attempt to also run e2e tests that needs gpus (#1070)

* attempt to also run e2e tests that needs gpus

* fix stray quote

* checkout specific github ref

* dockerfile for tests with proper checkout

ensure wandb is dissabled for docker pytests
clear wandb env after testing
clear wandb env after testing
make sure to provide a default val for pop
tryin skipping wandb validation tests
explicitly disable wandb in the e2e tests
explicitly report_to None to see if that fixes the docker e2e tests
split gpu from non-gpu unit tests
skip bf16 check in test for now
build docker w/o cache since it uses branch name ref
revert some changes now that caching is fixed
skip bf16 check if on gpu w support

* pytest skip for auto-gptq requirements

* skip mamba tests for now, split multipack and non packed lora llama tests

* split tests that use monkeypatches

* fix relative import for prev commit

* move other tests using monkeypatches to the correct run

tests/e2e/patched/test_fused_llama.py

@@ -15,7 +15,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from .utils import with_temp_dir
+from ..utils import with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"

tests/e2e/patched/test_lora_llama_multipack.py

@@ -0,0 +1,126 @@
+"""
+E2E tests for lora llama
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+import pytest
+from transformers.utils import is_auto_gptq_available, is_torch_bf16_gpu_available
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from ..utils import with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestLoraLlama(unittest.TestCase):
+    """
+    Test case for Llama models using LoRA w multipack
+    """
+
+    @with_temp_dir
+    def test_lora_packing(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 32,
+                "lora_alpha": 64,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+            }
+        )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
+
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.bin").exists()
+
+    @pytest.mark.skipif(not is_auto_gptq_available(), reason="auto-gptq not available")
+    @with_temp_dir
+    def test_lora_gptq_packed(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "TheBlokeAI/jackfram_llama-68m-GPTQ",
+                "model_type": "AutoModelForCausalLM",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "gptq": True,
+                "gptq_disable_exllama": True,
+                "lora_r": 32,
+                "lora_alpha": 64,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "save_steps": 0.5,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.bin").exists()

tests/e2e/patched/test_mistral_samplepack.py

@@ -15,7 +15,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from .utils import with_temp_dir
+from ..utils import with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"

tests/e2e/patched/test_mixtral_samplepack.py

@@ -15,7 +15,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from .utils import with_temp_dir
+from ..utils import with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"

tests/e2e/patched/test_model_patches.py

@@ -9,7 +9,7 @@ from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model, load_tokenizer
 
-from .utils import with_temp_dir
+from ..utils import with_temp_dir
 
 
 class TestModelPatches(unittest.TestCase):

tests/e2e/patched/test_resume.py

@@ -17,7 +17,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from .utils import most_recent_subdir, with_temp_dir
+from ..utils import most_recent_subdir, with_temp_dir
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
@@ -29,7 +29,7 @@ class TestResumeLlama(unittest.TestCase):
     """
 
     @with_temp_dir
-    def test_resume_qlora(self, temp_dir):
+    def test_resume_qlora_packed(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {

tests/e2e/test_lora_llama.py

@@ -65,96 +65,3 @@ class TestLoraLlama(unittest.TestCase):
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.bin").exists()
-
-    @with_temp_dir
-    def test_lora_packing(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
-                "sequence_len": 1024,
-                "sample_packing": True,
-                "flash_attention": True,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "lora_r": 32,
-                "lora_alpha": 64,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.1,
-                "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
-                },
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 2,
-                "micro_batch_size": 8,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "bf16": True,
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "adapter_model.bin").exists()
-
-    @with_temp_dir
-    def test_lora_gptq(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "TheBlokeAI/jackfram_llama-68m-GPTQ",
-                "model_type": "AutoModelForCausalLM",
-                "tokenizer_type": "LlamaTokenizer",
-                "sequence_len": 1024,
-                "sample_packing": True,
-                "flash_attention": True,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "gptq": True,
-                "gptq_disable_exllama": True,
-                "lora_r": 32,
-                "lora_alpha": 64,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0.1,
-                "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
-                },
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 2,
-                "save_steps": 0.5,
-                "micro_batch_size": 8,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "adapter_model.bin").exists()

tests/e2e/test_mamba.py

@@ -7,6 +7,8 @@ import os
 import unittest
 from pathlib import Path
 
+import pytest
+
 from axolotl.cli import load_datasets
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.train import train
@@ -19,9 +21,10 @@ LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
 
-class TestMistral(unittest.TestCase):
+@pytest.mark.skip(reason="skipping until upstreamed into transformers")
+class TestMamba(unittest.TestCase):
     """
-    Test case for Llama models using LoRA
+    Test case for Mamba models
     """
 
     @with_temp_dir

tests/e2e/test_phi.py

@@ -8,6 +8,7 @@ import unittest
 from pathlib import Path
 
 import pytest
+from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.cli import load_datasets
 from axolotl.common.cli import TrainerCliArgs
@@ -59,7 +60,6 @@ class TestPhi(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "paged_adamw_8bit",
                 "lr_scheduler": "cosine",
-                "bf16": True,
                 "flash_attention": True,
                 "max_steps": 10,
                 "save_steps": 10,
@@ -67,6 +67,10 @@ class TestPhi(unittest.TestCase):
                 "save_safetensors": True,
             }
         )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
         normalize_config(cfg)
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -110,9 +114,13 @@ class TestPhi(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_bnb_8bit",
                 "lr_scheduler": "cosine",
-                "bf16": True,
             }
         )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
+
         normalize_config(cfg)
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

tests/test_validation.py

@@ -6,6 +6,7 @@ import unittest
 from typing import Optional
 
 import pytest
+from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.utils.config import validate_config
 from axolotl.utils.dict import DictDefault
@@ -354,6 +355,10 @@ class ValidationTest(unittest.TestCase):
         with pytest.raises(ValueError, match=regex_exp):
             validate_config(cfg)
 
+    @pytest.mark.skipif(
+        is_torch_bf16_gpu_available(),
+        reason="test should only run on gpus w/o bf16 support",
+    )
     def test_merge_lora_no_bf16_fail(self):
         """
         This is assumed to be run on a CPU machine, so bf16 is not supported.
@@ -778,6 +783,15 @@ class ValidationWandbTest(ValidationTest):
         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):
         cfg = DictDefault({})
 
@@ -798,3 +812,6 @@ class ValidationWandbTest(ValidationTest):
         setup_wandb_env_vars(cfg)
 
         assert os.environ.get("WANDB_DISABLED", "") != "true"
+
+        os.environ.pop("WANDB_PROJECT", None)
+        os.environ.pop("WANDB_DISABLED", None)