Yuan2-2B-hf / yuan_hf_model_cpu.py

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	# coding=utf-8
	# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
	#
	# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
	# and OPT implementations in this library. It has been modified from its
	# original forms to accommodate minor architectural differences compared
	# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" PyTorch Yuan model."""
	import math
	from typing import List, Optional, Tuple, Union
	import torch.nn.functional as F
	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
	from .configuration_yuan import YuanConfig
	from einops import rearrange
	#from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
	#from flash_attn import flash_attn_func

	import copy

	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "YuanConfig"


	class LocalizedFiltering(torch.nn.Module):
	"""
	Mega's Exponential Moving Average layer, largely left unmodified from the original repo with the exception of
	variable names and moving away from the stateful representation of incremental decoding state. See
	"https://arxiv.org/abs/2209.10655" for more details.
	"""

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

	self.embed_dim = hidden_size
	self.lf_conv2d_group = 1
	self.lf_conv2d_num_pad = 1

	self.conv1 = torch.nn.Conv2d(self.embed_dim, self.embed_dim // 2, (2, 1), stride=(1, 1), padding=(self.lf_conv2d_num_pad, 0), groups=self.lf_conv2d_group)
	self.conv2 = torch.nn.Conv2d(self.embed_dim // 2, self.embed_dim, (2, 1), stride=(1, 1), padding=(self.lf_conv2d_num_pad, 0), groups=self.lf_conv2d_group)
	self.output_layernorm = YuanRMSNorm(self.embed_dim)

	def _train_forward(self, inputs):
	inputs = inputs.transpose(0,1)
	seq_len, bsz, embed_dim = inputs.size()
	if embed_dim != self.embed_dim:
	raise ValueError(
	f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
	)
	residual = inputs

	inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
	output1 = self.conv1(inputs)
	output1 = output1[:, :, :seq_len, :]

	output2 = self.conv2(output1)
	output2 = output2[:, :, :seq_len, :].permute(2, 3, 0, 1).contiguous()
	output2 = output2.view(seq_len, bsz, embed_dim)
	assert output2.shape == residual.shape

	lf_output = self.output_layernorm(output2 + residual)
	lf_output = lf_output.transpose(0,1)
	return lf_output

	def _inference_forward(self, inputs, before_hidden_states):

	if before_hidden_states is None:
	inputs = inputs.transpose(0,1)
	seq_len, bsz, embed_dim = inputs.size()
	if embed_dim != self.embed_dim:
	raise ValueError(
	f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}"
	)
	residual = inputs

	inputs = inputs.view(seq_len, 1, bsz, embed_dim).permute(2, 3, 0, 1)
	output1 = self.conv1(inputs)
	output1 = output1[:, :, :seq_len, :]

	output2 = self.conv2(output1)
	output2 = output2[:, :, :seq_len, :].permute(2, 3, 0, 1).contiguous()
	output2 = output2.view(seq_len, bsz, embed_dim)
	assert output2.shape == residual.shape

	lf_output = self.output_layernorm(output2 + residual)
	lf_output = lf_output.transpose(0,1)
	return lf_output
	else:
	inputs = inputs.transpose(0,1)
	before_hidden_states = before_hidden_states.transpose(0,1)
	residual = inputs

	seq_len, bsz, embed_dim = inputs.size()
	seq_len_before, _, _ = before_hidden_states.size()

	assert seq_len == 1 and seq_len_before == 2

	inputs = torch.cat((before_hidden_states, inputs), dim=0)
	inputs = inputs.view(3, 1, bsz, embed_dim).permute(2, 3, 0, 1)

	output1 = self.conv1(inputs)
	output2 = self.conv2(output1[:,:,1:-1,:])
	output2 = output2[:,:,1:-1,:]
	output2 = output2.view(1, bsz, embed_dim)
	assert output2.shape == residual.shape

	lf_output = self.output_layernorm(output2 + residual)
	lf_output = lf_output.transpose(0,1)

	return lf_output



	def forward(
	self,
	inputs,
	before_hidden_states
	) -> torch.Tensor:
	assert self.lf_conv2d_num_pad == 1
	if self.training:
	lf_output = self._train_forward(inputs)
	else:
	lf_output = self._inference_forward(inputs, before_hidden_states)

	return lf_output


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

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


	# Copied from transformers.models.bart.modeling_bart._expand_mask
	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	bsz, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len

	expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

	inverted_mask = 1.0 - expanded_mask

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


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


	def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
	# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
	cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
	sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
	cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
	sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed

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

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

	class YuanRotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):

	"""
	YuanRotaryEmbedding is equivalent to LlamaRotaryEmbedding in transformers v4.36
	"""

	super().__init__()

	self.dim = dim
	self.max_position_embeddings = max_position_embeddings
	self.base = base
	inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
	self.register_buffer("inv_freq", inv_freq, persistent=False)

	# Build here to make `torch.jit.trace` work.
	self._set_cos_sin_cache(
	seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
	)

	def _set_cos_sin_cache(self, seq_len, device, dtype):
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=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.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)

	def forward(self, x, seq_len=None):
	# x: [bs, num_attention_heads, seq_len, head_size]
	if seq_len > self.max_seq_len_cached:
	self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

	return (
	self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
	self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
	)

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

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

	class YuanAttention(nn.Module):
	"""Localized Filtering-based Attention 'YUAN 2.0: A Large Language Model with Localized Filtering-based Attention' paper"""

	def __init__(self, config: YuanConfig):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.hidden_size // self.num_heads
	self.max_position_embeddings = config.max_position_embeddings
	self.causal_mask = config.causal_mask
	self.softmax_scale = 1.0 / math.sqrt(self.head_dim)
	self.use_flash_attention = config.use_flash_attention
	try:
	self.use_shareqk = config.use_shareqk
	except Exception as e:
	self.use_shareqk=False
	self.dropout = 0.0
	if (self.head_dim * self.num_heads) != self.hidden_size:
	raise ValueError(
	f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
	f" and `num_heads`: {self.num_heads})."
	)
	self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
	self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
	self.rotary_emb = YuanRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
	if self.use_shareqk:
	self.qk_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
	self.qk_weight = nn.Parameter(torch.Tensor(2, self.hidden_size))
	self.qk_bias = nn.Parameter(torch.Tensor(2, self.hidden_size))
	else:
	self.lf_gate = LocalizedFiltering(self.hidden_size)
	self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
	self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)

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

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	bsz, q_len, _ = hidden_states.size()
	before_hidden_states = None
	is_first_step = False
	if use_cache:
	if past_key_value is None:
	#inference_hidden_states_memory = torch.empty(bsz, 2, hidden_states.shape[2], dtype=hidden_states.dtype ,device=torch.cuda.current_device())
	inference_hidden_states_memory = torch.empty(bsz, 2, hidden_states.shape[2], dtype=hidden_states.dtype)
	is_first_step = True
	else:
	before_hidden_states = past_key_value[2]

	if use_cache:
	if is_first_step:
	if q_len >= 2:
	inference_hidden_states_memory = hidden_states[ :, -2:, :]
	else:
	inference_hidden_states_memory[:, :, :] = 0
	inference_hidden_states_memory[:, -1:, :] = hidden_states[:, -1:, :]
	else:
	hidden_states_tmp = before_hidden_states[:, -1:, :]
	inference_hidden_states_memory = copy.deepcopy(torch.cat((hidden_states_tmp, hidden_states), dim=1))

	value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	if self.use_shareqk:
	qk_states = self.qk_proj(hidden_states).view(bsz, q_len, self.num_heads*self.head_dim)
	query_key = qk_states.unsqueeze(2) * self.qk_weight + self.qk_bias
	query_states, key_states = torch.unbind(query_key, dim=2)

	query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
	else:
	hidden_states = self.lf_gate(hidden_states,before_hidden_states)
	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	qk_states = torch.cat([query_states, key_states], dim=-1)
	qk_states = qk_states.view(bsz,q_len,self.num_heads,int(qk_states.shape[-1]//self.num_heads))
	(query_states,key_states) = torch.chunk(qk_states, 2, dim=-1)
	query_states = query_states.transpose(1, 2)
	key_states = key_states.transpose(1, 2)


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

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

	past_key_value = (key_states, value_states,inference_hidden_states_memory) if use_cache else None

	if self.use_flash_attention:
	attn_weights = None
	query_states = query_states.transpose(1, 2)
	key_states = key_states.transpose(1, 2)
	value_states = value_states.transpose(1, 2)

	batch_size, seqlen_q = query_states.shape[0], query_states.shape[1]
	seqlen_k = key_states.shape[1]

	q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [query_states, key_states, value_states]]

	cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int,
	device=q.device)

	if self.training:
	assert seqlen_k == seqlen_q
	cu_seqlens_k = cu_seqlens_q
	is_causal = self.causal_mask
	else:
	is_causal = seqlen_q == seqlen_k
	cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int,
	device=q.device)
	self.dropout=0

	output = flash_attn_unpadded_func(
	q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k, self.dropout, causal=is_causal
	)

	attn_output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
	else:
	attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

	if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
	raise ValueError(
	f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
	f" {attn_weights.size()}"
	)
	if attention_mask is not None:
	if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
	)
	attn_weights = attn_weights + attention_mask
	attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))

	# upcast attention to fp32
	attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
	attn_output = torch.matmul(attn_weights, value_states)

	if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
	f" {attn_output.size()}"
	)

	attn_output = attn_output.transpose(1, 2)

	attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

	attn_output = self.o_proj(attn_output)

	if not output_attentions:
	attn_weights = None
	return attn_output, attn_weights, past_key_value


	class YuanDecoderLayer(nn.Module):
	def __init__(self, config: YuanConfig):
	super().__init__()
	self.hidden_size = config.hidden_size
	self.self_attn = YuanAttention(config=config)
	self.mlp = YuanMLP(
	hidden_size=self.hidden_size,
	intermediate_size=config.intermediate_size,
	hidden_act=config.hidden_act,
	)
	self.input_layernorm = YuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_attention_layernorm = YuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`, optional): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	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`).
	past_key_value (`Tuple(torch.FloatTensor)`, optional): cached past key and value projection states
	"""

	residual = hidden_states
	hidden_states = self.input_layernorm(hidden_states)

	# Self Attention
	hidden_states, self_attn_weights, present_key_value = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)
	hidden_states = residual + hidden_states

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

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (present_key_value,)

	return outputs


	YUAN_START_DOCSTRING = r"""
	This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)

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

	Parameters:
	config ([`YuanConfig`]):
	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.
	"""


	@add_start_docstrings(
	"The bare Yuan Model outputting raw hidden-states without any specific head on top.",
	YUAN_START_DOCSTRING,
	)
	class YuanPreTrainedModel(PreTrainedModel):
	config_class = YuanConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["YuanDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"
	_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

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

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


	YUAN_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

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

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, 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)

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

	If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
	`past_key_values`).

	If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
	and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
	information on the default strategy.

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.
	position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	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)`.

	Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-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)`.
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, 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.
	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 (`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 Yuan Model outputting raw hidden-states without any specific head on top.",
	YUAN_START_DOCSTRING,
	)
	class YuanModel(YuanPreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`YuanDecoderLayer`]

	Args:
	config: YuanConfig
	"""

	def __init__(self, config: YuanConfig):
	super().__init__(config)
	self.padding_idx = config.pad_token_id
	self.vocab_size = config.vocab_size

	#TODO: control it by config
	self.eod_token = config.eod_token
	self.reset_attention_mask = config.reset_attention_mask
	self.reset_position_ids = config.reset_position_ids
	self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
	self.layers = nn.ModuleList([YuanDecoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.norm = YuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.embed_tokens

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

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

	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
	inputs_embeds.device
	)
	combined_attention_mask = (
	expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
	)

	return combined_attention_mask

	def _prepare_decoder_attention_mask_training(self, input_id, inputs_embeds, eod_token, reset_mask_flag ,reset_attention_mask=True, reset_position_ids=True):

	micro_batch_size, seq_length = input_id.size()

	attention_mask = torch.tril(torch.ones(
	(micro_batch_size, seq_length, seq_length), device=inputs_embeds.device)).view(
	micro_batch_size, 1, seq_length, seq_length)

	position_ids = torch.arange(seq_length, dtype=torch.long,
	device=inputs_embeds.device)
	position_ids = position_ids.unsqueeze(0).expand_as(input_id)

	if reset_position_ids:
	position_ids = position_ids.clone()

	if reset_position_ids or reset_attention_mask:
	# Loop through the batches:
	for b in range(micro_batch_size):

	# Find indecies where EOD token is.
	eod_index = position_ids[b, input_id[b] == eod_token]

	# Detach indecies from positions if going to modify positions.
	if reset_position_ids:
	eod_index = eod_index.clone()
	# Loop through EOD indecies:
	prev_index = 0
	for j in range(eod_index.size()[0]):
	i = eod_index[j]
	# Mask attention loss.
	if reset_attention_mask:
	attention_mask[b, 0, (i + 1):, :(i + 1)] = 0
	# Reset positions.
	if reset_position_ids:
	position_ids[b, (i + 1):] -= (i + 1 - prev_index)
	prev_index = i + 1

	inverted_mask = 1 - attention_mask
	output_attn_mask = inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min)
	if reset_mask_flag:
	output_attn_mask = output_attn_mask[:,:,-1:,:]
	return output_attn_mask, position_ids

	@add_start_docstrings_to_model_forward(YUAN_INPUTS_DOCSTRING)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPast]:
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	input_ids1 = copy.deepcopy(input_ids)
	reset_mask_flag = False
	if past_key_values:
	input_ids = input_ids[:, -1:]
	if use_cache:
	reset_mask_flag = True
	# retrieve input_ids and inputs_embeds
	if input_ids is not None and inputs_embeds is not None:
	raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
	elif input_ids is not None:
	batch_size, seq_length = input_ids.shape
	elif inputs_embeds is not None:
	batch_size, seq_length, _ = inputs_embeds.shape
	else:
	raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

	seq_length_with_past = seq_length
	past_key_values_length = 0

	if past_key_values is not None:
	past_key_values_length = past_key_values[0][0].shape[2]
	seq_length_with_past = seq_length_with_past + past_key_values_length

	if position_ids is None:
	device = input_ids.device if input_ids is not None else inputs_embeds.device
	position_ids = torch.arange(
	past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
	)
	position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
	else:
	position_ids = position_ids.view(-1, seq_length).long()
	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids)
	if self.training or self.reset_position_ids:
	attention_mask, _ = self._prepare_decoder_attention_mask_training(input_ids1, inputs_embeds, self.eod_token, reset_mask_flag, self.reset_attention_mask, self.reset_position_ids)

	else:
	if attention_mask is None:
	attention_mask = torch.ones(
	(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
	)
	attention_mask = self._prepare_decoder_attention_mask(
	attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
	)

	hidden_states = inputs_embeds

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

	# decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	next_decoder_cache = () if use_cache else None

	for idx, decoder_layer in enumerate(self.layers):
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	past_key_value = past_key_values[idx] if past_key_values is not None else None

	if self.gradient_checkpointing and self.training:

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

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	position_ids,
	None,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)

	hidden_states = layer_outputs[0]

	if use_cache:
	next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

	if output_attentions:
	all_self_attns += (layer_outputs[1],)
	hidden_states = self.norm(hidden_states)

	# add hidden states from the last decoder layer
	if output_hidden_states:
	all_hidden_states += (hidden_states,)
	next_cache = next_decoder_cache if use_cache else None
	if not return_dict:
	return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
	return BaseModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	)


	class YuanForCausalLM(YuanPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.eod_token = config.eod_token
	self.sep_token = config.sep_token
	self.use_loss_mask = config.use_loss_mask
	self.model = YuanModel(config)

	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

	def get_input_embeddings(self):
	return self.model.embed_tokens

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

	def get_output_embeddings(self):
	return self.lm_head

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

	def set_decoder(self, decoder):
	self.model = decoder

	def get_decoder(self):
	return self.model

	def get_loss_mask(self, input_ids, labels, eod_token, sep_token):
	micro_batch_size, seq_length = input_ids.size()
	loss_mask = torch.ones(input_ids.size(), dtype=torch.float, device=input_ids.device)

	position_ids = torch.arange(seq_length, dtype=torch.long,
	device=input_ids.device)
	position_ids = position_ids.unsqueeze(0).expand_as(input_ids)


	"""modify loss_mask to only calculate the loss of the answer (separated with [SEP])"""

	for b in range(micro_batch_size):
	eod_indexs = position_ids[b, input_ids[b] == eod_token]
	sep_indexs = position_ids[b, input_ids[b] == sep_token]

	if len(eod_indexs) == 0 or len(sep_indexs) == 0:
	loss_mask[b] = 1.0
	else:
	if eod_indexs[0] > sep_indexs[0]:
	loss_mask[b, 0:sep_indexs[0]] = 0

	if len(eod_indexs) == len(sep_indexs):
	for ii, eod_index in enumerate(eod_indexs):
	start_index = eod_index
	if ii == (len(sep_indexs) - 1):
	stop_index = seq_length
	else:
	stop_index = sep_indexs[ii + 1]
	loss_mask[b, start_index:stop_index] = 0.0
	else:
	if len(eod_indexs) > len(sep_indexs):
	loss_mask[b,:] = 1.0
	else:
	for ii, eod_index in enumerate(eod_indexs):
	start_index = eod_index
	stop_index = sep_indexs[ii + 1]

	loss_mask[b, start_index:stop_index] = 0.0

	elif eod_indexs[0] < sep_indexs[0]:

	if len(eod_indexs) == len(sep_indexs):
	for ii, eod_index in enumerate(eod_indexs):
	start_index = eod_index
	stop_index = sep_indexs[ii]
	loss_mask[b, start_index:stop_index] = 0.0

	else:
	if len(eod_indexs) < len(sep_indexs):
	loss_mask[b,:] = 1.0
	else:
	for ii, eod_index in enumerate(eod_indexs):
	start_index = eod_index
	if ii >= len(sep_indexs):
	stop_index = seq_length
	else:
	stop_index = sep_indexs[ii]
	loss_mask[b, start_index:stop_index] = 0.0

	loss_mask[input_ids == eod_token] = 1.0
	return loss_mask
	@add_start_docstrings_to_model_forward(YUAN_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithPast]:
	r"""
	Args:
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
	config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
	(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, YuanForCausalLM

	>>> model = YuanForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
	>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

	>>> prompt = "Hey, are you consciours? Can you talk to me?"
	>>> inputs = tokenizer(prompt, return_tensors="pt")

	>>> # Generate
	>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
	>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
	```"""

	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
	outputs = self.model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = outputs[0]
	logits = self.lm_head(hidden_states)
	loss = None
	if labels is not None:
	if self.use_loss_mask:
	loss_mask = self.get_loss_mask(input_ids, labels, self.eod_token, self.sep_token)
	# Shift so that tokens < n predict n
	shift_logits = logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	if self.use_loss_mask:
	loss_fct = CrossEntropyLoss(reduction='none')
	shift_logits = shift_logits.view(-1, self.config.vocab_size)
	shift_labels = shift_labels.view(-1)
	# Enable model parallelism
	shift_labels = shift_labels.to(shift_logits.device)
	loss = loss_fct(shift_logits, shift_labels)
	loss = torch.sum(loss * loss_mask) / loss_mask.sum()
	else:
	loss_fct = CrossEntropyLoss()
	shift_logits = shift_logits.view(-1, self.config.vocab_size)
	shift_labels = shift_labels.view(-1)
	# Enable model parallelism
	shift_labels = shift_labels.to(shift_logits.device)
	loss = loss_fct(shift_logits, shift_labels)
	if not return_dict:
	output = (logits,) + outputs[1:]
	return (loss,) + output if loss is not None else output

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

	def prepare_inputs_for_generation(
	self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
	):

	position_ids = kwargs.get("position_ids", None)
	if attention_mask is not None and position_ids is None:
	# create position_ids on the fly for batch generation
	position_ids = attention_mask.long().cumsum(-1) - 1
	position_ids.masked_fill_(attention_mask == 0, 1)
	if past_key_values:
	position_ids = position_ids[:, -1].unsqueeze(-1)

	# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
	if inputs_embeds is not None and past_key_values is None:
	model_inputs = {"inputs_embeds": inputs_embeds}
	else:
	model_inputs = {"input_ids": input_ids}

	model_inputs.update(
	{
	"position_ids": position_ids,
	"past_key_values": past_key_values,
	"use_cache": kwargs.get("use_cache"),
	"attention_mask": attention_mask,
	}
	)
	return model_inputs

	@staticmethod
	def _reorder_cache(past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
	return reordered_past


	@add_start_docstrings(
	"""
	The Yuan Model transformer with a sequence classification head on top (linear layer).

	[`YuanForSequenceClassification`] uses the last token in order to do the classification, as other causal models
	(e.g. GPT-2) do.

	Since it does classification on the last token, it requires to know the position of the last token. If a
	`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
	no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
	padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
	each row of the batch).
	""",
	YUAN_START_DOCSTRING,
	)
	class YuanForSequenceClassification(YuanPreTrainedModel):
	_keys_to_ignore_on_load_missing = [r"lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.model = YuanModel(config)
	self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

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

	def get_input_embeddings(self):
	return self.model.embed_tokens

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

	@add_start_docstrings_to_model_forward(YUAN_INPUTS_DOCSTRING)
	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, SequenceClassifierOutputWithPast]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size,)`, optional):
	Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
	config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
	`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	transformer_outputs = self.model(
	input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]
	logits = self.score(hidden_states)

	if input_ids is not None:
	batch_size = input_ids.shape[0]
	else:
	batch_size = inputs_embeds.shape[0]

	if self.config.pad_token_id is None and batch_size != 1:
	raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
	if self.config.pad_token_id is None:
	sequence_lengths = -1
	else:
	if input_ids is not None:
	sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
	else:
	sequence_lengths = -1

	pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

	loss = None
	if labels is not None:
	labels = labels.to(logits.device)
	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(pooled_logits.squeeze(), labels.squeeze())
	else:
	loss = loss_fct(pooled_logits, labels)
	elif self.config.problem_type == "single_label_classification":
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
	elif self.config.problem_type == "multi_label_classification":
	loss_fct = BCEWithLogitsLoss()
	loss = loss_fct(pooled_logits, labels)
	if not return_dict:
	output = (pooled_logits,) + transformer_outputs[1:]
	return ((loss,) + output) if loss is not None else output

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