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51defdc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 | #!/usr/bin/env python3
# SPDX-License-Identifier: Apache-2.0
"""Minimal standalone repro (plain ttnn, no METEOR code): the separable bilinear x2 resize of the METEOR lift,
captured in one trace and replayed in a tight loop. On a Blackhole p150 with ETH dispatch the replay intermittently
never completes (logs/meteor/hangfix/ETH_DISPATCH_HANG_ISSUE.md).
bin/devrun -t 600 -- python -u repro_resize2d_eth.py --dispatch eth --iters 3000 --jsonl out.jsonl
bin/devrun -t 600 -- python -u repro_resize2d_eth.py --dispatch worker ... # A/B
Op chain (= ttaw.ops.upsample.Resize2d, in (400, 250) -> out (800, 500), C = 96, N = 1, fp32 operands, HiFi4 + fp32
accumulation), on an input x [1, 1, 100000, 96] bf16 TILE in DRAM:
RM -> reshape [1, 400, 250, 96] -> TILE -> typecast fp32 -> transpose(-2, -1) [1, 400, 96, 250]
-> matmul A_w^T [250, 500] -> transpose(-2, -1) [1, 400, 500, 96] -> RM -> reshape [1, 1, 400, 48000] -> TILE
-> matmul A_h [800, 400] @ [1, 1, 400, 48000] -> [1, 1, 800, 48000] fp32 -> typecast bf16 -> RM
-> reshape [1, 1, 400000, 96] -> TILE
``--part w`` stops after the W pass, ``--part h`` runs only the H-pass matmul (on a persistent [1, 1, 400, 48000]
fp32 TILE input). Each replay is followed by ``synchronize_device`` and a fsync'd JSON heartbeat; outputs are hashed
every ``--check-every`` replays; faulthandler dumps the stack when a replay stalls. No tt-triage, no op timeout.
"""
from __future__ import annotations
import argparse
import faulthandler
import hashlib
import json
import os
import sys
import time
import numpy as np
H, W, H2, W2 = 400, 250, 800, 500
def interp(n_in: int, n_out: int) -> np.ndarray:
"""1-D bilinear, align_corners=False (half-pixel, clamped at 0), as F.interpolate."""
s = n_in / n_out
src = np.maximum((np.arange(n_out) + 0.5) * s - 0.5, 0.0)
i0 = np.minimum(np.floor(src).astype(np.int64), n_in - 1)
i1 = np.minimum(i0 + 1, n_in - 1)
lam = src - i0
a = np.zeros((n_out, n_in))
np.add.at(a, (np.arange(n_out), i0), 1.0 - lam)
np.add.at(a, (np.arange(n_out), i1), lam)
return a.astype(np.float32)
def log(fh, **rec):
rec["t"] = round(time.time(), 3)
line = json.dumps(rec)
print(line, flush=True)
if fh:
fh.write(line + "\n")
fh.flush()
os.fsync(fh.fileno())
def main() -> int:
ap = argparse.ArgumentParser()
ap.add_argument("--dispatch", choices=["eth", "worker"], default="eth")
ap.add_argument("--cqs", type=int, default=1)
ap.add_argument("--iters", type=int, default=3000)
ap.add_argument("--per-trace", type=int, default=1, help="resize chains per trace")
ap.add_argument("--part", choices=["full", "w", "h", "w_rm", "wh", "tail", "mid", "mid_h", "h_tail",
"t_typecast", "t_untilize", "t_reshape", "t_tilize", "t_reshape_tile"], default="full",
help="w: up to the W-pass transpose; w_rm: + RM/reshape/TILE; wh: + H matmul (no tail); "
"tail: typecast/RM/reshape/TILE of a persistent z; mid: RM/reshape/TILE of a persistent "
"[1,400,500,C] y; mid_h: mid + H matmul; h_tail: H matmul + tail; t_*: ONE tail op on a "
"persistent input (typecast fp32->bf16 TILE [800, 500C]; untilize bf16 [800, 500C]; RM "
"reshape [800, 500C] -> [400000, C]; tilize RM [400000, C])")
ap.add_argument("--channels", type=int, default=96, help="C (the H-pass matmul N is 500 * C; 96 -> 48000)")
ap.add_argument("--dtype", choices=["float32", "bfloat16"], default="float32", help="matmul operand dtype")
ap.add_argument("--fidelity", choices=["HiFi4", "HiFi3", "HiFi2", "LoFi"], default="HiFi4")
ap.add_argument("--no-fp32-acc", action="store_true", help="fp32_dest_acc_en=False")
ap.add_argument("--dest-cols", type=int, default=0,
help="t_reshape: destination last dim (default C: [800, 500C] -> [400000, C]); dest page = 2 B x it")
ap.add_argument("--reshape", choices=["rm", "tile", "tail_tile"], default="rm",
help="rm: TILE -> RM -> reshape -> TILE (as Resize2d); tile: ttnn.reshape on the TILE tensor")
ap.add_argument("--check-every", type=int, default=200)
ap.add_argument("--stall-s", type=float, default=60.0)
ap.add_argument("--jsonl")
a = ap.parse_args()
faulthandler.enable()
fh = open(a.jsonl, "a") if a.jsonl else None
faulthandler.dump_traceback_later(600, repeat=True)
import torch
import ttnn
core = ttnn.DispatchCoreType.ETH if a.dispatch == "eth" else ttnn.DispatchCoreType.WORKER
dev = ttnn.open_device(device_id=0, dispatch_core_config=ttnn.DispatchCoreConfig(core),
num_command_queues=a.cqs, l1_small_size=32768, trace_region_size=64 << 20)
rc = 0
try:
g = dev.compute_with_storage_grid_size()
C = a.channels
dt = ttnn.float32 if a.dtype == "float32" else ttnn.bfloat16
log(fh, ev="open", dispatch=a.dispatch, cqs=a.cqs, grid=f"{g.x}x{g.y}", part=a.part, per_trace=a.per_trace,
channels=C, n=W2 * C, dtype=a.dtype, fidelity=a.fidelity, fp32_acc=not a.no_fp32_acc,
reshape=a.reshape, dest_cols=a.dest_cols or None)
cfg = ttnn.init_device_compute_kernel_config(dev.arch(), math_fidelity=getattr(ttnn.MathFidelity, a.fidelity),
fp32_dest_acc_en=not a.no_fp32_acc, packer_l1_acc=False,
math_approx_mode=False)
to_dev = lambda arr, dt, lay: ttnn.from_torch(torch.from_numpy(np.ascontiguousarray(arr)), dtype=dt, # noqa
layout=lay, device=dev, memory_config=ttnn.DRAM_MEMORY_CONFIG)
rng = np.random.default_rng(0)
aw_t = to_dev(interp(W, W2).T, dt, ttnn.TILE_LAYOUT) # [250, 500]
ah = to_dev(interp(H, H2), dt, ttnn.TILE_LAYOUT) # [800, 400]
x_in = to_dev(rng.standard_normal((1, 1, H * W, C)).astype(np.float32), ttnn.bfloat16, ttnn.TILE_LAYOUT)
y_in = to_dev(rng.standard_normal((1, 1, H, W2 * C)).astype(np.float32), dt, ttnn.TILE_LAYOUT)
z_in = to_dev(rng.standard_normal((1, 1, H2, W2 * C)).astype(np.float32), dt, ttnn.TILE_LAYOUT) \
if a.part == "tail" else None
yw_in = to_dev(rng.standard_normal((1, H, W2, C)).astype(np.float32), dt, ttnn.TILE_LAYOUT) \
if a.part in ("mid", "mid_h") else None
one = None
if a.part.startswith("t_"):
z32 = rng.standard_normal((1, 1, H2, W2 * C)).astype(np.float32)
one = {"t_typecast": lambda: to_dev(z32, ttnn.float32, ttnn.TILE_LAYOUT),
"t_untilize": lambda: to_dev(z32, ttnn.bfloat16, ttnn.TILE_LAYOUT),
"t_reshape": lambda: to_dev(z32, ttnn.bfloat16, ttnn.ROW_MAJOR_LAYOUT),
"t_tilize": lambda: to_dev(z32.reshape(1, 1, H2 * W2, C), ttnn.bfloat16, ttnn.ROW_MAJOR_LAYOUT),
"t_reshape_tile": lambda: to_dev(z32, ttnn.bfloat16, ttnn.TILE_LAYOUT),
}[a.part]()
def tail(z):
if z.dtype != ttnn.bfloat16:
z = ttnn.typecast(z, ttnn.bfloat16)
if a.reshape in ("tile", "tail_tile"):
return ttnn.reshape(z, (1, 1, H2 * W2, C))
z = ttnn.to_layout(z, ttnn.ROW_MAJOR_LAYOUT)
z = ttnn.reshape(z, (1, 1, H2 * W2, C))
return ttnn.to_layout(z, ttnn.TILE_LAYOUT)
def mid(y):
if a.reshape == "tile":
return ttnn.reshape(y, (1, 1, H, W2 * C))
y = ttnn.to_layout(y, ttnn.ROW_MAJOR_LAYOUT)
y = ttnn.reshape(y, (1, 1, H, W2 * C))
return ttnn.to_layout(y, ttnn.TILE_LAYOUT)
def chain():
if a.part == "t_typecast":
return ttnn.typecast(one, ttnn.bfloat16)
if a.part == "t_untilize":
return ttnn.to_layout(one, ttnn.ROW_MAJOR_LAYOUT)
if a.part == "t_reshape":
d = a.dest_cols or C
return ttnn.reshape(one, (1, 1, H2 * W2 * C // d, d))
if a.part == "t_reshape_tile":
return ttnn.reshape(one, (1, 1, H2 * W2, C))
if a.part == "t_tilize":
return ttnn.to_layout(one, ttnn.TILE_LAYOUT)
if a.part == "h":
return ttnn.matmul(ah, y_in, compute_kernel_config=cfg)
if a.part == "h_tail":
return tail(ttnn.matmul(ah, y_in, compute_kernel_config=cfg))
if a.part == "tail":
return tail(z_in)
if a.part == "mid":
return mid(yw_in)
if a.part == "mid_h":
return ttnn.matmul(ah, mid(yw_in), compute_kernel_config=cfg)
x = ttnn.to_layout(x_in, ttnn.ROW_MAJOR_LAYOUT)
x = ttnn.reshape(x, (1, H, W, C))
x = ttnn.to_layout(x, ttnn.TILE_LAYOUT)
if dt != ttnn.bfloat16:
x = ttnn.typecast(x, dt)
xt = ttnn.transpose(x, -2, -1)
y = ttnn.matmul(xt, aw_t, compute_kernel_config=cfg)
y = ttnn.transpose(y, -2, -1)
if a.part == "w":
return y
y = mid(y)
if a.part == "w_rm":
return y
z = ttnn.matmul(ah, y, compute_kernel_config=cfg)
if a.part == "wh":
return z
return tail(z)
warm = [chain() for _ in range(a.per_trace)] # compile, then capture
ttnn.synchronize_device(dev)
del warm
tid = ttnn.begin_trace_capture(dev, cq_id=0)
outs = [chain() for _ in range(a.per_trace)]
ttnn.end_trace_capture(dev, tid, cq_id=0)
ttnn.synchronize_device(dev)
log(fh, ev="captured")
ref = None
for i in range(a.iters):
faulthandler.dump_traceback_later(a.stall_s, repeat=True)
t0 = time.perf_counter()
ttnn.execute_trace(dev, tid, cq_id=0, blocking=False)
ttnn.synchronize_device(dev)
rec = {"ev": "iter", "i": i, "ms": round((time.perf_counter() - t0) * 1e3, 2)}
if i % a.check_every == 0 or i == a.iters - 1:
h = hashlib.sha256()
for t in outs:
h.update(ttnn.to_torch(t).float().numpy().tobytes())
d = h.hexdigest()[:16]
rec["digest"] = d
ref = ref or d
if d != ref:
rec["MISMATCH"] = ref
rc = 1
log(fh, **rec)
faulthandler.cancel_dump_traceback_later()
log(fh, ev="done", iters=a.iters, rc=rc)
ttnn.release_trace(dev, tid)
finally:
faulthandler.dump_traceback_later(300, repeat=True)
ttnn.close_device(dev)
faulthandler.cancel_dump_traceback_later()
log(fh, ev="closed")
return rc
if __name__ == "__main__":
sys.exit(main())
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