add basic support for Baichuan-M1-14B-Instruct (#12808)
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Yishuo Wang
GitHub
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2 changed files with 261 additions and 0 deletions
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@ -1062,6 +1062,11 @@ def _optimize_pre(model, qtype=None):
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from ipex_llm.transformers.models.glm import merge_qkv, split_mlp
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model.apply(merge_qkv)
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model.apply(split_mlp)
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elif model.config.model_type == "baichuan_m1":
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from ipex_llm.transformers.models.baichuan_m1 import pre_register_inv_freq
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model.apply(pre_register_inv_freq)
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elif model.config.model_type == "multi_modality":
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pass
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return model
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@ -1994,5 +1999,21 @@ def _optimize_post(model):
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model.llm.config.rope_scaling = {"rope_type": "default"}
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_optimize_post(model.llm)
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model.llm.config.model_type = "megrezo"
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elif model.config.model_type == "baichuan_m1":
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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from ipex_llm.transformers.models.common import rms_norm_forward
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from ipex_llm.transformers.models.baichuan_m1 import model_forward
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from ipex_llm.transformers.models.baichuan_m1 import eager_attention_forward
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convert_forward(model, module.BaichuanModel, model_forward)
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convert_forward(model, module.BaichuanRMSNorm, rms_norm_forward)
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convert_forward(model, module.BaichuanAttention, eager_attention_forward)
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elif model.config.model_type == "multi_modality":
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# vision
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vpm_modeling_module_name = model.vision_model.vision_tower.__class__.__module__
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vpm_module = importlib.import_module(vpm_modeling_module_name)
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from ipex_llm.transformers.models.janus import vision_attention_forward
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convert_forward(model.vision_model, vpm_module.Attention, vision_attention_forward)
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return model
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240
python/llm/src/ipex_llm/transformers/models/baichuan_m1.py
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240
python/llm/src/ipex_llm/transformers/models/baichuan_m1.py
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@ -0,0 +1,240 @@
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#
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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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# This file is adapted from
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# https://huggingface.co/baichuan-inc/Baichuan-M1-14B-Instruct/blob/main/modeling_baichuan.py
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import math
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import torch
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import torch.nn.functional as F
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from typing import Optional, Tuple, Union
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from transformers.cache_utils import Cache
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.models.utils import should_use_fuse_rope, repeat_kv
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from ipex_llm.transformers.models.common import attention_softmax
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from ipex_llm.transformers.models.common import scaled_dot_product_attention
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from ipex_llm.transformers.kv import DynamicNormalCache
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def pre_register_inv_freq(module: torch.nn.Module):
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if module.__class__.__name__ == "RotaryEmbedding":
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inv_freq = module.inv_freq
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del module.inv_freq
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module.register_buffer("inv_freq", inv_freq, persistent=False)
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# copied from Baichuan M1
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def custom_convolution(U, K):
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"""
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U: Input matrix, shape (bs, seq, h, d)
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K: Convolution kernel, shape (w, h)
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Returns: Output matrix V, shape (bs, seq, h, d)
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"""
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# h, w = K.shape
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w = K.size(-1)
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padding = (w - 1, 0)
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U_padded = F.pad(U, (0, 0, 0, 0, *padding)) # Shape becomes (bs, seq+w-1, h, d)
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U_unfolded = U_padded.unfold(1, w, 1) # Shape becomes (bs, seq+w-1, h, d, w)
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V_unfolded = U_unfolded * K # Shape remains (bs, seq, h, d, w)
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V = V_unfolded.sum(dim=-1) # Shape becomes (bs, seq, h, d)
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return V
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def model_forward(
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self,
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input_ids: torch.LongTensor = 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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seqlens: 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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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = (
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output_attentions if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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invalidInputError((input_ids is None) ^ (inputs_embeds is None),
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"You cannot specify both input_ids and inputs_embeds at the same time, "
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"and must specify either one")
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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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 = True if inputs_embeds.device.type == "xpu" else use_cache
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# IPEX-LLM changes start: remove batch multi-pack and use ipex-llm's kv cache
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# kept for BC (non `Cache` `past_key_values` inputs)
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if use_cache 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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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1],
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device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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causal_mask = self._update_causal_mask(
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attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
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)
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hidden_states = inputs_embeds
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# create position embeddings to be shared across the decoder layers
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# position_embeddings = self.rotary_emb(hidden_states, position_ids)
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position_embeddings = None
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=causal_mask,
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position_ids=position_ids,
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seqlens=None,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
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if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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def eager_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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seqlens: 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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):
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invalidInputError(seqlens is None, "`seq_lens` must be None")
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bsz, q_len, _ = hidden_states.size()
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qkv = self.W_pack(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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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=2)
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# q, k, v: [bsz, seq_len, num_heads, head_dim]
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if past_key_value is None or past_key_value.get_seq_length(self.layer_idx) == 0: # prefill
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self.last_k = key_states[:, -1:]
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self.last_v = value_states[:, -1:]
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key_states = custom_convolution(key_states, self.conv_k)
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value_states = custom_convolution(value_states, self.conv_v)
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else:
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new_key_states = (self.conv_k[0, 0, :, 0, :1] * self.last_k +
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self.conv_k[0, 0, :, 0, 1:] * key_states)
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self.last_k = key_states
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key_states = new_key_states
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new_value_states = (self.conv_v[0, 0, :, 0, : 1] * self.last_v +
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self.conv_v[0, 0, :, 0, 1:] * value_states)
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self.last_v = value_states
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value_states = new_value_states
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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# q, k, v: [bsz, num_heads, seq_len, head_dim]
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invalidInputError(should_use_fuse_rope(hidden_states, position_ids, self.training),
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"fuse rope must be used")
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import xe_addons
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xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
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query_states, key_states)
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# ignore sliding window
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key_states, value_states = past_key_value.update(key_states, value_states,
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self.layer_idx, None)
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if self.head_dim <= 128:
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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)
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)
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else:
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n_rep = self.num_heads // self.num_key_value_heads
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key_states = repeat_kv(key_states, n_rep)
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value_states = repeat_kv(value_states, n_rep)
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attn_weights = torch.matmul(query_states,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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attn_weights = attention_softmax(attn_weights)
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attn_output = torch.matmul(attn_weights, value_states)
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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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