lerobot/tests/policies/pi052/test_pi052_bucketed_ce.py

#!/usr/bin/env python

# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pytest
import torch

pytest.importorskip("transformers")

from lerobot.policies.pi052.modeling_pi052 import _lin_ce_flat, _shifted_lin_ce


def test_shifted_ce_none_retains_distinct_per_sample_losses():
    hidden = torch.tensor(
        [
            [[8.0, 0.0], [0.0, 8.0], [0.0, 0.0]],
            [[0.0, 8.0], [8.0, 0.0], [0.0, 0.0]],
        ]
    )
    labels = torch.tensor([[0, 0, 1], [0, 0, 1]])
    losses = _shifted_lin_ce(hidden, torch.eye(2), labels, reduction="none")

    assert losses.shape == (2,)
    assert losses[0] < losses[1]


def test_checkpoint_resolution_forwards_explicit_hub_options(monkeypatch, tmp_path):
    import lerobot.policies.pi05.modeling_pi05 as modeling_pi05

    checkpoint = tmp_path / "model.safetensors"
    checkpoint.touch()
    calls = []

    def fake_cached_file(model_id, filename, **kwargs):
        calls.append((model_id, filename, kwargs))
        return None if filename.endswith("index.json") else str(checkpoint)

    monkeypatch.setattr(modeling_pi05, "cached_file", fake_cached_file)
    files = modeling_pi05._resolve_weight_files(
        "org/model",
        force_download=True,
        resume_download=True,
        proxies={"https": "proxy"},
        token="secret",
        cache_dir=tmp_path / "cache",
        local_files_only=True,
        revision="commit",
    )

    assert files == [checkpoint]
    for _model_id, _filename, kwargs in calls:
        assert kwargs["revision"] == "commit"
        assert kwargs["cache_dir"] == tmp_path / "cache"
        assert kwargs["force_download"] is True
        assert kwargs["resume_download"] is True
        assert kwargs["proxies"] == {"https": "proxy"}
        assert kwargs["token"] == "secret"
        assert kwargs["local_files_only"] is True


def test_checkpoint_resolution_rejects_local_directory_without_weights(tmp_path):
    import lerobot.policies.pi05.modeling_pi05 as modeling_pi05

    with pytest.raises(FileNotFoundError, match="model.safetensors"):
        modeling_pi05._resolve_weight_files(
            tmp_path,
            force_download=False,
            resume_download=None,
            proxies=None,
            token=None,
            cache_dir=None,
            local_files_only=False,
            revision=None,
        )


@pytest.mark.parametrize("z_loss_weight", [0.0, 1e-4])
@pytest.mark.parametrize("rows,valid_rows", [(24, 9), (48, 25)])
def test_bucketed_ce_matches_dense_loss_and_gradients(z_loss_weight, rows, valid_rows):
    generator = torch.Generator().manual_seed(23)
    hidden_size, vocab_size = 7, 19
    hidden_ref = torch.randn(rows, hidden_size, generator=generator, dtype=torch.float64, requires_grad=True)
    weight_ref = torch.randn(
        vocab_size, hidden_size, generator=generator, dtype=torch.float64, requires_grad=True
    )
    labels = torch.full((rows,), -100, dtype=torch.long)
    valid_indices = torch.randperm(rows, generator=generator)[:valid_rows]
    labels[valid_indices] = torch.randint(0, vocab_size, (valid_rows,), generator=generator)
    hidden_bucketed = hidden_ref.detach().clone().requires_grad_(True)
    weight_bucketed = weight_ref.detach().clone().requires_grad_(True)

    import lerobot.policies.pi052.modeling_pi052 as modeling_pi052

    loss_ref = _lin_ce_flat(hidden_ref, weight_ref, labels, z_loss_weight=z_loss_weight)
    old_limit = modeling_pi052._LOGITS_CE_MAX_POSITIONS
    modeling_pi052._LOGITS_CE_MAX_POSITIONS = 16
    try:
        loss_bucketed = _lin_ce_flat(
            hidden_bucketed,
            weight_bucketed,
            labels,
            z_loss_weight=z_loss_weight,
        )
    finally:
        modeling_pi052._LOGITS_CE_MAX_POSITIONS = old_limit

    loss_ref.backward()
    loss_bucketed.backward()

    torch.testing.assert_close(loss_bucketed, loss_ref, rtol=1e-6, atol=1e-6)
    torch.testing.assert_close(hidden_bucketed.grad, hidden_ref.grad, rtol=1e-12, atol=1e-12)
    torch.testing.assert_close(weight_bucketed.grad, weight_ref.grad, rtol=1e-12, atol=1e-12)


def test_bucketed_ce_all_ignored_preserves_zero_gradients():
    hidden = torch.randn(24, 7, dtype=torch.float64, requires_grad=True)
    weight = torch.randn(19, 7, dtype=torch.float64, requires_grad=True)
    labels = torch.full((24,), -100, dtype=torch.long)

    import lerobot.policies.pi052.modeling_pi052 as modeling_pi052

    old_limit = modeling_pi052._LOGITS_CE_MAX_POSITIONS
    modeling_pi052._LOGITS_CE_MAX_POSITIONS = 16
    try:
        loss = _lin_ce_flat(hidden, weight, labels)
    finally:
        modeling_pi052._LOGITS_CE_MAX_POSITIONS = old_limit
    loss.backward()

    assert loss.item() == 0.0
    assert hidden.grad is not None
    assert weight.grad is not None
    assert torch.count_nonzero(hidden.grad) == 0
    assert torch.count_nonzero(weight.grad) == 0