forked from mindspore-Ecosystem/mindspore
!47529 delete constexpr note
Merge pull request !47529 from NaCN/constexpr_remove
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Gitee
commit
7ccb517428
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@ -52,7 +52,7 @@ def _cal_repeat_dims(x_rank, rep, expand_axis):
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return tuple(rep_dims)
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#remove constexpr
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@constexpr
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def _cal_reshape(x_shape, rep, axis):
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x_reshape = list(x_shape)
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x_reshape[axis] *= rep
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@ -13,7 +13,6 @@
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# limitations under the License.
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# ============================================================================
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"""math Operations."""
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import numpy as np
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from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
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from mindspore.common import dtype as mstype
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from mindspore._checkparam import Validator as validator
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@ -24,7 +23,6 @@ from mindspore.ops.function.math_func import cummin as cummin_
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from mindspore.ops import operations as P
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#TODO: remove comment
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@constexpr
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def _check_validate_axis(axis, name):
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if isinstance(axis, (tuple, list)):
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@ -34,14 +32,12 @@ def _check_validate_axis(axis, name):
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return axis
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#TODO: remove comment
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@constexpr
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def _check_validate_keepdims(keep_dims, name):
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keep_dims = validator.check_value_type('keep_dims', keep_dims, [bool], name)
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return keep_dims
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#TODO: remove comment
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@constexpr
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def is_const(x):
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return x is not None
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@ -119,7 +115,6 @@ def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
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return nonzero_num
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#TODO: remove comment
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@constexpr
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def _int_to_tuple_conv(axes):
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"""
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@ -131,7 +126,6 @@ def _int_to_tuple_conv(axes):
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return axes
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#TODO: remove comment
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@constexpr
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def _check_axes(axes, prim_name=None):
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"""
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@ -152,7 +146,6 @@ def _check_axes(axes, prim_name=None):
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return axes
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#TODO: remove comment
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@constexpr
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def _typecheck_input(x1_type, x2_type, prim_name=None):
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"""
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@ -166,7 +159,7 @@ def _typecheck_input(x1_type, x2_type, prim_name=None):
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f"and x2_type: {x2_type}.")
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#remove constexpr
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@constexpr
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def _axes_int_check(x1_shape, x2_shape, axes, prim_name=None):
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"""
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Convert from single int axes to 2d tuple if required
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@ -182,7 +175,6 @@ def _axes_int_check(x1_shape, x2_shape, axes, prim_name=None):
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return axes
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#TODO: remove comment
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@constexpr
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def _calc_new_shape(shape, axes, position=0):
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"""
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@ -190,10 +182,14 @@ def _calc_new_shape(shape, axes, position=0):
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'position' refers to whether tensor is first or second in the op.
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"""
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contraction_axes = tuple(i if i >= 0 else i + len(shape) for i in axes[position])
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prod_contraction = int(np.prod([shape[i] for i in contraction_axes]))
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prod_contraction = 1
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for i in contraction_axes:
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prod_contraction *= shape[i]
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free_axes = tuple(i for i in range(len(shape)) if i not in contraction_axes)
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free_dims = tuple(shape[i] if shape[i] is not None else -1 for i in free_axes)
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prod_free = int(np.prod(free_dims))
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prod_free = 1
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for free_dim in free_dims:
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prod_free *= free_dim
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transpose_perm = contraction_axes + free_axes if position else free_axes + contraction_axes
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new_shape = (prod_contraction, prod_free) if position else (prod_free, prod_contraction)
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@ -272,7 +268,6 @@ def tensor_dot(x1, x2, axes):
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return final_result
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#TODO: remove comment
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@constexpr
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def _typecheck_input_dot(x1_type, x2_type, prim_name=None):
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"""
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@ -286,7 +281,7 @@ def _typecheck_input_dot(x1_type, x2_type, prim_name=None):
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f"x1_type: {x1_type} and x2_type: {x2_type}.")
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#remove constexpr
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@constexpr
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def _get_transpose_shape(x2_shape):
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x2_shape_range = tuple(range(len(x2_shape)))
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x2_shape_transpose = x2_shape_range[-2:-1] + x2_shape_range[:-2] + x2_shape_range[-1:]
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@ -385,7 +380,7 @@ def dot(x1, x2):
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return matmul_op(x1, x2)
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#remove constexpr
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@constexpr
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def _get_batch_size(x1_shape, x2_shape, prim_name=None):
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"""
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Get batch sizes from two inputs
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@ -393,7 +388,6 @@ def _get_batch_size(x1_shape, x2_shape, prim_name=None):
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return x1_shape[0], x2_shape[0]
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#TODO: remove comment
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@constexpr
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def _typecheck_input_batch_dot(x1_type, x2_type, prim_name=None):
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"""
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@ -407,7 +401,7 @@ def _typecheck_input_batch_dot(x1_type, x2_type, prim_name=None):
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f"x2_type: {x2_type}.")
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#remove constexpr
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@constexpr
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def _check_axes_for_batch_dot(x1_shape, x2_shape, axes, prim_name=None):
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"""
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Check whether axes are valid and cast axes from tuple to list
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@ -442,10 +436,14 @@ def _calc_new_shape_batchdot(shape, axes, position=0):
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"""
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axis = axes[position]
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contraction_axes = tuple([axis])
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prod_contraction = int(np.prod([shape[i] for i in contraction_axes]))
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prod_contraction = 1
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for i in contraction_axes:
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prod_contraction *= shape[i]
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free_axes = tuple(i for i in range(1, len(shape)) if i not in contraction_axes)
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free_dims = tuple(shape[i] for i in free_axes)
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prod_free = int(np.prod(free_dims))
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prod_free = 1
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for free_dim in free_dims:
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prod_free *= free_dim
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transpose_perm = contraction_axes + free_axes if position else free_axes + contraction_axes
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transpose_perm = tuple([0]) + transpose_perm
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@ -454,7 +452,6 @@ def _calc_new_shape_batchdot(shape, axes, position=0):
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return new_shape, transpose_perm, free_dims
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#TODO: remove comment
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@constexpr
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def _check_batch_size(x1_batch_size, x2_batch_size, prim_name=None):
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"""
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@ -466,7 +463,6 @@ def _check_batch_size(x1_batch_size, x2_batch_size, prim_name=None):
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f"'x1_batch_size': {x1_batch_size} and 'x2_batch_size': {x2_batch_size}.")
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#TODO: remove comment
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@constexpr
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def _get_output_shape(batch_size, x1_ret, x2_ret):
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"""
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@ -114,7 +114,6 @@ def get_x_shape(x_shape):
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return (s,)
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#TODO: remove comment
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@constexpr
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def _check_attr_dtype(param_name, input_dtype, allow_dtypes, cls_name):
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validator.check_value_type(param_name, input_dtype, allow_dtypes, cls_name)
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@ -1488,7 +1487,6 @@ def flatten(input_x):
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return _flatten(input_x)
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#TODO: remove comment
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@constexpr
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def _check_select_type_match(scalar, tensor_type, scalar_name, tensor_name):
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if isinstance(scalar, int) and tensor_type != mstype.int32:
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@ -1499,7 +1497,6 @@ def _check_select_type_match(scalar, tensor_type, scalar_name, tensor_name):
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f"then the input[{tensor_name}] must be a Tensor of float32.")
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#TODO: remove comment
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@constexpr
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def _check_select_type(is_cond_tensor, is_x_scalar, is_y_scalar, is_x_tensor, is_y_tensor):
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if not is_cond_tensor:
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@ -5517,7 +5514,6 @@ def expand(input_x, size):
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return expand_op(input_x, size)
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#TODO: remove comment
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@constexpr
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def _check_fold_param(param, param_name):
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"""Check the parameters of fold op."""
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@ -5579,7 +5575,6 @@ def fold(input, output_size, kernel_size, dilation=1, padding=0, stride=1):
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return fold_op(input, output_size)
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#TODO: remove comment
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@constexpr
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def _check_unfold_params(param, param_name, param_size):
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"""Check the parameters of unfold op."""
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@ -5646,7 +5641,6 @@ def unfold(input, kernel_size, dilation=1, padding=0, stride=1):
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return unfold_op(input)
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#TODO: remove comment
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@constexpr
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def _check_diagonal_axes(dim1, dim2, x_ndim):
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"""Check the parameters of unfold op."""
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@ -76,7 +76,6 @@ from mindspore._c_expression import Tensor as Tensor_
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import mindspore.ops.function as F
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#TODO: remove comment
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@constexpr
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def _make_tensor(val, dtype):
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"""Returns the tensor with value `val` and dtype `dtype`."""
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@ -5421,7 +5420,7 @@ def frac(x):
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#####################################
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#remove constexpr
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@constexpr
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def _create_cummin_perm(axis, x_shape):
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"""Insure axis is in [-len(x_shape),len(s_shape)-1]"""
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len_axis = len(x_shape)
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@ -6571,7 +6570,6 @@ def hann_window(window_length, periodic=True):
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return Tensor(w[:-1]) if periodic else Tensor(w)
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#TODO: remove comment
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@constexpr
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def _type_convert(force, obj):
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"""
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@ -7353,7 +7351,7 @@ def _min(*args):
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return min(*args)
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#remove constexpr
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@constexpr
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def _infer_shape_rem(shape1, shape2, ndim1, ndim2, transpose_b):
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"""Infers the shape of the last two dimensions after performing matmul."""
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shape_rem = []
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@ -7368,7 +7366,7 @@ def _infer_shape_rem(shape1, shape2, ndim1, ndim2, transpose_b):
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return tuple(shape_rem)
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#remove constexpr
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@constexpr
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def _check_matmul_shapes(shape1, shape2, prim_name=None):
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"""Checks shape1 and shape2 are valid to perform matmul, and returns output shape after broadcasting."""
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shape_out = list()
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@ -7383,7 +7381,7 @@ def _check_matmul_shapes(shape1, shape2, prim_name=None):
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return tuple(shape_out)
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#remove constexpr
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@constexpr
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def _tile_size(shape, out_shape, ndim):
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"""Returns tile_size such that shape*tile_size = out_shape"""
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size = [1] * ndim
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@ -7393,7 +7391,7 @@ def _tile_size(shape, out_shape, ndim):
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return tuple(size)
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#remove constexpr
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@constexpr
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def _expand(x, ndim):
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"""Expand x to ndim from axis, which can be 0 or -1."""
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rank_op = _get_cache_prim(P.Rank)()
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@ -203,7 +203,6 @@ def adaptive_avg_pool3d(input_x, output_size):
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return adaptive_avg_pool3d_(input_x)
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#TODO: remove comment
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@constexpr
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def _check_avgpool_1d_type_and_int(kernel_size, stride, ceil_mode, count_include_pad):
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"""Checks the type of avgpool1d input"""
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@ -215,7 +214,6 @@ def _check_avgpool_1d_type_and_int(kernel_size, stride, ceil_mode, count_include
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validator.check_int(stride, 1, Rel.GE, "stride", 'avg_pool1d')
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#TODO: remove comment
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@constexpr
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def check_non_negative_int(arg_value, arg_name=None, prim_name=None):
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"""Check argument is non-negative integer, which mean arg_value >= 0."""
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@ -304,7 +302,6 @@ def avg_pool1d(input_x, kernel_size=1, stride=1, padding=0, ceil_mode=False, cou
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return input_x
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#TODO: remove comment
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@constexpr
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def _check_avgpool_2d_kernel_size(kernel_size):
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"""check and calculate the avgpool2d kernel_size"""
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@ -322,7 +319,6 @@ def _check_avgpool_2d_kernel_size(kernel_size):
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return kernel_size
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#TODO: remove comment
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@constexpr
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def _check_avgpool_2d_stride(stride):
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"""check and calculate the avgpool2d stride"""
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@ -340,7 +336,6 @@ def _check_avgpool_2d_stride(stride):
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return stride
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#TODO: remove comment
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@constexpr
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def _check_avgpool_2d_padding(padding):
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"""check and calculate the avgpool2d padding"""
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@ -358,7 +353,6 @@ def _check_avgpool_2d_padding(padding):
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return padding
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#TODO: remove comment
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@constexpr
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def _check_avg_pool2d_type_and_value(ceil_mode, count_include_pad, divisor_override):
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"""check the type of avgpool2d input"""
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@ -453,7 +447,6 @@ def avg_pool2d(input_x, kernel_size=1, stride=1, padding=0, ceil_mode=False, cou
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return input_x
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#TODO: remove comment
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@constexpr
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def _check_avg_pool3d_padding(padding):
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"""Check the padding value in avg_pool3d op."""
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@ -1333,7 +1326,6 @@ def fast_gelu(x):
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return fast_gelu_(x)
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#TODO: remove comment
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@constexpr
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def _check_float_range_inc_right(arg_value, lower_limit, upper_limit, arg_name=None, prim_name=None):
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"""
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@ -1656,7 +1648,6 @@ def hardshrink(x, lambd=0.5):
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return hshrink_op(x)
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#TODO: remove comment
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@constexpr
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def _check_axis_in_range(axis, ndim):
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"""Checks axes are with the bounds of ndim"""
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@ -1667,7 +1658,6 @@ def _check_axis_in_range(axis, ndim):
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return axis % ndim
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#TODO: remove comment
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@constexpr
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def _check_axis_valid(axes, ndim):
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"""
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@ -1694,7 +1684,6 @@ def _get_flip_end(ndim, shape, axes):
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return tuple([-shape[i] - 1 if i in axes else shape[i] + 1 for i in range(ndim)])
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#TODO: remove comment
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@constexpr
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def _get_flip_strides(ndim, axes):
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"""Calculate the strides of flip"""
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@ -1892,7 +1881,6 @@ def hardswish(x):
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return hardswish_(x)
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#TODO: remove comment
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@constexpr
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def _check_interpolate_inputs(input_dims, roi, scales, sizes, coordinate_transformation_mode, mode,
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prim_name):
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@ -2459,7 +2447,6 @@ def pdist(x, p=2.0):
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return pdist_(x)
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#TODO: remove comment
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@constexpr
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def _check_pad_inputs(padding):
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"""check the input of pad"""
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@ -3432,14 +3419,12 @@ def mish(x):
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return mish_(x)
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#TODO: remove comment
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@constexpr
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def _check_value_type(arg_name, arg_value, valid_types, prim_name=None):
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"""Checks whether a value is instance of some types."""
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return validator.check_value_type(arg_name, arg_value, valid_types, prim_name)
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#TODO: remove comment
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@constexpr(check=False)
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def _check_is_tensor(param_name, input_data, cls_name):
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"""Internal function, used to check whether the input data is Tensor."""
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@ -3448,7 +3433,6 @@ def _check_is_tensor(param_name, input_data, cls_name):
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f"but got '{ops.typeof(input_data)}'")
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#TODO: remove comment
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@constexpr
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def _check_number_gt_value(arg_name, arg_value, value, cls_name):
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"""Internal function, used to judge whether arg_value is greater than or equal to value."""
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@ -3646,7 +3630,6 @@ def grid_sample(input_x, grid, interpolation_mode='bilinear', padding_mode='zero
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return _grid_sampler_3d(input_x, grid)
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#TODO: remove comment
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@constexpr
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def _check_ctc_loss_inputs(blank, reduction, zero_infinity, prim_name):
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validator.check_value_type("blank", blank, [int], prim_name)
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@ -3723,7 +3706,6 @@ def ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reducti
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return (loss, log_alpha)
|
||||
|
||||
|
||||
#TODO: remove comment
|
||||
@constexpr
|
||||
def _check_gaussian_nll_loss(full, eps, reduction):
|
||||
validator.check_value_type('full', full, [bool], 'gaussian_nll_loss')
|
||||
|
|
@ -4366,7 +4348,6 @@ def adaptive_avg_pool1d(input_x, output_size):
|
|||
return input_x
|
||||
|
||||
|
||||
#TODO: remove comment
|
||||
@constexpr
|
||||
def _check_adaptive_max_pool1d_output_size(output_size):
|
||||
"""Check the output_size value in adaptive_max_pool1d op."""
|
||||
|
|
@ -4739,7 +4720,6 @@ def conv3d(inputs, weight, pad_mode="valid", padding=0, stride=1, dilation=1, gr
|
|||
return output
|
||||
|
||||
|
||||
#TODO: remove comment
|
||||
@constexpr
|
||||
def _check_positive_int(arg_value, arg_name=None, prim_name=None):
|
||||
validator.check_positive_int(arg_value, arg_name=arg_name, prim_name=prim_name)
|
||||
|
|
|
|||
|
|
@ -73,7 +73,6 @@ def random_gamma(shape, alpha, seed=None):
|
|||
return output
|
||||
|
||||
|
||||
#TODO: remove comment
|
||||
@constexpr(reuse_result=False)
|
||||
def _get_seed(op_seed, kernel_name):
|
||||
"""Get the graph-level seed."""
|
||||
|
|
|
|||
Loading…
Reference in New Issue