Added LGamma op

mindspore/nn/layer/math.py

@@ -14,16 +14,18 @@
 # ============================================================================
 """math"""
 import math
+import numpy as np
 from mindspore.ops import operations as P
 from mindspore.ops.operations import _inner_ops as inner
 from mindspore.common.tensor import Tensor
+from mindspore.ops.primitive import constexpr
 from ..cell import Cell
 from ...common import dtype as mstype
 from ..._checkparam import Validator as validator
 from ..._checkparam import Rel
 
 
-__all__ = ['ReduceLogSumExp', 'Range', 'LinSpace']
+__all__ = ['ReduceLogSumExp', 'Range', 'LinSpace', 'LGamma']
 
 
 class ReduceLogSumExp(Cell):
@@ -169,3 +171,134 @@ class LinSpace(Cell):
 
         lin_space_out = self.lin_space(self.assist, self.start, self.stop, self.num)
         return lin_space_out
+
+@constexpr
+def check_tensors_dtype_same(data_dtype, value_dtype, op_name):
+    """Check tensors data type same."""
+    if data_dtype in value_dtype:
+        return True
+    raise TypeError(f"For '{op_name}', the value data type '{value_dtype}' "
+                    f"is not consistent with assigned tensor data type {data_dtype}.")
+
+class LGamma(Cell):
+    r"""
+    Calculate LGamma using Lanczos' approximation refering to "A Precision Approximationof the Gamma Function".
+    The algorithm is:
+
+    .. math::
+        lgamma(z + 1) = \frac{(\log(2) + \log(pi))}{2} + (z + 1/2) * log(t(z)) - t(z) + A(z)
+
+        t(z) = z + kLanczosGamma + 1/2
+
+        A(z) = kBaseLanczosCoeff + \sum_{k=1}^n \frac{kLanczosCoefficients[i]}{z + k}
+
+    However, if the input is less than 0.5 use Euler's reflection formula:
+
+    .. math::
+
+        lgamma(x) = \log(pi) - lgamma(1-x) - \log(abs(sin(pi * x)))
+
+    And please note that
+
+    .. math::
+
+        lgamma(+/-inf) = +inf
+
+    Thus, the behaviour of LGamma follows:
+    when x > 0.5, return log(Gamma(x))
+    when x < 0.5 and is not an interger, return the real part of Log(Gamma(x)) where Log is the complex logarithm
+    when x is an integer less or equal to 0, return +inf
+    when x = +/- inf, return +inf
+
+    Inputs:
+        - **input_x** (Tensor[Number]) - The input tensor. Only float16, float32 are supported.
+
+    Outputs:
+        Tensor, has the same shape and dtype as the 'input_x'.
+
+    Examples:
+        >>> input_x = Tensor(np.array(2, 3, 4).astype(np.float32))
+        >>> op = nn.LGamma()
+        >>> output = op(input_x)
+    """
+
+    def __init__(self):
+        super(LGamma, self).__init__()
+        # const numbers
+        self.k_lanczos_gamma = 7
+        self.k_base_lanczos_coeff = 0.99999999999980993227684700473478
+        self.k_lanczos_coefficients = [676.520368121885098567009190444019,
+                                       -1259.13921672240287047156078755283,
+                                       771.3234287776530788486528258894,
+                                       -176.61502916214059906584551354,
+                                       12.507343278686904814458936853,
+                                       -0.13857109526572011689554707,
+                                       9.984369578019570859563e-6,
+                                       1.50563273514931155834e-7]
+        self.one_half = 0.5
+        self.one = 1
+        self.two = 2
+        self.inf = np.inf
+        self.pi = np.pi
+        self.log_2 = np.log(self.two)
+        self.log_pi = np.log(np.pi)
+        self.log_sqrt_two_pi = (self.log_2 + self.log_pi) / self.two
+        self.lanczos_gamma_plus_one_half = self.k_lanczos_gamma + 0.5
+        self.log_lanczos_gamma_plus_one_half = np.log(self.lanczos_gamma_plus_one_half)
+
+        # operations
+        self.log = P.Log()
+        self.log1p = P.Log1p()
+        self.abs = P.Abs()
+        self.shape = P.Shape()
+        self.dtype = P.DType()
+        self.fill = P.Fill()
+        self.floor = P.Floor()
+        self.equal = P.Equal()
+        self.greater = P.Greater()
+        self.less = P.Less()
+        self.lessequal = P.LessEqual()
+        self.select = P.Select()
+        self.sin = P.Sin()
+        self.isfinite = P.IsFinite()
+
+    def construct(self, input_x):
+        input_dtype = self.dtype(input_x)
+        check_tensors_dtype_same(input_dtype, [mstype.float16, mstype.float32], "LGamma")
+        infinity = self.fill(input_dtype, self.shape(input_x), self.inf)
+
+        need_to_reflect = self.less(input_x, 0.5)
+        neg_input = -input_x
+        z = self.select(need_to_reflect, neg_input, input_x - 1)
+
+        @constexpr
+        def _calculate_x(z, k_base_lanczos_coeff, k_lanczos_coefficients):
+            x = k_base_lanczos_coeff
+            for i in range(8):
+                product_ = k_lanczos_coefficients[i] / (z + i + 1)
+                x = product_ + x
+            return  x
+        x = _calculate_x(z, self.k_base_lanczos_coeff, self.k_lanczos_coefficients)
+
+        t = z + self.lanczos_gamma_plus_one_half
+        log_t = self.log1p(z / self.lanczos_gamma_plus_one_half) + self.log_lanczos_gamma_plus_one_half
+
+        log_y = self.log(x) + (z + self.one_half - t / log_t) * log_t + self.log_sqrt_two_pi
+
+        abs_input = self.abs(input_x)
+        abs_frac_input = abs_input - self.floor(abs_input)
+        input_x = self.select(self.lessequal(input_x, 0.0),
+                              self.select(self.equal(abs_frac_input, 0.0),
+                                          infinity, input_x),
+                              input_x)
+        reduced_frac_input = self.select(self.greater(abs_frac_input, 0.5),
+                                         1 - abs_frac_input, abs_frac_input)
+        reflection_denom = self.log(self.sin(self.pi * reduced_frac_input))
+
+        reflection = self.select(self.isfinite(reflection_denom),
+                                 -reflection_denom - log_y + self.log_pi,
+                                 -reflection_denom)
+
+        result = self.select(need_to_reflect, reflection, log_y)
+
+        return self.select(self.isfinite(input_x), result, infinity)

tests/ut/python/ops/test_nn_ops.py

@@ -584,7 +584,10 @@ test_cases = [
     ('ReduceLogSumExp', {
         'block': nn.ReduceLogSumExp((0,), False),
         'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
-        'desc_bprop': [Tensor(np.array([1, 2, 3, 4]).astype(np.float32))],
+        'skip': ['backward']}),
+    ('LGamma', {
+        'block': nn.LGamma(),
+        'desc_inputs': [Tensor(np.array([3, 4, 5, 6]).astype(np.float32))],
         'skip': ['backward']}),
     ('FlattenNet', {
         'block': FlattenNet(),