size_t>(1),
std::multiplies<std::size_t>()) *
std::accumulate(std::begin(filter_spatial_lengths_),
std::begin(filter_spatial_lengths_) + num_dim_spatial_,
static_cast<std::size_t>(1),
std::multiplies<std::size_t>());
ck::accumulate_n<std::size_t>(
std::begin(output_spatial_lengths_), num_dim_spatial_, 1, std::multiplies<>()) *
ck::accumulate_n<std::size_t>(
std::begin(filter_spatial_lengths_), num_dim_spatial_, 1, std::multiplies<>());
}
std::string get_conv_param_parser_helper_msg()
......
profiler/CMakeLists.txt
View file @ 05ee41c3
......@@ -20,12 +20,14 @@ set(PROFILER_SOURCE
src/profile_conv_fwd_bias_relu.cpp
src/profile_conv_fwd_bias_relu_add.cpp
src/profile_conv_bwd_data.cpp
src/profile_conv_bwd_weight.cpp
src/profile_grouped_conv_fwd.cpp
src/profile_grouped_conv_bwd_weight.cpp
src/profile_reduce.cpp
src/profile_groupnorm.cpp
src/profile_layernorm.cpp
src/profile_softmax.cpp
src/profile_batchnorm_fwd.cpp
src/profile_batchnorm_bwd.cpp
)
add_executable(ckProfiler ${PROFILER_SOURCE})
......@@ -49,11 +51,14 @@ target_link_libraries(ckProfiler PRIVATE device_grouped_conv3d_fwd_instance)
target_link_libraries(ckProfiler PRIVATE device_conv1d_bwd_data_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_bwd_data_instance)
target_link_libraries(ckProfiler PRIVATE device_conv3d_bwd_data_instance)
target_link_libraries(ckProfiler PRIVATE device_conv1d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_conv3d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_grouped_conv1d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_grouped_conv2d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_grouped_conv3d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_add_instance)
target_link_libraries(ckProfiler PRIVATE device_normalization_instance)
target_link_libraries(ckProfiler PRIVATE device_softmax_instance)
target_link_libraries(ckProfiler PRIVATE device_reduce_instance)
target_link_libraries(ckProfiler PRIVATE device_batchnorm_instance)
rocm_install(TARGETS ckProfiler COMPONENT profiler)
profiler/include/profile_batched_gemm_add_relu_gemm_add_impl.hpp
View file @ 05ee41c3
......@@ -14,6 +14,7 @@
#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/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
namespace ck {
......@@ -111,15 +112,15 @@ bool profile_batched_gemm_add_relu_gemm_add_impl(bool do_verification,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), Row>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, stride, 1}));
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, 1, stride}));
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
}
};
......@@ -330,8 +331,7 @@ bool profile_batched_gemm_add_relu_gemm_add_impl(bool do_verification,
{
e1_g_m_o_device_buf.FromDevice(e1_g_m_o_device_result.mData.data());
pass = pass & ck::utils::check_err(e1_g_m_o_device_result.mData,
e1_g_m_o_host_result.mData);
pass = pass & ck::utils::check_err(e1_g_m_o_device_result, e1_g_m_o_host_result);
if(do_log)
{
......
profiler/include/profile_batched_gemm_gemm_impl.hpp
View file @ 05ee41c3
......@@ -16,6 +16,7 @@
#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/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
namespace ck {
......@@ -105,15 +106,15 @@ bool profile_batched_gemm_gemm_impl(bool do_verification,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), Row>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, stride, 1}));
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, 1, stride}));
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
}
};
......@@ -283,8 +284,7 @@ bool profile_batched_gemm_gemm_impl(bool do_verification,
{
c_g_m_o_device_buf.FromDevice(c_g_m_o_device_result.mData.data());
pass = pass &
ck::utils::check_err(c_g_m_o_device_result.mData, c_g_m_o_host_result.mData);
pass = pass & ck::utils::check_err(c_g_m_o_device_result, c_g_m_o_host_result);
if(do_log)
{
......
profiler/include/profile_batched_gemm_impl.hpp
View file @ 05ee41c3
......@@ -16,6 +16,7 @@
#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/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
namespace ck {
......@@ -50,15 +51,15 @@ bool profile_batched_gemm_impl(int do_verification,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
using namespace ck::literals;
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, stride, 1}));
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, 1, stride}));
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
}
};
......@@ -202,8 +203,7 @@ bool profile_batched_gemm_impl(int do_verification,
{
c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
pass = pass &
ck::utils::check_err(c_g_m_n_device_result.mData, c_g_m_n_host_result.mData);
pass = pass & ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
if(do_log)
{
......
profiler/include/profile_batched_gemm_reduce_impl.hpp
View file @ 05ee41c3
......@@ -14,6 +14,7 @@
#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/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
namespace ck {
......@@ -78,15 +79,15 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
std::size_t col,
std::size_t stride,
auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({row * stride, stride, 1}));
return HostTensorDescriptor({batch_count, row, col}, {row * stride, stride, 1_uz});
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({col * stride, 1, stride}));
return HostTensorDescriptor({batch_count, row, col}, {col * stride, 1_uz, stride});
}
};
......@@ -95,17 +96,13 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
Tensor<CDataType> c_g_m_n_host_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> d0_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
Tensor<ReduceDataType> d1_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
Tensor<ReduceDataType> d0_g_m_host_result({BatchCount, M});
Tensor<ReduceDataType> d1_g_m_host_result({BatchCount, M});
Tensor<CDataType> c_g_m_n_device_result(
f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
Tensor<ReduceDataType> d0_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
Tensor<ReduceDataType> d1_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
{static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
Tensor<ReduceDataType> d0_g_m_device_result({BatchCount, M});
Tensor<ReduceDataType> d1_g_m_device_result({BatchCount, M});
std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
......@@ -319,12 +316,9 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
reduce0_device_buf.FromDevice(d0_g_m_device_result.mData.data());
reduce1_device_buf.FromDevice(d1_g_m_device_result.mData.data());
bool c_error =
ck::utils::check_err(c_g_m_n_device_result.mData, c_g_m_n_host_result.mData);
bool d0_error =
ck::utils::check_err(d0_g_m_device_result.mData, d0_g_m_host_result.mData);
bool d1_error =
ck::utils::check_err(d1_g_m_device_result.mData, d1_g_m_host_result.mData);
bool c_error = ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
bool d0_error = ck::utils::check_err(d0_g_m_device_result, d0_g_m_host_result);
bool d1_error = ck::utils::check_err(d1_g_m_device_result, d1_g_m_host_result);
pass = pass && (c_error == true);
pass = pass && (d0_error == true);
......
profiler/include/profile_batched_gemm_softmax_gemm_impl.hpp
View file @ 05ee41c3
......@@ -16,6 +16,7 @@
#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/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
......@@ -113,15 +114,15 @@ bool profile_batched_gemm_softmax_gemm_impl(bool do_verification,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), Row>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, stride, 1}));
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
std::vector<std::size_t>({batch_stride, 1, stride}));
return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
}
};
......@@ -307,8 +308,7 @@ bool profile_batched_gemm_softmax_gemm_impl(bool do_verification,
{
c_g_m_o_device_buf.FromDevice(c_g_m_o_device_result.mData.data());
pass = pass &
ck::utils::check_err(c_g_m_o_device_result.mData, c_g_m_o_host_result.mData);
pass = pass & ck::utils::check_err(c_g_m_o_device_result, c_g_m_o_host_result);
if(do_log)
{
......
profiler/include/profile_batched_gemm_softmax_gemm_permute_impl.hpp
View file @ 05ee41c3
......@@ -16,6 +16,7 @@
#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/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
......@@ -308,8 +309,25 @@ bool profile_batched_gemm_softmax_gemm_permute_impl(bool do_verification,
{
c_device_buf.FromDevice(c_gs_ms_os_device_result.mData.data());
pass = pass & ck::utils::check_err(c_gs_ms_os_device_result.mData,
c_gs_ms_os_host_result.mData);
// default absolute error and relative error is 0.001
double rtol = 1e-3;
double atol = 1e-3;
// when BF16 is taken, set absolute error and relative error to 0.01
if(std::is_same_v<ADataType, ck::bhalf_t> &&
std::is_same_v<B0DataType, ck::bhalf_t> &&
std::is_same_v<B1DataType, ck::bhalf_t> &&
std::is_same_v<CDataType, ck::bhalf_t>)
{
rtol = 1e-2;
atol = 1e-2;
}
pass = pass & ck::utils::check_err(c_gs_ms_os_device_result,
c_gs_ms_os_host_result,
"Error: Incorrect results!",
rtol,
atol);
if(do_log)
{
......
profiler/include/profile_batchnorm_backward_impl.hpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iomanip>
#include <stdexcept>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.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/tensor_operation_instance/gpu/batchnorm_backward.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_backward.hpp"
namespace ck {
namespace profiler {
template <typename XDataType,
typename DxDataType,
typename DyDataType,
typename AccDataType,
typename ScaleDataType,
typename DscaleDbiasDataType,
typename MeanVarDataType,
index_t Rank,
index_t NumBatchNormReduceDim>
bool profile_batchnorm_backward_impl(bool do_verification,
int init_method,
bool do_dumpout,
bool time_kernel,
const std::vector<size_t> inOutLengths,
const std::vector<int> reduceDims,
bool haveSavedMeanInvVar,
double epsilon)
{
if(inOutLengths.size() != Rank || reduceDims.size() != NumBatchNormReduceDim)
{
throw std::runtime_error("Invalid tensor lengths or number of reduce dimensions!");
};
std::vector<size_t> scaleBiasMeanVarLengths;
// used for calculating the effective transferred bytes by each operation
size_t total_length;
size_t invariant_length = 1;
total_length =
std::accumulate(inOutLengths.begin(), inOutLengths.end(), 1, std::multiplies<size_t>{});
if(std::any_of(reduceDims.begin(), reduceDims.end(), [](int d) { return d < 0 || d >= Rank; }))
throw std::runtime_error("Invalid reduce dimensions!");
for(int dim = 0; dim < Rank; dim++)
{
if(std::none_of(reduceDims.begin(), reduceDims.end(), [&](int d) { return dim == d; }))
{
scaleBiasMeanVarLengths.push_back(inOutLengths[dim]);
invariant_length *= inOutLengths[dim];
};
}
// input data of the batchnorm backward algorithm
Tensor<XDataType> x(inOutLengths);
Tensor<DyDataType> dy(inOutLengths);
Tensor<ScaleDataType> bnScale(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> savedMean(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> savedInvVar(scaleBiasMeanVarLengths);
// savedVariance is only used for initializing savedInvVar
Tensor<MeanVarDataType> savedVariance(scaleBiasMeanVarLengths);
// output data of the batchnorm backward algorithm
Tensor<DxDataType> dx_ref(inOutLengths);
Tensor<DxDataType> dx(inOutLengths);
Tensor<DscaleDbiasDataType> dscale(scaleBiasMeanVarLengths);
Tensor<DscaleDbiasDataType> dbias(scaleBiasMeanVarLengths);
Tensor<DscaleDbiasDataType> dscale_ref(scaleBiasMeanVarLengths);
Tensor<DscaleDbiasDataType> dbias_ref(scaleBiasMeanVarLengths);
auto inOutStrides = x.mDesc.GetStrides();
auto scaleBiasMeanVarStrides = bnScale.mDesc.GetStrides();
std::size_t num_thread = std::thread::hardware_concurrency();
if(haveSavedMeanInvVar)
{
const float x_mean = 0.0f;
const float x_stddev = 1.0f;
const float noise_stddev = 0.0001f;
// input data in normal distribution
x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
// initialize the savedMean to be values with tiny variation to the mean of the x values
savedMean.GenerateTensorValue(GeneratorTensor_4<MeanVarDataType>{x_mean, noise_stddev},
num_thread);
// initialize the variance to be values with tiny variation to the variance of the x values
savedVariance.GenerateTensorValue(
GeneratorTensor_4<MeanVarDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
auto it_src = savedVariance.mData.begin();
auto it_dst = savedInvVar.mData.begin();
float tmp_epsilon = std::numeric_limits<float>::epsilon();
while(it_src != savedVariance.mData.end())
{
*it_dst = type_convert<AccDataType>(
1.0f / std::sqrtf(type_convert<float>(*it_src) + tmp_epsilon));
it_src++;
it_dst++;
};
}
else
{
const float x_mean = 0.0f;
const float x_stddev = 1.0f;
// input data in normal distribution
x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
};
if(do_verification)
{
switch(init_method)
{
case 0:
dy.GenerateTensorValue(GeneratorTensor_0<DyDataType>{}, num_thread);
bnScale.GenerateTensorValue(GeneratorTensor_0<ScaleDataType>{}, num_thread);
break;
case 1:
dy.GenerateTensorValue(GeneratorTensor_1<DyDataType>{1}, num_thread);
bnScale.GenerateTensorValue(GeneratorTensor_1<ScaleDataType>{1}, num_thread);
break;
case 2:
dy.GenerateTensorValue(GeneratorTensor_2<DyDataType>{-2, 2}, num_thread);
bnScale.GenerateTensorValue(GeneratorTensor_2<ScaleDataType>{-5, 5}, num_thread);
break;
default:
dy.GenerateTensorValue(GeneratorTensor_3<DyDataType>{-0.2f, 0.2f}, num_thread);
bnScale.GenerateTensorValue(GeneratorTensor_3<ScaleDataType>{-0.5f, 0.5f}, num_thread);
}
};
// input data of the batchnorm backward algorithm
DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
DeviceMem dy_dev(sizeof(DyDataType) * dy.mDesc.GetElementSpaceSize());
DeviceMem bnScale_dev(sizeof(ScaleDataType) * bnScale.mDesc.GetElementSpaceSize());
DeviceMem savedMean_dev(sizeof(MeanVarDataType) * savedMean.mDesc.GetElementSpaceSize());
DeviceMem savedInvVar_dev(sizeof(MeanVarDataType) * savedInvVar.mDesc.GetElementSpaceSize());
// output data of the batchnorm backward algorithm
DeviceMem dx_dev(sizeof(DxDataType) * dx.mDesc.GetElementSpaceSize());
DeviceMem dscale_dev(sizeof(DscaleDbiasDataType) * dscale.mDesc.GetElementSpaceSize());
DeviceMem dbias_dev(sizeof(DscaleDbiasDataType) * dbias.mDesc.GetElementSpaceSize());
x_dev.ToDevice(x.mData.data());
dy_dev.ToDevice(dy.mData.data());
bnScale_dev.ToDevice(bnScale.mData.data());
if(haveSavedMeanInvVar)
{
savedMean_dev.ToDevice(savedMean.mData.data());
savedInvVar_dev.ToDevice(savedInvVar.mData.data());
};
std::array<index_t, Rank> arrInOutLengths;
std::array<index_t, Rank> arrInOutStrides;
std::array<index_t, Rank - NumBatchNormReduceDim> arrScaleBiasMeanVarLengths;
std::array<index_t, Rank - NumBatchNormReduceDim> arrScaleBiasMeanVarStrides;
std::array<int, NumBatchNormReduceDim> arrReduceDims;
std::copy(inOutLengths.begin(), inOutLengths.end(), arrInOutLengths.begin());
std::copy(inOutStrides.begin(), inOutStrides.end(), arrInOutStrides.begin());
std::copy(scaleBiasMeanVarLengths.begin(),
scaleBiasMeanVarLengths.end(),
arrScaleBiasMeanVarLengths.begin());
std::copy(scaleBiasMeanVarStrides.begin(),
scaleBiasMeanVarStrides.end(),
arrScaleBiasMeanVarStrides.begin());
std::copy(reduceDims.begin(), reduceDims.end(), arrReduceDims.begin());
using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
// add device batchnorm-backward instances
using DeviceOp = ck::tensor_operation::device::DeviceBatchNormBwd<XDataType,
DxDataType,
DxDataType,
AccDataType,
ScaleDataType,
DscaleDbiasDataType,
MeanVarDataType,
PassThroughOp,
Rank,
NumBatchNormReduceDim>;
// get device op instances
const auto instance_ptrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
std::string best_instance_name;
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
if(do_verification)
{
using ReferenceBatchNormBwdInstance =
ck::tensor_operation::host::ReferenceBatchNormBwd<XDataType,
DxDataType,
DyDataType,
AccDataType,
ScaleDataType,
DscaleDbiasDataType,
MeanVarDataType,
PassThroughOp,
Rank,
NumBatchNormReduceDim>;
auto batchNormBwd_ref = ReferenceBatchNormBwdInstance{};
auto argument_ptr_ref = batchNormBwd_ref.MakeArgumentPointer(
arrInOutLengths,
arrInOutStrides,
arrInOutStrides,
arrInOutStrides,
arrReduceDims,
arrScaleBiasMeanVarLengths,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
x.mData.data(),
dy.mData.data(),
bnScale.mData.data(),
haveSavedMeanInvVar ? savedMean.mData.data() : nullptr,
haveSavedMeanInvVar ? savedInvVar.mData.data() : nullptr,
epsilon,
PassThroughOp{},
dx_ref.mData.data(),
dscale_ref.mData.data(),
dbias_ref.mData.data());
if(!batchNormBwd_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reference instance, exiting!"
<< std::endl;
return (false);
};
auto invoker_ptr_ref = batchNormBwd_ref.MakeInvokerPointer();
(void)invoker_ptr_ref->Run(argument_ptr_ref.get());
}
int num_kernel = 0;
bool pass = true;
for(auto& inst_ptr : instance_ptrs)
{
auto argument_ptr = inst_ptr->MakeArgumentPointer(
arrInOutLengths,
arrInOutStrides,
arrInOutStrides,
arrInOutStrides,
arrReduceDims,
arrScaleBiasMeanVarLengths,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
x_dev.GetDeviceBuffer(),
dy_dev.GetDeviceBuffer(),
bnScale_dev.GetDeviceBuffer(),
haveSavedMeanInvVar ? savedMean_dev.GetDeviceBuffer() : nullptr,
haveSavedMeanInvVar ? savedInvVar_dev.GetDeviceBuffer() : nullptr,
epsilon,
PassThroughOp{},
dx_dev.GetDeviceBuffer(),
dscale_dev.GetDeviceBuffer(),
dbias_dev.GetDeviceBuffer());
if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
{
num_kernel++;
}
else
{
if(time_kernel)
{
std::cout << inst_ptr->GetTypeString()
<< " skipped due to unsupported argument: " << std::endl;
}
continue;
};
size_t workspace_sz = inst_ptr->GetWorkSpaceSize(argument_ptr.get());
DeviceMem workspace_dev(workspace_sz);
inst_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
auto invoker_ptr = inst_ptr->MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
size_t num_bytes = 0;
// inputing of x, dy, scale, outputing of dx, dscale, dbias
num_bytes += total_length * (sizeof(XDataType) + sizeof(DyDataType) + sizeof(DxDataType)) +
invariant_length * sizeof(DscaleDbiasDataType) * 2;
// inputting of savedMean, savedInvVariance
if(haveSavedMeanInvVar)
num_bytes += invariant_length * sizeof(MeanVarDataType) * 2;
float gb_per_sec = num_bytes / 1.E6 / avg_time;
if(time_kernel)
std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, "
<< inst_ptr->GetTypeString() << std::endl;
if(avg_time < best_avg_time)
{
best_instance_name = inst_ptr->GetTypeString();
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
using ck::utils::check_err;
bool single_pass = true;
dx_dev.FromDevice(dx.mData.data());
dscale_dev.FromDevice(dscale.data());
dbias_dev.FromDevice(dbias.data());
// clang-format off
single_pass = single_pass && ck::utils::check_err(dx.mData, dx_ref.mData, "dx result:", 5e-4, 5e-4);
single_pass = single_pass && ck::utils::check_err(dscale.mData, dscale_ref.mData, "dScale result:", 3e-3, 3e-3);
single_pass = single_pass && ck::utils::check_err(dbias.mData, dbias_ref.mData, "dBias result:", 3e-3, 3e-3);
// clang-format on
pass = pass && single_pass;
};
if(do_dumpout)
{
using ck::host_common::dumpBufferToFile;
// clang-format off
dumpBufferToFile("dump_x.bin", x.mData.data(), x.mDesc.GetElementSize());
dumpBufferToFile("dump_dy.bin", dy.mData.data(), dy.mDesc.GetElementSize());
dumpBufferToFile("dump_dx.bin", dx.mData.data(), dx.mDesc.GetElementSize());
dumpBufferToFile("dump_dx_ref.bin", dx_ref.mData.data(), dx_ref.mDesc.GetElementSize());
dumpBufferToFile("dump_dscale.bin", dscale.mData.data(), dscale.mDesc.GetElementSize());
dumpBufferToFile("dump_dscale_ref.bin", dscale_ref.mData.data(), dscale_ref.mDesc.GetElementSize());
// clang-format off
};
}
if(time_kernel)
{
std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_instance_name << std::endl;
}
if(num_kernel == 0)
{
std::cout << "Error: No kernel is applicable" << std::endl;
return false;
}
return pass;
}
} // namespace profiler
} // namespace ck
profiler/include/profile_batchnorm_forward_impl.hpp 0 → 100644
View file @ 05ee41c3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iomanip>
#include <stdexcept>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.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/tensor_operation_instance/gpu/batchnorm_forward.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_forward.hpp"
namespace ck {
namespace profiler {
template <typename XDataType,
typename YDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
index_t Rank,
index_t NumBatchNormReduceDim>
bool profile_batchnorm_forward_impl(int do_verification,
int init_method,
bool do_dumpout,
bool time_kernel,
const std::vector<size_t> inOutLengths,
const std::vector<int> reduceDims,
bool updateMovingAverage,
bool saveMeanAndInvVariance,
double averageFactor,
double epsilon)
{
if(inOutLengths.size() != Rank || reduceDims.size() != NumBatchNormReduceDim)
{
throw std::runtime_error("Invalid tensor lengths or number of reduce dimensions!");
};
std::vector<size_t> scaleBiasMeanVarLengths;
// used for calculating the effective transferred bytes by each operation
size_t total_length;
size_t invariant_length = 1;
total_length =
std::accumulate(inOutLengths.begin(), inOutLengths.end(), 1, std::multiplies<size_t>{});
if(std::any_of(reduceDims.begin(), reduceDims.end(), [](int d) { return d < 0 || d >= Rank; }))
throw std::runtime_error("Invalid reduce dimensions!");
for(int dim = 0; dim < Rank; dim++)
{
if(std::none_of(reduceDims.begin(), reduceDims.end(), [&](int d) { return dim == d; }))
{
scaleBiasMeanVarLengths.push_back(inOutLengths[dim]);
invariant_length *= inOutLengths[dim];
};
}
// input data of the batchnorm forward algorithm
Tensor<XDataType> x(inOutLengths);
Tensor<ScaleDataType> bnScale(scaleBiasMeanVarLengths);
Tensor<BiasDataType> bnBias(scaleBiasMeanVarLengths);
// output data of the batchnorm forward algorithm
Tensor<YDataType> y_ref(inOutLengths);
Tensor<YDataType> y(inOutLengths);
Tensor<MeanVarDataType> resultSaveMean_ref(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultSaveInvVariance_ref(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultRunningMean_ref(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultRunningVariance_ref(scaleBiasMeanVarLengths);
auto inOutStrides = x.mDesc.GetStrides();
auto scaleBiasMeanVarStrides = bnScale.mDesc.GetStrides();
std::size_t num_thread = std::thread::hardware_concurrency();
if(updateMovingAverage)
{
const float x_mean = 0.0f;
const float x_stddev = 1.0f;
const float noise_stddev = 0.04f;
// input data in normal distribution
x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
// initialize the runningMean to be values with tiny variation to the mean of the x
// values
resultRunningMean_ref.GenerateTensorValue(
GeneratorTensor_4<MeanVarDataType>{x_mean, noise_stddev}, num_thread);
// initialize the runningVariance to be values with tiny variation to the variance of
// the x values
resultRunningVariance_ref.GenerateTensorValue(
GeneratorTensor_4<MeanVarDataType>{x_stddev * x_stddev, noise_stddev}, num_thread);
}
else
{
if constexpr(ck::is_same_v<XDataType, int8_t>)
x.GenerateTensorValue(GeneratorTensor_2<XDataType>{-5, 5}, num_thread);
else
x.GenerateTensorValue(GeneratorTensor_3<XDataType>{-1.0f, 1.0f}, num_thread);
};
if(do_verification)
{
switch(init_method)
{
case 0:
bnScale.GenerateTensorValue(GeneratorTensor_0<ScaleDataType>{}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_0<BiasDataType>{}, num_thread);
break;
case 1:
bnScale.GenerateTensorValue(GeneratorTensor_1<ScaleDataType>{1}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_1<BiasDataType>{0}, num_thread);
break;
case 2:
bnScale.GenerateTensorValue(GeneratorTensor_2<ScaleDataType>{-5, 5}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-5, 5}, num_thread);
break;
default:
bnScale.GenerateTensorValue(GeneratorTensor_3<ScaleDataType>{-1.0f, 1.0f}, num_thread);
bnBias.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{-1.0f, 1.0f}, num_thread);
}
};
// these buffers are usually provided by the user application
DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
DeviceMem y_dev(sizeof(XDataType) * y.mDesc.GetElementSpaceSize());
DeviceMem bnScale_dev(sizeof(ScaleDataType) * bnScale.mDesc.GetElementSpaceSize());
DeviceMem bnBias_dev(sizeof(BiasDataType) * bnBias.mDesc.GetElementSpaceSize());
// mean_dev or resultSaveMean_dev
DeviceMem resultSaveMean_dev(sizeof(MeanVarDataType) *
resultSaveMean_ref.mDesc.GetElementSpaceSize());
// meansquare_dev or resultSaveInvVariance_dev
DeviceMem resultSaveInvVariance_dev(sizeof(MeanVarDataType) *
resultSaveInvVariance_ref.mDesc.GetElementSpaceSize());
// resultRunningMean_dev
DeviceMem resultRunningMean_dev(sizeof(MeanVarDataType) *
resultRunningMean_ref.mDesc.GetElementSpaceSize());
// resultRunningVariance_dev
DeviceMem resultRunningVariance_dev(sizeof(MeanVarDataType) *
resultRunningVariance_ref.mDesc.GetElementSpaceSize());
x_dev.ToDevice(x.mData.data());
bnScale_dev.ToDevice(bnScale.mData.data());
bnBias_dev.ToDevice(bnBias.mData.data());
if(updateMovingAverage)
{
resultRunningMean_dev.ToDevice(resultRunningMean_ref.mData.data());
resultRunningVariance_dev.ToDevice(resultRunningVariance_ref.mData.data());
};
// used for storing the device result for verification when updateMovingAverage is enabled
Tensor<MeanVarDataType> resultRunningMean(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultRunningVariance(scaleBiasMeanVarLengths);
// used for storing the device result for verification when saveMeanAndInvVariance is enabled
Tensor<MeanVarDataType> resultSaveMean(scaleBiasMeanVarLengths);
Tensor<MeanVarDataType> resultSaveInvVariance(scaleBiasMeanVarLengths);
std::array<index_t, Rank> arrInOutLengths;
std::array<index_t, Rank> arrInOutStrides;
std::array<index_t, Rank - NumBatchNormReduceDim> arrScaleBiasMeanVarLengths;
std::array<index_t, Rank - NumBatchNormReduceDim> arrScaleBiasMeanVarStrides;
std::array<int, NumBatchNormReduceDim> arrReduceDims;
std::copy(inOutLengths.begin(), inOutLengths.end(), arrInOutLengths.begin());
std::copy(inOutStrides.begin(), inOutStrides.end(), arrInOutStrides.begin());
std::copy(scaleBiasMeanVarLengths.begin(),
scaleBiasMeanVarLengths.end(),
arrScaleBiasMeanVarLengths.begin());
std::copy(scaleBiasMeanVarStrides.begin(),
scaleBiasMeanVarStrides.end(),
arrScaleBiasMeanVarStrides.begin());
std::copy(reduceDims.begin(), reduceDims.end(), arrReduceDims.begin());
using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
// add device batchnorm-forward instances
using DeviceOp = ck::tensor_operation::device::DeviceBatchNormFwd<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
PassThroughOp,
Rank,
NumBatchNormReduceDim>;
// get device op instances
const auto instance_ptrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
std::string best_instance_name;
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
if(do_verification)
{
using ReferenceBatchNormFwdInstance =
ck::tensor_operation::host::ReferenceBatchNormFwd<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
PassThroughOp,
Rank,
NumBatchNormReduceDim>;
auto batchNormFwd_ref = ReferenceBatchNormFwdInstance{};
auto argument_ptr_ref = batchNormFwd_ref.MakeArgumentPointer(
arrInOutLengths,
arrInOutStrides,
arrInOutStrides,
arrReduceDims,
arrScaleBiasMeanVarLengths,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
x.mData.data(),
bnScale.mData.data(),
bnBias.mData.data(),
epsilon,
PassThroughOp{},
y_ref.mData.data(),
saveMeanAndInvVariance ? resultSaveMean_ref.mData.data() : nullptr,
saveMeanAndInvVariance ? resultSaveInvVariance_ref.mData.data() : nullptr,
averageFactor,
updateMovingAverage ? resultRunningMean_ref.mData.data() : nullptr,
updateMovingAverage ? resultRunningVariance_ref.mData.data() : nullptr);
if(!batchNormFwd_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reference instance, exiting!"
<< std::endl;
return (false);
};
auto invoker_ptr_ref = batchNormFwd_ref.MakeInvokerPointer();
(void)invoker_ptr_ref->Run(argument_ptr_ref.get());
}
int num_kernel = 0;
bool pass = true;
for(auto& inst_ptr : instance_ptrs)
{
auto argument_ptr = inst_ptr->MakeArgumentPointer(
arrInOutLengths,
arrInOutStrides,
arrInOutStrides,
arrReduceDims,
arrScaleBiasMeanVarLengths,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
arrScaleBiasMeanVarStrides,
x_dev.GetDeviceBuffer(),
bnScale_dev.GetDeviceBuffer(),
bnBias_dev.GetDeviceBuffer(),
epsilon,
PassThroughOp{},
y_dev.GetDeviceBuffer(),
saveMeanAndInvVariance ? resultSaveMean_dev.GetDeviceBuffer() : nullptr,
saveMeanAndInvVariance ? resultSaveInvVariance_dev.GetDeviceBuffer() : nullptr,
averageFactor,
updateMovingAverage ? resultRunningMean_dev.GetDeviceBuffer() : nullptr,
updateMovingAverage ? resultRunningVariance_dev.GetDeviceBuffer() : nullptr);
if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
{
num_kernel++;
}
else
{
if(time_kernel)
{
std::cout << inst_ptr->GetTypeString()
<< " skipped due to unsupported argument: " << std::endl;
}
continue;
};
size_t workspace_sz = inst_ptr->GetWorkSpaceSize(argument_ptr.get());
DeviceMem workspace_dev(workspace_sz);
inst_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_dev.GetDeviceBuffer());
auto invoker_ptr = inst_ptr->MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
size_t num_bytes = 0;
// inputing of x, scale, bias, outputing of y
num_bytes += total_length * (sizeof(XDataType) + sizeof(YDataType)) +
invariant_length * (sizeof(ScaleDataType) + sizeof(BiasDataType));
// outputing of mean, inv-variance
num_bytes += saveMeanAndInvVariance ? invariant_length * sizeof(MeanVarDataType) * 2 : 0;
// updating of moving mean, variance
num_bytes += updateMovingAverage ? invariant_length * sizeof(MeanVarDataType) * 4 : 0;
float gb_per_sec = num_bytes / 1.E6 / avg_time;
if(time_kernel)
std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, "
<< inst_ptr->GetTypeString() << std::endl;
if(avg_time < best_avg_time)
{
best_instance_name = inst_ptr->GetTypeString();
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
using ck::utils::check_err;
bool single_pass;
y_dev.FromDevice(y.mData.data());
if constexpr(ck::is_same_v<YDataType, ck::bhalf_t>)
single_pass = check_err(y.mData, y_ref.mData, "y results", 1e-2, 1e-2);
else
single_pass = check_err(y.mData, y_ref.mData, "y results", 4e-3, 4e-3);
if(updateMovingAverage)
{
resultRunningMean_dev.FromDevice(resultRunningMean.mData.data());
resultRunningVariance_dev.FromDevice(resultRunningVariance.mData.data());
// clang-format off
single_pass = single_pass && check_err(resultRunningMean.mData, resultRunningMean_ref.mData, "average mean results", 1.5e-5, 1.5e-5);
single_pass = single_pass && check_err(resultRunningVariance.mData, resultRunningVariance_ref.mData, "average variance results", 1e-5, 1e-5);
// clang-format on
};
if(saveMeanAndInvVariance)
{
resultSaveMean_dev.FromDevice(resultSaveMean.mData.data());
resultSaveInvVariance_dev.FromDevice(resultSaveInvVariance.mData.data());
// clang-format off
single_pass = single_pass && check_err(resultSaveMean.mData, resultSaveMean_ref.mData, "mean results", 3e-5, 3e-5);
single_pass = single_pass && check_err(resultSaveInvVariance.mData, resultSaveInvVariance_ref.mData, "inv-variance results", 7e-5, 7e-5);
// clang-format on
};
pass = pass && single_pass;
};
if(do_dumpout)
{
using ck::host_common::dumpBufferToFile;
// clang-format off
dumpBufferToFile("dump_x.bin", x.mData.data(), x.mDesc.GetElementSize());
dumpBufferToFile("dump_y.bin", y.mData.data(), y.mDesc.GetElementSize());
dumpBufferToFile("dump_y_ref.bin", y_ref.mData.data(), y_ref.mDesc.GetElementSize());
// clang-format off
if(saveMeanAndInvVariance)
{
// clang-format off
dumpBufferToFile("dump_mean.bin", resultSaveMean.mData.data(), resultSaveMean.mDesc.GetElementSize());
dumpBufferToFile("dump_mean_ref.bin", resultSaveMean_ref.mData.data(), resultSaveMean_ref.mDesc.GetElementSize());
dumpBufferToFile("dump_invvar.bin", resultSaveInvVariance.mData.data(), resultSaveInvVariance.mDesc.GetElementSize());
dumpBufferToFile("dump_invvar_ref.bin", resultSaveInvVariance_ref.mData.data(), resultSaveInvVariance_ref.mDesc.GetElementSize());
// clang-format on
};
};
}
if(time_kernel)
{
std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_instance_name << std::endl;
}
if(num_kernel == 0)
{
std::cout << "Error: No kernel is applicable" << std::endl;
return false;
}
return pass;
}
} // namespace profiler
} // namespace ck
profiler/include/profile_conv_bwd_data_impl.hpp
View file @ 05ee41c3
......@@ -209,8 +209,7 @@ bool profile_conv_bwd_data_impl(int do_verification,
{
in_device_buf.FromDevice(input_device_result.mData.data());
pass =
pass & ck::utils::check_err(input_device_result.mData, input_host_result.mData);
pass = pass & ck::utils::check_err(input_device_result, input_host_result);
if(do_log)
{
......
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