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""" Implementing the Deepseek Multi Latent Attention (MLA) module. Reference:
https://github.com/huggingface/transformers/blob/main/src/transformers/models/deepseek_v3/modeling_deepseek_v3.py#L328
"""
from __future__ import annotations
import math
import warnings
from typing import TYPE_CHECKING
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from transformers.utils import logging
from fla.layers.utils import pad_input, unpad_input
from fla.modules import RMSNorm, RotaryEmbedding
from fla.ops.utils.index import prepare_lens_from_mask
if TYPE_CHECKING:
from fla.models.utils import Cache
try:
from flash_attn import flash_attn_func, flash_attn_varlen_func
except ImportError:
warnings.warn(
"Flash Attention is not installed. Please install it via `pip install flash-attn --no-build-isolation`",
category=ImportWarning,
)
flash_attn_func = None
logger = logging.get_logger(__name__)
def yarn_get_mscale(scale=1, mscale=1):
if scale <= 1:
return 1.0
return 0.1 * mscale * math.log(scale) + 1.0
class MultiheadLatentAttention(nn.Module):
r"""
Multi-headed attention from [Deepseek V2](https://arxiv.org/abs/2405.04434)
"""
def __init__(
self,
hidden_size: int = 2048,
num_heads: int = 16,
q_lora_rank: int | None = 1536, # q lora rank is optional, None indicates no q lora
qk_rope_head_dim: int = 64,
kv_lora_rank: int = 512, # following the original Deepseek paper
v_head_dim: int = 128,
qk_nope_head_dim: int = 128,
qk_head_dim: int | None = 192, # qk_nope_head_dim + qk_rope_head_dim
window_size: int | None = None,
rope_theta: float = 10000.,
max_position_embeddings: int | None = None,
rope_scaling: dict | None = None,
layer_idx: int = None,
) -> MultiheadLatentAttention:
super().__init__()
# sanity check
if qk_head_dim is not None:
assert qk_head_dim == qk_nope_head_dim + qk_rope_head_dim, \
f"qk_head_dim {qk_head_dim} != qk_nope_head_dim {qk_nope_head_dim} + qk_rope_head_dim {qk_rope_head_dim}"
else:
qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
# attention params info
self.hidden_size = hidden_size
self.num_heads = num_heads
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.kv_lora_rank = kv_lora_rank
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_head_dim = qk_head_dim
self.window_size = window_size
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.layer_idx = layer_idx
if flash_attn_func is None:
raise ImportError("Please install Flash Attention via `pip install flash-attn --no-build-isolation` first")
if q_lora_rank is not None:
self.q_proj = nn.Sequential(
nn.Linear(hidden_size, q_lora_rank, bias=False),
RMSNorm(q_lora_rank),
nn.Linear(q_lora_rank, self.num_heads * self.qk_head_dim, bias=False),
)
else:
self.q_proj = nn.Linear(hidden_size, self.num_heads * self.qk_head_dim, bias=False)
self.k_rope = nn.Linear(hidden_size, self.qk_rope_head_dim, bias=False)
self.kv_proj = nn.Sequential(
nn.Linear(hidden_size, self.kv_lora_rank, bias=False),
RMSNorm(self.kv_lora_rank),
nn.Linear(self.kv_lora_rank, self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), bias=False),
)
self.o_proj = nn.Linear(self.num_heads * self.v_head_dim, hidden_size, bias=False)
self.scaling = self.qk_head_dim ** (-0.5)
if rope_scaling is not None:
mscale_all_dim = rope_scaling.get("mscale_all_dim", 0)
scaling_factor = rope_scaling["factor"]
if mscale_all_dim:
mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
self.scaling = self.scaling * mscale * mscale
self.rotary = RotaryEmbedding(dim=self.qk_rope_head_dim, base=self.rope_theta)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor | None,
past_key_values: Cache | None = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
# if attention_mask is not None, this is doing inference
if attention_mask is not None:
assert len(attention_mask.shape) == 2, (
"Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] "
"for padding purposes (0 indicating padding). "
"Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed."
)
# prepare q, k, v
batch_size, q_len, _ = hidden_states.shape
q_states = self.q_proj(hidden_states)
q_states = rearrange(q_states, '... (h d) -> ... h d', d=self.qk_head_dim)
q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
k_pass, k_rot = self.kv_proj(hidden_states), self.k_rope(hidden_states)
k_rot = rearrange(k_rot, 'b t d -> b t 1 d')
k_pass = rearrange(k_pass, '... (h d) -> ... h d', d=self.qk_nope_head_dim + self.v_head_dim)
k_pass, v = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
# apply rotary position embedding
seqlen_offset, max_seqlen = 0, q_len
if past_key_values is not None:
seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
max_seqlen = q_len + seqlen_offset
if attention_mask is not None:
seqlen_offset = seqlen_offset + prepare_lens_from_mask(attention_mask) - attention_mask.shape[-1]
max_seqlen = q_len + max(seqlen_offset)
if self.max_position_embeddings is not None:
max_seqlen = max(max_seqlen, self.max_position_embeddings)
cu_seqlens = kwargs.get("cu_seqlens")
q_rot, k_rot = self.rotary(
q_rot, k_rot, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens,
)
k_rot = repeat(k_rot, 'b t 1 d -> b t h d', h=self.num_heads)
q = torch.cat((q_pass, q_rot), dim=-1)
k = torch.cat((k_pass, k_rot), dim=-1)
# TODO: instead of caching the full k, v, we can actually only cache the compressed_kv and k_rot
# and recover the full k, v from compressed_kv and k_rot
if past_key_values is not None:
cache_has_content = past_key_values.get_seq_length(self.layer_idx) > 0
k_cached, v_cached = past_key_values.update(
attn_state=(k, v),
layer_idx=self.layer_idx,
offset=q_len,
)['attn_state']
if cache_has_content:
k, v = k_cached, v_cached
# Head dim match to use flash-attn
if self.qk_head_dim != self.v_head_dim:
v = F.pad(v, [0, self.qk_head_dim - self.v_head_dim])
# Contains at least one padding token in the sequence
if attention_mask is not None:
if q.shape[1] == 1 and self.window_size is not None:
attention_mask = attention_mask[:, -self.window_size:]
q, (k, v), indices_q, cu_seqlens, max_seq_lens = unpad_input(q, (k, v), attention_mask, q_len)
cu_seqlens_q, cu_seqlens_k = cu_seqlens
max_seqlen_q, max_seqlen_k = max_seq_lens
o = flash_attn_varlen_func(
q, k, v,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_q,
max_seqlen_k=max_seqlen_k,
causal=True,
window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0),
)
o = pad_input(o, indices_q, batch_size, q_len)
elif cu_seqlens is not None:
o = flash_attn_varlen_func(
q.squeeze(0), k.squeeze(0), v.squeeze(0),
cu_seqlens_q=cu_seqlens,
cu_seqlens_k=cu_seqlens,
max_seqlen_q=max_seqlen,
max_seqlen_k=max_seqlen,
causal=True,
window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0),
).unsqueeze(0)
else:
o = flash_attn_func(
q, k, v,
causal=True,
window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0),
)
if self.qk_head_dim != self.v_head_dim:
o = o[:, :, :, :self.v_head_dim]
o = o.reshape(batch_size, q_len, -1)
o = self.o_proj(o)
return o, None, past_key_values
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