refine attentionlstm infershape

Author: tensor-tang

paddle/fluid/operators/attention_lstm_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/attention_lstm_op.h"
 #include <string>
+#include "paddle/fluid/framework/shape_runtime_infer.h"
 #include "paddle/fluid/operators/math/blas.h"
 #include "paddle/fluid/operators/math/cpu_vec.h"
 #include "paddle/fluid/operators/math/fc_compute.h"
@@ -23,29 +24,60 @@ namespace paddle {
 namespace operators {
 
 void AttentionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
-  PADDLE_ENFORCE(ctx->HasInput("X"),
-                 "Input(X) of AttentionLSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasInput("C0"),
-                 "Input(C0) of AttentionLSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasInput("LSTMWeight"),
-                 "Input(LSTMWeight) of AttentionLSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasInput("LSTMBias"),
-                 "Input(LSTMBias) of AttentionLSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasInput("AttentionWeight"),
-                 "Input(AttentionWeight) of AttentionLSTM should not be null.");
-
-  PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
-                 "Output(Hidden) of AttentionLSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("Cell"),
-                 "Output(Cell) of AttentionLSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("AttentionedX"),
-                 "Output(AttentionedX) of AttentionLSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("AttentionFCOut"),
-                 "Output(AttentionFCOut) of AttentionLSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("LSTMX"),
-                 "Output(LSTMX) of AttentionLSTM should not be null.");
-  PADDLE_ENFORCE(ctx->HasOutput("LSTMOUT"),
-                 "Output(LSTMOUT) of AttentionLSTM should not be null.");
+  auto* runtime_ctx = dynamic_cast<framework::RuntimeInferShapeContext*>(ctx);
+  if (runtime_ctx == nullptr) {
+    LOG(FATAL) << "Should have runtime infer context";
+  }
+  const auto& ins = runtime_ctx->OpBase().Inputs();
+  const auto& outs = runtime_ctx->OpBase().Outputs();
+  const auto& scope = runtime_ctx->InferScope();
+  const auto ins_end = ins.end();
+  const auto outs_end = outs.end();
+  auto fair_input = [&](const std::string& name) -> bool {
+    auto it = ins.find(name);
+    if (it == ins_end) {
+      return false;
+    }
+    const auto& in = it->second;
+    if (in.size() != 1 || in[0] == framework::kEmptyVarName) {
+      return false;
+    }
+    return scope.FindVar(in[0]) != nullptr;
+  };
+  auto fair_output = [&](const std::string& name) -> bool {
+    auto it = outs.find(name);
+    if (it == outs_end) {
+      return false;
+    }
+    const auto& out = it->second;
+    if (out.size() != 1 || out[0] == framework::kEmptyVarName) {
+      return false;
+    }
+    return scope.FindVar(out[0]) != nullptr;
+  };
+
+  PADDLE_ENFORCE(fair_input("X"), "Assert only one Input(X) of AttentionLSTM.");
+  PADDLE_ENFORCE(fair_input("C0"),
+                 "Assert only one Input(C0) of AttentionLSTM.");
+  PADDLE_ENFORCE(fair_input("LSTMWeight"),
+                 "Assert only one Input(LSTMWeight) of AttentionLSTM.");
+  PADDLE_ENFORCE(fair_input("LSTMBias"),
+                 "Assert only one Input(LSTMBias) of AttentionLSTM.");
+  PADDLE_ENFORCE(fair_input("AttentionWeight"),
+                 "Assert only one Input(AttentionWeight) of AttentionLSTM.");
+
+  PADDLE_ENFORCE(fair_output("Hidden"),
+                 "Assert only one Output(Hidden) of AttentionLSTM.");
+  PADDLE_ENFORCE(fair_output("Cell"),
+                 "Assert only one Output(Cell) of AttentionLSTM.");
+  PADDLE_ENFORCE(fair_output("AttentionedX"),
+                 "Assert only one Output(AttentionedX) of AttentionLSTM.");
+  PADDLE_ENFORCE(fair_output("AttentionFCOut"),
+                 "Assert only one Output(AttentionFCOut) of AttentionLSTM.");
+  PADDLE_ENFORCE(fair_output("LSTMX"),
+                 "Assert only one Output(LSTMX) of AttentionLSTM.");
+  PADDLE_ENFORCE(fair_output("LSTMOUT"),
+                 "Assert only one Output(LSTMOUT) of AttentionLSTM.");
 
   auto x_dims = ctx->GetInputDim("X");
   const int M = x_dims[1];
@@ -65,7 +97,7 @@ void AttentionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
   auto c_dims = ctx->GetInputDim("C0");
   PADDLE_ENFORCE_EQ(c_dims.size(), 2, "Input(C0)'s rank must be 2.");
   PADDLE_ENFORCE_EQ(c_dims[1], D, "C0 dims should be N x %d.", D);
-  if (ctx->HasInput("H0")) {
+  if (fair_input("H0")) {
     auto h_dims = ctx->GetInputDim("H0");
     PADDLE_ENFORCE(h_dims == c_dims,
                    "The dimension of Input(H0) and Input(C0) "
@@ -79,7 +111,7 @@ void AttentionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
                     "AttentionWeight shapes must be (%d + %d) * 1.", M, D);
   PADDLE_ENFORCE_EQ(atten_w_dims[1], 1,
                     "AttentionWeight shapes must be (%d + %d) * 1.", M, D);
-  if (ctx->HasInput("AttentionBias")) {
+  if (fair_input("AttentionBias")) {
     auto atten_b_dims = ctx->GetInputDim("AttentionBias");
     PADDLE_ENFORCE_EQ(atten_b_dims.size(), 2,
                       "Input(AttentionBias)'s rank must be 2.");
@@ -89,18 +121,18 @@ void AttentionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
                       "AttentionBias shapes must be 1 * 1.");
   }
 
-  if (ctx->HasInput("AttentionScalar")) {
+  if (fair_input("AttentionScalar")) {
     auto dims = ctx->GetInputDim("AttentionScalar");
     PADDLE_ENFORCE_EQ(dims.size(), 2,
                       "Input(AttentionScalar)'s rank must be 2.");
     PADDLE_ENFORCE_EQ(dims[0], 1, "AttentionScalar shapes must be 1 * 1.");
     PADDLE_ENFORCE_EQ(dims[1], 1, "AttentionScalar shapes must be 1 * 1.");
   }
 
-  if (ctx->HasInput("AttentionScalarBias")) {
+  if (fair_input("AttentionScalarBias")) {
     auto dims = ctx->GetInputDim("AttentionScalarBias");
     PADDLE_ENFORCE(
-        ctx->HasInput("AttentionScalar"),
+        fair_input("AttentionScalar"),
         "AttentionScalar should not be null when have AttentionScalarBias.");
     PADDLE_ENFORCE_EQ(dims.size(), 2,
                       "Input(AttentionScalarBias)'s rank must be 2.");