bench_linalg.py

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import numpy

import dpnp

from .common import TYPES1, Benchmark, get_indexes_rand, get_squares_


class Eindot(Benchmark):
    params = [
        [dpnp, numpy],
        [16, 32, 64, 128, 256, 512, 1024],
        ["float64", "float32", "int64", "int32"],
    ]
    param_names = ["executor", "size", "dtype"]

    def setup(self, np, size, dtype):
        dt = getattr(np, dtype)
        # self.a = np.arange(60000.0).reshape(150, 400)
        self.a = np.arange(size * size, dtype=dt).reshape((size, size))

        # self.ac = self.a.copy()
        # self.at = self.a.T
        # self.atc = self.a.T.copy()
        # self.b = np.arange(240000.0).reshape(400, 600)
        self.b = np.arange(size * size, dtype=dt).reshape((size, size))
        # self.c = np.arange(600)
        # self.d = np.arange(400)

        # self.a3 = np.arange(480000.).reshape(60, 80, 100)
        # self.b3 = np.arange(192000.).reshape(80, 60, 40)

    def time_dot_a_b(self, np):
        np.dot(self.a, self.b)

    def time_dot_d_dot_b_c(self, np, *args):
        np.dot(self.d, np.dot(self.b, self.c))

    def time_dot_trans_a_at(self, np, *args):
        np.dot(self.a, self.at)

    def time_dot_trans_a_atc(self, np, *args):
        np.dot(self.a, self.atc)

    def time_dot_trans_at_a(self, np, *args):
        np.dot(self.at, self.a)

    def time_dot_trans_atc_a(self, np, *args):
        np.dot(self.atc, self.a)

    def time_einsum_i_ij_j(self, np, *args):
        np.einsum("i,ij,j", self.d, self.b, self.c)

    def time_einsum_ij_jk_a_b(self, np, *args):
        np.einsum("ij,jk", self.a, self.b)

    def time_einsum_ijk_jil_kl(self, np, *args):
        np.einsum("ijk,jil->kl", self.a3, self.b3)

    def time_inner_trans_a_a(self, np, *args):
        np.inner(self.a, self.a)

    def time_inner_trans_a_ac(self, np, *args):
        np.inner(self.a, self.ac)

    def time_matmul_a_b(self, np, *args):
        np.matmul(self.a, self.b)

    def time_matmul_d_matmul_b_c(self, np, *args):
        np.matmul(self.d, np.matmul(self.b, self.c))

    def time_matmul_trans_a_at(self, np, *args):
        np.matmul(self.a, self.at)

    def time_matmul_trans_a_atc(self, np, *args):
        np.matmul(self.a, self.atc)

    def time_matmul_trans_at_a(self, np, *args):
        np.matmul(self.at, self.a)

    def time_matmul_trans_atc_a(self, np, *args):
        np.matmul(self.atc, self.a)

    def time_tensordot_a_b_axes_1_0_0_1(self, np, *args):
        np.tensordot(self.a3, self.b3, axes=([1, 0], [0, 1]))


class Linalg(Benchmark):
    params = [[dpnp, numpy], ["svd", "pinv", "det", "norm"], TYPES1]
    param_names = ["executor", "op", "type"]

    def setup(self, np, op, typename):
        np.seterr(all="ignore")

        self.func = getattr(np.linalg, op)

        if op == "cholesky":
            # we need a positive definite
            self.a = np.dot(
                get_squares_()[typename], get_squares_()[typename].T
            )
        else:
            self.a = get_squares_()[typename]

        # check that dtype is supported at all
        try:
            self.func(self.a[:2, :2])
        except TypeError:
            raise NotImplementedError()

    def time_op(self, np, op, typename):
        self.func(self.a)


class Lstsq(Benchmark):
    params = [dpnp, numpy]
    param_names = ["executor"]

    def setup(self, np):
        self.a = get_squares_()["float64"]
        self.b = get_indexes_rand()[:100].astype(np.float64)

    def time_numpy_linalg_lstsq_a__b_float64(self, np):
        np.linalg.lstsq(self.a, self.b, rcond=-1)


# class Einsum(Benchmark):
# param_names = ['dtype']
# params = [[np.float64]]
# def setup(self, dtype):
# self.a = np.arange(2900, dtype=dtype)
# self.b = np.arange(3000, dtype=dtype)
# self.c = np.arange(24000, dtype=dtype).reshape(20, 30, 40)
# self.c1 = np.arange(1200, dtype=dtype).reshape(30, 40)
# self.d = np.arange(10000, dtype=dtype).reshape(10,100,10)

# #outer(a,b): trigger sum_of_products_contig_stride0_outcontig_two
# def time_einsum_outer(self, dtype):
# np.einsum("i,j", self.a, self.b, optimize=True)

# # multiply(a, b):trigger sum_of_products_contig_two
# def time_einsum_multiply(self, dtype):
# np.einsum("..., ...", self.c1, self.c , optimize=True)

# # sum and multiply:trigger sum_of_products_contig_stride0_outstride0_two
# def time_einsum_sum_mul(self, dtype):
# np.einsum(",i...->", 300, self.d, optimize=True)

# # sum and multiply:trigger sum_of_products_stride0_contig_outstride0_two
# def time_einsum_sum_mul2(self, dtype):
# np.einsum("i...,->", self.d, 300, optimize=True)