Remove prints and add proper asserts
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78454
Approved by: https://github.com/kulinseth
diff --git a/test/test_mps.py b/test/test_mps.py
index 94e084f..f49e1fb 100644
--- a/test/test_mps.py
+++ b/test/test_mps.py
@@ -28,7 +28,6 @@
TestCase = object # noqa: F811
NNTestCase = object # noqa: F811
-
class MPSReluTest(TestCase):
def _npRelu(self, np_features):
return np.maximum(np_features, np.zeros(np_features.shape)).astype(np_features.dtype)
@@ -218,7 +217,6 @@
def test_exp1(self, device="mps", dtype=torch.float):
input = torch.tensor([-0.1, 3.0, -0.9]).to('mps')
output = torch.exp(input).to('cpu')
- print(output)
def _testLeakyRelu(self, np_features, negative_slope, device):
cpu_x = torch.from_numpy(np_features).requires_grad_()
@@ -508,7 +506,6 @@
net = torch.nn.AdaptiveAvgPool2d((1, 1))
out = net(x)
ref_out = x.contiguous().mean((-1, -2)).view((x.size(0), x.size(1), 1, 1))
- print(ref_out)
out.sum().backward() # make sure it doesn't crash
@@ -1196,8 +1193,6 @@
cpu_x.transpose_(0, 1)
mps_x.transpose_(0, 1)
- print(cpu_x)
- print(mps_x.to('cpu'))
self.assertEqual(cpu_x, mps_x.to('cpu'))
def test_slice(self):
@@ -1518,7 +1513,6 @@
all_sum.backward()
all_sum_cpu.backward()
- print(torch.ones(1, device="mps").expand(10).clone())
self.assertEqual(all_sum, all_sum_cpu)
self.assertEqual(x.grad, cpu_x.grad)
@@ -3396,8 +3390,6 @@
cpu_idx = torch.tensor(index, device='cpu', dtype=idx_dtype)
idx = cpu_idx.detach().clone().to('mps')
- print(cpu_idx.shape)
-
idx_result = torch.index_select(x, dim=dim, index=idx)
idx_result_cpu = torch.index_select(cpu_x, dim=dim, index=cpu_idx)
@@ -3826,15 +3818,16 @@
prob_tensor = cpu_prob_tensor.detach().clone().to('mps')
mps_out = torch.bernoulli(prob_tensor)
- # Compare "real" with theoretical values
- print(mps_out.to('cpu').mean(), prob)
- print(mps_out.to('cpu').std() ** 2, prob * (1 - prob))
+ # We can't check reliably the mean and std.
+ # Just make sure we don't return constant values
+ self.assertNotEqual(mps_out.to('cpu').mean(), 0.)
+ self.assertNotEqual(mps_out.to('cpu').std() ** 2, 0.)
mps_out = torch.zeros(shape, device='mps')
mps_out = torch.bernoulli(mps_out, prob)
- print(mps_out.to('cpu').mean(), prob)
- print(mps_out.to('cpu').std() ** 2, prob * (1 - prob))
+ self.assertNotEqual(mps_out.to('cpu').mean(), 0.)
+ self.assertNotEqual(mps_out.to('cpu').std(), 0.)
helper((100, 100), 0.50)
helper((100, 100), 0.76)
@@ -3844,11 +3837,12 @@
def test_random(self):
def helper(shape, low, high, dtype=torch.int32):
- print(low, high)
mps_out = torch.randint(low, high, shape, dtype=dtype, device='mps')
- print(mps_out.to('cpu').float().mean(), (low + (high - 1)) / 2.)
- print(mps_out.to('cpu').float().std() ** 2, ((high - low)**2 - 1) / 12.)
+ # We can't check reliably the mean and std.
+ # Just make sure we don't return constant values
+ self.assertNotEqual(mps_out.to('cpu').float().mean(), 0.)
+ self.assertNotEqual(mps_out.to('cpu').float().std(), 0.)
helper([100, 100], 0, 10)
helper([100, 100], 23, 89)
@@ -3886,8 +3880,6 @@
cpu_out = torch.add(cpu_x, cpu_y, alpha=alpha)
out = torch.add(x, y, alpha=alpha)
- print(out.to('cpu'))
-
self.assertEqual(out, cpu_out)
helper()
@@ -4166,35 +4158,40 @@
input = torch.randn(2, 3, 1, device="cpu")
hx = torch.zeros(2, 3, 4, device="cpu")
cx = torch.zeros(2, 3, 4, device="cpu")
- outputs = []
- for device in [torch.device("cpu"), torch.device("mps")]:
- rnn = rnn.to(device)
- input = input.to(device)
- hx = hx.to(device)
- cx = cx.to(device)
- weight_list = []
- output, _ = rnn(input, (hx, cx))
- print(output.to('cpu'))
+ cpu_output, _ = rnn(input, (hx, cx))
+
+ device = torch.device("mps")
+ rnn = rnn.to(device)
+ input = input.to(device)
+ hx = hx.to(device)
+ cx = cx.to(device)
+ output, _ = rnn(input, (hx, cx))
+ self.assertEqual(cpu_output, output)
+
+ @unittest.skipIf(True, "Backward of lstm returns wrong result")
def test_lstm_2(self, device="mps", dtype=torch.float32):
- rnn = nn.LSTM(1, 4, 1, device="cpu")
- input = torch.randn(2, 3, 1, device="cpu", requires_grad=True)
- hx = torch.zeros(1, 3, 4, device="cpu")
- cx = torch.zeros(1, 3, 4, device="cpu")
- outputs = []
- for device in [torch.device("cpu"), torch.device("mps")]:
- rnn = rnn.to(device)
- input = input.to(device)
- input.retain_grad()
- hx = hx.to(device)
- cx = cx.to(device)
+ def get_results(device):
+ rnn = nn.LSTM(1, 4, 1, device=device)
+ inp = torch.randn(2, 3, 1, device=device, requires_grad=True)
+ hx = torch.zeros(1, 3, 4, device=device)
+ cx = torch.zeros(1, 3, 4, device=device)
- output, _ = rnn(input, (hx, cx))
- # Test by passing ones as the gradient from the loss.
- output.backward(torch.ones_like(output))
+ output, _ = rnn(inp, (hx, cx))
+ output.sum().backward()
- print(rnn.weight_ih_l0.grad)
- # Gradient on GPU is 2x the CPU gradient???
+ weight_grad = rnn.weight_ih_l0.grad.clone()
+ input_grad = inp.grad.clone()
+
+ return output, weight_grad, input_grad
+
+
+ cpu_output, cpu_weight_grad, cpu_input_grad = get_results("cpu")
+ mps_output, mps_weight_grad, mps_input_grad = get_results("mps")
+
+ self.assertEqual(cpu_output, mps_output)
+ self.assertEqual(cpu_input_grad, mps_input_grad)
+ self.assertEqual(cpu_weight_grad, mps_weight_grad)
class TestFallbackWarning(TestCase):
def test_no_warning_on_import(self):
