LLM: add fuse optimization for Mistral. (#9184)
* add fuse optimization for mistral. * fix. * fix * fix style. * fix. * fix error. * fix style. * fix style.
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Cengguang Zhang
GitHub
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3 changed files with 160 additions and 1 deletions
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@ -348,4 +348,15 @@ def optimize(model):
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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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elif model.config.model_type == "mistral":
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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 bigdl.llm.transformers.models.mistral import mistral_attention_forward
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convert_forward(model,
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module.MistralAttention,
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mistral_attention_forward
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)
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convert_forward(model,
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module.MistralRMSNorm,
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llama_rms_norm_forward)
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return model
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148
python/llm/src/bigdl/llm/transformers/models/mistral.py
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148
python/llm/src/bigdl/llm/transformers/models/mistral.py
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@ -0,0 +1,148 @@
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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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#
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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py
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#
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# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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""" PyTorch Mistral model."""
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import math
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from typing import Optional, Tuple
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import torch
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from torch import nn
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from bigdl.llm.utils.common import invalidInputError
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from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb,\
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apply_rotary_pos_emb_no_cache_xpu
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
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The hidden states go from (batch, num_key_value_heads, seqlen, head_dim)
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to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
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n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def mistral_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[Tuple[torch.Tensor]]=None,
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output_attentions: bool=False,
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use_cache: bool=False,
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padding_mask: Optional[torch.Tensor]=None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len,
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self.num_key_value_heads, self.head_dim).transpose(1, 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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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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"mistral")
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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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query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
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cos, sin, position_ids, "mistral")
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if past_key_value is not None:
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# reuse k, v, self_attention
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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past_key_value = (key_states, value_states) if use_cache else None
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(
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query_states,
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key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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invalidInputError(
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False,
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)},"
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f" but is {attn_weights.size()}"
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
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invalidInputError(
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False,
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
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f" but is {attention_mask.size()}"
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)
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.\
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softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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invalidInputError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)},"
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f" but is {attn_output.size()}"
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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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@ -98,7 +98,7 @@ def apply_rotary_pos_emb_no_cache_xpu(q, k, position_ids, model_family):
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import linear_q4_0
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q_embed = torch.empty(q.shape, dtype=q.dtype, device=q.device)
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k_embed = torch.empty(k.shape, dtype=k.dtype, device=k.device)
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if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox"]:
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if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox", "mistral"]:
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linear_q4_0.apply_rotary_embedding_half_qk(q, k, position_ids, q_embed, k_embed)
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return q_embed, k_embed
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else:
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