Split inplace_abn & batch_norm infershape (#23755)

* Fix elementwise compile error, test=develop

* split inplace_abn & batch_norm InferShape. test=develop

* fix type. test=develop

* fix message. test=develop

* fix ENFORCE. test=develop
Co-authored-by: Nzhaoyuchen <zhaoyuchen01@baidu.com>

paddle/fluid/operators/batch_norm_op.cc

@@ -468,30 +468,21 @@ void BatchNormGradOp::InferShape(framework::InferShapeContext *ctx) const {
             "in gradient op kernel of batch_norm_mkldnn_op now."));
   }
 
-  // batch_norm_grad with inplace takes Y as input, without inplace
-  // takes X as input. HasInput will throw exception in compile time,
-  // so only infer shape in run time here.
-  if (ctx->IsRuntime()) {
-    PADDLE_ENFORCE_EQ(ctx->HasInput("X") || ctx->HasInput("Y"), true,
-                      platform::errors::NotFound(
-                          "Input(X) and Input(Y) should not be all null."));
-    auto input_name = "Y";
-    if (ctx->HasInput("X")) input_name = "X";
-    const auto x_dims = ctx->GetInputDim(input_name);
-    const DataLayout data_layout = framework::StringToDataLayout(
-        ctx->Attrs().Get<std::string>("data_layout"));
+  OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "BatchNormGrad");
+  const auto x_dims = ctx->GetInputDim("X");
+  const DataLayout data_layout = framework::StringToDataLayout(
+      ctx->Attrs().Get<std::string>("data_layout"));
 
-    const int C =
-        ((this->IsMKLDNNType() == true) || (data_layout == DataLayout::kNCHW)
-             ? x_dims[1]
-             : x_dims[x_dims.size() - 1]);
-
-    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
-    // has_scale_grad == has_bias_grad, judge has_scale_grad is enough
-    if (has_scale_grad) {
-      ctx->SetOutputDim(framework::GradVarName("Scale"), {C});
-      ctx->SetOutputDim(framework::GradVarName("Bias"), {C});
-    }
+  const int C =
+      ((this->IsMKLDNNType() == true) || (data_layout == DataLayout::kNCHW)
+           ? x_dims[1]
+           : x_dims[x_dims.size() - 1]);
+
+  ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+  // has_scale_grad == has_bias_grad, judge has_scale_grad is enough
+  if (has_scale_grad) {
+    ctx->SetOutputDim(framework::GradVarName("Scale"), {C});
+    ctx->SetOutputDim(framework::GradVarName("Bias"), {C});
   }
 }
 

paddle/fluid/operators/inplace_abn_op.cc

@@ -62,6 +62,58 @@ class InplaceABNGradOp : public paddle::operators::BatchNormGradOp {
  public:
   using paddle::operators::BatchNormGradOp::BatchNormGradOp;
 
+  void InferShape(framework::InferShapeContext* ctx) const {
+    // check input
+    OP_INOUT_CHECK(ctx->HasInput("Scale"), "Input", "Scale", "InplaceABNGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")), "Input",
+                   "Y@GRAD", "InplaceABNGrad");
+    OP_INOUT_CHECK(ctx->HasInput("SavedMean"), "Input", "SavedMean",
+                   "InplaceABNGrad");
+    OP_INOUT_CHECK(ctx->HasInput("SavedVariance"), "Input", "SavedVariance",
+                   "InplaceABNGrad");
+
+    // check output
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   "X@GRAD", "InplaceABNGrad");
+
+    const bool has_scale_grad = ctx->HasOutput(framework::GradVarName("Scale"));
+    const bool has_bias_grad = ctx->HasOutput(framework::GradVarName("Bias"));
+
+    PADDLE_ENFORCE_EQ(
+        has_scale_grad, has_bias_grad,
+        platform::errors::InvalidArgument(
+            "Output(Scale@GRAD) and Output(Bias@GRAD) must be null "
+            "or not be null at same time. But now, "
+            "has Scale@Grad=[%d], has Bias@GRAD=[%d]",
+            has_scale_grad, has_bias_grad));
+
+    const bool use_global_stats = ctx->Attrs().Get<bool>("use_global_stats");
+    if (use_global_stats) {
+      PADDLE_ENFORCE_EQ(
+          !ctx->Attrs().Get<bool>("use_mkldnn"), true,
+          platform::errors::InvalidArgument(
+              "Using global stats during training is not supported "
+              "in gradient op kernel of batch_norm_mkldnn_op now."));
+    }
+
+    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "InplaceABNGrad");
+    const auto y_dims = ctx->GetInputDim("Y");
+    const DataLayout data_layout = framework::StringToDataLayout(
+        ctx->Attrs().Get<std::string>("data_layout"));
+
+    const int C =
+        ((this->IsMKLDNNType() == true) || (data_layout == DataLayout::kNCHW)
+             ? y_dims[1]
+             : y_dims[y_dims.size() - 1]);
+
+    ctx->SetOutputDim(framework::GradVarName("X"), y_dims);
+    // has_scale_grad == has_bias_grad, judge has_scale_grad is enough
+    if (has_scale_grad) {
+      ctx->SetOutputDim(framework::GradVarName("Scale"), {C});
+      ctx->SetOutputDim(framework::GradVarName("Bias"), {C});
+    }
+  }
+
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {