jiang-chat / modeling_gpt_jiang.py

Upload 2 files

a53fab2 11 months ago

No virus

35.5 kB

	# coding=utf-8
	# Copyright 2023 EleutherAI The HuggingFace Inc. team. and JIANG.ai All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" PyTorch GPTJiang model."""

	from typing import Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import CrossEntropyLoss
	import torch.nn.functional as F

	from transformers.activations import ACT2FN
	from transformers.file_utils import (
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	replace_return_docstrings,
	)
	from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import logging
	from .configuration_gpt_jiang import GPTJiangConfig


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "GPTJiangConfig"
	GPT_JIANG_PRETRAINED_MODEL_ARCHIVE_LIST = []


	class RMSNorm(torch.nn.Module):
	def __init__(self, dim: int, eps: float=1e-5):
	super().__init__()
	self.eps = eps
	self.weight = nn.Parameter(torch.ones(dim))

	def _norm(self, x):
	return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

	def forward(self, x):
	output = self._norm(x.float()).type_as(x)
	return output * self.weight


	class GPTJiangPreTrainedModel(PreTrainedModel):
	"""
	An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
	models.
	"""
	config_class = GPTJiangConfig
	base_model_prefix = "gpt_jiang"
	supports_gradient_checkpointing = True
	_no_split_modules = ["GPTJiangLayer"]

	def _init_weights(self, module):
	"""Initialize the weights"""
	if isinstance(module, GatedLinear):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.fill_(1.0)
	elif isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()
	elif isinstance(module, RMSNorm):
	# module.bias.data.zero_()
	module.weight.data.fill_(1.0)

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


	class GPTJiangAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.max_position_embeddings = config.max_position_embeddings
	self.num_attention_heads = config.num_attention_heads
	self.hidden_size = config.hidden_size
	self.head_size = self.hidden_size // self.num_attention_heads
	self.rotary_ndims = int(self.head_size * config.rotary_pct)
	self.rotary_emb = RotaryEmbedding(
	self.rotary_ndims,
	config.max_position_embeddings,
	base=config.rotary_emb_base
	)
	self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size)
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.causal_mask_cached = None

	def causal_mask(self, x, seq_len):
	if self.causal_mask_cached is None or seq_len > self.causal_mask_cached.shape[2]:
	cache_size = max(self.max_position_embeddings, seq_len)
	self.causal_mask_cached = torch.ones(
	cache_size,
	cache_size,
	dtype=torch.bool
	).tril().view(1, 1, cache_size, cache_size)
	return self.causal_mask_cached[:, :, :seq_len, :seq_len].to(x.device)

	def forward(
	self,
	hidden_states,
	attention_mask,
	head_mask=None,
	layer_past=None,
	use_cache=False,
	output_attentions=False
	):
	has_layer_past = layer_past is not None

	# Compute QKV
	# Attention heads [batch, seq_len, hidden_size]
	# --> [batch, seq_len, (np * 3 * head_size)]
	qkv = self.query_key_value(hidden_states)

	# [batch, seq_len, (num_heads * 3 * head_size)]
	# --> [batch, seq_len, num_heads, 3 * head_size]
	new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size)
	qkv = qkv.view(*new_qkv_shape)

	# [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
	query = qkv[..., : self.head_size].permute(0, 2, 1, 3)
	key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3)
	value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3)

	# Compute rotary embeddings on rotary_ndims
	# query_rot = query[..., : self.rotary_ndims]
	# query_pass = query[..., self.rotary_ndims :]
	# key_rot = key[..., : self.rotary_ndims]
	# key_pass = key[..., self.rotary_ndims :]

	# Compute token offset for rotary embeddings (when decoding)
	seq_len = key.shape[-2]
	offset = 0
	if has_layer_past:
	offset = layer_past[0].shape[-2]
	seq_len += offset
	cos, sin = self.rotary_emb(value, seq_len=seq_len)

	query, key = apply_rotary_pos_emb(query, key, cos, sin, offset=offset)
	# query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, offset=offset)
	# query = torch.cat((query, query_pass), dim=-1)
	# key = torch.cat((key, key_pass), dim=-1)

	# Cache QKV values
	if has_layer_past:
	past_key = layer_past[0]
	past_value = layer_past[1]
	key = torch.cat((past_key, key), dim=-2)
	value = torch.cat((past_value, value), dim=-2)
	present = (key, value,) if use_cache else None

	query = query.type_as(hidden_states)
	key = key.type_as(hidden_states)
	value = value.type_as(hidden_states)

	if output_attentions:
	# Use custom attention method to get attn_weights
	attn_output, attn_weights = self._attn(
	query, key, value,
	attention_mask=attention_mask,
	head_mask=head_mask
	)
	else:
	if layer_past is not None and attention_mask is None:
	# Must calculate attention_mask, or scaled_dot_product_attention will wrong
	batch_size = query.size(0)
	attention_mask = torch.ones(batch_size, seq_len, dtype=torch.bool)[:, None, None, :]

	if attention_mask is not None:
	attn_mask = attention_mask.transpose(2, 3) * attention_mask
	query_length = query.size(-2)
	key_length = key.size(-2)
	if query_length > 1:
	causal_mask = self.causal_mask(query, seq_len)
	causal_mask = causal_mask[:, :, -query_length:, :]
	attn_mask = (attn_mask[:, :, -query_length:, :] * causal_mask).to(torch.bool)
	else:
	attn_mask = attn_mask[:, :, -query_length:, :].to(torch.bool)

	attn_output = F.scaled_dot_product_attention(
	query,
	key,
	value,
	attn_mask=attn_mask,
	is_causal=False
	)
	else:
	attn_output = F.scaled_dot_product_attention(
	query,
	key,
	value,
	attn_mask=None,
	is_causal=True
	)
	attn_weights = None

	# Reshape outputs
	# attn_output == [bs, num_attention_heads, seq_len, attn_head_size]
	attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size)
	# tensor [bs, seq_len, num_attention_heads * attn_head_size]
	attn_output = self.dense(attn_output)

	outputs = (attn_output, present)
	if output_attentions:
	outputs += (attn_weights,)

	return outputs

	@classmethod
	def _calculate_attn_output_loss(self, attn_output):
	bs, num_attention_heads, seq_len, attn_head_size = attn_output.size()
	attn_output_out = attn_output.view(bs, num_attention_heads, -1)
	attn_output_out_norm = attn_output_out / torch.max(
	attn_output_out.norm(dim=2, keepdim=True),
	1e-8 * torch.ones_like(attn_output_out)
	)
	sim = torch.bmm(attn_output_out_norm, attn_output_out_norm.permute(0, 2, 1))
	attn_output_loss = sim.sum() / sim.numel()
	return attn_output_loss

	@classmethod
	def _split_heads(cls, tensor, num_attention_heads, attn_head_size):
	"""
	Splits hidden dim into attn_head_size and num_attention_heads
	"""
	# tensor: [bs, seq_len, hidden_size]
	new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
	# -> [bs, seq_len, num_attention_heads, attn_head_size]
	tensor = tensor.view(new_shape)
	# -> [bs, num_attention_heads, seq_len, attn_head_size]
	tensor = tensor.permute(0, 2, 1, 3)
	return tensor

	@classmethod
	def _merge_heads(cls, tensor, num_attention_heads, attn_head_size):
	"""
	Merges attn_head_size dim and num_attn_heads dim into hidden dim
	"""
	# tensor [bs, num_attention_heads, seq_len, attn_head_size]
	tensor = tensor.permute(0, 2, 1, 3).contiguous()
	# -> [bs, seq_len, num_attention_heads, attn_head_size]
	tensor = tensor.view(tensor.size(0), tensor.size(1), num_attention_heads * attn_head_size)
	# -> [bs, seq_len, hidden_size]
	return tensor

	def create_upper_triangular_matrix(self, q, k):
	size = max(q, k)
	# 创建一个单位矩阵
	identity = torch.eye(size)
	# 创建一个矩阵，其中每个元素都是它的行索引
	row_indices = torch.arange(size).view(-1, 1).expand(size, size)
	# 创建一个矩阵，其中每个元素都是它的列索引
	col_indices = torch.arange(size).view(1, -1).expand(size, size)
	# 比较行和列索引，如果行索引小于列索引，则0，否则1
	upper_triangular_matrix = torch.where(row_indices < col_indices, 0, 1)
	return upper_triangular_matrix[-q:, -k:].to(torch.bool)

	def _attn(self, query, key, value, attention_mask=None, head_mask=None):
	# q, k, v: [bs, num_attention_heads, seq_len, attn_head_size]
	# compute causal mask from causal mask buffer
	batch_size, num_attention_heads, query_length, attn_head_size = query.size()
	key_length = key.size(-2)

	# 避免使用tril
	# causal_mask = torch.ones(
	# query_length, key_length,
	# dtype=torch.bool,
	# device=query.device
	# ).tril(
	# diagonal=key_length - query_length
	# ).view(1, 1, query_length, key_length)
	causal_mask = self.create_upper_triangular_matrix(
	query_length, key_length
	).view(1, 1, query_length, key_length).to(query.device)

	query = query.view(batch_size * num_attention_heads, query_length, attn_head_size)
	key = key.view(batch_size * num_attention_heads, key_length, attn_head_size)
	attn_scores = torch.zeros(
	batch_size * num_attention_heads,
	query_length,
	key_length,
	dtype=query.dtype,
	device=key.device,
	)
	norm_factor = self.head_size ** 0.5
	attn_scores = torch.baddbmm(
	attn_scores,
	query,
	key.transpose(1, 2),
	beta=1.0,
	alpha=(torch.tensor(1.0, dtype=query.dtype, device=query.device) / norm_factor),
	)
	attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length)

	mask_value = torch.finfo(attn_scores.dtype).min
	# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
	# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
	mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype).to(attn_scores.device)
	attn_scores = torch.where(causal_mask, attn_scores, mask_value)

	if attention_mask is not None:
	# Apply the attention mask
	attn_scores = attn_scores + attention_mask

	attn_weights = nn.functional.softmax(attn_scores.float(), dim=-1).type_as(value)

	# Mask heads if we want to
	if head_mask is not None:
	attn_weights = attn_weights * head_mask

	attn_output = torch.matmul(attn_weights, value)
	return attn_output, attn_weights


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

	# Build here to make `torch.jit.trace` work.
	self.max_seq_len_cached = max_position_embeddings
	t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.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)
	self.cos_cached = emb.cos()[None, None, :, :]
	self.sin_cached = emb.sin()[None, None, :, :]

	def forward(self, x, seq_len=None):
	# x: [bs, num_attention_heads, seq_len, head_size]
	# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
	if seq_len > self.max_seq_len_cached:
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=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)
	self.cos_cached = emb.cos()[None, None, :, :]
	self.sin_cached = emb.sin()[None, None, :, :]
	return self.cos_cached[:seq_len, ...].to(x.device), self.sin_cached[:seq_len, ...].to(x.device)


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


	def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0):
	cos = cos[..., offset : q.shape[-2] + offset, :]
	sin = sin[..., offset : q.shape[-2] + offset, :]
	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed


	class GatedLinear(nn.Linear):
	pass


	class GPTJiangMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.mlp_bias)
	self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_bias)
	self.gated = config.gated
	if config.gated:
	self.dense_h_to_4h_gate = GatedLinear(config.hidden_size, config.intermediate_size, bias=config.mlp_bias)
	self.act = ACT2FN[config.hidden_act]

	def forward(self, hidden_states):

	if self.gated:
	# pseudocode:
	# g is activation function, W and V are weights, * is element-wised product
	# x = g(Wx) * Vx
	hidden_states = self.act(self.dense_h_to_4h(hidden_states)) * self.dense_h_to_4h_gate(hidden_states)
	else:
	# pseudocode:
	# x = g(Wx)
	hidden_states = self.act(self.dense_h_to_4h(hidden_states))
	hidden_states = self.dense_4h_to_h(hidden_states)
	return hidden_states


	class GPTJiangLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.use_parallel_residual = config.use_parallel_residual
	self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.attention = GPTJiangAttention(config)
	self.mlp = GPTJiangMLP(config)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	use_cache=False,
	layer_past=None,
	output_attentions=False,
	):
	attention_layer_outputs = self.attention(
	self.input_layernorm(hidden_states),
	attention_mask=attention_mask,
	layer_past=layer_past,
	head_mask=head_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights), (attentions_output_loss)
	outputs = attention_layer_outputs[1:]

	# Default True in multiple models, faster
	if self.use_parallel_residual:
	# pseudocode:
	# x = x + attn(ln1(x)) + mlp(ln2(x))
	mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
	hidden_states = mlp_output + attn_output + hidden_states
	else:
	# pseudocode:
	# x = x + attn(ln1(x))
	# x = x + mlp(ln2(x))
	attn_output = attn_output + hidden_states
	mlp_output = self.mlp(self.post_attention_layernorm(attn_output))
	hidden_states = mlp_output + attn_output

	if use_cache:
	outputs = (hidden_states,) + outputs # hidden_states, present, (attn_weights), (attentions_output_loss)
	else:
	outputs = (hidden_states,) + outputs[1:] # hidden_states, (attn_weights), (attentions_output_loss)

	return outputs


	GPT_JIANG_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 ([`~GPTJiangConfig`]): 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.
	"""

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

	Indices can be obtained using [`AutoTokenizer`]. 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)
	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 [`~file_utils.ModelOutput`] instead of a plain tuple.
	"""


	@add_start_docstrings(
	"The bare GPTJiang Model transformer outputting raw hidden-states without any specific head on top.",
	GPT_JIANG_START_DOCSTRING,
	)
	class GPTJiangModel(GPTJiangPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.config = config

	self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
	self.layers = nn.ModuleList([GPTJiangLayer(config) for _ in range(config.num_hidden_layers)])
	self.final_layer_norm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)

	self.gradient_checkpointing = False

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

	def get_input_embeddings(self):
	return self.embed_in

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

	@add_start_docstrings_to_model_forward(GPT_JIANG_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	output_type=BaseModelOutputWithPast,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPast]:
	r"""
	past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
	Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
	"""
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	use_cache = use_cache if use_cache is not None else self.config.use_cache

	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:
	input_shape = input_ids.size()
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	batch_size, seq_length = input_shape

	if past_key_values is None:
	past_key_values = tuple([None] * self.config.num_hidden_layers)

	# Attention mask.
	if attention_mask is not None:
	assert batch_size > 0, "batch_size has to be defined and > 0"
	attention_mask = attention_mask.view(batch_size, -1)
	# We create a 3D attention mask from a 2D tensor mask.
	# Sizes are [batch_size, 1, 1, to_seq_length]
	# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
	# this attention mask is more simple than the triangular masking of causal attention
	# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
	attention_mask = attention_mask[:, None, None, :]

	# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
	# masked positions, this operation will create a tensor which is 0.0 for
	# positions we want to attend and the dtype's smallest value for masked positions.
	# Since we are adding it to the raw scores before the softmax, this is
	# effectively the same as removing these entirely.
	attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
	# attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x n_heads x N x N
	# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
	# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
	head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

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

	hidden_states = inputs_embeds

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	presents = () if use_cache else None
	all_attentions = () if output_attentions else None
	all_hidden_states = () if output_hidden_states else None
	for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)):

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	# None for layer_past
	return module(*inputs, use_cache, None, output_attentions)

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(layer),
	hidden_states,
	attention_mask,
	head_mask[i],
	)
	else:
	outputs = layer(
	hidden_states,
	attention_mask=attention_mask,
	head_mask=head_mask[i],
	layer_past=layer_past,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	hidden_states = outputs[0]
	if use_cache is True:
	presents = presents + (outputs[1],)
	if output_attentions:
	all_attentions = all_attentions + (outputs[2 if use_cache else 1],)

	hidden_states = self.final_layer_norm(hidden_states)
	# Add last hidden state
	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_attentions] if v is not None)

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


	@add_start_docstrings(
	"""GPTJiang Model with a `language modeling` head on top for CLM fine-tuning.""", GPT_JIANG_START_DOCSTRING
	)
	class GPTJiangForCausalLM(GPTJiangPreTrainedModel):
	_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

	def __init__(self, config):
	super().__init__(config)

	self.gpt_kdf = GPTJiangModel(config)
	self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

	def get_output_embeddings(self):
	return self.embed_out

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

	@add_start_docstrings_to_model_forward(GPT_JIANG_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.FloatTensor] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithPast]:
	r"""
	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
	`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
	`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are
	only required when the model is used as a decoder in a Sequence to Sequence model.

	Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see
	`past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
	don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
	`decoder_input_ids` of shape `(batch_size, sequence_length)`.
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
	`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
	ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).

	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, GPTJiangForCausalLM, GPTJiangConfig
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
	>>> config = GPTJiangConfig.from_pretrained("EleutherAI/gpt-neox-20b")
	>>> config.is_decoder = True
	>>> model = GPTJiangForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", config=config)

	>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
	>>> outputs = model(**inputs)

	>>> prediction_logits = outputs.logits
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	outputs = self.gpt_kdf(
	input_ids,
	attention_mask=attention_mask,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	past_key_values=past_key_values,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = outputs[0]
	lm_logits = self.embed_out(hidden_states)

	lm_loss = None
	attn_output_loss = None
	if labels is not None:
	# we are doing next-token prediction; shift prediction scores and input ids by one
	shift_logits = lm_logits[:, :-1, :].contiguous()
	labels = labels[:, 1:].contiguous()
	loss_fct = CrossEntropyLoss()
	lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))

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

	ret = CausalLMOutputWithPast(
	loss=lm_loss,
	logits=lm_logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)
	return ret

	def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
	input_shape = input_ids.shape

	# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
	if attention_mask is None:
	attention_mask = input_ids.new_ones(input_shape)

	# cut decoder_input_ids if past is used
	if past_key_values and past_key_values[0] is not None:
	input_ids = input_ids[:, -1:]

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

	def _reorder_cache(self, past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (
	tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
	)
	return reordered_past