test_reduce_map.py 3.44 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.
17 18 19 20 21 22 23 24 25 26 27 28 29 30
import os
import tvm
from tvm.contrib import nvcc
import numpy as np

import topi


TASK = "reduce_map"
USE_MANUAL_CODE = False


@tvm.register_func
def tvm_callback_cuda_compile(code):
31
    ptx = nvcc.compile_cuda(code, target="ptx")
32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
    return ptx


def write_code(code, fname):
    with open(fname, "w") as f:
        f.write(code)


@tvm.register_func
def tvm_callback_cuda_postproc(code):
    if not os.path.exists("perf"):
        os.mkdir("perf")
    write_code(code, "perf/%s_generated.cu" % TASK)
    if USE_MANUAL_CODE:
        code = open("perf/%s_manual.cu" % TASK).read()
    return code


def test_reduce_map(in_shape, axis, keepdims, type="sum", test_id=0):
    global TASK
    # Build the logic and compile the function
    A = tvm.placeholder(shape=in_shape, name="A")
    if type == "sum":
        TASK = "sum_map_id%d" %test_id
        B = topi.sum(A, axis=axis, keepdims=keepdims)
    elif type == "max":
        TASK = "max_map_id%d" %test_id
        B = topi.max(A, axis=axis, keepdims=keepdims)
    elif type == "min":
        TASK = "min_map_id%d" %test_id
        B = topi.min(A, axis=axis, keepdims=keepdims)
    else:
        raise NotImplementedError
    s = topi.cuda.schedule_reduce(B)
    with tvm.build_config(auto_unroll_max_step=16,
                          auto_unroll_min_depth=0):
        fcuda = tvm.build(s, [A, B], "cuda", name="sum")

    # Test
    in_npy = np.random.normal(size=in_shape).astype(np.float32)
    if type == "sum":
        out_npy = in_npy.sum(axis=axis, keepdims=keepdims)
    elif type == "max":
        out_npy = in_npy.max(axis=axis, keepdims=keepdims)
    elif type == "min":
        out_npy = in_npy.min(axis=axis, keepdims=keepdims)
    else:
        raise NotImplementedError

    data_tvm = tvm.nd.array(in_npy, ctx=tvm.gpu())
    out_tvm = tvm.nd.empty(shape=out_npy.shape, ctx=tvm.gpu())

    for _ in range(2):
        fcuda(data_tvm, out_tvm)
86
    tvm.testing.assert_allclose(out_tvm.asnumpy(), out_npy, rtol=4e-4, atol=4e-4)
87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108

if __name__ == "__main__":
    test_reduce_map(in_shape=(128, 24, 128, 24),
                    axis=(1, 2, 3),
                    keepdims=True,
                    type="sum",
                    test_id=0)
    test_reduce_map(in_shape=(128, 24 * 128 * 24),
                    axis=(1,),
                    keepdims=False,
                    type="max",
                    test_id=1)
    test_reduce_map(in_shape=(32, 128, 24),
                    axis=None,
                    keepdims=True,
                    type="sum",
                    test_id=2)
    test_reduce_map(in_shape=(128, 24, 128, 24),
                    axis=(0, 2),
                    keepdims=False,
                    type="min",
                    test_id=3)