308 lines
14 KiB
Python
308 lines
14 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://huggingface.co/THUDM/chatglm2-6b/blob/8eb45c842594b8473f291d0f94e7bbe86ffc67d8/modeling_chatglm.py
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#
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import math
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import torch
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from typing import Optional, Tuple
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from ipex_llm.utils.common.log4Error import invalidInputError
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from ipex_llm.transformers.models.utils import restore_fp8_kv_cache, update_past_key_value
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, use_sdp, use_sdp_causal
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from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb
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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). The hidden states
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go from (batch, num_key_value_heads, seqlen, head_dim) to
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(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 chatglm_rms_norm_forward(self, hidden_states):
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if hidden_states.device.type == "xpu" and not (self.training and hidden_states.requires_grad):
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import xe_addons
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x_2d = hidden_states.reshape(-1, hidden_states.size(-1)).contiguous()
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output = xe_addons.rms_norm(self.weight, x_2d, self.eps)
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return output.reshape(hidden_states.shape)
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
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return self.weight * hidden_states.to(input_dtype)
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def chatglm2_model_forward(
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self,
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input_ids,
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position_ids: Optional[torch.Tensor]=None,
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attention_mask: Optional[torch.BoolTensor]=None,
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full_attention_mask: Optional[torch.BoolTensor]=None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]=None,
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inputs_embeds: Optional[torch.Tensor]=None,
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use_cache: 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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):
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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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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if inputs_embeds is None:
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batch_size, seq_length = input_ids.shape
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inputs_embeds = self.embedding(input_ids)
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else:
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inputs_embeds = inputs_embeds.transpose(0, 1).contiguous()
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seq_length, batch_size, _ = inputs_embeds.shape
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input_ids = torch.empty((batch_size, seq_length),
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dtype=inputs_embeds.dtype, device=inputs_embeds.device)
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if full_attention_mask is None:
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if (attention_mask is not None and not attention_mask.all()) or (
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past_key_values and seq_length != 1):
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full_attention_mask = self.get_masks(input_ids,
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past_key_values,
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padding_mask=attention_mask)
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# ipex-llm changes begin
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# 1. replace `rotary_pos_emb` with `inv_freq` and `position_ids`
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# 2. generate `causal_mask` and replace `full_attention_mask` with it
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if position_ids is None:
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if past_key_values is None:
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position_ids = torch.arange(seq_length, dtype=torch.int64, device=inputs_embeds.device)
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else:
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kv_length = past_key_values[0][0].size(0)
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position_ids = torch.arange(kv_length, kv_length + seq_length,
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dtype=torch.int64, device=inputs_embeds.device)
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position_ids = position_ids.repeat(batch_size, 1)
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if not getattr(self.rotary_pos_emb, "cached", False):
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rot_dim = self.rotary_pos_emb.dim
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base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1)
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inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2,
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dtype=torch.float,
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device=inputs_embeds.device) / rot_dim))
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inv_freq = inv_freq.to(inputs_embeds.dtype)
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self.rotary_pos_emb.register_buffer("inv_freq", inv_freq, persistent=False)
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self.rotary_pos_emb.cached = True
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# `full_attention_mask` is not None only when
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# `past_key_values` is not None and `seq_length` > 1
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if full_attention_mask is not None:
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causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)],
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dtype=inputs_embeds.dtype, device=inputs_embeds.device)
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mask_value = torch.finfo(inputs_embeds.dtype).min
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causal_mask.masked_fill_(full_attention_mask, mask_value)
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elif self.training or (inputs_embeds.device.type != "xpu" and past_key_values is None):
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full_attention_mask = self.get_masks(input_ids,
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past_key_values,
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padding_mask=attention_mask)
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causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)],
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dtype=inputs_embeds.dtype, device=inputs_embeds.device)
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mask_value = torch.finfo(inputs_embeds.dtype).min
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causal_mask.masked_fill_(full_attention_mask, mask_value)
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else:
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causal_mask = None
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# Run encoder.
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hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
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inputs_embeds, causal_mask,
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rotary_pos_emb=(self.rotary_pos_emb.inv_freq, position_ids),
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kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
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)
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# ipex-llm changes end
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if not return_dict:
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return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions]
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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=presents,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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)
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# remove code which stores first token's kv cache by tensor format
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# to fix chatglm2-32k and chatglm3-128k
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def chatglm2_encoder_forward(
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self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None,
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use_cache: Optional[bool] = True,
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output_hidden_states: Optional[bool] = False,
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):
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if not kv_caches:
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kv_caches = [None for _ in range(self.num_layers)]
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presents = () if use_cache else None
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if self.gradient_checkpointing and self.training:
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use_cache = False
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all_self_attentions = None
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all_hidden_states = () if output_hidden_states else None
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for index in range(self.num_layers):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer = self._get_layer(index)
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if self.gradient_checkpointing and self.training:
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layer_ret = torch.utils.checkpoint.checkpoint(
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layer,
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hidden_states,
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attention_mask,
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rotary_pos_emb,
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kv_caches[index],
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use_cache
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)
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else:
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layer_ret = layer(
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hidden_states,
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attention_mask,
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rotary_pos_emb,
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kv_cache=kv_caches[index],
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use_cache=use_cache
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)
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hidden_states, kv_cache = layer_ret
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if use_cache:
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presents = presents + (kv_cache,)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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# Final layer norm.
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if self.post_layer_norm:
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states, presents, all_hidden_states, all_self_attentions
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def chatglm2_attention_forward(
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self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
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):
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# hidden_states: [seq_len, bsz, head_dim]
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q_len, bsz, _ = hidden_states.size()
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# kv_cache: [seq_len, bsz, n_kv_head, head_dim] ->
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# past_key_value: [bsz, n_kv_head, seq_len, head_dim]
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past_key_value = None if kv_cache is None else (kv_cache[0].permute(1, 2, 0, 3),
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kv_cache[1].permute(1, 2, 0, 3))
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n_head = self.num_attention_heads_per_partition
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n_kv_head = self.num_multi_query_groups_per_partition if self.multi_query_attention else n_head
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head_dim = self.hidden_size_per_attention_head
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qkv = self.query_key_value(hidden_states)
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qkv = qkv.view(q_len, bsz, n_head + 2 * n_kv_head, head_dim)
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# [seq_len, bsz, n_head, head_dim] -> [bsz, n_head, seq_len, head_dim]
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qkv = qkv.permute(1, 2, 0, 3)
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query_states, key_states, value_states = qkv.split([n_head,
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n_kv_head,
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n_kv_head], dim=1)
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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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# IPEX-LLM OPT: fuse rope
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inv_freq, position_ids = rotary_pos_emb
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rot_dim = inv_freq.size(-1) * 2
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if should_use_fuse_rope(hidden_states, rotary_pos_emb[1], self.training):
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import xe_addons
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xe_addons.rotary_two_inplaced(inv_freq, position_ids,
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query_states[..., :rot_dim], key_states[..., :rot_dim])
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else:
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idx_theta = torch.outer(position_ids[0].float(),
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inv_freq.float()).to(hidden_states.dtype)
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idx_theta = idx_theta.unsqueeze(0).unsqueeze(0)
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cos = torch.cos(idx_theta).repeat_interleave(2, -1)
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sin = torch.sin(idx_theta).repeat_interleave(2, -1)
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q_rot, k_rot = apply_rotary_pos_emb(query_states[..., :rot_dim], key_states[..., :rot_dim],
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cos, sin, position_ids, "chatglm")
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query_states[..., :rot_dim] = q_rot[...]
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key_states[..., :rot_dim] = k_rot[...]
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# IPEX-LLM OPT: kv cache and quantize kv
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use_quantize_kv = use_quantize_kv_cache(self.query_key_value, query_states)
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key_states, value_states = update_past_key_value(
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past_key_value, key_states, value_states,
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kv_seq_len, use_quantize_kv, hidden_states.device
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)
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# past_key_value: [bsz, n_kv_head, seq_len, head_dim] -> [seq_len, bsz, n_kv_head, head_dim]
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past_key_value = (key_states.permute(2, 0, 1, 3),
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value_states.permute(2, 0, 1, 3)) if use_cache else None
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# IPEX-LLM OPT: sdp
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attn_weights = None
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if use_sdp(q_len, kv_seq_len, head_dim, query_states):
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import xe_addons
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if use_quantize_kv:
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attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states, attention_mask)
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else:
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attn_output = xe_addons.sdp(query_states, key_states, value_states, attention_mask)
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elif use_sdp_causal(q_len, kv_seq_len, head_dim, query_states, self.training):
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import xe_addons
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if use_quantize_kv:
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attn_output = xe_addons.sdp_fp8_causal(query_states, key_states, value_states,
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attention_mask)
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else:
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attn_output = xe_addons.sdp_causal(query_states, key_states, value_states,
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attention_mask)
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elif query_states.device.type == "cpu":
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, n_head // n_kv_head)
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value_states = repeat_kv(value_states, n_head // n_kv_head)
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if q_len == kv_seq_len:
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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query_states, key_states, value_states, is_causal=True
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)
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else:
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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query_states, key_states, value_states, attention_mask
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)
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else:
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if use_quantize_kv:
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key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
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query_states.dtype)
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, n_head // n_kv_head)
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value_states = repeat_kv(value_states, n_head // n_kv_head)
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attn_weights = torch.matmul(query_states,
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key_states.transpose(2, 3)) / math.sqrt(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 = torch.nn.functional.softmax(attn_weights, dim=-1,
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dtype=torch.float32).to(value_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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# context_layer's shape: [bsz, n_head, seq_len, head_dim] -> [seq_len, bsz, n_head * head_dim]
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attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(q_len, bsz, n_head * head_dim)
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output = self.dense(attn_output)
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return output, past_key_value
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