refine and clean code

a79a77ee · tensor-tang · c459fb5b · a79a77ee
隐藏空白更改
内联并排

Showing with 51 addition and 79 deletion

paddle/fluid/operators/fusion_lstm_op.cc paddle/fluid/operators/fusion_lstm_op.cc +51 -79

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--- a/paddle/fluid/operators/fusion_lstm_op.cc
+++ b/paddle/fluid/operators/fusion_lstm_op.cc
@@ -215,46 +215,53 @@ This operator fuse the X into LSTM, more details can refer to LSTM op.
 template <typename T>
 class FuisonLSTMKernel : public framework::OpKernel<T> {
 public:
+#define INIT_VEC_FUNC                                                          \
+  std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand; \
+  auto& act_gate_str = ctx.Attr<std::string>("gate_activation");               \
+  auto& act_cell_str = ctx.Attr<std::string>("cell_activation");               \
+  auto& act_cand_str = ctx.Attr<std::string>("candidate_activation");          \
+  if (platform::jit::MayIUse(platform::jit::avx)) {                            \
+    math::VecActivations<T, platform::jit::avx> act_functor;                   \
+    act_gate = act_functor(act_gate_str);                                      \
+    act_cell = act_functor(act_cell_str);                                      \
+    act_cand = act_functor(act_cand_str);                                      \
+  } else {                                                                     \
+    math::VecActivations<T, platform::jit::isa_any> act_functor;               \
+    act_gate = act_functor(act_gate_str);                                      \
+    act_cell = act_functor(act_cell_str);                                      \
+    act_cand = act_functor(act_cand_str);                                      \
+  }
+
+#define INIT_BASE_INPUT_OUTPUT                        \
+  auto* x = ctx.Input<LoDTensor>("X");                \
+  auto* h0 = ctx.Input<Tensor>("H0");                 \
+  auto* c0 = ctx.Input<Tensor>("C0");                 \
+  auto* wx = ctx.Input<Tensor>("WeightX");            \
+  auto* wh = ctx.Input<Tensor>("WeightH");            \
+  auto* bias = ctx.Input<Tensor>("Bias");             \
+  auto* xx = ctx.Output<LoDTensor>("XX");             \
+  auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
+  auto* cell_out = ctx.Output<LoDTensor>("Cell");     \
+  bool is_reverse = ctx.Attr<bool>("is_reverse");
+
+#define INIT_BASE_SIZES                  \
+  auto x_dims = x->dims();   /* T x M*/  \
+  auto wh_dims = wh->dims(); /* D x 4D*/ \
+  const int M = x_dims[1];               \
+  const int D = wh_dims[0];              \
+  const int D2 = D * 2;                  \
+  const int D3 = D * 3;                  \
+  const int D4 = wh_dims[1];
+
  void SeqCompute(const framework::ExecutionContext& ctx) const {
    using DeviceContext = paddle::platform::CPUDeviceContext;
-    auto* x = ctx.Input<LoDTensor>("X");
-    auto* h0 = ctx.Input<Tensor>("H0");
-    auto* c0 = ctx.Input<Tensor>("C0");
-    auto* wx = ctx.Input<Tensor>("WeightX");
-    auto* wh = ctx.Input<Tensor>("WeightH");
-    auto* bias = ctx.Input<Tensor>("Bias");
-
-    auto* xx = ctx.Output<LoDTensor>("XX");
-    auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
-    auto* cell_out = ctx.Output<LoDTensor>("Cell");
-    bool is_reverse = ctx.Attr<bool>("is_reverse");
-
-    std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand;
-    auto& act_gate_str = ctx.Attr<std::string>("gate_activation");
-    auto& act_cell_str = ctx.Attr<std::string>("cell_activation");
-    auto& act_cand_str = ctx.Attr<std::string>("candidate_activation");
-    if (platform::jit::MayIUse(platform::jit::avx)) {
-      math::VecActivations<T, platform::jit::avx> act_functor;
-      act_gate = act_functor(act_gate_str);
-      act_cell = act_functor(act_cell_str);
-      act_cand = act_functor(act_cand_str);
-    } else {
-      math::VecActivations<T, platform::jit::isa_any> act_functor;
-      act_gate = act_functor(act_gate_str);
-      act_cell = act_functor(act_cell_str);
-      act_cand = act_functor(act_cand_str);
-    }
+    INIT_BASE_INPUT_OUTPUT
+    INIT_BASE_SIZES
+    INIT_VEC_FUNC

    auto x_lod = x->lod();
-    auto x_dims = x->dims();    // T x M
-    auto wh_dims = wh->dims();  // D x 4D
    const int total_T = x_dims[0];
    const int N = x_lod[0].size() - 1;  // batch size
-    const int M = x_dims[1];            // x frame size
-    const int D = wh_dims[0];
-    const int D2 = D * 2;
-    const int D3 = D * 3;
-    const int D4 = wh_dims[1];

    const T* x_data = x->data<T>();
    const T* h0_data = h0 ? h0->data<T>() : NULL;
@@ -343,52 +350,18 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {

  void BatchCompute(const framework::ExecutionContext& ctx) const {
    using DeviceContext = platform::CPUDeviceContext;
-    auto* x = ctx.Input<LoDTensor>("X");
-    auto* wx = ctx.Input<Tensor>("WeightX");
-    auto* wh = ctx.Input<Tensor>("WeightH");
-    auto* bias = ctx.Input<Tensor>("Bias");
-    auto* h0 = ctx.Input<Tensor>("H0");
-    auto* c0 = ctx.Input<Tensor>("C0");
-
-    auto* xx = ctx.Output<LoDTensor>("XX");
+    INIT_BASE_INPUT_OUTPUT
+    if (x->lod()[0].size() == 2) {  // batch size == 1
+      SeqCompute(ctx);
+    }
+    INIT_BASE_SIZES
+    INIT_VEC_FUNC
+
    auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
    auto* reordered_c0 = ctx.Output<Tensor>("ReorderedC0");
    auto* batched_input = ctx.Output<LoDTensor>("BatchedInput");
    auto* batched_c_out = ctx.Output<LoDTensor>("BatchedCell");
    auto* batched_h_out = ctx.Output<LoDTensor>("BatchedHidden");
-    auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
-    auto* cell_out = ctx.Output<LoDTensor>("Cell");
-    bool is_reverse = ctx.Attr<bool>("is_reverse");
-
-    std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand;
-    auto& act_gate_str = ctx.Attr<std::string>("gate_activation");
-    auto& act_cell_str = ctx.Attr<std::string>("cell_activation");
-    auto& act_cand_str = ctx.Attr<std::string>("candidate_activation");
-    if (platform::jit::MayIUse(platform::jit::avx)) {
-      math::VecActivations<T, platform::jit::avx> act_functor;
-      act_gate = act_functor(act_gate_str);
-      act_cell = act_functor(act_cell_str);
-      act_cand = act_functor(act_cand_str);
-    } else {
-      math::VecActivations<T, platform::jit::isa_any> act_functor;
-      act_gate = act_functor(act_gate_str);
-      act_cell = act_functor(act_cell_str);
-      act_cand = act_functor(act_cand_str);
-    }
-
-    auto x_dims = x->dims();    // T x M
-    auto wh_dims = wh->dims();  // D x 4D
-
-    // auto x_lod = x->lod();
-    // const int N = x_lod[0].size() - 1;  // batch size
-    // if (N == 1) {
-    //   SeqCompute(ctx);
-    // }
-    const int M = x_dims[1];
-    const int D = wh_dims[0];
-    const int D2 = D * 2;
-    const int D3 = D * 3;
-    const int D4 = wh_dims[1];

    const T* x_data = x->data<T>();
    const T* wx_data = wx->data<T>();
@@ -485,16 +458,12 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
        // W_ch, W_ih, W_fh, W_oh
        act_gate(D3, cur_in_data + D, cur_in_data + D);
        act_cand(D, cur_in_data, cur_in_data);
-
        // a = forget * prev_cell
        blas.VMUL(D, cur_in_data + D2, cur_prev_c_data, cur_in_data + D2);
-
        // b = input * tilde
        blas.VMUL(D, cur_in_data, cur_in_data + D, cur_in_data + D);
-
        // cell out= a+b
        blas.VADD(D, cur_in_data + D, cur_in_data + D2, cur_c_out_data);
-
        // hidden out= act_state(cellout) * outgate
        act_cell(D, cur_c_out_data, cur_in_data + D2);
        blas.VMUL(D, cur_in_data + D2, cur_in_data + D3, cur_h_out_data);
@@ -526,6 +495,9 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
      BatchCompute(ctx);
    }
  }
+#undef INIT_BASE_SIZES
+#undef INIT_BASE_INPUT_OUTPUT
+#undef INIT_VEC_FUNC
 };

 }  // namespace operators