ggml: fix conv_1d result layout for batched input ( #28738 )
ggml: fix conv_1d result layout for batched input
The GEMM behind conv_1d folds the batch into its rows, so the product
sits in memory as [OL, N, OC] while the reshape claims [OL, OC, N].
The shape is right and the contents are transposed, so nothing fires:
no assert, no abort, the graph just runs and the channels come out
interleaved with the batches. With N == 1 the two readings describe
the same buffer, which is why the three vocoders in the tree never
hit it, they all squeeze to 2d right after the call.
Reshape to the real layout and permute OC and N back. The N == 1 path
keeps its direct reshape, so the current callers build the same graph
and pay for no extra copy.
tests/test-conv-1d.cpp compares one batched convolution against N
single batch convolutions built from the same weight and the same
input slices. Five of the seven geometries fail before this change and
all seven pass after. N == 1 and OC == 1 pass either way, since
swapping two dimensions when one of them is 1 changes nothing.
The batched path matters to downstream audio projects where several
synthesis lanes share a single graph. Nothing in mtmd batches a
vocoder today, each one streams one flow and carries its left context
between calls, but the op is correct now if one of them grows a lane
dimension.
ggml: build the same conv_1d graph for every batch size
Address review from ggerganov: drop the N == 1 branch of ggml_conv_1d.
The GEMM result is always reshaped to [OC, N, OL] and permuted back,
as ggml_conv_2d does, so every batch size builds the same graph. For
N == 1 this adds a cont on an already ordered tensor.
Website:
https://llama.app
Attestations:
https://github.com/ggml-org/llama.cpp/attestations/56061987
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