def _apply_model_parallel(self, tensor, encoder_output, encoder_mask, incr_state):
"""
Pipeline application of model parallelism.
"""
chunks = PipelineHelper.split(
(tensor, encoder_output, encoder_mask, incr_state)
)
work_items = PipelineHelper.schedule_work_items(self.layers, chunks)
new_incr_state = {i: [] for i, _ in enumerate(self.layers)}
for chunk_idx, layer_nos, next_device in work_items:
s_tensor, s_enc_out, s_enc_mask, s_incr_state = chunks[chunk_idx]
for layer_no in layer_nos:
s_tensor, nis = self.layers[layer_no](
x=s_tensor,
encoder_output=s_enc_out,
encoder_mask=s_enc_mask,
incr_state=s_incr_state.get(layer_no),
)
new_incr_state[layer_no].append(nis)
# don't move incr state, it's always on the correct device
s_tensor, s_enc_out, s_enc_mask = PipelineHelper.chunk_to(
(s_tensor, s_enc_out, s_enc_mask), next_device
)
chunks[chunk_idx] = (s_tensor, s_enc_out, s_enc_mask, s_incr_state)
tensor_out = PipelineHelper.join([c[0] for c in chunks])
new_incr_state = {
layer_no: PipelineHelper.join(pieces)
for layer_no, pieces in new_incr_state.items()
}
return tensor_out, new_incr_state
def _apply_model_parallel(self, tensor, encoder_output, encoder_mask,
incr_state):
"""
Pipeline application of model parallelism.
"""
chunks = PipelineHelper.split(
(tensor, encoder_output, encoder_mask, incr_state))
work_items = PipelineHelper.schedule_work_items(self.layers, chunks)
new_incr_state = [{} for _ in chunks]
for chunk_idx, layer_nos, next_device in work_items:
s_tensor, s_enc_out, s_enc_mask, s_incr_state = chunks[chunk_idx]
for layer_no in layer_nos:
s_tensor, new_incr_state[chunk_idx][layer_no] = self.layers[
layer_no](
x=s_tensor,
encoder_output=s_enc_out,
encoder_mask=s_enc_mask,
incr_state=s_incr_state.get(layer_no),
)
chunks[chunk_idx] = PipelineHelper.chunk_to(
(s_tensor, s_enc_out, s_enc_mask, s_incr_state), next_device)
tensor_out = PipelineHelper.join([c[0] for c in chunks])
new_incr_state = PipelineHelper.join(new_incr_state)
return tensor_out, new_incr_state
def _apply_model_parallel_with_extra(
self,
tensor,
encoder_output,
encoder_mask,
incr_state,
extra_output: torch.Tensor = None,
extra_mask: torch.Tensor = None,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Copy paste from TransformerDecoder._apply_model_parallel while incorporating the
extra output/extra mask.
"""
chunks = PipelineHelper.split(
(tensor, encoder_output, encoder_mask, incr_state, extra_output, extra_mask)
)
work_items = PipelineHelper.schedule_work_items(self.layers, chunks)
new_incr_state = {i: [] for i, _ in enumerate(self.layers)}
for chunk_idx, layer_nos, next_device in work_items:
s_tensor, s_enc_out, s_enc_mask, s_incr_state, s_extra_out, s_extra_mask = chunks[
chunk_idx
]
for layer_no in layer_nos:
s_tensor, nis = self.layers[layer_no](
x=s_tensor,
encoder_output=s_enc_out,
encoder_mask=s_enc_mask,
incr_state=s_incr_state.get(layer_no),
extra_output=s_extra_out,
extra_mask=s_extra_mask,
)
new_incr_state[layer_no].append(nis)
# don't move incr state, it's always on the correct device
s_tensor, s_enc_out, s_enc_mask, s_extra_out, s_extra_mask = PipelineHelper.chunk_to(
(s_tensor, s_enc_out, s_enc_mask, s_extra_out, s_extra_mask),
next_device,
)
chunks[chunk_idx] = (
s_tensor,
s_enc_out,
s_enc_mask,
s_incr_state,
s_extra_out,
s_extra_mask,
)
tensor_out = PipelineHelper.join([c[0] for c in chunks])
new_incr_state = {
layer_no: PipelineHelper.join(pieces)
for layer_no, pieces in new_incr_state.items()
}
return tensor_out, new_incr_state # type: ignore
def _apply_model_parallel(self, tensor, mask):
"""
Pipeline application of model parallelism.
"""
chunks = PipelineHelper.split((tensor, mask))
work_items = PipelineHelper.schedule_work_items(self.layers, chunks)
for chunk_idx, layer_nos, next_device in work_items:
s_tensor, s_mask = chunks[chunk_idx]
for layer_no in layer_nos:
s_tensor = self.layers[layer_no](s_tensor, s_mask)
chunks[chunk_idx] = PipelineHelper.chunk_to((s_tensor, s_mask), next_device)
tensor_out, mask_out = PipelineHelper.join(chunks)
return tensor_out
def _apply_model_parallel(self, tensor, mask,
**kwargs) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Override to return attention weights.
"""
chunks = PipelineHelper.split((tensor, mask))
work_items = PipelineHelper.schedule_work_items(self.layers, chunks)
for chunk_idx, layer_nos, next_device in work_items:
s_weights = None
try:
s_tensor, s_mask = chunks[chunk_idx]
except ValueError:
s_tensor, s_mask, s_weights = chunks[chunk_idx]
for layer_no in layer_nos:
s_tensor, s_weights = self.layers[layer_no](s_tensor, s_mask,
**kwargs)
chunks[chunk_idx] = PipelineHelper.chunk_to(
(s_tensor, s_mask, s_weights), next_device)
joined = PipelineHelper.join(chunks)
tensor_out, out_mask, weights = joined
return tensor_out, weights
