[PyTorch] Fix issues for MCore DDP in grouped GEMM. (#1609)

fix mcore DDP error
Signed-off-by: lit <lit@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

transformer_engine/pytorch/module/grouped_linear.py

@@ -78,8 +78,8 @@ class _GroupedLinear(torch.autograd.Function):
         skip_fp8_weight_update,
         *weights_and_biases,
     ) -> torch.Tensor:
-
         # pylint: disable=missing-function-docstring
+
         num_gemms = len(m_splits)
         weights = weights_and_biases[:num_gemms]
         biases = weights_and_biases[num_gemms:]
@@ -180,7 +180,12 @@ class _GroupedLinear(torch.autograd.Function):
 
             ctx.weights_shape_1 = weights[0].shape[1]
 
-            tensors_to_save, tensor_objects = prepare_for_saving(*inputmats, *weights_fp8, *biases)
+            tensors_to_save, tensor_objects = prepare_for_saving(
+                *inputmats,
+                *weights_fp8,
+                *weights,
+                *biases,
+            )
             ctx.save_for_backward(*tensors_to_save)
             ctx.tensor_objects = tensor_objects
 
@@ -220,7 +225,8 @@ class _GroupedLinear(torch.autograd.Function):
             N = ctx.num_gemms
             inputmats = saved_tensors[:N]
             weights = saved_tensors[N : 2 * N]
-            biases = saved_tensors[2 * N : 3 * N]
+            origin_weights = saved_tensors[2 * N : 3 * N]
+            biases = saved_tensors[3 * N : 4 * N]
             main_grads = ctx.main_grads
 
             if ctx.cpu_offloading and ctx.fuse_wgrad_accumulation:  # TOSO
@@ -311,21 +317,24 @@ class _GroupedLinear(torch.autograd.Function):
                 # Deallocate input tensor
                 clear_tensor_data(*inputmats)
 
-                def handle_custom_ddp_from_mcore(w, wgrad):
+                def handle_custom_ddp_from_mcore(weight, wgrad):
                     if ctx.weights_requires_grad:
-                        if ctx.fuse_wgrad_accumulation and hasattr(w, "grad_added_to_main_grad"):
-                            w.grad_added_to_main_grad = True
-                            if getattr(w, "zero_out_wgrad", False):
+                        # Handle custom DDP from mcore.
+                        if ctx.fuse_wgrad_accumulation and hasattr(
+                            weight, "grad_added_to_main_grad"
+                        ):
+                            weight.grad_added_to_main_grad = True
+                            if getattr(weight, "zero_out_wgrad", False):
                                 wgrad = torch.zeros(
-                                    w.main_grad.shape,
-                                    dtype=w.dtype,
+                                    weight.main_grad.shape,
+                                    dtype=weight.dtype,
                                     device=torch.cuda.current_device(),
                                     requires_grad=False,
                                 )
                             else:
                                 wgrad = torch.empty(
-                                    w.main_grad.shape,
-                                    dtype=w.dtype,
+                                    weight.main_grad.shape,
+                                    dtype=weight.dtype,
                                     device=torch.cuda.current_device(),
                                     requires_grad=False,
                                 )
@@ -336,7 +345,8 @@ class _GroupedLinear(torch.autograd.Function):
                     return wgrad
 
                 wgrad_list = [
-                    handle_custom_ddp_from_mcore(w, wgrad) for w, wgrad in zip(weights, wgrad_list)
+                    handle_custom_ddp_from_mcore(weight, wgrad)
+                    for weight, wgrad in zip(origin_weights, wgrad_list)
                 ]
             else:
                 wgrad_list = [None] * ctx.num_gemms