Merge pull request #36 from kvcache-ai/develop-0.1.2

Release v0.1.2

ktransformers/ktransformers_ext/cuda/custom_gguf/binding.cpp

@@ -12,14 +12,22 @@ int test(){
 }
 
 torch::Tensor dequantize_q6_k(torch::Tensor data, int blk_size, torch::Device device);
+torch::Tensor dequantize_q5_k(torch::Tensor data, int blk_size, torch::Device device);
+torch::Tensor dequantize_q2_k(torch::Tensor data, int blk_size, torch::Device device);
 
 PYBIND11_MODULE(cudaops, m) {
     m.def("dequantize_q8_0", &dequantize_q8_0, "Function to dequantize q8_0 data.",
           py::arg("data"), py::arg("blk_size"), py::arg("device"));
     m.def("dequantize_q6_k", &dequantize_q6_k, "Function to dequantize q6_k data.",
           py::arg("data"), py::arg("blk_size"), py::arg("device"));
+    m.def("dequantize_q5_k", &dequantize_q5_k, "Function to dequantize q5_k data.",
+          py::arg("data"), py::arg("blk_size"), py::arg("device"));
     m.def("dequantize_q4_k",  &dequantize_q4_k, "Function to dequantize q4_k data.",
           py::arg("data"), py::arg("blk_size"), py::arg("device"));
+    m.def("dequantize_q3_k",  &dequantize_q3_k, "Function to dequantize q3_k data.",
+            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("test", &test, "Function to test.");
     
 }

ktransformers/ktransformers_ext/cuda/custom_gguf/custom_ggml.h

-
-
-
-#include <cuda_fp16.h>
-
-
-__device__ float ggml_compute_fp16_to_fp32(uint16_t h) {
-    return __uint2float_rd(h);
-}
-
-static inline float ggml_compute_fp16_to_fp32(uint16_t h) {
-    uint16_t tmp;
-    memcpy(&tmp, &h, sizeof(ggml_fp16_t));
-    return (float)tmp;
-}
-
-// define the global table for fp16 to fp32 conversion
-__device__ float ggml_table_f32_f16[1 << 16];
-
-// CUDA Kernel to init the table
-__global__ void init_fp16_to_fp32_table() {
-    int idx = blockIdx.x * blockDim.x + threadIdx.x;
-    for (auto blk_id = idx; blk_id<(1 << 16); blk_id+=blockDim.x * gridDim.x){
-        ggml_table_f32_f16[blk_id] = GGML_COMPUTE_FP16_TO_FP32(blk_id);
-    }
-}
-
-#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
-
-extern __device__ float ggml_table_f32_f16[1 << 16]; // Declare as __device__ if used within device code
-
-// This version of the function is designed to be called from within a CUDA kernel
-#if !defined(GGML_FP16_TO_FP32)
-__device__ float ggml_lookup_fp16_to_fp32(uint16_t f) {
-    return ggml_table_f32_f16[f];
-}
-
-#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
-#endif
\ No newline at end of file

ktransformers/ktransformers_ext/cuda/custom_gguf/dequant.cu

@@ -3,8 +3,8 @@
  * @Author       : Azure-Tang, Boxin Zhang
  * @Date         : 2024-07-25 13:38:30
  * @Version      : 1.0.0
- * @LastEditors  : Azure 
- * @LastEditTime : 2024-07-26 11:58:50
+ * @LastEditors  : kkk1nak0
+ * @LastEditTime : 2024-08-12 04:18:04
  * Adapted from https://github.com/ggerganov/ggml/blob/fca1caafea7de9fbd7efc733b9818f9cf2da3050/src/ggml-quants.c
  * Copyright (c) 2023-2024 The ggml authors
  * Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
@@ -14,6 +14,7 @@
 #include <torch/extension.h>
 #include <torch/torch.h>
 #include <cstdint>
+#include <c10/cuda/CUDAGuard.h>
 
 __global__ void dequantize_q8_0_kernel(float* output, const float* scales, const int8_t* qs, int num_blocks, int blk_size) {
     int global_idx = blockIdx.x * blockDim.x + threadIdx.x;
@@ -35,6 +36,97 @@ __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t * __restrict_
     }
 }
 
+__global__ void dequantize_q2_k_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 + 80)));
+        const float min = __half2float(*(reinterpret_cast<half*>(data + block_id * blk_size + 82)));
+
+        const uint8_t * __restrict__ q = (uint8_t*)(data + block_id * blk_size + 16);
+
+        int is = 0;
+        float dl, ml;
+
+        for (int n = 0; n < 256; n += 128) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+                uint8_t* scales = (uint8_t*)(data + block_id * blk_size + (is++));
+                uint8_t sc = *scales;
+                dl = d * (sc & 0xF); ml = min * (sc >> 4);
+                for (int l = 0; l < 16; ++l) *output_blk++ = dl * ((int8_t)((q[l] >> shift) & 3)) - ml;
+
+                scales = (uint8_t*)(data + block_id * blk_size + (is++));
+                sc = *scales;
+
+                dl = d * (sc & 0xF); ml = min * (sc >> 4);
+                for (int l = 0; l < 16; ++l) *output_blk++ = dl * ((int8_t)((q[l+16] >> shift) & 3)) - ml;
+
+                shift += 2;
+            }
+            q += 32;
+        }
+    }
+}
+
+__global__ void dequantize_q3_k_kernel(int8_t* data, float* output, int blk_size, int num_blocks) {
+    
+    int global_idx = blockIdx.x * blockDim.x + threadIdx.x;    
+    const uint32_t kmask1 = 0x03030303;
+    const uint32_t kmask2 = 0x0f0f0f0f;
+    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);
+
+        uint32_t aux[4];
+        const int8_t * scales = (const int8_t*)aux;
+        const float d_all = __half2float(*(reinterpret_cast<half*>(data + block_id * blk_size + 108)));
+
+        const uint8_t * __restrict__ q  = (uint8_t*)(data + block_id * blk_size + 32);
+        const uint8_t * __restrict__ hm = (uint8_t*)(data + block_id * blk_size + 0);
+        uint8_t m = 1;
+
+
+        uint8_t* block_scales = (uint8_t*)(data + block_id * blk_size + 96);
+
+        for (int i = 0; i < 3; i++) {  
+            aux[i] = 0;  
+            for (int j = 0; j < 4; j++) {  
+                aux[i] |= ((uint32_t)block_scales[i * 4 + j]) << (j * 8);
+            }
+        }
+
+        uint32_t tmp = aux[2];
+        aux[2] = ((aux[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        aux[3] = ((aux[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        aux[0] = (aux[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        aux[1] = (aux[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+
+        int is = 0;
+        float dl;
+        for (int n = 0; n < 256; n += 128) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+
+                dl = d_all * (scales[is++] - 32);
+                for (int l = 0; l < 16; ++l) {
+                    *output_blk++ = dl * ((int8_t)((q[l+ 0] >> shift) & 3) - ((hm[l+ 0] & m) ? 0 : 4));
+                }
+
+                dl = d_all * (scales[is++] - 32);
+                for (int l = 0; l < 16; ++l) {
+                    *output_blk++ = dl * ((int8_t)((q[l+16] >> shift) & 3) - ((hm[l+16] & m) ? 0 : 4));
+                }
+
+                shift += 2;
+                m <<= 1;
+            }
+            q += 32;
+        }
+    }
+}
+
+
 __global__ void dequantize_q4_k_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){
@@ -59,6 +151,35 @@ __global__ void dequantize_q4_k_kernel(int8_t* data, float* output, int blk_size
     }
 }
 
+__global__ void dequantize_q5_k_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 + 0)));
+        const float min = __half2float(*(reinterpret_cast<half*>(data + block_id * blk_size + 2)));
+
+        const uint8_t * __restrict__ qh = (uint8_t*)(data + block_id * blk_size + 16);
+        const uint8_t * __restrict__ ql = (uint8_t*)(data + block_id * blk_size + 48);
+
+        int is = 0;
+        uint8_t sc, m;
+        uint8_t u1 = 1, u2 = 2;
+        uint8_t* scales = (uint8_t*)(data + block_id * blk_size + 4);
+
+        for (int j = 0; j < 256; j += 64) {
+            get_scale_min_k4(is + 0, scales, &sc, &m);
+            const float d1 = d * sc; const float m1 = min * m;
+            get_scale_min_k4(is + 1, scales, &sc, &m);
+            const float d2 = d * sc; const float m2 = min * m;
+            for (int l = 0; l < 32; ++l) *output_blk++ = d1 * ((ql[l] & 0xF) + (qh[l] & u1 ? 16 : 0)) - m1;
+            for (int l = 0; l < 32; ++l) *output_blk++ = d2 * ((ql[l]  >> 4) + (qh[l] & u2 ? 16 : 0)) - m2;
+            ql += 32; is += 2;
+            u1 <<= 2; u2 <<= 2;
+        }
+    }
+}
+
 __global__ void dequantize_q6_k_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){
@@ -94,6 +215,7 @@ __global__ void dequantize_q6_k_kernel(int8_t* data, float* output, int blk_size
 
 torch::Tensor dequantize_q8_0(torch::Tensor data, int blk_size, torch::Device device) {
     int num_blocks = data.numel() / blk_size;
+    const at::cuda::OptionalCUDAGuard device_guard(device);
     // create gpu
     auto options_scales = torch::TensorOptions().dtype(torch::kFloat32).device(device).memory_format(torch::MemoryFormat::Contiguous);
     auto options_qs = torch::TensorOptions().dtype(torch::kInt8).device(device).memory_format(torch::MemoryFormat::Contiguous);
@@ -128,6 +250,7 @@ torch::Tensor dequantize_q6_k(torch::Tensor data, int blk_size, torch::Device de
     // data.numel%blk_size should be 0, else raise err
     int num_blocks = data.numel() / blk_size;
 
+    const at::cuda::OptionalCUDAGuard device_guard(device);
     auto options = torch::TensorOptions().dtype(torch::kInt8).device(device).memory_format(torch::MemoryFormat::Contiguous);
     auto data_gpu = torch::empty({data.numel()}, options);
 
@@ -144,9 +267,28 @@ torch::Tensor dequantize_q6_k(torch::Tensor data, int blk_size, torch::Device de
     return output;
 }
 
+torch::Tensor dequantize_q5_k(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_q5_k_kernel<<< 512, 256 >>>(data_gpu.data_ptr<int8_t>(), output.data_ptr<float>(), blk_size, num_blocks);
+
+    cudaDeviceSynchronize();
+    return output;
+}
+
 torch::Tensor dequantize_q4_k(torch::Tensor data, int blk_size, torch::Device device) {
     // data.numel%blk_size should be 0, else raise err
     int num_blocks = data.numel() / blk_size;
+    const at::cuda::OptionalCUDAGuard device_guard(device);
 
     auto options = torch::TensorOptions().dtype(torch::kInt8).device(device).memory_format(torch::MemoryFormat::Contiguous);
     auto data_gpu = torch::empty({data.numel()}, options);
@@ -162,3 +304,39 @@ torch::Tensor dequantize_q4_k(torch::Tensor data, int blk_size, torch::Device de
     cudaDeviceSynchronize();
     return output;
 }
+
+torch::Tensor dequantize_q3_k(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_q3_k_kernel<<< 512, 256 >>>(data_gpu.data_ptr<int8_t>(), output.data_ptr<float>(), blk_size, num_blocks);
+
+    cudaDeviceSynchronize();
+    return output;
+}
+
+torch::Tensor dequantize_q2_k(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_q2_k_kernel<<< 512, 256 >>>(data_gpu.data_ptr<int8_t>(), output.data_ptr<float>(), blk_size, num_blocks);
+
+    cudaDeviceSynchronize();
+    return output;
+}
\ No newline at end of file

ktransformers/ktransformers_ext/cuda/custom_gguf/ops.h

@@ -3,8 +3,8 @@
  * @Author       : Azure-Tang
  * @Date         : 2024-07-22 09:27:55
  * @Version      : 1.0.0
- * @LastEditors  : Azure 
- * @LastEditTime : 2024-07-26 08:38:20
+ * @LastEditors  : kkk1nak0
+ * @LastEditTime : 2024-08-12 03:48:46
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 **/
 #pragma once
@@ -15,4 +15,7 @@
 
 torch::Tensor dequantize_q8_0(torch::Tensor data, int blk_size, torch::Device device);
 torch::Tensor dequantize_q6_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_q5_k(torch::Tensor data, int blk_size, torch::Device device);
\ No newline at end of file
+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);
\ No newline at end of file

ktransformers/ktransformers_ext/cuda/gptq_marlin/gptq_marlin.cu

@@ -23,7 +23,7 @@
  */
 #include "gptq_marlin.cuh"
 #include "gptq_marlin_dtypes.cuh"
-
+#include <c10/cuda/CUDAGuard.h>
 #define STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t)               \
   static_assert(std::is_same<scalar_t, half>::value ||          \
                     std::is_same<scalar_t, nv_bfloat16>::value, \
@@ -1774,6 +1774,7 @@ torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
                                torch::Tensor& perm, torch::Tensor& workspace,
                                int64_t num_bits, int64_t size_m, int64_t size_n,
                                int64_t size_k, bool is_k_full) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
   // Verify num_bits
   TORCH_CHECK(num_bits == 4 || num_bits == 8,
               "num_bits must be 4 or 8. Got = ", num_bits);
@@ -1816,7 +1817,6 @@ torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
   TORCH_CHECK(perm.is_contiguous(), "perm is not contiguous");
 
   // Alloc buffers
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
   auto options = torch::TensorOptions().dtype(a.dtype()).device(a.device());
   torch::Tensor c = torch::empty({size_m, size_n}, options);
   torch::Tensor a_tmp = torch::empty({size_m, size_k}, options);

ktransformers/ktransformers_ext/cuda/setup.py

@@ -2,17 +2,25 @@
 from setuptools import setup, Extension
 from torch.utils import cpp_extension
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension
-
-# setup marlin gemm
-setup(name='KTransformersOps',
-      ext_modules=[
-          CUDAExtension('KTransformersOps', [
+setup(
+    name='KTransformersOps',
+    ext_modules=[
+        CUDAExtension(
+            'KTransformersOps', [
                 'custom_gguf/dequant.cu',
                 'binding.cpp',
                 'gptq_marlin/gptq_marlin.cu',
-               # 'gptq_marlin_repack.cu',
-      ])
-      ],
-      cmdclass={'build_ext': BuildExtension
-})
-
+                # 'gptq_marlin_repack.cu',
+            ],
+            extra_compile_args={
+                'cxx': ['-O3'],
+                'nvcc': [
+                    '-O3',
+                    '--use_fast_math',
+                    '-Xcompiler', '-fPIC',
+                ]
+            },
+        )
+    ],
+    cmdclass={'build_ext': BuildExtension}
+)
\ No newline at end of file

ktransformers/ktransformers_ext/examples/test_linear.py

@@ -6,7 +6,7 @@ Author       : chenht2022
 Date         : 2024-07-25 10:32:05
 Version      : 1.0.0
 LastEditors  : chenht2022 
-LastEditTime : 2024-07-25 10:34:00
+LastEditTime : 2024-08-06 10:36:59
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 import os, sys
@@ -15,23 +15,23 @@ sys.path.append(os.path.dirname(__file__) + '/../build')
 import cpuinfer_ext
 import torch
 
-with torch.inference_mode(mode=True):
-    input_size = 16384
-    output_size = 5120
-    stride = 32
-    proj_type = 1 # ggml_type::GGML_TYPE_F16
-    hidden_type = 1 # ggml_type::GGML_TYPE_F16
-    layer_num = 10
-    CPUInfer = cpuinfer_ext.CPUInfer(48)
-    validation_iter = 100
-    warm_up_iter = 1000
-    test_iter = 10000
+input_size = 16384
+output_size = 5120
+stride = 32
+group_max_len = 1024
+proj_type = 1 # ggml_type::GGML_TYPE_F16
+hidden_type = 1 # ggml_type::GGML_TYPE_F16
+qlen = 30
+layer_num = 10
+CPUInfer = cpuinfer_ext.CPUInfer(48)
+validation_iter = 100
 
+with torch.inference_mode(mode=True):
     linears = []
     projs = []
     for _ in range(layer_num):
         proj = torch.randn((output_size, input_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
-        config = cpuinfer_ext.linear.LinearConfig(input_size, output_size, stride, proj.data_ptr(), proj_type, hidden_type)
+        config = cpuinfer_ext.linear.LinearConfig(input_size, output_size, stride, group_max_len, proj.data_ptr(), proj_type, hidden_type)
         linear = cpuinfer_ext.linear.Linear(config)
         projs.append(proj)
         linears.append(linear)
@@ -39,11 +39,17 @@ with torch.inference_mode(mode=True):
     # validation
     for i in range(validation_iter):
         linear = linears[i % layer_num]
-        input = torch.randn((1, input_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, output_size), dtype=torch.float16).contiguous()
+        input = torch.randn((qlen, input_size), dtype=torch.float16).contiguous()
+        output = torch.empty((qlen, output_size), dtype=torch.float16).contiguous()
         input = input / 100
 
-        CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
+        CPUInfer.submit(
+            linear.forward(
+                qlen,
+                input.data_ptr(),
+                output.data_ptr()
+            )
+        )
         CPUInfer.sync()
         # print('cpuinfer output', output)
 
@@ -54,30 +60,3 @@ with torch.inference_mode(mode=True):
         diff = torch.mean(torch.abs(output - t_output)) / torch.mean(torch.abs(t_output))
         print('diff = ', diff)
         assert(diff < 0.001)
-
-    # warm up
-    for i in range(warm_up_iter):
-        linear = linears[i % layer_num]
-        input = torch.randn((1, input_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, output_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-
-    # test
-    total_time = 0
-    for i in range(test_iter):
-        linear = linears[i % layer_num]
-        input = torch.randn((1, input_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, output_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        start = time.perf_counter()
-        CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-        end = time.perf_counter()
-        total_time += end - start
-    print('Time: ', total_time)
-    print('Iteration: ', test_iter) 
-    print('Time per iteration: ', total_time / test_iter)
-    print('Bandwidth: ', input_size * output_size * 2 * test_iter / total_time / 1000 / 1000 / 1000, 'GB/s')
-    print("All tasks completed.")
\ No newline at end of file

ktransformers/ktransformers_ext/examples/test_mlp.py

@@ -6,7 +6,7 @@ Author       : chenht2022
 Date         : 2024-07-25 10:32:05
 Version      : 1.0.0
 LastEditors  : chenht2022 
-LastEditTime : 2024-07-25 10:34:03
+LastEditTime : 2024-08-06 10:37:28
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 import os, sys
@@ -15,20 +15,30 @@ sys.path.append(os.path.dirname(__file__) + '/../build')
 import cpuinfer_ext
 import torch
 
-with torch.inference_mode(mode=True):
-    hidden_size = 5120
-    intermediate_size = 3072
-    stride = 32
-    gate_type = 1 # ggml_type::GGML_TYPE_F16
-    up_type = 1 # ggml_type::GGML_TYPE_F16
-    down_type = 1 # ggml_type::GGML_TYPE_F16
-    hidden_type = 1 # ggml_type::GGML_TYPE_F16
-    layer_num = 10
-    CPUInfer = cpuinfer_ext.CPUInfer(48)
-    validation_iter = 100
-    warm_up_iter = 1000
-    test_iter = 10000
+hidden_size = 5120
+intermediate_size = 3072
+stride = 32
+group_max_len = 1024
+gate_type = 1 # ggml_type::GGML_TYPE_F16
+up_type = 1 # ggml_type::GGML_TYPE_F16
+down_type = 1 # ggml_type::GGML_TYPE_F16
+hidden_type = 1 # ggml_type::GGML_TYPE_F16
+qlen = 30
+layer_num = 10
+CPUInfer = cpuinfer_ext.CPUInfer(48)
+validation_iter = 100
+
+def act_fn(x):
+    return x / (1.0 + torch.exp(-x))
+
+def mlp_torch(input, gate_proj, up_proj, down_proj):
+    gate_buf = torch.mm(input, gate_proj.t())
+    up_buf = torch.mm(input, up_proj.t())
+    intermediate = act_fn(gate_buf) * up_buf
+    ret = torch.mm(intermediate, down_proj.t())
+    return ret
 
+with torch.inference_mode(mode=True):
     mlps = []
     gate_projs = []
     up_projs = []
@@ -37,7 +47,7 @@ with torch.inference_mode(mode=True):
         gate_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
         up_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
         down_proj = torch.randn((hidden_size, intermediate_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
-        config = cpuinfer_ext.mlp.MLPConfig(hidden_size, intermediate_size, stride, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
+        config = cpuinfer_ext.mlp.MLPConfig(hidden_size, intermediate_size, stride, group_max_len, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
         mlp = cpuinfer_ext.mlp.MLP(config)
         gate_projs.append(gate_proj)
         up_projs.append(up_proj)
@@ -47,52 +57,26 @@ with torch.inference_mode(mode=True):
     # validation
     for i in range(validation_iter):
         mlp = mlps[i % layer_num]
-        input = torch.randn((1, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, hidden_size), dtype=torch.float16).contiguous()
+        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
+        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
         input = input / 100
 
-        CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
+        CPUInfer.submit(
+            mlp.forward(
+                qlen,
+                input.data_ptr(), 
+                output.data_ptr()
+            )
+        )
         CPUInfer.sync()
         # print('cpuinfer output', output)
 
-        def act_fn(x):
-            return x / (1.0 + torch.exp(-x))
         gate_proj = gate_projs[i%layer_num]
         up_proj = up_projs[i%layer_num]
         down_proj = down_projs[i%layer_num]
-        gate_buf = torch.mm(input, gate_proj.t())
-        up_buf = torch.mm(input, up_proj.t())
-        intermediate = act_fn(gate_buf) * up_buf
-        t_output = torch.mm(intermediate, down_proj.t())
+        t_output = mlp_torch(input, gate_proj, up_proj, down_proj)
         # print('torch output', t_output)
 
         diff = torch.mean(torch.abs(output - t_output)) / torch.mean(torch.abs(t_output))
         print('diff = ', diff)
         assert(diff < 0.001)
-
-    # warm up
-    for i in range(warm_up_iter):
-        mlp = mlps[i % layer_num]
-        input = torch.randn((1, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, hidden_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-
-    # test
-    total_time = 0
-    for i in range(test_iter):
-        mlp = mlps[i % layer_num]
-        input = torch.randn((1, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((1, hidden_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        start = time.time()
-        CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-        end = time.time()
-        total_time += end - start
-    print('Time: ', total_time)
-    print('Iteration: ', test_iter) 
-    print('Time per iteration: ', total_time / test_iter)
-    print('Bandwidth: ', hidden_size * intermediate_size * 3 * 2 * test_iter / total_time / 1024 / 1024 / 1024, 'GB/s')
-    print("All tasks completed.")
\ No newline at end of file

ktransformers/ktransformers_ext/examples/test_moe.py

@@ -6,7 +6,7 @@ Author       : chenht2022
 Date         : 2024-07-25 10:32:05
 Version      : 1.0.0
 LastEditors  : chenht2022 
-LastEditTime : 2024-07-25 10:34:06
+LastEditTime : 2024-08-06 10:38:05
 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
 '''
 import os, sys
@@ -15,25 +15,64 @@ sys.path.append(os.path.dirname(__file__) + '/../build')
 import cpuinfer_ext
 import torch
 
-with torch.inference_mode(mode=True):
-    expert_num = 10
-    hidden_size = 5120
-    intermediate_size = 1536
-    stride = 32
-    group_min_len = 10
-    group_max_len = 1024
-    gate_type = 1 # ggml_type::GGML_TYPE_F16
-    up_type = 1 # ggml_type::GGML_TYPE_F16
-    down_type = 1 # ggml_type::GGML_TYPE_F16
-    hidden_type = 1 # ggml_type::GGML_TYPE_F16
-    n_routed_experts = 6
-    qlen = 30
-    layer_num = 10
-    CPUInfer = cpuinfer_ext.CPUInfer(48)
-    validation_iter = 100
-    warm_up_iter = 1000
-    test_iter = 10000
+expert_num = 160
+hidden_size = 5120
+intermediate_size = 1536
+stride = 32
+group_min_len = 10
+group_max_len = 1024
+gate_type = 1 # ggml_type::GGML_TYPE_F16
+up_type = 1 # ggml_type::GGML_TYPE_F16
+down_type = 1 # ggml_type::GGML_TYPE_F16
+hidden_type = 1 # ggml_type::GGML_TYPE_F16
+n_routed_experts = 6
+qlen = 30
+layer_num = 10
+CPUInfer = cpuinfer_ext.CPUInfer(48)
+validation_iter = 100
+
+def act_fn(x):
+    return x / (1.0 + torch.exp(-x))
+
+def mlp_torch(input, gate_proj, up_proj, down_proj):
+    gate_buf = torch.mm(input, gate_proj.t())
+    up_buf = torch.mm(input, up_proj.t())
+    intermediate = act_fn(gate_buf) * up_buf
+    ret = torch.mm(intermediate, down_proj.t())
+    return ret
+
+def moe_torch(input, expert_ids, weights, gate_proj, up_proj, down_proj):
+    cnts = expert_ids.new_zeros((expert_ids.shape[0], expert_num))
+    cnts.scatter_(1, expert_ids, 1)
+    tokens_per_expert = cnts.sum(dim=0)
+    idxs = expert_ids.view(-1).argsort()
+    sorted_tokens = input[idxs // expert_ids.shape[1]]
+
+    outputs = []
+    start_idx = 0
+    for i, num_tokens in enumerate(tokens_per_expert):
+        end_idx = start_idx + num_tokens
+        if num_tokens == 0:
+            continue
+        tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+        expert_out = mlp_torch(tokens_for_this_expert, gate_proj[i], up_proj[i], down_proj[i])
+        outputs.append(expert_out)
+        start_idx = end_idx
+
+    outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+
+    new_x = torch.empty_like(outs)
+    new_x[idxs] = outs
+    t_output = (
+        new_x.view(*expert_ids.shape, -1)
+        .type(weights.dtype)
+        .mul_(weights.unsqueeze(dim=-1))
+        .sum(dim=1)
+        .type(new_x.dtype)
+    )
+    return t_output
 
+with torch.inference_mode(mode=True):
     moes = []
     gate_projs = []
     up_projs = []
@@ -51,63 +90,32 @@ with torch.inference_mode(mode=True):
 
     # validation
     for i in range(validation_iter):
-        moe = moes[i % layer_num]
-        expert_ids = torch.randint(0, expert_num, (qlen, n_routed_experts), dtype=torch.int64).contiguous()
+        expert_ids = torch.stack([torch.randperm(expert_num)[:n_routed_experts] for _ in range(qlen)]).contiguous()
         weights = torch.rand((qlen, n_routed_experts), dtype=torch.float32).contiguous()
-        input = torch.randn((qlen, 1, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((qlen, 1, hidden_size), dtype=torch.float16).contiguous()
+        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
+        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
         input = input / 100
         
-        CPUInfer.submit(moe.forward, qlen, n_routed_experts, expert_ids.data_ptr(), weights.data_ptr(), input.data_ptr(), output.data_ptr())
+        moe = moes[i % layer_num]
+        CPUInfer.submit(
+            moe.forward( 
+                qlen,
+                n_routed_experts, 
+                expert_ids.data_ptr(), 
+                weights.data_ptr(), 
+                input.data_ptr(), 
+                output.data_ptr()
+            )
+        )
         CPUInfer.sync()
         # print('cpuinfer output', output)
 
-        def act_fn(x):
-            return x / (1.0 + torch.exp(-x))
-        t_output = torch.zeros((qlen, 1, hidden_size), dtype=torch.float32).contiguous()
         gate_proj = gate_projs[i%layer_num]
         up_proj = up_projs[i%layer_num]
         down_proj = down_projs[i%layer_num]
-        for token_idx in range(qlen):
-            for i, expert_id in enumerate(expert_ids[token_idx]):
-                gate_buf = torch.mm(input[token_idx], gate_proj[expert_id].t())
-                up_buf = torch.mm(input[token_idx], up_proj[expert_id].t())
-                intermediate = act_fn(gate_buf) * up_buf
-                expert_output = torch.mm(intermediate, down_proj[expert_id].t())
-                t_output[token_idx] += weights[token_idx][i] * expert_output
+        t_output = moe_torch(input, expert_ids, weights, gate_proj, up_proj, down_proj)
         # print('torch output', t_output)
 
         diff = torch.mean(torch.abs(output - t_output)) / torch.mean(torch.abs(t_output))
         print('diff = ', diff)
         assert(diff < 0.001)
-
-    # warm up
-    for i in range(warm_up_iter):
-        moe = moes[i % layer_num]
-        expert_ids = torch.randint(0, expert_num, (qlen, n_routed_experts), dtype=torch.int64).contiguous()
-        weights = torch.rand((qlen, n_routed_experts), dtype=torch.float32).contiguous()
-        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        CPUInfer.submit(moe.forward, qlen, n_routed_experts, expert_ids.data_ptr(), weights.data_ptr(), input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-
-    # test
-    total_time = 0
-    for i in range(test_iter):
-        moe = moes[i % layer_num]
-        expert_ids = torch.randint(0, expert_num, (qlen, n_routed_experts), dtype=torch.int64).contiguous()
-        weights = torch.rand((qlen, n_routed_experts), dtype=torch.float32).contiguous()
-        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
-        input = input / 100
-        start = time.perf_counter()
-        CPUInfer.submit(moe.forward, qlen, n_routed_experts, expert_ids.data_ptr(), weights.data_ptr(), input.data_ptr(), output.data_ptr())
-        CPUInfer.sync()
-        end = time.perf_counter()
-        total_time += end - start
-    print('Time: ', total_time)
-    print('Iteration: ', test_iter) 
-    print('Time per iteration: ', total_time / test_iter)
-    print('Bandwidth: ', hidden_size * intermediate_size * 3 * n_routed_experts * 2 * test_iter / total_time / 1000 / 1000 / 1000, 'GB/s')
-    print("All tasks completed.")
\ No newline at end of file

ktransformers/ktransformers_ext/ext_bindings.cpp

@@ -3,8 +3,8 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-22 02:03:22
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
- * @LastEditTime : 2024-07-25 10:34:23
+ * @LastEditors  : chenht2022
+ * @LastEditTime : 2024-08-07 10:39:37
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
 // Python bindings
@@ -12,7 +12,6 @@
 #include <iostream>
 #include <memory>
 #include "cpu_backend/cpuinfer.h"
-#include "cuda_runtime.h"
 #include "device_launch_parameters.h"
 #include "llamafile/flags.h"
 #include "operators/llamafile/linear.h"
@@ -26,239 +25,155 @@
 namespace py = pybind11;
 using namespace pybind11::literals;
 
-// Binding functions for the Linear class
 class LinearBindings {
    public:
-    static void bind_forward(CPUInfer& cpuinfer, Linear* linear, py::args args, py::kwargs kwargs) {
-        auto input = args[0].cast<intptr_t>();
-        auto output = args[1].cast<intptr_t>();
-        cpuinfer.submit(&Linear::forward, linear,
-                        (const void*)input, (void*)output);
-    }
-
-    static void bind_warm_up(CPUInfer& cpuinfer, Linear* linear, py::args args, py::kwargs kwargs) {
-        cpuinfer.submit(&Linear::warm_up, linear);
-    }
-
-    static void bind_functions(CPUInfer& cpuinfer, py::object func, py::args args, py::kwargs kwargs) {
-        auto linear = func.attr("__self__").cast<Linear*>();
-        std::string func_name = py::str(func.attr("__func__").attr("__name__"));
-
-        if (func_name == "forward") {
-            bind_forward(cpuinfer, linear, args, kwargs);
-        } else if (func_name == "warm_up") {
-            bind_warm_up(cpuinfer, linear, args, kwargs);
-        } else {
-            throw py::value_error("Unsupported function: " +
-                                  std::string(func_name));
+    class WarmUpBindinds {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            Linear* linear;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&Linear::warm_up, args_->linear);
+        }
+        static std::pair<intptr_t, intptr_t> cpuinfer_interface(Linear& linear) {
+            Args* args = new Args{nullptr, &linear};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
+    class ForwardBindings {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            Linear* linear;
+            int qlen;
+            const void* input;
+            void* output;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&Linear::forward, args_->linear, args_->qlen, args_->input, args_->output);
         }
-    }
+        static std::pair<intptr_t, intptr_t> cpuinfer_interface(Linear& linear, int qlen, intptr_t input, intptr_t output) {
+            Args* args = new Args{nullptr, &linear, qlen, (const void*)input, (void*)output};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
 };
 
-// Binding functions for the MLP class
 class MLPBindings {
    public:
-    static void bind_forward(CPUInfer& cpuinfer, MLP* mlp, py::args args, py::kwargs kwargs) {
-        auto input = args[0].cast<intptr_t>();
-        auto output = args[1].cast<intptr_t>();
-        cpuinfer.submit(&MLP::forward, mlp,
-                        (const void*)input, (void*)output);
-    }
-
-    static void bind_warm_up(CPUInfer& cpuinfer, MLP* mlp, py::args args, py::kwargs kwargs) {
-        cpuinfer.submit(&MLP::warm_up, mlp);
-    }
-
-    static void bind_functions(CPUInfer& cpuinfer, py::object func, py::args args, py::kwargs kwargs) {
-        auto mlp = func.attr("__self__").cast<MLP*>();
-        std::string func_name = py::str(func.attr("__func__").attr("__name__"));
-
-        if (func_name == "forward") {
-            bind_forward(cpuinfer, mlp, args, kwargs);
-        } else if (func_name == "warm_up") {
-            bind_warm_up(cpuinfer, mlp, args, kwargs);
-        } else {
-            throw py::value_error("Unsupported function: " +
-                                  std::string(func_name));
+    class WarmUpBindinds {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            MLP* mlp;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&MLP::warm_up, args_->mlp);
         }
-    }
+        static std::pair<intptr_t, intptr_t> cpuinfer_interface(MLP& mlp) {
+            Args* args = new Args{nullptr, &mlp};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
+    class ForwardBindings {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            MLP* mlp;
+            int qlen;
+            const void* input;
+            void* output;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&MLP::forward, args_->mlp, args_->qlen, args_->input, args_->output);
+        }
+        static std::pair<intptr_t, intptr_t> cpuinfer_interface(MLP& mlp, int qlen, intptr_t input, intptr_t output) {
+            Args* args = new Args{nullptr, &mlp, qlen, (const void*)input, (void*)output};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
 };
 
-// Binding functions for the MOE class
 class MOEBindings {
    public:
-    static void bind_forward(CPUInfer& cpuinfer, MOE* moe, py::args args, py::kwargs kwargs) {
-        int qlen = args[0].cast<int>();
-        int k = args[1].cast<int>();
-        auto expert_ids = args[2].cast<intptr_t>();
-        auto weights = args[3].cast<intptr_t>();
-        auto input = args[4].cast<intptr_t>();
-        auto output = args[5].cast<intptr_t>();
-        cpuinfer.submit(&MOE::forward, moe,
-                        qlen, k, (const uint64_t*)expert_ids, (const float*)weights, (const void*)input, (void*)output);
-    }
-
-    static void bind_warm_up(CPUInfer& cpuinfer, MOE* moe, py::args args, py::kwargs kwargs) {
-        cpuinfer.submit(&MOE::warm_up, moe);
-    }
-
-    static void bind_functions(CPUInfer& cpuinfer, py::object func, py::args args, py::kwargs kwargs) {
-        auto moe = func.attr("__self__").cast<MOE*>();
-        std::string func_name = py::str(func.attr("__func__").attr("__name__"));
-
-        if (func_name == "forward") {
-            bind_forward(cpuinfer, moe, args, kwargs);
-        } else if (func_name == "warm_up") {
-            bind_warm_up(cpuinfer, moe, args, kwargs);
-        } else {
-            throw py::value_error("Unsupported function: " +
-                                  std::string(func_name));
+    class WarmUpBindinds {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            MOE* moe;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&MOE::warm_up, args_->moe);
         }
-    }
-};
-
-struct MOEForwardArgs {
-    CPUInfer* cpuinfer;
-    MOE* moe;
-    int qlen;
-    int k;
-    uint64_t* expert_ids;
-    float* weights;
-    void* input;
-    void* output;
+        static std::pair<intptr_t, intptr_t> cpuinfer_interface(MOE& moe) {
+            Args* args = new Args{nullptr, &moe};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
+    class ForwardBindings {
+       public:
+        struct Args {
+            CPUInfer* cpuinfer;
+            MOE* moe;
+            int qlen;
+            int k;
+            const uint64_t* expert_ids;
+            const float* weights;
+            const void* input;
+            void* output;
+        };
+        static void inner(void* args) {
+            Args* args_ = (Args*)args;
+            args_->cpuinfer->enqueue(&MOE::forward, args_->moe, args_->qlen, args_->k, args_->expert_ids, args_->weights, args_->input, args_->output);
+        }
+        static std::pair<intptr_t, intptr_t> cpuinfer_interface(MOE& moe, int qlen, int k, intptr_t expert_ids, intptr_t weights, intptr_t input, intptr_t output) {
+            Args* args = new Args{nullptr, &moe, qlen, k, (const uint64_t*)expert_ids, (const float*)weights, (const void*)input, (void*)output};
+            return std::make_pair((intptr_t)&inner, (intptr_t)args);
+        }
+    };
 };
 
-void submit_moe_forward_with_host_args_ptr(void* host_args_ptr) {
-    MOEForwardArgs* host_args = (MOEForwardArgs*)host_args_ptr;
-    host_args->cpuinfer->submit(&MOE::forward, host_args->moe,
-                                host_args->qlen, host_args->k, host_args->expert_ids, host_args->weights, host_args->input, host_args->output);
-}
-
-void cpuinfer_sync(void* host_args_ptr) {
-    CPUInfer* cpuinfer = (CPUInfer*)host_args_ptr;
-    cpuinfer->sync();
-}
-
 PYBIND11_MODULE(cpuinfer_ext, m) {
-    auto linear_module = m.def_submodule("linear");
+    py::class_<CPUInfer>(m, "CPUInfer")
+        .def(py::init<int>())
+        .def("submit", &CPUInfer::submit)
+        .def("submit_with_cuda_stream", &CPUInfer::submit_with_cuda_stream)
+        .def("sync", &CPUInfer::sync)
+        .def("sync_with_cuda_stream", &CPUInfer::sync_with_cuda_stream);
 
+    auto linear_module = m.def_submodule("linear");
     py::class_<LinearConfig>(linear_module, "LinearConfig")
-        .def(py::init([](int hidden_size, int intermediate_size, int stride, intptr_t proj, int proj_type, int hidden_type) {
-            return LinearConfig(hidden_size, intermediate_size, stride, (void*)proj, (ggml_type)proj_type, (ggml_type)hidden_type);
+        .def(py::init([](int hidden_size, int intermediate_size, int stride, int group_max_len, intptr_t proj, int proj_type, int hidden_type) {
+            return LinearConfig(hidden_size, intermediate_size, stride, group_max_len, (void*)proj, (ggml_type)proj_type, (ggml_type)hidden_type);
         }));
-
     py::class_<Linear>(linear_module, "Linear")
         .def(py::init<LinearConfig>())
-        .def("warm_up", [](Linear& linear) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        })
-        .def("forward", [](Linear& linear, intptr_t input, intptr_t output) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        });
+        .def("warm_up", &LinearBindings::WarmUpBindinds::cpuinfer_interface)
+        .def("forward", &LinearBindings::ForwardBindings::cpuinfer_interface);
 
     auto mlp_module = m.def_submodule("mlp");
-
     py::class_<MLPConfig>(mlp_module, "MLPConfig")
-        .def(py::init([](int hidden_size, int intermediate_size, int stride, intptr_t gate_proj, intptr_t up_proj, intptr_t down_proj, int gate_type, int up_type, int down_type, int hidden_type) {
-            return MLPConfig(hidden_size, intermediate_size, stride, (void*)gate_proj, (void*)up_proj, (void*)down_proj, (ggml_type)gate_type, (ggml_type)up_type, (ggml_type)down_type, (ggml_type)hidden_type);
+        .def(py::init([](int hidden_size, int intermediate_size, int stride, int group_max_len, intptr_t gate_proj, intptr_t up_proj, intptr_t down_proj, int gate_type, int up_type, int down_type, int hidden_type) {
+            return MLPConfig(hidden_size, intermediate_size, stride, group_max_len, (void*)gate_proj, (void*)up_proj, (void*)down_proj, (ggml_type)gate_type, (ggml_type)up_type, (ggml_type)down_type, (ggml_type)hidden_type);
         }));
-
     py::class_<MLP>(mlp_module, "MLP")
         .def(py::init<MLPConfig>())
-        .def("warm_up", [](MLP& mlp) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        })
-        .def("forward", [](MLP& mlp, intptr_t input, intptr_t output) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        });
+        .def("warm_up", &MLPBindings::WarmUpBindinds::cpuinfer_interface)
+        .def("forward", &MLPBindings::ForwardBindings::cpuinfer_interface);
 
     auto moe_module = m.def_submodule("moe");
-
     py::class_<MOEConfig>(moe_module, "MOEConfig")
         .def(py::init([](int expert_num, int routed_expert_num, int hidden_size, int intermediate_size, int stride, int group_min_len, int group_max_len, intptr_t gate_proj, intptr_t up_proj, intptr_t down_proj, int gate_type, int up_type, int down_type, int hidden_type) {
             return MOEConfig(expert_num, routed_expert_num, hidden_size, intermediate_size, stride, group_min_len, group_max_len, (void*)gate_proj, (void*)up_proj, (void*)down_proj, (ggml_type)gate_type, (ggml_type)up_type, (ggml_type)down_type, (ggml_type)hidden_type);
         }));
-
     py::class_<MOE>(moe_module, "MOE")
         .def(py::init<MOEConfig>())
-        .def("warm_up", [](MOE& moe) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        })
-        .def("forward", [](MOE& moe, int k, uint64_t expert_ids, intptr_t weights, intptr_t input, intptr_t output) {
-            throw std::runtime_error("!!! Doing nothing, please use CPUInfer.submit to call it!!!\n");
-        });
-
-    py::class_<CPUInfer>(m, "CPUInfer")
-        .def(py::init<int>())
-        .def("submit",
-             [linear_module, mlp_module, moe_module](CPUInfer& cpuinfer, py::object func, py::args args, py::kwargs kwargs) {
-                 if (py::hasattr(func, "__self__") &&
-                     py::hasattr(func, "__func__")) {
-                     std::string class_name = py::str(func.attr("__self__")
-                                                          .attr("__class__")
-                                                          .attr("__name__"));
-                     if (class_name == "Linear") {
-                         LinearBindings::bind_functions(cpuinfer, func,
-                                                        args, kwargs);
-                     } else if (class_name == "MLP") {
-                         MLPBindings::bind_functions(cpuinfer, func,
-                                                     args, kwargs);
-                     } else if (class_name == "MOE") {
-                         MOEBindings::bind_functions(cpuinfer, func,
-                                                     args, kwargs);
-                     } else {
-                         // handle other classes
-                         throw py::type_error("Unsupported class type: " +
-                                              class_name);
-                     }
-                 } else {
-                     // handle cases where func does not have __self__ or
-                     // __func__
-                     throw py::type_error(
-                         "Invalid function object: missing "
-                         "__self__ or __func__ attribute.");
-                 }
-             })
-        .def("submit_with_cuda_stream",
-             [linear_module, mlp_module, moe_module](CPUInfer& cpuinfer, intptr_t user_cuda_stream, py::object func, py::args args, py::kwargs kwargs) {
-                 if (py::hasattr(func, "__self__") &&
-                     py::hasattr(func, "__func__")) {
-                     std::string class_name = py::str(func.attr("__self__")
-                                                          .attr("__class__")
-                                                          .attr("__name__"));
-                     if (class_name == "MOE") {
-                         std::string func_name = py::str(func.attr("__func__").attr("__name__"));
-                         if (func_name == "forward") {
-                             auto moe = func.attr("__self__").cast<MOE*>();
-                             int qlen = args[0].cast<int>();
-                             int k = args[1].cast<int>();
-                             auto expert_ids = args[2].cast<intptr_t>();
-                             auto weights = args[3].cast<intptr_t>();
-                             auto input = args[4].cast<intptr_t>();
-                             auto output = args[5].cast<intptr_t>();
-                             MOEForwardArgs* moe_forward_args = new MOEForwardArgs{&cpuinfer, moe, qlen, k, (uint64_t*)expert_ids, (float*)weights, (void*)input, (void*)output};
-                             // submit_moe_forward_with_host_args_ptr(moe_forward_args);
-                             cudaLaunchHostFunc((cudaStream_t)user_cuda_stream, (cudaHostFn_t)submit_moe_forward_with_host_args_ptr, moe_forward_args);
-                         } else {
-                             throw py::value_error("Unsupported function: " +
-                                                   std::string(func_name));
-                         }
-                     } else {
-                         // handle other classes
-                         throw py::type_error("Unsupported class type: " +
-                                              class_name);
-                     }
-                 } else {
-                     // handle cases where func does not have __self__ or
-                     // __func__
-                     throw py::type_error(
-                         "Invalid function object: missing "
-                         "__self__ or __func__ attribute.");
-                 }
-             })
-        .def("sync_with_cuda_stream", [](CPUInfer& cpuinfer, intptr_t user_cuda_stream) {
-            // cpuinfer_sync((void*)(&cpuinfer));
-            cudaLaunchHostFunc((cudaStream_t)user_cuda_stream, (cudaHostFn_t)cpuinfer_sync, (void*)(&cpuinfer));
-        })
-        .def("sync", &CPUInfer::sync);
+        .def("warm_up", &MOEBindings::WarmUpBindinds::cpuinfer_interface)
+        .def("forward", &MOEBindings::ForwardBindings::cpuinfer_interface);
 }

ktransformers/ktransformers_ext/operators/custom_marlin/quantize/gptq.py

-import math
-import os
-import time
-from logging import getLogger
-
-import torch
-import torch.nn as nn
-import transformers
-
-from .quantizer import Quantizer
-
-
-logger = getLogger(__name__)
-
-torch.backends.cuda.matmul.allow_tf32 = False
-torch.backends.cudnn.allow_tf32 = False
-
-
-class GPTQ:
-    def __init__(self, layer):
-        self.layer = layer
-        self.dev = self.layer.weight.device
-        W = layer.weight.data.clone()
-        if isinstance(self.layer, nn.Conv2d):
-            W = W.flatten(1)
-        if isinstance(self.layer, transformers.pytorch_utils.Conv1D):
-            W = W.t()
-        self.rows = W.shape[0]
-        self.columns = W.shape[1]
-        self.H = torch.zeros((self.columns, self.columns), device=self.dev)
-        self.nsamples = 0
-        self.quantizer = Quantizer()
-
-    def add_batch(self, inp, out):
-        if os.environ.get("DEBUG"):
-            self.inp1 = inp
-            self.out1 = out
-        if len(inp.shape) == 2:
-            inp = inp.unsqueeze(0)
-        tmp = inp.shape[0]
-        if isinstance(self.layer, nn.Linear) or isinstance(self.layer, transformers.Conv1D):
-            if len(inp.shape) == 3:
-                inp = inp.reshape((-1, inp.shape[-1]))
-            inp = inp.t()
-        if isinstance(self.layer, nn.Conv2d):
-            unfold = nn.Unfold(
-                self.layer.kernel_size,
-                dilation=self.layer.dilation,
-                padding=self.layer.padding,
-                stride=self.layer.stride,
-            )
-            inp = unfold(inp)
-            inp = inp.permute([1, 0, 2])
-            inp = inp.flatten(1)
-        self.H *= self.nsamples / (self.nsamples + tmp)
-        self.nsamples += tmp
-        # inp = inp.float()
-        inp = math.sqrt(2 / self.nsamples) * inp.float()
-        # self.H += 2 / self.nsamples * inp.matmul(inp.t())
-        self.H += inp.matmul(inp.t())
-
-    def fasterquant(
-        self,
-        blocksize=128,
-        percdamp=0.01,
-        group_size=-1,
-        actorder=False,
-        static_groups=False,
-    ):
-        W = self.layer.weight.data.clone()
-        if isinstance(self.layer, nn.Conv2d):
-            W = W.flatten(1)
-        if isinstance(self.layer, transformers.Conv1D):
-            W = W.t()
-        W = W.float()
-
-        tick = time.time()
-
-        if not self.quantizer.ready():
-            self.quantizer.find_params(W, weight=True)
-
-        H = self.H
-        del self.H
-        dead = torch.diag(H) == 0
-        H[dead, dead] = 1
-        W[:, dead] = 0
-
-        g_idx = []
-        scale = []
-        zero = []
-        now_idx = 1
-
-        if static_groups:
-            import copy
-
-            groups = []
-            for i in range(0, self.columns, group_size):
-                quantizer = copy.deepcopy(self.quantizer)
-                quantizer.find_params(W[:, i : (i + group_size)], weight=True)
-                scale.append(quantizer.scale)
-                zero.append(quantizer.zero)
-                groups.append(quantizer)
-
-        if actorder:
-            perm = torch.argsort(torch.diag(H), descending=True)
-            W = W[:, perm]
-            H = H[perm][:, perm]
-            invperm = torch.argsort(perm)
-
-        Losses = torch.zeros_like(W)
-        Q = torch.zeros_like(W)
-
-        damp = percdamp * torch.mean(torch.diag(H))
-        diag = torch.arange(self.columns, device=self.dev)
-        H[diag, diag] += damp
-        H = torch.linalg.cholesky(H)
-        H = torch.cholesky_inverse(H)
-        H = torch.linalg.cholesky(H, upper=True)
-        Hinv = H
-
-        for i1 in range(0, self.columns, blocksize):
-            i2 = min(i1 + blocksize, self.columns)
-            count = i2 - i1
-
-            W1 = W[:, i1:i2].clone()
-            Q1 = torch.zeros_like(W1)
-            Err1 = torch.zeros_like(W1)
-            Losses1 = torch.zeros_like(W1)
-            Hinv1 = Hinv[i1:i2, i1:i2]
-
-            for i in range(count):
-                w = W1[:, i]
-                d = Hinv1[i, i]
-
-                if group_size != -1:
-                    if not static_groups:
-                        if (i1 + i) % group_size == 0:
-                            self.quantizer.find_params(W[:, (i1 + i) : (i1 + i + group_size)], weight=True)
-
-                        if ((i1 + i) // group_size) - now_idx == -1:
-                            scale.append(self.quantizer.scale)
-                            zero.append(self.quantizer.zero)
-                            now_idx += 1
-                    else:
-                        idx = i1 + i
-                        if actorder:
-                            idx = perm[idx]
-                        self.quantizer = groups[idx // group_size]
-
-                q = self.quantizer.quantize(w.unsqueeze(1)).flatten()
-                Q1[:, i] = q
-                Losses1[:, i] = (w - q) ** 2 / d**2
-
-                err1 = (w - q) / d
-                W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
-                Err1[:, i] = err1
-
-            Q[:, i1:i2] = Q1
-            Losses[:, i1:i2] = Losses1 / 2
-
-            W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])
-
-            if os.environ.get("DEBUG"):
-                self.layer.weight.data[:, :i2] = Q[:, :i2]
-                self.layer.weight.data[:, i2:] = W[:, i2:]
-                logger.debug(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
-                logger.debug(torch.sum(Losses))
-
-        torch.cuda.synchronize()
-        logger.info(f"duration: {(time.time() - tick)}")
-        logger.info(f"avg loss: {torch.sum(Losses).item() / self.nsamples}")
-
-        group_size = group_size if group_size != -1 else self.columns
-        if static_groups and actorder:
-            g_idx = [perm[i] // group_size for i in range(self.columns)]
-        else:
-            g_idx = [i // group_size for i in range(self.columns)]
-        g_idx = torch.tensor(g_idx, dtype=torch.int32, device=Q.device)
-        if actorder:
-            Q = Q[:, invperm]
-            g_idx = g_idx[invperm]
-
-        if isinstance(self.layer, transformers.Conv1D):
-            Q = Q.t()
-        self.layer.weight.data = Q.reshape(self.layer.weight.shape).type_as(self.layer.weight.data)
-        if os.environ.get("DEBUG"):
-            logger.debug(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
-
-        if scale == []:
-            scale.append(self.quantizer.scale)
-            zero.append(self.quantizer.zero)
-        scale = torch.cat(scale, dim=1)
-        zero = torch.cat(zero, dim=1)
-        return scale, zero, g_idx
-
-    def free(self):
-        if os.environ.get("DEBUG"):
-            self.inp1 = None
-            self.out1 = None
-        self.H = None
-        self.Losses = None
-        self.Trace = None
-        torch.cuda.empty_cache()
-
-
-__all__ = ["GPTQ"]

ktransformers/ktransformers_ext/operators/custom_marlin/quantize/gptq_marlin.py

-import enum
-from enum import Enum
-from typing import Any, Dict, List, Optional
-
-import torch
-from torch.nn.parameter import Parameter
-
-from vllm import _custom_ops as ops
-from vllm.logger import init_logger
-from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
-                                               set_weight_attrs)
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
-
-logger = init_logger(__name__)
-
-GPTQ_MARLIN_TILE = 16
-GPTQ_MARLIN_MIN_THREAD_N = 64
-GPTQ_MARLIN_MIN_THREAD_K = 128
-GPTQ_MARLIN_MAX_PARALLEL = 16
-
-GPTQ_MARLIN_SUPPORTED_NUM_BITS = [4, 8]
-GPTQ_MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
-GPTQ_MARLIN_SUPPORTED_SYM = [True]
-
-
-# Permutations for Marlin scale shuffling
-def get_scale_perms(num_bits: int):
-    scale_perm: List[int] = []
-    for i in range(8):
-        scale_perm.extend([i + 8 * j for j in range(8)])
-    scale_perm_single: List[int] = []
-    for i in range(4):
-        scale_perm_single.extend(
-            [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
-    return scale_perm, scale_perm_single
-
-
-def get_pack_factor(num_bits: int):
-    assert (num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS
-            ), f"Unsupported num_bits = {num_bits}"
-    return 32 // num_bits
-
-
-def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int,
-                          group_size: int, num_bits: int):
-    scale_perm, scale_perm_single = get_scale_perms(num_bits)
-    if group_size < size_k and group_size != -1:
-        s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
-    else:
-        s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
-    s = s.reshape((-1, size_n)).contiguous()
-
-    return s
-
-
-class GPTQMarlinConfig(QuantizationConfig):
-    """Config class for GPTQ Marlin"""
-
-    def __init__(self, weight_bits: int, group_size: int, desc_act: bool,
-                 is_sym: bool) -> None:
-        if desc_act and group_size == -1:
-            # In this case, act_order == True is the same as act_order == False
-            # (since we have only one group per output channel)
-            desc_act = False
-
-        self.weight_bits = weight_bits
-        self.group_size = group_size
-        self.desc_act = desc_act
-        self.is_sym = is_sym
-
-        # Verify
-        if self.weight_bits not in GPTQ_MARLIN_SUPPORTED_NUM_BITS:
-            raise ValueError(
-                f"Marlin does not support weight_bits = {self.weight_bits}. "
-                f"Only weight_bits = {GPTQ_MARLIN_SUPPORTED_NUM_BITS} "
-                "are supported.")
-        if self.group_size not in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES:
-            raise ValueError(
-                f"Marlin does not support group_size = {self.group_size}. "
-                f"Only group_sizes = {GPTQ_MARLIN_SUPPORTED_GROUP_SIZES} "
-                "are supported.")
-        if self.is_sym not in GPTQ_MARLIN_SUPPORTED_SYM:
-            raise ValueError(
-                f"Marlin does not support is_sym = {self.is_sym}. "
-                f"Only sym = {GPTQ_MARLIN_SUPPORTED_SYM} are supported.")
-
-        # Init
-        self.pack_factor = get_pack_factor(weight_bits)
-        self.tile_size = GPTQ_MARLIN_TILE
-        self.min_thread_n = GPTQ_MARLIN_MIN_THREAD_N
-        self.min_thread_k = GPTQ_MARLIN_MIN_THREAD_K
-        self.max_parallel = GPTQ_MARLIN_MAX_PARALLEL
-
-    def __repr__(self) -> str:
-        return (f"GPTQMarlinConfig(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_marlin"
-
-    @classmethod
-    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
-        return [torch.half, torch.bfloat16]
-
-    @classmethod
-    def get_min_capability(cls) -> int:
-        return 80
-
-    @classmethod
-    def get_config_filenames(cls) -> List[str]:
-        return ["quantize_config.json"]
-
-    @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "GPTQMarlinConfig":
-        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"])
-        is_sym = cls.get_from_keys(config, ["sym"])
-        return cls(weight_bits, group_size, desc_act, is_sym)
-
-    @classmethod
-    def override_quantization_method(cls, hf_quant_cfg,
-                                     user_quant) -> Optional[str]:
-        can_convert = cls.is_marlin_compatible(hf_quant_cfg)
-
-        is_valid_user_quant = (user_quant is None or user_quant == "marlin")
-
-        if can_convert and is_valid_user_quant:
-            msg = ("The model is convertible to {} during runtime."
-                   " Using {} kernel.".format(cls.get_name(), cls.get_name()))
-            logger.info(msg)
-            return cls.get_name()
-
-        if can_convert and user_quant == "gptq":
-            logger.info("Detected that the model can run with gptq_marlin"
-                        ", however you specified quantization=gptq explicitly,"
-                        " so forcing gptq. Use quantization=gptq_marlin for"
-                        " faster inference")
-        return None
-
-    def get_quant_method(
-            self,
-            layer: torch.nn.Module) -> Optional["GPTQMarlinLinearMethod"]:
-        if isinstance(layer, LinearBase):
-            return GPTQMarlinLinearMethod(self)
-        return None
-
-    def get_scaled_act_names(self) -> List[str]:
-        return []
-
-    @classmethod
-    def is_marlin_compatible(cls, quant_config: Dict[str, Any]):
-        # Extract data from quant config.
-        num_bits = quant_config.get("bits", None)
-        group_size = quant_config.get("group_size", None)
-        sym = quant_config.get("sym", None)
-        desc_act = quant_config.get("desc_act", None)
-
-        # If we cannot find the info needed in the config, cannot convert.
-        if (num_bits is None or group_size is None or sym is None
-                or desc_act is None):
-            return False
-
-        # If the capability of the device is too low, cannot convert.
-        major, minor = torch.cuda.get_device_capability()
-        device_capability = major * 10 + minor
-        if device_capability < cls.get_min_capability():
-            return False
-
-        # Otherwise, can convert if model satisfies marlin constraints.
-        return (num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS
-                and group_size in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES
-                and sym in GPTQ_MARLIN_SUPPORTED_SYM)
-
-
-class GPTQMarlinState(Enum):
-    REPACK = enum.auto()
-    READY = enum.auto()
-
-
-class GPTQMarlinLinearMethod(LinearMethodBase):
-    """Linear method for GPTQ Marlin.
-
-    Args:
-        quant_config: The GPTQ Marlin quantization config.
-    """
-
-    def __init__(self, quant_config: GPTQMarlinConfig) -> None:
-        self.quant_config = quant_config
-
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        input_size_per_partition: int,
-        output_partition_sizes: List[int],
-        input_size: int,
-        output_size: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ) -> None:
-        del output_size
-
-        # Normalize group_size
-        if self.quant_config.group_size != -1:
-            group_size = self.quant_config.group_size
-        else:
-            group_size = input_size
-
-        # Validate dtype
-        if params_dtype not in [torch.float16, torch.bfloat16]:
-            raise ValueError(f"The params dtype must be float16 "
-                             f"or bfloat16, but got {params_dtype}")
-
-        # Validate output_size_per_partition
-        output_size_per_partition = sum(output_partition_sizes)
-        if output_size_per_partition % self.quant_config.min_thread_n != 0:
-            raise ValueError(
-                f"Weight output_size_per_partition = "
-                f"{output_size_per_partition} is not divisible by "
-                f" min_thread_n = {self.quant_config.min_thread_n}.")
-
-        # Validate input_size_per_partition
-        if input_size_per_partition % self.quant_config.min_thread_k != 0:
-            raise ValueError(
-                f"Weight input_size_per_partition = "
-                f"{input_size_per_partition} is not divisible "
-                f"by min_thread_k = {self.quant_config.min_thread_k}.")
-
-        if (group_size < input_size
-                and input_size_per_partition % group_size != 0):
-            raise ValueError(
-                f"Weight input_size_per_partition = {input_size_per_partition}"
-                f" is not divisible by group_size = {group_size}.")
-
-        # Detect sharding of scales/zp
-
-        # By default, no sharding over "input dim"
-        scales_and_zp_size = input_size // group_size
-        scales_and_zp_input_dim = None
-
-        if self.quant_config.desc_act:
-            # Act-order case
-            assert self.quant_config.group_size != -1
-
-            is_k_full = input_size_per_partition == input_size
-
-        else:
-            # No act-order case
-
-            # K is always full due to full alignment with
-            # group-size and shard of scales/zp
-            is_k_full = True
-
-            # If this is a row-parallel case, then shard scales/zp
-            if (input_size != input_size_per_partition
-                    and self.quant_config.group_size != -1):
-                scales_and_zp_size = input_size_per_partition // group_size
-                scales_and_zp_input_dim = 0
-
-        # Init buffers
-
-        # Quantized weights
-        qweight = Parameter(
-            torch.empty(
-                input_size_per_partition // self.quant_config.pack_factor,
-                output_size_per_partition,
-                dtype=torch.int32,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            qweight,
-            {
-                **extra_weight_attrs,
-                "input_dim": 0,
-                "output_dim": 1,
-                "packed_dim": 0,
-                "pack_factor": self.quant_config.pack_factor,
-            },
-        )
-
-        # Activation order
-        g_idx = Parameter(
-            torch.empty(
-                input_size_per_partition,
-                dtype=torch.int32,
-            ),
-            requires_grad=False,
-        )
-        # Ignore warning from fused linear layers such as QKVParallelLinear.
-        set_weight_attrs(
-            g_idx,
-            {
-                **extra_weight_attrs, "input_dim": 0,
-                "ignore_warning": True
-            },
-        )
-
-        g_idx_sort_indices = torch.empty(
-            g_idx.shape,
-            dtype=torch.int32,
-        )
-
-        # Scales
-        scales = Parameter(
-            torch.empty(
-                scales_and_zp_size,
-                output_size_per_partition,
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            scales,
-            {
-                **extra_weight_attrs,
-                "input_dim": scales_and_zp_input_dim,
-                "output_dim": 1,
-            },
-        )
-
-        # Quantized zero-points
-        qzeros = Parameter(
-            torch.empty(
-                scales_and_zp_size,
-                output_size_per_partition // self.quant_config.pack_factor,
-                dtype=torch.int32,
-                device="meta",
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            qzeros,
-            {
-                **extra_weight_attrs,
-                "input_dim": scales_and_zp_input_dim,
-                "output_dim": 1,
-                "packed_dim": 1,
-                "pack_factor": self.quant_config.pack_factor,
-            },
-        )
-
-        # Allocate marlin workspace
-        max_workspace_size = (
-            output_size_per_partition //
-            self.quant_config.min_thread_n) * self.quant_config.max_parallel
-        workspace = torch.zeros(max_workspace_size,
-                                dtype=torch.int,
-                                requires_grad=False)
-
-        layer.register_parameter("qweight", qweight)
-        layer.register_parameter("g_idx", g_idx)
-        layer.register_parameter("scales", scales)
-        layer.register_parameter("qzeros", qzeros)
-        layer.g_idx_sort_indices = g_idx_sort_indices
-        layer.workspace = workspace
-        layer.input_size_per_partition = input_size_per_partition
-        layer.output_size_per_partition = output_size_per_partition
-        layer.input_size = input_size
-        layer.is_k_full = is_k_full
-        layer.marlin_state = GPTQMarlinState.REPACK
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        reshaped_x = x.reshape(-1, x.shape[-1])
-
-        size_m = reshaped_x.shape[0]
-        part_size_n = layer.output_size_per_partition
-        part_size_k = layer.input_size_per_partition
-        full_size_k = layer.input_size
-
-        out_shape = x.shape[:-1] + (part_size_n, )
-
-        if layer.marlin_state == GPTQMarlinState.REPACK:
-            layer.marlin_state = GPTQMarlinState.READY
-
-            # Newly generated tensors need to replace existing tensors that are
-            # already registered as parameters by vLLM (and won't be freed)
-            def replace_tensor(name, new_t):
-                # It is important to use resize_() here since it ensures
-                # the same buffer is reused
-                getattr(layer, name).resize_(new_t.shape)
-                getattr(layer, name).copy_(new_t)
-                del new_t
-
-            cur_device = layer.qweight.device
-
-            # Process act_order
-            if self.quant_config.desc_act:
-                # Get sorting based on g_idx
-                g_idx_sort_indices = torch.argsort(layer.g_idx).to(torch.int)
-
-                sorted_g_idx = layer.g_idx[g_idx_sort_indices]
-
-                replace_tensor("g_idx", sorted_g_idx)
-                replace_tensor("g_idx_sort_indices", g_idx_sort_indices)
-
-            else:
-                # Reset g_idx related tensors
-                layer.g_idx = Parameter(
-                    torch.empty(0, dtype=torch.int, device=cur_device),
-                    requires_grad=False,
-                )
-                layer.g_idx_sort_indices = Parameter(
-                    torch.empty(0, dtype=torch.int, device=cur_device),
-                    requires_grad=False,
-                )
-
-            # Repack weights
-            marlin_qweight = ops.gptq_marlin_repack(
-                layer.qweight,
-                layer.g_idx_sort_indices,
-                part_size_k,
-                part_size_n,
-                self.quant_config.weight_bits,
-            )
-            replace_tensor("qweight", marlin_qweight)
-
-            # Permute scales
-            scales_size_k = part_size_k
-            scales_size_n = part_size_n
-            if self.quant_config.desc_act:
-                scales_size_k = full_size_k
-
-            marlin_scales = marlin_permute_scales(
-                layer.scales,
-                scales_size_k,
-                scales_size_n,
-                self.quant_config.group_size,
-                self.quant_config.weight_bits,
-            )
-            replace_tensor("scales", marlin_scales)
-
-        output = ops.gptq_marlin_gemm(
-            reshaped_x,
-            layer.qweight,
-            layer.scales,
-            layer.g_idx,
-            layer.g_idx_sort_indices,
-            layer.workspace,
-            self.quant_config.weight_bits,
-            size_m,
-            part_size_n,
-            part_size_k,
-            layer.is_k_full,
-        )
-
-        if bias is not None:
-            output.add_(bias)  # In-place add
-
-        return output.reshape(out_shape)

ktransformers/ktransformers_ext/operators/custom_marlin/quantize/quantizer.py

-from logging import getLogger
-
-import torch
-import torch.nn as nn
-
-
-logger = getLogger(__name__)
-
-
-def quantize(x, scale, zero, maxq):
-    if maxq < 0:
-        return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero
-    q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
-    return scale * (q - zero)
-
-
-class Quantizer(nn.Module):
-    def __init__(self, shape=1):
-        super(Quantizer, self).__init__()
-        self.register_buffer("maxq", torch.tensor(0))
-        self.register_buffer("scale", torch.zeros(shape))
-        self.register_buffer("zero", torch.zeros(shape))
-
-    def configure(
-        self,
-        bits,
-        perchannel=False,
-        sym=True,
-        mse=False,
-        norm=2.4,
-        grid=100,
-        maxshrink=0.8,
-        trits=False,
-    ):
-        self.maxq = torch.tensor(2**bits - 1)
-        self.perchannel = perchannel
-        self.sym = sym
-        self.mse = mse
-        self.norm = norm
-        self.grid = grid
-        self.maxshrink = maxshrink
-        if trits:
-            self.maxq = torch.tensor(-1)
-
-    def find_params(self, x, weight=False):
-        dev = x.device
-        self.maxq = self.maxq.to(dev)
-
-        shape = x.shape
-        if self.perchannel:
-            if weight:
-                x = x.flatten(1)
-            else:
-                if len(shape) == 4:
-                    x = x.permute([1, 0, 2, 3])
-                    x = x.flatten(1)
-                if len(shape) == 3:
-                    x = x.reshape((-1, shape[-1])).t()
-                if len(shape) == 2:
-                    x = x.t()
-        else:
-            x = x.flatten().unsqueeze(0)
-
-        tmp = torch.zeros(x.shape[0], device=dev)
-        xmin = torch.minimum(x.min(1)[0], tmp)
-        xmax = torch.maximum(x.max(1)[0], tmp)
-
-        if self.sym:
-            xmax = torch.maximum(torch.abs(xmin), xmax)
-            tmp = xmin < 0
-            if torch.any(tmp):
-                xmin[tmp] = -xmax[tmp]
-        tmp = (xmin == 0) & (xmax == 0)
-        xmin[tmp] = -1
-        xmax[tmp] = +1
-
-        if self.maxq < 0:
-            self.scale = xmax
-            self.zero = xmin
-        else:
-            self.scale = (xmax - xmin) / self.maxq
-            if self.sym:
-                self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)
-            else:
-                self.zero = torch.round(-xmin / self.scale)
-
-        if self.mse:
-            best = torch.full([x.shape[0]], float("inf"), device=dev)
-            for i in range(int(self.maxshrink * self.grid)):
-                p = 1 - i / self.grid
-                xmin1 = p * xmin
-                xmax1 = p * xmax
-                scale1 = (xmax1 - xmin1) / self.maxq
-                zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
-                q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)
-                q -= x
-                q.abs_()
-                q.pow_(self.norm)
-                err = torch.sum(q, 1)
-                tmp = err < best
-                if torch.any(tmp):
-                    best[tmp] = err[tmp]
-                    self.scale[tmp] = scale1[tmp]
-                    self.zero[tmp] = zero1[tmp]
-        if not self.perchannel:
-            if weight:
-                tmp = shape[0]
-            else:
-                tmp = shape[1] if len(shape) != 3 else shape[2]
-            self.scale = self.scale.repeat(tmp)
-            self.zero = self.zero.repeat(tmp)
-
-        if weight:
-            shape = [-1] + [1] * (len(shape) - 1)
-            self.scale = self.scale.reshape(shape)
-            self.zero = self.zero.reshape(shape)
-            return
-        if len(shape) == 4:
-            self.scale = self.scale.reshape((1, -1, 1, 1))
-            self.zero = self.zero.reshape((1, -1, 1, 1))
-        if len(shape) == 3:
-            self.scale = self.scale.reshape((1, 1, -1))
-            self.zero = self.zero.reshape((1, 1, -1))
-        if len(shape) == 2:
-            self.scale = self.scale.unsqueeze(0)
-            self.zero = self.zero.unsqueeze(0)
-
-    def quantize(self, x):
-        if self.ready():
-            return quantize(x, self.scale, self.zero, self.maxq)
-        return x
-
-    def enabled(self):
-        return self.maxq > 0
-
-    def ready(self):
-        return torch.all(self.scale != 0)
-
-
-__all__ = ["Quantizer"]

ktransformers/ktransformers_ext/operators/custom_marlin/quantize/repack.py

-import torch
-import enum
-from enum import Enum
-from typing import Any, Dict, List, Optional
-from torch.nn.parameter import Parameter
-
-def apply(
-    self,
-    layer: torch.nn.Module,
-    x: torch.Tensor,
-    bias: Optional[torch.Tensor] = None,
-) -> torch.Tensor:
-    reshaped_x = x.reshape(-1, x.shape[-1])
-
-    size_m = reshaped_x.shape[0]
-    part_size_n = layer.output_size_per_partition
-    part_size_k = layer.input_size_per_partition
-    full_size_k = layer.input_size
-
-    out_shape = x.shape[:-1] + (part_size_n, )
-
-    if layer.marlin_state == GPTQMarlinState.REPACK:
-        layer.marlin_state = GPTQMarlinState.READY
-
-        # Newly generated tensors need to replace existing tensors that are
-        # already registered as parameters by vLLM (and won't be freed)
-        def replace_tensor(name, new_t):
-            # It is important to use resize_() here since it ensures
-            # the same buffer is reused
-            getattr(layer, name).resize_(new_t.shape)
-            getattr(layer, name).copy_(new_t)
-            del new_t
-
-        cur_device = layer.qweight.device
-
-        # Process act_order
-        if self.quant_config.desc_act:
-            # Get sorting based on g_idx
-            g_idx_sort_indices = torch.argsort(layer.g_idx).to(torch.int)
-
-            sorted_g_idx = layer.g_idx[g_idx_sort_indices]
-
-            replace_tensor("g_idx", sorted_g_idx)
-            replace_tensor("g_idx_sort_indices", g_idx_sort_indices)
-
-        else:
-            # Reset g_idx related tensors
-            layer.g_idx = Parameter(
-                torch.empty(0, dtype=torch.int, device=cur_device),
-                requires_grad=False,
-            )
-            layer.g_idx_sort_indices = Parameter(
-                torch.empty(0, dtype=torch.int, device=cur_device),
-                requires_grad=False,
-            )
-
-        # Repack weights
-        marlin_qweight = ops.gptq_marlin_repack(
-            layer.qweight,
-            layer.g_idx_sort_indices,
-            part_size_k,
-            part_size_n,
-            self.quant_config.weight_bits,
-        )
-        replace_tensor("qweight", marlin_qweight)
-
-        # Permute scales
-        scales_size_k = part_size_k
-        scales_size_n = part_size_n
-        if self.quant_config.desc_act:
-            scales_size_k = full_size_k
-
-        marlin_scales = marlin_permute_scales(
-            layer.scales,
-            scales_size_k,
-            scales_size_n,
-            self.quant_config.group_size,
-            self.quant_config.weight_bits,
-        )
-        replace_tensor("scales", marlin_scales)
-
-    output = ops.gptq_marlin_gemm(
-        reshaped_x,
-        layer.qweight,
-        layer.scales,
-        layer.g_idx,
-        layer.g_idx_sort_indices,
-        layer.workspace,
-        self.quant_config.weight_bits,
-        size_m,
-        part_size_n,
-        part_size_k,
-        layer.is_k_full,
-    )
-
-    if bias is not None:
-        output.add_(bias)  # In-place add
-
-    return output.reshape(out_shape)

ktransformers/ktransformers_ext/operators/custom_marlin/quantize/utils/marlin_utils.py

@@ -220,7 +220,7 @@ def compute_max_diff(output, output_ref):
 
 class MarlinWorkspace:
 
-    def __init__(self, out_features, min_thread_n, max_parallel):
+    def __init__(self, out_features, min_thread_n, max_parallel, device):
         assert (out_features % min_thread_n == 0), (
             "out_features = {} is undivisible by min_thread_n = {}".format(
                 out_features, min_thread_n))
@@ -229,4 +229,4 @@ class MarlinWorkspace:
 
         self.scratch = torch.zeros(max_workspace_size,
                                    dtype=torch.int,
-                                   device="cuda")
+                                   device=device)

ktransformers/ktransformers_ext/operators/llamafile/linear.cpp

@@ -3,7 +3,7 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-12 10:07:58
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:34:58
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
@@ -13,9 +13,15 @@ Linear::Linear(LinearConfig config) {
     config_ = config;
     proj_ = config_.proj;
 
-    input_fp32_.resize(config_.input_size);
-    proj_input_.resize(config_.input_size * 4);
-    proj_output_.resize(config_.output_size);
+    std::vector<std::pair<void**, uint64_t>> mem_requests;
+    mem_requests.push_back({(void**)&input_fp32_, sizeof(float) * config_.group_max_len * config_.input_size});
+    mem_requests.push_back({(void**)&proj_input_, config_.group_max_len * config_.input_size * ggml_type_size(ggml_internal_get_type_traits(config_.proj_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.proj_type).vec_dot_type)});
+    mem_requests.push_back({(void**)&proj_output_, sizeof(float) * config_.group_max_len * config_.output_size});
+    shared_mem_buffer.alloc(this, mem_requests);
+}
+
+Linear::~Linear() {
+    shared_mem_buffer.dealloc(this);
 }
 
 void Linear::warm_up(Backend* backend) {
@@ -26,22 +32,42 @@ void Linear::warm_up(Backend* backend) {
         input_fp32[i] = 0;
     }
     from_float(input_fp32.data(), input.data(), config_.input_size, config_.hidden_type);
-    forward(input.data(), output.data(), backend);
+    forward_many(1, input.data(), output.data(), backend);
 }
 
-void Linear::forward(const void* input, void* output, Backend* backend) {
+void Linear::forward_many(int qlen, const void* input, void* output, Backend* backend) {
     const void* proj_input_ptr;
     if (config_.hidden_type == ggml_internal_get_type_traits(config_.proj_type).vec_dot_type) {
         proj_input_ptr = input;
     } else {
-        to_float(input, input_fp32_.data(), config_.input_size, config_.hidden_type);
-        from_float(input_fp32_.data(), proj_input_.data(), config_.input_size, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type);
-        proj_input_ptr = proj_input_.data();
+        to_float(input, input_fp32_, qlen * config_.input_size, config_.hidden_type);
+        from_float(input_fp32_, proj_input_, qlen * config_.input_size, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type);
+        proj_input_ptr = proj_input_;
     }
     int nth = config_.output_size / config_.stride;
     backend->do_work_stealing_job(nth, [&](int task_id) {
-        int ith = task_id % nth;
-        llamafile_sgemm(config_.output_size, 1, config_.input_size / ggml_blck_size(config_.proj_type), proj_, config_.input_size / ggml_blck_size(config_.proj_type), proj_input_ptr, config_.input_size / ggml_blck_size(config_.proj_type), proj_output_.data(), config_.output_size, ith, nth, GGML_TASK_TYPE_COMPUTE, config_.proj_type, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        int ith = task_id;
+        void* proj_ptr = (uint8_t*)proj_ + ith * config_.stride * config_.input_size * ggml_type_size(config_.proj_type) / ggml_blck_size(config_.proj_type);
+        float* proj_output_ptr = proj_output_ + ith * config_.stride;
+        llamafile_sgemm(config_.stride, qlen, config_.input_size / ggml_blck_size(config_.proj_type), proj_ptr, config_.input_size / ggml_blck_size(config_.proj_type), proj_input_ptr, config_.input_size / ggml_blck_size(config_.proj_type), proj_output_ptr, config_.output_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.proj_type, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        if (config_.stride % ggml_blck_size(config_.hidden_type) == 0) {
+            for (int i = 0; i < qlen; i++) {
+                float* output_fp32_ptr = proj_output_ + i * config_.output_size + ith * config_.stride;
+                void* output_ptr = (uint8_t*)output + i * config_.output_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type) + ith * config_.stride * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type);
+                from_float(output_fp32_ptr, output_ptr, config_.stride, config_.hidden_type);
+            }
+        }
     });
-    from_float(proj_output_.data(), output, config_.output_size, config_.hidden_type);
+    if (config_.stride % ggml_blck_size(config_.hidden_type) != 0) {
+        from_float(proj_output_, output, qlen * config_.output_size, config_.hidden_type);
+    }
+}
+
+void Linear::forward(int qlen, const void* input, void* output, Backend* backend) {
+    if (qlen <= 0) {
+        return;
+    }
+    int forward_len = std::min(qlen, config_.group_max_len);
+    forward_many(forward_len, input, output, backend);
+    forward(qlen - forward_len, (uint8_t*)input + forward_len * config_.input_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), (uint8_t*)output + forward_len * config_.output_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), backend);
 }
\ No newline at end of file

ktransformers/ktransformers_ext/operators/llamafile/linear.h

@@ -3,7 +3,7 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-12 10:07:58
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:35:00
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
@@ -22,34 +22,38 @@
 #include "llama.cpp/ggml-quants.h"
 #include "llama.cpp/ggml.h"
 #include "llamafile/sgemm.h"
+#include "shared_mem_buffer.h"
 
 struct LinearConfig {
     int input_size;
     int output_size;
     int stride;
+    int group_max_len;
     void* proj;
     ggml_type proj_type;
     ggml_type hidden_type;
 
     LinearConfig() {}
 
-    LinearConfig(int input_size, int output_size, int stride, void* proj, ggml_type proj_type, ggml_type hidden_type)
-        : input_size(input_size), output_size(output_size), stride(stride), proj(proj), proj_type(proj_type), hidden_type(hidden_type) {}
+    LinearConfig(int input_size, int output_size, int stride, int group_max_len, void* proj, ggml_type proj_type, ggml_type hidden_type)
+        : input_size(input_size), output_size(output_size), stride(stride), group_max_len(group_max_len), proj(proj), proj_type(proj_type), hidden_type(hidden_type) {}
 };
 
 class Linear {
    public:
     Linear(LinearConfig);
+    ~Linear();
     void warm_up(Backend* backend);
-    void forward(const void* input, void* output, Backend* backend);
+    void forward_many(int qlen, const void* input, void* output, Backend* backend);
+    void forward(int qlen, const void* input, void* output, Backend* backend);
 
    private:
     LinearConfig config_;
     void* proj_;  // [output_size * input_size ( /32 if quantized)]
 
-    std::vector<float> input_fp32_;    // [input_size]
-    std::vector<uint8_t> proj_input_;  // [input_size * 4]
-    std::vector<float> proj_output_;   // [output_size]
+    float* input_fp32_;    // [group_max_len * input_size]
+    uint8_t* proj_input_;  // [group_max_len * input_size * ggml_type_size(ggml_internal_get_type_traits(proj_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(proj_type).vec_dot_type)]
+    float* proj_output_;   // [group_max_len * output_size]
 };
 
 #endif
\ No newline at end of file

ktransformers/ktransformers_ext/operators/llamafile/mlp.cpp

@@ -3,7 +3,7 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-16 10:43:18
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:35:04
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
@@ -15,14 +15,20 @@ MLP::MLP(MLPConfig config) {
     up_proj_ = config_.up_proj;
     down_proj_ = config_.down_proj;
 
-    input_fp32_.resize(config_.hidden_size);
-    gate_input_.resize(config_.hidden_size * 4);
-    up_input_.resize(config_.hidden_size * 4);
-    gate_output_.resize(config_.intermediate_size);
-    up_output_.resize(config_.intermediate_size);
-    intermediate_fp32_.resize(config_.intermediate_size);
-    down_input_.resize(config_.intermediate_size * 4);
-    down_output_.resize(config_.hidden_size);
+    std::vector<std::pair<void**, uint64_t>> mem_requests;
+    mem_requests.push_back({(void**)&input_fp32_, sizeof(float) * config_.group_max_len * config_.hidden_size});
+    mem_requests.push_back({(void**)&gate_input_, config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type)});
+    mem_requests.push_back({(void**)&up_input_, config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type)});
+    mem_requests.push_back({(void**)&gate_output_, sizeof(float) * config_.group_max_len * config_.intermediate_size});
+    mem_requests.push_back({(void**)&up_output_, sizeof(float) * config_.group_max_len * config_.intermediate_size});
+    mem_requests.push_back({(void**)&intermediate_fp32_, sizeof(float) * config_.group_max_len * config_.intermediate_size});
+    mem_requests.push_back({(void**)&down_input_, config_.group_max_len * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type)});
+    mem_requests.push_back({(void**)&down_output_, sizeof(float) * config_.group_max_len * config_.hidden_size});
+    shared_mem_buffer.alloc(this, mem_requests);
+}
+
+MLP::~MLP() {
+    shared_mem_buffer.dealloc(this);
 }
 
 void MLP::warm_up(Backend* backend) {
@@ -33,33 +39,33 @@ void MLP::warm_up(Backend* backend) {
         input_fp32[i] = 0;
     }
     from_float(input_fp32.data(), input.data(), config_.hidden_size, config_.hidden_type);
-    forward(input.data(), output.data(), backend);
+    forward_many(1, input.data(), output.data(), backend);
 }
 
 static float act_fn(float x) {
     return x / (1.0f + expf(-x));
 }
 
-void MLP::forward(const void* input, void* output, Backend* backend) {
+void MLP::forward_many(int qlen, const void* input, void* output, Backend* backend) {
     const void* gate_input_ptr;
     const void* up_input_ptr;
     if (config_.hidden_type == ggml_internal_get_type_traits(config_.gate_type).vec_dot_type && config_.hidden_type == ggml_internal_get_type_traits(config_.up_type).vec_dot_type) {
         gate_input_ptr = up_input_ptr = input;
     } else {
-        to_float(input, input_fp32_.data(), config_.hidden_size, config_.hidden_type);
+        to_float(input, input_fp32_, qlen * config_.hidden_size, config_.hidden_type);
         if (ggml_internal_get_type_traits(config_.gate_type).vec_dot_type == ggml_internal_get_type_traits(config_.up_type).vec_dot_type) {
-            from_float(input_fp32_.data(), gate_input_.data(), config_.hidden_size, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-            gate_input_ptr = up_input_ptr = gate_input_.data();
+            from_float(input_fp32_, gate_input_, qlen * config_.hidden_size, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
+            gate_input_ptr = up_input_ptr = gate_input_;
         } else {
             if (config_.hidden_type != ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) {
-                from_float(input_fp32_.data(), gate_input_.data(), config_.hidden_size, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-                gate_input_ptr = gate_input_.data();
+                from_float(input_fp32_, gate_input_, qlen * config_.hidden_size, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
+                gate_input_ptr = gate_input_;
             } else {
                 gate_input_ptr = input;
             }
             if (config_.hidden_type != ggml_internal_get_type_traits(config_.up_type).vec_dot_type) {
-                from_float(input_fp32_.data(), up_input_.data(), config_.hidden_size, ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
-                up_input_ptr = up_input_.data();
+                from_float(input_fp32_, up_input_, qlen * config_.hidden_size, ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
+                up_input_ptr = up_input_;
             } else {
                 up_input_ptr = input;
             }
@@ -69,35 +75,49 @@ void MLP::forward(const void* input, void* output, Backend* backend) {
     backend->do_work_stealing_job(nth, [&](int task_id) {
         int ith = task_id;
         void* gate_proj_ptr = (uint8_t*)gate_proj_ + ith * config_.stride * config_.hidden_size * ggml_type_size(config_.gate_type) / ggml_blck_size(config_.gate_type);
-        float* gate_output_ptr = gate_output_.data() + ith * config_.stride;
-        llamafile_sgemm(config_.stride, 1, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_proj_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_input_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_output_ptr, config_.stride, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.gate_type, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        float* gate_output_ptr = gate_output_ + ith * config_.stride;
+        llamafile_sgemm(config_.stride, qlen, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_proj_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_input_ptr, config_.hidden_size / ggml_blck_size(config_.gate_type), gate_output_ptr, config_.intermediate_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.gate_type, ggml_internal_get_type_traits(config_.gate_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
         void* up_proj_ptr = (uint8_t*)up_proj_ + ith * config_.stride * config_.hidden_size * ggml_type_size(config_.up_type) / ggml_blck_size(config_.up_type);
-        float* up_output_ptr = up_output_.data() + ith * config_.stride;
-        llamafile_sgemm(config_.stride, 1, config_.hidden_size / ggml_blck_size(config_.up_type), up_proj_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_input_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_output_ptr, config_.stride, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.up_type, ggml_internal_get_type_traits(config_.up_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
-        for (int i = ith * config_.stride; i < (ith + 1) * config_.stride; i++) {
-            intermediate_fp32_[i] = act_fn(gate_output_[i]) * up_output_[i];
-        }
-        if (config_.stride % ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) == 0) {
-            float* intermediate_fp32_ptr = intermediate_fp32_.data() + ith * config_.stride;
-            void* down_input_ptr = (uint8_t*)down_input_.data() + ith * config_.stride * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
-            from_float(intermediate_fp32_ptr, down_input_ptr, config_.stride, ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
+        float* up_output_ptr = up_output_ + ith * config_.stride;
+        llamafile_sgemm(config_.stride, qlen, config_.hidden_size / ggml_blck_size(config_.up_type), up_proj_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_input_ptr, config_.hidden_size / ggml_blck_size(config_.up_type), up_output_ptr, config_.intermediate_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.up_type, ggml_internal_get_type_traits(config_.up_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        for (int i = 0; i < qlen; i++) {
+            for (int j = ith * config_.stride; j < (ith + 1) * config_.stride; j++) {
+                intermediate_fp32_[i * config_.intermediate_size + j] = act_fn(gate_output_[i * config_.intermediate_size + j]) * up_output_[i * config_.intermediate_size + j];
+            }
+            if (config_.stride % ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) == 0) {
+                float* intermediate_fp32_ptr = intermediate_fp32_ + i * config_.intermediate_size + ith * config_.stride;
+                void* down_input_ptr = (uint8_t*)down_input_ + i * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) + ith * config_.stride * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
+                from_float(intermediate_fp32_ptr, down_input_ptr, config_.stride, ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
+            }
         }
     });
     if (config_.stride % ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) != 0) {
-        from_float(intermediate_fp32_.data(), down_input_.data(), config_.intermediate_size, ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
+        from_float(intermediate_fp32_, down_input_, qlen * config_.intermediate_size, ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
     }
     nth = config_.hidden_size / config_.stride;
     backend->do_work_stealing_job(nth, [&](int task_id) {
         int ith = task_id;
         void* down_proj_ptr = (uint8_t*)down_proj_ + ith * config_.stride * config_.intermediate_size * ggml_type_size(config_.down_type) / ggml_blck_size(config_.down_type);
-        float* down_output_ptr = down_output_.data() + ith * config_.stride;
-        llamafile_sgemm(config_.stride, 1, config_.intermediate_size / ggml_blck_size(config_.down_type), down_proj_ptr, config_.intermediate_size / ggml_blck_size(config_.down_type), down_input_.data(), config_.intermediate_size / ggml_blck_size(config_.down_type), down_output_ptr, config_.stride, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.down_type, ggml_internal_get_type_traits(config_.down_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
+        float* down_output_ptr = down_output_ + ith * config_.stride;
+        llamafile_sgemm(config_.stride, qlen, config_.intermediate_size / ggml_blck_size(config_.down_type), down_proj_ptr, config_.intermediate_size / ggml_blck_size(config_.down_type), down_input_, config_.intermediate_size / ggml_blck_size(config_.down_type), down_output_ptr, config_.hidden_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.down_type, ggml_internal_get_type_traits(config_.down_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
         if (config_.stride % ggml_blck_size(config_.hidden_type) == 0) {
-            void* output_ptr = (uint8_t*)output + ith * config_.stride * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type);
-            from_float(down_output_ptr, output_ptr, config_.stride, config_.hidden_type);
+            for (int i = 0; i < qlen; i++) {
+                float* output_fp32_ptr = down_output_ + i * config_.hidden_size + ith * config_.stride;
+                void* output_ptr = (uint8_t*)output + i * config_.hidden_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type) + ith * config_.stride * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type);
+                from_float(output_fp32_ptr, output_ptr, config_.stride, config_.hidden_type);
+            }
         }
     });
     if (config_.stride % ggml_blck_size(config_.hidden_type) != 0) {
-        from_float(down_output_.data(), output, config_.hidden_size, config_.hidden_type);
+        from_float(down_output_, output, qlen * config_.hidden_size, config_.hidden_type);
     }
 }
+
+void MLP::forward(int qlen, const void* input, void* output, Backend* backend) {
+    if (qlen <= 0) {
+        return;
+    }
+    int forward_len = std::min(qlen, config_.group_max_len);
+    forward_many(forward_len, input, output, backend);
+    forward(qlen - forward_len, (uint8_t*)input + forward_len * config_.hidden_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), (uint8_t*)output + forward_len * config_.hidden_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), backend);
+}
\ No newline at end of file

ktransformers/ktransformers_ext/operators/llamafile/mlp.h

@@ -3,7 +3,7 @@
  * @Author       : chenht2022
  * @Date         : 2024-07-12 10:07:58
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:35:06
  * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
  **/
@@ -22,11 +22,13 @@
 #include "llama.cpp/ggml-quants.h"
 #include "llama.cpp/ggml.h"
 #include "llamafile/sgemm.h"
+#include "shared_mem_buffer.h"
 
 struct MLPConfig {
     int hidden_size;
     int intermediate_size;
     int stride;
+    int group_max_len;
     void* gate_proj;
     void* up_proj;
     void* down_proj;
@@ -37,15 +39,17 @@ struct MLPConfig {
 
     MLPConfig() {}
 
-    MLPConfig(int hidden_size, int intermediate_size, int stride, void* gate_proj, void* up_proj, void* down_proj, ggml_type gate_type, ggml_type up_type, ggml_type down_type, ggml_type hidden_type)
-        : hidden_size(hidden_size), intermediate_size(intermediate_size), stride(stride), gate_proj(gate_proj), up_proj(up_proj), down_proj(down_proj), gate_type(gate_type), up_type(up_type), down_type(down_type), hidden_type(hidden_type) {}
+    MLPConfig(int hidden_size, int intermediate_size, int stride, int group_max_len, void* gate_proj, void* up_proj, void* down_proj, ggml_type gate_type, ggml_type up_type, ggml_type down_type, ggml_type hidden_type)
+        : hidden_size(hidden_size), intermediate_size(intermediate_size), stride(stride), group_max_len(group_max_len), gate_proj(gate_proj), up_proj(up_proj), down_proj(down_proj), gate_type(gate_type), up_type(up_type), down_type(down_type), hidden_type(hidden_type) {}
 };
 
 class MLP {
    public:
     MLP(MLPConfig);
+    ~MLP();
     void warm_up(Backend* backend);
-    void forward(const void* input, void* output, Backend* backend);
+    void forward_many(int qlen, const void* input, void* output, Backend* backend);
+    void forward(int qlen, const void* input, void* output, Backend* backend);
 
    private:
     MLPConfig config_;
@@ -53,14 +57,14 @@ class MLP {
     void* up_proj_;    // [intermediate_size * hidden_size ( /32 if quantized)]
     void* down_proj_;  // [hidden_size * intermediate_size ( /32 if quantized)]
 
-    std::vector<float> input_fp32_;         // [hidden_size]
-    std::vector<uint8_t> gate_input_;       // [hidden_size * 4]
-    std::vector<uint8_t> up_input_;         // [hidden_size * 4]
-    std::vector<float> gate_output_;        // [intermediate_size]
-    std::vector<float> up_output_;          // [intermediate_size]
-    std::vector<float> intermediate_fp32_;  // [intermediate_size]
-    std::vector<uint8_t> down_input_;       // [intermediate_size * 4]
-    std::vector<float> down_output_;        // [hidden_size]
+    float* input_fp32_;         // [group_max_len * hidden_size]
+    uint8_t* gate_input_;       // [group_max_len * hidden_size * ggml_type_size(ggml_internal_get_type_traits(gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(gate_type).vec_dot_type)]
+    uint8_t* up_input_;         // [group_max_len * hidden_size * ggml_type_size(ggml_internal_get_type_traits(up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(up_type).vec_dot_type)]
+    float* gate_output_;        // [group_max_len * intermediate_size]
+    float* up_output_;          // [group_max_len * intermediate_size]
+    float* intermediate_fp32_;  // [group_max_len * intermediate_size]
+    uint8_t* down_input_;       // [group_max_len * intermediate_size * ggml_type_size(ggml_internal_get_type_traits(down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(down_type).vec_dot_type)]
+    float* down_output_;        // [group_max_len * hidden_size]
 };
 
 #endif
\ No newline at end of file

ktransformers/ktransformers_ext/operators/llamafile/moe.cpp

 /**
- * @Description  :  
+ * @Description  :
  * @Author       : chenht2022
  * @Date         : 2024-07-22 02:03:22
  * @Version      : 1.0.0
- * @LastEditors  : chenht2022 
+ * @LastEditors  : chenht2022
  * @LastEditTime : 2024-07-25 10:35:07
- * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
-**/
+ * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+ **/
 #include "moe.h"
 #include <iostream>
 #include <cstdint>
 
-uint8_t* MOE::buffer_ = nullptr;
-
 MOE::MOE(MOEConfig config) {
     config_ = config;
     gate_proj_ = config_.gate_proj;
     up_proj_ = config_.up_proj;
     down_proj_ = config_.down_proj;
 
-    if (MOE::buffer_ == nullptr) {
-        uint64_t buffer_size = 0;
-        buffer_size += sizeof(float) * config_.group_max_len * config_.hidden_size;
-        buffer_size += config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-        buffer_size += config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
-        buffer_size += config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-        buffer_size += config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
-        buffer_size += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-        buffer_size += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-        buffer_size += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-        buffer_size += config_.routed_expert_num * config_.group_max_len * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
-        buffer_size += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.hidden_size;
-        buffer_size += sizeof(float) * config_.group_max_len * config_.hidden_size;
-        buffer_ = (uint8_t*)malloc(buffer_size);
-    }
-
-    uint64_t offset = 0;
-    s_input_fp32_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.hidden_size;
-    s_gate_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-    s_up_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
+    std::vector<std::pair<void**, uint64_t>> s_mem_requests;
+    s_mem_requests.push_back({(void**)&s_input_fp32_, sizeof(float) * config_.hidden_size});
+    s_mem_requests.push_back({(void**)&s_gate_input_, config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type)});
+    s_mem_requests.push_back({(void**)&s_up_input_, config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type)});
     s_gate_output_.resize(config_.routed_expert_num);
     s_up_output_.resize(config_.routed_expert_num);
     s_intermediate_fp32_.resize(config_.routed_expert_num);
     s_down_input_.resize(config_.routed_expert_num);
     s_down_output_.resize(config_.routed_expert_num);
     for (int i = 0; i < config_.routed_expert_num; i++) {
-        s_gate_output_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.intermediate_size;
-        s_up_output_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.intermediate_size;
-        s_intermediate_fp32_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.intermediate_size;
-        s_down_input_[i] = (uint8_t*)(buffer_ + offset);
-        offset += config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
-        s_down_output_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.hidden_size;
+        s_mem_requests.push_back({(void**)&s_gate_output_[i], sizeof(float) * config_.intermediate_size});
+        s_mem_requests.push_back({(void**)&s_up_output_[i], sizeof(float) * config_.intermediate_size});
+        s_mem_requests.push_back({(void**)&s_intermediate_fp32_[i], sizeof(float) * config_.intermediate_size});
+        s_mem_requests.push_back({(void**)&s_down_input_[i], config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type)});
+        s_mem_requests.push_back({(void**)&s_down_output_[i], sizeof(float) * config_.hidden_size});
     }
-    s_output_fp32_ = (float*)(buffer_ + offset);
+    s_mem_requests.push_back({(void**)&s_output_fp32_, sizeof(float) * config_.hidden_size});
+    shared_mem_buffer.alloc(this, s_mem_requests);
 
-    offset = 0;
+    std::vector<std::pair<void**, uint64_t>> m_mem_requests;
     m_input_fp32_.resize(config_.group_max_len);
     m_gate_input_.resize(config_.group_max_len);
     m_up_input_.resize(config_.group_max_len);
     for (int i = 0; i < config_.group_max_len; i++) {
-        m_input_fp32_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.hidden_size;
-        m_gate_input_[i] = (uint8_t*)(buffer_ + offset);
-        offset += config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-        m_up_input_[i] = (uint8_t*)(buffer_ + offset);
-        offset += config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
+        m_mem_requests.push_back({(void**)&m_input_fp32_[i], sizeof(float) * config_.hidden_size});
+        m_mem_requests.push_back({(void**)&m_gate_input_[i], config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type)});
+        m_mem_requests.push_back({(void**)&m_up_input_[i], config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type)});
     }
-    m_local_gate_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type);
-    m_local_up_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type);
-    m_local_gate_output_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-    m_local_up_output_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-    m_local_intermediate_fp32_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size;
-    m_local_down_input_ = (uint8_t*)(buffer_ + offset);
-    offset += config_.routed_expert_num * config_.group_max_len * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type);
-    m_local_down_output_ = (float*)(buffer_ + offset);
-    offset += sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.hidden_size;
+    m_mem_requests.push_back({(void**)&m_local_gate_input_, config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.gate_type).vec_dot_type)});
+    m_mem_requests.push_back({(void**)&m_local_up_input_, config_.routed_expert_num * config_.group_max_len * config_.hidden_size * ggml_type_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.up_type).vec_dot_type)});
+    m_mem_requests.push_back({(void**)&m_local_gate_output_, sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size});
+    m_mem_requests.push_back({(void**)&m_local_up_output_, sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size});
+    m_mem_requests.push_back({(void**)&m_local_intermediate_fp32_, sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.intermediate_size});
+    m_mem_requests.push_back({(void**)&m_local_down_input_, config_.routed_expert_num * config_.group_max_len * config_.intermediate_size * ggml_type_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.down_type).vec_dot_type)});
+    m_mem_requests.push_back({(void**)&m_local_down_output_, sizeof(float) * config_.routed_expert_num * config_.group_max_len * config_.hidden_size});
     m_output_fp32_.resize(config_.group_max_len);
     for (int i = 0; i < config_.group_max_len; i++) {
-        m_output_fp32_[i] = (float*)(buffer_ + offset);
-        offset += sizeof(float) * config_.hidden_size;
+        m_mem_requests.push_back({(void**)&m_output_fp32_[i], sizeof(float) * config_.hidden_size});
     }
+    shared_mem_buffer.alloc(this, m_mem_requests);
 
     m_local_pos_.resize(config_.group_max_len);
     for (int i = 0; i < config_.group_max_len; i++) {
@@ -107,6 +72,10 @@ MOE::MOE(MOEConfig config) {
     m_local_down_output_ptr_.resize(config_.expert_num);
 }
 
+MOE::~MOE() {
+    shared_mem_buffer.dealloc(this);
+}
+
 void MOE::warm_up(Backend* backend) {
     std::vector<float> input_fp32(config_.hidden_size);
     std::vector<uint8_t> input(config_.hidden_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type));