LLM: Add fuse rope and norm optimization for Aquila. (#9161)
* add fuse norm optimization. * add fuse rope optimization
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2 changed files with 13 additions and 3 deletions
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@ -331,4 +331,7 @@ def optimize(model):
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module.AquilaAttention,
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module.AquilaAttention,
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aquila_attention_forward
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aquila_attention_forward
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)
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)
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convert_forward(model,
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module.AquilaRMSNorm,
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llama_rms_norm_forward)
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return model
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return model
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@ -44,6 +44,7 @@ from torch import nn
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from bigdl.llm.transformers.models.utils import extend_kv_cache, init_kv_cache, append_kv_cache
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from bigdl.llm.transformers.models.utils import extend_kv_cache, init_kv_cache, append_kv_cache
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from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb
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from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb
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from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
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from bigdl.llm.utils.common import log4Error
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from bigdl.llm.utils.common import log4Error
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KV_CACHE_ALLOC_BLOCK_LENGTH = 256
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KV_CACHE_ALLOC_BLOCK_LENGTH = 256
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@ -73,6 +74,12 @@ def aquila_attention_forward(
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kv_seq_len = key_states.shape[-2]
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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kv_seq_len += past_key_value[0].shape[-2]
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if query_states.device.type == "xpu" and not (self.training and query_states.requires_grad):
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query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
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key_states,
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position_ids,
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"aquila")
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else:
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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cos, sin, position_ids, "aquila")
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cos, sin, position_ids, "aquila")
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