Add Important sampling, only use replacement, remove beta smoothing

lerobot/common/datasets/sampler.py

@@ -80,7 +80,7 @@ class SumTree:
 
     def initialize_tree(self, priorities: List[float]):
         """
-        Efficiently initializes the sum tree in O(n).
+        Initializes the sum tree
         """
         # Set leaf values
         for i, priority in enumerate(priorities):
@@ -132,32 +132,42 @@ class PrioritizedSampler(Sampler[int]):
         self,
         data_len: int,
         alpha: float = 0.6,
-        beta: float = 0.1,
         eps: float = 1e-6,
-        replacement: bool = True,
         num_samples_per_epoch: Optional[int] = None,
+        beta_start: float = 0.4,
+        beta_end: float = 1.0,
+        total_steps: int = 1,
     ):
         """
         Args:
             data_len: Total number of samples in the dataset.
             alpha: Exponent for priority scaling. Default is 0.6.
-            beta: Smoothing offset to avoid excluding low-priority samples.
             eps: Small constant to avoid zero priorities.
             replacement: Whether to sample with replacement.
             num_samples_per_epoch: Number of samples per epoch (default is data_len).
         """
         self.data_len = data_len
         self.alpha = alpha
-        self.beta = beta
         self.eps = eps
-        self.replacement = replacement
         self.num_samples_per_epoch = num_samples_per_epoch or data_len
+        self.beta_start = beta_start
+        self.beta_end = beta_end
+        self.total_steps = total_steps
+        self._beta = self.beta_start
 
         # Initialize difficulties and sum-tree
-        self.difficulties = [1.0] * data_len  # Default difficulty = 1.0
-        initial_priorities = [(1.0 + eps) ** alpha + beta] * data_len  # Compute initial priorities
+        self.difficulties = [1.0] * data_len
+        self.priorities = [0.0] * data_len
+        initial_priorities = [(1.0 + eps) ** alpha] * data_len
+
         self.sumtree = SumTree(data_len)
-        self.sumtree.initialize_tree(initial_priorities)  # Bulk load in O(n)
+        self.sumtree.initialize_tree(initial_priorities)
+        for i, p in enumerate(initial_priorities):
+            self.priorities[i] = p
+
+    def update_beta(self, current_step: int):
+        frac = min(1.0, current_step / self.total_steps)
+        self._beta = self.beta_start + (self.beta_end - self.beta_start) * frac
 
     def update_priorities(self, indices: List[int], difficulties: List[float]):
         """
@@ -165,7 +175,8 @@ class PrioritizedSampler(Sampler[int]):
         """
         for idx, diff in zip(indices, difficulties, strict=False):
             self.difficulties[idx] = diff
-            new_priority = (diff + self.eps) ** self.alpha + self.beta
+            new_priority = (diff + self.eps) ** self.alpha
+            self.priorities[idx] = new_priority
             self.sumtree.update(idx, new_priority)
 
     def __iter__(self) -> Iterator[int]:
@@ -173,19 +184,21 @@ class PrioritizedSampler(Sampler[int]):
         Samples indices based on their priority weights.
         """
         total_p = self.sumtree.total_priority()
-        sampled_indices = set() if not self.replacement else None
 
         for _ in range(self.num_samples_per_epoch):
             r = random.random() * total_p
             idx = self.sumtree.sample(r)
 
-            if not self.replacement:
-                while idx in sampled_indices:
-                    r = random.random() * total_p
-                    idx = self.sumtree.sample(r)
-                sampled_indices.add(idx)
-
             yield idx
 
     def __len__(self) -> int:
         return self.num_samples_per_epoch
+
+    def compute_is_weights(self, indices: List[int]) -> torch.Tensor:
+        w = []
+        total_p = self.sumtree.total_priority()
+        for idx in indices:
+            p = self.priorities[idx] / total_p
+            w.append((p * self.data_len) ** (-self._beta))
+        w = torch.tensor(w, dtype=torch.float32)
+        return w / w.max()

lerobot/common/policies/act/modeling_act.py

@@ -161,7 +161,6 @@ class ACTPolicy(PreTrainedPolicy):
 
         l1_loss = elementwise_l1.mean()
 
-        # mean over time+action_dim => per-sample array of shape (B,)
         l1_per_sample = elementwise_l1.mean(dim=(1, 2))
 
         if self.config.use_vae:
@@ -175,13 +174,13 @@ class ACTPolicy(PreTrainedPolicy):
             loss_dict = {
                 "l1_loss": l1_loss.item(),
                 "kld_loss": mean_kld.item(),
-                "per_sample_l1": l1_per_sample,  # shape (B,)
+                "per_sample_l1": l1_per_sample,
             }
             loss = l1_loss + mean_kld * self.config.kl_weight
         else:
             loss_dict = {
                 "l1_loss": l1_loss.item(),
-                "per_sample_l1": l1_per_sample,  # shape (B,)
+                "per_sample_l1": l1_per_sample,
             }
             loss = l1_loss
 

lerobot/scripts/train.py

@@ -70,6 +70,17 @@ def update_policy(
     with torch.autocast(device_type=device.type) if use_amp else nullcontext():
         loss, output_dict = policy.forward(batch)
         # TODO(rcadene): policy.unnormalize_outputs(out_dict)
+
+        # Apply importance-sampling if available
+        if "is_weights" in batch and "per_sample_l1" in output_dict:
+            per_sample_l1 = output_dict["per_sample_l1"]
+            l1_per_item = per_sample_l1.mean(dim=-1)
+            w = batch["is_weights"].to(device)
+            weighted_loss = (l1_per_item * w).mean()
+            if policy.config.use_vae and "kld_loss" in output_dict:
+                weighted_loss += output_dict["kld_loss"] * policy.config.kl_weight
+            loss = weighted_loss
+
     grad_scaler.scale(loss).backward()
 
     # Unscale the gradient of the optimizer's assigned params in-place **prior to gradient clipping**.
@@ -180,10 +191,11 @@ def train(cfg: TrainPipelineConfig):
     sampler = PrioritizedSampler(
         data_len=data_len,
         alpha=0.6,
-        beta=0.1,
         eps=1e-6,
-        replacement=True,
         num_samples_per_epoch=data_len,
+        beta_start=0.4,
+        beta_end=1.0,
+        total_steps=cfg.steps,
     )
 
     dataloader = torch.utils.data.DataLoader(
@@ -221,6 +233,11 @@ def train(cfg: TrainPipelineConfig):
             if isinstance(batch[key], torch.Tensor):
                 batch[key] = batch[key].to(device, non_blocking=True)
 
+        if "indices" in batch:
+            sampler.update_beta(step)
+            is_weights = sampler.compute_is_weights(batch["indices"].cpu().tolist())
+            batch["is_weights"] = is_weights
+
         train_tracker, output_dict = update_policy(
             train_tracker,
             policy,
@@ -232,11 +249,11 @@ def train(cfg: TrainPipelineConfig):
             use_amp=cfg.policy.use_amp,
         )
 
-        # If we have 'indices' and 'per_sample_l1' then update sampler
+        # Update sampler
         if "indices" in batch and "per_sample_l1" in output_dict:
-            indices = batch["indices"].detach().cpu().tolist()  # shape (B,)
-            difficulties = output_dict["per_sample_l1"].detach().cpu().tolist()  # shape (B,)
-            sampler.update_priorities(indices, difficulties)
+            idxs = batch["indices"].cpu().tolist()
+            diffs = output_dict["per_sample_l1"].detach().cpu().tolist()
+            sampler.update_priorities(idxs, diffs)
 
         # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
         # increment `step` here.