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multi-gpu: fix tensor device placements for various models #35763

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multi-gpu: fix tensor device placements for various models #35763

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dd484cd
milti-gpu: fix inputs_embeds + position_embeds
dvrogozh Jan 18, 2025
e713fb9
multi-gpu: fix tensor device placements for various models
dvrogozh Jan 18, 2025
eae8379
Apply make fix-copies
dvrogozh Jan 22, 2025
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4 changes: 3 additions & 1 deletion src/transformers/models/aria/modeling_aria.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1113,7 +1113,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 2 additions & 2 deletions src/transformers/models/bart/modeling_bart.py
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Original file line number Diff line number Diff line change
Expand Up @@ -495,8 +495,8 @@ def forward(
# but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
key_states.to(query_states.device),
value_states.to(query_states.device),
attn_mask=attention_mask,
dropout_p=self.dropout if self.training else 0.0,
is_causal=is_causal,
Expand Down
4 changes: 2 additions & 2 deletions src/transformers/models/biogpt/modeling_biogpt.py
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Expand Up @@ -329,8 +329,8 @@ def forward(
# but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
key_states.to(query_states.device),
value_states.to(query_states.device),
attn_mask=attention_mask,
dropout_p=self.dropout if self.training else 0.0,
is_causal=is_causal,
Expand Down
4 changes: 2 additions & 2 deletions src/transformers/models/blenderbot/modeling_blenderbot.py
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Expand Up @@ -310,15 +310,15 @@ def forward(
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = residual + hidden_states.to(residual.device)

residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = residual + hidden_states.to(residual.device)

if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/bloom/modeling_bloom.py
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Expand Up @@ -845,7 +845,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/chameleon/modeling_chameleon.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1490,7 +1490,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/codegen/modeling_codegen.py
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Original file line number Diff line number Diff line change
Expand Up @@ -688,7 +688,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/cohere/modeling_cohere.py
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Original file line number Diff line number Diff line change
Expand Up @@ -763,7 +763,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/cohere2/modeling_cohere2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -734,7 +734,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 2 additions & 2 deletions src/transformers/models/data2vec/modeling_data2vec_audio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -691,8 +691,8 @@ def forward(
# but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
key_states.to(query_states.device),
value_states.to(query_states.device),
attn_mask=attention_mask,
dropout_p=self.dropout if self.training else 0.0,
is_causal=is_causal,
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/dbrx/modeling_dbrx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1216,7 +1216,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/diffllama/modeling_diffllama.py
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Expand Up @@ -1002,7 +1002,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/emu3/modeling_emu3.py
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Expand Up @@ -1582,7 +1582,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/falcon/modeling_falcon.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1146,7 +1146,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
1 change: 1 addition & 0 deletions src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
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Expand Up @@ -309,6 +309,7 @@ def slow_forward(
) # [batch, intermediate_size, seq_len]
else:
conv_state = cache_params.update_conv_state(self.layer_idx, hidden_states, cache_position)
conv_state = conv_state.to(self.conv1d.weight.device)
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FYI reviewers, this fix was basically taken from here:

transformers/src/transformers/models/mamba2/modeling_mamba2.py

Line 482 in 5fa3534

conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device)

hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
if self.use_conv_bias:
hidden_states += self.conv1d.bias
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/gemma/modeling_gemma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -734,7 +734,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/gemma2/modeling_gemma2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -741,7 +741,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/glm/modeling_glm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -744,7 +744,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
2 changes: 1 addition & 1 deletion src/transformers/models/gpt2/modeling_gpt2.py
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Expand Up @@ -818,7 +818,7 @@ def forward(
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
hidden_states = inputs_embeds + position_embeds.to(inputs_embeds.device)
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FYI reviewers, this fix was taken from here:

transformers/src/transformers/models/whisper/modeling_whisper.py

Line 1270 in 5fa3534

hidden_states = inputs_embeds + positions.to(inputs_embeds.device)

See #30836 (comment) for associated discussion.


# Attention mask.
_use_sdpa = self._attn_implementation == "sdpa" and output_attentions is False and head_mask is None
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2 changes: 1 addition & 1 deletion src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py
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Expand Up @@ -959,7 +959,7 @@ def forward(
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
hidden_states = inputs_embeds + position_embeds.to(inputs_embeds.device)

if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
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4 changes: 3 additions & 1 deletion src/transformers/models/gpt_neo/modeling_gpt_neo.py
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Expand Up @@ -897,7 +897,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/gpt_neox/modeling_gpt_neox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1005,7 +1005,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
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Expand Up @@ -768,7 +768,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/gptj/modeling_gptj.py
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Expand Up @@ -996,7 +996,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/granite/modeling_granite.py
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Expand Up @@ -747,7 +747,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/granitemoe/modeling_granitemoe.py
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Expand Up @@ -1236,7 +1236,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/helium/modeling_helium.py
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Expand Up @@ -731,7 +731,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
4 changes: 2 additions & 2 deletions src/transformers/models/hubert/modeling_hubert.py
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Expand Up @@ -765,8 +765,8 @@ def forward(
# but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
key_states.to(query_states.device),
value_states.to(query_states.device),
attn_mask=attention_mask,
dropout_p=self.dropout if self.training else 0.0,
is_causal=is_causal,
Expand Down
4 changes: 3 additions & 1 deletion src/transformers/models/idefics/modeling_idefics.py
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Expand Up @@ -1468,7 +1468,9 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
Expand Down
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