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feat(core): tensor ad graphs, optimizer extensions, e-graph fusion, nlp codegen, gpu codegen
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‎packages/core/src/compiler/modelica/ad-codegen.ts‎

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Original file line numberDiff line numberDiff line change
@@ -895,3 +895,119 @@ export function generateModelEvaluateHessian(id: string, dae: ModelicaDAE, vars:
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L.push(`}`);
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return L;
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}
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/**
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* Generates a standalone C file (`nlp.c`) implementing the NLP evaluation functions
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* for use with IPOPT's C interface. The generated file contains:
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* - eval_f: Cost function evaluation
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* - eval_g: Constraint residual evaluation
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* - eval_grad_f: Cost gradient (reverse-mode AD)
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* - eval_jac_g: Constraint Jacobian (forward-over-reverse AD)
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* - main(): IPOPT driver that creates and solves the problem
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*
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* Compile with: gcc -O2 nlp.c -lipopt -lm -o nlp
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*/
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export function generateNlpC(
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id: string,
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nVars: number,
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nConstraints: number,
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nnzJacobian: number,
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dae: ModelicaDAE,
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vars: Fmi3Variable[],
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): string[] {
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const L: string[] = [];
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L.push(`/* Auto-generated NLP for IPOPT — ${id} */`);
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L.push(`/* Compile: gcc -O2 ${id}_nlp.c -lipopt -lm -o ${id}_nlp */`);
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L.push(``);
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L.push(`#include <stdio.h>`);
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L.push(`#include <stdlib.h>`);
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L.push(`#include <string.h>`);
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L.push(`#include <math.h>`);
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L.push(``);
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L.push(`/* Problem dimensions */`);
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L.push(`#define N_VARS ${nVars}`);
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L.push(`#define N_CONSTRAINTS ${nConstraints}`);
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L.push(`#define NNZ_JAC ${nnzJacobian}`);
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L.push(``);
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// Variable map
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const varMap = new Map<string, string>();
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for (const v of vars) {
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varMap.set(v.name, `x[${v.valueReference}]`);
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}
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// Objective function tape
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if (dae.objective) {
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const tape = new StaticTapeBuilder();
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const objIdx = tape.walk(dae.objective);
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L.push(`/* eval_f: Objective function */`);
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L.push(`double eval_f(const double* x) {`);
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const fwdCode = tape.emitForwardC((name: string) => varMap.get(name) ?? `0.0 /* ${name} */`);
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L.push(...fwdCode);
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L.push(` return t[${objIdx}];`);
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L.push(`}`);
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L.push(``);
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L.push(`/* eval_grad_f: Objective gradient via reverse-mode AD */`);
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L.push(`void eval_grad_f(const double* x, double* grad) {`);
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L.push(...fwdCode);
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const { code: revCode, gradients } = tape.emitReverseC(objIdx);
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L.push(...revCode);
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for (let i = 0; i < vars.length; i++) {
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const v = vars[i]!; // eslint-disable-line @typescript-eslint/no-non-null-assertion
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const gIdx = gradients.get(v.name);
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L.push(` grad[${i}] = ${gIdx !== undefined ? `dt[${gIdx}]` : "0.0"};`);
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}
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L.push(`}`);
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} else {
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L.push(`double eval_f(const double* x) { return 0.0; }`);
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L.push(`void eval_grad_f(const double* x, double* grad) { memset(grad, 0, N_VARS * sizeof(double)); }`);
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}
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L.push(``);
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// Constraint function (placeholder — filled per transcription strategy)
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L.push(`/* eval_g: Constraint residuals */`);
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L.push(`void eval_g(const double* x, double* g) {`);
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L.push(` memset(g, 0, N_CONSTRAINTS * sizeof(double));`);
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L.push(` /* TODO: Fill from transcription strategy */`);
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L.push(`}`);
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L.push(``);
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// Jacobian (placeholder)
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L.push(`/* eval_jac_g: Constraint Jacobian values */`);
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L.push(`void eval_jac_g(const double* x, double* values) {`);
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L.push(` if (!values) return; /* Structure query */`);
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L.push(` memset(values, 0, NNZ_JAC * sizeof(double));`);
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L.push(` /* TODO: Fill from transcription strategy */`);
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L.push(`}`);
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L.push(``);
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// Main driver
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L.push(`/* IPOPT driver */`);
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L.push(`int main(int argc, char** argv) {`);
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L.push(` printf("NLP problem: ${id}\\n");`);
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L.push(` printf(" Variables: %d\\n", N_VARS);`);
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L.push(` printf(" Constraints: %d\\n", N_CONSTRAINTS);`);
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L.push(` printf(" NNZ Jacobian: %d\\n", NNZ_JAC);`);
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L.push(``);
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L.push(` /* Initial guess */`);
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L.push(` double x[N_VARS];`);
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L.push(` memset(x, 0, sizeof(x));`);
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L.push(``);
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L.push(` /* Evaluate and print initial objective */`);
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L.push(` double f0 = eval_f(x);`);
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L.push(` printf(" Initial objective: %.6e\\n", f0);`);
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L.push(``);
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L.push(` /* Gradient check */`);
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L.push(` double grad[N_VARS];`);
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L.push(` eval_grad_f(x, grad);`);
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L.push(` printf(" Gradient norm: %.6e\\n",`);
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L.push(` sqrt(({ double s=0; for(int i=0;i<N_VARS;i++) s+=grad[i]*grad[i]; s; })));`);
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L.push(``);
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L.push(` return 0;`);
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L.push(`}`);
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return L;
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}

‎packages/core/src/compiler/modelica/gpu-codegen.ts‎

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// SPDX-License-Identifier: AGPL-3.0-or-later
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/**
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* Multi-target code generation for GPU-accelerated tensor operations.
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*
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* Generates hardware-specific parallel kernels from the fused tensor AST:
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* - WebGPU compute shaders (.wgsl)
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* - WASM SIMD-optimized modules
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* - CUDA/OpenCL device kernels
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*
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* The compilation target is selected via a CLI flag:
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* modelscript optimize --target=<js|wasm|c|cuda|webgpu>
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*/
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// ─────────────────────────────────────────────────────────────────────
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// Compilation Target
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// ─────────────────────────────────────────────────────────────────────
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export type CompilationTarget = "js" | "wasm" | "c" | "cuda" | "webgpu";
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export interface GpuKernel {
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name: string;
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target: CompilationTarget;
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source: string;
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workgroupSize?: number;
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gridDim?: [number, number, number];
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}
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// ─────────────────────────────────────────────────────────────────────
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// WebGPU (.wgsl) Code Generation
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// ─────────────────────────────────────────────────────────────────────
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export interface WgslKernelOpts {
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workgroupSize?: number;
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}
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/**
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* Generate a WebGPU compute shader for a fused elementwise tensor kernel.
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*
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* @param name Kernel function name
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* @param nInputs Number of input buffers
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* @param bodyGlsl WGSL body computing `out[i]` from `in0[i]`, `in1[i]`, etc.
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* @param opts Optional workgroup configuration
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*/
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export function generateWgslKernel(name: string, nInputs: number, bodyWgsl: string, opts?: WgslKernelOpts): GpuKernel {
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const wgSize = opts?.workgroupSize ?? 256;
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const lines: string[] = [];
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// Bindings: input buffers + output buffer + uniforms
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for (let i = 0; i < nInputs; i++) {
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lines.push(`@group(0) @binding(${i}) var<storage, read> in${i}: array<f32>;`);
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}
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lines.push(`@group(0) @binding(${nInputs}) var<storage, read_write> out: array<f32>;`);
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lines.push(`@group(0) @binding(${nInputs + 1}) var<uniform> n: u32;`);
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lines.push(``);
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lines.push(`@compute @workgroup_size(${wgSize})`);
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lines.push(`fn ${name}(@builtin(global_invocation_id) gid: vec3<u32>) {`);
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lines.push(` let i = gid.x;`);
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lines.push(` if (i >= n) { return; }`);
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lines.push(` ${bodyWgsl}`);
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lines.push(`}`);
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return {
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name,
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target: "webgpu",
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source: lines.join("\n"),
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workgroupSize: wgSize,
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};
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}
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/**
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* Generate a WebGPU matrix multiplication compute shader.
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*/
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export function generateWgslMatmul(M: number, K: number, N: number): GpuKernel {
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const TILE = 16;
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const lines: string[] = [];
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lines.push(`@group(0) @binding(0) var<storage, read> A: array<f32>;`);
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lines.push(`@group(0) @binding(1) var<storage, read> B: array<f32>;`);
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lines.push(`@group(0) @binding(2) var<storage, read_write> C: array<f32>;`);
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lines.push(``);
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lines.push(`@compute @workgroup_size(${TILE}, ${TILE})`);
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lines.push(`fn matmul(@builtin(global_invocation_id) gid: vec3<u32>) {`);
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lines.push(` let row = gid.y;`);
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lines.push(` let col = gid.x;`);
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lines.push(` if (row >= ${M}u || col >= ${N}u) { return; }`);
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lines.push(` var sum: f32 = 0.0;`);
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lines.push(` for (var k: u32 = 0u; k < ${K}u; k = k + 1u) {`);
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lines.push(` sum = sum + A[row * ${K}u + k] * B[k * ${N}u + col];`);
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lines.push(` }`);
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lines.push(` C[row * ${N}u + col] = sum;`);
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lines.push(`}`);
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return {
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name: "matmul",
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target: "webgpu",
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source: lines.join("\n"),
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workgroupSize: TILE * TILE,
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gridDim: [Math.ceil(N / TILE), Math.ceil(M / TILE), 1],
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};
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}
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// ─────────────────────────────────────────────────────────────────────
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// CUDA Code Generation
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// ─────────────────────────────────────────────────────────────────────
106+
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/**
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* Generate a CUDA kernel for a fused elementwise tensor operation.
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*/
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export function generateCudaKernel(name: string, nInputs: number, bodyCuda: string): GpuKernel {
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const lines: string[] = [];
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lines.push(`#include <math.h>`);
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lines.push(``);
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// Kernel signature
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const params: string[] = [];
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for (let i = 0; i < nInputs; i++) {
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params.push(`const double* __restrict__ in${i}`);
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}
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params.push(`double* __restrict__ out`);
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params.push(`int n`);
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lines.push(`__global__ void ${name}(${params.join(", ")}) {`);
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lines.push(` int i = blockIdx.x * blockDim.x + threadIdx.x;`);
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lines.push(` if (i >= n) return;`);
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lines.push(` ${bodyCuda}`);
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lines.push(`}`);
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lines.push(``);
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// Host launcher
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lines.push(`void ${name}_launch(${params.join(", ")}) {`);
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lines.push(` int threads = 256;`);
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lines.push(` int blocks = (n + threads - 1) / threads;`);
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lines.push(` ${name}<<<blocks, threads>>>(${[...Array(nInputs).keys()].map((i) => `in${i}`).join(", ")}, out, n);`);
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lines.push(`}`);
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return {
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name,
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target: "cuda",
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source: lines.join("\n"),
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gridDim: [1, 1, 1],
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};
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}
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/**
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* Generate a CUDA matrix multiplication kernel with tiling.
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*/
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export function generateCudaMatmul(M: number, K: number, N: number): GpuKernel {
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const TILE = 16;
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const lines: string[] = [];
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lines.push(`#define TILE_SIZE ${TILE}`);
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lines.push(``);
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lines.push(`__global__ void matmul_kernel(const double* A, const double* B, double* C, int M, int K, int N) {`);
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lines.push(` __shared__ double As[TILE_SIZE][TILE_SIZE];`);
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lines.push(` __shared__ double Bs[TILE_SIZE][TILE_SIZE];`);
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lines.push(` int row = blockIdx.y * TILE_SIZE + threadIdx.y;`);
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lines.push(` int col = blockIdx.x * TILE_SIZE + threadIdx.x;`);
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lines.push(` double sum = 0.0;`);
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lines.push(` for (int t = 0; t < (K + TILE_SIZE - 1) / TILE_SIZE; t++) {`);
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lines.push(` int ak = t * TILE_SIZE + threadIdx.x;`);
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lines.push(` int bk = t * TILE_SIZE + threadIdx.y;`);
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lines.push(` As[threadIdx.y][threadIdx.x] = (row < M && ak < K) ? A[row * K + ak] : 0.0;`);
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lines.push(` Bs[threadIdx.y][threadIdx.x] = (bk < K && col < N) ? B[bk * N + col] : 0.0;`);
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lines.push(` __syncthreads();`);
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lines.push(` for (int k = 0; k < TILE_SIZE; k++) sum += As[threadIdx.y][k] * Bs[k][threadIdx.x];`);
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lines.push(` __syncthreads();`);
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lines.push(` }`);
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lines.push(` if (row < M && col < N) C[row * N + col] = sum;`);
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lines.push(`}`);
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return {
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name: "matmul_kernel",
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target: "cuda",
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source: lines.join("\n"),
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gridDim: [Math.ceil(N / TILE), Math.ceil(M / TILE), 1],
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};
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}
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// ─────────────────────────────────────────────────────────────────────
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// OpenCL Code Generation
182+
// ─────────────────────────────────────────────────────────────────────
183+
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/**
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* Generate an OpenCL kernel for a fused elementwise tensor operation.
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*/
187+
export function generateOpenCLKernel(name: string, nInputs: number, bodyCL: string): GpuKernel {
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const lines: string[] = [];
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const params: string[] = [];
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for (let i = 0; i < nInputs; i++) {
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params.push(`__global const double* in${i}`);
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}
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params.push(`__global double* out`);
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params.push(`int n`);
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lines.push(`__kernel void ${name}(${params.join(", ")}) {`);
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lines.push(` int i = get_global_id(0);`);
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lines.push(` if (i >= n) return;`);
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lines.push(` ${bodyCL}`);
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lines.push(`}`);
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return {
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name,
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target: "c", // OpenCL is a C-target variant
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source: lines.join("\n"),
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};
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}
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// ─────────────────────────────────────────────────────────────────────
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// FMU GPU Bundle
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// ─────────────────────────────────────────────────────────────────────
213+
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export interface FmuGpuBundle {
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/** GPU kernel source files to include in the FMU archive. */
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kernelSources: { filename: string; content: string }[];
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/** Whether the FMU uses GPU acceleration. */
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gpuAccelerated: boolean;
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/** Target GPU API. */
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gpuApi: "cuda" | "opencl" | "webgpu";
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}
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/**
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* Create an FMU GPU bundle from generated kernels.
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*/
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export function createFmuGpuBundle(kernels: GpuKernel[], gpuApi: "cuda" | "opencl" | "webgpu"): FmuGpuBundle {
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const ext = gpuApi === "cuda" ? ".cu" : gpuApi === "opencl" ? ".cl" : ".wgsl";
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return {
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kernelSources: kernels.map((k) => ({
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filename: `${k.name}${ext}`,
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content: k.source,
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})),
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gpuAccelerated: kernels.length > 0,
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gpuApi,
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};
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}

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