CodeShell-7B-Chat / modeling_codeshell.py

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	# coding=utf-8
	# Copyright 2023 WisdomShell Inc. 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.

	# This code is based on Bigcode's GPTBigCode model. It has been modified from
	# its original forms to accommodate minor architectural differences compared to
	# GPTBigCode model that trained the model.

	# Copyright 2023 The Bigcode team and HuggingFace Inc. team.
	# 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 CodeShell model."""
	import os
	import math
	from typing import List, Optional, Tuple, Union, Callable
	from threading import Thread
	from queue import Queue


	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

	from transformers import LogitsProcessorList, StoppingCriteriaList, StoppingCriteria, PreTrainedModel, PretrainedConfig
	from transformers.generation.utils import GenerationConfig

	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import (
	BaseModelOutputWithPastAndCrossAttentions,
	CausalLMOutputWithCrossAttentions,
	)
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import (
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	)
	from .configuration_codeshell import CodeShellConfig

	# Fused kernels
	# Use separate functions for each case because conditionals prevent kernel fusion.
	# TODO: Could have better fused kernels depending on scaling, dropout and head mask.
	# Is it doable without writing 32 functions?
	@torch.jit.script
	def upcast_masked_softmax(
	x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor, scale: float, softmax_dtype: torch.dtype
	):
	input_dtype = x.dtype
	x = x.to(softmax_dtype) * scale
	x = torch.where(mask, x, mask_value)
	x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
	return x


	@torch.jit.script
	def upcast_softmax(x: torch.Tensor, scale: float, softmax_dtype: torch.dtype):
	input_dtype = x.dtype
	x = x.to(softmax_dtype) * scale
	x = torch.nn.functional.softmax(x, dim=-1).to(input_dtype)
	return x


	@torch.jit.script
	def masked_softmax(x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor):
	x = torch.where(mask, x, mask_value)
	x = torch.nn.functional.softmax(x, dim=-1)
	return x


	class CodeShellRotaryEmbedding(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)

	# 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 CodeShellLinearScalingRotaryEmbedding(CodeShellRotaryEmbedding):
	"""CodeShellRotaryEmbedding 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 CodeShellDynamicNTKScalingRotaryEmbedding(CodeShellRotaryEmbedding):
	"""ShellRotaryEmbedding 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)

	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 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

	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 CodeShellAttention(nn.Module):
	def __init__(self, config, layer_idx=None):
	super().__init__()
	self.mask_value = None

	self.position_embedding_type = config.position_embedding_type
	self.rope_scaling = config.rope_scaling
	self.max_position_embeddings = config.max_position_embeddings

	self.group_query_attention = config.group_query_attention
	self.num_query_groups = config.num_query_groups
	self.num_key_value_groups = config.num_attention_heads // config.num_query_groups

	self.embed_dim = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.embed_dim // self.num_heads
	self.kv_heads = config.num_query_groups if self.group_query_attention else self.num_heads
	self.kv_dim = self.kv_heads * self.head_dim
	self.split_size = self.embed_dim
	if self.head_dim * self.num_heads != self.embed_dim:
	raise ValueError(
	f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
	f" {self.num_heads})."
	)

	self.layer_idx = layer_idx

	self.c_attn = nn.Linear(self.embed_dim, self.embed_dim + 2 * self.kv_dim)
	self.c_proj = nn.Linear(self.embed_dim, self.embed_dim)

	self.attn_dropout = nn.Dropout(config.attn_pdrop)
	self.resid_dropout = nn.Dropout(config.resid_pdrop)

	if self.position_embedding_type == "rope":
	self._init_rope()

	def _init_rope(self):
	if self.rope_scaling is None:
	self.rotary_emb = CodeShellRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
	else:
	scaling_type = self.rope_scaling["type"]
	scaling_factor = self.rope_scaling["factor"]
	if scaling_type == "linear":
	self.rotary_emb = CodeShellLinearScalingRotaryEmbedding(
	self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
	)
	elif scaling_type == "dynamic":
	self.rotary_emb = CodeShellDynamicNTKScalingRotaryEmbedding(
	self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
	)
	else:
	raise ValueError(f"Unknown RoPE scaling type {scaling_type}")


	def _get_mask_value(self, device, dtype):
	# torch.where expects a tensor. We use a cache to avoid recreating it every time.
	if self.mask_value is None or self.mask_value.dtype != dtype or self.mask_value.device != device:
	self.mask_value = torch.full([], torch.finfo(dtype).min, dtype=dtype, device=device)
	return self.mask_value

	def forward(
	self,
	hidden_states: torch.Tensor,
	layer_past: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[
	Tuple[torch.Tensor, Optional[torch.Tensor]],
	Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[torch.Tensor, ...]],
	]:
	bsz, q_len, _ = hidden_states.size()
	query_states, key_states, value_states = self.c_attn(hidden_states).split((self.embed_dim, self.kv_dim, self.kv_dim), 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_query_groups, self.head_dim).transpose(1, 2)
	value_states = value_states.view(bsz, q_len, self.num_query_groups, self.head_dim).transpose(1, 2)

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

	layer_past = (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_heads // self.kv_heads)
	value_states = repeat_kv(value_states, self.num_heads // self.kv_heads)

	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()}"
	)
	mask_value = self._get_mask_value(attn_weights.device, attn_weights.dtype)
	# The fused kernel is very slow when the key length is not a multiple of 8, so we skip fusion.
	attn_weights = torch.where(attention_mask, attn_weights, mask_value)

	# upcast attention to fp32
	attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
	attn_weights = self.attn_dropout(attn_weights)
	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).contiguous()
	attn_output = attn_output.reshape(bsz, q_len, self.embed_dim)

	attn_output = self.c_proj(attn_output)
	attn_output = self.resid_dropout(attn_output)

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

	return outputs # a, present, (attentions)


	class CodeShellMLP(nn.Module):
	def __init__(self, intermediate_size, config):
	super().__init__()
	embed_dim = config.hidden_size
	self.c_fc = nn.Linear(embed_dim, intermediate_size)
	self.c_proj = nn.Linear(intermediate_size, embed_dim)
	self.act = ACT2FN[config.activation_function]
	self.dropout = nn.Dropout(config.resid_pdrop)

	# Copied from transformers.models.gpt2.modeling_gpt2.GPT2MLP.forward
	def forward(self, hidden_states: Optional[Tuple[torch.Tensor]]) -> torch.Tensor:
	hidden_states = self.c_fc(hidden_states)
	hidden_states = self.act(hidden_states)
	hidden_states = self.c_proj(hidden_states)
	hidden_states = self.dropout(hidden_states)
	return hidden_states


	class CodeShellBlock(nn.Module):
	def __init__(self, config, layer_idx=None):
	super().__init__()
	hidden_size = config.hidden_size
	self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

	self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
	self.attn = CodeShellAttention(config, layer_idx=layer_idx)
	self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

	self.mlp = CodeShellMLP(self.inner_dim, config)

	def forward(
	self,
	hidden_states: Optional[Tuple[torch.Tensor]],
	layer_past: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = False,
	output_attentions: Optional[bool] = False,
	) -> Union[
	Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
	]:
	residual = hidden_states
	hidden_states = self.ln_1(hidden_states)
	attn_outputs = self.attn(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	position_ids=position_ids,
	head_mask=head_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)
	attn_output = attn_outputs[0] # output_attn: a, present, (attentions)

	outputs = attn_outputs[1:]
	# residual connection
	hidden_states = attn_output + residual

	residual = hidden_states
	hidden_states = self.ln_2(hidden_states)
	feed_forward_hidden_states = self.mlp(hidden_states)
	# residual connection
	hidden_states = residual + feed_forward_hidden_states

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

	return outputs # hidden_states, present, (attentions, cross_attentions)


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

	config_class = CodeShellConfig
	base_model_prefix = "transformer"
	supports_gradient_checkpointing = True
	_no_split_modules = ["ShellBlock"]
	_skip_keys_device_placement = "past_key_values"

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

	def _init_weights(self, module):
	"""Initialize the weights."""
	if isinstance(module, (CodeShellMLP, CodeShellAttention)):
	# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
	# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
	# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
	# > -- GPT-2 :: https://openai.com/blog/better-language-models/
	#
	# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
	module.c_proj.weight.data.normal_(
	mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
	)
	module.c_proj._is_hf_initialized = True
	elif isinstance(module, nn.Linear):
	# Slightly different from the TF version which uses truncated_normal for initialization
	# cf https://github.com/pytorch/pytorch/pull/5617
	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, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)

	# Copied from transformers.models.gpt2.modeling_gpt2.GPT2PreTrainedModel._set_gradient_checkpointing with GPT2->Shell
	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, CodeShellModel):
	module.gradient_checkpointing = value


	GPT_BIGCODE_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 ([`CodeShellConfig`]): 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_BIGCODE_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.Tensor` of shape `(batch_size, input_ids_length)`):
	`input_ids_length` = `sequence_length` if `past_key_values` is `None` else
	`past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
	sequence tokens in the vocabulary.
	If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
	`input_ids`.
	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.
	[What are input IDs?](../glossary#input-ids)
	past_key_values (`Tuple[torch.Tensor]` of length `config.n_layers`):
	Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
	`past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
	their past given to this model should not be passed as `input_ids` as they have already been computed.
	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.
	If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for
	`past_key_values`. In other words, the `attention_mask` always has to have the length:
	`len(past_key_values) + len(input_ids)`
	[What are attention masks?](../glossary#attention-mask)
	token_type_ids (`torch.Tensor` of shape `(batch_size, input_ids_length)`, optional):
	Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
	1]`:
	- 0 corresponds to a sentence A token,
	- 1 corresponds to a sentence B token.
	[What are token type IDs?](../glossary#token-type-ids)
	position_ids (`torch.Tensor` 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.max_position_embeddings - 1]`.
	[What are position IDs?](../glossary#position-ids)
	head_mask (`torch.Tensor` 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.Tensor` 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.
	If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
	`past_key_values`).
	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 GPT_BIGCODE Model transformer outputting raw hidden-states without any specific head on top.",
	GPT_BIGCODE_START_DOCSTRING,
	)
	class CodeShellModel(CodeShellPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.group_query_attention = config.group_query_attention
	self.num_query_groups = config.num_query_groups
	self.position_embedding_type = config.position_embedding_type
	self.embed_dim = config.hidden_size

	self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
	if self.position_embedding_type == "learned_absolute":
	self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
	else:
	pass

	self.drop = nn.Dropout(config.embd_pdrop)
	self.h = nn.ModuleList([CodeShellBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
	self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

	max_positions = config.max_position_embeddings
	self.register_buffer(
	"bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)), persistent=False
	)

	self.gradient_checkpointing = False

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

	def get_input_embeddings(self):
	return self.wte

	def set_input_embeddings(self, new_embeddings):
	self.wte = new_embeddings

	@add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
	output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
	output_hidden_states = (
	output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	if 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()
	input_ids = input_ids.reshape(-1, input_shape[-1])
	batch_size = input_ids.shape[0]
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	batch_size = inputs_embeds.shape[0]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	if batch_size <= 0:
	raise ValueError("batch_size has to be defined and > 0")

	device = input_ids.device if input_ids is not None else inputs_embeds.device

	if token_type_ids is not None:
	token_type_ids = token_type_ids.reshape(-1, input_shape[-1])
	if position_ids is not None:
	position_ids = position_ids.reshape(-1, input_shape[-1])

	if past_key_values is None:
	past_length = 0
	past_key_values = tuple([None] * len(self.h))
	else:
	past_length = past_key_values[0][0].size(-2)

	if attention_mask is not None and len(attention_mask.shape) == 2 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_length > 0:
	position_ids = position_ids[:, past_length : input_shape[-1] + past_length :]
	elif position_ids is None:
	position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
	position_ids = position_ids.unsqueeze(0).reshape(-1, input_shape[-1])

	# Self-attention mask.
	query_length = input_shape[-1]
	key_length = past_length + query_length
	self_attention_mask = self.bias[None, key_length - query_length : key_length, :key_length]

	if attention_mask is not None:
	self_attention_mask = self_attention_mask * attention_mask.reshape(batch_size, 1, -1).to(
	dtype=torch.bool, device=self_attention_mask.device
	)

	# MQA models: (batch_size, query_length, n_heads, key_length)
	# MHA models: (batch_size, n_heads, query_length, key_length)
	attention_mask = self_attention_mask.unsqueeze(1)

	encoder_attention_mask = None

	# 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
	# head_mask has shape n_layer x batch x n_heads x N x N
	head_mask = self.get_head_mask(head_mask, self.config.n_layer)

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

	hidden_states = inputs_embeds
	if self.position_embedding_type == "learned_absolute":
	position_embeds = self.wpe(position_ids)
	hidden_states = hidden_states + position_embeds

	if token_type_ids is not None:
	token_type_embeds = self.wte(token_type_ids)
	hidden_states = hidden_states + token_type_embeds

	hidden_states = self.drop(hidden_states)

	output_shape = input_shape + (hidden_states.size(-1),)

	presents = [] if use_cache else None
	all_self_attentions = () if output_attentions else None
	all_hidden_states = () if output_hidden_states else None
	for i, (block, layer_past) in enumerate(zip(self.h, 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 past_key_value
	return module(*inputs, use_cache, output_attentions)

	return custom_forward

	outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(block),
	hidden_states,
	None,
	attention_mask,
	position_ids,
	head_mask[i],
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	outputs = block(
	hidden_states,
	layer_past=layer_past,
	attention_mask=attention_mask,
	position_ids=position_ids,
	head_mask=head_mask[i],
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	)

	hidden_states = outputs[0]
	if use_cache:
	presents.append(outputs[1])

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

	hidden_states = self.ln_f(hidden_states)
	hidden_states = hidden_states.reshape(output_shape)
	# 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_self_attentions]
	if v is not None
	)

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

	class EndOfFunctionCriteria(StoppingCriteria):
	"""Custom `StoppingCriteria` which checks if all generated functions in the batch are completed."""
	def __init__(self, input_lengths, eof_strings, tokenizer):
	self.input_lengths = input_lengths
	self.eof_strings = eof_strings
	self.tokenizer = tokenizer

	def __call__(self, input_ids, scores, **kwargs):
	"""Returns true if all generated sequences contain any of the end-of-function strings."""
	decoded_generations = []
	for _input_ids, input_length in zip(input_ids, self.input_lengths):
	decoded_generations.append(self.tokenizer.decode(_input_ids[input_length:]))
	done = []
	for decoded_generation in decoded_generations:
	done.append(
	any(
	[
	stop_string in decoded_generation
	for stop_string in self.eof_strings
	]
	)
	)
	return all(done)

	class TextIterStreamer:
	def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
	self.tokenizer = tokenizer
	self.skip_prompt = skip_prompt
	self.skip_special_tokens = skip_special_tokens
	self.tokens = []
	self.text_queue = Queue()
	self.next_tokens_are_prompt = True

	def put(self, value):
	if self.skip_prompt and self.next_tokens_are_prompt:
	self.next_tokens_are_prompt = False
	else:
	if len(value.shape) > 1:
	value = value[0]
	self.tokens.extend(value.tolist())
	self.text_queue.put(
	self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens))

	def end(self):
	self.text_queue.put(None)

	def __iter__(self):
	return self

	def __next__(self):
	value = self.text_queue.get()
	if value is None:
	raise StopIteration()
	else:
	return value


	@add_start_docstrings(
	"""
	The GPT_BIGCODE Model transformer with a language modeling head on top (linear layer with weights tied to the input
	embeddings).
	""",
	GPT_BIGCODE_START_DOCSTRING,
	)
	class CodeShellForCausalLM(CodeShellPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.transformer = CodeShellModel(config)
	self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

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

	def quantize(self, bits: int):
	try:
	import bitsandbytes
	from .quantizer import quantize
	except ImportError:
	raise ImportError(f"Needs bitsandbytes to run quantize.")
	return quantize(self, bits)

	def get_output_embeddings(self):
	return self.lm_head

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

	def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
	token_type_ids = kwargs.get("token_type_ids", None)
	# only last token for inputs_ids if past is defined in kwargs
	if past_key_values:
	input_ids = input_ids[:, -1].unsqueeze(-1)
	if token_type_ids is not None:
	token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

	attention_mask = kwargs.get("attention_mask", None)
	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)
	else:
	position_ids = None

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

	@add_start_docstrings_to_model_forward(GPT_BIGCODE_INPUTS_DOCSTRING)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	token_type_ids: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
	r"""
	labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set
	`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
	are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	transformer_outputs = self.transformer(
	input_ids,
	past_key_values=past_key_values,
	attention_mask=attention_mask,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	hidden_states = transformer_outputs[0]
	lm_logits = self.lm_head(hidden_states)
	loss = None
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = lm_logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous().to(shift_logits.device)
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	loss = loss_fct(shift_logits.reshape(-1, shift_logits.size(-1)), shift_labels.reshape(-1))

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

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

	@staticmethod
	def _reorder_cache(past_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 build_chat_input(self, query, history, tokenizer, max_new_tokens=None):
	user_name = "## human:"
	ai_name = "## assistant: "
	stop = '\|<end>\|'

	prompt = ''
	for q, r in history:
	prompt += f"{user_name}{q}{stop}"
	prompt += f"{ai_name}{r}{stop}"
	prompt += f"{user_name}{query}{stop}"
	prompt += ai_name.rstrip()

	max_new_tokens = max_new_tokens or self.generation_config.max_new_tokens
	max_new_tokens = max_new_tokens or 128
	max_input_tokens = self.config.n_positions - max_new_tokens

	input_tokens = tokenizer.encode(prompt)
	input_tokens = input_tokens[-max_input_tokens:] # truncate left
	return torch.LongTensor([input_tokens]).to(self.device)

	def chat(self, query, history, tokenizer, stream=False,
	generation_config: Optional[GenerationConfig]=None):
	generation_config = generation_config or self.generation_config
	input_ids = self.build_chat_input(query, history, tokenizer, generation_config.max_new_tokens)
	stopping_criteria = StoppingCriteriaList(
	[EndOfFunctionCriteria([len(input_ids[0])], ['\|<end>\|', '\|end\|', '<\|endoftext\|>', '## human'], tokenizer)]
	)

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

	def generate_stream(self, prompt, tokenizer, generation_config=None, **kwargs):
	generation_config = generation_config or self.generation_config
	max_input_tokens = self.config.n_positions - self.generation_config.max_new_tokens

	input_ids = tokenizer.encode(prompt)
	input_ids = input_ids[-max_input_tokens:] # truncate left

	stopping_criteria = StoppingCriteriaList(
	[EndOfFunctionCriteria([len(input_ids[0])], ['\|<end>\|', '\|end\|', '<\|endoftext\|>', '## human'], tokenizer)]
	)

	streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
	Thread(target=self.generate, kwargs=dict(
	inputs=input_ids, stopping_criteria=stopping_criteria, **kwargs
	)).start()
	return streamer


	class CodeShell4bitForCausalLM(CodeShellForCausalLM):
	def __init__(self, config):
	CodeShellPreTrainedModel.__init__(self, config)
	self.transformer = CodeShellModel(config)
	self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

	try:
	import bitsandbytes
	from .quantizer import quantize_offline
	quantize_offline(self)
	except ImportError:
	raise ImportError(f"Needs bitsandbytes to run quantize.")

	self.post_init()

	@classmethod
	def from_pretrained(
	cls,
	pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
	*model_args,
	config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
	cache_dir: Optional[Union[str, os.PathLike]] = None,
	ignore_mismatched_sizes: bool = False,
	force_download: bool = False,
	local_files_only: bool = False,
	token: Optional[Union[str, bool]] = None,
	revision: str = "main",
	use_safetensors: bool = None,
	**kwargs,
	):
	if not isinstance(config, PretrainedConfig):
	config_path = config if config is not None else pretrained_model_name_or_path
	config, _ = cls.config_class.from_pretrained(
	config_path,
	cache_dir=cache_dir,
	return_unused_kwargs=True,
	force_download=force_download,
	resume_download=False,
	proxies=None,
	local_files_only=local_files_only,
	token=token,
	revision=revision,
	subfolder="",
	_from_auto=False,
	_from_pipeline=None,
	**kwargs,
	)

	# Load config if we don't provide a configuration
	from .quantizer import load_state_dict_for_qunantied_model
	model = cls(config)
	state_dict = torch.load(os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin'), map_location="cpu")
	model = load_state_dict_for_qunantied_model(model, state_dict)
	model.eval()

	# If it is a model with generation capabilities, attempt to load the generation config
	if model.can_generate():
	try:
	model.generation_config = GenerationConfig.from_pretrained(
	pretrained_model_name_or_path,
	cache_dir=cache_dir,
	force_download=force_download,
	resume_download=False,
	proxies=None,
	local_files_only=local_files_only,
	token=token,
	revision=revision,
	subfolder="",
	_from_auto=False,
	_from_pipeline=None,
	**kwargs,
	)
	except (OSError, TypeError):
	pass

	device_map = kwargs.pop("device_map", None)
	if device_map is not None:
	model = model.to(torch.device(device_map))

	return model