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fix llama2 (#10710)

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Yishuo Wang 2024-04-09 17:28:37 +08:00 committed by GitHub
parent e10040b7f1
commit 8f45e22072
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2 changed files with 6 additions and 2 deletions
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6
python/llm/src/ipex_llm/transformers/models/llama.py
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@ -1011,8 +1011,10 @@ def llama_attention_forward_4_36_quantized(
kv_seq_len = key_states.shape[-2] kv_seq_len = key_states.shape[-2]
if len(past_key_value.key_cache) <= self.layer_idx: if len(past_key_value.key_cache) <= self.layer_idx:
repeated_key_states = repeat_kv(key_states, self.num_key_value_groups)
repeated_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) repeated_key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
invalidInputError( invalidInputError(
@ -1038,7 +1040,7 @@ def llama_attention_forward_4_36_quantized(
# upcast attention to fp32 # upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, attn_weights = nn.functional.softmax(attn_weights, dim=-1,
dtype=torch.float32).to(query_states.dtype) dtype=torch.float32).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states) attn_output = torch.matmul(attn_weights, repeated_value_states)
if use_cache: if use_cache:
cache_kwargs = None cache_kwargs = None
key_states, value_states = past_key_value.update(key_states, value_states, key_states, value_states = past_key_value.update(key_states, value_states,

2
python/llm/src/ipex_llm/transformers/models/utils.py
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@ -395,6 +395,8 @@ def use_fused_layer_norm(x: torch.Tensor, training: bool):
def fp16_fusion_check(proj, x, training): def fp16_fusion_check(proj, x, training):
# only use fp16 fusion on PVC inference # only use fp16 fusion on PVC inference
if not hasattr(proj, "qtype"):
return False
if proj.qtype != ggml_tensor_qtype["fp16"]: if proj.qtype != ggml_tensor_qtype["fp16"]:
return False return False
if proj.weight_type != 2: if proj.weight_type != 2: