File size: 4,234 Bytes
dfb775d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | """XTrainConfig YAML round-trip and validation against the canonical 10-section schema."""
from __future__ import annotations
import pytest
import yaml
from pydantic import ValidationError
from mindxtrain.config.loader import list_recipes, load_config, render_recipe
from mindxtrain.config.schema import LoraMethod, XTrainConfig
def test_qwen3_8b_sft_lora_validates(tmp_path):
yaml_text = render_recipe("qwen3_8b_sft_lora")
cfg_path = tmp_path / "run.yaml"
cfg_path.write_text(yaml_text)
cfg = load_config(cfg_path)
assert cfg.meta.run_name == "qwen3_8b_sft_lora"
assert cfg.meta.seed == 2048
assert cfg.model.name == "Qwen/Qwen3-8B"
assert cfg.hardware.gfx_arch == "gfx942"
assert cfg.hardware.gpus == 1
assert cfg.autotune.policy == "aot_only"
assert cfg.train.backend == "axolotl"
assert isinstance(cfg.train.method, LoraMethod)
assert cfg.train.method.r == 16
assert cfg.train.flash_attention.backend == "ck"
assert cfg.train.env["PYTORCH_ROCM_ARCH"] == "gfx942"
assert "mmlu" in cfg.eval.harness.tasks
assert cfg.quantize.scheme == "quark_fp8"
assert cfg.quantize.ptpc is True
assert cfg.serve.backend == "vllm-rocm"
assert cfg.serve.reasoning_parser == "qwen3"
def test_instella_template_validates(tmp_path):
yaml_text = render_recipe("instella_3b_lora")
cfg_path = tmp_path / "run.yaml"
cfg_path.write_text(yaml_text)
cfg = load_config(cfg_path)
assert cfg.model.name == "amd/Instella-3B-Instruct"
assert cfg.data.max_samples == 5000
def test_demo_qwen3_8b_sft_example_validates():
"""The hero config copied verbatim from the production blueprint must load."""
cfg = load_config("examples/demo_qwen3_8b_sft.yaml")
assert cfg.meta.run_name == "qwen3_8b_sft_demo"
assert isinstance(cfg.train.method, LoraMethod)
assert cfg.train.method.alpha == 32
assert cfg.publish.enabled is True
assert cfg.publish.billing.x402.network == "algorand"
assert cfg.publish.billing.x402.asset == "USDC"
def test_all_recipes_validate():
"""Every shipped recipe must round-trip against XTrainConfig."""
for name in list_recipes():
text = render_recipe(name)
cfg = XTrainConfig.model_validate(yaml.safe_load(text))
assert cfg.meta.run_name
def test_unknown_template_raises():
with pytest.raises(FileNotFoundError):
render_recipe("does_not_exist")
def test_xgmi_2gpu_rejected(tmp_path):
"""hardware.gpus must be 1 or 8 — xGMI asymmetry on 2/4-GPU MI300X."""
bad = yaml.safe_load(render_recipe("qwen3_32b_full_fsdp"))
bad["hardware"]["gpus"] = 2
cfg_path = tmp_path / "bad.yaml"
cfg_path.write_text(yaml.safe_dump(bad))
with pytest.raises(ValidationError):
load_config(cfg_path)
def test_extra_field_forbidden(tmp_path):
bad = yaml.safe_load(render_recipe("qwen3_8b_sft_lora"))
bad["meta"]["nonexistent"] = "foo"
cfg_path = tmp_path / "bad.yaml"
cfg_path.write_text(yaml.safe_dump(bad))
with pytest.raises(ValidationError):
load_config(cfg_path)
def test_method_discriminator_rejects_unknown_kind(tmp_path):
bad = yaml.safe_load(render_recipe("qwen3_8b_sft_lora"))
bad["train"]["method"] = {"kind": "not_a_real_method"}
cfg_path = tmp_path / "bad.yaml"
cfg_path.write_text(yaml.safe_dump(bad))
with pytest.raises(ValidationError):
load_config(cfg_path)
def test_dpo_method_round_trip():
text = render_recipe("qwen3_32b_dpo")
cfg = XTrainConfig.model_validate(yaml.safe_load(text))
assert cfg.train.method.kind == "dpo"
assert cfg.train.method.beta == 0.1
assert cfg.hardware.gpus == 8
def test_grpo_method_round_trip():
text = render_recipe("qwen3_32b_grpo")
cfg = XTrainConfig.model_validate(yaml.safe_load(text))
assert cfg.train.method.kind == "grpo"
assert cfg.train.method.num_generations == 4
def test_round_trip_via_model_dump(tmp_path):
cfg_path = tmp_path / "run.yaml"
cfg_path.write_text(render_recipe("qwen3_8b_sft_lora"))
cfg = load_config(cfg_path)
redumped = yaml.safe_dump(cfg.model_dump(mode="json"))
cfg2 = XTrainConfig.model_validate(yaml.safe_load(redumped))
assert cfg2 == cfg
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