chatglm-6b-int8 / modeling_chatglm.py

Sync with chatglm-6b

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	""" PyTorch ChatGLM model. """

	import math
	import copy
	import os
	import warnings
	import re
	import sys

	import torch
	import torch.utils.checkpoint
	import torch.nn.functional as F
	from torch import nn
	from torch.nn import CrossEntropyLoss, LayerNorm
	from torch.nn.utils import skip_init
	from typing import Optional, Tuple, Union, List, Callable, Dict, Any

	from transformers.utils import (
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	)
	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	BaseModelOutputWithPastAndCrossAttentions,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import logging
	from transformers.generation.logits_process import LogitsProcessor
	from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput

	from .configuration_chatglm import ChatGLMConfig


	# flags required to enable jit fusion kernels

	if sys.platform != 'darwin':
	torch._C._jit_set_profiling_mode(False)
	torch._C._jit_set_profiling_executor(False)
	torch._C._jit_override_can_fuse_on_cpu(True)
	torch._C._jit_override_can_fuse_on_gpu(True)

	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM-6B"
	_CONFIG_FOR_DOC = "ChatGLM6BConfig"

	CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"THUDM/chatglm-6b",
	# See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm
	]


	class InvalidScoreLogitsProcessor(LogitsProcessor):
	def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
	if torch.isnan(scores).any() or torch.isinf(scores).any():
	scores.zero_()
	scores[..., 5] = 5e4
	return scores


	def load_tf_weights_in_chatglm_6b(model, config, tf_checkpoint_path):
	"""Load tf checkpoints in a pytorch model."""
	try:
	import re

	import numpy as np
	import tensorflow as tf
	except ImportError:
	logger.error(
	"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
	"https://www.tensorflow.org/install/ for installation instructions."
	)
	raise
	tf_path = os.path.abspath(tf_checkpoint_path)
	logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
	# Load weights from TF model
	init_vars = tf.train.list_variables(tf_path)
	names = []
	arrays = []
	for name, shape in init_vars:
	logger.info(f"Loading TF weight {name} with shape {shape}")
	array = tf.train.load_variable(tf_path, name)
	names.append(name)
	arrays.append(array)

	for name, array in zip(names, arrays):
	name = name.split("/")
	# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
	# which are not required for using pretrained model
	if any(
	n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
	for n in name
	):
	logger.info(f"Skipping {'/'.join(name)}")
	continue
	pointer = model
	for m_name in name:
	if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
	scope_names = re.split(r"_(\d+)", m_name)
	else:
	scope_names = [m_name]
	if scope_names[0] == "kernel" or scope_names[0] == "gamma":
	pointer = getattr(pointer, "weight")
	elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
	pointer = getattr(pointer, "bias")
	elif scope_names[0] == "output_weights":
	pointer = getattr(pointer, "weight")
	elif scope_names[0] == "squad":
	pointer = getattr(pointer, "classifier")
	else:
	try:
	pointer = getattr(pointer, scope_names[0])
	except AttributeError:
	logger.info(f"Skipping {'/'.join(name)}")
	continue
	if len(scope_names) >= 2:
	num = int(scope_names[1])
	pointer = pointer[num]
	if m_name[-11:] == "_embeddings":
	pointer = getattr(pointer, "weight")
	elif m_name == "kernel":
	array = np.transpose(array)
	try:
	assert (
	pointer.shape == array.shape
	), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
	except AssertionError as e:
	e.args += (pointer.shape, array.shape)
	raise
	logger.info(f"Initialize PyTorch weight {name}")
	pointer.data = torch.from_numpy(array)
	return model


	class PrefixEncoder(torch.nn.Module):
	"""
	The torch.nn model to encode the prefix
	Input shape: (batch-size, prefix-length)
	Output shape: (batch-size, prefix-length, 2layershidden)
	"""

	def __init__(self, config):
	super().__init__()
	self.prefix_projection = config.prefix_projection
	if self.prefix_projection:
	# Use a two-layer MLP to encode the prefix
	self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size)
	self.trans = torch.nn.Sequential(
	torch.nn.Linear(config.hidden_size, config.hidden_size),
	torch.nn.Tanh(),
	torch.nn.Linear(config.hidden_size, config.num_layers * config.hidden_size * 2)
	)
	else:
	self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.hidden_size * 2)

	def forward(self, prefix: torch.Tensor):
	if self.prefix_projection:
	prefix_tokens = self.embedding(prefix)
	past_key_values = self.trans(prefix_tokens)
	else:
	past_key_values = self.embedding(prefix)
	return past_key_values


	@torch.jit.script
	def gelu_impl(x):
	"""OpenAI's gelu implementation."""
	return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
	(1.0 + 0.044715 * x * x)))


	def gelu(x):
	return gelu_impl(x)


	class RotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, base=10000, precision=torch.half, learnable=False):
	super().__init__()
	inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
	inv_freq = inv_freq.half()
	self.learnable = learnable
	if learnable:
	self.inv_freq = torch.nn.Parameter(inv_freq)
	self.max_seq_len_cached = None
	else:
	self.register_buffer('inv_freq', inv_freq)
	self.max_seq_len_cached = None
	self.cos_cached = None
	self.sin_cached = None
	self.precision = precision

	def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
	error_msgs):
	pass

	def forward(self, x, seq_dim=1, seq_len=None):
	if seq_len is None:
	seq_len = x.shape[seq_dim]
	if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached):
	self.max_seq_len_cached = None if self.learnable else seq_len
	t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
	freqs = torch.einsum('i,j->ij', t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
	if self.precision == torch.bfloat16:
	emb = emb.float()

	# [sx, 1 (b * np), hn]
	cos_cached = emb.cos()[:, None, :]
	sin_cached = emb.sin()[:, None, :]
	if self.precision == torch.bfloat16:
	cos_cached = cos_cached.bfloat16()
	sin_cached = sin_cached.bfloat16()
	if self.learnable:
	return cos_cached, sin_cached
	self.cos_cached, self.sin_cached = cos_cached, sin_cached
	return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]

	def _apply(self, fn):
	if self.cos_cached is not None:
	self.cos_cached = fn(self.cos_cached)
	if self.sin_cached is not None:
	self.sin_cached = fn(self.sin_cached)
	return super()._apply(fn)

	def rotate_half(x):
	x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
	return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions


	@torch.jit.script
	def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
	# position_id: [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn]
	cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), \
	F.embedding(position_id, sin.squeeze(1)).unsqueeze(2)
	q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
	return q, k


	def attention_fn(
	self,
	query_layer,
	key_layer,
	value_layer,
	attention_mask,
	hidden_size_per_partition,
	layer_id,
	layer_past=None,
	scaling_attention_score=True,
	use_cache=False,
	):
	if layer_past is not None:
	past_key, past_value = layer_past[0], layer_past[1]
	key_layer = torch.cat((past_key, key_layer), dim=0)
	value_layer = torch.cat((past_value, value_layer), dim=0)

	# seqlen, batch, num_attention_heads, hidden_size_per_attention_head
	seq_len, b, nh, hidden_size = key_layer.shape

	if use_cache:
	present = (key_layer, value_layer)
	else:
	present = None

	query_key_layer_scaling_coeff = float(layer_id + 1)
	if scaling_attention_score:
	query_layer = query_layer / (math.sqrt(hidden_size) * query_key_layer_scaling_coeff)

	# ===================================
	# Raw attention scores. [b, np, s, s]
	# ===================================

	# [b, np, sq, sk]
	output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))

	# [sq, b, np, hn] -> [sq, b * np, hn]
	query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
	# [sk, b, np, hn] -> [sk, b * np, hn]
	key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)

	matmul_result = torch.zeros(
	1, 1, 1,
	dtype=query_layer.dtype,
	device=query_layer.device,
	)

	matmul_result = torch.baddbmm(
	matmul_result,
	query_layer.transpose(0, 1), # [b * np, sq, hn]
	key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
	beta=0.0,
	alpha=1.0,
	)

	# change view to [b, np, sq, sk]
	attention_scores = matmul_result.view(*output_size)

	if self.scale_mask_softmax:
	self.scale_mask_softmax.scale = query_key_layer_scaling_coeff
	attention_probs = self.scale_mask_softmax(attention_scores, attention_mask.contiguous())
	else:
	if not (attention_mask == 0).all():
	# if auto-regressive, skip
	attention_scores.masked_fill_(attention_mask, -10000.0)
	dtype = attention_scores.dtype
	attention_scores = attention_scores.float()
	attention_scores = attention_scores * query_key_layer_scaling_coeff

	attention_probs = F.softmax(attention_scores, dim=-1)

	attention_probs = attention_probs.type(dtype)

	# =========================
	# Context layer. [sq, b, hp]
	# =========================

	# value_layer -> context layer.
	# [sk, b, np, hn] --> [b, np, sq, hn]

	# context layer shape: [b, np, sq, hn]
	output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))

	# change view [sk, b * np, hn]
	value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)

	# change view [b * np, sq, sk]
	attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)

	# matmul: [b * np, sq, hn]
	context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))

	# change view [b, np, sq, hn]
	context_layer = context_layer.view(*output_size)

	# [b, np, sq, hn] --> [sq, b, np, hn]
	context_layer = context_layer.permute(2, 0, 1, 3).contiguous()

	# [sq, b, np, hn] --> [sq, b, hp]
	new_context_layer_shape = context_layer.size()[:-2] + (hidden_size_per_partition,)
	context_layer = context_layer.view(*new_context_layer_shape)

	outputs = (context_layer, present, attention_probs)

	return outputs


	def default_init(cls, args, *kwargs):
	return cls(args, *kwargs)


	class SelfAttention(torch.nn.Module):
	def __init__(self, hidden_size, num_attention_heads,
	layer_id, hidden_size_per_attention_head=None, bias=True,
	params_dtype=torch.float, position_encoding_2d=True, empty_init=True):
	if empty_init:
	init_method = skip_init
	else:
	init_method = default_init
	super(SelfAttention, self).__init__()

	self.layer_id = layer_id
	self.hidden_size = hidden_size
	self.hidden_size_per_partition = hidden_size
	self.num_attention_heads = num_attention_heads
	self.num_attention_heads_per_partition = num_attention_heads
	self.position_encoding_2d = position_encoding_2d
	self.rotary_emb = RotaryEmbedding(
	self.hidden_size // (self.num_attention_heads * 2)
	if position_encoding_2d
	else self.hidden_size // self.num_attention_heads,
	base=10000,
	precision=torch.half,
	learnable=False,
	)

	self.scale_mask_softmax = None

	if hidden_size_per_attention_head is None:
	self.hidden_size_per_attention_head = hidden_size // num_attention_heads
	else:
	self.hidden_size_per_attention_head = hidden_size_per_attention_head

	self.inner_hidden_size = num_attention_heads * self.hidden_size_per_attention_head

	# Strided linear layer.
	self.query_key_value = init_method(
	torch.nn.Linear,
	hidden_size,
	3 * self.inner_hidden_size,
	bias=bias,
	dtype=params_dtype,
	)

	self.dense = init_method(
	torch.nn.Linear,
	self.inner_hidden_size,
	hidden_size,
	bias=bias,
	dtype=params_dtype,
	)

	@staticmethod
	def attention_mask_func(attention_scores, attention_mask):
	attention_scores.masked_fill_(attention_mask, -10000.0)
	return attention_scores

	def split_tensor_along_last_dim(self, tensor, num_partitions,
	contiguous_split_chunks=False):
	"""Split a tensor along its last dimension.
	Arguments:
	tensor: input tensor.
	num_partitions: number of partitions to split the tensor
	contiguous_split_chunks: If True, make each chunk contiguous
	in memory.
	"""
	# Get the size and dimension.
	last_dim = tensor.dim() - 1
	last_dim_size = tensor.size()[last_dim] // num_partitions
	# Split.
	tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
	# Note: torch.split does not create contiguous tensors by default.
	if contiguous_split_chunks:
	return tuple(chunk.contiguous() for chunk in tensor_list)

	return tensor_list

	def forward(
	self,
	hidden_states: torch.Tensor,
	position_ids,
	attention_mask: torch.Tensor,
	layer_id,
	layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
	use_cache: bool = False,
	output_attentions: bool = False,
	):
	"""
	hidden_states: [seq_len, batch, hidden_size]
	attention_mask: [(1, 1), seq_len, seq_len]
	"""

	# [seq_len, batch, 3 * hidden_size]
	mixed_raw_layer = self.query_key_value(hidden_states)

	# [seq_len, batch, 3 * hidden_size] --> [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head]
	new_tensor_shape = mixed_raw_layer.size()[:-1] + (
	self.num_attention_heads_per_partition,
	3 * self.hidden_size_per_attention_head,
	)
	mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape)

	# [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
	(query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3)

	if self.position_encoding_2d:
	q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1))
	k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1))
	cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1)
	position_ids, block_position_ids = position_ids[:, 0, :].transpose(0, 1).contiguous(), \
	position_ids[:, 1, :].transpose(0, 1).contiguous()
	q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids)
	q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids)
	query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1))
	key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1))
	else:
	position_ids = position_ids.transpose(0, 1)
	cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1)
	# [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
	query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids)

	# [seq_len, batch, hidden_size]
	context_layer, present, attention_probs = attention_fn(
	self=self,
	query_layer=query_layer,
	key_layer=key_layer,
	value_layer=value_layer,
	attention_mask=attention_mask,
	hidden_size_per_partition=self.hidden_size_per_partition,
	layer_id=layer_id,
	layer_past=layer_past,
	use_cache=use_cache
	)

	output = self.dense(context_layer)

	outputs = (output, present)

	if output_attentions:
	outputs += (attention_probs,)

	return outputs # output, present, attention_probs


	class GEGLU(torch.nn.Module):
	def __init__(self):
	super().__init__()
	self.activation_fn = F.gelu

	def forward(self, x):
	# dim=-1 breaks in jit for pt<1.10
	x1, x2 = x.chunk(2, dim=(x.ndim - 1))
	return x1 * self.activation_fn(x2)


	class GLU(torch.nn.Module):
	def __init__(self, hidden_size, inner_hidden_size=None,
	layer_id=None, bias=True, activation_func=gelu, params_dtype=torch.float, empty_init=True):
	super(GLU, self).__init__()
	if empty_init:
	init_method = skip_init
	else:
	init_method = default_init
	self.layer_id = layer_id
	self.activation_func = activation_func

	# Project to 4h.
	self.hidden_size = hidden_size
	if inner_hidden_size is None:
	inner_hidden_size = 4 * hidden_size
	self.inner_hidden_size = inner_hidden_size
	self.dense_h_to_4h = init_method(
	torch.nn.Linear,
	self.hidden_size,
	self.inner_hidden_size,
	bias=bias,
	dtype=params_dtype,
	)
	# Project back to h.
	self.dense_4h_to_h = init_method(
	torch.nn.Linear,
	self.inner_hidden_size,
	self.hidden_size,
	bias=bias,
	dtype=params_dtype,
	)

	def forward(self, hidden_states):
	"""
	hidden_states: [seq_len, batch, hidden_size]
	"""

	# [seq_len, batch, inner_hidden_size]
	intermediate_parallel = self.dense_h_to_4h(hidden_states)

	intermediate_parallel = self.activation_func(intermediate_parallel)

	output = self.dense_4h_to_h(intermediate_parallel)

	return output


	class GLMBlock(torch.nn.Module):
	def __init__(
	self,
	hidden_size,
	num_attention_heads,
	layernorm_epsilon,
	layer_id,
	inner_hidden_size=None,
	hidden_size_per_attention_head=None,
	layernorm=LayerNorm,
	use_bias=True,
	params_dtype=torch.float,
	num_layers=28,
	position_encoding_2d=True,
	empty_init=True
	):
	super(GLMBlock, self).__init__()
	# Set output layer initialization if not provided.

	self.layer_id = layer_id

	# Layernorm on the input data.
	self.input_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)

	self.position_encoding_2d = position_encoding_2d

	# Self attention.
	self.attention = SelfAttention(
	hidden_size,
	num_attention_heads,
	layer_id,
	hidden_size_per_attention_head=hidden_size_per_attention_head,
	bias=use_bias,
	params_dtype=params_dtype,
	position_encoding_2d=self.position_encoding_2d,
	empty_init=empty_init
	)

	# Layernorm on the input data.
	self.post_attention_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)

	self.num_layers = num_layers

	# GLU
	self.mlp = GLU(
	hidden_size,
	inner_hidden_size=inner_hidden_size,
	bias=use_bias,
	layer_id=layer_id,
	params_dtype=params_dtype,
	empty_init=empty_init
	)

	def forward(
	self,
	hidden_states: torch.Tensor,
	position_ids,
	attention_mask: torch.Tensor,
	layer_id,
	layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
	use_cache: bool = False,
	output_attentions: bool = False,
	):
	"""
	hidden_states: [seq_len, batch, hidden_size]
	attention_mask: [(1, 1), seq_len, seq_len]
	"""

	# Layer norm at the begining of the transformer layer.
	# [seq_len, batch, hidden_size]
	attention_input = self.input_layernorm(hidden_states)

	# Self attention.
	attention_outputs = self.attention(
	attention_input,
	position_ids,
	attention_mask=attention_mask,
	layer_id=layer_id,
	layer_past=layer_past,
	use_cache=use_cache,
	output_attentions=output_attentions
	)

	attention_output = attention_outputs[0]

	outputs = attention_outputs[1:]

	# Residual connection.
	alpha = (2 * self.num_layers) ** 0.5
	hidden_states = attention_input * alpha + attention_output

	mlp_input = self.post_attention_layernorm(hidden_states)

	# MLP.
	mlp_output = self.mlp(mlp_input)

	# Second residual connection.
	output = mlp_input * alpha + mlp_output

	if use_cache:
	outputs = (output,) + outputs
	else:
	outputs = (output,) + outputs[1:]

	return outputs # hidden_states, present, attentions


	class ChatGLMPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and
	a simple interface for downloading and loading pretrained models.
	"""

	is_parallelizable = False
	supports_gradient_checkpointing = True
	config_class = ChatGLMConfig
	base_model_prefix = "transformer"
	_no_split_modules = ["GLMBlock"]

	def __init__(self, inputs, *kwargs):
	super().__init__(inputs, *kwargs)

	def _init_weights(self, module: nn.Module):
	"""Initialize the weights."""
	return

	def get_masks(self, input_ids, device):
	batch_size, seq_length = input_ids.shape
	context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
	attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device)
	attention_mask.tril_()
	for i, context_length in enumerate(context_lengths):
	attention_mask[i, :, :context_length] = 1
	attention_mask.unsqueeze_(1)
	attention_mask = (attention_mask < 0.5).bool()

	return attention_mask

	def get_position_ids(self, input_ids, mask_positions, device, use_gmasks=None):
	batch_size, seq_length = input_ids.shape
	if use_gmasks is None:
	use_gmasks = [False] * batch_size
	context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
	if self.position_encoding_2d:
	position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
	for i, context_length in enumerate(context_lengths):
	position_ids[i, context_length:] = mask_positions[i]
	block_position_ids = [torch.cat((
	torch.zeros(context_length, dtype=torch.long, device=device),
	torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1
	)) for context_length in context_lengths]
	block_position_ids = torch.stack(block_position_ids, dim=0)
	position_ids = torch.stack((position_ids, block_position_ids), dim=1)
	else:
	position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
	for i, context_length in enumerate(context_lengths):
	if not use_gmasks[i]:
	position_ids[context_length:] = mask_positions[i]

	return position_ids

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


	CHATGLM_6B_START_DOCSTRING = r"""
	This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
	usage and behavior.

	Parameters:
	config ([`~ChatGLM6BConfig`]): Model configuration class with all the parameters of the model.
	Initializing with a config file does not load the weights associated with the model, only the configuration.
	Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
	"""

	CHATGLM_6B_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `({0})`):
	Indices of input sequence tokens in the vocabulary.

	Indices can be obtained using [`ChatGLM6BTokenizer`].
	See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.FloatTensor` of shape `({0})`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	token_type_ids (`torch.LongTensor` of shape `({0})`, optional):
	Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:

	- 0 corresponds to a sentence A token,
	- 1 corresponds to a sentence B token.

	[What are token type IDs?](../glossary#token-type-ids)
	position_ids (`torch.LongTensor` of shape `({0})`, optional):
	Indices of positions of each input sequence tokens in the position embeddings.
	Selected in the range `[0, config.max_position_embeddings - 1]`.

	[What are position IDs?](../glossary#position-ids)
	head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional):
	Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert input_ids indices into associated vectors
	than the model's internal embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
	tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
	more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""


	@add_start_docstrings(
	"The bare ChatGLM-6B Model transformer outputting raw hidden-states without any specific head on top.",
	CHATGLM_6B_START_DOCSTRING,
	)
	class ChatGLMModel(ChatGLMPreTrainedModel):
	"""

	The model can behave as an encoder (with only self-attention) as well
	as a decoder, in which case a layer of cross-attention is added between
	the self-attention layers, following the architecture described in [Attention is
	all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani,
	Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

	To behave as an decoder the model needs to be initialized with the
	`is_decoder` argument of the configuration set to `True`.
	To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder`
	argument and `add_cross_attention` set to `True`; an
	`encoder_hidden_states` is then expected as an input to the forward pass.
	"""

	def __init__(self, config: ChatGLMConfig, empty_init=True):
	super().__init__(config)
	if empty_init:
	init_method = skip_init
	else:
	init_method = default_init
	# recording parameters
	self.max_sequence_length = config.max_sequence_length
	self.hidden_size = config.hidden_size
	self.params_dtype = torch.half
	self.num_attention_heads = config.num_attention_heads
	self.vocab_size = config.vocab_size
	self.num_layers = config.num_layers
	self.layernorm_epsilon = config.layernorm_epsilon
	self.inner_hidden_size = config.inner_hidden_size
	self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads
	self.position_encoding_2d = config.position_encoding_2d
	self.pre_seq_len = config.pre_seq_len
	self.prefix_projection = config.prefix_projection

	self.word_embeddings = init_method(
	torch.nn.Embedding,
	num_embeddings=self.vocab_size, embedding_dim=self.hidden_size,
	dtype=self.params_dtype
	)
	self.gradient_checkpointing = False

	def get_layer(layer_id):
	return GLMBlock(
	self.hidden_size,
	self.num_attention_heads,
	self.layernorm_epsilon,
	layer_id,
	inner_hidden_size=self.inner_hidden_size,
	hidden_size_per_attention_head=self.hidden_size_per_attention_head,
	layernorm=LayerNorm,
	use_bias=True,
	params_dtype=self.params_dtype,
	position_encoding_2d=self.position_encoding_2d,
	empty_init=empty_init
	)

	self.layers = torch.nn.ModuleList(
	[get_layer(layer_id) for layer_id in range(self.num_layers)]
	)

	# Final layer norm before output.
	self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon)

	if self.pre_seq_len is not None:
	for param in self.parameters():
	param.requires_grad = False
	self.prefix_tokens = torch.arange(self.pre_seq_len).long()
	self.prefix_encoder = PrefixEncoder(config)
	self.dropout = torch.nn.Dropout(0.1)

	# total_params = sum(p.numel() for p in self.parameters())
	# trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
	# print("Using p-tuning v2: # trainable_params = {} / {}".format(trainable_params, total_params))

	def get_input_embeddings(self):
	return self.word_embeddings

	def set_input_embeddings(self, new_embeddings: torch.Tensor):
	self.word_embeddings = new_embeddings

	def get_prompt(self, batch_size, device, dtype=torch.half):
	prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
	past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
	past_key_values = past_key_values.view(
	batch_size,
	self.pre_seq_len,
	self.num_layers * 2,
	self.num_attention_heads,
	self.hidden_size // self.num_attention_heads
	)
	# seq_len, b, nh, hidden_size
	past_key_values = self.dropout(past_key_values)
	past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
	# past_key_values = [(v[0], v[1]) for v in past_key_values]
	return past_key_values

	@add_start_docstrings_to_model_forward(CHATGLM_6B_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=BaseModelOutputWithPastAndCrossAttentions,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
	inputs_embeds: 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[torch.Tensor, ...], 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

	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

	if input_ids is not None and inputs_embeds is not None:
	raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
	elif input_ids is not None:
	batch_size, seq_length = input_ids.shape[:2]
	elif inputs_embeds is not None:
	batch_size, seq_length = inputs_embeds.shape[:2]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	if inputs_embeds is None:
	inputs_embeds = self.word_embeddings(input_ids)

	if past_key_values is None:
	if self.pre_seq_len is not None:
	past_key_values = self.get_prompt(batch_size=input_ids.shape[0], device=input_ids.device,
	dtype=inputs_embeds.dtype)
	else:
	past_key_values = tuple([None] * len(self.layers))

	if attention_mask is None:
	attention_mask = self.get_masks(
	input_ids,
	device=input_ids.device
	)


	if position_ids is None:
	MASK, gMASK = self.config.mask_token_id, self.config.gmask_token_id
	seqs = input_ids.tolist()

	mask_positions, use_gmasks = [], []
	for seq in seqs:
	mask_token = gMASK if gMASK in seq else MASK
	use_gmask = mask_token == gMASK
	mask_positions.append(seq.index(mask_token))
	use_gmasks.append(use_gmask)

	position_ids = self.get_position_ids(
	input_ids,
	mask_positions=mask_positions,
	device=input_ids.device,
	use_gmasks=use_gmasks
	)

	if self.pre_seq_len is not None and attention_mask is not None:
	prefix_attention_mask = torch.ones(batch_size, 1, input_ids.size(-1), self.pre_seq_len).to(
	attention_mask.device)
	prefix_attention_mask = (prefix_attention_mask < 0.5).bool()
	attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=3)

	# [seq_len, batch, hidden_size]
	hidden_states = inputs_embeds.transpose(0, 1)

	presents = () if use_cache else None
	all_self_attentions = () if output_attentions else None
	all_hidden_states = () if output_hidden_states else None

	if attention_mask is None:
	attention_mask = torch.zeros(1, 1, device=input_ids.device).bool()
	else:
	attention_mask = attention_mask.to(hidden_states.device)

	for i, layer in enumerate(self.layers):

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)
	layer_past = past_key_values[i]

	if self.gradient_checkpointing and self.training:
	layer_ret = torch.utils.checkpoint.checkpoint(
	layer,
	hidden_states,
	position_ids,
	attention_mask,
	torch.tensor(i),
	layer_past,
	use_cache,
	output_attentions
	)
	else:
	layer_ret = layer(
	hidden_states,
	position_ids=position_ids,
	attention_mask=attention_mask,
	layer_id=torch.tensor(i),
	layer_past=layer_past,
	use_cache=use_cache,
	output_attentions=output_attentions
	)

	hidden_states = layer_ret[0]

	if use_cache:
	presents = presents + (layer_ret[1],)

	if output_attentions:
	all_self_attentions = all_self_attentions + (layer_ret[2 if use_cache else 1],)

	# Final layer norm.
	hidden_states = self.final_layernorm(hidden_states)

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)

	return BaseModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=presents,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	)


	class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
	def __init__(self, config: ChatGLMConfig, empty_init=True):
	super().__init__(config)
	if empty_init:
	init_method = skip_init
	else:
	init_method = default_init

	# self.hidden_size = config.hidden_size
	# self.params_dtype = torch.half
	# self.vocab_size = config.vocab_size
	self.max_sequence_length = config.max_sequence_length

	self.position_encoding_2d = config.position_encoding_2d

	self.transformer = ChatGLMModel(config, empty_init=empty_init)

	self.lm_head = init_method(
	nn.Linear,
	config.hidden_size,
	config.vocab_size,
	bias=False,
	dtype=torch.half
	)

	self.config = config

	self.quantized = False

	if self.config.quantization_bit:
	self.quantize(self.config.quantization_bit, self.config.quantization_embeddings, use_quantization_cache=True, empty_init=True)

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	def _update_model_kwargs_for_generation(
	self,
	outputs: ModelOutput,
	model_kwargs: Dict[str, Any],
	is_encoder_decoder: bool = False,
	standardize_cache_format: bool = False,
	) -> Dict[str, Any]:
	# update past_key_values
	model_kwargs["past_key_values"] = self._extract_past_from_model_output(
	outputs, standardize_cache_format=standardize_cache_format
	)

	# update attention mask
	if "attention_mask" in model_kwargs:
	attention_mask = model_kwargs["attention_mask"]
	if attention_mask is not None and attention_mask.dtype == torch.bool:
	attention_mask = torch.cat(
	[attention_mask, attention_mask.new_ones((*attention_mask.shape[:3], 1))], dim=3)
	new_attention_mask = attention_mask[:, :, -1:].clone()
	new_attention_mask[..., -1] = False
	model_kwargs["attention_mask"] = torch.cat(
	[attention_mask, new_attention_mask], dim=2
	)

	# update position ids
	if "position_ids" in model_kwargs:
	position_ids = model_kwargs["position_ids"]
	new_position_id = position_ids[..., -1:].clone()
	new_position_id[:, 1, :] += 1
	model_kwargs["position_ids"] = torch.cat(
	[position_ids, new_position_id], dim=-1
	)

	return model_kwargs

	def prepare_inputs_for_generation(
	self,
	input_ids: torch.LongTensor,
	past: Optional[torch.Tensor] = None,
	past_key_values: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	**kwargs
	) -> dict:
	batch_size, seq_length = input_ids.shape
	MASK, gMASK = self.config.mask_token_id, self.config.gmask_token_id
	seqs = input_ids.tolist()
	mask_positions, use_gmasks = [], []
	for seq in seqs:
	mask_token = gMASK if gMASK in seq else MASK
	use_gmask = mask_token == gMASK
	mask_positions.append(seq.index(mask_token))
	use_gmasks.append(use_gmask)

	# only last token for input_ids if past is not None
	if past is not None or past_key_values is not None:
	last_token = input_ids[:, -1].unsqueeze(-1)
	if attention_mask is not None and attention_mask.dtype == torch.bool:
	attention_mask = attention_mask[:, :, -1:]
	else:
	attention_mask = None
	if position_ids is not None:
	position_ids = position_ids[..., -1:]
	else:
	context_lengths = [seq.index(self.config.bos_token_id) for seq in seqs]
	if self.position_encoding_2d:
	position_ids = torch.tensor(
	[[mask_position, seq_length - context_length] for mask_position, context_length in
	zip(mask_positions, context_lengths)], dtype=torch.long, device=input_ids.device).unsqueeze(-1)
	else:
	position_ids = torch.tensor([mask_position for mask_position in mask_positions], dtype=torch.long,
	device=input_ids.device).unsqueeze(-1)

	if past is None:
	past = past_key_values
	return {
	"input_ids": last_token,
	"past_key_values": past,
	"position_ids": position_ids,
	"attention_mask": attention_mask
	}
	else:
	if attention_mask is not None and attention_mask.dtype != torch.bool:
	logger.warning_once(f"The dtype of attention mask ({attention_mask.dtype}) is not bool")
	attention_mask = None
	if attention_mask is None:
	attention_mask = self.get_masks(
	input_ids,
	device=input_ids.device
	)
	if position_ids is None:
	position_ids = self.get_position_ids(
	input_ids,
	device=input_ids.device,
	mask_positions=mask_positions,
	use_gmasks=use_gmasks
	)

	return {
	"input_ids": input_ids,
	"past_key_values": past,
	"position_ids": position_ids,
	"attention_mask": attention_mask
	}

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	):
	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

	transformer_outputs = self.transformer(
	input_ids=input_ids,
	position_ids=position_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 = transformer_outputs[0]

	lm_logits = self.lm_head(hidden_states).permute(1, 0, 2).contiguous()

	loss = None
	if labels is not None:
	lm_logits = lm_logits.to(torch.float32)

	# Shift so that tokens < n predict n
	shift_logits = lm_logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss(ignore_index=-100)
	loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

	lm_logits = lm_logits.to(hidden_states.dtype)
	loss = loss.to(hidden_states.dtype)

	if not return_dict:
	output = (lm_logits,) + transformer_outputs[1:]
	return ((loss,) + output) if loss is not None else output

	return CausalLMOutputWithPast(
	loss=loss,
	logits=lm_logits,
	past_key_values=transformer_outputs.past_key_values,
	hidden_states=transformer_outputs.hidden_states,
	attentions=transformer_outputs.attentions,
	)

	@staticmethod
	def _reorder_cache(
	past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
	) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
	"""
	This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
	[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
	beam_idx at every generation step.

	Output shares the same memory storage as `past`.
	"""
	return tuple(
	(
	layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)),
	layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
	)
	for layer_past in 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

	@torch.no_grad()
	def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048, num_beams=1,
	do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs):
	if history is None:
	history = []
	if logits_processor is None:
	logits_processor = LogitsProcessorList()
	logits_processor.append(InvalidScoreLogitsProcessor())
	gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
	"temperature": temperature, "logits_processor": logits_processor, **kwargs}
	if not history:
	prompt = query
	else:
	prompt = ""
	for i, (old_query, response) in enumerate(history):
	prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
	prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
	inputs = tokenizer([prompt], return_tensors="pt")
	inputs = inputs.to(self.device)
	outputs = self.generate(inputs, gen_kwargs)
	outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
	response = tokenizer.decode(outputs)
	response = self.process_response(response)
	history = history + [(query, response)]
	return response, history

	@torch.no_grad()
	def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048,
	do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs):
	if history is None:
	history = []
	if logits_processor is None:
	logits_processor = LogitsProcessorList()
	logits_processor.append(InvalidScoreLogitsProcessor())
	gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
	"temperature": temperature, "logits_processor": logits_processor, **kwargs}
	if not history:
	prompt = query
	else:
	prompt = ""
	for i, (old_query, response) in enumerate(history):
	prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
	prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
	inputs = tokenizer([prompt], return_tensors="pt")
	inputs = inputs.to(self.device)
	for outputs in self.stream_generate(inputs, gen_kwargs):
	outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
	response = tokenizer.decode(outputs)
	response = self.process_response(response)
	new_history = history + [(query, response)]
	yield response, new_history

	@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

	def quantize(self, bits: int, quantize_embeddings=False, use_quantization_cache=False, empty_init=False, **kwargs):
	if bits == 0:
	return

	from .quantization import quantize, QuantizedEmbedding, QuantizedLinear, load_cpu_kernel

	if self.quantized:
	if self.device == torch.device("cpu"):
	logger.info("Already quantized, reloading cpu kernel.")
	load_cpu_kernel(**kwargs)
	else:
	logger.info("Already quantized.")
	return self

	self.quantized = True

	self.config.quantization_bit = bits
	self.config.quantization_embeddings = quantize_embeddings

	self.transformer = quantize(self.transformer, bits, use_quantization_cache=use_quantization_cache, empty_init=empty_init, **kwargs)

	if self.device == torch.device("cpu"):
	dtype = torch.float32
	else:
	dtype = torch.half

	if quantize_embeddings:
	logger.info("Applying quantization to embeddings")
	self.transformer.word_embeddings = QuantizedEmbedding(
	weight_bit_width=bits,
	weight_tensor=self.transformer.word_embeddings.weight.to(self.device),
	num_embeddings=self.transformer.word_embeddings.num_embeddings,
	embedding_dim=self.transformer.word_embeddings.embedding_dim,
	dtype=dtype,
	empty_init=empty_init,
	device=self.transformer.word_embeddings.weight.device,
	)
	self.lm_head = QuantizedLinear(
	weight_bit_width=bits,
	weight_tensor=self.lm_head.weight.to(self.device),
	bias_tensor=None,
	in_features=self.lm_head.in_features,
	out_features=self.lm_head.out_features,
	bias=False,
	quantized_weight=self.transformer.word_embeddings.weight,
	quantized_weight_scale=self.transformer.word_embeddings.weight_scale,
	dtype=dtype,
	empty_init=empty_init,
	device=self.lm_head.weight.device,
	)

	return self