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use smaller pretrained models for ci (#3620) [skip ci]

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* use smaller pretrained models for ci

* more steps for loss check

* fix tests

* more train steps

* fix losses
This commit is contained in:
Wing Lian
2026-04-27 13:22:56 -04:00
committed by GitHub
parent 798c8fba89
commit ac77da96da
24 changed files with 716 additions and 288 deletions
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51
tests/e2e/multigpu/test_dist_muon_fsdp2.py
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@@ -1,23 +1,22 @@
"""Test module for DistMuon optimizer with FSDP2 multi-GPU functionality."""
import os
from pathlib import Path
import torch
import yaml
from accelerate.test_utils import execute_subprocess_async
from tbparse import SummaryReader
from transformers.testing_utils import get_torch_dist_unique_port
from axolotl.utils.dict import DictDefault
from tests.e2e.utils import most_recent_subdir, require_torch_2_7_0
from tests.e2e.utils import check_tensorboard_loss_decreased, require_torch_2_7_0
AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
def verify_training_success(temp_dir):
"""Verify that training completed successfully by checking artifacts and loss."""
"""Verify that training completed successfully — artifacts, no-NaN, loss
stayed in qwen2-pretraining scale (tiny-qwen2-129m final pretrain CE ~3.92).
"""
output_path = Path(temp_dir)
model_files = list(output_path.glob("*.bin")) + list(
@@ -30,19 +29,13 @@ def verify_training_success(temp_dir):
"No checkpoint files found - training may have failed"
)
tb_log_path = most_recent_subdir(temp_dir + "/runs")
if tb_log_path:
event_files = sorted(os.listdir(tb_log_path))
if event_files:
event_file = os.path.join(tb_log_path, event_files[0])
reader = SummaryReader(event_file)
df = reader.scalars
train_loss_df = df[df.tag == "train/train_loss"]
if len(train_loss_df) > 0:
final_loss = train_loss_df.value.values[-1]
assert not torch.isnan(torch.tensor(final_loss)), (
f"Training loss is NaN: {final_loss}"
)
check_tensorboard_loss_decreased(
temp_dir + "/runs",
initial_window=10,
final_window=10,
max_initial=5.0,
max_final=4.7,
)
class TestDistMuon:
@@ -52,7 +45,7 @@ class TestDistMuon:
def test_fft_sft(self, temp_dir):
cfg = DictDefault(
{
"base_model": "Qwen/Qwen2.5-0.5B",
"base_model": "axolotl-ai-co/tiny-qwen2-129m",
"sequence_len": 2048,
"val_set_size": 0.01,
"datasets": [
@@ -63,11 +56,12 @@ class TestDistMuon:
},
],
"num_epochs": 1,
"max_steps": 2,
"max_steps": 80,
"warmup_steps": 5,
"micro_batch_size": 2,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.02,
"learning_rate": 2e-3,
"optimizer": "muon",
"weight_decay": 0.01,
"lr_scheduler": "cosine",
@@ -82,6 +76,9 @@ class TestDistMuon:
"reshard_after_forward": True,
},
"use_tensorboard": True,
"seed": 42,
"sample_packing": True,
"pad_to_sequence_len": True,
"bf16": True,
}
)
@@ -109,7 +106,7 @@ class TestDistMuon:
def test_lora_sft(self, temp_dir):
cfg = DictDefault(
{
"base_model": "Qwen/Qwen2.5-0.5B",
"base_model": "axolotl-ai-co/tiny-qwen2-129m",
"sequence_len": 2048,
"val_set_size": 0.01,
"datasets": [
@@ -122,14 +119,15 @@ class TestDistMuon:
"adapter": "lora",
"lora_r": 8,
"lora_alpha": 16,
"lora_dropout": 0.05,
"lora_dropout": 0.0,
"lora_target_linear": True,
"num_epochs": 1,
"max_steps": 2,
"max_steps": 80,
"warmup_steps": 5,
"micro_batch_size": 2,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.02,
"learning_rate": 2e-3,
"optimizer": "muon",
"weight_decay": 0.01,
"lr_scheduler": "cosine",
@@ -144,6 +142,9 @@ class TestDistMuon:
"reshard_after_forward": True,
},
"use_tensorboard": True,
"seed": 42,
"sample_packing": True,
"pad_to_sequence_len": True,
"bf16": True,
}
)