* ci : only trigger release jobs for tags
This commit removes the building of the release jobs on pushed to
master.
The motivation for this is that it can be confusing at the momement when
releasing that the push to master also triggers the release jobs but
the actual release will be skipped. With this change the release job is
only run when a tag is pushed which should result in a single Release
github actions job and make it easier to follow.
* ci : add GGML_NATIVE=OFF for ubuntu-22-gcc
Adds a `--version` option to whisper-cli that prints the library version
via `whisper_version()` and exits, plus a corresponding entry in the help
output. Mirrors the existing `-h`/`--help` handling.
Closes#608
* Make ggml_gated_delta_net take only the initial recurrent state (D, 1, n_seqs) and passes the snapshot count K as an op parameter instead of inferring it from state->ne[1].
Remove the padding hack and copy all emitted snapshots into the recurrent cache with a single strided ggml_cpy
* Make GDN changes in all backends. Address review comments.
* Fix CI build errors
* vulkan: add support for valve fp16 dot2 extension
* use macro for dot2 path choice
* properly check for the feature
* add dot_product abstraction to reduce preprocessor branching
* 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.
* 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
commit 8b92060 switched ct.convert() to mlprogram, but did not update
the --quantize path. quantize_weights() from
neural_network.quantization_utils only works with the legacy
neuralnetwork format. Running with --quantize crashed with:
Exception: MLModel of type mlProgram cannot be loaded just from the
model spec object. It also needs the path to the weights file.
Fix: pass compute_precision=ct.precision.FLOAT16 into ct.convert() when
--quantize is set. This matches the original intent of nbits=16 (F16
storage) without changing the quantization scheme or model accuracy.
Also fix the three boolean CLI flags (--encoder-only, --quantize,
--optimize-ane) to use a _str_to_bool helper so that both
--flag True
and
--flag False
parse correctly. The type=bool form accepted "False" as True because
bool("False") == True.
Remove the "currently broken" label from --optimize-ane: the ANE path
(WhisperANE with Conv2d attention and LayerNormANE) converts and loads
correctly with both PyTorch 2.x and coremltools 9.x.
* vulkan: add fwht support for Intel with shmem reduction
* don't use N as workgroup size
* disable subgroup shuffle on MoltenVK AMD
* disable fwht shader on Intel Windows due to driver bug
mmvq:
Port the ncols_dst optimization from ggml-cuda/mmvq.cu to SYCL.
Read weights once per dispatch instead of once per column.
Covers all standard quant types + reorder paths for Q4_0, Q8_0,
Q3_K, Q4_K, Q5_K, Q6_K. IQ types (except IQ4_XS) excluded due to
incompatible vec_dot signatures.
ggml-sycl:
The weight reorder was only bootstrapped on single-token mat-vec
(ne[1] == 1). Speculative / MTP verify issues only multi-column mat-vec,
so it never triggered the reorder and ran on the slower non-reorder
kernel. Bootstrap it on small multi-column batches (ne[1] <= 8) too.
* ggml: vectorize ggml_vec_dot_q4_1_q8_1 with WASM SIMD128
Optimize the inner loop of ggml_vec_dot_q4_1_q8_1_generic using
WASM SIMD128 intrinsics, gated behind #ifdef __wasm_simd128__ so
non-wasm builds are completely unaffected.
Approach:
- single wasm_v128_load covers all 32 packed 4-bit weights
- nibbles unpacked via AND/SHR into two u8x16 registers
- widened to i16 before multiply (WASM SIMD has no i8*i8 instruction)
- 4x wasm_i32x4_dot_i16x8 calls accumulate all 32 element pairs
- horizontal reduce via 4x wasm_i32x4_extract_lane
Benchmark (node v25, emcc -O3 -msimd128, 64 blocks x QK8_1=32,
200k iterations):
| impl | ns/call | speedup |
|--------|---------|---------|
| scalar | 880.7 | 1.00x |
| simd | 257.8 | 3.42x |
Correctness verified against scalar reference across 10 random seeds
with exact output match.
* ggml: move q4_1_q8_1 WASM SIMD implementation to wasm backend
Relocate the SIMD128 implementation of ggml_vec_dot_q4_1_q8_1 to ggml/src/ggml-cpu/arch/wasm/quants.c to follow architecture-specific layout. Restore the generic implementation in ggml/src/ggml-cpu/quants.c.
Move for loop in the else block.
* ggml: use generic q4_1_q8_1 fallback in wasm backend
Kitaiti Makoto
Daniel Bevenius
Rum Nguyen
Georgi Gerganov
ZihaoMu
shaofeiqi
Jeff Bolz
Winston Ma
Kevin Liu
Gaurav Garg
Oliver Simons
Ruben Ortlam
Pascal
Yash Raj Pandey
ravel7524
Reese Levine
Masashi Yoshimura
Nikhil Jain
Christopher Albert
Harkirat Gill
Xuan-Son Nguyen
lhez
Charles Xu
Mason Milburn
Kartik Sirohi