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LLM: support int4 fp16 chatglm2-6b 8k input. (#10648)

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Cengguang Zhang 2024-04-07 09:39:21 +08:00 committed by GitHub
parent ab87b6ab21
commit 1a9b8204a4
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1 changed files with 17 additions and 4 deletions
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python/llm/src/ipex_llm/transformers/models/chatglm2.py
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@ -512,6 +512,19 @@ def core_attn_forward_8eb45c(query_layer, key_layer, value_layer, attention_mask
query_layer = query_layer.permute(1, 2, 0, 3) query_layer = query_layer.permute(1, 2, 0, 3)
L, S = query_layer.shape[2], key_layer.shape[2] L, S = query_layer.shape[2], key_layer.shape[2]
if attention_mask is None and L == S: if attention_mask is None and L == S:
# split tensor for memory block limitation
# support fp16 and set input length threshold at 5000 for now
if query_layer.dtype == torch.float16 and L >= 5000:
# split first dim 32 -> 8
query_sp = torch.split(query_layer.to(key_layer.dtype), 8, dim=1)
key_sp = torch.split(key_layer, 8, dim=1)
value_sp = torch.split(value_layer, 8, dim=1)
results = []
for q, k, v in zip(query_sp, key_sp, value_sp):
result = F.scaled_dot_product_attention(q, k, v, is_causal=True).to(k.dtype)
results.append(result)
context_layer = torch.cat(results, dim=1)
else:
context_layer = F.scaled_dot_product_attention(query_layer.to(key_layer.dtype), context_layer = F.scaled_dot_product_attention(query_layer.to(key_layer.dtype),
key_layer, key_layer,
value_layer, value_layer,