diff --git a/.github/workflows/wai-ops.yml b/.github/workflows/wai-ops.yml new file mode 100644 index 000000000..712fae639 --- /dev/null +++ b/.github/workflows/wai-ops.yml @@ -0,0 +1,229 @@ +name: wai-ops integer operators + +# The integer operator set (wai/extensions/integer-operators.md), checked on every +# fabric leg the corpus has today. Every leg recomputes every vector file of +# wai/ops-conformance/ from its inputs and requires the committed bytes: +# +# - Linux x86_64, Linux arm64 and macOS arm64, natively, debug and release: the +# unit tests, the goldens (every committed parameter set lowered, loaded and +# decoded through the interpreter to its committed output digest), the step-8 +# test and the doc tests, each in the default build and with the `uncertified` +# switch, and `wai-ops vectors verify`, which also re-lowers every set and +# regenerates the whole corpus; +# - WASI (wasm32-wasip2) under wasmtime, a 32-bit target: the same unit tests, +# goldens and step-8 test in both builds, and the same `vectors verify`; +# - a browser's engine (wasm32-unknown-unknown) under Node: the recomputation of +# every vector file; +# - the independent checker, tools/wai_ops_check.py: Python standard library only, +# sharing no code with wai-ops, rewrites every vector file and every lowered graph. +# +# There is no Windows leg: the Windows runner has been offline since 2026-08-28. + +# One run per branch at a time. A push supersedes the run before it. +# +# There is one `macos` runner and it is the dev Mac, so every macOS job in this repo +# queues on it; only the newest commit's verdict is wanted, so older runs are +# cancelled. `cancel-in-progress: true` also applies to `main`, which advances by +# merge commits whose tree contains the commits before them. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +on: + pull_request: + paths: + - 'wai/wai-ops/**' + - 'wai/ops-conformance/**' + - 'wai/tools/wai_ops_*.py' + # The text the checker is held to. + - 'wai/extensions/integer-operators.md' + # Every committed parameter set the corpus lowers, and the digests the goldens + # compare against (neural-conformance/sha256.json among them). + - 'wai/neural-conformance/**' + - 'wai/wai-rs/tests/vectors/int_hyper/**' + - 'wai/wai-rs/tests/vectors/int_audio/**' + - 'wai/wai-rs/tests/vectors/int_splat/**' + - 'wai/wai-rs/tests/vectors/int_mlicpp/**' + - 'wai/wai-rs/tests/vectors/int_world/**' + - 'wai/wai-rs/tests/vectors/int_world2/**' + - '.github/workflows/wai-ops.yml' + push: + branches: + - main + paths: + - 'wai/wai-ops/**' + - 'wai/ops-conformance/**' + - 'wai/tools/wai_ops_*.py' + - 'wai/extensions/integer-operators.md' + - 'wai/neural-conformance/**' + - 'wai/wai-rs/tests/vectors/int_hyper/**' + - 'wai/wai-rs/tests/vectors/int_audio/**' + - 'wai/wai-rs/tests/vectors/int_splat/**' + - 'wai/wai-rs/tests/vectors/int_mlicpp/**' + - 'wai/wai-rs/tests/vectors/int_world/**' + - 'wai/wai-rs/tests/vectors/int_world2/**' + - '.github/workflows/wai-ops.yml' + +jobs: + native: + name: ${{ matrix.label }} + runs-on: ${{ matrix.os }} + timeout-minutes: 40 + strategy: + fail-fast: false + matrix: + include: + - { os: linux, label: linux-x86_64 } + - { os: linux-arm64, label: linux-aarch64 } + - { os: macos, label: macos-arm64 } + steps: + - uses: actions/checkout@v4 + + # The docker images ship no Rust; the macOS host has it, but launchd's PATH omits + # ~/.cargo/bin. + - name: Install Rust 1.98.0 + shell: bash + run: | + export PATH="$HOME/.cargo/bin:$PATH" + command -v rustup >/dev/null 2>&1 || \ + curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --profile minimal --default-toolchain 1.98.0 + rustup toolchain install 1.98.0 --profile minimal + rustup default 1.98.0 + echo "$HOME/.cargo/bin" >> "$GITHUB_PATH" + + # Without the `uncertified` switch, load step 8 refuses every graph: the default + # build is what a decoder would link before the certifier exists. Its tests show + # that, and the doc tests that a graph runs only once loaded. + - name: Unit, step-8 and doc tests, default build + working-directory: wai/wai-ops + shell: bash + run: cargo test --locked + + - name: Unit tests and goldens, debug + working-directory: wai/wai-ops + shell: bash + run: cargo test --locked --features uncertified + + - name: Unit tests and goldens, release + working-directory: wai/wai-ops + shell: bash + run: cargo test --locked --release --features uncertified + + - name: Every vector file recomputed, every set re-lowered, the corpus regenerated + working-directory: wai/wai-ops + shell: bash + run: cargo run --locked --release --features uncertified --bin wai-ops -- vectors verify .. + + wasip2: + name: wasm32-wasip2 (wasmtime) + runs-on: linux + timeout-minutes: 30 + steps: + - uses: actions/checkout@v4 + + - name: Install Rust 1.98.0 with the wasm32-wasip2 target + shell: bash + run: | + export PATH="$HOME/.cargo/bin:$PATH" + command -v rustup >/dev/null 2>&1 || \ + curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --profile minimal --default-toolchain 1.98.0 + rustup toolchain install 1.98.0 --profile minimal + rustup default 1.98.0 + rustup target add wasm32-wasip2 + echo "$HOME/.cargo/bin" >> "$GITHUB_PATH" + + - name: Install wasmtime 38.0.3 + shell: bash + run: | + set -euo pipefail + v=38.0.3 + curl --proto '=https' --tlsv1.2 -sSfL -o "$RUNNER_TEMP/wasmtime.tar.xz" \ + "https://github.com/bytecodealliance/wasmtime/releases/download/v$v/wasmtime-v$v-x86_64-linux.tar.xz" + tar -C "$RUNNER_TEMP" -xf "$RUNNER_TEMP/wasmtime.tar.xz" + echo "$RUNNER_TEMP/wasmtime-v$v-x86_64-linux" >> "$GITHUB_PATH" + + # The tests read committed files through CARGO_MANIFEST_DIR/.., so the wai + # directory is preopened at its own absolute path. + - name: Unit and step-8 tests, default build, under WASI + working-directory: wai/wai-ops + shell: bash + run: | + set -euo pipefail + W=$(cd .. && pwd) + CARGO_TARGET_WASM32_WASIP2_RUNNER="wasmtime run --dir $W::$W" \ + cargo test --locked --release --target wasm32-wasip2 + + - name: Unit tests, goldens and step-8 test, under WASI + working-directory: wai/wai-ops + shell: bash + run: | + set -euo pipefail + W=$(cd .. && pwd) + CARGO_TARGET_WASM32_WASIP2_RUNNER="wasmtime run --dir $W::$W" \ + cargo test --locked --release --features uncertified --target wasm32-wasip2 + + - name: Build the tool for wasm32-wasip2 + working-directory: wai/wai-ops + shell: bash + run: cargo build --locked --release --features uncertified --target wasm32-wasip2 --bin wai-ops + + - name: Every vector file recomputed, every set re-lowered, the corpus regenerated, under WASI + working-directory: wai + shell: bash + run: | + set -euo pipefail + wasmtime --version + wasmtime run --dir .::/wai wai-ops/target/wasm32-wasip2/release/wai-ops.wasm vectors verify /wai + + node: + name: wasm32 under Node + runs-on: linux + timeout-minutes: 30 + steps: + - uses: actions/checkout@v4 + + - name: Install Rust 1.98.0 with the wasm32 target + shell: bash + run: | + export PATH="$HOME/.cargo/bin:$PATH" + command -v rustup >/dev/null 2>&1 || \ + curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --profile minimal --default-toolchain 1.98.0 + rustup toolchain install 1.98.0 --profile minimal + rustup default 1.98.0 + rustup target add wasm32-unknown-unknown + echo "$HOME/.cargo/bin" >> "$GITHUB_PATH" + + - uses: actions/setup-node@v4 + with: + node-version: '22' + + - name: Build the recomputation for wasm32-unknown-unknown + working-directory: wai/wai-ops/node + shell: bash + run: cargo build --locked --release --target wasm32-unknown-unknown + + - name: Every vector file recomputed under Node + working-directory: wai/wai-ops/node + shell: bash + run: node verify.mjs target/wasm32-unknown-unknown/release/wai_ops_node.wasm ../../ops-conformance + + checker: + name: independent checker (Python) + runs-on: linux + timeout-minutes: 20 + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: '3.11' + + # Standard library only, so it needs nothing installed. + - name: wai_ops_check.py rewrites every vector file and every lowered graph + working-directory: wai + shell: bash + run: python3 -I tools/wai_ops_check.py ops-conformance + + - name: Every workflow declares a concurrency group + shell: bash + run: bash scripts/check-workflow-concurrency.sh diff --git a/wai/ops-conformance/.gitattributes b/wai/ops-conformance/.gitattributes new file mode 100644 index 000000000..289b89b5e --- /dev/null +++ b/wai/ops-conformance/.gitattributes @@ -0,0 +1,2 @@ +# Compared as bytes: a line-ending conversion on checkout would change every file. +* -text diff --git a/wai/ops-conformance/README.md b/wai/ops-conformance/README.md new file mode 100644 index 000000000..44fe269a3 --- /dev/null +++ b/wai/ops-conformance/README.md @@ -0,0 +1,120 @@ +# ops-conformance — the integer operator set + +Vectors for `wai-ops/1`, the integer operator set +([`extensions/integer-operators.md`](../extensions/integer-operators.md)): its graph +operators (§5), its stage operators (§5E), the `WHG1` graph form (§10), and the +canonical lowerings of the registered integer network forms (§13). + +**Provenance.** Generated here by `wai-ops vectors gen` from fixed SplitMix64 +sequences: every graph, parameter, input and stream under `vectors/`, `stage/`, +`graphs/` and `sets/` is generated integers, with no media. The lowered graphs under +`certificates/` are those of the sets this corpus generates and of the parameter sets +other corpora commit, which keep their own provenance (`certificates/index.json` +names each one's corpus). + +**Equivalence.** An operator implementation of a profile (§7) conforms when it +reproduces every vector tagged with that profile, refusals included: the same `load` +outcome, and for every decode the same output integers or the same refusal code. A +lowering conforms when it writes every lowered graph byte for byte. + +## Layout + +- `vectors/.jsonl` — graph vectors whose graph has one operator (§14.1). +- `stage/.jsonl` — stage-operator vectors (§14.1). +- `graphs/.jsonl` — graph vectors whose graph has several operators (§14.2): + - `phase` — a transposed convolution and the same computed as phase convolutions, + interleaved by `pixel_shuffle` and cropped (§9.3), as the graph's two outputs; + - `state` — state inputs carried across decodes (§10.3), a refused decode among + them; + - `load` — a graph refused at each load check of §10.5, steps 1–7, and a `crop` + planned at load (§5.10); + - `run` — a decode refused at each step of §4's run, and the order between them + and within step 3 (the caps, then the shape rule, then `max_elems`); + - `lower_
` — each lowering of §13 on a small set from `sets/`. +- `sets//` — the small parameter sets the `lower_*` vectors lower, one per + lowering, in their registered forms. +- `certificates///.whg1` — each lowered graph (§14.3). +- `certificates/index.json` — every set lowered here: its capability, its lowering, + its members (the name in the set and the file, relative to `wai/`, that holds it), + its digest, and the path of each lowered graph, in §13.0's role order. + +Files are compared as bytes; `.gitattributes` turns off line-ending conversion. + +This corpus has no `sha256.json` sidecar (SPEC §7 *Digests*). Every vector states its +output integers in full, or its refusal code, and never a digest of them. The only +digest in `certificates/` is each set's SHA-256, which identifies a parameter set. The +output digests that a capability reproduces through its lowering (integer-operators §14.4) +stay in that capability's own corpus. + +## The vector files + +Every file follows integer-operators §14.1, which is normative for every field: the +graph vector and its decodes, the stage vector with its `modes`, each operator's +`in` and `expect` (the predictors' layouts included), and the refusal index `at`. +In short: + +- Every file is JSON Lines: one canonical JSON value per line (§8.5's rules), each + line ending with one line feed. +- A graph vector's decodes list the graph's **data** inputs only; the decodes of one + vector are one sequence, and a refused decode changes no state (§10.3). +- `load` is the outcome of load steps 1–8 (§10.5); every vector here loads or is + refused at steps 1–7 (see below). A vector whose load is refused has no decodes and + lists both profiles; one that loads lists exactly the profiles its verdict meets. +- `at` is −1 for a refusal before any symbol or element, the symbol's index for + `rans64_decode` (the number of symbols for a refusal after the last one), and, for + `mans_decode`, the position whose step refuses, or, for a flush that belongs to no + single position, the last position whose `in` step ran. +- A vector of `graphs/lower_.jsonl` has the id `///…`: its + graph is that role of the set `sets//` as lowered. + +## What this corpus does not cover yet + +Load step 8 has no implementation in `wai-ops` yet: its bounds pass and verdict land +with the certifier. Until then the `uncertified` switch skips it, and the generator +writes no vector whose outcome depends on it; a generated case whose graph meets no +profile is dropped, and `vectors gen` reports the count. So these coverage classes +of §14.1 item 6, each a load refusal at step 8, are not here yet: + +- `sigmoid` and `elu` on an input range that reaches `−2^63`, refused at load (the + corpus has the decoding side, ranges that stop at `−(2^63 − 1)`); +- an `rq` whose `q` passes 64 bits while `y` fits, refused at load under `acc64` + (`conv`, `conv_transpose`, `rescale`, `chscale`, `mul`, `gram`, `apply`); +- a `lut16` whose input range passes `[−32767, 32767]`, and a `check` range that no + value of its producer can meet (§8.1); +- a graph refused at step 8 in `graphs/load.jsonl`. + +The independent checker already runs step 8 and recomputes every vector's +`profiles` from its own bounds pass, so these vectors are checked the day they land. + +## The lowered sets + +`certificates/index.json` lists every set. A set of one file is identified by its +file's SHA-256, and a set of several files by the SHA-256 of its `wai.prior.set` +listing over the member names the index gives (SPEC §3.1). `wai.world.model` has no +registered set yet: its two networks are named `T.whm` and `g_s.whm` here, and its +lowering's latent channel count, which the initial latent fixes, is the entry's `c`. + +## Generation and verification + + cd wai/wai-ops + cargo run --release --features uncertified --bin wai-ops -- vectors gen .. + +writes every file here but this README and `.gitattributes`. + + cargo run --release --features uncertified --bin wai-ops -- vectors verify .. + +recomputes every vector file from its inputs, re-lowers every set, and regenerates +the corpus, and requires every byte to equal the committed one. The same check runs +built for `wasm32-wasip2` under a WASI runtime, and, for the vector files, +`wai-ops/node/verify.mjs` runs the recomputation as a `wasm32-unknown-unknown` +module under a browser's engine. + + python3 -I tools/wai_ops_check.py ops-conformance + +is the independent checker: Python standard library only, sharing no code with +`wai-ops`, and held to the extension's text: where the two disagree, the text +decides, and the disagreement is a bug in one of them. It rewrites every vector file +from its inputs, `load` (steps 1–8) and `profiles` from its own bounds pass and +verdict (§8.1–§8.5) included; re-lowers every set and requires each lowered graph, +and that each loads; and requires each lowering vector's graph to be the role its id +names. 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diff --git a/wai/ops-conformance/sets/jpegai/weights/hs_uv.json b/wai/ops-conformance/sets/jpegai/weights/hs_uv.json new file mode 100644 index 000000000..43aa2098a --- /dev/null +++ b/wai/ops-conformance/sets/jpegai/weights/hs_uv.json @@ -0,0 +1 @@ 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diff --git a/wai/ops-conformance/sets/jpegai/weights/hs_y.json b/wai/ops-conformance/sets/jpegai/weights/hs_y.json new file mode 100644 index 000000000..c5f17f481 --- /dev/null +++ b/wai/ops-conformance/sets/jpegai/weights/hs_y.json @@ -0,0 +1 @@ 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diff --git a/wai/ops-conformance/sets/mlicpp/model.wmb b/wai/ops-conformance/sets/mlicpp/model.wmb new file mode 100644 index 000000000..784bad5ad Binary files /dev/null and b/wai/ops-conformance/sets/mlicpp/model.wmb differ diff --git a/wai/ops-conformance/sets/synth/int_synth.json b/wai/ops-conformance/sets/synth/int_synth.json new file mode 100644 index 000000000..84a1ebbae --- /dev/null +++ b/wai/ops-conformance/sets/synth/int_synth.json @@ -0,0 +1 @@ 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Written from the +extension's text (and, for the source forms it lowers, integer-payloads), it: + + - recomputes every vector file under ops-conformance/vectors/, stage/ and graphs/: + each graph vector's `load` (WHG1 parse and load steps 1-8, section 10.5), its + `profiles` (section 14.1, from the bounds pass and verdict of sections 8.1-8.5), + and each decode's `expect` (the run of section 4, every graph operator of + section 5), and each stage vector's `expect` (section 5E); it writes each file + again canonically (sections 8.5 and 14.1) and requires the bytes of the + committed file; + - re-lowers every parameter set certificates/index.json lists, by the canonical + lowerings of section 13, and requires each lowered graph's bytes, each set's + digest (SPEC 3.1), and that every lowered graph loads; and requires the graph + of each graphs/lower_*.jsonl vector to be the role of the set its id names, as + re-lowered. + +The bounds pass decides the verdict only. It does not write the certificate of +section 8.5, which the certifier half of this checker will. + + python3 -I tools/wai_ops_check.py ops-conformance # check + python3 -I tools/wai_ops_check.py ops-conformance --write DIR # also write the files + +Exit status 1 on any difference or failure. +""" +import hashlib +import json +import math +import os +import struct +import sys + +CAP = 1 << 26 +I64_MIN, I64_MAX = -(1 << 63), (1 << 63) - 1 +I32_MIN, I32_MAX = -(1 << 31), (1 << 31) - 1 + + +class Refuse(Exception): + """A refusal with a code of section 4 or 5E, and, for a stage operator, `at`.""" + + def __init__(self, code, at=None): + super().__init__(code) + self.code = code + self.at = at + + +# --------------------------------------------------------------- section 2 + + +def R(p, s, mode): + """R_floor, R_half_up, R_half_even (section 2).""" + if s == 0: + return p + if mode == 0: + return p >> s + if mode == 1: + return (p + (1 << (s - 1))) >> s + q = p >> s + r = p - (q << s) + h = 1 << (s - 1) + if r > h or (r == h and q % 2 == 1): + return q + 1 + return q + + +def exp_neg_q16(x): + """exp_neg_q16(x), x <= 0, in the order section 2 writes it.""" + u = -x + n = u // 45426 + g = 45426 * n + r = x + g + t = r + 88670 + t2 = t * t + q = t2 >> 16 + c = 23495 * q + y = (c >> 16) + 22544 + return 0 if n >= 63 else y >> n + + +def rsqrt_q16(v): + r = math.isqrt(v) + return 0 if r == 0 else (1 << 32) // r + + +def trunc_div(a, b): + q = abs(a) // abs(b) + return q if (a >= 0) == (b > 0) else -q + + +# --------------------------------------------------------------- WHG1 (section 10) + +NAMES = { + 0x01: "conv", 0x02: "rescale", 0x03: "chscale", 0x04: "lut16", 0x05: "add", + 0x06: "concat", 0x07: "chunk_add", 0x08: "split", 0x09: "pixel_shuffle", + 0x0A: "pixel_unshuffle", 0x0B: "clamp", 0x0C: "relu", 0x0D: "leaky", + 0x0E: "pad_rep", 0x0F: "crop_to", 0x10: "mul", 0x11: "conv_transpose", + 0x12: "igdn", 0x13: "abs", 0x14: "crop", 0x15: "subpos_split", + 0x16: "subpos_merge", 0x17: "fill", 0x18: "layernorm_c", 0x19: "l2norm", + 0x1A: "softmax", 0x1B: "sigmoid", 0x1C: "elu", 0x1D: "gram", 0x1E: "apply", +} +CODE = {v: k for k, v in NAMES.items()} +TWO_IN = {"add", "crop_to", "mul", "gram", "apply"} + + +def arity(name): + if name in TWO_IN: + return (2, 1) + if name == "concat": + return (None, 1) + if name == "subpos_split": + return (1, 4) + if name == "subpos_merge": + return (4, 1) + return (1, 1) + + +class Reader: + def __init__(self, b, pos=0): + self.b, self.p = b, pos + + def take(self, n): + if n < 0 or self.p + n > len(self.b): + raise Refuse("model_mismatch") + s = self.b[self.p:self.p + n] + self.p += n + return s + + def u(self, n): + return int.from_bytes(self.take(n), "little") + + def i(self, n): + return int.from_bytes(self.take(n), "little", signed=True) + + def ints(self, count, width): + raw = self.take(count * width) + return [int.from_bytes(raw[k:k + width], "little", signed=True) for k in range(0, len(raw), width)] + + def zeros(self, n): + if any(self.take(n)): + raise Refuse("model_mismatch") + + +def bad(): + raise Refuse("model_mismatch") + + +def read_rq(rd, C): + """The RQ block (section 10.4) over C channels.""" + rq = {} + rq["round"], rq["bias"], rq["m_kind"], rq["s_kind"] = rd.u(1), rd.u(1), rd.u(1), rd.u(1) + rq["m_bytes"], rq["b_bytes"] = rd.u(1), rd.u(1) + if rd.u(2) != 0: + bad() + if rq["round"] > 2 or rq["bias"] > 2 or rq["m_kind"] > 2 or rq["s_kind"] not in (1, 2): + bad() + if rq["m_kind"] == 0: + if rq["m_bytes"] != 0: + bad() + elif rq["m_bytes"] not in (4, 8): + bad() + if rq["bias"] == 0: + if rq["b_bytes"] != 0: + bad() + elif rq["b_bytes"] not in (4, 8): + bad() + nm = {0: 0, 1: 1, 2: C}[rq["m_kind"]] + rq["mult"] = rd.ints(nm, rq["m_bytes"]) if nm else [] + ns = 1 if rq["s_kind"] == 1 else C + rq["shift"] = list(rd.take(ns)) + if any(s > 127 for s in rq["shift"]): + bad() + rq["biasv"] = rd.ints(C, rq["b_bytes"]) if rq["bias"] else [] + return rq + + +def rq_m(rq, o): + return None if rq["m_kind"] == 0 else rq["mult"][0 if rq["m_kind"] == 1 else o] + + +def rq_s(rq, o): + return rq["shift"][0 if rq["s_kind"] == 1 else o] + + +def rq_apply(rq, o, v): + """rq_o(v), section 5.1.""" + t = v + (rq["biasv"][o] if rq["bias"] == 1 else 0) + m = rq_m(rq, o) + p = t * (1 if m is None else m) + q = R(p, rq_s(rq, o), rq["round"]) + return q + (rq["biasv"][o] if rq["bias"] == 2 else 0) + + +def read_operand(rd, n_scalars): + kind = rd.u(1) + rd.zeros(3) + v = rd.u(4) + if kind == 0: + return ("const", v) + if kind == 1 and v < n_scalars: + return ("scalar", v) + bad() + + +def read_params(name, pb, cin, n_scalars): + rd = Reader(pb) + P = {} + if name == "conv": + for k in ("cout", "kh", "kw", "sh", "sw", "ph", "pw", "groups"): + P[k] = rd.u(4) + P["w_bytes"] = rd.u(1) + rd.zeros(3) + if min(P["cout"], P["kh"], P["kw"], P["sh"], P["sw"], P["groups"]) == 0 or P["w_bytes"] not in (1, 2, 4, 8): + bad() + if cin % P["groups"] or P["cout"] % P["groups"]: + bad() + P["rq"] = read_rq(rd, P["cout"]) + P["w"] = rd.ints(P["cout"] * (cin // P["groups"]) * P["kh"] * P["kw"], P["w_bytes"]) + elif name == "conv_transpose": + for k in ("cout", "kh", "kw", "sh", "sw", "ph", "pw", "oph", "opw"): + P[k] = rd.u(4) + P["w_bytes"] = rd.u(1) + rd.zeros(3) + if min(P["cout"], P["kh"], P["kw"], P["sh"], P["sw"]) == 0 or P["w_bytes"] not in (1, 2, 4, 8): + bad() + P["rq"] = read_rq(rd, P["cout"]) + P["w"] = rd.ints(cin * P["cout"] * P["kh"] * P["kw"], P["w_bytes"]) + elif name == "rescale": + P["rq"] = read_rq(rd, cin) + elif name == "chscale": + P["scalar"], P["n_rows"] = rd.u(4), rd.u(4) + P["round"], P["m_bytes"], P["shift"] = rd.u(1), rd.u(1), rd.u(1) + rd.zeros(1) + if P["scalar"] >= n_scalars or P["n_rows"] == 0 or P["m_bytes"] not in (4, 8) or P["shift"] > 127 or P["round"] > 2: + bad() + P["M"] = rd.ints(P["n_rows"] * cin, P["m_bytes"]) + elif name == "mul": + P["rq"] = read_rq(rd, cin) + if P["rq"]["bias"] != 0: + bad() + elif name == "lut16": + P["e_in"], P["e_out"] = rd.i(1), rd.i(1) + rd.zeros(2) + P["T"] = rd.ints(65536, 2) + if P["T"][0] != 0: + bad() + elif name in ("add", "concat", "chunk_add", "relu", "abs", "crop_to", "softmax", "sigmoid"): + pass + elif name == "split": + P["start"], P["count"] = rd.u(4), rd.u(4) + if P["count"] == 0: + bad() + elif name in ("pixel_shuffle", "pixel_unshuffle"): + P["r"] = rd.u(4) + if P["r"] == 0: + bad() + elif name == "clamp": + P["lo"], P["hi"] = rd.i(8), rd.i(8) + if P["lo"] > P["hi"]: + bad() + elif name == "leaky": + P["a"], P["f"] = rd.i(8), rd.u(4) + if P["f"] > 63: + bad() + elif name == "pad_rep": + P["m"] = rd.u(4) + if P["m"] == 0: + bad() + elif name == "crop": + P["ops"] = [read_operand(rd, n_scalars) for _ in range(4)] + elif name in ("subpos_split", "subpos_merge"): + P["order"] = list(rd.take(4)) + if sorted(P["order"]) != [0, 1, 2, 3]: + bad() + elif name == "fill": + P["kind"] = rd.u(1) + rd.zeros(7) + P["value"] = rd.i(8) + if not (P["kind"] == 0 or (P["kind"] == 1 and 0 <= P["value"] < n_scalars)): + bad() + elif name == "igdn": + P["gf"], P["frac"] = rd.u(4), rd.u(4) + P["g_bytes"] = rd.u(1) + rd.zeros(7) + if P["gf"] > 127 or P["frac"] > 127 or P["g_bytes"] not in (4, 8): + bad() + P["gamma"] = rd.ints(cin * cin, P["g_bytes"]) + P["beta"] = rd.ints(cin, 8) + elif name == "layernorm_c": + P["eps"] = rd.u(8) + P["g_bytes"] = rd.u(1) + rd.zeros(7) + if P["eps"] == 0 or P["g_bytes"] not in (4, 8): + bad() + P["gamma"] = rd.ints(cin, P["g_bytes"]) + P["beta"] = rd.ints(cin, 8) + elif name == "l2norm": + P["eps"] = rd.u(8) + if P["eps"] == 0: + bad() + elif name == "elu": + P["alpha"] = rd.i(8) + elif name in ("gram", "apply"): + P["groups"] = rd.u(4) + P["rq"] = read_rq(rd, cin) + if P["groups"] == 0 or P["rq"]["m_kind"] != 0 or P["rq"]["bias"] != 0 or P["rq"]["s_kind"] != 1: + bad() + if rd.p != len(pb): + bad() + return P + + +def parse_whg1(b): + """Load steps 1-6 of section 10.5. Every refusal is model_mismatch.""" + if len(b) < 32 or b[0:4] != b"WHG1" or b[4] != 1 or b[5] != 1: + bad() + rd = Reader(b, 6) + r0 = rd.u(2) + n_t, n_o = rd.u(4), rd.u(4) + n_i, n_out, n_s = rd.u(2), rd.u(2), rd.u(2) + r1 = rd.u(2) + max_elems = rd.u(4) + r2 = rd.u(4) + if r0 or r1 or r2: + bad() + if not (n_t and n_o and n_i and n_out) or not (1 <= max_elems <= CAP): + bad() + if 32 + 16 * n_s + 32 * n_i + 4 * n_out + 24 * n_t + 12 * n_o > len(b): + bad() + g = {"max_elems": max_elems, "scalars": [], "inputs": [], "outputs": [], "tensors": [], "ops": []} + for _ in range(n_s): + lo, hi = rd.i(8), rd.i(8) + if lo > hi: + bad() + g["scalars"].append((lo, hi)) + for _ in range(n_i): + t, mh, mw = rd.u(4), rd.u(4), rd.u(4) + kind = rd.u(1) + rd.zeros(3) + src = rd.u(4) + rd.zeros(4) + init = rd.i(8) + if (mh == 0) != (mw == 0) or kind > 1 or t >= n_t: + bad() + if kind == 0 and (src or init): + bad() + g["inputs"].append({"tensor": t, "max_h": mh, "max_w": mw, "kind": kind, "src": src, "init": init}) + for _ in range(n_out): + t = rd.u(4) + if t >= n_t: + bad() + g["outputs"].append(t) + for _ in range(n_t): + ch = rd.u(4) + ex = rd.i(1) + pol = rd.u(1) + rd.zeros(2) + lo, hi = rd.i(8), rd.i(8) + if ch == 0 or pol > 2: + bad() + if pol == 0 and (lo or hi): + bad() + if pol != 0 and lo > hi: + bad() + g["tensors"].append({"c": ch, "e": ex, "policy": pol, "lo": lo, "hi": hi}) + for _ in range(n_o): + code, ni, no = rd.u(1), rd.u(1), rd.u(1) + rd.zeros(1) + name = NAMES.get(code) + if name is None: + bad() + ai, ao = arity(name) + if no != ao or (ai is None and ni < 2) or (ai is not None and ni != ai): + bad() + ins = [rd.u(4) for _ in range(ni)] + outs = [rd.u(4) for _ in range(no)] + if any(t >= n_t for t in ins + outs): + bad() + plen = rd.u(4) + pb = rd.take(plen) + cin = g["tensors"][ins[0]]["c"] + g["ops"].append({"name": name, "ins": ins, "outs": outs, "p": read_params(name, pb, cin, n_s)}) + if rd.p != len(b): + bad() + rules(g) + return g + + +def rules(g): + """Load step 6: the graph rules of section 10.3, state, exponents (3, 5.6).""" + T = g["tensors"] + defined = [False] * len(T) + for i in g["inputs"]: + if defined[i["tensor"]] or T[i["tensor"]]["policy"] == 0: + bad() + defined[i["tensor"]] = True + if g["inputs"][0]["kind"] != 0: + bad() + if len({i["max_h"] != 0 for i in g["inputs"]}) != 1: + bad() + for op in g["ops"]: + if not all(defined[t] for t in op["ins"]): + bad() + for t in op["outs"]: + if defined[t]: + bad() + defined[t] = True + if not all(defined): + bad() + for i in g["inputs"]: + if i["kind"] == 1: + if i["src"] >= len(g["outputs"]): + bad() + rec, out = T[i["tensor"]], T[g["outputs"][i["src"]]] + if out["c"] != rec["c"] or not (rec["lo"] <= i["init"] <= rec["hi"]): + bad() + for op in g["ops"]: + n, P = op["name"], op["p"] + C = T[op["ins"][0]]["c"] + cs = [T[t]["c"] for t in op["ins"]] + want = C + if n in ("conv", "conv_transpose"): + want = P["cout"] + elif n == "add": + want = C if cs[1] == C else None + elif n == "mul": + want = C if cs[1] in (C, 1) else None + elif n == "concat": + want = sum(cs) + elif n == "chunk_add": + want = C // 2 if C % 2 == 0 else None + elif n == "split": + want = P["count"] if P["start"] + P["count"] <= C else None + elif n == "pixel_shuffle": + want = C // (P["r"] ** 2) if C % (P["r"] ** 2) == 0 else None + elif n == "pixel_unshuffle": + want = C * P["r"] ** 2 + elif n == "subpos_merge": + want = C if all(c == C for c in cs) else None + elif n == "fill": + want = T[op["outs"][0]]["c"] + elif n in ("gram", "apply"): + want = C if cs[1] == C and C % P["groups"] == 0 else None + elif n == "chscale": + lo, hi = g["scalars"][P["scalar"]] + want = C if lo >= 0 and hi <= P["n_rows"] - 1 else None + if want is None or any(T[t]["c"] != want for t in op["outs"]): + bad() + if n in ("add", "concat", "chunk_add"): + es = [T[t]["e"] for t in op["ins"] + op["outs"]] + if not (all(e == -128 for e in es) or (all(e != -128 for e in es) and len(set(es)) == 1)): + bad() + if n == "lut16": + if P["e_in"] != T[op["ins"][0]]["e"] or P["e_out"] != T[op["outs"][0]]["e"]: + bad() + + +# --------------------------------------------------------------- plans (section 4, 10.5 step 7) + + +class Big(Exception): + pass + + +class Shape(Exception): + pass + + +def capped(v): + if v > CAP: + raise Big() + return v + + +def op_sizes(g, op, hw, scal): + """The output sizes of one operator, from its inputs' sizes. `scal` is None at load + (scalars by their ranges) or the scalar values.""" + n, P = op["name"], op["p"] + h, w = hw[op["ins"][0]] + same = lambda o: hw[o] == (h, w) + if n == "conv": + hp, wp = capped(h + 2 * P["ph"]), capped(w + 2 * P["pw"]) + if hp < P["kh"] or wp < P["kw"]: + raise Shape() + return [((hp - P["kh"]) // P["sh"] + 1, (wp - P["kw"]) // P["sw"] + 1)] + if n == "conv_transpose": + uh = capped((h - 1) * P["sh"] + P["kh"] + P["oph"]) + uw = capped((w - 1) * P["sw"] + P["kw"] + P["opw"]) + capped(P["cout"] * uh * uw) + if uh <= 2 * P["ph"] or uw <= 2 * P["pw"]: + raise Shape() + return [(uh - 2 * P["ph"], uw - 2 * P["pw"])] + if n in ("add", "mul"): + if not same(op["ins"][1]): + raise Shape() + return [(h, w)] + if n in ("concat", "subpos_merge"): + # subpos_merge's output sides are sizes it forms: capped before the shape rule. + out = (capped(2 * h), capped(2 * w)) if n == "subpos_merge" else (h, w) + if not all(same(t) for t in op["ins"]): + raise Shape() + return [out] + if n == "pixel_shuffle": + return [(capped(h * P["r"]), capped(w * P["r"]))] + if n == "pixel_unshuffle": + if h % P["r"] or w % P["r"]: + raise Shape() + return [(h // P["r"], w // P["r"])] + if n == "pad_rep": + m = P["m"] + return [(capped(-(-h // m) * m), capped(-(-w // m) * m))] + if n == "crop_to": + rh, rw = hw[op["ins"][1]] + if rh > h or rw > w: + raise Shape() + return [(rh, rw)] + if n == "crop": + y0, x0, ch, cw = P["ops"] + + def side(start, length, size): + if scal is not None: + val = lambda o: o[1] if o[0] == "const" else scal[o[1]] + s0, ln = val(start), val(length) + if s0 < 0 or ln < 1 or s0 + ln > size: + raise Shape() + return ln + s0 = start[1] if start[0] == "const" else 0 + ln = length[1] if length[0] == "const" else min(g["scalars"][length[1]][1], size - s0) + if ln < 1 or s0 + ln > size: + raise Shape() + return ln + + return [(side(y0, ch, h), side(x0, cw, w))] + if n == "subpos_split": + if h % 2 or w % 2: + raise Shape() + return [(h // 2, w // 2)] * 4 + if n == "gram": + if not same(op["ins"][1]): + raise Shape() + return [(1, g["tensors"][op["ins"][0]]["c"] // P["groups"])] + if n == "apply": + d = g["tensors"][op["ins"][0]]["c"] // P["groups"] + if (h, w) != (1, d): + raise Shape() + return [hw[op["ins"][1]]] + return [(h, w)] + + +def walk(g, hw, scal, load): + for op in g["ops"]: + try: + outs = op_sizes(g, op, hw, scal) + except Big: + raise Refuse("too_large") + except Shape: + raise Refuse("model_mismatch" if load else "geometry_mismatch") + for t, (oh, ow) in zip(op["outs"], outs): + if g["tensors"][t]["c"] * oh * ow > g["max_elems"]: + raise Refuse("too_large") + hw[t] = (oh, ow) + + +def load(b): + """Load steps 1-8 of section 10.5. Step 8 is the bounds pass and the verdict + (section 8): a graph whose verdict meets neither profile is refused, so a graph + that loads meets acc64 at least, and g["verdict"] says whether it meets acc32.""" + g = parse_whg1(b) + if g["inputs"][0]["max_h"] == 0: + if any(op["name"] in ("gram", "l2norm", "softmax") for op in g["ops"]): + bad() + g["sizes"] = None + else: + hw = {} + for i in g["inputs"]: + c = g["tensors"][i["tensor"]]["c"] + if c * i["max_h"] * i["max_w"] > g["max_elems"]: + raise Refuse("too_large") + hw[i["tensor"]] = (i["max_h"], i["max_w"]) + walk(g, hw, None, True) + g["sizes"] = hw + g["verdict"] = bounds(g) + if not g["verdict"]["acc64"]: + bad() + return g + + +# --------------------------------------------------------------- bounds (section 8) + +SAT = 1 << 255 +# Section 7.1: the width of each class under each profile, in bits. +WIDTH = { + "acc64": {"tensor": 64, "acc": 64, "wide": 128, "product": 128}, + "acc32": {"tensor": 32, "acc": 32, "wide": 32, "product": 64}, +} +# The class of each named intermediate (sections 5 and 2). +CLASS = { + "conv.term": "acc", "conv.acc": "acc", "convT.term": "acc", "convT.acc": "acc", + "rq.t": "acc", "rq.p": "product", "rq.q": "acc", "rq.y": "acc", + "mul.p": "acc", "leaky.p": "acc", + "gram.term": "acc", "gram.acc": "acc", "apply.term": "acc", "apply.acc": "acc", + "igdn.x2": "wide", "igdn.gx2": "product", "igdn.term": "wide", "igdn.N": "wide", + "igdn.r": "acc", "igdn.xr": "wide", + "ln.S": "wide", "ln.T": "wide", "ln.m": "wide", "ln.v": "wide", "ln.dk": "wide", "ln.ng": "wide", + "ln.d": "acc", "rsq.num": "acc", "ln.k": "acc", "ln.n": "acc", + "l2.T": "wide", "l2.vk": "wide", "l2.k": "acc", + "sm.diff": "acc", "sm.sum": "acc", "sm.num": "acc", "sig.num": "acc", "elu.p": "wide", + "exp.negx": "acc", "exp.nl": "acc", "exp.t2": "acc", "exp.c": "acc", +} + + +def fits_bits(iv, n): + return -(1 << (n - 1)) <= iv[0] and iv[1] <= (1 << (n - 1)) - 1 + + +def hull(ivs): + return (min(v[0] for v in ivs), max(v[1] for v in ivs)) + + +def isqrt_ceil(n): + r = math.isqrt(n) + return r if r * r == n else r + 1 + + +class Node: + """One node of the bounds pass. Every +, -, x and floor division (a shift + included) goes through sat (section 8.1), and the node is saturated when sat + changes any result. min, max, |.|, negation and isqrt cannot leave + [-2^255, 2^255] when their operands lie in it. `rec` holds each named + intermediate's interval, hulled over channels.""" + + def __init__(self): + self.saturated = False + self.rec = {} + + def sat(self, v): + if v > SAT: + self.saturated = True + return SAT + if v < -SAT: + self.saturated = True + return -SAT + return v + + def add(self, a, b): + return self.sat(a + b) + + def sub(self, a, b): + return self.sat(a - b) + + def mul(self, a, b): + return self.sat(a * b) + + def div(self, a, b): + return self.sat(a // b) + + def shr(self, a, s): + return self.sat(a >> s) + + def note(self, key, iv): + old = self.rec.get(key) + self.rec[key] = iv if old is None else (min(old[0], iv[0]), max(old[1], iv[1])) + return iv + + def term(self, w, x): + """The term interval of a weight w over x (section 8.2).""" + a, b = self.mul(w, x[0]), self.mul(w, x[1]) + return (min(a, b), max(a, b)) + + def corner(self, x, y): + c = [self.mul(x[0], y[0]), self.mul(x[0], y[1]), self.mul(x[1], y[0]), self.mul(x[1], y[1])] + return (min(c), max(c)) + + def A(self, terms, b): + """The partial-sum interval A(K, b) of section 8.2.""" + lo, hi = min(0, b), max(0, b) + for t in terms: + lo = self.add(lo, min(0, t[0])) + hi = self.add(hi, max(0, t[1])) + return (lo, hi) + + def R(self, p, s, mode): + """R_floor, R_half_up, R_half_even (section 2), as written there.""" + if s == 0: + return p + if mode == 0: + return self.shr(p, s) + if mode == 1: + return self.shr(self.add(p, 1 << (s - 1)), s) + q = self.shr(p, s) + r = self.sub(p, self.mul(q, 1 << s)) + h = 1 << (s - 1) + return self.add(q, 1) if r > h or (r == h and q % 2 == 1) else q + + def rq(self, t, m, s, mode, post): + """The rq.p, rq.q and rq.y rules of section 8.3 for one channel.""" + self.note("rq.t", t) + P = self.term(m, t) + a = (1 << (s - 1)) if mode != 0 and s >= 1 else 0 + self.note("rq.p", (P[0], self.add(P[1], a))) + q = (self.R(P[0], s, mode), self.R(P[1], s, mode)) + self.note("rq.q", q) + return self.note("rq.y", (self.add(q[0], post), self.add(q[1], post))) + + def exp(self, u): + """The exp_neg_q16 rule of section 8.3, given the interval of u = -x.""" + self.note("exp.negx", u) + self.note("exp.nl", (self.mul(45426, self.div(u[0], 45426)), self.mul(45426, self.div(u[1], 45426)))) + self.note("exp.t2", (self.mul(43245, 43245), self.mul(88670, 88670))) + self.note("exp.c", (0, self.mul(23495, self.div(self.mul(88670, 88670), 1 << 16)))) + + +def bpre(rq, o): + return rq["biasv"][o] if rq["bias"] == 1 else 0 + + +def bpost(rq, o): + return rq["biasv"][o] if rq["bias"] == 2 else 0 + + +def rq_mult(rq, o): + m = rq_m(rq, o) + return 1 if m is None else m + + +def node_bounds(nb, g, op, iv): + """The producer results of one operator (section 8.3), recording its named + intermediates in nb. Refuses (model_mismatch) when lut16's input range is not + within [-32767, 32767] (section 5.6).""" + T = g["tensors"] + n, P = op["name"], op["p"] + x = iv[op["ins"][0]] + l, h = x + C = T[op["ins"][0]]["c"] + if n == "conv": + rq, cg, kk = P["rq"], C // P["groups"], P["kh"] * P["kw"] + outs = [] + for o in range(P["cout"]): + taps = [nb.term(P["w"][(o * cg + ci) * kk + k], x) for ci in range(cg) for k in range(kk)] + nb.note("conv.term", hull(taps)) + acc = nb.note("conv.acc", nb.A(taps, bpre(rq, o))) + outs.append(nb.rq(acc, rq_mult(rq, o), rq_s(rq, o), rq["round"], bpost(rq, o))) + return [hull(outs)] + if n == "conv_transpose": + rq, cout, kh, kw, sh, sw = P["rq"], P["cout"], P["kh"], P["kw"], P["sh"], P["sw"] + outs = [] + for o in range(cout): + every, by_res = [], {} + for ci in range(C): + for i in range(kh): + for j in range(kw): + t = nb.term(P["w"][((ci * cout + o) * kh + i) * kw + j], x) + every.append(t) + by_res.setdefault((i % sh, j % sw), []).append(t) + nb.note("convT.term", hull(every)) + b = bpre(rq, o) + accs = [nb.A(by_res[r], b) for r in sorted(by_res)] + if len(by_res) < sh * sw: + # A residue that no tap has: A over no terms. + accs.append(nb.A([], b)) + acc = nb.note("convT.acc", hull(accs)) + outs.append(nb.rq(acc, rq_mult(rq, o), rq_s(rq, o), rq["round"], bpost(rq, o))) + return [hull(outs)] + if n == "rescale": + rq = P["rq"] + t = lambda c: (nb.add(l, bpre(rq, c)), nb.add(h, bpre(rq, c))) + return [hull([nb.rq(t(c), rq_mult(rq, c), rq_s(rq, c), rq["round"], bpost(rq, c)) for c in range(C)])] + if n == "chscale": + lo_r, hi_r = g["scalars"][P["scalar"]] + s, mode = P["shift"], P["round"] + a = (1 << (s - 1)) if mode != 0 and s >= 1 else 0 + outs = [] + for c in range(C): + ps, qs = [], [] + for r in range(lo_r, hi_r + 1): + Pm = nb.term(P["M"][r * C + c], x) + ps.append((Pm[0], nb.add(Pm[1], a))) + qs.append((nb.R(Pm[0], s, mode), nb.R(Pm[1], s, mode))) + nb.note("rq.t", x) + nb.note("rq.p", hull(ps)) + q = nb.note("rq.q", hull(qs)) + outs.append(nb.note("rq.y", q)) + return [hull(outs)] + if n == "mul": + rq = P["rq"] + mp = nb.note("mul.p", nb.corner(x, iv[op["ins"][1]])) + return [hull([nb.rq(mp, rq_mult(rq, c), rq_s(rq, c), rq["round"], 0) for c in range(C)])] + if n in ("gram", "apply"): + rq = P["rq"] + t = nb.note(n + ".term", nb.corner(x, iv[op["ins"][1]])) + if n == "gram": + L = max(g["sizes"][u][0] * g["sizes"][u][1] for u in op["ins"]) + else: + L = C // P["groups"] + acc = nb.note(n + ".acc", (nb.mul(L, min(0, t[0])), nb.mul(L, max(0, t[1])))) + return [nb.rq(acc, 1, rq_s(rq, 0), rq["round"], 0)] + if n == "add": + b = iv[op["ins"][1]] + return [(nb.add(l, b[0]), nb.add(h, b[1]))] + if n == "chunk_add": + return [(nb.mul(2, l), nb.mul(2, h))] + if n == "clamp": + return [(min(max(l, P["lo"]), P["hi"]), min(max(h, P["lo"]), P["hi"]))] + if n == "relu": + return [(max(l, 0), max(h, 0))] + if n == "abs": + return [(l, h) if l >= 0 else ((-h, -l) if h <= 0 else (0, max(-l, h)))] + if n == "leaky": + parts = [] + if l < 0: + p = nb.note("leaky.p", nb.corner((l, min(h, -1)), (P["a"], P["a"]))) + parts.append((nb.shr(p[0], P["f"]), nb.shr(p[1], P["f"]))) + else: + nb.note("leaky.p", (0, 0)) + if h >= 0: + parts.append((max(l, 0), h)) + return [hull(parts)] + if n == "lut16": + if l < -32767 or h > 32767: + bad() + vals = P["T"][l + 32768:h + 32769] + return [(min(vals), max(vals))] + if n == "igdn": + l2, h2 = nb.mul(l, l), nb.mul(h, h) + x2 = nb.note("igdn.x2", (0, max(l2, h2)) if l <= 0 <= h else (min(l2, h2), max(l2, h2))) + outs = [] + for i in range(C): + terms = [] + for j in range(C): + gm = P["gamma"][i * C + j] + gx = nb.note("igdn.gx2", nb.corner((gm, gm), x2)) + terms.append(nb.note("igdn.term", (nb.shr(gx[0], P["gf"]), nb.shr(gx[1], P["gf"])))) + nb.note("igdn.N", nb.A(terms, P["beta"][i])) + # The final norm, a left fold over j from beta_i; not recorded. + f_lo = f_hi = P["beta"][i] + for t in terms: + f_lo, f_hi = nb.add(f_lo, t[0]), nb.add(f_hi, t[1]) + r = nb.note("igdn.r", (math.isqrt(max(0, f_lo)), math.isqrt(max(0, f_hi)))) + xr = nb.note("igdn.xr", nb.corner(x, r)) + outs.append((nb.shr(xr[0], P["frac"]), nb.shr(xr[1], P["frac"]))) + return [hull(outs)] + if n == "layernorm_c": + e = P["eps"] + r0 = math.isqrt(e) + nb.note("ln.S", (nb.mul(C, min(0, l)), nb.mul(C, max(0, h)))) + half = C // 2 + nb.note("ln.m", (nb.sub(nb.mul(C, l), half), nb.add(nb.mul(C, h), half))) + d = nb.sub(h, l) + nb.note("ln.d", (nb.sub(l, h), d)) + dd = nb.mul(d, d) + nb.note("ln.T", (0, nb.mul(C, dd))) + nb.note("ln.v", (e, nb.add(dd, e))) + nb.note("rsq.num", (1 << 32, 1 << 32)) + k = nb.div(1 << 32, r0) + nb.note("ln.k", (0, k)) + ic = isqrt_ceil(C) + big = nb.mul(nb.mul(ic, 1 << 32), nb.add(r0, 1)) + D = min(nb.mul(d, k), -nb.div(-big, r0)) + nb.note("ln.dk", (-D, D)) + nmax = nb.add(nb.div(nb.mul(nb.mul(ic, 1 << 16), nb.add(r0, 1)), r0), 1) + N = min(nb.add(nb.shr(nb.mul(d, k), 16), 1), nmax) + nb.note("ln.n", (-N, N)) + outs = [] + for c in range(C): + gm = P["gamma"][c] + ng = nb.note("ln.ng", nb.corner((-N, N), (gm, gm))) + outs.append((nb.add(nb.shr(ng[0], 16), P["beta"][c]), nb.add(nb.shr(ng[1], 16), P["beta"][c]))) + return [hull(outs)] + if n == "l2norm": + e = P["eps"] + hh, ww = g["sizes"][op["ins"][0]] + X = max(abs(l), abs(h)) + nb.note("l2.T", (0, nb.add(nb.mul(nb.mul(hh, ww), nb.mul(X, X)), e))) + nb.note("rsq.num", (1 << 32, 1 << 32)) + nb.note("l2.k", (0, nb.div(1 << 32, math.isqrt(e)))) + nb.note("l2.vk", (-(1 << 32), 1 << 32)) + return [(-(1 << 16), 1 << 16)] + if n == "softmax": + nb.note("sm.diff", (nb.sub(l, h), 0)) + nb.exp((0, nb.sub(h, l))) + wm = g["sizes"][op["ins"][0]][1] + nb.note("sm.sum", (0, nb.mul(wm, 65553))) + nb.note("sm.num", (0, nb.add(nb.mul(65553, 1 << 16), nb.div(nb.mul(wm, 65553), 2)))) + return [(0, 1 << 16)] + if n == "sigmoid": + nb.exp((l, h) if l >= 0 else ((-h, -l) if h <= 0 else (0, max(-l, h)))) + nb.note("sig.num", (0, nb.mul(65553, 1 << 16))) + return [(0, 1 << 16)] + if n == "elu": + nb.exp((max(0, -h), max(0, -l))) + al = P["alpha"] + p = nb.note("elu.p", nb.corner((al, al), (-(1 << 16), 17))) + parts = [] + if h > 0: + parts.append((max(l, 1), h)) + if l <= 0: + parts.append((nb.shr(p[0], 16), nb.shr(p[1], 16))) + return [hull(parts)] + if n in ("concat", "subpos_merge"): + return [hull([iv[t] for t in op["ins"]])] + if n == "subpos_split": + return [x] * 4 + if n == "fill": + return [(P["value"], P["value"]) if P["kind"] == 0 else g["scalars"][P["value"]]] + # split, pixel_shuffle, pixel_unshuffle, pad_rep, crop_to (its first input), crop. + return [x] + + +def params32(op): + """The parameter conditions of section 7.2: whether the node meets every one.""" + n, P = op["name"], op["p"] + f32 = lambda v: I32_MIN <= v <= I32_MAX + ok = True + rq = P.get("rq") + if rq is not None: + ok = all(map(f32, rq["mult"])) and all(s <= 63 for s in rq["shift"]) and (rq["bias"] != 2 or all(map(f32, rq["biasv"]))) + if n in ("conv", "conv_transpose"): + ok = ok and all(map(f32, P["w"])) + elif n == "chscale": + ok = all(map(f32, P["M"])) and P["shift"] <= 63 + elif n == "igdn": + ok = all(map(f32, P["gamma"])) and P["gf"] <= 63 and P["frac"] <= 63 + elif n == "leaky": + ok = f32(P["a"]) + return ok + + +def bounds(g): + """The bounds pass (sections 8.1-8.4) and the verdict (section 8.5) of a graph + that passed load steps 1-7: {"acc32": bool, "acc64": bool}. Refuses + (model_mismatch) where the pass itself refuses the graph at load: a `check` + interval that is empty (section 8.1), or lut16's input range (section 5.6).""" + T = g["tensors"] + ok = {"acc32": True, "acc64": True} + iv = {} + declared = [(T[i["tensor"]]["lo"], T[i["tensor"]]["hi"]) for i in g["inputs"]] + list(g["scalars"]) + for r in declared: + for p in ok: + ok[p] = ok[p] and fits_bits(r, WIDTH[p]["tensor"]) + for i in g["inputs"]: + iv[i["tensor"]] = (T[i["tensor"]]["lo"], T[i["tensor"]]["hi"]) + for op in g["ops"]: + nb = Node() + results = node_bounds(nb, g, op, iv) + fit = {p: not nb.saturated for p in ok} + for p in ok: + for k, r in nb.rec.items(): + fit[p] = fit[p] and fits_bits(r, WIDTH[p][CLASS[k]]) + for r in results: + fit[p] = fit[p] and fits_bits(r, WIDTH[p]["tensor"]) + fit["acc32"] = fit["acc32"] and params32(op) + for t, (a, b) in zip(op["outs"], results): + rec = T[t] + if rec["policy"] == 0: + iv[t] = (a, b) + elif rec["policy"] == 1: + iv[t] = (min(max(a, rec["lo"]), rec["hi"]), min(max(b, rec["lo"]), rec["hi"])) + else: + fit["acc32"] = False + iv[t] = (max(a, rec["lo"]), min(b, rec["hi"])) + if iv[t][0] > iv[t][1]: + bad() + for p in ok: + ok[p] = ok[p] and fit[p] + return ok + + +# --------------------------------------------------------------- the operators (section 5) + + +class T: + """A tensor [c, h, w], elements in CHW order.""" + + def __init__(self, c, h, w, d): + self.c, self.h, self.w, self.d = c, h, w, d + + def at(self, c, y, x): + return self.d[(c * self.h + y) * self.w + x] + + +def ev_conv(x, P, oh, ow): + cin, H, W = x.c, x.h, x.w + G = P["groups"] + cg, og = cin // G, P["cout"] // G + kh, kw = P["kh"], P["kw"] + d = [] + for o in range(P["cout"]): + base = (o // og) * cg + for y in range(oh): + for xx in range(ow): + acc = 0 + for ci in range(cg): + for i in range(kh): + u = y * P["sh"] + i - P["ph"] + if not 0 <= u < H: + continue + for j in range(kw): + v = xx * P["sw"] + j - P["pw"] + if 0 <= v < W: + acc += P["w"][((o * cg + ci) * kh + i) * kw + j] * x.at(base + ci, u, v) + d.append(rq_apply(P["rq"], o, acc)) + return T(P["cout"], oh, ow, d) + + +def ev_conv_t(x, P, oh, ow): + """The gather form of section 5.3.""" + cin, H, W = x.c, x.h, x.w + kh, kw, sh, sw, cout = P["kh"], P["kw"], P["sh"], P["sw"], P["cout"] + d = [] + for o in range(cout): + for y in range(oh): + for xx in range(ow): + acc = 0 + for i in range(kh): + ty = y + P["ph"] - i + if ty < 0 or ty % sh or ty // sh >= H: + continue + for j in range(kw): + tx = xx + P["pw"] - j + if tx < 0 or tx % sw or tx // sw >= W: + continue + for ci in range(cin): + acc += P["w"][((ci * cout + o) * kh + i) * kw + j] * x.at(ci, ty // sh, tx // sw) + d.append(rq_apply(P["rq"], o, acc)) + return T(cout, oh, ow, d) + + +def per_channel(x, f): + plane = x.h * x.w + return T(x.c, x.h, x.w, [f(k // plane, v) for k, v in enumerate(x.d)]) + + +def ev(g, op, vals, hw, scal): + n, P = op["name"], op["p"] + x = vals[op["ins"][0]] + oh, ow = hw[op["outs"][0]] + oc = [g["tensors"][t]["c"] for t in op["outs"]] + if n == "conv": + return [ev_conv(x, P, oh, ow)] + if n == "conv_transpose": + return [ev_conv_t(x, P, oh, ow)] + if n == "rescale": + return [per_channel(x, lambda c, v: rq_apply(P["rq"], c, v))] + if n == "chscale": + r = scal[P["scalar"]] + return [per_channel(x, lambda c, v: R(v * P["M"][r * x.c + c], P["shift"], P["round"]))] + if n == "mul": + b = vals[op["ins"][1]] + plane = x.h * x.w + d = [] + for c in range(x.c): + cb = c if b.c == x.c else 0 + for k in range(plane): + d.append(rq_apply(P["rq"], c, x.d[c * plane + k] * b.d[cb * plane + k])) + return [T(x.c, x.h, x.w, d)] + if n == "lut16": + return [T(x.c, x.h, x.w, [P["T"][v + 32768] if -32768 <= v <= 32767 else None for v in x.d])] + if n == "add": + b = vals[op["ins"][1]] + return [T(x.c, x.h, x.w, [p + q for p, q in zip(x.d, b.d)])] + if n == "concat": + d = [] + for t in op["ins"]: + d += vals[t].d + return [T(oc[0], x.h, x.w, d)] + if n == "chunk_add": + half = (x.c // 2) * x.h * x.w + return [T(x.c // 2, x.h, x.w, [x.d[k] + x.d[k + half] for k in range(half)])] + if n == "split": + plane = x.h * x.w + return [T(P["count"], x.h, x.w, x.d[P["start"] * plane:(P["start"] + P["count"]) * plane])] + if n == "pixel_shuffle": + r = P["r"] + co = x.c // (r * r) + d = [0] * (co * oh * ow) + for c in range(co): + for i in range(r): + for j in range(r): + for hh in range(x.h): + for ww in range(x.w): + d[(c * oh + hh * r + i) * ow + ww * r + j] = x.at(c * r * r + i * r + j, hh, ww) + return [T(co, oh, ow, d)] + if n == "pixel_unshuffle": + r = P["r"] + d = [0] * (x.c * r * r * oh * ow) + for c in range(x.c): + for i in range(r): + for j in range(r): + for hh in range(oh): + for ww in range(ow): + d[((c * r * r + i * r + j) * oh + hh) * ow + ww] = x.at(c, hh * r + i, ww * r + j) + return [T(x.c * r * r, oh, ow, d)] + if n == "clamp": + return [T(x.c, x.h, x.w, [min(max(v, P["lo"]), P["hi"]) for v in x.d])] + if n == "relu": + return [T(x.c, x.h, x.w, [max(v, 0) for v in x.d])] + if n == "leaky": + return [T(x.c, x.h, x.w, [v if v >= 0 else (v * P["a"]) >> P["f"] for v in x.d])] + if n == "abs": + return [T(x.c, x.h, x.w, [abs(v) for v in x.d])] + if n == "pad_rep": + return [T(x.c, oh, ow, [x.at(c, min(y, x.h - 1), min(xx, x.w - 1)) for c in range(x.c) for y in range(oh) for xx in range(ow)])] + if n in ("crop_to", "crop"): + y0 = x0 = 0 + if n == "crop": + val = lambda o: o[1] if o[0] == "const" else scal[o[1]] + y0, x0 = val(P["ops"][0]), val(P["ops"][1]) + return [T(x.c, oh, ow, [x.at(c, y0 + y, x0 + xx) for c in range(x.c) for y in range(oh) for xx in range(ow)])] + if n == "subpos_split": + outs = [] + for k in range(4): + a, b2 = P["order"][k] >> 1, P["order"][k] & 1 + outs.append(T(x.c, oh, ow, [x.at(c, 2 * y + a, 2 * xx + b2) for c in range(x.c) for y in range(oh) for xx in range(ow)])) + return outs + if n == "subpos_merge": + d = [0] * (x.c * oh * ow) + for k in range(4): + a, b2 = P["order"][k] >> 1, P["order"][k] & 1 + t = vals[op["ins"][k]] + for c in range(x.c): + for y in range(t.h): + for xx in range(t.w): + d[(c * oh + 2 * y + a) * ow + 2 * xx + b2] = t.at(c, y, xx) + return [T(x.c, oh, ow, d)] + if n == "fill": + v = P["value"] if P["kind"] == 0 else scal[P["value"]] + return [T(oc[0], x.h, x.w, [v] * (oc[0] * x.h * x.w))] + if n == "igdn": + C, plane = x.c, x.h * x.w + out = [0] * (C * plane) + for p in range(plane): + xs = [x.d[j * plane + p] for j in range(C)] + for i in range(C): + N = P["beta"][i] + sum((P["gamma"][i * C + j] * xs[j] * xs[j]) // (1 << P["gf"]) for j in range(C)) + r = math.isqrt(max(0, N)) + out[i * plane + p] = (xs[i] * r) // (1 << P["frac"]) + return [T(C, x.h, x.w, out)] + if n == "layernorm_c": + C, plane = x.c, x.h * x.w + out = [0] * (C * plane) + for p in range(plane): + xs = [x.d[c * plane + p] for c in range(C)] + S = sum(xs) + sg = (S > 0) - (S < 0) + M = S + (C // 2) * sg + m = trunc_div(M, C) + ds = [v - m for v in xs] + V = sum(d * d for d in ds) // C + k = rsqrt_q16(V + P["eps"]) + for c in range(C): + nn = (ds[c] * k) >> 16 + out[c * plane + p] = ((nn * P["gamma"][c]) >> 16) + P["beta"][c] + return [T(C, x.h, x.w, out)] + if n == "l2norm": + plane = x.h * x.w + out = [] + for c in range(x.c): + vs = x.d[c * plane:(c + 1) * plane] + k = rsqrt_q16(sum(v * v for v in vs) + P["eps"]) + out += [(v * k) >> 16 for v in vs] + return [T(x.c, x.h, x.w, out)] + if n == "softmax": + out = [] + for row in range(x.c * x.h): + vs = x.d[row * x.w:(row + 1) * x.w] + m = max(vs) + es = [exp_neg_q16(v - m) for v in vs] + s = sum(es) + out += [(e * 65536 + s // 2) // s for e in es] + return [T(x.c, x.h, x.w, out)] + if n == "sigmoid": + def sig(v): + if v >= 0: + return (1 << 32) // (65536 + exp_neg_q16(-v)) + e = exp_neg_q16(v) + return (e * 65536) // (65536 + e) + return [T(x.c, x.h, x.w, [sig(v) for v in x.d])] + if n == "elu": + return [T(x.c, x.h, x.w, [v if v > 0 else (P["alpha"] * (exp_neg_q16(v) - 65536)) >> 16 for v in x.d])] + if n == "gram": + b = vals[op["ins"][1]] + G = P["groups"] + dd = x.c // G + plane = x.h * x.w + out = [0] * (x.c * dd) + for gi in range(G): + for i in range(dd): + for j in range(dd): + s = sum(x.d[(gi * dd + i) * plane + p] * b.d[(gi * dd + j) * plane + p] for p in range(plane)) + out[(gi * dd + i) * dd + j] = R(s, P["rq"]["shift"][0], P["rq"]["round"]) + return [T(x.c, 1, dd, out)] + if n == "apply": + V = vals[op["ins"][1]] + G = P["groups"] + dd = x.c // G + plane = V.h * V.w + out = [0] * (V.c * plane) + for gi in range(G): + for i in range(dd): + for p in range(plane): + s = sum(x.d[(gi * dd + i) * dd + j] * V.d[(gi * dd + j) * plane + p] for j in range(dd)) + out[(gi * dd + i) * plane + p] = R(s, P["rq"]["shift"][0], P["rq"]["round"]) + return [T(V.c, V.h, V.w, out)] + raise Refuse("model_mismatch") + + +def apply_policy(rec, t): + """Returns False when a `check` tensor holds an element outside its range.""" + if rec["policy"] == 1: + t.d = [min(max(v, rec["lo"]), rec["hi"]) for v in t.d] + elif rec["policy"] == 2: + return all(rec["lo"] <= v <= rec["hi"] for v in t.d) + return True + + +def run(g, data_inputs, scalars, state): + """One decode (section 4): the data inputs in input-table order; each state input + from `state` (section 10.3), which the decode updates when it completes.""" + allin = [] + di = 0 + si = 0 + if len(data_inputs) != sum(1 for i in g["inputs"] if i["kind"] == 0): + raise Refuse("model_mismatch") + for i in g["inputs"]: + if i["kind"] == 0: + allin.append(data_inputs[di]) + di += 1 + else: + if state.get("s") is None: + x0 = data_inputs[0] + c = g["tensors"][i["tensor"]]["c"] + allin.append(T(c, x0.h, x0.w, [i["init"]] * (c * x0.h * x0.w))) + else: + allin.append(state["s"][si]) + si += 1 + hw = {} + # Step 1: each input's channel count, then its size. + for i, t in zip(g["inputs"], allin): + if t.c != g["tensors"][i["tensor"]]["c"] or t.h < 1 or t.w < 1: + raise Refuse("model_mismatch") + if i["max_h"] and (t.h > i["max_h"] or t.w > i["max_w"]): + raise Refuse("too_large") + if t.c * t.h * t.w > g["max_elems"]: + raise Refuse("too_large") + hw[i["tensor"]] = (t.h, t.w) + # Step 2: each scalar against its range. + if len(scalars) != len(g["scalars"]): + raise Refuse("model_mismatch") + for v, (lo, hi) in zip(scalars, g["scalars"]): + if not lo <= v <= hi: + raise Refuse("field_range") + # Step 3: the run-time plan. + walk(g, hw, scalars, False) + # Step 4: evaluation, the inputs' policies first. + vals = {} + for i, t in zip(g["inputs"], allin): + t = T(t.c, t.h, t.w, list(t.d)) + if not apply_policy(g["tensors"][i["tensor"]], t): + raise Refuse("field_range") + vals[i["tensor"]] = t + for op in g["ops"]: + outs = ev(g, op, vals, hw, scalars) + for t, ten in zip(op["outs"], outs): + # A value outside i64 is one load step 8 excludes; before the certifier + # exists, such a decode is refused. + if any(v is None or not I64_MIN <= v <= I64_MAX for v in ten.d): + raise Refuse("model_mismatch") + if not apply_policy(g["tensors"][t], ten): + raise Refuse("field_range") + vals[t] = ten + outs = [vals[t] for t in g["outputs"]] + state["s"] = [outs[i["src"]] for i in g["inputs"] if i["kind"] == 1] + return outs + + +# --------------------------------------------------------------- canonical JSON (8.5, 14.1) + + +def cw(v): + """The canonical form: ints as decimal strings, members sorted by key bytes.""" + if v is None: + return "null" + if v is True: + return "true" + if v is False: + return "false" + if isinstance(v, int): + return '"%d"' % v + if isinstance(v, str): + return '"' + v.replace("\\", "\\\\").replace('"', '\\"') + '"' + if isinstance(v, list): + return "[" + ",".join(cw(x) for x in v) + "]" + if isinstance(v, dict): + keys = sorted(v, key=lambda k: k.encode()) + return "{" + ",".join(cw(k) + ":" + cw(v[k]) for k in keys) + "}" + raise TypeError(v) + + +def dec(s): + """A canonical decimal integer string.""" + if not isinstance(s, str) or not s or s == "-0": + raise ValueError("not a canonical integer: %r" % (s,)) + d = s[1:] if s[0] == "-" else s + if not d.isdigit() or (len(d) > 1 and d[0] == "0") or not d.isascii(): + raise ValueError("not a canonical integer: %r" % (s,)) + return int(s) + + +def tensor_in(v): + c, h, w = (dec(x) for x in v["shape"]) + d = [dec(x) for x in v["data"]] + if len(d) != c * h * w: + raise ValueError("a tensor's data does not fill its shape") + return T(c, h, w, d) + + +def tensor_out(t): + return {"data": list(t.d), "shape": [t.c, t.h, t.w]} + + +def graph_line(v): + """A graph vector, recomputed: `load` (steps 1-8), `profiles` (section 14.1: both + when the load is refused, else exactly the profiles the verdict meets) and every + decode's `expect`.""" + b = bytes.fromhex(v["graph"]) + out = {"graph": b.hex(), "id": v["id"], "decodes": []} + try: + g = load(b) + out["load"] = "ok" + out["profiles"] = [p for p in ("acc32", "acc64") if g["verdict"][p]] + except Refuse as e: + out["load"] = e.code + out["profiles"] = ["acc32", "acc64"] + g = None + if g is not None: + state = {} + for d in v["decodes"]: + ins = [tensor_in(t) for t in d["inputs"]] + sc = [dec(s) for s in d["scalars"]] + try: + res = run(g, ins, sc, state) + exp = {"outputs": [tensor_out(t) for t in res]} + except Refuse as e: + exp = {"refuse": e.code} + out["decodes"].append({"expect": exp, "inputs": [tensor_out(t) for t in ins], "scalars": sc}) + return out + + +# --------------------------------------------------------------- stage operators (5E) + + +class Rans: + """integer-payloads section 2's decoder state.""" + + def __init__(self, b): + self.b = b + self.x = (int.from_bytes(b[4:8], "little") << 32) | int.from_bytes(b[0:4], "little") + self.i = 2 + self.out = False + + def renorm(self): + if self.x < (1 << 31): + if 4 * self.i + 4 <= len(self.b): + self.x = (self.x << 32) | int.from_bytes(self.b[4 * self.i:4 * self.i + 4], "little") + self.i += 1 + else: + self.out = True + + def bits(self, n): + v = self.x & ((1 << n) - 1) + self.x >>= n + self.renorm() + return v + + +def sat32(v): + return max(I32_MIN, min(I32_MAX, v)) + + +def rans64_decode(i, m): + table_rule, escape, end = m["table"], m["escape"], m["end"] + lo = hi = None + if escape == "strict": + lo, hi = dec(m["lo"]), dec(m["hi"]) + if not (I32_MIN <= lo <= hi <= I32_MAX): + raise Refuse("model_mismatch", -1) + tables = [([dec(c) for c in t["cdf"]], dec(t["offset"])) for t in i["tables"]] + reads = [dec(r) for r in i["reads"]] + for cdf, o in tables: + n = len(cdf) + ok_n = 3 <= n <= 4097 if table_rule == "t4097" else 2 <= n <= 65537 + if not ok_n or cdf[0] != 0 or cdf[-1] != 65536 or any(cdf[k] >= cdf[k + 1] for k in range(n - 1)) or not I32_MIN <= o <= I32_MAX: + raise Refuse("model_mismatch", -1) + if any(not 0 <= r < len(tables) for r in reads): + raise Refuse("model_mismatch", -1) + stream = bytes.fromhex(i["stream"]) + if len(stream) < 8 or len(stream) % 4: + raise Refuse("stream_exhausted", 0) + d = Rans(stream) + syms = [] + for k, r in enumerate(reads): + cdf, o = tables[r] + n = len(cdf) + cum = d.x & 0xFFFF + s = max(j for j in range(n - 1) if cdf[j] <= cum) + d.x = (cdf[s + 1] - cdf[s]) * (d.x >> 16) + cum - cdf[s] + d.renorm() + code = None + if s < n - 2: + sym = s + o + if escape == "lenient": + sym = sat32(sym) + elif not lo <= sym <= hi: + code = "field_range" + elif escape == "lenient": + v = d.bits(4) + count = v + groups = 0 + while v == 15 and groups < 8 and not d.out: + v = d.bits(4) + count += v + groups += 1 + count = min(count, 8) + raw = 0 + for j in range(count): + raw |= d.bits(4) << (4 * j) + value = -(raw >> 1) - 1 if raw & 1 else (raw >> 1) + (n - 2) + sym = sat32(sat32(value) + o) + else: + count = d.bits(4) + raw, last = 0, None + if count <= 8: + for j in range(count): + last = d.bits(4) + raw += last << (4 * j) + value = -(raw // 2) - 1 if raw % 2 else raw // 2 + (n - 2) + sym = value + o + if count > 8 or (count >= 1 and last == 0): + code = "stream_noncanonical" + elif not lo <= sym <= hi: + code = "field_range" + if end == "canonical" and d.out: + raise Refuse("stream_exhausted", k) + if code: + raise Refuse(code, k) + syms.append(sym) + if end == "canonical": + if d.out or d.x != (1 << 31) or 4 * d.i != len(stream): + raise Refuse("stream_noncanonical", len(reads)) + elif d.out: + raise Refuse("stream_exhausted", len(reads)) + return {"symbols": syms} + + +class Win: + """E8's window and states over memory M (8 zero bytes, then the stream).""" + + def __init__(self, stream): + self.M = bytes(8) + stream + self.p = len(stream) + self.h = self.le64(self.p) + self.pos = 0 + self.last_in = 0 + if self.h == 0: + raise Refuse("stream_exhausted", 0) + self.b = self.h.bit_length() - 1 + self.h -= 1 << self.b + self.s = [0, 0] + try: + self.s[0] = self.read(8) + self.s[1] = self.read(8) + self.flush() + except Refuse: + raise Refuse("stream_exhausted", 0) + + def le64(self, i): + return int.from_bytes(self.M[i:i + 8], "little") + + def read(self, k): + if k > self.b: + raise Refuse("stream_exhausted", self.pos) + self.b -= k + r = self.h >> self.b + self.h -= r << self.b + return r + + def flush(self): + d = 7 ^ (self.b // 8) + if d > self.p: + raise Refuse("stream_exhausted", self.pos) + self.p -= d + self.h = self.le64(self.p) % (1 << (56 + self.b % 8)) + self.b = 56 + self.b % 8 + + def step(self, which, t): + self.s[which] = ((t >> 16) | self.read(t >> 24)) % 256 + + def at(self, k): + """The `in` or `out` step of position k runs: a refusal in it is at k.""" + self.pos = k + + def at_in(self, k): + self.pos = k + self.last_in = k + + def group_flush(self): + """A flush that belongs to no single position (section 14.1, `at`): it reports + the last position whose `in` step ran.""" + self.pos = self.last_in + self.flush() + + +def wrap16(a): + return ((a + 32768) % 65536) - 32768 + + +def latent_tables(pmf): + w = len(pmf[0]) if pmf else 0 + if w < 1 or any(len(r) != w or min(r) < 0 or sum(r) != 256 for r in pmf): + raise Refuse("model_mismatch", -1) + tabs = [] + for P in pmf: + C = [0] + for p in P: + C.append(C[-1] + p) + F = lambda k: C[k] - C[k] // 2 + G = lambda k: C[k] // 2 + 128 + sym = lambda k: k // 2 if k % 2 == 0 else -((k + 1) // 2) + slot_sym, slot_next = [None] * 256, [None] * 256 + k = 0 + while C[k] < 256: + for u in range(F(k), F(k + 1)): + slot_sym[u], slot_next[u] = sym(k), P[k] + (u - F(k)) + for u in range(G(k), G(k + 1)): + slot_sym[u], slot_next[u] = sym(k), P[k] + (F(k + 1) - F(k)) + (u - G(k)) + k += 1 + t = [] + for u in range(256): + nb = 0 + while slot_next[u] * (1 << nb) < 256: + nb += 1 + base = (slot_next[u] * (1 << nb)) ^ 256 + t.append(nb * (1 << 24) + base * (1 << 16) + slot_sym[u] % 65536) + tabs.append(t) + return tabs + + +def mans_sgm(i): + pmf = [[dec(x) for x in r] for r in i["pmf"]] + bounds = [dec(x) for x in i["bounds"]] + spans = [([bool(b) for b in s["mask"]], [dec(x) for x in s["sigma"]]) for s in i["spans"]] + tabs = latent_tables(pmf) + if any(not 0 <= b <= 255 for b in bounds): + raise Refuse("model_mismatch", -1) + nd = min(len(tabs), len(bounds)) + if any(len(m) != len(sg) or any(not 0 <= x < nd for x in sg) for m, sg in spans): + raise Refuse("model_mismatch", -1) + w = Win(bytes.fromhex(i["memory"])) + out = [] + base = 0 + for mask, sigma in spans: + L = len(mask) + sy = [0] * L + + def inb(which, k): + w.at_in(base + k) + if mask[k]: + t = tabs[sigma[k]][w.s[which]] + w.step(which, t) + sy[k] = wrap16(t % 65536) + + def outb(k): + w.at(base + k) + if mask[k]: + beta = bounds[sigma[k]] + if sy[k] + beta == 0: + f = w.read(1) + v = w.read(3 if f == 1 else 16) + sy[k] = wrap16(v // 2 + beta) if v % 2 == 0 else wrap16(-(v // 2) - beta) + w.flush() + + k = 0 + while k + 3 < L: + inb(0, k); inb(1, k + 1); inb(0, k + 2); inb(1, k + 3) + w.group_flush() + for q in range(4): + outb(k + q) + k += 4 + if L % 4 >= 2: + inb(0, k); outb(k); inb(1, k + 1); outb(k + 1) + k += 2 + w.group_flush() + if L % 2 == 1: + inb(0, k); outb(k) + w.group_flush() + out.append(sy) + base += L + return {"symbols": out} + + +def mans_fac(i): + rows = [[dec(x) for x in r] for r in i["rows"]] + lens = [dec(x) for x in i["lengths"]] + if len(rows) != len(lens) or any(len(r) != 63 or r[0] < 0 or r[62] > 255 or any(r[k] > r[k + 1] for k in range(62)) for r in rows): + raise Refuse("model_mismatch", -1) + Zp = [0] * 512 + nb = 0 + for i_ in range(511, 0, -1): + if i_ * (1 << nb) < 256: + nb += 1 + Zp[i_] = ((i_ * (1 << nb)) ^ 256) + 256 * nb + w = Win(bytes.fromhex(i["memory"])) + Tz = [0] * 256 + out = [] + base = 0 + for r, L in zip(rows, lens): + prev = 0 + for a in range(63): + k = r[a] - prev + for j in range(prev, r[a]): + Tz[j] = Zp[k + j - prev] * 65536 + a + prev = r[a] + Tz[255] = Zp[1] * 65536 + 63 + sy = [0] * L + + def inb(which, k): + w.at_in(base + k) + t = Tz[w.s[which]] + w.step(which, t) + sy[k] = t % 256 + + def outb(k): + w.at(base + k) + if sy[k] == 63: + sy[k] = w.read(6) + + k = 0 + while k + 3 < L: + inb(0, k); inb(1, k + 1); inb(0, k + 2); inb(1, k + 3) + for q in range(4): + outb(k + q) + w.group_flush() + k += 4 + if L % 4 >= 2: + inb(0, k); outb(k); inb(1, k + 1); outb(k + 1) + k += 2 + if L % 2 == 1: + inb(0, k); outb(k) + w.group_flush() + out.append(sy) + base += L + return {"symbols": out} + + +def rdiv(n, d): + if d < 1: + return 0 + sg = (n > 0) - (n < 0) + return sg * ((2 * abs(n) + d) // (2 * d)) + + +def stage_expect(op, i, m): + D = lambda k: dec(i[k]) + if op == "rans64_decode": + return rans64_decode(i, m) + if op == "dequantize": + f, s, b = D("f"), D("sym"), D("base") + if not 0 <= f <= 62: + raise Refuse("field_range", 0) + v = s * (1 << f) + b + if not I64_MIN <= v <= I64_MAX: + raise Refuse("field_range", 0) + return {"value": v} + if op == "bucket": + sigma, e = D("sigma"), [dec(x) for x in i["edges"]] + if m["bucket"] == "ceil": + if not e: + raise Refuse("model_mismatch", -1) + s2 = max(sigma, e[0]) + return {"index": (len(e) - 1) - sum(1 for k in range(len(e) - 1) if s2 <= e[k])} + if not e or any(e[k] > e[k + 1] for k in range(len(e) - 1)): + raise Refuse("model_mismatch", -1) + return {"index": sum(1 for x in e if x <= sigma)} + if op == "coded": + return {"coded": D("v") > D("thr")} + if op == "scale_index": + n, s = D("n"), D("s") + if n < 1 or not 0 <= s <= 127: + raise Refuse("model_mismatch", -1) + return {"index": min(max(R(D("v"), s, 1), 0), n - 1)} + if op == "block_stat": + x = tensor_in(i["x"]) + b, s, lo, hi = D("b"), D("s"), D("lo"), D("hi") + if b < 1 or not 0 <= s <= 127 or lo > hi: + raise Refuse("model_mismatch", -1) + d = list(x.d) + for c in range(x.c): + for by in range(0, x.h, b): + for bx in range(0, x.w, b): + cells = [(y, xx) for y in range(by, min(by + b, x.h)) for xx in range(bx, min(bx + b, x.w))] + v = min(max(R(sum(x.at(c, y, xx) for y, xx in cells), s, 1), lo), hi) + for y, xx in cells: + d[(c * x.h + y) * x.w + xx] = v + return {"out": tensor_out(T(x.c, x.h, x.w, d))} + if op == "band_lookup": + e = [dec(x) for x in i["edges"]] + table = [[dec(x) for x in r] for r in i["table"]] + row = D("row") + if not 0 <= row < len(table) or len(table[row]) != len(e) + 1: + raise Refuse("model_mismatch", -1) + return {"value": table[row][sum(1 for k in e if D("v") >= k)]} + if op == "mans_decode": + return mans_sgm(i) if i["mode"] == "sgm" else mans_fac(i) + if op == "residual_step": + pred, r, q, lo, hi = D("pred"), D("r"), D("q"), D("lo"), D("hi") + if lo >= hi or not lo <= pred <= hi or not 1 <= q <= 65535 or not I32_MIN <= r <= I32_MAX: + raise Refuse("model_mismatch", -1) + return {"value": min(max(pred + r * q, lo), hi)} + if op == "mc_predict": + f = tensor_in(i["frame"]) + y, x, c, dy, dx, pr = D("y"), D("x"), D("c"), D("dy"), D("dx"), D("precision") + if not (0 <= c < f.c and 0 <= y < f.h and 0 <= x < f.w) or pr not in (1, 2): + raise Refuse("model_mismatch", -1) + p = lambda Y, X: f.at(c, min(max(Y, 0), f.h - 1), min(max(X, 0), f.w - 1)) + if pr == 1: + return {"value": p(y + dy, x + dx)} + iy, fy, ix, fx = y + (dy >> 1), dy & 1, x + (dx >> 1), dx & 1 + if not fy and not fx: + v = p(iy, ix) + elif not fy: + v = (p(iy, ix) + p(iy, ix + 1) + 1) >> 1 + elif not fx: + v = (p(iy, ix) + p(iy + 1, ix) + 1) >> 1 + else: + v = (p(iy, ix) + p(iy, ix + 1) + p(iy + 1, ix) + p(iy + 1, ix + 1) + 2) >> 2 + return {"value": v} + if op == "up2_predict": + prev = tensor_in(i["prev"]) + d0, d1 = D("d0"), D("d1") + hp, wp = -(-d0 // 2), -(-d1 // 2) + if d0 < 1 or d1 < 1 or (prev.h, prev.w) != (hp, wp): + raise Refuse("geometry_mismatch", -1) + p = lambda c, Y, X: prev.at(c, min(Y, hp - 1), min(X, wp - 1)) + d = [] + for c in range(prev.c): + for y in range(d0): + for x in range(d1): + iy, fy, ix, fx = y >> 1, y & 1, x >> 1, x & 1 + if not fy and not fx: + v = p(c, iy, ix) + elif not fy: + v = (p(c, iy, ix) + p(c, iy, ix + 1) + 1) >> 1 + elif not fx: + v = (p(c, iy, ix) + p(c, iy + 1, ix) + 1) >> 1 + else: + v = (p(c, iy, ix) + p(c, iy, ix + 1) + p(c, iy + 1, ix) + p(c, iy + 1, ix + 1) + 2) >> 2 + d.append(v) + return {"out": tensor_out(T(prev.c, d0, d1, d))} + if op == "rate_predict": + prev = tensor_in(i["prev"]) + num, den, d0 = D("rate_num"), D("rate_den"), D("d0") + n = prev.w + if prev.h != 1 or num < 1 or den < 1 or n * num // den != d0: + raise Refuse("geometry_mismatch", -1) + d = [] + for c in range(prev.c): + for k in range(d0): + s = k * den + lo, rho = s // num, s % num + hi = min(lo + 1, n - 1) + a, b = prev.at(c, 0, lo), prev.at(c, 0, hi) + d.append(rdiv(a * num + (b - a) * rho, num)) + return {"out": tensor_out(T(prev.c, 1, d0, d))} + if op == "coding_order": + c, h, w = (dec(x) for x in i["shape"]) + cb = None if i["order"] == "raster" else i["anchor"] + idx = [(cc * h + y) * w + x for cc in range(c) for y in range(h) for x in range(w) if cb is None or (((y + x) % 2 == 0) == cb)] + return {"indices": idx} + raise ValueError("unknown stage operator %r" % op) + + +def stage_line(v): + try: + exp = stage_expect(v["op"], v["in"], v["modes"]) + except Refuse as e: + exp = {"at": e.at, "refuse": e.code} + return {"expect": exp, "id": v["id"], "in": v["in"], "modes": v["modes"], "op": v["op"]} + + +# --------------------------------------------------------------- lowerings (section 13) + + +class WB: + """A WHG1 graph under construction with section 13.0's numbering.""" + + def __init__(self): + self.scalars, self.inputs, self.tensors, self.ops = [], [], [], [] + + def tensor(self, c, policy=0, lo=0, hi=0): + self.tensors.append((c, policy, lo, hi)) + return len(self.tensors) - 1 + + def input(self, c, lo, hi, mh=0, mw=0): + t = self.tensor(c, 2, lo, hi) + self.inputs.append((t, mh, mw)) + return t + + def op(self, code, ins, out_rec, params): + t = self.tensor(*out_rec) + self.ops.append((code, ins, [t], params)) + return t + + def bytes(self, outputs): + b = bytearray(b"WHG1") + b += bytes([1, 1, 0, 0]) + b += struct.pack(" 127 or len(w) != ic * oc * k * k or len(b) != oc: + lref() + rq = rq_block(0, 1, [], 0, 0, [sh], 1, b, 4) + x = wb.op(0x11, [x], (oc,), p_convt(oc, k, k, s, s, p, p, op_, op_, 4, rq, w)) + if relu: + x = wb.op(0x0C, [x], (oc,), b"") + c = oc + x = wb.op(0x0B, [x], (c,), p_clamp(0, 255)) + return [("synth", wb.bytes([x]))] + + +def parse_net(b, magic, audio): + """WHM1 / WAM1 (integer-payloads section 9).""" + if b[:4] != magic: + lref() + rd = Reader(b, 4) + try: + afrac, gf, shift, n = rd.u(4), rd.u(4), rd.u(4), rd.u(4) + layers = [] + for _ in range(n): + tag = rd.u(1) + if tag == 0: + inc, out, k, s, p, op_ = (rd.u(4) for _ in range(6)) + w = rd.ints(inc * out * k * (1 if audio else k), 2) + layers.append(("convT", inc, out, k, s, p, op_, w, rd.ints(out, 8), rd.ints(out, 8))) + elif tag == 1 and not audio: + inc, out, k, s, p = (rd.u(4) for _ in range(5)) + w = rd.ints(out * inc * k * k, 2) + layers.append(("conv", inc, out, k, s, p, 0, w, rd.ints(out, 8), rd.ints(out, 8))) + elif tag == 2: + c = rd.u(4) + layers.append(("igdn", c, rd.ints(c * c, 4), rd.ints(c, 8))) + elif tag == 3 and not audio: + layers.append(("relu",)) + elif tag == 4 and not audio: + layers.append(("leaky", rd.i(8), rd.u(4))) + else: + lref() + except Refuse: + lref() + if rd.p != len(b): + lref() + return {"afrac": afrac, "gf": gf, "shift": shift, "layers": layers, "audio": audio} + + +def largest_x(pred): + """The largest X in [0, 2^63] with pred(X), pred monotone; None if pred(0) fails.""" + if not pred(0): + return None + lo, hi = 0, 1 << 63 + if pred(hi): + return hi + while hi - lo > 1: + mid = (lo + hi) // 2 + if pred(mid): + lo = mid + else: + hi = mid + return lo + + +def x_l(net, layer, c): + """X_l of section 13.2 for a layer of tag 0, 1, 2 or 4 reading c channels; the set + is refused when P_l(0) fails.""" + I64, I128 = (1 << 63) - 1, (1 << 127) - 1 + kind = layer[0] + if kind in ("conv", "convT"): + k = layer[3] + terms = c * k * (1 if net["audio"] else k) + Wm, Mm, Bm = max(map(abs, layer[7]), default=0), max(map(abs, layer[8]), default=0), max(map(abs, layer[9]), default=0) + p = lambda X: terms * Wm * X <= I64 and (terms * Wm * X * Mm) // (1 << net["shift"]) + 1 + Bm <= I64 + elif kind == "igdn": + G, Bb = max(map(abs, layer[2]), default=0), max(map(abs, layer[3]), default=0) + + def p(X): + if G * X * X > I128: + return False + N = Bb + c * ((G * X * X) // (1 << net["gf"]) + 1) + if N > I128: + return False + r = math.isqrt(N) + return r <= I64 and (X * r) // (1 << net["afrac"]) + 1 <= I64 + else: + a = abs(layer[1]) + p = lambda X: X * a <= I64 + x = largest_x(p) + if x is None: + lref() + return x + + +def x_record(net, k, c): + """The tensor record of x_k on c channels (section 13.2, Range checks). x_k is read + by layer k, if there is one. When that layer is of tag 0, 1, 2 or 4, x_k is + check [-X_k, min(X_k, I64)]; that includes the network's input. A network input + that no such layer reads is check [-2^63, I64]. Every other tensor is derived.""" + L = net["layers"] + if k < len(L) and L[k][0] in ("convT", "conv", "igdn", "leaky"): + x = x_l(net, L[k], c) + return (c, 2, -x, min(x, I64_MAX)) + if k == 0: + return (c, 2, I64_MIN, I64_MAX) + return (c,) + + +def net_layers(wb, net, x): + L = net["layers"] + for k, l in enumerate(L): + c = wb.tensors[x][0] + oc = l[2] if l[0] in ("conv", "convT") else c + rec = x_record(net, k + 1, oc) + if l[0] in ("conv", "convT"): + _, inc, out, kk, s, p, op_, w, mult, bias = l + if inc != c or out == 0 or kk == 0 or s == 0: + lref() + rq = rq_block(0, 2, mult, 2, 8, [net["shift"]], 1, bias, 8) + if l[0] == "conv": + x = wb.op(0x01, [x], rec, p_conv(out, kk, kk, s, s, p, p, 1, 2, rq, w)) + elif net["audio"]: + x = wb.op(0x11, [x], rec, p_convt(out, 1, kk, 1, s, 0, p, 0, op_, 2, rq, w)) + else: + x = wb.op(0x11, [x], rec, p_convt(out, kk, kk, s, s, p, p, op_, op_, 2, rq, w)) + elif l[0] == "igdn": + if l[1] != c: + lref() + x = wb.op(0x12, [x], rec, struct.pack(" 63: + lref() + x = wb.op(0x0D, [x], rec, struct.pack(" 127 or net["gf"] > 127 or not 1 <= net["afrac"] <= 30 or not net["layers"]: + lref() + for l in net["layers"]: + if l[0] in ("conv", "convT"): + return l[1] + if l[0] == "igdn": + return l[1] + lref() + + +def rgb8(wb, x, afrac): + c = wb.tensors[x][0] + t = wb.op(0x0B, [x], (c,), p_clamp(0, 1 << afrac)) + return wb.op(0x02, [t], (c,), rq_block(1, 0, [255], 1, 4, [afrac], 1, [], 0)) + + +def whm1_lower(b, output_step): + net = parse_net(b, b"WHM1", False) + c0 = net_check(net) + wb = WB() + r = x_record(net, 0, c0) + x = wb.input(r[0], r[2], r[3]) + x = net_layers(wb, net, x) + if output_step: + x = rgb8(wb, x, net["afrac"]) + return wb.bytes([x]) + + +def wam1_lower(b): + net = parse_net(b, b"WAM1", True) + c0 = net_check(net) + wb = WB() + r = x_record(net, 0, c0) + x = wb.input(r[0], r[2], r[3]) + wb.scalars.append((1, 1 << 26)) + x = net_layers(wb, net, x) + c = wb.tensors[x][0] + lo = -((1 << 63) // 32767) + hi = (I64_MAX - (1 << (net["afrac"] - 1))) // 32767 + crop = struct.pack(" v[r * 63 + j + 1] for r in range(rows) for j in range(62)): + lref() + gy, guv = ints(norm.get("gain_y")), ints(norm.get("gain_uv")) + if len(gy) != 160 or len(guv) != 96 or any(not I64_MIN <= v <= I64_MAX for v in gy + guv): + lref() + + def member(o, k, dims): + m = o.get(k) + if not isinstance(m, dict) or m.get("dims") != dims: + lref() + v = ints(m.get("v")) + n = 1 + for d in dims: + n *= d + if len(v) != n or any(not I64_MIN <= x <= I64_MAX for x in v): + lref() + return v + + def plane(o, cz, gains): + if not isinstance(o, dict): + lref() + + def d0(k): + m = o.get(k) + dims = m.get("dims") if isinstance(m, dict) else None + if not isinstance(dims, list) or not dims or not isint(dims[0]) or not 1 <= dims[0] <= 0xFFFFFFFF: + lref() + return dims[0] + + c1, c2 = d0("conv1.weight"), d0("depthwise.weight") + c3 = 16 * cz + wb = WB() + x = wb.input(cz, -31, 32, 4, 4) + blocks = (("conv1", c1, cz, 1, 0, "onnx::Pow_85"), ("depthwise", c2, c1, 3, 1, "onnx::Pow_91"), ("pointwise", c3, c2, 1, 0, "onnx::Pow_97")) + for bi, (name, cout, cin, k, pad, sh) in enumerate(blocks): + w = member(o, name + ".weight", [cout, cin, k, k]) + bias = member(o, name + ".bias", [cout]) + shifts = member(o, sh, [1, cout, 1, 1]) + if any(not 0 <= s <= 127 for s in shifts): + lref() + x = wb.op(0x0B, [x], (wb.tensors[x][0],), p_clamp(-128, 127)) + rq = rq_block(0, 1, [], 0, 0, shifts, 2, bias, smallest(bias, (4, 8))) + wbytes = smallest(w, (1, 2, 4, 8)) + x = wb.op(0x01, [x], (cout,), p_conv(cout, k, k, 1, 1, pad, pad, 1, wbytes, rq, w)) + if bi < 2: + x = wb.op(0x0C, [x], (cout,), b"") + x = wb.op(0x09, [x], (cz,), struct.pack(".jsonl has the id ///...: its + graph must be that role of that synthetic set as re-lowered here, and every role + of every synthetic set must have one. `lowered` maps (file, set, role) to bytes.""" + failures = 0 + seen = set() + d = os.path.join(root, "graphs") + for name in sorted(os.listdir(d)): + if not (name.startswith("lower_") and name.endswith(".jsonl")): + continue + f = name[:-len(".jsonl")] + for k, line in enumerate(open(os.path.join(d, name), "rb").read().decode("ascii").split("\n")[:-1]): + v = json.loads(line) + parts = v["id"].split("/") + key = tuple(parts[:3]) + if len(parts) < 4 or parts[0] != f or key not in lowered: + failures += 1 + print("FAIL graphs/%s line %d: %s names no lowered role" % (name, k + 1, v["id"])) + elif bytes.fromhex(v["graph"]) != lowered[key]: + failures += 1 + print("FAIL graphs/%s line %d: %s is not the role as lowered" % (name, k + 1, v["id"])) + seen.add(key) + for key in sorted(set(lowered) - seen): + failures += 1 + print("FAIL %s: no lowering vector for this role" % "/".join(key)) + print("matched %d lowered roles to their lowering vectors" % len(seen & set(lowered))) + return failures + + +def main(argv): + if len(argv) not in (2, 4) or (len(argv) == 4 and argv[2] != "--write"): + print(__doc__) + return 2 + write = argv[3] if len(argv) == 4 else None + failures = check(argv[1], write) + if failures: + print("%d failures" % failures) + return 1 + print("ops-conformance: every vector file and every lowered graph rewritten byte for byte") + return 0 + + +if __name__ == "__main__": + sys.exit(main(sys.argv)) diff --git a/wai/wai-ops/Cargo.lock b/wai/wai-ops/Cargo.lock new file mode 100644 index 000000000..b89c00775 --- /dev/null +++ b/wai/wai-ops/Cargo.lock @@ -0,0 +1,7 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "wai-ops" +version = "0.1.0" diff --git a/wai/wai-ops/Cargo.toml b/wai/wai-ops/Cargo.toml new file mode 100644 index 000000000..2df213ad8 --- /dev/null +++ b/wai/wai-ops/Cargo.toml @@ -0,0 +1,46 @@ +# The reference implementation of `wai-ops/1`, the integer operator set +# (`extensions/integer-operators.md`): the `WHG1` graph form, the load and run-time +# plans, the interpreter, the stage operators, the canonical lowerings of the +# registered integer network forms, and the conformance vectors of +# `../ops-conformance/`. +# +# `no_std` with `alloc`, and no dependencies, so that a model checker can include +# its sources by `#[path]` the way `wai-verify` includes the other integer sources. +[package] +name = "wai-ops" +version = "0.1.0" +edition = "2024" +rust-version = "1.97" +license = "Apache-2.0" +publish = false +description = "WAI integer operator set wai-ops/1: WHG1 graphs, plans, interpreter, stage operators, lowerings and conformance vectors" + +[lib] +name = "wai_ops" +path = "src/lib.rs" + +# The vector and lowering tool. It reads and writes files, so it is `std`; the +# library it drives is not. +[[bin]] +name = "wai-ops" +path = "src/bin/wai-ops.rs" +required-features = ["uncertified"] + +[features] +# Load step 8 (the bounds pass and the profile verdict, §10.5) is not built yet. +# Without this switch a graph cannot complete its load, and since the interpreter +# runs only a graph `graph::load` returned, the default build decodes nothing; with +# it, step 8 is skipped, so the vectors and the lowerings run before the certifier +# exists. The tests, the conformance tool and its browser-engine build (`node/`) +# enable it, and no decoder may. wai-ops is a workspace of its own, so no other +# crate's features unify with it. The switch goes when the certifier lands. +uncertified = [] + +[lints.rust] +unexpected_cfgs = { level = "warn", check-cfg = ["cfg(kani)"] } + +[dependencies] +# none + +[profile.release] +debug = false diff --git a/wai/wai-ops/node/Cargo.lock b/wai/wai-ops/node/Cargo.lock new file mode 100644 index 000000000..2d704e4a5 --- /dev/null +++ b/wai/wai-ops/node/Cargo.lock @@ -0,0 +1,14 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "wai-ops" +version = "0.1.0" + +[[package]] +name = "wai-ops-node" +version = "0.1.0" +dependencies = [ + "wai-ops", +] diff --git a/wai/wai-ops/node/Cargo.toml b/wai/wai-ops/node/Cargo.toml new file mode 100644 index 000000000..919fbfa45 --- /dev/null +++ b/wai/wai-ops/node/Cargo.toml @@ -0,0 +1,20 @@ +# The vector recomputation of `wai-ops`, as a WebAssembly module for a browser's +# engine (`wasm32-unknown-unknown`), with a C ABI and no bindings generator, so that +# `verify.mjs` runs every vector file under Node with no dependency. +[package] +name = "wai-ops-node" +version = "0.1.0" +edition = "2024" +rust-version = "1.97" +license = "Apache-2.0" +publish = false + +[lib] +crate-type = ["cdylib"] +path = "src/lib.rs" + +[dependencies] +wai-ops = { path = "..", features = ["uncertified"] } + +[profile.release] +debug = false diff --git a/wai/wai-ops/node/src/lib.rs b/wai/wai-ops/node/src/lib.rs new file mode 100644 index 000000000..745bf0e2e --- /dev/null +++ b/wai/wai-ops/node/src/lib.rs @@ -0,0 +1,58 @@ +//! `wai_ops::vectors::recompute_file` behind a C ABI: the caller writes a file into +//! memory from [`wai_ops_alloc`], calls [`wai_ops_recompute`], and reads the result +//! from [`wai_ops_out_ptr`] and [`wai_ops_out_len`]. + +use std::cell::RefCell; + +thread_local! { + static OUT: RefCell> = const { RefCell::new(Vec::new()) }; +} + +/// A buffer of `n` bytes for the caller to fill; freed by [`wai_ops_free`]. +#[unsafe(no_mangle)] +pub extern "C" fn wai_ops_alloc(n: usize) -> *mut u8 { + let mut v = vec![0u8; n]; + let p = v.as_mut_ptr(); + std::mem::forget(v); + p +} + +/// Free a buffer from [`wai_ops_alloc`]. +/// +/// # Safety +/// `p` and `n` are a pointer and length [`wai_ops_alloc`] returned and was given. +#[unsafe(no_mangle)] +pub unsafe extern "C" fn wai_ops_free(p: *mut u8, n: usize) { + drop(unsafe { Vec::from_raw_parts(p, n, n) }); +} + +/// Recompute the vector file of `n` bytes at `p`: `kind` 0 for `vectors/` and +/// `graphs/`, 1 for `stage/`. Returns 0 with the recomputed file as the output, or 1 +/// with the error as the output. +/// +/// # Safety +/// `p` points to `n` readable bytes. +#[unsafe(no_mangle)] +pub unsafe extern "C" fn wai_ops_recompute(kind: u32, p: *const u8, n: usize) -> u32 { + let input = unsafe { std::slice::from_raw_parts(p, n) }; + let kind = if kind == 1 { wai_ops::vectors::Kind::Stage } else { wai_ops::vectors::Kind::Graph }; + let (status, out) = match std::str::from_utf8(input) { + Ok(text) => match wai_ops::vectors::recompute_file(kind, text) { + Ok(s) => (0, s.into_bytes()), + Err(e) => (1, e.into_bytes()), + }, + Err(_) => (1, b"not UTF-8".to_vec()), + }; + OUT.with(|o| *o.borrow_mut() = out); + status +} + +#[unsafe(no_mangle)] +pub extern "C" fn wai_ops_out_ptr() -> *const u8 { + OUT.with(|o| o.borrow().as_ptr()) +} + +#[unsafe(no_mangle)] +pub extern "C" fn wai_ops_out_len() -> usize { + OUT.with(|o| o.borrow().len()) +} diff --git a/wai/wai-ops/node/verify.mjs b/wai/wai-ops/node/verify.mjs new file mode 100644 index 000000000..fa3a90007 --- /dev/null +++ b/wai/wai-ops/node/verify.mjs @@ -0,0 +1,46 @@ +// Recompute every vector file of ops-conformance under this engine's WebAssembly, and +// require each to equal the committed file byte for byte. +// +// node verify.mjs +import { readFileSync, readdirSync } from 'node:fs'; +import { join } from 'node:path'; + +const [wasmPath, root] = process.argv.slice(2); +if (!wasmPath || !root) { + console.error('usage: node verify.mjs '); + process.exit(2); +} +const { instance } = await WebAssembly.instantiate(readFileSync(wasmPath), {}); +const x = instance.exports; + +function recompute(kind, bytes) { + const p = x.wai_ops_alloc(bytes.length); + new Uint8Array(x.memory.buffer, p, bytes.length).set(bytes); + const status = x.wai_ops_recompute(kind, p, bytes.length); + x.wai_ops_free(p, bytes.length); + const out = new Uint8Array(x.memory.buffer, x.wai_ops_out_ptr(), x.wai_ops_out_len()).slice(); + return { status, out }; +} + +let files = 0, vectors = 0, bad = 0; +for (const [dir, kind] of [['vectors', 0], ['stage', 1], ['graphs', 0]]) { + for (const f of readdirSync(join(root, dir)).filter((n) => n.endsWith('.jsonl')).sort()) { + const bytes = readFileSync(join(root, dir, f)); + files += 1; + vectors += bytes.reduce((n, b) => n + (b === 10 ? 1 : 0), 0); + const { status, out } = recompute(kind, bytes); + if (status !== 0) { + bad += 1; + console.error(`FAIL ${dir}/${f}: ${new TextDecoder().decode(out)}`); + } else if (Buffer.compare(Buffer.from(out), bytes) !== 0) { + bad += 1; + console.error(`DIFF ${dir}/${f}: the recomputed file differs`); + } + } +} +console.log(`recomputed ${files} vector files, ${vectors} vectors, under ${process.version}`); +if (bad > 0) { + console.error(`${bad} files failed`); + process.exit(1); +} +console.log('ops-conformance: every vector file verifies'); diff --git a/wai/wai-ops/src/bin/wai-ops.rs b/wai/wai-ops/src/bin/wai-ops.rs new file mode 100644 index 000000000..66d00f4c5 --- /dev/null +++ b/wai/wai-ops/src/bin/wai-ops.rs @@ -0,0 +1,225 @@ +//! `wai-ops`: the vector and lowering tool of `wai/ops-conformance/`. +//! +//! ```text +//! wai-ops vectors gen write every file of /ops-conformance +//! wai-ops vectors verify recompute every vector file and every lowered +//! graph, and regenerate the corpus; every byte +//! must equal the committed one +//! wai-ops vectors recompute recompute one vector file to stdout +//! ``` +//! +//! Exit status: 0 when everything verifies, 1 on a difference, 2 on a usage or I/O +//! error. + +use std::path::{Path, PathBuf}; +use std::process::ExitCode; + +use wai_ops::generate; +use wai_ops::json; +use wai_ops::vectors::{self, Kind}; + +fn usage() -> ExitCode { + eprintln!("usage: wai-ops vectors gen|verify | wai-ops vectors recompute "); + ExitCode::from(2) +} + +fn kind_of(rel: &str) -> Option { + if !rel.ends_with(".jsonl") { + return None; + } + if rel.starts_with("stage/") { Some(Kind::Stage) } else { Some(Kind::Graph) } +} + +fn reader(wai: &Path) -> impl Fn(&str) -> Option> + '_ { + move |p: &str| std::fs::read(wai.join(p)).ok() +} + +fn write_corpus(wai: &Path) -> ExitCode { + let read = reader(wai); + let (files, dropped) = match generate::all(&read) { + Ok(x) => x, + Err(e) => { + eprintln!("gen: {e}"); + return ExitCode::from(2); + } + }; + let root = wai.join("ops-conformance"); + for (rel, bytes) in &files { + let p = root.join(rel); + if let Some(d) = p.parent() { + std::fs::create_dir_all(d).expect("create directory"); + } + std::fs::write(&p, bytes).expect("write"); + } + let lines: usize = files.iter().filter(|(r, _)| r.ends_with(".jsonl")).map(|(_, b)| b.iter().filter(|&&c| c == b'\n').count()).sum(); + eprintln!("wrote {} files, {} vectors", files.len(), lines); + for (f, d) in dropped.iter().filter(|(_, d)| *d > 0) { + eprintln!(" {f}: {d} cases met no profile and were not written"); + } + ExitCode::SUCCESS +} + +fn list(dir: &Path, base: &Path, out: &mut Vec) { + let Ok(rd) = std::fs::read_dir(dir) else { return }; + let mut v: Vec = rd.filter_map(|e| e.ok().map(|e| e.path())).collect(); + v.sort(); + for p in v { + if p.is_dir() { + list(&p, base, out); + } else { + out.push(p.strip_prefix(base).expect("below base").to_path_buf()); + } + } +} + +fn verify(wai: &Path) -> ExitCode { + let root = wai.join("ops-conformance"); + let mut bad = 0usize; + // 1. Every committed vector file, recomputed from its inputs. + let mut committed = Vec::new(); + list(&root, &root, &mut committed); + let (mut nfiles, mut nlines) = (0usize, 0usize); + for rel in &committed { + let r = rel.to_string_lossy().replace('\\', "/"); + let Some(kind) = kind_of(&r) else { continue }; + let text = std::fs::read_to_string(root.join(rel)).expect("read"); + nfiles += 1; + nlines += text.lines().count(); + match vectors::recompute_file(kind, &text) { + Ok(out) if out == text => {} + Ok(out) => { + bad += 1; + let n = out.lines().zip(text.lines()).position(|(a, b)| a != b).map_or(out.lines().count().min(text.lines().count()), |n| n); + eprintln!("DIFF {r}: line {} differs when recomputed", n + 1); + } + Err(e) => { + bad += 1; + eprintln!("FAIL {r}: {e}"); + } + } + } + eprintln!("recomputed {nfiles} vector files, {nlines} vectors"); + // 2. Every lowered graph, from its set, as the index lists it. + match std::fs::read(root.join("certificates/index.json")) { + Ok(b) => match json::parse(&b) { + Ok(idx) => { + let read = reader(wai); + let mut n = 0; + for s in idx.get("sets").and_then(|v| v.as_arr()).unwrap_or(&[]) { + n += 1; + if let Err(e) = check_set(s, &read, wai) { + bad += 1; + eprintln!("FAIL lowering {}: {e}", s.get("name").and_then(|v| v.as_str()).unwrap_or("?")); + } + } + eprintln!("re-lowered {n} sets"); + } + Err(_) => { + bad += 1; + eprintln!("FAIL certificates/index.json does not parse"); + } + }, + Err(e) => { + bad += 1; + eprintln!("FAIL certificates/index.json: {e}"); + } + } + // 3. The generator writes the committed corpus again, byte for byte, and nothing + // is committed that it does not write (the README aside). + let read = reader(wai); + match generate::all(&read) { + Ok((files, _)) => { + let mut want: Vec = Vec::new(); + for (rel, bytes) in &files { + want.push(rel.clone()); + match std::fs::read(root.join(rel)) { + Ok(c) if &c == bytes => {} + Ok(_) => { + bad += 1; + eprintln!("DIFF {rel}: the generator writes other bytes"); + } + Err(_) => { + bad += 1; + eprintln!("MISSING {rel}"); + } + } + } + for rel in &committed { + let r = rel.to_string_lossy().replace('\\', "/"); + if r != "README.md" && r != ".gitattributes" && !want.contains(&r) { + bad += 1; + eprintln!("EXTRA {r}: committed, but the generator does not write it"); + } + } + eprintln!("regenerated {} files", files.len()); + } + Err(e) => { + bad += 1; + eprintln!("FAIL gen: {e}"); + } + } + if bad == 0 { + eprintln!("ops-conformance: every file verifies"); + ExitCode::SUCCESS + } else { + eprintln!("ops-conformance: {bad} failures"); + ExitCode::from(1) + } +} + +fn check_set(s: &json::Value, read: &dyn Fn(&str) -> Option>, wai: &Path) -> Result<(), String> { + use wai_ops::generate::sets; + let name = s.get("name").and_then(|v| v.as_str()).ok_or("name")?; + let cap = s.get("capability").and_then(|v| v.as_str()).ok_or("capability")?; + let lowering = s.get("lowering").and_then(|v| v.as_str()).ok_or("lowering")?; + let c = s.get("c").and_then(|v| v.as_dec()).and_then(|v| v.to_i64()).map(|v| v as u32); + let members: Vec<(String, String)> = s.get("members").and_then(|v| v.as_obj()).ok_or("members")? + .iter().map(|(k, v)| (k.clone(), v.as_str().unwrap_or("").to_string())).collect(); + let mut bytes = Vec::new(); + for (k, p) in &members { + bytes.push((k.clone(), read(p).ok_or_else(|| format!("cannot read {p}"))?)); + } + let m: Vec<(&str, Vec)> = bytes.iter().map(|(k, b)| (k.as_str(), b.clone())).collect(); + let digest = sets::set_digest(&m); + if Some(digest.as_str()) != s.get("set_sha256").and_then(|v| v.as_str()) { + return Err(format!("set digest {digest} differs")); + } + let leak = |x: &str| -> &'static str { Box::leak(x.to_string().into_boxed_str()) }; + let set = sets::Set { name: leak(name), capability: leak(cap), lowering: leak(lowering), members: vec![], c, note: "" }; + let l = sets::lower_set(&set, &m)?; + let paths: Vec<&str> = s.get("roles").and_then(|v| v.as_arr()).ok_or("roles")?.iter().filter_map(|v| v.as_str()).collect(); + if paths.len() != l.roles.len() { + return Err("the roles differ".into()); + } + for ((role, g), p) in l.roles.iter().zip(paths) { + let want = std::fs::read(wai.join(p)).map_err(|e| format!("{p}: {e}"))?; + if want != g.to_bytes() { + return Err(format!("{role}: the lowered graph differs from {p}")); + } + } + Ok(()) +} + +fn main() -> ExitCode { + let a: Vec = std::env::args().collect(); + match a.iter().map(String::as_str).collect::>().as_slice() { + [_, "vectors", "gen", dir] => write_corpus(Path::new(dir)), + [_, "vectors", "verify", dir] => verify(Path::new(dir)), + [_, "vectors", "recompute", file] => { + let p = Path::new(file); + let rel = p.to_string_lossy(); + let kind = if rel.contains("/stage/") || rel.starts_with("stage/") { Kind::Stage } else { Kind::Graph }; + match std::fs::read_to_string(p).map_err(|e| e.to_string()).and_then(|t| vectors::recompute_file(kind, &t)) { + Ok(out) => { + print!("{out}"); + ExitCode::SUCCESS + } + Err(e) => { + eprintln!("{e}"); + ExitCode::from(2) + } + } + } + _ => usage(), + } +} diff --git a/wai/wai-ops/src/code.rs b/wai/wai-ops/src/code.rs new file mode 100644 index 000000000..adfd43426 --- /dev/null +++ b/wai/wai-ops/src/code.rs @@ -0,0 +1,78 @@ +//! Refusal codes (§4, §5E) and where a refusal arose. + +/// The codes this extension gives. A capability whose payload extension reports one +/// code for every refusal maps these onto it (§4). +#[derive(Clone, Copy, Debug, PartialEq, Eq, PartialOrd, Ord)] +pub enum Code { + ModelMismatch, + TooLarge, + FieldRange, + GeometryMismatch, + StreamExhausted, + StreamNoncanonical, +} + +impl Code { + /// The code as the vector files write it. + pub fn as_str(self) -> &'static str { + match self { + Code::ModelMismatch => "model_mismatch", + Code::TooLarge => "too_large", + Code::FieldRange => "field_range", + Code::GeometryMismatch => "geometry_mismatch", + Code::StreamExhausted => "stream_exhausted", + Code::StreamNoncanonical => "stream_noncanonical", + } + } + + /// The inverse of [`Code::as_str`]. + pub fn parse(s: &str) -> Option { + Some(match s { + "model_mismatch" => Code::ModelMismatch, + "too_large" => Code::TooLarge, + "field_range" => Code::FieldRange, + "geometry_mismatch" => Code::GeometryMismatch, + "stream_exhausted" => Code::StreamExhausted, + "stream_noncanonical" => Code::StreamNoncanonical, + _ => return None, + }) + } +} + +/// Where a refusal arose. Informational: two conforming implementations agree on +/// the [`Code`], and on the symbol or element index a stage vector records. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum Site { + /// A load step of §10.5 (1–8). + Load(u8), + /// A graph input, by its position in the input table. + Input(u32), + /// A scalar, by its position in the scalar table. + Scalar(u32), + /// An operator, by its position in graph order. + Op(u32), + /// A stage operator's symbol or element, by its index. + Symbol(u64), + /// A stage operator's table, refused before any symbol. + Table(u32), + /// A lowering (§13). + Lowering, +} + +/// A refusal: its code and its site. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub struct Refusal { + pub code: Code, + pub at: Site, +} + +impl Refusal { + pub const fn new(code: Code, at: Site) -> Refusal { + Refusal { code, at } + } +} + +/// Shorthand for a load refusal at `step`. +pub(crate) const fn at_load(code: Code, step: u8) -> Refusal { + Refusal { code, at: Site::Load(step) } +} diff --git a/wai/wai-ops/src/generate/build.rs b/wai/wai-ops/src/generate/build.rs new file mode 100644 index 000000000..5f9cb079a --- /dev/null +++ b/wai/wai-ops/src/generate/build.rs @@ -0,0 +1,109 @@ +//! Building graphs and vector lines. + +use alloc::string::String; +use alloc::vec; +use alloc::vec::Vec; + +use super::tag; +use crate::graph::{self, CAP, Graph, InputRec, Op, Params, ScalarRec, TensorRec}; +use crate::interp::Tensor; +use crate::json::{self, C, hex}; +use crate::vectors; + +/// One decode of a vector: the data inputs and the scalars. +#[derive(Clone, Debug)] +pub struct Decode { + pub inputs: Vec, + pub scalars: Vec, +} + +impl Decode { + pub fn new(inputs: Vec, scalars: Vec) -> Decode { + Decode { inputs, scalars } + } +} + +/// An input: its record and its declared sizes. +pub struct In { + pub rec: TensorRec, + pub max_h: u32, + pub max_w: u32, +} + +/// A graph of one operator reading `ins` (tensors `0..n`) and writing `outs`. +pub fn one_op(code: u8, ins: Vec, outs: Vec, p: Params, scalars: Vec) -> Graph { + let n = ins.len() as u32; + let mut tensors = Vec::new(); + let mut inputs = Vec::new(); + for (i, x) in ins.into_iter().enumerate() { + tensors.push(x.rec); + inputs.push(InputRec { tensor: i as u32, max_h: x.max_h, max_w: x.max_w, kind: 0, src: 0, init: 0 }); + } + let no = outs.len() as u32; + tensors.extend(outs); + let op = Op { code, ins: (0..n).collect(), outs: (n..n + no).collect(), p }; + Graph { max_elems: CAP as u32, scalars, inputs, outputs: (n..n + no).collect(), tensors, ops: vec![op] } +} + +/// The profiles a graph's vector is tagged with: both when its load refuses (steps +/// 1–7 do not depend on a profile); otherwise those its verdict meets, or `None` when +/// it meets neither (a load-refusal vector of D2b, not written here). +pub fn profiles(bytes: &[u8]) -> Option> { + let g = match graph::Graph::parse(bytes) { + Ok(g) => g, + Err(_) => return Some(vec!["acc32", "acc64"]), + }; + let sizes = match crate::plan::load_plan(&g) { + Ok(s) => s, + Err(_) => return Some(vec!["acc32", "acc64"]), + }; + let v = tag::verdict(&g, sizes.as_deref()); + match (v.acc32, v.acc64) { + (true, true) => Some(vec!["acc32", "acc64"]), + (false, true) => Some(vec!["acc64"]), + _ => None, + } +} + +/// The vector line for a graph and its decodes, with `load` and every `expect` +/// computed by [`vectors::graph_line`]; `None` when the graph meets no profile. +pub fn line(id: &str, bytes: &[u8], decodes: &[Decode]) -> Option { + let prof = profiles(bytes)?; + let ds: Vec = decodes + .iter() + .map(|d| C::obj(vec![("inputs", C::Arr(d.inputs.iter().map(vectors::tensor_c).collect())), ("scalars", C::ints(d.scalars.iter().map(|&s| s as i128)))])) + .collect(); + let c = C::obj(vec![ + ("decodes", C::Arr(ds)), + ("graph", C::Str(hex(bytes))), + ("id", C::s(id)), + ("profiles", C::Arr(prof.iter().map(|p| C::s(p)).collect())), + ]); + let v = json::parse(json::to_string(&c).as_bytes()).ok()?; + let out = vectors::graph_line(&v).ok()?; + Some(json::to_string(&out)) +} + +/// A file of lines. +#[derive(Default)] +pub struct File { + pub lines: Vec, + pub dropped: usize, +} + +impl File { + pub fn push(&mut self, l: Option) { + match l { + Some(l) => self.lines.push(l), + None => self.dropped += 1, + } + } + pub fn text(&self) -> String { + let mut s = String::new(); + for l in &self.lines { + s.push_str(l); + s.push('\n'); + } + s + } +} diff --git a/wai/wai-ops/src/generate/graphs.rs b/wai/wai-ops/src/generate/graphs.rs new file mode 100644 index 000000000..b50936f24 --- /dev/null +++ b/wai/wai-ops/src/generate/graphs.rs @@ -0,0 +1,569 @@ +//! `graphs/.jsonl`: vectors whose graphs have several operators (§14.2). + +use alloc::format; +use alloc::string::String; +use alloc::vec; +use alloc::vec::Vec; + +use super::build::{Decode, File, line}; +use super::rng::Rng; +use crate::graph::{BiasKind, CAP, Graph, InputRec, Op, Operand, Params, Policy, Round, Rq, ScalarRec, TensorRec, opcode::*}; +use crate::interp::{Sequence, Tensor, run}; +use crate::lower::Lowered; + +fn der(c: u32) -> TensorRec { + TensorRec::derived(c) +} +fn chk(c: u32, lo: i64, hi: i64) -> TensorRec { + TensorRec::check(c, lo, hi) +} +fn data(t: u32, mh: u32, mw: u32) -> InputRec { + InputRec { tensor: t, max_h: mh, max_w: mw, kind: 0, src: 0, init: 0 } +} +fn op(code: u8, ins: Vec, outs: Vec, p: Params) -> Op { + Op { code, ins, outs, p } +} +fn tensor(r: &mut Rng, c: u32, h: u32, w: u32, lo: i64, hi: i64) -> Tensor { + Tensor::new(c, h, w, (0..c * h * w).map(|_| r.value(lo, hi)).collect()) +} + +struct G { + file: File, + name: &'static str, + n: usize, +} +impl G { + fn new(name: &'static str) -> G { + G { file: File::default(), name, n: 0 } + } + fn emit(&mut self, class: &str, b: &[u8], d: Vec) { + let id = format!("{}/{}/{}", self.name, class, self.n); + self.n += 1; + let l = line(&id, b, &d); + assert!(l.is_some(), "{id}: a graph vector meets no profile"); + self.file.push(l); + } +} + +// ------------------------------------------------------------------ phase form + +/// A transposed convolution, and the same computed as `s²` phase convolutions +/// interleaved by `pixel_shuffle` and cropped (§9.3): the graph's two outputs. Each +/// phase is a stride-1 convolution with the flipped sub-kernel of `T = ⌈k/s⌉` taps +/// (missing taps are zero weights), padded by `T − 1 + e`, where `e` extra rows and +/// columns cover output padding past what the phases reach. The crop starts at +/// `p + s·e` and takes the transposed convolution's output size, scalars 0 and 1. +fn phase_graph(cin: u32, cout: u32, k: u32, s: u32, p: u32, op_: u32, w: &[i64], rq: &Rq) -> Graph { + use crate::lower::Builder; + let t = k.div_ceil(s); + let e = (k + op_).saturating_sub(p + s * t).div_ceil(s); + let mut b = Builder::new(); + let x = b.input(chk(cin, -300, 300), 0, 0); + let (sh0, sw0) = (b.scalar(1, 64), b.scalar(1, 64)); + let ct = b.op(CONV_TRANSPOSE, vec![x], der(cout), Params::ConvT { cout, kh: k, kw: k, sh: s, sw: s, ph: p, pw: p, oph: op_, opw: op_, w_bytes: 2, rq: rq.clone(), w: w.to_vec() }); + let mut phases = Vec::new(); + for ry in 0..s { + for rx in 0..s { + let mut pw_ = vec![0i64; (cout * cin * t * t) as usize]; + for o in 0..cout { + for ci in 0..cin { + for i in 0..t { + for j in 0..t { + let (ki, kj) = (ry + s * (t - 1 - i), rx + s * (t - 1 - j)); + if ki < k && kj < k { + pw_[(((o * cin + ci) * t + i) * t + j) as usize] = w[(((ci * cout + o) * k + ki) * k + kj) as usize]; + } + } + } + } + } + let pad = t - 1 + e; + phases.push(b.op(CONV, vec![x], der(cout), Params::Conv { cout, kh: t, kw: t, sh: 1, sw: 1, ph: pad, pw: pad, groups: 1, w_bytes: 2, rq: rq.clone(), w: pw_ })); + } + } + // Channel o·s² + ρy·s + ρx, so that pixel_shuffle puts residue ρ at offset ρ. + let mut parts = Vec::new(); + for o in 0..cout { + for &ph in &phases { + parts.push(b.op(SPLIT, vec![ph], der(1), Params::Split { start: o, count: 1 })); + } + } + let cat = b.op(CONCAT, parts, der(cout * s * s), Params::None); + let shuf = b.op(PIXEL_SHUFFLE, vec![cat], der(cout), Params::Shuffle { r: s }); + let y0 = p + s * e; + let out = b.op(CROP, vec![shuf], der(cout), Params::Crop { y0: Operand::Const(y0), x0: Operand::Const(y0), h: Operand::Scalar(sh0), w: Operand::Scalar(sw0) }); + b.finish(vec![ct, out]).expect("the phase graph loads") +} + +fn phase() -> File { + let mut g = G::new("phase"); + let mut r = Rng::named("phase"); + for (cin, cout, k, s, p, op_) in [(1u32, 1u32, 3u32, 2u32, 1u32, 1u32), (2, 2, 4, 2, 1, 0), (1, 2, 5, 3, 2, 1), (2, 1, 2, 2, 0, 0), (1, 1, 3, 3, 0, 2), (2, 3, 3, 2, 1, 1)] { + let w: Vec = (0..cin * cout * k * k).map(|_| r.int(-300, 300)).collect(); + let rq = Rq { round: Round::HalfUp, bias_kind: BiasKind::Post, m_kind: 2, s_kind: 1, m_bytes: 4, b_bytes: 4, mult: (0..cout).map(|_| r.int(1, 99)).collect(), shift: vec![6], bias: (0..cout).map(|_| r.int(-9, 9)).collect() }; + let gr = phase_graph(cin, cout, k, s, p, op_, &w, &rq); + for (h, wd) in [(1u32, 1u32), (2, 3), (3, 2)] { + let ho = (h - 1) * s + k + op_ - 2 * p; + let wo = (wd - 1) * s + k + op_ - 2 * p; + let x = tensor(&mut r, cin, h, wd, -300, 300); + let d = vec![Decode::new(vec![x.clone()], vec![ho as i64, wo as i64])]; + // The two outputs agree, or the phase form is not what §9.3 says. + let b = gr.to_bytes(); + let gl = crate::graph::load(&b).expect("phase graph loads"); + match run(&gl, &[x], &[ho as i64, wo as i64], &mut Sequence::new()) { + Ok(outs) => assert_eq!(outs[0], outs[1], "the phase form differs from conv_transpose"), + Err(e) => panic!("phase graph refused: {e:?}"), + } + g.emit("phase", &b, d); + } + } + g.file +} + +// ------------------------------------------------------------------ state + +fn state() -> File { + let mut g = G::new("state"); + let mut r = Rng::named("state"); + // x + s, where s starts at init and then carries the previous decode's output. + let base = |init: i64, lo: i64, hi: i64, out: TensorRec| Graph { + max_elems: CAP as u32, + scalars: vec![], + inputs: vec![data(0, 0, 0), InputRec { tensor: 1, max_h: 0, max_w: 0, kind: 1, src: 0, init }], + outputs: vec![2], + tensors: vec![chk(2, -100, 100), chk(2, lo, hi), out], + ops: vec![op(ADD, vec![0, 1], vec![2], Params::None)], + }; + let three = |r: &mut Rng, h: u32, w: u32| (0..3).map(|_| Decode::new(vec![tensor(r, 2, h, w, -100, 100)], vec![])).collect::>(); + g.emit("carry", &base(5, -1000, 1000, der(2)).to_bytes(), three(&mut r, 2, 3)); + g.emit("carry", &base(-7, -1000, 1000, TensorRec::clamp(2, -150, 150)).to_bytes(), three(&mut r, 1, 4)); + // The state's check range refuses on the third decode. + let gr = base(0, -150, 150, der(2)); + let d = vec![ + Decode::new(vec![Tensor::filled(2, 1, 2, 100)], vec![]), + Decode::new(vec![Tensor::filled(2, 1, 2, 60)], vec![]), + Decode::new(vec![Tensor::filled(2, 1, 2, 1)], vec![]), + ]; + g.emit("check", &gr.to_bytes(), d); + // Input 0's size changes: the carried state no longer matches (geometry_mismatch). + let d = vec![Decode::new(vec![Tensor::filled(2, 2, 2, 1)], vec![]), Decode::new(vec![Tensor::filled(2, 2, 3, 1)], vec![])]; + g.emit("shape", &base(1, -1000, 1000, der(2)).to_bytes(), d); + // A recurrent pair: s' = clamp(s + x) feeds both a second output and the state. + let gr = Graph { + max_elems: CAP as u32, + scalars: vec![], + inputs: vec![data(0, 0, 0), InputRec { tensor: 1, max_h: 0, max_w: 0, kind: 1, src: 1, init: 3 }], + outputs: vec![3, 2], + tensors: vec![chk(1, -50, 50), chk(1, -400, 400), TensorRec::clamp(1, -400, 400), der(1)], + ops: vec![op(ADD, vec![0, 1], vec![2], Params::None), op(RESCALE, vec![2], vec![3], Params::Rescale(Rq::plain(Round::HalfEven, 1)))], + }; + g.emit("carry", &gr.to_bytes(), (0..3).map(|_| Decode::new(vec![tensor(&mut r, 1, 3, 3, -50, 50)], vec![])).collect()); + // A refused decode changes no state (§10.3). The second decode is refused by the + // output's check range after the add has run; the third reads the first's output, + // not the refused sum and not `init`. + let gr = base(0, -1000, 1000, chk(2, -50, 50)); + let d = vec![ + Decode::new(vec![Tensor::filled(2, 1, 2, 5)], vec![]), + Decode::new(vec![Tensor::filled(2, 1, 2, 100)], vec![]), + Decode::new(vec![Tensor::filled(2, 1, 2, 1)], vec![]), + ]; + g.emit("refused", &gr.to_bytes(), d); + // Until a decode completes, a state input is `init`, sized by input 0 of the decode + // being made: the first decode is refused (an input past its range), and the + // second, at another size, reads `init`. + let gr = base(7, -1000, 1000, der(2)); + let d = vec![Decode::new(vec![Tensor::filled(2, 1, 2, 101)], vec![]), Decode::new(vec![Tensor::filled(2, 2, 3, 1)], vec![])]; + g.emit("refused", &gr.to_bytes(), d); + g.file +} + +// ------------------------------------------------------------------ load refusals + +/// The base graph the load refusals change: `x → conv → relu`, with a scalar. +fn load_base() -> Graph { + Graph { + max_elems: CAP as u32, + scalars: vec![ScalarRec { lo: 0, hi: 3 }], + inputs: vec![data(0, 0, 0)], + outputs: vec![2], + tensors: vec![chk(2, -9, 9), der(3), der(3)], + ops: vec![ + op(CONV, vec![0], vec![1], Params::Conv { cout: 3, kh: 1, kw: 1, sh: 1, sw: 1, ph: 0, pw: 0, groups: 1, w_bytes: 1, rq: Rq::plain(Round::Floor, 1), w: vec![1, 2, 3, 4, 5, 6] }), + op(RELU, vec![1], vec![2], Params::None), + ], + } +} + +fn load_refusals() -> File { + let mut g = G::new("load"); + let ok = load_base(); + let b = ok.to_bytes(); + g.emit("ok", &b, vec![Decode::new(vec![Tensor::new(2, 1, 2, vec![-9, 9, 3, -3])], vec![0])]); + let patch = |off: usize, v: &[u8]| { + let mut x = b.clone(); + x[off..off + v.len()].copy_from_slice(v); + x + }; + // Step 1: magic, version, operator set, too short. + for x in [patch(0, b"WHG2"), patch(4, &[2]), patch(5, &[2]), b[..31].to_vec()] { + g.emit("step1", &x, vec![]); + } + // Step 2: the header's reserved fields. + for off in [6usize, 22, 28] { + g.emit("step2", &patch(off, &[1]), vec![]); + } + // Step 3: counts against the file's length; zero counts; max_elems. + for (off, v) in [(8usize, 0u32), (12, 0), (8, 1000), (12, 1000), (24, 0), (24, (1 << 26) + 1)] { + g.emit("step3", &patch(off, &v.to_le_bytes()), vec![]); + } + for (off, v) in [(16usize, 0u16), (18, 0), (20, 9)] { + g.emit("step3", &patch(off, &v.to_le_bytes()), vec![]); + } + let mut longer = b.clone(); + longer.push(0); + g.emit("step5", &longer, vec![]); + // Steps 4–7, built from changed graphs. + let mut cases: Vec<(&str, Graph)> = Vec::new(); + let mut m = ok.clone(); + m.scalars[0] = ScalarRec { lo: 4, hi: 3 }; + cases.push(("step4", m)); + let mut m = ok.clone(); + m.inputs[0].max_h = 3; + cases.push(("step4", m)); + let mut m = ok.clone(); + m.inputs[0].kind = 2; + cases.push(("step4", m)); + let mut m = ok.clone(); + m.inputs[0].src = 1; + cases.push(("step4", m)); + let mut m = ok.clone(); + m.inputs[0].tensor = 3; + cases.push(("step4", m)); + let mut m = ok.clone(); + m.outputs[0] = 3; + cases.push(("step4", m)); + let mut m = ok.clone(); + m.tensors[1].channels = 0; + cases.push(("step4", m)); + let mut m = ok.clone(); + m.tensors[1].lo = 1; + cases.push(("step4", m)); + let mut m = ok.clone(); + m.tensors[0].lo = 10; + cases.push(("step4", m)); + // Step 5: opcodes, arity, parameter ranges. + let mut m = ok.clone(); + m.ops[1].code = 0x1F; + cases.push(("step5", m)); + let mut m = ok.clone(); + m.ops[1].code = 0x00; + cases.push(("step5", m)); + let mut m = ok.clone(); + m.ops[1].ins.push(0); + cases.push(("step5", m)); + let mut m = ok.clone(); + m.ops[0].ins[0] = 9; + cases.push(("step5", m)); + for f in [ + |p: &mut Params| if let Params::Conv { cout, .. } = p { *cout = 0 }, + |p: &mut Params| if let Params::Conv { w_bytes, .. } = p { *w_bytes = 3 }, + |p: &mut Params| if let Params::Conv { groups, .. } = p { *groups = 3 }, + |p: &mut Params| if let Params::Conv { kh, .. } = p { *kh = 0 }, + |p: &mut Params| if let Params::Conv { rq, .. } = p { rq.shift[0] = 128 }, + |p: &mut Params| if let Params::Conv { rq, .. } = p { rq.m_kind = 3 }, + |p: &mut Params| if let Params::Conv { rq, .. } = p { rq.s_kind = 0 }, + |p: &mut Params| if let Params::Conv { rq, .. } = p { rq.m_kind = 1; rq.m_bytes = 2; rq.mult = vec![1] }, + |p: &mut Params| if let Params::Conv { rq, .. } = p { rq.bias_kind = BiasKind::Pre; rq.b_bytes = 0 }, + ] { + let mut m = ok.clone(); + f(&mut m.ops[0].p); + cases.push(("step5", m)); + } + // Step 6: the graph rules. + let mut m = ok.clone(); + m.ops[1].outs[0] = 0; + cases.push(("step6", m)); + let mut m = ok.clone(); + m.tensors.push(der(1)); + cases.push(("step6", m)); + let mut m = ok.clone(); + m.ops.swap(0, 1); + cases.push(("step6", m)); + let mut m = ok.clone(); + m.tensors[0].policy = Policy::Derived; + m.tensors[0].lo = 0; + m.tensors[0].hi = 0; + cases.push(("step6", m)); + let mut m = ok.clone(); + m.tensors[1].channels = 2; + cases.push(("step6", m)); + // A state input whose source has other channels, one whose init is out of its + // range, input 0 of kind state, and inputs that disagree on declaring sizes. + let st = |src: u32, init: i64, ch: u32, first_state: bool, sizes: (u32, u32)| -> Graph { + let mut m = ok.clone(); + m.tensors.push(chk(ch, -5, 5)); + let s = InputRec { tensor: 3, max_h: sizes.0, max_w: sizes.1, kind: 1, src, init }; + if first_state { + m.inputs.insert(0, s); + } else { + m.inputs.push(s); + } + m.ops.push(op(ADD, vec![2, 3], vec![4], Params::None)); + m.tensors.push(der(3)); + m + }; + cases.push(("step6", st(1, 0, 3, false, (0, 0)))); + cases.push(("step6", st(0, 0, 2, false, (0, 0)))); + cases.push(("step6", st(0, 6, 3, false, (0, 0)))); + cases.push(("step6", st(0, 0, 3, true, (0, 0)))); + cases.push(("step6", st(0, 0, 3, false, (2, 2)))); + let mut m = ok.clone(); + m.tensors[1].exponent = 3; + m.tensors[2].exponent = 3; + m.ops.push(op(ADD, vec![1, 2], vec![3], Params::None)); + m.tensors.push(der(3)); + cases.push(("step6", m)); + // Step 7: an input past max_elems; a kernel that cannot fit the declared sizes; a + // gram, l2norm or softmax whose inputs declare no sizes; an output past max_elems. + let mut m = ok.clone(); + m.inputs[0].max_h = 4; + m.inputs[0].max_w = 4; + m.max_elems = 31; + cases.push(("step7", m)); + let mut m = ok.clone(); + m.inputs[0].max_h = 1; + m.inputs[0].max_w = 1; + if let Params::Conv { kh, w, .. } = &mut m.ops[0].p { + *kh = 2; + *w = vec![1; 12]; + } + cases.push(("step7", m)); + let mut m = ok.clone(); + m.ops[1] = op(SOFTMAX, vec![1], vec![2], Params::None); + cases.push(("step7", m)); + let mut m = ok.clone(); + m.inputs[0].max_h = 3; + m.inputs[0].max_w = 3; + m.max_elems = 20; + cases.push(("step7", m)); + for (class, m) in cases { + g.emit(class, &m.to_bytes(), vec![]); + } + // Step 5: chscale's m_bytes and layernorm_c's g_bytes are 4 or 8 (§10.4). Width 2 + // is refused at load; width 4 loads and decodes. + let one_in = |scalars: Vec, o: Op| Graph { + max_elems: CAP as u32, + scalars, + inputs: vec![data(0, 0, 0)], + outputs: vec![1], + tensors: vec![chk(2, -9, 9), der(2)], + ops: vec![o], + }; + let x = || Tensor::new(2, 1, 2, vec![-9, 9, 3, -4]); + for m_bytes in [2u8, 4] { + let p = Params::Chscale { scalar: 0, n_rows: 2, round: Round::Floor, m_bytes, shift: 1, m: vec![1, -2, 3, -4] }; + let gr = one_in(vec![ScalarRec { lo: 0, hi: 1 }], op(CHSCALE, vec![0], vec![1], p)); + g.emit("step5", &gr.to_bytes(), vec![Decode::new(vec![x()], vec![1])]); + } + for g_bytes in [2u8, 4] { + let p = Params::LayerNorm { eps: 1, g_bytes, gamma: vec![1, -1], beta: vec![0, 0] }; + let gr = one_in(vec![], op(LAYERNORM_C, vec![0], vec![1], p)); + g.emit("step5", &gr.to_bytes(), vec![Decode::new(vec![x()], vec![])]); + } + // A crop's sides planned at load from the smallest start (§5.10): a scalar start + // is planned at 0 whatever its range, so a length of the whole side loads, and + // every decode, whose start is at least 1, is refused at run. + let gr = Graph { + max_elems: CAP as u32, + scalars: vec![ScalarRec { lo: 1, hi: 3 }], + inputs: vec![data(0, 4, 4)], + outputs: vec![1], + tensors: vec![chk(1, -9, 9), der(1)], + ops: vec![op(CROP, vec![0], vec![1], Params::Crop { y0: Operand::Scalar(0), x0: Operand::Const(0), h: Operand::Const(4), w: Operand::Const(1) })], + }; + g.emit("crop_plan", &gr.to_bytes(), vec![Decode::new(vec![Tensor::filled(1, 4, 4, 2)], vec![1]), Decode::new(vec![Tensor::filled(1, 4, 4, 2)], vec![3])]); + g.file +} + +// ------------------------------------------------------------------ run refusals + +fn run_refusals() -> File { + let mut g = G::new("run"); + // x [2, ≤4, ≤4] check [−9, 9] → crop by scalars (h, w) → pixel_shuffle? no: the crop + // feeds a check tensor; a second input y must share x's size (add). + let gr = Graph { + max_elems: 48, + scalars: vec![ScalarRec { lo: 1, hi: 4 }, ScalarRec { lo: 1, hi: 4 }], + inputs: vec![data(0, 4, 4), data(1, 4, 4)], + outputs: vec![4], + tensors: vec![chk(2, -9, 9), chk(2, -9, 9), der(2), der(2), chk(2, -10, 10)], + ops: vec![ + op(ADD, vec![0, 1], vec![2], Params::None), + op(CROP, vec![2], vec![3], Params::Crop { y0: Operand::Const(0), x0: Operand::Const(0), h: Operand::Scalar(0), w: Operand::Scalar(1) }), + op(RELU, vec![3], vec![4], Params::None), + ], + }; + let b = gr.to_bytes(); + let t = |c: u32, h: u32, w: u32, v: i64| Tensor::filled(c, h, w, v); + let cases: Vec<(&str, Vec, Vec)> = vec![ + ("ok", vec![t(2, 3, 3, 4), t(2, 3, 3, -2)], vec![2, 3]), + // Step 1: the channel count, then the declared sizes, then max_elems. + ("step1", vec![t(3, 3, 3, 1), t(2, 3, 3, 1)], vec![1, 1]), + ("step1", vec![t(2, 5, 3, 1), t(2, 5, 3, 1)], vec![1, 1]), + ("step1", vec![t(2, 4, 4, 1), t(2, 4, 4, 1)], vec![1, 1]), + // A size past the caps comes before a scalar out of its range. + ("order", vec![t(2, 5, 1, 1), t(2, 5, 1, 1)], vec![9, 9]), + // Step 2: each scalar against its range. + ("step2", vec![t(2, 3, 3, 1), t(2, 3, 3, 1)], vec![0, 1]), + ("step2", vec![t(2, 3, 3, 1), t(2, 3, 3, 1)], vec![1, 5]), + // A scalar out of its range comes before a shape that disagrees. + ("order", vec![t(2, 3, 3, 1), t(2, 2, 3, 1)], vec![0, 1]), + // Step 3: shapes that disagree; a crop that does not fit. + ("step3", vec![t(2, 3, 3, 1), t(2, 2, 3, 1)], vec![1, 1]), + ("step3", vec![t(2, 2, 3, 1), t(2, 2, 3, 1)], vec![3, 1]), + // A shape refused at run comes before a value out of a check range. + ("order", vec![t(2, 2, 3, 99), t(2, 2, 3, 1)], vec![3, 1]), + // Step 4: an input out of its check range; an output out of its check range. + ("step4", vec![t(2, 3, 3, 10), t(2, 3, 3, 0)], vec![1, 1]), + ("step4", vec![t(2, 3, 3, 9), t(2, 3, 3, 9)], vec![1, 1]), + ("step4", vec![t(2, 3, 3, 5), t(2, 3, 3, 5)], vec![1, 1]), + ]; + for (class, ins, sc) in cases { + g.emit(class, &b, vec![Decode::new(ins, sc)]); + } + // An input past max_elems at run time: reachable only when the inputs declare no + // sizes (declared sizes are checked against max_elems at load). + let gr = Graph { + max_elems: 16, + scalars: vec![], + inputs: vec![data(0, 0, 0)], + outputs: vec![1], + tensors: vec![chk(2, -9, 9), der(2)], + ops: vec![op(RELU, vec![0], vec![1], Params::None)], + }; + g.emit("step1", &gr.to_bytes(), vec![Decode::new(vec![t(2, 2, 4, 1)], vec![]), Decode::new(vec![t(2, 3, 3, 1)], vec![])]); + // A size an operator forms past the cap at run time: pixel_shuffle into a tensor + // past max_elems, on inputs that declare no sizes. + let gr = Graph { + max_elems: 64, + scalars: vec![], + inputs: vec![data(0, 0, 0)], + outputs: vec![1], + tensors: vec![chk(4, -9, 9), der(1)], + ops: vec![op(PIXEL_SHUFFLE, vec![0], vec![1], Params::Shuffle { r: 2 })], + }; + g.emit("step3", &gr.to_bytes(), vec![Decode::new(vec![t(4, 4, 4, 1)], vec![]), Decode::new(vec![t(4, 4, 5, 1)], vec![])]); + // Step 1 runs input by input (§4): input 0 is past its declared size and input 1 + // has the wrong channel count, so the decode is too_large, not model_mismatch. + // An input with a dimension of 0 is model_mismatch. + let gr = Graph { + max_elems: CAP as u32, + scalars: vec![], + inputs: vec![data(0, 2, 2), data(1, 2, 2)], + outputs: vec![2], + tensors: vec![chk(1, -9, 9), chk(1, -9, 9), der(1)], + ops: vec![op(ADD, vec![0, 1], vec![2], Params::None)], + }; + g.emit("step1", &gr.to_bytes(), vec![Decode::new(vec![t(1, 3, 3, 1), t(2, 2, 2, 1)], vec![])]); + g.emit("step1", &gr.to_bytes(), vec![Decode::new(vec![t(1, 2, 2, 1), Tensor::new(1, 0, 2, vec![])], vec![])]); + // conv_transpose at run step 3 (§4, §5.3), on a [1, 1, 1] input: kh = kw = 1, + // stride 1, so the uncropped sides are 1 + oph and 1 + opw. + let ct = |cout: u32, ph: u32, pw: u32, oph: u32, opw: u32| Graph { + max_elems: CAP as u32, + scalars: vec![], + inputs: vec![data(0, 0, 0)], + outputs: vec![1], + tensors: vec![chk(1, -9, 9), der(cout)], + ops: vec![op(CONV_TRANSPOSE, vec![0], vec![1], Params::ConvT { cout, kh: 1, kw: 1, sh: 1, sw: 1, ph, pw, oph, opw, w_bytes: 1, rq: Rq::plain(Round::Floor, 0), w: vec![1; cout as usize] })], + }; + let x1 = || vec![Decode::new(vec![t(1, 1, 1, 3)], vec![])]; + // The uncropped output is cout planes: 3·5001·5001 passes 2^26 though one plane + // does not, and the cropped output is [3, 1, 1]. + g.emit("caps", &ct(3, 2500, 2500, 5000, 5000).to_bytes(), x1()); + // The same planes with a crop that leaves nothing: the cap comes before the shape. + g.emit("caps", &ct(3, 2501, 2501, 5000, 5000).to_bytes(), x1()); + // An uncropped side past 2^26 with a crop that leaves nothing: the side's cap + // comes before the shape. + g.emit("caps", &ct(1, 1 << 26, 0, (1 << 26) + 10, 0).to_bytes(), x1()); + // One plane within the cap decodes. + g.emit("caps", &ct(1, 2500, 2500, 5000, 5000).to_bytes(), x1()); + // The shape rule comes before max_elems: crop_to with a ref larger than its input, + // whose output would also pass max_elems, is geometry_mismatch. + let gr = Graph { + max_elems: 64, + scalars: vec![], + inputs: vec![data(0, 0, 0), data(1, 0, 0)], + outputs: vec![2], + tensors: vec![chk(4, -9, 9), chk(1, -9, 9), der(4)], + ops: vec![op(CROP_TO, vec![0, 1], vec![2], Params::None)], + }; + g.emit("order", &gr.to_bytes(), vec![Decode::new(vec![t(4, 2, 2, 1), t(1, 8, 8, 1)], vec![])]); + // A crop's start is at least 0 (§5.10): a scalar start of −1 is refused, though + // −1 + h ≤ H; a start of 2 decodes. + let gr = Graph { + max_elems: CAP as u32, + scalars: vec![ScalarRec { lo: -4, hi: 4 }], + inputs: vec![data(0, 0, 0)], + outputs: vec![1], + tensors: vec![chk(1, -9, 9), der(1)], + ops: vec![op(CROP, vec![0], vec![1], Params::Crop { y0: Operand::Scalar(0), x0: Operand::Const(0), h: Operand::Const(1), w: Operand::Const(1) })], + }; + let x = Tensor::new(1, 3, 3, vec![1, 2, 3, 4, 5, 6, 7, 8, 9]); + g.emit("step3", &gr.to_bytes(), vec![Decode::new(vec![x.clone()], vec![-1]), Decode::new(vec![x], vec![2])]); + g.file +} + +// ------------------------------------------------------------------ lowerings + +/// Vectors for each lowered graph of the small sets. +pub fn lowered(name: &'static str, set: &str, l: &Lowered) -> File { + let mut g = G::new(name); + let mut r = Rng::named(name); + for (role, gr) in &l.roles { + let b = gr.to_bytes(); + let i0 = gr.inputs[0]; + let rec = gr.tensors[i0.tensor as usize]; + let c = rec.channels; + let (lo, hi) = (rec.lo.max(-300), rec.hi.min(300)); + let mut ds = Vec::new(); + let sizes: Vec<(u32, u32)> = if i0.max_h != 0 { vec![(i0.max_h, i0.max_w)] } else { vec![(2, 2), (3, 2), (1, 4)] }; + for (h, w) in sizes { + let x = tensor(&mut r, c, h, w, lo, hi); + let sc: Vec = gr.scalars.iter().map(|s| r.int(s.lo.max(-(1 << 13)), s.hi.min(1 << 13)).max(s.lo)).collect(); + // The PCM step's scalar is t_out: a length the synthesis can give. + let sc = if *role == "g_s" && name == "lower_wam1" { vec![(w as i64 * 2).min(4)] } else { sc }; + ds.push(Decode::new(vec![x], sc)); + } + g.emit(&format!("{set}/{role}"), &b, ds); + // A value one past the input's check range. + if rec.policy == Policy::Check && rec.hi < i64::MAX { + let (h, w) = if i0.max_h != 0 { (i0.max_h, i0.max_w) } else { (2, 2) }; + let mut x = tensor(&mut r, c, h, w, lo, hi); + x.data[0] = rec.hi + 1; + let sc: Vec = gr.scalars.iter().map(|s| s.lo.max(0).min(s.hi).max(s.lo)).collect(); + let sc = if *role == "g_s" && name == "lower_wam1" { vec![1] } else { sc }; + g.emit(&format!("{set}/{role}/range"), &b, vec![Decode::new(vec![x], sc)]); + } + } + // The PCM step's t_out at its range's lower end and past the synthesis's length. + if name == "lower_wam1" + && let Some((_, gr)) = l.roles.first() + { + let b = gr.to_bytes(); + let c = gr.tensors[0].channels; + let x = tensor(&mut r, c, 1, 3, -300, 300); + for t_out in [1i64, 6, 7, 0] { + g.emit(&format!("{set}/g_s/t_out"), &b, vec![Decode::new(vec![x.clone()], vec![t_out])]); + } + } + g.file +} + +/// Every graph file other than the lowerings': `(name, text, dropped)`. +pub fn all() -> Vec<(&'static str, String, usize)> { + let mut v = Vec::new(); + for (n, f) in [("phase", phase()), ("state", state()), ("load", load_refusals()), ("run", run_refusals())] { + v.push((n, f.text(), f.dropped)); + } + v +} diff --git a/wai/wai-ops/src/generate/mod.rs b/wai/wai-ops/src/generate/mod.rs new file mode 100644 index 000000000..520170871 --- /dev/null +++ b/wai/wai-ops/src/generate/mod.rs @@ -0,0 +1,84 @@ +//! The generator of `../ops-conformance/` (§14.1, Part 3.1 of the design). +//! +//! It writes every vector file, inputs and expectations alike, from one input corpus +//! that only it generates: its case lists and its SplitMix64 sequences (one seed per +//! file) are its own, and nothing outside it needs to know them. The committed files +//! are the artifact. An independent checker recomputes every `load` and `expect` from +//! the graph bytes and the inputs, and every lowered graph from its set, and must +//! write the same bytes. +//! +//! It runs the interpreter, so it needs the `uncertified` feature, as every decode +//! does before the certifier exists. + +use alloc::format; +use alloc::string::String; +use alloc::vec::Vec; + +pub mod build; +pub mod graphs; +pub mod ops; +pub mod rng; +pub mod sets; +pub mod stages; +pub mod tag; + +/// Every file of `ops-conformance/` the generator writes, by its path below +/// `ops-conformance/`, with the count of cases dropped because they met no profile. +/// `read` reads a committed file by its path below `wai/`. +pub fn all(read: &dyn Fn(&str) -> Option>) -> Result<(Vec<(String, Vec)>, Vec<(String, usize)>), String> { + let mut files: Vec<(String, Vec)> = Vec::new(); + let mut dropped: Vec<(String, usize)> = Vec::new(); + for (name, text, d) in ops::all() { + files.push((format!("vectors/{name}.jsonl"), text.into_bytes())); + dropped.push((format!("vectors/{name}"), d)); + } + for (name, text, d) in stages::all() { + files.push((format!("stage/{name}.jsonl"), text.into_bytes())); + dropped.push((format!("stage/{name}"), d)); + } + for (name, text, d) in graphs::all() { + files.push((format!("graphs/{name}.jsonl"), text.into_bytes())); + dropped.push((format!("graphs/{name}"), d)); + } + // The lowerings: every committed set, then the synthetic sets, whose members are + // written under sets/ and whose lowered graphs also get graph vectors. + let mut index = Vec::new(); + for s in sets::committed() { + let mut m = Vec::new(); + for (n, p) in &s.members { + m.push((*n, read(p).ok_or_else(|| format!("cannot read {p}"))?)); + } + let l = sets::lower_set(&s, &m)?; + let digest = sets::set_digest(&m); + let roles: Vec<&str> = l.roles.iter().map(|(r, _)| *r).collect(); + for (r, g) in &l.roles { + files.push((format!("certificates/{}/{digest}/{r}.whg1", s.capability), g.to_bytes())); + } + index.push(sets::index_entry(&s, &digest, &roles, false)); + } + for (s, m) in sets::synthetic() { + for (n, b) in &m { + files.push((format!("sets/{}/{n}", s.name), b.clone())); + } + let l = sets::lower_set(&s, &m)?; + let digest = sets::set_digest(&m); + let roles: Vec<&str> = l.roles.iter().map(|(r, _)| *r).collect(); + for (r, g) in &l.roles { + files.push((format!("certificates/{}/{digest}/{r}.whg1", s.capability), g.to_bytes())); + } + index.push(sets::index_entry(&s, &digest, &roles, true)); + let vname: &'static str = match s.lowering { + "int_synth/1" => "lower_int_synth", + "whm1/1" => "lower_whm1", + "wam1/1" => "lower_wam1", + "wmb1/1" => "lower_wmb1", + "world/1" => "lower_world", + _ => "lower_jpegai", + }; + let f = graphs::lowered(vname, s.name, &l); + files.push((format!("graphs/{vname}.jsonl"), f.text().into_bytes())); + dropped.push((format!("graphs/{vname}"), f.dropped)); + } + files.push((String::from("certificates/index.json"), sets::index_json(index))); + Ok((files, dropped)) +} diff --git a/wai/wai-ops/src/generate/ops.rs b/wai/wai-ops/src/generate/ops.rs new file mode 100644 index 000000000..18841e009 --- /dev/null +++ b/wai/wai-ops/src/generate/ops.rs @@ -0,0 +1,1003 @@ +//! `vectors/.jsonl`: one-operator graph vectors (§14.1). + +use alloc::format; +use alloc::string::String; +use alloc::vec; +use alloc::vec::Vec; + +use super::build::{Decode, File, In, line, one_op}; +use super::rng::Rng; +use crate::graph::{BiasKind, Graph, Operand, Params, Round, Rq, ScalarRec, TensorRec, opcode::*, smallest_width}; +use crate::interp::Tensor; + +const I32MIN: i64 = i32::MIN as i64; +const I32MAX: i64 = i32::MAX as i64; + +// ------------------------------------------------------------------ helpers + +fn chk(c: u32, lo: i64, hi: i64) -> TensorRec { + TensorRec::check(c, lo, hi) +} +fn der(c: u32) -> TensorRec { + TensorRec::derived(c) +} +fn inp(rec: TensorRec, max_h: u32, max_w: u32) -> In { + In { rec, max_h, max_w } +} +fn tensor(r: &mut Rng, c: u32, h: u32, w: u32, lo: i64, hi: i64) -> Tensor { + let n = (c * h * w) as usize; + Tensor::new(c, h, w, (0..n).map(|_| r.value(lo, hi)).collect()) +} +fn konst(c: u32, h: u32, w: u32, v: i64) -> Tensor { + Tensor::filled(c, h, w, v) +} + +/// A declared input range, from one of several magnitudes. +fn range(r: &mut Rng) -> (i64, i64) { + match r.below(9) { + 0 | 1 => (-r.int(0, 300), r.int(0, 300)), + 2 => (-128, 127), + 3 => (0, r.int(0, 255)), + 4 => (-r.int(1, 1 << 20), r.int(1, 1 << 20)), + 5 => (-r.int(1 << 20, 1 << 30), r.int(1 << 20, 1 << 30)), + 6 => (I32MIN, I32MAX), + 7 => (-r.int(1 << 32, 1 << 50), r.int(1 << 32, 1 << 50)), + _ => { + let lo = r.int(-300, 300); + (lo, lo + r.int(0, 50)) + } + } +} + +/// A weight magnitude. +fn wmag(r: &mut Rng) -> i64 { + r.pick(&[1, 2, 3, 7, 127, 255, 1 << 15, (1 << 15) - 1, 1 << 20, I32MAX]) +} + +fn shift_pick(r: &mut Rng) -> u8 { + r.pick(&[0, 0, 1, 1, 2, 3, 4, 5, 6, 8, 10, 12, 16, 20, 31, 32, 33, 40, 62, 63, 64, 100, 127]) +} + +/// A random `RQ` over `c` channels. +fn rq(r: &mut Rng, c: usize, bias: bool, mult: bool) -> Rq { + let round = r.pick(&[Round::Floor, Round::HalfUp, Round::HalfEven]); + let bias_kind = if bias { r.pick(&[BiasKind::None, BiasKind::Pre, BiasKind::Post]) } else { BiasKind::None }; + let m_kind = if mult { r.u(0, 2) as u8 } else { 0 }; + let s_kind = r.u(1, 2) as u8; + let mm = r.pick(&[1i64, 3, 255, 1 << 15, I32MAX, 1 << 40]); + let nm = match m_kind { + 0 => 0, + 1 => 1, + _ => c, + }; + let multv: Vec = (0..nm).map(|_| r.int(-mm, mm)).collect(); + let shift: Vec = (0..if s_kind == 1 { 1 } else { c }).map(|_| shift_pick(r)).collect(); + let bm = r.pick(&[3i64, 1000, 1 << 20, I32MAX, 1 << 40]); + let biasv: Vec = if bias_kind == BiasKind::None { vec![] } else { (0..c).map(|_| r.int(-bm, bm)).collect() }; + let m_bytes = if m_kind == 0 { 0 } else if r.chance(1, 4) { 8 } else { smallest_width(&multv, &[4, 8]) }; + let b_bytes = if bias_kind == BiasKind::None { 0 } else if r.chance(1, 4) { 8 } else { smallest_width(&biasv, &[4, 8]) }; + Rq { round, bias_kind, m_kind, s_kind, m_bytes, b_bytes, mult: multv, shift, bias: biasv } +} + +/// An output record: mostly derived, sometimes `clamp` or `check`. +fn out_rec(r: &mut Rng, c: u32) -> TensorRec { + match r.below(10) { + 0 => { + let (lo, hi) = range(r); + TensorRec::clamp(c, lo, hi) + } + 1 => { + let (lo, hi) = range(r); + TensorRec::check(c, lo, hi) + } + _ => der(c), + } +} + +fn sizes(r: &mut Rng) -> (u32, u32) { + (r.u(1, 7), r.u(1, 7)) +} + +/// Declared sizes for decodes of `(h, w)`: none, or at least those. +fn declare(r: &mut Rng, h: u32, w: u32) -> (u32, u32) { + if r.chance(1, 2) { (0, 0) } else { (h + r.u(0, 2), w + r.u(0, 2)) } +} + +struct Gen { + rng: Rng, + file: File, + name: &'static str, + n: usize, +} + +impl Gen { + fn new(name: &'static str) -> Gen { + Gen { rng: Rng::named(name), file: File::default(), name, n: 0 } + } + /// Write one vector; returns whether it was kept. + fn emit(&mut self, class: &str, g: &Graph, d: Vec) -> bool { + let id = format!("{}/{}/{}", self.name, class, self.n); + self.n += 1; + let l = line(&id, &g.to_bytes(), &d); + let kept = l.is_some(); + self.file.push(l); + kept + } + /// Write `count` random vectors from `f`, retrying cases that meet no profile. + fn random(&mut self, count: usize, mut f: impl FnMut(&mut Rng) -> (Graph, Vec)) { + let mut made = 0; + let mut tries = 0; + while made < count && tries < count * 20 { + tries += 1; + let (g, d) = f(&mut self.rng); + let id = format!("{}/random/{}", self.name, self.n); + if let Some(l) = line(&id, &g.to_bytes(), &d) { + self.n += 1; + self.file.lines.push(l); + made += 1; + } + } + } +} + +/// Decodes of one input tensor of `c` channels in `[lo, hi]`, at `n` random sizes +/// within the declared ones (or any small size). +fn decodes1(r: &mut Rng, c: u32, lo: i64, hi: i64, mh: u32, mw: u32, n: usize) -> Vec { + (0..n) + .map(|_| { + let (h, w) = if mh == 0 { sizes(r) } else { (r.u(1, mh), r.u(1, mw)) }; + Decode::new(vec![tensor(r, c, h, w, lo, hi)], vec![]) + }) + .collect() +} + +/// A one-input elementwise operator's random case. +fn unary(r: &mut Rng, code: u8, p: Params, ranges: (i64, i64)) -> (Graph, Vec) { + let c = r.u(1, 4); + let (lo, hi) = ranges; + let (h, w) = sizes(r); + let (mh, mw) = declare(r, h, w); + let g = one_op(code, vec![inp(chk(c, lo, hi), mh, mw)], vec![out_rec(r, c)], p, vec![]); + let n = r.u(1, 2) as usize; + let d = decodes1(r, c, lo, hi, mh, mw, n); + (g, d) +} + +// ------------------------------------------------------------------ the files + +/// Every operator's file: `(name, text, dropped)`. +pub fn all() -> Vec<(&'static str, String, usize)> { + let mut v = Vec::new(); + for code in crate::graph::OPCODES { + let name = crate::graph::op_name(code).unwrap_or("?"); + let f = file(code, name); + v.push((name, f.text(), f.dropped)); + } + v +} + +fn file(code: u8, name: &'static str) -> File { + let mut g = Gen::new(name); + match code { + CONV => conv(&mut g), + CONV_TRANSPOSE => conv_t(&mut g), + RESCALE => rescale(&mut g), + CHSCALE => chscale(&mut g), + MUL => mul(&mut g), + LUT16 => lut16(&mut g), + ADD => add(&mut g), + CONCAT => concat(&mut g), + CHUNK_ADD => chunk_add(&mut g), + SPLIT => split(&mut g), + PIXEL_SHUFFLE | PIXEL_UNSHUFFLE => shuffle(&mut g, code), + CLAMP => clamp(&mut g), + RELU => relu_abs(&mut g, RELU), + ABS => relu_abs(&mut g, ABS), + LEAKY => leaky(&mut g), + PAD_REP => pad_rep(&mut g), + CROP_TO => crop_to(&mut g), + CROP => crop(&mut g), + SUBPOS_SPLIT | SUBPOS_MERGE => subpos(&mut g, code), + FILL => fill(&mut g), + IGDN => igdn(&mut g), + LAYERNORM_C => layernorm(&mut g), + L2NORM => l2norm(&mut g), + SOFTMAX => softmax(&mut g), + SIGMOID => sigmoid(&mut g), + ELU => elu(&mut g), + GRAM => gram(&mut g), + APPLY => apply(&mut g), + _ => {} + } + g.file +} + +// ------------------------------------------------------------------ conv + +fn conv_params(r: &mut Rng, cin: u32) -> (Params, u32, u32, u32, u32, u32) { + let divs: Vec = (1..=cin).filter(|d| cin % d == 0).collect(); + let groups = r.pick(&divs); + let cout = groups * r.u(1, 3); + let (kh, kw) = (r.u(1, 5), r.u(1, 5)); + let (sh, sw) = (r.u(1, 3), r.u(1, 3)); + let (ph, pw) = (r.u(0, 2), r.u(0, 2)); + let m = wmag(r); + let n = (cout * (cin / groups) * kh * kw) as usize; + let w: Vec = (0..n).map(|_| r.int(-m, m)).collect(); + let w_bytes = smallest_width(&w, &[1, 2, 4, 8]); + let q = rq(r, cout as usize, true, true); + (Params::Conv { cout, kh, kw, sh, sw, ph, pw, groups, w_bytes, rq: q, w }, cout, kh, kw, ph, pw) +} + +fn conv(g: &mut Gen) { + g.random(90, |r| { + let cin = r.u(1, 4); + let (p, cout, kh, kw, ph, pw) = conv_params(r, cin); + let (lo, hi) = range(r); + let h = r.u(kh.saturating_sub(2 * ph).max(1), 8); + let w = r.u(kw.saturating_sub(2 * pw).max(1), 8); + let (mh, mw) = declare(r, h, w); + let gr = one_op(CONV, vec![inp(chk(cin, lo, hi), mh, mw)], vec![out_rec(r, cout)], p, vec![]); + let d = vec![Decode::new(vec![tensor(r, cin, h, w, lo, hi)], vec![])]; + (gr, d) + }); + bound_attaining_1x1(g, CONV); + geometry_conv(g); + edges32(g, CONV); + check_ranges(g); +} + +/// A 1×1 convolution whose input, at every position, attains the upper end of output +/// channel 0's partial-sum interval, and one that attains the lower end (§14.1 item 1). +fn bound_attaining_1x1(g: &mut Gen, code: u8) { + for (lo, hi, m) in [(-100i64, 100i64, 50i64), (I32MIN, I32MAX, 1), (-(1 << 40), 1 << 40, 3), (-5, 9, 1 << 15)] { + for b in [0i64, 7, -7] { + let cin = 3u32; + let w: Vec = vec![m, -m, 0]; + let q = Rq { round: Round::Floor, bias_kind: BiasKind::Pre, m_kind: 0, s_kind: 1, m_bytes: 0, b_bytes: 8, mult: vec![], shift: vec![0], bias: vec![b] }; + let p = if code == CONV { + Params::Conv { cout: 1, kh: 1, kw: 1, sh: 1, sw: 1, ph: 0, pw: 0, groups: 1, w_bytes: smallest_width(&w, &[1, 2, 4, 8]), rq: q, w } + } else { + Params::ConvT { cout: 1, kh: 1, kw: 1, sh: 1, sw: 1, ph: 0, pw: 0, oph: 0, opw: 0, w_bytes: smallest_width(&w, &[1, 2, 4, 8]), rq: q, w } + }; + let gr = one_op(code, vec![inp(chk(cin, lo, hi), 0, 0)], vec![der(1)], p, vec![]); + // Upper end: h where the weight is positive, l where negative, 0 at the zero + // weight. Lower end: the reverse. + let up = Tensor::new(3, 2, 2, [vec![hi; 4], vec![lo; 4], vec![0; 4]].concat()); + let dn = Tensor::new(3, 2, 2, [vec![lo; 4], vec![hi; 4], vec![0; 4]].concat()); + g.emit("bound", &gr, vec![Decode::new(vec![up], vec![]), Decode::new(vec![dn], vec![])]); + } + } +} + +fn simple_rq() -> Rq { + Rq::plain(Round::Floor, 0) +} + +/// Kernels larger than the input, strides larger than kernels, a 1×1 plane, odd sizes, +/// and every run-time shape refusal of `conv` (§14.1 items 3 and 5). +fn geometry_conv(g: &mut Gen) { + let w = |n: usize| (0..n).map(|i| (i as i64 % 5) - 2).collect::>(); + let cases: [(u32, u32, u32, u32, u32, u32, u32, u32); 8] = [ + // kh, kw, sh, sw, ph, pw, H, W + (5, 5, 1, 1, 2, 2, 1, 1), + (5, 3, 1, 1, 2, 1, 3, 2), + (2, 2, 3, 3, 0, 0, 7, 5), + (1, 1, 1, 1, 0, 0, 1, 1), + (3, 3, 2, 2, 1, 1, 7, 7), + (4, 2, 3, 1, 0, 1, 5, 3), + (5, 5, 1, 1, 1, 1, 2, 2), // refused at run: H + 2ph < kh + (3, 1, 1, 1, 0, 0, 2, 4), // refused at run: H < kh + ]; + for (kh, kw, sh, sw, ph, pw, h, wd) in cases { + let p = Params::Conv { cout: 2, kh, kw, sh, sw, ph, pw, groups: 1, w_bytes: 1, rq: simple_rq(), w: w((2 * 2 * kh * kw) as usize) }; + let gr = one_op(CONV, vec![inp(chk(2, -9, 9), 0, 0)], vec![der(2)], p, vec![]); + let d = vec![Decode::new(vec![Tensor::new(2, h, wd, (0..2 * h * wd).map(|i| (i as i64 * 7) % 19 - 9).collect())], vec![])]; + g.emit("geometry", &gr, d); + } + // Depthwise, and the load-time plan on declared sizes. + let p = Params::Conv { cout: 3, kh: 3, kw: 3, sh: 1, sw: 1, ph: 1, pw: 1, groups: 3, w_bytes: 1, rq: simple_rq(), w: w(27) }; + let gr = one_op(CONV, vec![inp(chk(3, -50, 50), 4, 4)], vec![der(3)], p.clone(), vec![]); + g.emit("geometry", &gr, vec![Decode::new(vec![Tensor::new(3, 4, 3, (0..36).map(|i| i as i64 - 18).collect())], vec![])]); + // Declared sizes the kernel cannot fit: refused at load. + let p = Params::Conv { cout: 1, kh: 5, kw: 5, sh: 1, sw: 1, ph: 0, pw: 0, groups: 1, w_bytes: 1, rq: simple_rq(), w: w(25) }; + let gr = one_op(CONV, vec![inp(chk(1, -5, 5), 3, 3)], vec![der(1)], p, vec![]); + g.emit("geometry", &gr, vec![]); +} + +/// Intermediates at exactly the i32 limits, and one past them (§14.1 item 4): a 1×1 +/// convolution (or a rescale) of one channel with weight 1 and no shift, whose +/// accumulator's interval is the input's. +fn edges32(g: &mut Gen, code: u8) { + for (lo, hi) in [(I32MIN, I32MAX), (I32MIN - 1, I32MAX), (I32MIN, I32MAX + 1), (-I32MAX, I32MAX)] { + let q = simple_rq(); + let p = match code { + CONV => Params::Conv { cout: 1, kh: 1, kw: 1, sh: 1, sw: 1, ph: 0, pw: 0, groups: 1, w_bytes: 1, rq: q, w: vec![1] }, + CONV_TRANSPOSE => Params::ConvT { cout: 1, kh: 1, kw: 1, sh: 1, sw: 1, ph: 0, pw: 0, oph: 0, opw: 0, w_bytes: 1, rq: q, w: vec![1] }, + _ => Params::Rescale(q), + }; + let gr = one_op(code, vec![inp(chk(1, lo, hi), 0, 0)], vec![der(1)], p, vec![]); + g.emit("edge32", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 3, vec![lo, 0, hi])], vec![])]); + } +} + +/// Each `check` range at its ends and one past them: on the input, and on the output. +fn check_ranges(g: &mut Gen) { + let p = Params::Conv { cout: 1, kh: 1, kw: 1, sh: 1, sw: 1, ph: 0, pw: 0, groups: 1, w_bytes: 1, rq: simple_rq(), w: vec![2] }; + let gr = one_op(CONV, vec![inp(chk(1, -10, 20), 0, 0)], vec![chk(1, -6, 30)], p, vec![]); + let d = |v: i64| Decode::new(vec![konst(1, 1, 2, v)], vec![]); + g.emit("check", &gr, vec![d(-10), d(20), d(-3), d(15)]); + g.emit("check", &gr, vec![d(-11)]); + g.emit("check", &gr, vec![d(21)]); + g.emit("check", &gr, vec![d(-4)]); + g.emit("check", &gr, vec![d(16)]); + // A clamp policy on the output. + let p = Params::Conv { cout: 1, kh: 1, kw: 1, sh: 1, sw: 1, ph: 0, pw: 0, groups: 1, w_bytes: 1, rq: simple_rq(), w: vec![3] }; + let gr = one_op(CONV, vec![inp(TensorRec::clamp(1, -10, 20), 0, 0)], vec![TensorRec::clamp(1, -6, 30)], p, vec![]); + g.emit("check", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 4, vec![-100, -3, 9, 100])], vec![])]); +} + +// ------------------------------------------------------------------ conv_transpose + +fn conv_t(g: &mut Gen) { + g.random(90, |r| { + let cin = r.u(1, 3); + let cout = r.u(1, 3); + let (kh, kw) = (r.u(1, 5), r.u(1, 5)); + let (sh, sw) = (r.u(1, 3), r.u(1, 3)); + let (ph, pw) = (r.u(0, 2), r.u(0, 2)); + let (oph, opw) = (r.u(0, 3), r.u(0, 3)); + let m = wmag(r); + let w: Vec = (0..(cin * cout * kh * kw) as usize).map(|_| r.int(-m, m)).collect(); + let w_bytes = smallest_width(&w, &[1, 2, 4, 8]); + let q = rq(r, cout as usize, true, true); + let p = Params::ConvT { cout, kh, kw, sh, sw, ph, pw, oph, opw, w_bytes, rq: q, w }; + let (lo, hi) = range(r); + let (h, wd) = (r.u(1, 5), r.u(1, 5)); + let (mh, mw) = declare(r, h, wd); + let gr = one_op(CONV_TRANSPOSE, vec![inp(chk(cin, lo, hi), mh, mw)], vec![out_rec(r, cout)], p, vec![]); + (gr, vec![Decode::new(vec![tensor(r, cin, h, wd, lo, hi)], vec![])]) + }); + bound_attaining_1x1(g, CONV_TRANSPOSE); + edges32(g, CONV_TRANSPOSE); + // Geometry: output padding at least the stride, a stride larger than the kernel, + // a 1×1 plane, odd sizes, and a crop that leaves nothing (refused at run). + let cases: [(u32, u32, u32, u32, u32, u32, u32, u32, u32, u32); 7] = [ + // kh, kw, sh, sw, ph, pw, oph, opw, H, W + (3, 3, 2, 2, 1, 1, 2, 3, 3, 3), + (2, 2, 3, 3, 0, 0, 0, 0, 3, 2), + (1, 1, 1, 1, 0, 0, 0, 0, 1, 1), + (5, 3, 2, 1, 2, 1, 1, 0, 3, 5), + (4, 4, 2, 2, 1, 1, 1, 1, 5, 3), + (3, 3, 1, 1, 2, 2, 0, 0, 1, 1), // refused at run: Ho = 3 − 4 < 1 + (1, 5, 1, 3, 0, 2, 0, 1, 1, 4), // one-dimensional form + ]; + for (kh, kw, sh, sw, ph, pw, oph, opw, h, wd) in cases { + let w: Vec = (0..(2 * 2 * kh * kw) as usize).map(|i| (i as i64 * 3) % 11 - 5).collect(); + let p = Params::ConvT { cout: 2, kh, kw, sh, sw, ph, pw, oph, opw, w_bytes: 1, rq: Rq { round: Round::HalfUp, bias_kind: BiasKind::Post, m_kind: 2, s_kind: 1, m_bytes: 4, b_bytes: 4, mult: vec![3, -2], shift: vec![1], bias: vec![5, -5] }, w }; + let gr = one_op(CONV_TRANSPOSE, vec![inp(chk(2, -20, 20), 0, 0)], vec![der(2)], p, vec![]); + let d = vec![Decode::new(vec![Tensor::new(2, h, wd, (0..2 * h * wd).map(|i| (i as i64 * 5) % 41 - 20).collect())], vec![])]; + g.emit("geometry", &gr, d); + } +} + +// ------------------------------------------------------------------ requantisation + +/// `rescale` vectors that hit ties exactly, positive and negative, at every rounding, +/// for each shift of §14.1 item 2, and with every bias placement. +fn rounding_grid(g: &mut Gen) { + for s in [0u8, 1, 31, 32, 62, 63, 64, 127] { + for round in [Round::Floor, Round::HalfUp, Round::HalfEven] { + for bias in [BiasKind::None, BiasKind::Pre, BiasKind::Post] { + // A multiplier and inputs whose products are ties at shift s (or the + // nearest product the input range allows), with neighbours either side. + let (t, m) = tie(s); + let b = match bias { + BiasKind::None => vec![], + BiasKind::Pre => vec![0], + BiasKind::Post => vec![5], + }; + let q = Rq { round, bias_kind: bias, m_kind: 1, s_kind: 1, m_bytes: smallest_width(&[m], &[4, 8]), b_bytes: if bias == BiasKind::None { 0 } else { 4 }, mult: vec![m], shift: vec![s], bias: b }; + let vals = vec![t, -t, t + 1, -t - 1, t.saturating_sub(1), 1 - t, 3 * t / 2, -3 * t / 2]; + let lo = *vals.iter().min().unwrap(); + let hi = *vals.iter().max().unwrap(); + let gr = one_op(RESCALE, vec![inp(chk(1, lo, hi), 0, 0)], vec![der(1)], Params::Rescale(q), vec![]); + g.emit("rounding", &gr, vec![Decode::new(vec![Tensor::new(1, 1, vals.len() as u32, vals)], vec![])]); + } + } + } +} + +/// An input `t` and a multiplier `m` with `t·m = 2^(s−1)`, a tie at shift `s`, both +/// within `i32` where the shift allows it. At shift 127 no tie lies within the widths +/// `acc64` allows (`p + 2^126` would leave `i128`), so the pair gives large non-ties. +fn tie(s: u8) -> (i64, i64) { + match s { + 0 => (3, 1), + 1..=31 => (1i64 << (s - 1), 1), + 32 => (1 << 30, 2), + 33..=61 => (1 << 30, 1i64 << (s - 31)), + 62 => (-(1 << 30), -(1i64 << 31)), + 63 => (-(1i64 << 31), -(1i64 << 31)), + 64 => (-(1i64 << 31), -(1i64 << 32)), + _ => (1i64 << 62, i64::MAX), + } +} + +fn rescale(g: &mut Gen) { + g.random(70, |r| { + let c = r.u(1, 4); + let q = rq(r, c as usize, true, true); + let (lo, hi) = range(r); + let (h, w) = sizes(r); + let (mh, mw) = declare(r, h, w); + let gr = one_op(RESCALE, vec![inp(chk(c, lo, hi), mh, mw)], vec![out_rec(r, c)], Params::Rescale(q), vec![]); + let d = decodes1(r, c, lo, hi, mh, mw, 2); + (gr, d) + }); + rounding_grid(g); + edges32(g, RESCALE); + // The i32 × i32 product, rounded, with a post bias, narrowed (§3.1 table (4)). + // q and y within i32 at shifts 32, 62 and 63; at shift 31, q reaches 2^31 (acc64 + // only, one past). + for (m, b, s) in [(I32MAX, (1 << 30) - 1, 32u8), (I32MIN, -(1 << 30), 32), (I32MAX, I32MIN, 62), (I32MIN, I32MAX, 63), (I32MAX, 0, 31)] { + for round in [Round::Floor, Round::HalfUp, Round::HalfEven] { + let q = Rq { round, bias_kind: BiasKind::Post, m_kind: 1, s_kind: 1, m_bytes: 4, b_bytes: 4, mult: vec![m], shift: vec![s], bias: vec![b] }; + let gr = one_op(RESCALE, vec![inp(chk(1, I32MIN, I32MAX), 0, 0)], vec![der(1)], Params::Rescale(q), vec![]); + g.emit("product32", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 5, vec![I32MIN, I32MAX, -1, 0, 12345])], vec![])]); + } + } + // An odd post bias under half_even (the post-bias identity of §9.4 does not hold). + let q = Rq { round: Round::HalfEven, bias_kind: BiasKind::Post, m_kind: 0, s_kind: 1, m_bytes: 0, b_bytes: 4, mult: vec![], shift: vec![1], bias: vec![3] }; + let gr = one_op(RESCALE, vec![inp(chk(1, -9, 9), 0, 0)], vec![der(1)], Params::Rescale(q), vec![]); + g.emit("rounding", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 8, vec![-9, -5, -3, -1, 1, 3, 5, 9])], vec![])]); +} + +fn chscale(g: &mut Gen) { + g.random(60, |r| { + let c = r.u(1, 4); + let n_rows = r.u(1, 4); + let mm = r.pick(&[3i64, 255, I32MAX, 1 << 40]); + let m: Vec = (0..(n_rows * c) as usize).map(|_| r.int(-mm, mm)).collect(); + let m_bytes = if r.chance(1, 3) { 8 } else { smallest_width(&m, &[4, 8]) }; + let shift = shift_pick(r); + let round = r.pick(&[Round::Floor, Round::HalfUp, Round::HalfEven]); + let (slo, shi) = (r.int(0, n_rows as i64 - 1), n_rows as i64 - 1); + let (lo, hi) = range(r); + let (h, w) = sizes(r); + let p = Params::Chscale { scalar: 0, n_rows, round, m_bytes, shift, m }; + let gr = one_op(CHSCALE, vec![inp(chk(c, lo, hi), 0, 0)], vec![out_rec(r, c)], p, vec![ScalarRec { lo: slo, hi: shi }]); + let d = (0..2).map(|_| Decode::new(vec![tensor(r, c, h, w, lo, hi)], vec![r.int(slo, shi)])).collect(); + (gr, d) + }); + // The scalar at its range's ends and one past them. + let p = Params::Chscale { scalar: 0, n_rows: 4, round: Round::HalfUp, m_bytes: 4, shift: 1, m: vec![1, 2, 3, 4, 5, 6, 7, 8] }; + let gr = one_op(CHSCALE, vec![inp(chk(2, -9, 9), 0, 0)], vec![der(2)], p, vec![ScalarRec { lo: 1, hi: 2 }]); + let x = Tensor::new(2, 1, 3, vec![-9, 0, 9, 3, -3, 5]); + for s in [1i64, 2, 0, 3] { + g.emit("scalar", &gr, vec![Decode::new(vec![x.clone()], vec![s])]); + } +} + +fn mul(g: &mut Gen) { + g.random(70, |r| { + let c = r.u(1, 4); + let bc = if r.chance(1, 3) { 1 } else { c }; + let q = rq(r, c as usize, false, true); + let (alo, ahi) = range(r); + let (blo, bhi) = range(r); + let (h, w) = sizes(r); + let (mh, mw) = declare(r, h, w); + let gr = one_op(MUL, vec![inp(chk(c, alo, ahi), mh, mw), inp(chk(bc, blo, bhi), mh, mw)], vec![out_rec(r, c)], Params::Mul(q), vec![]); + let d = vec![Decode::new(vec![tensor(r, c, h, w, alo, ahi), tensor(r, bc, h, w, blo, bhi)], vec![])]; + (gr, d) + }); + // mul.p at the i32 limits, and the sizes disagreeing (refused at run). + for (a, b) in [((I32MIN, I32MAX), (1, 1)), ((I32MIN, I32MAX), (-1, 1)), ((-46340, 46340), (-46341, 46341))] { + let gr = one_op(MUL, vec![inp(chk(1, a.0, a.1), 0, 0), inp(chk(1, b.0, b.1), 0, 0)], vec![der(1)], Params::Mul(simple_rq()), vec![]); + g.emit("edge32", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 2, vec![a.0, a.1]), Tensor::new(1, 1, 2, vec![b.1, b.0])], vec![])]); + } + let gr = one_op(MUL, vec![inp(chk(2, -5, 5), 0, 0), inp(chk(1, -5, 5), 0, 0)], vec![der(2)], Params::Mul(Rq::plain(Round::HalfUp, 1)), vec![]); + g.emit("geometry", &gr, vec![Decode::new(vec![konst(2, 2, 3, 3), konst(1, 3, 2, -3)], vec![])]); + g.emit("geometry", &gr, vec![Decode::new(vec![konst(2, 2, 3, 3), Tensor::new(1, 2, 3, vec![-5, -3, -1, 1, 3, 5])], vec![])]); +} + +// ------------------------------------------------------------------ lut16 + +fn lut16(g: &mut Gen) { + let mut r = Rng::named("lut16-tables"); + // Each graph carries its table (65,536 entries), so the cases are few. + for n in 0..2 { + let mut t: Vec = (0..65536).map(|i| ((i as i64 * 2654435761) >> 7) as i16).collect(); + t[0] = 0; + if n % 3 == 0 { + for (i, v) in t.iter_mut().enumerate() { + *v = ((i as i32 - 32768) / 3) as i16; + } + t[0] = 0; + } + let (lo, hi) = if n == 0 { (-32767, 32767) } else { (-300, 4000) }; + let (e_in, e_out) = if n == 0 { (-128i8, -128i8) } else { (4, 8) }; + let c = 2; + let mut rin = chk(c, lo, hi); + rin.exponent = e_in; + let mut rout = der(c); + rout.exponent = e_out; + let gr = one_op(LUT16, vec![inp(rin, 0, 0)], vec![rout], Params::Lut16 { e_in, e_out, t }, vec![]); + let mut x = tensor(&mut r, c, 3, 3, lo, hi); + x.data[0] = lo; + x.data[1] = hi; + let mut y = tensor(&mut r, c, 2, 5, lo, hi); + y.data[0] = 0; + g.emit("table", &gr, vec![Decode::new(vec![x], vec![]), Decode::new(vec![y], vec![])]); + } + // The exponent rules at load: e_in against the input's, e_out against the output's. + let t = { + let mut t = vec![1i16; 65536]; + t[0] = 0; + t + }; + for (ein, xin, eout, xout) in [(4i8, 5i8, -128i8, -128i8), (-128, -128, 2, -128)] { + let mut rin = chk(1, -9, 9); + rin.exponent = xin; + let mut rout = der(1); + rout.exponent = xout; + let gr = one_op(LUT16, vec![inp(rin, 0, 0)], vec![rout], Params::Lut16 { e_in: ein, e_out: eout, t: t.clone() }, vec![]); + let d = vec![Decode::new(vec![Tensor::new(1, 1, 3, vec![-9, 0, 9])], vec![])]; + g.emit("exponent", &gr, d); + } + // A table whose entry 0 is not 0: refused at load. + let mut t2 = t.clone(); + t2[0] = 1; + let gr = one_op(LUT16, vec![inp(chk(1, -9, 9), 0, 0)], vec![der(1)], Params::Lut16 { e_in: -128, e_out: -128, t: t2 }, vec![]); + g.emit("table", &gr, vec![]); +} + +// ------------------------------------------------------------------ elementwise + +fn add(g: &mut Gen) { + g.random(60, |r| { + let c = r.u(1, 4); + let (alo, ahi) = range(r); + let (blo, bhi) = range(r); + let (h, w) = sizes(r); + let (mh, mw) = declare(r, h, w); + let gr = one_op(ADD, vec![inp(chk(c, alo, ahi), mh, mw), inp(chk(c, blo, bhi), mh, mw)], vec![out_rec(r, c)], Params::None, vec![]); + (gr, vec![Decode::new(vec![tensor(r, c, h, w, alo, ahi), tensor(r, c, h, w, blo, bhi)], vec![])]) + }); + // Sums at the i32 limits and one past; the i64 limits; a saturating sum (a clamp + // policy on the output); the sizes disagreeing; exponents. + for (a, b) in [((I32MIN, 0), (0, I32MAX)), ((I32MIN, 0), (-1, I32MAX)), ((i64::MIN / 2, i64::MAX / 2), (i64::MIN / 2, i64::MAX / 2 + 1))] { + let gr = one_op(ADD, vec![inp(chk(1, a.0, a.1), 0, 0), inp(chk(1, b.0, b.1), 0, 0)], vec![der(1)], Params::None, vec![]); + g.emit("edge", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 2, vec![a.0, a.1]), Tensor::new(1, 1, 2, vec![b.0, b.1])], vec![])]); + } + let gr = one_op(ADD, vec![inp(chk(1, -200, 200), 0, 0), inp(chk(1, -200, 200), 0, 0)], vec![TensorRec::clamp(1, -128, 127)], Params::None, vec![]); + g.emit("saturating", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 4, vec![-200, 100, 27, 200]), Tensor::new(1, 1, 4, vec![-200, 100, 100, 0])], vec![])]); + g.emit("geometry", &gr, vec![Decode::new(vec![konst(1, 2, 2, 1), konst(1, 2, 3, 1)], vec![])]); + for (ea, eb, eo) in [(3i8, 3i8, 3i8), (-128, -128, -128), (3, 4, 3), (3, -128, 3), (-128, -128, 2)] { + let (mut a, mut b, mut o) = (chk(1, -5, 5), chk(1, -5, 5), der(1)); + a.exponent = ea; + b.exponent = eb; + o.exponent = eo; + let gr = one_op(ADD, vec![inp(a, 0, 0), inp(b, 0, 0)], vec![o], Params::None, vec![]); + g.emit("exponent", &gr, vec![Decode::new(vec![konst(1, 1, 1, 2), konst(1, 1, 1, 3)], vec![])]); + } +} + +fn chunk_add(g: &mut Gen) { + g.random(40, |r| { + let c = 2 * r.u(1, 3); + let (lo, hi) = range(r); + unary_with(r, CHUNK_ADD, Params::None, c, c / 2, lo, hi) + }); + for (lo, hi) in [(I32MIN / 2, I32MAX / 2), (I32MIN / 2 - 1, I32MAX / 2 + 1)] { + let gr = one_op(CHUNK_ADD, vec![inp(chk(2, lo, hi), 0, 0)], vec![der(1)], Params::None, vec![]); + g.emit("edge32", &gr, vec![Decode::new(vec![Tensor::new(2, 1, 2, vec![lo, hi, lo, hi])], vec![])]); + } + let gr = one_op(CHUNK_ADD, vec![inp(chk(3, -5, 5), 0, 0)], vec![der(1)], Params::None, vec![]); + g.emit("channels", &gr, vec![]); +} + +fn unary_with(r: &mut Rng, code: u8, p: Params, cin: u32, cout: u32, lo: i64, hi: i64) -> (Graph, Vec) { + let (h, w) = sizes(r); + let (mh, mw) = declare(r, h, w); + let gr = one_op(code, vec![inp(chk(cin, lo, hi), mh, mw)], vec![out_rec(r, cout)], p, vec![]); + let d = decodes1(r, cin, lo, hi, mh, mw, 2); + (gr, d) +} + +fn clamp(g: &mut Gen) { + g.random(50, |r| { + let (lo, hi) = range(r); + let (a, b) = range(r); + let p = Params::Clamp { lo: a, hi: b }; + unary(r, CLAMP, p, (lo, hi)) + }); + // One-sided clamps, and lo > hi (refused at load). + for (lo, hi) in [(i64::MIN, 0), (0, i64::MAX), (-7, -7), (5, 4)] { + let gr = one_op(CLAMP, vec![inp(chk(1, -10, 10), 0, 0)], vec![der(1)], Params::Clamp { lo, hi }, vec![]); + let d = if lo <= hi { vec![Decode::new(vec![Tensor::new(1, 1, 3, vec![-10, 0, 10])], vec![])] } else { vec![] }; + g.emit("param", &gr, d); + } +} + +fn relu_abs(g: &mut Gen, code: u8) { + g.random(40, |r| { + let rg = range(r); + unary(r, code, Params::None, rg) + }); + // The i64 limits: |−2^63| does not fit i64, so an input reaching −2^63 meets no + // profile (a D2b vector); one stopping at −(2^63 − 1) decodes. + let gr = one_op(code, vec![inp(chk(1, -i64::MAX, i64::MAX), 0, 0)], vec![der(1)], Params::None, vec![]); + g.emit("edge", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 3, vec![-i64::MAX, 0, i64::MAX])], vec![])]); + let gr = one_op(code, vec![inp(chk(1, I32MIN, I32MAX), 0, 0)], vec![der(1)], Params::None, vec![]); + g.emit("edge32", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 3, vec![I32MIN, -1, I32MAX])], vec![])]); +} + +fn leaky(g: &mut Gen) { + g.random(50, |r| { + let a = r.pick(&[1i64, 3, 655, -655, 13107, I32MAX, -(1 << 20), 1 << 40]); + let f = r.u(0, 63); + let rg = range(r); + unary(r, LEAKY, Params::Leaky { a, f }, rg) + }); + // Negative values that floor away from zero; a negative slope; f = 63; f = 64 + // (refused at load). + for (a, f) in [(3i64, 2u32), (-5, 1), (1, 63), (655, 12), (1, 64)] { + let gr = one_op(LEAKY, vec![inp(chk(1, -1000, 1000), 0, 0)], vec![der(1)], Params::Leaky { a, f }, vec![]); + let d = if f <= 63 { vec![Decode::new(vec![Tensor::new(1, 1, 6, vec![-1000, -7, -5, -1, 0, 1000])], vec![])] } else { vec![] }; + g.emit("floor", &gr, d); + } +} + +// ------------------------------------------------------------------ movement + +fn concat(g: &mut Gen) { + g.random(40, |r| { + let n = r.u(2, 4) as usize; + let (h, w) = sizes(r); + let (mh, mw) = declare(r, h, w); + let mut ins = Vec::new(); + let mut xs = Vec::new(); + let mut tot = 0; + for _ in 0..n { + let c = r.u(1, 3); + tot += c; + let (lo, hi) = range(r); + ins.push(inp(chk(c, lo, hi), mh, mw)); + xs.push(tensor(r, c, h, w, lo, hi)); + } + let gr = one_op(CONCAT, ins, vec![der(tot)], Params::None, vec![]); + (gr, vec![Decode::new(xs, vec![])]) + }); + let gr = one_op(CONCAT, vec![inp(chk(1, -5, 5), 0, 0), inp(chk(2, -5, 5), 0, 0)], vec![der(3)], Params::None, vec![]); + g.emit("geometry", &gr, vec![Decode::new(vec![konst(1, 2, 2, 1), konst(2, 2, 1, 2)], vec![])]); + let gr = one_op(CONCAT, vec![inp(chk(1, -5, 5), 0, 0), inp(chk(2, -5, 5), 0, 0)], vec![der(4)], Params::None, vec![]); + g.emit("channels", &gr, vec![]); +} + +fn split(g: &mut Gen) { + g.random(30, |r| { + let c = r.u(1, 5); + let start = r.u(0, c - 1); + let count = r.u(1, c - start); + let (lo, hi) = range(r); + unary_with(r, SPLIT, Params::Split { start, count }, c, count, lo, hi) + }); + let gr = one_op(SPLIT, vec![inp(chk(3, -5, 5), 0, 0)], vec![der(2)], Params::Split { start: 2, count: 2 }, vec![]); + g.emit("channels", &gr, vec![]); +} + +fn shuffle(g: &mut Gen, code: u8) { + g.random(40, |r| { + let rr = r.u(1, 3); + let (lo, hi) = range(r); + let base = r.u(1, 2); + if code == PIXEL_SHUFFLE { + let c = base * rr * rr; + unary_with(r, code, Params::Shuffle { r: rr }, c, base, lo, hi) + } else { + let (h, w) = (rr * r.u(1, 3), rr * r.u(1, 3)); + let (mh, mw) = declare(r, h, w); + let gr = one_op(code, vec![inp(chk(base, lo, hi), mh, mw)], vec![der(base * rr * rr)], Params::Shuffle { r: rr }, vec![]); + (gr, vec![Decode::new(vec![tensor(r, base, h, w, lo, hi)], vec![])]) + } + }); + if code == PIXEL_UNSHUFFLE { + let gr = one_op(code, vec![inp(chk(1, -5, 5), 0, 0)], vec![der(4)], Params::Shuffle { r: 2 }, vec![]); + g.emit("geometry", &gr, vec![Decode::new(vec![Tensor::new(1, 3, 2, (0..6).collect())], vec![])]); + } else { + let gr = one_op(code, vec![inp(chk(3, -5, 5), 0, 0)], vec![der(1)], Params::Shuffle { r: 2 }, vec![]); + g.emit("channels", &gr, vec![]); + } +} + +fn pad_rep(g: &mut Gen) { + g.random(30, |r| { + let m = r.u(1, 4); + let c = r.u(1, 3); + let rg = range(r); + unary_with(r, PAD_REP, Params::PadRep { m }, c, c, rg.0, rg.1) + }); + for m in [1u32, 2, 3, 5] { + let gr = one_op(PAD_REP, vec![inp(chk(1, -50, 50), 0, 0)], vec![der(1)], Params::PadRep { m }, vec![]); + g.emit("multiple", &gr, vec![Decode::new(vec![Tensor::new(1, 3, 4, (0..12).map(|i| i * 3 - 17).collect())], vec![])]); + } +} + +fn crop_to(g: &mut Gen) { + g.random(30, |r| { + let c = r.u(1, 3); + let (h, w) = (r.u(1, 6), r.u(1, 6)); + let (rh, rw) = (r.u(1, h), r.u(1, w)); + let (lo, hi) = range(r); + let rc = r.u(1, 3); + let gr = one_op(CROP_TO, vec![inp(chk(c, lo, hi), 0, 0), inp(chk(rc, -1, 1), 0, 0)], vec![der(c)], Params::None, vec![]); + (gr, vec![Decode::new(vec![tensor(r, c, h, w, lo, hi), konst(rc, rh, rw, 0)], vec![])]) + }); + let gr = one_op(CROP_TO, vec![inp(chk(1, -5, 5), 0, 0), inp(chk(1, -5, 5), 0, 0)], vec![der(1)], Params::None, vec![]); + g.emit("geometry", &gr, vec![Decode::new(vec![konst(1, 2, 2, 1), konst(1, 3, 1, 0)], vec![])]); +} + +fn crop(g: &mut Gen) { + g.random(30, |r| { + let c = r.u(1, 3); + let (h, w) = (r.u(1, 6), r.u(1, 6)); + let (ch, cw) = (r.u(1, h), r.u(1, w)); + let (y0, x0) = (r.u(0, h - ch), r.u(0, w - cw)); + let (lo, hi) = range(r); + let p = Params::Crop { y0: Operand::Const(y0), x0: Operand::Const(x0), h: Operand::Const(ch), w: Operand::Const(cw) }; + let gr = one_op(CROP, vec![inp(chk(c, lo, hi), 0, 0)], vec![der(c)], p, vec![]); + (gr, vec![Decode::new(vec![tensor(r, c, h, w, lo, hi)], vec![])]) + }); + // Scalar operands: every operand from a scalar, the scalars at their ends and one + // past them (field_range), and a crop that does not fit (geometry_mismatch). + let p = Params::Crop { y0: Operand::Scalar(0), x0: Operand::Scalar(1), h: Operand::Scalar(2), w: Operand::Scalar(3) }; + let sc = vec![ScalarRec { lo: 0, hi: 2 }, ScalarRec { lo: 0, hi: 3 }, ScalarRec { lo: 1, hi: 4 }, ScalarRec { lo: 1, hi: 5 }]; + let gr = one_op(CROP, vec![inp(chk(2, -99, 99), 6, 6)], vec![der(2)], p, sc); + let x = Tensor::new(2, 5, 6, (0..60).map(|i| i - 30).collect()); + for s in [vec![0, 0, 1, 1], vec![2, 3, 3, 3], vec![1, 0, 4, 5], vec![0, 0, 1, 6], vec![3, 0, 1, 1], vec![0, 0, 0, 1], vec![2, 3, 4, 4]] { + g.emit("scalar", &gr, vec![Decode::new(vec![x.clone()], s)]); + } + // The PCM step's form: constant y0, x0 and h; w from a scalar. + let p = Params::Crop { y0: Operand::Const(0), x0: Operand::Const(0), h: Operand::Const(1), w: Operand::Scalar(0) }; + let gr = one_op(CROP, vec![inp(chk(1, -99, 99), 0, 0)], vec![TensorRec::check(1, -50, 50)], p, vec![ScalarRec { lo: 1, hi: 1 << 26 }]); + let x = Tensor::new(1, 1, 6, vec![-50, 50, 51, 0, 1, 2]); + for s in [2i64, 3, 7, 0] { + g.emit("scalar", &gr, vec![Decode::new(vec![x.clone()], vec![s])]); + } +} + +fn subpos(g: &mut Gen, code: u8) { + let orders: [[u8; 4]; 4] = [[0, 1, 2, 3], [0, 3, 1, 2], [3, 2, 1, 0], [1, 0, 3, 2]]; + g.random(30, |r| { + let c = r.u(1, 3); + let order = r.pick(&orders); + let (lo, hi) = range(r); + if code == SUBPOS_SPLIT { + let (h, w) = (2 * r.u(1, 3), 2 * r.u(1, 3)); + let gr = one_op(code, vec![inp(chk(c, lo, hi), 0, 0)], vec![der(c), der(c), der(c), der(c)], Params::Subpos { order }, vec![]); + (gr, vec![Decode::new(vec![tensor(r, c, h, w, lo, hi)], vec![])]) + } else { + let (h, w) = (r.u(1, 3), r.u(1, 3)); + let ins = (0..4).map(|_| inp(chk(c, lo, hi), 0, 0)).collect(); + let xs = (0..4).map(|_| tensor(r, c, h, w, lo, hi)).collect(); + let gr = one_op(code, ins, vec![der(c)], Params::Subpos { order }, vec![]); + (gr, vec![Decode::new(xs, vec![])]) + } + }); + if code == SUBPOS_SPLIT { + let gr = one_op(code, vec![inp(chk(1, -5, 5), 0, 0)], vec![der(1), der(1), der(1), der(1)], Params::Subpos { order: [0, 3, 1, 2] }, vec![]); + g.emit("geometry", &gr, vec![Decode::new(vec![Tensor::new(1, 3, 2, (0..6).collect())], vec![])]); + } else { + let ins = (0..4).map(|_| inp(chk(1, -5, 5), 0, 0)).collect(); + let gr = one_op(code, ins, vec![der(1)], Params::Subpos { order: [0, 3, 1, 2] }, vec![]); + g.emit("geometry", &gr, vec![Decode::new(vec![konst(1, 1, 2, 1), konst(1, 1, 2, 2), konst(1, 2, 1, 3), konst(1, 1, 2, 4)], vec![])]); + } + // An order that is not a permutation: refused at load. + let gr = one_op(SUBPOS_SPLIT, vec![inp(chk(1, -5, 5), 0, 0)], vec![der(1), der(1), der(1), der(1)], Params::Subpos { order: [0, 1, 1, 2] }, vec![]); + g.emit("param", &gr, vec![]); +} + +fn fill(g: &mut Gen) { + g.random(30, |r| { + let c = r.u(1, 3); + let oc = r.u(1, 3); + let (lo, hi) = range(r); + let (h, w) = sizes(r); + if r.chance(1, 2) { + let v = r.value(lo, hi); + let gr = one_op(FILL, vec![inp(chk(c, -5, 5), 0, 0)], vec![der(oc)], Params::Fill { kind: 0, value: v }, vec![]); + (gr, vec![Decode::new(vec![konst(c, h, w, 1)], vec![])]) + } else { + let gr = one_op(FILL, vec![inp(chk(c, -5, 5), 0, 0)], vec![der(oc)], Params::Fill { kind: 1, value: 0 }, vec![ScalarRec { lo, hi }]); + let s = r.value(lo, hi); + (gr, vec![Decode::new(vec![konst(c, h, w, 1)], vec![s])]) + } + }); + let gr = one_op(FILL, vec![inp(chk(1, -5, 5), 0, 0)], vec![der(1)], Params::Fill { kind: 1, value: 0 }, vec![ScalarRec { lo: -3, hi: 3 }]); + for s in [-3i64, 3, -4, 4] { + g.emit("scalar", &gr, vec![Decode::new(vec![konst(1, 2, 2, 0)], vec![s])]); + } +} + +// ------------------------------------------------------------------ normalisation + +fn igdn(g: &mut Gen) { + g.random(60, |r| { + let c = r.u(1, 3); + let gf = r.pick(&[0u32, 4, 8, 12, 16, 30]); + let frac = r.pick(&[0u32, 4, 8, 12, 16]); + let gm = r.pick(&[3i64, 1 << 12, 1 << 20, I32MAX]); + let gamma: Vec = (0..(c * c) as usize).map(|_| r.int(-gm, gm)).collect(); + let bm = r.pick(&[1i64, 1 << 16, 1 << 40]); + let beta: Vec = (0..c as usize).map(|_| r.int(-bm, bm)).collect(); + let g_bytes = if r.chance(1, 3) { 8 } else { smallest_width(&gamma, &[4, 8]) }; + let (lo, hi) = range(r); + unary_with(r, IGDN, Params::Igdn { gf, frac, g_bytes, gamma, beta }, c, c, lo, hi) + }); + // Flooring each term against flooring the sum: γ·x² of 1, 1 and 1 at gf = 1 floor to + // 0 each, where their sum would floor to 1. A positive β with γ ≥ 0: the final norm, + // not igdn.N, sets r. C = 1, 2, 3. + for c in 1..=3u32 { + let gamma = vec![1i64; (c * c) as usize]; + let beta: Vec = (0..c as i64).map(|i| 4 + i).collect(); + let p = Params::Igdn { gf: 1, frac: 0, g_bytes: 4, gamma, beta }; + let gr = one_op(IGDN, vec![inp(chk(c, -3, 3), 0, 0)], vec![der(c)], p, vec![]); + let x = Tensor::new(c, 1, 3, (0..3 * c as i64).map(|i| [1, -1, 3, -3, 2, 0, 1, 1, -2][i as usize]).collect()); + g.emit("floor", &gr, vec![Decode::new(vec![x], vec![])]); + } + // igdn.gx2 near the 64-bit edge. + for (x, gm) in [(46340i64, I32MAX), (1 << 31, I32MAX), (I32MAX, I32MAX)] { + let p = Params::Igdn { gf: 31, frac: 16, g_bytes: 4, gamma: vec![gm], beta: vec![0] }; + let gr = one_op(IGDN, vec![inp(chk(1, -x, x), 0, 0)], vec![der(1)], p, vec![]); + g.emit("edge", &gr, vec![Decode::new(vec![Tensor::new(1, 1, 3, vec![-x, 0, x])], vec![])]); + } +} + +fn layernorm(g: &mut Gen) { + g.random(50, |r| { + let c = r.u(1, 5); + let eps = r.pick(&[1u64, 42949, 1 << 20, u64::MAX]); + let gm = r.pick(&[1i64 << 16, 3, 1 << 30]); + let gamma: Vec = (0..c as usize).map(|_| r.int(-gm, gm)).collect(); + let beta: Vec = (0..c as usize).map(|_| r.int(-1000, 1000)).collect(); + let g_bytes = if r.chance(1, 3) { 8 } else { smallest_width(&gamma, &[4, 8]) }; + let (lo, hi) = match r.below(3) { + 0 => (-(1 << 16), 1 << 16), + 1 => (-300, 300), + _ => (-(1 << 30), 1 << 30), + }; + unary_with(r, LAYERNORM_C, Params::LayerNorm { eps, g_bytes, gamma, beta }, c, c, lo, hi) + }); + // Odd C with a mean that is exactly a half (M rounds half away from zero); ε at + // 2^64 − 1, where V + ε passes 64 bits. + for (eps, x) in [(1u64, vec![1i64, 2, 2]), (1, vec![-1, -2, -2]), (42949, vec![0, 1, 1, 0, 0]), (u64::MAX, vec![5, -5, 1])] { + let c = x.len() as u32; + let p = Params::LayerNorm { eps, g_bytes: 4, gamma: vec![1 << 16; c as usize], beta: vec![0; c as usize] }; + let gr = one_op(LAYERNORM_C, vec![inp(chk(c, -10, 10), 0, 0)], vec![der(c)], p, vec![]); + g.emit("mean", &gr, vec![Decode::new(vec![Tensor::new(c, 1, 1, x)], vec![])]); + } +} + +fn l2norm(g: &mut Gen) { + g.random(40, |r| { + let c = r.u(1, 3); + let eps = r.pick(&[1u64, 42949, 1 << 30, u64::MAX]); + let (lo, hi) = match r.below(3) { + 0 => (-(1 << 16), 1 << 16), + 1 => (-300, 300), + _ => (-(1 << 24), 1 << 24), + }; + let (h, w) = (r.u(1, 5), r.u(1, 5)); + let gr = one_op(L2NORM, vec![inp(chk(c, lo, hi), h, w)], vec![der(c)], Params::L2norm { eps }, vec![]); + let (rh, rw) = (r.u(1, h), r.u(1, w)); + (gr, vec![Decode::new(vec![tensor(r, c, rh, rw, lo, hi)], vec![])]) + }); + // Without declared sizes: refused at load. + let gr = one_op(L2NORM, vec![inp(chk(1, -5, 5), 0, 0)], vec![der(1)], Params::L2norm { eps: 1 }, vec![]); + g.emit("sizes", &gr, vec![]); +} + +fn softmax(g: &mut Gen) { + g.random(40, |r| { + let c = r.u(1, 3); + let (lo, hi) = match r.below(4) { + 0 => (-(1 << 20), 1 << 20), + 1 => (-300, 300), + 2 => (-(1 << 40), 1 << 40), + _ => (i64::MIN / 2, i64::MAX / 2), + }; + let (h, w) = (r.u(1, 4), r.u(1, 7)); + let gr = one_op(SOFTMAX, vec![inp(chk(c, lo, hi), h, w)], vec![der(c)], Params::None, vec![]); + let (rh, rw) = (r.u(1, h), r.u(1, w)); + (gr, vec![Decode::new(vec![tensor(r, c, rh, rw, lo, hi)], vec![])]) + }); + // Rows that do not sum to 2^16; differences at n = 62 and n = 63 of exp_neg_q16. + let gr = one_op(SOFTMAX, vec![inp(chk(1, -(1 << 40), 1 << 40), 1, 5)], vec![der(1)], Params::None, vec![]); + for row in [vec![0i64, 0, 0], vec![1, 2, 3, 4, 5], vec![0, -(45426 * 62), -(45426 * 63), -(45426 * 63) + 1, 65536]] { + let w = row.len() as u32; + g.emit("rows", &gr, vec![Decode::new(vec![Tensor::new(1, 1, w, row)], vec![])]); + } + let gr = one_op(SOFTMAX, vec![inp(chk(1, -5, 5), 0, 0)], vec![der(1)], Params::None, vec![]); + g.emit("sizes", &gr, vec![]); +} + +fn sigmoid(g: &mut Gen) { + g.random(30, |r| { + let rg = match r.below(3) { + 0 => (-(1 << 20), 1 << 20), + 1 => (-300, 300), + _ => (-i64::MAX, i64::MAX), + }; + unary(r, SIGMOID, Params::None, rg) + }); + // An input range that stops at −(2^63 − 1), which decodes; n = 62 and 63. + let gr = one_op(SIGMOID, vec![inp(chk(1, -i64::MAX, i64::MAX), 0, 0)], vec![der(1)], Params::None, vec![]); + let x = vec![-i64::MAX, -(45426 * 63), -(45426 * 62), -1, 0, 1, 45426 * 62, 45426 * 63, i64::MAX]; + g.emit("edge", &gr, vec![Decode::new(vec![Tensor::new(1, 1, x.len() as u32, x)], vec![])]); +} + +fn elu(g: &mut Gen) { + g.random(30, |r| { + let alpha = r.pick(&[1i64 << 16, 1, -(1 << 16), 3, I32MAX, 1 << 40]); + let rg = match r.below(3) { + 0 => (-(1 << 20), 1 << 20), + 1 => (-300, 300), + _ => (-i64::MAX, i64::MAX), + }; + unary(r, ELU, Params::Elu { alpha }, rg) + }); + // elu(0) = ⌊17·α / 2^16⌋. + for alpha in [1i64 << 16, 1, -1, 3855, -3856] { + let gr = one_op(ELU, vec![inp(chk(1, -i64::MAX, i64::MAX), 0, 0)], vec![der(1)], Params::Elu { alpha }, vec![]); + let x = vec![0, -1, 1, -(45426 * 63), -i64::MAX, i64::MAX]; + g.emit("zero", &gr, vec![Decode::new(vec![Tensor::new(1, 1, x.len() as u32, x)], vec![])]); + } +} + +// ------------------------------------------------------------------ contractions + +fn gram(g: &mut Gen) { + g.random(40, |r| { + let groups = r.u(1, 2); + let c = groups * r.u(1, 2); + let s = shift_pick(r).min(40); + let round = r.pick(&[Round::Floor, Round::HalfUp, Round::HalfEven]); + let (lo, hi) = match r.below(3) { + 0 => (-300, 300), + 1 => (-(1 << 16), 1 << 16), + _ => (-(1 << 28), 1 << 28), + }; + let (h, w) = (r.u(1, 4), r.u(1, 4)); + let p = Params::Gram { groups, rq: Rq::plain(round, s) }; + let gr = one_op(GRAM, vec![inp(chk(c, lo, hi), h, w), inp(chk(c, lo, hi), h, w)], vec![der(c)], p, vec![]); + let (rh, rw) = (r.u(1, h), r.u(1, w)); + (gr, vec![Decode::new(vec![tensor(r, c, rh, rw, lo, hi), tensor(r, c, rh, rw, lo, hi)], vec![])]) + }); + let gr = one_op(GRAM, vec![inp(chk(2, -5, 5), 3, 3), inp(chk(2, -5, 5), 3, 3)], vec![der(2)], Params::Gram { groups: 1, rq: Rq::plain(Round::HalfUp, 1) }, vec![]); + g.emit("geometry", &gr, vec![Decode::new(vec![konst(2, 2, 2, 1), konst(2, 2, 3, 1)], vec![])]); +} + +fn apply(g: &mut Gen) { + g.random(40, |r| { + let groups = r.u(1, 2); + let d = r.u(1, 2); + let c = groups * d; + let s = shift_pick(r).min(40); + let round = r.pick(&[Round::Floor, Round::HalfUp, Round::HalfEven]); + let (lo, hi) = match r.below(3) { + 0 => (-300, 300), + 1 => (-(1 << 16), 1 << 16), + _ => (-(1 << 30), 1 << 30), + }; + let (h, w) = (r.u(1, 4), r.u(1, 4)); + let (mh, mw) = declare(r, h, w); + let p = Params::Apply { groups, rq: Rq::plain(round, s) }; + let gr = one_op(APPLY, vec![inp(chk(c, -(1 << 16), 1 << 16), if mh == 0 { 0 } else { 1 }, if mh == 0 { 0 } else { d }), inp(chk(c, lo, hi), mh, mw)], vec![der(c)], p, vec![]); + (gr, vec![Decode::new(vec![tensor(r, c, 1, d, -(1 << 16), 1 << 16), tensor(r, c, h, w, lo, hi)], vec![])]) + }); + let gr = one_op(APPLY, vec![inp(chk(2, -5, 5), 0, 0), inp(chk(2, -5, 5), 0, 0)], vec![der(2)], Params::Apply { groups: 1, rq: Rq::plain(Round::HalfUp, 1) }, vec![]); + g.emit("geometry", &gr, vec![Decode::new(vec![konst(2, 1, 3, 1), konst(2, 2, 2, 1)], vec![])]); + g.emit("geometry", &gr, vec![Decode::new(vec![konst(2, 1, 2, 1), konst(2, 2, 2, 1)], vec![])]); +} diff --git a/wai/wai-ops/src/generate/rng.rs b/wai/wai-ops/src/generate/rng.rs new file mode 100644 index 000000000..cd5a13f05 --- /dev/null +++ b/wai/wai-ops/src/generate/rng.rs @@ -0,0 +1,59 @@ +//! SplitMix64: the generator's only source of variety. Its seeds are per file, so a +//! change to one operator's cases leaves every other file's bytes alone. + +pub struct Rng(u64); + +impl Rng { + pub fn new(seed: u64) -> Rng { + Rng(seed) + } + /// A seed from a name (FNV-1a), so each file's sequence is its own. + pub fn named(name: &str) -> Rng { + let mut h: u64 = 0xcbf2_9ce4_8422_2325; + for b in name.bytes() { + h ^= b as u64; + h = h.wrapping_mul(0x0000_0100_0000_01b3); + } + Rng(h) + } + pub fn next(&mut self) -> u64 { + self.0 = self.0.wrapping_add(0x9e37_79b9_7f4a_7c15); + let mut z = self.0; + z = (z ^ (z >> 30)).wrapping_mul(0xbf58_476d_1ce4_e5b9); + z = (z ^ (z >> 27)).wrapping_mul(0x94d0_49bb_1331_11eb); + z ^ (z >> 31) + } + /// Uniform in `[lo, hi]` (inclusive), for any `lo ≤ hi` in `i128`'s range of + /// differences below `2^127`. + pub fn range(&mut self, lo: i128, hi: i128) -> i128 { + debug_assert!(lo <= hi); + let span = (hi - lo) as u128 + 1; + let r = ((self.next() as u128) << 64) | self.next() as u128; + lo + if span == 0 { r as i128 } else { (r % span) as i128 } + } + pub fn int(&mut self, lo: i64, hi: i64) -> i64 { + self.range(lo as i128, hi as i128) as i64 + } + pub fn u(&mut self, lo: u32, hi: u32) -> u32 { + self.range(lo as i128, hi as i128) as u32 + } + pub fn below(&mut self, n: usize) -> usize { + (self.next() % n as u64) as usize + } + pub fn chance(&mut self, num: u64, den: u64) -> bool { + self.next() % den < num + } + pub fn pick(&mut self, v: &[T]) -> T { + v[self.below(v.len())] + } + /// A value of `[lo, hi]`: an end a quarter of the time each way, zero (when in + /// range) a tenth of the time, otherwise uniform. + pub fn value(&mut self, lo: i64, hi: i64) -> i64 { + match self.next() % 20 { + 0..=4 => lo, + 5..=9 => hi, + 10..=11 if lo <= 0 && 0 <= hi => 0, + _ => self.int(lo, hi), + } + } +} diff --git a/wai/wai-ops/src/generate/sets.rs b/wai/wai-ops/src/generate/sets.rs new file mode 100644 index 000000000..8e1ec8dfe --- /dev/null +++ b/wai/wai-ops/src/generate/sets.rs @@ -0,0 +1,456 @@ +//! The parameter sets whose lowered graphs the corpus holds (§14.3): every committed +//! set, and a small synthetic set for each lowering, written here. + +use alloc::format; +use alloc::string::String; +use alloc::vec; +use alloc::vec::Vec; + +use super::rng::Rng; +use crate::json::{self, C, hex}; +use crate::lower::{self, Lowered}; +use crate::sha256::sha256; + +/// One set: its capability, its lowering, its members (the name in the set, then the +/// path below `wai/` that holds it), and any argument the lowering takes. +pub struct Set { + pub name: &'static str, + pub capability: &'static str, + pub lowering: &'static str, + pub members: Vec<(&'static str, String)>, + /// `world/1`'s latent channel count. + pub c: Option, + /// Why the set is here, for the index. + pub note: &'static str, +} + +/// The committed sets. +pub fn committed() -> Vec { + let mut v = Vec::new(); + for n in ["gradient", "bloom", "facade", "photo"] { + v.push(Set { + name: n, + capability: "wai.neural.int_synth", + lowering: "int_synth/1", + members: vec![("int_synth.json", format!("neural-conformance/vectors/{n}/int_synth.json"))], + c: None, + note: if n == "photo" { + "neural-conformance; its decoder was trained on images the corpus does not record (see that corpus's provenance)" + } else { + "neural-conformance, generated" + }, + }); + } + let tiny = vec![ + ("entropy.json", String::from("wai-rs/tests/vectors/int_hyper/tiny_entropy.json")), + ("g_s.whm", String::from("wai-rs/tests/vectors/int_hyper/tiny_g_s.bin")), + ("h_s.whm", String::from("wai-rs/tests/vectors/int_hyper/tiny_h_s.bin")), + ]; + v.push(Set { name: "tiny", capability: "wai.neural.int_hyper", lowering: "whm1/1", members: tiny.clone(), c: None, note: "int-payload-conformance `tiny`" }); + v.push(Set { name: "tiny", capability: "wai.video.int_hyper", lowering: "whm1/1", members: tiny, c: None, note: "int-payload-conformance `video_nk`, whose keyframe decodes against `tiny`" }); + v.push(Set { name: "audio", capability: "wai.audio.int_codec", lowering: "wam1/1", members: vec![("model.wam", String::from("wai-rs/tests/vectors/int_audio/audio_model.bin"))], c: None, note: "int-payload-conformance `audio`" }); + v.push(Set { name: "splat", capability: "wai.splat.int_codec", lowering: "whm1/1", members: vec![("g_s.whm", String::from("wai-rs/tests/vectors/int_splat/splat_gs.bin"))], c: None, note: "int-payload-conformance `splat`" }); + v.push(Set { name: "int_mlicpp", capability: "wai.neural.int_mlicpp", lowering: "wmb1/1", members: vec![("model.wmb", String::from("wai-rs/tests/vectors/int_mlicpp/model.wmb"))], c: None, note: "wai-rs/tests/vectors/int_mlicpp" }); + v.push(Set { + name: "world", + capability: "wai.world.model", + lowering: "world/1", + members: vec![("T.whm", String::from("wai-rs/tests/vectors/int_world/world_T.bin")), ("g_s.whm", String::from("wai-rs/tests/vectors/int_world/world_gs.bin"))], + c: Some(24), + note: "wai-rs/tests/vectors/int_world, one agent", + }); + v.push(Set { + name: "world2", + capability: "wai.world.model", + lowering: "world/1", + members: vec![("T.whm", String::from("wai-rs/tests/vectors/int_world2/world2_T.bin")), ("g_s.whm", String::from("wai-rs/tests/vectors/int_world2/world2_gs.bin"))], + c: Some(24), + note: "wai-rs/tests/vectors/int_world2, two agents", + }); + v +} + +/// The synthetic sets: their members' bytes, under `ops-conformance/sets//`. +pub fn synthetic() -> Vec<(Set, Vec<(&'static str, Vec)>)> { + let mut r = Rng::named("sets"); + let base = |n: &str, m: &str| format!("ops-conformance/sets/{n}/{m}"); + let mut v = Vec::new(); + let synth = int_synth_json(&mut r); + v.push(( + Set { name: "synth", capability: "wai.neural.int_synth", lowering: "int_synth/1", members: vec![("int_synth.json", base("synth", "int_synth.json"))], c: None, note: "synthetic" }, + vec![("int_synth.json", synth)], + )); + let (hs, gs, ent) = (whm1_h_s(&mut r), whm1_g_s(&mut r), entropy_json(&mut r, 3, 2)); + v.push(( + Set { + name: "hyper", + capability: "wai.neural.int_hyper", + lowering: "whm1/1", + members: vec![("entropy.json", base("hyper", "entropy.json")), ("g_s.whm", base("hyper", "g_s.whm")), ("h_s.whm", base("hyper", "h_s.whm"))], + c: None, + note: "synthetic", + }, + vec![("entropy.json", ent), ("g_s.whm", gs), ("h_s.whm", hs)], + )); + let wam = wam1_bytes(&mut r); + v.push(( + Set { name: "audio", capability: "wai.audio.int_codec", lowering: "wam1/1", members: vec![("model.wam", base("audio", "model.wam"))], c: None, note: "synthetic" }, + vec![("model.wam", wam)], + )); + let wmb = wmb1_bytes(&mut r); + v.push(( + Set { name: "mlicpp", capability: "wai.neural.int_mlicpp", lowering: "wmb1/1", members: vec![("model.wmb", base("mlicpp", "model.wmb"))], c: None, note: "synthetic" }, + vec![("model.wmb", wmb)], + )); + let (t, g) = (world_t(&mut r), world_gs(&mut r)); + v.push(( + Set { name: "world", capability: "wai.world.model", lowering: "world/1", members: vec![("T.whm", base("world", "T.whm")), ("g_s.whm", base("world", "g_s.whm"))], c: Some(2), note: "synthetic" }, + vec![("T.whm", t), ("g_s.whm", g)], + )); + let (norm, hy, huv) = jpegai_bundle(&mut r); + v.push(( + Set { + name: "jpegai", + capability: "wai.image.jpegai", + lowering: "jpegai-int/1", + members: vec![("norm.json", base("jpegai", "norm.json")), ("weights/hs_uv.json", base("jpegai", "weights/hs_uv.json")), ("weights/hs_y.json", base("jpegai", "weights/hs_y.json"))], + c: None, + note: "synthetic", + }, + vec![("norm.json", norm), ("weights/hs_uv.json", huv), ("weights/hs_y.json", hy)], + )); + v +} + +/// The set's digest (SPEC §3.1): one file's SHA-256, or its listing's. +pub fn set_digest(members: &[(&str, Vec)]) -> String { + if members.len() == 1 { + return hex(&sha256(&members[0].1)); + } + let mut lines: Vec<(&str, String)> = members.iter().map(|(n, b)| (*n, hex(&sha256(b)))).collect(); + lines.sort_by(|a, b| a.0.as_bytes().cmp(b.0.as_bytes())); + let mut listing = String::new(); + for (n, d) in lines { + listing.push_str(&d); + listing.push_str(" "); + listing.push_str(n); + listing.push('\n'); + } + hex(&sha256(listing.as_bytes())) +} + +/// Lower a set from its members' bytes. +pub fn lower_set(s: &Set, m: &[(&str, Vec)]) -> Result { + let get = |n: &str| m.iter().find(|(k, _)| *k == n).map(|(_, b)| b.as_slice()).ok_or_else(|| format!("{}: no member {n}", s.name)); + let r = match s.lowering { + "int_synth/1" => lower::int_synth(get("int_synth.json")?), + "whm1/1" if s.capability == "wai.splat.int_codec" => lower::splat(get("g_s.whm")?), + "whm1/1" => lower::int_hyper(get("h_s.whm")?, get("g_s.whm")?), + "wam1/1" => lower::wam1(get("model.wam")?).map(|g| Lowered { roles: vec![("g_s", g)] }), + "wmb1/1" => lower::wmb1(get("model.wmb")?), + "world/1" => lower::world(get("T.whm")?, get("g_s.whm")?, s.c.unwrap_or(0)), + "jpegai-int/1" => lower::jpegai(get("norm.json")?, get("weights/hs_y.json")?, get("weights/hs_uv.json")?), + _ => return Err(format!("{}: no lowering {}", s.name, s.lowering)), + }; + r.map_err(|e| format!("{}: the lowering refused ({:?})", s.name, e)) +} + +/// The index entry of a set. +pub fn index_entry(s: &Set, digest: &str, roles: &[&str], synthetic: bool) -> C { + let mut m = vec![ + ("capability", C::s(s.capability)), + ("lowering", C::s(s.lowering)), + ("members", C::Obj(s.members.iter().map(|(n, p)| (String::from(*n), C::s(p))).collect())), + ("name", C::s(s.name)), + ("note", C::s(s.note)), + ("roles", C::Arr(roles.iter().map(|r| C::s(&format!("ops-conformance/certificates/{}/{}/{}.whg1", s.capability, digest, r))).collect())), + ("set_sha256", C::s(digest)), + ("synthetic", C::Bool(synthetic)), + ]; + if let Some(c) = s.c { + m.push(("c", C::int(c as i128))); + } + C::obj(m) +} + +// ------------------------------------------------------------------ writers + +fn ints(v: &[i64]) -> String { + let mut s = String::from("["); + for (i, x) in v.iter().enumerate() { + if i > 0 { + s.push(','); + } + s.push_str(&format!("{x}")); + } + s.push(']'); + s +} + +fn int_synth_json(r: &mut Rng) -> Vec { + // Two layers: 2 → 3 (k 3, stride 2, pad 1, out_pad 1, ReLU), then 3 → 3. + let l = |r: &mut Rng, i: usize, o: usize, k: usize, s: usize, p: usize, op: usize, shift: u32, relu: bool| -> String { + let w: Vec = (0..i * o * k * k).map(|_| r.int(-40, 40)).collect(); + let b: Vec = (0..o).map(|_| r.int(-200, 200)).collect(); + let mut s_ = format!("{{\"in_ch\":{i},\"out_ch\":{o},\"k\":{k},\"stride\":{s},\"pad\":{p},\"out_pad\":{op},\"weight\":{},\"bias\":{},\"shift\":{shift}", ints(&w), ints(&b)); + if relu { + s_.push_str(",\"relu\":true"); + } + s_.push('}'); + s_ + }; + let a = l(r, 2, 3, 3, 2, 1, 1, 3, true); + let b = l(r, 3, 3, 3, 1, 1, 0, 4, false); + format!("{{\"layers\":[{a},{b}]}}\n").into_bytes() +} + +struct W(Vec); +impl W { + fn u32(&mut self, v: u32) { + self.0.extend_from_slice(&v.to_le_bytes()); + } + fn i16s(&mut self, v: &[i64]) { + for &x in v { + self.0.extend_from_slice(&(x as i16).to_le_bytes()); + } + } + fn i32s(&mut self, v: &[i64]) { + for &x in v { + self.0.extend_from_slice(&(x as i32).to_le_bytes()); + } + } + fn i64s(&mut self, v: &[i64]) { + for &x in v { + self.0.extend_from_slice(&x.to_le_bytes()); + } + } +} + +fn rv(r: &mut Rng, n: usize, m: i64) -> Vec { + (0..n).map(|_| r.int(-m, m)).collect() +} + +/// A WHM1 or WAM1 header: magic, afrac, gf, shift, the layer count. +fn head(magic: &[u8; 4], afrac: u32, gf: u32, shift: u32, n: u32) -> W { + let mut w = W(magic.to_vec()); + w.u32(afrac); + w.u32(gf); + w.u32(shift); + w.u32(n); + w +} + +fn conv_layer(w: &mut W, r: &mut Rng, transposed: bool, inc: u32, out: u32, k: u32, stride: u32, pad: u32, opad: u32) { + w.0.push(if transposed { 0 } else { 1 }); + for v in [inc, out, k, stride, pad] { + w.u32(v); + } + if transposed { + w.u32(opad); + } + w.i16s(&rv(r, (inc * out * k * k) as usize, 300)); + w.i64s(&(0..out).map(|_| r.int(1, 1 << 12)).collect::>()); + w.i64s(&rv(r, out as usize, 1 << 10)); +} + +fn igdn_layer(w: &mut W, r: &mut Rng, c: u32) { + w.0.push(2); + w.u32(c); + w.i32s(&(0..c * c).map(|_| r.int(0, 1 << 10)).collect::>()); + w.i64s(&(0..c).map(|_| r.int(1 << 14, 1 << 16)).collect::>()); +} + +fn whm1_h_s(r: &mut Rng) -> Vec { + // z (2 channels) → conv 2→4 (k 3, pad 1), ReLU, convT 4→4 (k 3, stride 2, pad 1, + // opad 1): σ‖μ for m = 2. + let mut w = head(b"WHM1", 8, 10, 12, 3); + conv_layer(&mut w, r, false, 2, 4, 3, 1, 1, 0); + w.0.push(3); + conv_layer(&mut w, r, true, 4, 4, 3, 2, 1, 1); + w.0 +} + +fn whm1_g_s(r: &mut Rng) -> Vec { + // y (2 channels) → convT 2→3 (k 3, stride 2, pad 1, opad 1), IGDN, leaky, conv 3→3. + let mut w = head(b"WHM1", 8, 10, 12, 4); + conv_layer(&mut w, r, true, 2, 3, 3, 2, 1, 1); + igdn_layer(&mut w, r, 3); + w.0.push(4); + w.i64s(&[655]); + w.u32(12); + conv_layer(&mut w, r, false, 3, 3, 1, 1, 0, 0); + w.0 +} + +/// An `entropy.json` (integer-payloads §9) with `n` scale edges and `k` hyper-latent +/// tables. +fn entropy_json(r: &mut Rng, n: usize, k: usize) -> Vec { + let table = |r: &mut Rng, len: usize| -> Vec { + let mut inner: Vec = Vec::new(); + while inner.len() < len - 2 { + let v = r.int(1, 65535); + if !inner.contains(&v) { + inner.push(v); + } + } + inner.sort_unstable(); + let mut t = vec![0]; + t.extend(inner); + t.push(65536); + t + }; + let rows = |r: &mut Rng, n: usize| -> (Vec, Vec) { + let mut v = Vec::new(); + let mut lens = Vec::new(); + for _ in 0..n { + let len = r.u(3, 9) as usize; + v.push(ints(&table(r, len))); + lens.push(len as i64); + } + (v, lens) + }; + let (c, cl) = rows(r, n); + let (zc, zl) = rows(r, k); + let mut edges: Vec = (0..n).map(|_| r.int(1, 1 << 12)).collect(); + edges.sort_unstable(); + format!( + "{{\"scale_edges\":{},\"cdfs\":[{}],\"cdf_lengths\":{},\"offsets\":{},\"z_cdfs\":[{}],\"z_cdf_lengths\":{},\"z_offsets\":{},\"z_median_q\":{}}}\n", + ints(&edges), + c.join(","), + ints(&cl), + ints(&(0..n).map(|_| r.int(-3, 0)).collect::>()), + zc.join(","), + ints(&zl), + ints(&(0..k).map(|_| r.int(-3, 0)).collect::>()), + ints(&(0..k).map(|_| r.int(-500, 500)).collect::>()), + ) + .into_bytes() +} + +fn wam1_bytes(r: &mut Rng) -> Vec { + // [2, 1, T] → convT1d 2→2 (k 4, stride 2, pad 1), IGDN, convT1d 2→1 (k 3, pad 1). + let mut w = head(b"WAM1", 10, 12, 12, 3); + for (inc, out, k, s, p, op) in [(2u32, 2u32, 4u32, 2u32, 1u32, 0u32)] { + w.0.push(0); + for v in [inc, out, k, s, p, op] { + w.u32(v); + } + w.i16s(&rv(r, (inc * out * k) as usize, 400)); + w.i64s(&(0..out).map(|_| r.int(1, 1 << 12)).collect::>()); + w.i64s(&rv(r, out as usize, 1 << 10)); + } + igdn_layer(&mut w, r, 2); + w.0.push(0); + for v in [2u32, 1, 3, 1, 1, 0] { + w.u32(v); + } + w.i16s(&rv(r, 6, 400)); + w.i64s(&[r.int(1, 1 << 12)]); + w.i64s(&rv(r, 1, 1 << 10)); + w.0 +} + +fn wmb1_bytes(r: &mut Rng) -> Vec { + // h_s: z (1) → [2·Cy = 4]; entropy: [3·Cy = 6] → [4]; g_s: [2] → [3]; afrac 8. + let mut h = head(b"WHM1", 8, 10, 12, 2); + conv_layer(&mut h, r, true, 1, 4, 2, 2, 0, 0); + h.0.push(3); + let mut e = head(b"WHM1", 8, 10, 12, 2); + conv_layer(&mut e, r, false, 6, 4, 3, 1, 1, 0); + e.0.push(4); + e.i64s(&[-13107]); + e.u32(16); + let mut g = head(b"WHM1", 8, 10, 12, 2); + conv_layer(&mut g, r, true, 2, 3, 3, 2, 1, 1); + igdn_layer(&mut g, r, 3); + let mut tables = entropy_json(r, 4, 1); + // The tables section also carries `afrac`. + tables.truncate(tables.len() - 2); + tables.extend_from_slice(b",\"afrac\":8}"); + let mut out = b"WMB1".to_vec(); + for s in [&h.0, &e.0, &g.0, &tables] { + out.extend_from_slice(&(s.len() as u32).to_le_bytes()); + out.extend_from_slice(s); + } + out +} + +fn world_t(r: &mut Rng) -> Vec { + // C = 2 latent channels, A = 3 planes: 5 → 4 (k 3, pad 1), leaky, 4 → 2 (k 1). + let mut w = head(b"WHM1", 8, 10, 12, 3); + conv_layer(&mut w, r, false, 5, 4, 3, 1, 1, 0); + w.0.push(4); + w.i64s(&[655]); + w.u32(12); + conv_layer(&mut w, r, false, 4, 2, 1, 1, 0, 0); + w.0 +} + +fn world_gs(r: &mut Rng) -> Vec { + let mut w = head(b"WHM1", 8, 10, 12, 2); + conv_layer(&mut w, r, true, 2, 3, 3, 2, 1, 1); + igdn_layer(&mut w, r, 3); + w.0 +} + +/// A synthetic `wai.image.jpegai` bundle: `norm.json` and the two scale networks, at +/// the channel counts §13.6.1 fixes (`Cz` 160 and 96) and two channels inside. +fn jpegai_bundle(r: &mut Rng) -> (Vec, Vec, Vec) { + let mut pmf = Vec::new(); + for _ in 0..32 { + let mut row = vec![0i64; 4]; + for _ in 0..256 { + row[r.below(4)] += 1; + } + pmf.push(ints(&row)); + } + let cdfs = |r: &mut Rng, rows: usize| -> Vec { + let mut v = Vec::new(); + for _ in 0..rows { + let mut row: Vec = (0..63).map(|_| r.int(0, 255)).collect(); + row.sort_unstable(); + v.extend(row); + } + v + }; + let norm = format!( + "{{\"pmf\":[{}],\"bounds\":{},\"per_channel\":16,\"cdfs_y\":{},\"cdfs_uv\":{},\"gain_y\":{},\"gain_uv\":{},\"float_member\":[0.5,1.25e2,-2.0]}}\n", + pmf.join(","), + ints(&(0..32).map(|_| r.int(0, 255)).collect::>()), + ints(&cdfs(r, 160)), + ints(&cdfs(r, 96)), + ints(&(0..160).map(|_| r.int(-687, 6)).collect::>()), + ints(&(0..96).map(|_| r.int(-687, 6)).collect::>()), + ); + let net = |r: &mut Rng, cz: usize| -> Vec { + let (c1, c2, c3) = (2usize, 2usize, 16 * cz); + let t = |dims: &[usize], v: &[i64]| -> String { + format!("{{\"dims\":{},\"v\":{}}}", ints(&dims.iter().map(|&d| d as i64).collect::>()), ints(v)) + }; + format!( + "{{\"conv1.weight\":{},\"conv1.bias\":{},\"onnx::Pow_85\":{},\"depthwise.weight\":{},\"depthwise.bias\":{},\"onnx::Pow_91\":{},\"pointwise.weight\":{},\"pointwise.bias\":{},\"onnx::Pow_97\":{}}}\n", + t(&[c1, cz, 1, 1], &rv(r, c1 * cz, 128)), + t(&[c1], &(0..c1).map(|_| r.int(1, 8)).collect::>()), + t(&[1, c1, 1, 1], &(0..c1).map(|_| r.int(1, 4)).collect::>()), + t(&[c2, c1, 3, 3], &rv(r, c2 * c1 * 9, 128)), + t(&[c2], &(0..c2).map(|_| r.int(-44270, 21049)).collect::>()), + t(&[1, c2, 1, 1], &(0..c2).map(|_| r.int(7, 12)).collect::>()), + t(&[c3, c2, 1, 1], &rv(r, c3 * c2, 128)), + t(&[c3], &(0..c3).map(|_| r.int(1, 64)).collect::>()), + t(&[1, c3, 1, 1], &(0..c3).map(|_| r.int(1, 7)).collect::>()), + ) + .into_bytes() + }; + let hy = net(r, 160); + let huv = net(r, 96); + (norm.into_bytes(), hy, huv) +} + +/// Write the index (`certificates/index.json`): pretty enough to read, and +/// canonical in its member order. +pub fn index_json(entries: Vec) -> Vec { + let c = C::obj(vec![("format", C::s("wai-ops-lowered/1")), ("sets", C::Arr(entries))]); + let mut s = json::to_string(&c); + s.push('\n'); + s.into_bytes() +} diff --git a/wai/wai-ops/src/generate/stages.rs b/wai/wai-ops/src/generate/stages.rs new file mode 100644 index 000000000..496cef25b --- /dev/null +++ b/wai/wai-ops/src/generate/stages.rs @@ -0,0 +1,660 @@ +//! `stage/.jsonl`: the stage operators' vectors (§14.1). + +use alloc::format; +use alloc::string::String; +use alloc::vec; +use alloc::vec::Vec; + +use super::build::File; +use super::rng::Rng; +use crate::interp::Tensor; +use crate::json::{self, C, hex}; +use crate::vectors; + +const L: u64 = 1 << 31; + +/// One step of a rANS stream, in decode order. +#[derive(Clone, Copy)] +enum Step { + Sym { start: u64, freq: u64 }, + Bits { n: u32, v: u64 }, +} + +/// Encode `steps` (in decode order) from the state `init`: the inverse of +/// integer-payloads §2's decode. The stream is the final state's two words, then the +/// words the encoder emitted, last emitted first. +fn encode(steps: &[Step], init: u64) -> Vec { + let mut x = init; + let mut words: Vec = Vec::new(); + for s in steps.iter().rev() { + match *s { + Step::Sym { start, freq } => { + let x_max = ((L >> 16) << 32) * freq; + if x >= x_max { + words.push(x as u32); + x >>= 32; + } + x = ((x / freq) << 16) + (x % freq) + start; + } + Step::Bits { n, v } => { + let x_max = (L >> n) << 32; + if x >= x_max { + words.push(x as u32); + x >>= 32; + } + x = (x << n) | v; + } + } + } + let mut out = Vec::new(); + out.extend_from_slice(&(x as u32).to_le_bytes()); + out.extend_from_slice(&((x >> 32) as u32).to_le_bytes()); + for w in words.iter().rev() { + out.extend_from_slice(&w.to_le_bytes()); + } + out +} + +/// A random table of `n` entries. +fn table(r: &mut Rng, n: usize) -> Vec { + let mut pts: Vec = Vec::with_capacity(n); + pts.push(0); + // n − 2 distinct interior points in (0, 65536). + let mut inner: Vec = Vec::new(); + while inner.len() < n - 2 { + let v = r.int(1, 65535); + if !inner.contains(&v) { + inner.push(v); + } + } + inner.sort_unstable(); + pts.extend(inner); + pts.push(65536); + pts +} + +/// A table with the escape slot `[65535, 65536)`, of `n ≥ 2` entries. +fn esc1_table(n: usize) -> Vec { + let mut t: Vec = (0..n - 1).map(|i| (i as i64 * 65535) / (n as i64 - 1).max(1)).collect(); + t[n - 2] = 65535; + t.push(65536); + t +} + +/// The steps that decode `value` from table `cdf` (with offset subtracted already), +/// minimal escape. +fn steps_for(cdf: &[i64], value: i64, r: &mut Rng) -> Vec { + let n = cdf.len() as i64; + let e = n - 2; + if (0..e).contains(&value) { + let s = value as usize; + let (start, freq) = (cdf[s] as u64, (cdf[s + 1] - cdf[s]) as u64); + let _ = r; + return vec![Step::Sym { start, freq }]; + } + let raw: u64 = if value >= e { 2 * (value - e) as u64 } else { 2 * (-value - 1) as u64 + 1 }; + let mut count = 0u32; + while count < 8 && (raw >> (4 * count)) != 0 { + count += 1; + } + let mut v = vec![Step::Sym { start: cdf[e as usize] as u64, freq: (cdf[e as usize + 1] - cdf[e as usize]) as u64 }, Step::Bits { n: 4, v: count as u64 }]; + for j in 0..count { + v.push(Step::Bits { n: 4, v: (raw >> (4 * j)) & 15 }); + } + v +} + +fn ints(v: &[i64]) -> C { + C::ints(v.iter().map(|&x| x as i128)) +} + +fn tensor_c(t: &Tensor) -> C { + vectors::tensor_c(t) +} + +struct S { + file: File, + op: &'static str, + n: usize, +} + +impl S { + fn new(op: &'static str) -> S { + S { file: File::default(), op, n: 0 } + } + /// Write a vector with `in_` and `modes`, its `expect` computed. + fn emit(&mut self, class: &str, in_: C, modes: C) { + let id = format!("{}/{}/{}", self.op, class, self.n); + self.n += 1; + let c = C::obj(vec![("id", C::Str(id)), ("in", in_), ("modes", modes), ("op", C::s(self.op))]); + let v = json::parse(json::to_string(&c).as_bytes()).expect("a generated line parses"); + let l = vectors::stage_line(&v).ok().map(|c| json::to_string(&c)); + self.file.push(l); + } +} + +fn modes_rans(table: &str, escape: Option<(i64, i64)>, end: &str) -> C { + let mut m = vec![("table", C::s(table)), ("end", C::s(end))]; + match escape { + None => m.push(("escape", C::s("lenient"))), + Some((lo, hi)) => { + m.push(("escape", C::s("strict"))); + m.push(("lo", C::int(lo as i128))); + m.push(("hi", C::int(hi as i128))); + } + } + C::obj(m) +} + +fn rans_in(stream: &[u8], tables: &[(Vec, i64)], reads: &[usize]) -> C { + C::obj(vec![ + ("reads", C::ints(reads.iter().map(|&x| x as i128))), + ("stream", C::Str(hex(stream))), + ("tables", C::Arr(tables.iter().map(|(c, o)| C::obj(vec![("cdf", ints(c)), ("offset", C::int(*o as i128))])).collect())), + ]) +} + +/// Every stage file: `(name, text, dropped)`. +pub fn all() -> Vec<(&'static str, String, usize)> { + let mut v = Vec::new(); + for (name, f) in [ + ("rans64_decode", rans64()), + ("dequantize", dequantize()), + ("bucket", bucket()), + ("coded", coded()), + ("scale_index", scale_index()), + ("block_stat", block_stat()), + ("band_lookup", band_lookup()), + ("mans_decode", mans()), + ("residual_step", residual_step()), + ("mc_predict", mc_predict()), + ("up2_predict", up2_predict()), + ("rate_predict", rate_predict()), + ("coding_order", coding_order()), + ] { + v.push((name, f.text(), f.dropped)); + } + v +} + +// ------------------------------------------------------------------ E1 + +fn rans64() -> File { + let mut s = S::new("rans64_decode"); + let mut r = Rng::named("rans64_decode"); + let ends = ["canonical", "final_only"]; + // Encoded streams under every mode: random tables (t4097 and t2 sizes) and values, + // escapes among them. + for k in 0..160 { + let t2 = k % 2 == 1; + let ntab = r.u(1, 3) as usize; + let tables: Vec<(Vec, i64)> = (0..ntab) + .map(|_| { + let n = if t2 && r.chance(1, 4) { 2 } else { r.pick(&[3usize, 4, 5, 9, 17, 64, 300]) }; + let off = r.pick(&[0i64, -8, 5, i32::MIN as i64, i32::MAX as i64 - 3, -1000]); + (table(&mut r, n), off) + }) + .collect(); + let nsym = r.u(0, 30) as usize; + let mut steps = Vec::new(); + let mut reads = Vec::new(); + for _ in 0..nsym { + let t = r.below(ntab); + let n = tables[t].0.len() as i64; + let value = match r.below(6) { + 0 => r.int(-200, -1), + 1 => r.int(n - 2, n + 5000), + 2 => r.pick(&[-(1i64 << 31), (1 << 31) - 1, n - 2 + (1 << 31) - 1, -(1 << 20)]), + _ => r.int(0, (n - 3).max(0)), + }; + steps.extend(steps_for(&tables[t].0, value, &mut r)); + reads.push(t); + } + let mut stream = encode(&steps, L); + let class = match r.below(8) { + 0 => { + // A word after the last symbol's. + stream.extend_from_slice(&r.next().to_le_bytes()[..4]); + "extra_word" + } + 1 => { + // An end state the encoder did not start from. + stream = encode(&steps, L + 1 + r.next() % 1000); + "end_state" + } + 2 if stream.len() > 8 => { + // The stream cut short: it runs out before its last symbol. + let cut = 4 * r.u(1, ((stream.len() - 8) / 4) as u32) as usize; + stream.truncate(stream.len() - cut); + "short" + } + _ => "encoded", + }; + let in_ = rans_in(&stream, &tables, &reads); + for end in ends { + s.emit(class, in_.clone(), modes_rans(if t2 { "t2" } else { "t4097" }, None, end)); + } + } + // The lenient escape's continuation: counts of 15 that continue, a count past 8 + // that is clamped, and the stream running out within the count groups. + let cdf = esc1_table(5); + for nibbles in [vec![15u64, 3, 1, 2, 3, 4, 5, 6, 7, 8], vec![15, 15, 15, 15, 15, 15, 15, 15, 15, 9, 1, 1, 1, 1, 1, 1, 1, 1], vec![12, 1, 2, 3, 4, 5, 6, 7, 8], vec![9, 0, 0, 0, 0, 0, 0, 0, 1]] { + let mut steps = vec![Step::Sym { start: 65535, freq: 1 }]; + steps.extend(nibbles.iter().map(|&v| Step::Bits { n: 4, v })); + let stream = encode(&steps, L); + for end in ends { + s.emit("lenient_count", rans_in(&stream, &[(cdf.clone(), 0)], &[0]), modes_rans("t4097", None, end)); + } + } + // Strict: each refusal alone, and each pair one symbol can give, under both end + // rules. The run-out cases are 8-byte streams whose state, after the escape's table + // step, is below 2^31 with no word left: every later read is from that state. + let n = 5usize; + let cdf = esc1_table(n); + let direct = |nibbles: &[u64]| -> Vec { + let mut a: u64 = 0; + for (i, &v) in nibbles.iter().enumerate() { + a |= v << (4 * i); + } + let x = (a << 16) | 0xFFFF; + let mut b = Vec::new(); + b.extend_from_slice(&(x as u32).to_le_bytes()); + b.extend_from_slice(&((x >> 32) as u32).to_le_bytes()); + b + }; + let enc = |nibbles: &[u64]| -> Vec { + let mut steps = vec![Step::Sym { start: 65535, freq: 1 }]; + steps.extend(nibbles.iter().map(|&v| Step::Bits { n: 4, v })); + encode(&steps, L) + }; + // (value of the nibbles once decoded under strict, for the range): raw 6 → esc + 3; + // raw 5 → −3; count 9 → esc. + let cases: [(&str, bool, Vec, (i64, i64)); 10] = [ + ("exhausted", true, vec![1, 6], (-128, 127)), + ("count", false, vec![9], (-128, 127)), + ("minimal", false, vec![2, 5, 0], (-128, 127)), + ("range", false, vec![1, 6], (-2, 2)), + ("range_plain", false, vec![], (-2, 1)), + ("exhausted+count", true, vec![9], (-128, 127)), + ("exhausted+minimal", true, vec![2, 5, 0], (-128, 127)), + ("exhausted+range", true, vec![1, 6], (-2, 2)), + ("count+range", false, vec![9], (-2, 2)), + ("minimal+range", false, vec![2, 5, 0], (-2, 2)), + ]; + for (name, out, nib, rng) in cases.iter() { + let stream = if *out { + // Run out in the table step; the groups are read from the state left. + direct(nib) + } else if nib.is_empty() { + // A plain symbol, slot 2 (value 2), outside [lo, hi] = [−2, 1]. + encode(&[Step::Sym { start: cdf[2] as u64, freq: (cdf[3] - cdf[2]) as u64 }], L) + } else { + enc(nib) + }; + for end in ends { + s.emit(&format!("strict_{name}"), rans_in(&stream, &[(cdf.clone(), 0)], &[0]), modes_rans("t4097", Some(*rng), end)); + } + } + // Strict over longer encoded streams, minimal escapes, values in and out of range. + for _ in 0..40 { + let tn = r.pick(&[3usize, 5, 9, 33]); + let tab = table(&mut r, tn); + let off = r.int(-5, 5); + let (lo, hi) = (r.int(-300, 0), r.int(0, 300)); + let nsym = r.u(1, 20) as usize; + let mut steps = Vec::new(); + for _ in 0..nsym { + let v = r.int(lo - off - 20, hi - off + 20); + steps.extend(steps_for(&tab, v, &mut r)); + } + let stream = encode(&steps, L); + for end in ends { + s.emit("strict", rans_in(&stream, &[(tab.clone(), off)], &vec![0; nsym]), modes_rans(if r.chance(1, 2) { "t2" } else { "t4097" }, Some((lo, hi)), end)); + } + } + // Table rules: n = 2 under t2 (every symbol escapes) and under t4097 (refused); + // n = 4097 and 4098; a table not strictly increasing, or not ending at 65536. + let two = vec![0i64, 65536]; + let st = encode(&[Step::Sym { start: 0, freq: 65536 }, Step::Bits { n: 4, v: 1 }, Step::Bits { n: 4, v: 3 }], L); + s.emit("table", rans_in(&st, &[(two.clone(), 7)], &[0]), modes_rans("t2", None, "canonical")); + s.emit("table", rans_in(&st, &[(two, 7)], &[0]), modes_rans("t4097", None, "canonical")); + for n in [4097usize, 4098] { + let t = table(&mut r, n); + let st = encode(&steps_for(&t, 7, &mut r), L); + for rule in ["t4097", "t2"] { + s.emit("table", rans_in(&st, &[(t.clone(), 0)], &[0]), modes_rans(rule, None, "canonical")); + } + } + for bad in [vec![0i64, 10, 10, 65536], vec![0, 10, 65535], vec![1, 10, 65536]] { + let st = encode(&[Step::Sym { start: 0, freq: 10 }], L); + s.emit("table", rans_in(&st, &[(bad, 0)], &[0]), modes_rans("t2", None, "canonical")); + } + // A stream of no symbol: the state 2^31 alone, and with a word after it. + s.emit("empty", rans_in(&encode(&[], L), &[(esc1_table(3), 0)], &[]), modes_rans("t4097", None, "canonical")); + let mut st = encode(&[], L); + st.extend_from_slice(&[0, 0, 0, 0]); + s.emit("empty", rans_in(&st, &[(esc1_table(3), 0)], &[]), modes_rans("t4097", None, "canonical")); + s.file +} + +// ------------------------------------------------------------------ E2–E7 + +fn dequantize() -> File { + let mut s = S::new("dequantize"); + let mut r = Rng::named("dequantize"); + let one = |sym: i64, f: i64, base: i64| C::obj(vec![("base", C::int(base as i128)), ("f", C::int(f as i128)), ("sym", C::int(sym as i128))]); + for _ in 0..60 { + let f = r.int(0, 62); + let (a, b) = (r.int(-1000, 1000), r.int(-(1 << 33), 1 << 33)); + let sym = r.pick(&[a, i32::MIN as i64, i32::MAX as i64, b]); + let m = r.int(-(1 << 47), 1 << 47); + let base = r.pick(&[0, m, i64::MIN, i64::MAX]); + s.emit("random", one(sym, f, base), C::obj(vec![])); + } + // f past 62; the value at the i64 limits and one past. + for (sym, f, base) in [(1, 63, 0), (1, 62, (1 << 62) - 1), (1, 62, 1 << 62), (-2, 62, 0), (-2, 62, -1), (i32::MAX as i64, 32, 0), (1 << 33, 30, 0)] { + s.emit("edge", one(sym, f, base), C::obj(vec![])); + } + s.file +} + +fn bucket() -> File { + let mut s = S::new("bucket"); + let mut r = Rng::named("bucket"); + for k in 0..60 { + let rule = if k % 2 == 0 { "ceil" } else { "floor" }; + let n = r.u(1, 6) as usize; + let mut e: Vec = (0..n).map(|_| r.int(-1000, 1000)).collect(); + e.sort_unstable(); + let sigma = if r.chance(1, 2) { e[r.below(n)] } else { r.int(-1200, 1200) }; + s.emit("random", C::obj(vec![("edges", ints(&e)), ("sigma", C::int(sigma as i128))]), C::obj(vec![("bucket", C::s(rule))])); + } + // Exactly at each edge, below and above all; unsorted edges (ceil reads any; floor + // refuses); no edges. + let e = vec![-5i64, 0, 0, 7, 100]; + for sigma in [-6i64, -5, -4, 0, 1, 7, 99, 100, 101, i64::MIN, i64::MAX] { + for rule in ["ceil", "floor"] { + s.emit("edge", C::obj(vec![("edges", ints(&e)), ("sigma", C::int(sigma as i128))]), C::obj(vec![("bucket", C::s(rule))])); + } + } + for rule in ["ceil", "floor"] { + s.emit("edges", C::obj(vec![("edges", ints(&[9, -3, 4])), ("sigma", C::int(4))]), C::obj(vec![("bucket", C::s(rule))])); + s.emit("edges", C::obj(vec![("edges", ints(&[])), ("sigma", C::int(4))]), C::obj(vec![("bucket", C::s(rule))])); + s.emit("edges", C::obj(vec![("edges", ints(&[3])), ("sigma", C::int(4))]), C::obj(vec![("bucket", C::s(rule))])); + } + s.file +} + +fn coded() -> File { + let mut s = S::new("coded"); + for (v, t) in [(383i64, 382i64), (382, 382), (381, 382), (i64::MIN, i64::MIN), (i64::MAX, i64::MIN), (0, -1), (-1, 0)] { + s.emit("edge", C::obj(vec![("thr", C::int(t as i128)), ("v", C::int(v as i128))]), C::obj(vec![])); + } + s.file +} + +fn scale_index() -> File { + let mut s = S::new("scale_index"); + let mut r = Rng::named("scale_index"); + let one = |v: i64, sh: i64, n: i64| C::obj(vec![("n", C::int(n as i128)), ("s", C::int(sh as i128)), ("v", C::int(v as i128))]); + for _ in 0..40 { + let sh = r.int(0, 10); + s.emit("random", one(r.int(-5000, 5000), sh, r.int(1, 40)), C::obj(vec![])); + } + for (v, sh, n) in [(64i64, 7i64, 32i64), (63, 7, 32), (-64, 7, 32), (-65, 7, 32), (3967 + 4351, 7, 32), (i64::MAX, 7, 32), (i64::MIN, 0, 1), (5, 0, 0)] { + s.emit("edge", one(v, sh, n), C::obj(vec![])); + } + s.file +} + +fn block_stat() -> File { + let mut s = S::new("block_stat"); + let mut r = Rng::named("block_stat"); + for _ in 0..30 { + let (c, h, w) = (r.u(1, 2), r.u(1, 12), r.u(1, 12)); + let x = Tensor::new(c, h, w, (0..c * h * w).map(|_| r.int(-500, 4351)).collect()); + let b = r.u(1, 9) as i64; + let sh = r.int(0, 8); + s.emit("random", C::obj(vec![("b", C::int(b as i128)), ("hi", C::int(4351)), ("lo", C::int(0)), ("s", C::int(sh as i128)), ("x", tensor_c(&x))]), C::obj(vec![])); + } + // The jpegai settings: 8×8 blocks, shift 6, a partial last block; b = 0 refused. + let x = Tensor::new(1, 16, 13, (0..208).map(|i| (i * 37) % 4400).collect()); + for b in [8i64, 0] { + s.emit("edge", C::obj(vec![("b", C::int(b as i128)), ("hi", C::int(4351)), ("lo", C::int(0)), ("s", C::int(6)), ("x", tensor_c(&x))]), C::obj(vec![])); + } + s.file +} + +fn band_lookup() -> File { + let mut s = S::new("band_lookup"); + let table = vec![vec![-31i64, 20, 122, 181], vec![-80, -31, 71, 130]]; + let tc = C::Arr(table.iter().map(|r| ints(r)).collect()); + for v in [0i64, 1002, 1003, 1004, 1351, 1352, 1936, 1937, 1938, 4351] { + for row in [0i64, 1] { + s.emit("edge", C::obj(vec![("edges", ints(&[1003, 1352, 1937])), ("row", C::int(row as i128)), ("table", tc.clone()), ("v", C::int(v as i128))]), C::obj(vec![])); + } + } + s.emit("edge", C::obj(vec![("edges", ints(&[1003, 1352, 1937])), ("row", C::int(2)), ("table", tc.clone()), ("v", C::int(0))]), C::obj(vec![])); + s.emit("edge", C::obj(vec![("edges", ints(&[1003, 1352])), ("row", C::int(0)), ("table", tc), ("v", C::int(0))]), C::obj(vec![])); + s.file +} + +// ------------------------------------------------------------------ E8 + +fn mans() -> File { + let mut s = S::new("mans_decode"); + let mut r = Rng::named("mans_decode"); + // A pmf row of width w summing to 256. + let row = |r: &mut Rng, w: usize| -> Vec { + let mut v = vec![0i64; w]; + for _ in 0..256 { + let k = if r.chance(1, 2) { r.below(w.min(8)) } else { r.below(w) }; + v[k] += 1; + } + v + }; + let sgm = |mem: &[u8], pmf: &[Vec], bounds: &[i64], spans: &[(Vec, Vec)]| -> C { + C::obj(vec![ + ("bounds", ints(bounds)), + ("memory", C::Str(hex(mem))), + ("mode", C::s("sgm")), + ("pmf", C::Arr(pmf.iter().map(|p| ints(p)).collect())), + ("spans", C::Arr(spans.iter().map(|(m, g)| C::obj(vec![("mask", C::Arr(m.iter().map(|&b| C::Bool(b)).collect())), ("sigma", ints(g))])).collect())), + ]) + }; + let fac = |mem: &[u8], rows: &[Vec], lens: &[i64]| -> C { + C::obj(vec![("lengths", ints(lens)), ("memory", C::Str(hex(mem))), ("mode", C::s("fac")), ("rows", C::Arr(rows.iter().map(|p| ints(p)).collect()))]) + }; + let bytes = |r: &mut Rng, n: usize| -> Vec { (0..n).map(|_| r.next() as u8).collect() }; + // sgm: random tables and memory; spans of every length mod 4; masks. + for _ in 0..60 { + let nd = r.u(1, 4) as usize; + let w = r.pick(&[1usize, 2, 5, 16, 64, 256]); + let pmf: Vec> = (0..nd).map(|_| row(&mut r, w)).collect(); + let bounds: Vec = (0..nd).map(|_| r.pick(&[0i64, 1, 4, 30, 128, 255])).collect(); + let nspan = r.u(1, 3) as usize; + let spans: Vec<(Vec, Vec)> = (0..nspan) + .map(|_| { + let len = r.u(0, 13) as usize; + ((0..len).map(|_| r.chance(3, 4)).collect(), (0..len).map(|_| r.int(0, nd as i64 - 1)).collect()) + }) + .collect(); + let total: usize = spans.iter().map(|(m, _)| m.len()).sum(); + let len = if r.chance(1, 4) { r.below(12) } else { 3 * total + 16 }; + let mem = bytes(&mut r, len); + s.emit("sgm", sgm(&mem, &pmf, &bounds, &spans), C::obj(vec![])); + } + // Escapes at a bound of 128 and of 255: every slot of the row holds the symbol −β. + for (beta, w) in [(128i64, 256usize), (255, 510)] { + let mut p = vec![0i64; w]; + p[w - 1] = 256; + for _ in 0..4 { + let len = r.u(1, 9) as usize; + let spans = vec![(vec![true; len], vec![0i64; len])]; + let mem = bytes(&mut r, 8 * len + 24); + s.emit("escape", sgm(&mem, &[p.clone()], &[beta], &spans), C::obj(vec![])); + } + } + // A zero tail (no sentinel bit) and a short stream: both refused. + let p = vec![row(&mut r, 8)]; + let spans = vec![(vec![true; 6], vec![0i64; 6])]; + let mut zt = bytes(&mut r, 24); + for b in zt.iter_mut().skip(16) { + *b = 0; + } + s.emit("refuse", sgm(&zt, &p, &[4], &spans), C::obj(vec![])); + s.emit("refuse", sgm(&[0x80], &p, &[4], &spans), C::obj(vec![])); + s.emit("refuse", sgm(&[], &p, &[4], &spans), C::obj(vec![])); + // Table rules: a row not summing to 256, rows of different widths, a negative entry, + // a bound past 255, a distribution past the tables. + for (pmf, bounds, sig) in [ + (vec![vec![255i64, 0]], vec![1i64], 0i64), + (vec![vec![256i64, 0], vec![256]], vec![1, 1], 0), + (vec![vec![257i64, -1]], vec![1], 0), + (vec![vec![256i64]], vec![256], 0), + (vec![vec![256i64]], vec![1], 1), + ] { + let mem = bytes(&mut r, 32); + s.emit("table", sgm(&mem, &pmf, &bounds, &[(vec![true], vec![sig])]), C::obj(vec![])); + } + // fac: random rows and memory; rows of every length mod 4; escapes (symbol 63). + for _ in 0..50 { + let nrows = r.u(1, 4) as usize; + let rows: Vec> = (0..nrows) + .map(|_| { + let mut c: Vec = (0..63).map(|_| r.int(0, 255)).collect(); + c.sort_unstable(); + if r.chance(1, 3) { + c[62] = 255; + } + c + }) + .collect(); + let lens: Vec = (0..nrows).map(|_| r.int(0, 17)).collect(); + let total: i64 = lens.iter().sum(); + let len = if r.chance(1, 5) { r.below(12) } else { 3 * total as usize + 16 }; + let mem = bytes(&mut r, len); + s.emit("fac", fac(&mem, &rows, &lens), C::obj(vec![])); + } + let mut bad: Vec = (0..63).map(|i| i * 4).collect(); + bad[10] = 0; + s.emit("table", fac(&bytes(&mut r, 40), &[bad], &[4]), C::obj(vec![])); + let mut big: Vec = (0..63).map(|i| i * 4).collect(); + big[62] = 256; + s.emit("table", fac(&bytes(&mut r, 40), &[big], &[4]), C::obj(vec![])); + s.file +} + +// ------------------------------------------------------------------ E9–E13 + +fn residual_step() -> File { + let mut s = S::new("residual_step"); + let mut r = Rng::named("residual_step"); + let one = |pred: i64, rr: i64, q: i64, lo: i64, hi: i64| { + C::obj(vec![("hi", C::int(hi as i128)), ("lo", C::int(lo as i128)), ("pred", C::int(pred as i128)), ("q", C::int(q as i128)), ("r", C::int(rr as i128))]) + }; + for &(lo, hi) in &[(0i64, 255i64), (-32768, 32767)] { + for q in [1i64, 2, 3, 255, 256, 4097, 65535] { + let big_r = (hi - lo) / q + 1; + for rr in [0, big_r, -big_r, big_r + 1, -big_r - 1, i32::MAX as i64, i32::MIN as i64] { + let pred = r.int(lo, hi); + s.emit("edge", one(pred, rr, q, lo, hi), C::obj(vec![])); + } + } + } + for (pred, rr, q, lo, hi) in [(5i64, 1i64, 0i64, 0i64, 255i64), (5, 1, 65536, 0, 255), (300, 1, 1, 0, 255), (5, 1, 1, 5, 5), (5, 1 << 31, 1, 0, 255)] { + s.emit("domain", one(pred, rr, q, lo, hi), C::obj(vec![])); + } + // At the i32 limits: ranges of width 65535 that end at i32::MAX or start at + // i32::MIN, where E9's 32-bit form would form values past i32 (§5E E9, *Width*). + for &(lo, hi) in &[(i32::MAX as i64 - 65535, i32::MAX as i64), (i32::MIN as i64, i32::MIN as i64 + 65535)] { + for q in [1i64, 65535] { + let big_r = (hi - lo) / q + 1; + for rr in [big_r, -big_r, big_r + 1, -big_r - 1] { + for pred in [lo, hi] { + s.emit("limits", one(pred, rr, q, lo, hi), C::obj(vec![])); + } + } + } + } + s.file +} + +fn mc_predict() -> File { + let mut s = S::new("mc_predict"); + let mut r = Rng::named("mc_predict"); + for _ in 0..60 { + let (h, w) = (r.u(1, 6), r.u(1, 6)); + let f = Tensor::new(3, h, w, (0..3 * h * w).map(|_| r.int(0, 255)).collect()); + let p = r.int(1, 2); + let (y, x, c) = (r.int(0, h as i64 - 1), r.int(0, w as i64 - 1), r.int(0, 2)); + let (dy, dx) = (r.int(-9, 9), r.int(-9, 9)); + let in_ = C::obj(vec![ + ("c", C::int(c as i128)), + ("dx", C::int(dx as i128)), + ("dy", C::int(dy as i128)), + ("frame", tensor_c(&f)), + ("precision", C::int(p as i128)), + ("x", C::int(x as i128)), + ("y", C::int(y as i128)), + ]); + s.emit("random", in_, C::obj(vec![])); + } + s.file +} + +fn up2_predict() -> File { + let mut s = S::new("up2_predict"); + let mut r = Rng::named("up2_predict"); + for _ in 0..30 { + let (d0, d1, c) = (r.u(1, 9), r.u(1, 9), r.u(1, 3)); + let p = Tensor::new(c, d0.div_ceil(2), d1.div_ceil(2), (0..c * d0.div_ceil(2) * d1.div_ceil(2)).map(|_| r.int(0, 255)).collect()); + s.emit("random", C::obj(vec![("d0", C::int(d0 as i128)), ("d1", C::int(d1 as i128)), ("prev", tensor_c(&p))]), C::obj(vec![])); + } + let p = Tensor::new(1, 2, 2, vec![0, 255, 255, 0]); + s.emit("geometry", C::obj(vec![("d0", C::int(5)), ("d1", C::int(4)), ("prev", tensor_c(&p))]), C::obj(vec![])); + s.file +} + +fn rate_predict() -> File { + let mut s = S::new("rate_predict"); + let mut r = Rng::named("rate_predict"); + for _ in 0..30 { + let (num, den) = (r.u(1, 7), r.u(1, 7)); + let n = r.u(1, 9); + let d0 = (n as u64 * num as u64 / den as u64) as u32; + if d0 == 0 { + continue; + } + let c = r.u(1, 2); + let p = Tensor::new(c, 1, n, (0..c * n).map(|_| r.int(-32768, 32767)).collect()); + s.emit("random", C::obj(vec![("d0", C::int(d0 as i128)), ("prev", tensor_c(&p)), ("rate_den", C::int(den as i128)), ("rate_num", C::int(num as i128))]), C::obj(vec![])); + } + // Half-way points that round away from zero, both signs. + let p = Tensor::new(1, 1, 3, vec![-3, 0, 3]); + s.emit("rounding", C::obj(vec![("d0", C::int(6)), ("prev", tensor_c(&p)), ("rate_den", C::int(1)), ("rate_num", C::int(2))]), C::obj(vec![])); + s.emit("geometry", C::obj(vec![("d0", C::int(7)), ("prev", tensor_c(&p)), ("rate_den", C::int(1)), ("rate_num", C::int(2))]), C::obj(vec![])); + // i16 extremes with rates near 2^32, where (b − a)·ρ nears 2^48 and rdiv's + // 2|n| + d passes it (§5E E12). + let num = u32::MAX as i128; + for (data, c, den, d0) in [ + (vec![-32768i64, 32767], 1u32, num - 1, 2i128), + (vec![32767, -32768], 1, num - 1, 2), + (vec![-32768, 32767, 32767, -32768], 2, 1i128 << 31, 3), + ] { + let n = data.len() as u32 / c; + let p = Tensor::new(c, 1, n, data); + s.emit("extremes", C::obj(vec![("d0", C::int(d0)), ("prev", tensor_c(&p)), ("rate_den", C::int(den)), ("rate_num", C::int(num))]), C::obj(vec![])); + } + s.file +} + +fn coding_order() -> File { + let mut s = S::new("coding_order"); + for (c, h, w) in [(1i64, 1i64, 1i64), (2, 3, 4), (1, 4, 4), (3, 2, 5)] { + for (order, anchor) in [("raster", false), ("checkerboard", true), ("checkerboard", false)] { + s.emit("shape", C::obj(vec![("anchor", C::Bool(anchor)), ("order", C::s(order)), ("shape", ints(&[c, h, w]))]), C::obj(vec![])); + } + } + s.file +} diff --git a/wai/wai-ops/src/generate/tag.rs b/wai/wai-ops/src/generate/tag.rs new file mode 100644 index 000000000..89e2a3f61 --- /dev/null +++ b/wai/wai-ops/src/generate/tag.rs @@ -0,0 +1,511 @@ +//! The profiles a generated graph meets (§7, §8.3), for the `profiles` member of its +//! vector. +//! +//! This is the generator's own tagger, not the certifier: it applies the bound rules +//! of §8.3 and the classes and parameter conditions of §7 to the graphs the generator +//! writes, in `i128` with every step checked. A value that leaves `i128` lies outside +//! every class width of both profiles, so treating it as a failure gives the same +//! verdict as saturating it (§8.1). The certifier (D2b) computes the same verdicts +//! from the text, and must agree with every tag written here. +//! +//! What a node fits follows §8.5: its intermediates and its producer results. A +//! tensor's interval after a `clamp` policy is not classed by that rule, although +//! §7.1 calls every tensor element class `tensor`; no vector here depends on the +//! difference. + +use alloc::vec; +use alloc::vec::Vec; + +use crate::graph::{BiasKind, Graph, Operand, Params, Policy, Round, Rq, opcode::*}; +use crate::interp::round; + +/// An interval. +type Iv = (i128, i128); + +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub struct Verdict { + pub acc32: bool, + pub acc64: bool, +} + +/// Widths of a class under each profile: (acc32, acc64), in bits. +#[derive(Clone, Copy)] +enum Class { + Tensor, + Acc, + Wide, + Product, +} + +fn widths(c: Class) -> (u32, u32) { + match c { + Class::Tensor | Class::Acc => (32, 64), + Class::Wide => (32, 128), + Class::Product => (64, 128), + } +} + +fn fits(v: i128, n: u32) -> bool { + if n >= 128 { + return true; + } + let lim = 1i128 << (n - 1); + -lim <= v && v < lim +} + +struct Acc { + a32: bool, + a64: bool, + dead: bool, +} + +impl Acc { + fn class(&mut self, iv: Option, c: Class) { + match iv { + None => self.dead = true, + Some((lo, hi)) => { + let (w32, w64) = widths(c); + if !(fits(lo, w32) && fits(hi, w32)) { + self.a32 = false; + } + if !(fits(lo, w64) && fits(hi, w64)) { + self.a64 = false; + } + } + } + } +} + +fn hull(a: Iv, b: Iv) -> Iv { + (a.0.min(b.0), a.1.max(b.1)) +} +fn hull_opt(a: Option, b: Iv) -> Option { + Some(match a { + None => b, + Some(a) => hull(a, b), + }) +} +fn mul(a: i128, b: i128) -> Option { + a.checked_mul(b) +} +fn add(a: i128, b: i128) -> Option { + a.checked_add(b) +} +fn term(w: i128, l: i128, h: i128) -> Option { + let (x, y) = (mul(w, l)?, mul(w, h)?); + Some((x.min(y), x.max(y))) +} +fn corner(a: Iv, b: Iv) -> Option { + let c = [mul(a.0, b.0)?, mul(a.0, b.1)?, mul(a.1, b.0)?, mul(a.1, b.1)?]; + Some((*c.iter().min()?, *c.iter().max()?)) +} +/// `A(K, b)` (§8.2). +fn psum(terms: &[Iv], b: i128) -> Option { + let mut lo = b.min(0); + let mut hi = b.max(0); + for t in terms { + lo = add(lo, t.0.min(0))?; + hi = add(hi, t.1.max(0))?; + } + Some((lo, hi)) +} +fn floor_sh(v: i128, s: u32) -> i128 { + if s >= 127 { if v < 0 { -1 } else { 0 } } else { v >> s } +} + +/// The `rq` rules for one channel: `rq.p`, `rq.q`, `rq.y` from `rq.t`. +fn rq_rules(t: Iv, m: Option, s: u32, r: Round, post: i64) -> Option<(Iv, Iv, Iv)> { + let m = m.unwrap_or(1) as i128; + let p = term(m, t.0, t.1)?; + let a = if r != Round::Floor && s >= 1 { 1i128.checked_shl(s - 1).filter(|_| s - 1 < 127)? } else { 0 }; + let rp = (p.0, add(p.1, a)?); + let q = (round(p.0, s, r)?, round(p.1, s, r)?); + let y = (add(q.0, post as i128)?, add(q.1, post as i128)?); + Some((rp, q, y)) +} + +/// The intermediates and the producer result of a whole `rq`, hulled over channels, +/// given each channel's `rq.t`. +fn rq_all(acc: &mut Acc, ts: &[Option], rq: &Rq) -> Option { + let (mut t, mut p, mut q, mut y) = (None, None, None, None); + for (o, ti) in ts.iter().enumerate() { + let ti = (*ti)?; + let (rp, rq_, ry) = rq_rules(ti, rq.mult(o), rq.shift(o), rq.round, rq.post(o))?; + t = hull_opt(t, ti); + p = hull_opt(p, rp); + q = hull_opt(q, rq_); + y = hull_opt(y, ry); + } + acc.class(t, Class::Acc); + acc.class(p, Class::Product); + acc.class(q, Class::Acc); + acc.class(y, Class::Acc); + y +} + +fn exp_rule(acc: &mut Acc, u: Iv) { + acc.class(Some(u), Class::Acc); + acc.class(Some((45426 * (u.0 / 45426), 45426 * (u.1 / 45426))), Class::Acc); + acc.class(Some((43245 * 43245, 88670 * 88670)), Class::Acc); + acc.class(Some((0, 23495 * ((88670i128 * 88670) >> 16))), Class::Acc); +} + +fn fits64(v: i128) -> bool { + fits(v, 64) +} + +/// The profiles `g` meets, given the sizes the load plan gave (`None` when the +/// inputs declare none). +pub fn verdict(g: &Graph, sizes: Option<&[(u64, u64)]>) -> Verdict { + let mut a = Acc { a32: true, a64: true, dead: false }; + // Inputs and scalars: their declared ranges. + for i in &g.inputs { + let t = &g.tensors[i.tensor as usize]; + a.class(Some((t.lo as i128, t.hi as i128)), Class::Tensor); + } + for s in &g.scalars { + a.class(Some((s.lo as i128, s.hi as i128)), Class::Tensor); + } + let mut iv: Vec> = vec![None; g.tensors.len()]; + for i in &g.inputs { + let t = &g.tensors[i.tensor as usize]; + iv[i.tensor as usize] = Some((t.lo as i128, t.hi as i128)); + } + for op in &g.ops { + let outs = node(&mut a, g, op, &iv, sizes); + for (k, &t) in op.outs.iter().enumerate() { + let rec = &g.tensors[t as usize]; + let r = outs.as_ref().and_then(|o| o.get(k).copied().flatten()); + a.class(r, Class::Tensor); + let after = r.map(|(lo, hi)| match rec.policy { + Policy::Derived => (lo, hi), + Policy::Clamp => (lo.clamp(rec.lo as i128, rec.hi as i128), hi.clamp(rec.lo as i128, rec.hi as i128)), + Policy::Check => (lo.max(rec.lo as i128), hi.min(rec.hi as i128)), + }); + if let Some((lo, hi)) = after + && lo > hi + { + a.dead = true; + } + if rec.policy == Policy::Check { + a.a32 = false; + } + iv[t as usize] = after; + } + params32(&mut a, op); + } + Verdict { acc32: a.a32 && a.a64 && !a.dead, acc64: a.a64 && !a.dead } +} + +/// The parameter conditions of §7.2. +fn params32(a: &mut Acc, op: &crate::graph::Op) { + let f32_ = |v: i64| fits(v as i128, 32); + let rq_ok = |rq: &Rq| rq.mult.iter().all(|&m| f32_(m)) && rq.shift.iter().all(|&s| s <= 63) && (rq.bias_kind != BiasKind::Post || rq.bias.iter().all(|&b| f32_(b))); + let ok = match &op.p { + Params::Conv { rq, w, .. } | Params::ConvT { rq, w, .. } => rq_ok(rq) && w.iter().all(|&x| f32_(x)), + Params::Rescale(rq) | Params::Mul(rq) | Params::Gram { rq, .. } | Params::Apply { rq, .. } => rq_ok(rq), + Params::Chscale { shift, m, .. } => *shift <= 63 && m.iter().all(|&x| f32_(x)), + Params::Igdn { gf, frac, gamma, .. } => *gf <= 63 && *frac <= 63 && gamma.iter().all(|&x| f32_(x)), + Params::Leaky { a: s, .. } => f32_(*s), + _ => true, + }; + if !ok { + a.a32 = false; + } +} + +/// One node: classes its intermediates into `a` and returns its producer results. +fn node(a: &mut Acc, g: &Graph, op: &crate::graph::Op, iv: &[Option], sizes: Option<&[(u64, u64)]>) -> Option>> { + let x = iv[op.ins[0] as usize]; + let Some((l, h)) = x else { + a.dead = true; + return None; + }; + let one = |r: Option| Some(vec![r]); + let cin = g.tensors[op.ins[0] as usize].channels as usize; + match (&op.p, op.code) { + (Params::Conv { cout, kh, kw, groups, rq, w, .. }, _) => { + let (cout, kk, cg) = (*cout as usize, (*kh * *kw) as usize, cin / *groups as usize); + let mut tm: Option = None; + let mut accs: Option = None; + let mut ts = Vec::with_capacity(cout); + for o in 0..cout { + let mut terms = Vec::with_capacity(cg * kk); + for k in 0..cg * kk { + let t = term(w[o * cg * kk + k] as i128, l, h); + match t { + Some(t) => { + terms.push(t); + tm = hull_opt(tm, t); + } + None => a.dead = true, + } + } + let ac = psum(&terms, rq.pre(o) as i128); + if let Some(ac) = ac { + accs = hull_opt(accs, ac); + } else { + a.dead = true; + } + ts.push(ac); + } + a.class(tm.or(Some((0, 0))), Class::Acc); + a.class(accs, Class::Acc); + one(rq_all(a, &ts, rq)) + } + (Params::ConvT { cout, kh, kw, sh, sw, rq, w, .. }, _) => { + let (cout, kh, kw, sh, sw) = (*cout as usize, *kh as usize, *kw as usize, *sh as usize, *sw as usize); + let mut tm: Option = None; + let mut accs: Option = None; + let mut ts = Vec::with_capacity(cout); + for o in 0..cout { + let mut all: Option = None; + for ry in 0..sh { + for rx in 0..sw { + let mut terms = Vec::new(); + for ci in 0..cin { + for i in (ry..kh).step_by(sh) { + for j in (rx..kw).step_by(sw) { + match term(w[((ci * cout + o) * kh + i) * kw + j] as i128, l, h) { + Some(t) => { + terms.push(t); + tm = hull_opt(tm, t); + } + None => a.dead = true, + } + } + } + } + match psum(&terms, rq.pre(o) as i128) { + Some(p) => all = hull_opt(all, p), + None => a.dead = true, + } + } + } + accs = match (accs, all) { + (Some(x), Some(y)) => Some(hull(x, y)), + (None, y) => y, + (x, None) => x, + }; + ts.push(all); + } + a.class(tm.or(Some((0, 0))), Class::Acc); + a.class(accs, Class::Acc); + one(rq_all(a, &ts, rq)) + } + (Params::Rescale(rq), _) => { + let ts: Vec> = (0..cin).map(|c| Some((add(l, rq.pre(c) as i128)?, add(h, rq.pre(c) as i128)?))).collect(); + one(rq_all(a, &ts, rq)) + } + (Params::Chscale { scalar, round: r, shift, m, .. }, _) => { + let s = g.scalars[*scalar as usize]; + let (mut p, mut q) = (None, None); + for c in 0..cin { + for row in s.lo..=s.hi { + let (rp, rq_, _) = rq_rules((l, h), Some(m[row as usize * cin + c]), *shift as u32, *r, 0)?; + p = hull_opt(p, rp); + q = hull_opt(q, rq_); + } + } + a.class(Some((l, h)), Class::Acc); + a.class(p, Class::Product); + a.class(q, Class::Acc); + a.class(q, Class::Acc); + one(q) + } + (Params::Mul(rq), _) => { + let b = iv[op.ins[1] as usize]?; + let mp = corner((l, h), b); + a.class(mp, Class::Acc); + let ts = vec![mp; cin]; + one(rq_all(a, &ts, rq)) + } + (Params::Gram { rq, .. }, _) | (Params::Apply { rq, .. }, _) => { + let b = iv[op.ins[1] as usize]?; + let t = corner((l, h), b); + a.class(t, Class::Acc); + let n = match op.code { + GRAM => { + let s = sizes?; + let (h0, w0) = s[op.ins[0] as usize]; + let (h1, w1) = s[op.ins[1] as usize]; + (h0 * w0).max(h1 * w1) as i128 + } + _ => (cin / match op.p { Params::Apply { groups, .. } => groups as usize, _ => 1 }) as i128, + }; + let acc = t.and_then(|t| Some((mul(n, t.0.min(0))?, mul(n, t.1.max(0))?))); + a.class(acc, Class::Acc); + let ts = vec![acc; cin]; + one(rq_all(a, &ts, rq)) + } + (_, ADD) => { + let b = iv[op.ins[1] as usize]?; + one(Some((add(l, b.0)?, add(h, b.1)?))) + } + (_, CHUNK_ADD) => one(Some((mul(2, l)?, mul(2, h)?))), + (Params::Clamp { lo, hi }, _) => one(Some((l.clamp(*lo as i128, *hi as i128), h.clamp(*lo as i128, *hi as i128)))), + (_, RELU) => one(Some((l.max(0), h.max(0)))), + (_, ABS) => one(Some(if l >= 0 { (l, h) } else if h <= 0 { (-h, -l) } else { (0, (-l).max(h)) })), + (Params::Leaky { a: s, f }, _) => { + let mut r: Option = None; + if l < 0 { + let p = corner((l, h.min(-1)), (*s as i128, *s as i128)); + a.class(p, Class::Acc); + let p = p?; + r = Some((floor_sh(p.0, *f), floor_sh(p.1, *f))); + } else { + a.class(Some((0, 0)), Class::Acc); + } + if h >= 0 { + r = hull_opt(r, (l.max(0), h)); + } + one(r) + } + (Params::Lut16 { t, .. }, _) => { + if l < -32767 || h > 32767 { + a.dead = true; + return None; + } + let vals = (l..=h).map(|u| t[(u + 32768) as usize] as i128); + let (mn, mx) = vals.fold((i128::MAX, i128::MIN), |(a, b), v| (a.min(v), b.max(v))); + one(Some((mn, mx))) + } + (Params::Igdn { gf, frac, gamma, beta, .. }, _) => { + let x2 = if l <= 0 && h >= 0 { (0, mul(l, l)?.max(mul(h, h)?)) } else { (mul(l, l)?.min(mul(h, h)?), mul(l, l)?.max(mul(h, h)?)) }; + a.class(Some(x2), Class::Wide); + let (mut gx, mut tm, mut nn, mut rr) = (None, None, None, None); + for i in 0..cin { + let mut terms = Vec::with_capacity(cin); + let (mut flo, mut fhi) = (beta[i] as i128, beta[i] as i128); + for j in 0..cin { + let g2 = corner((gamma[i * cin + j] as i128, gamma[i * cin + j] as i128), x2)?; + gx = hull_opt(gx, g2); + let t = (floor_sh(g2.0, *gf), floor_sh(g2.1, *gf)); + tm = hull_opt(tm, t); + terms.push(t); + flo = add(flo, t.0)?; + fhi = add(fhi, t.1)?; + } + nn = hull_opt(nn, psum(&terms, beta[i] as i128)?); + let r = ((flo.max(0) as u128).isqrt() as i128, (fhi.max(0) as u128).isqrt() as i128); + rr = hull_opt(rr, r); + } + a.class(gx, Class::Product); + a.class(tm, Class::Wide); + a.class(nn, Class::Wide); + a.class(rr, Class::Acc); + let xr = corner((l, h), rr?); + a.class(xr, Class::Wide); + let xr = xr?; + one(Some((floor_sh(xr.0, *frac), floor_sh(xr.1, *frac)))) + } + (Params::LayerNorm { eps, gamma, beta, .. }, _) => { + let c = cin as i128; + let e = *eps as i128; + let r0 = (e as u128).isqrt() as i128; + let k = (1i128 << 32) / r0; + let d = h - l; + a.class(Some((mul(c, l.min(0))?, mul(c, h.max(0))?)), Class::Wide); + a.class(Some((mul(c, l)? - c / 2, add(mul(c, h)?, c / 2)?)), Class::Wide); + a.class(Some((-d, d)), Class::Acc); + a.class(mul(d, d).and_then(|dd| Some((0, mul(c, dd)?))), Class::Wide); + a.class(mul(d, d).and_then(|dd| Some((e, add(dd, e)?))), Class::Wide); + a.class(Some((1 << 32, 1 << 32)), Class::Acc); + a.class(Some((0, k)), Class::Acc); + let ic = (c as u128).isqrt() as i128; + let icc = if ic * ic == c { ic } else { ic + 1 }; + let dmax_l = mul(d, k); + let dmax_r = mul(icc, 1i128 << 32).and_then(|v| mul(v, r0 + 1)).map(|v| (v + r0 - 1) / r0); + let dd = match (dmax_l, dmax_r) { + (Some(x), Some(y)) => Some(x.min(y)), + (x, y) => x.or(y), + }; + a.class(dd.map(|v| (-v, v)), Class::Wide); + let nmax = mul(icc, 1i128 << 16).and_then(|v| mul(v, r0 + 1)).map(|v| v / r0 + 1); + let n1 = mul(d, k).map(|v| (v >> 16) + 1); + let n = match (n1, nmax) { + (Some(x), Some(y)) => x.min(y), + (x, y) => x.or(y)?, + }; + a.class(Some((-n, n)), Class::Acc); + let (mut ng, mut pr) = (None, None); + for cc in 0..cin { + let gv = gamma[cc] as i128; + let t = corner((-n, n), (gv, gv))?; + ng = hull_opt(ng, t); + pr = hull_opt(pr, (add(t.0 >> 16, beta[cc] as i128)?, add(t.1 >> 16, beta[cc] as i128)?)); + } + a.class(ng, Class::Wide); + one(pr) + } + (Params::L2norm { eps }, _) => { + let s = sizes?; + let (hh, ww) = s[op.ins[0] as usize]; + let xm = l.abs().max(h.abs()); + let e = *eps as i128; + a.class(mul(xm, xm).and_then(|x2| mul((hh * ww) as i128, x2)).and_then(|v| add(v, e)).map(|v| (0, v)), Class::Wide); + a.class(Some((1 << 32, 1 << 32)), Class::Acc); + a.class(Some((0, (1i128 << 32) / (e as u128).isqrt() as i128)), Class::Acc); + a.class(Some((-(1 << 32), 1 << 32)), Class::Wide); + one(Some((-(1 << 16), 1 << 16))) + } + (_, SOFTMAX) => { + let s = sizes?; + let wm = s[op.ins[0] as usize].1 as i128; + a.class(Some((l - h, 0)), Class::Acc); + exp_rule(a, (0, h - l)); + a.class(Some((0, wm * 65553)), Class::Acc); + a.class(Some((0, 65553 * 65536 + (wm * 65553) / 2)), Class::Acc); + one(Some((0, 1 << 16))) + } + (_, SIGMOID) => { + let u = if l >= 0 { (l, h) } else if h <= 0 { (-h, -l) } else { (0, (-l).max(h)) }; + exp_rule(a, u); + a.class(Some((0, 65553 * 65536)), Class::Acc); + one(Some((0, 1 << 16))) + } + (Params::Elu { alpha }, _) => { + exp_rule(a, ((-h).max(0), (-l).max(0))); + let p = corner((*alpha as i128, *alpha as i128), (-(1 << 16), 17)); + a.class(p, Class::Wide); + let mut r: Option = None; + if h > 0 { + r = Some((l.max(1), h)); + } + if l <= 0 { + let p = p?; + r = hull_opt(r, (p.0 >> 16, p.1 >> 16)); + } + one(r) + } + (_, CONCAT) | (_, SUBPOS_MERGE) => { + let mut r = None; + for &t in &op.ins { + r = hull_opt(r, iv[t as usize]?); + } + one(r) + } + (Params::Fill { kind, value }, _) => { + if *kind == 0 { + one(Some((*value as i128, *value as i128))) + } else { + let s = g.scalars[*value as usize]; + one(Some((s.lo as i128, s.hi as i128))) + } + } + (_, SUBPOS_SPLIT) => Some(vec![Some((l, h)); 4]), + (Params::Crop { .. }, _) => one(Some((l, h))), + _ => one(Some((l, h))), + } +} + +/// Whether an `Operand` is a scalar; used by the generator to know whether a crop +/// needs a scalar. +pub fn is_scalar(o: Operand) -> bool { + matches!(o, Operand::Scalar(_)) +} + +/// Whether `v` fits `i64`. +pub fn fits_i64(v: i128) -> bool { + fits64(v) +} diff --git a/wai/wai-ops/src/graph.rs b/wai/wai-ops/src/graph.rs new file mode 100644 index 000000000..aa66e5b42 --- /dev/null +++ b/wai/wai-ops/src/graph.rs @@ -0,0 +1,1084 @@ +//! `WHG1`, the graph form (§10): the records, the operator parameters, parse and +//! write, and load steps 1–6 of §10.5. Every refusal here is `model_mismatch`. + +use alloc::vec; +use alloc::vec::Vec; + +use crate::code::{Code, Refusal, at_load}; + +pub const MAGIC: &[u8; 4] = b"WHG1"; +/// The cap of §3: `2^26` elements. +pub const CAP: u64 = 1 << 26; + +/// A tensor's policy (§3). +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum Policy { + Derived, + Clamp, + Check, +} + +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub struct ScalarRec { + pub lo: i64, + pub hi: i64, +} + +/// An input record (§10.2). `kind` 0 is data, 1 is state. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub struct InputRec { + pub tensor: u32, + pub max_h: u32, + pub max_w: u32, + pub kind: u8, + pub src: u32, + pub init: i64, +} + +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub struct TensorRec { + pub channels: u32, + /// −128 is "no exponent". + pub exponent: i8, + pub policy: Policy, + pub lo: i64, + pub hi: i64, +} + +impl TensorRec { + pub fn derived(channels: u32) -> TensorRec { + TensorRec { channels, exponent: -128, policy: Policy::Derived, lo: 0, hi: 0 } + } + pub fn check(channels: u32, lo: i64, hi: i64) -> TensorRec { + TensorRec { channels, exponent: -128, policy: Policy::Check, lo, hi } + } + pub fn clamp(channels: u32, lo: i64, hi: i64) -> TensorRec { + TensorRec { channels, exponent: -128, policy: Policy::Clamp, lo, hi } + } +} + +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum Round { + Floor, + HalfUp, + HalfEven, +} + +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum BiasKind { + None, + Pre, + Post, +} + +/// The requantisation block `RQ` (§5.1, §10.4), over `C` channels. +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Rq { + pub round: Round, + pub bias_kind: BiasKind, + /// 0 absent, 1 one value, 2 one per channel. + pub m_kind: u8, + /// 1 one value, 2 one per channel. + pub s_kind: u8, + pub m_bytes: u8, + pub b_bytes: u8, + pub mult: Vec, + pub shift: Vec, + pub bias: Vec, +} + +impl Rq { + /// Channel `o`'s multiplier, or `None` when it is absent (which means 1). + pub fn mult(&self, o: usize) -> Option { + match self.m_kind { + 0 => None, + 1 => Some(self.mult[0]), + _ => Some(self.mult[o]), + } + } + pub fn shift(&self, o: usize) -> u32 { + (if self.s_kind == 1 { self.shift[0] } else { self.shift[o] }) as u32 + } + pub fn pre(&self, o: usize) -> i64 { + if self.bias_kind == BiasKind::Pre { self.bias[o] } else { 0 } + } + pub fn post(&self, o: usize) -> i64 { + if self.bias_kind == BiasKind::Post { self.bias[o] } else { 0 } + } + /// A block with no multiplier, one shift, no bias. + pub fn plain(round: Round, shift: u8) -> Rq { + Rq { round, bias_kind: BiasKind::None, m_kind: 0, s_kind: 1, m_bytes: 0, b_bytes: 0, mult: vec![], shift: vec![shift], bias: vec![] } + } +} + +/// A `crop` operand: a constant, or the index of a scalar. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum Operand { + Const(u32), + Scalar(u32), +} + +/// Each operator's parameters (§10.4). +#[derive(Clone, Debug, PartialEq, Eq)] +pub enum Params { + Conv { cout: u32, kh: u32, kw: u32, sh: u32, sw: u32, ph: u32, pw: u32, groups: u32, w_bytes: u8, rq: Rq, w: Vec }, + ConvT { cout: u32, kh: u32, kw: u32, sh: u32, sw: u32, ph: u32, pw: u32, oph: u32, opw: u32, w_bytes: u8, rq: Rq, w: Vec }, + Rescale(Rq), + Chscale { scalar: u32, n_rows: u32, round: Round, m_bytes: u8, shift: u8, m: Vec }, + Mul(Rq), + Lut16 { e_in: i8, e_out: i8, t: Vec }, + /// `add`, `concat`, `chunk_add`, `relu`, `abs`, `crop_to`, `softmax`, `sigmoid`. + None, + Split { start: u32, count: u32 }, + /// `pixel_shuffle` and `pixel_unshuffle`. + Shuffle { r: u32 }, + Clamp { lo: i64, hi: i64 }, + Leaky { a: i64, f: u32 }, + PadRep { m: u32 }, + Crop { y0: Operand, x0: Operand, h: Operand, w: Operand }, + Subpos { order: [u8; 4] }, + /// `kind` 0 a constant `value`, 1 the scalar whose index is `value`. + Fill { kind: u8, value: i64 }, + Igdn { gf: u32, frac: u32, g_bytes: u8, gamma: Vec, beta: Vec }, + LayerNorm { eps: u64, g_bytes: u8, gamma: Vec, beta: Vec }, + L2norm { eps: u64 }, + Elu { alpha: i64 }, + Gram { groups: u32, rq: Rq }, + Apply { groups: u32, rq: Rq }, +} + +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Op { + pub code: u8, + pub ins: Vec, + pub outs: Vec, + pub p: Params, +} + +/// A `WHG1` graph: parsed, lowered or built in place. A `Graph` is data and cannot +/// run: the interpreter takes a [`Loaded`], which only [`load`] makes, from the +/// graph's bytes. +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Graph { + pub max_elems: u32, + pub scalars: Vec, + pub inputs: Vec, + pub outputs: Vec, + pub tensors: Vec, + pub ops: Vec, +} + +pub mod opcode { + pub const CONV: u8 = 0x01; + pub const RESCALE: u8 = 0x02; + pub const CHSCALE: u8 = 0x03; + pub const LUT16: u8 = 0x04; + pub const ADD: u8 = 0x05; + pub const CONCAT: u8 = 0x06; + pub const CHUNK_ADD: u8 = 0x07; + pub const SPLIT: u8 = 0x08; + pub const PIXEL_SHUFFLE: u8 = 0x09; + pub const PIXEL_UNSHUFFLE: u8 = 0x0A; + pub const CLAMP: u8 = 0x0B; + pub const RELU: u8 = 0x0C; + pub const LEAKY: u8 = 0x0D; + pub const PAD_REP: u8 = 0x0E; + pub const CROP_TO: u8 = 0x0F; + pub const MUL: u8 = 0x10; + pub const CONV_TRANSPOSE: u8 = 0x11; + pub const IGDN: u8 = 0x12; + pub const ABS: u8 = 0x13; + pub const CROP: u8 = 0x14; + pub const SUBPOS_SPLIT: u8 = 0x15; + pub const SUBPOS_MERGE: u8 = 0x16; + pub const FILL: u8 = 0x17; + pub const LAYERNORM_C: u8 = 0x18; + pub const L2NORM: u8 = 0x19; + pub const SOFTMAX: u8 = 0x1A; + pub const SIGMOID: u8 = 0x1B; + pub const ELU: u8 = 0x1C; + pub const GRAM: u8 = 0x1D; + pub const APPLY: u8 = 0x1E; +} + +/// An operator's name (§5), or `None` for an opcode that is not one. +pub fn op_name(code: u8) -> Option<&'static str> { + Some(match code { + 0x01 => "conv", + 0x02 => "rescale", + 0x03 => "chscale", + 0x04 => "lut16", + 0x05 => "add", + 0x06 => "concat", + 0x07 => "chunk_add", + 0x08 => "split", + 0x09 => "pixel_shuffle", + 0x0A => "pixel_unshuffle", + 0x0B => "clamp", + 0x0C => "relu", + 0x0D => "leaky", + 0x0E => "pad_rep", + 0x0F => "crop_to", + 0x10 => "mul", + 0x11 => "conv_transpose", + 0x12 => "igdn", + 0x13 => "abs", + 0x14 => "crop", + 0x15 => "subpos_split", + 0x16 => "subpos_merge", + 0x17 => "fill", + 0x18 => "layernorm_c", + 0x19 => "l2norm", + 0x1A => "softmax", + 0x1B => "sigmoid", + 0x1C => "elu", + 0x1D => "gram", + 0x1E => "apply", + _ => return None, + }) +} + +/// Every opcode, in table order. +pub const OPCODES: [u8; 30] = [ + 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0A, 0x0B, 0x0C, 0x0D, 0x0E, 0x0F, 0x10, 0x11, 0x12, 0x13, 0x14, + 0x15, 0x16, 0x17, 0x18, 0x19, 0x1A, 0x1B, 0x1C, 0x1D, 0x1E, +]; + +/// The operator's arity: `(n_in, n_out)`, with `n_in = 0` for `concat` (at least 2). +pub fn arity(code: u8) -> (u8, u8) { + use opcode::*; + match code { + ADD | CROP_TO | MUL | GRAM | APPLY => (2, 1), + CONCAT => (0, 1), + SUBPOS_SPLIT => (1, 4), + SUBPOS_MERGE => (4, 1), + _ => (1, 1), + } +} + +// ---------------------------------------------------------------- reading + +struct R<'a> { + b: &'a [u8], + p: usize, + end: usize, +} + +type Res = Result; + +impl<'a> R<'a> { + fn take(&mut self, n: usize, step: u8) -> Res<&'a [u8]> { + let e = self.p.checked_add(n).filter(|&e| e <= self.end).ok_or(at_load(Code::ModelMismatch, step))?; + let s = &self.b[self.p..e]; + self.p = e; + Ok(s) + } + fn u8(&mut self, st: u8) -> Res { + Ok(self.take(1, st)?[0]) + } + fn i8(&mut self, st: u8) -> Res { + Ok(self.take(1, st)?[0] as i8) + } + fn u16(&mut self, st: u8) -> Res { + let s = self.take(2, st)?; + Ok(u16::from_le_bytes([s[0], s[1]])) + } + fn u32(&mut self, st: u8) -> Res { + let s = self.take(4, st)?; + Ok(u32::from_le_bytes([s[0], s[1], s[2], s[3]])) + } + fn i64(&mut self, st: u8) -> Res { + let s = self.take(8, st)?; + Ok(i64::from_le_bytes([s[0], s[1], s[2], s[3], s[4], s[5], s[6], s[7]])) + } + fn u64(&mut self, st: u8) -> Res { + Ok(self.i64(st)? as u64) + } + /// `n` signed integers of `width` bytes each (1, 2, 4 or 8). + fn ints(&mut self, n: u64, width: u8, st: u8) -> Res> { + let len = n.checked_mul(width as u64).and_then(|v| usize::try_from(v).ok()).ok_or(at_load(Code::ModelMismatch, st))?; + let s = self.take(len, st)?; + Ok(s.chunks_exact(width as usize) + .map(|c| match width { + 1 => c[0] as i8 as i64, + 2 => i16::from_le_bytes([c[0], c[1]]) as i64, + 4 => i32::from_le_bytes([c[0], c[1], c[2], c[3]]) as i64, + _ => i64::from_le_bytes([c[0], c[1], c[2], c[3], c[4], c[5], c[6], c[7]]), + }) + .collect()) + } + fn zero(&mut self, n: usize, st: u8) -> Res<()> { + if self.take(n, st)?.iter().all(|&b| b == 0) { Ok(()) } else { Err(at_load(Code::ModelMismatch, st)) } + } +} + +fn mm(step: u8) -> Res { + Err(at_load(Code::ModelMismatch, step)) +} + +fn round_of(v: u8, st: u8) -> Res { + Ok(match v { + 0 => Round::Floor, + 1 => Round::HalfUp, + 2 => Round::HalfEven, + _ => return mm(st), + }) +} + +fn round_code(r: Round) -> u8 { + match r { + Round::Floor => 0, + Round::HalfUp => 1, + Round::HalfEven => 2, + } +} + +fn read_rq(r: &mut R, c: u64, st: u8) -> Res { + let round = round_of(r.u8(st)?, st)?; + let bias_kind = match r.u8(st)? { + 0 => BiasKind::None, + 1 => BiasKind::Pre, + 2 => BiasKind::Post, + _ => return mm(st), + }; + let m_kind = r.u8(st)?; + let s_kind = r.u8(st)?; + let m_bytes = r.u8(st)?; + let b_bytes = r.u8(st)?; + if r.u16(st)? != 0 { + return mm(st); + } + if m_kind > 2 || !(1..=2).contains(&s_kind) { + return mm(st); + } + let m_ok = if m_kind == 0 { m_bytes == 0 } else { m_bytes == 4 || m_bytes == 8 }; + let b_ok = if bias_kind == BiasKind::None { b_bytes == 0 } else { b_bytes == 4 || b_bytes == 8 }; + if !m_ok || !b_ok { + return mm(st); + } + let nm = match m_kind { + 0 => 0, + 1 => 1, + _ => c, + }; + let mult = r.ints(nm, m_bytes.max(1), st)?; + let ns = if s_kind == 1 { 1 } else { c }; + let shift: Vec = r.take(usize::try_from(ns).map_err(|_| at_load(Code::ModelMismatch, st))?, st)?.to_vec(); + if shift.iter().any(|&s| s > 127) { + return mm(st); + } + let bias = if bias_kind == BiasKind::None { vec![] } else { r.ints(c, b_bytes, st)? }; + Ok(Rq { round, bias_kind, m_kind, s_kind, m_bytes, b_bytes, mult, shift, bias }) +} + +fn read_operand(r: &mut R, n_scalars: usize, st: u8) -> Res { + let kind = r.u8(st)?; + r.zero(3, st)?; + let v = r.u32(st)?; + match kind { + 0 => Ok(Operand::Const(v)), + 1 if (v as usize) < n_scalars => Ok(Operand::Scalar(v)), + _ => mm(st), + } +} + +fn width_ok(w: u8, allowed: &[u8]) -> bool { + allowed.contains(&w) +} + +/// Read one operator's parameters from exactly its `plen` bytes. `cin` is the first +/// input tensor's channel count (§10.4: `cin` and `C`). +fn read_params(code: u8, b: &[u8], cin: u64, n_scalars: usize) -> Res { + use opcode::*; + const ST: u8 = 5; + let mut r = R { b, p: 0, end: b.len() }; + let p = match code { + CONV => { + let (cout, kh, kw, sh, sw, ph, pw, groups) = + (r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?); + let w_bytes = r.u8(ST)?; + r.zero(3, ST)?; + if cout == 0 || kh == 0 || kw == 0 || sh == 0 || sw == 0 || groups == 0 || !width_ok(w_bytes, &[1, 2, 4, 8]) { + return mm(ST); + } + if cin % groups as u64 != 0 || cout % groups != 0 { + return mm(ST); + } + let rq = read_rq(&mut r, cout as u64, ST)?; + let n = (cout as u64).checked_mul(cin / groups as u64).and_then(|v| v.checked_mul(kh as u64)).and_then(|v| v.checked_mul(kw as u64)); + let w = r.ints(n.ok_or(at_load(Code::ModelMismatch, ST))?, w_bytes, ST)?; + Params::Conv { cout, kh, kw, sh, sw, ph, pw, groups, w_bytes, rq, w } + } + CONV_TRANSPOSE => { + let (cout, kh, kw, sh, sw, ph, pw, oph, opw) = + (r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?, r.u32(ST)?); + let w_bytes = r.u8(ST)?; + r.zero(3, ST)?; + if cout == 0 || kh == 0 || kw == 0 || sh == 0 || sw == 0 || !width_ok(w_bytes, &[1, 2, 4, 8]) { + return mm(ST); + } + let rq = read_rq(&mut r, cout as u64, ST)?; + let n = cin.checked_mul(cout as u64).and_then(|v| v.checked_mul(kh as u64)).and_then(|v| v.checked_mul(kw as u64)); + let w = r.ints(n.ok_or(at_load(Code::ModelMismatch, ST))?, w_bytes, ST)?; + Params::ConvT { cout, kh, kw, sh, sw, ph, pw, oph, opw, w_bytes, rq, w } + } + RESCALE => Params::Rescale(read_rq(&mut r, cin, ST)?), + CHSCALE => { + let scalar = r.u32(ST)?; + let n_rows = r.u32(ST)?; + let round = round_of(r.u8(ST)?, ST)?; + let m_bytes = r.u8(ST)?; + let shift = r.u8(ST)?; + r.zero(1, ST)?; + if scalar as usize >= n_scalars || n_rows == 0 || !width_ok(m_bytes, &[4, 8]) || shift > 127 { + return mm(ST); + } + let n = (n_rows as u64).checked_mul(cin).ok_or(at_load(Code::ModelMismatch, ST))?; + let m = r.ints(n, m_bytes, ST)?; + Params::Chscale { scalar, n_rows, round, m_bytes, shift, m } + } + MUL => { + let rq = read_rq(&mut r, cin, ST)?; + if rq.bias_kind != BiasKind::None { + return mm(ST); + } + Params::Mul(rq) + } + LUT16 => { + let e_in = r.i8(ST)?; + let e_out = r.i8(ST)?; + r.zero(2, ST)?; + let t: Vec = r.ints(65536, 2, ST)?.into_iter().map(|v| v as i16).collect(); + if t[0] != 0 { + return mm(ST); + } + Params::Lut16 { e_in, e_out, t } + } + ADD | CONCAT | CHUNK_ADD | RELU | ABS | CROP_TO | SOFTMAX | SIGMOID => Params::None, + SPLIT => { + let (start, count) = (r.u32(ST)?, r.u32(ST)?); + if count == 0 { + return mm(ST); + } + Params::Split { start, count } + } + PIXEL_SHUFFLE | PIXEL_UNSHUFFLE => { + let rr = r.u32(ST)?; + if rr == 0 { + return mm(ST); + } + Params::Shuffle { r: rr } + } + CLAMP => { + let (lo, hi) = (r.i64(ST)?, r.i64(ST)?); + if lo > hi { + return mm(ST); + } + Params::Clamp { lo, hi } + } + LEAKY => { + let a = r.i64(ST)?; + let f = r.u32(ST)?; + if f > 63 { + return mm(ST); + } + Params::Leaky { a, f } + } + PAD_REP => { + let m = r.u32(ST)?; + if m == 0 { + return mm(ST); + } + Params::PadRep { m } + } + CROP => { + let y0 = read_operand(&mut r, n_scalars, ST)?; + let x0 = read_operand(&mut r, n_scalars, ST)?; + let h = read_operand(&mut r, n_scalars, ST)?; + let w = read_operand(&mut r, n_scalars, ST)?; + Params::Crop { y0, x0, h, w } + } + SUBPOS_SPLIT | SUBPOS_MERGE => { + let s = r.take(4, ST)?; + let order = [s[0], s[1], s[2], s[3]]; + let mut seen = [false; 4]; + for &o in &order { + if o > 3 || seen[o as usize] { + return mm(ST); + } + seen[o as usize] = true; + } + Params::Subpos { order } + } + FILL => { + let kind = r.u8(ST)?; + r.zero(7, ST)?; + let value = r.i64(ST)?; + match kind { + 0 => {} + 1 if value >= 0 && (value as u64) < n_scalars as u64 => {} + _ => return mm(ST), + } + Params::Fill { kind, value } + } + IGDN => { + let (gf, frac) = (r.u32(ST)?, r.u32(ST)?); + let g_bytes = r.u8(ST)?; + r.zero(7, ST)?; + if gf > 127 || frac > 127 || !width_ok(g_bytes, &[4, 8]) { + return mm(ST); + } + let n = cin.checked_mul(cin).ok_or(at_load(Code::ModelMismatch, ST))?; + let gamma = r.ints(n, g_bytes, ST)?; + let beta = r.ints(cin, 8, ST)?; + Params::Igdn { gf, frac, g_bytes, gamma, beta } + } + LAYERNORM_C => { + let eps = r.u64(ST)?; + let g_bytes = r.u8(ST)?; + r.zero(7, ST)?; + if eps == 0 || !width_ok(g_bytes, &[4, 8]) { + return mm(ST); + } + let gamma = r.ints(cin, g_bytes, ST)?; + let beta = r.ints(cin, 8, ST)?; + Params::LayerNorm { eps, g_bytes, gamma, beta } + } + L2NORM => { + let eps = r.u64(ST)?; + if eps == 0 { + return mm(ST); + } + Params::L2norm { eps } + } + ELU => Params::Elu { alpha: r.i64(ST)? }, + GRAM | APPLY => { + let groups = r.u32(ST)?; + let rq = read_rq(&mut r, cin, ST)?; + if groups == 0 || rq.m_kind != 0 || rq.bias_kind != BiasKind::None || rq.s_kind != 1 { + return mm(ST); + } + if code == GRAM { Params::Gram { groups, rq } } else { Params::Apply { groups, rq } } + } + _ => return mm(ST), + }; + if r.p != b.len() { + return mm(ST); + } + Ok(p) +} + +impl Graph { + /// Parse `b` and make load steps 1–6 of §10.5. Step 7 is [`crate::plan::load_plan`]. + /// The result cannot run: [`load`] makes every step, and only what it returns does. + pub fn parse(b: &[u8]) -> Res { + // 1. magic, version, operator set. + if b.len() < 32 || &b[0..4] != MAGIC || b[4] != 1 || b[5] != 1 { + return mm(1); + } + let mut r = R { b, p: 6, end: b.len() }; + // 2. the header's reserved fields. + let res0 = r.u16(2)?; + let n_tensors = r.u32(2)?; + let n_ops = r.u32(2)?; + let n_inputs = r.u16(2)?; + let n_outputs = r.u16(2)?; + let n_scalars = r.u16(2)?; + let res1 = r.u16(2)?; + let max_elems = r.u32(2)?; + let res2 = r.u32(2)?; + if res0 != 0 || res1 != 0 || res2 != 0 { + return mm(2); + } + // 3. the counts against the file's length. + if n_tensors == 0 || n_ops == 0 || n_inputs == 0 || n_outputs == 0 || max_elems == 0 || max_elems as u64 > CAP { + return mm(3); + } + let fixed = 32u64 + 16 * n_scalars as u64 + 32 * n_inputs as u64 + 4 * n_outputs as u64 + 24 * n_tensors as u64 + 12 * n_ops as u64; + if fixed > b.len() as u64 { + return mm(3); + } + // 4. each table's records. + let mut scalars = Vec::with_capacity(n_scalars as usize); + for _ in 0..n_scalars { + let (lo, hi) = (r.i64(4)?, r.i64(4)?); + if lo > hi { + return mm(4); + } + scalars.push(ScalarRec { lo, hi }); + } + let mut inputs = Vec::with_capacity(n_inputs as usize); + for _ in 0..n_inputs { + let tensor = r.u32(4)?; + let (max_h, max_w) = (r.u32(4)?, r.u32(4)?); + let kind = r.u8(4)?; + r.zero(3, 4)?; + let src = r.u32(4)?; + r.zero(4, 4)?; + let init = r.i64(4)?; + if (max_h == 0) != (max_w == 0) || kind > 1 || tensor >= n_tensors { + return mm(4); + } + if kind == 0 && (src != 0 || init != 0) { + return mm(4); + } + inputs.push(InputRec { tensor, max_h, max_w, kind, src, init }); + } + let mut outputs = Vec::with_capacity(n_outputs as usize); + for _ in 0..n_outputs { + let t = r.u32(4)?; + if t >= n_tensors { + return mm(4); + } + outputs.push(t); + } + let mut tensors = Vec::with_capacity(n_tensors as usize); + for _ in 0..n_tensors { + let channels = r.u32(4)?; + let exponent = r.i8(4)?; + let policy = match r.u8(4)? { + 0 => Policy::Derived, + 1 => Policy::Clamp, + 2 => Policy::Check, + _ => return mm(4), + }; + r.zero(2, 4)?; + let (lo, hi) = (r.i64(4)?, r.i64(4)?); + if channels == 0 { + return mm(4); + } + match policy { + Policy::Derived if lo != 0 || hi != 0 => return mm(4), + Policy::Clamp | Policy::Check if lo > hi => return mm(4), + _ => {} + } + tensors.push(TensorRec { channels, exponent, policy, lo, hi }); + } + // 5. each operator: opcode, arity, parameter length, parameter ranges. + let mut ops = Vec::with_capacity(n_ops as usize); + for _ in 0..n_ops { + let code = r.u8(5)?; + let n_in = r.u8(5)?; + let n_out = r.u8(5)?; + r.zero(1, 5)?; + if op_name(code).is_none() { + return mm(5); + } + let (ai, ao) = arity(code); + if n_out != ao || (ai == 0 && n_in < 2) || (ai != 0 && n_in != ai) { + return mm(5); + } + let mut ins = Vec::with_capacity(n_in as usize); + for _ in 0..n_in { + let t = r.u32(5)?; + if t >= n_tensors { + return mm(5); + } + ins.push(t); + } + let mut outs = Vec::with_capacity(n_out as usize); + for _ in 0..n_out { + let t = r.u32(5)?; + if t >= n_tensors { + return mm(5); + } + outs.push(t); + } + let plen = r.u32(5)?; + let pb = r.take(plen as usize, 5)?; + let cin = tensors[ins[0] as usize].channels as u64; + let p = read_params(code, pb, cin, scalars.len())?; + ops.push(Op { code, ins, outs, p }); + } + // Nothing follows the last operator. + if r.p != b.len() { + return mm(5); + } + let g = Graph { max_elems, scalars, inputs, outputs, tensors, ops }; + g.rules()?; + Ok(g) + } + + /// Load step 6: the graph rules (§10.3), the state rules and the exponent rules. + fn rules(&self) -> Res<()> { + use opcode::*; + const ST: u8 = 6; + let nt = self.tensors.len(); + // Every tensor is a graph input or the output of exactly one operator; an + // operator reads only graph inputs and earlier operators' outputs. + let mut defined = vec![false; nt]; + for i in &self.inputs { + if defined[i.tensor as usize] { + return mm(ST); + } + defined[i.tensor as usize] = true; + if self.tensors[i.tensor as usize].policy == Policy::Derived { + return mm(ST); + } + } + if self.inputs[0].kind != 0 { + return mm(ST); + } + let sized = self.inputs[0].max_h != 0; + if self.inputs.iter().any(|i| (i.max_h != 0) != sized) { + return mm(ST); + } + for op in &self.ops { + if op.ins.iter().any(|&t| !defined[t as usize]) { + return mm(ST); + } + for &t in &op.outs { + if defined[t as usize] { + return mm(ST); + } + defined[t as usize] = true; + } + } + if defined.iter().any(|&d| !d) { + return mm(ST); + } + // State inputs. + for i in &self.inputs { + if i.kind == 1 { + if i.src as usize >= self.outputs.len() { + return mm(ST); + } + let rec = &self.tensors[i.tensor as usize]; + let out = &self.tensors[self.outputs[i.src as usize] as usize]; + if out.channels != rec.channels || i.init < rec.lo || i.init > rec.hi { + return mm(ST); + } + } + } + // Each operator's output channel count, and the exponent rules. + let ch = |t: u32| self.tensors[t as usize].channels as u64; + let ex = |t: u32| self.tensors[t as usize].exponent; + for op in &self.ops { + let c = ch(op.ins[0]); + let want: Option = match (&op.p, op.code) { + (Params::Conv { cout, .. }, _) | (Params::ConvT { cout, .. }, _) => Some(*cout as u64), + (_, ADD) => (ch(op.ins[1]) == c).then_some(c), + (_, MUL) => (ch(op.ins[1]) == c || ch(op.ins[1]) == 1).then_some(c), + (_, CONCAT) => Some(op.ins.iter().map(|&t| ch(t)).sum()), + (_, CHUNK_ADD) => (c % 2 == 0).then_some(c / 2), + (Params::Split { start, count }, _) => ((*start as u64) + (*count as u64) <= c).then_some(*count as u64), + (Params::Shuffle { r }, PIXEL_SHUFFLE) => { + let r2 = (*r as u64) * (*r as u64); + (c % r2 == 0).then_some(c / r2) + } + (Params::Shuffle { r }, _) => c.checked_mul((*r as u64) * (*r as u64)), + (_, SUBPOS_MERGE) => op.ins.iter().all(|&t| ch(t) == c).then_some(c), + (_, FILL) => Some(ch(op.outs[0])), + (Params::Gram { groups, .. }, _) => (ch(op.ins[1]) == c && c % *groups as u64 == 0).then_some(c), + (Params::Apply { groups, .. }, _) => (ch(op.ins[1]) == c && c % *groups as u64 == 0).then_some(c), + (Params::Chscale { scalar, n_rows, .. }, _) => { + let s = self.scalars[*scalar as usize]; + (s.lo >= 0 && s.hi <= *n_rows as i64 - 1).then_some(c) + } + _ => Some(c), + }; + match want { + Some(w) if op.outs.iter().all(|&t| ch(t) == w) => {} + _ => return mm(ST), + } + let all_or_none = |ts: &mut dyn Iterator| -> bool { + let v: Vec = ts.map(ex).collect(); + v.iter().all(|&e| e == -128) || (v.iter().all(|&e| e != -128) && v.windows(2).all(|w| w[0] == w[1])) + }; + match op.code { + ADD | CONCAT | CHUNK_ADD => { + if !all_or_none(&mut op.ins.iter().copied().chain(op.outs.iter().copied())) { + return mm(ST); + } + } + LUT16 => { + if let Params::Lut16 { e_in, e_out, .. } = op.p { + if e_in != ex(op.ins[0]) || e_out != ex(op.outs[0]) { + return mm(ST); + } + } + } + _ => {} + } + } + Ok(()) + } + + /// The graph's bytes. + pub fn to_bytes(&self) -> Vec { + let mut o = Vec::new(); + o.extend_from_slice(MAGIC); + o.extend_from_slice(&[1, 1, 0, 0]); + o.extend_from_slice(&(self.tensors.len() as u32).to_le_bytes()); + o.extend_from_slice(&(self.ops.len() as u32).to_le_bytes()); + o.extend_from_slice(&(self.inputs.len() as u16).to_le_bytes()); + o.extend_from_slice(&(self.outputs.len() as u16).to_le_bytes()); + o.extend_from_slice(&(self.scalars.len() as u16).to_le_bytes()); + o.extend_from_slice(&[0, 0]); + o.extend_from_slice(&self.max_elems.to_le_bytes()); + o.extend_from_slice(&[0, 0, 0, 0]); + for s in &self.scalars { + o.extend_from_slice(&s.lo.to_le_bytes()); + o.extend_from_slice(&s.hi.to_le_bytes()); + } + for i in &self.inputs { + o.extend_from_slice(&i.tensor.to_le_bytes()); + o.extend_from_slice(&i.max_h.to_le_bytes()); + o.extend_from_slice(&i.max_w.to_le_bytes()); + o.extend_from_slice(&[i.kind, 0, 0, 0]); + o.extend_from_slice(&i.src.to_le_bytes()); + o.extend_from_slice(&[0, 0, 0, 0]); + o.extend_from_slice(&i.init.to_le_bytes()); + } + for t in &self.outputs { + o.extend_from_slice(&t.to_le_bytes()); + } + for t in &self.tensors { + o.extend_from_slice(&t.channels.to_le_bytes()); + o.push(t.exponent as u8); + o.push(match t.policy { + Policy::Derived => 0, + Policy::Clamp => 1, + Policy::Check => 2, + }); + o.extend_from_slice(&[0, 0]); + o.extend_from_slice(&t.lo.to_le_bytes()); + o.extend_from_slice(&t.hi.to_le_bytes()); + } + for op in &self.ops { + o.extend_from_slice(&[op.code, op.ins.len() as u8, op.outs.len() as u8, 0]); + for t in op.ins.iter().chain(&op.outs) { + o.extend_from_slice(&t.to_le_bytes()); + } + let p = write_params(&op.p); + o.extend_from_slice(&(p.len() as u32).to_le_bytes()); + o.extend_from_slice(&p); + } + o + } +} + +fn put_ints(o: &mut Vec, v: &[i64], w: u8) { + for &x in v { + o.extend_from_slice(&x.to_le_bytes()[..w as usize]); + } +} + +fn write_rq(o: &mut Vec, rq: &Rq) { + o.push(round_code(rq.round)); + o.push(match rq.bias_kind { + BiasKind::None => 0, + BiasKind::Pre => 1, + BiasKind::Post => 2, + }); + o.extend_from_slice(&[rq.m_kind, rq.s_kind, rq.m_bytes, rq.b_bytes, 0, 0]); + put_ints(o, &rq.mult, rq.m_bytes); + o.extend_from_slice(&rq.shift); + put_ints(o, &rq.bias, rq.b_bytes); +} + +fn write_operand(o: &mut Vec, x: Operand) { + let (k, v) = match x { + Operand::Const(v) => (0u8, v), + Operand::Scalar(v) => (1u8, v), + }; + o.extend_from_slice(&[k, 0, 0, 0]); + o.extend_from_slice(&v.to_le_bytes()); +} + +/// One operator's parameter bytes (§10.4). +pub fn write_params(p: &Params) -> Vec { + let mut o = Vec::new(); + let u = |o: &mut Vec, v: u32| o.extend_from_slice(&v.to_le_bytes()); + match p { + Params::Conv { cout, kh, kw, sh, sw, ph, pw, groups, w_bytes, rq, w } => { + for v in [*cout, *kh, *kw, *sh, *sw, *ph, *pw, *groups] { + u(&mut o, v); + } + o.extend_from_slice(&[*w_bytes, 0, 0, 0]); + write_rq(&mut o, rq); + put_ints(&mut o, w, *w_bytes); + } + Params::ConvT { cout, kh, kw, sh, sw, ph, pw, oph, opw, w_bytes, rq, w } => { + for v in [*cout, *kh, *kw, *sh, *sw, *ph, *pw, *oph, *opw] { + u(&mut o, v); + } + o.extend_from_slice(&[*w_bytes, 0, 0, 0]); + write_rq(&mut o, rq); + put_ints(&mut o, w, *w_bytes); + } + Params::Rescale(rq) | Params::Mul(rq) => write_rq(&mut o, rq), + Params::Chscale { scalar, n_rows, round, m_bytes, shift, m } => { + u(&mut o, *scalar); + u(&mut o, *n_rows); + o.extend_from_slice(&[round_code(*round), *m_bytes, *shift, 0]); + put_ints(&mut o, m, *m_bytes); + } + Params::Lut16 { e_in, e_out, t } => { + o.extend_from_slice(&[*e_in as u8, *e_out as u8, 0, 0]); + for &v in t { + o.extend_from_slice(&v.to_le_bytes()); + } + } + Params::None => {} + Params::Split { start, count } => { + u(&mut o, *start); + u(&mut o, *count); + } + Params::Shuffle { r } => u(&mut o, *r), + Params::Clamp { lo, hi } => { + o.extend_from_slice(&lo.to_le_bytes()); + o.extend_from_slice(&hi.to_le_bytes()); + } + Params::Leaky { a, f } => { + o.extend_from_slice(&a.to_le_bytes()); + u(&mut o, *f); + } + Params::PadRep { m } => u(&mut o, *m), + Params::Crop { y0, x0, h, w } => { + for x in [*y0, *x0, *h, *w] { + write_operand(&mut o, x); + } + } + Params::Subpos { order } => o.extend_from_slice(order), + Params::Fill { kind, value } => { + o.extend_from_slice(&[*kind, 0, 0, 0, 0, 0, 0, 0]); + o.extend_from_slice(&value.to_le_bytes()); + } + Params::Igdn { gf, frac, g_bytes, gamma, beta } => { + u(&mut o, *gf); + u(&mut o, *frac); + o.extend_from_slice(&[*g_bytes, 0, 0, 0, 0, 0, 0, 0]); + put_ints(&mut o, gamma, *g_bytes); + put_ints(&mut o, beta, 8); + } + Params::LayerNorm { eps, g_bytes, gamma, beta } => { + o.extend_from_slice(&eps.to_le_bytes()); + o.extend_from_slice(&[*g_bytes, 0, 0, 0, 0, 0, 0, 0]); + put_ints(&mut o, gamma, *g_bytes); + put_ints(&mut o, beta, 8); + } + Params::L2norm { eps } => o.extend_from_slice(&eps.to_le_bytes()), + Params::Elu { alpha } => o.extend_from_slice(&alpha.to_le_bytes()), + Params::Gram { groups, rq } | Params::Apply { groups, rq } => { + u(&mut o, *groups); + write_rq(&mut o, rq); + } + } + o +} + +/// The smallest width of `allowed` (ascending) that holds every value of `v`. +pub fn smallest_width(v: &[i64], allowed: &[u8]) -> u8 { + for &w in allowed { + let (lo, hi) = if w == 8 { (i64::MIN, i64::MAX) } else { (-(1i64 << (8 * w - 1)), (1i64 << (8 * w - 1)) - 1) }; + if v.iter().all(|&x| lo <= x && x <= hi) { + return w; + } + } + 8 +} + +/// A graph that passed every load step of §10.5. Only [`load`] makes one, and +/// [`crate::interp::run`] takes nothing else, so no graph decodes without passing +/// load step 8: a [`Graph`] that was parsed, lowered or built in place runs only +/// after `load` of its bytes. +/// +/// ``` +/// # #[cfg(feature = "uncertified")] { +/// use wai_ops::{Sequence, Tensor, graph, lower, run}; +/// let set = lower::int_synth(br#"{"layers":[{"in_ch":1,"out_ch":3,"k":1,"stride":1,"pad":0,"weight":[1,2,3],"bias":[0,0,0]}]}"#).unwrap(); +/// let g = graph::load(&set.get("synth").unwrap().to_bytes()).unwrap(); +/// let out = run(&g, &[Tensor::new(1, 1, 1, vec![5])], &[], &mut Sequence::new()).unwrap(); +/// assert_eq!(out[0].data, vec![5, 10, 15]); +/// # } +/// ``` +/// +/// A lowered graph that has not been loaded does not compile as an argument to `run`: +/// +/// ```compile_fail,E0308 +/// use wai_ops::{Sequence, Tensor, lower, run}; +/// let set = lower::int_synth(br#"{"layers":[{"in_ch":1,"out_ch":3,"k":1,"stride":1,"pad":0,"weight":[1,2,3],"bias":[0,0,0]}]}"#).unwrap(); +/// let g = set.get("synth").unwrap(); +/// let _ = run(g, &[Tensor::new(1, 1, 1, vec![5])], &[], &mut Sequence::new()); +/// ``` +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Loaded { + g: Graph, +} + +impl Loaded { + /// The graph. + pub fn graph(&self) -> &Graph { + &self.g + } +} + +impl core::ops::Deref for Loaded { + type Target = Graph; + fn deref(&self) -> &Graph { + &self.g + } +} + +/// Load a graph: steps 1–7 of §10.5, then step 8. +/// +/// Step 8 (the bounds pass, `lut16`'s range check, and the capability's profile +/// against the verdict) is not built yet. Without the `uncertified` feature it +/// refuses every graph (`model_mismatch` at step 8), so no [`Loaded`] exists and +/// nothing decodes; with the feature, which only the tests, the conformance tool and +/// its browser-engine build enable, it is skipped. +pub fn load(b: &[u8]) -> Res { + let g = Graph::parse(b)?; + crate::plan::load_plan(&g)?; + step8(&g)?; + Ok(Loaded { g }) +} + +#[cfg(feature = "uncertified")] +fn step8(_g: &Graph) -> Res<()> { + Ok(()) +} + +#[cfg(not(feature = "uncertified"))] +fn step8(_g: &Graph) -> Res<()> { + Err(Refusal::new(Code::ModelMismatch, crate::code::Site::Load(8))) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn tiny() -> Graph { + Graph { + max_elems: CAP as u32, + scalars: vec![], + inputs: vec![InputRec { tensor: 0, max_h: 0, max_w: 0, kind: 0, src: 0, init: 0 }], + outputs: vec![1], + tensors: vec![TensorRec::check(1, -5, 5), TensorRec::derived(1)], + ops: vec![Op { code: opcode::RELU, ins: vec![0], outs: vec![1], p: Params::None }], + } + } + + #[test] + fn a_graph_round_trips() { + let b = tiny().to_bytes(); + assert_eq!(Graph::parse(&b).unwrap(), tiny()); + assert_eq!(Graph::parse(&b).unwrap().to_bytes(), b); + } + + /// Without the `uncertified` switch, load step 8 refuses every graph: nothing + /// decodes through an uncertified graph outside the tests and the tool. + #[cfg(not(feature = "uncertified"))] + #[test] + fn step_8_refuses_every_graph_until_the_certifier_exists() { + let r = load(&tiny().to_bytes()).unwrap_err(); + assert_eq!(r, Refusal::new(Code::ModelMismatch, crate::code::Site::Load(8))); + } + + #[cfg(feature = "uncertified")] + #[test] + fn the_switch_skips_step_8() { + assert!(load(&tiny().to_bytes()).is_ok()); + } +} diff --git a/wai/wai-ops/src/interp.rs b/wai/wai-ops/src/interp.rs new file mode 100644 index 000000000..446bf89c5 --- /dev/null +++ b/wai/wai-ops/src/interp.rs @@ -0,0 +1,613 @@ +//! The reference interpreter of the graph operators (§5). +//! +//! Tensors hold `i64`. Every intermediate is formed in `i128`, which holds every +//! value a graph certified for `acc64` forms (§7.1), and every step is checked: a +//! graph whose arithmetic would leave `i128`, or whose producer result would leave +//! `i64`, is one that load step 8 refuses once the certifier exists, and here, where +//! step 8 is not built yet, the decode is refused (`model_mismatch`) at that operator +//! instead of wrapping. No certified graph reaches that refusal. + +use alloc::vec; +use alloc::vec::Vec; + +use crate::code::{Code, Refusal, Site}; +use crate::graph::{Graph, Loaded, Operand, Params, Policy, Rq, Round, opcode::*}; +use crate::plan::run_plan; + +/// A tensor `[c, h, w]`, its elements in CHW order. +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Tensor { + pub c: u32, + pub h: u32, + pub w: u32, + pub data: Vec, +} + +impl Tensor { + pub fn new(c: u32, h: u32, w: u32, data: Vec) -> Tensor { + Tensor { c, h, w, data } + } + pub fn filled(c: u32, h: u32, w: u32, v: i64) -> Tensor { + Tensor { c, h, w, data: vec![v; c as usize * h as usize * w as usize] } + } + fn at(&self, c: usize, y: usize, x: usize) -> i64 { + self.data[(c * self.h as usize + y) * self.w as usize + x] + } +} + +/// The state of a sequence of decodes (§10.3): the tensors carried into the state +/// inputs, or nothing before the first decode. +#[derive(Clone, Debug, Default)] +pub struct Sequence { + state: Option>, +} + +impl Sequence { + pub fn new() -> Sequence { + Sequence { state: None } + } +} + +type Res = Result; + +/// The refusal an uncertified graph meets where its arithmetic leaves the widths a +/// certified one keeps to. See the module documentation. +fn wide(at: u32) -> Refusal { + Refusal::new(Code::ModelMismatch, Site::Op(at)) +} + +/// `R_round(p, s)` (§2). +pub fn round(p: i128, s: u32, r: Round) -> Option { + if s == 0 { + return Some(p); + } + match r { + Round::Floor => Some(p >> s), + Round::HalfUp => Some(p.checked_add(1i128 << (s - 1))? >> s), + Round::HalfEven => { + let q = p >> s; + let rem = (p as u128) & (u128::MAX >> (128 - s)); + let h = 1u128 << (s - 1); + Some(if rem > h || (rem == h && q & 1 == 1) { q + 1 } else { q }) + } + } +} + +/// `rq_o(v)` (§5.1). +fn rq_apply(rq: &Rq, o: usize, v: i128) -> Option { + let t = v.checked_add(rq.pre(o) as i128)?; + let p = match rq.mult(o) { + Some(m) => t.checked_mul(m as i128)?, + None => t, + }; + let q = round(p, rq.shift(o), rq.round)?; + q.checked_add(rq.post(o) as i128) +} + +/// `exp_neg_q16(x)` (§2), for `x ≤ 0`. +pub fn exp_neg_q16(x: i128) -> i128 { + let u = -x; + let n = u / 45426; + let g = 45426 * n; + let r = x + g; + let t = r + 88670; + let t2 = t * t; + let q = t2 >> 16; + let c = 23495 * q; + let y = (c >> 16) + 22544; + if n >= 63 { 0 } else { y >> n } +} + +/// `rsqrt_q16(v)` (§2), for `v ≥ 0`. +pub fn rsqrt_q16(v: u128) -> i128 { + let r = v.isqrt(); + if r == 0 { 0 } else { ((1u128 << 32) / r) as i128 } +} + +fn to64(v: i128, at: u32) -> Res { + i64::try_from(v).map_err(|_| wide(at)) +} + +/// Run one decode of `g`, a graph that passed every load step ([`crate::graph::load`]). +/// `inputs` holds the data inputs, in input-table order; each state input is taken +/// from `seq` (§10.3). `scalars` holds every scalar's value. Returns the graph's +/// outputs, in output-table order. A refused decode leaves `seq` as it was. +pub fn run(g: &Loaded, inputs: &[Tensor], scalars: &[i64], seq: &mut Sequence) -> Res> { + let g: &Graph = g.graph(); + // The inputs, data and state, in input-table order. + let n_data = g.inputs.iter().filter(|i| i.kind == 0).count(); + if inputs.len() != n_data { + return Err(Refusal::new(Code::ModelMismatch, Site::Input(inputs.len().min(n_data) as u32))); + } + let mut all: Vec = Vec::with_capacity(g.inputs.len()); + let (mut d, mut s) = (0usize, 0usize); + for rec in &g.inputs { + if rec.kind == 0 { + all.push(inputs[d].clone()); + d += 1; + } else { + let t = match &seq.state { + Some(st) => st[s].clone(), + None => { + let i0 = &inputs[0]; + Tensor::filled(g.tensors[rec.tensor as usize].channels, i0.h, i0.w, rec.init) + } + }; + all.push(t); + s += 1; + } + } + for (k, t) in all.iter().enumerate() { + if t.data.len() as u64 != t.c as u64 * t.h as u64 * t.w as u64 { + return Err(Refusal::new(Code::ModelMismatch, Site::Input(k as u32))); + } + } + let shapes: Vec<(u64, u64, u64)> = all.iter().map(|t| (t.c as u64, t.h as u64, t.w as u64)).collect(); + let hw = run_plan(g, &shapes, scalars)?; + + let mut vals: Vec> = vec![None; g.tensors.len()]; + // Step 4 begins with the inputs: each input's policy applies to it. + for (k, (rec, mut t)) in g.inputs.iter().zip(all).enumerate() { + let tr = &g.tensors[rec.tensor as usize]; + if !apply_policy(tr.policy, tr.lo, tr.hi, &mut t.data) { + return Err(Refusal::new(Code::FieldRange, Site::Input(k as u32))); + } + vals[rec.tensor as usize] = Some(t); + } + for (i, op) in g.ops.iter().enumerate() { + let outs = eval(g, op, i as u32, &vals, &hw, scalars)?; + for (&t, mut ten) in op.outs.iter().zip(outs) { + let tr = &g.tensors[t as usize]; + if !apply_policy(tr.policy, tr.lo, tr.hi, &mut ten.data) { + return Err(Refusal::new(Code::FieldRange, Site::Op(i as u32))); + } + vals[t as usize] = Some(ten); + } + } + let outputs: Vec = g.outputs.iter().map(|&t| vals[t as usize].clone().expect("every tensor is defined")).collect(); + let next: Vec = g.inputs.iter().filter(|i| i.kind == 1).map(|i| outputs[i.src as usize].clone()).collect(); + seq.state = Some(next); + Ok(outputs) +} + +/// Apply a policy: clamp in place, or report whether every element lies in range. +fn apply_policy(p: Policy, lo: i64, hi: i64, d: &mut [i64]) -> bool { + match p { + Policy::Derived => true, + Policy::Clamp => { + for v in d.iter_mut() { + *v = (*v).clamp(lo, hi); + } + true + } + Policy::Check => d.iter().all(|&v| lo <= v && v <= hi), + } +} + +fn get<'a>(vals: &'a [Option], t: u32) -> &'a Tensor { + vals[t as usize].as_ref().expect("an operator reads only defined tensors") +} + +/// Map every element of `x` through `f`. +fn map(x: &Tensor, at: u32, f: impl Fn(i128) -> Option) -> Res { + let mut d = Vec::with_capacity(x.data.len()); + for &v in &x.data { + d.push(to64(f(v as i128).ok_or(wide(at))?, at)?); + } + Ok(Tensor::new(x.c, x.h, x.w, d)) +} + +/// Map every element of channel `c` of `x` through `f(c, v)`. +fn map_c(x: &Tensor, at: u32, f: impl Fn(usize, i128) -> Option) -> Res { + let plane = x.h as usize * x.w as usize; + let mut d = Vec::with_capacity(x.data.len()); + for (k, &v) in x.data.iter().enumerate() { + d.push(to64(f(k / plane.max(1), v as i128).ok_or(wide(at))?, at)?); + } + Ok(Tensor::new(x.c, x.h, x.w, d)) +} + +fn eval(g: &Graph, op: &crate::graph::Op, at: u32, vals: &[Option], hw: &[(u64, u64)], sc: &[i64]) -> Res> { + let x = get(vals, op.ins[0]); + let (c, h, w) = (x.c as usize, x.h as usize, x.w as usize); + let out_c = |k: usize| g.tensors[op.outs[k] as usize].channels; + let (oh, ow) = hw[op.outs[0] as usize]; + let (oh, ow) = (oh as usize, ow as usize); + let one = |t: Tensor| Ok(vec![t]); + match (&op.p, op.code) { + (Params::Conv { cout, kh, kw, sh, sw, ph, pw, groups, rq, w: wt, .. }, _) => { + let (cout, kh, kw, sh, sw, ph, pw, groups) = + (*cout as usize, *kh as usize, *kw as usize, *sh as usize, *sw as usize, *ph as usize, *pw as usize, *groups as usize); + let (cg, og) = (c / groups, cout / groups); + let mut d = vec![0i64; cout * oh * ow]; + for o in 0..cout { + let base = (o / og) * cg; + for y in 0..oh { + for xx in 0..ow { + let mut acc: i128 = 0; + for ci in 0..cg { + for i in 0..kh { + let iy = y * sh + i; + if iy < ph || iy - ph >= h { + continue; + } + for j in 0..kw { + let ix = xx * sw + j; + if ix < pw || ix - pw >= w { + continue; + } + let wv = wt[((o * cg + ci) * kh + i) * kw + j] as i128; + let term = wv * x.at(base + ci, iy - ph, ix - pw) as i128; + acc = acc.checked_add(term).ok_or(wide(at))?; + } + } + } + d[(o * oh + y) * ow + xx] = to64(rq_apply(rq, o, acc).ok_or(wide(at))?, at)?; + } + } + } + one(Tensor::new(cout as u32, oh as u32, ow as u32, d)) + } + (Params::ConvT { cout, kh, kw, sh, sw, ph, pw, oph, opw, rq, w: wt, .. }, _) => { + let (cout, kh, kw, sh, sw, ph, pw) = + (*cout as usize, *kh as usize, *kw as usize, *sh as usize, *sw as usize, *ph as usize, *pw as usize); + let uh = (h - 1) * sh + kh + *oph as usize; + let uw = (w - 1) * sw + kw + *opw as usize; + // The scatter form: the same products as §5.3's gather, summed exactly. + let mut plane = vec![0i128; cout * uh * uw]; + for ci in 0..c { + for u in 0..h { + for v in 0..w { + let xv = x.at(ci, u, v) as i128; + if xv == 0 { + continue; + } + for o in 0..cout { + for i in 0..kh { + let row = (o * uh + u * sh + i) * uw + v * sw; + for j in 0..kw { + let wv = wt[((ci * cout + o) * kh + i) * kw + j] as i128; + let cell = &mut plane[row + j]; + *cell = cell.checked_add(wv * xv).ok_or(wide(at))?; + } + } + } + } + } + } + let mut d = vec![0i64; cout * oh * ow]; + for o in 0..cout { + for y in 0..oh { + for xx in 0..ow { + let acc = plane[(o * uh + y + ph) * uw + xx + pw]; + d[(o * oh + y) * ow + xx] = to64(rq_apply(rq, o, acc).ok_or(wide(at))?, at)?; + } + } + } + one(Tensor::new(cout as u32, oh as u32, ow as u32, d)) + } + (Params::Rescale(rq), _) => one(map_c(x, at, |o, v| rq_apply(rq, o, v))?), + (Params::Chscale { scalar, round: r, shift, m, .. }, _) => { + let row = sc[*scalar as usize] as usize; + one(map_c(x, at, |o, v| round(v.checked_mul(m[row * c + o] as i128)?, *shift as u32, *r))?) + } + (Params::Mul(rq), _) => { + let b = get(vals, op.ins[1]); + let plane = h * w; + let mut d = Vec::with_capacity(x.data.len()); + for o in 0..c { + let bo = if b.c as usize == c { o } else { 0 }; + for k in 0..plane { + let p = (x.data[o * plane + k] as i128) * (b.data[bo * plane + k] as i128); + d.push(to64(rq_apply(rq, o, p).ok_or(wide(at))?, at)?); + } + } + one(Tensor::new(x.c, x.h, x.w, d)) + } + (Params::Lut16 { t, .. }, _) => one(map(x, at, |v| { + let i = v + 32768; + if (0..65536).contains(&i) { Some(t[i as usize] as i128) } else { None } + })?), + (_, ADD) => { + let b = get(vals, op.ins[1]); + let mut d = Vec::with_capacity(x.data.len()); + for (&p, &q) in x.data.iter().zip(&b.data) { + d.push(to64(p as i128 + q as i128, at)?); + } + one(Tensor::new(x.c, x.h, x.w, d)) + } + (_, CONCAT) => { + let mut d = Vec::new(); + for &t in &op.ins { + d.extend_from_slice(&get(vals, t).data); + } + one(Tensor::new(out_c(0), x.h, x.w, d)) + } + (_, CHUNK_ADD) => { + let half = (c / 2) * h * w; + let mut d = Vec::with_capacity(half); + for k in 0..half { + d.push(to64(x.data[k] as i128 + x.data[k + half] as i128, at)?); + } + one(Tensor::new((c / 2) as u32, x.h, x.w, d)) + } + (Params::Split { start, count }, _) => { + let plane = h * w; + let s = *start as usize * plane; + one(Tensor::new(*count, x.h, x.w, x.data[s..s + *count as usize * plane].to_vec())) + } + (Params::Shuffle { r }, PIXEL_SHUFFLE) => { + let r = *r as usize; + let co = c / (r * r); + let mut d = vec![0i64; c * h * w]; + for cc in 0..co { + for y in 0..h { + for xx in 0..w { + for i in 0..r { + for j in 0..r { + d[(cc * oh + y * r + i) * ow + xx * r + j] = x.at(cc * r * r + i * r + j, y, xx); + } + } + } + } + } + one(Tensor::new(co as u32, oh as u32, ow as u32, d)) + } + (Params::Shuffle { r }, _) => { + let r = *r as usize; + let mut d = vec![0i64; c * h * w]; + for cc in 0..c { + for y in 0..oh { + for xx in 0..ow { + for i in 0..r { + for j in 0..r { + d[((cc * r * r + i * r + j) * oh + y) * ow + xx] = x.at(cc, y * r + i, xx * r + j); + } + } + } + } + } + one(Tensor::new((c * r * r) as u32, oh as u32, ow as u32, d)) + } + (Params::Clamp { lo, hi }, _) => one(map(x, at, |v| Some(v.clamp(*lo as i128, *hi as i128)))?), + (_, RELU) => one(map(x, at, |v| Some(v.max(0)))?), + (Params::Leaky { a, f }, _) => one(map(x, at, |v| Some(if v >= 0 { v } else { (v * *a as i128) >> *f } ))?), + (_, ABS) => one(map(x, at, |v| Some(v.abs()))?), + (Params::PadRep { .. }, _) => { + let mut d = Vec::with_capacity(c * oh * ow); + for cc in 0..c { + for y in 0..oh { + for xx in 0..ow { + d.push(x.at(cc, y.min(h - 1), xx.min(w - 1))); + } + } + } + one(Tensor::new(x.c, oh as u32, ow as u32, d)) + } + (_, CROP_TO) => one(crop(x, 0, 0, oh, ow)), + (Params::Crop { y0, x0, .. }, _) => { + let v = |o: &Operand| match *o { + Operand::Const(k) => k as usize, + Operand::Scalar(s) => sc[s as usize] as usize, + }; + one(crop(x, v(y0), v(x0), oh, ow)) + } + (Params::Subpos { order }, SUBPOS_SPLIT) => { + let mut outs = Vec::with_capacity(4); + for &o in order { + let (a, b) = ((o >> 1) as usize, (o & 1) as usize); + let mut d = Vec::with_capacity(c * oh * ow); + for cc in 0..c { + for y in 0..oh { + for xx in 0..ow { + d.push(x.at(cc, 2 * y + a, 2 * xx + b)); + } + } + } + outs.push(Tensor::new(x.c, oh as u32, ow as u32, d)); + } + Ok(outs) + } + (Params::Subpos { order }, _) => { + let mut d = vec![0i64; c * oh * ow]; + for (k, &o) in order.iter().enumerate() { + let (a, b) = ((o >> 1) as usize, (o & 1) as usize); + let t = get(vals, op.ins[k]); + for cc in 0..c { + for y in 0..h { + for xx in 0..w { + d[(cc * oh + 2 * y + a) * ow + 2 * xx + b] = t.at(cc, y, xx); + } + } + } + } + one(Tensor::new(x.c, oh as u32, ow as u32, d)) + } + (Params::Fill { kind, value }, _) => { + let v = if *kind == 0 { *value } else { sc[*value as usize] }; + one(Tensor::filled(out_c(0), x.h, x.w, v)) + } + (Params::Igdn { gf, frac, gamma, beta, .. }, _) => { + let mut d = vec![0i64; c * h * w]; + let plane = h * w; + let mut x2 = vec![0i128; c]; + for p in 0..plane { + for j in 0..c { + let v = x.data[j * plane + p] as i128; + x2[j] = v * v; + } + for i in 0..c { + let mut n = beta[i] as i128; + for j in 0..c { + let gx2 = (gamma[i * c + j] as i128).checked_mul(x2[j]).ok_or(wide(at))?; + n = n.checked_add(gx2 >> *gf).ok_or(wide(at))?; + } + let r = (n.max(0) as u128).isqrt() as i128; + let xr = (x.data[i * plane + p] as i128).checked_mul(r).ok_or(wide(at))?; + d[i * plane + p] = to64(xr >> *frac, at)?; + } + } + one(Tensor::new(x.c, x.h, x.w, d)) + } + (Params::LayerNorm { eps, gamma, beta, .. }, _) => { + let plane = h * w; + let mut d = vec![0i64; c * plane]; + let ci = c as i128; + for p in 0..plane { + let mut s: i128 = 0; + for k in 0..c { + s = s.checked_add(x.data[k * plane + p] as i128).ok_or(wide(at))?; + } + let m_num = s.checked_add((ci / 2) * s.signum()).ok_or(wide(at))?; + let m = m_num / ci; + let mut t: i128 = 0; + let mut dev = vec![0i128; c]; + for k in 0..c { + let dk = (x.data[k * plane + p] as i128) - m; + dev[k] = dk; + t = t.checked_add(dk.checked_mul(dk).ok_or(wide(at))?).ok_or(wide(at))?; + } + let v = t / ci; + let u = v.checked_add(*eps as i128).ok_or(wide(at))?; + let k = rsqrt_q16(u as u128); + for kk in 0..c { + let n = dev[kk].checked_mul(k).ok_or(wide(at))? >> 16; + let ng = n.checked_mul(gamma[kk] as i128).ok_or(wide(at))?; + d[kk * plane + p] = to64((ng >> 16).checked_add(beta[kk] as i128).ok_or(wide(at))?, at)?; + } + } + one(Tensor::new(x.c, x.h, x.w, d)) + } + (Params::L2norm { eps }, _) => { + let plane = h * w; + let mut d = vec![0i64; c * plane]; + for cc in 0..c { + let mut t: i128 = 0; + for p in 0..plane { + let v = x.data[cc * plane + p] as i128; + t = t.checked_add(v * v).ok_or(wide(at))?; + } + let u = t.checked_add(*eps as i128).ok_or(wide(at))?; + let k = rsqrt_q16(u as u128); + for p in 0..plane { + let vk = (x.data[cc * plane + p] as i128).checked_mul(k).ok_or(wide(at))?; + d[cc * plane + p] = to64(vk >> 16, at)?; + } + } + one(Tensor::new(x.c, x.h, x.w, d)) + } + (_, SOFTMAX) => { + let mut d = vec![0i64; c * h * w]; + let mut e = vec![0i128; w]; + for row in 0..c * h { + let r = &x.data[row * w..(row + 1) * w]; + let m = *r.iter().max().expect("a row has at least one element") as i128; + let mut sum: i128 = 0; + for (k, &v) in r.iter().enumerate() { + e[k] = exp_neg_q16(v as i128 - m); + sum += e[k]; + } + for k in 0..w { + d[row * w + k] = to64(((e[k] << 16) + (sum >> 1)) / sum, at)?; + } + } + one(Tensor::new(x.c, x.h, x.w, d)) + } + (_, SIGMOID) => one(map(x, at, |v| { + Some(if v >= 0 { + let e = exp_neg_q16(-v); + (1i128 << 32) / ((1 << 16) + e) + } else { + let e = exp_neg_q16(v); + (e << 16) / ((1 << 16) + e) + }) + })?), + (Params::Elu { alpha }, _) => one(map(x, at, |v| { + Some(if v > 0 { v } else { ((*alpha as i128).checked_mul(exp_neg_q16(v) - (1 << 16))?) >> 16 }) + })?), + (Params::Gram { groups, rq }, _) => { + let b = get(vals, op.ins[1]); + let d_ = c / *groups as usize; + let plane = h * w; + let mut d = vec![0i64; c * d_]; + for gi in 0..*groups as usize { + for i in 0..d_ { + for j in 0..d_ { + let (ca, cb) = (gi * d_ + i, gi * d_ + j); + let mut acc: i128 = 0; + for p in 0..plane { + let t = (x.data[ca * plane + p] as i128) * (b.data[cb * plane + p] as i128); + acc = acc.checked_add(t).ok_or(wide(at))?; + } + d[ca * d_ + j] = to64(round(acc, rq.shift(0), rq.round).ok_or(wide(at))?, at)?; + } + } + } + one(Tensor::new(x.c, 1, d_ as u32, d)) + } + (Params::Apply { groups, rq }, _) => { + let v = get(vals, op.ins[1]); + let d_ = c / *groups as usize; + let plane = v.h as usize * v.w as usize; + let mut d = vec![0i64; c * plane]; + for gi in 0..*groups as usize { + for i in 0..d_ { + for p in 0..plane { + let mut acc: i128 = 0; + for j in 0..d_ { + let t = (x.data[(gi * d_ + i) * d_ + j] as i128) * (v.data[(gi * d_ + j) * plane + p] as i128); + acc = acc.checked_add(t).ok_or(wide(at))?; + } + d[(gi * d_ + i) * plane + p] = to64(round(acc, rq.shift(0), rq.round).ok_or(wide(at))?, at)?; + } + } + } + one(Tensor::new(v.c, v.h, v.w, d)) + } + _ => Err(wide(at)), + } +} + +fn crop(x: &Tensor, y0: usize, x0: usize, oh: usize, ow: usize) -> Tensor { + let mut d = Vec::with_capacity(x.c as usize * oh * ow); + for cc in 0..x.c as usize { + for y in 0..oh { + for xx in 0..ow { + d.push(x.at(cc, y0 + y, x0 + xx)); + } + } + } + Tensor::new(x.c, oh as u32, ow as u32, d) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn rounding_modes() { + // floor, half up, half even, at shift 1: −3/2 = −1.5. + assert_eq!(round(-3, 1, Round::Floor), Some(-2)); + assert_eq!(round(-3, 1, Round::HalfUp), Some(-1)); + assert_eq!(round(-3, 1, Round::HalfEven), Some(-2)); + assert_eq!(round(-1, 1, Round::HalfEven), Some(0)); + assert_eq!(round(5, 1, Round::HalfEven), Some(2)); + assert_eq!(round(7, 1, Round::HalfEven), Some(4)); + assert_eq!(round(i128::MIN, 127, Round::HalfEven), Some(-1)); + assert_eq!(round(i128::MAX, 127, Round::Floor), Some(0)); + assert_eq!(round(-(1 << 126), 127, Round::HalfEven), Some(0)); + // p + 2^(s−1) = 2^127 leaves i128: rq.p's interval fails its class under acc64, + // so no certified graph forms it, and the uncertified run refuses. + assert_eq!(round(1 << 126, 127, Round::HalfUp), None); + assert_eq!(round((1 << 126) - 1, 127, Round::HalfUp), Some(0)); + } + + #[test] + fn exp_constants() { + assert_eq!(exp_neg_q16(0), 65553); + assert_eq!(exp_neg_q16(-(45426 * 63)), 0); + assert!(exp_neg_q16(-(45426 * 62)) >= 0); + assert_eq!(rsqrt_q16(0), 0); + assert_eq!(rsqrt_q16(1), 1 << 32); + } +} diff --git a/wai/wai-ops/src/json.rs b/wai/wai-ops/src/json.rs new file mode 100644 index 000000000..e175a59d8 --- /dev/null +++ b/wai/wai-ops/src/json.rs @@ -0,0 +1,471 @@ +//! JSON: a reader for the JSON source forms and the vector files, and the canonical +//! writer of §8.5 and §14.1. +//! +//! The reader keeps a number as it was written. [`Value::as_int`] reads an integer +//! (no fraction, no exponent) of any size up to 511 bits; a number with a fraction or +//! an exponent is a value the integer forms never read, so it is not converted, and +//! members such forms ignore may hold one. + +use alloc::boxed::Box; +use alloc::string::String; +use alloc::vec::Vec; + +use crate::xint::I512; + +#[derive(Clone, Debug, PartialEq, Eq)] +pub enum Value { + Null, + Bool(bool), + /// A number exactly as written. + Num(String), + Str(String), + Arr(Vec), + /// Members in the order written. + Obj(Vec<(String, Value)>), +} + +/// Why a JSON text did not parse. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub struct JsonError { + pub at: usize, +} + +const MAX_DEPTH: usize = 256; + +struct P<'a> { + b: &'a [u8], + i: usize, +} + +impl<'a> P<'a> { + fn err(&self) -> Result { + Err(JsonError { at: self.i }) + } + fn ws(&mut self) { + while let Some(&c) = self.b.get(self.i) { + if c == b' ' || c == b'\t' || c == b'\n' || c == b'\r' { + self.i += 1; + } else { + break; + } + } + } + fn lit(&mut self, s: &[u8]) -> bool { + if self.b.get(self.i..self.i + s.len()) == Some(s) { + self.i += s.len(); + true + } else { + false + } + } + fn value(&mut self, depth: usize) -> Result { + if depth > MAX_DEPTH { + return self.err(); + } + self.ws(); + match self.b.get(self.i) { + None => self.err(), + Some(b'n') => if self.lit(b"null") { Ok(Value::Null) } else { self.err() }, + Some(b't') => if self.lit(b"true") { Ok(Value::Bool(true)) } else { self.err() }, + Some(b'f') => if self.lit(b"false") { Ok(Value::Bool(false)) } else { self.err() }, + Some(b'"') => Ok(Value::Str(self.string()?)), + Some(b'[') => { + self.i += 1; + let mut v = Vec::new(); + self.ws(); + if self.b.get(self.i) == Some(&b']') { + self.i += 1; + return Ok(Value::Arr(v)); + } + loop { + v.push(self.value(depth + 1)?); + self.ws(); + match self.b.get(self.i) { + Some(b',') => self.i += 1, + Some(b']') => { + self.i += 1; + return Ok(Value::Arr(v)); + } + _ => return self.err(), + } + } + } + Some(b'{') => { + self.i += 1; + let mut v = Vec::new(); + self.ws(); + if self.b.get(self.i) == Some(&b'}') { + self.i += 1; + return Ok(Value::Obj(v)); + } + loop { + self.ws(); + if self.b.get(self.i) != Some(&b'"') { + return self.err(); + } + let k = self.string()?; + self.ws(); + if self.b.get(self.i) != Some(&b':') { + return self.err(); + } + self.i += 1; + let val = self.value(depth + 1)?; + v.push((k, val)); + self.ws(); + match self.b.get(self.i) { + Some(b',') => self.i += 1, + Some(b'}') => { + self.i += 1; + return Ok(Value::Obj(v)); + } + _ => return self.err(), + } + } + } + Some(&c) if c == b'-' || c.is_ascii_digit() => self.number(), + _ => self.err(), + } + } + fn digits(&mut self) -> usize { + let s = self.i; + while self.b.get(self.i).is_some_and(|c| c.is_ascii_digit()) { + self.i += 1; + } + self.i - s + } + fn number(&mut self) -> Result { + let s = self.i; + if self.b.get(self.i) == Some(&b'-') { + self.i += 1; + } + match self.b.get(self.i) { + Some(b'0') => self.i += 1, + Some(c) if c.is_ascii_digit() => { + self.digits(); + } + _ => return self.err(), + } + if self.b.get(self.i) == Some(&b'.') { + self.i += 1; + if self.digits() == 0 { + return self.err(); + } + } + if matches!(self.b.get(self.i), Some(b'e') | Some(b'E')) { + self.i += 1; + if matches!(self.b.get(self.i), Some(b'+') | Some(b'-')) { + self.i += 1; + } + if self.digits() == 0 { + return self.err(); + } + } + // The slice is ASCII by construction. + let raw = core::str::from_utf8(&self.b[s..self.i]).map_err(|_| JsonError { at: s })?; + Ok(Value::Num(String::from(raw))) + } + fn hex4(&mut self) -> Result { + let mut v = 0u32; + for _ in 0..4 { + let c = *self.b.get(self.i).ok_or(JsonError { at: self.i })?; + let d = match c { + b'0'..=b'9' => c - b'0', + b'a'..=b'f' => c - b'a' + 10, + b'A'..=b'F' => c - b'A' + 10, + _ => return self.err(), + }; + v = v * 16 + d as u32; + self.i += 1; + } + Ok(v) + } + fn string(&mut self) -> Result { + self.i += 1; // the opening quote + let mut out: Vec = Vec::new(); + loop { + let c = *self.b.get(self.i).ok_or(JsonError { at: self.i })?; + match c { + b'"' => { + self.i += 1; + return String::from_utf8(out).map_err(|_| JsonError { at: self.i }); + } + b'\\' => { + self.i += 1; + let e = *self.b.get(self.i).ok_or(JsonError { at: self.i })?; + self.i += 1; + match e { + b'"' => out.push(b'"'), + b'\\' => out.push(b'\\'), + b'/' => out.push(b'/'), + b'b' => out.push(8), + b'f' => out.push(12), + b'n' => out.push(b'\n'), + b'r' => out.push(b'\r'), + b't' => out.push(b'\t'), + b'u' => { + let mut cp = self.hex4()?; + if (0xD800..0xDC00).contains(&cp) { + if !self.lit(b"\\u") { + return self.err(); + } + let lo = self.hex4()?; + if !(0xDC00..0xE000).contains(&lo) { + return self.err(); + } + cp = 0x10000 + ((cp - 0xD800) << 10) + (lo - 0xDC00); + } else if (0xDC00..0xE000).contains(&cp) { + return self.err(); + } + let ch = char::from_u32(cp).ok_or(JsonError { at: self.i })?; + let mut buf = [0u8; 4]; + out.extend_from_slice(ch.encode_utf8(&mut buf).as_bytes()); + } + _ => return self.err(), + } + } + 0..=0x1F => return self.err(), + _ => { + out.push(c); + self.i += 1; + } + } + } + } +} + +/// Parse one JSON text. Whitespace may surround it; nothing else may follow. +pub fn parse(b: &[u8]) -> Result { + let mut p = P { b, i: 0 }; + let v = p.value(0)?; + p.ws(); + if p.i != b.len() { + return p.err(); + } + Ok(v) +} + +impl Value { + /// The first member named `k` of an object. + pub fn get(&self, k: &str) -> Option<&Value> { + match self { + Value::Obj(m) => m.iter().find(|(n, _)| n == k).map(|(_, v)| v), + _ => None, + } + } + pub fn as_arr(&self) -> Option<&[Value]> { + match self { + Value::Arr(a) => Some(a), + _ => None, + } + } + pub fn as_obj(&self) -> Option<&[(String, Value)]> { + match self { + Value::Obj(m) => Some(m), + _ => None, + } + } + pub fn as_str(&self) -> Option<&str> { + match self { + Value::Str(s) => Some(s), + _ => None, + } + } + pub fn as_bool(&self) -> Option { + match self { + Value::Bool(b) => Some(*b), + _ => None, + } + } + /// A JSON number written as an integer: no fraction and no exponent. + pub fn as_int(&self) -> Option { + match self { + Value::Num(r) if !r.bytes().any(|c| c == b'.' || c == b'e' || c == b'E') => I512::from_dec(r), + _ => None, + } + } + pub fn as_i64(&self) -> Option { + self.as_int()?.to_i64() + } + pub fn as_i128(&self) -> Option { + self.as_int()?.to_i128() + } + pub fn as_u64(&self) -> Option { + let v = self.as_int()?.to_i128()?; + u64::try_from(v).ok() + } + pub fn as_u32(&self) -> Option { + u32::try_from(self.as_u64()?).ok() + } + /// A JSON string holding a canonical decimal integer (§8.5): digits, no leading + /// zero, a `-` only before a nonzero value. + pub fn as_dec(&self) -> Option { + let s = self.as_str()?; + if !is_canonical_dec(s) { + return None; + } + I512::from_dec(s) + } + pub fn as_dec_i64(&self) -> Option { + self.as_dec()?.to_i64() + } + pub fn as_dec_i128(&self) -> Option { + self.as_dec()?.to_i128() + } +} + +/// Whether `s` is a canonical decimal integer: `0`, or an optional `-` followed by a +/// nonzero digit and further digits. +pub fn is_canonical_dec(s: &str) -> bool { + let d = s.strip_prefix('-').unwrap_or(s); + if d.is_empty() || !d.bytes().all(|c| c.is_ascii_digit()) { + return false; + } + if d.len() > 1 && d.starts_with('0') { + return false; + } + !(s.starts_with('-') && d == "0") +} + +/// A canonical JSON value (§8.5): integers are written as decimal strings by the +/// caller, so a value is a string, a boolean, null, an array or an object. +#[derive(Clone, Debug, PartialEq, Eq)] +pub enum C { + Null, + Bool(bool), + Str(String), + Arr(Vec), + /// Members in any order; [`write`] sorts them by their keys' bytes. + Obj(Vec<(String, C)>), +} + +impl C { + pub fn int(v: i128) -> C { + C::Str(alloc::format!("{v}")) + } + pub fn big(v: I512) -> C { + C::Str(v.to_dec()) + } + pub fn s(v: &str) -> C { + C::Str(String::from(v)) + } + pub fn ints>(it: I) -> C { + C::Arr(it.into_iter().map(C::int).collect()) + } + pub fn obj(members: Vec<(&str, C)>) -> C { + C::Obj(members.into_iter().map(|(k, v)| (String::from(k), v)).collect()) + } + pub fn boxed(self) -> Box { + Box::new(self) + } +} + +/// Write `c` canonically into `out`: no whitespace, object members in ascending order +/// of their keys' bytes, strings with `"` written `\"` and `\` written `\\` and no +/// other escape. +pub fn write(c: &C, out: &mut String) { + match c { + C::Null => out.push_str("null"), + C::Bool(true) => out.push_str("true"), + C::Bool(false) => out.push_str("false"), + C::Str(s) => write_str(s, out), + C::Arr(a) => { + out.push('['); + for (i, v) in a.iter().enumerate() { + if i > 0 { + out.push(','); + } + write(v, out); + } + out.push(']'); + } + C::Obj(m) => { + let mut idx: Vec = (0..m.len()).collect(); + idx.sort_by(|&a, &b| m[a].0.as_bytes().cmp(m[b].0.as_bytes())); + out.push('{'); + for (i, &j) in idx.iter().enumerate() { + if i > 0 { + out.push(','); + } + write_str(&m[j].0, out); + out.push(':'); + write(&m[j].1, out); + } + out.push('}'); + } + } +} + +fn write_str(s: &str, out: &mut String) { + out.push('"'); + for ch in s.chars() { + debug_assert!((' '..='~').contains(&ch), "a canonical string holds printable ASCII only"); + match ch { + '"' => out.push_str("\\\""), + '\\' => out.push_str("\\\\"), + _ => out.push(ch), + } + } + out.push('"'); +} + +/// `c`, written canonically. +pub fn to_string(c: &C) -> String { + let mut s = String::new(); + write(c, &mut s); + s +} + +/// Lowercase hexadecimal. +pub fn hex(b: &[u8]) -> String { + const D: &[u8; 16] = b"0123456789abcdef"; + let mut s = String::with_capacity(b.len() * 2); + for &x in b { + s.push(D[(x >> 4) as usize] as char); + s.push(D[(x & 15) as usize] as char); + } + s +} + +/// Lowercase hexadecimal back to bytes; `None` on an odd length or another character. +pub fn unhex(s: &str) -> Option> { + let b = s.as_bytes(); + if b.len() % 2 != 0 { + return None; + } + let d = |c: u8| match c { + b'0'..=b'9' => Some(c - b'0'), + b'a'..=b'f' => Some(c - b'a' + 10), + _ => None, + }; + b.chunks(2).map(|p| Some(d(p[0])? << 4 | d(p[1])?)).collect() +} + +#[cfg(test)] +mod tests { + use super::*; + use alloc::vec; + + #[test] + fn parse_and_read() { + let v = parse(br#" {"a": [1, -2, 3.5, 1e3, 99999999999999999999999], "b": "x\"\u00e9", "c": true, "d": null} "#).unwrap(); + let a = v.get("a").unwrap().as_arr().unwrap(); + assert_eq!(a[0].as_i64(), Some(1)); + assert_eq!(a[1].as_i64(), Some(-2)); + assert_eq!(a[2].as_int(), None); + assert_eq!(a[3].as_int(), None); + assert_eq!(a[4].as_i128(), Some(99999999999999999999999)); + assert_eq!(v.get("b").unwrap().as_str(), Some("x\"é")); + assert!(parse(b"[1,]").is_err()); + assert!(parse(b"01").is_err()); + assert!(parse(b"{} x").is_err()); + assert!(parse(b"\"\\ud800\"").is_err()); + } + + #[test] + fn canonical() { + let c = C::obj(vec![("b", C::int(-5)), ("a", C::Arr(vec![C::Bool(true), C::Null, C::s("q\"\\")]))]); + assert_eq!(to_string(&c), r#"{"a":[true,null,"q\"\\"],"b":"-5"}"#); + assert!(is_canonical_dec("0") && is_canonical_dec("-12") && !is_canonical_dec("-0") && !is_canonical_dec("012")); + assert_eq!(unhex(&hex(&[0, 255, 16])), Some(vec![0, 255, 16])); + } +} diff --git a/wai/wai-ops/src/lib.rs b/wai/wai-ops/src/lib.rs new file mode 100644 index 000000000..c37396bae --- /dev/null +++ b/wai/wai-ops/src/lib.rs @@ -0,0 +1,41 @@ +//! `wai-ops/1`, the WAI integer operator set (`extensions/integer-operators.md`). +//! +//! - [`xint`]: a 512-bit signed integer with the saturation of §8.1. +//! - [`json`]: a JSON reader (integers of any size, numbers kept as written) and the +//! canonical writer of §8.5. +//! - [`graph`]: `WHG1` (§10), parsed and written, with load steps 1–6 of §10.5. +//! - [`plan`]: the load-time plan (§10.5 step 7) and the run-time plan (§4). +//! - [`interp`]: the reference interpreter of the graph operators (§5). +//! - [`stage`]: the stage operators E1–E13 (§5E). +//! - [`lower`]: the canonical lowerings of §13. +//! - [`vectors`]: the conformance vector files of §14.1, recomputed from their inputs. +//! - [`generate`]: the generator of `../ops-conformance/`, which writes those files. +//! +//! Every value is an exact integer. Nothing here uses floating point, wraps, or +//! depends on the target's word size. +//! +//! **Load step 8**, the bounds pass and the profile verdict (§8, §10.5 step 8), is +//! not built yet. Without the `uncertified` feature a graph never completes its load; +//! with it, step 8 is skipped. See [`graph::load`]. The interpreter runs only a +//! [`Loaded`] graph, which only `load` makes, so no entry point decodes past step 8. +#![no_std] +#![forbid(unsafe_code)] + +extern crate alloc; + +pub mod code; +#[cfg(feature = "uncertified")] +pub mod generate; +pub mod graph; +pub mod interp; +pub mod json; +pub mod lower; +pub mod plan; +pub mod sha256; +pub mod stage; +pub mod vectors; +pub mod xint; + +pub use code::{Code, Refusal, Site}; +pub use graph::{Graph, Loaded}; +pub use interp::{Sequence, Tensor, run}; diff --git a/wai/wai-ops/src/lower/jpegai.rs b/wai/wai-ops/src/lower/jpegai.rs new file mode 100644 index 000000000..f9da1174a --- /dev/null +++ b/wai/wai-ops/src/lower/jpegai.rs @@ -0,0 +1,136 @@ +//! `jpegai-int/1` (§13.6.1): the scale graphs `hs_y` and `hs_uv` of +//! `wai.image.jpegai`, from the bundle's `norm.json` and `weights/hs_{y,uv}.json`. + +use alloc::vec; +use alloc::vec::Vec; + +use super::{Builder, Lowered, Res, refuse}; +use crate::graph::{BiasKind, Graph, Params, Round, Rq, TensorRec, opcode, smallest_width}; +use crate::json::{self, Value}; + +fn ints(v: &Value) -> Res> { + let a = v.as_arr().ok_or(()).or_else(|_| refuse())?; + a.iter().map(|x| x.as_i64().ok_or(()).or_else(|_| refuse())).collect() +} + +fn member<'a>(o: &'a Value, k: &str) -> Res<&'a Value> { + o.get(k).ok_or(()).or_else(|_| refuse()) +} + +/// The load checks of §13.6.1 on `norm.json`; returns `(gain_y, gain_uv)`. +fn norm_checks(norm: &Value) -> Res<(Vec, Vec)> { + if norm.as_obj().is_none() { + return refuse(); + } + let pmf = member(norm, "pmf")?.as_arr().ok_or(()).or_else(|_| refuse())?; + if pmf.len() < 32 { + return refuse(); + } + let mut width = None; + for row in pmf { + let r = ints(row)?; + if r.is_empty() || r.iter().any(|&p| p < 0) || r.iter().sum::() != 256 || width.is_some_and(|w| w != r.len()) { + return refuse(); + } + width = Some(r.len()); + } + let bounds = ints(member(norm, "bounds")?)?; + if bounds.len() < 32 || bounds.iter().any(|&b| !(0..=255).contains(&b)) { + return refuse(); + } + if member(norm, "per_channel")?.as_i64() != Some(16) { + return refuse(); + } + for (k, rows) in [("cdfs_y", 160usize), ("cdfs_uv", 96)] { + let c = ints(member(norm, k)?)?; + if c.len() != rows * 63 || c.iter().any(|&v| !(0..=255).contains(&v)) || c.chunks(63).any(|r| r.windows(2).any(|p| p[0] > p[1])) { + return refuse(); + } + } + let gy = ints(member(norm, "gain_y")?)?; + let guv = ints(member(norm, "gain_uv")?)?; + if gy.len() != 160 || guv.len() != 96 { + return refuse(); + } + Ok((gy, guv)) +} + +/// A member `{ "dims": [...], "v": [...] }` with the dims `want`. +fn tensor(o: &Value, k: &str, want: &[u64]) -> Res> { + let m = member(o, k)?; + let dims: Vec = member(m, "dims")?.as_arr().ok_or(()).or_else(|_| refuse())? + .iter().map(|d| d.as_u64().ok_or(()).or_else(|_| refuse())).collect::>()?; + if dims.as_slice() != want { + return refuse(); + } + let v = ints(member(m, "v")?)?; + if v.len() as u64 != want.iter().product::() { + return refuse(); + } + Ok(v) +} + +/// The width of one dimension, read from the member's dims before checking it. +fn dim(o: &Value, k: &str, i: usize) -> Res { + member(member(o, k)?, "dims")?.as_arr().and_then(|d| d.get(i)).and_then(Value::as_u64).filter(|&d| d >= 1 && d <= u32::MAX as u64) + .ok_or(()).or_else(|_| refuse()) +} + +fn plane(hs: &Value, cz: u64, gains: &[i64]) -> Res { + if hs.as_obj().is_none() { + return refuse(); + } + let c1 = dim(hs, "conv1.weight", 0)?; + let c2 = dim(hs, "depthwise.weight", 0)?; + let c3 = 16 * cz; + let blocks = [ + ("conv1", c1, cz, 1u32, 0u32, "onnx::Pow_85"), + ("depthwise", c2, c1, 3, 1, "onnx::Pow_91"), + ("pointwise", c3, c2, 1, 0, "onnx::Pow_97"), + ]; + let mut b = Builder::new(); + let mut x = b.input(TensorRec::check(cz as u32, -31, 32), 4, 4); + for (i, (name, cout, cin, k, pad, sh)) in blocks.iter().enumerate() { + let w = tensor(hs, &alloc::format!("{name}.weight"), &[*cout, *cin, *k as u64, *k as u64])?; + let bias = tensor(hs, &alloc::format!("{name}.bias"), &[*cout])?; + let shifts = tensor(hs, sh, &[1, *cout, 1, 1])?; + if shifts.iter().any(|&s| !(0..=127).contains(&s)) { + return refuse(); + } + let cin_t = b.channels(x); + x = b.op(opcode::CLAMP, vec![x], TensorRec::derived(cin_t), Params::Clamp { lo: -128, hi: 127 }); + let rq = Rq { + round: Round::Floor, + bias_kind: BiasKind::Pre, + m_kind: 0, + s_kind: 2, + m_bytes: 0, + b_bytes: smallest_width(&bias, &[4, 8]), + mult: vec![], + shift: shifts.iter().map(|&s| s as u8).collect(), + bias, + }; + let w_bytes = smallest_width(&w, &[1, 2, 4, 8]); + let p = Params::Conv { cout: *cout as u32, kh: *k, kw: *k, sh: 1, sw: 1, ph: *pad, pw: *pad, groups: 1, w_bytes, rq, w }; + x = b.op(opcode::CONV, vec![x], TensorRec::derived(*cout as u32), p); + if i < 2 { + x = b.op(opcode::RELU, vec![x], TensorRec::derived(*cout as u32), Params::None); + } + } + let c = cz as u32; + x = b.op(opcode::PIXEL_SHUFFLE, vec![x], TensorRec::derived(c), Params::Shuffle { r: 4 }); + x = b.op(opcode::ABS, vec![x], TensorRec::derived(c), Params::None); + x = b.op(opcode::CLAMP, vec![x], TensorRec::derived(c), Params::Clamp { lo: 0, hi: 3967 }); + let rq = Rq { round: Round::Floor, bias_kind: BiasKind::Pre, m_kind: 0, s_kind: 1, m_bytes: 0, b_bytes: smallest_width(gains, &[4, 8]), mult: vec![], shift: vec![0], bias: gains.to_vec() }; + x = b.op(opcode::RESCALE, vec![x], TensorRec::derived(c), Params::Rescale(rq)); + b.finish(vec![x]) +} + +/// Lower a `wai.image.jpegai` bundle's scale path: `hs_y`, then `hs_uv`. +pub fn jpegai(norm: &[u8], hs_y: &[u8], hs_uv: &[u8]) -> Res { + let nv = json::parse(norm).or_else(|_| refuse())?; + let (gy, guv) = norm_checks(&nv)?; + let y = json::parse(hs_y).or_else(|_| refuse())?; + let uv = json::parse(hs_uv).or_else(|_| refuse())?; + Ok(Lowered { roles: vec![("hs_y", plane(&y, 160, &gy)?), ("hs_uv", plane(&uv, 96, &guv)?)] }) +} diff --git a/wai/wai-ops/src/lower/mod.rs b/wai/wai-ops/src/lower/mod.rs new file mode 100644 index 000000000..1299d09f8 --- /dev/null +++ b/wai/wai-ops/src/lower/mod.rs @@ -0,0 +1,110 @@ +//! The canonical lowerings of §13: each maps a parameter set in a registered form to +//! `WHG1` graphs, writing exactly one byte sequence for a set (§13.0). +//! +//! Every refusal of a lowering is `model_mismatch`; a capability whose payload +//! extension reports one code for every refusal (`int_synth`) maps it. + +use alloc::vec; +use alloc::vec::Vec; + +use crate::code::{Code, Refusal, Site}; +use crate::graph::{CAP, Graph, InputRec, Op, Params, ScalarRec, TensorRec, op_name}; + +mod jpegai; +mod synth; +mod whm; + +pub use jpegai::jpegai; +pub use synth::int_synth; +pub use whm::{Net, NetLayer, int_hyper, parse_wam1, parse_whm1, splat, wam1, whm1, wmb1, world}; + +/// A lowered set: its graphs by role, in §13.0's order. +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Lowered { + pub roles: Vec<(&'static str, Graph)>, +} + +impl Lowered { + pub fn get(&self, role: &str) -> Option<&Graph> { + self.roles.iter().find(|(r, _)| *r == role).map(|(_, g)| g) + } +} + +pub(crate) type Res = Result; + +pub(crate) fn refuse() -> Res { + Err(Refusal::new(Code::ModelMismatch, Site::Lowering)) +} + +/// A graph under construction with §13.0's numbering: input `i` is tensor `i`, and +/// each operator's outputs take the next unused indices as the operator is added. +pub(crate) struct Builder { + pub scalars: Vec, + pub inputs: Vec, + pub tensors: Vec, + pub ops: Vec, +} + +impl Builder { + pub fn new() -> Builder { + Builder { scalars: vec![], inputs: vec![], tensors: vec![], ops: vec![] } + } + /// A data input of `rec`, with the driver contract's sizes. + pub fn input(&mut self, rec: TensorRec, max_h: u32, max_w: u32) -> u32 { + let t = self.tensors.len() as u32; + self.tensors.push(rec); + self.inputs.push(InputRec { tensor: t, max_h, max_w, kind: 0, src: 0, init: 0 }); + t + } + pub fn scalar(&mut self, lo: i64, hi: i64) -> u32 { + self.scalars.push(ScalarRec { lo, hi }); + (self.scalars.len() - 1) as u32 + } + /// Add an operator writing one tensor of `out`; returns that tensor. + pub fn op(&mut self, code: u8, ins: Vec, out: TensorRec, p: Params) -> u32 { + let t = self.tensors.len() as u32; + self.tensors.push(out); + self.ops.push(Op { code, ins, outs: vec![t], p }); + t + } + pub fn channels(&self, t: u32) -> u32 { + self.tensors[t as usize].channels + } + /// The graph with `outputs`, after checking that it loads (steps 1–7): a lowering + /// writes only graphs that parse and plan. + pub fn finish(self, outputs: Vec) -> Res { + let g = Graph { max_elems: CAP as u32, scalars: self.scalars, inputs: self.inputs, outputs, tensors: self.tensors, ops: self.ops }; + let b = g.to_bytes(); + match Graph::parse(&b) { + Ok(back) if back == g => {} + _ => return refuse(), + } + crate::plan::load_plan(&g).map_err(|_| Refusal::new(Code::ModelMismatch, Site::Lowering))?; + debug_assert!(g.ops.iter().all(|o| op_name(o.code).is_some())); + Ok(g) + } +} + +/// The largest `X ∈ [0, 2^63]` for which `p(X)` holds, `p` monotone (it holds at `X` +/// whenever it holds at a larger value); `None` when `p(0)` fails. +pub(crate) fn largest(p: impl Fn(u128) -> bool) -> Option { + if !p(0) { + return None; + } + let (mut lo, mut hi) = (0u128, 1u128 << 63); + if p(hi) { + return Some(hi); + } + while hi - lo > 1 { + let mid = lo + (hi - lo) / 2; + if p(mid) { lo = mid } else { hi = mid } + } + Some(lo) +} + +/// `check [−X, min(X, 2^63 − 1)]` over `channels`. +pub(crate) fn check_x(channels: u32, x: u128) -> TensorRec { + let lo = -(x as i128) as i64; + let hi = x.min(i64::MAX as u128) as i64; + TensorRec::check(channels, lo, hi) +} diff --git a/wai/wai-ops/src/lower/synth.rs b/wai/wai-ops/src/lower/synth.rs new file mode 100644 index 000000000..a4b576964 --- /dev/null +++ b/wai/wai-ops/src/lower/synth.rs @@ -0,0 +1,74 @@ +//! `int_synth/1` (§13.1): `int_synth.json` (integer-payloads §13) to the graph +//! `synth`. + +use alloc::vec; +use alloc::vec::Vec; + +use super::{Builder, Lowered, Res, refuse}; +use crate::graph::{BiasKind, Params, Round, Rq, TensorRec, opcode}; +use crate::json::{self, Value}; + +fn uint(l: &Value, k: &str, default: Option) -> Res { + match l.get(k) { + None => default.ok_or(()).or_else(|_| refuse()), + Some(v) => v.as_u32().ok_or(()).or_else(|_| refuse()), + } +} + +fn i32s(l: &Value, k: &str) -> Res> { + let a = l.get(k).and_then(Value::as_arr).ok_or(()).or_else(|_| refuse())?; + let mut v = Vec::with_capacity(a.len()); + for x in a { + match x.as_i64() { + Some(n) if i32::try_from(n).is_ok() => v.push(n), + _ => return refuse(), + } + } + Ok(v) +} + +/// Lower `int_synth.json`. +pub fn int_synth(bytes: &[u8]) -> Res { + let root = json::parse(bytes).or_else(|_| refuse())?; + let layers = root.get("layers").and_then(Value::as_arr).ok_or(()).or_else(|_| refuse())?; + if layers.is_empty() { + return refuse(); + } + let mut b = Builder::new(); + let c0 = uint(&layers[0], "in_ch", None)?; + if c0 == 0 { + return refuse(); + } + let mut x = b.input(TensorRec::check(c0, -128, 127), 0, 0); + let mut c = c0; + for l in layers { + if l.as_obj().is_none() { + return refuse(); + } + let (in_ch, out_ch, k, stride, pad) = (uint(l, "in_ch", None)?, uint(l, "out_ch", None)?, uint(l, "k", None)?, uint(l, "stride", None)?, uint(l, "pad", None)?); + let out_pad = uint(l, "out_pad", Some(0))?; + let shift = uint(l, "shift", Some(0))?; + let relu = match l.get("relu") { + None => false, + Some(v) => v.as_bool().ok_or(()).or_else(|_| refuse())?, + }; + let weight = i32s(l, "weight")?; + let bias = i32s(l, "bias")?; + if in_ch != c || out_ch == 0 || k == 0 || stride == 0 || shift > 127 { + return refuse(); + } + let nw = in_ch as u64 * out_ch as u64 * k as u64 * k as u64; + if weight.len() as u64 != nw || bias.len() as u64 != out_ch as u64 { + return refuse(); + } + let rq = Rq { round: Round::Floor, bias_kind: BiasKind::Pre, m_kind: 0, s_kind: 1, m_bytes: 0, b_bytes: 4, mult: vec![], shift: vec![shift as u8], bias }; + let p = Params::ConvT { cout: out_ch, kh: k, kw: k, sh: stride, sw: stride, ph: pad, pw: pad, oph: out_pad, opw: out_pad, w_bytes: 4, rq, w: weight }; + x = b.op(opcode::CONV_TRANSPOSE, vec![x], TensorRec::derived(out_ch), p); + if relu { + x = b.op(opcode::RELU, vec![x], TensorRec::derived(out_ch), Params::None); + } + c = out_ch; + } + let out = b.op(opcode::CLAMP, vec![x], TensorRec::derived(c), Params::Clamp { lo: 0, hi: 255 }); + Ok(Lowered { roles: vec![("synth", b.finish(vec![out])?)] }) +} diff --git a/wai/wai-ops/src/lower/whm.rs b/wai/wai-ops/src/lower/whm.rs new file mode 100644 index 000000000..2427896ac --- /dev/null +++ b/wai/wai-ops/src/lower/whm.rs @@ -0,0 +1,398 @@ +//! `whm1/1` (§13.2), `wam1/1` (§13.3), `wmb1/1` (§13.4) and `world/1` (§13.5). + +use alloc::vec; +use alloc::vec::Vec; + +use super::{Builder, Lowered, Res, check_x, largest, refuse}; +use crate::graph::{BiasKind, Graph, Operand, Params, Round, Rq, TensorRec, opcode}; +use crate::xint::I512; + +/// One layer of a `WHM1` or `WAM1` network, as the file holds it. +#[derive(Clone, Debug, PartialEq, Eq)] +pub enum NetLayer { + /// Tag 0. For `WAM1`, `k` is the one-dimensional kernel and the weights are + /// `[inc][out][k]`. + ConvT { inc: u32, out: u32, k: u32, stride: u32, pad: u32, opad: u32, w: Vec, mult: Vec, bias: Vec }, + /// Tag 1 (`WHM1` only); weights `[out][inc][k][k]`. + Conv { inc: u32, out: u32, k: u32, stride: u32, pad: u32, w: Vec, mult: Vec, bias: Vec }, + /// Tag 2. + Igdn { c: u32, gamma: Vec, beta: Vec }, + /// Tag 3 (`WHM1` only). + Relu, + /// Tag 4 (`WHM1` only). + Leaky { slope_q: i64, slope_frac: u32 }, +} + +/// A parsed `WHM1` or `WAM1` network. +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Net { + pub afrac: u32, + pub gf: u32, + pub shift: u32, + pub layers: Vec, + /// `WAM1`: one-dimensional transposed convolutions. + pub audio: bool, +} + +struct Cur<'a> { + b: &'a [u8], + p: usize, +} + +impl<'a> Cur<'a> { + fn take(&mut self, n: u64) -> Res<&'a [u8]> { + let e = usize::try_from(n).ok().and_then(|n| self.p.checked_add(n)).filter(|&e| e <= self.b.len()); + match e { + Some(e) => { + let s = &self.b[self.p..e]; + self.p = e; + Ok(s) + } + None => refuse(), + } + } + fn u8(&mut self) -> Res { + Ok(self.take(1)?[0]) + } + fn u32(&mut self) -> Res { + let s = self.take(4)?; + Ok(u32::from_le_bytes([s[0], s[1], s[2], s[3]])) + } + fn ints(&mut self, n: u64, w: u64) -> Res> { + let s = self.take(n.checked_mul(w).map_or(u64::MAX, |v| v))?; + Ok(s.chunks_exact(w as usize) + .map(|c| match w { + 2 => i16::from_le_bytes([c[0], c[1]]) as i64, + 4 => i32::from_le_bytes([c[0], c[1], c[2], c[3]]) as i64, + _ => i64::from_le_bytes([c[0], c[1], c[2], c[3], c[4], c[5], c[6], c[7]]), + }) + .collect()) + } +} + +fn prod(d: &[u32]) -> u64 { + d.iter().fold(1u64, |a, &x| a.saturating_mul(x as u64)) +} + +fn parse(b: &[u8], magic: &[u8; 4], audio: bool) -> Res { + if b.get(0..4) != Some(&magic[..]) { + return refuse(); + } + let mut c = Cur { b, p: 4 }; + let (afrac, gf, shift, n) = (c.u32()?, c.u32()?, c.u32()?, c.u32()?); + let mut layers = Vec::new(); + for _ in 0..n { + let tag = c.u8()?; + layers.push(match (tag, audio) { + (0, _) => { + let (inc, out) = (c.u32()?, c.u32()?); + let (k, stride, pad, opad) = (c.u32()?, c.u32()?, c.u32()?, c.u32()?); + let nw = if audio { prod(&[inc, out, k]) } else { prod(&[inc, out, k, k]) }; + let w = c.ints(nw, 2)?; + let mult = c.ints(out as u64, 8)?; + let bias = c.ints(out as u64, 8)?; + NetLayer::ConvT { inc, out, k, stride, pad, opad, w, mult, bias } + } + (1, false) => { + let (inc, out) = (c.u32()?, c.u32()?); + let (k, stride, pad) = (c.u32()?, c.u32()?, c.u32()?); + let w = c.ints(prod(&[out, inc, k, k]), 2)?; + let mult = c.ints(out as u64, 8)?; + let bias = c.ints(out as u64, 8)?; + NetLayer::Conv { inc, out, k, stride, pad, w, mult, bias } + } + (2, _) => { + let cc = c.u32()?; + let gamma = c.ints(prod(&[cc, cc]), 4)?; + let beta = c.ints(cc as u64, 8)?; + NetLayer::Igdn { c: cc, gamma, beta } + } + (3, false) => NetLayer::Relu, + (4, false) => { + let slope_q = c.ints(1, 8)?[0]; + NetLayer::Leaky { slope_q, slope_frac: c.u32()? } + } + _ => return refuse(), + }); + } + if c.p != b.len() { + return refuse(); + } + Ok(Net { afrac, gf, shift, layers, audio }) +} + +/// Parse a `WHM1` network (integer-payloads §9). +pub fn parse_whm1(b: &[u8]) -> Res { + parse(b, b"WHM1", false) +} + +/// Parse a `WAM1` network (integer-payloads §9). +pub fn parse_wam1(b: &[u8]) -> Res { + parse(b, b"WAM1", true) +} + +fn maxabs(v: &[i64]) -> I512 { + v.iter().map(|&x| I512::from_i128((x as i128).abs())).max().unwrap_or(I512::ZERO) +} + +const I64: I512 = I512::from_i128(i64::MAX as i128); +const I128: I512 = I512::from_i128(i128::MAX); + +/// `X_ℓ` for a layer reading `c` channels (§13.2), or `None` for a layer no `check` +/// range precedes (a ReLU). Refuses when `P_ℓ(0)` fails. +fn x_of(net: &Net, l: &NetLayer, c: u32) -> Res> { + let le = |a: I512, b: I512| a <= b; + let x = match l { + NetLayer::ConvT { k, w, mult, bias, .. } | NetLayer::Conv { k, w, mult, bias, .. } => { + let kk = if net.audio { *k as u64 } else { *k as u64 * *k as u64 }; + let terms = I512::from_u64(c as u64).mul(I512::from_u64(kk)); + let (wm, mm, bm) = (maxabs(w), maxabs(mult), maxabs(bias)); + let s = net.shift; + largest(|x| { + let a = terms.mul(wm).mul(I512::from_u128(x)); + le(a, I64) && le(a.mul(mm).shr_floor(s).add(I512::ONE).add(bm), I64) + }) + } + NetLayer::Igdn { gamma, beta, .. } => { + let (g, bb, cc) = (maxabs(gamma), maxabs(beta), I512::from_u64(c as u64)); + let (gf, af) = (net.gf, net.afrac); + largest(|x| { + let x = I512::from_u128(x); + let gx2 = g.mul(x).mul(x); + if !le(gx2, I128) { + return false; + } + let n = bb.add(cc.mul(gx2.shr_floor(gf).add(I512::ONE))); + if !le(n, I128) { + return false; + } + let r = n.isqrt(); + le(r, I64) && le(x.mul(r).shr_floor(af).add(I512::ONE), I64) + }) + } + NetLayer::Leaky { slope_q, .. } => { + let a = I512::from_i128((*slope_q as i128).abs()); + largest(|x| le(I512::from_u128(x).mul(a), I64)) + } + NetLayer::Relu => return Ok(None), + }; + match x { + Some(x) => Ok(Some(x)), + None => refuse(), + } +} + +/// The input channel count of the first layer of tag 0, 1 or 2. +fn in_channels(net: &Net) -> Res { + for l in &net.layers { + match l { + NetLayer::ConvT { inc, .. } | NetLayer::Conv { inc, .. } => return Ok(*inc), + NetLayer::Igdn { c, .. } => return Ok(*c), + _ => {} + } + } + refuse() +} + +/// The layers' load checks (§13.2): `shift < 128`, `gf < 128`, `afrac` in `1..=30`. +fn header_ok(net: &Net) -> Res<()> { + if net.shift > 127 || net.gf > 127 || !(1..=30).contains(&net.afrac) { + return refuse(); + } + Ok(()) +} + +/// The record of `x_ℓ` on `c` channels, read by layer `l` (§13.2, *Range checks*): +/// `check [−X_ℓ, min(X_ℓ, I64)]` when `l` is of tag 0, 1, 2 or 4. When `l` is a ReLU, +/// the network's input is `check [−2^63, I64]`, since a graph input is never +/// `derived`, and every other `x_ℓ` is `derived`. +fn x_record(net: &Net, l: &NetLayer, c: u32, network_input: bool) -> Res { + Ok(match x_of(net, l, c)? { + Some(x) => check_x(c, x), + None if network_input => TensorRec::check(c, i64::MIN, i64::MAX), + None => TensorRec::derived(c), + }) +} + +/// Append the network's layers to `b`, reading `x0`, whose record the caller wrote +/// with [`x_record`] of layer 0. Returns the last tensor. +fn layers(b: &mut Builder, net: &Net, x0: u32) -> Res { + let mut x = x0; + for (i, l) in net.layers.iter().enumerate() { + let c = b.channels(x); + let out_c = match l { + NetLayer::ConvT { out, .. } | NetLayer::Conv { out, .. } => *out, + _ => c, + }; + let rec = match net.layers.get(i + 1) { + Some(next) => x_record(net, next, out_c, false)?, + None => TensorRec::derived(out_c), + }; + let rq = |mult: &[i64], bias: &[i64]| Rq { + round: Round::Floor, + bias_kind: BiasKind::Post, + m_kind: 2, + s_kind: 1, + m_bytes: 8, + b_bytes: 8, + mult: mult.to_vec(), + shift: vec![net.shift as u8], + bias: bias.to_vec(), + }; + x = match l { + NetLayer::ConvT { inc, out, k, stride, pad, opad, w, mult, bias } => { + if *inc != c || *out == 0 || *k == 0 || *stride == 0 { + return refuse(); + } + let p = if net.audio { + Params::ConvT { cout: *out, kh: 1, kw: *k, sh: 1, sw: *stride, ph: 0, pw: *pad, oph: 0, opw: *opad, w_bytes: 2, rq: rq(mult, bias), w: w.clone() } + } else { + Params::ConvT { cout: *out, kh: *k, kw: *k, sh: *stride, sw: *stride, ph: *pad, pw: *pad, oph: *opad, opw: *opad, w_bytes: 2, rq: rq(mult, bias), w: w.clone() } + }; + b.op(opcode::CONV_TRANSPOSE, vec![x], rec, p) + } + NetLayer::Conv { inc, out, k, stride, pad, w, mult, bias } => { + if *inc != c || *out == 0 || *k == 0 || *stride == 0 { + return refuse(); + } + let p = Params::Conv { cout: *out, kh: *k, kw: *k, sh: *stride, sw: *stride, ph: *pad, pw: *pad, groups: 1, w_bytes: 2, rq: rq(mult, bias), w: w.clone() }; + b.op(opcode::CONV, vec![x], rec, p) + } + NetLayer::Igdn { c: cc, gamma, beta } => { + if *cc != c { + return refuse(); + } + let p = Params::Igdn { gf: net.gf, frac: net.afrac, g_bytes: 4, gamma: gamma.clone(), beta: beta.clone() }; + b.op(opcode::IGDN, vec![x], rec, p) + } + NetLayer::Relu => b.op(opcode::RELU, vec![x], rec, Params::None), + NetLayer::Leaky { slope_q, slope_frac } => { + if *slope_frac > 63 { + return refuse(); + } + b.op(opcode::LEAKY, vec![x], rec, Params::Leaky { a: *slope_q, f: *slope_frac }) + } + }; + } + Ok(x) +} + +/// The RGB8 step (§13.0): `clamp(0, 2^afrac)`, then `rescale` by 255 with shift +/// `afrac`, rounding half up. +fn rgb8(b: &mut Builder, x: u32, afrac: u32) -> u32 { + let c = b.channels(x); + let t = b.op(opcode::CLAMP, vec![x], TensorRec::derived(c), Params::Clamp { lo: 0, hi: 1i64 << afrac }); + let rq = Rq { round: Round::HalfUp, bias_kind: BiasKind::None, m_kind: 1, s_kind: 1, m_bytes: 4, b_bytes: 0, mult: vec![255], shift: vec![afrac as u8], bias: vec![] }; + b.op(opcode::RESCALE, vec![t], TensorRec::derived(c), Params::Rescale(rq)) +} + +fn net_graph(net: &Net, output_step: bool) -> Res { + header_ok(net)?; + let c0 = in_channels(net)?; + let mut b = Builder::new(); + let first = net.layers.first().ok_or(()).or_else(|_| refuse())?; + let x0 = b.input(x_record(net, first, c0, true)?, 0, 0); + let mut x = layers(&mut b, net, x0)?; + if output_step { + x = rgb8(&mut b, x, net.afrac); + } + b.finish(vec![x]) +} + +/// `whm1/1`: one `WHM1` network, with the RGB8 step when `output_step` is set. +pub fn whm1(bytes: &[u8], output_step: bool) -> Res { + net_graph(&parse_whm1(bytes)?, output_step) +} + +/// `whm1/1` for `wai.neural.int_hyper` and the keyframe of `wai.video.int_hyper`: +/// `h_s`, then `g_s` with the RGB8 step. +pub fn int_hyper(h_s: &[u8], g_s: &[u8]) -> Res { + Ok(Lowered { roles: vec![("h_s", whm1(h_s, false)?), ("g_s", whm1(g_s, true)?)] }) +} + +/// `whm1/1` for `wai.splat.int_codec`: `g_s` with the RGB8 step. +pub fn splat(g_s: &[u8]) -> Res { + Ok(Lowered { roles: vec![("g_s", whm1(g_s, true)?)] }) +} + +/// `wam1/1` (§13.3): the `WAM1` synthesis with the PCM step. +pub fn wam1(bytes: &[u8]) -> Res { + let net = parse_wam1(bytes)?; + header_ok(&net)?; + let c0 = in_channels(&net)?; + let mut b = Builder::new(); + let first = net.layers.first().ok_or(()).or_else(|_| refuse())?; + let x0 = b.input(x_record(&net, first, c0, true)?, 0, 0); + let t_out = b.scalar(1, 1 << 26); + let x = layers(&mut b, &net, x0)?; + let c = b.channels(x); + // The range in which v·32767 + 2^(afrac−1) fits i64. + let lo = -((1i128 << 63) / 32767) as i64; // ⌈−2^63 / 32767⌉ + let hi = ((i64::MAX as i128 - (1i128 << (net.afrac - 1))) / 32767) as i64; + let crop = Params::Crop { y0: Operand::Const(0), x0: Operand::Const(0), h: Operand::Const(1), w: Operand::Scalar(t_out) }; + let t = b.op(opcode::CROP, vec![x], TensorRec::check(c, lo, hi), crop); + let rq = Rq { round: Round::HalfUp, bias_kind: BiasKind::None, m_kind: 1, s_kind: 1, m_bytes: 4, b_bytes: 0, mult: vec![32767], shift: vec![net.afrac as u8], bias: vec![] }; + let t = b.op(opcode::RESCALE, vec![t], TensorRec::derived(c), Params::Rescale(rq)); + let t = b.op(opcode::CLAMP, vec![t], TensorRec::derived(c), Params::Clamp { lo: -32768, hi: 32767 }); + b.finish(vec![t]) +} + +/// `wmb1/1` (§13.4): a `WMB1` bundle's `h_s`, `entropy` and `g_s` (with the RGB8 +/// step). The tables section is the stage operators' data; its `afrac` must be the +/// networks'. +pub fn wmb1(bytes: &[u8]) -> Res { + if bytes.get(0..4) != Some(&b"WMB1"[..]) { + return refuse(); + } + let mut c = Cur { b: bytes, p: 4 }; + let mut sec = || -> Res<&[u8]> { + let n = c.u32()?; + c.take(n as u64) + }; + let (h, e, g, t) = (sec()?, sec()?, sec()?, sec()?); + if c.p != bytes.len() { + return refuse(); + } + let (hn, en, gn) = (parse_whm1(h)?, parse_whm1(e)?, parse_whm1(g)?); + if hn.afrac != gn.afrac || en.afrac != gn.afrac { + return refuse(); + } + let tables = crate::json::parse(t).or_else(|_| refuse())?; + match tables.get("afrac").and_then(|a| a.as_u32()) { + Some(a) if a == gn.afrac && tables.as_obj().is_some() => {} + _ => return refuse(), + } + Ok(Lowered { roles: vec![("h_s", net_graph(&hn, false)?), ("entropy", net_graph(&en, false)?), ("g_s", net_graph(&gn, true)?)] }) +} + +/// `world/1` (§13.5, informative): the transition `T` with `A` action planes, then +/// the synthesis `g_s` with the RGB8 step. `c` is the latent's channel count. +pub fn world(t: &[u8], g_s: &[u8], c: u32) -> Res { + let tn = parse_whm1(t)?; + let gn = parse_whm1(g_s)?; + header_ok(&tn)?; + header_ok(&gn)?; + if tn.afrac != gn.afrac { + return refuse(); + } + let cin = in_channels(&tn)?; + if c == 0 || cin <= c { + return refuse(); + } + let a = cin - c; + let mut b = Builder::new(); + let z = b.input(TensorRec::check(c, i64::MIN, i64::MAX), 0, 0); + let mut parts = vec![z]; + for _ in 0..a { + let s = b.scalar(i64::MIN, i64::MAX); + parts.push(b.op(opcode::FILL, vec![z], TensorRec::derived(1), Params::Fill { kind: 1, value: s as i64 })); + } + let first = tn.layers.first().ok_or(()).or_else(|_| refuse())?; + let x0 = b.op(opcode::CONCAT, parts, x_record(&tn, first, cin, true)?, Params::None); + let x = layers(&mut b, &tn, x0)?; + let one = 1i64 << tn.afrac; + let cc = b.channels(x); + let out = b.op(opcode::CLAMP, vec![x], TensorRec::derived(cc), Params::Clamp { lo: -one, hi: one }); + let tg = b.finish(vec![out])?; + Ok(Lowered { roles: vec![("T", tg), ("g_s", net_graph(&gn, true)?)] }) +} diff --git a/wai/wai-ops/src/plan.rs b/wai/wai-ops/src/plan.rs new file mode 100644 index 000000000..e37327249 --- /dev/null +++ b/wai/wai-ops/src/plan.rs @@ -0,0 +1,262 @@ +//! Shapes: the load-time plan (§10.5 step 7) and the run-time plan (§4, run step 3). +//! +//! One walk serves both. At load it runs on the inputs' declared sizes, and a shape +//! rule that fails is `model_mismatch`; at run time it runs on the sizes and scalar +//! values of one decode, and a shape rule that fails is `geometry_mismatch`. In both, +//! for each operator in graph order (§4, run step 3), the sizes it forms before its +//! shape rule is read (`conv`'s padded sides; `conv_transpose`'s uncropped sides and +//! its uncropped output of `cout` planes; the output sides of `pixel_shuffle`, +//! `pad_rep` and `subpos_merge`) are checked against `2^26` first (`too_large`), then +//! the shape rule, then each output tensor against the graph's `max_elems` +//! (`too_large`). Every size is formed in 64-bit +//! arithmetic with overflow checked, and a size past 64 bits is past the cap. + +use alloc::vec; +use alloc::vec::Vec; + +use crate::code::{Code, Refusal, Site, at_load}; +use crate::graph::{CAP, Graph, Operand, Params, opcode::*}; + +type Res = Result; + +/// How a scalar is seen by the plan: at load, its declared range; at run time, its +/// value. +#[derive(Clone, Copy)] +enum Mode<'a> { + Load, + Run(&'a [i64]), +} + +/// The sizes `(H, W)` of every tensor, indexed by tensor. +pub type Sizes = Vec<(u64, u64)>; + +#[derive(Clone, Copy)] +enum Fail { + Big, + Shape, +} + +fn mul(a: u64, b: u64) -> Result { + a.checked_mul(b).ok_or(Fail::Big) +} +fn add(a: u64, b: u64) -> Result { + a.checked_add(b).ok_or(Fail::Big) +} +fn capped(v: u64) -> Result { + if v > CAP { Err(Fail::Big) } else { Ok(v) } +} + +/// One operator's output sizes from its inputs' sizes. +fn op_sizes(g: &Graph, op: &crate::graph::Op, hw: &[(u64, u64)], mode: Mode) -> Result, Fail> { + let (h, w) = hw[op.ins[0] as usize]; + let ch = |t: u32| g.tensors[t as usize].channels as u64; + let same = |o: (u64, u64)| o.0 == h && o.1 == w; + let shape = Err(Fail::Shape); + Ok(match (&op.p, op.code) { + (Params::Conv { kh, kw, sh, sw, ph, pw, .. }, _) => { + let hp = capped(add(h, mul(2, *ph as u64)?)?)?; + let wp = capped(add(w, mul(2, *pw as u64)?)?)?; + if hp < *kh as u64 || wp < *kw as u64 { + return shape; + } + vec![((hp - *kh as u64) / *sh as u64 + 1, (wp - *kw as u64) / *sw as u64 + 1)] + } + (Params::ConvT { cout, kh, kw, sh, sw, ph, pw, oph, opw, .. }, _) => { + let uh = capped(add(add(mul(h - 1, *sh as u64)?, *kh as u64)?, *oph as u64)?)?; + let uw = capped(add(add(mul(w - 1, *sw as u64)?, *kw as u64)?, *opw as u64)?)?; + capped(mul(mul(*cout as u64, uh)?, uw)?)?; + let (ch2, cw2) = (2 * *ph as u64, 2 * *pw as u64); + if uh <= ch2 || uw <= cw2 { + return shape; + } + vec![(uh - ch2, uw - cw2)] + } + (_, ADD) | (_, MUL) => { + if !same(hw[op.ins[1] as usize]) { + return shape; + } + vec![(h, w)] + } + (_, CONCAT) => { + if !op.ins.iter().all(|&t| same(hw[t as usize])) { + return shape; + } + vec![(h, w)] + } + (Params::Shuffle { r }, PIXEL_SHUFFLE) => { + let r = *r as u64; + vec![(capped(mul(h, r)?)?, capped(mul(w, r)?)?)] + } + (Params::Shuffle { r }, _) => { + let r = *r as u64; + if h % r != 0 || w % r != 0 { + return shape; + } + vec![(h / r, w / r)] + } + (Params::PadRep { m }, _) => { + let m = *m as u64; + vec![(capped(mul(h.div_ceil(m), m)?)?, capped(mul(w.div_ceil(m), m)?)?)] + } + (_, CROP_TO) => { + let (rh, rw) = hw[op.ins[1] as usize]; + if rh > h || rw > w { + return shape; + } + vec![(rh, rw)] + } + (Params::Crop { y0, x0, h: ch_, w: cw_ }, _) => { + let side = |start: &Operand, len: &Operand, n: u64| -> Result { + match mode { + Mode::Run(sv) => { + let v = |o: &Operand| -> i128 { + match *o { + Operand::Const(c) => c as i128, + Operand::Scalar(s) => sv[s as usize] as i128, + } + }; + let (s0, l) = (v(start), v(len)); + if s0 < 0 || l < 1 || s0 + l > n as i128 { + return Err(Fail::Shape); + } + Ok(l as u64) + } + Mode::Load => { + // The output is planned on the largest length the scalar's range + // allows within the input, from the smallest start. + let s0 = match *start { + Operand::Const(c) => c as i128, + Operand::Scalar(_) => 0, + }; + let l = match *len { + Operand::Const(c) => c as i128, + Operand::Scalar(s) => (g.scalars[s as usize].hi as i128).min(n as i128 - s0), + }; + if l < 1 || s0 + l > n as i128 { + return Err(Fail::Shape); + } + Ok(l as u64) + } + } + }; + vec![(side(y0, ch_, h)?, side(x0, cw_, w)?)] + } + (_, SUBPOS_SPLIT) => { + if h % 2 != 0 || w % 2 != 0 { + return shape; + } + vec![(h / 2, w / 2); 4] + } + (_, SUBPOS_MERGE) => { + // The output sides it forms are capped before its shape rule (§4). + let out = (capped(mul(h, 2)?)?, capped(mul(w, 2)?)?); + if !op.ins.iter().all(|&t| same(hw[t as usize])) { + return shape; + } + vec![out] + } + (Params::Gram { groups, .. }, _) => { + if !same(hw[op.ins[1] as usize]) { + return shape; + } + vec![(1, ch(op.ins[0]) / *groups as u64)] + } + (Params::Apply { groups, .. }, _) => { + let d = ch(op.ins[0]) / *groups as u64; + if (h, w) != (1, d) { + return shape; + } + vec![hw[op.ins[1] as usize]] + } + // Every other operator keeps its first input's sizes. + _ => vec![(h, w)], + }) +} + +/// Walk the operators on `hw` (the inputs' sizes filled in), filling every output. +fn walk(g: &Graph, hw: &mut Sizes, mode: Mode) -> Res<()> { + let shape_code = match mode { + Mode::Load => Code::ModelMismatch, + Mode::Run(_) => Code::GeometryMismatch, + }; + for (i, op) in g.ops.iter().enumerate() { + let at = match mode { + Mode::Load => Site::Load(7), + Mode::Run(_) => Site::Op(i as u32), + }; + let outs = op_sizes(g, op, hw, mode).map_err(|f| match f { + Fail::Big => Refusal::new(Code::TooLarge, at), + Fail::Shape => Refusal::new(shape_code, at), + })?; + for (&t, &(oh, ow)) in op.outs.iter().zip(&outs) { + let n = (g.tensors[t as usize].channels as u64).checked_mul(oh).and_then(|v| v.checked_mul(ow)); + if n.is_none_or(|n| n > g.max_elems as u64) { + return Err(Refusal::new(Code::TooLarge, at)); + } + hw[t as usize] = (oh, ow); + } + } + Ok(()) +} + +/// Whether the graph has an operator that reduces over a picture-sized length. +fn reduces(g: &Graph) -> bool { + g.ops.iter().any(|o| matches!(o.code, GRAM | L2NORM | SOFTMAX)) +} + +/// Load step 7. Channel counts were checked with the graph rules (step 6). When the +/// inputs declare their sizes, every shape is planned on `max_h` and `max_w`. +pub fn load_plan(g: &Graph) -> Res> { + let sized = g.inputs[0].max_h != 0; + if !sized { + if reduces(g) { + return Err(at_load(Code::ModelMismatch, 7)); + } + return Ok(None); + } + let mut hw: Sizes = vec![(0, 0); g.tensors.len()]; + for i in &g.inputs { + let c = g.tensors[i.tensor as usize].channels as u64; + let n = c.checked_mul(i.max_h as u64).and_then(|v| v.checked_mul(i.max_w as u64)); + if n.is_none_or(|n| n > g.max_elems as u64) { + return Err(at_load(Code::TooLarge, 7)); + } + hw[i.tensor as usize] = (i.max_h as u64, i.max_w as u64); + } + walk(g, &mut hw, Mode::Load)?; + Ok(Some(hw)) +} + +/// Run steps 1 to 3 of §4 for one decode: every input's channel count and size +/// (`input_shapes[i]` is input `i`'s `(C, H, W)`, state inputs included), every +/// scalar against its range, then the run-time plan. Returns every tensor's sizes. +pub fn run_plan(g: &Graph, input_shapes: &[(u64, u64, u64)], scalars: &[i64]) -> Res { + if input_shapes.len() != g.inputs.len() { + return Err(Refusal::new(Code::ModelMismatch, Site::Input(input_shapes.len() as u32))); + } + let mut hw: Sizes = vec![(0, 0); g.tensors.len()]; + for (k, (i, &(c, h, w))) in g.inputs.iter().zip(input_shapes).enumerate() { + let at = Site::Input(k as u32); + if c != g.tensors[i.tensor as usize].channels as u64 || h == 0 || w == 0 { + return Err(Refusal::new(Code::ModelMismatch, at)); + } + if i.max_h != 0 && (h > i.max_h as u64 || w > i.max_w as u64) { + return Err(Refusal::new(Code::TooLarge, at)); + } + let n = c.checked_mul(h).and_then(|v| v.checked_mul(w)); + if n.is_none_or(|n| n > g.max_elems as u64) { + return Err(Refusal::new(Code::TooLarge, at)); + } + hw[i.tensor as usize] = (h, w); + } + if scalars.len() != g.scalars.len() { + return Err(Refusal::new(Code::ModelMismatch, Site::Scalar(scalars.len() as u32))); + } + for (k, (s, &v)) in g.scalars.iter().zip(scalars).enumerate() { + if v < s.lo || v > s.hi { + return Err(Refusal::new(Code::FieldRange, Site::Scalar(k as u32))); + } + } + walk(g, &mut hw, Mode::Run(scalars))?; + Ok(hw) +} diff --git a/wai/wai-ops/src/sha256.rs b/wai/wai-ops/src/sha256.rs new file mode 100644 index 000000000..1663ca0ae --- /dev/null +++ b/wai/wai-ops/src/sha256.rs @@ -0,0 +1,78 @@ +//! SHA-256 (FIPS 180-4): a graph's identity (§10.5), a set's digest (SPEC §3.1). + +use alloc::vec::Vec; + +const K: [u32; 64] = [ + 0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5, + 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, + 0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da, + 0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967, + 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, + 0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070, + 0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3, + 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2, +]; + +/// The SHA-256 of `data`. +pub fn sha256(data: &[u8]) -> [u8; 32] { + let mut h: [u32; 8] = [ + 0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a, 0x510e527f, 0x9b05688c, 0x1f83d9ab, 0x5be0cd19, + ]; + let mut m: Vec = Vec::with_capacity(data.len() + 72); + m.extend_from_slice(data); + m.push(0x80); + while m.len() % 64 != 56 { + m.push(0); + } + m.extend_from_slice(&((data.len() as u64).wrapping_mul(8)).to_be_bytes()); + for block in m.chunks_exact(64) { + let mut w = [0u32; 64]; + for i in 0..16 { + w[i] = u32::from_be_bytes([block[4 * i], block[4 * i + 1], block[4 * i + 2], block[4 * i + 3]]); + } + for i in 16..64 { + let s0 = w[i - 15].rotate_right(7) ^ w[i - 15].rotate_right(18) ^ (w[i - 15] >> 3); + let s1 = w[i - 2].rotate_right(17) ^ w[i - 2].rotate_right(19) ^ (w[i - 2] >> 10); + w[i] = w[i - 16].wrapping_add(s0).wrapping_add(w[i - 7]).wrapping_add(s1); + } + let [mut a, mut b, mut c, mut d, mut e, mut f, mut g, mut hh] = h; + for i in 0..64 { + let s1 = e.rotate_right(6) ^ e.rotate_right(11) ^ e.rotate_right(25); + let ch = (e & f) ^ (!e & g); + let t1 = hh.wrapping_add(s1).wrapping_add(ch).wrapping_add(K[i]).wrapping_add(w[i]); + let s0 = a.rotate_right(2) ^ a.rotate_right(13) ^ a.rotate_right(22); + let maj = (a & b) ^ (a & c) ^ (b & c); + let t2 = s0.wrapping_add(maj); + hh = g; + g = f; + f = e; + e = d.wrapping_add(t1); + d = c; + c = b; + b = a; + a = t1.wrapping_add(t2); + } + for (x, v) in h.iter_mut().zip([a, b, c, d, e, f, g, hh]) { + *x = x.wrapping_add(v); + } + } + let mut out = [0u8; 32]; + for (i, v) in h.iter().enumerate() { + out[4 * i..4 * i + 4].copy_from_slice(&v.to_be_bytes()); + } + out +} + +#[cfg(test)] +mod tests { + use super::sha256; + use crate::json::hex; + + #[test] + fn known_digests() { + assert_eq!(hex(&sha256(b"")), "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"); + assert_eq!(hex(&sha256(b"abc")), "ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad"); + let long = [b'a'; 1000]; + assert_eq!(hex(&sha256(&long)), "41edece42d63e8d9bf515a9ba6932e1c20cbc9f5a5d134645adb5db1b9737ea3"); + } +} diff --git a/wai/wai-ops/src/stage/mans.rs b/wai/wai-ops/src/stage/mans.rs new file mode 100644 index 000000000..3c7394217 --- /dev/null +++ b/wai/wai-ops/src/stage/mans.rs @@ -0,0 +1,310 @@ +//! E8 `mans_decode` (§5E): a two-state masked table-ANS decode over a backward +//! 64-bit bit window. Every refusal during a decode is `stream_exhausted`; a table +//! that breaks its load check is `model_mismatch`, before any symbol. + +use alloc::vec; +use alloc::vec::Vec; + +use crate::code::Code; +use crate::stage::rans64::StageRefusal; + +/// The window and the two states. +struct Win { + m: Vec, + p: usize, + h: u64, + b: u32, + s: [u8; 2], + /// The position a refusal is reported at (§14.1, `at`): the position whose `in` + /// or `out` step is running, and between steps the last one whose step ran. A + /// flush that belongs to no single position runs after an `in` or `out` step of + /// the last position whose `in` step ran, so it reports that position. + pos: i64, +} + +type R = Result; + +impl Win { + fn le64(&self, i: usize) -> u64 { + let mut b = [0u8; 8]; + b.copy_from_slice(&self.m[i..i + 8]); + u64::from_le_bytes(b) + } + fn init(stream: &[u8]) -> R { + let mut m = vec![0u8; 8]; + m.extend_from_slice(stream); + let mut w = Win { m, p: stream.len(), h: 0, b: 0, s: [0, 0], pos: 0 }; + w.h = w.le64(w.p); + if w.h == 0 { + return Err(()); + } + w.b = 63 - w.h.leading_zeros(); + w.h -= 1u64 << w.b; + w.s[0] = w.read(8)? as u8; + w.s[1] = w.read(8)? as u8; + w.flush()?; + Ok(w) + } + fn read(&mut self, k: u32) -> R { + if k > self.b { + return Err(()); + } + self.b -= k; + let r = self.h >> self.b; + self.h -= r << self.b; + Ok(r) + } + fn flush(&mut self) -> R<()> { + let d = (7 ^ (self.b / 8)) as usize; + if d > self.p { + return Err(()); + } + self.p -= d; + let keep = 56 + (self.b % 8); + self.h = self.le64(self.p) & ((1u64 << keep) - 1); + self.b = keep; + Ok(()) + } + /// One transition: `t` from the state's table entry; the new state and the low bits. + fn step(&mut self, which: usize, t: u32) -> R<()> { + let r = self.read(t >> 24)?; + self.s[which] = (((t >> 16) as u64 | r) % 256) as u8; + Ok(()) + } +} + +fn wrap16(v: i64) -> i64 { + (v + 32768).rem_euclid(65536) - 32768 +} + +/// The `sgm` tables built from the rows `pmf` (§5E E8, *Building the latent tables*): +/// 256 entries of 32 bits per row. `None` when a row breaks the load checks: every row +/// of one width `w ≥ 1`, non-negative, summing to exactly 256. +pub fn build_latent_tables(pmf: &[Vec]) -> Option> { + let w = pmf.first()?.len(); + if w == 0 { + return None; + } + let mut out = Vec::with_capacity(pmf.len()); + for row in pmf { + if row.len() != w || row.iter().any(|&p| p < 0) || row.iter().sum::() != 256 { + return None; + } + let mut c = vec![0i64; w + 1]; + for k in 0..w { + c[k + 1] = c[k] + row[k]; + } + let f = |k: usize| c[k] - c[k] / 2; + let g = |k: usize| c[k] / 2 + 128; + let sym = |k: usize| -> i64 { if k % 2 == 0 { (k / 2) as i64 } else { -(((k + 1) / 2) as i64) } }; + let mut symbols = [0i64; 256]; + let mut next = [0i64; 256]; + let mut k = 0; + while c[k] < 256 { + for u in f(k)..f(k + 1) { + symbols[u as usize] = sym(k); + next[u as usize] = row[k] + (u - f(k)); + } + for u in g(k)..g(k + 1) { + symbols[u as usize] = sym(k); + next[u as usize] = row[k] + (f(k + 1) - f(k)) + (u - g(k)); + } + k += 1; + } + let mut t = [0u32; 256]; + for u in 0..256 { + // Every slot is written and next ∈ [1, 511], so nb ≤ 8; the bound only keeps + // the loop total. + let mut nb = 0u32; + while nb < 9 && next[u] << nb < 256 { + nb += 1; + } + let base = ((next[u] << nb) ^ 256) as u32; + t[u] = (nb << 24) | (base << 16) | (symbols[u].rem_euclid(65536) as u32); + } + out.push(t); + } + Some(out) +} + +/// One `sgm` span: the masked positions and their distributions. +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Span { + pub mask: Vec, + pub sigma: Vec, +} + +/// The latent decode (`sgm`): `pmf` (the rows), `bounds` (`B_y`, each in `[0, 255]`), +/// and the spans, decoded in order from one window. Returns one array per span. +pub fn decode_sgm(stream: &[u8], pmf: &[Vec], bounds: &[i64], spans: &[Span]) -> Result>, StageRefusal> { + let before = StageRefusal { code: Code::ModelMismatch, at: -1 }; + let tables = build_latent_tables(pmf).ok_or(before)?; + if bounds.iter().any(|&b| !(0..=255).contains(&b)) { + return Err(before); + } + let nd = tables.len().min(bounds.len()); + for s in spans { + if s.mask.len() != s.sigma.len() || s.sigma.iter().any(|&g| g >= nd) { + return Err(before); + } + } + let mut w = Win::init(stream).map_err(|_| StageRefusal { code: Code::StreamExhausted, at: 0 })?; + let mut out = Vec::with_capacity(spans.len()); + let mut base: i64 = 0; + for s in spans { + let r = sgm_span(&mut w, &tables, bounds, s, base); + match r { + Ok(v) => out.push(v), + Err(()) => return Err(StageRefusal { code: Code::StreamExhausted, at: w.pos }), + } + base += s.mask.len() as i64; + } + Ok(out) +} + +fn sgm_span(w: &mut Win, t: &[[u32; 256]], bounds: &[i64], s: &Span, base: i64) -> R> { + let n = s.mask.len(); + let mut sym = vec![0i64; n]; + let inb = |w: &mut Win, which: usize, i: usize, sym: &mut [i64]| -> R<()> { + w.pos = base + i as i64; + if !s.mask[i] { + return Ok(()); + } + let e = t[s.sigma[i]][w.s[which] as usize]; + w.step(which, e)?; + sym[i] = wrap16((e % 65536) as i64); + Ok(()) + }; + let outb = |w: &mut Win, i: usize, sym: &mut [i64]| -> R<()> { + w.pos = base + i as i64; + if !s.mask[i] { + return Ok(()); + } + let beta = bounds[s.sigma[i]]; + if sym[i] + beta == 0 { + let f = w.read(1)?; + let v = w.read(if f == 1 { 3 } else { 16 })? as i64; + sym[i] = if v % 2 == 0 { wrap16(v / 2 + beta) } else { wrap16(-(v / 2) - beta) }; + w.flush()?; + } + Ok(()) + }; + let mut i = 0; + while i + 3 < n { + inb(w, 0, i, &mut sym)?; + inb(w, 1, i + 1, &mut sym)?; + inb(w, 0, i + 2, &mut sym)?; + inb(w, 1, i + 3, &mut sym)?; + w.flush()?; + for k in 0..4 { + outb(w, i + k, &mut sym)?; + } + i += 4; + } + if n % 4 >= 2 { + inb(w, 0, i, &mut sym)?; + outb(w, i, &mut sym)?; + inb(w, 1, i + 1, &mut sym)?; + outb(w, i + 1, &mut sym)?; + i += 2; + w.flush()?; + } + if n % 2 == 1 { + inb(w, 0, i, &mut sym)?; + outb(w, i, &mut sym)?; + w.flush()?; + } + Ok(sym) +} + +/// The preprocessing table `Zp[1..=511]` (§5E E8, *Hyper-latent decode*). +fn zp() -> [u32; 512] { + let mut z = [0u32; 512]; + let mut nb = 0u32; + for i in (1..512u32).rev() { + if (i << nb) < 256 { + nb += 1; + } + z[i as usize] = ((i << nb) ^ 256) + 256 * nb; + } + z +} + +/// The hyper-latent decode (`fac`): `rows`, each 63 cumulative counts, and +/// `lengths`, the symbols of each row. Returns one array per row. +pub fn decode_fac(stream: &[u8], rows: &[Vec], lengths: &[usize]) -> Result>, StageRefusal> { + let before = StageRefusal { code: Code::ModelMismatch, at: -1 }; + if rows.len() != lengths.len() { + return Err(before); + } + for r in rows { + if r.len() != 63 || r[0] < 0 || r[62] > 255 || r.windows(2).any(|p| p[0] > p[1]) { + return Err(before); + } + } + let z = zp(); + let mut w = Win::init(stream).map_err(|_| StageRefusal { code: Code::StreamExhausted, at: 0 })?; + let mut tz = [0u32; 256]; + let mut out = Vec::with_capacity(rows.len()); + let mut base: i64 = 0; + for (r, &n) in rows.iter().zip(lengths) { + let mut prev = 0i64; + for (a, &ca) in r.iter().enumerate() { + let k = ca - prev; + for j in prev..ca { + tz[j as usize] = (z[(k + j - prev) as usize] << 16) + a as u32; + } + prev = ca; + } + tz[255] = (z[1] << 16) + 63; + match fac_row(&mut w, &tz, n, base) { + Ok(v) => out.push(v), + Err(()) => return Err(StageRefusal { code: Code::StreamExhausted, at: w.pos }), + } + base += n as i64; + } + Ok(out) +} + +fn fac_row(w: &mut Win, tz: &[u32; 256], n: usize, base: i64) -> R> { + let mut sym = vec![0i64; n]; + let inb = |w: &mut Win, which: usize, i: usize, sym: &mut [i64]| -> R<()> { + w.pos = base + i as i64; + let e = tz[w.s[which] as usize]; + w.step(which, e)?; + sym[i] = (e % 256) as i64; + Ok(()) + }; + let outb = |w: &mut Win, i: usize, sym: &mut [i64]| -> R<()> { + w.pos = base + i as i64; + if sym[i] == 63 { + sym[i] = w.read(6)? as i64; + } + Ok(()) + }; + let mut i = 0; + while i + 3 < n { + inb(w, 0, i, &mut sym)?; + inb(w, 1, i + 1, &mut sym)?; + inb(w, 0, i + 2, &mut sym)?; + inb(w, 1, i + 3, &mut sym)?; + for k in 0..4 { + outb(w, i + k, &mut sym)?; + } + w.flush()?; + i += 4; + } + if n % 4 >= 2 { + inb(w, 0, i, &mut sym)?; + outb(w, i, &mut sym)?; + inb(w, 1, i + 1, &mut sym)?; + outb(w, i + 1, &mut sym)?; + i += 2; + } + if n % 2 == 1 { + inb(w, 0, i, &mut sym)?; + outb(w, i, &mut sym)?; + } + w.flush()?; + Ok(sym) +} diff --git a/wai/wai-ops/src/stage/mod.rs b/wai/wai-ops/src/stage/mod.rs new file mode 100644 index 000000000..a2fde5a30 --- /dev/null +++ b/wai/wai-ops/src/stage/mod.rs @@ -0,0 +1,286 @@ +//! The stage operators E1–E13 (§5E). They run in a capability's driver, outside any +//! graph, at the widths §5E lists; here every value is formed in `i128` or narrower +//! where it provably fits. + +use alloc::vec; +use alloc::vec::Vec; + +use crate::code::Code; +use crate::graph::Round; +use crate::interp::{Tensor, round}; + +pub mod mans; +pub mod rans64; + +pub use rans64::StageRefusal; + +/// E2 `dequantize(sym, f, base)`: `sym·2^f + base`, refused (`field_range`) when `f` +/// is past 62 or the value does not fit `i64`. +pub fn dequantize(sym: i64, f: u32, base: i64) -> Result { + if f > 62 { + return Err(Code::FieldRange); + } + i64::try_from(((sym as i128) << f) + base as i128).map_err(|_| Code::FieldRange) +} + +/// E3 `bucket`, rule `ceil`: `N ≥ 1` edges `e_0 … e_{N−1}`. +pub fn bucket_ceil(sigma: i64, edges: &[i64]) -> Option { + let n = edges.len(); + if n == 0 { + return None; + } + let s = sigma.max(edges[0]); + Some((n - 1) - edges[..n - 1].iter().filter(|&&e| s <= e).count()) +} + +/// E3 `bucket`, rule `floor`: `N − 1 ≥ 1` edges `e_1 … e_{N−1}`, non-decreasing. +pub fn bucket_floor(sigma: i64, edges: &[i64]) -> Option { + if edges.is_empty() || edges.windows(2).any(|p| p[0] > p[1]) { + return None; + } + Some(edges.iter().filter(|&&e| e <= sigma).count()) +} + +/// E4 `coded(v, thr)`. +pub fn coded(v: i64, thr: i64) -> bool { + v > thr +} + +/// E5 `scale_index(v, s, n) = clamp(R_half_up(v, s), 0, n − 1)`. +pub fn scale_index(v: i64, s: u32, n: i64) -> Option { + if n < 1 || s > 127 { + return None; + } + let r = round(v as i128, s, Round::HalfUp)?; + Some(r.clamp(0, (n - 1) as i128) as i64) +} + +/// E6 `block_stat(x; b, s, lo, hi)`. +pub fn block_stat(x: &Tensor, b: u32, s: u32, lo: i64, hi: i64) -> Option { + if b == 0 || s > 127 || lo > hi { + return None; + } + let (c, h, w, b) = (x.c as usize, x.h as usize, x.w as usize, b as usize); + let mut d = vec![0i64; c * h * w]; + for cc in 0..c { + for by in (0..h).step_by(b) { + for bx in (0..w).step_by(b) { + let mut sum: i128 = 0; + for y in by..(by + b).min(h) { + for xx in bx..(bx + b).min(w) { + sum += x.data[(cc * h + y) * w + xx] as i128; + } + } + let v = round(sum, s, Round::HalfUp)?.clamp(lo as i128, hi as i128) as i64; + for y in by..(by + b).min(h) { + for xx in bx..(bx + b).min(w) { + d[(cc * h + y) * w + xx] = v; + } + } + } + } + } + Some(Tensor::new(x.c, x.h, x.w, d)) +} + +/// E7 `band_lookup(v, e, table, row)`: `table[row][#{k : v ≥ e_k}]`. +pub fn band_lookup(v: i64, edges: &[i64], table: &[Vec], row: usize) -> Option { + let r = table.get(row)?; + if r.len() != edges.len() + 1 { + return None; + } + Some(r[edges.iter().filter(|&&e| v >= e).count()]) +} + +/// E9 `residual_step(pred, r, q, lo, hi) = clamp(pred + r·q, lo, hi)`, for `lo < hi`, +/// `pred ∈ [lo, hi]`, `q ∈ [1, 65535]`, `r` fitting `i32`. +pub fn residual_step(pred: i64, r: i64, q: i64, lo: i64, hi: i64) -> Option { + if lo >= hi || pred < lo || pred > hi || !(1..=65535).contains(&q) || r < i32::MIN as i64 || r > i32::MAX as i64 { + return None; + } + Some(((pred as i128) + (r as i128) * (q as i128)).clamp(lo as i128, hi as i128) as i64) +} + +/// E9's 32-bit form: `clamp(pred + clamp(r, −R, R)·q, lo, hi)` with +/// `R = ⌊(hi − lo)/q⌋ + 1`. Every value it forms fits `i32` when `hi − lo ≤ 65535` +/// and both `lo − 131070` and `hi + 131070` fit `i32` (§5E E9, *Width*); outside +/// those conditions it is not E9, and [`residual_step`] is. +pub fn residual_step_i32(pred: i32, r: i32, q: i32, lo: i32, hi: i32) -> i32 { + debug_assert!(i32_form_fits(lo, hi), "E9's 32-bit form outside its conditions"); + let big_r = (hi - lo) / q + 1; + (pred + r.clamp(-big_r, big_r) * q).clamp(lo, hi) +} + +/// Whether E9's 32-bit form keeps every value it forms in `i32` on `[lo, hi]`. +pub fn i32_form_fits(lo: i32, hi: i32) -> bool { + let (lo, hi) = (lo as i64, hi as i64); + lo < hi && hi - lo <= 65535 && lo - 131070 >= i32::MIN as i64 && hi + 131070 <= i32::MAX as i64 +} + +/// E10 `mc_predict` (integer-payloads §3) for one sample: `frame` is the previous +/// decoded frame `[3, h, w]`, `(y, x)` the pixel, `c` the channel, `(dy, dx)` its +/// vector in units of `1/precision` pel. +#[allow(clippy::too_many_arguments)] +pub fn mc_predict(frame: &Tensor, y: i64, x: i64, c: usize, dy: i64, dx: i64, precision: u32) -> Option { + let (h, w) = (frame.h as i64, frame.w as i64); + if c >= frame.c as usize || !(0..h).contains(&y) || !(0..w).contains(&x) { + return None; + } + let p = |yy: i64, xx: i64| frame.data[(c * frame.h as usize + yy.clamp(0, h - 1) as usize) * frame.w as usize + xx.clamp(0, w - 1) as usize] as i64; + match precision { + 1 => Some(p(y + dy, x + dx)), + 2 => { + let (iy, fy, ix, fx) = (y + (dy >> 1), dy & 1, x + (dx >> 1), dx & 1); + Some(match (fy, fx) { + (0, 0) => p(iy, ix), + (0, _) => (p(iy, ix) + p(iy, ix + 1) + 1) >> 1, + (_, 0) => (p(iy, ix) + p(iy + 1, ix) + 1) >> 1, + _ => (p(iy, ix) + p(iy, ix + 1) + p(iy + 1, ix) + p(iy + 1, ix + 1) + 2) >> 2, + }) + } + _ => None, + } +} + +/// E11 `up2_predict` (staged-delivery A.4, `Up2`): `prev` is `[c, ⌈d0/2⌉, ⌈d1/2⌉]`; +/// the prediction is `[c, d0, d1]`. +pub fn up2_predict(prev: &Tensor, d0: u32, d1: u32) -> Option { + let (hp, wp) = (d0.div_ceil(2), d1.div_ceil(2)); + if d0 == 0 || d1 == 0 || prev.h != hp || prev.w != wp { + return None; + } + let (c, hp, wp) = (prev.c as usize, hp as usize, wp as usize); + let p = |cc: usize, yy: usize, xx: usize| prev.data[(cc * hp + yy.min(hp - 1)) * wp + xx.min(wp - 1)] as i64; + let mut d = Vec::with_capacity(c * d0 as usize * d1 as usize); + for cc in 0..c { + for y in 0..d0 as usize { + for x in 0..d1 as usize { + let (iy, fy, ix, fx) = (y >> 1, y & 1, x >> 1, x & 1); + d.push(match (fy, fx) { + (0, 0) => p(cc, iy, ix), + (0, _) => (p(cc, iy, ix) + p(cc, iy, ix + 1) + 1) >> 1, + (_, 0) => (p(cc, iy, ix) + p(cc, iy + 1, ix) + 1) >> 1, + _ => (p(cc, iy, ix) + p(cc, iy, ix + 1) + p(cc, iy + 1, ix) + p(cc, iy + 1, ix + 1) + 2) >> 2, + }); + } + } + } + Some(Tensor::new(prev.c, d0, d1, d)) +} + +/// `rdiv(n, d)` (E12): 0 for `d < 1`, else `sgn(n)·⌊(2|n| + d) / 2d⌋`. +pub fn rdiv(n: i128, d: i128) -> i128 { + if d < 1 { + return 0; + } + n.signum() * ((2 * n.abs() + d) / (2 * d)) +} + +/// E12 `rate_predict` (staged-delivery A.4, `Rate`): `prev` is `[c, 1, n′]`, `n′` +/// samples of `c` channels; the prediction is `[c, 1, d0]`, with +/// `⌊n′·rate_num/rate_den⌋ = d0`. +pub fn rate_predict(prev: &Tensor, rate_num: u32, rate_den: u32, d0: u32) -> Option { + let n = prev.w as u64; + if prev.h != 1 || rate_num == 0 || rate_den == 0 || n * rate_num as u64 / rate_den as u64 != d0 as u64 { + return None; + } + let (num, den) = (rate_num as i128, rate_den as i128); + let mut d = Vec::with_capacity(prev.c as usize * d0 as usize); + for cc in 0..prev.c as usize { + let row = &prev.data[cc * n as usize..(cc + 1) * n as usize]; + for i in 0..d0 as i128 { + let s = i * den; + let lo = s / num; + let rho = s % num; + let hi = (lo + 1).min(n as i128 - 1); + let (a, b) = (row[lo as usize] as i128, row[hi as usize] as i128); + d.push(rdiv(a * num + (b - a) * rho, num) as i64); + } + } + Some(Tensor::new(prev.c, 1, d0, d)) +} + +/// E13 `coding_order`: the CHW indices of `[c, h, w]` in `raster` order, or those of +/// one `checkerboard` pass (`anchor`: `(row + col)` even), each in raster order. +pub fn coding_order(c: u32, h: u32, w: u32, checkerboard: Option) -> Vec { + let mut v = Vec::new(); + for cc in 0..c as u64 { + for y in 0..h as u64 { + for x in 0..w as u64 { + let keep = match checkerboard { + None => true, + Some(anchor) => ((y + x) % 2 == 0) == anchor, + }; + if keep { + v.push((cc * h as u64 + y) * w as u64 + x); + } + } + } + } + v +} + +/// A stage refusal before any element, for a table or operand that breaks a load +/// check. +pub fn before(code: Code) -> StageRefusal { + StageRefusal { code, at: -1 } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn residual_identity_holds() { + // The registered ranges, and the ranges nearest the i32 limits on which the + // 32-bit form still fits (§5E E9, *Width*). + let near_max = i32::MAX - 131070; + let near_min = i32::MIN + 131070; + for &(lo, hi) in &[(0i32, 255i32), (-32768, 32767), (near_max - 65535, near_max), (near_min, near_min + 65535)] { + assert!(i32_form_fits(lo, hi)); + for &q in &[1i32, 2, 3, 255, 256, 1000, 65535] { + let big_r = (hi - lo) / q + 1; + for &r in &[0i32, 1, -1, big_r, -big_r, big_r + 1, -big_r - 1, i32::MAX, i32::MIN, 12345, -54321] { + for &pred in &[lo, hi, lo + (hi - lo) / 2] { + let a = residual_step(pred as i64, r as i64, q as i64, lo as i64, hi as i64).unwrap(); + assert_eq!(a, residual_step_i32(pred, r, q, lo, hi) as i64); + } + } + } + } + } + + /// One step past those ranges the 32-bit form leaves i32: a 32-bit fabric must not + /// use it there. The exact form still gives the clamped value. + #[test] + fn residual_form_condition_is_tight() { + let (lo, hi) = (i32::MAX - 65535, i32::MAX); + assert!(!i32_form_fits(lo, hi)); + assert!(i32_form_fits(lo - 131070, hi - 131070)); + assert!(!i32_form_fits(lo - 131069, hi - 131069)); + let (q, pred, r) = (65535i64, hi as i64, 2i64); + let big_r = (hi as i64 - lo as i64) / q + 1; + assert_eq!(big_r, 2); + assert!(pred + r.clamp(-big_r, big_r) * q > i32::MAX as i64); + assert_eq!(residual_step(pred, r, q, lo as i64, hi as i64), Some(i32::MAX as i64)); + let (lo, hi) = (i32::MIN, i32::MIN + 65535); + assert!(!i32_form_fits(lo, hi)); + assert!(i32_form_fits(lo + 131070, hi + 131070)); + assert_eq!(residual_step(lo as i64, -2, q, lo as i64, hi as i64), Some(i32::MIN as i64)); + } + + #[test] + fn buckets() { + assert_eq!(bucket_ceil(5, &[1, 5, 9]), Some(1)); + assert_eq!(bucket_ceil(-100, &[1, 5, 9]), Some(0)); + assert_eq!(bucket_ceil(100, &[1, 5, 9]), Some(2)); + assert_eq!(bucket_floor(5, &[1, 5, 9]), Some(2)); + assert_eq!(bucket_floor(4, &[1, 5, 9]), Some(1)); + assert_eq!(bucket_floor(4, &[5, 1]), None); + assert_eq!(rdiv(-3, 2), -2); + assert_eq!(rdiv(3, 2), 2); + assert_eq!(rdiv(1, 2), 1); + assert_eq!(rdiv(-1, 2), -1); + assert_eq!(rdiv(5, 0), 0); + } +} diff --git a/wai/wai-ops/src/stage/rans64.rs b/wai/wai-ops/src/stage/rans64.rs new file mode 100644 index 000000000..5a9cc2499 --- /dev/null +++ b/wai/wai-ops/src/stage/rans64.rs @@ -0,0 +1,220 @@ +//! E1 `rans64_decode` (§5E): integer-payloads §2's decode, with the table, escape and +//! end modes a capability fixes. + +use alloc::vec::Vec; + +use crate::code::{Code, Refusal, Site}; + +/// The table rule. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum TableRule { + /// `3 ≤ n ≤ 4097`. + T4097, + /// `2 ≤ n ≤ 65537`. + T2, +} + +/// The escape rule. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum Escape { + Lenient, + /// `strict(lo, hi)`, both fitting `i32`. + Strict { lo: i32, hi: i32 }, +} + +/// The end rule. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum End { + Canonical, + FinalOnly, +} + +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub struct Modes { + pub table: TableRule, + pub escape: Escape, + pub end: End, +} + +/// A table: its cumulative counts `cdf[0..n)` and its offset `o`. +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Table { + pub cdf: Vec, + pub offset: i64, +} + +/// Whether `cdf` is a table under `rule`: its length, `cdf[0] = 0`, +/// `cdf[n − 1] = 65536`, strictly increasing. +pub fn table_ok(cdf: &[i64], rule: TableRule) -> bool { + let n = cdf.len(); + let len_ok = match rule { + TableRule::T4097 => (3..=4097).contains(&n), + TableRule::T2 => (2..=65537).contains(&n), + }; + len_ok && cdf[0] == 0 && cdf[n - 1] == 65536 && cdf.windows(2).all(|p| p[0] < p[1]) +} + +const L: u64 = 1 << 31; + +/// The decoder state of integer-payloads §2. +pub struct Rans<'a> { + words: &'a [u8], + idx: usize, + x: u64, + /// Set when a renormalisation found no word left. + ran_out: bool, +} + +impl<'a> Rans<'a> { + /// Seed from a stream of `L` bytes, `L` a multiple of 4 and at least 8. + pub fn new(words: &'a [u8]) -> Option> { + if words.len() < 8 || words.len() % 4 != 0 { + return None; + } + let w = |k: usize| u32::from_le_bytes([words[4 * k], words[4 * k + 1], words[4 * k + 2], words[4 * k + 3]]) as u64; + Some(Rans { words, idx: 2, x: (w(1) << 32) | w(0), ran_out: false }) + } + fn renorm(&mut self) { + if self.x < L { + let off = self.idx * 4; + match self.words.get(off..off + 4) { + Some(b) => { + self.x = (self.x << 32) | u32::from_le_bytes([b[0], b[1], b[2], b[3]]) as u64; + self.idx += 1; + } + None => self.ran_out = true, + } + } + } + fn bits(&mut self, n: u32) -> u64 { + let v = self.x & ((1u64 << n) - 1); + self.x >>= n; + self.renorm(); + v + } + pub fn ran_out(&self) -> bool { + self.ran_out + } + /// Every word read, and the state `2^31`. + pub fn ends_canonically(&self) -> bool { + !self.ran_out && self.x == L && self.idx * 4 == self.words.len() + } + + /// One symbol against `t`. Under `strict`, returns the refusal the symbol's own + /// checks 2–4 give (check 1, the end rule's, is the caller's). + pub fn symbol(&mut self, t: &Table, escape: Escape) -> Result { + let n = t.cdf.len(); + let cum = (self.x & 0xFFFF) as i64; + // The largest s in [0, n − 2] with cdf[s] ≤ cum. + let (mut lo, mut hi) = (0usize, n - 1); + while hi - lo > 1 { + let mid = (lo + hi) / 2; + if t.cdf[mid] <= cum { lo = mid } else { hi = mid } + } + let s = lo; + let (start, freq) = (t.cdf[s] as u64, (t.cdf[s + 1] - t.cdf[s]) as u64); + self.x = freq * (self.x >> 16) + (cum as u64) - start; + self.renorm(); + let n2 = (n - 2) as i64; + if (s as i64) < n2 { + let sym = s as i64 + t.offset; + return match escape { + Escape::Lenient => Ok(sym.clamp(i32::MIN as i64, i32::MAX as i64)), + Escape::Strict { lo, hi } => { + if sym < lo as i64 || sym > hi as i64 { Err(Code::FieldRange) } else { Ok(sym) } + } + }; + } + match escape { + Escape::Lenient => { + let mut v = self.bits(4); + let mut count = v; + let mut groups = 0; + while v == 15 && groups < 8 && !self.ran_out { + v = self.bits(4); + count += v; + groups += 1; + } + let count = count.min(8); + let mut raw: u64 = 0; + for j in 0..count { + raw |= self.bits(4) << (4 * j); + } + let value: i64 = if raw & 1 == 1 { -((raw >> 1) as i64) - 1 } else { (raw >> 1) as i64 + n2 }; + let value = value.clamp(i32::MIN as i64, i32::MAX as i64); + Ok((value + t.offset).clamp(i32::MIN as i64, i32::MAX as i64)) + } + Escape::Strict { lo, hi } => { + let count = self.bits(4); + let mut raw: u64 = 0; + let mut last = 0; + if count <= 8 { + for j in 0..count { + last = self.bits(4); + raw |= last << (4 * j); + } + } + let value: i64 = if raw & 1 == 1 { -((raw >> 1) as i64) - 1 } else { (raw >> 1) as i64 + n2 }; + let sym = value + t.offset; + if count > 8 || (count >= 1 && last == 0) { + // Check 1 (run-out under `canonical`) is the caller's and comes first. + return Err(Code::StreamNoncanonical); + } + if sym < lo as i64 || sym > hi as i64 { Err(Code::FieldRange) } else { Ok(sym) } + } + } + } +} + +/// A refusal of the decode at symbol `at` (`at` = the number of symbols for a +/// refusal after the last one, −1 for one before the first). +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub struct StageRefusal { + pub code: Code, + pub at: i64, +} + +impl StageRefusal { + pub fn refusal(&self) -> Refusal { + let at = if self.at < 0 { Site::Table(0) } else { Site::Symbol(self.at as u64) }; + Refusal::new(self.code, at) + } +} + +/// Decode the symbols `reads` names (each the index of the table it decodes against) +/// from `stream`, under `modes`. +pub fn decode(stream: &[u8], tables: &[Table], reads: &[usize], modes: Modes) -> Result, StageRefusal> { + let before = |code| StageRefusal { code, at: -1 }; + for t in tables { + if !table_ok(&t.cdf, modes.table) || t.offset < i32::MIN as i64 || t.offset > i32::MAX as i64 { + return Err(before(Code::ModelMismatch)); + } + } + if let Escape::Strict { lo, hi } = modes.escape + && lo > hi + { + return Err(before(Code::ModelMismatch)); + } + if reads.iter().any(|&r| r >= tables.len()) { + return Err(before(Code::ModelMismatch)); + } + let mut d = Rans::new(stream).ok_or(StageRefusal { code: Code::StreamExhausted, at: 0 })?; + let mut out = Vec::with_capacity(reads.len()); + for (i, &r) in reads.iter().enumerate() { + let at = i as i64; + let res = d.symbol(&tables[r], modes.escape); + if modes.end == End::Canonical && d.ran_out() { + return Err(StageRefusal { code: Code::StreamExhausted, at }); + } + match res { + Ok(v) => out.push(v), + Err(code) => return Err(StageRefusal { code, at }), + } + } + let end = reads.len() as i64; + match modes.end { + End::Canonical if !d.ends_canonically() => Err(StageRefusal { code: Code::StreamNoncanonical, at: end }), + End::FinalOnly if d.ran_out() => Err(StageRefusal { code: Code::StreamExhausted, at: end }), + _ => Ok(out), + } +} diff --git a/wai/wai-ops/src/vectors.rs b/wai/wai-ops/src/vectors.rs new file mode 100644 index 000000000..89eb15ca2 --- /dev/null +++ b/wai/wai-ops/src/vectors.rs @@ -0,0 +1,323 @@ +//! The vector files of §14.1, recomputed from their inputs. +//! +//! [`recompute_file`] reads a JSON Lines file, keeps every line's inputs, recomputes +//! every line's `load` and `expect` values, and writes the file again canonically. A +//! conforming implementation's file is byte-identical to the committed one; every leg +//! of CI runs this same function, natively, under WASI and under a browser engine. + +use alloc::string::{String, ToString}; +use alloc::vec; +use alloc::vec::Vec; + +use crate::code::Code; +use crate::graph; +use crate::interp::{self, Sequence, Tensor}; +use crate::json::{self, C, Value, hex, unhex}; +use crate::stage::{self, rans64, mans, StageRefusal}; + +/// Which directory a file belongs to: `vectors/` and `graphs/` share the graph +/// vector format, `stage/` has its own. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum Kind { + Graph, + Stage, +} + +/// Why a line could not be recomputed: it is not a vector of its kind. +pub type Bad = String; + +fn bad(what: &str) -> Result { + Err(what.to_string()) +} + +fn dec(v: &Value) -> Result { + v.as_dec_i64().ok_or_else(|| "an integer outside i64".to_string()) +} +fn dec_u(v: &Value) -> Result { + let d = dec(v)?; + u64::try_from(d).map_err(|_| "a negative size".to_string()) +} +fn get<'a>(v: &'a Value, k: &str) -> Result<&'a Value, Bad> { + v.get(k).ok_or_else(|| alloc::format!("no member {k}")) +} +fn arr<'a>(v: &'a Value, k: &str) -> Result<&'a [Value], Bad> { + get(v, k)?.as_arr().ok_or_else(|| alloc::format!("{k} is not an array")) +} +fn decs(v: &Value, k: &str) -> Result, Bad> { + arr(v, k)?.iter().map(dec).collect() +} + +/// A tensor `{ "data": [...], "shape": [C, H, W] }`. +pub fn read_tensor(v: &Value) -> Result { + let s = arr(v, "shape")?; + if s.len() != 3 { + return bad("a shape has three dimensions"); + } + let (c, h, w) = (dec_u(&s[0])?, dec_u(&s[1])?, dec_u(&s[2])?); + let data = decs(v, "data")?; + let (c, h, w) = (u32::try_from(c).map_err(|_| "size")?, u32::try_from(h).map_err(|_| "size")?, u32::try_from(w).map_err(|_| "size")?); + Ok(Tensor::new(c, h, w, data)) +} + +pub fn tensor_c(t: &Tensor) -> C { + C::obj(vec![ + ("data", C::ints(t.data.iter().map(|&v| v as i128))), + ("shape", C::ints([t.c as i128, t.h as i128, t.w as i128])), + ]) +} + +/// Recompute one graph vector (§14.1): `load`, and every decode's `expect`. +pub fn graph_line(v: &Value) -> Result { + let hexg = get(v, "graph")?.as_str().ok_or("graph is not a string")?; + let bytes = unhex(hexg).ok_or("graph is not lowercase hexadecimal")?; + let id = get(v, "id")?.as_str().ok_or("id is not a string")?; + let profiles = arr(v, "profiles")?; + let decodes = arr(v, "decodes")?; + let loaded = graph::load(&bytes); + let load = match &loaded { + Ok(_) => C::s("ok"), + Err(r) => C::s(r.code.as_str()), + }; + let mut out = Vec::new(); + if let Ok(g) = &loaded { + let mut seq = Sequence::new(); + for d in decodes { + let inputs: Vec = arr(d, "inputs")?.iter().map(read_tensor).collect::>()?; + let scalars = decs(d, "scalars")?; + let expect = match interp::run(g, &inputs, &scalars, &mut seq) { + Ok(outs) => C::obj(vec![("outputs", C::Arr(outs.iter().map(tensor_c).collect()))]), + Err(r) => C::obj(vec![("refuse", C::s(r.code.as_str()))]), + }; + out.push(C::obj(vec![ + ("expect", expect), + ("inputs", C::Arr(inputs.iter().map(tensor_c).collect())), + ("scalars", C::ints(scalars.iter().map(|&s| s as i128))), + ])); + } + } + Ok(C::obj(vec![ + ("decodes", C::Arr(out)), + ("graph", C::Str(hex(&bytes))), + ("id", C::s(id)), + ("load", load), + ("profiles", C::Arr(profiles.iter().map(|p| C::s(p.as_str().unwrap_or("?"))).collect())), + ])) +} + +fn refused(r: StageRefusal) -> C { + C::obj(vec![("at", C::int(r.at as i128)), ("refuse", C::s(r.code.as_str()))]) +} + +fn str_of<'a>(v: &'a Value, k: &str) -> Result<&'a str, Bad> { + get(v, k)?.as_str().ok_or_else(|| alloc::format!("{k} is not a string")) +} + +/// The modes of E1 from a vector's `modes`. +pub fn rans_modes(m: &Value) -> Result { + let table = match str_of(m, "table")? { + "t4097" => rans64::TableRule::T4097, + "t2" => rans64::TableRule::T2, + _ => return bad("table rule"), + }; + let escape = match str_of(m, "escape")? { + "lenient" => rans64::Escape::Lenient, + "strict" => { + let lo = i32::try_from(dec(get(m, "lo")?)?).map_err(|_| "lo")?; + let hi = i32::try_from(dec(get(m, "hi")?)?).map_err(|_| "hi")?; + rans64::Escape::Strict { lo, hi } + } + _ => return bad("escape rule"), + }; + let end = match str_of(m, "end")? { + "canonical" => rans64::End::Canonical, + "final_only" => rans64::End::FinalOnly, + _ => return bad("end rule"), + }; + Ok(rans64::Modes { table, escape, end }) +} + +/// Compute a stage vector's `expect` from its `op`, `in` and `modes`. +pub fn stage_expect(op: &str, i: &Value, m: &Value) -> Result { + let one = |k: &str, v: C| C::obj(vec![(k, v)]); + let before = |code: Code| refused(stage::before(code)); + let at0 = |code: Code| refused(StageRefusal { code, at: 0 }); + Ok(match op { + "rans64_decode" => { + let modes = rans_modes(m)?; + let stream = unhex(str_of(i, "stream")?).ok_or("stream hex")?; + let tables: Vec = arr(i, "tables")? + .iter() + .map(|t| Ok(rans64::Table { cdf: decs(t, "cdf")?, offset: dec(get(t, "offset")?)? })) + .collect::>()?; + let reads: Vec = decs(i, "reads")?.iter().map(|&r| usize::try_from(r).unwrap_or(usize::MAX)).collect(); + match rans64::decode(&stream, &tables, &reads, modes) { + Ok(s) => one("symbols", C::ints(s.iter().map(|&v| v as i128))), + Err(r) => refused(r), + } + } + "dequantize" => { + let (f, sym, base) = (dec(get(i, "f")?)?, dec(get(i, "sym")?)?, dec(get(i, "base")?)?); + match u32::try_from(f).ok().map(|f| stage::dequantize(sym, f, base)) { + Some(Ok(v)) => one("value", C::int(v as i128)), + _ => at0(Code::FieldRange), + } + } + "bucket" => { + let sigma = dec(get(i, "sigma")?)?; + let edges = decs(i, "edges")?; + let r = match str_of(m, "bucket")? { + "ceil" => stage::bucket_ceil(sigma, &edges), + "floor" => stage::bucket_floor(sigma, &edges), + _ => return bad("bucket rule"), + }; + match r { + Some(b) => one("index", C::int(b as i128)), + None => before(Code::ModelMismatch), + } + } + "coded" => one("coded", C::Bool(stage::coded(dec(get(i, "v")?)?, dec(get(i, "thr")?)?))), + "scale_index" => { + let s = u32::try_from(dec(get(i, "s")?)?).unwrap_or(u32::MAX); + match stage::scale_index(dec(get(i, "v")?)?, s, dec(get(i, "n")?)?) { + Some(v) => one("index", C::int(v as i128)), + None => before(Code::ModelMismatch), + } + } + "block_stat" => { + let x = read_tensor(get(i, "x")?)?; + let b = u32::try_from(dec(get(i, "b")?)?).unwrap_or(0); + let s = u32::try_from(dec(get(i, "s")?)?).unwrap_or(u32::MAX); + match stage::block_stat(&x, b, s, dec(get(i, "lo")?)?, dec(get(i, "hi")?)?) { + Some(t) => one("out", tensor_c(&t)), + None => before(Code::ModelMismatch), + } + } + "band_lookup" => { + let table: Vec> = arr(i, "table")?.iter().map(|r| r.as_arr().ok_or("row").map(|r| r.iter().map(dec).collect::, _>>())).collect::, _>>()??; + let row = usize::try_from(dec(get(i, "row")?)?).unwrap_or(usize::MAX); + match stage::band_lookup(dec(get(i, "v")?)?, &decs(i, "edges")?, &table, row) { + Some(v) => one("value", C::int(v as i128)), + None => before(Code::ModelMismatch), + } + } + "mans_decode" => { + let mem = unhex(str_of(i, "memory")?).ok_or("memory hex")?; + let r = match str_of(i, "mode")? { + "sgm" => { + let pmf: Vec> = arr(i, "pmf")?.iter().map(|r| r.as_arr().ok_or("pmf").map(|r| r.iter().map(dec).collect::, _>>())).collect::, _>>()??; + let spans: Vec = arr(i, "spans")? + .iter() + .map(|s| { + let mask = arr(s, "mask")?.iter().map(|b| b.as_bool().ok_or_else(|| "mask".to_string())).collect::, _>>()?; + let sigma = decs(s, "sigma")?.iter().map(|&g| usize::try_from(g).unwrap_or(usize::MAX)).collect(); + Ok(mans::Span { mask, sigma }) + }) + .collect::>()?; + mans::decode_sgm(&mem, &pmf, &decs(i, "bounds")?, &spans) + } + "fac" => { + let rows: Vec> = arr(i, "rows")?.iter().map(|r| r.as_arr().ok_or("rows").map(|r| r.iter().map(dec).collect::, _>>())).collect::, _>>()??; + let lengths: Vec = decs(i, "lengths")?.iter().map(|&l| usize::try_from(l).unwrap_or(usize::MAX)).collect(); + mans::decode_fac(&mem, &rows, &lengths) + } + _ => return bad("mans mode"), + }; + match r { + Ok(s) => one("symbols", C::Arr(s.iter().map(|row| C::ints(row.iter().map(|&v| v as i128))).collect())), + Err(r) => refused(r), + } + } + "residual_step" => { + let g = |k| dec(get(i, k)?); + match stage::residual_step(g("pred")?, g("r")?, g("q")?, g("lo")?, g("hi")?) { + Some(v) => one("value", C::int(v as i128)), + None => before(Code::ModelMismatch), + } + } + "mc_predict" => { + let f = read_tensor(get(i, "frame")?)?; + let g = |k| dec(get(i, k)?); + let c = usize::try_from(g("c")?).unwrap_or(usize::MAX); + let p = u32::try_from(g("precision")?).unwrap_or(0); + match stage::mc_predict(&f, g("y")?, g("x")?, c, g("dy")?, g("dx")?, p) { + Some(v) => one("value", C::int(v as i128)), + None => before(Code::ModelMismatch), + } + } + "up2_predict" => { + let p = read_tensor(get(i, "prev")?)?; + let d0 = u32::try_from(dec(get(i, "d0")?)?).unwrap_or(0); + let d1 = u32::try_from(dec(get(i, "d1")?)?).unwrap_or(0); + match stage::up2_predict(&p, d0, d1) { + Some(t) => one("out", tensor_c(&t)), + None => before(Code::GeometryMismatch), + } + } + "rate_predict" => { + let p = read_tensor(get(i, "prev")?)?; + let u = |k| u32::try_from(dec(get(i, k)?)?).map_err(|_| "u32".to_string()); + match stage::rate_predict(&p, u("rate_num")?, u("rate_den")?, u("d0")?) { + Some(t) => one("out", tensor_c(&t)), + None => before(Code::GeometryMismatch), + } + } + "coding_order" => { + let s = decs(i, "shape")?; + if s.len() != 3 || s.iter().any(|&d| !(1..=u32::MAX as i64).contains(&d)) { + return bad("shape"); + } + let cb = match str_of(i, "order")? { + "raster" => None, + "checkerboard" => Some(get(i, "anchor")?.as_bool().ok_or("anchor")?), + _ => return bad("order"), + }; + one("indices", C::ints(stage::coding_order(s[0] as u32, s[1] as u32, s[2] as u32, cb).iter().map(|&v| v as i128))) + } + _ => return bad("an unknown stage operator"), + }) +} + +/// Recompute one stage vector. +pub fn stage_line(v: &Value) -> Result { + let op = str_of(v, "op")?; + let i = get(v, "in")?; + let m = get(v, "modes")?; + let id = str_of(v, "id")?; + let expect = stage_expect(op, i, m)?; + Ok(C::obj(vec![("expect", expect), ("id", C::s(id)), ("in", value_to_c(i)?), ("modes", value_to_c(m)?), ("op", C::s(op))])) +} + +/// A canonical value read back: strings, booleans, arrays, objects and null. A JSON +/// number cannot appear in a canonical file. +pub fn value_to_c(v: &Value) -> Result { + Ok(match v { + Value::Null => C::Null, + Value::Bool(b) => C::Bool(*b), + Value::Str(s) => C::s(s), + Value::Num(_) => return bad("a number in a canonical file"), + Value::Arr(a) => C::Arr(a.iter().map(value_to_c).collect::>()?), + Value::Obj(m) => C::Obj(m.iter().map(|(k, x)| Ok((k.clone(), value_to_c(x)?))).collect::>()?), + }) +} + +/// Recompute one line. +pub fn recompute_line(kind: Kind, line: &str) -> Result { + let v = json::parse(line.as_bytes()).map_err(|e| alloc::format!("JSON at byte {}", e.at))?; + let c = match kind { + Kind::Graph => graph_line(&v)?, + Kind::Stage => stage_line(&v)?, + }; + Ok(json::to_string(&c)) +} + +/// Recompute every line of a file. Every line, the last included, ends with one +/// line feed. +pub fn recompute_file(kind: Kind, text: &str) -> Result { + let mut out = String::with_capacity(text.len()); + for (n, line) in text.split_terminator('\n').enumerate() { + let r = recompute_line(kind, line).map_err(|e| alloc::format!("line {}: {e}", n + 1))?; + out.push_str(&r); + out.push('\n'); + } + Ok(out) +} diff --git a/wai/wai-ops/src/xint.rs b/wai/wai-ops/src/xint.rs new file mode 100644 index 000000000..53fc45721 --- /dev/null +++ b/wai/wai-ops/src/xint.rs @@ -0,0 +1,435 @@ +//! A 512-bit signed integer, two's complement, eight little-endian 64-bit limbs. +//! +//! The bounds pass of §8.1 saturates after every primitive operation at `±2^255`, so +//! every operand it meets lies in `[−2^255, 2^255]`; a product then lies in +//! `[−2^510, 2^510]` and a sum in `[−2^256, 2^256]`, and 512 bits hold each exactly. +//! The lowerings of §13.2 evaluate their predicates `P_ℓ` here too: every value they +//! form is below `2^255`. +//! +//! The arithmetic operations are exact while their result lies in +//! `[−2^511, 2^511 − 1]`. Every caller in this crate stays inside that range, which +//! [`I512::sat`] and the argument above guarantee; the operations themselves never +//! panic, and outside the range they wrap, which no caller relies on. + +use alloc::string::String; +use alloc::vec::Vec; +use core::cmp::Ordering; + +const N: usize = 8; + +#[derive(Clone, Copy, PartialEq, Eq, Hash, Debug)] +pub struct I512([u64; N]); + +impl Default for I512 { + fn default() -> Self { + I512::ZERO + } +} + +impl I512 { + pub const ZERO: I512 = I512([0; N]); + pub const ONE: I512 = I512([1, 0, 0, 0, 0, 0, 0, 0]); + + pub const fn from_i128(v: i128) -> I512 { + let u = v as u128; + let e = if v < 0 { u64::MAX } else { 0 }; + I512([u as u64, (u >> 64) as u64, e, e, e, e, e, e]) + } + pub const fn from_u128(v: u128) -> I512 { + I512([v as u64, (v >> 64) as u64, 0, 0, 0, 0, 0, 0]) + } + pub const fn from_i64(v: i64) -> I512 { + I512::from_i128(v as i128) + } + pub const fn from_u64(v: u64) -> I512 { + I512::from_u128(v as u128) + } + + pub fn is_neg(&self) -> bool { + self.0[N - 1] >> 63 == 1 + } + pub fn is_zero(&self) -> bool { + self.0.iter().all(|&l| l == 0) + } + + fn not(self) -> I512 { + let mut r = self.0; + for l in &mut r { + *l = !*l; + } + I512(r) + } + + fn wadd(a: I512, b: I512) -> I512 { + let mut r = [0u64; N]; + let mut carry = 0u128; + for (i, slot) in r.iter_mut().enumerate() { + let s = a.0[i] as u128 + b.0[i] as u128 + carry; + *slot = s as u64; + carry = s >> 64; + } + I512(r) + } + + pub fn add(self, o: I512) -> I512 { + I512::wadd(self, o) + } + pub fn neg(self) -> I512 { + I512::wadd(self.not(), I512::ONE) + } + pub fn sub(self, o: I512) -> I512 { + I512::wadd(self, o.neg()) + } + pub fn abs(self) -> I512 { + if self.is_neg() { self.neg() } else { self } + } + + /// The low 512 bits of the product of two magnitudes. + fn mul_mag(a: &[u64; N], b: &[u64; N]) -> [u64; N] { + let mut r = [0u64; N]; + for i in 0..N { + if a[i] == 0 { + continue; + } + let mut carry = 0u128; + for j in 0..(N - i) { + let cur = r[i + j] as u128 + (a[i] as u128) * (b[j] as u128) + carry; + r[i + j] = cur as u64; + carry = cur >> 64; + } + } + r + } + + pub fn mul(self, o: I512) -> I512 { + let neg = self.is_neg() != o.is_neg(); + let m = I512(I512::mul_mag(&self.abs().0, &o.abs().0)); + if neg { m.neg() } else { m } + } + + /// `self · 2^k`. + pub fn shl(self, k: u32) -> I512 { + if k >= 512 { + return I512::ZERO; + } + let (w, b) = ((k / 64) as usize, k % 64); + let mut r = [0u64; N]; + for i in (w..N).rev() { + let lo = self.0[i - w]; + r[i] = if b == 0 { lo } else { (lo << b) | if i > w { self.0[i - w - 1] >> (64 - b) } else { 0 } }; + } + I512(r) + } + + /// `⌊self / 2^k⌋`: an arithmetic shift right. + pub fn shr_floor(self, k: u32) -> I512 { + let fill = if self.is_neg() { u64::MAX } else { 0 }; + if k >= 512 { + return I512([fill; N]); + } + let (w, b) = ((k / 64) as usize, k % 64); + let mut r = [fill; N]; + for (i, slot) in r.iter_mut().enumerate().take(N - w) { + let lo = self.0[i + w]; + let hi = if i + w + 1 < N { self.0[i + w + 1] } else { fill }; + *slot = if b == 0 { lo } else { (lo >> b) | (hi << (64 - b)) }; + } + I512(r) + } + + /// `2^k`, for `k ≤ 510`. + pub fn pow2(k: u32) -> I512 { + I512::ONE.shl(k) + } + + fn bit(&self, i: u32) -> bool { + (self.0[(i / 64) as usize] >> (i % 64)) & 1 == 1 + } + + /// The number of significant bits of a non-negative value (0 for 0). + fn bit_len(&self) -> u32 { + for i in (0..N).rev() { + if self.0[i] != 0 { + return 64 * i as u32 + (64 - self.0[i].leading_zeros()); + } + } + 0 + } + + /// Quotient and remainder of two non-negative values, `b > 0`. + fn divmod_mag(a: I512, b: I512) -> (I512, I512) { + let mut q = I512::ZERO; + let mut r = I512::ZERO; + let n = a.bit_len(); + for i in (0..n).rev() { + r = r.shl(1); + if a.bit(i) { + r.0[0] |= 1; + } + if r.cmp(&b) != Ordering::Less { + r = r.sub(b); + q.0[(i / 64) as usize] |= 1u64 << (i % 64); + } + } + (q, r) + } + + /// `⌊self / d⌋`, for `d > 0`. + pub fn div_floor(self, d: I512) -> I512 { + debug_assert!(!d.is_neg() && !d.is_zero()); + let (q, r) = I512::divmod_mag(self.abs(), d); + if !self.is_neg() { + q + } else if r.is_zero() { + q.neg() + } else { + q.neg().sub(I512::ONE) + } + } + + /// `trunc(self / d)`, for `d > 0`. + pub fn div_trunc(self, d: I512) -> I512 { + let (q, _) = I512::divmod_mag(self.abs(), d); + if self.is_neg() { q.neg() } else { q } + } + + /// `isqrt(self)`, for `self ≥ 0`: the largest `r ≥ 0` with `r² ≤ self`. + pub fn isqrt(self) -> I512 { + debug_assert!(!self.is_neg()); + if self.is_zero() { + return I512::ZERO; + } + let mut x = self; + let mut r = I512::ZERO; + let top = (self.bit_len() - 1) & !1; + let mut bit = I512::pow2(top); + while !bit.is_zero() { + let t = r.add(bit); + if x.cmp(&t) != Ordering::Less { + x = x.sub(t); + r = r.shr_floor(1).add(bit); + } else { + r = r.shr_floor(1); + } + bit = bit.shr_floor(2); + } + r + } + + /// `isqrt_ceil(self)`, for `self ≥ 0`: the smallest `r ≥ 0` with `r² ≥ self`. + pub fn isqrt_ceil(self) -> I512 { + let r = self.isqrt(); + if r.mul(r) == self { r } else { r.add(I512::ONE) } + } + + pub fn min(self, o: I512) -> I512 { + if self.cmp(&o) == Ordering::Greater { o } else { self } + } + pub fn max(self, o: I512) -> I512 { + if self.cmp(&o) == Ordering::Less { o } else { self } + } + + /// The value as an `i128`, if it fits. + pub fn to_i128(self) -> Option { + let lo = (self.0[0] as u128) | ((self.0[1] as u128) << 64); + let v = lo as i128; + let e = if v < 0 { u64::MAX } else { 0 }; + if self.0[2..].iter().all(|&l| l == e) { Some(v) } else { None } + } + pub fn to_i64(self) -> Option { + self.to_i128().and_then(|v| i64::try_from(v).ok()) + } + + /// Whether the value fits iN (§2): `−2^(N−1) ≤ v ≤ 2^(N−1) − 1`. + pub fn fits(self, n: u32) -> bool { + self.bits_one() <= n + } + + /// The smallest `N ≥ 1` such that the value fits iN. + pub fn bits_one(self) -> u32 { + let m = if self.is_neg() { self.not() } else { self }; + m.bit_len() + 1 + } + + /// `bits([lo, hi])` (§2). + pub fn bits(lo: I512, hi: I512) -> u32 { + lo.bits_one().max(hi.bits_one()) + } + + /// `sat(v) = clamp(v, −2^255, 2^255)` (§8.1), and whether it changed `v`. + pub fn sat(self) -> (I512, bool) { + let cap = I512::pow2(255); + if self.cmp(&cap) == Ordering::Greater { + (cap, true) + } else if self.cmp(&cap.neg()) == Ordering::Less { + (cap.neg(), true) + } else { + (self, false) + } + } + + /// Divide a non-negative value by a small divisor, returning the remainder. + fn divrem_small(&mut self, d: u64) -> u64 { + let mut rem = 0u128; + for i in (0..N).rev() { + let cur = (rem << 64) | self.0[i] as u128; + self.0[i] = (cur / d as u128) as u64; + rem = cur % d as u128; + } + rem as u64 + } + + /// The decimal form: digits, with `-` before a negative value. + pub fn to_dec(self) -> String { + let neg = self.is_neg(); + let mut m = self.abs(); + if m.is_zero() { + return String::from("0"); + } + let mut chunks: Vec = Vec::new(); + while !m.is_zero() { + chunks.push(m.divrem_small(10_000_000_000_000_000_000)); + } + let mut s = String::new(); + if neg { + s.push('-'); + } + for (i, c) in chunks.iter().rev().enumerate() { + let digits = alloc::format!("{c}"); + if i > 0 { + for _ in digits.len()..19 { + s.push('0'); + } + } + s.push_str(&digits); + } + s + } + + /// Parse `-?[0-9]+`. `None` on any other character, an empty digit string, or a + /// magnitude of `2^511` or more. + pub fn from_dec(s: &str) -> Option { + let (neg, d) = match s.strip_prefix('-') { + Some(r) => (true, r), + None => (false, s), + }; + if d.is_empty() { + return None; + } + let mut m = I512::ZERO; + let ten = I512::from_u64(10); + let limit = I512::pow2(510); + for ch in d.bytes() { + if !ch.is_ascii_digit() { + return None; + } + // m·10 + digit stays below 2^511 only if m < 2^510 / 5 roughly; check exactly. + if m.cmp(&limit) != Ordering::Less { + return None; + } + m = m.mul(ten).add(I512::from_u64((ch - b'0') as u64)); + if m.is_neg() { + return None; + } + } + Some(if neg { m.neg() } else { m }) + } +} + +impl PartialOrd for I512 { + fn partial_cmp(&self, o: &I512) -> Option { + Some(self.cmp(o)) + } +} + +impl Ord for I512 { + fn cmp(&self, o: &I512) -> Ordering { + match (self.is_neg(), o.is_neg()) { + (true, false) => Ordering::Less, + (false, true) => Ordering::Greater, + _ => { + for i in (0..N).rev() { + match self.0[i].cmp(&o.0[i]) { + Ordering::Equal => continue, + x => return x, + } + } + Ordering::Equal + } + } + } +} + +#[cfg(test)] +mod tests { + use super::I512; + use alloc::string::ToString; + + fn x(v: i128) -> I512 { + I512::from_i128(v) + } + + #[test] + fn arithmetic_matches_i128() { + let vals = [0i128, 1, -1, 7, -7, 1 << 62, -(1 << 62), i64::MAX as i128, i64::MIN as i128, 123456789012345678, -98765432109876543]; + for &a in &vals { + for &b in &vals { + assert_eq!(x(a).add(x(b)).to_i128(), a.checked_add(b)); + assert_eq!(x(a).sub(x(b)).to_i128(), a.checked_sub(b)); + if let Some(p) = a.checked_mul(b) { + assert_eq!(x(a).mul(x(b)).to_i128(), Some(p)); + } + if b > 0 { + assert_eq!(x(a).div_floor(x(b)).to_i128(), Some(floor_div(a, b))); + assert_eq!(x(a).div_trunc(x(b)).to_i128(), Some(a / b)); + } + assert_eq!(x(a).cmp(&x(b)), a.cmp(&b)); + } + for k in [0u32, 1, 5, 63, 64, 65, 100] { + assert_eq!(x(a).shr_floor(k).to_i128(), Some(a >> k.min(127))); + } + } + } + + fn floor_div(a: i128, b: i128) -> i128 { + let q = a / b; + if (a % b != 0) && ((a < 0) != (b < 0)) { q - 1 } else { q } + } + + #[test] + fn wide_values() { + let p = I512::pow2(255); + assert_eq!(p.mul(p), I512::pow2(510)); + assert_eq!(p.neg().mul(p), I512::pow2(510).neg()); + assert_eq!(I512::pow2(300).shr_floor(299), x(2)); + assert_eq!(I512::pow2(300).neg().shr_floor(400), x(-1)); + assert_eq!(I512::pow2(300).isqrt(), I512::pow2(150)); + assert_eq!(I512::pow2(300).sub(I512::ONE).isqrt(), I512::pow2(150).sub(I512::ONE)); + assert_eq!(x(17).isqrt(), x(4)); + assert_eq!(x(17).isqrt_ceil(), x(5)); + assert_eq!(x(16).isqrt_ceil(), x(4)); + assert_eq!(x(0).isqrt_ceil(), x(0)); + assert_eq!(I512::pow2(256).sat(), (I512::pow2(255), true)); + assert_eq!(I512::pow2(255).sat(), (I512::pow2(255), false)); + assert_eq!(I512::from_dec("-115792089237316195423570985008687907853269984665640564039457584007913129639936").unwrap(), I512::pow2(256).neg()); + assert_eq!(I512::pow2(256).to_dec(), "115792089237316195423570985008687907853269984665640564039457584007913129639936"); + assert_eq!(x(-1000000000000000000000).to_dec(), "-1000000000000000000000"); + assert_eq!(x(0).to_dec(), "0"); + assert_eq!(x(i64::MIN as i128).to_dec(), (i64::MIN).to_string()); + assert!(I512::from_dec("12a").is_none()); + assert!(I512::from_dec("").is_none()); + assert!(I512::from_dec("-").is_none()); + } + + #[test] + fn bits_rule() { + assert_eq!(I512::bits(x(0), x(0)), 1); + assert_eq!(I512::bits(x(-1), x(0)), 1); + assert_eq!(I512::bits(x(-128), x(127)), 8); + assert_eq!(I512::bits(x(-129), x(127)), 9); + assert_eq!(I512::bits(x(0), x(128)), 9); + assert_eq!(I512::bits(x(i32::MIN as i128), x(i32::MAX as i128)), 32); + assert!(x(i64::MAX as i128).fits(64)); + assert!(!x(i64::MAX as i128 + 1).fits(64)); + } +} diff --git a/wai/wai-ops/tests/goldens.rs b/wai/wai-ops/tests/goldens.rs new file mode 100644 index 000000000..c342762ca --- /dev/null +++ b/wai/wai-ops/tests/goldens.rs @@ -0,0 +1,454 @@ +//! Every committed parameter set, lowered (§13) and decoded through the interpreter +//! and the stage operators, reproduces its committed output digest. +//! +//! The drivers here are test drivers, written from the payload extensions: they are +//! what a capability's driver does around its graphs (§13.5). They check the lowerings +//! and the operators against outputs that were fixed before either existed. +//! +//! Every graph a driver runs goes through `graph::load` of its bytes, as a sink's +//! does. That is why these tests need the `uncertified` switch: until the certifier +//! exists, load step 8 refuses every graph without it. +#![cfg(feature = "uncertified")] + +use std::path::{Path, PathBuf}; + +use wai_ops::graph; +use wai_ops::interp::{Sequence, Tensor, run}; +use wai_ops::json::{self, Value, hex}; +use wai_ops::lower; +use wai_ops::sha256::sha256; +use wai_ops::stage::{self, rans64}; + +fn wai() -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("..") +} +fn read(p: &str) -> Vec { + std::fs::read(wai().join(p)).unwrap_or_else(|e| panic!("{p}: {e}")) +} +fn jread(p: &str) -> Value { + json::parse(&read(p)).unwrap() +} +fn sha(b: &[u8]) -> String { + hex(&sha256(b)) +} +fn hwc(t: &Tensor) -> Vec { + let (c, h, w) = (t.c as usize, t.h as usize, t.w as usize); + let mut o = vec![0u8; c * h * w]; + for cc in 0..c { + for p in 0..h * w { + o[p * c + cc] = u8::try_from(t.data[cc * h * w + p]).unwrap(); + } + } + o +} +/// One decode of a lowered graph with one data input: loaded from its bytes, as a sink +/// loads it, then run. +fn one(g: &wai_ops::Graph, x: Tensor, sc: &[i64]) -> Tensor { + let g = graph::load(&g.to_bytes()).expect("a lowered graph loads"); + run(&g, &[x], sc, &mut Sequence::new()).unwrap().remove(0) +} +fn u32le(b: &[u8], o: usize) -> u32 { + u32::from_le_bytes(b[o..o + 4].try_into().unwrap()) +} +fn i64s(v: &Value) -> Vec { + v.as_arr().unwrap().iter().map(|x| x.as_i64().unwrap()).collect() +} +fn sidecar(p: &str, name: &str) -> String { + jread(p).get(name).unwrap().get("sha256").unwrap().as_str().unwrap().to_string() +} + +#[test] +fn int_synth_sets_reproduce_their_digests() { + for name in ["gradient", "bloom", "facade", "photo"] { + let set = lower::int_synth(&read(&format!("neural-conformance/vectors/{name}/int_synth.json"))).unwrap(); + let g = set.get("synth").unwrap(); + let p = read(&format!("neural-conformance/vectors/{name}/latent.bin")); + let (h, w, c, s) = (u32le(&p, 0), u32le(&p, 4), u16::from_le_bytes([p[8], p[9]]) as u32, 1u32 << p[10]); + let lat: Vec = p[12..].iter().map(|&b| b as i8 as i64).collect(); + let out = one(g, Tensor::new(c, s, s, lat), &[]); + assert_eq!((out.c, out.h, out.w), (3, h, w), "{name}: output shape"); + assert_eq!(sha(&hwc(&out)), sidecar("neural-conformance/sha256.json", name), "{name}"); + } +} + +fn world_clip(dir: &str, json_name: &str, t: &str, gs: &str, two: bool) -> Vec { + const ACTIONS: [(i64, i64); 5] = [(0, 0), (1, 0), (-1, 0), (0, 1), (0, -1)]; + let j = jread(&format!("{dir}/{json_name}")); + let c = j.get("c").unwrap().as_u32().unwrap(); + let lat = j.get("lat").unwrap().as_u32().unwrap(); + let afrac = j.get("afrac").unwrap().as_u32().unwrap(); + let set = lower::world(&read(&format!("{dir}/{t}")), &read(&format!("{dir}/{gs}")), c).unwrap(); + let (tg, gg) = (set.get("T").unwrap(), set.get("g_s").unwrap()); + let q = |d: i64| stage::dequantize(d, afrac, 0).unwrap(); + let rows: Vec> = if two { + j.get("pairs").unwrap().as_arr().unwrap().iter().map(|p| { + let a = i64s(p); + let ((xa, ya), (xb, yb)) = (ACTIONS[a[0] as usize], ACTIONS[a[1] as usize]); + vec![q(xa), q(ya), q(xb), q(yb)] + }).collect() + } else { + i64s(j.get("actions").unwrap()).iter().map(|&a| { + let (dx, dy) = ACTIONS[a as usize]; + vec![q(dx), q(dy), 0] + }).collect() + }; + let mut z = Tensor::new(c, lat, lat, i64s(j.get("z0").unwrap())); + let mut frames = vec![sha(&hwc(&one(gg, z.clone(), &[])))]; + for r in &rows { + z = one(tg, z, r); + frames.push(sha(&hwc(&one(gg, z.clone(), &[])))); + } + frames +} + +#[test] +fn world_sets_reproduce_every_frame() { + for (dir, js, t, gs, two, key) in [ + ("wai-rs/tests/vectors/int_world", "world.json", "world_T.bin", "world_gs.bin", false, "world"), + ("wai-rs/tests/vectors/int_world2", "world2.json", "world2_T.bin", "world2_gs.bin", true, "world2"), + ] { + let got = world_clip(dir, js, t, gs, two); + let want: Vec = jread(&format!("{dir}/sha256.json")).get(key).unwrap().get("frames_sha256").unwrap() + .as_arr().unwrap().iter().map(|v| v.as_str().unwrap().to_string()).collect(); + assert_eq!(got, want, "{dir}"); + } +} + +/// A `t4097`/`lenient`/`canonical` stream of `n` symbols, each against `tables[pick(i)]`. +fn decode_stream(stream: &[u8], tables: &[rans64::Table], reads: &[usize], modes: rans64::Modes) -> Vec { + rans64::decode(stream, tables, reads, modes).unwrap() +} + +const WIH1_MODES: rans64::Modes = rans64::Modes { table: rans64::TableRule::T4097, escape: rans64::Escape::Lenient, end: rans64::End::Canonical }; + +fn table(cdf: &[i64], len: usize, off: i64) -> rans64::Table { + rans64::Table { cdf: cdf[..len].to_vec(), offset: off } +} + +#[test] +fn int_hyper_tiny_reproduces_its_digest() { + let d = "wai-rs/tests/vectors/int_hyper"; + let set = lower::int_hyper(&read(&format!("{d}/tiny_h_s.bin")), &read(&format!("{d}/tiny_g_s.bin"))).unwrap(); + let afrac = lower::parse_whm1(&read(&format!("{d}/tiny_g_s.bin"))).unwrap().afrac; + let e = jread(&format!("{d}/tiny_entropy.json")); + let p = read(&format!("{d}/tiny_bitstream.wih1")); + assert_eq!(&p[0..4], b"WIH1"); + let f = |k: usize| u32le(&p, 8 + 4 * k); + let (m, sh, sw, nz, zh, zw) = (f(0), f(1), f(2), f(3), f(4), f(5)); + let lz = u32le(&p, 32) as usize; + let zs = &p[36..36 + lz]; + let ly = u32le(&p, 36 + lz) as usize; + let ys = &p[40 + lz..40 + lz + ly]; + let rows = |k: &str| -> Vec> { e.get(k).unwrap().as_arr().unwrap().iter().map(i64s).collect() }; + let (zc, zl, zo, zm) = (rows("z_cdfs"), i64s(e.get("z_cdf_lengths").unwrap()), i64s(e.get("z_offsets").unwrap()), i64s(e.get("z_median_q").unwrap())); + let ztabs: Vec<_> = (0..nz as usize).map(|k| table(&zc[k], zl[k] as usize, zo[k])).collect(); + let reads: Vec = (0..nz as usize).flat_map(|k| std::iter::repeat_n(k, (zh * zw) as usize)).collect(); + let zsym = decode_stream(zs, &ztabs, &reads, WIH1_MODES); + let z: Vec = zsym.iter().enumerate().map(|(i, &s)| stage::dequantize(s, afrac, zm[i / (zh * zw) as usize]).unwrap()).collect(); + let hyper = one(set.get("h_s").unwrap(), Tensor::new(nz, zh, zw, z), &[]); + assert_eq!((hyper.c, hyper.h, hyper.w), (2 * m, sh, sw)); + let n = (m * sh * sw) as usize; + let (sigma, mu) = hyper.data.split_at(n); + let (yc, yl, yo, edges) = (rows("cdfs"), i64s(e.get("cdf_lengths").unwrap()), i64s(e.get("offsets").unwrap()), i64s(e.get("scale_edges").unwrap())); + let ytabs: Vec<_> = (0..edges.len()).map(|b| table(&yc[b], yl[b] as usize, yo[b])).collect(); + let yreads: Vec = sigma.iter().map(|&s| stage::bucket_ceil(s, &edges).unwrap()).collect(); + let ysym = decode_stream(ys, &ytabs, &yreads, WIH1_MODES); + let y: Vec = ysym.iter().zip(mu).map(|(&r, &u)| stage::dequantize(r, afrac, u).unwrap()).collect(); + let rgb = one(set.get("g_s").unwrap(), Tensor::new(m, sh, sw, y), &[]); + assert_eq!(sha(&hwc(&rgb)), sidecar(&format!("{d}/sha256.json"), "tiny")); +} + +#[test] +fn splat_reproduces_its_digest() { + let d = "wai-rs/tests/vectors/int_splat"; + let set = lower::splat(&read(&format!("{d}/splat_gs.bin"))).unwrap(); + let p = read(&format!("{d}/splat.wis1")); + assert_eq!(&p[0..4], b"WIS1"); + let afrac = p[5] as u32; + let f = |k: usize| u32le(&p, 8 + 4 * k); + let (c, sh, sw, s, attr) = (f(0), f(1), f(2), f(3), f(4)); + let mut o = 32 + 8 * attr as usize; + let lens: Vec = (0..c as usize).map(|k| u16::from_le_bytes([p[o + 2 * k], p[o + 2 * k + 1]]) as usize).collect(); + o += 2 * c as usize; + let offs: Vec = (0..c as usize).map(|k| i32::from_le_bytes(p[o + 4 * k..o + 4 * k + 4].try_into().unwrap()) as i64).collect(); + o += 4 * c as usize; + let meds: Vec = (0..c as usize).map(|k| i64::from_le_bytes(p[o + 8 * k..o + 8 * k + 8].try_into().unwrap())).collect(); + o += 8 * c as usize; + let mut tabs = Vec::new(); + for k in 0..c as usize { + let cdf: Vec = (0..lens[k]).map(|j| u32le(&p, o + 4 * j) as i64).collect(); + o += 4 * lens[k]; + tabs.push(rans64::Table { cdf, offset: offs[k] }); + } + let l = u32le(&p, o) as usize; + let st = &p[o + 4..o + 4 + l]; + let per = (sh * sw) as usize; + let reads: Vec = (0..c as usize).flat_map(|k| std::iter::repeat_n(k, per)).collect(); + let sym = decode_stream(st, &tabs, &reads, WIH1_MODES); + let y: Vec = sym.iter().enumerate().map(|(i, &v)| stage::dequantize(v, afrac, meds[i / per]).unwrap()).collect(); + let out = one(set.get("g_s").unwrap(), Tensor::new(c, sh, sw, y), &[]); + assert_eq!((out.c, out.h, out.w), (attr, s, s)); + let bytes: Vec = out.data.iter().map(|&v| u8::try_from(v).unwrap()).collect(); + assert_eq!(sha(&bytes), sidecar(&format!("{d}/sha256.json"), "splat")); +} + +#[test] +fn audio_reproduces_its_digest() { + let d = "wai-rs/tests/vectors/int_audio"; + let g = lower::wam1(&read(&format!("{d}/audio_model.bin"))).unwrap(); + let p = read(&format!("{d}/audio_bitstream.wia1")); + assert_eq!(&p[0..4], b"WIA1"); + let afrac = p[5] as u32; + let (n_ch, lat_len, t_out) = (u32le(&p, 12), u32le(&p, 16), u32le(&p, 20)); + let offset = i32::from_le_bytes(p[24..28].try_into().unwrap()) as i64; + let cdf_len = u16::from_le_bytes([p[28], p[29]]) as usize; + let mut o = 32; + let meds: Vec = (0..n_ch as usize).map(|k| i64::from_le_bytes(p[o + 8 * k..o + 8 * k + 8].try_into().unwrap())).collect(); + o += 8 * n_ch as usize; + let mut tabs = Vec::new(); + for _ in 0..n_ch { + tabs.push(rans64::Table { cdf: (0..cdf_len).map(|j| u32le(&p, o + 4 * j) as i64).collect(), offset }); + o += 4 * cdf_len; + } + let l = u32le(&p, o) as usize; + let st = &p[o + 4..o + 4 + l]; + let per = lat_len as usize; + let reads: Vec = (0..n_ch as usize).flat_map(|k| std::iter::repeat_n(k, per)).collect(); + let sym = decode_stream(st, &tabs, &reads, WIH1_MODES); + let y: Vec = sym.iter().enumerate().map(|(i, &v)| stage::dequantize(v, afrac, meds[i / per]).unwrap()).collect(); + let out = one(&g, Tensor::new(n_ch, 1, lat_len, y), &[t_out as i64]); + assert_eq!((out.c, out.h, out.w), (1, 1, t_out)); + let pcm: Vec = out.data.iter().flat_map(|&v| i16::try_from(v).unwrap().to_le_bytes()).collect(); + assert_eq!(sha(&pcm), sidecar(&format!("{d}/sha256.json"), "audio")); +} + +#[test] +fn int_mlicpp_reproduces_its_digest() { + let d = "wai-rs/tests/vectors/int_mlicpp"; + let bundle = read(&format!("{d}/model.wmb")); + let set = lower::wmb1(&bundle).unwrap(); + let exp = jread(&format!("{d}/expected.json")); + // The tables section. + let mut o = 4; + let mut secs = Vec::new(); + for _ in 0..4 { + let n = u32le(&bundle, o) as usize; + secs.push(&bundle[o + 4..o + 4 + n]); + o += 4 + n; + } + let t = json::parse(secs[3]).unwrap(); + let afrac = t.get("afrac").unwrap().as_u32().unwrap(); + let rows = |k: &str| -> Vec> { t.get(k).unwrap().as_arr().unwrap().iter().map(i64s).collect() }; + // The payload: a WAI1 envelope holds its manifest, then the payload, each after + // its u32 length. + let env = read(&format!("{d}/stream.wai")); + let ml = u32le(&env, 4) as usize; + let pl = u32le(&env, 8 + ml) as usize; + let p = &env[12 + ml..12 + ml + pl]; + assert_eq!(sha(p), exp.get("payload_sha256").unwrap().as_str().unwrap()); + assert_eq!(&p[0..4], b"WIM1"); + let h16 = |k: usize| u16::from_le_bytes([p[k], p[k + 1]]) as u32; + let (cz, zh, zw, cy, yh, yw, kk) = (h16(12), h16(14), h16(16), h16(18), h16(20), h16(22), h16(24)); + let (lz, ly) = (u32le(p, 28) as usize, u32le(p, 32) as usize); + let (zs, ys) = (&p[36..36 + lz], &p[36 + lz..36 + lz + ly]); + let modes = rans64::Modes { table: rans64::TableRule::T2, escape: rans64::Escape::Lenient, end: rans64::End::FinalOnly }; + let (zc, zl, zo, zm) = (rows("z_cdfs"), i64s(t.get("z_cdf_lengths").unwrap()), i64s(t.get("z_offsets").unwrap()), i64s(t.get("z_median_q").unwrap())); + let ztabs: Vec<_> = (0..cz as usize).map(|k| table(&zc[k], zl[k] as usize, zo[k])).collect(); + let per = (zh * zw) as usize; + let reads: Vec = (0..cz as usize).flat_map(|k| std::iter::repeat_n(k, per)).collect(); + let zsym = rans64::decode(zs, &ztabs, &reads, modes).unwrap(); + let z: Vec = zsym.iter().enumerate().map(|(i, &s)| stage::dequantize(s, afrac, zm[i / per]).unwrap()).collect(); + let hyper = one(set.get("h_s").unwrap(), Tensor::new(cz, zh, zw, z), &[]); + // The y stream: every slice's two passes from one decoder, in order. + let (yc, yl, yo, edges) = (rows("cdfs"), i64s(t.get("cdf_lengths").unwrap()), i64s(t.get("offsets").unwrap()), i64s(t.get("scale_edges").unwrap())); + let ytabs: Vec<_> = (0..edges.len()).map(|b| table(&yc[b], yl[b] as usize, yo[b])).collect(); + let mut dec = rans64::Rans::new(ys).unwrap(); + let plane = (yh * yw) as usize; + let mut y = vec![0i64; cy as usize * plane]; + let per_slice = (cy / kk) as usize; + for j in 0..kk as usize { + for anchor in [true, false] { + let mut inp = y.clone(); + inp.extend_from_slice(&hyper.data); + let sm = one(set.get("entropy").unwrap(), Tensor::new(3 * cy, yh, yw, inp), &[]); + let n = cy as usize * plane; + for ch in j * per_slice..(j + 1) * per_slice { + for idx in stage::coding_order(1, yh, yw, Some(anchor)) { + let e = ch * plane + idx as usize; + let b = stage::bucket_ceil(sm.data[e], &edges).unwrap(); + let r = dec.symbol(&ytabs[b], rans64::Escape::Lenient).unwrap(); + y[e] = stage::dequantize(r, afrac, sm.data[n + e]).unwrap(); + } + } + } + } + assert!(!dec.ran_out()); + let rgb = one(set.get("g_s").unwrap(), Tensor::new(cy, yh, yw, y), &[]); + assert_eq!(sha(&hwc(&rgb)), exp.get("rgb_sha256").unwrap().as_str().unwrap()); +} + +/// The jpegai scale path on the real bundle, where one is supplied: lowered, and run on +/// every hyper-latent value in range, against the legacy decoder's arithmetic restated. +/// The bundle is not committed, so the test is ignored unless asked for: +/// `WAI_OPS_JPEGAI_BUNDLE` names a directory holding `norm.json`, `hs_y.json` and +/// `hs_uv.json`. +#[test] +#[ignore = "needs a jpegai bundle that is not committed: set WAI_OPS_JPEGAI_BUNDLE and run with --ignored"] +fn jpegai_scale_path_on_a_supplied_bundle() { + let dir = std::env::var("WAI_OPS_JPEGAI_BUNDLE").expect("WAI_OPS_JPEGAI_BUNDLE names the bundle"); + let rd = |n: &str| std::fs::read(Path::new(&dir).join(n)).unwrap(); + let set = lower::jpegai(&rd("norm.json"), &rd("hs_y.json"), &rd("hs_uv.json")).unwrap(); + for (role, file, cz) in [("hs_y", "hs_y.json", 160u32), ("hs_uv", "hs_uv.json", 96)] { + let g = set.get(role).unwrap(); + let net = json::parse(&rd(file)).unwrap(); + let norm = json::parse(&rd("norm.json")).unwrap(); + let gains = i64s(norm.get(if role == "hs_y" { "gain_y" } else { "gain_uv" }).unwrap()); + let z: Vec = (0..cz as i64 * 16).map(|i| (i * 7919 + 13) % 64 - 31).collect(); + let got = one(g, Tensor::new(cz, 4, 4, z.clone()), &[]); + let want = legacy_scale(&net, &z, cz as usize, &gains); + assert_eq!(got.data, want, "{role}"); + eprintln!("{role}: {} values equal", want.len()); + } +} + +/// The hyper-scale arithmetic as the legacy decoder writes it: clip, integer +/// convolution with its bias, an arithmetic right shift per channel, ReLU, depth to +/// space (CRD), abs, clip to 3967, then the gain. +fn legacy_scale(net: &Value, z: &[i64], cz: usize, gains: &[i64]) -> Vec { + let t = |k: &str| i64s(net.get(k).unwrap().get("v").unwrap()); + let block = |x: &[i64], cin: usize, name: &str, k: usize, pad: usize, sh: &str, relu: bool| -> (Vec, usize) { + let (w, b, s) = (t(&format!("{name}.weight")), t(&format!("{name}.bias")), t(sh)); + let cout = b.len(); + let mut y = vec![0i64; cout * 16]; + for o in 0..cout { + for oy in 0..4 { + for ox in 0..4 { + let mut acc = b[o]; + for i in 0..cin { + for ky in 0..k { + for kx in 0..k { + let (iy, ix) = (oy as i64 + ky as i64 - pad as i64, ox as i64 + kx as i64 - pad as i64); + if (0..4).contains(&iy) && (0..4).contains(&ix) { + acc += w[((o * cin + i) * k + ky) * k + kx] * x[i * 16 + (iy * 4 + ix) as usize].clamp(-128, 127); + } + } + } + } + let mut r = acc >> s[o]; + if relu && r < 0 { + r = 0; + } + y[o * 16 + oy * 4 + ox] = r; + } + } + } + (y, cout) + }; + let (x1, c1) = block(z, cz, "conv1", 1, 0, "onnx::Pow_85", true); + let (x2, c2) = block(&x1, c1, "depthwise", 3, 1, "onnx::Pow_91", true); + let (x3, _) = block(&x2, c2, "pointwise", 1, 0, "onnx::Pow_97", false); + let mut out = vec![0i64; cz * 256]; + for c in 0..cz { + for y in 0..16 { + for x in 0..16 { + let src = (c * 16 + (y % 4) * 4 + (x % 4)) * 16 + (y / 4) * 4 + (x / 4); + out[(c * 16 + y) * 16 + x] = x3[src].abs().clamp(0, 3967) + gains[c]; + } + } + } + out +} + +/// §13.6.1 end to end against the legacy decoder's intermediates on a real codestream: +/// the latent tables, then for each plane the hyper-latent (`fac`), the scale graph, +/// `coded`, `block_stat`, `band_lookup`, `scale_index` and the latent (`sgm`). Neither +/// the bundle nor the codestream is committed: `WAI_OPS_JPEGAI_BUNDLE` names the bundle +/// and `WAI_OPS_JPEGAI_DUMP` a JSON file of the legacy decoder's values for one +/// codestream (its segment payloads and every intermediate above). +#[test] +#[ignore = "needs a bundle and a legacy dump that are not committed: set WAI_OPS_JPEGAI_BUNDLE and WAI_OPS_JPEGAI_DUMP and run with --ignored"] +fn jpegai_entropy_path_matches_the_legacy_decoder() { + let dir = std::env::var("WAI_OPS_JPEGAI_BUNDLE").expect("WAI_OPS_JPEGAI_BUNDLE names the bundle"); + let dump = std::env::var("WAI_OPS_JPEGAI_DUMP").expect("WAI_OPS_JPEGAI_DUMP names the legacy dump"); + let rd = |n: &str| std::fs::read(Path::new(&dir).join(n)).unwrap(); + let norm = json::parse(&rd("norm.json")).unwrap(); + let d = json::parse(&std::fs::read(&dump).unwrap()).unwrap(); + let set = lower::jpegai(&rd("norm.json"), &rd("hs_y.json"), &rd("hs_uv.json")).unwrap(); + let pmf: Vec> = norm.get("pmf").unwrap().as_arr().unwrap().iter().map(i64s).collect(); + let tables = stage::mans::build_latent_tables(&pmf).unwrap(); + let want: Vec> = d.get("tables").unwrap().as_arr().unwrap().iter().map(i64s).collect(); + for (s, (t, w)) in tables.iter().zip(&want).enumerate() { + assert_eq!(t.iter().map(|&v| v as i64).collect::>(), *w, "table {s}"); + } + let bounds = i64s(norm.get("bounds").unwrap()); + let seg = |k: &str| -> Vec { i64s(d.get("segments").unwrap().get(k).unwrap()).iter().map(|&b| b as u8).collect() }; + let rows_of = |k: &str| -> Vec> { i64s(norm.get(k).unwrap()).chunks(63).map(|r| r.to_vec()).collect() }; + let (ry, ruv) = (rows_of("cdfs_y"), rows_of("cdfs_uv")); + for (plane, role, c, skip, gain_k, lat) in [("y", "hs_y", 160usize, 0usize, "gain_y", "SORP"), ("uv", "hs_uv", 96, 160, "gain_uv", "SORS")] { + let p = d.get("planes").unwrap().get(plane).unwrap(); + let gains = i64s(norm.get(gain_k).unwrap()); + let mut rows: Vec> = ry[..skip].to_vec(); + rows.extend_from_slice(if plane == "y" { &ry[..c] } else { &ruv[..c] }); + let lens = vec![16usize; skip + c]; + let syms = stage::mans::decode_fac(&seg("SOZ"), &rows, &lens).unwrap(); + let z: Vec = syms[skip..].iter().flatten().map(|&s| s - 31).collect(); + assert_eq!(z, i64s(p.get("z").unwrap()), "{plane}: z"); + let v = one(set.get(role).unwrap(), Tensor::new(c as u32, 4, 4, z), &[]); + let is: Vec = v.data.iter().enumerate().map(|(i, &x)| x - gains[i / 256]).collect(); + assert_eq!(is, i64s(p.get("i_s").unwrap()), "{plane}: I_s"); + let vt = Tensor::new(c as u32, 16, 16, v.data.clone()); + let likely = stage::block_stat(&vt, 8, 6, 0, 4351).unwrap(); + assert_eq!(likely.data, i64s(p.get("likely").unwrap()), "{plane}: likely"); + let grfs: Vec = p.get("grfs").unwrap().as_arr().unwrap().iter().map(|b| b.as_bool().unwrap()).collect(); + let table = vec![vec![-31, 20, 122, 181], vec![-80, -31, 71, 130]]; + let rvs: Vec = likely.data.iter().enumerate().map(|(i, &l)| stage::band_lookup(l, &[1003, 1352, 1937], &table, if grfs[i / 256] { 0 } else { 1 }).unwrap()).collect(); + assert_eq!(rvs, i64s(p.get("rvs").unwrap()), "{plane}: rvs"); + let sigma: Vec = v.data.iter().zip(&rvs).map(|(&x, &r)| stage::scale_index(x + r, 7, 32).unwrap()).collect(); + assert_eq!(sigma, i64s(p.get("sigma").unwrap()), "{plane}: sigma"); + let mask: Vec = v.data.iter().map(|&x| stage::coded(x, 382)).collect(); + let want_mask: Vec = p.get("mask").unwrap().as_arr().unwrap().iter().map(|b| b.as_bool().unwrap()).collect(); + assert_eq!(mask, want_mask, "{plane}: mask"); + let spans: Vec = (0..c).map(|ch| stage::mans::Span { + mask: mask[ch * 256..(ch + 1) * 256].to_vec(), + sigma: sigma[ch * 256..(ch + 1) * 256].iter().map(|&s| s as usize).collect(), + }).collect(); + let yq: Vec = stage::mans::decode_sgm(&seg(lat), &pmf, &bounds[..32], &spans).unwrap().concat(); + assert_eq!(yq, i64s(p.get("y_q").unwrap()), "{plane}: y_q"); + eprintln!("{plane}: z, I_s, likely, rvs, sigma, mask and y_q equal the legacy decoder's"); + } +} + +/// E8 against streams the reference encoder wrote, and the decode tables it built: +/// `WAI_OPS_MANS_GOLDENS` names the fixture (`jpegai_mans_goldens.json`, not +/// committed). +#[test] +#[ignore = "needs a fixture that is not committed: set WAI_OPS_MANS_GOLDENS and run with --ignored"] +fn mans_matches_reference_encoder_streams() { + let f = std::env::var("WAI_OPS_MANS_GOLDENS").expect("WAI_OPS_MANS_GOLDENS names the fixture"); + let v = json::parse(&std::fs::read(f).unwrap()).unwrap(); + let pmf: Vec> = v.get("pmf").unwrap().as_arr().unwrap().iter().map(i64s).collect(); + let built = stage::mans::build_latent_tables(&pmf).unwrap(); + let want: Vec> = v.get("decode_transitions").unwrap().as_arr().unwrap().iter().map(i64s).collect(); + for (t, w) in built.iter().zip(&want) { + assert_eq!(t.iter().map(|&x| x as i64).collect::>(), *w); + } + let bounds = i64s(v.get("bounds").unwrap()); + let bytes = |x: &Value| -> Vec { i64s(x).iter().map(|&b| b as u8).collect() }; + let y = v.get("y").unwrap(); + let mask: Vec = y.get("masks").unwrap().as_arr().unwrap().iter().map(|b| b.as_bool().unwrap()).collect(); + let sigma: Vec = i64s(y.get("sigma").unwrap()).iter().map(|&s| s as usize).collect(); + let got = stage::mans::decode_sgm(&bytes(y.get("memory").unwrap()), &pmf, &bounds, &[stage::mans::Span { mask: mask.clone(), sigma }]).unwrap(); + let exp = i64s(y.get("expected").unwrap()); + for i in 0..exp.len() { + if mask[i] { + assert_eq!(got[0][i], exp[i] as i16 as i64, "y symbol {i}"); + } + } + let z = v.get("z").unwrap(); + let rows: Vec> = z.get("cdfs").unwrap().as_arr().unwrap().iter().map(i64s).collect(); + let exp: Vec> = z.get("expected").unwrap().as_arr().unwrap().iter().map(i64s).collect(); + let lens: Vec = exp.iter().map(Vec::len).collect(); + let got = stage::mans::decode_fac(&bytes(z.get("memory").unwrap()), &rows, &lens).unwrap(); + assert_eq!(got, exp); + eprintln!("mans: tables, the sgm stream and the fac stream equal the reference encoder's"); +} diff --git a/wai/wai-ops/tests/step8.rs b/wai/wai-ops/tests/step8.rs new file mode 100644 index 000000000..378c818e1 --- /dev/null +++ b/wai/wai-ops/tests/step8.rs @@ -0,0 +1,33 @@ +//! Without the `uncertified` switch, which only the tests, the conformance tool and +//! its browser-engine build enable, load step 8 refuses every graph (§10.5): until the +//! certifier exists, the default build decodes nothing. The interpreter takes only a +//! `Loaded` graph, which only `graph::load` makes, so a lowered set's graphs cannot be +//! run around that refusal. (That `run` rejects a `Graph` at compile time is the +//! `compile_fail` example on `Loaded`.) + +use std::path::PathBuf; + +use wai_ops::code::{Code, Refusal, Site}; +use wai_ops::{graph, lower}; + +fn read(p: &str) -> Vec { + let path = PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("..").join(p); + std::fs::read(&path).unwrap_or_else(|e| panic!("{p}: {e}")) +} + +/// Every lowered graph of the committed int_synth sets loads through steps 1–7, and +/// then step 8 refuses it in the default build and passes it with the switch. +#[test] +fn a_lowered_set_decodes_only_through_load_step_8() { + for name in ["gradient", "bloom", "facade", "photo"] { + let set = lower::int_synth(&read(&format!("neural-conformance/vectors/{name}/int_synth.json"))).unwrap(); + for (role, g) in &set.roles { + let r = graph::load(&g.to_bytes()); + if cfg!(feature = "uncertified") { + assert!(r.is_ok(), "{name}/{role}"); + } else { + assert_eq!(r.unwrap_err(), Refusal::new(Code::ModelMismatch, Site::Load(8)), "{name}/{role}"); + } + } + } +} diff --git a/wai/wai-rs/tests/int_kernels_vectors.rs b/wai/wai-rs/tests/int_kernels_vectors.rs index a7bf108ab..a9963bda9 100644 --- a/wai/wai-rs/tests/int_kernels_vectors.rs +++ b/wai/wai-rs/tests/int_kernels_vectors.rs @@ -221,6 +221,7 @@ fn every_decoded_output_states_a_sha256() { ("oer-conformance", "OER/2 receipts: receipt content, no decoded output"), ("pack-conformance", "encode/expected.json states the SHA-256 of every decoded frame (RGB8); the rest are policies, refusals, conversions and scene cuts, with no decoded output"), ("pin-conformance", "parameter-set pins: every digest there is a SHA-256 of a parameter set"), + ("ops-conformance", "integer operators: every operator, stage and graph vector, the lower_* decodes of the sets under sets/ among them, states its output integers in full or its refusal code, never a digest of them; certificates/ states each lowered set's SHA-256 (a parameter-set identity) and holds the lowered graphs as bytes, compared byte for byte; the output digests a capability decoded through its lowering reproduces (integer-operators §14.4) are stated by that capability's own corpus"), ("signer-conformance", "key logs, grants and statuses: signed documents, no decoded output"), ("http-conformance", "objects and head proofs for transport: no case decodes a payload, and the component a fetch case picks is stated by its SHA-256"), ("cache-conformance", "cache rules: a driver, its controls and their reports, with no objects of its own"),