1530 lines
59 KiB
Python
1530 lines
59 KiB
Python
import os
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple, Union
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import json
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import tempfile
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from functools import partial
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import numpy as np
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import jax
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import jax.numpy as jnp
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from jax import lax
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from jax.sharding import PartitionSpec as PS
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import flax.linen as nn
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from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
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from flax.linen import combine_masks, make_causal_mask
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from flax.linen.attention import dot_product_attention_weights
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from flax.traverse_util import flatten_dict, unflatten_dict
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from flax.linen import partitioning as nn_partitioning
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import einops
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import sentencepiece as spm
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from transformers import AutoTokenizer
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from transformers.convert_slow_tokenizer import import_protobuf
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
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from transformers.modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
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from transformers.modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
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from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
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from ml_collections import ConfigDict
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from ml_collections.config_dict import config_dict
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from mlxu import function_args_to_config, load_pickle, open_file
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from EasyLM.bpt import blockwise_ffn, blockwise_attn
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from EasyLM.jax_utils import (
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with_sharding_constraint, get_jax_mesh, get_gradient_checkpoint_policy
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)
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LLAMA_STANDARD_CONFIGS = {
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'small': {
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'vocab_size': 64256,
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'hidden_size': 768,
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'intermediate_size': 3072,
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'num_hidden_layers': 12,
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'num_attention_heads': 12,
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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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'medium': {
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'vocab_size': 64256,
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'hidden_size': 1024,
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'intermediate_size': 4096,
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'num_hidden_layers': 24,
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'num_attention_heads': 16,
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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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'large': {
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'vocab_size': 64256,
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'hidden_size': 1536,
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'intermediate_size': 6144,
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'num_hidden_layers': 24,
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'num_attention_heads': 16,
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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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'xlarge': {
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'vocab_size': 64256,
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'hidden_size': 2048,
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'intermediate_size': 8192,
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'num_hidden_layers': 24,
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'num_attention_heads': 32,
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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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'1b': {
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'vocab_size': 64256,
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'hidden_size': 2048,
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'intermediate_size': 5504,
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'num_hidden_layers': 22,
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'num_attention_heads': 16,
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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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'3b': {
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'vocab_size': 64256,
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'hidden_size': 3200,
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'intermediate_size': 8640,
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'num_hidden_layers': 26,
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'num_attention_heads': 32,
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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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'7b': {
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'vocab_size': 64256,
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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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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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'13b': {
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'vocab_size': 64256,
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'hidden_size': 5120,
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'intermediate_size': 13824,
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'num_hidden_layers': 40,
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'num_attention_heads': 40,
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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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'30b': {
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'vocab_size': 64256,
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'hidden_size': 6656,
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'intermediate_size': 17920,
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'num_hidden_layers': 60,
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'num_attention_heads': 52,
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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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'65b': {
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'vocab_size': 64256,
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'hidden_size': 8192,
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'intermediate_size': 22016,
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'num_hidden_layers': 80,
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'num_attention_heads': 64,
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'max_sequence_length': 2048,
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'initializer_range': 0.02,
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'rms_norm_eps': 1e-5,
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'use_cache': True,
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'tie_word_embeddings': False,
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},
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'debug': { # A small model for debugging
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'vocab_size': 64256,
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'hidden_size': 128,
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'intermediate_size': 256,
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'num_hidden_layers': 2,
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'num_attention_heads': 4,
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'max_sequence_length': 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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'tie_word_embeddings': False,
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},
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}
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class LLaMAConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`~LLaMAModel`]. 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 [`~LLaMAModel`] or [`~TFLLaMAModel`].
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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 encoder.
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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 encoder.
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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_sequence_length (`int`, *optional*, defaults to 2048):
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Max sequence length for model (for RoPE computation)
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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-12):
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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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tie_word_embeddings(`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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Example:
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```python
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>>> from transformers import LLaMAModel, LLaMAConfig
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>>> # Initializing a LLaMA llama-7b style configuration
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>>> configuration = LLaMAConfig()
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>>> # Initializing a model from the llama-7b style configuration
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>>> model = LLaMAModel(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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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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max_sequence_length=2048,
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rms_norm_eps=1e-6,
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initializer_range=0.02,
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use_cache=True,
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# pad_token_id=-1,
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bos_token_id=0,
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eos_token_id=1,
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attn_pdrop=0.0,
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tie_word_embeddings=False,
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remat_block='nothing_saveable',
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remat_attention='',
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remat_mlp='',
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scan_attention=False,
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scan_mlp=False,
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scan_query_chunk_size=1024,
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scan_key_chunk_size=1024,
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scan_mlp_chunk_size=1024,
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fcm_min_ratio=0.0,
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fcm_max_ratio=0.0,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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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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self.max_sequence_length = max_sequence_length
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attn_pdrop = attn_pdrop
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self.remat_block = remat_block
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self.remat_attention = remat_attention
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self.remat_mlp = remat_mlp
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self.scan_attention = scan_attention
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self.scan_mlp = scan_mlp
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self.scan_query_chunk_size = scan_query_chunk_size
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self.scan_key_chunk_size = scan_key_chunk_size
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self.scan_mlp_chunk_size = scan_mlp_chunk_size
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self.fcm_min_ratio = fcm_min_ratio
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self.fcm_max_ratio = fcm_max_ratio
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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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@classmethod
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def get_default_config(cls, updates=None):
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config = function_args_to_config(cls.__init__)
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if updates is not None:
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config.update(ConfigDict(updates).copy_and_resolve_references())
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return config
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@staticmethod
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def get_jax_mesh(axis_dims):
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return get_jax_mesh(axis_dims, ('dp', 'fsdp', 'mp'))
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@staticmethod
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def get_partition_rules():
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""" Parition rules for GPTJ. Note that these rules are orderd, so that
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the beginning rules match first. It is important to use
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PartitionSpec() instead of None here because JAX does not treat
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None as a pytree leaf.
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"""
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return (
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# embeddings
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("transformer/wte/embedding", PS("mp", "fsdp")),
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# atention
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("attention/(wq|wk|wv)/kernel", PS("fsdp", "mp")),
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("attention/wo/kernel", PS("mp", "fsdp")),
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# mlp
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("feed_forward/w1/kernel", PS("fsdp", "mp")),
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("feed_forward/w2/kernel", PS("mp", "fsdp")),
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("feed_forward/w3/kernel", PS("fsdp", "mp")),
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# layer norms
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("attention_norm/kernel", PS(None)),
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("ffn_norm/kernel", PS(None)),
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# output head
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("transformer/ln_f/kernel", PS(None)),
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("lm_head/kernel", PS("fsdp", "mp")),
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('.*', PS(None)),
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)
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@staticmethod
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def get_weight_decay_exclusions():
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return (
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"attention_norm/kernel",
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"ffn_norm/kernel",
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"transformer/ln_f/kernel",
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)
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@staticmethod
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def rng_keys():
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return ('params', 'dropout', 'fcm')
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@staticmethod
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def get_tokenizer_config(updates=None):
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config = ConfigDict()
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config.vocab_file = ''
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config.pretrained_model_name_or_path = ''
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config.add_bos_token = False
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config.add_eos_token = False
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if updates is not None:
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config.update(ConfigDict(updates).copy_and_resolve_references())
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return config
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@classmethod
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def get_tokenizer(cls, config, padding_side='left', truncation_side='right'):
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config = cls.get_tokenizer_config(config)
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if config.vocab_file == '':
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assert config.pretrained_model_name_or_path != '', 'vocab_file or pretrained_model_name_or_path must be specified'
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if config.pretrained_model_name_or_path != '':
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tokenizer = AutoTokenizer.from_pretrained(
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config.pretrained_model_name_or_path,
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add_bos_token=config.add_bos_token,
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add_eos_token=config.add_eos_token,
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padding_side=padding_side,
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truncation_side=truncation_side,
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)
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else:
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tokenizer = LlamaTokenizer(
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vocab_file=config.vocab_file,
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add_bos_token=config.add_bos_token,
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add_eos_token=config.add_eos_token,
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padding_side=padding_side,
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truncation_side=truncation_side,
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)
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return tokenizer
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@classmethod
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def load_config(cls, path):
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if path in LLAMA_STANDARD_CONFIGS:
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return cls.from_dict(LLAMA_STANDARD_CONFIGS[path])
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load_type, load_path = path.split('::', 1)
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if load_type == 'pickle':
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return cls.from_dict(load_pickle(load_path)['llama_config'])
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elif load_type == 'json':
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with open_file(load_path, 'r') as fin:
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raw_config = fin.read()
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return cls.from_dict(json.loads(raw_config))
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else:
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raise ValueError(f'Unsupported load config type: {load_type}')
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remat = nn_partitioning.remat
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logger = logging.get_logger(__name__)
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class RMSNorm(nn.Module):
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dim: int
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eps: float=1e-6
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dtype: jnp.dtype=jnp.float32
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param_dtype: jnp.dtype=jnp.float32
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def setup(self) -> None:
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self.weight = self.param(
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'kernel',
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nn.initializers.ones,
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(self.dim,),
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self.param_dtype,
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)
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def _norm(self, x: jnp.ndarray) -> jnp.ndarray:
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return x * jax.lax.rsqrt(jnp.square(x).mean(-1, keepdims=True) + self.eps)
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def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
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x = x.astype(jnp.promote_types(self.dtype, jnp.float32))
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output = self._norm(x).astype(self.dtype)
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weight = jnp.asarray(self.weight, self.dtype)
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return output * weight
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def precompute_freqs_cis(dim: int, end: int, theta: float=10000.0, dtype: jnp.dtype=jnp.float32) -> jnp.ndarray:
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freqs = 1.0 / (theta ** (np.arange(0, dim, 2)[: (dim // 2)].astype(dtype) / dim))
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t = np.arange(end) # type: ignore
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freqs = np.outer(t, freqs).astype(dtype) # type: ignore
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sin, cos = np.sin(freqs), np.cos(freqs)
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freqs_cis = np.complex64(cos + 1j * sin)
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return jnp.asarray(freqs_cis)
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def apply_rotary_emb(
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xq: jnp.ndarray,
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xk: jnp.ndarray,
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freqs_cis: jnp.ndarray,
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dtype: jnp.dtype=jnp.float32,
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) -> Tuple[jnp.ndarray, jnp.ndarray]:
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reshape_xq = xq.astype(jnp.float32).reshape(*xq.shape[:-1], -1, 2)
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reshape_xk = xk.astype(jnp.float32).reshape(*xk.shape[:-1], -1, 2)
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xq_ = jax.lax.complex(reshape_xq[..., 0], reshape_xq[..., 1])
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xk_ = jax.lax.complex(reshape_xk[..., 0], reshape_xk[..., 1])
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# add head dim
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freqs_cis = jnp.reshape(freqs_cis, (*freqs_cis.shape[:2], 1, *freqs_cis.shape[2:]))
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xq_out = xq_ * freqs_cis
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xq_out = jnp.stack((jnp.real(xq_out), jnp.imag(xq_out)), axis=-1).reshape(*xq_out.shape[:-1], -1)
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xk_out = xk_ * freqs_cis
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xk_out = jnp.stack((jnp.real(xk_out), jnp.imag(xk_out)), axis=-1).reshape(*xk_out.shape[:-1], -1)
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return xq_out.astype(dtype), xk_out.astype(dtype)
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class FlaxLLaMAAttention(nn.Module):
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config: LLaMAConfig
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dtype: jnp.dtype=jnp.float32
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param_dtype: jnp.dtype=jnp.float32
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precision: Optional[Union[jax.lax.Precision, str]]=None
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def setup(self):
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config = self.config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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self.wq = nn.Dense(
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config.num_attention_heads*self.head_dim,
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dtype=self.dtype,
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param_dtype=self.param_dtype,
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use_bias=False,
|
|
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
|
|
precision=self.precision,
|
|
)
|
|
self.wk = nn.Dense(
|
|
config.num_attention_heads*self.head_dim,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
use_bias=False,
|
|
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
|
|
precision=self.precision,
|
|
)
|
|
self.wv = nn.Dense(
|
|
config.num_attention_heads*self.head_dim,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
use_bias=False,
|
|
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
|
|
precision=self.precision,
|
|
)
|
|
self.wo = nn.Dense(
|
|
config.hidden_size,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
use_bias=False,
|
|
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
|
|
precision=self.precision,
|
|
)
|
|
|
|
self.resid_dropout = nn.Dropout(rate=config.resid_pdrop)
|
|
|
|
self.causal_mask = make_causal_mask(jnp.ones((1, config.max_sequence_length), dtype="bool"), dtype="bool")
|
|
|
|
self.freqs_cis = precompute_freqs_cis(
|
|
self.head_dim,
|
|
config.max_sequence_length * 2,
|
|
dtype=self.dtype,
|
|
)
|
|
|
|
def _split_heads(self, hidden_states):
|
|
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
|
|
|
|
def _merge_heads(self, hidden_states):
|
|
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
|
|
|
|
@nn.compact
|
|
def _concatenate_to_cache(self, key, value, query, attention_mask):
|
|
"""
|
|
This function takes projected key, value states from a single input token and concatenates the states to cached
|
|
states from previous steps. This function is slighly adapted from the official Flax repository:
|
|
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
|
|
"""
|
|
# detect if we're initializing by absence of existing cache data.
|
|
is_initialized = self.has_variable("cache", "cached_key")
|
|
cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
|
|
cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
|
|
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
|
|
|
|
if is_initialized:
|
|
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
|
|
# update key, value caches with our new 1d spatial slices
|
|
cur_index = cache_index.value
|
|
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
|
|
key = lax.dynamic_update_slice(cached_key.value, key, indices)
|
|
value = lax.dynamic_update_slice(cached_value.value, value, indices)
|
|
cached_key.value = key
|
|
cached_value.value = value
|
|
num_updated_cache_vectors = query.shape[1]
|
|
cache_index.value = cache_index.value + num_updated_cache_vectors
|
|
# causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
|
|
pad_mask = jnp.broadcast_to(
|
|
jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
|
|
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
|
|
)
|
|
attention_mask = combine_masks(pad_mask, attention_mask)
|
|
return key, value, attention_mask
|
|
|
|
def __call__(
|
|
self,
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids,
|
|
deterministic: bool = True,
|
|
init_cache: bool = False,
|
|
output_attentions: bool = False,
|
|
fcm_mask=None,
|
|
):
|
|
xq, xk, xv = self.wq(hidden_states), self.wk(hidden_states), self.wv(hidden_states)
|
|
|
|
xq = with_sharding_constraint(xq, PS(("dp", "fsdp"), None, "mp"))
|
|
xk = with_sharding_constraint(xk, PS(("dp", "fsdp"), None, "mp"))
|
|
xv = with_sharding_constraint(xv, PS(("dp", "fsdp"), None, "mp"))
|
|
|
|
xq = self._split_heads(xq)
|
|
xk = self._split_heads(xk)
|
|
xv = self._split_heads(xv)
|
|
|
|
freqs_cis = jnp.take(self.freqs_cis, position_ids, axis=0)
|
|
|
|
xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis, dtype=self.dtype)
|
|
|
|
dropout_rng = None
|
|
if not deterministic and self.config.attn_pdrop > 0.0:
|
|
dropout_rng = self.make_rng("dropout")
|
|
|
|
if self.config.scan_attention and not (self.has_variable("cache", "cached_key") or init_cache):
|
|
# doesn't need blockwise attention if we are doing autoregressive decoding since no quadratic memory
|
|
|
|
# attention mask without nxn materlization, blockwise_attn will handle the rest
|
|
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
|
|
# transform boolean mask into float mask
|
|
attention_bias = lax.select(
|
|
attention_mask > 0,
|
|
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
|
|
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
|
|
)
|
|
attn_weights = None
|
|
attn_output = blockwise_attn(
|
|
xq,
|
|
xk,
|
|
xv,
|
|
bias=attention_bias,
|
|
deterministic=deterministic,
|
|
dropout_rng=dropout_rng,
|
|
attn_pdrop=self.config.attn_pdrop,
|
|
causal=True,
|
|
query_chunk_size=self.config.scan_query_chunk_size,
|
|
key_chunk_size=self.config.scan_key_chunk_size,
|
|
dtype=self.dtype,
|
|
policy=get_gradient_checkpoint_policy('nothing_saveable'),
|
|
precision=self.precision,
|
|
float32_logits=True,
|
|
prevent_cse=True,
|
|
)
|
|
attn_output = with_sharding_constraint(attn_output, PS(("dp", "fsdp"), None, "mp", None))
|
|
else:
|
|
query_length, key_length = xq.shape[1], xk.shape[1]
|
|
|
|
if self.has_variable("cache", "cached_key"):
|
|
mask_shift = self.variables["cache"]["cache_index"]
|
|
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
|
|
causal_mask = lax.dynamic_slice(
|
|
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
|
|
)
|
|
else:
|
|
causal_mask = self.causal_mask[:, :, :query_length, :key_length]
|
|
|
|
batch_size = hidden_states.shape[0]
|
|
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
|
|
|
|
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
|
|
attention_mask = combine_masks(attention_mask, causal_mask, fcm_mask)
|
|
|
|
# During fast autoregressive decoding, we feed one position at a time,
|
|
# and cache the keys and values step by step.
|
|
if self.has_variable("cache", "cached_key") or init_cache:
|
|
xk, xv, attention_mask = self._concatenate_to_cache(xk, xv, xq, attention_mask)
|
|
|
|
# transform boolean mask into float mask
|
|
attention_bias = lax.select(
|
|
attention_mask > 0,
|
|
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
|
|
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
|
|
)
|
|
attn_weights = dot_product_attention_weights(
|
|
xq,
|
|
xk,
|
|
bias=attention_bias,
|
|
dropout_rng=dropout_rng,
|
|
dropout_rate=self.config.attn_pdrop,
|
|
deterministic=deterministic,
|
|
dtype=jnp.promote_types(self.dtype, jnp.float32),
|
|
precision=self.precision,
|
|
)
|
|
attn_weights = with_sharding_constraint(attn_weights, PS(("dp", "fsdp"), "mp", None, None))
|
|
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, xv, precision=self.precision)
|
|
|
|
attn_output = self._merge_heads(attn_output)
|
|
attn_output = self.wo(attn_output)
|
|
attn_output = self.resid_dropout(attn_output, deterministic=deterministic)
|
|
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
|
|
return outputs
|
|
|
|
|
|
class FlaxLLaMAMLP(nn.Module):
|
|
config: LLaMAConfig
|
|
dtype: jnp.dtype=jnp.float32
|
|
param_dtype: jnp.dtype=jnp.float32
|
|
precision: Optional[Union[jax.lax.Precision, str]]=None
|
|
|
|
def setup(self) -> None:
|
|
config = self.config
|
|
|
|
self.w1 = nn.Dense(
|
|
config.intermediate_size,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
use_bias=False,
|
|
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
|
|
precision=self.precision,
|
|
)
|
|
self.w2 = nn.Dense(
|
|
config.hidden_size,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
use_bias=False,
|
|
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
|
|
precision=self.precision,
|
|
)
|
|
self.w3 = nn.Dense(
|
|
config.intermediate_size,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
use_bias=False,
|
|
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
|
|
precision=self.precision,
|
|
)
|
|
self.dropout = nn.Dropout(rate=self.config.resid_pdrop)
|
|
|
|
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
|
|
x = self.w2(nn.silu(self.w1(x)) * self.w3(x))
|
|
x = self.dropout(x, deterministic=deterministic)
|
|
return x
|
|
|
|
|
|
class FlaxLLaMABlock(nn.Module):
|
|
config: LLaMAConfig
|
|
dtype: jnp.dtype=jnp.float32
|
|
param_dtype: jnp.dtype=jnp.float32
|
|
precision: Optional[Union[jax.lax.Precision, str]]=None
|
|
|
|
def setup(self) -> None:
|
|
attention_module = FlaxLLaMAAttention
|
|
mlp_module = FlaxLLaMAMLP
|
|
if self.config.remat_attention != '':
|
|
attention_module = remat(
|
|
FlaxLLaMAAttention, static_argnums=(3, 4, 5),
|
|
policy=get_gradient_checkpoint_policy(self.config.remat_attention),
|
|
prevent_cse=True,
|
|
)
|
|
if self.config.remat_mlp != '':
|
|
mlp_module = remat(
|
|
FlaxLLaMAMLP, static_argnums=(1,),
|
|
policy=get_gradient_checkpoint_policy(self.config.remat_mlp),
|
|
prevent_cse=True,
|
|
)
|
|
|
|
self.attention = attention_module(
|
|
self.config,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
precision=self.precision,
|
|
)
|
|
self.feed_forward = mlp_module(
|
|
self.config,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
precision=self.precision,
|
|
)
|
|
self.attention_norm = RMSNorm(
|
|
self.config.hidden_size,
|
|
eps=self.config.rms_norm_eps,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
)
|
|
self.ffn_norm = RMSNorm(
|
|
self.config.hidden_size,
|
|
eps=self.config.rms_norm_eps,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
)
|
|
|
|
def __call__(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
position_ids=None,
|
|
deterministic: bool = True,
|
|
init_cache: bool = False,
|
|
output_attentions: bool = False,
|
|
fcm_mask: Optional[jnp.ndarray] = None,
|
|
):
|
|
attn_outputs = self.attention(
|
|
self.attention_norm(hidden_states),
|
|
attention_mask,
|
|
position_ids,
|
|
deterministic,
|
|
init_cache,
|
|
output_attentions,
|
|
fcm_mask,
|
|
)
|
|
attn_output = attn_outputs[0]
|
|
hidden_states = hidden_states + attn_output
|
|
|
|
feed_forward_input = self.ffn_norm(hidden_states)
|
|
|
|
if self.config.scan_mlp:
|
|
feed_forward_hidden_states = blockwise_ffn(
|
|
self.feed_forward,
|
|
feed_forward_input,
|
|
self.config.scan_mlp_chunk_size,
|
|
deterministic,
|
|
)
|
|
else:
|
|
feed_forward_hidden_states = self.feed_forward(
|
|
feed_forward_input,
|
|
deterministic,
|
|
)
|
|
feed_forward_hidden_states = with_sharding_constraint(feed_forward_hidden_states, PS(("dp", "fsdp"), None, "mp"))
|
|
|
|
hidden_states = hidden_states + feed_forward_hidden_states
|
|
|
|
return (hidden_states,) + attn_outputs[1:]
|
|
|
|
|
|
class FlaxLLaMAPreTrainedModel(FlaxPreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = LLaMAConfig
|
|
base_model_prefix = "transformer"
|
|
module_class: nn.Module = None
|
|
|
|
def __init__(
|
|
self,
|
|
config: LLaMAConfig,
|
|
input_shape: Tuple = (1, 1),
|
|
seed: int = 0,
|
|
dtype: jnp.dtype = jnp.float32,
|
|
_do_init: bool = True,
|
|
**kwargs,
|
|
):
|
|
module = self.module_class(config=config, dtype=dtype, **kwargs)
|
|
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
|
|
|
|
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
|
|
# init input tensors
|
|
input_ids = jnp.zeros(input_shape, dtype="i4")
|
|
attention_mask = jnp.ones_like(input_ids)
|
|
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
|
|
params_rng, dropout_rng = jax.random.split(rng)
|
|
rngs = {"params": params_rng, "dropout": dropout_rng}
|
|
|
|
if self.config.add_cross_attention:
|
|
encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
|
|
encoder_attention_mask = attention_mask
|
|
module_init_outputs = self.module.init(
|
|
rngs,
|
|
input_ids,
|
|
attention_mask,
|
|
position_ids,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
return_dict=False,
|
|
)
|
|
else:
|
|
module_init_outputs = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False)
|
|
|
|
random_params = module_init_outputs["params"]
|
|
|
|
if params is not None:
|
|
random_params = flatten_dict(unfreeze(random_params))
|
|
params = flatten_dict(unfreeze(params))
|
|
for missing_key in self._missing_keys:
|
|
params[missing_key] = random_params[missing_key]
|
|
self._missing_keys = set()
|
|
return freeze(unflatten_dict(params))
|
|
else:
|
|
return random_params
|
|
|
|
def init_cache(self, batch_size, max_length):
|
|
r"""
|
|
Args:
|
|
batch_size (`int`):
|
|
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
|
|
max_length (`int`):
|
|
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
|
|
cache.
|
|
"""
|
|
# init input variables to retrieve cache
|
|
input_ids = jnp.ones((batch_size, max_length))
|
|
attention_mask = jnp.ones_like(input_ids)
|
|
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
|
|
|
|
init_variables = self.module.init(
|
|
jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
|
|
)
|
|
return init_variables["cache"]
|
|
|
|
@add_start_docstrings_to_model_forward("")
|
|
def __call__(
|
|
self,
|
|
input_ids,
|
|
attention_mask=None,
|
|
position_ids=None,
|
|
params: dict = None,
|
|
past_key_values: dict = None,
|
|
dropout_rng: jax.random.PRNGKey = None,
|
|
train: bool = False,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.return_dict
|
|
|
|
batch_size, sequence_length = input_ids.shape
|
|
|
|
if position_ids is None:
|
|
if past_key_values is not None:
|
|
raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
|
|
|
|
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
|
|
|
|
if attention_mask is None:
|
|
attention_mask = jnp.ones((batch_size, sequence_length))
|
|
|
|
# Handle any PRNG if needed
|
|
rngs = {}
|
|
if dropout_rng is not None:
|
|
rngs["dropout"] = dropout_rng
|
|
|
|
inputs = {"params": params or self.params}
|
|
|
|
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxGPTJAttention module
|
|
if past_key_values:
|
|
inputs["cache"] = past_key_values
|
|
mutable = ["cache"]
|
|
else:
|
|
mutable = False
|
|
|
|
outputs = self.module.apply(
|
|
inputs,
|
|
jnp.array(input_ids, dtype="i4"),
|
|
jnp.array(attention_mask, dtype="i4"),
|
|
jnp.array(position_ids, dtype="i4"),
|
|
not train,
|
|
False,
|
|
output_attentions,
|
|
output_hidden_states,
|
|
return_dict,
|
|
rngs=rngs,
|
|
mutable=mutable,
|
|
)
|
|
|
|
# add updated cache to model output
|
|
if past_key_values is not None and return_dict:
|
|
outputs, past_key_values = outputs
|
|
outputs["past_key_values"] = unfreeze(past_key_values["cache"])
|
|
return outputs
|
|
elif past_key_values is not None and not return_dict:
|
|
outputs, past_key_values = outputs
|
|
outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
|
|
|
|
return outputs
|
|
|
|
|
|
class FlaxLLaMABlockCollection(nn.Module):
|
|
config: LLaMAConfig
|
|
dtype: jnp.dtype = jnp.float32
|
|
param_dtype: jnp.dtype=jnp.float32
|
|
precision: Optional[Union[jax.lax.Precision, str]]=None
|
|
|
|
def setup(self):
|
|
block = FlaxLLaMABlock
|
|
if self.config.remat_block != '':
|
|
block = remat(
|
|
FlaxLLaMABlock, static_argnums=(3, 4, 5),
|
|
policy=get_gradient_checkpoint_policy(self.config.remat_block)
|
|
)
|
|
self.blocks = [
|
|
block(
|
|
self.config,
|
|
name=str(i),
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
precision=self.precision
|
|
) for i in range(self.config.num_hidden_layers)
|
|
]
|
|
|
|
def __call__(
|
|
self,
|
|
hidden_states,
|
|
attention_mask=None,
|
|
position_ids=None,
|
|
deterministic: bool = True,
|
|
init_cache: bool = False,
|
|
output_attentions: bool = False,
|
|
output_hidden_states: bool = False,
|
|
return_dict: bool = True,
|
|
):
|
|
all_attentions = () if output_attentions else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
|
|
if not deterministic and self.config.fcm_max_ratio > 0:
|
|
# Apply forgetful causal mask
|
|
batch_size, seq_length = hidden_states.shape[0], hidden_states.shape[1]
|
|
fcm_ratio = jax.random.uniform(
|
|
self.make_rng('fcm'), shape=(batch_size, 1, 1, 1),
|
|
minval=self.config.fcm_min_ratio,
|
|
maxval=self.config.fcm_max_ratio
|
|
)
|
|
fcm_mask = jax.random.uniform(
|
|
self.make_rng('fcm'),
|
|
shape=(batch_size, 1, 1, seq_length)
|
|
) > fcm_ratio
|
|
fcm_mask = fcm_mask.at[:, :, :, 0].set(True)
|
|
fcm_mask = fcm_mask.astype('bool')
|
|
else:
|
|
fcm_mask = None
|
|
|
|
for block in self.blocks:
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
layer_outputs = block(
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids,
|
|
deterministic,
|
|
init_cache,
|
|
output_attentions,
|
|
fcm_mask,
|
|
)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_attentions += (layer_outputs[1],)
|
|
|
|
# this contains possible `None` values - `FlaxGPTJModule` will filter them out
|
|
outputs = (hidden_states, all_hidden_states, all_attentions)
|
|
|
|
return outputs
|
|
|
|
|
|
class FlaxLLaMAModule(nn.Module):
|
|
config: LLaMAConfig
|
|
dtype: jnp.dtype = jnp.float32
|
|
param_dtype: jnp.dtype=jnp.float32
|
|
precision: Optional[Union[jax.lax.Precision, str]]=None
|
|
|
|
def setup(self):
|
|
self.embed_dim = self.config.hidden_size
|
|
|
|
self.wte = nn.Embed(
|
|
self.config.vocab_size,
|
|
self.config.hidden_size,
|
|
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
)
|
|
self.dropout = nn.Dropout(rate=self.config.embd_pdrop)
|
|
self.h = FlaxLLaMABlockCollection(self.config, dtype=self.dtype, param_dtype=self.param_dtype, precision=self.precision)
|
|
self.ln_f = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps, dtype=self.dtype, param_dtype=self.param_dtype)
|
|
|
|
def __call__(
|
|
self,
|
|
input_ids,
|
|
attention_mask,
|
|
position_ids,
|
|
deterministic=True,
|
|
init_cache: bool = False,
|
|
output_attentions: bool = False,
|
|
output_hidden_states: bool = False,
|
|
return_dict: bool = True,
|
|
):
|
|
input_embeds = self.wte(input_ids.astype("i4"))
|
|
|
|
hidden_states = self.dropout(input_embeds, deterministic=deterministic)
|
|
|
|
outputs = self.h(
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids=position_ids,
|
|
deterministic=deterministic,
|
|
init_cache=init_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
hidden_states = self.ln_f(hidden_states)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = outputs[1] + (hidden_states,)
|
|
outputs = (hidden_states, all_hidden_states) + outputs[2:]
|
|
else:
|
|
outputs = (hidden_states,) + outputs[1:]
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in outputs if v is not None)
|
|
|
|
return FlaxBaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=outputs[1],
|
|
attentions=outputs[-1],
|
|
)
|
|
|
|
@add_start_docstrings("", "")
|
|
class FlaxLLaMAModel(FlaxLLaMAPreTrainedModel):
|
|
module_class = FlaxLLaMAModule
|
|
|
|
# append_call_sample_docstring(
|
|
# FlaxLLaMAModel,
|
|
# _TOKENIZER_FOR_DOC,
|
|
# _CHECKPOINT_FOR_DOC,
|
|
# FlaxCausalLMOutput,
|
|
# _CONFIG_FOR_DOC,
|
|
# )
|
|
|
|
class FlaxLLaMAForCausalLMModule(nn.Module):
|
|
config: LLaMAConfig
|
|
dtype: jnp.dtype = jnp.float32
|
|
param_dtype: jnp.dtype=jnp.float32
|
|
precision: Optional[Union[jax.lax.Precision, str]]=None
|
|
|
|
def setup(self):
|
|
self.transformer = FlaxLLaMAModule(self.config, dtype=self.dtype)
|
|
self.lm_head = nn.Dense(
|
|
self.config.vocab_size,
|
|
dtype=self.dtype,
|
|
param_dtype=self.param_dtype,
|
|
use_bias=False,
|
|
kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
|
|
precision=self.precision,
|
|
)
|
|
|
|
def __call__(
|
|
self,
|
|
input_ids,
|
|
attention_mask=None,
|
|
position_ids=None,
|
|
deterministic: bool = True,
|
|
init_cache: bool = False,
|
|
output_attentions: bool = False,
|
|
output_hidden_states: bool = False,
|
|
return_dict: bool = True,
|
|
):
|
|
batch_size, seq_length = input_ids.shape
|
|
if attention_mask is None:
|
|
attention_mask = jnp.ones_like(input_ids)
|
|
if position_ids is None:
|
|
position_ids = jnp.broadcast_to(
|
|
jnp.clip(jnp.cumsum(attention_mask, axis=-1) - 1, a_min=0),
|
|
(batch_size, seq_length)
|
|
)
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask,
|
|
position_ids,
|
|
deterministic=deterministic,
|
|
init_cache=init_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
|
|
if self.config.tie_word_embeddings:
|
|
shared_kernel = self.transformer.variables["params"]["wte"]["embedding"].T
|
|
lm_logits = self.lm_head.apply({"params": {"kernel": shared_kernel}}, hidden_states)
|
|
else:
|
|
lm_logits = self.lm_head(hidden_states)
|
|
|
|
if not return_dict:
|
|
return (lm_logits,) + outputs[1:]
|
|
|
|
return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
|
|
|
|
|
|
@add_start_docstrings("", "")
|
|
class FlaxLLaMAForCausalLM(FlaxLLaMAPreTrainedModel):
|
|
module_class = FlaxLLaMAForCausalLMModule
|
|
|
|
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
|
|
# initializing the cache
|
|
batch_size, seq_length = input_ids.shape
|
|
|
|
past_key_values = self.init_cache(batch_size, max_length)
|
|
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
|
|
# But since GPTJ uses a causal mask, those positions are masked anyways.
|
|
# Thus we can create a single static attention_mask here, which is more efficient for compilation
|
|
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
|
|
if attention_mask is not None:
|
|
position_ids = attention_mask.cumsum(axis=-1) - 1
|
|
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
|
|
else:
|
|
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
|
|
|
|
return {
|
|
"past_key_values": past_key_values,
|
|
"attention_mask": extended_attention_mask,
|
|
"position_ids": position_ids,
|
|
}
|
|
|
|
def update_inputs_for_generation(self, model_outputs, model_kwargs):
|
|
model_kwargs["past_key_values"] = model_outputs.past_key_values
|
|
model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
|
|
return model_kwargs
|
|
|
|
# append_call_sample_docstring(
|
|
# FlaxGPTJForCausalLM,
|
|
# _TOKENIZER_FOR_DOC,
|
|
# _CHECKPOINT_FOR_DOC,
|
|
# FlaxCausalLMOutput,
|
|
# _CONFIG_FOR_DOC,
|
|
# )
|
|
|
|
|
|
|
|
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
|
|
|
|
PRETRAINED_VOCAB_FILES_MAP = {}
|
|
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
|
|
SPIECE_UNDERLINE = "▁"
|
|
|
|
|
|
class LlamaTokenizer(PreTrainedTokenizer):
|
|
"""
|
|
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
|
|
no padding token in the original model.
|
|
|
|
Args:
|
|
vocab_file (`str`):
|
|
Path to the vocabulary file.
|
|
unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
|
|
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
|
token instead.
|
|
bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
|
|
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
|
|
eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
|
|
The end of sequence token.
|
|
pad_token (`str` or `tokenizers.AddedToken`, *optional*):
|
|
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
|
|
attention mechanisms or loss computation.
|
|
sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
|
|
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
|
|
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
|
|
to set:
|
|
|
|
- `enable_sampling`: Enable subword regularization.
|
|
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
|
|
|
|
- `nbest_size = {0,1}`: No sampling is performed.
|
|
- `nbest_size > 1`: samples from the nbest_size results.
|
|
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
|
|
using forward-filtering-and-backward-sampling algorithm.
|
|
|
|
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
|
|
BPE-dropout.
|
|
|
|
add_bos_token (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to add an `bos_token` at the start of sequences.
|
|
add_eos_token (`bool`, *optional*, defaults to `False`):
|
|
Whether or not to add an `eos_token` at the end of sequences.
|
|
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
|
|
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
|
|
extra spaces.
|
|
use_default_system_prompt (`bool`, *optional*, defaults to `False`):
|
|
Whether or not the default system prompt for Llama should be used.
|
|
spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
|
|
Whether or not to add spaces between special tokens.
|
|
legacy (`bool`, *optional*):
|
|
Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
|
|
and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple
|
|
example:
|
|
|
|
- `legacy=True`:
|
|
```python
|
|
>>> from transformers import T5Tokenizer
|
|
|
|
>>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=True)
|
|
>>> tokenizer.encode("Hello <extra_id_0>.")
|
|
[8774, 32099, 3, 5, 1]
|
|
```
|
|
- `legacy=False`:
|
|
```python
|
|
>>> from transformers import T5Tokenizer
|
|
|
|
>>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False)
|
|
>>> tokenizer.encode("Hello <extra_id_0>.") # the extra space `[3]` is no longer here
|
|
[8774, 32099, 5, 1]
|
|
```
|
|
Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
|
|
|
|
"""
|
|
|
|
vocab_files_names = VOCAB_FILES_NAMES
|
|
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
|
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
|
model_input_names = ["input_ids", "attention_mask"]
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_file,
|
|
unk_token="<unk>",
|
|
bos_token="<s>",
|
|
eos_token="</s>",
|
|
pad_token=None,
|
|
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
|
add_bos_token=False,
|
|
add_eos_token=False,
|
|
clean_up_tokenization_spaces=False,
|
|
use_default_system_prompt=False,
|
|
spaces_between_special_tokens=False,
|
|
legacy=None,
|
|
**kwargs,
|
|
):
|
|
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
|
bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
|
|
eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
|
|
unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
|
|
pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
|
|
|
|
if legacy is None:
|
|
logger.warning_once(
|
|
f"You are using the default legacy behaviour of the {self.__class__}. This is"
|
|
" expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
|
|
" If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
|
|
" means, and thoroughly read the reason why this was added as explained in"
|
|
" https://github.com/huggingface/transformers/pull/24565"
|
|
)
|
|
legacy = True
|
|
|
|
self.legacy = legacy
|
|
self.vocab_file = vocab_file
|
|
self.add_bos_token = add_bos_token
|
|
self.add_eos_token = add_eos_token
|
|
self.use_default_system_prompt = use_default_system_prompt
|
|
self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
|
|
|
|
super().__init__(
|
|
bos_token=bos_token,
|
|
eos_token=eos_token,
|
|
unk_token=unk_token,
|
|
pad_token=pad_token,
|
|
add_bos_token=add_bos_token,
|
|
add_eos_token=add_eos_token,
|
|
sp_model_kwargs=self.sp_model_kwargs,
|
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
|
use_default_system_prompt=use_default_system_prompt,
|
|
spaces_between_special_tokens=spaces_between_special_tokens,
|
|
legacy=legacy,
|
|
**kwargs,
|
|
)
|
|
|
|
@property
|
|
def unk_token_length(self):
|
|
return len(self.sp_model.encode(str(self.unk_token)))
|
|
|
|
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
|
|
def get_spm_processor(self, from_slow=False):
|
|
tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
|
if self.legacy or from_slow: # no dependency on protobuf
|
|
tokenizer.Load(self.vocab_file)
|
|
return tokenizer
|
|
|
|
with open(self.vocab_file, "rb") as f:
|
|
sp_model = f.read()
|
|
model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
|
|
model = model_pb2.ModelProto.FromString(sp_model)
|
|
normalizer_spec = model_pb2.NormalizerSpec()
|
|
normalizer_spec.add_dummy_prefix = False
|
|
model.normalizer_spec.MergeFrom(normalizer_spec)
|
|
sp_model = model.SerializeToString()
|
|
tokenizer.LoadFromSerializedProto(sp_model)
|
|
return tokenizer
|
|
|
|
def __getstate__(self):
|
|
state = self.__dict__.copy()
|
|
state["sp_model"] = None
|
|
state["sp_model_proto"] = self.sp_model.serialized_model_proto()
|
|
return state
|
|
|
|
def __setstate__(self, d):
|
|
self.__dict__ = d
|
|
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
|
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
|
|
|
|
@property
|
|
def vocab_size(self):
|
|
"""Returns vocab size"""
|
|
return self.sp_model.get_piece_size()
|
|
|
|
def get_vocab(self):
|
|
"""Returns vocab as a dict"""
|
|
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
|
vocab.update(self.added_tokens_encoder)
|
|
return vocab
|
|
|
|
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
|
|
def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> List[str]:
|
|
"""
|
|
Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
|
|
first token is special.
|
|
"""
|
|
if self.legacy or len(text) == 0:
|
|
return super().tokenize(text, **kwargs)
|
|
|
|
tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs)
|
|
|
|
if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
|
|
tokens = tokens[1:]
|
|
return tokens
|
|
|
|
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
|
|
def _tokenize(self, text, **kwargs):
|
|
"""
|
|
Returns a tokenized string.
|
|
|
|
We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
|
|
SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
|
|
`['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
|
|
`unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
|
|
`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
|
|
"""
|
|
tokens = self.sp_model.encode(text, out_type=str)
|
|
if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
|
|
return tokens
|
|
|
|
# 1. Encode string + prefix ex: "<unk> Hey"
|
|
tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
|
|
# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
|
|
return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
|
|
|
|
def _convert_token_to_id(self, token):
|
|
"""Converts a token (str) in an id using the vocab."""
|
|
return self.sp_model.piece_to_id(token)
|
|
|
|
def _convert_id_to_token(self, index):
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
|
token = self.sp_model.IdToPiece(index)
|
|
return token
|
|
|
|
def convert_tokens_to_string(self, tokens):
|
|
"""Converts a sequence of tokens (string) in a single string."""
|
|
# since we manually add the prefix space, we have to remove it when decoding
|
|
if tokens[0].startswith(SPIECE_UNDERLINE):
|
|
tokens[0] = tokens[0][1:]
|
|
|
|
current_sub_tokens = []
|
|
out_string = ""
|
|
prev_is_special = False
|
|
for i, token in enumerate(tokens):
|
|
# make sure that special tokens are not decoded using sentencepiece model
|
|
if token in self.all_special_tokens:
|
|
if not prev_is_special and i != 0 and self.legacy:
|
|
out_string += " "
|
|
out_string += self.sp_model.decode(current_sub_tokens) + token
|
|
prev_is_special = True
|
|
current_sub_tokens = []
|
|
else:
|
|
current_sub_tokens.append(token)
|
|
prev_is_special = False
|
|
out_string += self.sp_model.decode(current_sub_tokens)
|
|
return out_string
|
|
|
|
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
|
"""
|
|
Save the vocabulary and special tokens file to a directory.
|
|
|
|
Args:
|
|
save_directory (`str`):
|
|
The directory in which to save the vocabulary.
|
|
|
|
Returns:
|
|
`Tuple(str)`: Paths to the files saved.
|
|
"""
|
|
if not os.path.isdir(save_directory):
|
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
|
return
|
|
out_vocab_file = os.path.join(
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
|
)
|
|
|
|
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
|
copyfile(self.vocab_file, out_vocab_file)
|
|
elif not os.path.isfile(self.vocab_file):
|
|
with open(out_vocab_file, "wb") as fi:
|
|
content_spiece_model = self.sp_model.serialized_model_proto()
|
|
fi.write(content_spiece_model)
|
|
|
|
return (out_vocab_file,)
|
|
|
|
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
|
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
|
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
|
|
|
output = bos_token_id + token_ids_0 + eos_token_id
|
|
|
|
if token_ids_1 is not None:
|
|
output = output + bos_token_id + token_ids_1 + eos_token_id
|
|
|
|
return output
|
|
|
|
def get_special_tokens_mask(
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
|
) -> List[int]:
|
|
"""
|
|
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
|
special tokens using the tokenizer `prepare_for_model` method.
|
|
|
|
Args:
|
|
token_ids_0 (`List[int]`):
|
|
List of IDs.
|
|
token_ids_1 (`List[int]`, *optional*):
|
|
Optional second list of IDs for sequence pairs.
|
|
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
|
Whether or not the token list is already formatted with special tokens for the model.
|
|
|
|
Returns:
|
|
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
|
"""
|
|
if already_has_special_tokens:
|
|
return super().get_special_tokens_mask(
|
|
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
|
)
|
|
|
|
bos_token_id = [1] if self.add_bos_token else []
|
|
eos_token_id = [1] if self.add_eos_token else []
|
|
|
|
if token_ids_1 is None:
|
|
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
|
|
return (
|
|
bos_token_id
|
|
+ ([0] * len(token_ids_0))
|
|
+ eos_token_id
|
|
+ bos_token_id
|
|
+ ([0] * len(token_ids_1))
|
|
+ eos_token_id
|
|
)
|
|
|
|
def create_token_type_ids_from_sequences(
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
|
) -> List[int]:
|
|
"""
|
|
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
|
|
sequence pair mask has the following format:
|
|
|
|
```
|
|
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
|
| first sequence | second sequence |
|
|
```
|
|
|
|
if token_ids_1 is None, only returns the first portion of the mask (0s).
|
|
|
|
Args:
|
|
token_ids_0 (`List[int]`):
|
|
List of ids.
|
|
token_ids_1 (`List[int]`, *optional*):
|
|
Optional second list of IDs for sequence pairs.
|
|
|
|
Returns:
|
|
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
|
"""
|
|
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
|
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
|
|
|
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
|
|
|
|
if token_ids_1 is not None:
|
|
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
|
|
|
|
return output |