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Fix reshape LSTM states (#112) 

* Fix LSTM states reshape

* Fix warnings and update changelog

* Remove unused variable

* Fix runtime error when using n_lstm_layers > 1
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Antonin RAFFIN 2022-10-26 18:03:45 +02:00 committed by GitHub
parent c75ad7dd58
commit a9735b9f31
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5 changed files with 30 additions and 22 deletions
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4
docs/misc/changelog.rst
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@ -3,7 +3,7 @@
Changelog
==========
Release 1.7.0a0 (WIP)
Release 1.7.0a1 (WIP)
--------------------------
Breaking Changes:
@ -17,6 +17,8 @@ New Features:
Bug Fixes:
^^^^^^^^^^
- Fixed a bug in ``RecurrentPPO`` where the lstm states where incorrectly reshaped for ``n_lstm_layers > 1`` (thanks @kolbytn)
- Fixed ``RuntimeError: rnn: hx is not contiguous`` while predicting terminal values for ``RecurrentPPO`` when ``n_lstm_layers > 1``
Deprecations:
^^^^^^^^^^^^^

36
sb3_contrib/common/recurrent/buffers.py
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@ -206,7 +206,6 @@ class RecurrentRolloutBuffer(RolloutBuffer):
self.episode_starts[batch_inds], env_change[batch_inds], self.device
)
n_layers = self.hidden_states_pi.shape[1]
# Number of sequences
n_seq = len(self.seq_start_indices)
max_length = self.pad(self.actions[batch_inds]).shape[1]
@ -214,17 +213,19 @@ class RecurrentRolloutBuffer(RolloutBuffer):
# We retrieve the lstm hidden states that will allow
# to properly initialize the LSTM at the beginning of each sequence
lstm_states_pi = (
# (n_steps, n_layers, n_envs, dim) -> (n_layers, n_seq, dim)
self.hidden_states_pi[batch_inds][self.seq_start_indices].reshape(n_layers, n_seq, -1),
self.cell_states_pi[batch_inds][self.seq_start_indices].reshape(n_layers, n_seq, -1),
# 1. (n_envs * n_steps, n_layers, dim) -> (batch_size, n_layers, dim)
# 2. (batch_size, n_layers, dim) -> (n_seq, n_layers, dim)
# 3. (n_seq, n_layers, dim) -> (n_layers, n_seq, dim)
self.hidden_states_pi[batch_inds][self.seq_start_indices].swapaxes(0, 1),
self.cell_states_pi[batch_inds][self.seq_start_indices].swapaxes(0, 1),
)
lstm_states_vf = (
# (n_steps, n_layers, n_envs, dim) -> (n_layers, n_seq, dim)
self.hidden_states_vf[batch_inds][self.seq_start_indices].reshape(n_layers, n_seq, -1),
self.cell_states_vf[batch_inds][self.seq_start_indices].reshape(n_layers, n_seq, -1),
# (n_envs * n_steps, n_layers, dim) -> (n_layers, n_seq, dim)
self.hidden_states_vf[batch_inds][self.seq_start_indices].swapaxes(0, 1),
self.cell_states_vf[batch_inds][self.seq_start_indices].swapaxes(0, 1),
)
lstm_states_pi = (self.to_torch(lstm_states_pi[0]), self.to_torch(lstm_states_pi[1]))
lstm_states_vf = (self.to_torch(lstm_states_vf[0]), self.to_torch(lstm_states_vf[1]))
lstm_states_pi = (self.to_torch(lstm_states_pi[0]).contiguous(), self.to_torch(lstm_states_pi[1]).contiguous())
lstm_states_vf = (self.to_torch(lstm_states_vf[0]).contiguous(), self.to_torch(lstm_states_vf[1]).contiguous())
return RecurrentRolloutBufferSamples(
# (batch_size, obs_dim) -> (n_seq, max_length, obs_dim) -> (n_seq * max_length, obs_dim)
@ -349,24 +350,23 @@ class RecurrentDictRolloutBuffer(DictRolloutBuffer):
self.episode_starts[batch_inds], env_change[batch_inds], self.device
)
n_layers = self.hidden_states_pi.shape[1]
n_seq = len(self.seq_start_indices)
max_length = self.pad(self.actions[batch_inds]).shape[1]
padded_batch_size = n_seq * max_length
# We retrieve the lstm hidden states that will allow
# to properly initialize the LSTM at the beginning of each sequence
lstm_states_pi = (
# (n_steps, n_layers, n_envs, dim) -> (n_layers, n_seq, dim)
self.hidden_states_pi[batch_inds][self.seq_start_indices].reshape(n_layers, n_seq, -1),
self.cell_states_pi[batch_inds][self.seq_start_indices].reshape(n_layers, n_seq, -1),
# (n_envs * n_steps, n_layers, dim) -> (n_layers, n_seq, dim)
self.hidden_states_pi[batch_inds][self.seq_start_indices].swapaxes(0, 1),
self.cell_states_pi[batch_inds][self.seq_start_indices].swapaxes(0, 1),
)
lstm_states_vf = (
# (n_steps, n_layers, n_envs, dim) -> (n_layers, n_seq, dim)
self.hidden_states_vf[batch_inds][self.seq_start_indices].reshape(n_layers, n_seq, -1),
self.cell_states_vf[batch_inds][self.seq_start_indices].reshape(n_layers, n_seq, -1),
# (n_envs * n_steps, n_layers, dim) -> (n_layers, n_seq, dim)
self.hidden_states_vf[batch_inds][self.seq_start_indices].swapaxes(0, 1),
self.cell_states_vf[batch_inds][self.seq_start_indices].swapaxes(0, 1),
)
lstm_states_pi = (self.to_torch(lstm_states_pi[0]), self.to_torch(lstm_states_pi[1]))
lstm_states_vf = (self.to_torch(lstm_states_vf[0]), self.to_torch(lstm_states_vf[1]))
lstm_states_pi = (self.to_torch(lstm_states_pi[0]).contiguous(), self.to_torch(lstm_states_pi[1]).contiguous())
lstm_states_vf = (self.to_torch(lstm_states_vf[0]).contiguous(), self.to_torch(lstm_states_vf[1]).contiguous())
observations = {key: self.pad(obs[batch_inds]) for (key, obs) in self.observations.items()}
observations = {key: obs.reshape((padded_batch_size,) + self.obs_shape[key]) for (key, obs) in observations.items()}

4
sb3_contrib/ppo_recurrent/ppo_recurrent.py
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@ -277,8 +277,8 @@ class RecurrentPPO(OnPolicyAlgorithm):
terminal_obs = self.policy.obs_to_tensor(infos[idx]["terminal_observation"])[0]
with th.no_grad():
terminal_lstm_state = (
lstm_states.vf[0][:, idx : idx + 1, :],
lstm_states.vf[1][:, idx : idx + 1, :],
lstm_states.vf[0][:, idx : idx + 1, :].contiguous(),
lstm_states.vf[1][:, idx : idx + 1, :].contiguous(),
)
# terminal_lstm_state = None
episode_starts = th.tensor([False]).float().to(self.device)

2
sb3_contrib/version.txt
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@ -1 +1 @@
1.7.0a0
1.7.0a1

6
tests/test_lstm.py
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@ -66,11 +66,13 @@ class CartPoleNoVelEnv(CartPoleEnv):
enable_critic_lstm=True,
lstm_hidden_size=4,
lstm_kwargs=dict(dropout=0.5),
n_lstm_layers=2,
),
dict(
enable_critic_lstm=False,
lstm_hidden_size=4,
lstm_kwargs=dict(dropout=0.5),
n_lstm_layers=2,
),
],
)
@ -95,11 +97,13 @@ def test_cnn(policy_kwargs):
enable_critic_lstm=True,
lstm_hidden_size=4,
lstm_kwargs=dict(dropout=0.5),
n_lstm_layers=2,
),
dict(
enable_critic_lstm=False,
lstm_hidden_size=4,
lstm_kwargs=dict(dropout=0.5),
n_lstm_layers=2,
),
],
)
@ -162,11 +166,13 @@ def test_run_sde():
enable_critic_lstm=True,
lstm_hidden_size=4,
lstm_kwargs=dict(dropout=0.5),
n_lstm_layers=2,
),
dict(
enable_critic_lstm=False,
lstm_hidden_size=4,
lstm_kwargs=dict(dropout=0.5),
n_lstm_layers=2,
),
],
)