forked from huawei/mindspore2022
Fix sparse check and refactor the test cases of cg method.
This commit is contained in:
hezhenhao1
parent
98888f6cd8
commit
28e74b1e5f
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@ -164,30 +164,36 @@ def _sparse_check(func_name, a, m, b, x0):
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_mstype_check(func_name, x0, mstype.tensor_type, 'x0')
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# Checking shape and dtype
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if b.ndim != 1 or (b.ndim == 2 and b.shape[1] != 1):
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if (b.ndim != 1 and b.ndim != 2) or (b.ndim == 2 and b.shape[1] != 1):
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_raise_value_error(
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"For: '", func_name, "', the shape of b should be like (N,) or (N, 1), bug got ", b.shape, ".")
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"For: '", func_name, "', the shape of 'b' should be like (N,) or (N, 1), bug got ", b.shape, ".")
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if (x0.ndim != 1 and x0.ndim != 2) or (x0.ndim == 2 and x0.shape[1] != 1):
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_raise_value_error(
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"For: '", func_name, "', the shape of 'x0' should be like (N,) or (N, 1), bug got ", x0.shape, ".")
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_dtype_check(func_name, b, [mstype.int32, mstype.int64, mstype.float32, mstype.float64], 'b')
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_super_check((b.dtype, x0.dtype), (func_name, 'b', 'x0', 'data type'), '==', 'match', None, True)
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_super_check((b.shape, x0.shape), (func_name, 'b', 'x0', 'shape'), '==', 'match', None, True)
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_dtype_check(func_name, x0, [mstype.int32, mstype.int64, mstype.float32, mstype.float64], 'x0')
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def _check(arg, arg_name):
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if _callable_const(F.typeof(arg)):
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return arg
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_solve_check(func_name, arg, b, arg_name, 'b', True)
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if isinstance(arg, CSRTensor):
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_dtype_check(func_name, arg.indptr, [mstype.int32], arg_name)
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_dtype_check(func_name, arg.indices, [mstype.int32], arg_name)
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_dtype_check(func_name, arg.values, [mstype.float32], arg_name)
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else:
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_dtype_check(func_name, arg, [mstype.int32, mstype.int64, mstype.float32, mstype.float64], arg_name)
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if F.dtype(arg) in (mstype.int32, mstype.int64):
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arg = F.cast(arg, mstype.float64)
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_solve_check(func_name, arg, b, arg_name, 'b', True)
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_solve_check(func_name, arg, x0, arg_name, 'x0', True)
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if isinstance(arg, Tensor) and F.dtype(arg) in (mstype.int32, mstype.int64):
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arg = F.cast(arg, mstype.float64)
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return arg
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a = _check(a, 'A')
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m = _check(m, 'M')
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b = b.ravel()
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x0 = x0.ravel()
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if F.dtype(b) in (mstype.int32, mstype.int64):
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b = F.cast(b, mstype.float64)
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x0 = F.cast(x0, mstype.float64)
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@ -21,9 +21,8 @@ import mindspore.ops as ops
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import mindspore.nn as nn
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import mindspore.scipy as msp
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from mindspore import context
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from mindspore.common import Tensor, CSRTensor
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from tests.st.scipy_st.utils import create_sym_pos_matrix, create_full_rank_matrix, create_sym_pos_sparse_matrix, \
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to_tensor
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from mindspore.common import Tensor
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from tests.st.scipy_st.utils import create_sym_pos_matrix, create_full_rank_matrix, to_tensor
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def _fetch_preconditioner(preconditioner, A):
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@ -48,14 +47,15 @@ def _fetch_preconditioner(preconditioner, A):
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('dtype, tol', [(onp.float32, 1e-5), (onp.float64, 1e-12)])
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@pytest.mark.parametrize('shape', [(4, 4), (7, 7)])
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@pytest.mark.parametrize('tensor_type, dtype, tol', [('Tensor', onp.float32, 1e-5), ('Tensor', onp.float64, 1e-12),
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('CSRTensor', onp.float32, 1e-5)])
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@pytest.mark.parametrize('shape', [(7, 7)])
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@pytest.mark.parametrize('preconditioner', [None, 'identity', 'exact', 'random'])
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@pytest.mark.parametrize('maxiter', [1, 3])
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def test_cg_against_scipy(dtype, tol, shape, preconditioner, maxiter):
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@pytest.mark.parametrize('maxiter', [3, None])
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def test_cg_against_scipy(tensor_type, dtype, tol, shape, preconditioner, maxiter):
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"""
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Feature: ALL TO ALL
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Description: test cases for cg
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Description: test cases for cg using function way in pynative/graph mode
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Expectation: the result match scipy
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"""
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onp.random.seed(0)
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@ -64,9 +64,9 @@ def test_cg_against_scipy(dtype, tol, shape, preconditioner, maxiter):
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m = _fetch_preconditioner(preconditioner, a)
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osp_res = scipy.sparse.linalg.cg(a, b, M=m, maxiter=maxiter, atol=tol, tol=tol)
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a = Tensor(a)
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a = to_tensor((a, tensor_type))
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b = Tensor(b)
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m = Tensor(m) if m is not None else m
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m = to_tensor((m, tensor_type)) if m is not None else m
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# using PYNATIVE MODE
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context.set_context(mode=context.PYNATIVE_MODE)
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@ -117,17 +117,17 @@ def test_cg_against_numpy(dtype, shape):
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('dtype, tol', [(onp.float32, 1e-5), (onp.float64, 1e-12)])
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@pytest.mark.parametrize('tensor_type, dtype, tol', [('Tensor', onp.float32, 1e-5), ('Tensor', onp.float64, 1e-12),
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('CSRTensor', onp.float32, 1e-5)])
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@pytest.mark.parametrize('shape', [(7, 7)])
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@pytest.mark.parametrize('preconditioner', [None, 'identity', 'exact', 'random'])
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@pytest.mark.parametrize('maxiter', [3])
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def test_cg_against_scipy_graph(dtype, tol, shape, preconditioner, maxiter):
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@pytest.mark.parametrize('maxiter', [3, None])
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def test_cg_against_scipy_graph(tensor_type, dtype, tol, shape, preconditioner, maxiter):
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"""
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Feature: ALL TO ALL
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Description: test cases for cg within Cell object
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Description: test cases for cg within Cell object in pynative/graph mode
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Expectation: the result match scipy
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"""
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context.set_context(mode=context.GRAPH_MODE)
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class Net(nn.Cell):
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def construct(self, a, b, m, maxiter, tol):
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@ -139,52 +139,23 @@ def test_cg_against_scipy_graph(dtype, tol, shape, preconditioner, maxiter):
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m = _fetch_preconditioner(preconditioner, a)
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osp_res = scipy.sparse.linalg.cg(a, b, M=m, maxiter=maxiter, atol=tol, tol=tol)
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a = Tensor(a)
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a = to_tensor((a, tensor_type))
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b = Tensor(b)
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m = Tensor(m) if m is not None else m
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msp_res = Net()(a, b, m, maxiter, tol)
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m = to_tensor((m, tensor_type)) if m is not None else m
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kw = {"atol": tol, "rtol": tol}
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onp.testing.assert_allclose(osp_res[0], msp_res[0].asnumpy(), **kw)
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assert osp_res[1] == msp_res[1].asnumpy().item()
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# using PYNATIVE MODE
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context.set_context(mode=context.PYNATIVE_MODE)
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msp_res_dyn = Net()(a, b, m, maxiter, tol)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('dtype, tol', [(onp.float32, 1e-5)])
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@pytest.mark.parametrize('shape', [(7, 7)])
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@pytest.mark.parametrize('preconditioner', [None, 'identity', 'random'])
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@pytest.mark.parametrize('maxiter', [3])
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def test_cg_against_scipy_sparse(dtype, tol, shape, preconditioner, maxiter):
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"""
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Feature: ALL TO ALL
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Description: test cases of CSRTensor for cg
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Expectation: the result match scipy.
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"""
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# using GRAPH MODE
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context.set_context(mode=context.GRAPH_MODE)
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class Net(nn.Cell):
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def construct(self, a, b, m, maxiter, tol):
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return msp.sparse.linalg.cg(a, b, M=m, maxiter=maxiter, atol=tol, tol=tol)
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onp.random.seed(0)
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# scipy
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a = create_sym_pos_sparse_matrix(shape, dtype)
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b = onp.random.random(shape[:1]).astype(dtype)
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m = _fetch_preconditioner(preconditioner, a)
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osp_res = scipy.sparse.linalg.cg(a, b, M=m, maxiter=maxiter, atol=tol, tol=tol)
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# mindspore
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a = CSRTensor(Tensor(a.indptr), Tensor(a.indices), Tensor(a.data), shape)
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b = Tensor(b)
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m = Tensor(m) if m is not None else m
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msp_res = Net()(a, b, m, maxiter, tol)
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msp_res_sta = Net()(a, b, m, maxiter, tol)
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kw = {"atol": tol, "rtol": tol}
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onp.testing.assert_allclose(osp_res[0], msp_res[0].asnumpy(), **kw)
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assert osp_res[1] == msp_res[1].asnumpy().item()
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onp.testing.assert_allclose(osp_res[0], msp_res_dyn[0].asnumpy(), **kw)
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onp.testing.assert_allclose(osp_res[0], msp_res_sta[0].asnumpy(), **kw)
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assert osp_res[1] == msp_res_dyn[1].asnumpy().item()
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assert osp_res[1] == msp_res_sta[1].asnumpy().item()
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@pytest.mark.level0
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@ -14,18 +14,17 @@
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# ============================================================================
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"""utility functions for mindspore.scipy st tests"""
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from typing import List
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from functools import cmp_to_key, partial
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from functools import cmp_to_key
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import numpy as onp
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import scipy as osp
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import scipy.sparse.linalg
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from mindspore import Tensor, CSRTensor
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import mindspore.ops as ops
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import mindspore.numpy as mnp
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from mindspore.common import dtype as mstype
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def to_tensor(obj, dtype=None):
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def to_tensor(obj, dtype=None, indice_dtype=onp.int32):
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"""
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This function is used to initialize Tensor or CSRTensor.
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'obj' can be three type:
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@ -45,13 +44,14 @@ def to_tensor(obj, dtype=None):
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dtype = obj.dtype
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if isinstance(obj, onp.ndarray):
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tensor_fn = partial(Tensor, input_data=obj.astype(dtype))
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else:
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tensor_fn = partial(CSRTensor, indptr=Tensor(obj.indptr), indices=Tensor(obj.indices),
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values=Tensor(obj.data.astype(dtype)), shape=obj.shape)
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obj = Tensor(obj.astype(dtype))
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elif isinstance(obj, osp.sparse.csr_matrix):
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obj = CSRTensor(indptr=Tensor(obj.indptr.astype(indice_dtype)),
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indices=Tensor(obj.indices.astype(indice_dtype)),
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values=Tensor(obj.data.astype(dtype)),
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shape=obj.shape)
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res = tensor_fn()
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return res
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return obj
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def match_array(actual, expected, error=0, err_msg=''):
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@ -117,18 +117,6 @@ def create_sym_pos_matrix(shape, dtype):
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return (onp.matmul(x, x.T) + onp.eye(n)).astype(dtype)
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def create_sym_pos_sparse_matrix(shape, dtype, indice_dtype=onp.int32):
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if len(shape) != 2 or shape[0] != shape[1]:
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raise ValueError(
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'Symmetric positive definite matrix must be a square matrix, but has shape: ', shape)
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n = shape[-1]
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indptr = onp.arange(n + 1).astype(indice_dtype)
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indices = onp.arange(n).astype(indice_dtype)
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values = onp.random.random(n).astype(dtype)
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return scipy.sparse.csr_matrix((values, indices, indptr), shape=shape)
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def gradient_check(x, net, epsilon=1e-3, symmetric=False, enumerate_fn=onp.ndenumerate):
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# Some utils
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def _tensor_to_numpy(arg: List[Tensor]) -> List[onp.ndarray]:
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