chatglm2-6b / modeling_chatglm.py

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Fix use_cache=False

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	""" PyTorch ChatGLM model. """

	import math
	import copy
	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 import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss
	from torch.nn.utils import skip_init
	from typing import Optional, Tuple, Union, List, Callable, Dict, Any

	from transformers.modeling_outputs import (
	BaseModelOutputWithPast,
	CausalLMOutputWithPast,
	SequenceClassifierOutputWithPast,
	)
	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/ChatGLM2-6B"
	_CONFIG_FOR_DOC = "ChatGLM6BConfig"

	CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"THUDM/chatglm2-6b",
	# See all ChatGLM models at https://huggingface.co/models?filter=chatglm
	]


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


	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


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


	def split_tensor_along_last_dim(
	tensor: torch.Tensor,
	num_partitions: int,
	contiguous_split_chunks: bool = False,
	) -> List[torch.Tensor]:
	"""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.

	Returns:
	A list of Tensors
	"""
	# 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


	class RotaryEmbedding(nn.Module):
	def __init__(self, dim, original_impl=False, device=None, dtype=None):
	super().__init__()
	inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim))
	self.register_buffer("inv_freq", inv_freq)
	self.dim = dim
	self.original_impl = original_impl

	def forward_impl(
	self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000
	):
	"""Enhanced Transformer with Rotary Position Embedding.

	Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
	transformers/rope/__init__.py. MIT License:
	https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
	"""
	# $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
	theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem))

	# Create position indexes `[0, 1, ..., seq_len - 1]`
	seq_idx = torch.arange(seq_len, dtype=dtype, device=device)

	# Calculate the product of position index and $\theta_i$
	idx_theta = torch.outer(seq_idx, theta).float()

	cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)

	# this is to mimic the behaviour of complex32, else we will get different results
	if dtype in (torch.float16, torch.bfloat16, torch.int8):
	cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
	return cache

	def forward(self, max_seq_len, offset=0):
	return self.forward_impl(
	max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device
	)


	@torch.jit.script
	def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
	# x: [sq, b, np, hn]
	sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
	rot_dim = rope_cache.shape[-2] * 2
	x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
	# truncate to support variable sizes
	rope_cache = rope_cache[:sq]
	xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
	rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
	x_out2 = torch.stack(
	[
	xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
	xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
	],
	-1,
	)
	x_out2 = x_out2.flatten(3)
	return torch.cat((x_out2, x_pass), dim=-1)


	class RMSNorm(torch.nn.Module):
	def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
	super().__init__()
	self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
	self.eps = eps

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

	return (self.weight * hidden_states).to(input_dtype)


	class CoreAttention(torch.nn.Module):
	def __init__(self, config: ChatGLMConfig, layer_number):
	super(CoreAttention, self).__init__()

	self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
	self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
	if self.apply_query_key_layer_scaling:
	self.attention_softmax_in_fp32 = True
	self.layer_number = max(1, layer_number)

	projection_size = config.kv_channels * config.num_attention_heads

	# Per attention head and per partition values.
	self.hidden_size_per_partition = projection_size
	self.hidden_size_per_attention_head = projection_size // config.num_attention_heads
	self.num_attention_heads_per_partition = config.num_attention_heads

	coeff = None
	self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
	if self.apply_query_key_layer_scaling:
	coeff = self.layer_number
	self.norm_factor *= coeff
	self.coeff = coeff

	self.attention_dropout = torch.nn.Dropout(config.attention_dropout)

	def forward(self, query_layer, key_layer, value_layer, attention_mask):
	pytorch_major_version = int(torch.__version__.split('.')[0])
	if pytorch_major_version >= 2:
	query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]]
	if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
	context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
	is_causal=True)
	else:
	if attention_mask is not None:
	attention_mask = ~attention_mask
	context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
	attention_mask)
	context_layer = context_layer.permute(2, 0, 1, 3)
	new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
	context_layer = context_layer.reshape(*new_context_layer_shape)
	else:
	# Raw attention scores

	# [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)

	# preallocting input tensor: [b * np, sq, sk]
	matmul_input_buffer = torch.empty(
	output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype,
	device=query_layer.device
	)

	# Raw attention scores. [b * np, sq, sk]
	matmul_result = torch.baddbmm(
	matmul_input_buffer,
	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 / self.norm_factor),
	)

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

	# ===========================
	# Attention probs and dropout
	# ===========================

	# attention scores and attention mask [b, np, sq, sk]
	if self.attention_softmax_in_fp32:
	attention_scores = attention_scores.float()
	if self.coeff is not None:
	attention_scores = attention_scores * self.coeff
	if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]:
	attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3],
	device=attention_scores.device, dtype=torch.bool)
	attention_mask.tril_()
	attention_mask = ~attention_mask
	if attention_mask is not None:
	attention_scores = attention_scores.masked_fill(attention_mask, float("-inf"))
	attention_probs = F.softmax(attention_scores, dim=-1)
	attention_probs = attention_probs.type_as(value_layer)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs = self.attention_dropout(attention_probs)
	# =========================
	# 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] + (self.hidden_size_per_partition,)
	context_layer = context_layer.view(*new_context_layer_shape)

	return context_layer


	class SelfAttention(torch.nn.Module):
	"""Parallel self-attention layer abstract class.

	Self-attention layer takes input with size [s, b, h]
	and returns output of the same size.
	"""

	def __init__(self, config: ChatGLMConfig, layer_number, device=None):
	super(SelfAttention, self).__init__()
	self.layer_number = max(1, layer_number)

	self.projection_size = config.kv_channels * config.num_attention_heads

	# Per attention head and per partition values.
	self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
	self.num_attention_heads_per_partition = config.num_attention_heads

	self.multi_query_attention = config.multi_query_attention
	self.qkv_hidden_size = 3 * self.projection_size
	if self.multi_query_attention:
	self.num_multi_query_groups_per_partition = config.multi_query_group_num
	self.qkv_hidden_size = (
	self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
	)
	self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size,
	bias=config.add_bias_linear or config.add_qkv_bias,
	device=device, **_config_to_kwargs(config)
	)

	self.core_attention = CoreAttention(config, self.layer_number)

	# Output.
	self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear,
	device=device, **_config_to_kwargs(config)
	)

	def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
	if self.multi_query_attention:
	num_attention_heads = self.num_multi_query_groups_per_partition
	else:
	num_attention_heads = self.num_attention_heads_per_partition
	return torch.empty(
	inference_max_sequence_len,
	batch_size,
	num_attention_heads,
	self.hidden_size_per_attention_head,
	dtype=dtype,
	device=device,
	)

	def forward(
	self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
	):
	# hidden_states: [sq, b, h]

	# =================================================
	# Pre-allocate memory for key-values for inference.
	# =================================================
	# =====================
	# Query, Key, and Value
	# =====================

	# Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
	mixed_x_layer = self.query_key_value(hidden_states)

	if self.multi_query_attention:
	(query_layer, key_layer, value_layer) = mixed_x_layer.split(
	[
	self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
	self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
	self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
	],
	dim=-1,
	)
	query_layer = query_layer.view(
	query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
	)
	key_layer = key_layer.view(
	key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
	)
	value_layer = value_layer.view(
	value_layer.size()[:-1]
	+ (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
	)
	else:
	new_tensor_shape = mixed_x_layer.size()[:-1] + \
	(self.num_attention_heads_per_partition,
	3 * self.hidden_size_per_attention_head)
	mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)

	# [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
	(query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)

	# apply relative positional encoding (rotary embedding)
	if rotary_pos_emb is not None:
	query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
	key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)

	# adjust key and value for inference
	if kv_cache is not None:
	cache_k, cache_v = kv_cache
	key_layer = torch.cat((cache_k, key_layer), dim=0)
	value_layer = torch.cat((cache_v, value_layer), dim=0)
	if use_cache:
	kv_cache = (key_layer, value_layer)
	else:
	kv_cache = None

	if self.multi_query_attention:
	key_layer = key_layer.unsqueeze(-2)
	key_layer = key_layer.expand(
	-1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
	)
	key_layer = key_layer.contiguous().view(
	key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
	)
	value_layer = value_layer.unsqueeze(-2)
	value_layer = value_layer.expand(
	-1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
	)
	value_layer = value_layer.contiguous().view(
	value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
	)

	# ==================================
	# core attention computation
	# ==================================

	context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)

	# =================
	# Output. [sq, b, h]
	# =================

	output = self.dense(context_layer)

	return output, kv_cache


	def _config_to_kwargs(args):
	common_kwargs = {
	"dtype": args.torch_dtype,
	}
	return common_kwargs


	class MLP(torch.nn.Module):
	"""MLP.

	MLP will take the input with h hidden state, project it to 4*h
	hidden dimension, perform nonlinear transformation, and project the
	state back into h hidden dimension.
	"""

	def __init__(self, config: ChatGLMConfig, device=None):
	super(MLP, self).__init__()

	self.add_bias = config.add_bias_linear

	# Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
	self.dense_h_to_4h = nn.Linear(
	config.hidden_size,
	config.ffn_hidden_size * 2,
	bias=self.add_bias,
	device=device,
	**_config_to_kwargs(config)
	)

	def swiglu(x):
	x = torch.chunk(x, 2, dim=-1)
	return F.silu(x[0]) * x[1]

	self.activation_func = swiglu

	# Project back to h.
	self.dense_4h_to_h = nn.Linear(
	config.ffn_hidden_size,
	config.hidden_size,
	bias=self.add_bias,
	device=device,
	**_config_to_kwargs(config)
	)

	def forward(self, hidden_states):
	# [s, b, 4hp]
	intermediate_parallel = self.dense_h_to_4h(hidden_states)
	intermediate_parallel = self.activation_func(intermediate_parallel)
	# [s, b, h]
	output = self.dense_4h_to_h(intermediate_parallel)
	return output


	class GLMBlock(torch.nn.Module):
	"""A single transformer layer.

	Transformer layer takes input with size [s, b, h] and returns an
	output of the same size.
	"""

	def __init__(self, config: ChatGLMConfig, layer_number, device=None):
	super(GLMBlock, self).__init__()
	self.layer_number = layer_number

	self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm

	self.fp32_residual_connection = config.fp32_residual_connection

	LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
	# Layernorm on the input data.
	self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
	dtype=config.torch_dtype)

	# Self attention.
	self.self_attention = SelfAttention(config, layer_number, device=device)
	self.hidden_dropout = config.hidden_dropout

	# Layernorm on the attention output
	self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
	dtype=config.torch_dtype)

	# MLP
	self.mlp = MLP(config, device=device)

	def forward(
	self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True,
	):
	# hidden_states: [s, b, h]

	# Layer norm at the beginning of the transformer layer.
	layernorm_output = self.input_layernorm(hidden_states)
	# Self attention.
	attention_output, kv_cache = self.self_attention(
	layernorm_output,
	attention_mask,
	rotary_pos_emb,
	kv_cache=kv_cache,
	use_cache=use_cache
	)

	# Residual connection.
	if self.apply_residual_connection_post_layernorm:
	residual = layernorm_output
	else:
	residual = hidden_states

	layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training)
	layernorm_input = residual + layernorm_input

	# Layer norm post the self attention.
	layernorm_output = self.post_attention_layernorm(layernorm_input)

	# MLP.
	mlp_output = self.mlp(layernorm_output)

	# Second residual connection.
	if self.apply_residual_connection_post_layernorm:
	residual = layernorm_output
	else:
	residual = layernorm_input

	output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training)
	output = residual + output

	return output, kv_cache


	class GLMTransformer(torch.nn.Module):
	"""Transformer class."""

	def __init__(self, config: ChatGLMConfig, device=None):
	super(GLMTransformer, self).__init__()

	self.fp32_residual_connection = config.fp32_residual_connection
	self.post_layer_norm = config.post_layer_norm

	# Number of layers.
	self.num_layers = config.num_layers

	# Transformer layers.
	def build_layer(layer_number):
	return GLMBlock(config, layer_number, device=device)

	self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)])

	if self.post_layer_norm:
	LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
	# Final layer norm before output.
	self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
	dtype=config.torch_dtype)

	self.gradient_checkpointing = False

	def _get_layer(self, layer_number):
	return self.layers[layer_number]

	def forward(
	self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None,
	use_cache: Optional[bool] = True,
	output_hidden_states: Optional[bool] = False,
	):
	if not kv_caches:
	kv_caches = [None for _ in range(self.num_layers)]
	presents = () if use_cache else None
	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

	all_self_attentions = None
	all_hidden_states = () if output_hidden_states else None
	for index in range(self.num_layers):
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	layer = self._get_layer(index)
	if self.gradient_checkpointing and self.training:
	layer_ret = torch.utils.checkpoint.checkpoint(
	layer,
	hidden_states,
	attention_mask,
	rotary_pos_emb,
	kv_caches[index],
	use_cache
	)
	else:
	layer_ret = layer(
	hidden_states,
	attention_mask,
	rotary_pos_emb,
	kv_cache=kv_caches[index],
	use_cache=use_cache
	)
	hidden_states, kv_cache = layer_ret
	if use_cache:
	presents = presents + (kv_cache,)

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	# Final layer norm.
	if self.post_layer_norm:
	hidden_states = self.final_layernorm(hidden_states)

	return hidden_states, presents, all_hidden_states, all_self_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_weights(self, module: nn.Module):
	"""Initialize the weights."""
	return

	def get_masks(self, input_ids, past_key_values, padding_mask=None):
	batch_size, seq_length = input_ids.shape
	full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
	full_attention_mask.tril_()
	past_length = 0
	if past_key_values:
	past_length = past_key_values[0][0].shape[0]
	if past_length:
	full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length,
	device=input_ids.device), full_attention_mask), dim=-1)
	if padding_mask is not None:
	full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1)
	if not past_length and padding_mask is not None:
	full_attention_mask -= padding_mask.unsqueeze(-1) - 1
	full_attention_mask = (full_attention_mask < 0.5).bool()
	full_attention_mask.unsqueeze_(1)
	return full_attention_mask

	def get_position_ids(self, input_ids, device):
	batch_size, seq_length = input_ids.shape
	position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
	return position_ids

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


	class Embedding(torch.nn.Module):
	"""Language model embeddings."""

	def __init__(self, config: ChatGLMConfig, device=None):
	super(Embedding, self).__init__()

	self.hidden_size = config.hidden_size
	# Word embeddings (parallel).
	self.word_embeddings = nn.Embedding(
	config.padded_vocab_size,
	self.hidden_size,
	dtype=config.torch_dtype,
	device=device
	)
	self.fp32_residual_connection = config.fp32_residual_connection

	def forward(self, input_ids):
	# Embeddings.
	words_embeddings = self.word_embeddings(input_ids)
	embeddings = words_embeddings
	# Data format change to avoid explicit tranposes : [b s h] --> [s b h].
	embeddings = embeddings.transpose(0, 1).contiguous()
	# If the input flag for fp32 residual connection is set, convert for float.
	if self.fp32_residual_connection:
	embeddings = embeddings.float()
	return embeddings


	class ChatGLMModel(ChatGLMPreTrainedModel):
	def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
	super().__init__(config)
	if empty_init:
	init_method = skip_init
	else:
	init_method = default_init
	init_kwargs = {}
	if device is not None:
	init_kwargs["device"] = device
	self.embedding = init_method(Embedding, config, **init_kwargs)
	self.num_layers = config.num_layers
	self.multi_query_group_num = config.multi_query_group_num
	self.kv_channels = config.kv_channels

	# Rotary positional embeddings
	self.seq_length = config.seq_length
	rotary_dim = (
	config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels
	)

	self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device,
	dtype=config.torch_dtype)
	self.encoder = init_method(GLMTransformer, config, **init_kwargs)
	self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False,
	dtype=config.torch_dtype, **init_kwargs)
	self.pre_seq_len = config.pre_seq_len
	self.prefix_projection = config.prefix_projection
	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)

	def get_input_embeddings(self):
	return self.embedding.word_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.multi_query_group_num,
	self.kv_channels
	)
	# 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)
	return past_key_values

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

	batch_size, seq_length = input_ids.shape

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

	if self.pre_seq_len is not None:
	if past_key_values is None:
	past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device,
	dtype=inputs_embeds.dtype)
	if attention_mask is not None:
	attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)),
	attention_mask], dim=-1)

	if full_attention_mask is None:
	if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
	full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)

	# Rotary positional embeddings
	rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
	if position_ids is not None:
	rotary_pos_emb = rotary_pos_emb[position_ids]
	else:
	rotary_pos_emb = rotary_pos_emb[None, :seq_length]
	rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()

	# Run encoder.
	hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
	inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb,
	kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_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,
	)

	def quantize(self, weight_bit_width: int):
	from .quantization import quantize
	quantize(self.encoder, weight_bit_width)
	return self


	class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
	def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
	super().__init__(config)

	self.max_sequence_length = config.max_length
	self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
	self.config = config
	self.quantized = False

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

	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"]
	model_kwargs["attention_mask"] = torch.cat(
	[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
	)

	# 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
	model_kwargs["position_ids"] = torch.cat(
	[position_ids, new_position_id], dim=-1
	)

	model_kwargs["is_first_forward"] = False
	return model_kwargs

	def prepare_inputs_for_generation(
	self,
	input_ids: torch.LongTensor,
	past_key_values: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	is_first_forward: bool = True,
	**kwargs
	) -> dict:
	# only last token for input_ids if past is not None
	if position_ids is None:
	position_ids = self.get_position_ids(input_ids, device=input_ids.device)
	if not is_first_forward:
	if past_key_values is not None:
	position_ids = position_ids[..., -1:]
	input_ids = input_ids[:, -1:]
	return {
	"input_ids": input_ids,
	"past_key_values": past_key_values,
	"position_ids": position_ids,
	"attention_mask": attention_mask,
	"return_last_logit": True,
	"use_cache": use_cache
	}

	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,
	return_last_logit: Optional[bool] = False,
	):
	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_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = transformer_outputs[0]
	if return_last_logit:
	hidden_states = hidden_states[-1:]
	lm_logits = self.transformer.output_layer(hidden_states)
	lm_logits = lm_logits.transpose(0, 1).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")
	return response

	def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None):
	prompt = tokenizer.build_prompt(query, history=history)
	inputs = tokenizer([prompt], return_tensors="pt")
	inputs = inputs.to(self.device)
	return inputs

	def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None):
	if history:
	prompt = "\n\n[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query)
	input_ids = tokenizer.encode(prompt, add_special_tokens=False)
	input_ids = input_ids[1:]
	inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False)
	else:
	prompt = "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query)
	inputs = tokenizer([prompt], return_tensors="pt")
	inputs = inputs.to(self.device)
	return inputs

	@torch.inference_mode()
	def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 8192, num_beams=1,
	do_sample=True, top_p=0.8, temperature=0.8, 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}
	inputs = self.build_inputs(tokenizer, query, history=history)
	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.inference_mode()
	def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, past_key_values=None,
	max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None,
	return_past_key_values=False, **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 past_key_values is None and not return_past_key_values:
	inputs = self.build_inputs(tokenizer, query, history=history)
	else:
	inputs = self.build_stream_inputs(tokenizer, query, history=history)
	if past_key_values is not None:
	past_length = past_key_values[0][0].shape[0]
	if self.transformer.pre_seq_len is not None:
	past_length -= self.transformer.pre_seq_len
	inputs.position_ids += past_length
	attention_mask = inputs.attention_mask
	attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1)
	inputs['attention_mask'] = attention_mask
	for outputs in self.stream_generate(**inputs, past_key_values=past_key_values,
	return_past_key_values=return_past_key_values, **gen_kwargs):
	if return_past_key_values:
	outputs, past_key_values = outputs
	outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
	response = tokenizer.decode(outputs)
	if response and response[-1] != "�":
	response = self.process_response(response)
	new_history = history + [(query, response)]
	if return_past_key_values:
	yield response, new_history, past_key_values
	else:
	yield response, new_history

	@torch.inference_mode()
	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,
	return_past_key_values=False,
	**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)
	model_kwargs["use_cache"] = generation_config.use_cache
	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())
	if return_past_key_values:
	yield input_ids, outputs.past_key_values
	else:
	yield input_ids
	# 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

	def quantize(self, bits: int, empty_init=False, device=None, **kwargs):
	if bits == 0:
	return

	from .quantization import quantize

	if self.quantized:
	logger.info("Already quantized.")
	return self

	self.quantized = True

	self.config.quantization_bit = bits

	self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device,
	**kwargs)
	return self


	class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel):
	def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
	super().__init__(config)

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

	self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half)
	if config.classifier_dropout is not None:
	self.dropout = nn.Dropout(config.classifier_dropout)
	else:
	self.dropout = None
	self.config = config

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

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	full_attention_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
	inputs_embeds: Optional[torch.LongTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]:
	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,
	full_attention_mask=full_attention_mask,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = transformer_outputs[0]
	pooled_hidden_states = hidden_states[-1]
	if self.dropout is not None:
	pooled_hidden_states = self.dropout(pooled_hidden_states)
	logits = self.classifier_head(pooled_hidden_states)

	loss = None
	if labels is not None:
	if self.config.problem_type is None:
	if self.num_labels == 1:
	self.config.problem_type = "regression"
	elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
	self.config.problem_type = "single_label_classification"
	else:
	self.config.problem_type = "multi_label_classification"

	if self.config.problem_type == "regression":
	loss_fct = MSELoss()
	if self.num_labels == 1:
	loss = loss_fct(logits.squeeze().float(), labels.squeeze())
	else:
	loss = loss_fct(logits.float(), labels)
	elif self.config.problem_type == "single_label_classification":
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1))
	elif self.config.problem_type == "multi_label_classification":
	loss_fct = BCEWithLogitsLoss()
	loss = loss_fct(logits.float(), labels.view(-1, self.num_labels))

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

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