From 8a37fe05eb428af2219c26872ce98a360c4ec5ef Mon Sep 17 00:00:00 2001 From: linear3735 Date: Tue, 6 Oct 2026 15:38:37 +0800 Subject: [PATCH 1/8] cuda: add prefix-continuation kernels for hybrid qwen3_5 models Copy helpers (cs1_copy_dd/copy2d), f32-state GDN continuation (cs1_gdn_prefill_x) and windowed gated attention (cs1_attention_gated_prefix) so a forward can resume from a precomputed prefix state bitwise-equivalent to a one-shot pass (except GEMM M-shape). Library ABI stays at version 4; existing workers are unaffected. Evidence and conditions: ThinkFlowLab/system1-omni run /aifs4su/shiheming/jev/runs/20261006-jev-vl-native-r2 (H800, single-pass versus two-stage bitwise checks in tests/qwen3_5/kernels.rs). Co-Authored-By: Claude Code --- Cargo.lock | 79 ++- Cargo.toml | 2 +- src/backends/cuda/qwen3_5/attention.cu | 28 +- src/backends/cuda/qwen3_5/gdn_prefill.cu | 60 +- src/backends/cuda/qwen3_5/ops.h | 24 +- src/backends/cuda/qwen3_5/runtime.cu | 13 + src/models/qwen3_5/native/src/cuda.rs | 49 +- src/models/qwen3_5/native/src/lib.rs | 2 + src/models/qwen3_5/native/src/model.rs | 702 +++++++++++++++++++++-- tests/qwen3_5/kernels.rs | 199 +++++++ 10 files changed, 1081 insertions(+), 77 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index dcba5bb4..65d45fe5 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -143,6 +143,15 @@ dependencies = [ "generic-array", ] +[[package]] +name = "block-buffer" +version = "0.12.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d2f6c7dbe95a6ed67ad9f18e57daf93a2f034c524b99fd2b76d18fdfeb6660aa" +dependencies = [ + "hybrid-array", +] + [[package]] name = "bumpalo" version = "3.20.3" @@ -212,6 +221,12 @@ dependencies = [ "static_assertions", ] +[[package]] +name = "const-oid" +version = "0.10.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a6ef517f0926dd24a1582492c791b6a4818a4d94e789a334894aa15b0d12f55c" + [[package]] name = "cpufeatures" version = "0.2.17" @@ -271,6 +286,15 @@ dependencies = [ "typenum", ] +[[package]] +name = "crypto-common" +version = "0.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ce6e4c961d6cd6c9a86db418387425e8bdeaf05b3c8bc1411e6dca4c252f1453" +dependencies = [ + "hybrid-array", +] + [[package]] name = "daachorse" version = "3.0.3" @@ -358,8 +382,19 @@ version = "0.10.7" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9ed9a281f7bc9b7576e61468ba615a66a5c8cfdff42420a70aa82701a3b1e292" dependencies = [ - "block-buffer", - "crypto-common", + "block-buffer 0.10.4", + "crypto-common 0.1.7", +] + +[[package]] +name = "digest" +version = "0.11.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f1dd6dbb5841937940781866fa1281a1ff7bd3bf827091440879f9994983d5c2" +dependencies = [ + "block-buffer 0.12.1", + "const-oid", + "crypto-common 0.2.2", ] [[package]] @@ -630,6 +665,15 @@ version = "1.0.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "df3b46402a9d5adb4c86a0cf463f42e19994e3ee891101b1841f30a545cb49a9" +[[package]] +name = "hybrid-array" +version = "0.4.15" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "27f864f10dfb56725ce5ce5472bc52252c8f93a4ab86327122cebf62c5f59a17" +dependencies = [ + "typenum", +] + [[package]] name = "hyper" version = "1.11.1" @@ -1022,6 +1066,22 @@ dependencies = [ "tokio", ] +[[package]] +name = "omni-jev-vl-native" +version = "0.1.0" +dependencies = [ + "anyhow", + "axum", + "half", + "omni-qwen3-5-native", + "omni-runtime", + "safetensors 0.8.0", + "serde_json", + "sha2 0.11.0", + "tokenizers 0.22.2", + "tokio", +] + [[package]] name = "omni-laya" version = "0.1.0" @@ -1035,7 +1095,7 @@ dependencies = [ "safetensors 0.6.2", "serde", "serde_json", - "sha2", + "sha2 0.10.9", "tempfile", "tokenizers 0.23.2", "tokio", @@ -1572,7 +1632,18 @@ checksum = "a7507d819769d01a365ab707794a4084392c824f54a7a6a7862f8c3d0892b283" dependencies = [ "cfg-if", "cpufeatures 0.2.17", - "digest", + "digest 0.10.7", +] + +[[package]] +name = "sha2" +version = "0.11.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "446ba717509524cb3f22f17ecc096f10f4822d76ab5c0b9822c5f9c284e825f4" +dependencies = [ + "cfg-if", + "cpufeatures 0.3.1", + "digest 0.11.3", ] [[package]] diff --git a/Cargo.toml b/Cargo.toml index 242d51ca..dc0bef34 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -1,3 +1,3 @@ [workspace] -members = ["src/frontend", "src/runtime", "src/models/clm", "src/models/cua_s1/native", "src/models/qwen3_5/native", "src/models/open_jev/native", "src/models/laya", "src/backends/cuda"] +members = ["src/frontend", "src/runtime", "src/models/clm", "src/models/cua_s1/native", "src/models/qwen3_5/native", "src/models/open_jev/native", "src/models/jev_vl/native", "src/models/laya", "src/backends/cuda"] resolver = "3" diff --git a/src/backends/cuda/qwen3_5/attention.cu b/src/backends/cuda/qwen3_5/attention.cu index 20ffceed..71cf6e83 100644 --- a/src/backends/cuda/qwen3_5/attention.cu +++ b/src/backends/cuda/qwen3_5/attention.cu @@ -78,17 +78,20 @@ constexpr int D = 256, BM = 64, BN = 32, THREADS = 128; constexpr int LDS = D + 8; // shared row stride in elements: 528 bytes keeps ldmatrix conflict-free constexpr int SMEM_BYTES = (BM + 2 * BN) * LDS * 2; +// q_base offsets row 0 of the q/gate/out buffers: this launch covers the window +// [q_base, T_total) while k/v cover [0, T_total). Used by the cached-prefix +// continuation; q_base = 0 reduces to the plain full pass. template __global__ void __launch_bounds__(THREADS) flash_kernel(const bf16* __restrict__ q, const bf16* __restrict__ k, const bf16* __restrict__ v, int ldv, const bf16* __restrict__ gate, bf16* __restrict__ out, int T, int Hq, int Hk, - float scale_log2) { + float scale_log2, int q_base) { extern __shared__ __align__(16) unsigned char smem[]; bf16* qs = reinterpret_cast(smem); bf16* ks = qs + BM * LDS; bf16* vs = ks + BN * LDS; const int h = blockIdx.y, hk = h / (Hq / Hk); - const int q0 = (gridDim.x - 1 - blockIdx.x) * BM; // the longest blocks first + const int q0 = q_base + (gridDim.x - 1 - blockIdx.x) * BM; // the longest blocks first const int tid = threadIdx.x, warp = tid / 32, lane = tid % 32; const int g = lane / 4, t = lane % 4; const int row0 = q0 + warp * 16; // this warp's first query @@ -232,20 +235,21 @@ __global__ void __launch_bounds__(THREADS) template int launch(const void* q, const void* k, const void* v, int ldv, const void* gate, void* out, - int T, int Hq, int Hk, int Dh, float scale, void* stream) { + int T, int Hq, int Hk, int Dh, float scale, int q_base, void* stream) { if (Dh != D || Hk <= 0 || Hq <= 0 || Hq % Hk != 0 || ldv % 8 != 0 || ldv < Hk * Dh || T < 0) return cudaErrorInvalidValue; - if (T == 0) return cudaSuccess; + if (q_base < 0 || q_base % BM || q_base > T) return cudaErrorInvalidValue; + if (T == q_base) return cudaSuccess; if (Gated && gate == nullptr) return cudaErrorInvalidValue; // Once per specialization (for the device current at the first call). static const cudaError_t configured = cudaFuncSetAttribute( flash_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, SMEM_BYTES); if (configured != cudaSuccess) return configured; constexpr float LOG2E = 1.4426950408889634f; - flash_kernel<<<<(stream)>>>( static_cast(q), static_cast(k), static_cast(v), ldv, - static_cast(gate), static_cast(out), T, Hq, Hk, scale * LOG2E); + static_cast(gate), static_cast(out), T, Hq, Hk, scale * LOG2E, q_base); return cudaGetLastError(); } @@ -274,10 +278,18 @@ extern "C" int cs1_attn_prep(const void* qg, const void* kr, int ld, const void* extern "C" int cs1_attention(const void* q, const void* k, const void* v, int ldv, void* out, int T, int Hq, int Hk, int Dh, float scale, void* stream) { - return flash::launch(q, k, v, ldv, nullptr, out, T, Hq, Hk, Dh, scale, stream); + return flash::launch(q, k, v, ldv, nullptr, out, T, Hq, Hk, Dh, scale, 0, stream); } extern "C" int cs1_attention_gated(const void* q, const void* k, const void* v, int ldv, const void* gate, void* out, int T, int Hq, int Hk, int Dh, float scale, void* stream) { - return flash::launch(q, k, v, ldv, gate, out, T, Hq, Hk, Dh, scale, stream); + return flash::launch(q, k, v, ldv, gate, out, T, Hq, Hk, Dh, scale, 0, stream); +} + +// Same layout contract as the full pass; only the covered query rows differ: +// q_base is a multiple of 64 and rows below it keep whatever was already in k/v. +extern "C" int cs1_attention_gated_prefix(const void* q, const void* k, const void* v, int ldv, const void* gate, + void* out, int T, int Hq, int Hk, int Dh, float scale, int q_base, + void* stream) { + return flash::launch(q, k, v, ldv, gate, out, T, Hq, Hk, Dh, scale, q_base, stream); } diff --git a/src/backends/cuda/qwen3_5/gdn_prefill.cu b/src/backends/cuda/qwen3_5/gdn_prefill.cu index 676ce446..79fbd2a6 100644 --- a/src/backends/cuda/qwen3_5/gdn_prefill.cu +++ b/src/backends/cuda/qwen3_5/gdn_prefill.cu @@ -337,7 +337,8 @@ constexpr int SS_LD = BVS + 8; // bfloat16 row stride of the S copy and v_n constexpr int STAGE = C * WS_LD; // elements of one staged w or kd constexpr size_t SMEM2_BYTES = (4 * STAGE + K * SS_LD + C * SS_LD) * 2; -__global__ void __launch_bounds__(ST_THREADS) gdn_chunk_state(Work ws, int NC) { +__global__ void __launch_bounds__(ST_THREADS) gdn_chunk_state(Work ws, int NC, const float* __restrict__ S_IN, + float* __restrict__ S_OUT) { extern __shared__ __align__(128) unsigned char sm[]; bf16* wbuf = reinterpret_cast(sm); // [2][C][WS_LD] bf16* kbuf = wbuf + 2 * STAGE; // [2][C][WS_LD] @@ -362,6 +363,21 @@ __global__ void __launch_bounds__(ST_THREADS) gdn_chunk_state(Work ws, int NC) { for (int mt = 0; mt < 2; mt++) #pragma unroll for (int nt = 0; nt < 4; nt++) st[mt][nt][0] = st[mt][nt][1] = st[mt][nt][2] = st[mt][nt][3] = 0.f; + if (S_IN != nullptr) { + // float32 state seeded for a continuation: identical to the registers a + // full pass would carry into this chunk, so the scan repeats one pass. + const float* si = S_IN + (size_t)h * K * V; +#pragma unroll + for (int mt = 0; mt < 2; mt++) +#pragma unroll + for (int nt = 0; nt < 4; nt++) +#pragma unroll + for (int e = 0; e < 4; e++) { + const int row = warp * 32 + mt * 16 + g + (e >> 1) * 8; + const int col = vb0 + nt * 8 + 2 * t + (e & 1); + st[mt][nt][e] = si[(size_t)row * V + col]; + } + } load(0, 0); for (int c = 0; c < NC; c++) { @@ -438,6 +454,20 @@ __global__ void __launch_bounds__(ST_THREADS) gdn_chunk_state(Work ws, int NC) { } } } + if (S_OUT != nullptr) { + float* so = S_OUT + (size_t)h * K * V; +#pragma unroll + for (int mt = 0; mt < 2; mt++) +#pragma unroll + for (int nt = 0; nt < 4; nt++) +#pragma unroll + for (int r = 0; r < 2; r++) { + const int row = warp * 32 + mt * 16 + g + r * 8; + const int col = vb0 + nt * 8 + 2 * t; + *reinterpret_cast(so + (size_t)row * V + col) = + make_float2(st[mt][nt][2 * r], st[mt][nt][2 * r + 1]); + } + } } // ---- kernel 3: the output, per chunk ---- @@ -535,10 +565,19 @@ extern "C" { size_t cs1_gdn_workspace_floats(int T, int H) { return (Layout(T, H).total + 3) / 4; } -int cs1_gdn_prefill(const void* q, const void* k, const void* v, const float* g, const void* beta, - void* o, float* workspace, int T, int H, int HK, float scale, void* stream) { +static int gdn_prefill_run(const void* q, const void* k, const void* v, const float* g, + const void* beta, void* o, float* workspace, int T, int H, int HK, + float scale, const void* s_in, void* s_out, void* stream) { if (T < 0 || HK <= 0 || H % HK != 0) return cudaErrorInvalidValue; - if (T == 0) return cudaSuccess; + const float* si = static_cast(s_in); + float* so = static_cast(s_out); + if (T == 0) { + // No tokens: the state passes through unchanged, for symmetric capture. + if (so != nullptr && si != nullptr && + cudaMemcpy(so, si, (size_t)H * K * V * 4, cudaMemcpyDeviceToDevice) != cudaSuccess) + return cudaGetLastError(); + return cudaSuccess; + } // once per process (for the device current at the first call) static const cudaError_t configured = [] { cudaError_t e = cudaFuncSetAttribute(gdn_chunk_prep, cudaFuncAttributeMaxDynamicSharedMemorySize, @@ -558,9 +597,20 @@ int cs1_gdn_prefill(const void* q, const void* k, const void* v, const float* g, gdn_chunk_prep<<>>( static_cast(q), static_cast(k), static_cast(v), g, static_cast(beta), ws, T, H, HK, scale); - gdn_chunk_state<<>>(ws, NC); + gdn_chunk_state<<>>(ws, NC, si, so); gdn_chunk_out<<>>(ws, static_cast(o), T, H); return cudaGetLastError(); } +int cs1_gdn_prefill(const void* q, const void* k, const void* v, const float* g, const void* beta, + void* o, float* workspace, int T, int H, int HK, float scale, void* stream) { + return gdn_prefill_run(q, k, v, g, beta, o, workspace, T, H, HK, scale, nullptr, nullptr, stream); +} + +int cs1_gdn_prefill_x(const void* q, const void* k, const void* v, const float* g, const void* beta, + void* o, float* workspace, int T, int H, int HK, float scale, + const void* s_in, void* s_out, void* stream) { + return gdn_prefill_run(q, k, v, g, beta, o, workspace, T, H, HK, scale, s_in, s_out, stream); +} + } // extern "C" diff --git a/src/backends/cuda/qwen3_5/ops.h b/src/backends/cuda/qwen3_5/ops.h index ae5ce4db..8248c153 100644 --- a/src/backends/cuda/qwen3_5/ops.h +++ b/src/backends/cuda/qwen3_5/ops.h @@ -12,7 +12,8 @@ #include #include -// Bumped whenever the required interface below changes. +// Bumped whenever the required interface below changes. The R2d prefix-cache entry +// points below are ADDITIVE; existing callers keep working with the ABI-4 interface. #define CS1_ABI_VERSION 4 #ifdef __cplusplus @@ -35,6 +36,11 @@ int cs1_graph_destroy(void* exec); // Copy and wait for the copy. int cs1_upload(void* dst, const void* src, size_t bytes, void* stream); int cs1_download(void* dst, const void* src, size_t bytes, void* stream); +// Device-to-device copy of `bytes` (queue-only completion). +int cs1_copy_dd(void* dst, const void* src, size_t bytes, void* stream); +// Pitched copy both ways (default direction = device-to-device): +// height rows of `width`, output pitch dpitch, input pitch spitch, in bytes. +int cs1_copy2d(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, void* stream); // ---- operations ---- @@ -69,6 +75,15 @@ size_t cs1_gdn_workspace_floats(int T, int H); int cs1_gdn_prefill(const void* q, const void* k, const void* v, const float* g, const void* beta, void* o, float* workspace, int T, int H, int HK, float scale, void* stream); +// Same scan with an explicit state: s_in is the [H, K, V] float32 state before +// this window (null = zero), s_out receives the [H, K, V] float32 state after +// it (null = skip). The float32 state makes a continuation starting exactly at +// a chunk boundary repeat the arithmetic of one full pass bit for bit. Used +// for the cached-prefix continuation and for its capture. +int cs1_gdn_prefill_x(const void* q, const void* k, const void* v, const float* g, const void* beta, + void* o, float* workspace, int T, int H, int HK, float scale, + const void* s_in, void* s_out, void* stream); + // Attention inputs: q and gate from qg [T, Hq, 2*Dh], k from kr [T, Hk, Dh], both in rows // of ld; per-head zero-centred RMSNorm, then rotary embedding on the first 2*half dims // using cos/sin [T, half] (bfloat16). Writes q [T, Hq, Dh], gate [T, Hq*Dh], k [T, Hk, Dh]. @@ -87,6 +102,13 @@ int cs1_attention(const void* q, const void* k, const void* v, int ldv, void* ou int cs1_attention_gated(const void* q, const void* k, const void* v, int ldv, const void* gate, void* out, int T, int Hq, int Hk, int Dh, float scale, void* stream); +// Windowed variant for the cached prefix: k and v cover rows [0, T) (the cached +// prefix must already be in place); q, gate and out cover rows [q_base, T) only. +// q_base must be a multiple of 64; q_base = 0 reduces to cs1_attention_gated. +int cs1_attention_gated_prefix(const void* q, const void* k, const void* v, int ldv, const void* gate, + void* out, int T, int Hq, int Hk, int Dh, float scale, int q_base, + void* stream); + // x = x * sigmoid(gate), n elements. int cs1_sigmoid_gate(void* x, const void* gate, size_t n, void* stream); diff --git a/src/backends/cuda/qwen3_5/runtime.cu b/src/backends/cuda/qwen3_5/runtime.cu index 02a7db6f..1757a0e0 100644 --- a/src/backends/cuda/qwen3_5/runtime.cu +++ b/src/backends/cuda/qwen3_5/runtime.cu @@ -34,6 +34,19 @@ int cs1_download(void* dst, const void* src, size_t bytes, void* stream) { return e != cudaSuccess ? e : cudaStreamSynchronize(st); } +int cs1_copy_dd(void* dst, const void* src, size_t bytes, void* stream) { + const cudaError_t e = + cudaMemcpyAsync(dst, src, bytes, cudaMemcpyDeviceToDevice, static_cast(stream)); + return e != cudaSuccess ? e : cudaGetLastError(); +} + +int cs1_copy2d(void* dst, size_t dpitch, const void* src, size_t spitch, size_t width, size_t height, + void* stream) { + const cudaError_t e = cudaMemcpy2DAsync(dst, dpitch, src, spitch, width, height, + cudaMemcpyDeviceToDevice, static_cast(stream)); + return e != cudaSuccess ? e : cudaGetLastError(); +} + int cs1_graph_begin(void* stream) { const cudaError_t e = cudaStreamBeginCapture(static_cast(stream), cudaStreamCaptureModeThreadLocal); if (e != cudaSuccess) (void)cudaGetLastError(); diff --git a/src/models/qwen3_5/native/src/cuda.rs b/src/models/qwen3_5/native/src/cuda.rs index 7433ee67..cceb6aac 100644 --- a/src/models/qwen3_5/native/src/cuda.rs +++ b/src/models/qwen3_5/native/src/cuda.rs @@ -8,7 +8,8 @@ use std::sync::OnceLock; use anyhow::{Context, Result, bail, ensure}; -/// `CS1_ABI_VERSION` in ops.h. +/// `CS1_ABI_VERSION` in ops.h. The R2d prefix-cache functions are additive on top +/// of the ABI-4 interface, which the sibling cua_s1 / open_jev workers also resolve. const ABI_VERSION: u32 = 4; pub const LIBRARY: &str = "libqwen3_5_cuda.so"; @@ -61,6 +62,8 @@ api! { cs1_graph_destroy(exec: *mut c_void) -> c_int; cs1_upload(dst: *mut c_void, src: *const c_void, bytes: usize, stream: Stream) -> c_int; cs1_download(dst: *mut c_void, src: *const c_void, bytes: usize, stream: Stream) -> c_int; + cs1_copy_dd(dst: *mut c_void, src: *const c_void, bytes: usize, stream: Stream) -> c_int; + cs1_copy2d(dst: *mut c_void, dpitch: usize, src: *const c_void, spitch: usize, width: usize, height: usize, stream: Stream) -> c_int; cs1_embed(ids: *const i32, table: *const c_void, out: *mut c_void, t: c_int, d: c_int, stream: Stream) -> c_int; cs1_rms_norm( x: *const c_void, w: *const c_void, out: *mut c_void, rows: c_int, d: c_int, eps: f32, stream: Stream, @@ -86,6 +89,11 @@ api! { q: *const c_void, k: *const c_void, v: *const c_void, g: *const f32, beta: *const c_void, o: *mut c_void, workspace: *mut f32, t: c_int, h: c_int, hk: c_int, scale: f32, stream: Stream, ) -> c_int; + cs1_gdn_prefill_x( + q: *const c_void, k: *const c_void, v: *const c_void, g: *const f32, beta: *const c_void, o: *mut c_void, + workspace: *mut f32, t: c_int, h: c_int, hk: c_int, scale: f32, + s_in: *const c_void, s_out: *mut c_void, stream: Stream, + ) -> c_int; cs1_attn_prep( qg: *const c_void, kr: *const c_void, ld: c_int, qw: *const c_void, kw: *const c_void, cos: *const c_void, sin: *const c_void, q: *mut c_void, gate: *mut c_void, k: *mut c_void, t: c_int, hq: c_int, hk: c_int, @@ -99,6 +107,10 @@ api! { q: *const c_void, k: *const c_void, v: *const c_void, ldv: c_int, gate: *const c_void, out: *mut c_void, t: c_int, hq: c_int, hk: c_int, dh: c_int, scale: f32, stream: Stream, ) -> c_int; + cs1_attention_gated_prefix( + q: *const c_void, k: *const c_void, v: *const c_void, ldv: c_int, gate: *const c_void, + out: *mut c_void, t: c_int, hq: c_int, hk: c_int, dh: c_int, scale: f32, q_base: c_int, stream: Stream, + ) -> c_int; cs1_sigmoid_gate(x: *mut c_void, gate: *const c_void, n: usize, stream: Stream) -> c_int; cs1_silu_mul(gate_up: *const c_void, ld: c_int, out: *mut c_void, t: c_int, i: c_int, stream: Stream) -> c_int; cs1_gemm_create(workspace_bytes: usize) -> *mut c_void; @@ -246,6 +258,41 @@ pub unsafe fn download(dst: &mut [u8], src: *const c_void, stream: Stream) -> Re ) } +/// Queue a device-to-device copy of `bytes`; completion is only stream-ordered. +/// +/// # Safety +/// Both ranges of `bytes` must be valid device allocations, non-overlapping. +pub unsafe fn copy_dd( + dst: *mut c_void, + src: *const c_void, + bytes: usize, + stream: Stream, +) -> Result<()> { + check( + unsafe { (api().cs1_copy_dd)(dst, src, bytes, stream) }, + "device copy", + ) +} + +/// Queue a pitched device-to-device copy: `height` rows of `width` bytes. +/// +/// # Safety +/// `src`/`dst` must be device allocations with the given pitches and heights. +pub unsafe fn copy2d( + dst: *mut c_void, + dpitch: usize, + src: *const c_void, + spitch: usize, + width: usize, + height: usize, + stream: Stream, +) -> Result<()> { + check( + unsafe { (api().cs1_copy2d)(dst, dpitch, src, spitch, width, height, stream) }, + "pitched device copy", + ) +} + pub struct Graph { exec: *mut c_void, } diff --git a/src/models/qwen3_5/native/src/lib.rs b/src/models/qwen3_5/native/src/lib.rs index 929435b5..04b1f87f 100644 --- a/src/models/qwen3_5/native/src/lib.rs +++ b/src/models/qwen3_5/native/src/lib.rs @@ -3,3 +3,5 @@ pub mod cuda; pub mod json; pub mod model; + +pub mod inputs; diff --git a/src/models/qwen3_5/native/src/model.rs b/src/models/qwen3_5/native/src/model.rs index c8ab5d60..2fac8cbd 100644 --- a/src/models/qwen3_5/native/src/model.rs +++ b/src/models/qwen3_5/native/src/model.rs @@ -6,8 +6,8 @@ //! The order of operations follows `modeling_qwen3_5.py`, and so do the points where //! it rounds to bfloat16, except inside attention and the Gated DeltaNet prefill (see //! their kernels). Text prompts use one position per -//! token, so the multimodal rotary sections all get the same position and the -//! rotary embedding is the plain one. +//! token. The explicit multimodal boundary inserts adapted image rows and supplies +//! the interleaved temporal/height/width rotary positions to the same layer loop. use std::collections::{HashMap, VecDeque}; use std::ffi::c_void; @@ -17,6 +17,7 @@ use anyhow::{Context, Result, bail, ensure}; use serde_json::Value as Json; use crate::cuda::{self, DeviceBuffer, Stream, check}; +use crate::inputs::{MultimodalInput, rotary_tables}; const ALIGN: usize = 256; const BF16: usize = 2; @@ -35,6 +36,9 @@ pub struct Config { /// Half the number of rotary dims (rotate_half pairs dim i with dim i + half). pub rotary_half: usize, pub rope_theta: f64, + pub mrope_section: [usize; 3], + pub max_positions: usize, + pub image_token_id: Option, pub lin_k_heads: usize, pub lin_v_heads: usize, pub lin_k_dim: usize, @@ -69,6 +73,31 @@ impl Config { other => bail!("unknown layer type {other:?}"), }) .collect::>>()?; + let sections = rope + .get("mrope_section") + .cloned() + .unwrap_or(serde_json::json!([11, 11, 10])); + let sections = sections + .as_array() + .context("mrope_section must be an array")? + .iter() + .map(|v| { + v.as_u64() + .and_then(|n| usize::try_from(n).ok()) + .context("mrope_section must contain integers") + }) + .collect::>>()?; + let mrope_section: [usize; 3] = sections + .try_into() + .map_err(|_| anyhow::anyhow!("mrope_section must have three entries"))?; + let image_token_id = root + .get("image_token_id") + .map(|v| { + v.as_u64() + .and_then(|id| u32::try_from(id).ok()) + .context("image_token_id must be a u32") + }) + .transpose()?; let cfg = Config { hidden: int("hidden_size")?, intermediate: int("intermediate_size")?, @@ -81,6 +110,9 @@ impl Config { .as_f64() .or(c["rope_theta"].as_f64()) .context("rope_theta")?, + mrope_section, + max_positions: int("max_position_embeddings")?, + image_token_id, lin_k_heads: int("linear_num_key_heads")?, lin_v_heads: int("linear_num_value_heads")?, lin_k_dim: int("linear_key_head_dim")?, @@ -115,6 +147,25 @@ impl Config { cfg.lin_v_dim ); ensure!(cfg.rotary_half == 32, "{} rotary dims", 2 * cfg.rotary_half); + ensure!( + cfg.rope_theta.is_finite() && cfg.rope_theta > 0.0, + "invalid rope_theta" + ); + ensure!( + rope["mrope_interleaved"].as_bool() != Some(false), + "non-interleaved mrope is unsupported" + ); + ensure!( + cfg.mrope_section + .iter() + .try_fold(0usize, |sum, &x| sum.checked_add(x)) + == Some(cfg.rotary_half), + "mrope sections must sum to rotary_half" + ); + ensure!( + cfg.max_positions > 0 && cfg.max_positions <= (1 << 24), + "unsupported max_position_embeddings" + ); ensure!( cfg.kv_heads > 0 && cfg.heads.is_multiple_of(cfg.kv_heads), "attention heads" @@ -385,6 +436,8 @@ struct Scratch { act: usize, cos: usize, sin: usize, + custom_cos: usize, + custom_sin: usize, } impl Scratch { @@ -423,6 +476,8 @@ impl Scratch { take(cap * cfg.intermediate * BF16), take(cap * cfg.rotary_half * BF16), take(cap * cfg.rotary_half * BF16), + take(cap * cfg.rotary_half * BF16), + take(cap * cfg.rotary_half * BF16), ]; let buf = DeviceBuffer::new(next)?; let [ @@ -448,24 +503,16 @@ impl Scratch { act, cos, sin, + custom_cos, + custom_sin, ] = offsets; - // Rotary tables close to how Qwen3_5TextRotaryEmbedding builds them: inv_freq and - // freqs = inv_freq * position in float32, cos and sin rounded to bfloat16. Here - // cos and sin are taken in float64 on the host rather than in float32 on the - // GPU, so a few of the rounded values can differ by one bfloat16 step. - let half = cfg.rotary_half; - let inv: Vec = (0..half) - .map(|i| 1.0f32 / (cfg.rope_theta as f32).powf((2 * i) as f32 / (2 * half) as f32)) - .collect(); - let mut cos_t = Vec::with_capacity(cap * half * BF16); - let mut sin_t = Vec::with_capacity(cap * half * BF16); - for pos in 0..cap { - for &f in &inv { - let freq = (f * pos as f32) as f64; - cos_t.extend(half::bf16::from_f32(freq.cos() as f32).to_le_bytes()); - sin_t.extend(half::bf16::from_f32(freq.sin() as f32).to_le_bytes()); - } - } + let positions: Vec = (0..cap as i64).collect(); + let (cos_t, sin_t) = rotary_tables( + [&positions; 3], + cfg.rotary_half, + cfg.rope_theta, + cfg.mrope_section, + ); // SAFETY: both tables were laid out for cap * rotary_half bfloat16 values. unsafe { cuda::upload(buf.at(cos), &cos_t, stream)?; @@ -496,6 +543,8 @@ impl Scratch { act, cos, sin, + custom_cos, + custom_sin, }) } @@ -504,6 +553,25 @@ impl Scratch { } } +/// The cached device-side state of one token prefix (rows `[0, len)`) for the R2d +/// hybrid path: per full-attention layer the post-prep K rows plus the pre-GEMM V +/// rows, per Gated DeltaNet layer the float32 recurrent state and the three +/// pre-conv projection columns feeding the conv window. States belong to the +/// prefix itself; a continuation seeds its own scratch from them, read-only. +pub struct PrefixState { + /// Tokens this prefix covers; always a multiple of 64 (the GDN chunk length), + /// which also aligns the flash-attention key tiles of the one-shot pass. + pub len: usize, + /// Post-prep K [len, Hk*Dh] and raw V [len, Hk*Dh] rows per full-attention layer. + attn_kv: Vec<(DeviceBuffer, DeviceBuffer)>, + /// Float32 [H, K, V] recurrent states, one per Gated DeltaNet layer. + gdn_state: Vec, + /// Three pre-conv projection columns [3, gdn_in width], one per Gated DeltaNet layer. + conv_tail: Vec, + /// Total device bytes, for cache-budget accounting by the caller. + pub bytes: usize, +} + pub struct Model { pub cfg: Config, _weights: Weights, @@ -632,15 +700,7 @@ impl Model { ) } - /// The final-norm hidden state at the last position, as float32. - pub fn forward(&mut self, ids: &[u32]) -> Result> { - let t = ids.len(); - ensure!(t > 0, "empty prompt"); - let (vocab, h) = (self.embed.shape[0], self.cfg.hidden); - ensure!( - ids.iter().all(|&i| (i as usize) < vocab), - "token id outside the vocabulary" - ); + fn prepare_scratch(&mut self, t: usize) -> Result<()> { cuda::set_device(0)?; if self.scratch.as_ref().is_none_or(|s| t > s.cap) { self.graphs.clear(); @@ -651,16 +711,33 @@ impl Model { self.stream, )?); } + Ok(()) + } + + /// The final-norm hidden state at the last position, as float32. + pub fn forward(&mut self, ids: &[u32]) -> Result> { + let t = ids.len(); + ensure!( + t > 0 && t <= self.cfg.max_positions, + "empty or oversized prompt" + ); + ensure!( + ids.iter().all(|&i| (i as usize) < self.embed.shape[0]), + "token id outside the vocabulary" + ); + self.prepare_scratch(t)?; let s = self.scratch.as_ref().unwrap(); - let ids32: Vec = ids.iter().flat_map(|&i| (i as i32).to_le_bytes()).collect(); - // SAFETY: the ids buffer holds at least t int32 values. - unsafe { cuda::upload(s.at(s.ids), &ids32, self.stream)? }; + self.embed_tokens(s, ids)?; if self.graph_enabled { - if !self.graphs.iter().any(|(length, _)| *length == t) { - // Initialize every cuBLASLt plan before stream capture. - self.run(s, t)?; + if let Some((_, graph)) = self.graphs.iter().find(|(length, _)| *length == t) { + graph.launch(self.stream)?; + } else { + // Warm GEMM plans and keep this eager result for the cache miss. + // run() advances s.res in place and no longer embeds tokens, so + // launching the new graph here would advance the residual twice. + self.run(s, t, false)?; cuda::synchronize(self.stream)?; - match cuda::Graph::capture(self.stream, || self.run(s, t)) { + match cuda::Graph::capture(self.stream, || self.run(s, t, false)) { Ok(graph) => { if self.graphs.len() == 64 { self.graphs.pop_front(); @@ -669,27 +746,238 @@ impl Model { } Err(error) => { // Capture records without executing: the eager result is valid. - // Disable graphs for this worker rather than retrying failures. eprintln!("CUDA Graph capture failed; using eager execution: {error:#}"); self.graph_enabled = false; self.graphs.clear(); } } } - if self.graph_enabled { - self.graphs - .iter() - .find(|(length, _)| *length == t) - .unwrap() - .1 - .launch(self.stream)?; - } } else { - self.run(s, t)?; + self.run(s, t, false)?; + } + self.last_hidden(s, t) + } + + /// Prefill one unpadded prompt with already-adapted BF16 image embeddings and + /// explicit `[3, 1, sequence]` T/H/W positions. No vision tower runs here. + /// Load a checkpoint with the matching multimodal language adapter merged. + pub fn forward_multimodal(&mut self, input: &MultimodalInput<'_>) -> Result> { + let image_token = self + .cfg + .image_token_id + .context("checkpoint has no image_token_id")?; + input.validate( + self.cfg.hidden, + self.embed.shape[0], + image_token, + self.cfg.max_positions, + )?; + let t = input.token_ids.len(); + self.prepare_scratch(t)?; + let s = self.scratch.as_ref().unwrap(); + self.upload_positions(s, input.position_ids)?; + self.embed_tokens(s, input.token_ids)?; + self.overwrite_image_rows(s, input, 0)?; + self.run(s, t, true)?; + self.last_hidden(s, t) + } + + fn upload_positions(&self, s: &Scratch, positions: [&[i64]; 3]) -> Result<()> { + let (cos, sin) = rotary_tables( + positions, + self.cfg.rotary_half, + self.cfg.rope_theta, + self.cfg.mrope_section, + ); + // SAFETY: tables contain at most s.cap rows of rotary_half BF16 values. + unsafe { + cuda::upload(s.at(s.custom_cos), &cos, self.stream)?; + cuda::upload(s.at(s.custom_sin), &sin, self.stream)?; + } + Ok(()) + } + + /// Overwrite the residual rows of validated image placeholders with the adapted + /// image embeddings. `qb` is the absolute row base of the passed slice inside + /// the residual buffer (0 for a full prompt, the cached prefix length for a + /// continuation — indices are local to the slice in both cases). + fn overwrite_image_rows( + &self, + s: &Scratch, + input: &MultimodalInput<'_>, + qb: usize, + ) -> Result<()> { + let bytes: Vec = input + .image_embeddings + .iter() + .flat_map(|x| x.to_le_bytes()) + .collect(); + // Coalesce adjacent placeholders. Text rows remain those of embed_tokens. + let indices = input.image_token_indices; + let mut begin = 0; + while begin < indices.len() { + let mut end = begin + 1; + while end < indices.len() && indices[end] == indices[end - 1] + 1 { + end += 1; + } + let row_bytes = self.cfg.hidden * BF16; + // SAFETY: validated indices lie in the residual buffer, and features + // contain exactly one hidden-size BF16 row per placeholder. + unsafe { + cuda::upload( + s.at(s.res + (qb + indices[begin]) * row_bytes), + &bytes[begin * row_bytes..end * row_bytes], + self.stream, + )?; + } + begin = end; + } + Ok(()) + } + + /// Allocate the cache entry for a prefix of `len` tokens (a multiple of 64). + pub fn alloc_prefix(&self, len: usize) -> Result { + ensure!( + len >= 64 && len.is_multiple_of(64), + "cached prefix length must be a positive multiple of 64" + ); + let kvrow = self.cfg.kv_heads * self.cfg.head_dim * BF16; + let w = Widths::of(&self.cfg); + let nfull = self.cfg.full_attention.iter().filter(|&&f| f).count(); + let ngdn = self.cfg.full_attention.len() - nfull; + let mut bytes = 0usize; + let mut attn_kv = Vec::with_capacity(nfull); + for _ in 0..nfull { + let k = DeviceBuffer::new(len * kvrow)?; + let v = DeviceBuffer::new(len * kvrow)?; + bytes += 2 * len * kvrow; + attn_kv.push((k, v)); } - let mut last = vec![0u8; h * BF16]; + let state_floats = self.cfg.lin_v_heads * self.cfg.lin_k_dim * self.cfg.lin_v_dim; + let tail_bytes = 3 * w.gdn_in * BF16; + let mut gdn_state = Vec::with_capacity(ngdn); + let mut conv_tail = Vec::with_capacity(ngdn); + for _ in 0..ngdn { + gdn_state.push(DeviceBuffer::new(state_floats * F32)?); + conv_tail.push(DeviceBuffer::new(tail_bytes)?); + bytes += state_floats * F32 + tail_bytes; + } + Ok(PrefixState { + len, + attn_kv, + gdn_state, + conv_tail, + bytes, + }) + } + + /// Run the rows `[0, state.len)` of one prompt, collecting the per-layer + /// prefix state (post-prep K/V columns, float32 GDN states, conv tails) into + /// `state`. The hidden states of these rows are computed but not returned. + pub fn forward_multimodal_capture( + &mut self, + input: &MultimodalInput<'_>, + state: &mut PrefixState, + ) -> Result<()> { + let image_token = self + .cfg + .image_token_id + .context("checkpoint has no image_token_id")?; + input.validate( + self.cfg.hidden, + self.embed.shape[0], + image_token, + self.cfg.max_positions, + )?; + let t = input.token_ids.len(); + ensure!( + t == state.len, + "capture input must cover the cached prefix exactly" + ); + self.prepare_scratch(t)?; + let s = self.scratch.as_ref().unwrap(); + self.upload_positions(s, input.position_ids)?; + self.embed_tokens(s, input.token_ids)?; + self.overwrite_image_rows(s, input, 0)?; + self.run_window(s, 0, t, true, Some(state), None) + } + + /// Run rows `[prefix.len, prefix.len + input.len)` of one prompt seeded from a + /// captured prefix; returns the final-norm hidden state at the last position. + pub fn forward_multimodal_continue( + &mut self, + input: &MultimodalInput<'_>, + prefix: &PrefixState, + ) -> Result> { + let image_token = self + .cfg + .image_token_id + .context("checkpoint has no image_token_id")?; + input.validate( + self.cfg.hidden, + self.embed.shape[0], + image_token, + self.cfg.max_positions, + )?; + let rows = input.token_ids.len(); + let tend = prefix.len + rows; + self.prepare_scratch(tend)?; + let s = self.scratch.as_ref().unwrap(); + self.upload_positions(s, input.position_ids)?; + self.embed_tokens_at(s, input.token_ids, prefix.len)?; + self.overwrite_image_rows(s, input, prefix.len)?; + self.run_window(s, prefix.len, tend, true, None, Some(prefix))?; + let mut last = vec![0u8; self.cfg.hidden * BF16]; + // SAFETY: x holds at least tend rows of the hidden size. + unsafe { + cuda::download( + &mut last, + s.at(s.x + (tend - 1) * self.cfg.hidden * BF16), + self.stream, + )?; + } + let (pairs, _) = last.as_chunks::<2>(); + Ok(pairs + .iter() + .map(|&b| half::bf16::from_le_bytes(b).to_f32()) + .collect()) + } + + fn embed_tokens(&self, s: &Scratch, ids: &[u32]) -> Result<()> { + self.embed_tokens_at(s, ids, 0) + } + + /// Embed `ids` into the residual buffer rows starting at absolute row `qb`. + fn embed_tokens_at(&self, s: &Scratch, ids: &[u32], qb: usize) -> Result<()> { + let ids32: Vec = ids.iter().flat_map(|&i| (i as i32).to_le_bytes()).collect(); + // SAFETY: IDs were checked against the vocabulary; scratch holds qb + t rows. + unsafe { + cuda::upload(s.at(s.ids), &ids32, self.stream)?; + check( + (cuda::api().cs1_embed)( + s.at(s.ids).cast(), + self.embed.ptr, + s.at(s.res + qb * self.cfg.hidden * BF16), + ids.len() as i32, + self.cfg.hidden as i32, + self.stream, + ), + "embed", + )?; + } + Ok(()) + } + + fn last_hidden(&self, s: &Scratch, t: usize) -> Result> { + let mut last = vec![0u8; self.cfg.hidden * BF16]; // SAFETY: x holds at least t rows of the hidden size. - unsafe { cuda::download(&mut last, s.at(s.x + (t - 1) * h * BF16), self.stream)? }; + unsafe { + cuda::download( + &mut last, + s.at(s.x + (t - 1) * self.cfg.hidden * BF16), + self.stream, + )?; + } let (pairs, _) = last.as_chunks::<2>(); Ok(pairs .iter() @@ -697,9 +985,10 @@ impl Model { .collect()) } - /// Queue one forward pass over the first `t` ids in `s`. The final-norm hidden - /// states end up in `s.x`. - fn run(&self, s: &Scratch, t: usize) -> Result<()> { + /// Queue language layers over prepared embeddings in `s.res`, with rotary + /// tables in immutable text buffers or separate explicit-position buffers. + /// Final-norm hidden states end up in `s.x`. + fn run(&self, s: &Scratch, t: usize, custom_positions: bool) -> Result<()> { let cfg = &self.cfg; let st = self.stream; let (ti, hi, eps) = (t as i32, cfg.hidden as i32, cfg.eps); @@ -707,13 +996,14 @@ impl Model { let (hq, hk, hd) = (cfg.heads as i32, cfg.kv_heads as i32, cfg.head_dim as i32); let w = Widths::of(cfg); let p = |off: usize| s.at(off); - // SAFETY (every kernel call below): the pointers are weights in the arena or + let (cos, sin) = if custom_positions { + (s.custom_cos, s.custom_sin) + } else { + (s.cos, s.sin) + }; + // SAFETY (every kernel call below): pointers are weights in the arena or // scratch buffers laid out for at least t tokens with the widths used here. unsafe { - check( - (cuda::api().cs1_embed)(p(s.ids).cast(), self.embed.ptr, p(s.res), ti, hi, st), - "embed", - )?; check( (cuda::api().cs1_rms_norm)( p(s.res), @@ -814,8 +1104,8 @@ impl Model { ld, fa.q_norm.ptr, fa.k_norm.ptr, - p(s.cos), - p(s.sin), + p(cos), + p(sin), p(s.aq), p(s.agate), p(s.ak), @@ -902,4 +1192,302 @@ impl Model { } Ok(()) } + + /// The R2d prefix-cache pass: run rows `[qb, tend)` of one prompt with + /// explicit positions, touching per-layer buffers only for those rows. + /// + /// `capture` (qb must be 0): collect post-prep K and pre-GEMM V columns of each + /// full-attention layer plus the float32 GDN state and the three pre-conv + /// projection columns of each Gated DeltaNet layer. `prefix` (qb = prefix.len): + /// seed the window from a captured entry — the conv tail columns are copied + /// back into place before the convolution, and the prefix K/V columns before + /// the attention. Everything else is `run()` with shifted row bases, so a + /// captured+continued pass repeats the one-shot arithmetic token for token + /// (qb is a multiple of the 64-row GDN chunk; the only remaining divergence + /// is cuBLASLt's M-shaped plan choice, an f32-accumulation-order ulp). + fn run_window( + &self, + s: &Scratch, + qb: usize, + tend: usize, + custom_positions: bool, + capture: Option<&mut PrefixState>, + prefix: Option<&PrefixState>, + ) -> Result<()> { + ensure!( + qb <= tend && (qb == 0) == prefix.is_none() && (qb == 0 || capture.is_none()), + "prefix window shape" + ); + let cfg = &self.cfg; + let st = self.stream; + let rows = tend - qb; + let (ti, hi, eps) = (rows as i32, cfg.hidden as i32, cfg.eps); + let (kd, vd, hv) = (cfg.key_dim(), cfg.value_dim(), cfg.lin_v_heads); + let (hq, hk, hd) = (cfg.heads as i32, cfg.kv_heads as i32, cfg.head_dim as i32); + let w = Widths::of(cfg); + let p = |off: usize| s.at(off); + let (cos, sin) = if custom_positions { + (s.custom_cos, s.custom_sin) + } else { + (s.cos, s.sin) + }; + let hb = cfg.hidden * BF16; + let ob = cfg.heads * cfg.head_dim * BF16; // post-prep q/gate and ao row bytes + let kvb = cfg.kv_heads * cfg.head_dim * BF16; // k and v row bytes + let ldb = w.gdn_in; // gdn_in row, elements + let ldbb = ldb * BF16; + let ab = w.attn_in * BF16; // attn_in row bytes + let kb = kd * BF16; // linear q/k row bytes + let vb = vd * BF16; // linear v row bytes + let scale = (cfg.head_dim as f32).powf(-0.5); + let mut fa_i = 0usize; + let mut la_i = 0usize; + // SAFETY (every kernel call below): pointers are weights in the arena or + // scratch buffers laid out for tend tokens at the widths used here; prefix + // buffers hold exactly the shapes allocated by alloc_prefix(qb). + unsafe { + check( + (cuda::api().cs1_rms_norm)( + p(s.res + qb * hb), + self.layers[0].input_norm.ptr, + p(s.x + qb * hb), + ti, + hi, + eps, + st, + ), + "input norm", + )?; + } + for (i, layer) in self.layers.iter().enumerate() { + match &layer.mixer { + Mixer::Linear(la) => { + self.gemm(s, s.x + qb * hb, &la.in_proj, s.gdn_in + qb * ldbb, rows)?; + let ld = ldb as i32; + let z = s.gdn_in + w.conv * BF16; + let b = z + vd * BF16; + let a = b + hv * BF16; + let (s_in, s_out): (*const c_void, *mut c_void) = + match (&prefix, capture.as_deref()) { + (Some(pre), _) => { + (pre.gdn_state[la_i].at(0).cast_const(), std::ptr::null_mut()) + } + (None, Some(cap)) => (std::ptr::null(), cap.gdn_state[la_i].at(0)), + (None, None) => (std::ptr::null(), std::ptr::null_mut()), + }; + unsafe { + if let Some(cap) = capture.as_deref() { + // conv window tail: the last three pre-conv columns. + cuda::copy_dd( + cap.conv_tail[la_i].at(0), + p(s.gdn_in + (rows - 3) * ldbb), + 3 * ldbb, + st, + )?; + } + if qb > 0 { + cuda::copy_dd( + p(s.gdn_in + (qb - 3) * ldbb), + prefix.unwrap().conv_tail[la_i].at(0), + 3 * ldbb, + st, + )?; + } + // The shifted qkv base makes the untouched conv kernel read + // absolute rows qb-3..tend-1 — the three restored tail rows. + check( + (cuda::api().cs1_gdn_conv)( + p(s.gdn_in + qb * ldbb), + ld, + la.conv.ptr, + p(s.lq + qb * kb), + p(s.lk + qb * kb), + p(s.lv + qb * vb), + ti, + kd as i32, + vd as i32, + st, + ), + "gdn conv", + )?; + check( + (cuda::api().cs1_gdn_gates)( + p(b + qb * ldbb), + p(a + qb * ldbb), + ld, + la.a_log.ptr, + la.dt_bias.ptr, + p(s.beta + qb * hv * BF16), + p(s.g + qb * hv * F32).cast(), + ti, + hv as i32, + st, + ), + "gdn gates", + )?; + check( + (cuda::api().cs1_gdn_prefill_x)( + p(s.lq + qb * kb), + p(s.lk + qb * kb), + p(s.lv + qb * vb), + p(s.g + qb * hv * F32).cast(), + p(s.beta + qb * hv * BF16), + p(s.lo + qb * vb), + p(s.workspace).cast(), + ti, + hv as i32, + cfg.lin_k_heads as i32, + (cfg.lin_k_dim as f32).powf(-0.5), + s_in, + s_out, + st, + ), + "gdn prefill", + )?; + check( + (cuda::api().cs1_gated_rms_norm)( + p(s.lo + qb * vb), + p(z + qb * ldbb), + ld, + la.norm.ptr, + p(s.ln + qb * vb), + ti, + hv as i32, + cfg.lin_v_dim as i32, + eps, + st, + ), + "gated norm", + )?; + } + self.gemm(s, s.ln + qb * vb, &la.out, s.delta + qb * hb, rows)?; + la_i += 1; + } + Mixer::Full(fa) => { + self.gemm(s, s.x + qb * hb, &fa.qkv, s.attn_in + qb * ab, rows)?; + let k = s.attn_in + w.attn_q * BF16; + let v = k + kvb; + let (q_base, t_flash) = (qb as i32, tend as i32); + unsafe { + if qb > 0 { + let pre = prefix.unwrap(); + // Restore the prefix V columns and post-prep K rows in place; + // the windowed flash then sees the identical k/v layout. + cuda::copy2d(p(v), ab, pre.attn_kv[fa_i].1.at(0), kvb, kvb, qb, st)?; + cuda::copy_dd(p(s.ak), pre.attn_kv[fa_i].0.at(0), qb * kvb, st)?; + } + check( + (cuda::api().cs1_attn_prep)( + p(s.attn_in + qb * ab), + p(k + qb * ab), + w.attn_in as i32, + fa.q_norm.ptr, + fa.k_norm.ptr, + p(cos), + p(sin), + p(s.aq + qb * ob), + p(s.agate + qb * ob), + p(s.ak + qb * kvb), + ti, + hq, + hk, + hd, + cfg.rotary_half as i32, + eps, + st, + ), + "attention prep", + )?; + if let Some(cap) = capture.as_deref() { + // K must be post-prep (normed + rotary); V is the raw column. + cuda::copy_dd(cap.attn_kv[fa_i].0.at(0), p(s.ak), tend * kvb, st)?; + cuda::copy2d(cap.attn_kv[fa_i].1.at(0), kvb, p(v), ab, kvb, tend, st)?; + } + check( + (cuda::api().cs1_attention_gated_prefix)( + p(s.aq), + p(s.ak), + p(v), + w.attn_in as i32, + p(s.agate), + p(s.ao), + t_flash, + hq, + hk, + hd, + scale, + q_base, + st, + ), + "gated attention", + )?; + } + self.gemm(s, s.ao + qb * ob, &fa.o, s.delta + qb * hb, rows)?; + fa_i += 1; + } + } + unsafe { + check( + (cuda::api().cs1_add_rms_norm)( + p(s.res + qb * hb), + p(s.delta + qb * hb), + layer.post_norm.ptr, + p(s.x + qb * hb), + ti, + hi, + eps, + st, + ), + "post-attention norm", + )?; + } + self.gemm( + s, + s.x + qb * hb, + &layer.gate_up, + s.gate_up + qb * 2 * cfg.intermediate * BF16, + rows, + )?; + unsafe { + check( + (cuda::api().cs1_silu_mul)( + p(s.gate_up + qb * 2 * cfg.intermediate * BF16), + (2 * cfg.intermediate) as i32, + p(s.act + qb * cfg.intermediate * BF16), + ti, + cfg.intermediate as i32, + st, + ), + "silu mul", + )?; + } + self.gemm( + s, + s.act + qb * cfg.intermediate * BF16, + &layer.down, + s.delta + qb * hb, + rows, + )?; + let next = self + .layers + .get(i + 1) + .map_or(&self.final_norm, |l| &l.input_norm); + unsafe { + check( + (cuda::api().cs1_add_rms_norm)( + p(s.res + qb * hb), + p(s.delta + qb * hb), + next.ptr, + p(s.x + qb * hb), + ti, + hi, + eps, + st, + ), + "input norm", + )?; + } + } + Ok(()) + } } diff --git a/tests/qwen3_5/kernels.rs b/tests/qwen3_5/kernels.rs index 99c9b8ea..b1b93ad0 100644 --- a/tests/qwen3_5/kernels.rs +++ b/tests/qwen3_5/kernels.rs @@ -617,3 +617,202 @@ fn gated_delta_rule_matches_recurrent_reference() { assert!(worst <= 2e-2 * scale, "t = {t}: {worst} vs scale {scale}"); } } + +fn f32_from_device(buf: &DeviceBuffer, n: usize, st: Stream) -> Vec { + let mut bytes = vec![0u8; n * 4]; + // SAFETY: the buffer holds n float32 values. + unsafe { cuda::download(&mut bytes, buf.at(0), st).unwrap() }; + let (quad, _) = bytes.as_chunks::<4>(); + quad.iter().map(|&b| f32::from_le_bytes(b)).collect() +} + +#[test] +#[ignore = "needs a GPU and CUA_S1_CUDA_LIB"] +fn device_copy_helpers_roundtrip() { + let st = setup(); + // copy_dd keeps every bit (odd size exercises plain memcpy). + let a = to_device(&random(4099, 1, 2.0), st); + let b = DeviceBuffer::new(4099 * 2).unwrap(); + // SAFETY: same size, non-overlapping device allocations. + unsafe { cuda::copy_dd(b.at(0), a.at(0), 4099 * 2, st).unwrap() }; + assert_eq!(from_device(&a, 4099, st), from_device(&b, 4099, st)); + // copy2d copies width-sized windows out of pitched rows. + let (rows, ld, w) = (5usize, 300usize, 256usize); + let src = to_device(&random(rows * ld, 2, 1.0), st); + let dst = DeviceBuffer::new(rows * w * 2).unwrap(); + // SAFETY: dst holds rows of w, src rows of ld. + unsafe { + cuda::copy2d(dst.at(0), w * 2, src.at(0), ld * 2, w * 2, rows, st).unwrap(); + } + let full = from_device(&src, rows * ld, st); + let want: Vec = full.chunks_exact(ld).flat_map(|r| &r[..w]).copied().collect(); + assert_eq!(from_device(&dst, rows * w, st), want); +} + +#[test] +#[ignore = "needs a GPU and CUA_S1_CUDA_LIB"] +fn windowed_attention_matches_full_pass_bit_for_bit() { + let st = setup(); + let (hq, hk, dh) = (24usize, 4usize, 256usize); + for (t, bases) in [ + (139usize, vec![64usize]), + (712, vec![64, 128]), + (972, vec![64, 896]), + (2048, vec![64, 1024]), + ] { + // gated path with a strided V buffer, exactly the model's shape + let ldv = hk * dh + 16; + let q = to_device(&random(t * hq * dh, 1, 2.0), st); + let k = to_device(&random(t * hk * dh, 2, 2.0), st); + let v = to_device(&random(t * ldv, 3, 1.0), st); + let gate = to_device(&random(t * hq * dh, 4, 3.0), st); + let full = DeviceBuffer::new(t * hq * dh * 2).unwrap(); + // SAFETY: every buffer has complete rows of the shapes above. + unsafe { + check( + (api().cs1_attention_gated)( + q.at(0), k.at(0), v.at(0), ldv as i32, gate.at(0), full.at(0), + t as i32, hq as i32, hk as i32, dh as i32, 0.0625, st, + ), + "full pass", + ) + .unwrap(); + } + let full_rows = from_device(&full, t * hq * dh, st); + for qb in &bases { + let win = DeviceBuffer::new(t * hq * dh * 2).unwrap(); + // SAFETY: the window reads the same buffers; rows < q_base stay unwritten. + unsafe { + check( + (api().cs1_attention_gated_prefix)( + q.at(0), k.at(0), v.at(0), ldv as i32, gate.at(0), win.at(0), + t as i32, hq as i32, hk as i32, dh as i32, 0.0625, *qb as i32, st, + ), + "windowed pass", + ) + .unwrap(); + } + let win_rows = from_device(&win, t * hq * dh, st); + let mut bad = 0usize; + for row in *qb..t { + for e in 0..hq * dh { + if full_rows[row * hq * dh + e] != win_rows[row * hq * dh + e] { + bad += 1; + } + } + } + assert_eq!(bad, 0, "t = {t}, q_base = {qb}: {bad} values differ"); + eprintln!("windowed attention t = {t}, q_base = {qb}: rows >= q_base bitwise equal"); + } + } +} + +#[test] +#[ignore = "needs a GPU and CUA_S1_CUDA_LIB"] +fn gdn_prefill_two_phase_matches_one_shot_bit_for_bit() { + let st = setup(); + let d = 128usize; + for (t, h, hk, qk_amp, decay_scale) in [ + (107usize, 4usize, 2usize, 1.0f32, 1.0f32), + (936, 4, 2, 0.01, 1.0), + (3399, 3, 1, 0.01, 1.0), + (65, 4, 2, 0.01, 1e-9), + ] { + let k = random(t * hk * d, 12, qk_amp); + let q: Vec = k + .iter() + .zip(random(t * hk * d, 11, qk_amp)) + .map(|(k, n)| bf16::from_f32(0.8 * k.to_f32() + 0.2 * n.to_f32())) + .collect(); + let v = random(t * h * d, 13, 1.0); + let g: Vec = random(t * h, 14, 1.0) + .iter() + .map(|x| (x.to_f32() - 1.0) * decay_scale) + .collect(); + let beta: Vec = random(t * h, 15, 0.5) + .iter() + .map(|x| bf16::from_f32(x.to_f32() + 0.5)) + .collect(); + let (qd, kd, vd, gd, bd) = ( + to_device(&q, st), + to_device(&k, st), + to_device(&v, st), + f32_to_device(&g, st), + to_device(&beta, st), + ); + let floats = unsafe { (api().cs1_gdn_workspace_floats)(t as i32, h as i32) }; + let ws0 = DeviceBuffer::new(floats * 4).unwrap(); + let o_full = DeviceBuffer::new(t * h * d * 2).unwrap(); + // SAFETY: every buffer holds t rows of the widths above; workspace its size. + unsafe { + check( + (api().cs1_gdn_prefill)( + qd.at(0), kd.at(0), vd.at(0), gd.at(0).cast(), bd.at(0), + o_full.at(0), ws0.at(0).cast(), t as i32, h as i32, hk as i32, + (d as f32).powf(-0.5), st, + ), + "one-shot prefill", + ) + .unwrap(); + } + // phase 1 over rows [0, p) with the float32 state dump, phase 2 continues. + let p = ((t as f64 * 0.6) as usize / 64 * 64).max(64); + let state = DeviceBuffer::new(h * d * d * 4).unwrap(); + let o_p1 = DeviceBuffer::new(p * h * d * 2).unwrap(); + let ws1 = DeviceBuffer::new(unsafe { (api().cs1_gdn_workspace_floats)(p as i32, h as i32) } * 4).unwrap(); + // SAFETY: the p-row prefix of the same inputs; the state buffer is [H][K][V] f32. + unsafe { + check( + (api().cs1_gdn_prefill_x)( + qd.at(0), kd.at(0), vd.at(0), gd.at(0).cast(), bd.at(0), + o_p1.at(0), ws1.at(0).cast(), p as i32, h as i32, hk as i32, + (d as f32).powf(-0.5), std::ptr::null(), state.at(0).cast(), st, + ), + "capture phase", + ) + .unwrap(); + } + let t2 = t - p; + let o_p2 = DeviceBuffer::new(t2 * h * d * 2).unwrap(); + let ws2 = DeviceBuffer::new(unsafe { (api().cs1_gdn_workspace_floats)(t2 as i32, h as i32) } * 4).unwrap(); + // SAFETY: the t2-row suffix of the same inputs; the captured state feeds it. + unsafe { + check( + (api().cs1_gdn_prefill_x)( + qd.at(p * hk * d * 2), kd.at(p * hk * d * 2), vd.at(p * h * d * 2), + gd.at(p * h * 4).cast(), bd.at(p * h * 2), + o_p2.at(0), ws2.at(0).cast(), t2 as i32, h as i32, hk as i32, + (d as f32).powf(-0.5), state.at(0).cast_const(), std::ptr::null_mut(), st, + ), + "continuation phase", + ) + .unwrap(); + } + let full = from_device(&o_full, t * h * d, st); + let first = from_device(&o_p1, p * h * d, st); + let second = from_device(&o_p2, t2 * h * d, st); + assert_eq!(first, full[..p * h * d], "capture rows diverge at t = {t}"); + assert_eq!(second, full[p * h * d..], "continuation rows diverge at t = {t}"); + eprintln!("gdn two-phase t = {t}, split = {p}: both phases bitwise equal to one-shot"); + // capture-only, T = 0: the state passes through untouched. + let passthru = DeviceBuffer::new(h * d * d * 4).unwrap(); + let empty = DeviceBuffer::new(4).unwrap(); + // SAFETY: T = 0 touches only the state buffers. + unsafe { + check( + (api().cs1_gdn_prefill_x)( + empty.at(0), empty.at(0), empty.at(0), empty.at(0).cast(), empty.at(0), + empty.at(0), empty.at(0).cast(), 0, h as i32, hk as i32, + 1.0, state.at(0).cast_const(), passthru.at(0).cast(), st, + ), + "state passthrough", + ) + .unwrap(); + } + assert_eq!( + f32_from_device(&state, h * d * d, st), + f32_from_device(&passthru, h * d * d, st), + "T = 0 must copy the state through" + ); + } +} From c4f3474216c5d11cfb1c2af3ddcb6d432c9702d1 Mon Sep 17 00:00:00 2001 From: linear3735 Date: Tue, 6 Oct 2026 15:38:37 +0800 Subject: [PATCH 2/8] qwen3_5/native: multimodal inputs and prefix-state capture/continue Add inputs.rs (multimodal prompt assembly: placeholder rows overlaid with precomputed bf16 image embeddings plus custom 3D-position rotary tables via HF get_rope_index semantics) and PrefixState capture/continue/run_window in model.rs (f32 state, window-aligned to the 64-token GDN chunking). Existing text-only callers keep the same behavior; kernel-side occurrences are covered by tests/qwen3_5/kernels.rs. Co-Authored-By: Claude Code --- src/models/qwen3_5/native/src/inputs.rs | 95 +++++++++++++++++++ tests/qwen3_5/kernels.rs | 116 +++++++++++++++++++----- 2 files changed, 190 insertions(+), 21 deletions(-) create mode 100644 src/models/qwen3_5/native/src/inputs.rs diff --git a/src/models/qwen3_5/native/src/inputs.rs b/src/models/qwen3_5/native/src/inputs.rs new file mode 100644 index 00000000..bf719eca --- /dev/null +++ b/src/models/qwen3_5/native/src/inputs.rs @@ -0,0 +1,95 @@ +//! Batch-one, unpadded inputs at the adapted-vision / language-model boundary. + +use anyhow::{Result, ensure}; +use half::bf16; + +/// Image rows are already adapted to the language hidden size, in placeholder +/// order. Positions are the T/H/W slices of an int64 `[3, 1, sequence]` tensor. +/// The caller owns preprocessing, vision execution and the position calculation. +pub struct MultimodalInput<'a> { + pub token_ids: &'a [u32], + pub image_token_indices: &'a [usize], + pub image_embeddings: &'a [bf16], + pub position_ids: [&'a [i64]; 3], +} + +impl MultimodalInput<'_> { + /// Check the entire boundary before allocating buffers or launching CUDA. + pub fn validate( + &self, + hidden: usize, + vocab: usize, + image_token: u32, + max_position: usize, + ) -> Result<()> { + let t = self.token_ids.len(); + ensure!(t > 0 && t <= max_position, "empty or oversized prompt"); + ensure!( + self.token_ids.iter().all(|&id| (id as usize) < vocab), + "token id outside the vocabulary" + ); + let expected: Vec = self + .token_ids + .iter() + .enumerate() + .filter_map(|(i, &id)| (id == image_token).then_some(i)) + .collect(); + ensure!( + self.image_token_indices == expected, + "image indices must exactly match the ordered placeholders" + ); + ensure!( + Some(self.image_embeddings.len()) == expected.len().checked_mul(hidden), + "image embedding shape mismatch" + ); + ensure!( + self.image_embeddings.iter().all(|x| x.is_finite()), + "non-finite image embedding" + ); + ensure!( + self.position_ids.iter().all(|axis| axis.len() == t), + "position_ids must have shape [3, 1, sequence]" + ); + ensure!( + self.position_ids + .iter() + .flat_map(|axis| axis.iter()) + .all(|&p| p >= 0 && (p as u64) < max_position as u64), + "position outside the configured range" + ); + Ok(()) + } +} + +/// Qwen3.5's interleaved recomposition: overwrite H at 1::3 and W at 2::3 up +/// to section[axis] * 3, retaining T elsewhere. The second rotary half repeats +/// these frequencies, which the existing attention-prep kernel handles. +/// Float32 inverse frequencies/products and host float64 trig preserve the +/// original native text table rounding when all three axes are equal. +pub(crate) fn rotary_tables( + positions: [&[i64]; 3], + half: usize, + theta: f64, + sections: [usize; 3], +) -> (Vec, Vec) { + let inv: Vec = (0..half) + .map(|i| 1f32 / (theta as f32).powf((2 * i) as f32 / (2 * half) as f32)) + .collect(); + let mut cos = Vec::with_capacity(positions[0].len() * half * 2); + let mut sin = Vec::with_capacity(cos.capacity()); + for (t, _) in positions[0].iter().enumerate() { + for (i, &f) in inv.iter().enumerate() { + let axis = if i % 3 == 1 && i < sections[1] * 3 { + 1 + } else if i % 3 == 2 && i < sections[2] * 3 { + 2 + } else { + 0 + }; + let angle = (f * positions[axis][t] as f32) as f64; + cos.extend(bf16::from_f32(angle.cos() as f32).to_le_bytes()); + sin.extend(bf16::from_f32(angle.sin() as f32).to_le_bytes()); + } + } + (cos, sin) +} diff --git a/tests/qwen3_5/kernels.rs b/tests/qwen3_5/kernels.rs index b1b93ad0..32aa69bd 100644 --- a/tests/qwen3_5/kernels.rs +++ b/tests/qwen3_5/kernels.rs @@ -645,7 +645,11 @@ fn device_copy_helpers_roundtrip() { cuda::copy2d(dst.at(0), w * 2, src.at(0), ld * 2, w * 2, rows, st).unwrap(); } let full = from_device(&src, rows * ld, st); - let want: Vec = full.chunks_exact(ld).flat_map(|r| &r[..w]).copied().collect(); + let want: Vec = full + .chunks_exact(ld) + .flat_map(|r| &r[..w]) + .copied() + .collect(); assert_eq!(from_device(&dst, rows * w, st), want); } @@ -671,8 +675,18 @@ fn windowed_attention_matches_full_pass_bit_for_bit() { unsafe { check( (api().cs1_attention_gated)( - q.at(0), k.at(0), v.at(0), ldv as i32, gate.at(0), full.at(0), - t as i32, hq as i32, hk as i32, dh as i32, 0.0625, st, + q.at(0), + k.at(0), + v.at(0), + ldv as i32, + gate.at(0), + full.at(0), + t as i32, + hq as i32, + hk as i32, + dh as i32, + 0.0625, + st, ), "full pass", ) @@ -685,8 +699,19 @@ fn windowed_attention_matches_full_pass_bit_for_bit() { unsafe { check( (api().cs1_attention_gated_prefix)( - q.at(0), k.at(0), v.at(0), ldv as i32, gate.at(0), win.at(0), - t as i32, hq as i32, hk as i32, dh as i32, 0.0625, *qb as i32, st, + q.at(0), + k.at(0), + v.at(0), + ldv as i32, + gate.at(0), + win.at(0), + t as i32, + hq as i32, + hk as i32, + dh as i32, + 0.0625, + *qb as i32, + st, ), "windowed pass", ) @@ -747,9 +772,18 @@ fn gdn_prefill_two_phase_matches_one_shot_bit_for_bit() { unsafe { check( (api().cs1_gdn_prefill)( - qd.at(0), kd.at(0), vd.at(0), gd.at(0).cast(), bd.at(0), - o_full.at(0), ws0.at(0).cast(), t as i32, h as i32, hk as i32, - (d as f32).powf(-0.5), st, + qd.at(0), + kd.at(0), + vd.at(0), + gd.at(0).cast(), + bd.at(0), + o_full.at(0), + ws0.at(0).cast(), + t as i32, + h as i32, + hk as i32, + (d as f32).powf(-0.5), + st, ), "one-shot prefill", ) @@ -759,14 +793,27 @@ fn gdn_prefill_two_phase_matches_one_shot_bit_for_bit() { let p = ((t as f64 * 0.6) as usize / 64 * 64).max(64); let state = DeviceBuffer::new(h * d * d * 4).unwrap(); let o_p1 = DeviceBuffer::new(p * h * d * 2).unwrap(); - let ws1 = DeviceBuffer::new(unsafe { (api().cs1_gdn_workspace_floats)(p as i32, h as i32) } * 4).unwrap(); + let ws1 = + DeviceBuffer::new(unsafe { (api().cs1_gdn_workspace_floats)(p as i32, h as i32) } * 4) + .unwrap(); // SAFETY: the p-row prefix of the same inputs; the state buffer is [H][K][V] f32. unsafe { check( (api().cs1_gdn_prefill_x)( - qd.at(0), kd.at(0), vd.at(0), gd.at(0).cast(), bd.at(0), - o_p1.at(0), ws1.at(0).cast(), p as i32, h as i32, hk as i32, - (d as f32).powf(-0.5), std::ptr::null(), state.at(0).cast(), st, + qd.at(0), + kd.at(0), + vd.at(0), + gd.at(0).cast(), + bd.at(0), + o_p1.at(0), + ws1.at(0).cast(), + p as i32, + h as i32, + hk as i32, + (d as f32).powf(-0.5), + std::ptr::null(), + state.at(0).cast(), + st, ), "capture phase", ) @@ -774,15 +821,27 @@ fn gdn_prefill_two_phase_matches_one_shot_bit_for_bit() { } let t2 = t - p; let o_p2 = DeviceBuffer::new(t2 * h * d * 2).unwrap(); - let ws2 = DeviceBuffer::new(unsafe { (api().cs1_gdn_workspace_floats)(t2 as i32, h as i32) } * 4).unwrap(); + let ws2 = + DeviceBuffer::new(unsafe { (api().cs1_gdn_workspace_floats)(t2 as i32, h as i32) } * 4) + .unwrap(); // SAFETY: the t2-row suffix of the same inputs; the captured state feeds it. unsafe { check( (api().cs1_gdn_prefill_x)( - qd.at(p * hk * d * 2), kd.at(p * hk * d * 2), vd.at(p * h * d * 2), - gd.at(p * h * 4).cast(), bd.at(p * h * 2), - o_p2.at(0), ws2.at(0).cast(), t2 as i32, h as i32, hk as i32, - (d as f32).powf(-0.5), state.at(0).cast_const(), std::ptr::null_mut(), st, + qd.at(p * hk * d * 2), + kd.at(p * hk * d * 2), + vd.at(p * h * d * 2), + gd.at(p * h * 4).cast(), + bd.at(p * h * 2), + o_p2.at(0), + ws2.at(0).cast(), + t2 as i32, + h as i32, + hk as i32, + (d as f32).powf(-0.5), + state.at(0).cast_const(), + std::ptr::null_mut(), + st, ), "continuation phase", ) @@ -792,7 +851,11 @@ fn gdn_prefill_two_phase_matches_one_shot_bit_for_bit() { let first = from_device(&o_p1, p * h * d, st); let second = from_device(&o_p2, t2 * h * d, st); assert_eq!(first, full[..p * h * d], "capture rows diverge at t = {t}"); - assert_eq!(second, full[p * h * d..], "continuation rows diverge at t = {t}"); + assert_eq!( + second, + full[p * h * d..], + "continuation rows diverge at t = {t}" + ); eprintln!("gdn two-phase t = {t}, split = {p}: both phases bitwise equal to one-shot"); // capture-only, T = 0: the state passes through untouched. let passthru = DeviceBuffer::new(h * d * d * 4).unwrap(); @@ -801,9 +864,20 @@ fn gdn_prefill_two_phase_matches_one_shot_bit_for_bit() { unsafe { check( (api().cs1_gdn_prefill_x)( - empty.at(0), empty.at(0), empty.at(0), empty.at(0).cast(), empty.at(0), - empty.at(0), empty.at(0).cast(), 0, h as i32, hk as i32, - 1.0, state.at(0).cast_const(), passthru.at(0).cast(), st, + empty.at(0), + empty.at(0), + empty.at(0), + empty.at(0).cast(), + empty.at(0), + empty.at(0), + empty.at(0).cast(), + 0, + h as i32, + hk as i32, + 1.0, + state.at(0).cast_const(), + passthru.at(0).cast(), + st, ), "state passthrough", ) From 127b20bd89823cacea488e346788ef6a7aab6c76 Mon Sep 17 00:00:00 2001 From: linear3735 Date: Tue, 6 Oct 2026 15:38:37 +0800 Subject: [PATCH 3/8] jev_vl: add native multimodal worker with multi-level decision caches Serve autotrust/JEV-27B-VL System-1 decisions through /v1/systemone with a new omni-jev-vl-native crate: streaming LoRA merge export (backbone and lm_head merged separately, 256 verbalizer label rows), official (logprob(label)+bias)/per-kind-temperature readout semantics, and three Rust-side caches (L1 structural prefix, L2 vision-encoder assets by media hash, L3 KV prefix captured per structure key). Open-Jev and Cua-S1 sources are untouched. Measured on one H800 GPU, warm, two passes against the official vLLM serve path (R1 frozen reference): text manifest 36/36 and image set 12/12 decisions identical (48/48, max |dp| <= 0.0215); single-request p50 28.2ms vs 94.1ms; same-image multi-question cache hit vs miss p50 34.5ms vs 104.2ms (3.02x), with L3 prefix reuse the dominant term. Full protocol, raw samples and conditions: /aifs4su/shiheming/jev/runs/20261006-jev-vl-native-r2/. Co-Authored-By: Claude Code --- Cargo.lock | 2 +- recipe/jev_vl/export_merged.py | 213 ++++++++++ recipe/jev_vl/preencode.py | 112 ++++++ src/models/jev_vl/native/Cargo.toml | 34 ++ src/models/jev_vl/native/src/caches.rs | 377 ++++++++++++++++++ src/models/jev_vl/native/src/contract.rs | 390 ++++++++++++++++++ src/models/jev_vl/native/src/engine.rs | 80 ++++ src/models/jev_vl/native/src/executor.rs | 436 +++++++++++++++++++++ src/models/jev_vl/native/src/images.rs | 188 +++++++++ src/models/jev_vl/native/src/lib.rs | 11 + src/models/jev_vl/native/src/main.rs | 164 ++++++++ src/models/jev_vl/native/src/processing.rs | 415 ++++++++++++++++++++ tests/jev_vl/contract.rs | 161 ++++++++ tests/jev_vl/images.rs | 79 ++++ tests/jev_vl/prefix.rs | 177 +++++++++ tests/jev_vl/readout.rs | 71 ++++ 16 files changed, 2909 insertions(+), 1 deletion(-) create mode 100644 recipe/jev_vl/export_merged.py create mode 100644 recipe/jev_vl/preencode.py create mode 100644 src/models/jev_vl/native/Cargo.toml create mode 100644 src/models/jev_vl/native/src/caches.rs create mode 100644 src/models/jev_vl/native/src/contract.rs create mode 100644 src/models/jev_vl/native/src/engine.rs create mode 100644 src/models/jev_vl/native/src/executor.rs create mode 100644 src/models/jev_vl/native/src/images.rs create mode 100644 src/models/jev_vl/native/src/lib.rs create mode 100644 src/models/jev_vl/native/src/main.rs create mode 100644 src/models/jev_vl/native/src/processing.rs create mode 100644 tests/jev_vl/contract.rs create mode 100644 tests/jev_vl/images.rs create mode 100644 tests/jev_vl/prefix.rs create mode 100644 tests/jev_vl/readout.rs diff --git a/Cargo.lock b/Cargo.lock index 65d45fe5..4c96610e 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -1029,7 +1029,7 @@ dependencies = [ "safetensors 0.6.2", "serde", "serde_json", - "sha2", + "sha2 0.10.9", ] [[package]] diff --git a/recipe/jev_vl/export_merged.py b/recipe/jev_vl/export_merged.py new file mode 100644 index 00000000..87ff5a33 --- /dev/null +++ b/recipe/jev_vl/export_merged.py @@ -0,0 +1,213 @@ +"""Export the merged JEV-27B-VL text backbone plus the verbalizer label head, on CPU. + +Streaming merge, low RAM: the script never materializes the whole model. It walks +the base checkpoint shard by shard, adds the trained vLLM-LoRA deltas +(``scale * (lora_B @ lora_A)``, ``scale = lora_alpha / r``) computed in float32 and +rounded once back to bfloat16, and rewrites only the ``model.language_model.*`` +tensors under their original names, so the native backend's prefix autodetection +loads the output unchanged. ``lm_head.weight`` is merged the same way and the rows +the System-1 verbalizer readout needs (the union of the trained 24 head slots and +the tokenizer's single-token option labels) land in ``label_head.safetensors`` +(float32). ``jev_vl_export.json`` — written last — carries the decision semantics: +per-kind temperatures, the 24-slot bias/ranges, the exported label list, the +``enable_thinking=True`` chat template split (for later System-2 work; the raw +System-1 prompt does not use a chat template), and the checkpoint pins. + +The reference environment (``/aifs4su/shiheming/jev/envs/jev-vl``) provides +torch + transformers + safetensors; no PEFT and no GPU are needed. +""" + +import argparse +import hashlib +import json +import shutil +import string +from pathlib import Path + +import torch +from safetensors import safe_open +from safetensors.torch import save_file +from transformers import AutoTokenizer + +EXPORT_FORMAT = "jev-vl-text-merged/1" +MODEL_ID = "autotrust/JEV-27B-VL" +PROTOCOL = "jev27-bare-v1" +MAX_OPTIONS = 256 +TOKENIZER_FILES = [ + "tokenizer.json", + "tokenizer_config.json", + "vocab.json", + "merges.txt", + "special_tokens_map.json", + "added_tokens.json", + "chat_template.jinja", +] + + +def sha256(path: Path) -> str: + h = hashlib.sha256() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(1 << 22), b""): + h.update(chunk) + return h.hexdigest() + + +def hf_revision(model: Path) -> str | None: + meta = model / ".cache/huggingface/download/config.json.metadata" + try: + return meta.read_text().splitlines()[0].strip() + except OSError: + return None + + +def load_adapter(adapter: Path) -> tuple[dict[str, tuple[torch.Tensor, torch.Tensor]], float]: + cfg = json.loads((adapter / "adapter_config.json").read_text()) + scale = cfg["lora_alpha"] / cfg["r"] + assert cfg["peft_type"] == "LORA" and cfg["inference_mode"] and cfg["lora_dropout"] == 0.0 + pairs: dict[str, dict[str, torch.Tensor]] = {} + with safe_open(str(adapter / "adapter_model.safetensors"), framework="pt") as f: + for key in f.keys(): + assert key.startswith("base_model.model.") and key.endswith(".weight"), key + base = key[len("base_model.model."):] + name, kind = base.rsplit(".lora_", 1) + name += ".weight" + pairs.setdefault(name, {})[kind.split(".")[0].lower()] = f.get_tensor(key) + out = {} + for name, ab in pairs.items(): + assert set(ab) == {"a", "b"}, f"{name}: adapter pair incomplete" + a, b = ab["a"].float(), ab["b"].float() + assert a.shape[0] == cfg["r"] and b.shape[1] == cfg["r"], f"{name}: rank mismatch" + out[name] = (a, b) + return out, scale + + +def single_token_labels(tok, context: str) -> list[tuple[str, int]]: + """serve_decide.py's option-label scan, verbatim.""" + out = [] + for lab in list(string.ascii_uppercase) + [a + b for a in string.ascii_uppercase + for b in string.ascii_uppercase]: + t = tok.encode(lab, add_special_tokens=False) + if len(t) == 1 and t[0] in tok.encode(context.format(lab), add_special_tokens=False): + out.append((lab, t[0])) + if len(out) == MAX_OPTIONS: + break + return out + + +def main(): + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument("--model", required=True, type=Path) + parser.add_argument("--out", required=True, type=Path) + parser.add_argument("--max-length", type=int, default=16384) + args = parser.parse_args() + model, out = args.model, args.out + if out.exists(): + raise ValueError("output already exists; choose a new export directory") + if not 1 <= args.max_length <= 32768: + raise ValueError("max length must be within 1..=32768") + adapter = model / "adapter_vllm" + head_cfg = json.loads((adapter / "decision_head.json").read_text()) + temperatures = json.loads((model / "calibration.json").read_text())["per_kind"] + assert sorted(head_cfg["slots"]["ranges"]) == ["choice", "noul", "score"] + assert head_cfg["slots"]["ranges"]["noul"] == [0, 2] + assert head_cfg["slots"]["ranges"]["choice"] == [8, 24] + assert len(head_cfg["verbalizer_ids"]) == len(head_cfg["bias"]) == 24 + deltas, scale = load_adapter(adapter) + assert "lm_head.weight" in deltas, "adapter must train lm_head" + lm_delta = deltas.pop("lm_head.weight") + + tokenizer = AutoTokenizer.from_pretrained(model, local_files_only=True) + labels = single_token_labels(tokenizer, "x\n{}) y") + assert len(labels) == MAX_OPTIONS, f"only {len(labels)} single-token labels" + lo, hi = head_cfg["slots"]["ranges"]["choice"] + assert [t for _, t in labels[: hi - lo]] == head_cfg["verbalizer_ids"][lo:hi], \ + "first labels must be the trained A-P head" + marker = "\x00OMNI_JEV_VL\x00" + chat = tokenizer.apply_chat_template( + [{"role": "user", "content": marker}], tokenize=False, + add_generation_prompt=True, enable_thinking=True, + ) + assert chat.count(marker) == 1, "expected a single-user text chat template" + chat_prefix, chat_suffix = chat.split(marker) + + index = json.loads((model / "model.safetensors.index.json").read_text()) + weight_map: dict[str, str] = index["weight_map"] + files = sorted(set(weight_map.values())) + out.mkdir(parents=True) + consumed: set[str] = set() + new_map: dict[str, list[str]] = {} + print(f"merging with LoRA scale={scale} from {adapter}", flush=True) + for shard in files: + merged: dict[str, torch.Tensor] = {} + with safe_open(str(model / shard), framework="pt") as f: + for name in sorted(f.keys()): + if not name.startswith("model.language_model."): + continue + w = f.get_tensor(name) + if name in deltas: + a, b = deltas[name] + w = (w.float() + scale * (b @ a)).to(torch.bfloat16) + consumed.add(name) + merged[name] = w.contiguous() + if merged: + save_file(merged, str(out / shard)) + for name in merged: + new_map.setdefault(shard, []).append(name) + print(f" {shard}: {len(merged)} language tensors", flush=True) + missing = set(deltas) - consumed + assert not missing, f"adapter tensors without a base tensor: {sorted(missing)[:4]}" + (out / "model.safetensors.index.json").write_text(json.dumps({ + "metadata": {"total_size": 0, "jev_vl_export": EXPORT_FORMAT}, + "weight_map": {name: shard for shard, names in sorted(new_map.items()) for name in sorted(names)}, + }) + "\n") + + # The merged label rows for the verbalizer readout (float32). + head_ids = list(dict.fromkeys(head_cfg["verbalizer_ids"] + [t for _, t in labels])) + lm_shard = weight_map["lm_head.weight"] + with safe_open(str(model / lm_shard), framework="pt") as f: + lm_head = f.get_tensor("lm_head.weight") + a, b = lm_delta + idx = torch.tensor(head_ids) + rows = lm_head[idx].float() + scale * (b[idx] @ a) + assert torch.isfinite(rows).all(), "non-finite merged label rows" + save_file({"rows": rows.contiguous(), + "ids": idx.to(torch.int64)}, str(out / "label_head.safetensors")) + + for name in TOKENIZER_FILES: + src = model / name + if src.exists(): + shutil.copy2(src, out / name) + shutil.copy2(model / "config.json", out / "config.json") + + manifest = { + "format": EXPORT_FORMAT, + "model_id": MODEL_ID, + "protocol": PROTOCOL, + "hf_revision": hf_revision(model), + "pins": { + "model_index_sha256": sha256(model / "model.safetensors.index.json"), + "adapter_config_sha256": sha256(adapter / "adapter_config.json"), + "adapter_model_sha256": sha256(adapter / "adapter_model.safetensors"), + "decision_head_sha256": sha256(adapter / "decision_head.json"), + "calibration_sha256": sha256(model / "calibration.json"), + }, + "merge": {"lora_scale": scale, "delta_dtype": "float32", "backbone_dtype": "bfloat16"}, + "temperatures": temperatures, + "verbalizer_ids": head_cfg["verbalizer_ids"], + "verbalizer_bias": head_cfg["bias"], + "slots": head_cfg["slots"], + "labels": [l for l, _ in labels], + "label_ids": [t for _, t in labels], + "label_head": {"file": "label_head.safetensors", "ids": head_ids, "dtype": "float32"}, + "max_length": args.max_length, + "chat_prefix": chat_prefix, + "chat_suffix": chat_suffix, + "image_placeholder": "<|vision_start|><|image_pad|><|vision_end|>", + } + # Written last: the native worker refuses incomplete exports or plain base weights. + (out / "jev_vl_export.json").write_text(json.dumps(manifest, ensure_ascii=False) + "\n") + print(f"exported {len(new_map)} shards + {len(head_ids)} label rows -> {out}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/recipe/jev_vl/preencode.py b/recipe/jev_vl/preencode.py new file mode 100644 index 00000000..eee41361 --- /dev/null +++ b/recipe/jev_vl/preencode.py @@ -0,0 +1,112 @@ +"""Pre-encode manifest images into the (url-hash keyed) adapted-embedding assets. + +Runs the official HF vision tower (same model the vLLM reference path used) on the +GPU once per unique image in a manifest, and stores per-image +``//{emb.safetensors,grid.json}`` with the adapted rows +(``model.model.visual(pixel_values, grid_thw).pooler_output``, shape +[n = prod(grid)/merge^2, 5120], bfloat16) plus the patch grid. This asset IS the +R2d L2 cache content; the native worker never decodes images. +""" +import argparse +import hashlib +import io +import json +from pathlib import Path + +import safetensors +import torch +from safetensors.torch import save_file +from transformers import AutoConfig, AutoProcessor + + +def load_vision(model_dir: Path) -> torch.nn.Module: + """Hand-assemble the vision tower: no accelerate, no full-model device map.""" + from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5VisionModel + + cfg = AutoConfig.from_pretrained(model_dir, local_files_only=True) + vis = Qwen3_5VisionModel(cfg.vision_config) + index = json.loads((model_dir / "model.safetensors.index.json").read_text())["weight_map"] + names = {n for n in index if n.startswith("model.visual.")} + tensors = {} + for f in sorted({index[n] for n in names}): + with safetensors.safe_open(str(model_dir / f), framework="pt") as h: + for n in h.keys(): + if n in names: + tensors[n[len("model.visual."):]] = h.get_tensor(n) + missing, unexpected = vis.load_state_dict(tensors, strict=True) + assert not missing and not unexpected, (missing, unexpected) + return vis.to(torch.bfloat16).to("cuda").eval() + + +def images_of(manifest: Path) -> list[str]: + urls = [] + for line in manifest.read_text().splitlines(): + if not line.strip(): + continue + r = json.loads(line)["request"] + state = r.get("state", "") + if isinstance(state, list): + for p in state: + url = None + if isinstance(p, dict): + url = p.get("image") or (p.get("image_url") or {}).get("url") + if url: + urls.append(url) + return sorted(set(urls)) + + +def decode_url(url: str): + from PIL import Image + + if not url.startswith("data:"): + raise ValueError("recipe supports data: URIs (manifest uses them)") + header, payload = url.split(",", 1) + if ";base64" not in header: + raise ValueError("data URI without base64 encoding") + import base64 + + return Image.open(io.BytesIO(base64.b64decode(payload))).convert("RGB") + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument("--model", required=True, type=Path) + ap.add_argument("--manifest", required=True, type=Path) + ap.add_argument("--out", required=True, type=Path) + args = ap.parse_args() + urls = images_of(args.manifest) + print(f"{len(urls)} unique images", flush=True) + model_dir = args.model + processor = AutoProcessor.from_pretrained(model_dir, local_files_only=True) + model = load_vision(model_dir) + args.out.mkdir(parents=True, exist_ok=True) + for url in urls: + key = hashlib.sha256(url.encode()).hexdigest() + dest = args.out / key + if (dest / "emb.safetensors").exists(): + print(f"{key}: cached", flush=True) + continue + im = decode_url(url) + inputs = processor(images=[im], return_tensors="pt") + grid = inputs["image_grid_thw"][0].tolist() + pixel_values = inputs["pixel_values"].to("cuda", dtype=torch.bfloat16) + grid_thw = torch.tensor([grid], device="cuda") + with torch.no_grad(): + emb = model(pixel_values, grid_thw=grid_thw).pooler_output + emb = emb.detach().cpu().to(torch.bfloat16) + n = torch.prod(torch.tensor(grid)).item() // model.spatial_merge_size**2 + assert emb.shape == (n, 5120), f"{key}: {emb.shape} vs n={n}" + assert torch.isfinite(emb.float()).all(), f"{key}: non-finite" + dest.mkdir(parents=True) + save_file({"rows": emb.contiguous()}, str(dest / "emb.safetensors")) + (dest / "grid.json").write_text(json.dumps({ + "url_sha256": key, "grid_thw": grid, "n_tokens": n, "dtype": "bfloat16", + "shape": [int(emb.shape[0]), int(emb.shape[1])], + "model_index_sha256": hashlib.sha256((model_dir / "model.safetensors.index.json") + .read_bytes()).hexdigest(), + }) + "\n") + print(f"{key}: grid={grid} n={n} rows={tuple(emb.shape)}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/src/models/jev_vl/native/Cargo.toml b/src/models/jev_vl/native/Cargo.toml new file mode 100644 index 00000000..43163ce8 --- /dev/null +++ b/src/models/jev_vl/native/Cargo.toml @@ -0,0 +1,34 @@ +[package] +name = "omni-jev-vl-native" +version = "0.1.0" +edition = "2024" +publish = false +description = "Native Rust/CUDA System-1 worker for autotrust/JEV-27B-VL (verbalizer readout)" + +[dependencies] +anyhow = "1.0.100" +axum = "0.8.8" +half = "2.7.1" +sha2 = "0.11" +omni-qwen3-5-native = { path = "../../qwen3_5/native" } +omni-runtime = { path = "../../../runtime" } +safetensors = "0.8.0" +serde_json = { version = "1.0.149", features = ["float_roundtrip", "preserve_order"] } +tokenizers = { version = "=0.22.2", default-features = false, features = ["onig"] } +tokio = { version = "1.49.0", features = ["macros", "net", "rt-multi-thread", "sync"] } + +[[test]] +name = "jev_vl_contract" +path = "../../../../tests/jev_vl/contract.rs" + +[[test]] +name = "jev_vl_images" +path = "../../../../tests/jev_vl/images.rs" + +[[test]] +name = "jev_vl_readout" +path = "../../../../tests/jev_vl/readout.rs" + +[[test]] +name = "jev_vl_prefix" +path = "../../../../tests/jev_vl/prefix.rs" diff --git a/src/models/jev_vl/native/src/caches.rs b/src/models/jev_vl/native/src/caches.rs new file mode 100644 index 00000000..a603cb3d --- /dev/null +++ b/src/models/jev_vl/native/src/caches.rs @@ -0,0 +1,377 @@ +//! R2d multi-level prefix caches (see runs/20261006-jev-vl-native-r2/docs/ +//! r2d-multilevel-cache.md): +//! +//! - L1 processor: a per-prefix-structure record. Key = sha256 over the exact +//! request structure (kind + every state part: text verbatim / image url). +//! Value = the compiled prefix geometry (pad run, cached-prefix length P, +//! position bases, the expanded prefix ids/positions), so a hit skips the +//! prompt-head assembly, tokenization, expansion and meshgrid work; the suffix +//! (`<|vision_end|>` onward) is always tokenized fresh per question. +//! - L2 vision: parsed image assets (adapter output rows + grid) keyed by +//! sha256(url) — the same key form as the offline imgcache directory. A hit +//! skips the disk read + safetensors parse + validation (= the worker-side +//! stand-in for the encoder run: the ViT itself lives in offline preencode). +//! - L3 KV prefix: the device-side prefix state (per-full-attention-layer +//! post-prep K and raw V rows; per-GDN-layer float32 recurrent state + conv +//! tail). Holders are `Arc`; a continuation reads it while the +//! device buffers stay alive. Full-attention KV is per-prefix; the GDN/conv +//! states are per-prefix too — identical token prefix ⇒ identical state, and +//! they never blend across different prefixes (hybrid constraint). +//! +//! All collections are process-local, LRU + budgeted; everything is disabled by +//! JEV_VL_CACHE=0 and per level by JEV_VL_L1/L2/L3=0 (A/B decomposition). + +use std::collections::{HashMap, VecDeque}; +use std::sync::{Arc, Mutex}; + +use omni_qwen3_5_native::model::PrefixState; + +use crate::images::ImageAsset; + +/// Env-read switches + budgets. +pub struct CacheCfg { + pub enabled: bool, + pub l1: bool, + pub l2: bool, + pub l3: bool, + /// Max structural records. + pub l1_max: usize, + /// L2 parsed-asset budget in bytes. + pub l2_bytes: usize, + /// L3 device-state budget in bytes. + pub l3_bytes: usize, +} + +impl CacheCfg { + pub fn from_env() -> Self { + let flag = |name: &str, default: bool| { + std::env::var(name).map_or(default, |v| v != "0" && !v.eq_ignore_ascii_case("false")) + }; + let num = |name: &str, default: usize| { + std::env::var(name) + .ok() + .and_then(|v| v.parse().ok()) + .unwrap_or(default) + }; + Self { + enabled: flag("JEV_VL_CACHE", true), + l1: flag("JEV_VL_L1", true), + l2: flag("JEV_VL_L2", true), + l3: flag("JEV_VL_L3", true), + l1_max: num("JEV_VL_L1_MAX", 256), + l2_bytes: num("JEV_VL_L2_BYTES", 1 << 30), + l3_bytes: num("JEV_VL_L3_BYTES", 2 << 30), + } + } +} + +#[derive(Clone, Default)] +pub struct CacheStats { + pub l1_hit: u64, + pub l1_miss: u64, + pub l2_hit: u64, + pub l2_miss: u64, + pub l3_hit: u64, + pub l3_miss: u64, + pub l3_populate: u64, + pub l3_fallback_full: u64, + pub l1_records: u64, + pub l1_bytes: u64, + pub l2_records: u64, + pub l2_bytes: u64, + pub l3_records: u64, + pub l3_bytes: u64, +} + +/// The compiled geometry of one structural prefix (small; see module doc). +pub struct L1Meta { + /// Global row of the last image block's first pad; also its position base. + pub pads_start: usize, + /// Global row one past the last image block's last pad. + pub pads_end: usize, + /// Cached-prefix length, a multiple of 64: floor(pads_end / 64) * 64. + pub p: usize, + /// Position base of the pads (text-position base for the meshgrid offset). + pub base_pad: i64, + /// mrope advance after the image: max(grid_h, grid_w) / 2. + pub advance: i64, + /// sha256(url) of the last image's asset. + pub asset_key: String, + /// Expanded ids of rows [0, p) (needed by the L1-only mode's full rebuild). + pub ids_prefix: Vec, + /// Absolute positions of rows [0, p). + pub positions_prefix: [Vec; 3], +} + +impl L1Meta { + fn bytes(&self) -> usize { + self.ids_prefix.len() * 4 + + 3 * self.positions_prefix[0].len() * 8 + + self.asset_key.len() + + 96 + } +} + +struct Registry { + map: HashMap>, + lru: VecDeque, + meta_bytes: usize, + state_bytes: usize, + max_records: usize, + state_budget: usize, +} + +/// One structural record: compiled prefix geometry + (lazily) its device state. +pub struct PrefixRecord { + pub key: u64, + pub meta: L1Meta, + state: Mutex>>, +} + +impl PrefixRecord { + pub fn state(&self) -> Option> { + self.state.lock().ok().and_then(|s| s.clone()) + } +} + +/// Parsed image assets (L2). +struct L2Store { + map: HashMap>, + lru: VecDeque, + bytes: usize, + budget: usize, +} + +/// All R2d cache state, shared by the processor and the executor. +pub struct Caches { + pub cfg: CacheCfg, + l2: Mutex, + registry: Mutex, + stats: Mutex, +} + +impl Caches { + pub fn new(cfg: CacheCfg) -> Arc { + Arc::new(Self { + l2: Mutex::new(L2Store { + map: HashMap::new(), + lru: VecDeque::new(), + bytes: 0, + budget: cfg.l2_bytes, + }), + registry: Mutex::new(Registry { + map: HashMap::new(), + lru: VecDeque::new(), + meta_bytes: 0, + state_bytes: 0, + max_records: cfg.l1_max, + state_budget: cfg.l3_bytes, + }), + cfg, + stats: Mutex::new(CacheStats::default()), + }) + } + + fn bump(&self, f: impl FnOnce(&mut CacheStats)) { + if let Ok(mut s) = self.stats.lock() { + f(&mut s) + } + } + + pub fn snapshot(&self) -> CacheStats { + let mut s = self.stats.lock().map(|s| s.clone()).unwrap_or_default(); + if let Ok(r) = self.registry.lock() { + s.l1_records = r.map.len() as u64; + s.l1_bytes = r.meta_bytes as u64; + s.l3_records = r.map.values().filter(|rec| rec.state().is_some()).count() as u64; + s.l3_bytes = r.state_bytes as u64; + } + if let Ok(l2) = self.l2.lock() { + s.l2_records = l2.map.len() as u64; + s.l2_bytes = l2.bytes as u64; + } + s + } + + pub fn reset_stats(&self) { + if let Ok(mut s) = self.stats.lock() { + *s = CacheStats::default(); + } + } + + // ---- L2: parsed image assets ---- + + pub fn l2_get(&self, key: &str) -> Option> { + if !(self.cfg.enabled && self.cfg.l2) { + self.bump(|s| s.l2_miss += 1); + return None; + } + let hit = self.l2.lock().ok().and_then(|mut st| { + let asset = st.map.get(key).cloned(); + if asset.is_some() { + st.lru.retain(|k| k != key); + st.lru.push_back(key.to_owned()); + } + asset + }); + match &hit { + Some(_) => self.bump(|s| s.l2_hit += 1), + None => self.bump(|s| s.l2_miss += 1), + } + hit + } + + /// Insert a freshly loaded asset, evicting LRU entries over the byte budget. + pub fn l2_insert(&self, key: String, asset: Arc) { + if !(self.cfg.enabled && self.cfg.l2) { + return; + } + let bytes = asset.embeddings.len() * 2 + 64; + if bytes <= self.cfg.l2_bytes + && let Ok(mut st) = self.l2.lock() + { + st.lru.retain(|k| k != &key); + st.lru.push_back(key.clone()); + if let Some(old) = st.map.insert(key, asset) { + st.bytes = st.bytes.saturating_sub(old.embeddings.len() * 2 + 64); + } + st.bytes += bytes; + while st.bytes > st.budget && st.lru.len() > 1 { + let evict = st.lru.pop_front().unwrap(); + if let Some(old) = st.map.remove(&evict) { + st.bytes = st.bytes.saturating_sub(old.embeddings.len() * 2 + 64); + } + } + } + } + + // ---- L1 + L3: structural records ---- + + pub fn record_get(&self, key: u64) -> Option> { + if !(self.cfg.enabled && self.cfg.l1) { + self.bump(|s| s.l1_miss += 1); + return None; + } + let hit = self.registry.lock().ok().and_then(|mut r| { + let rec = r.map.get(&key).cloned(); + if rec.is_some() { + r.lru.retain(|k| *k != key); + r.lru.push_back(key); + } + rec + }); + match &hit { + Some(_) => self.bump(|s| s.l1_hit += 1), + None => self.bump(|s| s.l1_miss += 1), + } + hit + } + + /// Insert a new structural record, evicting LRU records over the record + /// count or the L3 state budget (evicted device states drop with their Arc). + pub fn record_insert(&self, key: u64, meta: L1Meta) -> Arc { + let rec = Arc::new(PrefixRecord { + key, + state: Mutex::new(None), + meta, + }); + if !(self.cfg.enabled && self.cfg.l1) { + return rec; + } + if let Ok(mut r) = self.registry.lock() { + r.meta_bytes = r.meta_bytes.saturating_add(rec.meta.bytes()); + r.lru.retain(|k| *k != key); + r.lru.push_back(key); + if let Some(old) = r.map.insert(key, rec.clone()) { + r.meta_bytes = r.meta_bytes.saturating_sub(old.meta.bytes()); + if let Some(s) = old.state() { + r.state_bytes = r.state_bytes.saturating_sub(s.bytes); + } + } + loop { + let over = r.map.len() > r.max_records + || (r.state_budget > 0 && r.state_bytes > r.state_budget); + if !over || r.lru.len() <= 1 { + break; + } + let evict = r.lru.pop_front().unwrap(); + if evict == key { + r.lru.push_front(evict); + break; + } + if let Some(old) = r.map.remove(&evict) { + r.meta_bytes = r.meta_bytes.saturating_sub(old.meta.bytes()); + if let Some(s) = old.state() { + r.state_bytes = r.state_bytes.saturating_sub(s.bytes); + } + } + } + } + rec + } + + /// Publish the device state for a record the executor just populated; + /// budgets accounted. No-op when the record was already populated or evicted. + pub fn record_publish_state(&self, key: u64, state: PrefixState) { + let arc = Arc::new(state); + if let Some(rec) = self + .registry + .lock() + .ok() + .and_then(|r| r.map.get(&key).cloned()) + { + let bytes = arc.bytes; + if let Ok(mut slot) = rec.state.lock() { + if slot.is_none() { + *slot = Some(arc); + if let Ok(mut r) = self.registry.lock() { + r.state_bytes = r.state_bytes.saturating_add(bytes); + } + } + } + } + } + + pub fn l3_hit(&self) { + self.bump(|s| s.l3_hit += 1); + } + + pub fn l3_miss(&self) { + self.bump(|s| s.l3_miss += 1); + } + + pub fn l3_populate(&self) { + self.bump(|s| s.l3_populate += 1); + } + + pub fn l3_fallback_full(&self) { + self.bump(|s| s.l3_fallback_full += 1); + } +} + +/// Key of one structural prefix: kind, then every state part verbatim +/// (text body / image url). Two requests share L1..L3 state only through an +/// identical key — the "same image, many questions" anchor. +pub fn structure_key(kind: &str, parts: &[crate::contract::Part]) -> u64 { + use sha2::Digest; + let mut h = sha2::Sha256::new(); + h.update(b"jev27-prefix-v1\0"); + h.update((kind.len() as u32).to_le_bytes()); + h.update(kind.as_bytes()); + for part in parts { + match part { + crate::contract::Part::Text(t) => { + h.update([1u8]); + h.update((t.len() as u64).to_le_bytes()); + h.update(t.as_bytes()); + } + crate::contract::Part::Image(url) => { + h.update([2u8]); + h.update((url.len() as u64).to_le_bytes()); + h.update(url.as_bytes()); + } + } + } + let digest = h.finalize(); + u64::from_le_bytes(digest[..8].try_into().unwrap()) +} diff --git a/src/models/jev_vl/native/src/contract.rs b/src/models/jev_vl/native/src/contract.rs new file mode 100644 index 00000000..6aa8050e --- /dev/null +++ b/src/models/jev_vl/native/src/contract.rs @@ -0,0 +1,390 @@ +//! JEV-27B-VL System-1 request contract and prompt compilation. +//! +//! The request is the official /v1/decide shape ({kind, state, question, +//! options}); the raw System-1 prompt mirrors serve_decide.py's `s1_pass`: +//! `[kind] {kind}\n[state] {state}\n[question] {question}\n[options]\n{lines}\n[decision]:` +//! with Python `json.dumps(..., ensure_ascii=False)` rendering for structured +//! state parts. Rejections carry the official response envelope +//! ({"error": {message, type, param, code}}) with the upstream status codes, so +//! the frozen boundary probes compare equal under digit normalization. + +use omni_qwen3_5_native::json; +use serde_json::{Map, Value, json}; + +pub const MODEL_ID: &str = "autotrust/JEV-27B-VL"; +pub const PROTOCOL: &str = "jev27-bare-v1"; +pub const IMAGE_PLACEHOLDER: &str = "<|vision_start|><|image_pad|><|vision_end|>"; + +/// A rejection keeping the official status code and body envelope. +#[derive(Debug)] +pub struct Reject { + pub status: u16, + pub body: Value, +} + +impl Reject { + fn new(status: u16, message: String, type_: &str, param: Option<&str>) -> Self { + let mut error = json!({"message": message, "type": type_}); + if let Some(param) = param { + error["param"] = json!(param); + } + error["code"] = json!(status); + Self { + status, + body: json!({"error": error}), + } + } + + /// serve_decide.py's `_err`: semantic rejections from the decide layer. + pub fn bad_request(message: impl Into) -> Self { + Self::new(400, message.into(), "BadRequestError", None) + } + + /// The upstream pydantic-validation envelope ("Bad Request"). + pub fn validation(message: String, param: &str) -> Self { + Self::new(400, message, "Bad Request", Some(param)) + } + + fn missing(field: &str, items: usize) -> Self { + Self::validation( + format!( + "1 validation error:\n {{'type': 'missing', 'loc': 'body.{field}', 'msg': 'Field required', 'input': ''}}" + ), + field, + ) + } + + pub fn unknown_model(model: &str) -> Self { + Self::new( + 404, + format!("The model `{model}` does not exist."), + "NotFoundError", + Some("model"), + ) + } +} + +#[derive(Clone, Copy, PartialEq, Debug)] +pub enum Kind { + Noul, + Score, + Choice, +} + +impl Kind { + pub fn as_str(self) -> &'static str { + match self { + Kind::Noul => "noul", + Kind::Score => "score", + Kind::Choice => "choice", + } + } +} + +#[derive(Clone, Debug)] +pub enum Part { + Text(String), + Image(String), +} + +#[derive(Debug)] +pub struct Compiled { + pub kind: Kind, + pub parts: Vec, + pub question: String, + pub options: Vec, + /// The raw System-1 prompt; image parts become the placeholder text. + pub prompt: String, + pub images: Vec, +} + +/// Python repr() of a JSON scalar, for pydantic-style error interpolation. +fn py_repr(value: &Value) -> String { + match value { + Value::String(s) => { + if !s.contains('\'') { + format!("'{s}'") + } else { + format!("\"{}\"", s.replace('"', "\\\"")) + } + } + Value::Null => "None".into(), + other => json::dumps(other), + } +} + +/// `json.dumps(value, ensure_ascii=False)` for dict/other state parts. +fn render(value: &Value) -> String { + if let Some(text) = value.as_str() { + return text.to_owned(); + } + json::dumps(value) +} + +/// serve_decide.py's `_parts`: state -> text/image parts + has_image. +pub fn parts(state: &Value) -> (Vec, Vec) { + match state { + Value::String(text) => (vec![Part::Text(text.clone())], Vec::new()), + Value::Object(_) => (vec![Part::Text(render(state))], Vec::new()), + Value::Array(list) => { + let mut out = Vec::with_capacity(list.len()); + let mut images = Vec::new(); + for p in list { + match p { + Value::String(text) => out.push(Part::Text(text.clone())), + Value::Object(map) if map.contains_key("image") => { + let url = map["image"].as_str().unwrap_or_default().to_owned(); + images.push(url); + out.push(Part::Image(images.last().unwrap().clone())); + } + Value::Object(map) + if map.get("type").and_then(Value::as_str) == Some("image") => + { + let url = map + .get("image_url") + .and_then(|v| v.get("url")) + .and_then(Value::as_str) + .unwrap_or_default() + .to_owned(); + images.push(url.clone()); + out.push(Part::Image(url)); + } + Value::Object(map) + if map.get("type").and_then(Value::as_str) == Some("text") => + { + out.push(Part::Text( + map.get("text") + .and_then(Value::as_str) + .unwrap_or_default() + .to_owned(), + )); + } + other => out.push(Part::Text(render(other))), + } + } + (out, images) + } + _ => (vec![Part::Text(render(state))], Vec::new()), + } +} + +/// Validate and compile a request; mirrors the /v1/decide entrance checks. +/// `labels` is the exported single-token option-label list (its length is the +/// advertised option bound, 256). +pub fn compile(raw: &[u8], labels: &[String]) -> Result { + let request: Map = match json::parse(raw) { + Ok(map) => map, + Err(_) => { + return Err(Reject::validation( + format!( + "1 validation error:\n {{'type': 'model_attributes_type', 'loc': 'body', 'msg': 'Input should be a valid dictionary or object to extract fields from', 'input': ''}}", + raw.len() + ), + "body", + )); + } + }; + let items = request.len(); + if let Some(model) = request.get("model").and_then(Value::as_str) + && model != MODEL_ID + { + return Err(Reject::unknown_model(model)); + } + let kind = match request.get("kind") { + None => return Err(Reject::missing("kind", items)), + Some(v) => match v.as_str() { + Some("noul") => Kind::Noul, + Some("score") => Kind::Score, + Some("choice") => Kind::Choice, + Some(_) => { + return Err(Reject::validation( + format!( + "1 validation error:\n {{'type': 'literal_error', 'loc': 'body.kind', 'msg': \"Input should be 'noul', 'score' or 'choice'\", 'input': {}, 'ctx': {{'expected': \"'noul', 'score' or 'choice'\"}}}}", + py_repr(v) + ), + "kind", + )); + } + None => { + return Err(Reject::validation( + format!( + "1 validation error:\n {{'type': 'literal_error', 'loc': 'body.kind', 'msg': \"Input should be 'noul', 'score' or 'choice'\", 'input': {}, 'ctx': {{'expected': \"'noul', 'score' or 'choice'\"}}}}", + py_repr(v) + ), + "kind", + )); + } + }, + }; + let question = match request.get("question") { + None => return Err(Reject::missing("question", items)), + Some(Value::String(q)) => q.clone(), + _ => { + return Err(Reject::bad_request("question must be a string".to_owned())); + } + }; + if let Some(thinking) = request.get("thinking").and_then(Value::as_str) { + match thinking { + "default" | "off" => {} + "auto" | "on" => { + if kind == Kind::Score { + return Err(Reject::bad_request( + "thinking is supported for noul and choice", + )); + } + return Err(Reject::bad_request( + "adaptive thinking is not implemented by this worker", + )); + } + _ => { + return Err(Reject::bad_request(format!( + "thinking must be one of default, off, auto, on; got {thinking:?}" + ))); + } + } + } + if let Some(strategy) = request.get("strategy").and_then(Value::as_str) + && !matches!(strategy, "auto" | "single") + { + return Err(Reject::bad_request(format!( + "strategy {strategy:?} is not implemented by this worker" + ))); + } + let options: Vec = match kind { + Kind::Noul => vec!["false".into(), "true".into()], + Kind::Score => (0..6).map(|i| i.to_string()).collect(), + Kind::Choice => match request.get("options") { + Some(Value::Array(list)) => { + let mut opts = Vec::with_capacity(list.len()); + for (i, v) in list.iter().enumerate() { + match v.as_str() { + Some(o) => opts.push(o.to_owned()), + None => { + return Err(Reject::bad_request(format!( + "options[{i}] must be a string" + ))); + } + } + } + opts + } + Some(Value::Null) | None => Vec::new(), + Some(_) => { + return Err(Reject::bad_request("options must be a list of strings")); + } + }, + }; + if kind == Kind::Choice && !(2..=labels.len()).contains(&options.len()) { + return Err(Reject::bad_request(format!( + "choice needs 2-{} options, got {}", + labels.len(), + options.len() + ))); + } + let state = request + .get("state") + .cloned() + .unwrap_or(Value::String(String::new())); + let (parts, images) = parts(&state); + let lines: Vec = match kind { + Kind::Choice => options + .iter() + .enumerate() + .map(|(i, o)| format!("{}) {o}", labels[i])) + .collect(), + _ => options.clone(), + }; + let mut prompt = format!("[kind] {}\n[state] ", kind.as_str()); + for part in &parts { + match part { + Part::Text(t) => prompt.push_str(t), + Part::Image(_) => prompt.push_str(IMAGE_PLACEHOLDER), + } + } + prompt.push_str(&format!( + "\n[question] {question}\n[options]\n{}\n[decision]:", + lines.join("\n") + )); + Ok(Compiled { + kind, + parts, + question, + options, + prompt, + images, + }) +} + +/// The prompt tail after the last image placeholder, rebuilt exactly as +/// `compile` would have it. The R2d L1 hit path tokenizes only this suffix; +/// `<|vision_end|>` is a special token (always atomic to BPE), so the split +/// concatenation equals a whole-prompt tokenization bit for bit. +/// None when the state carries no image (=> no prefix structure). +pub fn tail_after_last_image(compiled: &Compiled, labels: &[String]) -> Option { + let idx = compiled + .parts + .iter() + .rposition(|p| matches!(p, Part::Image(_)))?; + let mut tail = String::new(); + for part in &compiled.parts[idx + 1..] { + match part { + Part::Text(t) => tail.push_str(t), + Part::Image(_) => return None, + } + } + let lines: Vec = match compiled.kind { + Kind::Choice => compiled + .options + .iter() + .enumerate() + .map(|(i, o)| format!("{}) {o}", labels[i])) + .collect(), + _ => compiled.options.clone(), + }; + tail.push_str(&format!( + "\n[question] {}\n[options]\n{}\n[decision]:", + compiled.question, + lines.join("\n") + )); + Some(tail) +} + +/// The /v1/systemone answer, field-aligned with the official /v1/decide body. +pub fn answer( + kind: Kind, + options: &[String], + probabilities: &[f64], + input_tokens: usize, + elapsed_seconds: f64, +) -> Value { + let k = (1..probabilities.len()).fold(0, |m, i| { + if probabilities[i] > probabilities[m] { + i + } else { + m + } + }); + let adaptation = if kind != Kind::Choice || options.len() <= 16 { + "native" + } else { + "single" + }; + json!({ + "kind": kind.as_str(), + "effective_kind": kind.as_str(), + "options": options, + "probabilities": probabilities, + "choice_index": k, + "choice": options[k], + "adaptation": adaptation, + "protocol": PROTOCOL, + "model": MODEL_ID, + "usage": { + "prompt_tokens": input_tokens, + "completion_tokens": 1, + "total_tokens": input_tokens + 1, + }, + "elapsed_seconds": elapsed_seconds, + "num_model_requests": 1, + }) +} diff --git a/src/models/jev_vl/native/src/engine.rs b/src/models/jev_vl/native/src/engine.rs new file mode 100644 index 00000000..2d4d9237 --- /dev/null +++ b/src/models/jev_vl/native/src/engine.rs @@ -0,0 +1,80 @@ +//! Assemble the processor and model executor from one pinned JEV-27B-VL export. + +use std::path::Path; +use std::sync::Arc; + +use anyhow::{Context, Result, ensure}; +use omni_runtime::SerialScheduler; +use serde_json::Value; + +use crate::caches::{CacheCfg, Caches}; +use crate::executor::{Executor, LabelHead}; +use crate::processing::Processor; + +pub struct Engine { + pub manifest: Value, + pub head: Arc, + pub processor: Processor, + pub scheduler: SerialScheduler, + pub executor: Executor, + /// R2d multi-level caches: processors/executors consult it; the HTTP layer + /// exposes its counters on /v1/cache/stats. + pub caches: Arc, +} + +impl Engine { + pub async fn load(dir: &Path, library: &Path) -> Result { + let manifest: Value = serde_json::from_slice( + &std::fs::read(dir.join("jev_vl_export.json")) + .context("export the merged checkpoint; see recipe/jev_vl/export_merged.py")?, + )?; + ensure!( + manifest["format"] == "jev-vl-text-merged/1" + && manifest["model_id"] == crate::contract::MODEL_ID + && manifest["protocol"] == crate::contract::PROTOCOL, + "expected a pinned JEV-27B-VL text export" + ); + let ranges: Vec = serde_json::from_value(manifest["slots"]["ranges"]["noul"].clone())?; + let score: Vec = serde_json::from_value(manifest["slots"]["ranges"]["score"].clone())?; + let choice: Vec = + serde_json::from_value(manifest["slots"]["ranges"]["choice"].clone())?; + ensure!( + ranges == [0, 2] && score == [2, 8] && choice == [8, 24], + "unexpected verbalizer slot ranges" + ); + ensure!( + manifest["verbalizer_ids"].as_array().map(Vec::len) == Some(24) + && manifest["verbalizer_bias"].as_array().map(Vec::len) == Some(24), + "24-slot verbalizer head" + ); + let labels: Vec = serde_json::from_value(manifest["labels"].clone())?; + let label_ids: Vec = serde_json::from_value(manifest["label_ids"].clone())?; + ensure!( + labels.len() == label_ids.len() && !labels.is_empty(), + "label table" + ); + for t in ["noul", "score", "choice"] { + let temp = manifest["temperatures"][t] + .as_f64() + .context("temperatures")?; + ensure!(temp.is_finite() && temp > 0.0, "invalid temperature"); + } + let max_length = manifest["max_length"].as_u64().context("max_length")? as usize; + ensure!( + (1..=32768).contains(&max_length), + "max_length must be within 1..=32768" + ); + let head = Arc::new(LabelHead::load(dir, &manifest)?); + let caches = Caches::new(CacheCfg::from_env()); + let processor = Processor::load(dir, labels, max_length, caches.clone())?; + let executor = Executor::load(dir, library, head.clone(), caches.clone()).await?; + Ok(Self { + manifest, + head, + processor, + scheduler: SerialScheduler::default(), + executor, + caches, + }) + } +} diff --git a/src/models/jev_vl/native/src/executor.rs b/src/models/jev_vl/native/src/executor.rs new file mode 100644 index 00000000..2aea4265 --- /dev/null +++ b/src/models/jev_vl/native/src/executor.rs @@ -0,0 +1,436 @@ +//! Verbalizer readout: merged label-head rows on the CPU in f64 plus slot bias +//! and per-kind calibration temperature. +//! +//! The official readout uses full-vocabulary logprobs; the per-kind softmax only +//! sees `(logprob(t) + bias_slot) / T_kind`, where `logprob = logit - logZ` shares +//! one constant per request — so softmax((logit + bias)/T) is identical. The +//! export stores the merged lm_head rows for every exported label id, hence one +//! dot product per candidate token instead of a vocabulary GEMM. + +use std::collections::HashMap; +use std::path::Path; +use std::sync::{Arc, Mutex}; + +use omni_qwen3_5_native::inputs::MultimodalInput; + +use anyhow::{Context, Result, ensure}; +use omni_qwen3_5_native::model::{Config, Model}; +use omni_runtime::SerialScheduler; +use serde_json::Value; + +use crate::images::ImageAsset; + +/// Per-request readout setup: which label tokens, with which bias, divided by +/// which temperature, matching decision_head.json's slots + calibration.json. +#[derive(Clone)] +pub struct Readout { + pub token_ids: Vec, + pub bias: Vec, + pub temperature: f64, +} + +/// The merged lm_head rows for the exported label ids, row-major float32. +pub struct LabelHead { + width: usize, + rows: Vec, + index: HashMap, +} + +impl LabelHead { + /// Test/dev construction without a safetensors file. + pub fn from_parts(ids: Vec, rows: Vec, width: usize) -> Self { + assert_eq!(rows.len(), ids.len() * width); + Self { + width, + rows, + index: ids.iter().enumerate().map(|(i, &t)| (t, i)).collect(), + } + } + + /// Test helper: drop one row and rebuild the index (rejection path). + pub fn drop_row(&mut self, token: u32) { + let i = self.index.remove(&token).expect("row exists"); + self.rows.drain(i * self.width..(i + 1) * self.width); + self.index.values_mut().for_each(|v| { + if *v > i { + *v -= 1; + } + }); + } +} + +impl LabelHead { + pub fn load(dir: &Path, manifest: &Value) -> Result { + let cfg = Config::load(dir)?; + ensure!( + ( + cfg.hidden, + cfg.intermediate, + cfg.full_attention.len(), + cfg.heads, + cfg.kv_heads, + cfg.lin_k_heads, + cfg.lin_v_heads + ) == (5120, 17408, 64, 24, 4, 16, 48), + "expected the Qwen3.8-27B backbone dimensions" + ); + let spec = manifest["label_head"].as_object().context("label_head")?["file"] + .as_str() + .context("label_head.file")?; + let data = std::fs::read(dir.join(spec)).context("read label_head safetensors")?; + let st = safetensors::SafeTensors::deserialize(&data)?; + let rows = st.tensor("rows")?; + ensure!( + rows.dtype() == safetensors::Dtype::F32 + && rows.shape().len() == 2 + && rows.shape()[1] == cfg.hidden, + "label_head rows must be float32 [n, {}]", + cfg.hidden + ); + let ids_t = st.tensor("ids")?; + ensure!( + ids_t.dtype() == safetensors::Dtype::I64 && ids_t.shape() == [rows.shape()[0]], + "label_head ids must be int64 [n]" + ); + let rows_f: Vec = rows + .data() + .chunks_exact(4) + .map(|b| f32::from_le_bytes(b.try_into().unwrap())) + .collect(); + let ids: Vec = ids_t + .data() + .chunks_exact(8) + .map(|b| i64::from_le_bytes(b.try_into().unwrap())) + .collect(); + let exported: Vec = serde_json::from_value(manifest["label_head"]["ids"].clone())?; + ensure!( + ids == exported, + "label_head ids do not match the export manifest" + ); + ensure!( + rows_f.iter().all(|v| v.is_finite()), + "non-finite label head" + ); + Ok(Self { + width: cfg.hidden, + rows: rows_f, + index: ids + .iter() + .enumerate() + .map(|(i, &t)| (t as u32, i)) + .collect(), + }) + } + + /// Slot bias from the trained 24-slot head: [slot_lo, slot_hi), then zeros. + pub fn readout(&self, manifest: &Value, kind_token: &str, count: usize) -> Result { + let temps = manifest["temperatures"] + .as_object() + .context("temperatures")?; + let temperature = temps[kind_token].as_f64().context("temperature")?; + ensure!( + temperature.is_finite() && temperature > 0.0, + "invalid temperature" + ); + let bias: Vec = manifest["verbalizer_bias"] + .as_array() + .context("verbalizer_bias")? + .iter() + .map(|v| v.as_f64().context("numeric bias")) + .collect::>()?; + let verbalizer: Vec = serde_json::from_value(manifest["verbalizer_ids"].clone())?; + ensure!(bias.len() == 24 && verbalizer.len() == 24, "24-slot head"); + let label_ids: Vec = serde_json::from_value(manifest["label_ids"].clone())?; + let (token_ids, slot_bias): (Vec, Vec) = match kind_token { + "noul" => (verbalizer[0..2].to_vec(), bias[0..2].to_vec()), + "score" => (verbalizer[2..8].to_vec(), bias[2..8].to_vec()), + "choice" => { + ensure!(count <= label_ids.len(), "choice options above label table"); + let mut b: Vec = (0..count) + .map(|i| if i < 16 { bias[8 + i] } else { 0.0 }) + .collect(); + b.truncate(count); + (label_ids[..count].to_vec(), b) + } + other => anyhow::bail!("unknown kind {other}"), + }; + ensure!(token_ids.len() == slot_bias.len(), "readout shape"); + for &t in &token_ids { + ensure!( + self.index.contains_key(&(t as u32)), + "token {t} missing from the label head" + ); + } + Ok(Readout { + token_ids: token_ids.iter().map(|&t| t as u32).collect(), + bias: slot_bias, + temperature, + }) + } +} + +impl LabelHead { + /// One candidate distribution: `(logit + bias) / T` per slot then a stable + /// softmax. `last` is the final-norm hidden state as float32. + pub fn probabilities(&self, last: &[f32], readout: &Readout) -> Result> { + ensure!(last.len() == self.width, "hidden width"); + let w = self.width; + let mut z = Vec::with_capacity(readout.token_ids.len()); + for (t, b) in readout.token_ids.iter().zip(&readout.bias) { + let i = *self + .index + .get(t) + .with_context(|| format!("token {t} missing from the label head"))?; + let row = &self.rows[i * w..(i + 1) * w]; + z.push( + (row.iter() + .zip(last) + .map(|(&w, &h)| w as f64 * h as f64) + .sum::() + + b) + / readout.temperature, + ); + } + let base = z.iter().copied().fold(f64::NEG_INFINITY, f64::max); + let wgt: Vec = z.iter().map(|&v| (v - base).exp()).collect(); + let total: f64 = wgt.iter().sum(); + ensure!(total > 0.0 && total.is_finite(), "degenerate readout"); + Ok(wgt.iter().map(|v| v / total).collect()) + } +} + +/// One image block in the expanded request (adapted rows borrowed from L2). +pub struct ImgBlock { + /// Global expanded-id row of the first pad. + pub start: usize, + /// Global expanded-id row one past the last pad. + pub end: usize, + /// mrope meshgrid position base of the block. + pub base: i64, + /// mrope advance after the block: max(grid_h, grid_w) / 2. + pub advance: i64, + /// Parsed adapter output (grid + rows), shared with the L2 cache. + pub asset: Arc, +} + +/// Full expanded ids + absolute positions + image blocks (global coordinates). +pub struct PreparedMm { + pub ids: Vec, + pub positions: [Vec; 3], + pub blocks: Vec, +} + +/// The cached-prefix continuation slice of one request. +pub struct ContinueMm { + /// Rows [p, T): (pads_end - p) pads, <|vision_end|>, fresh tail tokens. + pub ids: Vec, + /// Absolute positions for those rows. + pub positions: [Vec; 3], + /// The image rows within the slice (the last block's tail), if any. + pub block: Option, + /// Cached prefix length (a multiple of 64); the continuation starts here. + pub p: usize, + /// L3 device state of the prefix. + pub state: Arc, +} + +/// The image rows of one continuation slice: an offset range of one asset's rows. +pub struct SuffixBlock { + pub asset: Arc, + /// First adapted-row index feeding the slice (= p - pads_start). + pub row_offset: usize, + /// Number of rows = number of pads leading the slice. + pub rows: usize, +} + +/// How the executor serves one request (see the R2d design doc). +pub enum MmPlan { + /// Text-only prompt. + Text { ids: Vec }, + /// One-shot multimodal prefill (unchanged R2c path). + Full(PreparedMm), + /// Structure recorded but its device state is not built yet: run the capture + /// phase over rows [0, p) followed by the continuation over [p, T), then + /// publish the state so later requests go straight to `Continue`. + Populate { + mm: PreparedMm, + record: Arc, + }, + /// Cached-prefix hit: only the continuation slice runs. + Continue(ContinueMm), +} + +pub struct Executor { + model: Arc>, + head: Arc, + caches: Arc, +} + +impl Executor { + pub(crate) async fn load( + dir: &Path, + library: &Path, + head: Arc, + caches: Arc, + ) -> Result { + let (d, lib) = (dir.to_path_buf(), library.to_path_buf()); + let model = tokio::task::spawn_blocking(move || Model::load(&d, &lib)).await??; + Ok(Self { + model: Arc::new(Mutex::new(model)), + head, + caches, + }) + } + + /// One forward, then `(logit + bias) / T` per slot and a stable softmax, + /// matching serve_decide.py's s1_pass ordering. + pub async fn execute( + &self, + scheduler: &SerialScheduler, + plan: MmPlan, + readout: Readout, + ) -> Result> { + let model = self.model.clone(); + let head = self.head.clone(); + let caches = self.caches.clone(); + scheduler + .run(move || { + let mut model = model + .lock() + .map_err(|_| anyhow::anyhow!("poisoned model"))?; + let last = match plan { + MmPlan::Text { ids } => model.forward(&ids)?, + MmPlan::Full(mm) => { + let one = slice_mm(&mm, 0, mm.ids.len()); + let input = one.input(); + model.forward_multimodal(&input)? + } + MmPlan::Populate { mm, record } => { + let p = record.meta.p; + let populated = (|| -> Result> { + let mut state = model.alloc_prefix(p)?; + let pf = slice_mm(&mm, 0, p); + let input = pf.input(); + model.forward_multimodal_capture(&input, &mut state)?; + let cf = slice_mm(&mm, p, mm.ids.len()); + let input = cf.input(); + let last = model.forward_multimodal_continue(&input, &state)?; + caches.record_publish_state(record.key, state); + Ok(last) + })(); + match populated { + Ok(last) => { + caches.l3_populate(); + last + } + Err(e) => { + // Device allocation or capture failed: serve the + // request on the proven one-shot path instead. + eprintln!("prefix populate failed; one-shot fallback: {e:#}"); + caches.l3_fallback_full(); + let one = slice_mm(&mm, 0, mm.ids.len()); + let input = one.input(); + model.forward_multimodal(&input)? + } + } + } + MmPlan::Continue(c) => { + let indices: Vec = match &c.block { + Some(b) => (0..b.rows).collect(), + None => Vec::new(), + }; + let embeddings: &[half::bf16] = match &c.block { + Some(b) => { + &b.asset.embeddings + [b.row_offset * 5120..(b.row_offset + b.rows) * 5120] + } + None => &[], + }; + let input = MultimodalInput { + token_ids: &c.ids, + image_token_indices: &indices, + image_embeddings: embeddings, + position_ids: [ + c.positions[0].as_slice(), + c.positions[1].as_slice(), + c.positions[2].as_slice(), + ], + }; + model.forward_multimodal_continue(&input, &c.state)? + } + }; + head.probabilities(&last, &readout) + }) + .await + } +} + +/// A slice of one request's expanded data for rows [from, to) in slice-local +/// coordinates: borrowed ids/positions, local pad indices, and the adapted rows +/// those pads need — borrowed straight out of the L2 asset when they form one +/// contiguous run (the usual single-image case), else concatenated once. +struct SlicedMm<'a> { + ids: &'a [u32], + positions: [&'a [i64]; 3], + indices: Vec, + borrowed: Option<&'a [half::bf16]>, + owned: Option>, +} + +impl<'a> SlicedMm<'a> { + fn input(&'a self) -> MultimodalInput<'a> { + MultimodalInput { + token_ids: self.ids, + image_token_indices: &self.indices, + image_embeddings: self.borrowed.or(self.owned.as_deref()).unwrap_or(&[]), + position_ids: self.positions, + } + } +} + +fn slice_mm<'a>(mm: &'a PreparedMm, from: usize, to: usize) -> SlicedMm<'a> { + let indices: Vec = mm + .blocks + .iter() + .flat_map(|b| (b.start.max(from)..b.end.min(to)).map(move |g| g - from)) + .collect(); + let overlapping: Vec<&ImgBlock> = mm + .blocks + .iter() + .filter(|b| b.end > from && b.start < to) + .collect(); + let (borrowed, owned) = match overlapping.as_slice() { + [] => (None, None), + [one] => { + let rows = (one.start.max(from) - one.start)..(one.end.min(to) - one.start); + ( + Some(&one.asset.embeddings[rows.start * 5120..rows.end * 5120]), + None, + ) + } + _ => ( + None, + Some( + overlapping + .iter() + .flat_map(|b| { + let rows = (b.start.max(from) - b.start)..(b.end.min(to) - b.start); + &b.asset.embeddings[rows.start * 5120..rows.end * 5120] + }) + .copied() + .collect::>(), + ), + ), + }; + SlicedMm { + ids: &mm.ids[from..to], + positions: [ + &mm.positions[0][from..to], + &mm.positions[1][from..to], + &mm.positions[2][from..to], + ], + indices, + borrowed, + owned, + } +} diff --git a/src/models/jev_vl/native/src/images.rs b/src/models/jev_vl/native/src/images.rs new file mode 100644 index 00000000..e9d9ad5c --- /dev/null +++ b/src/models/jev_vl/native/src/images.rs @@ -0,0 +1,188 @@ +//! Adapted image assets (pre-encoded by recipe/jev_vl/preencode.py) and the +//! language-side position expansion for Qwen mrope, ported from HF transformers +//! modeling_qwen3_5 get_rope_index / get_vision_position_ids. + +use std::path::Path; +use std::sync::Arc; + +use anyhow::{Context, Result, ensure}; + +/// One adapted image: merger output rows in placeholder order + the patch grid. +pub struct ImageAsset { + pub grid_thw: [i64; 3], + pub embeddings: Vec, +} + +impl ImageAsset { + pub fn n_tokens(&self) -> usize { + let [t, h, w] = self.grid_thw; + (t * h * w / 4) as usize + } + + pub fn load(dir: &Path) -> Result> { + let grid: serde_json::Value = serde_json::from_slice( + &std::fs::read(dir.join("grid.json")).context("imgcache asset grid.json")?, + )?; + let thw: Vec = serde_json::from_value(grid["grid_thw"].clone())?; + ensure!( + thw.len() == 3 && thw.iter().all(|&v| v >= 1), + "invalid grid_thw" + ); + let asset_n = (thw[0] * thw[1] * thw[2] / 4) as usize; + ensure!( + grid["n_tokens"].as_i64() == Some(asset_n as i64), + "grid.json n_tokens mismatch" + ); + let data = std::fs::read(dir.join("emb.safetensors")).context("imgcache asset emb")?; + let st = safetensors::SafeTensors::deserialize(&data)?; + let rows = st.tensor("rows")?; + ensure!( + rows.dtype() == safetensors::Dtype::BF16 && rows.shape() == [asset_n, 5120], + "image rows vs grid mismatch" + ); + let embeddings: Vec = rows + .data() + .chunks_exact(2) + .map(|b| half::bf16::from_le_bytes(b.try_into().unwrap())) + .collect(); + ensure!( + embeddings.iter().all(|x| x.is_finite()), + "non-finite image embedding" + ); + Ok(Arc::new(Self { + grid_thw: [thw[0], thw[1], thw[2]], + embeddings, + })) + } +} + +/// The token-expanded multimodal prompt for the language-side boundary. +pub struct Expanded { + pub ids: Vec, + pub image_token_indices: Vec, + pub positions: [Vec; 3], + pub embeddings: Vec, + /// Each image's pad run in expanded-id coordinates plus its position base + /// and mrope advance — the R2d cache anchor geometry. + pub blocks: Vec, +} + +/// One image block's pad run in expanded coordinates. +#[derive(Clone, Debug)] +pub struct ImageBlock { + /// ids row of the block's first pad. + pub start: usize, + /// ids row one past the block's last pad. + pub end: usize, + /// The meshgrid position base of the block (equals `start` for the first + /// image; earlier images' mrope advances make it smaller for later ones). + pub base: i64, + /// mrope advance after the image: max(grid_h, grid_w) / 2. + pub advance: i64, + /// Index of the image in the request's image list. + pub asset: usize, +} + +/// mrope meshgrid triples for the image rows [from, from + rows) of a grid, +/// offset by `base` — a slice of what `expand` would build for the full block. +pub fn meshgrid_positions(grid: [i64; 3], base: i64, from: usize, rows: usize) -> [Vec; 3] { + let [gt, gh, gw] = grid; + let (lh, lw) = ((gh / 2) as usize, (gw / 2) as usize); + let _ = gt; + let mut out = [ + Vec::with_capacity(rows), + Vec::with_capacity(rows), + Vec::with_capacity(rows), + ]; + for l in from..from + rows { + let w = l % lw; + let h = (l / lw) % lh; + let t = l / (lh * lw); + out[0].push(base + t as i64); + out[1].push(base + h as i64); + out[2].push(base + w as i64); + } + out +} + +/// Tokens for one image block: the raw prompt text carries one literal +/// `<|vision_start|><|image_pad|><|vision_end|>` per image; the token sequence +/// repeats image_pad n = prod(grid)/merge² (merge=2) times. The three mrope +/// axes follow HF's meshgrid (t outer, h mid, w fastest), each offset by the +/// running start position; the next text token continues at +/// start + max(grid_h, grid_w) / merge. +pub fn expand(text_ids: &[u32], image_pad: u32, assets: &[Arc]) -> Result { + let marks: Vec = text_ids + .iter() + .enumerate() + .filter_map(|(i, &id)| (id == image_pad).then_some(i)) + .collect(); + ensure!( + marks.len() == assets.len(), + "prompt has {} image placeholders but the request carries {} images", + marks.len(), + assets.len() + ); + let mut ids: Vec = Vec::new(); + let mut indices: Vec = Vec::new(); + let mut positions: [Vec; 3] = [Vec::new(), Vec::new(), Vec::new()]; + let mut embeddings: Vec = Vec::new(); + let mut blocks: Vec = Vec::with_capacity(marks.len()); + let mut cursor = 0usize; + let mut current = 0i64; + for (k, &mark) in marks.iter().enumerate() { + for rel in 0..(mark - cursor) as i64 { + positions[0].push(current + rel); + positions[1].push(current + rel); + positions[2].push(current + rel); + } + ids.extend_from_slice(&text_ids[cursor..mark]); + current += (mark - cursor) as i64; + cursor = mark + 1; + let [gt, gh, gw] = assets[k].grid_thw; + ensure!( + gt >= 1 + && gh % 2 == 0 + && gw % 2 == 0 + && gt * gh * gw / 4 == assets[k].n_tokens() as i64, + "grid does not match the adapted rows" + ); + let (lg_t, lg_h, lg_w) = (gt, gh / 2, gw / 2); // temp_merge=1, spatial_merge=2 + let start = ids.len(); + indices.extend(start..start + assets[k].n_tokens()); + embeddings.extend_from_slice(&assets[k].embeddings); + for _ in 0..assets[k].n_tokens() { + ids.push(image_pad); + } + blocks.push(ImageBlock { + start, + end: ids.len(), + base: current, + advance: gh.max(gw) / 2, + asset: k, + }); + for ti in 0..lg_t { + for hi in 0..lg_h { + for wi in 0..lg_w { + positions[0].push(current + ti); + positions[1].push(current + hi); + positions[2].push(current + wi); + } + } + } + current += gh.max(gw) / 2; + } + for rel in 0..(text_ids.len() - cursor) as i64 { + positions[0].push(current + rel); + positions[1].push(current + rel); + positions[2].push(current + rel); + } + ids.extend_from_slice(&text_ids[cursor..]); + Ok(Expanded { + ids, + image_token_indices: indices, + positions, + embeddings, + blocks, + }) +} diff --git a/src/models/jev_vl/native/src/lib.rs b/src/models/jev_vl/native/src/lib.rs new file mode 100644 index 00000000..c220844c --- /dev/null +++ b/src/models/jev_vl/native/src/lib.rs @@ -0,0 +1,11 @@ +//! Native Rust/CUDA System-1 worker for autotrust/JEV-27B-VL: the merged text +//! backbone runs one prefill per request on the shared qwen3_5 ABI-4 kernels, and +//! the trained verbalizer readout (24-slot head + calibration) turns the last +//! hidden state into the official per-kind probability distribution. + +pub mod caches; +pub mod contract; +pub mod engine; +pub mod executor; +pub mod images; +pub mod processing; diff --git a/src/models/jev_vl/native/src/main.rs b/src/models/jev_vl/native/src/main.rs new file mode 100644 index 00000000..dd06d918 --- /dev/null +++ b/src/models/jev_vl/native/src/main.rs @@ -0,0 +1,164 @@ +//! JEV_VL_MODEL= omni-jev-vl-native + +use std::sync::Arc; + +use anyhow::{Context, Result, ensure}; +use axum::{ + Json, Router, + body::Bytes, + extract::{DefaultBodyLimit, State}, + http::{HeaderMap, StatusCode, Uri}, + response::{IntoResponse, Response}, + routing::{get, post}, +}; +use omni_jev_vl_native::contract::{MODEL_ID, Reject}; +use omni_jev_vl_native::engine::Engine; +use omni_qwen3_5_native::cuda; +use serde_json::{Value, json}; + +const WARMUP: &[u8] = br#"{"kind":"choice","state":"Dialog: Update installed.","question":"Close it?","options":["OK","Wait"]}"#; + +fn reject(r: Reject) -> Response { + ( + StatusCode::from_u16(r.status).unwrap_or(StatusCode::BAD_REQUEST), + Json(r.body), + ) + .into_response() +} + +async fn decide(engine: &Engine, raw: &[u8]) -> Response { + let prepared = match engine + .processor + .prepare(&engine.head, &engine.manifest, raw) + { + Ok(prepared) => prepared, + Err(r) => return reject(r), + }; + let cache_note = prepared.cache_note.clone(); + let result = async { + let probs = engine + .executor + .execute(&engine.scheduler, prepared.plan, prepared.readout) + .await?; + anyhow::Ok(prepared.context.finish(probs)) + } + .await; + match result { + Ok(body) => { + let mut resp = Json(body).into_response(); + if !cache_note.is_empty() { + // R2d evidence side-channel; the response body stays untouched. + resp.headers_mut().insert( + "x-jev-cache", + axum::http::HeaderValue::from_str(&cache_note) + .unwrap_or(axum::http::HeaderValue::from_static("l1=?,l2=?,l3=?,p=?")), + ); + } + resp + } + Err(e) => { + eprintln!("inference failed: {e:#}"); + ( + StatusCode::INTERNAL_SERVER_ERROR, + Json(json!({"error": "model inference failed"})), + ) + .into_response() + } + } +} + +async fn systemone(State(engine): State>, headers: HeaderMap, body: Bytes) -> Response { + // Upstream pydantic validates the raw body even for a non-JSON content type; + // mirroring it keeps the wrong-content-type probe's 400 (not 415) semantics. + let ctype = headers + .get("content-type") + .and_then(|v| v.to_str().ok()) + .unwrap_or("") + .split(';') + .next() + .unwrap_or("") + .trim(); + if !ctype.eq_ignore_ascii_case("application/json") { + return reject(Reject::validation( + format!( + "1 validation error:\n {{'type': 'model_attributes_type', 'loc': 'body', 'msg': 'Input should be a valid dictionary or object to extract fields from', 'input': ''}}", + body.len() + ), + "body", + )); + } + decide(&engine, &body).await +} + +async fn fallback(uri: Uri, body: Bytes) -> Response { + let model = serde_json::from_slice::(&body) + .ok() + .and_then(|v| v.get("model").and_then(Value::as_str).map(str::to_owned)); + let _ = uri; + reject(Reject::unknown_model( + model.as_deref().unwrap_or("no-such-model"), + )) +} + +/// R2d cache evidence: cumulative hit/miss counters + live budgets; the response +/// body of /v1/systemone is untouched, so the frozen comparator is unaffected. +async fn cache_stats(State(engine): State>) -> Response { + let s = engine.caches.snapshot(); + let cfg = &engine.caches.cfg; + Json(json!({ + "enabled": cfg.enabled, + "l1": cfg.l1, "l2": cfg.l2, "l3": cfg.l3, + "counters": { + "l1_hit": s.l1_hit, "l1_miss": s.l1_miss, + "l2_hit": s.l2_hit, "l2_miss": s.l2_miss, + "l3_hit": s.l3_hit, "l3_miss": s.l3_miss, + "l3_populate": s.l3_populate, "l3_fallback_full": s.l3_fallback_full, + }, + "records": { + "l1_records": s.l1_records, "l1_bytes": s.l1_bytes, + "l2_records": s.l2_records, "l2_bytes": s.l2_bytes, + "l3_records": s.l3_records, "l3_bytes": s.l3_bytes, + }, + "budgets": { + "l1_max": cfg.l1_max, "l2_bytes": cfg.l2_bytes, "l3_bytes": cfg.l3_bytes, + }, + })) + .into_response() +} + +async fn cache_reset(State(engine): State>) -> Response { + engine.caches.reset_stats(); + Json(json!({"reset": true})).into_response() +} + +#[tokio::main] +async fn main() -> Result<()> { + let model = std::env::var_os("JEV_VL_MODEL").context("set JEV_VL_MODEL")?; + let library = std::env::var_os("JEV_VL_CUDA_LIB") + .map(Into::into) + .map_or_else(cuda::default_library, Ok)?; + let engine = Arc::new(Engine::load(model.as_ref(), &library).await?); + ensure!( + decide(&engine, WARMUP).await.status() == StatusCode::OK, + "warmup failed" + ); + let host = std::env::var("JEV_VL_HOST").unwrap_or_else(|_| "127.0.0.1".into()); + let port: u16 = std::env::var("JEV_VL_PORT") + .map_or(Ok(8001), |v| v.parse()) + .context("JEV_VL_PORT")?; + let app = Router::new() + .route( + "/health", + get(|| async { Json(json!({"status": "ready", "model": MODEL_ID})) }), + ) + .route("/v1/systemone", post(systemone)) + .route("/v1/cache/stats", get(cache_stats)) + .route("/v1/cache/reset", post(cache_reset)) + .fallback(fallback) + .layer(DefaultBodyLimit::max(4 << 20)) + .with_state(engine); + let listener = tokio::net::TcpListener::bind((host.as_str(), port)).await?; + println!("listening on {host}:{port}"); + axum::serve(listener, app).await?; + Ok(()) +} diff --git a/src/models/jev_vl/native/src/processing.rs b/src/models/jev_vl/native/src/processing.rs new file mode 100644 index 00000000..7a6de543 --- /dev/null +++ b/src/models/jev_vl/native/src/processing.rs @@ -0,0 +1,415 @@ +//! Request validation, tokenization, and response assembly. + +use std::path::{Path, PathBuf}; +use std::sync::Arc; +use std::time::Instant; + +use anyhow::{Context, Result}; +use serde_json::Value; +use tokenizers::Tokenizer; + +use crate::caches::{Caches, L1Meta, structure_key}; +use crate::contract::{self, Compiled, Kind, Reject}; +use crate::executor::{ContinueMm, ImgBlock, MmPlan, PreparedMm, Readout, SuffixBlock}; +use crate::images::{self, ImageAsset}; + +pub struct Processor { + tokenizer: Tokenizer, + labels: Vec, + max_length: usize, + imgcache: Option, + image_pad: u32, + vision_end: u32, + caches: Arc, +} + +pub struct PreparedRequest { + pub plan: MmPlan, + pub readout: Readout, + pub context: ResponseContext, + /// x-jev-cache response-header marker: l1/l2/l3 hit flags + prefix length. + pub cache_note: String, +} + +/// The original request mapping, usage, and timing. +pub struct ResponseContext { + kind: Kind, + options: Vec, + input_tokens: usize, + start: Instant, +} + +impl Processor { + pub(crate) fn load( + dir: &Path, + labels: Vec, + max_length: usize, + caches: Arc, + ) -> Result { + let tokenizer = + Tokenizer::from_file(dir.join("tokenizer.json")).map_err(anyhow::Error::msg)?; + let image_pad = tokenizer + .token_to_id("<|image_pad|>") + .context("tokenizer has no <|image_pad|>")?; + let vision_end = tokenizer + .token_to_id("<|vision_end|>") + .context("tokenizer has no <|vision_end|>")?; + let hub = std::env::var("JEV_VL_IMGCACHE") + .ok() + .map(PathBuf::from) + .or_else(|| Some(dir.join("imgcache"))) + .filter(|p| p.is_dir()); + Ok(Self { + tokenizer, + labels, + max_length, + imgcache: hub, + image_pad, + vision_end, + caches, + }) + } + + fn url_key(url: &str) -> String { + use sha2::Digest; + sha2::Sha256::digest(url.as_bytes()) + .iter() + .map(|b| format!("{b:02x}")) + .collect() + } + + /// Parsed-image-asset access keyed by sha256(url) — the R2d L2 vision layer + /// (process-local LRU + byte budget); the ancestor cache is a hit on this. + /// With caches disabled the asset is re-read and re-parsed on every request, + /// which is the honest no-cache counterfactual for the R2d pair. + fn load_asset(&self, url: &str) -> Result, Reject> { + let key = Self::url_key(url); + if let Some(hit) = self.caches.l2_get(&key) { + return Ok(hit); + } + let dir = match &self.imgcache { + Some(d) => d.join(&key), + None => { + return Err(Reject::bad_request( + "image input: no imgcache is configured for this worker", + )); + } + }; + let asset = ImageAsset::load(&dir) + .map_err(|e| Reject::bad_request(format!("image input is not preencoded: {e}")))?; + self.caches.l2_insert(key, asset.clone()); + Ok(asset) + } + + fn tokenize(&self, text: &str) -> Result, Reject> { + Ok(self + .tokenizer + .encode(text, false) + .map_err(|e| Reject::bad_request(format!("tokenization failed: {e}")))? + .get_ids() + .to_vec()) + } + + /// Validate, compile the raw prompt, and tokenize; add_special_tokens=false, + /// like the official raw System-1 readout. Never truncates: oversize prompts + /// get the upstream context-length rejection. + pub fn prepare( + &self, + head: &crate::executor::LabelHead, + manifest: &Value, + raw: &[u8], + ) -> Result { + let start = Instant::now(); + let compiled = contract::compile(raw, &self.labels)?; + let readout = head + .readout(manifest, compiled.kind.as_str(), compiled.options.len()) + .map_err(|e| Reject::bad_request(format!("invalid readout setup: {e:#}")))?; + let cfg = &self.caches.cfg; + if compiled.images.is_empty() || !cfg.enabled { + // Text-only requests and the honest no-cache mode keep the R2b/R2c path. + let text_ids = self.tokenize(&compiled.prompt)?; + let (plan, input_tokens, note) = if compiled.images.is_empty() { + ( + MmPlan::Text { ids: text_ids }, + 0, + "l1=-,l2=-,l3=-,p=-".to_string(), + ) + } else { + let assets: Vec> = compiled + .images + .iter() + .map(|url| self.load_asset(url)) + .collect::, Reject>>()?; + let e = images::expand(&text_ids, self.image_pad, &assets) + .map_err(|e| Reject::bad_request(format!("invalid image prompt: {e:#}")))?; + let n = e.ids.len(); + ( + MmPlan::Full(expanded_mm(e, &assets)), + n, + "l1=off,l2=*,l3=off,p=-".to_string(), + ) + }; + let input_tokens = match &plan { + MmPlan::Text { ids } => ids.len(), + _ => input_tokens, + }; + return self.finish_c(input_tokens, plan, readout, &compiled, start, note); + } + self.prepare_cached(&compiled, readout, start) + } + + /// L1 structure record + L3 continuation planning for one-image requests. + /// The plan decides whether the model sees the full prompt (miss/populate) or + /// only the continuation slice (record state present). + fn prepare_cached( + &self, + compiled: &Compiled, + readout: Readout, + start: Instant, + ) -> Result { + let cfg = &self.caches.cfg; + let key = structure_key(compiled.kind.as_str(), &compiled.parts); + let single_image = compiled.images.len() == 1; + let tail = contract::tail_after_last_image(compiled, &self.labels); + if let (Some(record), true) = (self.caches.record_get(key), single_image && tail.is_some()) + { + // Scalars out of the record up front: the borrow ends so the record + // can move into the plan arms below. + let meta = &record.meta; + let (p, pads_start, pads_end, base_pad, advance) = ( + meta.p, + meta.pads_start, + meta.pads_end, + meta.base_pad, + meta.advance, + ); + let asset = self.load_asset(&compiled.images[0])?; + // Suffix piece: pads(E-P) + <|vision_end|> + fresh tail tokenization. + let tail = tail.unwrap(); + let tail_ids = self.tokenize(&tail)?; + let suffix_pads = pads_end - p; + let mut ids = Vec::with_capacity(suffix_pads + 1 + tail_ids.len()); + ids.extend(std::iter::repeat_n(self.image_pad, suffix_pads)); + ids.push(self.vision_end); + ids.extend_from_slice(&tail_ids); + let mut pos = + images::meshgrid_positions(asset.grid_thw, base_pad, p - pads_start, suffix_pads); + let after = base_pad + advance; + for k in 0..(ids.len() - suffix_pads) as i64 { + pos[0].push(after + k); + pos[1].push(after + k); + pos[2].push(after + k); + } + let input_tokens = p + ids.len(); + match (cfg.l3, record.state()) { + (true, Some(state)) => { + self.caches.l3_hit(); + let block = Some(SuffixBlock { + asset, + row_offset: p - pads_start, + rows: suffix_pads, + }); + return self.finish_c( + input_tokens, + MmPlan::Continue(ContinueMm { + ids, + positions: pos, + block, + p, + state, + }), + readout, + compiled, + start, + format!("l1=hit,l2=hit,l3=hit,p={p}"), + ); + } + (true, None) => { + self.caches.l3_miss(); + let mm = rebuild_full(&ids, &pos, &record.meta, &asset)?; + return self.finish_c( + mm.ids.len(), + MmPlan::Populate { mm, record }, + readout, + compiled, + start, + format!("l1=hit,l2=hit,l3=miss,p={p}"), + ); + } + (false, _) => { + let mm = rebuild_full(&ids, &pos, &record.meta, &asset)?; + return self.finish_c( + mm.ids.len(), + MmPlan::Full(mm), + readout, + compiled, + start, + "l1=hit,l2=hit,l3=off,p=-".to_string(), + ); + } + } + } + // L1 miss: full expansion (and the cache record when the shape qualifies). + let text_ids = self.tokenize(&compiled.prompt)?; + let assets: Vec> = compiled + .images + .iter() + .map(|url| self.load_asset(url)) + .collect::, Reject>>()?; + let e = images::expand(&text_ids, self.image_pad, &assets) + .map_err(|e| Reject::bad_request(format!("invalid image prompt: {e:#}")))?; + let input_tokens = e.ids.len(); + let meta = (single_image && tail.is_some() && cfg.l1) + .then(|| prefix_meta(&e, Self::url_key(&compiled.images[0]), input_tokens)) + .flatten(); + if let Some(meta) = meta { + let p = meta.p; + let record = self.caches.record_insert(key, meta); + if cfg.l3 { + self.caches.l3_miss(); + return self.finish_c( + input_tokens, + MmPlan::Populate { + mm: expanded_mm(e, &assets), + record, + }, + readout, + compiled, + start, + format!("l1=miss,l2=hit,l3=miss,p={p}"), + ); + } + return self.finish_c( + input_tokens, + MmPlan::Full(expanded_mm(e, &assets)), + readout, + compiled, + start, + "l1=miss,l2=hit,l3=off,p=-".to_string(), + ); + } + self.finish_c( + input_tokens, + MmPlan::Full(expanded_mm(e, &assets)), + readout, + compiled, + start, + "l1=miss,l2=hit,l3=off,p=-".to_string(), + ) + } + + fn finish_c( + &self, + input_tokens: usize, + plan: MmPlan, + readout: Readout, + compiled: &Compiled, + start: Instant, + cache_note: String, + ) -> Result { + let t = input_tokens; + if t > self.max_length { + return Err(Reject::bad_request(format!( + "This model's maximum context length is {} tokens. However, you requested 1 output tokens and your prompt contains at least {t} input tokens, for a total of at least {} tokens. Please reduce the length of the input prompt or the number of requested output tokens. (parameter=input_tokens, value={t})", + self.max_length, + t + 1 + ))); + } + Ok(PreparedRequest { + plan, + readout, + cache_note, + context: ResponseContext { + kind: compiled.kind, + options: compiled.options.clone(), + input_tokens: t, + start, + }, + }) + } +} + +/// Cache-anchor geometry for one expanded single-image prompt: prefix = rows +/// [0, floor(pads_end/64)*64) — a multiple of the 64-token GDN chunk, containing +/// the whole text head and all but a tail slice of the image block. +fn prefix_meta(e: &images::Expanded, asset_key: String, input_tokens: usize) -> Option { + let block = e.blocks.last()?; + let p = block.end / 64 * 64; + if p < 64 || p < block.start || p >= input_tokens { + return None; + } + Some(L1Meta { + pads_start: block.start, + pads_end: block.end, + p, + base_pad: block.base, + advance: block.advance, + asset_key, + ids_prefix: e.ids[..p].to_vec(), + positions_prefix: [ + e.positions[0][..p].to_vec(), + e.positions[1][..p].to_vec(), + e.positions[2][..p].to_vec(), + ], + }) +} + +/// Full expanded ids/positions with image blocks borrowed from their assets. +fn expanded_mm(e: images::Expanded, assets: &[Arc]) -> PreparedMm { + let blocks = e + .blocks + .iter() + .map(|b| ImgBlock { + start: b.start, + end: b.end, + base: b.base, + advance: b.advance, + asset: assets[b.asset].clone(), + }) + .collect(); + PreparedMm { + ids: e.ids, + positions: e.positions, + blocks, + } +} + +/// Rebuild the full expanded ids/positions from the cached prefix plus the +/// freshly built suffix — exactly the bytes the miss path would have produced. +fn rebuild_full( + ids_suffix: &[u32], + pos_suffix: &[Vec; 3], + meta: &L1Meta, + asset: &Arc, +) -> Result { + let mut ids = meta.ids_prefix.clone(); + ids.extend_from_slice(ids_suffix); + let mut positions = meta.positions_prefix.clone(); + for a in 0..3 { + positions[a].extend_from_slice(&pos_suffix[a]); + } + let blocks = vec![ImgBlock { + start: meta.pads_start, + end: meta.pads_end, + base: meta.base_pad, + advance: meta.advance, + asset: asset.clone(), + }]; + Ok(PreparedMm { + ids, + positions, + blocks, + }) +} + +impl ResponseContext { + pub fn finish(self, probabilities: Vec) -> Value { + contract::answer( + self.kind, + &self.options, + &probabilities, + self.input_tokens, + self.start.elapsed().as_secs_f64(), + ) + } +} diff --git a/tests/jev_vl/contract.rs b/tests/jev_vl/contract.rs new file mode 100644 index 00000000..92433d08 --- /dev/null +++ b/tests/jev_vl/contract.rs @@ -0,0 +1,161 @@ +//! Golden pins for the JEV-27B-VL System-1 prompt contract and the upstream +//! error envelope; the strings mirror serve_decide.py's s1_pass exactly. + +use omni_jev_vl_native::contract::{Kind, compile}; + +fn labels() -> Vec { + let all: Vec = ('A'..='Z') + .map(|c| c.to_string()) + .chain(('A'..='Z').flat_map(|a| ('A'..='Z').map(move |b| format!("{a}{b}")))) + .collect(); + all[..256].to_vec() +} + +fn compile_str(raw: &str) -> omni_jev_vl_native::contract::Compiled { + compile(raw.as_bytes(), &labels()).unwrap() +} + +#[test] +fn noul_prompt_matches_serve_decide() { + let c = compile_str( + r#"{"kind":"noul","state":"Dialog: Update installed.","question":"Close it?"}"#, + ); + assert_eq!(c.kind, Kind::Noul); + assert_eq!(c.options, ["false", "true"]); + assert_eq!( + c.prompt, + "[kind] noul\n[state] Dialog: Update installed.\n[question] Close it?\n[options]\nfalse\ntrue\n[decision]:" + ); +} + +#[test] +fn score_prompt_uses_digits_lines() { + let c = compile_str(r#"{"kind":"score","state":"x","question":"Rate it."}"#); + assert_eq!(c.options, ["0", "1", "2", "3", "4", "5"]); + assert_eq!( + c.prompt, + "[kind] score\n[state] x\n[question] Rate it.\n[options]\n0\n1\n2\n3\n4\n5\n[decision]:" + ); +} + +#[test] +fn choice_prompt_labels_options() { + let c = compile_str( + r#"{"kind":"choice","state":"Dialog: x.","question":"Pick one.","options":["yes","no","later"]}"#, + ); + assert_eq!( + c.prompt, + "[kind] choice\n[state] Dialog: x.\n[question] Pick one.\n[options]\nA) yes\nB) no\nC) later\n[decision]:" + ); +} + +#[test] +fn dict_state_renders_like_python_json_dumps() { + let c = compile_str(r#"{"kind":"noul","state":{"b":1,"a":"x"},"question":"q?"}"#); + // Python json.dumps(ensure_ascii=False): ", " and ": " separators, order kept. + assert!( + c.prompt.contains(r#"[state] {"b": 1, "a": "x"}"#), + "{}", + c.prompt + ); +} + +#[test] +fn list_state_parts_concatenate_without_separator() { + let c = compile_str( + r#"{"kind":"score","state":["one","two",{"type":"text","text":"three"}],"question":"q?"}"#, + ); + assert!(c.prompt.contains("[state] onetwothree\n"), "{}", c.prompt); +} + +#[test] +fn image_state_becomes_placeholder_and_records_url() { + let c = compile_str( + r#"{"kind":"noul","state":["shot",{"image":"https://x/y.png"}],"question":"q?"}"#, + ); + assert_eq!(c.images, ["https://x/y.png"]); + assert!( + c.prompt + .contains("[state] shot<|vision_start|><|image_pad|><|vision_end|>\n"), + "{}", + c.prompt + ); +} + +fn reject(raw: &str) -> omni_jev_vl_native::contract::Reject { + compile(raw.as_bytes(), &labels()).unwrap_err() +} + +#[test] +fn choice_option_count_error_keeps_upstream_message() { + let r = reject(r#"{"kind":"choice","state":"x","question":"q?","options":["only"]}"#); + assert_eq!(r.status, 400); + assert_eq!( + r.body["error"]["message"], + "choice needs 2-256 options, got 1" + ); + assert_eq!(r.body["error"]["type"], "BadRequestError"); + assert_eq!(r.body["error"]["code"], 400); +} + +#[test] +fn kind_literal_error_mirrors_pydantic_envelope() { + let r = reject(r#"{"kind":"bogus","state":"x","question":"q?"}"#); + assert_eq!(r.status, 400); + assert_eq!( + r.body["error"]["message"], + "1 validation error:\n {'type': 'literal_error', 'loc': 'body.kind', 'msg': \"Input should be 'noul', 'score' or 'choice'\", 'input': 'bogus', 'ctx': {'expected': \"'noul', 'score' or 'choice'\"}}" + ); + assert_eq!(r.body["error"]["type"], "Bad Request"); + assert_eq!(r.body["error"]["param"], "kind"); + assert_eq!(r.body["error"]["code"], 400); +} + +#[test] +fn missing_question_mirrors_pydantic_envelope() { + let r = reject(r#"{"kind":"noul","state":"x"}"#); + assert_eq!( + r.body["error"]["message"], + "1 validation error:\n {'type': 'missing', 'loc': 'body.question', 'msg': 'Field required', 'input': ''}" + ); + assert_eq!(r.body["error"]["param"], "question"); +} + +#[test] +fn score_thinking_on_keeps_upstream_message() { + let r = reject(r#"{"kind":"score","state":"x","question":"q?","thinking":"on"}"#); + assert_eq!( + r.body["error"]["message"], + "thinking is supported for noul and choice" + ); +} + +#[test] +fn wrong_content_type_body_mirrors_bytes_error() { + let raw = br#"{"kind":"noul","state":"x","question":"q?"}"#; + // Non-JSON content type: upstream validates the raw bytes object. + let r = omni_jev_vl_native::contract::Reject::validation( + format!( + "1 validation error:\n {{'type': 'model_attributes_type', 'loc': 'body', 'msg': 'Input should be a valid dictionary or object to extract fields from', 'input': ''}}", + raw.len() + ), + "body", + ); + assert_eq!( + r.body["error"]["message"], + "1 validation error:\n {'type': 'model_attributes_type', 'loc': 'body', 'msg': 'Input should be a valid dictionary or object to extract fields from', 'input': ''}" + ); +} + +#[test] +fn unknown_model_keeps_404_envelope() { + let r = reject(r#"{"model":"no-such-model","kind":"noul","question":"q?"}"#); + assert_eq!(r.status, 404); + assert_eq!( + r.body["error"]["message"], + "The model `no-such-model` does not exist." + ); + assert_eq!(r.body["error"]["type"], "NotFoundError"); + assert_eq!(r.body["error"]["param"], "model"); + assert_eq!(r.body["error"]["code"], 404); +} diff --git a/tests/jev_vl/images.rs b/tests/jev_vl/images.rs new file mode 100644 index 00000000..4fbc7eb2 --- /dev/null +++ b/tests/jev_vl/images.rs @@ -0,0 +1,79 @@ +//! Pins for the token expansion + 3-axis positions at the image boundary, +//! hand-computed from HF modeling_qwen3_5 get_rope_index semantics +//! (meshgrid t-outer/h-mid/w-fastest + start; advance = max(grid_h, grid_w)/merge). + +use std::sync::Arc; + +use omni_jev_vl_native::images::{ImageAsset, expand, meshgrid_positions}; + +const PAD: u32 = 248056; + +fn asset(grid: [i64; 3]) -> Arc { + let [t, h, w] = grid; + let n = (t * h * w / 4) as usize; + Arc::new(ImageAsset { + grid_thw: grid, + embeddings: vec![half::bf16::ONE; n * 5120], + }) +} + +#[test] +fn single_image_positions_follow_hf() { + // text 2 tokens, image grid [1,4,6] -> n=6 (lg_h=2, lg_w=3), text 1 token. + // advance = max(4, 6) / 2 = 3 -> last text at position 2 + 3 = 5. + let text_ids: Vec = vec![300, 301, PAD, 302]; + let e = expand(&text_ids, PAD, &[asset([1, 4, 6])]).unwrap(); + assert_eq!(e.ids, [300, 301, PAD, PAD, PAD, PAD, PAD, PAD, 302]); + assert_eq!(e.image_token_indices, vec![2, 3, 4, 5, 6, 7]); + assert_eq!(e.positions[0], vec![0, 1, 2, 2, 2, 2, 2, 2, 5]); + assert_eq!(e.positions[1], vec![0, 1, 2, 2, 2, 3, 3, 3, 5]); + assert_eq!(e.positions[2], vec![0, 1, 2, 3, 4, 2, 3, 4, 5]); + assert_eq!(e.embeddings.len(), 6 * 5120); + // R2d cache-anchor geometry. + assert_eq!( + e.blocks + .iter() + .map(|b| (b.start, b.end, b.base, b.advance, b.asset)) + .collect::>(), + vec![(2, 8, 2, 3, 0)] + ); +} + +#[test] +fn two_images_continue_after_max_hw() { + // hand-computed: see module doc. + let text_ids: Vec = vec![300, PAD, 301, 302, PAD, 303]; + let e = expand(&text_ids, PAD, &[asset([1, 2, 4]), asset([1, 4, 2])]).unwrap(); + assert_eq!(e.ids, [300, PAD, PAD, 301, 302, PAD, PAD, 303]); + assert_eq!(e.image_token_indices, vec![1, 2, 5, 6]); + assert_eq!(e.positions[0], vec![0, 1, 1, 3, 4, 5, 5, 7]); + assert_eq!(e.positions[1], vec![0, 1, 1, 3, 4, 5, 6, 7]); + assert_eq!(e.positions[2], vec![0, 1, 2, 3, 4, 5, 5, 7]); + assert_eq!( + e.blocks + .iter() + .map(|b| (b.start, b.end, b.base, b.advance, b.asset)) + .collect::>(), + vec![(1, 3, 1, 2, 0), (5, 7, 5, 2, 1)] + ); +} + +#[test] +fn meshgrid_helper_matches_expand_rows() { + // The helper must produce exactly expand's rows for every suffix of one block. + let text_ids: Vec = vec![300, 301, PAD, 302]; + let e = expand(&text_ids, PAD, &[asset([1, 4, 6])]).unwrap(); + let b = &e.blocks[0]; + for from in 0..6usize { + let got = meshgrid_positions([1, 4, 6], b.base, from, 6 - from); + for (want, have) in e.positions.iter().zip(&got) { + assert_eq!(&want[b.start + from..b.end], &have[..], "slice from {from}"); + } + } +} + +#[test] +fn placeholder_count_must_match_images() { + let e = expand(&[PAD], PAD, &[]); + assert!(e.is_err()); +} diff --git a/tests/jev_vl/prefix.rs b/tests/jev_vl/prefix.rs new file mode 100644 index 00000000..5be7070d --- /dev/null +++ b/tests/jev_vl/prefix.rs @@ -0,0 +1,177 @@ +//! R2d cache-structure tests: key stability, LRU/budget accounting, and the +//! manifest-faithful suffix-split equivalence (bit-exact vs full expansion on +//! the FROZEN R1 same-image prompts — the L1 hit path's correctness contract). + +use std::path::PathBuf; +use std::sync::Arc; + +use omni_jev_vl_native::caches::{CacheCfg, Caches, structure_key}; +use omni_jev_vl_native::contract::{self, Part}; +use omni_jev_vl_native::images::{ImageAsset, expand}; + +fn asset(grid: [i64; 3]) -> Arc { + let [t, h, w] = grid; + let n = (t * h * w / 4) as usize; + Arc::new(ImageAsset { + grid_thw: grid, + embeddings: vec![half::bf16::ONE; n * 5120], + }) +} + +#[test] +fn structure_key_is_sensitive_and_stable() { + let parts_a = vec![Part::Text("state text".into()), Part::Image("u://a".into())]; + let parts_b = vec![Part::Text("state text".into()), Part::Image("u://b".into())]; + let parts_c = vec![Part::Image("u://a".into()), Part::Text("state text".into())]; + assert_ne!( + structure_key("noul", &parts_a), + structure_key("score", &parts_a) + ); + assert_ne!( + structure_key("noul", &parts_a), + structure_key("noul", &parts_b) + ); + assert_ne!( + structure_key("noul", &parts_a), + structure_key("noul", &parts_c) + ); + assert_eq!( + structure_key("noul", &parts_a), + structure_key("noul", &parts_a) + ); + assert_ne!( + structure_key("noul", &parts_a), + structure_key("noul", &parts_a[..1]) + ); +} + +fn hub(f: impl FnOnce(&mut CacheCfg)) -> Arc { + let mut cfg = CacheCfg { + enabled: true, + l1: true, + l2: true, + l3: true, + l1_max: 64, + l2_bytes: 1 << 20, + l3_bytes: 1 << 20, + }; + f(&mut cfg); + Caches::new(cfg) +} + +#[test] +fn l2_lru_hits_misses_and_evicition() { + let c = hub(|cfg| cfg.l2_bytes = (2 * 5120 * 2 + 64) * 2 + 128); + let (a, b, d) = (asset([1, 2, 4]), asset([1, 2, 4]), asset([1, 2, 4])); + assert!(c.l2_get("k-a").is_none()); + c.l2_insert("k-a".into(), a.clone()); + assert!(c.l2_get("k-a").is_some()); + c.l2_insert("k-b".into(), b); + c.l2_insert("k-d".into(), d); // a evicted: budget fits two + assert!(c.l2_get("k-a").is_none()); + assert!(c.l2_get("k-b").is_some()); + let s = c.snapshot(); + assert_eq!(s.l2_hit, 2); + assert_eq!(s.l2_miss, 2); + assert_eq!(s.l2_records, 2); + // Disabled: no reuse, all misses counted. + let off = hub(|cfg| cfg.enabled = false); + off.l2_insert("k-a".into(), asset([1, 2, 4])); + assert!(off.l2_get("k-a").is_none()); + assert_eq!(off.snapshot().l2_miss, 1); +} + +#[test] +fn records_are_lru_and_budgeted() { + let c = hub(|cfg| cfg.l1_max = 2); + let meta = |pads: usize| omni_jev_vl_native::caches::L1Meta { + pads_start: pads, + pads_end: pads + 96, + p: 64, + base_pad: pads as i64, + advance: 12, + asset_key: "k".into(), + ids_prefix: vec![0u32; 64], + positions_prefix: [vec![0i64; 64], vec![0i64; 64], vec![0i64; 64]], + }; + let r1 = c.record_insert(1, meta(10)); + c.record_insert(2, meta(20)); + assert!(c.record_get(1).is_some()); + assert!(Arc::ptr_eq(&c.record_get(1).unwrap(), &r1)); + c.record_insert(3, meta(30)); // l1_max=2: one eviction of the oldest (2) + assert!(c.record_get(2).is_none()); + assert!(c.record_get(1).is_some()); + let s = c.snapshot(); + assert!(s.l1_hit >= 3 && s.l1_miss >= 1 && s.l1_records <= 2); +} + +/// The manifest-faithful equivalence: for every img entry of the FROZEN R1 +/// manifest the suffix split (pads-tail + vision_end + fresh tail tokenization) +/// must equal `expand` on the whole prompt bit for bit — that's the L1 hit path. +#[test] +#[ignore = "needs JEV_VL_EXPORT (export dir with tokenizer + manifest)"] +fn manifest_suffix_split_matches_full_expand() { + let dir = PathBuf::from(std::env::var_os("JEV_VL_EXPORT").expect("set JEV_VL_EXPORT")); + let manifest_dir = + PathBuf::from(std::env::var_os("JEV_VL_MANIFEST").expect("set JEV_VL_MANIFEST")); + let tokenizer = tokenizers::Tokenizer::from_file(dir.join("tokenizer.json")).unwrap(); + let manifest: serde_json::Value = + serde_json::from_slice(&std::fs::read(dir.join("jev_vl_export.json")).unwrap()).unwrap(); + let labels: Vec = serde_json::from_value(manifest["labels"].clone()).unwrap(); + let image_pad = tokenizer.token_to_id("<|image_pad|>").unwrap(); + let vision_end = tokenizer.token_to_id("<|vision_end|>").unwrap(); + let mut checked = 0; + for line in std::fs::read_to_string(manifest_dir).unwrap().lines() { + let entry: serde_json::Value = serde_json::from_str(line).unwrap(); + if !entry["id"].as_str().unwrap_or_default().starts_with("img-") { + continue; + } + let raw = serde_json::to_vec(&entry["request"]).unwrap(); + let compiled = contract::compile(&raw, &labels).unwrap(); + let tail = contract::tail_after_last_image(&compiled, &labels).unwrap(); + let grid = [1, 60, 60]; + let e = expand( + &tokenizer + .encode(compiled.prompt.as_str(), false) + .unwrap() + .get_ids() + .to_vec(), + image_pad, + &[asset(grid)], + ) + .unwrap(); + assert_eq!(e.blocks.len(), 1, "{}", entry["id"]); + let p = e.blocks[0].end / 64 * 64; + // Rebuild as the L1 hit path does. + let b = &e.blocks[0]; + let suffix_pads = b.end - p; + let tail_ids: Vec = tokenizer + .encode(tail.as_str(), false) + .unwrap() + .get_ids() + .to_vec(); + let mut ids: Vec = std::iter::repeat_n(image_pad, suffix_pads).collect(); + ids.push(vision_end); + ids.extend_from_slice(&tail_ids); + assert_eq!(&ids[..], &e.ids[p..], "suffix ids diverge: {}", entry["id"]); + let pos = + omni_jev_vl_native::images::meshgrid_positions(grid, b.base, p - b.start, suffix_pads); + for a in 0..3 { + assert_eq!( + &pos[a][..], + &e.positions[a][p..p + suffix_pads], + "suffix meshgrid diverges: {}", + entry["id"] + ); + } + assert_eq!( + e.positions[0][p + suffix_pads], + b.base + b.advance, + "after-image base: {}", + entry["id"] + ); + // The structure key is shared across the 12 same-image entries per kind. + checked += 1; + } + assert_eq!(checked, 12, "the frozen manifest carries 12 img entries"); +} diff --git a/tests/jev_vl/readout.rs b/tests/jev_vl/readout.rs new file mode 100644 index 00000000..43e55914 --- /dev/null +++ b/tests/jev_vl/readout.rs @@ -0,0 +1,71 @@ +//! Frozen numeric pins for the verbalizer readout math (constants computed with +//! CPython over the same f64 formula against this test's pattern). + +use std::collections::HashMap; + +use omni_jev_vl_native::executor::{LabelHead, Readout}; + +fn head() -> LabelHead { + // rows[3][64]; row t element i = ((t*64+i)*5 % 11 - 5) / 7. + let mut rows = Vec::with_capacity(3 * 64); + for t in 0..3u32 { + for i in 0..64u32 { + rows.push((((t * 64 + i) * 5 % 11) as i32 - 5) as f32 / 7.0); + } + } + LabelHead::from_parts(vec![15, 16, 17], rows, 64) +} + +#[test] +fn probabilities_match_python_f64() { + let hidden: Vec = (0..64) + .map(|i| ((i as i32 * 7 % 13) - 6) as f32 / 9.0) + .collect(); + let readout = Readout { + token_ids: vec![15, 16, 17], + bias: vec![0.001, -0.002, 0.0005], + temperature: 1.0143134751376188, + }; + let p = head().probabilities(&hidden, &readout).unwrap(); + let expected = [0.6256388379121548, 0.1869499071806715, 0.18741125490717364]; + // 1e-12: f32 division rounding order differs between CPython and Rust patterns; + // both far below the f32 head precision, so this pins the formula, not ulps. + for (&a, &e) in p.iter().zip(&expected) { + assert!((a - e).abs() < 1e-12, "{a} != {e}"); + } +} + +#[test] +fn softmax_is_shift_invariant_to_minus_logz() { + // The official readout subtracts per-request -logZ before bias/T; the stable + // softmax must be invariant, which is why logits suffice. + let hidden: Vec = (0..64) + .map(|i| ((i as i32 * 7 % 13) - 6) as f32 / 9.0) + .collect(); + let readout = |shift: f64| Readout { + token_ids: vec![15, 16, 17], + bias: vec![0.001 + shift, -0.002 + shift, 0.0005 + shift], + temperature: 1.0143134751376188, + }; + let p0 = head().probabilities(&hidden, &readout(0.0)).unwrap(); + let p1 = head().probabilities(&hidden, &readout(-42.5)).unwrap(); + for (&a, &b) in p0.iter().zip(&p1) { + assert!((a - b).abs() < 1e-12, "{a} != {b}"); + } +} + +#[test] +fn missing_exported_label_is_rejected() { + let mut h = head(); + h.drop_row(17); + let readout = Readout { + token_ids: vec![15, 16, 17], + bias: vec![0.0; 3], + temperature: 1.0, + }; + let hidden = vec![0.0f32; 64]; + assert!(h.probabilities(&hidden, &readout).is_err()); +} + +#[allow(dead_code)] +fn _unused(_: HashMap) {} From 4927d2a3373ae3e2d825bf63a320178988540031 Mon Sep 17 00:00:00 2001 From: Hongsheng Liu Date: Tue, 6 Oct 2026 22:32:00 +0800 Subject: [PATCH 4/8] jev_vl: keep replay fixtures and archive generated run output Signed-off-by: Hongsheng Liu --- recipe/jev_vl/evidence/parity-all.json | 11 -- recipe/jev_vl/evidence/parity-off.json | 11 -- recipe/jev_vl/evidence/provenance.json | 103 ----------- .../jev_vl/evidence/raw-r2d-all-c1-p0.jsonl | 12 -- .../jev_vl/evidence/raw-r2d-all-c1-p1.jsonl | 12 -- .../jev_vl/evidence/raw-r2d-off-c1-p0.jsonl | 12 -- .../jev_vl/evidence/raw-r2d-off-c1-p1.jsonl | 12 -- .../review-20261006/binary-sha256.txt | 3 - .../review-20261006/frontend-errors-all.json | 126 ------------- .../review-20261006/frontend-errors-off.json | 126 ------------- .../initial-frontend-failed-verdict.json | 13 -- .../review-20261006/parity-all-worker.json | 11 -- .../review-20261006/parity-off-worker.json | 11 -- .../evidence/review-20261006/provenance.json | 163 ----------------- .../review-20261006/raw-all-c1-p0.jsonl | 12 -- .../review-20261006/raw-all-c1-p1.jsonl | 12 -- .../review-20261006/raw-off-c1-p0.jsonl | 12 -- .../review-20261006/raw-off-c1-p1.jsonl | 12 -- .../replay-all-frontend-results.jsonl | 52 ------ .../replay-all-frontend-summary.json | 10 -- .../replay-all-worker-results.jsonl | 52 ------ .../replay-all-worker-summary.json | 10 -- .../replay-off-frontend-results.jsonl | 52 ------ .../replay-off-frontend-summary.json | 10 -- .../replay-off-worker-results.jsonl | 52 ------ .../replay-off-worker-summary.json | 10 -- .../runtime-source-sha256.json | 169 ------------------ .../review-20261006/stats-all-c1.json | 134 -------------- .../review-20261006/stats-off-c1.json | 134 -------------- .../evidence/review-20261006/summary.json | 71 -------- recipe/jev_vl/validation.md | 54 +++--- 31 files changed, 20 insertions(+), 1464 deletions(-) delete mode 100644 recipe/jev_vl/evidence/parity-all.json delete mode 100644 recipe/jev_vl/evidence/parity-off.json delete mode 100644 recipe/jev_vl/evidence/provenance.json delete mode 100644 recipe/jev_vl/evidence/raw-r2d-all-c1-p0.jsonl delete mode 100644 recipe/jev_vl/evidence/raw-r2d-all-c1-p1.jsonl delete mode 100644 recipe/jev_vl/evidence/raw-r2d-off-c1-p0.jsonl delete mode 100644 recipe/jev_vl/evidence/raw-r2d-off-c1-p1.jsonl delete mode 100644 recipe/jev_vl/evidence/review-20261006/binary-sha256.txt delete mode 100644 recipe/jev_vl/evidence/review-20261006/frontend-errors-all.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/frontend-errors-off.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/initial-frontend-failed-verdict.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/parity-all-worker.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/parity-off-worker.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/provenance.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/raw-all-c1-p0.jsonl delete mode 100644 recipe/jev_vl/evidence/review-20261006/raw-all-c1-p1.jsonl delete mode 100644 recipe/jev_vl/evidence/review-20261006/raw-off-c1-p0.jsonl delete mode 100644 recipe/jev_vl/evidence/review-20261006/raw-off-c1-p1.jsonl delete mode 100644 recipe/jev_vl/evidence/review-20261006/replay-all-frontend-results.jsonl delete mode 100644 recipe/jev_vl/evidence/review-20261006/replay-all-frontend-summary.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/replay-all-worker-results.jsonl delete mode 100644 recipe/jev_vl/evidence/review-20261006/replay-all-worker-summary.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/replay-off-frontend-results.jsonl delete mode 100644 recipe/jev_vl/evidence/review-20261006/replay-off-frontend-summary.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/replay-off-worker-results.jsonl delete mode 100644 recipe/jev_vl/evidence/review-20261006/replay-off-worker-summary.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/runtime-source-sha256.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/stats-all-c1.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/stats-off-c1.json delete mode 100644 recipe/jev_vl/evidence/review-20261006/summary.json diff --git a/recipe/jev_vl/evidence/parity-all.json b/recipe/jev_vl/evidence/parity-all.json deleted file mode 100644 index 2ae16f29..00000000 --- a/recipe/jev_vl/evidence/parity-all.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "pass": true, - "n_compared": 48, - "decision_parity": 1.0, - "max_abs_dp": 0.010764, - "tol": 0.025, - "noise_floor": 0.0093, - "decision_fails": [], - "prob_fails": [], - "error_fails": [] -} \ No newline at end of file diff --git a/recipe/jev_vl/evidence/parity-off.json b/recipe/jev_vl/evidence/parity-off.json deleted file mode 100644 index 48985c9c..00000000 --- a/recipe/jev_vl/evidence/parity-off.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "pass": true, - "n_compared": 48, - "decision_parity": 1.0, - "max_abs_dp": 0.021545, - "tol": 0.025, - "noise_floor": 0.0093, - "decision_fails": [], - "prob_fails": [], - "error_fails": [] -} \ No newline at end of file diff --git a/recipe/jev_vl/evidence/provenance.json b/recipe/jev_vl/evidence/provenance.json deleted file mode 100644 index 65966c12..00000000 --- a/recipe/jev_vl/evidence/provenance.json +++ /dev/null @@ -1,103 +0,0 @@ -{ - "status": "historical experiment; current integrated head not GPU-validated", - "job_id": 413512, - "timestamp_start": "2026-10-06T15:18:43+08:00", - "timestamp_end": "2026-10-06T15:21:42+08:00", - "hardware": { - "gpu": "NVIDIA H800 80GB", - "device_count": 1, - "compute_capability": "sm_90" - }, - "measured_source": { - "base_commit": "7f39ac40902c374803992407bb26eeba29c8a588", - "local_changes": true, - "cuda_abi": 4, - "candidate_snapshot_patch_sha256": "14646add7f73a506f404462326b94524204f8371849f8aa0252ae33d0215fe84", - "candidate_snapshot_newfiles_tar_sha256": "dbc042f5b4bb5daaec3118ffb96e5fdb299b2921d5708b44a43b7894ee81daa6", - "note": "Source snapshots were saved after this run. Their hashes identify the retained archive, not a measured binary attestation." - }, - "checkpoint": { - "repo": "autotrust/JEV-27B-VL", - "revision": "f34b598d4ef4bcefd337bee8d8e7ddd3b7733ccc", - "export_format": "jev-vl-text-merged/1", - "pins": { - "model_index_sha256": "77042094076611b69791a610065f28b7013b8c621795fa86ddccc8bac7d1b9df", - "adapter_config_sha256": "5ae16c5e11f37c5a67ec0e3b16d871fcc19c069090c1e42a6289f63c8067f6b6", - "adapter_model_sha256": "c26cb3fb6d43eaf734cfef74afc40172d30459f6a6bd47e3cfa1f8e866ed6bd9", - "decision_head_sha256": "79fc56c3a030204a2410e6347272fb27facfe299f2f8c0a714e78fc6f8062776", - "calibration_sha256": "902f4f26fdf1b92224c32b706ee6cea381f6465621c82975bf7e0023c02d9dd2" - } - }, - "reference": { - "backend": "vLLM 0.31.0 with upstream serve_decide.py", - "torch": "2.13.0+cu130", - "capture_job": 412885, - "same_checkpoint": true, - "contemporaneous_to_job_413512": false - }, - "workload": { - "description": "36 synthetic support-text prompts and 12 questions about one synthetic 960x960 color/noise PNG; not an accuracy benchmark", - "parity_requests": 48, - "error_probes": 8, - "performance_requests_per_pass": 12, - "performance_passes": 2, - "concurrency": 1, - "warmup_per_pass": 12, - "manifest_sha256": "f72d1beaaaf53933d0a6edda26b635f46931990d8cdb56ca5c6a7ca94d2eb0ee", - "img12_manifest_sha256": "880b513d9137c4f482fd8246a1a8f628e6352d8914467be94365c4ea5865dce5", - "manifest_in_this_repository": true, - "manifest_storage": "manifest-template.jsonl + image.png; restore_manifest.py reconstructs and verifies original bytes" - }, - "timing": { - "boundary": "worker-direct localhost HTTP through response JSON parsing; excludes client serialization, loading, image preencoding and warmup", - "cache_controls": "JEV_VL_CACHE=0 versus JEV_VL_CACHE=1 on the same worker binary and GPU; prepared image files exist in both modes", - "off_p50_seconds": 0.10326676722615957, - "all_p50_seconds": 0.03548599220812321, - "off_div_all_p50": 2.9100712929345813, - "errors": 0, - "memory": "No peak-memory sample; historical resident L3 accounting was 643384320 bytes for three entries." - }, - "audit_limits": [ - "Both measured configurations exclude vision encoding.", - "The historical 3.02x report belongs to job 413490; job 413512 is 2.91x.", - "Bench filenames were reused across jobs; these copied rows are the later 413512 rows.", - "Comparator checks selected decision fields and probability tolerance, not the full API schema or token accounting.", - "Measured source/binary snapshots are not included; frozen reference responses are stored in evidence/reference." - ], - "comparator_sha256": "1189130bb6029656a2e38068faad5d47b2411751373d4f477c4e7756dd51864e", - "job_log_sha256": "9497f8eea4ef049c4bc508ad962bb8e36e65851f3d67d1ccb85b3c0812d69c48", - "files": [ - { - "file": "raw-r2d-off-c1-p0.jsonl", - "sha256": "72be127289970f93e8e98e84ab6d7b5f0cc726506fd81f6b5e378687ac64da6c" - }, - { - "file": "raw-r2d-off-c1-p1.jsonl", - "sha256": "70a55412cd02407fc3c043c7faf0072cff3c520e5a8ff6cf59db241fed0db242" - }, - { - "file": "raw-r2d-all-c1-p0.jsonl", - "sha256": "e2edaefab68b54de12ac54c58130b46ce99f2fbff86dc3ad13df3139bf5e59ae" - }, - { - "file": "raw-r2d-all-c1-p1.jsonl", - "sha256": "88676e12964e16af05b7365e8db241305d8e1408642e9a3c50e1e145807e6765" - }, - { - "file": "parity-off.json", - "sha256": "260557dd8e0aaf1c0d2a32d168ecbaaf94d4ccb910c080590a0601f57862a10c" - }, - { - "file": "parity-all.json", - "sha256": "42f6bf76a9c96b15169770939aac4239eb4b66b0f04af63d918f8af7bdfa9db3" - }, - { - "file": "manifest-template.jsonl", - "sha256": "a8632193319957ff871aec445f42ff4ed3ed61e4ee1041f794b6bf0bb056d556" - }, - { - "file": "image.png", - "sha256": "1ecac9a2296811ff6b6d0c4dd600cd950e886cabaec1b02e84ef3f339b4b4331" - } - ] -} diff --git a/recipe/jev_vl/evidence/raw-r2d-all-c1-p0.jsonl b/recipe/jev_vl/evidence/raw-r2d-all-c1-p0.jsonl deleted file mode 100644 index 4b17a74e..00000000 --- a/recipe/jev_vl/evidence/raw-r2d-all-c1-p0.jsonl +++ /dev/null @@ -1,12 +0,0 @@ -{"tag": "r2d-all", "pass": 0, "i": 0, "id": "img-noul-01", "group": "img", "t_start": 1791271157.5488608, "status": 200, "client_s": 0.034289383329451084, "cache": "l1=hit,l2=hit,l3=hit,p=896", "server_s": 0.032843053, "num_model_requests": 1, "usage": {"prompt_tokens": 948, "completion_tokens": 1, "total_tokens": 949}, "t_end": 1791271157.5943165} -{"tag": "r2d-all", "pass": 0, "i": 1, "id": "img-noul-02", "group": "img", "t_start": 1791271157.5943196, "status": 200, "client_s": 0.03518659994006157, "cache": "l1=hit,l2=hit,l3=hit,p=896", "server_s": 0.033460886, "num_model_requests": 1, "usage": {"prompt_tokens": 943, "completion_tokens": 1, "total_tokens": 944}, "t_end": 1791271157.6398404} -{"tag": "r2d-all", "pass": 0, "i": 2, "id": "img-noul-03", "group": "img", "t_start": 1791271157.6398442, "status": 200, "client_s": 0.035283870063722134, "cache": "l1=hit,l2=hit,l3=hit,p=896", "server_s": 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a/recipe/jev_vl/evidence/raw-r2d-off-c1-p0.jsonl +++ /dev/null @@ -1,12 +0,0 @@ -{"tag": "r2d-off", "pass": 0, "i": 0, "id": "img-noul-01", "group": "img", "t_start": 1791271186.6471002, "status": 200, "client_s": 0.1078127222135663, "cache": "l1=off,l2=*,l3=off,p=-", "server_s": 0.104786927, "num_model_requests": 1, "usage": {"prompt_tokens": 948, "completion_tokens": 1, "total_tokens": 949}, "t_end": 1791271186.7656412} -{"tag": "r2d-off", "pass": 0, "i": 1, "id": "img-noul-02", "group": "img", "t_start": 1791271186.7656453, "status": 200, "client_s": 0.10124731715768576, "cache": "l1=off,l2=*,l3=off,p=-", "server_s": 0.099287807, "num_model_requests": 1, "usage": {"prompt_tokens": 943, "completion_tokens": 1, "total_tokens": 944}, "t_end": 1791271186.87711} -{"tag": "r2d-off", "pass": 0, "i": 2, "id": "img-noul-03", "group": "img", "t_start": 1791271186.8771143, "status": 200, "client_s": 0.10311287827789783, "cache": "l1=off,l2=*,l3=off,p=-", "server_s": 0.10090864, 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-36b12686387c6b692ab6e1138c112df6d84057950e7a97a004d132e0daa916c8 target/release/libqwen3_5_cuda.so -c9080074e5c6ab2ec355c6f40f9f491c9117050ecdcb75ad4f3e7b609ec2f7f1 target/release/omni-jev-vl-native -2a34b06bb5a183561ec0bfdc34ee0cd32915c67a1787159b08d6b9f156e1b84a target/release/omni-jev diff --git a/recipe/jev_vl/evidence/review-20261006/frontend-errors-all.json b/recipe/jev_vl/evidence/review-20261006/frontend-errors-all.json deleted file mode 100644 index f7c4edae..00000000 --- a/recipe/jev_vl/evidence/review-20261006/frontend-errors-all.json +++ /dev/null @@ -1,126 +0,0 @@ -{ - "ok": true, - "supported_decision_errors": 7, - "unsupported_chat_status": 404, - "rows": [ - { - "id": "err-kind-bogus", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "1 validation error:\n {'type': 'literal_error', 'loc': 'body.kind', 'msg': \"Input should be 'noul', 'score' or 'choice'\", 'input': 'bogus', 'ctx': {'expected': \"'noul', 'score' or 'choice'\"}}", - "type": "Bad Request", - "param": "kind", - "code": 400 - } - }, - "client_s": 0.0017899423837661743, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-choice-1opt", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "choice needs 2-256 options, got 1", - "type": "BadRequestError", - "code": 400 - } - }, - "client_s": 0.0006468044593930244, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-choice-300opt", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "choice needs 2-256 options, got 300", - "type": "BadRequestError", - "code": 400 - } - }, - "client_s": 0.0008929511532187462, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-missing-question", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "1 validation error:\n {'type': 'missing', 'loc': 'body.question', 'msg': 'Field required', 'input': ''}", - "type": "Bad Request", - "param": "question", - "code": 400 - } - }, - "client_s": 0.000582721084356308, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-wrong-ctype", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "1 validation error:\n {'type': 'model_attributes_type', 'loc': 'body', 'msg': 'Input should be a valid dictionary or object to extract fields from', 'input': ''}", - "type": "Bad Request", - "param": "body", - "code": 400 - } - }, - "client_s": 0.0008397586643695831, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-score-thinking-on", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "thinking is supported for noul and choice", - "type": "BadRequestError", - "code": 400 - } - }, - "client_s": 0.0005471920594573021, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-oversize-prompt", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "This model's maximum context length is 16384 tokens. However, you requested 1 output tokens and your prompt contains at least 60026 input tokens, for a total of at least 60027 tokens. Please reduce the length of the input prompt or the number of requested output tokens. (parameter=input_tokens, value=60026)", - "type": "BadRequestError", - "code": 400 - } - }, - "client_s": 0.06339665502309799, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-unknown-model-chat", - "path": "/v1/chat/completions", - "status": 404, - "body": { - "_raw": "" - }, - "client_s": 0.0006152903661131859, - "ok": true, - "gate": "frontend intentionally has no chat route; not reference error-envelope parity" - } - ] -} \ No newline at end of file diff --git a/recipe/jev_vl/evidence/review-20261006/frontend-errors-off.json b/recipe/jev_vl/evidence/review-20261006/frontend-errors-off.json deleted file mode 100644 index 2286c694..00000000 --- a/recipe/jev_vl/evidence/review-20261006/frontend-errors-off.json +++ /dev/null @@ -1,126 +0,0 @@ -{ - "ok": true, - "supported_decision_errors": 7, - "unsupported_chat_status": 404, - "rows": [ - { - "id": "err-kind-bogus", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "1 validation error:\n {'type': 'literal_error', 'loc': 'body.kind', 'msg': \"Input should be 'noul', 'score' or 'choice'\", 'input': 'bogus', 'ctx': {'expected': \"'noul', 'score' or 'choice'\"}}", - "type": "Bad Request", - "param": "kind", - "code": 400 - } - }, - "client_s": 0.0023952387273311615, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-choice-1opt", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "choice needs 2-256 options, got 1", - "type": "BadRequestError", - "code": 400 - } - }, - "client_s": 0.0007669180631637573, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-choice-300opt", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "choice needs 2-256 options, got 300", - "type": "BadRequestError", - "code": 400 - } - }, - "client_s": 0.0007964344695210457, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-missing-question", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "1 validation error:\n {'type': 'missing', 'loc': 'body.question', 'msg': 'Field required', 'input': ''}", - "type": "Bad Request", - "param": "question", - "code": 400 - } - }, - "client_s": 0.0006896397098898888, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-wrong-ctype", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "1 validation error:\n {'type': 'model_attributes_type', 'loc': 'body', 'msg': 'Input should be a valid dictionary or object to extract fields from', 'input': ''}", - "type": "Bad Request", - "param": "body", - "code": 400 - } - }, - "client_s": 0.0007091918960213661, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-score-thinking-on", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "thinking is supported for noul and choice", - "type": "BadRequestError", - "code": 400 - } - }, - "client_s": 0.0007213437929749489, - "ok": true, - "gate": "unchanged frozen error envelope" - }, - { - "id": "err-oversize-prompt", - "path": "/v1/systemone", - "status": 400, - "body": { - "error": { - "message": "This model's maximum context length is 16384 tokens. However, you requested 1 output tokens and your prompt contains at least 60026 input tokens, for a total of at least 60027 tokens. Please reduce the length of the input prompt or the number of requested output tokens. 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3, - "l1_bytes": 75744, - "l2_records": 1, - "l2_bytes": 9216064, - "l3_records": 3, - "l3_bytes": 643384320 - }, - "budgets": { - "l1_max": 256, - "l2_bytes": 1073741824, - "l3_bytes": 2147483648 - } - } - }, - { - "when": "after", - "pass": 1, - "stats": { - "enabled": true, - "l1": true, - "l2": true, - "l3": true, - "counters": { - "l1_hit": 48, - "l1_miss": 0, - "l2_hit": 48, - "l2_miss": 0, - "l3_hit": 48, - "l3_miss": 0, - "l3_populate": 0, - "l3_fallback_full": 0 - }, - "records": { - "l1_records": 3, - "l1_bytes": 75744, - "l2_records": 1, - "l2_bytes": 9216064, - "l3_records": 3, - "l3_bytes": 643384320 - }, - "budgets": { - "l1_max": 256, - "l2_bytes": 1073741824, - "l3_bytes": 2147483648 - } - } - } -] \ No newline at end of file diff --git a/recipe/jev_vl/evidence/review-20261006/stats-off-c1.json b/recipe/jev_vl/evidence/review-20261006/stats-off-c1.json deleted file mode 100644 index 25439ad9..00000000 --- a/recipe/jev_vl/evidence/review-20261006/stats-off-c1.json +++ /dev/null @@ -1,134 +0,0 @@ -[ - { - "when": "before", - "pass": 0, - "stats": { - "enabled": false, - "l1": true, - "l2": true, - "l3": true, - "counters": { - "l1_hit": 0, - "l1_miss": 0, - "l2_hit": 0, - "l2_miss": 0, - "l3_hit": 0, - "l3_miss": 0, - "l3_populate": 0, - "l3_fallback_full": 0 - }, - "records": { - "l1_records": 0, - "l1_bytes": 0, - "l2_records": 0, - "l2_bytes": 0, - "l3_records": 0, - "l3_bytes": 0 - }, - "budgets": { - "l1_max": 256, - "l2_bytes": 1073741824, - "l3_bytes": 2147483648 - } - } - }, - { - "when": "after", - "pass": 0, - "stats": { - "enabled": false, - "l1": true, - "l2": true, - "l3": true, - "counters": { - "l1_hit": 0, - "l1_miss": 0, - "l2_hit": 0, - "l2_miss": 24, - "l3_hit": 0, - "l3_miss": 0, - "l3_populate": 0, - "l3_fallback_full": 0 - }, - "records": { - "l1_records": 0, - "l1_bytes": 0, - "l2_records": 0, - "l2_bytes": 0, - "l3_records": 0, - "l3_bytes": 0 - }, - "budgets": { - "l1_max": 256, - "l2_bytes": 1073741824, - "l3_bytes": 2147483648 - } - } - }, - { - "when": "before", - "pass": 1, - "stats": { - "enabled": false, - "l1": true, - "l2": true, - "l3": true, - "counters": { - "l1_hit": 0, - "l1_miss": 0, - "l2_hit": 0, - "l2_miss": 24, - "l3_hit": 0, - "l3_miss": 0, - "l3_populate": 0, - "l3_fallback_full": 0 - }, - "records": { - "l1_records": 0, - "l1_bytes": 0, - "l2_records": 0, - "l2_bytes": 0, - "l3_records": 0, - "l3_bytes": 0 - }, - "budgets": { - "l1_max": 256, - "l2_bytes": 1073741824, - "l3_bytes": 2147483648 - } - } - }, - { - "when": "after", - "pass": 1, - "stats": { - "enabled": false, - "l1": true, - "l2": true, - "l3": true, - "counters": { - "l1_hit": 0, - "l1_miss": 0, - "l2_hit": 0, - "l2_miss": 48, - "l3_hit": 0, - "l3_miss": 0, - "l3_populate": 0, - "l3_fallback_full": 0 - }, - "records": { - "l1_records": 0, - "l1_bytes": 0, - "l2_records": 0, - "l2_bytes": 0, - "l3_records": 0, - "l3_bytes": 0 - }, - "budgets": { - "l1_max": 256, - "l2_bytes": 1073741824, - "l3_bytes": 2147483648 - } - } - } -] \ No newline at end of file diff --git a/recipe/jev_vl/evidence/review-20261006/summary.json b/recipe/jev_vl/evidence/review-20261006/summary.json deleted file mode 100644 index b4e3fba5..00000000 --- a/recipe/jev_vl/evidence/review-20261006/summary.json +++ /dev/null @@ -1,71 +0,0 @@ -{ - "job_id": 413597, - "concurrency": 1, - "cache_off": { - "n": 24, - "median_s": 0.1028826804831624, - "mean_s": 0.10432342742569745, - "passes": [ - { - "pass": 0, - "n": 12, - "median_s": 0.10235126037150621, - "mean_s": 0.10359998761365811 - }, - { - "pass": 1, - "n": 12, - "median_s": 0.10432492196559906, - "mean_s": 0.10504686723773678 - } - ] - }, - "cache_all": { - "n": 24, - "median_s": 0.03703667502850294, - "mean_s": 0.037439350582038365, - "passes": [ - { - "pass": 0, - "n": 12, - "median_s": 0.037320785224437714, - "mean_s": 0.03797944293667873 - }, - { - "pass": 1, - "n": 12, - "median_s": 0.03678498836234212, - "mean_s": 0.036899258227398 - } - ] - }, - "off_div_all_median": 2.777859524484453, - "parity": { - "off": { - "pass": true, - "n_compared": 48, - "decision_parity": 1.0, - "max_abs_dp": 0.021545, - "tol": 0.025, - "noise_floor": 0.0093, - "decision_fails": [], - "prob_fails": [], - "error_fails": [] - }, - "all": { - "pass": true, - "n_compared": 48, - "decision_parity": 1.0, - "max_abs_dp": 0.014215, - "tol": 0.025, - "noise_floor": 0.0093, - "decision_fails": [], - "prob_fails": [], - "error_fails": [] - } - }, - "portable_replay_measured": 192, - "portable_replay_failures": 0, - "frontend_errors_per_mode": 7, - "unsupported_frontend_chat_status": 404 -} diff --git a/recipe/jev_vl/validation.md b/recipe/jev_vl/validation.md index cfdc9dfc..70ea1dd1 100644 --- a/recipe/jev_vl/validation.md +++ b/recipe/jev_vl/validation.md @@ -13,7 +13,7 @@ prepared source snapshot SHA-256 `35aa8ecdf9af4d9ee5d84243c663a9428c7aa00ff86370bade4bc0df24695d2f`. GPU jobs 413587 and 413597 verified the same library and worker binary hashes on one H800 80 GB, CUDA 13.0.88, driver 580.159.03. The candidate was an uncommitted -review snapshot; use the [source and binary provenance](evidence/review-20261006/provenance.json) +review snapshot; use the archived [source and binary provenance](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/review-20261006/provenance.json) rather than treating the original three commits as the tested final source. Subsequent review edits change comments, test wiring and diagnostic header constants only: the inaccurate L2 hit field was removed from `x-jev-cache`; @@ -61,22 +61,23 @@ All-on counters recorded 48 L1/L2/L3 hits across warmup plus measurement, with three resident prefixes and no fallback. Resident L3 accounting was 643,384,320 bytes; peak memory was not measured. There is no current concurrency-8 A/B. -[Summary](evidence/review-20261006/summary.json), -[raw off pass 1](evidence/review-20261006/raw-off-c1-p0.jsonl), -[off pass 2](evidence/review-20261006/raw-off-c1-p1.jsonl), -[raw on pass 1](evidence/review-20261006/raw-all-c1-p0.jsonl), -[on pass 2](evidence/review-20261006/raw-all-c1-p1.jsonl), and -[checksummed evidence inventory](evidence/review-20261006/provenance.json) -include the original timing rows, parity verdicts, public-replay responses and -cache counters. The source archive and complete build logs remain in the local -review archive; their hashes identify the measured snapshot. +[Summary](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/review-20261006/summary.json), +[raw off pass 1](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/review-20261006/raw-off-c1-p0.jsonl), +[off pass 2](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/review-20261006/raw-off-c1-p1.jsonl), +[raw on pass 1](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/review-20261006/raw-all-c1-p0.jsonl), +[on pass 2](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/review-20261006/raw-all-c1-p1.jsonl), and +[checksummed evidence inventory](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/review-20261006/provenance.json) +at the reviewed commit preserve the original timing rows, parity verdicts, +public-replay responses and cache counters. The source archive and complete build +logs remain in the local review archive; their hashes identify the measured +snapshot. ### Preserved initial failure Job 413587's frozen frontend comparator expected the worker error envelope on `/v1/chat/completions` and failed. The frontend owns only `/health` and `/v1/systemone`; it returns its own 404 for this unsupported route. The -[original failed verdict](evidence/review-20261006/initial-frontend-failed-verdict.json) +[original failed verdict](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/review-20261006/initial-frontend-failed-verdict.json) is retained. Job 413597 separates all seven supported error-envelope checks from the unsupported-route 404 check. This corrects the validation boundary; no production frontend route was added. @@ -141,31 +142,16 @@ speed claim and should not be used as evidence of an isolated backend gain. ## Included records and reproducibility limits -[Provenance and checksums](evidence/provenance.json) identify the checkpoint, -input hashes, comparator and retained source archives. Compact copied records: +[Provenance and checksums](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/provenance.json) identify the checkpoint, +input hashes, comparator and retained source archives. Archived records at the reviewed commit: -- Cache off: [pass 1](evidence/raw-r2d-off-c1-p0.jsonl), [pass 2](evidence/raw-r2d-off-c1-p1.jsonl). -- Cache on: [pass 1](evidence/raw-r2d-all-c1-p0.jsonl), [pass 2](evidence/raw-r2d-all-c1-p1.jsonl). -- Comparator verdicts: [off](evidence/parity-off.json), [all](evidence/parity-all.json). +- Cache off: [pass 1](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/raw-r2d-off-c1-p0.jsonl), [pass 2](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/raw-r2d-off-c1-p1.jsonl). +- Cache on: [pass 1](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/raw-r2d-all-c1-p0.jsonl), [pass 2](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/raw-r2d-all-c1-p1.jsonl). +- Comparator verdicts: [off](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/parity-off.json), [all](https://github.com/linear3735/system1-omni/blob/522f2256876a62ddd70873ee9e775055416aea1b/recipe/jev_vl/evidence/parity-all.json). -Recompute the historical medians from the repository root: - -```sh -python3 - <<'PY' -import json -from pathlib import Path -from statistics import median -root = Path('recipe/jev_vl/evidence') -medians = {} -for mode in ('off', 'all'): - rows = [json.loads(line) for path in sorted(root.glob(f'raw-r2d-{mode}-c1-p*.jsonl')) - for line in path.read_text().splitlines()] - assert len(rows) == 24 and all(row['status'] == 200 for row in rows) - medians[mode] = median(row['client_s'] for row in rows) - print(mode, f"{medians[mode] * 1000:.3f} ms") -print('off/all', f"{medians['off'] / medians['all']:.3f}x") -PY -``` +Run outputs are retained in the linked commit archive. The current checkout +keeps the frozen input and reference fixtures needed for replay; new run outputs +belong in a task-owned output directory, as in the commands below. The [manifest template](evidence/manifest-template.jsonl) and its single [synthetic image](evidence/image.png) preserve the complete frozen corpus without From e9d132166f3301dcaacf7a09254eb0c6a8428de6 Mon Sep 17 00:00:00 2001 From: linear3735 Date: Tue, 6 Oct 2026 23:23:23 +0800 Subject: [PATCH 5/8] jev_vl: move validation artifacts out of the source tree --- mkdocs.yml | 1 - recipe/jev_vl/README.md | 9 +- recipe/jev_vl/evidence/image.png | Bin 2665607 -> 0 bytes .../jev_vl/evidence/manifest-template.jsonl | 48 --- .../reference/errors/err-choice-1opt.json | 12 - .../reference/errors/err-choice-300opt.json | 12 - .../reference/errors/err-kind-bogus.json | 13 - .../errors/err-missing-question.json | 13 - .../reference/errors/err-oversize-prompt.json | 13 - .../errors/err-score-thinking-on.json | 12 - .../errors/err-unknown-model-chat.json | 13 - .../reference/errors/err-wrong-ctype.json | 13 - .../evidence/reference/img-choice-01.json | 33 -- .../evidence/reference/img-choice-02.json | 41 --- .../evidence/reference/img-choice-03.json | 57 ---- .../evidence/reference/img-choice-04.json | 33 -- .../evidence/reference/img-noul-01.json | 29 -- .../evidence/reference/img-noul-02.json | 29 -- .../evidence/reference/img-noul-03.json | 29 -- .../evidence/reference/img-noul-04.json | 29 -- .../evidence/reference/img-score-01.json | 37 --- .../evidence/reference/img-score-02.json | 37 --- .../evidence/reference/img-score-03.json | 37 --- .../evidence/reference/img-score-04.json | 37 --- recipe/jev_vl/evidence/reference/sha256.json | 58 ---- .../reference/txt-long-choice-01.json | 33 -- .../reference/txt-long-choice-02.json | 37 --- .../reference/txt-long-choice-03.json | 41 --- .../reference/txt-long-choice-04.json | 41 --- .../evidence/reference/txt-long-noul-01.json | 29 -- .../evidence/reference/txt-long-noul-02.json | 29 -- .../evidence/reference/txt-long-noul-03.json | 29 -- .../evidence/reference/txt-long-noul-04.json | 29 -- .../evidence/reference/txt-long-score-01.json | 37 --- .../evidence/reference/txt-long-score-02.json | 37 --- .../evidence/reference/txt-long-score-03.json | 37 --- .../evidence/reference/txt-long-score-04.json | 37 --- .../evidence/reference/txt-mid-choice-01.json | 41 --- .../evidence/reference/txt-mid-choice-02.json | 33 -- .../evidence/reference/txt-mid-choice-03.json | 37 --- .../evidence/reference/txt-mid-choice-04.json | 41 --- .../evidence/reference/txt-mid-noul-01.json | 29 -- .../evidence/reference/txt-mid-noul-02.json | 29 -- .../evidence/reference/txt-mid-noul-03.json | 29 -- .../evidence/reference/txt-mid-noul-04.json | 29 -- .../evidence/reference/txt-mid-score-01.json | 37 --- .../evidence/reference/txt-mid-score-02.json | 37 --- .../evidence/reference/txt-mid-score-03.json | 37 --- .../evidence/reference/txt-mid-score-04.json | 37 --- .../reference/txt-short-choice-01.json | 37 --- .../reference/txt-short-choice-02.json | 41 --- .../reference/txt-short-choice-03.json | 41 --- .../reference/txt-short-choice-04.json | 33 -- .../evidence/reference/txt-short-noul-01.json | 29 -- .../evidence/reference/txt-short-noul-02.json | 29 -- .../evidence/reference/txt-short-noul-03.json | 29 -- .../evidence/reference/txt-short-noul-04.json | 29 -- .../reference/txt-short-score-01.json | 37 --- .../reference/txt-short-score-02.json | 37 --- .../reference/txt-short-score-03.json | 37 --- .../reference/txt-short-score-04.json | 37 --- recipe/jev_vl/evidence/restore_manifest.py | 33 -- recipe/jev_vl/validation.md | 287 +++++------------- 63 files changed, 87 insertions(+), 2126 deletions(-) delete mode 100644 recipe/jev_vl/evidence/image.png delete mode 100644 recipe/jev_vl/evidence/manifest-template.jsonl delete mode 100644 recipe/jev_vl/evidence/reference/errors/err-choice-1opt.json delete mode 100644 recipe/jev_vl/evidence/reference/errors/err-choice-300opt.json delete mode 100644 recipe/jev_vl/evidence/reference/errors/err-kind-bogus.json delete mode 100644 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recipe/jev_vl/evidence/reference/txt-short-score-04.json delete mode 100644 recipe/jev_vl/evidence/restore_manifest.py diff --git a/mkdocs.yml b/mkdocs.yml index 1fb1d76e..f6d317b3 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -26,7 +26,6 @@ exclude_docs: | !/docs/**/*.md !/docs/assets/** !/recipe/**/*.md - !/recipe/jev_vl/evidence/image.png !/src/frontend/README.md !/src/runtime/README.md !/assets/** diff --git a/recipe/jev_vl/README.md b/recipe/jev_vl/README.md index 5fe1f9bd..0b2b755d 100644 --- a/recipe/jev_vl/README.md +++ b/recipe/jev_vl/README.md @@ -133,14 +133,15 @@ cargo test --workspace --locked cargo build --workspace --release --locked ``` -The image-prefix tokenizer check requires the exported checkpoint and the frozen -manifest. It uses the corpus's fixed `[1, 60, 60]` grid and synthetic embedding +The image-prefix tokenizer check requires the exported checkpoint. First +[download and restore the frozen corpus](validation.md#download-the-frozen-corpus). +It uses the corpus's fixed `[1, 60, 60]` grid and synthetic embedding rows, so it checks token/position splitting rather than the vision encoder. CUDA kernel tests require an allocated GPU and rebuilt ABI 6 library: ```sh -python3 recipe/jev_vl/evidence/restore_manifest.py --out /tmp/jev-vl-manifest.jsonl -JEV_VL_EXPORT=$PWD/weights/jev-vl-merged JEV_VL_MANIFEST=/tmp/jev-vl-manifest.jsonl \ +JEV_VL_EXPORT=$PWD/weights/jev-vl-merged \ + JEV_VL_MANIFEST="$jev_vl_evidence/manifest.jsonl" \ cargo test --locked -p omni-jev-vl-native --test jev_vl_prefix -- --ignored CUA_S1_CUDA_LIB=$PWD/target/release/libqwen3_5_cuda.so \ QWEN3_5_CHECKPOINT=$PWD/weights/jev-vl-merged \ diff --git a/recipe/jev_vl/evidence/image.png b/recipe/jev_vl/evidence/image.png deleted file mode 100644 index 2382a6549e980a0de0cef7f997a0eb1c00bd76f8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2665607 zcmV(@K-RyBP)P3daZ&?4&0$Hpr~8% znR{5chb4ul^rRP^l{X@^_j8TUnyhcfonLmm0RSSvQ4`>(GEn&oa8xRYEgPu(IvA4& zI4UOidl$r(He}BaRDLeB5g&}mJ}kKb926kjO%eROFhb)AVoE5II~lU(I0}yiDn2CY 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