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# pragma once
// ggml-backend internal header
# include "ggml-backend.h"
# ifdef __cplusplus
extern " C " {
# endif
//
// Backend buffer
//
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// buffer type
typedef void * ggml_backend_buffer_type_context_t ;
struct ggml_backend_buffer_type_i {
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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
ggml : add Vulkan backend (llama/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 18:03:59 +01:00
size_t ( * GGML_CALL get_max_size ) ( ggml_backend_buffer_type_t buft ) ; // allocation max size
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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
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// check if tensor data is in host memory
// should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
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bool ( * GGML_CALL is_host ) ( ggml_backend_buffer_type_t buft ) ;
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} ;
struct ggml_backend_buffer_type {
struct ggml_backend_buffer_type_i iface ;
ggml_backend_buffer_type_context_t context ;
} ;
// buffer
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typedef void * ggml_backend_buffer_context_t ;
struct ggml_backend_buffer_i {
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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
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} ;
struct ggml_backend_buffer {
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struct ggml_backend_buffer_i iface ;
ggml_backend_buffer_type_t buft ;
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ggml_backend_buffer_context_t context ;
size_t size ;
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enum ggml_backend_buffer_usage usage ;
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} ;
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GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init (
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ggml_backend_buffer_type_t buft ,
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struct ggml_backend_buffer_i iface ,
ggml_backend_buffer_context_t context ,
size_t size ) ;
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// 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 ) ;
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ggml : add Vulkan backend (llama/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 18:03:59 +01:00
// 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 ) ;
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//
// Backend
//
typedef void * ggml_backend_context_t ;
struct ggml_backend_i {
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const char * ( * GGML_CALL get_name ) ( ggml_backend_t backend ) ;
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void ( * GGML_CALL free ) ( ggml_backend_t backend ) ;
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// buffer allocation
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ggml_backend_buffer_type_t ( * GGML_CALL get_default_buffer_type ) ( ggml_backend_t backend ) ;
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// (optional) asynchronous tensor data access
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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 ) ;
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bool ( * GGML_CALL cpy_tensor_async ) ( ggml_backend_t backend_src , ggml_backend_t backend_dst , const struct ggml_tensor * src , struct ggml_tensor * dst ) ;
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// (optional) complete all pending operations
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void ( * GGML_CALL synchronize ) ( ggml_backend_t backend ) ;
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// compute graph with a plan (not used currently)
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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 ) ;
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// compute graph with a plan
enum ggml_status ( * GGML_CALL graph_plan_compute ) ( ggml_backend_t backend , ggml_backend_graph_plan_t plan ) ;
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// compute graph without a plan (async)
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enum ggml_status ( * GGML_CALL graph_compute ) ( ggml_backend_t backend , struct ggml_cgraph * cgraph ) ;
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// check if the backend supports an operation
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bool ( * GGML_CALL supports_op ) ( ggml_backend_t backend , const struct ggml_tensor * op ) ;
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// check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
// these should be expensive operations with large batch sizes that may benefit from running on this backend
// even if the weight has to be copied from the CPU temporarily
bool ( * GGML_CALL offload_op ) ( ggml_backend_t backend , const struct ggml_tensor * op ) ;
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// (optional) event synchronization
ggml_backend_event_t ( * GGML_CALL event_new ) ( ggml_backend_t backend ) ;
void ( * GGML_CALL event_free ) ( ggml_backend_event_t event ) ;
void ( * GGML_CALL event_record ) ( ggml_backend_event_t event ) ;
void ( * GGML_CALL event_wait ) ( ggml_backend_t backend , ggml_backend_event_t event ) ;
void ( * GGML_CALL event_synchronize ) ( ggml_backend_event_t event ) ;
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} ;
struct ggml_backend {
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ggml_guid_t guid ;
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struct ggml_backend_i iface ;
ggml_backend_context_t context ;
} ;
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struct ggml_backend_event {
ggml_backend_t backend ;
void * context ;
} ;
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//
// Backend registry
//
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typedef ggml_backend_t ( * GGML_CALL ggml_backend_init_fn ) ( const char * params , void * user_data ) ;
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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 ) ;
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# ifdef __cplusplus
}
# endif