Configurable sparse prediction threshold

convert.py

@@ -3,6 +3,7 @@
 
 import argparse
 import concurrent.futures
+import dataclasses
 import enum
 import faulthandler
 import functools
@@ -138,6 +139,28 @@ def type_for_tensor(self, name: str, tensor: LazyTensor) -> DataType:
 # hparams loading
 #
 
+@dataclass
+class PredictorParams:
+    sparse_threshold: float | None = None
+
+    @staticmethod
+    def loadPredictorJson(model: LazyModel, config_path: Path) -> PredictorParams:
+        config = json.load(open(config_path))
+        return PredictorParams(
+            sparse_threshold = config.get("sparse_threshold"),
+        )
+
+    @staticmethod
+    def load(model_plus: ModelPlus) -> PredictorParams:
+        config_path   = model_plus.paths[0].parent / "config.json"
+
+        if config_path.exists():
+            params = PredictorParams.loadPredictorJson(model_plus.model, config_path)
+        else:
+            params = PredictorParams()
+
+        return params
+
 @dataclass
 class Params:
     n_vocab:    int
@@ -160,6 +183,9 @@ class Params:
     # path to the directory containing the model files
     path_model: Path | None = None
 
+    # MLP predictor parameters
+    predictor_params: PredictorParams = dataclasses.field(default_factory=PredictorParams)
+
     @staticmethod
     def guessed(model: LazyModel) -> Params:
         # try transformer naming first
@@ -843,6 +869,9 @@ def add_meta_arch(self, params: Params) -> None:
         if params.ftype is not None:
             self.gguf.add_file_type(params.ftype)
 
+        if params.predictor_params.sparse_threshold is not None:
+            self.gguf.add_sparse_threshold(params.predictor_params.sparse_threshold)
+
     def add_meta_vocab(self, vocab: Vocab) -> None:
         tokens = []
         scores = []
@@ -1181,10 +1210,13 @@ def main(args_in: list[str] | None = None) -> None:
 
     if not args.vocab_only:
         model_plus = load_some_model(args.model)
+        params = Params.load(model_plus)
         mlp_predictor_plus = load_mlp_model(args.mlp_model)
+        params.predictor_params = PredictorParams.load(mlp_predictor_plus)
         model_plus = merge_multifile_models([model_plus, mlp_predictor_plus])
     else:
         model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)
+        params = Params.load(model_plus)
 
     if args.dump:
         do_dump_model(model_plus)
@@ -1193,7 +1225,6 @@ def main(args_in: list[str] | None = None) -> None:
     if args.bigendian:
         endianess = gguf.GGUFEndian.BIG
 
-    params = Params.load(model_plus)
     if params.n_ctx == -1:
         if args.ctx is None:
             raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n"

ggml-cuda.cu

@@ -108,6 +108,8 @@
 // max batch size to use MMQ kernels when tensor cores are available
 #define MMQ_MAX_BATCH_SIZE 32
 
+__constant__ float dev_sparse_threshold;
+
 #if defined(GGML_USE_HIPBLAS)
 #define __CUDA_ARCH__ 1300
 
@@ -4483,7 +4485,7 @@ static __global__ void dequantize_mul_mat_axpy_sparse(const void * __restrict__
     //     printf("row in gpu %d cols %d, value %d %d %d\n", id, ncols, *d, *(d+1), *(d+4095));
     // }
     // int id = row;
-    if (idx[id] < 0.0f) {
+    if (idx[id] < dev_sparse_threshold) {
         return;
     }
 
@@ -4552,12 +4554,7 @@ static __global__ void dequantize_mul_mat_axpy_sparse_batch(const void * __restr
         return;
     }
     int id = lst[row];
-    // int id = row;
-    // if (idx[id] < 0.0f) {
-    //     return;
-    // }
     const int bid = blockIdx.y;
-    // if (bid == 0) global_lock = 0;
 
     extern __shared__ float shared_dst[]; // TODO:dynamic
 
@@ -4578,7 +4575,7 @@ static __global__ void dequantize_mul_mat_axpy_sparse_batch(const void * __restr
     // __syncthreads();
     for (int col_id = 0; col_id < src1_ncols; col_id++) {
         __syncthreads();
-        if (loop_idx[id] < 0.0f) {
+        if (loop_idx[id] < dev_sparse_threshold) {
             loop_dst += ncols;
             loop_idx += src1_ne0;
             loop_y += src1_ne0;
@@ -4640,7 +4637,7 @@ static __global__ void dequantize_axpy_sparse(const void * __restrict__ vx, cons
         return;
     }
     int id = lst[row];
-    if (idx[id] < 0.0f) {
+    if (idx[id] < dev_sparse_threshold) {
         return;
     }
 
@@ -4689,8 +4686,7 @@ static __global__ void dequantize_mul_mat_vec_sparse(const void * __restrict__ v
         return;
     }
     int id = lst[row];
-    // int id = row;
-    if (idx[id] < 0.0f) {
+    if (idx[id] < dev_sparse_threshold) {
         return;
     }
 
@@ -4782,7 +4778,7 @@ static __global__ void dequantize_mul_mat_batch_sparse(const void * __restrict__
     {
         __syncthreads();
         tmp = 0.0f;
-        if (loop_idx[id] < 0.0f)
+        if (loop_idx[id] < dev_sparse_threshold)
         {
             loop_dst += dst_ne0;
             loop_idx += dst_ne0;
@@ -9618,3 +9614,6 @@ ggml_backend_t ggml_backend_cuda_init() {
     return cuda_backend;
 }
 
+void ggml_cuda_set_device_constants(float sparse_pred_threshold) {
+    CUDA_CHECK(cudaMemcpyToSymbol(dev_sparse_threshold, &sparse_pred_threshold, sizeof(float)));
+}

ggml-cuda.h

@@ -53,6 +53,8 @@ GGML_API int    ggml_cuda_get_device_count(void);
 GGML_API void   ggml_cuda_get_device_description(int device, char * description, size_t description_size);
 GGML_API size_t ggml_cuda_get_free_memory(int device);
 
+GGML_API void   ggml_cuda_set_device_constants(float sparse_pred_threshold);
+
 // backend API
 GGML_API ggml_backend_t ggml_backend_cuda_init(void); // TODO: take a list of devices to use
 

ggml.c

@@ -14059,6 +14059,8 @@ static void ggml_compute_forward_mul_mat_sparse(
     enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
     ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
 
+    const float threshold = sparse_pred_threshold;
+
     GGML_ASSERT(ne0 == ne01);
     GGML_ASSERT(ne1 == ne11);
     GGML_ASSERT(ne2 == ne12);
@@ -14262,7 +14264,7 @@ static void ggml_compute_forward_mul_mat_sparse(
                     float *dst_col = (float *)((char *)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));
 
                     // if (ffdata[ir0] <= 0.0f) {
-                    if (gid[ir0] == 1 || ffdata[ir0] < -0.0f) {
+                    if (gid[ir0] == 1 || ffdata[ir0] < threshold) {
                         dst_col[ir0] = 0;
                         continue;
                     }
@@ -14413,11 +14415,6 @@ static void ggml_compute_forward_mul_mat_axpy_dense(
         const int ir0 = atomic_fetch_add(params->aic, dr);
         for (int64_t ir1 = ir0; ir1 < ir0+dr; ir1++) {
             if (ir1 >= nr) break;
-            // if (gid[ir1] == 1)
-            //     continue;
-            // if (idx[ir1] < 0.0f)
-            //     continue;
-            // ggml_axpy_normal_f16(ne00, src0_row+nb01*ir1, vy, vy, wdata[ir1]);
             ggml_axpy_avx_f16(ne00, (ggml_fp16_t *)(src0_row+nb01*ir1), (ggml_fp16_t *)vy, vy, wdata[ir1]);
         }
         if (ir0 + dr >= nr)
@@ -14482,6 +14479,8 @@ static void ggml_compute_forward_mul_mat_axpy(
     enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
     ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
 
+    const float threshold = sparse_pred_threshold;
+
     // GGML_ASSERT(ne0 == ne01);
     // GGML_ASSERT(ne1 == ne11);
     // GGML_ASSERT(ne2 == ne12);
@@ -14569,7 +14568,7 @@ static void ggml_compute_forward_mul_mat_axpy(
                 if (gid[ir1] == 1) {
                     continue;
                 }
-                if (idx[ir1] < -0.0f)
+                if (idx[ir1] < threshold)
                     continue;
                 // ggml_axpy_normal_f16(ne00, src0_row+nb01*ir1, vy, vy, wdata[ir1]);
                 ggml_axpy_avx_f16(ne00, (ggml_fp16_t *)(src0_row+nb01*ir1), (ggml_fp16_t *)vy, vy, src1_ptr[ir1]);
@@ -14632,6 +14631,8 @@ static void ggml_compute_forward_mul_mat_axpy_q4_0(
     enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
     ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
 
+    const float threshold = sparse_pred_threshold;
+
     // GGML_ASSERT(ne0 == ne01);
     // GGML_ASSERT(ne1 == ne11);
     // GGML_ASSERT(ne2 == ne12);
@@ -14713,7 +14714,7 @@ static void ggml_compute_forward_mul_mat_axpy_q4_0(
                 break;
             if (gid[ir1] == 1)
                 continue;
-            if (idx[ir1] < 0.0f)
+            if (idx[ir1] < threshold)
                 continue;
             int bid = ir1 / QK8_0;
             int qsid = ir1 % QK8_0;

ggml.h

@@ -2196,6 +2196,12 @@ extern "C" {
     GGML_API int ggml_cpu_has_ssse3      (void);
     GGML_API int ggml_cpu_has_vsx        (void);
 
+    //
+    // global variables
+    // 
+    // TODO: these should be moved to the context
+    extern float sparse_pred_threshold;
+
     //
     // Internal types and functions exposed for tests and benchmarks
     //

gguf-py/gguf/constants.py

@@ -70,6 +70,9 @@ class Tokenizer:
         ADD_EOS    = "tokenizer.ggml.add_eos_token"
         HF_JSON    = "tokenizer.huggingface.json"
         RWKV       = "tokenizer.rwkv.world"
+
+    class PowerInfer:
+        SPARSE_THRESHOLD = "powerinfer.sparse_threshold"
 
 
 #

gguf-py/gguf/gguf_writer.py

@@ -399,6 +399,9 @@ def add_add_bos_token(self, value: bool) -> None:
     def add_add_eos_token(self, value: bool) -> None:
         self.add_bool(Keys.Tokenizer.ADD_EOS, value)
 
+    def add_sparse_threshold(self, value: float) -> None:
+        self.add_float32(Keys.PowerInfer.SPARSE_THRESHOLD, value)
+
     def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes:
         pack_prefix = ''
         if not skip_pack_prefix:

llama.cpp

@@ -93,6 +93,13 @@
 
 #define LLAMA_MAX_NODES 4096
 
+// 
+// global variables
+// 
+
+// sparsity threshold for sparse matrix multiplication prediction
+float sparse_pred_threshold = 0.;
+
 //
 // logging
 //
@@ -257,6 +264,8 @@ enum llm_kv {
     LLM_KV_TOKENIZER_PAD_ID,
     LLM_KV_TOKENIZER_HF_JSON,
     LLM_KV_TOKENIZER_RWKV,
+
+    LLM_KV_SPARSE_THRESHOLD,
 };
 
 static std::map<llm_kv, std::string> LLM_KV_NAMES = {
@@ -305,6 +314,8 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_PAD_ID,              "tokenizer.ggml.padding_token_id"   },
     { LLM_KV_TOKENIZER_HF_JSON,             "tokenizer.huggingface.json"        },
     { LLM_KV_TOKENIZER_RWKV,                "tokenizer.rwkv.world"              },
+
+    { LLM_KV_SPARSE_THRESHOLD,              "powerinfer.sparse_threshold" },
 };
 
 struct LLM_KV {
@@ -1150,6 +1161,9 @@ struct llama_hparams {
 
     float f_clamp_kqv;
     float f_max_alibi_bias;
+
+    // sparse predictor threshold if sparse inference is enabled
+    float sparse_pred_threshold = atof(getenv("LLAMA_SPARSE_PRED_THRESHOLD") ?: "0.0");
 
     bool operator!=(const llama_hparams & other) const {
         if (this->vocab_only  != other.vocab_only)  return true;
@@ -2220,6 +2234,11 @@ static void llm_load_hparams(
         // gpt-j n_rot = rotary_dim
     }
 
+    if (gguf_get_sparse_deriv(ctx)) {
+        // read sparse threshold override if sparse deriv is enabled
+        GGUF_GET_KEY(ctx, hparams.sparse_pred_threshold, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_SPARSE_THRESHOLD));
+    }
+
     // arch-specific KVs
     switch (model.arch) {
         case LLM_ARCH_LLAMA:
@@ -2607,6 +2626,9 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
     if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
     if (vocab.linefeed_id    != -1) { LLAMA_LOG_INFO( "%s: LF token  = %d '%s'\n", __func__, vocab.linefeed_id,    vocab.id_to_token[vocab.linefeed_id].text.c_str() );    }
+
+    // sparse inference
+    LLAMA_LOG_INFO("%s: sparse_pred_threshold = %.2f\n", __func__, hparams.sparse_pred_threshold);
 }
 
 
@@ -2808,7 +2830,7 @@ struct llama_augmentation_model_loader {
             return NULL;
         }
         // allocate and copy selected weights to gpu
-        #ifdef GGML_USE_CUBLAS
+#ifdef GGML_USE_CUBLAS
         int64_t row_len = src->ne[0];
         int64_t gpu_rows = gpu_bucket->ne[0];
         if (gpu_rows == 0)
@@ -2841,10 +2863,9 @@ struct llama_augmentation_model_loader {
         ggml_set_no_alloc(aux_ctx, false);
 
         return gpu_dst;
-        #else
-            printf("As you do not support CUDA. Split to GPU is not allowed.\n");
+#else
         return NULL;
-        #endif
+#endif
     }
 
     void slice_ffn_mat_to_gpu(llama_layer & layer) {
@@ -2882,22 +2903,11 @@ struct llama_augmentation_model_loader {
         const int64_t t_start_aug_us = ggml_time_us();
         std::vector<uint8_t> work_buffer;
 
-        // transpose ffn_down to use axpy
-        // ggml_cgraph * tmp_transpose_gf = ggml_new_graph(aux_ctx);
-        // for (llama_layer &model_layer : model -> layers) {
-        //     // gpu_w2 transpose load
-        //     ggml_tensor * ffn_down_t = ggml_cont(aux_ctx, ggml_transpose(aux_ctx, model_layer.ffn_down));
-        //     ggml_build_forward_expand(tmp_transpose_gf, ffn_down_t);
-        //     model_layer.ffn_down_t = ffn_down_t;
-        //     LLAMA_LOG_INFO(".");
-        // }
-        // ggml_graph_compute_helper(work_buffer, tmp_transpose_gf, 2);
-        // for (llama_layer &model_layer : model -> layers) {
-        //     model_layer.ffn_down_t->op = GGML_OP_NONE;
-        //     model_layer.ffn_down_t->src[0] = NULL;
-        //     model_layer.ffn_down_t->src[1] = NULL;
-        //     model_layer.ffn_down_t->src[2] = NULL;
-        // }
+        // Set sparsity threshold via global virables
+        sparse_pred_threshold = model->hparams.sparse_pred_threshold;
+#if defined (GGML_USE_CUBLAS)
+        ggml_cuda_set_device_constants(model->hparams.sparse_pred_threshold);
+#endif
 
         // load gpu_idx and slice mat to gpu
         for (llama_layer &model_layer : model -> layers) {

requirements.txt

@@ -1,3 +1,3 @@
 numpy==1.24.4
 sentencepiece==0.1.98
-gguf>=0.1.0
+-e ./gguf-py