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Fix, pipeline model with moe cause error when send grad #7055

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Fix, pipeline model with moe cause error when send grad #7055

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Fix, pipeline model with moe, send and recv grad use same buffer will…
Feb 19, 2025
fbd7182
Merge branch 'master' into fix-pipe-act-grad-comm
hwchen2017 Feb 20, 2025
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20 changes: 4 additions & 16 deletions deepspeed/runtime/pipe/engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -992,7 +992,7 @@ def _recv_tensor_meta(self, send_stage):
recv_dtype = self.ID_TO_DTYPE[buffer[1].item()]
recv_ndims = buffer[2].item()
recv_shape = buffer[3:3 + recv_ndims].tolist()
return self._allocate_or_extend_buffers(0, recv_shape, recv_dtype)
return self._allocate_buffer(recv_shape, dtype=recv_dtype, num_buffers=1)[0]

# List or tuple of tensors (recv_type == 1 (list) is currently unused)
elif recv_type == 1 or recv_type == 2:
Expand All @@ -1006,7 +1006,7 @@ def _recv_tensor_meta(self, send_stage):
recv_shape = buffer[offset + 2:offset + 2 + recv_ndims].tolist()
offset += 2 + recv_ndims

buffers.append(self._allocate_or_extend_buffers(idx, recv_shape, recv_dtype))
buffers.append(self._allocate_buffer(recv_shape, dtype=recv_dtype, num_buffers=1)[0])

# Convert to tuples if requested.
if recv_type == 2:
Expand Down Expand Up @@ -1172,7 +1172,7 @@ def _exec_recv_grads(self, buffer_id):
# Allocate gradient if necessary
if self.dynamic_shape or self.grad_layer is None:
if isinstance(outputs, torch.Tensor):
self.grad_layer = self._allocate_or_extend_buffers(0, list(outputs.size()), outputs.dtype)
self.grad_layer = self._allocate_buffer(list(outputs.size()), dtype=outputs.dtype, num_buffers=1)[0]
else:
# XXX This is a HACK
# When we exchange activations/gradients, the two pipe stages
Expand All @@ -1196,7 +1196,7 @@ def _exec_recv_grads(self, buffer_id):
sizes_and_dtypes = [(list(t.size()), t.dtype) for t in outputs if t.is_floating_point()]

self.grad_layer = [
self._allocate_or_extend_buffers(i, size, dtype)
self._allocate_buffer(size, dtype=dtype, num_buffers=1)[0]
for i, (size, dtype) in enumerate(sizes_and_dtypes)
]

Expand Down Expand Up @@ -1279,18 +1279,6 @@ def _allocate_buffer(self, shape, num_buffers=-1, **kwargs):
buffers.append(self._allocate_zeros(shape, **kwargs))
return buffers

def _allocate_or_extend_buffers(self, idx, shape, dtype):
numel = reduce(mul, shape) if len(shape) > 0 else 1
if len(self._grad_layer_buf) <= idx or self._grad_layer_buf[idx].numel() < numel:
new_buf = self._allocate_buffer(shape, dtype=dtype, num_buffers=1)[0]
if len(self._grad_layer_buf) <= idx:
self._grad_layer_buf.append(new_buf)
else:
self._grad_layer_buf[idx] = new_buf
return self._grad_layer_buf[idx]
else:
return self._grad_layer_buf[idx].flatten()[:numel].view(shape)

def forward(self, *args, **kwargs):
"""Disabled for pipeline parallel training. See ``train_batch()``. """
raise PipelineError("Only train_batch() is accessible in pipeline mode.")
Expand Down
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