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https://github.com/deepset-ai/haystack.git
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b60d9a2cbf
* no dpr test seems worth mocking * move distillation tests * pylint * mypy * pylint * move feature_extraction tests as well * move feature_extraction tests as well * merge feature extractor suites * get_language_model tests and adaptive model tests * duplicate test * moving fixtures * mypy * mypy-again * trigger * un-mock integration test * review feedback * feedback * pylint
104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
import torch
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from haystack.nodes import FARMReader
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from haystack.modeling.data_handler.processor import UnlabeledTextProcessor
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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_prediction_layer_distillation(samples_path):
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student = FARMReader(model_name_or_path="prajjwal1/bert-mini", 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) == 38
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student_weights.pop(-2) # pooler is not updated due to different attention head
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student.distil_prediction_layer_from(teacher, data_dir=samples_path / "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(-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_intermediate_layer_distillation(samples_path):
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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.distil_intermediate_layers_from(
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teacher_model=teacher, data_dir=samples_path / "squad", train_filename="tiny.json"
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)
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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)
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def test_intermediate_layer_distillation_from_scratch(samples_path):
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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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processor = UnlabeledTextProcessor(
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tokenizer=teacher.inferencer.processor.tokenizer,
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max_seq_len=128,
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train_filename="doc_2.txt",
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data_dir=samples_path / "docs",
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)
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student.distil_intermediate_layers_from(
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teacher_model=teacher, data_dir=samples_path / "squad", train_filename="tiny.json", processor=processor
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)
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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)
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