Fix bug when enabling --overlap-grad-reduce in mcore (#2142)

* fix bugs when enabling --overlap-grad-reduce in mcore
Signed-off-by: Hongbin Liu <hongbinl@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fix CI
Signed-off-by: Hongbin Liu <hongbinl@nvidia.com>

* format
Signed-off-by: Hongbin Liu <hongbinl@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci



---------
Signed-off-by: Hongbin Liu <hongbinl@nvidia.com>
Co-authored-by: Hongbin Liu <hongbinl@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

transformer_engine/pytorch/module/base.py

@@ -1482,7 +1482,6 @@ class TransformerEngineBaseModule(torch.nn.Module, ABC):
             (wgrad, bgrad), _ = self.wgrad_store.pop()
             if not self.fuse_wgrad_accumulation:
                 weight_tensor = noop_cat(self._get_weight_tensors())
-                if weight_tensor.grad is None:
                 weight_tensor.grad = wgrad.to(weight_tensor.dtype)
             if self.use_bias:
                 bias_tensor = noop_cat([getattr(self, name) for name in self.bias_names])

transformer_engine/pytorch/module/grouped_linear.py

@@ -452,9 +452,6 @@ class _GroupedLinear(torch.autograd.Function):
             else:
                 wgrad_list = [None] * ctx.num_gemms
 
-            if ctx.wgrad_store is not None and ctx.wgrad_store.delay_wgrad_compute():
-                wgrad_list = [None] * ctx.num_gemms
-
             if not ctx.use_bias or (
                 ctx.wgrad_store is not None
                 and ctx.wgrad_store.delay_wgrad_compute()
@@ -829,7 +826,6 @@ class GroupedLinear(TransformerEngineBaseModule):
             bias_params = [getattr(self, f"bias{i}") for i in range(self.num_gemms)]
             if not self.fuse_wgrad_accumulation:
                 for i in range(self.num_gemms):
-                    if weight_params[i].grad is None:
                     weight_params[i].grad = wgrad_list[i].to(weight_params[i].dtype)
             if self.use_bias:
                 for i in range(self.num_gemms):

transformer_engine/pytorch/module/layernorm_mlp.py

@@ -1197,7 +1197,6 @@ class _LayerNormMLP(torch.autograd.Function):
                             "with Userbuffers (tensor-parallel communication overlapping)"
                         )
                     ctx.wgrad_store.put([ln_out_total, dact], fc1_wgrad_gemm)
-                    fc1_wgrad = None
                     if fuse_gemm_and_bias_fc1_wgrad:
                         fc1_bias_grad = None
                 else:
@@ -2168,9 +2167,7 @@ class LayerNormMLP(TransformerEngineBaseModule):
                 if self.fc1_bias.grad is None:
                     self.fc1_bias.grad = fc1_bias_grad.to(self.fc1_bias.dtype)
             if not self.fuse_wgrad_accumulation:
-                if self.fc2_weight.grad is None:
                 self.fc2_weight.grad = fc2_wgrad.to(self.fc2_weight.dtype)
-                if self.fc1_weight.grad is None:
                 self.fc1_weight.grad = fc1_wgrad.to(self.fc1_weight.dtype)
             del fc2_bias_grad_
             del fc2_wgrad