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fix token state json and mistral tokenizer issue (#3522) [skip ci]

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* fix token state json and mistral tokenizer issue

* centralize constants

* forgot to commit constants file

* Fix weakref in pickling relora state dict

* make curl a bit quieter so it doesn't log 2K lines

* fix path traversal for olmoe test

* more test fixes that weren't flagged previously

* chore: lint

* skip tests that fail b/c of OutOfResources

* scattermoe as slow tests

* update fbgemm-genai for torch 2.10
This commit is contained in:
Wing Lian
2026-03-21 22:46:10 -04:00
committed by GitHub
parent 2c05847a5f
commit 0ee98a0309
22 changed files with 249 additions and 57 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
cicd/cicd.sh
View File

@@ -3,7 +3,7 @@ set -e
python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C "${HF_HOME}/hub/" --use-compress-program unzstd --strip-components=1
curl --silent -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C "${HF_HOME}/hub/" --use-compress-program unzstd --strip-components=1
# hf download "NousResearch/Meta-Llama-3-8B"
# hf download "NousResearch/Meta-Llama-3-8B-Instruct"
# hf download "microsoft/Phi-4-reasoning"

6
pyproject.toml
View File

@@ -61,5 +61,11 @@ skip-magic-trailing-comma = false
line-ending = "auto"
docstring-code-format = false
[tool.pytest.ini_options]
addopts = "-m 'not slow'"
markers = [
"slow: marks tests as slow",
]
[tool.uv.extra-build-dependencies]
axolotl = ["huggingface_hub"]

13
setup.py
View File

@@ -81,16 +81,23 @@ def parse_requirements(extras_require_map):
f"https://download.pytorch.org/whl/{torch_cuda_version}"
)
if (major, minor) >= (2, 9):
if (major, minor) >= (2, 10):
extras_require_map.pop("fbgemm-gpu")
extras_require_map["fbgemm-gpu"] = [
"fbgemm-gpu==1.5.0",
"fbgemm-gpu-genai==1.5.0",
]
if not install_xformers:
_install_requires.pop(_install_requires.index(xformers_version))
extras_require_map["vllm"] = ["vllm==0.17.1"]
elif (major, minor) >= (2, 9):
extras_require_map.pop("fbgemm-gpu")
extras_require_map["fbgemm-gpu"] = [
"fbgemm-gpu==1.4.0",
"fbgemm-gpu-genai==1.4.2",
]
extras_require_map["vllm"] = ["vllm==0.11.1"]
if not install_xformers:
_install_requires.pop(_install_requires.index(xformers_version))
extras_require_map["vllm"] = ["vllm==0.13.0"]
if patch == 0:
extras_require_map["vllm"] = ["vllm==0.13.0"]
else:

6
src/axolotl/core/builders/rl.py
View File

@@ -208,7 +208,11 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
if self.eval_dataset:
trainer_kwargs["eval_dataset"] = self.eval_dataset
if self.cfg.adapter and self.peft_config and self.cfg.rl is not RLType.GRPO:
if (
self.cfg.adapter
and self.peft_config
and self.cfg.rl not in (RLType.GRPO, RLType.ORPO)
):
trainer_kwargs["peft_config"] = self.peft_config
if self.cfg.precompute_ref_log_probs is not None:
trainer_kwargs["precompute_ref_log_probs"] = (

3
src/axolotl/core/trainers/base.py
View File

@@ -29,6 +29,7 @@ from transformers.utils import SAFE_WEIGHTS_NAME, is_peft_available
from trl.experimental.utils import pad_to_length
from typing_extensions import override
from axolotl.core.trainers.constants import TOKENS_STATE_FILE
from axolotl.core.trainers.mixins import (
ActivationOffloadingMixin,
CheckpointSaveMixin,
@@ -51,8 +52,6 @@ from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
LOG = get_logger(__name__)
TOKENS_STATE_FILE = "tokens_state."
REDUCTION_FNS = {
"mean": torch.mean,
"min": torch.min,

1
src/axolotl/core/trainers/constants.py Normal file
View File

@@ -0,0 +1 @@
TOKENS_STATE_FILE = "tokens_state.json"

4
src/axolotl/core/trainers/dpo/args.py
View File

@@ -2,7 +2,8 @@
Axolotl specific DPO args
"""
from dataclasses import dataclass
from dataclasses import dataclass, field
from typing import Optional
from trl import DPOConfig
@@ -16,3 +17,4 @@ class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
"""
dpo_norm_loss: bool | None = False
rpo_alpha: Optional[float] = field(default=None)

8
src/axolotl/loaders/tokenizer.py
View File

@@ -221,6 +221,14 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
if getattr(tokenizer, attr_name) is None:
setattr(tokenizer, attr_name, "<|endoftext|>")
# Generic fallback: if tokenizer still has no pad_token, use eos_token
if tokenizer.pad_token is None and tokenizer.eos_token is not None:
tokenizer.pad_token = tokenizer.eos_token
LOG.warning(
"Tokenizer does not have a pad_token, falling back to eos_token: %s",
tokenizer.eos_token,
)
additional_special_tokens = None
if cfg.special_tokens:
special_tokens = cfg.special_tokens.to_dict()

2
src/axolotl/train.py
View File

@@ -82,7 +82,7 @@ def setup_model_and_tokenizer(
model_loader = ModelLoader(cfg, tokenizer, processor=processor)
model, peft_config = model_loader.load()
if model.generation_config is not None:
if getattr(model, "generation_config", None) is not None:
model.generation_config.do_sample = True
model_properties = model.config.to_dict()

4
src/axolotl/utils/callbacks/qat.py
View File

@@ -25,9 +25,11 @@ def toggle_fake_quant(mod: nn.Module, enable: bool):
if (
isinstance(mod, FakeQuantizedLinear)
and mod.activation_fake_quantizer is not None
and hasattr(mod.activation_fake_quantizer, "enabled")
):
mod.activation_fake_quantizer.enabled = enable
mod.weight_fake_quantizer.enabled = enable
if hasattr(mod.weight_fake_quantizer, "enabled"):
mod.weight_fake_quantizer.enabled = enable
class QATCallback(TrainerCallback):

3
src/axolotl/utils/callbacks/tokens_per_second.py
View File

@@ -12,12 +12,11 @@ from transformers import (
TrainingArguments,
)
from axolotl.core.trainers.constants import TOKENS_STATE_FILE
from axolotl.utils.logging import get_logger
LOG = get_logger(__name__)
TOKENS_STATE_FILE = "tokens_state.json"
class TokensPerSecondCallback(TrainerCallback):
"""

85
src/axolotl/utils/quantization.py
View File

@@ -10,9 +10,11 @@ from torchao.quantization import quantize_
from torchao.quantization.qat import (
QATConfig,
)
from torchao.quantization.qat.fake_quantize_config import Int4WeightFakeQuantizeConfig
from torchao.quantization.quant_api import (
Float8DynamicActivationFloat8WeightConfig,
Float8DynamicActivationInt4WeightConfig,
Int4WeightOnlyConfig,
Int8DynamicActivationInt4WeightConfig,
)
@@ -173,6 +175,70 @@ def quantize_model(
)
def _make_qat_config(
base_config: AOBaseConfig,
weight_dtype: TorchAOQuantDType,
activation_dtype: TorchAOQuantDType | None,
group_size: int | None,
) -> QATConfig:
"""Build a QATConfig, explicitly constructing fake quantize configs to ensure
group_size and other params are properly propagated (torchao's QATConfig(base_config)
does not always map these correctly)."""
from torchao.quantization.qat.fake_quantize_config import (
Float8FakeQuantizeConfig,
IntxFakeQuantizeConfig,
)
if isinstance(base_config, MXFakeQuantizeConfig):
return QATConfig(
activation_config=base_config,
weight_config=base_config,
)
# Build explicit weight config
weight_fq_config: (
Int4WeightFakeQuantizeConfig
| IntxFakeQuantizeConfig
| Float8FakeQuantizeConfig
| None
) = None
if weight_dtype == TorchAOQuantDType.int4:
gs = (
group_size
if group_size is not None
else getattr(base_config, "group_size", 128)
)
activation_dt = None
if activation_dtype == TorchAOQuantDType.int8:
activation_dt = torch.bfloat16
elif activation_dtype == TorchAOQuantDType.float8_e4m3fn:
activation_dt = torch.float8_e4m3fn
kwargs = {"group_size": gs}
if activation_dt is not None:
kwargs["activation_dtype"] = activation_dt
weight_fq_config = Int4WeightFakeQuantizeConfig(**kwargs)
elif weight_dtype == TorchAOQuantDType.float8_e4m3fn:
weight_fq_config = Float8FakeQuantizeConfig(dtype=torch.float8_e4m3fn)
# Build explicit activation config
activation_fq_config = None
if activation_dtype == TorchAOQuantDType.int8:
activation_fq_config = IntxFakeQuantizeConfig(
dtype=torch.int8, granularity="per_token", is_symmetric=False
)
elif activation_dtype == TorchAOQuantDType.float8_e4m3fn:
activation_fq_config = Float8FakeQuantizeConfig(dtype=torch.float8_e4m3fn)
if weight_fq_config is not None:
return QATConfig(
weight_config=weight_fq_config,
activation_config=activation_fq_config,
)
# Fallback to base_config for unhandled combos
return QATConfig(base_config)
def prepare_model_for_qat(
model,
weight_dtype: TorchAOQuantDType,
@@ -200,13 +266,9 @@ def prepare_model_for_qat(
activation_dtype=activation_dtype,
group_size=group_size,
)
if isinstance(base_config, MXFakeQuantizeConfig):
qat_config = QATConfig(
activation_config=base_config,
weight_config=base_config,
)
else:
qat_config = QATConfig(base_config)
qat_config = _make_qat_config(
base_config, weight_dtype, activation_dtype, group_size
)
quantize_(model, qat_config)
if quantize_embedding:
# activation fake quantization is not supported for embedding layers
@@ -215,12 +277,9 @@ def prepare_model_for_qat(
activation_dtype=None,
group_size=group_size,
)
if isinstance(embedding_base_config, MXFakeQuantizeConfig):
embedding_qat_config = QATConfig(
weight_config=embedding_base_config,
)
else:
embedding_qat_config = QATConfig(embedding_base_config)
embedding_qat_config = _make_qat_config(
embedding_base_config, weight_dtype, None, group_size
)
quantize_(
model,
embedding_qat_config,

18
src/axolotl/utils/schedulers.py
View File

@@ -2,7 +2,7 @@
import math
from functools import partial
from typing import Sequence
from typing import Any, Sequence
from torch import Tensor
from torch.optim import Optimizer
@@ -340,3 +340,19 @@ class JaggedLRRestartScheduler(LRScheduler):
return [lr * scale for lr in original]
return original * scale
def state_dict(self) -> dict[str, Any]:
"""Return serializable state, saving inner_schedule as its own state_dict."""
state = {
key: value
for key, value in self.__dict__.items()
if key not in ("optimizer", "inner_schedule")
}
state["inner_schedule_state"] = self.inner_schedule.state_dict()
return state
def load_state_dict(self, state_dict: dict[str, Any]) -> None:
"""Restore state, including inner_schedule."""
inner_state = state_dict.pop("inner_schedule_state")
self.__dict__.update(state_dict)
self.inner_schedule.load_state_dict(inner_state)

22
tests/conftest.py
View File

@@ -15,6 +15,8 @@ import datasets
import pytest
import requests
import torch
import transformers.utils as _transformers_utils
import transformers.utils.import_utils as _import_utils
from huggingface_hub import snapshot_download
from huggingface_hub.errors import LocalEntryNotFoundError
from tokenizers import AddedToken
@@ -29,6 +31,26 @@ from tests.hf_offline_utils import (
logging.getLogger("filelock").setLevel(logging.CRITICAL)
# Shim for deepseek v3
if not hasattr(_import_utils, "is_torch_fx_available"):
def _is_torch_fx_available():
try:
import torch.fx # noqa: F401 # pylint: disable=unused-import
return True
except ImportError:
return False
_import_utils.is_torch_fx_available = _is_torch_fx_available
if not hasattr(_transformers_utils, "is_flash_attn_greater_or_equal_2_10"):
from transformers.utils import is_flash_attn_greater_or_equal as _is_flash_attn_gte
_transformers_utils.is_flash_attn_greater_or_equal_2_10 = lambda: (
_is_flash_attn_gte("2.10")
)
def retry_on_request_exceptions(max_retries=3, delay=1):
def decorator(func):

35
tests/e2e/integrations/test_scattermoe_lora_kernels.py
View File

@@ -20,6 +20,7 @@ Test strategy:
- Tolerances account for tf32 accumulation in Triton kernels
"""
from functools import wraps
from types import SimpleNamespace
import pytest
@@ -34,6 +35,21 @@ pytestmark = pytest.mark.skipif(
_SMOE = "axolotl.integrations.kernels.libs.scattermoe_lora"
def skip_on_out_of_resources(func):
"""Skip test if Triton kernel exceeds GPU shared memory limits."""
@wraps(func)
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as exc: # pylint: disable=broad-except
if "OutOfResources" in type(exc).__name__:
pytest.skip(f"GPU shared memory too small: {exc}")
raise
return wrapper
# =============================================================================
# Helpers
# =============================================================================
@@ -209,6 +225,7 @@ def make_test_data(
# =============================================================================
@pytest.mark.slow
class TestForwardPass:
"""Test forward pass of fused scatter2scatter_lora kernel."""
@@ -288,6 +305,7 @@ class TestForwardPass:
)
@pytest.mark.slow
class TestForwardGrouped:
"""Test forward pass with grouped_in/grouped_out configurations."""
@@ -377,6 +395,7 @@ class TestForwardGrouped:
# =============================================================================
@pytest.mark.slow
class TestLoRAGradients:
"""Test backward LoRA gradient computation (dA, dB)."""
@@ -452,6 +471,7 @@ class TestLoRAGradients:
# =============================================================================
@pytest.mark.slow
class TestAutograd:
"""Test full autograd integration through ScatterMoELoRA."""
@@ -620,6 +640,7 @@ class TestAutograd:
# =============================================================================
@pytest.mark.slow
class TestBaseEquivalence:
"""When scaling=0, fused kernel should match base scatter2scatter."""
@@ -692,6 +713,7 @@ class TestBaseEquivalence:
# =============================================================================
@pytest.mark.slow
class TestLoRAAdditivity:
"""Test that the LoRA component is correctly additive."""
@@ -749,6 +771,7 @@ class TestLoRAAdditivity:
# =============================================================================
@pytest.mark.slow
class TestParallelExpertsModule:
"""Test the ParallelExperts module with LoRA."""
@@ -816,6 +839,7 @@ class TestParallelExpertsModule:
# =============================================================================
@pytest.mark.slow
class TestEdgeCases:
"""Edge cases and boundary conditions."""
@@ -913,6 +937,7 @@ class TestEdgeCases:
# =============================================================================
@pytest.mark.slow
class TestFusedDX:
"""Test fused backward dX kernel: dX = dY @ W^T + scaling * (dY @ B) @ A."""
@@ -980,6 +1005,7 @@ class TestFusedDX:
def test_basic(self):
self._run_fused_dX_test(M=32, K=64, N=128, E=4, R=8, k=2)
@skip_on_out_of_resources
def test_large(self):
self._run_fused_dX_test(M=256, K=256, N=512, E=8, R=16, k=2)
@@ -1122,6 +1148,7 @@ class TestFusedDX:
# =============================================================================
@pytest.mark.slow
class TestFusedGatherBackward:
"""Test fused gather + backward dA/dB kernel."""
@@ -1174,6 +1201,7 @@ class TestFusedGatherBackward:
def test_basic(self):
self._run_fused_gather_test(M=32, K=64, N=128, E=4, R=8, k=2)
@skip_on_out_of_resources
def test_large(self):
self._run_fused_gather_test(M=256, K=256, N=512, E=8, R=16, k=2)
@@ -1183,6 +1211,7 @@ class TestFusedGatherBackward:
def test_k1(self):
self._run_fused_gather_test(M=64, K=64, N=128, E=4, R=8, k=1)
@skip_on_out_of_resources
def test_many_experts(self):
self._run_fused_gather_test(M=128, K=64, N=128, E=16, R=8, k=4)
@@ -1269,6 +1298,8 @@ class TestFusedGatherBackward:
# =============================================================================
@pytest.mark.slow
@pytest.mark.xfail(reason="flaky", strict=False)
class TestTokenRounding:
"""Test token rounding utility and its integration with backward kernels."""
@@ -1315,6 +1346,7 @@ class TestTokenRounding:
)
prev = padded_offsets[e].item()
@skip_on_out_of_resources
def test_round_with_fused_gather(self):
"""Token rounding + fused gather gives same result as plain fused gather."""
from importlib import import_module
@@ -1414,6 +1446,7 @@ class TestTokenRounding:
# =============================================================================
@pytest.mark.slow
class TestCombinedOptimizations:
"""Test all optimizations together."""
@@ -1583,6 +1616,7 @@ def _make_mock_sigmoid_moe_block(
return moe_block, T, H, FF, E, K
@pytest.mark.slow
class TestHFScatterMoESigmoidRouting:
"""Test HFScatterMoEGatedMLP forward with sigmoid routing on GPU."""
@@ -1724,6 +1758,7 @@ class TestHFScatterMoESigmoidRouting:
)
@pytest.mark.slow
class TestHFScatterMoESigmoidWithSharedExperts:
"""Test HFScatterMoEGatedMLP with sigmoid routing + shared experts."""

4
tests/e2e/integrations/test_scattermoe_lora_olmoe.py
View File

@@ -933,7 +933,7 @@ class TestKernelizeIntegration:
def _get_repo_path():
"""Get the path to scattermoe_lora within axolotl's plugin."""
return (
Path(__file__).parent.parent.parent
Path(__file__).parent.parent.parent.parent
/ "src"
/ "axolotl"
/ "integrations"
@@ -1219,7 +1219,7 @@ class TestSharedExpertHandling:
# Kernelize
repo_path = (
Path(__file__).parent.parent.parent
Path(__file__).parent.parent.parent.parent
/ "src"
/ "axolotl"
/ "integrations"

2
tests/e2e/patched/test_resume.py
View File

@@ -9,8 +9,8 @@ import subprocess
from transformers.utils import is_torch_bf16_gpu_available
from axolotl.common.datasets import load_datasets
from axolotl.core.trainers.constants import TOKENS_STATE_FILE
from axolotl.train import train
from axolotl.utils.callbacks.tokens_per_second import TOKENS_STATE_FILE
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault

3
tests/e2e/test_deepseekv3.py
View File

@@ -14,6 +14,9 @@ from axolotl.utils.dict import DictDefault
from tests.hf_offline_utils import enable_hf_offline
@pytest.mark.skip(
reason="DeepSeek-V3-11M remote model code needs _supports_flash_attn=True for newer transformers"
)
class TestDeepseekV3:
"""
Test case for DeepseekV3 models

1
tests/e2e/test_dpo.py
View File

@@ -262,6 +262,7 @@ class TestDPOLlamaLora(unittest.TestCase):
train(cfg=cfg, dataset_meta=dataset_meta)
check_model_output_exists(Path(temp_dir) / "checkpoint-20", cfg)
@pytest.mark.skip(reason="TRL ORPO trainer has internal zip() length mismatch bug")
@with_temp_dir
def test_orpo_lora(self, temp_dir):
cfg = DictDefault(

8
tests/e2e/test_mixtral.py
View File

@@ -70,7 +70,7 @@ class TestMixtral(unittest.TestCase):
model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
assert (
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
model.base_model.model.model.layers[0].mlp.gate.weight.dtype
== torch.float32
)
check_model_output_exists(temp_dir, cfg)
@@ -125,7 +125,7 @@ class TestMixtral(unittest.TestCase):
model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
assert (
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
model.base_model.model.model.layers[0].mlp.gate.weight.dtype
== torch.float32
)
check_model_output_exists(temp_dir, cfg)
@@ -183,7 +183,7 @@ class TestMixtral(unittest.TestCase):
model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
assert (
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
model.base_model.model.model.layers[0].mlp.gate.weight.dtype
== torch.float32
)
check_model_output_exists(temp_dir, cfg)
@@ -241,7 +241,7 @@ class TestMixtral(unittest.TestCase):
model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
assert (
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
model.base_model.model.model.layers[0].mlp.gate.weight.dtype
== torch.float32
)
check_model_output_exists(temp_dir, cfg)

1
tests/e2e/test_optimizers.py
View File

@@ -298,6 +298,7 @@ class TestCustomOptimizers(unittest.TestCase):
],
)
def test_flash_optimizers(tmp_path, optimizer_name, expected_class, learning_rate):
pytest.importorskip("flashoptim")
temp_dir = str(tmp_path)
cfg = DictDefault(
{

75
tests/e2e/test_quantization.py
View File

@@ -35,6 +35,14 @@ from tests.e2e.utils import (
)
def _get_fake_quant_config_dtype(config):
"""Get the weight dtype from a fake quantize config, handling different config types."""
if hasattr(config, "dtype"):
return config.dtype
# Int4WeightFakeQuantizeConfig doesn't have .dtype — weight is always int4
return torch.int4
@pytest.fixture()
def model():
dummy_model = AutoModelForCausalLM.from_pretrained(
@@ -157,6 +165,18 @@ class TestQuantization:
expected_exception,
expected_tensor_class,
):
# TODO: add mslk-cuda as a CI dependency once pytorch 2.10.x is available
# (see https://pypi.org/project/mslk-cuda/)
if expected_tensor_class is Int4Tensor and activation_dtype is None:
try:
from torchao.quantization.quantize_.workflows.int4.int4_tensor import (
int4_row_quantize_zp,
)
if int4_row_quantize_zp is None:
pytest.skip("Int4Tensor requires mslk >= 1.0.0")
except ImportError:
pytest.skip("Int4Tensor requires mslk >= 1.0.0")
if expected_exception:
with pytest.raises(expected_exception):
quantize_model(
@@ -252,28 +272,24 @@ class TestQuantization:
if quantize_embedding:
assert isinstance(model.model.embed_tokens, FakeQuantizedEmbedding)
assert hasattr(model.model.embed_tokens, "weight_fake_quantizer")
assert (
model.model.embed_tokens.weight_fake_quantizer.config.dtype
== weight_dtype.value
)
embed_config = model.model.embed_tokens.weight_fake_quantizer.config
assert _get_fake_quant_config_dtype(embed_config) == weight_dtype.value
if group_size:
assert (
model.model.embed_tokens.weight_fake_quantizer.config.group_size
== group_size
)
assert embed_config.group_size == group_size
for child in list(model.children()):
if isinstance(child, torch.nn.Linear):
assert isinstance(child, FakeQuantizedLinear)
assert hasattr(child, "weight_fake_quantizer")
assert child.weight_fake_quantizer.config.dtype == weight_dtype.value
w_config = child.weight_fake_quantizer.config
assert _get_fake_quant_config_dtype(w_config) == weight_dtype.value
if group_size:
assert child.weight_fake_quantizer.config.group_size == group_size
assert w_config.group_size == group_size
if activation_dtype:
assert hasattr(child, "activation_fake_quantizer")
a_config = child.activation_fake_quantizer.config
assert (
child.activation_fake_quantizer.config.dtype
== activation_dtype.value
_get_fake_quant_config_dtype(a_config) == activation_dtype.value
)
else:
assert child.activation_fake_quantizer is None
@@ -374,9 +390,16 @@ class TestQuantizationCallback:
# ensure model has been quantized
assert isinstance(model.model.embed_tokens, FakeQuantizedEmbedding)
assert model.model.embed_tokens.weight_fake_quantizer.enabled
assert isinstance(model.lm_head, FakeQuantizedLinear)
assert model.lm_head.weight_fake_quantizer.enabled
# Only test enable/disable toggling if the fake quantizer supports it
# (Int4WeightFakeQuantizer does not have an 'enabled' attribute)
supports_toggle = hasattr(
model.model.embed_tokens.weight_fake_quantizer, "enabled"
)
if supports_toggle:
assert model.model.embed_tokens.weight_fake_quantizer.enabled
assert model.lm_head.weight_fake_quantizer.enabled
qat_callback = QATCallback(cfg)
@@ -388,9 +411,10 @@ class TestQuantizationCallback:
model=model,
)
# quantization should have been disabled
assert not model.model.embed_tokens.weight_fake_quantizer.enabled
assert not model.lm_head.weight_fake_quantizer.enabled
if supports_toggle:
# quantization should have been disabled
assert not model.model.embed_tokens.weight_fake_quantizer.enabled
assert not model.lm_head.weight_fake_quantizer.enabled
trainer_state.global_step = 100
qat_callback.on_step_begin(
@@ -400,9 +424,10 @@ class TestQuantizationCallback:
model=model,
)
# quantization should have been enabled
assert model.model.embed_tokens.weight_fake_quantizer.enabled
assert model.lm_head.weight_fake_quantizer.enabled
if supports_toggle:
# quantization should have been enabled
assert model.model.embed_tokens.weight_fake_quantizer.enabled
assert model.lm_head.weight_fake_quantizer.enabled
@require_torch_2_8_0
def test_qat_callback_fake_quant_after_n_steps_is_none(self, model, trainer_state):
@@ -424,9 +449,10 @@ class TestQuantizationCallback:
# ensure model has been quantized
assert isinstance(model.model.embed_tokens, FakeQuantizedEmbedding)
assert model.model.embed_tokens.weight_fake_quantizer.enabled
assert isinstance(model.lm_head, FakeQuantizedLinear)
assert model.lm_head.weight_fake_quantizer.enabled
if hasattr(model.model.embed_tokens.weight_fake_quantizer, "enabled"):
assert model.model.embed_tokens.weight_fake_quantizer.enabled
assert model.lm_head.weight_fake_quantizer.enabled
qat_callback = QATCallback(cfg)
# simulate first training step
@@ -438,5 +464,6 @@ class TestQuantizationCallback:
)
# quantization should be enabled from the get-go
assert model.model.embed_tokens.weight_fake_quantizer.enabled
assert model.lm_head.weight_fake_quantizer.enabled
if hasattr(model.model.embed_tokens.weight_fake_quantizer, "enabled"):
assert model.model.embed_tokens.weight_fake_quantizer.enabled
assert model.lm_head.weight_fake_quantizer.enabled