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configuration_mistral.py

@@ -190,8 +190,6 @@ class MistralConfig(PretrainedConfig):
         self.use_complex_think_head = use_complex_think_head
         self.use_complex_talk_head = use_complex_talk_head
         self.use_weighted_talk_head = use_weighted_talk_head
-
-
         
         super().__init__(
             pad_token_id=pad_token_id,

modeling_mistral.py

@@ -36,25 +36,31 @@ 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 ...activations import ACT2FN
-from ...cache_utils import Cache, DynamicCache
-from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
-from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
-from ...modeling_utils import PreTrainedModel
-from ...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 transformers.generation.utils import GenerationMixin
+from transformers.generation.stopping_criteria import StoppingCriteriaList, validate_stopping_criteria
+from transformers import TextStreamer, AutoTokenizer
+import transformers
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+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_mistral import MistralConfig
 
 
@@ -64,6 +70,14 @@ if is_flash_attn_2_available():
 
     _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
 
+from .configuration_quiet import QuietConfig
+
+import time
+from typing import Optional, List
+
+
+
+
 
 logger = logging.get_logger(__name__)
 
@@ -134,6 +148,116 @@ def save_tokens_with_rewards_to_pdf(input_ids, token_rewards, tokenizer, output_
             previous_text = current_text
     c.showPage()
     c.save()
+def _prepare_4d_causal_attention_mask_for_sdpa(attention_mask, input_shape, inputs_embeds, past_key_values_length):
+	# Compute the attention mask correctly
+	bsz, tgt_len = input_shape
+
+	# Create a 4D attention mask from a 2D tensor mask.
+	# The shape of the output attention mask is (batch_size, 1, tgt_len, src_len)
+	# The values are either 0 or 1, where 0 means padding and 1 means non-padding.
+	combined_attention_mask = None
+	if attention_mask is not None:
+		# What if attention_mask is not None and has a shape of (batch_size, 1, tgt_len, src_len)
+		# In this case, we can just use it directly.
+		if attention_mask.dim() == 4:
+			combined_attention_mask = attention_mask
+		# What if attention_mask is not None and has a shape of (batch_size, 1, tgt_len)
+		# In this case, we need to expand it to (batch_size, 1, tgt_len, src_len)
+		elif attention_mask.dim() == 3:
+			expanded_attn_mask = attention_mask[:, None, :, :]
+			combined_attention_mask = expanded_attn_mask
+		# What if attention_mask is not None and has a shape of (batch_size, tgt_len)
+		# In this case, we need to expand it to (batch_size, 1, tgt_len, src_len)
+		elif attention_mask.dim() == 2:
+			# Provided a padding mask of dimensions [batch_size, seq_length]
+			# - if the model is a decoder, apply a causal mask in addition to the padding mask
+			# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+			if past_key_values_length > 0:
+				attention_mask = attention_mask.to(dtype=torch.long)
+				attention_mask = attention_mask[:, past_key_values_length:]
+			expanded_attn_mask = attention_mask[:, None, None, :]
+			combined_attention_mask = expanded_attn_mask
+		else:
+			raise ValueError(
+				"Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
+					input_shape, attention_mask.shape
+				)
+			)
+
+	# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+	# masked positions, this operation will create a tensor which is 0.0 for
+	# positions we want to attend and -10000.0 for masked positions.
+	# Since we are adding it to the raw scores before the softmax, this is
+	# effectively the same as removing these entirely.
+	if combined_attention_mask is not None:
+		# Ensure the attention mask values are within a reasonable range
+		combined_attention_mask = combined_attention_mask.clamp(min=0, max=1)
+
+		# Convert the attention mask to bfloat16
+		combined_attention_mask = combined_attention_mask.to(torch.bfloat16)
+
+		# Normalize the attention mask values to be between 0 and 1
+		combined_attention_mask = (1.0 - combined_attention_mask) * -10000.0
+	else:
+		combined_attention_mask = torch.zeros(
+			(bsz, 1, tgt_len, tgt_len), dtype=torch.bfloat16, device=inputs_embeds.device
+		)
+
+	return combined_attention_mask
+
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Quiet
+class QuietRMSNorm(nn.Module):
+	def __init__(self, hidden_size, eps=1e-6):
+		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->Quiet
+class QuietRotaryEmbedding(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._get_unpad_data
@@ -148,6 +272,88 @@ def _get_unpad_data(attention_mask):
         max_seqlen_in_batch,
     )
 
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+def _make_sliding_window_causal_mask(
+    input_ids_shape: torch.Size,
+    dtype: torch.dtype,
+    device: torch.device,
+    past_key_values_length: int = 0,
+    sliding_window: int = 4096,
+):
+    """
+    Make causal mask used for sliding window attention
+    """
+    bsz, tgt_len = input_ids_shape
+
+    tensor = torch.full(
+        (tgt_len, tgt_len),
+        fill_value=1,
+        device=device,
+    )
+    mask = torch.tril(tensor, diagonal=0)
+    # make the mask banded to account for sliding window
+    mask = torch.triu(mask, diagonal=-sliding_window)
+    mask = torch.log(mask).to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+# Inverse dim formula to find dim based on number of rotations
+def _yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
+    return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base))
+
+# Find dim range bounds based on rotations
+def _yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
+    low = math.floor(_yarn_find_correction_dim(
+        low_rot, dim, base, max_position_embeddings))
+    high = math.ceil(_yarn_find_correction_dim(
+        high_rot, dim, base, max_position_embeddings))
+    return max(low, 0), min(high, dim-1)  # Clamp values just in case
+
+def _yarn_linear_ramp_mask(min, max, dim):
+    if min == max:
+        max += 0.001  # Prevent singularity
+
+    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+    ramp_func = torch.clamp(linear_func, 0, 1)
+    return ramp_func
+
+def _yarn_get_mscale(scale=1):
+    if scale <= 1:
+        return 1.0
+    return 0.07 * math.log(scale) + 1.0
 
 # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mistral
 class MistralRMSNorm(nn.Module):
@@ -476,10 +682,11 @@ class MistralAttention(nn.Module):
         self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
 
         self._init_rope()
-        )
+        
 
     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 _init_rope(self):
         if self.config.rope_scaling is None:
             self.rotary_emb = MistralRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta)
@@ -2574,3 +2781,1847 @@ class MistralForSequenceClassification(MistralPreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+class QuietMLP(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)
+	"""
+
+	# pdb.set_trace()
+	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 QuietAttention(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: QuietConfig, 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
+		self._attn_implementation = config._attn_implementation
+
+		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 = QuietRotaryEmbedding(
+			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 self._attn_implementation == "flash_attention_2":
+			# Prepare attention mask for flash-attn
+			attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+		elif self._attn_implementation == "sdpa":
+			# Prepare attention mask for SDPA
+			if attention_mask is None or attention_mask.dim() == 2:
+				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,
+				)
+		else:
+			# Prepare attention mask for other implementations
+			if attention_mask is None or attention_mask.dim() == 2:
+				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,
+				)
+				
+		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
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Quiet
+class QuietSdpaAttention(QuietAttention):
+	"""
+	Quiet attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+	`QuietAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+	SDPA API.
+	"""
+
+	# Adapted from QuietAttention.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(
+				"QuietModel is using QuietSdpaAttention, 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()}"
+				)
+			attention_mask = attention_mask.to(query_states.dtype)
+		# 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
+
+
+QUIET_ATTENTION_CLASSES = {
+	"eager": QuietAttention,
+	"sdpa": QuietSdpaAttention,
+}
+
+
+class QuietDecoderLayer(nn.Module):
+	def __init__(self, config: QuietConfig, layer_idx: int):
+		super().__init__()
+		self.hidden_size = config.hidden_size
+
+		self.self_attn = QUIET_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+
+		self.mlp = QuietMLP(config)
+		self.input_layernorm = QuietRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+		self.post_attention_layernorm = QuietRMSNorm(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
+
+
+QUIET_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 ([`QuietConfig`]):
+			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 Quiet Model outputting raw hidden-states without any specific head on top.",
+	QUIET_START_DOCSTRING,
+)
+class QuietPreTrainedModel(PreTrainedModel):
+	config_class = QuietConfig
+	base_model_prefix = "model"
+	supports_gradient_checkpointing = True
+	_no_split_modules = ["QuietDecoderLayer"]
+	_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_()
+
+
+QUIET_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 Quiet Model outputting raw hidden-states without any specific head on top.",
+	QUIET_START_DOCSTRING,
+)
+class QuietModel(QuietPreTrainedModel):
+	"""
+	Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`QuietDecoderLayer`]
+	Args:
+		config: QuietConfig
+	"""
+
+	def __init__(self, config: QuietConfig):
+		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(
+			[QuietDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+		)
+		self._attn_implementation = config._attn_implementation
+		self.norm = QuietRMSNorm(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(QUIET_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 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 is not None and attention_mask.dim() == 2:
+			# 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 is not None and 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 QuietForCausalLM(QuietPreTrainedModel, GenerationMixin):
+	_tied_weights_keys = ["lm_head.weight"]
+
+	def __init__(self, config):
+		super().__init__(config)
+		self.model = QuietModel(config)
+		self.vocab_size = config.vocab_size
+		self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+		# self.router_aux_loss_coef = config.router_aux_loss_coef
+		# self.num_experts = config.num_experts
+		# self.num_experts_per_tok = config.num_experts_per_tok
+		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 = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q")
+		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.loss_mean = loss_mean
+
+		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.temperature = nn.Parameter(torch.ones(1))
+		self.max_temperature = config.max_temperature
+		self.reinforce_temperature = 3
+		self.base_loss_beta = 1
+		self.thinking_usefulness_head = nn.Linear(self.model.config.hidden_size, 1)
+		self.thinking_threshold = 0.5
+		self.thinking_usefulness_loss_weight = 1e-2
+
+		# 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 = False
+	
+		# 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)
+				)])
+				
+		self.apply(self._init_weights)
+
+		# Add dropout regularization
+		self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+		# Initialize weights and apply final processing
+		self.post_init()
+
+	def get_input_embeddings(self):
+		return self.model.embed_tokens
+
+	def set_input_embeddings(self, value):
+		self.model.embed_tokens = value
+
+	def get_output_embeddings(self):
+		return self.lm_head
+
+	def set_output_embeddings(self, new_embeddings):
+		self.lm_head = new_embeddings
+
+	def set_decoder(self, decoder):
+		self.model = decoder
+
+	def get_decoder(self):
+		return self.model
+	
+	def _init_weights(self, module):
+		if isinstance(module, nn.Linear):
+			nn.init.xavier_uniform_(module.weight)
+			if module.bias is not None:
+				nn.init.constant_(module.bias, 0)
+		elif isinstance(module, nn.Embedding):
+			nn.init.xavier_uniform_(module.weight)
+
+	@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
+		
+		# Initialize next_token_id with a default value
+		next_token_id = torch.zeros(batch_size, dtype=torch.long).to(input_ids.device)
+		
+		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]] * batch_size).to(input_ids.device)], dim=-1),
+			attention_mask=torch.cat([attention_mask[:, -1:], torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1),
+			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
+
+	@torch.no_grad()
+	def generate(
+		self,
+		input_ids: torch.LongTensor = torch.LongTensor(),
+		attention_mask: Optional[torch.Tensor] = None,
+		max_new_tokens: Optional[int] = None,
+		temperature: float = 1.1,
+		**kwargs,
+	):	
+		if isinstance(input_ids, str):
+			input_ids = self.tokenizer(input_ids, return_tensors="pt").input_ids
+
+		if attention_mask is None:
+			# Create a default attention mask if not provided
+			attention_mask = torch.ones_like(input_ids)
+
+		from .generate import generate
+		return generate(self, input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, temperature=temperature, **kwargs)
+
+	@add_start_docstrings_to_model_forward(QUIET_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,
+		max_new_tokens: Optional[int] = None,
+		temperature: Optional[float] = None,
+		temperature_last: Optional[float] = None,
+		dynamic_temperature: Optional[float] = None,
+		dynatemp_low: Optional[float] = None,
+		dynatemp_high: Optional[float] = None,
+		dynatemp_exponent: Optional[float] = None,
+		smoothing_factor: Optional[float] = None,
+		smoothing_curve: Optional[str] = None,
+		top_p: Optional[float] = None,
+		min_p: Optional[float] = None,
+		top_k: Optional[int] = None,
+		repetition_penalty: Optional[float] = None,
+		presence_penalty: Optional[float] = None,
+		frequency_penalty: Optional[float] = None,
+		repetition_penalty_range: Optional[int] = None,
+		typical_p: Optional[float] = None,
+		tfs: Optional[float] = None,
+		top_a: Optional[float] = None,
+		guidance_scale: Optional[float] = None,
+		penalty_alpha: Optional[float] = None,
+		mirostat_mode: Optional[int] = None,
+		mirostat_tau: Optional[float] = None,
+		mirostat_eta: Optional[float] = None,
+		do_sample: Optional[bool] = None,
+		encoder_repetition_penalty: Optional[float] = None,
+		no_repeat_ngram_size: Optional[int] = None,
+		sampler_priority: Optional[List[str]] = None,
+		negative_prompt_ids: Optional[List[int]] = None,
+		prompt_lookup_num_tokens: Optional[int] = None,
+		epsilon_cutoff: Optional[float] = None,
+		eta_cutoff: Optional[float] = None,
+		max_length: Optional[int] = None,
+		suppress_tokens: Optional[List[int]] = None,
+		synced_gpus: Optional[bool] = None,
+		eos_token_id: Optional[List[int]] = None,
+		stopping_criteria: Optional[transformers.StoppingCriteriaList] = None,
+		logits_processor: Optional[transformers.LogitsProcessorList] = None,
+		inputs: Optional[torch.LongTensor] = 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, QuietForCausalLM
+		>>> model = QuietForCausalLM.from_pretrained("quietai/Quiet-7B-v0.1")
+		>>> tokenizer = AutoTokenizer.from_pretrained("quietai/Quiet-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."
+		```"""
+
+		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
+		
+		# aux_loss = None
+		# output_router_logits = output_router_logits if output_router_logits is not None else self.config.output_router_logits
+		# if output_router_logits:
+		#     router_logits = outputs.router_logits if return_dict else outputs[-1]
+		#     if router_logits is not None:
+		#         aux_loss = load_balancing_loss_func(
+		#             router_logits,
+		#             self.num_experts,
+		#             self.num_experts_per_tok,
+		#             attention_mask,
+		#         )
+		#         if labels is not None:
+		#             loss += self.router_aux_loss_coef * aux_loss.to(loss.device)
+					
+		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):
+					# pdb.set_trace()
+					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 = []
+
+		temperature = self.temperature
+
+		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 * temperature
+				end_embedding = self.end_embedding[0].unsqueeze(0) * self.embedding_scale * temperature
+			else:
+				start_embedding = self.start_embedding * self.embedding_scale * temperature
+				end_embedding = self.end_embedding * self.embedding_scale * temperature
+			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,
+				# output_router_logits=output_router_logits,
+				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")
+						# print("Shift logits before:", shift_logits)
+						shift_logits = shift_logits.view(-1, self.config.vocab_size)
+						shift_labels = shift_labels.view(-1).clone()
+						# print("shift logits after:", shift_logits)
+						# 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
+						)
+						
+						# print((new_rm_tokens > self.vocab_size - 1).any().item())
+						new_rm_tokens = torch.clamp(new_rm_tokens, 0, self.vocab_size - 1)
+
+						# Now safely 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)
+					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) * temperature)
+				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)
+				
+				# Predict the usefulness of thinking at each token position
+				thinking_usefulness = self.thinking_usefulness_head(hidden_states).squeeze(-1)
+
+				# Apply a threshold to decide where to generate thoughts
+				generate_thought_mask = thinking_usefulness > self.thinking_threshold
+
+				# Compute the regularization loss for thinking usefulness prediction
+				thinking_usefulness_loss = torch.mean(thinking_usefulness * (1 - generate_thought_mask.float()))
+
+				# Add the regularization loss to the total loss
+				if loss is not None:
+					loss = loss + self.thinking_usefulness_loss_weight * thinking_usefulness_loss
+				else:
+					loss = self.thinking_usefulness_loss_weight * thinking_usefulness_loss
+
+
+				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)
+					# print("Loss:", unreduced_loss.item())  # Print the loss before checking for NaN values
+					if torch.any(unreduced_loss != unreduced_loss):
+						# pdb.set_trace()
+						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
+							reward_scale = 1.0
+							original_dqn_reward = torch.sign(previous_loss - unreduced_loss).detach() * reward_scale
+							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
+
+						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()
+
+							# Normalize rewards
+							fixed_policy_reward = (fixed_policy_reward - fixed_policy_reward.mean()) / (fixed_policy_reward.std() + 1e-8)
+							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 + thinking_usefulness_loss
+			
+			base_loss_scale = 0.6
+			policy_loss_scale = 0.03
+
+			loss = loss * base_loss_scale
+
+		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 * policy_loss_scale
+				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()
+
+		if not self.training:
+			self.n_ahead_talk = n_ahead_talk_to_restore
+			self.n_passes = n_passes_to_restore
+		
+		del start_embedding
+		del end_embedding
+		torch.cuda.empty_cache()
+		
+
+		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 Quiet Model transformer with a sequence classification head on top (linear layer).
+	[`QuietForSequenceClassification`] 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).
+	""",
+	QUIET_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Quiet, LLAMA->QUIET
+class QuietForSequenceClassification(QuietPreTrainedModel):
+	def __init__(self, config):
+		super().__init__(config)
+		self.num_labels = config.num_labels
+		self.model = QuietModel(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(QUIET_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,
+		)