Merge pull request #12737 from tensor-tang/feature/op/fusion_lstm

add fusion lstm

paddle/fluid/operators/fc_op.cc

@@ -15,8 +15,7 @@ limitations under the License. */
 #include "paddle/fluid/operators/fc_op.h"
 #include <vector>
 #include "paddle/fluid/operators/math/blas.h"
-
-DECLARE_int32(paddle_num_threads);
+#include "paddle/fluid/operators/math/fc_compute.h"
 
 namespace paddle {
 namespace operators {
@@ -110,13 +109,8 @@ void FCOpMaker::Make() {
   AddComment(R"DOC(
   Fully Connected Operator.
 
-  The fully connected operation calculates the output based on the input, weights and bias attribute.
+  The fully connected operation calculates the output based on the input, weights and bias.
   The size of each dimension of the parameters checked in the infer-shape.
-  The matrix of bias is generated by the mkldnn framework, when the bias_attr is True.
-  Additional parametrs are use_mkldnn and bias_attr.
-  The input(X) size and output(Out) size may be diffrent.
-
-  The fully connected layer only supports MKLDNN version
 )DOC");
 }
 
@@ -133,26 +127,15 @@ class FCOpKernel : public framework::OpKernel<T> {
     auto in_dims = input->dims();
     auto w_dims = w->dims();
 
-    auto& dev_ctx = ctx.template device_context<platform::CPUDeviceContext>();
-    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(dev_ctx);
     const T* input_data = input->data<T>();
     const T* w_data = w->data<T>();
     T* output_data = output->mutable_data<T>(ctx.GetPlace());
+    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(ctx);
+    math::FCCompute<platform::CPUDeviceContext, T>(
+        blas, in_dims[0], w_dims[1], w_dims[0], input_data, w_data, output_data,
+        bias ? bias->data<T>() : NULL);
 
-    blas.GEMM(CblasNoTrans, CblasNoTrans, in_dims[0], w_dims[1], w_dims[0],
-              static_cast<T>(1), input_data, w_data, static_cast<T>(0),
-              output_data);
-
-    if (bias) {
-      const T* bias_data = bias->data<T>();
-#ifdef PADDLE_WITH_MKLML
-#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
-#endif
-      for (int bs = 0; bs < in_dims[0]; bs++) {
-        blas.AXPY(w_dims[1], static_cast<T>(1), bias_data,
-                  output_data + bs * w_dims[1]);
-      }
-    }
+    // TODO(TJ): fuse act
   }
 };
 

paddle/fluid/operators/fusion_lstm_op.h

@@ -0,0 +1,42 @@
+/* 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. */
+
+#pragma once
+// #include <string>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+using Tensor = framework::Tensor;
+
+class FusionLSTMOp : 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 FusionLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override;
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/fc_compute.h

@@ -0,0 +1,43 @@
+/* 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. */
+
+#pragma once
+
+#include "paddle/fluid/operators/math/blas.h"
+
+DECLARE_int32(paddle_num_threads);
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename DeviceContext, typename T>
+inline void FCCompute(const BlasT<DeviceContext, T>& blas, const int M,
+                      const int N, const int K, const T* X, const T* W, T* Y,
+                      const T* B = NULL) {
+  blas.GEMM(CblasNoTrans, CblasNoTrans, M, N, K, static_cast<T>(1), X, W,
+            static_cast<T>(0), Y);
+  if (B) {
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
+#endif
+    for (int i = 0; i < M; i++) {
+      blas.AXPY(N, static_cast<T>(1), B, Y + i * N);
+    }
+  }
+}
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

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

@@ -64,27 +64,47 @@ class TestFCOp(OpTest):
         self.check_output()
 
 
-class TestFCOpBiasBoth(TestFCOp):
+class TestFCOpNoBias(TestFCOp):
     def init_shapes(self, mb, ic, oc, h, w):
-        for with_bias in {True, False}:
-            self.with_bias = with_bias
-            self.matrix = MatrixGenerate(mb, ic, oc, h, w)
+        self.with_bias = False
+        self.matrix = MatrixGenerate(mb, ic, oc, h, w)
 
 
-class TestFCOp1(TestFCOpBiasBoth):
+class TestFCOpWithBias(TestFCOp):
+    def init_shapes(self, mb, ic, oc, h, w):
+        self.with_bias = True
+        self.matrix = MatrixGenerate(mb, ic, oc, h, w)
+
+
+class TestFCOp1(TestFCOpNoBias):
     def init_op_type(self):
         self.init_shapes(2, 8, 10, 1, 1)
 
 
-class TestFCOp2(TestFCOpBiasBoth):
+class TestFCOp2(TestFCOpNoBias):
     def init_op_type(self):
         self.init_shapes(4, 5, 6, 2, 2)
 
 
-class TestFCOp4(TestFCOpBiasBoth):
+class TestFCOp4(TestFCOpNoBias):
     def init_op_type(self):
         self.init_shapes(1, 32, 64, 3, 3)
 
 
+class TestFCOpWithBias1(TestFCOpWithBias):
+    def init_op_type(self):
+        self.init_shapes(3, 8, 10, 2, 1)
+
+
+class TestFCOpWithBias2(TestFCOpWithBias):
+    def init_op_type(self):
+        self.init_shapes(4, 5, 6, 2, 2)
+
+
+class TestFCOpWithBias3(TestFCOpWithBias):
+    def init_op_type(self):
+        self.init_shapes(1, 64, 32, 3, 3)
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -0,0 +1,151 @@
+#   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_lstm_op import lstm, ACTIVATION
+
+
+def fc(x, w, b):
+    return np.dot(x, w) + b
+
+
+def fusion_lstm(
+        x,  # T x M
+        lod,  # 1 x N
+        wx=None,  # M x 4D
+        bx=None,  # 1 x 4D
+        h0=None,  # N x D
+        c0=None,  # N x D
+        w_h=None,  # D x 4D
+        w_b=None,  # 1 x 4D
+        w_c=None,  # 1 x 3D
+        is_reverse=False,
+        act_gate=None,
+        act_cell=None,
+        act_cand=None):
+    return lstm(
+        fc(x, wx, bx), lod, h0, c0, w_h, w_b, w_c, is_reverse, act_gate,
+        act_cell, act_cand)
+
+
+class TestLstmOp(OpTest):
+    def set_argument(self):
+        self.lod = [[2, 3, 2]]
+
+    def setUp(self):
+        self.op_type = 'fusion_lstm'
+        self.lod = [[2, 3, 2]]
+        self.M = 8
+        self.D = 16
+        self.has_initial_state = False
+        self.is_reverse = False
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+        self.use_peepholes = False
+        self.set_argument()
+
+        T = sum(self.lod[0])
+        bs = len(self.lod[0])
+
+        x = np.random.normal(size=(T, self.M)).astype('float64')
+        if self.has_initial_state:
+            h0 = np.random.normal(size=(bs, self.D)).astype('float64')
+            c0 = np.random.normal(size=(bs, self.D)).astype('float64')
+        else:
+            h0 = np.zeros((bs, self.D)).astype('float64')
+            c0 = np.zeros((bs, self.D)).astype('float64')
+
+        wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float64')
+
+        if self.use_peepholes:
+            b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
+        else:
+            b = np.random.normal(size=(1, 4 * self.D)).astype('float64')
+        w_b = np.copy(b[:, 0:4 * self.D])
+        w_c = b[:, 4 * self.D:] if self.use_peepholes else None
+
+        # this is the weight of fc
+        wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float64')
+        # this is the bias of fc
+        # and it should be manually added into the bias of this fusion LSTM
+        bx = np.random.normal(size=(1, 4 * self.D)).astype('float64')
+        b[0, 0:4 * self.D] += bx[0, :]
+        h, c = fusion_lstm(x, self.lod, wx, bx, h0, c0, wh, w_b, w_c,
+                           self.is_reverse, ACTIVATION[self.act_gate],
+                           ACTIVATION[self.act_cell], ACTIVATION[self.act_cand])
+
+        self.inputs = {
+            'X': (x, self.lod),
+            'WeightX': wx,
+            'WeightH': wh,
+            'Bias': b
+        }
+
+        if self.has_initial_state:
+            self.inputs['H0'] = h0
+            self.inputs['C0'] = c0
+
+        self.outputs = {
+            'Hidden': (h, self.lod),
+            'Cell': (c, self.lod),
+        }
+        self.attrs = {
+            'use_peepholes': self.use_peepholes,
+            'is_reverse': self.is_reverse,
+            'gate_activation': self.act_gate,
+            'cell_activation': self.act_cell,
+            'candidate_activation': self.act_cand
+        }
+
+    def test_check_output(self):
+        self.check_output(atol=1e-8)
+
+
+class TestLstmOpInitReverse(TestLstmOp):
+    def set_argument(self):
+        self.has_initial_state = True
+        self.is_reverse = True
+
+
+class TestLstmOpMD1(TestLstmOp):
+    def set_argument(self):
+        self.M = 36
+        self.D = 8
+
+
+class TestLstmOpMD2(TestLstmOp):
+    def set_argument(self):
+        self.M = 8
+        self.D = 8
+
+
+class TestLstmOpMD3(TestLstmOp):
+    def set_argument(self):
+        self.M = 15
+        self.D = 3
+
+
+class TestLstmOpBS1(TestLstmOp):
+    def set_argument(self):
+        self.lod = [[3]]
+        self.D = 16
+
+
+if __name__ == '__main__':
+    unittest.main()