apply_adagrad_run.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
from akg.utils import kernel_exec as utils
from test_op import apply_adagrad
from tensorio import compare_tensor
from base import get_rtol_atol
from gen_random import random_gaussian


def apply_adagrad_execute(shape, dtype, update_slots, attrs=None):
    if attrs is None:
        attrs = {}
    mod = apply_adagrad_compile(shape, dtype, update_slots, attrs)
    exp_output, inputs, args = gen_data(dtype, update_slots, shape)
    acu_output = utils.mod_launch(mod, args, outputs=(-2, -1), expect=exp_output)
    # compare result
    rtol, atol = get_rtol_atol("apply_adagrad", dtype)
    results = list(map(lambda x, y: compare_tensor(x, y, rtol=rtol, atol=atol), acu_output, exp_output))
    return inputs, acu_output, exp_output, all(results)


def gen_data(dtype, update_slots, shape):
    var = random_gaussian(shape, miu=1, sigma=0.1).astype(dtype)
    accum = random_gaussian(shape, miu=1, sigma=0.1).astype(dtype)
    lr = random_gaussian((1,), miu=1, sigma=0.1).astype(dtype)
    grad = random_gaussian(shape, miu=1, sigma=0.1).astype(dtype)
    inputs = [var, accum, lr, grad]
    accum_out = accum + grad * grad if update_slots else accum
    var_out = var - (lr * grad / np.sqrt(accum_out))
    exp_output = (var_out, accum_out)
    outputs = [np.full(e.shape, np.nan, dtype) for e in exp_output]
    args = [*inputs, *outputs]

    return exp_output, inputs, args


def apply_adagrad_compile(shape, dtype, update_slots, attrs, kernel_name="apply_adagrad", tuning=False):
    shapes = [shape, shape, (1,), shape]
    dtypes = [dtype] * len(shapes)
    return utils.op_build_test(apply_adagrad.apply_adagrad, shapes, dtypes, [update_slots],
                               kernel_name=kernel_name, attrs=attrs, tuning=tuning)