Spaces:
Runtime error
Runtime error
File size: 5,085 Bytes
8a42f8f |
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 |
import unittest
import functools as ft
import itertools as it
from apex import amp
import torch
from torch import nn
import torch.nn.functional as F
from utils import common_init, HALF, FLOAT,\
ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
def run_layer_test(test_case, fns, expected, input_shape, test_backward=True):
for fn, typ in it.product(fns, expected.keys()):
x = torch.randn(input_shape, dtype=typ).requires_grad_()
y = fn(x)
test_case.assertEqual(y.type(), expected[typ])
if test_backward:
y.float().sum().backward()
test_case.assertEqual(x.grad.type(), MATCH_INPUT[typ])
class TestBasicCasts(unittest.TestCase):
def setUp(self):
self.handle = amp.init(enabled=True)
common_init(self)
def tearDown(self):
self.handle._deactivate()
def test_linear_is_half(self):
m = nn.Linear(self.h, self.h)
f = ft.partial(F.linear, weight=m.weight, bias=m.bias)
run_layer_test(self, [m, f], ALWAYS_HALF, (self.b, self.h))
def test_conv2d_is_half(self):
m = nn.Conv2d(self.c, self.c, self.k)
f = ft.partial(F.conv2d, weight=m.weight, bias=m.bias)
run_layer_test(self, [m, f], ALWAYS_HALF, (self.b, self.c, self.h, self.h))
def test_softmax_is_float(self):
m = nn.Softmax(dim=1)
f = ft.partial(F.softmax, dim=1)
run_layer_test(self, [m, f], ALWAYS_FLOAT, (self.b, self.h))
def test_group_norm_is_float(self):
m = nn.GroupNorm(num_groups=4, num_channels=self.c)
run_layer_test(self, [m], ALWAYS_FLOAT, (self.b, self.c, self.h, self.h))
def test_mse_loss_is_float(self):
shape = (self.b, self.h)
target = torch.randn(shape)
mod = nn.MSELoss()
m = lambda x: mod(x, target)
f = ft.partial(F.mse_loss, target=target)
run_layer_test(self, [m], ALWAYS_FLOAT, shape)
def test_relu_is_match(self):
run_layer_test(self, [nn.ReLU(), F.relu], MATCH_INPUT, (self.b, self.h))
def test_batch_norm_is_match(self):
m = nn.BatchNorm2d(num_features=self.c)
f = ft.partial(F.batch_norm, running_mean=m.running_mean, running_var=m.running_var,
weight=m.weight, bias=m.bias, training=True)
run_layer_test(self, [m], MATCH_INPUT, (self.b, self.c, self.h, self.h))
# Test forward-only for BN inference
m.eval()
f = ft.partial(F.batch_norm, running_mean=m.running_mean, running_var=m.running_var,
weight=m.weight, bias=m.bias, training=False)
run_layer_test(self, [m, f], MATCH_INPUT, (self.b, self.c, self.h, self.h),
test_backward=False)
class TestBannedMethods(unittest.TestCase):
def setUp(self):
self.handle = amp.init(enabled=True)
common_init(self)
def tearDown(self):
self.handle._deactivate()
def bce_common(self, assertion):
shape = (self.b, self.h)
target = torch.rand(shape)
mod = nn.BCELoss()
m = lambda x: mod(x, target)
f = ft.partial(F.binary_cross_entropy, target=target)
for fn in [m, f]:
x = torch.rand(shape, dtype=torch.half)
assertion(fn, x)
def test_bce_raises_by_default(self):
assertion = lambda fn, x: self.assertRaises(NotImplementedError, fn, x)
self.bce_common(assertion)
def test_bce_is_float_with_allow_banned(self):
self.handle._deactivate()
self.handle = amp.init(enabled=True, allow_banned=True)
assertion = lambda fn, x: self.assertEqual(fn(x).type(), FLOAT)
self.bce_common(assertion)
class TestTensorCasts(unittest.TestCase):
def setUp(self):
self.handle = amp.init(enabled=True)
common_init(self)
def tearDown(self):
self.handle._deactivate()
def test_matmul_method_is_half(self):
other = torch.randn(self.h, self.h)
lhs = lambda x: x.matmul(other)
rhs = lambda x: other.matmul(x)
run_layer_test(self, [lhs, rhs], ALWAYS_HALF, (self.h, self.h))
def test_matmul_op_is_half(self):
other = torch.randn(self.h, self.h)
lhs = lambda x: x @ other
rhs = lambda x: other @ x
run_layer_test(self, [lhs, rhs], ALWAYS_HALF, (self.h, self.h))
def test_pow_method_is_float(self):
fn = lambda x: x.pow(2.)
run_layer_test(self, [fn], ALWAYS_FLOAT, (self.b, self.h))
def test_pow_op_is_float(self):
fn = lambda x: x ** 2.
run_layer_test(self, [fn], ALWAYS_FLOAT, (self.b, self.h))
def test_cpu_is_float(self):
fn = lambda x: x.cpu()
always_cpu_float = {torch.float: 'torch.FloatTensor',
torch.half: 'torch.FloatTensor'}
run_layer_test(self, [fn], always_cpu_float, (self.b, self.h))
def test_sum_is_float(self):
fn = lambda x: x.sum()
run_layer_test(self, [fn], ALWAYS_FLOAT, (self.b, self.h))
# TODO: maybe more tests on disabled casting?
if __name__ == '__main__':
unittest.main()
|