wehub-resource-sync
248 lines
8.9 KiB
Python
248 lines
8.9 KiB
Python
# 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 unittest
|
|
|
|
import numpy as np
|
|
from utils import static_guard
|
|
|
|
import paddle
|
|
from paddle.base.framework import in_dygraph_mode
|
|
from paddle.incubate.nn import FusedTransformerEncoderLayer
|
|
from paddle.nn import TransformerEncoderLayer
|
|
|
|
|
|
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
|
|
|
|
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()
|
|
|
|
self.rtol = 1e-3
|
|
# FIXME(limin29): Because there is a problem with the test precision
|
|
# on A100, atol is temporarily set to 1e-2, and it will be
|
|
# changed back after the precision problem is solved.
|
|
self.atol = 1e-2
|
|
if "V100" in paddle.device.cuda.get_device_name():
|
|
self.atol = 1e-4
|
|
|
|
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):
|
|
if in_dygraph_mode():
|
|
return
|
|
paddle.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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
fused_out = fused_encoder(
|
|
paddle.to_tensor(src, stop_gradient=False), attn_mask_tensor
|
|
)
|
|
paddle.autograd.backward([fused_out], [paddle.to_tensor(dout)], True)
|
|
|
|
correct_ffn_str = f'd_model={self.d_model}, dim_feedforward={self.dim_feedforward}, dropout_rate={self.dropout_rate}, epsilon={fused_encoder.ffn._epsilon}, activation={self.activation}, act_dropout_rate={self.dropout_rate}, normalize_before={self.pre_layer_norm}, dtype={self.dtype}'
|
|
self.assertTrue(fused_encoder.ffn.extra_repr(), correct_ffn_str)
|
|
|
|
correct_attn_str = f'embed_dim={self.embed_dim}, num_heads={self.num_heads}, dropout_rate={self.dropout_rate}, attn_dropout_rate={self.dropout_rate}, epsilon={fused_encoder.fused_attn._epsilon}, kdim={None}, vdim={None}, normalize_before={self.pre_layer_norm}, need_weights={False}, dtype={self.dtype}'
|
|
self.assertTrue(fused_encoder.fused_attn.extra_repr(), correct_attn_str)
|
|
|
|
np.testing.assert_allclose(
|
|
fused_out.numpy(), base_out.numpy(), rtol=self.rtol, atol=self.atol
|
|
)
|
|
np.testing.assert_allclose(
|
|
fused_out.grad.numpy(),
|
|
base_out.grad.numpy(),
|
|
rtol=self.rtol,
|
|
atol=self.atol,
|
|
)
|
|
|
|
|
|
class TestFusedTransformerEncoderLayerAct(TestFusedTransformerEncoderLayer):
|
|
def setActivation(self):
|
|
self.activation = 'relu'
|
|
|
|
|
|
class TestFusedTransformerEncoderLayerPreLayerNorm(
|
|
TestFusedTransformerEncoderLayer
|
|
):
|
|
def setPreLayerNorm(self):
|
|
self.pre_layer_norm = True
|
|
|
|
|
|
class TestFusedTransformerEncoderLayerAttnMaskIsNone(
|
|
TestFusedTransformerEncoderLayer
|
|
):
|
|
def setAttnMask(self):
|
|
self.has_attn_mask = False
|
|
|
|
|
|
class TestFusedTransformerEncoderLayerPreLnTrueAttnMaskIsNone(
|
|
TestFusedTransformerEncoderLayer
|
|
):
|
|
def setPreLayerNorm(self):
|
|
self.pre_layer_norm = True
|
|
|
|
def setAttnMask(self):
|
|
self.has_attn_mask = False
|
|
|
|
|
|
class TestPirFusedTransformerEncoderLayer(unittest.TestCase):
|
|
def run_program(self):
|
|
with static_guard():
|
|
paddle.seed(1)
|
|
startup = paddle.static.Program()
|
|
main = paddle.static.Program()
|
|
with paddle.static.program_guard(main, startup):
|
|
enc_input = paddle.rand((2, 4, 128))
|
|
attn_mask = paddle.rand((2, 2, 4, 4))
|
|
encoder_layer = FusedTransformerEncoderLayer(128, 2, 512)
|
|
enc_output = encoder_layer(enc_input, attn_mask)
|
|
|
|
exe = paddle.static.Executor()
|
|
exe.run(startup)
|
|
out = exe.run(feed={}, fetch_list=[enc_output])
|
|
return out
|
|
|
|
def test_pir(self):
|
|
out1 = self.run_program()
|
|
with paddle.pir_utils.IrGuard():
|
|
out2 = self.run_program()
|
|
np.testing.assert_allclose(out1, out2)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|