forked from mindspore-Ecosystem/mindspore
225 lines
7.9 KiB
Python
225 lines
7.9 KiB
Python
# Copyright 2019 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.
|
|
# ============================================================================
|
|
import numpy as np
|
|
|
|
import mindspore as ms
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore import context
|
|
from mindspore.common.api import _cell_graph_executor
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops import operations as P
|
|
from tests.ut.python.ops.test_math_ops import VirtualLoss
|
|
|
|
|
|
grad_all = C.GradOperation(get_all=True)
|
|
|
|
|
|
class NetWithLoss(nn.Cell):
|
|
def __init__(self, network):
|
|
super(NetWithLoss, self).__init__()
|
|
self.loss = VirtualLoss()
|
|
self.network = network
|
|
|
|
def construct(self, x, y):
|
|
predict = self.network(x, y)
|
|
return self.loss(predict)
|
|
|
|
|
|
class GradWrap(nn.Cell):
|
|
def __init__(self, network):
|
|
super(GradWrap, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, x, y):
|
|
return grad_all(self.network)(x, y)
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self, axis=0, strategy1=None, strategy2=None, shape=None, target=""):
|
|
super().__init__()
|
|
if shape is None:
|
|
shape = [64, 64]
|
|
self.gatherv2 = P.SparseGatherV2().shard(strategy1).add_prim_attr("primitive_target", target)
|
|
self.mul = P.Mul().shard(strategy2)
|
|
self.index = Tensor(np.ones(shape), dtype=ms.int32)
|
|
self.axis = axis
|
|
|
|
def construct(self, x, y):
|
|
out = self.gatherv2(x, self.index, self.axis)
|
|
out = self.mul(out, y)
|
|
return out
|
|
|
|
|
|
def test_gatherv2_semi_auto0():
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((8, 1), (1, 1))
|
|
strategy2 = ((4, 2, 1), (4, 2, 1))
|
|
net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
|
|
net.set_auto_parallel()
|
|
|
|
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_semi_auto1():
|
|
"""
|
|
Feature: distribute operator SparseGatherV2 in auto parallel.
|
|
Description: gather net with strategy in semi auto parallel, gather axis is 1.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((1, 8), (1, 1))
|
|
strategy2 = ((4, 2, 1), (4, 2, 1))
|
|
net = GradWrap(NetWithLoss(Net(1, strategy1, strategy2)))
|
|
net.set_auto_parallel()
|
|
|
|
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_semi_auto2():
|
|
"""
|
|
Feature: distribute operator SparseGatherV2 in auto parallel.
|
|
Description: gather net with strategy in semi auto parallel, gather axis is 1.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((8, 1), (1, 1))
|
|
strategy2 = ((4, 2, 1), (4, 2, 1))
|
|
net = GradWrap(NetWithLoss(Net(1, strategy1, strategy2)))
|
|
net.set_auto_parallel()
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_semi_auto3():
|
|
"""
|
|
Feature: distribute operator SparseGatherV2 in auto parallel.
|
|
Description: gather net with strategy in semi auto parallel, gather axis is 1.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((2, 4), (1, 1))
|
|
strategy2 = ((4, 2, 1), (4, 2, 1))
|
|
net = GradWrap(NetWithLoss(Net(1, strategy1, strategy2)))
|
|
net.set_auto_parallel()
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_semi_auto4():
|
|
"""
|
|
Feature: distribute operator SparseGatherV2 in auto parallel.
|
|
Description: gather net with strategy in semi auto parallel, gather axis is 0.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(dataset_strategy="full_batch")
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy2 = ((4, 2, 1), (4, 2, 1))
|
|
net = GradWrap(NetWithLoss(Net(0, None, strategy2)))
|
|
net.set_auto_parallel()
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_semi_auto5():
|
|
"""
|
|
Feature: distribute operator SparseGatherV2 in auto parallel.
|
|
Description: gather net with strategy in semi auto parallel, gather axis is 1.
|
|
Expectation: compile done without error.
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy2 = ((4, 2, 1), (4, 2, 1))
|
|
net = GradWrap(NetWithLoss(Net(1, None, strategy2)))
|
|
net.set_auto_parallel()
|
|
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_auto0():
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel")
|
|
net = GradWrap(NetWithLoss(Net(0)))
|
|
net.set_auto_parallel()
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_auto1():
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel")
|
|
net = GradWrap(NetWithLoss(Net(1)))
|
|
net.set_auto_parallel()
|
|
x = Tensor(np.ones([64, 32]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_cpu0():
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((8, 1), (1, 1))
|
|
strategy2 = ((4, 2, 1), (4, 2, 1))
|
|
net = NetWithLoss(Net(0, strategy1, strategy2, None, "CPU"))
|
|
net.set_auto_parallel()
|
|
|
|
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_cpu1():
|
|
context.set_auto_parallel_context(device_num=16, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((16, 1), (1, 1))
|
|
strategy2 = ((4, 2, 1), (4, 2, 1))
|
|
net = NetWithLoss(Net(0, strategy1, strategy2, None, "CPU"))
|
|
net.set_auto_parallel()
|
|
|
|
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|
|
|
|
|
|
def test_gatherv2_cpu2():
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
|
|
strategy1 = ((1, 8), (1, 1))
|
|
strategy2 = ((4, 2, 1), (4, 2, 1))
|
|
net = NetWithLoss(Net(0, strategy1, strategy2, None, "CPU"))
|
|
net.set_auto_parallel()
|
|
|
|
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
|
|
net.set_train()
|
|
_cell_graph_executor.compile(net, x, y)
|