* vulkan: fix GPU deduplication logic.
As reported in https://github.com/ggml-org/llama.cpp/issues/19221, the
(same uuid, same driver) logic is problematic for windows+intel igpu.
Let's just avoid filtering for MoltenVK which is apple-specific, and
keep the logic the same as before 88d23ad5 - just dedup based on UUID.
Verified that MacOS + 4xVega still reports 4 GPUs with this version.
* vulkan: only skip dedup when both drivers are moltenVk
* vulkan: use coopmat for flash attention p*v matrix multiplication
* fix P loading issue
* fix barrier position
* remove reduction that is no longer needed
* move max thread reduction into loop
* remove osh padding
* add bounds checks and padding
* remove unused code
* fix shmem sizes, loop duration and accesses
* don't overwrite Qf, add new shared psh buffer instead
* add missing bounds checks
* use subgroup reductions
* optimize
* move bounds check, reduce barriers
* support other Bc values and other subgroup sizes
* remove D_split
* replace Of register array with shared memory Ofsh array
* parallelize HSV across the rowgroups
* go back to Of in registers, not shmem
* vectorize sfsh
* don't store entire K tile in shmem
* fixes
* load large k tiles to shmem on Nvidia
* adapt shared memory host check function to shader changes
* remove Bc 32 case
* remove unused variable
* fix missing mask reduction tmspsh barrier
* fix mask bounds check
* fix rowmax f16 under/overflow to inf
* fix flash_attn_cm2 BLOCK_SIZE preprocessor directives
Deduplication here relied on the fact that vulkan would return unique
UUID for different physical GPUs. It is at the moment not always the case.
On Mac Pro 2019 running Mac OS, with 2 Vega II Duo cards (so, 4 GPU total),
MotlenVK would assign same UUID to pairs of GPUs, unless they
are connected with Infinity Fabric.
See more details here: KhronosGroup/MoltenVK#2683.
The right way is to fix that in MoltenVK, but until it is fixed,
llama.cpp would only recognize 2 of 4 GPUs in such configuration.
The deduplication logic here is changed to only filter GPUs if UUID is
same but driver is different.
* vulkan: Remove transfer_ctx, do everything in compute_ctx.
We had a bug where a set_tensor_async (using transfer_ctx) didn't get
submitted before the graph_compute (using compute_ctx) that came after
it. To avoid this sort of issue, just do everything in compute_ctx.
Remove transfer_cmd_pool, which was already unused.
* fix crash with perf logger
Change ggml_vk_mul_mat_vec_id_q_f16 to loop over the batch dimension and
update the indexing calculations in get_offsets.
Mat-vec is faster than mat-mat for small values of n. We don't get the same
reuse of the weights as in the non-ID path, but with this the cost is linear
in n rather than n>1 being far slower than n==1.
This fixes incoherent output in Llama-4-Maverick-17B-128E-PAB-Q8_0, which
has a mul_mat_id with an A matrix that's Q8_0 8192 x 5120 x 128.
This should work when the number of blocks in the A matrix is less than 2^32
(for mul_mat_vec or mul_mm_cm2), or for mul_mm I think the limit is like
2^32*LOAD_VEC_A elements.
- Divide batch_stride by QUANT_K earlier, so the block index calculation works in 32b.
- Each vk_pipeline_struct has a linked list of pipelines that will allow it to handle
variants. So far this change just adds a single use case for this, compiling with the
e64BitIndexingEXT flag.
- Use the 64b indexing variant when the A matrix is larger than maxStorageBufferRange.
64-bit indexing has some cost - around 3-5% in MoE models, so it's worth the effort
to avoid enabling it unconditionally.
* vulkan: Enable and optimize large matmul parameter combination for AMD
* limit tuning to AMD GPUs with coopmat support
* use tx_m values instead of _l
* ggml: add env var GGML_OP_OFFLOAD_MIN_BATCH
* makes the min_batch_size for triggering op offload configurable via env var, defaulting to the prior hardcoded value of 32
* ggml: read GGML_OP_OFFLOAD_MIN_BATCH once and store to dev ctx
* cann: forward declaration of device context struct
* cann: move offload op check after device context declaration
* cuda: fix whitespace
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
---------
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* modify warptile tuning for xe3
* intel vendor check w/ coopmat support
* fix back formatting
* fix formatting change 2
* move intel check to chip specific tuning part
* Change to support both windows and linux
* modify m_warptile to l_warptile for intel
* modify warptile tuning for bf16 matmuls to fix regression (m_warptile to l_warptile)
* Code style changes
* Code style changes (2)
* Code style changes (3)
* vulkan: support buffer_from_host_ptr
* hacky use of buffer_from_host_ptr for directio
* disable buffer_from_host_ptr cap
* use external memory for ggml_vk_host_malloc, revert model loader changes
* disable external_memory_host for MoltenVK
* take buffer memory types into account
* don't use external_memory_host for ggml_vk_host_malloc
* vulkan: Optimize GGML_OP_CUMSUM
There are two paths: The preexisting one that does a whole row per workgroup
in a single shader, and one that splits each row into multiple blocks and does
two passes. The first pass computes partials within a block, the second adds
the block partials to compute the final result. The multipass shader is used
when there are a small number of large rows.
In the whole-row shader, handle multiple elements per invocation.
* use 2 ELEM_PER_THREAD for AMD/Intel
* address feedback
* vulkan: extend topk_moe to handle sigmoid w/exp_probs_b for nemotron
Also handle GGML_OP_SCALE at the end (nemotron, deepseek2).
Fewer pipeline variants and spec constants, just use push constants.
In test_topk_moe, change exp_probs_b to be 1D, matching real networks.
Update test-backend-ops and ggml-backend to allow verifying multiple outputs
in a fusion test (topk_moe has two outputs). Previously only the final node
was verified.
* change test_topk_moe to allow results in arbitrary order
* disable sigmoid fusion for moltenvk
* vulkan: Use BK=32 for coopmat2 mul_mat_id
* vulkan: optimize decodeFuncB in coopmat2 mul_mat_id shader
Disable robustness, remove the OOB check in decodeFuncB, and initialize the
row_ids to zero to avoid OOB access.
Don't slice/offset the B matrix to ic * BN, only to adjust the coord back down
to the range [0, BN) in decodeFuncB. Instead just slice with a row offset of
zero and remove the '& (BN - 1)'. This allows the compiler to common some of
the shared memory loads.
The goal is to enable the async loading code paths in
llama_model_loader::load_all_data, originally from #7896. This works and the
loads themselves are faster, but with host visible vidmem I think the cost of
allocating/mapping vidmem moves and becomes more expensive, and I don't see a
benefit by default. But with GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM=1 I do see a
significant improvement in model loading time.
I updated test_topk_moe to more closely match llm_graph_context::build_moe_ffn
and added coverage for exp_probs_b and some other missing combinations. This
exposed a bug in both CUDA and Vulkan backends where they were assuming the
input to argsort and the input to get_rows are the same. I'd like to optimize
this graph in another change, but for now just get it functional.
CUDA also had a bug where it got n_experts from the wrong place, leading to
GGML_ASSERT failures in some of the new tests.
* Some improvement on mul_mat_iq2_xs
Refactor calculations for db values and grid data to optimize performance and reduce redundancy.
* Fix trailing whitespace
This implements a variation of the perf logger where rather than timing each
operation individually with effectively a barrier in between, we put the
timing boundaries where we already synchronize and time the groups of work
that normally overlap. This can be useful to help understand whether
individual operations need to be optimized, or if the group is already running
efficiently.
GGML_VK_PERF_LOGGER_CONCURRENT=1 enables the new mode (when
GGML_VK_PERF_LOGGER is also set).
GGML_VK_SYNC_LOGGER=1 replaces the ENABLE_SYNC_LOGGING compile time switch.
Oleksandr Kuvshynov
Jeff Bolz
Ruben Ortlam
Christian Kastner
Simon Redman
Masato Nakasaka
Georgi Gerganov
Ruben Ortlam
Doctor Shotgun
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