Revert "Integration of TurboMind AWQ" (#2866)

python/pyproject.toml

@@ -28,7 +28,7 @@ runtime_common = [
 srt = [
     "sglang[runtime_common]", "cuda-python",
     "sgl-kernel>=0.0.2.post11", "torch", "vllm>=0.6.3.post1,<=0.6.4.post1",
-    "flashinfer==0.1.6", "turbomind"
+    "flashinfer==0.1.6"
 ]
 
 # HIP (Heterogeneous-computing Interface for Portability) for AMD

python/sglang/srt/configs/model_config.py

@@ -14,7 +14,6 @@
 
 import json
 import logging
-import sys
 from enum import IntEnum, auto
 from typing import List, Optional, Set, Union
 
@@ -231,7 +230,7 @@ class ModelConfig:
         # Parse quantization method from the HF model config, if available.
         quant_cfg = self._parse_quant_hf_config()
 
-        if quant_cfg is not None and not quantization_in_turbomind(self.quantization):
+        if quant_cfg is not None:
             quant_method = quant_cfg.get("quant_method", "").lower()
 
             # Detect which checkpoint is it
@@ -402,10 +401,3 @@ def is_multimodal_model(model_architectures: List[str]):
 
 def is_encoder_decoder_model(model_architectures: List[str]):
     return "MllamaForConditionalGeneration" in model_architectures
-
-
-def quantization_in_turbomind(quantization: str) -> bool:
-    if quantization in ["awq_turbomind"]:
-        return True
-    else:
-        return False

python/sglang/srt/layers/linear.py

@@ -48,7 +48,6 @@ WEIGHT_LOADER_V2_SUPPORTED = [
     "GPTQLinearMethod",
     "FBGEMMFp8LinearMethod",
     "ModelOptFp8LinearMethod",
-    "AWQTurbomindLinearMethod",
     "IPEXAWQLinearMethod",
 ]
 

python/sglang/srt/layers/quantization/__init__.py

@@ -20,7 +20,6 @@ from vllm.model_executor.layers.quantization.marlin import MarlinConfig
 from vllm.model_executor.layers.quantization.qqq import QQQConfig
 from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
 
-from sglang.srt.layers.quantization.awq_turbomind import AWQTurbomindConfig
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.fp8 import Fp8Config
 from sglang.srt.layers.quantization.modelopt_quant import ModelOptFp8Config
@@ -38,7 +37,6 @@ QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "gptq_marlin_24": GPTQMarlin24Config,
     "gptq_marlin": GPTQMarlinConfig,
     "awq_marlin": AWQMarlinConfig,
-    "awq_turbomind": AWQTurbomindConfig,
     "gptq": GPTQConfig,
     "compressed-tensors": CompressedTensorsConfig,
     "bitsandbytes": BitsAndBytesConfig,

python/sglang/srt/layers/quantization/awq_turbomind.py

-import logging
-import os
-import sys
-from typing import Any, Dict, List, Optional
-
-import torch
-import turbomind
-from torch.nn import Parameter
-
-turbomind_dir = os.path.split(turbomind.__file__)[0]
-sys.path.append(os.path.join(turbomind_dir, "lib"))
-import _turbomind_ext
-from vllm.model_executor.layers.linear import LinearBase
-
-from sglang.srt.layers.linear import LinearMethodBase, UnquantizedLinearMethod
-from sglang.srt.layers.parameter import GroupQuantScaleParameter, PackedvLLMParameter
-from sglang.srt.layers.quantization.base_config import (
-    QuantizationConfig,
-    QuantizeMethodBase,
-)
-from sglang.srt.layers.quantization.turbomind_utils import (
-    get_u4_slices,
-    is_layer_skipped_awq,
-    pack_u4_row,
-    unpack_awq_gemm,
-    verify_turbomind_supported,
-)
-from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
-from sglang.srt.utils import is_cuda, set_weight_attrs
-
-logger = logging.getLogger(__name__)
-
-
-class AWQTurbomindConfig(QuantizationConfig):
-    """Config class for AWQ Turbomind"""
-
-    def __init__(
-        self,
-        weight_bits: int,
-        group_size: int,
-        zero_point: bool,
-        lm_head_quantized: bool,
-        modules_to_not_convert: Optional[List[str]] = None,
-    ) -> None:
-        self.pack_factor = 32 // weight_bits  # packed into int32
-        self.group_size = group_size
-        self.zero_point = zero_point
-        self.lm_head_quantized = lm_head_quantized
-        self.weight_bits = weight_bits
-        self.modules_to_not_convert = modules_to_not_convert or []
-
-        verify_turbomind_supported(self.weight_bits, self.group_size)
-
-    def __repr__(self) -> str:
-        return (
-            f"AWQTurbomindConfig(weight_bits={self.weight_bits}, "
-            f"group_size={self.group_size}, "
-            f"zero_point={self.zero_point}, "
-            f"lm_head_quantized={self.lm_head_quantized}, "
-            f"modules_to_not_convert={self.modules_to_not_convert})"
-        )
-
-    @classmethod
-    def get_name(cls) -> str:
-        return "awq_turbomind"
-
-    @classmethod
-    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
-        return [torch.half, torch.bfloat16]
-
-    @classmethod
-    def get_min_capability(cls) -> int:
-        return 70
-
-    @classmethod
-    def get_config_filenames(cls) -> List[str]:
-        return ["quantize_config.json"]
-
-    @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "AWQTurbomindConfig":
-        weight_bits = cls.get_from_keys(config, ["bits"])
-        group_size = cls.get_from_keys(config, ["group_size"])
-        zero_point = cls.get_from_keys(config, ["zero_point"])
-        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False)
-        modules_to_not_convert = cls.get_from_keys_or(
-            config, ["modules_to_not_convert"], None
-        )
-        return cls(
-            weight_bits,
-            group_size,
-            zero_point,
-            lm_head_quantized,
-            modules_to_not_convert,
-        )
-
-    @classmethod
-    def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
-        can_convert = cls.is_awq_turbomind_compatible(hf_quant_cfg)
-        is_valid_user_quant = user_quant is None or user_quant == "awq_turbomind"
-
-        if can_convert and is_valid_user_quant:
-            msg = f"The model is convertible to {cls.get_name()} during runtime. Using {cls.get_name()} kernel."
-            logger.info(msg)
-            return cls.get_name()
-
-        if can_convert and user_quant == "awq":
-            logger.info(
-                "Detected that the model can run with awq_turbomind"
-                ", however you specified quantization=awq explicitly,"
-                " so forcing awq. Use quantization=awq_turbomind for"
-                " faster inference"
-            )
-        return None
-
-    def get_quant_method(
-        self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
-        if isinstance(layer, LinearBase) or (
-            isinstance(layer, ParallelLMHead) and self.lm_head_quantized
-        ):
-            if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
-                return UnquantizedLinearMethod()
-            return AWQTurbomindLinearMethod(self)
-
-        return None
-
-    @classmethod
-    def is_awq_turbomind_compatible(cls, quant_config: Dict[str, Any]):
-        if not is_cuda():
-            return False
-
-        # Extract data from quant config.
-        quant_method = quant_config.get("quant_method", "").lower()
-        num_bits = quant_config.get("bits")
-        group_size = quant_config.get("group_size")
-        zero_point = quant_config.get("zero_point")
-
-        if quant_method != "awq":
-            return False
-
-        # If we cannot find the info needed in the config, cannot convert.
-        if num_bits is None or group_size is None or zero_point is None:
-            return False
-
-        return verify_turbomind_supported(quant_bit=num_bits, group_size=group_size)
-
-    def get_scaled_act_names(self) -> List[str]:
-        return []
-
-
-class AWQTurbomindLinearMethod(LinearMethodBase):
-    """Linear method for AWQ Turbomind.
-
-    Args:
-        quant_config: The AWQ Turbomind quantization config.
-    """
-
-    def __init__(self, quant_config: AWQTurbomindConfig) -> None:
-        self.quant_config = quant_config
-
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        input_size_per_partition: int,
-        output_partition_sizes: List[int],
-        input_size: int,
-        output_size: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ) -> None:
-
-        output_size_per_partition = sum(output_partition_sizes)
-        weight_loader = extra_weight_attrs.get("weight_loader")
-
-        # Normalize group_size
-        if self.quant_config.group_size != -1:
-            group_size = self.quant_config.group_size
-        else:
-            group_size = input_size
-
-        qweight = PackedvLLMParameter(
-            data=torch.empty(
-                input_size_per_partition,
-                output_size_per_partition // self.quant_config.pack_factor,
-                dtype=torch.int32,
-            ),
-            input_dim=0,
-            output_dim=1,
-            packed_dim=1,
-            packed_factor=self.quant_config.pack_factor,
-            weight_loader=weight_loader,
-        )
-
-        num_groups = input_size_per_partition // group_size
-
-        qzeros = PackedvLLMParameter(
-            data=torch.empty(
-                num_groups,
-                output_size_per_partition // self.quant_config.pack_factor,
-                dtype=torch.int32,
-            ),
-            input_dim=0,
-            output_dim=1,
-            packed_dim=1,
-            packed_factor=self.quant_config.pack_factor,
-            weight_loader=weight_loader,
-        )
-
-        scales = GroupQuantScaleParameter(
-            data=torch.empty(
-                num_groups,
-                output_size_per_partition,
-                dtype=params_dtype,
-            ),
-            input_dim=0,
-            output_dim=1,
-            weight_loader=weight_loader,
-        )
-
-        layer.register_parameter("qweight", qweight)
-        layer.register_parameter("qzeros", qzeros)
-        layer.register_parameter("scales", scales)
-
-        layer.input_size_per_partition = input_size_per_partition
-        layer.output_size_per_partition = output_size_per_partition
-        layer.num_groups = num_groups
-
-    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
-
-        qweight_turbomind = unpack_awq_gemm(layer.qweight.data)
-        qzeros_turbomind = unpack_awq_gemm(layer.qzeros.data)
-        scales_turbomind = layer.scales.data
-
-        qweight_turbomind = pack_u4_row(qweight_turbomind)
-        qzeros_turbomind = qzeros_turbomind.to(torch.half)
-
-        device_id = layer.qweight.device.index
-        properties = torch.cuda.get_device_properties(device_id)
-
-        def is_16xx_series(name):
-            import re
-
-            pattern = r"GTX 16\d\d"
-            return bool(re.search(pattern, name))
-
-        simt = is_16xx_series(properties.name)
-        qweight_turbomind = qweight_turbomind.contiguous()
-        scales_turbomind = scales_turbomind.contiguous()
-        qzeros_turbomind = qzeros_turbomind.contiguous()
-
-        self.linear = _turbomind_ext.Linear(
-            layer.input_size_per_partition,
-            layer.output_size_per_partition,
-            self.quant_config.weight_bits,
-            self.quant_config.group_size,
-        )
-
-        self.linear.post_init(
-            qweight_turbomind, scales_turbomind, qzeros_turbomind, simt
-        )
-
-        layer.qweight = Parameter(qweight_turbomind, requires_grad=False)
-        layer.scales = Parameter(scales_turbomind, requires_grad=False)
-        layer.qzeros = Parameter(qzeros_turbomind, requires_grad=False)
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-
-        x = x.view(-1, x.shape[-1])
-        out_shape = x.shape[:-1] + (layer.output_size_per_partition,)
-        out = torch.empty(
-            (x.shape[0], layer.output_size_per_partition),
-            dtype=torch.float16,
-            device=x.device,
-        )
-        stream = torch.cuda.current_stream()
-
-        self.linear.forward(x, out, stream.cuda_stream)
-        out = torch.from_dlpack(out)
-        if bias is not None:
-            out.add_(bias)
-
-        return out.view(out_shape)

python/sglang/srt/layers/quantization/turbomind_utils.py

-# Copyright (c) OpenMMLab. All rights reserved.
-
-from typing import List
-
-import torch
-
-from sglang.srt.utils import get_device_capability
-
-
-def get_u4_slices(x: torch.Tensor, dtype: torch.dtype) -> List[torch.Tensor]:
-    assert x.dtype == torch.int32
-    xs = []
-    for _ in range(8):
-        xs.append((x & 15).to(dtype))
-        x = x >> 4
-    return xs
-
-
-def unpack_awq_gemm(x: torch.Tensor) -> torch.Tensor:
-    """
-    The int4 weights are packed into int32:
-    bit:       31-28  27-24  23-20  19-16  15-12  11-8   7-4    3-0
-    weight:    int4_1 int4_2 int4_3 int4_4 int4_5 int4_6 int4_7 int4_8
-    """
-    xs = get_u4_slices(x, torch.uint8)
-    order = [0, 4, 1, 5, 2, 6, 3, 7]
-    ys = [xs[i] for i in order]
-    return torch.stack(ys, dim=-1).view(*x.shape[:-1], -1)
-
-
-def pack_u4_row(x: torch.Tensor) -> torch.Tensor:
-    assert x.dtype == torch.uint8
-    xs = x.view(*x.shape[:-1], -1, 8).split(1, dim=-1)
-    a = torch.zeros(xs[0].shape, dtype=torch.int32, device=x.device)
-    for t in reversed(xs):
-        a = (a << 4) | t
-    return a.squeeze(dim=-1)
-
-
-def verify_turbomind_supported(quant_bit: int, group_size: int) -> bool:
-
-    if quant_bit not in [4]:
-        raise NotImplementedError(
-            f"[Tubomind] Only 4-bit is supported for now, but got {quant_bit} bit"
-        )
-    if group_size != 128:
-        raise NotImplementedError(
-            f"[Tubomind] Only group_size 128 is supported for now, "
-            f"but got group_size {group_size}"
-        )
-
-    major, minor = get_device_capability()
-    capability = major * 10 + minor
-    if capability < 70:
-        raise NotImplementedError(
-            f"[Tubomind] Only capability >= 70 is supported for now, but got {capability}"
-        )
-
-    return True
-
-
-def is_layer_skipped_awq(prefix: str, modules_to_not_convert: List[str]):
-    return any(module_name in prefix for module_name in modules_to_not_convert)

python/sglang/srt/server_args.py

@@ -375,7 +375,6 @@ class ServerArgs:
                 "marlin",
                 "gptq_marlin",
                 "awq_marlin",
-                "awq_turbomind",
                 "bitsandbytes",
                 "gguf",
                 "modelopt",

test/srt/test_turbomind_awq.py

-import unittest
-from types import SimpleNamespace
-
-from sglang.srt.utils import kill_process_tree
-from sglang.test.run_eval import run_eval
-from sglang.test.test_utils import (
-    DEFAULT_MODEL_NAME_FOR_TEST,
-    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-    DEFAULT_URL_FOR_TEST,
-    popen_launch_server,
-)
-
-
-class TestMLA(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        cls.model = DEFAULT_MODEL_NAME_FOR_TEST
-        cls.base_url = DEFAULT_URL_FOR_TEST
-        cls.process = popen_launch_server(
-            cls.model,
-            cls.base_url,
-            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-            other_args=[
-                "--quantization",
-                "awq_turbomind",
-            ],
-        )
-
-    @classmethod
-    def tearDownClass(cls):
-        kill_process_tree(cls.process.pid)
-
-    def test_mgsm_en(self):
-        args = SimpleNamespace(
-            base_url=self.base_url,
-            model=self.model,
-            eval_name="mgsm_en",
-            num_examples=None,
-            num_threads=1024,
-        )
-
-        metrics = run_eval(args)
-        assert metrics["score"] >= 0.5
-
-
-if __name__ == "__main__":
-    unittest.main()