Spaces:
Runtime error
Runtime error
| import argparse | |
| import unittest | |
| from typing import Any, Dict, Sequence | |
| import torch | |
| from fairseq.models import transformer | |
| from tests.test_roberta import FakeTask | |
| def mk_sample(tok: Sequence[int] = None, batch_size: int = 2) -> Dict[str, Any]: | |
| if not tok: | |
| tok = [10, 11, 12, 13, 14, 15, 2] | |
| batch = torch.stack([torch.tensor(tok, dtype=torch.long)] * batch_size) | |
| sample = { | |
| "net_input": { | |
| "src_tokens": batch, | |
| "prev_output_tokens": batch, | |
| "src_lengths": torch.tensor( | |
| [len(tok)] * batch_size, dtype=torch.long, device=batch.device | |
| ), | |
| }, | |
| "target": batch[:, 1:], | |
| } | |
| return sample | |
| def mk_transformer(**extra_args: Any): | |
| overrides = { | |
| # Use characteristics dimensions | |
| "encoder_embed_dim": 12, | |
| "encoder_ffn_embed_dim": 14, | |
| "decoder_embed_dim": 12, | |
| "decoder_ffn_embed_dim": 14, | |
| # Disable dropout so we have comparable tests. | |
| "dropout": 0, | |
| "attention_dropout": 0, | |
| "activation_dropout": 0, | |
| "encoder_layerdrop": 0, | |
| } | |
| overrides.update(extra_args) | |
| # Overrides the defaults from the parser | |
| args = argparse.Namespace(**overrides) | |
| transformer.tiny_architecture(args) | |
| torch.manual_seed(0) | |
| task = FakeTask(args) | |
| return transformer.TransformerModel.build_model(args, task) | |
| class TransformerTestCase(unittest.TestCase): | |
| def test_forward_backward(self): | |
| model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=12) | |
| sample = mk_sample() | |
| o, _ = model.forward(**sample["net_input"]) | |
| loss = o.sum() | |
| loss.backward() | |
| def test_different_encoder_decoder_embed_dim(self): | |
| model = mk_transformer(encoder_embed_dim=12, decoder_embed_dim=16) | |
| sample = mk_sample() | |
| o, _ = model.forward(**sample["net_input"]) | |
| loss = o.sum() | |
| loss.backward() | |