forked from mindspore-Ecosystem/mindspore
336 lines
13 KiB
Python
336 lines
13 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.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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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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def test_multihead_attention_wrong_batch():
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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((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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with pytest.raises(ValueError):
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_cell_graph_executor.compile(model, from_tensor, to_tensor, to_tensor, attention_mask)
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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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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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_cell_graph_executor.compile(model, tensor)
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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'
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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'
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# Force to generate the logger again
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# pylint: disable=W0212
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mindspore.log._global_logger = None
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def teardown_method(self):
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for env in self.log_envs:
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if self.log_envs.get(env, False):
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os.environ[env] = self.log_envs.get(env, "False")
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shutil.rmtree(os.path.join(self.log_path))
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def check_warning_log(self):
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cmd = f'cd {self.log_path} && grep WARNING rank_0/logs/mindspore.log.* |wc -l'
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file_count = os.popen(cmd).read().strip()
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assert file_count == "0"
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def test_cross_entory_no_warning(self):
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"""
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Feature: Test the warning log
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Description: Test a forward compile has no warning error
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Expectation: To compile passed
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"""
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# Force to rebuild the logger
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test_cross_entroy()
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self.check_warning_log()
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def test_transformer_encoder_no_warning(self):
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"""
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Feature: Test the warning log
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Description: Test a forward compile has no warning error
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Expectation: To compile passed
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"""
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# Force to rebuild the logger
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test_transformer_encoder_only()
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self.check_warning_log()
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def test_transformer_decoder_no_warning(self):
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"""
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Feature: Test the warning log
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Description: Test a forward compile has no warning error
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Expectation: To compile passed
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"""
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# Force to rebuild the logger
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test_transformer_decoder()
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self.check_warning_log()
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