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mindspore2022/tests/ut/python/fallback/test_graph_fallback_numpy.py

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# 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 graph fallback """
import pytest
import numpy as np
import numpy.random as rand
from mindspore import ms_function, context, Tensor
context.set_context(mode=context.GRAPH_MODE)
def test_np_array_1():
"""
Feature: JIT Fallback
Description: Test numpy with ndarray in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_1():
a = np.array([1, 2, 3])
return Tensor(a)
res = np_array_1()
expect_res = np.array([1, 2, 3])
assert np.all(res.asnumpy() == expect_res)
def test_np_array_2():
"""
Feature: JIT Fallback
Description: Test numpy with ndarray in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_2():
a = np.array([[1, 2], [3, 4]])
return Tensor(a)
res = np_array_2()
expect_res = np.array([[1, 2], [3, 4]])
assert np.all(res.asnumpy() == expect_res)
def test_np_array_3():
"""
Feature: JIT Fallback
Description: Test numpy with ndarray in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_3():
a = np.array([1, 2, 3, 4, 5], ndmin=2)
return Tensor(a)
res = np_array_3()
expect_res = np.array([[1, 2, 3, 4, 5]])
assert np.all(res.asnumpy() == expect_res)
def test_np_array_4():
"""
Feature: JIT Fallback
Description: Test numpy with ndarray in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_4():
a = np.array([1, 2, 3], dtype=complex)
return Tensor(a)
res = np_array_4()
expect_res = np.array([1+0j, 2+0j, 3+0j])
assert np.all(res.asnumpy() == expect_res)
def test_np_dtype_1():
"""
Feature: JIT Fallback
Description: Test numpy with dtype in graph mode.
Expectation: No exception.
"""
@ms_function
def np_dtype_1():
t = np.dtype(np.int32)
return Tensor(np.array([1, 2, 3], dtype=t))
res = np_dtype_1()
expect_res = np.array([1, 2, 3], dtype=np.int32)
assert np.all(res.asnumpy() == expect_res)
def test_np_dtype_2():
"""
Feature: JIT Fallback
Description: Test numpy with dtype in graph mode.
Expectation: No exception.
"""
@ms_function
def np_dtype_2():
t = np.dtype('i4')
return Tensor(np.array([1, 2, 3], dtype=t))
res = np_dtype_2()
expect_res = np.array([1, 2, 3], dtype=np.int32)
assert np.all(res.asnumpy() == expect_res)
def test_np_array_ndim():
"""
Feature: JIT Fallback
Description: Test numpy with array ndim in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_ndim():
a = np.arange(24)
return Tensor(a.ndim)
res = np_array_ndim()
assert res == 1
def test_np_array_reshape_1():
"""
Feature: JIT Fallback
Description: Test numpy with array reshape in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_reshape_1():
a = np.array([[1, 2, 3], [4, 5, 6]])
b = a.reshape(3, 2)
return Tensor(b.ndim)
res = np_array_reshape_1()
assert res == 2
@pytest.mark.skip(reason='Not support graph fallback feature yet')
def test_np_array_reshape_2():
"""
Feature: JIT Fallback
Description: Test numpy with array reshape in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_reshape_2():
a = np.array([[1, 2, 3], [4, 5, 6]])
a.shape = (3, 2)
return a
res = np_array_reshape_2()
print("res:", res)
def test_np_array_itemsize():
"""
Feature: JIT Fallback
Description: Test numpy with array reshape in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_itemsize():
a = np.array([1, 2, 3, 4, 5], dtype=np.int8)
return Tensor(a.itemsize)
res = np_array_itemsize()
print("res:", res)
assert res == 1
def test_np_empty_zeros_ones():
"""
Feature: JIT Fallback
Description: Test numpy with array empty, zeros, ones in graph mode.
Expectation: No exception.
"""
@ms_function
def np_empty_zeros_ones():
x = np.empty([3, 2], dtype=np.int)
y = np.zeros(x.shape, dtype=np.int)
z = np.ones(x.shape, dtype=np.int)
return Tensor(y + z)
res = np_empty_zeros_ones()
except_res = np.ones([3, 2], dtype=np.int)
assert np.all(res.asnumpy() == except_res)
def test_np_asarray_list():
"""
Feature: JIT Fallback
Description: Test numpy with list to array in graph mode.
Expectation: No exception.
"""
@ms_function
def np_asarray_list():
x = [1, 2, 3]
y = np.asarray(x)
return Tensor(y)
res = np_asarray_list()
except_res = np.asarray([1, 2, 3])
assert np.all(res.asnumpy() == except_res)
def test_np_asarray_tuple():
"""
Feature: JIT Fallback
Description: Test numpy with tuple to array in graph mode.
Expectation: No exception.
"""
@ms_function
def np_asarray_tuple():
x = (1, 2, 3)
y = np.asarray(x)
return Tensor(y)
res = np_asarray_tuple()
except_res = np.asarray((1, 2, 3))
assert np.all(res.asnumpy() == except_res)
@pytest.mark.skip(reason='Not support graph fallback feature yet')
def test_np_frombuffer():
"""
Feature: JIT Fallback
Description: Test numpy with frombuffer in graph mode.
Expectation: No exception.
"""
@ms_function
def np_frombuffer():
s = b'Hello World'
a = np.frombuffer(s, dtype='S1')
return a
res = np_frombuffer()
print("res:", res)
def test_np_fromiter():
"""
Feature: JIT Fallback
Description: Test numpy with fromiter in graph mode.
Expectation: No exception.
"""
@ms_function
def np_fromiter():
l = range(5)
it = iter(l)
x = np.fromiter(it, dtype=float)
return Tensor(x)
res = np_fromiter()
except_res = np.asarray([0., 1., 2., 3., 4.])
assert np.all(res.asnumpy() == except_res)
def test_np_arange():
"""
Feature: JIT Fallback
Description: Test numpy with arange in graph mode.
Expectation: No exception.
"""
@ms_function
def np_arange():
x = np.arange(5, dtype=float)
y = np.arange(10, 20, 2)
return Tensor(x + y)
res = np_arange()
except_res = np.asarray([10., 13., 16., 19., 22.])
assert np.all(res.asnumpy() == except_res)
def test_np_logspace():
"""
Feature: JIT Fallback
Description: Test numpy with logspace in graph mode.
Expectation: No exception.
"""
@ms_function
def np_logspace():
a = np.logspace(0, 9, 10, base=2)
return Tensor(a)
res = np_logspace()
except_res = np.array([1., 2., 4., 8., 16., 32., 64., 128., 256., 512.])
assert np.all(res.asnumpy() == except_res)
def test_np_array_shape():
"""
Feature: JIT Fallback
Description: Test numpy with array shape in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_shape():
a = np.array([[1, 2, 3], [4, 5, 6]])
return Tensor(a.shape)
res = np_array_shape()
print("res:", res)
def test_np_array_size():
"""
Feature: JIT Fallback
Description: Test numpy with array size in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_size():
a = np.array([[1, 2, 3], [4, 5, 6]])
return Tensor(a.size)
res = np_array_size()
print("res:", res)
def test_np_array_real():
"""
Feature: JIT Fallback
Description: Test numpy with complex in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_real():
a = np.array([1, 2, 3], dtype=complex)
return Tensor(a.real)
res = np_array_real()
print("res:", res)
def test_np_array_imag():
"""
Feature: JIT Fallback
Description: Test numpy with complex in graph mode.
Expectation: No exception.
"""
@ms_function
def np_array_imag():
a = np.array([1, 2, 3], dtype=complex)
return Tensor(a.imag)
res = np_array_imag()
print("res:", res)
def test_np_binop():
"""
Feature: JIT Fallback
Description: Test numpy's binary operation in graph mode.
Expectation: No exception.
"""
@ms_function
def np_binop():
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
c = a + b
return Tensor(c)
res = np_binop()
assert np.all(res.asnumpy() == np.array([5, 7, 9]))
def test_np_binop_2():
"""
Feature: JIT Fallback
Description: Test numpy's binary operation in graph mode.
Expectation: No exception.
"""
@ms_function
def np_binop():
a = np.int_(1)
b = 4 + a
return Tensor(b)
res = np_binop()
assert res == 5
def test_np_compare():
"""
Feature: JIT Fallback
Description: Test numpy's compare operation in graph mode.
Expectation: No exception.
"""
@ms_function
def np_compare():
a = np.array([1, 2, 3])
b = np.array([0, 2, 4])
c = a > b
return Tensor(c)
res = np_compare()
assert np.all(res.asnumpy() == np.array([True, False, False]))
def test_np_compare_2():
"""
Feature: JIT Fallback
Description: Test numpy's compare operation in graph mode.
Expectation: No exception.
"""
@ms_function
def np_compare():
a = 1
b = np.int_(3)
c = a < b
return Tensor(c)
res = np_compare()
assert res
def test_np_bool_and():
"""
Feature: JIT Fallback
Description: Test AND operation in graph mode.
Expectation: No exception.
"""
@ms_function
def np_bool_and():
a = np.bool_(True)
b = np.bool_(False)
c = a and b
return Tensor(c)
res = np_bool_and()
assert not res
def test_np_bool_or():
"""
Feature: JIT Fallback
Description: Test OR operation in graph mode.
Expectation: No exception.
"""
@ms_function
def np_bool_or():
a = np.bool_(True)
b = np.bool_(False)
c = a or b
return Tensor(c)
res = np_bool_or()
assert res
def test_np_bool_or_2():
"""
Feature: JIT Fallback
Description: Test OR operation in graph mode.
Expectation: No exception.
"""
@ms_function
def np_bool_or():
out = 0 or np.bool_(True)
return Tensor(out)
res = np_bool_or()
assert res
def test_np_bool_not():
"""
Feature: JIT Fallback
Description: Test NOT operation in graph mode.
Expectation: No exception.
"""
@ms_function
def np_bool_not():
a = np.bool_(True)
b = not a
return Tensor(b)
res = np_bool_not()
assert not res
def test_np_augassign():
"""
Feature: JIT Fallback
Description: Test augassign method in graph mode.
Expectation: No exception.
"""
@ms_function
def np_augassign():
value_add = np.array([1, 2, 3])
value_add += np.array([4, 5, 6])
value_sub = np.array([5, 5, 5])
value_sub -= np.array([1, 2, 3])
value_mul = np.int_(2)
value_mul *= np.int_(3)
value_div = np.int_(10)
value_div /= np.int_(5)
value_floordiv = np.int_(5)
value_floordiv //= np.int_(2)
return Tensor(value_add), Tensor(value_sub), Tensor(value_mul), Tensor(value_div), Tensor(value_floordiv)
out_add, out_sub, out_mul, out_div, out_floordiv = np_augassign()
assert np.all(out_add.asnumpy() == np.array([5, 7, 9]))
assert np.all(out_sub.asnumpy() == np.array([4, 3, 2]))
assert out_mul == 6
assert out_div == 2
assert out_floordiv == 2
def test_np_augassign_2():
"""
Feature: JIT Fallback
Description: Test augassign method in graph mode.
Expectation: No exception.
"""
@ms_function
def np_augassign():
value_mod = np.int_(5)
value_mod %= np.int_(2)
value_pow = np.int_(3)
value_pow **= np.int_(2)
value_lshift = np.int_(4)
value_lshift <<= 1
value_rshift = np.int_(4)
value_rshift >>= 1
value_bitxor = np.int_(0)
value_bitxor ^= 1
return Tensor(value_mod), Tensor(value_pow), Tensor(value_lshift), Tensor(value_rshift), Tensor(value_bitxor)
out_mod, out_pow, out_lshift, out_rshift, out_bitxor = np_augassign()
assert out_mod == 1
assert out_pow == 9
assert out_lshift == 8
assert out_rshift == 2
assert out_bitxor == 1
def test_np_subscript():
"""
Feature: JIT Fallback
Description: Test subscript method in graph mode.
Expectation: No exception.
"""
@ms_function
def np_subscript():
a = np.array([1, 2, 3])
b = a[np.int32(1)]
return Tensor(b)
res = np_subscript()
assert res == 2
def test_np_slice():
"""
Feature: JIT Fallback
Description: Test slice method in graph mode.
Expectation: No exception.
"""
@ms_function
def np_slice():
a = np.arange(10)
b = a[1:5]
return Tensor(b)
res = np_slice()
assert np.all(res.asnumpy() == np.array([1, 2, 3, 4]))
def test_np_random():
"""
Feature: JIT Fallback
Description: Test numpy.random module in graph mode.
Expectation: No exception.
"""
@ms_function
def np_random():
a = rand.randint(100, size=(5))
b = a[1:5]
return Tensor(b)
res = np_random()
print(res)
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