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* Name the token embedding for remote code whose inherited accessor cannot find it (EXAONE 3.5) * Assert the embedding repair instead of skipping when transformers already resolves it * Install the embedding getter and setter on the class so the repair works on transformers 4.52 and 4.53
131 lines
3.9 KiB
Python
131 lines
3.9 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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"""Remote code keeping its token embedding at `wte` with no accessor of its own (EXAONE 3.5, unsloth#1406)."""
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import sys
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import pytest
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import torch
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transformers = pytest.importorskip("transformers")
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from transformers import PreTrainedModel, PretrainedConfig
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class WteConfig(PretrainedConfig):
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model_type = "tiny_wte_remote"
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def __init__(
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self,
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vocab_size = 32,
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hidden_size = 8,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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super().__init__(**kwargs)
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class WteModel(PreTrainedModel):
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config_class = WteConfig
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base_model_prefix = "transformer"
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def __init__(self, config):
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super().__init__(config)
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self.wte = torch.nn.Embedding(config.vocab_size, config.hidden_size)
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self.proj = torch.nn.Linear(config.hidden_size, config.hidden_size, bias = False)
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def forward(
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self,
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input_ids = None,
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**kwargs,
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):
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return self.proj(self.wte(input_ids))
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class WteForCausalLM(PreTrainedModel):
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config_class = WteConfig
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base_model_prefix = "transformer"
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def __init__(self, config):
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super().__init__(config)
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self.transformer = WteModel(config)
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self.lm_head = torch.nn.Linear(config.hidden_size, config.vocab_size, bias = False)
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def forward(
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self,
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input_ids = None,
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**kwargs,
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):
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return self.lm_head(self.transformer(input_ids))
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WteModel.__module__ = WteForCausalLM.__module__ = "transformers_modules.tiny_wte.modeling_tiny"
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sys.modules.setdefault(WteModel.__module__, sys.modules[__name__])
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def _fresh_classes():
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# The shim patches the class: give each test its own classes.
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inner = type("WteModel", (WteModel,), {"__module__": WteModel.__module__})
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outer = type("WteForCausalLM", (WteForCausalLM,), {"__module__": WteForCausalLM.__module__})
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def init(self, config):
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PreTrainedModel.__init__(self, config)
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self.transformer = inner(config)
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self.lm_head = torch.nn.Linear(config.hidden_size, config.vocab_size, bias = False)
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outer.__init__ = init
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return inner, outer
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def _default_accessor_raises(model):
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try:
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model.get_input_embeddings()
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except NotImplementedError:
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return True
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return False
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def test_wte_accessor_is_named_and_peft_attaches():
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from unsloth.models.remote_code_shims import apply_remote_code_shims
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_, outer = _fresh_classes()
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model = outer(WteConfig())
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broken = _default_accessor_raises(model)
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repaired = apply_remote_code_shims(model)
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assert ("WteModel.get_input_embeddings" in repaired) == broken
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assert model.get_input_embeddings() is model.transformer.wte
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assert model.transformer.get_input_embeddings() is model.transformer.wte
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new = torch.nn.Embedding(32, 8)
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model.set_input_embeddings(new)
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assert model.transformer.wte is new
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peft = pytest.importorskip("peft")
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lora = peft.get_peft_model(model, peft.LoraConfig(r = 2, target_modules = ["proj"]))
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assert any("lora_A" in name for name, _ in lora.named_parameters())
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def test_second_pass_is_a_no_op():
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from unsloth.models.remote_code_shims import apply_remote_code_shims
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_, outer = _fresh_classes()
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apply_remote_code_shims(outer(WteConfig()))
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assert apply_remote_code_shims(outer(WteConfig())) == []
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def test_native_and_own_accessor_classes_are_left_alone():
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from unsloth.models.remote_code_shims import apply_remote_code_shims
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native = type(
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"NativeWte", (WteModel,), {"__module__": "transformers.models.fake.modeling_fake"}
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)
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apply_remote_code_shims(native(WteConfig()))
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assert "get_input_embeddings" not in native.__dict__
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own = type(
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"OwnAccessor",
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(WteModel,),
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{"__module__": WteModel.__module__, "get_input_embeddings": lambda self: self.wte},
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)
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assert apply_remote_code_shims(own(WteConfig())) == []
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assert "set_input_embeddings" not in own.__dict__
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