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optimize phi3 1st token performance (#10981)

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Yishuo Wang 2024-05-10 17:33:46 +08:00 committed by GitHub
parent cfed76b2ed
commit ad96f32ce0
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2 changed files with 26 additions and 13 deletions
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31
python/llm/src/ipex_llm/transformers/models/phi3.py
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@ -42,6 +42,7 @@ from ipex_llm.transformers.models.utils import (
from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
from ipex_llm.transformers.models.utils import use_new_esimd_sdp_fp16, use_quantize_kv_cache 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.models.utils import use_sdp_fp8, restore_fp8_kv_cache
from ipex_llm.transformers.models.utils import use_sdp_causal
from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache
from typing import Optional, Tuple, List from typing import Optional, Tuple, List
@ -148,22 +149,26 @@ def attention_forward(
if isinstance(past_key_value, DynamicFp8Cache): if isinstance(past_key_value, DynamicFp8Cache):
key_states, value_states = restore_fp8_kv_cache(key_states, value_states, key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
query_states.dtype) query_states.dtype)
# repeat k/v heads if n_kv_heads < n_heads if use_sdp_causal(q_len, kv_seq_len, query_states, self.training):
key_states = repeat_kv(key_states, self.num_key_value_groups) import linear_q4_0
value_states = repeat_kv(value_states, self.num_key_value_groups) attn_output = linear_q4_0.sdp_causal(query_states, key_states, value_states)
else:
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim) key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None: if attention_mask is not None:
attn_weights = attn_weights + attention_mask attn_weights = attn_weights + attention_mask
# upcast attention to fp32 # upcast attention to fp32
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
dtype=torch.float32).to(value_states.dtype) dtype=torch.float32).to(value_states.dtype)
attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout, attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
training=self.training) training=self.training)
attn_output = torch.matmul(attn_weights, value_states) attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

8
python/llm/src/ipex_llm/transformers/models/utils.py
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@ -384,6 +384,14 @@ def use_sdp_fp8(q_len, k_len, query_states):
return True return True
def use_sdp_causal(q_len, kv_len, query_states, training):
return (
q_len == kv_len # first token
and query_states.device.type == "xpu" # GPU
and not query_states.requires_grad and not training # no training
)
def mlp_fusion_check(x, qtype, training): def mlp_fusion_check(x, qtype, training):
invalidInputError(x.dim() == 2, invalidInputError(x.dim() == 2,
"Here input x's dim should be 2.") "Here input x's dim should be 2.")