Init

2025-09-07 14:59:17 +08:00
commit 24dbdbd24b
40 changed files with 10754 additions and 0 deletions
@@ -0,0 +1,80 @@
+import torch
+from wall_x.model.qwen2_5_based.modeling_qwen2_5_vl_act import Qwen2_5_VLMoEForAction
+
+model_path = "/path/to/model"
+model = Qwen2_5_VLMoEForAction.from_pretrained(model_path)
+model.eval()
+
+# Gen Fake data
+batch_size = 1
+seq_length = 50
+
+torch.manual_seed(0)
+fake_input_ids = torch.randint(0, len(model.processor.tokenizer), (batch_size, seq_length), dtype=torch.long)
+fake_attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long)
+fake_moe_token_types = torch.zeros((batch_size, seq_length), dtype=torch.long)
+fake_position_ids = torch.arange(seq_length, dtype=torch.long).unsqueeze(0).expand(batch_size, -1)
+fake_proprioception = torch.randn((batch_size, 1, 20), dtype=torch.float32)
+fake_agent_pos_mask = torch.ones((batch_size, 1, 20), dtype=torch.float32)
+fake_dof_mask = torch.ones((batch_size, 32, 20), dtype=torch.float32)
+fake_dataset_names = ["x2_normal"]
+
+
+device = "cuda"
+
+model = model.to(device)
+model = model.bfloat16()
+
+fake_input_ids = fake_input_ids.to(device)
+fake_attention_mask = fake_attention_mask.to(device)
+fake_moe_token_types = fake_moe_token_types.to(device)
+fake_position_ids = fake_position_ids.to(device)
+fake_proprioception = fake_proprioception.to(device).bfloat16()
+fake_agent_pos_mask = fake_agent_pos_mask.to(device).bfloat16()
+fake_dof_mask = fake_dof_mask.to(device).bfloat16()
+
+try:
+    with torch.no_grad():
+        outputs = model(
+            input_ids=fake_input_ids,
+            attention_mask=fake_attention_mask,
+            moe_token_types=fake_moe_token_types,
+            position_ids=fake_position_ids,
+            proprioception=fake_proprioception,
+            agent_pos_mask=fake_agent_pos_mask,
+            dof_mask=fake_dof_mask,
+            dataset_names=fake_dataset_names,
+            mode="validate"
+        )
+    
+    print("✅ Fake inference test successful!")
+    print(f"Output logits shape: {outputs.logits.shape}")
+    print(f"Output logits dtype: {outputs.logits.dtype}")
+    print(f"Output logits device: {outputs.logits.device}")
+    
+    # Check if output is reasonable
+    if outputs.logits.shape == (batch_size, seq_length, model.config.vocab_size):
+        print("✅ Output shape correct")
+    else:
+        print("❌ Output shape incorrect")
+        
+    if not torch.isnan(outputs.logits).any():
+        print("✅ Output contains no NaN values")
+    else:
+        print("❌ Output contains NaN values")
+        
+    if not torch.isinf(outputs.logits).any():
+        print("✅ Output contains no infinity values")
+    else:
+        print("❌ Output contains infinity values")
+        
+    print(f"Output logits statistics:")
+    print(f"  Min value: {outputs.logits.min().item():.4f}")
+    print(f"  Max value: {outputs.logits.max().item():.4f}")
+    print(f"  Mean: {outputs.logits.mean().item():.4f}")
+    print(f"  Standard deviation: {outputs.logits.std().item():.4f}")
+    
+except Exception as e:
+    print(f"❌ Fake inference test failed: {e}")
+    import traceback
+    traceback.print_exc()