Merge pull request #6 from vincentccc/main

Fix model load/save && open-loop script

README.md

@@ -76,6 +76,7 @@ Training script path configuration
 - Robot DOF configuration
 - Training hyperparameters
 
+Download the Flow/FAST pretrained model and run:
 ```bash
 bash ./workspace/lerobot_example/run.sh
 ```

scripts/draw_openloop_plot.py

@@ -38,8 +38,7 @@ gt_traj = torch.zeros((total_frames, action_dim))
 pred_traj = torch.zeros((total_frames, action_dim))
 
 for idx, batch in enumerate(dataloader):
-    gt_traj[idx] = batch['action_chunk'][0, 0,:action_dim]
+    if idx % pred_horizon ==0 and idx + pred_horizon < total_frames:
-    if idx % 32 ==0 and idx + 32 < total_frames:
         batch = batch.to("cuda")
         with torch.no_grad():
             outputs = model(
@@ -50,7 +49,13 @@ for idx, batch in enumerate(dataloader):
                 predict_mode="fast"
             )
         pred_traj[idx : idx + pred_horizon] = outputs['predict_action'].detach().cpu()
-
+        
+        # Denormalize ground truth actions
+        gt_action_chunk = batch['action_chunk'][:, :, :action_dim]
+        dof_mask = batch["dof_mask"].to(gt_action_chunk.dtype)
+        denormalized_gt = model.action_preprocessor.normalizer_action.unnormalize_data(gt_action_chunk, ["x2_normal"], dof_mask)
+        gt_traj[idx : idx + pred_horizon] = denormalized_gt.detach().cpu()
+        
 
 gt_traj_np = gt_traj.numpy()
 pred_traj_np = pred_traj.numpy()

wall_x/data/load_lerobot_dataset.py

@@ -207,7 +207,7 @@ class DataCollator:
         self.load_processor()
 
     def load_processor(self):
-        processor_path = self.config["processor_path"]
+        processor_path = self.config["pretrained_qwen_vl_path"]
         action_tokenizer_path = self.config["action_tokenizer_path"]
 
         # Use cached processors if available

wall_x/model/qwen2_5_based/modeling_qwen2_5_vl_act.py

@@ -1,6 +1,7 @@
 import os
 import torch
 import numpy as np
+import glob
 import torch.nn as nn
 from torchdiffeq import odeint
 from dataclasses import dataclass
@@ -685,7 +686,6 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
         """
         
         # Load model components from pretrained path
-        model_path = os.path.join(pretrained_model_path, "model.safetensors")
         config_path = os.path.join(pretrained_model_path, "config.json")
         config = cls.config_class.from_pretrained(config_path)
         processor = AutoProcessor.from_pretrained(pretrained_model_path, use_fast=True)
@@ -703,8 +703,13 @@ class Qwen2_5_VLMoEForAction(Qwen2_5_VLForConditionalGeneration):
         model.resize_token_embeddings(len(processor.tokenizer))
         
         # Load model state dict from safetensors file
-        state_dict = load_file(model_path, device="cpu")
+        safetensor_files = glob.glob(os.path.join(pretrained_model_path, "*.safetensors"))
-        msg = model.load_state_dict(state_dict, strict=False)
+        state_dict = {}
+        for file in safetensor_files:
+            sd = load_file(file, device="cpu")
+            state_dict.update(sd)
+
+        model.load_state_dict(state_dict, strict=False)
         
         return model
 

wall_x/trainer/qwen_vl_act_trainer.py

@@ -108,7 +108,7 @@ class QwenVlAct_Trainer:
             ValueError: If required configuration keys are missing
         """
         # Validate required configuration keys
-        required_keys = ["processor_path", "qwen_vl_act_config_path", "learning_rate", "num_epoch"]
+        required_keys = ["learning_rate", "num_epoch"]
         for key in required_keys:
             if key not in config:
                 raise ValueError(f"Missing required configuration key: {key}")
@@ -197,6 +197,9 @@ class QwenVlAct_Trainer:
             self.train_loop(epoch)
             self.accelerator.wait_for_everyone()
             
+            if (epoch + 1) % self.config.get("epoch_save_interval", 10) == 0:
+                self.save_checkpoint(epoch)
+            
             # Validation after each epoch
             self.val_loop()
             self.accelerator.wait_for_everyone()

workspace/README.md

@@ -2,6 +2,12 @@
 
 This document explains the key configuration parameters that can be modified for Wall-X training.
 
+## Enable FAST tokenizer
+To fine-tune using the FAST tokenizer, please download the repository and update the `action_tokenizer_path`. Make sure to set `use_fast_tokenizer` to `true`:
+```bash
+git clone https://huggingface.co/physical-intelligence/fast
+```
+
 ## Quick Start Checklist
 1. **Update run.sh**: Set `code_dir` and `config_path` to your actual paths
 2. **Configure GPUs**: Set `CUDA_VISIBLE_DEVICES` for your available GPUs  

workspace/lerobot_example/config_qact.yml

@@ -5,9 +5,8 @@
 log_name: "robotic_training"
 log_project: "vla_training"
 model_type: qwen2_5
-processor_path: "/path/to/model/"
+pretrained_qwen_vl_path: "/path/to/wallx_model/"
-pretrained_qwen_vl_path: "/path/to/qwen_vl_model/"
+use_fast_tokenizer: false # True: train FAST, False: train Flow
-qwen_vl_act_config_path: "/path/to/config.json"
 action_tokenizer_path: "/path/to/fast/"
 save_path: "/path/to/workspace/"
 
@@ -27,6 +26,7 @@ num_epoch: 100
 gradient_accumulation_steps: 32
 batch_size_per_gpu: 8
 padding_side: left
+epoch_save_interval: 10
 
 # Robot configuration - Define degrees of freedom for each component
 dof_config:
@@ -52,10 +52,10 @@ agent_pos_config:
   height: 1
   car_pose: 3
 
-# Checkpoint resuming configuration
+# # Checkpoint resuming configuration
-resume:
+# resume:
-  ckpt: "/path/to/resume_model/"
+#   ckpt: "/path/to/resume_model/"
-  load_ckpt_only: true
+#   load_ckpt_only: true
 
 # Data configuration
 data: