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optimize phi3 again: use quantize kv if possible (#10953)

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Yishuo Wang 2024-05-07 17:26:19 +08:00 committed by GitHub
parent aa2fa9fde1
commit c801c37bc6
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1 changed files with 18 additions and 4 deletions
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python/llm/src/ipex_llm/transformers/models/phi3.py
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@ -39,8 +39,10 @@ from ipex_llm.transformers.models.utils import (
rotate_half, should_use_fuse_rope, rotate_half, should_use_fuse_rope,
apply_rotary_pos_emb_cache_freq_xpu apply_rotary_pos_emb_cache_freq_xpu
) )
from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU, use_new_esimd_sdp_fp16 from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
from ipex_llm.transformers.kv import DynamicNormalCache from ipex_llm.transformers.models.utils import use_new_esimd_sdp_fp16, use_quantize_kv_cache
from ipex_llm.transformers.models.utils import use_sdp_fp8, restore_fp8_kv_cache
from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache
from typing import Optional, Tuple, List from typing import Optional, Tuple, List
from transformers.models.phi.modeling_phi import repeat_kv from transformers.models.phi.modeling_phi import repeat_kv
@ -93,10 +95,18 @@ def attention_forward(
key_states, value_states = past_key_value.update(key_states, value_states, key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, None) self.layer_idx, None)
if use_new_esimd_sdp_fp16(q_len, kv_seq_len, self.head_dim, query_states): if (isinstance(past_key_value, DynamicFp8Cache) and
use_sdp_fp8(q_len, kv_seq_len, query_states)):
import linear_q4_0
attn_output = linear_q4_0.sdp_fp8(query_states, key_states, value_states, attention_mask)
elif (isinstance(past_key_value, DynamicNormalCache) and
use_new_esimd_sdp_fp16(q_len, kv_seq_len, self.head_dim, query_states)):
import linear_q4_0 import linear_q4_0
attn_output = linear_q4_0.sdp_fp16(query_states, key_states, value_states, attention_mask) attn_output = linear_q4_0.sdp_fp16(query_states, key_states, value_states, attention_mask)
else: else:
if isinstance(past_key_value, DynamicFp8Cache):
key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
query_states.dtype)
# repeat k/v heads if n_kv_heads < n_heads # repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups) key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups)
@ -179,8 +189,12 @@ def model_forward_wrapper(origin_model_forward):
): ):
# IPEX-LLM OPT: kv cache but no sdp (its head_dim 96, cannot use sdp) # IPEX-LLM OPT: kv cache but no sdp (its head_dim 96, cannot use sdp)
use_cache = use_cache if use_cache is not None else self.config.use_cache use_cache = use_cache if use_cache is not None else self.config.use_cache
use_quantize_kv = (use_quantize_kv_cache(self.layers[0].mlp.down_proj, input_ids) and
self.config.hidden_size // self.config.num_attention_heads in [64, 128])
if use_cache: if use_cache:
if not isinstance(past_key_values, DynamicNormalCache): if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
if not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values) past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
return origin_model_forward( return origin_model_forward(
self=self, self=self,