diff --git a/sl2610-function-calling/build.gradle.kts b/sl2610-function-calling/build.gradle.kts index ead21f0..79395f0 100644 --- a/sl2610-function-calling/build.gradle.kts +++ b/sl2610-function-calling/build.gradle.kts @@ -56,6 +56,8 @@ kotlin { implementation(libs.skainet.compile.opt) // DtypeForwardPropagationPass implementation(libs.skainet.transformers.core) // transformer Modules (MHA/RoPE/FFN) for trace tooling implementation(libs.skainet.transformers.inference.moonshine) // moonshineEncoder() DSL, self-compiled here + // Torq NPU target passes (host export-time only; ENC_TORQ=1). Never enters the linuxArm64 binary. + implementation("sk.ainet.vendors:synaptics-torq:0.1.0") } } } diff --git a/sl2610-function-calling/docs/synaptics-support/README.md b/sl2610-function-calling/docs/synaptics-support/README.md new file mode 100644 index 0000000..13e85df --- /dev/null +++ b/sl2610-function-calling/docs/synaptics-support/README.md @@ -0,0 +1,110 @@ +# Torq compiler: StableHLO encoder fragments into ~40 dispatches (returns zeros on SL2610), where your shipped encoder is 1 fused dispatch + +**Where to file this** +- **Primary (recommended):** GitHub issue on the compiler repo — https://github.com/synaptics-torq/torq-compiler/issues + (public, Apache-2.0, Issues enabled — this is the compiler team's repo). Attach `encoder_1layer.mlir`. +- **Formal support ticket:** Astra Support Portal — https://synacsm.atlassian.net/servicedesk/customer/portal/543 +- Docs referenced: https://synaptics-torq.github.io/torq-compiler/v/latest/ + +--- + +## Message + +Hi Torq team, + +First — thank you for shipping the compiler and runtime as open IREE/MLIR. We're building our own +ML framework (**SKaiNET**, a Kotlin-multiplatform NN library): we author models in a small NN DSL, +trace them to a compute graph, and emit **StableHLO**, which we then hand to your `iree-compile` +(Torq fork) for the SL2610 (Astra Machina, Astra SDK `scarthgap_6.12_v2.4.0`). The open toolchain is +exactly what let us get this far, and we'd love your guidance on one issue. + +**Symptom.** Our self-authored Moonshine-tiny **encoder** StableHLO compiles fine but fragments into +**~40 dispatches**, and on the SL2610 NPU (`--device=torq --torq_hw_type=astra_machina`) it returns +**all zeros**. Your own shipped encoder for the same model +(`.../models/Synaptics/moonshine-tiny-bf16-torq/encoder.vmfb`) compiles to a **single fused dispatch** +and runs correctly on the same board. So the model math is fine — the difference is dispatch +segmentation/fusion. + +**What we've verified (SL2610, board + your runtimes):** + +| graph | dispatches | result on `--device=torq` | +|---|---|---| +| your `encoder.vmfb` (moonshine-tiny-bf16-torq) | **1** | RUNS (nonzero, sane) | +| our 2-matmul chain / matmul→softmax→matmul | 1 / 2 | RUNS | +| **our 1-layer encoder** (`encoder_1layer.mlir`, attached) | **41** | **ALL ZEROS** | +| our full 6-layer encoder | ~240 | ALL ZEROS | + +- Dispatch count is the discriminator: 1–2 dispatch graphs run; the many-dispatch encoder returns zeros. +- The dtype recipe matches yours (we cast matmul inputs to bf16 → `bf16×bf16→f32` projections + bf16 + attention interior + f32 LayerNorm reductions) — the zeros persist, so it isn't a dtype issue. +- It isn't the NPU clock (`devmem 0xf7e104b0 32 0x216`): your encoder runs bit-identically at the boot + clock and 0x216. +- If we **disable tiling** (compile the attention block un-tiled to reduce dispatch count), the compiler + instead **crashes in CSS codegen** — `iree-lld: cannot open /tmp/css_linalg.generic_*.o` after an + abort, i.e. it can't lower the un-tiled bf16 attention/softmax as one kernel. So we're stuck between + "tiled → too many dispatches → runtime zeros" and "un-tiled → CSS codegen crash." + +**Our questions:** +1. **Fusion:** How do we get `iree-compile` to fuse the encoder into one (or few) dispatch(es), the way + your `encoder.vmfb` is built? Is there a flag / pass / pipeline (segmentation-fusion, dispatch-region + policy) we should be enabling? +2. **Runtime limit:** Is there a per-invocation **dispatch-count limit** on the SL2610 runtime, and what + is it? (Our 1–2 dispatch graphs run; ~40 return zeros silently, with no error.) +3. **Import path:** Does importing from **StableHLO** vs ONNX change dispatch segmentation? Is there a + recommended StableHLO shape/op form for good fusion on Torq? +4. **CSS codegen crash:** Is the `css_linalg.generic` codegen abort (un-tiled bf16 attention/softmax) a + known issue, and is it fixed in a newer build? +5. **Compiler build:** The public **v2.0.0** compiler fragments our graph; the build that produced your + moonshine `encoder.vmfb` fuses it to 1 dispatch. Is that build/config available, or can you share the + compile recipe/flags used for the shipped moonshine encoder? + +Repro attached (`encoder_1layer.mlir`, self-contained StableHLO, weights as inputs). Exact commands and +observed output below. Happy to provide the 6-layer graph, the board runtime logs, or a smaller synthetic +if useful. Thanks a lot! + +— [your name], SKaiNET + +--- + +## Reproducer + +`encoder_1layer.mlir` — one Moonshine-tiny encoder layer (pre-norm attention + GELU FFN), authored in +the SKaiNET DSL and emitted as StableHLO. Self-contained; weights are function inputs. bf16 activations, +f32 LayerNorm reductions (the vendor recipe). + +### Compile (Torq `iree-compile`, v2.0.0) +``` +iree-compile \ + --iree-input-type=stablehlo \ + --iree-hal-target-device=torq --torq-hw=SL2610 \ + --torq-fallback-f32-to-host \ + encoder_1layer.mlir -o encoder_1layer.vmfb +``` + +### Observed: 41 dispatches +``` +strings encoder_1layer.vmfb | grep -oE 'dispatch_[0-9]+' | sort -u | wc -l +# -> 41 +``` + +### Run on the SL2610 NPU → all zeros +``` +iree-run-module --module=encoder_1layer.vmfb --function=main \ + --device=torq --torq_hw_type=astra_machina \ + --input=... (weights + [1,165,288] bf16 activation) --output=@out.bin +# out.bin is all zeros; your 1-dispatch encoder.vmfb on the same board is nonzero/correct. +``` + +### Contrast (same board): your fused encoder runs +``` +strings .../moonshine-tiny-bf16-torq/encoder.vmfb | grep -oE 'dispatch_[0-9]+' | sort -u | wc -l +# -> 1 (runs correctly, in bf16[1,288,207] -> out bf16[1,207,288], nonzero) +``` + +### The un-tiled variant instead crashes the compiler +Compiling the same layer with attention **un-tiled** (one large block) aborts in CSS codegen: +``` +iree-lld: error: cannot find linker script /tmp/css_linalg.generic_0-*.ld +iree-lld: error: cannot open /tmp/css_linalg.generic_0-*.o: No such file or directory +... Aborted (core dumped) +``` diff --git a/sl2610-function-calling/docs/synaptics-support/encoder_1layer.mlir b/sl2610-function-calling/docs/synaptics-support/encoder_1layer.mlir new file mode 100644 index 0000000..921df41 --- /dev/null +++ b/sl2610-function-calling/docs/synaptics-support/encoder_1layer.mlir @@ -0,0 +1,357 @@ +module { + func.func @moonshine_encoder(%arg0: tensor<1x165x288xbf16>, %arg1: tensor<288xbf16>, %arg2: tensor<288xbf16>, %arg3: tensor<288x288xbf16>, %arg4: tensor<288x288xbf16>, %arg5: tensor<288x288xbf16>, %arg6: tensor<288x288xbf16>, %arg7: tensor<288xbf16>, %arg8: tensor<288xbf16>, %arg9: tensor<1152x288xbf16>, %arg10: tensor<1152xbf16>, %arg11: tensor<288x1152xbf16>, %arg12: tensor<288xbf16>, %arg13: tensor<288xbf16>, %arg14: tensor<288xbf16>) -> (tensor<165x288xf32>) { + // input n101_input: t0 : tensor<1x165x288xbf16> + // input n102_input: t14 : tensor<288xbf16> + // input n103_input: t16 : tensor<288xbf16> + // input n104_input: t18 : tensor<288x288xbf16> + // input n105_input: t21 : tensor<288x288xbf16> + // input n106_input: t24 : tensor<288x288xbf16> + // input n111_input: t72 : tensor<288x288xbf16> + // input n112_input: t88 : tensor<288xbf16> + // input n113_input: t90 : tensor<288xbf16> + // input n114_input: t92 : tensor<1152x288xbf16> + // input n115_input: t95 : tensor<1152xbf16> + // input n116_input: t98 : tensor<288x1152xbf16> + // input n117_input: t101 : tensor<288xbf16> + // input n118_input: t116 : tensor<288xbf16> + // input n119_input: t118 : tensor<288xbf16> + // weight n107_weight: frozen parameter + %v0 = stablehlo.constant dense<[[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [0.5403023, 0.5403023, 0.84600914, 0.84600914, 0.95041525, 0.95041525, 0.9842302, 0.9842302, 0.9950042, 0.9950042, 0.9984193, 0.9984193, 0.99950004, 0.99950004, 0.99984187, 0.99984187, 0.99995, 0.99995, 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0.14647116, -0.08257005, 0.08257005, -0.046468742, 0.046468742, -0.02613773, 0.02613773, -0.0, 0.0, -0.0, 0.0], [0.3383334, -0.3383334, -0.9996701, 0.9996701, -0.31663665, 0.31663665, -0.9267974, 0.9267974, -0.78825194, 0.78825194, -0.8921702, 0.8921702, 0.999481, -0.999481, -0.48794952, 0.48794952, -0.99588084, 0.99588084, -0.73945814, 0.73945814, -0.45111755, 0.45111755, -0.26015753, 0.26015753, -0.1474603, 0.1474603, -0.083130464, 0.083130464, -0.04678463, 0.04678463, -0.026315497, 0.026315497, -0.0, 0.0, -0.0, 0.0], [0.97464865, -0.97464865, -0.8594256, 0.8594256, -0.005954588, 0.005954588, -0.97861594, 0.97861594, -0.72288096, 0.72288096, -0.86537224, 0.86537224, 0.9999998, -0.9999998, -0.47235116, 0.47235116, -0.9967378, 0.9967378, -0.7432322, 0.7432322, -0.45393747, 0.45393747, -0.26187417, 0.26187417, -0.14844929, 0.14844929, -0.08369085, 0.08369085, -0.04710051, 0.04710051, -0.026493264, 0.026493264, -0.0, 0.0, -0.0, 0.0], [0.7148764, -0.7148764, -0.45449513, 0.45449513, 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-0.7544126, 0.7544126, -0.46237, 0.46237, -0.2670191, 0.2670191, -0.15141538, 0.15141538, -0.08537183, 0.08537183, -0.048048116, 0.048048116, -0.027026556, 0.027026556, -0.0, 0.0, -0.0, 0.0], [-0.8064006, 0.8064006, 0.9374466, -0.9374466, 0.95177084, -0.95177084, -0.87559193, 0.87559193, -0.3967404, 0.3967404, -0.7317978, 0.7317978, 0.9920856, -0.9920856, -0.40851375, 0.40851375, -0.9991679, 0.9991679, -0.75809187, 0.75809187, -0.46517166, 0.46517166, -0.26873237, 0.26873237, -0.15240379, 0.15240379, -0.085932106, 0.085932106, -0.048363976, 0.048363976, -0.027204318, 0.027204318, -0.0, 0.0, -0.0, 0.0], [0.061920337, -0.061920337, 0.97870016, -0.97870016, 0.9999904, -0.9999904, -0.7763361, 0.7763361, -0.3031178, 0.3031178, -0.6923363, 0.6923363, 0.9876195, -0.9876195, -0.39221862, 0.39221862, -0.99952585, 0.99952585, -0.7617472, 0.7617472, -0.4679686, 0.4679686, -0.2704448, 0.2704448, -0.15339203, 0.15339203, -0.08649235, 0.08649235, -0.048679832, 0.048679832, -0.02738208, 0.02738208, -0.0, 0.0, -0.0, 0.0], [0.873312, -0.873312, 0.7185335, -0.7185335, 0.9490407, -0.9490407, -0.652595, 0.652595, -0.20646748, 0.20646748, -0.65068674, 0.65068674, 0.98216593, -0.98216593, -0.37579966, 0.37579966, -0.99978375, 0.99978375, -0.76537836, 0.76537836, -0.47076094, 0.47076094, -0.2721564, 0.2721564, -0.1543801, 0.1543801, -0.087052576, 0.087052576, -0.04899568, 0.04899568, -0.027559841, 0.027559841, -0.0, 0.0, -0.0, 0.0], [0.8817846, -0.8817846, 0.23706543, -0.23706543, 0.8039757, -0.8039757, -0.5082697, 0.5082697, -0.10775327, 0.10775327, -0.6069793, 0.6069793, 0.9757303, -0.9757303, -0.35926187, 0.35926187, -0.9999417, 0.9999417, -0.7689854, 0.7689854, -0.47354853, 0.47354853, -0.2738671, 0.2738671, -0.15536803, 0.15536803, -0.08761277, 0.08761277, -0.04931153, 0.04931153, -0.0277376, 0.0277376, -0.0, 0.0, -0.0, 0.0], [0.079548545, -0.079548545, -0.3174088, 0.3174088, 0.57918125, -0.57918125, -0.34791535, 0.34791535, -0.007963374, 0.007963374, -0.56135297, 0.56135297, 0.9683191, -0.9683191, -0.34261027, 0.34261027, -0.9999997, 0.9999997, -0.77256805, 0.77256805, -0.4763314, 0.4763314, -0.27557698, 0.27557698, -0.15635581, 0.15635581, -0.088172935, 0.088172935, -0.04962737, 0.04962737, -0.02791536, 0.02791536, -0.0, 0.0, -0.0, 0.0], [-0.7958241, 0.7958241, -0.7741274, 0.7741274, 0.29695004, -0.29695004, -0.17658788, 0.17658788, 0.09190704, -0.09190704, -0.5139519, 0.5139519, 0.95993954, -0.95993954, -0.32585055, 0.32585055, -0.9999576, 0.9999576, -0.7761263, 0.7761263, -0.4791095, 0.4791095, -0.27728596, 0.27728596, -0.15734345, 0.15734345, -0.08873307, 0.08873307, -0.0499432, 0.0499432, -0.028093116, 0.028093116, -0.0, 0.0, -0.0, 0.0], [-0.9395197, 0.9395197, -0.99243015, 0.99243015, -0.014733227, 0.014733227, 3.0906202E-4, -3.0906202E-4, 0.19085914, -0.19085914, -0.4649268, 0.4649268, 0.95060015, -0.95060015, -0.30898756, 0.30898756, -0.99981564, 0.99981564, -0.77966005, 0.77966005, -0.4818828, 0.4818828, -0.27899408, 0.27899408, -0.1583309, 0.1583309, -0.089293174, 0.089293174, -0.050259035, 0.050259035, -0.028270874, 0.028270874, -0.0, 0.0, -0.0, 0.0], [-0.21942526, 0.21942526, -0.90508413, 0.90508413, -0.32495177, 0.32495177, 0.17719626, -0.17719626, 0.2879033, -0.2879033, -0.4144311, 0.4144311, 0.9403102, -0.9403102, -0.2920271, 0.2920271, -0.9995736, 0.9995736, -0.7831691, 0.7831691, -0.4846513, 0.4846513, -0.2807013, 0.2807013, -0.15931822, 0.15931822, -0.089853264, 0.089853264, -0.05057486, 0.05057486, -0.028448632, 0.028448632, -0.0, 0.0, -0.0, 0.0], [0.7024078, -0.7024078, -0.53898376, 0.53898376, -0.60294515, 0.60294515, 0.34849477, -0.34849477, 0.38207176, -0.38207176, -0.36262515, 0.36262515, 0.92908, -0.92908, -0.27497405, 0.27497405, -0.99923164, 0.99923164, -0.78665334, 0.78665334, -0.48741493, 0.48741493, -0.28240764, 0.28240764, -0.16030537, 0.16030537, -0.09041332, 0.09041332, -0.05089068, 0.05089068, -0.028626386, 0.028626386, -0.0, 0.0, -0.0, 0.0], [0.97845036, -0.97845036, -0.0068903295, 0.0068903295, -0.8211452, 0.8211452, 0.50880194, -0.50880194, 0.47242266, -0.47242266, -0.30967277, 0.30967277, 0.916921, -0.916921, -0.25783426, 0.25783426, -0.9987897, 0.9987897, -0.79011273, 0.79011273, -0.49017367, 0.49017367, -0.28411308, 0.28411308, -0.16129234, 0.16129234, -0.09097334, 0.09097334, -0.0512065, 0.0512065, -0.028804142, 0.028804142, -0.0, 0.0, -0.0, 0.0], [0.35491017, -0.35491017, 0.5273252, -0.5273252, -0.95791304, 0.95791304, 0.65306175, -0.65306175, 0.5580532, -0.5580532, -0.2557414, 0.2557414, 0.90384495, -0.90384495, -0.24061273, 0.24061273, -0.998248, 0.998248, -0.7935472, 0.7935472, -0.49292758, 0.49292758, -0.28581762, 0.28581762, -0.16227917, 0.16227917, -0.09153333, 0.09153333, -0.051522307, 0.051522307, -0.028981896, 0.028981896, -0.0, 0.0, -0.0, 0.0], [-0.5949328, 0.5949328, 0.89913505, -0.89913505, -0.9996856, 0.9996856, 0.77672434, -0.77672434, 0.6381064, -0.6381064, -0.20100242, 0.20100242, 0.88986516, -0.88986516, -0.22331534, 0.22331534, -0.9976064, 0.9976064, -0.79695654, 0.79695654, -0.4956765, 0.4956765, -0.2875213, 0.2875213, -0.16326584, 0.16326584, -0.0920933, 0.0920933, -0.05183811, 0.05183811, -0.029159648, 0.029159648, -0.0, 0.0, -0.0, 0.0]]> : tensor<165x36xbf16> + %v4 = stablehlo.constant dense<0.16666667> : tensor<4x83x165xbf16> + %v5 = stablehlo.constant dense<0.16666667> : tensor<4x82x165xbf16> + %v6 = stablehlo.constant dense<0.16666667> : tensor<4x83x165xbf16> + %v7 = stablehlo.constant dense<0.16666667> : tensor<4x82x165xbf16> + // Squeeze dimension 0 + %v8 = stablehlo.reshape %arg0 : (tensor<1x165x288xbf16>) -> tensor<165x288xbf16> + %v9 = stablehlo.transpose %arg3, dims = [1, 0] : (tensor<288x288xbf16>) -> tensor<288x288xbf16> + %v10 = stablehlo.transpose %arg4, dims = [1, 0] : (tensor<288x288xbf16>) -> tensor<288x288xbf16> + %v11 = stablehlo.transpose %arg5, dims = [1, 0] : (tensor<288x288xbf16>) -> tensor<288x288xbf16> + %v12 = stablehlo.reshape %v2 : (tensor<165x36xbf16>) -> tensor<165x1x36xbf16> + %v13 = stablehlo.reshape %v3 : (tensor<165x36xbf16>) -> tensor<165x1x36xbf16> + %v14 = stablehlo.transpose %arg6, dims = [1, 0] : (tensor<288x288xbf16>) -> tensor<288x288xbf16> + %v15 = stablehlo.transpose %arg9, dims = [1, 0] : (tensor<1152x288xbf16>) -> tensor<288x1152xbf16> + %v16 = stablehlo.slice %arg10 [0:288:1] : (tensor<1152xbf16>) -> tensor<288xbf16> + %v17 = stablehlo.slice %arg10 [288:576:1] : (tensor<1152xbf16>) -> tensor<288xbf16> + %v18 = stablehlo.slice %arg10 [576:864:1] : (tensor<1152xbf16>) -> tensor<288xbf16> + %v19 = stablehlo.slice %arg10 [864:1152:1] : (tensor<1152xbf16>) -> tensor<288xbf16> + %v20 = stablehlo.transpose %arg11, dims = [1, 0] : (tensor<288x1152xbf16>) -> tensor<1152x288xbf16> + %v21 = stablehlo.convert %v8 : (tensor<165x288xbf16>) -> tensor<165x288xf32> + %v22 = stablehlo.slice %v15 [0:288:1, 0:288:1] : (tensor<288x1152xbf16>) -> tensor<288x288xbf16> + %v23 = stablehlo.slice %v15 [0:288:1, 288:576:1] : (tensor<288x1152xbf16>) -> tensor<288x288xbf16> + %v24 = stablehlo.slice %v15 [0:288:1, 576:864:1] : (tensor<288x1152xbf16>) -> tensor<288x288xbf16> + %v25 = stablehlo.slice %v15 [0:288:1, 864:1152:1] : (tensor<288x1152xbf16>) -> tensor<288x288xbf16> + %v26 = stablehlo.slice %v20 [0:288:1, 0:288:1] : (tensor<1152x288xbf16>) -> tensor<288x288xbf16> + %v27 = stablehlo.slice %v20 [288:576:1, 0:288:1] : (tensor<1152x288xbf16>) -> tensor<288x288xbf16> + %v28 = stablehlo.slice %v20 [576:864:1, 0:288:1] : (tensor<1152x288xbf16>) -> tensor<288x288xbf16> + %v29 = stablehlo.slice %v20 [864:1152:1, 0:288:1] : (tensor<1152x288xbf16>) -> tensor<288x288xbf16> + %v30 = stablehlo.constant dense<0.0> : tensor + %v31 = stablehlo.reduce(%v21 init: %v30) applies stablehlo.add across dimensions = [1] : (tensor<165x288xf32>, tensor) -> tensor<165xf32> + %v32 = stablehlo.constant dense<288.0> : tensor<165xf32> + %v33 = stablehlo.divide %v31, %v32 : tensor<165xf32> + // Unsqueeze at dimension 1 + %v34 = stablehlo.reshape %v33 : (tensor<165xf32>) -> tensor<165x1xf32> + %v35 = stablehlo.broadcast_in_dim %v34, dims = [0, 1] : (tensor<165x1xf32>) -> tensor<165x288xf32> + %v36 = stablehlo.subtract %v21, %v35 : tensor<165x288xf32> + %v37 = stablehlo.broadcast_in_dim %v34, dims = [0, 1] : (tensor<165x1xf32>) -> tensor<165x288xf32> + %v38 = stablehlo.subtract %v21, %v37 : tensor<165x288xf32> + %v39 = stablehlo.multiply %v36, %v36 : tensor<165x288xf32> + %v40 = stablehlo.constant dense<0.0> : tensor + %v41 = stablehlo.reduce(%v39 init: %v40) applies stablehlo.add across dimensions = [1] : (tensor<165x288xf32>, tensor) -> tensor<165xf32> + %v42 = stablehlo.constant dense<288.0> : tensor<165xf32> + %v43 = stablehlo.divide %v41, %v42 : tensor<165xf32> + // Unsqueeze at dimension 1 + %v44 = stablehlo.reshape %v43 : (tensor<165xf32>) -> tensor<165x1xf32> + %v45 = stablehlo.constant dense<9.999999747378752E-6> : tensor<165x1xf32> + %v46 = stablehlo.add %v44, %v45 : tensor<165x1xf32> + %v47 = stablehlo.sqrt %v46 : tensor<165x1xf32> + %v48 = stablehlo.broadcast_in_dim %v47, dims = [0, 1] : (tensor<165x1xf32>) -> tensor<165x288xf32> + %v49 = stablehlo.divide %v38, %v48 : tensor<165x288xf32> + %v50 = stablehlo.convert %v49 : (tensor<165x288xf32>) -> tensor<165x288xf32> + %v51 = stablehlo.convert %arg1 : (tensor<288xbf16>) -> tensor<288xf32> + %v52 = stablehlo.broadcast_in_dim %v51, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v53 = stablehlo.multiply %v50, %v52 : tensor<165x288xf32> + %v54 = stablehlo.convert %arg2 : (tensor<288xbf16>) -> tensor<288xf32> + %v55 = stablehlo.broadcast_in_dim %v54, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v56 = stablehlo.add %v53, %v55 : tensor<165x288xf32> + %v57 = stablehlo.convert %v56 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v58 = stablehlo.convert %v56 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v59 = stablehlo.convert %v56 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v60 = stablehlo.dot_general %v57, %v9, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v61 = stablehlo.dot_general %v58, %v10, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v62 = stablehlo.dot_general %v59, %v11, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v63 = stablehlo.reshape %v60 : (tensor<165x288xf32>) -> tensor<165x8x36xf32> + %v64 = stablehlo.reshape %v61 : (tensor<165x288xf32>) -> tensor<165x8x36xf32> + %v65 = stablehlo.reshape %v62 : (tensor<165x288xf32>) -> tensor<165x8x36xf32> + %v66 = stablehlo.convert %v63 : (tensor<165x8x36xf32>) -> tensor<165x8x36xbf16> + %v67 = stablehlo.convert %v64 : (tensor<165x8x36xf32>) -> tensor<165x8x36xf32> + %v68 = stablehlo.convert %v65 : (tensor<165x8x36xf32>) -> tensor<165x8x36xbf16> + %v69 = stablehlo.transpose %v66, dims = [1, 0, 2] : (tensor<165x8x36xbf16>) -> tensor<8x165x36xbf16> + %v70 = stablehlo.reshape %v67 : (tensor<165x8x36xf32>) -> tensor<165x8x18x2xf32> + %v71 = stablehlo.convert %v12 : (tensor<165x1x36xbf16>) -> tensor<165x1x36xf32> + %v72 = stablehlo.broadcast_in_dim %v71, dims = [0, 1, 2] : (tensor<165x1x36xf32>) -> tensor<165x8x36xf32> + %v73 = stablehlo.multiply %v67, %v72 : tensor<165x8x36xf32> + %v74 = stablehlo.transpose %v68, dims = [1, 0, 2] : (tensor<165x8x36xbf16>) -> tensor<8x165x36xbf16> + %v75 = stablehlo.convert %v69 : (tensor<8x165x36xbf16>) -> tensor<8x165x36xbf16> + %v76 = stablehlo.slice %v70 [0:165:1, 0:8:1, 0:18:1, 1:2:1] : (tensor<165x8x18x2xf32>) -> tensor<165x8x18x1xf32> + %v77 = stablehlo.slice %v70 [0:165:1, 0:8:1, 0:18:1, 0:1:1] : (tensor<165x8x18x2xf32>) -> tensor<165x8x18x1xf32> + %v78 = stablehlo.slice %v74 [0:4:1, 0:165:1, 0:36:1] : (tensor<8x165x36xbf16>) -> tensor<4x165x36xbf16> + %v79 = stablehlo.slice %v74 [4:8:1, 0:165:1, 0:36:1] : (tensor<8x165x36xbf16>) -> tensor<4x165x36xbf16> + %v80 = stablehlo.reshape %v75 : (tensor<8x165x36xbf16>) -> tensor<8x165x18x2xbf16> + %v81 = stablehlo.broadcast_in_dim %v0, dims = [1, 2] : (tensor<165x36xbf16>) -> tensor<8x165x36xbf16> + %v82 = stablehlo.multiply %v75, %v81 : tensor<8x165x36xbf16> + %v83 = stablehlo.reshape %v76 : (tensor<165x8x18x1xf32>) -> tensor<165x8x18xf32> + %v84 = stablehlo.reshape %v77 : (tensor<165x8x18x1xf32>) -> tensor<165x8x18xf32> + %v85 = stablehlo.slice %v80 [0:8:1, 0:165:1, 0:18:1, 0:1:1] : (tensor<8x165x18x2xbf16>) -> tensor<8x165x18x1xbf16> + %v86 = stablehlo.slice %v80 [0:8:1, 0:165:1, 0:18:1, 1:2:1] : (tensor<8x165x18x2xbf16>) -> tensor<8x165x18x1xbf16> + // Unsqueeze at dimension 3 + %v87 = stablehlo.reshape %v83 : (tensor<165x8x18xf32>) -> tensor<165x8x18x1xf32> + // Unsqueeze at dimension 3 + %v88 = stablehlo.reshape %v84 : (tensor<165x8x18xf32>) -> tensor<165x8x18x1xf32> + %v89 = stablehlo.reshape %v85 : (tensor<8x165x18x1xbf16>) -> tensor<8x165x18xbf16> + %v90 = stablehlo.reshape %v86 : (tensor<8x165x18x1xbf16>) -> tensor<8x165x18xbf16> + %v91 = stablehlo.concatenate %v87, %v88, dim = 3 : (tensor<165x8x18x1xf32>, tensor<165x8x18x1xf32>) -> tensor<165x8x18x2xf32> + // Unsqueeze at dimension 3 + %v92 = stablehlo.reshape %v89 : (tensor<8x165x18xbf16>) -> tensor<8x165x18x1xbf16> + // Unsqueeze at dimension 3 + %v93 = stablehlo.reshape %v90 : (tensor<8x165x18xbf16>) -> tensor<8x165x18x1xbf16> + %v94 = stablehlo.reshape %v91 : (tensor<165x8x18x2xf32>) -> tensor<165x8x36xf32> + %v95 = stablehlo.concatenate %v93, %v92, dim = 3 : (tensor<8x165x18x1xbf16>, tensor<8x165x18x1xbf16>) -> tensor<8x165x18x2xbf16> + %v96 = stablehlo.convert %v13 : (tensor<165x1x36xbf16>) -> tensor<165x1x36xf32> + %v97 = stablehlo.broadcast_in_dim %v96, dims = [0, 1, 2] : (tensor<165x1x36xf32>) -> tensor<165x8x36xf32> + %v98 = stablehlo.multiply %v94, %v97 : tensor<165x8x36xf32> + %v99 = stablehlo.reshape %v95 : (tensor<8x165x18x2xbf16>) -> tensor<8x165x36xbf16> + %v100 = stablehlo.add %v73, %v98 : tensor<165x8x36xf32> + %v101 = stablehlo.broadcast_in_dim %v1, dims = [1, 2] : (tensor<165x36xbf16>) -> tensor<8x165x36xbf16> + %v102 = stablehlo.multiply %v99, %v101 : tensor<8x165x36xbf16> + %v103 = stablehlo.convert %v100 : (tensor<165x8x36xf32>) -> tensor<165x8x36xf32> + %v104 = stablehlo.add %v82, %v102 : tensor<8x165x36xbf16> + %v105 = stablehlo.convert %v103 : (tensor<165x8x36xf32>) -> tensor<165x8x36xbf16> + %v106 = stablehlo.convert %v104 : (tensor<8x165x36xbf16>) -> tensor<8x165x36xbf16> + %v107 = stablehlo.transpose %v105, dims = [1, 2, 0] : (tensor<165x8x36xbf16>) -> tensor<8x36x165xbf16> + %v108 = stablehlo.slice %v106 [0:4:1, 0:165:1, 0:36:1] : (tensor<8x165x36xbf16>) -> tensor<4x165x36xbf16> + %v109 = stablehlo.slice %v106 [4:8:1, 0:165:1, 0:36:1] : (tensor<8x165x36xbf16>) -> tensor<4x165x36xbf16> + %v110 = stablehlo.slice %v107 [0:4:1, 0:36:1, 0:165:1] : (tensor<8x36x165xbf16>) -> tensor<4x36x165xbf16> + %v111 = stablehlo.slice %v107 [4:8:1, 0:36:1, 0:165:1] : (tensor<8x36x165xbf16>) -> tensor<4x36x165xbf16> + %v112 = stablehlo.slice %v108 [0:4:1, 0:83:1, 0:36:1] : (tensor<4x165x36xbf16>) -> tensor<4x83x36xbf16> + %v113 = stablehlo.slice %v108 [0:4:1, 83:165:1, 0:36:1] : (tensor<4x165x36xbf16>) -> tensor<4x82x36xbf16> + %v114 = stablehlo.slice %v109 [0:4:1, 0:83:1, 0:36:1] : (tensor<4x165x36xbf16>) -> tensor<4x83x36xbf16> + %v115 = stablehlo.slice %v109 [0:4:1, 83:165:1, 0:36:1] : (tensor<4x165x36xbf16>) -> tensor<4x82x36xbf16> + %v116 = stablehlo.dot_general %v112, %v110, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x83x36xbf16>, tensor<4x36x165xbf16>) -> tensor<4x83x165xbf16> + %v117 = stablehlo.dot_general %v113, %v110, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x82x36xbf16>, tensor<4x36x165xbf16>) -> tensor<4x82x165xbf16> + %v118 = stablehlo.dot_general %v114, %v111, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x83x36xbf16>, tensor<4x36x165xbf16>) -> tensor<4x83x165xbf16> + %v119 = stablehlo.dot_general %v115, %v111, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x82x36xbf16>, tensor<4x36x165xbf16>) -> tensor<4x82x165xbf16> + %v120 = stablehlo.multiply %v116, %v4 : tensor<4x83x165xbf16> + %v121 = stablehlo.multiply %v117, %v5 : tensor<4x82x165xbf16> + %v122 = stablehlo.multiply %v118, %v6 : tensor<4x83x165xbf16> + %v123 = stablehlo.multiply %v119, %v7 : tensor<4x82x165xbf16> + %v124 = stablehlo.constant dense<0xFF80> : tensor + %v125 = stablehlo.reduce(%v120 init: %v124) applies stablehlo.maximum across dimensions = [2] : (tensor<4x83x165xbf16>, tensor) -> tensor<4x83xbf16> + %v126 = stablehlo.broadcast_in_dim %v125, dims = [0, 1] : (tensor<4x83xbf16>) -> tensor<4x83x165xbf16> + %v127 = stablehlo.subtract %v120, %v126 : tensor<4x83x165xbf16> + %v128 = stablehlo.exponential %v127 : tensor<4x83x165xbf16> + %v129 = stablehlo.constant dense<0.0> : tensor + %v130 = stablehlo.reduce(%v128 init: %v129) applies stablehlo.add across dimensions = [2] : (tensor<4x83x165xbf16>, tensor) -> tensor<4x83xbf16> + %v131 = stablehlo.broadcast_in_dim %v130, dims = [0, 1] : (tensor<4x83xbf16>) -> tensor<4x83x165xbf16> + %v132 = stablehlo.divide %v128, %v131 : tensor<4x83x165xbf16> + %v133 = stablehlo.constant dense<0xFF80> : tensor + %v134 = stablehlo.reduce(%v121 init: %v133) applies stablehlo.maximum across dimensions = [2] : (tensor<4x82x165xbf16>, tensor) -> tensor<4x82xbf16> + %v135 = stablehlo.broadcast_in_dim %v134, dims = [0, 1] : (tensor<4x82xbf16>) -> tensor<4x82x165xbf16> + %v136 = stablehlo.subtract %v121, %v135 : tensor<4x82x165xbf16> + %v137 = stablehlo.exponential %v136 : tensor<4x82x165xbf16> + %v138 = stablehlo.constant dense<0.0> : tensor + %v139 = stablehlo.reduce(%v137 init: %v138) applies stablehlo.add across dimensions = [2] : (tensor<4x82x165xbf16>, tensor) -> tensor<4x82xbf16> + %v140 = stablehlo.broadcast_in_dim %v139, dims = [0, 1] : (tensor<4x82xbf16>) -> tensor<4x82x165xbf16> + %v141 = stablehlo.divide %v137, %v140 : tensor<4x82x165xbf16> + %v142 = stablehlo.constant dense<0xFF80> : tensor + %v143 = stablehlo.reduce(%v122 init: %v142) applies stablehlo.maximum across dimensions = [2] : (tensor<4x83x165xbf16>, tensor) -> tensor<4x83xbf16> + %v144 = stablehlo.broadcast_in_dim %v143, dims = [0, 1] : (tensor<4x83xbf16>) -> tensor<4x83x165xbf16> + %v145 = stablehlo.subtract %v122, %v144 : tensor<4x83x165xbf16> + %v146 = stablehlo.exponential %v145 : tensor<4x83x165xbf16> + %v147 = stablehlo.constant dense<0.0> : tensor + %v148 = stablehlo.reduce(%v146 init: %v147) applies stablehlo.add across dimensions = [2] : (tensor<4x83x165xbf16>, tensor) -> tensor<4x83xbf16> + %v149 = stablehlo.broadcast_in_dim %v148, dims = [0, 1] : (tensor<4x83xbf16>) -> tensor<4x83x165xbf16> + %v150 = stablehlo.divide %v146, %v149 : tensor<4x83x165xbf16> + %v151 = stablehlo.constant dense<0xFF80> : tensor + %v152 = stablehlo.reduce(%v123 init: %v151) applies stablehlo.maximum across dimensions = [2] : (tensor<4x82x165xbf16>, tensor) -> tensor<4x82xbf16> + %v153 = stablehlo.broadcast_in_dim %v152, dims = [0, 1] : (tensor<4x82xbf16>) -> tensor<4x82x165xbf16> + %v154 = stablehlo.subtract %v123, %v153 : tensor<4x82x165xbf16> + %v155 = stablehlo.exponential %v154 : tensor<4x82x165xbf16> + %v156 = stablehlo.constant dense<0.0> : tensor + %v157 = stablehlo.reduce(%v155 init: %v156) applies stablehlo.add across dimensions = [2] : (tensor<4x82x165xbf16>, tensor) -> tensor<4x82xbf16> + %v158 = stablehlo.broadcast_in_dim %v157, dims = [0, 1] : (tensor<4x82xbf16>) -> tensor<4x82x165xbf16> + %v159 = stablehlo.divide %v155, %v158 : tensor<4x82x165xbf16> + %v160 = stablehlo.dot_general %v132, %v78, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x83x165xbf16>, tensor<4x165x36xbf16>) -> tensor<4x83x36xbf16> + %v161 = stablehlo.dot_general %v141, %v78, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x82x165xbf16>, tensor<4x165x36xbf16>) -> tensor<4x82x36xbf16> + %v162 = stablehlo.dot_general %v150, %v79, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x83x165xbf16>, tensor<4x165x36xbf16>) -> tensor<4x83x36xbf16> + %v163 = stablehlo.dot_general %v159, %v79, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x82x165xbf16>, tensor<4x165x36xbf16>) -> tensor<4x82x36xbf16> + %v164 = stablehlo.concatenate %v160, %v161, dim = 1 : (tensor<4x83x36xbf16>, tensor<4x82x36xbf16>) -> tensor<4x165x36xbf16> + %v165 = stablehlo.concatenate %v162, %v163, dim = 1 : (tensor<4x83x36xbf16>, tensor<4x82x36xbf16>) -> tensor<4x165x36xbf16> + %v166 = stablehlo.concatenate %v164, %v165, dim = 0 : (tensor<4x165x36xbf16>, tensor<4x165x36xbf16>) -> tensor<8x165x36xbf16> + %v167 = stablehlo.convert %v166 : (tensor<8x165x36xbf16>) -> tensor<8x165x36xf32> + %v168 = stablehlo.reshape %v167 : (tensor<8x165x36xf32>) -> tensor<1x8x165x36xf32> + %v169 = stablehlo.convert %v168 : (tensor<1x8x165x36xf32>) -> tensor<1x8x165x36xbf16> + // Squeeze dimension 0 + %v170 = stablehlo.reshape %v169 : (tensor<1x8x165x36xbf16>) -> tensor<8x165x36xbf16> + %v171 = stablehlo.convert %v170 : (tensor<8x165x36xbf16>) -> tensor<8x165x36xf32> + %v172 = stablehlo.transpose %v171, dims = [1, 0, 2] : (tensor<8x165x36xf32>) -> tensor<165x8x36xf32> + %v173 = stablehlo.reshape %v172 : (tensor<165x8x36xf32>) -> tensor<165x288xf32> + %v174 = stablehlo.convert %v173 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v175 = stablehlo.dot_general %v174, %v14, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v176 = stablehlo.convert %v8 : (tensor<165x288xbf16>) -> tensor<165x288xf32> + %v177 = stablehlo.add %v175, %v176 : tensor<165x288xf32> + %v178 = stablehlo.convert %v177 : (tensor<165x288xf32>) -> tensor<165x288xf32> + %v179 = stablehlo.constant dense<0.0> : tensor + %v180 = stablehlo.reduce(%v178 init: %v179) applies stablehlo.add across dimensions = [1] : (tensor<165x288xf32>, tensor) -> tensor<165xf32> + %v181 = stablehlo.constant dense<288.0> : tensor<165xf32> + %v182 = stablehlo.divide %v180, %v181 : tensor<165xf32> + // Unsqueeze at dimension 1 + %v183 = stablehlo.reshape %v182 : (tensor<165xf32>) -> tensor<165x1xf32> + %v184 = stablehlo.broadcast_in_dim %v183, dims = [0, 1] : (tensor<165x1xf32>) -> tensor<165x288xf32> + %v185 = stablehlo.subtract %v178, %v184 : tensor<165x288xf32> + %v186 = stablehlo.broadcast_in_dim %v183, dims = [0, 1] : (tensor<165x1xf32>) -> tensor<165x288xf32> + %v187 = stablehlo.subtract %v178, %v186 : tensor<165x288xf32> + %v188 = stablehlo.multiply %v185, %v185 : tensor<165x288xf32> + %v189 = stablehlo.constant dense<0.0> : tensor + %v190 = stablehlo.reduce(%v188 init: %v189) applies stablehlo.add across dimensions = [1] : (tensor<165x288xf32>, tensor) -> tensor<165xf32> + %v191 = stablehlo.constant dense<288.0> : tensor<165xf32> + %v192 = stablehlo.divide %v190, %v191 : tensor<165xf32> + // Unsqueeze at dimension 1 + %v193 = stablehlo.reshape %v192 : (tensor<165xf32>) -> tensor<165x1xf32> + %v194 = stablehlo.constant dense<9.999999747378752E-6> : tensor<165x1xf32> + %v195 = stablehlo.add %v193, %v194 : tensor<165x1xf32> + %v196 = stablehlo.sqrt %v195 : tensor<165x1xf32> + %v197 = stablehlo.broadcast_in_dim %v196, dims = [0, 1] : (tensor<165x1xf32>) -> tensor<165x288xf32> + %v198 = stablehlo.divide %v187, %v197 : tensor<165x288xf32> + %v199 = stablehlo.convert %v198 : (tensor<165x288xf32>) -> tensor<165x288xf32> + %v200 = stablehlo.convert %arg7 : (tensor<288xbf16>) -> tensor<288xf32> + %v201 = stablehlo.broadcast_in_dim %v200, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v202 = stablehlo.multiply %v199, %v201 : tensor<165x288xf32> + %v203 = stablehlo.convert %arg8 : (tensor<288xbf16>) -> tensor<288xf32> + %v204 = stablehlo.broadcast_in_dim %v203, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v205 = stablehlo.add %v202, %v204 : tensor<165x288xf32> + %v206 = stablehlo.convert %v205 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v207 = stablehlo.convert %v205 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v208 = stablehlo.convert %v205 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v209 = stablehlo.convert %v205 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v210 = stablehlo.dot_general %v206, %v22, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v211 = stablehlo.dot_general %v207, %v23, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v212 = stablehlo.dot_general %v208, %v24, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v213 = stablehlo.dot_general %v209, %v25, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v214 = stablehlo.convert %v16 : (tensor<288xbf16>) -> tensor<288xf32> + %v215 = stablehlo.broadcast_in_dim %v214, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v216 = stablehlo.add %v210, %v215 : tensor<165x288xf32> + %v217 = stablehlo.convert %v17 : (tensor<288xbf16>) -> tensor<288xf32> + %v218 = stablehlo.broadcast_in_dim %v217, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v219 = stablehlo.add %v211, %v218 : tensor<165x288xf32> + %v220 = stablehlo.convert %v18 : (tensor<288xbf16>) -> tensor<288xf32> + %v221 = stablehlo.broadcast_in_dim %v220, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v222 = stablehlo.add %v212, %v221 : tensor<165x288xf32> + %v223 = stablehlo.convert %v19 : (tensor<288xbf16>) -> tensor<288xf32> + %v224 = stablehlo.broadcast_in_dim %v223, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v225 = stablehlo.add %v213, %v224 : tensor<165x288xf32> + %v226 = stablehlo.constant dense<0.5> : tensor<165x288xf32> + %v227 = stablehlo.constant dense<1.0> : tensor<165x288xf32> + %v228 = stablehlo.constant dense<0.7978845608> : tensor<165x288xf32> + %v229 = stablehlo.constant dense<0.044715> : tensor<165x288xf32> + %v230 = stablehlo.multiply %v216, %v216 : tensor<165x288xf32> + %v231 = stablehlo.multiply %v230, %v216 : tensor<165x288xf32> + %v232 = stablehlo.multiply %v229, %v231 : tensor<165x288xf32> + %v233 = stablehlo.add %v216, %v232 : tensor<165x288xf32> + %v234 = stablehlo.multiply %v228, %v233 : tensor<165x288xf32> + %v235 = stablehlo.tanh %v234 : tensor<165x288xf32> + %v236 = stablehlo.add %v227, %v235 : tensor<165x288xf32> + %v237 = stablehlo.multiply %v226, %v216 : tensor<165x288xf32> + %v238 = stablehlo.multiply %v237, %v236 : tensor<165x288xf32> + %v239 = stablehlo.constant dense<0.5> : tensor<165x288xf32> + %v240 = stablehlo.constant dense<1.0> : tensor<165x288xf32> + %v241 = stablehlo.constant dense<0.7978845608> : tensor<165x288xf32> + %v242 = stablehlo.constant dense<0.044715> : tensor<165x288xf32> + %v243 = stablehlo.multiply %v219, %v219 : tensor<165x288xf32> + %v244 = stablehlo.multiply %v243, %v219 : tensor<165x288xf32> + %v245 = stablehlo.multiply %v242, %v244 : tensor<165x288xf32> + %v246 = stablehlo.add %v219, %v245 : tensor<165x288xf32> + %v247 = stablehlo.multiply %v241, %v246 : tensor<165x288xf32> + %v248 = stablehlo.tanh %v247 : tensor<165x288xf32> + %v249 = stablehlo.add %v240, %v248 : tensor<165x288xf32> + %v250 = stablehlo.multiply %v239, %v219 : tensor<165x288xf32> + %v251 = stablehlo.multiply %v250, %v249 : tensor<165x288xf32> + %v252 = stablehlo.constant dense<0.5> : tensor<165x288xf32> + %v253 = stablehlo.constant dense<1.0> : tensor<165x288xf32> + %v254 = stablehlo.constant dense<0.7978845608> : tensor<165x288xf32> + %v255 = stablehlo.constant dense<0.044715> : tensor<165x288xf32> + %v256 = stablehlo.multiply %v222, %v222 : tensor<165x288xf32> + %v257 = stablehlo.multiply %v256, %v222 : tensor<165x288xf32> + %v258 = stablehlo.multiply %v255, %v257 : tensor<165x288xf32> + %v259 = stablehlo.add %v222, %v258 : tensor<165x288xf32> + %v260 = stablehlo.multiply %v254, %v259 : tensor<165x288xf32> + %v261 = stablehlo.tanh %v260 : tensor<165x288xf32> + %v262 = stablehlo.add %v253, %v261 : tensor<165x288xf32> + %v263 = stablehlo.multiply %v252, %v222 : tensor<165x288xf32> + %v264 = stablehlo.multiply %v263, %v262 : tensor<165x288xf32> + %v265 = stablehlo.constant dense<0.5> : tensor<165x288xf32> + %v266 = stablehlo.constant dense<1.0> : tensor<165x288xf32> + %v267 = stablehlo.constant dense<0.7978845608> : tensor<165x288xf32> + %v268 = stablehlo.constant dense<0.044715> : tensor<165x288xf32> + %v269 = stablehlo.multiply %v225, %v225 : tensor<165x288xf32> + %v270 = stablehlo.multiply %v269, %v225 : tensor<165x288xf32> + %v271 = stablehlo.multiply %v268, %v270 : tensor<165x288xf32> + %v272 = stablehlo.add %v225, %v271 : tensor<165x288xf32> + %v273 = stablehlo.multiply %v267, %v272 : tensor<165x288xf32> + %v274 = stablehlo.tanh %v273 : tensor<165x288xf32> + %v275 = stablehlo.add %v266, %v274 : tensor<165x288xf32> + %v276 = stablehlo.multiply %v265, %v225 : tensor<165x288xf32> + %v277 = stablehlo.multiply %v276, %v275 : tensor<165x288xf32> + %v278 = stablehlo.convert %v238 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v279 = stablehlo.convert %v251 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v280 = stablehlo.convert %v264 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v281 = stablehlo.convert %v277 : (tensor<165x288xf32>) -> tensor<165x288xbf16> + %v282 = stablehlo.dot_general %v278, %v26, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v283 = stablehlo.dot_general %v279, %v27, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v284 = stablehlo.dot_general %v280, %v28, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v285 = stablehlo.dot_general %v281, %v29, contracting_dims = [1] x [0] : (tensor<165x288xbf16>, tensor<288x288xbf16>) -> tensor<165x288xf32> + %v286 = stablehlo.add %v282, %v283 : tensor<165x288xf32> + %v287 = stablehlo.add %v284, %v285 : tensor<165x288xf32> + %v288 = stablehlo.add %v286, %v287 : tensor<165x288xf32> + %v289 = stablehlo.convert %arg12 : (tensor<288xbf16>) -> tensor<288xf32> + %v290 = stablehlo.broadcast_in_dim %v289, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v291 = stablehlo.add %v288, %v290 : tensor<165x288xf32> + %v292 = stablehlo.add %v291, %v177 : tensor<165x288xf32> + %v293 = stablehlo.convert %v292 : (tensor<165x288xf32>) -> tensor<165x288xf32> + %v294 = stablehlo.constant dense<0.0> : tensor + %v295 = stablehlo.reduce(%v293 init: %v294) applies stablehlo.add across dimensions = [1] : (tensor<165x288xf32>, tensor) -> tensor<165xf32> + %v296 = stablehlo.constant dense<288.0> : tensor<165xf32> + %v297 = stablehlo.divide %v295, %v296 : tensor<165xf32> + // Unsqueeze at dimension 1 + %v298 = stablehlo.reshape %v297 : (tensor<165xf32>) -> tensor<165x1xf32> + %v299 = stablehlo.broadcast_in_dim %v298, dims = [0, 1] : (tensor<165x1xf32>) -> tensor<165x288xf32> + %v300 = stablehlo.subtract %v293, %v299 : tensor<165x288xf32> + %v301 = stablehlo.broadcast_in_dim %v298, dims = [0, 1] : (tensor<165x1xf32>) -> tensor<165x288xf32> + %v302 = stablehlo.subtract %v293, %v301 : tensor<165x288xf32> + %v303 = stablehlo.multiply %v300, %v300 : tensor<165x288xf32> + %v304 = stablehlo.constant dense<0.0> : tensor + %v305 = stablehlo.reduce(%v303 init: %v304) applies stablehlo.add across dimensions = [1] : (tensor<165x288xf32>, tensor) -> tensor<165xf32> + %v306 = stablehlo.constant dense<288.0> : tensor<165xf32> + %v307 = stablehlo.divide %v305, %v306 : tensor<165xf32> + // Unsqueeze at dimension 1 + %v308 = stablehlo.reshape %v307 : (tensor<165xf32>) -> tensor<165x1xf32> + %v309 = stablehlo.constant dense<9.999999747378752E-6> : tensor<165x1xf32> + %v310 = stablehlo.add %v308, %v309 : tensor<165x1xf32> + %v311 = stablehlo.sqrt %v310 : tensor<165x1xf32> + %v312 = stablehlo.broadcast_in_dim %v311, dims = [0, 1] : (tensor<165x1xf32>) -> tensor<165x288xf32> + %v313 = stablehlo.divide %v302, %v312 : tensor<165x288xf32> + %v314 = stablehlo.convert %v313 : (tensor<165x288xf32>) -> tensor<165x288xf32> + %v315 = stablehlo.convert %arg13 : (tensor<288xbf16>) -> tensor<288xf32> + %v316 = stablehlo.broadcast_in_dim %v315, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v317 = stablehlo.multiply %v314, %v316 : tensor<165x288xf32> + %v318 = stablehlo.convert %arg14 : (tensor<288xbf16>) -> tensor<288xf32> + %v319 = stablehlo.broadcast_in_dim %v318, dims = [1] : (tensor<288xf32>) -> tensor<165x288xf32> + %v320 = stablehlo.add %v317, %v319 : tensor<165x288xf32> + return %v320 : tensor<165x288xf32> + } +} diff --git a/sl2610-function-calling/scripts/add_argmax_perpos.py b/sl2610-function-calling/scripts/add_argmax_perpos.py deleted file mode 100644 index 42d30e0..0000000 --- a/sl2610-function-calling/scripts/add_argmax_perpos.py +++ /dev/null @@ -1,32 +0,0 @@ -"""Rewrite the seq=24 baked gemma so @gemma returns the PER-POSITION argmax token -ids (tensor<24xi32>) — argmax over vocab at every sequence position. Tiny output -(24 ints) the board can emit; the host decode loop reads the argmax at the last -real position each greedy step. Tie-break to lowest index (numpy/greedy). -""" -import re -B = "/home/miso/projects/coral/build-mlir" -src = open(f"{B}/gemma-baked.mlir").read() -m = re.search(r"return (%v\d+) : tensor<1x24x262153xf32>", src) -assert m, "seq=24 return not found" -logits = m.group(1) -V = 262153 -S = 24 -tail = f""" %am_flat = stablehlo.reshape {logits} : (tensor<1x{S}x{V}xf32>) -> tensor<{S}x{V}xf32> - %am_iota = stablehlo.iota dim = 1 : tensor<{S}x{V}xi32> - %am_ninf = stablehlo.constant dense<0xFF800000> : tensor - %am_zero = stablehlo.constant dense<0> : tensor - %am_mv, %am_mi = stablehlo.reduce(%am_flat init: %am_ninf), (%am_iota init: %am_zero) across dimensions = [1] : (tensor<{S}x{V}xf32>, tensor<{S}x{V}xi32>, tensor, tensor) -> (tensor<{S}xf32>, tensor<{S}xi32>) - reducer(%lv: tensor, %rv: tensor) (%li: tensor, %ri: tensor) {{ - %gt = stablehlo.compare GT, %lv, %rv : (tensor, tensor) -> tensor - %eq = stablehlo.compare EQ, %lv, %rv : (tensor, tensor) -> tensor - %mn = stablehlo.minimum %li, %ri : tensor - %sv = stablehlo.select %gt, %lv, %rv : tensor, tensor - %i1 = stablehlo.select %eq, %mn, %ri : tensor, tensor - %si = stablehlo.select %gt, %li, %i1 : tensor, tensor - stablehlo.return %sv, %si : tensor, tensor - }} - return %am_mi : tensor<{S}xi32>""" -src = src.replace(f" return {logits} : tensor<1x{S}x{V}xf32>", tail) -src = src.replace(f"-> (tensor<1x{S}x{V}xf32>) {{", f"-> (tensor<{S}xi32>) {{") -open(f"{B}/gemma-gen.mlir", "w").write(src) -print("wrote gemma-gen.mlir; logits=", logits) diff --git a/sl2610-function-calling/scripts/add_argmax_tail.py b/sl2610-function-calling/scripts/add_argmax_tail.py deleted file mode 100644 index 676acf9..0000000 --- a/sl2610-function-calling/scripts/add_argmax_tail.py +++ /dev/null @@ -1,38 +0,0 @@ -"""Rewrite gemma-baked.mlir so @gemma returns the last-position argmax token id -(tensor<1xi32>) instead of the full [1,4,262153] logits — a tiny output the -board's iree-run-module can print without OOM. Tie-break to the lowest index -to match numpy.argmax / greedy top_k=1. -""" -import re, sys - -src = open("/home/miso/projects/coral/build-mlir/gemma-baked.mlir").read() - -# final logits SSA + return line -m = re.search(r"return (%v\d+) : tensor<1x4x262153xf32>", src) -assert m, "return not found" -logits = m.group(1) -VOCAB = 262153 - -tail = f""" %am_last = stablehlo.slice {logits} [0:1:1, 3:4:1, 0:{VOCAB}:1] : (tensor<1x4x262153xf32>) -> tensor<1x1x262153xf32> - %am_flat = stablehlo.reshape %am_last : (tensor<1x1x262153xf32>) -> tensor<{VOCAB}xf32> - %am_iota = stablehlo.iota dim = 0 : tensor<{VOCAB}xi32> - %am_ninf = stablehlo.constant dense<0xFF800000> : tensor - %am_zero = stablehlo.constant dense<0> : tensor - %am_mv, %am_mi = stablehlo.reduce(%am_flat init: %am_ninf), (%am_iota init: %am_zero) across dimensions = [0] : (tensor<{VOCAB}xf32>, tensor<{VOCAB}xi32>, tensor, tensor) -> (tensor, tensor) - reducer(%lv: tensor, %rv: tensor) (%li: tensor, %ri: tensor) {{ - %gt = stablehlo.compare GT, %lv, %rv : (tensor, tensor) -> tensor - %eq = stablehlo.compare EQ, %lv, %rv : (tensor, tensor) -> tensor - %mn = stablehlo.minimum %li, %ri : tensor - %sv = stablehlo.select %gt, %lv, %rv : tensor, tensor - %i1 = stablehlo.select %eq, %mn, %ri : tensor, tensor - %si = stablehlo.select %gt, %li, %i1 : tensor, tensor - stablehlo.return %sv, %si : tensor, tensor - }} - %am_out = stablehlo.reshape %am_mi : (tensor) -> tensor<1xi32> - return %am_out : tensor<1xi32>""" - -src = src.replace(f" return {logits} : tensor<1x4x262153xf32>", tail) -src = src.replace("-> (tensor<1x4x262153xf32>) {", "-> (tensor<1xi32>) {") - -open("/home/miso/projects/coral/build-mlir/gemma-argmax.mlir", "w").write(src) -print("wrote gemma-argmax.mlir; logits=", logits) diff --git a/sl2610-function-calling/scripts/compile-gemma.sh b/sl2610-function-calling/scripts/compile-gemma.sh new file mode 100755 index 0000000..f893a1b --- /dev/null +++ b/sl2610-function-calling/scripts/compile-gemma.sh @@ -0,0 +1,60 @@ +#!/usr/bin/env bash +# Self-compile FunctionGemma from the SKaiNET DSL — ONE command, NO Python. +# +# GEMMA_GGUF=/path/functiongemma-physical-ai-v10-Q5_K_M.gguf \ +# scripts/compile-gemma.sh [host|aarch64] +# +# DSL gemmaNetwork() (argMax tail) -> gemma-gen.mlir + bf16 gemma.safetensors (kgemma exporter) +# -> gemma-gen.irpa (iree-convert-parameters) +# -> gemma-gen.vmfb (iree-compile llvm-cpu) +# +# TARGET: +# host (default) — x64 llvm-cpu via the stock IREE docker image; validates the pipeline. +# board — aarch64+NEON llvm-cpu via the g165 Torq-fork iree-compile (TORQ_PKG). REQUIRED +# for the SL2610: the board's iree-run-module rejects the "Ch" bytecode feature +# stock IREE 3.x emits (verified). Produces a board-runnable gemma-gen.vmfb. +# TORQ_PKG: g165 torq_compiler package dir (default /home/miso/projects/coral/build-mlir/torqpkg). +# IREE_IMAGE: stock iree docker image for the host target (default iree-cpu-toolchain:3.11.0). +# +# Replaces the old test+Python chain (RealGemmaBakeIrpaTest + add_argmax_perpos.py + make_f16.py): +# - the argmax tail is now the DSL `argMax` op (in the emitted StableHLO), and +# - weights are emitted as bf16 externals directly by the exporter. +set -euo pipefail + +TARGET="${1:-host}" +: "${GEMMA_GGUF:?set GEMMA_GGUF to the FunctionGemma Q5_K_M .gguf}" +ROOT="$(cd "$(dirname "$0")/.." && pwd)" # sl2610-function-calling +TF="$(cd "$ROOT/../../SKaiNET-transformers" && pwd)" # SKaiNET-transformers (kgemma exporter) +MLIR="$ROOT/build/mlir"; mkdir -p "$MLIR" +IREE_IMAGE="${IREE_IMAGE:-iree-cpu-toolchain:3.11.0}" + +echo ">> [1/3] DSL -> gemma-gen.mlir + bf16 gemma.safetensors (kgemma FunctionGemmaExport)" +( cd "$TF" && GEMMA_GGUF="$GEMMA_GGUF" GEMMA_OUT_DIR="$MLIR" \ + ./gradlew -PuseLocalSkainet=true :llm-runtime:kgemma:exportFunctionGemma -q ) + +echo ">> [2/3] gemma.safetensors -> gemma-gen.irpa (iree-convert-parameters)" +docker run --rm --user "$(id -u):$(id -g)" -v "$MLIR:/work" "$IREE_IMAGE" \ + iree-convert-parameters --parameters=model=/work/gemma.safetensors --output=/work/gemma-gen.irpa + +echo ">> [3/3] iree-compile (llvm-cpu, $TARGET) -> gemma-gen.vmfb" +if [ "$TARGET" = board ]; then + # g165 Torq-fork compiler (local, not docker): aarch64+NEON, board-compatible bytecode. + TORQ_PKG="${TORQ_PKG:-/home/miso/projects/coral/build-mlir/torqpkg}" + MLIRLIBS="$TORQ_PKG/iree/compiler/_mlir_libs" + [ -x "$MLIRLIBS/iree-compile" ] || { echo "error: g165 iree-compile not at $MLIRLIBS (set TORQ_PKG)" >&2; exit 1; } + SHIM="$(mktemp -d)"; printf '#!/usr/bin/env bash\nexec "%s/iree-lld" -flavor gnu "$@"\n' "$MLIRLIBS" > "$SHIM/ld"; chmod +x "$SHIM/ld" + PATH="$SHIM:$PATH" LD_LIBRARY_PATH="$MLIRLIBS" "$MLIRLIBS/iree-compile" \ + --iree-input-type=stablehlo \ + --iree-hal-target-device=local --iree-hal-local-target-device-backends=llvm-cpu \ + --iree-llvmcpu-target-triple=aarch64-unknown-linux-gnu --iree-llvmcpu-target-cpu-features=+neon \ + "$MLIR/gemma-gen.mlir" -o "$MLIR/gemma-gen.vmfb" + rm -rf "$SHIM" +else + docker run --rm --user "$(id -u):$(id -g)" -v "$MLIR:/work" "$IREE_IMAGE" \ + iree-compile /work/gemma-gen.mlir --iree-hal-target-device=local \ + --iree-hal-local-target-device-backends=llvm-cpu -o /work/gemma-gen.vmfb +fi + +echo ">> done: $MLIR/gemma-gen.{vmfb,irpa}" +echo " deploy both to /home/root/ireetest/ on the board — the demo's GemmaDecoder loads them." +echo " verified: 'turn the light on' -> [262146,236769,3255,718,498,1373,262152,106] = (state=\"on\")" diff --git a/sl2610-function-calling/scripts/make_f16.py b/sl2610-function-calling/scripts/make_f16.py deleted file mode 100644 index 4d4a335..0000000 --- a/sl2610-function-calling/scripts/make_f16.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Build f16 weights + an f16-global MLIR from the f32 baked artifacts, to halve -the board .irpa (1.74GB -> ~0.87GB). (1) rewrite gemma.safetensors f32->f16; -(2) rewrite gemma-argmax.mlir so every weight util.global is f16 and each load is -followed by stablehlo.convert f16->f32 (compute stays f32; board has no EXT_F16 -but the convert lowers to int/f32 ops, proven on-device). -""" -import json, struct, re, sys -import numpy as np - -B = "/home/miso/projects/coral/build-mlir" -MLIR_IN = sys.argv[1] if len(sys.argv) > 1 else f"{B}/gemma-argmax.mlir" -MLIR_OUT = sys.argv[2] if len(sys.argv) > 2 else f"{B}/gemma-argmax-f16.mlir" - -# --- (1) safetensors f32 -> f16 --- -with open(f"{B}/gemma.safetensors", "rb") as f: - n = struct.unpack(" f16 + convert-on-load --- -mlir = open(MLIR_IN).read() - -# global decls: only parameter.named ones, switch their elem type f32 -> f16 -def glob_sub(m): - return m.group(1) + "f16>" -mlir = re.sub(r'(util\.global private @\w+ = #flow\.parameter\.named<"[^"]*"::"[^"]*"> : tensor<[0-9x]*x)f32>', - glob_sub, mlir) - -# loads of those globals: load f16, then convert to f32 under the original SSA name -def load_sub(m): - ssa, shape = m.group(1), m.group(2) - return (f"{ssa}_h = util.global.load @{m.group(3)} : tensor<{shape}xf16>\n" - f" {ssa} = stablehlo.convert {ssa}_h : (tensor<{shape}xf16>) -> tensor<{shape}xf32>") -mlir = re.sub(r'(%\w+) = util\.global\.load @(\w+) : tensor<([0-9x]*)xf32>', - lambda m: f"{m.group(1)}_h = util.global.load @{m.group(2)} : tensor<{m.group(3)}xf16>\n" - f" {m.group(1)} = stablehlo.convert {m.group(1)}_h : (tensor<{m.group(3)}xf16>) -> tensor<{m.group(3)}xf32>", - mlir) -open(MLIR_OUT, "w").write(mlir) -print("wrote gemma-argmax-f16.mlir; f16 globals:", mlir.count("xf16>")) diff --git a/sl2610-function-calling/settings.gradle.kts b/sl2610-function-calling/settings.gradle.kts index 94e9342..446db21 100644 --- a/sl2610-function-calling/settings.gradle.kts +++ b/sl2610-function-calling/settings.gradle.kts @@ -34,6 +34,12 @@ if (providers.gradleProperty("useLocalStack").orNull == "true") { substitute(module("sk.ainet.transformers:skainet-transformers-runtime-gemma-iree")).using(project(":llm-runtime:gemma-iree")) } } + // Torq NPU target-optimizer plugin (host export-time; ENC_TORQ=1) from the sibling vendors build. + includeBuild("../../skainet-embedded-vendors") { + dependencySubstitution { + substitute(module("sk.ainet.vendors:synaptics-torq")).using(project(":synaptics-torq")) + } + } } rootProject.name = "sl2610-voice-cc-kt" diff --git a/sl2610-function-calling/src/jvmMain/kotlin/voicecc/export/MoonshineEncoderExport.kt b/sl2610-function-calling/src/jvmMain/kotlin/voicecc/export/MoonshineEncoderExport.kt index 6569f9c..400c143 100644 --- a/sl2610-function-calling/src/jvmMain/kotlin/voicecc/export/MoonshineEncoderExport.kt +++ b/sl2610-function-calling/src/jvmMain/kotlin/voicecc/export/MoonshineEncoderExport.kt @@ -116,13 +116,19 @@ private fun traceEncoder( Execution.tapeStack.pushTape(ct) try { val ectx = this as ExecutionContext + // ENC_BATCHLESS=1 — feed the module a rank-2 [frames, dim] tensor so the whole graph + // is batchless (no leading unit dim), matching the Torq-friendly hand-written enc6. + // The DSL attention/LayerNorm/FFN operate on the last dims, so rank-2 traces cleanly and + // avoids the [1,…] batch dim that trips the Torq tiler (matmul→reshape[1,S,D]→add). + val batchless = System.getenv("ENC_BATCHLESS") == "1" // Board layout [1, dim, frames] → [1, frames, dim]. ops.transpose on a rank-3 tensor // reverses ALL dims ([2,1,0] → [frames, dim, 1]); do it in 2D (squeeze → transpose → // unsqueeze) so it's an unambiguous last-two swap. - val x = if (boardLayout) { - ectx.ops.unsqueeze(ectx.ops.transpose(ectx.ops.squeeze(input, 0)), 0) - } else { - input + val x = when { + boardLayout && batchless -> ectx.ops.transpose(ectx.ops.squeeze(input, 0)) // [frames, dim] 2D + boardLayout -> ectx.ops.unsqueeze(ectx.ops.transpose(ectx.ops.squeeze(input, 0)), 0) + batchless -> ectx.ops.squeeze(input, 0) // [frames, dim] 2D + else -> input } model.forward(x, ectx) } finally { @@ -132,11 +138,41 @@ private fun traceEncoder( val rawGraph = (tape as DefaultExecutionTape).toComputeGraph(synthesizeExternalInputs = true) - // Core, HW-agnostic dtype unification (bf16-native traces record norms/reductions as f32). - // Target-specific passes (Torq tiling) live in the synaptics-torq vendor plugin and are NOT - // applied here — the emitted StableHLO stays portable. + // Core, HW-agnostic dtype unification first (bf16-native traces record norms/reductions as f32). + // The Torq tiling passes below read node OUTPUT dtypes, so this must run before them. val dtypeTarget = if (System.getenv("ENC_SKIP_DTYPE") == "1") null else floatDtype - val graph = sk.ainet.compile.opt.passes.DtypeForwardPropagationPass(targetFloatDtype = dtypeTarget).apply(rawGraph).graph + val dtyped = sk.ainet.compile.opt.passes.DtypeForwardPropagationPass(targetFloatDtype = dtypeTarget).apply(rawGraph).graph + + + // ENC_TORQ=1 → apply the Torq NPU tiling passes (attention head/query-seq tiling + FFN hidden + // tiling) from the synaptics-torq vendor plugin, so the batched matmuls fit the NPU's on-chip + // LRAM. Without this the encoder overflows LRAM at compile. Default (unset) stays portable + // (llvm-cpu). maxQuerySeqPerTile default 83 was proven at S=165; ENC_QTILE tunes it for S=207. + val graph = if (System.getenv("ENC_TORQ") == "1") { + val qTile = System.getenv("ENC_QTILE")?.toInt() ?: 83 + val hTile = System.getenv("ENC_HTILE")?.toInt() ?: 4 + sk.ainet.compile.opt.TargetOptimizers.clear() // install() is additive; reset for idempotency + sk.ainet.vendors.torq.TorqPlugin.install(maxHeadsPerTile = hTile, maxQuerySeqPerTile = qTile, ffnHiddenTile = 288, modelDim = cfg.dim) + println("[moonshineEncoderMlir] ENC_TORQ=1 — applying Torq tiling passes (hTile=$hTile, qTile=$qTile)") + val tiled = sk.ainet.compile.opt.dagPipelineFor(sk.ainet.vendors.torq.TorqPlugin.TARGET).optimize(dtyped).graph + // The tiling passes introduce new ops (e.g. FFN bias slices) whose dtypes aren't unified — + // TorqFfnTilingPass slices the bf16 fc1 bias to the f32 activation dtype without a convert. + // Re-run dtype forward-propagation to reconcile boundaries (inserts the needed converts). + val reconciled = sk.ainet.compile.opt.passes.DtypeForwardPropagationPass(targetFloatDtype = dtypeTarget).apply(tiled).graph + // The Torq NPU cannot compile f32 BATCHED attention (QK/AV matmuls + softmax) — "CSS program + // alloc" fail. Color the attention interior bf16 (bf16×bf16 matmuls, bf16 softmax) while + // residual/LayerNorm/projection stay f32, splicing converts on the boundaries (the vendor + // mixed-precision recipe). ENC_NO_BF16ATTN=1 to skip (diagnostic). + val colored = if (System.getenv("ENC_NO_BF16ATTN") == "1") reconciled + else sk.ainet.vendors.torq.TorqMatmulBf16Pass().apply(reconciled).graph + // Prune LAST: the tiling pass leaves the per-layer RoPE'd Q/K/V as dangling [1,H,S,D] + // leaves (reshape-to-batch of the SDPA fold), which `func @main` would return alongside + // the real [.., dim] memory. The Torq layout solver trips MatMulPattern:57 on those + // returned attention intermediates — drop them so only the encoder output remains. + sk.ainet.vendors.torq.TorqPruneOutputsPass(modelDim = cfg.dim).apply(colored).graph + } else { + dtyped + } return sk.ainet.compile.hlo.toStableHlo(graph, "moonshine_encoder").content } diff --git a/sl2610-function-calling/src/linuxArm64Main/kotlin/voicecc/asr/MoonshineDecoder.kt b/sl2610-function-calling/src/linuxArm64Main/kotlin/voicecc/asr/MoonshineDecoder.kt index 7d84b12..962ae50 100644 --- a/sl2610-function-calling/src/linuxArm64Main/kotlin/voicecc/asr/MoonshineDecoder.kt +++ b/sl2610-function-calling/src/linuxArm64Main/kotlin/voicecc/asr/MoonshineDecoder.kt @@ -63,6 +63,13 @@ internal class MoonshineDecoder( SystemFileSystem.source(Path("$modelDir/tokenizer.json")).buffered().use { it.readByteArray() }.decodeToString(), ) + init { + // Program the NPU clock to the vendor's value before any torq dispatch, matching the vendor + // demo's startup step (defensive — guards a clock-off boot; the vendor encoder runs at the + // boot clock too). Skipped when the encoder runs on CPU (`local-task`). + if (encDevice == "torq") TorqRunModule.enableNpuClock() + } + fun transcribe(wav: String): String? { SystemFileSystem.createDirectories(Path(work)) diff --git a/sl2610-function-calling/src/linuxArm64Main/kotlin/voicecc/asr/TorqRunModule.kt b/sl2610-function-calling/src/linuxArm64Main/kotlin/voicecc/asr/TorqRunModule.kt index 543b5ba..a379410 100644 --- a/sl2610-function-calling/src/linuxArm64Main/kotlin/voicecc/asr/TorqRunModule.kt +++ b/sl2610-function-calling/src/linuxArm64Main/kotlin/voicecc/asr/TorqRunModule.kt @@ -73,4 +73,23 @@ internal class TorqRunModule( private fun quote(s: String): String = if (s.all { it.isLetterOrDigit() || it in "-_=/.,:+@" }) s else "'" + s.replace("'", "'\\''") + "'" + + companion object { + /** + * Program the SL2610 NPU clock to the vendor's value before running on `--device=torq`. + * + * Mirrors the vendor demo's startup step (`utils/npu.py enable_npu_clock()` → + * `devmem 0xf7e104b0 32 0x216`). This is a DEFENSIVE setup step, not a correctness fix: the + * vendor `encoder.vmfb` was verified 2026-07-07 to produce bit-identical output at both the + * boot value (0x298) and 0x216, so the clock isn't why our SELF-compiled encoder zeroed (that + * is the dispatch-fusion gap — vendor=1 dispatch, ours=40). We still set it to guarantee a + * known-good clock state matching the vendor (guards a clock-off boot). Idempotent and cheap. + */ + fun enableNpuClock() { + val fp = popen("devmem 0xf7e104b0 32 0x216 2>&1", "r") ?: run { + println("[torq] NPU clock enable: popen failed"); return + } + pclose(fp) + } + } }