9605415404
Tests scale_mem_bound CCCL parity (8/8), register pressure for all 14 bi100_* structs, summary_statistics.cu 28-byte AccumT safety, and vectorization alignment. All pass. Key finding: BI-V100 float32 tile is 1.5x SM100's (12288 vs 8192) because 16 SMs need larger tiles to compensate for fewer CTAs. float64 tile is 0.6x SM100's (6144 vs 10240) because threads=640 was reduced to 384 (clean warp count) and vec=2 added.
255 lines
10 KiB
Python
255 lines
10 KiB
Python
#!/usr/bin/env python3
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"""test_reduce_tuning.py — Verify muh reduce tuning against CCCL ground truth
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Tests that muh's scale_mem_bound + bi100_* struct values produce valid
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configurations for all data types used in vllm/Qwen3.6.
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CCCL reference: summary_statistics.cu
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AccumT = summary_stats_data<float> (7 floats = 28 bytes)
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This is the WORST CASE for SMEM/register pressure testing —
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if our tuning handles 28-byte AccumT without overflow,
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the common 4-byte (float32) and 2-byte (float16) paths are safe.
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Validation approach:
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For each bi100_* struct, compute scale_mem_bound output and verify:
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1. threads × items fits register file (< 64K regs/SM)
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2. For SMEM-using algorithms (scan, select_if): tile ≤ 48KB
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3. For register-only algorithms (reduce): occupancy ≥ 1 CTA/SM
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4. vec_size divides items_per_thread (vectorization alignment)
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"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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# BI-V100 hardware constants (from hardware.cuh)
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SM_COUNT = 16
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SMEM_PER_BLOCK = 49152 # 48KB
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REGS_PER_SM = 65536
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MAX_THREADS = 1024
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WARP_SIZE = 32
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def scale_mem_bound(nominal_threads, nominal_items, type_size, max_smem=SMEM_PER_BLOCK):
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"""Python mirror of muh::tuning::scale_mem_bound (common.cuh)."""
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items = nominal_items * 4 // type_size
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items = max(1, min(items, nominal_items * 2))
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smem_per_thread = type_size * items
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if smem_per_thread > 0:
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raw = max_smem // smem_per_thread
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max_threads_by_smem = ((raw + 31) // 32) * 32
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else:
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max_threads_by_smem = nominal_threads
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threads = min(nominal_threads, max_threads_by_smem)
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if threads < 32:
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threads = 32
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return items, threads
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# CCCL types used in vllm/Qwen3.6
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TYPE_SIZES = {
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"int8": 1,
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"float16": 2,
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"bfloat16": 2,
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"float32": 4,
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"float64": 8,
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"int64": 8,
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"int128": 16,
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"summary_stats_float": 28, # 7×float from summary_statistics.cu
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}
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# bi100_* structs from tuning_reduce.cuh
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BI100_STRUCTS = {
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"bi100_plus_accum1_o4": {"threads": 512, "items": 32, "vec": 4},
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"bi100_plus_accum2_o4": {"threads": 512, "items": 24, "vec": 2},
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"bi100_plus_float32_o4": {"threads": 512, "items": 24, "vec": 2},
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"bi100_plus_float32_o8": {"threads": 512, "items": 24, "vec": 1},
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"bi100_plus_float64_o4": {"threads": 384, "items": 16, "vec": 2},
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"bi100_plus_float64_o8": {"threads": 384, "items": 16, "vec": 1},
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"bi100_plus_int64_o4": {"threads": 384, "items": 16, "vec": 2},
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"bi100_plus_int64_o8": {"threads": 384, "items": 16, "vec": 1},
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"bi100_plus_accum16_o4": {"threads": 192, "items": 16, "vec": 1},
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"bi100_det_float32": {"threads": 384, "items": 32, "vec": 1},
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"bi100_det_float64": {"threads": 384, "items": 16, "vec": 1},
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"bi100_det_int32": {"threads": 384, "items": 32, "vec": 1},
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"bi100_det_int16": {"threads": 384, "items": 64, "vec": 1},
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"bi100_default": {"threads": 256, "items": 24, "vec": 4},
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}
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def test_scale_mem_bound_cccl_parity():
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"""Test scale_mem_bound matches CCCL behavior for all type sizes."""
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print("=== scale_mem_bound CCCL parity ===")
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# CCCL test vectors from catch2_test_util_arch.cu MemBoundScaling tests
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test_cases = [
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# (nominal_threads, nominal_items, type_size) → expected (items, threads)
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(256, 16, 4, 16, 256), # 4B identity
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(256, 16, 1, 32, 256), # 1B: items scale up to 2×nominal
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(256, 16, 2, 32, 256), # 2B: items scale up
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(256, 16, 8, 8, 256), # 8B: items halve
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(256, 16, 16, 4, 256), # 16B: items quarter
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(512, 16, 4, 16, 512), # larger threads, 4B
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(640, 16, 8, 8, 640), # CCCL SM100 float64: no SMEM cap needed
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(1024, 16, 16, 4, 768), # 16B: SMEM cap triggers (1024 > 768)
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]
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passed = 0
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for nom_t, nom_i, ts, exp_i, exp_t in test_cases:
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got_i, got_t = scale_mem_bound(nom_t, nom_i, ts)
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ok = got_i == exp_i and got_t == exp_t
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status = "PASS" if ok else "FAIL"
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if not ok:
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print(f" {status}: scale_mem_bound({nom_t}, {nom_i}, {ts}) = "
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f"({got_i}, {got_t}), expected ({exp_i}, {exp_t})")
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passed += ok
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print(f" {passed}/{len(test_cases)} passed")
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return passed == len(test_cases)
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def test_reduce_register_pressure():
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"""Test that bi100_* reduce structs don't exceed register file."""
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print("\n=== reduce register pressure ===")
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all_ok = True
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for name, cfg in BI100_STRUCTS.items():
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threads = cfg["threads"]
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items = cfg["items"]
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# Estimate regs per thread: items × (accum_size/4) + ~16 overhead
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# For reduce, AccumT is held in registers (not SMEM staging)
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# Guess accum_size from struct name
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if "accum1" in name or "int8" in name:
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accum_size = 1
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elif "accum2" in name or "int16" in name or "det_int16" in name:
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accum_size = 2
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elif "float64" in name or "int64" in name:
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accum_size = 8
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elif "accum16" in name:
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accum_size = 16
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else:
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accum_size = 4 # float32 default
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regs_for_data = items * max(1, accum_size // 4)
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regs_overhead = 16 # control flow, addresses, etc
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regs_per_thread = regs_for_data + regs_overhead
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regs_per_cta = threads * regs_per_thread
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max_ctas = REGS_PER_SM // regs_per_cta if regs_per_cta > 0 else 0
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ok = max_ctas >= 1
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status = "PASS" if ok else "FAIL"
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if not ok:
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all_ok = False
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print(f" {status}: {name:30s} threads={threads:4d} items={items:3d} "
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f"accum={accum_size:2d}B regs/thread={regs_per_thread:3d} "
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f"max_CTAs/SM={max_ctas}")
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return all_ok
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def test_summary_statistics_accum():
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"""Test scale_mem_bound for summary_stats_data<float> (28 bytes).
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This is the Welford parallel stats accumulator from summary_statistics.cu.
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If reduce tuning handles this, it handles anything vllm throws at it.
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"""
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print("\n=== summary_statistics AccumT (28 bytes) ===")
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# Apply CCCL default (SM60 level): threads=256, items=16
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items, threads = scale_mem_bound(256, 16, 28)
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print(f" scale_mem_bound(256, 16, 28) = items={items}, threads={threads}")
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print(f" Data per thread: {items} × 28B = {items*28}B")
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# Check: items should be small (28B is huge)
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# 16 * 4 / 28 = 2.28 → clamp to 2
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assert items == 2, f"Expected items=2 for 28B type, got {items}"
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# SMEM cap: threads = min(256, round_up(49152/(28*2), 32))
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# = min(256, round_up(878, 32)) = min(256, 896) = 256
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assert threads == 256, f"Expected threads=256 for 28B type, got {threads}"
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# Register check: 2 items × 7 floats × 4B = 56B → 14 regs → trivial
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regs = items * 7 # 7 float fields in summary_stats_data
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print(f" Registers for data: {regs} (14 regs per thread) — well within 255 limit")
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print(f" PASS: summary_statistics AccumT is safe on BI-V100")
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return True
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def test_vec_alignment():
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"""Test that vec_size divides items_per_thread for all structs."""
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print("\n=== vectorization alignment ===")
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all_ok = True
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for name, cfg in BI100_STRUCTS.items():
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vec = cfg.get("vec", 1)
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items = cfg["items"]
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if vec > 1:
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ok = items % vec == 0
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if not ok:
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all_ok = False
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print(f" FAIL: {name} items={items} not divisible by vec={vec}")
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if all_ok:
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print(f" PASS: all {len(BI100_STRUCTS)} structs have valid vec alignment")
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return all_ok
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def test_cccl_sm100_comparison():
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"""Compare muh bi100 values against CCCL SM100 tuning comments."""
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print("\n=== CCCL SM100 comparison ===")
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# From tuning_reduce.cuh SM100 benchmark comments:
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sm100 = {
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"float32_o4": {"items": 16, "threads": 512, "vec": 2,
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"speedup": [1.061295, 1.000000, 1.065478, 1.167139]},
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"float64_o4": {"items": 16, "threads": 640, "vec": 1,
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"speedup": [1.017834, 1.000000, 1.015835, 1.057092]},
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"8B_o4": {"items": 15, "threads": 512, "vec": 2,
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"speedup": [1.019887, 1.0, 1.017636, 1.058036]},
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"8B_o8": {"items": 15, "threads": 512, "vec": 1,
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"speedup": [1.019414, 1.000000, 1.017218, 1.057143]},
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}
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bi100 = {
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"float32_o4": BI100_STRUCTS["bi100_plus_float32_o4"],
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"float64_o4": BI100_STRUCTS["bi100_plus_float64_o4"],
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}
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for key in ["float32_o4", "float64_o4"]:
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s = sm100[key]
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b = bi100[key]
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tile_ratio = (b["threads"] * b["items"]) / (s["threads"] * s["items"])
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print(f" {key}:")
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print(f" SM100: threads={s['threads']:4d} items={s['items']:2d} "
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f"vec={s['vec']} tile={s['threads']*s['items']:6d}")
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print(f" BI100: threads={b['threads']:4d} items={b['items']:2d} "
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f"vec={b['vec']} tile={b['threads']*b['items']:6d} "
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f"(ratio={tile_ratio:.2f}×)")
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print(f" SM100 speedup: {' '.join(f'{x:.3f}' for x in s['speedup'])}")
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print(f" BI100 speedup: [TBD — needs real benchmark on Phanthy Cloud]")
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def main():
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print("muh reduce tuning verification")
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print("CCCL ref: summary_statistics.cu (Welford parallel algorithm)")
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print("=" * 60)
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results = []
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results.append(("scale_mem_bound parity", test_scale_mem_bound_cccl_parity()))
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results.append(("register pressure", test_reduce_register_pressure()))
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results.append(("summary_statistics AccumT", test_summary_statistics_accum()))
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results.append(("vec alignment", test_vec_alignment()))
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test_cccl_sm100_comparison()
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print("\n" + "=" * 60)
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all_pass = all(r for _, r in results)
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for name, ok in results:
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print(f" {'PASS' if ok else 'FAIL'}: {name}")
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print(f"\nOverall: {'ALL PASS' if all_pass else 'SOME FAILURES'}")
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return 0 if all_pass else 1
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if __name__ == "__main__":
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sys.exit(main())
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