360Zhinao-7B-Chat-360K / modeling_zhinao.py

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	# Copyright (c) 360zhinao and the HuggingFace Inc. team. All rights reserved.
	# This code is built upon Huggingface's transformers repository.

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

	import torch
	import torch.nn.functional as F
	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 logging
	from transformers.generation.utils import GenerationConfig
	from transformers.generation.logits_process import LogitsProcessorList
	from .configuration_zhinao import ZhinaoConfig
	from .generation_utils import TextIterStreamer, make_context, OutputRepetitionPenaltyLogitsProcessor, parse_pot_no_stream


	try:
	from flash_attn import flash_attn_varlen_func
	from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
	except:
	flash_attn_varlen_func = None
	index_first_axis, pad_input, unpad_input = None, None, None


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "ZhinaoConfig"


	def _get_unpad_data(attention_mask):
	seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
	indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
	max_seqlen_in_batch = seqlens_in_batch.max().item()
	cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
	return (
	indices,
	cu_seqlens,
	max_seqlen_in_batch,
	)


	# 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.finfo(dtype).min, 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)


	class ZhinaoRMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	ZhinaoRMSNorm is equivalent to T5LayerNorm
	"""
	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 ZhinaoRotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
	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 ZhinaoLinearScalingRotaryEmbedding(ZhinaoRotaryEmbedding):
	"""ZhinaoRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
	self.scaling_factor = scaling_factor
	super().__init__(dim, max_position_embeddings, base, device)

	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)
	t = t / self.scaling_factor

	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)


	class ZhinaoDynamicNTKScalingRotaryEmbedding(ZhinaoRotaryEmbedding):
	"""ZhinaoRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
	self.scaling_factor = scaling_factor
	super().__init__(dim, max_position_embeddings, base, device)

	def _set_cos_sin_cache(self, seq_len, device, dtype):
	self.max_seq_len_cached = seq_len

	if seq_len > self.max_position_embeddings:
	base = self.base * (
	(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
	) ** (self.dim / (self.dim - 2))
	inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
	self.register_buffer("inv_freq", inv_freq, persistent=False)

	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)


	class ZhinaoNTKScalingRotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, scaling_factor=100, device=None):
	super().__init__()

	self.dim = dim
	self.max_position_embeddings = max_position_embeddings
	self.base = base * scaling_factor
	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)
	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):
	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),
	)


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

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


	def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
	"""
	This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
	num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
	"""
	batch, num_key_value_heads, slen, head_dim = hidden_states.shape
	if n_rep == 1:
	return hidden_states
	hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
	return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


	class ZhinaoAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(self, config: ZhinaoConfig):
	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.num_key_value_heads = config.num_key_value_heads
	self.num_key_value_groups = self.num_heads // self.num_key_value_heads
	self.max_position_embeddings = config.max_position_embeddings
	self.rope_theta = config.rope_theta
	self.is_causal = True
	self.dropout = 0.0
	self.use_flash_attn = config.use_flash_attn

	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.qkv_hidden_size = (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim
	self.qkv_proj = nn.Linear(self.hidden_size, self.qkv_hidden_size, bias=True)
	self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
	self._init_rope()

	def _init_rope(self):
	if self.config.rope_scaling is None:
	self.rotary_emb = ZhinaoRotaryEmbedding(
	self.head_dim,
	max_position_embeddings=self.max_position_embeddings,
	base=self.rope_theta,
	)
	else:
	scaling_type = self.config.rope_scaling["type"]
	scaling_factor = self.config.rope_scaling["factor"]
	if scaling_type == "linear":
	self.rotary_emb = ZhinaoLinearScalingRotaryEmbedding(
	self.head_dim,
	max_position_embeddings=self.max_position_embeddings,
	scaling_factor=scaling_factor,
	base=self.rope_theta,
	)
	elif scaling_type == "dynamic":
	self.rotary_emb = ZhinaoDynamicNTKScalingRotaryEmbedding(
	self.head_dim,
	max_position_embeddings=self.max_position_embeddings,
	scaling_factor=scaling_factor,
	base=self.rope_theta,
	)
	elif scaling_type == "ntk":
	self.rotary_emb = ZhinaoNTKScalingRotaryEmbedding(
	self.head_dim,
	max_position_embeddings=self.max_position_embeddings,
	scaling_factor=scaling_factor,
	base=self.rope_theta,
	)
	else:
	raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

	def raw_attention(self, query_states, key_states, value_states, attention_mask):
	attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

	if attention_mask is not None:
	attn_weights = attn_weights + attention_mask

	# 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)

	attn_output = attn_output.transpose(1, 2).contiguous()

	return attn_output

	def flash_attention(self, query_states, key_states, value_states, attention_mask):
	# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
	# to be able to avoid many of these transpose/reshape/view.
	query_states = query_states.transpose(1, 2)
	key_states = key_states.transpose(1, 2)
	value_states = value_states.transpose(1, 2)

	batch_size, query_length = query_states.shape[0], query_states.shape[1]
	query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
	query_states, key_states, value_states, attention_mask, query_length
	)

	cu_seqlens_q, cu_seqlens_k = cu_seq_lens
	max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

	attn_output_unpad = flash_attn_varlen_func(
	query_states,
	key_states,
	value_states,
	cu_seqlens_q=cu_seqlens_q,
	cu_seqlens_k=cu_seqlens_k,
	max_seqlen_q=max_seqlen_in_batch_q,
	max_seqlen_k=max_seqlen_in_batch_k,
	dropout_p=self.dropout,
	softmax_scale=None,
	causal=self.is_causal,
	)
	attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
	return attn_output

	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()

	mixed_x_layer = self.qkv_proj(hidden_states)
	new_tensor_shape = mixed_x_layer.size()[:-1] + \
	(self.num_key_value_heads, ((self.num_heads // self.num_key_value_heads + 2) * self.head_dim))
	mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
	query, key_states, value_states = torch.split(
	mixed_x_layer,
	[self.num_heads // self.num_key_value_heads * self.head_dim, self.head_dim, self.head_dim],
	dim=3
	)
	# [sq, b, ng, np/ng * hn] -> [sq, b, np, hn]
	query_states = query.contiguous().view(query.size(0), query.size(1), -1, self.head_dim)

	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_key_value_heads, self.head_dim).transpose(1, 2)
	value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

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

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

	past_key_value = (key_states, value_states) if use_cache else None

	# repeat k/v heads if n_kv_heads < n_heads
	key_states = repeat_kv(key_states, self.num_key_value_groups)
	value_states = repeat_kv(value_states, self.num_key_value_groups)

	# q, k, v: [b, n, s, h]
	# check attention mask
	if self.use_flash_attn:
	if attention_mask is not None and attention_mask.size() != (bsz, kv_seq_len):
	raise ValueError(f"Attention mask should be of size {(bsz, kv_seq_len)}, but is {attention_mask.size()}")
	attn_output = self.flash_attention(query_states, key_states, value_states, attention_mask)
	else:
	if attention_mask is not None and 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_output = self.raw_attention(query_states, key_states, value_states, attention_mask)

	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

	# Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
	def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
	batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape

	# On the first iteration we need to properly re-create the padding mask
	# by slicing it on the proper place
	if kv_seq_len != attention_mask.shape[-1]:
	attention_mask_num_tokens = attention_mask.shape[-1]
	attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]

	indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)

	key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
	value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)

	if query_length == kv_seq_len:
	query_layer = index_first_axis(
	query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
	)
	cu_seqlens_q = cu_seqlens_k
	max_seqlen_in_batch_q = max_seqlen_in_batch_k
	indices_q = indices_k
	elif query_length == 1:
	max_seqlen_in_batch_q = 1
	cu_seqlens_q = torch.arange(
	batch_size + 1, dtype=torch.int32, device=query_layer.device
	) # There is a memcpy here, that is very bad.
	indices_q = cu_seqlens_q[:-1]
	query_layer = query_layer.squeeze(1)
	else:
	# The -q_len: slice assumes left padding.
	attention_mask = attention_mask[:, -query_length:]

	query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
	return (
	query_layer,
	key_layer,
	value_layer,
	indices_q,
	(cu_seqlens_q, cu_seqlens_k),
	(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
	)


	class ZhinaoDecoderLayer(nn.Module):
	def __init__(self, config: ZhinaoConfig):
	super().__init__()
	self.hidden_size = config.hidden_size

	self.self_attn = ZhinaoAttention(config=config)
	self.mlp = ZhinaoMLP(config)
	self.input_layernorm = ZhinaoRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_attention_layernorm = ZhinaoRMSNorm(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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
	query_sequence_length, key_sequence_length)` if default attention is used.
	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


	class ZhinaoPreTrainedModel(PreTrainedModel):
	config_class = ZhinaoConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["ZhinaoDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"

	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, ZhinaoModel):
	module.gradient_checkpointing = value


	class ZhinaoModel(ZhinaoPreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`ZhinaoDecoderLayer`]

	Args:
	config: ZhinaoConfig
	"""

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

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

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

	def get_input_embeddings(self):
	return self.embed_tokens

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

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

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

	return combined_attention_mask

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPast]:
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache

	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

	seq_length_with_past = seq_length
	past_key_values_length = 0

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

	if position_ids is None:
	device = input_ids.device if input_ids is not None else inputs_embeds.device
	position_ids = torch.arange(
	past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
	)
	position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
	else:
	position_ids = position_ids.view(-1, seq_length).long()

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids)
	# embed positions
	if attention_mask is None:
	attention_mask = torch.ones(
	(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
	)

	# (batch_size, 1, seq_length, seq_length)` if default attention is used
	if not self.use_flash_attn:
	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, past_key_value, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	position_ids,
	)
	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 ZhinaoForCausalLM(ZhinaoPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.model = ZhinaoModel(config)
	self.vocab_size = config.vocab_size
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

	# Initialize weights and apply final processing
	if config.bf16:
	self.model.bfloat16()
	self.lm_head.bfloat16()
	if config.fp16:
	self.model.half()
	self.lm_head.half()

	if config.use_flash_attn == "auto":
	if flash_attn_varlen_func:
	if config.bf16 or config.fp16:
	logger.warn("Try importing flash-attention.")
	config.use_flash_attn = True
	else:
	config.use_flash_attn = False
	logger.warn("Flash attention will be disabled because it does NOT support fp32.")
	else:
	config.use_flash_attn = False
	logger.warn("Please install FlashAttention first, " "e.g., with pip install flash-attn")

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

	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
	outputs = self.model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = outputs[0]
	logits = self.lm_head(hidden_states)

	# warn：Huge gpu memory
	logits = logits.float()

	loss = None
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	shift_logits = shift_logits.view(-1, self.config.vocab_size)
	shift_labels = shift_labels.view(-1)
	# 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=outputs.hidden_states,
	attentions=outputs.attentions,
	)

	def prepare_inputs_for_generation(
	self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
	):
	if past_key_values:
	input_ids = input_ids[:, -1:]

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

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

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

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


	def generate(
	self,
	inputs: Optional[torch.Tensor] = None,
	generation_config: Optional[GenerationConfig] = None,
	streamer = None,
	**kwargs,
	):
	repetition_penalty = kwargs.pop("repetition_penalty", generation_config.repetition_penalty)
	generation_config.repetition_penalty = 1.0

	logits_processor = None
	if repetition_penalty > 1.0:
	warnings.warn("We highly recommend using OpenAI's frequency and presence penalty instead of the original repetition penalty. The original repetition penalty penalizes prompt tokens, which may lead to various potential issues. Therefore, your repetition penalty coefficient will be transformed into frequency penalty and presence penalty.", UserWarning)
	presence_penalty = repetition_penalty - 1.0
	frequency_penalty = repetition_penalty - 1.0
	logits_processor = LogitsProcessorList(
	[OutputRepetitionPenaltyLogitsProcessor(inputs.size(1), presence_penalty, frequency_penalty, 1.0)]
	)

	response = super().generate(
	inputs,
	generation_config=generation_config,
	logits_processor=logits_processor,
	streamer=streamer,
	**kwargs,
	)
	generation_config.repetition_penalty = repetition_penalty
	return response


	def chat(
	self,
	tokenizer,
	messages: List[dict],
	system: str = "You are a helpful assistant.",
	stream=False,
	use_pot=True,
	generation_config: Optional[GenerationConfig]=None):

	generation_config = generation_config or self.generation_config
	input_ids = make_context(
	model=self, tokenizer=tokenizer, messages=messages,
	system=system, max_new_tokens=generation_config.max_new_tokens
	)

	if stream:
	streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, use_pot=use_pot)
	Thread(target=self.generate, kwargs=dict(
	inputs=input_ids, streamer=streamer,
	generation_config=generation_config,
	)).start()
	return streamer
	else:
	outputs = self.generate(input_ids, generation_config=generation_config)
	response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
	if use_pot:
	response = parse_pot_no_stream(response)
	return response


	class ZhinaoForSequenceClassification(ZhinaoPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.model = ZhinaoModel(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

	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]:


	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.eq(input_ids, self.config.pad_token_id).long().argmax(-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,
	)