Commit
•
8e27989
1
Parent(s):
7e51525
Upload 17 files
Browse files- .gitattributes +1 -0
- added_tokens.json +4 -0
- bitnet_b1_58-large-q8_0.gguf +3 -0
- config.json +31 -0
- configuration_bitnet.py +195 -0
- eval_ppl.py +67 -0
- eval_task.py +63 -0
- eval_utils.py +133 -0
- generation_config.json +7 -0
- model.safetensors.index.json +321 -0
- modeling_bitnet.py +1387 -0
- special_tokens_map.json +33 -0
- tokenization_bitnet.py +482 -0
- tokenizer (1).model +3 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +62 -0
- utils_quant.py +48 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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bitnet_b1_58-large-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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added_tokens.json
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{
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"</line>": 32001,
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"<pad>": 32000
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}
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bitnet_b1_58-large-q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:d9200f0b47f1d774d843b8f65c88c1413cf7e59d1632b42590da51964fe84c4d
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size 775766112
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config.json
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{
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"_name_or_path": "1bitLLM/bitnet_b1_58-3B",
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"architectures": [
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"BitnetForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 3200,
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"initializer_range": 0.02,
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"input_bits": 8,
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"intermediate_size": 8640,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 26,
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"num_key_value_heads": 32,
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"pad_token_id": 32000,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "float16",
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"transformers_version": "4.39.0",
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"use_cache": true,
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"vocab_size": 32002,
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"weight_bits": 1
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}
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configuration_bitnet.py
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# coding=utf-8
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# Copyright 2022 EleutherAI 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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""" LLaMA model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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class BitnetConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BitnetModel`]. It is used to instantiate an LLaMA
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the LLaMA-7B.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`BitnetModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 11008):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Bitnet 1 supports up to 2048 tokens,
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Bitnet 2 up to 4096, CodeBitnet up to 16384.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
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document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
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necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
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issue](https://github.com/pytorch/pytorch/issues/76232).
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
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experimental feature, subject to breaking API changes in future versions.
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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```python
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>>> from transformers import BitnetModel, BitnetConfig
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>>> # Initializing a LLaMA llama-7b style configuration
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>>> configuration = BitnetConfig()
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>>> # Initializing a model from the llama-7b style configuration
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>>> model = BitnetModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "llama"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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hidden_act="silu",
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max_position_embeddings=2048,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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pretraining_tp=1,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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weight_bits=1,
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input_bits=8,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self._rope_scaling_validation()
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.weight_bits = weight_bits
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self.input_bits = input_bits
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
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f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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eval_ppl.py
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import math
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import argparse
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import torch
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import random
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from eval_utils import get_test_dataset
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from .modeling_bitnet import BitnetForCausalLM
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from .tokenization_bitnet import BitnetTokenizer
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from tqdm import tqdm
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torch.set_grad_enabled(False)
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parser = argparse.ArgumentParser()
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parser.add_argument('--seed', default=0, type=int)
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parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str)
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parser.add_argument('--seqlen', default=2048, type=int)
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def calulate_loss(model, input, loss_fct):
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+
output = model(input,
|
21 |
+
use_cache=False,
|
22 |
+
output_hidden_states=False,
|
23 |
+
output_attentions=False)[0]
|
24 |
+
shift_logits = output[:, :-1, :].contiguous()
|
25 |
+
shift_labels = input[:, 1:]
|
26 |
+
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
27 |
+
return loss
|
28 |
+
|
29 |
+
|
30 |
+
def main(args):
|
31 |
+
datasets = ['c4', 'wikitext2']
|
32 |
+
model = BitnetForCausalLM.from_pretrained(
|
33 |
+
args.hf_path,
|
34 |
+
device_map='auto',
|
35 |
+
low_cpu_mem_usage=True,
|
36 |
+
use_flash_attention_2=True,
|
37 |
+
torch_dtype=torch.float16,
|
38 |
+
).half()
|
39 |
+
tokenizer = BitnetTokenizer.from_pretrained(args.hf_path, use_fast=False)
|
40 |
+
loss_fct = torch.nn.CrossEntropyLoss(reduction="sum").cuda()
|
41 |
+
|
42 |
+
ppl = []
|
43 |
+
for dataset in datasets:
|
44 |
+
testdata = get_test_dataset(dataset, tokenizer, seqlen=args.seqlen)
|
45 |
+
acc_loss, count = 0.0, 0
|
46 |
+
progress = tqdm(range(len(testdata)))
|
47 |
+
for ii in progress:
|
48 |
+
input = torch.Tensor(testdata[ii]).long().cuda().view(1, -1)
|
49 |
+
loss = calulate_loss(model, input, loss_fct)
|
50 |
+
count += (input.size(-1) - 1)
|
51 |
+
acc_loss += loss.item()
|
52 |
+
progress.set_description(f"avg_loss = {acc_loss/ count / math.log(2)}")
|
53 |
+
|
54 |
+
avg_loss = acc_loss / count / math.log(2)
|
55 |
+
ppl.append(2 ** avg_loss)
|
56 |
+
print("{} PPL: {}".format(dataset, ppl[-1]))
|
57 |
+
|
58 |
+
print(ppl)
|
59 |
+
print("Avg PPL:", sum(ppl) / len(ppl))
|
60 |
+
|
61 |
+
|
62 |
+
if __name__ == '__main__':
|
63 |
+
torch.set_grad_enabled(False)
|
64 |
+
args = parser.parse_args()
|
65 |
+
random.seed(args.seed)
|
66 |
+
torch.random.manual_seed(args.seed)
|
67 |
+
main(args)
|
eval_task.py
ADDED
@@ -0,0 +1,63 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import json
|
3 |
+
import argparse
|
4 |
+
import torch
|
5 |
+
import random
|
6 |
+
import glog
|
7 |
+
|
8 |
+
from lm_eval import evaluator
|
9 |
+
from eval_utils import LMEvalAdaptor
|
10 |
+
from .tokenization_bitnet import BitnetTokenizer
|
11 |
+
from .modeling_bitnet import BitnetForCausalLM
|
12 |
+
|
13 |
+
|
14 |
+
parser = argparse.ArgumentParser()
|
15 |
+
parser.add_argument('--seed', default=0, type=int)
|
16 |
+
parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str)
|
17 |
+
parser.add_argument('--batch_size', type=int, default=1, help='batch size')
|
18 |
+
parser.add_argument("--tasks", type=str)
|
19 |
+
parser.add_argument("--output_path", default=None, type=str)
|
20 |
+
parser.add_argument('--num_fewshot', type=int, default=0)
|
21 |
+
parser.add_argument('--ctx_size', default=2048, type=int)
|
22 |
+
|
23 |
+
|
24 |
+
def main(args):
|
25 |
+
model_str = args.hf_path
|
26 |
+
model = BitnetForCausalLM.from_pretrained(
|
27 |
+
args.hf_path,
|
28 |
+
device_map='auto',
|
29 |
+
low_cpu_mem_usage=True,
|
30 |
+
use_flash_attention_2=True,
|
31 |
+
torch_dtype=torch.float16,
|
32 |
+
).half()
|
33 |
+
|
34 |
+
tokenizer = BitnetTokenizer.from_pretrained(args.hf_path, use_fast=False)
|
35 |
+
glog.info('loaded model!')
|
36 |
+
|
37 |
+
task_names = args.tasks.split(",")
|
38 |
+
|
39 |
+
lm_eval_model = LMEvalAdaptor(model_str, model, tokenizer, args.batch_size, args.ctx_size)
|
40 |
+
results = evaluator.simple_evaluate(
|
41 |
+
model=lm_eval_model,
|
42 |
+
tasks=task_names,
|
43 |
+
batch_size=args.batch_size,
|
44 |
+
no_cache=True,
|
45 |
+
num_fewshot=args.num_fewshot,
|
46 |
+
)
|
47 |
+
|
48 |
+
print(evaluator.make_table(results))
|
49 |
+
|
50 |
+
if args.output_path is not None:
|
51 |
+
os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
|
52 |
+
# otherwise cannot save
|
53 |
+
results["config"]["model"] = args.hf_path
|
54 |
+
with open(args.output_path, "w") as f:
|
55 |
+
json.dump(results, f, indent=2)
|
56 |
+
|
57 |
+
|
58 |
+
if __name__ == '__main__':
|
59 |
+
torch.set_grad_enabled(False)
|
60 |
+
args = parser.parse_args()
|
61 |
+
random.seed(args.seed)
|
62 |
+
torch.random.manual_seed(args.seed)
|
63 |
+
main(args)
|
eval_utils.py
ADDED
@@ -0,0 +1,133 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
|
3 |
+
import numpy as np
|
4 |
+
import torch.nn.functional as F
|
5 |
+
|
6 |
+
from lm_eval.base import BaseLM
|
7 |
+
from datasets import load_dataset
|
8 |
+
|
9 |
+
|
10 |
+
def set_seed(seed):
|
11 |
+
np.random.seed(seed)
|
12 |
+
torch.random.manual_seed(seed)
|
13 |
+
|
14 |
+
def get_test_dataset(dataset_name, tokenizer, seqlen=2048):
|
15 |
+
if dataset_name == "wikitext2":
|
16 |
+
testdata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
|
17 |
+
testdata = "".join(testdata['text']).split('\n')
|
18 |
+
elif dataset_name == "c4":
|
19 |
+
testdata = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')['text']
|
20 |
+
else:
|
21 |
+
raise NotImplementedError
|
22 |
+
|
23 |
+
testdata = [item for item in testdata if item != ""]
|
24 |
+
tokenized_text = [tokenizer(item, add_special_tokens=False)['input_ids'] + [tokenizer.eos_token_id] for item in testdata]
|
25 |
+
|
26 |
+
data, doc = [], [tokenizer.bos_token_id]
|
27 |
+
for sen in tokenized_text:
|
28 |
+
if len(sen) > seqlen:
|
29 |
+
continue
|
30 |
+
if len(doc) + len(sen) > seqlen:
|
31 |
+
data.append(doc)
|
32 |
+
doc = [tokenizer.bos_token_id]
|
33 |
+
doc.extend(sen)
|
34 |
+
if len(doc) > 1 and len(doc) <= seqlen:
|
35 |
+
data.append(doc)
|
36 |
+
return data
|
37 |
+
|
38 |
+
|
39 |
+
class LMEvalAdaptor(BaseLM):
|
40 |
+
def __init__(self, model_name, model, tokenizer, batch_size=1, max_length=-1):
|
41 |
+
super().__init__()
|
42 |
+
|
43 |
+
assert isinstance(batch_size, int)
|
44 |
+
|
45 |
+
self.model_name = model_name
|
46 |
+
self.model = model
|
47 |
+
self.model.eval()
|
48 |
+
|
49 |
+
self.tokenizer = tokenizer
|
50 |
+
|
51 |
+
self.vocab_size = self.tokenizer.vocab_size
|
52 |
+
|
53 |
+
self._batch_size = batch_size
|
54 |
+
|
55 |
+
self._max_length = max_length
|
56 |
+
|
57 |
+
@property
|
58 |
+
def eot_token_id(self):
|
59 |
+
# we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
|
60 |
+
return self.tokenizer.eos_token_id
|
61 |
+
|
62 |
+
@property
|
63 |
+
def max_length(self):
|
64 |
+
if self._max_length != -1:
|
65 |
+
return self._max_length
|
66 |
+
if hasattr(self.model.config, "n_ctx"):
|
67 |
+
return self.model.config.n_ctx
|
68 |
+
elif hasattr(self.model.config, "max_position_embeddings"):
|
69 |
+
return self.model.config.max_position_embeddings
|
70 |
+
elif hasattr(self.model.config, "n_positions"):
|
71 |
+
return self.model.config.n_positions
|
72 |
+
elif "bloom" in self.model_name:
|
73 |
+
return 2048
|
74 |
+
elif "llama" in self.model_name:
|
75 |
+
return 2048 # TODO: did not check this
|
76 |
+
elif "mpt" in self.model_name:
|
77 |
+
return 2048
|
78 |
+
elif "falcon" in self.model_name:
|
79 |
+
return 2048
|
80 |
+
else:
|
81 |
+
print(self.model.config)
|
82 |
+
raise NotImplementedError
|
83 |
+
|
84 |
+
@property
|
85 |
+
def max_gen_toks(self):
|
86 |
+
return 256
|
87 |
+
|
88 |
+
@property
|
89 |
+
def batch_size(self):
|
90 |
+
return self._batch_size
|
91 |
+
|
92 |
+
@property
|
93 |
+
def device(self):
|
94 |
+
return "cuda"
|
95 |
+
|
96 |
+
def tok_encode(self, string: str, add_special_tokens=True):
|
97 |
+
return self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
|
98 |
+
|
99 |
+
def tok_decode(self, tokens):
|
100 |
+
return self.tokenizer.decode(tokens)
|
101 |
+
|
102 |
+
def loglikelihood(self, requests):
|
103 |
+
new_reqs = []
|
104 |
+
for context, continuation in requests:
|
105 |
+
context, continuation = context.strip(), continuation.strip()
|
106 |
+
if context == "":
|
107 |
+
# end of text as context
|
108 |
+
context_enc = [self.eot_token_id]
|
109 |
+
else:
|
110 |
+
context_enc = self.tok_encode(context, add_special_tokens=True)
|
111 |
+
|
112 |
+
continuation_enc = self.tok_encode(continuation, add_special_tokens=False)
|
113 |
+
|
114 |
+
new_reqs.append(((context, continuation), context_enc, continuation_enc))
|
115 |
+
|
116 |
+
return self._loglikelihood_tokens(new_reqs)
|
117 |
+
|
118 |
+
def _model_call(self, inps):
|
119 |
+
"""
|
120 |
+
inps: a torch tensor of shape [batch, sequence]
|
121 |
+
the size of sequence may vary from call to call
|
122 |
+
|
123 |
+
returns: a torch tensor of shape [batch, sequence, vocab] with the
|
124 |
+
logits returned from the model
|
125 |
+
"""
|
126 |
+
with torch.no_grad():
|
127 |
+
out = self.model(inps)[0]
|
128 |
+
return out
|
129 |
+
|
130 |
+
def _model_generate(self, context, max_length, eos_token_id):
|
131 |
+
return self.model.generate(
|
132 |
+
context, max_length=max_length, eos_token_id=eos_token_id, do_sample=False
|
133 |
+
)
|
generation_config.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
"bos_token_id": 0,
|
4 |
+
"eos_token_id": 2,
|
5 |
+
"pad_token_id": 1,
|
6 |
+
"transformers_version": "4.39.0"
|
7 |
+
}
|
model.safetensors.index.json
ADDED
@@ -0,0 +1,321 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
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|
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|
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|
|
|
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|
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|
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|
|
|
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|
|
|
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|
|
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modeling_bitnet.py
ADDED
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|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
5 |
+
# and OPT implementations in this library. It has been modified from its
|
6 |
+
# original forms to accommodate minor architectural differences compared
|
7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
"""PyTorch LLaMA model."""
|
21 |
+
|
22 |
+
import math
|
23 |
+
import warnings
|
24 |
+
from typing import List, Optional, Tuple, Union
|
25 |
+
|
26 |
+
import torch
|
27 |
+
import torch.nn.functional as F
|
28 |
+
import torch.utils.checkpoint
|
29 |
+
from torch import nn
|
30 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
31 |
+
|
32 |
+
from transformers.activations import ACT2FN
|
33 |
+
from transformers.cache_utils import Cache, DynamicCache, StaticCache
|
34 |
+
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
|
35 |
+
from transformers.modeling_outputs import (
|
36 |
+
BaseModelOutputWithPast,
|
37 |
+
CausalLMOutputWithPast,
|
38 |
+
QuestionAnsweringModelOutput,
|
39 |
+
SequenceClassifierOutputWithPast,
|
40 |
+
)
|
41 |
+
from transformers.modeling_utils import PreTrainedModel
|
42 |
+
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
|
43 |
+
from transformers.utils import (
|
44 |
+
add_start_docstrings,
|
45 |
+
add_start_docstrings_to_model_forward,
|
46 |
+
is_flash_attn_2_available,
|
47 |
+
is_flash_attn_greater_or_equal_2_10,
|
48 |
+
logging,
|
49 |
+
replace_return_docstrings,
|
50 |
+
)
|
51 |
+
from .configuration_bitnet import BitnetConfig
|
52 |
+
from .utils_quant import BitLinear
|
53 |
+
|
54 |
+
|
55 |
+
if is_flash_attn_2_available():
|
56 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
57 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
58 |
+
|
59 |
+
|
60 |
+
logger = logging.get_logger(__name__)
|
61 |
+
|
62 |
+
_CONFIG_FOR_DOC = "BitnetConfig"
|
63 |
+
|
64 |
+
|
65 |
+
def _get_unpad_data(attention_mask):
|
66 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
67 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
68 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
69 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
|
70 |
+
return (
|
71 |
+
indices,
|
72 |
+
cu_seqlens,
|
73 |
+
max_seqlen_in_batch,
|
74 |
+
)
|
75 |
+
|
76 |
+
|
77 |
+
class BitnetRMSNorm(nn.Module):
|
78 |
+
def __init__(self, hidden_size, eps=1e-6):
|
79 |
+
"""
|
80 |
+
BitnetRMSNorm is equivalent to T5LayerNorm
|
81 |
+
"""
|
82 |
+
super().__init__()
|
83 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
84 |
+
self.variance_epsilon = eps
|
85 |
+
|
86 |
+
def forward(self, hidden_states):
|
87 |
+
input_dtype = hidden_states.dtype
|
88 |
+
hidden_states = hidden_states.to(torch.float32)
|
89 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
90 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
91 |
+
return self.weight * hidden_states.to(input_dtype)
|
92 |
+
|
93 |
+
|
94 |
+
ALL_LAYERNORM_LAYERS.append(BitnetRMSNorm)
|
95 |
+
|
96 |
+
|
97 |
+
class BitnetRotaryEmbedding(nn.Module):
|
98 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
99 |
+
super().__init__()
|
100 |
+
self.scaling_factor = scaling_factor
|
101 |
+
self.dim = dim
|
102 |
+
self.max_position_embeddings = max_position_embeddings
|
103 |
+
self.base = base
|
104 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
|
105 |
+
self.register_buffer("inv_freq", inv_freq)
|
106 |
+
# For BC we register cos and sin cached
|
107 |
+
self.max_seq_len_cached = max_position_embeddings
|
108 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
|
109 |
+
t = t / self.scaling_factor
|
110 |
+
freqs = torch.outer(t, self.inv_freq)
|
111 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
112 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
113 |
+
self.register_buffer("_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False)
|
114 |
+
self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False)
|
115 |
+
|
116 |
+
@property
|
117 |
+
def sin_cached(self):
|
118 |
+
logger.warning_once(
|
119 |
+
"The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
|
120 |
+
"the forward method of RoPE from now on instead. It is not used in the `BitnetAttention` class"
|
121 |
+
)
|
122 |
+
return self._sin_cached
|
123 |
+
|
124 |
+
@property
|
125 |
+
def cos_cached(self):
|
126 |
+
logger.warning_once(
|
127 |
+
"The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
|
128 |
+
"the forward method of RoPE from now on instead. It is not used in the `BitnetAttention` class"
|
129 |
+
)
|
130 |
+
return self._cos_cached
|
131 |
+
|
132 |
+
@torch.no_grad()
|
133 |
+
def forward(self, x, position_ids):
|
134 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
135 |
+
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
136 |
+
position_ids_expanded = position_ids[:, None, :].float()
|
137 |
+
# Force float32 since bfloat16 loses precision on long contexts
|
138 |
+
# See https://github.com/huggingface/transformers/pull/29285
|
139 |
+
device_type = x.device.type
|
140 |
+
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
141 |
+
with torch.autocast(device_type=device_type, enabled=False):
|
142 |
+
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
143 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
144 |
+
cos = emb.cos()
|
145 |
+
sin = emb.sin()
|
146 |
+
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
147 |
+
|
148 |
+
|
149 |
+
def rotate_half(x):
|
150 |
+
"""Rotates half the hidden dims of the input."""
|
151 |
+
x1 = x[..., : x.shape[-1] // 2]
|
152 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
153 |
+
return torch.cat((-x2, x1), dim=-1)
|
154 |
+
|
155 |
+
|
156 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
157 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
158 |
+
|
159 |
+
Args:
|
160 |
+
q (`torch.Tensor`): The query tensor.
|
161 |
+
k (`torch.Tensor`): The key tensor.
|
162 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
163 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
164 |
+
position_ids (`torch.Tensor`, *optional*):
|
165 |
+
Deprecated and unused.
|
166 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
167 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
168 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
169 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
170 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
171 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
172 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
173 |
+
Returns:
|
174 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
175 |
+
"""
|
176 |
+
cos = cos.unsqueeze(unsqueeze_dim)
|
177 |
+
sin = sin.unsqueeze(unsqueeze_dim)
|
178 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
179 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
180 |
+
return q_embed, k_embed
|
181 |
+
|
182 |
+
|
183 |
+
class BitnetMLP(nn.Module):
|
184 |
+
def __init__(self, config):
|
185 |
+
super().__init__()
|
186 |
+
self.config = config
|
187 |
+
self.hidden_size = config.hidden_size
|
188 |
+
self.intermediate_size = config.intermediate_size
|
189 |
+
self.gate_proj = BitLinear(
|
190 |
+
self.hidden_size, self.intermediate_size, bias=False,
|
191 |
+
weight_bits=config.weight_bits, input_bits=config.input_bits,
|
192 |
+
)
|
193 |
+
self.up_proj = BitLinear(
|
194 |
+
self.hidden_size, self.intermediate_size, bias=False,
|
195 |
+
weight_bits=config.weight_bits, input_bits=config.input_bits,
|
196 |
+
)
|
197 |
+
self.down_proj = BitLinear(
|
198 |
+
self.intermediate_size, self.hidden_size, bias=False,
|
199 |
+
weight_bits=config.weight_bits, input_bits=config.input_bits,
|
200 |
+
)
|
201 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
202 |
+
self.ffn_layernorm = BitnetRMSNorm(self.intermediate_size, eps=config.rms_norm_eps)
|
203 |
+
|
204 |
+
def forward(self, x):
|
205 |
+
x = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
|
206 |
+
x = self.ffn_layernorm(x)
|
207 |
+
x = self.down_proj(x)
|
208 |
+
return x
|
209 |
+
|
210 |
+
|
211 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
212 |
+
"""
|
213 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
214 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
215 |
+
"""
|
216 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
217 |
+
if n_rep == 1:
|
218 |
+
return hidden_states
|
219 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
220 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
221 |
+
|
222 |
+
|
223 |
+
class BitnetAttention(nn.Module):
|
224 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
225 |
+
|
226 |
+
def __init__(self, config: BitnetConfig, layer_idx: Optional[int] = None):
|
227 |
+
super().__init__()
|
228 |
+
self.config = config
|
229 |
+
self.layer_idx = layer_idx
|
230 |
+
if layer_idx is None:
|
231 |
+
logger.warning_once(
|
232 |
+
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
|
233 |
+
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
|
234 |
+
"when creating this class."
|
235 |
+
)
|
236 |
+
|
237 |
+
self.attention_dropout = config.attention_dropout
|
238 |
+
self.hidden_size = config.hidden_size
|
239 |
+
self.num_heads = config.num_attention_heads
|
240 |
+
self.head_dim = self.hidden_size // self.num_heads
|
241 |
+
self.num_key_value_heads = config.num_key_value_heads
|
242 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
243 |
+
self.max_position_embeddings = config.max_position_embeddings
|
244 |
+
self.rope_theta = config.rope_theta
|
245 |
+
self.is_causal = True
|
246 |
+
|
247 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
248 |
+
raise ValueError(
|
249 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
250 |
+
f" and `num_heads`: {self.num_heads})."
|
251 |
+
)
|
252 |
+
|
253 |
+
self.q_proj = BitLinear(
|
254 |
+
self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias,
|
255 |
+
weight_bits=config.weight_bits, input_bits=config.input_bits,
|
256 |
+
)
|
257 |
+
self.k_proj = BitLinear(
|
258 |
+
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias,
|
259 |
+
weight_bits=config.weight_bits, input_bits=config.input_bits,
|
260 |
+
)
|
261 |
+
self.v_proj = BitLinear(
|
262 |
+
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias,
|
263 |
+
weight_bits=config.weight_bits, input_bits=config.input_bits,
|
264 |
+
)
|
265 |
+
self.o_proj = BitLinear(
|
266 |
+
self.hidden_size, self.hidden_size, bias=config.attention_bias,
|
267 |
+
weight_bits=config.weight_bits, input_bits=config.input_bits,
|
268 |
+
)
|
269 |
+
self._init_rope()
|
270 |
+
self.inner_attn_ln = BitnetRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
|
271 |
+
|
272 |
+
def _init_rope(self):
|
273 |
+
if self.config.rope_scaling is None:
|
274 |
+
self.rotary_emb = BitnetRotaryEmbedding(
|
275 |
+
self.head_dim,
|
276 |
+
max_position_embeddings=self.max_position_embeddings,
|
277 |
+
base=self.rope_theta,
|
278 |
+
)
|
279 |
+
else:
|
280 |
+
raise NotImplementedError
|
281 |
+
|
282 |
+
def forward(
|
283 |
+
self,
|
284 |
+
hidden_states: torch.Tensor,
|
285 |
+
attention_mask: Optional[torch.Tensor] = None,
|
286 |
+
position_ids: Optional[torch.LongTensor] = None,
|
287 |
+
past_key_value: Optional[Cache] = None,
|
288 |
+
output_attentions: bool = False,
|
289 |
+
use_cache: bool = False,
|
290 |
+
cache_position: Optional[torch.LongTensor] = None,
|
291 |
+
**kwargs,
|
292 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
293 |
+
bsz, q_len, _ = hidden_states.size()
|
294 |
+
|
295 |
+
query_states = self.q_proj(hidden_states)
|
296 |
+
key_states = self.k_proj(hidden_states)
|
297 |
+
value_states = self.v_proj(hidden_states)
|
298 |
+
|
299 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
300 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
301 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
302 |
+
|
303 |
+
past_key_value = getattr(self, "past_key_value", past_key_value)
|
304 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
305 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
306 |
+
|
307 |
+
if past_key_value is not None:
|
308 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
309 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
310 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
311 |
+
|
312 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
313 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
314 |
+
|
315 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
316 |
+
|
317 |
+
if attention_mask is not None: # no matter the length, we just slice it
|
318 |
+
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
319 |
+
attn_weights = attn_weights + causal_mask
|
320 |
+
|
321 |
+
# upcast attention to fp32
|
322 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
323 |
+
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
324 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
325 |
+
|
326 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
327 |
+
raise ValueError(
|
328 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
329 |
+
f" {attn_output.size()}"
|
330 |
+
)
|
331 |
+
|
332 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
333 |
+
|
334 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
335 |
+
|
336 |
+
attn_output = self.inner_attn_ln(attn_output)
|
337 |
+
attn_output = self.o_proj(attn_output)
|
338 |
+
|
339 |
+
if not output_attentions:
|
340 |
+
attn_weights = None
|
341 |
+
|
342 |
+
return attn_output, attn_weights, past_key_value
|
343 |
+
|
344 |
+
|
345 |
+
class BitnetFlashAttention2(BitnetAttention):
|
346 |
+
"""
|
347 |
+
Bitnet flash attention module. This module inherits from `BitnetAttention` as the weights of the module stays
|
348 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
349 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
350 |
+
"""
|
351 |
+
|
352 |
+
def __init__(self, *args, **kwargs):
|
353 |
+
super().__init__(*args, **kwargs)
|
354 |
+
|
355 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
356 |
+
# 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.
|
357 |
+
# 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).
|
358 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
359 |
+
|
360 |
+
def forward(
|
361 |
+
self,
|
362 |
+
hidden_states: torch.Tensor,
|
363 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
364 |
+
position_ids: Optional[torch.LongTensor] = None,
|
365 |
+
past_key_value: Optional[Cache] = None,
|
366 |
+
output_attentions: bool = False,
|
367 |
+
use_cache: bool = False,
|
368 |
+
cache_position: Optional[torch.LongTensor] = None,
|
369 |
+
**kwargs,
|
370 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
371 |
+
output_attentions = False
|
372 |
+
|
373 |
+
bsz, q_len, _ = hidden_states.size()
|
374 |
+
|
375 |
+
query_states = self.q_proj(hidden_states)
|
376 |
+
key_states = self.k_proj(hidden_states)
|
377 |
+
value_states = self.v_proj(hidden_states)
|
378 |
+
|
379 |
+
# Flash attention requires the input to have the shape
|
380 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
381 |
+
# therefore we just need to keep the original shape
|
382 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
383 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
384 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
385 |
+
|
386 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
387 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
388 |
+
|
389 |
+
past_key_value = getattr(self, "past_key_value", past_key_value)
|
390 |
+
|
391 |
+
if past_key_value is not None:
|
392 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
393 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
394 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
395 |
+
|
396 |
+
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
397 |
+
# to be able to avoid many of these transpose/reshape/view.
|
398 |
+
query_states = query_states.transpose(1, 2)
|
399 |
+
key_states = key_states.transpose(1, 2)
|
400 |
+
value_states = value_states.transpose(1, 2)
|
401 |
+
|
402 |
+
dropout_rate = self.attention_dropout if self.training else 0.0
|
403 |
+
|
404 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
405 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
406 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
407 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
408 |
+
# in fp32. (BitnetRMSNorm handles it correctly)
|
409 |
+
|
410 |
+
input_dtype = query_states.dtype
|
411 |
+
if input_dtype == torch.float32:
|
412 |
+
if torch.is_autocast_enabled():
|
413 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
414 |
+
# Handle the case where the model is quantized
|
415 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
416 |
+
target_dtype = self.config._pre_quantization_dtype
|
417 |
+
else:
|
418 |
+
target_dtype = self.q_proj.weight.dtype
|
419 |
+
|
420 |
+
logger.warning_once(
|
421 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
422 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
423 |
+
f" {target_dtype}."
|
424 |
+
)
|
425 |
+
|
426 |
+
query_states = query_states.to(target_dtype)
|
427 |
+
key_states = key_states.to(target_dtype)
|
428 |
+
value_states = value_states.to(target_dtype)
|
429 |
+
|
430 |
+
attn_output = self._flash_attention_forward(
|
431 |
+
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
|
432 |
+
)
|
433 |
+
|
434 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
435 |
+
attn_output = self.inner_attn_ln(attn_output)
|
436 |
+
attn_output = self.o_proj(attn_output)
|
437 |
+
|
438 |
+
if not output_attentions:
|
439 |
+
attn_weights = None
|
440 |
+
|
441 |
+
return attn_output, attn_weights, past_key_value
|
442 |
+
|
443 |
+
def _flash_attention_forward(
|
444 |
+
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
445 |
+
):
|
446 |
+
"""
|
447 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
448 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
449 |
+
|
450 |
+
Args:
|
451 |
+
query_states (`torch.Tensor`):
|
452 |
+
Input query states to be passed to Flash Attention API
|
453 |
+
key_states (`torch.Tensor`):
|
454 |
+
Input key states to be passed to Flash Attention API
|
455 |
+
value_states (`torch.Tensor`):
|
456 |
+
Input value states to be passed to Flash Attention API
|
457 |
+
attention_mask (`torch.Tensor`):
|
458 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
459 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
460 |
+
dropout (`float`):
|
461 |
+
Attention dropout
|
462 |
+
softmax_scale (`float`, *optional*):
|
463 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
464 |
+
"""
|
465 |
+
if not self._flash_attn_uses_top_left_mask:
|
466 |
+
causal = self.is_causal
|
467 |
+
else:
|
468 |
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in BitnetFlashAttention2 __init__.
|
469 |
+
causal = self.is_causal and query_length != 1
|
470 |
+
|
471 |
+
# Contains at least one padding token in the sequence
|
472 |
+
if attention_mask is not None:
|
473 |
+
batch_size = query_states.shape[0]
|
474 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
475 |
+
query_states, key_states, value_states, attention_mask, query_length
|
476 |
+
)
|
477 |
+
|
478 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
479 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
480 |
+
|
481 |
+
attn_output_unpad = flash_attn_varlen_func(
|
482 |
+
query_states,
|
483 |
+
key_states,
|
484 |
+
value_states,
|
485 |
+
cu_seqlens_q=cu_seqlens_q,
|
486 |
+
cu_seqlens_k=cu_seqlens_k,
|
487 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
488 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
489 |
+
dropout_p=dropout,
|
490 |
+
softmax_scale=softmax_scale,
|
491 |
+
causal=causal,
|
492 |
+
)
|
493 |
+
|
494 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
495 |
+
else:
|
496 |
+
attn_output = flash_attn_func(
|
497 |
+
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
498 |
+
)
|
499 |
+
|
500 |
+
return attn_output
|
501 |
+
|
502 |
+
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
503 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
504 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
505 |
+
|
506 |
+
key_layer = index_first_axis(
|
507 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
508 |
+
)
|
509 |
+
value_layer = index_first_axis(
|
510 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
511 |
+
)
|
512 |
+
if query_length == kv_seq_len:
|
513 |
+
query_layer = index_first_axis(
|
514 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
515 |
+
)
|
516 |
+
cu_seqlens_q = cu_seqlens_k
|
517 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
518 |
+
indices_q = indices_k
|
519 |
+
elif query_length == 1:
|
520 |
+
max_seqlen_in_batch_q = 1
|
521 |
+
cu_seqlens_q = torch.arange(
|
522 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
523 |
+
) # There is a memcpy here, that is very bad.
|
524 |
+
indices_q = cu_seqlens_q[:-1]
|
525 |
+
query_layer = query_layer.squeeze(1)
|
526 |
+
else:
|
527 |
+
# The -q_len: slice assumes left padding.
|
528 |
+
attention_mask = attention_mask[:, -query_length:]
|
529 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
530 |
+
|
531 |
+
return (
|
532 |
+
query_layer,
|
533 |
+
key_layer,
|
534 |
+
value_layer,
|
535 |
+
indices_q,
|
536 |
+
(cu_seqlens_q, cu_seqlens_k),
|
537 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
538 |
+
)
|
539 |
+
|
540 |
+
|
541 |
+
|
542 |
+
LLAMA_ATTENTION_CLASSES = {
|
543 |
+
"eager": BitnetAttention,
|
544 |
+
"flash_attention_2": BitnetFlashAttention2,
|
545 |
+
}
|
546 |
+
|
547 |
+
|
548 |
+
class BitnetDecoderLayer(nn.Module):
|
549 |
+
def __init__(self, config: BitnetConfig, layer_idx: int):
|
550 |
+
super().__init__()
|
551 |
+
self.hidden_size = config.hidden_size
|
552 |
+
|
553 |
+
self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
554 |
+
|
555 |
+
self.mlp = BitnetMLP(config)
|
556 |
+
self.input_layernorm = BitnetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
557 |
+
self.post_attention_layernorm = BitnetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
558 |
+
|
559 |
+
def forward(
|
560 |
+
self,
|
561 |
+
hidden_states: torch.Tensor,
|
562 |
+
attention_mask: Optional[torch.Tensor] = None,
|
563 |
+
position_ids: Optional[torch.LongTensor] = None,
|
564 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
565 |
+
output_attentions: Optional[bool] = False,
|
566 |
+
use_cache: Optional[bool] = False,
|
567 |
+
cache_position: Optional[torch.LongTensor] = None,
|
568 |
+
**kwargs,
|
569 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
570 |
+
"""
|
571 |
+
Args:
|
572 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
573 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
574 |
+
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
575 |
+
query_sequence_length, key_sequence_length)` if default attention is used.
|
576 |
+
output_attentions (`bool`, *optional*):
|
577 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
578 |
+
returned tensors for more detail.
|
579 |
+
use_cache (`bool`, *optional*):
|
580 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
581 |
+
(see `past_key_values`).
|
582 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
583 |
+
"""
|
584 |
+
if "padding_mask" in kwargs:
|
585 |
+
warnings.warn(
|
586 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
587 |
+
)
|
588 |
+
|
589 |
+
residual = hidden_states
|
590 |
+
|
591 |
+
hidden_states = self.input_layernorm(hidden_states)
|
592 |
+
|
593 |
+
# Self Attention
|
594 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
595 |
+
hidden_states=hidden_states,
|
596 |
+
attention_mask=attention_mask,
|
597 |
+
position_ids=position_ids,
|
598 |
+
past_key_value=past_key_value,
|
599 |
+
output_attentions=output_attentions,
|
600 |
+
use_cache=use_cache,
|
601 |
+
cache_position=cache_position,
|
602 |
+
**kwargs,
|
603 |
+
)
|
604 |
+
hidden_states = residual + hidden_states
|
605 |
+
|
606 |
+
# Fully Connected
|
607 |
+
residual = hidden_states
|
608 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
609 |
+
hidden_states = self.mlp(hidden_states)
|
610 |
+
hidden_states = residual + hidden_states
|
611 |
+
|
612 |
+
outputs = (hidden_states,)
|
613 |
+
|
614 |
+
if output_attentions:
|
615 |
+
outputs += (self_attn_weights,)
|
616 |
+
|
617 |
+
if use_cache:
|
618 |
+
outputs += (present_key_value,)
|
619 |
+
|
620 |
+
return outputs
|
621 |
+
|
622 |
+
|
623 |
+
LLAMA_START_DOCSTRING = r"""
|
624 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
625 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
626 |
+
etc.)
|
627 |
+
|
628 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
629 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
630 |
+
and behavior.
|
631 |
+
|
632 |
+
Parameters:
|
633 |
+
config ([`BitnetConfig`]):
|
634 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
635 |
+
load the weights associated with the model, only the configuration. Check out the
|
636 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
637 |
+
"""
|
638 |
+
|
639 |
+
|
640 |
+
@add_start_docstrings(
|
641 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
642 |
+
LLAMA_START_DOCSTRING,
|
643 |
+
)
|
644 |
+
class BitnetPreTrainedModel(PreTrainedModel):
|
645 |
+
config_class = BitnetConfig
|
646 |
+
base_model_prefix = "model"
|
647 |
+
supports_gradient_checkpointing = True
|
648 |
+
_no_split_modules = ["BitnetDecoderLayer"]
|
649 |
+
_skip_keys_device_placement = ["past_key_values"]
|
650 |
+
_supports_flash_attn_2 = True
|
651 |
+
_supports_sdpa = False
|
652 |
+
_supports_cache_class = True
|
653 |
+
|
654 |
+
def _init_weights(self, module):
|
655 |
+
std = self.config.initializer_range
|
656 |
+
if isinstance(module, nn.Linear):
|
657 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
658 |
+
if module.bias is not None:
|
659 |
+
module.bias.data.zero_()
|
660 |
+
elif isinstance(module, nn.Embedding):
|
661 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
662 |
+
if module.padding_idx is not None:
|
663 |
+
module.weight.data[module.padding_idx].zero_()
|
664 |
+
|
665 |
+
def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None):
|
666 |
+
if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache:
|
667 |
+
raise ValueError(
|
668 |
+
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
|
669 |
+
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
|
670 |
+
)
|
671 |
+
|
672 |
+
for layer in self.model.layers:
|
673 |
+
device = layer.input_layernorm.weight.device
|
674 |
+
if hasattr(self.config, "_pre_quantization_dtype"):
|
675 |
+
dtype = self.config._pre_quantization_dtype
|
676 |
+
else:
|
677 |
+
dtype = layer.self_attn.o_proj.weight.dtype
|
678 |
+
layer.self_attn.past_key_value = cache_cls(
|
679 |
+
self.config, max_batch_size, max_cache_len, device=device, dtype=dtype
|
680 |
+
)
|
681 |
+
|
682 |
+
def _reset_cache(self):
|
683 |
+
for layer in self.model.layers:
|
684 |
+
layer.self_attn.past_key_value = None
|
685 |
+
|
686 |
+
|
687 |
+
LLAMA_INPUTS_DOCSTRING = r"""
|
688 |
+
Args:
|
689 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
690 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
691 |
+
it.
|
692 |
+
|
693 |
+
Indices can be obtained using [`BitnetTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
694 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
695 |
+
|
696 |
+
[What are input IDs?](../glossary#input-ids)
|
697 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
698 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
699 |
+
|
700 |
+
- 1 for tokens that are **not masked**,
|
701 |
+
- 0 for tokens that are **masked**.
|
702 |
+
|
703 |
+
[What are attention masks?](../glossary#attention-mask)
|
704 |
+
|
705 |
+
Indices can be obtained using [`BitnetTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
706 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
707 |
+
|
708 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
709 |
+
`past_key_values`).
|
710 |
+
|
711 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
712 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
713 |
+
information on the default strategy.
|
714 |
+
|
715 |
+
- 1 indicates the head is **not masked**,
|
716 |
+
- 0 indicates the head is **masked**.
|
717 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
718 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
719 |
+
config.n_positions - 1]`.
|
720 |
+
|
721 |
+
[What are position IDs?](../glossary#position-ids)
|
722 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
723 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
724 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
725 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
726 |
+
|
727 |
+
Two formats are allowed:
|
728 |
+
- a [`~cache_utils.Cache`] instance;
|
729 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
730 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
731 |
+
cache format.
|
732 |
+
|
733 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
734 |
+
legacy cache format will be returned.
|
735 |
+
|
736 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
737 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
738 |
+
of shape `(batch_size, sequence_length)`.
|
739 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
740 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
741 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
742 |
+
model's internal embedding lookup matrix.
|
743 |
+
use_cache (`bool`, *optional*):
|
744 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
745 |
+
`past_key_values`).
|
746 |
+
output_attentions (`bool`, *optional*):
|
747 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
748 |
+
tensors for more detail.
|
749 |
+
output_hidden_states (`bool`, *optional*):
|
750 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
751 |
+
more detail.
|
752 |
+
return_dict (`bool`, *optional*):
|
753 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
754 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
755 |
+
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
|
756 |
+
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
|
757 |
+
the complete sequence length.
|
758 |
+
"""
|
759 |
+
|
760 |
+
|
761 |
+
@add_start_docstrings(
|
762 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
763 |
+
LLAMA_START_DOCSTRING,
|
764 |
+
)
|
765 |
+
class BitnetModel(BitnetPreTrainedModel):
|
766 |
+
"""
|
767 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BitnetDecoderLayer`]
|
768 |
+
|
769 |
+
Args:
|
770 |
+
config: BitnetConfig
|
771 |
+
"""
|
772 |
+
|
773 |
+
def __init__(self, config: BitnetConfig):
|
774 |
+
super().__init__(config)
|
775 |
+
self.padding_idx = config.pad_token_id
|
776 |
+
self.vocab_size = config.vocab_size
|
777 |
+
|
778 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
779 |
+
self.layers = nn.ModuleList(
|
780 |
+
[BitnetDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
781 |
+
)
|
782 |
+
self.norm = BitnetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
783 |
+
self.gradient_checkpointing = False
|
784 |
+
|
785 |
+
# Initialize weights and apply final processing
|
786 |
+
self.post_init()
|
787 |
+
|
788 |
+
def get_input_embeddings(self):
|
789 |
+
return self.embed_tokens
|
790 |
+
|
791 |
+
def set_input_embeddings(self, value):
|
792 |
+
self.embed_tokens = value
|
793 |
+
|
794 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
795 |
+
def forward(
|
796 |
+
self,
|
797 |
+
input_ids: torch.LongTensor = None,
|
798 |
+
attention_mask: Optional[torch.Tensor] = None,
|
799 |
+
position_ids: Optional[torch.LongTensor] = None,
|
800 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
801 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
802 |
+
use_cache: Optional[bool] = None,
|
803 |
+
output_attentions: Optional[bool] = None,
|
804 |
+
output_hidden_states: Optional[bool] = None,
|
805 |
+
return_dict: Optional[bool] = None,
|
806 |
+
cache_position: Optional[torch.LongTensor] = None,
|
807 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
808 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
809 |
+
output_hidden_states = (
|
810 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
811 |
+
)
|
812 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
813 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
814 |
+
|
815 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
816 |
+
raise ValueError(
|
817 |
+
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
|
818 |
+
)
|
819 |
+
|
820 |
+
if self.gradient_checkpointing and self.training and use_cache:
|
821 |
+
logger.warning_once(
|
822 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
823 |
+
)
|
824 |
+
use_cache = False
|
825 |
+
|
826 |
+
if inputs_embeds is None:
|
827 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
828 |
+
|
829 |
+
past_seen_tokens = 0
|
830 |
+
if use_cache: # kept for BC (cache positions)
|
831 |
+
if not isinstance(past_key_values, StaticCache):
|
832 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
833 |
+
past_seen_tokens = past_key_values.get_seq_length()
|
834 |
+
|
835 |
+
if cache_position is None:
|
836 |
+
if isinstance(past_key_values, StaticCache):
|
837 |
+
raise ValueError("cache_position is a required argument when using StaticCache.")
|
838 |
+
cache_position = torch.arange(
|
839 |
+
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
840 |
+
)
|
841 |
+
|
842 |
+
if position_ids is None:
|
843 |
+
position_ids = cache_position.unsqueeze(0)
|
844 |
+
|
845 |
+
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position)
|
846 |
+
|
847 |
+
# embed positions
|
848 |
+
hidden_states = inputs_embeds
|
849 |
+
|
850 |
+
# decoder layers
|
851 |
+
all_hidden_states = () if output_hidden_states else None
|
852 |
+
all_self_attns = () if output_attentions else None
|
853 |
+
next_decoder_cache = None
|
854 |
+
|
855 |
+
for decoder_layer in self.layers:
|
856 |
+
if output_hidden_states:
|
857 |
+
all_hidden_states += (hidden_states,)
|
858 |
+
|
859 |
+
if self.gradient_checkpointing and self.training:
|
860 |
+
layer_outputs = self._gradient_checkpointing_func(
|
861 |
+
decoder_layer.__call__,
|
862 |
+
hidden_states,
|
863 |
+
causal_mask,
|
864 |
+
position_ids,
|
865 |
+
past_key_values,
|
866 |
+
output_attentions,
|
867 |
+
use_cache,
|
868 |
+
cache_position,
|
869 |
+
)
|
870 |
+
else:
|
871 |
+
layer_outputs = decoder_layer(
|
872 |
+
hidden_states,
|
873 |
+
attention_mask=causal_mask,
|
874 |
+
position_ids=position_ids,
|
875 |
+
past_key_value=past_key_values,
|
876 |
+
output_attentions=output_attentions,
|
877 |
+
use_cache=use_cache,
|
878 |
+
cache_position=cache_position,
|
879 |
+
)
|
880 |
+
|
881 |
+
hidden_states = layer_outputs[0]
|
882 |
+
|
883 |
+
if use_cache:
|
884 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
885 |
+
|
886 |
+
if output_attentions:
|
887 |
+
all_self_attns += (layer_outputs[1],)
|
888 |
+
|
889 |
+
hidden_states = self.norm(hidden_states)
|
890 |
+
|
891 |
+
# add hidden states from the last decoder layer
|
892 |
+
if output_hidden_states:
|
893 |
+
all_hidden_states += (hidden_states,)
|
894 |
+
|
895 |
+
next_cache = None
|
896 |
+
if use_cache:
|
897 |
+
next_cache = (
|
898 |
+
next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
|
899 |
+
)
|
900 |
+
if not return_dict:
|
901 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
902 |
+
return BaseModelOutputWithPast(
|
903 |
+
last_hidden_state=hidden_states,
|
904 |
+
past_key_values=next_cache,
|
905 |
+
hidden_states=all_hidden_states,
|
906 |
+
attentions=all_self_attns,
|
907 |
+
)
|
908 |
+
|
909 |
+
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
|
910 |
+
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
|
911 |
+
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
|
912 |
+
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
|
913 |
+
def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
|
914 |
+
if self.config._attn_implementation == "flash_attention_2":
|
915 |
+
if attention_mask is not None and 0.0 in attention_mask:
|
916 |
+
return attention_mask
|
917 |
+
return None
|
918 |
+
|
919 |
+
dtype, device = input_tensor.dtype, input_tensor.device
|
920 |
+
min_dtype = torch.finfo(dtype).min
|
921 |
+
sequence_length = input_tensor.shape[1]
|
922 |
+
if hasattr(self.layers[0].self_attn, "past_key_value"): # static cache
|
923 |
+
target_length = self.config.max_position_embeddings
|
924 |
+
else: # dynamic cache
|
925 |
+
target_length = (
|
926 |
+
attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else cache_position[-1] + 1
|
927 |
+
)
|
928 |
+
|
929 |
+
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
|
930 |
+
if sequence_length != 1:
|
931 |
+
causal_mask = torch.triu(causal_mask, diagonal=1)
|
932 |
+
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
|
933 |
+
causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
|
934 |
+
if attention_mask is not None:
|
935 |
+
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
936 |
+
if attention_mask.dim() == 2:
|
937 |
+
mask_length = attention_mask.shape[-1]
|
938 |
+
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
|
939 |
+
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
|
940 |
+
elif attention_mask.dim() == 4:
|
941 |
+
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
|
942 |
+
# cache. In that case, the 4D attention mask attends to the newest tokens only.
|
943 |
+
if attention_mask.shape[-2] < cache_position[0] + sequence_length:
|
944 |
+
offset = cache_position[0]
|
945 |
+
else:
|
946 |
+
offset = 0
|
947 |
+
mask_shape = attention_mask.shape
|
948 |
+
mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
|
949 |
+
causal_mask[
|
950 |
+
: mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]
|
951 |
+
] = mask_slice
|
952 |
+
|
953 |
+
return causal_mask
|
954 |
+
|
955 |
+
|
956 |
+
class BitnetForCausalLM(BitnetPreTrainedModel):
|
957 |
+
_tied_weights_keys = ["lm_head.weight"]
|
958 |
+
|
959 |
+
def __init__(self, config):
|
960 |
+
super().__init__(config)
|
961 |
+
self.model = BitnetModel(config)
|
962 |
+
self.vocab_size = config.vocab_size
|
963 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
964 |
+
|
965 |
+
# Initialize weights and apply final processing
|
966 |
+
self.post_init()
|
967 |
+
|
968 |
+
def get_input_embeddings(self):
|
969 |
+
return self.model.embed_tokens
|
970 |
+
|
971 |
+
def set_input_embeddings(self, value):
|
972 |
+
self.model.embed_tokens = value
|
973 |
+
|
974 |
+
def get_output_embeddings(self):
|
975 |
+
return self.lm_head
|
976 |
+
|
977 |
+
def set_output_embeddings(self, new_embeddings):
|
978 |
+
self.lm_head = new_embeddings
|
979 |
+
|
980 |
+
def set_decoder(self, decoder):
|
981 |
+
self.model = decoder
|
982 |
+
|
983 |
+
def get_decoder(self):
|
984 |
+
return self.model
|
985 |
+
|
986 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
987 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
988 |
+
def forward(
|
989 |
+
self,
|
990 |
+
input_ids: torch.LongTensor = None,
|
991 |
+
attention_mask: Optional[torch.Tensor] = None,
|
992 |
+
position_ids: Optional[torch.LongTensor] = None,
|
993 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
994 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
995 |
+
labels: Optional[torch.LongTensor] = None,
|
996 |
+
use_cache: Optional[bool] = None,
|
997 |
+
output_attentions: Optional[bool] = None,
|
998 |
+
output_hidden_states: Optional[bool] = None,
|
999 |
+
return_dict: Optional[bool] = None,
|
1000 |
+
cache_position: Optional[torch.LongTensor] = None,
|
1001 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1002 |
+
r"""
|
1003 |
+
Args:
|
1004 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1005 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
1006 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1007 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
1008 |
+
|
1009 |
+
Returns:
|
1010 |
+
|
1011 |
+
Example:
|
1012 |
+
|
1013 |
+
```python
|
1014 |
+
>>> from transformers import LlamaTokenizer, LlamaForCausalLM
|
1015 |
+
|
1016 |
+
>>> model = LlamaForCausalLM.from_pretrained("meta-llama/Bitnet-2-7b-hf")
|
1017 |
+
>>> tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Bitnet-2-7b-hf")
|
1018 |
+
|
1019 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
1020 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
1021 |
+
|
1022 |
+
>>> # Generate
|
1023 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
1024 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
1025 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
1026 |
+
```"""
|
1027 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1028 |
+
output_hidden_states = (
|
1029 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1030 |
+
)
|
1031 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1032 |
+
|
1033 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
1034 |
+
outputs = self.model(
|
1035 |
+
input_ids=input_ids,
|
1036 |
+
attention_mask=attention_mask,
|
1037 |
+
position_ids=position_ids,
|
1038 |
+
past_key_values=past_key_values,
|
1039 |
+
inputs_embeds=inputs_embeds,
|
1040 |
+
use_cache=use_cache,
|
1041 |
+
output_attentions=output_attentions,
|
1042 |
+
output_hidden_states=output_hidden_states,
|
1043 |
+
return_dict=return_dict,
|
1044 |
+
cache_position=cache_position,
|
1045 |
+
)
|
1046 |
+
|
1047 |
+
hidden_states = outputs[0]
|
1048 |
+
logits = self.lm_head(hidden_states)
|
1049 |
+
logits = logits.float()
|
1050 |
+
|
1051 |
+
loss = None
|
1052 |
+
if labels is not None:
|
1053 |
+
# Shift so that tokens < n predict n
|
1054 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
1055 |
+
shift_labels = labels[..., 1:].contiguous()
|
1056 |
+
# Flatten the tokens
|
1057 |
+
loss_fct = CrossEntropyLoss()
|
1058 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1059 |
+
shift_labels = shift_labels.view(-1)
|
1060 |
+
# Enable model parallelism
|
1061 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
1062 |
+
loss = loss_fct(shift_logits, shift_labels)
|
1063 |
+
|
1064 |
+
if not return_dict:
|
1065 |
+
output = (logits,) + outputs[1:]
|
1066 |
+
return (loss,) + output if loss is not None else output
|
1067 |
+
|
1068 |
+
return CausalLMOutputWithPast(
|
1069 |
+
loss=loss,
|
1070 |
+
logits=logits,
|
1071 |
+
past_key_values=outputs.past_key_values,
|
1072 |
+
hidden_states=outputs.hidden_states,
|
1073 |
+
attentions=outputs.attentions,
|
1074 |
+
)
|
1075 |
+
|
1076 |
+
def prepare_inputs_for_generation(
|
1077 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, **kwargs
|
1078 |
+
):
|
1079 |
+
# With static cache, the `past_key_values` is None
|
1080 |
+
# TODO joao: standardize interface for the different Cache classes and remove of this if
|
1081 |
+
has_static_cache = False
|
1082 |
+
if past_key_values is None:
|
1083 |
+
past_key_values = getattr(self.model.layers[0].self_attn, "past_key_value", None)
|
1084 |
+
has_static_cache = past_key_values is not None
|
1085 |
+
|
1086 |
+
past_length = 0
|
1087 |
+
if past_key_values is not None:
|
1088 |
+
if isinstance(past_key_values, Cache):
|
1089 |
+
past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
|
1090 |
+
max_cache_length = (
|
1091 |
+
torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
|
1092 |
+
if past_key_values.get_max_length() is not None
|
1093 |
+
else None
|
1094 |
+
)
|
1095 |
+
cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
|
1096 |
+
# TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
|
1097 |
+
else:
|
1098 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
1099 |
+
max_cache_length = None
|
1100 |
+
|
1101 |
+
# Keep only the unprocessed tokens:
|
1102 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
1103 |
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
1104 |
+
# input)
|
1105 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
1106 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
1107 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
1108 |
+
# input_ids based on the past_length.
|
1109 |
+
elif past_length < input_ids.shape[1]:
|
1110 |
+
input_ids = input_ids[:, past_length:]
|
1111 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
1112 |
+
|
1113 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
1114 |
+
if (
|
1115 |
+
max_cache_length is not None
|
1116 |
+
and attention_mask is not None
|
1117 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
1118 |
+
):
|
1119 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
1120 |
+
|
1121 |
+
position_ids = kwargs.get("position_ids", None)
|
1122 |
+
if attention_mask is not None and position_ids is None:
|
1123 |
+
# create position_ids on the fly for batch generation
|
1124 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
1125 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
1126 |
+
if past_key_values:
|
1127 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
1128 |
+
|
1129 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1130 |
+
if inputs_embeds is not None and past_key_values is None:
|
1131 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
1132 |
+
else:
|
1133 |
+
# The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
|
1134 |
+
# recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
|
1135 |
+
# TODO: use `next_tokens` directly instead.
|
1136 |
+
model_inputs = {"input_ids": input_ids.contiguous()}
|
1137 |
+
|
1138 |
+
input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
|
1139 |
+
if cache_position is None:
|
1140 |
+
cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
|
1141 |
+
else:
|
1142 |
+
cache_position = cache_position[-input_length:]
|
1143 |
+
|
1144 |
+
if has_static_cache:
|
1145 |
+
past_key_values = None
|
1146 |
+
|
1147 |
+
model_inputs.update(
|
1148 |
+
{
|
1149 |
+
"position_ids": position_ids,
|
1150 |
+
"cache_position": cache_position,
|
1151 |
+
"past_key_values": past_key_values,
|
1152 |
+
"use_cache": kwargs.get("use_cache"),
|
1153 |
+
"attention_mask": attention_mask,
|
1154 |
+
}
|
1155 |
+
)
|
1156 |
+
return model_inputs
|
1157 |
+
|
1158 |
+
@staticmethod
|
1159 |
+
def _reorder_cache(past_key_values, beam_idx):
|
1160 |
+
reordered_past = ()
|
1161 |
+
for layer_past in past_key_values:
|
1162 |
+
reordered_past += (
|
1163 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
1164 |
+
)
|
1165 |
+
return reordered_past
|
1166 |
+
|
1167 |
+
|
1168 |
+
@add_start_docstrings(
|
1169 |
+
"""
|
1170 |
+
The LLaMa Model transformer with a sequence classification head on top (linear layer).
|
1171 |
+
|
1172 |
+
[`BitnetForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
1173 |
+
(e.g. GPT-2) do.
|
1174 |
+
|
1175 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
1176 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
1177 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
1178 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
1179 |
+
each row of the batch).
|
1180 |
+
""",
|
1181 |
+
LLAMA_START_DOCSTRING,
|
1182 |
+
)
|
1183 |
+
class BitnetForSequenceClassification(BitnetPreTrainedModel):
|
1184 |
+
def __init__(self, config):
|
1185 |
+
super().__init__(config)
|
1186 |
+
self.num_labels = config.num_labels
|
1187 |
+
self.model = BitnetModel(config)
|
1188 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
1189 |
+
|
1190 |
+
# Initialize weights and apply final processing
|
1191 |
+
self.post_init()
|
1192 |
+
|
1193 |
+
def get_input_embeddings(self):
|
1194 |
+
return self.model.embed_tokens
|
1195 |
+
|
1196 |
+
def set_input_embeddings(self, value):
|
1197 |
+
self.model.embed_tokens = value
|
1198 |
+
|
1199 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
1200 |
+
def forward(
|
1201 |
+
self,
|
1202 |
+
input_ids: torch.LongTensor = None,
|
1203 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1204 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1205 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1206 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1207 |
+
labels: Optional[torch.LongTensor] = None,
|
1208 |
+
use_cache: Optional[bool] = None,
|
1209 |
+
output_attentions: Optional[bool] = None,
|
1210 |
+
output_hidden_states: Optional[bool] = None,
|
1211 |
+
return_dict: Optional[bool] = None,
|
1212 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
1213 |
+
r"""
|
1214 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1215 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1216 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1217 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1218 |
+
"""
|
1219 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1220 |
+
|
1221 |
+
transformer_outputs = self.model(
|
1222 |
+
input_ids,
|
1223 |
+
attention_mask=attention_mask,
|
1224 |
+
position_ids=position_ids,
|
1225 |
+
past_key_values=past_key_values,
|
1226 |
+
inputs_embeds=inputs_embeds,
|
1227 |
+
use_cache=use_cache,
|
1228 |
+
output_attentions=output_attentions,
|
1229 |
+
output_hidden_states=output_hidden_states,
|
1230 |
+
return_dict=return_dict,
|
1231 |
+
)
|
1232 |
+
hidden_states = transformer_outputs[0]
|
1233 |
+
logits = self.score(hidden_states)
|
1234 |
+
|
1235 |
+
if input_ids is not None:
|
1236 |
+
batch_size = input_ids.shape[0]
|
1237 |
+
else:
|
1238 |
+
batch_size = inputs_embeds.shape[0]
|
1239 |
+
|
1240 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
1241 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
1242 |
+
if self.config.pad_token_id is None:
|
1243 |
+
sequence_lengths = -1
|
1244 |
+
else:
|
1245 |
+
if input_ids is not None:
|
1246 |
+
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
1247 |
+
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
1248 |
+
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
1249 |
+
sequence_lengths = sequence_lengths.to(logits.device)
|
1250 |
+
else:
|
1251 |
+
sequence_lengths = -1
|
1252 |
+
|
1253 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
1254 |
+
|
1255 |
+
loss = None
|
1256 |
+
if labels is not None:
|
1257 |
+
labels = labels.to(logits.device)
|
1258 |
+
if self.config.problem_type is None:
|
1259 |
+
if self.num_labels == 1:
|
1260 |
+
self.config.problem_type = "regression"
|
1261 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
1262 |
+
self.config.problem_type = "single_label_classification"
|
1263 |
+
else:
|
1264 |
+
self.config.problem_type = "multi_label_classification"
|
1265 |
+
|
1266 |
+
if self.config.problem_type == "regression":
|
1267 |
+
loss_fct = MSELoss()
|
1268 |
+
if self.num_labels == 1:
|
1269 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
1270 |
+
else:
|
1271 |
+
loss = loss_fct(pooled_logits, labels)
|
1272 |
+
elif self.config.problem_type == "single_label_classification":
|
1273 |
+
loss_fct = CrossEntropyLoss()
|
1274 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
1275 |
+
elif self.config.problem_type == "multi_label_classification":
|
1276 |
+
loss_fct = BCEWithLogitsLoss()
|
1277 |
+
loss = loss_fct(pooled_logits, labels)
|
1278 |
+
if not return_dict:
|
1279 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
1280 |
+
return ((loss,) + output) if loss is not None else output
|
1281 |
+
|
1282 |
+
return SequenceClassifierOutputWithPast(
|
1283 |
+
loss=loss,
|
1284 |
+
logits=pooled_logits,
|
1285 |
+
past_key_values=transformer_outputs.past_key_values,
|
1286 |
+
hidden_states=transformer_outputs.hidden_states,
|
1287 |
+
attentions=transformer_outputs.attentions,
|
1288 |
+
)
|
1289 |
+
|
1290 |
+
|
1291 |
+
@add_start_docstrings(
|
1292 |
+
"""
|
1293 |
+
The Bitnet Model transformer with a span classification head on top for extractive question-answering tasks like
|
1294 |
+
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
1295 |
+
""",
|
1296 |
+
LLAMA_START_DOCSTRING,
|
1297 |
+
)
|
1298 |
+
class BitnetForQuestionAnswering(BitnetPreTrainedModel):
|
1299 |
+
base_model_prefix = "transformer"
|
1300 |
+
|
1301 |
+
# Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Bitnet
|
1302 |
+
def __init__(self, config):
|
1303 |
+
super().__init__(config)
|
1304 |
+
self.transformer = BitnetModel(config)
|
1305 |
+
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
1306 |
+
|
1307 |
+
# Initialize weights and apply final processing
|
1308 |
+
self.post_init()
|
1309 |
+
|
1310 |
+
def get_input_embeddings(self):
|
1311 |
+
return self.transformer.embed_tokens
|
1312 |
+
|
1313 |
+
def set_input_embeddings(self, value):
|
1314 |
+
self.transformer.embed_tokens = value
|
1315 |
+
|
1316 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
1317 |
+
def forward(
|
1318 |
+
self,
|
1319 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1320 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
1321 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1322 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1323 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1324 |
+
start_positions: Optional[torch.LongTensor] = None,
|
1325 |
+
end_positions: Optional[torch.LongTensor] = None,
|
1326 |
+
output_attentions: Optional[bool] = None,
|
1327 |
+
output_hidden_states: Optional[bool] = None,
|
1328 |
+
return_dict: Optional[bool] = None,
|
1329 |
+
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
1330 |
+
r"""
|
1331 |
+
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1332 |
+
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
1333 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1334 |
+
are not taken into account for computing the loss.
|
1335 |
+
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1336 |
+
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
1337 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1338 |
+
are not taken into account for computing the loss.
|
1339 |
+
"""
|
1340 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1341 |
+
|
1342 |
+
outputs = self.transformer(
|
1343 |
+
input_ids,
|
1344 |
+
attention_mask=attention_mask,
|
1345 |
+
position_ids=position_ids,
|
1346 |
+
past_key_values=past_key_values,
|
1347 |
+
inputs_embeds=inputs_embeds,
|
1348 |
+
output_attentions=output_attentions,
|
1349 |
+
output_hidden_states=output_hidden_states,
|
1350 |
+
return_dict=return_dict,
|
1351 |
+
)
|
1352 |
+
|
1353 |
+
sequence_output = outputs[0]
|
1354 |
+
|
1355 |
+
logits = self.qa_outputs(sequence_output)
|
1356 |
+
start_logits, end_logits = logits.split(1, dim=-1)
|
1357 |
+
start_logits = start_logits.squeeze(-1).contiguous()
|
1358 |
+
end_logits = end_logits.squeeze(-1).contiguous()
|
1359 |
+
|
1360 |
+
total_loss = None
|
1361 |
+
if start_positions is not None and end_positions is not None:
|
1362 |
+
# If we are on multi-GPU, split add a dimension
|
1363 |
+
if len(start_positions.size()) > 1:
|
1364 |
+
start_positions = start_positions.squeeze(-1).to(start_logits.device)
|
1365 |
+
if len(end_positions.size()) > 1:
|
1366 |
+
end_positions = end_positions.squeeze(-1).to(end_logits.device)
|
1367 |
+
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
1368 |
+
ignored_index = start_logits.size(1)
|
1369 |
+
start_positions = start_positions.clamp(0, ignored_index)
|
1370 |
+
end_positions = end_positions.clamp(0, ignored_index)
|
1371 |
+
|
1372 |
+
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
1373 |
+
start_loss = loss_fct(start_logits, start_positions)
|
1374 |
+
end_loss = loss_fct(end_logits, end_positions)
|
1375 |
+
total_loss = (start_loss + end_loss) / 2
|
1376 |
+
|
1377 |
+
if not return_dict:
|
1378 |
+
output = (start_logits, end_logits) + outputs[2:]
|
1379 |
+
return ((total_loss,) + output) if total_loss is not None else output
|
1380 |
+
|
1381 |
+
return QuestionAnsweringModelOutput(
|
1382 |
+
loss=total_loss,
|
1383 |
+
start_logits=start_logits,
|
1384 |
+
end_logits=end_logits,
|
1385 |
+
hidden_states=outputs.hidden_states,
|
1386 |
+
attentions=outputs.attentions,
|
1387 |
+
)
|
special_tokens_map.json
ADDED
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"</line>"
|
4 |
+
],
|
5 |
+
"bos_token": {
|
6 |
+
"content": "<s>",
|
7 |
+
"lstrip": false,
|
8 |
+
"normalized": false,
|
9 |
+
"rstrip": false,
|
10 |
+
"single_word": false
|
11 |
+
},
|
12 |
+
"eos_token": {
|
13 |
+
"content": "</s>",
|
14 |
+
"lstrip": false,
|
15 |
+
"normalized": false,
|
16 |
+
"rstrip": false,
|
17 |
+
"single_word": false
|
18 |
+
},
|
19 |
+
"pad_token": {
|
20 |
+
"content": "<pad>",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false
|
25 |
+
},
|
26 |
+
"unk_token": {
|
27 |
+
"content": "<unk>",
|
28 |
+
"lstrip": false,
|
29 |
+
"normalized": false,
|
30 |
+
"rstrip": false,
|
31 |
+
"single_word": false
|
32 |
+
}
|
33 |
+
}
|
tokenization_bitnet.py
ADDED
@@ -0,0 +1,482 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
5 |
+
# and OPT implementations in this library. It has been modified from its
|
6 |
+
# original forms to accommodate minor architectural differences compared
|
7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
|
21 |
+
"""Tokenization classes for LLaMA."""
|
22 |
+
import os
|
23 |
+
from shutil import copyfile
|
24 |
+
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
|
25 |
+
|
26 |
+
import sentencepiece as spm
|
27 |
+
|
28 |
+
from transformers.convert_slow_tokenizer import import_protobuf
|
29 |
+
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
|
30 |
+
from transformers.utils import logging
|
31 |
+
|
32 |
+
|
33 |
+
if TYPE_CHECKING:
|
34 |
+
from transformers.tokenization_utils_base import TextInput
|
35 |
+
|
36 |
+
logger = logging.get_logger(__name__)
|
37 |
+
|
38 |
+
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
|
39 |
+
|
40 |
+
PRETRAINED_VOCAB_FILES_MAP = {
|
41 |
+
"vocab_file": {
|
42 |
+
"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
|
43 |
+
},
|
44 |
+
"tokenizer_file": {
|
45 |
+
"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
|
46 |
+
},
|
47 |
+
}
|
48 |
+
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
|
49 |
+
"hf-internal-testing/llama-tokenizer": 2048,
|
50 |
+
}
|
51 |
+
SPIECE_UNDERLINE = "▁"
|
52 |
+
|
53 |
+
B_INST, E_INST = "[INST]", "[/INST]"
|
54 |
+
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
|
55 |
+
|
56 |
+
# fmt: off
|
57 |
+
DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
|
58 |
+
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
|
59 |
+
that your responses are socially unbiased and positive in nature.
|
60 |
+
|
61 |
+
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
|
62 |
+
correct. If you don't know the answer to a question, please don't share false information."""
|
63 |
+
# fmt: on
|
64 |
+
|
65 |
+
|
66 |
+
class BitnetTokenizer(PreTrainedTokenizer):
|
67 |
+
"""
|
68 |
+
Construct a Bitnet tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
|
69 |
+
no padding token in the original model.
|
70 |
+
|
71 |
+
Args:
|
72 |
+
vocab_file (`str`):
|
73 |
+
Path to the vocabulary file.
|
74 |
+
unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
|
75 |
+
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
76 |
+
token instead.
|
77 |
+
bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
|
78 |
+
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
|
79 |
+
eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
|
80 |
+
The end of sequence token.
|
81 |
+
pad_token (`str` or `tokenizers.AddedToken`, *optional*):
|
82 |
+
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
|
83 |
+
attention mechanisms or loss computation.
|
84 |
+
sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
|
85 |
+
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
|
86 |
+
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
|
87 |
+
to set:
|
88 |
+
|
89 |
+
- `enable_sampling`: Enable subword regularization.
|
90 |
+
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
|
91 |
+
|
92 |
+
- `nbest_size = {0,1}`: No sampling is performed.
|
93 |
+
- `nbest_size > 1`: samples from the nbest_size results.
|
94 |
+
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
|
95 |
+
using forward-filtering-and-backward-sampling algorithm.
|
96 |
+
|
97 |
+
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
|
98 |
+
BPE-dropout.
|
99 |
+
|
100 |
+
add_bos_token (`bool`, *optional*, defaults to `True`):
|
101 |
+
Whether or not to add an `bos_token` at the start of sequences.
|
102 |
+
add_eos_token (`bool`, *optional*, defaults to `False`):
|
103 |
+
Whether or not to add an `eos_token` at the end of sequences.
|
104 |
+
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
|
105 |
+
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
|
106 |
+
extra spaces.
|
107 |
+
use_default_system_prompt (`bool`, *optional*, defaults to `False`):
|
108 |
+
Whether or not the default system prompt for Bitnet should be used.
|
109 |
+
spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
|
110 |
+
Whether or not to add spaces between special tokens.
|
111 |
+
legacy (`bool`, *optional*):
|
112 |
+
Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
|
113 |
+
and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple
|
114 |
+
example:
|
115 |
+
|
116 |
+
- `legacy=True`:
|
117 |
+
```python
|
118 |
+
>>> from transformers import T5Tokenizer
|
119 |
+
|
120 |
+
>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=True)
|
121 |
+
>>> tokenizer.encode("Hello <extra_id_0>.")
|
122 |
+
[8774, 32099, 3, 5, 1]
|
123 |
+
```
|
124 |
+
- `legacy=False`:
|
125 |
+
```python
|
126 |
+
>>> from transformers import T5Tokenizer
|
127 |
+
|
128 |
+
>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False)
|
129 |
+
>>> tokenizer.encode("Hello <extra_id_0>.") # the extra space `[3]` is no longer here
|
130 |
+
[8774, 32099, 5, 1]
|
131 |
+
```
|
132 |
+
Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
|
133 |
+
add_prefix_space (`bool`, *optional*, defaults to `True`):
|
134 |
+
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
|
135 |
+
other word.
|
136 |
+
|
137 |
+
"""
|
138 |
+
|
139 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
140 |
+
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
141 |
+
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
142 |
+
model_input_names = ["input_ids", "attention_mask"]
|
143 |
+
|
144 |
+
def __init__(
|
145 |
+
self,
|
146 |
+
vocab_file,
|
147 |
+
unk_token="<unk>",
|
148 |
+
bos_token="<s>",
|
149 |
+
eos_token="</s>",
|
150 |
+
pad_token=None,
|
151 |
+
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
152 |
+
add_bos_token=True,
|
153 |
+
add_eos_token=False,
|
154 |
+
clean_up_tokenization_spaces=False,
|
155 |
+
use_default_system_prompt=False,
|
156 |
+
spaces_between_special_tokens=False,
|
157 |
+
legacy=None,
|
158 |
+
add_prefix_space=True,
|
159 |
+
**kwargs,
|
160 |
+
):
|
161 |
+
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
162 |
+
bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
|
163 |
+
eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
|
164 |
+
unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
|
165 |
+
pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
|
166 |
+
|
167 |
+
if legacy is None:
|
168 |
+
logger.warning_once(
|
169 |
+
f"You are using the default legacy behaviour of the {self.__class__}. This is"
|
170 |
+
" expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
|
171 |
+
" If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
|
172 |
+
" means, and thoroughly read the reason why this was added as explained in"
|
173 |
+
" https://github.com/huggingface/transformers/pull/24565"
|
174 |
+
)
|
175 |
+
legacy = True
|
176 |
+
|
177 |
+
self.legacy = legacy
|
178 |
+
self.vocab_file = vocab_file
|
179 |
+
self.add_bos_token = add_bos_token
|
180 |
+
self.add_eos_token = add_eos_token
|
181 |
+
self.use_default_system_prompt = use_default_system_prompt
|
182 |
+
self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
|
183 |
+
self.add_prefix_space = add_prefix_space
|
184 |
+
|
185 |
+
super().__init__(
|
186 |
+
bos_token=bos_token,
|
187 |
+
eos_token=eos_token,
|
188 |
+
unk_token=unk_token,
|
189 |
+
pad_token=pad_token,
|
190 |
+
add_bos_token=add_bos_token,
|
191 |
+
add_eos_token=add_eos_token,
|
192 |
+
sp_model_kwargs=self.sp_model_kwargs,
|
193 |
+
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
194 |
+
use_default_system_prompt=use_default_system_prompt,
|
195 |
+
spaces_between_special_tokens=spaces_between_special_tokens,
|
196 |
+
legacy=legacy,
|
197 |
+
add_prefix_space=add_prefix_space,
|
198 |
+
**kwargs,
|
199 |
+
)
|
200 |
+
|
201 |
+
@property
|
202 |
+
def unk_token_length(self):
|
203 |
+
return len(self.sp_model.encode(str(self.unk_token)))
|
204 |
+
|
205 |
+
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
|
206 |
+
def get_spm_processor(self, from_slow=False):
|
207 |
+
tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
208 |
+
if self.legacy or from_slow: # no dependency on protobuf
|
209 |
+
tokenizer.Load(self.vocab_file)
|
210 |
+
return tokenizer
|
211 |
+
|
212 |
+
with open(self.vocab_file, "rb") as f:
|
213 |
+
sp_model = f.read()
|
214 |
+
model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
|
215 |
+
model = model_pb2.ModelProto.FromString(sp_model)
|
216 |
+
normalizer_spec = model_pb2.NormalizerSpec()
|
217 |
+
normalizer_spec.add_dummy_prefix = False
|
218 |
+
model.normalizer_spec.MergeFrom(normalizer_spec)
|
219 |
+
sp_model = model.SerializeToString()
|
220 |
+
tokenizer.LoadFromSerializedProto(sp_model)
|
221 |
+
return tokenizer
|
222 |
+
|
223 |
+
def __getstate__(self):
|
224 |
+
state = self.__dict__.copy()
|
225 |
+
state["sp_model"] = None
|
226 |
+
state["sp_model_proto"] = self.sp_model.serialized_model_proto()
|
227 |
+
return state
|
228 |
+
|
229 |
+
def __setstate__(self, d):
|
230 |
+
self.__dict__ = d
|
231 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
232 |
+
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
|
233 |
+
|
234 |
+
@property
|
235 |
+
def vocab_size(self):
|
236 |
+
"""Returns vocab size"""
|
237 |
+
return self.sp_model.get_piece_size()
|
238 |
+
|
239 |
+
def get_vocab(self):
|
240 |
+
"""Returns vocab as a dict"""
|
241 |
+
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
242 |
+
vocab.update(self.added_tokens_encoder)
|
243 |
+
return vocab
|
244 |
+
|
245 |
+
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
|
246 |
+
def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
|
247 |
+
"""
|
248 |
+
Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
|
249 |
+
first token is special.
|
250 |
+
"""
|
251 |
+
if self.legacy or len(text) == 0:
|
252 |
+
return super().tokenize(text, **kwargs)
|
253 |
+
|
254 |
+
text = text.replace(SPIECE_UNDERLINE, " ")
|
255 |
+
if self.add_prefix_space:
|
256 |
+
text = SPIECE_UNDERLINE + text
|
257 |
+
|
258 |
+
tokens = super().tokenize(text, **kwargs)
|
259 |
+
|
260 |
+
if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
|
261 |
+
tokens = tokens[1:]
|
262 |
+
return tokens
|
263 |
+
|
264 |
+
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
|
265 |
+
def _tokenize(self, text, **kwargs):
|
266 |
+
"""
|
267 |
+
Returns a tokenized string.
|
268 |
+
|
269 |
+
We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
|
270 |
+
SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
|
271 |
+
`['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
|
272 |
+
`unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
|
273 |
+
`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
|
274 |
+
"""
|
275 |
+
tokens = self.sp_model.encode(text, out_type=str)
|
276 |
+
if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
|
277 |
+
return tokens
|
278 |
+
|
279 |
+
# 1. Encode string + prefix ex: "<unk> Hey"
|
280 |
+
tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
|
281 |
+
# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
|
282 |
+
return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
|
283 |
+
|
284 |
+
def _convert_token_to_id(self, token):
|
285 |
+
"""Converts a token (str) in an id using the vocab."""
|
286 |
+
return self.sp_model.piece_to_id(token)
|
287 |
+
|
288 |
+
def _convert_id_to_token(self, index):
|
289 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
290 |
+
token = self.sp_model.IdToPiece(index)
|
291 |
+
return token
|
292 |
+
|
293 |
+
def convert_tokens_to_string(self, tokens):
|
294 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
295 |
+
# since we manually add the prefix space, we have to remove it when decoding
|
296 |
+
if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
|
297 |
+
tokens[0] = tokens[0][1:]
|
298 |
+
|
299 |
+
current_sub_tokens = []
|
300 |
+
out_string = ""
|
301 |
+
prev_is_special = False
|
302 |
+
for i, token in enumerate(tokens):
|
303 |
+
# make sure that special tokens are not decoded using sentencepiece model
|
304 |
+
if token in self.all_special_tokens:
|
305 |
+
if not prev_is_special and i != 0 and self.legacy:
|
306 |
+
out_string += " "
|
307 |
+
out_string += self.sp_model.decode(current_sub_tokens) + token
|
308 |
+
prev_is_special = True
|
309 |
+
current_sub_tokens = []
|
310 |
+
else:
|
311 |
+
current_sub_tokens.append(token)
|
312 |
+
prev_is_special = False
|
313 |
+
out_string += self.sp_model.decode(current_sub_tokens)
|
314 |
+
return out_string
|
315 |
+
|
316 |
+
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
317 |
+
"""
|
318 |
+
Save the vocabulary and special tokens file to a directory.
|
319 |
+
|
320 |
+
Args:
|
321 |
+
save_directory (`str`):
|
322 |
+
The directory in which to save the vocabulary.
|
323 |
+
|
324 |
+
Returns:
|
325 |
+
`Tuple(str)`: Paths to the files saved.
|
326 |
+
"""
|
327 |
+
if not os.path.isdir(save_directory):
|
328 |
+
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
329 |
+
return
|
330 |
+
out_vocab_file = os.path.join(
|
331 |
+
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
332 |
+
)
|
333 |
+
|
334 |
+
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
335 |
+
copyfile(self.vocab_file, out_vocab_file)
|
336 |
+
elif not os.path.isfile(self.vocab_file):
|
337 |
+
with open(out_vocab_file, "wb") as fi:
|
338 |
+
content_spiece_model = self.sp_model.serialized_model_proto()
|
339 |
+
fi.write(content_spiece_model)
|
340 |
+
|
341 |
+
return (out_vocab_file,)
|
342 |
+
|
343 |
+
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
344 |
+
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
345 |
+
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
346 |
+
|
347 |
+
output = bos_token_id + token_ids_0 + eos_token_id
|
348 |
+
|
349 |
+
if token_ids_1 is not None:
|
350 |
+
output = output + bos_token_id + token_ids_1 + eos_token_id
|
351 |
+
|
352 |
+
return output
|
353 |
+
|
354 |
+
def get_special_tokens_mask(
|
355 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
356 |
+
) -> List[int]:
|
357 |
+
"""
|
358 |
+
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
359 |
+
special tokens using the tokenizer `prepare_for_model` method.
|
360 |
+
|
361 |
+
Args:
|
362 |
+
token_ids_0 (`List[int]`):
|
363 |
+
List of IDs.
|
364 |
+
token_ids_1 (`List[int]`, *optional*):
|
365 |
+
Optional second list of IDs for sequence pairs.
|
366 |
+
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
367 |
+
Whether or not the token list is already formatted with special tokens for the model.
|
368 |
+
|
369 |
+
Returns:
|
370 |
+
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
371 |
+
"""
|
372 |
+
if already_has_special_tokens:
|
373 |
+
return super().get_special_tokens_mask(
|
374 |
+
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
375 |
+
)
|
376 |
+
|
377 |
+
bos_token_id = [1] if self.add_bos_token else []
|
378 |
+
eos_token_id = [1] if self.add_eos_token else []
|
379 |
+
|
380 |
+
if token_ids_1 is None:
|
381 |
+
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
|
382 |
+
return (
|
383 |
+
bos_token_id
|
384 |
+
+ ([0] * len(token_ids_0))
|
385 |
+
+ eos_token_id
|
386 |
+
+ bos_token_id
|
387 |
+
+ ([0] * len(token_ids_1))
|
388 |
+
+ eos_token_id
|
389 |
+
)
|
390 |
+
|
391 |
+
def create_token_type_ids_from_sequences(
|
392 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
393 |
+
) -> List[int]:
|
394 |
+
"""
|
395 |
+
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
|
396 |
+
sequence pair mask has the following format:
|
397 |
+
|
398 |
+
```
|
399 |
+
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
400 |
+
| first sequence | second sequence |
|
401 |
+
```
|
402 |
+
|
403 |
+
if token_ids_1 is None, only returns the first portion of the mask (0s).
|
404 |
+
|
405 |
+
Args:
|
406 |
+
token_ids_0 (`List[int]`):
|
407 |
+
List of ids.
|
408 |
+
token_ids_1 (`List[int]`, *optional*):
|
409 |
+
Optional second list of IDs for sequence pairs.
|
410 |
+
|
411 |
+
Returns:
|
412 |
+
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
413 |
+
"""
|
414 |
+
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
415 |
+
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
416 |
+
|
417 |
+
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
|
418 |
+
|
419 |
+
if token_ids_1 is not None:
|
420 |
+
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
|
421 |
+
|
422 |
+
return output
|
423 |
+
|
424 |
+
@property
|
425 |
+
def default_chat_template(self):
|
426 |
+
"""
|
427 |
+
LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
|
428 |
+
Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
|
429 |
+
user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
|
430 |
+
rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
|
431 |
+
results in an unusual token ordering when it is present. This template should definitely be changed if you wish
|
432 |
+
to fine-tune a model with more flexible role ordering!
|
433 |
+
|
434 |
+
The output should look something like:
|
435 |
+
|
436 |
+
<bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
|
437 |
+
<bos>[INST] Prompt [/INST]
|
438 |
+
|
439 |
+
The reference for this chat template is [this code
|
440 |
+
snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
|
441 |
+
in the original repository.
|
442 |
+
"""
|
443 |
+
logger.warning_once(
|
444 |
+
"\nNo chat template is defined for this tokenizer - using the default template "
|
445 |
+
f"for the {self.__class__.__name__} class. If the default is not appropriate for "
|
446 |
+
"your model, please set `tokenizer.chat_template` to an appropriate template. "
|
447 |
+
"See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
|
448 |
+
)
|
449 |
+
template = (
|
450 |
+
"{% if messages[0]['role'] == 'system' %}"
|
451 |
+
"{% set loop_messages = messages[1:] %}" # Extract system message if it's present
|
452 |
+
"{% set system_message = messages[0]['content'] %}"
|
453 |
+
"{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
|
454 |
+
"{% set loop_messages = messages %}" # Or use the default system message if the flag is set
|
455 |
+
"{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
|
456 |
+
"{% else %}"
|
457 |
+
"{% set loop_messages = messages %}"
|
458 |
+
"{% set system_message = false %}"
|
459 |
+
"{% endif %}"
|
460 |
+
"{% for message in loop_messages %}" # Loop over all non-system messages
|
461 |
+
"{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
|
462 |
+
"{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
|
463 |
+
"{% endif %}"
|
464 |
+
"{% if loop.index0 == 0 and system_message != false %}" # Embed system message in first message
|
465 |
+
"{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
|
466 |
+
"{% else %}"
|
467 |
+
"{% set content = message['content'] %}"
|
468 |
+
"{% endif %}"
|
469 |
+
"{% if message['role'] == 'user' %}" # After all of that, handle messages/roles in a fairly normal way
|
470 |
+
"{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
|
471 |
+
"{% elif message['role'] == 'system' %}"
|
472 |
+
"{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
|
473 |
+
"{% elif message['role'] == 'assistant' %}"
|
474 |
+
"{{ ' ' + content.strip() + ' ' + eos_token }}"
|
475 |
+
"{% endif %}"
|
476 |
+
"{% endfor %}"
|
477 |
+
)
|
478 |
+
template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
|
479 |
+
default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
|
480 |
+
template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
|
481 |
+
|
482 |
+
return template
|
tokenizer (1).model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
3 |
+
size 499723
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
3 |
+
size 499723
|
tokenizer_config.json
ADDED
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"add_bos_token": true,
|
3 |
+
"add_eos_token": false,
|
4 |
+
"add_prefix_space": true,
|
5 |
+
"added_tokens_decoder": {
|
6 |
+
"0": {
|
7 |
+
"content": "<unk>",
|
8 |
+
"lstrip": false,
|
9 |
+
"normalized": false,
|
10 |
+
"rstrip": false,
|
11 |
+
"single_word": false,
|
12 |
+
"special": true
|
13 |
+
},
|
14 |
+
"1": {
|
15 |
+
"content": "<s>",
|
16 |
+
"lstrip": false,
|
17 |
+
"normalized": false,
|
18 |
+
"rstrip": false,
|
19 |
+
"single_word": false,
|
20 |
+
"special": true
|
21 |
+
},
|
22 |
+
"2": {
|
23 |
+
"content": "</s>",
|
24 |
+
"lstrip": false,
|
25 |
+
"normalized": false,
|
26 |
+
"rstrip": false,
|
27 |
+
"single_word": false,
|
28 |
+
"special": true
|
29 |
+
},
|
30 |
+
"32000": {
|
31 |
+
"content": "<pad>",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false,
|
36 |
+
"special": true
|
37 |
+
},
|
38 |
+
"32001": {
|
39 |
+
"content": "</line>",
|
40 |
+
"lstrip": false,
|
41 |
+
"normalized": false,
|
42 |
+
"rstrip": false,
|
43 |
+
"single_word": false,
|
44 |
+
"special": true
|
45 |
+
}
|
46 |
+
},
|
47 |
+
"additional_special_tokens": [
|
48 |
+
"</line>"
|
49 |
+
],
|
50 |
+
"bos_token": "<s>",
|
51 |
+
"clean_up_tokenization_spaces": false,
|
52 |
+
"eos_token": "</s>",
|
53 |
+
"legacy": false,
|
54 |
+
"model_max_length": 1000000000000000019884624838656,
|
55 |
+
"pad_token": "<pad>",
|
56 |
+
"padding_side": "right",
|
57 |
+
"sp_model_kwargs": {},
|
58 |
+
"spaces_between_special_tokens": false,
|
59 |
+
"tokenizer_class": "BitnetTokenizer",
|
60 |
+
"unk_token": "<unk>",
|
61 |
+
"use_default_system_prompt": false
|
62 |
+
}
|
utils_quant.py
ADDED
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
|
5 |
+
|
6 |
+
def weight_quant(weight, num_bits=1):
|
7 |
+
dtype = weight.dtype
|
8 |
+
weight = weight.float()
|
9 |
+
s = 1 / weight.abs().mean().clamp(min=1e-5)
|
10 |
+
result = (weight * s).round().clamp(-1, 1) / s
|
11 |
+
return result.type(dtype)
|
12 |
+
|
13 |
+
|
14 |
+
def activation_quant(x, num_bits=8):
|
15 |
+
dtype = x.dtype
|
16 |
+
x = x.float()
|
17 |
+
Qn = -2 ** (num_bits - 1)
|
18 |
+
Qp = 2 ** (num_bits - 1) - 1
|
19 |
+
s = Qp / x.abs().max(dim=-1, keepdim=True).values.clamp(min=1e-5)
|
20 |
+
result = (x * s).round().clamp(Qn, Qp) / s
|
21 |
+
return result.type(dtype)
|
22 |
+
|
23 |
+
|
24 |
+
class BitLinear(nn.Linear):
|
25 |
+
|
26 |
+
def __init__(self,
|
27 |
+
*kargs,
|
28 |
+
weight_bits=1,
|
29 |
+
input_bits=8,
|
30 |
+
**kwargs
|
31 |
+
):
|
32 |
+
super(BitLinear, self).__init__(*kargs, **kwargs)
|
33 |
+
"""
|
34 |
+
RMSNorm is placed outside BitLinear
|
35 |
+
"""
|
36 |
+
self.weight_bits = weight_bits
|
37 |
+
self.input_bits = input_bits
|
38 |
+
|
39 |
+
def forward(self, input):
|
40 |
+
|
41 |
+
quant_input = input + (activation_quant(input, self.input_bits) - input).detach()
|
42 |
+
quant_weight = self.weight + (weight_quant(self.weight, self.weight_bits) - self.weight).detach()
|
43 |
+
|
44 |
+
out = nn.functional.linear(quant_input, quant_weight)
|
45 |
+
if not self.bias is None:
|
46 |
+
out += self.bias.view(1, -1).expand_as(out)
|
47 |
+
|
48 |
+
return out
|