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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/10/09 01:37:00 UTC
[GitHub] anirudh2290 closed pull request #12637: [MXNET-912] Refactoring ctc
loss operator
anirudh2290 closed pull request #12637: [MXNET-912] Refactoring ctc loss operator
URL: https://github.com/apache/incubator-mxnet/pull/12637
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diff --git a/src/operator/contrib/ctc_include/LICENSE b/3rdparty/ctc_include/LICENSE
similarity index 100%
rename from src/operator/contrib/ctc_include/LICENSE
rename to 3rdparty/ctc_include/LICENSE
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/LICENSE b/3rdparty/ctc_include/contrib/moderngpu/LICENSE
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/LICENSE
rename to 3rdparty/ctc_include/contrib/moderngpu/LICENSE
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctaloadbalance.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/ctaloadbalance.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctaloadbalance.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/ctaloadbalance.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctamerge.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/ctamerge.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctamerge.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/ctamerge.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctascan.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/ctascan.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctascan.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/ctascan.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasearch.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/ctasearch.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasearch.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/ctasearch.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasegreduce.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/ctasegreduce.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasegreduce.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/ctasegreduce.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasegscan.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/ctasegscan.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasegscan.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/ctasegscan.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasegsort.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/ctasegsort.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasegsort.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/ctasegsort.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasortedsearch.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/ctasortedsearch.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/ctasortedsearch.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/ctasortedsearch.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/devicetypes.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/devicetypes.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/devicetypes.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/devicetypes.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/deviceutil.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/deviceutil.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/deviceutil.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/deviceutil.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/intrinsics.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/intrinsics.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/intrinsics.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/intrinsics.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/loadstore.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/loadstore.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/loadstore.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/loadstore.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/serialsets.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/serialsets.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/serialsets.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/serialsets.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/device/sortnetwork.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/device/sortnetwork.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/device/sortnetwork.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/device/sortnetwork.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/mgpudevice.cuh b/3rdparty/ctc_include/contrib/moderngpu/include/mgpudevice.cuh
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/mgpudevice.cuh
rename to 3rdparty/ctc_include/contrib/moderngpu/include/mgpudevice.cuh
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/mgpuenums.h b/3rdparty/ctc_include/contrib/moderngpu/include/mgpuenums.h
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/mgpuenums.h
rename to 3rdparty/ctc_include/contrib/moderngpu/include/mgpuenums.h
diff --git a/src/operator/contrib/ctc_include/contrib/moderngpu/include/util/static.h b/3rdparty/ctc_include/contrib/moderngpu/include/util/static.h
similarity index 100%
rename from src/operator/contrib/ctc_include/contrib/moderngpu/include/util/static.h
rename to 3rdparty/ctc_include/contrib/moderngpu/include/util/static.h
diff --git a/src/operator/contrib/ctc_include/detail/cpu_ctc.h b/3rdparty/ctc_include/detail/cpu_ctc.h
similarity index 100%
rename from src/operator/contrib/ctc_include/detail/cpu_ctc.h
rename to 3rdparty/ctc_include/detail/cpu_ctc.h
diff --git a/src/operator/contrib/ctc_include/detail/ctc_helper.h b/3rdparty/ctc_include/detail/ctc_helper.h
similarity index 100%
rename from src/operator/contrib/ctc_include/detail/ctc_helper.h
rename to 3rdparty/ctc_include/detail/ctc_helper.h
diff --git a/src/operator/contrib/ctc_include/detail/gpu_ctc.h b/3rdparty/ctc_include/detail/gpu_ctc.h
similarity index 100%
rename from src/operator/contrib/ctc_include/detail/gpu_ctc.h
rename to 3rdparty/ctc_include/detail/gpu_ctc.h
diff --git a/src/operator/contrib/ctc_include/detail/gpu_ctc_kernels.h b/3rdparty/ctc_include/detail/gpu_ctc_kernels.h
similarity index 100%
rename from src/operator/contrib/ctc_include/detail/gpu_ctc_kernels.h
rename to 3rdparty/ctc_include/detail/gpu_ctc_kernels.h
diff --git a/src/operator/contrib/ctc_include/detail/hostdevice.h b/3rdparty/ctc_include/detail/hostdevice.h
similarity index 100%
rename from src/operator/contrib/ctc_include/detail/hostdevice.h
rename to 3rdparty/ctc_include/detail/hostdevice.h
diff --git a/python/mxnet/gluon/loss.py b/python/mxnet/gluon/loss.py
index 2be43981a64..7e4d3457763 100644
--- a/python/mxnet/gluon/loss.py
+++ b/python/mxnet/gluon/loss.py
@@ -468,10 +468,10 @@ def hybrid_forward(self, F, pred, label,
pred = F.swapaxes(pred, 0, 1)
if self._batch_axis == 1:
label = F.swapaxes(label, 0, 1)
- loss = F.contrib.CTCLoss(pred, label, pred_lengths, label_lengths,
- use_data_lengths=pred_lengths is not None,
- use_label_lengths=label_lengths is not None,
- blank_label='last')
+ loss = F.CTCLoss(pred, label, pred_lengths, label_lengths,
+ use_data_lengths=pred_lengths is not None,
+ use_label_lengths=label_lengths is not None,
+ blank_label='last')
return _apply_weighting(F, loss, self._weight, sample_weight)
diff --git a/src/operator/contrib/ctc_loss-inl.h b/src/operator/contrib/ctc_loss-inl.h
deleted file mode 100644
index c8a8b263740..00000000000
--- a/src/operator/contrib/ctc_loss-inl.h
+++ /dev/null
@@ -1,591 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements. See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership. The ASF licenses this file
- * to you 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.
- */
-
-/*!
- * Copyright (c) 2016 by Contributors
- * \file ctc_loss-inl.h
- * \brief
- * \author Sebastian Bodenstien
-*/
-
-#ifndef MXNET_OPERATOR_CONTRIB_CTC_LOSS_INL_H_
-#define MXNET_OPERATOR_CONTRIB_CTC_LOSS_INL_H_
-
-#include <dmlc/logging.h>
-#include <dmlc/parameter.h>
-#include <mxnet/operator.h>
-#include <algorithm>
-#include <map>
-#include <vector>
-#include <string>
-#include <utility>
-#include <ctime>
-#include <cstring>
-#include <iostream>
-#include "../operator_common.h"
-#include "../sequence_op_common.h"
-#include "../mshadow_op.h"
-#include "../nn/sequence_mask-inl.h"
-
-#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
-#define CUDNN_LABEL_LENGTH_LIMIT 256
-#include "../nn/softmax-inl.h"
-#endif // CUDNN
-
-namespace mxnet {
-namespace op {
-
-namespace ctc_loss {
-enum CTCLossOpInputs { kData, kLabel };
-enum CTCLossOpOutputs { kOut, kGrad };
-enum CTCLossOpForwardResource { kTempSpace };
-}
-
-template <typename T>
-inline void get_workspace_size(std::vector<int> *label_lengths,
- std::vector<int> *data_lengths,
- int alphabet_size, int minibatch, bool gpu,
- size_t *size_bytes) {
- // This is the max of all S and T for all examples in the minibatch.
- int maxL = *std::max_element(label_lengths->data(),
- label_lengths->data() + minibatch);
- int maxT = *std::max_element(data_lengths->data(),
- data_lengths->data() + minibatch);
-
- const int S = 2 * maxL + 1;
-
- *size_bytes = 0;
-
- if (gpu) {
- // GPU storage
- // nll_forward, nll_backward
- *size_bytes += 2 * sizeof(T) * minibatch;
-
- // repeats
- *size_bytes += sizeof(int) * minibatch;
-
- // label offsets
- *size_bytes += sizeof(int) * minibatch;
-
- // utt_length
- *size_bytes += sizeof(int) * minibatch;
-
- // label lengths
- *size_bytes += sizeof(int) * minibatch;
-
- // labels without blanks - overallocate for now
- *size_bytes += sizeof(int) * maxL * minibatch;
-
- // labels with blanks
- *size_bytes += sizeof(int) * S * minibatch;
-
- // alphas
- *size_bytes += sizeof(T) * S * maxT * minibatch;
-
- // denoms
- *size_bytes += sizeof(T) * maxT * minibatch;
-
- // probs (since we will pass in activations)
- *size_bytes += sizeof(T) * alphabet_size * maxT * minibatch;
-
- } else {
- // cpu can eventually replace all minibatch with
- // max number of concurrent threads if memory is
- // really tight
-
- // per minibatch memory
- size_t per_minibatch_bytes = 0;
-
- // output
- per_minibatch_bytes += sizeof(T) * alphabet_size;
-
- // alphas
- per_minibatch_bytes += sizeof(T) * S * maxT;
-
- // betas
- per_minibatch_bytes += sizeof(T) * S;
-
- // labels w/blanks, e_inc, s_inc
- per_minibatch_bytes += 3 * sizeof(int) * S;
-
- *size_bytes = per_minibatch_bytes * minibatch;
-
- // probs
- *size_bytes += sizeof(T) * alphabet_size * maxT * minibatch;
- }
-}
-
-// Takes a tensor of labels, and interprets 0-elements at the end of the vector
-// as padding. The tensor is packed into an std::vector without padding
-// characters. The label sequence lengths are also inferred from the padding chars.
-// When cudnn is enabled, the return value signifies whether the cudnn length limit is exceeded.
-template <typename DType, typename xpu>
-inline bool LabelTensorToPackedVector(mshadow::Tensor<xpu, 2, DType> labels,
- int padding_mask,
- std::vector<int> *packed_labels,
- std::vector<int> *label_lengths) {
- int batch = labels.size(0);
- int max_num_labels = labels.size(1);
- bool exceed_limit = false;
-
- std::vector<int> cpu_labels(max_num_labels*batch);
- mshadow::Tensor<xpu, 1, DType> flat_labels = labels.FlatTo1D();
- IndexTensorToVector(flat_labels, &cpu_labels);
-
- for (int b = 0; b < batch; ++b) {
- auto start = cpu_labels.data()+b*max_num_labels;
- auto res = std::find(start, start+max_num_labels, padding_mask);
- int len = std::distance(start, res);
-#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
- exceed_limit = exceed_limit || len > CUDNN_LABEL_LENGTH_LIMIT;
-#endif
- std::copy(start, start + len,
- std::back_inserter(*packed_labels));
- label_lengths->at(b) = len;
- }
- return exceed_limit;
-}
-
-// Takes a tensor of labels, and a vector which specifies the actual length of each label
-// The tensor is packed into an std::vector without padding characters.
-// The label length vector is copied into an std::vector.
-// When cudnn is enabled, the return value signifies whether the cudnn length limit is exceeded.
-template <typename DType, typename xpu>
-inline bool PackLabelByLength(mshadow::Tensor<xpu, 2, DType> labels,
- mshadow::Tensor<xpu, 1, DType> in_label_lengths,
- std::vector<int> *packed_labels,
- std::vector<int> *label_lengths) {
- int batch = labels.size(0);
- int max_num_labels = labels.size(1);
- bool exceed_limit = false;
-
- IndexTensorToVector(in_label_lengths, label_lengths);
-
- std::vector<int> cpu_labels(max_num_labels*batch);
- mshadow::Tensor<xpu, 1, DType> flat_labels = labels.FlatTo1D();
- IndexTensorToVector(flat_labels, &cpu_labels);
-
- for (int b = 0; b < batch; ++b) {
- auto start = cpu_labels.data()+b*max_num_labels;
- int len = label_lengths->at(b);
-#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
- exceed_limit = exceed_limit || len > CUDNN_LABEL_LENGTH_LIMIT;
-#endif
- std::copy(start, start + len,
- std::back_inserter(*packed_labels));
- }
- return exceed_limit;
-}
-
-struct CTCLossParam : public dmlc::Parameter<CTCLossParam> {
- bool use_data_lengths;
- bool use_label_lengths;
- int blank_label;
- DMLC_DECLARE_PARAMETER(CTCLossParam) {
- DMLC_DECLARE_FIELD(use_data_lengths).set_default(false)
- .describe("Whether the data lenghts are decided by `data_lengths`. "
- "If false, the lengths are equal to the max sequence length.");
- DMLC_DECLARE_FIELD(use_label_lengths).set_default(false)
- .describe("Whether the label lenghts are decided by "
- "`label_lengths`, or derived from `padding_mask`. "
- "If false, the lengths are derived from the "
- "first occurrence of the value of `padding_mask`. "
- "The value of `padding_mask` is ``0`` when first CTC label is reserved for blank, "
- "and ``-1`` when last label is reserved for blank. See `blank_label`.");
- DMLC_DECLARE_FIELD(blank_label)
- .add_enum("first", 0)
- .add_enum("last", 1)
- .set_default(0)
- .describe("Set the label that is reserved for blank label."
- "If \"first\", 0-th label is reserved, and "
- "label values for tokens in the vocabulary are "
- "between ``1`` and ``alphabet_size-1``, and the padding mask is ``-1``. "
- "If \"last\", last label value ``alphabet_size-1`` "
- "is reserved for blank label instead, "
- "and label values for tokens in the vocabulary are "
- "between ``0`` and ``alphabet_size-2``, and the padding mask is ``0``.");
- }
-};
-
-template <typename xpu>
-class CTCLossOp : public Operator {
- public:
- explicit CTCLossOp(CTCLossParam p) {
- this->param_ = p;
- exceed_cudnn_limit = false;
-#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
- CUDNN_CALL(cudnnCreateCTCLossDescriptor(&ctc_desc_));
- CUDNN_CALL(cudnnSetCTCLossDescriptor(ctc_desc_, CUDNN_DATA_FLOAT));
- CUDNN_CALL(cudnnCreateTensorDescriptor(&prob_desc_));
- CUDNN_CALL(cudnnCreateTensorDescriptor(&grad_desc_));
-#endif
- }
-
- ~CTCLossOp() {
-#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
- CUDNN_CALL(cudnnDestroyCTCLossDescriptor(ctc_desc_));
- CUDNN_CALL(cudnnDestroyTensorDescriptor(prob_desc_));
- CUDNN_CALL(cudnnDestroyTensorDescriptor(grad_desc_));
-#endif
- }
-
- virtual void Forward(const OpContext &ctx, const std::vector<TBlob> &in_data,
- const std::vector<OpReqType> &req,
- const std::vector<TBlob> &out_data,
- const std::vector<TBlob> &aux_args) {
- using namespace mshadow;
- using namespace mshadow::expr;
- CHECK_EQ(in_data.size(), 2U+param_.use_data_lengths+param_.use_label_lengths);
- CHECK_EQ(out_data.size(), 2U);
- exceed_cudnn_limit = false;
- Stream<xpu> *s = ctx.get_stream<xpu>();
-
- MSHADOW_TYPE_SWITCH(in_data[ctc_loss::kLabel].type_flag_, DType, {
- Tensor<xpu, 3, real_t> data =
- in_data[ctc_loss::kData].get<xpu, 3, real_t>(s);
- Tensor<xpu, 2, DType> labels =
- in_data[ctc_loss::kLabel].get<xpu, 2, DType>(s);
-
- Tensor<xpu, 1, real_t> costs =
- out_data[ctc_loss::kOut].get<xpu, 1, real_t>(s);
- Tensor<xpu, 3, real_t> grad =
- out_data[ctc_loss::kGrad].get<xpu, 3, real_t>(s);
-
- int max_seq_len = data.size(0);
- int batch_size = data.size(1);
- int alphabet_size = data.size(2);
-
- // data_lengths
- std::vector<int> data_lengths(batch_size, max_seq_len);
- if (param_.use_data_lengths) {
- int kInputLength = 2;
- IndexTensorToVector(in_data[kInputLength].get<xpu, 1, real_t>(s), &data_lengths);
- }
-
- // label_lengths
- std::vector<int> packed_labels;
- std::vector<int> label_lengths(batch_size);
-
- if (param_.use_label_lengths) {
- int kLabelLength = 2 + param_.use_data_lengths;
- exceed_cudnn_limit =
- PackLabelByLength(labels, in_data[kLabelLength].get<xpu, 1, DType>(s),
- &packed_labels, &label_lengths);
- } else {
- exceed_cudnn_limit = LabelTensorToPackedVector(labels, param_.blank_label == 0 ? 0 : -1,
- &packed_labels, &label_lengths);
- }
-
- // CUDNN is disabled due to lack of support for input lengths
- /* #if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7 */
- /* if (!exceed_cudnn_limit) { */
- /* cudnn_forward(ctx, s, data, costs, grad, */
- /* &data_lengths, &label_lengths, &packed_labels, */
- /* max_seq_len, batch_size, alphabet_size, */
- /* req[ctc_loss::kGrad] != mxnet::kNullOp); */
- /* } else { */
- /* baidu_forward(ctx, s, data, costs, grad, */
- /* &data_lengths, &label_lengths, &packed_labels, */
- /* batch_size, alphabet_size, req[ctc_loss::kGrad] != mxnet::kNullOp);*/
- /* } */
- /* #else */
-
- baidu_forward(ctx, s, data, costs, grad,
- &data_lengths, &label_lengths, &packed_labels,
- batch_size, alphabet_size, req[ctc_loss::kGrad] != mxnet::kNullOp);
-
- if (param_.use_data_lengths) {
- // baidu warp CTC implementation sometimes includes undefined gradients
- // for data outside of length mask. Setting to 0 to make it consistent
- // with CPU implementation.
- int kInputLength = 2;
- mxnet_op::SequenceMask(grad, in_data[kInputLength].get<xpu, 1, real_t>(s),
- static_cast<real_t>(0));
- }
- });
- }
-
- virtual void Backward(const OpContext &ctx,
- const std::vector<TBlob> &out_grad,
- const std::vector<TBlob> &in_data,
- const std::vector<TBlob> &out_data,
- const std::vector<OpReqType> &req,
- const std::vector<TBlob> &in_grad,
- const std::vector<TBlob> &aux_args) {
- using namespace mshadow;
- using namespace mshadow::expr;
-
- Stream<xpu> *s = ctx.get_stream<xpu>();
-
- Tensor<xpu, 3, real_t> data_grad =
- in_grad[ctc_loss::kData].get<xpu, 3, real_t>(s);
- Tensor<xpu, 1, real_t> output_grad =
- out_grad[ctc_loss::kOut].get<xpu, 1, real_t>(s);
-
- Tensor<xpu, 3, real_t> data_grad_computed =
- out_data[ctc_loss::kGrad].get<xpu, 3, real_t>(s);
-
- Assign(data_grad, req[ctc_loss::kData],
- mshadow::expr::broadcast<1>(output_grad, data_grad.shape_) * data_grad_computed);
- }
-
- private:
- CTCLossParam param_;
- bool exceed_cudnn_limit;
-
-#if defined(__CUDACC__) && MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 7
- cudnnDataType_t dtype_;
- cudnnCTCLossDescriptor_t ctc_desc_;
- cudnnTensorDescriptor_t prob_desc_, grad_desc_;
-
- inline virtual void cudnn_forward(const OpContext &ctx,
- mshadow::Stream<xpu>* s,
- mshadow::Tensor<xpu, 3, real_t> data,
- mshadow::Tensor<xpu, 1, real_t> costs,
- mshadow::Tensor<xpu, 3, real_t> grad,
- std::vector<int>* data_lengths,
- std::vector<int>* label_lengths,
- std::vector<int>* packed_labels,
- int max_seq_len,
- int batch_size,
- int alphabet_size,
- bool req_grad) {
- using namespace mshadow;
-
- // call cudnn to calculate ctc loss
- dtype_ = CUDNN_DATA_FLOAT;
- int dims[3], strides[3];
- size_t workspace_bytes;
- int workspace_size;
- dims[0] = max_seq_len;
- dims[1] = batch_size;
- dims[2] = alphabet_size;
- strides[0] = batch_size*alphabet_size;
- strides[1] = alphabet_size;
- strides[2] = 1;
- cudnnCTCLossAlgo_t ctc_algo = CUDNN_CTC_LOSS_ALGO_DETERMINISTIC;
- CUDNN_CALL(cudnnSetTensorNdDescriptor(prob_desc_,
- dtype_,
- 3,
- dims,
- strides));
- CUDNN_CALL(cudnnSetTensorNdDescriptor(grad_desc_,
- dtype_,
- 3,
- dims,
- strides));
- CUDNN_CALL(cudnnGetCTCLossWorkspaceSize(s->dnn_handle_,
- prob_desc_,
- req_grad?grad_desc_:NULL,
- packed_labels->data(),
- label_lengths->data(),
- data_lengths->data(),
- ctc_algo,
- ctc_desc_,
- &workspace_bytes));
- workspace_size = (workspace_bytes + sizeof(real_t) - 1)/sizeof(real_t);
-
- Tensor<xpu, 1, real_t> temp_space =
- ctx.requested[ctc_loss::kTempSpace].get_space_typed<xpu, 1, real_t>(
- mshadow::Shape1(workspace_size+data.shape_.FlatTo1D()[0]), s);
-
- Tensor<gpu, 1, real_t> work_space(temp_space.dptr_,
- mshadow::Shape1(workspace_size), s);
- Tensor<xpu, 3, real_t> prob(temp_space.dptr_+workspace_size,
- data.shape_, s);
-
- // since the input is activation before softmax and cudnn ctc takes softmax
- // apply softmax to inputs first.
- mxnet_op::Softmax<mxnet_op::softmax_fwd, false>(
- s, data.dptr_, prob.dptr_, data.shape_, 2, 1.0);
-
- CUDNN_CALL(cudnnCTCLoss(s->dnn_handle_,
- prob_desc_,
- prob.dptr_,
- packed_labels->data(),
- label_lengths->data(),
- data_lengths->data(),
- costs.dptr_,
- req_grad?grad_desc_:NULL,
- req_grad?grad.dptr_:NULL,
- ctc_algo,
- ctc_desc_,
- work_space.dptr_,
- workspace_bytes));
-
- if (req_grad) {
- mxnet_op::SoftmaxGrad<mshadow_op::mul, mxnet_op::softmax_bwd, kWriteTo, false>(
- s, prob.dptr_, grad.dptr_, grad.dptr_, data.shape_, 2, 1.0);
- Assign(grad, mxnet::kWriteInplace, grad * alphabet_size);
- }
- }
-#endif // __CUDACC__ && CUDNN
-
- inline void baidu_forward(const OpContext &ctx,
- mshadow::Stream<xpu>* s,
- mshadow::Tensor<xpu, 3, real_t> data,
- mshadow::Tensor<xpu, 1, real_t> costs,
- mshadow::Tensor<xpu, 3, real_t> grad,
- std::vector<int>* data_lengths,
- std::vector<int>* label_lengths,
- std::vector<int>* packed_labels,
- int batch_size,
- int alphabet_size,
- bool req_grad) {
- using namespace mshadow;
- // allocate temporary workspace
- size_t size_bytes;
- bool gpu = data.kDevCPU ? false : true;
- get_workspace_size<real_t>(label_lengths, data_lengths, alphabet_size,
- batch_size, gpu, &size_bytes);
-
- // round-up so there are enough elems in memory
- int num_tmp_elems = (size_bytes + sizeof(real_t) - 1) / sizeof(real_t);
- Tensor<xpu, 1, real_t> workspace =
- ctx.requested[ctc_loss::kTempSpace].get_space_typed<xpu, 1, real_t>(
- Shape1(num_tmp_elems), s);
-
- compute_ctc_cost(data, costs.dptr_, grad.dptr_, packed_labels->data(),
- label_lengths->data(), data_lengths->data(),
- workspace.dptr_, req_grad,
- param_.blank_label == 0 ? 0 : (alphabet_size-1));
- }
-}; // class CTCLossOp
-
-template <typename xpu>
-Operator *CreateOp(CTCLossParam param, int dtype);
-
-#if DMLC_USE_CXX11
-class CTCLossProp : public OperatorProperty {
- public:
- int NumVisibleOutputs() const override { return 1; }
-
- int NumOutputs() const override { return 2; }
-
- std::vector<std::string> ListArguments() const override {
- if (param_.use_data_lengths && param_.use_label_lengths) {
- return {"data", "label", "data_lengths", "label_lengths"};
- } else if (param_.use_data_lengths) {
- return {"data", "label", "data_lengths"};
- } else if (param_.use_label_lengths) {
- return {"data", "label", "label_lengths"};
- } else {
- return {"data", "label"};
- }
- }
-
- std::vector<std::string> ListOutputs() const override {
- return {"output", "grad"};
- }
-
- void Init(const std::vector<std::pair<std::string, std::string>> &kwargs) override {
- param_.Init(kwargs);
- }
-
- std::map<std::string, std::string> GetParams() const override {
- return param_.__DICT__();
- }
-
- bool InferShape(std::vector<TShape> *in_shape, std::vector<TShape> *out_shape,
- std::vector<TShape> *aux_shape) const override {
- using namespace mshadow;
- index_t expected_inputs = 2+param_.use_data_lengths+param_.use_label_lengths;
- CHECK_EQ(in_shape->size(), expected_inputs)
- << "Expect " << expected_inputs << " inputs to the symbol.";
-
- const TShape &dshape = (*in_shape)[ctc_loss::kData];
- const TShape &lshape = (*in_shape)[ctc_loss::kLabel];
- CHECK_EQ(dshape.ndim(), 3U) << "The data array must be of rank 3.";
- CHECK_EQ(lshape.ndim(), 2U) << "The labels array must be of rank 2.";
- CHECK_EQ(dshape[1], lshape[0])
- << "The batch size for the labels and data arrays must be the same.";
- if (param_.use_data_lengths) {
- int kInputLength = 2;
- const TShape &dlshape = (*in_shape)[kInputLength];
- CHECK_EQ(dlshape.ndim(), 1U) << "Data length array must be a vector.";
- CHECK_EQ(dlshape[0], dshape[1])
- << "The batch size for the data and data lengths must be the same.";
- }
- if (param_.use_label_lengths) {
- int kLabelLength = 2+param_.use_data_lengths;
- const TShape &llshape = (*in_shape)[kLabelLength];
- CHECK_EQ(llshape.ndim(), 1U) << "Label length array must be a vector.";
- CHECK_EQ(llshape[0], lshape[0])
- << "The batch size for the labels and label lengths must be the same.";
- }
-
- CHECK_GE(dshape[0], lshape[1]) << "The max number of labels cannot exceed "
- "the maximum sequence length of the "
- "data.";
-
- TShape oshape(1);
- oshape[0] = dshape[1]; // batch size
- out_shape->clear();
- out_shape->push_back(oshape); // forward output
- out_shape->push_back(dshape); // grad output
- return true;
- }
-
- bool InferType(std::vector<int> *in_type,
- std::vector<int> *out_type,
- std::vector<int> *aux_type) const override {
- CHECK_LE(in_type->size(), this->ListArguments().size());
- int dtype = (*in_type)[ctc_loss::kData];
- CHECK_NE(dtype, -1) << "Input data must have specified type";
-
- out_type->clear();
- out_type->push_back(dtype); // forward output
- out_type->push_back(dtype); // grad output
- return true;
- }
-
- OperatorProperty *Copy() const override {
- auto ptr = new CTCLossProp();
- ptr->param_ = param_;
- return ptr;
- }
-
- std::string TypeString() const override { return "_contrib_CTCLoss"; }
-
- std::vector<ResourceRequest> ForwardResource(
- const std::vector<TShape> &in_shape) const override {
- return {ResourceRequest::kTempSpace};
- }
-
- std::vector<int> DeclareBackwardDependency(
- const std::vector<int> &out_grad, const std::vector<int> &in_data,
- const std::vector<int> &out_data) const override {
- return {out_grad[ctc_loss::kOut], out_data[ctc_loss::kGrad]};
- }
-
- Operator *CreateOperator(Context ctx) const override {
- LOG(FATAL) << "Not Implemented.";
- return NULL;
- }
-
- Operator *CreateOperatorEx(Context ctx, std::vector<TShape> *in_shape,
- std::vector<int> *in_type) const override;
-
- private:
- CTCLossParam param_;
-}; // class CTCLossProp
-#endif // DMLC_USE_CXX11
-} // namespace op
-} // namespace mxnet
-#endif // MXNET_OPERATOR_CONTRIB_CTC_LOSS_INL_H_
diff --git a/src/operator/nn/ctc_loss-inl.h b/src/operator/nn/ctc_loss-inl.h
new file mode 100644
index 00000000000..754cf8471b5
--- /dev/null
+++ b/src/operator/nn/ctc_loss-inl.h
@@ -0,0 +1,397 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * Copyright (c) 2017 by Contributors
+ * \file ctc_loss-inl.h
+ * \brief CTC Loss operator
+*/
+
+#ifndef MXNET_OPERATOR_NN_CTC_LOSS_INL_H_
+#define MXNET_OPERATOR_NN_CTC_LOSS_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include <algorithm>
+#include <string>
+#include "../mshadow_op.h"
+#include "./sequence_mask-inl.h"
+#include "../sequence_op_common.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+namespace ctc_loss {
+enum CTCLossOpInputs { kData, kLabel };
+enum CTCLossOpOutputs { kOut, kGrad };
+}
+
+template <typename T>
+inline void get_workspace_size(const std::vector<int> *label_lengths,
+ const std::vector<int> *data_lengths,
+ int alphabet_size, int minibatch, bool isGPU,
+ size_t *size_bytes) {
+ // This is the max of all S and T for all examples in the minibatch.
+ int maxL = *std::max_element(label_lengths->data(),
+ label_lengths->data() + minibatch);
+ int maxT = *std::max_element(data_lengths->data(),
+ data_lengths->data() + minibatch);
+
+ const int S = 2 * maxL + 1;
+
+ *size_bytes = 0;
+
+ if (isGPU) {
+ // GPU storage
+ // nll_forward, nll_backward
+ *size_bytes += 2 * sizeof(T) * minibatch;
+
+ // repeats
+ *size_bytes += sizeof(int) * minibatch;
+
+ // label offsets
+ *size_bytes += sizeof(int) * minibatch;
+
+ // utt_length
+ *size_bytes += sizeof(int) * minibatch;
+
+ // label lengths
+ *size_bytes += sizeof(int) * minibatch;
+
+ // labels without blanks - overallocate for now
+ *size_bytes += sizeof(int) * maxL * minibatch;
+
+ // labels with blanks
+ *size_bytes += sizeof(int) * S * minibatch;
+
+ // alphas
+ *size_bytes += sizeof(T) * S * maxT * minibatch;
+
+ // denoms
+ *size_bytes += sizeof(T) * maxT * minibatch;
+
+ // probs (since we will pass in activations)
+ *size_bytes += sizeof(T) * alphabet_size * maxT * minibatch;
+
+ } else {
+ // cpu can eventually replace all minibatch with
+ // max number of concurrent threads if memory is
+ // really tight
+
+ // per minibatch memory
+ size_t per_minibatch_bytes = 0;
+
+ // output
+ per_minibatch_bytes += sizeof(T) * alphabet_size;
+
+ // alphas
+ per_minibatch_bytes += sizeof(T) * S * maxT;
+
+ // betas
+ per_minibatch_bytes += sizeof(T) * S;
+
+ // labels w/blanks, e_inc, s_inc
+ per_minibatch_bytes += 3 * sizeof(int) * S;
+
+ *size_bytes = per_minibatch_bytes * minibatch;
+
+ // probs
+ *size_bytes += sizeof(T) * alphabet_size * maxT * minibatch;
+ }
+}
+
+// Takes a tensor of labels, and interprets 0-elements at the end of the vector
+// as padding. The tensor is packed into an std::vector without padding
+// characters. The label sequence lengths are also inferred from the padding chars.
+template <typename DType, typename xpu>
+inline void LabelTensorToPackedVector(mshadow::Tensor<xpu, 2, DType> labels,
+ int padding_mask,
+ std::vector<int> *packed_labels,
+ std::vector<int> *label_lengths) {
+ int batch = labels.size(0);
+ int max_num_labels = labels.size(1);
+
+ std::vector<int> cpu_labels(max_num_labels * batch);
+ mshadow::Tensor<xpu, 1, DType> flat_labels = labels.FlatTo1D();
+ IndexTensorToVector(flat_labels, &cpu_labels);
+
+ for (int b = 0; b < batch; ++b) {
+ auto start = cpu_labels.data() + b * max_num_labels;
+ auto res = std::find(start, start + max_num_labels, padding_mask);
+ int len = std::distance(start, res);
+ std::copy(start, start + len,
+ std::back_inserter(*packed_labels));
+ label_lengths->at(b) = len;
+ }
+}
+
+// Takes a tensor of labels, and a vector which specifies the actual length of each label
+// The tensor is packed into an std::vector without padding characters.
+// The label length vector is copied into an std::vector.
+template <typename DType, typename xpu>
+inline void PackLabelByLength(mshadow::Tensor<xpu, 2, DType> labels,
+ mshadow::Tensor<xpu, 1, DType> in_label_lengths,
+ std::vector<int> *packed_labels,
+ std::vector<int> *label_lengths) {
+ int batch = labels.size(0);
+ int max_num_labels = labels.size(1);
+
+ IndexTensorToVector(in_label_lengths, label_lengths);
+
+ std::vector<int> cpu_labels(max_num_labels * batch);
+ mshadow::Tensor<xpu, 1, DType> flat_labels = labels.FlatTo1D();
+ IndexTensorToVector(flat_labels, &cpu_labels);
+
+ for (int b = 0; b < batch; ++b) {
+ auto start = cpu_labels.data() + b * max_num_labels;
+ int len = label_lengths->at(b);
+ std::copy(start, start + len,
+ std::back_inserter(*packed_labels));
+ }
+}
+
+struct CTCLossOpParam : public dmlc::Parameter<CTCLossOpParam> {
+ bool use_data_lengths;
+ bool use_label_lengths;
+ int blank_label;
+ DMLC_DECLARE_PARAMETER(CTCLossOpParam) {
+ DMLC_DECLARE_FIELD(use_data_lengths).set_default(false)
+ .describe("Whether the data lenghts are decided by `data_lengths`. "
+ "If false, the lengths are equal to the max sequence length.");
+ DMLC_DECLARE_FIELD(use_label_lengths).set_default(false)
+ .describe("Whether the label lenghts are decided by "
+ "`label_lengths`, or derived from `padding_mask`. "
+ "If false, the lengths are derived from the "
+ "first occurrence of the value of `padding_mask`. "
+ "The value of `padding_mask` is ``0`` when first CTC label is reserved for blank, "
+ "and ``-1`` when last label is reserved for blank. See `blank_label`.");
+ DMLC_DECLARE_FIELD(blank_label)
+ .add_enum("first", 0)
+ .add_enum("last", 1)
+ .set_default(0)
+ .describe("Set the label that is reserved for blank label."
+ "If \"first\", 0-th label is reserved, and "
+ "label values for tokens in the vocabulary are "
+ "between ``1`` and ``alphabet_size-1``, and the padding mask is ``-1``. "
+ "If \"last\", last label value ``alphabet_size-1`` "
+ "is reserved for blank label instead, "
+ "and label values for tokens in the vocabulary are "
+ "between ``0`` and ``alphabet_size-2``, and the padding mask is ``0``.");
+ }
+};
+
+// By default, the inputs must include data array and label array
+// if use_data_lengths parameter is set, user should also supply
+// data_lengths array; if use_label_lengths parameter is set, user
+// should also specify label_lengths array.
+inline uint32_t CTCLossOpNumInputs(const NodeAttrs& attrs) {
+ const CTCLossOpParam& param = nnvm::get<CTCLossOpParam>(attrs.parsed);
+ return 2U + param.use_data_lengths + param.use_label_lengths;
+}
+
+inline bool CTCLossOpShape(const nnvm::NodeAttrs &attrs,
+ std::vector<TShape>* in_attrs,
+ std::vector<TShape>* out_attrs) {
+ const CTCLossOpParam& param = nnvm::get<CTCLossOpParam>(attrs.parsed);
+ CHECK_EQ(in_attrs->size(), CTCLossOpNumInputs(attrs));
+ CHECK_EQ(out_attrs->size(), 2U);
+
+ const TShape &dshape = (*in_attrs)[ctc_loss::kData];
+ const TShape &lshape = (*in_attrs)[ctc_loss::kLabel];
+ CHECK_EQ(dshape.ndim(), 3U) << "The number of dimensions of data array must be 3.";
+ CHECK_EQ(lshape.ndim(), 2U) << "The number of dimensions of labels array must be 2.";
+ CHECK_EQ(dshape[1], lshape[0])
+ << "The batch size for the labels and data arrays must be the same.";
+
+ if (param.use_data_lengths) {
+ int kInputLength = 2;
+ const TShape &dlshape = (*in_attrs)[kInputLength];
+ CHECK_EQ(dlshape.ndim(), 1U) << "Data length array must be a vector.";
+ CHECK_EQ(dlshape[0], dshape[1])
+ << "The batch size for the data and data lengths must be the same.";
+ }
+ if (param.use_label_lengths) {
+ int kLabelLength = 2 + param.use_data_lengths;
+ const TShape &llshape = (*in_attrs)[kLabelLength];
+ CHECK_EQ(llshape.ndim(), 1U) << "Label length array must be a vector.";
+ CHECK_EQ(llshape[0], lshape[0])
+ << "The batch size for the labels and label lengths must be the same.";
+ }
+ CHECK_GE(dshape[0], lshape[1]) << "The max number of labels cannot exceed "
+ "the maximum sequence length of the "
+ "data.";
+
+ TShape oshape(1);
+ oshape[0] = dshape[1]; // batch size
+ SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape); // forward output
+ SHAPE_ASSIGN_CHECK(*out_attrs, 1, dshape); // grad output
+ return true;
+}
+
+inline bool CTCLossOpType(const nnvm::NodeAttrs& attrs,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_GE(in_attrs->size(), 2U);
+ CHECK_EQ(out_attrs->size(), 2U);
+ int dtype = (*in_attrs)[ctc_loss::kData];
+ CHECK_NE(dtype, -1) << "Input data must have specified type";
+
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0)); // forward output
+ TYPE_ASSIGN_CHECK(*out_attrs, 1, in_attrs->at(0)); // grad output
+ return true;
+}
+
+inline bool CTCLossOpStorageType(const nnvm::NodeAttrs& attrs,
+ const int dev_mask,
+ DispatchMode* dispatch_mode,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_GE(in_attrs->size(), 2U);
+ CHECK_EQ(out_attrs->size(), 2U);
+ const int in_stype = in_attrs->at(0);
+ bool dispatched = false;
+ if (!dispatched && in_stype == kDefaultStorage) {
+ // dns -> dns
+ dispatched = storage_type_assign(out_attrs, kDefaultStorage,
+ dispatch_mode, DispatchMode::kFCompute);
+ }
+ if (!dispatched) {
+ dispatched = dispatch_fallback(out_attrs, dispatch_mode);
+ }
+ return dispatched;
+}
+
+
+inline std::vector<std::string> CTCLossOpListInputNames(const NodeAttrs& attrs) {
+ const CTCLossOpParam& param = nnvm::get<CTCLossOpParam>(attrs.parsed);
+ if (param.use_data_lengths && param.use_label_lengths) {
+ return {"data", "label", "data_lengths", "label_lengths"};
+ } else if (param.use_data_lengths) {
+ return {"data", "label", "data_lengths"};
+ } else if (param.use_label_lengths) {
+ return {"data", "label", "label_lengths"};
+ } else {
+ return {"data", "label"};
+ }
+}
+
+template<typename xpu>
+void CTCLossOpForward(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ using namespace mshadow;
+ using namespace mshadow::expr;
+
+ const CTCLossOpParam& param = nnvm::get<CTCLossOpParam>(attrs.parsed);
+ CHECK_EQ(inputs.size(), CTCLossOpNumInputs(attrs));
+ CHECK_EQ(outputs.size(), 2U);
+ CHECK_EQ(req.size(), 2U);
+
+ const TBlob& in_data = inputs[ctc_loss::kData];
+ const TBlob& in_label = inputs[ctc_loss::kLabel];
+ const TBlob& out_data = outputs[ctc_loss::kOut];
+ const TBlob& out_grad = outputs[ctc_loss::kGrad];
+
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+ MSHADOW_TYPE_SWITCH(inputs[ctc_loss::kLabel].type_flag_, DType, {
+ Tensor<xpu, 3, real_t> data = in_data.get<xpu, 3, real_t>(s);
+ Tensor<xpu, 2, DType> labels = in_label.get<xpu, 2, DType>(s);
+ Tensor<xpu, 1, real_t> costs = out_data.get<xpu, 1, real_t>(s);
+ Tensor<xpu, 3, real_t> grad = out_grad.get<xpu, 3, real_t>(s);
+
+ int max_seq_len = data.size(0);
+ int batch_size = data.size(1);
+ int alphabet_size = data.size(2);
+
+ // data_lengths
+ std::vector<int> data_lengths(batch_size, max_seq_len);
+ if (param.use_data_lengths) {
+ int kInputLength = 2;
+ IndexTensorToVector(inputs[kInputLength].get<xpu, 1, real_t>(s), &data_lengths);
+ }
+
+ // label_lengths
+ std::vector<int> packed_labels;
+ std::vector<int> label_lengths(batch_size);
+
+ if (param.use_label_lengths) {
+ int kLabelLength = 2 + param.use_data_lengths;
+ PackLabelByLength(labels, inputs[kLabelLength].get<xpu, 1, DType>(s),
+ &packed_labels, &label_lengths);
+ } else {
+ LabelTensorToPackedVector(labels, param.blank_label == 0 ? 0 : -1,
+ &packed_labels, &label_lengths);
+ }
+
+ size_t size_bytes;
+ get_workspace_size<real_t>(&label_lengths, &data_lengths, alphabet_size,
+ batch_size, data.kDevCPU ? false : true, &size_bytes);
+
+ // round-up so there are enough elems in memory
+ int num_tmp_elems = (size_bytes + sizeof(real_t) - 1) / sizeof(real_t);
+ Tensor<xpu, 1, real_t> workspace =
+ ctx.requested[0].get_space_typed<xpu, 1, real_t>(Shape1(num_tmp_elems), s);
+
+ compute_ctc_cost(data, costs.dptr_, grad.dptr_, packed_labels.data(),
+ label_lengths.data(), data_lengths.data(),
+ workspace.dptr_, req[ctc_loss::kGrad] != mxnet::kNullOp,
+ param.blank_label == 0 ? 0 : (alphabet_size - 1));
+
+ if (param.use_data_lengths) {
+ // baidu warp CTC implementation sometimes includes undefined gradients
+ // for data outside of length mask. Setting to 0 to make it consistent
+ // with CPU implementation.
+ int kInputLength = 2;
+ mxnet_op::SequenceMask(grad, inputs[kInputLength].get<xpu, 1, real_t>(s),
+ static_cast<real_t>(0));
+ }
+ });
+}
+
+template<typename xpu>
+void CTCLossOpBackward(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+ const TBlob& in_grad = outputs[0];
+ const TBlob& out_grad = inputs[0];
+ const TBlob& grad_computed = inputs[3]; // grad computed in the forward step
+
+ Tensor<xpu, 3, real_t> igrad_data = in_grad.get<xpu, 3, real_t>(s);
+ Tensor<xpu, 1, real_t> ograd_data = out_grad.get<xpu, 1, real_t>(s);
+ Tensor<xpu, 3, real_t> computed_grad_data = grad_computed.get<xpu, 3, real_t>(s);
+
+ Assign(igrad_data, req[0],
+ mshadow::expr::broadcast<1>(ograd_data, computed_grad_data.shape_) * computed_grad_data);
+}
+
+} // namespace op
+} // namespace mxnet
+
+#endif // MXNET_OPERATOR_NN_CTC_LOSS_INL_H_
+
diff --git a/src/operator/contrib/ctc_loss.cc b/src/operator/nn/ctc_loss.cc
similarity index 64%
rename from src/operator/contrib/ctc_loss.cc
rename to src/operator/nn/ctc_loss.cc
index 32e8e629f09..c381677b3ce 100644
--- a/src/operator/contrib/ctc_loss.cc
+++ b/src/operator/nn/ctc_loss.cc
@@ -18,26 +18,22 @@
*/
/*!
- * Copyright (c) 2015 by Contributors
* \file ctc_loss.cc
- * \brief
- * \author Sebastian Bodenstein
-*/
-
+ * \brief CPU Implementation of CTC Loss op
+ */
#include "./ctc_loss-inl.h"
-#include "./ctc_include/detail/cpu_ctc.h"
+#include "../../../3rdparty/ctc_include/detail/cpu_ctc.h"
namespace mshadow {
-
template <typename DType>
ctcStatus_t compute_ctc_cost(const Tensor<cpu, 3, DType> activations,
DType *costs, DType *grads, int *labels,
int *label_lengths, int *data_lengths,
- void *workspace, int train, int blank_label) {
+ void *workspace, bool isTraining, int blank_label) {
int minibatch = static_cast<int>(activations.size(1));
int alphabet_size = static_cast<int>(activations.size(2));
mxnet_warpctc::CpuCTC<DType> ctc(alphabet_size, minibatch, workspace, blank_label);
- if (train) {
+ if (isTraining) {
return ctc.cost_and_grad(activations.dptr_, grads, costs, labels,
label_lengths, data_lengths);
} else {
@@ -45,32 +41,18 @@ ctcStatus_t compute_ctc_cost(const Tensor<cpu, 3, DType> activations,
data_lengths);
}
}
-
} // namespace mshadow
namespace mxnet {
namespace op {
-template <>
-Operator *CreateOp<cpu>(CTCLossParam param, int dtype) {
- return new CTCLossOp<cpu>(param);
-}
-
-// DO_BIND_DISPATCH comes from operator_common.h
-Operator *CTCLossProp::CreateOperatorEx(Context ctx,
- std::vector<TShape> *in_shape,
- std::vector<int> *in_type) const {
- std::vector<TShape> out_shape, aux_shape;
- std::vector<int> out_type, aux_type;
- CHECK(InferType(in_type, &out_type, &aux_type));
- CHECK(InferShape(in_shape, &out_shape, &aux_shape));
- DO_BIND_DISPATCH(CreateOp, param_, (*in_type)[0]);
-}
-
-DMLC_REGISTER_PARAMETER(CTCLossParam);
-MXNET_REGISTER_OP_PROPERTY(_contrib_CTCLoss, CTCLossProp)
- .describe(R"code(Connectionist Temporal Classification Loss.
+DMLC_REGISTER_PARAMETER(CTCLossOpParam);
+NNVM_REGISTER_OP(CTCLoss)
+.add_alias("ctc_loss")
+.add_alias("_contrib_CTCLoss")
+.add_alias("_contrib_ctc_loss")
+.describe(R"code(Connectionist Temporal Classification Loss.
The shapes of the inputs and outputs:
- **data**: `(sequence_length, batch_size, alphabet_size)`
@@ -113,18 +95,41 @@ Sequence Data with Recurrent Neural Networks*, A. Graves *et al*. for more
information on the definition and the algorithm.
)code" ADD_FILELINE)
- .add_argument("data", "NDArray-or-Symbol", "Input data to the ctc_loss op.")
- .add_argument("label", "NDArray-or-Symbol",
- "Ground-truth labels for the loss.")
- .add_argument("data_lengths", "NDArray-or-Symbol",
- "Lengths of data for each of the samples. Only required "
- "when use_data_lengths is true.")
- .add_argument("label_lengths", "NDArray-or-Symbol",
- "Lengths of labels for each of the samples. Only required "
- "when use_label_lengths is true.")
- .add_arguments(CTCLossParam::__FIELDS__());
-
-NNVM_REGISTER_OP(_contrib_CTCLoss).add_alias("_contrib_ctc_loss");
+.set_attr_parser(ParamParser<CTCLossOpParam>)
+.set_num_inputs(CTCLossOpNumInputs)
+.set_num_outputs(2)
+.set_attr<nnvm::FListInputNames>("FListInputNames", CTCLossOpListInputNames)
+.set_attr<nnvm::FListOutputNames>("FListOutputNAmes",
+ [](const NodeAttrs& attrs) {
+ return std::vector<std::string>{"out", "grad"};
+ })
+.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs",
+ [](const NodeAttrs& attrs) {
+ return 1;
+ })
+.set_attr<nnvm::FInferShape>("FInferShape", CTCLossOpShape)
+.set_attr<nnvm::FInferType>("FInferType", CTCLossOpType)
+.set_attr<FInferStorageType>("FInferStorageType", CTCLossOpStorageType)
+.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
+ { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
+.set_attr<FCompute>("FCompute<cpu>", CTCLossOpForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_ctc_loss"})
+.add_argument("data", "NDArray-or-Symbol", "Input ndarray")
+.add_argument("label", "NDArray-or-Symbol", "Ground-truth labels for the loss.")
+.add_argument("data_lengths", "NDArray-or-Symbol",
+ "Lengths of data for each of the samples. Only required "
+ "when use_data_lengths is true.")
+.add_argument("label_lengths", "NDArray-or-Symbol",
+ "Lengths of labels for each of the samples. Only required "
+ "when use_label_lengths is true.")
+.add_arguments(CTCLossOpParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_ctc_loss)
+.set_attr_parser(ParamParser<CTCLossOpParam>)
+.set_num_inputs(1)
+.set_num_outputs(CTCLossOpNumInputs)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FCompute>("FCompute<cpu>", CTCLossOpBackward<cpu>);
} // namespace op
} // namespace mxnet
diff --git a/src/operator/contrib/ctc_loss.cu b/src/operator/nn/ctc_loss.cu
similarity index 81%
rename from src/operator/contrib/ctc_loss.cu
rename to src/operator/nn/ctc_loss.cu
index 3f5f12ca439..a4491bf6986 100644
--- a/src/operator/contrib/ctc_loss.cu
+++ b/src/operator/nn/ctc_loss.cu
@@ -18,14 +18,13 @@
*/
/*!
- * Copyright (c) 2015 by Contributors
+ * Copyright (c) 2018 by Contributors
* \file ctc_loss.cu
- * \brief
- * \author Sebastian Bodenstein
-*/
-#include <algorithm>
+ * \brief GPU Implementation of ctc_loss op
+ */
+
#include "./ctc_loss-inl.h"
-#include "./ctc_include/detail/gpu_ctc.h"
+#include "../../../3rdparty/ctc_include/detail/gpu_ctc.h"
namespace mshadow {
@@ -45,17 +44,19 @@ ctcStatus_t compute_ctc_cost(const Tensor<gpu, 3, DType> activations,
return ctc.score_forward(activations.dptr_, costs, labels,
label_lengths, input_lengths);
}
-
} // namespace mshadow
-////////////////////////////////////////////////////////////////////////////////
-
namespace mxnet {
namespace op {
-template <>
-Operator *CreateOp<gpu>(CTCLossParam param, int dtype) {
- return new CTCLossOp<gpu>(param);
-}
+
+NNVM_REGISTER_OP(CTCLoss)
+.add_alias("ctc_loss")
+.add_alias("_contrib_ctc_loss")
+.add_alias("_contrib_CTCLoss")
+.set_attr<FCompute>("FCompute<gpu>", CTCLossOpForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_ctc_loss)
+.set_attr<FCompute>("FCompute<gpu>", CTCLossOpBackward<gpu>);
} // namespace op
} // namespace mxnet
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index b17562c1d94..5332517fa68 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -4491,11 +4491,34 @@ def test_pick_helper(index_type=np.int32):
def check_ctc_loss(acts, labels, loss_truth):
in_var = mx.sym.Variable('input')
labels_var = mx.sym.Variable('labels')
- ctc = mx.sym.contrib.ctc_loss(in_var, labels_var)
+ ctc = mx.sym.ctc_loss(in_var, labels_var)
acts_nd = mx.nd.array(acts, ctx=default_context())
labels_nd = mx.nd.array(labels, ctx=default_context())
exe = ctc.bind(ctx=default_context(), args=[acts_nd, labels_nd])
+ # test forward with grad calc
+ exe.forward(is_train=True)
+ outTest = exe.outputs[0]
# test forward without grad calc
+ exe.forward(is_train=False)
+ outTrain = exe.outputs[0]
+ # make sure losses calculated with both modes are the same
+ assert_almost_equal(outTest.asnumpy(), outTrain.asnumpy())
+
+ # test against ground truth, if available
+ if loss_truth is not None:
+ assert_almost_equal(outTest.asnumpy(), loss_truth)
+ # test grad
+ check_numeric_gradient(ctc, [acts, labels], grad_nodes=['input'], rtol=0.05, atol=1e-3)
+
+# check contrib operator for backward compatibility
+def check_contrib_ctc_loss(acts, labels, loss_truth):
+ in_var = mx.sym.Variable('input')
+ labels_var = mx.sym.Variable('labels')
+ ctc = mx.sym.contrib.ctc_loss(in_var, labels_var)
+ acts_nd = mx.nd.array(acts, ctx=default_context())
+ labels_nd = mx.nd.array(labels, ctx=default_context())
+ exe = ctc.bind(ctx=default_context(), args=[acts_nd, labels_nd])
+ # test forward with grad calc
exe.forward(is_train=True)
outTest = exe.outputs[0]
# test forward without grad calc
@@ -4503,6 +4526,7 @@ def check_ctc_loss(acts, labels, loss_truth):
outTrain = exe.outputs[0]
# make sure losses calculated with both modes are the same
assert_almost_equal(outTest.asnumpy(), outTrain.asnumpy())
+
# test against ground truth, if available
if loss_truth is not None:
assert_almost_equal(outTest.asnumpy(), loss_truth)
@@ -4520,6 +4544,8 @@ def test_ctc_loss():
labels = np.array([[2, 3, 0], [2, 3, 0]])
true_loss = np.array([4.04789, 4.04789], dtype=np.float32) # from Torch
check_ctc_loss(acts, labels, true_loss)
+ check_contrib_ctc_loss(acts, labels, true_loss)
+
# Test 2:
acts2 = np.array([
[[-5, -4, -3, -2, -1], [1.2, 3.4, 1.2, -0.1, -2.34]],
@@ -4528,11 +4554,13 @@ def test_ctc_loss():
labels2 = np.array([[2, 3, 1], [2, 0, 0]], dtype=np.float32)
true_loss = np.array([7.3557, 5.4091], dtype=np.float32) # from Torch
check_ctc_loss(acts2, labels2, true_loss)
+ check_contrib_ctc_loss(acts2, labels2, true_loss)
# Test 3: check use integer type as label
labels3 = np.array([[2, 3, 1], [2, 0, 0]], dtype=np.int32)
true_loss = np.array([7.3557, 5.4091], dtype=np.float32) # from Torch
check_ctc_loss(acts2, labels3, true_loss)
+ check_contrib_ctc_loss(acts2, labels3, true_loss)
@with_seed()
def test_ctc_loss_with_large_classes():
@@ -4550,7 +4578,7 @@ def test_ctc_loss_with_large_classes():
[1000, 2000, 3000, 4000, 0, 5000, 0, 0]], dtype=np.int32)
nd_data = mx.nd.array(data)
nd_label = mx.nd.array(label)
- loss = mx.nd.contrib.ctc_loss(data=nd_data, label=nd_label)
+ loss = mx.nd.ctc_loss(data=nd_data, label=nd_label)
expected_loss = np.array([688.02826, 145.34462])
assert_almost_equal(loss.asnumpy(), expected_loss)
@@ -4619,6 +4647,85 @@ def check_ctc_loss_grad(blank_label): # from tf
label_lens = np.array([5, 4], dtype=np.int32)
loss_truth = np.array([-loss_log_prob_0, -loss_log_prob_1], np.float32)
+ with default_context():
+ data = mx.nd.array(inputs)
+ label = mx.nd.array(labels)
+ data.attach_grad()
+ with mx.autograd.record():
+ l = mx.ndarray.CTCLoss(data, label,
+ use_data_lengths=True,
+ use_label_lengths=True,
+ data_lengths=mx.nd.array(seq_lens),
+ label_lengths=mx.nd.array(label_lens),
+ blank_label=blank_label)
+ l.backward()
+ assert_almost_equal(l.asnumpy(), loss_truth, atol=1e-5, rtol=1e-5)
+ assert_almost_equal(data.grad.asnumpy(), grad_truth, atol=1e-5, rtol=1e-5)
+
+ # check contrib operator for backward compatibility
+ def check_contrib_ctc_loss_grad(blank_label): # from tf
+ vocab_size = 5
+ max_label_len = 5
+ padding_mask = -1+ (blank_label=='first')
+
+ targets_0 = [0, 1, 2, 1, 0]
+ loss_log_prob_0 = -3.34211
+ input_prob_matrix_0 = np.asarray(
+ [[0.633766, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553],
+ [0.111121, 0.588392, 0.278779, 0.0055756, 0.00569609, 0.010436],
+ [0.0357786, 0.633813, 0.321418, 0.00249248, 0.00272882, 0.0037688],
+ [0.0663296, 0.643849, 0.280111, 0.00283995, 0.0035545, 0.00331533],
+ [0.458235, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107]],
+ dtype=np.float32)
+ gradient_log_prob_0 = np.asarray(
+ [[-0.366234, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553],
+ [0.111121, -0.411608, 0.278779, 0.0055756, 0.00569609, 0.010436],
+ [0.0357786, 0.633813, -0.678582, 0.00249248, 0.00272882, 0.0037688],
+ [0.0663296, -0.356151, 0.280111, 0.00283995, 0.0035545, 0.00331533],
+ [-0.541765, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107]],
+ dtype=np.float32)
+
+ targets_1 = [0, 1, 1, 0]
+ loss_log_prob_1 = -5.42262
+ input_prob_matrix_1 = np.asarray(
+ [[0.30176, 0.28562, 0.0831517, 0.0862751, 0.0816851, 0.161508],
+ [0.24082, 0.397533, 0.0557226, 0.0546814, 0.0557528, 0.19549],
+ [0.230246, 0.450868, 0.0389607, 0.038309, 0.0391602, 0.202456],
+ [0.280884, 0.429522, 0.0326593, 0.0339046, 0.0326856, 0.190345],
+ [0.423286, 0.315517, 0.0338439, 0.0393744, 0.0339315, 0.154046]],
+ dtype=np.float32)
+ gradient_log_prob_1 = np.asarray(
+ [[-0.69824, 0.28562, 0.0831517, 0.0862751, 0.0816851, 0.161508],
+ [0.24082, -0.602467, 0.0557226, 0.0546814, 0.0557528, 0.19549],
+ [0.230246, 0.450868, 0.0389607, 0.038309, 0.0391602, -0.797544],
+ [0.280884, -0.570478, 0.0326593, 0.0339046, 0.0326856, 0.190345],
+ [-0.576714, 0.315517, 0.0338439, 0.0393744, 0.0339315, 0.154046]],
+ dtype=np.float32)
+
+ inputs = [
+ np.vstack(
+ [input_prob_matrix_0[t, :], input_prob_matrix_1[t, :]])
+ for t in range(5)
+ ] + 2 * [np.nan * np.ones((2, vocab_size+1), np.float32)]
+ inputs = np.log(np.asarray(inputs, dtype=np.float32))
+
+ grad_truth = np.array([
+ np.vstack(
+ [gradient_log_prob_0[t, :], gradient_log_prob_1[t, :]])
+ for t in range(5)
+ ] + 2 * [np.zeros((2, vocab_size+1), np.float32)])
+
+ if blank_label == 'first':
+ inputs = np.roll(inputs, 1, axis=2)
+ grad_truth = np.roll(grad_truth, 1, axis=2)
+
+ labels = (np.asarray([x + [padding_mask]*(max_label_len-len(x))
+ for x in [targets_0, targets_1]])+(blank_label == 'first'))
+
+ seq_lens = np.array([5, 5], dtype=np.int32)
+ label_lens = np.array([5, 4], dtype=np.int32)
+ loss_truth = np.array([-loss_log_prob_0, -loss_log_prob_1], np.float32)
+
with default_context():
data = mx.nd.array(inputs)
label = mx.nd.array(labels)
@@ -4634,8 +4741,11 @@ def check_ctc_loss_grad(blank_label): # from tf
assert_almost_equal(l.asnumpy(), loss_truth, atol=1e-5, rtol=1e-5)
assert_almost_equal(data.grad.asnumpy(), grad_truth, atol=1e-5, rtol=1e-5)
+
check_ctc_loss_grad('first')
check_ctc_loss_grad('last')
+ check_contrib_ctc_loss_grad('first')
+ check_contrib_ctc_loss_grad('last')
@with_seed()
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