mirror of
https://github.com/huggingface/lerobot.git
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refactor(rewards): clean up TOPReward processor/model
This commit is contained in:
Khalil Meftah
@@ -16,67 +16,71 @@
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from __future__ import annotations
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import numpy as np
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from types import SimpleNamespace
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import pytest
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import torch
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from lerobot.configs.rewards import RewardModelConfig
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from lerobot.rewards.factory import get_reward_model_class, make_reward_model_config
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from lerobot.rewards.topreward import TOPRewardConfig
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from lerobot.rewards.topreward.modeling_topreward import minmax_normalize_rewards
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from lerobot.rewards.topreward.processor_topreward import TOPREWARD_FEATURE_PREFIX
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from tests.utils import skip_if_package_missing
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class _FakeTokenizer:
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"""Minimal tokenizer surface used by ``TOPRewardModel._compute_log_prob_reward``."""
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eos_token = "<|endoftext|>"
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class _FakeProcessor:
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"""Stand-in for the Qwen ``AutoProcessor`` returned by ``from_pretrained``."""
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def __init__(self) -> None:
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self.tokenizer = _FakeTokenizer()
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@classmethod
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def from_pretrained(cls, *args, **kwargs): # noqa: ARG003
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return cls()
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class _FakeQwenModel(torch.nn.Module):
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"""Stand-in for ``Qwen3VLForConditionalGeneration``.
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Provides the minimum surface ``TOPRewardModel`` touches at construction
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time (a ``parameters()`` iterator for device inference). Actual
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``_compute_log_prob_reward`` calls are bypassed by monkey-patching the
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method directly in the tests, so we never invoke ``self.model(...)``.
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Returns a ``SimpleNamespace`` with ``logits`` of a controlled shape so
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the log-prob extraction path in ``compute_reward`` can be exercised
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without downloading real VLM weights.
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"""
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def __init__(self) -> None:
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super().__init__()
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self._param = torch.nn.Parameter(torch.zeros(1))
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self._reward_value: float = -1.5
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@classmethod
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def from_pretrained(cls, *args, **kwargs): # noqa: ARG003
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return cls()
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def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): # noqa: ARG002
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batch_size, seq_len = input_ids.shape
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vocab_size = 1000
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logits = torch.zeros(batch_size, seq_len, vocab_size)
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# Place a controlled log-prob at the target token position so the
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# model returns a predictable reward value.
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# The label-masked suffix is the last token (prompt_length = seq_len - 1).
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# After the causal-LM shift (logits[:, :-1], labels[:, 1:]) the scored
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# position is logits[:, -2, :] predicting labels[:, -1].
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# We set logits so that log_softmax at the target token ≈ _reward_value.
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if labels is not None:
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for i in range(batch_size):
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target_idx = int(input_ids[i, -1].item())
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logits[i, -2, target_idx] = self._reward_value * -10 # high logit -> high log-prob
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return SimpleNamespace(logits=logits)
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def _patch_build(monkeypatch) -> None:
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"""Stub out HF AutoX so TOPReward construction is cheap and offline."""
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from lerobot.rewards.topreward import modeling_topreward
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monkeypatch.setattr(modeling_topreward, "Qwen3VLForConditionalGeneration", _FakeQwenModel)
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monkeypatch.setattr(modeling_topreward, "AutoProcessor", _FakeProcessor)
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def _make_batch(frames: list[np.ndarray], tasks: list[str]) -> dict[str, list]:
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def _make_batch(
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input_ids: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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prompt_length: torch.Tensor | None = None,
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) -> dict[str, torch.Tensor]:
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"""Build a ``compute_reward``-ready batch using TOPReward's namespaced keys."""
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return {
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f"{TOPREWARD_FEATURE_PREFIX}frames": frames,
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f"{TOPREWARD_FEATURE_PREFIX}task": tasks,
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}
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batch: dict[str, torch.Tensor] = {f"{TOPREWARD_FEATURE_PREFIX}input_ids": input_ids}
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if attention_mask is not None:
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batch[f"{TOPREWARD_FEATURE_PREFIX}attention_mask"] = attention_mask
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if prompt_length is not None:
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batch[f"{TOPREWARD_FEATURE_PREFIX}prompt_length"] = prompt_length
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return batch
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# ---------------------------------------------------------------------------
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@@ -121,32 +125,6 @@ def test_topreward_config_rejects_suffix_without_instruction_placeholder():
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TOPRewardConfig(device="cpu", prompt_suffix_template="no placeholder here")
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# ---------------------------------------------------------------------------
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# minmax_normalize_rewards — pure math helper
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# ---------------------------------------------------------------------------
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def test_minmax_normalize_rewards_maps_min_and_max_to_zero_and_one():
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values = minmax_normalize_rewards([-3.0, -1.0, 0.0, -2.0])
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assert values.shape == (4,)
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assert values[0] == pytest.approx(0.0)
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assert values[2] == pytest.approx(1.0)
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# Monotonicity preserved within the input range.
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assert values[3] == pytest.approx(1.0 / 3.0, abs=1e-6)
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def test_minmax_normalize_rewards_handles_singleton_and_flat_inputs():
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# Single element -> mapped to 1.0 (no information to scale).
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assert minmax_normalize_rewards([42.0]).tolist() == [1.0]
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# All-equal values -> all ones (avoid divide-by-zero).
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assert minmax_normalize_rewards([0.5, 0.5, 0.5]).tolist() == [1.0, 1.0, 1.0]
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def test_minmax_normalize_rewards_empty_input_returns_empty_array():
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out = minmax_normalize_rewards([])
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assert out.shape == (0,)
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# ---------------------------------------------------------------------------
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# compute_reward
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# ---------------------------------------------------------------------------
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@@ -154,55 +132,43 @@ def test_minmax_normalize_rewards_empty_input_returns_empty_array():
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@skip_if_package_missing("transformers")
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def test_topreward_compute_reward_returns_one_scalar_per_sample(monkeypatch):
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"""``compute_reward`` must return a ``(B,)`` float32 tensor with one
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log-prob reward per sample, consuming pre-encoded Qwen-VL tensors."""
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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_patch_build(monkeypatch)
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cfg = TOPRewardConfig(device="cpu")
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model = TOPRewardModel(cfg)
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captured = []
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def fake_log_prob(self, frames, instruction): # noqa: ARG002
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captured.append((frames.shape, instruction))
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return -1.5
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monkeypatch.setattr(TOPRewardModel, "_compute_log_prob_reward", fake_log_prob)
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frames_a = np.zeros((4, 8, 8, 3), dtype=np.uint8)
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frames_b = np.zeros((6, 8, 8, 3), dtype=np.uint8)
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batch = _make_batch([frames_a, frames_b], ["pick the cube", "open the drawer"])
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input_ids = torch.randint(0, 100, (2, 10))
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attention_mask = torch.ones(2, 10, dtype=torch.long)
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prompt_length = torch.tensor([9, 9]) # unmask only the last token
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batch = _make_batch(input_ids, attention_mask, prompt_length)
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rewards = model.compute_reward(batch)
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assert rewards.shape == (2,)
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assert rewards.dtype == torch.float32
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assert torch.allclose(rewards, torch.tensor([-1.5, -1.5]))
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# `_compute_log_prob_reward` was called once per sample with the right tasks.
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assert [task for _, task in captured] == ["pick the cube", "open the drawer"]
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assert [shape[0] for shape, _ in captured] == [4, 6]
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@skip_if_package_missing("transformers")
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def test_topreward_compute_reward_applies_success_threshold(monkeypatch):
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"""When ``success_threshold`` is finite, the model returns binary success
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instead of the raw log-prob — useful as a drop-in success detector."""
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"""When ``success_threshold`` is finite, the model returns binary success."""
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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_patch_build(monkeypatch)
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cfg = TOPRewardConfig(device="cpu", success_threshold=-2.0)
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cfg = TOPRewardConfig(device="cpu", success_threshold=0.0)
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model = TOPRewardModel(cfg)
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rewards_in = iter([-1.5, -3.0]) # first above threshold, second below
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monkeypatch.setattr(
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TOPRewardModel,
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"_compute_log_prob_reward",
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lambda _self, _frames, _instr: next(rewards_in),
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)
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input_ids = torch.randint(0, 100, (2, 10))
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attention_mask = torch.ones(2, 10, dtype=torch.long)
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prompt_length = torch.tensor([9, 9])
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frames = [np.zeros((2, 8, 8, 3), dtype=np.uint8), np.zeros((2, 8, 8, 3), dtype=np.uint8)]
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rewards = model.compute_reward(_make_batch(frames, ["task", "task"]))
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batch = _make_batch(input_ids, attention_mask, prompt_length)
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rewards = model.compute_reward(batch)
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assert torch.equal(rewards, torch.tensor([1.0, 0.0]))
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assert rewards.shape == (2,)
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assert set(rewards.tolist()).issubset({0.0, 1.0})
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@skip_if_package_missing("transformers")
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@@ -213,137 +179,10 @@ def test_topreward_compute_reward_errors_when_inputs_missing(monkeypatch):
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cfg = TOPRewardConfig(device="cpu")
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model = TOPRewardModel(cfg)
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with pytest.raises(KeyError, match=r"observation\.topreward\."):
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with pytest.raises(KeyError, match=r"observation\.topreward\.input_ids"):
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model.compute_reward({})
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@skip_if_package_missing("transformers")
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def test_topreward_compute_reward_errors_when_batch_sizes_mismatch(monkeypatch):
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"""frames and task lists must have matching lengths — a stale processor
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that produces only one task for a multi-sample batch should surface as
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an explicit error, not a silent zip truncation."""
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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_patch_build(monkeypatch)
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cfg = TOPRewardConfig(device="cpu")
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model = TOPRewardModel(cfg)
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monkeypatch.setattr(
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TOPRewardModel,
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"_compute_log_prob_reward",
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lambda _self, _frames, _instr: 0.0,
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)
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frames = [np.zeros((2, 8, 8, 3), dtype=np.uint8), np.zeros((2, 8, 8, 3), dtype=np.uint8)]
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with pytest.raises(ValueError, match="task batch size"):
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model.compute_reward(_make_batch(frames, ["only one task"]))
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# ---------------------------------------------------------------------------
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# predict_curves
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# ---------------------------------------------------------------------------
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@skip_if_package_missing("transformers")
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def test_topreward_predict_curves_runs_one_forward_per_prefix(monkeypatch):
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"""``predict_curves`` must call the VLM once per prefix length per
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trajectory and write min-max-normalised values back into the curve."""
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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_patch_build(monkeypatch)
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cfg = TOPRewardConfig(device="cpu")
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model = TOPRewardModel(cfg)
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# Simulate a strictly increasing log-prob curve as the prefix grows.
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call_log: list[int] = []
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def fake_log_prob(self, frames, instruction): # noqa: ARG002
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call_log.append(int(frames.shape[0]))
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return float(frames.shape[0]) # log-prob = prefix length
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monkeypatch.setattr(TOPRewardModel, "_compute_log_prob_reward", fake_log_prob)
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frames = np.zeros((5, 8, 8, 3), dtype=np.uint8)
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batch = _make_batch([frames], ["lift the cup"])
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out = model.predict_curves(batch)
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# One forward per prefix length, in order.
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assert call_log == [1, 2, 3, 4, 5]
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# (B, T_max) shape, padded with NaN beyond each trajectory's length.
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assert out["progress"].shape == (1, 5)
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# Strictly increasing raw rewards -> min-max-normalised to [0, 1] linearly.
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expected = torch.tensor([[0.0, 0.25, 0.5, 0.75, 1.0]])
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assert torch.allclose(out["progress"], expected, atol=1e-6)
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@skip_if_package_missing("transformers")
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def test_topreward_predict_curves_sparse_dense_interpolates_to_full_resolution(monkeypatch):
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"""With ``num_prefixes < N`` the model should score only the requested
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number of anchor prefixes and linearly interpolate between them — the
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upstream sparse-dense pattern (``num_samples=15``)."""
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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_patch_build(monkeypatch)
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cfg = TOPRewardConfig(device="cpu")
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model = TOPRewardModel(cfg)
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call_log: list[int] = []
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def fake_log_prob(self, frames, instruction): # noqa: ARG002
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call_log.append(int(frames.shape[0]))
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return float(frames.shape[0])
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monkeypatch.setattr(TOPRewardModel, "_compute_log_prob_reward", fake_log_prob)
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frames = np.zeros((9, 8, 8, 3), dtype=np.uint8)
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out = model.predict_curves(_make_batch([frames], ["lift the cup"]), num_prefixes=3)
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# 3 anchors at linspace(1, 9, 3) -> [1, 5, 9] -> 3 VLM forwards instead of 9.
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assert call_log == [1, 5, 9]
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# Returned curve is full resolution (9 frames) and monotone in [0, 1].
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assert out["progress"].shape == (1, 9)
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curve = out["progress"][0].numpy()
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assert curve[0] == pytest.approx(0.0)
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assert curve[-1] == pytest.approx(1.0)
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assert np.all(np.diff(curve) >= 0)
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@skip_if_package_missing("transformers")
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def test_topreward_predict_curves_rejects_invalid_num_prefixes(monkeypatch):
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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_patch_build(monkeypatch)
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model = TOPRewardModel(TOPRewardConfig(device="cpu"))
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batch = _make_batch([np.zeros((3, 8, 8, 3), dtype=np.uint8)], ["task"])
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with pytest.raises(ValueError, match="num_prefixes must be"):
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model.predict_curves(batch, num_prefixes=0)
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@skip_if_package_missing("transformers")
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def test_topreward_predict_curves_right_pads_with_nan_for_variable_lengths(monkeypatch):
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"""Trajectories of different lengths in the same batch are right-padded
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with ``NaN`` so the output is a regular ``(B, T_max)`` tensor."""
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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_patch_build(monkeypatch)
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cfg = TOPRewardConfig(device="cpu")
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model = TOPRewardModel(cfg)
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monkeypatch.setattr(
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TOPRewardModel,
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"_compute_log_prob_reward",
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lambda _self, frames, _instr: float(frames.shape[0]),
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)
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frames_short = np.zeros((2, 8, 8, 3), dtype=np.uint8)
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frames_long = np.zeros((4, 8, 8, 3), dtype=np.uint8)
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out = model.predict_curves(_make_batch([frames_short, frames_long], ["a", "b"]))
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assert out["progress"].shape == (2, 4)
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# Trailing entries for the shorter trajectory are NaN.
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assert torch.isnan(out["progress"][0, 2:]).all()
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# The longer trajectory has no NaNs.
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assert not torch.isnan(out["progress"][1]).any()
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# ---------------------------------------------------------------------------
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# Save / load — config-only checkpoint
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# ---------------------------------------------------------------------------
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@@ -351,10 +190,6 @@ def test_topreward_predict_curves_right_pads_with_nan_for_variable_lengths(monke
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@skip_if_package_missing("transformers")
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def test_topreward_save_pretrained_writes_only_config_json(monkeypatch, tmp_path):
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"""A TOPReward "checkpoint" is just ``config.json``. Writing
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``model.safetensors`` would only duplicate ~16 GB of Qwen weights for
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no benefit, so :meth:`_save_pretrained` must skip it entirely.
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"""
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from huggingface_hub.constants import CONFIG_NAME, SAFETENSORS_SINGLE_FILE
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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@@ -371,13 +206,11 @@ def test_topreward_save_pretrained_writes_only_config_json(monkeypatch, tmp_path
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model.save_pretrained(str(tmp_path))
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assert (tmp_path / CONFIG_NAME).exists()
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# Zero-shot model: no safetensors written by `_save_pretrained`.
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assert not (tmp_path / SAFETENSORS_SINGLE_FILE).exists()
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@skip_if_package_missing("transformers")
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def test_topreward_from_pretrained_local_dir_roundtrips_config(monkeypatch, tmp_path):
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"""Save a TOPRewardConfig locally and reload it — user knobs must survive."""
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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_patch_build(monkeypatch)
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@@ -387,7 +220,6 @@ def test_topreward_from_pretrained_local_dir_roundtrips_config(monkeypatch, tmp_
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reduction="sum",
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fps=4.0,
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image_key="observation.images.front",
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use_video_description=True,
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add_chat_template=True,
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success_threshold=-1.5,
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)
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@@ -400,16 +232,12 @@ def test_topreward_from_pretrained_local_dir_roundtrips_config(monkeypatch, tmp_
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assert reloaded.config.reduction == "sum"
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assert reloaded.config.fps == 4.0
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assert reloaded.config.image_key == "observation.images.front"
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assert reloaded.config.use_video_description is True
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assert reloaded.config.add_chat_template is True
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assert reloaded.config.success_threshold == -1.5
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@skip_if_package_missing("transformers")
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def test_topreward_is_not_trainable(monkeypatch):
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"""The whole point of TOPReward is that it is zero-shot.
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``is_trainable`` must therefore be ``False`` and ``forward(...)`` must
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raise the base-class ``NotImplementedError``."""
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from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
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_patch_build(monkeypatch)
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@@ -23,11 +23,11 @@ import torch
|
||||
from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
|
||||
from lerobot.rewards.topreward.processor_topreward import (
|
||||
TOPREWARD_FEATURE_PREFIX,
|
||||
TOPRewardEncoderProcessorStep,
|
||||
_expand_tasks,
|
||||
_video_to_numpy,
|
||||
)
|
||||
from lerobot.types import TransitionKey
|
||||
from tests.utils import skip_if_package_missing
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# _video_to_numpy — pure (T, C, H, W) -> (T, H, W, C) uint8 conversion
|
||||
@@ -35,7 +35,7 @@ from lerobot.types import TransitionKey
|
||||
|
||||
|
||||
def test_video_to_numpy_chw_float_is_converted_to_thwc_uint8():
|
||||
video = torch.rand(4, 3, 8, 8) # (T, C, H, W) floats in [0, 1]
|
||||
video = torch.rand(4, 3, 8, 8)
|
||||
array = _video_to_numpy(video, max_frames=None)
|
||||
|
||||
assert array.shape == (4, 8, 8, 3)
|
||||
@@ -52,7 +52,6 @@ def test_video_to_numpy_already_thwc_uint8_passes_through():
|
||||
|
||||
|
||||
def test_video_to_numpy_max_frames_tail_crops_recent_frames():
|
||||
"""``max_frames`` should keep the **last** K frames (most recent)."""
|
||||
video = torch.zeros(10, 3, 4, 4)
|
||||
for t in range(10):
|
||||
video[t] = t / 9.0
|
||||
@@ -70,8 +69,6 @@ def test_video_to_numpy_rejects_3d_input():
|
||||
|
||||
|
||||
def test_video_to_numpy_floats_above_one_pass_through_without_rescaling():
|
||||
"""If ``array.max() > 1`` the helper assumes the tensor is already in the
|
||||
uint8 range; values pass through unchanged (but are still clipped to 255)."""
|
||||
video = torch.full((1, 3, 2, 2), 5.0)
|
||||
array = _video_to_numpy(video, max_frames=None)
|
||||
|
||||
@@ -127,50 +124,80 @@ def test_expand_tasks_wrong_type_raises():
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Encoder step — input/output shapes + dataclass surface
|
||||
# Encoder step — stubbed AutoProcessor + process_vision_info
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _skip_if_topreward_extras_missing(func):
|
||||
func = skip_if_package_missing("qwen-vl-utils", import_name="qwen_vl_utils")(func)
|
||||
func = skip_if_package_missing("transformers")(func)
|
||||
return func
|
||||
|
||||
|
||||
class _FakeTokenizer:
|
||||
eos_token = "<|endoftext|>"
|
||||
pad_token = "<|endoftext|>"
|
||||
|
||||
def __call__(self, *args, **kwargs):
|
||||
return {"input_ids": torch.zeros(1, 10, dtype=torch.long)}
|
||||
|
||||
|
||||
class _FakeAutoProcessor:
|
||||
def __init__(self) -> None:
|
||||
self.tokenizer = _FakeTokenizer()
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, *args, **kwargs): # noqa: ARG003
|
||||
return cls()
|
||||
|
||||
def apply_chat_template(self, messages, **kwargs): # noqa: ARG002
|
||||
return "fake_prompt_text"
|
||||
|
||||
def __call__(self, text=None, images=None, videos=None, **kwargs): # noqa: ARG002
|
||||
seq_len = 10
|
||||
return {
|
||||
"input_ids": torch.randint(0, 100, (1, seq_len)),
|
||||
"attention_mask": torch.ones(1, seq_len, dtype=torch.long),
|
||||
}
|
||||
|
||||
|
||||
def _build_step(monkeypatch, **overrides):
|
||||
import importlib
|
||||
import sys
|
||||
import types
|
||||
|
||||
from lerobot.rewards.topreward import processor_topreward
|
||||
from lerobot.utils import import_utils
|
||||
|
||||
monkeypatch.setattr(processor_topreward, "AutoProcessor", _FakeAutoProcessor)
|
||||
|
||||
# Stub qwen_vl_utils as a real module object (not MagicMock) so
|
||||
# ``require_package`` / ``find_spec`` don't choke on a missing ``__spec__``.
|
||||
fake_qwen_vl = types.ModuleType("qwen_vl_utils")
|
||||
fake_qwen_vl.process_vision_info = lambda messages: (None, None) # type: ignore[attr-defined]
|
||||
fake_qwen_vl.__spec__ = importlib.machinery.ModuleSpec("qwen_vl_utils", None)
|
||||
monkeypatch.setitem(sys.modules, "qwen_vl_utils", fake_qwen_vl)
|
||||
|
||||
# Clear the require_package cache so the stub is picked up.
|
||||
import_utils._require_package_cache.pop("qwen_vl_utils", None)
|
||||
|
||||
return processor_topreward.TOPRewardEncoderProcessorStep(**overrides)
|
||||
|
||||
|
||||
def _make_transition(observation: dict, complementary: dict | None = None) -> dict:
|
||||
"""Build a tiny ``EnvTransition`` dict for the encoder step."""
|
||||
transition: dict = {TransitionKey.OBSERVATION: observation}
|
||||
if complementary is not None:
|
||||
transition[TransitionKey.COMPLEMENTARY_DATA] = complementary
|
||||
return transition
|
||||
|
||||
|
||||
def test_encoder_step_writes_namespaced_frames_and_task():
|
||||
"""The encoder step's output is the contract the model reads from. It
|
||||
must populate exactly two namespaced keys: ``frames`` and ``task``."""
|
||||
step = TOPRewardEncoderProcessorStep(
|
||||
image_key="observation.images.top",
|
||||
task_key="task",
|
||||
max_frames=None,
|
||||
)
|
||||
@_skip_if_topreward_extras_missing
|
||||
def test_encoder_step_emits_input_ids_and_prompt_length(monkeypatch):
|
||||
"""The processor must emit Qwen-VL tensors including ``input_ids`` and
|
||||
``prompt_length`` under the ``observation.topreward.*`` namespace."""
|
||||
step = _build_step(monkeypatch)
|
||||
|
||||
frames_batch = torch.zeros(2, 4, 3, 8, 8) # (B=2, T=4, C, H, W)
|
||||
out = step(
|
||||
_make_transition(
|
||||
observation={"observation.images.top": frames_batch},
|
||||
complementary={"task": ["pick", "place"]},
|
||||
)
|
||||
)
|
||||
|
||||
obs_out = out[TransitionKey.OBSERVATION]
|
||||
frames_out = obs_out[f"{TOPREWARD_FEATURE_PREFIX}frames"]
|
||||
tasks_out = obs_out[f"{TOPREWARD_FEATURE_PREFIX}task"]
|
||||
|
||||
assert len(frames_out) == 2
|
||||
assert all(arr.shape == (4, 8, 8, 3) and arr.dtype == np.uint8 for arr in frames_out)
|
||||
assert tasks_out == ["pick", "place"]
|
||||
|
||||
|
||||
def test_encoder_step_adds_singleton_time_dim_for_4d_input():
|
||||
"""A ``(B, C, H, W)`` observation is the single-frame case; the encoder
|
||||
must unsqueeze the time dim so the model still sees a video."""
|
||||
step = TOPRewardEncoderProcessorStep(image_key="observation.images.top", max_frames=None)
|
||||
|
||||
frames_batch = torch.zeros(1, 3, 8, 8) # (B=1, C, H, W) — no time dim
|
||||
frames_batch = torch.zeros(1, 4, 3, 8, 8)
|
||||
out = step(
|
||||
_make_transition(
|
||||
observation={"observation.images.top": frames_batch},
|
||||
@@ -178,76 +205,60 @@ def test_encoder_step_adds_singleton_time_dim_for_4d_input():
|
||||
)
|
||||
)
|
||||
|
||||
frames_out = out[TransitionKey.OBSERVATION][f"{TOPREWARD_FEATURE_PREFIX}frames"]
|
||||
assert len(frames_out) == 1
|
||||
assert frames_out[0].shape == (1, 8, 8, 3) # (T=1, H, W, C)
|
||||
obs_out = out[TransitionKey.OBSERVATION]
|
||||
assert f"{TOPREWARD_FEATURE_PREFIX}input_ids" in obs_out
|
||||
assert f"{TOPREWARD_FEATURE_PREFIX}attention_mask" in obs_out
|
||||
assert f"{TOPREWARD_FEATURE_PREFIX}prompt_length" in obs_out
|
||||
|
||||
prompt_length = obs_out[f"{TOPREWARD_FEATURE_PREFIX}prompt_length"]
|
||||
assert prompt_length.dtype == torch.long
|
||||
assert prompt_length.shape == (1,)
|
||||
|
||||
|
||||
def test_encoder_step_uses_default_task_when_complementary_is_missing():
|
||||
step = TOPRewardEncoderProcessorStep(
|
||||
image_key="observation.images.top",
|
||||
default_task="perform the task",
|
||||
)
|
||||
|
||||
frames_batch = torch.zeros(1, 2, 3, 4, 4)
|
||||
out = step(_make_transition(observation={"observation.images.top": frames_batch}))
|
||||
|
||||
tasks_out = out[TransitionKey.OBSERVATION][f"{TOPREWARD_FEATURE_PREFIX}task"]
|
||||
assert tasks_out == ["perform the task"]
|
||||
|
||||
|
||||
def test_encoder_step_rejects_missing_image_key():
|
||||
step = TOPRewardEncoderProcessorStep(image_key="observation.images.top")
|
||||
with pytest.raises(KeyError, match="image key"):
|
||||
step(_make_transition(observation={}, complementary={"task": "pick"}))
|
||||
|
||||
|
||||
def test_encoder_step_rejects_non_dict_observation():
|
||||
step = TOPRewardEncoderProcessorStep()
|
||||
with pytest.raises(ValueError, match="observation dict"):
|
||||
step({TransitionKey.OBSERVATION: torch.zeros(1, 3, 8, 8)})
|
||||
|
||||
|
||||
def test_encoder_step_rejects_3d_or_6d_input():
|
||||
"""The encoder accepts ``(B,C,H,W)`` or ``(B,T,C,H,W)`` only."""
|
||||
step = TOPRewardEncoderProcessorStep(image_key="observation.images.top")
|
||||
with pytest.raises(ValueError, match=r"\(B,C,H,W\)"):
|
||||
step(
|
||||
_make_transition(
|
||||
observation={"observation.images.top": torch.zeros(8, 8, 3)},
|
||||
complementary={"task": "pick"},
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def test_encoder_step_get_config_roundtrips_user_fields():
|
||||
"""``get_config`` must serialise every user-tunable field — these are
|
||||
what the processor pipeline saves under ``preprocessor_config.json``."""
|
||||
step = TOPRewardEncoderProcessorStep(
|
||||
@_skip_if_topreward_extras_missing
|
||||
def test_encoder_step_get_config_roundtrips_user_fields(monkeypatch):
|
||||
step = _build_step(
|
||||
monkeypatch,
|
||||
vlm_name="Qwen/Qwen3-VL-8B-Instruct",
|
||||
image_key="observation.images.cam_top",
|
||||
task_key="task",
|
||||
default_task="do the thing",
|
||||
max_frames=8,
|
||||
fps=4.0,
|
||||
add_chat_template=True,
|
||||
max_length=2048,
|
||||
)
|
||||
|
||||
assert step.get_config() == {
|
||||
"image_key": "observation.images.cam_top",
|
||||
"task_key": "task",
|
||||
"default_task": "do the thing",
|
||||
"max_frames": 8,
|
||||
}
|
||||
cfg = step.get_config()
|
||||
assert cfg["vlm_name"] == "Qwen/Qwen3-VL-8B-Instruct"
|
||||
assert cfg["image_key"] == "observation.images.cam_top"
|
||||
assert cfg["default_task"] == "do the thing"
|
||||
assert cfg["max_frames"] == 8
|
||||
assert cfg["fps"] == 4.0
|
||||
assert cfg["add_chat_template"] is True
|
||||
assert cfg["max_length"] == 2048
|
||||
|
||||
|
||||
def test_encoder_step_transform_features_is_identity():
|
||||
"""The encoder writes plain Python objects (numpy arrays / strings)
|
||||
into ``observation`` at call time but does NOT advertise new typed
|
||||
features at pipeline-build time — the model reads them via the
|
||||
``TOPREWARD_FEATURE_PREFIX`` namespace, not via the typed feature map.
|
||||
"""
|
||||
step = TOPRewardEncoderProcessorStep()
|
||||
@_skip_if_topreward_extras_missing
|
||||
def test_encoder_step_transform_features_is_identity(monkeypatch):
|
||||
step = _build_step(monkeypatch)
|
||||
features = {
|
||||
PipelineFeatureType.OBSERVATION: {
|
||||
"observation.images.top": PolicyFeature(shape=(3, 224, 224), type=FeatureType.VISUAL),
|
||||
}
|
||||
}
|
||||
assert step.transform_features(features) == features
|
||||
|
||||
|
||||
@_skip_if_topreward_extras_missing
|
||||
def test_encoder_step_rejects_missing_image_key(monkeypatch):
|
||||
step = _build_step(monkeypatch, image_key="observation.images.top")
|
||||
with pytest.raises(KeyError, match="image key"):
|
||||
step(_make_transition(observation={}, complementary={"task": "pick"}))
|
||||
|
||||
|
||||
@_skip_if_topreward_extras_missing
|
||||
def test_encoder_step_rejects_non_dict_observation(monkeypatch):
|
||||
step = _build_step(monkeypatch)
|
||||
with pytest.raises(ValueError, match="observation dict"):
|
||||
step({TransitionKey.OBSERVATION: torch.zeros(1, 3, 8, 8)})
|
||||
|
||||
Reference in New Issue
Block a user