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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Optional, Tuple
import torch
import torch.distributed as dist
from mmengine import MessageHub
from transformers.models.llama.modeling_llama import (apply_rotary_pos_emb,
repeat_kv)
from transformers.utils import is_flash_attn_greater_or_equal_2_10
from .attention import (SUPPORT_FLASH2, flash_attn_w_mask, flash_attn_wo_mask,
varlen_flash_attn)
from .triton_kernels import apply_rotary_emb
try:
from transformers.cache_utils import Cache
except ImportError:
class Cache:
pass
def repeat_kv_bshd(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""The hidden states go from (batch, seqlen, num_key_value_heads, head_dim)
to (batch, seqlen, num_attention_heads, head_dim)"""
batch, slen, num_key_value_heads, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, :,
None, :].expand(batch, slen,
num_key_value_heads, n_rep,
head_dim)
return hidden_states.reshape(batch, slen, num_key_value_heads * n_rep,
head_dim)
def llama_attn_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
):
# Modified from https://github.com/huggingface/transformers/blob/66ce9593fdb8e340df546ddd0774eb444f17a12c/src/transformers/models/llama/modeling_llama.py#L422 # noqa:E501
output_attentions = False
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)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
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)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin)
past_key_value = getattr(self, 'past_key_value', past_key_value)
if past_key_value is not None:
# sin and cos are specific to RoPE models;
# cache_position needed for the static cache
cache_kwargs = {
'sin': sin,
'cos': cos,
'cache_position': cache_position
}
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
assert SUPPORT_FLASH2
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
# In PEFT, usually we cast the layer norms in float32 for training
# stability reasons therefore the input hidden states gets silently
# casted in float32. Hence, we need cast them back in the correct dtype
# just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not
# cast the LayerNorms in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, '_pre_quantization_dtype'):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
dropout_rate = self.attention_dropout if self.training else 0.0
if is_flash_attn_greater_or_equal_2_10():
causal = self.is_causal
else:
# TODO: Remove the `q_len != 1` check once Flash Attention for RoCm
# is bumped to 2.1. For details, please see the comment in
# LlamaFlashAttention2 __init__.
causal = self.is_causal and q_len != 1
# the shape of attention_mask used by flash_attn and
# F.scaled_dot_product_attention are different
assert attention_mask is None or attention_mask.ndim == 2, \
('When using flash_attn, attention_mask.ndim should equal to 2.'
f'But got attention_mask.shape = {attention_mask.shape}.'
'We can pass the `attn_implementation="flash_attention_2"` flag '
'to `.from_pretrained` method when instantiating a Internlm2 '
'model.')
if attention_mask is not None:
attn_output = flash_attn_w_mask(
query_states,
key_states,
value_states,
attention_mask,
causal=causal,
dropout_p=dropout_rate,
training=self.training)
else:
attn_output = flash_attn_wo_mask(
query_states,
key_states,
value_states,
causal=causal,
dropout_p=dropout_rate,
training=self.training)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def llama_attn_forward_legacy(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
Optional[Tuple[torch.Tensor]]]:
# Modified from https://github.com/huggingface/transformers/blob/ced9fd86f55ebb6b656c273f6e23f8ba50652f83/src/transformers/models/llama/modeling_llama.py#L331 # noqa:E501
if 'padding_mask' in kwargs:
warnings.warn(
'Passing `padding_mask` is deprecated and will be removed in '
'v4.37. Please make sure use `attention_mask` instead.`')
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:
if self.layer_idx is None:
raise ValueError(
'The cache structure has changed since version v4.36. '
f'If you are using {self.__class__.__name__} '
'for auto-regressive decoding with k/v caching, '
'please make sure to initialize the attention class '
'with a layer index.')
kv_seq_len += past_key_value.get_usable_length(kv_seq_len,
self.layer_idx)
assert position_ids is not None
if self.training:
cos, sin = self.rotary_emb(
value_states, seq_len=position_ids.max() + 1)
else:
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)
if past_key_value is not None:
cache_kwargs = {'sin': sin, 'cos': cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
assert SUPPORT_FLASH2
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
# In PEFT, usually we cast the layer norms in float32 for training
# stability reasons therefore the input hidden states gets silently
# casted in float32. Hence, we need cast them back in the correct dtype
# just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not
# cast the LayerNorms in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, '_pre_quantization_dtype'):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
dropout_rate = self.attention_dropout if self.training else 0.0
if is_flash_attn_greater_or_equal_2_10():
causal = self.is_causal
else:
# TODO: Remove the `q_len != 1` check once Flash Attention for RoCm
# is bumped to 2.1. For details, please see the comment in
# LlamaFlashAttention2 __init__.
causal = self.is_causal and q_len != 1
# the shape of attention_mask used by flash_attn and
# F.scaled_dot_product_attention are different
assert attention_mask is None or attention_mask.ndim == 2, \
('When using flash_attn, attention_mask.ndim should equal to 2.'
f'But got attention_mask.shape = {attention_mask.shape}.'
'We can pass the `attn_implementation="flash_attention_2"` flag '
'to `.from_pretrained` method when instantiating a Internlm2 '
'model.')
if attention_mask is not None:
attn_output = flash_attn_w_mask(
query_states,
key_states,
value_states,
attention_mask=attention_mask,
causal=causal,
dropout_p=dropout_rate,
training=self.training)
else:
attn_output = flash_attn_wo_mask(
query_states,
key_states,
value_states,
causal=causal,
dropout_p=dropout_rate,
training=self.training)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
# Due to the implementation of the PyTorch version of flash attention,
# even when the output_attentions flag is set to True, it is not possible
# to return the attn_weights.
return attn_output, None, past_key_value
def llama_varlen_attn_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
Optional[Tuple[torch.Tensor]]]:
message_hub = MessageHub.get_instance('varlen_attn_args')
rank = dist.get_rank()
cumulative_len = message_hub.get_info(f'cumulative_len_rank_{rank}')
max_seqlen = message_hub.get_info(f'max_seqlen_rank_{rank}')
use_varlen_atten = (cumulative_len is not None)
if 'padding_mask' in kwargs:
warnings.warn('Passing `padding_mask` is deprecated and will be '
'removed in v4.37. Please make sure use '
'`attention_mask` instead.`')
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)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin)
past_key_value = getattr(self, 'past_key_value', past_key_value)
if past_key_value is not None:
# sin and cos are specific to RoPE models;
# cache_position needed for the static cache
cache_kwargs = {
'sin': sin,
'cos': cos,
'cache_position': cache_position
}
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
# repeat kv for sequence parallel
key_states = repeat_kv_bshd(key_states, self.num_key_value_groups)
value_states = repeat_kv_bshd(value_states, self.num_key_value_groups)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training
# stability reasons therefore the input hidden states gets silently casted
# in float32. Hence, we need cast them back in the correct dtype
# just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not
# cast the LayerNorms in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, '_pre_quantization_dtype'):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
assert SUPPORT_FLASH2
if use_varlen_atten:
attn_output = varlen_flash_attn(
query_states,
key_states,
value_states,
cumulative_len,
max_seqlen,
causal=True,
dropout_p=dropout_rate,
training=self.training)
else:
attn_output = flash_attn_wo_mask(
query_states,
key_states,
value_states,
causal=True,
training=self.training)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
def llama_varlen_attn_forward_legacy(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
Optional[Tuple[torch.Tensor]]]:
message_hub = MessageHub.get_instance('varlen_attn_args')
rank = dist.get_rank()
cumulative_len = message_hub.get_info(f'cumulative_len_rank_{rank}')
max_seqlen = message_hub.get_info(f'max_seqlen_rank_{rank}')
use_varlen_atten = (cumulative_len is not None)
if 'padding_mask' in kwargs:
warnings.warn('Passing `padding_mask` is deprecated and will be '
'removed in v4.37. Please make sure use '
'`attention_mask` instead.`')
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)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
self.head_dim)
kv_seq_len = key_states.shape[-3]
if past_key_value is not None:
if self.layer_idx is None:
raise ValueError(
'The cache structure has changed since version v4.36. '
f'If you are using {self.__class__.__name__} '
'for auto-regressive decoding with k/v caching, '
'please make sure to initialize the attention class '
'with a layer index.')
kv_seq_len += past_key_value.get_usable_length(kv_seq_len,
self.layer_idx)
if use_varlen_atten:
cos, sin = self.rotary_emb(value_states, max_seqlen)
# position_ids (1, seq_len)
# cos, sin (1, seq_len, dim) -> (seq_len, dim)
cos = cos[position_ids].squeeze(0)
sin = sin[position_ids].squeeze(0)
query_states = apply_rotary_emb(query_states, cos, sin)
key_states = apply_rotary_emb(key_states, cos, sin)
else:
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
cos, sin = self.rotary_emb(value_states, kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
cache_kwargs = {'sin': sin, 'cos': cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(
key_states, value_states, self.layer_idx, cache_kwargs)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
# repeat kv for sequence parallel
key_states = repeat_kv_bshd(key_states, self.num_key_value_groups)
value_states = repeat_kv_bshd(value_states, self.num_key_value_groups)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training
# stability reasons therefore the input hidden states gets silently casted
# in float32. Hence, we need cast them back in the correct dtype
# just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not
# cast the LayerNorms in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, '_pre_quantization_dtype'):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
assert SUPPORT_FLASH2
if use_varlen_atten:
attn_output = varlen_flash_attn(
query_states,
key_states,
value_states,
cumulative_len,
max_seqlen,
causal=True,
dropout_p=dropout_rate,
training=self.training)
else:
attn_output = flash_attn_wo_mask(
query_states,
key_states,
value_states,
causal=True,
dropout_p=dropout_rate,
training=self.training)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
# Due to the implementation of the PyTorch version of flash attention,
# even when the output_attentions flag is set to True, it is not possible
# to return the attn_weights.
return attn_output, None, past_key_value
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