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未验证 提交 ec9bc1bd 编写于 作者: T Tao Luo 提交者： GitHub
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paddle::framework::vectorize() templatization (#19730) 

remove unused accuracy-diff warpctc-cudnn implementation

test=develop
上级 aa63d5ac
隐藏空白更改
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Showing with 2 addition and 251 deletion
paddle/fluid/API.spec
浏览文件 @ ec9bc1bd
......@@ -159,7 +159,7 @@ paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized',
paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', 'c1df110ea65998984f564c5c10abc54a'))
paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', '3720b4a386585094435993deb028b592'))
paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e50940f3ce5a08cc477b72f517491bf3'))
paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn', 'input_length', 'label_length'], varargs=None, keywords=None, defaults=(0, False, False, None, None)), ('document', 'ba27f25141adf24706536d179fabdf17'))
paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'input_length', 'label_length'], varargs=None, keywords=None, defaults=(0, False, None, None)), ('document', 'a5be881ada816e47ea7a6ee4396da357'))
paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'f568714a876425004aca4ea2d4a27701'))
paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8e72db173d4c082e27cb11f31d8c9bfa'))
paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '33134416fc27dd65a767e5f15116ee16'))
......
paddle/fluid/framework/ddim.cc
浏览文件 @ ec9bc1bd
......@@ -48,15 +48,6 @@ bool DDim::operator==(const DDim& d) const {
bool DDim::operator!=(const DDim& d) const { return !(*this == d); }
// NOTE: framework::vectorize converts to type int64_t
// which does not fit cudnn inputs.
std::vector<int> vectorize2int(const DDim& ddim) {
std::vector<int> result(DDim::kMaxRank);
dynamic_dim_assign(ddim.Get(), result.data(), ddim.size());
result.resize(ddim.size());
return result;
}
struct ProductVisitor {
template <int D>
inline int64_t operator()(const Dim<D>& dim) {
......
paddle/fluid/framework/ddim.h
浏览文件 @ ec9bc1bd
......@@ -177,7 +177,6 @@ std::vector<T> vectorize(const DDim& ddim) {
result.resize(ddim.size());
return result;
}
std::vector<int> vectorize2int(const DDim& ddim);
int64_t product(const DDim& ddim);
......
paddle/fluid/operators/warpctc_cudnn_op.cu.cc 已删除 100644 → 0
浏览文件 @ aa63d5ac
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/operators/math/softmax.h"
#include "paddle/fluid/operators/warpctc_op.h"
#include "paddle/fluid/platform/cudnn_helper.h"
namespace paddle {
namespace operators {
#if CUDNN_VERSION >= 7001
using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
using ScopedCTCLossDescriptor = platform::ScopedCTCLossDescriptor;
using DataLayout = platform::DataLayout;
template <typename DeviceContext, typename T>
class CudnnCTCKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
// =====================Copied code from warpctc===========================
auto* logits = ctx.Input<LoDTensor>("Logits");
auto* label = ctx.Input<LoDTensor>("Label");
auto* warpctc_grad = ctx.Output<LoDTensor>("WarpCTCGrad");
auto* loss = ctx.Output<LoDTensor>("Loss");
const size_t level = 0;
auto logits_lod = framework::ToAbsOffset(logits->lod());
auto logits_dims = logits->dims();
PADDLE_ENFORCE_EQ(logits_dims[0],
static_cast<int64_t>(logits_lod[level].back()),
"The first dimension of Input(Logits) should be equal to "
"the sum of all sequences' lengths.");
auto label_lod = framework::ToAbsOffset(label->lod());
auto label_dims = label->dims();
PADDLE_ENFORCE_EQ(
label_dims[0], label->numel(),
"The width of each timestep in Input(Label) should be 1.");
const size_t num_sequences = logits_lod[level].size() - 1;
PADDLE_ENFORCE_EQ(num_sequences, label_lod[level].size() - 1,
"The number of sequences of Input(Logits) should be "
"equal to that of Input(Label).");
PADDLE_ENFORCE_LE(num_sequences, 256,
"The labelLengths must less than 256 for cudnn call.");
const size_t sequence_width = logits->numel() / logits_dims[0];
auto loss_dims =
framework::make_ddim({static_cast<int64_t>(num_sequences), 1});
// NOTE: cudnn takes softmax input, calculate softmax first, then do padding
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
LoDTensor softmax_logits;
softmax_logits.mutable_data<T>(logits->dims(), ctx.GetPlace());
softmax_logits.set_lod(logits_lod);
int rank = logits->dims().size();
int axis_dim = logits->dims()[rank - 1];
Tensor in_2d = framework::ReshapeToMatrix(*logits, rank - 1);
Tensor out_2d = framework::ReshapeToMatrix(softmax_logits, rank - 1);
math::SoftmaxFunctor<DeviceContext, T, false>()(dev_ctx, axis_dim, &in_2d,
&out_2d);
// ctc needs sequences data stored in transposed padding format
// logits and grad using padding data of layout 'TNC'
// T: max_sequence_length
// N: batch_size (num_sequences)
// C: width
LoDTensor warpctc_logits;
const size_t max_sequence_length =
math::MaximumSequenceLength(logits_lod[level]);
auto warpctc_logits_dims =
framework::make_ddim({static_cast<int64_t>(max_sequence_length),
static_cast<int64_t>(num_sequences),
static_cast<int64_t>(sequence_width)});
warpctc_logits.mutable_data<T>(warpctc_logits_dims, ctx.GetPlace());
LoDTensor cpu_pad_value;
T* pad_value_data =
cpu_pad_value.mutable_data<T>({1}, platform::CPUPlace());
*pad_value_data = static_cast<T>(0);
LoDTensor pad_value;
if (platform::is_cpu_place(ctx.GetPlace())) {
pad_value = cpu_pad_value;
} else {
TensorCopySync(cpu_pad_value, ctx.GetPlace(), &pad_value);
}
math::PaddingLoDTensorFunctor<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), softmax_logits,
&warpctc_logits, pad_value, -1, 0, false /* norm_by_times */,
math::kLengthBatchWidth);
const T* warpctc_logits_data = warpctc_logits.data<T>();
std::vector<int> warpctc_label_lengths(num_sequences);
std::vector<int> warpctc_logits_lengths(num_sequences);
for (size_t i = 0; i < num_sequences; ++i) {
warpctc_label_lengths[i] = label_lod[level][i + 1] - label_lod[level][i];
warpctc_logits_lengths[i] =
logits_lod[level][i + 1] - logits_lod[level][i];
}
T* warpctc_grad_data =
warpctc_grad->mutable_data<T>(warpctc_logits.dims(), ctx.GetPlace());
math::SetConstant<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), warpctc_grad,
static_cast<T>(0));
Tensor warpctc_label;
TensorCopySync(*label, platform::CPUPlace(), &warpctc_label);
const int* warpctc_label_data = warpctc_label.data<int>();
// ========================================================================
ScopedTensorDescriptor logits_desc;
ScopedTensorDescriptor grad_desc;
ScopedCTCLossDescriptor ctcloss_desc;
// layout here doesn't have effect.
DataLayout layout = DataLayout::kNCHW;
auto cu_logits_desc = logits_desc.descriptor<T>(
layout, framework::vectorize2int(warpctc_logits.dims()));
auto cu_grad_desc = grad_desc.descriptor<T>(
layout, framework::vectorize2int(warpctc_grad->dims()));
auto cu_ctcloss_desc = ctcloss_desc.descriptor<T>();
auto handle = dev_ctx.cudnn_handle();
size_t workspace_size;
CUDNN_ENFORCE(platform::dynload::cudnnGetCTCLossWorkspaceSize(
handle, cu_logits_desc, cu_grad_desc, warpctc_label_data,
warpctc_label_lengths.data(), warpctc_logits_lengths.data(),
CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, cu_ctcloss_desc, &workspace_size));
T* loss_data = loss->mutable_data<T>(loss_dims, ctx.GetPlace());
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnCTCLoss(
handle, cu_logits_desc, warpctc_logits_data, warpctc_label_data,
warpctc_label_lengths.data(), warpctc_logits_lengths.data(),
loss_data, cu_grad_desc, warpctc_grad_data,
CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, cu_ctcloss_desc, cudnn_workspace,
workspace_size));
};
workspace_handle.RunFunc(cudnn_func, workspace_size);
}
};
template <typename DeviceContext, typename T>
class CudnnCTCGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* warpctc_grad = ctx.Input<LoDTensor>("WarpCTCGrad");
auto* logits_grad = ctx.Output<LoDTensor>(framework::GradVarName("Logits"));
const Tensor* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
logits_grad->mutable_data<T>(ctx.GetPlace());
bool norm_by_times = ctx.Attr<bool>("norm_by_times");
math::UnpaddingLoDTensorFunctor<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), *warpctc_grad,
logits_grad, -1, 0, norm_by_times, math::kLengthBatchWidth);
const T* loss_grad_data = loss_grad->data<T>();
math::ScaleLoDTensorFunctor<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), loss_grad_data,
logits_grad);
}
};
#endif
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
#if CUDNN_VERSION >= 7001
REGISTER_OP_KERNEL(
warpctc, CUDNN, plat::CUDAPlace,
ops::CudnnCTCKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_KERNEL(
warpctc_grad, CUDNN, plat::CUDAPlace,
ops::CudnnCTCGradKernel<paddle::platform::CUDADeviceContext, float>);
#endif
paddle/fluid/operators/warpctc_op.cc
浏览文件 @ ec9bc1bd
......@@ -59,20 +59,6 @@ class WarpCTCOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
#if CUDA_VERSION >= 9000
LOG(WARNING)
<< "The cudnnCTCLoss of CUDNN7 have some diff between "
"CUDA9/CUDA10 and CUDA8. You can close use_cudnn option to "
"use "
"baidu-research/warp-ctc(https://github.com/baidu-research/"
"warp-ctc)";
#endif
library_ = framework::LibraryType::kCUDNN;
}
#endif
framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
return framework::OpKernelType(ctx.Input<Tensor>("Logits")->type(),
ctx.device_context(), layout_, library_);
......@@ -129,10 +115,6 @@ class WarpCTCOpMaker : public framework::OpProtoAndCheckerMaker {
"normalize the gradients by the number of time-step, "
"which is also the sequence's length.")
.SetDefault(false);
AddAttr<bool>("use_cudnn",
"(bool, default: false), whether to "
"use cudnn kernel.")
.SetDefault(false);
AddComment(R"DOC(
An operator integrating the open-source
[warp-ctc](https://github.com/baidu-research/warp-ctc) library, which is used in
......
python/paddle/fluid/layers/nn.py
浏览文件 @ ec9bc1bd
......@@ -5793,7 +5793,6 @@ def warpctc(input,
label,
blank=0,
norm_by_times=False,
use_cudnn=False,
input_length=None,
label_length=None):
"""
......@@ -5825,7 +5824,6 @@ def warpctc(input,
by the number of time-step, which is also the sequence's length.
There is no need to normalize the gradients if warpctc layer was
follewed by a mean_op.
use_cudnn (bool, default false): Whether to use cudnn.
input_length(Variable): The length for each input sequence if it is
of Tensor type, it should have shape `[batch_size]` and dtype int64.
label_length(Variable): The length for each label sequence if it is
......@@ -5885,7 +5883,6 @@ def warpctc(input,
attrs={
'blank': blank,
'norm_by_times': norm_by_times,
'use_cudnn': use_cudnn
})
return loss_out
......
python/paddle/fluid/tests/unittests/test_warpctc_op.py
浏览文件 @ ec9bc1bd
......@@ -183,7 +183,6 @@ class TestWarpCTCOp(OpTest):
self.labels_lod = [[3, 1, 4, 4]]
self.blank = self.num_classes - 1
self.norm_by_times = False
self.use_cudnn = False
def setUp(self):
self.op_type = "warpctc"
......@@ -219,7 +218,6 @@ class TestWarpCTCOp(OpTest):
self.attrs = {
"blank": self.blank,
"norm_by_times": self.norm_by_times,
"use_cudnn": self.use_cudnn
}
def test_check_output(self):
......@@ -238,7 +236,6 @@ class TestWarpCTCOpCase1(TestWarpCTCOp):
self.labels_lod = [[3, 1, 4, 4]]
self.blank = 0
self.norm_by_times = False
self.use_cudnn = False
class TestWarpCTCOpWithPadding(OpTest):
......@@ -251,7 +248,6 @@ class TestWarpCTCOpWithPadding(OpTest):
self.labels_length = np.array([3, 1, 4, 4], dtype=np.int64)
self.blank = self.num_classes - 1
self.norm_by_times = False
self.use_cudnn = False
def setUp(self):
self.op_type = "warpctc"
......@@ -315,7 +311,6 @@ class TestWarpCTCOpWithPadding(OpTest):
self.attrs = {
"blank": self.blank,
"norm_by_times": self.norm_by_times,
"use_cudnn": self.use_cudnn
}
def test_check_output(self):
......@@ -336,23 +331,7 @@ class TestWarpCTCOpWithPaddingCase1(TestWarpCTCOpWithPadding):
self.labels_length = np.array([3, 1, 4, 4], dtype=np.int64)
self.blank = 0
self.norm_by_times = False
self.use_cudnn = False
# TODO: fix this test failed cuda9/10 manylinux images
# class TestCudnnCTCOp(TestWarpCTCOp):
# def config(self):
# self.batch_size = 4
# self.num_classes = 8
# self.logits_lod = [[4, 1, 3, 3]]
# self.labels_lod = [[3, 1, 4, 4]]
# self.blank = 0
# self.norm_by_times = False
# self.use_cudnn = True
# def test_check_grad(self):
# if sys.version_info < (3, 0):
# self.outputs['WarpCTCGrad'] = self.gradient
# self.check_grad(["Logits"], "Loss", max_relative_error=0.01)
if __name__ == "__main__":
unittest.main()
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