# Advanced Features

JIT compilation, model export, graph transformations, quantization, and deployment utilities for optimizing and deploying PyTorch models in production environments.

## Capabilities

### JIT Compilation (torch.jit)

TorchScript compilation for model optimization and deployment.

```python { .api }
def jit.script(obj, optimize=None, _frames_up=0, _rcb=None):
    """
    Compile Python code to TorchScript.
    
    Parameters:
    - obj: Function, method, or class to compile
    - optimize: Whether to apply optimizations
    
    Returns:
    ScriptModule or ScriptFunction
    """

def jit.trace(func, example_inputs, optimize=None, check_trace=True, check_inputs=None, check_tolerance=1e-5, strict=True, _force_outplace=False, _module_class=None, _compilation_unit=None):
    """
    Trace function execution to create TorchScript.
    
    Parameters:
    - func: Function or module to trace
    - example_inputs: Example inputs for tracing
    - optimize: Whether to apply optimizations
    - check_trace: Whether to verify trace correctness
    - strict: Whether to record all operations
    
    Returns:
    TracedModule or function
    """

def jit.load(f, map_location=None, _extra_files=None):
    """Load TorchScript model from file."""

def jit.save(m, f, _extra_files=None):
    """Save TorchScript model to file."""

class jit.ScriptModule(nn.Module):
    """TorchScript compiled module."""
    def save(self, f, _extra_files=None): ...
    def code(self) -> str: ...
    def graph(self): ...
    def code_with_constants(self) -> Tuple[str, List[Tensor]]: ...

def jit.freeze(mod, preserved_attrs=None, optimize_numerics=True):
    """Freeze TorchScript module for inference."""

def jit.optimize_for_inference(mod, other_methods=None):
    """Optimize TorchScript module for inference."""

def jit.enable_onednn_fusion(enabled: bool):
    """Enable/disable OneDNN fusion optimization."""

def jit.set_fusion_strategy(strategy: List[Tuple[str, bool]]):
    """Set fusion strategy for optimization."""
```

### Model Export (torch.export)

Export PyTorch models for deployment and optimization.

```python { .api }
def export.export(mod: nn.Module, args, kwargs=None, *, dynamic_shapes=None, strict=True) -> ExportedProgram:
    """
    Export PyTorch module to exportable format.
    
    Parameters:
    - mod: Module to export
    - args: Example arguments
    - kwargs: Example keyword arguments
    - dynamic_shapes: Dynamic shape specifications
    - strict: Whether to enforce strict export
    
    Returns:
    ExportedProgram
    """

class export.ExportedProgram:
    """Exported PyTorch program."""
    def module(self) -> nn.Module: ...
    def graph_module(self): ...
    def graph_signature(self): ...
    def call_spec(self): ...
    def verifier(self): ...
    def state_dict(self) -> Dict[str, Any]: ...
    def named_parameters(self): ...
    def named_buffers(self): ...

def export.save(ep: ExportedProgram, f) -> None:
    """Save exported program to file."""

def export.load(f) -> ExportedProgram:
    """Load exported program from file."""
```

### Model Compilation (torch.compile)

Compile PyTorch models for performance optimization.

```python { .api }
def compile(model=None, *, fullgraph=False, dynamic=None, backend="inductor", mode=None, options=None, disable=False):
    """
    Compile PyTorch model for optimization.
    
    Parameters:
    - model: Model to compile (or use as decorator)
    - fullgraph: Whether to compile the entire graph
    - dynamic: Enable dynamic shapes
    - backend: Compilation backend ("inductor", "aot_eager", etc.)
    - mode: Compilation mode ("default", "reduce-overhead", "max-autotune")
    - options: Backend-specific options
    - disable: Disable compilation
    
    Returns:
    Compiled model
    """

@compile
def compiled_function(x):
    """Example of function compilation."""
    return x * 2 + 1

# Alternative usage
compiled_model = torch.compile(model, mode="max-autotune")
```

### Graph Transformations (torch.fx)

Symbolic tracing and graph manipulation for model analysis and optimization.

```python { .api }
class fx.GraphModule(nn.Module):
    """Module with FX graph representation."""
    def __init__(self, root, graph, class_name='GraphModule'): ...
    def recompile(self): ...
    def code(self) -> str: ...
    def graph(self): ...
    def print_readable(self, print_output=True): ...

def fx.symbolic_trace(root, concrete_args=None, meta_args=None, _force_outplace=False) -> GraphModule:
    """
    Symbolically trace PyTorch module.
    
    Parameters:
    - root: Module or function to trace
    - concrete_args: Arguments to keep concrete
    - meta_args: Meta tensor arguments
    
    Returns:
    GraphModule with traced computation graph
    """

class fx.Tracer:
    """Tracer for symbolic execution."""
    def trace(self, root, concrete_args=None): ...
    def call_module(self, m, forward, args, kwargs): ...
    def call_function(self, target, args, kwargs): ...
    def call_method(self, target, args, kwargs): ...

class fx.Graph:
    """Computational graph representation."""
    def nodes(self): ...
    def create_node(self, op, target, args=None, kwargs=None, name=None, type_expr=None): ...
    def erase_node(self, to_erase): ...
    def inserting_before(self, n): ...
    def inserting_after(self, n): ...
    def lint(self): ...
    def print_tabular(self): ...

class fx.Node:
    """Node in FX graph."""
    def replace_all_uses_with(self, replace_with): ...
    def replace_input_with(self, old_input, new_input): ...
    def append(self, x): ...
    def prepend(self, x): ...

def fx.replace_pattern(gm: GraphModule, pattern, replacement) -> List[Match]:
    """Replace patterns in graph."""

class fx.Interpreter:
    """Base class for FX graph interpreters."""
    def run(self, *args, **kwargs): ...
    def run_node(self, n): ...
    def call_function(self, target, args, kwargs): ...
    def call_method(self, target, args, kwargs): ...
    def call_module(self, target, args, kwargs): ...
```

### Quantization (torch.quantization)

Model quantization for efficient deployment.

```python { .api }
def quantization.quantize_dynamic(model, qconfig_spec=None, dtype=torch.qint8, mapping=None, inplace=False, remove_qconfig=True):
    """
    Dynamic quantization of model.
    
    Parameters:
    - model: Model to quantize
    - qconfig_spec: Quantization configuration
    - dtype: Target quantized data type
    - mapping: Custom op mapping
    - inplace: Whether to modify model in-place
    
    Returns:
    Quantized model
    """

def quantization.quantize(model, run_fn, run_args, mapping=None, inplace=False):
    """Post-training static quantization."""

def quantization.prepare(model, inplace=False, allow_list=None, observer_non_leaf_module_list=None, prepare_custom_config_dict=None):
    """Prepare model for quantization aware training."""

def quantization.convert(model, mapping=None, inplace=False, remove_qconfig=True, convert_custom_config_dict=None):
    """Convert prepared model to quantized version."""

def quantization.prepare_qat(model, mapping=None, inplace=False):
    """Prepare model for quantization aware training."""

class quantization.QuantStub(nn.Module):
    """Quantization stub for marking quantization points."""
    def __init__(self, qconfig=None): ...
    def forward(self, x): ...

class quantization.DeQuantStub(nn.Module):
    """Dequantization stub for marking dequantization points."""
    def __init__(self): ...
    def forward(self, x): ...

class quantization.QConfig:
    """Quantization configuration."""
    def __init__(self, activation, weight): ...

def quantization.get_default_qconfig(backend='fbgemm'):
    """Get default quantization configuration."""

def quantization.get_default_qat_qconfig(backend='fbgemm'):
    """Get default QAT quantization configuration."""

class quantization.FakeQuantize(nn.Module):
    """Fake quantization for QAT."""
    def __init__(self, observer=MinMaxObserver, quant_min=0, quant_max=255, **observer_kwargs): ...
    def forward(self, X): ...
    def calculate_qparams(self): ...
```

### ONNX Export (torch.onnx)

Export PyTorch models to ONNX format for interoperability.

```python { .api }
def onnx.export(model, args, f, export_params=True, verbose=False, training=TrainingMode.EVAL, 
                input_names=None, output_names=None, operator_export_type=OperatorExportTypes.ONNX, 
                opset_version=None, do_constant_folding=True, dynamic_axes=None, keep_initializers_as_inputs=None, 
                custom_opsets=None, enable_onnx_checker=True, use_external_data_format=False):
    """
    Export PyTorch model to ONNX format.
    
    Parameters:
    - model: PyTorch model to export
    - args: Model input arguments
    - f: File path or file-like object to save to
    - export_params: Whether to export parameters
    - verbose: Enable verbose output
    - training: Training mode (EVAL, TRAINING, PRESERVE)
    - input_names: Names for input nodes
    - output_names: Names for output nodes
    - opset_version: ONNX opset version
    - dynamic_axes: Dynamic input/output axes
    - custom_opsets: Custom operator sets
    """

def onnx.dynamo_export(model, *model_args, export_options=None, **model_kwargs) -> ONNXProgram:
    """Export using torch.export and Dynamo."""

class onnx.ONNXProgram:
    """ONNX program representation."""
    def save(self, destination): ...
    def model_proto(self): ...

def onnx.load(f) -> ModelProto:
    """Load ONNX model."""

def onnx.save(model, f, export_params=True):
    """Save ONNX model to file."""

class onnx.TrainingMode(Enum):
    """Training mode for ONNX export."""
    EVAL = 0
    TRAINING = 1
    PRESERVE = 2

class onnx.OperatorExportTypes(Enum):
    """Operator export types."""
    ONNX = 0
    ONNX_ATEN = 1
    ONNX_ATEN_FALLBACK = 2
```

### Mobile Deployment (torch.utils.mobile_optimizer)

Optimization utilities for mobile deployment.

```python { .api }
def utils.mobile_optimizer.optimize_for_mobile(script_module, optimization_blocklist=None, preserved_methods=None, backend='CPU'):
    """
    Optimize TorchScript module for mobile deployment.
    
    Parameters:
    - script_module: TorchScript module to optimize
    - optimization_blocklist: Operations to exclude from optimization
    - preserved_methods: Methods to preserve during optimization
    - backend: Target backend ('CPU', 'Vulkan', 'Metal')
    
    Returns:
    Optimized TorchScript module
    """

class utils.mobile_optimizer.LiteScriptModule:
    """Lightweight script module for mobile."""
    def forward(self, *args): ...
    def get_debug_info(self): ...
```

### TensorRT Integration

NVIDIA TensorRT integration for GPU inference optimization.

```python { .api }
def tensorrt.compile(model, inputs, enabled_precisions={torch.float}, workspace_size=1 << 22, 
                    min_block_size=3, torch_executed_ops=None, torch_executed_modules=None):
    """
    Compile model with TensorRT.
    
    Parameters:
    - model: PyTorch model to compile
    - inputs: Example inputs for compilation
    - enabled_precisions: Allowed precision types
    - workspace_size: TensorRT workspace size
    - min_block_size: Minimum block size for TensorRT subgraphs
    
    Returns:
    TensorRT compiled model
    """
```

### Automatic Mixed Precision (torch.amp)

Automatic mixed precision training for performance and memory optimization.

```python { .api }
class amp.GradScaler:
    """Gradient scaler for mixed precision training."""
    def __init__(self, init_scale=2**16, growth_factor=2.0, backoff_factor=0.5, growth_interval=2000, enabled=True):
        """
        Parameters:
        - init_scale: Initial scale factor
        - growth_factor: Scale growth factor
        - backoff_factor: Scale reduction factor
        - growth_interval: Steps between scale increases
        - enabled: Whether scaler is enabled
        """
    
    def scale(self, outputs): ...
    def step(self, optimizer): ...
    def update(self): ...
    def unscale_(self, optimizer): ...
    def get_scale(self): ...
    def get_growth_factor(self): ...
    def set_growth_factor(self, new_factor): ...
    def get_backoff_factor(self): ...
    def set_backoff_factor(self, new_factor): ...
    def get_growth_interval(self): ...
    def set_growth_interval(self, new_interval): ...
    def is_enabled(self): ...
    def state_dict(self): ...
    def load_state_dict(self, state_dict): ...

def amp.autocast(device_type='cuda', dtype=None, enabled=True, cache_enabled=None):
    """
    Context manager for automatic mixed precision.
    
    Parameters:
    - device_type: Device type ('cuda', 'cpu', 'xpu')
    - dtype: Target dtype (torch.float16, torch.bfloat16)
    - enabled: Whether autocast is enabled
    - cache_enabled: Whether to cache autocast state
    """
```

### Model Optimization and Pruning (torch.ao)

Advanced optimization techniques including pruning and sparsity.

```python { .api }
def ao.pruning.prune_low_magnitude(model, amount, importance_scores=None, structured=False, dim=None):
    """
    Prune model by removing low magnitude weights.
    
    Parameters:
    - model: Model to prune
    - amount: Fraction of weights to prune
    - importance_scores: Custom importance scores
    - structured: Whether to use structured pruning
    - dim: Dimension for structured pruning
    
    Returns:
    Pruned model
    """

class ao.pruning.WeightNormSparsifier:
    """Weight norm based sparsifier."""
    def __init__(self, sparsity_level=0.5): ...
    def update_mask(self, module, tensor_name, **kwargs): ...

class ao.quantization.QConfigMapping:
    """Quantization configuration mapping."""
    def set_global(self, qconfig): ...
    def set_object_type(self, object_type, qconfig): ...
    def set_module_name(self, module_name, qconfig): ...

def ao.quantization.get_default_qconfig_mapping(backend='x86'):
    """Get default quantization configuration mapping."""

class ao.quantization.FusedMovingAvgObsFakeQuantize(nn.Module):
    """Fused moving average observer fake quantize."""
    def __init__(self, observer=MovingAverageMinMaxObserver, **observer_kwargs): ...
```

## Usage Examples

### TorchScript Compilation

```python
import torch
import torch.nn as nn

# Define model
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.linear = nn.Linear(10, 5)
    
    def forward(self, x):
        return torch.relu(self.linear(x))

model = SimpleModel()
model.eval()

# Script compilation
scripted_model = torch.jit.script(model)
print(scripted_model.code)

# Trace compilation
example_input = torch.randn(1, 10)
traced_model = torch.jit.trace(model, example_input)

# Save/load
torch.jit.save(scripted_model, 'model_scripted.pt')
loaded_model = torch.jit.load('model_scripted.pt')

# Optimization for inference
optimized_model = torch.jit.optimize_for_inference(scripted_model)

print("TorchScript compilation completed")
```

### Model Export and Deployment

```python
import torch
import torch.nn as nn
from torch.export import export

# Define model
class ExportModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(3, 16, 3, padding=1)
        self.pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(16, 10)
    
    def forward(self, x):
        x = torch.relu(self.conv(x))
        x = self.pool(x)
        x = x.flatten(1)
        return self.fc(x)

model = ExportModel()
example_input = torch.randn(1, 3, 32, 32)

# Export to ExportedProgram
exported_program = export(model, (example_input,))

# Save exported program
torch.export.save(exported_program, 'exported_model.pt2')

# Load exported program
loaded_program = torch.export.load('exported_model.pt2')

# Use exported program
output = loaded_program.module()(example_input)
print(f"Export completed, output shape: {output.shape}")
```

### Torch Compile Usage

```python
import torch
import torch.nn as nn

# Define model
model = nn.Sequential(
    nn.Linear(100, 200),
    nn.ReLU(),
    nn.Linear(200, 100),
    nn.ReLU(),
    nn.Linear(100, 10)
)

# Compile with different modes
default_compiled = torch.compile(model)
fast_compiled = torch.compile(model, mode="reduce-overhead")
optimal_compiled = torch.compile(model, mode="max-autotune")

# Use as decorator
@torch.compile
def custom_function(x, y):
    return x.matmul(y) + x.sum()

# Example usage
x = torch.randn(32, 100)
y = torch.randn(100, 50)

# Compiled function
result = custom_function(x, y)

# Compiled model
output = optimal_compiled(x)

print(f"Torch compile completed, output shape: {output.shape}")
```

### Quantization Example

```python
import torch
import torch.nn as nn
import torch.quantization as quant

# Define model
class QuantModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.quant = quant.QuantStub()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.relu2 = nn.ReLU()
        self.pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(64, 10)
        self.dequant = quant.DeQuantStub()
    
    def forward(self, x):
        x = self.quant(x)
        x = self.relu1(self.conv1(x))
        x = self.relu2(self.conv2(x))
        x = self.pool(x)
        x = x.flatten(1)
        x = self.fc(x)
        x = self.dequant(x)
        return x

model = QuantModel()
model.eval()

# Dynamic quantization
quantized_model = quant.quantize_dynamic(
    model, {nn.Linear}, dtype=torch.qint8
)

# Post-training static quantization
model.qconfig = quant.get_default_qconfig('fbgemm')
prepared_model = quant.prepare(model)

# Calibration (example data)
for _ in range(10):
    calibration_data = torch.randn(1, 3, 32, 32)
    prepared_model(calibration_data)

# Convert to quantized model
quantized_static_model = quant.convert(prepared_model)

print("Quantization completed")
print(f"Original model size: {sum(p.numel() for p in model.parameters())}")
print(f"Quantized model parameters: {sum(p.numel() for p in quantized_model.parameters())}")
```

### ONNX Export

```python
import torch
import torch.nn as nn
import torch.onnx

# Define model
class ONNXModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.backbone = nn.Sequential(
            nn.Conv2d(3, 64, 7, stride=2, padding=3),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(64, 1000)
        )
    
    def forward(self, x):
        return self.backbone(x)

model = ONNXModel()
model.eval()

# Example input
dummy_input = torch.randn(1, 3, 224, 224)

# Export to ONNX
torch.onnx.export(
    model,
    dummy_input,
    "model.onnx",
    export_params=True,
    opset_version=11,
    do_constant_folding=True,
    input_names=['input'],
    output_names=['output'],
    dynamic_axes={
        'input': {0: 'batch_size'},
        'output': {0: 'batch_size'}
    }
)

print("ONNX export completed")
```

### FX Graph Manipulation

```python
import torch
import torch.nn as nn
import torch.fx as fx

# Define model
class FXModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.relu = nn.ReLU()
        self.pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(64, 10)
    
    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        x = x.flatten(1)
        x = self.fc(x)
        return x

# Symbolic tracing
model = FXModel()
traced = fx.symbolic_trace(model)

# Print graph
print("Original graph:")
traced.graph.print_tabular()

# Graph manipulation - replace ReLU with GELU
for node in traced.graph.nodes:
    if node.target == torch.relu:
        with traced.graph.inserting_after(node):
            new_node = traced.graph.call_function(torch.nn.functional.gelu, args=(node.args[0],))
            node.replace_all_uses_with(new_node)
        traced.graph.erase_node(node)

# Recompile
traced.recompile()

print("\nModified graph:")
traced.graph.print_tabular()

# Test modified model
test_input = torch.randn(1, 3, 32, 32)
output = traced(test_input)
print(f"FX transformation completed, output shape: {output.shape}")
```

### Mixed Precision Training

```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.cuda.amp import autocast, GradScaler

# Define model and training setup
model = nn.Sequential(
    nn.Linear(1000, 500),
    nn.ReLU(),
    nn.Linear(500, 100),
    nn.ReLU(),
    nn.Linear(100, 10)
).cuda()

optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
scaler = GradScaler()

# Training loop with mixed precision
model.train()
for epoch in range(5):
    for batch_idx in range(100):  # Simulate 100 batches
        # Generate dummy data
        data = torch.randn(32, 1000).cuda()
        targets = torch.randint(0, 10, (32,)).cuda()
        
        optimizer.zero_grad()
        
        # Forward pass with autocast
        with autocast():
            outputs = model(data)
            loss = criterion(outputs, targets)
        
        # Backward pass with gradient scaling
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
        
        if batch_idx % 25 == 0:
            print(f"Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item():.4f}, Scale: {scaler.get_scale()}")

print("Mixed precision training completed")
```