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Merge pull request #6299 from wangbluo/upgrade_bloom

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Upgrade bloom
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Hanks 2025-05-14 10:19:44 +08:00 committed by GitHub
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2 changed files with 239 additions and 131 deletions
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colossalai/shardformer/modeling/bloom.py
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@ -6,7 +6,7 @@ import torch.distributed as dist
from torch.distributed import ProcessGroup
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch.nn import functional as F
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
@ -21,6 +21,7 @@ from transformers.models.bloom.modeling_bloom import (
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
dropout_add,
)
from transformers.utils import logging
@ -108,7 +109,7 @@ class BloomPipelineForwards:
def bloom_model_forward(
self: BloomModel,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
past_key_values: Optional[Union[Cache, 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,
@ -116,6 +117,7 @@ class BloomPipelineForwards:
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,
@ -151,6 +153,8 @@ class BloomPipelineForwards:
if use_cache:
logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
use_cache = False
past_key_values = None
# 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
@ -161,46 +165,60 @@ class BloomPipelineForwards:
# case: First stage of training
if stage_manager.is_first_stage():
# check input_ids and inputs_embeds
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 inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
# initialize in the first stage and then pass to the next stage
else:
input_shape = hidden_states.shape[:-1]
batch_size, seq_length = input_shape
# extra recording tensor should be generated in the first stage
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:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
if past_key_values is None:
past_key_values = tuple([None] * len(self.h))
# Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
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] # source_len
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
batch_size, seq_length, _ = inputs_embeds.shape
past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
if cache_position is None:
cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)
# initialize in the first stage and then pass to the next stage
else:
input_shape = hidden_states.shape[:-1]
batch_size, seq_length = input_shape
past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
if cache_position is None:
cache_position = torch.arange(past_length, past_length + seq_length, device=hidden_states.device)
# extra recording tensor should be generated in the first stage
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True
if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
# Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
past_length = 0
seq_length_with_past = seq_length + past_length
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:
@ -209,13 +227,10 @@ class BloomPipelineForwards:
alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
# causal_mask is constructed every stage and its input is passed through different stages
causal_mask = _prepare_4d_causal_attention_mask(
attention_mask,
input_shape=(batch_size, seq_length),
inputs_embeds=hidden_states,
past_key_values_length=past_key_values_length,
causal_mask = self._update_causal_mask(
attention_mask, hidden_states, cache_position, past_key_values, output_attentions
)
causal_mask = causal_mask.bool()
# split the input tensor along sequence dimension
# [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
if shard_config and shard_config.enable_sequence_parallelism:
@ -228,9 +243,7 @@ class BloomPipelineForwards:
)
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
):
for i, block in enumerate(self.h[start_idx:end_idx], start=start_idx):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
@ -240,26 +253,28 @@ class BloomPipelineForwards:
hidden_states,
alibi,
causal_mask,
layer_past,
past_key_values,
head_mask[i],
use_cache,
output_attentions,
cache_position,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
layer_past=past_key_values,
attention_mask=causal_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
alibi=alibi,
cache_position=cache_position,
)
hidden_states = outputs[0]
if use_cache:
next_decoder_cache = outputs[1]
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],)
@ -277,20 +292,23 @@ class BloomPipelineForwards:
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
# TODO(jianghai): deal with all_hidden_states, all_self_attentions, presents
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
if stage_manager.is_last_stage():
if not return_dict:
return tuple(
v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None
v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
)
# attention_mask is not returned ; presents = past_key_values
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
@ -718,35 +736,24 @@ def get_jit_fused_bloom_attention_forward():
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
cache_position: Optional[torch.LongTensor] = None,
):
batch_size, q_length, _ = hidden_states.shape
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
# 3 x [batch_size, num_heads, seq_length, head_dim]
query_layer, key_layer, value_layer = self._reshape(fused_qkv)
# 3 x [batch_size, seq_length, num_heads, head_dim]
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
batch_size, q_length, _, _ = query_layer.shape
query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
key_layer = key_layer.permute(0, 2, 3, 1).reshape(batch_size * self.num_heads, self.head_dim, q_length)
value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
if layer_past is not None:
past_key, past_value = layer_past
# concatenate along seq_length dimension:
# - key: [batch_size * self.num_heads, head_dim, kv_length]
# - value: [batch_size * self.num_heads, kv_length, head_dim]
key_layer = torch.cat((past_key, key_layer), dim=2)
value_layer = torch.cat((past_value, value_layer), dim=1)
cache_kwargs = {"cache_position": cache_position}
key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
_, _, kv_length = key_layer.shape
if use_cache is True:
present = (key_layer, value_layer)
else:
present = None
# reshape qkv for further computations
query_layer = query_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
key_layer = key_layer.reshape(batch_size * self.num_heads, -1, self.head_dim).transpose(-1, -2)
value_layer = value_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
# [batch_size * num_heads, q_length, kv_length]
# we use `torch.Tensor.baddbmm` instead of `torch.baddbmm` as the latter isn't supported by TorchScript v1.11
matmul_result = alibi.baddbmm(
attention_scores = alibi.baddbmm(
batch1=query_layer,
batch2=key_layer,
beta=self.beta,
@ -754,15 +761,13 @@ def get_jit_fused_bloom_attention_forward():
)
# change view to [batch_size, num_heads, q_length, kv_length]
attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
attn_weights = attention_scores.view(batch_size, self.num_heads, q_length, -1)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_layer.shape[-1]]
attn_weights = attn_weights + causal_mask
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
input_dtype = attention_scores.dtype
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
if input_dtype == torch.float16:
attention_scores = attention_scores.to(torch.float)
attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(input_dtype)
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype
attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_layer.dtype)
# [batch_size, num_heads, q_length, kv_length]
attention_probs = self.attention_dropout(attention_probs)
@ -771,12 +776,12 @@ def get_jit_fused_bloom_attention_forward():
attention_probs = attention_probs * head_mask
# change view [batch_size x num_heads, q_length, kv_length]
attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, -1)
# matmul: [batch_size * num_heads, q_length, head_dim]
context_layer = torch.bmm(attention_probs_reshaped, value_layer)
# change view [batch_size, num_heads, q_length, head_dim]
# change view [batch_size, q_length, num_heads * head_dim]
context_layer = self._merge_heads(context_layer)
# aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
@ -791,9 +796,9 @@ def get_jit_fused_bloom_attention_forward():
else:
output_tensor = self.dense(context_layer)
output_tensor = self.dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
outputs = (output_tensor, present)
outputs = (output_tensor, layer_past)
if output_attentions:
outputs += (attention_probs,)
@ -839,13 +844,99 @@ def get_jit_fused_bloom_gelu_forward():
return forward
# Fixed the q_length args when doing the sequence parallelism in bloom model.
def get_bloom_sequence_parallel_attention_forward(shard_config: ShardConfig):
from transformers.models.bloom.modeling_bloom import BloomAttention
def forward(
self: BloomAttention,
hidden_states: torch.Tensor,
residual: torch.Tensor,
alibi: torch.Tensor,
attention_mask: torch.Tensor,
layer_past: Optional[Cache] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
cache_position: Optional[torch.LongTensor] = None,
):
batch_size, q_length, _ = hidden_states.shape
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
# 3 x [batch_size, num_heads, seq_length, head_dim]
query_layer, key_layer, value_layer = self._reshape(fused_qkv)
if layer_past is not None:
cache_kwargs = {"cache_position": cache_position}
key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
# reshape qkv for further computations
query_layer = query_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
key_layer = key_layer.reshape(batch_size * self.num_heads, -1, self.head_dim).transpose(-1, -2)
value_layer = value_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
# [batch_size * num_heads, q_length, kv_length]
attention_scores = alibi.baddbmm(
batch1=query_layer,
batch2=key_layer,
beta=self.beta,
alpha=self.inv_norm_factor,
)
if shard_config.enable_sequence_parallelism:
_, q_length, _ = query_layer.shape
# change view to [batch_size, num_heads, q_length, kv_length]
attn_weights = attention_scores.view(batch_size, self.num_heads, q_length, -1)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_layer.shape[-1]]
attn_weights = attn_weights + causal_mask
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype
attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_layer.dtype)
# [batch_size, num_heads, q_length, kv_length]
attention_probs = self.attention_dropout(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
# change view [batch_size x num_heads, q_length, kv_length]
attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, -1)
# matmul: [batch_size * num_heads, q_length, head_dim]
context_layer = torch.bmm(attention_probs_reshaped, value_layer)
# change view [batch_size, q_length, num_heads * head_dim]
context_layer = self._merge_heads(context_layer)
# aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
if self.pretraining_tp > 1 and self.slow_but_exact:
slices = self.hidden_size / self.pretraining_tp
output_tensor = torch.zeros_like(context_layer)
for i in range(self.pretraining_tp):
output_tensor = output_tensor + F.linear(
context_layer[:, :, int(i * slices) : int((i + 1) * slices)],
self.dense.weight[:, int(i * slices) : int((i + 1) * slices)],
)
else:
output_tensor = self.dense(context_layer)
output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
outputs = (output_tensor, layer_past)
if output_attentions:
outputs += (attention_probs,)
return outputs
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,
past_key_values: Optional[Union[Cache, 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,
@ -853,6 +944,7 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**deprecated_arguments,
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
if deprecated_arguments.pop("position_ids", False) is not False:
@ -864,7 +956,6 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
)
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
@ -872,62 +963,60 @@ def get_bloom_sequence_parallel_forward_fn(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
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 (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if past_key_values is None:
past_key_values = tuple([None] * len(self.h))
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True
if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
batch_size, seq_length, _ = inputs_embeds.shape
past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
seq_length_with_past = seq_length + past_length
if cache_position is None:
cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)
# 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
next_decoder_cache = 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 = _prepare_4d_causal_attention_mask(
attention_mask,
input_shape=(batch_size, seq_length),
inputs_embeds=hidden_states,
past_key_values_length=past_key_values_length,
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
causal_mask = causal_mask.bool()
# 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,
@ -935,7 +1024,7 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
fp8_communication=shard_config.fp8_communication,
)
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
for i, block in enumerate(self.h):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
@ -945,25 +1034,27 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
hidden_states,
alibi,
causal_mask,
layer_past,
past_key_values,
head_mask[i],
use_cache,
output_attentions,
cache_position,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
layer_past=past_key_values,
attention_mask=causal_mask,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
alibi=alibi,
cache_position=cache_position,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if use_cache:
next_decoder_cache = outputs[1]
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
@ -975,18 +1066,25 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
process_group=shard_config.tensor_parallel_process_group,
fp8_communication=shard_config.fp8_communication,
)
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
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 tuple(
v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)

10
colossalai/shardformer/policies/bloom.py
View File

@ -11,6 +11,7 @@ import colossalai.shardformer.layer as col_nn
from ..modeling.bloom import (
BloomPipelineForwards,
build_bloom_alibi_tensor_fn,
get_bloom_sequence_parallel_attention_forward,
get_bloom_sequence_parallel_forward_fn,
get_jit_fused_bloom_attention_forward,
get_jit_fused_bloom_gelu_forward,
@ -61,6 +62,15 @@ class BloomPolicy(Policy):
use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv
if self.shard_config.enable_sequence_parallelism:
self.append_or_create_method_replacement(
description={
"forward": get_bloom_sequence_parallel_attention_forward(self.shard_config),
},
policy=policy,
target_key=BloomAttention,
)
if self.shard_config.enable_tensor_parallelism:
assert (
self.model.config.n_head % self.shard_config.tensor_parallel_size == 0