github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-06-26 07:22:12 +00:00
Code Issues Packages Projects Releases Wiki Activity

[upgrade]upgrade mistral (#6296)

Browse Source 
* upgrade mistral

* fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
flybird11111 2025-05-21 16:14:45 +08:00 committed by GitHub
parent 04516bb756
commit 6875a8a1cf
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 79 additions and 160 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

218
colossalai/shardformer/modeling/mistral.py
View File

@ -4,10 +4,6 @@ from typing import List, Optional, Tuple, Union
import torch
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import (
_prepare_4d_causal_attention_mask,
_prepare_4d_causal_attention_mask_for_sdpa,
)
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
@ -36,7 +32,7 @@ class MistralForwards:
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
@ -50,8 +46,6 @@ class MistralForwards:
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
# retrieve input_ids and inputs_embeds
if stage_manager.is_first_stage():
if input_ids is not None and inputs_embeds is not None:
@ -67,20 +61,23 @@ class MistralForwards:
else:
input_shape = hidden_states.shape[:-1]
batch_size, seq_length = input_shape
device = hidden_states.device
hidden_states.device
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 use_cache and past_key_values is None:
past_key_values = DynamicCache()
if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
if attention_mask is not None and self.config._attn_implementation == "flash_attention_2" and use_cache:
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
if is_padding_right:
raise ValueError(
@ -100,27 +97,9 @@ class MistralForwards:
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,
)
attention_mask = self._update_causal_mask(
attention_mask, hidden_states, cache_position, past_key_values, output_attentions
)
if self.gradient_checkpointing and self.training:
if use_cache:
@ -133,6 +112,8 @@ class MistralForwards:
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
position_embeddings = self.rotary_emb(hidden_states, position_ids)
start_idx, end_idx = stage_index[0], stage_index[1]
num_ckpt_layers = 0
if self.gradient_checkpointing and self.training:
@ -156,11 +137,13 @@ class MistralForwards:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
else:
layer_outputs = decoder_layer(
@ -170,6 +153,8 @@ class MistralForwards:
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
hidden_states = layer_outputs[0]
@ -189,8 +174,6 @@ class MistralForwards:
next_cache = 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,
@ -212,7 +195,8 @@ class MistralForwards:
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
@ -248,7 +232,6 @@ class MistralForwards:
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 = MistralForwards.mistral_model_forward(
@ -261,7 +244,7 @@ class MistralForwards:
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
stage_manager=stage_manager,
hidden_states=hidden_states,
stage_index=stage_index,
@ -278,10 +261,6 @@ class MistralForwards:
if labels is not None:
loss = dist_cross_entropy(labels, logits, shard_config, self.lm_head.out_features, self.model.dtype)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
@ -305,7 +284,6 @@ class MistralForwards:
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,
@ -317,7 +295,6 @@ class MistralForwards:
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).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = MistralForwards.mistral_model_forward(
self.model,
@ -329,7 +306,6 @@ class MistralForwards:
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,
@ -383,9 +359,6 @@ class MistralForwards:
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
else:
hidden_states = transformer_outputs.get("hidden_states")
return {"hidden_states": hidden_states}
@ -413,7 +386,8 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**flash_attn_kwargs,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
@ -421,8 +395,6 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
)
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")
@ -433,27 +405,22 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
past_key_values_length = 0
if use_cache:
use_legacy_cache = not isinstance(past_key_values, Cache)
if use_legacy_cache:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
past_key_values_length = past_key_values.get_usable_length(seq_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 inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
if attention_mask is not None and self.config._attn_implementation == "flash_attention_2" and use_cache:
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
if is_padding_right:
raise ValueError(
@ -471,31 +438,11 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
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),
inputs_embeds,
past_key_values_length,
sliding_window=self.config.sliding_window,
)
hidden_states = inputs_embeds
position_embeddings = self.rotary_emb(hidden_states, position_ids)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
@ -506,37 +453,25 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
# 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:
for decoder_layer in self.layers[: self.config.num_hidden_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,
)
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,
cache_position=cache_position,
position_embeddings=position_embeddings,
**flash_attn_kwargs,
)
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],)
@ -546,15 +481,12 @@ def get_mistral_model_forward_for_flash_attn(shard_config: ShardConfig):
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = None
if use_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
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)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
@ -568,11 +500,10 @@ def get_mistral_flash_attention_forward(shard_config: ShardConfig):
def forward(
self: MistralAttention,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if "padding_mask" in kwargs:
@ -585,9 +516,9 @@ def get_mistral_flash_attention_forward(shard_config: ShardConfig):
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)
query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
@ -598,11 +529,12 @@ def get_mistral_flash_attention_forward(shard_config: ShardConfig):
"with a layer index."
)
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # 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
@ -613,11 +545,11 @@ def get_mistral_flash_attention_forward(shard_config: ShardConfig):
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 = attn_output.reshape(bsz, q_len, -1)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
return attn_output, None
return forward

19
colossalai/shardformer/policies/mistral.py
View File

@ -38,24 +38,10 @@ class MistralPolicy(Policy):
return self.model
def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
from transformers.models.mistral.modeling_mistral import (
MistralAttention,
MistralDecoderLayer,
MistralFlashAttention2,
MistralModel,
MistralSdpaAttention,
)
ATTN_IMPLEMENTATION = {
"eager": MistralAttention,
"flash_attention_2": MistralFlashAttention2,
"sdpa": MistralSdpaAttention,
}
from transformers.models.mistral.modeling_mistral import MistralAttention, MistralDecoderLayer, MistralModel
policy = {}
attn_cls = ATTN_IMPLEMENTATION[self.model.config._attn_implementation]
embedding_cls = None
if self.shard_config.enable_tensor_parallelism:
embedding_cls = VocabParallelEmbedding1D
@ -258,7 +244,7 @@ class MistralPolicy(Policy):
"forward": get_mistral_flash_attention_forward(self.shard_config),
},
policy=policy,
target_key=attn_cls,
target_key=MistralAttention,
)
if self.pipeline_stage_manager is None:
# replace llama model forward method
@ -316,6 +302,7 @@ class MistralPolicy(Policy):
stage_manager = self.pipeline_stage_manager
held_layers = []
held_layers.append(module.rotary_emb)
if stage_manager.is_interleave:
assert stage_manager.num_model_chunks is not None
layers_per_stage = stage_manager.distribute_layers(len(module.layers))

2
tests/test_shardformer/test_model/test_shard_mistral.py
View File

@ -23,6 +23,7 @@ from tests.test_shardformer.test_model._utils import (
os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
@clear_cache_before_run()
def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
model_fn, loss_fn, test_config
@ -176,7 +177,6 @@ def check_mistral(rank, world_size, port):
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_mistral():
spawn(check_mistral, 4)