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