Add integration with gemlite weight only quant (#2528)

python/pyproject.toml

@@ -21,7 +21,7 @@ runtime_common = ["aiohttp", "decord", "fastapi",
     "orjson", "outlines>=0.0.44,<0.1.0",
     "packaging", "pillow", "prometheus-client>=0.20.0",
     "psutil", "pydantic", "python-multipart",
-    "pyzmq>=25.1.2", "torchao>=0.7.0", "uvicorn", "uvloop",
+    "pyzmq>=25.1.2", "torchao>=0.7.0", "gemlite", "uvicorn", "uvloop",
     "xgrammar>=0.1.6"]
 srt = ["sglang[runtime_common]", "torch", "vllm>=0.6.3.post1,<=0.6.4.post1", "cuda-python", "flashinfer==0.1.6"]
 

python/sglang/bench_offline_throughput.py

@@ -322,6 +322,18 @@ def throughput_test(
         )
         time.sleep(0.5)
 
+    try:
+        import os
+        import pwd
+
+        from gemlite.core import GemLiteLinearTriton
+
+        GemLiteLinearTriton.cache_config(
+            f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json"
+        )
+    except ImportError:
+        pass
+
     logging.info("\nBenchmark...")
     result = throughput_test_once(
         backend_name=bench_args.backend,

python/sglang/bench_one_batch.py

@@ -385,6 +385,19 @@ def latency_test(
         8,  # shorter decoding to speed up the warmup
         server_args.device,
     )
+
+    try:
+        import os
+        import pwd
+
+        from gemlite.core import GemLiteLinearTriton
+
+        GemLiteLinearTriton.cache_config(
+            f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json"
+        )
+    except ImportError:
+        pass
+
     rank_print("Benchmark ...")
 
     # Run the sweep

python/sglang/srt/layers/torchao_utils.py

@@ -47,6 +47,41 @@ def apply_torchao_config_to_model(
             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:
+        # gemlite-<packing_bitwidth>-<bit_width>-<group_size> or
+        # gemlite-<bit_width>-<group_size> (packing_bitwidth defaults to 32)
+        import os
+        import pwd
+
+        import gemlite
+        from gemlite.core import GemLiteLinearTriton, set_autotune
+
+        try:
+            from torchao.quantization import gemlite_uintx_weight_only
+        except:
+            print(
+                f"import `gemlite_uintx_weight_only` failed, please use torchao nightly to use gemlite quantization"
+            )
+            return model
+
+        _quant_args = torchao_config.split("-")
+        bit_width = int(_quant_args[-2])
+        group_size = None if _quant_args[-1] == "None" else int(_quant_args[-1])
+        try:
+            packing_bitwidth = int(_quant_args[-3])
+        except:
+            # if only 2 inputs found, use default value
+            packing_bitwidth = 32
+
+        quantize_(
+            model, gemlite_uintx_weight_only(group_size, bit_width, packing_bitwidth)
+        )
+
+        # try to load gemlite kernel config
+        GemLiteLinearTriton.load_config(
+            f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json"
+        )
+
     elif "fp8wo" in torchao_config:
         # this requires newer hardware
         # [rank0]: AssertionError: fp8e4nv data type is not supported on CUDA arch < 89