Support IQ4_XS dequantize

ktransformers/ktransformers_ext/cuda/binding.cpp

@@ -31,6 +31,8 @@ PYBIND11_MODULE(KTransformersOps, m) {
             py::arg("data"), py::arg("blk_size"), py::arg("device"));
       m.def("dequantize_q2_k",  &dequantize_q2_k, "Function to dequantize q2_k data.",
             py::arg("data"), py::arg("blk_size"), py::arg("device"));
+      m.def("dequantize_iq4_xs",  &dequantize_iq4_xs, "Function to dequantize iq4_xs data.",
+            py::arg("data"), py::arg("blk_size"), py::arg("device"));
       m.def("gptq_marlin_gemm", &gptq_marlin_gemm, "Function to perform GEMM using Marlin quantization.",
             py::arg("a"), py::arg("b_q_weight"), py::arg("b_scales"), py::arg("g_idx"),
             py::arg("perm"), py::arg("workspace"), py::arg("num_bits"), py::arg("size_m"),

ktransformers/ktransformers_ext/cuda/custom_gguf/binding.cpp

@@ -28,6 +28,8 @@ PYBIND11_MODULE(cudaops, m) {
             py::arg("data"), py::arg("blk_size"), py::arg("device"));
     m.def("dequantize_q2_k",  &dequantize_q2_k, "Function to dequantize q2_k data.",
           py::arg("data"), py::arg("blk_size"), py::arg("device"));
+    m.def("dequantize_iq4_xs",  &dequantize_iq4_xs, "Function to dequantize iq4_xs data.",
+          py::arg("data"), py::arg("blk_size"), py::arg("device"));
     m.def("test", &test, "Function to test.");
     
 }

ktransformers/ktransformers_ext/cuda/custom_gguf/dequant.cu

@@ -212,6 +212,29 @@ __global__ void dequantize_q6_k_kernel(int8_t* data, float* output, int blk_size
     }
 }
 
+static constexpr __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
+
+__global__ void dequantize_iq4_xs_kernel(int8_t* data, float* output, int blk_size, int num_blocks) {
+    int global_idx = blockIdx.x * blockDim.x + threadIdx.x;
+    for (auto block_id=global_idx; block_id<num_blocks; block_id+=blockDim.x * gridDim.x) {
+        float* __restrict__ output_blk = (float*)(output + block_id * 256);
+        const float d = __half2float(*(reinterpret_cast<half*>(data + block_id * blk_size)));
+        const uint16_t scales_h = *(reinterpret_cast<uint16_t*>(data + block_id * blk_size + 2));
+        const uint8_t* scales_l = (uint8_t*)(data + block_id * blk_size + 2 + 2);
+        const uint8_t* qs = (uint8_t*)(data + block_id * blk_size + 2 + 2 + 4);
+
+        for (int ib = 0; ib < 8; ++ib) {
+            const int ls = ((scales_l[ib / 2] >> 4 * (ib % 2)) & 0xf) | (((scales_h >> 2 * ib) & 3) << 4);
+            const float dl = d * (ls - 32);
+            for (int j = 0; j < 16; ++j) {
+                output_blk[j + 0] = dl * kvalues_iq4nl[qs[j] & 0xf];
+                output_blk[j + 16] = dl * kvalues_iq4nl[qs[j] >> 4];
+            }
+            output_blk += 32;
+            qs += 16;
+        }
+    }
+}
 
 torch::Tensor dequantize_q8_0(torch::Tensor data, int blk_size, torch::Device device) {
     int num_blocks = data.numel() / blk_size;
@@ -339,4 +362,22 @@ torch::Tensor dequantize_q2_k(torch::Tensor data, int blk_size, torch::Device de
 
     cudaDeviceSynchronize();
     return output;
-}
+}
\ No newline at end of file
+
+torch::Tensor dequantize_iq4_xs(torch::Tensor data, int blk_size, torch::Device device) {
+    int num_blocks = data.numel() / blk_size;
+
+    auto options = torch::TensorOptions().dtype(torch::kInt8).device(device).memory_format(torch::MemoryFormat::Contiguous);
+    auto data_gpu = torch::empty({data.numel()}, options);
+
+    data_gpu.copy_(data, false);
+
+    // Create output tensor
+    auto output = torch::zeros({num_blocks, 256}, torch::dtype(torch::kFloat32).device(device));
+
+    // Launch kernel
+    dequantize_iq4_xs_kernel<<< 512, 256 >>>(data_gpu.data_ptr<int8_t>(), output.data_ptr<float>(), blk_size, num_blocks);
+
+    cudaDeviceSynchronize();
+    return output;
+}

ktransformers/ktransformers_ext/cuda/custom_gguf/ops.h

@@ -18,4 +18,5 @@ torch::Tensor dequantize_q6_k(torch::Tensor data, int blk_size, torch::Device de
 torch::Tensor dequantize_q5_k(torch::Tensor data, int blk_size, torch::Device device);
 torch::Tensor dequantize_q4_k(torch::Tensor data, int blk_size, torch::Device device);
 torch::Tensor dequantize_q3_k(torch::Tensor data, int blk_size, torch::Device device);
-torch::Tensor dequantize_q2_k(torch::Tensor data, int blk_size, torch::Device device);
+torch::Tensor dequantize_q2_k(torch::Tensor data, int blk_size, torch::Device device);
\ No newline at end of file
+torch::Tensor dequantize_iq4_xs(torch::Tensor data, int blk_size, torch::Device device);

ktransformers/util/custom_gguf.py

@@ -108,6 +108,7 @@ GGML_TYPES = {
     "Q4_K": 12,
     "Q5_K": 13,
     "Q6_K": 14,
+    "IQ4_XS": 23,
 }
 
 GGML_NAMES = {ggml_type: name for name, ggml_type in GGML_TYPES.items()}
@@ -123,6 +124,7 @@ GGML_BLOCK_SIZES = {
     "Q4_K": 2 + 2 + 12 + 256 // 2,
     "Q5_K": 2 + 2 + 12 + 256 // 8 + 256 // 2,
     "Q6_K": 256 // 2 + 256 // 4 + 256 // 16 + 2,
+    "IQ4_XS": 2 + 2 + 256 // 2 + 256 // 64,
 }
 
 GGML_ELEMENTS_PER_BLOCK = {
@@ -136,6 +138,7 @@ GGML_ELEMENTS_PER_BLOCK = {
     "Q4_K": 256,
     "Q5_K": 256,
     "Q6_K": 256,
+    "IQ4_XS": 256,
 }
 
 DATA_TYPES = {
@@ -601,6 +604,46 @@ def dequantize_q6_k_gpu(data: np.ndarray, device:str = "cuda"):
     data = torch.from_numpy(data)
     return KTransformersOps.dequantize_q6_k(data, block_size, device)
 
+kvalues_iq4nl = np.array([-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113], dtype=np.int8)
+
+def dequantize_iq4_xs(data):
+    # C implementation
+    # https://github.com/ggerganov/ggml/blob/21d3a308fcb7f31cb9beceaeebad4fb622f3c337/src/ggml-quants.c#L3568
+    # C struct definition
+    # https://github.com/ggerganov/ggml/blob/21d3a308fcb7f31cb9beceaeebad4fb622f3c337/src/ggml-common.h#L393
+    block_size = GGML_BLOCK_SIZES["IQ4_XS"]
+    num_blocks = len(data) // block_size
+
+    d = np.frombuffer(data, dtype=np.float16)[0::block_size//2].astype(np.float32).reshape(num_blocks, 1)
+    scales_h = np.frombuffer(data, dtype=np.uint16)[1::block_size//2].reshape(num_blocks, 1)
+    data_u8 = np.frombuffer(data, dtype=np.uint8).reshape(num_blocks, block_size)[:, 4:]
+    scales_l = data_u8[:, :4].reshape(num_blocks, 4)
+    qs = data_u8[:, 4:].reshape(num_blocks, block_size - 8)
+
+    ls = np.zeros((num_blocks, QK_K // 32), dtype=np.int8)
+    for ib in range(QK_K // 32):
+        ls[:, ib] = ((scales_l[:, ib // 2] >> 4 * (ib % 2)) & 0xf) | (((scales_h[:, 0] >> 2 * ib) & 3) << 4)
+
+    dl = (d * (ls - 32)).reshape(num_blocks, -1, 1)
+
+    qs_lo_4 = qs[:, :QK_K // 2].reshape(num_blocks, -1, 16) & 0xf
+    qs_hi_4 = qs[:, :QK_K // 2].reshape(num_blocks, -1, 16) >> 4
+
+    y = np.zeros((num_blocks, QK_K), dtype=np.float32)
+    for ib in range(QK_K // 32):
+        y[:, ib*32:(ib*32)+16] = dl[:, ib] * kvalues_iq4nl[qs_lo_4[:, ib]]
+        y[:, (ib*32)+16:(ib*32)+32] = dl[:, ib] * kvalues_iq4nl[qs_hi_4[:, ib]]
+
+    return y.flatten()
+
+def dequantize_iq4_xs_gpu(data: np.ndarray, device:str = "cuda"):
+    block_size = GGML_BLOCK_SIZES["IQ4_XS"]
+    device = torch.device(device)
+    num_blocks = len(data) // block_size
+    data = np.frombuffer(data, dtype=data.dtype)
+    data = torch.from_numpy(data)
+    return KTransformersOps.dequantize_iq4_xs(data, block_size, device)
+
 def dequantize_q4_0(data):
     # C implementation
     # https://github.com/ggerganov/ggml/blob/a3c0188a4b5d3dec052ff87c9f773baa53631d70/src/ggml-quants.c#L1515
@@ -693,6 +736,7 @@ GGML_DEQUANTIZE = {
     "Q4_K": dequantize_q4_k,
     "Q5_K": dequantize_q5_k,
     "Q6_K": dequantize_q6_k,
+    "IQ4_XS": dequantize_iq4_xs,
 }
 
 GGML_DEQUANTIZE_GPU = {
@@ -706,6 +750,7 @@ GGML_DEQUANTIZE_GPU = {
     "Q4_K": dequantize_q4_k_gpu,
     "Q5_K": dequantize_q5_k_gpu,
     "Q6_K": dequantize_q6_k_gpu,
+    "IQ4_XS": dequantize_iq4_xs_gpu,
 }