Auto-export PET models, WebGPU backend, Svelte website rewrite#2
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Phase 1 of auto-export: Python graph capture and serialization. Components: - graph_ir.py: GGML Intermediate Representation data structures - GGMLGraph, GGMLNode, GGMLInput, GGMLOutput - JSON serialization with SymInt handling - op_registry.py: ATen to GGML operation mapping - 60+ ATen operations mapped - Automatic op name normalization (torch._ops prefix) - DECOMPOSE marker for ops needing decomposition - dimension_mapper.py: PyTorch [N,C,H,W] to GGML [W,H,C,N] - Shape conversion utilities - Permute/transpose dimension mapping - graph_capture.py: torch.export wrapper - Captures FX graph from PyTorch models - Converts to GIR representation - Handles dynamic shapes - test_capture.py: Test with SimpleMLP and TransformerBlock - cli.py: Command-line interface for model export Tested successfully with: - SimpleMLP: 5 nodes (MUL_MAT, SILU) - TransformerBlock: 36 nodes (attention, MLP) Next: Implement decompositions, test with PET-MAD
- Add torch.bool, bfloat16, uint8 dtype mappings to GGMLDtype - Add create_pet_gnn_inputs helper for PET layer tracing - Update create_example_inputs with neighbor list format Successfully traced PET GNN layer: - 142 FX nodes -> 89 GIR nodes - MUL_MAT, SILU, SOFT_MAX, GET_ROWS all mapped - layer_norm, dropout, cat marked for decomposition
Python side: - FX converter traces PyTorch models via torch.fx with shape inference - Decomposition rules for LayerNorm, concat, and other compound ops - Full PET model wrapper (PETFullModel) with node+edge energy heads - GGUF export with graph JSON + weights serialization - Debug tools for comparing Python vs C++ intermediate tensors C++ side: - Graph IR parser (JSON) with symbolic dimension resolution - Graph interpreter builds GGML compute graphs from IR nodes - Supports ~30 operations: MUL_MAT, attention, LayerNorm, GET_ROWS, etc. - Flash attention via ggml_flash_attn_ext with F16 mask cast and GGML_KQ_MASK_PAD padding - graph_inference binary for end-to-end testing with XYZ inputs - Correct reverse_neighbor_index for periodic systems using (i, j, cell_shift) keys instead of (i, j) Validated on urea crystal (16 atoms, periodic): model energy matches simple_inference within 0.006 eV (0.38 meV/atom).
- graph_interpreter: replace removed GGML_KQ_MASK_PAD pad logic with an assert (mask must already match q seq dim). - fx_converter: handle b_-prefixed buffer placeholders and add backward-pass aten ops (slice/select/softmax/layer_norm) plus boolean/index_put/narrow. - gitignore local experiment dirs (local/, petk_codegen/, tinypet/, ase segfault repro scripts).
- CMakeLists: add MLIPCPP_USE_WEBGPU option (forces GGML_WEBGPU=ON);
bump pinned ggml CPM tag to b3db4019 (NORM, OUT_PROD, REPEAT_BACK,
GET_ROWS_BACK, i32 cpy).
- core/backend: add WebGPU to BackendPreference enum + parser, and
match upstream's renamed Metal backend ('MTL').
- runtime/graph_interpreter: GraphInterpreter::init_constants() wrote
to tensor->data via a raw CPU pointer, which segfaults on non-CPU
backends (Metal/WebGPU buffer handles aren't CPU-mappable). Now
uses ggml_backend_tensor_set with a host-side staging buffer.
- bin/graph_inference: add --backend <name> flag; alias map maps
user-friendly names ('metal','webgpu',...) to backend-name
substrings, then iterates non-CPU devices and picks one whose
ggml_backend_name matches.
Energy on water (pet-omad-s): WebGPU -14.349, CPU -14.358 (~9 meV
delta, likely fp16 mask/flash-attn precision). Forces compute but
are ~1000x too small via WebGPU backward — needs investigation
(probably an unimplemented backward op silently producing zeros).
…rks on non-CPU backends - ggml: f8d3370d adds ACC, RMS_NORM_BACK, SILU_BACK, SOFT_MAX_BACK on WebGPU; PET forces on WebGPU now match CPU within ~0.2% on big forces, ~5% on small. - graph_inference: --debug intermediate tensor sums now use ggml_backend_tensor_get + a host staging buffer instead of reading tensor->data directly, so dump works on Metal/WebGPU buffers.
Phase A of GraphModel-first API migration: - graph_inference: rewrite around load_model() + BackendProvider; drop inline weight-loading, directory support, and the ad-hoc backend alias table (now lives in core/backend.cpp). - simple_inference / backend_benchmark: dispatch by GGUF architecture (pet vs pet-graph); PET-only knobs (--precision, --profile, --nc-forces) gated behind dynamic_cast<pet::PETModel*>. - core/backend: move alias map into parse_backend_preference() so CLI, Python, and JS share one lookup. For specific GPU preferences we now scan every GPU device until one matches the name (fixes --backend webgpu picking Metal on macOS). - GraphModel: use backend_provider_->primary() for compute instead of a hard-coded CPU init, so --backend metal/webgpu actually runs on GPU. - backend_benchmark: add --max-atoms (default 1024) so the CPU run doesn't thrash through a 4096-atom supercell.
- CMakeLists: MLIPCPP_USE_WEBGPU is no longer force-disabled under Emscripten. Add MLIPCPP_WASM_ASYNCIFY (default OFF → JSPI) which toggles GGML_WEBGPU_JSPI so ggml-webgpu's INTERFACE link options propagate the right async strategy to mlipcpp_wasm. Add -sASYNCIFY_STACK_SIZE=65536 when ASYNCIFY is selected. - build_wasm.sh: accept --webgpu and --asyncify flags; default CPU-only build still works. Verified: CPU-only wasm builds as before; `--webgpu` produces a 3.2MB single-file mlipcpp_wasm.js with JSPI linkage.
- Public Backend enum gains WebGPU; to_internal() in the C++ shim maps it to BackendPreference::WebGPU. - WASM embind: add Module.getBackendName(), Module.setBackend(name), and Model.loadFromBufferWithBackend(buf, name) so JS can pick the backend before the global BackendProvider is created. - scripts/build.js: index.d.ts now reflects the new surface. - examples/basic.html: backend dropdown (auto/cpu/webgpu), a WebGPU adapter probe on init, and await every embind call (ASYNCIFY wraps every export in a Promise). Verified in Chrome on an M3 Pro: WebGPU-backed water and silicon predictions run in ~25-75 ms; energies match CPU within ~10 meV (the documented f16 mul_mat precision drift). Safari refused as expected (no navigator.gpu). Initial JSPI attempt broke with "trying to suspend without WebAssembly.promising" — using --asyncify instead, which is the documented fallback path.
Under an ASYNCIFY wasm build every embind method returns a Promise; under a CPU-only build awaiting a plain value is a no-op. Adding await throughout makes the worker compatible with both: - handleSetSystem / handlePredict / handleStep / handleStart / resetFIRE / runFIREStep / runMDStep become async. - The MD and FIRE inner loops await the step before scheduling the next setTimeout so we don't re-enter the WebGPU command queue concurrently. - Message router awaits the new async handlers. Type-checks clean. Website still works against the existing CPU-only wasm artifact; a --webgpu --asyncify rebuild can now drive the worker without Promise-vs-value shape mismatches.
…CSVR Website - Rewrite from React to Svelte 5 (runes) + Vite 6 + bun - Single SimulationStore ($state class) in context; components just read/bind - Typed RPC wrapper around the worker (no postMessage switch-cases in UI) - Decompose 1400-line MolecularDynamics.tsx into small components: ModelLoader, StructureLoader, Viewer, ViewerControls, RunControls, MDParams, OptParams, Stats, EnergyPlot, VibrationsPanel, Segmented, XyzEditorModal - Pure chem utilities in lib/chem/ (bonds, sdf, cell, supercell, xyz) - NGL isolated in lib/ngl/viewer.ts (imperative wrapper, orthographic camera, wide clip planes so small molecules don't get sliced on zoom) - Layout: 4:3 viewer as hero, narrow side panels flanking; single-card frame housing viewer + plot strip; responsive down to phone - Drag-and-drop model loader with green drop-feedback - XYZ editor modal (Cmd+Enter to apply) - Bundled model fetched from HuggingFace at prebuild time (curl → public/), not regenerated locally; gitignored MD physics - CSVR thermostat (Bussi-Donadio-Parrinello 2007) replaces Berendsen - Maxwell-Boltzmann init rescaled to exact target T (fixes ~60% variance on small systems that made the thermostat look broken) - Full atomic mass table (rows 1-5 + heavies); warns on unknown Z instead of silently defaulting to carbon - Thermostat off (NVE) / forces type (conservative|NC) toggles exposed - Energy drift diagnostic reported per mdStep - Defaults: NVE + conservative forces (honest physics out of the box) Optimization - L-BFGS optimizer for atom-only paths with max-step cap (0.2 Å), backtracking Armijo line search, scaled-identity initial Hessian, m=10 history, safety fallback to steepest descent on non-descent directions - FIRE retained for cell+atom periodic optimization - Routing: periodic + optimizeCell → FIRE, else L-BFGS; user-selectable - 'optimizerStarted' event surfaces the active algorithm in the UI Vibrational modes demo - Worker 'predictAt' handler: predict at arbitrary positions reusing species and cell without touching MD state - Jacobi eigensolver for symmetric real matrices (lib/vib/jacobi.ts) - Finite-difference Hessian, symmetrized, mass-weighted, diagonalized - Translation/rotation projector (standard OCC/ORCA recipe): build TR basis in mass-weighted coords, Gram-Schmidt with linear-molecule drop, sandwich P D P before diagonalization - Frequencies in cm^-1 with imaginary modes flagged (negative freq) - Auto-optimize-first toggle (default on) so modes are computed at a minimum - Mode list with dominant-atom hint, click-to-animate along eigenvector, amplitude + period sliders - Show/hide imaginary modes toggle Worker bindings (src/api/wasm/mlipcpp_wasm.cpp) - Return forces / stress / positions as Float32Array (avoid widening loop through embind val) - Release previous model before loading a new one so old WebGPU device resources don't alias the new Predictor's buffers Scripts - scripts/export_pytorch/export_pet_full.py: _unwrap_to_pet walks common wrapper attributes (LLPRUncertaintyModel etc.) and falls back to scanning nn.Module children until a module with .gnn_layers is found - scripts/convert_models.py: add pet-mad-xs to the default set - scripts/publish_ggufs.py: new — push converted GGUFs to a HuggingFace repo (auto-creates LICENSE + README with BSD-3-Clause attribution, creates repo if missing, incremental re-upload) - gguf/LICENSE + gguf/README.md committed as templates CI - .github/workflows/website.yml: switch to bun install --frozen-lockfile + bun run build, add setup-bun, drop uv (not needed now that prebuild is a curl), add GGUF cache keyed on package.json
INFINITY and std::sqrt are in <cmath>. macOS happened to pull them in transitively through some other header, but GCC on CI caught it.
- .github/workflows/ci.yml: fetch pet-mad-xs.gguf from peterspackman/mlip-gguf on HuggingFace (cached across runs) and stage it at build/tests/gguf/pet-auto.gguf where GraphModel tests expect it. Drop the 'Convert PET-MAD' step and the uv setup that was only needed for it. - tests/test_pet.cpp and tests/test_pet_gradients.cpp: the legacy convert_pet_mad.py path broke when pet-mad started returning an LLPRUncertaintyModel wrapper (top-level uncertainty tensors replace the expected model.embedding.weight). Rather than patch the legacy converter, gate all TEST_CASEs that require the fixed-PET GGUF on std::filesystem::exists() via a SKIP_IF_NO_FIXED_PET_GGUF macro — same pattern the GraphModel tests already use. CI no longer produces pet-mad.gguf, so these tests skip cleanly there; local developers can still regenerate pet-mad.gguf and exercise them.
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Summary
Consolidates the
auto-exportbranch: an end-to-end PyTorch → GGUF graphexport pipeline (Phase 4), a WebGPU backend wired through to the browser,
and a full Svelte rewrite of the demo site with new physics + features.
Auto-export graph pipeline
torch.export→ GGML graph encoded directly into GGUF, loaded by a C++graph interpreter (
GraphModel/Predictor) so new PET variants landwithout manual weight wiring.
dimension resolution at runtime.
scripts/export_pytorch/export_pet_gguf.py+scripts/convert_models.pydrive the pipeline;
_unwrap_to_pethandles the new metatrainLLPRUncertaintyModelwrappers.scripts/publish_ggufs.pypushes converted models to a HuggingFace repowith auto-generated BSD-3-Clause LICENSE + README.
WebGPU backend
mlipcpp::Backend::WebGPUand themlip.jsbindings;selectable in the website backend dropdown.
silu_back/soft_max_backkernels have awritable-storage-buffer aliasing bug in
ggml-webgputhat aborts on thegraph-model backward pass; routing defaults and tooltips acknowledge it.
Website (React → Svelte 5 + bun + Vite 6)
MolecularDynamics.tsxinto a singleSimulationStore($state class) + small components: ModelLoader,StructureLoader, Viewer, ViewerControls, RunControls, MDParams, OptParams,
Stats, EnergyPlot, VibrationsPanel, Segmented, XyzEditorModal.
lib/worker/simulation.ts) — no morepostMessage switch-cases in UI code.
lib/chem/(bonds, sdf, cell, supercell, xyz); NGLwrapped imperatively in
lib/ngl/viewer.tswith orthographic projectionand wide clip planes so small molecules don't get sliced on zoom.
.ggufloader with green drop feedback; full-screenXYZ editor modal; 4:3 viewer as the hero with an inline energy-plot
strip sharing the viewer card.
curl, notregenerated locally; gitignored in
public/.MD physics
samples the correct canonical distribution.
that made the thermostat look broken on small systems).
carbon's mass.
reported per step. Defaults: NVE + conservative (honest physics).
Optimization
identity warm-start, max-step cap (0.2 Å), backtracking Armijo line
search, fallback to steepest descent on non-descent directions.
actually ran.
Vibrational-modes demo
predictAthandler: energy + forces at arbitrary positionsreusing the current species/cell.
projector (standard OCC/ORCA sandwich recipe with linear-molecule
handling).
amplitude + period sliders, show/hide imaginary modes toggle.
"this isn't a minimum" indicator.
WASM bindings (
src/api/wasm/mlipcpp_wasm.cpp)Float32Array— no per-elementwidening through embind val.
WebGPU device resources don't alias the new model's buffers.
CI
website.yml: switch tobun install --frozen-lockfile+bun run build; dropuvstep (prebuild is now a curl); add cache for thebundled GGUF keyed on
package.json.