Fix bisection with start/end versions. (#2132)

Summary:
At the compile time, pytorch compilation will link with the system libstdcxx. At runtime, it will use the version provided by conda.

After the upgrade, Ubuntu 22.04 ships with libstdcxx 12.3.0, but the default conda version of libstdcxx-ng is 11.2.0.
Install libstdcxx-ng 12.3.0 to provide the ABI symbol GLIBCXX_3.4.30 at runtime.

Ubuntu 22.04 ships with glib 2.78.3, so it does not need the glib 2.69 trick anymore.

Pull Request resolved: #2132

Test Plan: https://github.com/pytorch/benchmark/actions/runs/7663583650

Reviewed By: suez1224

Differential Revision: D53095193

Pulled By: xuzhao9

fbshipit-source-id: ad5bf51d423c933dd063ce006a4099f8ef9ba65c

bisection.py

@@ -185,8 +185,12 @@ class BisectionTargetRepo:
     commits: List[Commit]
     # Map from commit SHA to its index in commits
     commit_dict: Dict[str, int]
-    def __init__(self, repo: TorchRepo, start: str, end: str,
-                 start_version: str, end_version: str,
+    def __init__(self,
+                 repo: TorchRepo,
+                 start: str,
+                 end: str,
+                 start_version: str,
+                 end_version: str,
                  non_target_repos: List[TorchRepo]):
         self.repo = repo
         self.start = start
@@ -414,6 +418,8 @@ def __init__(
         target_repo: TorchRepo,
         start: str,
         end: str,
+        start_version: str,
+        end_version: str,
         bisect_config: TorchBenchABTestResult,
         output_json: str,
         debug: bool = False,
@@ -428,7 +434,12 @@ def __init__(
         )
         torchbench_repo_key = "torchbench" if not IS_FBCODE else "fbcode"
         self.target_repo = BisectionTargetRepo(
-            repo=target_repo, start=start, end=end, non_target_repos=non_target_repos
+            repo=target_repo,
+            start=start,
+            end=end,
+            start_version=start_version,
+            end_version=end_version,
+            non_target_repos=non_target_repos,
         )
         self.torchbench = TorchBenchRepo(
             repo=torch_repos[torchbench_repo_key],

utils/cuda_utils.py

@@ -113,12 +113,14 @@ def install_torch_build_deps(cuda_version: str):
     pip_deps = [ f"numpy=={PIN_NUMPY_VERSION}" ]
     cmd = ["pip", "install"] + pip_deps
     subprocess.check_call(cmd)
+    # conda forge deps
+    # ubuntu 22.04 comes with libstdcxx6 12.3.0
+    # we need to install the same library version in conda
+    conda_deps = ["libstdcxx-ng=12.3.0"]
+    cmd = ["conda", "install", "-y", "-c", "conda-forge"] + conda_deps
+    subprocess.check_call(cmd)
 
 def install_torchbench_deps():
-    # weasyprint requires ffi7, which requires glib > 2.69 on ubuntu 20.04
-    conda_deps = ["glib"]
-    cmd = ["conda", "install", "-y"] + conda_deps
-    subprocess.check_call(cmd)
     cmd = ["pip", "install", "unittest-xml-reporting", "boto3"]
     subprocess.check_call(cmd)