Extend torch.compile benchmark to dynamic=True; Add assert_indirect_indexing = False

CHANGELOG.md

@@ -13,6 +13,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Disable device asserts during `torch_geometric.compile` ([#8220](https://github.com/pyg-team/pytorch_geometric/pull/8220))
+
 ### Deprecated
 
 ### Fixed

test/nn/models/test_basic_gnn.py

@@ -1,5 +1,6 @@
 import os
 import os.path as osp
+import random
 import sys
 import warnings
 
@@ -388,11 +389,24 @@ def test_basic_gnn_cache():
     parser = argparse.ArgumentParser()
     parser.add_argument('--device', type=str, default='cuda')
     parser.add_argument('--backward', action='store_true')
+    parser.add_argument('--dynamic', action='store_true')
     args = parser.parse_args()
 
-    num_nodes, num_edges = 10_000, 200_000
-    x = torch.randn(num_nodes, 64, device=args.device)
-    edge_index = torch.randint(num_nodes, (2, num_edges), device=args.device)
+    if args.dynamic:
+        min_num_nodes, max_num_nodes = 10_000, 15_000
+        min_num_edges, max_num_edges = 200_000, 300_000
+    else:
+        min_num_nodes, max_num_nodes = 10_000, 10_000
+        min_num_edges, max_num_edges = 200_000, 200_000
+
+    def gen_args():
+        N = random.randint(min_num_nodes, max_num_nodes)
+        E = random.randint(min_num_edges, max_num_edges)
+
+        x = torch.randn(N, 64, device=args.device)
+        edge_index = torch.randint(N, (2, E), device=args.device)
+
+        return x, edge_index
 
     for Model in [GCN, GraphSAGE, GIN, EdgeCNN]:
         print(f'Model: {Model.__name__}')
@@ -403,7 +417,7 @@ def test_basic_gnn_cache():
         benchmark(
             funcs=[model, compiled_model],
             func_names=['Vanilla', 'Compiled'],
-            args=(x, edge_index),
+            args=gen_args,
             num_steps=50 if args.device == 'cpu' else 500,
             num_warmups=10 if args.device == 'cpu' else 100,
             backward=args.backward,

torch_geometric/compile.py

@@ -51,6 +51,9 @@ def compile(model: Optional[Callable] = None, *args, **kwargs) -> Callable:
        jittable instances
        (see :meth:`torch_geometric.nn.conv.MessagePassing.jittable`)
 
+    3. disables generation of device asserts during fused gather/scatter calls
+       to avoid performance impacts
+
     .. note::
         Without these adjustments, :meth:`torch.compile` may currently fail to
         correctly optimize your :pyg:`PyG` model.
@@ -89,6 +92,9 @@ def fn(model: Callable) -> Callable:
     # Replace instances of `MessagePassing` by their jittable version:
     model = to_jittable(model)
 
+    # Do not generate device asserts which may slow down model execution:
+    torch._inductor.config.triton.assert_indirect_indexing = False
+
     # Finally, run `torch.compile` to create an optimized version:
     out = torch.compile(model, *args, **kwargs)
 

torch_geometric/profile/benchmark.py

@@ -38,6 +38,8 @@ def benchmark(
         args ((Any, ) or [(Any, )]): The arguments to pass to the functions.
             Can be a list of arguments for each function in :obj:`funcs` in
             case their headers differ.
+            Alternatively, you can pass in functions that generate arguments
+            on-the-fly (e.g., useful for benchmarking models on various sizes).
         num_steps (int): The number of steps to run the benchmark.
         func_names ([str], optional): The names of the functions. If not given,
             will try to infer the name from the function itself.
@@ -69,17 +71,18 @@ def benchmark(
                          f"'func_names' (got {len(func_names)}) must be equal")
 
     # Zero-copy `args` for each function (if necessary):
-    args_list = [args] * len(funcs) if isinstance(args, tuple) else args
+    args_list = [args] * len(funcs) if not isinstance(args, list) else args
 
     iterator = zip(funcs, args_list, func_names)
     if progress_bar:
         from tqdm import tqdm
         iterator = tqdm(iterator, total=len(funcs))
 
     ts: List[List[str]] = []
-    for func, args, name in iterator:
+    for func, inputs, name in iterator:
         t_forward = t_backward = 0
         for i in range(num_warmups + num_steps):
+            args = inputs() if callable(inputs) else inputs
             args = require_grad(args, backward)
 
             if torch.cuda.is_available():