backend-cpu: add online flow for aarch64 Q4_0 GEMV/GEMM kernels

Author: chaxu01

common/arg.cpp

@@ -2047,6 +2047,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             common_log_set_timestamps(common_log_main(), true);
         }
     ).set_env("LLAMA_LOG_TIMESTAMPS"));
+    add_opt(common_arg(
+        {"-rtrp", "--runtime-repack"},
+        string_format("Allow runtime requantization and repacking of Q4_0 to enable optimized GEMM and GEMV kernels (default: %d)", params.runtime_repack),
+        [](common_params & params) {
+            params.runtime_repack = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
 
     return ctx_arg;
 }

common/common.cpp

@@ -983,7 +983,7 @@ struct llama_model_params common_model_params_to_llama(const common_params & par
     mparams.main_gpu        = params.main_gpu;
     mparams.split_mode      = params.split_mode;
     mparams.tensor_split    = params.tensor_split;
-    mparams.use_mmap        = params.use_mmap;
+    mparams.use_mmap        = params.use_mmap && !params.runtime_repack;
     mparams.use_mlock       = params.use_mlock;
     mparams.check_tensors   = params.check_tensors;
     if (params.kv_overrides.empty()) {
@@ -1056,6 +1056,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.offload_kqv       = !params.no_kv_offload;
     cparams.flash_attn        = params.flash_attn;
     cparams.no_perf           = params.no_perf;
+    cparams.runtime_repack   = params.runtime_repack;
 
     if (params.reranking) {
         cparams.embeddings    = true;

common/common.h

@@ -271,6 +271,8 @@ struct common_params {
     bool warmup            = true;  // warmup run
     bool check_tensors     = false; // validate tensor data
 
+    bool runtime_repack    = false; // runtime repack weight for optimized kernels
+
     std::string cache_type_k = "f16"; // KV cache data type for the K
     std::string cache_type_v = "f16"; // KV cache data type for the V
 

examples/llama-bench/llama-bench.cpp

@@ -305,7 +305,19 @@ extern "C" {
     GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
     GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);
 
-    // CPU buffer types are always available
+    //
+    // CPU backend
+    //
+
+    GGML_API ggml_backend_t ggml_backend_cpu_init(void);
+
+    GGML_API bool ggml_backend_is_cpu                (ggml_backend_t backend);
+    GGML_API void ggml_backend_cpu_set_n_threads     (ggml_backend_t backend_cpu, int n_threads);
+    GGML_API void ggml_backend_cpu_set_threadpool    (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool);
+    GGML_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
+    GGML_API void ggml_backend_cpu_set_runtime_repack(ggml_backend_t backend_cpu, bool runtime_repack);
+
+    // Create a backend buffer from an existing pointer
     GGML_API ggml_backend_buffer_t      ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
     GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
 

ggml/include/ggml-backend.h

@@ -3476,3 +3476,102 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
         }
     }
 }
+
+static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor *t, int interleave_block, uint8_t **pmem, size_t *psize) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(t->ne[0] % 8 == 0);
+    GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
+
+    // Do in-place transformation. Allocate scratch buffer
+    size_t size = sizeof(block_q4_0x4) * t->ne[0] / QK4_0;
+    if (size > *psize) {
+        uint8_t *new_mem = realloc(*pmem, size);
+        if (!new_mem) {
+            return -1;
+        }
+        *pmem = new_mem;
+        *psize = size;
+    }
+    block_q4_0x4 *dst = (block_q4_0x4*) *pmem;
+    block_q4_0 *src = (block_q4_0*) t->data;
+    block_q4_0 dst_tmp[4];
+    int n = t->ne[0];
+    int nrow = t->ne[1]; // Number of rows
+    int nrows_interleaved = 4;
+    int nblocks = t->ne[0] / QK4_0;
+    for (int b = 0; b < (nrow * n); b += nrows_interleaved * n) {
+        int cnt = 0;
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i  = 0; i < nrows_interleaved; i++ ) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            dst[cnt++] = make_block_q4_0x4(dst_tmp, interleave_block, 0x88);
+        }
+        memcpy(src, dst, size);
+        src += cnt * 4;
+    }
+    return 0;
+}
+
+static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block,  uint8_t **pmem, size_t *psize) {
+    GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(t->ne[0] % 8 == 0);
+    GGML_ASSERT(interleave_block == 8);
+
+    // Do in-place transformation. Allocate scratch buffer
+    size_t size = sizeof(block_q4_0x8) * t->ne[0] / QK4_0;
+    if (size > *psize) {
+        uint8_t *new_mem = realloc(*pmem, size);
+        if (!new_mem) {
+            return -1;
+        }
+        *pmem = new_mem;
+        *psize = size;
+    }
+    block_q4_0x8 *dst = (block_q4_0x8*) *pmem;
+    block_q4_0 *src = (block_q4_0*) t->data;
+    block_q4_0 dst_tmp[8];
+    int n = t->ne[0];
+    int nrow = t->ne[1]; // Number of rows
+    int nrows_interleaved = 8;
+    int nblocks = t->ne[0] / QK4_0;
+    for (int b = 0; b < (nrow * n); b += nrows_interleaved * n) {
+        int cnt = 0;
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i  = 0; i < nrows_interleaved; i++ ) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            dst[cnt++] = make_block_q4_0x8(dst_tmp, interleave_block, 0x88);
+        }
+        memcpy(src, dst, size);
+        src += cnt * 4;
+    }
+    return 0;
+}
+
+// Prepare for optimized kernels if applicable
+void ggml_prepare_optimal_kernel(struct ggml_tensor *cur, uint8_t **pmem, size_t *psize) {
+    UNUSED(cur);
+    UNUSED(pmem);
+    UNUSED(psize);
+
+#if defined(__ARM_ARCH)
+    if (cur->type == GGML_TYPE_Q4_0) {
+        if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) {
+            if (repack_q4_0_to_q4_0_8_bl(cur, 8, pmem, psize) == 0) {
+                cur->type = GGML_TYPE_Q4_0_8_8;
+            }
+        }
+        else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
+            if (repack_q4_0_to_q4_0_4_bl(cur, 8, pmem, psize) == 0) {
+                cur->type = GGML_TYPE_Q4_0_4_8;
+            }
+        }
+        else if (ggml_cpu_has_neon()) {
+            if (repack_q4_0_to_q4_0_4_bl(cur, 4, pmem, psize) == 0) {
+                cur->type = GGML_TYPE_Q4_0_4_4;
+            }
+        }
+    }
+#endif
+}

ggml/src/ggml-aarch64.c

@@ -33,6 +33,8 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
+void ggml_prepare_optimal_kernel(struct ggml_tensor *cur, uint8_t **pmem, size_t *psize);
+
 #ifdef __cplusplus
 }
 #endif