Support W4A8 quantization for vllm (#5218)

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-QQQ.yaml

+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
+model_name: "HandH1998/QQQ-Llama-3-8b-g128"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.409
+  - name: "exact_match,flexible-extract"
+    value: 0.406
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/models-small.txt

@@ -7,3 +7,4 @@ Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml
 Minitron-4B-Base.yaml
 Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml
 Qwen2-1.5B-Instruct-FP8W8.yaml
+Meta-Llama-3-8B-QQQ.yaml

CMakeLists.txt

@@ -170,6 +170,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     "csrc/quantization/awq/gemm_kernels.cu"
     "csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
     "csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
+    "csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu"
     "csrc/quantization/gptq_marlin/gptq_marlin.cu"
     "csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
     "csrc/quantization/gptq_marlin/awq_marlin_repack.cu"

csrc/ops.h

@@ -115,6 +115,13 @@ void cutlass_scaled_mm(torch::Tensor& out, torch::Tensor const& a,
                        torch::Tensor const& b_scales,
                        c10::optional<torch::Tensor> const& bias);
 
+torch::Tensor marlin_qqq_gemm(torch::Tensor const& a,
+                              torch::Tensor const& b_q_weight,
+                              torch::Tensor const& s_tok,
+                              torch::Tensor const& s_ch,
+                              torch::Tensor const& s_group,
+                              torch::Tensor& workspace, int64_t size_m,
+                              int64_t size_n, int64_t size_k);
 #endif
 
 void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,

csrc/quantization/marlin/dense/common/base.h

+/*
+ * Modified by HandH1998
+ * Modified by Neural Magic
+ * Copyright (C) Marlin.2024 Elias Frantar
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *         http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+constexpr int ceildiv(int a, int b) { return (a + b - 1) / b; }
+
+// Instances of `Vec` are used to organize groups of >>registers<<, as needed
+// for instance as inputs to tensor core operations. Consequently, all
+// corresponding index accesses must be compile-time constants, which is why we
+// extensively use `#pragma unroll` throughout the kernel code to guarantee
+// this.
+template <typename T, int n>
+struct Vec {
+  T elems[n];
+  __device__ T& operator[](int i) { return elems[i]; }
+};

csrc/quantization/marlin/dense/common/mem.h

+/*
+ * Modified by HandH1998
+ * Modified by Neural Magic
+ * Copyright (C) Marlin.2024 Elias Frantar
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *         http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+// Predicated asynchronous global->shared copy; used for inputs A where we apply
+// predication to handle batchsizes that are not multiples of 16.
+__device__ inline void cp_async4_pred(void* smem_ptr, const void* glob_ptr,
+                                      bool pred = true) {
+  const int BYTES = 16;
+  uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
+  asm volatile(
+      "{\n"
+      "   .reg .pred p;\n"
+      "   setp.ne.b32 p, %0, 0;\n"
+      "   @p cp.async.cg.shared.global [%1], [%2], %3;\n"
+      "}\n" ::"r"((int)pred),
+      "r"(smem), "l"(glob_ptr), "n"(BYTES));
+}
+
+// Asynchronous global->shared copy
+__device__ inline void cp_async4(void* smem_ptr, const void* glob_ptr) {
+  const int BYTES = 16;
+  uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
+  asm volatile(
+      "{\n"
+      "   cp.async.cg.shared.global [%0], [%1], %2;\n"
+      "}\n" ::"r"(smem),
+      "l"(glob_ptr), "n"(BYTES));
+}
+
+// Async copy fence.
+__device__ inline void cp_async_fence() {
+  asm volatile("cp.async.commit_group;\n" ::);
+}
+
+// Wait until at most `n` async copy stages are still pending.
+template <int n>
+__device__ inline void cp_async_wait() {
+  asm volatile("cp.async.wait_group %0;\n" ::"n"(n));
+}
+
+// Wait until barrier reaches `count`, then lock for current threadblock.
+__device__ inline void barrier_acquire(int* lock, int count) {
+  if (threadIdx.x == 0) {
+    int state = -1;
+    do
+      // Guarantee that subsequent writes by this threadblock will be visible
+      // globally.
+      asm volatile("ld.global.acquire.gpu.b32 %0, [%1];\n"
+                   : "=r"(state)
+                   : "l"(lock));
+    while (state != count);
+  }
+  __syncthreads();
+}
+
+// Release barrier and increment visitation count.
+__device__ inline void barrier_release(int* lock, bool reset = false) {
+  __syncthreads();
+  if (threadIdx.x == 0) {
+    if (reset) {
+      lock[0] = 0;
+      return;
+    }
+    int val = 1;
+    // Make sure that all writes since acquiring this barrier are visible
+    // globally, while releasing the barrier.
+    asm volatile("fence.acq_rel.gpu;\n");
+    asm volatile("red.relaxed.gpu.global.add.s32 [%0], %1;\n"
+                 :
+                 : "l"(lock), "r"(val));
+  }
+}

csrc/quantization/marlin/dense/marlin_cuda_kernel.cu

@@ -25,6 +25,12 @@
 
 #include <iostream>
 
+#include "common/base.h"
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+  #include "common/mem.h"
+#endif
+
 template <typename T>
 inline std::string str(T x) {
   return std::to_string(x);
@@ -32,23 +38,9 @@ inline std::string str(T x) {
 
 namespace marlin_dense {
 
-constexpr int ceildiv(int a, int b) { return (a + b - 1) / b; }
-
 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
 
-// Instances of `Vec` are used to organize groups of >>registers<<, as needed
-// for instance as inputs to tensor core operations. Consequently, all
-// corresponding index accesses must be compile-time constants, which is why we
-// extensively use `#pragma unroll` throughout the kernel code to guarantee
-// this.
-template <typename T, int n>
-struct Vec {
-  T elems[n];
-  __device__ T& operator[](int i) { return elems[i]; }
-};
-
 using I4 = Vec<int, 4>;
-
 // Matrix fragments for tensor core instructions; their precise layout is
 // documented here:
 // https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-fragments-for-mma-m16n8k16-with-floating-point-type
@@ -57,43 +49,6 @@ using FragB = Vec<half2, 2>;
 using FragC = Vec<float, 4>;
 using FragS = Vec<half2, 1>;  // quantization scales
 
-// Predicated asynchronous global->shared copy; used for inputs A where we apply
-// predication to handle batchsizes that are not multiples of 16.
-__device__ inline void cp_async4_pred(void* smem_ptr, const void* glob_ptr,
-                                      bool pred = true) {
-  const int BYTES = 16;
-  uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
-  asm volatile(
-      "{\n"
-      "   .reg .pred p;\n"
-      "   setp.ne.b32 p, %0, 0;\n"
-      "   @p cp.async.cg.shared.global [%1], [%2], %3;\n"
-      "}\n" ::"r"((int)pred),
-      "r"(smem), "l"(glob_ptr), "n"(BYTES));
-}
-
-// Asynchronous global->shared copy
-__device__ inline void cp_async4(void* smem_ptr, const void* glob_ptr) {
-  const int BYTES = 16;
-  uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
-  asm volatile(
-      "{\n"
-      "   cp.async.cg.shared.global [%0], [%1], %2;\n"
-      "}\n" ::"r"(smem),
-      "l"(glob_ptr), "n"(BYTES));
-}
-
-// Async copy fence.
-__device__ inline void cp_async_fence() {
-  asm volatile("cp.async.commit_group;\n" ::);
-}
-
-// Wait until at most `n` async copy stages are still pending.
-template <int n>
-__device__ inline void cp_async_wait() {
-  asm volatile("cp.async.wait_group %0;\n" ::"n"(n));
-}
-
 // m16n8k16 tensor core mma instruction with fp16 inputs and fp32
 // output/accumulation.
 __device__ inline void mma(const FragA& a_frag, const FragB& frag_b,
@@ -164,39 +119,6 @@ __device__ inline void scale(FragB& frag_b, FragS& frag_s, int i) {
   frag_b[1] = __hmul2(frag_b[1], s);
 }
 
-// Wait until barrier reaches `count`, then lock for current threadblock.
-__device__ inline void barrier_acquire(int* lock, int count) {
-  if (threadIdx.x == 0) {
-    int state = -1;
-    do
-      // Guarantee that subsequent writes by this threadblock will be visible
-      // globally.
-      asm volatile("ld.global.acquire.gpu.b32 %0, [%1];\n"
-                   : "=r"(state)
-                   : "l"(lock));
-    while (state != count);
-  }
-  __syncthreads();
-}
-
-// Release barrier and increment visitation count.
-__device__ inline void barrier_release(int* lock, bool reset = false) {
-  __syncthreads();
-  if (threadIdx.x == 0) {
-    if (reset) {
-      lock[0] = 0;
-      return;
-    }
-    int val = 1;
-    // Make sure that all writes since acquiring this barrier are visible
-    // globally, while releasing the barrier.
-    asm volatile("fence.acq_rel.gpu;\n");
-    asm volatile("red.relaxed.gpu.global.add.s32 [%0], %1;\n"
-                 :
-                 : "l"(lock), "r"(val));
-  }
-}
-
 template <const int threads,          // number of threads in a threadblock
           const int thread_m_blocks,  // number of 16x16 blocks in the m
                                       // dimension (batchsize) of the

csrc/torch_bindings.cpp

@@ -149,6 +149,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("fp8_marlin_gemm", &fp8_marlin_gemm);
   ops.impl("fp8_marlin_gemm", torch::kCUDA, &fp8_marlin_gemm);
 
+  // marlin_qqq_gemm for QQQ.
+  ops.def("marlin_qqq_gemm", &marlin_qqq_gemm);
+  ops.impl("marlin_qqq_gemm", torch::kCUDA, &marlin_qqq_gemm);
+
   // CUTLASS w8a8 GEMM, supporting symmetric per-tensor or per-row/column
   // quantization.
   ops.def(

tests/kernels/test_marlin_gemm.py

@@ -10,6 +10,9 @@ from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
     GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N,
     GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_NUM_BITS)
+from vllm.model_executor.layers.quantization.qqq import (
+    MARLIN_QQQ_MAX_PARALLEL, MARLIN_QQQ_MIN_THREAD_N,
+    MARLIN_QQQ_SUPPORTED_GROUP_SIZES, MARLIN_QQQ_SUPPORTED_NUM_BITS)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N,
     MARLIN_SUPPORTED_GROUP_SIZES, MARLIN_SUPPORTED_NUM_BITS,
@@ -21,6 +24,8 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
     marlin_weights)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test_24 import (
     marlin_24_quantize)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_test_qqq import (  # noqa: E501
+    marlin_qqq_quantize)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     awq_pack, gptq_pack, quantize_weights, quantize_weights_with_zp,
     sort_weights)
@@ -425,3 +430,64 @@ def test_awq_marlin_gemm(
     print("max_diff = {}".format(max_diff))
 
     assert max_diff < 0.04
+
+
+@pytest.mark.skipif(not is_quant_method_supported("qqq"),
+                    reason="Marlin is not supported on this GPU type.")
+@pytest.mark.parametrize("k_chunk", MARLIN_K_CHUNKS)
+@pytest.mark.parametrize("n_chunk", MARLIN_N_CHUNKS)
+@pytest.mark.parametrize("num_bits", MARLIN_QQQ_SUPPORTED_NUM_BITS)
+@pytest.mark.parametrize("group_size", MARLIN_QQQ_SUPPORTED_GROUP_SIZES)
+@pytest.mark.parametrize("mnk_factors", MNK_FACTORS)
+def test_marlin_qqq_gemm(
+    k_chunk,
+    n_chunk,
+    num_bits,
+    group_size,
+    mnk_factors,
+):
+    int8_traits = torch.iinfo(torch.int8)
+    m_factor, n_factor, k_factor = mnk_factors
+
+    size_m = m_factor
+    size_k = k_chunk * k_factor
+    size_n = n_chunk * n_factor
+
+    print(f"MNK = {size_m} {size_n} {size_k}")
+    print(f"groupsize = {group_size}")
+
+    a_input = rand_data((size_m, size_k))
+    b_weight = rand_data((size_k, size_n))
+
+    # Quantize activations
+    s_a = a_input.abs().max(dim=-1, keepdim=True)[0].div(int8_traits.max).to(
+        torch.float)
+    q_a = (a_input / s_a).round().clamp(int8_traits.min,
+                                        int8_traits.max).to(torch.int8)
+
+    # Quantize weights
+    w_ref, marlin_qqq_q_w, marlin_qqq_s_group, marlin_qqq_s_channel = \
+    marlin_qqq_quantize(b_weight, num_bits, group_size)
+
+    workspace = MarlinWorkspace(size_n, MARLIN_QQQ_MIN_THREAD_N,
+                                MARLIN_QQQ_MAX_PARALLEL)
+
+    output = ops.marlin_qqq_gemm(
+        q_a,
+        marlin_qqq_q_w,
+        s_a,
+        marlin_qqq_s_channel,
+        marlin_qqq_s_group,
+        workspace.scratch,
+        a_input.shape[0],
+        b_weight.shape[1],
+        a_input.shape[1],
+    )
+    output_ref = torch.matmul(q_a.half() * s_a.half(), w_ref)
+
+    torch.cuda.synchronize()
+
+    max_diff = compute_max_diff(output, output_ref)
+    print("max_diff = {}".format(max_diff))
+
+    assert max_diff < 0.04

vllm/_custom_ops.py

@@ -389,6 +389,15 @@ def scaled_int8_quant(
     return output, input_scales
 
 
+# qqq ops
+def marlin_qqq_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
+                    s_tok: torch.Tensor, s_ch: torch.Tensor,
+                    s_group: torch.Tensor, workspace: torch.Tensor,
+                    size_m: int, size_n: int, size_k: int) -> torch.Tensor:
+    return torch.ops._C.marlin_qqq_gemm(a, b_q_weight, s_tok, s_ch, s_group,
+                                        workspace, size_m, size_n, size_k)
+
+
 # moe
 def moe_align_block_size(topk_ids: torch.Tensor, num_experts: int,
                          block_size: int, sorted_token_ids: torch.Tensor,

vllm/model_executor/layers/quantization/__init__.py

@@ -19,6 +19,7 @@ from vllm.model_executor.layers.quantization.gptq_marlin import (
 from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
     GPTQMarlin24Config)
 from vllm.model_executor.layers.quantization.marlin import MarlinConfig
+from vllm.model_executor.layers.quantization.qqq import QQQConfig
 from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
 
 QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
@@ -37,6 +38,7 @@ QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "squeezellm": SqueezeLLMConfig,
     "compressed-tensors": CompressedTensorsConfig,
     "bitsandbytes": BitsAndBytesConfig,
+    "qqq": QQQConfig,
 }
 
 

vllm/model_executor/layers/quantization/qqq.py

+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
+
+logger = init_logger(__name__)
+
+MARLIN_QQQ_TILE = 16
+MARLIN_QQQ_MIN_THREAD_N = 64
+MARLIN_QQQ_MIN_THREAD_K = 128
+MARLIN_QQQ_MAX_PARALLEL = 16
+
+MARLIN_QQQ_SUPPORTED_NUM_BITS = [4]
+MARLIN_QQQ_SUPPORTED_GROUP_SIZES = [-1, 128]
+MARLIN_QQQ_SUPPORTED_SYM = [True]
+
+
+class QQQConfig(QuantizationConfig):
+    """Config class for QQQ
+    
+    Reference: https://arxiv.org/pdf/2406.09904
+    """
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        is_sym: bool = True,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.is_sym = is_sym
+
+        # Verify
+        if self.weight_bits not in MARLIN_QQQ_SUPPORTED_NUM_BITS:
+            raise ValueError(
+                f"QQQ does not support weight_bits = {self.weight_bits}. "
+                f"Only weight_bits = {MARLIN_QQQ_SUPPORTED_NUM_BITS} "
+                "are supported.")
+        if self.group_size not in MARLIN_QQQ_SUPPORTED_GROUP_SIZES:
+            raise ValueError(
+                f"QQQ does not support group_size = {self.group_size}. "
+                f"Only group_sizes = {MARLIN_QQQ_SUPPORTED_GROUP_SIZES} "
+                "are supported.")
+        if self.is_sym not in MARLIN_QQQ_SUPPORTED_SYM:
+            raise ValueError(
+                f"QQQ does not support is_sym = {self.is_sym}. "
+                f"Only sym = {MARLIN_QQQ_SUPPORTED_SYM} are supported.")
+
+        # 4 Bits packed into 32 bit datatype.
+        self.pack_factor = 32 // self.weight_bits
+
+        # Tile size used by QQQ kernels.
+        self.tile_size = MARLIN_QQQ_TILE
+
+        # Min out_features dim
+        self.min_n_threads = MARLIN_QQQ_MIN_THREAD_N
+
+        # Min in_features dim
+        self.min_k_threads = MARLIN_QQQ_MIN_THREAD_K
+
+        # Max parallel problems to solve at once (improves large
+        # batch performance)
+        self.max_parallel = MARLIN_QQQ_MAX_PARALLEL
+
+        # Permutation length used by the QQQ kernels.
+        self.perm_len = 1024
+
+    def __repr__(self) -> str:
+        return "QQQConfig(weight_bits={}, group_size={})".format(
+            self.weight_bits, self.group_size)
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "qqq"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        """List of filenames to search for in the model directory."""
+        return [
+            "quant_config.json",
+            "quantize_config.json",
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "QQQConfig":
+        weight_bits = cls.get_from_keys(config, ["wbits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        return cls(weight_bits, group_size)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QQQLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return QQQLinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class QQQLinearMethod(LinearMethodBase):
+    """Linear method for QQQ.
+
+    Args:
+        quant_config: The QQQ quantization config.
+    """
+
+    def __init__(self, quant_config: QQQConfig):
+        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,
+    ):
+        if params_dtype != torch.float16:
+            raise ValueError(
+                f"The params dtype must be float16, but got {params_dtype}")
+
+        # Validate output_size_per_partition
+        output_size_per_partition = sum(output_partition_sizes)
+        if output_size_per_partition % self.quant_config.min_n_threads != 0:
+            raise ValueError(
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"min_n_threads = {self.quant_config.min_n_threads}.")
+        if output_size_per_partition % self.quant_config.pack_factor != 0:
+            raise ValueError(
+                f"Weight output_size_per_partition = "
+                f"{output_size_per_partition} is not divisible by "
+                f"pack_factor = {self.quant_config.pack_factor}.")
+
+        # Validate input_size_per_partition
+        if input_size_per_partition % self.quant_config.min_k_threads != 0:
+            raise ValueError(
+                f"Weight input_size_per_partition = "
+                f"{input_size_per_partition} is not divisible by "
+                f"min_k_threads = {self.quant_config.min_k_threads}.")
+        if (self.quant_config.group_size != -1 and
+                input_size_per_partition % self.quant_config.group_size != 0):
+            raise ValueError(f"Weight input_size_per_partition = "
+                             f"{input_size_per_partition} is not divisible by "
+                             f"group_size = {self.quant_config.group_size}.")
+
+        # Check that we have at least 4 tiles horizontally in the shard
+        num_tiles_per_perm = self.quant_config.perm_len // (
+            self.quant_config.tile_size**2)
+        if output_size_per_partition % num_tiles_per_perm != 0:
+            raise ValueError(
+                "Each permutation group must reside on the same gpu")
+
+        # Quantized 4Bit weights packed into Int32.
+        qweight = Parameter(
+            torch.empty(
+                input_size_per_partition // self.quant_config.tile_size,
+                output_size_per_partition * self.quant_config.tile_size //
+                self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(
+            qweight,
+            {
+                "input_dim": 0,
+                "output_dim": 1,
+                "packed_dim": 1,
+                "pack_factor": self.quant_config.pack_factor,
+                "marlin_tile_size": self.quant_config.tile_size,
+            },
+        )
+
+        s_channel = Parameter(
+            torch.empty(
+                1,
+                output_size_per_partition,
+                device="cuda",
+                dtype=torch.float,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(
+            s_channel,
+            {
+                "input_dim": None,
+                "output_dim": 1,
+            },
+        )
+
+        if self.quant_config.group_size == -1:
+            s_group = Parameter(
+                torch.tensor(
+                    [],
+                    device="cuda",
+                    dtype=torch.half,
+                ),
+                requires_grad=False,
+            )
+        else:
+            s_group = Parameter(
+                torch.empty(
+                    input_size_per_partition // self.quant_config.group_size,
+                    output_size_per_partition,
+                    device="cuda",
+                    dtype=torch.half,
+                ),
+                requires_grad=False,
+            )
+
+        set_weight_attrs(
+            s_group,
+            {
+                "input_dim": None if self.quant_config.group_size == -1 else 0,
+                "output_dim":
+                None if self.quant_config.group_size == -1 else 1,
+            },
+        )
+
+        # Allocate workspace (Used for internal locking mechanism)
+        max_workspace_size = (
+            output_size_per_partition //
+            self.quant_config.min_n_threads) * self.quant_config.max_parallel
+        workspace = Parameter(torch.zeros(max_workspace_size,
+                                          device="cuda",
+                                          dtype=torch.int),
+                              requires_grad=False)
+
+        layer.register_parameter("B", qweight)
+        set_weight_attrs(qweight, extra_weight_attrs)
+        layer.register_parameter("s_channel", s_channel)
+        set_weight_attrs(s_channel, extra_weight_attrs)
+        layer.register_parameter("s_group", s_group)
+        set_weight_attrs(s_group, extra_weight_attrs)
+        layer.register_parameter("workspace", workspace)
+        set_weight_attrs(workspace, extra_weight_attrs)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        qweight = layer.B
+        s_ch = layer.s_channel
+        s_group = layer.s_group
+        workspace = layer.workspace
+
+        x_2d = x.view(-1, x.shape[-1])
+
+        size_m = x_2d.shape[0]
+        size_k = x_2d.shape[1]
+        size_n = s_ch.shape[1]
+
+        x_int8, s_tok = ops.scaled_int8_quant(x_2d)
+
+        output_2d = ops.marlin_qqq_gemm(x_int8, qweight, s_tok, s_ch, s_group,
+                                        workspace, size_m, size_n, size_k)
+
+        output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
+
+        if bias is not None:
+            output.add_(bias)  # In-place add
+
+        return output

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

+from typing import List
+
+import numpy
+import torch
+
+from .marlin_utils_test import marlin_permute_weights
+from .quant_utils import get_pack_factor, qqq_quantize_weights
+
+
+def marlin_qqq_weights(q_w, size_k, size_n, num_bits, perm, group_size):
+    # Permute
+    q_w = marlin_permute_weights(q_w, size_k, size_n, perm)
+
+    # Pack
+    pack_factor = get_pack_factor(num_bits)
+    orig_device = q_w.device
+
+    q_w = q_w.cpu().numpy().astype(numpy.uint32)
+
+    q_packed = numpy.zeros((q_w.shape[0], q_w.shape[1] // pack_factor),
+                           dtype=numpy.uint32)
+    if group_size == size_k:
+        for i in range(pack_factor):
+            q_packed |= (q_w[:, i::pack_factor] & 0xF) << num_bits * i
+    else:
+        for i in range(pack_factor):
+            q_packed |= q_w[:, i::pack_factor] << num_bits * i
+
+    q_packed = torch.from_numpy(q_packed.astype(numpy.int32)).to(orig_device)
+
+    return q_packed
+
+
+def get_qqq_scale_perms():
+    scale_perm: List[int] = []
+    for i in range(8):
+        scale_perm.extend([i + 8 * j for j in range(8)])
+    scale_perm_single: List[int] = []
+    for i in range(4):
+        scale_perm_single.extend(
+            [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
+    return scale_perm, scale_perm_single
+
+
+# NOTE(HandH1998): QQQ employs different perms for per-group and per-channel weight quantization. # noqa: E501
+def get_qqq_weight_perm(num_bits: int, quant_type: str):
+    perm_list: List[int] = []
+    for i in range(32):
+        perm1: List[int] = []
+        col = i // 4
+        for block in [0, 1]:
+            for row in [
+                    4 * (i % 4),
+                    4 * (i % 4) + 1,
+                    4 * (i % 4) + 2,
+                    4 * (i % 4) + 3,
+            ]:
+                perm1.append(16 * row + col + 8 * block)
+        for j in range(4):
+            perm_list.extend([p + 256 * j for p in perm1])
+
+    perm = numpy.array(perm_list)
+
+    assert quant_type in ["per-channel",
+                          "per-group"], "not supported quantization type"
+    if num_bits == 4:
+        if quant_type == "per-channel":
+            interleave = numpy.array([4, 0, 5, 1, 6, 2, 7, 3])
+        else:
+            interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7])
+    else:
+        raise Exception("num_bits must be 4, got {}".format(num_bits))
+
+    perm = perm.reshape((-1, len(interleave)))[:, interleave].ravel()
+    perm = torch.from_numpy(perm)
+    return perm
+
+
+def marlin_qqq_permute_scales(s_group, s_channel, size_k, size_n, group_size):
+    scale_perm, scale_perm_single = get_qqq_scale_perms()
+    if group_size < size_k and group_size != -1:
+        s_group = s_group.reshape((-1, len(scale_perm)))[:, scale_perm]
+        s_channel = s_channel.reshape(
+            (-1, len(scale_perm_single)))[:, scale_perm_single]
+        s_group = s_group.reshape((-1, size_n)).contiguous()
+    else:
+        s_channel = s_channel.reshape(
+            (-1, len(scale_perm_single)))[:, scale_perm_single]
+    s_channel = s_channel.reshape((-1, size_n)).contiguous()
+
+    return s_group, s_channel
+
+
+def marlin_qqq_quantize(
+    w: torch.Tensor,
+    num_bits: int,
+    group_size: int,
+):
+    size_k, size_n = w.shape
+
+    # Normalize group_size
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+    quant_type = "per-channel" if group_size == size_k else "per-group"
+
+    # Quantize
+    w_ref, q_w, s_group, s_channel = qqq_quantize_weights(
+        w, num_bits, group_size)
+
+    # Reformat to marlin_qqq
+    weight_perm = get_qqq_weight_perm(num_bits, quant_type)
+    marlin_qqq_q_w = marlin_qqq_weights(q_w, size_k, size_n, num_bits,
+                                        weight_perm, group_size)
+    marlin_qqq_s_group, marlin_qqq_s_channel = marlin_qqq_permute_scales(
+        s_group, s_channel, size_k, size_n, group_size)
+
+    # Create result
+    res_list = [
+        w_ref, marlin_qqq_q_w, marlin_qqq_s_group, marlin_qqq_s_channel
+    ]
+    for i in range(len(res_list)):
+        res_list[i] = res_list[i].to(w.device)
+
+    return res_list

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

@@ -205,6 +205,88 @@ def quantize_weights_with_zp(w: torch.Tensor, num_bits: int, group_size: int):
     )
 
 
+# QQQ employs different quant schemes for per-group and
+# per-channel quantization.
+def qqq_quantize_weights(w: torch.Tensor, num_bits: int, group_size: int):
+    orig_device = w.device
+    size_k, size_n = w.shape
+
+    assert w.is_floating_point(), "w must be float"
+    assert num_bits in SUPPORTED_NUM_BITS, f"Unsupported num_bits = {num_bits}"
+    assert group_size in SUPPORTED_GROUP_SIZES + [
+        size_k
+    ], f"Unsupported groupsize = {group_size}"
+
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    if group_size < size_k:
+        # Reshape to [groupsize, -1]
+        w = w.reshape((-1, group_size, size_n))
+        w = w.permute(1, 0, 2)
+        w = w.reshape((group_size, -1))
+
+        max_q_val = 2**num_bits - 1
+        half_q_val = (max_q_val + 1) // 2
+
+        # Compute scale for each group
+        s_group = torch.max(torch.abs(w), 0, keepdim=True)[0]
+        s_group *= 2 / max_q_val  # 2 => symmetric
+
+        # Quantize
+        q_w = torch.round(w / s_group).int()
+        q_w += half_q_val
+        q_w = torch.clamp(q_w, 0, max_q_val)
+        # Compute ref (dequantized)
+        w_ref = (q_w - half_q_val).half() * s_group
+
+        # Restore original shapes
+        def reshape_w(w):
+            w = w.reshape((group_size, -1, size_n))
+            w = w.permute(1, 0, 2)
+            w = w.reshape((size_k, size_n)).contiguous()
+            return w
+
+        q_w = reshape_w(q_w)
+        w_ref = reshape_w(w_ref)
+
+        # Compute int8 quantization scale for each channel
+        s_channel = torch.max(torch.abs(w_ref), 0, keepdim=True)[0]
+        s_channel /= 127.0
+        t_int8 = (w_ref / s_channel).round().clamp(-128, 127).to(torch.int8)
+        w_ref = t_int8.half() * s_channel
+        s_channel = s_channel.reshape(1, -1).to(dtype=torch.float)
+
+        # Fuse scales
+        s_group = (s_group.reshape(-1, size_n).contiguous() /
+                   s_channel).to(dtype=torch.half)
+    else:
+        max_q_val = 2**(num_bits - 1) - 1
+
+        # Compute scale for each channel
+        s_channel = torch.max(torch.abs(w), 0, keepdim=True)[0]
+        s_channel /= max_q_val
+
+        # Quantize
+        q_w = torch.round(w / s_channel).int()
+        q_w = torch.clamp(q_w, -max_q_val, max_q_val)
+        # Compute ref (dequantized)
+        w_ref = q_w.half() * s_channel
+
+        s_group = torch.tensor([], dtype=torch.half)
+        # div 2 ** (8 - self.bits)) to offset right shift in unpacking
+        s_channel /= (2**(8 - num_bits))
+        s_channel = s_channel.reshape(-1, size_n).contiguous().to(torch.float)
+
+    return (
+        w_ref.to(device=orig_device),
+        q_w.to(device=orig_device),
+        s_group.to(device=orig_device),
+        s_channel.to(device=orig_device),
+    )
+
+
 def sort_weights(q_w: torch.Tensor, g_idx: torch.Tensor):
     orig_device = q_w.device