fused linear grad add bug fix and perf optim (#56094)

* skip CopyOrAdd when tmp grad is None (#55679)

* Optim fused linear grad add (#55927)

Author: FeixLiu

paddle/fluid/eager/accumulation/accumulation_node.cc

@@ -124,7 +124,10 @@ GradNodeAccumulation::operator()(
 
   if (!weak_grad_.expired() && !is_new_grad) {
     auto grad = weak_grad_.lock();
-    CopyOrAddTensor(grad.get(), grad_out, is_fake_empty_);
+    if (grad_out.defined() && grad_out.initialized()) {
+      CopyOrAddTensor(grad.get(), grad_out, is_fake_empty_);
+    }
+    // else { do nothing since there is no valid value in grad out tensor }
     is_fake_empty_ = false;
   }
 

paddle/phi/api/yaml/fused_ops.yaml

@@ -45,7 +45,7 @@
   support_dygraph_mode : true
 
 - op : fused_linear_param_grad_add
-  args : (Tensor x, Tensor dout, Tensor dweight, Tensor dbias, bool multi_precision = true)
+  args : (Tensor x, Tensor dout, Tensor dweight, Tensor dbias, bool multi_precision = true, bool has_bias = true)
   output : Tensor(dweight_out), Tensor(dbias_out)
   infer_meta:
     func : FusedLinearParamGradAddInferMeta

paddle/phi/infermeta/multiary.cc

@@ -1259,6 +1259,7 @@ void FusedLinearParamGradAddInferMeta(const MetaTensor& x,
                                       const MetaTensor& dweight,
                                       const MetaTensor& dbias,
                                       bool multi_precision,
+                                      bool has_bias,
                                       MetaTensor* dweight_out,
                                       MetaTensor* dbias_out) {
   const auto dtype = dout.dtype();
@@ -1302,7 +1303,7 @@ void FusedLinearParamGradAddInferMeta(const MetaTensor& x,
           ? DataType::FLOAT32
           : dtype;
 
-  if (dbias_out) {
+  if (has_bias && dbias_out) {
     dbias_out->set_dims({weight_dims[1]});
     dbias_out->set_dtype(multi_precision ? mp_dtype : dtype);
   }

paddle/phi/infermeta/multiary.h

@@ -265,6 +265,7 @@ void FusedLinearParamGradAddInferMeta(const MetaTensor& x,
                                       const MetaTensor& dweight,
                                       const MetaTensor& dbias,
                                       bool multi_precision,
+                                      bool has_bias,
                                       MetaTensor* dweight_out,
                                       MetaTensor* dbias_out);
 

paddle/phi/kernels/fusion/gpu/fused_linear_param_grad_add_kernel.cu

@@ -40,6 +40,7 @@ void FusedLinearParamGradAddImpl(const Context &ctx,
                                  int64_t K,
                                  int64_t N,
                                  bool use_addto,
+                                 bool has_bias,
                                  DenseTensor *dweight_out,
                                  DenseTensor *dbias_out) {
   constexpr bool kIsMultiPrecision = !std::is_same<T, MT>::value;
@@ -65,7 +66,7 @@ void FusedLinearParamGradAddImpl(const Context &ctx,
         use_addto);
   }
 
-  if (dbias_out == nullptr) return;
+  if (!has_bias) return;
 
   if (!fuse_bias_grad) {
     auto dout_copy = dout;
@@ -126,6 +127,7 @@ void FusedLinearParamGradAdd(const Context &ctx,
                              const paddle::optional<DenseTensor> &dweight,
                              const paddle::optional<DenseTensor> &dbias,
                              bool multi_precision,
+                             bool has_bias,
                              DenseTensor *dweight_out,
                              DenseTensor *dbias_out) {
   using MT = typename phi::dtype::MPTypeTrait<T>::Type;
@@ -159,7 +161,7 @@ void FusedLinearParamGradAdd(const Context &ctx,
     multi_precision = false;
   }
 
-  if (dbias_out) {
+  if (has_bias && dbias_out) {
     ctx.template Alloc<T>(dbias_out);
   }
 
@@ -176,18 +178,37 @@ void FusedLinearParamGradAdd(const Context &ctx,
     PrintMeta<kLogLevel>(dweight_out, "dweight_out");
     PrintMeta<kLogLevel>(dbias_out, "dbias_out");
     VLOG(kLogLevel) << "multi_precision = " << multi_precision;
+    VLOG(kLogLevel) << "has_bias = " << has_bias;
     VLOG(kLogLevel) << "use_addto = " << use_addto;
     VLOG(kLogLevel) << "M = " << M;
     VLOG(kLogLevel) << "N = " << N;
     VLOG(kLogLevel) << "K = " << K;
   }
 
   if (multi_precision) {
-    FusedLinearParamGradAddImpl<T, MT, Context>(
-        ctx, x, dout, dbias, M, K, N, use_addto, dweight_out, dbias_out);
+    FusedLinearParamGradAddImpl<T, MT, Context>(ctx,
+                                                x,
+                                                dout,
+                                                dbias,
+                                                M,
+                                                K,
+                                                N,
+                                                use_addto,
+                                                has_bias,
+                                                dweight_out,
+                                                dbias_out);
   } else {
-    FusedLinearParamGradAddImpl<T, T, Context>(
-        ctx, x, dout, dbias, M, K, N, use_addto, dweight_out, dbias_out);
+    FusedLinearParamGradAddImpl<T, T, Context>(ctx,
+                                               x,
+                                               dout,
+                                               dbias,
+                                               M,
+                                               K,
+                                               N,
+                                               use_addto,
+                                               has_bias,
+                                               dweight_out,
+                                               dbias_out);
   }
 }
 
@@ -199,6 +220,7 @@ void FusedLinearParamGradAdd(const Context &ctx,
                              const paddle::optional<DenseTensor> &dweight,
                              const paddle::optional<DenseTensor> &dbias,
                              bool multi_precision,
+                             bool has_bias,
                              DenseTensor *dweight_out,
                              DenseTensor *dbias_out) {
   PADDLE_THROW(phi::errors::Unimplemented(