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
2024-01-09 21:23:23 -05:00
parent 9be92d1448
commit 788649fe95
13 changed files with 214 additions and 105 deletions
--- a/tests/e2e/patched/test_mixtral_samplepack.py
+++ b/tests/e2e/patched/test_mixtral_samplepack.py
@@ -0,0 +1,123 @@
+"""
+E2E tests for mixtral
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+from transformers.utils import 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 TestMixtral(unittest.TestCase):
+    """
+    Test case for Llama models using LoRA
+    """
+
+    @with_temp_dir
+    def test_qlora(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "hf-internal-testing/Mixtral-tiny",
+                "tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
+                "flash_attention": True,
+                "sequence_len": 2048,
+                "load_in_4bit": True,
+                "adapter": "qlora",
+                "lora_r": 16,
+                "lora_alpha": 32,
+                "lora_dropout": 0.1,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {},
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "eval_steps": 10,
+                "sample_packing": 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)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.bin").exists()
+
+    @with_temp_dir
+    def test_ft(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "hf-internal-testing/Mixtral-tiny",
+                "tokenizer_config": "mistralai/Mixtral-8x7B-v0.1",
+                "flash_attention": True,
+                "sequence_len": 2048,
+                "val_set_size": 0.1,
+                "special_tokens": {},
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "eval_steps": 10,
+                "sample_packing": 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)
+
+        model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (
+            "axolotl.monkeypatch.mixtral.modeling_mixtral"
+            in model.model.layers[0].self_attn.__class__.__module__
+        )
+        assert (
+            "MixtralMultipackFlashAttention2"
+            in model.model.layers[0].self_attn.__class__.__name__
+        )
+        assert (Path(temp_dir) / "pytorch_model.bin").exists()