Add GPTQ support (#916)

benchmarks/benchmark_latency.py

@@ -84,7 +84,7 @@ if __name__ == '__main__':
     parser.add_argument('--tokenizer', type=str, default=None)
     parser.add_argument('--quantization',
                         '-q',
-                        choices=['awq', 'squeezellm', None],
+                        choices=['awq', 'gptq', 'squeezellm', None],
                         default=None)
     parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
     parser.add_argument('--input-len', type=int, default=32)

benchmarks/benchmark_throughput.py

@@ -244,7 +244,7 @@ if __name__ == "__main__":
     parser.add_argument("--tokenizer", type=str, default=None)
     parser.add_argument('--quantization',
                         '-q',
-                        choices=['awq', 'squeezellm', None],
+                        choices=['awq', 'gptq', 'squeezellm', None],
                         default=None)
     parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
     parser.add_argument("--n",

csrc/ops.h

@@ -77,3 +77,15 @@ void squeezellm_gemm(
   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,
+  bool use_exllama);
+
+void gptq_shuffle(
+  torch::Tensor q_weight,
+  torch::Tensor q_perm);

csrc/pybind.cpp

@@ -52,8 +52,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   // Quantization ops
   ops.def("awq_gemm", &awq_gemm, "Quantized GEMM for AWQ");
 #endif
-
+  ops.def("gptq_gemm", &gptq_gemm, "Quantized GEMM for GPTQ");
-
+  ops.def("gptq_shuffle", &gptq_shuffle, "Post processing for GPTQ");
   ops.def("squeezellm_gemm", &squeezellm_gemm, "Quantized GEMM for SqueezeLLM");
 
   // Cache ops

csrc/quantization/gptq/compat.cuh

@@ -0,0 +1,64 @@
+/*
+Copied from https://github.com/turboderp/exllamav2
+*/
+
+#ifndef _compat_cuh
+#define _compat_cuh
+
+namespace vllm {
+namespace gptq {
+// atomicAdd for half types, to support CC < 7.x
+
+__device__ __forceinline__ void atomicAdd_half(half* address, half val)
+{
+    unsigned int * address_as_ui = (unsigned int *) ((char *)address - ((size_t)address & 2));
+    unsigned int old = *address_as_ui;
+    unsigned int assumed;
+
+    do
+    {
+        assumed = old;
+        __half_raw hsum;
+        hsum.x = (size_t)address & 2 ? (old >> 16) : (old & 0xffff);
+        half tmpres = __hadd(hsum, val);
+        hsum = __half_raw(tmpres);
+        old = (size_t)address & 2 ? (old & 0xffff) | (hsum.x << 16) : (old & 0xffff0000) | hsum.x;
+        old = atomicCAS(address_as_ui, assumed, old);
+    }
+    while (assumed != old);
+}
+
+// atomicAdd for half2 types
+
+__device__ __forceinline__ void atomicAdd_half2(half2* address, half2 val)
+{
+    unsigned int* address_as_ui = (unsigned int*)address;
+    unsigned int old = *address_as_ui;
+    unsigned int assumed;
+    do
+    {
+        assumed = old;
+        half2 old_val = *((half2*)&old);
+        half2 new_val = __hadd2(old_val, val);
+        old = atomicCAS(address_as_ui, assumed, *((unsigned int*)&new_val));
+    }
+    while (assumed != old);
+}
+
+//
+
+#if defined(__CUDA_ARCH__) || defined(USE_ROCM)
+#if __CUDA_ARCH__ < 700 || defined(USE_ROCM)
+
+__device__ __forceinline__ void atomicAdd(half* address, half val) { atomicAdd_half(address, val); }
+
+#if __CUDA_ARCH__ < 600 || defined(USE_ROCM)
+__device__ __forceinline__ void atomicAdd(half2* address, half2 val) { atomicAdd_half2(address, val); }
+#endif
+
+#endif
+#endif
+
+}  // namespace gptq
+}  // namespace vllm
+#endif

csrc/quantization/gptq/matrix_view.cuh

@@ -0,0 +1,151 @@
+/*
+Adapted from https://github.com/turboderp/exllamav2 and https://github.com/turboderp/exllama
+*/
+
+#ifndef _matrix_view_cuh
+#define _matrix_view_cuh
+
+#include <cuda_runtime.h>
+#include <cuda_fp16.h>
+
+#include "qdq_util.cuh"
+
+namespace vllm {
+namespace gptq {
+
+class MatrixView_half
+{
+public:
+    const half* data;
+    const int height;
+    const int width;
+
+    __device__ __forceinline__ MatrixView_half(const half* data, const int height, const int width)
+        : data(data), height(height), width(width)
+    { }
+
+    __device__ __forceinline__ half item(int row, int column) const { return data[row * width + column]; }
+    __device__ __forceinline__ half2 item_half2(int row, int column) const { return ((half2*)data)[(row * width + column) / 2]; }
+    __device__ __forceinline__ half2 item_half2half2(int row, int column) const { return __half2half2(data[row * width + column]); }
+    __device__ __forceinline__ const half* item_ptr(int row, int column) const { return &data[row * width + column]; }
+
+    __device__ __forceinline__ void item4(half (&items)[4], int row, int column) const
+    {
+        half2* ptr = (half2*) item_ptr(row, column);
+        half2 i01 = ptr[0];
+        half2 i23 = ptr[1];
+        items[0] = __low2half(i01);
+        items[1] = __high2half(i01);
+        items[2] = __low2half(i23);
+        items[3] = __high2half(i23);
+    }
+    __device__ __forceinline__ void item4_f(float (&items)[4], int row, int column) const
+    {
+        half2* ptr = (half2*)item_ptr(row, column);
+        half2 i01 = ptr[0];
+        half2 i23 = ptr[1];
+        items[0] = __half2float(__low2half(i01));
+        items[1] = __half2float(__high2half(i01));
+        items[2] = __half2float(__low2half(i23));
+        items[3] = __half2float(__high2half(i23));
+    }
+
+    __device__ __forceinline__ void item4_h2(half2 (&items)[4], int row, int column) const
+    {
+        half2* ptr = (half2*)item_ptr(row, column);
+        half2 i01 = ptr[0];
+        half2 i23 = ptr[1];
+        items[0] = __half2half2(__low2half(i01));
+        items[1] = __half2half2(__high2half(i01));
+        items[2] = __half2half2(__low2half(i23));
+        items[3] = __half2half2(__high2half(i23));
+    }
+};
+
+class MatrixView_half_rw
+{
+public:
+    half* data;
+    const int height;
+    const int width;
+
+    __device__ __forceinline__ MatrixView_half_rw(half* data, const int height, const int width)
+        : data(data), height(height), width(width)
+    { }
+
+    __device__ __forceinline__ half item(int row, int column) const { return data[row * width + column]; }
+    __device__ __forceinline__ half2 item_half2(int row, int column) const { return ((half2*)data)[(row * width + column) / 2]; }
+    __device__ __forceinline__ half* item_ptr(int row, int column) { return &data[row * width + column]; }
+    __device__ __forceinline__ void set(int row, int column, half value) { data[row * width + column] = value; }
+    __device__ __forceinline__ void set_half2(int row, int column, half2 value) { ((half2*)data)[(row * width + column) / 2] = value; }
+
+    __device__ __forceinline__ void set4(int row, int column, half v0, half v1, half v2, half v3)
+    {
+        half2 v01 = __halves2half2(v0, v1);
+        half2 v23 = __halves2half2(v2, v3);
+        half2* ptr = (half2*) item_ptr(row, column);
+        ptr[0] = v01;
+        ptr[1] = v23;
+    }
+};
+
+class MatrixView_q4_row
+{
+public:
+    const uint32_t* data;
+    const int height;
+    const int width;
+
+    __device__ __forceinline__ MatrixView_q4_row(const uint32_t* data, const int height, const int width)
+        : data(data), height(height), width(width)
+    { }
+
+    __device__ __forceinline__ int item(int row, int column) const
+    {
+        int shift = (column & 0x07) * 4;
+        return (data[row * width / 8 + column / 8] >> shift) & 0x0f;
+    }
+
+    __device__ __forceinline__ void item2(int (&items)[2], int row, int column) const
+    {
+        int shift = (column & 0x07) * 4;
+        uint32_t d = data[row * width / 8 + column / 8] >> shift;
+        items[0] = d & 0x0f;
+        items[1] = (d >> 4) & 0x0f;
+    }
+
+    __device__ __forceinline__ void item4(int (&items)[4], int row, int column) const
+    {
+        int shift = (column & 0x07) * 4;
+        uint32_t d = data[row * width / 8 + column / 8] >> shift;
+        items[0] = d & 0x0f;
+        items[1] = (d >> 4) & 0x0f;
+        items[2] = (d >> 8) & 0x0f;
+        items[3] = (d >> 12) & 0x0f;
+    }
+};
+
+class MatrixView_q4_column
+{
+public:
+    const uint32_t* data;
+    const int height;
+    const int width;
+
+    __device__ __forceinline__ MatrixView_q4_column(const uint32_t* data, const int height, const int width)
+        : data(data), height(height), width(width)
+    { }
+
+    __device__ __forceinline__ int item(int row, int column) const
+    {
+        int shift = (row & 0x07) * 4;
+        return (data[row / 8 * width + column] >> shift) & 0x0f;
+    }
+
+    __device__ __forceinline__ uint32_t item_uint32_t(int row, int column) { return data[row / 8 * width + column]; }
+    __device__ __forceinline__ const uint32_t* item_uint32_ptr(int row, int column) { return &data[row / 8 * width + column]; }
+};
+
+}  // namespace gptq
+}  // namespace vllm
+#endif

csrc/quantization/gptq/q_gemm.cu

@@ -0,0 +1,859 @@
+/*
+Adapted from https://github.com/turboderp/exllamav2 and https://github.com/qwopqwop200/GPTQ-for-LLaMa
+*/
+
+#include <cstdint>
+#include <cstdio>
+
+#include <torch/extension.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda_runtime.h>
+#include <cuda_fp16.h>
+
+#include "compat.cuh"
+#include "matrix_view.cuh"
+#include "qdq_4.cuh"
+
+namespace vllm {
+namespace gptq {
+
+#define BLOCK_KN_SIZE 128
+#define BLOCK_M_SIZE_MAX 8
+#define MAX_GROUPS_IN_BLOCK (BLOCK_KN_SIZE / 32)
+#define MAX_Q_GEMM_ROWS 50
+#define MAX_ALT_GEMM_ROWS 8
+#define THREADS_X 32
+#define THREADS_Y 32
+#define DIVIDE(x, size) (((x) + (size) - 1) / (size))
+
+#if defined(USE_ROCM)
+__host__ __forceinline__ hipblasStatus_t __compat_hipblasHgemm(hipblasHandle_t    handle,
+                                                               hipblasOperation_t transA,
+                                                               hipblasOperation_t transB,
+                                                               int                m,
+                                                               int                n,
+                                                               int                k,
+                                                               const half*        alpha,
+                                                               const half*        AP,
+                                                               int                lda,
+                                                               const half*        BP,
+                                                               int                ldb,
+                                                               const half*        beta,
+                                                               half*              CP,
+                                                               int                ldc) {
+    return hipblasHgemm(handle, transA, transB, m, n, k,
+                        reinterpret_cast<const hipblasHalf *>(alpha),
+                        reinterpret_cast<const hipblasHalf *>(AP), lda,
+                        reinterpret_cast<const hipblasHalf *>(BP), ldb,
+                        reinterpret_cast<const hipblasHalf *>(beta),
+                        reinterpret_cast<hipblasHalf *>(CP), ldc);
+}
+#define hipblasHgemm __compat_hipblasHgemm
+
+// Previous version of PyTorch were converting to rocBLAS instead of hipBLAS.
+#define rocblas_operation_none HIPBLAS_OP_N
+#define rocblas_hgemm __compat_hipblasHgemm
+#endif
+
+__forceinline__ __device__ half2 dot22_8(half2(&dq)[4], const half* a_ptr, const half2 g_result)
+{
+    half2 result = {};
+    const half2* a2_ptr = (const half2*)a_ptr;
+    #pragma unroll
+    for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
+    return __hadd2(result, g_result);
+}
+
+__forceinline__ __device__ float dot22_8_f(half2(&dq)[4], const half* a_ptr)
+{
+    half2 result = {};
+    const half2* a2_ptr = (const half2*)a_ptr;
+    #pragma unroll
+    for (int i = 0; i < 4; i++) result = __hfma2(dq[i], *a2_ptr++, result);
+    return __half2float(__low2half(result)) + __half2float(__high2half(result));
+}
+
+typedef void (*fp_gemm_half_q_half_gptq_kernel)
+(
+    const half*,
+    const uint32_t*,
+    const uint32_t*,
+    const half*,
+    half*,
+    const int,
+    const int,
+    const int,
+    const int,
+    const int*
+);
+
+template <bool first_block, int m_count>
+__global__ void gemm_half_q_half_gptq_kernel
+(
+    const half* __restrict__ a,
+    const uint32_t* __restrict__ b_q_weight,
+    const uint32_t* __restrict__ b_gptq_qzeros,
+    const half* __restrict__ b_gptq_scales,
+    half* __restrict__ c,
+    const int size_m,
+    const int size_n,
+    const int size_k,
+    const int groups,
+    const int* __restrict__ b_q_perm
+)
+{
+    MatrixView_half a_(a, size_m, size_k);
+    MatrixView_half_rw c_(c, size_m, size_n);
+    MatrixView_q4_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
+    MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
+
+    int t = threadIdx.x;
+
+    // Block
+    int offset_n = blockIdx.x * BLOCK_KN_SIZE * 4;
+    int offset_m = blockIdx.y * m_count;
+    int offset_k = blockIdx.z * BLOCK_KN_SIZE;
+
+    int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
+    int end_m = min(offset_m + m_count, size_m);
+    int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
+
+    int n = offset_n + t * 4;
+
+    // Preload block_a
+    __shared__ half block_a[m_count][BLOCK_KN_SIZE];
+
+    if (offset_k + t < end_k)
+    {
+        for (int m = 0; m < m_count; ++m)
+        {
+            const half* a_ptr = a_.item_ptr(offset_m + m, 0);
+            half* block_a_ptr = block_a[m];
+
+            half a0;
+            if (b_q_perm) a0 = a_ptr[b_q_perm[offset_k + t]];
+            else a0 = a_ptr[offset_k + t];
+            block_a_ptr[t] = a0;
+        }
+    }
+
+    // Zero output
+    if (n >= size_n) return;
+
+    if (blockIdx.z == 0)
+    {
+        for (int m = 0; m < m_count; m++)
+            *((uint64_t*)c_.item_ptr(offset_m + m, n)) = 0;
+    }
+
+    __syncthreads();
+
+    // Find initial group
+    int groupsize = size_k / groups;
+    int group = offset_k / groupsize;
+    int nextgroup = offset_k + groupsize;
+
+    // a, b offset
+    int qk = offset_k / (32 / 4);
+
+    const uint32_t* b_ptr = b_q_weight + qk * size_n + n;
+    const half* a_ptr = &block_a[0][0];
+    int a_stride = BLOCK_KN_SIZE;
+
+    // Initial group
+    int zeros[4];
+    float scales[4];
+    half2 z1z16[4][2];
+    half2 y1y16[4][2];
+    b_gptq_qzeros_.item4(zeros, group, n);
+    b_gptq_scales_.item4_f(scales, group, n);
+    dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
+    dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
+    dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
+    dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
+
+    // Column result
+    float block_c[m_count][4] = {};
+
+    // Dequantize and multiply
+    int k = offset_k;
+    while (k < end_k)
+    {
+        if (k == nextgroup)
+        {
+            group++;
+            nextgroup += groupsize;
+            b_gptq_qzeros_.item4(zeros, group, n);
+            b_gptq_scales_.item4_f(scales, group, n);
+            dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
+            dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
+            dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
+            dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
+        }
+
+        #pragma unroll
+        for (int j = 0; j < 4; j++)
+        {
+            const int4* b_ptr4 = (int4*) b_ptr;
+            int4 load_int4 = *b_ptr4;
+
+            half2 dq[4][4];
+            dequant_4bit_8_gptq(load_int4.x, dq[0], z1z16[0], y1y16[0], size_n, false);
+            dequant_4bit_8_gptq(load_int4.y, dq[1], z1z16[1], y1y16[1], size_n, false);
+            dequant_4bit_8_gptq(load_int4.z, dq[2], z1z16[2], y1y16[2], size_n, false);
+            dequant_4bit_8_gptq(load_int4.w, dq[3], z1z16[3], y1y16[3], size_n, false);
+
+            #pragma unroll
+            for (int m = 0; m < m_count; m++)
+            {
+                block_c[m][0] = fma(dot22_8_f(dq[0], a_ptr + m * a_stride), scales[0], block_c[m][0]);
+                block_c[m][1] = fma(dot22_8_f(dq[1], a_ptr + m * a_stride), scales[1], block_c[m][1]);
+                block_c[m][2] = fma(dot22_8_f(dq[2], a_ptr + m * a_stride), scales[2], block_c[m][2]);
+                block_c[m][3] = fma(dot22_8_f(dq[3], a_ptr + m * a_stride), scales[3], block_c[m][3]);
+            }
+
+            b_ptr += size_n;
+            a_ptr += 8;
+        }
+
+        k += 32;
+    }
+
+    for (int m = 0; m < m_count; m++)
+    {
+        half2 *out = (half2*) c_.item_ptr(offset_m + m, n);
+        half2 result01 = __halves2half2(__float2half_rn(block_c[m][0]), __float2half_rn(block_c[m][1]));
+        half2 result23 = __halves2half2(__float2half_rn(block_c[m][2]), __float2half_rn(block_c[m][3]));
+        atomicAdd(out    , result01);
+        atomicAdd(out + 1, result23);
+    }
+}
+
+
+fp_gemm_half_q_half_gptq_kernel pick_gemm_half_q_half_gptq_kernel(bool first_block, const int m_count)
+{
+    #if BLOCK_M_SIZE_MAX >= 1
+    if (m_count == 1) return gemm_half_q_half_gptq_kernel<true, 1>;
+    #endif
+    #if BLOCK_M_SIZE_MAX >= 2
+    if (m_count == 2) return gemm_half_q_half_gptq_kernel<true, 2>;
+    #endif
+    #if BLOCK_M_SIZE_MAX >= 3
+    if (m_count == 3) return gemm_half_q_half_gptq_kernel<true, 3>;
+    #endif
+    #if BLOCK_M_SIZE_MAX >= 4
+    if (m_count == 4) return gemm_half_q_half_gptq_kernel<true, 4>;
+    #endif
+    #if BLOCK_M_SIZE_MAX >= 5
+    if (m_count == 5) return gemm_half_q_half_gptq_kernel<true, 5>;
+    #endif
+    #if BLOCK_M_SIZE_MAX >= 6
+    if (m_count == 6) return gemm_half_q_half_gptq_kernel<true, 6>;
+    #endif
+    #if BLOCK_M_SIZE_MAX >= 7
+    if (m_count == 7) return gemm_half_q_half_gptq_kernel<true, 7>;
+    #endif
+    #if BLOCK_M_SIZE_MAX >= 8
+    if (m_count == 8) return gemm_half_q_half_gptq_kernel<true, 8>;
+    #endif
+    return NULL;
+}
+
+
+void gemm_half_q_half_cuda_part
+(
+    const half* a,
+    const uint32_t* b_q_weight,
+    const uint32_t* b_gptq_qzeros,
+    const half* b_gptq_scales,
+    const int* b_q_perm,
+    half* c,
+    int size_m,
+    int size_n,
+    int size_k,
+    int m_count,
+    int groups
+)
+{
+    dim3 blockDim, gridDim;
+    blockDim.x = BLOCK_KN_SIZE;
+    blockDim.y = 1;
+    blockDim.z = 1;
+    gridDim.x = DIVIDE(size_n, BLOCK_KN_SIZE * 4);
+    gridDim.y = DIVIDE(size_m, m_count);
+    gridDim.z = DIVIDE(size_k, BLOCK_KN_SIZE);
+
+    fp_gemm_half_q_half_gptq_kernel kernel = pick_gemm_half_q_half_gptq_kernel(true, m_count);
+
+    kernel<<<gridDim, blockDim>>>
+    (
+        a,
+        b_q_weight,
+        b_gptq_qzeros,
+        b_gptq_scales,
+        c,
+        size_m,
+        size_n,
+        size_k,
+        groups,
+        b_q_perm
+    );
+}
+
+
+__global__ void reconstruct_exllama_kernel
+(
+    const uint32_t* __restrict__ b_q_weight,
+    const int* __restrict__ b_q_perm,
+    const uint32_t* __restrict__ b_gptq_qzeros,
+    const half* __restrict__ b_gptq_scales,
+    const int size_k,
+    const int size_n,
+    const int groups,
+    half* __restrict__ b
+)
+{
+    MatrixView_half_rw b_(b, size_k, size_n);
+    MatrixView_q4_row b_gptq_qzeros_(b_gptq_qzeros, groups, size_n);
+    MatrixView_half b_gptq_scales_(b_gptq_scales, groups, size_n);
+
+    int offset_k = BLOCK_KN_SIZE * blockIdx.y;
+    int offset_n = BLOCK_KN_SIZE * blockIdx.x * 4;
+
+    int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
+
+    // Preload remapping table
+    __shared__ int perm[BLOCK_KN_SIZE];
+    int t = threadIdx.x;
+
+    if (b_q_perm)
+    {
+        if (offset_k + t < size_k)
+            perm[t] = b_q_perm[offset_k + t];
+    }
+
+    // Column
+    int n = offset_n + t * 4;
+    if (n >= size_n) return;
+
+    // Find initial group
+    int groupsize = size_k / groups;
+    int group = offset_k / groupsize;
+    int nextgroup = offset_k + groupsize;
+
+    // b offset
+    int qk = offset_k / (32 / 4);
+
+    const uint32_t* b_ptr = b_q_weight + qk * size_n + n;
+
+    // Initial zeros/scale
+    int zeros[4];
+    half2 scales[4];
+    half2 z1z16[4][2];
+    half2 y1y16[4][2];
+    b_gptq_qzeros_.item4(zeros, group, n);
+    b_gptq_scales_.item4_h2(scales, group, n);
+    dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
+    dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
+    dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
+    dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
+
+    __syncthreads();
+
+    int k = offset_k;
+    int lk = 0;
+
+    while (k < end_k)
+    {
+        if (k == nextgroup)
+        {
+            group++;
+            nextgroup += groupsize;
+            b_gptq_qzeros_.item4(zeros, group, n);
+            b_gptq_scales_.item4_h2(scales, group, n);
+            dequant_4bit_8_prep_zero(zeros[0] + 1, z1z16[0], y1y16[0]);
+            dequant_4bit_8_prep_zero(zeros[1] + 1, z1z16[1], y1y16[1]);
+            dequant_4bit_8_prep_zero(zeros[2] + 1, z1z16[2], y1y16[2]);
+            dequant_4bit_8_prep_zero(zeros[3] + 1, z1z16[3], y1y16[3]);
+        }
+
+        for (int p = 0; p < 4; p++)
+        {
+            half2 dq[4][4];
+            const int4* b_ptr4 = (int4*) b_ptr;
+            int4 load_int4 = *b_ptr4;
+
+            dequant_4bit_8_gptq(load_int4.x, dq[0], z1z16[0], y1y16[0], size_n, false);
+            dequant_4bit_8_gptq(load_int4.y, dq[1], z1z16[1], y1y16[1], size_n, false);
+            dequant_4bit_8_gptq(load_int4.z, dq[2], z1z16[2], y1y16[2], size_n, false);
+            dequant_4bit_8_gptq(load_int4.w, dq[3], z1z16[3], y1y16[3], size_n, false);
+
+            b_ptr += size_n;
+            //half* dqh = (half*)dq;
+            if (b_q_perm)
+            {
+                for (int j = 0; j < 4; j++)
+                {
+                    for (int v = 0; v < 4; v++) dq[v][j] = __hmul2(scales[v], dq[v][j]);
+                    b_.set4(perm[lk++], n, __low2half(dq[0][j]), __low2half(dq[1][j]), __low2half(dq[2][j]), __low2half(dq[3][j]));
+                    b_.set4(perm[lk++], n, __high2half(dq[0][j]), __high2half(dq[1][j]), __high2half(dq[2][j]), __high2half(dq[3][j]));
+                }
+            }
+            else
+            {
+                for (int j = 0; j < 4; j++)
+                {
+                    for (int v = 0; v < 4; v++) dq[v][j] = __hmul2(scales[v], dq[v][j]);
+                    b_.set4(offset_k + lk++, n, __low2half(dq[0][j]), __low2half(dq[1][j]), __low2half(dq[2][j]), __low2half(dq[3][j]));
+                    b_.set4(offset_k + lk++, n, __high2half(dq[0][j]), __high2half(dq[1][j]), __high2half(dq[2][j]), __high2half(dq[3][j]));
+                }
+            }
+        }
+        k += 32;
+    }
+}
+
+
+void reconstruct_exllama
+(
+    const uint32_t* b_q_weight,
+    const uint32_t* b_gptq_qzeros,
+    const half* b_gptq_scales,
+    const int* b_q_perm,
+    half* out,
+    int height,
+    int width,
+    int groups
+)
+{
+    dim3 blockDim, gridDim;
+    blockDim.x = BLOCK_KN_SIZE;
+    blockDim.y = 1;
+    gridDim.y = DIVIDE(height, BLOCK_KN_SIZE);
+    gridDim.x = DIVIDE(width, BLOCK_KN_SIZE);
+
+    reconstruct_exllama_kernel<<<gridDim, blockDim>>>
+    (
+        b_q_weight,
+        b_q_perm,
+        b_gptq_qzeros,
+        b_gptq_scales,
+        height,
+        width,
+        groups,
+        out
+    );
+}
+
+
+__global__ void gemm_half_q_half_alt_kernel(
+    const half2* __restrict__ vec,
+    const uint32_t* __restrict__ mat,
+    half* __restrict__ mul,
+    const half* __restrict__ scales,
+    const uint32_t* __restrict__ zeros,
+    const int* __restrict__ g_idx,
+    int batch,
+    int height,
+    int width
+)
+{
+    int zero_width = width / 8;
+    int vec_height = height * 4;
+    const int blockwidth2 = BLOCK_KN_SIZE / 2;
+    int b = blockIdx.y * BLOCK_M_SIZE_MAX;
+    int b_end = min(BLOCK_M_SIZE_MAX, batch - b);
+    int h = BLOCK_KN_SIZE * blockIdx.z / 8;
+    int h_end = min(BLOCK_KN_SIZE / 8, height - h) * 4;
+    int w = BLOCK_KN_SIZE * blockIdx.x + threadIdx.x;
+
+    __shared__ half2 blockvec[BLOCK_M_SIZE_MAX][blockwidth2];
+    if (threadIdx.x < h_end) {
+        for (int m = 0; m < b_end; ++m) {
+          blockvec[m][threadIdx.x] =
+              vec[(m + b) * vec_height + blockIdx.z * BLOCK_KN_SIZE / 2 +
+                  threadIdx.x];
+        }
+    }
+
+    __shared__ half2 deq2[256][8];
+    int val = threadIdx.x / 8;
+    int off = threadIdx.x % 8;
+    for (; val < 256; val += BLOCK_KN_SIZE / 8) {
+        deq2[val][off] = __halves2half2(
+            __int2half_rn(val & 0xF), __int2half_rn(val >> 4)
+        );
+    }
+
+    if (blockIdx.z == 0)
+    {
+        for (int m = 0; m < b_end; m++)
+            mul[(b + m) * width + w] = __int2half_rn(0);
+    }
+    __syncthreads();
+
+    int i = width * h + w;
+    int g_h = h * 8;
+    int k = 0;
+    int z_w = w / 8;
+    int z_mod = (w % 8) * 4;
+    half2 res2;
+    half res[BLOCK_M_SIZE_MAX] = {};
+
+    unsigned int tmp;
+    while (k < h_end) {
+        tmp = mat[i];
+        half2 scales_tmp[4];
+        half2 zeros_tmp[4];
+        for (int tmp_k = 0; tmp_k < 4; tmp_k++) {
+            int g = g_idx[g_h + (k + tmp_k) * 2];
+            int g2 = g_idx[g_h + (k + tmp_k) * 2 + 1];
+            half scale_f = scales[g * width + w];
+            half scale_f2 = scales[g2 * width + w];
+            half2 scale = __halves2half2(scale_f, scale_f2);
+            half2 zero = __halves2half2(
+                __hmul(scale_f, __int2half_rn(-((zeros[g * zero_width + z_w] >> z_mod) & 0xF) - 1)),
+                __hmul(scale_f2, __int2half_rn(-((zeros[g2 * zero_width + z_w] >> z_mod) & 0xF) - 1))
+            );
+            scales_tmp[tmp_k] = scale;
+            zeros_tmp[tmp_k] = zero;
+        }
+        for (int m = 0; m < b_end; m++) {
+            res2 = {};
+            res2 = __hfma2(__hfma2(deq2[(tmp >>  0) & 0xff][off], scales_tmp[0], zeros_tmp[0]), blockvec[m][k + 0], res2);
+            res2 = __hfma2(__hfma2(deq2[(tmp >>  8) & 0xff][off], scales_tmp[1], zeros_tmp[1]), blockvec[m][k + 1], res2);
+            res2 = __hfma2(__hfma2(deq2[(tmp >> 16) & 0xff][off], scales_tmp[2], zeros_tmp[2]), blockvec[m][k + 2], res2);
+            res2 = __hfma2(__hfma2(deq2[(tmp >> 24) & 0xff][off], scales_tmp[3], zeros_tmp[3]), blockvec[m][k + 3], res2);
+            res[m] = __hadd(res[m], __hadd(res2.x, res2.y));
+        }
+        i += width;
+        k += 4;
+    }
+    for (int m = 0; m < b_end; m++) {
+        atomicAdd(&mul[(b + m) * width + w], res[m]);
+    }
+}
+
+
+void gemm_half_q_half_alt
+(
+    const half* a,
+    const uint32_t* b_q_weight,
+    const uint32_t* b_gptq_qzeros,
+    const half* b_gptq_scales,
+    const int* b_g_idx,
+    half* c,
+    int size_m,
+    int size_n,
+    int size_k
+)
+{
+    dim3 blockDim, gridDim;
+    blockDim.x = BLOCK_KN_SIZE;
+    blockDim.y = 1;
+    blockDim.z = 1;
+    gridDim.x = DIVIDE(size_n, BLOCK_KN_SIZE);
+    gridDim.y = DIVIDE(size_m, BLOCK_M_SIZE_MAX);
+    gridDim.z = DIVIDE(size_k, BLOCK_KN_SIZE);
+
+    gemm_half_q_half_alt_kernel<<<gridDim, blockDim>>>
+    (
+        (const half2*) a,
+        b_q_weight,
+        c,
+        b_gptq_scales,
+        b_gptq_qzeros,
+        b_g_idx,
+        size_m,
+        size_k / 8,
+        size_n
+    );
+}
+
+
+__global__ void reconstruct_gptq_kernel
+(
+    const uint32_t* __restrict__ w,
+    const half* __restrict__ w_scales,
+    const uint32_t* __restrict__ w_zeros,
+    const int* __restrict__ g_idx,
+    const int height,
+    const int width,
+    const int group,
+    half* __restrict__ out
+)
+{
+    // Start of block
+
+    int column = BLOCK_KN_SIZE * blockIdx.x + threadIdx.x;
+    int row = blockIdx.y * 8;
+    if (column >= width) return;
+
+    // Views
+
+    MatrixView_q4_column w_(w, height, width);
+    MatrixView_half_rw out_(out, height, width);
+    MatrixView_half w_scales_(w_scales, group, width);
+    MatrixView_q4_row w_zeros_(w_zeros, group, width);
+
+    uint32_t w_read = w_.item_uint32_t(row, column);
+    half* out_ptr = out_.item_ptr(row, column);
+
+    #pragma unroll
+    for (int s = 0; s < 32; s += 4)
+    {
+        int group = g_idx[row + s / 4];
+        half w_scale = w_scales_.item(group, column);
+        uint32_t w_zero = w_zeros_.item(group, column) + 1;
+        half w_item = __hmul(__int2half_rn((int)((w_read >> s) & 0x0f) - w_zero), w_scale);
+        *out_ptr = w_item; out_ptr += out_.width;
+    }
+}
+
+
+void reconstruct_gptq
+(
+    const uint32_t* b_q_weight,
+    const uint32_t* b_gptq_qzeros,
+    const half* b_gptq_scales,
+    const int* b_g_idx,
+    half* out,
+    int height,
+    int width,
+    int groups
+)
+{
+    dim3 blockDim, gridDim;
+    blockDim.x = BLOCK_KN_SIZE;
+    blockDim.y = 1;
+    gridDim.y = DIVIDE(height, 8);
+    gridDim.x = DIVIDE(width, BLOCK_KN_SIZE);
+    reconstruct_gptq_kernel<<<gridDim, blockDim>>>
+    (
+        b_q_weight,
+        b_gptq_scales,
+        b_gptq_qzeros,
+        b_g_idx,
+        height,
+        width,
+        groups,
+        out
+    );
+}
+
+
+void gemm_half_q_half_cuda
+(
+    cublasHandle_t cublas_handle,
+    const half* a,
+    const uint32_t* b_q_weight,
+    const uint32_t* b_gptq_qzeros,
+    const half* b_gptq_scales,
+    const int* b_g_idx,
+    half* c,
+    half* temp_dq,
+    int size_m,
+    int size_n,
+    int size_k,
+    int groups,
+    bool use_exllama
+)
+{
+    if ((use_exllama && size_m > MAX_Q_GEMM_ROWS) || (!use_exllama && size_m > MAX_ALT_GEMM_ROWS)) {
+        // Reconstruct FP16 matrix, then cuBLAS
+        if (use_exllama) {
+            reconstruct_exllama(b_q_weight, b_gptq_qzeros, b_gptq_scales, b_g_idx, temp_dq,
+                                size_k, size_n, groups);
+        }
+        else
+        {
+            reconstruct_gptq(b_q_weight, b_gptq_qzeros, b_gptq_scales, b_g_idx,
+                             temp_dq, size_k, size_n, groups);
+        }
+
+        const half alpha = __float2half(1.0f);
+        const half beta = __float2half(0.0f);
+        cublasHgemm(cublas_handle,
+                    CUBLAS_OP_N,
+                    CUBLAS_OP_N,
+                    size_n, size_m, size_k,
+                    &alpha, temp_dq, size_n,
+                            a,       size_k,
+                    &beta,  c,       size_n);
+    }
+    else if (use_exllama)
+    {
+        // Quantized matmul
+        int max_chunks = size_m / BLOCK_M_SIZE_MAX;
+        int last_chunk = max_chunks * BLOCK_M_SIZE_MAX;
+        int last_chunk_size = size_m - last_chunk;
+
+        if (max_chunks)
+        {
+            gemm_half_q_half_cuda_part(a, b_q_weight, b_gptq_qzeros, b_gptq_scales, b_g_idx,
+                                        c, last_chunk, size_n, size_k, BLOCK_M_SIZE_MAX,
+                                        groups);
+        }
+
+        if (last_chunk_size)
+        {
+            gemm_half_q_half_cuda_part(a + last_chunk * size_k, b_q_weight, b_gptq_qzeros,
+                                        b_gptq_scales, b_g_idx, c + last_chunk * size_n,
+                                        last_chunk_size, size_n, size_k, last_chunk_size,
+                                        groups);
+        }
+    }
+    else
+    {
+        gemm_half_q_half_alt(a, b_q_weight, b_gptq_qzeros, b_gptq_scales, b_g_idx,
+                             c, size_m, size_n, size_k);
+    }
+}
+
+
+__global__ void shuffle_kernel
+(
+    uint32_t* __restrict__ b_q_weight,
+    const int size_k,
+    const int size_n
+)
+{
+    int n = blockIdx.x * THREADS_X + threadIdx.x;
+    if (n >= size_n) return;
+    int k = 0;
+    uint32_t* b_ptr = b_q_weight + n;
+    while (k < size_k) { shuffle_4bit_8 (b_ptr, size_n); b_ptr += 1 * size_n; k +=  8; }
+}
+
+
+__global__ void make_sequential_kernel
+(
+    const uint32_t* __restrict__ w,
+    uint32_t* __restrict__ w_new,
+    const int* __restrict__ q_perm,
+    const int w_height,
+    const int w_width
+)
+{
+    const uint64_t* w2 = (uint64_t*) w;
+    uint64_t* w_new2 = (uint64_t*) w_new;
+    int w2_stride = w_width >> 1;
+    int w2_column = THREADS_X * blockIdx.x + threadIdx.x;
+    if (w2_column >= w2_stride) return;
+    int w_new2_row = blockIdx.y;
+    int q_perm_idx = w_new2_row << 3;
+    uint64_t dst = 0;
+
+    #pragma unroll
+    for (int i = 0; i < 8; i++)
+    {
+        int source_row = q_perm[q_perm_idx++];
+
+        int w2_row = source_row >> 3;
+        int w2_subrow = source_row & 0x07;
+        int w2_row_shift = w2_subrow << 2;
+        int wnew2_row_shift = i << 2;
+
+        uint64_t src = w2[w2_row * w2_stride + w2_column];
+        src >>= w2_row_shift;
+        src &= 0x0000000f0000000f;
+        src <<= wnew2_row_shift;
+        dst |= src;
+    }
+    w_new2[w_new2_row * w2_stride + w2_column] = dst;
+}
+
+
+void shuffle_exllama_weight
+(
+    uint32_t* q_weight,
+    int* q_perm,
+    int height,
+    int width
+)
+{
+    if (q_perm)
+    {
+        uint32_t* new_qweight = NULL;
+        cudaMalloc(&new_qweight, height / 8 * width * sizeof(uint32_t));
+
+        dim3 blockDim, gridDim;
+        blockDim.x = THREADS_X;
+        blockDim.y = 1;
+        gridDim.x = DIVIDE(width, THREADS_X);
+        gridDim.y = height / 8;
+
+        make_sequential_kernel<<<gridDim, blockDim>>>
+        (
+            q_weight,
+            new_qweight,
+            q_perm,
+            height / 8,
+            width
+        );
+        // Replace qweights
+        cudaMemcpyAsync(q_weight, new_qweight, height / 8 * width * sizeof(uint32_t), cudaMemcpyDeviceToDevice);
+        // Cleanup
+        cudaDeviceSynchronize();
+        cudaFree(new_qweight);
+    }
+    dim3 blockDim, gridDim;
+    blockDim.x = THREADS_X;
+    blockDim.y = 1;
+    gridDim.x = DIVIDE(width, THREADS_X);
+    gridDim.y = 1;
+    shuffle_kernel<<<gridDim, blockDim>>>(q_weight, height, width);
+}
+
+}  // namespace gptq
+}  // namespace vllm
+
+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,
+    bool use_exllama
+)
+{
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
+    auto options = torch::TensorOptions().dtype(a.dtype()).device(a.device());
+    at::Tensor c = torch::empty({a.size(0), b_q_weight.size(1)}, options);
+    at::Tensor temp_dq = torch::empty({b_q_weight.size(0) * 8, b_q_weight.size(1)}, options);
+
+    vllm::gptq::gemm_half_q_half_cuda
+    (
+        at::cuda::getCurrentCUDABlasHandle(),
+        (const half*) a.data_ptr(),
+        (const uint32_t*) b_q_weight.data_ptr(),
+        (const uint32_t*)b_gptq_qzeros.data_ptr(),
+        (const half*) b_gptq_scales.data_ptr(),
+        b_g_idx.device().is_meta() ? NULL : (const int*) b_g_idx.data_ptr(),
+        (half*) c.data_ptr(),
+        (half*) temp_dq.data_ptr(),
+        c.size(0),  // m
+        c.size(1),  // n
+        a.size(1),  // k
+        b_gptq_qzeros.size(0),  // group number
+        use_exllama
+    );
+    return c;
+}
+
+void gptq_shuffle
+(
+    torch::Tensor q_weight,
+    torch::Tensor q_perm
+)
+{
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(q_weight));
+    vllm::gptq::shuffle_exllama_weight(
+        (uint32_t*) q_weight.data_ptr(),
+        q_perm.device().is_meta() ? NULL : (int*) q_perm.data_ptr(),
+        q_weight.size(0) * 8,
+        q_weight.size(1)
+    );
+}

csrc/quantization/gptq/qdq_4.cuh

@@ -0,0 +1,235 @@
+/*
+Copied from https://github.com/turboderp/exllamav2
+*/
+
+#ifndef _qdq_4_cuh
+#define _qdq_4_cuh
+
+#include "qdq_util.cuh"
+
+namespace vllm {
+namespace gptq {
+// Permutation:
+//
+// 77775555 33331111  66664444 22220000
+
+__forceinline__ __device__ void shuffle_4bit_8
+(
+    uint32_t* q,
+    int stride
+)
+{
+    uint32_t qa = q[0];
+    uint32_t qb = 0;
+
+    #pragma unroll
+    for (int i = 0; i < 4; i++)
+    {
+        uint32_t qa0 = qa & 0x0f;
+        uint32_t qa1 = (qa & 0xf0) >> 4;
+        qa >>= 8;
+        qb |= (qa1 << (i * 4 + 16));
+        qb |= (qa0 << (i * 4));
+    }
+    q[0] = qb;
+}
+
+__forceinline__ __device__ void dequant_4bit_8
+(
+    const uint32_t q_0,
+    half2 (&dq)[4],
+    int stride
+)
+{
+    const uint32_t c0 = 0x64006400;
+    const half y16_ = __float2half_rn(1.0f / 16.0f);
+    const half2 y16 = __halves2half2(y16_, y16_);
+    const half z1_  = __float2half_rn(-1024.0f         - 8.0f);
+    const half z16_ = __float2half_rn(-1024.0f / 16.0f - 8.0f);
+    const half2 z1  = __halves2half2(z1_,  z1_);
+    const half2 z16 = __halves2half2(z16_, z16_);
+
+    uint32_t qa = q_0;
+    half2_uint32 q0((qa & 0x000f000f) | c0); // half2(q[ 0], q[ 1])      + 1024
+    half2_uint32 q1((qa & 0x00f000f0) | c0); // half2(q[ 2], q[ 3]) * 16 + 1024
+    qa >>= 8;
+    half2_uint32 q2((qa & 0x000f000f) | c0); // half2(q[ 4], q[ 5])      + 1024
+    half2_uint32 q3((qa & 0x00f000f0) | c0); // half2(q[ 6], q[ 7]) * 16 + 1024
+
+    dq[0] = __hadd2(q0.as_half2, z1);
+    dq[1] = __hfma2(q1.as_half2, y16, z16);
+    dq[2] = __hadd2(q2.as_half2, z1);
+    dq[3] = __hfma2(q3.as_half2, y16, z16);
+}
+
+__forceinline__ __device__ void dequant_4bit_8_prep_zero_scale
+(
+    const uint32_t zero,
+    const half scale,
+    half2 (&z1z16)[2],
+    half2 (&y1y16)[2]
+)
+{
+    half_uint16 z1(0xe400 | zero); // half(-1024.0f - zero);
+    half z16 = __hsub(__int2half_rn(-64), __int2half_rn(zero));
+
+    half2 scale2 = __half2half2(scale);
+
+    z1z16[0] = __hmul2(scale2, __half2half2(z1.as_half));
+    z1z16[1] = __hmul2(scale2, __half2half2(z16));
+
+    const half y1 = __float2half_rn(1.0f);
+    const half y16 = __float2half_rn(1.0f / 16.0f);
+
+    y1y16[0] = __hmul2(scale2, __half2half2(y1));
+    y1y16[1] = __hmul2(scale2, __half2half2(y16));
+}
+
+__forceinline__ __device__ void dequant_4bit_8_prep_zero
+(
+    const uint32_t zero,
+    half2(&z1z16)[2],
+    half2(&y1y16)[2]
+)
+{
+    half_uint16 z1(0xe400 | zero); // half(-1024.0f - zero);
+    half z16 = __hsub(__int2half_rn(-64), __int2half_rn(zero));
+
+    z1z16[0] = __half2half2(z1.as_half);
+    z1z16[1] = __half2half2(z16);
+
+    const half y1 = __float2half_rn(1.0f);
+    const half y16 = __float2half_rn(1.0f / 16.0f);
+
+    y1y16[0] = __half2half2(y1);
+    y1y16[1] = __half2half2(y16);
+}
+
+
+__forceinline__ __device__ void dequant_4bit_8_gptq
+(
+    const uint32_t q_0,
+    half2 (&dq)[4],
+    half2 (&z1z16)[2],
+    half2 (&y1y16)[2],
+    int stride,
+    bool scaled
+)
+{
+    const uint32_t c0 = 0x64006400;
+
+    uint32_t qa = q_0;
+    half2_uint32 q0((qa & 0x000f000f) | c0); // half2( q[0]      + 1024, q[1]      + 1024 )
+    half2_uint32 q1((qa & 0x00f000f0) | c0); // half2( q[2] * 16 + 1024, q[3] * 16 + 1024 )
+    qa >>= 8;
+    half2_uint32 q2((qa & 0x000f000f) | c0); // half2( q[4]      + 1024, q[5]      + 1024 )
+    half2_uint32 q3((qa & 0x00f000f0) | c0); // half2( q[6] * 16 + 1024, q[7] * 16 + 1024 )
+
+    if (scaled)
+    {
+        dq[0] = __hfma2(q0.as_half2, y1y16[0], z1z16[0]);  // half2( q[0] * s - z * s, q[1] * s - z * s)
+        dq[1] = __hfma2(q1.as_half2, y1y16[1], z1z16[1]);  // half2( q[2] * s - z * s, q[3] * s - z * s)
+        dq[2] = __hfma2(q2.as_half2, y1y16[0], z1z16[0]);
+        dq[3] = __hfma2(q3.as_half2, y1y16[1], z1z16[1]);
+    }
+    else
+    {
+        dq[0] = __hadd2(q0.as_half2,           z1z16[0]);  // half2( q[0] - z, q[1] - z )
+        dq[1] = __hfma2(q1.as_half2, y1y16[1], z1z16[1]);  // half2( q[2] - z, q[3] - z )
+        dq[2] = __hadd2(q2.as_half2,           z1z16[0]);  // half2( q[4] - z, q[5] - z )
+        dq[3] = __hfma2(q3.as_half2, y1y16[1], z1z16[1]);  // half2( q[6] - z, q[7] - z )
+    }
+}
+}  // namespace gptq
+}  // namespace vllm
+
+#else
+
+namespace vllm {
+namespace gptq {
+__forceinline__ __device__ void shuffle_4bit_8
+(
+    uint32_t* q,
+    int stride
+)
+{
+}
+
+__forceinline__ __device__ void dequant_4bit_8
+(
+    const uint32_t q_0,
+    half2 (&dq)[4],
+    int stride
+)
+{
+    half dqh[8];
+    for (int i = 0; i < 8; i++) dqh[i] = dq_ns(exb(q_0, i * 4, 0x0f), 8);
+
+    for (int i = 0; i < 4; i++) dq[i] = __halves2half2(dqh[i * 2], dqh[i * 2 + 1]);
+}
+
+__forceinline__ __device__ void dequant_4bit_8_prep_zero_scale
+(
+    const uint32_t zero,
+    const half scale,
+    half2 (&z1)[2],
+    half2 (&y1)[2]
+)
+{
+    half z = __int2half_rn(-((int)zero));
+    z = __hmul(z, scale);
+    z1[0] = __half2half2(z);
+    y1[0] = __half2half2(scale);
+}
+
+__forceinline__ __device__ void dequant_4bit_8_prep_zero
+(
+    const uint32_t zero,
+    half2(&z1)[2],
+    half2(&y1)[2]
+)
+{
+    half z = __int2half_rn(-((int)zero));
+    z1[0] = __half2half2(z);
+}
+
+__forceinline__ __device__ void dequant_4bit_8_gptq
+(
+    const uint32_t q_0,
+    half2 (&dq)[4],
+    half2 (&z1)[2],
+    half2 (&y1)[2],
+    int stride,
+    bool scaled
+)
+{
+    half2 dqh2[8];
+
+    uint32_t qa = q_0;
+    for (int i = 0; i < 4; i++)
+    {
+        half d0 = __int2half_rn(qa & 0x0f); qa >>= 4;
+        half d1 = __int2half_rn(qa & 0x0f); qa >>= 4;
+        dqh2[i] = __halves2half2(d0, d1);
+    }
+
+    if (scaled)
+    {
+        dq[0] = __hfma2(dqh2[0], y1[0], z1[0]);
+        dq[1] = __hfma2(dqh2[1], y1[0], z1[0]);
+        dq[2] = __hfma2(dqh2[2], y1[0], z1[0]);
+        dq[3] = __hfma2(dqh2[3], y1[0], z1[0]);
+    }
+    else
+    {
+        dq[0] = __hadd2(dqh2[0], z1[0]);
+        dq[1] = __hadd2(dqh2[1], z1[0]);
+        dq[2] = __hadd2(dqh2[2], z1[0]);
+        dq[3] = __hadd2(dqh2[3], z1[0]);
+    }
+}
+
+}  // namespace gptq
+}  // namespace vllm
+
+#endif

csrc/quantization/gptq/qdq_util.cuh

@@ -0,0 +1,60 @@
+/*
+Copied from https://github.com/turboderp/exllamav2
+*/
+
+#ifndef _qdq_util_cuh
+#define _qdq_util_cuh
+
+namespace vllm {
+namespace gptq {
+
+union half2_uint32
+{
+    uint32_t as_uint32;
+    half2 as_half2;
+    __device__ half2_uint32(uint32_t val) : as_uint32(val) {}
+    __device__ half2_uint32(half2 val) : as_half2(val) {}
+};
+
+union half_uint16
+{
+    uint16_t as_uint16;
+    half as_half;
+    __device__ half_uint16(uint16_t val) : as_uint16(val) {}
+    __device__ half_uint16(half val) : as_half(val) {}
+};
+
+// Max_scale premultiplied by 1/256
+
+__forceinline__ __device__ half dq_scale(const int qs, const half max_scale)
+{
+    int qs_i = qs + 1;
+    half qs_h = __int2half_rn(qs_i * qs_i);
+    qs_h = __hmul(qs_h, max_scale);
+    return qs_h;
+}
+
+__forceinline__ __device__ half dq(const int q, const int qzero, const half scale)
+{
+    return __hmul(__int2half_rn(q - qzero), scale);
+}
+
+__forceinline__ __device__ half dq_ns(const int q, const int qzero)
+{
+    //return __hsub(__int2half_rn(q), __int2half_rn(qzero));
+    return __int2half_rn(q - qzero);
+}
+
+__forceinline__ __device__ int exb(const uint32_t q, const int shift, const int mask)
+{
+    return (int)((q >> shift) & mask);
+}
+
+__forceinline__ __device__ int exb(const uint32_t q1, const uint32_t q0, const int shift, const int mask)
+{
+    return (int)(__funnelshift_rc(q0, q1, shift) & mask);
+}
+
+}  // namespace gptq
+}  // namespace vllm
+#endif

setup.py

@@ -219,6 +219,7 @@ vllm_extension_sources = [
     "csrc/activation_kernels.cu",
     "csrc/layernorm_kernels.cu",
     "csrc/quantization/squeezellm/quant_cuda_kernel.cu",
+    "csrc/quantization/gptq/q_gemm.cu",
     "csrc/cuda_utils_kernels.cu",
     "csrc/pybind.cpp",
 ]

vllm/config.py

@@ -142,7 +142,7 @@ class ModelConfig:
         self.tokenizer_mode = tokenizer_mode
 
     def _verify_quantization(self) -> None:
-        supported_quantization = ["awq", "squeezellm"]
+        supported_quantization = ["awq", "gptq", "squeezellm"]
         rocm_not_supported_quantization = ["awq"]
         if self.quantization is not None:
             self.quantization = self.quantization.lower()

vllm/engine/arg_utils.py

@@ -179,7 +179,7 @@ class EngineArgs:
         parser.add_argument('--quantization',
                             '-q',
                             type=str,
-                            choices=['awq', 'squeezellm', None],
+                            choices=['awq', 'gptq', 'squeezellm', None],
                             default=None,
                             help='Method used to quantize the weights')
         return parser

vllm/entrypoints/llm.py

@@ -38,8 +38,9 @@ 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". If None, we assume the model weights are not
+            we support "awq", "gptq" and "squeezellm". If None, we assume the
-            quantized and use `dtype` to determine the data type of the weights.
+            model weights are not quantized and use `dtype` to determine the
+            data type of the weights.
         revision: The specific model version to use. It can be a branch name,
             a tag name, or a commit id.
         tokenizer_revision: The specific tokenizer version to use. It can be a

vllm/model_executor/layers/linear.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
+from typing import Any, Dict, List, Optional
 
 import torch
 import torch.nn.functional as F
@@ -21,8 +21,10 @@ class LinearMethodBase(ABC):
     """Base class for different (maybe quantized) linear methods."""
 
     @abstractmethod
-    def create_weights(self, input_size: int, output_size: int,
+    def create_weights(self, input_size_per_partition: int,
-                       params_dtype: torch.dtype) -> Dict[str, torch.Tensor]:
+                       output_size_per_partition: int, input_size: int,
+                       output_size: int,
+                       params_dtype: torch.dtype) -> Dict[str, Any]:
         """Create weights for a linear layer."""
         raise NotImplementedError
 
@@ -46,10 +48,12 @@ class UnquantizedLinearMethod(LinearMethodBase):
     def __init__(self, separate_bias_add: bool = False):
         self.separate_bias_add = separate_bias_add
 
-    def create_weights(self, input_size: int, output_size: int,
+    def create_weights(self, input_size_per_partition: int,
-                       params_dtype: torch.dtype) -> Dict[str, torch.Tensor]:
+                       output_size_per_partition: int, input_size: int,
-        weight = Parameter(torch.empty(output_size,
+                       output_size: int,
-                                       input_size,
+                       params_dtype: torch.dtype) -> Dict[str, Any]:
+        weight = Parameter(torch.empty(output_size_per_partition,
+                                       input_size_per_partition,
                                        device=torch.cuda.current_device(),
                                        dtype=params_dtype),
                            requires_grad=False)
@@ -102,9 +106,11 @@ class ReplicatedLinear(torch.nn.Module):
             linear_method = UnquantizedLinearMethod()
         self.linear_method = linear_method
         self.linear_weights = self.linear_method.create_weights(
-            self.input_size, self.output_size, self.params_dtype)
+            self.input_size, self.output_size, self.input_size,
+            self.output_size, self.params_dtype)
         for name, weight in self.linear_weights.items():
-            self.register_parameter(name, weight)
+            if isinstance(weight, torch.Tensor):
+                self.register_parameter(name, weight)
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size,
@@ -168,10 +174,12 @@ class ColumnParallelLinear(torch.nn.Module):
             linear_method = UnquantizedLinearMethod()
         self.linear_method = linear_method
         self.linear_weights = self.linear_method.create_weights(
-            self.input_size, self.output_size_per_partition, self.params_dtype)
+            self.input_size, self.output_size_per_partition, self.input_size,
+            self.output_size, self.params_dtype)
         for name, weight in self.linear_weights.items():
-            self.register_parameter(name, weight)
+            if isinstance(weight, torch.Tensor):
-            set_weight_attrs(weight, {"weight_loader": self.weight_loader})
+                self.register_parameter(name, weight)
+                set_weight_attrs(weight, {"weight_loader": self.weight_loader})
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size_per_partition,
@@ -295,10 +303,12 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                  shard_size)
         else:
-            logger.warning(
+            ignore_warning = getattr(param, "ignore_warning", False)
-                "Loading a weight without `output_dim` attribute in "
+            if not ignore_warning:
-                "MergedColumnParallelLinear, assume the weight is "
+                logger.warning(
-                "the same for all partitions.")
+                    "Loading a weight without `output_dim` attribute in "
+                    "MergedColumnParallelLinear, assume the weight is "
+                    "the same for all partitions.")
         assert param_data.shape == loaded_weight.shape
         param_data.copy_(loaded_weight)
 
@@ -418,10 +428,12 @@ class QKVParallelLinear(ColumnParallelLinear):
             loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                  shard_size)
         else:
-            logger.warning(
+            ignore_warning = getattr(param, "ignore_warning", False)
-                "Loading a weight without `output_dim` attribute in "
+            if not ignore_warning:
-                "QKVParallelLinear, assume the weight is the same "
+                logger.warning(
-                "for all partitions.")
+                    "Loading a weight without `output_dim` attribute in "
+                    "QKVParallelLinear, assume the weight is the same "
+                    "for all partitions.")
         assert param_data.shape == loaded_weight.shape
         param_data.copy_(loaded_weight)
 
@@ -481,10 +493,12 @@ class RowParallelLinear(torch.nn.Module):
             linear_method = UnquantizedLinearMethod()
         self.linear_method = linear_method
         self.linear_weights = self.linear_method.create_weights(
-            self.input_size_per_partition, self.output_size, self.params_dtype)
+            self.input_size_per_partition, self.output_size, self.input_size,
+            self.output_size, self.params_dtype)
         for name, weight in self.linear_weights.items():
-            self.register_parameter(name, weight)
+            if isinstance(weight, torch.Tensor):
-            set_weight_attrs(weight, {"weight_loader": self.weight_loader})
+                self.register_parameter(name, weight)
+                set_weight_attrs(weight, {"weight_loader": self.weight_loader})
 
         if not reduce_results and (bias and not skip_bias_add):
             raise ValueError("When not reduce the results, adding bias to the "

vllm/model_executor/layers/quantization/__init__.py

@@ -1,11 +1,13 @@
 from typing import Type
 
-from vllm.model_executor.layers.quantization.awq import AWQConfig
-from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
 from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
 
 _QUANTIZATION_CONFIG_REGISTRY = {
     "awq": AWQConfig,
+    "gptq": GPTQConfig,
     "squeezellm": SqueezeLLMConfig,
 }
 

vllm/model_executor/layers/quantization/awq.py

@@ -77,14 +77,16 @@ class AWQLinearMethod(LinearMethodBase):
     def __init__(self, quant_config: AWQConfig):
         self.quant_config = quant_config
 
-    def create_weights(self, input_size: int, output_size: int,
+    def create_weights(self, input_size_per_partition: int,
-                       params_dtype: torch.dtype) -> Dict[str, torch.Tensor]:
+                       output_size_per_partition: int, input_size: int,
-        if input_size % self.quant_config.group_size != 0:
+                       output_size: int,
+                       params_dtype: torch.dtype) -> Dict[str, Any]:
+        if input_size_per_partition % self.quant_config.group_size != 0:
             raise ValueError(
                 "The input size is not aligned with the quantized "
                 "weight shape. This can be caused by too large "
                 "tensor parallel size.")
-        if output_size % self.quant_config.pack_factor != 0:
+        if output_size_per_partition % self.quant_config.pack_factor != 0:
             raise ValueError(
                 "The output size is not aligned with the quantized "
                 "weight shape. This can be caused by too large "
@@ -92,8 +94,8 @@ class AWQLinearMethod(LinearMethodBase):
 
         qweight = Parameter(
             torch.empty(
-                input_size,
+                input_size_per_partition,
-                output_size // self.quant_config.pack_factor,
+                output_size_per_partition // self.quant_config.pack_factor,
                 device="cuda",
                 dtype=torch.int32,
             ),
@@ -108,8 +110,8 @@ class AWQLinearMethod(LinearMethodBase):
             })
         qzeros = Parameter(
             torch.empty(
-                input_size // self.quant_config.group_size,
+                input_size_per_partition // self.quant_config.group_size,
-                output_size // self.quant_config.pack_factor,
+                output_size_per_partition // self.quant_config.pack_factor,
                 device="cuda",
                 dtype=torch.int32,
             ),
@@ -124,8 +126,8 @@ class AWQLinearMethod(LinearMethodBase):
             })
         scales = Parameter(
             torch.empty(
-                input_size // self.quant_config.group_size,
+                input_size_per_partition // self.quant_config.group_size,
-                output_size,
+                output_size_per_partition,
                 device="cuda",
                 dtype=params_dtype,
             ),
@@ -142,7 +144,7 @@ class AWQLinearMethod(LinearMethodBase):
         }
 
     def apply_weights(self,
-                      weights: Dict[str, torch.Tensor],
+                      weights: Dict[str, Any],
                       x: torch.Tensor,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         qweight = weights["qweight"]

vllm/model_executor/layers/quantization/gptq.py

@@ -0,0 +1,215 @@
+import enum
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm._C import ops
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               set_weight_attrs)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+
+
+class GPTQConfig(QuantizationConfig):
+    """Config class for GPTQ.
+
+    Reference: https://arxiv.org/abs/2210.17323
+    """
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        desc_act: bool,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.desc_act = desc_act
+        self.pack_factor = 32 // self.weight_bits
+        # exllama kernel v1 only supports 4 bit
+        if self.weight_bits != 4:
+            raise ValueError(
+                "Currently, only 4-bit weight quantization is supported for "
+                f"GPTQ, but got {self.weight_bits} bits.")
+
+    def __repr__(self) -> str:
+        return (f"GPTQConfig(weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}, "
+                f"desc_act={self.desc_act})")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "gptq"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half]
+
+    @classmethod
+    # Need to figure it out
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "GPTQConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        desc_act = cls.get_from_keys(config, ["desc_act"])
+        return cls(weight_bits, group_size, desc_act)
+
+    def get_linear_method(self) -> "GPTQLinearMethod":
+        return GPTQLinearMethod(self)
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class ExllamaState(Enum):
+
+    UNUSED = enum.auto()
+    UNINITIALIZED = enum.auto()
+    READY = enum.auto()
+
+
+class GPTQLinearMethod(LinearMethodBase):
+    """Linear method for GPTQ.
+
+    Args:
+        quant_config: The GPTQ quantization config.
+    """
+
+    def __init__(self, quant_config: GPTQConfig):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        input_size_per_partition: int,
+        output_size_per_partition: int,
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+    ) -> Dict[str, Any]:
+        del output_size  # Unused.
+        if input_size_per_partition % self.quant_config.group_size != 0:
+            raise ValueError(
+                "The input size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+        if output_size_per_partition % self.quant_config.pack_factor != 0:
+            raise ValueError(
+                "The output size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+
+        if self.quant_config.group_size != -1:
+            group_size = self.quant_config.group_size
+        else:
+            group_size = input_size
+        exllama_state = ExllamaState.UNINITIALIZED
+        scale_and_zero_size = input_size // group_size
+        scale_and_zero_input_dim = None
+        if input_size != input_size_per_partition and self.quant_config.group_size != -1:
+            # For act-order models, we cannot use Exllama for row parallel layer
+            if self.quant_config.desc_act:
+                exllama_state = ExllamaState.UNUSED
+            else:
+                # we need to partition qzeros and scales for exllama kernel
+                scale_and_zero_size = input_size_per_partition // group_size
+                scale_and_zero_input_dim = 0
+
+        qweight = Parameter(
+            torch.empty(
+                input_size_per_partition // self.quant_config.pack_factor,
+                output_size_per_partition,
+                device="cuda",
+                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,
+            })
+        g_idx = Parameter(
+            torch.tensor(
+                [
+                    i // self.quant_config.group_size
+                    for i in range(input_size_per_partition)
+                ],
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        # Ignore warning from fused linear layers such as QKVParallelLinear.
+        set_weight_attrs(g_idx, {"input_dim": 0, "ignore_warning": True})
+        qzeros = Parameter(
+            torch.empty(
+                scale_and_zero_size,
+                output_size_per_partition // self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(
+            qzeros, {
+                "input_dim": scale_and_zero_input_dim,
+                "output_dim": 1,
+                "packed_dim": 1,
+                "pack_factor": self.quant_config.pack_factor,
+            })
+        scales = Parameter(
+            torch.empty(
+                scale_and_zero_size,
+                output_size_per_partition,
+                device="cuda",
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(scales, {
+            "input_dim": scale_and_zero_input_dim,
+            "output_dim": 1,
+        })
+        return {
+            "qweight": qweight,
+            "g_idx": g_idx,
+            "qzeros": qzeros,
+            "scales": scales,
+            "exllama_state": exllama_state,
+        }
+
+    def apply_weights(self,
+                      weights: Dict[str, Any],
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        qweight = weights["qweight"]
+        out_shape = x.shape[:-1] + (qweight.shape[-1], )
+        reshaped_x = x.reshape(-1, x.shape[-1])
+        # exllama needs to shuffle the weight after the weight is loaded
+        # here we do the shuffle on first forward pass
+        if weights["exllama_state"] == ExllamaState.UNINITIALIZED:
+            if self.quant_config.desc_act:
+                weights["g_idx"] = torch.argsort(weights["g_idx"]).to(
+                    torch.int)
+            else:
+                weights["g_idx"] = torch.empty((1, 1), device="meta")
+            weights["exllama_state"] = ExllamaState.READY
+            ops.gptq_shuffle(weights["qweight"], weights["g_idx"])
+        output = ops.gptq_gemm(reshaped_x, weights["qweight"],
+                               weights["qzeros"], weights["scales"],
+                               weights["g_idx"],
+                               weights["exllama_state"] == ExllamaState.READY)
+        if bias is not None:
+            output = output + bias
+        return output.reshape(out_shape)

vllm/model_executor/layers/quantization/squeezellm.py

@@ -67,17 +67,19 @@ class SqueezeLLMLinearMethod(LinearMethodBase):
     def __init__(self, quant_config: SqueezeLLMConfig):
         self.quant_config = quant_config
 
-    def create_weights(self, input_size: int, output_size: int,
+    def create_weights(self, input_size_per_partition: int,
-                       params_dtype: torch.dtype) -> Dict[str, torch.Tensor]:
+                       output_size_per_partition: int, input_size: int,
-        if input_size % self.quant_config.pack_factor != 0:
+                       output_size: int,
+                       params_dtype: torch.dtype) -> Dict[str, Any]:
+        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.")
         qweight = Parameter(
             torch.empty(
-                input_size // self.quant_config.pack_factor,
+                input_size_per_partition // self.quant_config.pack_factor,
-                output_size,
+                output_size_per_partition,
                 device="cuda",
                 dtype=torch.int32,
             ),
@@ -108,7 +110,7 @@ class SqueezeLLMLinearMethod(LinearMethodBase):
         }
 
     def apply_weights(self,
-                      weights: Dict[str, torch.Tensor],
+                      weights: Dict[str, Any],
                       x: torch.Tensor,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
         qweight = weights["qweight"]

vllm/model_executor/models/aquila.py

@@ -332,11 +332,18 @@ class AquilaForCausalLM(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/models/baichuan.py

@@ -355,11 +355,18 @@ class BaiChuanBaseForCausalLM(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/models/chatglm.py

@@ -377,6 +377,9 @@ class ChatGLMForCausalLM(nn.Module):
                 continue
             if "word_embeddings" in name:
                 name = name.replace(".word_embeddings", "")
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
             param = params_dict[name]
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)

vllm/model_executor/models/falcon.py

@@ -425,27 +425,32 @@ class FalconForCausalLM(nn.Module):
         params_dict = dict(self.named_parameters())
         for name, loaded_weight in hf_model_weights_iterator(
                 model_name_or_path, cache_dir, load_format, revision):
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
             param = params_dict[name]
             if "query_key_value" in name:
                 output_dim = getattr(param, "output_dim", None)
                 loaded_weight_shape = loaded_weight.shape
-                loaded_weight = loaded_weight.view(
+                if output_dim is not None:
-                    loaded_weight_shape[:output_dim] +
+                    loaded_weight = loaded_weight.view(
-                    (total_num_kv_heads, num_query_heads_per_kv_head + 2, -1) +
+                        loaded_weight_shape[:output_dim] +
-                    loaded_weight_shape[output_dim + 1:])
+                        (total_num_kv_heads, num_query_heads_per_kv_head + 2,
-                wq = loaded_weight.narrow(
+                         -1) + loaded_weight_shape[output_dim + 1:])
-                    output_dim + 1, 0, num_query_heads_per_kv_head).reshape(
+                    wq = loaded_weight.narrow(
-                        *loaded_weight_shape[:output_dim], -1,
+                        output_dim + 1, 0,
-                        *loaded_weight_shape[output_dim + 1:])
+                        num_query_heads_per_kv_head).reshape(
-                wk = loaded_weight.narrow(
+                            *loaded_weight_shape[:output_dim], -1,
-                    output_dim + 1, num_query_heads_per_kv_head,
+                            *loaded_weight_shape[output_dim + 1:])
-                    1).reshape(*loaded_weight_shape[:output_dim], -1,
+                    wk = loaded_weight.narrow(
-                               *loaded_weight_shape[output_dim + 1:])
+                        output_dim + 1, num_query_heads_per_kv_head,
-                wv = loaded_weight.narrow(
+                        1).reshape(*loaded_weight_shape[:output_dim], -1,
-                    output_dim + 1, num_query_heads_per_kv_head + 1,
+                                   *loaded_weight_shape[output_dim + 1:])
-                    1).reshape(*loaded_weight_shape[:output_dim], -1,
+                    wv = loaded_weight.narrow(
-                               *loaded_weight_shape[output_dim + 1:])
+                        output_dim + 1, num_query_heads_per_kv_head + 1,
-                loaded_weight = torch.cat([wq, wk, wv], dim=output_dim)
+                        1).reshape(*loaded_weight_shape[:output_dim], -1,
+                                   *loaded_weight_shape[output_dim + 1:])
+                    loaded_weight = torch.cat([wq, wk, wv], dim=output_dim)
 
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)

vllm/model_executor/models/gpt2.py

@@ -275,7 +275,6 @@ class GPT2LMHeadModel(nn.Module):
                 if not name.endswith(".weight"):
                     continue
                 loaded_weight = loaded_weight.t()
-
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)
             weight_loader(param, loaded_weight)

vllm/model_executor/models/gpt_j.py

@@ -274,11 +274,18 @@ class GPTJForCausalLM(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/models/gpt_neox.py

@@ -72,7 +72,6 @@ class GPTNeoXAttention(nn.Module):
             config.hidden_size,
             linear_method=linear_method,
         )
-
         scaling = self.head_size**-0.5
         rotary_dim = int(self.head_size * config.rotary_pct)
         assert rotary_dim % 2 == 0

vllm/model_executor/models/internlm.py

@@ -289,11 +289,18 @@ class InternLMForCausalLM(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/models/llama.py

@@ -330,11 +330,18 @@ class LlamaForCausalLM(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/models/mistral.py

@@ -321,11 +321,18 @@ class MistralForCausalLM(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/models/mixtral.py

@@ -153,7 +153,7 @@ class MixtralMoE(nn.Module):
         self.gate = ReplicatedLinear(config.hidden_size,
                                      self.num_total_experts,
                                      bias=False,
-                                     linear_method=linear_method)
+                                     linear_method=None)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         batch_size, sequence_length, hidden_dim = hidden_states.shape
@@ -418,11 +418,18 @@ class MixtralForCausalLM(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/models/mpt.py

@@ -297,6 +297,9 @@ class MPTForCausalLM(nn.Module):
         params_dict = dict(self.named_parameters(remove_duplicate=False))
         for name, loaded_weight in hf_model_weights_iterator(
                 model_name_or_path, cache_dir, load_format, revision):
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
             param = params_dict[name]
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)

vllm/model_executor/models/opt.py

@@ -345,11 +345,18 @@ class OPTForCausalLM(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/models/phi_1_5.py

@@ -305,6 +305,9 @@ class PhiForCausalLM(nn.Module):
             if "rotary_emb.inv_freq" in name:
                 continue
 
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
             # pylint: disable=E1136
             param = params_dict[name]
             weight_loader = getattr(param, "weight_loader",

vllm/model_executor/models/qwen.py

@@ -82,7 +82,6 @@ class QWenAttention(nn.Module):
         self.num_heads = (self.total_num_heads //
                           tensor_model_parallel_world_size)
         self.head_dim = hidden_size // self.total_num_heads
-
         self.c_attn = QKVParallelLinear(
             hidden_size,
             self.head_dim,
@@ -279,11 +278,18 @@ class QWenLMHeadModel(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/models/yi.py

@@ -320,11 +320,18 @@ class YiForCausalLM(nn.Module):
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)

vllm/model_executor/weight_utils.py

@@ -287,4 +287,5 @@ def initialize_dummy_weights(
     values between -1e-3 and 1e-3 works well for most models.
     """
     for param in model.state_dict().values():
-        param.data.uniform_(low, high)
+        if torch.is_floating_point(param):
+            param.data.uniform_(low, high)