forked from mindspore-Ecosystem/mindspore
196 lines
7.3 KiB
Python
196 lines
7.3 KiB
Python
# Copyright 2021 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 vjp in graph mode"""
|
|
import numpy as np
|
|
import pytest
|
|
import mindspore.nn as nn
|
|
import mindspore.context as context
|
|
from mindspore import Tensor
|
|
from mindspore import jit
|
|
from mindspore.ops.functional import vjp
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
|
|
|
|
class SingleInputNet(nn.Cell):
|
|
def construct(self, x):
|
|
return x ** 3
|
|
|
|
|
|
class MultipleInputsOutputNet(nn.Cell):
|
|
def construct(self, x, y):
|
|
return 2 * x, y ** 3
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_vjp_single_input_graph():
|
|
"""
|
|
Features: Function vjp
|
|
Description: Test vjp with single input, single output and default v in graph mode.
|
|
Expectation: No exception.
|
|
"""
|
|
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
|
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
|
net = SingleInputNet()
|
|
expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
|
|
expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
|
|
primal, grad_fn = vjp(net, x)
|
|
gradient = grad_fn(v)
|
|
assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
|
|
assert np.allclose(gradient[0].asnumpy(), expect_grad.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_vjp_multiple_inputs_default_v_graph():
|
|
"""
|
|
Features: Function vjp
|
|
Description: Test vjp with single input, single output and default v in graph mode.
|
|
Expectation: No exception.
|
|
"""
|
|
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
|
y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
|
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
|
net = MultipleInputsOutputNet()
|
|
expect_primal_0 = Tensor(np.array([[2, 4], [6, 8]]).astype(np.float32))
|
|
expect_primal_1 = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
|
|
expect_grad_0 = Tensor(np.array([[2, 2], [2, 2]]).astype(np.float32))
|
|
expect_grad_1 = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
|
|
primal, grad_fn = vjp(net, x, y)
|
|
gradient = grad_fn(v, v)
|
|
assert isinstance(primal, tuple)
|
|
assert len(primal) == 2
|
|
assert np.allclose(primal[0].asnumpy(), expect_primal_0.asnumpy())
|
|
assert np.allclose(primal[1].asnumpy(), expect_primal_1.asnumpy())
|
|
assert isinstance(gradient, tuple)
|
|
assert len(gradient) == 2
|
|
assert np.allclose(gradient[0].asnumpy(), expect_grad_0.asnumpy())
|
|
assert np.allclose(gradient[1].asnumpy(), expect_grad_1.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_vjp_jit_function_single_input_single_output_default_v_graph():
|
|
"""
|
|
Features: Function vjp
|
|
Description: Test vjp with @jit decorated function, single input, single output and default v in graph mode.
|
|
Expectation: No exception.
|
|
"""
|
|
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
|
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
|
net = SingleInputNet()
|
|
|
|
@jit
|
|
def vjp_with_jit_function(inputs, vectors):
|
|
output, grad_fn = vjp(net, inputs)
|
|
vjp_grad = grad_fn(vectors)
|
|
return output, vjp_grad
|
|
|
|
primal, gradient = vjp_with_jit_function(x, v)
|
|
expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
|
|
expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
|
|
assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
|
|
assert np.allclose(gradient[0].asnumpy(), expect_grad.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_vjp_input_function_single_input_single_output_default_v_graph():
|
|
"""
|
|
Features: Function vjp
|
|
Description: Test vjp with function, single input, single output and default v in graph mode.
|
|
Expectation: No exception.
|
|
"""
|
|
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
|
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
|
|
|
def test_function(inputs):
|
|
return inputs ** 3
|
|
|
|
primal, grad_fn = vjp(test_function, x)
|
|
gradient = grad_fn(v)
|
|
expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
|
|
expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
|
|
assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
|
|
assert np.allclose(gradient[0].asnumpy(), expect_grad.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_vjp_construct_single_input_single_output_default_v_graph():
|
|
"""
|
|
Features: Function vjp
|
|
Description: Test vjp with function, single input, single output and default v in graph mode.
|
|
Expectation: No exception.
|
|
"""
|
|
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
|
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self, network):
|
|
super(Net, self).__init__()
|
|
self.net = network
|
|
|
|
def construct(self, inputs, vectors):
|
|
net_out, grad_fn = vjp(self.net, inputs)
|
|
vjp_out = grad_fn(vectors)
|
|
return net_out, vjp_out
|
|
|
|
test_net_graph = Net(SingleInputNet())
|
|
primal, gradient = test_net_graph(x, v)
|
|
expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
|
|
expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
|
|
assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
|
|
assert np.allclose(gradient[0].asnumpy(), expect_grad.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_vjp_multiple_outputs_with_has_aux_graph():
|
|
"""
|
|
Features: Function vjp
|
|
Description: Test vjp with multiple inputs, multiple outputs with set_aux as True in graph mode.
|
|
Expectation: No exception.
|
|
"""
|
|
|
|
def fn(x, y):
|
|
return 2 * x + y, y ** 3
|
|
|
|
def fn2(*args):
|
|
return fn(*args)
|
|
|
|
x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
|
y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
|
|
v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
|
expect_primal = Tensor(np.array([[3, 6], [9, 12]]).astype(np.float32))
|
|
expect_aux = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
|
|
expect_grad_0 = Tensor(np.array([[2, 2], [2, 2]]).astype(np.float32))
|
|
expect_grad_1 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
|
|
primal, grad_fn, aux = vjp(fn2, x, y, has_aux=True)
|
|
gradient = grad_fn(v)
|
|
assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
|
|
assert np.allclose(aux.asnumpy(), expect_aux.asnumpy())
|
|
assert isinstance(gradient, tuple)
|
|
assert len(gradient) == 2
|
|
assert np.allclose(gradient[0].asnumpy(), expect_grad_0.asnumpy())
|
|
assert np.allclose(gradient[1].asnumpy(), expect_grad_1.asnumpy())
|