File size: 7,674 Bytes
e2b41e5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
import copy
import logging
import time
from contextlib import nullcontext
import pytest
import torch
import torch.distributed as dist
from optimizer.muon import Muon, get_default_muon_param_groups
from torch.distributed.tensor import DTensor, Replicate
from torch.profiler import ProfilerActivity, profile
from .utils import (ParallelDims, assert_params_equal, parallelize_motif,
parallelize_qk_logits)
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
def apply_muon_step(
model: torch.nn.Module,
parallel_dims: ParallelDims | None,
grads: list[torch.Tensor],
warmup_step: int,
chunk_size: int,
qk_logits: dict[int, torch.Tensor] | None = None,
use_distributed_muon: bool = False,
measure_perf: bool = False,
do_profile: bool = False,
) -> tuple[torch.nn.Module, tuple[float, float] | None]:
""" apply single Muon step with optional QK clipping """
# 1. Apply gradients to model parameters
assert len(grads) == len(list(model.parameters()))
for grad, param in zip(grads, model.parameters()):
grad = grad.to(param.device)
if isinstance(param.data, DTensor):
unsharded_grad = DTensor.from_local(
grad,
device_mesh=param.data.device_mesh,
placements=[Replicate()] * param.data.device_mesh.ndim,
)
sharded_grad = unsharded_grad.redistribute(
device_mesh=param.data.device_mesh,
placements=param.data.placements)
param.grad = sharded_grad
else:
param.grad = grad
# 2. Setup Muon optimizer
params = get_default_muon_param_groups(model)
clip_config = dict({
"q_indices":
list(range(model.config.num_attention_heads)),
"k_indices":
list(range(model.config.num_attention_heads)),
"head_dim":
model.config.hidden_size // model.config.num_attention_heads,
"threshold":
0.5
})
optim = Muon(
params=params,
clip_config=clip_config if qk_logits is not None else None,
none_grad=False,
warmup_step=warmup_step,
chunk_size=chunk_size,
use_distributed_muon=use_distributed_muon,
)
optim.step(qk_logits=qk_logits)
timing_result: tuple[float, float] | None = None
if measure_perf:
# extra warm up
optim.step(qk_logits=qk_logits)
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
num_iters = 20
current_mem = torch.cuda.memory_allocated()
if do_profile:
context = profile(
activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
record_shapes=True)
else:
context = nullcontext()
with context as prof:
for _i in range(num_iters):
optim.step(qk_logits=qk_logits)
end.record()
end.synchronize()
if prof is not None and dist.get_rank() == 0:
date = time.strftime("%Y%m%d_%H%M%S", time.localtime())
profile_name = "trace"
profile_name += f"_{date}"
profile_name += f"_{parallel_dims}"
profile_name += f"_{chunk_size}"
profile_name += f"_{warmup_step}"
profile_name += f"_{qk_logits is not None}"
profile_name += f"_{use_distributed_muon}"
prof.export_chrome_trace(f"{profile_name}.json")
peak_memory = torch.cuda.max_memory_allocated() - current_mem
elapsed_time_ms = start.elapsed_time(end) / num_iters
timing_result = (elapsed_time_ms, peak_memory)
return model, timing_result
@pytest.fixture(scope="session")
def sequential_muon_result(
skip_verify, # from conftest.py
inputs # from conftest.py
) -> dict[bool, torch.nn.Module]:
"""Run Muon optimizer to sequential model for baseline results."""
if skip_verify:
logger.info("Skipping verification tests as per user request")
return None
model, grads, qk_logits = inputs
result = apply_muon_step(
model=copy.deepcopy(model).cuda(),
parallel_dims=None,
grads=grads,
warmup_step=-1,
chunk_size=-1,
qk_logits=None,
)[0].cpu()
result_qk_clip = apply_muon_step(
model=copy.deepcopy(model).cuda(),
parallel_dims=None,
grads=grads,
warmup_step=-1,
chunk_size=-1,
qk_logits=qk_logits,
)[0].cpu()
return {
False: result,
True: result_qk_clip,
}
OVERLAP_STEPS = [5]
CHUNK_SIZES = [8]
@pytest.mark.parametrize("parallel_dims", [
pytest.param(ParallelDims(8, 1, 1), id="base"),
pytest.param(ParallelDims(1, 8, 1), id="fsdp"),
pytest.param(ParallelDims(2, 4, 1), id="hsdp"),
pytest.param(ParallelDims(1, 1, 8), id="tp"),
pytest.param(ParallelDims(2, 2, 2), id="hsdp+tp"),
pytest.param(ParallelDims(1, 2, 4), id="fsdp+tp"),
])
@pytest.mark.parametrize("apply_qk_clip", [False, True])
@pytest.mark.parametrize("use_distributed_muon", [False])
@pytest.mark.parametrize("warmup_step", OVERLAP_STEPS)
@pytest.mark.parametrize("chunk_size", CHUNK_SIZES)
def test_parallel_muon(
request,
sequential_muon_result: dict[bool, torch.nn.Module],
parallel_dims: ParallelDims,
apply_qk_clip: bool,
use_distributed_muon: bool,
warmup_step: int,
chunk_size: int,
inputs: tuple[torch.nn.Module, list[torch.Tensor],
dict[int, torch.Tensor]], # from conftest.py
measure_perf, # from conftest.py
do_profile, # from conftest.py
) -> None:
if use_distributed_muon and chunk_size != CHUNK_SIZES[0]:
pytest.skip("Distributed Muon does not effected by chunk size")
if use_distributed_muon and warmup_step != OVERLAP_STEPS[0]:
pytest.skip("Distributed Muon does not effected by warmup step")
model, grads, qk_logits = inputs
if not apply_qk_clip:
qk_logits = None
# Deepcopy the model to avoid in-place modification
model = copy.deepcopy(model).cuda()
parallelized_model = parallelize_motif(model, parallel_dims)
if qk_logits is not None:
# Deepcopy the qk logits to avoid in-place modification
qk_logits = copy.deepcopy(qk_logits)
qk_logits = parallelize_qk_logits(qk_logits, parallel_dims)
parallelized_model, timing_result = apply_muon_step(
model=parallelized_model,
parallel_dims=parallel_dims,
grads=grads,
warmup_step=warmup_step,
chunk_size=chunk_size,
qk_logits=qk_logits,
use_distributed_muon=use_distributed_muon,
measure_perf=measure_perf,
do_profile=do_profile,
)
if measure_perf:
assert timing_result is not None
avg_time_ms, peak_memory = timing_result
logger.info(
f"\nParallel dims: {parallel_dims}, "
f"\nUse distributed Muon: {use_distributed_muon}, "
f"\nApply QK clip: {apply_qk_clip} => "
f"\nChunk Size, Warmup Step, Avg Time (ms), Peak Memory (MB):"
f"\n{chunk_size}, {warmup_step}, {avg_time_ms:.2f}, {peak_memory / (1024**2):.2f},"
)
if sequential_muon_result is None:
logger.info("Skipping correctness check as sequential result is None")
elif measure_perf:
logger.info("Skipping correctness check as timing is enabled")
else:
assert_params_equal(parallelized_model,
sequential_muon_result[apply_qk_clip])
|