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torcho gemlite integration #2498

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torcho gemlite integration #2498

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8 changes: 8 additions & 0 deletions python/sglang/bench_one_batch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -385,6 +385,14 @@ def latency_test(
8, # shorter decoding to speed up the warmup
server_args.device,
)

try:
from gemlite.core import GemLiteLinearTriton
import os, pwd
GemLiteLinearTriton.cache_config(f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json")
except ImportError:
pass

rank_print("Benchmark ...")

# Run the sweep
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15 changes: 15 additions & 0 deletions python/sglang/srt/layers/torchao_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@ def apply_torchao_config_to_model(
int8_weight_only,
quantize_,
)
from torchao.quantization.quant_api import gemlite_uintx_weight_only
from torchao.quantization.observer import PerRow, PerTensor

if filter_fn is None:
Expand All @@ -47,6 +48,20 @@ def filter_fn(module, fqn):
256,
], f"int4wo groupsize needs to be one of [32, 64, 128, 256] but got {group_size}"
quantize_(model, int4_weight_only(group_size=group_size), filter_fn=filter_fn)
elif "gemlite" in torchao_config:
_quant_args = torchao_config.split("-")
bit_width = int(_quant_args[-2])
group_size = None if _quant_args[-1] == 'None' else int(_quant_args[-1])
packing_bitwidth = int(_quant_args[-3])
quantize_(model, gemlite_uintx_weight_only(group_size, bit_width, packing_bitwidth), filter_fn=filter_fn)

import pwd
import os
from gemlite.core import GemLiteLinearTriton
try:
GemLiteLinearTriton.load_config(f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json")
except:
pass
elif "fp8wo" in torchao_config:
# this requires newer hardware
# [rank0]: AssertionError: fp8e4nv data type is not supported on CUDA arch < 89
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