You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
120 lines
4.0 KiB
120 lines
4.0 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# 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.
|
|
# ============================================================================
|
|
""" test distribute predict """
|
|
import numpy as np
|
|
import pytest
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor, Model
|
|
from mindspore.ops import operations as P
|
|
from mindspore import context
|
|
from mindspore.parallel._utils import _infer_rank_list
|
|
|
|
|
|
class Net(nn.Cell):
|
|
"""Net definition"""
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.fc1 = nn.Dense(128, 768, activation='relu')
|
|
self.fc2 = nn.Dense(128, 768, activation='relu')
|
|
self.fc3 = nn.Dense(128, 768, activation='relu')
|
|
self.fc4 = nn.Dense(768, 768, activation='relu')
|
|
self.relu4 = nn.ReLU()
|
|
self.relu5 = nn.ReLU()
|
|
self.transpose = P.Transpose()
|
|
self.matmul1 = P.MatMul()
|
|
self.matmul2 = P.MatMul()
|
|
|
|
def construct(self, x):
|
|
q = self.fc1(x)
|
|
k = self.fc2(x)
|
|
v = self.fc3(x)
|
|
k = self.transpose(k, (1, 0))
|
|
c = self.relu4(self.matmul1(q, k))
|
|
s = self.relu5(self.matmul2(c, v))
|
|
s = self.fc4(s)
|
|
return s
|
|
|
|
|
|
def test_distribute_predict():
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
|
|
inputs = Tensor(np.ones([32, 128]).astype(np.float32))
|
|
net = Net()
|
|
model = Model(net)
|
|
predict_map = model.infer_predict_layout(inputs)
|
|
output = model.predict(inputs)
|
|
context.reset_auto_parallel_context()
|
|
return predict_map, output
|
|
|
|
|
|
def test_edge_case():
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
inputs = Tensor(np.ones([32, 48]).astype(np.float32))
|
|
net = Net()
|
|
model = Model(net)
|
|
with pytest.raises(RuntimeError):
|
|
model.infer_predict_layout(inputs)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
with pytest.raises(RuntimeError):
|
|
model.infer_predict_layout(inputs)
|
|
context.set_auto_parallel_context(full_batch=True, enable_parallel_optimizer=True)
|
|
with pytest.raises(RuntimeError):
|
|
model.predict(inputs)
|
|
|
|
|
|
# standalone predict
|
|
def test_infer_rank_list1():
|
|
train_map = {'weight': [[4, 8], [-1, 0]]}
|
|
predict_map = None
|
|
rank_list = _infer_rank_list(train_map, predict_map)["weight"]
|
|
assert list(rank_list[0]) == [0, 1, 2, 3, 4, 5, 6, 7]
|
|
assert rank_list[1] is False
|
|
|
|
|
|
# similar layout: gpt3 prediction mode
|
|
def test_infer_rank_list2():
|
|
train_map = {'weight': [[4, 8], [-1, 0]]}
|
|
predict_map = {'weight': [[8], [-1, 0]]}
|
|
rank_list = _infer_rank_list(train_map, predict_map)
|
|
expect_map = {'weight': ([0], True)}
|
|
assert rank_list == expect_map
|
|
|
|
|
|
# same layout
|
|
def test_infer_rank_list3():
|
|
train_map = {'weight': [[4, 8], [-1, 0]]}
|
|
predict_map = {'weight': [[4, 8], [-1, 0]]}
|
|
rank_list = _infer_rank_list(train_map, predict_map)
|
|
expect_map = {'weight': ([0], True)}
|
|
assert rank_list == expect_map
|
|
|
|
|
|
# totally different layout
|
|
def test_infer_rank_list4():
|
|
train_map = {'weight': [[4, 8], [-1, 0]]}
|
|
predict_map = {'weight': [[2, 2], [1, 0]]}
|
|
rank_list = _infer_rank_list(train_map, predict_map)["weight"]
|
|
assert list(rank_list[0]) == [0, 1, 2, 3, 4, 5, 6, 7]
|
|
assert rank_list[1] is False
|
|
|
|
|
|
# full shape ckpt
|
|
def test_infer_rank_list5():
|
|
train_map = {'weight': [[8], [-1, -1]]}
|
|
predict_map = {'weight': [[2, 2], [1, 0]]}
|
|
rank_list = _infer_rank_list(train_map, predict_map)
|
|
expect_map = {'weight': ([0], False)}
|
|
assert rank_list == expect_map
|