Baichuan2-7B-Chat-4bits / modeling_baichuan.py

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	# Copyright 2023 Baichuan Inc. All Rights Reserved.

	# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
	#
	# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
	# and OPT implementations in this library. It has been modified from its
	# original forms to accommodate minor architectural differences compared
	# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


	from .configuration_baichuan import BaichuanConfig
	from .generation_utils import build_chat_input, TextIterStreamer

	import math
	from typing import List, Optional, Tuple, Union
	from threading import Thread

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
	from torch.nn import functional as F
	from transformers import PreTrainedModel, PretrainedConfig
	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
	from transformers.generation.utils import GenerationConfig
	from transformers.utils import logging, ContextManagers

	import os
	from contextlib import contextmanager
	logger = logging.get_logger(__name__)

	try:
	from xformers import ops as xops
	except ImportError:
	xops = None
	logger.warning(
	"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."
	)


	# Copied from transformers.models.bart.modeling_bart._make_causal_mask
	def _make_causal_mask(
	input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
	):
	"""
	Make causal mask used for bi-directional self-attention.
	"""
	bsz, tgt_len = input_ids_shape
	mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
	mask_cond = torch.arange(mask.size(-1), device=device)
	mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
	mask = mask.to(dtype)

	if past_key_values_length > 0:
	mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
	return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)

	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	if len(mask.size()) == 3:
	bsz, src_len, _ = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len
	expanded_mask = mask[:,None,:,:].expand(bsz, 1, tgt_len, src_len).to(dtype)
	else:
	bsz, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len
	expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

	inverted_mask = 1.0 - expanded_mask

	return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


	class RMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	RMSNorm is equivalent to T5LayerNorm
	"""
	super().__init__()
	self.weight = nn.Parameter(torch.ones(hidden_size))
	self.variance_epsilon = eps

	def forward(self, hidden_states):
	variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
	hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

	# convert into half-precision if necessary
	if self.weight.dtype in [torch.float16, torch.bfloat16]:
	hidden_states = hidden_states.to(self.weight.dtype)

	return self.weight * hidden_states


	class RotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
	super().__init__()
	self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
	self.max_seq_len_cached = max_position_embeddings
	t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
	freqs = torch.outer(t, self.inv_freq)
	emb = torch.cat((freqs, freqs), dim=-1)
	self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32)
	self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32)
	def forward(self, x, seq_len=None):
	# x: [bs, num_attention_heads, seq_len, head_size]
	# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
	if seq_len > self.max_seq_len_cached:
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
	freqs = torch.outer(t, self.inv_freq)
	emb = torch.cat((freqs, freqs), dim=-1)
	self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32).to(x.device)
	self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32).to(x.device)
	elif self.cos_cached.device != x.device:
	self.cos_cached = self.cos_cached.to(x.device)
	self.sin_cached = self.sin_cached.to(x.device)
	return (
	self.cos_cached[:, :, :seq_len, ...],
	self.sin_cached[:, :, :seq_len, ...],
	)


	def rotate_half(x):
	"""Rotates half the hidden dims of the input."""
	x1 = x[..., : x.shape[-1] // 2]
	x2 = x[..., x.shape[-1] // 2:]
	return torch.cat((-x2, x1), dim=-1)


	def apply_rotary_pos_emb(q, k, cos_, sin_, position_ids):
	cos = cos_.squeeze(1).squeeze(0) # [seq_len, dim]
	sin = sin_.squeeze(1).squeeze(0) # [seq_len, dim]
	cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
	sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
	q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin)
	k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin)
	return q_embed.to(q.dtype), k_embed.to(k.dtype)


	class MLP(nn.Module):
	def __init__(
	self,
	hidden_size: int,
	intermediate_size: int,
	hidden_act: str,
	):
	super().__init__()
	self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
	self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
	self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
	self.act_fn = ACT2FN[hidden_act]

	def forward(self, x):
	return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


	class Attention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""
	def __init__(self, config: BaichuanConfig):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.hidden_size // self.num_heads
	self.max_position_embeddings = config.max_position_embeddings

	if (self.head_dim * self.num_heads) != self.hidden_size:
	raise ValueError(
	f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
	f" and `num_heads`: {self.num_heads})."
	)
	self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
	self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
	self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)

	def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	bsz, q_len, _ = hidden_states.size()

	proj = self.W_pack(hidden_states)
	proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
	query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

	kv_seq_len = key_states.shape[-2]
	if past_key_value is not None:
	kv_seq_len += past_key_value[0].shape[-2]
	cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
	query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
	# [bsz, nh, t, hd]

	if past_key_value is not None:
	# reuse k, v, self_attention
	key_states = torch.cat([past_key_value[0], key_states], dim=2)
	value_states = torch.cat([past_key_value[1], value_states], dim=2)

	past_key_value = (key_states, value_states) if use_cache else None
	if xops is not None and self.training:
	attn_weights = None
	query_states = query_states.transpose(1, 2)
	key_states = key_states.transpose(1, 2)
	value_states = value_states.transpose(1, 2)
	attn_output = xops.memory_efficient_attention(
	query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask()
	)
	else:
	with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
	attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
	attn_output = attn_output.transpose(1, 2)
	attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
	attn_output = self.o_proj(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value


	class DecoderLayer(nn.Module):
	def __init__(self, config: BaichuanConfig):
	super().__init__()
	self.hidden_size = config.hidden_size
	self.self_attn = Attention(config=config)
	self.mlp = MLP(
	hidden_size=self.hidden_size,
	intermediate_size=config.intermediate_size,
	hidden_act=config.hidden_act,
	)
	self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:

	residual = hidden_states

	hidden_states = self.input_layernorm(hidden_states)

	# Self Attention
	hidden_states, self_attn_weights, present_key_value = self.self_attn(
	hidden_states=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 = residual + hidden_states

	# Fully Connected
	residual = hidden_states
	hidden_states = self.post_attention_layernorm(hidden_states)
	hidden_states = self.mlp(hidden_states)
	hidden_states = residual + hidden_states

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (present_key_value,)

	return outputs


	class BaichuanPreTrainedModel(PreTrainedModel):
	config_class = BaichuanConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["DecoderLayer"]
	_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

	def _init_weights(self, module):
	std = self.config.initializer_range
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, BaichuanModel):
	module.gradient_checkpointing = value


	class BaichuanModel(BaichuanPreTrainedModel):
	def __init__(self, config: BaichuanConfig):
	super().__init__(config)
	self.padding_idx = config.pad_token_id
	self.vocab_size = config.vocab_size

	self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
	self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.embed_tokens

	def set_input_embeddings(self, value):
	self.embed_tokens = value

	# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
	def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
	# create causal mask
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	combined_attention_mask = None
	if input_shape[-1] > 1:
	combined_attention_mask = _make_causal_mask(
	input_shape,
	inputs_embeds.dtype,
	device=inputs_embeds.device,
	past_key_values_length=past_key_values_length,
	)

	if attention_mask is not None:
	# [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 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)
	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']:
	try:
	from .quantizer import quantize_offline, init_model_weight_int4
	except ImportError:
	raise ImportError(f"Needs QLinear 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,
	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]:

	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)
	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 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 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 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