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

ghstack-source-id: a7d786fe77c2c12d5c8c85579123a64ef5c87cf2
Pull Request resolved: #1111

test/conftest.py

@@ -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)
 
 

test/test_tensordict.py

@@ -134,7 +134,7 @@
     ),
 ]
 
-mp_ctx = "fork" if (not torch.cuda.is_available() and not _IS_WINDOWS) else "spawn"
+mp_ctx = "spawn"
 
 
 @pytest.fixture

tutorials/sphinx_tuto/export.py

@@ -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:

tutorials/sphinx_tuto/tensordict_module.py

@@ -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"
 )
 
 ###############################################################################