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未验证 提交 3d5aa9d1 编写于 作者: Q Qi Li 提交者： GitHub
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[ROCM] fix conv2d and conv3d op, test=develop (#31553)

上级 f302bb4f
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Showing with 298 addition and 249 deletion
paddle/fluid/operators/conv_cudnn_op.cu
浏览文件 @ 3d5aa9d1
......@@ -249,6 +249,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
args.handle = handle;
#ifdef PADDLE_WITH_HIP
// MIOPEN need to set groups in cdesc in miopen_desc.h
args.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), groups);
#else
......@@ -264,6 +265,10 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
platform::dynload::cudnnSetConvolutionGroupCount(args.cdesc.desc(),
groups));
groups = 1;
#endif
#ifdef PADDLE_WITH_HIP
// MIOPEN do not set groups in wdesc after set groups in cdesc
groups = 1;
#endif
args.idesc.set(transformed_input, layout_format);
args.wdesc.set(transformed_filter_channel, layout_format, groups);
......@@ -292,12 +297,14 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
#ifdef PADDLE_WITH_HIP
miopenConvFwdAlgorithm_t algo{};
using search = SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size = search::GetWorkspaceSize(args);
algo = search::Find<T>(args, exhaustive_search, false, workspace_size, ctx);
#else
cudnnConvolutionFwdAlgo_t algo{};
using search = SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
#endif
algo = search::Find<T>(args, exhaustive_search, false, ctx);
workspace_size = search::GetWorkspaceSize(args, algo);
#endif
#if defined(PADDLE_WITH_CUDA) && CUDNN_VERSION_MIN(7, 0, 1)
// when groups > 1, SearchAlgorithm find algo is CUDNN_CONVOLUTION_\
......@@ -652,13 +659,17 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
#ifdef PADDLE_WITH_HIP
using search1 = SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size =
std::max(workspace_size, search1::GetWorkspaceSize(args1));
data_algo = search1::Find<T>(args1, exhaustive_search, deterministic,
workspace_size, ctx);
#else
using search1 = SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
#endif
data_algo =
search1::Find<T>(args1, exhaustive_search, deterministic, ctx);
workspace_size =
std::max(workspace_size, search1::GetWorkspaceSize(args1, data_algo));
#endif
}
if (filter_grad) {
......@@ -673,13 +684,17 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
platform::AllowTF32Cudnn(), c_groups);
#ifdef PADDLE_WITH_HIP
using search2 = SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2));
filter_algo = search2::Find<T>(args2, exhaustive_search, deterministic,
workspace_size, ctx);
#else
using search2 = SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
#endif
filter_algo =
search2::Find<T>(args2, exhaustive_search, deterministic, ctx);
workspace_size = std::max(workspace_size,
search2::GetWorkspaceSize(args2, filter_algo));
#endif
}
// ------------------- cudnn conv backward data ---------------------
......@@ -688,23 +703,22 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
VLOG(4) << "Conv_grad: use_addto = " << ctx.Attr<bool>("use_addto");
if (input_grad) {
// When beta is 0, it is unnecessary to reset input_grad.
// When beta is 1, the output cannot be reset since addt strategy used.
for (int i = 0; i < groups; i++) {
// When beta is 0, it is unnecessary to reset input_grad.
// When beta is 1, the output cannot be reset since addt strategy used.
#ifdef PADDLE_WITH_HIP
workspace_handle.RunFunc(
[&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardData(
handle, &alpha, args1.odesc.desc(),
output_grad_data + i * group_offset_out,
args1.wdesc.desc(), filter_data + i * group_offset_filter,
args1.cdesc.desc(), data_algo, &beta, args1.idesc.desc(),
transformed_input_grad_data + i * group_offset_in,
cudnn_workspace_ptr, workspace_size));
},
workspace_size);
workspace_handle.RunFunc(
[&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardData(
handle, &alpha, args1.odesc.desc(), output_grad_data,
args1.wdesc.desc(), filter_data, args1.cdesc.desc(),
data_algo, &beta, args1.idesc.desc(),
transformed_input_grad_data, cudnn_workspace_ptr,
workspace_size));
},
workspace_size);
#else
for (int i = 0; i < groups; i++) {
workspace_handle.RunFunc(
[&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
......@@ -717,9 +731,8 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
transformed_input_grad_data + i * group_offset_in));
},
workspace_size);
#endif
}
#endif
if (!is_sys_pad) {
std::vector<int> starts(transformed_input_channel.dims().size(), 0);
std::vector<int> axes(transformed_input_channel.dims().size(), 0);
......@@ -751,23 +764,20 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
ScalingParamType<T> beta_filter = 0.0f;
// ------------------- cudnn conv backward filter ---------------------
if (filter_grad) {
// Because beta is zero, it is unnecessary to reset filter_grad.
for (int i = 0; i < groups; i++) {
// Because beta is zero, it is unnecessary to reset filter_grad.
#ifdef PADDLE_WITH_HIP
workspace_handle.RunFunc(
[&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardWeights(
handle, &alpha, args2.odesc.desc(),
output_grad_data + i * group_offset_out,
args2.idesc.desc(), input_data + i * group_offset_in,
args2.cdesc.desc(), filter_algo, &beta,
args2.wdesc.desc(),
filter_grad_data + i * group_offset_filter,
cudnn_workspace_ptr, workspace_size));
},
workspace_size);
workspace_handle.RunFunc(
[&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardWeights(
handle, &alpha, args2.odesc.desc(), output_grad_data,
args2.idesc.desc(), input_data, args2.cdesc.desc(),
filter_algo, &beta, args2.wdesc.desc(), filter_grad_data,
cudnn_workspace_ptr, workspace_size));
},
workspace_size);
#else
for (int i = 0; i < groups; i++) {
workspace_handle.RunFunc(
[&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
......@@ -780,8 +790,8 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
filter_grad_data + i * group_offset_filter));
},
workspace_size);
#endif
}
#endif
if (compute_format == DataLayout::kNHWC) {
TransToChannelFirst<paddle::platform::CUDADeviceContext, T>(
......@@ -1080,32 +1090,37 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
#ifdef PADDLE_WITH_HIP
using search1 = SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size = search1::GetWorkspaceSize(args1);
fwd_algo1 = search1::Find<T>(args1, exhaustive_search, false,
workspace_size, ctx);
#else
using search1 = SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
#endif
fwd_algo1 = search1::Find<T>(args1, exhaustive_search, false, ctx);
workspace_size = search1::GetWorkspaceSize(args1, fwd_algo1);
#endif
}
if (ddW) {
ddw = ddW->data<T>();
args2.handle = handle;
args2.idesc.set(transformed_X, iwo_group);
args2.wdesc.set(*ddW, layout, iwo_group);
args2.odesc.set(transformed_ddO_channel, iwo_group);
args2.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), c_group);
#ifdef PADDLE_WITH_HIP
using search2 = SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2));
fwd_algo2 = search2::Find<T>(args2, exhaustive_search, false,
workspace_size, ctx);
#else
using search2 = SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
#endif
fwd_algo2 = search2::Find<T>(args2, exhaustive_search, false, ctx);
workspace_size = std::max(workspace_size,
search2::GetWorkspaceSize(args2, fwd_algo2));
#endif
}
}
......@@ -1114,21 +1129,23 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
args3.handle = handle;
args3.idesc.set(transformed_ddX, iwo_group);
args3.wdesc.set(*dW, layout, iwo_group);
args3.odesc.set(transformed_dO_channel, iwo_group);
args3.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), c_group);
#ifdef PADDLE_WITH_HIP
using search3 = SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size =
std::max(workspace_size, search3::GetWorkspaceSize(args3));
filter_algo = search3::Find<T>(args3, exhaustive_search, deterministic,
workspace_size, ctx);
#else
using search3 = SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
#endif
filter_algo =
search3::Find<T>(args3, exhaustive_search, deterministic, ctx);
workspace_size = std::max(workspace_size,
search3::GetWorkspaceSize(args3, filter_algo));
#endif
}
if (ddW && dX) {
......@@ -1143,13 +1160,17 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
#ifdef PADDLE_WITH_HIP
using search4 = SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size =
std::max(workspace_size, search4::GetWorkspaceSize(args4));
data_algo = search4::Find<T>(args4, exhaustive_search, deterministic,
workspace_size, ctx);
#else
using search4 = SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
#endif
data_algo =
search4::Find<T>(args4, exhaustive_search, deterministic, ctx);
workspace_size =
std::max(workspace_size, search4::GetWorkspaceSize(args4, data_algo));
#endif
}
int i_n, i_c, i_d, i_h, i_w;
......@@ -1176,21 +1197,19 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
if (ddO) {
if (ddX) {
ddx = transformed_ddX.data<T>();
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionForward(
handle, &alpha, args1.idesc.desc(),
ddx + i * group_offset_in, args1.wdesc.desc(),
w + i * group_offset_filter, args1.cdesc.desc(),
fwd_algo1, &beta, args1.odesc.desc(),
transformed_ddy_channel + i * group_offset_out,
workspace_ptr, workspace_size));
},
workspace_size);
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionForward(
handle, &alpha, args1.idesc.desc(), ddx,
args1.wdesc.desc(), w, args1.cdesc.desc(), fwd_algo1,
&beta, args1.odesc.desc(), transformed_ddy_channel,
workspace_ptr, workspace_size));
},
workspace_size);
#else
for (int i = 0; i < groups; i++) {
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
......@@ -1203,26 +1222,24 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
transformed_ddy_channel + i * group_offset_out));
},
workspace_size);
#endif
}
#endif
}
if (ddW) {
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
// MIOPEN ONLY support beta to be 0.0f
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionForward(
handle, &alpha, args2.idesc.desc(),
x + i * group_offset_in, args2.wdesc.desc(),
ddw + i * group_offset_filter, args2.cdesc.desc(),
fwd_algo2, &beta, args2.odesc.desc(),
transformed_ddy_channel + i * group_offset_out,
workspace_ptr, workspace_size));
},
workspace_size);
// MIOPEN ONLY support beta to be 0.0f
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionForward(
handle, &alpha, args2.idesc.desc(), x, args2.wdesc.desc(),
ddw, args2.cdesc.desc(), fwd_algo2, &beta,
args2.odesc.desc(), transformed_ddy_channel,
workspace_ptr, workspace_size));
},
workspace_size);
#else
for (int i = 0; i < groups; i++) {
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
......@@ -1235,8 +1252,8 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
transformed_ddy_channel + i * group_offset_out));
},
workspace_size);
#endif
}
#endif
}
if (channel_last) {
TransToChannelLast<paddle::platform::CUDADeviceContext, T>(
......@@ -1246,21 +1263,19 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
T* transformed_dy_channel = transformed_dO_channel.data<T>();
if (dW && ddX) {
ddx = transformed_ddX.data<T>();
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardWeights(
handle, &alpha, args3.odesc.desc(),
transformed_dy_channel + i * group_offset_out,
args3.idesc.desc(), ddx + i * group_offset_in,
args3.cdesc.desc(), filter_algo, &beta,
args3.wdesc.desc(), dw + i * group_offset_filter,
workspace_ptr, workspace_size));
},
workspace_size);
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardWeights(
handle, &alpha, args3.odesc.desc(), transformed_dy_channel,
args3.idesc.desc(), ddx, args3.cdesc.desc(), filter_algo,
&beta, args3.wdesc.desc(), dw, workspace_ptr,
workspace_size));
},
workspace_size);
#else
for (int i = 0; i < groups; i++) {
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
......@@ -1273,27 +1288,25 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
dw + i * group_offset_filter));
},
workspace_size);
#endif
}
#endif
}
if (dX && ddW) {
ddw = ddW->data<T>();
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardData(
handle, &alpha, args4.odesc.desc(),
transformed_dy_channel + i * group_offset_out,
args4.wdesc.desc(), ddw + i * group_offset_filter,
args4.cdesc.desc(), data_algo, &beta, args4.idesc.desc(),
transformed_dx + i * group_offset_in, workspace_ptr,
workspace_size));
},
workspace_size);
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardData(
handle, &alpha, args4.odesc.desc(), transformed_dy_channel,
args4.wdesc.desc(), ddw, args4.cdesc.desc(), data_algo,
&beta, args4.idesc.desc(), transformed_dx, workspace_ptr,
workspace_size));
},
workspace_size);
#else
for (int i = 0; i < groups; i++) {
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
......@@ -1306,8 +1319,8 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
transformed_dx + i * group_offset_in));
},
workspace_size);
#endif
}
#endif
if (!is_sys_pad) {
// reverse padded input
......
paddle/fluid/operators/conv_miopen_helper.h
浏览文件 @ 3d5aa9d1
......@@ -127,57 +127,52 @@ struct SearchAlgorithm<miopenConvFwdAlgorithm_t> {
template <typename T>
static algo_t Find(const ConvArgs& args, bool exhaustive_search,
bool deterministic,
bool deterministic, size_t workspace_size,
const framework::ExecutionContext& ctx) {
auto dtype = platform::CudnnDataType<T>::type;
bool has_got_workspace_size = true;
size_t workspace_size_limit = FLAGS_conv_workspace_size_limit * 1024 * 1024;
size_t workspace_size = 0;
algo_t algo;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
auto& temp = ctx.cuda_device_context();
AlgorithmsCache<algo_t>& algo_cache =
*(framework::ConvSearchCache::Instance().GetForward());
auto x_dims = framework::vectorize(args.x->dims());
auto w_dims = framework::vectorize(args.w->dims());
VLOG(10) << "miopenConvolutionFwdAlgoPerf_t:"
<< ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s"
<< args.s << ", args.p" << args.p << ", args.d" << args.d;
algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
int returned_algo_count;
std::array<perf_t, kNUM_CUDNN_FWD_ALGS> perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenFindConvolutionForwardAlgorithm(
args.handle, args.idesc.desc(), args.x->data<T>(),
args.wdesc.desc(), args.w->data<T>(), args.cdesc.desc(),
args.odesc.desc(), const_cast<T*>(args.o->data<T>()),
kNUM_CUDNN_FWD_ALGS, &returned_algo_count, perf_stat.data(),
cudnn_workspace_ptr, workspace_size_limit, false));
};
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size_limit);
VLOG(3) << "FwdAlgo Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
const auto& stat = perf_stat[i];
VLOG(3) << stat.fwd_algo;
}
return perf_stat[0].fwd_algo;
});
int find_count;
miopenConvAlgoPerf_t find_result;
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenFindConvolutionForwardAlgorithm(
args.handle, args.idesc.desc(), args.x->data<T>(),
args.wdesc.desc(), args.w->data<T>(), args.cdesc.desc(),
args.odesc.desc(), const_cast<T*>(args.o->data<T>()),
kNUM_CUDNN_FWD_ALGS, &find_count, &find_result,
cudnn_workspace_ptr, workspace_size, false));
};
if (!exhaustive_search && !deterministic) {
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size);
algo = find_result.fwd_algo;
} else {
auto& temp = ctx.cuda_device_context();
AlgorithmsCache<algo_t>& algo_cache =
*(framework::ConvSearchCache::Instance().GetForward());
auto x_dims = framework::vectorize(args.x->dims());
auto w_dims = framework::vectorize(args.w->dims());
VLOG(10) << "miopenConvolutionFwdAlgoPerf_t:"
<< ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s"
<< args.s << ", args.p" << args.p << ", args.d" << args.d;
algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size);
return find_result.fwd_algo;
});
}
VLOG(3) << "choose algo " << algo;
return algo;
}
static size_t GetWorkspaceSize(const ConvArgs& args, algo_t algo) {
static size_t GetWorkspaceSize(const ConvArgs& args) {
size_t workspace_size = 0;
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionForwardGetWorkSpaceSize(
......@@ -194,58 +189,51 @@ struct SearchAlgorithm<miopenConvBwdDataAlgorithm_t> {
template <typename T>
static algo_t Find(const ConvArgs& args, bool exhaustive_search,
bool deterministic,
bool deterministic, size_t workspace_size,
const framework::ExecutionContext& ctx) {
auto dtype = platform::CudnnDataType<T>::type;
size_t workspace_size_limit = FLAGS_conv_workspace_size_limit * 1024 * 1024;
size_t workspace_size = 0;
bool has_got_workspace_size = true;
algo_t algo;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
AlgorithmsCache<algo_t>& algo_cache =
*(framework::ConvSearchCache::Instance().GetBackwardData());
auto x_dims = framework::vectorize(args.x->dims());
auto w_dims = framework::vectorize(args.w->dims());
VLOG(10) << "miopenConvolutionFwdAlgoPerf_t"
<< ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s"
<< args.s << ", args.p" << args.p << ", args.d" << args.d;
algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
int returned_algo_count;
std::array<perf_t, kNUM_CUDNN_FWD_ALGS> perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenFindConvolutionBackwardDataAlgorithm(
args.handle, args.odesc.desc(), args.o->data<T>(),
args.wdesc.desc(), args.w->data<T>(), args.cdesc.desc(),
args.idesc.desc(), const_cast<T*>(args.x->data<T>()),
kNUM_CUDNN_BWD_DATA_ALGS, &returned_algo_count,
perf_stat.data(), cudnn_workspace_ptr, workspace_size_limit,
false));
};
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size_limit);
VLOG(3) << "BwdDataAlgo Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
const auto& stat = perf_stat[i];
VLOG(3) << stat.bwd_data_algo;
}
return perf_stat[0].bwd_data_algo;
});
int find_count;
miopenConvAlgoPerf_t find_result;
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenFindConvolutionBackwardDataAlgorithm(
args.handle, args.odesc.desc(), args.o->data<T>(),
args.wdesc.desc(), args.w->data<T>(), args.cdesc.desc(),
args.idesc.desc(), const_cast<T*>(args.x->data<T>()),
kNUM_CUDNN_BWD_DATA_ALGS, &find_count, &find_result,
cudnn_workspace_ptr, workspace_size, false));
};
if (!exhaustive_search && !deterministic) {
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size);
algo = find_result.bwd_data_algo;
} else {
AlgorithmsCache<algo_t>& algo_cache =
*(framework::ConvSearchCache::Instance().GetBackwardData());
auto x_dims = framework::vectorize(args.x->dims());
auto w_dims = framework::vectorize(args.w->dims());
VLOG(10) << "miopenConvolutionFwdAlgoPerf_t"
<< ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s"
<< args.s << ", args.p" << args.p << ", args.d" << args.d;
algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size);
return find_result.bwd_data_algo;
});
}
VLOG(3) << "choose algo " << algo;
return algo;
}
static size_t GetWorkspaceSize(const ConvArgs& args, algo_t algo) {
static size_t GetWorkspaceSize(const ConvArgs& args) {
size_t workspace_size = 0;
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardDataGetWorkSpaceSize(
......@@ -262,56 +250,51 @@ struct SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t> {
template <typename T>
static algo_t Find(const ConvArgs& args, bool exhaustive_search,
bool deterministic,
bool deterministic, size_t workspace_size,
const framework::ExecutionContext& ctx) {
auto dtype = platform::CudnnDataType<T>::type;
size_t workspace_size_limit = FLAGS_conv_workspace_size_limit * 1024 * 1024;
size_t workspace_size = 0;
bool has_got_workspace_size = true;
algo_t algo;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
AlgorithmsCache<algo_t>& algo_cache =
*(framework::ConvSearchCache::Instance().GetBackwardFilter());
auto x_dims = framework::vectorize(args.x->dims());
auto w_dims = framework::vectorize(args.w->dims());
VLOG(10) << "miopenConvolutionFwdAlgoPerf_t:"
<< ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s"
<< args.s << ", args.p" << args.p << ", args.d" << args.d;
algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
int returned_algo_count;
std::array<perf_t, kNUM_CUDNN_FWD_ALGS> perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::
miopenFindConvolutionBackwardWeightsAlgorithm(
args.handle, args.odesc.desc(), args.o->data<T>(),
args.idesc.desc(), args.x->data<T>(), args.cdesc.desc(),
args.wdesc.desc(), const_cast<T*>(args.w->data<T>()),
kNUM_CUDNN_BWD_FILTER_ALGS, &returned_algo_count,
perf_stat.data(), cudnn_workspace_ptr,
workspace_size_limit, false));
};
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size_limit);
VLOG(3) << "BwdFilterAlgo Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
const auto& stat = perf_stat[i];
VLOG(3) << stat.bwd_weights_algo;
}
return perf_stat[0].bwd_weights_algo;
});
int find_count;
miopenConvAlgoPerf_t find_result;
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenFindConvolutionBackwardWeightsAlgorithm(
args.handle, args.odesc.desc(), args.o->data<T>(),
args.idesc.desc(), args.x->data<T>(), args.cdesc.desc(),
args.wdesc.desc(), const_cast<T*>(args.w->data<T>()),
kNUM_CUDNN_BWD_FILTER_ALGS, &find_count, &find_result,
cudnn_workspace_ptr, workspace_size, false));
};
if (!exhaustive_search && !deterministic) {
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size);
algo = find_result.bwd_weights_algo;
} else {
AlgorithmsCache<algo_t>& algo_cache =
*(framework::ConvSearchCache::Instance().GetBackwardFilter());
auto x_dims = framework::vectorize(args.x->dims());
auto w_dims = framework::vectorize(args.w->dims());
VLOG(10) << "miopenConvolutionFwdAlgoPerf_t:"
<< ", x_dims:" << x_dims << ", w_dims:" << w_dims << ", args.s"
<< args.s << ", args.p" << args.p << ", args.d" << args.d;
algo = algo_cache.GetAlgorithm(
x_dims, w_dims, args.s, args.p, args.d, 0,
static_cast<int64_t>(args.cudnn_dtype), [&]() {
workspace_handle.RunFuncSync(cudnn_find_func, workspace_size);
return find_result.bwd_weights_algo;
});
}
VLOG(3) << "choose algo " << algo;
return algo;
}
static size_t GetWorkspaceSize(const ConvArgs& args, algo_t algo) {
static size_t GetWorkspaceSize(const ConvArgs& args) {
size_t workspace_size = 0;
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::miopenConvolutionBackwardWeightsGetWorkSpaceSize(
......
paddle/fluid/operators/conv_transpose_cudnn_op.cu
浏览文件 @ 3d5aa9d1
......@@ -244,13 +244,14 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
#ifdef PADDLE_WITH_HIP
using search = SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = std::max(workspace_size, search::GetWorkspaceSize(args));
algo = search::Find<T>(args, false, deterministic, workspace_size, ctx);
#else
using search = SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
#endif
algo = search::Find<T>(args, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search::GetWorkspaceSize(args, algo));
#endif
// ------------------- cudnn conv transpose forward ---------------------
int input_offset =
......@@ -504,12 +505,16 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
platform::AllowTF32Cudnn(), c_groups);
#ifdef PADDLE_WITH_HIP
using search1 = SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size =
std::max(workspace_size, search1::GetWorkspaceSize(args1));
data_algo =
search1::Find<T>(args1, false, deterministic, workspace_size, ctx);
#else
using search1 = SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
#endif
data_algo = search1::Find<T>(args1, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search1::GetWorkspaceSize(args1, data_algo));
#endif
}
if (filter_grad) {
......@@ -522,12 +527,16 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
platform::AllowTF32Cudnn(), c_groups);
#ifdef PADDLE_WITH_HIP
using search2 = SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2));
filter_algo =
search2::Find<T>(args2, false, deterministic, workspace_size, ctx);
#else
using search2 = SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
#endif
filter_algo = search2::Find<T>(args2, false, deterministic, ctx);
workspace_size = std::max(workspace_size,
search2::GetWorkspaceSize(args2, filter_algo));
#endif
}
// ------------------- cudnn conv backward data ---------------------
......@@ -942,11 +951,14 @@ class CUDNNConvTransposeDoubleGradOpKernel : public framework::OpKernel<T> {
args1.cdesc.set(dtype, padding_common, strides, dilations, c_group);
#ifdef PADDLE_WITH_HIP
using search1 = SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = search1::GetWorkspaceSize(args1);
bwd_algo1 =
search1::Find<T>(args1, false, deterministic, workspace_size, ctx);
#else
using search1 = SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
#endif
bwd_algo1 = search1::Find<T>(args1, false, deterministic, ctx);
workspace_size = search1::GetWorkspaceSize(args1, bwd_algo1);
#endif
}
if (ddW) {
......@@ -958,12 +970,16 @@ class CUDNNConvTransposeDoubleGradOpKernel : public framework::OpKernel<T> {
args2.cdesc.set(dtype, padding_common, strides, dilations, c_group);
#ifdef PADDLE_WITH_HIP
using search2 = SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2));
bwd_algo2 =
search2::Find<T>(args2, false, deterministic, workspace_size, ctx);
#else
using search2 = SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
#endif
bwd_algo2 = search2::Find<T>(args2, false, deterministic, ctx);
workspace_size = std::max(workspace_size,
search2::GetWorkspaceSize(args2, bwd_algo2));
#endif
}
}
......@@ -978,12 +994,16 @@ class CUDNNConvTransposeDoubleGradOpKernel : public framework::OpKernel<T> {
args3.cdesc.set(dtype, padding_common, strides, dilations, c_group);
#ifdef PADDLE_WITH_HIP
using search3 = SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size =
std::max(workspace_size, search3::GetWorkspaceSize(args3));
filter_algo =
search3::Find<T>(args3, false, deterministic, workspace_size, ctx);
#else
using search3 = SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
#endif
filter_algo = search3::Find<T>(args3, false, deterministic, ctx);
workspace_size = std::max(workspace_size,
search3::GetWorkspaceSize(args3, filter_algo));
#endif
}
if (ddW && dX) {
......@@ -996,12 +1016,16 @@ class CUDNNConvTransposeDoubleGradOpKernel : public framework::OpKernel<T> {
args4.cdesc.set(dtype, padding_common, strides, dilations, c_group);
#ifdef PADDLE_WITH_HIP
using search4 = SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size =
std::max(workspace_size, search4::GetWorkspaceSize(args4));
data_algo =
search4::Find<T>(args4, false, deterministic, workspace_size, ctx);
#else
using search4 = SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
#endif
data_algo = search4::Find<T>(args4, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search4::GetWorkspaceSize(args4, data_algo));
#endif
}
int i_n, i_c, i_d, i_h, i_w;
......
paddle/fluid/platform/miopen_desc.h
浏览文件 @ 3d5aa9d1
......@@ -199,19 +199,24 @@ class FilterDescriptor {
void set(const Tensor& tensor, const miopenTensorFormat_t format,
const int groups = 1) {
auto dims = framework::vectorize<int>(tensor.dims());
std::vector<int> transformed_dims;
PADDLE_ENFORCE_EQ(format, MIOPEN_TENSOR_NCHW,
platform::errors::InvalidArgument(
"format should ONLY be NCHW in MIOPEN."));
transformed_dims = dims;
// if (groups > 1) {
// transformed_dims[1] = transformed_dims[1] / groups;
// }
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::miopenSet4dTensorDescriptor(
(miopenTensorDescriptor_t)desc_.get(), ToCudnnDataType(tensor.type()),
transformed_dims[0], transformed_dims[1], transformed_dims[2],
transformed_dims[3]));
auto dims = framework::vectorize<int>(tensor.dims());
std::vector<int> strides(dims.size());
strides[dims.size() - 1] = 1;
for (int i = dims.size() - 2; i >= 0; i--) {
strides[i] = dims[i + 1] * strides[i + 1];
}
std::vector<int> dims_with_group(dims.begin(), dims.end());
if (groups > 1) {
dims_with_group[1] = dims_with_group[1] / groups;
}
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::miopenSetTensorDescriptor(
(miopenTensorDescriptor_t)(desc_.get()), ToCudnnDataType(tensor.type()),
static_cast<int>(dims_with_group.size()),
const_cast<int*>(dims_with_group.data()),
const_cast<int*>(strides.data())));
}
private:
......
python/paddle/fluid/tests/unittests/test_conv2d_op.py
浏览文件 @ 3d5aa9d1
......@@ -128,6 +128,8 @@ def create_test_cudnn_class(parent):
class TestCUDNNCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm(
) else np.float64
cls_name = "{0}_{1}".format(parent.__name__, "CUDNN")
TestCUDNNCase.__name__ = cls_name
......@@ -185,6 +187,8 @@ def create_test_cudnn_channel_last_class(parent):
class TestCudnnChannelLastCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm(
) else np.float64
def init_data_format(self):
self.data_format = "NHWC"
......@@ -264,6 +268,8 @@ def create_test_cudnn_padding_SAME_class(parent):
class TestCUDNNPaddingSMAECase(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm(
) else np.float64
def init_paddings(self):
self.pad = [1, 1]
......@@ -280,6 +286,8 @@ def create_test_cudnn_padding_VALID_class(parent):
class TestCUDNNPaddingVALIDCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm(
) else np.float64
def init_paddings(self):
self.pad = [1, 1]
......@@ -299,8 +307,7 @@ class TestConv2DOp(OpTest):
self.use_mkldnn = False
self.fuse_relu_before_depthwise_conv = False
self.data_format = "AnyLayout"
# explicilty use float32 for ROCm, as MIOpen does not yet support float64
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
self.dtype = np.float64
self.init_kernel_type()
self.init_group()
self.init_dilation()
......@@ -693,6 +700,7 @@ class TestCUDNNExhaustiveSearch(TestConv2DOp):
def init_kernel_type(self):
self.use_cudnn = True
self.exhaustive_search = True
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
class TestConv2DOpError(unittest.TestCase):
......@@ -734,8 +742,7 @@ class TestConv2DOp_v2(OpTest):
self.use_cuda = False
self.use_mkldnn = False
self.fuse_relu_before_depthwise_conv = False
# explicilty use float32 for ROCm, as MIOpen does not yet support float64
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
self.dtype = np.float64
self.init_kernel_type()
self.init_group()
self.init_dilation()
......
python/paddle/fluid/tests/unittests/test_conv3d_op.py
浏览文件 @ 3d5aa9d1
......@@ -135,6 +135,8 @@ def create_test_cudnn_class(parent):
class TestCUDNNCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm(
) else np.float64
cls_name = "{0}_{1}".format(parent.__name__, "CUDNN")
TestCUDNNCase.__name__ = cls_name
......@@ -169,6 +171,8 @@ def create_test_cudnn_padding_SAME_class(parent):
class TestCUDNNPaddingSMAECase(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm(
) else np.float64
def init_paddings(self):
self.pad = [1, 1, 1]
......@@ -185,6 +189,8 @@ def create_test_cudnn_padding_VALID_class(parent):
class TestCUDNNPaddingVALIDCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm(
) else np.float64
def init_paddings(self):
self.pad = [1, 1, 1]
......@@ -215,6 +221,8 @@ def create_test_cudnn_channel_last_class(parent):
class TestCudnnChannelLastCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm(
) else np.float64
def init_data_format(self):
self.data_format = "NDHWC"
......@@ -410,6 +418,7 @@ class TestWithDilation(TestConv3DOp):
class TestCUDNN(TestConv3DOp):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
@unittest.skipIf(not core.is_compiled_with_cuda(),
......@@ -431,6 +440,7 @@ class TestFP16CUDNN(TestConv3DOp):
class TestWithGroup1CUDNN(TestWithGroup1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
@unittest.skipIf(not core.is_compiled_with_cuda(),
......@@ -452,6 +462,7 @@ class TestFP16WithGroup1CUDNN(TestWithGroup1):
class TestWithGroup2CUDNN(TestWithGroup2):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
@unittest.skipIf(not core.is_compiled_with_cuda(),
......@@ -473,6 +484,7 @@ class TestFP16WithGroup2CUDNN(TestWithGroup2):
class TestWith1x1CUDNN(TestWith1x1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
@unittest.skipIf(not core.is_compiled_with_cuda(),
......@@ -494,6 +506,7 @@ class TestFP16With1x1CUDNN(TestWith1x1):
class TestWithInput1x1Filter1x1CUDNN(TestWithInput1x1Filter1x1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
@unittest.skipIf(not core.is_compiled_with_cuda(),
......@@ -514,6 +527,7 @@ class TestCUDNNExhaustiveSearch(TestCUDNN):
def init_kernel_type(self):
self.use_cudnn = True
self.exhaustive_search = True
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
# ---- test asymmetric padding ----
......
python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py
浏览文件 @ 3d5aa9d1
......@@ -50,7 +50,7 @@ class TestSyncBatchNormOpTraining(unittest.TestCase):
def setUp(self):
"""Setup."""
#self.dtype = np.float32
self.dtype = np.float64
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
self.N = 8
self.C = 16
self.H = 32
......@@ -92,7 +92,10 @@ class TestSyncBatchNormOpTraining(unittest.TestCase):
moving_variance_name='bn_moving_variance',
data_layout=layout,
is_test=only_forward)
bn = fluid.layers.cast(bn, 'float64')
if core.is_compiled_with_rocm():
bn = fluid.layers.cast(bn, 'float32')
else:
bn = fluid.layers.cast(bn, 'float64')
sigmoid = fluid.layers.sigmoid(bn)
out = fluid.layers.reduce_sum(sigmoid)
if not sync_bn:
......
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