* cpu: add GGML_OP_COL2IM_1D
Add the overlap-add (scatter-add) step of a 1D transposed convolution.
A ConvTranspose1d factorizes as a GEMM followed by col2im: a weight
pre-permuted to [IC, K*OC] is contracted against the [IC, T_in] input
with mul_mat to produce a column matrix [K*OC, T_in], and col2im_1d
scatters those columns back into the [T_out, OC] signal, with
T_out = (T_in - 1)*s0 + K - 2*p0.
Keeping the contraction as a plain mul_mat leaves the heavy work on the
optimized (and quantizable) matmul kernels, so col2im_1d only does the
cheap overlap-add.
CPU uses a gather formulation parallelized over output channels,
supporting F32, F16 and BF16 with an F32 accumulator.
* tests: add backend coverage for GGML_OP_COL2IM_1D
Add test_col2im_1d next to the conv_transpose_1d cases, covering F32,
F16 and BF16 across eight geometries: the canonical kernel = 2*stride
DAC upsampling shape, overlap, no overlap, cropping (p0 = 1 and
p0 = stride/2), kernel < stride with zeroed gaps, kernel not a
multiple of stride, and a single column unfold.
Perf mode gets three real vocoder stage shapes reporting memory
bandwidth. max_nmse_err relaxes to 5e-4 for F16 and BF16.
* cpu: harden GGML_OP_COL2IM_1D
ggml_col2im_1d validates s0, oc, p0 and input contiguity at graph
build time, before the oc division, protecting every backend at once.
The kernel asserts the contiguity its flat indexing assumes and its
doc states the full output length including the crop term.
The kernel parallelizes over the time axis: the split stays balanced
down to OC = 1, where the previous channel split was single threaded.
Values are bit identical on the three real vocoder chains, two out of
three improve.
* tests: extend the GGML_OP_COL2IM_1D grid
The eval grid grows to eleven geometries: OC = 1 (mono output stage),
K = 1 with stride > 1 (sparse scatter, every gap position zeroed) and
a crop down to T_out = 2 where all the gather bounds act at once.
* tests: add col2im_1d equivalence test
tests/test-col2im-1d.cpp proves mul_mat + col2im_1d matches the
native ggml_conv_transpose_1d on the CPU backend, F32 bit exact, F16
and BF16 through casts of the column matrix. test-backend-ops cannot
cover this for a CPU only op since the CPU backend is its own
reference there.
* rpc: bump protocol patch version for GGML_OP_COL2IM_1D
GGML_OP_COUNT goes from 96 to 97 with the new op, which trips the
static_assert in ggml-rpc.h. Bump RPC_PROTO_PATCH_VERSION since the
op is appended and no existing op code shifts.
* Always export idle slots to RAM
Without this, a slot's VRAM cache may not be written to RAM. If this
slot happens to be busy then later on, this triggers needless
preprocessing in another slot.
* cont : clean-up
---------
Co-authored-by: Christoph Weiss <weiss@wsoptics.de>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
A SWA-only draft head (e.g. StepFun MTP) leaves the base sub-cache
empty, so its kq_mask buffer stays null and asserts at load. Guard
each mask on its own buffer in set_input and can_reuse, base and swa.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Only run webgpu CI on my fork
* Add webgpu only workflow
* handle buffer overlap case for concat operator
* restore build-webgpu.yml
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Run clang-format
* Update ggml/src/ggml-webgpu/wgsl-shaders/concat.wgsl
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* Only run webgpu CI on my fork
* Add webgpu only workflow
* Implement 2d workgroups for more operations
* fix
* Fix type
* Move back to global_invocation_id
This allows vec4 loads of the B elements. Also increase BK to 64 when this is
enabled. Neither of these alone is consistently faster, but together these give
a nice speedup.
In ggml-vulkan.cpp, we need to make sure the B matrix alignment and stride are
multiples of 4.
* 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
* common : relax sampler name matching
Currently, in some cases, the alternative names for samplers (like
`top-k` and `min-p` instead of the canonical `top_k` and `min_p`) are
not always recognized by the `common_sampler_types_from_names` function
in `common/sampling.cpp`.
This PR changes the signature of this function to remove the `bool
allow_alt_names` flag, and removes all occurences of the flag from call
sites. Therefore, the function will now always match all known names.
I also changed the logic of the function to unconditionally check the
provided sampler names against both the canonical and alternative names,
and to be case-insensitive.
This fixes an issue I was seeing wherein samplers specified in the
`llama-server` UI were not recognized as valid when the alternative
names were used.
* add more alt names
* cont. fix
* cast to unsigned char for correctness
* common : unify sampler name mapping
* annotate canonical vs. alt sampler name mappings per @CISC
* Update common/sampling.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* common : auto-generate sampler name aliases per @ngxson
* use merged map for matching
* use `.merge` instead of iterating
* nit: simplify comment
* nit: use insert everywhere, not index assignment
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Mistral explicitly sets `moe` and `llama_4_scaling` to `null` in
params.json, breaking `key in dict` checks during conversion. Replace
with `dict.get(key) is not None` where this matters.
Fixes `convert-hf-to-gguf.py --mistral-format Mistral-Medium-3.5-128B`
A fitted target context can end up smaller than the draft default, the
oversized assistant views then overflow the shared K/V tensors and trip
the ggml_view_4d size assert during graph reserve.
Pascal
Concedo
fiesh
Sigbjørn Skjæret
Yash Raj Pandey
ravel7524
Reese Levine
Masashi Yoshimura
Max Krasnyansky
Aldehir Rojas
Nikhil Jain
Xuan-Son Nguyen
Wagner Bruna
Georgi Gerganov
Jeff Bolz
Ruben Ortlam
Harkirat Gill
Neo Zhang
ddh0
David Friehs
Aman Gupta
konradmb