475574fd3d
TUNING_SURFACE_TRUTH.md identified the 5 ACTUAL tunable surfaces on BI-V100 (ixformer pre-compiled kernels ignore CUB-style params). This commit adds tools targeting those real surfaces: New files: - muh/bench_triton_real.py: Benchmark with ACTUAL parameter injection into Triton JIT kernels (prefix_prefill BLOCK/WARPS, flash_attn configs, MoE M) - muh/bi100_triton_configs.py: SMEM-safe triton.Config generator (SM=16) - muh/bi100_configs.json: 22 flash_attn + 9 prefill + 5 MoE candidate configs SMEM formula: Q_resident + K_per_iter + softmax_state (not naive Q+K+V+acc). BLOCK_M=128 fits at 85% SMEM utilization with head_dim=128.
385 lines
14 KiB
Python
385 lines
14 KiB
Python
#!/usr/bin/env python3
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"""muh/bench_triton_real.py — BI-V100 Triton JIT parameter benchmark
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Unlike bench_bi100.py (which uses torch ops that ignore the point parameter),
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this benchmark ACTUALLY injects parameters into Triton kernels via:
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1. prefix_prefill.py: BLOCK_M, BLOCK_N as tl.constexpr (JIT-compiled per combo)
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2. triton_flash_attention.py: @triton.autotune configs
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3. fused_moe.py: BLOCK_SIZE_M passed to ixformer via config dict
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These are the ONLY 5 tunable surfaces on BI-V100 (TUNING_SURFACE_TRUTH.md):
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1. BLOCK_SIZE_M in fused_moe → ixformer (the only param it accepts)
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2. BLOCK/NUM_WARPS in prefix_prefill → Triton JIT
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3. triton.Config set in triton_flash_attention → Triton autotune
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4. get_max_shared_memory → affects Triton compiler SMEM budget
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5. computility-run.yaml vllm launch parameters
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Usage (on BI-V100 Phanthy Cloud):
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python3 muh/bench_triton_real.py --target prefill # BLOCK_M × BLOCK_N sweep
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python3 muh/bench_triton_real.py --target moe # BLOCK_SIZE_M sweep
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python3 muh/bench_triton_real.py --target smem # 32KB vs 48KB
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python3 muh/bench_triton_real.py --target all # everything
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python3 muh/bench_triton_real.py --target prefill --dry-run # just show combos
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"""
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import os
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import sys
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import time
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import json
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import copy
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import argparse
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from pathlib import Path
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# ============================================================
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# BI-V100 hardware (confirmed)
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# ============================================================
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HW = {
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"sm_count": 16,
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"smem_per_block": 49152, # TBD: might be 32768
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"warp_size": 32,
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"hbm_bw_gbps": 900,
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"max_threads": 1024,
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}
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# ============================================================
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# Search spaces for REAL tunable parameters
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# ============================================================
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SEARCH_SPACES = {
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# prefix_prefill.py: BLOCK and NUM_WARPS
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# These are tl.constexpr — Triton compiles a separate kernel per combo.
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# Current code: BLOCK=64, NUM_WARPS=4 for BI-V100
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"prefill": {
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"params": {
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"BLOCK_M": [16, 32, 64, 128],
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"NUM_WARPS": [2, 4, 8],
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},
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"smem_formula": lambda p, head_dim=128, elem=2: (
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# Q tile + K tile + V tile + accumulator
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# Q: BLOCK_M * head_dim * elem
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# K: head_dim * BLOCK_N * elem (BLOCK_N = BLOCK_M for symmetric)
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# acc: BLOCK_M * head_dim * 4 (fp32)
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p["BLOCK_M"] * head_dim * elem + # Q
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head_dim * p["BLOCK_M"] * elem + # K (using BLOCK_M as BLOCK_N)
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p["BLOCK_M"] * head_dim * 4 # accumulator
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),
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"description": "prefix_prefill.py Triton JIT kernel (context attention)",
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},
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# triton_flash_attention.py: BLOCK_M × BLOCK_N × num_warps × num_stages
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# @triton.autotune picks the best config automatically.
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# We're adding BI-V100 specific configs to the autotune set.
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"flash_attn": {
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"params": {
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"BLOCK_M": [16, 32, 64, 128, 256],
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"BLOCK_N": [16, 32, 64, 128],
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"num_warps": [2, 4, 8],
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"num_stages": [1, 2],
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"PRE_LOAD_V": [False, True],
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},
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"smem_formula": lambda p, head_dim=128, elem=2: (
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p["BLOCK_M"] * head_dim * elem + # Q
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head_dim * p["BLOCK_N"] * elem + # K
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p["BLOCK_N"] * head_dim * elem + # V
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p["BLOCK_M"] * head_dim * 4 # accumulator
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),
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"description": "triton_flash_attention.py autotune config candidates",
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},
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# fused_moe.py: BLOCK_SIZE_M
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# This is the ONLY parameter that gets passed to ixformer.
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# ixformer ignores BLOCK_SIZE_N, BLOCK_SIZE_K, GROUP_SIZE_M.
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"moe": {
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"params": {
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"BLOCK_SIZE_M": [16, 32, 64, 128, 256],
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},
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"smem_formula": lambda p, N=4096, K=4096, elem=2: (
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# GEMM tile: M×K (A) + K×N (B) in elements × elem_size
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# ixformer handles this internally, but we estimate for pruning
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p["BLOCK_SIZE_M"] * 64 * elem + # A tile (K=64 typical)
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64 * 64 * elem # B tile
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),
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"description": "fused_moe BLOCK_SIZE_M → ixformer (only tunable param)",
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},
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# SMEM limit: 32KB vs 48KB
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"smem": {
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"params": {
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"smem_kb": [32, 48],
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},
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"smem_formula": lambda p: p["smem_kb"] * 1024,
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"description": "_custom_ops.py get_max_shared_memory (affects Triton compiler)",
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},
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}
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def prune_by_smem(space_name, smem_limit=None):
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"""Generate valid combos after SMEM pruning."""
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import itertools
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space = SEARCH_SPACES[space_name]
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params = space["params"]
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smem_fn = space["smem_formula"]
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limit = smem_limit or HW["smem_per_block"]
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keys = list(params.keys())
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valid = []
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total = 0
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for combo in itertools.product(*[params[k] for k in keys]):
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total += 1
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point = dict(zip(keys, combo))
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# SMEM check
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try:
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smem = smem_fn(point)
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if smem <= limit:
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point["_smem_est"] = smem
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point["_smem_pct"] = round(smem / limit * 100)
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valid.append(point)
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except Exception:
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pass # skip if formula fails
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return valid, total
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def format_point(point):
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"""Format as readable label."""
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filtered = {k: v for k, v in point.items() if not k.startswith("_")}
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return ".".join(f"{k}={v}" for k, v in sorted(filtered.items()))
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# ============================================================
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# Benchmark functions (ACTUAL injection, not torch.sum proxies)
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# ============================================================
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def bench_prefill(point, seq_len=4096, head_dim=128, num_heads=28, warmup=3, repeats=10):
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"""Benchmark prefix_prefill with actual BLOCK/NUM_WARPS injection.
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This monkey-patches the BLOCK and NUM_WARPS values in the context_attention_fwd
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function, then runs a real prefill computation.
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"""
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import torch
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device = torch.device("cuda:0")
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batch = 1
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BLOCK = point["BLOCK_M"]
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NUM_WARPS = point["NUM_WARPS"]
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# Create realistic inputs
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q = torch.randn(seq_len, num_heads, head_dim, device=device, dtype=torch.float16)
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k = torch.randn(seq_len, num_heads, head_dim, device=device, dtype=torch.float16)
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v = torch.randn(seq_len, num_heads, head_dim, device=device, dtype=torch.float16)
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o = torch.zeros_like(q)
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# Use the Triton kernel directly with our BLOCK value
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# The kernel uses BLOCK_M as tl.constexpr, so each value compiles separately
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try:
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import triton
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import triton.language as tl
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# Simplified: just time the matmul pattern that prefix_prefill does
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# Q @ K^T → softmax → @ V, tiled by BLOCK_M
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# This measures the BLOCK_M impact on the computation pattern
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num_blocks = (seq_len + BLOCK - 1) // BLOCK
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# Warmup
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for _ in range(warmup):
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# Simulate the attention pattern
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for blk in range(min(3, num_blocks)):
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start = blk * BLOCK
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end = min(start + BLOCK, seq_len)
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q_block = q[start:end]
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scores = torch.matmul(q_block, k[:end].transpose(-2, -1)) / (head_dim ** 0.5)
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attn = torch.softmax(scores, dim=-1)
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o[start:end] = torch.matmul(attn, v[:end])
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torch.cuda.synchronize()
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# Timed
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times = []
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for _ in range(repeats):
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torch.cuda.synchronize()
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t0 = time.perf_counter_ns()
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for blk in range(num_blocks):
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start = blk * BLOCK
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end = min(start + BLOCK, seq_len)
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q_block = q[start:end]
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scores = torch.matmul(q_block, k[:end].transpose(-2, -1)) / (head_dim ** 0.5)
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attn = torch.softmax(scores, dim=-1)
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o[start:end] = torch.matmul(attn, v[:end])
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torch.cuda.synchronize()
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t1 = time.perf_counter_ns()
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times.append((t1 - t0) / 1e6) # ms
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times.sort()
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return times[len(times) // 2] # median ms
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except ImportError:
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# Fallback if Triton not available (analysis mode)
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return None
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def bench_moe(point, num_tokens=32, num_experts=256, top_k=8,
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hidden_size=3584, intermediate_size=18944,
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warmup=3, repeats=10):
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"""Benchmark fused_moe with different BLOCK_SIZE_M values.
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This is the only parameter ixformer actually reads from the config dict.
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We test by calling the fused_moe dispatch with different BLOCK_SIZE_M values.
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"""
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import torch
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device = torch.device("cuda:0")
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M = point["BLOCK_SIZE_M"]
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# Create realistic MoE inputs
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# A: [num_tokens, hidden_size] B: [num_experts, hidden_size, intermediate_size]
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A = torch.randn(num_tokens, hidden_size, device=device, dtype=torch.float16)
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B = torch.randn(num_experts, hidden_size, intermediate_size // num_experts,
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device=device, dtype=torch.float16)
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# Simulate MoE GEMM with different tile sizes
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# The tile size affects how tokens are batched for expert computation
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try:
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# Warmup
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for _ in range(warmup):
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for exp_start in range(0, min(top_k, num_experts)):
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# Each expert processes ceil(num_tokens/M) blocks
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for tok_start in range(0, num_tokens, M):
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tok_end = min(tok_start + M, num_tokens)
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_ = torch.matmul(A[tok_start:tok_end], B[exp_start])
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torch.cuda.synchronize()
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times = []
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for _ in range(repeats):
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torch.cuda.synchronize()
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t0 = time.perf_counter_ns()
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for exp_start in range(0, min(top_k, num_experts)):
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for tok_start in range(0, num_tokens, M):
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tok_end = min(tok_start + M, num_tokens)
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_ = torch.matmul(A[tok_start:tok_end], B[exp_start])
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torch.cuda.synchronize()
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t1 = time.perf_counter_ns()
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times.append((t1 - t0) / 1e6)
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times.sort()
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return times[len(times) // 2]
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except Exception as e:
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return None
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BENCH_FUNCS = {
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"prefill": bench_prefill,
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"moe": bench_moe,
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}
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def run_benchmark(target, dry_run=False, output_dir=None):
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"""Run parameter sweep for a target."""
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valid, total = prune_by_smem(target)
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print(f"\n{'='*70}")
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print(f"Target: {target} — {SEARCH_SPACES[target]['description']}")
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print(f"Total combos: {total}")
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print(f"After SMEM pruning: {len(valid)} ({len(valid)*100//max(total,1)}%)")
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print(f"{'='*70}")
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if dry_run:
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for p in valid:
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smem = p.get("_smem_est", 0)
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print(f" {format_point(p):50s} SMEM≈{smem:>6d} ({p.get('_smem_pct',0):>3d}%)")
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return valid
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bench_fn = BENCH_FUNCS.get(target)
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if not bench_fn:
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print(f" No benchmark function for {target} — showing combos only")
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for p in valid:
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print(f" {format_point(p)}")
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return valid
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# Run baseline (first point)
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baseline_point = valid[0]
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baseline_time = bench_fn(baseline_point)
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if baseline_time is None:
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print(" WARN: benchmark returned None (Triton/CUDA not available?)")
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return valid
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print(f" Baseline: {format_point(baseline_point)} → {baseline_time:.2f} ms")
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results = []
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best_speedup = 0
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best_point = None
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for i, point in enumerate(valid):
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t = bench_fn(point)
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if t is None or t <= 0:
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continue
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speedup = baseline_time / t
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results.append({
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"point": {k: v for k, v in point.items() if not k.startswith("_")},
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"time_ms": round(t, 3),
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"speedup": round(speedup, 4),
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"smem_est": point.get("_smem_est", 0),
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})
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marker = ""
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if speedup > best_speedup:
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best_speedup = speedup
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best_point = point
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marker = " ★"
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if (i + 1) % 5 == 0 or marker:
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print(f" [{i+1}/{len(valid)}] {format_point(point):45s} "
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f"{t:8.2f}ms {speedup:6.3f}x{marker}")
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# Sort by speedup
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results.sort(key=lambda r: -r["speedup"])
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print(f"\n{'='*70}")
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print(f"TOP 5 for {target}:")
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for j, r in enumerate(results[:5]):
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print(f" #{j+1}: {r['point']} {r['time_ms']}ms {r['speedup']}x")
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if output_dir:
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os.makedirs(output_dir, exist_ok=True)
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path = os.path.join(output_dir, f"triton_{target}.json")
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with open(path, "w") as f:
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json.dump({
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"target": target,
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"hardware": HW,
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"baseline_time_ms": round(baseline_time, 3),
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"results": results[:20], # top 20
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}, f, indent=2)
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print(f" Saved: {path}")
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return results
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def main():
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p = argparse.ArgumentParser(
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description="BI-V100 Triton JIT parameter benchmark (REAL injection)")
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p.add_argument("--target", required=True,
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choices=["prefill", "flash_attn", "moe", "smem", "all"],
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help="Which tunable surface to benchmark")
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p.add_argument("--dry-run", action="store_true",
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help="Just show valid combos, don't run")
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p.add_argument("-o", "--output", default=None,
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help="Output directory for results JSON")
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p.add_argument("--smem-limit", type=int, default=None,
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help="Override SMEM limit (49152 or 32768)")
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args = p.parse_args()
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if args.smem_limit:
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HW["smem_per_block"] = args.smem_limit
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targets = list(SEARCH_SPACES.keys()) if args.target == "all" else [args.target]
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for target in targets:
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run_benchmark(target, args.dry_run, args.output)
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if __name__ == "__main__":
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main()
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