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refactor(rewards): clean up TOPReward processor/model

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This commit is contained in:
Khalil Meftah
2026-05-20 17:39:21 +02:00
parent 70ad322676
commit f6ecb7b955
7 changed files with 568 additions and 928 deletions
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tests/rewards/test_modeling_topreward.py
+49 -221
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@@ -16,67 +16,71 @@
from __future__ import annotations
import numpy as np
from types import SimpleNamespace
import pytest
import torch
from lerobot.configs.rewards import RewardModelConfig
from lerobot.rewards.factory import get_reward_model_class, make_reward_model_config
from lerobot.rewards.topreward import TOPRewardConfig
from lerobot.rewards.topreward.modeling_topreward import minmax_normalize_rewards
from lerobot.rewards.topreward.processor_topreward import TOPREWARD_FEATURE_PREFIX
from tests.utils import skip_if_package_missing
class _FakeTokenizer:
"""Minimal tokenizer surface used by ``TOPRewardModel._compute_log_prob_reward``."""
eos_token = "<|endoftext|>"
class _FakeProcessor:
"""Stand-in for the Qwen ``AutoProcessor`` returned by ``from_pretrained``."""
def __init__(self) -> None:
self.tokenizer = _FakeTokenizer()
@classmethod
def from_pretrained(cls, *args, **kwargs): # noqa: ARG003
return cls()
class _FakeQwenModel(torch.nn.Module):
"""Stand-in for ``Qwen3VLForConditionalGeneration``.
Provides the minimum surface ``TOPRewardModel`` touches at construction
time (a ``parameters()`` iterator for device inference). Actual
``_compute_log_prob_reward`` calls are bypassed by monkey-patching the
method directly in the tests, so we never invoke ``self.model(...)``.
Returns a ``SimpleNamespace`` with ``logits`` of a controlled shape so
the log-prob extraction path in ``compute_reward`` can be exercised
without downloading real VLM weights.
"""
def __init__(self) -> None:
super().__init__()
self._param = torch.nn.Parameter(torch.zeros(1))
self._reward_value: float = -1.5
@classmethod
def from_pretrained(cls, *args, **kwargs): # noqa: ARG003
return cls()
def forward(self, input_ids, attention_mask=None, labels=None, **kwargs): # noqa: ARG002
batch_size, seq_len = input_ids.shape
vocab_size = 1000
logits = torch.zeros(batch_size, seq_len, vocab_size)
# Place a controlled log-prob at the target token position so the
# model returns a predictable reward value.
# The label-masked suffix is the last token (prompt_length = seq_len - 1).
# After the causal-LM shift (logits[:, :-1], labels[:, 1:]) the scored
# position is logits[:, -2, :] predicting labels[:, -1].
# We set logits so that log_softmax at the target token ≈ _reward_value.
if labels is not None:
for i in range(batch_size):
target_idx = int(input_ids[i, -1].item())
logits[i, -2, target_idx] = self._reward_value * -10 # high logit -> high log-prob
return SimpleNamespace(logits=logits)
def _patch_build(monkeypatch) -> None:
"""Stub out HF AutoX so TOPReward construction is cheap and offline."""
from lerobot.rewards.topreward import modeling_topreward
monkeypatch.setattr(modeling_topreward, "Qwen3VLForConditionalGeneration", _FakeQwenModel)
monkeypatch.setattr(modeling_topreward, "AutoProcessor", _FakeProcessor)
def _make_batch(frames: list[np.ndarray], tasks: list[str]) -> dict[str, list]:
def _make_batch(
input_ids: torch.Tensor,
attention_mask: torch.Tensor | None = None,
prompt_length: torch.Tensor | None = None,
) -> dict[str, torch.Tensor]:
"""Build a ``compute_reward``-ready batch using TOPReward's namespaced keys."""
return {
f"{TOPREWARD_FEATURE_PREFIX}frames": frames,
f"{TOPREWARD_FEATURE_PREFIX}task": tasks,
}
batch: dict[str, torch.Tensor] = {f"{TOPREWARD_FEATURE_PREFIX}input_ids": input_ids}
if attention_mask is not None:
batch[f"{TOPREWARD_FEATURE_PREFIX}attention_mask"] = attention_mask
if prompt_length is not None:
batch[f"{TOPREWARD_FEATURE_PREFIX}prompt_length"] = prompt_length
return batch
# ---------------------------------------------------------------------------
@@ -121,32 +125,6 @@ def test_topreward_config_rejects_suffix_without_instruction_placeholder():
TOPRewardConfig(device="cpu", prompt_suffix_template="no placeholder here")
# ---------------------------------------------------------------------------
# minmax_normalize_rewards — pure math helper
# ---------------------------------------------------------------------------
def test_minmax_normalize_rewards_maps_min_and_max_to_zero_and_one():
values = minmax_normalize_rewards([-3.0, -1.0, 0.0, -2.0])
assert values.shape == (4,)
assert values[0] == pytest.approx(0.0)
assert values[2] == pytest.approx(1.0)
# Monotonicity preserved within the input range.
assert values[3] == pytest.approx(1.0 / 3.0, abs=1e-6)
def test_minmax_normalize_rewards_handles_singleton_and_flat_inputs():
# Single element -> mapped to 1.0 (no information to scale).
assert minmax_normalize_rewards([42.0]).tolist() == [1.0]
# All-equal values -> all ones (avoid divide-by-zero).
assert minmax_normalize_rewards([0.5, 0.5, 0.5]).tolist() == [1.0, 1.0, 1.0]
def test_minmax_normalize_rewards_empty_input_returns_empty_array():
out = minmax_normalize_rewards([])
assert out.shape == (0,)
# ---------------------------------------------------------------------------
# compute_reward
# ---------------------------------------------------------------------------
@@ -154,55 +132,43 @@ def test_minmax_normalize_rewards_empty_input_returns_empty_array():
@skip_if_package_missing("transformers")
def test_topreward_compute_reward_returns_one_scalar_per_sample(monkeypatch):
"""``compute_reward`` must return a ``(B,)`` float32 tensor with one
log-prob reward per sample, consuming pre-encoded Qwen-VL tensors."""
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
_patch_build(monkeypatch)
cfg = TOPRewardConfig(device="cpu")
model = TOPRewardModel(cfg)
captured = []
def fake_log_prob(self, frames, instruction): # noqa: ARG002
captured.append((frames.shape, instruction))
return -1.5
monkeypatch.setattr(TOPRewardModel, "_compute_log_prob_reward", fake_log_prob)
frames_a = np.zeros((4, 8, 8, 3), dtype=np.uint8)
frames_b = np.zeros((6, 8, 8, 3), dtype=np.uint8)
batch = _make_batch([frames_a, frames_b], ["pick the cube", "open the drawer"])
input_ids = torch.randint(0, 100, (2, 10))
attention_mask = torch.ones(2, 10, dtype=torch.long)
prompt_length = torch.tensor([9, 9]) # unmask only the last token
batch = _make_batch(input_ids, attention_mask, prompt_length)
rewards = model.compute_reward(batch)
assert rewards.shape == (2,)
assert rewards.dtype == torch.float32
assert torch.allclose(rewards, torch.tensor([-1.5, -1.5]))
# `_compute_log_prob_reward` was called once per sample with the right tasks.
assert [task for _, task in captured] == ["pick the cube", "open the drawer"]
assert [shape[0] for shape, _ in captured] == [4, 6]
@skip_if_package_missing("transformers")
def test_topreward_compute_reward_applies_success_threshold(monkeypatch):
"""When ``success_threshold`` is finite, the model returns binary success
instead of the raw log-prob — useful as a drop-in success detector."""
"""When ``success_threshold`` is finite, the model returns binary success."""
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
_patch_build(monkeypatch)
cfg = TOPRewardConfig(device="cpu", success_threshold=-2.0)
cfg = TOPRewardConfig(device="cpu", success_threshold=0.0)
model = TOPRewardModel(cfg)
rewards_in = iter([-1.5, -3.0]) # first above threshold, second below
monkeypatch.setattr(
TOPRewardModel,
"_compute_log_prob_reward",
lambda _self, _frames, _instr: next(rewards_in),
)
input_ids = torch.randint(0, 100, (2, 10))
attention_mask = torch.ones(2, 10, dtype=torch.long)
prompt_length = torch.tensor([9, 9])
frames = [np.zeros((2, 8, 8, 3), dtype=np.uint8), np.zeros((2, 8, 8, 3), dtype=np.uint8)]
rewards = model.compute_reward(_make_batch(frames, ["task", "task"]))
batch = _make_batch(input_ids, attention_mask, prompt_length)
rewards = model.compute_reward(batch)
assert torch.equal(rewards, torch.tensor([1.0, 0.0]))
assert rewards.shape == (2,)
assert set(rewards.tolist()).issubset({0.0, 1.0})
@skip_if_package_missing("transformers")
@@ -213,137 +179,10 @@ def test_topreward_compute_reward_errors_when_inputs_missing(monkeypatch):
cfg = TOPRewardConfig(device="cpu")
model = TOPRewardModel(cfg)
with pytest.raises(KeyError, match=r"observation\.topreward\."):
with pytest.raises(KeyError, match=r"observation\.topreward\.input_ids"):
model.compute_reward({})
@skip_if_package_missing("transformers")
def test_topreward_compute_reward_errors_when_batch_sizes_mismatch(monkeypatch):
"""frames and task lists must have matching lengths — a stale processor
that produces only one task for a multi-sample batch should surface as
an explicit error, not a silent zip truncation."""
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
_patch_build(monkeypatch)
cfg = TOPRewardConfig(device="cpu")
model = TOPRewardModel(cfg)
monkeypatch.setattr(
TOPRewardModel,
"_compute_log_prob_reward",
lambda _self, _frames, _instr: 0.0,
)
frames = [np.zeros((2, 8, 8, 3), dtype=np.uint8), np.zeros((2, 8, 8, 3), dtype=np.uint8)]
with pytest.raises(ValueError, match="task batch size"):
model.compute_reward(_make_batch(frames, ["only one task"]))
# ---------------------------------------------------------------------------
# predict_curves
# ---------------------------------------------------------------------------
@skip_if_package_missing("transformers")
def test_topreward_predict_curves_runs_one_forward_per_prefix(monkeypatch):
"""``predict_curves`` must call the VLM once per prefix length per
trajectory and write min-max-normalised values back into the curve."""
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
_patch_build(monkeypatch)
cfg = TOPRewardConfig(device="cpu")
model = TOPRewardModel(cfg)
# Simulate a strictly increasing log-prob curve as the prefix grows.
call_log: list[int] = []
def fake_log_prob(self, frames, instruction): # noqa: ARG002
call_log.append(int(frames.shape[0]))
return float(frames.shape[0]) # log-prob = prefix length
monkeypatch.setattr(TOPRewardModel, "_compute_log_prob_reward", fake_log_prob)
frames = np.zeros((5, 8, 8, 3), dtype=np.uint8)
batch = _make_batch([frames], ["lift the cup"])
out = model.predict_curves(batch)
# One forward per prefix length, in order.
assert call_log == [1, 2, 3, 4, 5]
# (B, T_max) shape, padded with NaN beyond each trajectory's length.
assert out["progress"].shape == (1, 5)
# Strictly increasing raw rewards -> min-max-normalised to [0, 1] linearly.
expected = torch.tensor([[0.0, 0.25, 0.5, 0.75, 1.0]])
assert torch.allclose(out["progress"], expected, atol=1e-6)
@skip_if_package_missing("transformers")
def test_topreward_predict_curves_sparse_dense_interpolates_to_full_resolution(monkeypatch):
"""With ``num_prefixes < N`` the model should score only the requested
number of anchor prefixes and linearly interpolate between them — the
upstream sparse-dense pattern (``num_samples=15``)."""
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
_patch_build(monkeypatch)
cfg = TOPRewardConfig(device="cpu")
model = TOPRewardModel(cfg)
call_log: list[int] = []
def fake_log_prob(self, frames, instruction): # noqa: ARG002
call_log.append(int(frames.shape[0]))
return float(frames.shape[0])
monkeypatch.setattr(TOPRewardModel, "_compute_log_prob_reward", fake_log_prob)
frames = np.zeros((9, 8, 8, 3), dtype=np.uint8)
out = model.predict_curves(_make_batch([frames], ["lift the cup"]), num_prefixes=3)
# 3 anchors at linspace(1, 9, 3) -> [1, 5, 9] -> 3 VLM forwards instead of 9.
assert call_log == [1, 5, 9]
# Returned curve is full resolution (9 frames) and monotone in [0, 1].
assert out["progress"].shape == (1, 9)
curve = out["progress"][0].numpy()
assert curve[0] == pytest.approx(0.0)
assert curve[-1] == pytest.approx(1.0)
assert np.all(np.diff(curve) >= 0)
@skip_if_package_missing("transformers")
def test_topreward_predict_curves_rejects_invalid_num_prefixes(monkeypatch):
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
_patch_build(monkeypatch)
model = TOPRewardModel(TOPRewardConfig(device="cpu"))
batch = _make_batch([np.zeros((3, 8, 8, 3), dtype=np.uint8)], ["task"])
with pytest.raises(ValueError, match="num_prefixes must be"):
model.predict_curves(batch, num_prefixes=0)
@skip_if_package_missing("transformers")
def test_topreward_predict_curves_right_pads_with_nan_for_variable_lengths(monkeypatch):
"""Trajectories of different lengths in the same batch are right-padded
with ``NaN`` so the output is a regular ``(B, T_max)`` tensor."""
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
_patch_build(monkeypatch)
cfg = TOPRewardConfig(device="cpu")
model = TOPRewardModel(cfg)
monkeypatch.setattr(
TOPRewardModel,
"_compute_log_prob_reward",
lambda _self, frames, _instr: float(frames.shape[0]),
)
frames_short = np.zeros((2, 8, 8, 3), dtype=np.uint8)
frames_long = np.zeros((4, 8, 8, 3), dtype=np.uint8)
out = model.predict_curves(_make_batch([frames_short, frames_long], ["a", "b"]))
assert out["progress"].shape == (2, 4)
# Trailing entries for the shorter trajectory are NaN.
assert torch.isnan(out["progress"][0, 2:]).all()
# The longer trajectory has no NaNs.
assert not torch.isnan(out["progress"][1]).any()
# ---------------------------------------------------------------------------
# Save / load — config-only checkpoint
# ---------------------------------------------------------------------------
@@ -351,10 +190,6 @@ def test_topreward_predict_curves_right_pads_with_nan_for_variable_lengths(monke
@skip_if_package_missing("transformers")
def test_topreward_save_pretrained_writes_only_config_json(monkeypatch, tmp_path):
"""A TOPReward "checkpoint" is just ``config.json``. Writing
``model.safetensors`` would only duplicate ~16 GB of Qwen weights for
no benefit, so :meth:`_save_pretrained` must skip it entirely.
"""
from huggingface_hub.constants import CONFIG_NAME, SAFETENSORS_SINGLE_FILE
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
@@ -371,13 +206,11 @@ def test_topreward_save_pretrained_writes_only_config_json(monkeypatch, tmp_path
model.save_pretrained(str(tmp_path))
assert (tmp_path / CONFIG_NAME).exists()
# Zero-shot model: no safetensors written by `_save_pretrained`.
assert not (tmp_path / SAFETENSORS_SINGLE_FILE).exists()
@skip_if_package_missing("transformers")
def test_topreward_from_pretrained_local_dir_roundtrips_config(monkeypatch, tmp_path):
"""Save a TOPRewardConfig locally and reload it — user knobs must survive."""
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
_patch_build(monkeypatch)
@@ -387,7 +220,6 @@ def test_topreward_from_pretrained_local_dir_roundtrips_config(monkeypatch, tmp_
reduction="sum",
fps=4.0,
image_key="observation.images.front",
use_video_description=True,
add_chat_template=True,
success_threshold=-1.5,
)
@@ -400,16 +232,12 @@ def test_topreward_from_pretrained_local_dir_roundtrips_config(monkeypatch, tmp_
assert reloaded.config.reduction == "sum"
assert reloaded.config.fps == 4.0
assert reloaded.config.image_key == "observation.images.front"
assert reloaded.config.use_video_description is True
assert reloaded.config.add_chat_template is True
assert reloaded.config.success_threshold == -1.5
@skip_if_package_missing("transformers")
def test_topreward_is_not_trainable(monkeypatch):
"""The whole point of TOPReward is that it is zero-shot.
``is_trainable`` must therefore be ``False`` and ``forward(...)`` must
raise the base-class ``NotImplementedError``."""
from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
_patch_build(monkeypatch)