Extend test timeout (#727)

Summary:
Pull Request resolved: #727

Some health tests are still failing due to timeout. Extended timeout to 60,000 ms for all tests with a deadline, which is the maximum allowed run-time.

Reviewed By: HuanyuZhang

Differential Revision: D68980483

fbshipit-source-id: 8743e5630e22bb966885f7acacc9660982cc85c7

opacus/tests/accountants_test.py

@@ -136,7 +136,7 @@ def test_get_noise_multiplier_prv_steps(self) -> None:
         ),
         delta=st.sampled_from([1e-4, 1e-5, 1e-6]),
     )
-    @settings(deadline=40000)
+    @settings(deadline=60000)
     def test_get_noise_multiplier_overshoot(self, epsilon, epochs, sample_rate, delta):
         noise_multiplier = get_noise_multiplier(
             target_epsilon=epsilon,

opacus/tests/batch_memory_manager_test.py

@@ -59,7 +59,7 @@ def _init_training(self, batch_size=10, **data_loader_kwargs):
         batch_size=st.sampled_from([8, 16, 64]),
         max_physical_batch_size=st.sampled_from([4, 8]),
     )
-    @settings(suppress_health_check=list(HealthCheck), deadline=40000)
+    @settings(suppress_health_check=list(HealthCheck), deadline=60000)
     def test_basic(
         self,
         num_workers: int,
@@ -119,7 +119,7 @@ def test_basic(
         num_workers=st.integers(0, 4),
         pin_memory=st.booleans(),
     )
-    @settings(suppress_health_check=list(HealthCheck), deadline=40000)
+    @settings(suppress_health_check=list(HealthCheck), deadline=60000)
     def test_empty_batch(
         self,
         num_workers: int,

opacus/tests/dp_layers/dp_multihead_attention_test.py

@@ -57,7 +57,7 @@ class DPMultiheadAttention_test(DPModules_test):
         kdim=st.integers(2, 8) | st.none(),
         vdim=st.integers(2, 8) | st.none(),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_attn(
         self,
         batch_size: int,
@@ -142,7 +142,7 @@ def test_attn(
         kdim=st.integers(2, 8) | st.none(),
         vdim=st.integers(2, 8) | st.none(),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_dp_attn(
         self,
         batch_size: int,
@@ -227,7 +227,7 @@ def test_dp_attn(
         kdim=st.integers(2, 8) | st.none(),
         vdim=st.integers(2, 8) | st.none(),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_dp_attn_hook(
         self,
         batch_size: int,

opacus/tests/dp_layers/dp_rnn_test.py

@@ -57,7 +57,7 @@ class DPLSTM_test(DPModules_test):
         zero_init=st.booleans(),
         packed_input_flag=st.integers(0, 2),
     )
-    @settings(deadline=20000)
+    @settings(deadline=60000)
     def test_rnn(
         self,
         mode: str,

opacus/tests/grad_samples/conv1d_test.py

@@ -35,7 +35,7 @@ class Conv1d_test(GradSampleHooks_test):
         dilation=st.integers(1, 2),
         groups=st.integers(1, 12),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_conv1d(
         self,
         N: int,

opacus/tests/grad_samples/conv2d_test.py

@@ -40,7 +40,7 @@ class Conv2d_test(GradSampleHooks_test):
         dilation=st.integers(1, 3),
         groups=st.integers(1, 16),
     )
-    @settings(deadline=30000)
+    @settings(deadline=60000)
     def test_conv2d(
         self,
         N: int,
@@ -117,7 +117,7 @@ def test_conv2d(
         dilation_h=st.integers(1, 3),
         dilation_w=st.integers(1, 3),
     )
-    @settings(deadline=30000)
+    @settings(deadline=60000)
     def test_unfold2d(
         self,
         B: int,

opacus/tests/grad_samples/conv3d_test.py

@@ -37,7 +37,7 @@ class Conv3d_test(GradSampleHooks_test):
         dilation=st.sampled_from([1, (1, 2, 2)]),
         groups=st.integers(1, 16),
     )
-    @settings(deadline=30000)
+    @settings(deadline=60000)
     def test_conv3d(
         self,
         N: int,

opacus/tests/grad_samples/dp_multihead_attention_test.py

@@ -55,7 +55,7 @@ class MultiHeadAttention_test(GradSampleHooks_test):
         kv_dim=st.booleans(),
         test_or_check=st.integers(1, 2),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_multihead_attention(
         self,
         N: int,

opacus/tests/grad_samples/dp_rnn_test.py

@@ -60,7 +60,7 @@ class RNN_test(GradSampleHooks_test):
         using_packed_sequences=st.booleans(),
         packed_sequences_sorted=st.booleans(),
     )
-    @settings(deadline=30000)
+    @settings(deadline=60000)
     def test_rnn(
         self,
         model,

opacus/tests/grad_samples/embedding_bag_test.py

@@ -29,7 +29,7 @@ class Embedding_bag_test(GradSampleHooks_test):
         D=st.integers(10, 17),
         mode=st.sampled_from(["sum", "mean"]),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_input_across_dims(
         self,
         N: int,

opacus/tests/grad_samples/embedding_test.py

@@ -32,7 +32,7 @@ class Embedding_test(GradSampleHooks_test):
         dim=st.integers(2, 4),
         batch_first=st.booleans(),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_input_across_dims(
         self,
         N: int,

opacus/tests/grad_samples/group_norm_test.py

@@ -36,7 +36,7 @@ class GroupNorm_test(GradSampleHooks_test):
         W=st.integers(4, 8),
         num_groups=st.sampled_from([1, 4, "C"]),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_3d_input_groups(
         self,
         N: int,

opacus/tests/grad_samples/instance_norm1d_test.py

@@ -23,7 +23,7 @@
 
 class InstanceNorm1d_test(GradSampleHooks_test):
     @given(N=st.integers(1, 4), C=st.integers(1, 3), W=st.integers(5, 10))
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_3d_input(self, N: int, C: int, W: int):
         x = torch.randn([N, C, W])
         norm = nn.InstanceNorm1d(num_features=C, affine=True, track_running_stats=False)

opacus/tests/grad_samples/instance_norm2d_test.py

@@ -28,7 +28,7 @@ class InstanceNorm2d_test(GradSampleHooks_test):
         W=st.integers(5, 10),
         H=st.integers(4, 8),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_4d_input(self, N: int, C: int, W: int, H: int):
         x = torch.randn([N, C, H, W])
         norm = nn.InstanceNorm2d(num_features=C, affine=True, track_running_stats=False)

opacus/tests/grad_samples/instance_norm3d_test.py

@@ -29,7 +29,7 @@ class InstanceNorm3d_test(GradSampleHooks_test):
         H=st.integers(4, 8),
         Z=st.integers(1, 4),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_5d_input(self, N: int, C: int, W: int, H: int, Z: int):
         x = torch.randn([N, C, Z, H, W])
         norm = nn.InstanceNorm3d(num_features=C, affine=True, track_running_stats=False)

opacus/tests/grad_samples/layer_norm_test.py

@@ -30,7 +30,7 @@ class LayerNorm_test(GradSampleHooks_test):
         input_dim=st.integers(2, 4),
         norm_dim=st.integers(1, 3),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_input_norm(
         self, N: int, Z: int, W: int, H: int, input_dim: int, norm_dim: int
     ):

opacus/tests/grad_samples/linear_test.py

@@ -31,7 +31,7 @@ class Linear_test(GradSampleHooks_test):
         bias=st.booleans(),
         batch_first=st.booleans(),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_input_bias(
         self,
         N: int,

opacus/tests/grad_samples/sequence_bias_test.py

@@ -28,7 +28,7 @@ class SequenceBias_test(GradSampleHooks_test):
         D=st.integers(4, 8),
         batch_first=st.booleans(),
     )
-    @settings(deadline=10000)
+    @settings(deadline=60000)
     def test_batch_second(self, N: int, T: int, D: int, batch_first: bool):
         seqbias = SequenceBias(D, batch_first)
         if batch_first:

opacus/tests/per_sample_gradients_utils_test.py

@@ -71,7 +71,7 @@ def per_sample_grads_utils_test(
         groups=st.integers(1, 12),
         grad_sample_mode=st.sampled_from(get_grad_sample_modes(use_ew=True)),
     )
-    @settings(deadline=40000)
+    @settings(deadline=60000)
     def test_conv1d(
         self,
         N: int,
@@ -120,7 +120,7 @@ def test_conv1d(
         batch_first=st.booleans(),
         grad_sample_mode=st.sampled_from(get_grad_sample_modes(use_ew=True)),
     )
-    @settings(deadline=40000)
+    @settings(deadline=60000)
     def test_linear(
         self,
         N: int,