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[AMD][No-Merge] Simplify fused allreduce + RMSNorm and remove hidden_dim allowlist (#21986)

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Co-authored-by: HAI <hixiao@gmail.com>
This commit is contained in:
Hubert Lu
2026-04-11 23:47:08 -07:00
committed by GitHub
co-authored by HAI
parent 9a4e8089ff
commit edaa5973d4
4 changed files with 263 additions and 56 deletions
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test/registered/ops/test_aiter_allreduce_fusion_amd.py
+251 -38
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@@ -12,14 +12,142 @@ from sglang.test.ci.ci_register import register_amd_ci
register_amd_ci(est_time=240, suite="stage-c-test-large-8-gpu-amd")
HIDDEN_DIMS = [2880, 4096, 5120, 6144, 7168, 8192]
def _run_residual_accuracy_check():
"""Distributed entry point: bit-exact residual accuracy across 1-stage/2-stage.
Regression test for the 1-stage kernel accuracy bug (ROCm/aiter#2586):
allreduce_fusion_kernel_1stage accumulated in f32 and added the residual
before rounding to bf16, while the unfused path rounds allreduce to bf16
first. The 1-ULP divergence compounded across layers and caused a -2.6pp
GSM8K regression.
Must be launched via torchrun (multi-GPU).
"""
import torch.distributed as dist
from sglang.srt.distributed.communication_op import (
tensor_model_parallel_all_reduce,
tensor_model_parallel_fused_allreduce_rmsnorm,
)
from sglang.srt.distributed.parallel_state import (
destroy_distributed_environment,
destroy_model_parallel,
init_distributed_environment,
initialize_model_parallel,
set_custom_all_reduce,
)
rank = int(os.environ.get("RANK", "0"))
world_size = int(os.environ.get("WORLD_SIZE", "1"))
local_rank = int(os.environ.get("LOCAL_RANK", str(rank)))
torch.cuda.set_device(local_rank % torch.cuda.device_count())
device = torch.device(f"cuda:{local_rank % torch.cuda.device_count()}")
set_custom_all_reduce(True)
init_distributed_environment(
world_size=world_size,
rank=rank,
local_rank=local_rank,
distributed_init_method="env://",
backend="nccl",
)
initialize_model_parallel(tensor_model_parallel_size=world_size)
dtype = torch.bfloat16
eps = 1e-6
all_pass = True
test_cases = [(m, n) for n in HIDDEN_DIMS for m in [1, 4, 8, 16, 32, 64, 128]]
prev_n = None
for m, n in test_cases:
if n != prev_n:
prev_n = n
weight = torch.ones((n,), dtype=dtype, device=device)
if rank == 0:
print(f"\nhidden_dim={n}:")
torch.manual_seed(1234 + rank * 17 + m)
x = torch.randn((m, n), dtype=torch.float32, device=device).to(dtype)
residual = torch.randn((m, n), dtype=torch.float32, device=device).to(dtype)
zero_res = torch.zeros((m, n), dtype=dtype, device=device)
dist.barrier()
torch.cuda.synchronize()
fused_zero = tensor_model_parallel_fused_allreduce_rmsnorm(
x.clone(), zero_res.clone(), weight, eps
)
torch.cuda.synchronize()
if fused_zero is None:
if rank == 0:
print(f" {m:>5d}x{n}: SKIP (fused unavailable)")
continue
_, fused_ar = fused_zero
dist.barrier()
torch.cuda.synchronize()
fused_random = tensor_model_parallel_fused_allreduce_rmsnorm(
x.clone(), residual.clone(), weight, eps
)
torch.cuda.synchronize()
_, fused_res = fused_random
dist.barrier()
torch.cuda.synchronize()
unfused_ar = tensor_model_parallel_all_reduce(x.clone())
torch.cuda.synchronize()
expected = fused_ar + residual
diff = (fused_res.float() - expected.float()).abs()
ar_diff = (fused_ar.float() - unfused_ar.float()).abs()
max_diff = diff.max().item()
frac_nonzero = (diff > 0).float().mean().item()
nbytes = m * n * dtype.itemsize
stage = "1-stage" if nbytes <= 128 * 1024 else "2-stage"
passed = max_diff == 0.0
if not passed:
all_pass = False
if rank == 0:
status = "PASS" if passed else "FAIL"
print(
f" {m:>5d}x{n} ({stage:>7s}): max_diff={max_diff:.6e} "
f"frac_nonzero={frac_nonzero:.4f} "
f"AR_exact={'yes' if ar_diff.max().item() == 0 else 'no':>3s} "
f"[{status}]"
)
dist.barrier()
destroy_model_parallel()
destroy_distributed_environment()
if rank == 0:
print()
if all_pass:
print("ALL PASSED: fused residual output is bit-identical to unfused path.")
else:
print(
"FAILED: fused residual output diverges from unfused path for some shapes."
)
sys.exit(0 if all_pass else 1)
class TestAiterAllreduceFusionAmd(unittest.TestCase):
def test_fused_ar_rms_benchmark(self):
if not torch.cuda.is_available():
self.skipTest("CUDA/ROCm device is not available.")
if torch.cuda.device_count() < 8:
self.skipTest("This test requires at least 8 GPUs.")
@staticmethod
def _gpu_count():
return torch.cuda.device_count() if torch.cuda.is_available() else 0
def _run_benchmark(self, nproc, prefill_shapes, decode_shapes):
"""Run the benchmark subprocess and return parsed CSV rows."""
repo_root = Path(__file__).resolve().parents[3]
benchmark_script = (
repo_root
@@ -40,15 +168,14 @@ class TestAiterAllreduceFusionAmd(unittest.TestCase):
"-m",
"torch.distributed.run",
"--standalone",
"--nproc_per_node=8",
f"--nproc_per_node={nproc}",
str(benchmark_script),
"--dtype",
"bf16",
"--prefill-shapes",
# Include both <=64MiB and >64MiB shapes to verify default gate behavior.
"128x7168,512x7168,2048x7168,4096x7168,5120x7168",
prefill_shapes,
"--decode-shapes",
"1x7168,8x7168,64x7168,512x7168",
decode_shapes,
"--warmup",
"3",
"--iters",
@@ -84,39 +211,125 @@ class TestAiterAllreduceFusionAmd(unittest.TestCase):
rows = list(csv.DictReader(f))
self.assertGreater(len(rows), 0, "CSV contains no rows.")
return rows
eager_rows = [r for r in rows if r["mode"] == "eager"]
graph_rows = [r for r in rows if r["mode"] == "graph"]
self.assertGreater(len(eager_rows), 0, "Missing eager rows in CSV.")
self.assertGreater(len(graph_rows), 0, "Missing graph rows in CSV.")
def _assert_correctness(self, rows):
bad_rows = [r for r in rows if r["correctness_ok"] != "True"]
self.assertEqual(
[],
bad_rows,
f"Found correctness failures: {bad_rows}",
)
# Correctness should always pass regardless of fused availability.
bad_rows = [r for r in rows if r["correctness_ok"] != "True"]
self.assertEqual(
[],
bad_rows,
f"Found correctness failures: {bad_rows}",
def test_fused_ar_rms_benchmark(self):
if self._gpu_count() < 8:
self.skipTest("This test requires at least 8 GPUs.")
rows = self._run_benchmark(
nproc=8,
prefill_shapes="128x7168,512x7168,2048x7168,4096x7168,5120x7168",
decode_shapes="1x7168,8x7168,64x7168,512x7168",
)
eager_rows = [r for r in rows if r["mode"] == "eager"]
graph_rows = [r for r in rows if r["mode"] == "graph"]
self.assertGreater(len(eager_rows), 0, "Missing eager rows in CSV.")
self.assertGreater(len(graph_rows), 0, "Missing graph rows in CSV.")
self._assert_correctness(rows)
self.assertTrue(
any(r["fused_available"] == "True" for r in eager_rows),
"Expected at least one eager row with fused_available=True.",
)
self.assertTrue(
any(r["fused_available"] == "True" for r in graph_rows),
"Expected at least one graph row with fused_available=True.",
)
large_eager_rows = [
r for r in eager_rows if int(r["bytes_per_rank"]) > 64 * 1024 * 1024
]
self.assertTrue(
any(r["fused_available"] == "False" for r in large_eager_rows),
"Expected fused fallback for oversized eager shape(s) under default gate.",
)
def test_fused_ar_rms_multi_hidden_dim(self):
"""Correctness across hidden_dims from various models (TP=4)."""
nproc = min(self._gpu_count(), 4)
if nproc < 2:
self.skipTest("This test requires at least 2 GPUs.")
# hidden_dims: 2880 (GPT-OSS), 4096 (Qwen3.5), 5120, 6144 (Mixtral),
# 7168 (DeepSeek), 8192 (Llama-70B)
decode = ",".join(f"{m}x{n}" for n in HIDDEN_DIMS for m in [1, 4, 16])
prefill = ",".join(f"128x{n}" for n in HIDDEN_DIMS)
rows = self._run_benchmark(
nproc=nproc,
prefill_shapes=prefill,
decode_shapes=decode,
)
self._assert_correctness(rows)
fused_rows = [r for r in rows if r["fused_available"] == "True"]
self.assertEqual(
len(fused_rows),
len(rows),
f"Expected fused available for all shapes, but {len(rows) - len(fused_rows)} "
f"rows were not fused: "
f"{[r['shape'] for r in rows if r['fused_available'] != 'True']}",
)
def test_fused_ar_rms_residual_accuracy(self):
"""Bit-exact residual accuracy across 1-stage and 2-stage paths.
Regression test for ROCm/aiter#2586. Launches this file itself via
torchrun with --residual-accuracy to run the distributed check.
"""
nproc = min(self._gpu_count(), 4)
if nproc < 2:
self.skipTest("This test requires at least 2 GPUs.")
cmd = [
sys.executable,
"-m",
"torch.distributed.run",
"--standalone",
f"--nproc_per_node={nproc}",
__file__,
"--residual-accuracy",
]
result = subprocess.run(
cmd,
cwd=str(Path(__file__).resolve().parents[3]),
env=os.environ.copy(),
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
timeout=300,
)
if result.returncode != 0:
self.fail(
"Residual accuracy check failed.\n"
f"Return code: {result.returncode}\n"
f"Command: {' '.join(cmd)}\n"
f"Output:\n{result.stdout}"
)
# We should see fused path active for small shapes in both modes.
self.assertTrue(
any(r["fused_available"] == "True" for r in eager_rows),
"Expected at least one eager row with fused_available=True.",
)
self.assertTrue(
any(r["fused_available"] == "True" for r in graph_rows),
"Expected at least one graph row with fused_available=True.",
)
# Default gate should reject at least one oversized eager shape.
large_eager_rows = [
r for r in eager_rows if int(r["bytes_per_rank"]) > 64 * 1024 * 1024
]
self.assertTrue(
any(r["fused_available"] == "False" for r in large_eager_rows),
"Expected fused fallback for oversized eager shape(s) under default gate.",
)
self.assertIn(
"ALL PASSED",
result.stdout,
f"Expected 'ALL PASSED' in output, got:\n{result.stdout}",
)
if __name__ == "__main__":
unittest.main()
if "--residual-accuracy" in sys.argv:
_run_residual_accuracy_check()
else:
unittest.main()