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[fix]fix the bug of fused_attention and fused_feedforward (PaddlePadd…
…le#36972) * fix bug: 1. atten: set the default value of attn_dropout_rate to None 2. ffn: add activation parameter
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zhangkaihuo
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Nov 16, 2021
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188 changes: 188 additions & 0 deletions
188
python/paddle/fluid/tests/unittests/test_fused_transformer_encoder_layer.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,188 @@ | ||
| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # 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. | ||
| import numpy as np | ||
|
|
||
| import paddle | ||
| from paddle.incubate.nn import FusedTransformerEncoderLayer | ||
| from paddle.nn import TransformerEncoderLayer | ||
| from paddle.fluid.framework import default_main_program | ||
| import unittest | ||
|
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|
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| class TestFusedTransformerEncoderLayer(unittest.TestCase): | ||
| def setActivation(self): | ||
| self.activation = 'gelu' | ||
|
|
||
| def setPreLayerNorm(self): | ||
| self.pre_layer_norm = False | ||
|
|
||
| def setAttnMask(self): | ||
| self.has_attn_mask = True | ||
|
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||
| def setUp(self): | ||
| self.batch_size = np.random.randint(1, 8) | ||
| self.query_length = np.random.randint(1, 128) | ||
| self.nhead = 16 | ||
| self.head_dim = 4 | ||
| self.num_heads = self.nhead | ||
| self.d_model = self.head_dim * self.num_heads | ||
| self.embed_dim = self.d_model | ||
| self.dim_feedforward = np.random.randint(1, 32) | ||
| self.dropout_rate = 0 | ||
| self.attn_dropout_rate = None | ||
| self.act_dropout_rate = None | ||
| self.attn_mask_type = np.float64 | ||
| self.key_length = self.query_length | ||
| self.dtype = 'float32' | ||
| self.setActivation() | ||
| self.setPreLayerNorm() | ||
| self.setAttnMask() | ||
|
|
||
| def fused_weight(self, weight, num_head): | ||
| a = paddle.transpose(weight, perm=[1, 0]) | ||
| return paddle.reshape( | ||
| a, shape=[1, num_head, int(a.shape[0] / num_head), a.shape[1]]) | ||
|
|
||
| def fused_qkv(self, q, k, v, num_head): | ||
| fq = self.fused_weight(q, num_head) | ||
| fk = self.fused_weight(k, num_head) | ||
| fv = self.fused_weight(v, num_head) | ||
| return paddle.concat(x=[fq, fk, fv], axis=0) | ||
|
|
||
| def test_out(self): | ||
| default_main_program().random_seed = 42 | ||
| base_encoder = TransformerEncoderLayer( | ||
| self.d_model, self.nhead, self.dim_feedforward, self.dropout_rate, | ||
| self.activation, self.attn_dropout_rate, self.act_dropout_rate, | ||
| self.pre_layer_norm) | ||
| src = np.random.rand(self.batch_size, self.query_length, | ||
| self.embed_dim).astype(self.dtype) | ||
|
|
||
| if self.has_attn_mask: | ||
| attn_mask = np.ones( | ||
| (self.batch_size, self.num_heads, self.query_length, | ||
| self.key_length), | ||
| dtype=self.attn_mask_type) | ||
| attn_mask_tensor = paddle.to_tensor(attn_mask) | ||
| else: | ||
| attn_mask = None | ||
| attn_mask_tensor = None | ||
|
|
||
| dout = np.random.random(src.shape).astype(self.dtype) | ||
|
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| base_out = base_encoder( | ||
| paddle.to_tensor( | ||
| src, stop_gradient=False), attn_mask_tensor) | ||
| paddle.autograd.backward([base_out], [paddle.to_tensor(dout)], True) | ||
|
|
||
| fused_encoder = FusedTransformerEncoderLayer( | ||
| self.d_model, self.nhead, self.dim_feedforward, self.dropout_rate, | ||
| self.activation, self.attn_dropout_rate, self.act_dropout_rate, | ||
| self.pre_layer_norm) | ||
|
|
||
| fused_encoder.ffn._linear1_weight.set_value(base_encoder.linear1.weight) | ||
| fused_encoder.ffn._linear1_bias.set_value(base_encoder.linear1.bias) | ||
| fused_encoder.ffn._linear2_weight.set_value(base_encoder.linear2.weight) | ||
| fused_encoder.ffn._linear2_bias.set_value(base_encoder.linear2.bias) | ||
| if self.pre_layer_norm: | ||
| fused_encoder.ffn._ln1_scale.set_value(base_encoder.norm2.weight) | ||
| fused_encoder.ffn._ln1_bias.set_value(base_encoder.norm2.bias) | ||
| else: | ||
| fused_encoder.ffn._ln2_scale.set_value(base_encoder.norm2.weight) | ||
| fused_encoder.ffn._ln2_bias.set_value(base_encoder.norm2.bias) | ||
|
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||
| fused_encoder.fused_attn.linear_weight.set_value( | ||
| base_encoder.self_attn.out_proj.weight) | ||
| fused_encoder.fused_attn.linear_bias.set_value( | ||
| base_encoder.self_attn.out_proj.bias) | ||
| if self.pre_layer_norm: | ||
| fused_encoder.fused_attn.pre_ln_scale.set_value( | ||
| base_encoder.norm1.weight) | ||
| fused_encoder.fused_attn.pre_ln_bias.set_value( | ||
| base_encoder.norm1.bias) | ||
| else: | ||
| fused_encoder.fused_attn.ln_scale.set_value( | ||
| base_encoder.norm1.weight) | ||
| fused_encoder.fused_attn.ln_bias.set_value(base_encoder.norm1.bias) | ||
|
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||
| q = base_encoder.self_attn.q_proj.weight | ||
| q_bias = base_encoder.self_attn.q_proj.bias | ||
| k = base_encoder.self_attn.k_proj.weight | ||
| k_bias = base_encoder.self_attn.k_proj.bias | ||
| v = base_encoder.self_attn.v_proj.weight | ||
| v_bias = base_encoder.self_attn.v_proj.bias | ||
| qkv_weight = self.fused_qkv(q, k, v, self.num_heads) | ||
| fused_encoder.fused_attn.qkv_weight.set_value(qkv_weight) | ||
|
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||
| tmp = paddle.concat(x=[q_bias, k_bias, v_bias], axis=0) | ||
| qkv_bias = paddle.reshape( | ||
| tmp, | ||
| shape=[3, self.num_heads, int(tmp.shape[0] / 3 / self.num_heads)]) | ||
| fused_encoder.fused_attn.qkv_bias.set_value(qkv_bias) | ||
|
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| fused_out = fused_encoder( | ||
| paddle.to_tensor( | ||
| src, stop_gradient=False), attn_mask_tensor) | ||
| paddle.autograd.backward([fused_out], [paddle.to_tensor(dout)], True) | ||
|
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| correct_ffn_str = 'd_model={}, dim_feedforward={}, dropout_rate={}, epsilon={}, activation={}, act_dropout_rate={}, normalize_before={}, dtype={}'.format( | ||
| self.d_model, self.dim_feedforward, self.dropout_rate, | ||
| fused_encoder.ffn._epsilon, self.activation, self.dropout_rate, | ||
| self.pre_layer_norm, self.dtype) | ||
| self.assertTrue(fused_encoder.ffn.extra_repr(), correct_ffn_str) | ||
|
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| correct_attn_str = 'embed_dim={}, num_heads={}, dropout_rate={}, attn_dropout_rate={}, epsilon={}, kdim={}, vdim={}, normalize_before={}, need_weights={}, dtype={}'.format( | ||
| self.embed_dim, self.num_heads, self.dropout_rate, | ||
| self.dropout_rate, fused_encoder.fused_attn._epsilon, None, None, | ||
| self.pre_layer_norm, False, self.dtype) | ||
| self.assertTrue(fused_encoder.fused_attn.extra_repr(), correct_attn_str) | ||
|
|
||
| np.testing.assert_allclose( | ||
| fused_out.numpy(), base_out.numpy(), rtol=1e-3, atol=1e-4) | ||
| self.assertTrue( | ||
| np.allclose( | ||
| fused_out.grad.numpy(), | ||
| base_out.grad.numpy(), | ||
| rtol=1e-3, | ||
| atol=1e-4)) | ||
|
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| class TestFusedTransformerEncoderLayerAct(TestFusedTransformerEncoderLayer): | ||
| def setActivation(self): | ||
| self.activation = 'relu' | ||
|
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||
|
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| class TestFusedTransformerEncoderLayerPreLayerNorm( | ||
| TestFusedTransformerEncoderLayer): | ||
| def setPreLayerNorm(self): | ||
| self.pre_layer_norm = True | ||
|
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|
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| class TestFusedTransformerEncoderLayerAttnMaskIsNone( | ||
| TestFusedTransformerEncoderLayer): | ||
| def setAttnMask(self): | ||
| self.has_attn_mask = False | ||
|
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|
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| class TestFusedTransformerEncoderLayerPreLnTrueAttnMaskIsNone( | ||
| TestFusedTransformerEncoderLayer): | ||
| def setPreLayerNorm(self): | ||
| self.pre_layer_norm = True | ||
|
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| def setAttnMask(self): | ||
| self.has_attn_mask = False | ||
|
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|
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| if __name__ == "__main__": | ||
| unittest.main() |
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