* Optimize Metal Tensor API usage for matmul2d
Separates the Metal Tensor API (matmul2d) path in kernel_mul_mm into its own standalone kernel, gated by GGML_METAL_HAS_TENSOR.
The legacy simdgroup_matrix kernel is preserved under #else.
Previously both paths were interleaved via #ifdef blocks within a single kernel, forcing the tensor path to share the legacy kernel's data layout and threadgroup memory scheme. Splitting the kernel enabled memory and dispatch optimizations that weren't possible when the two paths shared code structure.
* cont : cleanup
* cont : cleanup
* cont : cleanup
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* nix: support unified apple-sdk
* Impl roll op for Metal
* Revert "nix: support unified apple-sdk"
This reverts commit abfa473360471532c547de8b202c780507924d4b.
* update ops.md
* update op docs
* initial Q1_0 Metal backend
* tuning q1_0 metal kernels
* add Q1_0 to test-backend-ops
* add Q1_0<->F32 copy test
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* metal:add conv_3d backend
Rebased with master and resolved conflicts.
* Resolved issues related to changes in variable names
* kernel void kernel_upscale_bilinear_f32 was missing in my branch, added back, should pass all tests now
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : enable chunked fused GDN path
* models : avoid Q and K repeats when using fused GDA
* cont : fix comment
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* cont : fix the fix
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* cont : fix
* metal : add GDN kernel (#20361)
* metal : add Metal backend for GGML_OP_GATED_DELTA_NET
Add a fused Metal kernel for the gated delta net recurrence op
(#19504), enabling GPU-accelerated inference for DeltaNet-based
models (Qwen3.5, etc.) on Apple Silicon.
Supports both GDA (scalar gate) and KDA (per-row gate) modes
with head_size 64 and 128. Unsupported configurations (head_size
32, non-contiguous tensors) gracefully fall back to CPU.
Performance: Qwen3.5-0.8B Q4_K_M on M4 Max
tg128: 170 -> 213 t/s (+25%)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* metal : validate contiguity of all input tensors in supports_op
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* metal : add algorithm equivalence comment for GDA decay path
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* cont : unslop + optimize
* cont : clean-up
---------
Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* CUDA: AR gated delta net improvements (#20391)
* Add FastDiv to gated_delta_net_cuda
* Shard columns across warps
This reduces register pressure (avoids spill for S_v = 128) and gives
the warp-scheduler more CTAs to schedule (thus hiding data-access
latencies).
* Remove unneded include in gated_delta_net.cu
* Improve comments
* Apply code-formating
* Make sharding HIP-compatible
1. Use ggml_cuda_get_physical_warp_size() to determine warp size flexibly
2. Add test with partial warp to test sum reduction on CUDA
* Remove fastdiv_s64, as we can treat neqk1 and rq3 as uint32_t
* Rename variables
* Enable GDN also for prefill, move TODO for chunked_GDN
* Actually remove the TODO from 206890897546bd16602c3b79394fd5ea09ef199f
* Get warp size at runtime
warp_size is not known at compile time in hip host code.
* Don't expose ggml_cuda_get_physical_warp_size on host
---------
Co-authored-by: uvos <devnull@uvos.xyz>
* llama : refactor llm_build_delta_net_base API
---------
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Oliver Simons <osimons@nvidia.com>
Co-authored-by: uvos <devnull@uvos.xyz>
Enable mul_mv_ext small-batch kernels (BS 2-8) for BF16, Q2_K,
and Q3_K quantization types. These types previously fell through
to the slower single-row mul_mv path.
BF16 uses the float4 dequantize path (like F16). Q2_K and Q3_K
use the float4x4 K-quant path (like Q4_K/Q5_K/Q6_K).
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* add count equal for metal
* remove trailing whitespace
* updated doc ops table
* changed shmem to i32
* added multi tg and templating
* removed BLAS support from Metal docs
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* add memset to set dst to 0
* metal : cleanup
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add a batched version of ssm_conv
This was done using Claude Code. It found a number of optimizations around
how the threads were organized, resulting in a huge performance boost!
Branch: Mamba2SSD
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Optimized SSM_SCAN kernel for metal
This used Claude Code and resulted in a modest performance improvement
while maintaining correctness.
Branch: Mamba2SSD
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* test: Add test-backend-ops perf tests for SSM_CONV
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* test: Real representitive tests for SSM_CONV
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Use function constant for ssm_conv batch size
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* test: backend op tests for ssm_scan from granite4 1b-h
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: remove commented out templates
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: float4 version of ssm_conv_batched
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add missing ggml_metal_cv_free
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat(wip): Port initial TRI impl from pervious work
The kernel does not work and is not optimized, but the
code compiles and runs, so this will be the starting point
now that the core op has been merged.
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove argument for constant val override
This was added in the original draft, but later removed. With this, the
kernel now passes tests.
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Move the ttype conditional to templating to avoid conditional in kernel
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Type fixes
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add softplus for metal
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add EXPM1 for metal
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add FILL for metal
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Branchless version of tri using _ggml_vec_tri_cmp as a mask
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unused arguments
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Use select instead of branch for softplus non-vec
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* optimise GGML_OP_SUM
* add non-contiguous tests by permuting the input
* change tests to require full contiguity of OP_SUM
* cuda : add check GGML_OP_SUM
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* scaffold to support opt step adamw on metal (not written so far)
* add opt-step-adamw kernel for metal
* pass op->src[4] as a separate buffer to the pipeline
* add bounds check to opt-step-adamw kernel
* complete scaffold for GGML_OP_SUM
* naive GGML_OP_SUM kernel
* remove unwanted comment
* change OP_SUM capability gate
* Add has_simdgroup_reduction to both ops to pass CI
* metal : better unroll in the FA kernels
* metal : index FA blocks
* tests : restore [no ci]
* metal : prevent division by zero in FA kernels
* metal : fix -INF detection logic
* metal : pad K, V and Mask when needed
* cont : simplify
* cuda : add TODO about KV padding requirement
* metal : add comments
* metal : remove mask padding requirement
* metal : ssm_scan minor opts
* metal : get_rows optimize
* metal : cpy optimize
* metal : ssm_conv opt
* metal : ssm_scan simplify
* metal : ssm_Scan opt
* metal : support mul_mm with src1->type == GGML_TYPE_F16
* metal : support mul_mm_id with src1->type == GGML_TYPE_F16
[no ci]
* metal : mul_mm support ne00 % 32 != 0
* metal : support mul_mm_id with ne00 % 32 != 0
* cont : remove unnecessary unrolls
* cont : simplify data loading
* metal : optimize mul_mm when output bounds checks are not needed
Kaloyan Nikolov