paddle::framework::vectorize() templatization (#19730)
remove unused accuracy-diff warpctc-cudnn implementation test=developexpand_as_op_1
Tao Luo
5 years ago
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GitHub
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commit
ec9bc1bd9f
@ -1,197 +0,0 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/framework/mixed_vector.h"
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#include "paddle/fluid/operators/math/softmax.h"
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#include "paddle/fluid/operators/warpctc_op.h"
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#include "paddle/fluid/platform/cudnn_helper.h"
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namespace paddle {
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namespace operators {
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#if CUDNN_VERSION >= 7001
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using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
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using ScopedCTCLossDescriptor = platform::ScopedCTCLossDescriptor;
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using DataLayout = platform::DataLayout;
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template <typename DeviceContext, typename T>
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class CudnnCTCKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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// =====================Copied code from warpctc===========================
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auto* logits = ctx.Input<LoDTensor>("Logits");
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auto* label = ctx.Input<LoDTensor>("Label");
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auto* warpctc_grad = ctx.Output<LoDTensor>("WarpCTCGrad");
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auto* loss = ctx.Output<LoDTensor>("Loss");
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const size_t level = 0;
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auto logits_lod = framework::ToAbsOffset(logits->lod());
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auto logits_dims = logits->dims();
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PADDLE_ENFORCE_EQ(logits_dims[0],
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static_cast<int64_t>(logits_lod[level].back()),
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"The first dimension of Input(Logits) should be equal to "
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"the sum of all sequences' lengths.");
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auto label_lod = framework::ToAbsOffset(label->lod());
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auto label_dims = label->dims();
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PADDLE_ENFORCE_EQ(
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label_dims[0], label->numel(),
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"The width of each timestep in Input(Label) should be 1.");
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const size_t num_sequences = logits_lod[level].size() - 1;
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PADDLE_ENFORCE_EQ(num_sequences, label_lod[level].size() - 1,
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"The number of sequences of Input(Logits) should be "
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"equal to that of Input(Label).");
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PADDLE_ENFORCE_LE(num_sequences, 256,
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"The labelLengths must less than 256 for cudnn call.");
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const size_t sequence_width = logits->numel() / logits_dims[0];
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auto loss_dims =
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framework::make_ddim({static_cast<int64_t>(num_sequences), 1});
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// NOTE: cudnn takes softmax input, calculate softmax first, then do padding
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auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
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LoDTensor softmax_logits;
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softmax_logits.mutable_data<T>(logits->dims(), ctx.GetPlace());
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softmax_logits.set_lod(logits_lod);
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int rank = logits->dims().size();
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int axis_dim = logits->dims()[rank - 1];
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Tensor in_2d = framework::ReshapeToMatrix(*logits, rank - 1);
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Tensor out_2d = framework::ReshapeToMatrix(softmax_logits, rank - 1);
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math::SoftmaxFunctor<DeviceContext, T, false>()(dev_ctx, axis_dim, &in_2d,
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&out_2d);
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// ctc needs sequences data stored in transposed padding format
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// logits and grad using padding data of layout 'TNC'
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// T: max_sequence_length
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// N: batch_size (num_sequences)
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// C: width
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LoDTensor warpctc_logits;
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const size_t max_sequence_length =
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math::MaximumSequenceLength(logits_lod[level]);
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auto warpctc_logits_dims =
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framework::make_ddim({static_cast<int64_t>(max_sequence_length),
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static_cast<int64_t>(num_sequences),
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static_cast<int64_t>(sequence_width)});
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warpctc_logits.mutable_data<T>(warpctc_logits_dims, ctx.GetPlace());
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LoDTensor cpu_pad_value;
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T* pad_value_data =
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cpu_pad_value.mutable_data<T>({1}, platform::CPUPlace());
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*pad_value_data = static_cast<T>(0);
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LoDTensor pad_value;
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if (platform::is_cpu_place(ctx.GetPlace())) {
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pad_value = cpu_pad_value;
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} else {
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TensorCopySync(cpu_pad_value, ctx.GetPlace(), &pad_value);
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}
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math::PaddingLoDTensorFunctor<DeviceContext, T>()(
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ctx.template device_context<DeviceContext>(), softmax_logits,
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&warpctc_logits, pad_value, -1, 0, false /* norm_by_times */,
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math::kLengthBatchWidth);
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const T* warpctc_logits_data = warpctc_logits.data<T>();
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std::vector<int> warpctc_label_lengths(num_sequences);
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std::vector<int> warpctc_logits_lengths(num_sequences);
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for (size_t i = 0; i < num_sequences; ++i) {
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warpctc_label_lengths[i] = label_lod[level][i + 1] - label_lod[level][i];
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warpctc_logits_lengths[i] =
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logits_lod[level][i + 1] - logits_lod[level][i];
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}
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T* warpctc_grad_data =
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warpctc_grad->mutable_data<T>(warpctc_logits.dims(), ctx.GetPlace());
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math::SetConstant<DeviceContext, T>()(
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ctx.template device_context<DeviceContext>(), warpctc_grad,
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static_cast<T>(0));
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Tensor warpctc_label;
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TensorCopySync(*label, platform::CPUPlace(), &warpctc_label);
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const int* warpctc_label_data = warpctc_label.data<int>();
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// ========================================================================
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ScopedTensorDescriptor logits_desc;
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ScopedTensorDescriptor grad_desc;
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ScopedCTCLossDescriptor ctcloss_desc;
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// layout here doesn't have effect.
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DataLayout layout = DataLayout::kNCHW;
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auto cu_logits_desc = logits_desc.descriptor<T>(
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layout, framework::vectorize2int(warpctc_logits.dims()));
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auto cu_grad_desc = grad_desc.descriptor<T>(
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layout, framework::vectorize2int(warpctc_grad->dims()));
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auto cu_ctcloss_desc = ctcloss_desc.descriptor<T>();
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auto handle = dev_ctx.cudnn_handle();
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size_t workspace_size;
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CUDNN_ENFORCE(platform::dynload::cudnnGetCTCLossWorkspaceSize(
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handle, cu_logits_desc, cu_grad_desc, warpctc_label_data,
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warpctc_label_lengths.data(), warpctc_logits_lengths.data(),
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CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, cu_ctcloss_desc, &workspace_size));
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T* loss_data = loss->mutable_data<T>(loss_dims, ctx.GetPlace());
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auto workspace_handle = dev_ctx.cudnn_workspace_handle();
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auto cudnn_func = [&](void* cudnn_workspace) {
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CUDNN_ENFORCE(platform::dynload::cudnnCTCLoss(
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handle, cu_logits_desc, warpctc_logits_data, warpctc_label_data,
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warpctc_label_lengths.data(), warpctc_logits_lengths.data(),
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loss_data, cu_grad_desc, warpctc_grad_data,
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CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, cu_ctcloss_desc, cudnn_workspace,
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workspace_size));
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};
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workspace_handle.RunFunc(cudnn_func, workspace_size);
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}
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};
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template <typename DeviceContext, typename T>
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class CudnnCTCGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* warpctc_grad = ctx.Input<LoDTensor>("WarpCTCGrad");
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auto* logits_grad = ctx.Output<LoDTensor>(framework::GradVarName("Logits"));
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const Tensor* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
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logits_grad->mutable_data<T>(ctx.GetPlace());
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bool norm_by_times = ctx.Attr<bool>("norm_by_times");
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math::UnpaddingLoDTensorFunctor<DeviceContext, T>()(
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ctx.template device_context<DeviceContext>(), *warpctc_grad,
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logits_grad, -1, 0, norm_by_times, math::kLengthBatchWidth);
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const T* loss_grad_data = loss_grad->data<T>();
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math::ScaleLoDTensorFunctor<DeviceContext, T>()(
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ctx.template device_context<DeviceContext>(), loss_grad_data,
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logits_grad);
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}
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};
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#endif
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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namespace plat = paddle::platform;
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#if CUDNN_VERSION >= 7001
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REGISTER_OP_KERNEL(
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warpctc, CUDNN, plat::CUDAPlace,
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ops::CudnnCTCKernel<paddle::platform::CUDADeviceContext, float>);
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REGISTER_OP_KERNEL(
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warpctc_grad, CUDNN, plat::CUDAPlace,
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ops::CudnnCTCGradKernel<paddle::platform::CUDADeviceContext, float>);
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#endif
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