init seqconv eltadd relu op

paddle/fluid/operators/CMakeLists.txt

@@ -86,7 +86,7 @@ function(op_library TARGET)
     # remove windows unsupported op, because windows has no nccl, no warpctc such ops.
     foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op" "hierarchical_sigmoid_op"
      "crf_decoding_op" "select_op" "lstmp_op" "gru_op" "fusion_gru_op" "lstm_op" "fusion_lstm_op" "cumsum_op"
-     "channel_send_op" "channel_create_op" "channel_close_op" "channel_recv_op")
+      "fusion_seqconv_eltadd_relu_op" "channel_send_op" "channel_create_op" "channel_close_op" "channel_recv_op")
         if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
           return()
         endif()

paddle/fluid/operators/fusion_seqconv_eltadd_relu_op.cc

+/* Copyright (c) 2018 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/operators/fusion_seqconv_eltadd_relu_op.h"
+#include <algorithm>  // for min, max
+#include <string>
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/fc_compute.h"
+
+namespace paddle {
+namespace operators {
+
+void FusionSeqConvEltAddReluOp::InferShape(
+    framework::InferShapeContext* ctx) const {
+  PADDLE_ENFORCE(ctx->HasInput("X"),
+                 "Input(X) of FusionSeqConvEltAddReluOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasInput("Filter"),
+      "Input(Filter) of FusionSeqConvEltAddReluOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasInput("Bias"),
+      "Input(Bias) of FusionSeqConvEltAddReluOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasOutput("Out"),
+      "Output(Out) of FusionSeqConvEltAddReluOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasOutput("ColMat"),
+      "Output(ColMat) of FusionSeqConvEltAddReluOp should not be null.");
+
+  auto x_dims = ctx->GetInputDim("X");
+  auto w_dims = ctx->GetInputDim("Filter");
+  PADDLE_ENFORCE(
+      ctx->Attrs().Get<int>("contextStride") == 1,
+      "Currently, FusionSeqConvEltAddReluOp only supports contextStride=1.");
+  PADDLE_ENFORCE(x_dims.size() == 2 && w_dims.size() == 2,
+                 "Input(X, Filter) should be 2-D tensor.");
+  PADDLE_ENFORCE(x_dims.size() == 2 && w_dims.size() == 2,
+                 "Input(X, Filter) should be 2-D tensor.");
+  PADDLE_ENFORCE(
+      w_dims[0] == ctx->Attrs().Get<int>("contextLength") * x_dims[1],
+      "Filter's height should be context_length * "
+      "input_hidden_size .");
+
+  ctx->SetOutputDim("Out", {x_dims[0], w_dims[1]});
+  ctx->SetOutputDim("ColMat", {x_dims[0], w_dims[0]});
+  ctx->ShareLoD("X", "Out");
+}
+
+framework::OpKernelType FusionSeqConvEltAddReluOp::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
+      ctx.device_context());
+}
+
+void FusionSeqConvEltAddReluOpMaker::Make() {
+  AddInput("X",
+           "(LoDTensor) the input is a LodTensor, which support "
+           "variable-time length input sequence. The underlying tensor in "
+           "this LoDTensor is a matrix with shape (T X M), where T is the "
+           "total time steps in this mini-batch, M is the dim size of x.");
+  // PaddingData only support false yet, should be ensured at pass.
+  AddInput("Filter",
+           "(Tensor) same as the input(Filter) of sequence conv op is an "
+           "learnable parameter."
+           "This is a tensor with shape (K, N), where K is the "
+           "context_length * dim size of x, N is the output feature size.");
+  AddInput("Bias",
+           "(Tensor) the learnable weights. shape (1, N), where N is the "
+           "output feature size");
+  AddOutput(
+      "Out",
+      "(LoDTensor) the output(Out) is a LodTensor, which support "
+      "variable-time length output sequence. The underlying tensor in "
+      "this LoDTensor is a matrix with shape (T, N), where, T is the "
+      "total time steps in this mini-batch, N is the output feature size.");
+  AddOutput("ColMat",
+            "(Tensor) (T, K), where T is where T is the "
+            "total time steps in this mini-batch, K is height of Filter")
+      .AsIntermediate();
+  AddAttr<int>("contextLength",
+               "(int) the contextLength of FusionSeqConvEltAddReluOp is the "
+               "height of the convolution kernel.")
+      .GreaterThan(0);
+  AddAttr<int>("contextStart",
+               "(int, default:0) the contextStart of FusionSeqConvEltAddReluOp "
+               "represents the beginning of the convolution of the number of "
+               "rows of sequence, which can be negative. The negative number "
+               "means to pad contextStart time-steps of zeros or learnable "
+               "parameters at the beginning of each instance. The positive "
+               "number means to skip contextStart time-steps of each "
+               "instance.")
+      .SetDefault(0);
+  AddAttr<int>(
+      "contextStride",
+      "(int, default:1) the contextStride of FusionSeqConvEltAddReluOp "
+      "represents the stride length of convolution kernel. "
+      "Currently, FusionSeqConvEltAddReluOp only supports"
+      "contextStride=1.")
+      .SetDefault(1)
+      .GreaterThan(0);
+  AddComment(R"DOC(
+Fusion Sequence Conv and ElementwiseAdd Operator.
+)DOC");
+}
+
+template <typename T>
+class FusionSeqConvEltAddReluKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    using DeviceContext = paddle::platform::CPUDeviceContext;
+    auto* x = ctx.Input<LoDTensor>("X");
+    auto* w = ctx.Input<Tensor>("Filter");
+    auto* b = ctx.Input<Tensor>("Bias");
+    auto* y = ctx.Output<LoDTensor>("Out");
+    auto* col = ctx.Output<Tensor>("ColMat");
+
+    auto x_lod = x->lod();
+    auto x_dims = x->dims();
+    auto w_dims = w->dims();
+    PADDLE_ENFORCE_EQ(b->numel(), w_dims[1],
+                      "bias size should be equal to output feature size.");
+    PADDLE_ENFORCE_EQ(x_lod.size(), 1UL,
+                      "Only support one level sequence now.");
+
+    const T* x_data = x->data<T>();
+    const T* w_data = w->data<T>();
+    const T* b_data = b->data<T>();
+    T* y_data = y->mutable_data<T>(ctx.GetPlace());
+    T* col_data = col->mutable_data<T>(ctx.GetPlace());
+
+    int context_start = ctx.Attr<int>("contextStart");
+    int context_length = ctx.Attr<int>("contextLength");
+    int up_pad = std::max(0, -context_start);
+    int down_pad = std::max(0, context_start + context_length - 1);
+    // im2col
+    int src_mat_w = static_cast<int>(x_dims[1]);
+    int src_mat_w_sz = src_mat_w * sizeof(T);
+    int col_mat_w = static_cast<int>(w_dims[0]);
+    int col_mat_w_sz = col_mat_w * sizeof(T);
+    for (int i = 0; i < static_cast<int>(x_lod[0].size()) - 1; ++i) {
+      int st = x_lod[0][i];
+      int ed = x_lod[0][i + 1];
+      const T* src_data = x_data + st * src_mat_w;
+      T* dst_data = col_data + st * col_mat_w;
+      int seq_len = ed - st;
+      if (seq_len > up_pad + down_pad) {
+        // zero all up_pad
+        std::memset(dst_data, 0, up_pad * col_mat_w_sz);
+        // fill up_pad data
+        dst_data = dst_data + up_pad * src_mat_w;
+        int copy_size = col_mat_w_sz - up_pad * src_mat_w_sz;
+        for (int j = 0; j < up_pad; ++j) {
+          // blas.VCOPY?
+          std::memcpy(dst_data, src_data, copy_size);
+          dst_data += (col_mat_w - src_mat_w);
+          copy_size += src_mat_w_sz;
+        }
+        // fill data
+        for (int j = 0; j < seq_len - up_pad - down_pad; ++j) {
+          std::memcpy(dst_data, src_data, copy_size);
+          dst_data += col_mat_w;
+          src_data += src_mat_w;
+        }
+        // zero all down_pad
+        std::memset(dst_data, 0, down_pad * col_mat_w_sz);
+        // fill down_pad data
+        copy_size -= src_mat_w_sz;
+        for (int j = 0; j < down_pad; ++j) {
+          std::memcpy(dst_data, src_data, copy_size);
+          dst_data += col_mat_w;
+          src_data += src_mat_w;
+          copy_size -= src_mat_w_sz;
+        }
+      } else {
+        PADDLE_ENFORCE_GE(context_length, up_pad + down_pad + 1);
+        std::memset(dst_data, 0, seq_len * col_mat_w_sz);
+        int zero_sz = up_pad * src_mat_w_sz;
+        int seq_len_size = seq_len * src_mat_w_sz;
+        for (int j = 0; j < std::min(up_pad, seq_len); ++j) {
+          int copy_size = std::min(seq_len_size, col_mat_w_sz - zero_sz);
+          std::memcpy(dst_data + zero_sz / sizeof(T), src_data, copy_size);
+          dst_data += col_mat_w;
+          zero_sz -= src_mat_w_sz;
+        }
+        zero_sz = down_pad * src_mat_w_sz;
+        dst_data = col_data + (ed - 1) * col_mat_w;
+        src_data = x_data + (ed - up_pad - 1) * src_mat_w;
+        for (int j = 0; j < std::min(0, seq_len - up_pad); ++j) {
+          int copy_size = std::min(seq_len_size, col_mat_w_sz - zero_sz);
+          std::memcpy(dst_data, src_data, copy_size);
+          dst_data -= col_mat_w;
+          src_data += src_mat_w;
+          zero_sz -= src_mat_w_sz;
+        }
+      }
+    }
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
+    math::FCCompute<DeviceContext, T>(blas, x_dims[0], w_dims[1], w_dims[0],
+                                      col_data, w_data, y_data, b_data, true);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(fusion_seqconv_eltadd_relu, ops::FusionSeqConvEltAddReluOp,
+                  ops::FusionSeqConvEltAddReluOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+
+REGISTER_OP_CPU_KERNEL(fusion_seqconv_eltadd_relu,
+                       ops::FusionSeqConvEltAddReluKernel<float>,
+                       ops::FusionSeqConvEltAddReluKernel<double>);

paddle/fluid/operators/fusion_seqconv_eltadd_relu_op.h

+/* Copyright (c) 2018 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. */
+
+#pragma once
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+using Tensor = framework::Tensor;
+
+class FusionSeqConvEltAddReluOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override;
+};
+
+class FusionSeqConvEltAddReluOpMaker
+    : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override;
+};
+
+}  // namespace operators
+}  // namespace paddle