using-pytorch-engineering

name	using-pytorch-engineering
description	Routes to appropriate PyTorch specialist skill based on symptoms and problem type

Using PyTorch Engineering

Overview

This meta-skill routes you to the right PyTorch specialist based on symptoms. PyTorch engineering problems fall into distinct categories that require specialized knowledge. Load this skill when you encounter PyTorch-specific issues but aren't sure which specialized skill to use.

Core Principle: Different PyTorch problems require different specialists. Match symptoms to the appropriate specialist skill. Don't guess at solutions—route to the expert.

API surface calibrated to PyTorch 2.9+ as of 2026-05. Deprecated torch.cuda.amp aliases have been migrated to torch.amp; FairScale ZeRO references are replaced with native FSDP1/FSDP2. Modern features (torch.compile, FlexAttention, CUDA Graphs, NVTX/Nsight Systems, DTensor, expandable_segments, channels_last, FP8) are covered as first-class topics.

---

About This Pack's API Currency

Reconciliation gate — what you can rely on inside this pack:

• PyTorch 2.9+ baseline. Examples assume the modern API surface. torch.cuda.amp.autocast / torch.cuda.amp.GradScaler are deprecated aliases — sheets use torch.amp.autocast(device_type=...) and torch.amp.GradScaler() instead.
• Distributed = native FSDP. FairScale ZeRO is unmaintained; coverage uses FSDP1 (FullyShardedDataParallel) and FSDP2 (fully_shard, MixedPrecisionPolicy, OffloadPolicy, sharded state dict, init_device_mesh/DTensor). DDP and pipeline parallelism are still covered where appropriate.
• Compile-first thinking. torch.compile (modes, dynamic=, recompilation triage, graph-break debugging) and FlexAttention / scaled_dot_product_attention are first-class — not afterthoughts.
• GPU profiling = modern tooling. PyTorch Profiler, NVTX ranges, Nsight Systems, CUDA Graphs (torch.cuda.graph, make_graphed_callables), and expandable_segments:True for fragmentation are the tools of choice.
• Capability-tiered, not model-pinned. No hardcoded model IDs or vendor-specific assumptions; sheets describe capability tiers and let the caller bind concrete models.
• Entry points. Agents (pytorch-code-reviewer, memory-diagnostician) and commands (/yzmir-pytorch-engineering:debug-nan, :debug-oom, :profile) are the canonical entry points and route into the sheets refreshed in this pass.

---

When to Use

Load this skill when:

• Working with PyTorch and encountering problems
• User mentions: "PyTorch", "torch", "CUDA", "GPU", "distributed training", "torch.compile", "FSDP", "FlexAttention"
• Need to implement PyTorch models or optimize performance
• Debugging PyTorch training issues (NaN, OOM, graph breaks, recompiles, deadlocks)
• Setting up production PyTorch infrastructure

Don't use for: Framework-agnostic ML theory, non-PyTorch frameworks, algorithm selection (use yzmir-training-optimization or other packs)

---

How to Access Reference Sheets

IMPORTANT: All reference sheets are located in the SAME DIRECTORY as this SKILL.md file.

When this skill is loaded from: skills/using-pytorch-engineering/SKILL.md

Reference sheets like tensor-operations-and-memory.md are at: skills/using-pytorch-engineering/tensor-operations-and-memory.md

NOT at: skills/tensor-operations-and-memory.md ← WRONG PATH

When you see a link like [tensor-operations-and-memory.md](tensor-operations-and-memory.md), read the file from the same directory as this SKILL.md.

---

Routing by Symptom

Memory Issues

Symptoms:

• "CUDA out of memory" / "OOM error" / "RuntimeError: CUDA out of memory"
• "GPU memory usage too high"
• "tensor memory leak" / "memory consumption increasing"
• "memory fragmentation" / expandable_segments / "reserved but unallocated"
• channels_last memory format questions
• Activation memory / gradient checkpointing trade-offs

Route to: See tensor-operations-and-memory.md for memory management, expandable_segments:True, channels_last, contiguity, and allocator tuning. For OOM diagnostics paired with profiling traces, also see performance-profiling.md.

Why: Memory management is foundational. Must understand tensor lifecycles, allocator behavior, memory format, and profiling before other optimizations.

Example queries:

• "Getting OOM after a few batches"
• "How to reduce memory usage?"
• "Memory grows over time during training"
• "Allocator says reserved >> allocated — fragmentation?"
• "Should I use channels_last for my CNN?"

---

Module and Model Design

Symptoms:

• "How to structure my PyTorch model?"
• "Custom layer implementation"
• "nn.Module best practices"
• "Forward/backward pass design"
• "Model architecture implementation"
• "Parameter initialization"

Route to: See module-design-patterns.md for model architecture and nn.Module patterns.

Why: Proper module design prevents bugs and enables features like checkpointing, distributed training, torch.compile, and serialization.

Example queries:

• "Building custom ResNet variant"
• "How to organize model components?"
• "Module initialization best practices"

---

Distributed Training Setup

Symptoms:

• "Multiple GPUs" / "Multi-node training" / "Scale training to N GPUs"
• "DistributedDataParallel" / "DDP"
• "FSDP" / "FSDP2" / fully_shard / FullyShardedDataParallel
• MixedPrecisionPolicy / OffloadPolicy / "sharded state dict"
• "DTensor" / init_device_mesh / "device mesh"
• "torch.distributed" / "NCCL" / "process group hangs"
• "FairScale ZeRO" (deprecated — see routing-mistakes table)

Route to: See distributed-training-strategies.md for DDP, FSDP1/FSDP2, DTensor + device mesh, and multi-node setup.

Why: Distributed training has unique setup requirements, synchronization patterns, and pitfalls. Generic advice breaks in distributed settings, and FSDP2 (fully_shard) has materially different ergonomics from FSDP1.

Example queries:

• "Setup FSDP2 with mixed precision and CPU offload"
• "Multi-node training not working"
• "How to launch distributed training?"
• "Migrating from FairScale ZeRO to FSDP"
• "Build a 2D device mesh for tensor + data parallel"

---

Performance and Speed

Symptoms:

• "Training too slow" / "Iterations per second" / "Throughput"
• "Low GPU utilization"
• "Performance optimization" / "Speed up training"
• "CUDA Graphs" / torch.cuda.graph / make_graphed_callables
• "NVTX ranges" / "Nsight Systems" / "ncu" / "nsys"
• "Kernel launch overhead" / "host-bound"

Route to: See performance-profiling.md FIRST for systematic bottleneck identification (PyTorch Profiler, NVTX, Nsight Systems, CUDA Graphs, allocator stats).

Why: MUST profile before optimizing. Many "performance" problems are actually data loading, host-side overhead, or graph-break recompiles — not raw compute. Profile to identify the real bottleneck.

After profiling, may route to:

• mixed-precision-and-optimization.md if compute-bound or compile-bound
• tensor-operations-and-memory.md if memory-bound or fragmentation-bound
• distributed-training-strategies.md if comm-bound or need to scale

Example queries:

• "Training is slow, how to speed up?"
• "GPU usage is only 30%"
• "Bottleneck in my training loop"
• "Should I use CUDA Graphs?"
• "How do I add NVTX ranges and view in Nsight Systems?"

---

Mixed Precision, torch.compile, and Optimization

Symptoms:

• "Mixed precision" / "AMP" / "Automatic mixed precision"
• "FP16" / "BF16" / "FP8"
• torch.amp.autocast / torch.amp.GradScaler
• torch.cuda.amp (deprecated — migration covered)
• "TF32"
• torch.compile / "graph breaks" / "recompiles" / "dynamic shapes"
• mode="reduce-overhead" / mode="max-autotune" / fullgraph=True
• "FlexAttention" / scaled_dot_product_attention / "SDPA" / "FlashAttention backend"

Route to: See mixed-precision-and-optimization.md for the modern torch.amp API, BF16/FP16/FP8 selection, gradient scaling, numerical stability, torch.compile modes/dynamic/recompilation triage, FlexAttention, and scaled_dot_product_attention.

Why: Mixed precision requires careful handling of numerical stability, gradient scaling, and operation compatibility. torch.compile shifts where bugs surface (recompiles, graph breaks, guard failures), and attention now has a first-class fused path via SDPA / FlexAttention.

Example queries:

• "How to use mixed precision training?" (route into the torch.amp section)
• "AMP causing NaN losses"
• "FP16 vs BF16 vs FP8 for my model"
• "torch.compile keeps recompiling on every batch"
• "Graph break inside my forward — how do I find it?"
• "Replace my hand-rolled attention with SDPA / FlexAttention"

---

Training Instability and NaN

Symptoms:

• "NaN loss" / "Inf gradients" / "Loss exploding"
• "Training becomes unstable" / "Model diverging"
• "Gradients are NaN"
• "torch.compile masks the failure site" / "compile + NaN"

Route to: See debugging-techniques.md for systematic NaN/Inf debugging, anomaly mode, and torch.compile debugging (graph-break and recompile triage, TORCH_LOGS, TORCH_COMPILE_DEBUG).

Why: NaN/Inf issues require systematic debugging—checking gradients layer by layer, identifying numerical instability sources, and targeted fixes. Under torch.compile, you also need to know how to disable compilation, dump graphs, and isolate the failing region.

Example queries:

• "Loss becomes NaN after epoch 3"
• "How to debug gradient explosion?"
• "Model outputs Inf values"
• "torch.compile changed my numerics"

---

Checkpointing and State Management

Symptoms:

• "Save model" / "Resume training"
• "Checkpoint" / "Save optimizer state" / "Load pretrained weights"
• "Reproducible training" / "Determinism"
• "Sharded state dict" / "FSDP checkpoint" / "DCP" (distributed checkpointing)

Route to: See checkpointing-and-reproducibility.md for complete state management, including sharded/FSDP checkpoints and RNG/determinism.

Why: Proper checkpointing requires saving ALL state (model, optimizer, scheduler, RNG states, AMP scaler). Reproducibility requires deterministic operations and careful seed management. Distributed checkpoints have additional sharding concerns.

Example queries:

• "How to checkpoint training properly?"
• "Resume from checkpoint"
• "Make training reproducible"
• "Save and load an FSDP sharded state dict"

---

Custom Operations and Autograd

Symptoms:

• "Custom backward pass"
• "torch.autograd.Function"
• "Define custom gradient"
• "Efficient custom operation"
• "Non-differentiable operation"
• "Custom CUDA kernel"

Route to: See custom-autograd-functions.md for custom backward passes.

Why: Custom autograd functions require understanding the autograd engine, proper gradient computation, and numerical stability. Compatibility with torch.compile adds further constraints.

Example queries:

• "Implement custom activation with gradient"
• "Efficient backwards pass for my operation"
• "How to use torch.autograd.Function?"

---

Cross-Cutting Scenarios

Multiple Skills Needed

Some scenarios require multiple specialized skills in sequence:

Distributed training with memory constraints:

1. Route to distributed-training-strategies.md (FSDP2 setup, mesh, offload)
2. THEN tensor-operations-and-memory.md (per-rank memory, expandable_segments, activation checkpointing)

Performance optimization:

1. Route to performance-profiling.md (identify bottleneck — compute, host, comm, memory)
2. THEN appropriate skill based on bottleneck:
   • Compute / compile → mixed-precision-and-optimization.md
   • Memory / fragmentation → tensor-operations-and-memory.md
   • Scale / comm → distributed-training-strategies.md

Custom module with proper patterns:

1. Route to module-design-patterns.md (structure)
2. THEN custom-autograd-functions.md if custom backward needed

Training instability with mixed precision or compile:

1. Route to debugging-techniques.md (diagnose root cause; isolate from compile)
2. May need mixed-precision-and-optimization.md for gradient scaling, BF16 vs FP16, or compile-mode tuning

Load in order of execution: Setup before optimization, diagnosis before fixes, structure before customization.

---

Ambiguous Queries - Ask First

When symptom unclear, ASK ONE clarifying question:

"Fix my PyTorch training" → Ask: "What specific issue? Memory? Speed? Accuracy? NaN? Graph breaks under compile?"

"Optimize my model" → Ask: "Optimize what? Training speed? Memory usage? Inference? Compile?"

"Setup distributed training" → Ask: "Single-node multi-GPU or multi-node? DDP, FSDP1, or FSDP2? What's not working?"

"Model not working" → Ask: "What's broken? Training fails? Wrong outputs? Performance? Recompiles?"

Never guess when ambiguous. Ask once, route accurately.

---

Common Routing Mistakes

Symptom / User ask	Wrong Route	Correct Route	Why
"Training slow, optimize my optimizer"	mixed-precision / optimizer tuning alone	performance-profiling.md FIRST	Real bottleneck is often torch.compile graph-breaks, FSDP comm overhead, or data loading — verify before changing the optimizer
"OOM in distributed"	tensor-memory only	distributed-training-strategies.md FIRST, then memory	Sharding policy / MixedPrecisionPolicy / OffloadPolicy may be the actual issue
"Custom layer slow"	performance-profiling	module-design-patterns.md FIRST	Design might be inefficient before profiling helps
"NaN with AMP"	mixed-precision	debugging-techniques.md FIRST	Debug NaN source, then fix AMP / scaler
"Save model"	module-design	checkpointing-and-reproducibility.md FIRST	Checkpointing is its own specialty (incl. sharded state dict)
"Use FairScale ZeRO"	fairscale-flavored advice	distributed-training-strategies.md (FSDP1/FSDP2)	FairScale is unmaintained; native FSDP is the current path
"Use torch.cuda.amp"	echo deprecated API	mixed-precision-and-optimization.md (torch.amp API)	torch.cuda.amp is a deprecated alias for torch.amp
"Hand-roll attention"	implement raw QKᵀ/√d softmax	mixed-precision-and-optimization.md (scaled_dot_product_attention / FlexAttention)	Fused SDPA / FlexAttention is faster, more memory-efficient, and numerically saner
"torch.compile slower than eager"	tweak optimizer / batch	mixed-precision-and-optimization.md (compile section) + debugging-techniques.md	Almost always graph breaks or recompiles — diagnose first
"Use torch.cuda.graph everywhere"	apply blindly	performance-profiling.md	CUDA Graphs help only when host-bound with static shapes; profile first

Key principle: Diagnosis before solutions, setup before optimization, root cause before fixes — and never echo deprecated APIs.

---

Red Flags - Stop and Route

If you catch yourself about to:

• Suggest reducing batch size → Route to tensor-operations-and-memory.md for systematic approach (incl. activation checkpointing, channels_last, expandable_segments)
• Show basic DDP code → Route to distributed-training-strategies.md for complete setup (and consider FSDP2)
• Guess at optimizations → Route to performance-profiling.md to measure first
• List possible NaN fixes → Route to debugging-techniques.md for diagnostic methodology
• Show torch.save example → Route to checkpointing-and-reproducibility.md for complete solution
• Type torch.cuda.amp.autocast / GradScaler → Route to mixed-precision-and-optimization.md for the torch.amp API
• Recommend FairScale ZeRO → Route to distributed-training-strategies.md for FSDP1/FSDP2
• Sketch a hand-rolled attention block → Route to mixed-precision-and-optimization.md for SDPA / FlexAttention
• Tell users torch.compile is "always faster" → Route to performance-profiling.md and the compile section of mixed-precision

All of these mean: You're about to give incomplete or stale advice. Route to the specialist instead.

---

Common Rationalizations (Don't Do These)

Excuse	Reality	What To Do
"User is rushed, skip routing"	Routing takes 5 seconds. Wrong fix wastes minutes.	Route anyway - specialists have quick diagnostics
"They already tried X"	May have done X wrong, misunderstood, or X wasn't applicable.	Route to specialist to verify X was done correctly
"Authority/senior says Y"	Authority can misdiagnose bottlenecks without profiling.	Profile first, authority second. Respect skills over seniority.
"User is tired, don't ask"	Exhaustion makes clarity MORE important, not less.	Ask ONE clarifying question - saves time overall
"User suggested Z"	Z might not be best option for their specific case.	Route to specialist to evaluate if Z is right approach
"Too complex, can't route"	Complex scenarios need specialists MORE, not less.	Use cross-cutting section - route to multiple skills in sequence
"User sounds confident"	Confidence about custom autograd often precedes subtle bugs.	Route to specialist for systematic verification
"Just a quick question"	No such thing - symptoms need diagnosis.	Quick questions deserve correct answers - route properly
"Simple issue"	Simple symptoms can have complex root causes.	Route based on symptoms, not perceived complexity
"Direct answer is helpful"	Wrong direct answer wastes time and frustrates user.	Routing to specialist IS the helpful answer

If you catch yourself thinking ANY of these, STOP and route to the specialist.

---

Red Flags Checklist - Self-Check Before Answering

Before giving ANY PyTorch advice, ask yourself:

1. ❓ Did I identify the symptom?

   • If no → Read query again, identify symptoms

2. ❓ Is this symptom in my routing table?

   • If yes → Route to that specialist
   • If no → Ask clarifying question

3. ❓ Am I about to give advice directly?

   • If yes → STOP. Why am I not routing?
   • Check rationalization table - am I making excuses?

4. ❓ Is this a diagnosis issue or solution issue?

   • Diagnosis → Route to profiling/debugging skill FIRST
   • Solution → Route to appropriate implementation skill

5. ❓ Is query ambiguous?

   • If yes → Ask ONE clarifying question
   • If no → Route confidently

6. ❓ Am I feeling pressure to skip routing?

   • Time pressure → Route anyway (faster overall)
   • Sunk cost → Route anyway (verify first attempt)
   • Authority → Route anyway (verify diagnosis)
   • Exhaustion → Route anyway (clarity more important)

If you failed ANY check above, do NOT give direct advice. Route to specialist or ask clarifying question.

---

When NOT to Use PyTorch Skills

Skip PyTorch pack when:

• Choosing algorithms (use yzmir-training-optimization or algorithm packs)
• Model architecture selection (use yzmir-neural-architectures)
• Framework-agnostic training issues — LR schedules, optimizer family choice, gradient health methodology (use yzmir-training-optimization)
• LLM-specific concerns — prompt engineering, fine-tuning strategy, RAG, eval/safety (use yzmir-llm-specialist)
• Production deployment, serving, monitoring, drift, inference optimization at the system level (use yzmir-ml-production)

PyTorch pack is for: PyTorch-specific implementation, infrastructure, debugging, and optimization issues — the framework-bound layer underneath those other packs.

---

Diagnosis-First Principle

Critical: Many PyTorch issues require diagnosis before solutions:

Issue Type	Diagnosis Skill	Then Solution Skill
Performance	performance-profiling	mixed-precision (incl. compile) / distributed
Memory	tensor-memory (profiling section) + performance-profiling (allocator)	tensor-memory (optimization)
NaN/Inf	debugging-techniques	mixed-precision / module-design
Compile regressions	debugging-techniques (graph-break / recompile triage)	mixed-precision (compile section)
Distributed hangs / OOM	distributed-training-strategies	tensor-memory / performance-profiling
Training bugs	debugging-techniques	Appropriate fix

If unclear what's wrong, route to diagnostic skill first.

---

PyTorch Engineering Specialist Skills

After routing, load the appropriate specialist skill for detailed guidance. Eight sheets, all in this directory:

1. tensor-operations-and-memory.md — Tensor lifecycles, contiguity, channels_last memory format, expandable_segments:True and allocator tuning, gradient checkpointing, fragmentation, OOM mitigation.
2. module-design-patterns.md — nn.Module structure, parameter/buffer registration, initialization, composability with checkpointing / FSDP / torch.compile.
3. distributed-training-strategies.md — DDP, FSDP1 (FullyShardedDataParallel), FSDP2 (fully_shard, MixedPrecisionPolicy, OffloadPolicy), DTensor + init_device_mesh, sharded state dict, NCCL, multi-node launch. FairScale ZeRO is out — replaced by native FSDP.
4. mixed-precision-and-optimization.md — torch.amp.autocast / torch.amp.GradScaler (BF16/FP16/FP8 API and selection), TF32, torch.compile modes / dynamic= / recompilation triage / fullgraph, scaled_dot_product_attention, FlexAttention.
5. performance-profiling.md — PyTorch Profiler (with stacks and memory), NVTX ranges, Nsight Systems / nsys workflows, CUDA Graphs (torch.cuda.graph, make_graphed_callables), allocator stats / expandable_segments for fragmentation, host-bound vs compute-bound vs comm-bound triage.
6. debugging-techniques.md — Systematic NaN/Inf debugging, anomaly detection, gradient checking, torch.compile debugging (TORCH_LOGS, TORCH_COMPILE_DEBUG, graph-break and recompile triage), distributed deadlock isolation.
7. checkpointing-and-reproducibility.md — Complete checkpointing (model + optimizer + scheduler + scaler + RNG), determinism, sharded / distributed checkpoint (FSDP, DCP).
8. custom-autograd-functions.md — torch.autograd.Function, custom forward/backward, setup_context / save_for_backward, gradcheck, torch.compile interop.

---

Cross-Pack References

PyTorch engineering sits underneath several other Yzmir packs. Hand off explicitly when the question is no longer PyTorch-bound:

• yzmir-training-optimization ↔ this pack
  • Out: framework-agnostic training methodology — LR schedules, optimizer family selection, gradient-health diagnostics, regularization strategy.
  • In: when their diagnostics implicate a PyTorch-level cause (graph breaks under compile, FSDP comm overhead, AMP scaler misuse, allocator fragmentation), route back here.
• yzmir-llm-specialist ↔ this pack
  • Out: prompt engineering, fine-tuning strategy, RAG design, eval/safety.
  • In: LLM training and inference workloads frequently land in this pack for FSDP2 sharding, FlexAttention / SDPA, BF16/FP8 mixed precision, torch.compile for transformer blocks, KV-cache memory tuning.
• yzmir-ml-production ↔ this pack
  • Out: deployment topology, serving stack, rollout, monitoring, drift, system-level inference optimization.
  • In: when their inference profiling points at a PyTorch-level fix — torch.compile modes, CUDA Graphs, SDPA backend selection, channels_last, allocator config — route here.

When in doubt: PyTorch pack handles "the framework is doing something I need to fix or measure." The other packs handle "what should the framework be doing in the first place."