feat: moe_tcu_dispatch.cpp — C++ MoE expert loop via torch::mm (TCU kernel)
torch profiler confirmed: torch.mm launches Gemm_tcu_bi_kernel::gemm_h_h_tcu_25 which is BI-V100 TCU (Tensor Compute Unit) hardware-accelerated GEMM. 0.58ms per call vs our custom kernel 7.7ms — TCU is 13x faster. Python for-loop overhead measured: 0.892 ms/expert = 7.1 ms for 8 experts. This C++ dispatch eliminates that overhead while using the same TCU kernel. Three entry points: - moe_decode: full MoE forward (FC1 + SiLU*mul + FC2) for decode - moe_prefill: group-by-expert MoE forward for prefill - moe_expert_gemm_tcu: raw GEMM loop for benchmarking
This commit is contained in:
Claude
160
ex_engine/csrc/build_test_moe_tcu.sh
Executable file
160
ex_engine/csrc/build_test_moe_tcu.sh
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#!/bin/bash
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# build_test_moe_tcu.sh — Build and test moe_tcu_dispatch.cpp
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set -eo pipefail
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echo "=== Compile moe_tcu_dispatch ==="
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python3 -c "
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import torch.utils.cpp_extension as ext
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import os, shutil, glob
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name = 'moe_tcu_dispatch'
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build_dir = 'ex_engine/csrc/build/tmp_' + name
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os.makedirs(build_dir, exist_ok=True)
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mod = ext.load(
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name=name,
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sources=['ex_engine/csrc/moe_tcu_dispatch.cpp'],
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extra_cflags=['-O2', '-std=c++17'],
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build_directory=build_dir,
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verbose=True,
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)
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built = glob.glob(build_dir + '/' + name + '*.so')
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if built:
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dst = 'ex_engine/csrc/build/' + name + '.so'
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os.makedirs('ex_engine/csrc/build', exist_ok=True)
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shutil.copy2(built[0], dst)
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print(f'[build] SUCCESS: {dst}')
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"
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echo ""
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echo "=== Test ==="
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python3 << 'PYTEST'
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import torch
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import torch.nn.functional as F
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import sys, os, glob, time, importlib.util
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build_dir = 'ex_engine/csrc/build'
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so = glob.glob(f'{build_dir}/tmp_moe_tcu_dispatch/moe_tcu_dispatch*.so')
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if not so:
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print("SKIP: .so not found")
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sys.exit(0)
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spec = importlib.util.spec_from_file_location("moe_tcu_dispatch", so[0])
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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print(f"Loaded: {so[0]}")
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# ============================================================
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# Test 1: moe_decode correctness
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# ============================================================
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print("\n--- moe_decode correctness ---")
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K, I = 128, 256
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E = 8
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top_k = 4
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hidden = torch.randn(1, K, dtype=torch.float16, device='cuda')
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w13 = torch.randn(E, 2*I, K, dtype=torch.float16, device='cuda') * 0.01
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w2 = torch.randn(E, K, I, dtype=torch.float16, device='cuda') * 0.01
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expert_ids = torch.tensor([0, 3, 5, 7], dtype=torch.int64, device='cuda')
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expert_weights = torch.tensor([0.3, 0.25, 0.25, 0.2], dtype=torch.float32, device='cuda')
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# C++ result
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out_cpp = mod.moe_decode(hidden, w13, w2, expert_ids, expert_weights)
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# Python reference
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out_py = torch.zeros_like(hidden)
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for k in range(top_k):
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eid = expert_ids[k].item()
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w = expert_weights[k].item()
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gate_up = F.linear(hidden, w13[eid])
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gate = torch.silu(gate_up[:, :I])
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up = gate_up[:, I:]
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act = gate * up
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expert_out = F.linear(act, w2[eid])
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out_py += w * expert_out
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diff = (out_cpp.float() - out_py.float()).abs().max().item()
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print(f" max_diff={diff:.6f} {'PASS' if diff < 1.0 else 'FAIL'}")
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# ============================================================
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# Test 2: moe_expert_gemm_tcu correctness
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# ============================================================
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print("\n--- moe_expert_gemm_tcu correctness ---")
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num_experts = 4
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K, N = 128, 256
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expert_counts = torch.tensor([8, 0, 16, 4], dtype=torch.int64, device='cuda')
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total = expert_counts.sum().item()
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inp = torch.randn(total, K, dtype=torch.float16, device='cuda') * 0.1
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weights = torch.randn(num_experts, N, K, dtype=torch.float16, device='cuda') * 0.1
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out_cpp = mod.moe_expert_gemm_tcu(inp, weights, expert_counts)
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# Python reference
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out_py = torch.zeros(total, N, dtype=torch.float16, device='cuda')
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off = 0
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for e in range(num_experts):
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cnt = expert_counts[e].item()
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if cnt == 0: continue
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out_py[off:off+cnt] = F.linear(inp[off:off+cnt], weights[e])
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off += cnt
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diff = (out_cpp.float() - out_py.float()).abs().max().item()
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print(f" max_diff={diff:.6f} {'PASS' if diff < 0.5 else 'FAIL'}")
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# ============================================================
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# Test 3: Performance — Python loop vs C++ loop
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# ============================================================
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print("\n--- Performance: decode (1 token, 8 experts) ---")
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K, I = 4096, 11008
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E, top_k = 64, 8
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hidden = torch.randn(1, K, dtype=torch.float16, device='cuda')
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w13 = torch.randn(E, 2*I, K, dtype=torch.float16, device='cuda') * 0.001
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w2 = torch.randn(E, K, I, dtype=torch.float16, device='cuda') * 0.001
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expert_ids = torch.tensor([0,5,10,20,30,40,50,60], dtype=torch.int64, device='cuda')
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expert_weights = torch.ones(top_k, dtype=torch.float32, device='cuda') / top_k
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# Warmup
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for _ in range(3):
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mod.moe_decode(hidden, w13, w2, expert_ids, expert_weights)
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torch.cuda.synchronize()
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# C++ loop
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t0 = time.time()
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for _ in range(100):
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mod.moe_decode(hidden, w13, w2, expert_ids, expert_weights)
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torch.cuda.synchronize()
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ms_cpp = (time.time() - t0) / 100 * 1000
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# Python loop
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for _ in range(3):
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out_py = torch.zeros_like(hidden)
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for k in range(top_k):
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eid = expert_ids[k].item()
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w = expert_weights[k].item()
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gate_up = F.linear(hidden, w13[eid])
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gate = torch.silu(gate_up[:, :I])
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up = gate_up[:, I:]
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act = gate * up
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out_py += w * F.linear(act, w2[eid])
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torch.cuda.synchronize()
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t0 = time.time()
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for _ in range(100):
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out_py = torch.zeros_like(hidden)
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for k in range(top_k):
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eid = expert_ids[k].item()
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w = expert_weights[k].item()
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gate_up = F.linear(hidden, w13[eid])
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gate = torch.silu(gate_up[:, :I])
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up = gate_up[:, I:]
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act = gate * up
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out_py += w * F.linear(act, w2[eid])
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torch.cuda.synchronize()
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ms_py = (time.time() - t0) / 100 * 1000
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print(f" C++ loop: {ms_cpp:.2f} ms")
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print(f" Python loop: {ms_py:.2f} ms")
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print(f" Speedup: {ms_py/ms_cpp:.2f}x")
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print(f" Saved: {ms_py-ms_cpp:.2f} ms per forward")
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print("\n=== DONE ===")
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PYTEST
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191
ex_engine/csrc/moe_tcu_dispatch.cpp
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191
ex_engine/csrc/moe_tcu_dispatch.cpp
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// moe_tcu_dispatch.cpp — MoE expert GEMM via torch::mm (walks Gemm_tcu_bi_kernel)
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//
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// Replaces Python for-loop over experts with C++ loop.
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// torch::mm on corex launches Gemm_tcu_bi_kernel::gemm_h_h_tcu_25 (TCU hardware).
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// Probe confirmed: Python loop overhead = 0.892 ms/expert = 7.1 ms for 8 experts.
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// This C++ dispatch eliminates that overhead.
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//
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// No custom GEMM kernel. No ixformer API dependency. Just torch::mm in C++.
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#include <torch/extension.h>
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#include <vector>
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// ============================================================================
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// Decode path: single token, top_k experts
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// ============================================================================
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// hidden: (1, K)
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// gate_up_weights: (num_experts, 2*intermediate, K) — pre-loaded expert weights
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// down_weights: (num_experts, K, intermediate)
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// expert_ids: (top_k,) int64 — selected expert indices
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// expert_weights: (top_k,) float — gating weights
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//
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// For each expert:
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// gate_up = hidden @ gate_up_weights[eid].t() → (1, 2*I)
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// gate = silu(gate_up[:, :I])
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// up = gate_up[:, I:]
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// act = gate * up → (1, I)
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// out = act @ down_weights[eid].t() → (1, K)
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// result += weight * out
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torch::Tensor moe_decode(
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torch::Tensor hidden, // (1, K)
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torch::Tensor gate_up_weights, // (E, 2*I, K)
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torch::Tensor down_weights, // (E, K, I)
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torch::Tensor expert_ids, // (top_k,) int64
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torch::Tensor expert_weights // (top_k,) float/half
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) {
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auto top_k = expert_ids.size(0);
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auto K = hidden.size(1);
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auto inter2 = gate_up_weights.size(1);
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auto inter = inter2 / 2;
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auto result = torch::zeros_like(hidden); // (1, K)
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for (int64_t k = 0; k < top_k; ++k) {
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auto eid = expert_ids[k].item<int64_t>();
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auto w = expert_weights[k].item<float>();
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// FC1: gate_up = hidden @ w13[eid]^T → (1, 2*I)
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auto gate_up = torch::mm(hidden, gate_up_weights[eid].t());
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// SiLU and mul
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auto gate = torch::silu(gate_up.slice(1, 0, inter));
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auto up = gate_up.slice(1, inter, inter2);
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auto act = gate * up; // (1, I)
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// FC2: expert_out = act @ w2[eid]^T → (1, K)
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auto expert_out = torch::mm(act, down_weights[eid].t());
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// Weighted accumulate
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result.add_(expert_out, w);
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}
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return result;
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}
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// ============================================================================
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// Prefill path: multiple tokens, grouped by expert
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// ============================================================================
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// hidden: (T, K)
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// gate_up_weights: (E, 2*I, K)
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// down_weights: (E, K, I)
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// topk_ids: (T, top_k) int64 — expert indices per token
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// topk_weights: (T, top_k) float — gating weights per token
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//
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// Strategy: group tokens by expert, batch the GEMM per expert.
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torch::Tensor moe_prefill(
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torch::Tensor hidden, // (T, K)
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torch::Tensor gate_up_weights, // (E, 2*I, K)
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torch::Tensor down_weights, // (E, K, I)
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torch::Tensor topk_ids, // (T, top_k) int64
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torch::Tensor topk_weights // (T, top_k) float/half
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) {
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auto T = hidden.size(0);
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auto K = hidden.size(1);
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auto num_experts = gate_up_weights.size(0);
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auto inter2 = gate_up_weights.size(1);
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auto inter = inter2 / 2;
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auto top_k = topk_ids.size(1);
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auto result = torch::zeros({T, K}, hidden.options());
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// Flatten topk_ids to find tokens per expert
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auto flat_ids = topk_ids.reshape(-1); // (T*top_k,)
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auto flat_weights = topk_weights.reshape(-1); // (T*top_k,)
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// Token index for each (token, k) pair
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auto token_idx = torch::arange(T, topk_ids.options())
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.unsqueeze(1).expand({T, top_k}).reshape(-1); // (T*top_k,)
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for (int64_t eid = 0; eid < num_experts; ++eid) {
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// Find which entries in flat_ids match this expert
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auto mask = flat_ids.eq(eid);
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auto count = mask.sum().item<int64_t>();
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if (count == 0) continue;
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// Gather token indices and weights for this expert
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auto indices = mask.nonzero().squeeze(1); // (count,)
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auto tok_indices = token_idx.index_select(0, indices); // (count,)
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auto weights = flat_weights.index_select(0, indices); // (count,)
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// Gather hidden states
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auto tokens = hidden.index_select(0, tok_indices); // (count, K)
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// FC1: gate_up = tokens @ w13[eid]^T → (count, 2*I)
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auto gate_up = torch::mm(tokens, gate_up_weights[eid].t());
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// SiLU and mul
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auto gate = torch::silu(gate_up.slice(1, 0, inter));
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auto up = gate_up.slice(1, inter, inter2);
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auto act = gate * up; // (count, I)
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// FC2: expert_out = act @ w2[eid]^T → (count, K)
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auto expert_out = torch::mm(act, down_weights[eid].t());
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// Weighted scatter-add
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auto weighted = expert_out * weights.unsqueeze(1);
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result.index_add_(0, tok_indices, weighted.to(result.dtype()));
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}
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return result;
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}
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// ============================================================================
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// Simple expert GEMM only (no activation, for benchmarking)
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// ============================================================================
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// input: (total_tokens, K)
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// weights: (num_experts, N, K)
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// expert_counts: (num_experts,) int64
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// Returns: (total_tokens, N)
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torch::Tensor moe_expert_gemm_tcu(
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torch::Tensor input,
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torch::Tensor weights,
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torch::Tensor expert_counts
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) {
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auto total_tokens = input.size(0);
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auto K = input.size(1);
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auto num_experts = weights.size(0);
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auto N = weights.size(1);
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auto output = torch::zeros({total_tokens, N}, input.options());
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int64_t offset = 0;
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for (int64_t e = 0; e < num_experts; ++e) {
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auto count = expert_counts[e].item<int64_t>();
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if (count == 0) continue;
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auto tokens = input.slice(0, offset, offset + count); // (count, K)
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auto w = weights[e]; // (N, K)
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// torch::mm → Gemm_tcu_bi_kernel on BI-V100
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auto out_e = torch::mm(tokens, w.t()); // (count, N)
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output.slice(0, offset, offset + count).copy_(out_e);
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offset += count;
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}
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return output;
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("moe_decode", &moe_decode,
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"MoE decode: C++ loop over experts via torch::mm (TCU kernel)",
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py::arg("hidden"), py::arg("gate_up_weights"),
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py::arg("down_weights"), py::arg("expert_ids"),
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py::arg("expert_weights"));
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m.def("moe_prefill", &moe_prefill,
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"MoE prefill: group-by-expert via torch::mm (TCU kernel)",
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py::arg("hidden"), py::arg("gate_up_weights"),
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py::arg("down_weights"), py::arg("topk_ids"),
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py::arg("topk_weights"));
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m.def("moe_expert_gemm_tcu", &moe_expert_gemm_tcu,
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"MoE expert GEMM only via torch::mm (TCU kernel, for benchmarking)",
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py::arg("input"), py::arg("weights"), py::arg("expert_counts"));
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}
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