diff --git "a/configuration_quiet.py" "b/configuration_quiet.py" --- "a/configuration_quiet.py" +++ "b/configuration_quiet.py" @@ -1,11 +1,6 @@ # coding=utf-8 # Copyright 2023 Quiet 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 @@ -17,2289 +12,155 @@ # 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 Quiet 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 -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_quiet import QuietConfig - - -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 +""" Quiet model configuration""" - _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging logger = logging.get_logger(__name__) -_CONFIG_FOR_DOC = "QuietConfig" - -from reportlab.pdfgen import canvas -from reportlab.lib.pagesizes import letter -from reportlab.lib.colors import HexColor - - -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._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->Quiet -class QuietRMSNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-6): - """ - QuietRMSNorm 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->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.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 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) - """ - 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 - - -class QuietFlashAttention2(QuietAttention): - """ - Quiet flash attention module. This module inherits from `QuietAttention` 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 - - # Ensure attention_mask has the correct shape and values - if attention_mask is not None: - if attention_mask.dim() == 4: - # Convert 4D attention mask to 2D - attention_mask = attention_mask.squeeze(1).squeeze(1) - elif attention_mask.dim() != 2: - raise ValueError( - f"Invalid attention mask dimension: {attention_mask.dim()}. Expected 2D or 4D mask." - ) - - # Ensure attention_mask has values of 0 and 1 - attention_mask = attention_mask.to(torch.bool).to(torch.int32) - - # 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->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()}" - ) - - # 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, - "flash_attention_2": QuietFlashAttention2, - "sdpa": QuietSdpaAttention, +QUIET_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "quietai/Quiet-7B-v0.1": "https://huggingface.co/quietai/Quiet-7B-v0.1/resolve/main/config.json", + "quietai/Quiet-7B-Instruct-v0.1": "https://huggingface.co/quietai/Quiet-7B-Instruct-v0.1/resolve/main/config.json", } -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""" +class QuietConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`QuietModel`]. It is used to instantiate an + Quiet model according to the specified arguments, defining the model architecture. Instantiating a configuration + with the defaults will yield a similar configuration to that of the Quiet-7B-v0.1 or Quiet-7B-Instruct-v0.1. + [quietai/Quiet-7B-v0.1](https://huggingface.co/quietai/Quiet-7B-v0.1) + [quietai/Quiet-7B-Instruct-v0.1](https://huggingface.co/quietai/Quiet-7B-Instruct-v0.1) + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. 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.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 QuietForCausalLM(QuietPreTrainedModel): - _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 = 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.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.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) - )]) - - # 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 - generated_tokens = [] - - 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) - print("Next token logits:", next_token_logits) - 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) - generated_tokens.append(next_token_id) - 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) - - # Update the position ids - if position_ids is not None: - position_ids = torch.cat([position_ids, (position_ids[:, -1] + 1).unsqueeze(-1)], 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) - - # Decode the logits to get the generated text - generated_tokens = torch.cat(generated_tokens, dim=-1) - generated_text = self.tokenizer.decode(generated_tokens.squeeze(), skip_special_tokens=True) - - return generated_text - - @add_start_docstrings_to_model_forward(QUIET_INPUTS_DOCSTRING) - @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) - def forward( + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the Quiet model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`QuietModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 14336): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*, defaults to 8): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to `4096*32`): + The maximum sequence length that this model might ever be used with. Quiet's sliding window attention + allows sequence of up to 4096*32 tokens. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + pad_token_id (`int`, *optional*): + The id of the padding token. + bos_token_id (`int`, *optional*, defaults to 1): + The id of the "beginning-of-sequence" token. + eos_token_id (`int`, *optional*, defaults to 2): + The id of the "end-of-sequence" token. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether the model's input and output word embeddings should be tied. + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + sliding_window (`int`, *optional*, defaults to 4096): + Sliding window attention window size. If not specified, will default to `4096`. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + ```python + >>> from transformers import QuietModel, QuietConfig + >>> # Initializing a Quiet 7B style configuration + >>> configuration = QuietConfig() + >>> # Initializing a model from the Quiet 7B style configuration + >>> model = QuietModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "quiet" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( 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_router_logits: 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, 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 = [] - - 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, - # 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)) - 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 - 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 - - 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 + thinking_usefulness_loss - - 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() - - 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 + vocab_size=32000, + hidden_size=4096, + intermediate_size=14336, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=8, + hidden_act="silu", + max_position_embeddings=4096 * 32, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=None, + bos_token_id=1, + eos_token_id=2, + tie_word_embeddings=False, + rope_theta=10000.0, + sliding_window=4096, + attention_dropout=0.0, + max_thoughts=16, + merged_talk_heads=True, + merged_lm_and_talk_heads=False, + merged_lm_and_think_heads=True, + use_concat_talk_head=True, + use_shallow_think=True, + use_shallow_talk=False, + use_complex_think_head=False, + use_complex_talk_head=True, + use_weighted_talk_head=True, + hidden_dropout_prob=0.0, + **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, + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.sliding_window = sliding_window + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.attention_dropout = attention_dropout + self.max_thoughts = max_thoughts + self.merged_talk_heads = merged_talk_heads + self.merged_lm_and_talk_heads = merged_lm_and_talk_heads + self.merged_lm_and_think_heads = merged_lm_and_think_heads + self.use_concat_talk_head = use_concat_talk_head + self.use_shallow_think = use_shallow_think + self.use_shallow_talk = use_shallow_talk + 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 + self.hidden_dropout_prob = hidden_dropout_prob + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, ) \ No newline at end of file