Upload apex-master/tests/L0/run_amp/test_basic_casts.py with huggingface_hub

apex-master/tests/L0/run_amp/test_basic_casts.py

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+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()