diff --git a/AGENTS.md b/AGENTS.md index e183c59..af738eb 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -29,6 +29,15 @@ uv sync --locked --all-groups - Treat the public version string `2.10.0` as the repository contract. Local build suffixes such as `+cu128` or `+cpu` should not appear in generated provenance. +## JAX Generator Dependencies + +- The JAX generator surface is a first-class part of the repository contract. +- Keep `jax` and `jaxlib` pinned to exact matching versions in `pyproject.toml`. +- The current exact JAX pins are `jax==0.9.1` and `jaxlib==0.9.1`. +- Run every JAX command through `uv run`, including generator smoke checks and any + validation commands added for JAX witnesses. +- When updating JAX-related dependencies, refresh `uv.lock` before syncing the environment. + ## Operational Notes - Do not run `uv lock` and `uv sync --locked --all-groups` in parallel. Update the lockfile first, then sync. @@ -38,6 +47,7 @@ uv sync --locked --all-groups - `uv run python scripts/verify_cases.py` - `uv run python scripts/check_replay.py` - `uv run python scripts/check_regeneration.py` +- Add any JAX-specific validation scripts under the same `uv run` discipline. - `scripts/check_regeneration.py` compares regenerated JSONL files semantically. Metadata must match exactly, while numeric tensors may differ only within the case-level `comparison.rtol` / `comparison.atol`. diff --git a/README.md b/README.md index baae807..ce6c10d 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,37 @@ validation: - mathematical AD notes - a machine-readable JSON oracle database +The repository is intentionally dual-backend: + +- PyTorch remains the baseline oracle source for the existing case DB +- JAX support is being added in later tasks as a parallel generator and witness + surface + +## Planned JAX Surface + +The following JAX-facing entrypoint and witness fields are planned for later +tasks and are not implemented yet: + +- `uv run python -m generators.jax_v1 --list` +- `jax_ref` +- `linearization` +- `transpose` + +These names document the intended future contract so the JAX backend can be +added without changing the vocabulary later. + +## JAX Witness Source Policy + +JAX witness materialization uses two source modes: + +- Prefer `jax_test` for families with a dedicated JAX internal harness. +- Fall back to `torch_aligned` when a published PyTorch case should be reused + as the exact serialized primal-input source. + +When a witness comes from a JAX internal harness, the top-level provenance +records the harness `source_file`, `source_function`, `seed`, and a +`comment` entry containing `harness_fullname=...`. + The oracle database covers both scalar-style `OpInfo` families and linear algebra operations. @@ -80,6 +111,11 @@ uv run python scripts/report_upstream_publish_coverage.py uv run python scripts/report_complex_support.py ``` +`uv run python scripts/validate_schema.py` is a repository-integrity and +publish-time check for maintainers and CI. Downstream consumers should treat +`schema/case.schema.json` as the contract and normally do not need to invoke +the repository script directly. + Repository-managed environment files: - `.python-version` @@ -90,6 +126,10 @@ The repository requires an exact PyTorch dependency pin: `torch==2.10.0`. Generated provenance stores the public version string `2.10.0`, not local build suffixes such as `+cpu` or `+cu128`. +The planned JAX backend will use exact version pins as well; those pins are +tracked in the repository contract now so the later generator work can rely on a +fixed runtime. + ## Math Notes The mathematical AD notes live under `docs/math/`. @@ -142,6 +182,12 @@ A case is defined by: - an `observable` - one or more paired derivative probes +Published JSONL files store materialized numeric tensor payloads directly. For +`success` cases this includes serialized inputs, probe directions, cotangents, +and numeric reference tensors such as `pytorch_ref`, `fd_ref`, and any present +`jax_ref` witness payloads. Downstream readers do not need PyTorch or JAX to +reconstruct those published numbers. + The database does not require raw decomposition outputs to be the comparison target. For spectral operations, the observable may be a processed output such as `U.abs()`, `S`, `Vh.abs()`, or `U @ Vh`, following the same derivative-relevant observables used by PyTorch AD tests. ## Oracle Policy diff --git a/cases/abs/identity.jsonl b/cases/abs/identity.jsonl index b19a8c8..a53bcd7 100644 --- a/cases/abs/identity.jsonl +++ b/cases/abs/identity.jsonl @@ -1,4 +1,4 @@ -{"case_id": "abs_f64_identity_001", "comparison": {"first_order": {"atol": 1e-08, "kind": "allclose", "rtol": 1e-05}, "second_order": {"atol": 1e-15, "kind": "allclose", "rtol": 1e-15}}, "dtype": "float64", "expected_behavior": "success", "family": "identity", "inputs": {"a": {"data": [-4.826984902407649, -7.582430495695129, -0.6046891126565539, -1.5411692216498662, -1.9719097652168658, -4.146041530864057, -5.4215280659972755, -4.784169829877467, -4.150805063878843, 7.380839390984031, 5.576059452216908, -8.315684908389088, 4.177597026003685, 2.047386382824099, -4.076012721760325, -3.063683029231029, 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[[0.000207506149308756, 1.3058055628789589e-05]], "dtype": "complex64", "order": "row_major", "shape": []}}}}], "provenance": {"fd_policy_version": "v1", "generator": "python-jax-v1", "seed": 28, "source_commit": "449b1768410104d3ed79d3bcfe4ba1d65c7f22c0", "source_file": "torch/testing/_internal/common_methods_invocations.py", "source_function": "sample_inputs_elementwise_unary", "source_repo": "pytorch", "torch_version": "2.10.0"}, "schema_version": 1} diff --git a/docs/math/cholesky.md b/docs/math/cholesky.md index cd0e300..6ed39a7 100644 --- a/docs/math/cholesky.md +++ b/docs/math/cholesky.md @@ -1,5 +1,72 @@ # Cholesky AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space Cholesky map before any DB observable projection. For complex +tensors, `Transpose` means the adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is the lower-triangular factor + +$$ +A \mapsto L, +\qquad +A = L L^{\mathsf{H}}, +\qquad +A = A^{\mathsf{H}} \succ 0. +$$ + +## Linearization + +With the helper + +$$ +\varphi(X) = \mathrm{tril}(X) - \tfrac{1}{2}\mathrm{diag}(X), +$$ + +the raw-output-space linearization is + +$$ +\dot{L} = L \, \varphi\!\bigl(L^{-1}\dot{A}\,L^{-\mathsf{H}}\bigr). +$$ + +## JVP + +The JVP is exactly the same tangent formula: + +$$ +\operatorname{jvp}(\operatorname{chol})(A;\dot{A}) += L \, \varphi\!\bigl(L^{-1}\dot{A}\,L^{-\mathsf{H}}\bigr). +$$ + +## Transpose + +For a raw output cotangent $\bar{L}$, the transpose map is + +$$ +\bar{A} = +L^{-\mathsf{H}} \, +\varphi^*\!\bigl(\mathrm{tril}(L^{\mathsf{H}}\bar{L})\bigr) +\, L^{-1}. +$$ + +## VJP (JAX convention) + +JAX reads the same raw transpose map directly as the cotangent rule on the +Cholesky factor. + +## VJP (PyTorch convention) + +PyTorch uses the same triangular-solve sandwich. For `cholesky_ex`, auxiliary +status outputs are treated as metadata, so the VJP applies only to the factor +output. + ## Forward Definition $$ diff --git a/docs/math/det.md b/docs/math/det.md index 2c88b0a..3582cfb 100644 --- a/docs/math/det.md +++ b/docs/math/det.md @@ -1,5 +1,70 @@ # Determinant AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space determinant maps before any DB observable projection. For +complex tensors, `Transpose` means the adjoint under the real Frobenius inner +product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +This note covers two raw operators: + +$$ +A \mapsto \det(A), +\qquad +A \mapsto (\operatorname{sign}, \operatorname{logabsdet}). +$$ + +## Linearization + +For `det`, + +$$ +\dot{d} = \det(A)\operatorname{tr}(A^{-1}\dot{A}). +$$ + +For `slogdet`, if $w = \operatorname{tr}(A^{-1}\dot{A})$, then + +$$ +\dot{\operatorname{logabsdet}} = \operatorname{Re}(w), +\qquad +\dot{\operatorname{sign}} = i\,\operatorname{Im}(w)\operatorname{sign}. +$$ + +## JVP + +The JVP is the same linearization evaluated at the tangent matrix $\dot{A}$. + +## Transpose + +For `det`, a raw output cotangent $\bar{d}$ gives + +$$ +\bar{A} = \overline{\bar{d}\det(A)}\,A^{-\mathsf{H}}, +$$ + +with the real case reducing to $A^{-\mathsf{T}}$. + +For `slogdet`, a raw output cotangent on the pair +$(\bar{\operatorname{sign}}, \bar{\operatorname{logabsdet}})$ yields the same +solve-style adjoint summarized later in the note. + +## VJP (JAX convention) + +JAX reads these transpose maps directly on the scalar or tuple output, with the +same singularity caveats as the raw determinant formulas. + +## VJP (PyTorch convention) + +PyTorch uses the same raw adjoint structure, together with the real-input +projection and the singular-matrix fallback discussed below. + ## 1. Determinant ### Forward Definition diff --git a/docs/math/eig.md b/docs/math/eig.md index 06e1f46..c7a8293 100644 --- a/docs/math/eig.md +++ b/docs/math/eig.md @@ -1,5 +1,96 @@ # General Eigen AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space eigendecomposition before any DB observable such as +`values_vectors_abs` is applied. For complex tensors, `Transpose` means the +adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is + +$$ +A \mapsto (\lambda, V), +\qquad +A V = V \operatorname{diag}(\lambda), +$$ + +with simple eigenvalues. + +## Linearization + +Let + +$$ +\Delta P = V^{-1}\dot{A}\,V. +$$ + +Then + +$$ +\dot{\lambda}_i = (\Delta P)_{ii}, +\qquad +Q_{ij} = \frac{(\Delta P)_{ij}}{\lambda_j - \lambda_i} \ \ (i \neq j), +\qquad +Q_{ii} = 0, +$$ + +and the normalized eigenvector tangent is + +$$ +\dot{V} = VQ - V\,\operatorname{diag}\!\left(\operatorname{Re}(V^\dagger VQ)\right). +$$ + +## JVP + +The JVP is exactly the linearization evaluated at $\dot{A}$, returning the raw +pair $(\dot{\lambda}, \dot{V})$ before any observable removes gauge freedom. + +## Transpose + +For raw output cotangents $(\bar{\lambda}, \bar{V})$, define + +$$ +\bar{V}_{\mathrm{adj}} = +\bar{V} +- V \, \operatorname{diag}\!\left(\operatorname{Re}(V^\dagger \bar{V})\right), +$$ + +then build + +$$ +G = V^\dagger \bar{V}_{\mathrm{adj}}, +\qquad +G_{ij} \leftarrow \frac{G_{ij}}{\overline{\lambda_j - \lambda_i}} +\ \ (i \neq j), +\qquad +G_{ii} = \bar{\lambda}_i, +$$ + +and finally + +$$ +\bar{A} = V^{-\dagger} G V^\dagger. +$$ + +## VJP (JAX convention) + +JAX reads the same raw transpose map for the eigendecomposition outputs. If a +downstream observable removes phase or normalization ambiguity, that observable +is applied after the raw rule. + +## VJP (PyTorch convention) + +PyTorch uses the same raw adjoint together with the explicit normalization and +gauge checks. For real inputs with complex outputs, the final cotangent is +projected back to the real domain. + ## Forward Definition $$ diff --git a/docs/math/eigen.md b/docs/math/eigen.md index 00b8a9b..32105b1 100644 --- a/docs/math/eigen.md +++ b/docs/math/eigen.md @@ -1,5 +1,77 @@ # Hermitian Eigen AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space Hermitian eigendecomposition before any DB observable such as +`values_vectors_abs` is applied. For complex tensors, `Transpose` means the +adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is + +$$ +A \mapsto (E, U), +\qquad +A = U \operatorname{diag}(E) U^\dagger, +\qquad +A = A^\dagger. +$$ + +## Linearization + +With the stabilized inverse-gap matrix $F$ defined later in the note, + +$$ +dE = \operatorname{diag}(U^\dagger dA U), +$$ + +$$ +dU = U \left(F \odot (U^\dagger dA U - \operatorname{diag}(dE))\right), +$$ + +with the skew-Hermitian gauge projected away. + +## JVP + +The JVP is the same linearization evaluated on the Hermitian tangent $dA$, +returning $(dE, dU)$. + +## Transpose + +For raw output cotangents $(\bar{E}, \bar{U})$, the transpose map uses the +inner matrix + +$$ +D = +\frac{1}{2} +\left( +F \odot (\bar{U}^\dagger U) ++ (F \odot (\bar{U}^\dagger U))^\dagger +\right) ++ \operatorname{diag}(\bar{E}), +$$ + +and returns + +$$ +\bar{A} = U D U^\dagger. +$$ + +## VJP (JAX convention) + +JAX reads the same raw transpose on the Hermitian eigendecomposition outputs. + +## VJP (PyTorch convention) + +PyTorch routes the Hermitian case through the same raw rule as a structured +specialization of the general eigendecomposition kernels. + ## Forward Definition $$ diff --git a/docs/math/index.md b/docs/math/index.md index 523e1da..c938d38 100644 --- a/docs/math/index.md +++ b/docs/math/index.md @@ -21,6 +21,36 @@ stable note anchor. For the human-facing explanation of that linkage, see [math-registry.md](../math-registry.md). +## Unified Note Model + +Each published operator note now starts with the same six sections: + +- `## Forward` +- `## Linearization` +- `## JVP` +- `## Transpose` +- `## VJP (JAX convention)` +- `## VJP (PyTorch convention)` + +The intent is: + +- `Forward` states the raw mathematical operator. +- `Linearization` and `Transpose` describe the raw-output-space differential and + adjoint before any DB observable is applied. +- `JVP` records the tangent-evaluation view of the same raw rule. +- `VJP (JAX convention)` and `VJP (PyTorch convention)` explain how the same + raw cotangent map is surfaced by each framework. + +Unless a note says otherwise, `Transpose` means the adjoint under the real +Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +Later sections keep the full migrated derivations, case splits, and family +anchors intact. + ## Core Linalg Notes - [svd.md](./svd.md) diff --git a/docs/math/inv.md b/docs/math/inv.md index f42ca5d..f2bf68c 100644 --- a/docs/math/inv.md +++ b/docs/math/inv.md @@ -1,5 +1,57 @@ # Inverse AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space inverse map. For complex tensors, `Transpose` means the adjoint +under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is + +$$ +A \mapsto B = A^{-1}. +$$ + +## Linearization + +Differentiating $A B = I$ gives + +$$ +\dot{B} = -B\,\dot{A}\,B. +$$ + +## JVP + +The JVP is the same linearization evaluated at $\dot{A}$: + +$$ +\operatorname{jvp}(\operatorname{inv})(A;\dot{A}) = -B\,\dot{A}\,B. +$$ + +## Transpose + +For a raw output cotangent $\bar{B}$, the transpose map is + +$$ +\bar{A} = -B^{\mathsf{H}}\,\bar{B}\,B^{\mathsf{H}}. +$$ + +## VJP (JAX convention) + +JAX exposes the same transpose rule directly on the inverse output cotangent. + +## VJP (PyTorch convention) + +PyTorch uses the same raw adjoint, typically via the corresponding `solve` +formula. Real inputs remain in the real domain after the final cotangent +projection. + ## Forward Definition $$ diff --git a/docs/math/lstsq.md b/docs/math/lstsq.md index 5f96e5a..eb1237d 100644 --- a/docs/math/lstsq.md +++ b/docs/math/lstsq.md @@ -1,5 +1,67 @@ # Least Squares AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space least-squares solution map. For complex tensors, `Transpose` +means the adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is + +$$ +(A, b) \mapsto x = \arg\min_x \|Ax - b\|_2^2, +\qquad +x = R^{-1}Q^\dagger b +$$ + +for the thin QR factorization $A = Q R$. + +## Linearization + +With residual $r = b - A x$, the raw-output-space linearization is + +$$ +dx = (A^\dagger A)^{-1}(A^\dagger db + dA^\dagger r - A^\dagger dA\,x). +$$ + +## JVP + +The JVP is that same linearization evaluated at $(dA, db)$. + +## Transpose + +For a raw output cotangent $\bar{x}$, define + +$$ +y = R^{-\dagger}\bar{x}, +\qquad +z = R^{-1}y. +$$ + +Then + +$$ +\bar{b} = Q y, +\qquad +\bar{A} = r z^\dagger - \bar{b} x^\dagger. +$$ + +## VJP (JAX convention) + +JAX reads the same raw transpose map on the least-squares solution output. + +## VJP (PyTorch convention) + +PyTorch currently routes the solution term through `pinv`-style helpers and +adds the residual correction separately, but the resulting raw cotangent map is +the same. + ## Forward Definition For the least-squares problem diff --git a/docs/math/lu.md b/docs/math/lu.md index f23e14e..8b6f2d4 100644 --- a/docs/math/lu.md +++ b/docs/math/lu.md @@ -1,5 +1,76 @@ # LU AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space LU factorization before any DB observable projection. For +complex tensors, `Transpose` means the adjoint under the real Frobenius inner +product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is + +$$ +A \mapsto (P, L, U), +\qquad +P A = L U, +$$ + +where $P$ is discrete metadata and only $(L, U)$ are differentiated. + +## Linearization + +In the square case, define + +$$ +\dot{F} = L^{-1} P \dot{A} U^{-1}. +$$ + +Then + +$$ +\dot{L} = L \, \mathrm{tril}_-(\dot{F}), +\qquad +\dot{U} = \mathrm{triu}(\dot{F}) \, U. +$$ + +Wide and tall cases use the same lower/upper triangular split on the leading +square block, with the extra block corrections recorded later in this note. + +## JVP + +The JVP is the same block-triangular linearization, returned on the raw factor +outputs $(L, U)$ while keeping pivots as metadata. + +## Transpose + +In the square case, raw output cotangents $(\bar{L}, \bar{U})$ give + +$$ +\bar{F} = \mathrm{tril}_-(L^\dagger \bar{L}) + \mathrm{triu}(\bar{U} U^\dagger), +$$ + +$$ +\bar{A} = P^T L^{-\dagger} \bar{F} U^{-\dagger}. +$$ + +Wide and tall cases use the same leading-block triangular adjoints, with the +explicit block formulas retained below. + +## VJP (JAX convention) + +JAX reads the same raw cotangent map on the differentiable factor outputs. + +## VJP (PyTorch convention) + +PyTorch uses the same block-triangular adjoint in `linalg_lu_backward`; pivots +and status metadata stay outside the differentiable surface. + ## Forward Definition $$ diff --git a/docs/math/matrix_exp.md b/docs/math/matrix_exp.md index 302947c..78bd615 100644 --- a/docs/math/matrix_exp.md +++ b/docs/math/matrix_exp.md @@ -1,5 +1,59 @@ # Matrix Exponential AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space matrix exponential map. For complex tensors, `Transpose` means +the adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is + +$$ +A \mapsto B = \exp(A). +$$ + +## Linearization + +The raw-output-space linearization is the Fr\'echet derivative + +$$ +\dot{B} = L(A, \dot{A}) += \int_0^1 \exp(sA)\,\dot{A}\,\exp((1-s)A)\,ds. +$$ + +## JVP + +The JVP is exactly the same Fr\'echet derivative applied to the tangent: + +$$ +\operatorname{jvp}(\operatorname{matrix\_exp})(A;\dot{A}) = L(A,\dot{A}). +$$ + +## Transpose + +For a raw output cotangent $\bar{B}$, the transpose map is + +$$ +\bar{A} = L(A^{\mathsf{H}}, \bar{B}). +$$ + +## VJP (JAX convention) + +JAX reads the same Fr\'echet-adjoint map as the VJP or `linear_transpose` +result. + +## VJP (PyTorch convention) + +PyTorch uses the same raw rule through +`differential_analytic_matrix_function`; the public VJP is the same adjoint map +packaged through the framework's cotangent convention. + ## Forward Definition $$ diff --git a/docs/math/norm.md b/docs/math/norm.md index 7ea4f19..c66c069 100644 --- a/docs/math/norm.md +++ b/docs/math/norm.md @@ -1,5 +1,92 @@ # Norm AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space norm map before any DB observable projection. For complex +tensors, `Transpose` means the adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +This note covers the raw scalar outputs of vector $p$-norms, Frobenius norm, +nuclear norm, and spectral norm. + +## Linearization + +Representative raw-output-space linearizations are: + +- vector $p$-norm: + +$$ +\dot{n} = +\frac{\sum_i |x_i|^{p-2}\operatorname{Re}(\bar{x}_i \dot{x}_i)} +{\|x\|_p^{p-1}} +$$ + +- Frobenius norm: + +$$ +\dot{n} = \frac{\operatorname{Re}\operatorname{tr}(A^\dagger \dot{A})}{\|A\|_F} +$$ + +- nuclear norm: + +$$ +\dot{n} = \operatorname{Re}\operatorname{tr}(U^\dagger \dot{A} V) +$$ + +- spectral norm: + +$$ +\dot{n} = \operatorname{Re}(u_1^\dagger \dot{A} v_1) +$$ + +## JVP + +The JVP is the same scalar linearization evaluated at the chosen tangent. + +## Transpose + +Representative transpose rules are: + +- vector $p$-norm: + +$$ +\bar{x}_i = \bar{n} \cdot \frac{x_i |x_i|^{p-2}}{\|x\|_p^{p-1}} +$$ + +- Frobenius norm: + +$$ +\bar{A} = \bar{n} \cdot \frac{A}{\|A\|_F} +$$ + +- nuclear norm: + +$$ +\bar{A} = \bar{n} \cdot U V^\dagger +$$ + +- spectral norm: + +$$ +\bar{A} = \bar{n} \cdot u_1 v_1^\dagger +$$ + +## VJP (JAX convention) + +JAX reads the same scalar-output transpose maps directly. Nonsmooth points keep +their subgradient caveats. + +## VJP (PyTorch convention) + +PyTorch uses the same raw adjoints, together with the same masking and +subgradient conventions at zeros, ties, and repeated singular values. + ## 1. Vector $p$-norm $$ diff --git a/docs/math/pinv.md b/docs/math/pinv.md index 7cb61bb..d068035 100644 --- a/docs/math/pinv.md +++ b/docs/math/pinv.md @@ -1,5 +1,62 @@ # Pseudoinverse AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space pseudoinverse map. For complex tensors, `Transpose` means the +adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is + +$$ +A \mapsto A^+ = \operatorname{pinv}(A), +$$ + +with locally constant rank. + +## Linearization + +The raw-output-space linearization is + +$$ +\dot{A}^+ = +-A^+ \dot{A} A^+ ++ (I - A^+ A)\dot{A}^\dagger (A^+)^\dagger A^+ ++ A^+ (A^+)^\dagger \dot{A}^\dagger (I - A A^+). +$$ + +## JVP + +The JVP is the same three-term pseudoinverse differential evaluated at +$\dot{A}$. + +## Transpose + +For a raw output cotangent $\bar{A}^+$, the transpose map is + +$$ +\bar{A} = +-(A^+)^\dagger \bar{A}^+ (A^+)^\dagger ++ (I - A A^+) (\bar{A}^+)^\dagger A^+ (A^+)^\dagger ++ (A^+)^\dagger A^+ (\bar{A}^+)^\dagger (I - A^+ A). +$$ + +## VJP (JAX convention) + +JAX reads the same projector-corrected transpose map directly on the +pseudoinverse output cotangent. + +## VJP (PyTorch convention) + +PyTorch implements algebraically equivalent branch-wise formulas that reduce +intermediate sizes, but the raw cotangent map is the same three-term adjoint. + ## Forward Definition $$ diff --git a/docs/math/qr.md b/docs/math/qr.md index 233efa8..3e7512d 100644 --- a/docs/math/qr.md +++ b/docs/math/qr.md @@ -4,6 +4,77 @@ This note covers the reduced QR rule that is materialized in the DB and keeps the transpose-dual LQ formulas from `tenferro-rs/docs/AD/qr.md` so that no derivation detail is lost in the migration. +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space QR factorization before any DB observable projection. For +complex tensors, `Transpose` means the adjoint under the real Frobenius inner +product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is + +$$ +A \mapsto (Q, R), +\qquad +A = Q R, +\qquad +Q^\dagger Q = I. +$$ + +## Linearization + +For $M \geq N$, + +$$ +dR = \operatorname{syminv}\!\left(\operatorname{sym}(Q^\dagger dA R^{-1})\right) R, +$$ + +$$ +dQ = (dA)R^{-1} - Q(dR R^{-1}). +$$ + +For $M < N$, the leading square block $R_1$ controls the constrained part and +the exact `trilIm` / `trilImInv` formulas are recorded below. + +## JVP + +The JVP is the same case-split linearization returned on the raw factors +$(dQ, dR)$. + +## Transpose + +For $M \geq N$, + +$$ +\bar{A} = +\left[\bar{Q} + Q \cdot \operatorname{copyltu}(R \bar{R}^\dagger - \bar{Q}^\dagger Q)\right] R^{-\dagger}. +$$ + +For $M < N$, + +$$ +\bar{A} = Q \bar{R} + \pi^\*\!\left( +Q \, \operatorname{trilImInvAdjSkew}(Q^\dagger \bar{Q} - \bar{R} R^\dagger) +R_1^{-\dagger}\right). +$$ + +## VJP (JAX convention) + +JAX reads the same raw transpose on the QR outputs before any downstream +observable repackages the factors. + +## VJP (PyTorch convention) + +PyTorch uses the same full-rank versus wide-reduced case split in +`linalg_qr_backward`. The transpose-dual LQ formulas are preserved later in +this note. + ## QR Forward Definition For diff --git a/docs/math/scalar_ops.md b/docs/math/scalar_ops.md index b5c116d..84756a1 100644 --- a/docs/math/scalar_ops.md +++ b/docs/math/scalar_ops.md @@ -1,5 +1,130 @@ # Scalar And Tensor Wrapper AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space operator before any DB observable projection. For complex +tensors, `Transpose` means the adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +This note groups raw scalar and tensor-wrapper operators of three common forms: + +$$ +y = f(x), +\qquad +y = h(x_1, x_2), +\qquad +y = r(x). +$$ + +Here `f` is a unary wrapper, `h` is a binary wrapper, and `r` is a reduction or +small tensor composite. + +## Linearization + +Representative raw-output-space linearizations are: + +- unary analytic wrapper: + +$$ +\dot{y} = f'(x)\dot{x} +$$ + +- addition and subtraction: + +$$ +\dot{y} = \dot{x}_1 \pm \dot{x}_2 +$$ + +- multiplication: + +$$ +\dot{y} = \dot{x}_1 x_2 + x_1 \dot{x}_2 +$$ + +- quotient: + +$$ +\dot{y} = \frac{\dot{x}_1 x_2 - x_1 \dot{x}_2}{x_2^2} +$$ + +- reductions: + +$$ +\dot{y}_{\mathrm{sum}} = \sum_i \dot{x}_i, +\qquad +\dot{y}_{\mathrm{mean}} = \frac{1}{n}\sum_i \dot{x}_i +$$ + +The same pattern extends to `var`, `std`, and tensor composites through the +scalar basis plus broadcast or reduction structure. + +## JVP + +The JVP is the linearization evaluated at the chosen tangent. Representative +examples are + +$$ +\operatorname{jvp}(\exp)(x;\dot{x}) = \exp(x)\dot{x}, +\qquad +\operatorname{jvp}(\mathrm{mul})(x_1, x_2;\dot{x}_1,\dot{x}_2) += \dot{x}_1 x_2 + x_1 \dot{x}_2. +$$ + +For reductions, JVP just applies the corresponding sum, averaging, or centered +residual rule to the tangent tensor. + +## Transpose + +For a raw output cotangent $\bar{y}$, representative transpose rules are: + +- unary analytic wrapper: + +$$ +\bar{x} = \overline{f'(x)} \odot \bar{y} +$$ + +- addition and subtraction: + +$$ +(\bar{x}_1, \bar{x}_2) = (\bar{y}, \pm \bar{y}) +$$ + +- multiplication: + +$$ +(\bar{x}_1, \bar{x}_2) = (\bar{y}\,\overline{x_2}, \bar{y}\,\overline{x_1}) +$$ + +- reductions: + +$$ +\bar{x}_{\mathrm{sum}} = \operatorname{broadcast}(\bar{y}), +\qquad +\bar{x}_{\mathrm{mean}} = \frac{1}{n}\operatorname{broadcast}(\bar{y}) +$$ + +`var` and `std` add the centered-residual correction recorded later in this +note. + +## VJP (JAX convention) + +JAX presents the same raw transpose map as the VJP or `linear_transpose` +result, interpreted under the real Frobenius inner product. Complex families +therefore follow the raw adjoint formulas directly. + +## VJP (PyTorch convention) + +PyTorch uses the same raw formulas but packages them through its +conjugate-Wirtinger convention. When a real input is embedded into complex +intermediates, the final cotangent is projected back to the real domain via +`handle_r_to_c`. + ## Scope This note records the shared scalar AD formulas implemented in diff --git a/docs/math/solve.md b/docs/math/solve.md index 7f91a1d..dd1673f 100644 --- a/docs/math/solve.md +++ b/docs/math/solve.md @@ -1,5 +1,70 @@ # Solve AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space solve map before any DB observable projection. For complex +tensors, `Transpose` means the adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is the solution map + +$$ +(A, B) \mapsto X, +\qquad +A X = B. +$$ + +## Linearization + +Differentiating the defining equation gives + +$$ +\dot{A} X + A \dot{X} = \dot{B}, +\qquad +\dot{X} = A^{-1}(\dot{B} - \dot{A}X). +$$ + +## JVP + +The JVP is the same tangent solve: + +$$ +\operatorname{jvp}(\operatorname{solve})(A,B;\dot{A},\dot{B}) += A^{-1}(\dot{B} - \dot{A}X). +$$ + +## Transpose + +For a raw output cotangent $\bar{X}$, define + +$$ +G = A^{-\mathsf{H}}\bar{X}. +$$ + +Then the transpose map is + +$$ +(\bar{A}, \bar{B}) = (-G X^{\mathsf{H}}, G). +$$ + +## VJP (JAX convention) + +JAX exposes the same raw transpose on the solution output. Right solves, +triangular solves, and `tensorsolve` are the same cotangent geometry with the +matching primal structure. + +## VJP (PyTorch convention) + +PyTorch uses the same raw adjoint formulas in `linalg_solve_backward` and its +triangular variants. For `solve_ex`, the status output remains nondifferentiable +metadata. + ## Forward Definition For the left solve diff --git a/docs/math/svd.md b/docs/math/svd.md index 6b4c4fd..a8d6271 100644 --- a/docs/math/svd.md +++ b/docs/math/svd.md @@ -1,5 +1,87 @@ # SVD AD Notes +## Conventions + +Unless noted otherwise, `Linearization` and `Transpose` are written for the +raw-output-space thin SVD before any DB observable such as `u_abs`, +`vh_abs`, or `uvh_product` is applied. For complex tensors, `Transpose` means +the adjoint under the real Frobenius inner product + +$$ +\langle X, Y \rangle_{\mathbb{R}} = \operatorname{Re}\operatorname{tr}(X^\dagger Y). +$$ + +## Forward + +The raw operator is + +$$ +A \mapsto (U, S, V^\dagger), +\qquad +A = U \operatorname{diag}(S) V^\dagger. +$$ + +## Linearization + +Let + +$$ +dP = U^\dagger (dA) V, +\qquad +dS = \operatorname{Re}(\operatorname{diag}(dP)), +\qquad +dX = dP - \operatorname{diag}(dS). +$$ + +Then the square-thin linearization is determined by the same spectral-gap +solves summarized later in the note: + +$$ +dU = U \left(\frac{\operatorname{sym}(dX \Sigma)}{E} ++ \operatorname{diag}\!\left(i \, \operatorname{Im}(\operatorname{diag}(dX)) \oslash (2 S)\right)\right), +$$ + +$$ +dV = V \left(\frac{\operatorname{sym}(\Sigma dX)}{E} +- \operatorname{diag}\!\left(i \, \operatorname{Im}(\operatorname{diag}(dX)) \oslash (2 S)\right)\right), +$$ + +together with the non-square corrections recorded below. + +## JVP + +The JVP is the same linearization evaluated at $dA$, returned on the raw factor +outputs $(dU, dS, dV^\dagger)$. + +## Transpose + +For raw output cotangents $(\bar{U}, \bar{S}, \bar{V})$, the transpose map is + +$$ +\bar{A} = U \Gamma V^\dagger ++ \mathbf{1}_{M > K}(I_M - U U^\dagger)\bar{U}\operatorname{diag}(S_{\text{inv}})V^\dagger ++ \mathbf{1}_{N > K}U\operatorname{diag}(S_{\text{inv}})\bar{V}^\dagger(I_N - V V^\dagger), +$$ + +with + +$$ +\Gamma = \Gamma_{\bar{U}} + \Gamma_{\bar{V}} + \Gamma_{\bar{S}} +$$ + +defined by the spectral-gap helpers below. + +## VJP (JAX convention) + +JAX reads the same raw transpose rule on the thin SVD outputs. Gauge-dependent +observables should be removed after the raw rule, not baked into it. + +## VJP (PyTorch convention) + +PyTorch uses the same raw adjoint, returned as cotangents for `U`, `S`, and +`Vh`. The public DB families avoid raw singular-vector gauge issues by +publishing gauge-insensitive observables. + ## Forward Definition For a real or complex matrix diff --git a/generators/__init__.py b/generators/__init__.py index 7d4b216..77b9ed2 100644 --- a/generators/__init__.py +++ b/generators/__init__.py @@ -5,7 +5,9 @@ "fd", "observables", "probes", + "jax_v1", "pytorch_v1", + "runtime_jax", "tolerance_audit", "upstream_scalar_inventory", "upstream_inventory", diff --git a/generators/jax_v1.py b/generators/jax_v1.py new file mode 100644 index 0000000..75718fd --- /dev/null +++ b/generators/jax_v1.py @@ -0,0 +1,585 @@ +"""JAX-backed v1 case materialization entrypoint.""" + +from __future__ import annotations + +import argparse +import copy +import json +import re +from dataclasses import dataclass +from pathlib import Path + +from . import probes, pytorch_v1, runtime_jax +from validators.encoding import decode_tensor_map + + +REPO_ROOT = Path(__file__).resolve().parents[1] +SCHEMA_PATH = REPO_ROOT / "schema" / "case.schema.json" + + +@dataclass(frozen=True) +class SimpleWitnessSpec: + op: str + family: str + input_name: str + input_value: float + direction_value: float + cotangent_value: float + raw_output_function: object + comment: str + + +@dataclass(frozen=True) +class HarnessWitnessSpec: + source_file: str + source_function: str + harness_fullname: str + + +def _simple_torch_observable(torch, spec: pytorch_v1.CaseFamilySpec, x): + if spec.op == "abs": + return torch.abs(x) + if spec.op == "exp": + return torch.exp(x) + raise ValueError(f"unsupported simple JAX smoke op: {spec.op}") + + +def _simple_raw_output_function(op: str, input_name: str): + _, jnp = runtime_jax.import_generation_runtime() + if op == "abs": + return lambda inputs: {"value": jnp.abs(inputs[input_name])} + if op == "exp": + return lambda inputs: {"value": jnp.exp(inputs[input_name])} + raise ValueError(f"unsupported simple JAX smoke op: {op}") + + +def _encode_torch_tensor_map(torch, tensor_map: dict[str, object]) -> dict[str, dict]: + return runtime_jax.encode_tensor_map( + {name: tensor.detach().clone().cpu() for name, tensor in tensor_map.items()} + ) + + +def _normalize_torch_tensor_map(torch, tensor_map: dict[str, object]) -> dict[str, object]: + normalized = {} + for name, tensor in tensor_map.items(): + norm = float(torch.linalg.vector_norm(tensor).item()) + normalized[name] = tensor.clone() if norm == 0.0 else tensor / norm + return normalized + + +def _jax_test_harness_specs() -> dict[tuple[str, str], HarnessWitnessSpec]: + source_file = "jax/_src/internal_test_util/test_harnesses.py" + source_function = "_make_unary_elementwise_harness" + return { + ("abs", "identity"): HarnessWitnessSpec( + source_file=source_file, + source_function=source_function, + harness_fullname=_unary_elementwise_harness_fullname("abs"), + ), + ("exp", "identity"): HarnessWitnessSpec( + source_file=source_file, + source_function=source_function, + harness_fullname=_unary_elementwise_harness_fullname("exp"), + ), + } + + +def _published_torch_source_case_paths() -> dict[tuple[str, str], Path]: + return { + ("abs", "identity"): REPO_ROOT / "cases" / "abs" / "identity.jsonl", + ("exp", "identity"): REPO_ROOT / "cases" / "exp" / "identity.jsonl", + } + + +def select_witness_source(op: str, family: str, *, prefer_jax_test: bool = True) -> str: + """Choose the preferred witness source for one JAX v1 case family.""" + key = (op, family) + if prefer_jax_test and key in _jax_test_harness_specs(): + return "jax_test" + if key in _published_torch_source_case_paths(): + return "torch_aligned" + if key in _jax_test_harness_specs(): + return "jax_test" + raise ValueError(f"unsupported JAX witness source selection key: {op}/{family}") + + +def _load_jsonl_case(path: Path, *, index: int = 0) -> dict: + lines = path.read_text(encoding="utf-8").splitlines() + if index >= len(lines): + raise IndexError(f"no case at line {index + 1} in {path}") + return json.loads(lines[index]) + + +def _load_jsonl_cases(path: Path) -> list[dict]: + return [ + json.loads(line) + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + + +def _decode_tensor_map_to_jax(jnp, encoded: dict[str, dict]) -> dict[str, object]: + return { + name: jnp.asarray(tensor.detach().cpu().numpy()) for name, tensor in decode_tensor_map(encoded).items() + } + + +def _unary_elementwise_harness_fullname( + prim: str, *, shape: tuple[int, ...] = (20, 20), dtype: str = "float64" +) -> str: + shape_part = ",".join(str(dim) for dim in shape) + raw_name = f"{prim}_shape={dtype}[{shape_part}]" + return re.sub(r'[ "\'\[\](){}<>=,._]+', "_", raw_name) + + +def build_case_families() -> dict[str, tuple[str, ...]]: + """Return the fixed JAX v1 op/family registry.""" + return pytorch_v1.build_case_families() + + +def build_case_spec_index(): + """Reuse the PyTorch v1 case index for JAX materialization.""" + return pytorch_v1.build_case_spec_index() + + +def build_provenance( + *, + source_commit: str, + seed: int, + source_repo: str = "jax", + source_file: str = "generators/jax_v1.py", + source_function: str = "materialize_case_family", + comment: str | None = None, + generator: str = "python-jax-v1", +) -> dict: + """Build the common provenance block for a JAX materialized case record.""" + return { + "source_repo": source_repo, + "source_file": source_file, + "source_function": source_function, + "source_commit": source_commit, + "generator": generator, + "seed": seed, + "torch_version": pytorch_v1.PINNED_TORCH_VERSION, + "fd_policy_version": pytorch_v1.FD_POLICY_VERSION, + **({"comment": comment} if comment is not None else {}), + } + + +def build_jax_ref_provenance( + *, + source_commit: str, + seed: int, + jax_version: str, + jaxlib_version: str, + witness_source: str, + backend: str = "cpu", + enable_x64: bool = True, +) -> dict: + """Build the probe-level JAX witness provenance block.""" + del source_commit, seed + return { + "source_backend": "jax", + "witness_source": witness_source, + "jax_version": jax_version, + "jaxlib_version": jaxlib_version, + "backend": backend, + "enable_x64": enable_x64, + } + + +def case_output_path(spec: pytorch_v1.CaseFamilySpec, *, cases_root: Path | None = None) -> Path: + root = cases_root if cases_root is not None else REPO_ROOT / "cases" + return root / spec.op / f"{spec.family}.jsonl" + + +def write_case_records( + spec: pytorch_v1.CaseFamilySpec, + records: list[dict], + *, + cases_root: Path | None = None, +) -> Path: + out_path = case_output_path(spec, cases_root=cases_root) + out_path.parent.mkdir(parents=True, exist_ok=True) + with out_path.open("w", encoding="utf-8") as handle: + for record in records: + handle.write(json.dumps(record, sort_keys=True)) + handle.write("\n") + return out_path + + +def make_success_case( + spec: pytorch_v1.CaseFamilySpec, + *, + case_id: str, + dtype: str, + inputs: dict, + comparison: dict, + probes_: list[dict], + provenance: dict, +) -> dict: + case = { + "schema_version": 1, + "case_id": case_id, + "op": spec.op, + "dtype": dtype, + "family": spec.family, + "expected_behavior": "success", + "inputs": inputs, + "observable": {"kind": spec.observable_kind}, + "comparison": comparison, + "probes": probes_, + "provenance": provenance, + } + return case + + +def _simple_witness_specs() -> dict[tuple[str, str], SimpleWitnessSpec]: + _, jnp = runtime_jax.import_generation_runtime() + return { + ("abs", "identity"): SimpleWitnessSpec( + op="abs", + family="identity", + input_name="x", + input_value=3.0, + direction_value=1.0, + cotangent_value=2.0, + raw_output_function=lambda inputs: {"value": jnp.abs(inputs["x"])}, + comment="jax-native abs/identity smoke witness", + ), + ("exp", "identity"): SimpleWitnessSpec( + op="exp", + family="identity", + input_name="x", + input_value=1.0, + direction_value=2.0, + cotangent_value=3.0, + raw_output_function=lambda inputs: {"value": jnp.exp(inputs["x"])}, + comment="jax-native exp/identity smoke witness", + ), + } + + +def _jax_test_comment(base_comment: str, harness_fullname: str) -> str: + return f"{base_comment}; harness_fullname={harness_fullname}" + + +def _supported_materialization_keys() -> tuple[tuple[str, str], ...]: + return tuple(_simple_witness_specs()) + + +def _case_id(spec: pytorch_v1.CaseFamilySpec, *, dtype: str, index: int) -> str: + dtype_tag = { + "float32": "f32", + "float64": "f64", + "complex64": "c64", + "complex128": "c128", + }[dtype] + return f"{spec.op}_{dtype_tag}_{spec.family}_{index:03d}" + + +def _materialize_simple_success_case( + spec: pytorch_v1.CaseFamilySpec, + witness_spec: SimpleWitnessSpec, + *, + seed: int, + index: int, + source_file: str | None = None, + source_function: str | None = None, + harness_fullname: str | None = None, +) -> dict: + jax, jnp = runtime_jax.import_generation_runtime() + import jaxlib + import torch + + dtype = jnp.float64 + inputs = {witness_spec.input_name: jnp.array([witness_spec.input_value], dtype=dtype)} + raw_direction = {witness_spec.input_name: jnp.array([witness_spec.direction_value], dtype=dtype)} + raw_cotangent = {"value": jnp.array([witness_spec.cotangent_value], dtype=dtype)} + direction = runtime_jax.normalize_raw_tensor_map(raw_direction) + cotangent = runtime_jax.normalize_raw_tensor_map(raw_cotangent) + + torch_input = torch.tensor( + [witness_spec.input_value], dtype=torch.float64, requires_grad=True + ) + torch_direction = _normalize_torch_tensor_map( + torch, + {"x": torch.tensor([witness_spec.direction_value], dtype=torch.float64)}, + )["x"] + torch_cotangent = _normalize_torch_tensor_map( + torch, + {"value": torch.tensor([witness_spec.cotangent_value], dtype=torch.float64)}, + )["value"] + + def torch_observable(x): + return _simple_torch_observable(torch, spec, x) + + torch_output = torch_observable(torch_input) + _, torch_jvp = torch.func.jvp( + torch_observable, + (torch_input,), + (torch_direction,), + ) + torch_vjp = torch.autograd.grad( + torch_output, + torch_input, + grad_outputs=torch_cotangent, + allow_unused=False, + )[0] + fd_step = 1e-6 + fd_jvp = ( + torch_observable(torch_input + fd_step * torch_direction) + - torch_observable(torch_input - fd_step * torch_direction) + ) / (2.0 * fd_step) + + jvp = runtime_jax.compute_jax_jvp(witness_spec.raw_output_function, inputs, direction) + vjp = runtime_jax.compute_jax_vjp(witness_spec.raw_output_function, inputs, cotangent) + linearization, linear_fn = runtime_jax.compute_jax_linearization( + witness_spec.raw_output_function, + inputs, + direction, + ) + transpose = runtime_jax.compute_jax_transpose(linear_fn, inputs, cotangent) + + adjoint_check = runtime_jax.compute_jax_adjoint_check( + runtime_jax.tensor_map_inner_product(cotangent, jvp), + runtime_jax.tensor_map_inner_product(transpose, direction), + ) + + probe = probes.make_probe_record( + probe_id="p0", + direction=runtime_jax.encode_tensor_map(direction), + cotangent=runtime_jax.encode_tensor_map(cotangent), + pytorch_jvp=_encode_torch_tensor_map(torch, {"value": torch_jvp}), + pytorch_vjp=_encode_torch_tensor_map(torch, {witness_spec.input_name: torch_vjp}), + fd_step=fd_step, + fd_jvp=_encode_torch_tensor_map(torch, {"value": fd_jvp}), + jax_jvp=runtime_jax.encode_tensor_map(jvp), + jax_vjp=runtime_jax.encode_tensor_map(vjp), + jax_linearization=runtime_jax.encode_tensor_map(linearization), + jax_raw_output_cotangent=runtime_jax.encode_tensor_map(cotangent), + jax_transpose=runtime_jax.encode_tensor_map(transpose), + jax_adjoint_check=adjoint_check, + jax_provenance=build_jax_ref_provenance( + source_commit=runtime_jax.normalize_jax_version(jax.__version__), + seed=seed, + jax_version=runtime_jax.normalize_jax_version(jax.__version__), + jaxlib_version=runtime_jax.normalize_jax_version(jaxlib.__version__), + witness_source="jax_test", + enable_x64=True, + ), + ) + + comparison = { + "first_order": { + "kind": "allclose", + "rtol": 1e-12, + "atol": 1e-12, + } + } + return make_success_case( + spec, + case_id=_case_id(spec, dtype="float64", index=index), + dtype="float64", + inputs=runtime_jax.encode_tensor_map(inputs), + comparison=comparison, + probes_=[probe], + provenance=build_provenance( + source_commit=runtime_jax.normalize_jax_version(jax.__version__), + seed=seed, + source_file=source_file or "generators/jax_v1.py", + source_function=source_function or "materialize_case_family", + comment=( + _jax_test_comment(witness_spec.comment, harness_fullname) + if harness_fullname is not None + else witness_spec.comment + ), + ), + ) + + +def enrich_torch_aligned_case_record( + spec: pytorch_v1.CaseFamilySpec, + source_case: dict, + *, + seed: int, +) -> dict: + """Attach a JAX witness to a published PyTorch case without changing its inputs.""" + jax, jnp = runtime_jax.import_generation_runtime() + import jaxlib + + enriched = copy.deepcopy(source_case) + + source_inputs = enriched["inputs"] + input_name = next(iter(source_inputs)) + inputs = _decode_tensor_map_to_jax(jnp, source_inputs) + probe = enriched["probes"][0] + direction = _decode_tensor_map_to_jax(jnp, probe["direction"]) + cotangent = _decode_tensor_map_to_jax(jnp, probe["cotangent"]) + raw_output_function = _simple_raw_output_function(spec.op, input_name) + jvp = runtime_jax.compute_jax_jvp(raw_output_function, inputs, direction) + vjp = runtime_jax.compute_jax_vjp(raw_output_function, inputs, cotangent) + linearization, linear_fn = runtime_jax.compute_jax_linearization( + raw_output_function, + inputs, + direction, + ) + transpose = runtime_jax.compute_jax_transpose(linear_fn, inputs, cotangent) + adjoint_check = runtime_jax.compute_jax_adjoint_check( + runtime_jax.tensor_map_inner_product(cotangent, jvp), + runtime_jax.tensor_map_inner_product(transpose, direction), + ) + + probe["jax_ref"] = { + "jvp": runtime_jax.encode_tensor_map(jvp), + "vjp": runtime_jax.encode_tensor_map(vjp), + "linearization": runtime_jax.encode_tensor_map(linearization), + "raw_output_cotangent": runtime_jax.encode_tensor_map(cotangent), + "transpose": runtime_jax.encode_tensor_map(transpose), + "adjoint_check": adjoint_check, + "provenance": build_jax_ref_provenance( + source_commit=runtime_jax.normalize_jax_version(jax.__version__), + seed=seed, + jax_version=runtime_jax.normalize_jax_version(jax.__version__), + jaxlib_version=runtime_jax.normalize_jax_version(jaxlib.__version__), + witness_source="torch_aligned", + enable_x64=True, + ), + } + + enriched["provenance"] = build_provenance( + source_repo=source_case["provenance"]["source_repo"], + source_file=source_case["provenance"]["source_file"], + source_function=source_case["provenance"]["source_function"], + source_commit=source_case["provenance"]["source_commit"], + seed=seed, + comment=source_case["provenance"].get("comment"), + generator="python-jax-v1", + ) + return enriched + + +def materialize_torch_aligned_case_family( + op: str, + family: str, + *, + limit: int = 1, + cases_root: Path | None = None, + source_case_path: Path | None = None, +) -> Path: + """Materialize a JAX witness from a published PyTorch source case.""" + spec = build_case_spec_index()[(op, family)] + key = (op, family) + if key not in _published_torch_source_case_paths(): + raise ValueError( + f"JAX v1 only has published torch-aligned source cases for: {op}/{family}" + ) + source_path = source_case_path or _published_torch_source_case_paths()[key] + source_cases = _load_jsonl_cases(source_path) + records = [ + enrich_torch_aligned_case_record( + spec, + source_case, + seed=17 + index, + ) + for index, source_case in enumerate(source_cases[:limit]) + ] + return write_case_records(spec, records, cases_root=cases_root) + + +def materialize_case_family( + op: str, + family: str, + *, + limit: int = 1, + cases_root: Path | None = None, +) -> Path: + spec = build_case_spec_index()[(op, family)] + key = (op, family) + supported = _simple_witness_specs() + if key not in supported: + raise ValueError(f"JAX v1 only materializes simple smoke witnesses in this task: {op}/{family}") + witness_source = select_witness_source(op, family) + if witness_source == "torch_aligned": + return materialize_torch_aligned_case_family( + op, + family, + limit=limit, + cases_root=cases_root, + ) + harness_spec = _jax_test_harness_specs().get(key) + records = [ + _materialize_simple_success_case( + spec, + supported[key], + seed=17 + index, + index=index + 1, + source_file=harness_spec.source_file if harness_spec is not None else None, + source_function=harness_spec.source_function if harness_spec is not None else None, + harness_fullname=harness_spec.harness_fullname if harness_spec is not None else None, + ) + for index in range(limit) + ] + return write_case_records(spec, records, cases_root=cases_root) + + +def materialize_all_case_families( + *, + limit: int = 1, + cases_root: Path | None = None, +) -> list[Path]: + paths: list[Path] = [] + for op, family in _supported_materialization_keys(): + paths.append( + materialize_case_family( + op, + family, + limit=limit, + cases_root=cases_root, + ) + ) + return paths + + +def _iter_registry_lines() -> list[str]: + case_families = build_case_families() + return [f"{op}: {', '.join(case_families[op])}" for op in case_families] + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--list", action="store_true", help="Print the fixed v1 op/family registry and exit.") + parser.add_argument("--materialize", choices=tuple(build_case_families()), help="Materialize one supported op family into JSONL.") + parser.add_argument("--materialize-all", action="store_true", help="Materialize the supported smoke witnesses into JSONL files.") + parser.add_argument("--family", help="Case family to materialize for the selected op.") + parser.add_argument("--limit", type=int, default=1, help="Maximum number of records to materialize.") + parser.add_argument("--cases-root", type=Path, default=None, help="Optional output root for generated cases.") + args = parser.parse_args(argv) + + if args.list: + for line in _iter_registry_lines(): + print(line) + return 0 + + if args.materialize: + if not args.family: + raise SystemExit("--family is required with --materialize") + out_path = materialize_case_family( + args.materialize, + args.family, + limit=args.limit, + cases_root=args.cases_root, + ) + print(out_path) + return 0 + + if args.materialize_all: + for path in materialize_all_case_families(limit=args.limit, cases_root=args.cases_root): + print(path) + return 0 + + raise SystemExit("JAX v1 case generation is not implemented yet.") + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/generators/probes.py b/generators/probes.py index 40ea03f..f79bbc2 100644 --- a/generators/probes.py +++ b/generators/probes.py @@ -62,8 +62,32 @@ def make_probe_record( fd_step: float, fd_jvp: dict[str, dict], fd_hvp: dict[str, dict] | None = None, + jax_jvp: dict[str, dict] | None = None, + jax_vjp: dict[str, dict] | None = None, + jax_linearization: dict[str, dict] | None = None, + jax_raw_output_cotangent: dict[str, dict] | None = None, + jax_transpose: dict[str, dict] | None = None, + jax_adjoint_check: dict[str, float] | None = None, + jax_provenance: dict[str, object] | None = None, ) -> dict: """Assemble one paired derivative probe record.""" + jax_values = { + "jvp": jax_jvp, + "vjp": jax_vjp, + "linearization": jax_linearization, + "raw_output_cotangent": jax_raw_output_cotangent, + "transpose": jax_transpose, + "adjoint_check": jax_adjoint_check, + "provenance": jax_provenance, + } + provided_jax_fields = [name for name, value in jax_values.items() if value is not None] + if provided_jax_fields and len(provided_jax_fields) != len(jax_values): + missing = [name for name, value in jax_values.items() if value is None] + raise ValueError( + "jax_ref requires all JAX witness fields when any JAX payload is provided; " + f"missing: {', '.join(missing)}" + ) + pytorch_ref = { "jvp": pytorch_jvp, "vjp": pytorch_vjp, @@ -80,10 +104,24 @@ def make_probe_record( if fd_hvp is not None: fd_ref["hvp"] = fd_hvp - return { + probe = { "probe_id": probe_id, "direction": direction, "cotangent": cotangent, "pytorch_ref": pytorch_ref, "fd_ref": fd_ref, } + jax_ref = {} + if provided_jax_fields: + jax_ref = { + "jvp": jax_jvp, + "vjp": jax_vjp, + "linearization": jax_linearization, + "raw_output_cotangent": jax_raw_output_cotangent, + "transpose": jax_transpose, + "adjoint_check": jax_adjoint_check, + "provenance": jax_provenance, + } + probe["jax_ref"] = jax_ref + + return probe diff --git a/generators/runtime_jax.py b/generators/runtime_jax.py new file mode 100644 index 0000000..83620f0 --- /dev/null +++ b/generators/runtime_jax.py @@ -0,0 +1,169 @@ +"""Shared JAX runtime helpers for v1 case generation.""" + +from __future__ import annotations + +import numpy as np + +PINNED_JAX_VERSION = "0.9.1" + + +def normalize_jax_version(version: str) -> str: + """Strip local build metadata from a JAX version string.""" + return version.split("+", 1)[0] + + +def ensure_pinned_jax_version(jax, jaxlib) -> None: + """Raise when the active JAX runtime does not match the repository pin.""" + actual_jax = normalize_jax_version(jax.__version__) + actual_jaxlib = normalize_jax_version(jaxlib.__version__) + if actual_jax != PINNED_JAX_VERSION or actual_jaxlib != PINNED_JAX_VERSION: + raise RuntimeError( + f"tensor-ad-oracles requires jax=={PINNED_JAX_VERSION} and jaxlib=={PINNED_JAX_VERSION}, " + f"got jax=={jax.__version__} and jaxlib=={jaxlib.__version__}" + ) + + +def import_generation_runtime(): + import jax + import jax.numpy as jnp + import jaxlib + + jax.config.update("jax_enable_x64", True) + ensure_pinned_jax_version(jax, jaxlib) + return jax, jnp + + +def dtype_name(dtype) -> str: + name = getattr(dtype, "name", None) + if isinstance(name, str) and name: + return name + name = getattr(dtype, "__name__", None) + if isinstance(name, str) and name: + return name + text = str(dtype) + for prefix in ("") + return text + + +def _raw_tensor_norm(jnp, tensor) -> float: + if tensor.size == 0: + return 0.0 + return float(jnp.linalg.norm(tensor).item()) + + +def normalize_raw_tensor(jnp, tensor): + array = jnp.asarray(tensor) + norm = _raw_tensor_norm(jnp, array) + if norm == 0.0: + return array + return array / norm + + +def normalize_raw_tensor_map(tensor_map: dict[str, object]) -> dict[str, object]: + jax, jnp = import_generation_runtime() + del jax + return {name: normalize_raw_tensor(jnp, tensor) for name, tensor in tensor_map.items()} + + +def tensor_map_inner_product(left: dict[str, object], right: dict[str, object]): + _, jnp = import_generation_runtime() + left_items = list(left.items()) + if not left_items: + return jnp.asarray(0.0) + total = None + for name, left_tensor in left_items: + right_tensor = right[name] + if left_tensor.size == 0: + continue + term = jnp.vdot(left_tensor.reshape(-1), right_tensor.reshape(-1)) + total = term if total is None else total + term + if total is None: + return jnp.asarray(0.0) + return jnp.real(total) + + +def _flatten_real_data(value) -> list[float]: + if isinstance(value, list): + flattened: list[float] = [] + for item in value: + flattened.extend(_flatten_real_data(item)) + return flattened + return [float(value)] + + +def _flatten_complex_data(value) -> list[list[float]]: + if isinstance(value, list): + flattened: list[list[float]] = [] + for item in value: + flattened.extend(_flatten_complex_data(item)) + return flattened + return [[float(value.real), float(value.imag)]] + + +def encode_tensor(tensor) -> dict: + _, jnp = import_generation_runtime() + materialized = np.asarray(jnp.asarray(tensor)) + dtype = dtype_name(materialized.dtype) + shape = list(materialized.shape) + raw = materialized.tolist() + data = ( + _flatten_complex_data(raw) + if np.iscomplexobj(materialized) + else _flatten_real_data(raw) + ) + return { + "dtype": dtype, + "shape": shape, + "order": "row_major", + "data": data, + } + + +def encode_tensor_map(tensors: dict[str, object]) -> dict[str, dict]: + return {name: encode_tensor(tensor) for name, tensor in tensors.items()} + + +def compute_jax_jvp(observable_fn, inputs, direction): + jax, _ = import_generation_runtime() + _, tangent = jax.jvp(observable_fn, (inputs,), (direction,)) + return tangent + + +def compute_jax_vjp(observable_fn, inputs, cotangent): + jax, _ = import_generation_runtime() + _, pullback = jax.vjp(observable_fn, inputs) + result = pullback(cotangent) + if isinstance(result, tuple) and len(result) == 1: + return result[0] + return result + + +def compute_jax_linearization(raw_output_fn, inputs, direction): + """Return the raw-output-space linearization and the cached linear map.""" + jax, _ = import_generation_runtime() + _, linear_fn = jax.linearize(raw_output_fn, inputs) + return linear_fn(direction), linear_fn + + +def compute_jax_transpose(linear_fn, inputs, cotangent): + jax, _ = import_generation_runtime() + result = jax.linear_transpose(linear_fn, inputs)(cotangent) + if isinstance(result, tuple) and len(result) == 1: + return result[0] + return result + + +def compute_jax_adjoint_check(lhs, rhs) -> dict[str, float]: + lhs_value = float(lhs.item() if hasattr(lhs, "item") else lhs) + rhs_value = float(rhs.item() if hasattr(rhs, "item") else rhs) + abs_err = abs(lhs_value - rhs_value) + rel_denom = max(abs(lhs_value), abs(rhs_value), 1e-300) + rel_err = abs_err / rel_denom + return { + "lhs": lhs_value, + "rhs": rhs_value, + "abs_err": abs_err, + "rel_err": rel_err, + } diff --git a/pyproject.toml b/pyproject.toml index 7c92ca2..8384df1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,6 +7,8 @@ license = "MIT OR Apache-2.0" requires-python = ">=3.12" dependencies = [ "expecttest>=0.3", + "jax==0.9.1", + "jaxlib==0.9.1", "jsonschema>=4.23", "numpy>=2.0", "torch==2.10.0", diff --git a/schema/case.schema.json b/schema/case.schema.json index 5709492..14aa03f 100644 --- a/schema/case.schema.json +++ b/schema/case.schema.json @@ -280,6 +280,108 @@ } } }, + "jax_ref": { + "type": "object", + "additionalProperties": false, + "minProperties": 1, + "required": [ + "jvp", + "vjp", + "linearization", + "raw_output_cotangent", + "transpose", + "adjoint_check", + "provenance" + ], + "properties": { + "jvp": { + "$ref": "#/$defs/tensor_map" + }, + "vjp": { + "$ref": "#/$defs/tensor_map" + }, + "linearization": { + "$ref": "#/$defs/tensor_map" + }, + "raw_output_cotangent": { + "$ref": "#/$defs/tensor_map" + }, + "transpose": { + "$ref": "#/$defs/tensor_map" + }, + "adjoint_check": { + "type": "object", + "additionalProperties": false, + "required": [ + "lhs", + "rhs", + "abs_err", + "rel_err" + ], + "properties": { + "lhs": { + "type": "number" + }, + "rhs": { + "type": "number" + }, + "abs_err": { + "type": "number", + "minimum": 0 + }, + "rel_err": { + "type": "number", + "minimum": 0 + } + } + }, + "provenance": { + "type": "object", + "additionalProperties": false, + "required": [ + "source_backend", + "witness_source", + "jax_version", + "jaxlib_version", + "backend", + "enable_x64" + ], + "properties": { + "source_backend": { + "type": "string", + "const": "jax" + }, + "witness_source": { + "type": "string", + "enum": [ + "torch_aligned", + "jax_test", + "jax_native" + ] + }, + "jax_version": { + "type": "string", + "minLength": 1 + }, + "jaxlib_version": { + "type": "string", + "minLength": 1 + }, + "backend": { + "type": "string", + "enum": [ + "cpu", + "gpu", + "tpu" + ] + }, + "enable_x64": { + "type": "boolean" + } + } + } + } + }, "fd_ref": { "type": "object", "additionalProperties": false, @@ -334,6 +436,9 @@ "pytorch_ref": { "$ref": "#/$defs/pytorch_ref" }, + "jax_ref": { + "$ref": "#/$defs/jax_ref" + }, "fd_ref": { "$ref": "#/$defs/fd_ref" } diff --git a/scripts/check_regeneration.py b/scripts/check_regeneration.py index d01b63f..61ba73e 100644 --- a/scripts/check_regeneration.py +++ b/scripts/check_regeneration.py @@ -13,6 +13,7 @@ if str(REPO_ROOT) not in sys.path: sys.path.insert(0, str(REPO_ROOT)) +from generators import jax_v1 from generators.pytorch_v1 import materialize_all_case_families @@ -20,6 +21,21 @@ def _relative_case_files(root: Path) -> set[Path]: return {path.relative_to(root) for path in root.rglob("*.jsonl")} +def _torch_aligned_jax_overlay_families() -> tuple[tuple[str, str], ...]: + return (("abs", "identity"), ("exp", "identity")) + + +def _overlay_torch_aligned_jax_refs(regenerated_root: Path) -> None: + for op, family in _torch_aligned_jax_overlay_families(): + jax_v1.materialize_torch_aligned_case_family( + op, + family, + limit=None, + cases_root=regenerated_root, + source_case_path=regenerated_root / op / f"{family}.jsonl", + ) + + def _record_tolerance(record: dict) -> tuple[float, float]: comparison = record.get("comparison", {}) if comparison.get("kind") == "allclose": @@ -191,13 +207,32 @@ def check_regeneration(cases_root: Path = CASES_ROOT) -> int: with tempfile.TemporaryDirectory() as tmpdir: regenerated_root = Path(tmpdir) / "cases" materialize_all_case_families(limit=None, cases_root=regenerated_root) + _overlay_torch_aligned_jax_refs(regenerated_root) compare_case_trees(cases_root, regenerated_root) return len(_relative_case_files(cases_root)) +def _materialize_jax_smoke_case_tree(root: Path) -> None: + jax_v1.materialize_case_family("abs", "identity", limit=1, cases_root=root) + jax_v1.materialize_case_family("exp", "identity", limit=1, cases_root=root) + + +def check_jax_smoke_regeneration() -> int: + """Regenerate the JAX smoke tree twice and require semantic equality.""" + with tempfile.TemporaryDirectory() as tmpdir: + left_root = Path(tmpdir) / "left" + right_root = Path(tmpdir) / "right" + _materialize_jax_smoke_case_tree(left_root) + _materialize_jax_smoke_case_tree(right_root) + compare_case_trees(left_root, right_root) + return len(_relative_case_files(left_root)) + + def main() -> int: compared = check_regeneration() + jax_compared = check_jax_smoke_regeneration() print(f"regeneration_checked_files={compared}") + print(f"jax_regeneration_checked_files={jax_compared}") return 0 diff --git a/scripts/check_replay.py b/scripts/check_replay.py index 9422431..6b12937 100644 --- a/scripts/check_replay.py +++ b/scripts/check_replay.py @@ -3,6 +3,7 @@ from __future__ import annotations import sys +import tempfile from pathlib import Path REPO_ROOT = Path(__file__).resolve().parents[1] @@ -10,15 +11,31 @@ if str(REPO_ROOT) not in sys.path: sys.path.insert(0, str(REPO_ROOT)) +from generators import jax_v1 from validators.replay import replay_case_tree +def _replay_jax_smoke_case_tree() -> object: + with tempfile.TemporaryDirectory() as tmpdir: + smoke_root = Path(tmpdir) + jax_v1.materialize_case_family("abs", "identity", limit=1, cases_root=smoke_root) + jax_v1.materialize_case_family("exp", "identity", limit=1, cases_root=smoke_root) + return replay_case_tree(smoke_root) + + def main() -> int: - result = replay_case_tree(CASES_ROOT) - if result.failures: - joined = "\n".join(result.failures) + published = replay_case_tree(CASES_ROOT) + if published.failures: + joined = "\n".join(published.failures) raise SystemExit(f"replay failed:\n{joined}") - print(f"replay_checked={result.checked}") + + jax_smoke = _replay_jax_smoke_case_tree() + if jax_smoke.failures: + joined = "\n".join(jax_smoke.failures) + raise SystemExit(f"jax smoke replay failed:\n{joined}") + + print(f"replay_checked={published.checked}") + print(f"jax_replay_checked={jax_smoke.checked}") return 0 diff --git a/scripts/verify_cases.py b/scripts/verify_cases.py index 75738ab..8d02517 100644 --- a/scripts/verify_cases.py +++ b/scripts/verify_cases.py @@ -4,6 +4,7 @@ import json import sys +import tempfile from pathlib import Path from typing import Iterable @@ -12,6 +13,7 @@ if str(REPO_ROOT) not in sys.path: sys.path.insert(0, str(REPO_ROOT)) +from generators import jax_v1 from validators.case_loader import iter_case_files @@ -48,9 +50,19 @@ def verify_case_tree(root: Path = CASES_ROOT) -> int: return sum(len(load_jsonl_records(path)) for path in paths) +def _verify_jax_smoke_case_tree() -> int: + with tempfile.TemporaryDirectory() as tmpdir: + smoke_root = Path(tmpdir) + jax_v1.materialize_case_family("abs", "identity", limit=1, cases_root=smoke_root) + jax_v1.materialize_case_family("exp", "identity", limit=1, cases_root=smoke_root) + return verify_case_tree(smoke_root) + + def main() -> int: verified = verify_case_tree() + smoke_verified = _verify_jax_smoke_case_tree() print(f"verified_case_records={verified}") + print(f"jax_verified_case_records={smoke_verified}") return 0 diff --git a/tests/test_db_replay.py b/tests/test_db_replay.py index 0b48c3f..99ea5b0 100644 --- a/tests/test_db_replay.py +++ b/tests/test_db_replay.py @@ -3,6 +3,7 @@ from unittest import mock from pathlib import Path from types import SimpleNamespace +import json import torch @@ -215,6 +216,166 @@ def test_replay_case_file_accepts_generated_scalar_case_family(self) -> None: self.assertEqual(result.checked, 4, msg=result.failures) self.assertEqual(result.failures, []) + def test_replay_case_file_accepts_generated_jax_smoke_case_family(self) -> None: + try: + import jax.numpy as jnp # noqa: F401 + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + with tempfile.TemporaryDirectory() as tmpdir: + cases_root = Path(tmpdir) + from generators import jax_v1 + + generated = jax_v1.materialize_case_family( + "abs", + "identity", + limit=1, + cases_root=cases_root, + ) + result = replay.replay_case_file(generated) + + self.assertEqual(result.checked, 1, msg=result.failures) + self.assertEqual(result.failures, []) + + def test_replay_case_file_rejects_jax_adjoint_check_drift(self) -> None: + try: + import jax.numpy as jnp # noqa: F401 + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + with tempfile.TemporaryDirectory() as tmpdir: + cases_root = Path(tmpdir) + from generators import jax_v1 + + generated = jax_v1.materialize_case_family( + "exp", + "identity", + limit=1, + cases_root=cases_root, + ) + record = json.loads(generated.read_text(encoding="utf-8").splitlines()[0]) + record["probes"][0]["jax_ref"]["adjoint_check"]["lhs"] += 1.0 + generated.write_text(json.dumps(record, sort_keys=True) + "\n", encoding="utf-8") + + result = replay.replay_case_file(generated) + + self.assertEqual(result.checked, 0) + self.assertEqual(len(result.failures), 1) + self.assertRegex(result.failures[0], "JAX adjoint check") + + def test_replay_case_file_rejects_pytorch_drift_in_jax_smoke_case(self) -> None: + try: + import jax.numpy as jnp # noqa: F401 + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + with tempfile.TemporaryDirectory() as tmpdir: + cases_root = Path(tmpdir) + from generators import jax_v1 + + generated = jax_v1.materialize_case_family( + "abs", + "identity", + limit=1, + cases_root=cases_root, + ) + record = json.loads(generated.read_text(encoding="utf-8").splitlines()[0]) + record["probes"][0]["pytorch_ref"]["jvp"]["value"]["data"][0] += 1.0 + generated.write_text(json.dumps(record, sort_keys=True) + "\n", encoding="utf-8") + + result = replay.replay_case_file(generated) + + self.assertEqual(result.checked, 0) + self.assertEqual(len(result.failures), 1) + self.assertRegex(result.failures[0], "PyTorch JVP") + + def test_replay_case_file_rejects_small_pytorch_drift_in_jax_smoke_case(self) -> None: + try: + import jax.numpy as jnp # noqa: F401 + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + with tempfile.TemporaryDirectory() as tmpdir: + cases_root = Path(tmpdir) + from generators import jax_v1 + + generated = jax_v1.materialize_case_family( + "abs", + "identity", + limit=1, + cases_root=cases_root, + ) + record = json.loads(generated.read_text(encoding="utf-8").splitlines()[0]) + record["probes"][0]["pytorch_ref"]["jvp"]["value"]["data"][0] += 5e-6 + generated.write_text(json.dumps(record, sort_keys=True) + "\n", encoding="utf-8") + + result = replay.replay_case_file(generated) + + self.assertEqual(result.checked, 0) + self.assertEqual(len(result.failures), 1) + self.assertRegex(result.failures[0], "PyTorch JVP") + + def test_replay_case_file_rejects_fd_drift_in_jax_smoke_case(self) -> None: + try: + import jax.numpy as jnp # noqa: F401 + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + with tempfile.TemporaryDirectory() as tmpdir: + cases_root = Path(tmpdir) + from generators import jax_v1 + + generated = jax_v1.materialize_case_family( + "exp", + "identity", + limit=1, + cases_root=cases_root, + ) + record = json.loads(generated.read_text(encoding="utf-8").splitlines()[0]) + record["probes"][0]["fd_ref"]["jvp"]["value"]["data"][0] += 1.0 + generated.write_text(json.dumps(record, sort_keys=True) + "\n", encoding="utf-8") + + result = replay.replay_case_file(generated) + + self.assertEqual(result.checked, 0) + self.assertEqual(len(result.failures), 1) + self.assertRegex(result.failures[0], "FD-JVP") + + def test_replay_case_file_runs_live_cross_check_for_jax_smoke_case(self) -> None: + try: + import jax.numpy as jnp # noqa: F401 + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + with tempfile.TemporaryDirectory() as tmpdir: + cases_root = Path(tmpdir) + from generators import jax_v1 + + generated = jax_v1.materialize_case_family( + "exp", + "identity", + limit=1, + cases_root=cases_root, + ) + real_validate = replay.validate_live_success_probe + call_count = 0 + + def counting_validate(*args, **kwargs): + nonlocal call_count + call_count += 1 + return real_validate(*args, **kwargs) + + with mock.patch.object( + replay, + "validate_live_success_probe", + side_effect=counting_validate, + ): + result = replay.replay_case_file(generated) + + self.assertEqual(result.failures, []) + self.assertEqual(result.checked, 1) + self.assertEqual(call_count, 1) + def test_replay_case_file_reuses_sample_inputs_for_same_spec_and_dtype(self) -> None: try: import expecttest # noqa: F401 diff --git a/tests/test_jax_v1.py b/tests/test_jax_v1.py new file mode 100644 index 0000000..84f8aae --- /dev/null +++ b/tests/test_jax_v1.py @@ -0,0 +1,147 @@ +import io +import json +import math +import tempfile +import unittest +from contextlib import redirect_stdout +from pathlib import Path + +from generators import jax_v1, pytorch_v1 +from validators.encoding import decode_tensor_map + + +REPO_ROOT = Path(__file__).resolve().parents[1] + + +class JaxV1Tests(unittest.TestCase): + def test_build_case_families_matches_pytorch_registry(self) -> None: + self.assertEqual(jax_v1.build_case_families(), pytorch_v1.build_case_families()) + + def test_select_witness_source_prefers_jax_test_for_unary_elementwise_families(self) -> None: + self.assertEqual(jax_v1.select_witness_source("abs", "identity"), "jax_test") + self.assertEqual( + jax_v1.select_witness_source("abs", "identity", prefer_jax_test=False), + "torch_aligned", + ) + + def test_build_jax_ref_provenance_preserves_witness_source(self) -> None: + provenance = jax_v1.build_jax_ref_provenance( + source_commit="deadbeef", + seed=17, + jax_version="0.9.1", + jaxlib_version="0.9.1", + witness_source="jax_test", + ) + + self.assertEqual(provenance["source_backend"], "jax") + self.assertEqual(provenance["witness_source"], "jax_test") + self.assertEqual(provenance["jax_version"], "0.9.1") + self.assertEqual(provenance["jaxlib_version"], "0.9.1") + + def test_main_materialize_smoke_witnesses_normalize_computation_and_serialization(self) -> None: + try: + import jax.numpy as jnp # noqa: F401 + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + with tempfile.TemporaryDirectory() as tmpdir: + with redirect_stdout(io.StringIO()): + exit_code = jax_v1.main( + ["--materialize-all", "--limit", "1", "--cases-root", tmpdir] + ) + + self.assertEqual(exit_code, 0) + for op, expected_linearization in ( + ("abs", 1.0), + ("exp", math.e), + ): + out_path = Path(tmpdir) / op / "identity.jsonl" + self.assertTrue(out_path.exists()) + record = json.loads(out_path.read_text(encoding="utf-8").splitlines()[0]) + probe = record["probes"][0] + + direction = decode_tensor_map(probe["direction"]) + cotangent = decode_tensor_map(probe["cotangent"]) + jvp = decode_tensor_map(probe["jax_ref"]["jvp"]) + vjp = decode_tensor_map(probe["jax_ref"]["vjp"]) + linearization = decode_tensor_map(probe["jax_ref"]["linearization"]) + transpose = decode_tensor_map(probe["jax_ref"]["transpose"]) + pytorch_jvp = decode_tensor_map(probe["pytorch_ref"]["jvp"]) + fd_jvp = decode_tensor_map(probe["fd_ref"]["jvp"]) + + self.assertEqual(direction["x"].tolist(), [1.0]) + self.assertEqual(cotangent["value"].tolist(), [1.0]) + self.assertAlmostEqual(float(linearization["value"].item()), expected_linearization, places=12) + self.assertAlmostEqual(float(jvp["value"].item()), expected_linearization, places=12) + self.assertAlmostEqual(float(pytorch_jvp["value"].item()), expected_linearization, places=8) + self.assertAlmostEqual(float(fd_jvp["value"].item()), expected_linearization, places=6) + self.assertAlmostEqual(float(vjp["x"].item()), float(transpose["x"].item()), places=12) + + lhs = float((cotangent["value"] * jvp["value"]).sum().item()) + rhs = float((transpose["x"] * direction["x"]).sum().item()) + self.assertAlmostEqual(lhs, rhs, places=12) + self.assertEqual(probe["jax_ref"]["provenance"]["witness_source"], "jax_test") + self.assertEqual( + record["provenance"]["source_file"], + "jax/_src/internal_test_util/test_harnesses.py", + ) + self.assertEqual( + record["provenance"]["source_function"], + "_make_unary_elementwise_harness", + ) + self.assertEqual(record["provenance"]["seed"], 17) + self.assertIn( + f"harness_fullname={op}_shape_float64_20_20_", + record["provenance"]["comment"], + ) + + def test_torch_aligned_materialization_preserves_published_inputs(self) -> None: + source_path = REPO_ROOT / "cases" / "abs" / "identity.jsonl" + source_case = json.loads(source_path.read_text(encoding="utf-8").splitlines()[0]) + + with tempfile.TemporaryDirectory() as tmpdir: + out_path = jax_v1.materialize_torch_aligned_case_family( + "abs", + "identity", + cases_root=Path(tmpdir), + ) + + generated = json.loads(out_path.read_text(encoding="utf-8").splitlines()[0]) + + self.assertEqual(generated["inputs"], source_case["inputs"]) + self.assertEqual( + generated["probes"][0]["jax_ref"]["provenance"]["witness_source"], + "torch_aligned", + ) + self.assertEqual(generated["provenance"]["source_file"], source_case["provenance"]["source_file"]) + self.assertEqual( + generated["provenance"]["source_function"], + source_case["provenance"]["source_function"], + ) + + def test_main_materialize_smoke_witnesses_keep_strict_first_order_replay_tolerances(self) -> None: + try: + import jax.numpy as jnp # noqa: F401 + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + expected = { + "abs": {"kind": "allclose", "rtol": 1e-12, "atol": 1e-12}, + "exp": {"kind": "allclose", "rtol": 1e-12, "atol": 1e-12}, + } + + with tempfile.TemporaryDirectory() as tmpdir: + with redirect_stdout(io.StringIO()): + exit_code = jax_v1.main( + ["--materialize-all", "--limit", "1", "--cases-root", tmpdir] + ) + + self.assertEqual(exit_code, 0) + for op, expected_first_order in expected.items(): + out_path = Path(tmpdir) / op / "identity.jsonl" + record = json.loads(out_path.read_text(encoding="utf-8").splitlines()[0]) + self.assertEqual(record["comparison"]["first_order"], expected_first_order) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_materialize.py b/tests/test_materialize.py index f2cb583..276725d 100644 --- a/tests/test_materialize.py +++ b/tests/test_materialize.py @@ -3,9 +3,11 @@ import unittest from pathlib import Path -from generators import pytorch_v1 +from generators import jax_v1, pytorch_v1 from tests.test_encoding import FakeTensor +REPO_ROOT = Path(__file__).resolve().parents[1] + class MaterializeTests(unittest.TestCase): def test_skippable_hvp_runtime_error_matches_forward_view_assertion(self) -> None: @@ -188,6 +190,48 @@ def test_materialize_success_case_encodes_raw_probe_payloads(self) -> None: self.assertEqual(case["observable"], {"kind": "identity"}) self.assertEqual(case["provenance"]["comment"], "solve materialization coverage") + def test_materialize_torch_aligned_case_family_preserves_all_source_records(self) -> None: + try: + import jax.numpy as jnp # noqa: F401 + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + source_path = REPO_ROOT / "cases" / "exp" / "identity.jsonl" + source_records = [ + json.loads(line) + for line in source_path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + + with tempfile.TemporaryDirectory() as tmpdir: + out_path = jax_v1.materialize_torch_aligned_case_family( + "exp", + "identity", + limit=3, + cases_root=Path(tmpdir), + source_case_path=source_path, + ) + generated_records = [ + json.loads(line) + for line in out_path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + + self.assertEqual(len(generated_records), 3) + for source_record, generated_record in zip(source_records[:3], generated_records): + self.assertEqual(generated_record["case_id"], source_record["case_id"]) + self.assertEqual(generated_record["dtype"], source_record["dtype"]) + self.assertEqual(generated_record["inputs"], source_record["inputs"]) + self.assertEqual( + generated_record["probes"][0]["pytorch_ref"], + source_record["probes"][0]["pytorch_ref"], + ) + self.assertEqual( + generated_record["probes"][0]["fd_ref"], + source_record["probes"][0]["fd_ref"], + ) + self.assertIn("jax_ref", generated_record["probes"][0]) + if __name__ == "__main__": unittest.main() diff --git a/tests/test_math_notes_structure.py b/tests/test_math_notes_structure.py new file mode 100644 index 0000000..6e4e666 --- /dev/null +++ b/tests/test_math_notes_structure.py @@ -0,0 +1,60 @@ +import re +import unittest +from pathlib import Path + +from validators import math_registry + + +REPO_ROOT = Path(__file__).resolve().parents[1] +REQUIRED_HEADINGS = ( + "## Forward", + "## Linearization", + "## JVP", + "## Transpose", + "## VJP (JAX convention)", + "## VJP (PyTorch convention)", +) + + +class MathNoteStructureTests(unittest.TestCase): + def test_published_note_paths_use_unified_heading_order(self) -> None: + entries = math_registry.load_registry(REPO_ROOT)["entries"] + note_paths = sorted({REPO_ROOT / row["note_path"] for row in entries}) + + for note_path in note_paths: + text = note_path.read_text(encoding="utf-8") + last_index = -1 + for heading in REQUIRED_HEADINGS: + current_index = text.find(heading) + self.assertNotEqual( + current_index, + -1, + msg=f"{note_path.name} is missing required heading {heading!r}", + ) + self.assertGreater( + current_index, + last_index, + msg=f"{note_path.name} does not preserve unified heading order", + ) + last_index = current_index + + def test_published_note_paths_state_raw_output_space_and_complex_convention(self) -> None: + entries = math_registry.load_registry(REPO_ROOT)["entries"] + note_paths = sorted({REPO_ROOT / row["note_path"] for row in entries}) + + for note_path in note_paths: + text = note_path.read_text(encoding="utf-8") + self.assertIn( + "raw-output-space", + text, + msg=f"{note_path.name} must describe raw-output-space rules", + ) + self.assertRegex( + text, + re.compile(r"real Frobenius inner\s+product"), + msg=f"{note_path.name} must state the transpose convention", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_math_registry.py b/tests/test_math_registry.py index 0417a27..a9b9d31 100644 --- a/tests/test_math_registry.py +++ b/tests/test_math_registry.py @@ -358,6 +358,16 @@ def test_repo_contains_remaining_known_rule_notes(self) -> None: self.assertTrue(expected.issubset({path.name for path in note_dir.glob("*.md")})) + def test_repo_math_index_describes_unified_note_model(self) -> None: + text = ( + Path(__file__).resolve().parents[1] / "docs" / "math" / "index.md" + ).read_text(encoding="utf-8") + + self.assertIn("raw-output-space", text) + self.assertIn("## Unified Note Model", text) + self.assertIn("VJP (JAX convention)", text) + self.assertIn("VJP (PyTorch convention)", text) + def test_repo_scalar_ops_note_exposes_representative_op_anchors(self) -> None: note_path = Path(__file__).resolve().parents[1] / "docs" / "math" / "scalar_ops.md" anchors = math_registry.extract_markdown_anchors(note_path.read_text(encoding="utf-8")) diff --git a/tests/test_probes.py b/tests/test_probes.py index b6fb076..2df7435 100644 --- a/tests/test_probes.py +++ b/tests/test_probes.py @@ -101,6 +101,141 @@ def test_make_probe_record_includes_optional_hvp_payloads(self) -> None: self.assertEqual(probe["pytorch_ref"]["hvp"], direction) self.assertEqual(probe["fd_ref"]["hvp"], direction) + def test_make_probe_record_includes_optional_jax_payloads(self) -> None: + direction = { + "a": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + } + cotangent = { + "value": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + } + jax_payload = { + "value": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + } + jax_adjoint_check = { + "lhs": 1.0, + "rhs": 1.0, + "abs_err": 0.0, + "rel_err": 0.0, + } + jax_provenance = { + "source_backend": "jax", + "witness_source": "torch_aligned", + "jax_version": "0.0.0", + "jaxlib_version": "0.0.0", + "backend": "cpu", + "enable_x64": True, + } + + probe = probes.make_probe_record( + probe_id="p0", + direction=direction, + cotangent=cotangent, + pytorch_jvp=cotangent, + pytorch_vjp=direction, + fd_step=1e-6, + fd_jvp=cotangent, + jax_jvp=jax_payload, + jax_vjp=direction, + jax_linearization=jax_payload, + jax_raw_output_cotangent=jax_payload, + jax_transpose=direction, + jax_adjoint_check=jax_adjoint_check, + jax_provenance=jax_provenance, + ) + + self.assertEqual(probe["jax_ref"]["jvp"], jax_payload) + self.assertEqual(probe["jax_ref"]["vjp"], direction) + self.assertEqual(probe["jax_ref"]["linearization"], jax_payload) + self.assertEqual(probe["jax_ref"]["raw_output_cotangent"], jax_payload) + self.assertEqual(probe["jax_ref"]["transpose"], direction) + self.assertEqual(probe["jax_ref"]["adjoint_check"], jax_adjoint_check) + self.assertEqual(probe["jax_ref"]["provenance"], jax_provenance) + self.assertEqual(probe["pytorch_ref"]["vjp"], direction) + self.assertEqual(probe["fd_ref"]["jvp"], cotangent) + + def test_make_probe_record_omits_jax_ref_for_pytorch_only_payloads(self) -> None: + direction = { + "a": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + } + cotangent = { + "value": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + } + + probe = probes.make_probe_record( + probe_id="p0", + direction=direction, + cotangent=cotangent, + pytorch_jvp=cotangent, + pytorch_vjp=direction, + fd_step=1e-6, + fd_jvp=cotangent, + ) + + self.assertNotIn("jax_ref", probe) + + def test_make_probe_record_rejects_partial_jax_payloads(self) -> None: + direction = { + "a": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + } + cotangent = { + "value": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + } + jax_provenance = { + "source_backend": "jax", + "witness_source": "torch_aligned", + "jax_version": "0.0.0", + "jaxlib_version": "0.0.0", + "backend": "cpu", + "enable_x64": True, + } + + with self.assertRaises(ValueError): + probes.make_probe_record( + probe_id="p0", + direction=direction, + cotangent=cotangent, + pytorch_jvp=cotangent, + pytorch_vjp=direction, + fd_step=1e-6, + fd_jvp=cotangent, + jax_provenance=jax_provenance, + ) + if __name__ == "__main__": unittest.main() diff --git a/tests/test_pytorch_v1.py b/tests/test_pytorch_v1.py index 1a2da16..a4399c5 100644 --- a/tests/test_pytorch_v1.py +++ b/tests/test_pytorch_v1.py @@ -8,6 +8,8 @@ from generators import pytorch_v1 +REPO_ROOT = Path(__file__).resolve().parents[1] + class PytorchV1RegistryTests(unittest.TestCase): def test_build_case_families_returns_expected_registry(self) -> None: @@ -327,6 +329,22 @@ def test_main_materialize_abs_identity_writes_file(self) -> None: self.assertEqual(exit_code, 0) self.assertTrue((Path(tmpdir) / "abs" / "identity.jsonl").exists()) + def test_published_abs_and_exp_cases_include_jax_refs(self) -> None: + for op in ("abs", "exp"): + path = REPO_ROOT / "cases" / op / "identity.jsonl" + records = [ + json.loads(line) + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + + self.assertGreater(len(records), 0) + for record in records: + self.assertEqual(record["expected_behavior"], "success") + self.assertIn("jax_ref", record["probes"][0], msg=f"{op} record missing jax_ref") + self.assertIn("pytorch_ref", record["probes"][0]) + self.assertIn("fd_ref", record["probes"][0]) + def test_main_materialize_all_writes_every_family(self) -> None: try: import torch # noqa: F401 diff --git a/tests/test_repo_config.py b/tests/test_repo_config.py index 831f896..1ba48be 100644 --- a/tests/test_repo_config.py +++ b/tests/test_repo_config.py @@ -22,6 +22,8 @@ def test_pyproject_declares_uv_managed_non_package_project(self) -> None: self.assertEqual(config["tool"]["uv"]["package"], False) self.assertIn("dev", config["dependency-groups"]) self.assertIn("torch==2.10.0", config["project"]["dependencies"]) + self.assertIn("jax==0.9.1", config["project"]["dependencies"]) + self.assertIn("jaxlib==0.9.1", config["project"]["dependencies"]) def test_readme_documents_uv_sync_and_uv_run(self) -> None: readme = (REPO_ROOT / "README.md").read_text(encoding="utf-8") @@ -48,6 +50,15 @@ def test_readme_documents_math_notes_and_registry(self) -> None: self.assertIn("mathematical AD notes", readme) self.assertIn("oracle database", readme) self.assertIn("docs/math/registry.json", readme) + self.assertIn("dual-backend", readme) + self.assertIn("added in later tasks", readme) + self.assertIn("Planned JAX Surface", readme) + self.assertIn("later tasks", readme) + self.assertIn("not implemented yet", readme) + self.assertIn("uv run python -m generators.jax_v1 --list", readme) + self.assertIn("jax_ref", readme) + self.assertIn("linearization", readme) + self.assertIn("transpose", readme) self.assertTrue((REPO_ROOT / "docs" / "math" / "index.md").exists()) self.assertTrue((REPO_ROOT / "docs" / "math" / "registry.json").exists()) @@ -79,6 +90,10 @@ def test_agents_documents_uv_interpreter_and_expecttest_rules(self) -> None: self.assertIn("3.12.12", agents) self.assertIn("expecttest", agents) self.assertIn("torch==2.10.0", agents) + self.assertIn("jax==0.9.1", agents) + self.assertIn("jaxlib==0.9.1", agents) + self.assertIn("The JAX generator surface is a first-class part of the repository contract.", agents) + self.assertIn("Run every JAX command through `uv run`", agents) def test_github_policy_files_are_present(self) -> None: self.assertTrue((REPO_ROOT / ".github" / "CODEOWNERS").exists()) diff --git a/tests/test_runtime_jax.py b/tests/test_runtime_jax.py new file mode 100644 index 0000000..095fb24 --- /dev/null +++ b/tests/test_runtime_jax.py @@ -0,0 +1,106 @@ +import unittest + +from generators import runtime_jax + + +class RuntimeJaxTests(unittest.TestCase): + def test_import_generation_runtime_returns_pinned_jax_modules(self) -> None: + try: + import jaxlib + + jax, jnp = runtime_jax.import_generation_runtime() + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + self.assertEqual(runtime_jax.normalize_jax_version(jax.__version__), "0.9.1") + self.assertEqual(runtime_jax.normalize_jax_version(jaxlib.__version__), "0.9.1") + + def test_dtype_name_normalizes_jax_dtype_objects(self) -> None: + try: + import jax.numpy as jnp + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + self.assertEqual(runtime_jax.dtype_name(jnp.float32), "float32") + self.assertEqual(runtime_jax.dtype_name(jnp.dtype("complex128")), "complex128") + + def test_normalize_raw_tensor_map_scales_each_array_independently(self) -> None: + try: + import jax.numpy as jnp + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + normalized = runtime_jax.normalize_raw_tensor_map( + { + "a": jnp.array([3.0, 4.0]), + "b": jnp.array([0.0, 0.0]), + } + ) + + self.assertAlmostEqual(float(jnp.linalg.norm(normalized["a"])), 1.0) + self.assertEqual(normalized["b"].tolist(), [0.0, 0.0]) + + def test_compute_jax_observable_jvp_and_vjp(self) -> None: + try: + import jax.numpy as jnp + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + def observable_fn(inputs): + return {"value": inputs["x"] ** 2 + 1.0} + + inputs = {"x": jnp.array([2.0])} + direction = {"x": jnp.array([3.0])} + cotangent = {"value": jnp.array([5.0])} + + jvp = runtime_jax.compute_jax_jvp(observable_fn, inputs, direction) + vjp = runtime_jax.compute_jax_vjp(observable_fn, inputs, cotangent) + + self.assertEqual(jvp["value"].tolist(), [12.0]) + self.assertEqual(vjp["x"].tolist(), [20.0]) + + def test_compute_jax_linearization_returns_directional_output(self) -> None: + try: + import jax.numpy as jnp + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + def raw_output_fn(inputs): + return {"value": jnp.abs(inputs["x"])} + + inputs = {"x": jnp.array([3.0])} + direction = {"x": jnp.array([1.0])} + + linearization, _ = runtime_jax.compute_jax_linearization( + raw_output_fn, + inputs, + direction, + ) + + self.assertEqual(linearization["value"].tolist(), [1.0]) + + def test_compute_jax_transpose_and_adjoint_check(self) -> None: + try: + import jax.numpy as jnp + except Exception as exc: + self.skipTest(f"jax runtime unavailable: {exc}") + + def linear_fn(tangent): + return {"value": tangent["x"] * 12.0} + + inputs = {"x": jnp.array([2.0])} + cotangent = {"value": jnp.array([5.0])} + direction = {"x": jnp.array([3.0])} + + transpose = runtime_jax.compute_jax_transpose(linear_fn, inputs, cotangent) + adjoint_check = runtime_jax.compute_jax_adjoint_check( + runtime_jax.tensor_map_inner_product(cotangent, linear_fn(direction)), + runtime_jax.tensor_map_inner_product(transpose, direction), + ) + + self.assertEqual(transpose["x"].tolist(), [60.0]) + self.assertEqual(adjoint_check, {"lhs": 180.0, "rhs": 180.0, "abs_err": 0.0, "rel_err": 0.0}) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_schema_contract.py b/tests/test_schema_contract.py index 35d2e63..7cde14a 100644 --- a/tests/test_schema_contract.py +++ b/tests/test_schema_contract.py @@ -110,6 +110,356 @@ def test_schema_accepts_minimal_success_case(self) -> None: jsonschema.validate(case, schema) + def test_schema_accepts_optional_jax_ref_in_success_case(self) -> None: + try: + import jsonschema + except ModuleNotFoundError as exc: + self.skipTest(f"jsonschema unavailable: {exc}") + + schema = json.loads(SCHEMA_PATH.read_text(encoding="utf-8")) + tensor = { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + case = { + "schema_version": 1, + "case_id": "solve_f64_identity_jax_001", + "op": "solve", + "dtype": "float64", + "family": "identity", + "expected_behavior": "success", + "inputs": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [2.0]}, + }, + "observable": {"kind": "identity"}, + "comparison": { + "first_order": {"kind": "allclose", "rtol": 1e-10, "atol": 1e-10} + }, + "probes": [ + { + "probe_id": "p0", + "direction": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}, + }, + "cotangent": { + "value": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + }, + "pytorch_ref": { + "jvp": {"value": tensor}, + "vjp": {"a": tensor, "b": tensor}, + }, + "fd_ref": { + "method": "central_difference", + "stencil_order": 2, + "step": 1e-6, + "jvp": {"value": tensor}, + }, + "jax_ref": { + "jvp": {"value": tensor}, + "vjp": {"a": tensor}, + "linearization": {"value": tensor}, + "raw_output_cotangent": {"value": tensor}, + "transpose": {"a": tensor}, + "adjoint_check": { + "lhs": 1.0, + "rhs": 1.0, + "abs_err": 0.0, + "rel_err": 0.0, + }, + "provenance": { + "source_backend": "jax", + "witness_source": "torch_aligned", + "jax_version": "0.0.0", + "jaxlib_version": "0.0.0", + "backend": "cpu", + "enable_x64": True, + }, + }, + } + ], + "provenance": { + "source_repo": "pytorch", + "source_file": "torch/testing/_internal/opinfo/definitions/linalg.py", + "source_function": "sample_inputs_linalg_solve", + "source_commit": "deadbeef", + "comment": "from PyTorch OpInfo", + "generator": "python-pytorch-v1", + "seed": 17, + "torch_version": "2.10.0", + "fd_policy_version": "v1", + }, + } + + jsonschema.validate(case, schema) + + def test_schema_rejects_incomplete_jax_ref(self) -> None: + try: + import jsonschema + except ModuleNotFoundError as exc: + self.skipTest(f"jsonschema unavailable: {exc}") + + schema = json.loads(SCHEMA_PATH.read_text(encoding="utf-8")) + tensor = { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + case = { + "schema_version": 1, + "case_id": "solve_f64_identity_jax_partial_001", + "op": "solve", + "dtype": "float64", + "family": "identity", + "expected_behavior": "success", + "inputs": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [2.0]}, + }, + "observable": {"kind": "identity"}, + "comparison": { + "first_order": {"kind": "allclose", "rtol": 1e-10, "atol": 1e-10} + }, + "probes": [ + { + "probe_id": "p0", + "direction": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}, + }, + "cotangent": { + "value": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + }, + "pytorch_ref": { + "jvp": {"value": tensor}, + "vjp": {"a": tensor, "b": tensor}, + }, + "fd_ref": { + "method": "central_difference", + "stencil_order": 2, + "step": 1e-6, + "jvp": {"value": tensor}, + }, + "jax_ref": { + "provenance": { + "source_backend": "jax", + "witness_source": "torch_aligned", + "jax_version": "0.0.0", + "jaxlib_version": "0.0.0", + "backend": "cpu", + "enable_x64": True, + } + }, + } + ], + "provenance": { + "source_repo": "pytorch", + "source_file": "torch/testing/_internal/opinfo/definitions/linalg.py", + "source_function": "sample_inputs_linalg_solve", + "source_commit": "deadbeef", + "comment": "from PyTorch OpInfo", + "generator": "python-pytorch-v1", + "seed": 17, + "torch_version": "2.10.0", + "fd_policy_version": "v1", + }, + } + + with self.assertRaises(jsonschema.ValidationError): + jsonschema.validate(case, schema) + + def test_schema_rejects_invalid_jax_witness_source(self) -> None: + try: + import jsonschema + except ModuleNotFoundError as exc: + self.skipTest(f"jsonschema unavailable: {exc}") + + schema = json.loads(SCHEMA_PATH.read_text(encoding="utf-8")) + case = { + "schema_version": 1, + "case_id": "solve_f64_identity_jax_bad_source_001", + "op": "solve", + "dtype": "float64", + "family": "identity", + "expected_behavior": "success", + "inputs": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [2.0]}, + }, + "observable": {"kind": "identity"}, + "comparison": { + "first_order": {"kind": "allclose", "rtol": 1e-10, "atol": 1e-10} + }, + "probes": [ + { + "probe_id": "p0", + "direction": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}, + }, + "cotangent": { + "value": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + }, + "pytorch_ref": { + "jvp": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + "vjp": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}, + }, + }, + "fd_ref": { + "method": "central_difference", + "stencil_order": 2, + "step": 1e-6, + "jvp": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + }, + "jax_ref": { + "jvp": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + "vjp": {"a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}}, + "linearization": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + "raw_output_cotangent": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + "transpose": {"a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}}, + "adjoint_check": { + "lhs": 1.0, + "rhs": 1.0, + "abs_err": 0.0, + "rel_err": 0.0, + }, + "provenance": { + "source_backend": "jax", + "witness_source": "not_a_valid_source", + "jax_version": "0.0.0", + "jaxlib_version": "0.0.0", + "backend": "cpu", + "enable_x64": True, + }, + }, + } + ], + "provenance": { + "source_repo": "pytorch", + "source_file": "torch/testing/_internal/opinfo/definitions/linalg.py", + "source_function": "sample_inputs_linalg_solve", + "source_commit": "deadbeef", + "comment": "from PyTorch OpInfo", + "generator": "python-pytorch-v1", + "seed": 17, + "torch_version": "2.10.0", + "fd_policy_version": "v1", + }, + } + + with self.assertRaises(jsonschema.ValidationError): + jsonschema.validate(case, schema) + + def test_schema_rejects_malformed_jax_adjoint_check(self) -> None: + try: + import jsonschema + except ModuleNotFoundError as exc: + self.skipTest(f"jsonschema unavailable: {exc}") + + schema = json.loads(SCHEMA_PATH.read_text(encoding="utf-8")) + case = { + "schema_version": 1, + "case_id": "solve_f64_identity_jax_bad_adjoint_001", + "op": "solve", + "dtype": "float64", + "family": "identity", + "expected_behavior": "success", + "inputs": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [2.0]}, + }, + "observable": {"kind": "identity"}, + "comparison": { + "first_order": {"kind": "allclose", "rtol": 1e-10, "atol": 1e-10} + }, + "probes": [ + { + "probe_id": "p0", + "direction": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}, + }, + "cotangent": { + "value": { + "dtype": "float64", + "shape": [1], + "order": "row_major", + "data": [1.0], + } + }, + "pytorch_ref": { + "jvp": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + "vjp": { + "a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}, + "b": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}, + }, + }, + "fd_ref": { + "method": "central_difference", + "stencil_order": 2, + "step": 1e-6, + "jvp": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + }, + "jax_ref": { + "jvp": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + "vjp": {"a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}}, + "linearization": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + "raw_output_cotangent": {"value": {"dtype": "float64", "shape": [1], "order": "row_major", "data": [1.0]}}, + "transpose": {"a": {"dtype": "float64", "shape": [1, 1], "order": "row_major", "data": [1.0]}}, + "adjoint_check": { + "lhs": 1.0, + "rhs": 1.0, + "abs_err": -0.1, + "rel_err": 0.0, + }, + "provenance": { + "source_backend": "jax", + "witness_source": "torch_aligned", + "jax_version": "0.0.0", + "jaxlib_version": "0.0.0", + "backend": "cpu", + "enable_x64": True, + }, + }, + } + ], + "provenance": { + "source_repo": "pytorch", + "source_file": "torch/testing/_internal/opinfo/definitions/linalg.py", + "source_function": "sample_inputs_linalg_solve", + "source_commit": "deadbeef", + "comment": "from PyTorch OpInfo", + "generator": "python-pytorch-v1", + "seed": 17, + "torch_version": "2.10.0", + "fd_policy_version": "v1", + }, + } + + with self.assertRaises(jsonschema.ValidationError): + jsonschema.validate(case, schema) + def test_schema_accepts_hvp_enabled_success_case(self) -> None: try: import jsonschema diff --git a/tests/test_scripts.py b/tests/test_scripts.py index d906710..04ced32 100644 --- a/tests/test_scripts.py +++ b/tests/test_scripts.py @@ -2,6 +2,7 @@ import tempfile import unittest from pathlib import Path +from types import SimpleNamespace from unittest.mock import patch from scripts import ( @@ -45,6 +46,11 @@ def test_load_jsonl_records_reads_multiple_lines(self) -> None: self.assertEqual([record["case_id"] for record in records], ["a", "b"]) + def test_main_also_verifies_jax_smoke_cases(self) -> None: + with patch.object(verify_cases, "verify_case_tree", side_effect=[7, 2]) as verify_case_tree: + self.assertEqual(verify_cases.main(), 0) + self.assertEqual(verify_case_tree.call_count, 2) + class CheckRegenerationTests(unittest.TestCase): def test_compare_case_trees_allows_numeric_drift_within_case_tolerance(self) -> None: @@ -248,6 +254,81 @@ def test_compare_case_trees_treats_nan_payloads_as_equal(self) -> None: check_regeneration.compare_case_trees(expected, actual) + def test_main_also_checks_jax_smoke_regeneration(self) -> None: + with patch.object(check_regeneration, "materialize_all_case_families", return_value=None): + with patch.object(check_regeneration, "_overlay_torch_aligned_jax_refs", return_value=None): + with patch.object(check_regeneration, "compare_case_trees", return_value=None) as compare_case_trees: + self.assertEqual(check_regeneration.main(), 0) + self.assertEqual(compare_case_trees.call_count, 2) + + def test_check_regeneration_overlays_torch_aligned_jax_refs(self) -> None: + published_record = { + "case_id": "abs_f64_identity_001", + "probes": [{"probe_id": "p0"}], + } + + with tempfile.TemporaryDirectory() as tmpdir: + cases_root = Path(tmpdir) / "published" + (cases_root / "abs").mkdir(parents=True) + (cases_root / "exp").mkdir(parents=True) + (cases_root / "abs" / "identity.jsonl").write_text( + json.dumps(published_record) + "\n", + encoding="utf-8", + ) + (cases_root / "exp" / "identity.jsonl").write_text( + json.dumps(published_record | {"case_id": "exp_f64_identity_001"}) + "\n", + encoding="utf-8", + ) + + def fake_materialize_all_case_families(*, limit, cases_root): + del limit + (cases_root / "abs").mkdir(parents=True, exist_ok=True) + (cases_root / "exp").mkdir(parents=True, exist_ok=True) + (cases_root / "abs" / "identity.jsonl").write_text( + json.dumps(published_record) + "\n", + encoding="utf-8", + ) + (cases_root / "exp" / "identity.jsonl").write_text( + json.dumps(published_record | {"case_id": "exp_f64_identity_001"}) + "\n", + encoding="utf-8", + ) + return [ + cases_root / "abs" / "identity.jsonl", + cases_root / "exp" / "identity.jsonl", + ] + + with patch.object( + check_regeneration, + "materialize_all_case_families", + side_effect=fake_materialize_all_case_families, + ): + with patch.object( + check_regeneration.jax_v1, + "materialize_torch_aligned_case_family", + return_value=None, + ) as overlay: + with patch.object(check_regeneration, "compare_case_trees", return_value=None): + compared = check_regeneration.check_regeneration(cases_root=cases_root) + + self.assertEqual(compared, 2) + self.assertEqual(overlay.call_count, 2) + self.assertEqual(overlay.call_args_list[0].args, ("abs", "identity")) + self.assertEqual(overlay.call_args_list[1].args, ("exp", "identity")) + for call in overlay.call_args_list: + self.assertIsNone(call.kwargs["limit"]) + self.assertTrue(str(call.kwargs["source_case_path"]).endswith("identity.jsonl")) + + def test_main_raises_when_jax_smoke_regeneration_fails(self) -> None: + with patch.object(check_regeneration, "materialize_all_case_families", return_value=None): + with patch.object(check_regeneration, "_overlay_torch_aligned_jax_refs", return_value=None): + with patch.object( + check_regeneration, + "compare_case_trees", + side_effect=[None, ValueError("jax smoke regeneration mismatch")], + ): + with self.assertRaisesRegex(ValueError, "jax smoke regeneration mismatch"): + check_regeneration.main() + class ValidateSchemaTests(unittest.TestCase): def test_require_jsonschema_dependency_raises_clear_error(self) -> None: @@ -285,6 +366,28 @@ def test_main_raises_on_failures(self) -> None: with self.assertRaisesRegex(SystemExit, "bad_case: mismatch"): check_replay.main() + def test_main_also_replays_jax_smoke_cases(self) -> None: + published = SimpleNamespace(checked=7, failures=[]) + jax_smoke = SimpleNamespace(checked=2, failures=[]) + with patch.object( + check_replay, + "replay_case_tree", + side_effect=[published, jax_smoke], + ) as replay_case_tree: + self.assertEqual(check_replay.main(), 0) + self.assertEqual(replay_case_tree.call_count, 2) + + def test_main_raises_when_jax_smoke_replay_fails(self) -> None: + published = SimpleNamespace(checked=7, failures=[]) + jax_smoke = SimpleNamespace(checked=2, failures=["jax_case: mismatch"]) + with patch.object( + check_replay, + "replay_case_tree", + side_effect=[published, jax_smoke], + ): + with self.assertRaisesRegex(SystemExit, "jax_case: mismatch"): + check_replay.main() + class CheckMathRegistryScriptTests(unittest.TestCase): def test_main_reports_success_for_valid_repo(self) -> None: diff --git a/tests/test_validator_encoding.py b/tests/test_validator_encoding.py index 107c209..e3cbf84 100644 --- a/tests/test_validator_encoding.py +++ b/tests/test_validator_encoding.py @@ -1,4 +1,6 @@ +import json import unittest +from pathlib import Path from validators import encoding @@ -59,3 +61,16 @@ def test_decode_complex64_tensor_round_trips_pairs(self) -> None: self.assertEqual(str(tensor.dtype), "torch.complex64") self.assertEqual(tuple(tensor.shape), (1,)) self.assertEqual(tensor.tolist(), [complex(1.0, -0.5)]) + + def test_decode_tensor_map_round_trips_published_jax_ref_payloads(self) -> None: + path = Path(__file__).resolve().parents[1] / "cases" / "abs" / "identity.jsonl" + record = json.loads(path.read_text(encoding="utf-8").splitlines()[0]) + jax_ref = record["probes"][0]["jax_ref"] + + linearization = encoding.decode_tensor_map(jax_ref["linearization"]) + transpose = encoding.decode_tensor_map(jax_ref["transpose"]) + raw_output_cotangent = encoding.decode_tensor_map(jax_ref["raw_output_cotangent"]) + + self.assertEqual(set(linearization), {"value"}) + self.assertEqual(set(transpose), {"a"}) + self.assertEqual(set(raw_output_cotangent), {"value"}) diff --git a/uv.lock b/uv.lock index ffd116f..347c3d9 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,10 @@ version = 1 revision = 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100644 --- a/validators/replay.py +++ b/validators/replay.py @@ -2,10 +2,12 @@ from __future__ import annotations +import math from dataclasses import dataclass, field from pathlib import Path from generators.pytorch_v1 import build_case_spec_index +from generators import runtime_jax from generators.runtime import ( apply_spec_observable, build_call_metadata, @@ -92,6 +94,133 @@ def _decode_record_inputs(record: dict) -> dict[str, object]: } +def _decode_record_inputs_jax(jnp, encoded: dict[str, object]) -> dict[str, object]: + return { + name: jnp.asarray(tensor.detach().cpu().numpy()) + for name, tensor in decode_tensor_map(encoded).items() + } + + +def _decode_tensor_map_jax(jnp, encoded: dict[str, object]) -> dict[str, object]: + return _decode_record_inputs_jax(jnp, encoded) + + +def _jax_map_close(jnp, expected: dict[str, object], actual: dict[str, object], *, comparison: dict) -> bool: + if expected.keys() != actual.keys(): + return False + first_order = _first_order_comparison(comparison) + rtol = max(float(first_order["rtol"]), 1e-8) + atol = max(float(first_order["atol"]), 1e-9) + return all( + expected[name].shape == actual[name].shape + and expected[name].dtype == actual[name].dtype + and bool( + jnp.allclose( + expected[name], + actual[name], + rtol=rtol, + atol=atol, + equal_nan=True, + ) + ) + for name in expected + ) + + +def _scalar_close(lhs: float, rhs: float, *, comparison: dict) -> bool: + first_order = _first_order_comparison(comparison) + rtol = max(float(first_order["rtol"]), 1e-8) + atol = max(float(first_order["atol"]), 1e-9) + return math.isclose(lhs, rhs, rel_tol=rtol, abs_tol=atol) + + +def _simple_jax_raw_output_function(op: str, input_name: str, *, family: str): + _, jnp = runtime_jax.import_generation_runtime() + if op == "abs": + return lambda inputs: {"value": jnp.abs(inputs[input_name])} + if op == "exp": + return lambda inputs: {"value": jnp.exp(inputs[input_name])} + raise ValueError(f"unsupported JAX-backed family for replay: {op}/{family}") + + +def _simple_torch_observable(op: str, input_name: str): + import torch + + if op == "abs": + return lambda x: torch.abs(x) + if op == "exp": + return lambda x: torch.exp(x) + raise ValueError(f"unsupported JAX-backed family for replay: {op}/identity") + + +def _replay_simple_success_case(record: dict) -> None: + import torch + + if (record["op"], record["family"]) not in {("abs", "identity"), ("exp", "identity")}: + raise ValueError(f"unsupported JAX-backed family for replay: {record['op']}/{record['family']}") + + inputs = _decode_record_inputs(record) + input_name = next(iter(inputs)) + direction = decode_tensor_map(record["probes"][0]["direction"]) + cotangent = decode_tensor_map(record["probes"][0]["cotangent"]) + stored_pytorch_jvp = decode_tensor_map(record["probes"][0]["pytorch_ref"]["jvp"]) + stored_pytorch_vjp = decode_tensor_map(record["probes"][0]["pytorch_ref"]["vjp"]) + stored_fd_jvp = decode_tensor_map(record["probes"][0]["fd_ref"]["jvp"]) + fd_step = float(record["probes"][0]["fd_ref"]["step"]) + first_order = _first_order_comparison(record["comparison"]) + + observable = _simple_torch_observable(record["op"], input_name) + output = observable(inputs[input_name]) + _, jvp_tuple = torch.func.jvp(observable, (inputs[input_name],), (direction[input_name],)) + pytorch_jvp = {"value": jvp_tuple} + pytorch_vjp = { + input_name: torch.autograd.grad( + output, + inputs[input_name], + grad_outputs=cotangent["value"], + allow_unused=False, + )[0] + } + plus_output = observable(inputs[input_name] + fd_step * direction[input_name]) + minus_output = observable(inputs[input_name] - fd_step * direction[input_name]) + fd_jvp = {"value": (plus_output - minus_output) / (2.0 * fd_step)} + + if not map_allclose( + torch, + stored_pytorch_jvp, + pytorch_jvp, + rtol=first_order["rtol"], + atol=first_order["atol"], + ): + raise ValueError("stored and replayed PyTorch JVP disagree") + if not map_allclose( + torch, + stored_pytorch_vjp, + pytorch_vjp, + rtol=first_order["rtol"], + atol=first_order["atol"], + ): + raise ValueError("stored and replayed PyTorch VJP disagree") + if not map_allclose( + torch, + stored_fd_jvp, + fd_jvp, + rtol=first_order["rtol"], + atol=first_order["atol"], + ): + raise ValueError("stored and replayed FD-JVP disagree") + + validate_live_success_probe( + torch, + comparison=_live_cross_check_comparison(record["comparison"]), + direction=direction, + cotangent=cotangent, + pytorch_jvp=pytorch_jvp, + pytorch_vjp=pytorch_vjp, + fd_jvp=fd_jvp, + ) + + def _decode_success_probe(record: dict) -> tuple[dict[str, object], dict[str, object], dict[str, object], dict[str, object], float]: probe = record["probes"][0] return ( @@ -121,6 +250,17 @@ def _second_order_comparison(comparison: dict) -> dict | None: return comparison.get("second_order") +def _live_cross_check_comparison(comparison: dict) -> dict: + first_order = _first_order_comparison(comparison) + return { + "first_order": { + **first_order, + "rtol": max(float(first_order["rtol"]), 1e-8), + "atol": max(float(first_order["atol"]), 1e-9), + } + } + + def validate_live_success_probe( torch, *, @@ -402,6 +542,57 @@ def _replay_success_case_for_sample( return None +def _replay_jax_success_case(record: dict) -> None: + _replay_simple_success_case(record) + + jax_ref = record["probes"][0]["jax_ref"] + if (record["op"], record["family"]) not in {("abs", "identity"), ("exp", "identity")}: + raise ValueError(f"unsupported JAX-backed family for replay: {record['op']}/{record['family']}") + + _, jnp = runtime_jax.import_generation_runtime() + inputs = _decode_record_inputs_jax(jnp, record["inputs"]) + direction = _decode_record_inputs_jax(jnp, record["probes"][0]["direction"]) + cotangent = _decode_record_inputs_jax(jnp, record["probes"][0]["cotangent"]) + stored_jvp = _decode_tensor_map_jax(jnp, jax_ref["jvp"]) + stored_vjp = _decode_tensor_map_jax(jnp, jax_ref["vjp"]) + stored_linearization = _decode_tensor_map_jax(jnp, jax_ref["linearization"]) + stored_raw_output_cotangent = _decode_tensor_map_jax(jnp, jax_ref["raw_output_cotangent"]) + stored_transpose = _decode_tensor_map_jax(jnp, jax_ref["transpose"]) + stored_adj = jax_ref["adjoint_check"] + + input_name = next(iter(inputs)) + raw_output_fn = _simple_jax_raw_output_function(record["op"], input_name, family=record["family"]) + jvp = runtime_jax.compute_jax_jvp(raw_output_fn, inputs, direction) + vjp = runtime_jax.compute_jax_vjp(raw_output_fn, inputs, cotangent) + linearization, linear_fn = runtime_jax.compute_jax_linearization( + raw_output_fn, + inputs, + direction, + ) + transpose = runtime_jax.compute_jax_transpose(linear_fn, inputs, cotangent) + adjoint_check = runtime_jax.compute_jax_adjoint_check( + runtime_jax.tensor_map_inner_product(cotangent, jvp), + runtime_jax.tensor_map_inner_product(transpose, direction), + ) + + if not _jax_map_close(jnp, stored_raw_output_cotangent, cotangent, comparison=record["comparison"]): + raise ValueError("stored and replayed JAX raw-output cotangent disagree") + if not _jax_map_close(jnp, stored_jvp, jvp, comparison=record["comparison"]): + raise ValueError("stored and replayed JAX JVP disagree") + if not _jax_map_close(jnp, stored_vjp, vjp, comparison=record["comparison"]): + raise ValueError("stored and replayed JAX VJP disagree") + if not _jax_map_close(jnp, stored_linearization, linearization, comparison=record["comparison"]): + raise ValueError("stored and replayed JAX linearization disagree") + if not _jax_map_close(jnp, stored_transpose, transpose, comparison=record["comparison"]): + raise ValueError("stored and replayed JAX transpose disagree") + + if not all( + _scalar_close(float(stored_adj[name]), float(adjoint_check[name]), comparison=record["comparison"]) + for name in ("lhs", "rhs", "abs_err", "rel_err") + ): + raise ValueError("stored and replayed JAX adjoint check disagree") + + def _replay_success_case( record: dict, *, @@ -510,10 +701,13 @@ def replay_case_file(path: Path, *, limit: int | None = None) -> ReplayResult: for record in records[:limit]: try: if record["expected_behavior"] == "success": - _replay_success_case( - record, - prepared_sample_cache=prepared_sample_cache, - ) + if record.get("probes") and record["probes"][0].get("jax_ref") is not None: + _replay_jax_success_case(record) + else: + _replay_success_case( + record, + prepared_sample_cache=prepared_sample_cache, + ) elif record["expected_behavior"] == "error": _replay_error_case(record) else: