174 lines
6.9 KiB
Python
174 lines
6.9 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This file is adapted from
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm/modeling_glm.py
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#
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2024 The GLM & ZhipuAI team and HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from typing import Optional, Tuple
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from transformers.cache_utils import Cache
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from transformers.models.glm.modeling_glm import apply_rotary_pos_emb
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from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache
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from ipex_llm.transformers.models.common import merge_qkv_base
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from ipex_llm.transformers.models.common import scaled_dot_product_attention
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from ipex_llm.transformers.models.utils import make_cache_contiguous_inplaced
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache
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def merge_qkv(module: torch.nn.Module):
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merge_qkv_base(module, "GlmAttention")
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merge_qkv_base(module, "SiglipAttention")
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def split_mlp(module: torch.nn.Module):
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if module.__class__.__name__ == "GlmMLP":
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gate_weight, up_weight = module.gate_up_proj.weight.data.chunk(2, dim=0)
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gate_proj = torch.nn.Linear(0, 0, bias=False)
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gate_proj.weight = torch.nn.Parameter(gate_weight, requires_grad=False)
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gate_proj.in_features = gate_weight.size(1)
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gate_proj.out_features = gate_weight.size(0)
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up_proj = torch.nn.Linear(0, 0, bias=False)
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up_proj.weight = torch.nn.Parameter(up_weight, requires_grad=False)
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up_proj.in_features = up_weight.size(1)
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up_proj.out_features = up_weight.size(0)
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module.gate_proj = gate_proj
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module.up_proj = up_proj
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del module.gate_up_proj
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# rename activation function
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module.act_fn = module.activation_fn
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def glm_attention_forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
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**kwargs,
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):
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bsz, q_len, _ = hidden_states.size()
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qkv = self.qkv_proj(hidden_states)
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qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
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qkv = qkv.transpose(1, 2)
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query_states, key_states, value_states = qkv.split([self.num_heads,
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self.num_key_value_heads,
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self.num_key_value_heads], dim=1)
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cos, sin = position_embeddings
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if query_states.device.type == "xpu":
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import xe_addons
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make_cache_contiguous_inplaced(cos, sin)
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xe_addons.rotary_two_with_cache_inplaced(query_states, key_states, cos, sin, True)
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else:
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states,
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self.layer_idx, cache_kwargs)
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attn_weights = None
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attn_output = scaled_dot_product_attention(
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query_states, key_states, value_states,
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attention_mask, q_len == key_states.size(2), self.scaling
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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def glm_model_forward_wrapper(origin_forward):
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def glm_model_forward(
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self,
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input_ids: torch.LongTensor = None,
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images: torch.Tensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Cache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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):
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# ipex-llm changes start
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# IPEX-LLM OPT: kv cache and quantize kv cache
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inputs = input_ids if input_ids is not None else inputs_embeds
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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use_cache = use_cache or inputs.device.type == 'xpu'
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use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, inputs,
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self.config.num_attention_heads //
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self.config.num_key_value_heads)
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if use_cache:
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if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
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past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
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elif not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
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past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
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# ipex-llm changes end
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return origin_forward(
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self=self,
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input_ids=input_ids,
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images=images,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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cache_position=cache_position,
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**kwargs,
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)
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return glm_model_forward
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