various tests fixes for flakey tests

cicd/cicd.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 set -e
 
-pytest -n8 --ignore=tests/e2e/ /workspace/axolotl/tests/
-pytest -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
-pytest --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/
+pytest --durations=10 -n8 --ignore=tests/e2e/ /workspace/axolotl/tests/
+pytest --durations=10 -n1 --dist loadfile -v /workspace/axolotl/tests/e2e/patched/ /workspace/axolotl/tests/e2e/integrations/
+pytest --durations=10 --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ /workspace/axolotl/tests/e2e/

requirements.txt

@@ -26,7 +26,7 @@ numpy>=1.24.4,<=2.0.1
 evaluate==0.4.1
 scipy
 scikit-learn==1.4.2
-pynvml
+nvidia-ml-py==12.560.30
 art
 gradio==3.50.2
 tensorboard

src/axolotl/utils/bench.py

@@ -1,13 +1,24 @@
 """Benchmarking and measurement utilities"""
 import functools
 
-import pynvml
 import torch
-from pynvml.nvml import NVMLError
 from transformers.utils.import_utils import is_torch_npu_available
 
 from axolotl.utils.distributed import get_device_type
 
+try:
+    from pynvml import (
+        NVMLError,
+        nvmlDeviceGetHandleByIndex,
+        nvmlDeviceGetMemoryInfo,
+        nvmlInit,
+    )
+except ImportError:
+    NVMLError = None
+    nvmlDeviceGetHandleByIndex = None
+    nvmlDeviceGetMemoryInfo = None
+    nvmlInit = None
+
 
 def check_cuda_device(default_value):
     """
@@ -68,10 +79,12 @@ def gpu_memory_usage_smi(device=0):
         device = device.index
     if isinstance(device, str) and device.startswith("cuda:"):
         device = int(device[5:])
+    if not nvmlInit:
+        return 0.0
     try:
-        pynvml.nvmlInit()
-        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
-        info = pynvml.nvmlDeviceGetMemoryInfo(handle)
+        nvmlInit()
+        handle = nvmlDeviceGetHandleByIndex(device)
+        info = nvmlDeviceGetMemoryInfo(handle)
         return info.used / 1024.0**3
     except NVMLError:
         return 0.0

src/axolotl/utils/data/sft.py

@@ -179,7 +179,7 @@ def load_tokenized_prepared_datasets(
                 + "|".join(
                     sorted(
                         [
-                            f"{d.path}: {d.type}: {d.shards}: {d.conversation}{d.split}"
+                            f"{d.path}:{d.type}:{d.shards}:{d.conversation}{d.split}"
                             for d in cfg_datasets
                         ]
                     )

tests/conftest.py

@@ -14,6 +14,12 @@ def download_smollm2_135m_model():
     snapshot_download("HuggingFaceTB/SmolLM2-135M")
 
 
+@pytest.fixture(scope="session", autouse=True)
+def download_llama_68m_random_model():
+    # download the model
+    snapshot_download("JackFram/llama-68m")
+
+
 @pytest.fixture(scope="session", autouse=True)
 def download_qwen_2_5_half_billion_model():
     # download the model
@@ -22,18 +28,26 @@ def download_qwen_2_5_half_billion_model():
 
 @pytest.fixture(scope="session", autouse=True)
 def download_tatsu_lab_alpaca_dataset():
-    # download the model
+    # download the dataset
     snapshot_download("tatsu-lab/alpaca", repo_type="dataset")
 
 
 @pytest.fixture(scope="session", autouse=True)
 def download_mhenrichsen_alpaca_2k_dataset():
-    # download the model
+    # download the dataset
     snapshot_download("mhenrichsen/alpaca_2k_test", repo_type="dataset")
 
 
+@pytest.fixture(scope="session", autouse=True)
+def download_mhenrichsen_alpaca_2k_w_revision_dataset():
+    # download the dataset
+    snapshot_download(
+        "mhenrichsen/alpaca_2k_test", repo_type="dataset", revision="d05c1cb"
+    )
+
+
 def download_mlabonne_finetome_100k_dataset():
-    # download the model
+    # download the dataset
     snapshot_download("mlabonne/FineTome-100k", repo_type="dataset")
 
 

tests/core/test_trainer_builder.py

@@ -14,9 +14,7 @@
 def fixture_cfg():
     cfg = DictDefault(
         {
-            "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
-            "model_type": "AutoModelForCausalLM",
-            "tokenizer_type": "LlamaTokenizer",
+            "base_model": "HuggingFaceTB/SmolLM2-135M",
             "micro_batch_size": 1,
             "gradient_accumulation_steps": 1,
             "learning_rate": 0.00005,
@@ -33,6 +31,9 @@ def fixture_cfg():
             "dataloader_num_workers": 1,
             "dataloader_pin_memory": True,
             "model_config_type": "llama",
+            "special_tokens": {
+                "pad_token": "<|endoftext|>",
+            },
         }
     )
 

tests/e2e/patched/test_fa_xentropy.py

@@ -51,11 +51,11 @@ def test_lora_packing_fa_cross_entropy(self, temp_dir, gradient_accumulation_ste
                 "flash_attn_cross_entropy": True,
                 "load_in_8bit": True,
                 "adapter": "lora",
-                "lora_r": 32,
-                "lora_alpha": 64,
+                "lora_r": 8,
+                "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "val_set_size": 0.2,
+                "val_set_size": 0.05,
                 "special_tokens": {
                     "pad_token": "<|endoftext|>",
                 },

tests/e2e/patched/test_resume.py

@@ -29,23 +29,24 @@ class TestResumeLlama(unittest.TestCase):
     """
 
     @with_temp_dir
-    def test_resume_qlora_packed(self, temp_dir):
+    def test_resume_lora_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 1024,
                 "sample_packing": True,
                 "flash_attention": True,
-                "load_in_4bit": True,
-                "adapter": "qlora",
-                "lora_r": 32,
-                "lora_alpha": 64,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "val_set_size": 0.1,
-                "special_tokens": {},
+                "val_set_size": 0.01,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
                 "datasets": [
                     {
                         "path": "vicgalle/alpaca-gpt4",
@@ -57,11 +58,11 @@ def test_resume_qlora_packed(self, temp_dir):
                 "gradient_accumulation_steps": 1,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
+                "optimizer": "adamw_8bit",
                 "lr_scheduler": "cosine",
-                "save_steps": 10,
+                "save_steps": 3,
                 "save_total_limit": 5,
-                "max_steps": 40,
+                "max_steps": 15,
                 "use_tensorboard": True,
             }
         )
@@ -77,7 +78,7 @@ def test_resume_qlora_packed(self, temp_dir):
 
         resume_cfg = cfg | DictDefault(
             {
-                "resume_from_checkpoint": f"{temp_dir}/checkpoint-30/",
+                "resume_from_checkpoint": f"{temp_dir}/checkpoint-9/",
             }
         )
         normalize_config(resume_cfg)
@@ -93,4 +94,4 @@ def test_resume_qlora_packed(self, temp_dir):
         )
         pattern = r"first_step\s+(\d+)"
         first_steps = int(re.findall(pattern, res.stdout)[0])
-        assert first_steps == 31
+        assert first_steps == 10

tests/e2e/patched/test_unsloth_qlora.py

@@ -42,7 +42,7 @@ def test_unsloth_llama_qlora_fa2(self, temp_dir, sample_packing):
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "val_set_size": 0.2,
+                "val_set_size": 0.05,
                 "special_tokens": {
                     "pad_token": "<|endoftext|>",
                 },
@@ -92,7 +92,7 @@ def test_unsloth_llama_qlora_unpacked(self, temp_dir):
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "val_set_size": 0.2,
+                "val_set_size": 0.05,
                 "special_tokens": {
                     "pad_token": "<|endoftext|>",
                 },
@@ -146,7 +146,7 @@ def test_unsloth_llama_qlora_unpacked_no_fa2_fp16(self, temp_dir, sdp_attention)
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "val_set_size": 0.2,
+                "val_set_size": 0.05,
                 "special_tokens": {
                     "pad_token": "<|endoftext|>",
                 },

tests/e2e/test_optimizers.py

@@ -94,6 +94,7 @@ def test_adopt_adamw(self, temp_dir):
                     },
                 ],
                 "num_epochs": 1,
+                "max_steps": 5,
                 "micro_batch_size": 8,
                 "gradient_accumulation_steps": 1,
                 "output_dir": temp_dir,
@@ -115,7 +116,7 @@ def test_fft_schedule_free_adamw(self, temp_dir):
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
                 "sequence_len": 1024,
-                "val_set_size": 0.1,
+                "val_set_size": 0.01,
                 "special_tokens": {
                     "pad_token": "<|endoftext|>",
                 },
@@ -126,13 +127,14 @@ def test_fft_schedule_free_adamw(self, temp_dir):
                     },
                 ],
                 "num_epochs": 1,
-                "micro_batch_size": 4,
+                "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "schedule_free_adamw",
                 "lr_scheduler": "constant",
                 "save_safetensors": True,
+                "max_steps": 10,
             }
         )
         # pylint: disable=duplicate-code

tests/e2e/test_relora_llama.py

@@ -52,6 +52,7 @@ def test_relora(self, temp_dir):
                 ],
                 "warmup_steps": 15,
                 "num_epochs": 2,
+                "max_steps": 51,  # at least 2x relora_steps
                 "micro_batch_size": 4,
                 "gradient_accumulation_steps": 1,
                 "output_dir": temp_dir,

tests/test_datasets.py

@@ -67,7 +67,7 @@ def test_load_hub(self):
     def test_load_local_hub(self):
         """Niche use case.  Verify that a local copy of a hub dataset can be loaded"""
         with tempfile.TemporaryDirectory() as tmp_dir:
-            tmp_ds_path = Path("mhenrichsen/alpaca_2k_test")
+            tmp_ds_path = Path(tmp_dir) / "mhenrichsen/alpaca_2k_test"
             tmp_ds_path.mkdir(parents=True, exist_ok=True)
             snapshot_download(
                 repo_id="mhenrichsen/alpaca_2k_test",
@@ -89,7 +89,7 @@ def test_load_local_hub(self):
                             "ds_type": "parquet",
                             "type": "alpaca",
                             "data_files": [
-                                "mhenrichsen/alpaca_2k_test/alpaca_2000.parquet",
+                                f"{tmp_ds_path}/alpaca_2000.parquet",
                             ],
                         },
                     ],

tests/test_perplexity.py

@@ -7,7 +7,7 @@
 
 from axolotl.utils.callbacks.perplexity import Perplexity
 
-MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+MODEL_NAME = "HuggingFaceTB/SmolLM2-135M"
 
 
 @fixture()
@@ -22,7 +22,9 @@ def model():
 
 @fixture()
 def tokenizer():
-    return AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+    tokenizer_ = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+    tokenizer_.add_special_tokens({"pad_token": "<|endoftext|>"})
+    return tokenizer_
 
 
 def test_perplexity_longer_than_stride(model, metric):
@@ -33,12 +35,12 @@ def test_perplexity_longer_than_stride(model, metric):
 """
     result = metric.compute(model, [sample_text])
     ppl = result["score"]
-    assert round(ppl, 2) == 5.37
+    assert round(ppl, 2) == 7.41
 
 
 def test_perplexity_short(model, metric):
     # taken from https://huggingface.co/datasets/roneneldan/TinyStories
     sample_text = "Once upon a time, there was a little car named Beep. Beep loved to go fast and play in the sun."
     result = metric.compute(model, [sample_text])
     ppl = result["score"]
-    assert round(ppl, 2) == 10.02
+    assert round(ppl, 2) == 10.33