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import functools |
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import subprocess |
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import sys |
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from contextlib import redirect_stdout |
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from io import StringIO |
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from pathlib import Path |
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from unittest import mock |
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from unittest.mock import Mock, call, ANY |
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import torch |
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wd = Path(__file__).parent.parent.absolute() |
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@functools.lru_cache(maxsize=1) |
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def load_generate_script(): |
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sys.path.append(str(wd)) |
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import generate |
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return generate |
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def test_generate(): |
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generate = load_generate_script() |
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from lit_llama.model import LLaMA, LLaMAConfig |
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T, C = 5, 3 |
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logits = torch.randn(T, C) |
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input_idx = torch.randint(10, size=(T,)) |
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config = LLaMAConfig(block_size=128, vocab_size=16, n_layer=1, n_head=4, n_embd=8) |
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model = LLaMA(config) |
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max_new_tokens = 20 |
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multinomial_results = [] |
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original_multinomial = torch.multinomial |
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def multinomial(*args, **kwargs): |
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out = original_multinomial(*args, **kwargs) |
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multinomial_results.append(out) |
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return out |
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with mock.patch("torch.multinomial", multinomial): |
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out = generate.generate(model, input_idx, max_new_tokens, max_seq_length=10, top_k=4) |
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assert out.size(0) == T + max_new_tokens |
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multinomial_results = torch.hstack(multinomial_results) |
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expected = torch.cat((input_idx, multinomial_results)) |
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assert out.shape == expected.shape |
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torch.testing.assert_close(out, expected) |
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@mock.patch("torch.cuda.is_bf16_supported", return_value=False) |
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def test_main(tmp_path, monkeypatch): |
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generate = load_generate_script() |
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checkpoint_path = tmp_path / "ckpt" |
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checkpoint_path.touch() |
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tokenizer_path = tmp_path / "tokenizer" |
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tokenizer_path.touch() |
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class FabricMock(Mock): |
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@property |
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def device(self): |
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return torch.device("cpu") |
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monkeypatch.setattr(generate.L, "Fabric", FabricMock) |
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model_mock = Mock() |
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monkeypatch.setattr(generate.LLaMA, "from_name", model_mock) |
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lookup_mock = Mock(return_value="1T") |
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monkeypatch.setattr(generate, "llama_model_lookup", lookup_mock) |
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load_mock = Mock() |
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load_mock.return_value = load_mock |
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load_mock.__enter__ = Mock() |
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load_mock.__exit__ = Mock() |
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monkeypatch.setattr(generate.torch, "load", load_mock) |
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monkeypatch.setattr(generate, "lazy_load", load_mock) |
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tokenizer_mock = Mock() |
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tokenizer_mock.return_value.encode.return_value = torch.tensor([[1, 2, 3]]) |
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tokenizer_mock.return_value.decode.return_value = "foo bar baz" |
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monkeypatch.setattr(generate, "Tokenizer", tokenizer_mock) |
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generate_mock = Mock() |
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generate_mock.return_value = torch.tensor([[3, 2, 1]]) |
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monkeypatch.setattr(generate, "generate", generate_mock) |
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num_samples = 2 |
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out = StringIO() |
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with redirect_stdout(out): |
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generate.main( |
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checkpoint_path=checkpoint_path, |
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tokenizer_path=tokenizer_path, |
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temperature=2.0, |
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top_k=2, |
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num_samples=num_samples, |
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) |
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model_mock.assert_called_once_with("1T") |
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load_mock.assert_called_once_with(checkpoint_path) |
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tokenizer_mock.assert_called_once_with(tokenizer_path) |
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assert len(tokenizer_mock.return_value.decode.mock_calls) == num_samples |
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assert torch.allclose(tokenizer_mock.return_value.decode.call_args[0][0], generate_mock.return_value) |
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assert generate_mock.mock_calls == [call(ANY, ANY, 50, ANY, temperature=2.0, top_k=2)] * num_samples |
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assert out.getvalue() == "foo bar baz\n" * num_samples |
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def test_cli(): |
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cli_path = wd / "generate.py" |
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output = subprocess.check_output([sys.executable, cli_path, "-h"]) |
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output = str(output.decode()) |
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assert "Generates text samples" in output |
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