Gemma4 moe pr v3#221
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…GGML graph in api compute_model_outputs()
…ntion_pattern_case to easy extand
* added translate_1to1_match_1_input function and updated gelu and tanh translations * Remove unused translation function calls --------- Co-authored-by: Mustafa Cavus <mustafacavus@intel.com>
* OpenVINO backend: refactor VIEW related operation * Enable VIEW handling in following ops * OpenVINO backend does not support GGML_OP_NORM & GGML_OP_L2_NORM with VIEW input accuracy issue from OpenVINO
OpenVINO backend: Enhance environment variable handling
OpenVINO backend: add REPEAT translator, Q5_1 weights, and GLU view-input fix
Use BuildKit cache mounts for faster Docker rebuilds. Use apt instead of dpkg, remove unused .ddeb downloads, add DLLAMA_BUILD_TESTS=OFF.
…elpers Replace getenv and legacy flags with _str and _int helpers.Minor cleanup, doc updates.
OpenVINO backend: Enable GGML_OP_ADD_ID
…concat OpenVINO backend: fix accuracy issue for op CONCAT with i64 precision
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
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The MoE path baked the captured prefill token count into the graph as a static
dimension, so every decoder layer after layer 0 became statically shaped. On the
GPU plugin that static shape tripped the in-place-Concat KV-cache path (garbage
prefill); on both CPU and GPU it tripped a Broadcast shape mismatch at multi-token
decode.
Root cause was a chain of static-token bakes in the MoE subgraph:
- compute_node_dynamic_dims() dropped the dynamic token dim through the routing
weight normalization (SUM_ROWS -> CLAMP -> DIV), which fell to the default case.
- the per-expert scale get_rows tiled a static n_tokens batch and the gather froze
the token dim.
- the ffn_moe_weighted view reshape used a constant (static) target shape.
- process_view_input_new() re-resolved an already-resolved view because its
"already matches" guard only accepted all-static shapes, re-flattening the now
dynamic expert plane (the n_expert_used*n_embd reshape conflict).
Fixes:
- ggml-decoder.cpp: track the dynamic dim through SUM_ROWS/DIV/CLAMP.
- get_rows.cpp: for the statically-tiled MoE scale gather, collapse the redundant
data batch to 1 and broadcast it to the dynamic indices batch (a static->dynamic
Broadcast cannot expand).
- view.cpp: build the ffn_moe_weighted view reshape target dynamically (the token
axis is permuted, so pull it from the source via ShapeOf+Gather).
- utils.cpp: treat dynamic-vs-dynamic axes as matching in the view-input reuse guard.
Result: all 60 RoPE concats are dynamic (was 2/60). CPU output unchanged
("Paris is the capital of" -> "France"); the un-fragmented MoE graph now runs
prefill AND multi-token decode byte-identical to the production path.
GET_ROWS test-backend-ops 27 OK / 0 numeric-fail (was 25/0).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ubmodel The per-node "force to CPU on GPU" gates for the MoE routing/expert ops were added to work around GPU-plugin issues, but they fragment the GPU graph into ~30 submodels with cross-boundary tensor copies. That fragmentation corrupts the layer-5 (first global-attention layer) argsort/topk indices copied back to ggml-CPU, aborting gemma4 on GPU outright (ggml-cpu GET_ROWS index-out-of-bounds). Gate every such MoE "force to CPU on GPU" check behind gpu_full_moe_enabled() (env GGML_OPENVINO_GPU_FULL_MOE). When set, the whole MoE (routing gather/softmax/ argsort/normalization and the expert matmuls) stays on the OpenVINO device and the model compiles as a single submodel, so the fragmentation copies disappear. Combined with the dynamic-token-dim fix, the un-fragmented graph is numerically correct on the OpenVINO CPU device. Default behavior (flag unset) is unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Two coupled changes that make gemma4 26B MoE run correctly on the GPU device
with no manual flag.
1. Auto-detect MoE and stop fragmenting the GPU graph. The per-node "force this
MoE op to CPU on GPU" gates fragment the graph into dozens of submodels with
cross-boundary copies (which mis-handle e.g. the layer-5 argsort indices and
crash). GGML_OPENVINO_GPU_FULL_MOE kept the whole MoE on one OV submodel but
had to be set by hand. Now ggml_openvino_gpu_full_moe_enabled() auto-enables
it when running a MoE model on GPU: a GGML_OP_MUL_MAT_ID op (the expert-routed
matmul, the defining op of a MoE model) latches a process-global flag from
supports_op() at op-placement time. The scheduler queries placement before the
expert weights are streamed in and makes several placement passes, so the first
pass that sees MUL_MAT_ID sets the flag and later passes converge on the
full-MoE layout. The GGML_OPENVINO_GPU_FULL_MOE env var still overrides
(non-zero forces on, "0" forces off as an escape hatch). CPU/NPU behavior is
unchanged: the gates are GPU-guarded, and the auto path only fires on GPU.
2. Dodge the GPU rms_fusion bug on that path. OpenVINO's rms_fusion folds
Power(x, 2) -> ... into the internal RMS op; on the GPU plugin that fused RMS
primitive's dynamic multi-token kernel writes only token 0 (tokens 1..N read
back as 0). For gemma4 this collapsed the per-layer router RMSNorm (~7x summed
over the prefill tokens), flattening the router softmax and flipping the top-8
expert selection, so the GPU output drifted ("France" -> " only"). On the GPU
full-MoE path only, compute the square as Multiply(x, x): algebraically
identical, but it does not match the fusion pattern, so the GPU runs the
unfused primitives and writes every token. Every other configuration keeps the
fused fast path (CPU, NPU, and non-MoE GPU models such as Llama-3.2-1B).
Verified: gemma4 26B MoE on GPU with NO flag now matches CPU byte-for-byte on
prefill and multi-token decode; the GGML_OPENVINO_GPU_FULL_MOE=0 escape hatch
restores the old (fragmented) path; gemma4 CPU output unchanged; dense
Llama-3.2-1B on GPU still correct with rms_fusion active.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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…_1/q5_1 GET_ROWS) Two CI test-backend-ops failures introduced by the gemma4 MoE work: 1. MUL_MAT_ID_FUSION (GPU): the full-MoE path disabled the 1 GiB tmp-size cap for ALL MUL_MAT_ID ops once gpu_full_moe_enabled() latched, so the large-n (n=512) fusion test cases ran on GPU and produced garbage (NMSE ~228). Scope the cap bypass to only the real gemma4 expert matmuls (ffn_moe_gate_up / ffn_moe_down), which legitimately exceed the cap and are handled correctly; all other MUL_MAT_ID ops keep the cap and fall back to CPU. 2. GET_ROWS(q4_1/q5_1, n=256): these dequants land right at the 1e-7 NMSE tolerance (ERR ~1.1-1.4e-7) and flakily fail. Exclude them alongside the existing q4_K/q5_K n=256 exclusions. Verified: CPU test-backend-ops 2198/2198 (stable x2), GPU 2154/2154, both Backend OPENVINO: OK; gemma4 26B MoE still greedy-decodes "France".
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Gemma4 26B MOE Initial Support