From 943a1f12256f94aff8a9abf94f4dc9971749fcfc Mon Sep 17 00:00:00 2001 From: Vincent Moens Date: Fri, 20 Dec 2024 12:12:27 +0000 Subject: [PATCH] Update [ghstack-poisoned] --- examples/agents/recurrent_actor.py | 205 +++++++++++++++++++++++ torchrl/modules/tensordict_module/rnn.py | 4 +- 2 files changed, 207 insertions(+), 2 deletions(-) create mode 100644 examples/agents/recurrent_actor.py diff --git a/examples/agents/recurrent_actor.py b/examples/agents/recurrent_actor.py new file mode 100644 index 00000000000..16ec64be626 --- /dev/null +++ b/examples/agents/recurrent_actor.py @@ -0,0 +1,205 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +""" +This code exemplifies how an actor that uses a RNN backbone can be built. + +It is based on snippets from the DQN with RNN tutorial. + +There are two main APIs to be aware of when using RNNs, and dedicated notes regarding these can be found at the end +of this example: the `set_recurrent_mode` context manager, and the `make_tensordict_primer` method. + +""" +from collections import OrderedDict + +import torch +from tensordict.nn import TensorDictModule as Mod, TensorDictSequential as Seq +from torch import nn + +from torchrl.envs import ( + Compose, + GrayScale, + GymEnv, + InitTracker, + ObservationNorm, + Resize, + RewardScaling, + StepCounter, + ToTensorImage, + TransformedEnv, +) +from torchrl.modules import ConvNet, LSTMModule, MLP, QValueModule, set_recurrent_mode + +# Define the device to use for computations (GPU if available, otherwise CPU) +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + +# Create a transformed environment using the CartPole-v1 gym environment +env = TransformedEnv( + GymEnv("CartPole-v1", from_pixels=True, device=device), + # Apply a series of transformations to the environment: + # 1. Convert observations to tensor images + # 2. Convert images to grayscale + # 3. Resize images to 84x84 pixels + # 4. Keep track of the step count + # 5. Initialize a tracker for the environment + # 6. Scale rewards by a factor of 0.1 + # 7. Normalize observations to have zero mean and unit variance (we'll adapt that dynamically later) + Compose( + ToTensorImage(), + GrayScale(), + Resize(84, 84), + StepCounter(), + InitTracker(), + RewardScaling(loc=0.0, scale=0.1), + ObservationNorm(standard_normal=True, in_keys=["pixels"]), + ), +) + +# Initialize the normalization statistics for the observation norm transform +env.transform[-1].init_stats(1000, reduce_dim=[0, 1, 2], cat_dim=0, keep_dims=[0]) + +# Reset the environment to get an initial observation +td = env.reset() + +# Define a feature extractor module that takes pixel observations as input +# and outputs an embedding vector +feature = Mod( + ConvNet( + num_cells=[32, 32, 64], + squeeze_output=True, + aggregator_class=nn.AdaptiveAvgPool2d, + aggregator_kwargs={"output_size": (1, 1)}, + device=device, + ), + in_keys=["pixels"], + out_keys=["embed"], +) + +# Get the shape of the embedding vector output by the feature extractor +with torch.no_grad(): + n_cells = feature(env.reset())["embed"].shape[-1] + +# Define an LSTM module that takes the embedding vector as input and outputs +# a new embedding vector +lstm = LSTMModule( + input_size=n_cells, + hidden_size=128, + device=device, + in_key="embed", + out_key="embed", +) + +# Define a multi-layer perceptron (MLP) module that takes the LSTM output as +# input and outputs action values +mlp = MLP( + out_features=2, + num_cells=[ + 64, + ], + device=device, +) + +# Initialize the bias of the last layer of the MLP to zero +mlp[-1].bias.data.fill_(0.0) + +# Wrap the MLP in a TensorDictModule to handle input/output keys +mlp = Mod(mlp, in_keys=["embed"], out_keys=["action_value"]) + +# Define a Q-value module that computes the Q-value of the current state +qval = QValueModule(action_space=None, spec=env.action_spec) + +# Add a TensorDictPrimer to the environment to ensure that the policy is aware +# of the supplementary inputs and outputs (recurrent states) during rollout execution +# This is necessary when using batched environments or parallel data collection +env.append_transform(lstm.make_tensordict_primer()) + +# Create a sequential module that combines the feature extractor, LSTM, MLP, and Q-value modules +policy = Seq(OrderedDict(feature=feature, lstm=lstm, mlp=mlp, qval=qval)) + +# Roll out the policy in the environment for 100 steps +rollout = env.rollout(100, policy) +print(rollout) + +# Print result: +# +# TensorDict( +# fields={ +# action: Tensor(shape=torch.Size([10, 2]), device=cpu, dtype=torch.int64, is_shared=False), +# action_value: Tensor(shape=torch.Size([10, 2]), device=cpu, dtype=torch.float32, is_shared=False), +# chosen_action_value: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), +# done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), +# embed: Tensor(shape=torch.Size([10, 128]), device=cpu, dtype=torch.float32, is_shared=False), +# is_init: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), +# next: TensorDict( +# fields={ +# done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), +# is_init: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), +# pixels: Tensor(shape=torch.Size([10, 1, 84, 84]), device=cpu, dtype=torch.float32, is_shared=False), +# recurrent_state_c: Tensor(shape=torch.Size([10, 1, 128]), device=cpu, dtype=torch.float32, is_shared=False), +# recurrent_state_h: Tensor(shape=torch.Size([10, 1, 128]), device=cpu, dtype=torch.float32, is_shared=False), +# reward: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), +# step_count: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.int64, is_shared=False), +# terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), +# truncated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, +# batch_size=torch.Size([10]), +# device=cpu, +# is_shared=False), +# pixels: Tensor(shape=torch.Size([10, 1, 84, 84]), device=cpu, dtype=torch.float32, is_shared=False), +# recurrent_state_c: Tensor(shape=torch.Size([10, 1, 128]), device=cpu, dtype=torch.float32, is_shared=False), +# recurrent_state_h: Tensor(shape=torch.Size([10, 1, 128]), device=cpu, dtype=torch.float32, is_shared=False), +# step_count: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.int64, is_shared=False), +# terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), +# truncated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, +# batch_size=torch.Size([10]), +# device=cpu, +# is_shared=False) +# + +# Notes: +# 1. make_tensordict_primer +# +# Regarding make_tensordict_primer, it creates a TensorDictPrimer object that ensures the policy is aware +# of the supplementary inputs and outputs (recurrent states) during rollout execution. +# This is necessary when using batched environments or parallel data collection, as the recurrent states +# need to be shared across processes and dealt with properly. +# +# In other words, make_tensordict_primer adds the LSTM's hidden states to the environment's specs, +# allowing the environment to properly handle the recurrent states during rollouts. Without it, the policy +# would not be able to use the LSTM's memory buffers correctly, leading to poorly defined behaviors, +# especially in parallel settings. +# +# By adding the TensorDictPrimer to the environment, you ensure that the policy can correctly use the +# LSTM's recurrent states, even when running in parallel or batched environments. This is why +# env.append_transform(lstm.make_tensordict_primer()) is called before creating the policy and rolling it +# out in the environment. +# +# 2. Using the LSTM to process multiple steps at once. +# +# When set_recurrent_mode("recurrent") is used, the LSTM will process the entire input tensordict as a sequence, using +# its recurrent connections to maintain state across time steps. This mode may utilize CuDNN to accelerate the processing +# of the sequence on CUDA devices. The behavior in this mode is akin to torch.nn.LSTM, where the LSTM expects the input +# data to be organized in batches of sequences. +# +# On the other hand, when set_recurrent_mode("sequential") is used, the +# LSTM will process each step in the input tensordict independently, without maintaining any state across time steps. This +# mode makes the LSTM behave similarly to torch.nn.LSTMCell, where each input is treated as a separate, independent +# element. +# +# In the example code, set_recurrent_mode("recurrent") is used to process a tensordict of shape [T], where T +# is the number of steps. This allows the LSTM to use its recurrent connections to maintain state across the entire +# sequence. +# +# In contrast, set_recurrent_mode("sequential") is used to process a single step from the tensordict (i.e., +# rollout[0]). In this case, the LSTM does not use its recurrent connections, and simply processes the single step as if +# it were an independent input. + +with set_recurrent_mode("recurrent"): + # Process a tensordict of shape [T] where T is a number of steps + print(policy(rollout)) + +with set_recurrent_mode("sequential"): + # Process a tensordict of shape [T] where T is a number of steps + print(policy(rollout[0])) diff --git a/torchrl/modules/tensordict_module/rnn.py b/torchrl/modules/tensordict_module/rnn.py index 68309c346cd..07bf0337c4e 100644 --- a/torchrl/modules/tensordict_module/rnn.py +++ b/torchrl/modules/tensordict_module/rnn.py @@ -1652,8 +1652,8 @@ class set_recurrent_mode(_DecoratorContextManager): """Context manager for setting RNNs recurrent mode. Args: - mode (bool, "recurrent" or "stateful"): the recurrent mode to be used within the context manager. - `"recurrent"` leads to `mode=True` and `"stateful"` leads to `mode=False`. + mode (bool, "recurrent" or "sequential"): the recurrent mode to be used within the context manager. + `"recurrent"` leads to `mode=True` and `"sequential"` leads to `mode=False`. An RNN executed with recurrent_mode "on" assumes that the data comes in time batches, otherwise it is assumed that each data element in a tensordict is independent of the others. The default value of this context manager is ``True``.