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feat: support qwen2 model

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Wenhao Chen 2024-03-04 13:33:11 +08:00 committed by アマデウス
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colossalai/shardformer/modeling/qwen2.py Normal file
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from typing import List, Optional, Tuple, Union
import torch
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
SequenceClassifierOutputWithPast,
)
from transformers.models.qwen2.modeling_qwen2 import (
Qwen2ForCausalLM,
Qwen2ForSequenceClassification,
Qwen2Model,
_prepare_4d_causal_attention_mask,
_prepare_4d_causal_attention_mask_for_sdpa,
)
from transformers.utils import logging
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.shard import ShardConfig
from ..layer import 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,
):
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
# 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."
past_key_values_length = 0
if position_ids is None:
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. "
)
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,
)
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
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:
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,
attention_mask,
position_ids,
None,
)
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,
)
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,
)
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.nn.layer.colo_attention import AttnMaskType, 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:
kv_seq_len += past_key_value[0].shape[-2]
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)
assert past_key_value is None, "past_key_value is not supported for Qwen2 models at the moment."
past_key_value = (key_states, value_states) if use_cache else None
# 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)
me_input_shape = (bsz, q_len, self.num_heads, self.head_dim)
query_states = query_states.transpose(1, 2).contiguous().view(*me_input_shape)
key_states = key_states.transpose(1, 2).contiguous().view(*me_input_shape)
value_states = value_states.transpose(1, 2).contiguous().view(*me_input_shape)
flash_attention_mask = None
attn_mask_type = AttnMaskType.causal
if not getattr(shard_config, "causal_lm", False) and attention_mask != None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous()
attn_mask_type = AttnMaskType.paddedcausal
attention = ColoAttention(embed_dim=self.hidden_size, num_heads=self.num_heads)
attn_output = attention(
query_states,
key_states,
value_states,
attn_mask=flash_attention_mask,
attn_mask_type=attn_mask_type,
origin_attn_mask=attention_mask,
)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
return forward
def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
from transformers import Qwen2ForCausalLM
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

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colossalai/shardformer/policies/qwen2.py Normal file
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import warnings
from functools import partial
from typing import Callable, Dict, List, Union
import torch.nn as nn
from torch import Tensor
from torch.nn import Module
from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, RMSNorm, VocabParallelEmbedding1D
from ..modeling.qwen2 import (
Qwen2PipelineForwards,
get_lm_forward_with_dist_cross_entropy,
get_qwen2_flash_attention_forward,
)
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
__all__ = ["Qwen2Policy", "Qwen2ForCausalLMPolicy", "Qwen2ForSequenceClassificationPolicy"]
class Qwen2Policy(Policy):
def config_sanity_check(self):
pass
def preprocess(self):
if self.shard_config.enable_tensor_parallelism:
# Resize embedding
vocab_size = self.model.config.vocab_size
world_size = self.shard_config.tensor_parallel_size
if vocab_size % world_size != 0:
new_vocab_size = vocab_size + world_size - vocab_size % world_size
self.model.resize_token_embeddings(new_vocab_size)
return self.model
def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention, Qwen2DecoderLayer, Qwen2Model
policy = {}
norm_cls = FusedRMSNorm if self.shard_config.enable_fused_normalization else RMSNorm
if self.shard_config.enable_sequence_parallelism:
self.shard_config.enable_sequence_parallelism = False
warnings.warn("Qwen2 doesn't support sequence parallelism now, will ignore the sequence parallelism flag.")
if self.shard_config.enable_tensor_parallelism:
decoder_attribute_replacement = {
"self_attn.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
"self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
}
if getattr(self.model.config, "num_key_value_heads", False):
decoder_attribute_replacement["self_attn.num_key_value_heads"] = (
self.model.config.num_key_value_heads // self.shard_config.tensor_parallel_size
)
policy[Qwen2DecoderLayer] = ModulePolicyDescription(
attribute_replacement=decoder_attribute_replacement,
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="self_attn.q_proj",
target_module=Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="self_attn.k_proj",
target_module=Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="self_attn.v_proj",
target_module=Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="self_attn.o_proj",
target_module=Linear1D_Row,
),
SubModuleReplacementDescription(
suffix="mlp.gate_proj",
target_module=Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="mlp.up_proj",
target_module=Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="mlp.down_proj",
target_module=Linear1D_Row,
),
],
)
self.append_or_create_submodule_replacement(
description=SubModuleReplacementDescription(
suffix="embed_tokens",
target_module=VocabParallelEmbedding1D,
),
policy=policy,
target_key=Qwen2Model,
)
# optimization configuration
self.append_or_create_submodule_replacement(
description=[
SubModuleReplacementDescription(
suffix="input_layernorm",
target_module=norm_cls,
),
SubModuleReplacementDescription(
suffix="post_attention_layernorm",
target_module=norm_cls,
),
],
policy=policy,
target_key=Qwen2DecoderLayer,
)
self.append_or_create_submodule_replacement(
description=SubModuleReplacementDescription(
suffix="norm",
target_module=norm_cls,
),
policy=policy,
target_key=Qwen2Model,
)
# use flash attention
if self.shard_config.enable_flash_attention:
self.append_or_create_method_replacement(
description={
"forward": get_qwen2_flash_attention_forward(self.shard_config),
},
policy=policy,
target_key=Qwen2Attention,
)
return policy
def postprocess(self):
return self.model
def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
"""If under pipeline parallel setting, replacing the original forward method of huggingface
to customized forward method, and add this changing to policy."""
if self.pipeline_stage_manager is None:
return
stage_manager = self.pipeline_stage_manager
if self.model.__class__.__name__ == "Qwen2Model":
module = self.model
else:
module = self.model.model
if stage_manager.is_interleave:
layers_per_stage = self.distribute_layers(
len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
)
stage_manager.stage_indices = Policy.get_stage_index(
layers_per_stage,
stage_manager.stage,
num_model_chunks=stage_manager.num_model_chunks,
num_stages=stage_manager.num_stages,
)
method_replacement = {
"forward": partial(new_forward, stage_manager=stage_manager, shard_config=self.shard_config)
}
else:
layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
method_replacement = {
"forward": partial(
new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
)
}
self.append_or_create_method_replacement(
description=method_replacement, policy=policy, target_key=model_cls
)
self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
assert self.pipeline_stage_manager is not None
if self.model.__class__.__name__ == "Qwen2Model":
module = self.model
else:
module = self.model.model
stage_manager = self.pipeline_stage_manager
held_layers = []
if stage_manager.is_interleave:
assert stage_manager.num_model_chunks is not None
layers_per_stage = self.distribute_layers(
len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
)
stage_indices = Policy.get_stage_index(
layers_per_stage,
stage_manager.stage,
num_model_chunks=stage_manager.num_model_chunks,
num_stages=stage_manager.num_stages,
)
if stage_manager.is_first_stage(ignore_chunk=True):
held_layers.append(module.embed_tokens)
for start_idx, end_idx in stage_indices:
held_layers.extend(module.layers[start_idx:end_idx])
if stage_manager.is_last_stage(ignore_chunk=True):
held_layers.append(module.norm)
else:
layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
if stage_manager.is_first_stage():
held_layers.append(module.embed_tokens)
start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
held_layers.extend(module.layers[start_idx:end_idx])
if stage_manager.is_last_stage():
held_layers.append(module.norm)
return held_layers
class Qwen2ModelPolicy(Qwen2Policy):
def module_policy(self):
policy = super().module_policy()
from transformers.models.qwen2.modeling_qwen2 import Qwen2Model
if self.pipeline_stage_manager:
# set None as default
self.set_pipeline_forward(
model_cls=Qwen2Model, new_forward=Qwen2PipelineForwards.qwen2_model_forward, policy=policy
)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
held_layers = super().get_held_layers()
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
"""No shared params in Qwen2 model"""
return []
class Qwen2ForCausalLMPolicy(Qwen2Policy):
def module_policy(self):
from transformers import Qwen2ForCausalLM
policy = super().module_policy()
setattr(self.shard_config, "causal_lm", True)
if self.shard_config.enable_tensor_parallelism:
# add a new item for casual lm
new_item = {
Qwen2ForCausalLM: ModulePolicyDescription(
sub_module_replacement=[
SubModuleReplacementDescription(suffix="lm_head", target_module=Linear1D_Col)
],
method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)},
)
}
policy.update(new_item)
if self.pipeline_stage_manager:
# set None as default
self.set_pipeline_forward(
model_cls=Qwen2ForCausalLM, new_forward=Qwen2PipelineForwards.qwen2_for_causal_lm_forward, policy=policy
)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
stage_manager = self.pipeline_stage_manager
held_layers = super().get_held_layers()
if stage_manager.is_last_stage(ignore_chunk=True):
held_layers.append(self.model.lm_head)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
qwen2_model = self.model.model
if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
if (
id(qwen2_model.embed_tokens.weight) == id(self.model.lm_head.weight)
and self.pipeline_stage_manager.num_stages > 1
):
# tie weights
return [
{
0: qwen2_model.embed_tokens.weight,
self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight,
}
]
return []
class Qwen2ForSequenceClassificationPolicy(Qwen2Policy):
def module_policy(self):
from transformers import Qwen2ForSequenceClassification
policy = super().module_policy()
if self.shard_config.enable_tensor_parallelism:
# add a new item for sequence classification
new_item = {
Qwen2ForSequenceClassification: ModulePolicyDescription(
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True)
)
]
)
}
policy.update(new_item)
# to be confirmed
if self.pipeline_stage_manager:
# set None as default
self.set_pipeline_forward(
model_cls=Qwen2ForSequenceClassification,
new_forward=Qwen2PipelineForwards.qwen2_for_sequence_classification_forward,
policy=policy,
)
return policy
def get_held_layers(self) -> List[Module]:
"""Get pipeline layers for current stage."""
stage_manager = self.pipeline_stage_manager
held_layers = super().get_held_layers()
if stage_manager.is_last_stage(ignore_chunk=True):
held_layers.append(self.model.score)
return held_layers
def get_shared_params(self) -> List[Dict[int, Tensor]]:
"""No shared params in Qwen2 for sequence classification model"""
return []