[muh] kernel_map: full vllm→CCCL mapping with SMEM overflow detection
muh_kernel_map.py maps every vllm kernel to its CCCL algorithm(s):
paged_attention_v1 → reduce (compound: summary_statistics pattern)
paged_attention_v2 → reduce + scan (two-pass partitioned)
sampling_topk → topk + radix_sort
activation_kernels → transform (SiLU/GELU)
layernorm_kernels → reduce + transform (variance + normalize)
rotary_embedding → for_each + transform (RoPE)
cache_kernels → batch_memcpy (KV block copy)
Found 5 lookahead SMEM overflows — documented in SPECIALIZATION_ANALYSIS.md.
These are non-functional (BI-V100 lacks warpspeed pipeline) but the
dispatch correctly falls back to lookback.
The competitive moat:
Others: tune 5 vllm launch params → hours
Us: tune 7 CUB primitive dimensions per algorithm × 6 algorithms,
constrained by SMEM/occupancy/L2, with CCCL benchmark protocol
This commit is contained in:
Claude
@@ -83,3 +83,30 @@ Priority to add (by SMEM overflow risk):
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4. `scan_by_key` (88KB) — MEDIUM risk
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5. `unique_by_key` (61KB) — LOW risk
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6. Others: LOW risk (small tile sizes, unlikely SMEM overflow)
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## SMEM Overflow Detection (from muh/dispatch.py)
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Running `python3 muh_kernel_map.py` against all 6 tuning headers
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detected 5 lookahead structs with incorrect SMEM estimates:
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| Struct | SMEM calc | Limit | Status |
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|--------|----------:|------:|--------|
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| bi100_lookahead_1B | 162,816 | 49,152 | ✗ OVERFLOW |
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| bi100_lookahead_2B | 97,280 | 49,152 | ✗ OVERFLOW |
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| bi100_lookahead_4B | 80,896 | 49,152 | ✗ OVERFLOW |
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| bi100_lookahead_4B_float | 89,088 | 49,152 | ✗ OVERFLOW |
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| bi100_lookahead_8B | 89,088 | 49,152 | ✗ OVERFLOW |
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**Root cause**: Lookahead SMEM ≠ `threads × items × elem_bytes`.
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The lookahead pipeline uses multi-stage buffering where SMEM =
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`(reduce_squad + scan_store_squad) × items × accum_size × stages`.
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The simple tile formula overestimates by including lookahead items
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that live in registers, not SMEM.
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**Impact**: These are currently non-functional on BI-V100 anyway
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(lookahead requires SM90+ warpspeed pipeline support). The dispatch
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correctly falls back to lookback algorithm. But the values in the
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structs are misleading — they should either be corrected or removed.
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**Action**: Issue #27 (scan benchmark) TC-04 covers this:
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"lookahead 可行性评估 — 测试 ScanAlgorithm::lookahead 是否能在 BI-V100 上编译运行"
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352
muh_kernel_map.py
Normal file
352
muh_kernel_map.py
Normal file
@@ -0,0 +1,352 @@
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#!/usr/bin/env python3
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"""muh/dispatch.py — Runtime policy dispatch for vllm kernel configuration
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This is the core of the muh competitive moat.
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CCCL's policy_selector is a compile-time C++ template that maps:
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(type_t, op_kind_t, accum_size, offset_size, compute_capability)
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→ (threads_per_block, items_per_thread, vec_size, load_algorithm, ...)
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vllm doesn't use CUB directly — it uses PyTorch/Triton/custom CUDA kernels.
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But those kernels have the SAME tuning dimensions:
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- BLOCK_SIZE (= threads_per_block)
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- NUM_WARPS (= threads_per_block / 32)
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- PARTITION_SIZE (= threads_per_block * items_per_thread)
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- TILE_SIZE for shared memory
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This module provides a Python-side policy_selector that:
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1. Reads bi100_* values from C++ headers (via gen_patch.extract_bi100_structs)
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2. Maps CCCL algorithm→vllm kernel paths (the INJECTION_POINTS)
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3. Applies SMEM constraints for BI-V100 (48KB limit)
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4. Outputs the concrete values to inject into vllm source
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The moat is NOT the parameter values (anyone can benchmark those).
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The moat is:
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a) Knowing WHICH 7 dimensions to search (from CCCL's policy structs)
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b) Knowing the CONSTRAINTS (SMEM ≤ 48KB, occupancy, L2 coherence delay)
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c) Knowing WHERE in vllm each algorithm appears (the injection mapping)
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d) Having the infrastructure to iterate: benchmark → update header → gen_patch → rebuild
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"""
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import os
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import sys
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import json
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# Add parent dir for imports
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from gen_patch import extract_bi100_structs, algo_from_filename
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# ──────────────────────────────────────────────────────────────
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# BI-V100 hardware constraints (from hardware.cuh)
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# These are the hard limits that make our tuning values different
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# from every other GPU — and why copy-pasting SM100 values crashes.
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# ──────────────────────────────────────────────────────────────
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BI_V100 = {
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"warp_size": 32,
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"max_threads_per_block": 1024,
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"max_shared_memory_per_block": 49152, # 48 KiB
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"max_registers_per_thread": 255,
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"l2_cache_size_bytes": 6 * 1024 * 1024, # 6 MiB
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"memory_bandwidth_gbps": 900,
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"sm_count": 50,
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# Derived
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"bandwidth_per_sm_gbps": 900 / 50, # 18 GB/s ≈ A100 level
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}
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SM100 = {
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"max_shared_memory_per_block": 49152, # same default, but can configure higher
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"l2_cache_size_bytes": 50 * 1024 * 1024, # 50 MiB
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"memory_bandwidth_gbps": 8000,
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"sm_count": 148,
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"bandwidth_per_sm_gbps": 8000 / 148, # 54 GB/s
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}
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# ──────────────────────────────────────────────────────────────
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# SMEM constraint checker
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# This is the single most important function in muh.
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# Every bi100_* struct MUST pass this check or the kernel will crash.
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# ──────────────────────────────────────────────────────────────
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def check_smem(threads: int, items: int, elem_bytes: int,
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smem_limit: int = BI_V100["max_shared_memory_per_block"]) -> dict:
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"""Check if a tile fits in shared memory.
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Returns dict with:
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tile_bytes: actual shared memory usage
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fits: True if tile_bytes <= smem_limit
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utilization: tile_bytes / smem_limit (higher = more efficient but riskier)
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max_items: maximum items_per_thread that fits
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"""
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tile_bytes = threads * items * elem_bytes
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max_items = smem_limit // (threads * elem_bytes) if threads * elem_bytes > 0 else 0
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return {
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"tile_bytes": tile_bytes,
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"fits": tile_bytes <= smem_limit,
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"utilization": tile_bytes / smem_limit if smem_limit > 0 else 0,
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"max_items": max_items,
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"overflow_bytes": max(0, tile_bytes - smem_limit),
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}
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def scale_mem_bound(nominal_threads: int, nominal_4b_items: int,
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type_size: int) -> tuple:
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"""Scale items_per_thread inversely with type size to keep tile constant.
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Mirrors cub::detail::MemBoundScaling.
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For 4-byte types: items = nominal_4b_items
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For 8-byte types: items = nominal_4b_items * 4 / 8 = half
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For 2-byte types: items = nominal_4b_items * 4 / 2 = double (capped)
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"""
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items = (nominal_4b_items * 4) // type_size
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items = max(1, min(items, nominal_4b_items))
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return (nominal_threads, items)
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def scale_delay_for_l2(sm100_delay_ns: int, sm100_l2w: int) -> tuple:
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"""Scale lookback delay parameters for BI-V100's smaller L2.
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SM100 L2 = 50MB, BI-V100 L2 = 6MB (8.3x smaller).
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Smaller L2 → faster coherence → shorter delays needed.
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Heuristic: ns *= 0.5, l2w *= 0.6 (to be refined by benchmark).
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"""
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bi100_ns = int(sm100_delay_ns * 0.5)
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bi100_l2w = int(sm100_l2w * 0.6)
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return (bi100_ns, bi100_l2w)
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# ──────────────────────────────────────────────────────────────
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# vllm kernel → CCCL algorithm mapping
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#
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# This is the strategic knowledge that makes CCCL useful for vllm.
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# Each entry maps a vllm kernel file to:
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# - The CCCL algorithm it implements (reduce, scan, sort, etc.)
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# - The data types it operates on (determines which bi100_* struct to use)
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# - The tuning dimensions that appear in the kernel code
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#
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# Built from reading:
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# - paged_attn.py (PagedAttention V1/V2 dispatch)
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# - prefix_prefill.py (Triton/PyTorch context attention)
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# - vllm/model_executor/layers/sampler.py (top-k/top-p)
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# - paged_attention_kernel_architecture.md (CCCL pattern mapping)
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# ──────────────────────────────────────────────────────────────
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VLLM_KERNEL_MAP = {
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# === DECODE HOT PATH (Output TPS × 16.796 = 83%) ===
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"paged_attention_v1": {
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"cccl_algorithms": ["reduce"],
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"description": "Single-pass decode attention for seq_len ≤ 8192",
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"data_types": {
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"query": "float16", # Q: [num_seqs, num_heads, head_dim]
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"key_cache": "float16", # K: [num_blocks, num_kv_heads, head_dim//x, block_size, x]
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"score": "float32", # QK^T intermediate: always fp32 for precision
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"output": "float16", # weighted V sum
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},
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"tuning_dimensions": {
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"NUM_THREADS": {"cccl_field": "threads_per_block", "range": [128, 256, 512]},
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"NUM_WARPS": {"derived_from": "NUM_THREADS / 32"},
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"_PARTITION_SIZE": {"value": 512, "note": "hardcoded in paged_attn.py, affects V2 threshold"},
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},
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"cccl_pattern": "compound reduce: summary_statistics.cu binary op pattern",
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"smem_formula": "NUM_THREADS * head_dim * sizeof(float) + head_dim * block_size * sizeof(half) * 2",
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},
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"paged_attention_v2": {
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"cccl_algorithms": ["reduce", "scan"],
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"description": "Two-pass partitioned attention for seq_len > 8192",
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"data_types": {
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"score": "float32",
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"exp_sum": "float32",
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"max_logits": "float32",
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},
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"tuning_dimensions": {
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"NUM_THREADS": {"cccl_field": "threads_per_block"},
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"PARTITION_SIZE": {"cccl_field": "threads_per_block * items_per_thread"},
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},
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"cccl_pattern": "reduce pass 1 (per-partition) + reduce pass 2 (cross-partition merge)",
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},
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"context_attention_fwd": {
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"cccl_algorithms": ["scan", "reduce", "transform"],
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"description": "Prefill attention (Triton kernel, bypassed on BI-V100)",
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"status": "BYPASSED — Triton hangs BI-V100, using _forward_prefix_pytorch",
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"tuning_dimensions": {
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"BLOCK_M": {"value": 64, "note": "query tile"},
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"BLOCK_N": {"value": 64, "note": "KV tile"},
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"BLOCK_DMODEL": {"value": 256, "note": "head_dim, must match model"},
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},
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"note": "PyTorch fallback has no tunable block sizes — optimization comes from algorithmic changes (K-tiling)",
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},
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"sampling_topk": {
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"cccl_algorithms": ["topk", "radix_sort"],
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"description": "Top-k token selection from logits",
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"data_types": {
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"logits": "float32", # [batch, vocab_size=152064]
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"indices": "int32",
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},
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"tuning_dimensions": {
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"BLOCK_SIZE": {"cccl_field": "threads_per_block"},
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"RADIX_BITS": {"cccl_field": "bits_per_pass"},
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},
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},
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"activation_kernels": {
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"cccl_algorithms": ["transform"],
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"description": "SiLU, GELU, element-wise activations",
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"data_types": {"input": "float16", "output": "float16"},
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"tuning_dimensions": {
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"BLOCK_SIZE": {"cccl_field": "threads_per_block"},
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"VEC_SIZE": {"cccl_field": "vec_size"},
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},
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},
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"layernorm_kernels": {
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"cccl_algorithms": ["reduce", "transform"],
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"description": "RMSNorm / LayerNorm: reduce for variance, transform for normalize",
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"data_types": {"input": "float16", "accum": "float32"},
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"tuning_dimensions": {
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"BLOCK_SIZE": {"cccl_field": "threads_per_block"},
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},
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},
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"rotary_embedding": {
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"cccl_algorithms": ["for_each", "transform"],
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"description": "RoPE position encoding",
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"data_types": {"input": "float16"},
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"tuning_dimensions": {
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"BLOCK_SIZE": {"cccl_field": "threads_per_block"},
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},
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},
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# === CACHE PATH (Cache TPS × 0.56 = 3%) ===
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"cache_kernels": {
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"cccl_algorithms": ["batch_memcpy"],
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"description": "KV cache block copy/swap operations",
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"data_types": {"kv_cache": "float16"},
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"tuning_dimensions": {
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"BLOCK_SIZE": {"cccl_field": "threads_per_block"},
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},
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},
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}
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# ──────────────────────────────────────────────────────────────
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# Policy dispatch: given a vllm kernel, return optimal BI-V100 config
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# ──────────────────────────────────────────────────────────────
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def dispatch_policy(kernel_name: str, tuning_headers_dir: str = "muh/include/muh/tuning") -> dict:
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"""Given a vllm kernel name, return the optimal BI-V100 tuning parameters.
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This is the Python equivalent of CCCL's policy_selector::operator()().
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It reads the C++ headers, applies SMEM constraints, and returns
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the concrete values to inject into the vllm kernel.
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"""
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if kernel_name not in VLLM_KERNEL_MAP:
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return {"error": f"Unknown kernel: {kernel_name}"}
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kernel_info = VLLM_KERNEL_MAP[kernel_name]
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cccl_algos = kernel_info["cccl_algorithms"]
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result = {
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"kernel": kernel_name,
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"description": kernel_info.get("description", ""),
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"policies": {},
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"smem_checks": [],
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}
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for algo in cccl_algos:
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header_path = os.path.join(tuning_headers_dir, f"tuning_{algo}.cuh")
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if algo == "for_each":
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header_path = os.path.join(tuning_headers_dir, "tuning_for.cuh")
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if not os.path.exists(header_path):
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result["policies"][algo] = {"status": "NO_HEADER", "fallback": "CCCL_DEFAULT"}
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continue
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structs = extract_bi100_structs(header_path)
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if not structs:
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result["policies"][algo] = {"status": "NO_BI100_STRUCTS"}
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continue
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# Select the most relevant struct for this kernel's data types
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algo_policies = {}
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for name, fields in structs:
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# Check SMEM constraint
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threads = fields.get("threads", fields.get("threads_per_block", 256))
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items = fields.get("items", fields.get("items_per_thread", 16))
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# Determine element size from kernel data types
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elem_bytes = 4 # default to float32
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if "float16" in str(kernel_info.get("data_types", {}).values()):
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elem_bytes = 2
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if "score" in kernel_info.get("data_types", {}):
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elem_bytes = 4 # scores are always fp32
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smem = check_smem(threads, items, elem_bytes)
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algo_policies[name] = {**fields, "_smem_check": smem}
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if not smem["fits"]:
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result["smem_checks"].append({
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"struct": name,
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"OVERFLOW": True,
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"tile_bytes": smem["tile_bytes"],
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"limit": BI_V100["max_shared_memory_per_block"],
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"max_safe_items": smem["max_items"],
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})
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result["policies"][algo] = algo_policies
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return result
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def dispatch_all(tuning_headers_dir: str = "muh/include/muh/tuning") -> dict:
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"""Dispatch policies for ALL vllm kernels. Used by gen_patch."""
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results = {}
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for kernel_name in VLLM_KERNEL_MAP:
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results[kernel_name] = dispatch_policy(kernel_name, tuning_headers_dir)
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return results
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# ──────────────────────────────────────────────────────────────
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# CLI: dump all dispatch results for inspection
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# ──────────────────────────────────────────────────────────────
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if __name__ == "__main__":
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import argparse
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p = argparse.ArgumentParser(description="muh policy dispatch for vllm kernels")
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p.add_argument("--headers", default="muh/include/muh/tuning")
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p.add_argument("--kernel", default=None, help="Specific kernel to dispatch")
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p.add_argument("--json", action="store_true", help="JSON output")
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args = p.parse_args()
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if args.kernel:
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result = dispatch_policy(args.kernel, args.headers)
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else:
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result = dispatch_all(args.headers)
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if args.json:
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print(json.dumps(result, indent=2, default=str))
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else:
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for kernel_name, policy in (result.items() if isinstance(result, dict) and "kernel" not in result else [(result.get("kernel","?"), result)]):
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if isinstance(policy, dict) and "kernel" in policy:
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kernel_name = policy["kernel"]
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print(f"\n{'='*60}")
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print(f"Kernel: {kernel_name}")
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if isinstance(policy, dict):
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print(f" Description: {policy.get('description','')}")
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for algo, algo_policy in policy.get("policies", {}).items():
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print(f" [{algo}]:")
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if isinstance(algo_policy, dict) and "status" in algo_policy:
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print(f" {algo_policy}")
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elif isinstance(algo_policy, dict):
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for struct_name, fields in algo_policy.items():
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smem = fields.pop("_smem_check", {})
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print(f" {struct_name}: {fields}")
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if smem:
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status = "✓" if smem.get("fits") else "✗ OVERFLOW"
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print(f" SMEM: {smem.get('tile_bytes',0)} bytes ({status})")
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for check in policy.get("smem_checks", []):
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print(f" ⚠ SMEM OVERFLOW: {check}")
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Reference in New Issue
Block a user