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[shardformer] chatglm support sequence parallel (#4482) 

* [shardformer] chatglm support sequence parallel

[shardformer] chatglm support sequence parallel

[shardformer] chatglm support sequence parallel

[shardformer] chatglm support sequence parallel

[shardformer] chatglm support sequence parallel

[shardformer] chatglm support sequence parallel

* fix

fix

fix

fix
pull/4498/head
flybird11111 1 year ago committed by GitHub
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11 changed files with 259 additions and 94 deletions
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  1. 10
      colossalai/shardformer/layer/linear.py
  2. 135
      colossalai/shardformer/modeling/chatglm2.py
  3. 18
      colossalai/shardformer/policies/bert.py
  4. 23
      colossalai/shardformer/policies/blip2.py
  5. 26
      colossalai/shardformer/policies/bloom.py
  6. 101
      colossalai/shardformer/policies/chatglm2.py
  7. 6
      colossalai/shardformer/policies/gpt2.py
  8. 6
      colossalai/shardformer/policies/llama.py
  9. 12
      colossalai/shardformer/policies/sam.py
  10. 12
      colossalai/shardformer/policies/vit.py
  11. 4
      tests/kit/model_zoo/transformers/chatglm2.py

10
colossalai/shardformer/layer/linear.py
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@ -74,6 +74,7 @@ class Linear1D_Col(ParallelModule):
process_group: ProcessGroup = None, process_group: ProcessGroup = None,
gather_output: bool = False, gather_output: bool = False,
seq_parallel: bool = False, seq_parallel: bool = False,
seq_parallel_dim: int = 1,
overlap: bool = False, overlap: bool = False,
skip_bias_add: bool = False, skip_bias_add: bool = False,
weight: Optional[Parameter] = None, weight: Optional[Parameter] = None,
@ -87,6 +88,7 @@ class Linear1D_Col(ParallelModule):
self.out_features = out_features self.out_features = out_features
self.gather_output = gather_output self.gather_output = gather_output
self.seq_parallel = seq_parallel self.seq_parallel = seq_parallel
self.seq_parallel_dim = seq_parallel_dim
self.overlap = overlap self.overlap = overlap
self.skip_bias_add = skip_bias_add self.skip_bias_add = skip_bias_add
self.device = device self.device = device
@ -190,7 +192,8 @@ class Linear1D_Col(ParallelModule):
bias = self.bias if not self.skip_bias_add else None bias = self.bias if not self.skip_bias_add else None
if self.seq_parallel: if self.seq_parallel:
output_parallel = linear_gather_forward_reducescatter_backward(input_parallel, self.weight, bias, output_parallel = linear_gather_forward_reducescatter_backward(input_parallel, self.weight, bias,
self.process_group, True, 1, self.overlap) self.process_group, True,
self.seq_parallel_dim, self.overlap)
else: else:
output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True) output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True)
@ -236,6 +239,7 @@ class Linear1D_Row(ParallelModule):
device: torch.device = None, device: torch.device = None,
process_group: ProcessGroup = None, process_group: ProcessGroup = None,
seq_parallel: bool = False, seq_parallel: bool = False,
seq_parallel_dim: int = 1,
parallel_input: bool = True, parallel_input: bool = True,
skip_bias_add: bool = False, skip_bias_add: bool = False,
weight: Optional[Parameter] = None, weight: Optional[Parameter] = None,
@ -254,6 +258,7 @@ class Linear1D_Row(ParallelModule):
self.skip_bias_add = skip_bias_add self.skip_bias_add = skip_bias_add
self.process_group = process_group self.process_group = process_group
self.seq_parallel = seq_parallel self.seq_parallel = seq_parallel
self.seq_parallel_dim = seq_parallel_dim
self.num_partitions = dist.get_world_size(self.process_group) self.num_partitions = dist.get_world_size(self.process_group)
if skip_bias_add and not bias: if skip_bias_add and not bias:
@ -390,7 +395,8 @@ class Linear1D_Row(ParallelModule):
else: else:
output_parallel = F.linear(input_, self.weight) output_parallel = F.linear(input_, self.weight)
if self.seq_parallel: if self.seq_parallel:
output = linear_reducescatter_forward_gather_backward(output_parallel, self.process_group, 1) output = linear_reducescatter_forward_gather_backward(output_parallel, self.process_group,
self.seq_parallel_dim)
else: else:
output = reduce_forward(output_parallel, self.process_group) output = reduce_forward(output_parallel, self.process_group)

135
colossalai/shardformer/modeling/chatglm2.py
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@ -9,6 +9,8 @@ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutpu
from transformers.utils import logging from transformers.utils import logging
from colossalai.pipeline.stage_manager import PipelineStageManager from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer import ShardConfig
from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ( from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import (
ChatGLMForConditionalGeneration, ChatGLMForConditionalGeneration,
@ -146,6 +148,7 @@ class ChatGLMPipelineForwards:
stage_manager: Optional[PipelineStageManager] = None, stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None, hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None, stage_index: Optional[List[int]] = None,
shard_config: ShardConfig = None,
): ):
logger = logging.get_logger(__name__) logger = logging.get_logger(__name__)
output_hidden_states = (output_hidden_states output_hidden_states = (output_hidden_states
@ -198,6 +201,11 @@ class ChatGLMPipelineForwards:
all_self_attentions = None all_self_attentions = None
all_hidden_states = () if output_hidden_states else None all_hidden_states = () if output_hidden_states else None
start_idx, end_idx = stage_index[0], stage_index[1] start_idx, end_idx = stage_index[0], stage_index[1]
if shard_config.enable_sequence_parallelism:
hidden_states = split_forward_gather_backward(hidden_states,
dim=0,
process_group=shard_config.tensor_parallel_process_group)
for idx in range(start_idx, end_idx): for idx in range(start_idx, end_idx):
layer = self.encoder._get_layer(idx) layer = self.encoder._get_layer(idx)
if output_hidden_states: if output_hidden_states:
@ -214,6 +222,11 @@ class ChatGLMPipelineForwards:
hidden_states, kv_cache = layer_ret hidden_states, kv_cache = layer_ret
if use_cache: if use_cache:
presents = presents + (kv_cache,) presents = presents + (kv_cache,)
if shard_config.enable_sequence_parallelism:
hidden_states = gather_forward_split_backward(hidden_states,
dim=0,
process_group=shard_config.tensor_parallel_process_group)
if output_hidden_states: if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) all_hidden_states = all_hidden_states + (hidden_states,)
if stage_manager.is_last_stage(): if stage_manager.is_last_stage():
@ -233,23 +246,22 @@ class ChatGLMPipelineForwards:
return {'hidden_states': hidden_states} return {'hidden_states': hidden_states}
@staticmethod @staticmethod
def chatglm_for_conditional_generation_forward( def chatglm_for_conditional_generation_forward(self: ChatGLMForConditionalGeneration,
self: ChatGLMForConditionalGeneration, input_ids: Optional[torch.Tensor] = None,
input_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
past_key_values: Optional[Tuple[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None,
use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
return_dict: Optional[bool] = None, return_last_logit: Optional[bool] = False,
return_last_logit: Optional[bool] = False, stage_manager: Optional[PipelineStageManager] = None,
stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None,
hidden_states: Optional[torch.FloatTensor] = None, stage_index: Optional[List[int]] = None,
stage_index: Optional[List[int]] = None, shard_config: ShardConfig = None):
):
logger = logging.get_logger(__name__) logger = logging.get_logger(__name__)
use_cache = use_cache if use_cache is not None else self.config.use_cache 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) return_dict = (return_dict if return_dict is not None else self.config.use_return_dict)
@ -266,6 +278,7 @@ class ChatGLMPipelineForwards:
stage_manager=stage_manager, stage_manager=stage_manager,
hidden_states=hidden_states, hidden_states=hidden_states,
stage_index=stage_index, stage_index=stage_index,
shard_config=shard_config,
) )
if stage_manager.is_last_stage(): if stage_manager.is_last_stage():
hidden_states = transformer_outputs[0] hidden_states = transformer_outputs[0]
@ -296,3 +309,91 @@ class ChatGLMPipelineForwards:
) )
else: else:
return transformer_outputs return transformer_outputs
def get_chatglm_sequence_parallel_forward_fn(shard_config: ShardConfig):
def forward(
self,
input_ids,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.BoolTensor] = None,
full_attention_mask: Optional[torch.BoolTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
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)
batch_size, seq_length = input_ids.shape
if inputs_embeds is None:
inputs_embeds = self.embedding(input_ids)
if self.pre_seq_len is not None:
if past_key_values is None:
past_key_values = self.get_prompt(
batch_size=batch_size,
device=input_ids.device,
dtype=inputs_embeds.dtype,
)
if attention_mask is not None:
attention_mask = torch.cat(
[
attention_mask.new_ones((batch_size, self.pre_seq_len)),
attention_mask,
],
dim=-1,
)
if full_attention_mask is None:
if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)
# Rotary positional embeddings
rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
if position_ids is not None:
rotary_pos_emb = rotary_pos_emb[position_ids]
else:
rotary_pos_emb = rotary_pos_emb[None, :seq_length]
rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
# Run encoder.
# [seq_len, batch_size, hidden_size] -> [seq_len/TP_size, batch_size, hidden_size]
inputs_embeds = split_forward_gather_backward(inputs_embeds,
dim=0,
process_group=shard_config.tensor_parallel_process_group)
hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
inputs_embeds,
full_attention_mask,
rotary_pos_emb=rotary_pos_emb,
kv_caches=past_key_values,
use_cache=use_cache,
output_hidden_states=output_hidden_states,
)
hidden_states = gather_forward_split_backward(hidden_states,
dim=0,
process_group=shard_config.tensor_parallel_process_group)
if not return_dict:
return tuple(v for v in [
hidden_states,
presents,
all_hidden_states,
all_self_attentions,
] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
return forward

18
colossalai/shardformer/policies/bert.py
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@ -155,20 +155,26 @@ class BertPolicy(Policy):
# use flash attention # use flash attention
if self.shard_config.enable_flash_attention: if self.shard_config.enable_flash_attention:
policy[BertSelfAttention] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_bert_flash_attention_forward(), 'forward': get_bert_flash_attention_forward(),
}) },
policy=policy,
target_key=BertSelfAttention)
# use jit operator # use jit operator
if self.shard_config.enable_jit_fused: if self.shard_config.enable_jit_fused:
policy[BertSelfOutput] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_jit_fused_bert_self_output_forward(), 'forward': get_jit_fused_bert_self_output_forward(),
'dropout_add': get_jit_fused_dropout_add_func(), 'dropout_add': get_jit_fused_dropout_add_func(),
}) },
policy[BertOutput] = ModulePolicyDescription(method_replacement={ policy=policy,
target_key=BertSelfOutput)
self.append_or_create_method_replacement(description={
'forward': get_jit_fused_bert_output_forward(), 'forward': get_jit_fused_bert_output_forward(),
'dropout_add': get_jit_fused_dropout_add_func(), 'dropout_add': get_jit_fused_dropout_add_func(),
}) },
policy=policy,
target_key=BertOutput)
return policy return policy

23
colossalai/shardformer/policies/blip2.py
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@ -285,21 +285,26 @@ class BlipPolicy(Policy):
# use flash attention # use flash attention
if self.shard_config.enable_flash_attention: if self.shard_config.enable_flash_attention:
policy[Blip2Attention] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_blip2_flash_attention_forward(), 'forward': get_blip2_flash_attention_forward(),
}) },
policy=policy,
target_key=Blip2Attention)
# use jit operator # use jit operator
if self.shard_config.enable_jit_fused: if self.shard_config.enable_jit_fused:
policy[Blip2QFormerSelfOutput] = ModulePolicyDescription( self.append_or_create_method_replacement(description={
method_replacement={ 'forward': get_jit_fused_blip2_QFormer_self_output_forward(),
'forward': get_jit_fused_blip2_QFormer_self_output_forward(), 'dropout_add': get_jit_fused_dropout_add_func(),
'dropout_add': get_jit_fused_dropout_add_func(), },
}) policy=policy,
policy[Blip2QFormerOutput] = ModulePolicyDescription(method_replacement={ target_key=Blip2QFormerSelfOutput)
self.append_or_create_method_replacement(description={
'forward': get_jit_fused_blip2_QFormer_output_forward(), 'forward': get_jit_fused_blip2_QFormer_output_forward(),
'dropout_add': get_jit_fused_dropout_add_func(), 'dropout_add': get_jit_fused_dropout_add_func(),
}) },
policy=policy,
target_key=Blip2QFormerOutput)
return policy return policy

26
colossalai/shardformer/policies/bloom.py
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@ -125,25 +125,33 @@ class BloomPolicy(Policy):
target_key=BloomModel) target_key=BloomModel)
if self.shard_config.enable_flash_attention: if self.shard_config.enable_flash_attention:
policy[BloomAttention] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_bloom_flash_attention_forward(), 'forward': get_bloom_flash_attention_forward(),
'dropout_add': get_dropout_add_func() 'dropout_add': get_dropout_add_func(),
}) },
policy=policy,
target_key=BloomAttention)
# enable jit fused operator # enable jit fused operator
if self.shard_config.enable_jit_fused: if self.shard_config.enable_jit_fused:
policy[BloomAttention] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_jit_fused_bloom_attention_forward(), 'forward': get_jit_fused_bloom_attention_forward(),
'dropout_add': get_jit_fused_dropout_add_func(), 'dropout_add': get_jit_fused_dropout_add_func(),
}) },
policy[BloomMLP] = ModulePolicyDescription(method_replacement={ policy=policy,
target_key=BloomAttention)
self.append_or_create_method_replacement(description={
'forward': get_jit_fused_bloom_mlp_forward(), 'forward': get_jit_fused_bloom_mlp_forward(),
'dropout_add': get_jit_fused_dropout_add_func(), 'dropout_add': get_jit_fused_dropout_add_func(),
}) },
policy[BloomGelu] = ModulePolicyDescription(method_replacement={ policy=policy,
target_key=BloomMLP)
self.append_or_create_method_replacement(description={
'forward': get_jit_fused_bloom_gelu_forward(), 'forward': get_jit_fused_bloom_gelu_forward(),
'bloom_gelu_forward': get_jit_fused_gelu_forward_func(), 'bloom_gelu_forward': get_jit_fused_gelu_forward_func(),
}) },
policy=policy,
target_key=BloomGelu)
return policy return policy

101
colossalai/shardformer/policies/chatglm2.py
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@ -15,7 +15,11 @@ from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import (
GLMBlock, GLMBlock,
) )
from ..modeling.chatglm2 import get_flash_core_attention_forward, get_jit_fused_glm_block_forward from ..modeling.chatglm2 import (
get_chatglm_sequence_parallel_forward_fn,
get_flash_core_attention_forward,
get_jit_fused_glm_block_forward,
)
from ..modeling.jit import get_jit_fused_dropout_add_func from ..modeling.jit import get_jit_fused_dropout_add_func
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
@ -45,8 +49,8 @@ class ChatGLMPolicy(Policy):
policy = {} policy = {}
use_sequence_parallel = self.shard_config.enable_sequence_parallelism
if self.shard_config.enable_tensor_parallelism: if self.shard_config.enable_tensor_parallelism:
policy[ChatGLMModel] = ModulePolicyDescription(attribute_replacement={}, policy[ChatGLMModel] = ModulePolicyDescription(attribute_replacement={},
sub_module_replacement=[ sub_module_replacement=[
SubModuleReplacementDescription( SubModuleReplacementDescription(
@ -55,36 +59,42 @@ class ChatGLMPolicy(Policy):
) )
]) ])
policy[GLMBlock] = ModulePolicyDescription(attribute_replacement={ policy[GLMBlock] = ModulePolicyDescription(
"self_attention.num_attention_heads_per_partition": attribute_replacement={
self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, "self_attention.num_attention_heads_per_partition":
"self_attention.projection_size": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
(self.model.config.kv_channels * self.model.config.num_attention_heads) // "self_attention.projection_size":
self.shard_config.tensor_parallel_size, (self.model.config.kv_channels * self.model.config.num_attention_heads) //
"self_attention.qkv_hidden_size": self.shard_config.tensor_parallel_size,
(self.model.config.kv_channels * self.model.config.num_attention_heads * 3) // "self_attention.qkv_hidden_size":
self.shard_config.tensor_parallel_size, (self.model.config.kv_channels * self.model.config.num_attention_heads * 3) //
"self_attention.core_attention.num_attention_heads_per_partition": self.shard_config.tensor_parallel_size,
self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, "self_attention.core_attention.num_attention_heads_per_partition":
"self_attention.core_attention.hidden_size_per_partition": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
self.model.config.kv_channels * self.model.config.num_attention_heads // "self_attention.core_attention.hidden_size_per_partition":
self.shard_config.tensor_parallel_size, self.model.config.kv_channels * self.model.config.num_attention_heads //
}, self.shard_config.tensor_parallel_size,
param_replacement=[], },
sub_module_replacement=[ param_replacement=[],
SubModuleReplacementDescription( sub_module_replacement=[
suffix="self_attention.query_key_value", SubModuleReplacementDescription(suffix="self_attention.query_key_value",
target_module=col_nn.Linear1D_Col, target_module=col_nn.Linear1D_Col,
), kwargs={
SubModuleReplacementDescription( 'seq_parallel': use_sequence_parallel,
suffix="self_attention.dense", 'seq_parallel_dim': 0
target_module=col_nn.Linear1D_Row, }),
), SubModuleReplacementDescription(suffix="self_attention.dense",
SubModuleReplacementDescription( target_module=col_nn.Linear1D_Row,
suffix="self_attention.core_attention.attention_dropout", kwargs={
target_module=col_nn.DropoutForParallelInput, 'seq_parallel': use_sequence_parallel,
), 'seq_parallel_dim': 0
]) }),
SubModuleReplacementDescription(
suffix="self_attention.core_attention.attention_dropout",
target_module=col_nn.DropoutForParallelInput,
),
])
# optimization configuration # optimization configuration
if self.shard_config.enable_fused_normalization: if self.shard_config.enable_fused_normalization:
if not self.model.config.rmsnorm: if not self.model.config.rmsnorm:
@ -124,16 +134,27 @@ class ChatGLMPolicy(Policy):
# use flash attention # use flash attention
if self.shard_config.enable_flash_attention: if self.shard_config.enable_flash_attention:
policy[CoreAttention] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_flash_core_attention_forward(), 'forward': get_flash_core_attention_forward(),
}) },
policy=policy,
target_key=CoreAttention)
# use sequence parallel
if use_sequence_parallel:
self.append_or_create_method_replacement(
description={'forward': get_chatglm_sequence_parallel_forward_fn(self.shard_config)},
policy=policy,
target_key=ChatGLMModel)
# use jit fused operator # use jit fused operator
if self.shard_config.enable_jit_fused: if self.shard_config.enable_jit_fused:
policy[GLMBlock] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_jit_fused_glm_block_forward(), 'forward': get_jit_fused_glm_block_forward(),
'dropout_add': get_jit_fused_dropout_add_func(), 'dropout_add': get_jit_fused_dropout_add_func(),
}) },
policy=policy,
target_key=GLMBlock)
return policy return policy
@ -178,7 +199,13 @@ class ChatGLMPolicy(Policy):
layers_per_stage = Policy.distribute_layers(module.num_layers, stage_manager.num_stages) layers_per_stage = Policy.distribute_layers(module.num_layers, stage_manager.num_stages)
stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) 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)} 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)

6
colossalai/shardformer/policies/gpt2.py
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@ -118,9 +118,11 @@ class GPT2Policy(Policy):
target_key=GPT2Block) target_key=GPT2Block)
if self.shard_config.enable_flash_attention: if self.shard_config.enable_flash_attention:
policy[GPT2Attention] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_gpt2_flash_attention_forward(), 'forward': get_gpt2_flash_attention_forward(),
}) },
policy=policy,
target_key=GPT2Attention)
if self.shard_config.enable_sequence_parallelism: if self.shard_config.enable_sequence_parallelism:
policy[GPT2Model].method_replacement = {"forward": gpt2_sequence_parallel_forward_fn(self.shard_config)} policy[GPT2Model].method_replacement = {"forward": gpt2_sequence_parallel_forward_fn(self.shard_config)}

6
colossalai/shardformer/policies/llama.py
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@ -105,9 +105,11 @@ class LlamaPolicy(Policy):
target_key=LlamaModel) target_key=LlamaModel)
if self.shard_config.enable_flash_attention: if self.shard_config.enable_flash_attention:
policy[LlamaAttention] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_llama_flash_attention_forward(), 'forward': get_llama_flash_attention_forward(),
}) },
policy=policy,
target_key=LlamaAttention)
return policy return policy

12
colossalai/shardformer/policies/sam.py
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@ -199,12 +199,16 @@ class SamPolicy(Policy):
# use flash attention # use flash attention
if self.shard_config.enable_flash_attention: if self.shard_config.enable_flash_attention:
policy[SamAttention] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_sam_flash_attention_forward(), 'forward': get_sam_flash_attention_forward(),
}) },
policy[SamVisionAttention] = ModulePolicyDescription(method_replacement={ policy=policy,
target_key=SamAttention)
self.append_or_create_method_replacement(description={
'forward': get_sam_vision_flash_attention_forward(), 'forward': get_sam_vision_flash_attention_forward(),
}) },
policy=policy,
target_key=SamVisionAttention)
return policy return policy

12
colossalai/shardformer/policies/vit.py
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@ -90,16 +90,20 @@ class ViTPolicy(Policy):
# use flash attention # use flash attention
if self.shard_config.enable_flash_attention: if self.shard_config.enable_flash_attention:
policy[ViTSelfAttention] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_vit_flash_self_attention_forward(), 'forward': get_vit_flash_self_attention_forward(),
}) },
policy=policy,
target_key=ViTSelfAttention)
# use jit fused operator # use jit fused operator
if self.shard_config.enable_jit_fused: if self.shard_config.enable_jit_fused:
policy[ViTOutput] = ModulePolicyDescription(method_replacement={ self.append_or_create_method_replacement(description={
'forward': get_jit_fused_vit_output_forward(), 'forward': get_jit_fused_vit_output_forward(),
'dropout_add': get_jit_fused_dropout_add_func(), 'dropout_add': get_jit_fused_dropout_add_func(),
}) },
policy=policy,
target_key=ViTOutput)
return policy return policy
def new_model_class(self): def new_model_class(self):

4
tests/kit/model_zoo/transformers/chatglm2.py
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@ -12,8 +12,8 @@ from ..registry import ModelAttribute, model_zoo
def data_gen(): def data_gen():
input_ids = torch.tensor([[5941, 15, 2670, 3543, 632, 2075]], dtype=torch.int64) input_ids = torch.tensor([[5941, 15, 2670, 3543, 632, 2075, 632, 2075]], dtype=torch.int64)
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]]) attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]])
return dict(input_ids=input_ids, attention_mask=attention_mask) return dict(input_ids=input_ids, attention_mask=attention_mask)

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