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未验证 提交 687ac358 编写于 作者: K kangguangli 提交者： GitHub
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remove test_auto_search_dist_matmul_op.py (#48794)

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python/paddle/fluid/tests/unittests/test_auto_search_dist_matmul_op.py 已删除 100644 → 0
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# 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 paddle
import paddle.fluid.core as core
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.static as static
import paddle.utils as utils
from paddle.distributed.auto_parallel.dist_attribute import (
OperatorDistributedAttribute,
)
from paddle.distributed.auto_parallel.dist_op import DistributedOperator
from paddle.distributed.auto_parallel.operators.common import (
get_distributed_operator_impl_container,
)
from paddle.fluid import layers
paddle.enable_static()
device = "gpu" if core.is_compiled_with_cuda() else "cpu"
class MLPLayer(nn.Layer):
def __init__(
self,
hidden_size=1024,
intermediate_size=4 * 1024,
initializer_range=0.02,
):
super().__init__()
d_model = hidden_size
dim_feedforward = intermediate_size
weight_attr = paddle.ParamAttr(
initializer=nn.initializer.Normal(mean=0.0, std=initializer_range)
)
bias_attr = None
self.linear0 = nn.Linear(
d_model, dim_feedforward, weight_attr, bias_attr=bias_attr
)
self.linear1 = nn.Linear(
dim_feedforward, d_model, weight_attr, bias_attr=bias_attr
)
self.norm = nn.LayerNorm(d_model, epsilon=1e-5)
def forward(self, input):
out = self.norm(input)
out = self.linear0(out)
out = F.gelu(out, approximate=True)
out = self.linear1(out)
return out
def mlp_forward(train_program, start_program):
with static.program_guard(
train_program, start_program
), utils.unique_name.guard():
batch_size = 4
hidden_size = 1024
sqrt_hidden_size = 32
double_hidden_size = 64
input = static.data(name="input", shape=[8, 8, 16], dtype='int32')
input = paddle.reshape(input, [hidden_size])
input = paddle.reshape(input, [sqrt_hidden_size, sqrt_hidden_size])
embedding = paddle.nn.Embedding(2, batch_size, sparse=True)
input = embedding(input)
input = paddle.reshape(input, [hidden_size, batch_size])
input = paddle.transpose(input, perm=[1, 0])
matmulinput = static.data(
name="matmulinput",
shape=[hidden_size, hidden_size],
dtype='float32',
)
input = layers.matmul(x=input, y=matmulinput)
label = static.data(
name="label", shape=[batch_size, 1], dtype='float32'
)
mlp = MLPLayer(
hidden_size=hidden_size,
intermediate_size=4 * hidden_size,
initializer_range=0.02,
)
predict = mlp(input)
error_cost = paddle.nn.functional.square_error_cost(predict, label)
loss = paddle.mean(error_cost)
m = paddle.nn.Softmax()
loss = m(loss)
return loss, train_program, start_program
@unittest.skipIf(True, "to delete later")
class TestCompatible(unittest.TestCase):
def test_matmulv2_matmul_2_compatible(self):
valid_op_dist_attr_list = []
program = paddle.static.Program()
startup_program = paddle.static.Program()
loss, program, start_program = mlp_forward(program, startup_program)
with static.program_guard(
program, start_program
), utils.unique_name.guard():
matmulx3 = static.data(
name="matmulx3", shape=[6, 2, 6], dtype='float32'
)
matmuly3 = static.data(
name="matmuly3", shape=[6, 6], dtype='float32'
)
output1 = paddle.matmul(x=matmulx3, y=matmuly3)
output_1 = layers.matmul(x=matmulx3, y=matmuly3)
matmulx4 = static.data(
name="matmulx4", shape=[6, 6, 2, 6], dtype='float32'
)
matmuly4 = static.data(
name="matmuly4", shape=[6, 6, 6, 6], dtype='float32'
)
output2 = paddle.matmul(x=matmulx4, y=matmuly4)
output_2 = layers.matmul(x=matmulx4, y=matmuly4)
ops = program.global_block().ops
vars = program.global_block().vars
for idx, op in enumerate(ops):
if op.type == 'matmul_v2' or op.type == 'matmul':
dist_op_impl_container = (
get_distributed_operator_impl_container(op.type)
)
impls = dist_op_impl_container.impls
op_dist_attr = OperatorDistributedAttribute()
X = op.input_arg_names[0]
Y = op.input_arg_names[1]
out = op.output_arg_names[0]
if len(vars[X].shape) == 2 and len(vars[Y].shape) == 2:
op_dist_attr.set_input_dims_mapping(X, [-1, -1])
op_dist_attr.set_input_dims_mapping(Y, [-1, -1])
op_dist_attr.set_output_dims_mapping(out, [-1, -1])
self.assertTrue(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [1, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [-1, 1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [1, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [-1, 1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, 1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [1, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
if len(vars[X].shape) == 3 and len(vars[Y].shape) == 2:
op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1])
op_dist_attr.set_input_dims_mapping(Y, [-1, -1])
op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1])
self.assertTrue(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [1, -1, -1])
op_dist_attr.set_input_dims_mapping(X, [-1, -1, 1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [1, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, 1, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
if len(vars[X].shape) == 4 and len(vars[Y].shape) == 4:
op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1, -1])
op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, -1])
op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1, -1])
self.assertTrue(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [0, -1, -1, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [0, -1, -1, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [-1, -1, 0, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, -1, 0, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, 1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, -1, 0, -1])
self.assertFalse(
impls[2].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
def test_matmulv2_matmul_1_compatible(self):
valid_op_dist_attr_list = []
program = paddle.static.Program()
startup_program = paddle.static.Program()
loss, program, start_program = mlp_forward(program, startup_program)
with static.program_guard(
program, start_program
), utils.unique_name.guard():
matmulx3 = static.data(
name="matmulx3", shape=[6, 2, 6], dtype='float32'
)
matmuly3 = static.data(
name="matmuly3", shape=[6, 6], dtype='float32'
)
output1 = paddle.matmul(x=matmulx3, y=matmuly3)
output_1 = layers.matmul(x=matmulx3, y=matmuly3)
matmulx4 = static.data(
name="matmulx4", shape=[6, 6, 6, 6], dtype='float32'
)
matmuly4 = static.data(
name="matmuly4", shape=[6, 6, 6, 6], dtype='float32'
)
output2 = paddle.matmul(x=matmulx4, y=matmuly4)
output_2 = layers.matmul(x=matmulx4, y=matmuly4)
ops = program.global_block().ops
vars = program.global_block().vars
for idx, op in enumerate(ops):
if op.type == 'matmul_v2' or op.type == 'matmul':
dist_op_impl_container = (
get_distributed_operator_impl_container(op.type)
)
impls = dist_op_impl_container.impls
op_dist_attr = OperatorDistributedAttribute()
X = op.input_arg_names[0]
Y = op.input_arg_names[1]
out = op.output_arg_names[0]
if len(vars[X].shape) == 2 and len(vars[Y].shape) == 2:
op_dist_attr.set_input_dims_mapping(X, [-1, 1])
op_dist_attr.set_input_dims_mapping(Y, [1, -1])
op_dist_attr.set_output_dims_mapping(out, [-1, -1])
dist_op = DistributedOperator(op, op_dist_attr)
op_dist_attr.set_output_dims_mapping(out, [1, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [-1, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [-1, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
if len(vars[X].shape) == 3 and len(vars[Y].shape) == 2:
op_dist_attr.set_input_dims_mapping(X, [-1, -1, 1])
op_dist_attr.set_input_dims_mapping(Y, [1, -1])
op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1])
self.assertTrue(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [1, -1, 1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(out, [-1, -1, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, 0, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
if len(vars[X].shape) == 4 and len(vars[Y].shape) == 4:
op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1, 1])
op_dist_attr.set_input_dims_mapping(Y, [-1, -1, 1, -1])
op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1, -1])
self.assertTrue(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [0, -1, -1, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [0, -1, -1, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [-1, -1, 0, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, -1, 0, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, 1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, -1, 0, -1])
self.assertFalse(
impls[1].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
def test_matmulv2_matmul_0_compatible(self):
valid_op_dist_attr_list = []
program = paddle.static.Program()
startup_program = paddle.static.Program()
loss, program, start_program = mlp_forward(program, startup_program)
with static.program_guard(
program, start_program
), utils.unique_name.guard():
matmulx3 = static.data(
name="matmulx3", shape=[6, 2, 6], dtype='float32'
)
matmuly3 = static.data(
name="matmuly3", shape=[6, 6], dtype='float32'
)
output1 = paddle.matmul(x=matmulx3, y=matmuly3)
output_1 = layers.matmul(x=matmulx3, y=matmuly3)
matmulx4 = static.data(
name="matmulx4", shape=[6, 6, 2, 6], dtype='float32'
)
matmuly4 = static.data(
name="matmuly4", shape=[6, 6, 6, 6], dtype='float32'
)
output2 = paddle.matmul(x=matmulx4, y=matmuly4)
output_2 = layers.matmul(x=matmulx4, y=matmuly4)
ops = program.global_block().ops
vars = program.global_block().vars
for idx, op in enumerate(ops):
if op.type == 'matmul_v2' or op.type == 'matmul':
dist_op_impl_container = (
get_distributed_operator_impl_container(op.type)
)
impls = dist_op_impl_container.impls
op_dist_attr = OperatorDistributedAttribute()
X = op.input_arg_names[0]
Y = op.input_arg_names[1]
out = op.output_arg_names[0]
if len(vars[X].shape) == 2 and len(vars[Y].shape) == 2:
op_dist_attr.set_input_dims_mapping(X, [-1, -1])
op_dist_attr.set_input_dims_mapping(Y, [-1, 1])
op_dist_attr.set_output_dims_mapping(out, [-1, 1])
self.assertTrue(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [-1, 1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [1, 1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [0, 0])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [0, -1])
op_dist_attr.set_output_dims_mapping(out, [1, 1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
if len(vars[X].shape) == 3 and len(vars[Y].shape) == 2:
op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1])
op_dist_attr.set_input_dims_mapping(Y, [-1, 1])
op_dist_attr.set_output_dims_mapping(out, [-1, -1, 1])
self.assertTrue(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [-1, 0, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [-1, 1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [-1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [1, -1, 1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, 1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
if len(vars[X].shape) == 4 and len(vars[Y].shape) == 4:
op_dist_attr.set_input_dims_mapping(X, [-1, -1, -1, -1])
op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, 1])
op_dist_attr.set_output_dims_mapping(out, [-1, -1, -1, 1])
self.assertTrue(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [0, -1, -1, 1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [-1, 1, 1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [-1, 1, -1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(X, [-1, -1, 1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [0, -1, -1, 1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, 1, 1, 1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [-1, -1, -1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_output_dims_mapping(out, [-1, -1, 1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
op_dist_attr.set_input_dims_mapping(Y, [-1, -1, 1, -1])
self.assertFalse(
impls[0].is_auto_compatible(
DistributedOperator(op, op_dist_attr)
)
)
if __name__ == "__main__":
unittest.main()
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