separate runtime infershape

test=develop

paddle/fluid/operators/attention_lstm_op.cc

@@ -54,17 +54,25 @@ void AttentionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
   auto w_dims = ctx->GetInputDim("LSTMWeight");
   const int D = w_dims[1] / 4;
   PADDLE_ENFORCE_EQ(w_dims.size(), 2, "Input(LSTMWeight)'s rank must be 2.");
+  if (ctx->IsRuntime()) {
     PADDLE_ENFORCE_EQ(w_dims[0], D + M,
                       "LSTMWeight dims should be (%d + %d) * %d.", D, M, 4 * D);
+  }
 
   auto b_dims = ctx->GetInputDim("LSTMBias");
   PADDLE_ENFORCE_EQ(b_dims.size(), 2, "Input(LSTMBias)'s rank must be 2.");
+  if (ctx->IsRuntime()) {
     PADDLE_ENFORCE_EQ(b_dims[0], 1, "LSTMBias dims should be 1 x %d.", 4 * D);
-  PADDLE_ENFORCE_EQ(b_dims[1], 4 * D, "LSTMBias dims should be 1 x %d.", 4 * D);
+    PADDLE_ENFORCE_EQ(b_dims[1], 4 * D, "LSTMBias dims should be 1 x %d.",
+                      4 * D);
+  }
 
   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(h_dims == c_dims,
@@ -75,27 +83,34 @@ void AttentionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
   auto atten_w_dims = ctx->GetInputDim("AttentionWeight");
   PADDLE_ENFORCE_EQ(atten_w_dims.size(), 2,
                     "Input(AttentionWeight)'s rank must be 2.");
+  if (ctx->IsRuntime()) {
     PADDLE_ENFORCE_EQ(atten_w_dims[0], M + D,
                       "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,
                       "Input(AttentionBias)'s rank must be 2.");
+    if (ctx->IsRuntime()) {
       PADDLE_ENFORCE_EQ(atten_b_dims[0], 1,
                         "AttentionBias shapes must be 1 * 1.");
       PADDLE_ENFORCE_EQ(atten_b_dims[1], 1,
                         "AttentionBias shapes must be 1 * 1.");
     }
+  }
 
   if (ctx->HasInput("AttentionScalar")) {
     auto dims = ctx->GetInputDim("AttentionScalar");
     PADDLE_ENFORCE_EQ(dims.size(), 2,
                       "Input(AttentionScalar)'s rank must be 2.");
+    if (ctx->IsRuntime()) {
       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")) {
     auto dims = ctx->GetInputDim("AttentionScalarBias");
@@ -104,8 +119,12 @@ void AttentionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
         "AttentionScalar should not be null when have AttentionScalarBias.");
     PADDLE_ENFORCE_EQ(dims.size(), 2,
                       "Input(AttentionScalarBias)'s rank must be 2.");
-    PADDLE_ENFORCE_EQ(dims[0], 1, "AttentionScalarBias shapes must be 1 * 1.");
-    PADDLE_ENFORCE_EQ(dims[1], 1, "AttentionScalarBias shapes must be 1 * 1.");
+    if (ctx->IsRuntime()) {
+      PADDLE_ENFORCE_EQ(dims[0], 1,
+                        "AttentionScalarBias shapes must be 1 * 1.");
+      PADDLE_ENFORCE_EQ(dims[1], 1,
+                        "AttentionScalarBias shapes must be 1 * 1.");
+    }
   }
 
   framework::DDim out_dims({x_dims[0], D});