llama : fix various warnings

I think these were affected by the removal of the `round` during quantization

.gitignore

@@ -16,6 +16,7 @@ build-debug/
 build-release/
 build-static/
 build-cublas/
+build-opencl/
 build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/

README.md

@@ -9,7 +9,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 **Hot topics:**
 
-- Qauntization formats `Q4` and `Q5` have changed - requantize any old models [(info)](https://github.com/ggerganov/llama.cpp/pull/1405)
+- Quantization formats `Q4` and `Q5` have changed - requantize any old models [(info)](https://github.com/ggerganov/llama.cpp/pull/1405)
 - [Roadmap May 2023](https://github.com/ggerganov/llama.cpp/discussions/1220)
 
 <details>
@@ -333,12 +333,12 @@ Several quantization methods are supported. They differ in the resulting model d
 
 | Model | Measure      | F16    | Q4_0   | Q4_1   | Q5_0   | Q5_1   | Q8_0   |
 |------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
-|    7B | perplexity   | 5.9066 | 6.1620 | 6.0910 | 5.9862 | 5.9481 | 5.9069 |
+|    7B | perplexity   | 5.9066 | 6.1565 | 6.0910 | 5.9862 | 5.9481 | 5.9069 |
 |    7B | file size    |  13.0G |   4.0G |   4.8G |   4.4G |   4.8G |   7.1G |
 |    7B | ms/tok @ 4th |    128 |     50 |     54 |     75 |     83 |     75 |
 |    7B | ms/tok @ 8th |    123 |     44 |     52 |     53 |     58 |     72 |
 |    7B | bits/weight  |   16.0 |    5.0 |    6.0 |    5.5 |    6.0 |    9.0 |
-|   13B | perplexity   | 5.2543 | 5.3863 | 5.3607 | 5.2856 | 5.2706 | 5.2548 |
+|   13B | perplexity   | 5.2543 | 5.3860 | 5.3607 | 5.2856 | 5.2706 | 5.2548 |
 |   13B | file size    |  25.0G |   7.6G |   9.1G |   8.4G |   9.1G |    14G |
 |   13B | ms/tok @ 4th |    239 |     93 |    101 |    150 |    164 |    141 |
 |   13B | ms/tok @ 8th |    240 |     81 |     96 |     96 |    104 |    136 |

examples/embedding/embedding.cpp

@@ -56,9 +56,6 @@ int main(int argc, char ** argv) {
     // tokenize the prompt
     auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
 
-    // determine newline token
-    auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
-
     if (params.verbose_prompt) {
         fprintf(stderr, "\n");
         fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());

ggml-opencl.c

@@ -12,109 +12,129 @@
 #define MULTILINE_QUOTE(...) #__VA_ARGS__
 const char * clblast_dequant = MULTILINE_QUOTE(
 
+typedef uchar uint8_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+constant uint QK4_0 = 32;
 struct block_q4_0
 {
     float d;
-    uchar qs[16];
+    uint8_t qs[QK4_0 / 2];
 };
 
-__kernel void dequantize_row_q4_0(__global struct block_q4_0* blocks, __global float* result) {
-    const uint i = get_global_id(0) / 32;
-    const uint l = get_local_id(0);
-
-    const float d = blocks[i].d;
-
-    const uchar vi = blocks[i].qs[l];
-
-    const uint index = i*32 + l*2;
-    result[index + 0] = ((vi & 0xf) - 8)*d;
-    result[index + 1] = ((vi >> 4) - 8)*d;
-}
-
+constant uint QK4_1 = 32;
 struct block_q4_1
 {
     float d;
     float m;
-    uchar qs[16];
+    uint8_t qs[QK4_1 / 2];
 };
 
-__kernel void dequantize_row_q4_1(__global struct block_q4_1* blocks, __global float* result) {
-    const uint i = get_global_id(0) / 32;
-    const uint l = get_local_id(0);
-
-    const float d = blocks[i].d;
-    const float m = blocks[i].m;
-
-    const uchar vi = blocks[i].qs[l];
-
-    const uint index = i*32 + l*2;
-    result[index + 0] = (vi & 0xf) * d + m;
-    result[index + 1] = (vi >> 4) * d + m;
-}
-
-struct block_q5_0
+constant uint QK5_0 = 32;
+struct __attribute__ ((packed)) block_q5_0
 {
-    float d;
-    uint qh;
-    uchar qs[16];
+    half d;
+    uint32_t qh;
+    uint8_t qs[QK5_0 / 2];
 };
 
-__kernel void dequantize_row_q5_0(__global struct block_q5_0* blocks, __global float* result) {
-    const uint i = get_global_id(0) / 32;
-    const uint l = get_local_id(0);
-
-    const float d = blocks[i].d;
-
-    const uchar vi = blocks[i].qs[l];
-
-    const uint l2 = l * 2;
-
-    const uchar vh0 = ((blocks[i].qh & (1 << (l2 + 0))) >> (l2 + 0)) << 4;
-    const uchar vh1 = ((blocks[i].qh & (1 << (l2 + 1))) >> (l2 + 1)) << 4;
-
-    const uint index = i*32 + l2;
-    result[index + 0] = (((vi & 0xf) | vh0) - 16)*d;
-    result[index + 1] = (((vi >>  4) | vh1) - 16)*d;
-}
-
+constant uint QK5_1 = 32;
 struct block_q5_1
 {
-    ushort d;
-    ushort m;
-    uint qh;
-    uchar qs[16];
+    half d;
+    half m;
+    uint32_t qh;
+    uint8_t qs[QK5_1 / 2];
 };
 
-__kernel void dequantize_row_q5_1(__global struct block_q5_1* blocks, __global float* result) {
-    const uint i = get_global_id(0) / 32;
-    const uint l = get_local_id(0);
-
-    const float d = vload_half(0, (__global half*) &blocks[i].d);
-    const float m = vload_half(0, (__global half*) &blocks[i].m);
-
-    const uchar vi = blocks[i].qs[l];
-
-    const uint l2 = l * 2;
-
-    const uchar vh0 = ((blocks[i].qh & (1 << (l2 + 0))) >> (l2 + 0)) << 4;
-    const uchar vh1 = ((blocks[i].qh & (1 << (l2 + 1))) >> (l2 + 1)) << 4;
-
-    const uint index = i*32 + l2;
-    result[index + 0] = ((vi & 0xf) | vh0)*d + m;
-    result[index + 1] = ((vi >>  4) | vh1)*d + m;
-}
-
+constant uint QK8_0 = 32;
 struct block_q8_0
 {
     float d;
-    char qs[32];
+    uint8_t qs[QK8_0];
 };
 
-__kernel void dequantize_row_q8_0(__global struct block_q8_0* blocks, __global float* result) {
-    const uint i = get_global_id(0) / 32;
-    const uint l = get_local_id(0);
 
-    result[i*32 + l] = blocks[i].qs[l] * blocks[i].d;
+__kernel void dequantize_row_q4_0(__global struct block_q4_0* x, __global float* y) {
+    constant uint qk = QK4_0;
+
+    const uint i = get_global_id(0) / qk;
+    const uint j = get_local_id(0);
+
+    const float d = x[i].d;
+
+    const int x0 = (x[i].qs[j] & 0xf) - 8;
+    const int x1 = (x[i].qs[j] >>  4) - 8;
+
+    y[i*qk + j + 0   ] = x0*d;
+    y[i*qk + j + qk/2] = x1*d;
+}
+
+__kernel void dequantize_row_q4_1(__global struct block_q4_1* x, __global float* y) {
+    constant uint qk = QK4_1;
+
+    const uint i = get_global_id(0) / qk;
+    const uint j = get_local_id(0);
+
+    const float d = x[i].d;
+    const float m = x[i].m;
+
+    const int x0 = (x[i].qs[j] & 0xf);
+    const int x1 = (x[i].qs[j] >>  4);
+
+    y[i*qk + j + 0   ] = x0*d + m;
+    y[i*qk + j + qk/2] = x1*d + m;
+}
+
+__kernel void dequantize_row_q5_0(__global struct block_q5_0* x, __global float* y) {
+    constant uint qk = QK5_0;
+
+    const uint i = get_global_id(0) / qk;
+    const uint j = get_local_id(0);
+
+    const float d = vload_half(0, (__global half*) &x[i].d);
+
+    uint32_t qh = x[i].qh;
+
+    const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+    const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+    const int32_t x0 = ((x[i].qs[j] & 0xf) | xh_0) - 16;
+    const int32_t x1 = ((x[i].qs[j] >>  4) | xh_1) - 16;
+
+    y[i*qk + j + 0   ] = x0*d;
+    y[i*qk + j + qk/2] = x1*d;
+}
+
+__kernel void dequantize_row_q5_1(__global struct block_q5_1* x, __global float* y) {
+    constant uint qk = QK5_1;
+
+    const uint i = get_global_id(0) / qk;
+    const uint j = get_local_id(0);
+
+    const float d = vload_half(0, (__global half*) &x[i].d);
+    const float m = vload_half(0, (__global half*) &x[i].m);
+
+    uint32_t qh = x[i].qh;
+
+    const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+    const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+    const int x0 = (x[i].qs[j] & 0xf) | xh_0;
+    const int x1 = (x[i].qs[j] >>  4) | xh_1;
+
+    y[i*qk + j + 0   ] = x0*d + m;
+    y[i*qk + j + qk/2] = x1*d + m;
+}
+
+__kernel void dequantize_row_q8_0(__global struct block_q8_0* x, __global float* y) {
+    constant uint qk = QK8_0;
+    const uint i = get_global_id(0) / qk;
+    const uint j = get_local_id(0);
+
+    const float d = x[i].d;
+    y[i*qk + j] = x[i].qs[j]*d;
 }
 
 );
@@ -128,20 +148,6 @@ __kernel void dequantize_row_q8_0(__global struct block_q8_0* blocks, __global f
         }                                                                                       \
     } while (0)
 
-#define QK5_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2]; // nibbles / quants
-} block_q5_0;
-
-
-typedef struct {
-    float d;                // delta
-    uint32_t qh;          // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2];  // nibbles / quants
-} cl_block_q5_0;
-
 static cl_platform_id platform;
 static cl_device_id device;
 static cl_context context;
@@ -252,7 +258,6 @@ void ggml_cl_sgemm_wrapper(
     cl_kernel kernel;
     size_t global = n * k, local, size_qb;
     bool dequant;
-    cl_block_q5_0* cl_host_b;
 
     switch (btype) {
     case GGML_TYPE_F32:
@@ -274,18 +279,7 @@ void ggml_cl_sgemm_wrapper(
         dequant = true;
         kernel = kernel_q5_0;
         local = 16;
-        // For some reason OpenCL seems to be incapable of working with structs of size 22.
-        // 20 and 24 bytes are fine. Workaround to do the fp16 to fp32 step on CPU...
-        // TODO Find the reason, fix and remove workaround.
-        const block_q5_0* b = (const block_q5_0*) host_b;
-        cl_host_b = (cl_block_q5_0*) malloc(sizeof(cl_block_q5_0) * global / 32);
-        for (size_t i = 0; i < global / 32; i++) {
-            cl_host_b[i].d = ggml_fp16_to_fp32(b[i].d);
-            memcpy(&cl_host_b[i].qh, b[i].qh, sizeof(uint32_t));
-            memcpy(&cl_host_b[i].qs, b[i].qs, QK5_0 / 2);
-        }
-        host_b = (const float*) cl_host_b;
-        size_qb = global * (sizeof(float) + sizeof(uint32_t) + local) / 32;
+        size_qb = global * (sizeof(ggml_fp16_t) + sizeof(uint32_t) + local) / 32;
         break;
     case GGML_TYPE_Q5_1:
         dequant = true;
@@ -364,7 +358,4 @@ void ggml_cl_sgemm_wrapper(
     clWaitForEvents(1, &ev_c);
     clReleaseEvent(ev_sgemm);
     clReleaseEvent(ev_c);
-    if (btype == GGML_TYPE_Q5_0) {
-        free((void*) cl_host_b);
-    }
 }

llama.cpp

@@ -50,49 +50,49 @@ static const size_t MB = 1024*1024;
 
 static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0()
 {
-    static std::map<e_model, size_t> _MEM_REQ_SCRATCH0 = {
+    static std::map<e_model, size_t> k_sizes = {
         { MODEL_7B,    512ull * MB },
         { MODEL_13B,   512ull * MB },
         { MODEL_30B,   512ull * MB },
         { MODEL_65B,  1024ull * MB },
     };
-    return _MEM_REQ_SCRATCH0;
+    return k_sizes;
 }
 
 static const std::map<e_model, size_t> & MEM_REQ_SCRATCH1()
 {
-    static std::map<e_model, size_t> _MEM_REQ_SCRATCH1 = {
+    static std::map<e_model, size_t> k_sizes = {
         { MODEL_7B,    512ull * MB },
         { MODEL_13B,   512ull * MB },
         { MODEL_30B,   512ull * MB },
         { MODEL_65B,  1024ull * MB },
     };
-    return _MEM_REQ_SCRATCH1;
+    return k_sizes;
 }
 
 // 2*n_embd*n_ctx*n_layer*sizeof(float16)
 static const std::map<e_model, size_t> & MEM_REQ_KV_SELF()
 {
-    static std::map<e_model, size_t> _MEM_REQ_KV_SELF = {
+    static std::map<e_model, size_t> k_sizes = {
         { MODEL_7B,   1026ull * MB },
         { MODEL_13B,  1608ull * MB },
         { MODEL_30B,  3124ull * MB },
         { MODEL_65B,  5120ull * MB },
     };
-    return _MEM_REQ_KV_SELF;
+    return k_sizes;
 }
 
 // this is mostly needed for temporary mul_mat buffers to dequantize the data
 // not actually needed if BLAS is disabled
 static const std::map<e_model, size_t> & MEM_REQ_EVAL()
 {
-    static std::map<e_model, size_t> _MEM_REQ_EVAL = {
+    static std::map<e_model, size_t> k_sizes = {
         { MODEL_7B,   768ull * MB },
         { MODEL_13B, 1024ull * MB },
         { MODEL_30B, 1280ull * MB },
         { MODEL_65B, 1536ull * MB },
     };
-    return _MEM_REQ_EVAL;
+    return k_sizes;
 }
 
 // default hparams (LLaMA 7B)
@@ -586,12 +586,12 @@ struct llama_model_loader {
     std::unique_ptr<llama_mmap> mapping;
 
     llama_model_loader(const std::string & fname_base, bool use_mmap, bool vocab_only) {
-        auto first_file = new llama_file_loader(fname_base.c_str(), 0, tensors_map);
+        auto * first_file = new llama_file_loader(fname_base.c_str(), 0, tensors_map);
         file_loaders.emplace_back(first_file);
         uint32_t n_parts = vocab_only ? 1 : guess_n_parts();
         for (uint32_t i = 1; i < n_parts; i++) {
             std::string fname = fname_base + "." + std::to_string(i);
-            auto ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
+            auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
             file_loaders.emplace_back(ith_file);
             if (ith_file->hparams != first_file->hparams) {
                 throw format("llama.cpp: hparams inconsistent between files");
@@ -638,7 +638,7 @@ struct llama_model_loader {
         }
     }
 
-    struct ggml_tensor * get_tensor(const std::string & name, std::vector<uint32_t> ne) {
+    struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne) {
         auto it = tensors_map.name_to_idx.find(name);
         if (it == tensors_map.name_to_idx.end()) {
             throw format("llama.cpp: tensor '%s' is missing from model", name.c_str());
@@ -667,7 +667,7 @@ struct llama_model_loader {
         return tensor;
     }
 
-    void done_getting_tensors() {
+    void done_getting_tensors() const {
         if (num_ggml_tensors_created != tensors_map.tensors.size()) {
             throw std::string("llama.cpp: file contained more tensors than expected");
         }
@@ -934,7 +934,8 @@ static void llama_model_load_internal(
 
     auto & ctx = model.ctx;
 
-    size_t ctx_size, mmapped_size;
+    size_t ctx_size;
+    size_t mmapped_size;
     ml->calc_sizes(&ctx_size, &mmapped_size);
     fprintf(stderr, "%s: ggml ctx size = %6.2f KB\n", __func__, ctx_size/1024.0);
 
@@ -1074,7 +1075,7 @@ static bool llama_eval_internal(
     const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
 
-    auto & kv_self = model.kv_self;
+    const auto & kv_self = model.kv_self;
 
     LLAMA_ASSERT(!!kv_self.ctx);
 
@@ -1318,7 +1319,7 @@ static bool llama_eval_internal(
     }
 
     // extract embeddings
-    if (lctx.embedding.size()) {
+    if (!lctx.embedding.empty()) {
         auto & embedding_out = lctx.embedding;
 
         embedding_out.resize(n_embd);
@@ -1369,6 +1370,8 @@ struct llama_sp_symbol {
     size_t n;
 };
 
+static_assert(std::is_trivially_copyable<llama_sp_symbol>::value, "llama_sp_symbol is not trivially copyable");
+
 struct llama_sp_bigram {
     struct comparator {
         bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
@@ -1401,7 +1404,7 @@ struct llama_tokenizer {
             sym.prev = index - 1;
             sym.next = offs == text.size() ? -1 : index + 1;
             index++;
-            symbols_.emplace_back(std::move(sym));
+            symbols_.emplace_back(sym);
         }
 
         // seed the work queue with all possible 2-character tokens.
@@ -1492,7 +1495,7 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co
     llama_tokenizer tokenizer(vocab);
     std::vector<llama_vocab::id> output;
 
-    if (text.size() == 0) {
+    if (text.empty()) {
         return output;
     }
 
@@ -1728,7 +1731,7 @@ void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_dat
     const int64_t t_start_sample_us = ggml_time_us();
 
     for (size_t i = 0; i < candidates->size; ++i) {
-        auto token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
+        const auto * token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
         if (token_iter == last_tokens + last_tokens_size) {
             continue;
         }
@@ -1872,7 +1875,7 @@ llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_da
     const int64_t t_start_sample_us = ggml_time_us();
 
     // Find max element
-    auto max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+    auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
         return a.logit < b.logit;
     });
 
@@ -1925,7 +1928,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         nthread = std::thread::hardware_concurrency();
     }
 
-    std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp.c_str(), /*use_mmap*/ false,
+    std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false,
                                                                             /*vocab_only*/ false));
     llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
 
@@ -1979,7 +1982,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             } else if (tensor.type == GGML_TYPE_F16) {
                 f32_conv_buf.resize(nelements * sizeof(float));
                 f32_data = (float *) f32_conv_buf.addr;
-                auto f16_data = (const ggml_fp16_t *) tensor.data;
+                const auto * f16_data = (const ggml_fp16_t *) tensor.data;
                 for (size_t i = 0; i < nelements; i++) {
                     f32_data[i] = ggml_fp16_to_fp32(f16_data[i]);
                 }
@@ -2010,21 +2013,31 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                         size_t first = counter; counter += chunk_size;
                         if (first >= nelements) {
                             if (!local_hist.empty()) {
-                                for (int j=0; j<int(local_hist.size()); ++j) hist_cur[j] += local_hist[j];
+                                for (int j=0; j<int(local_hist.size()); ++j) {
+                                    hist_cur[j] += local_hist[j];
+                                }
                                 new_size += local_size;
                             }
                             break;
                         }
                         lock.unlock();
                         size_t last = std::min(nelements, first + chunk_size);
-                        if (local_hist.empty()) local_hist.resize(hist_cur.size(), 0);
+                        if (local_hist.empty()) {
+                            local_hist.resize(hist_cur.size(), 0);
+                        }
                         local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
                     }
                 };
-                if (int(workers.size()) < nthread_use - 1) workers.resize(nthread_use - 1);
-                for (int it = 0; it < nthread_use - 1; ++it) workers[it] = std::thread(compute);
+                if ((int) workers.size() < nthread_use - 1) {
+                    workers.resize(nthread_use - 1);
+                }
+                for (int it = 0; it < nthread_use - 1; ++it) {
+                    workers[it] = std::thread(compute);
+                }
                 compute();
-                for (int it = 0; it < nthread_use - 1; ++it) workers[it].join();
+                for (int it = 0; it < nthread_use - 1; ++it) {
+                    workers[it].join();
+                }
             }
 
             printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
@@ -2222,7 +2235,8 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
         fprintf(stderr, "%s: loading base model from '%s'\n", __func__, path_base_model);
         model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*vocab_only*/ false));
 
-        size_t ctx_size, mmapped_size;
+        size_t ctx_size;
+        size_t mmapped_size;
         model_loader->calc_sizes(&ctx_size, &mmapped_size);
         base_buf.resize(ctx_size);
 
@@ -2261,8 +2275,12 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
             fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
         }
 
-        std::string name(length, 0);
-        fin.read(&name[0], length);
+        std::string name;
+        {
+            char buf[1024];
+            fin.read(buf, length);
+            name = std::string(buf, length);
+        }
 
         // check for lora suffix and get the type of tensor
         const std::string lora_suffix = ".lora";
@@ -2277,7 +2295,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
         base_name.erase(pos);
         // fprintf(stderr, "%s: %s => %s (lora type %s) ", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
 
-        if (model_tensors.find(base_name.data()) == model_tensors.end()) {
+        if (model_tensors.find(base_name) == model_tensors.end()) {
             fprintf(stderr, "%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
             return 1;
         }
@@ -2379,8 +2397,9 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
             lora_tensors.clear();
 
             n_tensors++;
-            if (n_tensors % 4 == 0)
+            if (n_tensors % 4 == 0) {
                 fprintf(stderr, ".");
+            }
         }
     }
 
@@ -2409,7 +2428,7 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
     return ctx->model.kv_self.n;
 }
 
-#define LLAMA_MAX_RNG_STATE 64*1024
+#define LLAMA_MAX_RNG_STATE (64*1024)
 
 void llama_set_rng_seed(struct llama_context * ctx, int seed) {
     if (seed < 0) {
@@ -2450,8 +2469,8 @@ size_t llama_get_state_size(const struct llama_context * ctx) {
 }
 
 // Copies the state to the specified destination address
-size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest) {
-    uint8_t * out = dest;
+size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
+    uint8_t * out = dst;
 
     // copy rng
     {
@@ -2511,7 +2530,9 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest) {
 
         if (kv_size) {
             const size_t elt_size = ggml_element_size(kv_self.k);
+
             char buffer[4096];
+
             ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
             ggml_cgraph gf{};
             gf.n_threads = 1;
@@ -2535,10 +2556,12 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest) {
             ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
             ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
             ggml_graph_compute(cpy_ctx, &gf);
+
+            ggml_free(cpy_ctx);
         }
     }
 
-    const size_t written  = out - dest;
+    const size_t written  = out - dst;
     const size_t max_size = llama_get_state_size(ctx);
 
     LLAMA_ASSERT(written <= max_size);
@@ -2548,15 +2571,15 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest) {
 
 // Sets the state reading from the specified source address
 size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
-    const uint8_t * in = src;
+    const uint8_t * inp = src;
 
     // set rng
     {
         size_t rng_size;
         char   rng_buf[LLAMA_MAX_RNG_STATE];
 
-        memcpy(&rng_size,   in, sizeof(rng_size));    in += sizeof(rng_size);
-        memcpy(&rng_buf[0], in, LLAMA_MAX_RNG_STATE); in += LLAMA_MAX_RNG_STATE;
+        memcpy(&rng_size,   inp, sizeof(rng_size));    inp += sizeof(rng_size);
+        memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE;
 
         std::stringstream rng_ss;
         rng_ss.str(std::string(&rng_buf[0], rng_size));
@@ -2570,30 +2593,30 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
         size_t logits_cap;
         size_t logits_size;
 
-        memcpy(&logits_cap,  in, sizeof(logits_cap));  in += sizeof(logits_cap);
-        memcpy(&logits_size, in, sizeof(logits_size)); in += sizeof(logits_size);
+        memcpy(&logits_cap,  inp, sizeof(logits_cap));  inp += sizeof(logits_cap);
+        memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
 
         LLAMA_ASSERT(ctx->logits.capacity() == logits_cap);
 
         if (logits_size) {
             ctx->logits.resize(logits_size);
-            memcpy(ctx->logits.data(), in, logits_size * sizeof(float));
+            memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
         }
 
-        in += logits_cap * sizeof(float);
+        inp += logits_cap * sizeof(float);
     }
 
     // set embeddings
     {
         size_t embedding_size;
 
-        memcpy(&embedding_size, in, sizeof(embedding_size)); in += sizeof(embedding_size);
+        memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
 
         LLAMA_ASSERT(ctx->embedding.capacity() == embedding_size);
 
         if (embedding_size) {
-            memcpy(ctx->embedding.data(), in, embedding_size * sizeof(float));
-            in += embedding_size * sizeof(float);
+            memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
+            inp += embedding_size * sizeof(float);
         }
     }
 
@@ -2608,25 +2631,27 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
         size_t kv_size;
         int kv_ntok;
 
-        memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size);
-        memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok);
+        memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size);
+        memcpy(&kv_ntok, inp, sizeof(kv_ntok)); inp += sizeof(kv_ntok);
 
         if (kv_size) {
             LLAMA_ASSERT(kv_self.buf.size == kv_size);
 
             const size_t elt_size = ggml_element_size(kv_self.k);
+
             char buffer[4096];
+
             ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
             ggml_cgraph gf{};
             gf.n_threads = 1;
 
             ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
-            kin3d->data = (void *) in;
-            in += ggml_nbytes(kin3d);
+            kin3d->data = (void *) inp;
+            inp += ggml_nbytes(kin3d);
 
             ggml_tensor * vin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
-            vin3d->data = (void *) in;
-            in += ggml_nbytes(vin3d);
+            vin3d->data = (void *) inp;
+            inp += ggml_nbytes(vin3d);
 
             ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
                 n_embd, kv_ntok, n_layer,
@@ -2639,12 +2664,14 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
             ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
             ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
             ggml_graph_compute(cpy_ctx, &gf);
+
+            ggml_free(cpy_ctx);
         }
 
         ctx->model.kv_self.n = kv_ntok;
     }
 
-    const size_t nread    = in - src;
+    const size_t nread    = inp - src;
     const size_t max_size = llama_get_state_size(ctx);
 
     LLAMA_ASSERT(nread <= max_size);
@@ -2660,7 +2687,7 @@ bool llama_load_session_file(struct llama_context * ctx, const char * path_sessi
         const uint32_t magic   = file.read_u32();
         const uint32_t version = file.read_u32();
 
-        if (!(magic == LLAMA_SESSION_MAGIC && version == LLAMA_SESSION_VERSION)) {
+        if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
             fprintf(stderr, "%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
             return false;
         }

llama.h

@@ -134,7 +134,7 @@ extern "C" {
     // Copies the state to the specified destination address.
     // Destination needs to have allocated enough memory.
     // Returns the number of bytes copied
-    LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest);
+    LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
 
     // Set the state reading from the specified address
     // Returns the number of bytes read