Quantized dot products for CUDA mul mat vec (#2067)

4 files changed, 425 insertions, 91 deletions

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4ac0f6f..a240454 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -68,8 +68,9 @@ option(LLAMA_ACCELERATE                      "llama: enable Accelerate framework
 option(LLAMA_BLAS                            "llama: use BLAS"                                  OFF)
 set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
 option(LLAMA_CUBLAS                          "llama: use cuBLAS"                                OFF)
+option(LLAMA_CUDA_FORCE_DMMV                 "llama: use dmmv instead of mmvq CUDA kernels"     OFF)
 set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
-set(LLAMA_CUDA_DMMV_Y       "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
+set(LLAMA_CUDA_MMV_Y        "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
 option(LLAMA_CUDA_DMMV_F16                   "llama: use 16 bit floats for dmmv CUDA kernels"   OFF)
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
@@ -246,8 +247,14 @@ if (LLAMA_CUBLAS)
         set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
 
         add_compile_definitions(GGML_USE_CUBLAS)
+        if (LLAMA_CUDA_FORCE_DMMV)
+            add_compile_definitions(GGML_CUDA_FORCE_DMMV)
+        endif()
         add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
-        add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
+        add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
+        if (DEFINED LLAMA_CUDA_DMMV_Y)
+            add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility
+        endif()
         if (LLAMA_CUDA_DMMV_F16)
             add_compile_definitions(GGML_CUDA_DMMV_F16)
         endif()
@@ -263,7 +270,7 @@ if (LLAMA_CUBLAS)
         if (LLAMA_CUDA_DMMV_F16)
             set(CMAKE_CUDA_ARCHITECTURES "61") # needed for f16 CUDA intrinsics
         else()
-            set(CMAKE_CUDA_ARCHITECTURES "52") # lowest CUDA 12 standard
+            set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
         endif()
     endif()
     message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
diff --git a/Makefile b/Makefile
index 8966a35..7141566 100644
--- a/Makefile
+++ b/Makefile
@@ -164,16 +164,21 @@ ifdef LLAMA_CUBLAS
 	OBJS      += ggml-cuda.o
 	NVCC      = nvcc
 	NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
+ifdef LLAMA_CUDA_FORCE_DMMV
+	NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
+endif # LLAMA_CUDA_FORCE_DMMV
 ifdef LLAMA_CUDA_DMMV_X
 	NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
 else
 	NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
 endif # LLAMA_CUDA_DMMV_X
-ifdef LLAMA_CUDA_DMMV_Y
-	NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y)
+ifdef LLAMA_CUDA_MMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
+else ifdef LLAMA_CUDA_DMMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
 else
-	NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
-endif # LLAMA_CUDA_DMMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
+endif # LLAMA_CUDA_MMV_Y
 ifdef LLAMA_CUDA_DMMV_F16
 	NVCCFLAGS += -DGGML_CUDA_DMMV_F16
 endif # LLAMA_CUDA_DMMV_F16
diff --git a/README.md b/README.md
index 6c2bb39..32f17c2 100644
--- a/README.md
+++ b/README.md
@@ -345,8 +345,9 @@ Building the program with BLAS support may lead to some performance improvements
 
   | Option                  | Legal values           | Default | Description |
   |-------------------------|------------------------|---------|-------------|
+  | LLAMA_CUDA_FORCE_DMMV   | Boolean                |   false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 7.0/Turing/RTX 2000 or higher). Does not affect k-quants. |
   | LLAMA_CUDA_DMMV_X       | Positive integer >= 32 |      32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
-  | LLAMA_CUDA_DMMV_Y       | Positive integer       |       1 | Block size in y direction for the CUDA dequantization + mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
+  | LLAMA_CUDA_MMV_Y       | Positive integer       |       1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
   | LLAMA_CUDA_DMMV_F16     | Boolean                |   false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. |
   | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 |       2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
 
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 0b12a9e..7965ff7 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -70,9 +70,11 @@ typedef void (*ggml_cuda_op_t)(
 
 // QK = number of values after dequantization
 // QR = QK / number of values before dequantization
+// QI = number of 32 bit integers before dequantization
 
 #define QK4_0 32
 #define QR4_0 2
+#define QI4_0 4
 typedef struct {
     half    d;              // delta
     uint8_t qs[QK4_0 / 2];  // nibbles / quants
@@ -81,6 +83,7 @@ static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0
 
 #define QK4_1 32
 #define QR4_1 2
+#define QI4_1 4
 typedef struct {
     half    d;              // delta
     half    m;              // min
@@ -90,6 +93,7 @@ static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong
 
 #define QK5_0 32
 #define QR5_0 2
+#define QI5_0 4
 typedef struct {
     half d;                 // delta
     uint8_t qh[4];          // 5-th bit of quants
@@ -99,6 +103,7 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5
 
 #define QK5_1 32
 #define QR5_1 2
+#define QI5_1 4
 typedef struct {
     half d;                 // delta
     half m;                 // min
@@ -109,12 +114,25 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) +
 
 #define QK8_0 32
 #define QR8_0 1
+#define QI8_0 8
 typedef struct {
     half    d;              // delta
     int8_t  qs[QK8_0];      // quants
 } block_q8_0;
 static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
 
+#define QK8_1 32
+#define QR8_1 1
+#define QI8_1 8
+typedef struct {
+    half    d;              // delta
+    half    s;              // unquantized sum
+    int8_t  qs[QK8_0];      // quants
+} block_q8_1;
+static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding");
+
+typedef float (*vec_dot_q_cuda_t)(const void * vbq, const block_q8_1 * bq8_1, const int iqs);
+
 //================================= k-quants
 
 #ifdef GGML_QKK_64
@@ -198,14 +216,15 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
 #define CUDA_SCALE_BLOCK_SIZE 256
 #define CUDA_ROPE_BLOCK_SIZE 256
 #define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
+#define CUDA_QUANTIZE_BLOCK_SIZE 256
 #define CUDA_DEQUANTIZE_BLOCK_SIZE 256
 
 // dmmv = dequantize_mul_mat_vec
 #ifndef GGML_CUDA_DMMV_X
 #define GGML_CUDA_DMMV_X 32
 #endif
-#ifndef GGML_CUDA_DMMV_Y
-#define GGML_CUDA_DMMV_Y 1
+#ifndef GGML_CUDA_MMV_Y
+#define GGML_CUDA_MMV_Y 1
 #endif
 
 #ifndef K_QUANTS_PER_ITERATION
@@ -270,7 +289,6 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol
     }
 
     // sum up partial sums
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -714,7 +732,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -819,7 +836,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -923,7 +939,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1028,7 +1043,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1139,7 +1153,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1158,6 +1171,41 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs,
     v.y = x[ib + iqs + 1];
 }
 
+static __global__ void quantize_q8_1(const float * x, void * vy, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    block_q8_1 * y = (block_q8_1 *) vy;
+
+    const int ib = i / QK8_0; // block index
+    const int iqs = i % QK8_0; // quant index
+
+    const float xi = x[i];
+    float amax = fabsf(xi);
+    float sum = xi;
+
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32));
+        sum += __shfl_xor_sync(0xffffffff, sum, mask, 32);
+    }
+
+    const float d = amax / 127;
+    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
+
+    y[ib].qs[iqs] = q;
+
+    if (iqs > 0) {
+        return;
+    }
+
+    y[ib].d = d;
+    y[ib].s = sum;
+}
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
 static __global__ void dequantize_block(const void * vx, float * y, const int k) {
     const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
@@ -1179,6 +1227,182 @@ static __global__ void dequantize_block(const void * vx, float * y, const int k)
     y[iybs + iqs + y_offset] = v.y;
 }
 
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+
+    int vi;
+    memcpy(&vi,  &bq4_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_0)]);
+
+    const float d = __half2float(bq4_0->d) * __half2float(bq8_1->d);
+
+    // subtract 8 from each quantized value
+    const int vi0 = __vsub4((vi >> 0) & 0x0F0F0F0F, 0x08080808);
+    const int vi1 = __vsub4((vi >> 4) & 0x0F0F0F0F, 0x08080808);
+
+    // SIMD dot product of quantized values
+    int sumi = __dp4a(vi0, ui0, 0);
+    sumi     = __dp4a(vi1, ui1, sumi);
+
+    return sumi*d;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+
+    const int vi  = *((int *) &bq4_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_1)]);
+
+    const float d = __half2float(bq4_1->d) * __half2float(bq8_1->d);
+    const float m = bq4_1->m;
+    const float s = bq8_1->s;
+
+    const int vi0 = (vi >> 0) & 0x0F0F0F0F;
+    const int vi1 = (vi >> 4) & 0x0F0F0F0F;
+
+    // SIMD dot product of quantized values
+    int sumi = __dp4a(vi0, ui0, 0);
+    sumi     = __dp4a(vi1, ui1, sumi);
+
+    return sumi*d + m*s / QI4_1; // scale sum by QI4_1 because there are QI4_1 threads working on this block
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+
+    int qs;
+    memcpy(&qs, &bq5_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
+    const int qh0 = bq5_0->qh[iqs/2 + 0] >> 4*(iqs%2);
+    const int qh1 = bq5_0->qh[iqs/2 + 2] >> 4*(iqs%2);
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_0)]);
+
+    const float d = __half2float(bq5_0->d) * __half2float(bq8_1->d);
+
+    int vi0 = (qs  >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
+    vi0    |= (qh0 <<  4) & 0x00000010; // 1 ->  5
+    vi0    |= (qh0 << 11) & 0x00001000; // 2 -> 13
+    vi0    |= (qh0 << 18) & 0x00100000; // 3 -> 21
+    vi0    |= (qh0 << 25) & 0x10000000; // 4 -> 29
+    vi0     = __vsub4(vi0,  0x10101010); // subtract 16 from quantized values
+    int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
+
+    int vi1 = (qs  >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
+    vi1    |= (qh1 <<  4) & 0x00000010; // 1 ->  5
+    vi1    |= (qh1 << 11) & 0x00001000; // 2 -> 13
+    vi1    |= (qh1 << 18) & 0x00100000; // 3 -> 21
+    vi1    |= (qh1 << 25) & 0x10000000; // 4 -> 29
+    vi1     = __vsub4(vi1,  0x10101010); // subtract 16 from quantized values
+    sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
+
+    return sumi*d;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+
+    const int qs  = *((int *) &bq5_1->qs[sizeof(int) * (iqs + 0)]);
+    const int qh0 = bq5_1->qh[iqs/2 + 0] >> 4*(iqs%2);
+    const int qh1 = bq5_1->qh[iqs/2 + 2] >> 4*(iqs%2);
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_1)]);
+
+    const float d = __half2float(bq5_1->d) * __half2float(bq8_1->d);
+    const float m = bq5_1->m;
+    const float s = bq8_1->s;
+
+    int vi0 = (qs  >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
+    vi0    |= (qh0 <<  4) & 0x00000010; // 1 ->  5
+    vi0    |= (qh0 << 11) & 0x00001000; // 2 -> 13
+    vi0    |= (qh0 << 18) & 0x00100000; // 3 -> 21
+    vi0    |= (qh0 << 25) & 0x10000000; // 4 -> 29
+    int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
+
+    int vi1 = (qs  >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
+    vi1    |= (qh1 <<  4) & 0x00000010; // 1 ->  5
+    vi1    |= (qh1 << 11) & 0x00001000; // 2 -> 13
+    vi1    |= (qh1 << 18) & 0x00100000; // 3 -> 21
+    vi1    |= (qh1 << 25) & 0x10000000; // 4 -> 29
+    sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
+
+    return sumi*d + m*s / QI5_1; // scale sum by QI5_1 because there are QI5_1 threads working on this block
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+
+    int vi;
+    memcpy(&vi,  &bq8_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
+    const int ui = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+
+    const float d = __half2float(bq8_0->d) * __half2float(bq8_1->d);
+
+    // SIMD dot product of quantized values
+    int sumi = __dp4a(vi, ui, 0);
+
+    return sumi*d;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+template <int qk, int qi, typename block_q_t, vec_dot_q_cuda_t vec_dot_q_cuda>
+static __global__ void mul_mat_vec_q(const void * vx, const void * vy, float * dst, const int ncols, const int nrows) {
+    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i + threadIdx.x / qi; // x block index
+
+        const int iby = i + threadIdx.x / qi; // y block index
+
+        const int iqs  = threadIdx.x % qi; // x block quant index when casting the quants to int
+
+        tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
 static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows) {
     // qk = quantized weights per x block
@@ -1233,7 +1457,6 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y,
     }
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1284,7 +1507,6 @@ static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, fl
     const int idst = channel*nrows_dst + row_dst;
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1330,7 +1552,6 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
     }
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1440,7 +1661,6 @@ static __global__ void soft_max_f32(const float * x, float * dst, const int ncol
     }
 
     // sum up partial sums
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1494,6 +1714,11 @@ static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, con
     rms_norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
 }
 
+static void quantize_row_q8_1_cuda(const float * x, void * vy, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    quantize_q8_1<<<num_blocks, CUDA_QUANTIZE_BLOCK_SIZE, 0, stream>>>(x, vy, k);
+}
+
 static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
     dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
@@ -1562,45 +1787,45 @@ static void dequantize_row_q6_K_cuda(const void * vx, float * y, const int k, cu
 
 static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -1647,6 +1872,51 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f
     dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
+static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, vec_dot_q4_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, vec_dot_q4_1_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, vec_dot_q5_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, vec_dot_q5_1_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, vec_dot_q8_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
 static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
     dequantize_block<1, 1, convert_f16><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
@@ -1654,9 +1924,9 @@ static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, c
 
 static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<1, 1, convert_f16>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -1822,6 +2092,7 @@ static size_t g_scratch_offset = 0;
 
 static int g_device_count = -1;
 static int g_main_device = 0;
+static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
 static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
 
 static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
@@ -1839,9 +2110,12 @@ void ggml_init_cublas() {
         for (int id = 0; id < g_device_count; ++id) {
             cudaDeviceProp prop;
             CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
-            fprintf(stderr, "  Device %d: %s\n", id, prop.name);
+            fprintf(stderr, "  Device %d: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor);
+
             g_tensor_split[id] = total_vram;
             total_vram += prop.totalGlobalMem;
+
+            g_compute_capabilities[id] = 100*prop.major + 10*prop.minor;
         }
         for (int id = 0; id < g_device_count; ++id) {
             g_tensor_split[id] /= total_vram;
@@ -2057,7 +2331,7 @@ inline void ggml_cuda_op_rms_norm(
     (void) i1;
 }
 
-inline void ggml_cuda_op_dequantize_mul_mat_vec(
+inline void ggml_cuda_op_mul_mat_vec(
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
     float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
     cudaStream_t & cudaStream_main){
@@ -2069,69 +2343,116 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec(
     const int64_t ne00 = src0->ne[0];
     const int64_t nrows = i01_high - i01_low;
 
-// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
-#ifdef GGML_CUDA_DMMV_F16
-    size_t ash;
-    dfloat * src1_dfloat = nullptr; // dfloat == half
+#ifdef GGML_CUDA_FORCE_DMMV
+    const bool use_mul_mat_vec_q = false;
+#else
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
 
-    bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
-        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
-        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
+    const bool mul_mat_vec_q_implemented = src0->type == GGML_TYPE_Q4_0 ||
+        src0->type == GGML_TYPE_Q4_1 ||
+        src0->type == GGML_TYPE_Q5_0 ||
+        src0->type == GGML_TYPE_Q5_1 ||
+        src0->type == GGML_TYPE_Q8_0;
 
-    if (src1_convert_f16) {
-        src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash);
-        ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00,
-                                ne00, 1, sizeof(float), 0, 0,
-                                ne00, 1, sizeof(half),  0, 0, cudaStream_main);
-    }
+    // The integer intrinsics used in mul_mat_vec_q are available with compute capability 6.
+    // However, they have bad performance with Pascal cards.
+    // Therefore, in a multi GPU setting decide at runtime which GPUs should use mul_mat_vec_q.
+    const bool use_mul_mat_vec_q = g_compute_capabilities[id] >= 700 && mul_mat_vec_q_implemented;
+#endif
+
+    if (use_mul_mat_vec_q) {
+        size_t as;
+        void * src1_q8_1 = ggml_cuda_pool_malloc(ne00*sizeof(block_q8_1)/QK8_1, &as);
+        quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne00, cudaStream_main);
+
+        switch (src0->type) {
+            case GGML_TYPE_Q4_0:
+                mul_mat_vec_q4_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q4_1:
+                mul_mat_vec_q4_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_0:
+                mul_mat_vec_q5_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_1:
+                mul_mat_vec_q5_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q8_0:
+                mul_mat_vec_q8_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+
+        ggml_cuda_pool_free(src1_q8_1, as);
+    } else {
+        // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
+#ifdef GGML_CUDA_DMMV_F16
+        size_t ash;
+        dfloat * src1_dfloat = nullptr; // dfloat == half
+
+        bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
+            src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
+            src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
+
+        if (src1_convert_f16) {
+            src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash);
+            ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00,
+                                    ne00, 1, sizeof(float), 0, 0,
+                                    ne00, 1, sizeof(half),  0, 0, cudaStream_main);
+        }
 #else
-    dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion
+        dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion
 #endif // GGML_CUDA_DMMV_F16
 
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q4_1:
-            dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q5_0:
-            dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q5_1:
-            dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q8_0:
-            dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q2_K:
-            dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q3_K:
-            dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q4_K:
-            dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q5_K:
-            dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q6_K:
-            dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_F16:
-            convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
+        switch (src0->type) {
+            case GGML_TYPE_Q4_0:
+                dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q4_1:
+                dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_0:
+                dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_1:
+                dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q8_0:
+                dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q2_K:
+                dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q3_K:
+                dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q4_K:
+                dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_K:
+                dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q6_K:
+                dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_F16:
+                convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
 
 #ifdef GGML_CUDA_DMMV_F16
-    if (src1_convert_f16) {
-        ggml_cuda_pool_free(src1_dfloat, ash);
-    }
+        if (src1_convert_f16) {
+            ggml_cuda_pool_free(src1_dfloat, ash);
+        }
 #endif // GGML_CUDA_DMMV_F16
+    }
 
     (void) src1;
     (void) dst;
@@ -2701,8 +3022,8 @@ void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_
     }else if (src0->type == GGML_TYPE_F32) {
         ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
     } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
-        if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src0->ne[1] % GGML_CUDA_DMMV_Y == 0) {
-            ggml_cuda_op(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false, false);
+        if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
+            ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_vec, false, false);
         } else {
             ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
         }