Merge pull request #16861 from tensor-tang/refine/infershape

separate runtime infershape

paddle/fluid/operators/attention_lstm_op.cc

@@ -64,13 +64,20 @@ 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.");
+  if (ctx->IsRuntime()) {
     PADDLE_ENFORCE_EQ(c_dims[1], D, "C0 dims should be N x %d.", D);
+  }
+
   if (ctx->HasInput("H0")) {
     auto h_dims = ctx->GetInputDim("H0");
+    PADDLE_ENFORCE_EQ(h_dims.size(), 2UL, "Input(H0)'s rank must be 2.");
+    if (ctx->IsRuntime() ||
+        (framework::product(c_dims) > 0 && framework::product(h_dims) > 0)) {
       PADDLE_ENFORCE(h_dims == c_dims,
                      "The dimension of Input(H0) and Input(C0) "
                      "should be the same.");
     }
+  }
 
   auto atten_w_dims = ctx->GetInputDim("AttentionWeight");
   PADDLE_ENFORCE_EQ(atten_w_dims.size(), 2,
@@ -79,6 +86,7 @@ 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")) {
     auto atten_b_dims = ctx->GetInputDim("AttentionBias");
     PADDLE_ENFORCE_EQ(atten_b_dims.size(), 2,