Files
llama.cpp-mtp-turboquant/ggml/src/ggml-virtgpu/ggml-backend-buffer.cpp
T
Johannes Gäßler d6f3030047 ggml: backend-agnostic tensor parallelism (experimental) (#19378)
* ggml: backend-agnostic tensor parallelism

* support for GPT-OSS, Qwen 3 MoE

* partial Vulkan fix

* add support for 4/8 GPUs

* unconditional peer access

* re-use buffers + ggml contexts

* fix output pattern

* NCCL support

* GGML: HIP: add RCCL support

* Remove shfl and AllReduce from backend interface

* move allocation workaround out of ggml-alloc.c

* 2d tensor set/get support

* Fix the seg fault without NCCL

* Apply suggestion from JohannesGaessler

* support for tensor dims % n_devs != 0

* fix view_offs scaling

* arbitrary num. of GPUs/tensor split

* fix compilation

* better granularity estimate

* Support device-specific host buffer types if all underlying backends expose the same type. This allows using pinned memory instead of pageable memory for CUDA.

Fix compilation errors.

* partial Qwen 3 Next support

* Fix qwen3 30b (#8)

* Fix crash with Qwen-30B-A3B Q4_0

Qwen-30B-A3B Q4_0 has an intermediate dimension of 768. Using a granularity of 256 forces an uneven split between GPUs, which is not supported by the current implementation.

* Decide block size based on tensor quantization type

* Fix crashes due to KV cache serialization (#9)

KV cache serialization requires non-zero offsets on the tensor. Add support in the meta backend to set/get a tensor with a non-zero offset.

* metal : fix build (#7)

* static memory allocations, fix usage count

* fix tensor granularity

* more even memory distribution

* use BF16 for allreduce

* rebase fixup

* better error message for unsupported architectures

* Fix device mismatch during scatter of allReduce. (#11)

There is a mismatch between the dst buffer device and the backend device, causing the use of sync copies

* Enable the previous allreduce implementation. It is better in both perf and stability (#12)

* delay AllReduce for Moe for less I/O

* build : clean-up compile warnings

* backend : move most of the meta backend API to ggml-backend-impl.h

* cont : hide unused public API in the implementation

* llama : use llama_device + remove ggml_backend_dev_is_meta()

* ggml-backend : remove unused alloc include

* minor : remove regex include

* ggml : introduce ggml-ext.h for staging new APIs

* rebase fixup

* fix tests

* llama : more robust logic for determining Meta devices (#16)

* llama : more robust logic for determining Meta devices

* cont : fix devs size check

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* cont : fix log type

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* disable roundtrip for meta backend

* fix arch selection

* Qwen 3.5 support

* fix Gemma 4 MoE

* fix OpenVino, SYCL

* fix test-llama-archs for CPU-only builds

* Fix Qwen 3.5 MoE

* disable meta backend tests for WebGPU

* tests : filter CPU-based devices from the Meta backend tests (#17)

* meta : formatting, naming, indentation (#18)

* formatting : llama-model.cpp

* formatting : ggml-ext.h

* formatting : ggml-backend-meta.cpp

* meta : add TODO

* add documentation

* better error messages

* fix GPT-OSS

---------

Co-authored-by: Carl Philipp Klemm <carl@uvos.xyz>
Co-authored-by: Gaurav Garg <gaugarg@nvidia.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2026-04-09 16:42:19 +02:00

124 lines
5.3 KiB
C++

#include "ggml-remoting.h"
#define BUFFER_TO_GPU(name) ((ggml_backend_remoting_buffer_context *) (name)->context)->gpu
static void * ggml_backend_remoting_buffer_get_base(ggml_backend_buffer_t buffer) {
ggml_backend_remoting_buffer_context * context = (ggml_backend_remoting_buffer_context *) buffer->context;
if (context->base) {
return context->base;
}
context->base = apir_buffer_get_base(BUFFER_TO_GPU(buffer), BUFFER_TO_APIR_CONTEXT(buffer));
return context->base;
}
static void ggml_backend_remoting_buffer_set_tensor(ggml_backend_buffer_t buffer,
ggml_tensor * tensor,
const void * data,
size_t offset,
size_t size) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer);
if (context->is_from_ptr) {
memcpy((char *) tensor->data + offset, data, size);
} else {
apir_buffer_set_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), tensor, data, offset, size);
}
return;
}
static void ggml_backend_remoting_buffer_get_tensor(ggml_backend_buffer_t buffer,
const ggml_tensor * tensor,
void * data,
size_t offset,
size_t size) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer);
if (context->is_from_ptr) {
memcpy(data, (const char *) tensor->data + offset, size);
} else {
apir_buffer_get_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), tensor, data, offset, size);
}
}
static void ggml_backend_remoting_buffer_set_tensor_from_ptr(ggml_backend_buffer_t buffer,
ggml_tensor * tensor,
const void * data,
size_t offset,
size_t size) {
UNUSED(buffer);
memcpy((char *) tensor->data + offset, data, size);
return;
}
static void ggml_backend_remoting_buffer_get_tensor_from_ptr(ggml_backend_buffer_t buffer,
const ggml_tensor * tensor,
void * data,
size_t offset,
size_t size) {
UNUSED(buffer);
memcpy(data, (const char *) tensor->data + offset, size);
}
static bool ggml_backend_remoting_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
const ggml_tensor * src,
ggml_tensor * dst) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
bool ret = apir_buffer_cpy_tensor(gpu, BUFFER_TO_APIR_CONTEXT(buffer), src, dst);
return ret;
}
static void ggml_backend_remoting_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
apir_buffer_clear(gpu, BUFFER_TO_APIR_CONTEXT(buffer), value);
return;
}
static void ggml_backend_remoting_buffer_free_buffer(ggml_backend_buffer_t buffer) {
virtgpu * gpu = BUFFER_TO_GPU(buffer);
apir_buffer_free_buffer(gpu, BUFFER_TO_APIR_CONTEXT(buffer));
ggml_backend_remoting_buffer_context * context = BUFFER_TO_GGML_CONTEXT(buffer);
free(context);
buffer->context = NULL;
}
const ggml_backend_buffer_i ggml_backend_remoting_buffer_interface = {
/* .free_buffer = */ ggml_backend_remoting_buffer_free_buffer,
/* .get_base = */ ggml_backend_remoting_buffer_get_base,
/* .init_tensor = */ NULL,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_remoting_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_remoting_buffer_get_tensor,
/* .set_tensor_2d = */ NULL,
/* .get_tensor_2d = */ NULL,
/* .cpy_tensor = */ ggml_backend_remoting_buffer_cpy_tensor,
/* .clear = */ ggml_backend_remoting_buffer_clear,
/* .reset = */ NULL,
};
const ggml_backend_buffer_i ggml_backend_remoting_buffer_from_ptr_interface = {
/* .free_buffer = */ ggml_backend_remoting_buffer_free_buffer,
/* .get_base = */ ggml_backend_remoting_buffer_get_base,
/* .init_tensor = */ NULL,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_remoting_buffer_set_tensor_from_ptr,
/* .get_tensor = */ ggml_backend_remoting_buffer_get_tensor_from_ptr,
/* .set_tensor_2d = */ NULL,
/* .get_tensor_2d = */ NULL,
/* .cpy_tensor = */ ggml_backend_remoting_buffer_cpy_tensor,
/* .clear = */ ggml_backend_remoting_buffer_clear,
/* .reset = */ NULL,
};