quietstar / modeling_quietstar.py

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	# coding=utf-8
	# Copyright 2023 Mistral AI 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 Mistral model."""
	import inspect
	import math
	import copy
	import os
	import time
	import pandas as pd
	import seaborn as sns
	import matplotlib.pyplot as plt
	import wandb
	from termcolor import colored
	from tqdm import tqdm
	import random
	import numpy as np
	from matplotlib.colors import LinearSegmentedColormap, LogNorm
	import warnings
	from collections import defaultdict
	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.cache_utils import Cache, DynamicCache
	from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
	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,
	is_flash_attn_2_available,
	is_flash_attn_greater_or_equal_2_10,
	logging,
	replace_return_docstrings,
	)
	from .configuration_quietstar import MistralConfig


	if is_flash_attn_2_available():
	from flash_attn import flash_attn_func, flash_attn_varlen_func
	from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa

	_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)


	logger = logging.get_logger(__name__)

	_CONFIG_FOR_DOC = "MistralConfig"

	from reportlab.pdfgen import canvas
	from reportlab.lib.pagesizes import letter
	from reportlab.lib.colors import HexColor

	def save_tokens_with_rewards_to_pdf(input_ids, token_rewards, tokenizer, output_file="text.pdf", eps=0.2, eps2=0.5):
	c = canvas.Canvas(output_file, pagesize=letter)
	c.setFont("Courier", 8)
	x, y = 50, 750
	previous_text = ""
	current_text = ""
	for token_idx, reward in enumerate(token_rewards):
	current_text = tokenizer.decode(input_ids[: token_idx + 1])
	if current_text != previous_text:
	diff_text = current_text[len(previous_text) :]
	if "\n" in diff_text:
	lines = diff_text.split("\n")
	for line_idx, line in enumerate(lines):
	if line_idx > 0:
	x = 50
	y -= 12
	if abs(reward) < eps:
	opacity = 0
	elif abs(reward) > eps2:
	opacity = 0.8
	else:
	opacity = 0.8 * (abs(reward) - eps) / (eps2 - eps)
	text_width = c.stringWidth(line)
	if reward > 0:
	highlight_color = HexColor("#4CCD99")
	else:
	highlight_color = HexColor("#FFC700")
	highlight_color.alpha = opacity
	c.setFillColor(highlight_color)
	c.rect(x, y - 2, text_width, 10, fill=True, stroke=False)
	c.setFillColor(HexColor("#000000"))
	c.drawString(x, y, line)
	x += text_width
	else:
	if abs(reward) < eps:
	opacity = 0
	elif abs(reward) > eps2:
	opacity = 0.8
	else:
	opacity = 0.8 * (abs(reward) - eps) / (eps2 - eps)
	text_width = c.stringWidth(diff_text)
	if reward > 0:
	highlight_color = HexColor("#4CCD99")
	else:
	highlight_color = HexColor("#FFC700")
	highlight_color.alpha = opacity
	c.setFillColor(highlight_color)
	c.rect(x, y - 2, text_width, 10, fill=True, stroke=False)
	c.setFillColor(HexColor("#000000"))
	c.drawString(x, y, diff_text)
	x += text_width
	if x > 550:
	x = 50
	y -= 12
	if y < 50:
	c.showPage()
	y = 750
	x = 50
	previous_text = current_text
	c.showPage()
	c.save()


	# Copied from transformers.models.llama.modeling_llama._get_unpad_data
	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.llama.modeling_llama.LlamaRMSNorm with Llama->Mistral
	class MistralRMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	"""
	MistralRMSNorm 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 hidden_states.to(input_dtype) * self.weight.to(hidden_states.device)


	# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Mistral
	class MistralRotaryEmbedding(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.outer(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().to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin().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),
	)


	# Copied from transformers.models.llama.modeling_llama.rotate_half
	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)


	# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
	def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
	"""Applies Rotary Position Embedding to the query and key tensors.

	Args:
	q (`torch.Tensor`): The query tensor.
	k (`torch.Tensor`): The key tensor.
	cos (`torch.Tensor`): The cosine part of the rotary embedding.
	sin (`torch.Tensor`): The sine part of the rotary embedding.
	position_ids (`torch.Tensor`):
	The position indices of the tokens corresponding to the query and key tensors. For example, this can be
	used to pass offsetted position ids when working with a KV-cache.
	unsqueeze_dim (`int`, optional, defaults to 1):
	The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
	sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
	that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
	k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
	cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
	the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
	Returns:
	`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
	"""
	cos = cos[position_ids].unsqueeze(unsqueeze_dim)
	sin = sin[position_ids].unsqueeze(unsqueeze_dim)
	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed


	class MistralMLP(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):
	return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


	# Copied from transformers.models.llama.modeling_llama.repeat_kv
	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 MistralAttention(nn.Module):
	"""
	Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
	and "Generating Long Sequences with Sparse Transformers".
	"""

	def __init__(self, config: MistralConfig, layer_idx: Optional[int] = None):
	super().__init__()
	self.config = config
	self.layer_idx = layer_idx
	if layer_idx is None:
	logger.warning_once(
	f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
	"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
	"when creating this class."
	)

	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.attention_dropout = config.attention_dropout

	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.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_key_value_heads * self.head_dim, bias=False)
	self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_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 = MistralRotaryEmbedding(
	self.head_dim,
	max_position_embeddings=self.max_position_embeddings,
	base=self.rope_theta,
	)

	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[Cache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	**kwargs,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	if "padding_mask" in kwargs:
	warnings.warn(
	"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
	)
	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	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:
	if self.layer_idx is None:
	raise ValueError(
	f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
	"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
	"with a layer index."
	)
	kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
	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:
	cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
	key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

	# 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)

	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

	# upcast attention to fp32
	attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
	attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
	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.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 MistralFlashAttention2(MistralAttention):
	"""
	Mistral flash attention module. This module inherits from `MistralAttention` as the weights of the module stays
	untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
	flash attention and deal with padding tokens in case the input contains any of them.
	"""

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
	def __init__(self, args, *kwargs):
	super().__init__(args, *kwargs)

	# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
	# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
	# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
	self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Cache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	**kwargs,
	):
	if "padding_mask" in kwargs:
	warnings.warn(
	"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
	)

	# overwrite attention_mask with padding_mask
	attention_mask = kwargs.pop("padding_mask")
	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	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:
	if self.layer_idx is None:
	raise ValueError(
	f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
	"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
	"with a layer index."
	)
	kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

	# Because the input can be padded, the absolute sequence length depends on the max position id.
	rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
	cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)

	query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

	use_sliding_windows = (
	_flash_supports_window_size
	and getattr(self.config, "sliding_window", None) is not None
	and kv_seq_len > self.config.sliding_window
	)

	if not _flash_supports_window_size:
	logger.warning_once(
	"The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
	" make sure to upgrade flash-attn library."
	)

	if past_key_value is not None:
	# Activate slicing cache only if the config has a value `sliding_windows` attribute
	cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
	if (
	getattr(self.config, "sliding_window", None) is not None
	and kv_seq_len > self.config.sliding_window
	and cache_has_contents
	):
	slicing_tokens = 1 - self.config.sliding_window

	past_key = past_key_value[self.layer_idx][0]
	past_value = past_key_value[self.layer_idx][1]

	past_key = past_key[:, :, slicing_tokens:, :].contiguous()
	past_value = past_value[:, :, slicing_tokens:, :].contiguous()

	if past_key.shape[-2] != self.config.sliding_window - 1:
	raise ValueError(
	f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
	f" {past_key.shape}"
	)

	if attention_mask is not None:
	attention_mask = attention_mask[:, slicing_tokens:]
	attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)

	cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
	key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

	# 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)
	dropout_rate = 0.0 if not self.training else self.attention_dropout

	# In PEFT, usually we cast the layer norms in float32 for training stability reasons
	# therefore the input hidden states gets silently casted in float32. Hence, we need
	# cast them back in float16 just to be sure everything works as expected.
	input_dtype = query_states.dtype
	if input_dtype == torch.float32:
	if torch.is_autocast_enabled():
	target_dtype = torch.get_autocast_gpu_dtype()
	# Handle the case where the model is quantized
	elif hasattr(self.config, "_pre_quantization_dtype"):
	target_dtype = self.config._pre_quantization_dtype
	else:
	target_dtype = self.q_proj.weight.dtype

	logger.warning_once(
	f"The input hidden states seems to be silently casted in float32, this might be related to"
	f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
	f" {target_dtype}."
	)

	query_states = query_states.to(target_dtype)
	key_states = key_states.to(target_dtype)
	value_states = value_states.to(target_dtype)

	# Reashape to the expected shape for Flash Attention
	query_states = query_states.transpose(1, 2)
	key_states = key_states.transpose(1, 2)
	value_states = value_states.transpose(1, 2)

	attn_output = self._flash_attention_forward(
	query_states,
	key_states,
	value_states,
	attention_mask,
	q_len,
	dropout=dropout_rate,
	use_sliding_windows=use_sliding_windows,
	)

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

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value

	def _flash_attention_forward(
	self,
	query_states,
	key_states,
	value_states,
	attention_mask,
	query_length,
	dropout=0.0,
	softmax_scale=None,
	use_sliding_windows=False,
	):
	"""
	Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
	first unpad the input, then computes the attention scores and pad the final attention scores.

	Args:
	query_states (`torch.Tensor`):
	Input query states to be passed to Flash Attention API
	key_states (`torch.Tensor`):
	Input key states to be passed to Flash Attention API
	value_states (`torch.Tensor`):
	Input value states to be passed to Flash Attention API
	attention_mask (`torch.Tensor`):
	The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
	position of padding tokens and 1 for the position of non-padding tokens.
	dropout (`int`, optional):
	Attention dropout
	softmax_scale (`float`, optional):
	The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
	use_sliding_windows (`bool`, optional):
	Whether to activate sliding window attention.
	"""
	if not self._flash_attn_uses_top_left_mask:
	causal = self.is_causal
	else:
	# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
	causal = self.is_causal and query_length != 1

	# Contains at least one padding token in the sequence
	if attention_mask is not None:
	batch_size = query_states.shape[0]
	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

	if not use_sliding_windows:
	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=dropout,
	softmax_scale=softmax_scale,
	causal=causal,
	)
	else:
	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=dropout,
	softmax_scale=softmax_scale,
	causal=causal,
	window_size=(self.config.sliding_window, self.config.sliding_window),
	)

	attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
	else:
	if not use_sliding_windows:
	attn_output = flash_attn_func(
	query_states,
	key_states,
	value_states,
	dropout,
	softmax_scale=softmax_scale,
	causal=causal,
	)
	else:
	attn_output = flash_attn_func(
	query_states,
	key_states,
	value_states,
	dropout,
	softmax_scale=softmax_scale,
	causal=causal,
	window_size=(self.config.sliding_window, self.config.sliding_window),
	)

	return attn_output

	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),
	)


	# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mistral
	class MistralSdpaAttention(MistralAttention):
	"""
	Mistral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
	`MistralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
	SDPA API.
	"""

	# Adapted from MistralAttention.forward
	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_value: Optional[Cache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	if output_attentions:
	# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
	logger.warning_once(
	"MistralModel is using MistralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
	'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
	)
	return super().forward(
	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,
	)

	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	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.get_usable_length(kv_seq_len, self.layer_idx)
	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:
	cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
	key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

	key_states = repeat_kv(key_states, self.num_key_value_groups)
	value_states = repeat_kv(value_states, self.num_key_value_groups)

	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()}"
	)

	# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
	# Reference: https://github.com/pytorch/pytorch/issues/112577.
	if query_states.device.type == "cuda" and attention_mask is not None:
	query_states = query_states.contiguous()
	key_states = key_states.contiguous()
	value_states = value_states.contiguous()

	attn_output = torch.nn.functional.scaled_dot_product_attention(
	query_states,
	key_states,
	value_states,
	attn_mask=attention_mask.to(query_states.device) if attention_mask is not None else None,
	dropout_p=self.attention_dropout if self.training else 0.0,
	# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
	is_causal=self.is_causal and attention_mask is None and q_len > 1,
	)

	attn_output = attn_output.transpose(1, 2).contiguous()
	attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

	attn_output = self.o_proj(attn_output)

	return attn_output, None, past_key_value


	MISTRAL_ATTENTION_CLASSES = {
	"eager": MistralAttention,
	"flash_attention_2": MistralFlashAttention2,
	"sdpa": MistralSdpaAttention,
	}


	class MistralDecoderLayer(nn.Module):
	def __init__(self, config: MistralConfig, layer_idx: int):
	super().__init__()
	self.hidden_size = config.hidden_size

	self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)

	self.mlp = MistralMLP(config)
	self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_attention_layernorm = MistralRMSNorm(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,
	**kwargs,
	) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
	if "padding_mask" in kwargs:
	warnings.warn(
	"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
	)
	"""
	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, sequence_length)` where padding elements are indicated by 0.
	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.to(hidden_states.device) + 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


	MISTRAL_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 ([`MistralConfig`]):
	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 Mistral Model outputting raw hidden-states without any specific head on top.",
	MISTRAL_START_DOCSTRING,
	)
	class MistralPreTrainedModel(PreTrainedModel):
	config_class = MistralConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["MistralDecoderLayer"]
	_skip_keys_device_placement = "past_key_values"
	_supports_flash_attn_2 = True
	_supports_sdpa = True
	_supports_cache_class = True

	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_()


	MISTRAL_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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, optional):
	Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
	returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

	Two formats are allowed:
	- a [`~cache_utils.Cache`] instance;
	- 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)`). This is also known as the legacy
	cache format.

	The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
	legacy cache format will be returned.

	If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
	have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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 Mistral Model outputting raw hidden-states without any specific head on top.",
	MISTRAL_START_DOCSTRING,
	)
	class MistralModel(MistralPreTrainedModel):
	"""
	Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [`MistralDecoderLayer`]

	Args:
	config: MistralConfig
	"""

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

	self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
	self.layers = nn.ModuleList(
	[MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
	)
	self._attn_implementation = config._attn_implementation
	self.norm = MistralRMSNorm(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

	@add_start_docstrings_to_model_forward(MISTRAL_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

	# 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")

	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

	past_key_values_length = 0

	if use_cache:
	use_legacy_cache = not isinstance(past_key_values, Cache)
	if use_legacy_cache:
	past_key_values = DynamicCache.from_legacy_cache(past_key_values)
	past_key_values_length = past_key_values.get_usable_length(seq_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 attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
	is_padding_right = attention_mask[:, -1].sum().item() != batch_size
	if is_padding_right:
	raise ValueError(
	"You are attempting to perform batched generation with padding_side='right'"
	" this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to "
	" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
	)

	if self._attn_implementation == "flash_attention_2":
	# 2d mask is passed through the layers
	attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
	elif self._attn_implementation == "sdpa" and not output_attentions and attention_mask.dim() == 2 and False:
	# output_attentions=True can not be supported when using SDPA, and we fall back on
	# the manual implementation that requires a 4D causal mask in all cases.
	attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
	attention_mask,
	(batch_size, seq_length),
	inputs_embeds,
	past_key_values_length,
	)
	elif attention_mask is None or attention_mask.dim() == 2:
	# 4d mask is passed through the layers
	attention_mask = _prepare_4d_causal_attention_mask(
	attention_mask,
	(batch_size, seq_length),
	inputs_embeds,
	past_key_values_length,
	sliding_window=self.config.sliding_window,
	)

	hidden_states = inputs_embeds

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

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

	if self.gradient_checkpointing and self.training:
	layer_outputs = self._gradient_checkpointing_func(
	decoder_layer.__call__,
	hidden_states,
	attention_mask,
	position_ids,
	past_key_values,
	output_attentions,
	use_cache,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_values,
	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 = None
	if use_cache:
	next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache

	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,
	)

	def nonzero_mean(x, axis=None):
	if axis is not None:
	return x.sum(axis) / (x != 0).sum(axis)
	return x.sum() / (x != 0).sum()

	def loss_mean(x):
	return x.sum() / (x != 0).sum()

	class MistralForCausalLM(MistralPreTrainedModel):
	_tied_weights_keys = ["lm_head.weight"]

	def __init__(self, config):
	super().__init__(config)
	self.model = MistralModel(config)
	self.vocab_size = config.vocab_size
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
	self.max_thoughts = config.max_thoughts
	self.merged_lm_and_talk_heads = config.merged_lm_and_talk_heads
	self.use_concat_talk_head = config.use_concat_talk_head
	self.use_shallow_talk = config.use_shallow_talk
	self.use_complex_talk_head = config.use_complex_talk_head
	self.use_weighted_talk_head = config.use_weighted_talk_head
	# the weighted head will output a single value, so it can't be passed to the lm head
	assert not (self.use_weighted_talk_head and self.use_shallow_talk)

	self.n_ahead = 1
	self.n_ahead_talk = 1
	self.n_passes = 1
	self.n_tokens_print = 1
	self.gradient_accumulation_steps = 1
	self.training_steps = 0
	self.tokenizer = None
	self.start_token_id = None
	self.end_token_id = None
	self.rm_initialized = False
	self.residual_talk_head = True
	self.thought_init_std_scale = 1e-2

	self.final_only_mode = False
	self.first_and_last_mode = True
	self.first_only = False
	self.original_loss_weight = 0.5

	self.cumulative_residual = False
	self.clever_residual = False
	self.skip_residual = False
	self.no_residual = True

	self.optimize_lm_head_only_at_start = False
	self.optimize_model_only_at_start = False

	if self.optimize_model_only_at_start:
	raise NotImplementedError
	self.train_only_thinking_embedding = False
	self.weighted_embeddings = False
	self.use_start_thought_token = True
	self.use_end_thought_token = True
	self.initialize_thought_embedding_to_normal = False
	self.initial_start_token = "---"
	self.initial_end_token = "---"
	self.output_logits_at_the_end = True

	self.wandb_enabled = False
	self.gumbel_temperature = 0.001

	self.use_policy_loss = True
	self.include_policy_loss = True
	self.trice_mode = True
	self.remove_negative_rewards = True
	self.use_policy_loss_for_end_thought = True

	self.base_original_mode = False
	self.original_mode = False

	self.thought_prefix = "(Let's think step by step"
	self.tokenized_thought_prefix = None
	self.log_dict = defaultdict(int)
	self.eval_log_dict = defaultdict(int)
	self.print_final_only = True
	self.loss_mean = loss_mean
	self.all_rewards = []
	self.all_unreduced_losses = []
	self.kill_after = 100

	self.start_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
	self.end_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))

	self.policy_loss_beta = 1e6
	self.embedding_scale = 1e2
	self.reinforce_temperature = 3
	self.base_loss_beta = 1

	# Not used in the paper:
	self.use_thought_prefix = False
	self.use_reparam_for_thought_embeddings = False
	self.use_upper_triangular = False
	self.subtract_mean_reward = False
	self.comparison_mode = False
	self.gumbel_detach = True

	# For visualization
	self.eval_mode = False

	num_talk = 1
	talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2
	if self.use_weighted_talk_head:
	talk_output_dim = 1
	else:
	talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size

	if not self.merged_lm_and_talk_heads:
	if self.use_complex_talk_head:
	self.talk_head = nn.ModuleList([nn.Sequential(
	nn.Linear(talk_input_dim, config.hidden_size),
	nn.ReLU(),
	nn.Linear(config.hidden_size, config.hidden_size),
	nn.ReLU(),
	nn.Linear(config.hidden_size, talk_output_dim, bias=False)
	)])
	else:
	self.talk_head = nn.ModuleList([nn.Sequential(
	nn.Linear(talk_input_dim, talk_output_dim, 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

	@torch.no_grad()
	def infer(
	self,
	input_ids: torch.LongTensor,
	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,
	):
	batch_size, seq_len = input_ids.shape

	# Save the original input_ids and attention_mask for later use
	original_input_ids = input_ids.clone()
	original_attention_mask = attention_mask.clone() if attention_mask is not None else None

	# Append the start thought token to the input sequence
	start_thought_token_id = self.tokenizer.convert_tokens_to_ids("<\|startthought\|>")
	input_ids = torch.cat([input_ids, torch.tensor([[start_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
	seq_len += 1

	# Update the attention mask
	if attention_mask is not None:
	attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)

	# Generate the continuation
	continuation_length = self.n_ahead - 2
	new_key_values = past_key_values

	start_time = time.time()
	for continuation_idx in range(continuation_length):
	outputs = self.model(
	input_ids=input_ids if continuation_idx == 0 else next_token_id.unsqueeze(-1).to(input_ids.device),
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=new_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=True,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	new_key_values = outputs.past_key_values

	hidden_states = outputs[0]

	logits = self.lm_head(hidden_states)
	logits = logits[:, -1, :] # Only consider the last token

	# Apply Gumbel-Softmax to the logits
	next_token_logits = F.gumbel_softmax(logits, tau=self.gumbel_temperature, hard=True, dim=-1)
	next_token_id = torch.argmax(next_token_logits, dim=-1)

	# Append the generated token to the input sequence
	input_ids = torch.cat([input_ids, next_token_id.unsqueeze(-1).to(input_ids.device)], dim=-1)
	seq_len += 1

	# Update the attention mask
	if attention_mask is not None:
	attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)

	# Append the end thought token to the input sequence
	end_thought_token_id = self.tokenizer.convert_tokens_to_ids("<\|endthought\|>")
	input_ids = torch.cat([input_ids, torch.tensor([[end_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
	seq_len += 1

	# Update the attention mask
	if attention_mask is not None:
	attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)

	# Get the hidden states before and after the thought
	outputs_before = self.model(
	input_ids=original_input_ids,
	attention_mask=original_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_before = outputs_before[0][:, -1:, :]

	# two new tokens: last continuation token and end thought token
	outputs_after = self.model(
	input_ids=torch.cat([next_token_id.unsqueeze(-1).to(input_ids.device), torch.tensor(end_thought_token_id).unsqueeze(-1).unsqueeze(-1).to(input_ids.device)], dim=-1),
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=new_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_after = outputs_after[0][:, -1:, :]

	# Apply the talk head to get the mixing weight
	mixing_weight = self.talk_head[0](torch.cat([hidden_states_before, hidden_states_after], dim=-1))

	# Apply the mixing weight to the hidden states
	mixed_hidden_states = (1 - mixing_weight) * hidden_states_before + mixing_weight * hidden_states_after

	# Apply the language model head to get the final logits
	logits = self.lm_head(mixed_hidden_states)
	return logits

	@add_start_docstrings_to_model_forward(MISTRAL_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, MistralForCausalLM

	>>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
	>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

	>>> prompt = "Hey, are you conscious? 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 conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
	```"""
	log_dict = self.log_dict if self.training else self.eval_log_dict

	if self.training and self.kill_after is not None and self.training_steps // self.gradient_accumulation_steps > self.kill_after:
	raise ValueError("Killed after")

	if not self.training:
	n_ahead_talk_to_restore = self.n_ahead_talk
	n_passes_to_restore = self.n_passes
	self.n_ahead_talk = 1
	self.n_passes = 1

	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

	assert self.cumulative_residual or self.clever_residual or self.skip_residual or self.no_residual
	assert not (self.skip_residual and self.use_policy_loss)

	if self.tokenized_thought_prefix is None and self.use_thought_prefix:
	self.tokenized_thought_prefix = self.tokenizer(self.thought_prefix, return_tensors="pt", add_special_tokens=False)["input_ids"]

	def apply_head(head, states, detach=False):
	if detach:
	head_weight = head.weight.detach()
	else:
	head_weight = head.weight
	head_weight = head_weight.to(states.device)
	return (head_weight @ states.transpose(-1, -2)).transpose(-1, -2).contiguous()

	def idx_if_sequential(head, idx=0):
	if isinstance(head, nn.Sequential) or isinstance(head, nn.ModuleList):
	return idx_if_sequential(head[idx], idx=idx)
	return head

	def none_repeat_interleave(x, n):
	if x is None:
	return x
	return x.repeat_interleave(n, dim=0)

	if self.n_passes > 1:
	input_ids = none_repeat_interleave(input_ids, self.n_passes)
	attention_mask = none_repeat_interleave(attention_mask, self.n_passes)
	position_ids = none_repeat_interleave(position_ids, self.n_passes)
	inputs_embeds = none_repeat_interleave(inputs_embeds, self.n_passes)
	labels = none_repeat_interleave(labels, self.n_passes)
	if past_key_values is not None:
	past_key_values = [none_repeat_interleave(p, self.n_passes) for p in past_key_values]
	cur_token_indices = torch.arange(input_ids.shape[1], device=input_ids.device)

	self.tokenizer_has_start_thought_token = True
	self.tokenizer_has_end_thought_token = True
	if self.start_token_id is None:
	self.start_token_id = self.tokenizer.convert_tokens_to_ids("<\|startthought\|>")
	if self.start_token_id == 0:
	self.start_token_id = self.tokenizer.bos_token_id
	self.tokenizer_has_start_thought_token = False
	elif self.use_start_thought_token:
	# base_start_id = self.tokenizer.convert_tokens_to_ids(self.initial_start_token)
	base_start_id = self.tokenizer.encode(self.initial_start_token, add_special_tokens=False)[0]
	if self.initialize_thought_embedding_to_normal:
	self.start_embedding.data = torch.zeros_like(self.start_embedding.data)
	else:
	self.start_embedding.data[0] = self.model.embed_tokens.weight.data[base_start_id].clone().detach() / self.embedding_scale
	self.start_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
	if self.end_token_id is None:
	self.end_token_id = self.tokenizer.convert_tokens_to_ids("<\|endthought\|>")
	if self.end_token_id == 0:
	self.end_token_id = self.tokenizer.eos_token_id
	self.tokenizer_has_end_thought_token = False
	elif self.use_end_thought_token:
	# base_end_id = self.tokenizer.convert_tokens_to_ids(self.initial_end_token)
	base_end_id = self.tokenizer.encode(self.initial_end_token, add_special_tokens=False)[0]
	if self.initialize_thought_embedding_to_normal:
	self.end_embedding.data = torch.zeros_like(self.end_embedding.data)
	else:
	self.end_embedding.data[0] = self.model.embed_tokens.weight.data[base_end_id].clone().detach() / self.embedding_scale
	self.end_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)

	if not self.rm_initialized and (self.n_ahead > 1 or not self.base_original_mode):
	self.rm_initialized = True
	if not self.use_shallow_talk:
	head = self.talk_head[0]
	cur_head = head[-1] if isinstance(head, nn.Sequential) else head
	talk_input_dim = cur_head.weight.data.shape[1]
	talk_output_dim = 1 if self.use_weighted_talk_head else self.lm_head.weight.data.shape[0]
	cur_head.weight.data = torch.zeros(talk_output_dim, talk_input_dim, device=cur_head.weight.device, dtype=cur_head.weight.dtype)
	else:
	# convert to identity transform
	def lambda_transform(cur_head):
	if cur_head.weight.data.shape[0] != cur_head.weight.data.shape[1]:
	return torch.cat([
	torch.eye(
	cur_head.weight.data.shape[0],
	device=cur_head.weight.device,
	dtype=cur_head.weight.dtype
	),
	torch.zeros(
	cur_head.weight.data.shape[0],
	cur_head.weight.data.shape[1] - cur_head.weight.data.shape[0],
	device=cur_head.weight.device,
	dtype=cur_head.weight.dtype
	)], dim=1)
	return torch.eye(
	cur_head.weight.data.shape[0],
	device=cur_head.weight.device,
	dtype=cur_head.weight.dtype
	)
	if isinstance(self.talk_head[0], nn.Sequential):
	for cur_head in self.talk_head[0]:
	# if it has weights
	if hasattr(cur_head, "weight"):
	cur_head.weight.data = lambda_transform(cur_head)
	else:
	self.talk_head[-1].weight.data = lambda_transform(self.talk_head[0])

	loss = None
	prev_rm_tokens = None
	cur_rm_tokens = None
	prev_rm_logits = None
	prev_sample_probs = None
	did_skip_sampling = None
	skip_sampling = None
	sample_probs = None
	hidden_states = None
	logits = None
	talk_kl_penalty = None
	rm_logits = None
	residual_logits = None
	probabilities_2d = None
	prev_probabilities_2d = None
	policy_reward = None
	logits_to_output = None
	batch_size, seq_len = input_ids.shape
	base_input_ids = input_ids.clone()
	loss_list = []
	dqn_loss_list = []
	sampled_token_history = []
	sample_probs_history = []
	action_loglikelihoods_list = []

	if self.use_end_thought_token or self.use_start_thought_token:
	if not self.use_reparam_for_thought_embeddings:
	start_embedding = self.start_embedding[0].unsqueeze(0) * self.embedding_scale
	end_embedding = self.end_embedding[0].unsqueeze(0) * self.embedding_scale
	else:
	start_embedding = self.start_embedding * self.embedding_scale
	end_embedding = self.end_embedding * self.embedding_scale
	base_embeddings = self.model.embed_tokens.weight
	if self.train_only_thinking_embedding:
	base_embeddings = base_embeddings.detach()
	# # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
	fwd_iters = 1 if self.original_mode else self.n_ahead + self.n_ahead_talk - 1
	for ahead_idx in range(fwd_iters):
	past_key_values_length = 0
	if past_key_values is not None:
	use_legacy_cache = not isinstance(past_key_values, Cache)
	if use_legacy_cache:
	past_key_values = DynamicCache.from_legacy_cache(past_key_values)
	past_key_values_length = past_key_values.get_usable_length(seq_len)

	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_len + past_key_values_length, dtype=torch.long, device=device
	)
	position_ids = position_ids.unsqueeze(0).view(-1, seq_len)
	else:
	position_ids = position_ids.view(-1, seq_len).long()

	if inputs_embeds is None:
	contains_start = self.use_start_thought_token and (input_ids == self.start_token_id).any()
	contains_end = self.use_end_thought_token and (input_ids == self.end_token_id).any()
	contains_thought = contains_start or contains_end
	if contains_thought:
	thought_id = self.start_token_id if contains_start else self.end_token_id
	cur_thought_embedding = start_embedding if contains_start else end_embedding
	if self.use_reparam_for_thought_embeddings:
	inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
	inputs_embeds = inputs_embeds.detach() * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
	if contains_start:
	sampled_start = inputs_embeds.clone().detach()
	if contains_end:
	sampled_end = inputs_embeds.clone().detach()
	else:
	inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
	else:
	with torch.set_grad_enabled(not self.train_only_thinking_embedding):
	inputs_embeds = self.model.embed_tokens(input_ids)

	if self.n_ahead != 1 or self.n_ahead_talk != 1 or self.comparison_mode:
	if attention_mask is None:
	base_attention_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=0).to(input_ids.device)
	base_attention_mask = base_attention_mask.view(1, 1, seq_len, seq_len)
	base_attention_mask = base_attention_mask.repeat(input_ids.shape[0], 1, 1, 1)
	attention_mask = base_attention_mask
	breakpoint()
	elif attention_mask.dim() == 2:
	if seq_len + past_key_values_length != attention_mask.shape[-1]:
	breakpoint()
	attention_mask = torch.cat(
	[torch.ones((attention_mask.shape[0], past_key_values_length), dtype=attention_mask.dtype, device=attention_mask.device), attention_mask],
	dim=-1
	)
	# # if the attention mask
	attention_mask = _prepare_4d_causal_attention_mask(
	attention_mask,
	(batch_size, seq_len),
	inputs_embeds,
	past_key_values_length,
	sliding_window=self.config.sliding_window,
	)

	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,
	)

	prev_hidden_states = hidden_states
	hidden_states = outputs[0]
	prev_rm_logits = rm_logits # for policy gradient
	prev_rm_tokens = cur_rm_tokens # for policy gradient

	if ahead_idx == 0:
	hidden_states_lm = hidden_states
	logits = self.lm_head(hidden_states_lm)
	base_hidden_states = hidden_states.clone()
	initial_loss_logits = logits.clone()
	if self.optimize_lm_head_only_at_start or self.optimize_model_only_at_start:
	logits = logits.detach()
	base_hidden_states = base_hidden_states.detach()
	if self.optimize_model_only_at_start:
	hidden_states = hidden_states.detach()
	base_logits = logits.clone()
	else:
	talk_hidden_states = hidden_states
	if self.merged_lm_and_talk_heads:
	assert self.no_residual
	residual_logits = self.lm_head(hidden_states)
	talk_hidden_states = hidden_states
	else:
	if ahead_idx > self.n_ahead - 1:
	cur_base_hidden = torch.cat([
	base_hidden_states[..., ahead_idx - self.n_ahead + 1:, :],
	base_hidden_states[..., :ahead_idx - self.n_ahead + 1, :]
	], dim=-2)
	else:
	cur_base_hidden = base_hidden_states

	if self.use_concat_talk_head:
	# concatenate the hidden states with the original hidden states
	head_input_hidden_states = torch.cat([cur_base_hidden, talk_hidden_states], dim=-1)
	else:
	head_input_hidden_states = talk_hidden_states

	residual_logits = self.talk_head[0](head_input_hidden_states)
	if self.use_shallow_talk:
	residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)
	residual_logits = residual_logits.to(logits.device)
	if self.use_weighted_talk_head:
	# combine the cur_base_hidden with the talk_hidden_states according to the weighted head
	residual_logits = cur_base_hidden * (1 - residual_logits) + talk_hidden_states * residual_logits
	residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)

	assert sum([self.cumulative_residual, self.clever_residual, self.skip_residual, self.no_residual]) == 1
	if self.clever_residual:
	if ahead_idx >= self.n_ahead - 1:
	# get the logits shifted according to the current talk ahead
	cur_base_logits = torch.cat([
	base_logits[..., ahead_idx - self.n_ahead + 1:, :],
	base_logits[..., :ahead_idx - self.n_ahead + 1, :]
	], dim=-2)
	if self.optimize_lm_head_only_at_start:
	cur_base_logits = cur_base_logits.detach()
	logits = cur_base_logits + residual_logits
	else:
	logits += residual_logits / self.n_ahead
	elif self.cumulative_residual:
	if self.residual_talk_head:
	if ahead_idx < self.n_ahead:
	logits += residual_logits
	else:
	# get the logits shifted according to the current talk ahead
	cur_base_logits = torch.cat([
	base_logits[..., ahead_idx - self.n_ahead + 1:, :],
	base_logits[..., :ahead_idx - self.n_ahead + 1, :]
	], dim=-2)
	if self.optimize_lm_head_only_at_start:
	cur_base_logits = cur_base_logits.detach()
	logits = cur_base_logits + residual_logits
	else:
	if ahead_idx < self.n_ahead:
	logits += residual_logits
	else:
	logits = residual_logits
	elif self.skip_residual:
	if ahead_idx >= self.n_ahead:
	# get the logits shifted according to the current talk ahead
	cur_base_logits = torch.cat([
	base_logits[..., ahead_idx - self.n_ahead + 1:, :],
	base_logits[..., :ahead_idx - self.n_ahead + 1, :]
	], dim=-2)
	if self.optimize_lm_head_only_at_start:
	cur_base_logits = cur_base_logits.detach()
	logits = cur_base_logits
	elif self.no_residual:
	logits = residual_logits
	else:
	logits = base_logits + residual_logits

	attempted = False
	talk_loss_list = []
	if self.original_mode or (self.n_ahead == 1) or (self.comparison_mode and ahead_idx == 0):# or (self.optimize_lm_head_only_at_start and ahead_idx == 0):
	loss = None
	attempted = True

	if labels is not None:
	for shift_amount in range(self.n_ahead_talk):
	# Shift so that tokens < n predict n
	# ab[cde]f
	# abc[def]
	if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
	loss_logits = initial_loss_logits
	else:
	loss_logits = logits
	shift_logits = loss_logits[..., shift_amount:-1, :].contiguous()
	shift_labels = labels[..., 1 + shift_amount:].contiguous()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss(reduction="none")
	shift_logits = shift_logits.view(-1, self.config.vocab_size)
	shift_labels = shift_labels.view(-1).clone()
	# Enable model parallelism
	shift_labels[shift_labels == self.tokenizer.pad_token_id] = -100
	shift_labels = shift_labels.to(shift_logits.device)
	loss = loss_fct(shift_logits, shift_labels)
	if not self.comparison_mode and not (self.optimize_lm_head_only_at_start and (self.n_ahead + self.n_ahead_talk > 2)) or self.original_mode:
	loss_list.append(loss)
	talk_loss_list.append(nonzero_mean(loss).detach())

	if not attempted or self.comparison_mode:
	rm_hidden_states = hidden_states
	# print("Magnitude of RM hidden states before RM head", rm_hidden_states.norm())
	rm_logits = apply_head(self.lm_head, rm_hidden_states, detach=self.optimize_lm_head_only_at_start)

	# don't allow it to predict the thinking token
	if self.tokenizer_has_start_thought_token:
	rm_logits[..., self.start_token_id] = -1e10
	if self.tokenizer_has_end_thought_token:
	rm_logits[..., self.end_token_id] = -1e10
	probabilities = rm_logits
	if probabilities_2d is not None:
	prev_probabilities_2d = probabilities_2d.clone()
	probabilities_2d = probabilities.view(-1, probabilities.size(-1))

	did_skip_sampling = skip_sampling
	skip_sampling = False
	if ahead_idx == 0 and self.use_start_thought_token:
	override_token = self.start_token_id
	elif self.use_thought_prefix and ahead_idx < self.tokenized_thought_prefix.shape[-1]:
	override_token = self.tokenized_thought_prefix[..., ahead_idx]
	elif ahead_idx == self.n_ahead - 2 and self.use_end_thought_token:
	override_token = self.end_token_id
	else:
	override_token = None
	if override_token is not None and self.n_ahead > 1:
	# always start with the start token
	probabilities_2d = torch.zeros_like(probabilities_2d)
	probabilities_2d[:, override_token] = 1.0
	skip_sampling = True
	elif ahead_idx >= self.n_ahead - 1:
	if labels is not None: # we're in the talk phase
	cur_talk_n = ahead_idx - (self.n_ahead - 1) + 1
	# print("Setting rm to labels", cur_talk_n, "during", ahead_idx)
	shift_labels = labels[..., cur_talk_n:].contiguous().to(probabilities_2d.device)
	padding = torch.full_like(
	labels[..., :cur_talk_n],
	self.tokenizer.pad_token_id,
	dtype=torch.long,
	device=shift_labels.device
	)
	new_rm_tokens = torch.cat(
	[shift_labels, padding],
	dim=-1
	)
	# convert rm tokens to one-hot
	probabilities_2d = F.one_hot(new_rm_tokens, num_classes=self.vocab_size).reshape(-1, self.vocab_size).to(probabilities_2d.dtype)
	skip_sampling = True
	else:
	continue
	temperature = self.gumbel_temperature if self.training else 0.001
	prev_sample_probs = sample_probs
	sample_probs = probabilities_2d
	if ahead_idx < self.n_ahead - 1 and not skip_sampling:
	probabilities_2d = F.gumbel_softmax(sample_probs, tau=temperature, hard=True, dim=-1)
	if self.gumbel_detach:
	probabilities_2d = probabilities_2d.detach()
	sampled_token_history.append(probabilities_2d.argmax(dim=-1).detach().cpu())
	# convert rm logits directly to embeddings
	contains_start = self.use_start_thought_token and (probabilities_2d[..., self.start_token_id].sum() > 0)
	contains_end = self.use_end_thought_token and (probabilities_2d[..., self.end_token_id].sum() > 0)
	contains_thought = contains_start or contains_end

	if not contains_thought:
	with torch.set_grad_enabled(not self.train_only_thinking_embedding):
	inputs_embeds = probabilities_2d @ (self.model.embed_tokens.weight.to(probabilities.device).to(probabilities.dtype))
	else:
	thought_id = self.start_token_id if contains_start else self.end_token_id
	cur_thought_embedding = start_embedding if contains_start else end_embedding
	if self.use_reparam_for_thought_embeddings:
	inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
	inputs_embeds = inputs_embeds * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
	if contains_start:
	sampled_start = inputs_embeds.clone().detach()
	else:
	sampled_end = inputs_embeds.clone().detach()
	else:
	inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
	inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)
	inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)

	if len(attention_mask.shape) == 2:
	breakpoint()
	else:
	original_attention = attention_mask[..., :attention_mask.shape[-2]]
	if self.use_upper_triangular:
	new_attention = original_attention
	else:
	original_attention = original_attention == attention_mask.max()
	# because eye isn't implemented for BF16, we need to handle the case
	if not attention_mask.dtype == torch.bfloat16:
	new_attention = torch.eye(
	seq_len, dtype=attention_mask.dtype, device=attention_mask.device
	)
	else:
	new_attention = torch.eye(
	seq_len, dtype=torch.float32, device=attention_mask.device
	).to(attention_mask.dtype)

	new_attention = new_attention.view(1, 1, seq_len, seq_len).repeat(input_ids.shape[0], 1, 1, 1)
	new_attention = new_attention * original_attention
	new_attention[new_attention == 0] = attention_mask.min()
	new_attention[new_attention == 1] = attention_mask.max()
	attention_mask = torch.cat([attention_mask, new_attention], dim=-1)
	past_key_values = outputs.past_key_values
	position_ids = position_ids + 1

	if labels is not None and (self.n_ahead > 1 or not self.base_original_mode):
	# Shift so that tokens < n predict n
	# logits: abcdef -> bcdef? -> cdef??
	# labels: abcdef -> ?bcdef -> ??cdef
	if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
	loss_logits = initial_loss_logits
	else:
	loss_logits = logits
	shift_idx = 1 + max(0, ahead_idx - (self.n_ahead - 1))
	shift_logits = loss_logits[..., :-shift_idx, :].contiguous()
	shift_labels = labels[..., shift_idx:].contiguous()
	# Flatten the tokens
	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)
	# if shift_labels.min() == self.tokenizer.pad_token_id:
	shift_labels = torch.where(shift_labels == self.tokenizer.pad_token_id, -100, shift_labels)
	unreduced_loss = loss_fct(shift_logits, shift_labels)
	if torch.any(unreduced_loss != unreduced_loss):
	raise ValueError("NaN loss")
	unreduced_loss = unreduced_loss.reshape(logits.shape[0], -1)
	loss_list.append(unreduced_loss)


	if self.use_policy_loss and ahead_idx > 0 and (ahead_idx > 1 or not self.use_start_thought_token):
	# we treat the change in loss as the reward
	previous_loss = loss_list[-2]
	# for example, suppose n_ahead = 3 and n_ahead_talk = 2
	# note that we end at self.n_ahead + self.n_ahead_talk - 2
	# in this case, 5 - 2 = 3, so we end at ahead_idx = 3
	# we also predict the next token at ahead_idx = 2
	# when we get to ahead_idx = 2, we predict ahead
	# so we shift by 1
	# note that this is ahead_idx = n_ahead - 1
	# when we get to ahead_idx = 3, we predict ahead
	# so we shift by 2
	# note that this is ahead_idx = n_ahead
	if ahead_idx < self.n_ahead - 1:
	shift_amount = 0
	original_dqn_reward = (previous_loss - unreduced_loss).detach()
	if self.first_and_last_mode:
	original_dqn_reward = original_dqn_reward * 0.0
	else:
	# logits vs cur_policy_shift_logits
	# let's look at rm_logits and prev_rm_logits
	shift_amount = max(0, ahead_idx - (self.n_ahead - 1))
	# let's say shift_amount = 2
	# abcdefg -> bcdefg? -> cdefg??
	# logits = [a b]c d e f[g]
	# labels = [a b c]d e f g
	cur_policy_shift_logits = initial_loss_logits[..., shift_amount:-1, :].contiguous().detach()
	cur_policy_shift_labels = labels[..., 1 + shift_amount:].contiguous()
	# Flatten the tokens
	cur_policy_loss_fct = CrossEntropyLoss(reduction="none")
	cur_policy_shift_logits = cur_policy_shift_logits.view(-1, self.config.vocab_size)
	cur_policy_shift_labels = cur_policy_shift_labels.view(-1).clone()
	# Enable model parallelism
	cur_policy_shift_labels[cur_policy_shift_labels == self.tokenizer.pad_token_id] = -100
	cur_policy_shift_labels = cur_policy_shift_labels.to(cur_policy_shift_labels.device)
	cur_policy_reward_base_loss = loss_fct(
	cur_policy_shift_logits, cur_policy_shift_labels.to(cur_policy_shift_logits.device)
	).reshape(logits.shape[0], -1)
	original_dqn_reward = cur_policy_reward_base_loss.detach() - unreduced_loss

	if not did_skip_sampling:
	nonzero_indices = prev_probabilities_2d.nonzero()
	action_loglikelihoods = F.log_softmax(prev_sample_probs / self.reinforce_temperature, dim=-1)[nonzero_indices[:, 0], nonzero_indices[:, 1]]
	action_loglikelihoods_2d = action_loglikelihoods.reshape(batch_size, -1)[:, :-1 - shift_amount]
	action_loglikelihoods_list.append(action_loglikelihoods_2d)
	if policy_reward is None:
	policy_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
	else:
	if self.n_ahead_talk > shift_amount:
	added_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
	else:
	added_reward = original_dqn_reward
	policy_reward += added_reward

	if self.use_policy_loss and ahead_idx == self.n_ahead + self.n_ahead_talk - 2:
	# only compute during the thinking phase
	if self.use_reparam_for_thought_embeddings and (self.use_start_thought_token or self.use_end_thought_token):
	# sampled_start, sampled_end
	# calculate the log likelihood of the start and end embeddings sampled from a multivariate normal distribution
	# with mean start_embedding[0] and standard deviation start_embedding[1]
	if self.use_start_thought_token:
	exp_start_std = torch.exp(start_embedding[1])
	start_loglikelihood = -0.5 * (sampled_start.detach() - start_embedding[0]) 2 / exp_start_std 2 - start_embedding[1] - 0.5 * math.log(2 * math.pi)
	start_loglikelihood = start_loglikelihood.mean(dim=-1)
	if self.use_end_thought_token:
	exp_end_std = torch.exp(end_embedding[1])
	end_loglikelihood = -0.5 * (sampled_end.detach() - end_embedding[0]) 2 / exp_end_std 2 - end_embedding[1] - 0.5 * math.log(2 * math.pi)
	end_loglikelihood = end_loglikelihood.mean(dim=-1)
	# we use the mean instead of the sum to prevent dependence on the dimensionality of the embeddings
	if self.use_end_thought_token and self.use_policy_loss_for_end_thought:
	action_loglikelihoods_list.append(end_loglikelihood)
	if self.use_start_thought_token:
	action_loglikelihoods_list.append(start_loglikelihood)

	if ahead_idx == self.n_ahead + self.n_ahead_talk - 2 and self.eval_mode:
	with torch.no_grad():
	# calculate the 0.75 quantile of the rewards
	filtered_tokens = input_ids[:, :policy_reward.shape[-1]].cpu().detach().numpy().flatten()
	filtered_tokens_mask = filtered_tokens != self.tokenizer.pad_token_id
	filtered_tokens = filtered_tokens[filtered_tokens_mask]
	filtered_rewards = policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten()
	filtered_rewards = filtered_rewards[filtered_tokens_mask]

	abs_reward_list = np.abs(policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten())
	abs_reward_list = abs_reward_list[filtered_tokens_mask]
	medium_quantile = np.quantile(abs_reward_list, 0.5)
	upper_quantile = np.quantile(abs_reward_list, 0.95)

	save_tokens_with_rewards_to_pdf(
	filtered_tokens,
	[0] + filtered_rewards.tolist(),
	self.tokenizer,
	output_file=f"texts/rewards_talk_{self.n_ahead_talk}_{self.training_steps}.pdf",
	eps=medium_quantile,
	eps2=upper_quantile,
	)

	def plot_kde(data, losses):
	sns.set(style="whitegrid")
	# Create the KDE plot
	sns.kdeplot(data, fill=True)
	# Set the plot title and labels
	plt.title("KDE Plot")
	plt.xlabel("Value")
	plt.ylabel("Density")
	# Save the plot
	plt.savefig(f"texts/kde_talk_{self.n_ahead_talk}_{self.training_steps}.pdf")
	# Close the plot
	plt.close()

	# Step 1: Create a base color palette
	base_colors = sns.color_palette("light:#5A9", n_colors=256) # More colors for a smoother gradient
	base_cmap = LinearSegmentedColormap.from_list("log_light", base_colors)
	log_norm = LogNorm(vmin=1e-3, vmax=10)

	sns.kdeplot(x=data, y=losses, fill=True, levels=20, norm=log_norm, cut=0, linewidths=0)
	# limit y to 0 to 25 and x to -1 to 1
	plt.xlim(-1, 1)
	plt.ylim(0, 25)
	plt.savefig(f"texts/jointer_talk_{self.n_ahead_talk}_{self.training_steps}.pdf")
	plt.close()

	self.all_rewards.extend(filtered_rewards)
	self.all_unreduced_losses.extend(unreduced_loss[:, :-1].flatten()[filtered_tokens_mask].float().flatten().cpu().detach().numpy())
	plot_kde(self.all_rewards, self.all_unreduced_losses)

	for action_loglikelihoods_2d in action_loglikelihoods_list:
	train_policy_reward = policy_reward

	# discard rewards below the mean
	if self.trice_mode and self.n_passes > 1:
	batched_policy_reward = train_policy_reward.reshape(-1, self.n_passes, train_policy_reward.shape[-1])
	# average over the passes
	train_policy_reward = batched_policy_reward - batched_policy_reward.mean(dim=1, keepdim=True)
	train_policy_reward = train_policy_reward.reshape(-1, train_policy_reward.shape[-1])

	if self.subtract_mean_reward:
	train_policy_reward = train_policy_reward - train_policy_reward.mean()
	if self.remove_negative_rewards:
	fixed_policy_reward = train_policy_reward.detach().clamp(min=0)
	else:
	fixed_policy_reward = train_policy_reward.detach()
	actor_loss = -fixed_policy_reward * action_loglikelihoods_2d[:, :policy_reward.shape[-1]].to(policy_reward.device)
	if action_loglikelihoods_2d.mean() < -1e4 and not self.use_policy_loss_just_for_thoughts:
	# This will only happen when we force the next token to be the end of thought token
	break
	dqn_loss_list.append(actor_loss.mean())

	if loss_list:
	if self.first_and_last_mode:
	loss = sum(
	self.loss_mean(loss_list[-(i + 1)]) for i in range(self.n_ahead_talk)
	) * (1 - self.original_loss_weight) / self.n_ahead_talk
	loss = loss + self.loss_mean(loss_list[0]) * self.original_loss_weight
	# Let's NaN out the others
	# e.g. if n_ahead_talk = 2 and the list is 5 long, we want to NaN out 1, 2 but keep 0, 3, 4
	for i in range(1, len(loss_list) - self.n_ahead_talk):
	loss_list[i] = loss_list[i] * math.nan
	elif self.first_only:
	loss = self.loss_mean(loss_list[0])
	elif self.final_only_mode:
	loss = sum(
	self.loss_mean(loss_list[-i]) for i in range(1, self.n_ahead_talk + 1)
	) / self.n_ahead_talk
	else:
	loss = None
	for i in range(len(loss_list)):
	cur_loss = self.loss_mean(loss_list[i])
	if loss is not None:
	loss = loss + cur_loss.to(loss.device)
	else:
	loss = cur_loss
	loss = loss / len(loss_list)

	loss = loss * self.base_loss_beta

	if dqn_loss_list:
	dqn_loss = sum(dqn_loss_list) / len(dqn_loss_list)
	if self.include_policy_loss:
	if loss is not None:
	loss += dqn_loss * self.policy_loss_beta
	else:
	loss = dqn_loss * self.policy_loss_beta

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

	base_log_dict = {
	f"loss_{i}": nonzero_mean(loss_list[i]) for i in range(len(loss_list))
	}

	if loss is not None:
	base_log_dict["loss_train"] = loss.item()

	for loss_key, loss_val in base_log_dict.items():
	log_dict[loss_key] += loss_val / self.n_tokens_print

	if self.use_policy_loss and policy_reward is not None:
	log_dict["policy_loss"] += dqn_loss / self.n_tokens_print
	log_dict["policy_reward"] += policy_reward.mean() / self.n_tokens_print

	if not loss_list:
	if loss is not None:
	log_dict["loss_0"] += loss / self.n_tokens_print
	else:
	log_dict["loss_final"] += nonzero_mean(loss_list[-1]) / self.n_tokens_print
	log_dict["loss_talk"] += sum(nonzero_mean(cur_loss_item) for cur_loss_item in loss_list[-self.n_ahead_talk:]) / self.n_ahead_talk / self.n_tokens_print

	# also log relative losses to loss_0
	if loss_list:
	for i in range(len(loss_list)):
	talk_idx = min(max(i - (self.n_ahead - 1), 0), len(talk_loss_list) - 1)
	if not talk_loss_list:
	cur_talk_loss = nonzero_mean(loss_list[0])
	else:
	cur_talk_loss = talk_loss_list[talk_idx]
	log_dict[f"rel_loss_{i}"] += (nonzero_mean(loss_list[i]) - cur_talk_loss) / self.n_tokens_print
	if self.training:
	self.training_steps += 1
	try:
	# if self.training_steps % (self.gradient_accumulation_steps * 256) == 0:
	if self.wandb_enabled:
	if self.training_steps % (self.n_tokens_print) == 0 or not self.training:# and "0" in str(loss.device):
	if not self.training:
	new_log_dict = {}
	for key in list(log_dict.keys()):
	new_log_dict["eval_" + key] = log_dict[key]
	log_dict = new_log_dict
	log_dict["training_steps"] = self.training_steps
	log_dict["batch_size"] = batch_size
	log_dict["example_steps"] = self.training_steps * batch_size * self.gradient_accumulation_steps
	if self.n_ahead > 1:
	log_dict["compute_steps"] = self.training_steps * batch_size * (self.n_ahead + self.n_ahead_talk - 1) * self.gradient_accumulation_steps
	else: # There's no overhead for talk tokens if there's no thinking
	log_dict["compute_steps"] = self.training_steps * batch_size * self.gradient_accumulation_steps
	# remove all nans
	for key in list(log_dict.keys()):
	if log_dict[key] != log_dict[key]:
	del log_dict[key]
	if self.training:
	wandb.log(log_dict)
	if self.training:
	self.log_dict = defaultdict(int)
	else:
	self.eval_log_dict = defaultdict(int)
	except Exception as e:
	pass

	if not self.training:
	self.n_ahead_talk = n_ahead_talk_to_restore
	self.n_passes = n_passes_to_restore
	return CausalLMOutputWithPast(
	loss=loss if loss is not None else None,
	logits=(rm_logits if self.n_ahead > 1 else logits) if not self.output_logits_at_the_end else 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
	):
	# Omit tokens covered by past_key_values
	if past_key_values is not None:
	if isinstance(past_key_values, Cache):
	cache_length = past_key_values.get_seq_length()
	past_length = past_key_values.seen_tokens
	max_cache_length = past_key_values.get_max_length()
	else:
	cache_length = past_length = past_key_values[0][0].shape[2]
	max_cache_length = None

	# Keep only the unprocessed tokens:
	# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
	# some of the inputs are exclusively passed as part of the cache (e.g. when passing inputs_embeds as
	# input)
	if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
	input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
	# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
	# input_ids based on the past_length.
	elif past_length < input_ids.shape[1]:
	input_ids = input_ids[:, past_length:]
	# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

	# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
	if (
	max_cache_length is not None
	and attention_mask is not None
	and cache_length + input_ids.shape[1] > max_cache_length
	):
	attention_mask = attention_mask[:, -max_cache_length:]

	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[:, -input_ids.shape[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


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

	[`MistralForSequenceClassification`] 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).
	""",
	MISTRAL_START_DOCSTRING,
	)
	# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Mistral, LLAMA->MISTRAL
	class MistralForSequenceClassification(MistralPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.num_labels = config.num_labels
	self.model = MistralModel(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(MISTRAL_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:
	# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
	sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
	sequence_lengths = sequence_lengths % input_ids.shape[-1]
	sequence_lengths = sequence_lengths.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,
	)