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fix conv_fusion in multi thread. (#55374)
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@jiweibo
jiweibo authored Jul 13, 2023
1 parent ce8455c commit ceb8356
Showing 1 changed file with 91 additions and 97 deletions.
188 changes: 91 additions & 97 deletions paddle/phi/kernels/fusion/gpu/conv_fusion_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -130,34 +130,32 @@ class CudnnConvDescManager {
XXH64_hash_t hash_key = XXH64_digest(state);
XXH64_freeState(state);

std::lock_guard<std::mutex> lock(cache_mutex_);
if (!cudnn_conv_cache_.count(hash_key)) {
std::lock_guard<std::mutex> lock(cache_mutex_);
if (!cudnn_conv_cache_.count(hash_key)) {
cudnn_conv_cache_[hash_key] = CudnnCacheInfo();
cudnn_conv_cache_[hash_key].x_desc =
GetTensorDescInfo(input_dims, input_dtype, format);
cudnn_conv_cache_[hash_key].w_desc =
GetFilterDescInfo(filter_dims, input_dtype, format);
cudnn_conv_cache_[hash_key].o_desc =
GetTensorDescInfo(output_dims, input_dtype, format);
cudnn_conv_cache_[hash_key].b_desc =
GetTensorDescInfo(bias_dims, input_dtype, format);
cudnn_conv_cache_[hash_key].conv_desc =
GetConvDescInfo(paddings, strides, dilations, groups, dtype);
cudnn_conv_cache_[hash_key].act_desc =
GetActivationDescInfo(act, value_max);

size_t workspace_size;
cudnnConvolutionFwdAlgo_t algo;
search_func(&algo,
&workspace_size,
cudnn_conv_cache_[hash_key].x_desc->desc(),
cudnn_conv_cache_[hash_key].w_desc->desc(),
cudnn_conv_cache_[hash_key].o_desc->desc(),
cudnn_conv_cache_[hash_key].conv_desc->desc());
cudnn_conv_cache_[hash_key].workspace_size = workspace_size;
cudnn_conv_cache_[hash_key].algo = algo;
}
cudnn_conv_cache_[hash_key] = CudnnCacheInfo();
cudnn_conv_cache_[hash_key].x_desc =
GetTensorDescInfo(input_dims, input_dtype, format);
cudnn_conv_cache_[hash_key].w_desc =
GetFilterDescInfo(filter_dims, input_dtype, format);
cudnn_conv_cache_[hash_key].o_desc =
GetTensorDescInfo(output_dims, input_dtype, format);
cudnn_conv_cache_[hash_key].b_desc =
GetTensorDescInfo(bias_dims, input_dtype, format);
cudnn_conv_cache_[hash_key].conv_desc =
GetConvDescInfo(paddings, strides, dilations, groups, dtype);
cudnn_conv_cache_[hash_key].act_desc =
GetActivationDescInfo(act, value_max);

size_t workspace_size;
cudnnConvolutionFwdAlgo_t algo;
search_func(&algo,
&workspace_size,
cudnn_conv_cache_[hash_key].x_desc->desc(),
cudnn_conv_cache_[hash_key].w_desc->desc(),
cudnn_conv_cache_[hash_key].o_desc->desc(),
cudnn_conv_cache_[hash_key].conv_desc->desc());
cudnn_conv_cache_[hash_key].workspace_size = workspace_size;
cudnn_conv_cache_[hash_key].algo = algo;
}

return &cudnn_conv_cache_.at(hash_key);
Expand Down Expand Up @@ -199,90 +197,86 @@ class CudnnConvDescManager {
XXH64_hash_t hash_key = XXH64_digest(state);
XXH64_freeState(state);

std::lock_guard<std::mutex> lock(attr_mutex_);
if (!conv_attr_cache_.count(hash_key)) {
std::lock_guard<std::mutex> lock(attr_mutex_);
if (!conv_attr_cache_.count(hash_key)) {
ConvAttrCacheInfo cache;
auto paddings = paddings_t;
auto dilations = dilations_t;
std::vector<int> in_data_dims(input_dims.size() - 2);
std::vector<int> ksize(filter_dims.size() - 2);
if (format == CUDNN_TENSOR_NHWC) {
for (size_t i = 1; i < input_dims.size() - 1; ++i) {
in_data_dims[i - 1] = input_dims[i];
}
for (size_t i = 1; i < filter_dims.size() - 1; ++i) {
ksize[i - 1] = filter_dims[i];
}
ConvAttrCacheInfo cache;
auto paddings = paddings_t;
auto dilations = dilations_t;
std::vector<int> in_data_dims(input_dims.size() - 2);
std::vector<int> ksize(filter_dims.size() - 2);
if (format == CUDNN_TENSOR_NHWC) {
for (size_t i = 1; i < input_dims.size() - 1; ++i) {
in_data_dims[i - 1] = input_dims[i];
}
for (size_t i = 1; i < filter_dims.size() - 1; ++i) {
ksize[i - 1] = filter_dims[i];
}
} else {
for (size_t i = 2; i < input_dims.size(); ++i) {
in_data_dims[i - 2] = input_dims[i];
}
for (size_t i = 2; i < filter_dims.size(); ++i) {
ksize[i - 2] = filter_dims[i];
}
}
phi::UpdatePaddingAndDilation(&paddings,
&dilations,
padding_algorithm,
make_ddim(in_data_dims),
strides,
ksize);

int data_dim = strides.size(); // 2d or 3d
bool is_sys_pad = funcs::IsSymmetricPadding(paddings, data_dim);
std::vector<int> padding_common(data_dim, 0);
if (!is_sys_pad) {
std::vector<int> padding_diff(data_dim);
std::vector<int> new_input_shape_vec(data_dim + 2);
new_input_shape_vec[0] = input_dims[0];

if (format == CUDNN_TENSOR_NCHW) {
new_input_shape_vec[1] = input_dims[1];
} else {
for (size_t i = 2; i < input_dims.size(); ++i) {
in_data_dims[i - 2] = input_dims[i];
}
for (size_t i = 2; i < filter_dims.size(); ++i) {
ksize[i - 2] = filter_dims[i];
}
new_input_shape_vec[data_dim + 1] = input_dims[data_dim + 1];
}
phi::UpdatePaddingAndDilation(&paddings,
&dilations,
padding_algorithm,
make_ddim(in_data_dims),
strides,
ksize);

int data_dim = strides.size(); // 2d or 3d
bool is_sys_pad = funcs::IsSymmetricPadding(paddings, data_dim);
std::vector<int> padding_common(data_dim, 0);
if (!is_sys_pad) {
std::vector<int> padding_diff(data_dim);
std::vector<int> new_input_shape_vec(data_dim + 2);
new_input_shape_vec[0] = input_dims[0];

std::vector<int> input_pad(input_dims.size() * 2, 0);
for (size_t i = 0; i < data_dim; ++i) {
padding_diff[i] = std::abs(paddings[2 * i] - paddings[2 * i + 1]);
padding_common[i] = std::min(paddings[2 * i], paddings[2 * i + 1]);
if (format == CUDNN_TENSOR_NCHW) {
new_input_shape_vec[1] = input_dims[1];
new_input_shape_vec[i + 2] = input_dims[i + 2] + padding_diff[i];
} else {
new_input_shape_vec[data_dim + 1] = input_dims[data_dim + 1];
new_input_shape_vec[i + 1] = input_dims[i + 1] + padding_diff[i];
}
if (format == CUDNN_TENSOR_NCHW) {
input_pad[2 * i + 4] = paddings[2 * i] - padding_common[i];
input_pad[2 * i + 4 + 1] = paddings[2 * i + 1] - padding_common[i];
} else {
input_pad[2 * i + 2] = paddings[2 * i] - padding_common[i];
input_pad[2 * i + 2 + 1] = paddings[2 * i + 1] - padding_common[i];
}
}

std::vector<int> input_pad(input_dims.size() * 2, 0);
cache.is_sys_pad = false;
cache.input_pad = input_pad;
cache.new_input_shape_vec = new_input_shape_vec;
} else {
cache.is_sys_pad = true;
if (paddings.size() == data_dim) {
for (size_t i = 0; i < data_dim; ++i) {
padding_diff[i] = std::abs(paddings[2 * i] - paddings[2 * i + 1]);
padding_common[i] = std::min(paddings[2 * i], paddings[2 * i + 1]);
if (format == CUDNN_TENSOR_NCHW) {
new_input_shape_vec[i + 2] = input_dims[i + 2] + padding_diff[i];
} else {
new_input_shape_vec[i + 1] = input_dims[i + 1] + padding_diff[i];
}
if (format == CUDNN_TENSOR_NCHW) {
input_pad[2 * i + 4] = paddings[2 * i] - padding_common[i];
input_pad[2 * i + 4 + 1] =
paddings[2 * i + 1] - padding_common[i];
} else {
input_pad[2 * i + 2] = paddings[2 * i] - padding_common[i];
input_pad[2 * i + 2 + 1] =
paddings[2 * i + 1] - padding_common[i];
}
padding_common[i] = paddings[i];
}

cache.is_sys_pad = false;
cache.input_pad = input_pad;
cache.new_input_shape_vec = new_input_shape_vec;
} else {
cache.is_sys_pad = true;
if (paddings.size() == data_dim) {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings[i];
}
} else {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings[2 * i];
}
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings[2 * i];
}
}

cache.dilations = dilations;
cache.paddings = padding_common;
conv_attr_cache_[hash_key] = cache;
}

cache.dilations = dilations;
cache.paddings = padding_common;
conv_attr_cache_[hash_key] = cache;
}

return &conv_attr_cache_.at(hash_key);
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