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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Optional, Tuple
import torch
import torch.distributed as dist
import torch.nn.functional as F
from einops import rearrange
from mmengine import MessageHub
from .attention import (SUPPORT_FLASH2, flash_attn_w_mask, flash_attn_wo_mask,
varlen_flash_attn)
from .triton_kernels import apply_rotary_emb
class InternLM2RotaryEmbedding(torch.nn.Module):
def __init__(self,
dim,
max_position_embeddings=2048,
base=1000000,
device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.inv_freq = 1.0 / (
base**(torch.arange(0, dim, 2).float().to(device) / dim))
# Build here to make `torch.jit.trace` work.
self.max_seq_len_cached = max_position_embeddings
t = torch.arange(
self.max_seq_len_cached,
device=self.inv_freq.device,
dtype=self.inv_freq.dtype)
freqs = torch.einsum('i,j->ij', t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
self.cos_cached = emb.cos()
self.sin_cached = emb.sin()
def forward(self, x, seq_len):
# x: [bs, num_attention_heads, seq_len, head_size]
if (seq_len > self.max_seq_len_cached
or self.cos_cached.device != x.device
or self.cos_cached.dtype != x.dtype):
self.max_seq_len_cached = seq_len
assert self.inv_freq.dtype == torch.float32
t = torch.arange(
self.max_seq_len_cached,
device=x.device,
dtype=self.inv_freq.dtype)
freqs = torch.einsum('i,j->ij', t, self.inv_freq.to(t.device))
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self.cos_cached = emb.cos().to(x.dtype)
self.sin_cached = emb.sin().to(x.dtype)
return (
self.cos_cached[:seq_len, ...],
self.sin_cached[:seq_len, ...],
)
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)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
sin = sin[position_ids].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 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)
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 internlm2_attn_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
):
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')
output_attentions = False
bsz, q_len, _ = hidden_states.size()
qkv_states = self.wqkv(hidden_states)
qkv_states = rearrange(
qkv_states,
'b q (h gs d) -> b q h gs d',
gs=2 + self.num_key_value_groups,
d=self.head_dim,
)
query_states = qkv_states[..., :self.num_key_value_groups, :]
query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
key_states = qkv_states[..., -2, :]
value_states = qkv_states[..., -1, :]
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
# This modification is necessary for sequential parallel
assert position_ids is not None and (position_ids.max() + 1) >= kv_seq_len
cos, sin = self.rotary_emb(value_states, seq_len=position_ids.max() + 1)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
cos, sin, position_ids)
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
# repeat kv for sequence parallel
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
if SUPPORT_FLASH2:
# 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.')
# flash attn 2 need (bs, seq_len, nhead, h_dim)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
causal = self.is_causal and q_len != 1
if attention_mask is not None:
attn_output = flash_attn_w_mask(
query_states,
key_states,
value_states,
attention_mask,
causal=causal,
training=self.training)
else:
attn_output = flash_attn_wo_mask(
query_states,
key_states,
value_states,
causal=causal,
training=self.training)
else:
# use flash attention implemented by pytorch
# do not support sequence parallel
attn_output = F.scaled_dot_product_attention(
query_states, key_states, value_states, attn_mask=attention_mask)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.wo(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def internlm2_varlen_attn_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor],
Optional[Tuple[torch.Tensor]]]:
# Modified from https://huggingface.co/internlm/internlm-7b/blob/939a68c0dc1bd5f35b63c87d44af05ce33379061/modeling_internlm.py#L161 # noqa:E501
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)
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}')
qkv_states = self.wqkv(hidden_states)
qkv_states = rearrange(
qkv_states,
'b q (h gs d) -> b q h gs d',
gs=2 + self.num_key_value_groups,
d=self.head_dim,
)
query_states = qkv_states[..., :self.num_key_value_groups, :]
query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
key_states = qkv_states[..., -2, :]
value_states = qkv_states[..., -1, :]
kv_seq_len = key_states.shape[-3]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
if use_varlen_atten:
cos, sin = self.rotary_emb(value_states, max_seqlen)
query_states = apply_rotary_emb(query_states,
cos[position_ids].squeeze(0),
sin[position_ids].squeeze(0))
key_states = apply_rotary_emb(key_states, cos[position_ids].squeeze(0),
sin[position_ids].squeeze(0))
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:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
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)
assert SUPPORT_FLASH2
if use_varlen_atten:
attn_output = varlen_flash_attn(
query_states,
key_states,
value_states,
cumulative_len,
max_seqlen,
training=self.training)
else:
attn_output = flash_attn_wo_mask(
query_states,
key_states,
value_states,
causal=True,
training=False)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.wo(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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