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[BugFix] Use 'spawn' mp context in all tests #1111

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Nov 26, 2024
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[BugFix] Use 'spawn' mp context in all tests #1111

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6 changes: 6 additions & 0 deletions test/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,18 @@
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import multiprocessing
import os
import time
from collections import defaultdict

import pytest

try:
multiprocessing.set_start_method("spawn")
except Exception:
assert multiprocessing.get_start_method() == "spawn"

CALL_TIMES = defaultdict(lambda: 0.0)


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2 changes: 1 addition & 1 deletion test/test_tensordict.py
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Expand Up @@ -134,7 +134,7 @@
),
]

mp_ctx = "fork" if (not torch.cuda.is_available() and not _IS_WINDOWS) else "spawn"
mp_ctx = "spawn"


@pytest.fixture
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4 changes: 2 additions & 2 deletions tutorials/sphinx_tuto/export.py
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Expand Up @@ -120,13 +120,13 @@

t0 = time.time()
model(x=x)
print(f"Time for TDModule: {(time.time()-t0)*1e6: 4.2f} micro-seconds")
print(f"Time for TDModule: {(time.time() - t0) * 1e6: 4.2f} micro-seconds")
exported = model_export.module()

# Exported version
t0 = time.time()
exported(x=x)
print(f"Time for exported module: {(time.time()-t0)*1e6: 4.2f} micro-seconds")
print(f"Time for exported module: {(time.time() - t0) * 1e6: 4.2f} micro-seconds")

##################################################
# and the FX graph:
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12 changes: 6 additions & 6 deletions tutorials/sphinx_tuto/tensordict_module.py
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Expand Up @@ -213,33 +213,33 @@ def forward(self, x):
from torch.utils.benchmark import Timer

print(
f"Regular: {Timer('block_notd(x=x)', globals=globals()).adaptive_autorange().median*1_000_000: 4.4f} us"
f"Regular: {Timer('block_notd(x=x)', globals=globals()).adaptive_autorange().median * 1_000_000: 4.4f} us"
)
print(
f"TDM: {Timer('block_tdm(x=x)', globals=globals()).adaptive_autorange().median*1_000_000: 4.4f} us"
f"TDM: {Timer('block_tdm(x=x)', globals=globals()).adaptive_autorange().median * 1_000_000: 4.4f} us"
)
print(
f"Sequential: {Timer('block_tds(x=x)', globals=globals()).adaptive_autorange().median*1_000_000: 4.4f} us"
f"Sequential: {Timer('block_tds(x=x)', globals=globals()).adaptive_autorange().median * 1_000_000: 4.4f} us"
)

print("Compiled versions")
block_notd_c = torch.compile(block_notd, mode="reduce-overhead")
for _ in range(5): # warmup
block_notd_c(x)
print(
f"Compiled regular: {Timer('block_notd_c(x=x)', globals=globals()).adaptive_autorange().median*1_000_000: 4.4f} us"
f"Compiled regular: {Timer('block_notd_c(x=x)', globals=globals()).adaptive_autorange().median * 1_000_000: 4.4f} us"
)
block_tdm_c = torch.compile(block_tdm, mode="reduce-overhead")
for _ in range(5): # warmup
block_tdm_c(x=x)
print(
f"Compiled TDM: {Timer('block_tdm_c(x=x)', globals=globals()).adaptive_autorange().median*1_000_000: 4.4f} us"
f"Compiled TDM: {Timer('block_tdm_c(x=x)', globals=globals()).adaptive_autorange().median * 1_000_000: 4.4f} us"
)
block_tds_c = torch.compile(block_tds, mode="reduce-overhead")
for _ in range(5): # warmup
block_tds_c(x=x)
print(
f"Compiled sequential: {Timer('block_tds_c(x=x)', globals=globals()).adaptive_autorange().median*1_000_000: 4.4f} us"
f"Compiled sequential: {Timer('block_tds_c(x=x)', globals=globals()).adaptive_autorange().median * 1_000_000: 4.4f} us"
)

###############################################################################
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