[Misc] Remove SqueezeLLM (#8220)

CMakeLists.txt

@@ -181,7 +181,6 @@ set(VLLM_EXT_SRC
   "csrc/pos_encoding_kernels.cu"
   "csrc/activation_kernels.cu"
   "csrc/layernorm_kernels.cu"
-  "csrc/quantization/squeezellm/quant_cuda_kernel.cu"
   "csrc/quantization/gptq/q_gemm.cu"
   "csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
   "csrc/quantization/fp8/common.cu"

csrc/ops.h

@@ -170,9 +170,6 @@ void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
 void dynamic_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
                                torch::Tensor& scales);
 
-void squeezellm_gemm(torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
-                     torch::Tensor lookup_table);
-
 torch::Tensor gptq_gemm(torch::Tensor a, torch::Tensor b_q_weight,
                         torch::Tensor b_gptq_qzeros,
                         torch::Tensor b_gptq_scales, torch::Tensor b_g_idx,

csrc/quantization/squeezellm/quant_cuda_kernel.cu

@@ -1,216 +0,0 @@
-#include <torch/all.h>
-#include <cuda.h>
-#include <cuda_runtime.h>
-#include <cuda_fp16.h>
-
-// half-tensor
-#include <c10/cuda/CUDAStream.h>
-#include <ATen/cuda/CUDATensorMethods.cuh>
-#include <c10/cuda/CUDAGuard.h>
-
-#define BLOCKWIDTH 128
-#define BLOCKHEIGHT4 16
-
-namespace vllm {
-namespace squeezellm {
-
-__device__ inline unsigned int as_unsigned(int i) {
-  return *reinterpret_cast<unsigned int*>(&i);
-}
-
-// 4-bit matvec kernel (LUT-based)
-__global__ void NUQ4MatMulKernel(
-#ifndef USE_ROCM
-    const half2* __restrict__ vec,
-#else
-    const __half2* __restrict__ vec,
-#endif
-    const int* __restrict__ mat,
-#ifndef USE_ROCM
-    half2* __restrict__ mul,
-#else
-    float2* __restrict__ mul,
-#endif
-    const __half* __restrict__ lookup_table, int height, int width, int batch,
-    int vec_height) {
-
-  const int blockwidth2 = BLOCKWIDTH / 2;
-
-  int row = BLOCKHEIGHT4 * blockIdx.x;
-  int col = BLOCKWIDTH * blockIdx.y + threadIdx.x;
-
-#ifndef USE_ROCM
-  __shared__ half2 blockvec[blockwidth2];
-#else
-  __shared__ __half2 blockvec[blockwidth2];
-#endif
-
-  __shared__ __half deq2[16][BLOCKWIDTH];
-  int off = threadIdx.x;
-  int column_offset = col * 16;
-  for (int val = 0; val < 16; val += 1) {
-    int lut_index = column_offset + val;
-    deq2[val][off] = lookup_table[lut_index];
-  }
-
-  __half res;
-#ifndef USE_ROCM
-  half2 res2;
-  half2 tmp2;
-#else
-  __half2 res2;
-  __half2 tmp2;
-#endif
-
-  int i;
-  int k;
-
-  unsigned int tmp1;
-  unsigned int lut_index1, lut_index2;
-
-  for (int b = 0; b < batch; ++b) {
-    i = width * row + col;
-    res = __int2half_rd(0);
-    k = 0;
-
-    __syncthreads();
-    if (threadIdx.x < blockwidth2)
-      blockvec[threadIdx.x] =
-          vec[b * vec_height / 2 + (row / BLOCKHEIGHT4) * blockwidth2 +
-              threadIdx.x];
-    __syncthreads();
-
-    while (k < blockwidth2) {
-      tmp1 = as_unsigned(mat[i]);
-
-#ifndef USE_ROCM
-      res2 = {};
-      tmp2 = {};
-#else
-      res2.x = __half_as_ushort(__float2half(0));
-      res2.y = __half_as_ushort(__float2half(0));
-      tmp2.x = __half_as_ushort(__float2half(0));
-      tmp2.y = __half_as_ushort(__float2half(0));
-#endif
-
-      lut_index1 = tmp1 & 0xF;
-      lut_index2 = (tmp1 >> 4) & 0xF;
-#ifndef USE_ROCM
-      tmp2.x = deq2[lut_index1][off];
-      tmp2.y = deq2[lut_index2][off];
-#else
-      tmp2.x = __half_as_ushort(deq2[lut_index1][off]);
-      tmp2.y = __half_as_ushort(deq2[lut_index2][off]);
-#endif
-      res2 = __hfma2(tmp2, blockvec[k + 0], res2);
-
-      lut_index1 = (tmp1 >> 8) & 0xF;
-      lut_index2 = (tmp1 >> 12) & 0xF;
-#ifndef USE_ROCM
-      tmp2.x = deq2[lut_index1][off];
-      tmp2.y = deq2[lut_index2][off];
-#else
-      tmp2.x = __half_as_ushort(deq2[lut_index1][off]);
-      tmp2.y = __half_as_ushort(deq2[lut_index2][off]);
-#endif
-      res2 = __hfma2(tmp2, blockvec[k + 1], res2);
-
-      lut_index1 = (tmp1 >> 16) & 0xF;
-      lut_index2 = (tmp1 >> 20) & 0xF;
-#ifndef USE_ROCM
-      tmp2.x = deq2[lut_index1][off];
-      tmp2.y = deq2[lut_index2][off];
-#else
-      tmp2.x = __half_as_ushort(deq2[lut_index1][off]);
-      tmp2.y = __half_as_ushort(deq2[lut_index2][off]);
-#endif
-      res2 = __hfma2(tmp2, blockvec[k + 2], res2);
-
-      lut_index1 = (tmp1 >> 24) & 0xF;
-      lut_index2 = (tmp1 >> 28) & 0xF;
-#ifndef USE_ROCM
-      tmp2.x = deq2[lut_index1][off];
-      tmp2.y = deq2[lut_index2][off];
-#else
-      tmp2.x = __half_as_ushort(deq2[lut_index1][off]);
-      tmp2.y = __half_as_ushort(deq2[lut_index2][off]);
-#endif
-      res2 = __hfma2(tmp2, blockvec[k + 3], res2);
-
-#ifndef USE_ROCM
-      res = __hadd(__hadd(res2.x, res2.y), res);
-#else
-      res = __hadd(__hadd(__ushort_as_half(res2.x), __ushort_as_half(res2.y)),
-                   res);
-#endif
-
-      i += width;
-      k += 4;
-    }
-
-    // col%2 -> only set one of the two values
-#ifndef USE_ROCM
-    half2 res3 = {};
-    if (col % 2 == 0) {
-      res3.x = res;
-    } else {
-      res3.y = res;
-    }
-#else
-    __half2 res3;
-    res3.x = __half_as_ushort(__float2half(0));
-    res3.y = __half_as_ushort(__float2half(0));
-    if (col % 2 == 0) {
-      res3.x = __half_as_ushort(res);
-    } else {
-      res3.y = __half_as_ushort(res);
-    }
-#endif
-
-#ifndef USE_ROCM
-    atomicAdd(&mul[b * width / 2 + col / 2], res3);
-#else
-    int tmp_addr = b * width / 2 + col / 2;
-    atomicAdd(&(mul[tmp_addr].x), __half2float(__ushort_as_half(res3.x)));
-    atomicAdd(&(mul[tmp_addr].y), __half2float(__ushort_as_half(res3.y)));
-#endif
-  }
-}
-
-}  // namespace squeezellm
-}  // namespace vllm
-
-// 4-bit matvec kernel (LUT-based)
-void squeezellm_gemm(torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
-                     torch::Tensor lookup_table) {
-  int height = mat.size(0);
-  int width = mat.size(1);
-
-  int batch = vec.size(0);
-  int vec_height = vec.size(1);
-
-  dim3 blocks((height + BLOCKHEIGHT4 - 1) / BLOCKHEIGHT4,
-              (width + BLOCKWIDTH - 1) / BLOCKWIDTH);
-  dim3 threads(BLOCKWIDTH);
-
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  vllm::squeezellm::NUQ4MatMulKernel<<<blocks, threads, 0, stream>>>(
-#ifndef USE_ROCM
-      (half2*)vec.data_ptr<at::Half>(),
-#else
-      (__half2*)vec.data_ptr<at::Half>(),
-#endif
-      mat.data_ptr<int>(),
-#ifndef USE_ROCM
-      (half2*)mul.data_ptr<at::Half>(),
-      (__half*)lookup_table.data_ptr<at::Half>(),
-#else
-      (float2*)mul.data_ptr<float>(),
-      (__half*)lookup_table.data_ptr<at::Half>(),
-#endif
-      height, width, batch, vec_height);
-}
-
-#undef BLOCKWIDTH
-#undef BLOCKHEIGHT4

csrc/torch_bindings.cpp

@@ -237,12 +237,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("gptq_shuffle(Tensor! q_weight, Tensor q_perm, int bit) -> ()");
   ops.impl("gptq_shuffle", torch::kCUDA, &gptq_shuffle);
 
-  // Quantized GEMM for SqueezeLLM.
-  ops.def(
-      "squeezellm_gemm(Tensor vec, Tensor mat, Tensor! mul, Tensor "
-      "lookup_table) -> ()");
-  ops.impl("squeezellm_gemm", torch::kCUDA, &squeezellm_gemm);
-
   // Compute FP8 quantized tensor for given scaling factor.
   ops.def(
       "static_scaled_fp8_quant(Tensor! out, Tensor input, Tensor scale) -> ()");

docs/source/quantization/supported_hardware.rst

@@ -119,17 +119,6 @@ The table below shows the compatibility of various quantization implementations
      - ✗
      - ✗
      - ✗
-   * - SqueezeLLM
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✅︎
-     - ✗
-     - ✗
-     - ✗
-     - ✗
-     - ✗
 
 Notes:
 ^^^^^^

examples/fp8/README.md

@@ -62,7 +62,7 @@ This script evaluates the inference throughput of language models using various
 
 python3 benchmarks/benchmark_throughput.py --help 
 usage: benchmark_throughput.py [-h] [--backend {vllm,hf,mii}] [--dataset DATASET] [--input-len INPUT_LEN] [--output-len OUTPUT_LEN] [--model MODEL]
-                               [--tokenizer TOKENIZER] [--quantization {awq,gptq,squeezellm,None}] [--tensor-parallel-size TENSOR_PARALLEL_SIZE] [--n N]
+                               [--tokenizer TOKENIZER] [--quantization {awq,gptq,None}] [--tensor-parallel-size TENSOR_PARALLEL_SIZE] [--n N]
                                [--use-beam-search] [--num-prompts NUM_PROMPTS] [--seed SEED] [--hf-max-batch-size HF_MAX_BATCH_SIZE] [--trust-remote-code]
                                [--max-model-len MAX_MODEL_LEN] [--dtype {auto,half,float16,bfloat16,float,float32}] [--enforce-eager] [--kv-cache-dtype {auto,fp8}]
                                [--quantization-param-path KV_CACHE_quantization_param_path]
@@ -76,7 +76,7 @@ optional arguments:
   --output-len OUTPUT_LEN  Output length for each request. Overrides the output length from the dataset.
   --model MODEL
   --tokenizer TOKENIZER
-  --quantization {awq,gptq,squeezellm,None}, -q {awq,gptq,squeezellm,None}
+  --quantization {awq,gptq,None}, -q {awq,gptq,None}
   --tensor-parallel-size TENSOR_PARALLEL_SIZE, -tp TENSOR_PARALLEL_SIZE
   --n N  Number of generated sequences per prompt.
   --use-beam-search

vllm/_custom_ops.py

@@ -209,12 +209,6 @@ def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor,
     torch.ops._C.gptq_shuffle(q_weight, q_perm, bit)
 
 
-# squeezellm
-def squeezellm_gemm(vec: torch.Tensor, mat: torch.Tensor, mul: torch.Tensor,
-                    lookup_table: torch.Tensor) -> None:
-    torch.ops._C.squeezellm_gemm(vec, mat, mul, lookup_table)
-
-
 # marlin
 def marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
                 b_scales: torch.Tensor, workspace: torch.Tensor, size_m: int,

vllm/config.py

@@ -277,7 +277,7 @@ class ModelConfig:
 
     def _verify_quantization(self) -> None:
         supported_quantization = [*QUANTIZATION_METHODS]
-        rocm_supported_quantization = ["awq", "gptq", "squeezellm", "fp8"]
+        rocm_supported_quantization = ["awq", "gptq", "fp8"]
         optimized_quantization_methods = [
             "fp8", "marlin", "gptq_marlin_24", "gptq_marlin", "awq_marlin",
             "fbgemm_fp8", "compressed_tensors", "compressed-tensors",
@@ -1537,7 +1537,7 @@ class LoRAConfig:
         if model_config.quantization and model_config.quantization not in [
                 "awq", "gptq"
         ]:
-            # TODO support marlin and squeezellm
+            # TODO support marlin
             logger.warning("%s quantization is not tested with LoRA yet.",
                            model_config.quantization)
 

vllm/entrypoints/llm.py

@@ -55,7 +55,7 @@ class LLM:
             However, if the `torch_dtype` in the config is `float32`, we will
             use `float16` instead.
         quantization: The method used to quantize the model weights. Currently,
-            we support "awq", "gptq", "squeezellm", and "fp8" (experimental).
+            we support "awq", "gptq", and "fp8" (experimental).
             If None, we first check the `quantization_config` attribute in the
             model config file. If that is None, we assume the model weights are
             not quantized and use `dtype` to determine the data type of

vllm/lora/layers.py

@@ -39,7 +39,7 @@ def _get_lora_device(base_layer: nn.Module) -> torch.device:
     # unquantizedLinear
     if hasattr(base_layer, "weight"):
         return base_layer.weight.device
-    # GPTQ/AWQ/SqueezeLLM
+    # GPTQ/AWQ
     elif hasattr(base_layer, "qweight"):
         return base_layer.qweight.device
     # marlin

vllm/model_executor/layers/quantization/__init__.py

@@ -25,7 +25,6 @@ from vllm.model_executor.layers.quantization.marlin import MarlinConfig
 from vllm.model_executor.layers.quantization.neuron_quant import (
     NeuronQuantConfig)
 from vllm.model_executor.layers.quantization.qqq import QQQConfig
-from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
 from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
 
 QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
@@ -43,7 +42,6 @@ QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "gptq_marlin": GPTQMarlinConfig,
     "awq_marlin": AWQMarlinConfig,
     "gptq": GPTQConfig,
-    "squeezellm": SqueezeLLMConfig,
     "compressed-tensors": CompressedTensorsConfig,
     "bitsandbytes": BitsAndBytesConfig,
     "qqq": QQQConfig,

vllm/model_executor/layers/quantization/squeezellm.py

@@ -1,138 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-import torch
-from torch.nn.parameter import Parameter
-
-from vllm import _custom_ops as ops
-from vllm.model_executor.layers.linear import LinearBase
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig, QuantizeMethodBase)
-from vllm.model_executor.utils import set_weight_attrs
-from vllm.utils import is_hip
-
-
-class SqueezeLLMConfig(QuantizationConfig):
-    """Config class for SqueezeLLM.
-
-    Reference: https://arxiv.org/pdf/2306.07629
-    """
-
-    def __init__(
-        self,
-        weight_bits: int,
-    ) -> None:
-        self.weight_bits = weight_bits
-
-        if self.weight_bits != 4:
-            raise ValueError(
-                "Currently, only 4-bit weight quantization is supported for "
-                f"SqueezeLLM, but got {self.weight_bits} bits.")
-
-        self.pack_factor = 32 // self.weight_bits
-
-    def __repr__(self) -> str:
-        return f"SqueezeLLMConfig(weight_bits={self.weight_bits})"
-
-    def get_name(self) -> str:
-        return "squeezellm"
-
-    def get_supported_act_dtypes(self) -> List[torch.dtype]:
-        return [torch.half]
-
-    @classmethod
-    def get_min_capability(cls) -> int:
-        return 70
-
-    @staticmethod
-    def get_config_filenames() -> List[str]:
-        return ["quant_config.json"]
-
-    @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "SqueezeLLMConfig":
-        weight_bits = cls.get_from_keys(config, ["wbits"])
-        return cls(weight_bits)
-
-    def get_quant_method(self, layer: torch.nn.Module,
-                         prefix: str) -> Optional[QuantizeMethodBase]:
-        if isinstance(layer, LinearBase):
-            return SqueezeLLMLinearMethod(self)
-        return None
-
-    def get_scaled_act_names(self) -> List[str]:
-        return []
-
-
-class SqueezeLLMLinearMethod(QuantizeMethodBase):
-    """Linear method for SqueezeLLM.
-
-    Args:
-        quant_config: The SqueezeLLM quantization config.
-    """
-
-    def __init__(self, quant_config: SqueezeLLMConfig):
-        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 input_size_per_partition % self.quant_config.pack_factor != 0:
-            raise ValueError(
-                "The input size is not aligned with the quantized "
-                "weight shape. This can be caused by too large "
-                "tensor parallel size.")
-
-        output_size_per_partition = sum(output_partition_sizes)
-        qweight = Parameter(
-            torch.empty(
-                input_size_per_partition // self.quant_config.pack_factor,
-                output_size_per_partition,
-                dtype=torch.int32,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            qweight, {
-                "input_dim": 0,
-                "output_dim": 1,
-                "packed_dim": 0,
-                "pack_factor": self.quant_config.pack_factor,
-            })
-        lookup_table = Parameter(
-            torch.empty(
-                output_size,
-                self.quant_config.weight_bits**2,
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(lookup_table, {
-            "output_dim": 0,
-        })
-
-        layer.register_parameter("qweight", qweight)
-        set_weight_attrs(qweight, extra_weight_attrs)
-        layer.register_parameter("lookup_table", lookup_table)
-        set_weight_attrs(lookup_table, extra_weight_attrs)
-
-    def apply(self,
-              layer: torch.nn.Module,
-              x: torch.Tensor,
-              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        qweight = layer.qweight
-        lookup_table = layer.lookup_table
-        out_shape = x.shape[:-1] + (qweight.shape[-1], )
-        reshaped_x = x.reshape(-1, x.shape[-1])
-        if is_hip():
-            out_f = torch.zeros(out_shape, dtype=torch.float)
-            ops.squeezellm_gemm(reshaped_x, qweight, out_f, lookup_table)
-            out = out_f.to(dtype=torch.float16)
-        else:
-            # NOTE: The output tensor should be zero-initialized.
-            out = torch.zeros(out_shape, dtype=torch.float16)
-            ops.squeezellm_gemm(reshaped_x, qweight, out, lookup_table)
-
-        if bias is not None:
-            out.add_(bias)
-        return out.reshape(out_shape)