mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-07 20:10:17 +00:00
[shardformer] rewrite tests for opt/bloom/llama/vit/chatglm (#4395)
* rewrite opt tests * rewrite llama tests * rewrite bloom & vit tests * rewrite chatglm tests * fix LinearCol for classfiers * add judge for other tp layers, fix lazy init in util
This commit is contained in:
Baizhou Zhang
committed by
Hongxin Liu
Hongxin Liu
parent
21e0a42fd1
commit
7711bd524a
@@ -1,7 +1,500 @@
|
||||
from typing import Optional, Tuple
|
||||
import random
|
||||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
||||
from transformers.modeling_outputs import (
|
||||
BaseModelOutputWithPast,
|
||||
CausalLMOutputWithPast,
|
||||
QuestionAnsweringModelOutput,
|
||||
SequenceClassifierOutputWithPast,
|
||||
)
|
||||
from transformers.models.opt.modeling_opt import (
|
||||
OPTForCausalLM,
|
||||
OPTForQuestionAnswering,
|
||||
OPTForSequenceClassification,
|
||||
OPTModel,
|
||||
)
|
||||
from transformers.utils import logging
|
||||
|
||||
from colossalai.pipeline.stage_manager import PipelineStageManager
|
||||
|
||||
|
||||
class OPTPipelineForwards:
|
||||
'''
|
||||
This class serves as a micro library for forward function substitution of OPT models
|
||||
under pipeline setting.
|
||||
'''
|
||||
|
||||
@staticmethod
|
||||
def _prepare_decoder_attention_mask(attention_mask, input_shape, _dtype, device, past_key_values_length):
|
||||
# create causal mask
|
||||
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
||||
from transformers.models.opt.modeling_opt import _make_causal_mask
|
||||
combined_attention_mask = None
|
||||
if input_shape[-1] > 1:
|
||||
combined_attention_mask = _make_causal_mask(
|
||||
input_shape,
|
||||
_dtype,
|
||||
device,
|
||||
past_key_values_length=past_key_values_length,
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
||||
expanded_attn_mask = OPTPipelineForwards._expand_mask(attention_mask, _dtype,
|
||||
tgt_len=input_shape[-1]).to(device)
|
||||
combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask +
|
||||
combined_attention_mask)
|
||||
|
||||
return combined_attention_mask
|
||||
|
||||
@staticmethod
|
||||
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
||||
"""
|
||||
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
||||
"""
|
||||
bsz, src_len = mask.size()
|
||||
tgt_len = tgt_len if tgt_len is not None else src_len
|
||||
|
||||
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
||||
|
||||
inverted_mask = 1.0 - expanded_mask
|
||||
|
||||
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
||||
|
||||
@staticmethod
|
||||
def opt_model_forward(
|
||||
self: OPTModel,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
head_mask: Optional[torch.Tensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
stage_manager: Optional[PipelineStageManager] = None,
|
||||
hidden_states: Optional[torch.FloatTensor] = None,
|
||||
stage_index: Optional[List[int]] = None,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
'''
|
||||
This forward method is modified based on transformers.models.opt.modeling_opt.OPTModel.forward
|
||||
'''
|
||||
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast
|
||||
from transformers.utils import logging
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (output_hidden_states
|
||||
if output_hidden_states is not None else self.config.output_hidden_states)
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
decoder = self.decoder
|
||||
if stage_manager.is_first_stage():
|
||||
# retrieve input_ids and inputs_embeds
|
||||
if input_ids is not None and inputs_embeds is not None:
|
||||
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
||||
elif input_ids is not None:
|
||||
input_shape = input_ids.size()
|
||||
input_ids = input_ids.view(-1, input_shape[-1])
|
||||
elif inputs_embeds is not None:
|
||||
input_shape = inputs_embeds.size()[:-1]
|
||||
else:
|
||||
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
||||
|
||||
batch_size, seq_length = input_shape
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = decoder.embed_tokens(input_ids)
|
||||
|
||||
if decoder.project_in is not None:
|
||||
inputs_embeds = decoder.project_in(inputs_embeds)
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
_dtype = inputs_embeds.dtype
|
||||
|
||||
else:
|
||||
if hidden_states is None:
|
||||
raise ValueError("hidden_states shouln't be None for intermediate stages.")
|
||||
input_shape = hidden_states.size()[:-1]
|
||||
batch_size, seq_length = input_shape[0], input_shape[1]
|
||||
device = hidden_states.device
|
||||
_dtype = hidden_states.dtype
|
||||
|
||||
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
||||
# required mask seq length can be calculated via length of past
|
||||
mask_seq_length = past_key_values_length + seq_length
|
||||
# embed positions
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones(batch_size, mask_seq_length, device=device)
|
||||
elif attention_mask.shape[1] != mask_seq_length:
|
||||
raise ValueError(
|
||||
f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
|
||||
f"{mask_seq_length} (sum of the lengths of current and past inputs)")
|
||||
|
||||
causal_attention_mask = OPTPipelineForwards._prepare_decoder_attention_mask(attention_mask, input_shape, _dtype,
|
||||
device, past_key_values_length)
|
||||
|
||||
if stage_manager.is_first_stage():
|
||||
pos_embeds = decoder.embed_positions(attention_mask, past_key_values_length)
|
||||
hidden_states = inputs_embeds + pos_embeds
|
||||
|
||||
if decoder.gradient_checkpointing and decoder.training:
|
||||
if use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
|
||||
use_cache = False
|
||||
|
||||
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
|
||||
if past_key_values:
|
||||
logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.')
|
||||
past_key_values = None
|
||||
if output_attentions:
|
||||
logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
|
||||
output_attentions = False
|
||||
if output_hidden_states:
|
||||
logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
|
||||
output_hidden_states = False
|
||||
if use_cache:
|
||||
logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
|
||||
use_cache = False
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
next_decoder_cache = () if use_cache else None
|
||||
|
||||
# check if head_mask has a correct number of layers specified if desired
|
||||
for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
|
||||
if attn_mask is not None:
|
||||
if attn_mask.size()[0] != (len(decoder.layers)):
|
||||
raise ValueError(
|
||||
f"The `{mask_name}` should be specified for {len(decoder.layers)} layers, but it is for"
|
||||
f" {head_mask.size()[0]}.")
|
||||
|
||||
start_idx, end_idx = stage_index[0], stage_index[1]
|
||||
|
||||
torch.cuda.set_device(device)
|
||||
|
||||
for idx in range(start_idx, end_idx):
|
||||
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
|
||||
decoder_layer = decoder.layers[idx]
|
||||
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
dropout_probability = random.uniform(0, 1)
|
||||
if decoder.training and (dropout_probability < decoder.layerdrop):
|
||||
continue
|
||||
|
||||
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
||||
|
||||
if decoder.gradient_checkpointing and decoder.training:
|
||||
|
||||
def create_custom_forward(module):
|
||||
|
||||
def custom_forward(*inputs):
|
||||
# None for past_key_value
|
||||
return module(*inputs, output_attentions, None)
|
||||
|
||||
return custom_forward
|
||||
|
||||
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(decoder_layer),
|
||||
hidden_states,
|
||||
causal_attention_mask,
|
||||
head_mask[idx] if head_mask is not None else None,
|
||||
None,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_attention_mask,
|
||||
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if use_cache:
|
||||
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
if stage_manager.is_last_stage():
|
||||
if decoder.final_layer_norm is not None:
|
||||
hidden_states = decoder.final_layer_norm(hidden_states)
|
||||
if decoder.project_out is not None:
|
||||
hidden_states = decoder.project_out(hidden_states)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
next_cache = next_decoder_cache if use_cache else None
|
||||
|
||||
if stage_manager.is_last_stage():
|
||||
if not return_dict:
|
||||
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
||||
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
)
|
||||
else:
|
||||
return {'hidden_states': hidden_states}
|
||||
|
||||
@staticmethod
|
||||
def opt_for_causal_lm_forward(
|
||||
self: OPTForCausalLM,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
head_mask: Optional[torch.Tensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
stage_manager: Optional[PipelineStageManager] = None,
|
||||
hidden_states: Optional[torch.FloatTensor] = None,
|
||||
stage_index: Optional[List[int]] = None,
|
||||
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||||
r"""
|
||||
This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForCausalLM.forward.
|
||||
Please refer to original code of transformers for more details.
|
||||
"""
|
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (output_hidden_states
|
||||
if output_hidden_states is not None else self.config.output_hidden_states)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||||
outputs = OPTPipelineForwards.opt_model_forward(
|
||||
self.model,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
stage_manager=stage_manager,
|
||||
hidden_states=hidden_states,
|
||||
stage_index=stage_index,
|
||||
)
|
||||
if stage_manager.is_last_stage():
|
||||
logits = self.lm_head(outputs[0]).contiguous()
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# move labels to correct device to enable model parallelism
|
||||
labels = labels.to(logits.device)
|
||||
# Shift so that tokens < n predict n
|
||||
shift_logits = logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
|
||||
if not return_dict:
|
||||
output = (logits,) + outputs[1:]
|
||||
return (loss,) + output if loss is not None else output
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
else:
|
||||
hidden_states = outputs.get('hidden_states')
|
||||
return {'hidden_states': hidden_states}
|
||||
|
||||
@staticmethod
|
||||
def opt_for_sequence_classification_forward(
|
||||
self: OPTForSequenceClassification,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
head_mask: Optional[torch.FloatTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
stage_manager: Optional[PipelineStageManager] = None,
|
||||
hidden_states: Optional[torch.FloatTensor] = None,
|
||||
stage_index: Optional[List[int]] = None,
|
||||
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
||||
r"""
|
||||
This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForSequenceClassification.forward.
|
||||
Please refer to original code of transformers for more details.
|
||||
"""
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model,
|
||||
input_ids,
|
||||
past_key_values=past_key_values,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
stage_manager=stage_manager,
|
||||
hidden_states=hidden_states,
|
||||
stage_index=stage_index)
|
||||
|
||||
if stage_manager.is_last_stage():
|
||||
hidden_states = transformer_outputs[0]
|
||||
logits = self.score(hidden_states)
|
||||
|
||||
batch_size = input_ids.shape[0] if input_ids is not None else hidden_states.shape[0]
|
||||
|
||||
if self.config.pad_token_id is None:
|
||||
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)
|
||||
else:
|
||||
sequence_lengths = -1
|
||||
logger.warning(
|
||||
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
||||
"unexpected if using padding tokens in conjunction with `inputs_embeds.`")
|
||||
|
||||
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
if self.config.problem_type is None:
|
||||
if self.num_labels == 1:
|
||||
self.config.problem_type = "regression"
|
||||
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
||||
self.config.problem_type = "single_label_classification"
|
||||
else:
|
||||
self.config.problem_type = "multi_label_classification"
|
||||
|
||||
if self.config.problem_type == "regression":
|
||||
loss_fct = MSELoss()
|
||||
if self.num_labels == 1:
|
||||
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
||||
else:
|
||||
loss = loss_fct(pooled_logits, labels)
|
||||
elif self.config.problem_type == "single_label_classification":
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
||||
elif self.config.problem_type == "multi_label_classification":
|
||||
loss_fct = BCEWithLogitsLoss()
|
||||
loss = loss_fct(pooled_logits, labels)
|
||||
|
||||
if not return_dict:
|
||||
output = (pooled_logits,) + transformer_outputs[1:]
|
||||
return ((loss,) + output) if loss is not None else output
|
||||
|
||||
return SequenceClassifierOutputWithPast(
|
||||
loss=loss,
|
||||
logits=pooled_logits,
|
||||
past_key_values=transformer_outputs.past_key_values,
|
||||
hidden_states=transformer_outputs.hidden_states,
|
||||
attentions=transformer_outputs.attentions,
|
||||
)
|
||||
else:
|
||||
hidden_states = transformer_outputs.get('hidden_states')
|
||||
return {'hidden_states': hidden_states}
|
||||
|
||||
@staticmethod
|
||||
def opt_for_question_answering_forward(
|
||||
self: OPTForQuestionAnswering,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
head_mask: Optional[torch.FloatTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
start_positions: Optional[torch.LongTensor] = None,
|
||||
end_positions: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
stage_manager: Optional[PipelineStageManager] = None,
|
||||
hidden_states: Optional[torch.FloatTensor] = None,
|
||||
stage_index: Optional[List[int]] = None,
|
||||
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
||||
r"""
|
||||
This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForQuestionAnswering.forward.
|
||||
Please refer to original code of transformers for more details.
|
||||
"""
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model,
|
||||
input_ids,
|
||||
past_key_values=past_key_values,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
stage_manager=stage_manager,
|
||||
hidden_states=hidden_states,
|
||||
stage_index=stage_index)
|
||||
if stage_manager.is_last_stage():
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
logits = self.qa_outputs(hidden_states)
|
||||
start_logits, end_logits = logits.split(1, dim=-1)
|
||||
start_logits = start_logits.squeeze(-1).contiguous()
|
||||
end_logits = end_logits.squeeze(-1).contiguous()
|
||||
|
||||
total_loss = None
|
||||
if start_positions is not None and end_positions is not None:
|
||||
# If we are on multi-GPU, split add a dimension
|
||||
if len(start_positions.size()) > 1:
|
||||
start_positions = start_positions.squeeze(-1)
|
||||
if len(end_positions.size()) > 1:
|
||||
end_positions = end_positions.squeeze(-1)
|
||||
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
||||
ignored_index = start_logits.size(1)
|
||||
start_positions = start_positions.clamp(0, ignored_index)
|
||||
end_positions = end_positions.clamp(0, ignored_index)
|
||||
|
||||
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
||||
start_loss = loss_fct(start_logits, start_positions)
|
||||
end_loss = loss_fct(end_logits, end_positions)
|
||||
total_loss = (start_loss + end_loss) / 2
|
||||
|
||||
if not return_dict:
|
||||
output = (start_logits, end_logits) + transformer_outputs[2:]
|
||||
return ((total_loss,) + output) if total_loss is not None else output
|
||||
|
||||
return QuestionAnsweringModelOutput(
|
||||
loss=total_loss,
|
||||
start_logits=start_logits,
|
||||
end_logits=end_logits,
|
||||
hidden_states=transformer_outputs.hidden_states,
|
||||
attentions=transformer_outputs.attentions,
|
||||
)
|
||||
else:
|
||||
hidden_states = transformer_outputs.get('hidden_states')
|
||||
return {'hidden_states': hidden_states}
|
||||
|
||||
|
||||
def get_opt_flash_attention_forward():
|
||||
|
||||
Reference in New Issue
Block a user