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提交 7b17c118 编写于 作者: M Megvii Engine Team
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refactor(dnn): make cudnn_frontend work 

GitOrigin-RevId: f089f934945790f1e01659b0a25a4615b87b7db2
上级 35e9cc98
隐藏空白更改
内联 并排
Showing with 163 addition and 226 deletion
dnn/CMakeLists.txt
浏览文件 @ 7b17c118
......@@ -54,7 +54,10 @@ if(MGE_WITH_CUDA)
add_library(cutlass INTERFACE)
target_include_directories(
cutlass
INTERFACE $<BUILD_INTERFACE:${PROJECT_SOURCE_DIR}/third_party/cutlass/include>)
INTERFACE
$<BUILD_INTERFACE:${PROJECT_SOURCE_DIR}/third_party/cutlass/include>
$<BUILD_INTERFACE:${PROJECT_SOURCE_DIR}/third_party/cutlass/tools/util/include>)
add_library(cudnn-frontend INTERFACE)
target_include_directories(
cudnn-frontend
......
dnn/include/megdnn/algorithm_cache.h
浏览文件 @ 7b17c118
......@@ -31,7 +31,7 @@ public:
}
};
class Key {
struct Key {
Handle* m_handle;
uint32_t m_opr_type;
const TensorLayout* m_inp_layouts_ptr;
......
dnn/src/cuda/conv_bias/algo.cpp
浏览文件 @ 7b17c118
......@@ -15,7 +15,7 @@ ConvBiasForwardImpl::AlgoPack::AlgoPack() {
non_cudnn_algos.push_back(&batched_matmul);
non_cudnn_algos.push_back(&int1_simple);
#if CUDNN_VERSION > 8004
#if CUDNN_VERSION >= 8020
all_algos.push_back(&cudnn_conv_v8);
all_algos.push_back(&cudnn_conv_bias_activation_v8);
#endif
......
dnn/src/cuda/conv_bias/algo.h
浏览文件 @ 7b17c118
......@@ -173,10 +173,10 @@ public:
bool is_cudnn() const override { return true; }
size_t get_preprocess_workspace_in_bytes(const SizeArgs& args) const override;
SmallVector<TensorLayout> deduce_preprocessed_filter_layout(
const SizeArgs& args) const override;
void exec_preprocess(const ExecArgs& args) const override;
// size_t get_preprocess_workspace_in_bytes(const SizeArgs& args) const override;
// SmallVector<TensorLayout> deduce_preprocessed_filter_layout(
// const SizeArgs& args) const override;
// void exec_preprocess(const ExecArgs& args) const override;
protected:
virtual size_t cudnn_get_workspace_in_bytes(const SizeArgs& args) const = 0;
......@@ -237,7 +237,7 @@ private:
CudnnAlgoPack::Attr m_attr;
};
#if CUDNN_VERSION > 8004
#if CUDNN_VERSION >= 8020
class ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationV8 final
: public AlgoCUDNNConvBiasActivationBase {
public:
......@@ -414,7 +414,7 @@ private:
CudnnAlgoPack::Attr m_attr;
};
#if CUDNN_VERSION > 8004
#if CUDNN_VERSION >= 8020
class ConvBiasForwardImpl::AlgoCUDNNConvV8 final : public AlgoCUDNNConvBase {
public:
AlgoCUDNNConvV8() : AlgoCUDNNConvBase() {
......@@ -1247,7 +1247,7 @@ public:
AlgoGroupConvGeneral group;
AlgoBFloat16 bfloat16;
AlgoSimpleInt1 int1_simple;
#if CUDNN_VERSION > 8004
#if CUDNN_VERSION >= 8020
AlgoCUDNNConvV8 cudnn_conv_v8;
AlgoCUDNNConvBiasActivationV8 cudnn_conv_bias_activation_v8;
#endif
......
dnn/src/cuda/conv_bias/cudnn_conv_base.cpp
浏览文件 @ 7b17c118
/**
* \file dnn/src/cuda/conv_bias/cudnn_conv_base.cpp
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#include "src/common/conv_bias.h"
#include "src/cuda/conv_bias/algo.h"
#include "src/cuda/utils.h"
......
dnn/src/cuda/conv_bias/cudnn_conv_bias_activation_base.cpp
浏览文件 @ 7b17c118
/**
* \file dnn/src/cuda/conv_bias/cudnn_conv_bias_activation_base.cpp
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#include "megdnn/oprs/general.h"
#include "./algo.h"
......@@ -26,19 +15,21 @@ size_t ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::get_workspace_in_by
const SizeArgs& args) const {
auto workspace_size = cudnn_get_workspace_in_bytes(args);
auto&& param = args.opr->param();
if (args.preprocessed_filter == nullptr) {
if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
// cudnn require bias to be float when executing CONFIG_INT
// convert bias to float if bias is not float at first
workspace_size += sizeof(float) * args.bias_layout->span().dist_elem();
}
if (param.format == param::ConvBias::Format::NCHW32) {
workspace_size += args.filter_layout->span().dist_byte() +
args.bias_layout->span().dist_byte();
}
// if (args.preprocessed_filter == nullptr) {
if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
// cudnn require bias to be float when executing CONFIG_INT
// convert bias to float if bias is not float at first
workspace_size += sizeof(float) * args.bias_layout->span().dist_elem();
}
// #if CUDNN_VERSION >= 7500
// auto&& param = args.opr->param();
// if (param.format == param::ConvBias::Format::NCHW32) {
// workspace_size += args.filter_layout->span().dist_byte() +
// args.bias_layout->span().dist_byte();
// }
// #endif
// }
return workspace_size;
}
......@@ -56,55 +47,62 @@ void ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::exec(
TensorND filter_tensor;
TensorND bias_tensor;
auto&& param = args.opr->param();
if (args.preprocessed_filter != nullptr) {
bias_tensor = TensorND{
args.bias_tensor->layout,
args.preprocessed_filter->tensors[0].raw_ptr()};
if (param.format == Param::Format::NCHW32) {
megdnn_assert(args.preprocessed_filter->tensors.size() == 2);
filter_tensor = TensorND{
args.filter_tensor->layout,
args.preprocessed_filter->tensors[1].raw_ptr()};
} else {
filter_tensor = *args.filter_tensor;
}
} else {
if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
auto cvt = args.handle->create_operator<TypeCvt>();
auto float_bias_layout = *args.bias_layout;
auto converted_bias_layout = *args.bias_layout;
converted_bias_layout.dtype = dtype::QuantizedS32(alpha);
float_bias_layout.dtype = dtype::Float32();
auto bias_size_in_bytes = float_bias_layout.span().dist_byte();
megdnn_assert(args.workspace.size >= bias_size_in_bytes);
cvt->exec(
{args.bias_tensor->raw_ptr(), converted_bias_layout},
TensorND{workspace_ptr, float_bias_layout});
bias_ptr = workspace_ptr;
workspace_ptr += bias_size_in_bytes;
workspace_size -= bias_size_in_bytes;
}
if (param.format == Param::Format::NCHW32) {
size_t reorder_workspace_size =
args.filter_tensor->layout.span().dist_byte() +
args.bias_tensor->layout.span().dist_byte();
auto reorder_filter_ptr = workspace_ptr;
auto reorder_bias_ptr =
workspace_ptr + args.filter_tensor->layout.span().dist_byte();
cudnn_reorder_filer_and_bias_nchw32(
cudnn_handle(args.opr->handle()), args.filter_tensor->raw_ptr(),
args.filter_meta, bias_ptr, reorder_filter_ptr, reorder_bias_ptr);
filter_tensor = TensorND(args.filter_tensor->layout, reorder_filter_ptr);
bias_ptr = reorder_bias_ptr;
workspace_ptr += reorder_workspace_size;
workspace_size -= reorder_workspace_size;
} else {
filter_tensor = *args.filter_tensor;
}
// if (args.preprocessed_filter != nullptr) {
// bias_tensor = TensorND{
// args.bias_tensor->layout,
// args.preprocessed_filter->tensors[0].raw_ptr()};
// // #if CUDNN_VERSION >= 7500
// // auto&& param = args.opr->param();
// // if (param.format == Param::Format::NCHW32) {
// // megdnn_assert(args.preprocessed_filter->tensors.size() == 2);
// // filter_tensor = TensorND{
// // args.filter_tensor->layout,
// // args.preprocessed_filter->tensors[1].raw_ptr()};
// // }
// // #else
// filter_tensor = *args.filter_tensor;
// // #endif
// } else {
if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
auto cvt = args.handle->create_operator<TypeCvt>();
auto float_bias_layout = *args.bias_layout;
auto converted_bias_layout = *args.bias_layout;
converted_bias_layout.dtype = dtype::QuantizedS32(alpha);
float_bias_layout.dtype = dtype::Float32();
auto bias_size_in_bytes = float_bias_layout.span().dist_byte();
megdnn_assert(args.workspace.size >= bias_size_in_bytes);
cvt->exec(
{args.bias_tensor->raw_ptr(), converted_bias_layout},
TensorND{workspace_ptr, float_bias_layout});
bias_ptr = workspace_ptr;
workspace_ptr += bias_size_in_bytes;
workspace_size -= bias_size_in_bytes;
}
// #if CUDNN_VERSION >= 7500
// auto&& param = args.opr->param();
// if (param.format == Param::Format::NCHW32) {
// size_t reorder_workspace_size =
// args.filter_tensor->layout.span().dist_byte() +
// args.bias_tensor->layout.span().dist_byte();
// auto reorder_filter_ptr = workspace_ptr;
// auto reorder_bias_ptr =
// workspace_ptr +
// args.filter_tensor->layout.span().dist_byte();
// cudnn_reorder_filter_and_bias_nchw32(
// cudnn_handle(args.opr->handle()),
// args.filter_tensor->raw_ptr(), args.filter_meta,
// bias_ptr, reorder_filter_ptr, reorder_bias_ptr);
// filter_tensor = TensorND(args.filter_tensor->layout,
// reorder_filter_ptr); bias_ptr = reorder_bias_ptr; workspace_ptr
// += reorder_workspace_size; workspace_size -=
// reorder_workspace_size;
// }
// #else
filter_tensor = *args.filter_tensor;
// #endif
// }
bias_tensor = TensorND{args.bias_tensor->layout, bias_ptr};
ExecArgs exec_args{
......@@ -153,58 +151,64 @@ void ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::exec(
}
}
size_t ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::
get_preprocess_workspace_in_bytes(const SizeArgs& args) const {
auto&& param = args.opr->param();
if (param.format == Param::Format::NCHW32) {
return args.bias_layout->span().dist_byte();
}
return 0_z;
}
SmallVector<TensorLayout> ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::
deduce_preprocessed_filter_layout(const SizeArgs& args) const {
auto&& param = args.opr->param();
if (param.format == Param::Format::NCHW32) {
return {args.bias_layout->collapse_contiguous(),
args.filter_layout->collapse_contiguous()};
} else {
return {args.bias_layout->collapse_contiguous()};
}
}
void ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::exec_preprocess(
const ExecArgs& args) const {
float alpha, beta;
std::tie(alpha, beta) = cudnn_get_conv_bias_act_scale_param(
args.src_tensor->layout, args.dst_tensor->layout,
args.filter_tensor->layout, args.bias_tensor->layout,
args.z_tensor->layout);
MEGDNN_MARK_USED_VAR(beta);
auto workspace_ptr = args.workspace.raw_ptr;
auto workspace_size = args.workspace.size;
auto bias_ptr = workspace_size > 0 ? workspace_ptr
: args.preprocessed_filter->tensors[0].raw_ptr();
if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
auto cvt = args.handle->create_operator<TypeCvt>();
auto float_bias_layout = *args.bias_layout;
auto converted_bias_layout = *args.bias_layout;
converted_bias_layout.dtype = dtype::QuantizedS32(alpha);
float_bias_layout.dtype = dtype::Float32();
cvt->exec(
{args.bias_tensor->raw_ptr(), converted_bias_layout},
TensorND{bias_ptr, float_bias_layout});
}
if (args.opr->param().format == Param::Format::NCHW32) {
auto reorder_filter_ptr = args.preprocessed_filter->tensors[1].raw_ptr();
auto reorder_bias_ptr = args.preprocessed_filter->tensors[0].raw_ptr();
cudnn_reorder_filer_and_bias_nchw32(
cudnn_handle(args.opr->handle()), args.filter_tensor->raw_ptr(),
args.filter_meta, bias_ptr, reorder_filter_ptr, reorder_bias_ptr);
}
}
// size_t ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::
// get_preprocess_workspace_in_bytes(const SizeArgs&) const {
// #if CUDNN_VERSION >= 7500
// auto&& param = args.opr->param();
// if (param.format == Param::Format::NCHW32) {
// return args.bias_layout->span().dist_byte();
// }
// #endif
// return 0_z;
// }
// SmallVector<TensorLayout> ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::
// deduce_preprocessed_filter_layout(const SizeArgs& args) const {
// #if CUDNN_VERSION >= 7500
// auto&& param = args.opr->param();
// if (param.format == Param::Format::NCHW32) {
// return {args.bias_layout->collapse_contiguous(),
// args.filter_layout->collapse_contiguous()};
// }
// #endif
// return {args.bias_layout->collapse_contiguous()};
// }
// void ConvBiasForwardImpl::AlgoCUDNNConvBiasActivationBase::exec_preprocess(
// const ExecArgs& args) const {
// float alpha, beta;
// std::tie(alpha, beta) = cudnn_get_conv_bias_act_scale_param(
// args.src_tensor->layout, args.dst_tensor->layout,
// args.filter_tensor->layout, args.bias_tensor->layout,
// args.z_tensor->layout);
// MEGDNN_MARK_USED_VAR(beta);
// auto workspace_ptr = args.workspace.raw_ptr;
// auto workspace_size = args.workspace.size;
// auto bias_ptr = workspace_size > 0 ? workspace_ptr
// :
// args.preprocessed_filter->tensors[0].raw_ptr();
// if (args.bias_layout && args.bias_layout->dtype != dtype::Float32() &&
// args.src_layout->dtype.category() != DTypeCategory::FLOAT) {
// auto cvt = args.handle->create_operator<TypeCvt>();
// auto float_bias_layout = *args.bias_layout;
// auto converted_bias_layout = *args.bias_layout;
// converted_bias_layout.dtype = dtype::QuantizedS32(alpha);
// float_bias_layout.dtype = dtype::Float32();
// cvt->exec(
// {args.bias_tensor->raw_ptr(), converted_bias_layout},
// TensorND{bias_ptr, float_bias_layout});
// }
// #if CUDNN_VERSION >= 7500
// if (args.opr->param().format == Param::Format::NCHW32) {
// auto reorder_filter_ptr = args.preprocessed_filter->tensors[1].raw_ptr();
// auto reorder_bias_ptr = args.preprocessed_filter->tensors[0].raw_ptr();
// cudnn_reorder_filter_and_bias_nchw32(
// cudnn_handle(args.opr->handle()), args.filter_tensor->raw_ptr(),
// args.filter_meta, bias_ptr, reorder_filter_ptr, reorder_bias_ptr);
// }
// #endif
// }
// vim: syntax=cpp.doxygen
dnn/src/cuda/conv_bias/cudnn_conv_bias_activation_v8.cpp
浏览文件 @ 7b17c118
/**
* \file dnn/src/cuda/conv_bias/cudnn_conv_bias_activation_v8.cpp
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#include "megdnn/oprs/general.h"
#include "./algo.h"
......@@ -17,7 +6,7 @@
#include "src/cuda/cudnn_wrapper_v8.h"
#include "src/cuda/utils.h"
#if CUDNN_VERSION >= 8004
#if CUDNN_VERSION >= 8020
using namespace megdnn;
using namespace cuda;
using namespace conv_bias;
......
dnn/src/cuda/conv_bias/cudnn_conv_v8.cpp
浏览文件 @ 7b17c118
/**
* \file dnn/src/cuda/conv_bias/cudnn_conv_v8.cpp
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#include "src/common/conv_bias.h"
#include "src/cuda/conv_bias/algo.h"
#include "src/cuda/cudnn_wrapper_v8.h"
#include "src/cuda/utils.h"
#if CUDNN_VERSION >= 8004
#if CUDNN_VERSION >= 8020
using namespace megdnn;
using namespace cuda;
using namespace conv_bias;
......
dnn/src/cuda/conv_bias/helper.cpp
浏览文件 @ 7b17c118
......@@ -239,7 +239,8 @@ std::pair<float, float> cudnn_get_conv_bias_act_scale_param(
return {alpha, beta};
}
void cudnn_reorder_filer_and_bias_nchw32(
#if CUDNN_VERSION >= 7500
void cudnn_reorder_filter_and_bias_nchw32(
const cudnnHandle_t& handle, const void* filter_ptr,
const CanonizedFilterMeta& fm, const void* bias_ptr, void* reordered_filter_ptr,
void* reordered_bias_ptr) {
......@@ -250,6 +251,8 @@ void cudnn_reorder_filer_and_bias_nchw32(
handle, filter_desc.desc, CUDNN_DEFAULT_REORDER, filter_ptr,
reordered_filter_ptr, reorder_bias, bias_ptr, reordered_bias_ptr));
}
#endif
} // namespace conv_bias
} // namespace cuda
} // namespace megdnn
......
dnn/src/cuda/conv_bias/helper.h
浏览文件 @ 7b17c118
......@@ -117,11 +117,12 @@ std::pair<float, float> cudnn_get_conv_bias_act_scale_param(
const TensorLayout& x, const TensorLayout& y, const TensorLayout& w,
const TensorLayout& b, const TensorLayout& z);
void cudnn_reorder_filer_and_bias_nchw32(
#if CUDNN_VERSION >= 7500
void cudnn_reorder_filter_and_bias_nchw32(
const cudnnHandle_t& handle, const void* filter_ptr,
const CanonizedFilterMeta& fm, const void* bias_ptr, void* reordered_filter_ptr,
void* reordered_bias_ptr);
#endif
} // namespace conv_bias
} // namespace cuda
} // namespace megdnn
......
dnn/src/cuda/conv_bias/opr_impl.cpp
浏览文件 @ 7b17c118
......@@ -47,7 +47,7 @@ ConvBiasForward::Algorithm* ConvBiasForwardImpl::get_algorithm_heuristic(
const AlgoAttribute& positive_attr, const AlgoAttribute& negative_attr) {
using namespace conv_bias;
AlgoBase::SizeArgs args{this, src, filter, bias, z, dst};
#if CUDNN_VERSION >= 8004
#if CUDNN_VERSION >= 8020
if (sm_algo_pack.cudnn_conv_v8.is_available_attribute(
args, positive_attr, negative_attr, workspace_limit_in_bytes)) {
return &sm_algo_pack.cudnn_conv_v8;
......
dnn/src/cuda/conv_bias/opr_impl.h
浏览文件 @ 7b17c118
......@@ -32,12 +32,10 @@ public:
const char* get_algorithm_set_name() const override;
class AlgoBase;
class AlgoCUDNNConvBiasActivation;
class AlgoChanwise;
class AlgoChanwiseSmall;
class AlgoDepthwiseLargeFilter;
class AlgoChanwise8x8x32;
class AlgoCUDNNConv;
class AlgoFallbackNCHWQS8;
class AlgoInplaceMatmul;
class AlgoMatmul;
......@@ -67,8 +65,10 @@ public:
class AlgoFloat32NCHWFMAImplicitBatchedGemm;
class AlgoFloat16NCHWHMMAImplicitBatchedGemm;
class AlgoCUDNNConvBase;
class AlgoCUDNNConv;
class AlgoCUDNNConvBiasActivationBase;
#if CUDNN_VERSION > 8004
class AlgoCUDNNConvBiasActivation;
#if CUDNN_VERSION >= 8020
class AlgoCUDNNConvV8;
class AlgoCUDNNConvBiasActivationV8;
#endif
......
dnn/src/cuda/cudnn_wrapper_v8.cpp
浏览文件 @ 7b17c118
/**
* \file dnn/src/cuda/cudnn_wrapper_v8.cpp
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#if CUDNN_VERSION >= 8020
#include "src/cuda/cudnn_wrapper_v8.h"
#include "src/cuda/cudnn_wrapper.h"
......@@ -19,7 +10,7 @@
#include "cudnn_frontend_EngineConfigGenerator.h"
#include "megdnn/heuristic_cache.h"
#include "megdnn/algorithm_cache.h"
using namespace megdnn;
using namespace cuda;
......@@ -240,9 +231,9 @@ auto make_activation_descriptor(
// high-level api for convolution execution
struct StaticData {
using Key = megdnn::HeuristicCache::Key;
using KeyStorage = megdnn::HeuristicCache::KeyStorage;
using KeyHash = megdnn::HeuristicCache::Hash;
using Key = megdnn::AlgorithmCache::Key;
using KeyStorage = megdnn::AlgorithmCache::KeyStorage;
using KeyHash = megdnn::AlgorithmCache::Hash;
using Result = cudnn_frontend::ExecutionPlan;
using CudnnFrontendExecutionPlanCache =
std::unordered_map<KeyStorage, Result, KeyHash>;
......@@ -682,4 +673,5 @@ void megdnn::cuda::run_conv_bias_act_with_plan(
handle, plan.get_raw_desc(), variant_pack.get_raw_desc()));
}
#endif
// vim: syntax=cpp.doxygen
dnn/src/cuda/cudnn_wrapper_v8.h
浏览文件 @ 7b17c118
/**
* \file dnn/src/cuda/cudnn_wrapper_v8.h
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#pragma once
#if CUDNN_VERSION >= 8020
#include "megdnn/basic_types.h"
#include "megdnn/oprs/nn.h"
#include "src/common/utils.h"
......@@ -67,4 +58,5 @@ void run_conv_bias_act_with_plan(
} // namespace cuda
} // namespace megdnn
#endif
// vim: syntax=cpp.doxygen
dnn/src/cuda/handle.cpp
浏览文件 @ 7b17c118
......@@ -58,11 +58,6 @@ HandleImpl::HandleImpl(megcoreComputingHandle_t comp_handle)
For example `export CUDA_CACHE_MAXSIZE=2147483647` and `export CUDA_CACHE_PATH=/data/.cuda_cache`)");
}
#endif
size_t free, tot;
cudaMemGetInfo(&free, &tot);
printf("before cudnn create, free: %.2f MB, tot: %.2f MB, allocated: %.2f MB\n",
free / 1024.0 / 1024.0, tot / 1024.0 / 1024.0,
(tot - free) / 1024.0 / 1024.0);
cudnn_check(cudnnCreate(&m_cudnn_handle));
cublas_check(cublasCreate(&m_cublas_handle));
#if CUDA_VERSION >= 10010
......@@ -74,11 +69,6 @@ HandleImpl::HandleImpl(megcoreComputingHandle_t comp_handle)
cudnn_check(cudnnSetStream(m_cudnn_handle, stream()));
cublas_check(cublasSetStream(m_cublas_handle, stream()));
#if CUDNN_VERSION >= 8004
// cudnn_check(cudnnOpsInferVersionCheck());
// cudnn_check(cudnnCnnInferVersionCheck());
#endif
// Note that all cublas scalars (alpha, beta) and scalar results such as dot
// output resides at device side.
cublas_check(cublasSetPointerMode(m_cublas_handle, CUBLAS_POINTER_MODE_DEVICE));
......@@ -92,11 +82,6 @@ HandleImpl::HandleImpl(megcoreComputingHandle_t comp_handle)
cudaMemcpyHostToDevice, stream()));
cuda_check(cudaStreamSynchronize(stream()));
cudaMemGetInfo(&free, &tot);
printf("after cudnn create, free: %.2f MB, tot: %.2f MB, allocated: %.2f MB\n",
free / 1024.0 / 1024.0, tot / 1024.0 / 1024.0,
(tot - free) / 1024.0 / 1024.0);
// check tk1
m_is_tegra_k1 = (strcmp(m_device_prop->name, "GK20A") == 0);
m_cusolver_handle = nullptr;
......
dnn/test/cuda/conv_v8.cpp
浏览文件 @ 7b17c118
/**
* \file dnn/test/cuda/conv_bias.cpp
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#include "megdnn/dtype.h"
#include "test/cuda/fixture.h"
......@@ -26,7 +16,7 @@ using namespace megdnn;
using namespace test;
using namespace conv_bias;
#if CUDNN_VERSION >= 8004
#if CUDNN_VERSION >= 8020
TEST_F(CUDA, CONV_V8_FLOAT) {
Checker<ConvBiasForward> checker(handle_cuda());
checker.set_before_exec_callback(
......
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