llama.cpp/ggml-backend-impl.h
0cc4m 2307523d32
ggml : add Vulkan backend (#2059)
* Vulkan loader code

* Fix matmul kernel, continue implementation

* Continue implementation

* Vulkan memory management

* Vulkan development

* Matmul call

* Add aligned malloc and free for VMA

* Continue implementation

* First matmul success

* GEMM Kernel optimization

* 1D Blocktiling

* 2D Blocktiling

* Write coalescing

* Continue vulkan implementation and optimization

* First FP16 attempt, disabled for now

* Code abstraction, FP16 implementation, fix kernel, add FP16 to FP32 kernel

* Enable device extensions properly, restore fp16 matmul op

* Fix mulmat_f16

* Output FP32 in fp16 matmul shader

* Fix f16_to_f32 kernel

* dequant_q4_0 kernel

* Add VMA library

* Avoid requesting dedicated memory, VMA can decide that by itself

* Add bounds checking to matmul kernels, improve implementation, fix command buffers not freed properly

* add cmake commands

* Add 2d write operation, profiling code

* Fix 2d write

* Fix queue selection for AMD RADV

* Fix trailing whitespace in vk_mem_alloc.h

* Add WIP warp tile mat mul shaders

* Disable glslc optimization

* Disable glslc optimization for CMake

* Optimize warptile matmul shader, replace blocktile with it

* Add split-k optimization for small matrix multiplication

Use semaphores for synchronization instead of fences or waitidle

Rework async write/read for synchronization

* Fix validation errors, improve compatibility with AMD GPUs

* Rework command buffer handling

* Variable matmul kernel using specialization constants

* Fix synchronization on AMD, add barriers for buffer ownership transfer, add debug flag and prints

* Reuse semaphores

* Handle stage flags during command buffer submission properly

* Increase matmul test runs for consistent results

* Fix F32 matmul

* Add vectorized loading and zeropadding for matrix multiplication

* Use pinned memory for f16 preprocessing

* Don't force aligned matmul

* Don't free before queue done

* Replace VMA library with native Vulkan buffer management

* Basic offloading support with mul_f32 and dmmv for q4_0

* Run glslc commands in parallel

* Unroll loops in dmmv shader

* Reduce usage of waitIdle

* Reuse pinned allocation for f16 conversion

* Handle devices with only a single queue

* Fix trailing whitespace in CMakeLists.txt

* Allow parallel execution of kernels, parallelize third and fourth dimension calls

* Add fallback for devices only supporting one DescriptorSet per DescriptorPool

* Move to graph function similar to CUDA implementation

* Use F16 kernel for most things, replace q_f32 with mul_mat_q_f16 function

* Add F32 dmmv shaders

* Batch submissions

* Add .spv to gitignore

* Split off matrix vector multiplication for separate optimization

* Use single command buffer for matrix vector multiplication ops

* Reduce overhead of mul_f32 calls by using a single command buffer

* Add submission batching to mul_f32

* Fix tests

* Add missing barrier

* Add further missing barrier

* Add further ops

* Replace vk::QueueFamilyIgnored with VK_QUEUE_FAMILY_IGNORED to support more Vulkan header versions

* Remove unnecessary cblas link

* Fix descriptor set pre-allocation assert

* Add runtime shader compilation, start transferring shaders to this approach

* Transfer remaining shaders to header and compile on runtime

* Fix fp32 fallback if device doesn't support fp16, add force disable env var GGML_VULKAN_DISABLE_F16

* Add support for q4_1, q5_0, q5_1 and q8_0

* Remove unnecessary scalar layout extension

* Parse graph early to pre-record command buffers

* Add q6_k support

* Add multi-submit for command buffers

* Fix q6_k dequant shader for AMD

* Fix q6_k for GPUs without fp16 support

* Simplify q6_k fp16 fix

* Minor fixes

* Fix wg_denom of m-mulmat shaders

* Add Python-based Vulkan shader generator

* Replace shaderc dependency with precompiled shaders

Fix python script to generate shaders

* Clean up code

* Fix shader generator script Windows compatibility

Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>

* Close file before deletion

* Fix vulkan shader fp32 name

* Add q2_k and q3_k support

Add validation check to compare shader results to cpu results

* Add q4_k support

* Add q5_k support

* Bake SPIR-V bytecode into the library instead of loading shaders from file

* Switch to signal semaphores for flexibility

Prepare broadcasting support for mul mat

* Finish broadcasting mul mat support for GQA

* Clean up unused functions

Add repeat op

* Add further ops, not yet enabled. Improve semaphore code

* Reduce number of used semaphores by utilizing timelines more properly

* Remove queue information

* Reuse timeline semaphores, allow parallel operation with binary semaphores to work around nvidia driver limitations

* Add Vulkan to llama-bench

* Remove cblas dependency

* Fix matmul k-split bug

* Fix q4_k dmmv K_QUANTS_PER_ITERATION 1 shader

* Add RMS Norm shader, rework op_f32 shader setup, fix matmul bug

* Fix issues with float16 overflows in shaders

* Fix issues with older Vulkan headers on Ubuntu 22.04

* Allow multi-op partial offloading by parsing the graph to preallocate enough between-op buffers

* Implement further ops, rework op_f32 calls, fix bugs

* Finish full offloading support, add last remaining ops, fix bugs, remove redundant code

* Upload generated file ggml-vulkan-shaders.hpp, remove redundant shaders

* Merge upstream changes, fix conflicts, adapt soft_max op

* Fix Python and shader header format

* Free model gpu buffers on exit

* Use single queue per device to simplify code

* Add matmul shader support for running multiple calculations in parallel

* Switch from semaphore-synchronized multiple command buffers per op to single command buffer for multiple ops, whole graph if possible

* Fix missing event cast

* Replace uint64_t(-1) with UINT64_MAX, rename function for clarity

* Fix warning about empty C function parameters

* Fix compiler warnings

* Properly implement Vulkan backend buffer handling

* Fix oversized host staging buffers

* Simplify barrier synchronization calls

* Fix gcc warnings

* Implement max_size for backend buffer types to limit the size of a single allocation

* Use min of maxMemoryAllocationSize and maxBufferSize for device max allocation size

* refactor multi buf

* Disable unsupported ops to fix tests

* Check for maintenance4 support before using it

* Handle devices with only a single queue

* Fix single queue logic

* propagate buffer usage in multi buffers

* Implement rope_neox op

* Cleanup header and other files

* Simplify gpu_extras by removing events and putting staging memcpys into contexts

* Move queue into context

Add not-yet-enabled async backend ops

* Simplify context use, optimize matmul shader for warp size 64 (AMD GCN), fix split_k matmul shader optimization

* Add get_max_size to SYCL backend.

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* llama : fix trailing whitespace

---------

Co-authored-by: Henri Vasserman <henv@hot.ee>
Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-28 19:03:59 +02:00

123 lines
5.7 KiB
C

#pragma once
// ggml-backend internal header
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
//
// Backend buffer
//
// buffer type
typedef void * ggml_backend_buffer_type_context_t;
struct ggml_backend_buffer_type_i {
const char * (*GGML_CALL get_name) (ggml_backend_buffer_type_t buft);
ggml_backend_buffer_t (*GGML_CALL alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
size_t (*GGML_CALL get_max_size) (ggml_backend_buffer_type_t buft); // allocation max size
size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
bool (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
// check if tensor data is in host memory
// should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
bool (*GGML_CALL is_host) (ggml_backend_buffer_type_t buft);
};
struct ggml_backend_buffer_type {
struct ggml_backend_buffer_type_i iface;
ggml_backend_buffer_type_context_t context;
};
// buffer
typedef void * ggml_backend_buffer_context_t;
struct ggml_backend_buffer_i {
const char * (*GGML_CALL get_name) (ggml_backend_buffer_t buffer);
void (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer);
void * (*GGML_CALL get_base) (ggml_backend_buffer_t buffer);
void (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
void (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
bool (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
void (*GGML_CALL clear) (ggml_backend_buffer_t buffer, uint8_t value);
void (*GGML_CALL reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
};
struct ggml_backend_buffer {
struct ggml_backend_buffer_i iface;
ggml_backend_buffer_type_t buft;
ggml_backend_buffer_context_t context;
size_t size;
enum ggml_backend_buffer_usage usage;
};
GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
ggml_backend_buffer_type_t buft,
struct ggml_backend_buffer_i iface,
ggml_backend_buffer_context_t context,
size_t size);
// do not use directly, use ggml_backend_tensor_copy instead
bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst);
// buffer that contains a collection of buffers
GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers);
GGML_CALL bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer);
GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
//
// Backend
//
typedef void * ggml_backend_context_t;
struct ggml_backend_i {
const char * (*GGML_CALL get_name)(ggml_backend_t backend);
void (*GGML_CALL free)(ggml_backend_t backend);
// buffer allocation
ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend);
// (optional) asynchronous tensor data access
void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
// (optional) complete all pending operations
void (*GGML_CALL synchronize)(ggml_backend_t backend);
// compute graph with a plan
ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
void (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// compute graph without a plan (async)
bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
// check if the backend supports an operation
bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
};
struct ggml_backend {
struct ggml_backend_i iface;
ggml_backend_context_t context;
};
//
// Backend registry
//
typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data);
GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
#ifdef __cplusplus
}
#endif