&`i/lddlmZmZmZmZddlZddlZddl Z ddl m Z m Z ddl mZddlmZddlmZddlmZddlmZdd lmZdd lmZdd lmZmZdd lmZdd l m!Z!ddl"m#Z#m$Z$ddl%m&Z&m'Z'ddl(m)Z)e\Z*Z+eGddeZ,eGdde,e+j-Z.dS))DictListTupleUnionN)Discrete MultiDiscrete)deprecation_warning)ModelV2)SlimFC) TorchModelV2)add_time_dimension) SampleBatch)ViewRequirement) OldAPIStackoverride)try_import_torch)get_base_struct_from_space)flatten_inputs_to_1d_tensorone_hot)ModelConfigDict TensorType)log_oncec eZdZdZeedeeefde edede ee effdZ dede edede ee effdZ d S) RecurrentNetworkaHelper class to simplify implementing RNN models with TorchModelV2. Instead of implementing forward(), you can implement forward_rnn() which takes batches with the time dimension added already. Here is an example implementation for a subclass ``MyRNNClass(RecurrentNetwork, nn.Module)``:: def __init__(self, obs_space, num_outputs): nn.Module.__init__(self) super().__init__(obs_space, action_space, num_outputs, model_config, name) self.obs_size = _get_size(obs_space) self.rnn_hidden_dim = model_config["lstm_cell_size"] self.fc1 = nn.Linear(self.obs_size, self.rnn_hidden_dim) self.rnn = nn.GRUCell(self.rnn_hidden_dim, self.rnn_hidden_dim) self.fc2 = nn.Linear(self.rnn_hidden_dim, num_outputs) self.value_branch = nn.Linear(self.rnn_hidden_dim, 1) self._cur_value = None @override(ModelV2) def get_initial_state(self): # Place hidden states on same device as model. h = [self.fc1.weight.new( 1, self.rnn_hidden_dim).zero_().squeeze(0)] return h @override(ModelV2) def value_function(self): assert self._cur_value is not None, "must call forward() first" return self._cur_value @override(RecurrentNetwork) def forward_rnn(self, input_dict, state, seq_lens): x = nn.functional.relu(self.fc1(input_dict["obs_flat"].float())) h_in = state[0].reshape(-1, self.rnn_hidden_dim) h = self.rnn(x, h_in) q = self.fc2(h) self._cur_value = self.value_branch(h).squeeze(1) return q, [h] input_dictstateseq_lensreturncdtdrtd|d}|jdd|_t ||d|j}||||\}}t |d |j g}||fS) zAdds time dimension to batch before sending inputs to forward_rnn(). You should implement forward_rnn() in your subclass.recurrent_network_tfz5ray.rllib.models.torch.recurrent_net.RecurrentNetwork)oldobs_flat _time_majorFtorch)r framework time_major) rr float model_configgetr&r forward_rnnr$reshape num_outputs)selfrrr flat_inputsinputsoutput new_states x/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/rllib/models/torch/recurrent_net.pyforwardzRecurrentNetwork.forwardFs * + +  K    !,2244 +// uEE#      !,,VUHEE vD,<'=>>y  r0c td)aCall the model with the given input tensors and state. Args: inputs: Observation tensor with shape [B, T, obs_size]. state: List of state tensors, each with shape [B, size]. seq_lens: 1D tensor holding input sequence lengths. Note: len(seq_lens) == B. Returns: (outputs, new_state): The model output tensor of shape [B, T, num_outputs] and the list of new state tensors each with shape [B, size]. Examples: def forward_rnn(self, inputs, state, seq_lens): model_out, h, c = self.rnn_model([inputs, seq_lens] + state) return model_out, [h, c] z(You must implement this for an RNN model)NotImplementedError)r.r0rrs r3r+zRecurrentNetwork.forward_rnngs*""LMMMr5N) __name__ __module__ __qualname____doc__rr rstrrrrr4r+r5r3rrs))VXg!j)!J! ! z4 ++ , !!!!@N N)-j)9NEON z4 ++ ,NNNNNNr5rc eZdZdZdejjdejjdedede f fd Z e e de e efd eed ed eeeefffd Ze e d ed eed ed eeeeffdZe ed eeejeeffdZe ed efdZxZS) LSTMWrapperzGAn LSTM wrapper serving as an interface for ModelV2s that set use_lstm. obs_space action_spacer-r)namec.tj|tt|||d|||j0t tj|j j |_|d|_ | dd|_ |d|_|d|_t!|j|_d|_t)j|jD]}t-|t.r|xj|jz c_-t-|t2r(|xjtj|jz c_j|j 5|xjt tj|j z c_|xjt t9|z c_|jr|xj|jz c_|jr|xjdz c_t|j|j |j |_||_t?|j |jdt@jj!j" |_#t?|j ddt@jj!j" |_$|dr3tKtLj'|jd |j(tLj)<|dr/tKtLj*d |j(tLj+<dSdS) Nlstm_cell_sizer#Flstm_use_prev_actionlstm_use_prev_rewardr) batch_first)in_sizeout_size activation_fn initializerr')spaceshift)rN),nnModule__init__superr?r-intnpprodr@shape cell_sizer*r&use_prev_actionuse_prev_rewardrrAaction_space_struct action_dimtreeflatten isinstancernrsumnveclenLSTMlstmr r$initxavier_uniform__logits_branch _value_branchrrACTIONSview_requirements PREV_ACTIONSREWARDS PREV_REWARDS)r.r@rAr-r)rBrM __class__s r3rQzLSTMWrapper.__init__s 4    k4  )) |T<      #"274>+?#@#@AAD %&67&**=%@@+,BC+,BC#=d>O#P#P \$":;; 3 3E%** 357*E=11 326%*#5#55(3rwu{';';#<#<<3s5zz??2   0    /     "    !  GG  dndo:M   '%N% 5     $N 5     . / ?N#4+ N/d.FRWd/(M1JKK,$V\\^^T5FGGFF#\\^^F fr4?6K L LMMM  3 4  OO j)ABHHJJRQRGTT     E))[MH$rs++++++++++++ 44444444777777,,,,,,......======>>>>>>555555==============666666IIIIIILLLLLLLL>>>>>>>>######     r bNbNbNbNbN|bNbN bNJ hGhGhGhGhG"BIhGhG hGhGhGr5