# pylint: disable=too-many-lines
"""Module for testing the validation module"""
import logging
import os
import warnings
from typing import Optional
import pytest
from pydantic import ValidationError
from axolotl.utils.config import validate_config
from axolotl.utils.config.models.input.v0_4_1 import AxolotlConfigWCapabilities
from axolotl.utils.dict import DictDefault
from axolotl.utils.models import check_model_config
from axolotl.utils.wandb_ import setup_wandb_env_vars
warnings.filterwarnings("error")
@pytest.fixture(name="minimal_cfg")
def fixture_cfg():
return DictDefault(
{
"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"learning_rate": 0.000001,
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
}
],
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
}
)
class BaseValidation:
"""
Base validation module to setup the log capture
"""
_caplog: Optional[pytest.LogCaptureFixture] = None
@pytest.fixture(autouse=True)
def inject_fixtures(self, caplog):
self._caplog = caplog
# pylint: disable=too-many-public-methods
class TestValidation(BaseValidation):
"""
Test the validation module
"""
def test_datasets_min_length(self):
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"learning_rate": 0.000001,
"datasets": [],
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
}
)
with pytest.raises(
ValidationError,
match=r".*List should have at least 1 item after validation*",
):
validate_config(cfg)
def test_datasets_min_length_empty(self):
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"learning_rate": 0.000001,
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
}
)
with pytest.raises(
ValueError, match=r".*either datasets or pretraining_dataset is required*"
):
validate_config(cfg)
def test_pretrain_dataset_min_length(self):
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"learning_rate": 0.000001,
"pretraining_dataset": [],
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
"max_steps": 100,
}
)
with pytest.raises(
ValidationError,
match=r".*List should have at least 1 item after validation*",
):
validate_config(cfg)
def test_valid_pretrain_dataset(self):
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"learning_rate": 0.000001,
"pretraining_dataset": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
}
],
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
"max_steps": 100,
}
)
validate_config(cfg)
def test_valid_sft_dataset(self):
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"learning_rate": 0.000001,
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
}
],
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
}
)
validate_config(cfg)
def test_batch_size_unused_warning(self):
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"learning_rate": 0.000001,
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
}
],
"micro_batch_size": 4,
"batch_size": 32,
}
)
with self._caplog.at_level(logging.WARNING):
validate_config(cfg)
assert "batch_size is not recommended" in self._caplog.records[0].message
def test_batch_size_more_params(self):
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"learning_rate": 0.000001,
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
}
],
"batch_size": 32,
}
)
with pytest.raises(ValueError, match=r".*At least two of*"):
validate_config(cfg)
def test_lr_as_float(self, minimal_cfg):
cfg = (
DictDefault( # pylint: disable=unsupported-binary-operation
{
"learning_rate": "5e-5",
}
)
| minimal_cfg
)
new_cfg = validate_config(cfg)
assert new_cfg.learning_rate == 0.00005
def test_model_config_remap(self, minimal_cfg):
cfg = (
DictDefault(
{
"model_config": {"model_type": "mistral"},
}
)
| minimal_cfg
)
new_cfg = validate_config(cfg)
assert new_cfg.overrides_of_model_config["model_type"] == "mistral"
def test_model_type_remap(self, minimal_cfg):
cfg = (
DictDefault(
{
"model_type": "AutoModelForCausalLM",
}
)
| minimal_cfg
)
new_cfg = validate_config(cfg)
assert new_cfg.type_of_model == "AutoModelForCausalLM"
def test_model_revision_remap(self, minimal_cfg):
cfg = (
DictDefault(
{
"model_revision": "main",
}
)
| minimal_cfg
)
new_cfg = validate_config(cfg)
assert new_cfg.revision_of_model == "main"
def test_qlora(self, minimal_cfg):
base_cfg = (
DictDefault(
{
"adapter": "qlora",
}
)
| minimal_cfg
)
cfg = (
DictDefault( # pylint: disable=unsupported-binary-operation
{
"load_in_8bit": True,
}
)
| base_cfg
)
with pytest.raises(ValueError, match=r".*8bit.*"):
validate_config(cfg)
cfg = (
DictDefault( # pylint: disable=unsupported-binary-operation
{
"gptq": True,
}
)
| base_cfg
)
with pytest.raises(ValueError, match=r".*gptq.*"):
validate_config(cfg)
cfg = (
DictDefault( # pylint: disable=unsupported-binary-operation
{
"load_in_4bit": False,
}
)
| base_cfg
)
with pytest.raises(ValueError, match=r".*4bit.*"):
validate_config(cfg)
cfg = (
DictDefault( # pylint: disable=unsupported-binary-operation
{
"load_in_4bit": True,
}
)
| base_cfg
)
validate_config(cfg)
def test_qlora_merge(self, minimal_cfg):
base_cfg = (
DictDefault(
{
"adapter": "qlora",
"merge_lora": True,
}
)
| minimal_cfg
)
cfg = (
DictDefault( # pylint: disable=unsupported-binary-operation
{
"load_in_8bit": True,
}
)
| base_cfg
)
with pytest.raises(ValueError, match=r".*8bit.*"):
validate_config(cfg)
cfg = (
DictDefault( # pylint: disable=unsupported-binary-operation
{
"gptq": True,
}
)
| base_cfg
)
with pytest.raises(ValueError, match=r".*gptq.*"):
validate_config(cfg)
cfg = (
DictDefault( # pylint: disable=unsupported-binary-operation
{
"load_in_4bit": True,
}
)
| base_cfg
)
with pytest.raises(ValueError, match=r".*4bit.*"):
validate_config(cfg)
def test_hf_use_auth_token(self, minimal_cfg):
cfg = (
DictDefault(
{
"push_dataset_to_hub": "namespace/repo",
}
)
| minimal_cfg
)
with pytest.raises(ValueError, match=r".*hf_use_auth_token.*"):
validate_config(cfg)
cfg = (
DictDefault(
{
"push_dataset_to_hub": "namespace/repo",
"hf_use_auth_token": True,
}
)
| minimal_cfg
)
validate_config(cfg)
def test_gradient_accumulations_or_batch_size(self):
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"learning_rate": 0.000001,
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
}
],
"gradient_accumulation_steps": 1,
"batch_size": 1,
}
)
with pytest.raises(
ValueError, match=r".*gradient_accumulation_steps or batch_size.*"
):
validate_config(cfg)
def test_falcon_fsdp(self, minimal_cfg):
regex_exp = r".*FSDP is not supported for falcon models.*"
# Check for lower-case
cfg = (
DictDefault(
{
"base_model": "tiiuae/falcon-7b",
"fsdp": ["full_shard", "auto_wrap"],
}
)
| minimal_cfg
)
with pytest.raises(ValueError, match=regex_exp):
validate_config(cfg)
# Check for upper-case
cfg = (
DictDefault(
{
"base_model": "Falcon-7b",
"fsdp": ["full_shard", "auto_wrap"],
}
)
| minimal_cfg
)
with pytest.raises(ValueError, match=regex_exp):
validate_config(cfg)
cfg = (
DictDefault(
{
"base_model": "tiiuae/falcon-7b",
}
)
| minimal_cfg
)
validate_config(cfg)
def test_mpt_gradient_checkpointing(self, minimal_cfg):
regex_exp = r".*gradient_checkpointing is not supported for MPT models*"
# Check for lower-case
cfg = (
DictDefault(
{
"base_model": "mosaicml/mpt-7b",
"gradient_checkpointing": True,
}
)
| minimal_cfg
)
with pytest.raises(ValueError, match=regex_exp):
validate_config(cfg)
def test_flash_optimum(self, minimal_cfg):
cfg = (
DictDefault(
{
"flash_optimum": True,
"adapter": "lora",
"bf16": False,
}
)
| minimal_cfg
)
with self._caplog.at_level(logging.WARNING):
validate_config(cfg)
assert any(
"BetterTransformers probably doesn't work with PEFT adapters"
in record.message
for record in self._caplog.records
)
cfg = (
DictDefault(
{
"flash_optimum": True,
"bf16": False,
}
)
| minimal_cfg
)
with self._caplog.at_level(logging.WARNING):
validate_config(cfg)
assert any(
"probably set bfloat16 or float16" in record.message
for record in self._caplog.records
)
cfg = (
DictDefault(
{
"flash_optimum": True,
"fp16": True,
}
)
| minimal_cfg
)
regex_exp = r".*AMP is not supported.*"
with pytest.raises(ValueError, match=regex_exp):
validate_config(cfg)
cfg = (
DictDefault(
{
"flash_optimum": True,
"bf16": True,
}
)
| minimal_cfg
)
regex_exp = r".*AMP is not supported.*"
with pytest.raises(ValueError, match=regex_exp):
validate_config(cfg)
def test_adamw_hyperparams(self, minimal_cfg):
cfg = (
DictDefault(
{
"optimizer": None,
"adam_epsilon": 0.0001,
}
)
| minimal_cfg
)
with self._caplog.at_level(logging.WARNING):
validate_config(cfg)
assert any(
"adamw hyperparameters found, but no adamw optimizer set"
in record.message
for record in self._caplog.records
)
cfg = (
DictDefault(
{
"optimizer": "adafactor",
"adam_beta1": 0.0001,
}
)
| minimal_cfg
)
with self._caplog.at_level(logging.WARNING):
validate_config(cfg)
assert any(
"adamw hyperparameters found, but no adamw optimizer set"
in record.message
for record in self._caplog.records
)
cfg = (
DictDefault(
{
"optimizer": "adamw_bnb_8bit",
"adam_beta1": 0.9,
"adam_beta2": 0.99,
"adam_epsilon": 0.0001,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"optimizer": "adafactor",
}
)
| minimal_cfg
)
validate_config(cfg)
def test_deprecated_packing(self, minimal_cfg):
cfg = (
DictDefault(
{
"max_packed_sequence_len": 1024,
}
)
| minimal_cfg
)
with pytest.raises(
DeprecationWarning,
match=r"`max_packed_sequence_len` is no longer supported",
):
validate_config(cfg)
def test_packing(self, minimal_cfg):
cfg = (
DictDefault(
{
"sample_packing": True,
"pad_to_sequence_len": None,
}
)
| minimal_cfg
)
with self._caplog.at_level(logging.WARNING):
validate_config(cfg)
assert any(
"`pad_to_sequence_len: true` is recommended when using sample_packing"
in record.message
for record in self._caplog.records
)
def test_merge_lora_no_bf16_fail(self, minimal_cfg):
"""
This is assumed to be run on a CPU machine, so bf16 is not supported.
"""
cfg = (
DictDefault(
{
"bf16": True,
"capabilities": {"bf16": False},
}
)
| minimal_cfg
)
with pytest.raises(ValueError, match=r".*AMP is not supported on this GPU*"):
AxolotlConfigWCapabilities(**cfg.to_dict())
cfg = (
DictDefault(
{
"bf16": True,
"merge_lora": True,
"capabilities": {"bf16": False},
}
)
| minimal_cfg
)
validate_config(cfg)
def test_sharegpt_deprecation(self, minimal_cfg):
cfg = (
DictDefault(
{"datasets": [{"path": "lorem/ipsum", "type": "sharegpt:chat"}]}
)
| minimal_cfg
)
with self._caplog.at_level(logging.WARNING):
new_cfg = validate_config(cfg)
assert any(
"`type: sharegpt:chat` will soon be deprecated." in record.message
for record in self._caplog.records
)
assert new_cfg.datasets[0].type == "sharegpt"
cfg = (
DictDefault(
{
"datasets": [
{"path": "lorem/ipsum", "type": "sharegpt_simple:load_role"}
]
}
)
| minimal_cfg
)
with self._caplog.at_level(logging.WARNING):
new_cfg = validate_config(cfg)
assert any(
"`type: sharegpt_simple` will soon be deprecated." in record.message
for record in self._caplog.records
)
assert new_cfg.datasets[0].type == "sharegpt:load_role"
def test_no_conflict_save_strategy(self, minimal_cfg):
cfg = (
DictDefault(
{
"save_strategy": "epoch",
"save_steps": 10,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError, match=r".*save_strategy and save_steps mismatch.*"
):
validate_config(cfg)
cfg = (
DictDefault(
{
"save_strategy": "no",
"save_steps": 10,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError, match=r".*save_strategy and save_steps mismatch.*"
):
validate_config(cfg)
cfg = (
DictDefault(
{
"save_strategy": "steps",
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"save_strategy": "steps",
"save_steps": 10,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"save_steps": 10,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"save_strategy": "no",
}
)
| minimal_cfg
)
validate_config(cfg)
def test_no_conflict_eval_strategy(self, minimal_cfg):
cfg = (
DictDefault(
{
"evaluation_strategy": "epoch",
"eval_steps": 10,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError, match=r".*evaluation_strategy and eval_steps mismatch.*"
):
validate_config(cfg)
cfg = (
DictDefault(
{
"evaluation_strategy": "no",
"eval_steps": 10,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError, match=r".*evaluation_strategy and eval_steps mismatch.*"
):
validate_config(cfg)
cfg = (
DictDefault(
{
"evaluation_strategy": "steps",
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"evaluation_strategy": "steps",
"eval_steps": 10,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"eval_steps": 10,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"evaluation_strategy": "no",
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"evaluation_strategy": "epoch",
"val_set_size": 0,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError,
match=r".*eval_steps and evaluation_strategy are not supported with val_set_size == 0.*",
):
validate_config(cfg)
cfg = (
DictDefault(
{
"eval_steps": 10,
"val_set_size": 0,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError,
match=r".*eval_steps and evaluation_strategy are not supported with val_set_size == 0.*",
):
validate_config(cfg)
cfg = (
DictDefault(
{
"val_set_size": 0,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"eval_steps": 10,
"val_set_size": 0.01,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"evaluation_strategy": "epoch",
"val_set_size": 0.01,
}
)
| minimal_cfg
)
validate_config(cfg)
def test_eval_table_size_conflict_eval_packing(self, minimal_cfg):
cfg = (
DictDefault(
{
"sample_packing": True,
"eval_table_size": 100,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError, match=r".*Please set 'eval_sample_packing' to false.*"
):
validate_config(cfg)
cfg = (
DictDefault(
{
"sample_packing": True,
"eval_sample_packing": False,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"sample_packing": False,
"eval_table_size": 100,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"sample_packing": True,
"eval_table_size": 100,
"eval_sample_packing": False,
}
)
| minimal_cfg
)
validate_config(cfg)
def test_load_in_x_bit_without_adapter(self, minimal_cfg):
cfg = (
DictDefault(
{
"load_in_4bit": True,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError,
match=r".*load_in_8bit and load_in_4bit are not supported without setting an adapter.*",
):
validate_config(cfg)
cfg = (
DictDefault(
{
"load_in_8bit": True,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError,
match=r".*load_in_8bit and load_in_4bit are not supported without setting an adapter.*",
):
validate_config(cfg)
cfg = (
DictDefault(
{
"load_in_4bit": True,
"adapter": "qlora",
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"load_in_8bit": True,
"adapter": "lora",
}
)
| minimal_cfg
)
validate_config(cfg)
def test_warmup_step_no_conflict(self, minimal_cfg):
cfg = (
DictDefault(
{
"warmup_steps": 10,
"warmup_ratio": 0.1,
}
)
| minimal_cfg
)
with pytest.raises(
ValueError,
match=r".*warmup_steps and warmup_ratio are mutually exclusive*",
):
validate_config(cfg)
cfg = (
DictDefault(
{
"warmup_steps": 10,
}
)
| minimal_cfg
)
validate_config(cfg)
cfg = (
DictDefault(
{
"warmup_ratio": 0.1,
}
)
| minimal_cfg
)
validate_config(cfg)
def test_unfrozen_parameters_w_peft_layers_to_transform(self, minimal_cfg):
cfg = (
DictDefault(
{
"adapter": "lora",
"unfrozen_parameters": [
"model.layers.2[0-9]+.block_sparse_moe.gate.*"
],
"peft_layers_to_transform": [0, 1],
}
)
| minimal_cfg
)
with pytest.raises(
ValueError,
match=r".*can have unexpected behavior*",
):
validate_config(cfg)
def test_hub_model_id_save_value_warns(self, minimal_cfg):
cfg = DictDefault({"hub_model_id": "test"}) | minimal_cfg
with self._caplog.at_level(logging.WARNING):
validate_config(cfg)
assert (
"set without any models being saved" in self._caplog.records[0].message
)
def test_hub_model_id_save_value(self, minimal_cfg):
cfg = DictDefault({"hub_model_id": "test", "saves_per_epoch": 4}) | minimal_cfg
with self._caplog.at_level(logging.WARNING):
validate_config(cfg)
assert len(self._caplog.records) == 0
class TestValidationCheckModelConfig(BaseValidation):
"""
Test the validation for the config when the model config is available
"""
def test_llama_add_tokens_adapter(self, minimal_cfg):
cfg = (
DictDefault(
{"adapter": "qlora", "load_in_4bit": True, "tokens": ["<|imstart|>"]}
)
| minimal_cfg
)
model_config = DictDefault({"model_type": "llama"})
with pytest.raises(
ValueError,
match=r".*`lora_modules_to_save` not properly set when adding new tokens*",
):
check_model_config(cfg, model_config)
cfg = (
DictDefault(
{
"adapter": "qlora",
"load_in_4bit": True,
"tokens": ["<|imstart|>"],
"lora_modules_to_save": ["embed_tokens"],
}
)
| minimal_cfg
)
with pytest.raises(
ValueError,
match=r".*`lora_modules_to_save` not properly set when adding new tokens*",
):
check_model_config(cfg, model_config)
cfg = (
DictDefault(
{
"adapter": "qlora",
"load_in_4bit": True,
"tokens": ["<|imstart|>"],
"lora_modules_to_save": ["embed_tokens", "lm_head"],
}
)
| minimal_cfg
)
check_model_config(cfg, model_config)
def test_phi_add_tokens_adapter(self, minimal_cfg):
cfg = (
DictDefault(
{"adapter": "qlora", "load_in_4bit": True, "tokens": ["<|imstart|>"]}
)
| minimal_cfg
)
model_config = DictDefault({"model_type": "phi"})
with pytest.raises(
ValueError,
match=r".*`lora_modules_to_save` not properly set when adding new tokens*",
):
check_model_config(cfg, model_config)
cfg = (
DictDefault(
{
"adapter": "qlora",
"load_in_4bit": True,
"tokens": ["<|imstart|>"],
"lora_modules_to_save": ["embd.wte", "lm_head.linear"],
}
)
| minimal_cfg
)
with pytest.raises(
ValueError,
match=r".*`lora_modules_to_save` not properly set when adding new tokens*",
):
check_model_config(cfg, model_config)
cfg = (
DictDefault(
{
"adapter": "qlora",
"load_in_4bit": True,
"tokens": ["<|imstart|>"],
"lora_modules_to_save": ["embed_tokens", "lm_head"],
}
)
| minimal_cfg
)
check_model_config(cfg, model_config)
class TestValidationWandb(BaseValidation):
"""
Validation test for wandb
"""
def test_wandb_set_run_id_to_name(self, minimal_cfg):
cfg = (
DictDefault(
{
"wandb_run_id": "foo",
}
)
| minimal_cfg
)
with self._caplog.at_level(logging.WARNING):
new_cfg = validate_config(cfg)
assert any(
"wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
in record.message
for record in self._caplog.records
)
assert new_cfg.wandb_name == "foo" and new_cfg.wandb_run_id == "foo"
cfg = (
DictDefault(
{
"wandb_name": "foo",
}
)
| minimal_cfg
)
new_cfg = validate_config(cfg)
assert new_cfg.wandb_name == "foo" and new_cfg.wandb_run_id is None
def test_wandb_sets_env(self, minimal_cfg):
cfg = (
DictDefault(
{
"wandb_project": "foo",
"wandb_name": "bar",
"wandb_run_id": "bat",
"wandb_entity": "baz",
"wandb_mode": "online",
"wandb_watch": "false",
"wandb_log_model": "checkpoint",
}
)
| minimal_cfg
)
new_cfg = validate_config(cfg)
setup_wandb_env_vars(new_cfg)
assert os.environ.get("WANDB_PROJECT", "") == "foo"
assert os.environ.get("WANDB_NAME", "") == "bar"
assert os.environ.get("WANDB_RUN_ID", "") == "bat"
assert os.environ.get("WANDB_ENTITY", "") == "baz"
assert os.environ.get("WANDB_MODE", "") == "online"
assert os.environ.get("WANDB_WATCH", "") == "false"
assert os.environ.get("WANDB_LOG_MODEL", "") == "checkpoint"
assert os.environ.get("WANDB_DISABLED", "") != "true"
os.environ.pop("WANDB_PROJECT", None)
os.environ.pop("WANDB_NAME", None)
os.environ.pop("WANDB_RUN_ID", None)
os.environ.pop("WANDB_ENTITY", None)
os.environ.pop("WANDB_MODE", None)
os.environ.pop("WANDB_WATCH", None)
os.environ.pop("WANDB_LOG_MODEL", None)
os.environ.pop("WANDB_DISABLED", None)
def test_wandb_set_disabled(self, minimal_cfg):
cfg = DictDefault({}) | minimal_cfg
new_cfg = validate_config(cfg)
setup_wandb_env_vars(new_cfg)
assert os.environ.get("WANDB_DISABLED", "") == "true"
cfg = (
DictDefault(
{
"wandb_project": "foo",
}
)
| minimal_cfg
)
new_cfg = validate_config(cfg)
setup_wandb_env_vars(new_cfg)
assert os.environ.get("WANDB_DISABLED", "") != "true"
os.environ.pop("WANDB_PROJECT", None)
os.environ.pop("WANDB_DISABLED", None)