Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tda45/TdAI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
| //#define GGML_ALLOCATOR_DEBUG | |
| //#define AT_PRINTF(...) GGML_LOG_DEBUG(__VA_ARGS__) | |
| // ops that return true for this function must not use restrict pointers for their backend implementations | |
| bool ggml_op_can_inplace(enum ggml_op op) { | |
| switch (op) { | |
| case GGML_OP_FILL: | |
| case GGML_OP_SCALE: | |
| case GGML_OP_DIAG_MASK_ZERO: | |
| case GGML_OP_DIAG_MASK_INF: | |
| case GGML_OP_ADD: | |
| case GGML_OP_ADD_ID: | |
| case GGML_OP_ADD1: | |
| case GGML_OP_SUB: | |
| case GGML_OP_MUL: | |
| case GGML_OP_DIV: | |
| case GGML_OP_SQR: | |
| case GGML_OP_SQRT: | |
| case GGML_OP_LOG: | |
| case GGML_OP_UNARY: | |
| case GGML_OP_ROPE: | |
| case GGML_OP_ROPE_BACK: | |
| case GGML_OP_SILU_BACK: | |
| case GGML_OP_RMS_NORM: | |
| case GGML_OP_RMS_NORM_BACK: | |
| case GGML_OP_SOFT_MAX: | |
| case GGML_OP_SOFT_MAX_BACK: | |
| return true; | |
| default: | |
| return false; | |
| } | |
| } | |
| static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) { | |
| assert(alignment && !(alignment & (alignment - 1))); // power of 2 | |
| size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment; | |
| return offset + align; | |
| } | |
| // tallocr | |
| struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) { | |
| void * base = ggml_backend_buffer_get_base(buffer); | |
| size_t align = ggml_backend_buffer_get_alignment(buffer); | |
| assert(align && !(align & (align - 1))); // power of 2 | |
| struct ggml_tallocr talloc = (struct ggml_tallocr) { | |
| /*.buffer = */ buffer, | |
| /*.base = */ base, | |
| /*.alignment = */ align, | |
| /*.offset = */ aligned_offset(base, 0, align), | |
| }; | |
| return talloc; | |
| } | |
| enum ggml_status ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) { | |
| size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor); | |
| size = GGML_PAD(size, talloc->alignment); | |
| if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) { | |
| GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n", | |
| __func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset); | |
| GGML_ABORT("not enough space in the buffer"); | |
| } | |
| void * addr = (char *)ggml_backend_buffer_get_base(talloc->buffer) + talloc->offset; | |
| talloc->offset += size; | |
| assert(((uintptr_t)addr % talloc->alignment) == 0); | |
| return ggml_backend_tensor_alloc(talloc->buffer, tensor, addr); | |
| } | |
| // dynamic tensor allocator | |
| // relative memory address within an allocation that can be split into multiple buffers (chunks) | |
| struct buffer_address { | |
| int chunk; // index of a backend buffer | |
| size_t offset; // local memory offset within the buffer | |
| }; | |
| static const struct buffer_address GGML_BUFFER_ADDRESS_INVALID = { -1, SIZE_MAX }; | |
| static bool ggml_buffer_address_less(struct buffer_address a, struct buffer_address b) { | |
| return a.chunk != b.chunk ? a.chunk < b.chunk : a.offset < b.offset; | |
| } | |
| struct free_block { | |
| size_t offset; | |
| size_t size; | |
| }; | |
| struct tallocr_chunk { | |
| struct free_block free_blocks[MAX_FREE_BLOCKS]; | |
| int n_free_blocks; | |
| size_t max_size; | |
| }; | |
| struct ggml_dyn_tallocr { | |
| size_t alignment; | |
| size_t max_chunk_size; | |
| struct tallocr_chunk * chunks[GGML_VBUFFER_MAX_CHUNKS]; | |
| int n_chunks; | |
| struct { | |
| const struct ggml_tensor * tensor; | |
| struct buffer_address addr; | |
| } allocated_tensors[1024]; | |
| }; | |
| static void ggml_dyn_tallocr_insert_block(struct tallocr_chunk * chunk, size_t offset, size_t size) { | |
| GGML_ASSERT(chunk->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks"); | |
| // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster) | |
| int insert_pos = 0; | |
| while (insert_pos < chunk->n_free_blocks && chunk->free_blocks[insert_pos].offset < offset) { | |
| insert_pos++; | |
| } | |
| // shift all blocks from insert_pos onward to make room for the new block | |
| for (int i = chunk->n_free_blocks; i > insert_pos; i--) { | |
| chunk->free_blocks[i] = chunk->free_blocks[i-1]; | |
| } | |
| // insert the new block | |
| chunk->free_blocks[insert_pos].offset = offset; | |
| chunk->free_blocks[insert_pos].size = size; | |
| chunk->n_free_blocks++; | |
| } | |
| static void ggml_dyn_tallocr_remove_block(struct tallocr_chunk * chunk, int idx) { | |
| // shift all elements after idx by 1 to the left, overwriting the element at idx | |
| for (int i = idx; i < chunk->n_free_blocks - 1; i++) { | |
| chunk->free_blocks[i] = chunk->free_blocks[i+1]; | |
| } | |
| chunk->n_free_blocks--; | |
| } | |
| static int ggml_dyn_tallocr_new_chunk(struct ggml_dyn_tallocr * alloc, size_t min_size) { | |
| if (alloc->n_chunks >= GGML_VBUFFER_MAX_CHUNKS) { | |
| return -1; | |
| } | |
| struct tallocr_chunk * chunk = calloc(1, sizeof(struct tallocr_chunk)); | |
| chunk->n_free_blocks = 1; | |
| chunk->free_blocks[0].offset = 0; | |
| // available space in a chunk is limited to max_chunk_size, but can be higher if: | |
| // 1. a single tensor exceeds the maximum, and cannot fit any other way | |
| // 2. we are running out of chunks | |
| // backends will either manage to allocate the larger size, or report an error. | |
| chunk->free_blocks[0].size = MAX(min_size, alloc->max_chunk_size); | |
| if (alloc->n_chunks == GGML_VBUFFER_MAX_CHUNKS - 1) { | |
| chunk->free_blocks[0].size = SIZE_MAX/2; | |
| } | |
| alloc->chunks[alloc->n_chunks] = chunk; | |
| alloc->n_chunks++; | |
| return alloc->n_chunks - 1; | |
| } | |
| static void add_allocated_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, const struct ggml_tensor * tensor) { | |
| for (int i = 0; i < 1024; i++) { | |
| if (alloc->allocated_tensors[i].tensor == NULL) { | |
| alloc->allocated_tensors[i].tensor = tensor; | |
| alloc->allocated_tensors[i].addr = addr; | |
| return; | |
| } | |
| } | |
| GGML_ABORT("out of allocated_tensors"); | |
| } | |
| static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, const struct ggml_tensor * tensor) { | |
| for (int i = 0; i < 1024; i++) { | |
| if (alloc->allocated_tensors[i].addr.chunk == addr.chunk && alloc->allocated_tensors[i].addr.offset == addr.offset) { | |
| alloc->allocated_tensors[i].tensor = NULL; | |
| return; | |
| } | |
| } | |
| GGML_ABORT("tried to free tensor %s not found\n", tensor->name); | |
| } | |
| static struct buffer_address ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t size, const struct ggml_tensor * tensor) { | |
| size = aligned_offset(NULL, size, alloc->alignment); | |
| AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size); | |
| int best_fit_chunk = -1; | |
| int best_fit_block = -1; | |
| size_t max_avail = 0; | |
| // find the best fitting free block besides the last block, within any chunk | |
| for (int c = 0; c < alloc->n_chunks; ++c) { | |
| struct tallocr_chunk * chunk = alloc->chunks[c]; | |
| size_t best_fit_size = SIZE_MAX; | |
| for (int i = 0; i < chunk->n_free_blocks - 1; i++) { | |
| struct free_block * block = &chunk->free_blocks[i]; | |
| max_avail = MAX(max_avail, block->size); | |
| if (block->size >= size && block->size <= best_fit_size) { | |
| best_fit_chunk = c; | |
| best_fit_block = i; | |
| best_fit_size = block->size; | |
| } | |
| } | |
| } | |
| if (best_fit_block == -1) { | |
| // no suitable block found, try the last block (this may grow a chunks size) | |
| int64_t best_reuse = INT64_MIN; | |
| for (int c = 0; c < alloc->n_chunks; ++c) { | |
| struct tallocr_chunk * chunk = alloc->chunks[c]; | |
| if (chunk->n_free_blocks > 0) { | |
| struct free_block * block = &chunk->free_blocks[chunk->n_free_blocks - 1]; | |
| max_avail = MAX(max_avail, block->size); | |
| int64_t reuse_factor = chunk->max_size - block->offset - size; | |
| // reuse_factor < 0 : amount of extra memory that needs to be allocated | |
| // reuse_factor = 0 : allocated free space exactly matches tensor size | |
| // reuse_factor > 0 : superfluous memory that will remain unused | |
| bool better_reuse = best_reuse < 0 && reuse_factor > best_reuse; | |
| bool better_fit = reuse_factor >= 0 && reuse_factor < best_reuse; | |
| if (block->size >= size && (better_reuse || better_fit)) { | |
| best_fit_chunk = c; | |
| best_fit_block = chunk->n_free_blocks - 1; | |
| best_reuse = reuse_factor; | |
| } | |
| } | |
| } | |
| } | |
| if (best_fit_block == -1) { | |
| // none of the existing chunks have enough space left | |
| best_fit_chunk = ggml_dyn_tallocr_new_chunk(alloc, size); | |
| best_fit_block = 0; | |
| } | |
| if (best_fit_chunk == -1) { | |
| // since the last chunk always has virtually endless memory, this should never happen | |
| GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n", | |
| __func__, size, max_avail); | |
| GGML_ABORT("graph allocation: failed to reserve memory"); | |
| } | |
| struct tallocr_chunk * chunk = alloc->chunks[best_fit_chunk]; | |
| struct free_block * block = &chunk->free_blocks[best_fit_block]; | |
| struct buffer_address addr = {.chunk = best_fit_chunk, .offset = block->offset }; | |
| block->offset += size; | |
| block->size -= size; | |
| if (block->size == 0) { | |
| // remove block if empty | |
| ggml_dyn_tallocr_remove_block(chunk, best_fit_block); | |
| } | |
| AT_PRINTF("block %d, offset %zu, chunk %d\n", best_fit_block, addr.offset, addr.chunk); | |
| add_allocated_tensor(alloc, addr, tensor); | |
| size_t cur_max = addr.offset + size; | |
| if (cur_max > chunk->max_size) { | |
| // sort allocated_tensors by chunk/offset | |
| for (int i = 0; i < 1024; i++) { | |
| for (int j = i + 1; j < 1024; j++) { | |
| if (ggml_buffer_address_less(alloc->allocated_tensors[j].addr, alloc->allocated_tensors[i].addr)) { | |
| const struct ggml_tensor * tmp_tensor = alloc->allocated_tensors[i].tensor; | |
| struct buffer_address tmp_addr = alloc->allocated_tensors[i].addr; | |
| alloc->allocated_tensors[i].tensor = alloc->allocated_tensors[j].tensor; | |
| alloc->allocated_tensors[i].addr = alloc->allocated_tensors[j].addr; | |
| alloc->allocated_tensors[j].tensor = tmp_tensor; | |
| alloc->allocated_tensors[j].addr = tmp_addr; | |
| } | |
| } | |
| } | |
| GGML_LOG_DEBUG("max_size[%d] = %.2f MB: tensors: ", addr.chunk, cur_max / 1024.0 / 1024.0); | |
| for (int i = 0; i < 1024; i++) { | |
| if (alloc->allocated_tensors[i].tensor) { | |
| GGML_LOG_DEBUG("%s [%d: %zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name, | |
| alloc->allocated_tensors[i].addr.chunk, | |
| alloc->allocated_tensors[i].addr.offset, | |
| alloc->allocated_tensors[i].addr.offset + ggml_nbytes(alloc->allocated_tensors[i].tensor), | |
| ggml_nbytes(alloc->allocated_tensors[i].tensor) / 1024.0 / 1024.0); | |
| } | |
| } | |
| GGML_LOG_DEBUG("\n"); | |
| } | |
| chunk->max_size = MAX(chunk->max_size, addr.offset + size); | |
| return addr; | |
| GGML_UNUSED(tensor); | |
| } | |
| // this is a very naive implementation, but for our case the number of free blocks should be very small | |
| static void ggml_dyn_tallocr_free_bytes(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, size_t size) { | |
| size = aligned_offset(NULL, size, alloc->alignment); | |
| struct tallocr_chunk * chunk = alloc->chunks[addr.chunk]; | |
| // see if we can merge with an existing block | |
| for (int i = 0; i < chunk->n_free_blocks; i++) { | |
| struct free_block * block = &chunk->free_blocks[i]; | |
| // check if ptr is at the end of the block | |
| if (block->offset + block->size == addr.offset) { | |
| block->size += size; | |
| // check if we can merge with the next block | |
| if (i < chunk->n_free_blocks - 1) { | |
| struct free_block * next = &chunk->free_blocks[i+1]; | |
| if (block->offset + block->size == next->offset) { | |
| block->size += next->size; | |
| ggml_dyn_tallocr_remove_block(chunk, i+1); | |
| } | |
| } | |
| return; | |
| } | |
| // check if ptr is at the beginning of the block | |
| if (addr.offset + size == block->offset) { | |
| block->offset = addr.offset; | |
| block->size += size; | |
| // check if we can merge with the previous block | |
| if (i > 0) { | |
| struct free_block * prev = &chunk->free_blocks[i-1]; | |
| if (prev->offset + prev->size == block->offset) { | |
| prev->size += block->size; | |
| ggml_dyn_tallocr_remove_block(chunk, i); | |
| } | |
| } | |
| return; | |
| } | |
| } | |
| // otherwise, add a new block | |
| ggml_dyn_tallocr_insert_block(chunk, addr.offset, size); | |
| } | |
| static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) { | |
| for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS; i++) { | |
| free(alloc->chunks[i]); | |
| alloc->chunks[i] = NULL; | |
| } | |
| alloc->n_chunks = 0; | |
| for (int i = 0; i < 1024; i++) { | |
| alloc->allocated_tensors[i].tensor = NULL; | |
| } | |
| } | |
| static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment, size_t max_buffer_size) { | |
| struct ggml_dyn_tallocr * alloc = (struct ggml_dyn_tallocr *)malloc(sizeof(struct ggml_dyn_tallocr)); | |
| *alloc = (struct ggml_dyn_tallocr) { | |
| /*.alignment = */ alignment, | |
| /*.max_chunk_size = */ MIN(max_buffer_size, SIZE_MAX/2), // clamp to avoid overflows | |
| /*.chunks = */ {NULL}, | |
| /*.n_chunks = */ 0, | |
| /*.allocated_tensors = */ {{0}}, | |
| }; | |
| ggml_dyn_tallocr_reset(alloc); | |
| return alloc; | |
| } | |
| static void ggml_dyn_tallocr_free(struct ggml_dyn_tallocr * alloc) { | |
| for (int i = 0; i < alloc->n_chunks; ++i) { | |
| free(alloc->chunks[i]); | |
| } | |
| free(alloc); | |
| } | |
| static size_t ggml_dyn_tallocr_max_size(struct ggml_dyn_tallocr * alloc, int chunk) { | |
| return chunk < alloc->n_chunks ? alloc->chunks[chunk]->max_size : 0; | |
| } | |
| // virtual buffer with contiguous memory range, split into multiple backend buffers (chunks) | |
| struct vbuffer { | |
| ggml_backend_buffer_t chunks[GGML_VBUFFER_MAX_CHUNKS]; | |
| }; | |
| static void ggml_vbuffer_free(struct vbuffer * buf) { | |
| if (buf == NULL) { | |
| return; | |
| } | |
| for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS; ++i) { | |
| ggml_backend_buffer_free(buf->chunks[i]); | |
| } | |
| free(buf); | |
| } | |
| static size_t ggml_vbuffer_chunk_size(struct vbuffer * buf, int chunk) { | |
| return buf->chunks[chunk] ? ggml_backend_buffer_get_size(buf->chunks[chunk]) : 0; | |
| } | |
| static size_t ggml_vbuffer_size(struct vbuffer * buf) { | |
| size_t size = 0; | |
| for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS && buf->chunks[i]; ++i) { | |
| size += ggml_backend_buffer_get_size(buf->chunks[i]); | |
| } | |
| return size; | |
| } | |
| static struct vbuffer * ggml_vbuffer_alloc(ggml_backend_buffer_type_t buft, const struct ggml_dyn_tallocr * talloc, enum ggml_backend_buffer_usage usage) { | |
| struct vbuffer * buf = (struct vbuffer *)calloc(1, sizeof(struct vbuffer)); | |
| if (buf == NULL) { | |
| return NULL; | |
| } | |
| for (int n = 0; n < talloc->n_chunks; n++) { | |
| size_t chunk_size = talloc->chunks[n]->max_size; | |
| buf->chunks[n] = ggml_backend_buft_alloc_buffer(buft, chunk_size); | |
| if (buf->chunks[n] == NULL) { | |
| ggml_vbuffer_free(buf); | |
| return NULL; | |
| } | |
| ggml_backend_buffer_set_usage(buf->chunks[n], usage); | |
| } | |
| return buf; | |
| } | |
| static void ggml_vbuffer_tensor_alloc(struct vbuffer * buf, struct ggml_tensor * tensor, struct buffer_address buf_addr) { | |
| void * base = ggml_backend_buffer_get_base(buf->chunks[buf_addr.chunk]); | |
| void * addr = (char *)base + buf_addr.offset; | |
| ggml_backend_tensor_alloc(buf->chunks[buf_addr.chunk], tensor, addr); | |
| } | |
| static void ggml_vbuffer_reset(struct vbuffer * buf) { | |
| for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS && buf->chunks[i]; ++i) { | |
| ggml_backend_buffer_reset(buf->chunks[i]); | |
| } | |
| } | |
| ///////////////////////////////////// | |
| // graph allocator | |
| struct hash_node { | |
| int n_children; | |
| int n_views; | |
| int buffer_id; | |
| struct buffer_address addr; | |
| bool allocated; | |
| }; | |
| struct tensor_alloc { | |
| int buffer_id; | |
| struct buffer_address addr; | |
| size_t size_max; // 0 = pre-allocated, unused, or view | |
| }; | |
| struct leaf_alloc { | |
| struct tensor_alloc leaf; | |
| }; | |
| struct node_alloc { | |
| struct tensor_alloc dst; | |
| struct tensor_alloc src[GGML_MAX_SRC]; | |
| }; | |
| struct ggml_gallocr { | |
| ggml_backend_buffer_type_t * bufts; // [n_buffers] | |
| struct vbuffer ** buffers; // [n_buffers] | |
| struct ggml_dyn_tallocr ** buf_tallocs; // [n_buffers] | |
| int n_buffers; | |
| struct ggml_hash_set hash_set; | |
| struct hash_node * hash_values; // [hash_set.size] | |
| struct node_alloc * node_allocs; // [n_nodes] | |
| int n_nodes; | |
| struct leaf_alloc * leaf_allocs; // [n_leafs] | |
| int n_leafs; | |
| }; | |
| ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) { | |
| ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(1, sizeof(struct ggml_gallocr)); | |
| GGML_ASSERT(galloc != NULL); | |
| galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t)); | |
| GGML_ASSERT(galloc->bufts != NULL); | |
| galloc->buffers = calloc(n_bufs, sizeof(struct vbuffer *)); | |
| GGML_ASSERT(galloc->buffers != NULL); | |
| galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *)); | |
| GGML_ASSERT(galloc->buf_tallocs != NULL); | |
| for (int i = 0; i < n_bufs; i++) { | |
| galloc->bufts[i] = bufts[i]; | |
| galloc->buffers[i] = NULL; | |
| // check if the same buffer type is used multiple times and reuse the same allocator | |
| for (int j = 0; j < i; j++) { | |
| if (bufts[i] == bufts[j]) { | |
| galloc->buf_tallocs[i] = galloc->buf_tallocs[j]; | |
| break; | |
| } | |
| } | |
| if (galloc->buf_tallocs[i] == NULL) { | |
| size_t alignment = ggml_backend_buft_get_alignment(bufts[i]); | |
| size_t max_size = ggml_backend_buft_get_max_size(bufts[i]); | |
| galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment, max_size); | |
| } | |
| } | |
| galloc->n_buffers = n_bufs; | |
| return galloc; | |
| } | |
| ggml_gallocr_t ggml_gallocr_new(ggml_backend_buffer_type_t buft) { | |
| return ggml_gallocr_new_n(&buft, 1); | |
| } | |
| void ggml_gallocr_free(ggml_gallocr_t galloc) { | |
| if (galloc == NULL) { | |
| return; | |
| } | |
| for (int i = 0; i < galloc->n_buffers; i++) { | |
| if (galloc->buffers != NULL) { | |
| // skip if already freed | |
| bool freed = false; | |
| for (int j = 0; j < i; j++) { | |
| if (galloc->buffers[j] == galloc->buffers[i]) { | |
| freed = true; | |
| break; | |
| } | |
| } | |
| if (!freed) { | |
| ggml_vbuffer_free(galloc->buffers[i]); | |
| } | |
| } | |
| if (galloc->buf_tallocs != NULL) { | |
| // skip if already freed | |
| bool freed = false; | |
| for (int j = 0; j < i; j++) { | |
| if (galloc->buf_tallocs[j] == galloc->buf_tallocs[i]) { | |
| freed = true; | |
| break; | |
| } | |
| } | |
| if (!freed) { | |
| ggml_dyn_tallocr_free(galloc->buf_tallocs[i]); | |
| } | |
| } | |
| } | |
| ggml_hash_set_free(&galloc->hash_set); | |
| free(galloc->hash_values); | |
| free(galloc->bufts); | |
| free(galloc->buffers); | |
| free(galloc->buf_tallocs); | |
| free(galloc->node_allocs); | |
| free(galloc->leaf_allocs); | |
| free(galloc); | |
| } | |
| typedef struct ggml_gallocr * ggml_gallocr_t; | |
| static struct hash_node * ggml_gallocr_hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) { | |
| size_t i = ggml_hash_find_or_insert(&galloc->hash_set, t); | |
| return &galloc->hash_values[i]; | |
| } | |
| static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) { | |
| return ggml_gallocr_hash_get(galloc, t)->allocated; | |
| } | |
| static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) { | |
| return t->data != NULL // tensor data already set externally | |
| || t->buffer // tensor on external buffer (but not yet allocated) | |
| || ggml_gallocr_is_own(galloc, t); // tensor will be allocated by galloc | |
| } | |
| // free the extra space at the end if the new tensor is smaller | |
| static void ggml_gallocr_free_extra_space(ggml_gallocr_t galloc, struct ggml_tensor * node, struct ggml_tensor * parent) { | |
| struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); | |
| struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent); | |
| size_t parent_size = ggml_backend_buft_get_alloc_size(galloc->bufts[p_hn->buffer_id], parent); | |
| size_t node_size = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node); | |
| GGML_ASSERT(parent_size >= node_size); | |
| // note: we want after the freeing the chunks to continue to be aligned | |
| struct ggml_dyn_tallocr * p_alloc = galloc->buf_tallocs[p_hn->buffer_id]; | |
| parent_size = aligned_offset(NULL, parent_size, p_alloc->alignment); | |
| node_size = aligned_offset(NULL, node_size, p_alloc->alignment); | |
| if (parent_size > node_size) { | |
| struct buffer_address p_addr = p_hn->addr; | |
| p_addr.offset += node_size; | |
| size_t extra_size = parent_size - node_size; | |
| AT_PRINTF("freeing extra %zu bytes from parent %s for %s\n", extra_size, parent->name, node->name); | |
| ggml_dyn_tallocr_free_bytes(p_alloc, p_addr, extra_size); | |
| } | |
| } | |
| static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) { | |
| GGML_ASSERT(buffer_id >= 0); | |
| struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); | |
| if (!ggml_gallocr_is_allocated(galloc, node) && !ggml_impl_is_view(node)) { | |
| hn->allocated = true; | |
| assert(hn->addr.offset == 0); | |
| // try to reuse a parent's buffer (inplace) | |
| if (ggml_op_can_inplace(node->op)) { | |
| for (int i = 0; i < GGML_MAX_SRC; i++) { | |
| struct ggml_tensor * parent = node->src[i]; | |
| if (parent == NULL) { | |
| continue; | |
| } | |
| // if the node's data is external, then we cannot re-use it | |
| if (!ggml_gallocr_is_own(galloc, parent)) { | |
| AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data); | |
| continue; | |
| } | |
| // outputs cannot be reused | |
| if (parent->flags & GGML_TENSOR_FLAG_OUTPUT || (parent->view_src != NULL && parent->view_src->flags & GGML_TENSOR_FLAG_OUTPUT)) { | |
| AT_PRINTF("not reusing parent %s for %s as it is an output\n", parent->name, node->name); | |
| continue; | |
| } | |
| if (!ggml_are_same_layout(node, parent)) { | |
| AT_PRINTF("not reusing parent %s for %s as layouts are different\n", parent->name, node->name); | |
| continue; | |
| } | |
| struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent); | |
| if (p_hn->n_children == 1 && p_hn->n_views == 0) { | |
| if (ggml_impl_is_view(parent)) { | |
| struct ggml_tensor * view_src = parent->view_src; | |
| struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src); | |
| if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) { | |
| AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name); | |
| assert(view_src_hn->addr.chunk == p_hn->addr.chunk && view_src_hn->addr.offset == p_hn->addr.offset); | |
| hn->buffer_id = p_hn->buffer_id; | |
| hn->addr = p_hn->addr; | |
| p_hn->allocated = false; // avoid freeing the parent | |
| view_src_hn->allocated = false; | |
| ggml_gallocr_free_extra_space(galloc, node, view_src); | |
| return; | |
| } | |
| } else { | |
| AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name); | |
| hn->buffer_id = p_hn->buffer_id; | |
| hn->addr = p_hn->addr; | |
| p_hn->allocated = false; // avoid freeing the parent | |
| ggml_gallocr_free_extra_space(galloc, node, parent); | |
| return; | |
| } | |
| } | |
| } | |
| } | |
| // allocate tensor from the buffer | |
| struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id]; | |
| ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id]; | |
| size_t size = ggml_backend_buft_get_alloc_size(buft, node); | |
| hn->buffer_id = buffer_id; | |
| hn->addr = ggml_dyn_tallocr_alloc(alloc, size, node); | |
| } | |
| } | |
| static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * node) { | |
| // graph outputs are never freed | |
| if (node->flags & GGML_TENSOR_FLAG_OUTPUT) { | |
| AT_PRINTF("not freeing output %s\n", node->name); | |
| return; | |
| } | |
| struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); | |
| int buffer_id = hn->buffer_id; | |
| struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id]; | |
| ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id]; | |
| size_t size = ggml_backend_buft_get_alloc_size(buft, node); | |
| AT_PRINTF("%s: freeing %s at {chunk=%d, offset=%zu} (%zu bytes) - n_free_blocks = %d\n", | |
| __func__, node->name, hn->addr.chunk, hn->addr.offset, size, alloc->chunks[hn->addr.chunk]->n_free_blocks); | |
| remove_allocated_tensor(alloc, hn->addr, node); | |
| ggml_dyn_tallocr_free_bytes(alloc, hn->addr, size); | |
| hn->allocated = false; | |
| } | |
| static int get_node_buffer_id(const int * node_buffer_ids, int i) { | |
| return node_buffer_ids ? node_buffer_ids[i] : 0; | |
| } | |
| static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) { | |
| // clear hash tables | |
| ggml_hash_set_reset(&galloc->hash_set); | |
| memset(galloc->hash_values, 0, sizeof(struct hash_node) * galloc->hash_set.size); | |
| // allocate leafs | |
| // these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes | |
| for (int i = 0; i < graph->n_leafs; i++) { | |
| struct ggml_tensor * leaf = graph->leafs[i]; | |
| ggml_gallocr_allocate_node(galloc, leaf, get_node_buffer_id(leaf_buffer_ids, i)); | |
| } | |
| // count number of children and views | |
| // allocate other graph inputs and leafs first to avoid overwriting them | |
| for (int i = 0; i < graph->n_nodes; i++) { | |
| struct ggml_tensor * node = graph->nodes[i]; | |
| // TODO: better way to add external dependencies | |
| // GGML_OP_NONE does not appear normally in the graph nodes, but is used by ggml-backend to add dependencies to | |
| // control when some tensors are allocated and freed. in this case, the dependencies are in `src`, but the node | |
| // itself is never used and should not be considered a dependency | |
| if (ggml_impl_is_view(node) && node->op != GGML_OP_NONE) { | |
| struct ggml_tensor * view_src = node->view_src; | |
| ggml_gallocr_hash_get(galloc, view_src)->n_views += 1; | |
| } | |
| if (node->flags & GGML_TENSOR_FLAG_INPUT) { | |
| ggml_gallocr_allocate_node(galloc, graph->nodes[i], get_node_buffer_id(node_buffer_ids, i)); | |
| } | |
| for (int j = 0; j < GGML_MAX_SRC; j++) { | |
| struct ggml_tensor * src = node->src[j]; | |
| if (src == NULL) { | |
| continue; | |
| } | |
| ggml_gallocr_hash_get(galloc, src)->n_children += 1; | |
| // allocate explicit inputs | |
| if (src->flags & GGML_TENSOR_FLAG_INPUT) { | |
| ggml_gallocr_allocate_node(galloc, src, get_node_buffer_id(node_buffer_ids, i)); | |
| } | |
| } | |
| } | |
| // allocate tensors | |
| for (int i = 0; i < graph->n_nodes; i++) { | |
| struct ggml_tensor * node = graph->nodes[i]; | |
| int buffer_id = get_node_buffer_id(node_buffer_ids, i); | |
| // allocate parents (only leafs need to be allocated at this point) | |
| for (int j = 0; j < GGML_MAX_SRC; j++) { | |
| struct ggml_tensor * parent = node->src[j]; | |
| if (parent == NULL) { | |
| continue; | |
| } | |
| ggml_gallocr_allocate_node(galloc, parent, buffer_id); | |
| } | |
| // allocate node | |
| ggml_gallocr_allocate_node(galloc, node, buffer_id); | |
| AT_PRINTF("exec: %s (%s) <= ", ggml_op_desc(node), node->name); | |
| for (int j = 0; j < GGML_MAX_SRC; j++) { | |
| struct ggml_tensor * parent = node->src[j]; | |
| if (parent == NULL) { | |
| continue; | |
| } | |
| AT_PRINTF("%s", parent->name); | |
| if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) { | |
| AT_PRINTF(", "); | |
| } | |
| } | |
| AT_PRINTF("\n"); | |
| // update parents | |
| for (int j = 0; j < GGML_MAX_SRC; j++) { | |
| struct ggml_tensor * parent = node->src[j]; | |
| if (parent == NULL) { | |
| continue; | |
| } | |
| struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent); | |
| p_hn->n_children -= 1; | |
| AT_PRINTF("parent %s: %d children, %d views, allocated: %d\n", | |
| parent->name, p_hn->n_children, p_hn->n_views, p_hn->allocated); | |
| if (p_hn->n_children == 0 && p_hn->n_views == 0) { | |
| if (ggml_impl_is_view(parent)) { | |
| struct ggml_tensor * view_src = parent->view_src; | |
| struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src); | |
| view_src_hn->n_views -= 1; | |
| AT_PRINTF("view_src %s: %d children, %d views\n", | |
| view_src->name, view_src_hn->n_children, view_src_hn->n_views); | |
| if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src_hn->allocated) { | |
| ggml_gallocr_free_node(galloc, view_src); | |
| } | |
| } | |
| else if (p_hn->allocated) { | |
| ggml_gallocr_free_node(galloc, parent); | |
| } | |
| } | |
| AT_PRINTF("\n"); | |
| } | |
| } | |
| } | |
| static bool ggml_gallocr_reserve_n_impl( | |
| ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, bool no_alloc) { | |
| size_t min_hash_size = graph->n_nodes + graph->n_leafs; | |
| // add 25% margin to avoid hash collisions | |
| min_hash_size += min_hash_size / 4; | |
| // initialize hash table | |
| if (galloc->hash_set.size < min_hash_size) { | |
| ggml_hash_set_free(&galloc->hash_set); | |
| galloc->hash_set = ggml_hash_set_new(min_hash_size); | |
| GGML_ASSERT(galloc->hash_set.keys != NULL); | |
| free(galloc->hash_values); | |
| galloc->hash_values = malloc(sizeof(struct hash_node) * galloc->hash_set.size); | |
| GGML_ASSERT(galloc->hash_values != NULL); | |
| } | |
| // reset allocators | |
| for (int i = 0; i < galloc->n_buffers; i++) { | |
| ggml_dyn_tallocr_reset(galloc->buf_tallocs[i]); | |
| } | |
| // allocate in hash table | |
| ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids); | |
| // set the node_allocs from the hash table | |
| if (galloc->n_nodes < graph->n_nodes) { | |
| free(galloc->node_allocs); | |
| galloc->node_allocs = calloc(graph->n_nodes, sizeof(struct node_alloc)); | |
| GGML_ASSERT(galloc->node_allocs != NULL); | |
| } | |
| galloc->n_nodes = graph->n_nodes; | |
| for (int i = 0; i < graph->n_nodes; i++) { | |
| struct ggml_tensor * node = graph->nodes[i]; | |
| struct node_alloc * node_alloc = &galloc->node_allocs[i]; | |
| if (node->view_src || node->data) { | |
| node_alloc->dst.buffer_id = -1; | |
| node_alloc->dst.addr = GGML_BUFFER_ADDRESS_INVALID; | |
| node_alloc->dst.size_max = 0; | |
| } else { | |
| struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); | |
| node_alloc->dst.buffer_id = hn->buffer_id; | |
| node_alloc->dst.addr = hn->addr; | |
| node_alloc->dst.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node); | |
| } | |
| for (int j = 0; j < GGML_MAX_SRC; j++) { | |
| struct ggml_tensor * src = node->src[j]; | |
| if (!src || src->view_src || src->data) { | |
| node_alloc->src[j].buffer_id = -1; | |
| node_alloc->src[j].addr = GGML_BUFFER_ADDRESS_INVALID; | |
| node_alloc->src[j].size_max = 0; | |
| } else { | |
| struct hash_node * hn = ggml_gallocr_hash_get(galloc, src); | |
| node_alloc->src[j].buffer_id = hn->buffer_id; | |
| node_alloc->src[j].addr = hn->addr; | |
| node_alloc->src[j].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], src); | |
| } | |
| } | |
| } | |
| if (galloc->n_leafs < graph->n_leafs) { | |
| free(galloc->leaf_allocs); | |
| galloc->leaf_allocs = calloc(graph->n_leafs, sizeof(galloc->leaf_allocs[0])); | |
| GGML_ASSERT(galloc->leaf_allocs != NULL); | |
| } | |
| galloc->n_leafs = graph->n_leafs; | |
| for (int i = 0; i < graph->n_leafs; i++) { | |
| struct ggml_tensor * leaf = graph->leafs[i]; | |
| struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf); | |
| if (leaf->view_src || leaf->data) { | |
| galloc->leaf_allocs[i].leaf.buffer_id = -1; | |
| galloc->leaf_allocs[i].leaf.addr = GGML_BUFFER_ADDRESS_INVALID; | |
| galloc->leaf_allocs[i].leaf.size_max = 0; | |
| } else { | |
| galloc->leaf_allocs[i].leaf.buffer_id = hn->buffer_id; | |
| galloc->leaf_allocs[i].leaf.addr = hn->addr; | |
| galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf); | |
| } | |
| } | |
| // reallocate buffers if needed | |
| for (int i = 0; i < galloc->n_buffers; i++) { | |
| // if the buffer type is used multiple times, we reuse the same buffer | |
| for (int j = 0; j < i; j++) { | |
| if (galloc->buf_tallocs[j] == galloc->buf_tallocs[i]) { | |
| galloc->buffers[i] = galloc->buffers[j]; | |
| break; | |
| } | |
| } | |
| // even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views | |
| bool realloc = galloc->buffers[i] == NULL; | |
| size_t new_size = 0; | |
| for (int c = 0; c < galloc->buf_tallocs[i]->n_chunks; c++) { | |
| size_t cur_chunk_size = galloc->buffers[i] ? ggml_vbuffer_chunk_size(galloc->buffers[i], c) : 0; | |
| size_t new_chunk_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i], c); | |
| new_size += new_chunk_size; | |
| if (new_chunk_size > cur_chunk_size) { | |
| realloc = true; | |
| } | |
| } | |
| if (realloc) { | |
| { | |
| size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0; | |
| if (cur_size > 0) { | |
| GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", | |
| __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); | |
| } | |
| } | |
| ggml_vbuffer_free(galloc->buffers[i]); | |
| if (no_alloc) { | |
| galloc->buffers[i] = NULL; | |
| } else { | |
| galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE); | |
| if (galloc->buffers[i] == NULL) { | |
| GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size); | |
| return false; | |
| } | |
| } | |
| } | |
| } | |
| return true; | |
| } | |
| void ggml_gallocr_reserve_n_size( | |
| ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, size_t * sizes) { | |
| GGML_ASSERT(ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ true)); | |
| for (int i = 0; i < galloc->n_buffers; i++) { | |
| sizes[i] = 0; | |
| for (int c = 0; c < galloc->buf_tallocs[i]->n_chunks; c++) { | |
| sizes[i] += galloc->buf_tallocs[i]->chunks[c]->max_size; | |
| } | |
| } | |
| } | |
| bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) { | |
| return ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ false); | |
| } | |
| bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) { | |
| return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL); | |
| } | |
| static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, struct tensor_alloc * tensor_alloc) { | |
| int buffer_id = tensor_alloc->buffer_id; | |
| assert(tensor->data || tensor->view_src || ggml_backend_buft_get_alloc_size(galloc->bufts[buffer_id], tensor) <= tensor_alloc->size_max); | |
| if (tensor->view_src != NULL) { | |
| if (tensor->buffer == NULL) { | |
| assert(tensor_alloc->addr.offset == SIZE_MAX); | |
| if (tensor->view_src->buffer == NULL) { | |
| // this tensor was allocated without ggml-backend | |
| return; | |
| } | |
| ggml_backend_view_init(tensor); | |
| } | |
| } else { | |
| if (tensor->data == NULL) { | |
| assert(tensor_alloc->addr.offset != SIZE_MAX); | |
| assert(ggml_backend_buft_get_alloc_size(galloc->bufts[buffer_id], tensor) <= tensor_alloc->size_max); | |
| ggml_vbuffer_tensor_alloc(galloc->buffers[buffer_id], tensor, tensor_alloc->addr); | |
| } else { | |
| if (tensor->buffer == NULL) { | |
| // this tensor was allocated without ggml-backend | |
| return; | |
| } | |
| } | |
| } | |
| } | |
| static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) { | |
| size_t node_size = 0; | |
| if (!node->data && !node->view_src) { | |
| // If we previously had data but don't now then reallocate | |
| if (talloc->buffer_id < 0) { | |
| return false; | |
| } | |
| node_size = ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node); | |
| } | |
| return talloc->size_max >= node_size; | |
| } | |
| static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph * graph) { | |
| if (galloc->n_nodes != graph->n_nodes) { | |
| GGML_LOG_DEBUG("%s: graph has different number of nodes\n", __func__); | |
| return true; | |
| } | |
| if (galloc->n_leafs != graph->n_leafs) { | |
| GGML_LOG_DEBUG("%s: graph has different number of leafs\n", __func__); | |
| return true; | |
| } | |
| for (int i = 0; i < graph->n_nodes; i++) { | |
| struct ggml_tensor * node = graph->nodes[i]; | |
| struct node_alloc * node_alloc = &galloc->node_allocs[i]; | |
| if (!ggml_gallocr_node_needs_realloc(galloc, node, &node_alloc->dst)) { | |
| GGML_LOG_DEBUG("%s: node %s is not valid\n", __func__, node->name); | |
| return true; | |
| } | |
| for (int j = 0; j < GGML_MAX_SRC; j++) { | |
| struct ggml_tensor * src = node->src[j]; | |
| if (src == NULL) { | |
| continue; | |
| } | |
| if (!ggml_gallocr_node_needs_realloc(galloc, src, &node_alloc->src[j])) { | |
| GGML_LOG_DEBUG("%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name); | |
| return true; | |
| } | |
| } | |
| } | |
| return false; | |
| } | |
| bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) { | |
| if (ggml_gallocr_needs_realloc(galloc, graph)) { | |
| if (galloc->n_buffers == 1) { | |
| GGML_LOG_DEBUG("%s: reallocating buffers automatically\n", __func__); | |
| if (!ggml_gallocr_reserve(galloc, graph)) { | |
| return false; | |
| } | |
| } else { | |
| GGML_LOG_DEBUG("%s: cannot reallocate multi buffer graph automatically, call reserve\n", __func__); | |
| return false; | |
| } | |
| } | |
| // reset buffers | |
| for (int i = 0; i < galloc->n_buffers; i++) { | |
| if (galloc->buffers[i] != NULL) { | |
| ggml_vbuffer_reset(galloc->buffers[i]); | |
| } | |
| } | |
| // allocate the graph tensors from the previous assignments | |
| // leafs | |
| for (int i = 0; i < graph->n_leafs; i++) { | |
| struct ggml_tensor * leaf = graph->leafs[i]; | |
| struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i]; | |
| ggml_gallocr_init_tensor(galloc, leaf, &leaf_alloc->leaf); | |
| } | |
| // nodes | |
| for (int i = 0; i < graph->n_nodes; i++) { | |
| struct ggml_tensor * node = graph->nodes[i]; | |
| struct node_alloc * node_alloc = &galloc->node_allocs[i]; | |
| for (int j = 0; j < GGML_MAX_SRC; j++) { | |
| struct ggml_tensor * src = node->src[j]; | |
| if (src == NULL) { | |
| continue; | |
| } | |
| ggml_gallocr_init_tensor(galloc, src, &node_alloc->src[j]); | |
| } | |
| ggml_gallocr_init_tensor(galloc, node, &node_alloc->dst); | |
| } | |
| return true; | |
| } | |
| size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) { | |
| GGML_ASSERT(buffer_id >= 0 && buffer_id < galloc->n_buffers); | |
| if (galloc->buffers[buffer_id] == NULL) { | |
| return 0; | |
| } | |
| for (int i = 0; i < buffer_id; i++) { | |
| if (galloc->buffers[i] == galloc->buffers[buffer_id]) { | |
| // this buffer is the same as a previous one due to the same buffer type being used multiple times | |
| // only return the buffer size the first time it appears to avoid double counting | |
| return 0; | |
| } | |
| } | |
| return ggml_vbuffer_size(galloc->buffers[buffer_id]); | |
| } | |
| // utils | |
| static void free_buffers(ggml_backend_buffer_t ** buffers, const size_t * n_buffers) { | |
| for (size_t i = 0; i < *n_buffers; i++) { | |
| ggml_backend_buffer_free((*buffers)[i]); | |
| } | |
| free(*buffers); | |
| } | |
| static bool alloc_tensor_range(struct ggml_context * ctx, | |
| struct ggml_tensor * first, struct ggml_tensor * last, | |
| ggml_backend_buffer_type_t buft, size_t size, | |
| ggml_backend_buffer_t ** buffers, size_t * n_buffers) { | |
| ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); | |
| if (buffer == NULL) { | |
| GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size); | |
| free_buffers(buffers, n_buffers); | |
| return false; | |
| } | |
| *buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1)); | |
| (*buffers)[(*n_buffers)++] = buffer; | |
| struct ggml_tallocr tallocr = ggml_tallocr_new(buffer); | |
| for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) { | |
| enum ggml_status status = GGML_STATUS_SUCCESS; | |
| if (t->data == NULL) { | |
| if (t->view_src == NULL) { | |
| status = ggml_tallocr_alloc(&tallocr, t); | |
| } else if (t->buffer == NULL) { | |
| status = ggml_backend_view_init(t); | |
| } | |
| } else { | |
| if (t->view_src != NULL && t->buffer == NULL) { | |
| // view of a pre-allocated tensor | |
| status = ggml_backend_view_init(t); | |
| } | |
| } | |
| if (status != GGML_STATUS_SUCCESS) { | |
| GGML_LOG_ERROR("%s: failed to initialize tensor %s\n", __func__, t->name); | |
| free_buffers(buffers, n_buffers); | |
| return false; | |
| } | |
| } | |
| return true; | |
| } | |
| static ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft_impl( | |
| struct ggml_context * ctx, ggml_backend_buffer_type_t buft, size_t * nbytes_total, bool no_alloc) { | |
| GGML_ASSERT(ggml_get_no_alloc(ctx) == true); | |
| size_t alignment = ggml_backend_buft_get_alignment(buft); | |
| size_t max_size = ggml_backend_buft_get_max_size(buft); | |
| ggml_backend_buffer_t * buffers = NULL; | |
| size_t n_buffers = 0; | |
| *nbytes_total = 0; | |
| size_t cur_buf_size = 0; | |
| struct ggml_tensor * first = ggml_get_first_tensor(ctx); | |
| for (struct ggml_tensor * t = first; t != NULL; t = ggml_get_next_tensor(ctx, t)) { | |
| size_t this_size = 0; | |
| if (t->data == NULL && t->view_src == NULL) { | |
| this_size = GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment); | |
| } | |
| if (cur_buf_size > 0 && (cur_buf_size + this_size) > max_size) { | |
| // allocate tensors in the current buffer | |
| if (!no_alloc && !alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) { | |
| return NULL; | |
| } | |
| first = t; | |
| *nbytes_total += cur_buf_size; | |
| cur_buf_size = this_size; | |
| } else { | |
| cur_buf_size += this_size; | |
| } | |
| } | |
| // allocate remaining tensors | |
| if (cur_buf_size > 0) { | |
| *nbytes_total += cur_buf_size; | |
| if (!no_alloc && !alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) { | |
| return NULL; | |
| } | |
| } | |
| if (no_alloc) { | |
| return NULL; | |
| } | |
| if (n_buffers == 0) { | |
| GGML_LOG_DEBUG("%s: all tensors in the context are already allocated\n", __func__); | |
| GGML_ASSERT(!buffers); | |
| return NULL; | |
| } | |
| ggml_backend_buffer_t buffer; | |
| if (n_buffers == 1) { | |
| buffer = buffers[0]; | |
| } else { | |
| buffer = ggml_backend_multi_buffer_alloc_buffer(buffers, n_buffers); | |
| } | |
| if (buffers) { | |
| free(buffers); // can be NULL if context is empty or no_alloc | |
| } | |
| return buffer; | |
| } | |
| size_t ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { | |
| size_t nbytes_total = 0; | |
| ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc=*/ true); | |
| GGML_ASSERT(!buf); | |
| return nbytes_total; | |
| } | |
| ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { | |
| size_t nbytes_total = 0; | |
| if (ggml_backend_buft_is_meta(buft)) { | |
| return ggml_backend_meta_alloc_ctx_tensors_from_buft(ctx, buft); | |
| } | |
| return ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc =*/ false); | |
| } | |
| ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) { | |
| return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend)); | |
| } | |