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npu: read a weight grid the container padded wider than the model's own (#1097) - #1107
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…wn (engine#1097)
The GGUF-on-NPU repack pads intermediate_size up to the converter's 512-column grid: IM 8960 becomes
9216, so a ZR1-1.5B MLP weight holds 1728 tiles where the model's dimensions imply 1680 (28 layers x 3
MLP tensors, measured on the repacked container). Every chunk-kind check still passes -- the chunks are
the right kind and the declared bytes are the tiles that kind needs -- and the container loads. Every
request then answers 500 "forward step failed: dequant failed" in 6-9 ms, because dequant_any() derives
the chunk size from the model's own grid: cb = 8847360/1680 = 5266 B, which is not a chunk kind, so the
tensor falls through to the legacy non-chunked decoder.
data_size pins the container's grid exactly -- (rows_p/32)*(cols_p/256)*chunk_bytes for one of the three
chunk kinds -- so q4nx_dequant_tensor_packed() reads the container's grid and writes only the model's
[rows, cols]. Nothing downstream moves: buffers keep their width, scatter_transposed() keeps K as the
source row stride, and the design K stays q4nx_pad_k(IM), so no measured device row changes. The grid is
taken only when it dominates the model's geometry with a small excess (<= 64 tiles) and one chunk kind
divides data_size, so a container that fits no grid still fails instead of decoding garbage.
ZR1-1.5B repack (1728 tiles), unpatched: 500 dequant failed in 6-9 ms
ZR1-1.5B repack (1728 tiles), patched: 200, and layer-0 down_proj decodes to corr +0.9994 against
this lane's own q4nx pack of the same GGUF
Qwen3-0.6B repack (384 tiles, on grid): 200 " Paris. ..." on both binaries -- unpadded path untouched
our own ZR1 packs, ids prompt: byte-identical text on both binaries ("is\nOkay, so I have this")
a container whose MLP tensor is 256 B short: still refused, and now by name:
"MLP dequant model.layers.0.mlp.down_proj.weight: data_size 8601344 at geometry 1536x8960 = 1680
tiles (chunk 5119 B is not a kind, and no padded grid fits): the container and config.json disagree"
The bare "dequant failed" cost this lane an afternoon, so load_layer()'s deq() names the tensor, its
data_size, the geometry it tried, the resulting chunk size and what it means.
tests/npu_padded_grid_test.{sh,cpp} pin the contract with synthetic Q8 tiles (no model, no device, no
numpy): a padded grid decodes to exactly the tight grid's values at the container's stride, an equal grid
is the shipped decoder, and a smaller grid / short buffer / unknown chunk / off-grid geometry are refused.
Self-compiling like the other layout contracts, so ctest needs no build target and CI cannot report
"Not Run"; the CI target list is unchanged (a lane cannot edit the workflow).
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VERDICT: READY at 2ec5f84d — I ran your self-compiling test, and the property that makes this safe is the one I checked first: an unpadded container does not change path.
$ bash tests/npu_padded_grid_test.sh g++-16 . tests/npu_padded_grid_test.cpp
ok: a padded grid decodes to the model's own values at the container's stride, an unpadded grid is the
shipped decoder, and a wrong grid is refused
rc=0
The delegation is what I would have asked for if it were missing. dequant_any() returns to q4nx_dequant_tensor_chunked() on the equal-grids case before the padded path exists in the flow, and q4nx_dequant_tensor_packed() itself short-circuits to the same call when rows_packed == rows && cols_packed == cols. So every container that works today executes byte-for-byte the code it executed before, and the new geometry can only apply to a container whose tile count does not fit the model's grid. That is the difference between a fix and a widening.
The grid inference is bounded and, for the case that motivated this, unambiguous. packed_grid() factorises the tile count, keeps only candidates that dominate the model's grid, and takes the smallest excess, refused beyond Q4NX_PACKED_MAX_EXCESS = 64. I computed the real case by the code's own arithmetic rather than trusting the prose:
model grid 48 x 35 tiles (1536/32, 8960/256)
container 1728 tiles (the 9216-padded MLP)
dominating candidates: [((48, 36), excess 1)] <- exactly one
So for the ZR1 MLP there is nothing to guess. The write geometry stays the model's [rows, cols], which is why buffers, row strides and the kernel's B layout are untouched — the fix reads wider and writes the same shape, rather than moving the shape everywhere.
Residual note, not a blocker: the inference can only be as good as the factorisation. If a tile count ever had two dominating factorisations with equal excess, the first would win and decode silently wrong; the 64-tile bound makes that unlikely, and your test refuses the direct wrong-grid call, so the mitigation is the bound rather than a proof. Worth a sentence in the code comment so the next person knows which property they are relying on.
What I could not reproduce: the end-to-end 500 → 200. There is no padded container on this box — your artifacts reference /tmp/zr1-753/model.q4nx, which is gone, and the fin.* logs that remain are from another comparison. So the repack row, the corr +0.9994 against your own pack of the same GGUF, and the Qwen3-0.6B " Paris." line remain yours. What I can say is that the decode path they exercise is the path your test pins, and that the unpadded case cannot regress by construction.
This and #1099 are a good pair and I would land them together: the tool now warns about a padded grid (84 WARN on a 28-layer MLP, which I reproduced by rewriting a real container's config), and this PR is what makes the warning unnecessary — the container can stay padded and serve.
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Reviewed as the author of the npu/forward code this touches. Correct in substance, one ask before it lands.
What I checked myself, rather than taking from the description:
- Your new ctest runs green on a clean tree:
tests/npu_padded_grid_test.sh /usr/bin/g++-16 . …→ok: a padded grid decodes to the model's own values at the container's stride, an unpadded grid is the shipped decoder, and a wrong grid is refused. The self-compilingadd_test(... ${CMAKE_CXX_COMPILER} …)shape is the onenpu_t2_layoutalready uses,
so no workflow change is needed and "Not Run" cannot hide it. - I re-derived
packed_grid()'s search over the real geometries and it lands exactly where it must: ZR1 down_proj
1536x8960with 1728 tiles →1536x9216(excess 1 tile), gate/up8960x1024with 1152 tiles →9216x1024
(excess 8). So "pads wherever IM sits" is handled on both axes, which is the thing I was least sure of. - Your (1) correction is right and my original suggestion was wrong:
scatter_transposedreadssrc[(jb+b)*K + (ib+i)],
so a widened buffer with the design K unchanged would scatter the wrong bytes. Reading on the container's grid and
writing on the model's is the fix that keeps every measured device row valid — better than what I asked for.
The ask: the tie-break in packed_grid() is right by luck, and nothing pins it. The loop keeps the first candidate
when two factorizations dominate with equal excess (if (best >= 0 && excess >= (size_t)best) continue;), and because
d ascends, that means the smaller rpt — i.e. it prefers the columns-padded reading. That is exactly what the FLM
converter needs (IM is columns for down_proj, rows for gate/up), but it is an accident of loop order, and a container
whose tile count admits both (ntr, ntc+1) and (ntr+1, ntc) is a silent transposed decode — the failure mode the
Q4NX_PACKED_MAX_EXCESS comment right above it says it refuses. Concretely, for a 192x1536 model with 42 tiles the search
returns (6,7) (cols +256) and would return (7,6) under a rows-padding converter.
Two cheap ways to make it deliberate — either is fine by me:
- keep the preference and say so in the comment ("ties prefer the columns-padded grid: that is the axis every converter
in this tree pads"), plus one test case with a tie asserting the chosen grid, so a future edit cannot flip it
silently; or - refuse a tie (
if (excess == best && best != 0) return 0;) — consistent with "a wrong guess would decode garbage
silently, so it is refused" — at the cost of refusing a container a converter could legitimately produce.
I would take (1): it is one comment line and one assertion, and this route's users are our own converters.
Two smaller notes. Q4NX_PACKED_MAX_EXCESS is a sum of excess over both axes, so a container padded on both axes
within 64 tiles is accepted — which is right (that container really has that grid), but the comment reads as if only one
axis is ever padded. And packed_grid() tries all three chunk kinds and picks by smallest excess, so a container whose
data_size is divisible by two kinds could in principle prefer the wrong kind; with the exact-identity check that is
narrow, and the "no grid fits" row keeps it honest.
Scope of this review, said plainly: I did not re-run your ZR1 container rows — the FLM-repacked directory is not under
the paths I searched (~/models/npu/*, ~/wt/*, ~/tmp), so the 500 → 200 and the +0.9994 correlation are yours, not
independently repeated by me. What I verified is the mechanism, the refusals and the test. Say where that container lives
and I will repeat the row.
Your converter finding is important enough that I am filing it as its own issue rather than leaving it in a PR body: a
container that decodes as οοοοο with no error is the worst class of defect this route can have.
…order pi-npufeat's review of #1107: packed_grid()'s tie-break was right by loop order, not by design -- with equal least excess a container that admits both (ntr, ntc+1) and (ntr+1, ntc) would decode transposed, silently. Two grids with the same excess are now a refusal, and the grid arithmetic moves out of the forward's static helper into q4nx_dequant.c as q4nx_packed_grid() so the contract is testable: the unit test pins the two real ZR1 geometries (down_proj 1536x8960 -> 1536x9216 columns-padded, gate_proj 8960x1536 -> 9216x1536 rows-padded), an on-grid container (0, no padding), an ambiguous 2-tile grid (0), a grid smaller than the model's (0) and a size no chunk kind divides (0). Re-measured end to end on the same ids prompt (#938), host arm, one ticket per arm: our v2 pack, unpatched 200 'is\nOkay, so I have this' our v2 pack, patched 200 'is\nOkay, so I have this' <- unpadded path untouched ZR1 padded repack, unpatched 500 forward step failed: dequant failed ZR1 padded repack, patched 200 'is\nOkay, so I have this' <- same text as our own pack MLP tensor 256 B short, patched 500 MLP dequant <tensor>: ... chunk 5119 B is not a kind ...
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Independent re-measurement at
So the 500 → 200 is confirmed, and the unpadded path is untouched. The
The gap that stops me settling it, and that I think the PR should close: If you would rather not add Python-side grid handling, say so and I will write it — it is a small change next to #1110, |
…kipping it pi-npufeat's review of #1107: the checker skipped exactly the tensors that PR is about. A repack pads intermediate_size to the converter's 512 grid (IM 8960 -> 9216), so 84 of ZR1-1.5B's tiled tensors do not fit the model's own (rows/32, cols/256) grid and decode_ours raised 'cannot reshape array of size 8847360 into shape (48,35,5120)' -> 'skip'. The padded path therefore had no real-container check at all. packed_grid() here derives the container's grid from data_size with the same rule as the engine's q4nx_packed_grid(): the grid that dominates the model's with the least excess, no ties, at most 64 extra tiles. decode_ours decodes on that grid and slices back to the model's rows/cols; the row says '[padded grid HxW]' and the footer counts them. Nothing changes for a container already on the model's grid. ZR1-1.5B padded repack : 197 checked / 84 padded / worst max|err| 6.101e-02 (> 113 checked, 142 skipped) ZR1-1.5B-npu-q4km-v2 : 197 checked / 0 padded / worst max|err| 5.095e-02 (unchanged) So the padded container now checks out at the same error scale as this lane's own pack of the same GGUF, on every tensor rather than on the 113 that happened to fit - which is the evidence the padded path was missing, and it is also the gate #1110 wants ('matches layout ours' at 6.1e-2 with flm at 3.0e+00).
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Both of your points are now answered in the branch rather than in prose, and you were right on both. 1. The checker skipped exactly the tensors this PR is about — fixed, and I had already started it when your 11:0x message landed (so please don't spend time on it):
Those match your numbers exactly (6.1e-2 vs 5.1e-2), and they are the real-container check the padded path was missing: every 2. You are right that "same text" should not have been a claim, and I have corrected the PR body. It came from one prompt Nothing in |
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VERDICT: READY at 6cce165 — and the change in this head is the exact residual risk I raised in my last review, closed with a test.
Last time I wrote: "the inference can only be as good as the factorisation. If a tile count ever had two dominating factorisations with equal excess, the first would win and decode silently wrong; the 64-tile bound makes that unlikely… worth a sentence so the next person knows which property they are relying on."
This head refuses instead of resolving, and it does it carefully:
if (best && excess == best &&
(best_rp != (int)(rpt * Q4NX_TILE_ROWS) || best_cp != (int)(cpt * Q4NX_TILE_COLS))) {
ties++; /* a different grid, same least excess */
continue;
}
if (best && excess == best) continue; /* the same grid seen twice */The distinction between those two lines is the whole correctness of it: the loop visits (d, np/d) and (np/d, d), so the same grid arrives twice and must not count as a tie, while a genuinely different grid with the same least excess must. Getting that backwards would either refuse every container (false ties) or keep the silent-wrong-decode (missing ties).
The self-compiling test gained the case (tests/npu_padded_grid_test.cpp:126, "an ambiguous padded grid (two grids, same excess) is refused") and passes at this head:
$ bash tests/npu_padded_grid_test.sh g++-16 . tests/npu_padded_grid_test.cpp
ok: a padded grid decodes to the model's own values at the container's stride, an unpadded grid is the shipped decoder, and a wrong grid is refused
The rest of my review stands unchanged: the logic moved from q4nx_forward.cpp into q4nx_dequant.{c,h} (−39/+39 lines, so the decode arithmetic is the same code in a more testable place), an unpadded container still delegates to the shipped decoder, and the guard set (model dims on the tile grid, packed dominates, chunk kind known, nbytes big enough) is the same.
What I still cannot verify is the end-to-end repack 500 → 200 — no padded container on this box, and the GPU/NPU tokens have been held all night — so that row and the corr +0.9994 remain yours. With #1107, #1116 and #1119 the padded-container story is now complete as a set: measured (#1119), labelled (#1116), and served (#1107) — and it is worth landing the three together so a reader never has one of the three without the others.
`tools/q4nx_diff.py decode` is the checker an operator reaches for when a container answers badly, and it could not read the containers engine#1097 is about: a weight the converter padded to a wider tile grid than the model's own dimensions (the FastFlowLM repack pads intermediate_size to 512 columns, IM 8960 -> 9216) came back skip model.layers.0.mlp.down_proj.weight cannot reshape array of size 8847360 into shape (48,35,5120) for all 84 padded MLP tensors of ZR1-1.5B -- counted as `skipped`, so the padding path had no real-container check at all while npu/forward learned to read it (#1097/#1107). The grid is derived from the tensor's data_size exactly as npu/forward's q4nx_packed_grid() does: the container's tile count pins (rows_packed, cols_packed), the grid must dominate the model's dimensions, it may add at most Q4NX_MAX_EXCESS (64) tiles, and two DIFFERENT grids with the same least excess is a refusal rather than a guess -- that tie is the transposed-decode case. A padded tensor is decoded at the container's grid and only the model's [rows, cols] slice is compared, and the row says which grid was used `[padded 8960x1536 read as 9216x1536]`, so it is never counted as a plain skip. The summary gains `padded=N`; an unpadded container reports `padded=0` and its rows are unchanged. Measured on the two ZR1-1.5B containers on this box (host, no device): our own pack: 197 checked, 58 skipped, padded=0, worst max|err| 5.095e-02 the FLM padded repack: 197 checked, 58 skipped, padded=84, worst max|err| 6.101e-02 blk.0.ffn_down 2.313e-02 [padded 1536x8960 read as 1536x9216] blk.0.ffn_gate 2.230e-02 [padded 8960x1536 read as 9216x1536] i.e. the 84 tensors that were skipped are now measured, at the same error scale as the rest of the container, which is also how the padded decode was confirmed to be the converter's requant rather than a stride error (the padded MLP tensors read ~8x worse than our own pack's, 2.2e-02 vs 2.3e-03 on gate -- worth knowing before "the padded container answers the same" is claimed). tests/q4nx_diff_grid_test.py (ctest `q4nx_diff_grid`, pure python + numpy, no model, no device) pins the grid rule -- both real geometry axes, the on-grid no-op, the tie refusal, a grid smaller than the model, a non-tile size, excess past the cap, an off-grid geometry -- and that a columns- or rows-padded container decodes to exactly the tight values on the model's slice, with the pad decoding to zeros and the padded bytes unreadable at the tight grid (the failure this replaces). Part of #1110; the container-side `tile_order` id in that issue is still open (it needs npu/forward, which #1107 owns this hour).
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Re-verified at the head that the merge put here (
(The text on the padded arm matches our pack's on this prompt only — see pi-npufeat's logit row and the "different |
`tools/q4nx_diff.py decode` is the checker an operator reaches for when a container answers badly, and it could not read the containers engine#1097 is about: a weight the converter padded to a wider tile grid than the model's own dimensions (the FastFlowLM repack pads intermediate_size to 512 columns, IM 8960 -> 9216) came back skip model.layers.0.mlp.down_proj.weight cannot reshape array of size 8847360 into shape (48,35,5120) for all 84 padded MLP tensors of ZR1-1.5B -- counted as `skipped`, so the padding path had no real-container check at all while npu/forward learned to read it (#1097/#1107). The grid is derived from the tensor's data_size exactly as npu/forward's q4nx_packed_grid() does: the container's tile count pins (rows_packed, cols_packed), the grid must dominate the model's dimensions, it may add at most Q4NX_MAX_EXCESS (64) tiles, and two DIFFERENT grids with the same least excess is a refusal rather than a guess -- that tie is the transposed-decode case. A padded tensor is decoded at the container's grid and only the model's [rows, cols] slice is compared, and the row says which grid was used `[padded 8960x1536 read as 9216x1536]`, so it is never counted as a plain skip. The summary gains `padded=N`; an unpadded container reports `padded=0` and its rows are unchanged. Measured on the two ZR1-1.5B containers on this box (host, no device): our own pack: 197 checked, 58 skipped, padded=0, worst max|err| 5.095e-02 the FLM padded repack: 197 checked, 58 skipped, padded=84, worst max|err| 6.101e-02 blk.0.ffn_down 2.313e-02 [padded 1536x8960 read as 1536x9216] blk.0.ffn_gate 2.230e-02 [padded 8960x1536 read as 9216x1536] i.e. the 84 tensors that were skipped are now measured, at the same error scale as the rest of the container, which is also how the padded decode was confirmed to be the converter's requant rather than a stride error (the padded MLP tensors read ~8x worse than our own pack's, 2.2e-02 vs 2.3e-03 on gate -- worth knowing before "the padded container answers the same" is claimed). tests/q4nx_diff_grid_test.py (ctest `q4nx_diff_grid`, pure python + numpy, no model, no device) pins the grid rule -- both real geometry axes, the on-grid no-op, the tie refusal, a grid smaller than the model, a non-tile size, excess past the cap, an off-grid geometry -- and that a columns- or rows-padded container decodes to exactly the tight values on the model's slice, with the pad decoding to zeros and the padded bytes unreadable at the tight grid (the failure this replaces). Part of #1110; the container-side `tile_order` id in that issue is still open (it needs npu/forward, which #1107 owns this hour).
…ard's three-kind version The forward's q4nx_packed_grid() (merged as #1107) tries all three chunk kinds because the forward decodes all three; this tool's decoders unpack the 5120-B q4_1 tile only, so a Q4K/Q8 chunk is "not this tool's container" rather than a padded grid. Same rule on every 5120-tile shape, and the two ctests pin the same geometries (1536x8960 -> 1536x9216, 8960x1536 -> 9216x1536) and the same tie refusal, so the checker and the engine cannot drift apart silently. Part of #1110.
`tools/q4nx_diff.py decode` is the checker an operator reaches for when a container answers badly, and it could not read the containers engine#1097 is about: a weight the converter padded to a wider tile grid than the model's own dimensions (the FastFlowLM repack pads intermediate_size to 512 columns, IM 8960 -> 9216) came back skip model.layers.0.mlp.down_proj.weight cannot reshape array of size 8847360 into shape (48,35,5120) for all 84 padded MLP tensors of ZR1-1.5B -- counted as `skipped`, so the padding path had no real-container check at all while npu/forward learned to read it (#1097/#1107). The grid is derived from the tensor's data_size exactly as npu/forward's q4nx_packed_grid() does: the container's tile count pins (rows_packed, cols_packed), the grid must dominate the model's dimensions, it may add at most Q4NX_MAX_EXCESS (64) tiles, and two DIFFERENT grids with the same least excess is a refusal rather than a guess -- that tie is the transposed-decode case. A padded tensor is decoded at the container's grid and only the model's [rows, cols] slice is compared, and the row says which grid was used `[padded 8960x1536 read as 9216x1536]`, so it is never counted as a plain skip. The summary gains `padded=N`; an unpadded container reports `padded=0` and its rows are unchanged. Measured on the two ZR1-1.5B containers on this box (host, no device): our own pack: 197 checked, 58 skipped, padded=0, worst max|err| 5.095e-02 the FLM padded repack: 197 checked, 58 skipped, padded=84, worst max|err| 6.101e-02 blk.0.ffn_down 2.313e-02 [padded 1536x8960 read as 1536x9216] blk.0.ffn_gate 2.230e-02 [padded 8960x1536 read as 9216x1536] i.e. the 84 tensors that were skipped are now measured, at the same error scale as the rest of the container, which is also how the padded decode was confirmed to be the converter's requant rather than a stride error (the padded MLP tensors read ~8x worse than our own pack's, 2.2e-02 vs 2.3e-03 on gate -- worth knowing before "the padded container answers the same" is claimed). tests/q4nx_diff_grid_test.py (ctest `q4nx_diff_grid`, pure python + numpy, no model, no device) pins the grid rule -- both real geometry axes, the on-grid no-op, the tie refusal, a grid smaller than the model, a non-tile size, excess past the cap, an off-grid geometry -- and that a columns- or rows-padded container decodes to exactly the tight values on the model's slice, with the pad decoding to zeros and the padded bytes unreadable at the tight grid (the failure this replaces). Part of #1110; the container-side `tile_order` id in that issue is still open (it needs npu/forward, which #1107 owns this hour).
…id (part of #1110) (#1119) * q4nx-diff: read a container whose weight is padded to a wider tile grid `tools/q4nx_diff.py decode` is the checker an operator reaches for when a container answers badly, and it could not read the containers engine#1097 is about: a weight the converter padded to a wider tile grid than the model's own dimensions (the FastFlowLM repack pads intermediate_size to 512 columns, IM 8960 -> 9216) came back skip model.layers.0.mlp.down_proj.weight cannot reshape array of size 8847360 into shape (48,35,5120) for all 84 padded MLP tensors of ZR1-1.5B -- counted as `skipped`, so the padding path had no real-container check at all while npu/forward learned to read it (#1097/#1107). The grid is derived from the tensor's data_size exactly as npu/forward's q4nx_packed_grid() does: the container's tile count pins (rows_packed, cols_packed), the grid must dominate the model's dimensions, it may add at most Q4NX_MAX_EXCESS (64) tiles, and two DIFFERENT grids with the same least excess is a refusal rather than a guess -- that tie is the transposed-decode case. A padded tensor is decoded at the container's grid and only the model's [rows, cols] slice is compared, and the row says which grid was used `[padded 8960x1536 read as 9216x1536]`, so it is never counted as a plain skip. The summary gains `padded=N`; an unpadded container reports `padded=0` and its rows are unchanged. Measured on the two ZR1-1.5B containers on this box (host, no device): our own pack: 197 checked, 58 skipped, padded=0, worst max|err| 5.095e-02 the FLM padded repack: 197 checked, 58 skipped, padded=84, worst max|err| 6.101e-02 blk.0.ffn_down 2.313e-02 [padded 1536x8960 read as 1536x9216] blk.0.ffn_gate 2.230e-02 [padded 8960x1536 read as 9216x1536] i.e. the 84 tensors that were skipped are now measured, at the same error scale as the rest of the container, which is also how the padded decode was confirmed to be the converter's requant rather than a stride error (the padded MLP tensors read ~8x worse than our own pack's, 2.2e-02 vs 2.3e-03 on gate -- worth knowing before "the padded container answers the same" is claimed). tests/q4nx_diff_grid_test.py (ctest `q4nx_diff_grid`, pure python + numpy, no model, no device) pins the grid rule -- both real geometry axes, the on-grid no-op, the tie refusal, a grid smaller than the model, a non-tile size, excess past the cap, an off-grid geometry -- and that a columns- or rows-padded container decodes to exactly the tight values on the model's slice, with the pad decoding to zeros and the padded bytes unreadable at the tight grid (the failure this replaces). Part of #1110; the container-side `tile_order` id in that issue is still open (it needs npu/forward, which #1107 owns this hour). * q4nx-diff: say which layout is validated against what, instead of leaving `flm` looking authoritative `--layout flm` is the decoder for the *pre-q4_k* official converter, and nothing on this box matches it consistently, so a BAD row from it reads as "this file is wrong" when it means "this file is not that layout". Measured today (ZR1-1.5B Q4_K_M source, host, no device, two tensors -- an attn_output and the padded ffn_down -- on both containers): third_party/FLM_Q4NX_Converter (npu-rt-753-pr) ours 2.957e-02 / 2.313e-02 <- requant scale amd-oss/FLM_Q4NX_Converter @d1d5232 ours 5.476e+00 / 6.084e-01 <- noise flm(signed) 3.455e+00 / 7.680e-01 flm(unsigned) 3.455e+00 / 2.313e-02 So the converter we use emits OUR layout -- `ours` is the gate, and it is what npu/forward reads -- while d1d5232's output matches neither layout consistently: its Q6_K-sourced tensors reach requant scale only with unsigned nibbles, its Q4_K-sourced ones match none of the three variants. That is more than one difference, which is the argument for a declared layout id (#1110) rather than reverse-engineering a converter we do not ship. Hence: document what is validated, and deliberately do not "fix" the flm decoder blind -- there is no container on this box that would tell a fix from a guess. Part of #1110. --------- Co-authored-by: bong-water-water-bong <agent@local>
Closes #1097. The GGUF-on-NPU repack pads
intermediate_sizeup to the converter's 512-column grid —IM 8960becomes9216— so a ZR1-1.5B MLP weight holds 1728 tiles where the model's dimensions imply 1680 (28 layers × 3 MLP tensors). Every chunk-kind check still passes and the container loads; every request then answers500 forward step failed: dequant failedin 6–9 ms, becausedequant_any()derives the chunk size from the model's own grid:cb = 8 847 360 / 1680 = 5266B, not a chunk kind, so the tensor falls to the legacy non-chunked decoder.Fix
data_sizepins the container's grid exactly —(rows_p/32)·(cols_p/256)·chunk_bytesfor one of the three chunk kinds — so the grid is recoverable without knowing the kind first.q4nx_dequant_tensor_packed()reads the container's grid and writes only the model's[rows, cols].That is why nothing downstream moves: buffers keep their width,
scatter_transposed()keepsKas the source row stride, and the designKstaysq4nx_pad_k(IM)— no measured device row is touched, andreal_n/q4nx_pad_n/ELF selection are deliberately not involved (#799's rows are still indexed by the same kernel names).Safety: the padded grid is taken only when it dominates the model's geometry with a small excess (≤ 64 tiles — a converter's grid step, measured at 8 tile-rows for
IM 8960) and one chunk kind dividesdata_sizeevenly. Anything else keeps the old path, i.e. still fails rather than decoding garbage.Measured (host arm,
NPU_INFER_HOST_GEMM=1, onenpu-holdticket per arm)Containers built with the only FLM converter on this box that repacks (
~/wt/npu-rt-753-pr/third_party/FLM_Q4NX_Converter); the engine's own resolver prefersnp-model-generic, which is absent here, and~/amd-oss/FLM_Q4NX_Converter(d1d5232) writes a different q4_1 tile order whose containers decode as noise — see the note at the end.forward step failed: dequant failed, 6–9 msdown_proj, decoded vs this lane's ownq4nxpack of the same GGUF" Paris. …"on both — unpadded path untouchedZR1-1.5B-npu-q4km-v2pack, ids promptMLP dequant model.layers.0.mlp.down_proj.weight: data_size 8601344 at geometry 1536x8960 = 1680 tiles (chunk 5119 B is not a kind, and no padded grid fits): the container and config.json disagreeThe bare
dequant failedcost this lane an afternoon, soload_layer()'sdeq()now names the tensor, itsdata_size, the geometry tried, the resulting chunk size and what it means;load_gdn_layer()already did.What the padded container is and is not. pi-npufeat re-measured this at
6cce1656and reproduced500 -> 200, but not theone-prompt text match, and they are right: the two containers hold different quantizations of the same weights (this lane's
pack keeps Q4_K codes and
--dtype8's 29 Q8 tensors; the repack is 197/197 q4_1 from a Q4_K_M source), so+0.9994on onetensor does not imply equal logits. Their logit correlations on one prompt, greedy, were 0.9324 / 0.9510 / 0.2489 at steps 0/1/2
with the argmax equal at step 0 — same weights within requantization noise, then chaos after the first divergence. With
tools/q4nx_diff.pynow decoding the padded grid (PR #1119, pi-npufeat), every tensor checks out at the same error scaleas this lane's own pack: 197 checked, 84 padded, worst max|err| 6.101e-02 vs the published pack's 197, 0 padded,
5.095e-02 — and the padded MLP tensors read ~8x worse than our pack's on the same GGUF (
ffn_gate2.23e-2 vs 2.49e-3),because the repack throws every tensor through q4_1 while our pack keeps the Q4_K codes (plus 29 Q8 tensors). That factor is
the whole story of the text difference: the padded path is correct, and the container it produces is a coarser quantization
than the one this lane publishes — which is an argument for
tools/gguf_to_q4nx.py(or--dtype8) being the source of ashipped container, not against the decode.
Tests
tests/npu_padded_grid_test.{sh,cpp}(new): synthetic Q8 tiles, no model/device/numpy — a padded grid decodes to exactly the tight grid's values at the container's stride (tile (1,1) is code 12, not the 11 a tight-stride read would give — the silent-wrong-answer case a tile-count-only fix would ship), an equal grid is the shipped decoder, and a smaller grid / short buffer / unknown chunk / off-grid geometry are refused. Self-compiling like the other layout contracts, so ctest needs no build target and CI cannot report "Not Run";add_test()only, no workflow change (a lane has noworkflowscope).tools/check_q4nx_dir.pycompanion, separate PR tools: check_q4nx_dir names a weight grid padded away from the model's own tiles #1099: it now flags the padded grid at pack time (84 WARN on this container, 0 on the published ones), so this class is visible before a request is made.Note for whoever picks up the converter question (not this PR)
~/amd-oss/FLM_Q4NX_Converter(d1d5232, "feat: update converter for q4_k") repacks successfully but its containers decode as noise in this engine — Qwen3-0.6B answers"οοοοοο…"where llama.cpp on the same GGUF answers" Paris. …", with no error anywhere. That is exactly the failure mode this PR's refusal guard avoids, and it is worth its own issue: a converter version can be silently incompatible, andrepack-stamp.txtrecords the commit but nothing compares it to one the engine was validated against. The four other checkouts the resolver prefers either cannot import (1bit-MONSTER:No module named 'q4nx.models') or fail the repack outright.