325 lines
14 KiB
Python
325 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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# This file is adapted from
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# https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/cb7fc748b78b7ea99772e4cf76db155729ce774e/modeling_baichuan.py
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# and
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# https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/blob/c6f8592a60b4ad73c210b28dd2ab3cca51abbf93/modeling_baichuan.py
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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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import torch.utils.checkpoint
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from torch.nn import functional as F
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from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
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from ipex_llm.transformers.models.utils import update_past_key_value
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from ipex_llm.transformers.models.utils import should_use_fuse_rope
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from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_causal
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from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, SILU
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from ipex_llm.transformers.models.utils import mlp_fusion_check
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import warnings
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def pre_compute_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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def baichuan_13b_rms_norm_forward(self, hidden_states):
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if hidden_states.device.type == "xpu" and not (self.training or 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.epsilon)
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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.epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def baichuan_mlp_forward(
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self,
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x: torch.Tensor,
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) -> torch.Tensor:
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x_2d = x.view(-1, x.shape[-1])
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qtype = getattr(self.gate_proj, "qtype", None)
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if mlp_fusion_check(x_2d, qtype, self.training) and not self.down_proj.enable_xetla:
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import xe_linear
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if not x_2d.is_contiguous():
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x_2d = x_2d.contiguous()
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return self.down_proj(xe_linear.mlp_forward_xpu(
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x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
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x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_len,
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SILU, qtype
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))
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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def baichuan_attention_forward_7b(
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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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):
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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qkv = self.W_pack(hidden_states)
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qkv = qkv.view(bsz, q_len, self.num_heads * 3, 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_heads,
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self.num_heads], 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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if should_use_fuse_rope(hidden_states, position_ids, self.training):
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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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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, "baichuan")
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query_states = query_states.to(hidden_states.dtype)
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key_states = key_states.to(hidden_states.dtype)
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# IPEX-LLM OPT: kv cache and quantize kv
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use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_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, device
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)
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past_key_value = (key_states, value_states) if use_cache else None
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if self.training:
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warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
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# IPEX-LLM OPT: sdp
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attn_weights = None
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if not self.training and not hidden_states.requires_grad and \
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use_flash_attention(query_states, key_states, attention_mask):
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attn_output = F.scaled_dot_product_attention(query_states.to(dtype=torch.float16),
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key_states.to(dtype=torch.float16),
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value_states.to(dtype=torch.float16),
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is_causal=True).to(hidden_states.dtype)
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elif use_sdp(q_len, kv_seq_len, self.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,
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attention_mask)
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else:
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attn_output = xe_addons.sdp(query_states, key_states, value_states,
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attention_mask)
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elif use_sdp_causal(q_len, kv_seq_len, self.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,
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value_states, attention_mask)
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else:
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attn_output = xe_addons.sdp_causal(query_states, key_states,
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value_states, attention_mask)
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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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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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# upcast attention to fp32
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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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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 baichuan_attention_forward_13b(
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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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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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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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device = hidden_states.device
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qkv = self.W_pack(hidden_states)
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qkv = qkv.view(bsz, q_len, self.num_heads * 3, 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_heads,
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self.num_heads], 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: kv cache and quantize kv
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use_quantize_kv = use_quantize_kv_cache(self.W_pack, hidden_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, device
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)
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past_key_value = (key_states, value_states) if use_cache else None
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if self.training:
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warnings.warn("xops is not supported on Intel GPU, so just use normal implementation")
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if attention_mask is not None:
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if len(attention_mask.size()) == 4:
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attention_mask = attention_mask[:, :, -q_len:, :]
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else:
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attention_mask = attention_mask[None, :, -q_len:, :]
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if use_sdp(q_len, kv_seq_len, self.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,
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attention_mask)
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else:
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attn_output = xe_addons.sdp(query_states, key_states, value_states,
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attention_mask)
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attn_weights = None
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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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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 = attn_weights.to(query_states.dtype)
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
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attn_output = torch.matmul(attn_weights.to(dtype=value_states.dtype), value_states)
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attn_output = attn_output.transpose(1, 2)
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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 _get_interleave(n):
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def _get_interleave_power_of_2(n):
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start = 2 ** (-(2 ** -(math.log2(n) - 3)))
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ratio = start
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return [start * ratio**i for i in range(n)]
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if math.log2(n).is_integer():
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return _get_interleave_power_of_2(n)
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else:
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closest_power_of_2 = 2 ** math.floor(math.log2(n))
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return (
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_get_interleave_power_of_2(closest_power_of_2)
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+ _get_interleave(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
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)
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def _fill_with_neg_inf(t):
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"""FP16-compatible function that fills a tensor with -inf."""
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return t.float().fill_(float("-inf")).type_as(t)
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def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
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_future_mask = torch.triu(_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1)
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_future_mask = _future_mask.unsqueeze(0) + alibi
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new_future_mask = _future_mask.to(tensor)
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return new_future_mask[: tensor.shape[0] * attn_heads, :maxpos, :maxpos]
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def baichuan_13b_gen_alibi_mask(tensor, n_head, max_pos):
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slopes = torch.Tensor(_get_interleave(n_head)).to(tensor.dtype)
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position_point = torch.arange(max_pos) - max_pos + 1
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position_point = position_point.unsqueeze(0).unsqueeze(0).expand(n_head, -1, -1)
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diag = torch.diag(position_point[0])
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position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(-1, -2)
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alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
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alibi = alibi.view(n_head, 1, max_pos)
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alibi_mask = torch.triu(
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_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1).to(tensor.dtype)
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alibi_mask = alibi_mask.unsqueeze(0) + alibi
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if tensor.device.type == "xpu":
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alibi_mask = alibi_mask.to(tensor.device)
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return alibi_mask
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MASK_BLOCK_SIZE = 512
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def baichuan_13b_get_alibi_mask(self, tensor, seq_length_with_past):
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if self.training:
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slopes = torch.Tensor(_get_interleave(self.n_head))
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position_point = (
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torch.arange(seq_length_with_past) - seq_length_with_past + 1
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)
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position_point = (
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position_point.unsqueeze(0)
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.unsqueeze(0)
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.expand(self.n_head, seq_length_with_past, -1)
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)
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diag = torch.diag(position_point[0])
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position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(
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-1, -2
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)
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alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
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mask = _buffered_future_mask(
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tensor, seq_length_with_past, alibi, self.n_head
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)
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else:
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if self.first_run:
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# Override the default max_cache_pos=4096 for memory considerations
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self.max_cache_pos = seq_length_with_past + MASK_BLOCK_SIZE
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self.first_run = False
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self.register_buffer(
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"future_mask",
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baichuan_13b_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos),
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persistent=False,
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)
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if seq_length_with_past > self.max_cache_pos:
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# When max_cache_pos is not enough for current sequence length,
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# increase by MASK_BLOCK_SIZE and recalculate future_mask.
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self.max_cache_pos = seq_length_with_past + MASK_BLOCK_SIZE
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self.register_buffer(
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"future_mask",
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baichuan_13b_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos),
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persistent=False,
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)
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mask = self.future_mask[
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: self.n_head, :seq_length_with_past, :seq_length_with_past
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]
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return mask
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