If a Cuda device has no or limited available memory, the actual call
to cudaMemGetInfo() itself can cause a fatal crash due to a cuda out
of memory error (there is not enough memory to actually query memory)
This causes an issue because we query memory for all devices at
startup even if the user isn't trying to use the device for inference.
Fix this by making the error non-fatal and assigning zero total/free
memory to the device. This will have the downstream effect of the fit
algorithm not trying to put any layers on it, which is desired outcome
vs hard crashing.
this also prevents crashes in cuda enabled builds when user explicitly
passes '-dev none'
* cuda: fix snake fusion type predicate, a and inv_b are F32
The matcher required a->type == x->type while launch_snake reads both
as const float *, matching the CPU and Metal contract where a and inv_b
stay F32. F16/BF16 chains never fused and fell back to the naive path,
and a hypothetical all F16 chain would have read F16 bits as float.
Aligns the predicate and the comment with ggml-cpu.c
* cuda: reject snake fusion on non-contiguous operands
The kernel reads x[idx] and a[c] / inv_b[c] linearly, so a
non-contiguous view passing the matcher would silently read wrong data.
Mirror the contiguity guard already present in the CPU, Vulkan and
Metal matchers.
* Update ggml-cuda.cu - Turing P2P access fix.
* Add original code as fallback behaviour when NCCL or P2P is not set/true.
* Update ggml/src/ggml-cuda/ggml-cuda.cu to add comment as per suggestion
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cuda : concat implementation for quantized types
* chore : apply am17an clever suggestion to shorten the code
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* Remove redundant CUDA copies after gated_delta_net.
Currently, GDN writes recurrent state snapshots into its output tail, then the graph immediately copies those snapshots into ssm_states_all. With MTP draft length 3, target decode uses K=4, so that becomes 4 extra ggml_cuda_cpy calls.
The change detects that gated_delta_net -> view -> cpy pattern and makes the CUDA GDN kernel write the state snapshot(s) directly into the recurrent cache, skipping the intermediate tail writes and copy kernels when safe.
* Address review comments
* CUDA: Improve performance via less synchronizations between token (#17795)
* Adds CPU-to-CUDA copy capability to
ggml_backend_cuda_cpy_tensor_async()
* Adds function to relax sync requirements between input copies on
supported backends (CUDA for now)
* Exchanges synchronous copy with async copy function.
* Adds macro guards to allow compilation in non-CUDA builds
* Reworked backend detection in ggml-backend.cpp to avoid linking
conflicts
* Relax requirement of checks in async CUDA copies from backend and buffer type to just buffer type, to avoid linking issues
* Minor cleanup
* Makes opt-in to relax use of explicit syncs more general. Backends like
vulkan which require a synchronization between HtoD copies and graph
execution could also adopt this change now.
* Reintroduces stricter check for CPU->CUDA backend async copy via
GGML_DEVICE_TYPE_CPU.
* Corrects initialization of ggml_backend_sync_mode in
ggml_backend_sched_split initialization
* Simplifies synchronizations to adhere to `saaasg` pattern.
* Apply suggestion from @ggerganov (src->buffer to buf_src)
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Apply suggestion from @ggerganov (src->buffer to buf_src) v2
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Apply suggestions from @johannesgaessler code review
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Adds single-GPU synchronizations to multi-GPU settings to fix hip backend pipeline parallel bugs.
* Scheduler Hardening: Exclude hip/MUSA from copy_from_host CPU split ->
GPU split optimization
* Scheduler Hardening: Re-adding original additional synchronizations for
non-async backends
* Adds disclaimer to hip/musa exclusion of copy_from_host. Highlights that it is out of
precaution, but that no perf-impact is visible, and that it can be
revisited separately anytime.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* vulkan: make SQR/SQRT/SIN/COS/CLAMP/LEAKY_RELU use unary.comp
* vulkan: make NORM support noncontig
* add noncontiguous row test cases for norm/l2_norm, handle this in the CPU backend and l2_norm.comp
* fix supports_op for cuda and webgpu
* cuda: add GGML_OP_COL2IM_1D, follow-up to the CPU op
* cuda: col2im_1d use fast_div_modulo for the index decomposition
* cuda: col2im_1d tighten supports_op, type match and contiguous dst
* cuda: reset device in get_memory function if no backend is active
* also count device and host buffers
* exclude hip and musa from counting and device reset
* use device mutex instead of atomic
* undo backend_free function move
* cuda: reserve space for quantize kv-cache at startup
* address review comments
* remove forward decl
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* remove assert in ggml-cuda.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cuda: tighten snake fusion type checks for all operands (defensive, sync vulkan)
* cuda: reject snake fusion when ne[2] or ne[3] > 1 (mirror vulkan PR review)
* cuda: merge type_ok and types_ok into a single types_ok (address am17an review)
* cuda: filter ADD/SUB/MUL/DIV in supports_op to F32/F16
bin_bcast only dispatches F32/F16 type triplets, mirror the
vulkan filter so unsupported types fall back through cpy
instead of aborting.
* test-backend-ops: extend snake_fuse to rank-4 with ne[2]/ne[3] > 1 cases
* ggml-cuda: add internal AllReduce provider for tensor parallelism
Introduces a NCCL-free AllReduce implementation for LLAMA_SPLIT_MODE_TENSOR
using a single-phase CUDA kernel that pipelines D2H copy, cross-GPU
handshake via pinned-memory volatile flags, and the reduction in one
kernel launch per GPU.
New files:
- ggml/src/ggml-cuda/comm.cuh — ggml_cuda_allreduce_provider enum
- ggml/src/ggml-cuda/allreduce.cuh — pipeline API declarations
- ggml/src/ggml-cuda/allreduce.cu — kernel + pipeline init/dispatch
ggml-cuda.cu changes:
- ggml_backend_cuda_comm_context gains ar_pipeline field
- Provider selection via GGML_CUDA_ALLREDUCE env var ("nccl" / "internal")
- INTERNAL provider initialises the pipeline at comm_init time
- Dispatch routes to ggml_cuda_ar_allreduce(); falls back to meta-backend
CPU reduce for unsupported sizes or GPU counts (> 2)
Current scope: 2 GPUs, FP32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* llama-bench: add --allreduce flag to select AllReduce provider
Adds --allreduce <auto|nccl|internal> to llama-bench (and via the shared
field pattern, consistent with other multi-value flags). Useful for
isolating hangs or regressions in tensor-parallel mode: pass --allreduce nccl
to force NCCL and bypass the internal provider.
Also fixes ggml_cuda_select_allreduce_provider() to treat an empty
GGML_CUDA_ALLREDUCE env var the same as unset (avoids spurious warning when
llama-bench sets it to "" for the "auto" case).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
xt gains ar_pipeline field
- Provider selection via GGML_CUDA_ALLREDUCE env var ("nccl" / "internal")
- INTERNAL provider initialises the pipeline at comm_init time
- Dispatch routes to ggml_cuda_ar_allreduce(); falls back to meta-backend
CPU reduce for unsupported sizes or GPU counts (> 2)
Current scope: 2 GPUs, FP32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* llama-bench: rename --allreduce to --reduction-provider / -rp
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
via the shared
field pattern, consistent with other multi-value flags). Useful for
isolating hangs or regressions in tensor-parallel mode: pass --allreduce nccl
to force NCCL and bypass the internal provider.
Also fixes ggml_cuda_select_allreduce_provider() to treat an empty
GGML_CUDA_ALLREDUCE env var the same as unset (avoids spurious warning when
llama-bench sets it to "" for the "auto" case).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
xt gains ar_pipeline field
- Provider selection via GGML_CUDA_ALLREDUCE env var ("nccl" / "internal")
- INTERNAL provider initialises the pipeline at comm_init time
- Dispatch routes to ggml_cuda_ar_allreduce(); falls back to meta-backend
CPU reduce for unsupported sizes or GPU counts (> 2)
Current scope: 2 GPUs, FP32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* llama-bench: pass WARN/ERROR log messages through in non-verbose mode
The null log callback was silently dropping all messages. WARN and ERROR
should always be visible since they indicate legitimate issues (e.g. a
requested reduction provider not being available).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
vider.
Also fixes ggml_cuda_select_allreduce_provider() to treat an empty
GGML_CUDA_ALLREDUCE env var the same as unset (avoids spurious warning when
llama-bench sets it to "" for the "auto" case).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
xt gains ar_pipeline field
- Provider selection via GGML_CUDA_ALLREDUCE env var ("nccl" / "internal")
- INTERNAL provider initialises the pipeline at comm_init time
- Dispatch routes to ggml_cuda_ar_allreduce(); falls back to meta-backend
CPU reduce for unsupported sizes or GPU counts (> 2)
Current scope: 2 GPUs, FP32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* cmake: improve NCCL detection for source-tree builds, add static/dynamic switch
FindNCCL.cmake now searches the cmake source-build layout used by the Windows
NCCL port (cmake/lib/Release for static, cmake/src/Release for dynamic import
lib) and also checks src/include for the generated nccl.h header.
New option GGML_CUDA_NCCL_STATIC (default OFF) selects static vs dynamic
linking and controls which paths and library names are searched.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
for the "auto" case).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
xt gains ar_pipeline field
- Provider selection via GGML_CUDA_ALLREDUCE env var ("nccl" / "internal")
- INTERNAL provider initialises the pipeline at comm_init time
- Dispatch routes to ggml_cuda_ar_allreduce(); falls back to meta-backend
CPU reduce for unsupported sizes or GPU counts (> 2)
Current scope: 2 GPUs, FP32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* ggml-cuda: add AllReduce hang watchdog (GGML_CUDA_AR_WATCHDOG)
When compiled with -DGGML_CUDA_AR_WATCHDOG=ON, uses a debug kernel
variant that writes per-GPU spin diagnostics to pinned host memory.
A host-side blocking poll (cudaEventQuery + volatile reads) detects
hangs and logs WARN with the last observed arrival counters and spin
counts, controlled by GGML_CUDA_AR_WATCHDOG (ms timeout) and
GGML_CUDA_AR_MAX_SPIN (kernel bailout) env vars at runtime.
Zero overhead on the production path — all debug code is behind #ifdef.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ar_pipeline field
- Provider selection via GGML_CUDA_ALLREDUCE env var ("nccl" / "internal")
- INTERNAL provider initialises the pipeline at comm_init time
- Dispatch routes to ggml_cuda_ar_allreduce(); falls back to meta-backend
CPU reduce for unsupported sizes or GPU counts (> 2)
Current scope: 2 GPUs, FP32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* ggml-cuda: fix intermittent AllReduce hang on Blackwell PCIe
Add __threadfence_system() before the arrival signal write in
signal_set to ensure D2H data is globally visible before the peer
observes the arrival flag. Without this fence, the peer could enter
Phase 3 host reads before the data had fully landed, causing an
intermittent deadlock on RTX 5090 (Blackwell, PCIe-only).
Also redesign the watchdog from a blocking dispatch-thread poll to a
non-blocking background thread, eliminating the ~20ms per-slot
latency the old design added.
Verified: 30/30 soak test runs clean at ~50 t/s (previously ~1-in-15
hang rate).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- INTERNAL provider initialises the pipeline at comm_init time
- Dispatch routes to ggml_cuda_ar_allreduce(); falls back to meta-backend
CPU reduce for unsupported sizes or GPU counts (> 2)
Current scope: 2 GPUs, FP32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* ggml-cuda: fix watchdog shutdown ordering and pipeline_free drain
- Stop watchdog thread BEFORE destroying GPU resources (events, streams)
to prevent polling destroyed handles → spurious "busy" readings
- Add cudaStreamSynchronize in pipeline_free to drain in-flight kernels
before freeing pinned host buffers they may still be reading
- Sleep-first watchdog polling: no +0ms noise, only logs when a kernel
is genuinely stuck past the poll interval
- Check wdog_stop in both outer and inner loops so join() returns
promptly instead of draining the entire queue
- Add Phase 3 breadcrumbs to debug[3] for hang localization
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
RNAL provider initialises the pipeline at comm_init time
- Dispatch routes to ggml_cuda_ar_allreduce(); falls back to meta-backend
CPU reduce for unsupported sizes or GPU counts (> 2)
Current scope: 2 GPUs, FP32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* ggml-cuda: replace event-based watchdog with per-GPU ring buffer
Completely rework the GGML_CUDA_AR_WATCHDOG system:
- Replace the shared debug_buf + event-polling + queue design with
per-GPU ring buffers in pinned host memory
- Kernel writes a debug record only on spin-limit bailout: claims a
ring slot via atomicAdd (single-GPU host atomics work on RTX 5090),
writes fields, fences, sets completion flag, then all threads exit
- Watchdog thread simply polls ring head counters every 1ms and prints
any new complete records — no CUDA event queries, no mutex, no queue
- Zero overhead on the dispatch path (no queue posting, no memset)
- Watchdog shutdown returns within ~1ms (atomic bool, no drain)
- On bailout the kernel skips Phase 3 entirely and exits cleanly
Verified: 20/20 prefill soak test clean at ~1112 t/s, no hangs.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
P32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: normalize line endings to LF (undo Windows CRLF conversion)
Five files were inadvertently converted to CRLF by the Windows
development environment, causing every line to show as changed in
diffs against master.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
imit bailout: claims a
ring slot via atomicAdd (single-GPU host atomics work on RTX 5090),
writes fields, fences, sets completion flag, then all threads exit
- Watchdog thread simply polls ring head counters every 1ms and prints
any new complete records — no CUDA event queries, no mutex, no queue
- Zero overhead on the dispatch path (no queue posting, no memset)
- Watchdog shutdown returns within ~1ms (atomic bool, no drain)
- On bailout the kernel skips Phase 3 entirely and exits cleanly
Verified: 20/20 prefill soak test clean at ~1112 t/s, no hangs.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
P32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* .gitattributes: force LF line endings to prevent Windows CRLF conversion
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
elopment environment, causing every line to show as changed in
diffs against master.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
imit bailout: claims a
ring slot via atomicAdd (single-GPU host atomics work on RTX 5090),
writes fields, fences, sets completion flag, then all threads exit
- Watchdog thread simply polls ring head counters every 1ms and prints
any new complete records — no CUDA event queries, no mutex, no queue
- Zero overhead on the dispatch path (no queue posting, no memset)
- Watchdog shutdown returns within ~1ms (atomic bool, no drain)
- On bailout the kernel skips Phase 3 entirely and exits cleanly
Verified: 20/20 prefill soak test clean at ~1112 t/s, no hangs.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
P32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* ggml-cuda: move GGML_CUDA_AR_WATCHDOG from CMake option to local define
The watchdog is development-only; a global CMake option is overkill.
Move the toggle to a #define at the top of allreduce.cu (set to 0 by
default) and remove the option from ggml/CMakeLists.txt and the CUDA
CMakeLists.txt add_compile_definitions block.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
fences, sets completion flag, then all threads exit
- Watchdog thread simply polls ring head counters every 1ms and prints
any new complete records — no CUDA event queries, no mutex, no queue
- Zero overhead on the dispatch path (no queue posting, no memset)
- Watchdog shutdown returns within ~1ms (atomic bool, no drain)
- On bailout the kernel skips Phase 3 entirely and exits cleanly
Verified: 20/20 prefill soak test clean at ~1112 t/s, no hangs.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
P32, tensors <= 256 KB. Notes in NOTES-allreduce.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* unify kernel debug paths
* use __threadfence_system explicitly (not in ggml_cuda_ar_signal_set)
* preferentially use internal reduction for <=2 GPUs
* templatize the main kernel to support fp16/bf16
* restore llama-bench.cpp changes
* revert CMakeLists changes
* remove notes from repo
* remove dead warmup code
* fix comments
* improve reduction provider fallback code
* add messages for allreduce fallback
* rework reduction provider init to not call ncclCommInitAll if using the internal provider
* fix case where a given tensor has not been computed
* add chunked mode to the kernel for unlimited vector size
* rework a few checks/fallbacks
* various small cleanups
* allow disabling CUDA reductions completely (falling back to the non-CUDA butterfly mode)
* simplify reduction provider selection
* minor simplifications
* more cleanups/fixes
* prototype alternate path for large reductions
* chunked version of large reduction path
* use bf16 for large reductions
* experimental reduction using cudaMemcpyPeerAsync (slightly slower)
* revert experimental change
* add combined conversion/reduction kernel
* add bf16 wire format for single kernel mode
* experimental on-stream small reduction kernel
* double buffer arrival slots, use token (incrementing) method
* double buffer host_buf for small reductions
* put in waits for use of host_mem in large reduction case (prevents stomping on in-use memory
* remove watchdog code
* various cleanups / dead code removal
* fix fp16 mode
* fix some comments/logging statements
* use increasing token scheme for arrival signals
* add top-level comment to allreduce.cu
* improve top-level comment in allreduce.cu
* fix comments in ggml_cuda_ar_kernel
* improve event handling for hostmem buffer usage tracking
* change ev_pool to fixed 2D array
* add chunked memcpy fallback for extra-large reductions (>32 MB)
* change thresholds for copy-engine path and bf16 demotion
* multi-block kernel test
* more fine-tuning for chukn-size, etc.
* various fixes for PR review
* more PR fixes
* fix semantics of all host mappings
* require ampere+
* small cleanups
* properly use host pointer for src/dst in cudaMemcpy calls
* allreduce: lazy-init the internal pipeline on first use
A config that lives entirely on NCCL never needs the chunked-kernel
pipeline (host_buf, host_large, dev_tmp, streams, events, arrival ring).
Defer pipeline creation to the first try_allreduce_internal call using the
same std::call_once pattern as ensure_nccl, so those resources stay
unallocated when only NCCL is in use.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* allreduce: assert n_backends == 2 instead of soft-fallback
ar_pipeline_init already requires n_devices == 2 and bails before any AR can
get here, so by the time we reach try_allreduce_internal we know we have
exactly two backends. Replace the runtime-debug-log fallback with a hard
assert.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
NCCL is in use.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* rework reduction provider selection. internal/nccl is OS dependent; most fallbacks are removed
* remove unneeded Turing arch check (llama.cpp doesn't even compile pre-Turing anyway)
* allreduce: ASCII-only comments and ggml_cuda_cast for value conversions
Replace non-ASCII characters in comments (em dashes, right arrows) with
ASCII equivalents (--, ->) so the source stays in the ggml/upstream norm.
In the kernel-side code, replace static_cast<Twire>/static_cast<Tdst>
with ggml_cuda_cast<...> so the BF16 conversions go through the fast
__float2bfloat16 / __bfloat162float intrinsics from convert.cuh. Pure
pointer and integer casts stay as static_cast.
Also drops two stray garbage tokens that snuck in from earlier merges
(a duplicated 'return ok; }' tail in allreduce.cu and a leftover '_reg)'
fragment in ggml-cuda.cu).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* allreduce: use ggml_cuda_memcpy_1 for the chunked-kernel vector copies
The chunked kernel's two 16-byte register<->host transfers (Phase 1 store
and Phase 3 load) used reinterpret_cast<float4 *> on both sides. Replace
with ggml_cuda_memcpy_1<sizeof(wire)>, which is the canonical helper for
this pattern and emits the same int4 LD/ST under the hood.
Conformance passes; 5x reruns of 70b internal pp512 show 1832-1836 t/s,
matching the prior matrix value of 1831 t/s -- no perf change as expected.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ok; }' tail in allreduce.cu and a leftover '_reg)'
fragment in ggml-cuda.cu).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* allreduce: assert cuda_ctx->device matches the pipeline's device
Both ggml_cuda_ar_pipeline and ggml_backend_cuda_context carry the device
they were created for; if they ever disagree, every cuda call that follows
runs on the wrong device. Add GGML_ASSERT at each cuda_ctx retrieval site
in the AR path so the misuse fails fast rather than silently corrupting.
Also: rename __nv_bfloat16 -> nv_bfloat16 (typedef alias) for consistency
with the rest of the file, and tighten one cudaGetLastError check to fire
only after the to_bf16 call that can actually fail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
gml-cuda.cu).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* allreduce: expand one-liner for loops to braced bodies
Code-style preference -- match the rest of the file by writing every for
loop with the body on its own braced line. Three sites in the copy-engine
typed dispatch.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
in the AR path so the misuse fails fast rather than silently corrupting.
Also: rename __nv_bfloat16 -> nv_bfloat16 (typedef alias) for consistency
with the rest of the file, and tighten one cudaGetLastError check to fire
only after the to_bf16 call that can actually fail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
gml-cuda.cu).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* allreduce: rename template parameters Tdst/Twire/Tsrc -> T_dst/T_wire/T_src
Code-style preference per PR review -- T_dst/T_wire/T_src is more
consistent with surrounding code. Whole-word rename across all 58 sites
in allreduce.cu (kernel definitions, internal uses, and comment text).
Realigned the parameter columns in three function signatures whose
T_src/T_dst lines shifted by 1 char relative to their non-templated
neighbors.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
to fire
only after the to_bf16 call that can actually fail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
gml-cuda.cu).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* allreduce: drop hyphen in 'chunked-kernel' across comments
Per PR review feedback -- 'chunked kernel' (no hyphen) reads more naturally
in running prose, especially for ESL readers. Pure comment-only change;
all 10 occurrences in allreduce.cu updated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
three function signatures whose
T_src/T_dst lines shifted by 1 char relative to their non-templated
neighbors.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
to fire
only after the to_bf16 call that can actually fail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
gml-cuda.cu).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* allreduce: use ggml_cuda_get_max_cpy_bytes() instead of hardcoded 16
The chunked kernel hardcoded a 16-byte vector unit; replace with the
ggml_cuda_get_max_cpy_bytes() helper that fattn-common.cuh uses for the
same purpose, so ELEMS_PER_VEC self-adjusts to the arch's widest
single-instruction copy.
Perf-neutral on supported targets (Volta+ returns 16).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
hbors.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
to fire
only after the to_bf16 call that can actually fail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
gml-cuda.cu).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* ggml-cuda: PR review fixes -- annotate #endif, fix stale comment, assert nbytes alignment
Three separate but minor changes from PR #22299 review feedback:
1. Annotate the five GGML_USE_NCCL #endif lines with the matching condition
so the pairing is visible without scrolling back.
2. The comment block on ggml_backend_cuda_comm_context claimed NCCL is
lazy-initialised; that was true at one point but the dispatch refactor
(727b141c0) made both NCCL and the internal pipeline eager. Rewrite
the comment to match current behaviour.
3. Assert in ggml_backend_cuda_comm_allreduce_internal that the tensor's
byte size is a 16-byte multiple. The chunked-kernel issues full-width
vector loads/stores, so this is a precondition; tensor-parallel splits
of hidden-dim-multiples satisfy it trivially, but a hard assert turns
any caller-side bug into a clear failure rather than UB.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
device's new AR
records its ev.ker -- otherwise the second device's wait sees the first
device's just-recorded event (the in-flight new AR) and creates a circular
dependency with the in-kernel peer signal. Two-pass dispatch (all waits,
then all launches) avoids this.
Bump POOL_SIZE 2 -> 8 (small memory cost, more breathing room for the
GPU's view of the event chain) and add a runtime env override for the
hybrid kernel chunk size (GGML_CUDA_AR_HYBRID_CHUNK_BYTES) for tuning.
One-shot stderr diagnostic at first AR prints the chosen path + sizing.
Result on 2x RTX 5090 Linux, 70b ub_sweep:
ub=64 (1 MB AR): 913 -> 1036 t/s (+13.5% vs old, +1.8% vs NCCL)
ub=128 (2 MB AR): 1056 -> 1181 (+11.9%, +3.7% vs NCCL)
ub=256 (4 MB AR): 1212 -> 1424 (+17.5%, +3.5% vs NCCL)
Internal now beats NCCL at every size (+1.8% to +15.6%), recovering all
ground in the 1-4 MB regime that was previously a 10-12% loss.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* simplify the init logic
* address some other PR requests
* ggml-cuda: stub internal AllReduce on HIP/MUSA, drop pre-Ampere mention, gate NCCL fallback warning on !HIP
The internal AllReduce relies on cudaHostAllocPortable/Mapped,
cudaHostGetDevicePointer, and __nanosleep -- none of which the HIP or
MUSA shims expose -- so wrap the implementation in
!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) and provide
nullptr/no-op/false stubs in the #else branch. The dispatcher already
treats a null pipeline as init failure and silently falls back to the
meta backend's generic AllReduce, so HIP/MUSA builds compile clean and
behave correctly without further call-site changes.
PR review follow-ups:
- drop "or pre-Ampere?" from the internal-init failure warning -- the
kernel doesn't require Ampere or newer.
- guard the "NCCL not compiled in" fallback warning behind
!defined(GGML_USE_HIP); the suggestion to install NCCL only makes
sense on NVIDIA builds.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
hind, now +6-8% ahead at ub=1024-4096.
Perplexity (32 chunks) matches NCCL bit-for-bit (3.4044 vs 3.4043).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* allreduce: guard __nanosleep on Volta+ and reject pre-Volta devices at init
__nanosleep is the only Volta-specific intrinsic in the kernel; wrap it
in #if __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA / NO_DEVICE_CODE so the file
still compiles cleanly when targeting older arches (the dispatcher's
init check below ensures the kernel is never actually launched on
pre-Volta).
Add a per-device compute-capability check in pipeline_init that returns
nullptr if any device is below sm70. The dispatcher already treats
nullptr as init failure and silently falls back to the meta backend's
generic AllReduce.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
rom the internal-init failure warning -- the
kernel doesn't require Ampere or newer.
- guard the "NCCL not compiled in" fallback warning behind
!defined(GGML_USE_HIP); the suggestion to install NCCL only makes
sense on NVIDIA builds.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
hind, now +6-8% ahead at ub=1024-4096.
Perplexity (32 chunks) matches NCCL bit-for-bit (3.4044 vs 3.4043).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* allreduce: fix CI -Werror warnings (sign-compare, format, restrict alias, maybe-uninitialized)
The CUDA CI builds with -Werror -Wsign-compare -Wformat -Wrestrict
-Wmaybe-uninitialized. Address each:
- n_devices is size_t; change `int i; i < n_devices` to size_t in the
three init loops, and the matching GGML_LOG_INFO format from %d to %zu.
- ggml_cuda_ar_kernel was launched with sendbuf == recvbuf (in-place
reduction), so the __restrict__ qualifiers on those parameters were
technically UB. Drop __restrict__ from sendbuf and recvbuf; an A/B
sweep showed <0.6% perf delta (within noise) on Linux.
- The buf/src/dst pointer arrays in ggml_cuda_ar_allreduce and the
per-iteration arrays in ggml_cuda_ar_allreduce_copy_outer were
declared with size GGML_CUDA_MAX_DEVICES but the loop only writes
indices [0, n_devices); zero-initialise so the compiler sees the
tail elements as defined.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
now +6-8% ahead at ub=1024-4096.
Perplexity (32 chunks) matches NCCL bit-for-bit (3.4044 vs 3.4043).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* ggml-cuda: drop unused-function warning by guarding try_allreduce_nccl behind GGML_USE_NCCL
The only call site (in init_nccl) is already inside #ifdef GGML_USE_NCCL,
so the function is unreferenced in non-NCCL builds and trips
nvcc's -Werror=unused-function check. Move the guard from inside the
function body to around the entire definition.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ce
reduction), so the __restrict__ qualifiers on those parameters were
technically UB. Drop __restrict__ from sendbuf and recvbuf; an A/B
sweep showed <0.6% perf delta (within noise) on Linux.
- The buf/src/dst pointer arrays in ggml_cuda_ar_allreduce and the
per-iteration arrays in ggml_cuda_ar_allreduce_copy_outer were
declared with size GGML_CUDA_MAX_DEVICES but the loop only writes
indices [0, n_devices); zero-initialise so the compiler sees the
tail elements as defined.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
now +6-8% ahead at ub=1024-4096.
Perplexity (32 chunks) matches NCCL bit-for-bit (3.4044 vs 3.4043).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* cuda: fuse snake activation (mul, sin, sqr, mul, add)
Add ggml_cuda_op_snake_fused with F32 / F16 / BF16 templates. The
matcher recognizes the naive 5 op decomposition emitted by audio
decoders (BigVGAN, Vocos) for snake activation
y = x + sin(a*x)^2 * inv_b and rewrites it to a single elementwise
kernel.
Add test_snake_fuse comparing CPU naive vs CUDA fused across
F32 / F16 / BF16.
* cuda: address review feedback from @am17an
Use ggml_cuda_cast for F32/F16/BF16 conversions and rename
kernel_snake to snake_kernel to match upstream conventions.
* cuda: snake fusion fastdiv on T_len, Suggested-by: @am17an
* Update tests/test-backend-ops.cpp
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* cuda: snake fusion check add->type matches x->type
Address review feedback from @am17an
* cuda: snake fusion check add->type matches x->type
Moved for readability (equivalent)
Address review feedback from @am17an
---------
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* ggml: add graph_reused
* use versioning instead of reuse flag
* increment version with atomic
* use top bits for split numbering
* add assert
* move counter to ggml.c
* set uid in split_graph only
* fix windows
* address further review comments
* get next_uid rather than doing bit manipulation
* rename + add comment about uid
* CUDA: manage NCCL communicators in context
* add check that all backends are CUDA
* remove unused vector, limit init to > 1 GPUs
* fix warnings
* fix cuda device, cache allreduce
* 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>
* Introduced NVFP4 generic MMQ kernel
* Added extra FP8 guard, hope to solve ci HIP failure
* Rename tiles and use HIP_FP8_AVAILABLE
* Removed remaning FP8 straggler and added const int
* Const
* Removed DECL_MMQ_CASE artifact
* Removed newline
* Removed space after else
* Changed HIP FP8 NVFP4 conversion gate
* Added new line to bottom of mmq.cu 270
* Removed extra spaces
* Removed single space in front of else on line 814
* Added NVFP4 to generate cu script so HIP can see it, further tightened logic
* Include generated mmq-instance-nvfp4.cu
* Added NVFP4 mmq to HIP Check ignore list
* Update ggml/src/ggml-cuda/mmq.cuh
Changed to Q3_K tile to read MMQ_MMA_TILE_X_K_NVFP4
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml/src/ggml-cuda/mmq.cuh
Changed to Q3_K tile to read MMQ_MMA_TILE_X_K_NVFP4 in tile assert
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml/src/ggml-cuda/mmq.cuh
Added function name ending for end if
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Added function names to closing endif
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Optimize MOE GEMV kernel for BS > 1.
The previous MOE kernel for BS > 1 had too many thread blocks (nrows_x, nchannels_dst, ncols_dst), with very little work per block. block of (32, 4) was doing inner dot product for a single row.
New mul_mat_vec_q_moe kernel is dedicated for MoE multi-token kernel with grid (ceil(nrows_x/rpb), nchannels_dst), block (warp_size, ncols_dst). Each warp handles two rows independently with warp-level reduction only (no shared memory sync).
This change doesn't increase any compilation time as a single template instance is needed per type. This also simplifies the original GEMV kernel and gets rid of `is_multi_token_id` specialization.
* Remove em-dashes
* Cherry-pick changes from @am17an PR https://github.com/ggml-org/llama.cpp/pull/20885 to enable small_k optimization only for cases where it benefits
Increase max batch size for MMVQ kernels for MUL_MAT_ID to 8
* Make the max batch size for MOE GEMV kernel configurable based on GPU arch and datatype
---------
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
Added check for dst_t to cuda_cast template for float
Restored ggml_cuda_ue4m3_to_fp32, changed vecdot ints to int32ts
Added CUDART/HIP Check and HIP/fp8 include
Added NVFP4 to Test-backend-ops
Added hip_fp8_e4m3 to __nv_fp8_e4m3 typedef
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* ggml/hip: fix APU compatibility - soft error handling for hipMemAdviseSetCoarseGrain
On AMD APU/iGPU devices (unified memory architecture), hipMemAdviseSetCoarseGrain
returns hipErrorInvalidValue because the hint is not applicable to UMA systems.
The previous CUDA_CHECK() call treated this as a fatal error, causing crashes on
APU systems such as AMD Strix Halo (gfx1151).
Fix: treat hipMemAdviseSetCoarseGrain as an optional performance hint - call it
without error checking and clear any resulting error with hipGetLastError().
Also add pre-allocation debug logging (GGML_LOG_DEBUG) to help diagnose memory
issues on APU systems, and store totalGlobalMem in device info.
Context: AMD APUs on Windows are affected by a ROCm runtime bug that limits
hipMallocManaged to ~64GB regardless of available system RAM. A fix has been
submitted upstream: https://github.com/ROCm/rocm-systems/pull/4077
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* ggml/hip: remove unrelated changes, keep only hipMemAdviseSetCoarseGrain fix
---------
Co-authored-by: moonshadow-25 <moonshadow-25@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
