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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 xtuner.parallel.sequence import (get_sequence_parallel_world_size,
post_process_for_sequence_parallel_attn,
pre_process_for_sequence_parallel_attn)
from .attention import flash_attn_wo_mask, varlen_flash_attn
try:
from transformers.cache_utils import Cache
except ImportError:
class Cache:
pass
import inspect
_flash_supports_window_size = False
try:
from flash_attn import flash_attn_func
_flash_supports_window_size = 'window_size' in list(
inspect.signature(flash_attn_func).parameters)
if not _flash_supports_window_size:
raise ValueError(
'Please update flash-attention to support window size.')
# else:
except ImportError:
pass
# Copied from https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/3a811845d89f3c1b3f41b341d0f9f05104769f35/modeling_phi3.py#L302 # noqa:E501
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""This is the equivalent of torch.repeat_interleave(x, dim=1,
repeats=n_rep).
The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to
(batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :,
None, :, :].expand(batch,
num_key_value_heads,
n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen,
head_dim)
# https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/3a811845d89f3c1b3f41b341d0f9f05104769f35/modeling_phi3.py#L247 # noqa:E501
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., :x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=-1)
# Copied from https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/3a811845d89f3c1b3f41b341d0f9f05104769f35/modeling_phi3.py#L255 # noqa:E501
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
""" # noqa:E501
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
def phi3_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,
):
if not _flash_supports_window_size:
raise ValueError(
'The current flash attention version does not support '
'sliding window attention.')
output_attentions = False
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.`')
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop('padding_mask')
bsz, q_len, _ = hidden_states.size()
qkv = self.qkv_proj(hidden_states)
query_pos = self.num_heads * self.head_dim
query_states = qkv[..., :query_pos]
key_states = qkv[..., query_pos:query_pos +
self.num_key_value_heads * self.head_dim]
value_states = qkv[...,
query_pos + self.num_key_value_heads * self.head_dim:]
# 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)
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)
rotary_seq_len = max(kv_seq_len, position_ids.max().item() + 1)
cos, sin = self.rotary_emb(
value_states, position_ids, seq_len=rotary_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin, position_ids)
use_sliding_windows = (
_flash_supports_window_size
and getattr(self.config, 'sliding_window', None) is not None
and kv_seq_len > self.config.sliding_window)
if past_key_value is not None:
# Activate slicing cache only if the config has a value
# `sliding_windows` attribute
cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
if (getattr(self.config, 'sliding_window', None) is not None
and kv_seq_len > self.config.sliding_window
and cache_has_contents):
slicing_tokens = 1 - self.config.sliding_window
past_key = past_key_value[self.layer_idx][0]
past_value = past_key_value[self.layer_idx][1]
past_key = past_key[:, :, slicing_tokens:, :].contiguous()
past_value = past_value[:, :, slicing_tokens:, :].contiguous()
if past_key.shape[-2] != self.config.sliding_window - 1:
raise ValueError(
'past key must have a shape of (`batch_size, num_heads, '
'self.config.sliding_window-1, head_dim`), got'
f' {past_key.shape}')
if attention_mask is not None:
attention_mask = attention_mask[:, slicing_tokens:]
attention_mask = torch.cat(
[attention_mask,
torch.ones_like(attention_mask[:, -1:])],
dim=-1)
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)
# 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)
attn_dropout = 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.
if query_states.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.qkv_proj.weight.dtype
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
# Reashape to the expected shape for Flash Attention
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
enable_sequence_parallel = (
dist.is_initialized() and get_sequence_parallel_world_size() > 1
and self.training)
if enable_sequence_parallel:
# (b, s // sp_world_size, nd, dim) -> (b, s, nd // sp_world_size, dim)
query_states, key_states, value_states = \
pre_process_for_sequence_parallel_attn(
query_states, key_states, value_states,
scatter_dim=2, gather_dim=1)
attn_output = self._flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
query_states.shape[1],
dropout=attn_dropout,
use_sliding_windows=use_sliding_windows,
)
if enable_sequence_parallel:
# (b, s, nd // sp_world_size, dim) -> (b, s // sp_world_size, nd, dim)
attn_output = post_process_for_sequence_parallel_attn(
attn_output, scatter_dim=1, gather_dim=2)
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 phi3_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]]]:
if not _flash_supports_window_size:
raise ValueError(
'The current flash attention version does not support '
'sliding window attention.')
output_attentions = False
is_training = self.training
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}')
assert is_training == (past_key_value is None)
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.`')
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop('padding_mask')
bsz, q_len, _ = hidden_states.size()
assert bsz == 1, (f'If utilizing local attention, the batch size should be'
f' set to 1, but got {bsz}')
# attention_mask is set to None if no padding token in input_ids
# varlen attn need data packing so no padding tokens in input_ids
assert attention_mask is None
qkv = self.qkv_proj(hidden_states)
query_pos = self.num_heads * self.head_dim
query_states = qkv[..., :query_pos]
key_states = qkv[..., query_pos:query_pos +
self.num_key_value_heads * self.head_dim]
value_states = qkv[...,
query_pos + self.num_key_value_heads * self.head_dim:]
# 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)
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
rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
cos, sin = self.rotary_emb(
value_states, position_ids, seq_len=rotary_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin, position_ids)
use_sliding_windows = (
_flash_supports_window_size
and getattr(self.config, 'sliding_window', None) is not None
and kv_seq_len > self.config.sliding_window)
if past_key_value is not None:
# Activate slicing cache only if the config has a value
# `sliding_windows` attribute
cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
if (getattr(self.config, 'sliding_window', None) is not None
and kv_seq_len > self.config.sliding_window
and cache_has_contents):
slicing_tokens = 1 - self.config.sliding_window
past_key = past_key_value[self.layer_idx][0]
past_value = past_key_value[self.layer_idx][1]
past_key = past_key[:, :, slicing_tokens:, :].contiguous()
past_value = past_value[:, :, slicing_tokens:, :].contiguous()
if past_key.shape[-2] != self.config.sliding_window - 1:
raise ValueError(
'past key must have a shape of (`batch_size, num_heads, '
'self.config.sliding_window-1, head_dim`), got'
f' {past_key.shape}')
if attention_mask is not None:
attention_mask = attention_mask[:, slicing_tokens:]
attention_mask = torch.cat(
[attention_mask,
torch.ones_like(attention_mask[:, -1:])],
dim=-1)
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)
# 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)
# 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 float16 just to be sure everything works as expected.
if query_states.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.qkv_proj.weight.dtype
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
# Reashape to the expected shape for Flash Attention
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
# ----------------- flash attention forward ------------------------#
if not self._flash_attn_uses_top_left_mask:
causal = self.is_causal
else:
causal = self.is_causal and q_len != 1
use_sliding_windows = (
_flash_supports_window_size
and getattr(self.config, 'sliding_window', None) is not None
and kv_seq_len > self.config.sliding_window)
window_size = (self.config.sliding_window,
self.config.sliding_window) if use_sliding_windows else (-1,
-1)
attn_dropout = self.attention_dropout if self.training else 0.0
if use_varlen_atten:
attn_output = varlen_flash_attn(
query_states,
key_states,
value_states,
cumulative_len,
max_seqlen,
causal=causal,
dropout_p=attn_dropout,
window_size=window_size,
training=self.training)
else:
attn_output = flash_attn_wo_mask(
query_states,
key_states,
value_states,
causal=causal,
dropout_p=attn_dropout,
window_size=window_size,
training=self.training)
# ---------------- flash attention forward end ------------------- #
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
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