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refactor baichuan2-13b (#11064)

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Yishuo Wang 2024-05-17 16:25:30 +08:00 committed by GitHub
parent 67db925112
commit 31ce3e0c13
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2 changed files with 88 additions and 234 deletions
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275
python/llm/src/ipex_llm/transformers/models/baichuan2.py
View file

@ -23,19 +23,13 @@ from typing import Optional, Tuple
import torch
import torch.utils.checkpoint
from torch.nn import functional as F
from ipex_llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_cache, \
restore_fp8_kv_cache, use_quantize_kv_cache
from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, \
append_kv_cache, is_enough_kv_cache_room_4_31
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
from ipex_llm.transformers.models.utils import update_past_key_value
from ipex_llm.transformers.models.utils import should_use_fuse_rope
from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_causal
from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, SILU
from ipex_llm.transformers.models.utils import mlp_fusion_check
import warnings
import os
KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
def pre_compute_inv_freq(module: torch.nn.Module):
@ -114,52 +108,16 @@ def baichuan_attention_forward_7b(
# IPEX-LLM OPT: kv cache and quantize kv
use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
if use_quantize_kv:
if past_key_value is None:
k_cache, v_cache = init_fp8_kv_cache(
bsz, self.num_heads, kv_seq_len, self.head_dim,
device=device
)
else:
k_cache, v_cache = past_key_value
key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
key_states, value_states)
else:
if past_key_value is None:
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
k_cache, v_cache = init_kv_cache(bsz,
self.num_heads,
self.head_dim,
kv_seq_len,
max_cache_length,
dtype=key_states.dtype,
device=device)
k_cache[...] = key_states
v_cache[...] = value_states
key_states = k_cache
value_states = v_cache
else:
k_cache, v_cache = past_key_value
if k_cache.stride(1) < kv_seq_len * k_cache.size(3):
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
new_k_cache, new_v_cache = extend_kv_cache(bsz,
self.num_heads,
self.head_dim,
k_cache.size(2),
max_cache_length,
dtype=k_cache.dtype,
device=device)
new_k_cache[...] = k_cache
new_v_cache[...] = v_cache
k_cache = new_k_cache
v_cache = new_v_cache
key_states, value_states = append_kv_cache(k_cache, v_cache, key_states, value_states)
key_states, value_states = update_past_key_value(
past_key_value, key_states, value_states,
kv_seq_len, use_quantize_kv, device
)
past_key_value = (key_states, value_states) if use_cache else None
if self.training:
warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
# IPEX-LLM OPT: sdp
attn_weights = None
if not self.training and not hidden_states.requires_grad and \
use_flash_attention(query_states, key_states, attention_mask):
@ -211,207 +169,56 @@ def baichuan_attention_forward_13b(
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]]]:
if use_quantize_kv_cache(self.W_pack, hidden_states):
forward_function = baichuan_attention_forward_13b_quantized
else:
forward_function = baichuan_attention_forward_13b_origin
return forward_function(
self=self,
hidden_states=hidden_states,
attention_mask=attention_mask,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
def baichuan_attention_forward_13b_quantized(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = 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]]]:
bsz, q_len, _ = hidden_states.size()
device = hidden_states.device
proj = self.W_pack(hidden_states)
proj = (
proj.unflatten(-1, (3, self.hidden_size))
.unsqueeze(0)
.transpose(0, -2)
.squeeze(-2)
)
query_states = (
proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
)
key_states = (
proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
)
value_states = (
proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
)
qkv = self.W_pack(hidden_states)
qkv = qkv.view(bsz, q_len, self.num_heads * 3, self.head_dim)
qkv = qkv.transpose(1, 2)
query_states, key_states, value_states = qkv.split([self.num_heads,
self.num_heads,
self.num_heads], dim=1)
if past_key_value is None:
kv_seq_len = key_states.shape[-2]
k_cache, v_cache = init_fp8_kv_cache(
bsz, self.num_heads, kv_seq_len, self.head_dim,
device=device
)
else:
k_cache, v_cache = past_key_value
key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
key_states, value_states)
past_key_value = (key_states, value_states)
if query_states.size(2) != 1 or device.type != 'xpu':
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
query_states.dtype)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
else:
import linear_q4_0
attn_weights = linear_q4_0.query_key_fp8_matmul(query_states, key_states)
attn_weights = attn_weights / math.sqrt(self.head_dim)
if attention_mask is not None:
if q_len == 1: # inference with cache
if len(attention_mask.size()) == 4:
attention_mask = attention_mask[:, :, -1:, :]
else:
attention_mask = attention_mask[:, -1:, :]
attn_weights = attn_weights + attention_mask
attn_weights = torch.max(attn_weights,
torch.tensor(torch.finfo(attn_weights.dtype).min))
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
attn_weights = attn_weights.to(hidden_states.dtype)
if query_states.size(2) != 1 or device.type != 'xpu':
attn_output = torch.matmul(attn_weights, value_states)
else:
import linear_q4_0
attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights, value_states)
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
def baichuan_attention_forward_13b_origin(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = 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]]]:
bsz, q_len, _ = hidden_states.size()
device = hidden_states.device
proj = self.W_pack(hidden_states)
proj = (
proj.unflatten(-1, (3, self.hidden_size))
.unsqueeze(0)
.transpose(0, -2)
.squeeze(-2)
)
query_states = (
proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
)
key_states = (
proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
)
value_states = (
proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
)
kv_seq_len = key_states.shape[-2]
enough_kv_room = True
kv_seq_len = key_states.shape[2]
if past_key_value is not None:
enough_kv_room = is_enough_kv_cache_room_4_31(past_key_value, seq_len=kv_seq_len)
kv_seq_len += past_key_value[0].shape[-2]
# 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 past_key_value is not None:
# reuse k, v, self_attention
cache_k = past_key_value[0]
cache_v = past_key_value[1]
if not enough_kv_room:
if device.type == 'xpu':
torch.xpu.empty_cache()
# allocate new
new_cache_k, new_cache_v = extend_kv_cache(bsz,
self.num_heads,
self.head_dim,
cache_k.size(2),
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
dtype=cache_k.dtype,
device=device)
new_cache_k[:] = cache_k
new_cache_v[:] = cache_v
cache_k = new_cache_k
cache_v = new_cache_v
key_states, value_states = append_kv_cache(cache_k, cache_v, key_states, value_states)
elif use_cache:
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
new_key_states, new_value_states = init_kv_cache(bsz,
self.num_heads,
self.head_dim,
kv_seq_len,
max_cache_length,
dtype=key_states.dtype,
device=device)
new_key_states[:] = key_states
new_value_states[:] = value_states
key_states = new_key_states
value_states = new_value_states
kv_seq_len += past_key_value[0].shape[2]
# IPEX-LLM OPT: kv cache and quantize kv
use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_states)
key_states, value_states = update_past_key_value(
past_key_value, key_states, value_states,
kv_seq_len, use_quantize_kv, device
)
past_key_value = (key_states, value_states) if use_cache else None
if self.training:
warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
attn_weights = torch.matmul(
query_states.to(dtype=key_states.dtype), key_states.transpose(2, 3)
) / math.sqrt(self.head_dim)
if attention_mask is not None:
if q_len == 1: # inference with cache
if len(attention_mask.size()) == 4:
attention_mask = attention_mask[:, :, -1:, :]
else:
attention_mask = attention_mask[:, -1:, :]
if attention_mask.shape[-2] == attn_weights.shape[-2]:
attn_weights = attn_weights + attention_mask
if len(attention_mask.size()) == 4:
attention_mask = attention_mask[:, :, -q_len:, :]
else:
# support for Baichuan/Baichuan2 13B Chat running speculative decoding
# split attention mask on dim -2
split_sizes = [attention_mask.shape[-2] - attn_weights.shape[-2],
attn_weights.shape[-2]]
# the last chunk of splited is the new attention mask
attention_mask = attention_mask.split(split_sizes, dim=-2)[-1]
attn_weights = attn_weights + attention_mask
attn_weights = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
)
attention_mask = attention_mask[:, None, -q_len:, :]
if use_quantize_kv and q_len == 1:
import linear_q4_0
attn_weights = linear_q4_0.query_key_fp8_matmul(query_states, key_states)
else:
if use_quantize_kv:
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
query_states.dtype)
attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3))
attn_weights = attn_weights / math.sqrt(self.head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
attn_output = torch.matmul(attn_weights.to(dtype=value_states.dtype), value_states)
if use_quantize_kv and q_len == 1:
import linear_q4_0
attn_output = linear_q4_0.attn_value_fp8_matmul(attn_weights, value_states)
else:
attn_output = torch.matmul(attn_weights.to(dtype=value_states.dtype), value_states)
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)

47
python/llm/src/ipex_llm/transformers/models/utils.py
View file

@ -24,6 +24,7 @@ from ipex_llm.transformers.low_bit_linear import SYM_INT4, SYM_INT8, FP8E5, IQ2_
from ipex_llm.transformers.convert import is_deepspeed_available
FP8_KV_ALLOC_LENGTH = 512
KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
# used in fused mlp forward
SILU = 0
@ -426,3 +427,49 @@ def fp16_fusion_check(proj, x, training):
if device_type != "pvc":
return False
return True
def update_past_key_value(past_key_value, key_states, value_states,
kv_seq_len, use_quantize_kv, device):
bsz, num_heads, _, head_dim = key_states.shape
if use_quantize_kv:
if past_key_value is None:
k_cache, v_cache = init_fp8_kv_cache(
bsz, num_heads, kv_seq_len, head_dim,
device=device
)
else:
k_cache, v_cache = past_key_value
key_states, value_states = append_fp8_kv_cache(k_cache, v_cache,
key_states, value_states)
else:
if past_key_value is None:
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
k_cache, v_cache = init_kv_cache(bsz,
num_heads,
head_dim,
kv_seq_len,
max_cache_length,
dtype=key_states.dtype,
device=device)
k_cache[...] = key_states
v_cache[...] = value_states
key_states = k_cache
value_states = v_cache
else:
k_cache, v_cache = past_key_value
if k_cache.stride(1) < kv_seq_len * k_cache.size(3):
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
new_k_cache, new_v_cache = extend_kv_cache(bsz,
num_heads,
head_dim,
k_cache.size(2),
max_cache_length,
dtype=k_cache.dtype,
device=device)
new_k_cache[...] = k_cache
new_v_cache[...] = v_cache
k_cache = new_k_cache
v_cache = new_v_cache
key_states, value_states = append_kv_cache(k_cache, v_cache, key_states, value_states)
return key_states, value_states