[MLU] fix TensorAdd for mlu (#39523)

paddle/fluid/imperative/gradient_accumulator.cc

@@ -35,6 +35,9 @@
 #ifdef PADDLE_WITH_ASCEND_CL
 #include "paddle/fluid/platform/device/npu/npu_op_runner.h"
 #endif
+#ifdef PADDLE_WITH_MLU
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+#endif
 
 namespace paddle {
 namespace imperative {
@@ -362,6 +365,35 @@ void TensorAdd(const VarType& src, VarType* dst) {
   }
 #endif
 
+#ifdef PADDLE_WITH_MLU
+  if (platform::is_mlu_place(place)) {
+    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+    platform::DeviceContext* ctx = pool.Get(place);
+    auto dev_ctx = dynamic_cast<platform::MLUDeviceContext*>(ctx);
+    if (data_type == framework::DataTypeTrait<float>::DataType()) {
+      dst_tensor->mutable_data<float>(place);
+    } else if (data_type ==
+               framework::DataTypeTrait<platform::float16>::DataType()) {
+      dst_tensor->mutable_data<platform::float16>(place);
+    } else {
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "Gradient accumulation of data type (%s) on place (%s) is not "
+          "supported in imperative mode",
+          framework::DataTypeToString(data_type), place));
+    }
+    static const float alpha = 1.f;
+    static const float beta = 1.f;
+    operators::MLUCnnlTensorDesc src_tensor_desc(src_tensor);
+    operators::MLUCnnlTensorDesc dst_tensor_desc(*dst_tensor);
+    PADDLE_ENFORCE_MLU_SUCCESS(cnnlAssignAdd(
+        dev_ctx->cnnl_handle(), static_cast<void*>(&alpha),
+        src_tensor_desc.get(), operators::GetBasePtr(&src_tensor), nullptr, 0,
+        static_cast<void*>(&beta), dst_tensor_desc.get(),
+        operators::GetBasePtr(dst_tensor)));
+    return;
+  }
+#endif
+
   PADDLE_TENSOR_ADD(float);
 
 #ifndef PADDLE_WITH_XPU

python/paddle/nn/functional/loss.py

@@ -1676,7 +1676,7 @@ def cross_entropy(input,
             if label_max >= input.shape[axis]:
                 raise ValueError("label should not out of bound, but got{}".
                                  format(label_max))
-        if core.is_compiled_with_npu():
+        if core.is_compiled_with_npu() or core.is_compiled_with_mlu():
             _, _, out = _C_ops.softmax_with_cross_entropy(
                 input, label, 'soft_label', soft_label, 'ignore_index',
                 ignore_index, 'numeric_stable_mode', True, 'axis', axis,