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PyTorch API Complete Study Guide

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Study Path Organization

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Phase 1: Fundamentals (2-3 weeks)

Goal: Master basic tensor operations and understand PyTorch’s core concepts

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Week 1: Tensor Basics

• Day 1-2: Tensor Creation
  • torch.tensor, torch.zeros, torch.ones, torch.arange, torch.linspace
  • torch.eye, torch.empty, torch.full
  • torch.from_numpy, torch.as_tensor, torch.asarray
• Day 3-4: Tensor Manipulation
  • torch.cat, torch.stack, torch.split, torch.chunk
  • torch.reshape, torch.transpose, torch.permute
  • torch.squeeze, torch.unsqueeze, torch.flatten
• Day 5-7: Indexing & Slicing
  • torch.index_select, torch.masked_select, torch.gather, torch.scatter
  • Boolean indexing, advanced indexing
  • torch.where, torch.nonzero

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Week 2: Mathematical Operations

• Day 1-2: Pointwise Operations
  • Arithmetic: add, sub, mul, div, pow
  • Trigonometric: sin, cos, tan, asin, acos, atan
  • Exponential: exp, log, sqrt, sigmoid, tanh
• Day 3-4: Reduction Operations
  • torch.sum, torch.mean, torch.std, torch.var
  • torch.max, torch.min, torch.argmax, torch.argmin
  • torch.prod, torch.median, torch.mode
• Day 5-7: Comparison & Logical Operations
  • Comparison: eq, ne, gt, ge, lt, le
  • Logical: logical_and, logical_or, logical_not, logical_xor
  • Bitwise: bitwise_and, bitwise_or, bitwise_xor

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Week 3: Linear Algebra Basics

• Day 1-3: Matrix Operations
  • torch.mm, torch.matmul, torch.bmm
  • torch.dot, torch.vdot, torch.outer, torch.inner
  • Broadcasting rules
• Day 4-7: Basic Linear Algebra
  • torch.linalg.norm, torch.linalg.det
  • torch.linalg.inv, torch.linalg.solve
  • torch.trace, matrix properties

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Phase 2: Neural Networks (3-4 weeks)

Goal: Build and train neural networks from scratch

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Week 4: Neural Network Fundamentals

• Day 1-2: Module System
  • torch.nn.Module architecture
  • torch.nn.Parameter, torch.nn.Buffer
  • Forward and backward passes
• Day 3-4: Basic Layers
  • torch.nn.Linear
  • torch.nn.Conv2d, torch.nn.Conv1d, torch.nn.Conv3d
  • torch.nn.MaxPool2d, torch.nn.AvgPool2d
• Day 5-7: Activation Functions
  • torch.nn.ReLU, torch.nn.LeakyReLU, torch.nn.ELU
  • torch.nn.Sigmoid, torch.nn.Tanh
  • torch.nn.GELU, torch.nn.SiLU, torch.nn.Softmax

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Week 5: Advanced Layers

• Day 1-3: Normalization
  • torch.nn.BatchNorm1d/2d/3d
  • torch.nn.LayerNorm, torch.nn.GroupNorm
  • torch.nn.InstanceNorm1d/2d/3d
• Day 4-7: Recurrent Networks
  • torch.nn.RNN, torch.nn.LSTM, torch.nn.GRU
  • torch.nn.RNNCell, torch.nn.LSTMCell, torch.nn.GRUCell
  • Sequence modeling

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Week 6: Transformers & Attention

• Day 1-4: Transformer Architecture
  • torch.nn.Transformer
  • torch.nn.TransformerEncoder/Decoder
  • torch.nn.TransformerEncoderLayer/DecoderLayer
• Day 5-7: Attention Mechanisms
  • torch.nn.MultiheadAttention
  • torch.nn.functional.scaled_dot_product_attention
  • Self-attention, cross-attention

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Week 7: Loss Functions & Optimization

• Day 1-3: Loss Functions
  • Regression: MSELoss, L1Loss, SmoothL1Loss, HuberLoss
  • Classification: CrossEntropyLoss, NLLLoss, BCELoss, BCEWithLogitsLoss
  • Embedding: CosineEmbeddingLoss, TripletMarginLoss
• Day 4-7: Optimizers
  • torch.optim.SGD, torch.optim.Adam, torch.optim.AdamW
  • torch.optim.RMSprop, torch.optim.Adagrad
  • Learning rate schedulers

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Phase 3: Automatic Differentiation (1-2 weeks)

Goal: Master autograd and gradient computation

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Week 8: Autograd Deep Dive

• Day 1-3: Gradient Computation
  • torch.autograd.backward, torch.autograd.grad
  • Tensor.backward(), Tensor.grad
  • Computational graphs
• Day 4-5: Gradient Contexts
  • torch.no_grad(), torch.enable_grad()
  • torch.set_grad_enabled(), torch.inference_mode()
  • When to use each context
• Day 6-7: Custom Autograd Functions
  • torch.autograd.Function
  • forward() and backward() methods
  • Custom gradient implementation

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Phase 4: Data Loading & Processing (1 week)

Goal: Efficiently load and preprocess data

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Week 9: Data Utilities

• Day 1-3: Datasets
  • torch.utils.data.Dataset
  • torch.utils.data.TensorDataset
  • Custom dataset creation
• Day 4-7: Data Loading
  • torch.utils.data.DataLoader
  • torch.utils.data.Sampler, torch.utils.data.BatchSampler
  • Multiprocessing, pin_memory, prefetching

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Phase 5: Advanced Training (2-3 weeks)

Goal: Implement production-ready training pipelines

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Week 10: Distributed Training

• Day 1-3: Data Parallel
  • torch.nn.DataParallel
  • torch.nn.parallel.DistributedDataParallel
  • Multi-GPU training
• Day 4-7: FSDP & Advanced Parallelism
  • torch.distributed.fsdp.FullyShardedDataParallel
  • Tensor parallelism, pipeline parallelism
  • Distributed optimization

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Week 11: Mixed Precision & Optimization

• Day 1-3: Automatic Mixed Precision
  • torch.cuda.amp.autocast
  • torch.cuda.amp.GradScaler
  • FP16/BF16 training
• Day 4-7: Gradient Accumulation & Clipping
  • torch.nn.utils.clip_grad_norm_
  • torch.nn.utils.clip_grad_value_
  • Memory-efficient training

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Week 12: Checkpointing & Serialization

• Day 1-4: Model Saving/Loading
  • torch.save, torch.load
  • State dict management
  • torch.nn.utils.checkpoint (gradient checkpointing)
• Day 5-7: Distributed Checkpointing
  • torch.distributed.checkpoint
  • Sharded checkpoints
  • Resuming training

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Phase 6: Performance & Deployment (2-3 weeks)

Goal: Optimize models for production

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Week 13: JIT Compilation

• Day 1-4: TorchScript
  • torch.jit.script, torch.jit.trace
  • torch.jit.ScriptModule
  • torch.jit.freeze, torch.jit.optimize_for_inference
• Day 5-7: JIT Optimization
  • Fusion optimization
  • Type refinement
  • Graph optimization

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Week 14: PyTorch 2.0 Compiler

• Day 1-4: torch.compile
  • torch.compile() modes
  • Backends: inductor, cudagraphs, onnxrt
  • Debugging compilation
• Day 5-7: Advanced Compilation
  • Dynamic shapes
  • AOT Autograd
  • Custom backends

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Week 15: Model Export & Quantization

• Day 1-3: ONNX Export
  • torch.onnx.export
  • torch.onnx.dynamo_export
  • ONNX runtime deployment
• Day 4-7: Quantization
  • torch.quantization.quantize_dynamic
  • torch.quantization.quantize_qat
  • Post-training quantization, QAT

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Phase 7: Advanced Topics (2-4 weeks)

Goal: Master advanced PyTorch features

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Week 16: Functional Transforms

• Day 1-4: torch.func
  • torch.func.vmap (vectorization)
  • torch.func.grad, torch.func.grad_and_value
  • torch.func.jacrev, torch.func.jacfwd
• Day 5-7: Functionalization
  • torch.func.functionalize
  • torch.func.functional_call
  • Pure functional transformations

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Week 17: Sparse Tensors

• Day 1-4: Sparse Formats
  • torch.sparse_coo_tensor
  • torch.sparse_csr_tensor, torch.sparse_csc_tensor
  • torch.sparse_bsr_tensor, torch.sparse_bsc_tensor
• Day 5-7: Sparse Operations
  • torch.sparse.mm, torch.sparse.addmm
  • torch.sparse.sum, torch.sparse.softmax
  • Sparse gradients

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Week 18: Signal Processing & FFT

• Day 1-4: FFT Operations
  • torch.fft.fft, torch.fft.ifft
  • torch.fft.rfft, torch.fft.irfft
  • 2D and N-D FFT
• Day 5-7: Spectral Analysis
  • torch.stft, torch.istft
  • Window functions
  • Audio/signal processing

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Week 19: Profiling & Debugging

• Day 1-4: Profiler
  • torch.profiler.profile
  • torch.profiler.record_function
  • Performance analysis
• Day 5-7: Debugging Tools
  • torch.autograd.detect_anomaly
  • torch.autograd.gradcheck, torch.autograd.gradgradcheck
  • Memory profiling

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Learning Resources by Category

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Essential APIs (Must Know)

1. Tensor Operations: 95% daily use
2. torch.nn.Module: Core building block
3. torch.optim: Training essentials
4. torch.autograd: Automatic differentiation
5. torch.utils.data: Data loading

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Important APIs (Should Know)

1. torch.nn.functional: Functional operations
2. torch.cuda: GPU management
3. torch.distributed: Multi-GPU/multi-node
4. torch.jit: Model optimization
5. torch.compile: PyTorch 2.0 compiler

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Advanced APIs (Nice to Know)

1. torch.func: Functional transforms
2. torch.export: Model export
3. torch.quantization: Model quantization
4. torch.sparse: Sparse tensors
5. torch.profiler: Performance profiling

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Specialized APIs (Domain Specific)

1. torch.fft: Signal processing
2. torch.linalg: Linear algebra
3. torch.special: Special functions
4. torch.nested: Nested tensors
5. torch.distributed.fsdp: Large model training

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Practice Projects by Phase

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Phase 1 Projects

1. Implement tensor operations from scratch
2. Build a simple neural network without nn.Module
3. Create custom data transformations

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Phase 2 Projects

1. Build CNN for image classification
2. Implement RNN for sequence prediction
3. Create a Transformer from scratch

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Phase 3 Projects

1. Implement custom loss function with autograd
2. Build custom optimizer
3. Create backward hooks for analysis

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Phase 4 Projects

1. Build efficient data pipeline for large datasets
2. Implement custom sampler
3. Create data augmentation pipeline

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Phase 5 Projects

1. Multi-GPU training pipeline
2. Mixed precision training
3. Distributed data parallel training

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Phase 6 Projects

1. Export model to ONNX
2. Quantize model for mobile
3. Optimize with torch.compile

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Phase 7 Projects

1. Use vmap for batch processing
2. Implement sparse neural network
3. Build audio processing pipeline with FFT

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Daily Study Routine

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Beginner (Weeks 1-4)

• 30 min: Read documentation
• 60 min: Code along with examples
• 30 min: Practice exercises
• Total: 2 hours/day

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Intermediate (Weeks 5-12)

• 20 min: Documentation review
• 90 min: Build projects
• 30 min: Debug and optimize
• Total: 2.5 hours/day

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Advanced (Weeks 13-19)

• 15 min: Read research papers
• 120 min: Advanced projects
• 15 min: Community engagement
• Total: 2.5 hours/day

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Assessment Checklist

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Phase 1: ✓

• Can create tensors in 5+ different ways
• Understand broadcasting rules
• Can manipulate tensor shapes efficiently
• Master indexing and slicing
• Perform matrix operations

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Phase 2: ✓

• Build custom nn.Module from scratch
• Implement CNN, RNN, Transformer
• Understand loss functions
• Configure optimizers and schedulers
• Debug training loops

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Phase 3: ✓

• Understand computational graphs
• Implement custom autograd functions
• Use gradient contexts appropriately
• Debug gradient flow

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Phase 4: ✓

• Build efficient data pipelines
• Implement custom datasets
• Optimize data loading
• Handle large datasets

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Phase 5: ✓

• Set up multi-GPU training
• Implement mixed precision
• Use FSDP for large models
• Manage checkpoints

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Phase 6: ✓

• Export models to production formats
• Optimize with JIT/compile
• Quantize models
• Profile and optimize performance

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Phase 7: ✓

• Use functional transforms
• Work with sparse tensors
• Implement signal processing
• Master debugging tools