Merge branch 'master' into gguf

ggml-ci

README.md

@@ -9,13 +9,13 @@
 
 Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
-**Hot topics:**
+### 🚧 Incoming breaking change + refactoring:
 
-- Simple web chat example: https://github.com/ggerganov/llama.cpp/pull/1998
+See PR https://github.com/ggerganov/llama.cpp/pull/2398 for more info.
-- k-quants now support super-block size of 64: https://github.com/ggerganov/llama.cpp/pull/2001
+
-- New roadmap: https://github.com/users/ggerganov/projects/7
+To devs: avoid making big changes to `llama.h` / `llama.cpp` until merged
-- Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985
+
-- p1 : LLM-based code completion engine at the edge : https://github.com/ggml-org/p1/discussions/1
+----
 
 <details>
   <summary>Table of Contents</summary>
@@ -99,6 +99,7 @@ as the main playground for developing new features for the [ggml](https://github
 - Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
 - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
 - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
+- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
 
 **UI:**
 

examples/perplexity/perplexity.cpp

@@ -5,6 +5,7 @@
 #include <cmath>
 #include <ctime>
 #include <sstream>
+#include <cstring>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -121,6 +122,27 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
     printf("\n");
 }
 
+std::vector<float> hellaswag_evaluate_tokens(llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch,
+        int n_vocab, int n_thread) {
+    std::vector<float> result;
+    result.reserve(tokens.size() * n_vocab);
+    size_t n_chunk = (tokens.size() + n_batch - 1)/n_batch;
+    for (size_t i_chunk = 0; i_chunk < n_chunk; ++i_chunk) {
+        size_t n_tokens = tokens.size() - i_chunk * n_batch;
+        n_tokens = std::min(n_tokens, size_t(n_batch));
+        if (llama_eval(ctx, tokens.data() + i_chunk * n_batch, n_tokens, n_past, n_thread)) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return {};
+        }
+
+        const auto logits = llama_get_logits(ctx);
+        result.insert(result.end(), logits, logits + n_tokens * n_vocab);
+
+        n_past += n_tokens;
+    }
+    return result;
+}
+
 void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     // Calculates hellaswag score (acc_norm) from prompt
     //
@@ -209,17 +231,19 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     double acc = 0.0f;
     const int n_vocab = llama_n_vocab(ctx);
 
+    std::vector<float> tok_logits(n_vocab);
+
     for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
 
         // Tokenize the context to count tokens
         std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
         size_t context_size = context_embd.size();
 
-        for (size_t ending_idx=0;ending_idx<4;ending_idx++) {
+        // Do the 1st ending
-
+        // In this case we include the context when evaluating
-            // Tokenize the query
+        auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], prepend_bos);
-            std::vector<int> query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[ending_idx], prepend_bos);
+        auto query_size = query_embd.size();
-            size_t query_size = query_embd.size();
+        //printf("First query: %d\n",(int)query_size);
 
         // Stop if query wont fit the ctx window
         if (query_size > (size_t)params.n_ctx) {
@@ -232,25 +256,66 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
             query_embd.resize(32);
         }
 
-            // Evaluate the query
+        auto logits = hellaswag_evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab, params.n_threads);
-            if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) {
+        if (logits.empty()) {
             fprintf(stderr, "%s : failed to eval\n", __func__);
             return;
         }
 
-            const auto query_logits = llama_get_logits(ctx);
+        std::memcpy(tok_logits.data(), logits.data() + (context_size-1)*n_vocab, n_vocab*sizeof(float));
-            std::vector<float> logits;
+        const auto first_probs = softmax(tok_logits);
-            logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab);
 
-            hs_data[task_idx].ending_logprob_count[ending_idx] = 0;
+        hs_data[task_idx].ending_logprob_count[0] = 1;
-            hs_data[task_idx].ending_logprob[ending_idx] = 0.0f;
+        hs_data[task_idx].ending_logprob[0] = std::log(first_probs[query_embd[context_size]]);
 
         // Calculate the logprobs over the ending
-            for (size_t j = context_size-1; j < query_size - 1; j++) {
+        for (size_t j = context_size; j < query_size - 1; j++) {
-                // Calculate probability of next token, given the previous ones.
+
-                const std::vector<float> tok_logits(
+            std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));
-                    logits.begin() + (j + 0) * n_vocab,
+
-                    logits.begin() + (j + 1) * n_vocab);
+            const float prob = softmax(tok_logits)[query_embd[j + 1]];
+
+            hs_data[task_idx].ending_logprob[0] += std::log(prob);
+            hs_data[task_idx].ending_logprob_count[0]++;
+        }
+
+        // Calculate the mean token logprob for acc_norm
+        hs_data[task_idx].ending_logprob[0] /= hs_data[task_idx].ending_logprob_count[0];
+
+        // Do the remaining endings
+        // For these, we use the bare ending with n_past = context_size
+        //
+        for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {
+
+            // Tokenize the query
+            query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false);
+            query_size = query_embd.size();
+
+            // Stop if query wont fit the ctx window
+            if (context_size + query_size > (size_t)params.n_ctx) {
+                fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
+                return;
+            }
+
+            // Speedup small evaluations by evaluating atleast 32 tokens
+            // No, resizing to 32 is actually slightly slower (at least on CUDA)
+            //if (query_size < 32) {
+            //    query_embd.resize(32);
+            //}
+
+            // Evaluate the query
+            logits = hellaswag_evaluate_tokens(ctx, query_embd, context_size, params.n_batch, n_vocab, params.n_threads);
+            if (logits.empty()) {
+                fprintf(stderr, "%s : failed to eval\n", __func__);
+                return;
+            }
+
+            hs_data[task_idx].ending_logprob_count[ending_idx] = 1;
+            hs_data[task_idx].ending_logprob[ending_idx] = std::log(first_probs[query_embd[0]]);
+
+            // Calculate the logprobs over the ending
+            for (size_t j = 0; j < query_size - 1; j++) {
+                std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));
 
                 const float prob = softmax(tok_logits)[query_embd[j + 1]];
 
@@ -267,9 +332,9 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         }
 
         // Find the ending with maximum logprob
-        size_t ending_logprob_max_idx = -1;
+        size_t ending_logprob_max_idx = 0;
-        double ending_logprob_max_val = -INFINITY;
+        double ending_logprob_max_val = hs_data[task_idx].ending_logprob[0];
-        for (size_t j=0; j < 4; j++) {
+        for (size_t j = 1; j < 4; j++) {
             if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {
                 ending_logprob_max_idx = j;
                 ending_logprob_max_val =  hs_data[task_idx].ending_logprob[j];

examples/server/deps.sh

@@ -11,8 +11,10 @@ echo >> $PUBLIC/index.js # add newline
 
 FILES=$(ls $PUBLIC)
 
+cd $PUBLIC
 for FILE in $FILES; do
-  func=$(echo $FILE | tr '.' '_')
+  echo "generate $FILE.hpp"
-  echo "generate $FILE.hpp ($func)"
+
-  xxd -n $func -i $PUBLIC/$FILE > $DIR/$FILE.hpp
+  # use simple flag for old version of xxd
+  xxd -i $FILE > $DIR/$FILE.hpp
 done

examples/server/public/index.html

@@ -144,12 +144,12 @@
     import { SchemaConverter } from '/json-schema-to-grammar.mjs';
 
     const session = signal({
-      prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.",
+      prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.",
       template: "{{prompt}}\n\n{{history}}\n{{char}}:",
       historyTemplate: "{{name}}: {{message}}",
       transcript: [],
       type: "chat",
-      char: "llama",
+      char: "Llama",
       user: "User",
     })
 

ggml-metal.metal

@@ -1898,10 +1898,11 @@ kernel void kernel_mul_mm(device const  uchar * src0,
         threadgroup float *temp_str = ((threadgroup float *)shared_memory) \
                                       + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M;
         for (int i = 0; i < 8; i++) {
+            threadgroup_barrier(mem_flags::mem_device);
             simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M);
         }
 
-        threadgroup_barrier(mem_flags::mem_threadgroup);
+        threadgroup_barrier(mem_flags::mem_device);
         device float *C = dst + BLOCK_SIZE_M * r0 + (BLOCK_SIZE_N * r1) * ne0 + im*ne1*ne0;
         if (sgitg==0) {
             for (int i = 0; i < n_rows; i++) {

ggml.c

@@ -1643,11 +1643,37 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
 static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 
 static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
+    [GGML_TYPE_I8] = {
+        .type_name                = "i8",
+        .blck_size                = 1,
+        .type_size                = sizeof(int8_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_I16] = {
+        .type_name                = "i16",
+        .blck_size                = 1,
+        .type_size                = sizeof(int16_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_I32] = {
+        .type_name                = "i32",
+        .blck_size                = 1,
+        .type_size                = sizeof(int32_t),
+        .is_quantized             = false,
+    },
     [GGML_TYPE_F32] = {
+        .type_name                = "f32",
+        .blck_size                = 1,
+        .type_size                = sizeof(float),
+        .is_quantized             = false,
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f32,
         .vec_dot_type             = GGML_TYPE_F32,
     },
     [GGML_TYPE_F16] = {
+        .type_name                = "f16",
+        .blck_size                = 1,
+        .type_size                = sizeof(ggml_fp16_t),
+        .is_quantized             = false,
         .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
         .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
         .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
@@ -1655,6 +1681,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_F16,
     },
     [GGML_TYPE_Q4_0] = {
+        .type_name                = "q4_0",
+        .blck_size                = QK4_0,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_0,
         .from_float               = quantize_row_q4_0,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
@@ -1662,6 +1692,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q4_1] = {
+        .type_name                = "q4_1",
+        .blck_size                = QK4_1,
+        .type_size                = sizeof(block_q4_1),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_1,
         .from_float               = quantize_row_q4_1,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
@@ -1669,6 +1703,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
     [GGML_TYPE_Q5_0] = {
+        .type_name                = "q5_0",
+        .blck_size                = QK5_0,
+        .type_size                = sizeof(block_q5_0),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_0,
         .from_float               = quantize_row_q5_0,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
@@ -1676,6 +1714,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q5_1] = {
+        .type_name                = "q5_1",
+        .blck_size                = QK5_1,
+        .type_size                = sizeof(block_q5_1),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_1,
         .from_float               = quantize_row_q5_1,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
@@ -1683,6 +1725,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
     [GGML_TYPE_Q8_0] = {
+        .type_name                = "q8_0",
+        .blck_size                = QK8_0,
+        .type_size                = sizeof(block_q8_0),
+        .is_quantized             = true,
         .to_float                 = dequantize_row_q8_0,
         .from_float               = quantize_row_q8_0,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
@@ -1690,12 +1736,20 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q8_1] = {
+        .type_name                = "q8_1",
+        .blck_size                = QK8_1,
+        .type_size                = sizeof(block_q8_1),
+        .is_quantized             = true,
         .from_float               = quantize_row_q8_1,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
 #ifdef GGML_USE_K_QUANTS
     [GGML_TYPE_Q2_K] = {
+        .type_name                = "q2_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q2_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q2_K,
         .from_float               = quantize_row_q2_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
@@ -1703,6 +1757,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q3_K] = {
+        .type_name                = "q3_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q3_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q3_K,
         .from_float               = quantize_row_q3_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
@@ -1710,6 +1768,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q4_K] = {
+        .type_name                = "q4_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q4_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_K,
         .from_float               = quantize_row_q4_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
@@ -1717,6 +1779,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q5_K] = {
+        .type_name                = "q5_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q5_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_K,
         .from_float               = quantize_row_q5_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
@@ -1724,6 +1790,10 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q6_K] = {
+        .type_name                = "q6_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q6_K),
+        .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q6_K,
         .from_float               = quantize_row_q6_K,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
@@ -1731,15 +1801,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q8_K] = {
+        .type_name                = "q8_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q8_K),
+        .is_quantized             = true,
         .from_float               = quantize_row_q8_K,
     }
 #endif
 };
 
 // For internal test use
-ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i) {
+ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
-    GGML_ASSERT(i < GGML_TYPE_COUNT);
+    GGML_ASSERT(type < GGML_TYPE_COUNT);
-    return type_traits[i];
+    return type_traits[type];
 }
 
 
@@ -3648,98 +3722,6 @@ inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) {
     *s = idx;
 }
 
-//
-// data types
-//
-
-static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
-    [GGML_TYPE_F32]  = 1,
-    [GGML_TYPE_F16]  = 1,
-    [GGML_TYPE_Q4_0] = QK4_0,
-    [GGML_TYPE_Q4_1] = QK4_1,
-    [GGML_TYPE_Q5_0] = QK5_0,
-    [GGML_TYPE_Q5_1] = QK5_1,
-    [GGML_TYPE_Q8_0] = QK8_0,
-    [GGML_TYPE_Q8_1] = QK8_1,
-#ifdef GGML_USE_K_QUANTS
-    [GGML_TYPE_Q2_K] = QK_K,
-    [GGML_TYPE_Q3_K] = QK_K,
-    [GGML_TYPE_Q4_K] = QK_K,
-    [GGML_TYPE_Q5_K] = QK_K,
-    [GGML_TYPE_Q6_K] = QK_K,
-    [GGML_TYPE_Q8_K] = QK_K,
-#endif
-    [GGML_TYPE_I8]   = 1,
-    [GGML_TYPE_I16]  = 1,
-    [GGML_TYPE_I32]  = 1,
-};
-static_assert(GGML_TYPE_COUNT == 19, "GGML_BLCK_SIZE is outdated");
-
-static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
-    [GGML_TYPE_F32]  = sizeof(float),
-    [GGML_TYPE_F16]  = sizeof(ggml_fp16_t),
-    [GGML_TYPE_Q4_0] = sizeof(block_q4_0),
-    [GGML_TYPE_Q4_1] = sizeof(block_q4_1),
-    [GGML_TYPE_Q5_0] = sizeof(block_q5_0),
-    [GGML_TYPE_Q5_1] = sizeof(block_q5_1),
-    [GGML_TYPE_Q8_0] = sizeof(block_q8_0),
-    [GGML_TYPE_Q8_1] = sizeof(block_q8_1),
-#ifdef GGML_USE_K_QUANTS
-    [GGML_TYPE_Q2_K] = sizeof(block_q2_K),
-    [GGML_TYPE_Q3_K] = sizeof(block_q3_K),
-    [GGML_TYPE_Q4_K] = sizeof(block_q4_K),
-    [GGML_TYPE_Q5_K] = sizeof(block_q5_K),
-    [GGML_TYPE_Q6_K] = sizeof(block_q6_K),
-    [GGML_TYPE_Q8_K] = sizeof(block_q8_K),
-#endif
-    [GGML_TYPE_I8]   = sizeof(int8_t),
-    [GGML_TYPE_I16]  = sizeof(int16_t),
-    [GGML_TYPE_I32]  = sizeof(int32_t),
-};
-static_assert(GGML_TYPE_COUNT == 19, "GGML_TYPE_SIZE is outdated");
-
-static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
-    [GGML_TYPE_F32]  = "f32",
-    [GGML_TYPE_F16]  = "f16",
-    [GGML_TYPE_Q4_0] = "q4_0",
-    [GGML_TYPE_Q4_1] = "q4_1",
-    [GGML_TYPE_Q5_0] = "q5_0",
-    [GGML_TYPE_Q5_1] = "q5_1",
-    [GGML_TYPE_Q8_0] = "q8_0",
-    [GGML_TYPE_Q8_1] = "q8_1",
-    [GGML_TYPE_Q2_K] = "q2_K",
-    [GGML_TYPE_Q3_K] = "q3_K",
-    [GGML_TYPE_Q4_K] = "q4_K",
-    [GGML_TYPE_Q5_K] = "q5_K",
-    [GGML_TYPE_Q6_K] = "q6_K",
-    [GGML_TYPE_Q8_K] = "q8_K",
-    [GGML_TYPE_I8]   = "i8",
-    [GGML_TYPE_I16]  = "i16",
-    [GGML_TYPE_I32]  = "i32",
-};
-static_assert(GGML_TYPE_COUNT == 19, "GGML_TYPE_NAME is outdated");
-
-static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
-    [GGML_TYPE_F32]  = false,
-    [GGML_TYPE_F16]  = false,
-    [GGML_TYPE_Q4_0] = true,
-    [GGML_TYPE_Q4_1] = true,
-    [GGML_TYPE_Q5_0] = true,
-    [GGML_TYPE_Q5_1] = true,
-    [GGML_TYPE_Q8_0] = true,
-    [GGML_TYPE_Q8_1] = true,
-    [GGML_TYPE_Q2_K] = true,
-    [GGML_TYPE_Q3_K] = true,
-    [GGML_TYPE_Q4_K] = true,
-    [GGML_TYPE_Q5_K] = true,
-    [GGML_TYPE_Q6_K] = true,
-    [GGML_TYPE_Q8_K] = true,
-    [GGML_TYPE_I8]   = false,
-    [GGML_TYPE_I16]  = false,
-    [GGML_TYPE_I32]  = false,
-};
-static_assert(GGML_TYPE_COUNT == 19, "GGML_IS_QUANTIZED is outdated");
-
 static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "NONE",
 
@@ -4109,7 +4091,7 @@ size_t ggml_nbytes(const struct ggml_tensor * tensor) {
     //
     // is enough, but just in case, adding the second part
 
-    return MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]);
+    return MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*ggml_type_size(tensor->type))/ggml_blck_size(tensor->type));
 }
 
 size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
@@ -4119,23 +4101,27 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
 size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
-    return (nrows_split*tensor->ne[0]*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type];
+    return (nrows_split*tensor->ne[0]*ggml_type_size(tensor->type))/ggml_blck_size(tensor->type);
 }
 
 int ggml_blck_size(enum ggml_type type) {
-    return GGML_BLCK_SIZE[type];
+    return type_traits[type].blck_size;
 }
 
 size_t ggml_type_size(enum ggml_type type) {
-    return GGML_TYPE_SIZE[type];
+    return type_traits[type].type_size;
 }
 
 float ggml_type_sizef(enum ggml_type type) {
-    return ((float)(GGML_TYPE_SIZE[type]))/GGML_BLCK_SIZE[type];
+    return ((float)(type_traits[type].type_size))/type_traits[type].blck_size;
 }
 
 const char * ggml_type_name(enum ggml_type type) {
-    return GGML_TYPE_NAME[type];
+    return type_traits[type].type_name;
+}
+
+bool ggml_is_quantized(enum ggml_type type) {
+    return type_traits[type].is_quantized;
 }
 
 const char * ggml_op_name(enum ggml_op op) {
@@ -4147,7 +4133,7 @@ const char * ggml_op_symbol(enum ggml_op op) {
 }
 
 size_t ggml_element_size(const struct ggml_tensor * tensor) {
-    return GGML_TYPE_SIZE[tensor->type];
+    return ggml_type_size(tensor->type);
 }
 
 static inline bool ggml_is_scalar(const struct ggml_tensor * tensor) {
@@ -4185,10 +4171,6 @@ static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct
         (t0->ne[3] == t1->ne[3]);
 }
 
-bool ggml_is_quantized(enum ggml_type type) {
-    return GGML_IS_QUANTIZED[type];
-}
-
 enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
     enum ggml_type wtype = GGML_TYPE_COUNT;
 
@@ -4226,8 +4208,8 @@ bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] &&
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
-        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/GGML_BLCK_SIZE[tensor->type] &&
+        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
         tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
@@ -4236,7 +4218,7 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] &&
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
         tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
@@ -4251,7 +4233,7 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
-        tensor->nb[0] == GGML_TYPE_SIZE[tensor->type] &&
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
         tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
@@ -4570,7 +4552,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
     size_t data_size = 0;
 
     if (data == NULL && !ctx->no_alloc) {
-        data_size += GGML_TYPE_SIZE[type]*(ne[0]/GGML_BLCK_SIZE[type]);
+        data_size += ggml_type_size(type)*(ne[0]/ggml_blck_size(type));
         for (int i = 1; i < n_dims; i++) {
             data_size *= ne[i];
         }
@@ -4625,8 +4607,8 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         result->ne[i] = ne[i];
     }
 
-    result->nb[0] = GGML_TYPE_SIZE[type];
+    result->nb[0] = ggml_type_size(type);
-    result->nb[1] = result->nb[0]*(result->ne[0]/GGML_BLCK_SIZE[type]);
+    result->nb[1] = result->nb[0]*(result->ne[0]/ggml_blck_size(type));
     for (int i = 2; i < GGML_MAX_DIMS; i++) {
         result->nb[i] = result->nb[i - 1]*result->ne[i - 1];
     }
@@ -7748,7 +7730,7 @@ static void ggml_compute_forward_dup_same_cont(
         memcpy(
             ((char *)  dst->data + ie0*nb0),
             ((char *) src0->data + ie0*nb00),
-            (ie1 - ie0) * GGML_TYPE_SIZE[src0->type]);
+            (ie1 - ie0) * ggml_type_size(src0->type));
     }
 
 }
@@ -7782,7 +7764,7 @@ static void ggml_compute_forward_dup_f16(
 
     if (src0->type == dst->type &&
         ne00 == ne0 &&
-        nb00 == GGML_TYPE_SIZE[src0->type] && nb0 == GGML_TYPE_SIZE[dst->type]) {
+        nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) {
         // copy by rows
         const size_t rs = ne00*nb00;
         for (int64_t i03 = 0; i03 < ne03; i03++) {
@@ -7840,7 +7822,7 @@ static void ggml_compute_forward_dup_f16(
                 float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;
 
                 size_t id = 0;
-                size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]);
+                size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type));
                 char * dst_ptr = (char *) dst->data;
 
                 for (int i03 = 0; i03 < ne03; i03++) {
@@ -8053,7 +8035,7 @@ static void ggml_compute_forward_dup_f32(
 
     if (src0->type == dst->type &&
         ne00 == ne0 &&
-        nb00 == GGML_TYPE_SIZE[src0->type] && nb0 == GGML_TYPE_SIZE[dst->type]) {
+        nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) {
         // copy by rows
         const size_t rs = ne00*nb00;
         for (int64_t i03 = 0; i03 < ne03; i03++) {
@@ -8092,7 +8074,7 @@ static void ggml_compute_forward_dup_f32(
                 ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
 
                 size_t id = 0;
-                size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]);
+                size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type));
                 char * dst_ptr = (char *) dst->data;
 
                 for (int i03 = 0; i03 < ne03; i03++) {
@@ -8504,7 +8486,7 @@ static void ggml_compute_forward_add_q_f32(
     ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
 
     // we don't support permuted src0 or src1
-    GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
+    GGML_ASSERT(nb00 == ggml_type_size(type));
     GGML_ASSERT(nb10 == sizeof(float));
 
     // dst cannot be transposed or permuted
@@ -8778,7 +8760,7 @@ static void ggml_compute_forward_add1_q_f32(
     ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
 
     // we don't support permuted src0
-    GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
+    GGML_ASSERT(nb00 == ggml_type_size(type));
 
     // dst cannot be transposed or permuted
     GGML_ASSERT(nb0 <= nb1);
@@ -10634,7 +10616,7 @@ static void ggml_compute_forward_mul_mat(
     GGML_ASSERT(ne3 == ne13);
 
     // we don't support permuted src0 or src1
-    GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
+    GGML_ASSERT(nb00 == ggml_type_size(type));
     GGML_ASSERT(nb10 == sizeof(float));
 
     // dst cannot be transposed or permuted
@@ -10717,7 +10699,7 @@ static void ggml_compute_forward_mul_mat(
     if (params->type == GGML_TASK_INIT) {
         if (src1->type != vec_dot_type) {
             char * wdata = params->wdata;
-            const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
+            const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
 
             for (int64_t i13 = 0; i13 < ne13; ++i13) {
                 for (int64_t i12 = 0; i12 < ne12; ++i12) {
@@ -10737,7 +10719,7 @@ static void ggml_compute_forward_mul_mat(
     }
 
     const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
-    const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
+    const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
 
     const int64_t nr0 = ne01;           // src0 rows
     const int64_t nr1 = ne11*ne12*ne13; // src1 rows
@@ -11210,7 +11192,7 @@ static void ggml_compute_forward_get_rows_q(
 
     assert( dst->ne[0] == nc);
     assert( dst->ne[1] == nr);
-    assert(src0->nb[0] == GGML_TYPE_SIZE[type]);
+    assert(src0->nb[0] == ggml_type_size(type));
 
     for (int i = 0; i < nr; ++i) {
         const int r = ((int32_t *) src1->data)[i];
@@ -16387,7 +16369,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
 
                     size_t cur = 0;
                     if (ggml_is_quantized(node->type)) {
-                        cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->ne[0] * n_tasks;
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
                     }
 
                     work_size = MAX(work_size, cur);
@@ -16400,7 +16382,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                     size_t cur = 0;
 
                     if (ggml_is_quantized(node->src[0]->type)) {
-                        cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->src[0]->ne[0] * n_tasks;
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
                     }
 
                     work_size = MAX(work_size, cur);
@@ -16412,7 +16394,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                     size_t cur = 0;
 
                     if (ggml_is_quantized(node->src[0]->type)) {
-                        cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->src[1]->ne[0] * n_tasks;
+                        cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
                     }
 
                     work_size = MAX(work_size, cur);
@@ -16495,12 +16477,12 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                                      //       the threads are still spinning
                         if (node->src[0]->type != GGML_TYPE_F32) {
                             // here we need memory just for single 2D matrix from src0
-                            cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src[0]->ne[0]*node->src[0]->ne[1]);
+                            cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);
                         }
                     } else
 #endif
                     if (node->src[1]->type != vec_dot_type) {
-                        cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src[1])/GGML_BLCK_SIZE[vec_dot_type];
+                        cur = ggml_type_size(vec_dot_type)*ggml_nelements(node->src[1])/ggml_blck_size(vec_dot_type);
                     } else {
                         cur = 0;
                     }
@@ -18306,8 +18288,8 @@ enum ggml_opt_result ggml_opt_resume(
         struct ggml_tensor * f) {
 
     // build forward + backward compute graphs
-    struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / GGML_TYPE_SIZE[GGML_TYPE_I32]+ (sizeof(struct ggml_cgraph) % GGML_TYPE_SIZE[GGML_TYPE_I32] ? 1 : 0));
+    struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
-    struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / GGML_TYPE_SIZE[GGML_TYPE_I32]+ (sizeof(struct ggml_cgraph) % GGML_TYPE_SIZE[GGML_TYPE_I32] ? 1 : 0));
+    struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
 
     struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data;
     struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data;

ggml.h

@@ -1856,6 +1856,10 @@ extern "C" {
     typedef void (*ggml_vec_dot_t)   (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
 
     typedef struct {
+        const char      * type_name;
+        int               blck_size;
+        size_t            type_size;
+        bool              is_quantized;
         ggml_to_float_t   to_float;
         ggml_from_float_t from_float;
         ggml_from_float_t from_float_reference;
@@ -1863,7 +1867,7 @@ extern "C" {
         enum ggml_type    vec_dot_type;
     } ggml_type_traits_t;
 
-    ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i);
+    ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
 
 #ifdef  __cplusplus
 }