forked from mindspore-Ecosystem/mindspore
624 lines
26 KiB
Python
624 lines
26 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test transformer"""
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import os
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import shutil
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import numpy as np
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import pytest
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import mindspore
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from mindspore import Tensor
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from mindspore.common import dtype
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from mindspore.ops import operations as ops
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from mindspore.parallel.nn import MultiHeadAttention, FeedForward, TransformerEncoderLayer, TransformerEncoder, \
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TransformerDecoder, TransformerDecoderLayer, Transformer, CrossEntropyLoss, AttentionMask, FixedSparseAttention
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from mindspore.common.api import _cell_graph_executor
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class MyActivation(mindspore.nn.Cell):
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def __init__(self):
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super(MyActivation, self).__init__()
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self.add = ops.Add()
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def construct(self, x):
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return self.add(x, 0.1)
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def activation_shard(self, parallel_config):
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self.add.shard(((parallel_config.data_parallel, parallel_config.model_parallel), ()))
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class MyActivationNoShard(mindspore.nn.Cell):
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def __init__(self):
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super(MyActivationNoShard, self).__init__()
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self.add = ops.Add()
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def construct(self, x):
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return self.add(x, 0.1)
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def test_transformer_encoder_only():
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model = Transformer(batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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encoder_layers=2,
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decoder_layers=0,
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hidden_size=64,
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ffn_hidden_size=64)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
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_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
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def test_transformer_encoder_log_softmax():
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with pytest.raises(ValueError):
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model = Transformer(batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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encoder_layers=2,
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decoder_layers=0,
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hidden_act='logsoftmax',
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hidden_size=64,
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ffn_hidden_size=64)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
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_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
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def test_transformer_encoder_leakyrelu():
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model = Transformer(batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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encoder_layers=2,
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decoder_layers=0,
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hidden_act='leakyrelu',
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hidden_size=64,
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ffn_hidden_size=64)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
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_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
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def test_transformer_encoder_logsigmoid():
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model = Transformer(batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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encoder_layers=2,
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decoder_layers=0,
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hidden_act='logsigmoid',
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hidden_size=64,
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ffn_hidden_size=64)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
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_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask)
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def test_encoder_and_decoder():
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model = Transformer(batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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encoder_layers=1,
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decoder_layers=2,
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hidden_size=64,
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ffn_hidden_size=64)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
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decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
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memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
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_cell_graph_executor.compile(model, encoder_input_value, encoder_input_mask,
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decoder_input_value,
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decoder_input_mask,
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memory_mask)
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def test_transformer_encoder():
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model = TransformerEncoder(batch_size=2,
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seq_length=16,
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num_layers=2,
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hidden_size=8,
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ffn_hidden_size=64,
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num_heads=2)
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encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
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_cell_graph_executor.compile(model,
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encoder_input_value,
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encoder_input_mask)
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def test_transformer_encoder_layer():
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model = TransformerEncoderLayer(batch_size=2, hidden_size=8, ffn_hidden_size=64, seq_length=16,
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num_heads=2)
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encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
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_cell_graph_executor.compile(model,
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encoder_input_value,
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encoder_input_mask)
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def test_transformer_encoder_layer_post_ture():
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model = TransformerEncoderLayer(batch_size=2,
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seq_length=16,
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hidden_size=8, ffn_hidden_size=64,
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num_heads=2, post_layernorm_residual=True)
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encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
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_cell_graph_executor.compile(model,
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encoder_input_value,
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encoder_input_mask)
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def test_transformer_decoder():
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model = TransformerDecoder(num_layers=1,
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batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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hidden_size=64,
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ffn_hidden_size=64,
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num_heads=2)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
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decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
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memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
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_cell_graph_executor.compile(model, decoder_input_value, decoder_input_mask,
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encoder_input_value,
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memory_mask)
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def test_transformer_decoder_layer():
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model = TransformerDecoderLayer(
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batch_size=2,
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src_seq_length=20,
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tgt_seq_length=10,
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hidden_size=64,
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ffn_hidden_size=64,
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num_heads=2)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
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decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
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memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
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_cell_graph_executor.compile(model, decoder_input_value, decoder_input_mask,
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encoder_input_value,
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memory_mask)
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def test_multihead_attention():
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model = MultiHeadAttention(hidden_size=15,
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src_seq_length=20,
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tgt_seq_length=20,
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batch_size=2,
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num_heads=3)
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from_tensor = Tensor(np.ones((2, 20, 15)), dtype.float32)
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to_tensor = Tensor(np.ones((2, 20, 15)), dtype.float16)
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attention_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
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_cell_graph_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
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@pytest.mark.parametrize('batch_size', [1, 2, None, 4])
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def test_multihead_attention_wrong_batch(batch_size):
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"""
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Feature: Test MultiHeadAttention with wrong batch for training
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Description: Test the batch size to be any int or None
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Expectation: No exception
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"""
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model = MultiHeadAttention(hidden_size=15,
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src_seq_length=20,
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tgt_seq_length=20,
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batch_size=batch_size,
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num_heads=3)
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from_tensor = Tensor(np.ones((3, 20, 15)), dtype.float32)
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to_tensor = Tensor(np.ones((3, 20, 15)), dtype.float16)
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attention_mask = Tensor(np.ones((3, 20, 20)), dtype.float16)
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_cell_graph_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
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@pytest.mark.parametrize('from_tensor,to_tensor', [(Tensor(np.ones((20, 15)), dtype.float32),
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Tensor(np.ones((20, 15)), dtype.float16)),
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(Tensor(np.ones((3, 20, 15)), dtype.float32),
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Tensor(np.ones((3, 20, 15)), dtype.float16))])
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def test_multihead_attention_no_mask_2d_or_3d_shape(from_tensor, to_tensor):
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"""
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Feature: Test MultiHeadAttention no mask
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Description: Test MultiHeadAttention no mask and 2d as inputs.
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Expectation: No exception
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"""
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model = MultiHeadAttention(hidden_size=15,
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src_seq_length=20,
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tgt_seq_length=20,
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batch_size=None,
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num_heads=3)
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_cell_graph_executor.compile(model, from_tensor, to_tensor, to_tensor, None)
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def test_multihead_attention_fp32_dtype():
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"""
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Feature: Test MultiHeadAttention with float32 as compute dtype
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Description: Test using float32 as computation for linear layer.
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Expectation: No exception
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"""
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model = MultiHeadAttention(hidden_size=15,
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src_seq_length=20,
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tgt_seq_length=20,
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compute_dtype=dtype.float32,
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batch_size=2,
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num_heads=3)
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from_tensor = Tensor(np.ones((2, 20, 15)), dtype.float32)
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to_tensor = Tensor(np.ones((2, 20, 15)), dtype.float32)
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attention_mask = Tensor(np.ones((2, 20, 20)), dtype.float32)
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_cell_graph_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
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@pytest.mark.parametrize('batch_size', [1, 2, None, 4])
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def test_transformerencoder_wrong_batch(batch_size):
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"""
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Feature: Test TransformerEncoderLayer with wrong batch for training
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Description: Test the batch size to be any int or None
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Expectation: No exception
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"""
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model = TransformerEncoderLayer(batch_size=batch_size, hidden_size=8, ffn_hidden_size=64, seq_length=16,
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num_heads=2)
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encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 16, 16)), dtype.float16)
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model(encoder_input_value, encoder_input_mask)
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@pytest.mark.parametrize('attention_mask', [Tensor(np.ones((2, 16, 16)), dtype.float16),
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None])
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def test_transformerencoder_no_mask(attention_mask):
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"""
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Feature: Test TransformerEncoderLayer with no mask
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Description: Test the attention mask is None
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Expectation: No exception
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"""
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model = TransformerEncoderLayer(batch_size=None, hidden_size=8, ffn_hidden_size=64, seq_length=16,
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num_heads=2)
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encoder_input_value = Tensor(np.ones((2, 16, 8)), dtype.float32)
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model(encoder_input_value, attention_mask)
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@pytest.mark.parametrize('shape', [(2, 16, 8), (32, 8)])
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def test_transformerencoder_2d_or_3d_shape(shape):
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"""
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Feature: Test TransformerEncoderLayer with 2d or 3d inputs
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Description: Test the attention mask is None
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Expectation: No exception
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"""
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model = TransformerEncoderLayer(batch_size=None, hidden_size=8, ffn_hidden_size=64, seq_length=16,
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num_heads=2)
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encoder_input_value = Tensor(np.ones(shape), dtype.float32)
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model(encoder_input_value, None)
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@pytest.mark.parametrize('batch_size', [1, 2, None, 4])
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def test_transformerdecoder_wrong_batch(batch_size):
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"""
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Feature: Test TransformerDecoderLayer with wrong batch for training
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Description: Test the batch size to be any int or None
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Expectation: No exception
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"""
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model = TransformerDecoderLayer(batch_size=batch_size, hidden_size=64, ffn_hidden_size=64, num_heads=2,
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src_seq_length=20, tgt_seq_length=10)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
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decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
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memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
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model(decoder_input_value, decoder_input_mask, encoder_input_value, memory_mask)
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@pytest.mark.parametrize('decoder_input_mask,memory_mask',
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[(None, None), (Tensor(np.ones((2, 10, 10)), dtype.float16), None),
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(None, Tensor(np.ones((2, 10, 20)), dtype.float16))])
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def test_transformerdecoder_mask(decoder_input_mask, memory_mask):
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"""
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Feature: Test TransformerDecoderLayer with empty mask
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Description: Test the mask is None
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Expectation: No exception
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"""
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model = TransformerDecoderLayer(batch_size=4, hidden_size=64, ffn_hidden_size=64, num_heads=2,
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src_seq_length=20, tgt_seq_length=10)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
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model(decoder_input_value, decoder_input_mask, encoder_input_value, memory_mask)
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@pytest.mark.parametrize('activation',
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[MyActivation, MyActivationNoShard])
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def test_transformerdecoder_custom_activation(activation):
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"""
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Feature: Test TransformerDecoderLayer custom activation
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Description: Test TransformerDecoderLayer custom activation
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Expectation: No exception
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"""
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model = TransformerDecoderLayer(batch_size=4, hidden_size=64, ffn_hidden_size=64, num_heads=2,
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hidden_act=activation,
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src_seq_length=20, tgt_seq_length=10)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
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model(decoder_input_value, None, encoder_input_value, None)
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@pytest.mark.parametrize('activation',
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[0, None, -1])
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def test_transformerdecoder_wrong_activation(activation):
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"""
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Feature: Test TransformerDecoderLayer with wrong activation
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Description: Test TransformerDecoderLayer with wrong activation type
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Expectation: No exception
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"""
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with pytest.raises(TypeError):
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TransformerDecoderLayer(batch_size=4, hidden_size=64, ffn_hidden_size=64, num_heads=2,
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hidden_act=activation,
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src_seq_length=20, tgt_seq_length=10)
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@pytest.mark.parametrize('encoder_shape,decoder_shape', [((2, 20, 64), (2, 10, 64)),
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((20, 64), (10, 64))])
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def test_transformerdecoder_2d_or_3d_shape(encoder_shape, decoder_shape):
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"""
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Feature: Test TransformerDecoderLayer with 2d or 3d inputs
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Description: Test the attention mask is None
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Expectation: No exception
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"""
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model = TransformerDecoderLayer(batch_size=None, hidden_size=64, ffn_hidden_size=64, num_heads=2,
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src_seq_length=20, tgt_seq_length=10)
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encoder_input_value = Tensor(np.ones(encoder_shape), dtype.float32)
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decoder_input_value = Tensor(np.ones(decoder_shape), dtype.float32)
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model(decoder_input_value, None, encoder_input_value, None)
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@pytest.mark.parametrize('hidden_act', [MyActivation, "relu"])
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def test_transformer_hidden_act(hidden_act):
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"""
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Feature: Test Transformer hidden activation with activation or None
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Description: Test the transformer hidden activation
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Expectation: No exception
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"""
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model = Transformer(batch_size=2, encoder_layers=1, decoder_layers=2, hidden_size=64,
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hidden_act=hidden_act,
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ffn_hidden_size=64, src_seq_length=20, tgt_seq_length=10)
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encoder_input_value = Tensor(np.ones((2, 20, 64)), dtype.float32)
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encoder_input_mask = Tensor(np.ones((2, 20, 20)), dtype.float16)
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decoder_input_value = Tensor(np.ones((2, 10, 64)), dtype.float32)
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decoder_input_mask = Tensor(np.ones((2, 10, 10)), dtype.float16)
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memory_mask = Tensor(np.ones((2, 10, 20)), dtype.float16)
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model(encoder_input_value, encoder_input_mask, decoder_input_value,
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decoder_input_mask, memory_mask)
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def test_transformer_hidden_act_with_wrong_hidden_act_wrong_lambda_func():
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"""
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Feature: Test Transformer hidden activation with activation or None
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Description: Test the transformer hidden activation
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Expectation: No exception
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"""
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with pytest.raises(TypeError):
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Transformer(batch_size=2, encoder_layers=1, decoder_layers=2, hidden_size=64,
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hidden_act=lambda x: x,
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ffn_hidden_size=64, src_seq_length=20, tgt_seq_length=10)
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def test_transformer_hidden_act_with_wrong_hidden_act_wrong_str():
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"""
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Feature: Test Transformer hidden activation with wrong activation
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Description: Test the transformer hidden activation
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Expectation: No exception
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"""
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with pytest.raises(KeyError):
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Transformer(batch_size=2, encoder_layers=1, decoder_layers=2, hidden_size=64,
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hidden_act="no_string",
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ffn_hidden_size=64, src_seq_length=20, tgt_seq_length=10)
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def test_feedforward_layer():
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model = FeedForward(hidden_size=15,
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ffn_hidden_size=30,
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dropout_rate=0.1,
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hidden_act='relu')
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tensor = Tensor(np.ones((2, 20, 15)), dtype.float32)
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|
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_cell_graph_executor.compile(model, tensor)
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|
|
|
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def test_cross_entroy():
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model = CrossEntropyLoss()
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logits = Tensor(np.array([[3, 5, 6, 9, 12, 33, 42, 12, 32, 72]]), dtype.float32)
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labels_np = np.array([1]).astype(np.int32)
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input_mask = Tensor(np.ones(1).astype(np.float32))
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labels = Tensor(labels_np)
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|
_cell_graph_executor.compile(model, logits, labels, input_mask)
|
|
|
|
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def test_attention_mask():
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model = AttentionMask(seq_length=19)
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inputs = Tensor(np.ones((2, 19)), dtype.float32)
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|
_cell_graph_executor.compile(model, inputs)
|
|
|
|
|
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def test_sparse_attention():
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model = FixedSparseAttention(batch_size=2,
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seq_length=1024,
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size_per_head=64,
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num_heads=8,
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block_size=64)
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q = Tensor(np.ones((2, 1024, 512)), dtype.float16)
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k = Tensor(np.ones((2, 1024, 512)), dtype.float16)
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v = Tensor(np.ones((2, 1024, 512)), dtype.float16)
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mask = Tensor(np.ones((2, 1024, 1024)), dtype.float32)
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_cell_graph_executor.compile(model, q, k, v, mask)
|
|
|
|
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class TestBasicWarningValidator:
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log_envs = dict(GLOG_v=None, GLOG_logtostderr=None, GLOG_log_dir=None, logger_maxBytes=None,
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logger_backupCount=None)
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log_path = './TestBasicWarningValidator'
|
|
|
|
def setup_method(self):
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|
for env in self.log_envs:
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self.log_envs[env] = os.environ.get(env, None)
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|
os.environ['GLOG_log_dir'] = self.log_path
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|
os.environ['GLOG_v'] = '1'
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|
os.environ['GLOG_logtostderr'] = '0'
|
|
# Force to generate the logger again
|
|
# pylint: disable=W0212
|
|
mindspore.log.GLOBAL_LOGGER = None
|
|
|
|
def teardown_method(self):
|
|
for env in self.log_envs:
|
|
if self.log_envs.get(env, False):
|
|
os.environ[env] = self.log_envs.get(env, "False")
|
|
shutil.rmtree(os.path.join(self.log_path))
|
|
|
|
def check_warning_log(self):
|
|
cmd = f'cd {self.log_path} && grep WARNING rank_0/logs/mindspore.log.* |wc -l'
|
|
file_count = os.popen(cmd).read().strip()
|
|
assert file_count == "0"
|
|
|
|
def test_cross_entory_no_warning(self):
|
|
"""
|
|
Feature: Test the warning log
|
|
Description: Test a forward compile has no warning error
|
|
Expectation: To compile passed
|
|
"""
|
|
# Force to rebuild the logger
|
|
test_cross_entroy()
|
|
self.check_warning_log()
|
|
|
|
@pytest.mark.skip(reason="random failures")
|
|
def test_transformer_encoder_no_warning(self):
|
|
"""
|
|
Feature: Test the warning log
|
|
Description: Test a forward compile has no warning error
|
|
Expectation: To compile passed
|
|
"""
|
|
# Force to rebuild the logger
|
|
test_transformer_encoder_only()
|
|
self.check_warning_log()
|
|
|
|
def test_transformer_decoder_no_warning(self):
|
|
"""
|
|
Feature: Test the warning log
|
|
Description: Test a forward compile has no warning error
|
|
Expectation: To compile passed
|
|
"""
|
|
# Force to rebuild the logger
|
|
test_transformer_decoder()
|
|
self.check_warning_log()
|
|
|
|
|
|
def test_attention_with_wrong_batch_3d_inputs():
|
|
"""
|
|
Feature: Test Transformer batch error when the input's batch size is different
|
|
Description: Test the input's batch size is different between the tensors. The input is 3d
|
|
Expectation: Raise a reshape error exception
|
|
"""
|
|
model = MultiHeadAttention(hidden_size=15, src_seq_length=20, tgt_seq_length=20,
|
|
batch_size=None, num_heads=3)
|
|
from_tensor = Tensor(np.ones((3, 20, 15)), dtype.float32)
|
|
to_tensor = Tensor(np.ones((5, 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_attention_with_wrong_batch_2d_inputs():
|
|
"""
|
|
Feature: Test Transformer batch error when the input's batch size is different
|
|
Description: Test the input's batch size is different between the tensors. The inputs is 2d
|
|
Expectation: Raise a reshape error exception
|
|
"""
|
|
model = MultiHeadAttention(hidden_size=15, src_seq_length=20, tgt_seq_length=20,
|
|
batch_size=None, num_heads=3)
|
|
from_tensor = Tensor(np.ones((60, 15)), dtype.float32)
|
|
to_tensor = Tensor(np.ones((100, 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_incremental_prediction_first_iterator():
|
|
"""
|
|
Feature: Test MultiHeadAttention with incremental prediction
|
|
Description: Test MultiHeadAttention with incremental prediction in the first iterator
|
|
Expectation: No Expectation
|
|
"""
|
|
# Step 1: set is_first_iteration=True, and input the full sequence length's state.
|
|
# We need to prepare the memory parameters for saving key and value states firstly.
|
|
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)
|
|
key_past = Tensor(np.zeros(shape=(2, 3, 5, 20)), dtype.float16)
|
|
value_past = Tensor(np.zeros(shape=(2, 3, 20, 5)), dtype.float16)
|
|
batch_valid_length = Tensor(np.ones((2,)), dtype.int32)
|
|
|
|
model = MultiHeadAttention(batch_size=2, hidden_size=15, src_seq_length=20, tgt_seq_length=20,
|
|
num_heads=3, use_past=True)
|
|
model.add_flags_recursive(is_first_iteration=True)
|
|
model(from_tensor, to_tensor, to_tensor, attention_mask, key_past, value_past,
|
|
batch_valid_length)
|
|
|
|
|
|
def test_incremental_prediction_second_iterator():
|
|
"""
|
|
Feature: Test MultiHeadAttention with incremental prediction
|
|
Description: Test MultiHeadAttention with incremental prediction in the second iterator
|
|
Expectation: No Expectation
|
|
"""
|
|
model = MultiHeadAttention(batch_size=2, hidden_size=15, src_seq_length=20, tgt_seq_length=20,
|
|
num_heads=3, use_past=True)
|
|
key_past = Tensor(np.zeros(shape=(2, 3, 5, 20)), dtype.float16)
|
|
value_past = Tensor(np.zeros(shape=(2, 3, 20, 5)), dtype.float16)
|
|
batch_valid_length = Tensor(np.ones((2,)), dtype.int32)
|
|
# Set is_first_iteration=True to generate the full memory states
|
|
from_tensor = Tensor(np.ones((2, 1, 15)), dtype.float32)
|
|
to_tensor = Tensor(np.ones((2, 1, 15)), dtype.float16)
|
|
attention_mask = Tensor(np.ones((2, 1, 20)), dtype.float16)
|
|
# Step 2: set is_first_iteration=False, and pass the single word to run the prediction rather than the
|
|
# full sequence.
|
|
model.add_flags_recursive(is_first_iteration=False)
|
|
model(from_tensor, to_tensor, to_tensor, attention_mask, key_past, value_past,
|
|
batch_valid_length)
|