205 lines
8 KiB
Python
205 lines
8 KiB
Python
import math
|
|
import torch
|
|
import torch.nn as nn
|
|
from torch.nn import functional as F
|
|
from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb
|
|
|
|
from ._fused_base import FusedBaseAttentionModule
|
|
from ..utils.import_utils import compare_pytorch_version, dynamically_import_QuantLinear
|
|
|
|
from logging import getLogger
|
|
logger = getLogger(__name__)
|
|
|
|
class FusedLlamaAttentionForQuantizedModel(FusedBaseAttentionModule):
|
|
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size,
|
|
num_heads,
|
|
qkv_proj,
|
|
o_proj,
|
|
rotary_emb,
|
|
):
|
|
super().__init__()
|
|
self.hidden_size = hidden_size
|
|
self.num_heads = num_heads
|
|
self.head_dim = hidden_size // num_heads
|
|
|
|
if self.head_dim * num_heads != self.hidden_size:
|
|
raise ValueError(
|
|
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
|
f" and `num_heads`: {num_heads})."
|
|
)
|
|
self.qkv_proj = qkv_proj
|
|
self.o_proj = o_proj
|
|
self.rotary_emb = rotary_emb
|
|
|
|
def _shape(self, tensor, seq_len, bsz):
|
|
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
past_key_value=None,
|
|
attention_mask=None,
|
|
position_ids=None,
|
|
output_attentions=False,
|
|
use_cache=False,
|
|
**kwargs
|
|
):
|
|
"""Input shape: Batch x Time x Channel"""
|
|
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
qkv_states = self.qkv_proj(hidden_states)
|
|
query_states, key_states, value_states = torch.split(qkv_states, self.hidden_size, dim=2)
|
|
|
|
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_heads, self.head_dim).transpose(1, 2)
|
|
value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).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]
|
|
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)
|
|
# [bsz, nh, t, hd]
|
|
|
|
is_causal = past_key_value is None
|
|
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)
|
|
|
|
if use_cache:
|
|
# Since qkv_proj is fused, query_states etc will hold a reference to the original qkv_states tensor
|
|
# which can cause excessive memory usage by the cache. `contiguous` is a convenient way to workaround this.
|
|
query_states = query_states.contiguous()
|
|
key_states = key_states.contiguous()
|
|
value_states = value_states.contiguous()
|
|
|
|
past_key_value = (key_states, value_states) if use_cache else None
|
|
|
|
if compare_pytorch_version("v2.0.0", op="ge"):
|
|
attn_output = F.scaled_dot_product_attention(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attn_mask=None if is_causal else attention_mask,
|
|
is_causal=is_causal
|
|
)
|
|
attn_weights = None
|
|
else:
|
|
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
|
raise ValueError(
|
|
f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
|
|
f" {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
raise ValueError(
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
|
)
|
|
attn_weights = attn_weights + attention_mask
|
|
attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
|
|
|
|
# upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
|
raise ValueError(
|
|
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
|
f" {attn_output.size()}"
|
|
)
|
|
|
|
attn_output = attn_output.transpose(1, 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
|
|
|
|
@classmethod
|
|
def inject_to_model(
|
|
cls,
|
|
model,
|
|
use_triton=False,
|
|
group_size=-1,
|
|
use_cuda_fp16=True,
|
|
desc_act=False,
|
|
trainable=False,
|
|
bits: int = 4,
|
|
disable_exllama=False,
|
|
**kwargs
|
|
):
|
|
"""
|
|
Replace all LlamaAttention modules with QuantLlamaAttention modules, fusing the q, k, v projections.
|
|
"""
|
|
QuantLinear = dynamically_import_QuantLinear(use_triton=use_triton, desc_act=desc_act, group_size=group_size, bits=bits, disable_exllama=disable_exllama)
|
|
if QuantLinear.QUANT_TYPE == "exllama" and desc_act:
|
|
# TODO: support it. The issue lies maybe in the line:
|
|
# int groups = qzeros.size(0);
|
|
# in exllama_ext.cpp
|
|
logger.warning(f"Exllama kernel does not support query/key/value fusion with act-order. Because of this, Fused attention is automatically disabled.")
|
|
return False
|
|
|
|
for name, m in model.named_modules():
|
|
if not isinstance(m, LlamaAttention):
|
|
continue
|
|
|
|
q_proj = m.q_proj
|
|
k_proj = m.k_proj
|
|
v_proj = m.v_proj
|
|
|
|
qweights = torch.cat([q_proj.qweight, k_proj.qweight, v_proj.qweight], dim=1)
|
|
qzeros = torch.cat([q_proj.qzeros, k_proj.qzeros, v_proj.qzeros], dim=1)
|
|
scales = torch.cat([q_proj.scales, k_proj.scales, v_proj.scales], dim=1)
|
|
|
|
if QuantLinear.QUANT_TYPE == "exllama":
|
|
g_idx = None
|
|
else:
|
|
g_idx = torch.cat([q_proj.g_idx, k_proj.g_idx, v_proj.g_idx], dim=0)
|
|
|
|
bias = torch.cat([q_proj.bias, k_proj.bias, v_proj.bias], dim=0) if q_proj.bias is not None else None
|
|
|
|
qlinear_args = (
|
|
q_proj.bits,
|
|
q_proj.group_size,
|
|
q_proj.infeatures,
|
|
q_proj.outfeatures + k_proj.outfeatures + v_proj.outfeatures,
|
|
True if q_proj.bias is not None else False,
|
|
)
|
|
qlinear_kwargs = {"trainable": trainable}
|
|
if (not desc_act or group_size == -1) and not use_triton:
|
|
qlinear_kwargs["use_cuda_fp16"] = use_cuda_fp16
|
|
qkv_layer = QuantLinear(*qlinear_args, **qlinear_kwargs)
|
|
qkv_layer.qweight = qweights
|
|
qkv_layer.qzeros = qzeros
|
|
qkv_layer.scales = scales
|
|
qkv_layer.g_idx = g_idx
|
|
qkv_layer.bias = bias
|
|
|
|
attn = cls(m.hidden_size, m.num_heads, qkv_layer, m.o_proj, m.rotary_emb)
|
|
|
|
if '.' in name:
|
|
parent_name = name.rsplit('.', 1)[0]
|
|
child_name = name[len(parent_name) + 1:]
|
|
parent = model.get_submodule(parent_name)
|
|
else:
|
|
parent_name = ''
|
|
parent = model
|
|
child_name = name
|
|
|
|
setattr(parent, child_name, attn)
|
|
return True
|
|
|
|
|
|
__all__ = ["FusedLlamaAttentionForQuantizedModel"]
|