mindspore/tests/st/ops/gpu/test_dynamic_ops.py

654 lines
21 KiB
Python

# Copyright 2022 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 pytest
from mindspore import ops, nn, ParameterTuple, context, set_seed, Tensor
from mindspore.train import DatasetHelper, connect_network_with_dataset
import mindspore.dataset as ds
import mindspore as ms
from mindspore.common.initializer import One
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
set_seed(2)
def np_type_to_ms(data_type):
if data_type == np.float32:
return ms.float32
if data_type == np.float64:
return ms.float64
if data_type == np.int32:
return ms.int32
if data_type == np.int64:
return ms.int64
raise ValueError("Unsupportted datatype: {}".format(data_type))
def _exec_preprocess(network, is_train, dataset, dataset_sink_mode, epoch_num, sink_size):
if dataset_sink_mode and not is_train:
dataset.__loop_size__ = 1
dataset_helper = DatasetHelper(
dataset, dataset_sink_mode, sink_size, epoch_num)
if dataset_sink_mode:
network = connect_network_with_dataset(network, dataset_helper)
return dataset_helper, network
def dynamic_shape_sink_process(network, dataset, is_train=True):
dataset_sink_mode = True
sink_size = 1
epoch_num = 1
dataset_helper, network = _exec_preprocess(
network, is_train, dataset, dataset_sink_mode, epoch_num, sink_size)
network.set_train(is_train)
for inputs in dataset_helper:
outputs = network(*inputs)
return outputs
def fixed_shape_process(network, dataset, is_train=True):
network.set_train(is_train)
for inputs in dataset.create_tuple_iterator():
outputs = network(*inputs)
return outputs
def dataset_generator(data_list):
for data in data_list:
yield data
def get_columns(tensor_num):
columns = []
for i in range(tensor_num):
columns.append("data" + str(i))
return columns
def compare(output, expect):
if isinstance(output, (tuple, list)):
assert isinstance(expect, (tuple, list))
for output_, expect_ in zip(output, expect):
if not compare(output_, expect_):
return False
else:
if not np.allclose(output.asnumpy(), expect.asnumpy(), rtol=1.0e-4, atol=1.0e-4):
return False
return True
class GradNetWrtX(nn.Cell):
def __init__(self, net):
super(GradNetWrtX, self).__init__()
self.net = net
self.grad_op = ops.GradOperation(
get_all=True, get_by_list=True, sens_param=True)
self.params = ParameterTuple(net.trainable_params())
def construct(self, *inputs):
gradient_function = self.grad_op(self.net, self.params)
return gradient_function(*inputs)
def comm_func(dyn_range, input_shp, data_type, op_net, num=None, output_compare_idx=None):
list_data = []
for i in dyn_range:
tmp_data = []
for data_shp in input_shp:
if num is None:
cur_shp = [dim if dim is not None else i for dim in data_shp]
else:
cur_shp = []
k = 0
for dim in data_shp:
if dim is not None:
cur_shp.append(dim)
elif k == 1:
cur_shp.append(num)
else:
cur_shp.append(i)
k = k + 1
tmp_data.append(np.random.random(cur_shp).astype(data_type))
list_data.append(tuple(tmp_data))
data_map = {}
dyn_tensors = []
for i, val in enumerate(input_shp):
data_map["data" + str(i + 1)] = val
if None in val:
dyn_tensors.append(Tensor(dtype=np_type_to_ms(data_type), shape=val))
else:
dyn_tensors.append(Tensor(dtype=np_type_to_ms(data_type), shape=val, init=One()))
dataset = ds.GeneratorDataset(list_data, list(data_map.keys()))
op_net.set_inputs(*dyn_tensors)
gradient = dynamic_shape_sink_process(op_net, dataset)
gradient_cmp = fixed_shape_process(op_net, dataset)
if output_compare_idx is None:
assert compare(gradient, gradient_cmp)
else:
assert compare(gradient[output_compare_idx], gradient_cmp[output_compare_idx])
class ConcatNet(nn.Cell):
def __init__(self, axis):
super(ConcatNet, self).__init__()
self.op = ops.Concat(axis)
def construct(self, x1, x2):
return self.op((x1, x2))
def dynamic_concat_run(is_grad):
axis = 1
dtype = np.float32
data_list = []
for i in [2, 64]:
data = []
data.append(np.random.rand(16, i).astype(dtype))
data.append(np.random.rand(16, i).astype(dtype))
if is_grad:
data.append(np.random.rand(16, i*2).astype(dtype))
data_list.append(tuple(data))
column_names = get_columns(len(data_list[0]))
dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
dynamic_columns = {column_names[0]: [
16, None], column_names[1]: [16, None]}
if is_grad:
dynamic_columns[column_names[-1]] = [16, None]
dyn_tensors = []
for val in dynamic_columns.values():
dyn_tensors.append(Tensor(dtype=ms.float32, shape=val))
net = ConcatNet(axis)
if is_grad:
net = GradNetWrtX(net)
net.set_inputs(*dyn_tensors)
output = dynamic_shape_sink_process(net, dataset)
output_cmp = fixed_shape_process(net, dataset)
assert compare(output, output_cmp)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_concat_forward():
"""
Feature: Test Concat.
Description: The shape of inputs is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_concat_run(False)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_concat_backward():
"""
Feature: Test backward of Concat.
Description: The shape of inputs is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_concat_run(True)
class BatchNormNet(nn.Cell):
def __init__(self, c):
super(BatchNormNet, self).__init__()
self.bn = nn.BatchNorm1d(c)
def construct(self, input_data):
x = self.bn(input_data)
return x
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_bachnorm():
"""
Feature: Test BatchNorm and its backward.
Description: The shape of inputs is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
c = 256
dtype = np.float32
data_list = []
for i in [2, 64]:
data = []
data.append(np.random.rand(i, c).astype(dtype))
data.append(np.random.rand(i, c).astype(dtype))
data_list.append(tuple(data))
column_names = get_columns(len(data_list[0]))
dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
t0 = Tensor(dtype=ms.float32, shape=[None, c])
t1 = Tensor(dtype=ms.float32, shape=[None, c])
net = GradNetWrtX(BatchNormNet(c))
net.set_inputs(t0, t1)
gradients = dynamic_shape_sink_process(net, dataset)
gradients_cmp = fixed_shape_process(net, dataset)
assert compare(gradients, gradients_cmp)
class ReshapeNet(nn.Cell):
def construct(self, x, y):
shape_of_y = ops.TensorShape()(y)
return ops.Reshape()(x, shape_of_y)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_reshape():
"""
Feature: Test Reshape.
Description: The shape of inputs is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dtype = np.float32
data_list = []
for i in [2, 96]:
data = []
data.append(np.random.rand(i, 64, 1).astype(dtype))
data.append(np.random.rand(i, 64).astype(dtype))
data_list.append(tuple(data))
column_names = get_columns(len(data_list[0]))
dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
net = ReshapeNet()
t0 = Tensor(dtype=ms.float32, shape=[None, 64, 1])
t1 = Tensor(dtype=ms.float32, shape=[None, 64])
net.set_inputs(t0, t1)
output = dynamic_shape_sink_process(net, dataset)
output_cmp = fixed_shape_process(net, dataset)
assert compare(output, output_cmp)
class ReduceSumInputAxisNet(nn.Cell):
def __init__(self):
super(ReduceSumInputAxisNet, self).__init__()
self.reduce = ops.ReduceSum()
def construct(self, x, y):
return self.reduce(x, y)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_reduce_sum_input_axis():
"""
Feature: Test ReduceSum with axis is input.
Description: The shape of inputs is dynamic.
Expectation: Assert that results are consistent with result of the numpy compute
"""
dtype = np.float32
data_list = []
for i in [2, 96]:
data = []
data.append(np.random.rand(i, 256).astype(dtype))
data.append(np.array([1], dtype=np.int64))
data_list.append(tuple(data))
column_names = get_columns(len(data_list[0]))
dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
net = ReduceSumInputAxisNet()
t0 = Tensor(dtype=ms.float32, shape=[None, 256])
t1 = Tensor(dtype=ms.int64, shape=[1], init=One())
net.set_inputs(t0, t1)
output = dynamic_shape_sink_process(net, dataset)
# Currently, the parameter axis of ReduceSum operator is dynamic(tensor) is
# not supported under the fixed shape, so numpy is used for comparison
inputs = data_list[0]
output_cmp = np.sum(inputs[0], inputs[1][0])
assert np.allclose(output.asnumpy(), output_cmp, rtol=1.0e-4, atol=1.0e-4)
class NopNet(nn.Cell):
def construct(self, x):
x1 = ops.squeeze(x)
y1 = ops.expand_dims(x1, 1)
return ops.sub(y1, x1)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_nop():
"""
Feature: Test Nop.
Description: The shape of inputs is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dtype = np.float32
data_list = []
for i in [2, 64]:
data = []
data.append(np.random.rand(i, 1).astype(dtype))
data_list.append(tuple(data))
column_names = get_columns(len(data_list[0]))
dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
net = NopNet()
t0 = Tensor(dtype=ms.float32, shape=[None, 1])
net.set_inputs(t0)
output = dynamic_shape_sink_process(net, dataset)
output_cmp = fixed_shape_process(net, dataset)
assert compare(output, output_cmp)
class ReduceSumNet(nn.Cell):
def __init__(self, axis=()):
super(ReduceSumNet, self).__init__()
self.reduce = ops.ReduceSum()
self.axis = axis
def construct(self, x):
return self.reduce(x, self.axis)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_reduce_sum():
"""
Feature: Test ReduceSum and its backward.
Description: The shape of inputs is dynamic.
Expectation: Assert that results are consistent with result of with fixed shape.
"""
dtype = np.float32
data_list = []
for i in [2, 96]:
data = []
data.append(np.random.rand(i, 256).astype(dtype))
data.append(np.array(1).astype(np.float32))
data_list.append(tuple(data))
column_names = get_columns(len(data_list[0]))
dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
net = GradNetWrtX(ReduceSumNet())
t0 = Tensor(dtype=ms.float32, shape=[None, 256])
t1 = Tensor(dtype=ms.float32, shape=[], init=One())
net.set_inputs(t0, t1)
output = dynamic_shape_sink_process(net, dataset)
output_cmp = fixed_shape_process(net, dataset)
assert compare(output, output_cmp)
class AddNet(nn.Cell):
def construct(self, x, y):
return ops.add(x, y)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_add():
"""
Feature: Test add and its backward.
Description: The shape of inputs is dynamic.
Expectation: Assert that results are consistent with result of with fixed shape.
"""
dtype = np.float32
data_list = []
for i in [2, 96]:
data = []
data.append(np.random.rand(i, 256).astype(dtype))
data.append(np.random.rand(i, 256).astype(dtype))
data.append(np.random.rand(i, 256).astype(dtype))
data_list.append(tuple(data))
column_names = get_columns(len(data_list[0]))
dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
net = GradNetWrtX(AddNet())
t0 = Tensor(dtype=ms.float32, shape=[None, 256])
t1 = Tensor(dtype=ms.float32, shape=[None, 256])
t2 = Tensor(dtype=ms.float32, shape=[None, 256])
net.set_inputs(t0, t1, t2)
output = dynamic_shape_sink_process(net, dataset)
output_cmp = fixed_shape_process(net, dataset)
assert compare(output, output_cmp)
class BatchNorm(nn.Cell):
def __init__(self):
super(BatchNorm, self).__init__()
self.batch_norm = ops.BatchNorm()
def construct(self, input_x, scale, bias, mean, variance):
out = self.batch_norm(input_x, scale, bias, mean, variance)
return out
class MaxPool(nn.Cell):
def __init__(self):
super(MaxPool, self).__init__()
self.maxpool = ops.MaxPool(pad_mode="VALID", kernel_size=2, strides=1)
def construct(self, x):
out = self.maxpool(x)
return out
class SigmoidCrossEntropyWithLogits(nn.Cell):
def __init__(self):
super(SigmoidCrossEntropyWithLogits, self).__init__()
self.op = ops.SigmoidCrossEntropyWithLogits()
def construct(self, x, y):
out = self.op(x, y)
return out
class Sigmoid(nn.Cell):
def __init__(self):
super(Sigmoid, self).__init__()
self.op = ops.Sigmoid()
def construct(self, x):
out = self.op(x)
return out
class ResizeNearestNeighbor(nn.Cell):
def __init__(self):
super(ResizeNearestNeighbor, self).__init__()
self.op = ops.ResizeNearestNeighbor((2, 2))
def construct(self, x):
out = self.op(x)
return out
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_batchnorm():
"""
Feature: Test Dynamic batchnorm and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(None, 64), (64,), (64,), (64,), (64,)]
net = BatchNorm()
comm_func(dynamic_range, input_shape, data_type, net, output_compare_idx=0)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_batchnorm2():
"""
Feature: Test Dynamic batchnorm and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(64, None), (None,), (None,), (None,), (None,)]
net = BatchNorm()
comm_func(dynamic_range, input_shape, data_type, net, output_compare_idx=0)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_maxpool1():
"""
Feature: Test Dynamic maxpool and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(32, 16, 32, None)]
net = MaxPool()
comm_func(dynamic_range, input_shape, data_type, net)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_maxpool2():
"""
Feature: Test Dynamic maxpool and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(32, 16, None, 8)]
net = MaxPool()
comm_func(dynamic_range, input_shape, data_type, net)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_maxpool3():
"""
Feature: Test Dynamic maxpool and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(32, None, 32, 8)]
net = MaxPool()
comm_func(dynamic_range, input_shape, data_type, net)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_maxpool4():
"""
Feature: Test Dynamic maxpool and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(None, 16, 32, 8)]
net = MaxPool()
comm_func(dynamic_range, input_shape, data_type, net)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_sigmoid_cross_entropy_with_logits():
"""
Feature: Test Dynamic SigmoidCrossEntropyWithLogits and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(None, 16, 32, 8), (None, 16, 32, 8)]
net = SigmoidCrossEntropyWithLogits()
comm_func(dynamic_range, input_shape, data_type, net)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_sigmoid_cross_entropy_with_logits_grad():
"""
Feature: Test Dynamic SigmoidCrossEntropyWithLogitsGrad and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(4, 16, None, 8), (4, 16, None, 8), (4, 16, None, 8)]
net = GradNetWrtX(SigmoidCrossEntropyWithLogits())
comm_func(dynamic_range, input_shape, data_type, net)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_sigmoid_grad():
"""
Feature: Test Dynamic SigmoidGrad and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(4, 16, None, 8), (4, 16, None, 8)]
net = GradNetWrtX(Sigmoid())
comm_func(dynamic_range, input_shape, data_type, net)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_resize_nearest_neighbor():
"""
Feature: Test Dynamic ResizeNearestNeighbor and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(4, 16, None, 8)]
net = ResizeNearestNeighbor()
comm_func(dynamic_range, input_shape, data_type, net)
@pytest.mark.level1
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_dynamic_resize_nearest_neighbor_grad():
"""
Feature: Test Dynamic ResizeNearestNeighborGrad and its backward. The input shape is dynamic.
Description: The input shape is dynamic.
Expectation: Assert that results are consistent with fixed shape.
"""
dynamic_range = range(2, 64)
data_type = np.float32
input_shape = [(4, 16, None, 8), (4, 16, 2, 2)]
net = GradNetWrtX(ResizeNearestNeighbor())
comm_func(dynamic_range, input_shape, data_type, net)