[CI] Add benchmarks to test runs

ghstack-source-id: 8d83ae870d3629117d857fdbe98a992ca56b7838
Pull Request resolved: #2410

.github/unittest/linux/scripts/environment.yml

@@ -15,6 +15,7 @@ dependencies:
     - pytest-cov
     - pytest-mock
     - pytest-instafail
+    - pytest-benchmark
     - pytest-rerunfailures
     - pytest-timeout
     - expecttest

.github/unittest/linux/scripts/run_all.sh

@@ -88,8 +88,7 @@ conda deactivate
 conda activate "${env_dir}"
 
 echo "installing gymnasium"
-pip3 install "gymnasium"
-pip3 install ale_py
+pip3 install "gymnasium[atari,accept-rom-license]"
 pip3 install mo-gymnasium[mujoco]  # requires here bc needs mujoco-py
 pip3 install "mujoco" -U
 
@@ -189,9 +188,14 @@ export MKL_THREADING_LAYER=GNU
 export CKPT_BACKEND=torch
 export MAX_IDLE_COUNT=100
 export BATCHED_PIPE_TIMEOUT=60
+export TORCHDYNAMO_INLINE_INBUILT_NN_MODULES=1
 
 pytest test/smoke_test.py -v --durations 200
 pytest test/smoke_test_deps.py -v --durations 200 -k 'test_gym or test_dm_control_pixels or test_dm_control or test_tb'
+
+# Check that benchmarks run
+python -m pytest benchmarks
+
 if [ "${CU_VERSION:-}" != cpu ] ; then
   python .github/unittest/helpers/coverage_run_parallel.py -m pytest test \
     --instafail --durations 200 -vv --capture no --ignore test/test_rlhf.py \

.github/unittest/linux_libs/scripts_gym/batch_scripts.sh

@@ -126,7 +126,7 @@ do
   conda activate ./cloned_env
 
   echo "Testing gym version: ${GYM_VERSION}"
-  pip3 install 'gymnasium[atari,accept-rom-license,ale-py]'==$GYM_VERSION
+  pip3 install 'gymnasium[atari,accept-rom-license]'==$GYM_VERSION
 
   $DIR/run_test.sh
 

torchrl/__init__.py

@@ -21,6 +21,10 @@
 
 from ._extension import _init_extension
 
+try:
+    from torch.compiler import is_dynamo_compiling
+except Exception:
+    from torch._dynamo import is_compiling as is_dynamo_compiling
 
 try:
     from .version import __version__
@@ -69,7 +73,7 @@ def _inv(self):
         inv = self._inv()
     if inv is None:
         inv = _InverseTransform(self)
-        if not torch.compiler.is_dynamo_compiling():
+        if not is_dynamo_compiling():
             self._inv = weakref.ref(inv)
     return inv
 
@@ -84,7 +88,7 @@ def _inv(self):
         inv = self._inv()
     if inv is None:
         inv = ComposeTransform([p.inv for p in reversed(self.parts)])
-        if not torch.compiler.is_dynamo_compiling():
+        if not is_dynamo_compiling():
             self._inv = weakref.ref(inv)
             inv._inv = weakref.ref(self)
         else:

torchrl/modules/distributions/continuous.py

@@ -33,6 +33,11 @@
 )
 from torchrl.modules.utils import mappings
 
+try:
+    from torch.compiler import is_dynamo_compiling
+except Exception:
+    from torch._dynamo import is_compiling as is_dynamo_compiling
+
 # speeds up distribution construction
 D.Distribution.set_default_validate_args(False)
 
@@ -112,7 +117,7 @@ def inv(self):
             inv = self._inv()
         if inv is None:
             inv = _InverseTransform(self)
-            if not torch.compiler.is_dynamo_compiling():
+            if not is_dynamo_compiling():
                 self._inv = weakref.ref(inv)
         return inv
 
@@ -334,7 +339,7 @@ def inv(self):
             inv = self._inv()
         if inv is None:
             inv = _PatchedComposeTransform([p.inv for p in reversed(self.parts)])
-            if not torch.compiler.is_dynamo_compiling():
+            if not is_dynamo_compiling():
                 self._inv = weakref.ref(inv)
                 inv._inv = weakref.ref(self)
         return inv
@@ -348,7 +353,7 @@ def inv(self):
             inv = self._inv()
         if inv is None:
             inv = _InverseTransform(self)
-            if not torch.compiler.is_dynamo_compiling():
+            if not is_dynamo_compiling():
                 self._inv = weakref.ref(inv)
         return inv
 
@@ -460,15 +465,13 @@ def __init__(
         self.high = high
 
         if safe_tanh:
-            if torch.compiler.is_dynamo_compiling():
+            if is_dynamo_compiling():
                 _err_compile_safetanh()
             t = SafeTanhTransform()
         else:
             t = D.TanhTransform()
         # t = D.TanhTransform()
-        if torch.compiler.is_dynamo_compiling() or (
-            self.non_trivial_max or self.non_trivial_min
-        ):
+        if is_dynamo_compiling() or (self.non_trivial_max or self.non_trivial_min):
             t = _PatchedComposeTransform(
                 [
                     t,
@@ -495,9 +498,7 @@ def update(self, loc: torch.Tensor, scale: torch.Tensor) -> None:
         if self.tanh_loc:
             loc = (loc / self.upscale).tanh() * self.upscale
             # loc must be rescaled if tanh_loc
-            if torch.compiler.is_dynamo_compiling() or (
-                self.non_trivial_max or self.non_trivial_min
-            ):
+            if is_dynamo_compiling() or (self.non_trivial_max or self.non_trivial_min):
                 loc = loc + (self.high - self.low) / 2 + self.low
         self.loc = loc
         self.scale = scale

torchrl/objectives/utils.py

@@ -26,6 +26,11 @@
         raise err_ft from err
 from torchrl.envs.utils import step_mdp
 
+try:
+    from torch.compiler import is_dynamo_compiling
+except Exception:
+    from torch._dynamo import is_compiling as is_dynamo_compiling
+
 _GAMMA_LMBDA_DEPREC_ERROR = (
     "Passing gamma / lambda parameters through the loss constructor "
     "is a deprecated feature. To customize your value function, "
@@ -460,7 +465,7 @@ def _cache_values(func):
 
     @functools.wraps(func)
     def new_func(self, netname=None):
-        if torch.compiler.is_dynamo_compiling():
+        if is_dynamo_compiling():
             if netname is not None:
                 return func(self, netname)
             else: