[Bugfix][Kernel] Add IQ1_M quantization implementation to GGUF kernel

csrc/quantization/gguf/dequantize.cuh

@@ -353,18 +353,47 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
 template<typename dst_t>
 static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
 
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
     const block_iq1_s * x = (const block_iq1_s  *) vx;
 
-    const int tid = threadIdx.x;
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    const int64_t tid = threadIdx.x;
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
+    const float d = __half2float(x[i].d) * (2*((x[i].qh[ib] >> 12) & 7) + 1);
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+    for (int j = 0; j < 8; ++j) {
+        y[j] = __float2half(d * (q[j] + delta));
+    }
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_iq1_m * x = (const block_iq1_m  *) vx;
+
+    const int64_t tid = threadIdx.x;
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
     dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const int i8 = 4*ib+il;
-    uint8_t h = x[i].scales[i8/2] >> 4*(i8%2);
-    const int8_t * grid = (const int8_t *)(iq1s_grid + (x[i].qs[i8] | ((h & 8) << 5)));
-    const float d = __half2float(x[i].d) * (2*(h & 7) + 1);
-    for (int j = 0; j < 8; ++j) y[j] = __float2half(d * grid[j]);
+    const uint16_t * sc = (const uint16_t *)x[i].scales;
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
+    const float d = __half2float(scale.f16) * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
+    const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+    for (int j = 0; j < 8; ++j) {
+        y[j] = __float2half(d * (q[j] + delta));
+    }
 }
 
 template<typename dst_t>
@@ -475,6 +504,12 @@ static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, c
     dequantize_block_iq1_s<<<nb, 32, 0, stream>>>(vx, y);
 }
 
+template<typename dst_t>
+static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq1_m<<<nb, 32, 0, stream>>>(vx, y);
+}
+
 template<typename dst_t>
 static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
     const int nb = (k + QK_K - 1) / QK_K;
@@ -525,6 +560,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(int64_t type) {
             return dequantize_row_iq2_s_cuda;
         case 23:
             return dequantize_row_iq4_xs_cuda;
+        case 29:
+            return dequantize_row_iq1_m_cuda;
         default:
             return nullptr;
     }

csrc/quantization/gguf/gguf_kernel.cu

@@ -166,6 +166,11 @@ torch::Tensor ggml_mul_mat_vec_a8(torch::Tensor W,  // quant weight
                                    (void*)quant_X.data_ptr(),
                                    (half*)Y.data_ptr(), col, row, stream);
       break;
+    case 29:
+      mul_mat_vec_iq1_m_q8_1_cuda((void*)W.data_ptr(),
+                                  (void*)quant_X.data_ptr(),
+                                  (half*)Y.data_ptr(), col, row, stream);
+      break;
   }
   return Y;
 }

csrc/quantization/gguf/mmvq.cuh

@@ -157,6 +157,14 @@ static void mul_mat_vec_iq1_s_q8_1_cuda(const void * vx, const void * vy, half *
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
+static void mul_mat_vec_iq1_m_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK_K, QI1_M, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
 static void mul_mat_vec_iq4_nl_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);

csrc/quantization/gguf/vecdotq.cuh

@@ -1,5 +1,18 @@
 // copied and adapted from https://github.com/ggerganov/llama.cpp/blob/b2899/ggml-cuda/vecdotq.cuh
 // and https://github.com/ggerganov/llama.cpp/blob/b2899/ggml-cuda/mmq.cu
+static __device__ __forceinline__ int get_int_b2(const void * x, const int & i32) {
+    const uint16_t * x16 = (const uint16_t *) x; // assume at least 2 byte alignment
+
+    int x32  = x16[2*i32 + 0] <<  0;
+    x32     |= x16[2*i32 + 1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_b4(const void * x, const int & i32) {
+    return ((const int *) x)[i32]; // assume at least 4 byte alignment
+}
+
 static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
     const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
     int x32 = 0;
@@ -1658,28 +1671,76 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
 
 static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 610
     const block_iq1_s * bq1 = (const block_iq1_s *) vbq;
 
-    const int ib32 = iqs;
-    int sumi1 = 0, sumi2 = 0, sumi3 = 0, sumi4 = 0;
-    const uint8_t h1 = bq1->scales[2*ib32+0];
-    const uint8_t h2 = bq1->scales[2*ib32+1];
-    const int * q8 = (const int *)bq8_1[ib32].qs;
-    const int * grid1 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+0] | ((h1 & 0x08) << 5)));
-    const int * grid2 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+1] | ((h1 & 0x80) << 1)));
-    const int * grid3 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+2] | ((h2 & 0x08) << 5)));
-    const int * grid4 = (const int *)(iq1s_grid + (bq1->qs[4*ib32+3] | ((h2 & 0x80) << 1)));
-    for (int j = 0; j < 2; ++j) {
-        sumi1 = __dp4a(q8[j+0], grid1[j], sumi1);
-        sumi2 = __dp4a(q8[j+2], grid2[j], sumi2);
-        sumi3 = __dp4a(q8[j+4], grid3[j], sumi3);
-        sumi4 = __dp4a(q8[j+6], grid4[j], sumi4);
-    }
-    const float d = __half2float(bq1->d) * __low2float(bq8_1[ib32].ds);
-    return d * (sumi1 * (2*(h1 & 7) + 1) + sumi2 * (2*((h1 >> 4) & 7) + 1) +
-                sumi3 * (2*(h2 & 7) + 1) + sumi4 * (2*((h2 >> 4) & 7) + 1));
-#endif
+    const int       qs_packed = get_int_b2(bq1->qs, iqs);
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    const int qh = bq1->qh[iqs];
+
+    int sumi = 0;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int grid = iq1s_grid_gpu[qs[l0/2] | (((qh >> 3*(l0/2)) & 0x07) << 8)];
+
+        const int grid0 = (grid >> 0) & 0x0F0F0F0F;
+        const int grid1 = (grid >> 4) & 0x0F0F0F0F;
+
+        const int u0 = get_int_b4(bq8_1[iqs].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs].qs, l0 + 1);
+
+        sumi = __dp4a(grid0, u0, sumi);
+        sumi = __dp4a(grid1, u1, sumi);
+    }
+
+    const float  d1q   = __half2float(bq1->d) * (((qh >> 11) & 0x0E) + 1);
+    const float  delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000);
+    const float2 ds    = __half22float2(bq8_1[iqs].ds);
+    return d1q * (ds.x*sumi + ds.y*delta);
+}
+
+static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_iq1_m * bq1 = (const block_iq1_m *) vbq;
+
+    const int       qs_packed = get_int_b4(bq1->qs, iqs);
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    int   sumi[2] = {0};
+    float sumf[2] = {0.0f};
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int qhl = bq1->qh[2*iqs + l0/4] >> (4 * ((l0/2) % 2));
+
+        const int grid = iq1s_grid_gpu[qs[l0/2] | ((qhl & 0x07) << 8)];
+
+        const int grid0 = (grid >> 0) & 0x0F0F0F0F;
+        const int grid1 = (grid >> 4) & 0x0F0F0F0F;
+
+        const int u0 = get_int_b4(bq8_1[iqs].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs].qs, l0 + 1);
+
+        sumi[l0/4] = __dp4a(grid0, u0, sumi[l0/4]);
+        sumi[l0/4] = __dp4a(grid1, u1, sumi[l0/4]);
+
+        const float delta = -1.0f + IQ1M_DELTA - (qhl & 0x08) * (2.0f*IQ1M_DELTA/0x08);
+        int sumy = 0;
+        sumy = __dp4a(u0, 0x01010101, sumy);
+        sumy = __dp4a(u1, 0x01010101, sumy);
+        sumf[l0/4] += delta*sumy;
+    }
+
+    const uint16_t * sc = (const uint16_t *) bq1->scales;
+
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00F0) | ((sc[2] >> 4) & 0x0F00) | (sc[3] & 0xF000);
+    const float d = __half2float(scale.f16) * __low2float(bq8_1[iqs].ds);
+
+    const int tmp = sc[iqs/2] >> (6*(iqs%2));
+    const int sc0 = 2*((tmp >> 0) & 0x07) + 1;
+    const int sc1 = 2*((tmp >> 3) & 0x07) + 1;
+    return d * ((sumi[0] + sumf[0]) * sc0 + (sumi[1] + sumf[1]) * sc1);
 }
 
 static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values,

requirements-common.txt

@@ -24,7 +24,7 @@ filelock >= 3.10.4 # filelock starts to support `mode` argument from 3.10.4
 partial-json-parser # used for parsing partial JSON outputs
 pyzmq
 msgspec
-gguf == 0.9.1
+gguf == 0.10.0
 importlib_metadata
 mistral_common >= 1.4.0
 pyyaml

tests/kernels/test_gguf.py

@@ -0,0 +1,126 @@
+from pathlib import Path
+from typing import List
+
+import pytest
+import torch
+from gguf import GGMLQuantizationType, GGUFReader, ReaderTensor, dequantize
+from huggingface_hub import snapshot_download
+
+import vllm._custom_ops as ops
+
+GGUF_SAMPLE = snapshot_download("Isotr0py/test-gguf-sample")
+
+
+def get_gguf_sample_tensors(
+        hidden_size: int,
+        quant_type: GGMLQuantizationType) -> List[ReaderTensor]:
+    sample_dir = GGUF_SAMPLE
+    filename = f"Quant_{quant_type.name}_{hidden_size}.gguf"
+    sample_file = Path(sample_dir) / filename
+    return GGUFReader(sample_file).tensors
+
+
+DTYPES = [torch.half]
+# Hidden_size for testing, must match the sample file in HF repo,
+# we have `hidden_size = 256, 1024` for test in HF repo currently.
+HIDDEN_SIZES = [256, 1024]
+NUM_TOKENS = [7, 83, 128, 2048]  # Arbitrary values for testing
+SEEDS = [0]
+QUANT_TYPES = [
+    # i-matrix
+    GGMLQuantizationType.IQ1_M,
+    GGMLQuantizationType.IQ1_S,
+    GGMLQuantizationType.IQ2_S,
+    GGMLQuantizationType.IQ2_XS,
+    GGMLQuantizationType.IQ3_S,
+    GGMLQuantizationType.IQ3_XXS,
+    GGMLQuantizationType.IQ4_NL,
+    GGMLQuantizationType.IQ4_XS,
+    # k-quants
+    GGMLQuantizationType.Q2_K,
+    GGMLQuantizationType.Q3_K,
+    GGMLQuantizationType.Q4_K,
+    GGMLQuantizationType.Q5_K,
+    GGMLQuantizationType.Q6_K,
+    # standard quantization
+    GGMLQuantizationType.Q4_0,
+    GGMLQuantizationType.Q5_0,
+    GGMLQuantizationType.Q8_0,
+]
+
+
+@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("quant_type", QUANT_TYPES)
+@torch.inference_mode()
+def test_dequantize(hidden_size: int, dtype: torch.dtype,
+                    quant_type: GGMLQuantizationType):
+    tensors = get_gguf_sample_tensors(hidden_size, quant_type)
+    for tensor in tensors:
+        shape_str = tensor.name.split("_")[-1]
+        shape = map(int, shape_str.split("x"))
+
+        ref_output = torch.tensor(dequantize(tensor.data, quant_type),
+                                  device="cuda").to(dtype)
+        output = ops.ggml_dequantize(torch.tensor(tensor.data, device="cuda"),
+                                     quant_type, *list(shape)).to(dtype)
+
+        torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=4e-2)
+
+
+@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("quant_type", QUANT_TYPES)
+@torch.inference_mode()
+def test_mmvq(hidden_size: int, dtype: torch.dtype,
+              quant_type: GGMLQuantizationType):
+    torch.cuda.manual_seed_all(0)
+
+    tensors = get_gguf_sample_tensors(hidden_size, quant_type)
+    x = torch.rand((1, hidden_size), dtype=dtype, device="cuda")
+    for tensor in tensors:
+        weight = torch.tensor(dequantize(tensor.data, quant_type),
+                              device="cuda").to(dtype)
+        ref_output = x @ weight.T
+
+        qweight = torch.tensor(tensor.data, device="cuda")
+        output = ops.ggml_mul_mat_vec_a8(qweight, x, quant_type,
+                                         qweight.shape[0]).to(dtype)
+
+        torch.testing.assert_close(output, ref_output, atol=1, rtol=1e-1)
+
+
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize(
+    "quant_type",
+    [
+        # k-quants
+        GGMLQuantizationType.Q2_K,
+        GGMLQuantizationType.Q3_K,
+        GGMLQuantizationType.Q4_K,
+        GGMLQuantizationType.Q5_K,
+        GGMLQuantizationType.Q6_K,
+        # standard quants
+        GGMLQuantizationType.Q4_0,
+        GGMLQuantizationType.Q5_0,
+        GGMLQuantizationType.Q8_0,
+    ])
+@torch.inference_mode()
+def test_mmq(num_tokens: int, hidden_size: int, dtype: torch.dtype,
+             quant_type: GGMLQuantizationType):
+    torch.cuda.manual_seed_all(0)
+
+    tensors = get_gguf_sample_tensors(hidden_size, quant_type)
+    x = torch.rand((num_tokens, hidden_size), dtype=dtype, device="cuda")
+    for tensor in tensors:
+        weight = torch.tensor(dequantize(tensor.data, quant_type),
+                              device="cuda").to(dtype)
+        ref_output = x @ weight.T
+
+        qweight = torch.tensor(tensor.data, device="cuda")
+        output = ops.ggml_mul_mat_a8(qweight, x, quant_type,
+                                     qweight.shape[0]).to(dtype)
+
+        torch.testing.assert_close(output, ref_output, atol=1, rtol=1e-1)

vllm/model_executor/layers/quantization/gguf.py

@@ -55,7 +55,10 @@ def get_scaled_act_names(self) -> List[str]:
 def _fuse_mul_mat(x: torch.Tensor, qweight: torch.Tensor,
                   qweight_type: int) -> torch.Tensor:
     # use dequantize mulmat for IQmatrix, mmq for k-quants
-    if qweight_type >= 16:
+    if x.shape[0] == 1:
+        # enable mmvq in contiguous batching
+        y = ops.ggml_mul_mat_vec_a8(qweight, x, qweight_type, qweight.shape[0])
+    elif qweight_type >= 16:
         block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
         shape = (qweight.shape[0], qweight.shape[1] // type_size * block_size)
         weight = ops.ggml_dequantize(qweight, qweight_type, *shape)