延迟生成 benchmark 形状

benchmark/bench_flash_mla.py

@@ -484,10 +484,27 @@ def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype):
     "flash_mla_triton",
 ]
 
-shape_configs = [
-    {"b": batch, "s_q": 1, "cache_seqlens": torch.tensor([seqlen + 2 * i for i in range(batch)], dtype=torch.int32, device="cuda"), "h_q": head, "h_kv": 1, "d": 512+64, "dv": 512, "causal": True, "dtype": torch.bfloat16}
-    for batch in [128] for seqlen in [1024, 2048, 4096, 8192, 8192*2, 8192*4] for head in [128]
-]
+def build_shape_configs(device="cuda"):
+    return [
+        {
+            "b": batch,
+            "s_q": 1,
+            "cache_seqlens": torch.tensor(
+                [seqlen + 2 * i for i in range(batch)],
+                dtype=torch.int32,
+                device=device,
+            ),
+            "h_q": head,
+            "h_kv": 1,
+            "d": 512 + 64,
+            "dv": 512,
+            "causal": True,
+            "dtype": torch.bfloat16,
+        }
+        for batch in [128]
+        for seqlen in [1024, 2048, 4096, 8192, 8192 * 2, 8192 * 4]
+        for head in [128]
+    ]
 
 
 def get_args():
@@ -504,6 +521,7 @@ def get_args():
 if __name__ == "__main__":
     args = get_args()
     benchmark_type = "all" if args.all else f"{args.baseline}_vs_{args.target}" if args.compare else args.target
+    shape_configs = build_shape_configs()
     with open(f"{benchmark_type}_perf.csv", "w") as fout:
         fout.write("name,batch,seqlen,head,bw\n")
         for shape in shape_configs:
@@ -517,4 +535,4 @@ def get_args():
                 fout.write(f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{prefb:.0f}\n')
             elif args.one:
                 perf = compare_a(args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], shape["causal"], shape["dtype"])
-                fout.write(f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n')
+                fout.write(f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n')

tests/test_benchmark_shapes.py

@@ -0,0 +1,57 @@
+import ast
+import unittest
+from pathlib import Path
+
+
+BENCHMARK = Path(__file__).parents[1] / "benchmark" / "bench_flash_mla.py"
+
+
+class BenchmarkShapeConfigTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.tree = ast.parse(BENCHMARK.read_text(encoding="utf-8"))
+
+    def test_shape_configs_are_not_created_at_import_time(self):
+        top_level_assigns = [
+            node
+            for node in self.tree.body
+            if isinstance(node, ast.Assign)
+            and any(
+                isinstance(target, ast.Name) and target.id == "shape_configs"
+                for target in node.targets
+            )
+        ]
+
+        self.assertEqual(top_level_assigns, [])
+
+    def test_shape_builder_accepts_device_argument(self):
+        builder = next(
+            node
+            for node in self.tree.body
+            if isinstance(node, ast.FunctionDef) and node.name == "build_shape_configs"
+        )
+
+        self.assertEqual(builder.args.args[0].arg, "device")
+        self.assertEqual(builder.args.defaults[0].value, "cuda")
+
+    def test_main_creates_shape_configs_lazily(self):
+        main_block = next(
+            node
+            for node in self.tree.body
+            if isinstance(node, ast.If)
+            and isinstance(node.test, ast.Compare)
+            and isinstance(node.test.left, ast.Name)
+            and node.test.left.id == "__name__"
+        )
+        calls = [node for node in ast.walk(main_block) if isinstance(node, ast.Call)]
+
+        self.assertTrue(
+            any(
+                isinstance(call.func, ast.Name) and call.func.id == "build_shape_configs"
+                for call in calls
+            )
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()