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WIP fix tests #661

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1 change: 0 additions & 1 deletion tests/test_allreduce.py
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Original file line number Diff line number Diff line change
Expand Up @@ -172,7 +172,6 @@ async def send_tensors(sender_index: int):
)
@pytest.mark.forked
@pytest.mark.asyncio
@pytest.mark.skip("Skipping test due to freezes in CI")
async def test_allreduce_protocol(peer_modes, averaging_weights, peer_fractions, part_size_bytes):
"""Run group allreduce protocol manually without grpc, see if the internal logic is working as intended"""

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2 changes: 0 additions & 2 deletions tests/test_moe.py
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Expand Up @@ -21,7 +21,6 @@


@pytest.mark.forked
@pytest.mark.skip("Skipping test due to freezes in CI")
def test_moe():
all_expert_uids = [
f"ffn.{np.random.randint(0, 3)}.{np.random.randint(0, 3)}.{np.random.randint(0, 3)}" for _ in range(10)
Expand Down Expand Up @@ -66,7 +65,6 @@ def test_no_experts():


@pytest.mark.forked
@pytest.mark.skip(reason="Skipping call_many test due to freezes")
def test_call_many(hidden_dim=16):
k_min = 1
timeout_after_k_min = None
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2 changes: 1 addition & 1 deletion tests/test_p2p_daemon.py
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Expand Up @@ -46,7 +46,7 @@ async def test_startup_error_message():
)

with pytest.raises(P2PDaemonError, match=r"Daemon failed to start in .+ seconds"):
await P2P.create(startup_timeout=0.01) # Test that startup_timeout works
await P2P.create(startup_timeout=0.1)


@pytest.mark.asyncio
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4 changes: 2 additions & 2 deletions tests/test_start_server.py
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Expand Up @@ -12,8 +12,8 @@
def cleanup_process(process, timeout=5):
try:
process.terminate()
process.wait(timeout=timeout) # Add timeout to wait
except: # noqa: E722
process.wait(timeout=timeout)
except (ProcessLookupError, TimeoutError):
process.kill()
process.wait(timeout=timeout)

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3 changes: 0 additions & 3 deletions tests/test_training.py
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Expand Up @@ -14,7 +14,6 @@


@pytest.mark.forked
@pytest.mark.skip("Skipping test due to freezes in CI")
def test_training(max_steps: int = 100, threshold: float = 0.9):
dataset = load_digits(n_class=2)
X_train, y_train = torch.tensor(dataset["data"], dtype=torch.float), torch.tensor(dataset["target"])
Expand Down Expand Up @@ -55,7 +54,6 @@ def test_training(max_steps: int = 100, threshold: float = 0.9):


@pytest.mark.forked
@pytest.mark.skip("Skipping test due to freezes in CI")
def test_moe_training(max_steps: int = 100, threshold: float = 0.9, num_experts=2):
dataset = load_digits(n_class=2)
X_train, y_train = torch.tensor(dataset["data"], dtype=torch.float), torch.tensor(dataset["target"])
Expand Down Expand Up @@ -108,7 +106,6 @@ def forward(self, x):


@pytest.mark.forked
@pytest.mark.skip("Skipping test due to freezes in CI")
def test_switch_training(max_steps: int = 10, threshold: float = 0.9, num_experts=5):
dataset = load_digits(n_class=2)
X_train, y_train = torch.tensor(dataset["data"], dtype=torch.float), torch.tensor(dataset["target"])
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