830 lines
32 KiB
Python
830 lines
32 KiB
Python
# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
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from .configuration_baichuan import BaichuanConfig
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from .generation_utils import build_chat_input, TextIterStreamer
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import math
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from threading import Thread
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from typing import List, Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from torch.nn import functional as F
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from transformers import PreTrainedModel, PretrainedConfig
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from transformers.activations import ACT2FN
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from transformers.generation.utils import GenerationConfig
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.utils import logging, ContextManagers
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import os
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from contextlib import contextmanager
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from accelerate import init_empty_weights
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logger = logging.get_logger(__name__)
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try:
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from xformers import ops as xops
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except ImportError:
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xops = None
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logger.warning(
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"Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
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)
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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 _gen_alibi_mask(tensor, n_head, max_pos):
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slopes = torch.Tensor(_get_interleave(n_head))
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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(_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1)
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alibi_mask = alibi_mask.unsqueeze(0) + alibi
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return alibi_mask
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class RMSNorm(torch.nn.Module):
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def __init__(self, hidden_size, epsilon=1e-6):
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super().__init__()
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self.weight = torch.nn.Parameter(torch.empty(hidden_size))
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self.epsilon = epsilon
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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.epsilon)
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# convert into half-precision
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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 MLP(torch.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 = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
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self.down_proj = torch.nn.Linear(intermediate_size, hidden_size, bias=False)
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self.up_proj = torch.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 BaichuanAttention(torch.nn.Module):
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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.model_max_length
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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 {self.hidden_size} is not divisible by num_heads {self.num_heads}"
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)
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self.W_pack = torch.nn.Linear(
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self.hidden_size, 3 * self.hidden_size, bias=False
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)
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self.o_proj = torch.nn.Linear(
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self.num_heads * self.head_dim, self.hidden_size, bias=False
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)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return (
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tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
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.transpose(1, 2)
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.contiguous()
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)
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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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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 = (
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proj.unflatten(-1, (3, self.hidden_size))
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.unsqueeze(0)
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.transpose(0, -2)
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.squeeze(-2)
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)
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query_states = (
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proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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key_states = (
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proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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value_states = (
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proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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if 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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if xops is not None and self.training:
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attn_weights = None
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# query_states = query_states.transpose(1, 2)
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# key_states = key_states.transpose(1, 2)
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# value_states = value_states.transpose(1, 2)
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# attn_output = xops.memory_efficient_attention(
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# query_states, key_states, value_states, attn_bias=attention_mask
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# )
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with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
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attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
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attn_output = attn_output.transpose(1, 2)
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else:
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attn_weights = torch.matmul(
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query_states, key_states.transpose(2, 3)
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) / math.sqrt(self.head_dim)
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if attention_mask is not None:
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if q_len == 1: # inference with cache
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if len(attention_mask.size()) == 4:
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attention_mask = attention_mask[:, :, -1:, :]
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else:
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attention_mask = attention_mask[:, -1:, :]
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attn_weights = attn_weights + attention_mask
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attn_weights = torch.max(
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attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
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)
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attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
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attn_output = torch.matmul(attn_weights, 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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class BaichuanLayer(torch.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 = BaichuanAttention(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, epsilon=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, epsilon=config.rms_norm_eps
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)
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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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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[
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torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
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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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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 use_cache:
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outputs += (present_key_value,)
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return outputs
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class BaichuanPreTrainedModel(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 = ["BaichuanLayer"]
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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, torch.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, torch.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, BaichuanModel):
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module.gradient_checkpointing = value
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class BaichuanModel(BaichuanPreTrainedModel):
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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.n_head = config.num_attention_heads
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self.embed_tokens = torch.nn.Embedding(
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config.vocab_size, config.hidden_size, self.padding_idx
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)
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self.layers = torch.nn.ModuleList(
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[BaichuanLayer(config) for _ in range(config.num_hidden_layers)]
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)
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self.norm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
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self.gradient_checkpointing = config.gradient_checkpointing
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self.post_init()
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self.max_cache_pos = config.model_max_length
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self.first_run = True
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self.alibi_mask = None
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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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def 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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self.first_run = False
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self.register_buffer(
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"future_mask",
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_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
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tensor
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),
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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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self.max_cache_pos = seq_length_with_past
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self.register_buffer(
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"future_mask",
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_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
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tensor
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),
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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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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = False,
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output_attentions: Optional[bool] = False,
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output_hidden_states: Optional[bool] = False,
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return_dict: Optional[bool] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError(
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"You cannot provide both input_ids and inputs_embeds simultaneously"
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)
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elif input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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raise ValueError("You need to provide input_ids or inputs_embeds")
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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 = (
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return_dict if return_dict is not None else self.config.use_return_dict
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)
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seq_length_with_past = seq_length
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if past_key_values is not None:
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past_key_values_length = past_key_values[0][0].shape[2]
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seq_length_with_past = seq_length_with_past + past_key_values_length
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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if self.training:
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if (
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self.alibi_mask is None
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or self.alibi_mask.shape[-1] != seq_length_with_past
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):
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self.alibi_mask = self.get_alibi_mask(
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inputs_embeds, seq_length_with_past
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)
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alibi_mask = self.alibi_mask
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else:
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alibi_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)
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if attention_mask is not None:
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if len(attention_mask.shape) == 2:
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expanded_mask = attention_mask.to(alibi_mask.dtype)
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expanded_mask = torch.tril(
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torch.gt(expanded_mask[:, :, None] * expanded_mask[:, None, :], 0)
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) * torch.eq(expanded_mask[:, :, None] - expanded_mask[:, None, :], 0)
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else:
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expanded_mask = attention_mask
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bsz = inputs_embeds.size(0)
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src_len, tgt_len = alibi_mask.size()[-2:]
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expanded_mask = (
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expanded_mask.unsqueeze(1)
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.expand(bsz, 1, src_len, tgt_len)
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.to(alibi_mask.dtype)
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)
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inverted_mask = 1.0 - expanded_mask
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inverted_mask = inverted_mask.masked_fill(
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inverted_mask.to(torch.bool), torch.finfo(alibi_mask.dtype).min
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)
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attention_mask = inverted_mask + alibi_mask.unsqueeze(0)
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else:
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attention_mask = alibi_mask
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hidden_states = inputs_embeds
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = () if use_cache else None
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for idx, decoder_layer in enumerate(self.layers):
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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past_key_value = (
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past_key_values[idx] if past_key_values is not None else None
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)
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if self.gradient_checkpointing and self.training:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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# None for past_key_value
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return module(*inputs, output_attentions, None)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(decoder_layer),
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hidden_states,
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attention_mask,
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None,
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|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
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 NormHead(nn.Module):
|
|
def __init__(self, hidden_size, vocab_size, bias=False):
|
|
super().__init__()
|
|
self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
|
|
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
|
|
self.first_flag = True
|
|
|
|
def forward(self, hidden_states):
|
|
if self.training:
|
|
norm_weight = nn.functional.normalize(self.weight)
|
|
self.first_flag = True
|
|
elif self.first_flag:
|
|
self.first_flag = False
|
|
self.weight = nn.Parameter(nn.functional.normalize(self.weight))
|
|
norm_weight = self.weight
|
|
else:
|
|
norm_weight = self.weight
|
|
return nn.functional.linear(hidden_states, norm_weight)
|
|
|
|
_init_weights = True
|
|
@contextmanager
|
|
def no_init_weights(_enable=True):
|
|
global _init_weights
|
|
old_init_weights = _init_weights
|
|
if _enable:
|
|
_init_weights = False
|
|
try:
|
|
yield
|
|
finally:
|
|
_init_weights = old_init_weights
|
|
|
|
|
|
class BaichuanForCausalLM(BaichuanPreTrainedModel):
|
|
def __init__(self, config, *model_args, **model_kwargs):
|
|
super().__init__(config, *model_args, **model_kwargs)
|
|
self.model = BaichuanModel(config)
|
|
self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
|
|
#if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
|
|
if hasattr(config, "quantization_config") and isinstance(config.quantization_config, dict) and config.quantization_config.get('load_in_4bit', False):
|
|
try:
|
|
from .quantizer import quantize_offline, init_model_weight_int4
|
|
except ImportError:
|
|
raise ImportError(f"Needs quantize_offline to run quantize.")
|
|
quantize_offline(self, 4)
|
|
# 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
|
|
|
|
@classmethod
|
|
def from_pretrained(
|
|
cls,
|
|
pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
|
|
*model_args,
|
|
config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
|
|
cache_dir: Optional[Union[str, os.PathLike]] = None,
|
|
ignore_mismatched_sizes: bool = False,
|
|
force_download: bool = False,
|
|
local_files_only: bool = False,
|
|
token: Optional[Union[str, bool]] = None,
|
|
revision: str = "main",
|
|
use_safetensors: bool = None,
|
|
**kwargs,
|
|
):
|
|
|
|
# Load config if we don't provide a configuration
|
|
if not isinstance(config, PretrainedConfig):
|
|
config_path = config if config is not None else pretrained_model_name_or_path
|
|
config, model_kwargs = cls.config_class.from_pretrained(
|
|
config_path,
|
|
cache_dir=cache_dir,
|
|
return_unused_kwargs=True,
|
|
force_download=force_download,
|
|
resume_download=False,
|
|
proxies=None,
|
|
local_files_only=local_files_only,
|
|
token=token,
|
|
revision=revision,
|
|
subfolder="",
|
|
_from_auto=False,
|
|
_from_pipeline=None,
|
|
**kwargs,
|
|
)
|
|
else:
|
|
model_kwargs = kwargs
|
|
|
|
if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
|
|
try:
|
|
from .quantizer import init_model_weight_int4
|
|
from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map
|
|
from accelerate.utils import CustomDtype
|
|
from accelerate.utils import get_balanced_memory
|
|
except ImportError:
|
|
raise ImportError(f"Needs import model weight init func to run quantize.")
|
|
# Instantiate model.
|
|
init_contexts = [no_init_weights(_enable=True)]
|
|
init_contexts.append(init_empty_weights())
|
|
with ContextManagers(init_contexts):
|
|
model = cls(config)
|
|
|
|
model_file = os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin')
|
|
state_dict = torch.load(model_file, map_location="cpu")
|
|
model.is_quantized = True
|
|
|
|
device_map = kwargs.pop("device_map", None)
|
|
torch_dtype = kwargs.pop("torch_dtype", None)
|
|
if device_map is not None:
|
|
kwargs = {"no_split_module_classes": model._no_split_modules}
|
|
target_dtype = CustomDtype.INT4
|
|
max_memory = get_balanced_memory(
|
|
model,
|
|
dtype=target_dtype,
|
|
low_zero=(device_map == "balanced_low_0"),
|
|
max_memory=None,
|
|
**kwargs,
|
|
)
|
|
kwargs["max_memory"] = max_memory
|
|
device_map = infer_auto_device_map(model, dtype=target_dtype, **kwargs)
|
|
model = init_model_weight_int4(config, model, state_dict)
|
|
|
|
# Set model in evaluation mode to deactivate DropOut modules by default
|
|
model.eval()
|
|
# If it is a model with generation capabilities, attempt to load the generation config
|
|
if model.can_generate():
|
|
try:
|
|
model.generation_config = GenerationConfig.from_pretrained(
|
|
pretrained_model_name_or_path,
|
|
cache_dir=cache_dir,
|
|
force_download=force_download,
|
|
resume_download=False,
|
|
proxies=None,
|
|
local_files_only=local_files_only,
|
|
token=token,
|
|
revision=revision,
|
|
subfolder="",
|
|
_from_auto=False,
|
|
_from_pipeline=None,
|
|
**kwargs,
|
|
)
|
|
except (OSError, TypeError):
|
|
logger.info(
|
|
"Generation config file not found, using a generation config created from the model config."
|
|
)
|
|
pass
|
|
|
|
if device_map is not None:
|
|
dispatch_model(model, device_map=device_map)
|
|
|
|
return model
|
|
|
|
return super(BaichuanForCausalLM, cls).from_pretrained(pretrained_model_name_or_path, *model_args,
|
|
config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes,
|
|
force_download=force_download, local_files_only=local_files_only, token=token, revision=revision,
|
|
use_safetensors=use_safetensors, **kwargs)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = 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] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
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,
|
|
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)
|
|
softmax_normalizer = shift_logits.max(-1).values ** 2
|
|
z_loss = self.config.z_loss_weight * softmax_normalizer.mean()
|
|
# Enable model parallelism
|
|
shift_labels = shift_labels.to(shift_logits.device)
|
|
loss = loss_fct(shift_logits, shift_labels) + z_loss
|
|
|
|
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 quantize(self, bits: int):
|
|
try:
|
|
from .quantizer import quantize_online
|
|
except ImportError:
|
|
raise ImportError(f"Needs QLinear to run quantize.")
|
|
return quantize_online(self, bits)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids: torch.LongTensor,
|
|
past_key_values: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
):
|
|
if past_key_values:
|
|
input_ids = input_ids[:, -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(
|
|
{
|
|
"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):
|
|
return tuple(
|
|
tuple(past_state.index_select(0, beam_idx) for past_state in layer_past)
|
|
for layer_past in past_key_values
|
|
)
|
|
|
|
def _build_chat_input(
|
|
self, tokenizer, messages: List[dict], max_new_tokens: int = 0
|
|
):
|
|
max_new_tokens = max_new_tokens or self.generation_config.max_new_tokens
|
|
max_input_tokens = self.config.model_max_length - max_new_tokens
|
|
max_input_tokens = max(self.config.model_max_length // 2, max_input_tokens)
|
|
total_input, round_input = [], []
|
|
for i, message in enumerate(messages[::-1]):
|
|
content_tokens = tokenizer.encode(message["content"])
|
|
if message["role"] == "user":
|
|
round_input = (
|
|
[self.generation_config.user_token_id]
|
|
+ content_tokens
|
|
+ round_input
|
|
)
|
|
if (
|
|
total_input
|
|
and len(total_input) + len(round_input) > max_input_tokens
|
|
):
|
|
break
|
|
else:
|
|
total_input = round_input + total_input
|
|
if len(total_input) >= max_input_tokens:
|
|
break
|
|
else:
|
|
round_input = []
|
|
elif message["role"] == "assistant":
|
|
round_input = (
|
|
[self.generation_config.assistant_token_id]
|
|
+ content_tokens
|
|
+ [self.generation_config.eos_token_id]
|
|
+ round_input
|
|
)
|
|
else:
|
|
raise ValueError(f"message role not supported yet: {message['role']}")
|
|
total_input = total_input[-max_input_tokens:] # truncate left
|
|
total_input.append(self.generation_config.assistant_token_id)
|
|
total_input = torch.LongTensor([total_input]).to(self.device)
|
|
return total_input
|
|
|
|
def chat(self, tokenizer, messages: List[dict], stream=False,
|
|
generation_config: Optional[GenerationConfig]=None):
|
|
generation_config = generation_config or self.generation_config
|
|
input_ids = build_chat_input(self, tokenizer, messages, generation_config.max_new_tokens)
|
|
if stream:
|
|
streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
|
|
Thread(target=self.generate, kwargs=dict(
|
|
inputs=input_ids, streamer=streamer,
|
|
generation_config=generation_config,
|
|
)).start()
|
|
return streamer
|
|
else:
|
|
outputs = self.generate(input_ids, generation_config=generation_config)
|
|
response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
|
|
return response
|