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5b6b0cef77
* add UnlabeledTextProcessor * allow choosing processor when finetuning or distilling * fix type hint * Add latest docstring and tutorial changes * improve segment id computation for UnlabeledTextProcessor * add text and documentation * change batch size parameter for intermediate layer distillation * Add latest docstring and tutorial changes * fix distillation dim mapping * remove unnecessary changes * removed confusing parameter * Add latest docstring and tutorial changes Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
88 lines
3.6 KiB
Python
88 lines
3.6 KiB
Python
from haystack.nodes import FARMReader
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from haystack.modeling.data_handler.processor import UnlabeledTextProcessor
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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_prediction_layer_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_prediction_layer_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_intermediate_layer_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.distil_intermediate_layers_from(teacher_model=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) == 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():
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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(tokenizer=teacher.inferencer.processor.tokenizer, max_seq_len=128, train_filename="doc_2.txt", data_dir="samples/docs")
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student.distil_intermediate_layers_from(teacher_model=teacher, data_dir="samples/squad", train_filename="tiny.json", processor=processor)
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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) |