WIP

Move dependencies to extra
Merge remote-tracking branch 'origin/main' into user/aliberts/2024_05_14_compare_policies
2024-05-21 16:31:48 +02:00 · 2024-05-21 16:29:44 +02:00 · 2024-05-21 10:14:10 +02:00 · 2024-05-20 18:57:54 +01:00 · 2024-05-20 18:27:54 +01:00 · 2024-05-16 19:31:57 +02:00
8 changed files with 839 additions and 197 deletions
--- a/38
+++ b/38
@@ -20,6 +20,8 @@ build-gpu:
 test-end-to-end:
 	${MAKE} test-act-ete-train
 	${MAKE} test-act-ete-eval
+	${MAKE} test-act-ete-train-amp
+	${MAKE} test-act-ete-eval-amp
 	${MAKE} test-diffusion-ete-train
 	${MAKE} test-diffusion-ete-eval
 	${MAKE} test-tdmpc-ete-train
@@ -29,6 +31,7 @@ test-end-to-end:
 test-act-ete-train:
 	python lerobot/scripts/train.py \
 		policy=act \
+		policy.dim_model=64 \
 		env=aloha \
 		wandb.enable=False \
 		training.offline_steps=2 \
@@ -51,9 +54,40 @@ test-act-ete-eval:
 		env.episode_length=8 \
 		device=cpu \

+test-act-ete-train-amp:
+	python lerobot/scripts/train.py \
+		policy=act \
+		policy.dim_model=64 \
+		env=aloha \
+		wandb.enable=False \
+		training.offline_steps=2 \
+		training.online_steps=0 \
+		eval.n_episodes=1 \
+		eval.batch_size=1 \
+		device=cpu \
+		training.save_model=true \
+		training.save_freq=2 \
+		policy.n_action_steps=20 \
+		policy.chunk_size=20 \
+		training.batch_size=2 \
+		hydra.run.dir=tests/outputs/act/ \
+		use_amp=true
+
+test-act-ete-eval-amp:
+	python lerobot/scripts/eval.py \
+		-p tests/outputs/act/checkpoints/000002 \
+		eval.n_episodes=1 \
+		eval.batch_size=1 \
+		env.episode_length=8 \
+		device=cpu \
+		use_amp=true
+
 test-diffusion-ete-train:
 	python lerobot/scripts/train.py \
 		policy=diffusion \
+		policy.down_dims=\[64,128,256\] \
+		policy.diffusion_step_embed_dim=32 \
+		policy.num_inference_steps=10 \
 		env=pusht \
 		wandb.enable=False \
 		training.offline_steps=2 \
@@ -74,6 +108,7 @@ test-diffusion-ete-eval:
 		env.episode_length=8 \
 		device=cpu \

+# TODO(alexander-soare): Restore online_steps to 2 when it is reinstated.
 test-tdmpc-ete-train:
 	python lerobot/scripts/train.py \
 		policy=tdmpc \
@@ -82,7 +117,7 @@ test-tdmpc-ete-train:
 		dataset_repo_id=lerobot/xarm_lift_medium \
 		wandb.enable=False \
 		training.offline_steps=2 \
-		training.online_steps=2 \
+		training.online_steps=0 \
 		eval.n_episodes=1 \
 		eval.batch_size=1 \
 		env.episode_length=2 \
@@ -100,7 +135,6 @@ test-tdmpc-ete-eval:
 		env.episode_length=8 \
 		device=cpu \

-
 test-default-ete-eval:
 	python lerobot/scripts/eval.py \
 		--config lerobot/configs/default.yaml \
--- a/lerobot/configs/default.yaml
+++ b/lerobot/configs/default.yaml
@@ -10,6 +10,9 @@ hydra:
    name: default

 device: cuda  # cpu
+# `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP,
+# automatic gradient scaling is used.
+use_amp: false
 # `seed` is used for training (eg: model initialization, dataset shuffling)
 # AND for the evaluation environments.
 seed: ???
@@ -17,6 +20,7 @@ dataset_repo_id: lerobot/pusht

 training:
  offline_steps: ???
+  # NOTE: `online_steps` is not implemented yet. It's here as a placeholder.
  online_steps: ???
  online_steps_between_rollouts: ???
  online_sampling_ratio: 0.5
--- a/lerobot/configs/policy/tdmpc.yaml
+++ b/lerobot/configs/policy/tdmpc.yaml
@@ -5,7 +5,8 @@ dataset_repo_id: lerobot/xarm_lift_medium

 training:
  offline_steps: 25000
-  online_steps: 25000
+  # TODO(alexander-soare): uncomment when online training gets reinstated
+  online_steps: 0  # 25000 not implemented yet
  eval_freq: 5000
  online_steps_between_rollouts: 1
  online_sampling_ratio: 0.5
--- a/lerobot/scripts/compare_policies.py
+++ b/lerobot/scripts/compare_policies.py
@@ -0,0 +1,340 @@
+"""Compare two policies on based on metrics computed from an eval.
+
+Usage example:
+
+You just made changes to a policy and you want to assess its new performance against
+the reference policy (i.e. before your changes).
+
+```
+python lerobot/scripts/compare_policies.py \
+    output/eval/ref_policy/eval_info.json \
+    output/eval/new_policy/eval_info.json
+```
+
+This script can accept `eval_info.json` dicts with identical seeds between each eval episode of ref_policy and
+new_policy (paired-samples) or from evals performed with different seeds (independent samples).
+
+The script will first perform normality tests to determine if parametric tests can be used or not, then
+evaluate if policies metrics are significantly different using the appropriate tests.
+
+CAVEATS: by default, this script will compare seeds numbers to determine if samples can be considered paired.
+If changes have been made to this environment in-between the ref_policy eval and the new_policy eval, you
+should use the `--independent` flag to override this and not pair the samples even if they have identical
+seeds.
+"""
+
+import argparse
+import json
+import logging
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.stats as stats
+from scipy.stats import anderson, kstest, mannwhitneyu, normaltest, shapiro, ttest_ind, ttest_rel, wilcoxon
+from statsmodels.stats.contingency_tables import mcnemar
+from termcolor import colored
+from terminaltables import AsciiTable
+
+
+def init_logging() -> None:
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(message)s",
+        handlers=[logging.StreamHandler()],
+    )
+    logging.getLogger("matplotlib.font_manager").disabled = True
+
+
+def log_section(title: str) -> None:
+    section_title = f"\n{'-'*21}\n {title.center(19)} \n{'-'*21}"
+    logging.info(section_title)
+
+
+def log_test(msg: str, p_value: float):
+    if p_value < 0.01:
+        color, interpretation = "red", "H_0 Rejected"
+    elif 0.01 <= p_value < 0.05:
+        color, interpretation = "yellow", "Inconclusive"
+    else:
+        color, interpretation = "green", "H_0 Not Rejected"
+    logging.info(
+        f"{msg}, p-value = {colored(f'{p_value:.3f}', color)} -> {colored(f'{interpretation}', color, attrs=['bold'])}"
+    )
+
+
+def get_eval_info_episodes(eval_info_path: Path) -> dict:
+    with open(eval_info_path) as f:
+        eval_info = json.load(f)
+
+    return {
+        "sum_rewards": np.array([ep_stat["sum_reward"] for ep_stat in eval_info["per_episode"]]),
+        "max_rewards": np.array([ep_stat["max_reward"] for ep_stat in eval_info["per_episode"]]),
+        "successes": np.array([ep_stat["success"] for ep_stat in eval_info["per_episode"]]),
+        "seeds": [ep_stat["seed"] for ep_stat in eval_info["per_episode"]],
+        "num_episodes": len(eval_info["per_episode"]),
+    }
+
+
+def append_table_metric(table: list, metric: str, ref_sample: dict, new_sample: dict, mean_std: bool = False):
+    if mean_std:
+        ref_metric = f"{np.mean(ref_sample[metric]):.3f} ({np.std(ref_sample[metric]):.3f})"
+        new_metric = f"{np.mean(new_sample[metric]):.3f} ({np.std(new_sample[metric]):.3f})"
+        row_header = f"{metric} - mean (std)"
+    else:
+        ref_metric = ref_sample[metric]
+        new_metric = new_sample[metric]
+        row_header = metric
+
+    row = [row_header, ref_metric, new_metric]
+    table.append(row)
+    return table
+
+
+def cohens_d(x, y):
+    return (np.mean(x) - np.mean(y)) / np.sqrt((np.std(x, ddof=1) ** 2 + np.std(y, ddof=1) ** 2) / 2)
+
+
+def normality_tests(array: np.ndarray, name: str):
+    ap_stat, ap_p = normaltest(array)
+    sw_stat, sw_p = shapiro(array)
+    ks_stat, ks_p = kstest(array, "norm", args=(np.mean(array), np.std(array)))
+    ad_stat = anderson(array)
+
+    log_test(f"{name} - D'Agostino and Pearson test: statistic = {ap_stat:.3f}", ap_p)
+    log_test(f"{name} - Shapiro-Wilk test: statistic = {sw_stat:.3f}", sw_p)
+    log_test(f"{name} - Kolmogorov-Smirnov test: statistic = {ks_stat:.3f}", ks_p)
+    logging.info(f"{name} - Anderson-Darling test: statistic = {ad_stat.statistic:.3f}")
+    for i in range(len(ad_stat.critical_values)):
+        cv, sl = ad_stat.critical_values[i], ad_stat.significance_level[i]
+        logging.info(f"    Critical value at {sl}%: {cv:.3f}")
+
+    return sw_p > 0.05 and ks_p > 0.05
+
+
+def perform_tests(ref_sample: dict, new_sample: dict, output_dir: Path, independent: bool = False):
+    seeds_a, seeds_b = ref_sample["seeds"], new_sample["seeds"]
+    if (seeds_a == seeds_b) and not independent:
+        logging.info("\nSamples are paired (identical seeds).")
+        paired = True
+    else:
+        logging.info("\nSamples are considered independent (seeds are different).")
+        paired = False
+
+    table_data = [["Metric", "Ref.", "New"]]
+    table_data = append_table_metric(table_data, "num_episodes", ref_sample, new_sample)
+    table_data = append_table_metric(table_data, "successes", ref_sample, new_sample, mean_std=True)
+    table_data = append_table_metric(table_data, "max_rewards", ref_sample, new_sample, mean_std=True)
+    table_data = append_table_metric(table_data, "sum_rewards", ref_sample, new_sample, mean_std=True)
+    table = AsciiTable(table_data)
+    print(table.table)
+
+    log_section("Effect Size")
+    d_max_reward = cohens_d(ref_sample["max_rewards"], new_sample["max_rewards"])
+    d_sum_reward = cohens_d(ref_sample["sum_rewards"], new_sample["sum_rewards"])
+    logging.info(f"Cohen's d for Max Reward: {d_max_reward:.3f}")
+    logging.info(f"Cohen's d for Sum Reward: {d_sum_reward:.3f}")
+
+    if paired:
+        paired_sample_tests(ref_sample, new_sample)
+    else:
+        independent_sample_tests(ref_sample, new_sample)
+
+    output_dir.mkdir(exist_ok=True, parents=True)
+
+    plot_boxplot(
+        ref_sample["max_rewards"],
+        new_sample["max_rewards"],
+        ["Ref Sample Max Reward", "New Sample Max Reward"],
+        "Boxplot of Max Rewards",
+        f"{output_dir}/boxplot_max_reward.png",
+    )
+    plot_boxplot(
+        ref_sample["sum_rewards"],
+        new_sample["sum_rewards"],
+        ["Ref Sample Sum Reward", "New Sample Sum Reward"],
+        "Boxplot of Sum Rewards",
+        f"{output_dir}/boxplot_sum_reward.png",
+    )
+
+    plot_histogram(
+        ref_sample["max_rewards"],
+        new_sample["max_rewards"],
+        ["Ref Sample Max Reward", "New Sample Max Reward"],
+        "Histogram of Max Rewards",
+        f"{output_dir}/histogram_max_reward.png",
+    )
+    plot_histogram(
+        ref_sample["sum_rewards"],
+        new_sample["sum_rewards"],
+        ["Ref Sample Sum Reward", "New Sample Sum Reward"],
+        "Histogram of Sum Rewards",
+        f"{output_dir}/histogram_sum_reward.png",
+    )
+
+    plot_qqplot(
+        ref_sample["max_rewards"],
+        "Q-Q Plot of Ref Sample Max Rewards",
+        f"{output_dir}/qqplot_sample_a_max_reward.png",
+    )
+    plot_qqplot(
+        new_sample["max_rewards"],
+        "Q-Q Plot of New Sample Max Rewards",
+        f"{output_dir}/qqplot_sample_b_max_reward.png",
+    )
+    plot_qqplot(
+        ref_sample["sum_rewards"],
+        "Q-Q Plot of Ref Sample Sum Rewards",
+        f"{output_dir}/qqplot_sample_a_sum_reward.png",
+    )
+    plot_qqplot(
+        new_sample["sum_rewards"],
+        "Q-Q Plot of New Sample Sum Rewards",
+        f"{output_dir}/qqplot_sample_b_sum_reward.png",
+    )
+
+
+def paired_sample_tests(ref_sample: dict, new_sample: dict):
+    log_section("Normality tests")
+    max_reward_diff = ref_sample["max_rewards"] - new_sample["max_rewards"]
+    sum_reward_diff = ref_sample["sum_rewards"] - new_sample["sum_rewards"]
+
+    normal_max_reward_diff = normality_tests(max_reward_diff, "Max Reward Difference")
+    normal_sum_reward_diff = normality_tests(sum_reward_diff, "Sum Reward Difference")
+
+    log_section("Paired-sample tests")
+    if normal_max_reward_diff:
+        t_stat_max_reward, p_val_max_reward = ttest_rel(ref_sample["max_rewards"], new_sample["max_rewards"])
+        log_test(f"Paired t-test for Max Reward: t-statistic = {t_stat_max_reward:.3f}", p_val_max_reward)
+    else:
+        w_stat_max_reward, p_wilcox_max_reward = wilcoxon(
+            ref_sample["max_rewards"], new_sample["max_rewards"]
+        )
+        log_test(f"Wilcoxon test for Max Reward: statistic = {w_stat_max_reward:.3f}", p_wilcox_max_reward)
+
+    if normal_sum_reward_diff:
+        t_stat_sum_reward, p_val_sum_reward = ttest_rel(ref_sample["sum_rewards"], new_sample["sum_rewards"])
+        log_test(f"Paired t-test for Sum Reward: t-statistic = {t_stat_sum_reward:.3f}", p_val_sum_reward)
+    else:
+        w_stat_sum_reward, p_wilcox_sum_reward = wilcoxon(
+            ref_sample["sum_rewards"], new_sample["sum_rewards"]
+        )
+        log_test(f"Wilcoxon test for Sum Reward: statistic = {w_stat_sum_reward:.3f}", p_wilcox_sum_reward)
+
+    table = np.array(
+        [
+            [
+                np.sum((ref_sample["successes"] == 1) & (new_sample["successes"] == 1)),
+                np.sum((ref_sample["successes"] == 1) & (new_sample["successes"] == 0)),
+            ],
+            [
+                np.sum((ref_sample["successes"] == 0) & (new_sample["successes"] == 1)),
+                np.sum((ref_sample["successes"] == 0) & (new_sample["successes"] == 0)),
+            ],
+        ]
+    )
+    mcnemar_result = mcnemar(table, exact=True)
+    log_test(f"McNemar's test for Success: statistic = {mcnemar_result.statistic:.3f}", mcnemar_result.pvalue)
+
+
+def independent_sample_tests(ref_sample: dict, new_sample: dict):
+    log_section("Normality tests")
+    normal_max_rewards_a = normality_tests(ref_sample["max_rewards"], "Max Rewards Ref Sample")
+    normal_max_rewards_b = normality_tests(new_sample["max_rewards"], "Max Rewards New Sample")
+    normal_sum_rewards_a = normality_tests(ref_sample["sum_rewards"], "Sum Rewards Ref Sample")
+    normal_sum_rewards_b = normality_tests(new_sample["sum_rewards"], "Sum Rewards New Sample")
+
+    log_section("Independent samples tests")
+    table = [["Test", "max_rewards", "sum_rewards"]]
+    if normal_max_rewards_a and normal_max_rewards_b:
+        table = append_independent_test(
+            table, ref_sample, new_sample, ttest_ind, "Two-Sample t-test", kwargs={"equal_var": False}
+        )
+        t_stat_max_reward, p_val_max_reward = ttest_ind(
+            ref_sample["max_rewards"], new_sample["max_rewards"], equal_var=False
+        )
+        log_test(f"Two-Sample t-test for Max Reward: t-statistic = {t_stat_max_reward:.3f}", p_val_max_reward)
+    else:
+        table = append_independent_test(table, ref_sample, new_sample, mannwhitneyu, "Mann-Whitney U")
+        u_stat_max_reward, p_u_max_reward = mannwhitneyu(ref_sample["max_rewards"], new_sample["max_rewards"])
+        log_test(f"Mann-Whitney U test for Max Reward: U-statistic = {u_stat_max_reward:.3f}", p_u_max_reward)
+
+    if normal_sum_rewards_a and normal_sum_rewards_b:
+        t_stat_sum_reward, p_val_sum_reward = ttest_ind(
+            ref_sample["sum_rewards"], new_sample["sum_rewards"], equal_var=False
+        )
+        log_test(f"Two-Sample t-test for Sum Reward: t-statistic = {t_stat_sum_reward:.3f}", p_val_sum_reward)
+    else:
+        u_stat_sum_reward, p_u_sum_reward = mannwhitneyu(ref_sample["sum_rewards"], new_sample["sum_rewards"])
+        log_test(f"Mann-Whitney U test for Sum Reward: U-statistic = {u_stat_sum_reward:.3f}", p_u_sum_reward)
+
+    table = AsciiTable(table)
+    print(table.table)
+
+
+def append_independent_test(
+    table: list,
+    ref_sample: dict,
+    new_sample: dict,
+    test: callable,
+    test_name: str,
+    kwargs: dict | None = None,
+) -> list:
+    kwargs = {} if kwargs is None else kwargs
+    row = [f"{test_name}: p-value ≥ alpha"]
+    for metric in table[0][1:]:
+        _, p_val = test(ref_sample[metric], new_sample[metric], **kwargs)
+        alpha = 0.05
+        status = "✅" if p_val >= alpha else "❌"
+        row.append(f"{status} {p_val:.3f} ≥ {alpha}")
+
+    table.append(row)
+    return table
+
+
+def plot_boxplot(data_a: np.ndarray, data_b: np.ndarray, labels: list[str], title: str, filename: str):
+    plt.boxplot([data_a, data_b], labels=labels)
+    plt.title(title)
+    plt.savefig(filename)
+    plt.close()
+
+
+def plot_histogram(data_a: np.ndarray, data_b: np.ndarray, labels: list[str], title: str, filename: str):
+    plt.hist(data_a, bins=30, alpha=0.7, label=labels[0])
+    plt.hist(data_b, bins=30, alpha=0.7, label=labels[1])
+    plt.title(title)
+    plt.legend()
+    plt.savefig(filename)
+    plt.close()
+
+
+def plot_qqplot(data: np.ndarray, title: str, filename: str):
+    stats.probplot(data, dist="norm", plot=plt)
+    plt.title(title)
+    plt.savefig(filename)
+    plt.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("ref_sample_path", type=Path, help="Path to the reference sample JSON file.")
+    parser.add_argument("new_sample_path", type=Path, help="Path to the new sample JSON file.")
+    parser.add_argument(
+        "--independent",
+        action="store_true",
+        help="Ignore seeds and consider samples to be independent (unpaired).",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=Path,
+        default=Path("outputs/compare/"),
+        help="Directory to save the output results. Defaults to outputs/compare/",
+    )
+    args = parser.parse_args()
+    init_logging()
+
+    ref_sample = get_eval_info_episodes(args.ref_sample_path)
+    new_sample = get_eval_info_episodes(args.new_sample_path)
+    perform_tests(ref_sample, new_sample, args.output_dir, args.independent)
--- a/lerobot/scripts/eval.py
+++ b/lerobot/scripts/eval.py
@@ -46,6 +46,7 @@ import json
 import logging
 import threading
 import time
+from contextlib import nullcontext
 from copy import deepcopy
 from datetime import datetime as dt
 from pathlib import Path
@@ -520,7 +521,7 @@ def eval(
        raise NotImplementedError()

    # Check device is available
-    get_safe_torch_device(hydra_cfg.device, log=True)
+    device = get_safe_torch_device(hydra_cfg.device, log=True)

    torch.backends.cudnn.benchmark = True
    torch.backends.cuda.matmul.allow_tf32 = True
@@ -539,16 +540,17 @@ def eval(
        policy = make_policy(hydra_cfg=hydra_cfg, dataset_stats=make_dataset(hydra_cfg).stats)
    policy.eval()

-    info = eval_policy(
-        env,
-        policy,
-        hydra_cfg.eval.n_episodes,
-        max_episodes_rendered=10,
-        video_dir=Path(out_dir) / "eval",
-        start_seed=hydra_cfg.seed,
-        enable_progbar=True,
-        enable_inner_progbar=True,
-    )
+    with torch.no_grad(), torch.autocast(device_type=device.type) if hydra_cfg.use_amp else nullcontext():
+        info = eval_policy(
+            env,
+            policy,
+            hydra_cfg.eval.n_episodes,
+            max_episodes_rendered=10,
+            video_dir=Path(out_dir) / "eval",
+            start_seed=hydra_cfg.seed,
+            enable_progbar=True,
+            enable_inner_progbar=True,
+        )
    print(info["aggregated"])

    # Save info
--- a/lerobot/scripts/train.py
+++ b/lerobot/scripts/train.py
@@ -15,15 +15,14 @@
 # limitations under the License.
 import logging
 import time
+from contextlib import nullcontext
 from copy import deepcopy
 from pathlib import Path

-import datasets
 import hydra
 import torch
-from datasets import concatenate_datasets
-from datasets.utils import disable_progress_bars, enable_progress_bars
 from omegaconf import DictConfig
+from torch.cuda.amp import GradScaler

 from lerobot.common.datasets.factory import make_dataset
 from lerobot.common.datasets.utils import cycle
@@ -31,6 +30,7 @@ from lerobot.common.envs.factory import make_env
 from lerobot.common.logger import Logger, log_output_dir
 from lerobot.common.policies.factory import make_policy
 from lerobot.common.policies.policy_protocol import PolicyWithUpdate
+from lerobot.common.policies.utils import get_device_from_parameters
 from lerobot.common.utils.utils import (
    format_big_number,
    get_safe_torch_device,
@@ -69,7 +69,6 @@ def make_optimizer_and_scheduler(cfg, policy):
            cfg.training.adam_eps,
            cfg.training.adam_weight_decay,
        )
-        assert cfg.training.online_steps == 0, "Diffusion Policy does not handle online training."
        from diffusers.optimization import get_scheduler

        lr_scheduler = get_scheduler(
@@ -87,21 +86,40 @@ def make_optimizer_and_scheduler(cfg, policy):
    return optimizer, lr_scheduler


-def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
+def update_policy(
+    policy,
+    batch,
+    optimizer,
+    grad_clip_norm,
+    grad_scaler: GradScaler,
+    lr_scheduler=None,
+    use_amp: bool = False,
+):
    """Returns a dictionary of items for logging."""
-    start_time = time.time()
+    start_time = time.perf_counter()
+    device = get_device_from_parameters(policy)
    policy.train()
-    output_dict = policy.forward(batch)
-    # TODO(rcadene): policy.unnormalize_outputs(out_dict)
-    loss = output_dict["loss"]
-    loss.backward()
+    with torch.autocast(device_type=device.type) if use_amp else nullcontext():
+        output_dict = policy.forward(batch)
+        # TODO(rcadene): policy.unnormalize_outputs(out_dict)
+        loss = output_dict["loss"]
+    grad_scaler.scale(loss).backward()
+
+    # Unscale the graident of the optimzer's assigned params in-place **prior to gradient clipping**.
+    grad_scaler.unscale_(optimizer)
+
    grad_norm = torch.nn.utils.clip_grad_norm_(
        policy.parameters(),
        grad_clip_norm,
        error_if_nonfinite=False,
    )

-    optimizer.step()
+    # Optimizer's gradients are already unscaled, so scaler.step does not unscale them,
+    # although it still skips optimizer.step() if the gradients contain infs or NaNs.
+    grad_scaler.step(optimizer)
+    # Updates the scale for next iteration.
+    grad_scaler.update()
+
    optimizer.zero_grad()

    if lr_scheduler is not None:
@@ -115,7 +133,7 @@ def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
        "loss": loss.item(),
        "grad_norm": float(grad_norm),
        "lr": optimizer.param_groups[0]["lr"],
-        "update_s": time.time() - start_time,
+        "update_s": time.perf_counter() - start_time,
        **{k: v for k, v in output_dict.items() if k != "loss"},
    }

@@ -211,103 +229,6 @@ def log_eval_info(logger, info, step, cfg, dataset, is_offline):
    logger.log_dict(info, step, mode="eval")


-def calculate_online_sample_weight(n_off: int, n_on: int, pc_on: float):
-    """
-    Calculate the sampling weight to be assigned to samples so that a specified percentage of the batch comes from online dataset (on average).
-
-    Parameters:
-    - n_off (int): Number of offline samples, each with a sampling weight of 1.
-    - n_on (int): Number of online samples.
-    - pc_on (float): Desired percentage of online samples in decimal form (e.g., 50% as 0.5).
-
-    The total weight of offline samples is n_off * 1.0.
-    The total weight of offline samples is n_on * w.
-    The total combined weight of all samples is n_off + n_on * w.
-    The fraction of the weight that is online is n_on * w / (n_off + n_on * w).
-    We want this fraction to equal pc_on, so we set up the equation n_on * w / (n_off + n_on * w) = pc_on.
-    The solution is w = - (n_off * pc_on) / (n_on * (pc_on - 1))
-    """
-    assert 0.0 <= pc_on <= 1.0
-    return -(n_off * pc_on) / (n_on * (pc_on - 1))
-
-
-def add_episodes_inplace(
-    online_dataset: torch.utils.data.Dataset,
-    concat_dataset: torch.utils.data.ConcatDataset,
-    sampler: torch.utils.data.WeightedRandomSampler,
-    hf_dataset: datasets.Dataset,
-    episode_data_index: dict[str, torch.Tensor],
-    pc_online_samples: float,
-):
-    """
-    Modifies the online_dataset, concat_dataset, and sampler in place by integrating
-    new episodes from hf_dataset into the online_dataset, updating the concatenated
-    dataset's structure and adjusting the sampling strategy based on the specified
-    percentage of online samples.
-
-    Parameters:
-    - online_dataset (torch.utils.data.Dataset): The existing online dataset to be updated.
-    - concat_dataset (torch.utils.data.ConcatDataset): The concatenated dataset that combines
-      offline and online datasets, used for sampling purposes.
-    - sampler (torch.utils.data.WeightedRandomSampler): A sampler that will be updated to
-      reflect changes in the dataset sizes and specified sampling weights.
-    - hf_dataset (datasets.Dataset): A Hugging Face dataset containing the new episodes to be added.
-    - episode_data_index (dict): A dictionary containing two keys ("from" and "to") associated to dataset indices.
-      They indicate the start index and end index of each episode in the dataset.
-    - pc_online_samples (float): The target percentage of samples that should come from
-      the online dataset during sampling operations.
-
-    Raises:
-    - AssertionError: If the first episode_id or index in hf_dataset is not 0
-    """
-    first_episode_idx = hf_dataset.select_columns("episode_index")[0]["episode_index"].item()
-    last_episode_idx = hf_dataset.select_columns("episode_index")[-1]["episode_index"].item()
-    first_index = hf_dataset.select_columns("index")[0]["index"].item()
-    last_index = hf_dataset.select_columns("index")[-1]["index"].item()
-    # sanity check
-    assert first_episode_idx == 0, f"{first_episode_idx=} is not 0"
-    assert first_index == 0, f"{first_index=} is not 0"
-    assert first_index == episode_data_index["from"][first_episode_idx].item()
-    assert last_index == episode_data_index["to"][last_episode_idx].item() - 1
-
-    if len(online_dataset) == 0:
-        # initialize online dataset
-        online_dataset.hf_dataset = hf_dataset
-        online_dataset.episode_data_index = episode_data_index
-    else:
-        # get the starting indices of the new episodes and frames to be added
-        start_episode_idx = last_episode_idx + 1
-        start_index = last_index + 1
-
-        def shift_indices(episode_index, index):
-            # note: we dont shift "frame_index" since it represents the index of the frame in the episode it belongs to
-            example = {"episode_index": episode_index + start_episode_idx, "index": index + start_index}
-            return example
-
-        disable_progress_bars()  # map has a tqdm progress bar
-        hf_dataset = hf_dataset.map(shift_indices, input_columns=["episode_index", "index"])
-        enable_progress_bars()
-
-        episode_data_index["from"] += start_index
-        episode_data_index["to"] += start_index
-
-        # extend online dataset
-        online_dataset.hf_dataset = concatenate_datasets([online_dataset.hf_dataset, hf_dataset])
-
-    # update the concatenated dataset length used during sampling
-    concat_dataset.cumulative_sizes = concat_dataset.cumsum(concat_dataset.datasets)
-
-    # update the sampling weights for each frame so that online frames get sampled a certain percentage of times
-    len_online = len(online_dataset)
-    len_offline = len(concat_dataset) - len_online
-    weight_offline = 1.0
-    weight_online = calculate_online_sample_weight(len_offline, len_online, pc_online_samples)
-    sampler.weights = torch.tensor([weight_offline] * len_offline + [weight_online] * len(online_dataset))
-
-    # update the total number of samples used during sampling
-    sampler.num_samples = len(concat_dataset)
-
-
 def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = None):
    if out_dir is None:
        raise NotImplementedError()
@@ -316,11 +237,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No

    init_logging()

-    if cfg.training.online_steps > 0 and cfg.eval.batch_size > 1:
-        logging.warning("eval.batch_size > 1 not supported for online training steps")
+    if cfg.training.online_steps > 0:
+        raise NotImplementedError("Online training is not implemented yet.")

    # Check device is available
-    get_safe_torch_device(cfg.device, log=True)
+    device = get_safe_torch_device(cfg.device, log=True)

    torch.backends.cudnn.benchmark = True
    torch.backends.cuda.matmul.allow_tf32 = True
@@ -338,6 +259,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
    # Create optimizer and scheduler
    # Temporary hack to move optimizer out of policy
    optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
+    grad_scaler = GradScaler(enabled=cfg.use_amp)

    num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
    num_total_params = sum(p.numel() for p in policy.parameters())
@@ -358,14 +280,15 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
    def evaluate_and_checkpoint_if_needed(step):
        if step % cfg.training.eval_freq == 0:
            logging.info(f"Eval policy at step {step}")
-            eval_info = eval_policy(
-                eval_env,
-                policy,
-                cfg.eval.n_episodes,
-                video_dir=Path(out_dir) / "eval",
-                max_episodes_rendered=4,
-                start_seed=cfg.seed,
-            )
+            with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
+                eval_info = eval_policy(
+                    eval_env,
+                    policy,
+                    cfg.eval.n_episodes,
+                    video_dir=Path(out_dir) / "eval",
+                    max_episodes_rendered=4,
+                    start_seed=cfg.seed,
+                )
            log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline)
            if cfg.wandb.enable:
                logger.log_video(eval_info["video_paths"][0], step, mode="eval")
@@ -389,23 +312,30 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
        num_workers=4,
        batch_size=cfg.training.batch_size,
        shuffle=True,
-        pin_memory=cfg.device != "cpu",
+        pin_memory=device.type != "cpu",
        drop_last=False,
    )
    dl_iter = cycle(dataloader)

    policy.train()
-    step = 0  # number of policy update (forward + backward + optim)
    is_offline = True
-    for offline_step in range(cfg.training.offline_steps):
-        if offline_step == 0:
+    for step in range(cfg.training.offline_steps):
+        if step == 0:
            logging.info("Start offline training on a fixed dataset")
        batch = next(dl_iter)

        for key in batch:
-            batch[key] = batch[key].to(cfg.device, non_blocking=True)
+            batch[key] = batch[key].to(device, non_blocking=True)

-        train_info = update_policy(policy, batch, optimizer, cfg.training.grad_clip_norm, lr_scheduler)
+        train_info = update_policy(
+            policy,
+            batch,
+            optimizer,
+            cfg.training.grad_clip_norm,
+            grad_scaler=grad_scaler,
+            lr_scheduler=lr_scheduler,
+            use_amp=cfg.use_amp,
+        )

        # TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
        if step % cfg.training.log_freq == 0:
@@ -415,11 +345,6 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
        # so we pass in step + 1.
        evaluate_and_checkpoint_if_needed(step + 1)

-        step += 1
-
-    # create an env dedicated to online episodes collection from policy rollout
-    online_training_env = make_env(cfg, n_envs=1)
-
    # create an empty online dataset similar to offline dataset
    online_dataset = deepcopy(offline_dataset)
    online_dataset.hf_dataset = {}
@@ -436,58 +361,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
        num_workers=4,
        batch_size=cfg.training.batch_size,
        sampler=sampler,
-        pin_memory=cfg.device != "cpu",
+        pin_memory=device.type != "cpu",
        drop_last=False,
    )
-    dl_iter = cycle(dataloader)
-
-    online_step = 0
-    is_offline = False
-    for env_step in range(cfg.training.online_steps):
-        if env_step == 0:
-            logging.info("Start online training by interacting with environment")
-
-        policy.eval()
-        with torch.no_grad():
-            eval_info = eval_policy(
-                online_training_env,
-                policy,
-                n_episodes=1,
-                return_episode_data=True,
-                start_seed=cfg.training.online_env_seed,
-                enable_progbar=True,
-            )
-
-        add_episodes_inplace(
-            online_dataset,
-            concat_dataset,
-            sampler,
-            hf_dataset=eval_info["episodes"]["hf_dataset"],
-            episode_data_index=eval_info["episodes"]["episode_data_index"],
-            pc_online_samples=cfg.training.online_sampling_ratio,
-        )
-
-        policy.train()
-        for _ in range(cfg.training.online_steps_between_rollouts):
-            batch = next(dl_iter)
-
-            for key in batch:
-                batch[key] = batch[key].to(cfg.device, non_blocking=True)
-
-            train_info = update_policy(policy, batch, optimizer, cfg.training.grad_clip_norm, lr_scheduler)
-
-            if step % cfg.training.log_freq == 0:
-                log_train_info(logger, train_info, step, cfg, online_dataset, is_offline)
-
-            # Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
-            # so we pass in step + 1.
-            evaluate_and_checkpoint_if_needed(step + 1)
-
-            step += 1
-            online_step += 1

    eval_env.close()
-    online_training_env.close()
    logging.info("End of training")


--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.

 [[package]]
 name = "absl-py"
@@ -442,6 +442,69 @@ files = [
    {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]

+[[package]]
+name = "contourpy"
+version = "1.2.1"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = true
+python-versions = ">=3.9"
+files = [
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+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+kiwisolver = ">=1.3.1"
+numpy = ">=1.21"
+packaging = ">=20.0"
+pillow = ">=8"
+pyparsing = ">=2.3.1"
+python-dateutil = ">=2.7"
+
 [[package]]
 name = "mkl"
 version = "2021.4.0"
@@ -2451,6 +2756,24 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.9.2)"]

+[[package]]
+name = "patsy"
+version = "0.5.6"
+description = "A Python package for describing statistical models and for building design matrices."
+optional = true
+python-versions = "*"
+files = [
+    {file = "patsy-0.5.6-py2.py3-none-any.whl", hash = "sha256:19056886fd8fa71863fa32f0eb090267f21fb74be00f19f5c70b2e9d76c883c6"},
+    {file = "patsy-0.5.6.tar.gz", hash = "sha256:95c6d47a7222535f84bff7f63d7303f2e297747a598db89cf5c67f0c0c7d2cdb"},
+]
+
+[package.dependencies]
+numpy = ">=1.4"
+six = "*"
+
+[package.extras]
+test = ["pytest", "pytest-cov", "scipy"]
+
 [[package]]
 name = "pettingzoo"
 version = "1.24.3"
@@ -3045,7 +3368,6 @@ files = [
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@@ -3677,6 +3999,51 @@ files = [
    {file = "soupsieve-2.5.tar.gz", hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690"},
 ]

+[[package]]
+name = "statsmodels"
+version = "0.14.2"
+description = "Statistical computations and models for Python"
+optional = true
+python-versions = ">=3.9"
+files = [
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+    {file = "statsmodels-0.14.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5827a12e3ede2b98a784476d61d6bec43011fedb64aa815f2098e0573bece257"},
+    {file = "statsmodels-0.14.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10f2b7611a61adb7d596a6d239abdf1a4d5492b931b00d5ed23d32844d40e48e"},
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+    {file = "statsmodels-0.14.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f36494df7c03d63168fccee5038a62f469469ed6a4dd6eaeb9338abedcd0d5f5"},
+    {file = "statsmodels-0.14.2-cp39-cp39-win_amd64.whl", hash = "sha256:8875823bdd41806dc853333cc4e1b7ef9481bad2380a999e66ea42382cf2178d"},
+    {file = "statsmodels-0.14.2.tar.gz", hash = "sha256:890550147ad3a81cda24f0ba1a5c4021adc1601080bd00e191ae7cd6feecd6ad"},
+]
+
+[package.dependencies]
+numpy = ">=1.22.3"
+packaging = ">=21.3"
+pandas = ">=1.4,<2.1.0 || >2.1.0"
+patsy = ">=0.5.6"
+scipy = ">=1.8,<1.9.2 || >1.9.2"
+
+[package.extras]
+build = ["cython (>=0.29.33)"]
+develop = ["colorama", "cython (>=0.29.33)", "cython (>=3.0.10,<4)", "flake8", "isort", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=8.0,<9.0)"]
+docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
+
 [[package]]
 name = "sympy"
 version = "1.12"
@@ -3718,6 +4085,17 @@ files = [
 [package.extras]
 tests = ["pytest", "pytest-cov"]

+[[package]]
+name = "terminaltables"
+version = "3.1.10"
+description = "Generate simple tables in terminals from a nested list of strings."
+optional = true
+python-versions = ">=2.6"
+files = [
+    {file = "terminaltables-3.1.10-py2.py3-none-any.whl", hash = "sha256:e4fdc4179c9e4aab5f674d80f09d76fa436b96fdc698a8505e0a36bf0804a874"},
+    {file = "terminaltables-3.1.10.tar.gz", hash = "sha256:ba6eca5cb5ba02bba4c9f4f985af80c54ec3dccf94cfcd190154386255e47543"},
+]
+
 [[package]]
 name = "tifffile"
 version = "2024.5.10"
@@ -4239,6 +4617,7 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p

 [extras]
 aloha = ["gym-aloha"]
+compare = ["matplotlib", "statsmodels", "terminaltables"]
 dev = ["debugpy", "pre-commit"]
 pusht = ["gym-pusht"]
 test = ["pytest", "pytest-cov"]
@@ -4248,4 +4627,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "e4834d67df32c8c617c259b0e59bb33ddaccde08fe940d771e74046cbffe3399"
+content-hash = "f85a71ac284fe1090a0b3188a6cfddeb346c8712c6d899f24c19b59962d557d4"
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,15 +58,19 @@ imagecodecs = { version = ">=2024.1.1", optional = true }
 pyav = ">=12.0.5"
 moviepy = ">=1.0.3"
 rerun-sdk = ">=0.15.1"
+statsmodels = {version = ">=0.14.2", optional = true}
+matplotlib = {version = ">=3.8.4", optional = true}
+terminaltables = {version = ">=3.1.10", optional = true}


 [tool.poetry.extras]
 pusht = ["gym-pusht"]
 xarm = ["gym-xarm"]
 aloha = ["gym-aloha"]
+umi = ["imagecodecs"]
+compare = ["statsmodels", "matplotlib", "terminaltables"]
 dev = ["pre-commit", "debugpy"]
 test = ["pytest", "pytest-cov"]
-umi = ["imagecodecs"]

 [tool.ruff]
 line-length = 110
Author	SHA1	Message	Date
Simon Alibert	c6a61e3ba2	WIP	2024-05-21 16:31:48 +02:00
Simon Alibert	62d3546f08	Move dependencies to extra	2024-05-21 16:29:44 +02:00
Simon Alibert	956f035d16	Merge remote-tracking branch 'origin/main' into user/aliberts/2024_05_14_compare_policies	2024-05-21 10:14:10 +02:00
Alexander Soare	b6c216b590	Add Automatic Mixed Precision option for training and evaluation. (#199 )	2024-05-20 18:57:54 +01:00
Alexander Soare	2b270d085b	Disable online training (#202 ) Co-authored-by: Remi <re.cadene@gmail.com>	2024-05-20 18:27:54 +01:00
Simon Alibert	eb530fa595	Add '--independent' flag	2024-05-16 19:31:57 +02:00
Simon Alibert	fe31b7f4b7	Merge remote-tracking branch 'origin/main' into user/aliberts/2024_05_14_compare_policies	2024-05-16 17:04:33 +02:00
Simon Alibert	8f5cfcd73d	Add argparse, refactor & cleanup	2024-05-16 16:55:40 +02:00
Simon Alibert	10036c1219	WIP add score tests	2024-05-15 17:50:12 +02:00