1055 lines
41 KiB
Python
1055 lines
41 KiB
Python
# coding=utf-8
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# Copyright 2021 The EleutherAI and The HuggingFace Inc. team.
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# Modifications copyright 2022 Xinyang Geng
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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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from functools import partial
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from typing import Optional, Tuple
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import json
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import numpy as np
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import flax.linen as nn
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import jax
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import jax.numpy as jnp
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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 jax import lax
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from flax.linen import partitioning as nn_partitioning
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from transformers.configuration_utils import PretrainedConfig
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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 transformers.generation.flax_logits_process import FlaxLogitsProcessorList
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from transformers import AutoTokenizer
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from jax.sharding import PartitionSpec
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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.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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"""
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The follow code is taken from
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transformers/src/transformers/models/gptj/configuration_gptj.py
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and modified to work with EasyLM.
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"""
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GPTJ_STANDARD_CONFIGS = {
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'6b': {
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"vocab_size": 50400,
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"n_positions": 2048,
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"n_embd": 4096,
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"n_layer": 28,
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"n_head": 16,
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"rotary_dim": 64,
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"n_inner": None,
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"activation_function": "gelu_new",
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"layer_norm_epsilon": 1e-5,
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"initializer_range": 0.02,
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"scale_attn_weights": True,
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"use_cache": True,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"tie_word_embeddings": False,
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"n_real_tokens": 50257,
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}
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}
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class GPTJConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J
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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 GPT-J
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[EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) architecture. Configuration objects inherit from
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[`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`]
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for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50400):
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Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`GPTJModel`].
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n_positions (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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n_embd (`int`, *optional*, defaults to 4096):
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Dimensionality of the embeddings and hidden states.
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n_layer (`int`, *optional*, defaults to 28):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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rotary_dim (`int`, *optional*, defaults to 64):
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Number of dimensions in the embedding that Rotary Position Embedding is applied to.
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n_inner (`int`, *optional*, defaults to 0):
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Dimensionality of the inner feed-forward layers. 0 will set it to 4 times n_embd
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activation_function (`str`, *optional*, defaults to `"gelu_new"`):
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Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
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resid_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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embd_pdrop (`int`, *optional*, defaults to 0.1):
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The dropout ratio for the embeddings.
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attn_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
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The epsilon to use in the layer normalization layers.
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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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scale_attn_weights (`bool`, *optional*, defaults to `True`):
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Scale attention weights by dividing by sqrt(hidden_size).
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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).
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Example:
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```python
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>>> from transformers import GPTJModel, GPTJConfig
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>>> # Initializing a GPT-J 6B configuration
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>>> configuration = GPTJConfig()
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>>> # Initializing a model from the configuration
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>>> model = GPTJModel(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 = "gptj"
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attribute_map = {
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"max_position_embeddings": "n_positions",
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"hidden_size": "n_embd",
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"num_attention_heads": "n_head",
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"num_hidden_layers": "n_layer",
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}
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def __init__(
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self,
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vocab_size=50400,
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n_positions=2048,
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n_embd=4096,
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n_layer=28,
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n_head=16,
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rotary_dim=64,
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n_inner=None,
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activation_function="gelu_new",
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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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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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scale_attn_weights=True,
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use_cache=True,
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bos_token_id=50256,
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eos_token_id=50256,
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tie_word_embeddings=False,
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gradient_checkpointing=True,
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gradient_checkpointing_policy='nothing_saveable',
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n_real_tokens=50257,
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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.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_head = n_head
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self.n_inner = n_inner
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self.rotary_dim = rotary_dim
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self.activation_function = activation_function
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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.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.scale_attn_weights = scale_attn_weights
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self.use_cache = use_cache
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self.gradient_checkpointing = gradient_checkpointing
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self.gradient_checkpointing_policy = gradient_checkpointing_policy
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self.n_real_tokens = n_real_tokens
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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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if self.n_real_tokens is None:
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self.n_real_tokens = self.vocab_size
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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super().__init__(
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bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **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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none_arg_types = dict(
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n_inner=int,
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rotary_dim=int,
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)
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config = function_args_to_config(cls.__init__, none_arg_types=none_arg_types)
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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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('transformer/wte/embedding', PartitionSpec('mp', 'fsdp')),
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('attn/(k_proj|q_proj|v_proj)/kernel', PartitionSpec('fsdp', 'mp')),
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('attn/out_proj/kernel', PartitionSpec('mp', 'fsdp')),
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('mlp/fc_in/kernel', PartitionSpec('fsdp', 'mp')),
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('mlp/fc_in/bias', PartitionSpec('mp')),
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('mlp/fc_out/kernel', PartitionSpec('mp', 'fsdp')),
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('mlp/fc_out/bias', PartitionSpec()),
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('ln_[0-9]+/bias', PartitionSpec()),
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('[0-9]+/ln_[0-9]+/scale', PartitionSpec()),
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('ln_f/bias', PartitionSpec()),
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('ln_f/scale', PartitionSpec()),
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('lm_head/kernel', PartitionSpec('fsdp', 'mp')),
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('lm_head/bias', PartitionSpec('mp')),
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('.*', PartitionSpec()),
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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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'ln_[0-9]+/bias', 'ln_[0-9]+/scale', 'ln_f/bias', 'ln_f/scale',
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'bias'
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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.name = 'EleutherAI/gpt-j-6B'
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config.bos_token = '<|endoftext|>'
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config.eos_token = '<|endoftext|>'
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config.pad_token = '<|extratoken_40|>'
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config.cls_token = '<|extratoken_41|>'
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config.mask_token = '<|extratoken_42|>'
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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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return AutoTokenizer.from_pretrained(
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config.name,
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bos_token=config.bos_token,
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eos_token=config.eos_token,
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pad_token=config.pad_token,
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cls_token=config.cls_token,
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mask_token=config.mask_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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@staticmethod
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def load_pretrained(name, dtype=jnp.float32):
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with jax.default_device(jax.devices("cpu")[0]):
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params = FlaxGPTJForCausalLM.from_pretrained(
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name, _do_init=False, dtype=dtype
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)[1]
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params = freeze({'params': params})
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return jax.device_get(params)
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@classmethod
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def load_config(cls, path):
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if path in GPTJ_STANDARD_CONFIGS:
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return cls.from_dict(GPTJ_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)['gptj_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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elif load_type == 'huggingface':
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return cls.from_pretrained(load_path)
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else:
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raise ValueError(f'Unsupported load config type: {load_type}')
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"""
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The follow code is taken from
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transformers/src/transformers/models/gptj/modeling_flax_gptj.py
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and modified to work with EasyLM.
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"""
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "gptj"
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_CONFIG_FOR_DOC = "GPTJConfig"
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remat = nn_partitioning.remat
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GPTJ_START_DOCSTRING = r"""
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This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
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etc.)
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This model is also a Flax Linen
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[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
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regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
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Finally, this model supports inherent JAX features such as:
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- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
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- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
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- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
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- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
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Parameters:
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config ([`GPTJConfig`]): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the
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configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
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dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
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The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
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`jax.numpy.bfloat16` (on TPUs).
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This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
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specified all the computation will be performed with the given `dtype`.
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**Note that this only specifies the dtype of the computation and does not influence the dtype of model
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parameters.**
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If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
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[`~FlaxPreTrainedModel.to_bf16`].
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"""
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GPTJ_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`):
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`input_ids_length` = `sequence_length`. Indices of input sequence tokens in the vocabulary.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
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config.max_position_embeddings - 1]`.
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past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
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Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
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auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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def create_sinusoidal_positions(num_pos, dim):
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inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2) / dim))
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sinusoid_inp = np.einsum("i , j -> i j", np.arange(num_pos), inv_freq).astype("float32")
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sin, cos = np.sin(sinusoid_inp), np.cos(sinusoid_inp)
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sentinel = dim // 2 + dim % 2
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out = np.zeros((num_pos, dim))
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out[:, 0:sentinel] = sin
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out[:, sentinel:] = cos
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return jnp.array(out)
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def rotate_every_two(tensor):
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rotate_half_tensor = jnp.stack((-tensor[:, :, :, 1::2], tensor[:, :, :, ::2]), axis=-1)
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rotate_half_tensor = rotate_half_tensor.reshape(rotate_half_tensor.shape[:-2] + (-1,))
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return rotate_half_tensor
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def apply_rotary_pos_emb(tensor, sincos):
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sin_pos, cos_pos = sincos
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sin_pos = sin_pos[:, :, None, :].repeat(2, 3)
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cos_pos = cos_pos[:, :, None, :].repeat(2, 3)
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return (tensor * cos_pos) + (rotate_every_two(tensor) * sin_pos)
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class FlaxGPTJAttention(nn.Module):
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config: GPTJConfig
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dtype: jnp.dtype = jnp.float32
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causal: bool = True
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is_cross_attention: bool = False
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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.rotary_dim = config.rotary_dim
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dense = partial(
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nn.Dense,
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self.embed_dim,
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use_bias=False,
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dtype=self.dtype,
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kernel_init=jax.nn.initializers.variance_scaling(
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scale=1.0, mode='fan_in',
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distribution='normal',
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)
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)
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self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
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self.out_proj = dense()
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self.resid_dropout = nn.Dropout(rate=config.resid_pdrop)
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self.causal_mask = make_causal_mask(jnp.ones((1, config.max_position_embeddings), dtype="bool"), dtype="bool")
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if self.rotary_dim is not None and self.rotary_dim > 0:
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pos_embd_dim = self.rotary_dim
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else:
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pos_embd_dim = self.embed_dim // self.num_heads
|
|
self.embed_positions = create_sinusoidal_positions(config.max_position_embeddings, pos_embd_dim)
|
|
|
|
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,
|
|
):
|
|
|
|
query = self.q_proj(hidden_states)
|
|
key = self.k_proj(hidden_states)
|
|
value = self.v_proj(hidden_states)
|
|
|
|
query = self._split_heads(query)
|
|
key = self._split_heads(key)
|
|
value = self._split_heads(value)
|
|
|
|
sincos = jnp.take(self.embed_positions, position_ids, axis=0)
|
|
sincos = jnp.split(sincos, 2, axis=-1)
|
|
# Rotary position embeddings induce some weird issues in multi-host environments, so we remove activation-sharding for keys/query vectors to fix this.
|
|
# key = with_sharding_constraint(key, PartitionSpec("dp", None, None, None))
|
|
# query = with_sharding_constraint(query, PartitionSpec("dp", None, None, None))
|
|
if self.rotary_dim is not None and self.rotary_dim > 0:
|
|
k_rot = key[:, :, :, : self.rotary_dim]
|
|
k_pass = key[:, :, :, self.rotary_dim :]
|
|
|
|
q_rot = query[:, :, :, : self.rotary_dim]
|
|
q_pass = query[:, :, :, self.rotary_dim :]
|
|
|
|
k_rot = apply_rotary_pos_emb(k_rot, sincos)
|
|
q_rot = apply_rotary_pos_emb(q_rot, sincos)
|
|
|
|
key = jnp.concatenate([k_rot, k_pass], axis=-1)
|
|
query = jnp.concatenate([q_rot, q_pass], axis=-1)
|
|
else:
|
|
key = apply_rotary_pos_emb(key, sincos)
|
|
query = apply_rotary_pos_emb(query, sincos)
|
|
|
|
query_length, key_length = query.shape[1], key.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)
|
|
if self.causal:
|
|
attention_mask = combine_masks(attention_mask, causal_mask, fcm_mask)
|
|
else:
|
|
attention_mask = attention_mask
|
|
|
|
dropout_rng = None
|
|
if not deterministic and self.config.attn_pdrop > 0.0:
|
|
dropout_rng = self.make_rng("dropout")
|
|
|
|
# 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:
|
|
key, value, attention_mask = self._concatenate_to_cache(key, value, query, 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, -1e9).astype(self.dtype),
|
|
)
|
|
|
|
# usual dot product attention
|
|
attn_weights = dot_product_attention_weights(
|
|
query,
|
|
key,
|
|
bias=attention_bias,
|
|
dropout_rng=dropout_rng,
|
|
dropout_rate=self.config.attn_pdrop,
|
|
deterministic=deterministic,
|
|
dtype=jnp.promote_types(self.dtype, jnp.float32),
|
|
precision=None,
|
|
)
|
|
|
|
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value)
|
|
attn_output = self._merge_heads(attn_output)
|
|
attn_output = self.out_proj(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 FlaxGPTJMLP(nn.Module):
|
|
config: GPTJConfig
|
|
intermediate_size: int
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
def setup(self):
|
|
embed_dim = self.config.hidden_size
|
|
kernel_init=jax.nn.initializers.variance_scaling(
|
|
scale=1.0, mode='fan_in',
|
|
distribution='normal',
|
|
)
|
|
|
|
self.fc_in = nn.Dense(self.intermediate_size, dtype=self.dtype, kernel_init=kernel_init)
|
|
self.fc_out = nn.Dense(embed_dim, dtype=self.dtype, kernel_init=kernel_init)
|
|
|
|
self.act = ACT2FN[self.config.activation_function]
|
|
self.dropout = nn.Dropout(rate=self.config.resid_pdrop)
|
|
|
|
def __call__(self, hidden_states, deterministic: bool = True):
|
|
hidden_states = self.fc_in(hidden_states)
|
|
hidden_states = self.act(hidden_states)
|
|
hidden_states = self.fc_out(hidden_states)
|
|
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
|
|
return hidden_states
|
|
|
|
|
|
class FlaxGPTJBlock(nn.Module):
|
|
config: GPTJConfig
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
def setup(self):
|
|
hidden_size = self.config.hidden_size
|
|
inner_dim = self.config.n_inner if self.config.n_inner is not None else 4 * hidden_size
|
|
|
|
self.ln_1 = nn.LayerNorm(
|
|
epsilon=self.config.layer_norm_epsilon,
|
|
dtype=jnp.promote_types(self.dtype, jnp.float32)
|
|
)
|
|
self.attn = FlaxGPTJAttention(self.config, dtype=self.dtype)
|
|
|
|
self.mlp = FlaxGPTJMLP(self.config, inner_dim, dtype=self.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=None,
|
|
):
|
|
residual = hidden_states
|
|
hidden_states = self.ln_1(hidden_states)
|
|
attn_outputs = self.attn(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
deterministic=deterministic,
|
|
init_cache=init_cache,
|
|
output_attentions=output_attentions,
|
|
fcm_mask=fcm_mask,
|
|
)
|
|
attn_output = attn_outputs[0]
|
|
|
|
feed_forward_hidden_states = self.mlp(hidden_states, deterministic=deterministic)
|
|
# residual connection
|
|
hidden_states = attn_output + feed_forward_hidden_states + residual
|
|
|
|
return (hidden_states,) + attn_outputs[1:]
|
|
|
|
|
|
class FlaxGPTJPreTrainedModel(FlaxPreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = GPTJConfig
|
|
base_model_prefix = "transformer"
|
|
module_class: nn.Module = None
|
|
|
|
def __init__(
|
|
self,
|
|
config: GPTJConfig,
|
|
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.n_embd,))
|
|
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"]
|
|
|
|
def _get_logits_processor(self,*args, **kwargs) -> FlaxLogitsProcessorList:
|
|
processors = super()._get_logits_processor(*args, **kwargs)
|
|
def squash_extra_tokens(input_ids, scores, cur_len):
|
|
return scores.at[:, self.config.n_real_tokens:].set(-float('inf'))
|
|
|
|
processors.append(squash_extra_tokens)
|
|
return processors
|
|
|
|
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING)
|
|
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 FlaxGPTJBlockCollection(nn.Module):
|
|
config: GPTJConfig
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
def setup(self):
|
|
block = FlaxGPTJBlock
|
|
if self.config.gradient_checkpointing:
|
|
FlaxGPT2CheckpointBlock = remat(
|
|
block, static_argnums=(3, 4, 5),
|
|
policy=get_gradient_checkpoint_policy(
|
|
self.config.gradient_checkpointing_policy
|
|
)
|
|
)
|
|
block = FlaxGPT2CheckpointBlock
|
|
self.blocks = [
|
|
block(self.config, name=str(i), dtype=self.dtype) 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, seq_length, 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 FlaxGPTJModule(nn.Module):
|
|
config: GPTJConfig
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
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),
|
|
)
|
|
self.dropout = nn.Dropout(rate=self.config.embd_pdrop)
|
|
self.h = FlaxGPTJBlockCollection(self.config, dtype=self.dtype)
|
|
self.ln_f = nn.LayerNorm(
|
|
epsilon=self.config.layer_norm_epsilon,
|
|
dtype=jnp.promote_types(self.dtype, jnp.float32)
|
|
)
|
|
|
|
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(
|
|
"The bare GPTJ Model transformer outputting raw hidden-states without any specific head on top.",
|
|
GPTJ_START_DOCSTRING,
|
|
)
|
|
class FlaxGPTJModel(FlaxGPTJPreTrainedModel):
|
|
module_class = FlaxGPTJModule
|
|
|
|
|
|
append_call_sample_docstring(
|
|
FlaxGPTJModel,
|
|
_CHECKPOINT_FOR_DOC,
|
|
FlaxCausalLMOutput,
|
|
_CONFIG_FOR_DOC,
|
|
)
|
|
|
|
|
|
class FlaxGPTJForCausalLMModule(nn.Module):
|
|
config: GPTJConfig
|
|
dtype: jnp.dtype = jnp.float32
|
|
|
|
def setup(self):
|
|
self.transformer = FlaxGPTJModule(self.config, dtype=self.dtype)
|
|
self.lm_head = nn.Dense(
|
|
self.config.vocab_size,
|
|
dtype=self.dtype,
|
|
kernel_init=jax.nn.initializers.variance_scaling(
|
|
scale=1.0, mode='fan_in',
|
|
distribution='normal',
|
|
)
|
|
)
|
|
|
|
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(
|
|
"""
|
|
The GPTJ Model transformer with a language modeling head on top.
|
|
""",
|
|
GPTJ_START_DOCSTRING,
|
|
)
|
|
class FlaxGPTJForCausalLM(FlaxGPTJPreTrainedModel):
|
|
module_class = FlaxGPTJForCausalLMModule
|
|
|
|
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = 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,
|
|
_CHECKPOINT_FOR_DOC,
|
|
FlaxCausalLMOutput,
|
|
_CONFIG_FOR_DOC,
|
|
)
|