mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-20 19:19:56 +00:00
Khalil Meftah
300 lines
12 KiB
Python
300 lines
12 KiB
Python
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for the in-tree Robometer reward model."""
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from __future__ import annotations
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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.robometer import RobometerConfig
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from lerobot.rewards.robometer.modeling_robometer import (
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ROBOMETER_FEATURE_PREFIX,
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convert_bins_to_continuous,
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decode_progress_outputs,
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)
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class _FakeQwenConfig:
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"""Stand-in for a Qwen3-VL config (the `model.config` attribute).
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``to_dict`` matches HF's ``PretrainedConfig.to_dict`` closely enough for
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``RobometerRewardModel._save_pretrained`` to snapshot a meaningful
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``vlm_config`` into the saved ``config.json`` and for the reload path
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to round-trip through ``AutoConfig.for_model``.
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"""
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def __init__(self, hidden_dim: int = 8, vocab_size: int = 100) -> None:
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self.text_config = SimpleNamespace(hidden_size=hidden_dim, vocab_size=vocab_size)
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self._hidden_dim = hidden_dim
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self._vocab_size = vocab_size
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def to_dict(self) -> dict:
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return {
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"model_type": "fake_qwen",
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"text_config": {
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"hidden_size": self._hidden_dim,
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"vocab_size": self._vocab_size,
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},
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}
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class _FakeEmbeddings(torch.nn.Module):
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def __init__(self, num_embeddings: int = 100) -> None:
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super().__init__()
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self.num_embeddings = num_embeddings
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class _FakeBaseModel(torch.nn.Module):
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"""Stand-in for the Qwen3-VL backbone during tests.
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Provides the minimum surface `RobometerRewardModel.__init__` and
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`_compute_rbm_logits` rely on: a `parameters()` iterator (for dtype +
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device), a `config.text_config.hidden_size`, a `config.to_dict()` so
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`_save_pretrained` can snapshot `vlm_config`,
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`get_input_embeddings()` / `resize_token_embeddings()` so the fresh-init
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embed resize is a no-op, and a forward that returns a `SimpleNamespace`
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with a `hidden_states` tuple.
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"""
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def __init__(self, hidden_dim: int = 8) -> None:
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super().__init__()
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self._param = torch.nn.Parameter(torch.zeros(1))
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self.hidden_dim = hidden_dim
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self.config = _FakeQwenConfig(hidden_dim)
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self._embeddings = _FakeEmbeddings()
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def get_input_embeddings(self) -> _FakeEmbeddings:
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return self._embeddings
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def resize_token_embeddings(self, new_size: int) -> None:
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self._embeddings.num_embeddings = new_size
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def forward(self, **kwargs): # noqa: ARG002 - intentional kwargs sink
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input_ids = kwargs["input_ids"]
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return SimpleNamespace(
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hidden_states=(torch.zeros(input_ids.shape[0], input_ids.shape[1], self.hidden_dim),),
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last_hidden_state=torch.zeros(input_ids.shape[0], input_ids.shape[1], self.hidden_dim),
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)
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class _FakeTokenizer:
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"""Minimal stand-in for an HF tokenizer.
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``RobometerConfig.__post_init__`` uses ``len(tokenizer)`` to compute the
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deterministic resize target ``len(tokenizer) + len(ROBOMETER_SPECIAL_TOKENS)``,
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so a working ``__len__`` is all we need.
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"""
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def __init__(self, length: int = 100) -> None:
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self._length = length
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def __len__(self) -> int:
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return self._length
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def _patch_build(monkeypatch) -> None:
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"""Stub out the HF AutoX calls so Robometer construction stays cheap in tests.
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Covers (EO-1 style — no model-side override hooks):
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* ``AutoConfig.from_pretrained`` (config side) — used by
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``RobometerConfig.__post_init__`` to snapshot the backbone config.
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* ``AutoTokenizer.from_pretrained`` (config side) — used by
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``__post_init__`` to compute ``len(tokenizer) + 5``.
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* ``AutoConfig.for_model`` — used by
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``RobometerConfig.vlm_backbone_config`` when rebuilding for ``from_config``.
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* ``AutoModelForImageTextToText.from_pretrained`` — fresh-training path
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(``pretrained_path is None``).
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* ``AutoModelForImageTextToText.from_config`` — checkpoint-reload path
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(``pretrained_path`` is set).
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"""
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from lerobot.rewards.robometer import configuration_robometer, modeling_robometer
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monkeypatch.setattr(
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modeling_robometer.AutoModelForImageTextToText,
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"from_pretrained",
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lambda *args, **kwargs: _FakeBaseModel(hidden_dim=8),
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)
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monkeypatch.setattr(
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modeling_robometer.AutoModelForImageTextToText,
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"from_config",
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lambda *args, **kwargs: _FakeBaseModel(hidden_dim=8),
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)
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monkeypatch.setattr(
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configuration_robometer.AutoConfig,
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"for_model",
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lambda *args, **kwargs: _FakeQwenConfig(hidden_dim=8),
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)
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monkeypatch.setattr(
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configuration_robometer.AutoConfig,
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"from_pretrained",
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lambda *args, **kwargs: _FakeQwenConfig(hidden_dim=8),
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)
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monkeypatch.setattr(
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configuration_robometer.AutoTokenizer,
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"from_pretrained",
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lambda *args, **kwargs: _FakeTokenizer(length=100),
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)
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def _make_batch(features: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
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"""Build a `compute_reward`-ready batch using Robometer's namespaced keys."""
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return {f"{ROBOMETER_FEATURE_PREFIX}{key}": value for key, value in features.items()}
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def test_robometer_config_registered(monkeypatch):
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_patch_build(monkeypatch)
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assert "robometer" in RewardModelConfig.get_known_choices()
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assert RewardModelConfig.get_choice_class("robometer") is RobometerConfig
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assert isinstance(make_reward_model_config("robometer", device="cpu"), RobometerConfig)
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def test_robometer_factory_returns_in_tree_class():
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from lerobot.rewards.robometer.modeling_robometer import RobometerRewardModel
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assert get_reward_model_class("robometer") is RobometerRewardModel
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def test_convert_bins_to_continuous_returns_expected_values():
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# Two frames: first peaks at bin 0 (center 0.0), second peaks at bin 9 (center 1.0).
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bin_logits = torch.full((2, 10), -10.0)
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bin_logits[0, 0] = 10.0
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bin_logits[1, -1] = 10.0
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values = convert_bins_to_continuous(bin_logits)
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assert values.shape == (2,)
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assert torch.allclose(values, torch.tensor([0.0, 1.0]), atol=1e-3)
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def test_decode_progress_outputs_returns_last_frame_values():
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progress = torch.tensor([[0.1, 0.9], [0.4, 0.6]])
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success_logits = torch.tensor([[0.0, 5.0], [0.0, -5.0]])
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outputs = decode_progress_outputs(progress, success_logits, is_discrete_mode=False)
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assert outputs["progress_pred"] == [pytest.approx([0.1, 0.9]), pytest.approx([0.4, 0.6])]
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assert outputs["success_probs"][0][-1] == pytest.approx(torch.sigmoid(torch.tensor(5.0)).item(), abs=1e-3)
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assert outputs["success_probs"][1][-1] == pytest.approx(
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torch.sigmoid(torch.tensor(-5.0)).item(), abs=1e-3
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)
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def test_decode_progress_outputs_discrete_mode_softmaxes_over_bins():
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# 2 frames, peaks at bin 0 and bin 9 → continuous predictions 0.0 and 1.0
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bin_logits = torch.full((1, 2, 10), -10.0)
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bin_logits[0, 0, 0] = 10.0
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bin_logits[0, 1, -1] = 10.0
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outputs = decode_progress_outputs(bin_logits, success_logits=None, is_discrete_mode=True)
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assert outputs["success_probs"] == []
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assert outputs["progress_pred"][0] == pytest.approx([0.0, 1.0], abs=1e-3)
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def test_robometer_compute_reward_reads_pre_encoded_inputs(monkeypatch):
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from lerobot.rewards.robometer.modeling_robometer import RobometerRewardModel
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progress = torch.tensor([[0.1, 0.9], [0.4, 0.6]])
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success_logits = torch.tensor([[0.0, 5.0], [0.0, -5.0]])
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_patch_build(monkeypatch)
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cfg = RobometerConfig(device="cpu", reward_output="progress", progress_loss_type="l2")
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model = RobometerRewardModel(cfg)
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# Bypass the Qwen3-VL forward + head extraction with deterministic logits.
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monkeypatch.setattr(model, "_compute_rbm_logits", lambda _inputs: (progress, success_logits))
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batch = _make_batch({"input_ids": torch.zeros(2, 2, dtype=torch.long)})
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rewards = model.compute_reward(batch)
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assert torch.allclose(rewards, torch.tensor([0.9, 0.6]))
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def test_robometer_compute_reward_can_return_binary_success(monkeypatch):
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from lerobot.rewards.robometer.modeling_robometer import RobometerRewardModel
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progress = torch.tensor([[0.1, 0.9], [0.4, 0.6]])
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success_logits = torch.tensor([[0.0, 5.0], [0.0, -5.0]]) # sigmoid(5) > 0.5; sigmoid(-5) < 0.5
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_patch_build(monkeypatch)
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cfg = RobometerConfig(
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device="cpu",
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reward_output="success",
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success_threshold=0.5,
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progress_loss_type="l2",
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)
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model = RobometerRewardModel(cfg)
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monkeypatch.setattr(model, "_compute_rbm_logits", lambda _inputs: (progress, success_logits))
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batch = _make_batch({"input_ids": torch.zeros(2, 2, dtype=torch.long)})
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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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def test_robometer_compute_reward_errors_when_inputs_missing(monkeypatch):
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from lerobot.rewards.robometer.modeling_robometer import RobometerRewardModel
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_patch_build(monkeypatch)
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cfg = RobometerConfig(device="cpu", progress_loss_type="l2")
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model = RobometerRewardModel(cfg)
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with pytest.raises(KeyError, match=r"observation\.robometer\.input_ids"):
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model.compute_reward({})
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def test_robometer_save_pretrained_roundtrips(monkeypatch, tmp_path):
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"""Saving and reloading a Robometer model in LeRobot HF format must produce
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a single ``model.safetensors`` + ``config.json`` (no Hydra ``config.yaml``).
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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.robometer.modeling_robometer import RobometerRewardModel
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_patch_build(monkeypatch)
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cfg = RobometerConfig(
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device="cpu",
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pretrained_path="robometer/Robometer-4B",
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# Knobs the user might tweak — must survive the round-trip.
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image_key="observation.images.cam_top",
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task_key="task",
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reward_output="success",
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success_threshold=0.7,
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progress_loss_type="l2",
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)
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model = RobometerRewardModel(cfg)
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model.save_pretrained(str(tmp_path))
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# Exactly the files LeRobot's HubMixin promises.
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assert (tmp_path / CONFIG_NAME).exists()
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assert (tmp_path / SAFETENSORS_SINGLE_FILE).exists()
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assert not (tmp_path / "config.yaml").exists() # we want HF-style, not Hydra
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# Reload from the local directory: no Hub fetch, no YAML overlay. The
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# base class drives subclass dispatch via the `type` field in config.json.
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reloaded_cfg = RewardModelConfig.from_pretrained(str(tmp_path))
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assert isinstance(reloaded_cfg, RobometerConfig)
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reloaded_cfg.pretrained_path = str(tmp_path) # mimic lerobot-train's `validate()`
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reloaded = RobometerRewardModel.from_pretrained(str(tmp_path), config=reloaded_cfg)
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assert reloaded.config.image_key == "observation.images.cam_top"
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assert reloaded.config.task_key == "task"
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assert reloaded.config.reward_output == "success"
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assert reloaded.config.success_threshold == 0.7
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assert reloaded.config.progress_loss_type == "l2" # came back from config.json
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