29 lines
898 B
Python
29 lines
898 B
Python
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import pytest
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from torchscale.architecture.config import EncoderConfig
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from torchscale.architecture.encoder import Encoder
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import torch
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testcases = [
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{},
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{"vocab_size": 64000},
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{"activation_fn": "relu"},
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{"drop_path_rate": 0.1},
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{"encoder_normalize_before": False},
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{"no_scale_embedding": False},
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{"layernorm_embedding": True},
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{"rel_pos_buckets": 32, "max_rel_pos": 256},
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{"deepnorm": True, "subln": False, "encoder_normalize_before": False},
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{"bert_init": True},
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{"multiway": True},
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{"share_encoder_input_output_embed": True},
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{"checkpoint_activations": True},
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{"fsdp": True}
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]
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@pytest.mark.parametrize("args", testcases)
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def test_encoder(args):
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config = EncoderConfig(**args)
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model = Encoder(config)
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token_embeddings = torch.rand(2, 10, config.encoder_embed_dim)
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model(src_tokens=None, token_embeddings=token_embeddings)
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