mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-05 02:51:59 +00:00
[Shardformer] Support the Qwen2 model (#5699)
* feat: support qwen2 model * fix: modify model config and add Qwen2RMSNorm * fix qwen2 model conflicts * test: add qwen2 shard test * to: add qwen2 auto policy * support qwen model * fix the conflicts * add try catch * add transformers version for qwen2 * add the ColoAttention for the qwen2 model * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add the unit test version check * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix the test input bug * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix the version check * fix the version check --------- Co-authored-by: Wenhao Chen <cwher@outlook.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Wang Binluo
GitHub
758
colossalai/shardformer/modeling/qwen2.py
Normal file
758
colossalai/shardformer/modeling/qwen2.py
Normal file
@@ -0,0 +1,758 @@
|
||||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
||||
from transformers.modeling_outputs import (
|
||||
BaseModelOutputWithPast,
|
||||
CausalLMOutputWithPast,
|
||||
SequenceClassifierOutputWithPast,
|
||||
)
|
||||
|
||||
try:
|
||||
from transformers.models.qwen2.modeling_qwen2 import (
|
||||
Qwen2ForCausalLM,
|
||||
Qwen2ForSequenceClassification,
|
||||
Qwen2Model,
|
||||
_prepare_4d_causal_attention_mask,
|
||||
_prepare_4d_causal_attention_mask_for_sdpa,
|
||||
)
|
||||
except ImportError:
|
||||
Qwen2Model = "Qwen2Model"
|
||||
Qwen2ForSequenceClassification = "Qwen2ForSequenceClassification"
|
||||
Qwen2ForCausalLM = "Qwen2ForCausalLM"
|
||||
|
||||
from transformers.utils import logging
|
||||
|
||||
from colossalai.pipeline.stage_manager import PipelineStageManager
|
||||
from colossalai.shardformer.shard import ShardConfig
|
||||
|
||||
from ..layer import ColoAttention, cross_entropy_1d
|
||||
|
||||
|
||||
class Qwen2PipelineForwards:
|
||||
"""
|
||||
This class serves as a micro library for forward function substitution of Qwen2 models
|
||||
under pipeline setting.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def qwen2_model_forward(
|
||||
self: Qwen2Model,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = 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,
|
||||
shard_config: ShardConfig = None,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
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
|
||||
|
||||
# retrieve input_ids and inputs_embeds
|
||||
if stage_manager.is_first_stage():
|
||||
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:
|
||||
batch_size, seq_length = input_ids.shape
|
||||
elif inputs_embeds is not None:
|
||||
batch_size, seq_length, _ = inputs_embeds.shape
|
||||
else:
|
||||
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
||||
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
hidden_states = inputs_embeds
|
||||
else:
|
||||
input_shape = hidden_states.shape[:-1]
|
||||
batch_size, seq_length = input_shape
|
||||
device = hidden_states.device
|
||||
|
||||
seq_length_with_past = seq_length
|
||||
past_key_values_length = 0
|
||||
|
||||
# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
|
||||
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
|
||||
|
||||
# assert past_key_values is None, "past_key_values is not supported for Qwen2 models at the moment."
|
||||
|
||||
if past_key_values is not None:
|
||||
past_key_values_length = past_key_values[0][0].shape[2]
|
||||
seq_length_with_past = seq_length_with_past + past_key_values_length
|
||||
|
||||
if position_ids is None:
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
position_ids = torch.arange(
|
||||
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
||||
)
|
||||
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
||||
else:
|
||||
position_ids = position_ids.view(-1, seq_length).long()
|
||||
|
||||
if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
|
||||
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
|
||||
if is_padding_right:
|
||||
raise ValueError(
|
||||
"You are attempting to perform batched generation with padding_side='right'"
|
||||
" this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to "
|
||||
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
|
||||
)
|
||||
# embed positions, for the first stage, hidden_states is the input embeddings,
|
||||
# for the other stages, hidden_states is the output of the previous stage
|
||||
if shard_config.enable_flash_attention:
|
||||
# in this case, attention_mask is a dict rather than a tensor
|
||||
mask_shape = (batch_size, 1, seq_length_with_past, seq_length_with_past)
|
||||
attention_mask = ColoAttention.prepare_attn_kwargs(
|
||||
mask_shape,
|
||||
hidden_states.dtype,
|
||||
hidden_states.device,
|
||||
q_padding_mask=attention_mask,
|
||||
is_causal=True,
|
||||
)
|
||||
else:
|
||||
if self._attn_implementation == "flash_attention_2":
|
||||
# 2d mask is passed through the layers
|
||||
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
||||
elif self._attn_implementation == "sdpa" and not output_attentions:
|
||||
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
||||
# the manual implementation that requires a 4D causal mask in all cases.
|
||||
|
||||
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
||||
attention_mask,
|
||||
(batch_size, seq_length),
|
||||
inputs_embeds,
|
||||
past_key_values_length,
|
||||
)
|
||||
else:
|
||||
# 4d mask is passed through the layers
|
||||
|
||||
attention_mask = _prepare_4d_causal_attention_mask(
|
||||
attention_mask,
|
||||
(batch_size, seq_length),
|
||||
hidden_states,
|
||||
past_key_values_length,
|
||||
sliding_window=self.config.sliding_window,
|
||||
)
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
next_decoder_cache = None
|
||||
|
||||
start_idx, end_idx = stage_index[0], stage_index[1]
|
||||
for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx):
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
decoder_layer.__call__,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
past_key_values,
|
||||
output_attentions,
|
||||
use_cache,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
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():
|
||||
hidden_states = self.norm(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,
|
||||
)
|
||||
# always return dict for imediate stage
|
||||
return {"hidden_states": hidden_states}
|
||||
|
||||
@staticmethod
|
||||
def qwen2_for_causal_lm_forward(
|
||||
self: Qwen2ForCausalLM,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = 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,
|
||||
shard_config: ShardConfig = None,
|
||||
):
|
||||
r"""
|
||||
Args:
|
||||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||||
|
||||
Returns:
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, Qwen2ForCausalLM
|
||||
|
||||
>>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
||||
|
||||
>>> prompt = "Hey, are you consciours? Can you talk to me?"
|
||||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||||
|
||||
>>> # Generate
|
||||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||||
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
|
||||
```"""
|
||||
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
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
|
||||
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
|
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||||
outputs = Qwen2PipelineForwards.qwen2_model_forward(
|
||||
self.model,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
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,
|
||||
shard_config=shard_config,
|
||||
)
|
||||
past_key_values = None
|
||||
|
||||
if stage_manager.is_last_stage():
|
||||
hidden_states = outputs[0]
|
||||
logits = self.lm_head(hidden_states)
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# Shift so that tokens < n predict n
|
||||
shift_logits = logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
shift_labels = shift_labels.view(-1)
|
||||
# Enable model parallelism
|
||||
shift_labels = shift_labels.to(shift_logits.device)
|
||||
if shard_config.enable_tensor_parallelism:
|
||||
new_vocab_size = logits.shape[-1]
|
||||
shift_logits = shift_logits.view(-1, new_vocab_size)
|
||||
loss = cross_entropy_1d(
|
||||
shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group
|
||||
)
|
||||
else:
|
||||
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
||||
loss = loss_fct(shift_logits, shift_labels)
|
||||
|
||||
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 qwen2_for_sequence_classification_forward(
|
||||
self: Qwen2ForSequenceClassification,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = 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,
|
||||
shard_config: ShardConfig = None,
|
||||
):
|
||||
r"""
|
||||
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||||
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
||||
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
||||
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
||||
"""
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
|
||||
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
|
||||
|
||||
transformer_outputs = Qwen2PipelineForwards.qwen2_model_forward(
|
||||
self.model,
|
||||
input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
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,
|
||||
shard_config=shard_config,
|
||||
)
|
||||
|
||||
if input_ids is not None:
|
||||
batch_size = input_ids.shape[0]
|
||||
elif inputs_embeds is not None:
|
||||
batch_size = inputs_embeds.shape[0]
|
||||
else:
|
||||
batch_size = hidden_states.shape[0]
|
||||
|
||||
if stage_manager.is_last_stage():
|
||||
hidden_states = transformer_outputs[0]
|
||||
logits = self.score(hidden_states)
|
||||
|
||||
if self.config.pad_token_id is None and batch_size != 1:
|
||||
print(self.config.pad_token_id)
|
||||
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
||||
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
|
||||
|
||||
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
labels = labels.to(logits.device)
|
||||
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}
|
||||
|
||||
|
||||
def get_qwen2_flash_attention_forward(shard_config: ShardConfig):
|
||||
from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention, apply_rotary_pos_emb, repeat_kv
|
||||
|
||||
from colossalai.shardformer.layer import ColoAttention
|
||||
|
||||
def forward(
|
||||
self: Qwen2Attention,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
**kwargs,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if past_key_value is not None:
|
||||
if self.layer_idx is None:
|
||||
raise ValueError(
|
||||
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
||||
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
||||
"with a layer index."
|
||||
)
|
||||
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
||||
# Because the input can be padded, the absolute sequence length depends on the max position id.
|
||||
rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
|
||||
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||||
|
||||
if past_key_value is not None:
|
||||
# Activate slicing cache only if the config has a value `sliding_windows` attribute
|
||||
cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
|
||||
if (
|
||||
getattr(self.config, "sliding_window", None) is not None
|
||||
and kv_seq_len > self.config.sliding_window
|
||||
and cache_has_contents
|
||||
):
|
||||
slicing_tokens = 1 - self.config.sliding_window
|
||||
|
||||
past_key = past_key_value[self.layer_idx][0]
|
||||
past_value = past_key_value[self.layer_idx][1]
|
||||
|
||||
past_key = past_key[:, :, slicing_tokens:, :].contiguous()
|
||||
past_value = past_value[:, :, slicing_tokens:, :].contiguous()
|
||||
|
||||
if past_key.shape[-2] != self.config.sliding_window - 1:
|
||||
raise ValueError(
|
||||
f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
|
||||
f" {past_key.shape}"
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
attention_mask = attention_mask[:, slicing_tokens:]
|
||||
attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
|
||||
|
||||
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
# repeat k/v heads if n_kv_heads < n_heads
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
assert isinstance(attention_mask, dict), "Flash Attention Error: attention_mask should be a dict."
|
||||
attn_output = ColoAttention.attention(query_states, key_states, value_states, **attention_mask)
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
return attn_output, None, past_key_value
|
||||
|
||||
return forward
|
||||
|
||||
|
||||
def get_qwen2_model_forward_for_flash_attn(shard_config: ShardConfig):
|
||||
logger = logging.get_logger(__name__)
|
||||
assert shard_config.enable_flash_attention, "Flash Attention is not enabled."
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = 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,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
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
|
||||
|
||||
# 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:
|
||||
batch_size, seq_length = input_ids.shape
|
||||
elif inputs_embeds is not None:
|
||||
batch_size, seq_length, _ = inputs_embeds.shape
|
||||
else:
|
||||
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
||||
|
||||
seq_length_with_past = seq_length
|
||||
past_key_values_length = 0
|
||||
|
||||
if position_ids is None:
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
position_ids = torch.arange(
|
||||
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
||||
)
|
||||
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
||||
else:
|
||||
position_ids = position_ids.view(-1, seq_length).long()
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
|
||||
# embed positions
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
# in this case, attention_mask is a dict rather than a tensor
|
||||
mask_shape = (batch_size, 1, seq_length_with_past, seq_length_with_past)
|
||||
attention_mask = ColoAttention.prepare_attn_kwargs(
|
||||
mask_shape,
|
||||
hidden_states.dtype,
|
||||
hidden_states.device,
|
||||
q_padding_mask=attention_mask,
|
||||
is_causal=True,
|
||||
)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
if use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||||
)
|
||||
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 = None
|
||||
|
||||
for decoder_layer in self.layers:
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
decoder_layer.__call__,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
past_key_values,
|
||||
output_attentions,
|
||||
use_cache,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
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],)
|
||||
|
||||
hidden_states = self.norm(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 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,
|
||||
)
|
||||
|
||||
return forward
|
||||
|
||||
|
||||
def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
|
||||
def forward(
|
||||
self: Qwen2ForCausalLM,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = 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,
|
||||
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||||
r"""
|
||||
Args:
|
||||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||||
|
||||
Returns:
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, Qwen2ForCausalLM
|
||||
|
||||
>>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
||||
|
||||
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
||||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||||
|
||||
>>> # Generate
|
||||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||||
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
||||
```"""
|
||||
|
||||
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 = self.model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
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,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
logits = self.lm_head(hidden_states)
|
||||
logits = logits.float()
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# Shift so that tokens < n predict n
|
||||
shift_logits = logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
shift_labels = shift_labels.view(-1)
|
||||
# Enable model parallelism
|
||||
shift_labels = shift_labels.to(shift_logits.device)
|
||||
if shard_config.enable_tensor_parallelism:
|
||||
new_vocab_size = logits.shape[-1]
|
||||
shift_logits = shift_logits.view(-1, new_vocab_size)
|
||||
loss = cross_entropy_1d(
|
||||
shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group
|
||||
)
|
||||
else:
|
||||
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
||||
loss = loss_fct(shift_logits, shift_labels)
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
return forward
|
||||
Reference in New Issue
Block a user