Add support for two training

lerobot/common/policies/dexvla/configuration_dexvla.py

@@ -23,6 +23,7 @@ from transformers import AutoConfig
 from lerobot.common.optim.optimizers import AdamWConfig
 from lerobot.common.optim.schedulers import (
     CosineDecayWithWarmupSchedulerConfig,
+    ConstantWithWarmupSchedulerConfig
 )
 from transformers.utils import logging
 from lerobot.configs.policies import PreTrainedConfig
@@ -45,9 +46,12 @@ class DexVLAConfig(PreTrainedConfig):
     n_obs_steps: int = 1
 
     hidden_size: int = 1536
-    qwen2_vl_path: str = None # '/media/rl/HDD/data/weights/Qwen2-VL-2B-Instruct'
+    qwen2_vl_path: str = None # '/media/rl/HDD/data/weights/Qwen2-VL-2B-Instruct', official weights of qwen2vl
 
-    pretrained_path: str = None # pretrained dexvla
+    pretrained_path: str = None # for loading pretrained weights of whole dexvla, usually for training stage3
+    pretrained_scaledp_path: str = None # for loading pretrained weights of ScaleDP(Stage1)
+
+    training_stage: int = 2 # specific training stage, [2, 3]
     using_film: bool = True
     llm_loss_weight: float = 1.0
     with_llm_head: bool = True
@@ -59,7 +63,7 @@ class DexVLAConfig(PreTrainedConfig):
     optimizer_eps: float = 1e-8
     optimizer_weight_decay: float = 1e-10
 
-    scheduler_warmup_steps: int = 1_000
+    scheduler_warmup_steps: int = 2_000
     scheduler_decay_steps: int = 30_000
     scheduler_decay_lr: float = 2.5e-6
 
@@ -110,6 +114,9 @@ class DexVLAConfig(PreTrainedConfig):
         else:
             raise ValueError(f'Policy head type {self.policy_head_type} not supported')
 
+        if self.training_stage not in [2,3]:
+            raise ValueError(f"Training stage must be 2 or 3. Got {self.training_stage}.")
+
         self.qwen2_vla_config = AutoConfig.from_pretrained(self.qwen2_vl_path)
 
     def validate_features(self) -> None:
@@ -134,12 +141,17 @@ class DexVLAConfig(PreTrainedConfig):
         )
 
     def get_scheduler_preset(self):
-        return CosineDecayWithWarmupSchedulerConfig(
-            peak_lr=self.optimizer_lr,
-            decay_lr=self.scheduler_decay_lr,
-            num_warmup_steps=self.scheduler_warmup_steps,
-            num_decay_steps=self.scheduler_decay_steps,
-        )
+        if self.training_stage == 3:
+            return CosineDecayWithWarmupSchedulerConfig(
+                peak_lr=self.optimizer_lr,
+                decay_lr=self.scheduler_decay_lr,
+                num_warmup_steps=self.scheduler_warmup_steps,
+                num_decay_steps=self.scheduler_decay_steps,
+            )
+        else:
+            return ConstantWithWarmupSchedulerConfig(
+                num_warmup_steps=self.scheduler_warmup_steps,
+            )
 
     @property
     def observation_delta_indices(self) -> None: