Download code/scripts/micro/mc_bench3.py from changh95/hamer-p150: direct link, hf CLI and curl.
- Browser
- Download file 2.76 kB
-
https://huggingface.co/changh95/hamer-p150/resolve/main/code/scripts/micro/mc_bench3.py
- Command line
-
hf download hf://changh95/hamer-p150/code/scripts/micro/mc_bench3.py
-
curl -L -o mc_bench3.py https://huggingface.co/changh95/hamer-p150/resolve/main/code/scripts/micro/mc_bench3.py
2.76 kB
| """raw multicast bandwidth on 12x10: usage mc_bench.py [eth|worker]""" | |
| import sys | |
| from pathlib import Path | |
| from common import * | |
| from open_device_12x10 import open_device | |
| K = str(Path(__file__).resolve().parents[2] / "hamer/kernels/mcbench/mc.cpp") | |
| disp = sys.argv[1] if len(sys.argv) > 1 else "eth" | |
| dev = open_device(grid="12x10", dispatch=disp, trace_region_size=64 << 20) | |
| try: | |
| cores = ttnn.CoreRangeSet([ttnn.CoreRange(ttnn.CoreCoord(0, 0), ttnn.CoreCoord(11, 9))]) | |
| dummy = ttnn.from_torch(torch.zeros(1, 1, 32, 32), dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=dev) | |
| phys = {(x, y): dev.worker_core_from_logical_core(ttnn.CoreCoord(x, y)) for x in range(12) for y in range(10)} | |
| def run(groups, size, reps, noc, lb=0, each=False): | |
| # groups: list of (sender logical, rect logical (x0,y0,x1,y1)) | |
| args = ttnn.RuntimeArgs() | |
| for x in range(12): | |
| for y in range(10): | |
| args[x][y] = [0, 0, 0, 0, 0, 0, 0, 0, 0] | |
| for (s, (x0, y0, x1, y1)) in groups: | |
| p0, p1 = phys[(x0, y0)], phys[(x1, y1)] | |
| n = (x1 - x0 + 1) * (y1 - y0 + 1) | |
| inside = x0 <= s[0] <= x1 and y0 <= s[1] <= y1 | |
| nd = n if lb else n - (1 if inside else 0) | |
| args[s[0]][s[1]] = [1, min(p0.x, p1.x), min(p0.y, p1.y), max(p0.x, p1.x), max(p0.y, p1.y), nd, size, reps, lb] | |
| cfg = ttnn.ReaderConfigDescriptor() if noc == 0 else ttnn.WriterConfigDescriptor() | |
| kd = ttnn.KernelDescriptor(kernel_source=K, source_type=ttnn.KernelDescriptor.SourceType.FILE_PATH, core_ranges=cores, | |
| compile_time_args=[], defines=[("MC_BARRIER_EACH", "1")] if each else [], runtime_args=args, config=cfg) | |
| cb = ttnn.CBDescriptor(total_size=size, core_ranges=cores, format_descriptors=[ | |
| ttnn.CBFormatDescriptor(buffer_index=0, data_format=ttnn.bfloat16, page_size=2048)]) | |
| prog = ttnn.ProgramDescriptor(kernels=[kd], semaphores=[], cbs=[cb]) | |
| f = lambda: (ttnn.generic_op([dummy, dummy], prog), None)[1] | |
| return time_op(dev, f, reps=10, iters=5) | |
| base = run([], 32768, 1, 0) | |
| print(f"[mc] empty {base:.2f} us", flush=True) | |
| MB = 696 * 1024 | |
| for noc in (0,): | |
| for size in (34816, 69632): | |
| reps = MB // size | |
| for each in (False, True): | |
| for lb in (0, 1): | |
| for sy in (0, 2, 5): | |
| t = run([((x, sy), (x, 0, x, 5)) for x in range(10)], size, reps, noc, lb=lb, each=each) | |
| print(f"[mc] noc{noc} 10 cols x6 sender y={sy} lb={lb} barrier_each={each} {size}B x{reps}: {t:.2f} us -> {(t-base)/reps:.2f} us/xfer {reps*size / (t - base) / 1e3:.1f} GB/s", flush=True) | |
| finally: | |
| ttnn.close_device(dev) | |