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LLM: fix accuracy issue of chatglm3 (#9830)

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* add attn mask for first token

* fix

* fix

* change attn calculation

* fix

* fix

* fix style

* fix style
This commit is contained in:
Ruonan Wang 2024-01-05 10:52:05 +08:00 committed by GitHub
parent 3147ebe63d
commit 5df31db773
1 changed files with 15 additions and 3 deletions
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18
python/llm/src/bigdl/llm/transformers/models/chatglm2.py
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@ -366,14 +366,26 @@ def core_attn_forward_8eb45c(self, query_layer, key_layer, value_layer, attentio
pytorch_major_version = int(torch.__version__.split('.')[0]) pytorch_major_version = int(torch.__version__.split('.')[0])
if pytorch_major_version >= 2 and (query_layer.device.type == 'xpu' or query_layer.size(0) > 1): if pytorch_major_version >= 2 and (query_layer.device.type == 'xpu' or query_layer.size(0) > 1):
query_layer = query_layer.permute(1, 2, 0, 3) query_layer = query_layer.permute(1, 2, 0, 3)
if attention_mask is None and use_flash_attention(query_layer): L, S = query_layer.shape[2], key_layer.shape[2]
if attention_mask is None and (use_flash_attention(query_layer) or
L == S and query_layer.device.type == "cpu"):
context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer,
key_layer, key_layer,
value_layer, value_layer,
is_causal=True) is_causal=True)
elif attention_mask is None: elif attention_mask is None:
scaling_factor = 1 / math.sqrt(query_layer.size(-1)) head_dim = query_layer.size(-1)
attn = torch.matmul(query_layer * scaling_factor, key_layer.transpose(-2, -1)) attn = torch.matmul(query_layer,
key_layer.transpose(2, 3)) / math.sqrt(head_dim)
if L == S:
# first token, need attention mask
attn_bias = torch.zeros(L, S, dtype=query_layer.dtype,
device=query_layer.device)
temp_mask = torch.ones(L, S, dtype=torch.bool,
device=query_layer.device).tril(diagonal=0)
attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
attn_bias.to(query_layer.dtype)
attn += attn_bias
attn = torch.softmax(attn, -1) attn = torch.softmax(attn, -1)
context_layer = torch.matmul(attn, value_layer) context_layer = torch.matmul(attn, value_layer)
else: else: