835 lines
36 KiB
Python
835 lines
36 KiB
Python
# coding=utf-8
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# Copyright 2022 EleutherAI 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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from .configuration_baichuan import BaiChuanConfig
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import copy
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import warnings
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import math
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from typing import *
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import re
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers import PreTrainedModel, add_start_docstrings
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \
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SequenceClassifierOutputWithPast
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from transformers.utils import logging, add_start_docstrings_to_model_forward, replace_return_docstrings
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from transformers.generation.logits_process import LogitsProcessor
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from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
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logger = logging.get_logger(__name__)
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# copied from https://huggingface.co/THUDM/chatglm-6b-int4/blob/main/modeling_chatglm.py
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class InvalidScoreLogitsProcessor(LogitsProcessor):
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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if torch.isnan(scores).any() or torch.isinf(scores).any():
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scores.zero_()
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scores[..., 5] = 5e4
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return scores
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def generate_prompt(input_text):
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return "Human: \n" + input_text + "\n\nAssistant:\n"
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# Copied from transformers.models.bart.modeling_bart._make_causal_mask
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def _make_causal_mask(
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input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
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):
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"""
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Make causal mask used for bi-directional self-attention.
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"""
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bsz, tgt_len = input_ids_shape
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mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
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mask_cond = torch.arange(mask.size(-1), device=device)
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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mask = mask.to(dtype)
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if past_key_values_length > 0:
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mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
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return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
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# Copied from transformers.models.bart.modeling_bart._expand_mask
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def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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class RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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RMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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# convert into half-precision if necessary
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if self.weight.dtype in [torch.float16, torch.bfloat16]:
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hidden_states = hidden_states.to(self.weight.dtype)
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return self.weight * hidden_states
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class RotaryEmbedding(torch.nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
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self.register_buffer("inv_freq", inv_freq)
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# Build here to make `torch.jit.trace` work.
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self.max_seq_len_cached = max_position_embeddings
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t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
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self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
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def forward(self, x, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
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if seq_len > self.max_seq_len_cached:
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
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self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
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self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
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return (
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self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
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self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
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)
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2:]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
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# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
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cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
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sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
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cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
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sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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class MLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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):
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super().__init__()
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self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
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self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
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self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
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self.act_fn = ACT2FN[hidden_act]
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def forward(self, x):
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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class Attention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: BaiChuanConfig):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.hidden_size // self.num_heads
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self.max_position_embeddings = config.max_position_embeddings
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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# self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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# self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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# self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def 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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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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proj = self.W_pack(hidden_states)
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proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
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query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1,
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2) # batch_size x source_len x hidden_size
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key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1,
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2) # batch_size x target_len x head_size
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value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1,
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2) # batch_size x source_len x hidden_size
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# query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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# key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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# value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_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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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, cos, sin, position_ids)
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# [bsz, nh, t, hd]
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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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attn_weights = torch.matmul(query_states, 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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raise ValueError(
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
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f" {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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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
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)
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attn_weights = attn_weights + attention_mask
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attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
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# upcast attention to fp32
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attn_weights = nn.functional.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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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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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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class DecoderLayer(nn.Module):
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def __init__(self, config: BaiChuanConfig):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = Attention(config=config)
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self.mlp = MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def 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: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
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`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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use_cache (`bool`, *optional*):
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
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(see `past_key_values`).
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past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
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"""
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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hidden_states, self_attn_weights, present_key_value = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (self_attn_weights,)
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if use_cache:
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outputs += (present_key_value,)
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return outputs
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class PreTrainedModel(PreTrainedModel):
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config_class = BaiChuanConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["DecoderLayer"]
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_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
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def _init_weights(self, module):
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std = self.config.initializer_range
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if isinstance(module, nn.Linear):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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def _set_gradient_checkpointing(self, module, value=False):
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if isinstance(module, Model):
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module.gradient_checkpointing = value
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class Model(PreTrainedModel):
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"""
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Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DecoderLayer`]
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Args:
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config: BaiChuanConfig
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"""
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def __init__(self, config: BaiChuanConfig):
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super().__init__(config)
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
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self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.gradient_checkpointing = False
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.embed_tokens
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def set_input_embeddings(self, value):
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self.embed_tokens = value
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# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
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def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
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# create causal mask
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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combined_attention_mask = None
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if input_shape[-1] > 1:
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combined_attention_mask = _make_causal_mask(
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input_shape,
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inputs_embeds.dtype,
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device=inputs_embeds.device,
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past_key_values_length=past_key_values_length,
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)
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if attention_mask is not None:
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
||
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
||
inputs_embeds.device
|
||
)
|
||
combined_attention_mask = (
|
||
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
||
)
|
||
|
||
return combined_attention_mask
|
||
|
||
def forward(
|
||
self,
|
||
input_ids: torch.LongTensor = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
output_attentions: Optional[bool] = None,
|
||
output_hidden_states: Optional[bool] = None,
|
||
return_dict: Optional[bool] = None,
|
||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
output_hidden_states = (
|
||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
)
|
||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||
|
||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
||
# retrieve input_ids and inputs_embeds
|
||
if input_ids is not None and inputs_embeds is not None:
|
||
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
||
elif input_ids is not None:
|
||
batch_size, seq_length = input_ids.shape
|
||
elif inputs_embeds is not None:
|
||
batch_size, seq_length, _ = inputs_embeds.shape
|
||
else:
|
||
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
||
|
||
seq_length_with_past = seq_length
|
||
past_key_values_length = 0
|
||
|
||
if past_key_values is not None:
|
||
past_key_values_length = past_key_values[0][0].shape[2]
|
||
seq_length_with_past = seq_length_with_past + past_key_values_length
|
||
|
||
if position_ids is None:
|
||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||
position_ids = torch.arange(
|
||
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
||
)
|
||
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
||
else:
|
||
position_ids = position_ids.view(-1, seq_length).long()
|
||
|
||
if inputs_embeds is None:
|
||
inputs_embeds = self.embed_tokens(input_ids)
|
||
# embed positions
|
||
if attention_mask is None:
|
||
attention_mask = torch.ones(
|
||
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
||
)
|
||
attention_mask = self._prepare_decoder_attention_mask(
|
||
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
||
)
|
||
|
||
hidden_states = inputs_embeds
|
||
|
||
if self.gradient_checkpointing and self.training:
|
||
if use_cache:
|
||
logger.warning_once(
|
||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||
)
|
||
use_cache = False
|
||
|
||
# decoder layers
|
||
all_hidden_states = () if output_hidden_states else None
|
||
all_self_attns = () if output_attentions else None
|
||
next_decoder_cache = () if use_cache else None
|
||
|
||
for idx, decoder_layer in enumerate(self.layers):
|
||
if output_hidden_states:
|
||
all_hidden_states += (hidden_states,)
|
||
|
||
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
||
|
||
if self.gradient_checkpointing and self.training:
|
||
|
||
def create_custom_forward(module):
|
||
def custom_forward(*inputs):
|
||
# None for past_key_value
|
||
return module(*inputs, output_attentions, None)
|
||
|
||
return custom_forward
|
||
|
||
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||
create_custom_forward(decoder_layer),
|
||
hidden_states,
|
||
attention_mask,
|
||
position_ids,
|
||
None,
|
||
)
|
||
else:
|
||
layer_outputs = decoder_layer(
|
||
hidden_states,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_value=past_key_value,
|
||
output_attentions=output_attentions,
|
||
use_cache=use_cache,
|
||
)
|
||
|
||
hidden_states = layer_outputs[0]
|
||
|
||
if use_cache:
|
||
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
||
|
||
if output_attentions:
|
||
all_self_attns += (layer_outputs[1],)
|
||
|
||
hidden_states = self.norm(hidden_states)
|
||
|
||
# add hidden states from the last decoder layer
|
||
if output_hidden_states:
|
||
all_hidden_states += (hidden_states,)
|
||
|
||
next_cache = next_decoder_cache if use_cache else None
|
||
if not return_dict:
|
||
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
||
return BaseModelOutputWithPast(
|
||
last_hidden_state=hidden_states,
|
||
past_key_values=next_cache,
|
||
hidden_states=all_hidden_states,
|
||
attentions=all_self_attns,
|
||
)
|
||
|
||
|
||
class BaiChuanForCausalLM(PreTrainedModel):
|
||
def __init__(self, config):
|
||
super().__init__(config)
|
||
self.model = Model(config)
|
||
|
||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||
|
||
# Initialize weights and apply final processing
|
||
self.post_init()
|
||
|
||
def get_input_embeddings(self):
|
||
return self.model.embed_tokens
|
||
|
||
def set_input_embeddings(self, value):
|
||
self.model.embed_tokens = value
|
||
|
||
def get_output_embeddings(self):
|
||
return self.lm_head
|
||
|
||
def set_output_embeddings(self, new_embeddings):
|
||
self.lm_head = new_embeddings
|
||
|
||
def set_decoder(self, decoder):
|
||
self.model = decoder
|
||
|
||
def get_decoder(self):
|
||
return self.model
|
||
|
||
def forward(
|
||
self,
|
||
input_ids: torch.LongTensor = None,
|
||
attention_mask: Optional[torch.Tensor] = None,
|
||
position_ids: Optional[torch.LongTensor] = None,
|
||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||
labels: Optional[torch.LongTensor] = None,
|
||
use_cache: Optional[bool] = None,
|
||
output_attentions: Optional[bool] = None,
|
||
output_hidden_states: Optional[bool] = None,
|
||
return_dict: Optional[bool] = None,
|
||
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||
r"""
|
||
Args:
|
||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||
|
||
Returns:
|
||
|
||
Example:
|
||
|
||
```python
|
||
>>> from transformers import AutoTokenizer, ModelForCausalLM
|
||
|
||
>>> model = ModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
||
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
||
|
||
>>> prompt = "Hey, are you consciours? Can you talk to me?"
|
||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||
|
||
>>> # Generate
|
||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
|
||
```"""
|
||
|
||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||
output_hidden_states = (
|
||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||
)
|
||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||
outputs = self.model(
|
||
input_ids=input_ids,
|
||
attention_mask=attention_mask,
|
||
position_ids=position_ids,
|
||
past_key_values=past_key_values,
|
||
inputs_embeds=inputs_embeds,
|
||
use_cache=use_cache,
|
||
output_attentions=output_attentions,
|
||
output_hidden_states=output_hidden_states,
|
||
return_dict=return_dict,
|
||
)
|
||
|
||
hidden_states = outputs[0]
|
||
logits = self.lm_head(hidden_states)
|
||
|
||
loss = None
|
||
if labels is not None:
|
||
# Shift so that tokens < n predict n
|
||
shift_logits = logits[..., :-1, :].contiguous()
|
||
shift_labels = labels[..., 1:].contiguous()
|
||
# Flatten the tokens
|
||
loss_fct = CrossEntropyLoss()
|
||
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
||
shift_labels = shift_labels.view(-1)
|
||
# Enable model parallelism
|
||
shift_labels = shift_labels.to(shift_logits.device)
|
||
loss = loss_fct(shift_logits, shift_labels)
|
||
|
||
if not return_dict:
|
||
output = (logits,) + outputs[1:]
|
||
return (loss,) + output if loss is not None else output
|
||
|
||
return CausalLMOutputWithPast(
|
||
loss=loss,
|
||
logits=logits,
|
||
past_key_values=outputs.past_key_values,
|
||
hidden_states=outputs.hidden_states,
|
||
attentions=outputs.attentions,
|
||
)
|
||
|
||
def prepare_inputs_for_generation(
|
||
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
||
):
|
||
if past_key_values:
|
||
input_ids = input_ids[:, -1:]
|
||
|
||
position_ids = kwargs.get("position_ids", None)
|
||
if attention_mask is not None and position_ids is None:
|
||
# create position_ids on the fly for batch generation
|
||
position_ids = attention_mask.long().cumsum(-1) - 1
|
||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||
if past_key_values:
|
||
position_ids = position_ids[:, -1].unsqueeze(-1)
|
||
|
||
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
||
if inputs_embeds is not None and past_key_values is None:
|
||
model_inputs = {"inputs_embeds": inputs_embeds}
|
||
else:
|
||
model_inputs = {"input_ids": input_ids}
|
||
|
||
model_inputs.update(
|
||
{
|
||
"position_ids": position_ids,
|
||
"past_key_values": past_key_values,
|
||
"use_cache": kwargs.get("use_cache"),
|
||
"attention_mask": attention_mask,
|
||
}
|
||
)
|
||
return model_inputs
|
||
|
||
@staticmethod
|
||
def _reorder_cache(past_key_values, beam_idx):
|
||
reordered_past = ()
|
||
for layer_past in past_key_values:
|
||
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
||
return reordered_past
|
||
|
||
def process_response(self, response):
|
||
response = response.strip()
|
||
response = response.replace("[[训练时间]]", "2023年")
|
||
punkts = [
|
||
[",", "，"],
|
||
["!", "！"],
|
||
[":", "："],
|
||
[";", "；"],
|
||
["\?", "？"],
|
||
]
|
||
for item in punkts:
|
||
response = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], response)
|
||
response = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], response)
|
||
return response
|
||
|
||
# copied from https://huggingface.co/THUDM/chatglm-6b-int4/blob/main/modeling_chatglm.py
|
||
@torch.no_grad()
|
||
def stream_chat(self, tokenizer, query: str, history: List = None,
|
||
gen_kwargs: dict = None, logits_processor=None):
|
||
if history is None:
|
||
history = []
|
||
if logits_processor is None:
|
||
logits_processor = LogitsProcessorList()
|
||
logits_processor.append(InvalidScoreLogitsProcessor())
|
||
|
||
if not history:
|
||
prompt = generate_prompt(query)
|
||
else:
|
||
history.append(generate_prompt(query))
|
||
prompt = "".join(history)
|
||
|
||
inputs = tokenizer(prompt, return_tensors="pt")
|
||
inputs = inputs.to(self.model.device)
|
||
for outputs in self.stream_generate(**inputs, **gen_kwargs):
|
||
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
|
||
response = tokenizer.decode(outputs, skip_special_tokens=True).split("Assistant:")[-1].strip()
|
||
response = self.process_response(response)
|
||
yield response
|
||
|
||
@torch.no_grad()
|
||
def stream_generate(
|
||
self,
|
||
input_ids,
|
||
generation_config: Optional[GenerationConfig] = None,
|
||
logits_processor: Optional[LogitsProcessorList] = None,
|
||
stopping_criteria: Optional[StoppingCriteriaList] = None,
|
||
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
|
||
**kwargs,
|
||
):
|
||
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
|
||
|
||
if generation_config is None:
|
||
generation_config = self.generation_config
|
||
generation_config = copy.deepcopy(generation_config)
|
||
model_kwargs = generation_config.update(**kwargs)
|
||
bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
|
||
|
||
if isinstance(eos_token_id, int):
|
||
eos_token_id = [eos_token_id]
|
||
|
||
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
|
||
if has_default_max_length and generation_config.max_new_tokens is None:
|
||
warnings.warn(
|
||
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
|
||
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
|
||
" recommend using `max_new_tokens` to control the maximum length of the generation.",
|
||
UserWarning,
|
||
)
|
||
elif generation_config.max_new_tokens is not None:
|
||
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
|
||
if not has_default_max_length:
|
||
logger.warn(
|
||
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
|
||
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
|
||
"Please refer to the documentation for more information. "
|
||
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
|
||
UserWarning,
|
||
)
|
||
|
||
if input_ids_seq_length >= generation_config.max_length:
|
||
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
|
||
logger.warning(
|
||
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
|
||
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
|
||
" increasing `max_new_tokens`."
|
||
)
|
||
|
||
# 2. Set generation parameters if not already defined
|
||
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
|
||
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
|
||
|
||
logits_processor = self._get_logits_processor(
|
||
generation_config=generation_config,
|
||
input_ids_seq_length=input_ids_seq_length,
|
||
encoder_input_ids=input_ids,
|
||
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
|
||
logits_processor=logits_processor,
|
||
)
|
||
|
||
stopping_criteria = self._get_stopping_criteria(
|
||
generation_config=generation_config, stopping_criteria=stopping_criteria
|
||
)
|
||
logits_warper = self._get_logits_warper(generation_config)
|
||
|
||
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
|
||
scores = None
|
||
while True:
|
||
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
|
||
# forward pass to get next token
|
||
outputs = self(
|
||
**model_inputs,
|
||
return_dict=True,
|
||
output_attentions=False,
|
||
output_hidden_states=False,
|
||
)
|
||
|
||
next_token_logits = outputs.logits[:, -1, :]
|
||
|
||
# pre-process distribution
|
||
next_token_scores = logits_processor(input_ids, next_token_logits)
|
||
next_token_scores = logits_warper(input_ids, next_token_scores)
|
||
|
||
# sample
|
||
probs = nn.functional.softmax(next_token_scores, dim=-1)
|
||
if generation_config.do_sample:
|
||
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
|
||
else:
|
||
next_tokens = torch.argmax(probs, dim=-1)
|
||
|
||
# update generated ids, model inputs, and length for next step
|
||
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
|
||
model_kwargs = self._update_model_kwargs_for_generation(
|
||
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
|
||
)
|
||
unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long())
|
||
|
||
# stop when each sentence is finished, or if we exceed the maximum length
|
||
if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
|
||
break
|
||
yield input_ids
|