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Summary: Add the model to canary as its CPU test exceeds time limit and GPU test will OOM on A100. Pull Request resolved: #2037 Reviewed By: aaronenyeshi Differential Revision: D51356557 Pulled By: xuzhao9 fbshipit-source-id: f462c27b787bb81fae3cf0631a001799ae462f6f
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from torchbenchmark.tasks import NLP | ||
from torchbenchmark.util.framework.huggingface.model_factory import HuggingFaceModel | ||
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class Model(HuggingFaceModel): | ||
task = NLP.LANGUAGE_MODELING | ||
# DEFAULT_TRAIN_BSIZE not specified since we're not implementing a train test | ||
# DEFAULT_TRAIN_BSIZE = 1 | ||
DEFAULT_EVAL_BSIZE = 1 | ||
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def __init__(self, test, device, batch_size=None, extra_args=[]): | ||
super().__init__(name="hf_Yi", test=test, device=device, batch_size=batch_size, extra_args=extra_args) | ||
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def train(self): | ||
return NotImplementedError("Not implemented") | ||
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def eval(self): | ||
super().eval() |
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import subprocess | ||
import sys | ||
import os | ||
from torchbenchmark.util.framework.huggingface.patch_hf import patch_transformers, cache_model | ||
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def pip_install_requirements(): | ||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', '-r', 'requirements.txt']) | ||
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if __name__ == '__main__': | ||
pip_install_requirements() | ||
patch_transformers() | ||
model_name = os.path.basename(os.path.dirname(os.path.abspath(__file__))) | ||
cache_model(model_name, trust_remote_code=True) |
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devices: | ||
NVIDIA A100-SXM4-40GB: | ||
eval_batch_size: 1 | ||
eval_benchmark: false | ||
eval_deterministic: false | ||
eval_nograd: true | ||
train_benchmark: false | ||
train_deterministic: false | ||
not_implemented: | ||
- device: NVIDIA A10G | ||
# - device: cpu |
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numba |
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