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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """ test transformer"""
- import numpy as np
- import pytest
- from mindspore import Tensor
- from mindspore.common import dtype
- from mindspore.parallel.nn import MultiHeadAttention, FeedForward, TransformerEncoderLayer, TransformerEncoder, \
- TransformerDecoder, TransformerDecoderLayer, Transformer, CrossEntropyLoss, AttentionMask, FixedSparseAttention
- from mindspore.common.api import _cell_graph_executor
-
-
- def test_transformer_encoder_only():
- model = Transformer(batch_size=2,
- src_seq_length=20,
- tgt_seq_length=10,
- encoder_layers=2,
- decoder_layers=0,
- hidden_size=64,
- ffn_hidden_size=64)
-
- encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
- encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
-
- _cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
-
-
- def test_transformer_encoder_log_softmax():
- with pytest.raises(ValueError):
- model = Transformer(batch_size=2,
- src_seq_length=20,
- tgt_seq_length=10,
- encoder_layers=2,
- decoder_layers=0,
- hidden_act='logsoftmax',
- hidden_size=64,
- ffn_hidden_size=64)
-
- encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
- encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
-
- _cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
-
-
- def test_transformer_encoder_leakyrelu():
- model = Transformer(batch_size=2,
- src_seq_length=20,
- tgt_seq_length=10,
- encoder_layers=2,
- decoder_layers=0,
- hidden_act='leakyrelu',
- hidden_size=64,
- ffn_hidden_size=64)
-
- encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
- encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
-
- _cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
-
-
- def test_transformer_encoder_logsigmoid():
- model = Transformer(batch_size=2,
- src_seq_length=20,
- tgt_seq_length=10,
- encoder_layers=2,
- decoder_layers=0,
- hidden_act='logsigmoid',
- hidden_size=64,
- ffn_hidden_size=64)
-
- encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
- encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
-
- _cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
-
-
- def test_encoder_and_decoder():
- model = Transformer(batch_size=2,
- src_seq_length=20,
- tgt_seq_length=10,
- encoder_layers=1,
- decoder_layers=2,
- hidden_size=64,
- ffn_hidden_size=64)
-
- encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
- encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
-
- decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
- decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
- memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
-
- _cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask,
- decoder_input_value,
- decoder_input_mask,
- memory_mask)
-
-
- def test_transformer_encoder():
- model = TransformerEncoder(batch_size=2,
- seq_length=16,
- num_layers=2,
- hidden_size=8,
- ffn_hidden_size=64,
- num_heads=2)
-
- encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
- encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
-
- _cell_graph_executor.compile(model,
- encoder_input_value,
- encoder_input_mask)
-
-
- def test_transformer_encoder_layer():
- model = TransformerEncoderLayer(batch_size=2, hidden_size=8, ffn_hidden_size=64, seq_length=16,
- num_heads=2)
-
- encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
- encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
-
- _cell_graph_executor.compile(model,
- encoder_input_value,
- encoder_input_mask)
-
-
- def test_transformer_encoder_layer_post_ture():
- model = TransformerEncoderLayer(batch_size=2,
- seq_length=16,
- hidden_size=8, ffn_hidden_size=64,
- num_heads=2, post_layernorm_residual=True)
-
- encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
- encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
-
- _cell_graph_executor.compile(model,
- encoder_input_value,
- encoder_input_mask)
-
-
- def test_transformer_decoder():
- model = TransformerDecoder(num_layers=1,
- batch_size=2,
- src_seq_length=20,
- tgt_seq_length=10,
- hidden_size=64,
- ffn_hidden_size=64,
- num_heads=2)
-
- encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
-
- decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
- decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
- memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
-
- _cell_graph_executor.compile(model, decoder_input_value, decoder_input_mask,
- encoder_input_value,
- memory_mask)
-
-
- def test_transformer_decoder_layer():
- model = TransformerDecoderLayer(
- batch_size=2,
- src_seq_length=20,
- tgt_seq_length=10,
- hidden_size=64,
- ffn_hidden_size=64,
- num_heads=2)
-
- encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
-
- decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
- decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
- memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
-
- _cell_graph_executor.compile(model, decoder_input_value, decoder_input_mask,
- encoder_input_value,
- memory_mask)
-
-
- def test_multihead_attention():
- model = MultiHeadAttention(hidden_size=15,
- src_seq_length=20,
- tgt_seq_length=20,
- batch_size=2,
- num_heads=3)
- from_tensor = Tensor(np.ones((2, 20, 15)), dtype.float32)
- to_tensor = Tensor(np.ones((2, 20, 15)), dtype.float16)
- attention_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
-
- _cell_graph_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
-
-
- def test_multihead_attention_wrong_batch():
- model = MultiHeadAttention(hidden_size=15,
- src_seq_length=20,
- tgt_seq_length=20,
- batch_size=2,
- num_heads=3)
- from_tensor = Tensor(np.ones((3, 20, 15)), dtype.float32)
- to_tensor = Tensor(np.ones((3, 20, 15)), dtype.float16)
- attention_mask = Tensor(np.ones((3, 20, 20)), dtype.float16)
-
- with pytest.raises(ValueError):
- _cell_graph_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
-
-
- def test_feedforward_layer():
- model = FeedForward(hidden_size=15,
- ffn_hidden_size=30,
- dropout_rate=0.1,
- hidden_act='relu')
- tensor = Tensor(np.ones((2, 20, 15)), dtype.float32)
-
- _cell_graph_executor.compile(model, tensor)
-
-
- def test_cross_entroy():
- model = CrossEntropyLoss()
- logits = Tensor(np.array([[3, 5, 6, 9, 12, 33, 42, 12, 32, 72]]), dtype.float32)
- labels_np = np.array([1]).astype(np.int32)
- input_mask = Tensor(np.ones(1).astype(np.float32))
- labels = Tensor(labels_np)
- _cell_graph_executor.compile(model, logits, labels, input_mask)
-
-
- def test_attention_mask():
- model = AttentionMask(seq_length=19)
- inputs = Tensor(np.ones((2, 19)), dtype.float32)
- _cell_graph_executor.compile(model, inputs)
-
-
- def test_sparse_attention():
- model = FixedSparseAttention(batch_size=2,
- seq_length=1024,
- size_per_head=64,
- num_heads=8,
- block_size=64)
- q = Tensor(np.ones((2, 1024, 512)), dtype.float16)
- k = Tensor(np.ones((2, 1024, 512)), dtype.float16)
- v = Tensor(np.ones((2, 1024, 512)), dtype.float16)
- mask = Tensor(np.ones((2, 1024, 1024)), dtype.float32)
- _cell_graph_executor.compile(model, q, k, v, mask)
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