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| from typing import List, Tuple, Union |
|
|
| import numpy as np |
| import torch |
| import transformers.models.t5.modeling_t5 |
|
|
| from transformer_deploy.backends.pytorch_utils import get_model_size |
| from transformer_deploy.benchmarks.utils import compare_outputs, generate_input, generate_multiple_inputs, to_numpy |
| from transformer_deploy.utils.accuracy import check_accuracy |
| from transformer_deploy.utils.fastseq import code_patcher |
|
|
|
|
| def generate_fake_outputs( |
| shape: Tuple[int, int], nb: int, factor: float, tensor_type: str |
| ) -> List[Union[np.ndarray, torch.Tensor]]: |
| results = list() |
| for _ in range(nb): |
| if tensor_type == "np": |
| tensor = np.arange(start=0, stop=shape[0] * shape[1]).reshape(shape) * factor |
| elif tensor_type == "torch": |
| tensor = torch.arange(start=0, end=shape[0] * shape[1], device="cpu").reshape(shape) * factor |
| else: |
| raise Exception(f"unknown: {tensor_type}") |
| results.append(tensor) |
| return results |
|
|
|
|
| def test_gap(): |
| shape = (1, 4) |
| pairs = [("np", "np"), ("np", "torch"), ("torch", "np"), ("torch", "torch")] |
| for t1_type, t2_type in pairs: |
| t1 = generate_fake_outputs(shape=shape, nb=1, factor=0.1, tensor_type=t1_type) |
| t2 = generate_fake_outputs(shape=shape, nb=1, factor=0.2, tensor_type=t2_type) |
| assert np.isclose(a=compare_outputs(pytorch_output=to_numpy(t1), engine_output=to_numpy(t2)), b=0.15, atol=1e-3) |
| check_accuracy(engine_name=f"test [{t1_type}/{t2_type}]", pytorch_output=t1, engine_output=t2, tolerance=0.16) |
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|
|
| def test_generate_input(): |
| inputs_pytorch = generate_input(seq_len=16, batch_size=4, input_names=["input_ids", "attention_mask"], device="cpu") |
| assert set(inputs_pytorch.keys()) == {"input_ids", "attention_mask"} |
| assert inputs_pytorch["input_ids"].shape == torch.Size([4, 16]) |
| inputs_pytorch = generate_input( |
| seq_len=1, batch_size=1, input_names=["input_ids", "attention_mask", "token_type_ids"], device="cpu" |
| ) |
| assert set(inputs_pytorch.keys()) == {"input_ids", "attention_mask", "token_type_ids"} |
|
|
|
|
| def test_multiple_generate_input(): |
| multiple_inputs_pytorch = generate_multiple_inputs( |
| seq_len=16, batch_size=4, input_names=["input_ids", "attention_mask"], nb_inputs_to_gen=4, device="cpu" |
| ) |
| assert len(multiple_inputs_pytorch) == 4 |
| assert set(multiple_inputs_pytorch[0].keys()) == {"input_ids", "attention_mask"} |
|
|
|
|
| def test_extract_model_info(): |
| models = [ |
| "philschmid/MiniLM-L6-H384-uncased-sst2", |
| "camembert-base", |
| "sentence-transformers/msmarco-distilbert-cos-v5", |
| ] |
| for m in models: |
| att, hidden_size = get_model_size(path=m) |
| assert att > 0 and hidden_size > 0 |
|
|
|
|
| def test_update_module(): |
| src_code = ( |
| "vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}\n" |
| " vocab.update(self.added_tokens_encoder)\n" |
| ) |
| code_patcher( |
| module_name="transformers.models.t5.tokenization_t5", |
| function=transformers.models.t5.tokenization_t5.T5Tokenizer.get_vocab, |
| new_function_name="new_vocab", |
| modifications={src_code: 'vocab = {"1": "success"}\n'}, |
| ) |
| transformers.models.t5.tokenization_t5.T5Tokenizer.get_vocab = transformers.models.t5.tokenization_t5.new_vocab |
| assert transformers.models.t5.tokenization_t5.T5Tokenizer.get_vocab(1) == {"1": "success"} |
|
|
