Merge pull request #12990 from tensor-tang/feature/op/fusion_expand_concat_fc

Feature fusion expand concat fc

paddle/fluid/operators/fusion_seqexpand_concat_fc_op.cc

+/* 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/operators/fusion_seqexpand_concat_fc_op.h"
+#include <string>
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/cpu_vec.h"
+#include "paddle/fluid/operators/math/fc_compute.h"
+#include "paddle/fluid/platform/cpu_info.h"
+
+namespace paddle {
+namespace operators {
+
+void FusionSeqExpandConcatFCOp::InferShape(
+    framework::InferShapeContext* ctx) const {
+  PADDLE_ENFORCE_GT(
+      ctx->Inputs("X").size(), 1UL,
+      "Inputs(X) of FusionSeqExpandConcatFCOp should larger than 1.");
+  PADDLE_ENFORCE(
+      ctx->HasInput("FCWeight"),
+      "Input(FCWeight) of FusionSeqExpandConcatFCOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasOutput("Out"),
+      "Output(Out) of FusionSeqExpandConcatFCOp should not be null.");
+  PADDLE_ENFORCE(
+      ctx->HasOutput("FCOut"),
+      "Output(FCOut) of FusionSeqExpandConcatFCOp should not be null.");
+
+  auto ins_dims = ctx->GetInputsDim("X");
+  auto w_dims = ctx->GetInputDim("FCWeight");  // (M0+M1+M2+..) x D
+  PADDLE_ENFORCE_EQ(w_dims.size(), 2UL, "Input(FCWeight)'s rank must be 2.");
+  const int D = w_dims[1];
+  int sum = ins_dims[0][1];
+  for (size_t i = 1; i < ins_dims.size(); ++i) {
+    sum += ins_dims[i][1];
+  }
+  PADDLE_ENFORCE_EQ(sum, w_dims[0],
+                    "FC height should be sum of all inputs width.");
+  if (ctx->HasInput("FCBias")) {
+    auto b_dims = ctx->GetInputDim("FCBias");
+    PADDLE_ENFORCE_EQ(b_dims.size(), 2, "Input(FCBias)'s rank must be 2.");
+    PADDLE_ENFORCE_EQ(b_dims[0], 1, "FCBias shapes must be 1 * %d.", D);
+    PADDLE_ENFORCE_EQ(b_dims[1], D, "FCBias shapes must be 1 * %d.", D);
+  }
+
+  ctx->SetOutputDim("Out", {ins_dims[0][0], D});
+  // fcout should be reshape when run since can not get lod in infershape
+  // explicit share the ref lod
+  ctx->ShareLoD("X", "Out", 0);
+}
+
+framework::OpKernelType FusionSeqExpandConcatFCOp::GetExpectedKernelType(
+    const framework::ExecutionContext& ctx) const {
+  return framework::OpKernelType(
+      framework::ToDataType(ctx.MultiInput<LoDTensor>("X")[0]->type()),
+      ctx.device_context());
+}
+
+void FusionSeqExpandConcatFCOpMaker::Make() {
+  AddInput("X",
+           "(LoDTensor) input LodDTensors, the first one must be have ref lod "
+           "for sequence expand, and the rest input should have same lod.")
+      .AsDuplicable();
+  AddInput("FCWeight", "(Tensor) the weights of fc.");
+  AddInput("FCBias", "(Tensor, optional) the bias of fc.").AsDispensable();
+  AddOutput("Out", "(LoDTensor) Output LodTensor.");
+  AddOutput(
+      "FCOut",
+      "(Tensor) the intermediate tensor to keep the result of fc."
+      "Shape is (N x D), where N is the batch size, D is the output dim of fc")
+      .AsIntermediate();
+  AddAttr<std::string>("fc_activation",
+                       "(string, default: identity)"
+                       "The activation for the result of fc."
+                       "`identity` by default.")
+      .SetDefault("identity")
+      .InEnum({"sigmoid", "tanh", "relu", "identity"});
+  AddComment(R"DOC(
+Fusion Sequence expand + concat + fc Operator.
+
+All below conditions should be meet:
+
+The ref_level of seq_expand should be 0.
+
+The ref lod of seq_expand level is the first input of concat.
+
+The other inputs should have same lod and same batch size of ref lod.
+
+The seq len of other inputs should be 1.
+
+The concat axis should be 1.
+
+)DOC");
+}
+
+template <typename T>
+class FusionSeqExpandConcatFCOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    using DeviceContext = paddle::platform::CPUDeviceContext;
+    auto ins = ctx.MultiInput<LoDTensor>("X");
+    auto* w = ctx.Input<Tensor>("FCWeight");
+    auto* b = ctx.Input<Tensor>("FCBias");
+    auto* out = ctx.Output<LoDTensor>("Out");
+    auto* fc_out = ctx.Output<Tensor>("FCOut");
+
+    auto* ref_in = ins[0];
+    auto ref_lod = ref_in->lod();
+    auto in1_lod = ins[1]->lod();
+    auto ref_dims = ref_in->dims();  // T x M0
+    auto in1_dims = ins[1]->dims();  // N x M1
+    auto w_dims = w->dims();
+    const int N = ref_lod[0].size() - 1;
+    const int total_T = ref_dims[0];
+    const int M0 = ref_dims[1];
+    const int M1 = in1_dims[1];
+    const int D = w_dims[1];
+
+    // some check and fcout should be reshape here
+    // since infershape can not get lod info
+    PADDLE_ENFORCE_EQ(ref_lod.size(), 1UL, "Only support input lod size is 1.");
+    PADDLE_ENFORCE_EQ(in1_lod.size(), 1UL, "Only support input lod size is 1.");
+    PADDLE_ENFORCE_EQ(in1_lod[0].size() - 1, N,
+                      "Batch size of all inputs should be equal.");
+    PADDLE_ENFORCE_EQ(in1_lod[0][N], N,
+                      "Seq_length of other inputs should be 1.");
+    PADDLE_ENFORCE_EQ(in1_dims[0], N, "input height should be batch size.");
+    for (size_t i = 2; i < ins.size(); ++i) {
+      PADDLE_ENFORCE_EQ(ins[i]->dims()[0], N,
+                        "All other inputs height should be equal");
+      PADDLE_ENFORCE_EQ(ins[i]->lod(), in1_lod,
+                        "All other inputs should have same lod");
+    }
+    fc_out->Resize({N, D});
+
+    std::function<void(const int, const T*, T*)> fc_act;
+    auto& fc_act_str = ctx.Attr<std::string>("fc_activation");
+    if (platform::jit::MayIUse(platform::jit::avx)) {
+      math::VecActivations<T, platform::jit::avx> act_functor;
+      fc_act = act_functor(fc_act_str);
+    } else {
+      math::VecActivations<T, platform::jit::isa_any> act_functor;
+      fc_act = act_functor(fc_act_str);
+    }
+
+    const T* ref_in_data = ref_in->data<T>();
+    const T* in1_data = ins[1]->data<T>();
+    const T* w_data = w->data<T>();
+    T* out_data = out->mutable_data<T>(ctx.GetPlace());
+    T* fc_out_data = fc_out->mutable_data<T>(ctx.GetPlace());
+
+    auto blas = math::GetBlas<DeviceContext, T>(ctx);
+    math::FCCompute<DeviceContext, T>(blas, total_T, D, M0, ref_in_data, w_data,
+                                      out_data, b ? b->data<T>() : NULL);
+    w_data = w_data + M0 * D;
+    // first write on
+    blas.MatMul(N, D, M1, in1_data, w_data, fc_out_data);
+    w_data = w_data + M1 * D;
+    for (size_t i = 2; i < ins.size(); ++i) {
+      // add on
+      const T* in_data = ins[i]->data<T>();
+      const int K = ins[i]->dims()[1];
+      blas.GEMM(CblasNoTrans, CblasNoTrans, N, D, K, static_cast<T>(1), in_data,
+                K, w_data, D, static_cast<T>(1), fc_out_data, D);
+      w_data = w_data + K * D;
+    }
+    T* cur_out_data = out_data;
+    for (int i = 0; i < N; ++i) {
+      int seq_len = ref_lod[0][i + 1] - ref_lod[0][i];
+      T* src = fc_out_data + i * D;
+      for (int step = 0; step < seq_len; ++step) {
+        blas.VADD(D, cur_out_data, src, cur_out_data);
+        cur_out_data = cur_out_data + D;
+      }
+    }
+    fc_act(total_T * D, out_data, out_data);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(fusion_seqexpand_concat_fc, ops::FusionSeqExpandConcatFCOp,
+                  ops::FusionSeqExpandConcatFCOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+
+REGISTER_OP_CPU_KERNEL(fusion_seqexpand_concat_fc,
+                       ops::FusionSeqExpandConcatFCOpKernel<float>,
+                       ops::FusionSeqExpandConcatFCOpKernel<double>);

paddle/fluid/operators/fusion_seqexpand_concat_fc_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 FusionSeqExpandConcatFCOp : 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 FusionSeqExpandConcatFCOpMaker
+    : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override;
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/tests/unittests/test_fusion_seqexpand_concat_fc_op.py

+#   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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+from test_fusion_lstm_op import fc, ACTIVATION
+
+
+def fusion_seqexpand_concat_fc(xs, lod, w, b, fc_act):
+
+    T = sum(lod[0])
+    N = len(lod[0])
+    num_inputs = len(xs)
+    D = w.shape[1]
+
+    expanded_inputs = [xs[0]]
+    for i in range(num_inputs - 1):
+        x = xs[i + 1]
+        assert x.shape[0] == N
+        expanded = np.repeat(x, lod[0], axis=0)
+        assert expanded.shape[0] == T
+        assert expanded.shape[1] == x.shape[1]
+        expanded_inputs.append(expanded)
+
+    fc_input = np.concatenate(expanded_inputs, axis=1)
+    assert fc_input.shape[0] == T
+    assert fc_input.shape[1] == w.shape[0]
+    fc_out = fc(fc_input, w, b)
+    fc_out = fc_act(fc_out)
+    assert fc_out.shape[0] == T
+    assert fc_out.shape[1] == D
+    return fc_out
+
+
+class TestFusionSeqExpandConcatFCOp(OpTest):
+    def set_conf(self):
+        pass
+
+    def setUp(self):
+        self.op_type = 'fusion_seqexpand_concat_fc'
+        self.lod = [[3, 5, 8, 2]]
+        self.inputs_M = [15, 10, 10]
+        self.D = 20
+        self.with_bias = True
+        self.fc_act = 'relu'
+        self.set_conf()
+
+        T = sum(self.lod[0])
+        bs = len(self.lod[0])
+        num_inputs = len(self.inputs_M)
+
+        x0 = np.random.normal(size=(T, self.inputs_M[0])).astype('float32')
+        xs = [x0]
+        for i in range(num_inputs - 1):
+            xi = np.random.normal(size=(bs,
+                                        self.inputs_M[i + 1])).astype('float32')
+            xs.append(xi)
+
+        # fc weight and bias
+        w = np.random.normal(size=(sum(self.inputs_M),
+                                   self.D)).astype('float32')
+        b = np.random.normal(size=(
+            1, self.D)).astype('float32') if self.with_bias else np.zeros(
+                (1, self.D)).astype('float32')
+
+        out = fusion_seqexpand_concat_fc(xs, self.lod, w, b,
+                                         ACTIVATION[self.fc_act])
+
+        self.inputs = {'X': [('x0', (x0, self.lod))], 'FCWeight': w}
+        normal_lod = [[1] * bs]
+        for i in range(num_inputs - 1):
+            self.inputs['X'].append(('x%d' % (i + 1), (xs[i + 1], normal_lod)))
+
+        if self.with_bias:
+            self.inputs['FCBias'] = b
+
+        self.outputs = {'Out': (out, self.lod)}
+        self.attrs = {'fc_activation': self.fc_act}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestFusionSECFCOpNonBias(TestFusionSeqExpandConcatFCOp):
+    def set_conf(self):
+        self.with_bias = False
+
+
+class TestFusionSECFCOpNonAct(TestFusionSeqExpandConcatFCOp):
+    def set_conf(self):
+        self.fc_act = 'identity'
+
+
+class TestFusionSECFCOpMD1(TestFusionSeqExpandConcatFCOp):
+    def set_conf(self):
+        self.inputs_M = [3, 4, 2, 1, 5]
+        self.D = 8
+
+
+class TestFusionSECFCOpMD2(TestFusionSeqExpandConcatFCOp):
+    def set_conf(self):
+        self.lod = [[5, 6]]
+        self.inputs_M = [1, 1]
+
+
+class TestFusionSECFCOpBS1_1(TestFusionSeqExpandConcatFCOp):
+    def set_conf(self):
+        self.lod = [[1]]
+        self.inputs_M = [3, 4, 2]
+
+
+class TestFusionSECFCOpBS1_2(TestFusionSeqExpandConcatFCOp):
+    def set_conf(self):
+        self.lod = [[1]]
+        self.inputs_M = [3, 4]
+
+
+class TestFusionSECFCOpBS1_3(TestFusionSeqExpandConcatFCOp):
+    def set_conf(self):
+        self.lod = [[5]]
+        self.inputs_M = [6, 3]
+
+
+if __name__ == '__main__':
+    unittest.main()