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[PyTorch] Fix wgrads for GroupedLinear when weights don't require grad #1258

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merged 2 commits into from
Oct 17, 2024
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[PyTorch] Fix wgrads for GroupedLinear when weights don't require grad #1258

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65199c6
Fix wgrad for GroupedLinear when weights doesn't require grad
yaox12 Oct 16, 2024
28c0626
Merge branch 'main' into xiny/fix_wgrad
ksivaman Oct 16, 2024
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56 changes: 29 additions & 27 deletions transformer_engine/pytorch/module/grouped_linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -443,36 +443,38 @@ def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None],
clear_tensor_data(*inputmats)
clear_tensor_data(*inputmats_t)

if not ctx.use_bias:
grad_biases = [None] * ctx.num_gemms

def handle_custom_ddp_from_mcore(w, wgrad):
if w.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):
wgrad = torch.zeros(
w.main_grad.shape,
dtype=w.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
)
def handle_custom_ddp_from_mcore(w, wgrad):
if w.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):
wgrad = torch.zeros(
w.main_grad.shape,
dtype=w.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
)
else:
wgrad = torch.empty(
w.main_grad.shape,
dtype=w.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
)
elif ctx.fuse_wgrad_accumulation:
wgrad = None
else:
wgrad = torch.empty(
w.main_grad.shape,
dtype=w.dtype,
device=torch.cuda.current_device(),
requires_grad=False,
)
elif ctx.fuse_wgrad_accumulation:
wgrad = None
wgrad = None
return wgrad

wgrad_list = [
handle_custom_ddp_from_mcore(w, wgrad) for w, wgrad in zip(weights, wgrad_list)
]
else:
wgrad = None
return wgrad
wgrad_list = [None] * ctx.num_gemms

wgrad_list = [
handle_custom_ddp_from_mcore(w, wgrad) for w, wgrad in zip(weights, wgrad_list)
]
if not ctx.use_bias:
grad_biases = [None] * ctx.num_gemms

if ctx.reduce_and_update_bwd_fp8_tensors and not is_graph_capturing():
FP8GlobalStateManager.reduce_and_update_fp8_tensors(forward=False)
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