test_pe_transform.py

#!/usr/bin/env python3

# Copyright (c) 2021 CINN Authors. All Rights Reserved.
#
# 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 unittest

import cinn
import numpy as np
from cinn import Target, ir, lang, pe, runtime
from cinn.common import *
from cinn.poly import create_stages


class TestPETransform(unittest.TestCase):
    def setUp(self):
        self.m = 100
        self.n = 32
        self.k = 16

        self.target = Target()
        self.target.arch = Target.Arch.X86
        self.target.bits = Target.Bit.k64
        self.target.os = Target.OS.Linux

        self.transform_data = []

    def test_transform_0(self):
        for fn_name, pe_fn, np_fn in [
            ("matmul", pe.matmul, np.matmul),
        ]:
            self.compiler = cinn.Compiler.create(self.target)
            self.transform_matmul_tester(fn_name, pe_fn, np_fn, False, False, 1)

    def test_transform_1(self):
        for fn_name, pe_fn, np_fn in [
            ("matmul", pe.matmul, np.matmul),
        ]:
            self.compiler = cinn.Compiler.create(self.target)
            self.transform_matmul_tester(fn_name, pe_fn, np_fn, False, True, 2)

    def transform_matmul_tester(
        self, fn_name, cinn_fn, np_fn, trans_a, trans_b, alpha
    ):
        m, n, k = [
            ir.Expr(_)
            for _ in (
                self.m,
                self.n,
                self.k,
            )
        ]
        x_shape_expr = [k, m] if trans_a else [m, k]
        y_shape_expr = [n, k] if trans_b else [k, n]
        x = lang.Placeholder("float32", "x", x_shape_expr)
        y = lang.Placeholder("float32", "y", y_shape_expr)
        func_name = "test_" + fn_name
        z = cinn_fn(x.to_tensor(), y.to_tensor(), trans_a, trans_b, alpha)
        tensor_args = [x.to_tensor(), y.to_tensor()]
        for out in z:
            tensor_args.append(out)
        stages = create_stages(tensor_args)
        func = lang.lower(func_name, stages, tensor_args)
        print(func)

        builder = lang.Module.Builder("transform_module", self.target)
        builder.add_function(func)

        module = builder.build()
        self.compiler.build(module)

        fn = self.compiler.lookup(func_name)

        x_data, y_data, x_buf, y_buf, out_buf, *args = self.create_data(
            (self.m, self.n), trans_a, trans_b, alpha
        )
        fn(args)

        np.testing.assert_allclose(
            out_buf.numpy(),
            self.create_target_data(
                np_fn, x_data, y_data, trans_a, trans_b, alpha
            ),
            atol=1e-4,
        )

    def create_target_data(
        self, np_target_fn, x_data, y_data, trans_a, trans_b, alpha
    ):
        x_data = np.transpose(x_data) if trans_a else x_data
        y_data = np.transpose(y_data) if trans_b else y_data
        return np_target_fn(x_data, y_data) * alpha

    def create_data(self, output_shape, trans_a, trans_b, alpha=1):
        if not self.transform_data:
            if trans_a:
                x_data = np.around(
                    np.random.randn(self.k, self.m).astype("float32"), 2
                )
            else:
                x_data = np.around(
                    np.random.randn(self.m, self.k).astype("float32"), 2
                )
            if trans_b:
                y_data = np.around(
                    np.random.randn(self.n, self.k).astype("float32"), 2
                )
            else:
                y_data = np.around(
                    np.random.randn(self.k, self.n).astype("float32"), 2
                )
            x = runtime.cinn_buffer_t(x_data, runtime.cinn_x86_device)
            y = runtime.cinn_buffer_t(y_data, runtime.cinn_x86_device)
            out = runtime.cinn_buffer_t(
                np.zeros(output_shape).astype("float32"),
                runtime.cinn_x86_device,
            )
            out1 = runtime.cinn_buffer_t(
                np.zeros(output_shape).astype("float32"),
                runtime.cinn_x86_device,
            )
            self.transform_data = [
                x_data,
                y_data,
                x,
                y,
                out,
                runtime.cinn_pod_value_t(x),
                runtime.cinn_pod_value_t(y),
                runtime.cinn_pod_value_t(out),
                runtime.cinn_pod_value_t(out1),
            ]

        return self.transform_data


if __name__ == "__main__":
    unittest.main()