ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

Fix qwen's position_ids no enough (#10572)

Browse source 
* fix position_ids

* fix position_ids
This commit is contained in:
Xin Qiu 2024-03-28 17:05:49 +08:00 committed by GitHub
parent 52a2135d83
commit 5963239b46
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 7 additions and 8 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
python/llm/src/ipex_llm/transformers/convert.py
View file

@ -595,7 +595,6 @@ def _optimize_pre(model):
from ipex_llm.transformers.models.bert import merge_qkv from ipex_llm.transformers.models.bert import merge_qkv
model.apply(merge_qkv) model.apply(merge_qkv)
if model.config.model_type == "qwen": if model.config.model_type == "qwen":
position_ids = torch.arange(0, model.config.max_position_embeddings)
rope_base = model.config.rotary_emb_base rope_base = model.config.rotary_emb_base
from accelerate.big_modeling import init_empty_weights from accelerate.big_modeling import init_empty_weights
@ -625,7 +624,6 @@ def _optimize_pre(model):
module.q_proj = q_proj module.q_proj = q_proj
module.k_proj = k_proj module.k_proj = k_proj
module.v_proj = v_proj module.v_proj = v_proj
module.position_ids = position_ids
module.rope_base = rope_base module.rope_base = rope_base
del module.c_attn del module.c_attn
model.apply(split_qkv_proj_func) model.apply(split_qkv_proj_func)

13
python/llm/src/ipex_llm/transformers/models/qwen.py
View file

@ -136,6 +136,8 @@ def qwen_attention_forward_original(
device = hidden_states.device device = hidden_states.device
# for flash attention # for flash attention
original_dtype = hidden_states.dtype original_dtype = hidden_states.dtype
position_ids = rotary_pos_emb_list[-1] # the last one is posisiton_ids
rotary_pos_emb_list = rotary_pos_emb_list[:-1]
use_fuse_rope = should_use_fuse_rope(self, hidden_states) use_fuse_rope = should_use_fuse_rope(self, hidden_states)
qtype_check = decoding_fast_path_qtype_check(self.q_proj) qtype_check = decoding_fast_path_qtype_check(self.q_proj)
@ -147,8 +149,6 @@ def qwen_attention_forward_original(
cache_v = cache_v.transpose(1, 2) cache_v = cache_v.transpose(1, 2)
kv_seq_len = cache_k.shape[-2] kv_seq_len = cache_k.shape[-2]
self.position_ids = self.position_ids.to(device)
position_ids = self.position_ids[kv_seq_len]
base = self.rope_base base = self.rope_base
if is_enough_kv_cache_room(layer_past, kv_seq_len): if is_enough_kv_cache_room(layer_past, kv_seq_len):
new_cache_k, new_cache_v = extend_kv_cache(bsz, new_cache_k, new_cache_v = extend_kv_cache(bsz,
@ -182,7 +182,7 @@ def qwen_attention_forward_original(
# query = self._split_heads(query, self.num_heads, self.head_dim) # query = self._split_heads(query, self.num_heads, self.head_dim)
# key = self._split_heads(key, self.num_heads, self.head_dim) # key = self._split_heads(key, self.num_heads, self.head_dim)
# value = self._split_heads(value, self.num_heads, self.head_dim) # value = self._split_heads(value, self.num_heads, self.head_dim)
if rotary_pos_emb_list is not None: if len(rotary_pos_emb_list) != 0:
cur_len = query.shape[1] cur_len = query.shape[1]
if len(rotary_pos_emb_list) == 1: if len(rotary_pos_emb_list) == 1:
rotary_pos_emb = rotary_pos_emb_list[0] rotary_pos_emb = rotary_pos_emb_list[0]
@ -332,6 +332,8 @@ def qwen_attention_forward_quantized(
bsz, q_len, _ = hidden_states.size() bsz, q_len, _ = hidden_states.size()
device = hidden_states.device device = hidden_states.device
position_ids = rotary_pos_emb_list[-1] # the last one is posisiton_ids
rotary_pos_emb_list = rotary_pos_emb_list[:-1]
use_fuse_rope = should_use_fuse_rope(self, hidden_states) use_fuse_rope = should_use_fuse_rope(self, hidden_states)
# qtype_check = decoding_fast_path_qtype_check(self.q_proj) # qtype_check = decoding_fast_path_qtype_check(self.q_proj)
@ -349,7 +351,6 @@ def qwen_attention_forward_quantized(
device=device device=device
) )
position_ids = self.position_ids[self.kv_seq_len].to(device)
base = self.rope_base base = self.rope_base
args = [hidden_states, self.q_proj.weight.data, self.k_proj.weight.data, args = [hidden_states, self.q_proj.weight.data, self.k_proj.weight.data,
@ -599,7 +600,7 @@ def qwen_model_forward(
if self.use_cache_quantization: if self.use_cache_quantization:
past_length = past_key_values[0][0][0].size(2) past_length = past_key_values[0][0][0].size(2)
else: else:
past_length = past_key_values[0][0].size(-2) past_length = past_key_values[0][0].size(1)
if position_ids is None: if position_ids is None:
position_ids = torch.arange( position_ids = torch.arange(
past_length, past_length,
@ -651,7 +652,7 @@ def qwen_model_forward(
self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list
rotary_pos_emb_list = [ rotary_pos_emb_list = [
self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list
] ] + [position_ids]
hidden_states = self.drop(hidden_states) hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),) output_shape = input_shape + (hidden_states.size(-1),)