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[shardformer] fix modeling of bloom and falcon #5796

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Merged
ver217 merged 1 commit into from
Jun 11, 2024
Merged

[shardformer] fix modeling of bloom and falcon #5796

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5 changes: 4 additions & 1 deletion colossalai/shardformer/modeling/bloom.py
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Original file line number Diff line number Diff line change
Expand Up @@ -475,7 +475,10 @@ def bloom_for_sequence_classification_forward(
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
sequence_lengths = sequence_lengths % input_ids.shape[-1]
sequence_lengths = sequence_lengths.to(logits.device)
else:
sequence_lengths = -1
logger.warning(
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14 changes: 8 additions & 6 deletions colossalai/shardformer/modeling/falcon.py
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Expand Up @@ -291,18 +291,17 @@ def falcon_model_forward(
if attention_mask_2d is None:
attention_mask = alibi / math.sqrt(self.config.hidden_size // self.num_heads)
else:
min_dtype = torch.finfo(alibi.dtype).min
attention_mask = torch.masked_fill(
alibi / math.sqrt(self.config.hidden_size // self.num_heads),
attention_mask < -1,
torch.finfo(alibi.dtype).min,
min_dtype,
)

# From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend
# produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213
if seq_length > 1:
attention_mask = AttentionMaskConverter._unmask_unattended(
attention_mask, attention_mask_2d, unmasked_value=0.0
)
if seq_length > 1 and attention_mask.device.type == "cuda":
attention_mask = AttentionMaskConverter._unmask_unattended(attention_mask, min_dtype=min_dtype)
else:
# PyTorch SDPA does not support head_mask, we fall back on the eager implementation in this case.
attention_mask = _prepare_4d_causal_attention_mask(
Expand Down Expand Up @@ -543,7 +542,10 @@ def falcon_for_sequence_classification_forward(
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1).to(logits.device)
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
sequence_lengths = sequence_lengths % input_ids.shape[-1]
sequence_lengths = sequence_lengths.to(logits.device)
else:
sequence_lengths = -1
logger.warning(
Expand Down