support for latest transformers release 4.48.1 (#2256)
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@@ -102,9 +102,5 @@ class TestMixtral(unittest.TestCase):
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cli_args = TrainerCliArgs()
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
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assert (
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"MixtralFlashAttention2"
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in model.model.layers[0].self_attn.__class__.__name__
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)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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@@ -49,12 +49,7 @@ class TestModelPatches(unittest.TestCase):
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)
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normalize_config(cfg)
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tokenizer = load_tokenizer(cfg)
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model, _ = load_model(cfg, tokenizer, inference=False)
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assert (
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"MixtralFlashAttention2"
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in model.model.layers[0].self_attn.__class__.__name__
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)
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load_model(cfg, tokenizer, inference=False)
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@with_temp_dir
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def test_mistral_multipack(self, temp_dir):
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@@ -3,8 +3,6 @@ import unittest
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import pytest
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from axolotl.monkeypatch.unsloth_ import check_self_attn_is_patchable
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@pytest.mark.skip(
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reason="Unsloth integration will be broken going into latest transformers"
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@@ -13,6 +11,8 @@ class TestUnslothIntegration(unittest.TestCase):
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"""Unsloth monkeypatch integration tests."""
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def test_is_self_attn_patchable(self):
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from axolotl.monkeypatch.unsloth_ import check_self_attn_is_patchable
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# ensures the current version of transformers has loss code that matches our patching code
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self.assertTrue(
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check_self_attn_is_patchable(),
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