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8fb9d5ece3
* feat(mining): add Pearl model conversion workflow * test(mining): tolerate missing Docker device request type * docs(mining): record Gemma and Qwen conversion evidence * docs(mining): record Qwen local validation evidence --------- Co-authored-by: krypticmouse <herumbshandilya123@gmail.com>
117 lines
3.7 KiB
Python
117 lines
3.7 KiB
Python
"""Tests for the experimental Pearl model converter script."""
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from __future__ import annotations
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import importlib.util
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import json
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from pathlib import Path
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import pytest
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torch = pytest.importorskip("torch")
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safetensors_torch = pytest.importorskip("safetensors.torch")
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load_file = safetensors_torch.load_file
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save_file = safetensors_torch.save_file
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def _load_converter():
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path = Path(__file__).parents[2] / "scripts" / "mining" / "pearl_model_converter.py"
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spec = importlib.util.spec_from_file_location("pearl_model_converter", path)
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assert spec is not None and spec.loader is not None
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return module
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def test_converter_quantizes_linear_weights_and_writes_pearl_config(
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tmp_path: Path,
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) -> None:
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converter = _load_converter()
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src = tmp_path / "src"
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out = tmp_path / "out"
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src.mkdir()
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(src / "config.json").write_text(
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json.dumps({"architectures": ["TinyForCausalLM"], "model_type": "tiny"})
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)
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(src / "tokenizer_config.json").write_text("{}")
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save_file(
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{
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"model.embed_tokens.weight": torch.ones(4, 4, dtype=torch.bfloat16),
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"model.layers.0.self_attn.q_proj.weight": torch.arange(
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16, dtype=torch.float32
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).reshape(4, 4),
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"model.layers.0.self_attn.o_proj.weight": torch.arange(
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16, dtype=torch.float32
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).reshape(4, 4),
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"model.layers.0.input_layernorm.weight": torch.ones(4),
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},
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src / "model.safetensors",
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)
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stats = converter.convert_checkpoint(
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str(src),
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out,
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hf_token_env="HF_TOKEN",
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device="cpu",
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chunk_rows=2,
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dry_run=False,
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)
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assert stats.non_mining == 1
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assert stats.mining == 1
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tensors = load_file(out / "model.safetensors")
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assert tensors["model.layers.0.self_attn.q_proj.weight"].dtype == torch.int8
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assert tensors["model.layers.0.self_attn.o_proj.weight"].dtype == torch.int8
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assert tensors["model.layers.0.self_attn.q_proj.weight_scale"].shape == (4, 1)
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assert tensors["model.layers.0.self_attn.o_proj.weight_scale"].shape == (4, 1)
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assert tensors["model.embed_tokens.weight"].dtype == torch.bfloat16
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config = json.loads((out / "config.json").read_text())
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assert config["quantization_config"]["quant_method"] == "pearl"
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assert (
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config["quantization_config"]["config_groups"]["group_1"]["weights"]["num_bits"]
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== 7
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)
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assert "re:.*visual.*" in config["quantization_config"]["ignore"]
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index = json.loads((out / "model.safetensors.index.json").read_text())
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assert "model.layers.0.self_attn.o_proj.weight_scale" in index["weight_map"]
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def test_converter_adds_gemma4_preprocessor_compat_file(
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tmp_path: Path,
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) -> None:
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converter = _load_converter()
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src = tmp_path / "src"
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out = tmp_path / "out"
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src.mkdir()
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(src / "config.json").write_text(
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json.dumps(
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{
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"architectures": ["Gemma4ForConditionalGeneration"],
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"model_type": "gemma4",
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}
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)
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)
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(src / "processor_config.json").write_text('{"processor_class":"Gemma4Processor"}')
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(src / "tokenizer_config.json").write_text("{}")
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save_file(
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{
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"language_model.model.layers.0.self_attn.q_proj.weight": torch.arange(
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16, dtype=torch.float32
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).reshape(4, 4),
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},
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src / "model.safetensors",
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)
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converter.convert_checkpoint(
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str(src),
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out,
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hf_token_env="HF_TOKEN",
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device="cpu",
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chunk_rows=2,
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dry_run=False,
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)
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assert (out / "processor_config.json").exists()
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assert (out / "preprocessor_config.json").exists()
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