Remove unused data and add fixes (#5154)

* Remove unused data and add fixes

* Add missing file

* Address review comments

* Replace the scope of vq allocation

common/sampling.cpp

@@ -13,6 +13,7 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
         // will be empty (default) if there are parse errors
         if (result->parsed_grammar.rules.empty()) {
             fprintf(stderr, "%s: failed to parse grammar\n", __func__);
+            delete result;
             return nullptr;
         }
 

examples/infill/infill.cpp

@@ -241,7 +241,7 @@ int main(int argc, char ** argv) {
     LOG("add_bos: %d\n", add_bos);
 
     bool suff_rm_leading_spc = params.escape;
-    if (suff_rm_leading_spc && params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) {
+    if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
         params.input_suffix.erase(0, 1);
         suff_rm_leading_spc = false;
     }

examples/llava/clip.cpp

@@ -1277,7 +1277,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
         ".*weight",
     };
 
-    std::vector<uint8_t> read_data(512);
     std::vector<uint8_t> work(512);
     std::vector<float> conv_buf(512);
     std::vector<int64_t> hist_all(1 << 4, 0);

examples/server/README.md

@@ -30,7 +30,8 @@ Command line options:
 -   `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled)
 -   `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
 -   `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
-
+-   `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`
+-   `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`
 ## Build
 
 server is build alongside everything else from the root of the project

examples/server/server.cpp

@@ -184,6 +184,12 @@ struct llama_client_slot
     struct llama_sampling_params sparams;
     llama_sampling_context *ctx_sampling = nullptr;
 
+    int32_t ga_i = 0;   // group-attention state
+    int32_t ga_n = 1;// group-attention factor
+    int32_t ga_w = 512; // group-attention width
+
+    int32_t n_past_se = 0; // self-extend
+
     // multimodal
     std::vector<slot_image> images;
 
@@ -212,7 +218,8 @@ struct llama_client_slot
         sent_count             = 0;
         sent_token_probs_index = 0;
         infill                 = false;
-
+        ga_i                   = 0;
+        n_past_se  = 0;
         generated_token_probs.clear();
 
         for (slot_image & img : images)
@@ -399,9 +406,26 @@ struct llama_server_context
 
             slot.id = i;
             slot.n_ctx = n_ctx_slot;
-            slot.reset();
 
             LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
+
+            const int ga_n = params.grp_attn_n;
+            const int ga_w = params.grp_attn_w;
+
+            if (ga_n != 1) {
+                GGML_ASSERT(ga_n > 0                    && "ga_n must be positive");                     // NOLINT
+                GGML_ASSERT(ga_w % ga_n == 0            && "ga_w must be a multiple of ga_n");     // NOLINT
+                //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of ga_w");    // NOLINT
+                //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
+                LOG_TEE(" -> Slot %i - self-extend: ga_n = %d, ga_w = %d\n", slot.id, ga_n, ga_w);
+            }
+
+            slot.ga_i = 0;
+            slot.ga_n = ga_n;
+            slot.ga_w = ga_w;
+
+            slot.reset();
+
             slots.push_back(slot);
         }
 
@@ -657,7 +681,7 @@ struct llama_server_context
                     while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
                         size_t end_prefix = pos;
                         pos += pattern.length();
-                        size_t end_pos = prompt.find("]", pos);
+                        size_t end_pos = prompt.find(']', pos);
                         if (end_pos != std::string::npos)
                         {
                             std::string image_id = prompt.substr(pos, end_pos - pos);
@@ -1349,32 +1373,35 @@ struct llama_server_context
 
         for (llama_client_slot &slot : slots)
         {
-            if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx)
+            if (slot.ga_n == 1)
             {
-                // Shift context
-                const int n_left    = slot.n_past - slot.params.n_keep - 1;
-                const int n_discard = n_left / 2;
-
-                LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
-                llama_kv_cache_seq_rm   (ctx, slot.id, slot.params.n_keep + 1            , slot.params.n_keep + n_discard + 1);
-                llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard);
-
-                for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
+                if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx)
                 {
-                    slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
+                    // Shift context
+                    const int n_left    = slot.n_past - slot.params.n_keep - 1;
+                    const int n_discard = n_left / 2;
+
+                    LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
+                    llama_kv_cache_seq_rm   (ctx, slot.id, slot.params.n_keep + 1            , slot.params.n_keep + n_discard + 1);
+                    llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard);
+
+                    for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
+                    {
+                        slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
+                    }
+
+                    slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
+
+                    slot.n_past -= n_discard;
+
+                    slot.truncated = true;
+
+                    LOG_VERBOSE("context shift", {
+                        { "n_ctx", n_ctx },
+                        { "n_keep", params.n_keep },
+                        { "n_left", n_left },
+                    });
                 }
-
-                slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
-
-                slot.n_past -= n_discard;
-
-                slot.truncated = true;
-
-                LOG_VERBOSE("context shift", {
-                                                {"n_ctx",  n_ctx},
-                                                {"n_keep", params.n_keep},
-                                                {"n_left", n_left},
-                                            });
             }
         }
 
@@ -1401,7 +1428,8 @@ struct llama_server_context
 
             slot.i_batch = batch.n_tokens;
 
-            llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
+            const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
+            llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true);
 
             slot.n_past += 1;
         }
@@ -1499,6 +1527,8 @@ struct llama_server_context
                         llama_sampling_reset(slot.ctx_sampling);
 
                         slot.n_past = 0;
+                        slot.n_past_se = 0;
+                        slot.ga_i = 0;
                         slot.num_prompt_tokens_processed = slot.num_prompt_tokens;
                     }
                     else
@@ -1512,6 +1542,25 @@ struct llama_server_context
                         slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
                         slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;
 
+                        if (slot.ga_n != 1)
+                        {
+                            int ga_i = 0;
+                            int32_t ga_n = slot.ga_n;
+                            int32_t ga_w = slot.ga_w;
+                            int32_t slot_npast = 0;
+                            for (int k = 0; k < slot.n_past; ++k)
+                            {
+                                while (slot_npast >= ga_i + ga_w) {
+                                    const int bd = (ga_w/ga_n)*(ga_n - 1);
+                                    slot_npast -= bd;
+                                    ga_i += ga_w/ga_n;
+                                }
+                                slot_npast++;
+                            }
+                            slot.n_past_se = slot_npast;
+                            slot.ga_i = ga_i;
+                        }
+
                         LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
                     }
 
@@ -1526,6 +1575,10 @@ struct llama_server_context
                         // we have to evaluate at least 1 token to generate logits.
                         LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
                         slot.n_past--;
+                        if (slot.ga_i > 0)
+                        {
+                            slot.n_past_se--;
+                        }
                     }
 
                     LOG_VERBOSE("prompt ingested", {
@@ -1538,9 +1591,22 @@ struct llama_server_context
 
                     // process the prefix of first image
                     std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
+                    int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
+                    int ga_i = slot.ga_i;
+                    int32_t ga_n = slot.ga_n;
+                    int32_t ga_w = slot.ga_w;
                     for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
                     {
-                       llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false);
+                        if (slot.ga_n != 1)
+                        {
+                            while (slot_npast >= ga_i + ga_w) {
+                                const int bd = (ga_w/ga_n)*(ga_n - 1);
+                                slot_npast -= bd;
+                                ga_i += ga_w/ga_n;
+                            }
+                        }
+                        llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false);
+                        slot_npast += 1;
                     }
 
                     if (has_images && !ingest_images(slot, n_batch))
@@ -1570,6 +1636,36 @@ struct llama_server_context
         for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
         {
             const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
+
+            for (auto & slot : slots)
+            {
+                if (slot.ga_n != 1)
+                {
+                    // context extension via Self-Extend
+                    while (slot.n_past_se >= slot.ga_i + slot.ga_w)
+                    {
+                        const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
+                        const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
+                        const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
+
+                        LOG_TEE("\n");
+                        LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
+                        LOG_TEE("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
+                        LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
+
+                        llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
+                        llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n);
+                        llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
+
+                        slot.n_past_se -= bd;
+
+                        slot.ga_i += slot.ga_w / slot.ga_n;
+
+                        LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
+                    }
+                    slot.n_past_se += n_tokens;
+                }
+            }
             llama_batch batch_view =
             {
                 n_tokens,
@@ -1583,6 +1679,7 @@ struct llama_server_context
             };
 
             const int ret = llama_decode(ctx, batch_view);
+
             if (ret != 0)
             {
                 if (n_batch == 1 || ret < 0)
@@ -1728,6 +1825,8 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("  --override-kv KEY=TYPE:VALUE\n");
     printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
     printf("                        types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("  -gan N, --grp-attn-n N    Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`");
+    printf("  -gaw N, --grp-attn-w N    Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`");
     printf("\n");
 }
 
@@ -1913,6 +2012,25 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             }
             params.n_threads = std::stoi(argv[i]);
         }
+        else if (arg == "--grp-attn-n" || arg == "-gan")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+
+            params.grp_attn_n = std::stoi(argv[i]);
+        }
+        else if (arg == "--grp-attn-w" || arg == "-gaw")
+        {
+            if (++i >= argc)
+            {
+                invalid_param = true;
+                break;
+            }
+
+            params.grp_attn_w = std::stoi(argv[i]);
+        }
         else if (arg == "--threads-batch" || arg == "-tb")
         {
             if (++i >= argc)

ggml-opencl.cpp

@@ -714,7 +714,6 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx,
         dst[row] = tmp[0];
     }
 }
-
 );
 
 
@@ -784,6 +783,7 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float
         dst[row] = tmp[0];
     }
 }
+
 );
 
 
@@ -799,6 +799,18 @@ __kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y
 }
 );
 
+std::string add_template = MULTILINE_QUOTE(
+__kernel void add_f32(__global float * x, const int x_offset, __global float * y, const int y_offset, __global float * dst, const int dst_offset, const int ky) {
+    const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
+
+    if (i >= get_global_size(0)) {
+        return;
+    }
+
+    dst[dst_offset + i] = x[x_offset + i] + y[y_offset + i%ky];
+}
+);
+
 #define CL_CHECK(err)                                               \
     do {                                                            \
         cl_int err_ = (err);                                        \
@@ -878,6 +890,7 @@ static std::string generate_kernels() {
         }
         src << mul_kernel << '\n';
     }
+    src << add_template << '\n';
 
     return src.str();
 }
@@ -893,6 +906,7 @@ static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl,
 static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl;
 static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl;
 static cl_kernel mul_f32_cl;
+static cl_kernel add_f32_cl;
 static bool fp16_support;
 
 static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
@@ -1100,9 +1114,10 @@ void ggml_cl_init(void) {
     char *ext_buffer = (char *)alloca(ext_str_size + 1);
     clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL);
     ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated
+    // Disabled due to faulty outputs
     // Check if ext_buffer contains cl_khr_fp16
-    fp16_support = strstr(ext_buffer, "cl_khr_fp16") != NULL;
-    fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false");
+    fp16_support = false;  // strstr(ext_buffer, "cl_khr_fp16") != NULL;
+    // fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false");
 
     cl_context_properties properties[] = {
         (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
@@ -1150,6 +1165,8 @@ void ggml_cl_init(void) {
 
     // mul kernel
     CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
+
+    CL_CHECK((add_f32_cl = clCreateKernel(program, "add_f32", &err), err));
 }
 
 static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
@@ -1458,6 +1475,70 @@ void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src
     ggml_cl_mul_f32(src0, src1, dst);
 }
 
+static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+    const int nb2  = dst->nb[2];
+    const int nb3  = dst->nb[3];
+    size_t x_size;
+    size_t d_size;
+
+    cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0
+    cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
+    cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst
+
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            cl_event ev;
+
+            // copy src0 to device
+            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev));
+
+            const int64_t i13 = i03%ne13;
+            const int64_t i12 = i02%ne12;
+            const int i1 = i13*ne12*ne11 + i12*ne11;
+
+            cl_int x_offset = 0;
+            cl_int y_offset = i1*ne10;
+            cl_int d_offset = 0;
+
+            size_t global = ne00 * ne01;
+            cl_int ky = ne10 * ne11;
+
+            CL_CHECK(clSetKernelArg(add_f32_cl, 0, sizeof(cl_mem), &d_X));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 1, sizeof(cl_int), &x_offset));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 2, sizeof(cl_mem), &d_Y));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 3, sizeof(cl_int), &y_offset));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 4, sizeof(cl_mem), &d_D));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 5, sizeof(cl_int), &d_offset));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 6, sizeof(cl_int), &ky));
+            CL_CHECK(clEnqueueNDRangeKernel(queue, add_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
+
+            CL_CHECK(clReleaseEvent(ev));
+            CL_CHECK(clFinish(queue));
+
+            // copy dst to host
+            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
+            CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
+        }
+    }
+    ggml_cl_pool_free(d_X, x_size);
+    ggml_cl_pool_free(d_D, d_size);
+}
+
+void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+    ggml_cl_add_f32(src0, src1, dst);
+}
+
 static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];

ggml-opencl.h

@@ -10,6 +10,7 @@ extern "C" {
 GGML_API void ggml_cl_init(void);
 
 GGML_API void   ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API void   ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
 GGML_API bool   ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst);
 GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
 GGML_API void   ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);

ggml.c

@@ -7207,6 +7207,17 @@ static void ggml_compute_forward_add_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
+#ifdef GGML_USE_CLBLAST
+    if (src1->backend == GGML_BACKEND_GPU) {
+        // TODO: OpenCL kernel support full broadcast
+        GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
+        if (ith == 0) {
+            ggml_cl_add(src0, src1, dst);
+        }
+        return;
+    }
+#endif
+
     const int nr  = ggml_nrows(src0);
 
     GGML_TENSOR_BINARY_OP_LOCALS

pocs/vdot/vdot.cpp

@@ -243,7 +243,6 @@ int main(int argc, char** argv) {
     if (useQ4_1) q41.resize(n4);
     else q40.resize(n4);
     std::vector<block_q8_0> q8(n8);
-    std::vector<int64_t> H(16, 0);
     double sumt = 0, sumt2 = 0, maxt = 0;
     double sumqt = 0, sumqt2 = 0, maxqt = 0;
     double sum = 0, sumq = 0, exactSum = 0;

tests/test-backend-ops.cpp

@@ -102,7 +102,6 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
                     } else if (t->type == GGML_TYPE_I8) {
                         tv.push_back((float)*(int8_t *) &buf[i]);
                     } else if (quantized) {
-                        std::vector<float> vq(ggml_blck_size(t->type));
                         tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type));
                         tv.insert(tv.end(), vq.begin(), vq.end());
                     } else {

tests/test-llama-grammar.cpp

@@ -190,7 +190,6 @@ int main()
         index++;
     }
 
-    std::vector<std::vector<const llama_grammar_element *>> next_stacks;
     std::vector<llama_grammar_candidate> next_candidates;
     next_candidates.resize(24);