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Commit fbfa9d82 authored by Casper Hansen's avatar Casper Hansen
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Attention works

parent 75710806
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Showing with 342 additions and 91 deletions
awq/models/llama.py
View file @ fbfa9d82
......@@ -70,7 +70,7 @@ from typing import List, Tuple, Union
from awq.utils.utils import set_module_name
from awq.modules.fused.mlp import QuantLlamaMLP
from awq.modules.fused.norm import FTLlamaRMSNorm
from awq.modules.fused.attn import QuantLlamaAttention
from awq.modules.fused.attn import QuantLlamaAttention, QuantLlamaAttentionFused
from awq.modules.linear import WQLinear_GEMM, WQLinear_GEMV
from transformers.models.llama.modeling_llama import LlamaAttention, LlamaRMSNorm, LlamaMLP
......@@ -96,11 +96,19 @@ class LlamaFuser:
def fuse_attention(self):
for name, module in self.attention_modules:
qkv_layer: Union[WQLinear_GEMM, WQLinear_GEMV] = self._fuse_qkv(module)
attn = QuantLlamaAttention(
qkv_layer: Union[WQLinear_GEMM, WQLinear_GEMV] = self._fuse_qkv2(module)
# attn = QuantLlamaAttention(
# module.hidden_size,
# module.num_heads,
# module.num_key_value_heads,
# qkv_layer,
# module.o_proj,
# next(iter(qkv_layer.state_dict().values())).device,
# self.model.config.max_new_tokens
# )
attn = QuantLlamaAttentionFused(
module.hidden_size,
module.num_heads,
module.num_key_value_heads,
qkv_layer,
module.o_proj,
next(iter(qkv_layer.state_dict().values())).device,
......@@ -108,6 +116,39 @@ class LlamaFuser:
)
set_module_name(self.model, name, attn)
def _fuse_qkv2(self, module: LlamaAttention):
q_proj = module.q_proj
k_proj = module.k_proj
v_proj = module.v_proj
qweights = torch.cat([q_proj.qweight, k_proj.qweight, v_proj.qweight], dim=0)
qzeros = torch.cat([q_proj.qzeros, k_proj.qzeros, v_proj.qzeros], dim=0)
scales = torch.cat([q_proj.scales, k_proj.scales, v_proj.scales], dim=0)
# g_idx = torch.cat([q_proj.g_idx, k_proj.g_idx, v_proj.g_idx], dim=0)
g_idx = None
bias = (
torch.cat([q_proj.bias, k_proj.bias, v_proj.bias], dim=0)
if q_proj.bias is not None
else None
)
qkv_layer = WQLinear_GEMV(
q_proj.w_bit,
q_proj.group_size,
q_proj.in_features,
q_proj.out_features + k_proj.out_features + v_proj.out_features,
q_proj.bias is not None,
q_proj.qweight.device,
)
qkv_layer.qweight = qweights
qkv_layer.qzeros = qzeros
qkv_layer.scales = scales
qkv_layer.bias = bias
qkv_layer.split_k_iters = q_proj.split_k_iters
return qkv_layer
def _fuse_qkv(self, module: LlamaAttention):
# get qkv and bias
q_proj, k_proj, v_proj = module.q_proj, module.k_proj, module.v_proj
......@@ -129,10 +170,11 @@ class LlamaFuser:
)
# replace buffers with real weights
qkv_layer.qweight = torch.cat([q_proj.qweight, k_proj.qweight, v_proj.qweight], dim=1)
qkv_layer.qzeros = torch.cat([q_proj.qzeros, k_proj.qzeros, v_proj.qzeros], dim=1)
qkv_layer.scales = torch.cat([q_proj.scales, k_proj.scales, v_proj.scales], dim=1)
qkv_layer.qweights = torch.cat([q_proj.qweight, k_proj.qweight, v_proj.qweight], dim=0)
qkv_layer.qzeros = torch.cat([q_proj.qzeros, k_proj.qzeros, v_proj.qzeros], dim=0)
qkv_layer.scales = torch.cat([q_proj.scales, k_proj.scales, v_proj.scales], dim=0)
qkv_layer.bias = bias
qkv_layer.split_k_iters = q_proj.split_k_iters
return qkv_layer
......
awq/modules/fused/attn.py
View file @ fbfa9d82
import math
import torch
import torch.nn as nn
import awq_inference_engine
from torch.nn import functional as F
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, LlamaRotaryEmbedding
try:
from flash_attn import flash_attn_func
FLASH_INSTALLED = True
except:
FLASH_INSTALLED = False
class QuantLlamaRotary(nn.Module):
def __init__(self, dim=4096, max_position_embeddings=2048, base=10000, device=None,
is_neox=True, num_heads=None, num_kv_heads=None):
super().__init__()
self.dim = dim
self.is_neox = is_neox
self.num_heads = num_heads
self.num_kv_heads = num_kv_heads
# create cache
inv_freq = 1.0 / (base**(torch.arange(0, dim, 2, device=device) / dim))
t = torch.arange(max_position_embeddings, device=device).float()
freqs = torch.einsum("i,j -> ij", t, inv_freq.float())
cos = freqs.cos()
sin = freqs.sin()
cache = torch.cat((cos, sin), dim=-1).to(torch.get_default_dtype())
# Embedding size: [max_position, rotary_dim]
self.register_buffer("cos_sin_cache", cache.half(), persistent=False)
def forward(
self,
qkv_states: torch.Tensor,
position_ids: torch.Tensor,
batch_size: int,
q_len: int
):
# get qkv
query, key, value = qkv_states.chunk(chunks=3, dim=-1)
del qkv_states
# [num_tokens, num_heads * head_size]
query_batch_size, query_len, _ = query.shape
query = query.view(query_len*query_batch_size, self.num_heads * self.dim)
# [num_tokens, num_kv_heads * head_size]
key_batch_size, key_len, _ = key.shape
key = key.view(key_len*key_batch_size, self.num_kv_heads * self.dim)
# [num_tokens]
positions = position_ids.view(-1).to(query.device)
# Apply rotary embedding to the query and key before passing them
# to the attention op.
query = query.contiguous()
key = key.contiguous()
awq_inference_engine.rotary_embedding(
positions,
query,
key,
self.dim,
self.cos_sin_cache,
self.is_neox
)
query = query.view(batch_size, q_len, self.num_heads, self.dim).transpose(1, 2)
key = key.view(batch_size, q_len, self.num_heads, self.dim).transpose(1, 2)
value = value.view(batch_size, q_len, self.num_heads, self.dim).transpose(1, 2)
return query, key, value
class QuantLlamaRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
......@@ -11,16 +80,22 @@ class QuantLlamaRotaryEmbedding(nn.Module):
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
inv_freq = 1.0 / (
self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
)
self.register_buffer("inv_freq", inv_freq)
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
seq_len=max_position_embeddings,
device=self.inv_freq.device,
dtype=torch.get_default_dtype(),
)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
t = torch.arange(
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
......@@ -30,7 +105,8 @@ class QuantLlamaRotaryEmbedding(nn.Module):
sin = freqs.sin()
cache = torch.cat((cos, sin), dim=-1)
# [max_position, rot_dim]
# self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
# self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
self.register_buffer("cos_sin_cache", cache.half(), persistent=False)
def forward(
......@@ -41,20 +117,72 @@ class QuantLlamaRotaryEmbedding(nn.Module):
):
# Apply rotary embedding to the query and key before passing them
# to the attention op.
# print(positions.shape, query.shape, key.shape, self.cos_sin_cache.shape)
query = query.contiguous()
key = key.contiguous()
awq_inference_engine.rotary_embedding(
positions,
query,
key,
self.dim,
self.cos_sin_cache,
True # is_neox
True
)
return query, key
class TorchAttention(nn.Module):
def __init__(self, hidden_size, use_flash=False):
super().__init__()
self.hidden_size = hidden_size
self.use_flash = use_flash
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
use_cache: bool,
past_key_value: torch.Tensor,
batch_size: int,
q_len: int
):
is_causal = past_key_value is None
kv_seq_len = q_len
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
value = value.to(key.device)
if past_key_value is not None:
# reuse k, v, self_attention
key = torch.cat([past_key_value[0], key], dim=2)
value = torch.cat([past_key_value[1], value], dim=2)
if use_cache:
# Since qkv_proj is fused, query 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.
key = key.contiguous()
value = value.contiguous()
query = query.contiguous()
past_key_value = (key, value) if use_cache else None
if self.use_flash and FLASH_INSTALLED:
query = query.transpose(1,2)
key = key.transpose(1,2)
value = value.transpose(1,2)
attn_output = flash_attn_func(query, key, value, causal=is_causal)
else:
attn_output = F.scaled_dot_product_attention(query, key, value, is_causal=is_causal)
del query, key, value
attn_output = attn_output.transpose(1, 2).reshape(batch_size, q_len, self.hidden_size)
return attn_output, past_key_value
class QuantLlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
......@@ -66,101 +194,182 @@ class QuantLlamaAttention(nn.Module):
qkv_proj,
o_proj,
dev,
max_new_tokens,
use_hf_rotary=False
max_new_tokens
):
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.num_kv_heads = num_kv_heads
self.head_dim = hidden_size // num_heads
self.use_hf_rotary = use_hf_rotary
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.attn = TorchAttention(hidden_size)
self.rotary_emb = QuantLlamaRotary(
dim=hidden_size // num_heads,
max_position_embeddings=max_new_tokens,
device=dev,
is_neox=True,
num_heads=num_heads,
num_kv_heads=num_heads
)
if use_hf_rotary:
self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_new_tokens, device=dev)
else:
self.rotary_emb = QuantLlamaRotaryEmbedding(self.head_dim, max_position_embeddings=max_new_tokens, device = dev)
def forward(self, hidden_states, past_key_value=None, attention_mask=None, position_ids=None, output_attentions=False, use_cache=False):
"""Input shape: Batch x Time x Channel"""
bsz, q_len, _ = hidden_states.size()
batch_size, q_len, _ = hidden_states.size()
qkv_states = self.qkv_proj(hidden_states)
query, key, value = self.rotary_emb(qkv_states, position_ids, batch_size, q_len)
attn_output, past_key_value = self.attn(query, key, value, use_cache, past_key_value, batch_size, q_len)
attn_output = self.o_proj(attn_output)
if self.use_hf_rotary:
# get qkv
qkv_states = qkv_states.view(bsz, q_len, 3, self.num_heads, self.head_dim)
query, key, value = torch.split(qkv_states, 1, dim=2)
del qkv_states
# reshape for hf rotary
query = query.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key = key.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value = value.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value, seq_len=kv_seq_len)
query, key = apply_rotary_pos_emb(query, key, cos, sin, position_ids)
else:
# get qkv
query, key, value = qkv_states.chunk(chunks=3, dim=-1)
del qkv_states
return attn_output, None, past_key_value
# [num_tokens, num_heads * head_size]
query_batch_size, query_len, _ = query.shape
query = query.view(query_len*query_batch_size, self.num_heads * self.head_dim)
def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device) # type: ignore
freqs = torch.outer(t, freqs).float() # type: ignore
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
return freqs_cis
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
ndim = x.ndim
assert 0 <= 1 < ndim
assert freqs_cis.shape == (x.shape[1], x.shape[-1])
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
return freqs_cis.view(*shape)
def apply_rotary_emb(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cis: torch.Tensor,
):
xq_ = torch.view_as_complex(
xq.float().reshape(*xq.shape[:-1], 2, -1).transpose(-2, -1).contiguous()
)
xk_ = torch.view_as_complex(
xk.float().reshape(*xk.shape[:-1], 2, -1).transpose(-2, -1).contiguous()
)
freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
xq_out = torch.view_as_real(xq_ * freqs_cis).transpose(-2, -1).flatten(3)
xk_out = torch.view_as_real(xk_ * freqs_cis).transpose(-2, -1).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
# [num_tokens, num_kv_heads * head_size]
key_batch_size, key_len, _ = key.shape
key = key.view(key_len*key_batch_size, self.num_kv_heads * self.head_dim)
# [num_tokens]
positions = position_ids.view(-1).to(query.device)
class QuantLlamaAttentionFused(nn.Module):
def __init__(self, hidden_size, num_heads, qkv_layer, o_proj, dev, max_position_embeddings):
super().__init__()
self.hidden_size = hidden_size
self.n_local_heads = num_heads
self.head_dim = self.hidden_size // num_heads
self.qkv_proj = qkv_layer
self.o_proj = o_proj
self.start_pos = 0
query, key = self.rotary_emb(query, key, positions)
self.freqs_cis = precompute_freqs_cis(
self.head_dim ,
max_position_embeddings * 2,
)
query = query.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key = key.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value = value.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
# following fastertransformer definition
self.cache_v = (
torch.zeros(
(
1,
self.n_local_heads,
max_position_embeddings,
self.head_dim,
)
)
.to(dev)
.half()
) # added to half
# 8: pack 8 fp16 in FT, if fp32 then use 4
self.cache_k = (
torch.zeros(
(
1,
self.n_local_heads,
self.head_dim // 8,
max_position_embeddings,
8,
)
)
.to(dev)
.half()
) # added to half
is_causal = past_key_value is None
# dummy
self.rotary_emb = QuantLlamaRotaryEmbedding(
hidden_size // num_heads, max_position_embeddings=max_position_embeddings, base=10000, device=dev
)
kv_seq_len = q_len
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
def forward(
self,
hidden_states, past_key_value=None, attention_mask=None, position_ids=None, output_attentions=False, use_cache=False
):
bsz, seqlen, _ = hidden_states.shape
xqkv = self.qkv_proj(hidden_states)
xqkv = xqkv.view(bsz, seqlen, -1, self.n_local_heads, self.head_dim)
xq = xqkv[:, :, 0]
xk = xqkv[:, :, 1]
xv = xqkv[:, :, 2]
if seqlen > 1:
xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
xk = xk.view(bsz, seqlen, self.n_local_heads, self.head_dim)
xv = xv.view(bsz, seqlen, self.n_local_heads, self.head_dim)
xq, xk = self.rotary_emb(xq, xk, position_ids)
self.cache_k = self.cache_k.to(xq)
self.cache_v = self.cache_v.to(xq)
values_store = xv.transpose(2, 1)
keys_store = (
xk.reshape(bsz, seqlen, self.n_local_heads, self.head_dim // 8, 8)
.permute(0, 2, 3, 1, 4)
.contiguous()
)
value = value.to(key.device)
self.cache_v[:bsz, :, self.start_pos : self.start_pos + seqlen, :] = values_store
self.cache_k[:bsz, :, :, self.start_pos : self.start_pos + seqlen, :] = keys_store
keys = xk
values = xv
past_key_value = (xk, xv) if use_cache else None
xq = xq.transpose(1, 2)
keys = keys.transpose(1, 2)
values = values.transpose(1, 2)
scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None:
scores = scores + attention_mask # (bs, n_local_heads, slen, cache_len + slen)
scores = F.softmax(scores.float(), dim=-1).type_as(xq)
output = torch.matmul(scores, values) # (bs, n_local_heads, slen, head_dim)
output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
else:
xq = xq[:, 0, :, :]
xk = xk[:, 0, :, :]
xv = xv[:, 0, :, :]
past_key_value = (xk, xv) if use_cache else None
output = awq_inference_engine.single_query_attention(
xq,
xk,
xv,
self.cache_k,
self.cache_v,
None,
None,
self.start_pos,
self.head_dim,
10000,
True,
)
output = output.reshape(bsz, 1, -1)
if past_key_value is not None:
# reuse k, v, self_attention
key = torch.cat([past_key_value[0], key], dim=2)
value = torch.cat([past_key_value[1], value], dim=2)
attn_output = self.o_proj(output)
if use_cache:
# Since qkv_proj is fused, query 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.
key = key.contiguous()
value = value.contiguous()
query = query.contiguous()
past_key_value = (key, value) if use_cache else None
# with torch.backends.cuda.sdp_kernel(enable_math=False):
attn_output = F.scaled_dot_product_attention(query, key, value, is_causal=is_causal)
del query, key, value
attn_output = attn_output.transpose(1, 2).reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
self.start_pos += seqlen
else:
self.start_pos = 0
return attn_output, None, past_key_value
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