From b0af7d54ff77daebc40fbef142b1e64162ce437c Mon Sep 17 00:00:00 2001
From: Claude <claude@anthropic.com>
Date: Sat, 15 Aug 2026 12:41:29 +0000
Subject: [PATCH] =?UTF-8?q?test:=20breakdown=20moe=5Fdecode=5Ffused=20timi?=
 =?UTF-8?q?ng=20by=20step=20=E2=80=94=20find=20the=20real=20bottleneck?=
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---
 qwen3_6_scripts/probe_moe_fused_breakdown.sh | 69 ++++++++++++++++++++
 1 file changed, 69 insertions(+)
 create mode 100755 qwen3_6_scripts/probe_moe_fused_breakdown.sh

diff --git a/qwen3_6_scripts/probe_moe_fused_breakdown.sh b/qwen3_6_scripts/probe_moe_fused_breakdown.sh
new file mode 100755
index 00000000..aa5416ca
--- /dev/null
+++ b/qwen3_6_scripts/probe_moe_fused_breakdown.sh
@@ -0,0 +1,69 @@
+#!/bin/bash
+# probe_moe_fused_breakdown.sh — Time each step of MoE decode
+
+python3 << 'PY'
+import torch
+import time
+import importlib.util
+
+spec = importlib.util.spec_from_file_location('m',
+    'qwen3_6_scripts/prebuilt/corex-3.2.3-ivcore10/corex_batched_gemm.so')
+mod = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(mod)
+
+K = 8
+H = 4096
+I = 2752
+
+x = torch.randn(1, H, dtype=torch.float16, device='cuda')
+w13 = torch.randn(K, 2*I, H, dtype=torch.float16, device='cuda')
+w2 = torch.randn(K, H, I, dtype=torch.float16, device='cuda')
+ws = torch.softmax(torch.randn(K, device='cuda'), 0).half()
+
+def time_fn(fn, name, iters=50):
+    for _ in range(5): fn()
+    torch.cuda.synchronize()
+    t0 = time.time()
+    for _ in range(iters): fn()
+    torch.cuda.synchronize()
+    ms = (time.time() - t0) / iters * 1000
+    print(f"  {name}: {ms:.3f} ms")
+    return ms
+
+print("=== Step-by-step breakdown ===")
+
+# Step 0: expand+contiguous
+time_fn(lambda: x.expand(K, 1, H).contiguous(), "expand+contiguous (1,H)->(K,1,H)")
+
+# Step 1: batched GEMM FC1 only
+x_exp = x.expand(K, 1, H).contiguous()
+time_fn(lambda: mod.batched_gemm_fp16(x_exp, w13), "batched_gemm FC1 (K,1,H)x(K,2I,H)")
+
+# Step 2: silu * mul
+gate_up = mod.batched_gemm_fp16(x_exp, w13).squeeze(1)
+chunks = gate_up.chunk(2, dim=1)
+time_fn(lambda: torch.sigmoid(chunks[0]) * chunks[0] * chunks[1], "silu*mul (K,I)")
+
+# Step 3: batched GEMM FC2 only
+act = (torch.sigmoid(chunks[0]) * chunks[0] * chunks[1]).unsqueeze(1)
+time_fn(lambda: mod.batched_gemm_fp16(act, w2), "batched_gemm FC2 (K,1,I)x(K,H,I)")
+
+# Step 4: weighted reduction
+eo = mod.batched_gemm_fp16(act, w2).squeeze(1)
+time_fn(lambda: (eo * ws.unsqueeze(1)).sum(0, True), "weighted_sum")
+
+# Full fused
+time_fn(lambda: mod.moe_decode_fused(x, w13, w2, ws), "moe_decode_fused (full)")
+
+# Comparison: 8x F.linear loop
+import torch.nn.functional as F
+def flinear_loop():
+    outs = []
+    for i in range(K):
+        gu = F.linear(x, w13[i])
+        g, u = gu.chunk(2, dim=-1)
+        a = F.silu(g) * u
+        outs.append(F.linear(a, w2[i]))
+    return sum(outs[i] * ws[i] for i in range(K))
+time_fn(flinear_loop, "F.linear loop (baseline)")
+PY