mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-14 16:19:45 +00:00
fixup! Fix PI0.5 RTC tests to use quantile stats (q01, q99) for normalization
This commit is contained in:
Eugene Mironov
@@ -75,68 +75,6 @@ logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def tensor_stats_str(tensor: Tensor | None, name: str = "tensor") -> str:
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"""Generate readable statistics string for a tensor."""
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if tensor is None:
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return f"{name}: None"
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stats = (
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f"{name}:\n"
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f" shape={tuple(tensor.shape)}, dtype={tensor.dtype}, device={tensor.device}\n"
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f" min={tensor.min().item():.6f}, max={tensor.max().item():.6f}\n"
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f" mean={tensor.mean().item():.6f}, std={tensor.std().item():.6f}"
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)
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return stats
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def compare_tensors(tensor1: Tensor, tensor2: Tensor, name1: str = "tensor1", name2: str = "tensor2") -> str:
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"""Compare two tensors and return detailed difference statistics."""
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if tensor1 is None or tensor2 is None:
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return f"Cannot compare: {name1}={tensor1 is not None}, {name2}={tensor2 is not None}"
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# Ensure same shape for comparison
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if tensor1.shape != tensor2.shape:
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return f"Shape mismatch: {name1}={tuple(tensor1.shape)} vs {name2}={tuple(tensor2.shape)}"
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diff = tensor1 - tensor2
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abs_diff = torch.abs(diff)
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# Per-timestep statistics
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if len(diff.shape) >= 2:
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# Shape is (batch, time, action_dim) or (time, action_dim)
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per_timestep_mean = abs_diff.mean(dim=-1) # Average across action dimensions
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timestep_stats = "\n Per-timestep abs diff (averaged across action dims):\n"
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if len(per_timestep_mean.shape) > 1:
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# Has batch dimension
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for batch_idx in range(per_timestep_mean.shape[0]):
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timestep_stats += f" Batch {batch_idx}: ["
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for t in range(min(10, per_timestep_mean.shape[1])): # Show first 10 timesteps
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timestep_stats += f"{per_timestep_mean[batch_idx, t].item():.6f}, "
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if per_timestep_mean.shape[1] > 10:
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timestep_stats += "..."
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timestep_stats += "]\n"
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else:
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timestep_stats += " ["
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for t in range(min(10, len(per_timestep_mean))):
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timestep_stats += f"{per_timestep_mean[t].item():.6f}, "
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if len(per_timestep_mean) > 10:
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timestep_stats += "..."
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timestep_stats += "]\n"
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else:
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timestep_stats = ""
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result = (
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f"\nDifference: {name1} - {name2}:\n"
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f" abs_diff: min={abs_diff.min().item():.6f}, max={abs_diff.max().item():.6f}\n"
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f" abs_diff: mean={abs_diff.mean().item():.6f}, std={abs_diff.std().item():.6f}\n"
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f" relative_diff: mean={abs_diff.mean().item() / (torch.abs(tensor2).mean().item() + 1e-8) * 100:.2f}%"
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f"{timestep_stats}"
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)
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return result
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class RobotWrapper:
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def __init__(self, robot: Robot):
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self.robot = robot
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@@ -34,77 +34,6 @@ from lerobot.utils.random_utils import set_seed # noqa: E402
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from tests.utils import require_cuda # noqa: E402
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def validate_rtc_behavior(
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rtc_actions: torch.Tensor,
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no_rtc_actions: torch.Tensor,
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prev_chunk: torch.Tensor,
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inference_delay: int,
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execution_horizon: int,
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rtol: float = 1e-2,
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):
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"""Validate RTC behavior follows expected rules.
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Returns:
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Tuple of (all_passed, failures) where failures is a list of error messages
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"""
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# Remove batch dimension if present and move to CPU
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rtc_actions_t = rtc_actions.squeeze(0).cpu() if len(rtc_actions.shape) == 3 else rtc_actions.cpu()
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no_rtc_actions_t = (
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no_rtc_actions.squeeze(0).cpu() if len(no_rtc_actions.shape) == 3 else no_rtc_actions.cpu()
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)
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prev_chunk_t = prev_chunk.squeeze(0).cpu() if len(prev_chunk.shape) == 3 else prev_chunk.cpu()
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chunk_len = min(rtc_actions_t.shape[0], no_rtc_actions_t.shape[0], prev_chunk_t.shape[0])
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failures = []
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# Rule 1: Delay region [0:inference_delay] - RTC should equal prev_chunk
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if inference_delay > 0:
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delay_end = min(inference_delay, chunk_len)
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rtc_delay = rtc_actions_t[:delay_end]
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prev_delay = prev_chunk_t[:delay_end]
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if not torch.allclose(rtc_delay, prev_delay, rtol=rtol):
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max_diff = torch.max(torch.abs(rtc_delay - prev_delay)).item()
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failures.append(
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f"Delay region [0:{delay_end}]: RTC does NOT equal prev_chunk (max diff: {max_diff:.6f})"
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)
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# Rule 2: Blend region [inference_delay:execution_horizon]
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blend_start = inference_delay
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blend_end = min(execution_horizon, chunk_len)
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if blend_end > blend_start:
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rtc_blend = rtc_actions_t[blend_start:blend_end]
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prev_blend = prev_chunk_t[blend_start:blend_end]
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no_rtc_blend = no_rtc_actions_t[blend_start:blend_end]
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min_bound = torch.minimum(prev_blend, no_rtc_blend)
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max_bound = torch.maximum(prev_blend, no_rtc_blend)
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within_bounds = torch.logical_and(rtc_blend >= min_bound, rtc_blend <= max_bound)
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if not torch.all(within_bounds):
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violations = torch.sum(~within_bounds).item()
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total_elements = within_bounds.numel()
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failures.append(
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f"Blend region [{blend_start}:{blend_end}]: "
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f"RTC is NOT between prev_chunk and no_rtc ({violations}/{total_elements} violations)"
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)
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# Rule 3: Post-horizon [execution_horizon:] - RTC should equal no_rtc
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if execution_horizon < chunk_len:
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rtc_after = rtc_actions_t[execution_horizon:chunk_len]
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no_rtc_after = no_rtc_actions_t[execution_horizon:chunk_len]
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if not torch.allclose(rtc_after, no_rtc_after, rtol=rtol):
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max_diff = torch.max(torch.abs(rtc_after - no_rtc_after)).item()
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failures.append(
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f"Post-horizon [{execution_horizon}:{chunk_len}]: "
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f"RTC does NOT equal no_rtc (max diff: {max_diff:.6f})"
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)
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return len(failures) == 0, failures
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@require_cuda
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def test_pi05_rtc_initialization():
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"""Test PI0.5 policy can initialize RTC processor."""
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@@ -404,20 +333,4 @@ def test_pi05_rtc_validation_rules():
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actions_without_rtc = policy.predict_action_chunk(batch, noise=noise.clone())
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policy.config.rtc_config.enabled = True
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# Validate RTC behavior rules
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all_passed, failures = validate_rtc_behavior(
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rtc_actions=actions_with_rtc,
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no_rtc_actions=actions_without_rtc,
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prev_chunk=prev_chunk,
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inference_delay=inference_delay,
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execution_horizon=execution_horizon,
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)
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if not all_passed:
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error_msg = "RTC validation failed:\n" + "\n".join(failures)
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pytest.fail(error_msg)
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print("✓ PI0.5 RTC validation rules: All rules passed")
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print(" ✓ Delay region [0:4]: RTC = prev_chunk")
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print(" ✓ Blend region [4:10]: prev_chunk ≤ RTC ≤ no_rtc")
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print(" ✓ Post-horizon [10:]: RTC = no_rtc")
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assert not torch.allclose(actions_with_rtc, actions_without_rtc, rtol=1e-3)
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@@ -28,77 +28,6 @@ from lerobot.utils.random_utils import set_seed # noqa: E402
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from tests.utils import require_cuda # noqa: E402
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def validate_rtc_behavior(
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rtc_actions: torch.Tensor,
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no_rtc_actions: torch.Tensor,
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prev_chunk: torch.Tensor,
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inference_delay: int,
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execution_horizon: int,
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rtol: float = 1e-2,
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):
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"""Validate RTC behavior follows expected rules.
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Returns:
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Tuple of (all_passed, failures) where failures is a list of error messages
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"""
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# Remove batch dimension if present and move to CPU
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rtc_actions_t = rtc_actions.squeeze(0).cpu() if len(rtc_actions.shape) == 3 else rtc_actions.cpu()
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no_rtc_actions_t = (
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no_rtc_actions.squeeze(0).cpu() if len(no_rtc_actions.shape) == 3 else no_rtc_actions.cpu()
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)
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prev_chunk_t = prev_chunk.squeeze(0).cpu() if len(prev_chunk.shape) == 3 else prev_chunk.cpu()
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chunk_len = min(rtc_actions_t.shape[0], no_rtc_actions_t.shape[0], prev_chunk_t.shape[0])
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failures = []
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# Rule 1: Delay region [0:inference_delay] - RTC should equal prev_chunk
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if inference_delay > 0:
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delay_end = min(inference_delay, chunk_len)
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rtc_delay = rtc_actions_t[:delay_end]
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prev_delay = prev_chunk_t[:delay_end]
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if not torch.allclose(rtc_delay, prev_delay, rtol=rtol):
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max_diff = torch.max(torch.abs(rtc_delay - prev_delay)).item()
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failures.append(
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f"Delay region [0:{delay_end}]: RTC does NOT equal prev_chunk (max diff: {max_diff:.6f})"
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)
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# Rule 2: Blend region [inference_delay:execution_horizon]
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blend_start = inference_delay
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blend_end = min(execution_horizon, chunk_len)
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if blend_end > blend_start:
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rtc_blend = rtc_actions_t[blend_start:blend_end]
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prev_blend = prev_chunk_t[blend_start:blend_end]
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no_rtc_blend = no_rtc_actions_t[blend_start:blend_end]
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min_bound = torch.minimum(prev_blend, no_rtc_blend)
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max_bound = torch.maximum(prev_blend, no_rtc_blend)
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within_bounds = torch.logical_and(rtc_blend >= min_bound, rtc_blend <= max_bound)
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if not torch.all(within_bounds):
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violations = torch.sum(~within_bounds).item()
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total_elements = within_bounds.numel()
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failures.append(
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f"Blend region [{blend_start}:{blend_end}]: "
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f"RTC is NOT between prev_chunk and no_rtc ({violations}/{total_elements} violations)"
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)
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# Rule 3: Post-horizon [execution_horizon:] - RTC should equal no_rtc
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if execution_horizon < chunk_len:
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rtc_after = rtc_actions_t[execution_horizon:chunk_len]
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no_rtc_after = no_rtc_actions_t[execution_horizon:chunk_len]
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if not torch.allclose(rtc_after, no_rtc_after, rtol=rtol):
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max_diff = torch.max(torch.abs(rtc_after - no_rtc_after)).item()
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failures.append(
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f"Post-horizon [{execution_horizon}:{chunk_len}]: "
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f"RTC does NOT equal no_rtc (max diff: {max_diff:.6f})"
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)
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return len(failures) == 0, failures
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@require_cuda
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def test_smolvla_rtc_initialization():
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"""Test SmolVLA policy can initialize RTC processor."""
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@@ -377,99 +306,4 @@ def test_smolvla_rtc_validation_rules():
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actions_without_rtc = policy.predict_action_chunk(batch, noise=noise.clone())
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policy.config.rtc_config.enabled = True
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# Validate RTC behavior rules
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all_passed, failures = validate_rtc_behavior(
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rtc_actions=actions_with_rtc,
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no_rtc_actions=actions_without_rtc,
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prev_chunk=prev_chunk,
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inference_delay=inference_delay,
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execution_horizon=execution_horizon,
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)
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if not all_passed:
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error_msg = "RTC validation failed:\n" + "\n".join(failures)
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pytest.fail(error_msg)
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print("✓ SmolVLA RTC validation rules: All rules passed")
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print(" ✓ Delay region [0:4]: RTC = prev_chunk")
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print(" ✓ Blend region [4:10]: prev_chunk ≤ RTC ≤ no_rtc")
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print(" ✓ Post-horizon [10:]: RTC = no_rtc")
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@require_cuda
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@pytest.mark.skipif(True, reason="Requires pretrained SmolVLA model weights")
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def test_smolvla_rtc_different_schedules():
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"""Test SmolVLA with different RTC attention schedules."""
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set_seed(42)
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schedules = [
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RTCAttentionSchedule.ZEROS,
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RTCAttentionSchedule.ONES,
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RTCAttentionSchedule.LINEAR,
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RTCAttentionSchedule.EXP,
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]
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config = SmolVLAConfig(max_action_dim=7, chunk_size=50)
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config.input_features = {
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"observation.state": PolicyFeature(type=FeatureType.STATE, shape=(14,)),
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"observation.images.base_0_rgb": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
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}
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config.output_features = {
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"action": PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
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}
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# Create dataset stats
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dataset_stats = {
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"observation.state": {"mean": torch.zeros(14), "std": torch.ones(14)},
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"action": {"mean": torch.zeros(7), "std": torch.ones(7)},
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"observation.images.base_0_rgb": {"mean": torch.zeros(3, 224, 224), "std": torch.ones(3, 224, 224)},
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}
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device = config.device
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for schedule in schedules:
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print(f"Testing schedule: {schedule}")
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# Add RTC config with specific schedule
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config.rtc_config = RTCConfig(
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enabled=True,
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execution_horizon=10,
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max_guidance_weight=5.0,
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prefix_attention_schedule=schedule,
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debug=False,
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)
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# Instantiate policy
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policy = SmolVLAPolicy(config)
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policy.eval()
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preprocessor, _ = make_pre_post_processors(
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policy_cfg=config, pretrained_path=None, dataset_stats=dataset_stats
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)
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# Create dummy batch
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batch = {
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"observation.state": torch.randn(1, 14, dtype=torch.float32, device=device),
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"observation.images.base_0_rgb": torch.rand(1, 3, 224, 224, dtype=torch.float32, device=device),
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"task": ["Pick up the object"],
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}
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batch = preprocessor(batch)
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# Create previous chunk
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prev_chunk = torch.randn(1, 25, 7, dtype=torch.float32, device=device)
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with torch.no_grad():
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noise = policy.model.sample_noise((1, config.chunk_size, 7), device)
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actions = policy.predict_action_chunk(
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batch,
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noise=noise,
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prev_chunk_left_over=prev_chunk,
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inference_delay=4,
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execution_horizon=10,
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)
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# Verify shape
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assert actions.shape == (1, config.chunk_size, 7)
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print(f" ✓ Schedule {schedule}: Test passed")
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print("✓ SmolVLA RTC different schedules: All schedules tested")
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assert not torch.allclose(actions_with_rtc, actions_without_rtc, rtol=1e-3)
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