codingutf8 2022 the trajectory transformers paper s and the huggingface inc team 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 orglicenseslicense2 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 trajectorytransformer model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername trajectorytransformerpretrainedconfigarchivemap carlcochettrajectorytransformerhalfcheetahmediumv2 https huggingface cocarlcochettrajectorytransformerhalfcheetahmediumv2resolvemainconfig json see all trajectorytransformer models at https huggingface comodels filtertrajectorytransformer class trajectorytransformerconfigpretrainedconfig r this is the configuration class to store the configuration of a trajectorytransformermodel it is used to instantiate an trajectorytransformer 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 trajectorytransformer carlcochettrajectorytransformerhalfcheetahmediumv2https huggingface cocarlcochettrajectorytransformerhalfcheetahmediumv2 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocabsize int optional defaults to 100 vocabulary size of the trajectorytransformer model defines the number of different tokens that can be represented by the trajectories passed when calling trajectorytransformermodel actionweight int optional defaults to 5 weight of the action in the loss function rewardweight int optional defaults to 1 weight of the reward in the loss function valueweight int optional defaults to 1 weight of the value in the loss function blocksize int optional defaults to 249 size of the blocks in the trajectory transformer actiondim int optional defaults to 6 dimension of the action space observationdim int optional defaults to 17 dimension of the observation space transitiondim int optional defaults to 25 dimension of the transition space nlayer int optional defaults to 4 number of hidden layers in the transformer encoder nhead int optional defaults to 4 number of attention heads for each attention layer in the transformer encoder nembd int optional defaults to 128 dimensionality of the embeddings and hidden states residpdrop float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler embdpdrop int optional defaults to 0 1 the dropout ratio for the embeddings attnpdrop float optional defaults to 0 1 the dropout ratio for the attention hiddenact str or function optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew are supported maxpositionembeddings int optional defaults to 512 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 initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e12 the epsilon used by the layer normalization layers kaiminginitializerrange float optional defaults to 1 a coefficient scaling the negative slope of the kaiming initializer rectifier for einlinear layers usecache bool optional defaults to true whether or not the model should return the last keyvalues attentions not used by all models only relevant if config isdecodertrue example python from transformers import trajectorytransformerconfig trajectorytransformermodel initializing a trajectorytransformer carlcochettrajectorytransformerhalfcheetahmediumv2 style configuration configuration trajectorytransformerconfig initializing a model with random weights from the carlcochettrajectorytransformerhalfcheetahmediumv2 style configuration model trajectorytransformermodelconfiguration accessing the model configuration configuration model config modeltype trajectorytransformer keystoignoreatinference pastkeyvalues attributemap hiddensize nembd numattentionheads nhead numhiddenlayers nlayer def init self vocabsize100 actionweight5 rewardweight1 valueweight1 blocksize249 actiondim6 observationdim17 transitiondim25 nlayer4 nhead4 nembd128 embdpdrop0 1 attnpdrop0 1 residpdrop0 1 learningrate0 0006 maxpositionembeddings512 initializerrange0 02 layernormeps1e12 kaiminginitializerrange1 usecachetrue padtokenid1 bostokenid50256 eostokenid50256 kwargs self vocabsize vocabsize self actionweight actionweight self rewardweight rewardweight self valueweight valueweight self maxpositionembeddings maxpositionembeddings self blocksize blocksize self actiondim actiondim self observationdim observationdim self transitiondim transitiondim self learningrate learningrate self nlayer nlayer self nhead nhead self nembd nembd self embdpdrop embdpdrop self attnpdrop attnpdrop self residpdrop residpdrop self initializerrange initializerrange self layernormeps layernormeps self kaiminginitializerrange kaiminginitializerrange self usecache usecache super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs coding utf 8 2022 the trajectory transformers paper s and the huggingface inc team 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 trajectorytransformer model configuration see all trajectorytransformer models at https huggingface co models filter trajectory_transformer this is the configuration class to store the configuration of a trajectorytransformermodel it is used to instantiate an trajectorytransformer 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 trajectorytransformer carlcochet trajectory transformer halfcheetah medium v2 https huggingface co carlcochet trajectory transformer halfcheetah medium v2 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocab_size int optional defaults to 100 vocabulary size of the trajectorytransformer model defines the number of different tokens that can be represented by the trajectories passed when calling trajectorytransformermodel action_weight int optional defaults to 5 weight of the action in the loss function reward_weight int optional defaults to 1 weight of the reward in the loss function value_weight int optional defaults to 1 weight of the value in the loss function block_size int optional defaults to 249 size of the blocks in the trajectory transformer action_dim int optional defaults to 6 dimension of the action space observation_dim int optional defaults to 17 dimension of the observation space transition_dim int optional defaults to 25 dimension of the transition space n_layer int optional defaults to 4 number of hidden layers in the transformer encoder n_head int optional defaults to 4 number of attention heads for each attention layer in the transformer encoder n_embd int optional defaults to 128 dimensionality of the embeddings and hidden states 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 hidden_act str or function optional defaults to gelu the non linear activation function function or string in the encoder and pooler if string gelu relu selu and gelu_new are supported max_position_embeddings int optional defaults to 512 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 initializer_range float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices layer_norm_eps float optional defaults to 1e 12 the epsilon used by the layer normalization layers kaiming_initializer_range float optional defaults to 1 a coefficient scaling the negative slope of the kaiming initializer rectifier for einlinear layers use_cache bool optional defaults to true whether or not the model should return the last key values attentions not used by all models only relevant if config is_decoder true example python from transformers import trajectorytransformerconfig trajectorytransformermodel initializing a trajectorytransformer carlcochet trajectory transformer halfcheetah medium v2 style configuration configuration trajectorytransformerconfig initializing a model with random weights from the carlcochet trajectory transformer halfcheetah medium v2 style configuration model trajectorytransformermodel configuration accessing the model configuration configuration model config
from ....configuration_utils import PretrainedConfig
from ....utils import logging


logger = logging.get_logger(__name__)

TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    
}


class TrajectoryTransformerConfig(PretrainedConfig):
    r

    model_type = "trajectory_transformer"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(
        self,
        vocab_size=100,
        action_weight=5,
        reward_weight=1,
        value_weight=1,
        block_size=249,
        action_dim=6,
        observation_dim=17,
        transition_dim=25,
        n_layer=4,
        n_head=4,
        n_embd=128,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        resid_pdrop=0.1,
        learning_rate=0.0006,
        max_position_embeddings=512,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        kaiming_initializer_range=1,
        use_cache=True,
        pad_token_id=1,
        bos_token_id=50256,
        eos_token_id=50256,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.action_weight = action_weight
        self.reward_weight = reward_weight
        self.value_weight = value_weight
        self.max_position_embeddings = max_position_embeddings
        self.block_size = block_size
        self.action_dim = action_dim
        self.observation_dim = observation_dim
        self.transition_dim = transition_dim
        self.learning_rate = learning_rate
        self.n_layer = n_layer
        self.n_head = n_head
        self.n_embd = n_embd
        self.embd_pdrop = embd_pdrop
        self.attn_pdrop = attn_pdrop
        self.resid_pdrop = resid_pdrop
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.kaiming_initializer_range = kaiming_initializer_range
        self.use_cache = use_cache
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)