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[shardformer] update t5 model #5524

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merged 1 commit into from
Apr 3, 2024
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[shardformer] update t5 model #5524

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26 changes: 10 additions & 16 deletions colossalai/shardformer/modeling/t5.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,15 +118,12 @@ def t5_stack_forward(
# required mask seq length can be calculated via length of past
mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length

if attention_mask is None:
attention_mask = torch.ones(batch_size, mask_seq_length, device=device)
if in_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
encoder_seq_length = encoder_hidden_states.shape[1]
encoder_attention_mask = torch.ones(batch_size, encoder_seq_length, device=device, dtype=torch.long)

# initialize past_key_values with `None` if past does not exist
if past_key_values is None:
past_key_values = [None] * len(self.block)

if attention_mask is None:
attention_mask = torch.ones(batch_size, mask_seq_length, device=device)

# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
Expand All @@ -138,7 +135,9 @@ def t5_stack_forward(
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
encoder_attention_mask = torch.ones(
encoder_hidden_shape, device=inputs_embeds.device, dtype=torch.long
)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
Expand All @@ -162,15 +161,8 @@ def t5_stack_forward(
torch.cuda.set_device(hidden_states.device)

if self.gradient_checkpointing and self.training:

def create_custom_forward(module):
def custom_forward(*inputs):
return tuple(module(*inputs, use_cache, output_attentions))

return custom_forward

layer_outputs = checkpoint(
create_custom_forward(layer_module),
layer_outputs = self._gradient_checkpointing_func(
layer_module.forward,
hidden_states,
extended_attention_mask,
position_bias,
Expand All @@ -180,6 +172,8 @@ def custom_forward(*inputs):
layer_head_mask,
cross_attn_layer_head_mask,
None, # past_key_value is always None with gradient checkpointing
use_cache,
output_attentions,
)
else:
layer_outputs = layer_module(
Expand Down