mirror of https://github.com/InternLM/InternLM
363 lines
18 KiB
Python
363 lines
18 KiB
Python
#!/usr/bin/env python
|
|
# -*- encoding: utf-8 -*-
|
|
|
|
import math
|
|
import warnings
|
|
from typing import Optional
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from einops import rearrange
|
|
|
|
try:
|
|
from flash_attn import flash_attn_unpadded_func
|
|
except ImportError:
|
|
try:
|
|
from flash_attn import (
|
|
flash_attn_unpadded_kvpacked_func as flash_attn_unpadded_func,
|
|
)
|
|
except ImportError:
|
|
try:
|
|
from flash_attn import (
|
|
flash_attn_varlen_kvpacked_func as flash_attn_unpadded_func,
|
|
)
|
|
except ImportError:
|
|
raise ImportError("Please check your flash_attn version >= 1.0.5.")
|
|
|
|
from flash_attn.modules.mha import (
|
|
CrossAttention,
|
|
FlashCrossAttention,
|
|
FlashSelfAttention,
|
|
SelfAttention,
|
|
_update_kv_cache,
|
|
)
|
|
from torch import nn
|
|
|
|
from internlm.core.context import IS_TENSOR_PARALLEL, ParallelMode
|
|
from internlm.core.context import global_context as gpc
|
|
from internlm.model.embedding import DynamicNTKScalingRotaryEmbedding, RotaryEmbedding
|
|
from internlm.model.linear import ColumnParallelLinearTorch, RowParallelLinearTorch
|
|
|
|
|
|
class MHA(nn.Module):
|
|
"""
|
|
Multi-head self-attention and cross-attention.
|
|
|
|
Args:
|
|
embed_dim (int): The dimention of hidden state.
|
|
num_heads (int): The number of attention heads.
|
|
process_group (torch.distributed.ProcessGroup): The group of the current device for `parallel_mode`.
|
|
bias (boolean): Whether the bias is needed for linears. Will be used when initializing QKV matrix and
|
|
output projection. True by default.
|
|
dropout (float): The dropout rate for cross attention and self attention. 0.0 by default.
|
|
softmax_scale (float): The temperature to use for the softmax attention.
|
|
causal (boolean): Whether to apply causal attention mask. False by default.
|
|
layer_idx (int): The index of current layer. None by default.
|
|
rotary_emb_dim (int): The dimention of Rotary Embedding. 0 by default.
|
|
rotary_emb_scale_base (int): The scaling factor of Rotary Embedding. If scale_base > 0, this implements
|
|
XPos(Sun et al., https://arxiv.org/abs/2212.10554). 0 by default.
|
|
use_flash_attn (boolean): Whether to use flash attention or not.If False, vanilla attention module will be used.
|
|
False by default.
|
|
sequence_parallel (boolean): If True, we're doing Tensor Parallel with sequence parallelism. An all_gather_raw
|
|
of x will be done before doing the matmul.
|
|
device (Optional[Union[str, torch.device]]): The device will be used.
|
|
dtype (Optional[torch.dtype]): The type of data.
|
|
use_flash_attn (bool): Whether to use flash-attn. True by default.
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
embed_dim: int,
|
|
num_heads: int,
|
|
process_group: Optional[torch.distributed.ProcessGroup],
|
|
max_position_embeddings: int = 2048,
|
|
dropout: float = 0.0,
|
|
softmax_scale: float = None,
|
|
causal: bool = False,
|
|
layer_idx: int = None,
|
|
use_dynamic_ntk_rope: bool = False,
|
|
rotary_emb_dim: int = 0,
|
|
rotary_emb_scale_base: int = 0,
|
|
use_flash_attn: bool = True,
|
|
device: Optional[torch.device] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
) -> None:
|
|
factory_kwargs = {"device": device, "dtype": dtype}
|
|
super().__init__()
|
|
self.embed_dim = embed_dim
|
|
self.causal = causal
|
|
self.layer_idx = layer_idx
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.use_dynamic_ntk_rope = use_dynamic_ntk_rope
|
|
self.rotary_emb_dim = rotary_emb_dim
|
|
self.use_flash_attn = use_flash_attn
|
|
self.num_heads = num_heads
|
|
assert self.embed_dim % num_heads == 0, "self.kdim must be divisible by num_heads"
|
|
self.head_dim = self.embed_dim // num_heads
|
|
|
|
if self.rotary_emb_dim > 0:
|
|
if self.use_dynamic_ntk_rope:
|
|
self.rotary_emb = DynamicNTKScalingRotaryEmbedding(
|
|
self.rotary_emb_dim,
|
|
scale_base=rotary_emb_scale_base,
|
|
device=device,
|
|
max_position_embeddings=max_position_embeddings,
|
|
scaling_factor=1.0, # Currently do not support dynamic scaling.
|
|
)
|
|
else:
|
|
self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, scale_base=rotary_emb_scale_base, device=device)
|
|
|
|
# notice here should change bias=True
|
|
self.Wqkv = ColumnParallelLinearTorch(
|
|
embed_dim,
|
|
3 * embed_dim,
|
|
process_group,
|
|
bias=True,
|
|
sequence_parallel=gpc.config.parallel.sequence_parallel,
|
|
**factory_kwargs,
|
|
) # according to https://spaces.ac.cn/archives/9577
|
|
|
|
inner_attn_cls = FlashSelfAttention if use_flash_attn else SelfAttention
|
|
inner_cross_attn_cls = FlashCrossAttention if use_flash_attn else CrossAttention
|
|
self.inner_attn = inner_attn_cls(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
|
|
self.inner_cross_attn = inner_cross_attn_cls(
|
|
causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout
|
|
)
|
|
|
|
# output projection always have the bias (for now)
|
|
self.out_proj = RowParallelLinearTorch(
|
|
embed_dim,
|
|
embed_dim,
|
|
process_group,
|
|
sequence_parallel=gpc.config.parallel.sequence_parallel,
|
|
**factory_kwargs,
|
|
)
|
|
# need to assign tp attribute so that internlm know it is tensor parallel module
|
|
if gpc.get_world_size(ParallelMode.TENSOR) > 1:
|
|
for name in ["out_proj", "Wqkv"]:
|
|
for param in getattr(self, name).parameters():
|
|
setattr(param, IS_TENSOR_PARALLEL, True)
|
|
|
|
def forward(self, x, seqlen=None, inference_params=None, **kwargs):
|
|
if kwargs.get("indexes", None) is not None:
|
|
return self._packed_forward(x=x, inference_params=inference_params, **kwargs)
|
|
else:
|
|
return self._forward(x=x, seqlen=seqlen, inference_params=inference_params, **kwargs)
|
|
|
|
def _forward(self, x, seqlen=None, inference_params=None, **kwargs): # pylint: disable=W0613
|
|
"""
|
|
Arguments:
|
|
x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if seqlen=None.
|
|
If seqlen is not None, x is (batch * seqlen, hidden_dim). This is so that when we
|
|
split x during sequence parallel, we split the batch * seqlen dimension
|
|
(in case batch is small).
|
|
"""
|
|
bsz, _, _ = x.shape
|
|
qkv = self.Wqkv(x)
|
|
if seqlen is None:
|
|
qkv = rearrange(qkv, "b s (three h d) -> b s three h d", three=3, d=self.head_dim)
|
|
else:
|
|
qkv = rearrange(qkv, "(b s) (three h d) -> b s three h d", s=seqlen, three=3, d=self.head_dim)
|
|
|
|
if inference_params is None:
|
|
kwargs["inference_params"] = inference_params
|
|
qkv = self.rotary_emb(qkv, **kwargs)
|
|
if gpc.config.model.dtype is torch.float32 and gpc.config.model.use_flash_attn:
|
|
with torch.cuda.amp.autocast(dtype=torch.bfloat16):
|
|
if qkv.dtype not in [torch.float16, torch.bfloat16]:
|
|
qkv = qkv.to(torch.bfloat16)
|
|
context = self.inner_attn(qkv).to(x.dtype)
|
|
else:
|
|
context = self.inner_attn(qkv)
|
|
|
|
else:
|
|
if self.use_dynamic_ntk_rope:
|
|
q = qkv[:, :, 0]
|
|
assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
|
|
kv = _update_kv_cache(qkv[:, :, 1:], inference_params, self.layer_idx)
|
|
if inference_params.sequence_len_offset != 0:
|
|
# q shape: [bsz, 1, nheads, head_dim]
|
|
# kv shape: [bsz, seqlen, 2, nheads, head_dim]
|
|
bsz, seq_len, _, nheads, head_dim = kv.shape
|
|
q = torch.cat([q.new_zeros(size=(bsz, seq_len - 1, nheads, head_dim)), q], dim=1).unsqueeze(2)
|
|
qkv = torch.cat([q, kv], dim=2)
|
|
if self.rotary_emb_dim > 0:
|
|
qkv = self.rotary_emb(qkv)
|
|
q = qkv[:, [-1], 0]
|
|
kv = qkv[:, :, 1:]
|
|
else:
|
|
if inference_params.sequence_len_offset > self.max_position_embeddings:
|
|
warnings.warn(
|
|
"Notice your prompt's length is longer than model's max_position_embeddings: "
|
|
f"{self.max_position_embeddings}, which will cause deviations in dynamic ntk calculations."
|
|
)
|
|
if self.rotary_emb_dim > 0:
|
|
kwargs["inference_params"] = inference_params
|
|
qkv = self.rotary_emb(qkv, **kwargs)
|
|
q = qkv[:, :, 0]
|
|
kv = qkv[:, :, 1:]
|
|
else:
|
|
assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
|
|
q, k, v = (x.squeeze(2) for x in qkv.chunk(chunks=3, dim=2))
|
|
kv = torch.stack([k, v], dim=2)
|
|
assert self.rotary_emb_dim > 0, "You should use rotary_emb."
|
|
|
|
if hasattr(inference_params, "attention_mask") and inference_params.attention_mask is not None:
|
|
empties = inference_params.attention_mask[..., -1].sum(dim=-1)
|
|
if inference_params.sequence_len_offset == 0:
|
|
moved_q = q.clone()
|
|
moved_k = k.clone()
|
|
for i in range(len(empties)):
|
|
if empties[i] != 0:
|
|
moved_q[i][: -empties[i]] = q[i][empties[i] :]
|
|
moved_k[i][: -empties[i]] = k[i][empties[i] :]
|
|
moved_q = self.rotary_emb._single_eval_forward(moved_q, seqlen_offset=0)
|
|
moved_k = self.rotary_emb._single_eval_forward(moved_k, seqlen_offset=0)
|
|
for i in range(len(empties)):
|
|
if empties[i] != 0:
|
|
q[i][empties[i] :] = moved_q[i][: -empties[i]]
|
|
k[i][empties[i] :] = moved_k[i][: -empties[i]]
|
|
else:
|
|
q[i] = moved_q[i]
|
|
k[i] = moved_k[i]
|
|
elif not self.use_dynamic_ntk_rope:
|
|
if inference_params.sequence_len_offset > self.max_position_embeddings:
|
|
warnings.warn(
|
|
"Notice your prompt's length is longer than model's max_position_embeddings: "
|
|
f"{self.max_position_embeddings}, may cause deviations in dynamic ntk calculations."
|
|
)
|
|
q = q.squeeze(1)
|
|
k = k.squeeze(1)
|
|
q = self.rotary_emb._single_forward(
|
|
q,
|
|
inference_params.sequence_len_offset
|
|
* torch.ones(q.size(0), dtype=torch.int, device=q.device)
|
|
- empties,
|
|
).unsqueeze(1)
|
|
k = self.rotary_emb._single_forward(
|
|
k,
|
|
inference_params.sequence_len_offset
|
|
* torch.ones(k.size(0), dtype=torch.int, device=k.device)
|
|
- empties,
|
|
).unsqueeze(1)
|
|
else:
|
|
q = q.squeeze(1)
|
|
q = self.rotary_emb._single_forward(
|
|
q,
|
|
inference_params.sequence_len_offset
|
|
* torch.ones(q.size(0), dtype=torch.int, device=q.device)
|
|
- empties,
|
|
).unsqueeze(1)
|
|
moved_k = k.clone()
|
|
for i in range(len(empties)):
|
|
if empties[i] != 0:
|
|
moved_k[i][: -empties[i]] = k[i][empties[i] :]
|
|
moved_k = self.rotary_emb._single_eval_forward(moved_k, seqlen_offset=0)
|
|
for i in range(len(empties)):
|
|
if empties[i] != 0:
|
|
k[i][empties[i] :] = moved_k[i][: -empties[i]]
|
|
else:
|
|
k[i] = moved_k[i]
|
|
else:
|
|
q = self.rotary_emb._single_forward(q, inference_params.sequence_len_offset)
|
|
k = self.rotary_emb._single_forward(k, inference_params.sequence_len_offset)
|
|
|
|
kv = torch.stack([k, v], dim=2)
|
|
kv = _update_kv_cache(kv, inference_params, self.layer_idx)
|
|
|
|
if hasattr(inference_params, "attention_mask") and inference_params.attention_mask is not None:
|
|
if inference_params.sequence_len_offset == 0: # First entrance, attnmask (bs*seqlen*seqlen)
|
|
attn_mask = inference_params.attention_mask[:, None, ...]
|
|
attn_mask = torch.logical_or(
|
|
torch.ones_like(attn_mask, dtype=torch.bool).triu(diagonal=1), attn_mask
|
|
)
|
|
attn_mask4flsh = ~attn_mask[:, :, -1, :].view(bsz, -1)
|
|
cu_seqlens = torch.concat(
|
|
[
|
|
torch.tensor([0], dtype=torch.int32, device=attn_mask4flsh.device),
|
|
attn_mask4flsh.sum(dim=-1).to(dtype=torch.int32),
|
|
],
|
|
dim=0,
|
|
)
|
|
cu_seqlens = cu_seqlens.cumsum(dim=0, dtype=torch.int32)
|
|
max_seqlen_q = attn_mask4flsh.shape[-1]
|
|
max_seqlen_k = attn_mask4flsh.shape[-1]
|
|
total_q = q.masked_select(attn_mask4flsh.view(bsz, -1, 1, 1)).view(-1, q.shape[-2], q.shape[-1])
|
|
total_kv = kv.masked_select(attn_mask4flsh.view(bsz, -1, 1, 1, 1)).view(
|
|
-1, kv.shape[-3], kv.shape[-2], kv.shape[-1]
|
|
)
|
|
|
|
if gpc.config.model.dtype is torch.float32 and gpc.config.model.use_flash_attn:
|
|
with torch.cuda.amp.autocast(dtype=torch.bfloat16):
|
|
if total_q.dtype not in [torch.float16, torch.bfloat16]:
|
|
total_q = total_q.to(torch.bfloat16)
|
|
if total_kv.dtype not in [torch.float16, torch.bfloat16]:
|
|
total_kv = total_kv.to(torch.bfloat16)
|
|
|
|
output = flash_attn_unpadded_func(
|
|
total_q, total_kv, cu_seqlens, cu_seqlens, max_seqlen_q, max_seqlen_k, 0.0, None, True, False
|
|
).to(x.dtype)
|
|
|
|
context = torch.zeros_like(q)
|
|
context = context.masked_scatter_(attn_mask4flsh.view(bsz, -1, 1, 1), output)
|
|
|
|
else:
|
|
attn_mask = inference_params.attention_mask[:, -1, :].view(bsz, 1, 1, -1)
|
|
|
|
k, v = torch.chunk(kv, 2, dim=2)
|
|
k = k.squeeze(2)
|
|
v = v.squeeze(2)
|
|
sp = k.shape
|
|
scores = torch.einsum(
|
|
"blhd,bnhd->bhln",
|
|
q,
|
|
k.reshape(sp[0], sp[1], q.size(2), sp[3]),
|
|
) / math.sqrt(q.size(-1))
|
|
scores = scores.masked_fill(attn_mask, -65000.0)
|
|
scores = F.softmax(scores, dim=-1) # bsz x h x L x L
|
|
context = torch.einsum(
|
|
"bhmn,bnhd->bmhd",
|
|
scores,
|
|
v.reshape(sp[0], sp[1], q.size(2), sp[3]),
|
|
)
|
|
else:
|
|
context = self.inner_cross_attn(q, kv, causal=True)
|
|
|
|
if seqlen is None:
|
|
context = rearrange(context, "b s h d -> b s (h d)")
|
|
else:
|
|
context = rearrange(context, "b s h d -> (b s) (h d)")
|
|
|
|
out = self.out_proj(context)
|
|
return out
|
|
|
|
def _packed_forward(self, x, inference_params=None, **kwargs):
|
|
"""
|
|
Arguments:
|
|
x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if seqlen=None.
|
|
If seqlen is not None, x is (batch * seqlen, hidden_dim). This is so that when we
|
|
split x during sequence parallel, we split the batch * seqlen dimension
|
|
(in case batch is small).
|
|
"""
|
|
qkv = self.Wqkv(x) # total x hsz'
|
|
qkv = rearrange(qkv, "t (three h d) -> t three h d", three=3, d=self.head_dim) # total x 3 x n_head x d
|
|
qkv = self.rotary_emb(qkv, **kwargs)
|
|
kwargs.pop("indexes")
|
|
|
|
if inference_params is None:
|
|
if gpc.config.model.dtype is torch.float32 and gpc.config.model.use_flash_attn:
|
|
with torch.cuda.amp.autocast(dtype=torch.bfloat16):
|
|
if qkv.dtype not in [torch.float16, torch.bfloat16]:
|
|
qkv = qkv.to(torch.bfloat16)
|
|
context = self.inner_attn(qkv, **kwargs).to(x.dtype)
|
|
else:
|
|
context = self.inner_attn(qkv, **kwargs)
|
|
|
|
else:
|
|
raise RuntimeError("Not support this right now")
|
|
|
|
context = rearrange(context, "b h d -> b (h d)") # recover the shape
|
|
out = self.out_proj(context)
|
|
return out
|