theimageconvert2
/
transformers_4_35_0
/models
/decision_transformer
/configuration_decision_transformer.py
# coding=utf-8 | |
# Copyright 2022 The HuggingFace Team and The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" Decision Transformer model configuration""" | |
from ...configuration_utils import PretrainedConfig | |
from ...utils import logging | |
logger = logging.get_logger(__name__) | |
DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
"edbeeching/decision-transformer-gym-hopper-medium": ( | |
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json" | |
), | |
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer | |
} | |
class DecisionTransformerConfig(PretrainedConfig): | |
""" | |
This is the configuration class to store the configuration of a [`DecisionTransformerModel`]. It is used to | |
instantiate a Decision Transformer model according to the specified arguments, defining the model architecture. | |
Instantiating a configuration with the defaults will yield a similar configuration to that of the standard | |
DecisionTransformer architecture. Many of the config options are used to instatiate the GPT2 model that is used as | |
part of the architecture. | |
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
documentation from [`PretrainedConfig`] for more information. | |
Args: | |
state_dim (`int`, *optional*, defaults to 17): | |
The state size for the RL environment | |
act_dim (`int`, *optional*, defaults to 4): | |
The size of the output action space | |
hidden_size (`int`, *optional*, defaults to 128): | |
The size of the hidden layers | |
max_ep_len (`int`, *optional*, defaults to 4096): | |
The maximum length of an episode in the environment | |
action_tanh (`bool`, *optional*, defaults to True): | |
Whether to use a tanh activation on action prediction | |
vocab_size (`int`, *optional*, defaults to 50257): | |
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the | |
`inputs_ids` passed when calling [`DecisionTransformerModel`]. | |
n_positions (`int`, *optional*, defaults to 1024): | |
The maximum sequence length that this model might ever be used with. Typically set this to something large | |
just in case (e.g., 512 or 1024 or 2048). | |
n_layer (`int`, *optional*, defaults to 3): | |
Number of hidden layers in the Transformer encoder. | |
n_head (`int`, *optional*, defaults to 1): | |
Number of attention heads for each attention layer in the Transformer encoder. | |
n_inner (`int`, *optional*): | |
Dimensionality of the inner feed-forward layers. If unset, will default to 4 times `n_embd`. | |
activation_function (`str`, *optional*, defaults to `"gelu"`): | |
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. | |
resid_pdrop (`float`, *optional*, defaults to 0.1): | |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
embd_pdrop (`int`, *optional*, defaults to 0.1): | |
The dropout ratio for the embeddings. | |
attn_pdrop (`float`, *optional*, defaults to 0.1): | |
The dropout ratio for the attention. | |
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): | |
The epsilon to use in the layer normalization layers. | |
initializer_range (`float`, *optional*, defaults to 0.02): | |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
scale_attn_weights (`bool`, *optional*, defaults to `True`): | |
Scale attention weights by dividing by sqrt(hidden_size).. | |
use_cache (`bool`, *optional*, defaults to `True`): | |
Whether or not the model should return the last key/values attentions (not used by all models). | |
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): | |
Whether to additionally scale attention weights by `1 / layer_idx + 1`. | |
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): | |
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention | |
dot-product/softmax to float() when training with mixed precision. | |
Example: | |
```python | |
>>> from transformers import DecisionTransformerConfig, DecisionTransformerModel | |
>>> # Initializing a DecisionTransformer configuration | |
>>> configuration = DecisionTransformerConfig() | |
>>> # Initializing a model (with random weights) from the configuration | |
>>> model = DecisionTransformerModel(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "decision_transformer" | |
keys_to_ignore_at_inference = ["past_key_values"] | |
attribute_map = { | |
"max_position_embeddings": "n_positions", | |
"num_attention_heads": "n_head", | |
"num_hidden_layers": "n_layer", | |
} | |
def __init__( | |
self, | |
state_dim=17, | |
act_dim=4, | |
hidden_size=128, | |
max_ep_len=4096, | |
action_tanh=True, | |
vocab_size=1, | |
n_positions=1024, | |
n_layer=3, | |
n_head=1, | |
n_inner=None, | |
activation_function="relu", | |
resid_pdrop=0.1, | |
embd_pdrop=0.1, | |
attn_pdrop=0.1, | |
layer_norm_epsilon=1e-5, | |
initializer_range=0.02, | |
scale_attn_weights=True, | |
use_cache=True, | |
bos_token_id=50256, | |
eos_token_id=50256, | |
scale_attn_by_inverse_layer_idx=False, | |
reorder_and_upcast_attn=False, | |
**kwargs, | |
): | |
self.state_dim = state_dim | |
self.act_dim = act_dim | |
self.hidden_size = hidden_size | |
self.max_ep_len = max_ep_len | |
self.action_tanh = action_tanh | |
self.vocab_size = vocab_size | |
self.n_positions = n_positions | |
self.n_layer = n_layer | |
self.n_head = n_head | |
self.n_inner = n_inner | |
self.activation_function = activation_function | |
self.resid_pdrop = resid_pdrop | |
self.embd_pdrop = embd_pdrop | |
self.attn_pdrop = attn_pdrop | |
self.layer_norm_epsilon = layer_norm_epsilon | |
self.initializer_range = initializer_range | |
self.scale_attn_weights = scale_attn_weights | |
self.use_cache = use_cache | |
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx | |
self.reorder_and_upcast_attn = reorder_and_upcast_attn | |
self.bos_token_id = bos_token_id | |
self.eos_token_id = eos_token_id | |
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |