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diff --git a/llama.cpp/ggml/src/ggml-impl.h b/llama.cpp/ggml/src/ggml-impl.h
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+#pragma once
+
+// GGML internal header
+
+#include "ggml.h"
+#include "gguf.h"
+
+#include <assert.h>
+#include <math.h>
+#include <stdlib.h> // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/
+#include <stdbool.h>
+#include <stdint.h>
+#include <string.h>
+
+#ifdef __ARM_FEATURE_SVE
+#include <arm_sve.h>
+#endif // __ARM_FEATURE_SVE
+
+#if defined(__ARM_NEON) && !defined(__CUDACC__) && !defined(__MUSACC__)
+// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
+//
+// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
+//
+#include <arm_neon.h>
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+void ggml_print_backtrace(void);
+
+#ifndef MIN
+# define MIN(a, b) ((a) < (b) ? (a) : (b))
+#endif
+
+#ifndef MAX
+# define MAX(a, b) ((a) > (b) ? (a) : (b))
+#endif
+
+// required for mmap as gguf only guarantees 32-byte alignment
+#define TENSOR_ALIGNMENT 32
+
+// static_assert should be a #define, but if it's not,
+// fall back to the _Static_assert C11 keyword.
+// if C99 - static_assert is noop
+// ref: https://stackoverflow.com/a/53923785/4039976
+#ifndef __cplusplus
+ #ifndef static_assert
+ #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
+ #define static_assert(cond, msg) _Static_assert(cond, msg)
+ #else
+ #define static_assert(cond, msg) struct global_scope_noop_trick
+ #endif
+ #endif
+#endif
+
+static inline int ggml_up32(int n) {
+ return (n + 31) & ~31;
+}
+
+//static inline int ggml_up64(int n) {
+// return (n + 63) & ~63;
+//}
+
+static inline int ggml_up(int n, int m) {
+ // assert m is a power of 2
+ GGML_ASSERT((m & (m - 1)) == 0);
+ return (n + m - 1) & ~(m - 1);
+}
+
+// TODO: move to ggml.h? (won't be able to inline)
+static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
+ if (a->type != b->type) {
+ return false;
+ }
+ for (int i = 0; i < GGML_MAX_DIMS; i++) {
+ if (a->ne[i] != b->ne[i]) {
+ return false;
+ }
+ if (a->nb[i] != b->nb[i]) {
+ return false;
+ }
+ }
+ return true;
+}
+
+static bool ggml_op_is_empty(enum ggml_op op) {
+ switch (op) {
+ case GGML_OP_NONE:
+ case GGML_OP_RESHAPE:
+ case GGML_OP_TRANSPOSE:
+ case GGML_OP_VIEW:
+ case GGML_OP_PERMUTE:
+ return true;
+ default:
+ return false;
+ }
+}
+
+static inline float ggml_compute_softplus_f32(float input) {
+ return (input > 20.0f) ? input : logf(1 + expf(input));
+}
+//
+// logging
+//
+
+GGML_ATTRIBUTE_FORMAT(2, 3)
+GGML_API void ggml_log_internal (enum ggml_log_level level, const char * format, ...);
+GGML_API void ggml_log_callback_default(enum ggml_log_level level, const char * text, void * user_data);
+
+#define GGML_LOG(...) ggml_log_internal(GGML_LOG_LEVEL_NONE , __VA_ARGS__)
+#define GGML_LOG_INFO(...) ggml_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
+#define GGML_LOG_WARN(...) ggml_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
+#define GGML_LOG_ERROR(...) ggml_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
+#define GGML_LOG_DEBUG(...) ggml_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
+#define GGML_LOG_CONT(...) ggml_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__)
+
+#define GGML_DEBUG 0
+
+#if (GGML_DEBUG >= 1)
+#define GGML_PRINT_DEBUG(...) GGML_LOG_DEBUG(__VA_ARGS__)
+#else
+#define GGML_PRINT_DEBUG(...)
+#endif
+
+#if (GGML_DEBUG >= 5)
+#define GGML_PRINT_DEBUG_5(...) GGML_LOG_DEBUG(__VA_ARGS__)
+#else
+#define GGML_PRINT_DEBUG_5(...)
+#endif
+
+#if (GGML_DEBUG >= 10)
+#define GGML_PRINT_DEBUG_10(...) GGML_LOG_DEBUG(__VA_ARGS__)
+#else
+#define GGML_PRINT_DEBUG_10(...)
+#endif
+
+// tensor params
+
+static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
+ GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings
+ assert(params_size <= GGML_MAX_OP_PARAMS);
+ memcpy(tensor->op_params, params, params_size);
+}
+
+static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) {
+ assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
+ return ((const int32_t *)(tensor->op_params))[i];
+}
+
+static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) {
+ assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
+ return ((const float *)(tensor->op_params))[i];
+}
+
+static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) {
+ assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
+ ((int32_t *)(tensor->op_params))[i] = value;
+}
+
+static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) {
+ assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
+ ((float *)(tensor->op_params))[i] = value;
+}
+
+struct ggml_map_custom1_op_params {
+ ggml_custom1_op_t fun;
+ int n_tasks;
+ void * userdata;
+};
+
+struct ggml_map_custom2_op_params {
+ ggml_custom2_op_t fun;
+ int n_tasks;
+ void * userdata;
+};
+
+struct ggml_map_custom3_op_params {
+ ggml_custom3_op_t fun;
+ int n_tasks;
+ void * userdata;
+};
+
+struct ggml_custom_op_params {
+ ggml_custom_op_t fun;
+ int n_tasks;
+ void * userdata;
+};
+
+// bitset
+
+typedef uint32_t ggml_bitset_t;
+
+static_assert(sizeof(ggml_bitset_t) == 4, "bitset_t constants must be updated");
+#define BITSET_SHR 5 // log2(sizeof(ggml_bitset_t)*8)
+#define BITSET_MASK (sizeof(ggml_bitset_t)*8 - 1)
+
+static size_t ggml_bitset_size(size_t n) {
+ return (n + BITSET_MASK) >> BITSET_SHR;
+}
+
+static inline bool ggml_bitset_get(const ggml_bitset_t * bitset, size_t i) {
+ return !!(bitset[i >> BITSET_SHR] & (1u << (i & BITSET_MASK)));
+}
+
+static inline void ggml_bitset_set(ggml_bitset_t * bitset, size_t i) {
+ bitset[i >> BITSET_SHR] |= (1u << (i & BITSET_MASK));
+}
+
+static inline void ggml_bitset_clear(ggml_bitset_t * bitset, size_t i) {
+ bitset[i >> BITSET_SHR] &= ~(1u << (i & BITSET_MASK));
+}
+
+// hash set
+
+#define GGML_HASHSET_FULL ((size_t)-1)
+#define GGML_HASHSET_ALREADY_EXISTS ((size_t)-2)
+
+struct ggml_hash_set {
+ size_t size;
+ ggml_bitset_t * used; // whether or not the keys are in use i.e. set
+ struct ggml_tensor ** keys; // actual tensors in the set, keys[i] is only defined if ggml_bitset_get(used, i)
+};
+
+struct ggml_hash_set ggml_hash_set_new(size_t size);
+void ggml_hash_set_free(struct ggml_hash_set * hash_set);
+
+// returns the minimum size for a hash set that can hold min_sz elements
+size_t ggml_hash_size(size_t min_sz);
+
+// remove all elements from the hash set
+void ggml_hash_set_reset(struct ggml_hash_set * hash_set);
+
+// returns true if key is in the hash set
+static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key);
+
+// returns GGML_HASHSET_FULL if table is full, otherwise the current index of the key or where it should be inserted
+static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key);
+
+// returns GGML_HASHSET_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
+static size_t ggml_hash_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key);
+
+// return index, asserts if table is full
+static size_t ggml_hash_find_or_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key);
+
+// hash function for ggml_tensor
+static inline size_t ggml_hash(const struct ggml_tensor * p) {
+ // the last 4 bits are always zero due to alignment
+ return (size_t)(uintptr_t)p >> 4;
+}
+
+static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key) {
+ size_t h = ggml_hash(key) % hash_set->size;
+
+ // linear probing
+ size_t i = h;
+ while (ggml_bitset_get(hash_set->used, i) && hash_set->keys[i] != key) {
+ i = (i + 1) % hash_set->size;
+ if (i == h) {
+ // visited all hash table entries -> not found
+ return GGML_HASHSET_FULL;
+ }
+ }
+ return i;
+}
+
+static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
+ size_t i = ggml_hash_find(hash_set, key);
+ return i != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, i);
+}
+
+static size_t ggml_hash_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
+ size_t h = ggml_hash(key) % hash_set->size;
+
+ // linear probing
+ size_t i = h;
+ do {
+ if (!ggml_bitset_get(hash_set->used, i)) {
+ ggml_bitset_set(hash_set->used, i);
+ hash_set->keys[i] = key;
+ return i;
+ }
+ if (hash_set->keys[i] == key) {
+ return GGML_HASHSET_ALREADY_EXISTS;
+ }
+ i = (i + 1) % hash_set->size;
+ } while (i != h);
+
+ // visited all hash table entries -> not found
+ GGML_ABORT("fatal error");
+}
+
+static size_t ggml_hash_find_or_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
+ size_t h = ggml_hash(key) % hash_set->size;
+
+ // linear probing
+ size_t i = h;
+ do {
+ if (!ggml_bitset_get(hash_set->used, i)) {
+ ggml_bitset_set(hash_set->used, i);
+ hash_set->keys[i] = key;
+ return i;
+ }
+ if (hash_set->keys[i] == key) {
+ return i;
+ }
+ i = (i + 1) % hash_set->size;
+ } while (i != h);
+
+ // visited all hash table entries -> not found
+ GGML_ABORT("fatal error");
+}
+
+// computation graph
+
+enum ggml_cgraph_eval_order {
+ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT = 0,
+ GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT,
+ GGML_CGRAPH_EVAL_ORDER_COUNT
+};
+
+struct ggml_cgraph {
+ int size; // maximum number of nodes/leafs/grads/grad_accs
+ int n_nodes; // number of nodes currently in use
+ int n_leafs; // number of leafs currently in use
+
+ struct ggml_tensor ** nodes; // tensors with data that can change if the graph is evaluated
+ struct ggml_tensor ** grads; // the outputs of these tensors are the gradients of the nodes
+ struct ggml_tensor ** grad_accs; // accumulators for node gradients
+ struct ggml_tensor ** leafs; // tensors with constant data
+ int32_t * use_counts;// number of uses of each tensor, indexed by hash table slot
+
+ struct ggml_hash_set visited_hash_set;
+
+ enum ggml_cgraph_eval_order order;
+};
+
+// returns a slice of cgraph with nodes [i0, i1)
+// the slice does not have leafs or gradients
+// if you need the gradients, get them from the original graph
+struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);
+
+// ggml-alloc.c: true if the operation can reuse memory from its sources
+GGML_API bool ggml_op_can_inplace(enum ggml_op op);
+
+
+// Memory allocation
+
+GGML_API void * ggml_aligned_malloc(size_t size);
+GGML_API void ggml_aligned_free(void * ptr, size_t size);
+
+// FP16 <-> FP32
+// ref: https://github.com/Maratyszcza/FP16
+
+static inline float fp32_from_bits(uint32_t w) {
+ union {
+ uint32_t as_bits;
+ float as_value;
+ } fp32;
+ fp32.as_bits = w;
+ return fp32.as_value;
+}
+
+static inline uint32_t fp32_to_bits(float f) {
+ union {
+ float as_value;
+ uint32_t as_bits;
+ } fp32;
+ fp32.as_value = f;
+ return fp32.as_bits;
+}
+
+static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
+ const uint32_t w = (uint32_t) h << 16;
+ const uint32_t sign = w & UINT32_C(0x80000000);
+ const uint32_t two_w = w + w;
+
+ const uint32_t exp_offset = UINT32_C(0xE0) << 23;
+#if (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)) && (!defined(__cplusplus) || __cplusplus >= 201703L)
+ const float exp_scale = 0x1.0p-112f;
+#else
+ const float exp_scale = fp32_from_bits(UINT32_C(0x7800000));
+#endif
+ const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale;
+
+ const uint32_t magic_mask = UINT32_C(126) << 23;
+ const float magic_bias = 0.5f;
+ const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias;
+
+ const uint32_t denormalized_cutoff = UINT32_C(1) << 27;
+ const uint32_t result = sign |
+ (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value));
+ return fp32_from_bits(result);
+}
+
+static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
+#if (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)) && (!defined(__cplusplus) || __cplusplus >= 201703L)
+ const float scale_to_inf = 0x1.0p+112f;
+ const float scale_to_zero = 0x1.0p-110f;
+#else
+ const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000));
+ const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000));
+#endif
+ float base = (fabsf(f) * scale_to_inf) * scale_to_zero;
+
+ const uint32_t w = fp32_to_bits(f);
+ const uint32_t shl1_w = w + w;
+ const uint32_t sign = w & UINT32_C(0x80000000);
+ uint32_t bias = shl1_w & UINT32_C(0xFF000000);
+ if (bias < UINT32_C(0x71000000)) {
+ bias = UINT32_C(0x71000000);
+ }
+
+ base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
+ const uint32_t bits = fp32_to_bits(base);
+ const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00);
+ const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF);
+ const uint32_t nonsign = exp_bits + mantissa_bits;
+ return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign);
+}
+
+#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
+
+#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
+#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
+
+static inline float ggml_e8m0_to_fp32(uint8_t x) {
+ uint32_t bits; // Stores the raw bit representation of the float
+
+ // Handle special case for minimum exponent (denormalized float)
+ if (x == 0) {
+ // Bit pattern for 2^(-127):
+ // - Sign bit: 0 (positive)
+ // - Exponent: 0 (denormalized number)
+ // - Mantissa: 0x400000 (0.5 in fractional form)
+ // Value = 0.5 * 2^(-126) = 2^(-127)
+ bits = 0x00400000;
+ }
+ // note: disabled as we don't need to handle NaNs
+ //// Handle special case for NaN (all bits set)
+ //else if (x == 0xFF) {
+ // // Standard quiet NaN pattern:
+ // // - Sign bit: 0
+ // // - Exponent: all 1s (0xFF)
+ // // - Mantissa: 0x400000 (quiet NaN flag)
+ // bits = 0x7FC00000;
+ //}
+ // Normalized values (most common case)
+ else {
+ // Construct normalized float by shifting exponent into position:
+ // - Exponent field: 8 bits (positions 30-23)
+ // - Mantissa: 0 (implicit leading 1)
+ // Value = 2^(x - 127)
+ bits = (uint32_t) x << 23;
+ }
+
+ float result; // Final float value
+ // Safely reinterpret bit pattern as float without type-punning issues
+ memcpy(&result, &bits, sizeof(float));
+ return result;
+}
+
+// Equal to ggml_e8m0_to_fp32/2
+// Useful with MXFP4 quantization since the E0M2 values are doubled
+static inline float ggml_e8m0_to_fp32_half(uint8_t x) {
+ uint32_t bits;
+
+ // For x < 2: use precomputed denormal patterns
+ if (x < 2) {
+ // 0x00200000 = 2^(-128), 0x00400000 = 2^(-127)
+ bits = 0x00200000 << x;
+ }
+ // For x >= 2: normalized exponent adjustment
+ else {
+ // 0.5 * 2^(x-127) = 2^(x-128) = normalized with exponent (x-1)
+ bits = (uint32_t)(x - 1) << 23;
+ }
+ // Note: NaNs are not handled here
+
+ float result;
+ memcpy(&result, &bits, sizeof(float));
+ return result;
+}
+
+#define GGML_E8M0_TO_FP32(x) ggml_e8m0_to_fp32(x)
+#define GGML_E8M0_TO_FP32_HALF(x) ggml_e8m0_to_fp32_half(x)
+
+/**
+ * Converts brain16 to float32.
+ *
+ * The bfloat16 floating point format has the following structure:
+ *
+ * ┌sign
+ * │
+ * │ ┌exponent
+ * │ │
+ * │ │ ┌mantissa
+ * │ │ │
+ * │┌──┴───┐┌─┴───┐
+ * 0b0000000000000000 brain16
+ *
+ * Since bf16 has the same number of exponent bits as a 32bit float,
+ * encoding and decoding numbers becomes relatively straightforward.
+ *
+ * ┌sign
+ * │
+ * │ ┌exponent
+ * │ │
+ * │ │ ┌mantissa
+ * │ │ │
+ * │┌──┴───┐┌─┴───────────────────┐
+ * 0b00000000000000000000000000000000 IEEE binary32
+ *
+ * For comparison, the standard fp16 format has fewer exponent bits.
+ *
+ * ┌sign
+ * │
+ * │ ┌exponent
+ * │ │
+ * │ │ ┌mantissa
+ * │ │ │
+ * │┌─┴─┐┌─┴──────┐
+ * 0b0000000000000000 IEEE binary16
+ *
+ * @see IEEE 754-2008
+ */
+static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
+ union {
+ float f;
+ uint32_t i;
+ } u;
+ u.i = (uint32_t)h.bits << 16;
+ return u.f;
+}
+
+/**
+ * Converts float32 to brain16.
+ *
+ * This is binary identical with Google Brain float conversion.
+ * Floats shall round to nearest even, and NANs shall be quiet.
+ * Subnormals aren't flushed to zero, except perhaps when used.
+ * This code should vectorize nicely if using modern compilers.
+ */
+static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
+ ggml_bf16_t h;
+ union {
+ float f;
+ uint32_t i;
+ } u;
+ u.f = s;
+ if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */
+ h.bits = (u.i >> 16) | 64; /* force to quiet */
+ return h;
+ }
+ h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16;
+ return h;
+}
+
+#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x)
+#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x)
+
+static inline int32_t ggml_node_get_use_count(const struct ggml_cgraph * cgraph, int node_idx) {
+ const struct ggml_tensor * node = cgraph->nodes[node_idx];
+
+ size_t hash_pos = ggml_hash_find(&cgraph->visited_hash_set, node);
+ if (!ggml_bitset_get(cgraph->visited_hash_set.used, hash_pos)) {
+ return 0;
+ }
+ return cgraph->use_counts[hash_pos];
+}
+
+// return true if the node's results are only used by N other nodes
+// and can be fused into their calculations.
+static inline bool ggml_node_has_n_uses(const struct ggml_cgraph * cgraph, int node_idx, int32_t n_uses) {
+ const struct ggml_tensor * node = cgraph->nodes[node_idx];
+
+ // check the use count against how many we're replacing
+ if (ggml_node_get_use_count(cgraph, node_idx) != n_uses) {
+ return false;
+ }
+
+ // if node is a view, some other node might be using the intermediate result
+ // via the view source.
+ if (node->view_src) {
+ return false;
+ }
+
+ // If the user requested output for the node, can't fuse
+ if (node->flags & GGML_TENSOR_FLAG_OUTPUT) {
+ return false;
+ }
+
+ return true;
+}
+
+// Returns true if nodes with indices { node_idxs } are the sequence of ggml_ops in ops[]
+// and are fusable. Nodes are considered fusable according to this function if:
+// - all nodes except the last have only one use and are not views/outputs (see ggml_node_has_N_uses).
+// - all nodes except the last are a src of the following node.
+// - all nodes are the same shape.
+// TODO: Consider allowing GGML_OP_NONE nodes in between
+static inline bool ggml_can_fuse_ext(const struct ggml_cgraph * cgraph, const int * node_idxs, const enum ggml_op * ops, int num_ops) {
+ for (int i = 0; i < num_ops; ++i) {
+ if (node_idxs[i] >= cgraph->n_nodes) {
+ return false;
+ }
+
+ struct ggml_tensor * node = cgraph->nodes[node_idxs[i]];
+ if (node->op != ops[i]) {
+ return false;
+ }
+ if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) {
+ return false;
+ }
+ if (i < num_ops - 1 && !ggml_node_has_n_uses(cgraph, node_idxs[i], 1)) {
+ return false;
+ }
+ if (i > 0) {
+ struct ggml_tensor * prev = cgraph->nodes[node_idxs[i - 1]];
+ if (node->src[0] != prev && node->src[1] != prev) {
+ return false;
+ }
+ if (!ggml_are_same_shape(node, prev)) {
+ return false;
+ }
+ }
+ }
+ return true;
+}
+
+// same as above, for sequential indices starting at node_idx
+static inline bool ggml_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, const enum ggml_op * ops, int num_ops) {
+ assert(num_ops < 32);
+
+ if (node_idx + num_ops > cgraph->n_nodes) {
+ return false;
+ }
+
+ int idxs[32];
+ for (int i = 0; i < num_ops; ++i) {
+ idxs[i] = node_idx + i;
+ }
+
+ return ggml_can_fuse_ext(cgraph, idxs, ops, num_ops);
+}
+
+GGML_API bool ggml_can_fuse_subgraph_ext(const struct ggml_cgraph * cgraph,
+ const int * node_idxs,
+ int count,
+ const enum ggml_op * ops,
+ const int * outputs,
+ int num_outputs);
+
+// Returns true if the subgraph formed by {node_idxs} can be fused
+// checks whethers all nodes which are not part of outputs can be elided
+// by checking if their num_uses are confined to the subgraph
+static inline bool ggml_can_fuse_subgraph(const struct ggml_cgraph * cgraph,
+ int node_idx,
+ int count,
+ const enum ggml_op * ops,
+ const int * outputs,
+ int num_outputs) {
+ GGML_ASSERT(count < 32);
+ if (node_idx + count > cgraph->n_nodes) {
+ return false;
+ }
+
+ int idxs[32];
+
+ for (int i = 0; i < count; ++i) {
+ idxs[i] = node_idx + i;
+ }
+
+ return ggml_can_fuse_subgraph_ext(cgraph, idxs, count, ops, outputs, num_outputs);
+}
+
+#ifdef __cplusplus
+}
+#endif
+
+#ifdef __cplusplus
+#include <array>
+#include <initializer_list>
+#include <vector>
+
+// nicer C++ syntax for ggml_can_fuse
+inline bool ggml_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops) {
+ return ggml_can_fuse(cgraph, node_idx, ops.begin(), (int)ops.size());
+}
+
+inline bool ggml_can_fuse_subgraph(const struct ggml_cgraph * cgraph,
+ int start_idx,
+ std::initializer_list<enum ggml_op> ops,
+ std::initializer_list<int> outputs = {}) {
+ return ggml_can_fuse_subgraph(cgraph, start_idx, ops.size(), ops.begin(), outputs.begin(), outputs.size());
+}
+
+// Return true if the edges in the graph match expectations.
+inline bool ggml_check_edges(const struct ggml_cgraph * cgraph,
+ int start_idx,
+ std::initializer_list<std::array<int, 3>> edges) {
+ for (const auto & edge : edges) {
+ int dst_node = edge[0];
+ int src_idx = edge[1];
+ int src_node = edge[2];
+ if (cgraph->nodes[start_idx + dst_node]->src[src_idx] != cgraph->nodes[start_idx + src_node]) {
+ return false;
+ }
+ }
+ return true;
+}
+
+// expose GGUF internals for test code
+GGML_API size_t gguf_type_size(enum gguf_type type);
+GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params);
+GGML_API void gguf_write_to_buf(const struct gguf_context * ctx, std::vector<int8_t> & buf, bool only_meta);
+#endif // __cplusplus
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