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cfb4b6dfb1
- Add core Gemma model (architecture, attention, GeGLU, RoPE, RMSNorm, etc) - Add configs for training and generation: gemma_train.json, gemma_generate.json - Add Gemma notebook for exploratory analysis and demonstration - Add __init__.py for Gemma submodule - Update run_llm_experiment.py to support Gemma experiment configs test(gemma): add comprehensive unit tests for Gemma - Test forward pass (with/without cache) - Test autoregressive generation (greedy, top-k, top-p) - Test shape correctness and max sequence length errors - Test multi-layer stack and token embeddings docs: add documentation notebook for Gemma usage and analysis Closes: #issue (if applicable)
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
# llm/tests/models/test_gemma.py
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import torch
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import pytest
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from llm.models.gemma.gemma import Gemma
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@pytest.fixture
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def config():
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return {
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"vocab_size": 100,
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"embed_dim": 32,
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"num_q_heads": 4,
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"num_layers": 2,
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"max_position_embeddings": 16,
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"dropout": 0.0,
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}
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@pytest.fixture
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def model(config):
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return Gemma(config)
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def test_forward_basic(model):
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x = torch.randint(0, 100, (2, 8))
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logits, cache = model(x)
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assert logits.shape == (2, 8, 100)
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assert isinstance(cache, list)
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assert len(cache) == model._decoders.__len__()
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def test_forward_with_cache(model):
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x = torch.randint(0, 100, (2, 4))
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logits, cache = model(x, use_cache=True)
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# Второй проход с cache и одним новым токеном
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x2 = torch.randint(0, 100, (2, 1))
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logits2, cache2 = model(x2, use_cache=True, cache=cache)
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assert logits2.shape == (2, 1, 100)
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assert isinstance(cache2, list)
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def test_generate_and_shape(model):
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x = torch.randint(0, 100, (1, 5))
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result = model.generate(x, max_new_tokens=3, do_sample=False)
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assert result.shape == (1, 8)
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def test_forward_sequence_too_long(model, config):
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x = torch.randint(0, 100, (1, config["max_position_embeddings"] + 1))
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with pytest.raises(ValueError):
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model(x)
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def test_generate_with_sampling_topk(model):
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x = torch.randint(0, 100, (1, 3))
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out = model.generate(x, max_new_tokens=2, do_sample=True, top_k=5)
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assert out.shape == (1, 5)
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def test_generate_with_sampling_topp(model):
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x = torch.randint(0, 100, (1, 3))
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out = model.generate(x, max_new_tokens=2, do_sample=True, top_p=0.8)
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assert out.shape == (1, 5)
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