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flybird11111 merged 1 commit into from
Aug 18, 2023
Merged

[shardformer] bloom support sequence parallel #4465

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184 changes: 181 additions & 3 deletions colossalai/shardformer/modeling/bloom.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,10 @@
from transformers.utils import logging

from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
from colossalai.shardformer.shard import ShardConfig

logger = logging.get_logger(__name__)


def build_bloom_alibi_tensor_fn(process_group: ProcessGroup) -> torch.Tensor:
Expand Down Expand Up @@ -111,6 +115,7 @@ def bloom_model_forward(
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
shard_config: ShardConfig = None,
**deprecated_arguments,
) -> Union[Tuple[torch.Tensor, ...], 'BaseModelOutputWithPastAndCrossAttentions']:

Expand Down Expand Up @@ -205,6 +210,13 @@ def bloom_model_forward(
past_key_values_length=past_key_values_length,
)

# split the input tensor along sequence dimension
# [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
if shard_config.enable_sequence_parallelism:
hidden_states = split_forward_gather_backward(hidden_states,
dim=1,
process_group=shard_config.tensor_parallel_process_group)

start_idx, end_idx = stage_index[0], stage_index[1]
for i, (block, layer_past) in enumerate(zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx]),
start=start_idx):
Expand Down Expand Up @@ -248,6 +260,12 @@ def custom_forward(*inputs):
all_self_attentions = all_self_attentions + \
(outputs[2 if use_cache else 1],)

# When sequence parallelism done, gather the output tensor in forward and split it in backward
if shard_config.enable_sequence_parallelism:
hidden_states = gather_forward_split_backward(hidden_states,
dim=1,
process_group=shard_config.tensor_parallel_process_group)

if stage_manager.is_last_stage():
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
Expand Down Expand Up @@ -287,6 +305,7 @@ def bloom_for_causal_lm_forward(self: BloomForCausalLM,
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
shard_config: ShardConfig = None,
**deprecated_arguments):
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Expand Down Expand Up @@ -327,7 +346,8 @@ def bloom_for_causal_lm_forward(self: BloomForCausalLM,
return_dict=return_dict,
stage_manager=stage_manager,
hidden_states=hidden_states,
stage_index=stage_index)
stage_index=stage_index,
shard_config=shard_config)
past_key_values = None
all_hidden_states = None
all_self_attentions = None
Expand Down Expand Up @@ -380,6 +400,7 @@ def bloom_for_sequence_classification_forward(
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
shard_config: ShardConfig = None,
**deprecated_arguments,
):
r"""
Expand Down Expand Up @@ -424,6 +445,7 @@ def bloom_for_sequence_classification_forward(
stage_manager=stage_manager,
hidden_states=hidden_states,
stage_index=stage_index,
shard_config=shard_config,
)
past_key_values = None
all_hidden_states = None
Expand Down Expand Up @@ -503,6 +525,7 @@ def bloom_for_token_classification_forward(
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
shard_config: ShardConfig = None,
**deprecated_arguments,
):
r"""
Expand Down Expand Up @@ -547,6 +570,7 @@ def bloom_for_token_classification_forward(
stage_manager=stage_manager,
hidden_states=hidden_states,
stage_index=stage_index,
shard_config=shard_config,
)
past_key_values = None
all_hidden_states = None
Expand Down Expand Up @@ -597,6 +621,7 @@ def bloom_for_question_answering_forward(
stage_manager: Optional[PipelineStageManager] = None,
hidden_states: Optional[torch.FloatTensor] = None,
stage_index: Optional[List[int]] = None,
shard_config: ShardConfig = None,
):
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Expand Down Expand Up @@ -632,6 +657,7 @@ def bloom_for_question_answering_forward(
stage_manager=stage_manager,
hidden_states=hidden_states,
stage_index=stage_index,
shard_config=shard_config,
)
past_key_values = None
all_hidden_states = None
Expand Down Expand Up @@ -700,8 +726,7 @@ def forward(

fused_qkv = self.query_key_value(hidden_states)
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
batch_size, tgt_len, _ = hidden_states.size()
assert tgt_len % 4 == 0, "Flash Attention Error: The sequence length should be a multiple of 4."
batch_size, tgt_len, _ = query_layer.size()

_, kv_length, _, _ = key_layer.size()

Expand Down Expand Up @@ -896,3 +921,156 @@ def forward(self: BloomGelu, x: torch.Tensor) -> torch.Tensor:
return self.bloom_gelu_forward(x, bias)

return forward


def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):

from transformers import BloomModel

def forward(
self: BloomModel,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: 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,
**deprecated_arguments,
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
if deprecated_arguments.pop("position_ids", False) is not False:
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
warnings.warn(
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
" passing `position_ids`.",
FutureWarning,
)
if len(deprecated_arguments) > 0:
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")

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

if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and 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 input_ids or inputs_embeds")

if past_key_values is None:
past_key_values = tuple([None] * len(self.h))

# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape batch_size x num_heads x N x N
# head_mask has shape n_layer x batch x num_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)

if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)

hidden_states = self.word_embeddings_layernorm(inputs_embeds)

presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None

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

# Compute alibi tensor: check build_alibi_tensor documentation
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values[0] 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 attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
else:
attention_mask = attention_mask.to(hidden_states.device)

alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)

causal_mask = self._prepare_attn_mask(
attention_mask,
input_shape=(batch_size, seq_length),
past_key_values_length=past_key_values_length,
)
# split the input tensor along sequence dimension
# [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
hidden_states = split_forward_gather_backward(hidden_states,
dim=1,
process_group=shard_config.tensor_parallel_process_group)

for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)

if self.gradient_checkpointing and self.training:

def create_custom_forward(module):

def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)

return custom_forward

outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
alibi,
causal_mask,
layer_past,
head_mask[i],
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=causal_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
alibi=alibi,
)

hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)

if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

# When sequence parallelism done, gather the output tensor in forward and split it in backward
hidden_states = gather_forward_split_backward(hidden_states,
dim=1,
process_group=shard_config.tensor_parallel_process_group)
# Add last hidden state
hidden_states = self.ln_f(hidden_states)

if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)

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 BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)

return forward
24 changes: 19 additions & 5 deletions colossalai/shardformer/policies/bloom.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
BloomPipelineForwards,
build_bloom_alibi_tensor_fn,
get_bloom_flash_attention_forward,
get_bloom_sequence_parallel_forward_fn,
get_jit_fused_bloom_attention_forward,
get_jit_fused_bloom_gelu_forward,
get_jit_fused_bloom_mlp_forward,
Expand Down Expand Up @@ -43,6 +44,7 @@ def module_policy(self):

policy = {}

use_sequence_parallel = self.shard_config.enable_sequence_parallelism
if self.shard_config.enable_tensor_parallelism:
policy[BloomBlock] = ModulePolicyDescription(attribute_replacement={
"self_attention.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
Expand All @@ -53,23 +55,23 @@ def module_policy(self):
SubModuleReplacementDescription(
suffix="self_attention.query_key_value",
target_module=col_nn.Linear1D_Col,
),
kwargs={'seq_parallel': use_sequence_parallel}),
SubModuleReplacementDescription(
suffix="self_attention.dense",
target_module=col_nn.Linear1D_Row,
),
kwargs={'seq_parallel': use_sequence_parallel}),
SubModuleReplacementDescription(
suffix="self_attention.attention_dropout",
target_module=col_nn.DropoutForParallelInput,
),
SubModuleReplacementDescription(
suffix="mlp.dense_h_to_4h",
target_module=col_nn.Linear1D_Col,
),
kwargs={'seq_parallel': use_sequence_parallel}),
SubModuleReplacementDescription(
suffix="mlp.dense_4h_to_h",
target_module=col_nn.Linear1D_Row,
),
kwargs={'seq_parallel': use_sequence_parallel}),
])

policy[BloomModel] = ModulePolicyDescription(
Expand Down Expand Up @@ -116,6 +118,12 @@ def module_policy(self):
policy=policy,
target_key=BloomBlock)

if use_sequence_parallel:
self.append_or_create_method_replacement(
description={'forward': get_bloom_sequence_parallel_forward_fn(self.shard_config)},
policy=policy,
target_key=BloomModel)

if self.shard_config.enable_flash_attention:
policy[BloomAttention] = ModulePolicyDescription(method_replacement={
'forward': get_bloom_flash_attention_forward(),
Expand Down Expand Up @@ -154,7 +162,13 @@ def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, poli

layers_per_stage = Policy.distribute_layers(len(module.h), 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)}
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)
Expand Down
1 change: 1 addition & 0 deletions colossalai/shardformer/shard/shard_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,3 +58,4 @@ def _turn_on_all_optimization(self):
self.enable_fused_normalization = True
self.enable_flash_attention = True
self.enable_jit_fused = True
self.enable_sequence_parallelism = True