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!29720 refine eigh API exception 

Merge pull request !29720 from zhujingxuan/master
This commit is contained in:
i-robot 2022-02-07 09:33:53 +00:00 committed by Gitee
parent 93c75b2dc4 aec3357a03
commit 26d54856f7
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GPG Key ID: 173E9B9CA92EEF8F
4 changed files with 98 additions and 28 deletions
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9
mindspore/ccsrc/backend/kernel_compiler/cpu/eigen/eigh_cpu_kernel.cc
View File

@ -29,16 +29,19 @@ constexpr size_t kOutputsNum = 2;
template <typename T>
void EighCpuKernelMod<T>::InitKernel(const CNodePtr &kernel_node) {
kernel_name_ = AnfAlgo::GetCNodeName(kernel_node);
dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
compute_eigen_vectors_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, C_EIEH_VECTOR);
lower_ = AnfAlgo::GetNodeAttr<bool>(kernel_node, LOWER);
auto A_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
if (A_shape.size() != kShape2dDims) {
MS_LOG(EXCEPTION) << "Wrong array shape, A should be 2D, but got [" << A_shape.size() << "] dimensions.";
MS_LOG(EXCEPTION) << "Wrong array shape. For '" << kernel_name_ << "', a should be 2D, but got [" << A_shape.size()
<< "] dimensions.";
}
if (A_shape[kDim0] != A_shape[kDim1]) {
MS_LOG(EXCEPTION) << "Wrong array shape, A should be a squre matrix like [N X N], but got shape [" << A_shape[kDim0]
<< " X " << A_shape[kDim1] << "].";
MS_LOG(EXCEPTION) << "Wrong array shape. For '" << kernel_name_
<< "', a should be a squre matrix like [N X N], but got [" << A_shape[kDim0] << " X "
<< A_shape[kDim1] << "].";
}
m_ = A_shape[kDim0];
}

4
mindspore/ccsrc/backend/kernel_compiler/gpu/math/eigh_gpu_kernel.h
View File

@ -64,8 +64,8 @@ class EighGpuKernelMod : public NativeGpuKernelMod {
<< "] dimensions.";
}
if (A_shape[kDim0] != A_shape[kDim1]) {
MS_LOG(EXCEPTION) << "Wrong array shape, For '" << kernel_name
<< "', a should be a squre matrix like [N X N], but got shape [" << A_shape[kDim0] << " X "
MS_LOG(EXCEPTION) << "Wrong array shape. For '" << kernel_name
<< "', a should be a squre matrix like [N X N], but got [" << A_shape[kDim0] << " X "
<< A_shape[kDim1] << "].";
}
m_ = A_shape[0];

9
mindspore/python/mindspore/scipy/linalg.py
View File

@ -493,12 +493,13 @@ def eigh(a, b=None, lower=True, eigvals_only=False, overwrite_a=False,
``CPU`` ``GPU``
Examples:
>>> import numpy as onp
>>> import mindspore.numpy as mnp
>>> from mindspore.common import Tensor
>>> from mindspore.common import Tensor, dtype
>>> from mindspore.scipy.linalg import eigh
>>> A = Tensor([[6., 3., 1., 5.], [3., 0., 5., 1.], [1., 5., 6., 2.], [5., 1., 2., 2.]])
>>> w, v = eigh(A)
>>> print(mnp.sum(mnp.dot(A, v) - mnp.dot(v, mnp.diag(w))) < 1e-10)
>>> a = Tensor([[6, 3, 1, 5], [3, 0, 5, 1], [1, 5, 6, 2], [5, 1, 2, 2]], dtype.float64)
>>> w, v = eigh(a)
>>> print(onp.allclose(mnp.dot(a, v).asnumpy(), mnp.dot(v, mnp.diag(w)).asnumpy(), 1e-5, 1e-8))
True
"""
_type_check('lower', lower, bool, 'eigh')

104
tests/st/scipy_st/test_linalg.py
View File

@ -378,32 +378,25 @@ def test_cholesky_solve(n: int, lower: bool, data_type):
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@pytest.mark.parametrize('n', [4, 6, 9, 20])
@pytest.mark.parametrize('data_type',
[(onp.int32, "f"), (onp.int64, "d"), (onp.float32, "f"), (onp.float64, "d")])
def test_eigh(n: int, data_type):
@pytest.mark.parametrize('lower', [True, False])
@pytest.mark.parametrize('data_type, rtol, atol',
[(onp.int32, 1e-5, 1e-8), (onp.int64, 1e-5, 1e-8), (onp.float32, 1e-3, 1e-4),
(onp.float64, 1e-5, 1e-8)])
def test_eigh(n: int, lower, data_type, rtol, atol):
"""
Feature: ALL TO ALL
Description: test cases for eigenvalues/eigenvector for symmetric/Hermitian matrix solver [N,N]
Expectation: the result match scipy eigenvalues
"""
a = create_sym_pos_matrix([n, n], data_type)
a_tensor = Tensor(onp.array(a))
# test for real scalar float
tol = {"f": (1e-3, 1e-4), "d": (1e-5, 1e-8)}
rtol = tol[data_type[1]][0]
atol = tol[data_type[1]][1]
A = create_sym_pos_matrix([n, n], data_type[0])
msp_wl, msp_vl = msp.linalg.eigh(Tensor(onp.array(A).astype(data_type[0])), lower=True, eigvals_only=False)
msp_wu, msp_vu = msp.linalg.eigh(Tensor(onp.array(A).astype(data_type[0])), lower=False, eigvals_only=False)
assert onp.allclose(A @ msp_vl.asnumpy() - msp_vl.asnumpy() @ onp.diag(msp_wl.asnumpy()), onp.zeros((n, n)),
rtol,
atol)
assert onp.allclose(A @ msp_vu.asnumpy() - msp_vu.asnumpy() @ onp.diag(msp_wu.asnumpy()), onp.zeros((n, n)),
rtol,
atol)
w, v = msp.linalg.eigh(a_tensor, lower=lower, eigvals_only=False)
assert onp.allclose(a @ v.asnumpy() - v.asnumpy() @ onp.diag(w.asnumpy()), onp.zeros((n, n)), rtol, atol)
# test for real scalar float no vector
msp_wl0 = msp.linalg.eigh(Tensor(onp.array(A).astype(data_type[0])), lower=True, eigvals_only=True)
msp_wu0 = msp.linalg.eigh(Tensor(onp.array(A).astype(data_type[0])), lower=False, eigvals_only=True)
assert onp.allclose(msp_wl.asnumpy() - msp_wl0.asnumpy(), onp.zeros((n, n)), rtol, atol)
assert onp.allclose(msp_wu.asnumpy() - msp_wu0.asnumpy(), onp.zeros((n, n)), rtol, atol)
w0 = msp.linalg.eigh(a_tensor, lower=lower, eigvals_only=True)
assert onp.allclose(w.asnumpy(), w0.asnumpy(), rtol, atol)
@pytest.mark.level0
@ -470,6 +463,79 @@ def test_eigh_error_type(dtype, argname, argtype, wrong_argvalue, wrong_argtype)
f"but got '{wrong_argvalue}' with type '{wrong_argtype}'."
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@pytest.mark.parametrize('dtype, dtype_name', [(onp.float16, 'Float16'), (onp.int8, 'Int8'), (onp.int16, 'Int16')])
def test_eigh_error_tensor_dtype(dtype, dtype_name):
"""
Feature: ALL TO ALL
Description: test cases for solve_triangular for triangular matrix solver [N,N]
Expectation: eigh raises expectated Exception
"""
a = onp.random.randint(low=-1024, high=1024, size=(10, 10)).astype(dtype)
with pytest.raises(TypeError) as err:
msp.linalg.eigh(Tensor(a))
msg = f"For 'Eigh', the type of `A_dtype` should be in " \
f"[mindspore.float32, mindspore.float64, mindspore.complex64, mindspore.complex128], but got {dtype_name}."
assert str(err.value) == msg
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
@pytest.mark.parametrize('n', [1, 3, 4, 6])
@pytest.mark.parametrize('dtype', [onp.float32, onp.float64, onp.int32, onp.int64])
def test_eigh_error_dims(n: int, dtype):
"""
Feature: ALL TO ALL
Description: test cases for solve_triangular for triangular matrix solver [N,N]
Expectation: eigh raises expectated Exception
"""
a = onp.random.randint(low=-1024, high=1024, size=(10,) * n).astype(dtype)
with pytest.raises(RuntimeError) as err:
msp.linalg.eigh(Tensor(a))
msg = f"Wrong array shape. For 'Eigh', a should be 2D, but got [{n}] dimensions."
assert match_runtime_exception(err, msg)
a = onp.random.randint(low=-1024, high=1024, size=(n, n + 1)).astype(dtype)
with pytest.raises(RuntimeError) as err:
msp.linalg.eigh(Tensor(a))
msg = f"Wrong array shape. For 'Eigh', a should be a squre matrix like [N X N], " \
f"but got [{n} X {n + 1}]."
assert match_runtime_exception(err, msg)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_eigh_error_not_implemented():
"""
Feature: ALL TO ALL
Description: test cases for solve_triangular for triangular matrix solver [N,N]
Expectation: eigh raises expectated Exception
"""
a = onp.random.randint(low=-1024, high=1024, size=(10, 10)).astype(onp.float32)
b = onp.random.randint(low=-1024, high=1024, size=(10, 10)).astype(onp.float32)
with pytest.raises(ValueError) as err:
msp.linalg.eigh(Tensor(a), Tensor(b))
msg = "Currently only case b=None of eigh is Implemented. Which means that b must be identity matrix."
assert str(err.value) == msg
with pytest.raises(ValueError) as err:
msp.linalg.eigh(Tensor(a), 42)
msg = "Currently only case b=None of eigh is Implemented. Which means that b must be identity matrix."
assert str(err.value) == msg
with pytest.raises(ValueError) as err:
msp.linalg.eigh(Tensor(a), eigvals=42)
msg = "Currently only case eigvals=None of eighis Implemented."
assert str(err.value) == msg
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_x86_cpu