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[RLlib] Remove framework_iterator from codebase. #47259

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Aug 22, 2024
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[RLlib] Remove framework_iterator from codebase. #47259

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sven1977 Aug 21, 2024
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sven1977 Aug 21, 2024
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sven1977 Aug 22, 2024
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7 changes: 0 additions & 7 deletions rllib/BUILD
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Original file line number Diff line number Diff line change
Expand Up @@ -1898,13 +1898,6 @@ py_test(
srcs = ["utils/postprocessing/tests/test_value_predictions.py"]
)

py_test(
name = "test_random_encoder",
tags = ["team:rllib", "utils"],
size = "large",
srcs = ["utils/exploration/tests/test_random_encoder.py"]
)

py_test(
name = "test_torch_utils",
tags = ["team:rllib", "utils", "gpu"],
Expand Down
94 changes: 42 additions & 52 deletions rllib/algorithms/appo/tests/test_appo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,11 +4,7 @@
import ray.rllib.algorithms.appo as appo
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.utils.metrics.learner_info import LEARNER_INFO, LEARNER_STATS_KEY
from ray.rllib.utils.test_utils import (
check_compute_single_action,
check_train_results,
framework_iterator,
)
from ray.rllib.utils.test_utils import check_compute_single_action, check_train_results


class TestAPPO(unittest.TestCase):
Expand All @@ -25,15 +21,14 @@ def test_appo_compilation(self):
config = appo.APPOConfig().env_runners(num_env_runners=1)
num_iterations = 2

for _ in framework_iterator(config):
algo = config.build(env="CartPole-v1")
for i in range(num_iterations):
results = algo.train()
print(results)
check_train_results(results)
algo = config.build(env="CartPole-v1")
for i in range(num_iterations):
results = algo.train()
print(results)
check_train_results(results)

check_compute_single_action(algo)
algo.stop()
check_compute_single_action(algo)
algo.stop()

def test_appo_compilation_use_kl_loss(self):
"""Test whether APPO can be built with kl_loss enabled."""
Expand All @@ -42,14 +37,13 @@ def test_appo_compilation_use_kl_loss(self):
)
num_iterations = 2

for _ in framework_iterator(config):
algo = config.build(env="CartPole-v1")
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)
check_compute_single_action(algo)
algo.stop()
algo = config.build(env="CartPole-v1")
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)
check_compute_single_action(algo)
algo.stop()

def test_appo_two_optimizers_two_lrs(self):
# Not explicitly setting this should cause a warning, but not fail.
Expand All @@ -71,14 +65,13 @@ def test_appo_two_optimizers_two_lrs(self):
num_iterations = 2

# Only supported for tf so far.
for _ in framework_iterator(config, frameworks=("torch", "tf2", "tf")):
algo = config.build(env="CartPole-v1")
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)
check_compute_single_action(algo)
algo.stop()
algo = config.build(env="CartPole-v1")
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)
check_compute_single_action(algo)
algo.stop()

def test_appo_entropy_coeff_schedule(self):
# Initial lr, doesn't really matter because of the schedule below.
Expand Down Expand Up @@ -122,19 +115,18 @@ def _step_n_times(algo, n: int):
"entropy_coeff"
]

for _ in framework_iterator(config, frameworks=("torch", "tf")):
algo = config.build(env="CartPole-v1")
algo = config.build(env="CartPole-v1")

coeff = _step_n_times(algo, 10) # 200 timesteps
# Should be close to the starting coeff of 0.01.
self.assertLessEqual(coeff, 0.01)
self.assertGreaterEqual(coeff, 0.001)
coeff = _step_n_times(algo, 10) # 200 timesteps
# Should be close to the starting coeff of 0.01.
self.assertLessEqual(coeff, 0.01)
self.assertGreaterEqual(coeff, 0.001)

coeff = _step_n_times(algo, 20) # 400 timesteps
# Should have annealed to the final coeff of 0.0001.
self.assertLessEqual(coeff, 0.001)
coeff = _step_n_times(algo, 20) # 400 timesteps
# Should have annealed to the final coeff of 0.0001.
self.assertLessEqual(coeff, 0.001)

algo.stop()
algo.stop()

def test_appo_learning_rate_schedule(self):
config = (
Expand Down Expand Up @@ -173,15 +165,14 @@ def _step_n_times(algo, n: int):
"cur_lr"
]

for _ in framework_iterator(config):
algo = config.build(env="CartPole-v1")
algo = config.build(env="CartPole-v1")

lr1 = _step_n_times(algo, 10) # 200 timesteps
lr2 = _step_n_times(algo, 10) # 200 timesteps
lr1 = _step_n_times(algo, 10) # 200 timesteps
lr2 = _step_n_times(algo, 10) # 200 timesteps

self.assertGreater(lr1, lr2)
self.assertGreater(lr1, lr2)

algo.stop()
algo.stop()

def test_appo_model_variables(self):
config = (
Expand All @@ -202,13 +193,12 @@ def test_appo_model_variables(self):
)
)

for _ in framework_iterator(config, frameworks=["tf2", "torch"]):
algo = config.build(env="CartPole-v1")
state = algo.get_policy(DEFAULT_POLICY_ID).get_state()
# Weights and Biases for the single hidden layer, the output layer
# of the policy and value networks. So 6 tensors in total.
# We should not get the tensors from the target model here.
self.assertEqual(len(state["weights"]), 6)
algo = config.build(env="CartPole-v1")
state = algo.get_policy(DEFAULT_POLICY_ID).get_state()
# Weights and Biases for the single hidden layer, the output layer
# of the policy and value networks. So 6 tensors in total.
# We should not get the tensors from the target model here.
self.assertEqual(len(state["weights"]), 6)


if __name__ == "__main__":
Expand Down
79 changes: 36 additions & 43 deletions rllib/algorithms/bc/tests/test_bc_old_api_stack.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,6 @@
from ray.rllib.utils.test_utils import (
check_compute_single_action,
check_train_results,
framework_iterator,
)


Expand Down Expand Up @@ -50,48 +49,42 @@ def test_bc_compilation_and_learning_from_offline_file(self):
num_iterations = 350
min_return_to_reach = 75.0

# Test for the following frameworks.
frameworks_to_test = ("torch", "tf")

for _ in framework_iterator(config, frameworks=frameworks_to_test):
for recurrent in [True, False]:
# We only test recurrent networks with RLModules.
if recurrent:
# TODO (Artur): We read input data without a time-dimensions.
# In order for a recurrent offline learning RL Module to
# work, the input data needs to be transformed do add a
# time-dimension.
continue

config.training(model={"use_lstm": recurrent})
algo = config.build(env="CartPole-v1")
learnt = False
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)

eval_results = results.get("evaluation")
if eval_results:
mean_return = eval_results[ENV_RUNNER_RESULTS][
EPISODE_RETURN_MEAN
]
print("iter={} R={}".format(i, mean_return))
# Learn until good reward is reached in the actual env.
if mean_return > min_return_to_reach:
print("learnt!")
learnt = True
break

if not learnt:
raise ValueError(
"`BC` did not reach {} reward from expert offline "
"data!".format(min_return_to_reach)
)

check_compute_single_action(algo, include_prev_action_reward=True)

algo.stop()
for recurrent in [True, False]:
# We only test recurrent networks with RLModules.
if recurrent:
# TODO (Artur): We read input data without a time-dimensions.
# In order for a recurrent offline learning RL Module to
# work, the input data needs to be transformed do add a
# time-dimension.
continue

config.training(model={"use_lstm": recurrent})
algo = config.build(env="CartPole-v1")
learnt = False
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)

eval_results = results.get("evaluation")
if eval_results:
mean_return = eval_results[ENV_RUNNER_RESULTS][EPISODE_RETURN_MEAN]
print("iter={} R={}".format(i, mean_return))
# Learn until good reward is reached in the actual env.
if mean_return > min_return_to_reach:
print("learnt!")
learnt = True
break

if not learnt:
raise ValueError(
"`BC` did not reach {} reward from expert offline "
"data!".format(min_return_to_reach)
)

check_compute_single_action(algo, include_prev_action_reward=True)

algo.stop()


if __name__ == "__main__":
Expand Down
114 changes: 41 additions & 73 deletions rllib/algorithms/cql/tests/test_cql.py
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Original file line number Diff line number Diff line change
@@ -1,23 +1,17 @@
import numpy as np
from pathlib import Path
import os
import unittest

import ray
from ray.rllib.algorithms import cql
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.metrics import (
ENV_RUNNER_RESULTS,
EPISODE_RETURN_MEAN,
EVALUATION_RESULTS,
)
from ray.rllib.utils.test_utils import (
check_compute_single_action,
check_train_results,
framework_iterator,
)
from ray.rllib.utils.test_utils import check_compute_single_action, check_train_results

tf1, tf, tfv = try_import_tf()
torch, _ = try_import_torch()


Expand Down Expand Up @@ -75,77 +69,51 @@ def test_cql_compilation(self):
)
num_iterations = 4

# Test for tf/torch frameworks.
for fw in framework_iterator(config):
algo = config.build()
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)
eval_results = results.get(EVALUATION_RESULTS)
if eval_results:
print(
f"iter={algo.iteration} "
f"R={eval_results[ENV_RUNNER_RESULTS][EPISODE_RETURN_MEAN]}"
)
check_compute_single_action(algo)

# Get policy and model.
pol = algo.get_policy()
cql_model = pol.model
if fw == "tf":
pol.get_session().__enter__()

# Example on how to do evaluation on the trained Algorithm
# using the data from CQL's global replay buffer.
# Get a sample (MultiAgentBatch).

batch = algo.env_runner.input_reader.next()
multi_agent_batch = batch.as_multi_agent()
# All experiences have been buffered for `default_policy`
batch = multi_agent_batch.policy_batches["default_policy"]

if fw == "torch":
obs = torch.from_numpy(batch["obs"])
else:
obs = batch["obs"]
batch["actions"] = batch["actions"].astype(np.float32)

# Pass the observations through our model to get the
# features, which then to pass through the Q-head.
model_out, _ = cql_model({"obs": obs})
# The estimated Q-values from the (historic) actions in the batch.
if fw == "torch":
q_values_old = cql_model.get_q_values(
model_out, torch.from_numpy(batch["actions"])
)
else:
q_values_old = cql_model.get_q_values(
tf.convert_to_tensor(model_out), batch["actions"]
algo = config.build()
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)
eval_results = results.get(EVALUATION_RESULTS)
if eval_results:
print(
f"iter={algo.iteration} "
f"R={eval_results[ENV_RUNNER_RESULTS][EPISODE_RETURN_MEAN]}"
)
check_compute_single_action(algo)

# The estimated Q-values for the new actions computed
# by our policy.
actions_new = pol.compute_actions_from_input_dict({"obs": obs})[0]
if fw == "torch":
q_values_new = cql_model.get_q_values(
model_out, torch.from_numpy(actions_new)
)
else:
q_values_new = cql_model.get_q_values(model_out, actions_new)
# Get policy and model.
pol = algo.get_policy()
cql_model = pol.model

if fw == "tf":
q_values_old, q_values_new = pol.get_session().run(
[q_values_old, q_values_new]
)
# Example on how to do evaluation on the trained Algorithm
# using the data from CQL's global replay buffer.
# Get a sample (MultiAgentBatch).

batch = algo.env_runner.input_reader.next()
multi_agent_batch = batch.as_multi_agent()
# All experiences have been buffered for `default_policy`
batch = multi_agent_batch.policy_batches["default_policy"]

obs = torch.from_numpy(batch["obs"])

# Pass the observations through our model to get the
# features, which then to pass through the Q-head.
model_out, _ = cql_model({"obs": obs})
# The estimated Q-values from the (historic) actions in the batch.
q_values_old = cql_model.get_q_values(
model_out, torch.from_numpy(batch["actions"])
)

print(f"Q-val batch={q_values_old}")
print(f"Q-val policy={q_values_new}")
# The estimated Q-values for the new actions computed
# by our policy.
actions_new = pol.compute_actions_from_input_dict({"obs": obs})[0]
q_values_new = cql_model.get_q_values(model_out, torch.from_numpy(actions_new))

if fw == "tf":
pol.get_session().__exit__(None, None, None)
print(f"Q-val batch={q_values_old}")
print(f"Q-val policy={q_values_new}")

algo.stop()
algo.stop()


if __name__ == "__main__":
Expand Down
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