diff --git "a/modeling_mistral.py" "b/modeling_mistral.py" new file mode 100644--- /dev/null +++ "b/modeling_mistral.py" @@ -0,0 +1,4627 @@ +# coding=utf-8 +# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Mistral model.""" +import inspect +import math +import copy +import os +import time +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import wandb +from termcolor import colored +from tqdm import tqdm +import random +import numpy as np +from matplotlib.colors import LinearSegmentedColormap, LogNorm +import warnings +from collections import defaultdict +from typing import List, Optional, Tuple, Union + + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.generation.utils import GenerationMixin +from transformers.generation.stopping_criteria import StoppingCriteriaList, validate_stopping_criteria +from transformers import TextStreamer, AutoTokenizer +import transformers + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache +from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) + +from .configuration_mistral import MistralConfig + + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) + +from .configuration_quiet import QuietConfig + +import time +from typing import Optional, List + + + + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "MistralConfig" + +from reportlab.pdfgen import canvas +from reportlab.lib.pagesizes import letter +from reportlab.lib.colors import HexColor + +def save_tokens_with_rewards_to_pdf(input_ids, token_rewards, tokenizer, output_file="text.pdf", eps=0.2, eps2=0.5): + c = canvas.Canvas(output_file, pagesize=letter) + c.setFont("Courier", 8) + x, y = 50, 750 + previous_text = "" + current_text = "" + for token_idx, reward in enumerate(token_rewards): + current_text = tokenizer.decode(input_ids[: token_idx + 1]) + if current_text != previous_text: + diff_text = current_text[len(previous_text) :] + if "\n" in diff_text: + lines = diff_text.split("\n") + for line_idx, line in enumerate(lines): + if line_idx > 0: + x = 50 + y -= 12 + if abs(reward) < eps: + opacity = 0 + elif abs(reward) > eps2: + opacity = 0.8 + else: + opacity = 0.8 * (abs(reward) - eps) / (eps2 - eps) + text_width = c.stringWidth(line) + if reward > 0: + highlight_color = HexColor("#4CCD99") + else: + highlight_color = HexColor("#FFC700") + highlight_color.alpha = opacity + c.setFillColor(highlight_color) + c.rect(x, y - 2, text_width, 10, fill=True, stroke=False) + c.setFillColor(HexColor("#000000")) + c.drawString(x, y, line) + x += text_width + else: + if abs(reward) < eps: + opacity = 0 + elif abs(reward) > eps2: + opacity = 0.8 + else: + opacity = 0.8 * (abs(reward) - eps) / (eps2 - eps) + text_width = c.stringWidth(diff_text) + if reward > 0: + highlight_color = HexColor("#4CCD99") + else: + highlight_color = HexColor("#FFC700") + highlight_color.alpha = opacity + c.setFillColor(highlight_color) + c.rect(x, y - 2, text_width, 10, fill=True, stroke=False) + c.setFillColor(HexColor("#000000")) + c.drawString(x, y, diff_text) + x += text_width + if x > 550: + x = 50 + y -= 12 + if y < 50: + c.showPage() + y = 750 + x = 50 + previous_text = current_text + c.showPage() + c.save() +def _prepare_4d_causal_attention_mask_for_sdpa(attention_mask, input_shape, inputs_embeds, past_key_values_length): + # Compute the attention mask correctly + bsz, tgt_len = input_shape + + # Create a 4D attention mask from a 2D tensor mask. + # The shape of the output attention mask is (batch_size, 1, tgt_len, src_len) + # The values are either 0 or 1, where 0 means padding and 1 means non-padding. + combined_attention_mask = None + if attention_mask is not None: + # What if attention_mask is not None and has a shape of (batch_size, 1, tgt_len, src_len) + # In this case, we can just use it directly. + if attention_mask.dim() == 4: + combined_attention_mask = attention_mask + # What if attention_mask is not None and has a shape of (batch_size, 1, tgt_len) + # In this case, we need to expand it to (batch_size, 1, tgt_len, src_len) + elif attention_mask.dim() == 3: + expanded_attn_mask = attention_mask[:, None, :, :] + combined_attention_mask = expanded_attn_mask + # What if attention_mask is not None and has a shape of (batch_size, tgt_len) + # In this case, we need to expand it to (batch_size, 1, tgt_len, src_len) + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if past_key_values_length > 0: + attention_mask = attention_mask.to(dtype=torch.long) + attention_mask = attention_mask[:, past_key_values_length:] + expanded_attn_mask = attention_mask[:, None, None, :] + combined_attention_mask = expanded_attn_mask + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + if combined_attention_mask is not None: + # Ensure the attention mask values are within a reasonable range + combined_attention_mask = combined_attention_mask.clamp(min=0, max=1) + + # Convert the attention mask to bfloat16 + combined_attention_mask = combined_attention_mask.to(torch.bfloat16) + + # Normalize the attention mask values to be between 0 and 1 + combined_attention_mask = (1.0 - combined_attention_mask) * -10000.0 + else: + combined_attention_mask = torch.zeros( + (bsz, 1, tgt_len, tgt_len), dtype=torch.bfloat16, device=inputs_embeds.device + ) + + return combined_attention_mask + + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Quiet +class QuietRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return hidden_states.to(input_dtype) * self.weight.to(hidden_states.device) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Quiet +class QuietRotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + +def _make_sliding_window_causal_mask( + input_ids_shape: torch.Size, + dtype: torch.dtype, + device: torch.device, + past_key_values_length: int = 0, + sliding_window: int = 4096, +): + """ + Make causal mask used for sliding window attention + """ + bsz, tgt_len = input_ids_shape + + tensor = torch.full( + (tgt_len, tgt_len), + fill_value=1, + device=device, + ) + mask = torch.tril(tensor, diagonal=0) + # make the mask banded to account for sliding window + mask = torch.triu(mask, diagonal=-sliding_window) + mask = torch.log(mask).to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +# Copied from transformers.models.bart.modeling_bart._expand_mask +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + +# Inverse dim formula to find dim based on number of rotations +def _yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048): + return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base)) + +# Find dim range bounds based on rotations +def _yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048): + low = math.floor(_yarn_find_correction_dim( + low_rot, dim, base, max_position_embeddings)) + high = math.ceil(_yarn_find_correction_dim( + high_rot, dim, base, max_position_embeddings)) + return max(low, 0), min(high, dim-1) # Clamp values just in case + +def _yarn_linear_ramp_mask(min, max, dim): + if min == max: + max += 0.001 # Prevent singularity + + linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min) + ramp_func = torch.clamp(linear_func, 0, 1) + return ramp_func + +def _yarn_get_mscale(scale=1): + if scale <= 1: + return 1.0 + return 0.07 * math.log(scale) + 1.0 + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mistral +class MistralRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + MistralRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return hidden_states.to(input_dtype) * self.weight.to(hidden_states.device) + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Mistral +class MistralRotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().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), + ) + +class MistralLinearScalingRotaryEmbedding(MistralRotaryEmbedding): + """MistralRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + t = t / self.scaling_factor + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.cos().to(dtype), persistent=False) + + +class MistralDynamicNTKScalingRotaryEmbedding(MistralRotaryEmbedding): + """MistralRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + +class MistralYaRNScaledRotaryEmbedding(torch.nn.Module): + """MistralRotaryEmbedding extended with YaRN. See: https://arxiv.org/abs/2309.00071""" + def __init__(self, dim, max_position_embeddings=2048, base=10000, scale=1, original_max_position_embeddings=2048, + extrapolation_factor=1, attn_factor=1, beta_fast=128, beta_slow=2, finetuned=False, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + self.scale = scale + self.original_max_position_embeddings = original_max_position_embeddings + self.extrapolation_factor = extrapolation_factor + self.attn_factor = attn_factor + self.beta_fast = beta_fast + self.beta_slow = beta_slow + + self.yarn(device) + + # Build here to make `torch.jit.trace` work. + self.max_seq_len_cached = max_position_embeddings + t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + dtype = torch.get_default_dtype() + + self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(dtype), persistent=False) + self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. + if seq_len > self.max_seq_len_cached: + self.max_seq_len_cached = seq_len + + t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + + self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(x.dtype), persistent=False) + self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(x.dtype), persistent=False) + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + def yarn(self, device): + pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) + inv_freq_extrapolation = 1.0 / pos_freqs + inv_freq_interpolation = 1.0 / (self.scale * pos_freqs) + + low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings) + inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation + inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.mscale = float(_yarn_get_mscale(self.scale) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation + + +class MistralDynamicYaRNScaledRotaryEmbedding(torch.nn.Module): + """MistralRotaryEmbedding extended with Dynamic YaRN. See: https://arxiv.org/abs/2309.00071""" + def __init__(self, dim, max_position_embeddings=2048, base=10000, original_max_position_embeddings=2048, + extrapolation_factor=1, attn_factor=1, beta_fast=128, beta_slow=2, finetuned=False, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + self.original_max_position_embeddings = original_max_position_embeddings + self.extrapolation_factor = extrapolation_factor + self.attn_factor = attn_factor + self.beta_fast = beta_fast + self.beta_slow = beta_slow + + if finetuned: + self.yarn(self.max_position_embeddings / self.original_max_position_embeddings, device) + else: + inv_freq = 1.0 / \ + (base ** (torch.arange(0, dim, 2).float().to(device) / dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.mscale = 1 + + # Build here to make `torch.jit.trace` work. + self.max_seq_len_cached = max_position_embeddings + t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + dtype = torch.get_default_dtype() + + self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(dtype), persistent=False) + self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. + if seq_len > self.max_seq_len_cached: + self.max_seq_len_cached = seq_len + + self.yarn(seq_len / self.max_position_embeddings, x.device) + + t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + + self.register_buffer("cos_cached", (emb.cos() * self.mscale).to(x.dtype), persistent=False) + self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(x.dtype), persistent=False) + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + def yarn(self, scale, device): + pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) + inv_freq_extrapolation = 1.0 / pos_freqs + inv_freq_interpolation = 1.0 / (scale * pos_freqs) + + low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings) + inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation + inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.mscale = float(_yarn_get_mscale(scale) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb +def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos[position_ids].unsqueeze(unsqueeze_dim) + sin = sin[position_ids].unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class MistralMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class MistralAttention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: MistralConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + self.attention_dropout = config.attention_dropout + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + + self._init_rope() + + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = MistralRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = MistralLinearScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, base=self.rope_theta, + ) + elif scaling_type == "dynamic": + self.rotary_emb = MistralDynamicNTKScalingRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "yarn": + original_max_position_embeddings = self.config.rope_scaling["original_max_position_embeddings"] + self.rotary_emb = MistralYaRNScaledRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, scale=scaling_factor, + original_max_position_embeddings=original_max_position_embeddings, base=self.rope_theta, + ) + elif scaling_type == "dynamic-yarn": + original_max_position_embeddings = self.config.rope_scaling["original_max_position_embeddings"] + self.rotary_emb = MistralDynamicYaRNScaledRotaryEmbedding( + self.head_dim, max_position_embeddings=self.max_position_embeddings, + original_max_position_embeddings=original_max_position_embeddings, base=self.rope_theta, + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class MistralFlashAttention2(MistralAttention): + """ + Mistral flash attention module. This module inherits from `MistralAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ): + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + + # Because the input can be padded, the absolute sequence length depends on the max position id. + rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 + cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + use_sliding_windows = ( + _flash_supports_window_size + and getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + ) + + if not _flash_supports_window_size: + logger.warning_once( + "The current flash attention version does not support sliding window attention, for a more memory efficient implementation" + " make sure to upgrade flash-attn library." + ) + + if past_key_value is not None: + # Activate slicing cache only if the config has a value `sliding_windows` attribute + cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0 + if ( + getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + and cache_has_contents + ): + slicing_tokens = 1 - self.config.sliding_window + + past_key = past_key_value[self.layer_idx][0] + past_value = past_key_value[self.layer_idx][1] + + past_key = past_key[:, :, slicing_tokens:, :].contiguous() + past_value = past_value[:, :, slicing_tokens:, :].contiguous() + + if past_key.shape[-2] != self.config.sliding_window - 1: + raise ValueError( + f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got" + f" {past_key.shape}" + ) + + if attention_mask is not None: + attention_mask = attention_mask[:, slicing_tokens:] + attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) + + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + dropout_rate = 0.0 if not self.training else self.attention_dropout + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in float16 just to be sure everything works as expected. + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + # Reashape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + use_sliding_windows=use_sliding_windows, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + use_sliding_windows=False, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`int`, *optional*): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + use_sliding_windows (`bool`, *optional*): + Whether to activate sliding window attention. + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if not use_sliding_windows: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + if not use_sliding_windows: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + return attn_output + + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape + + # On the first iteration we need to properly re-create the padding mask + # by slicing it on the proper place + if kv_seq_len != attention_mask.shape[-1]: + attention_mask_num_tokens = attention_mask.shape[-1] + attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :] + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + + key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mistral +class MistralSdpaAttention(MistralAttention): + """ + Mistral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `MistralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from MistralAttention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "MistralModel is using MistralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask.to(query_states.device) if attention_mask is not None else None, + dropout_p=self.attention_dropout if self.training else 0.0, + # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal=self.is_causal and attention_mask is None and q_len > 1, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +MISTRAL_ATTENTION_CLASSES = { + "eager": MistralAttention, + "flash_attention_2": MistralFlashAttention2, + "sdpa": MistralSdpaAttention, +} + + +class MistralDecoderLayer(nn.Module): + def __init__(self, config: MistralConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) + + self.mlp = MistralMLP(config) + self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual.to(hidden_states.device) + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +MISTRAL_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`MistralConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Mistral Model outputting raw hidden-states without any specific head on top.", + MISTRAL_START_DOCSTRING, +) +class MistralPreTrainedModel(PreTrainedModel): + config_class = MistralConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["MistralDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +MISTRAL_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare Mistral Model outputting raw hidden-states without any specific head on top.", + MISTRAL_START_DOCSTRING, +) +class MistralModel(MistralPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`] + + Args: + config: MistralConfig + """ + + def __init__(self, config: MistralConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + past_key_values_length = 0 + + if use_cache: + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + past_key_values_length = past_key_values.get_usable_length(seq_length) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: + is_padding_right = attention_mask[:, -1].sum().item() != batch_size + if is_padding_right: + raise ValueError( + "You are attempting to perform batched generation with padding_side='right'" + " this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to " + " call `tokenizer.padding_side = 'left'` before tokenizing the input. " + ) + + if self._attn_implementation == "flash_attention_2": + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._attn_implementation == "sdpa" and not output_attentions and attention_mask.dim() == 2 and False: + # output_attentions=True can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + elif attention_mask is None or attention_mask.dim() == 2: + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + +def nonzero_mean(x, axis=None): + if axis is not None: + return x.sum(axis) / (x != 0).sum(axis) + return x.sum() / (x != 0).sum() + +def loss_mean(x): + return x.sum() / (x != 0).sum() + +class MistralForCausalLM(MistralPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = MistralModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.max_thoughts = config.max_thoughts + self.merged_lm_and_talk_heads = config.merged_lm_and_talk_heads + self.use_concat_talk_head = config.use_concat_talk_head + self.use_shallow_talk = config.use_shallow_talk + self.use_complex_talk_head = config.use_complex_talk_head + self.use_weighted_talk_head = config.use_weighted_talk_head + # the weighted head will output a single value, so it can't be passed to the lm head + assert not (self.use_weighted_talk_head and self.use_shallow_talk) + + self.n_ahead = 1 + self.n_ahead_talk = 1 + self.n_passes = 1 + self.n_tokens_print = 1 + self.gradient_accumulation_steps = 1 + self.training_steps = 0 + self.tokenizer = None + self.start_token_id = None + self.end_token_id = None + self.rm_initialized = False + self.residual_talk_head = True + self.thought_init_std_scale = 1e-2 + + self.final_only_mode = False + self.first_and_last_mode = True + self.first_only = False + self.original_loss_weight = 0.5 + + self.cumulative_residual = False + self.clever_residual = False + self.skip_residual = False + self.no_residual = True + + self.optimize_lm_head_only_at_start = False + self.optimize_model_only_at_start = False + + if self.optimize_model_only_at_start: + raise NotImplementedError + self.train_only_thinking_embedding = False + self.weighted_embeddings = False + self.use_start_thought_token = True + self.use_end_thought_token = True + self.initialize_thought_embedding_to_normal = False + self.initial_start_token = "---" + self.initial_end_token = "---" + self.output_logits_at_the_end = True + + self.wandb_enabled = False + self.gumbel_temperature = 0.001 + + self.use_policy_loss = True + self.include_policy_loss = True + self.trice_mode = True + self.remove_negative_rewards = True + self.use_policy_loss_for_end_thought = True + + self.base_original_mode = False + self.original_mode = False + + self.thought_prefix = "(Let's think step by step" + self.tokenized_thought_prefix = None + self.log_dict = defaultdict(int) + self.eval_log_dict = defaultdict(int) + self.print_final_only = True + self.loss_mean = loss_mean + self.all_rewards = [] + self.all_unreduced_losses = [] + self.kill_after = 100 + + self.start_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size)) + self.end_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size)) + + self.policy_loss_beta = 1e6 + self.embedding_scale = 1e2 + self.reinforce_temperature = 3 + self.base_loss_beta = 1 + + # Not used in the paper: + self.use_thought_prefix = False + self.use_reparam_for_thought_embeddings = False + self.use_upper_triangular = False + self.subtract_mean_reward = False + self.comparison_mode = False + self.gumbel_detach = True + + # For visualization + self.eval_mode = False + + num_talk = 1 + talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2 + if self.use_weighted_talk_head: + talk_output_dim = 1 + else: + talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size + + if not self.merged_lm_and_talk_heads: + if self.use_complex_talk_head: + self.talk_head = nn.ModuleList([nn.Sequential( + nn.Linear(talk_input_dim, config.hidden_size), + nn.ReLU(), + nn.Linear(config.hidden_size, config.hidden_size), + nn.ReLU(), + nn.Linear(config.hidden_size, talk_output_dim, bias=False) + )]) + else: + self.talk_head = nn.ModuleList([nn.Sequential( + nn.Linear(talk_input_dim, talk_output_dim, bias=False) + )]) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @torch.no_grad() + def infer( + self, + input_ids: torch.LongTensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + batch_size, seq_len = input_ids.shape + + # Save the original input_ids and attention_mask for later use + original_input_ids = input_ids.clone() + original_attention_mask = attention_mask.clone() if attention_mask is not None else None + + # Append the start thought token to the input sequence + start_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>") + input_ids = torch.cat([input_ids, torch.tensor([[start_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1) + seq_len += 1 + + # Update the attention mask + if attention_mask is not None: + attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1) + + # Generate the continuation + continuation_length = self.n_ahead - 2 + new_key_values = past_key_values + + start_time = time.time() + for continuation_idx in range(continuation_length): + outputs = self.model( + input_ids=input_ids if continuation_idx == 0 else next_token_id.unsqueeze(-1).to(input_ids.device), + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=new_key_values, + inputs_embeds=inputs_embeds, + use_cache=True, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + new_key_values = outputs.past_key_values + + hidden_states = outputs[0] + + logits = self.lm_head(hidden_states) + logits = logits[:, -1, :] # Only consider the last token + + # Apply Gumbel-Softmax to the logits + next_token_logits = F.gumbel_softmax(logits, tau=self.gumbel_temperature, hard=True, dim=-1) + next_token_id = torch.argmax(next_token_logits, dim=-1) + + # Append the generated token to the input sequence + input_ids = torch.cat([input_ids, next_token_id.unsqueeze(-1).to(input_ids.device)], dim=-1) + seq_len += 1 + + # Update the attention mask + if attention_mask is not None: + attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1) + + # Append the end thought token to the input sequence + end_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>") + input_ids = torch.cat([input_ids, torch.tensor([[end_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1) + seq_len += 1 + + # Update the attention mask + if attention_mask is not None: + attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1) + + # Get the hidden states before and after the thought + outputs_before = self.model( + input_ids=original_input_ids, + attention_mask=original_attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states_before = outputs_before[0][:, -1:, :] + + # two new tokens: last continuation token and end thought token + outputs_after = self.model( + input_ids=torch.cat([next_token_id.unsqueeze(-1).to(input_ids.device), torch.tensor(end_thought_token_id).unsqueeze(-1).unsqueeze(-1).to(input_ids.device)], dim=-1), + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=new_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states_after = outputs_after[0][:, -1:, :] + + # Apply the talk head to get the mixing weight + mixing_weight = self.talk_head[0](torch.cat([hidden_states_before, hidden_states_after], dim=-1)) + + # Apply the mixing weight to the hidden states + mixed_hidden_states = (1 - mixing_weight) * hidden_states_before + mixing_weight * hidden_states_after + + # Apply the language model head to get the final logits + logits = self.lm_head(mixed_hidden_states) + return logits + + @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, MistralForCausalLM + + >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") + >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + log_dict = self.log_dict if self.training else self.eval_log_dict + + if self.training and self.kill_after is not None and self.training_steps // self.gradient_accumulation_steps > self.kill_after: + raise ValueError("Killed after") + + if not self.training: + n_ahead_talk_to_restore = self.n_ahead_talk + n_passes_to_restore = self.n_passes + self.n_ahead_talk = 1 + self.n_passes = 1 + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + assert self.cumulative_residual or self.clever_residual or self.skip_residual or self.no_residual + assert not (self.skip_residual and self.use_policy_loss) + + if self.tokenized_thought_prefix is None and self.use_thought_prefix: + self.tokenized_thought_prefix = self.tokenizer(self.thought_prefix, return_tensors="pt", add_special_tokens=False)["input_ids"] + + def apply_head(head, states, detach=False): + if detach: + head_weight = head.weight.detach() + else: + head_weight = head.weight + head_weight = head_weight.to(states.device) + return (head_weight @ states.transpose(-1, -2)).transpose(-1, -2).contiguous() + + def idx_if_sequential(head, idx=0): + if isinstance(head, nn.Sequential) or isinstance(head, nn.ModuleList): + return idx_if_sequential(head[idx], idx=idx) + return head + + def none_repeat_interleave(x, n): + if x is None: + return x + return x.repeat_interleave(n, dim=0) + + if self.n_passes > 1: + input_ids = none_repeat_interleave(input_ids, self.n_passes) + attention_mask = none_repeat_interleave(attention_mask, self.n_passes) + position_ids = none_repeat_interleave(position_ids, self.n_passes) + inputs_embeds = none_repeat_interleave(inputs_embeds, self.n_passes) + labels = none_repeat_interleave(labels, self.n_passes) + if past_key_values is not None: + past_key_values = [none_repeat_interleave(p, self.n_passes) for p in past_key_values] + cur_token_indices = torch.arange(input_ids.shape[1], device=input_ids.device) + + self.tokenizer_has_start_thought_token = True + self.tokenizer_has_end_thought_token = True + if self.start_token_id is None: + self.start_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>") + if self.start_token_id == 0: + self.start_token_id = self.tokenizer.bos_token_id + self.tokenizer_has_start_thought_token = False + elif self.use_start_thought_token: + # base_start_id = self.tokenizer.convert_tokens_to_ids(self.initial_start_token) + base_start_id = self.tokenizer.encode(self.initial_start_token, add_special_tokens=False)[0] + if self.initialize_thought_embedding_to_normal: + self.start_embedding.data = torch.zeros_like(self.start_embedding.data) + else: + self.start_embedding.data[0] = self.model.embed_tokens.weight.data[base_start_id].clone().detach() / self.embedding_scale + self.start_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale) + if self.end_token_id is None: + self.end_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>") + if self.end_token_id == 0: + self.end_token_id = self.tokenizer.eos_token_id + self.tokenizer_has_end_thought_token = False + elif self.use_end_thought_token: + # base_end_id = self.tokenizer.convert_tokens_to_ids(self.initial_end_token) + base_end_id = self.tokenizer.encode(self.initial_end_token, add_special_tokens=False)[0] + if self.initialize_thought_embedding_to_normal: + self.end_embedding.data = torch.zeros_like(self.end_embedding.data) + else: + self.end_embedding.data[0] = self.model.embed_tokens.weight.data[base_end_id].clone().detach() / self.embedding_scale + self.end_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale) + + if not self.rm_initialized and (self.n_ahead > 1 or not self.base_original_mode): + self.rm_initialized = True + if not self.use_shallow_talk: + head = self.talk_head[0] + cur_head = head[-1] if isinstance(head, nn.Sequential) else head + talk_input_dim = cur_head.weight.data.shape[1] + talk_output_dim = 1 if self.use_weighted_talk_head else self.lm_head.weight.data.shape[0] + cur_head.weight.data = torch.zeros(talk_output_dim, talk_input_dim, device=cur_head.weight.device, dtype=cur_head.weight.dtype) + else: + # convert to identity transform + def lambda_transform(cur_head): + if cur_head.weight.data.shape[0] != cur_head.weight.data.shape[1]: + return torch.cat([ + torch.eye( + cur_head.weight.data.shape[0], + device=cur_head.weight.device, + dtype=cur_head.weight.dtype + ), + torch.zeros( + cur_head.weight.data.shape[0], + cur_head.weight.data.shape[1] - cur_head.weight.data.shape[0], + device=cur_head.weight.device, + dtype=cur_head.weight.dtype + )], dim=1) + return torch.eye( + cur_head.weight.data.shape[0], + device=cur_head.weight.device, + dtype=cur_head.weight.dtype + ) + if isinstance(self.talk_head[0], nn.Sequential): + for cur_head in self.talk_head[0]: + # if it has weights + if hasattr(cur_head, "weight"): + cur_head.weight.data = lambda_transform(cur_head) + else: + self.talk_head[-1].weight.data = lambda_transform(self.talk_head[0]) + + loss = None + prev_rm_tokens = None + cur_rm_tokens = None + prev_rm_logits = None + prev_sample_probs = None + did_skip_sampling = None + skip_sampling = None + sample_probs = None + hidden_states = None + logits = None + talk_kl_penalty = None + rm_logits = None + residual_logits = None + probabilities_2d = None + prev_probabilities_2d = None + policy_reward = None + logits_to_output = None + batch_size, seq_len = input_ids.shape + base_input_ids = input_ids.clone() + loss_list = [] + dqn_loss_list = [] + sampled_token_history = [] + sample_probs_history = [] + action_loglikelihoods_list = [] + + if self.use_end_thought_token or self.use_start_thought_token: + if not self.use_reparam_for_thought_embeddings: + start_embedding = self.start_embedding[0].unsqueeze(0) * self.embedding_scale + end_embedding = self.end_embedding[0].unsqueeze(0) * self.embedding_scale + else: + start_embedding = self.start_embedding * self.embedding_scale + end_embedding = self.end_embedding * self.embedding_scale + base_embeddings = self.model.embed_tokens.weight + if self.train_only_thinking_embedding: + base_embeddings = base_embeddings.detach() + # # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + fwd_iters = 1 if self.original_mode else self.n_ahead + self.n_ahead_talk - 1 + for ahead_idx in range(fwd_iters): + past_key_values_length = 0 + if past_key_values is not None: + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + past_key_values_length = past_key_values.get_usable_length(seq_len) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_len + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_len) + else: + position_ids = position_ids.view(-1, seq_len).long() + + if inputs_embeds is None: + contains_start = self.use_start_thought_token and (input_ids == self.start_token_id).any() + contains_end = self.use_end_thought_token and (input_ids == self.end_token_id).any() + contains_thought = contains_start or contains_end + if contains_thought: + thought_id = self.start_token_id if contains_start else self.end_token_id + cur_thought_embedding = start_embedding if contains_start else end_embedding + if self.use_reparam_for_thought_embeddings: + inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype) + inputs_embeds = inputs_embeds.detach() * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0] + if contains_start: + sampled_start = inputs_embeds.clone().detach() + if contains_end: + sampled_end = inputs_embeds.clone().detach() + else: + inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1) + else: + with torch.set_grad_enabled(not self.train_only_thinking_embedding): + inputs_embeds = self.model.embed_tokens(input_ids) + + if self.n_ahead != 1 or self.n_ahead_talk != 1 or self.comparison_mode: + if attention_mask is None: + base_attention_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=0).to(input_ids.device) + base_attention_mask = base_attention_mask.view(1, 1, seq_len, seq_len) + base_attention_mask = base_attention_mask.repeat(input_ids.shape[0], 1, 1, 1) + attention_mask = base_attention_mask + breakpoint() + elif attention_mask.dim() == 2: + if seq_len + past_key_values_length != attention_mask.shape[-1]: + breakpoint() + attention_mask = torch.cat( + [torch.ones((attention_mask.shape[0], past_key_values_length), dtype=attention_mask.dtype, device=attention_mask.device), attention_mask], + dim=-1 + ) + # # if the attention mask + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_len), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + outputs = self.model( + # input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + prev_hidden_states = hidden_states + hidden_states = outputs[0] + prev_rm_logits = rm_logits # for policy gradient + prev_rm_tokens = cur_rm_tokens # for policy gradient + + if ahead_idx == 0: + hidden_states_lm = hidden_states + logits = self.lm_head(hidden_states_lm) + base_hidden_states = hidden_states.clone() + initial_loss_logits = logits.clone() + if self.optimize_lm_head_only_at_start or self.optimize_model_only_at_start: + logits = logits.detach() + base_hidden_states = base_hidden_states.detach() + if self.optimize_model_only_at_start: + hidden_states = hidden_states.detach() + base_logits = logits.clone() + else: + talk_hidden_states = hidden_states + if self.merged_lm_and_talk_heads: + assert self.no_residual + residual_logits = self.lm_head(hidden_states) + talk_hidden_states = hidden_states + else: + if ahead_idx > self.n_ahead - 1: + cur_base_hidden = torch.cat([ + base_hidden_states[..., ahead_idx - self.n_ahead + 1:, :], + base_hidden_states[..., :ahead_idx - self.n_ahead + 1, :] + ], dim=-2) + else: + cur_base_hidden = base_hidden_states + + if self.use_concat_talk_head: + # concatenate the hidden states with the original hidden states + head_input_hidden_states = torch.cat([cur_base_hidden, talk_hidden_states], dim=-1) + else: + head_input_hidden_states = talk_hidden_states + + residual_logits = self.talk_head[0](head_input_hidden_states) + if self.use_shallow_talk: + residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start) + residual_logits = residual_logits.to(logits.device) + if self.use_weighted_talk_head: + # combine the cur_base_hidden with the talk_hidden_states according to the weighted head + residual_logits = cur_base_hidden * (1 - residual_logits) + talk_hidden_states * residual_logits + residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start) + + assert sum([self.cumulative_residual, self.clever_residual, self.skip_residual, self.no_residual]) == 1 + if self.clever_residual: + if ahead_idx >= self.n_ahead - 1: + # get the logits shifted according to the current talk ahead + cur_base_logits = torch.cat([ + base_logits[..., ahead_idx - self.n_ahead + 1:, :], + base_logits[..., :ahead_idx - self.n_ahead + 1, :] + ], dim=-2) + if self.optimize_lm_head_only_at_start: + cur_base_logits = cur_base_logits.detach() + logits = cur_base_logits + residual_logits + else: + logits += residual_logits / self.n_ahead + elif self.cumulative_residual: + if self.residual_talk_head: + if ahead_idx < self.n_ahead: + logits += residual_logits + else: + # get the logits shifted according to the current talk ahead + cur_base_logits = torch.cat([ + base_logits[..., ahead_idx - self.n_ahead + 1:, :], + base_logits[..., :ahead_idx - self.n_ahead + 1, :] + ], dim=-2) + if self.optimize_lm_head_only_at_start: + cur_base_logits = cur_base_logits.detach() + logits = cur_base_logits + residual_logits + else: + if ahead_idx < self.n_ahead: + logits += residual_logits + else: + logits = residual_logits + elif self.skip_residual: + if ahead_idx >= self.n_ahead: + # get the logits shifted according to the current talk ahead + cur_base_logits = torch.cat([ + base_logits[..., ahead_idx - self.n_ahead + 1:, :], + base_logits[..., :ahead_idx - self.n_ahead + 1, :] + ], dim=-2) + if self.optimize_lm_head_only_at_start: + cur_base_logits = cur_base_logits.detach() + logits = cur_base_logits + elif self.no_residual: + logits = residual_logits + else: + logits = base_logits + residual_logits + + attempted = False + talk_loss_list = [] + if self.original_mode or (self.n_ahead == 1) or (self.comparison_mode and ahead_idx == 0):# or (self.optimize_lm_head_only_at_start and ahead_idx == 0): + loss = None + attempted = True + + if labels is not None: + for shift_amount in range(self.n_ahead_talk): + # Shift so that tokens < n predict n + # ab[cde]f + # abc[def] + if ahead_idx == 0 and self.optimize_lm_head_only_at_start: + loss_logits = initial_loss_logits + else: + loss_logits = logits + shift_logits = loss_logits[..., shift_amount:-1, :].contiguous() + shift_labels = labels[..., 1 + shift_amount:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(reduction="none") + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1).clone() + # Enable model parallelism + shift_labels[shift_labels == self.tokenizer.pad_token_id] = -100 + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + if not self.comparison_mode and not (self.optimize_lm_head_only_at_start and (self.n_ahead + self.n_ahead_talk > 2)) or self.original_mode: + loss_list.append(loss) + talk_loss_list.append(nonzero_mean(loss).detach()) + + if not attempted or self.comparison_mode: + rm_hidden_states = hidden_states + # print("Magnitude of RM hidden states before RM head", rm_hidden_states.norm()) + rm_logits = apply_head(self.lm_head, rm_hidden_states, detach=self.optimize_lm_head_only_at_start) + + # don't allow it to predict the thinking token + if self.tokenizer_has_start_thought_token: + rm_logits[..., self.start_token_id] = -1e10 + if self.tokenizer_has_end_thought_token: + rm_logits[..., self.end_token_id] = -1e10 + probabilities = rm_logits + if probabilities_2d is not None: + prev_probabilities_2d = probabilities_2d.clone() + probabilities_2d = probabilities.view(-1, probabilities.size(-1)) + + did_skip_sampling = skip_sampling + skip_sampling = False + if ahead_idx == 0 and self.use_start_thought_token: + override_token = self.start_token_id + elif self.use_thought_prefix and ahead_idx < self.tokenized_thought_prefix.shape[-1]: + override_token = self.tokenized_thought_prefix[..., ahead_idx] + elif ahead_idx == self.n_ahead - 2 and self.use_end_thought_token: + override_token = self.end_token_id + else: + override_token = None + if override_token is not None and self.n_ahead > 1: + # always start with the start token + probabilities_2d = torch.zeros_like(probabilities_2d) + probabilities_2d[:, override_token] = 1.0 + skip_sampling = True + elif ahead_idx >= self.n_ahead - 1: + if labels is not None: # we're in the talk phase + cur_talk_n = ahead_idx - (self.n_ahead - 1) + 1 + # print("Setting rm to labels", cur_talk_n, "during", ahead_idx) + shift_labels = labels[..., cur_talk_n:].contiguous().to(probabilities_2d.device) + padding = torch.full_like( + labels[..., :cur_talk_n], + self.tokenizer.pad_token_id, + dtype=torch.long, + device=shift_labels.device + ) + new_rm_tokens = torch.cat( + [shift_labels, padding], + dim=-1 + ) + # convert rm tokens to one-hot + probabilities_2d = F.one_hot(new_rm_tokens, num_classes=self.vocab_size).reshape(-1, self.vocab_size).to(probabilities_2d.dtype) + skip_sampling = True + else: + continue + temperature = self.gumbel_temperature if self.training else 0.001 + prev_sample_probs = sample_probs + sample_probs = probabilities_2d + if ahead_idx < self.n_ahead - 1 and not skip_sampling: + probabilities_2d = F.gumbel_softmax(sample_probs, tau=temperature, hard=True, dim=-1) + if self.gumbel_detach: + probabilities_2d = probabilities_2d.detach() + sampled_token_history.append(probabilities_2d.argmax(dim=-1).detach().cpu()) + # convert rm logits directly to embeddings + contains_start = self.use_start_thought_token and (probabilities_2d[..., self.start_token_id].sum() > 0) + contains_end = self.use_end_thought_token and (probabilities_2d[..., self.end_token_id].sum() > 0) + contains_thought = contains_start or contains_end + + if not contains_thought: + with torch.set_grad_enabled(not self.train_only_thinking_embedding): + inputs_embeds = probabilities_2d @ (self.model.embed_tokens.weight.to(probabilities.device).to(probabilities.dtype)) + else: + thought_id = self.start_token_id if contains_start else self.end_token_id + cur_thought_embedding = start_embedding if contains_start else end_embedding + if self.use_reparam_for_thought_embeddings: + inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype) + inputs_embeds = inputs_embeds * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0] + if contains_start: + sampled_start = inputs_embeds.clone().detach() + else: + sampled_end = inputs_embeds.clone().detach() + else: + inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1) + inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype) + inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype) + + if len(attention_mask.shape) == 2: + breakpoint() + else: + original_attention = attention_mask[..., :attention_mask.shape[-2]] + if self.use_upper_triangular: + new_attention = original_attention + else: + original_attention = original_attention == attention_mask.max() + # because eye isn't implemented for BF16, we need to handle the case + if not attention_mask.dtype == torch.bfloat16: + new_attention = torch.eye( + seq_len, dtype=attention_mask.dtype, device=attention_mask.device + ) + else: + new_attention = torch.eye( + seq_len, dtype=torch.float32, device=attention_mask.device + ).to(attention_mask.dtype) + + new_attention = new_attention.view(1, 1, seq_len, seq_len).repeat(input_ids.shape[0], 1, 1, 1) + new_attention = new_attention * original_attention + new_attention[new_attention == 0] = attention_mask.min() + new_attention[new_attention == 1] = attention_mask.max() + attention_mask = torch.cat([attention_mask, new_attention], dim=-1) + past_key_values = outputs.past_key_values + position_ids = position_ids + 1 + + if labels is not None and (self.n_ahead > 1 or not self.base_original_mode): + # Shift so that tokens < n predict n + # logits: abcdef -> bcdef? -> cdef?? + # labels: abcdef -> ?bcdef -> ??cdef + if ahead_idx == 0 and self.optimize_lm_head_only_at_start: + loss_logits = initial_loss_logits + else: + loss_logits = logits + shift_idx = 1 + max(0, ahead_idx - (self.n_ahead - 1)) + shift_logits = loss_logits[..., :-shift_idx, :].contiguous() + shift_labels = labels[..., shift_idx:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(reduction="none") + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + # if shift_labels.min() == self.tokenizer.pad_token_id: + shift_labels = torch.where(shift_labels == self.tokenizer.pad_token_id, -100, shift_labels) + unreduced_loss = loss_fct(shift_logits, shift_labels) + if torch.any(unreduced_loss != unreduced_loss): + raise ValueError("NaN loss") + unreduced_loss = unreduced_loss.reshape(logits.shape[0], -1) + loss_list.append(unreduced_loss) + + + if self.use_policy_loss and ahead_idx > 0 and (ahead_idx > 1 or not self.use_start_thought_token): + # we treat the change in loss as the reward + previous_loss = loss_list[-2] + # for example, suppose n_ahead = 3 and n_ahead_talk = 2 + # note that we end at self.n_ahead + self.n_ahead_talk - 2 + # in this case, 5 - 2 = 3, so we end at ahead_idx = 3 + # we also predict the next token at ahead_idx = 2 + # when we get to ahead_idx = 2, we predict ahead + # so we shift by 1 + # note that this is ahead_idx = n_ahead - 1 + # when we get to ahead_idx = 3, we predict ahead + # so we shift by 2 + # note that this is ahead_idx = n_ahead + if ahead_idx < self.n_ahead - 1: + shift_amount = 0 + original_dqn_reward = (previous_loss - unreduced_loss).detach() + if self.first_and_last_mode: + original_dqn_reward = original_dqn_reward * 0.0 + else: + # logits vs cur_policy_shift_logits + # let's look at rm_logits and prev_rm_logits + shift_amount = max(0, ahead_idx - (self.n_ahead - 1)) + # let's say shift_amount = 2 + # abcdefg -> bcdefg? -> cdefg?? + # logits = [a b]c d e f[g] + # labels = [a b c]d e f g + cur_policy_shift_logits = initial_loss_logits[..., shift_amount:-1, :].contiguous().detach() + cur_policy_shift_labels = labels[..., 1 + shift_amount:].contiguous() + # Flatten the tokens + cur_policy_loss_fct = CrossEntropyLoss(reduction="none") + cur_policy_shift_logits = cur_policy_shift_logits.view(-1, self.config.vocab_size) + cur_policy_shift_labels = cur_policy_shift_labels.view(-1).clone() + # Enable model parallelism + cur_policy_shift_labels[cur_policy_shift_labels == self.tokenizer.pad_token_id] = -100 + cur_policy_shift_labels = cur_policy_shift_labels.to(cur_policy_shift_labels.device) + cur_policy_reward_base_loss = loss_fct( + cur_policy_shift_logits, cur_policy_shift_labels.to(cur_policy_shift_logits.device) + ).reshape(logits.shape[0], -1) + original_dqn_reward = cur_policy_reward_base_loss.detach() - unreduced_loss + + if not did_skip_sampling: + nonzero_indices = prev_probabilities_2d.nonzero() + action_loglikelihoods = F.log_softmax(prev_sample_probs / self.reinforce_temperature, dim=-1)[nonzero_indices[:, 0], nonzero_indices[:, 1]] + action_loglikelihoods_2d = action_loglikelihoods.reshape(batch_size, -1)[:, :-1 - shift_amount] + action_loglikelihoods_list.append(action_loglikelihoods_2d) + if policy_reward is None: + policy_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)] + else: + if self.n_ahead_talk > shift_amount: + added_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)] + else: + added_reward = original_dqn_reward + policy_reward += added_reward + + if self.use_policy_loss and ahead_idx == self.n_ahead + self.n_ahead_talk - 2: + # only compute during the thinking phase + if self.use_reparam_for_thought_embeddings and (self.use_start_thought_token or self.use_end_thought_token): + # sampled_start, sampled_end + # calculate the log likelihood of the start and end embeddings sampled from a multivariate normal distribution + # with mean start_embedding[0] and standard deviation start_embedding[1] + if self.use_start_thought_token: + exp_start_std = torch.exp(start_embedding[1]) + start_loglikelihood = -0.5 * (sampled_start.detach() - start_embedding[0]) ** 2 / exp_start_std ** 2 - start_embedding[1] - 0.5 * math.log(2 * math.pi) + start_loglikelihood = start_loglikelihood.mean(dim=-1) + if self.use_end_thought_token: + exp_end_std = torch.exp(end_embedding[1]) + end_loglikelihood = -0.5 * (sampled_end.detach() - end_embedding[0]) ** 2 / exp_end_std ** 2 - end_embedding[1] - 0.5 * math.log(2 * math.pi) + end_loglikelihood = end_loglikelihood.mean(dim=-1) + # we use the mean instead of the sum to prevent dependence on the dimensionality of the embeddings + if self.use_end_thought_token and self.use_policy_loss_for_end_thought: + action_loglikelihoods_list.append(end_loglikelihood) + if self.use_start_thought_token: + action_loglikelihoods_list.append(start_loglikelihood) + + if ahead_idx == self.n_ahead + self.n_ahead_talk - 2 and self.eval_mode: + with torch.no_grad(): + # calculate the 0.75 quantile of the rewards + filtered_tokens = input_ids[:, :policy_reward.shape[-1]].cpu().detach().numpy().flatten() + filtered_tokens_mask = filtered_tokens != self.tokenizer.pad_token_id + filtered_tokens = filtered_tokens[filtered_tokens_mask] + filtered_rewards = policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten() + filtered_rewards = filtered_rewards[filtered_tokens_mask] + + abs_reward_list = np.abs(policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten()) + abs_reward_list = abs_reward_list[filtered_tokens_mask] + medium_quantile = np.quantile(abs_reward_list, 0.5) + upper_quantile = np.quantile(abs_reward_list, 0.95) + + save_tokens_with_rewards_to_pdf( + filtered_tokens, + [0] + filtered_rewards.tolist(), + self.tokenizer, + output_file=f"texts/rewards_talk_{self.n_ahead_talk}_{self.training_steps}.pdf", + eps=medium_quantile, + eps2=upper_quantile, + ) + + def plot_kde(data, losses): + sns.set(style="whitegrid") + # Create the KDE plot + sns.kdeplot(data, fill=True) + # Set the plot title and labels + plt.title("KDE Plot") + plt.xlabel("Value") + plt.ylabel("Density") + # Save the plot + plt.savefig(f"texts/kde_talk_{self.n_ahead_talk}_{self.training_steps}.pdf") + # Close the plot + plt.close() + + # Step 1: Create a base color palette + base_colors = sns.color_palette("light:#5A9", n_colors=256) # More colors for a smoother gradient + base_cmap = LinearSegmentedColormap.from_list("log_light", base_colors) + log_norm = LogNorm(vmin=1e-3, vmax=10) + + sns.kdeplot(x=data, y=losses, fill=True, levels=20, norm=log_norm, cut=0, linewidths=0) + # limit y to 0 to 25 and x to -1 to 1 + plt.xlim(-1, 1) + plt.ylim(0, 25) + plt.savefig(f"texts/jointer_talk_{self.n_ahead_talk}_{self.training_steps}.pdf") + plt.close() + + self.all_rewards.extend(filtered_rewards) + self.all_unreduced_losses.extend(unreduced_loss[:, :-1].flatten()[filtered_tokens_mask].float().flatten().cpu().detach().numpy()) + plot_kde(self.all_rewards, self.all_unreduced_losses) + + for action_loglikelihoods_2d in action_loglikelihoods_list: + train_policy_reward = policy_reward + + # discard rewards below the mean + if self.trice_mode and self.n_passes > 1: + batched_policy_reward = train_policy_reward.reshape(-1, self.n_passes, train_policy_reward.shape[-1]) + # average over the passes + train_policy_reward = batched_policy_reward - batched_policy_reward.mean(dim=1, keepdim=True) + train_policy_reward = train_policy_reward.reshape(-1, train_policy_reward.shape[-1]) + + if self.subtract_mean_reward: + train_policy_reward = train_policy_reward - train_policy_reward.mean() + if self.remove_negative_rewards: + fixed_policy_reward = train_policy_reward.detach().clamp(min=0) + else: + fixed_policy_reward = train_policy_reward.detach() + actor_loss = -fixed_policy_reward * action_loglikelihoods_2d[:, :policy_reward.shape[-1]].to(policy_reward.device) + if action_loglikelihoods_2d.mean() < -1e4 and not self.use_policy_loss_just_for_thoughts: + # This will only happen when we force the next token to be the end of thought token + break + dqn_loss_list.append(actor_loss.mean()) + + if loss_list: + if self.first_and_last_mode: + loss = sum( + self.loss_mean(loss_list[-(i + 1)]) for i in range(self.n_ahead_talk) + ) * (1 - self.original_loss_weight) / self.n_ahead_talk + loss = loss + self.loss_mean(loss_list[0]) * self.original_loss_weight + # Let's NaN out the others + # e.g. if n_ahead_talk = 2 and the list is 5 long, we want to NaN out 1, 2 but keep 0, 3, 4 + for i in range(1, len(loss_list) - self.n_ahead_talk): + loss_list[i] = loss_list[i] * math.nan + elif self.first_only: + loss = self.loss_mean(loss_list[0]) + elif self.final_only_mode: + loss = sum( + self.loss_mean(loss_list[-i]) for i in range(1, self.n_ahead_talk + 1) + ) / self.n_ahead_talk + else: + loss = None + for i in range(len(loss_list)): + cur_loss = self.loss_mean(loss_list[i]) + if loss is not None: + loss = loss + cur_loss.to(loss.device) + else: + loss = cur_loss + loss = loss / len(loss_list) + + loss = loss * self.base_loss_beta + + if dqn_loss_list: + dqn_loss = sum(dqn_loss_list) / len(dqn_loss_list) + if self.include_policy_loss: + if loss is not None: + loss += dqn_loss * self.policy_loss_beta + else: + loss = dqn_loss * self.policy_loss_beta + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + base_log_dict = { + f"loss_{i}": nonzero_mean(loss_list[i]) for i in range(len(loss_list)) + } + + if loss is not None: + base_log_dict["loss_train"] = loss.item() + + for loss_key, loss_val in base_log_dict.items(): + log_dict[loss_key] += loss_val / self.n_tokens_print + + if self.use_policy_loss and policy_reward is not None: + log_dict["policy_loss"] += dqn_loss / self.n_tokens_print + log_dict["policy_reward"] += policy_reward.mean() / self.n_tokens_print + + if not loss_list: + if loss is not None: + log_dict["loss_0"] += loss / self.n_tokens_print + else: + log_dict["loss_final"] += nonzero_mean(loss_list[-1]) / self.n_tokens_print + log_dict["loss_talk"] += sum(nonzero_mean(cur_loss_item) for cur_loss_item in loss_list[-self.n_ahead_talk:]) / self.n_ahead_talk / self.n_tokens_print + + # also log relative losses to loss_0 + if loss_list: + for i in range(len(loss_list)): + talk_idx = min(max(i - (self.n_ahead - 1), 0), len(talk_loss_list) - 1) + if not talk_loss_list: + cur_talk_loss = nonzero_mean(loss_list[0]) + else: + cur_talk_loss = talk_loss_list[talk_idx] + log_dict[f"rel_loss_{i}"] += (nonzero_mean(loss_list[i]) - cur_talk_loss) / self.n_tokens_print + if self.training: + self.training_steps += 1 + try: + # if self.training_steps % (self.gradient_accumulation_steps * 256) == 0: + if self.wandb_enabled: + if self.training_steps % (self.n_tokens_print) == 0 or not self.training:# and "0" in str(loss.device): + if not self.training: + new_log_dict = {} + for key in list(log_dict.keys()): + new_log_dict["eval_" + key] = log_dict[key] + log_dict = new_log_dict + log_dict["training_steps"] = self.training_steps + log_dict["batch_size"] = batch_size + log_dict["example_steps"] = self.training_steps * batch_size * self.gradient_accumulation_steps + if self.n_ahead > 1: + log_dict["compute_steps"] = self.training_steps * batch_size * (self.n_ahead + self.n_ahead_talk - 1) * self.gradient_accumulation_steps + else: # There's no overhead for talk tokens if there's no thinking + log_dict["compute_steps"] = self.training_steps * batch_size * self.gradient_accumulation_steps + # remove all nans + for key in list(log_dict.keys()): + if log_dict[key] != log_dict[key]: + del log_dict[key] + if self.training: + wandb.log(log_dict) + if self.training: + self.log_dict = defaultdict(int) + else: + self.eval_log_dict = defaultdict(int) + except Exception as e: + pass + + if not self.training: + self.n_ahead_talk = n_ahead_talk_to_restore + self.n_passes = n_passes_to_restore + return CausalLMOutputWithPast( + loss=loss if loss is not None else None, + logits=(rm_logits if self.n_ahead > 1 else logits) if not self.output_logits_at_the_end else logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + # Omit tokens covered by past_key_values + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + max_cache_length = past_key_values.get_max_length() + else: + cache_length = past_length = past_key_values[0][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing inputs_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + +@add_start_docstrings( + """ + The Mistral Model transformer with a sequence classification head on top (linear layer). + + [`MistralForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + MISTRAL_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Mistral, LLAMA->MISTRAL +class MistralForSequenceClassification(MistralPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = MistralModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + +class QuietMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + + # pdb.set_trace() + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class QuietAttention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: QuietConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + self.attention_dropout = config.attention_dropout + self._attn_implementation = config._attn_implementation + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + + self.rotary_emb = QuietRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + if self._attn_implementation == "flash_attention_2": + # Prepare attention mask for flash-attn + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._attn_implementation == "sdpa": + # Prepare attention mask for SDPA + if attention_mask is None or attention_mask.dim() == 2: + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + else: + # Prepare attention mask for other implementations + if attention_mask is None or attention_mask.dim() == 2: + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Quiet +class QuietSdpaAttention(QuietAttention): + """ + Quiet attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `QuietAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from QuietAttention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "QuietModel is using QuietSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attention_mask = attention_mask.to(query_states.dtype) + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask.to(query_states.device) if attention_mask is not None else None, + dropout_p=self.attention_dropout if self.training else 0.0, + # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. + is_causal=self.is_causal and attention_mask is None and q_len > 1, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +QUIET_ATTENTION_CLASSES = { + "eager": QuietAttention, + "sdpa": QuietSdpaAttention, +} + + +class QuietDecoderLayer(nn.Module): + def __init__(self, config: QuietConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = QUIET_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) + + self.mlp = QuietMLP(config) + self.input_layernorm = QuietRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = QuietRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = residual.to(hidden_states.device) + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +QUIET_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + Parameters: + config ([`QuietConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Quiet Model outputting raw hidden-states without any specific head on top.", + QUIET_START_DOCSTRING, +) +class QuietPreTrainedModel(PreTrainedModel): + config_class = QuietConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["QuietDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +QUIET_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + [What are attention masks?](../glossary#attention-mask) + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare Quiet Model outputting raw hidden-states without any specific head on top.", + QUIET_START_DOCSTRING, +) +class QuietModel(QuietPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`QuietDecoderLayer`] + Args: + config: QuietConfig + """ + + def __init__(self, config: QuietConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [QuietDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = QuietRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(QUIET_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + past_key_values_length = 0 + + if use_cache: + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + past_key_values_length = past_key_values.get_usable_length(seq_length) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if self._attn_implementation == "flash_attention_2": + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + elif self._attn_implementation == "sdpa" and not output_attentions and attention_mask is not None and attention_mask.dim() == 2: + # output_attentions=True can not be supported when using SDPA, and we fall back on + # the manual implementation that requires a 4D causal mask in all cases. + attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + ) + elif attention_mask is None or (attention_mask is not None and attention_mask.dim() == 2): + # 4d mask is passed through the layers + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_length), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + +def nonzero_mean(x, axis=None): + if axis is not None: + return x.sum(axis) / (x != 0).sum(axis) + return x.sum() / (x != 0).sum() + +def loss_mean(x): + return x.sum() / (x != 0).sum() + +class QuietForCausalLM(QuietPreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = QuietModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + # self.router_aux_loss_coef = config.router_aux_loss_coef + # self.num_experts = config.num_experts + # self.num_experts_per_tok = config.num_experts_per_tok + self.max_thoughts = config.max_thoughts + self.merged_lm_and_talk_heads = config.merged_lm_and_talk_heads + self.use_concat_talk_head = config.use_concat_talk_head + self.use_shallow_talk = config.use_shallow_talk + self.use_complex_talk_head = config.use_complex_talk_head + self.use_weighted_talk_head = config.use_weighted_talk_head + # the weighted head will output a single value, so it can't be passed to the lm head + assert not (self.use_weighted_talk_head and self.use_shallow_talk) + + self.n_ahead = 1 + self.n_ahead_talk = 1 + self.n_passes = 1 + self.n_tokens_print = 1 + self.gradient_accumulation_steps = 1 + self.training_steps = 0 + self.tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q") + self.start_token_id = None + self.end_token_id = None + self.rm_initialized = False + self.residual_talk_head = True + self.thought_init_std_scale = 1e-2 + + self.final_only_mode = False + self.first_and_last_mode = True + self.first_only = False + self.original_loss_weight = 0.5 + + self.cumulative_residual = False + self.clever_residual = False + self.skip_residual = False + self.no_residual = True + + self.optimize_lm_head_only_at_start = False + self.optimize_model_only_at_start = False + + if self.optimize_model_only_at_start: + raise NotImplementedError + self.train_only_thinking_embedding = False + self.weighted_embeddings = False + self.use_start_thought_token = True + self.use_end_thought_token = True + self.initialize_thought_embedding_to_normal = False + self.initial_start_token = "---" + self.initial_end_token = "---" + self.output_logits_at_the_end = True + + self.wandb_enabled = False + self.gumbel_temperature = 0.001 + + self.use_policy_loss = True + self.include_policy_loss = True + self.trice_mode = True + self.remove_negative_rewards = True + self.use_policy_loss_for_end_thought = True + + self.base_original_mode = False + self.original_mode = False + + self.thought_prefix = "(Let's think step by step" + self.tokenized_thought_prefix = None + self.log_dict = defaultdict(int) + self.eval_log_dict = defaultdict(int) + self.loss_mean = loss_mean + + self.start_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size)) + self.end_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size)) + + self.policy_loss_beta = 1e6 + self.embedding_scale = 1e2 + self.temperature = nn.Parameter(torch.ones(1)) + self.max_temperature = config.max_temperature + self.reinforce_temperature = 3 + self.base_loss_beta = 1 + self.thinking_usefulness_head = nn.Linear(self.model.config.hidden_size, 1) + self.thinking_threshold = 0.5 + self.thinking_usefulness_loss_weight = 1e-2 + + # Not used in the paper: + self.use_thought_prefix = False + self.use_reparam_for_thought_embeddings = False + self.use_upper_triangular = False + self.subtract_mean_reward = False + self.comparison_mode = False + self.gumbel_detach = False + + # For visualization + self.eval_mode = False + + num_talk = 1 + talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2 + if self.use_weighted_talk_head: + talk_output_dim = 1 + else: + talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size + + if not self.merged_lm_and_talk_heads: + if self.use_complex_talk_head: + self.talk_head = nn.ModuleList([nn.Sequential( + nn.Linear(talk_input_dim, config.hidden_size), + nn.ReLU(), + nn.Linear(config.hidden_size, config.hidden_size), + nn.ReLU(), + nn.Linear(config.hidden_size, talk_output_dim, bias=False) + )]) + else: + self.talk_head = nn.ModuleList([nn.Sequential( + nn.Linear(talk_input_dim, talk_output_dim, bias=False) + )]) + + self.apply(self._init_weights) + + # Add dropout regularization + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def _init_weights(self, module): + if isinstance(module, nn.Linear): + nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + elif isinstance(module, nn.Embedding): + nn.init.xavier_uniform_(module.weight) + + @torch.no_grad() + def infer( + self, + input_ids: torch.LongTensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + batch_size, seq_len = input_ids.shape + + # Save the original input_ids and attention_mask for later use + original_input_ids = input_ids.clone() + original_attention_mask = attention_mask.clone() if attention_mask is not None else None + + # Append the start thought token to the input sequence + start_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>") + input_ids = torch.cat([input_ids, torch.tensor([[start_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1) + seq_len += 1 + + # Update the attention mask + if attention_mask is not None: + attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1) + + # Generate the continuation + continuation_length = self.n_ahead - 2 + new_key_values = past_key_values + + # Initialize next_token_id with a default value + next_token_id = torch.zeros(batch_size, dtype=torch.long).to(input_ids.device) + + start_time = time.time() + for continuation_idx in range(continuation_length): + outputs = self.model( + input_ids=input_ids if continuation_idx == 0 else next_token_id.unsqueeze(-1).to(input_ids.device), + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=new_key_values, + inputs_embeds=inputs_embeds, + use_cache=True, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + new_key_values = outputs.past_key_values + + hidden_states = outputs[0] + + logits = self.lm_head(hidden_states) + logits = logits[:, -1, :] # Only consider the last token + + # Apply Gumbel-Softmax to the logits + next_token_logits = F.gumbel_softmax(logits, tau=self.gumbel_temperature, hard=True, dim=-1) + next_token_id = torch.argmax(next_token_logits, dim=-1) + + # Append the generated token to the input sequence + # input_ids = torch.cat([input_ids, next_token_id.unsqueeze(-1).to(input_ids.device)], dim=-1) + seq_len += 1 + + # Update the attention mask + if attention_mask is not None: + attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1) + + # Append the end thought token to the input sequence + end_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>") + input_ids = torch.cat([input_ids, torch.tensor([[end_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1) + seq_len += 1 + + # Update the attention mask + if attention_mask is not None: + attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1) + + # Get the hidden states before and after the thought + outputs_before = self.model( + input_ids=original_input_ids, + attention_mask=original_attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states_before = outputs_before[0][:, -1:, :] + + # two new tokens: last continuation token and end thought token + outputs_after = self.model( + input_ids=torch.cat([next_token_id.unsqueeze(-1).to(input_ids.device), torch.tensor([[end_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1), + attention_mask=torch.cat([attention_mask[:, -1:], torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1), + position_ids=position_ids, + past_key_values=new_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states_after = outputs_after[0][:, -1:, :] + + # Apply the talk head to get the mixing weight + mixing_weight = self.talk_head[0](torch.cat([hidden_states_before, hidden_states_after], dim=-1)) + + # Apply the mixing weight to the hidden states + mixed_hidden_states = (1 - mixing_weight) * hidden_states_before + mixing_weight * hidden_states_after + + # Apply the language model head to get the final logits + logits = self.lm_head(mixed_hidden_states) + return logits + + @torch.no_grad() + def generate( + self, + input_ids: torch.LongTensor = torch.LongTensor(), + attention_mask: Optional[torch.Tensor] = None, + max_new_tokens: Optional[int] = None, + temperature: float = 1.1, + **kwargs, + ): + if isinstance(input_ids, str): + input_ids = self.tokenizer(input_ids, return_tensors="pt").input_ids + + if attention_mask is None: + # Create a default attention mask if not provided + attention_mask = torch.ones_like(input_ids) + + from .generate import generate + return generate(self, input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, temperature=temperature, **kwargs) + + @add_start_docstrings_to_model_forward(QUIET_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + max_new_tokens: Optional[int] = None, + temperature: Optional[float] = None, + temperature_last: Optional[float] = None, + dynamic_temperature: Optional[float] = None, + dynatemp_low: Optional[float] = None, + dynatemp_high: Optional[float] = None, + dynatemp_exponent: Optional[float] = None, + smoothing_factor: Optional[float] = None, + smoothing_curve: Optional[str] = None, + top_p: Optional[float] = None, + min_p: Optional[float] = None, + top_k: Optional[int] = None, + repetition_penalty: Optional[float] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float] = None, + repetition_penalty_range: Optional[int] = None, + typical_p: Optional[float] = None, + tfs: Optional[float] = None, + top_a: Optional[float] = None, + guidance_scale: Optional[float] = None, + penalty_alpha: Optional[float] = None, + mirostat_mode: Optional[int] = None, + mirostat_tau: Optional[float] = None, + mirostat_eta: Optional[float] = None, + do_sample: Optional[bool] = None, + encoder_repetition_penalty: Optional[float] = None, + no_repeat_ngram_size: Optional[int] = None, + sampler_priority: Optional[List[str]] = None, + negative_prompt_ids: Optional[List[int]] = None, + prompt_lookup_num_tokens: Optional[int] = None, + epsilon_cutoff: Optional[float] = None, + eta_cutoff: Optional[float] = None, + max_length: Optional[int] = None, + suppress_tokens: Optional[List[int]] = None, + synced_gpus: Optional[bool] = None, + eos_token_id: Optional[List[int]] = None, + stopping_criteria: Optional[transformers.StoppingCriteriaList] = None, + logits_processor: Optional[transformers.LogitsProcessorList] = None, + inputs: Optional[torch.LongTensor] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + Returns: + Example: + ```python + >>> from transformers import AutoTokenizer, QuietForCausalLM + >>> model = QuietForCausalLM.from_pretrained("quietai/Quiet-7B-v0.1") + >>> tokenizer = AutoTokenizer.from_pretrained("quietai/Quiet-7B-v0.1") + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + if not self.training: + n_ahead_talk_to_restore = self.n_ahead_talk + n_passes_to_restore = self.n_passes + self.n_ahead_talk = 1 + self.n_passes = 1 + + # aux_loss = None + # output_router_logits = output_router_logits if output_router_logits is not None else self.config.output_router_logits + # if output_router_logits: + # router_logits = outputs.router_logits if return_dict else outputs[-1] + # if router_logits is not None: + # aux_loss = load_balancing_loss_func( + # router_logits, + # self.num_experts, + # self.num_experts_per_tok, + # attention_mask, + # ) + # if labels is not None: + # loss += self.router_aux_loss_coef * aux_loss.to(loss.device) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + assert self.cumulative_residual or self.clever_residual or self.skip_residual or self.no_residual + assert not (self.skip_residual and self.use_policy_loss) + + if self.tokenized_thought_prefix is None and self.use_thought_prefix: + self.tokenized_thought_prefix = self.tokenizer(self.thought_prefix, return_tensors="pt", add_special_tokens=False)["input_ids"] + + def apply_head(head, states, detach=False): + if detach: + head_weight = head.weight.detach() + else: + head_weight = head.weight + head_weight = head_weight.to(states.device) + return (head_weight @ states.transpose(-1, -2)).transpose(-1, -2).contiguous() + + def idx_if_sequential(head, idx=0): + if isinstance(head, nn.Sequential) or isinstance(head, nn.ModuleList): + return idx_if_sequential(head[idx], idx=idx) + return head + + def none_repeat_interleave(x, n): + if x is None: + return x + return x.repeat_interleave(n, dim=0) + + if self.n_passes > 1: + input_ids = none_repeat_interleave(input_ids, self.n_passes) + attention_mask = none_repeat_interleave(attention_mask, self.n_passes) + position_ids = none_repeat_interleave(position_ids, self.n_passes) + inputs_embeds = none_repeat_interleave(inputs_embeds, self.n_passes) + labels = none_repeat_interleave(labels, self.n_passes) + if past_key_values is not None: + past_key_values = [none_repeat_interleave(p, self.n_passes) for p in past_key_values] + cur_token_indices = torch.arange(input_ids.shape[1], device=input_ids.device) + + self.tokenizer_has_start_thought_token = True + self.tokenizer_has_end_thought_token = True + if self.start_token_id is None: + self.start_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>") + if self.start_token_id == 0: + self.start_token_id = self.tokenizer.bos_token_id + self.tokenizer_has_start_thought_token = False + elif self.use_start_thought_token: + # base_start_id = self.tokenizer.convert_tokens_to_ids(self.initial_start_token) + base_start_id = self.tokenizer.encode(self.initial_start_token, add_special_tokens=False)[0] + if self.initialize_thought_embedding_to_normal: + self.start_embedding.data = torch.zeros_like(self.start_embedding.data) + else: + self.start_embedding.data[0] = self.model.embed_tokens.weight.data[base_start_id].clone().detach() / self.embedding_scale + self.start_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale) + if self.end_token_id is None: + self.end_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>") + if self.end_token_id == 0: + self.end_token_id = self.tokenizer.eos_token_id + self.tokenizer_has_end_thought_token = False + elif self.use_end_thought_token: + # base_end_id = self.tokenizer.convert_tokens_to_ids(self.initial_end_token) + base_end_id = self.tokenizer.encode(self.initial_end_token, add_special_tokens=False)[0] + if self.initialize_thought_embedding_to_normal: + self.end_embedding.data = torch.zeros_like(self.end_embedding.data) + else: + self.end_embedding.data[0] = self.model.embed_tokens.weight.data[base_end_id].clone().detach() / self.embedding_scale + self.end_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale) + + if not self.rm_initialized and (self.n_ahead > 1 or not self.base_original_mode): + self.rm_initialized = True + if not self.use_shallow_talk: + head = self.talk_head[0] + cur_head = head[-1] if isinstance(head, nn.Sequential) else head + talk_input_dim = cur_head.weight.data.shape[1] + talk_output_dim = 1 if self.use_weighted_talk_head else self.lm_head.weight.data.shape[0] + cur_head.weight.data = torch.zeros(talk_output_dim, talk_input_dim, device=cur_head.weight.device, dtype=cur_head.weight.dtype) + else: + # convert to identity transform + def lambda_transform(cur_head): + # pdb.set_trace() + if cur_head.weight.data.shape[0] != cur_head.weight.data.shape[1]: + return torch.cat([ + torch.eye( + cur_head.weight.data.shape[0], + device=cur_head.weight.device, + dtype=cur_head.weight.dtype + ), + torch.zeros( + cur_head.weight.data.shape[0], + cur_head.weight.data.shape[1] - cur_head.weight.data.shape[0], + device=cur_head.weight.device, + dtype=cur_head.weight.dtype + )], dim=1) + return torch.eye( + cur_head.weight.data.shape[0], + device=cur_head.weight.device, + dtype=cur_head.weight.dtype + ) + if isinstance(self.talk_head[0], nn.Sequential): + for cur_head in self.talk_head[0]: + # if it has weights + if hasattr(cur_head, "weight"): + cur_head.weight.data = lambda_transform(cur_head) + else: + self.talk_head[-1].weight.data = lambda_transform(self.talk_head[0]) + + loss = None + prev_rm_tokens = None + cur_rm_tokens = None + prev_rm_logits = None + prev_sample_probs = None + did_skip_sampling = None + skip_sampling = None + sample_probs = None + hidden_states = None + logits = None + talk_kl_penalty = None + rm_logits = None + residual_logits = None + probabilities_2d = None + prev_probabilities_2d = None + policy_reward = None + logits_to_output = None + batch_size, seq_len = input_ids.shape + base_input_ids = input_ids.clone() + loss_list = [] + dqn_loss_list = [] + sampled_token_history = [] + sample_probs_history = [] + action_loglikelihoods_list = [] + + temperature = self.temperature + + if self.use_end_thought_token or self.use_start_thought_token: + if not self.use_reparam_for_thought_embeddings: + start_embedding = self.start_embedding[0].unsqueeze(0) * self.embedding_scale * temperature + end_embedding = self.end_embedding[0].unsqueeze(0) * self.embedding_scale * temperature + else: + start_embedding = self.start_embedding * self.embedding_scale * temperature + end_embedding = self.end_embedding * self.embedding_scale * temperature + base_embeddings = self.model.embed_tokens.weight + if self.train_only_thinking_embedding: + base_embeddings = base_embeddings.detach() + + # # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + fwd_iters = 1 if self.original_mode else self.n_ahead + self.n_ahead_talk - 1 + for ahead_idx in range(fwd_iters): + past_key_values_length = 0 + if past_key_values is not None: + use_legacy_cache = not isinstance(past_key_values, Cache) + if use_legacy_cache: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + past_key_values_length = past_key_values.get_usable_length(seq_len) + + if position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_len + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_len) + else: + position_ids = position_ids.view(-1, seq_len).long() + + if inputs_embeds is None: + contains_start = self.use_start_thought_token and (input_ids == self.start_token_id).any() + contains_end = self.use_end_thought_token and (input_ids == self.end_token_id).any() + contains_thought = contains_start or contains_end + if contains_thought: + thought_id = self.start_token_id if contains_start else self.end_token_id + cur_thought_embedding = start_embedding if contains_start else end_embedding + if self.use_reparam_for_thought_embeddings: + inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype) + inputs_embeds = inputs_embeds.detach() * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0] + if contains_start: + sampled_start = inputs_embeds.clone().detach() + if contains_end: + sampled_end = inputs_embeds.clone().detach() + else: + inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1) + else: + with torch.set_grad_enabled(not self.train_only_thinking_embedding): + inputs_embeds = self.model.embed_tokens(input_ids) + + if self.n_ahead != 1 or self.n_ahead_talk != 1 or self.comparison_mode: + if attention_mask is None: + base_attention_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=0).to(input_ids.device) + base_attention_mask = base_attention_mask.view(1, 1, seq_len, seq_len) + base_attention_mask = base_attention_mask.repeat(input_ids.shape[0], 1, 1, 1) + attention_mask = base_attention_mask + # breakpoint() + elif attention_mask.dim() == 2: + if seq_len + past_key_values_length != attention_mask.shape[-1]: + # breakpoint() + attention_mask = torch.cat( + [torch.ones((attention_mask.shape[0], past_key_values_length), dtype=attention_mask.dtype, device=attention_mask.device), attention_mask], + dim=-1 + ) + # # if the attention mask + attention_mask = _prepare_4d_causal_attention_mask( + attention_mask, + (batch_size, seq_len), + inputs_embeds, + past_key_values_length, + sliding_window=self.config.sliding_window, + ) + + outputs = self.model( + # input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + # output_router_logits=output_router_logits, + return_dict=return_dict, + ) + + prev_hidden_states = hidden_states + hidden_states = outputs[0] + prev_rm_logits = rm_logits # for policy gradient + prev_rm_tokens = cur_rm_tokens # for policy gradient + + if ahead_idx == 0: + hidden_states_lm = hidden_states + logits = self.lm_head(hidden_states_lm) + base_hidden_states = hidden_states.clone() + initial_loss_logits = logits.clone() + if self.optimize_lm_head_only_at_start or self.optimize_model_only_at_start: + logits = logits.detach() + base_hidden_states = base_hidden_states.detach() + if self.optimize_model_only_at_start: + hidden_states = hidden_states.detach() + base_logits = logits.clone() + else: + talk_hidden_states = hidden_states + if self.merged_lm_and_talk_heads: + assert self.no_residual + residual_logits = self.lm_head(hidden_states) + talk_hidden_states = hidden_states + else: + if ahead_idx > self.n_ahead - 1: + cur_base_hidden = torch.cat([ + base_hidden_states[..., ahead_idx - self.n_ahead + 1:, :], + base_hidden_states[..., :ahead_idx - self.n_ahead + 1, :] + ], dim=-2) + else: + cur_base_hidden = base_hidden_states + + if self.use_concat_talk_head: + # concatenate the hidden states with the original hidden states + head_input_hidden_states = torch.cat([cur_base_hidden, talk_hidden_states], dim=-1) + else: + head_input_hidden_states = talk_hidden_states + + residual_logits = self.talk_head[0](head_input_hidden_states) + if self.use_shallow_talk: + residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start) + residual_logits = residual_logits.to(logits.device) + if self.use_weighted_talk_head: + # combine the cur_base_hidden with the talk_hidden_states according to the weighted head + residual_logits = cur_base_hidden * (1 - residual_logits) + talk_hidden_states * residual_logits + residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start) + + assert sum([self.cumulative_residual, self.clever_residual, self.skip_residual, self.no_residual]) == 1 + if self.clever_residual: + if ahead_idx >= self.n_ahead - 1: + # get the logits shifted according to the current talk ahead + cur_base_logits = torch.cat([ + base_logits[..., ahead_idx - self.n_ahead + 1:, :], + base_logits[..., :ahead_idx - self.n_ahead + 1, :] + ], dim=-2) + if self.optimize_lm_head_only_at_start: + cur_base_logits = cur_base_logits.detach() + logits = cur_base_logits + residual_logits + else: + logits += residual_logits / self.n_ahead + elif self.cumulative_residual: + if self.residual_talk_head: + if ahead_idx < self.n_ahead: + logits += residual_logits + else: + # get the logits shifted according to the current talk ahead + cur_base_logits = torch.cat([ + base_logits[..., ahead_idx - self.n_ahead + 1:, :], + base_logits[..., :ahead_idx - self.n_ahead + 1, :] + ], dim=-2) + if self.optimize_lm_head_only_at_start: + cur_base_logits = cur_base_logits.detach() + logits = cur_base_logits + residual_logits + else: + if ahead_idx < self.n_ahead: + logits += residual_logits + else: + logits = residual_logits + elif self.skip_residual: + if ahead_idx >= self.n_ahead: + # get the logits shifted according to the current talk ahead + cur_base_logits = torch.cat([ + base_logits[..., ahead_idx - self.n_ahead + 1:, :], + base_logits[..., :ahead_idx - self.n_ahead + 1, :] + ], dim=-2) + if self.optimize_lm_head_only_at_start: + cur_base_logits = cur_base_logits.detach() + logits = cur_base_logits + elif self.no_residual: + logits = residual_logits + else: + logits = base_logits + residual_logits + + attempted = False + talk_loss_list = [] + if self.original_mode or (self.n_ahead == 1) or (self.comparison_mode and ahead_idx == 0):# or (self.optimize_lm_head_only_at_start and ahead_idx == 0): + loss = None + attempted = True + + if labels is not None: + for shift_amount in range(self.n_ahead_talk): + # Shift so that tokens < n predict n + # ab[cde]f + # abc[def] + if ahead_idx == 0 and self.optimize_lm_head_only_at_start: + loss_logits = initial_loss_logits + else: + loss_logits = logits + shift_logits = loss_logits[..., shift_amount:-1, :].contiguous() + shift_labels = labels[..., 1 + shift_amount:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(reduction="none") + # print("Shift logits before:", shift_logits) + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1).clone() + # print("shift logits after:", shift_logits) + # Enable model parallelism + shift_labels[shift_labels == self.tokenizer.pad_token_id] = -100 + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + if not self.comparison_mode and not (self.optimize_lm_head_only_at_start and (self.n_ahead + self.n_ahead_talk > 2)) or self.original_mode: + loss_list.append(loss) + talk_loss_list.append(nonzero_mean(loss).detach()) + + if not attempted or self.comparison_mode: + rm_hidden_states = hidden_states + # print("Magnitude of RM hidden states before RM head", rm_hidden_states.norm()) + rm_logits = apply_head(self.lm_head, rm_hidden_states, detach=self.optimize_lm_head_only_at_start) + + # don't allow it to predict the thinking token + if self.tokenizer_has_start_thought_token: + rm_logits[..., self.start_token_id] = -1e10 + if self.tokenizer_has_end_thought_token: + rm_logits[..., self.end_token_id] = -1e10 + probabilities = rm_logits + if probabilities_2d is not None: + prev_probabilities_2d = probabilities_2d.clone() + probabilities_2d = probabilities.view(-1, probabilities.size(-1)) + + did_skip_sampling = skip_sampling + skip_sampling = False + if ahead_idx == 0 and self.use_start_thought_token: + override_token = self.start_token_id + elif self.use_thought_prefix and ahead_idx < self.tokenized_thought_prefix.shape[-1]: + override_token = self.tokenized_thought_prefix[..., ahead_idx] + elif ahead_idx == self.n_ahead - 2 and self.use_end_thought_token: + override_token = self.end_token_id + else: + override_token = None + if override_token is not None and self.n_ahead > 1: + # always start with the start token + probabilities_2d = torch.zeros_like(probabilities_2d) + probabilities_2d[:, override_token] = 1.0 + skip_sampling = True + elif ahead_idx >= self.n_ahead - 1: + if labels is not None: # we're in the talk phase + cur_talk_n = ahead_idx - (self.n_ahead - 1) + 1 + # print("Setting rm to labels", cur_talk_n, "during", ahead_idx) + shift_labels = labels[..., cur_talk_n:].contiguous().to(probabilities_2d.device) + padding = torch.full_like( + labels[..., :cur_talk_n], + self.tokenizer.pad_token_id, + dtype=torch.long, + device=shift_labels.device + ) + new_rm_tokens = torch.cat( + [shift_labels, padding], + dim=-1 + ) + + # print((new_rm_tokens > self.vocab_size - 1).any().item()) + new_rm_tokens = torch.clamp(new_rm_tokens, 0, self.vocab_size - 1) + + # Now safely convert rm tokens to one-hot + probabilities_2d = F.one_hot(new_rm_tokens, num_classes=self.vocab_size).reshape(-1, self.vocab_size).to(probabilities_2d.dtype) + else: + continue + temperature = self.gumbel_temperature if self.training else 0.001 + prev_sample_probs = sample_probs + sample_probs = probabilities_2d + if ahead_idx < self.n_ahead - 1 and not skip_sampling: + probabilities_2d = F.gumbel_softmax(sample_probs, tau=temperature, hard=True, dim=-1) + if self.gumbel_detach: + probabilities_2d = probabilities_2d.detach() + sampled_token_history.append(probabilities_2d.argmax(dim=-1).detach().cpu()) + # convert rm logits directly to embeddings + contains_start = self.use_start_thought_token and (probabilities_2d[..., self.start_token_id].sum() > 0) + contains_end = self.use_end_thought_token and (probabilities_2d[..., self.end_token_id].sum() > 0) + contains_thought = contains_start or contains_end + + + if not contains_thought: + with torch.set_grad_enabled(not self.train_only_thinking_embedding): + inputs_embeds = probabilities_2d @ (self.model.embed_tokens.weight.to(probabilities.device).to(probabilities.dtype) * temperature) + else: + thought_id = self.start_token_id if contains_start else self.end_token_id + cur_thought_embedding = start_embedding if contains_start else end_embedding + if self.use_reparam_for_thought_embeddings: + inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype) + inputs_embeds = inputs_embeds * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0] + if contains_start: + sampled_start = inputs_embeds.clone().detach() + else: + sampled_end = inputs_embeds.clone().detach() + else: + inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1) + inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype) + inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype) + + # Predict the usefulness of thinking at each token position + thinking_usefulness = self.thinking_usefulness_head(hidden_states).squeeze(-1) + + # Apply a threshold to decide where to generate thoughts + generate_thought_mask = thinking_usefulness > self.thinking_threshold + + # Compute the regularization loss for thinking usefulness prediction + thinking_usefulness_loss = torch.mean(thinking_usefulness * (1 - generate_thought_mask.float())) + + # Add the regularization loss to the total loss + if loss is not None: + loss = loss + self.thinking_usefulness_loss_weight * thinking_usefulness_loss + else: + loss = self.thinking_usefulness_loss_weight * thinking_usefulness_loss + + + if len(attention_mask.shape) == 2: + breakpoint() + else: + original_attention = attention_mask[..., :attention_mask.shape[-2]] + if self.use_upper_triangular: + new_attention = original_attention + else: + original_attention = original_attention == attention_mask.max() + # because eye isn't implemented for BF16, we need to handle the case + if not attention_mask.dtype == torch.bfloat16: + new_attention = torch.eye( + seq_len, dtype=attention_mask.dtype, device=attention_mask.device + ) + else: + new_attention = torch.eye( + seq_len, dtype=torch.float32, device=attention_mask.device + ).to(attention_mask.dtype) + + new_attention = new_attention.view(1, 1, seq_len, seq_len).repeat(input_ids.shape[0], 1, 1, 1) + new_attention = new_attention * original_attention + new_attention[new_attention == 0] = attention_mask.min() + new_attention[new_attention == 1] = attention_mask.max() + attention_mask = torch.cat([attention_mask, new_attention], dim=-1) + past_key_values = outputs.past_key_values + position_ids = position_ids + 1 + + if labels is not None and (self.n_ahead > 1 or not self.base_original_mode): + # Shift so that tokens < n predict n + # logits: abcdef -> bcdef? -> cdef?? + # labels: abcdef -> ?bcdef -> ??cdef + if ahead_idx == 0 and self.optimize_lm_head_only_at_start: + loss_logits = initial_loss_logits + else: + loss_logits = logits + shift_idx = 1 + max(0, ahead_idx - (self.n_ahead - 1)) + shift_logits = loss_logits[..., :-shift_idx, :].contiguous() + shift_labels = labels[..., shift_idx:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss(reduction="none") + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + # if shift_labels.min() == self.tokenizer.pad_token_id: + shift_labels = torch.where(shift_labels == self.tokenizer.pad_token_id, -100, shift_labels) + unreduced_loss = loss_fct(shift_logits, shift_labels) + # print("Loss:", unreduced_loss.item()) # Print the loss before checking for NaN values + if torch.any(unreduced_loss != unreduced_loss): + # pdb.set_trace() + raise ValueError("NaN loss") + unreduced_loss = unreduced_loss.reshape(logits.shape[0], -1) + loss_list.append(unreduced_loss) + + + if self.use_policy_loss and ahead_idx > 0 and (ahead_idx > 1 or not self.use_start_thought_token): + # we treat the change in loss as the reward + previous_loss = loss_list[-2] + # for example, suppose n_ahead = 3 and n_ahead_talk = 2 + # note that we end at self.n_ahead + self.n_ahead_talk - 2 + # in this case, 5 - 2 = 3, so we end at ahead_idx = 3 + # we also predict the next token at ahead_idx = 2 + # when we get to ahead_idx = 2, we predict ahead + # so we shift by 1 + # note that this is ahead_idx = n_ahead - 1 + # when we get to ahead_idx = 3, we predict ahead + # so we shift by 2 + # note that this is ahead_idx = n_ahead + if ahead_idx < self.n_ahead - 1: + shift_amount = 0 + reward_scale = 1.0 + original_dqn_reward = torch.sign(previous_loss - unreduced_loss).detach() * reward_scale + if self.first_and_last_mode: + original_dqn_reward = original_dqn_reward * 0.0 + else: + # logits vs cur_policy_shift_logits + # let's look at rm_logits and prev_rm_logits + shift_amount = max(0, ahead_idx - (self.n_ahead - 1)) + # let's say shift_amount = 2 + # abcdefg -> bcdefg? -> cdefg?? + # logits = [a b]c d e f[g] + # labels = [a b c]d e f g + cur_policy_shift_logits = initial_loss_logits[..., shift_amount:-1, :].contiguous().detach() + cur_policy_shift_labels = labels[..., 1 + shift_amount:].contiguous() + # Flatten the tokens + cur_policy_loss_fct = CrossEntropyLoss(reduction="none") + cur_policy_shift_logits = cur_policy_shift_logits.view(-1, self.config.vocab_size) + cur_policy_shift_labels = cur_policy_shift_labels.view(-1).clone() + # Enable model parallelism + cur_policy_shift_labels[cur_policy_shift_labels == self.tokenizer.pad_token_id] = -100 + cur_policy_shift_labels = cur_policy_shift_labels.to(cur_policy_shift_labels.device) + cur_policy_reward_base_loss = loss_fct( + cur_policy_shift_logits, cur_policy_shift_labels.to(cur_policy_shift_logits.device) + ).reshape(logits.shape[0], -1) + original_dqn_reward = cur_policy_reward_base_loss.detach() - unreduced_loss + + if not did_skip_sampling: + nonzero_indices = prev_probabilities_2d.nonzero() + action_loglikelihoods = F.log_softmax(prev_sample_probs / self.reinforce_temperature, dim=-1)[nonzero_indices[:, 0], nonzero_indices[:, 1]] + action_loglikelihoods_2d = action_loglikelihoods.reshape(batch_size, -1)[:, :-1 - shift_amount] + action_loglikelihoods_list.append(action_loglikelihoods_2d) + if policy_reward is None: + policy_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)] + else: + if self.n_ahead_talk > shift_amount: + added_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)] + else: + added_reward = original_dqn_reward + policy_reward += added_reward + + for action_loglikelihoods_2d in action_loglikelihoods_list: + train_policy_reward = policy_reward + + # discard rewards below the mean + if self.trice_mode and self.n_passes > 1: + batched_policy_reward = train_policy_reward.reshape(-1, self.n_passes, train_policy_reward.shape[-1]) + # average over the passes + train_policy_reward = batched_policy_reward - batched_policy_reward.mean(dim=1, keepdim=True) + train_policy_reward = train_policy_reward.reshape(-1, train_policy_reward.shape[-1]) + + if self.subtract_mean_reward: + train_policy_reward = train_policy_reward - train_policy_reward.mean() + if self.remove_negative_rewards: + fixed_policy_reward = train_policy_reward.detach().clamp(min=0) + else: + fixed_policy_reward = train_policy_reward.detach() + + # Normalize rewards + fixed_policy_reward = (fixed_policy_reward - fixed_policy_reward.mean()) / (fixed_policy_reward.std() + 1e-8) + actor_loss = -fixed_policy_reward * action_loglikelihoods_2d[:, :policy_reward.shape[-1]].to(policy_reward.device) + if action_loglikelihoods_2d.mean() < -1e4 and not self.use_policy_loss_just_for_thoughts: + # This will only happen when we force the next token to be the end of thought token + break + dqn_loss_list.append(actor_loss.mean()) + + if loss_list: + if self.first_and_last_mode: + loss = sum( + self.loss_mean(loss_list[-(i + 1)]) for i in range(self.n_ahead_talk) + ) * (1 - self.original_loss_weight) / self.n_ahead_talk + loss = loss + self.loss_mean(loss_list[0]) * self.original_loss_weight + # Let's NaN out the others + # e.g. if n_ahead_talk = 2 and the list is 5 long, we want to NaN out 1, 2 but keep 0, 3, 4 + for i in range(1, len(loss_list) - self.n_ahead_talk): + loss_list[i] = loss_list[i] * math.nan + elif self.first_only: + loss = self.loss_mean(loss_list[0]) + elif self.final_only_mode: + loss = sum( + self.loss_mean(loss_list[-i]) for i in range(1, self.n_ahead_talk + 1) + ) / self.n_ahead_talk + else: + loss = None + for i in range(len(loss_list)): + cur_loss = self.loss_mean(loss_list[i]) + if loss is not None: + loss = loss + cur_loss.to(loss.device) + else: + loss = cur_loss + loss = loss / len(loss_list) + loss = loss + thinking_usefulness_loss + + base_loss_scale = 0.6 + policy_loss_scale = 0.03 + + loss = loss * base_loss_scale + + if dqn_loss_list: + dqn_loss = sum(dqn_loss_list) / len(dqn_loss_list) + if self.include_policy_loss: + if loss is not None: + loss += dqn_loss * policy_loss_scale + else: + loss = dqn_loss * self.policy_loss_beta + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + base_log_dict = { + f"loss_{i}": nonzero_mean(loss_list[i]) for i in range(len(loss_list)) + } + + if loss is not None: + base_log_dict["loss_train"] = loss.item() + + if not self.training: + self.n_ahead_talk = n_ahead_talk_to_restore + self.n_passes = n_passes_to_restore + + del start_embedding + del end_embedding + torch.cuda.empty_cache() + + + return CausalLMOutputWithPast( + loss=loss if loss is not None else None, + logits=(rm_logits if self.n_ahead > 1 else logits) if not self.output_logits_at_the_end else logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + # Omit tokens covered by past_key_values + if past_key_values is not None: + if isinstance(past_key_values, Cache): + cache_length = past_key_values.get_seq_length() + past_length = past_key_values.seen_tokens + max_cache_length = past_key_values.get_max_length() + else: + cache_length = past_length = past_key_values[0][0].shape[2] + max_cache_length = None + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing inputs_embeds as + # input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past + + + + +@add_start_docstrings( + """ + The Quiet Model transformer with a sequence classification head on top (linear layer). + [`QuietForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + QUIET_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Quiet, LLAMA->QUIET +class QuietForSequenceClassification(QuietPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = QuietModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + @add_start_docstrings_to_model_forward(QUIET_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) \ No newline at end of file