mirror of https://github.com/Jittor/Jittor
235 lines
8.2 KiB
Python
235 lines
8.2 KiB
Python
# ***************************************************************
|
|
# Copyright (c) 2020 Jittor. Authors: Dun Liang <randonlang@gmail.com>. All Rights Reserved.
|
|
# This file is subject to the terms and conditions defined in
|
|
# file 'LICENSE.txt', which is part of this source code package.
|
|
# ***************************************************************
|
|
import unittest
|
|
import jittor as jt
|
|
import numpy as np
|
|
|
|
class TestCodeOp(unittest.TestCase):
|
|
def test(self):
|
|
a = jt.random([10])
|
|
b = jt.code(a.shape, a.dtype, [a],
|
|
cpu_src='''
|
|
for (int i=0; i<in0_shape0; i++)
|
|
@out(i) = @in0(i)*@in0(i)*2;
|
|
''',
|
|
cpu_grad_src = ['''
|
|
for (int i=0; i<in0_shape0; i++) {
|
|
@out(i) = @dout(i)*@in0(i)*4;
|
|
}
|
|
'''])
|
|
na, nb = jt.fetch_sync([a,b])
|
|
assert np.allclose(na*na*2, nb)
|
|
|
|
c = jt.random([10])
|
|
da = jt.grad(c*b, a)
|
|
assert np.allclose(c.data*na*4, da.data), (c.data*na*4, da.data)
|
|
|
|
def test_multi_input(self):
|
|
a = jt.random([10])
|
|
b = jt.random([10])
|
|
c = jt.code(a.shape, a.dtype, [a,b],
|
|
cpu_src='''
|
|
for (int i=0; i<in0_shape0; i++)
|
|
@out(i) = @in0(i)*@in1(i);
|
|
''',
|
|
cpu_grad_src = ['''
|
|
for (int i=0; i<in0_shape0; i++) {
|
|
@out(i) = @dout(i)*@in1(i);
|
|
}
|
|
''', '''
|
|
for (int i=0; i<in0_shape0; i++) {
|
|
@out(i) = @dout(i)*@in0(i);
|
|
}
|
|
'''])
|
|
da, db = jt.grad(c, [a, b])
|
|
assert np.allclose(c.data, a.data*b.data), (c.data, a.data*b.data)
|
|
assert np.allclose(da.data, b.data)
|
|
assert np.allclose(db.data, a.data)
|
|
|
|
def test_header(self):
|
|
a = jt.array([3,2,1])
|
|
b = jt.code(a.shape, a.dtype, [a],
|
|
cpu_header="""
|
|
#include <algorithm>
|
|
@alias(a, in0)
|
|
@alias(b, out)
|
|
""",
|
|
cpu_src="""
|
|
for (int i=0; i<a_shape0; i++)
|
|
@b(i) = @a(i);
|
|
std::sort(&@b(0), &@b(in0_shape0));
|
|
"""
|
|
)
|
|
assert (b.data==[1,2,3]).all()
|
|
|
|
def test_multi_output(self):
|
|
a = jt.array([3,2,1])
|
|
b,c = jt.code([[2],[4]], ["float32", "float64"], [a],
|
|
cpu_src="""
|
|
@alias(a, in0)
|
|
@alias(b, out0)
|
|
@alias(c, out1)
|
|
for (int i=0; i<a_shape0; i++) {
|
|
if (i<b_shape0) @b(i) = @a(i);
|
|
if (i<c_shape0) @c(i) = @a(i);
|
|
}
|
|
"""
|
|
)
|
|
assert b.shape == [2]
|
|
assert c.shape == [4]
|
|
assert b.dtype == "float32"
|
|
assert c.dtype == "float64"
|
|
assert (b.data == [3,2]).all()
|
|
assert (c.data[:3] == [3,2,1]).all()
|
|
|
|
def test_multi_output2(self):
|
|
a = jt.array([3,2,1])
|
|
b,c = jt.code([(1,), (1,)], [a.dtype, a.dtype], [a],
|
|
cpu_header="""
|
|
#include <iostream>
|
|
using namespace std;
|
|
""",
|
|
cpu_src="""
|
|
@alias(a, in0)
|
|
@alias(b, out0)
|
|
@alias(c, out1)
|
|
@b(0) = @c(0) = @a(0);
|
|
for (int i=0; i<a_shape0; i++) {
|
|
@b(0) = std::min(@b(0), @a(i));
|
|
@c(0) = std::max(@c(0), @a(i));
|
|
}
|
|
cout << "min:" << @b(0) << " max:" << @c(0) << endl;
|
|
"""
|
|
)
|
|
assert b.data == 1, b
|
|
assert c.data == 3, c
|
|
|
|
def test_vary_shape(self):
|
|
a = jt.array([5,-4,3,-2,1])
|
|
|
|
# negtive shape for max size of vary dimension
|
|
b,c = jt.code([(-5,), (-5,)], [a.dtype, a.dtype], [a],
|
|
cpu_src="""
|
|
@alias(a, in0)
|
|
@alias(b, out0)
|
|
@alias(c, out1)
|
|
int num_b=0, num_c=0;
|
|
for (int i=0; i<a_shape0; i++) {
|
|
if (@a(i)>0)
|
|
@b(num_b++) = @a(i);
|
|
else
|
|
@c(num_c++) = @a(i);
|
|
}
|
|
b->set_shape({num_b});
|
|
c->set_shape({num_c});
|
|
"""
|
|
)
|
|
assert (b.data == [5,3,1]).all()
|
|
assert (c.data == [-4,-2]).all()
|
|
|
|
def test_comment(self):
|
|
a = jt.array([3,2,1])
|
|
b = jt.code(a.shape, a.dtype, [a],
|
|
cpu_header='''
|
|
#include <algorithm>
|
|
// asd
|
|
/* asd
|
|
*/
|
|
''',
|
|
cpu_src="""
|
|
// test comment
|
|
/*
|
|
multi line
|
|
*/
|
|
@alias(a, in0)
|
|
for (int i=0; i<a_shape0; i++)
|
|
@out(i) = @a(i);
|
|
std::sort(&@out(0), &@out(a_shape0));
|
|
"""
|
|
)
|
|
assert (b.data==[1,2,3]).all()
|
|
|
|
@unittest.skipIf(not jt.compiler.has_cuda, "No CUDA found")
|
|
@jt.flag_scope(use_cuda=1)
|
|
def test_cuda(self):
|
|
a = jt.random([100000])
|
|
b = jt.random([100000])
|
|
c = jt.code(a.shape, a.dtype, [a,b],
|
|
cuda_src='''
|
|
__global__ static void kernel1(@ARGS_DEF) {
|
|
@PRECALC
|
|
int i = threadIdx.x + blockIdx.x * blockDim.x;
|
|
int stride = blockDim.x * gridDim.x;
|
|
for (; i<in0_shape0; i+=stride)
|
|
@out(i) = @in0(i)*@in1(i);
|
|
}
|
|
kernel1<<<(in0_shape0-1)/1024+1, 1024>>>(@ARGS);
|
|
''',
|
|
cuda_grad_src = ['''
|
|
__global__ static void kernel2(@ARGS_DEF) {
|
|
@PRECALC
|
|
int i = threadIdx.x + blockIdx.x * blockDim.x;
|
|
int stride = blockDim.x * gridDim.x;
|
|
for (; i<in0_shape0; i+=stride)
|
|
@out(i) = @dout(i)*@in1(i);
|
|
}
|
|
kernel2<<<(in0_shape0-1)/1024+1, 1024>>>(@ARGS);
|
|
''', '''
|
|
__global__ static void kernel3(@ARGS_DEF) {
|
|
@PRECALC
|
|
int i = threadIdx.x + blockIdx.x * blockDim.x;
|
|
int stride = blockDim.x * gridDim.x;
|
|
for (; i<in0_shape0; i+=stride)
|
|
@out(i) = @dout(i)*@in0(i);
|
|
}
|
|
kernel3<<<(in0_shape0-1)/1024+1, 1024>>>(@ARGS);
|
|
'''])
|
|
da, db = jt.grad(c, [a, b])
|
|
assert np.allclose(c.data, a.data*b.data), (c.data, a.data*b.data)
|
|
assert np.allclose(da.data, b.data)
|
|
assert np.allclose(db.data, a.data)
|
|
|
|
@unittest.skipIf(not jt.compiler.has_cuda, "No CUDA found")
|
|
@jt.flag_scope(use_cuda=1)
|
|
def test_cuda2(self):
|
|
a = jt.random((100,100))
|
|
b = jt.random((100,100))
|
|
c = jt.code(a.shape, a.dtype, [a,b],
|
|
cuda_src='''
|
|
__global__ static void kernel1(@ARGS_DEF) {
|
|
@PRECALC
|
|
for (int i=blockIdx.x; i<in0_shape0; i+=gridDim.x)
|
|
for (int j=threadIdx.x; j<in0_shape1; j+=blockDim.x)
|
|
@out(i,j) = @in0(i,j)*@in1(i,j);
|
|
}
|
|
kernel1<<<32, 32>>>(@ARGS);
|
|
''',
|
|
cuda_grad_src = ['''
|
|
__global__ static void kernel(@ARGS_DEF) {
|
|
@PRECALC
|
|
for (int i=blockIdx.x; i<in0_shape0; i+=gridDim.x)
|
|
for (int j=threadIdx.x; j<in0_shape1; j+=blockDim.x)
|
|
@out(i,j) = @dout(i,j)*@in1(i,j);
|
|
}
|
|
kernel<<<32, 32>>>(@ARGS);
|
|
''', '''
|
|
__global__ static void kernel(@ARGS_DEF) {
|
|
@PRECALC
|
|
@pout(0,0);
|
|
for (int i=blockIdx.x; i<in0_shape0; i+=gridDim.x)
|
|
for (int j=threadIdx.x; j<in0_shape1; j+=blockDim.x)
|
|
@out(i,j) = @dout(i,j)*@in0(i,j);
|
|
}
|
|
kernel<<<32, 32>>>(@ARGS);
|
|
'''])
|
|
da, db = jt.grad(c, [a, b])
|
|
assert np.allclose(c.data, a.data*b.data), (c.data, a.data*b.data)
|
|
assert np.allclose(da.data, b.data)
|
|
assert np.allclose(db.data, a.data)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main() |