torch-compile

Plugin torch-compile from marketplace ai-marketplace in TorchedHat/ai-marketplace. Commands read from the repo's manifest — 19 days ago.

Skills (8)

• compile-bisect

  Use PyTorch's compiler bisector to automatically find which backend/subsystem/operation causes compilation failures. Binary searches through backends (eager → aot_eager → inductor) and subsystems (passes, lowerings, etc.) to pinpoint exact failing operations. Outputs backend/subsystem/debug_info tha

• compile-overview

  Reference documentation for torch.compile pipeline architecture, IR levels (Full ATen, Core ATen, Prims), stages, TORCH_LOGS flags, output files, and debugging tools. Use for understanding pipeline structure, operator IRs, and available debugging options.

• compile-trace-aot

  Debug PyTorch AOT Autograd stage - functionalization, decompositions, IR transformations, joint forward+backward graph (when requires_grad=True), partitioning/recomputation, and post-grad passes. Use for tracing AOT stage and understanding decomposition application.

• compile-trace-dynamo

  Debug PyTorch Dynamo stage - bytecode capture, FX graph construction, graph breaks, and pre-grad passes. Covers TORCH_LOGS for dynamo/graph_breaks/pre_grad_graphs, interpreting FX graph files, understanding graph break reasons, and pre-grad fusion patterns (Conv-BN, split-cat). Load after compile-bi

• compile-trace-inductor

  Debug PyTorch Inductor compiler backend - IR lowering, scheduler/fusion, loopbody ops, and Triton/C++ codegen. Covers TORCH_LOGS for fusion/schedule/ir_post_fusion/output_code, config.trace.enabled for IR dumps, interpreting Inductor IR nodes (Pointwise/Reduction/etc), fusion decisions, kernel gener

• pytorch-aot

  Expert guidance for PyTorch functorch and AOT Autograd development. Covers torch/_functorch architecture, AOT Autograd pipeline (functionalization, IR transformations - Full ATen → Core ATen, joint graphs, partitioning, post-grad passes), vmap/batching, functional transforms (grad, vjp, jvp, jacrev,

• pytorch-dynamo

  Expert guidance for PyTorch Dynamo (torch.compile) development and debugging. Covers symbolic execution, VariableTracker system, pytree integration, guard failures, guard generation, FakeTensors, SymInts, symbolic shapes, dynamic shapes, C++ ATen ops, .size() vs .sym_size(), .numel() vs .sym_numel()

• pytorch-inductor

  Expert guidance for PyTorch Inductor compiler backend development and optimization. Covers FX graph lowering, decomposition→lowering pipeline, kernel fusion, Triton codegen, TritonTemplate, TritonTemplateKernel, template system, Jinja2 templates, C++ codegen, scheduling, memory planning, select_algo

Subagents (4)

• aot-expert-agent

  AOT Autograd specialist for functionalization, decomposition, and gradient computation

• compile-debug

  Skill-driven torch.compile debugger. Orchestrates bisection, trace collection, and root cause analysis using stage-specific skills. Use when debugging compilation failures, errors, or incorrect output.

• dynamo-expert-agent

  Dynamo specialist for graph capture, guards, graph breaks, and VariableTracker system

• inductor-expert-agent

  Inductor specialist for lowerings, IR nodes, Triton codegen, fusion, and kernel optimization

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