2369 lines
115 KiB
Python
2369 lines
115 KiB
Python
# coding=utf-8
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# Copyright 2023 Quiet AI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch Quiet model."""
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import inspect
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import math
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import pdb
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import warnings
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from collections import defaultdict
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.generation.utils import GenerationMixin
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from transformers.generation.stopping_criteria import StoppingCriteriaList, validate_stopping_criteria
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from transformers import TextStreamer
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from transformers.activations import ACT2FN
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from transformers.cache_utils import Cache, DynamicCache
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from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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logging,
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replace_return_docstrings,
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)
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from .configuration_quiet import QuietConfig
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import time
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from typing import Optional, List
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "QuietConfig"
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def _prepare_4d_causal_attention_mask_for_sdpa(attention_mask, input_shape, inputs_embeds, past_key_values_length):
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# Compute the attention mask correctly
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bsz, tgt_len = input_shape
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# Create a 4D attention mask from a 2D tensor mask.
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# The shape of the output attention mask is (batch_size, 1, tgt_len, src_len)
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# The values are either 0 or 1, where 0 means padding and 1 means non-padding.
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combined_attention_mask = None
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if attention_mask is not None:
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# What if attention_mask is not None and has a shape of (batch_size, 1, tgt_len, src_len)
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# In this case, we can just use it directly.
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if attention_mask.dim() == 4:
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combined_attention_mask = attention_mask
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# What if attention_mask is not None and has a shape of (batch_size, 1, tgt_len)
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# In this case, we need to expand it to (batch_size, 1, tgt_len, src_len)
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elif attention_mask.dim() == 3:
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expanded_attn_mask = attention_mask[:, None, :, :]
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combined_attention_mask = expanded_attn_mask
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# What if attention_mask is not None and has a shape of (batch_size, tgt_len)
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# In this case, we need to expand it to (batch_size, 1, tgt_len, src_len)
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elif attention_mask.dim() == 2:
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# Provided a padding mask of dimensions [batch_size, seq_length]
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# - if the model is a decoder, apply a causal mask in addition to the padding mask
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# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
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if past_key_values_length > 0:
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attention_mask = attention_mask.to(dtype=torch.long)
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attention_mask = attention_mask[:, past_key_values_length:]
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expanded_attn_mask = attention_mask[:, None, None, :]
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combined_attention_mask = expanded_attn_mask
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else:
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raise ValueError(
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"Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
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input_shape, attention_mask.shape
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)
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)
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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# positions we want to attend and -10000.0 for masked positions.
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# Since we are adding it to the raw scores before the softmax, this is
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# effectively the same as removing these entirely.
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if combined_attention_mask is not None:
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# Ensure the attention mask values are within a reasonable range
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combined_attention_mask = combined_attention_mask.clamp(min=0, max=1)
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# Convert the attention mask to bfloat16
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combined_attention_mask = combined_attention_mask.to(torch.bfloat16)
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# Normalize the attention mask values to be between 0 and 1
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combined_attention_mask = (1.0 - combined_attention_mask) * -10000.0
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else:
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combined_attention_mask = torch.zeros(
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(bsz, 1, tgt_len, tgt_len), dtype=torch.bfloat16, device=inputs_embeds.device
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)
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return combined_attention_mask
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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def _get_unpad_data(attention_mask):
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
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max_seqlen_in_batch = seqlens_in_batch.max().item()
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
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return (
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indices,
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cu_seqlens,
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max_seqlen_in_batch,
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)
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# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Quiet
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class QuietRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return hidden_states.to(input_dtype) * self.weight.to(hidden_states.device)
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# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Quiet
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class QuietRotaryEmbedding(nn.Module):
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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# Build here to make `torch.jit.trace` work.
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self._set_cos_sin_cache(
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seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
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)
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def _set_cos_sin_cache(self, seq_len, device, dtype):
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self.max_seq_len_cached = seq_len
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
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freqs = torch.outer(t, self.inv_freq)
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# Different from paper, but it uses a different permutation in order to obtain the same calculation
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emb = torch.cat((freqs, freqs), dim=-1)
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
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def forward(self, x, seq_len=None):
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# x: [bs, num_attention_heads, seq_len, head_size]
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if seq_len > self.max_seq_len_cached:
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self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
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return (
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self.cos_cached[:seq_len].to(dtype=x.dtype),
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self.sin_cached[:seq_len].to(dtype=x.dtype),
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)
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# Copied from transformers.models.llama.modeling_llama.rotate_half
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`):
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The position indices of the tokens corresponding to the query and key tensors. For example, this can be
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used to pass offsetted position ids when working with a KV-cache.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos[position_ids].unsqueeze(unsqueeze_dim)
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sin = sin[position_ids].unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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class QuietMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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# Copied from transformers.models.llama.modeling_llama.repeat_kv
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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# pdb.set_trace()
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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class QuietAttention(nn.Module):
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"""
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Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
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and "Generating Long Sequences with Sparse Transformers".
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"""
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def __init__(self, config: QuietConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
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"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.hidden_size // self.num_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.is_causal = True
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self.attention_dropout = config.attention_dropout
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self._attn_implementation = config._attn_implementation
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads})."
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)
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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self.rotary_emb = QuietRotaryEmbedding(
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self.head_dim,
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max_position_embeddings=self.max_position_embeddings,
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base=self.rope_theta,
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)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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**kwargs,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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if "padding_mask" in kwargs:
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warnings.warn(
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"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
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)
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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if self.layer_idx is None:
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raise ValueError(
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f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
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"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
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"with a layer index."
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)
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kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
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f" {attn_weights.size()}"
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)
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if self._attn_implementation == "flash_attention_2":
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# Prepare attention mask for flash-attn
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attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
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elif self._attn_implementation == "sdpa":
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# Prepare attention mask for SDPA
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if attention_mask is None or attention_mask.dim() == 2:
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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sliding_window=self.config.sliding_window,
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)
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else:
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# Prepare attention mask for other implementations
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if attention_mask is None or attention_mask.dim() == 2:
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask,
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(batch_size, seq_length),
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inputs_embeds,
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past_key_values_length,
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sliding_window=self.config.sliding_window,
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
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)
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output, attn_weights, past_key_value
|
|
|
|
|
|
class QuietFlashAttention2(QuietAttention):
|
|
"""
|
|
Quiet flash attention module. This module inherits from `QuietAttention` as the weights of the module stays
|
|
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
|
flash attention and deal with padding tokens in case the input contains any of them.
|
|
"""
|
|
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
|
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
|
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
|
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
**kwargs,
|
|
):
|
|
if "padding_mask" in kwargs:
|
|
warnings.warn(
|
|
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
|
)
|
|
|
|
# overwrite attention_mask with padding_mask
|
|
attention_mask = kwargs.pop("padding_mask")
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
if self.layer_idx is None:
|
|
raise ValueError(
|
|
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
|
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
|
"with a layer index."
|
|
)
|
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
|
|
|
# Because the input can be padded, the absolute sequence length depends on the max position id.
|
|
rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
|
|
cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
|
|
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
|
|
|
use_sliding_windows = (
|
|
_flash_supports_window_size
|
|
and getattr(self.config, "sliding_window", None) is not None
|
|
and kv_seq_len > self.config.sliding_window
|
|
)
|
|
|
|
if not _flash_supports_window_size:
|
|
logger.warning_once(
|
|
"The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
|
|
" make sure to upgrade flash-attn library."
|
|
)
|
|
|
|
if past_key_value is not None:
|
|
# Activate slicing cache only if the config has a value `sliding_windows` attribute
|
|
cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
|
|
if (
|
|
getattr(self.config, "sliding_window", None) is not None
|
|
and kv_seq_len > self.config.sliding_window
|
|
and cache_has_contents
|
|
):
|
|
slicing_tokens = 1 - self.config.sliding_window
|
|
|
|
past_key = past_key_value[self.layer_idx][0]
|
|
past_value = past_key_value[self.layer_idx][1]
|
|
|
|
past_key = past_key[:, :, slicing_tokens:, :].contiguous()
|
|
past_value = past_value[:, :, slicing_tokens:, :].contiguous()
|
|
|
|
if past_key.shape[-2] != self.config.sliding_window - 1:
|
|
raise ValueError(
|
|
f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
|
|
f" {past_key.shape}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask[:, slicing_tokens:]
|
|
attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
|
|
|
|
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
# repeat k/v heads if n_kv_heads < n_heads
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
dropout_rate = 0.0 if not self.training else self.attention_dropout
|
|
|
|
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
|
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
|
# cast them back in float16 just to be sure everything works as expected.
|
|
input_dtype = query_states.dtype
|
|
if input_dtype == torch.float32:
|
|
if torch.is_autocast_enabled():
|
|
target_dtype = torch.get_autocast_gpu_dtype()
|
|
# Handle the case where the model is quantized
|
|
elif hasattr(self.config, "_pre_quantization_dtype"):
|
|
target_dtype = self.config._pre_quantization_dtype
|
|
else:
|
|
target_dtype = self.q_proj.weight.dtype
|
|
|
|
logger.warning_once(
|
|
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
|
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
|
f" {target_dtype}."
|
|
)
|
|
|
|
query_states = query_states.to(target_dtype)
|
|
key_states = key_states.to(target_dtype)
|
|
value_states = value_states.to(target_dtype)
|
|
|
|
# Reashape to the expected shape for Flash Attention
|
|
query_states = query_states.transpose(1, 2)
|
|
key_states = key_states.transpose(1, 2)
|
|
value_states = value_states.transpose(1, 2)
|
|
|
|
attn_output = self._flash_attention_forward(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attention_mask,
|
|
q_len,
|
|
dropout=dropout_rate,
|
|
use_sliding_windows=use_sliding_windows,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output, attn_weights, past_key_value
|
|
|
|
def _flash_attention_forward(
|
|
self,
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attention_mask,
|
|
query_length,
|
|
dropout=0.0,
|
|
softmax_scale=None,
|
|
use_sliding_windows=False,
|
|
):
|
|
"""
|
|
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
|
first unpad the input, then computes the attention scores and pad the final attention scores.
|
|
Args:
|
|
query_states (`torch.Tensor`):
|
|
Input query states to be passed to Flash Attention API
|
|
key_states (`torch.Tensor`):
|
|
Input key states to be passed to Flash Attention API
|
|
value_states (`torch.Tensor`):
|
|
Input value states to be passed to Flash Attention API
|
|
attention_mask (`torch.Tensor`):
|
|
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
|
position of padding tokens and 1 for the position of non-padding tokens.
|
|
dropout (`int`, *optional*):
|
|
Attention dropout
|
|
softmax_scale (`float`, *optional*):
|
|
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
|
use_sliding_windows (`bool`, *optional*):
|
|
Whether to activate sliding window attention.
|
|
"""
|
|
if not self._flash_attn_uses_top_left_mask:
|
|
causal = self.is_causal
|
|
else:
|
|
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
|
causal = self.is_causal and query_length != 1
|
|
|
|
# Ensure attention_mask has the correct shape and values
|
|
if attention_mask is not None:
|
|
if attention_mask.dim() == 4:
|
|
# Convert 4D attention mask to 2D
|
|
attention_mask = attention_mask.squeeze(1).squeeze(1)
|
|
elif attention_mask.dim() != 2:
|
|
raise ValueError(
|
|
f"Invalid attention mask dimension: {attention_mask.dim()}. Expected 2D or 4D mask."
|
|
)
|
|
|
|
# Ensure attention_mask has values of 0 and 1
|
|
attention_mask = attention_mask.to(torch.bool).to(torch.int32)
|
|
|
|
# Contains at least one padding token in the sequence
|
|
if attention_mask is not None:
|
|
batch_size = query_states.shape[0]
|
|
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
|
query_states, key_states, value_states, attention_mask, query_length
|
|
)
|
|
|
|
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
|
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
|
|
|
if not use_sliding_windows:
|
|
attn_output_unpad = flash_attn_varlen_func(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
cu_seqlens_k=cu_seqlens_k,
|
|
max_seqlen_q=max_seqlen_in_batch_q,
|
|
max_seqlen_k=max_seqlen_in_batch_k,
|
|
dropout_p=dropout,
|
|
softmax_scale=softmax_scale,
|
|
causal=causal,
|
|
)
|
|
else:
|
|
attn_output_unpad = flash_attn_varlen_func(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
cu_seqlens_k=cu_seqlens_k,
|
|
max_seqlen_q=max_seqlen_in_batch_q,
|
|
max_seqlen_k=max_seqlen_in_batch_k,
|
|
dropout_p=dropout,
|
|
softmax_scale=softmax_scale,
|
|
causal=causal,
|
|
window_size=(self.config.sliding_window, self.config.sliding_window),
|
|
)
|
|
|
|
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
|
else:
|
|
if not use_sliding_windows:
|
|
attn_output = flash_attn_func(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
dropout,
|
|
softmax_scale=softmax_scale,
|
|
causal=causal,
|
|
)
|
|
else:
|
|
attn_output = flash_attn_func(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
dropout,
|
|
softmax_scale=softmax_scale,
|
|
causal=causal,
|
|
window_size=(self.config.sliding_window, self.config.sliding_window),
|
|
)
|
|
|
|
return attn_output
|
|
|
|
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
|
batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
|
|
|
|
# On the first iteration we need to properly re-create the padding mask
|
|
# by slicing it on the proper place
|
|
if kv_seq_len != attention_mask.shape[-1]:
|
|
attention_mask_num_tokens = attention_mask.shape[-1]
|
|
attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
|
|
|
|
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
|
|
|
key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
|
|
value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
|
|
|
|
if query_length == kv_seq_len:
|
|
query_layer = index_first_axis(
|
|
query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
|
|
)
|
|
cu_seqlens_q = cu_seqlens_k
|
|
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
|
indices_q = indices_k
|
|
elif query_length == 1:
|
|
max_seqlen_in_batch_q = 1
|
|
cu_seqlens_q = torch.arange(
|
|
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
|
) # There is a memcpy here, that is very bad.
|
|
indices_q = cu_seqlens_q[:-1]
|
|
query_layer = query_layer.squeeze(1)
|
|
else:
|
|
# The -q_len: slice assumes left padding.
|
|
attention_mask = attention_mask[:, -query_length:]
|
|
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
|
|
|
return (
|
|
query_layer,
|
|
key_layer,
|
|
value_layer,
|
|
indices_q,
|
|
(cu_seqlens_q, cu_seqlens_k),
|
|
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Quiet
|
|
class QuietSdpaAttention(QuietAttention):
|
|
"""
|
|
Quiet attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
|
`QuietAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
|
SDPA API.
|
|
"""
|
|
|
|
# Adapted from QuietAttention.forward
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: bool = False,
|
|
use_cache: bool = False,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
if output_attentions:
|
|
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
|
logger.warning_once(
|
|
"QuietModel is using QuietSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
|
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
|
)
|
|
return super().forward(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
bsz, q_len, _ = hidden_states.size()
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.v_proj(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
|
|
|
kv_seq_len = key_states.shape[-2]
|
|
if past_key_value is not None:
|
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
|
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
|
|
|
if past_key_value is not None:
|
|
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
raise ValueError(
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
|
)
|
|
|
|
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
|
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
|
if query_states.device.type == "cuda" and attention_mask is not None:
|
|
query_states = query_states.contiguous()
|
|
key_states = key_states.contiguous()
|
|
value_states = value_states.contiguous()
|
|
|
|
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attn_mask=attention_mask.to(query_states.device) if attention_mask is not None else None,
|
|
dropout_p=self.attention_dropout if self.training else 0.0,
|
|
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
|
|
is_causal=self.is_causal and attention_mask is None and q_len > 1,
|
|
)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
|
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
return attn_output, None, past_key_value
|
|
|
|
|
|
QUIET_ATTENTION_CLASSES = {
|
|
"eager": QuietAttention,
|
|
"flash_attention_2": QuietFlashAttention2,
|
|
"sdpa": QuietSdpaAttention,
|
|
}
|
|
|
|
|
|
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.dim() == 2 and False:
|
|
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
|
# the manual implementation that requires a 4D causal mask in all cases.
|
|
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
|
attention_mask,
|
|
(batch_size, seq_length),
|
|
inputs_embeds,
|
|
past_key_values_length,
|
|
)
|
|
elif attention_mask is None or attention_mask.dim() == 2:
|
|
# 4d mask is passed through the layers
|
|
attention_mask = _prepare_4d_causal_attention_mask(
|
|
attention_mask,
|
|
(batch_size, seq_length),
|
|
inputs_embeds,
|
|
past_key_values_length,
|
|
sliding_window=self.config.sliding_window,
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = None
|
|
|
|
for decoder_layer in self.layers:
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
decoder_layer.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids,
|
|
past_key_values,
|
|
output_attentions,
|
|
use_cache,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = None
|
|
if use_cache:
|
|
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
def nonzero_mean(x, axis=None):
|
|
if axis is not None:
|
|
return x.sum(axis) / (x != 0).sum(axis)
|
|
return x.sum() / (x != 0).sum()
|
|
|
|
def loss_mean(x):
|
|
return x.sum() / (x != 0).sum()
|
|
|
|
class QuietForCausalLM(QuietPreTrainedModel, 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 = None
|
|
self.start_token_id = None
|
|
self.end_token_id = None
|
|
self.rm_initialized = False
|
|
self.residual_talk_head = True
|
|
self.thought_init_std_scale = 1e-2
|
|
|
|
self.final_only_mode = False
|
|
self.first_and_last_mode = True
|
|
self.first_only = False
|
|
self.original_loss_weight = 0.5
|
|
|
|
self.cumulative_residual = False
|
|
self.clever_residual = False
|
|
self.skip_residual = False
|
|
self.no_residual = True
|
|
|
|
self.optimize_lm_head_only_at_start = False
|
|
self.optimize_model_only_at_start = False
|
|
|
|
if self.optimize_model_only_at_start:
|
|
raise NotImplementedError
|
|
self.train_only_thinking_embedding = False
|
|
self.weighted_embeddings = False
|
|
self.use_start_thought_token = True
|
|
self.use_end_thought_token = True
|
|
self.initialize_thought_embedding_to_normal = False
|
|
self.initial_start_token = "---"
|
|
self.initial_end_token = "---"
|
|
self.output_logits_at_the_end = True
|
|
|
|
self.wandb_enabled = False
|
|
self.gumbel_temperature = 0.001
|
|
|
|
self.use_policy_loss = True
|
|
self.include_policy_loss = True
|
|
self.trice_mode = True
|
|
self.remove_negative_rewards = True
|
|
self.use_policy_loss_for_end_thought = True
|
|
|
|
self.base_original_mode = False
|
|
self.original_mode = False
|
|
|
|
self.thought_prefix = "(Let's think step by step"
|
|
self.tokenized_thought_prefix = None
|
|
self.log_dict = defaultdict(int)
|
|
self.eval_log_dict = defaultdict(int)
|
|
self.loss_mean = loss_mean
|
|
|
|
self.start_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
|
|
self.end_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
|
|
|
|
self.policy_loss_beta = 1e6
|
|
self.embedding_scale = 1e2
|
|
self.temperature = nn.Parameter(torch.ones(1))
|
|
self.max_temperature = config.max_temperature
|
|
self.complexity_factor = config.complexity_factor
|
|
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, *args, **kwargs):
|
|
# Call the infer method to get the logits
|
|
logits = self.infer(
|
|
input_ids=kwargs.pop("input_ids", None),
|
|
attention_mask=kwargs.pop("attention_mask", None),
|
|
position_ids=kwargs.pop("position_ids", None),
|
|
past_key_values=kwargs.pop("past_key_values", None),
|
|
inputs_embeds=kwargs.pop("inputs_embeds", None),
|
|
use_cache=kwargs.pop("use_cache", None),
|
|
output_attentions=kwargs.pop("output_attentions", None),
|
|
output_hidden_states=kwargs.pop("output_hidden_states", None),
|
|
return_dict=kwargs.pop("return_dict", None),
|
|
)
|
|
|
|
# Generate output using the logits
|
|
output_ids = torch.argmax(logits, dim=-1)
|
|
|
|
return output_ids
|
|
|
|
@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_router_logits: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
Args:
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
Returns:
|
|
Example:
|
|
```python
|
|
>>> from transformers import AutoTokenizer, QuietForCausalLM
|
|
>>> model = QuietForCausalLM.from_pretrained("quietai/Quiet-7B-v0.1")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("quietai/Quiet-7B-v0.1")
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
|
```"""
|
|
|
|
if not self.training:
|
|
n_ahead_talk_to_restore = self.n_ahead_talk
|
|
n_passes_to_restore = self.n_passes
|
|
self.n_ahead_talk = 1
|
|
self.n_passes = 1
|
|
|
|
# aux_loss = None
|
|
# output_router_logits = output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
# if output_router_logits:
|
|
# router_logits = outputs.router_logits if return_dict else outputs[-1]
|
|
# if router_logits is not None:
|
|
# aux_loss = load_balancing_loss_func(
|
|
# router_logits,
|
|
# self.num_experts,
|
|
# self.num_experts_per_tok,
|
|
# attention_mask,
|
|
# )
|
|
# if labels is not None:
|
|
# loss += self.router_aux_loss_coef * aux_loss.to(loss.device)
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
assert self.cumulative_residual or self.clever_residual or self.skip_residual or self.no_residual
|
|
assert not (self.skip_residual and self.use_policy_loss)
|
|
|
|
if self.tokenized_thought_prefix is None and self.use_thought_prefix:
|
|
self.tokenized_thought_prefix = self.tokenizer(self.thought_prefix, return_tensors="pt", add_special_tokens=False)["input_ids"]
|
|
|
|
def apply_head(head, states, detach=False):
|
|
if detach:
|
|
head_weight = head.weight.detach()
|
|
else:
|
|
head_weight = head.weight
|
|
head_weight = head_weight.to(states.device)
|
|
return (head_weight @ states.transpose(-1, -2)).transpose(-1, -2).contiguous()
|
|
|
|
def idx_if_sequential(head, idx=0):
|
|
if isinstance(head, nn.Sequential) or isinstance(head, nn.ModuleList):
|
|
return idx_if_sequential(head[idx], idx=idx)
|
|
return head
|
|
|
|
def none_repeat_interleave(x, n):
|
|
if x is None:
|
|
return x
|
|
return x.repeat_interleave(n, dim=0)
|
|
|
|
if self.n_passes > 1:
|
|
input_ids = none_repeat_interleave(input_ids, self.n_passes)
|
|
attention_mask = none_repeat_interleave(attention_mask, self.n_passes)
|
|
position_ids = none_repeat_interleave(position_ids, self.n_passes)
|
|
inputs_embeds = none_repeat_interleave(inputs_embeds, self.n_passes)
|
|
labels = none_repeat_interleave(labels, self.n_passes)
|
|
if past_key_values is not None:
|
|
past_key_values = [none_repeat_interleave(p, self.n_passes) for p in past_key_values]
|
|
cur_token_indices = torch.arange(input_ids.shape[1], device=input_ids.device)
|
|
|
|
self.tokenizer_has_start_thought_token = True
|
|
self.tokenizer_has_end_thought_token = True
|
|
if self.start_token_id is None:
|
|
self.start_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
|
|
if self.start_token_id == 0:
|
|
self.start_token_id = self.tokenizer.bos_token_id
|
|
self.tokenizer_has_start_thought_token = False
|
|
elif self.use_start_thought_token:
|
|
# base_start_id = self.tokenizer.convert_tokens_to_ids(self.initial_start_token)
|
|
base_start_id = self.tokenizer.encode(self.initial_start_token, add_special_tokens=False)[0]
|
|
if self.initialize_thought_embedding_to_normal:
|
|
self.start_embedding.data = torch.zeros_like(self.start_embedding.data)
|
|
else:
|
|
self.start_embedding.data[0] = self.model.embed_tokens.weight.data[base_start_id].clone().detach() / self.embedding_scale
|
|
self.start_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
|
|
if self.end_token_id is None:
|
|
self.end_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
|
|
if self.end_token_id == 0:
|
|
self.end_token_id = self.tokenizer.eos_token_id
|
|
self.tokenizer_has_end_thought_token = False
|
|
elif self.use_end_thought_token:
|
|
# base_end_id = self.tokenizer.convert_tokens_to_ids(self.initial_end_token)
|
|
base_end_id = self.tokenizer.encode(self.initial_end_token, add_special_tokens=False)[0]
|
|
if self.initialize_thought_embedding_to_normal:
|
|
self.end_embedding.data = torch.zeros_like(self.end_embedding.data)
|
|
else:
|
|
self.end_embedding.data[0] = self.model.embed_tokens.weight.data[base_end_id].clone().detach() / self.embedding_scale
|
|
self.end_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
|
|
|
|
if not self.rm_initialized and (self.n_ahead > 1 or not self.base_original_mode):
|
|
self.rm_initialized = True
|
|
if not self.use_shallow_talk:
|
|
head = self.talk_head[0]
|
|
cur_head = head[-1] if isinstance(head, nn.Sequential) else head
|
|
talk_input_dim = cur_head.weight.data.shape[1]
|
|
talk_output_dim = 1 if self.use_weighted_talk_head else self.lm_head.weight.data.shape[0]
|
|
cur_head.weight.data = torch.zeros(talk_output_dim, talk_input_dim, device=cur_head.weight.device, dtype=cur_head.weight.dtype)
|
|
else:
|
|
# convert to identity transform
|
|
def lambda_transform(cur_head):
|
|
# pdb.set_trace()
|
|
if cur_head.weight.data.shape[0] != cur_head.weight.data.shape[1]:
|
|
return torch.cat([
|
|
torch.eye(
|
|
cur_head.weight.data.shape[0],
|
|
device=cur_head.weight.device,
|
|
dtype=cur_head.weight.dtype
|
|
),
|
|
torch.zeros(
|
|
cur_head.weight.data.shape[0],
|
|
cur_head.weight.data.shape[1] - cur_head.weight.data.shape[0],
|
|
device=cur_head.weight.device,
|
|
dtype=cur_head.weight.dtype
|
|
)], dim=1)
|
|
return torch.eye(
|
|
cur_head.weight.data.shape[0],
|
|
device=cur_head.weight.device,
|
|
dtype=cur_head.weight.dtype
|
|
)
|
|
if isinstance(self.talk_head[0], nn.Sequential):
|
|
for cur_head in self.talk_head[0]:
|
|
# if it has weights
|
|
if hasattr(cur_head, "weight"):
|
|
cur_head.weight.data = lambda_transform(cur_head)
|
|
else:
|
|
self.talk_head[-1].weight.data = lambda_transform(self.talk_head[0])
|
|
|
|
loss = None
|
|
prev_rm_tokens = None
|
|
cur_rm_tokens = None
|
|
prev_rm_logits = None
|
|
prev_sample_probs = None
|
|
did_skip_sampling = None
|
|
skip_sampling = None
|
|
sample_probs = None
|
|
hidden_states = None
|
|
logits = None
|
|
talk_kl_penalty = None
|
|
rm_logits = None
|
|
residual_logits = None
|
|
probabilities_2d = None
|
|
prev_probabilities_2d = None
|
|
policy_reward = None
|
|
logits_to_output = None
|
|
batch_size, seq_len = input_ids.shape
|
|
base_input_ids = input_ids.clone()
|
|
loss_list = []
|
|
dqn_loss_list = []
|
|
sampled_token_history = []
|
|
sample_probs_history = []
|
|
action_loglikelihoods_list = []
|
|
|
|
complexity_scores = self.compute_complexity_scores(input_ids, attention_mask)
|
|
temperature = self.temperature * complexity_scores.unsqueeze(-1)
|
|
|
|
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 compute_complexity_scores(self, input_ids, attention_mask):
|
|
# Compute complexity scores based on input sequence characteristics
|
|
# Example: Normalize sequence lengths and consider the presence of rare tokens
|
|
seq_lengths = torch.sum(attention_mask, dim=-1)
|
|
max_length = torch.max(seq_lengths)
|
|
length_scores = seq_lengths / max_length
|
|
|
|
# Compute the proportion of rare tokens in each sequence
|
|
rare_token_ids = self.get_rare_token_ids()
|
|
rare_token_mask = torch.isin(input_ids, rare_token_ids)
|
|
rare_token_counts = torch.sum(rare_token_mask, dim=-1)
|
|
rare_token_scores = rare_token_counts / seq_lengths
|
|
|
|
# Combine length scores and rare token scores
|
|
complexity_scores = self.complexity_factor * length_scores + (1 - self.complexity_factor) * rare_token_scores
|
|
return complexity_scores
|
|
|
|
def get_rare_token_ids(self):
|
|
# Get the IDs of rare tokens based on a predefined frequency threshold
|
|
frequency_threshold = 1e-4
|
|
token_counts = torch.bincount(self.model.embed_tokens.weight.argmax(dim=-1))
|
|
total_tokens = torch.sum(token_counts)
|
|
rare_token_mask = token_counts / total_tokens < frequency_threshold
|
|
rare_token_ids = torch.nonzero(rare_token_mask).squeeze(-1)
|
|
return rare_token_ids
|
|
|
|
|
|
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,
|
|
) |