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f33c2b987a
* initial tinybertdistill commit * add tinybert distill loss * remove teacher caching for tinybert * add tinybert to distil_from method * Add latest docstring and tutorial changes * add dim mapping and fix type hints * fix type hints * fix dummy input * fix dim mapping for tinybert loss and add comments/doc strings * add test for tinybert loss * Add latest docstring and tutorial changes * add comment * fix BERT forward parameters * add doc string to AdaptiveModel forward method * remove unnecessary data silo * fix farm import Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
from haystack.nodes import FARMReader
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import torch
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def create_checkpoint(model):
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weights = []
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for name, weight in model.inferencer.model.named_parameters():
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if "weight" in name and weight.requires_grad:
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weights.append(torch.clone(weight))
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return weights
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def assert_weight_change(weights, new_weights):
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print([torch.equal(old_weight, new_weight) for old_weight, new_weight in zip(weights, new_weights)])
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assert not any(torch.equal(old_weight, new_weight) for old_weight, new_weight in zip(weights, new_weights))
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def test_distillation():
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student = FARMReader(model_name_or_path="prajjwal1/bert-tiny", num_processes=0)
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teacher = FARMReader(model_name_or_path="prajjwal1/bert-small", num_processes=0)
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# create a checkpoint of weights before distillation
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student_weights = create_checkpoint(student)
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assert len(student_weights) == 22
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student_weights.pop(-2) # pooler is not updated due to different attention head
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student.distil_from(teacher, data_dir="samples/squad", train_filename="tiny.json")
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# create new checkpoint
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new_student_weights = create_checkpoint(student)
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assert len(new_student_weights) == 22
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new_student_weights.pop(-2) # pooler is not updated due to different attention head
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# check if weights have changed
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assert_weight_change(student_weights, new_student_weights)
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def test_tinybert_distillation():
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student = FARMReader(model_name_or_path="huawei-noah/TinyBERT_General_4L_312D")
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teacher = FARMReader(model_name_or_path="bert-base-uncased")
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# create a checkpoint of weights before distillation
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student_weights = create_checkpoint(student)
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assert len(student_weights) == 38
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student_weights.pop(-1) # last layer is not affected by tinybert loss
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student_weights.pop(-1) # pooler is not updated due to different attention head
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student._training_procedure(teacher_model=teacher, tinybert=True, data_dir="samples/squad", train_filename="tiny.json")
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# create new checkpoint
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new_student_weights = create_checkpoint(student)
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assert len(new_student_weights) == 38
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new_student_weights.pop(-1) # last layer is not affected by tinybert loss
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new_student_weights.pop(-1) # pooler is not updated due to different attention head
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# check if weights have changed
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assert_weight_change(student_weights, new_student_weights) |