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/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)