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optimize phi3 again: use sdp if possible (#10951)

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Yishuo Wang 2024-05-07 15:53:08 +08:00 committed by GitHub
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commit aa2fa9fde1
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1 changed files with 18 additions and 14 deletions
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python/llm/src/ipex_llm/transformers/models/phi3.py
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@ -39,7 +39,7 @@ from ipex_llm.transformers.models.utils import (
rotate_half, should_use_fuse_rope,
apply_rotary_pos_emb_cache_freq_xpu
)
from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU, use_new_esimd_sdp_fp16
from ipex_llm.transformers.kv import DynamicNormalCache
from typing import Optional, Tuple, List
@ -93,22 +93,26 @@ def attention_forward(
key_states, value_states = past_key_value.update(key_states, value_states,
self.layer_idx, None)
# 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)
if use_new_esimd_sdp_fp16(q_len, kv_seq_len, self.head_dim, query_states):
import linear_q4_0
attn_output = linear_q4_0.sdp_fp16(query_states, key_states, value_states, attention_mask)
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,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
attn_weights = torch.matmul(query_states,
key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
dtype=torch.float32).to(value_states.dtype)
attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
# upcast attention to fp32
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
dtype=torch.float32).to(value_states.dtype)
attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)