[Quantization][Deprecation] Remove RTN (#32697)

Signed-off-by: Robert Shaw <robshaw@redhat.com>
Co-authored-by: Robert Shaw <robshaw@redhat.com>

tests/quantization/test_rtn.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-# Copyright © 2025, Oracle and/or its affiliates.
-"""Tests RTN quantization startup and generation,
-doesn't test correctness
-"""
-
-import pytest
-
-from tests.quantization.utils import is_quant_method_supported
-
-MODELS = [
-    "ai21labs/Jamba-tiny-dev",  # MoE model
-]
-
-
-@pytest.mark.skipif(
-    not is_quant_method_supported("rtn"),
-    reason="RTN is not supported on this GPU type.",
-)
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["bfloat16"])
-@pytest.mark.parametrize("max_tokens", [10])
-def test_model_rtn_startup(
-    hf_runner,
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-) -> None:
-    with vllm_runner(
-        model,
-        enforce_eager=True,
-        dtype=dtype,
-        quantization="rtn",
-        allow_deprecated_quantization=True,
-    ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)

vllm/model_executor/layers/quantization/__init__.py

@@ -31,7 +31,6 @@ QuantizationMethods = Literal[
     "quark",
     "moe_wna16",
     "torchao",
-    "rtn",
     "inc",
     "mxfp4",
     "petit_nvfp4",
@@ -49,7 +48,6 @@ DEPRECATED_QUANTIZATION_METHODS = [
     "gptq_bitblas",
     "experts_int8",
     "ipex",
-    "rtn",
     "petit_nvfp4",
 ]
 
@@ -138,7 +136,6 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
     from .mxfp4 import Mxfp4Config
     from .petit import PetitNvFp4Config
     from .ptpc_fp8 import PTPCFp8Config
-    from .rtn import RTNConfig
     from .torchao import TorchAOConfig
 
     method_to_config: dict[str, type[QuantizationConfig]] = {
@@ -163,7 +160,6 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
         "quark": QuarkConfig,
         "moe_wna16": MoeWNA16Config,
         "torchao": TorchAOConfig,
-        "rtn": RTNConfig,
         "auto-round": INCConfig,
         "inc": INCConfig,
         "mxfp4": Mxfp4Config,

vllm/model_executor/layers/quantization/utils/marlin_utils.py

@@ -650,64 +650,3 @@ def apply_awq_marlin_linear(
     )
 
     return output.reshape(out_shape)
-
-
-def apply_rtn_marlin_linear(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    weight_scale: torch.Tensor,
-    workspace: torch.Tensor,
-    quant_type: ScalarType,
-    output_size_per_partition: int,
-    input_size_per_partition: int,
-    input_global_scale: torch.Tensor | None = None,
-    bias: torch.Tensor | None = None,
-    use_fp32_reduce: bool = USE_FP32_REDUCE_DEFAULT,
-    input_dtype: torch.dtype | None = None,
-) -> torch.Tensor:
-    reshaped_x = input.reshape(-1, input.shape[-1])
-    out_shape = input.shape[:-1] + (output_size_per_partition,)
-
-    use_atomic_add = should_use_atomic_add_reduce(
-        m=reshaped_x.size(0),
-        n=output_size_per_partition,
-        k=reshaped_x.size(1),
-        device=input.device,
-        dtype=input.dtype,
-    )
-
-    a_scales = None
-    if input_dtype == torch.int8:
-        assert quant_type == scalar_types.uint4b8, (
-            "W8A8-INT8 is not supported by marlin kernel."
-        )
-        reshaped_x, a_scales = marlin_quant_input(reshaped_x, input_dtype)
-        a_scales = a_scales * input_global_scale
-    elif input_dtype == torch.float8_e4m3fn:
-        assert quant_type == scalar_types.uint4b8, (
-            "INT8 weight + FP8 activation is not supported."
-        )
-        reshaped_x, a_scales = marlin_quant_input(reshaped_x, input_dtype)
-
-    output = ops.gptq_marlin_gemm(
-        reshaped_x,
-        None,
-        weight,
-        bias,
-        weight_scale,
-        a_scales,
-        None,
-        None,
-        None,
-        None,
-        workspace,
-        quant_type,
-        size_m=reshaped_x.shape[0],
-        size_n=output_size_per_partition,
-        size_k=input_size_per_partition,
-        use_atomic_add=use_atomic_add,
-        use_fp32_reduce=use_fp32_reduce,
-        is_zp_float=False,
-    )
-
-    return output.reshape(out_shape)