Trouble loading GPTBigCode models
Closed this issue · 3 comments
arjunguha commented
I'm happy to try to debug this. But, in case the error is obvious to an nnsight hacker, here is an error I'm getting.
This is the model:
https://huggingface.co/bigcode/gpt_bigcode-santacoder
This is my code that raises the error below. I am able to load Pythia as shown in the tutorial.
from nnsight import LanguageModel
model = LanguageModel("bigcode/gpt_bigcode-santacoder", device_map='cuda:0')
Error:
/work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/utils/hub.py:123: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
warnings.warn(
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/utils/import_utils.py:1382, in _LazyModule._get_module(self, module_name)
1381 try:
-> 1382 return importlib.import_module("." + module_name, self.__name__)
1383 except Exception as e:
File ~/miniconda3/lib/python3.11/importlib/__init__.py:126, in import_module(name, package)
125 level += 1
--> 126 return _bootstrap._gcd_import(name[level:], package, level)
File <frozen importlib._bootstrap>:1204, in _gcd_import(name, package, level)
File <frozen importlib._bootstrap>:1176, in _find_and_load(name, import_)
File <frozen importlib._bootstrap>:1147, in _find_and_load_unlocked(name, import_)
File <frozen importlib._bootstrap>:690, in _load_unlocked(spec)
File <frozen importlib._bootstrap_external>:940, in exec_module(self, module)
File <frozen importlib._bootstrap>:241, in _call_with_frames_removed(f, *args, **kwds)
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py:64
60 # Fused kernels
61 # Use separate functions for each case because conditionals prevent kernel fusion.
62 # TODO: Could have better fused kernels depending on scaling, dropout and head mask.
63 # Is it doable without writing 32 functions?
---> 64 @torch.jit.script
65 def upcast_masked_softmax(
66 x: torch.Tensor, mask: torch.Tensor, mask_value: torch.Tensor, scale: float, softmax_dtype: torch.dtype
67 ):
68 input_dtype = x.dtype
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/torch/jit/_script.py:1381, in script(obj, optimize, _frames_up, _rcb, example_inputs)
1380 _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
-> 1381 fn = torch._C._jit_script_compile(
1382 qualified_name, ast, _rcb, get_default_args(obj)
1383 )
1384 # Forward docstrings
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/torch/jit/_recursive.py:1010, in try_compile_fn(fn, loc)
1009 rcb = _jit_internal.createResolutionCallbackFromClosure(fn)
-> 1010 return torch.jit.script(fn, _rcb=rcb)
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/torch/jit/_script.py:1378, in script(obj, optimize, _frames_up, _rcb, example_inputs)
1377 return maybe_already_compiled_fn
-> 1378 ast = get_jit_def(obj, obj.__name__)
1379 if _rcb is None:
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/torch/jit/frontend.py:331, in get_jit_def(fn, def_name, self_name, is_classmethod)
317 """
318 Build a JIT AST (TreeView) from the given function.
319
(...)
329 self_name: If this function is a method, what the type name of `self` is.
330 """
--> 331 parsed_def = parse_def(fn) if not isinstance(fn, _ParsedDef) else fn
332 type_line = torch.jit.annotations.get_type_line(parsed_def.source)
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/torch/_sources.py:120, in parse_def(fn)
119 def parse_def(fn):
--> 120 sourcelines, file_lineno, filename = get_source_lines_and_file(
121 fn, ErrorReport.call_stack()
122 )
123 sourcelines = normalize_source_lines(sourcelines)
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/torch/_sources.py:23, in get_source_lines_and_file(obj, error_msg)
22 filename = inspect.getsourcefile(obj)
---> 23 sourcelines, file_lineno = inspect.getsourcelines(obj)
24 except OSError as e:
File ~/miniconda3/lib/python3.11/inspect.py:1244, in getsourcelines(object)
1243 object = unwrap(object)
-> 1244 lines, lnum = findsource(object)
1246 if istraceback(object):
File ~/miniconda3/lib/python3.11/inspect.py:1063, in findsource(object)
1056 """Return the entire source file and starting line number for an object.
1057
1058 The argument may be a module, class, method, function, traceback, frame,
1059 or code object. The source code is returned as a list of all the lines
1060 in the file and the line number indexes a line in that list. An OSError
1061 is raised if the source code cannot be retrieved."""
-> 1063 file = getsourcefile(object)
1064 if file:
1065 # Invalidate cache if needed.
File ~/miniconda3/lib/python3.11/inspect.py:940, in getsourcefile(object)
937 """Return the filename that can be used to locate an object's source.
938 Return None if no way can be identified to get the source.
939 """
--> 940 filename = getfile(object)
941 all_bytecode_suffixes = importlib.machinery.DEBUG_BYTECODE_SUFFIXES[:]
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/torch/package/package_importer.py:696, in _patched_getfile(object)
695 return _package_imported_modules[object.__module__].__file__
--> 696 return _orig_getfile(object)
File ~/miniconda3/lib/python3.11/inspect.py:920, in getfile(object)
919 return object.co_filename
--> 920 raise TypeError('module, class, method, function, traceback, frame, or '
921 'code object was expected, got {}'.format(
922 type(object).__name__))
TypeError: module, class, method, function, traceback, frame, or code object was expected, got builtin_function_or_method
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
Cell In[1], line 3
1 from nnsight import LanguageModel
----> 3 model = LanguageModel("bigcode/gpt_bigcode-santacoder", device_map='cuda:0')
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/nnsight/models/LanguageModel.py:46, in LanguageModel.__init__(self, tokenizer, automodel, *args, **kwargs)
43 self.local_model: PreTrainedModel = None
44 self.automodel = automodel if not isinstance(automodel, str) else getattr(modeling_auto, automodel)
---> 46 super().__init__(*args, **kwargs)
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/nnsight/models/AbstractModel.py:104, in AbstractModel.__init__(self, repoid_path_model, dispatch, alter, *args, **kwargs)
99 self.meta_model: Module = Module.wrap(
100 copy.deepcopy(self.local_model).to("meta")
101 )
102 else:
103 self.meta_model: Module = Module.wrap(
--> 104 self._load_meta(self.repoid_path_clsname, *args, **kwargs).to(
105 "meta"
106 )
107 )
109 # Wrap all modules in our Module class.
110 for name, module in self.meta_model.named_children():
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/nnsight/models/LanguageModel.py:59, in LanguageModel._load_meta(self, repoid_or_path, *args, **kwargs)
54 self.tokenizer = AutoTokenizer.from_pretrained(
55 repoid_or_path, config=self.config, padding_side="left"
56 )
57 self.tokenizer.pad_token = self.tokenizer.eos_token
---> 59 return self.automodel.from_config(self.config, trust_remote_code=True)
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:440, in _BaseAutoModelClass.from_config(cls, config, **kwargs)
438 return model_class._from_config(config, **kwargs)
439 elif type(config) in cls._model_mapping.keys():
--> 440 model_class = _get_model_class(config, cls._model_mapping)
441 return model_class._from_config(config, **kwargs)
443 raise ValueError(
444 f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
445 f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
446 )
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:387, in _get_model_class(config, model_mapping)
386 def _get_model_class(config, model_mapping):
--> 387 supported_models = model_mapping[type(config)]
388 if not isinstance(supported_models, (list, tuple)):
389 return supported_models
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:740, in _LazyAutoMapping.__getitem__(self, key)
738 if model_type in self._model_mapping:
739 model_name = self._model_mapping[model_type]
--> 740 return self._load_attr_from_module(model_type, model_name)
742 # Maybe there was several model types associated with this config.
743 model_types = [k for k, v in self._config_mapping.items() if v == key.__name__]
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:754, in _LazyAutoMapping._load_attr_from_module(self, model_type, attr)
752 if module_name not in self._modules:
753 self._modules[module_name] = importlib.import_module(f".{module_name}", "transformers.models")
--> 754 return getattribute_from_module(self._modules[module_name], attr)
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:698, in getattribute_from_module(module, attr)
696 if isinstance(attr, tuple):
697 return tuple(getattribute_from_module(module, a) for a in attr)
--> 698 if hasattr(module, attr):
699 return getattr(module, attr)
700 # Some of the mappings have entries model_type -> object of another model type. In that case we try to grab the
701 # object at the top level.
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/utils/import_utils.py:1372, in _LazyModule.__getattr__(self, name)
1370 value = self._get_module(name)
1371 elif name in self._class_to_module.keys():
-> 1372 module = self._get_module(self._class_to_module[name])
1373 value = getattr(module, name)
1374 else:
File /work/arjunguha-research-group/arjun/venvs/jan2024/lib/python3.11/site-packages/transformers/utils/import_utils.py:1384, in _LazyModule._get_module(self, module_name)
1382 return importlib.import_module("." + module_name, self.__name__)
1383 except Exception as e:
-> 1384 raise RuntimeError(
1385 f"Failed to import {self.__name__}.{module_name} because of the following error (look up to see its"
1386 f" traceback):\n{e}"
1387 ) from e
RuntimeError: Failed to import transformers.models.gpt_bigcode.modeling_gpt_bigcode because of the following error (look up to see its traceback):
module, class, method, function, traceback, frame, or code object was expected, got builtin_function_or_method
JadenFiotto-Kaufman commented
@arjunguha What version is your transformers? I see this in the model card:
Model Summary
This is the same model as SantaCoder but it can be loaded with transformers >=4.28.1 to use the GPTBigCode architecture. We refer the reader to the SantaCoder model page for full documentation about this model
main: Uses the gpt_bigcode model. Requires the bigcode fork of transformers.
main_custom: Packaged with its modeling code. Requires transformers>=4.27. Alternatively, it can run on older versions by setting the configuration parameter activation_function = "gelu_pytorch_tanh".
arjunguha commented
I'm using transformers 4.36.2. So, the main branch and not the version that has its own modeling code.
cadentj commented
SantaCoder seems to work in the current release of nnsight.
from nnsight import LanguageModel
model = LanguageModel("bigcode/gpt_bigcode-santacoder", device_map='cuda:0')
with model.trace("# the following python function computes the sqrt"):
test = model.transformer.h[0].attn.output.save()Closing this issue.