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66c9c8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 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 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | # Copyright (c) 2022 NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import unittest
import numpy as np
import warp as wp
from warp.tests.unittest_utils import *
wp.init()
# atomic add function that memorizes which thread incremented the counter
# so that the correct counter value per thread can be used in the replay
# phase of the backward pass
@wp.func
def reversible_increment(
counter: wp.array(dtype=int), counter_index: int, value: int, thread_values: wp.array(dtype=int), tid: int
):
next_index = wp.atomic_add(counter, counter_index, value)
thread_values[tid] = next_index
return next_index
@wp.func_replay(reversible_increment)
def replay_reversible_increment(
counter: wp.array(dtype=int), counter_index: int, value: int, thread_values: wp.array(dtype=int), tid: int
):
return thread_values[tid]
def test_custom_replay_grad(test, device):
num_threads = 128
counter = wp.zeros(1, dtype=wp.int32, device=device)
thread_ids = wp.zeros(num_threads, dtype=wp.int32, device=device)
inputs = wp.array(np.arange(num_threads, dtype=np.float32), device=device, requires_grad=True)
outputs = wp.zeros_like(inputs)
@wp.kernel
def run_atomic_add(
input: wp.array(dtype=float),
counter: wp.array(dtype=int),
thread_values: wp.array(dtype=int),
output: wp.array(dtype=float),
):
tid = wp.tid()
idx = reversible_increment(counter, 0, 1, thread_values, tid)
output[idx] = input[idx] ** 2.0
tape = wp.Tape()
with tape:
wp.launch(
run_atomic_add, dim=num_threads, inputs=[inputs, counter, thread_ids], outputs=[outputs], device=device
)
tape.backward(grads={outputs: wp.array(np.ones(num_threads, dtype=np.float32), device=device)})
assert_np_equal(inputs.grad.numpy(), 2.0 * inputs.numpy(), tol=1e-4)
@wp.func
def overload_fn(x: float, y: float):
return x * 3.0 + y / 3.0, y**2.5
@wp.func_grad(overload_fn)
def overload_fn_grad(x: float, y: float, adj_ret0: float, adj_ret1: float):
wp.adjoint[x] += x * adj_ret0 * 42.0 + y * adj_ret1 * 10.0
wp.adjoint[y] += y * adj_ret1 * 3.0
@wp.struct
class MyStruct:
scalar: float
vec: wp.vec3
@wp.func
def overload_fn(x: MyStruct):
return x.vec[0] * x.vec[1] * x.vec[2] * 4.0, wp.length(x.vec), x.scalar**0.5
@wp.func_grad(overload_fn)
def overload_fn_grad(x: MyStruct, adj_ret0: float, adj_ret1: float, adj_ret2: float):
wp.adjoint[x.scalar] += x.scalar * adj_ret0 * 10.0
wp.adjoint[x.vec][0] += adj_ret0 * x.vec[1] * x.vec[2] * 20.0
wp.adjoint[x.vec][1] += adj_ret1 * x.vec[0] * x.vec[2] * 30.0
wp.adjoint[x.vec][2] += adj_ret2 * x.vec[0] * x.vec[1] * 40.0
@wp.kernel
def run_overload_float_fn(
xs: wp.array(dtype=float), ys: wp.array(dtype=float), output0: wp.array(dtype=float), output1: wp.array(dtype=float)
):
i = wp.tid()
out0, out1 = overload_fn(xs[i], ys[i])
output0[i] = out0
output1[i] = out1
@wp.kernel
def run_overload_struct_fn(xs: wp.array(dtype=MyStruct), output: wp.array(dtype=float)):
i = wp.tid()
out0, out1, out2 = overload_fn(xs[i])
output[i] = out0 + out1 + out2
def test_custom_overload_grad(test, device):
dim = 3
xs_float = wp.array(np.arange(1.0, dim + 1.0), dtype=wp.float32, requires_grad=True)
ys_float = wp.array(np.arange(10.0, dim + 10.0), dtype=wp.float32, requires_grad=True)
out0_float = wp.zeros(dim)
out1_float = wp.zeros(dim)
tape = wp.Tape()
with tape:
wp.launch(run_overload_float_fn, dim=dim, inputs=[xs_float, ys_float], outputs=[out0_float, out1_float])
tape.backward(
grads={
out0_float: wp.array(np.ones(dim), dtype=wp.float32),
out1_float: wp.array(np.ones(dim), dtype=wp.float32),
}
)
assert_np_equal(xs_float.grad.numpy(), xs_float.numpy() * 42.0 + ys_float.numpy() * 10.0)
assert_np_equal(ys_float.grad.numpy(), ys_float.numpy() * 3.0)
x0 = MyStruct()
x0.vec = wp.vec3(1.0, 2.0, 3.0)
x0.scalar = 4.0
x1 = MyStruct()
x1.vec = wp.vec3(5.0, 6.0, 7.0)
x1.scalar = -1.0
x2 = MyStruct()
x2.vec = wp.vec3(8.0, 9.0, 10.0)
x2.scalar = 19.0
xs_struct = wp.array([x0, x1, x2], dtype=MyStruct, requires_grad=True)
out_struct = wp.zeros(dim)
tape = wp.Tape()
with tape:
wp.launch(run_overload_struct_fn, dim=dim, inputs=[xs_struct], outputs=[out_struct])
tape.backward(grads={out_struct: wp.array(np.ones(dim), dtype=wp.float32)})
xs_struct_np = xs_struct.numpy()
struct_grads = xs_struct.grad.numpy()
# fmt: off
assert_np_equal(
np.array([g[0] for g in struct_grads]),
np.array([g[0] * 10.0 for g in xs_struct_np]))
assert_np_equal(
np.array([g[1][0] for g in struct_grads]),
np.array([g[1][1] * g[1][2] * 20.0 for g in xs_struct_np]))
assert_np_equal(
np.array([g[1][1] for g in struct_grads]),
np.array([g[1][0] * g[1][2] * 30.0 for g in xs_struct_np]))
assert_np_equal(
np.array([g[1][2] for g in struct_grads]),
np.array([g[1][0] * g[1][1] * 40.0 for g in xs_struct_np]))
# fmt: on
devices = get_test_devices()
class TestGradCustoms(unittest.TestCase):
pass
add_function_test(TestGradCustoms, "test_custom_replay_grad", test_custom_replay_grad, devices=devices)
add_function_test(TestGradCustoms, "test_custom_overload_grad", test_custom_overload_grad, devices=devices)
if __name__ == "__main__":
wp.build.clear_kernel_cache()
unittest.main(verbosity=2, failfast=False)
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