"""
Kernel Fusion Strategies for GPU Optimization

This module provides educational guidance and practical tools for identifying
and implementing kernel fusion opportunities in PyTorch neural networks.

Kernel fusion is one of the most impactful GPU optimization techniques:
- Reduces memory bandwidth requirements by 40-80%
- Eliminates kernel launch overhead
- Improves GPU cache utilization
- Enables higher arithmetic intensity

 FUSION PATTERN CATEGORIES:
- Element-wise fusion: Operations that can be combined element-wise
- Producer-consumer fusion: Output of one operation feeds directly into another
- Reduction fusion: Multiple reduction operations combined
- Mixed precision fusion: Operations that benefit from automatic casting

Learn to recognize fusion opportunities in your own models and apply
proven patterns that lead to measurable performance improvements.
"""

from collections.abc import Callable
from dataclasses import dataclass
from enum import Enum
from typing import Any

import torch
import torch.nn as nn
import torch.nn.functional as F


class FusionType(Enum):
    """Types of fusion patterns for GPU optimization."""
    ELEMENT_WISE = "element_wise"
    PRODUCER_CONSUMER = "producer_consumer"
    REDUCTION = "reduction"
    BROADCAST = "broadcast"
    MIXED_PRECISION = "mixed_precision"


@dataclass
class FusionPattern:
    """
    Data structure for describing fusion optimization patterns.

    By categorizing fusion opportunities into patterns, we can:
    - Systematically identify optimization opportunities
    - Apply proven optimization strategies
    - Measure and validate fusion effectiveness
    """
    name: str
    fusion_type: FusionType
    operations: list[str]
    memory_reduction: float  # Expected memory bandwidth reduction (0.0-1.0)
    compute_improvement: float  # Expected compute efficiency improvement
    description: str
    example_before: str
    example_after: str


#  EDUCATIONAL: Common fusion patterns found in neural networks
COMMON_FUSION_PATTERNS = [
    FusionPattern(
        name="Linear + Activation",
        fusion_type=FusionType.PRODUCER_CONSUMER,
        operations=["Linear", "ReLU/GELU/SiLU"],
        memory_reduction=0.5,
        compute_improvement=0.3,
        description="Fuse linear layer output directly with activation function",
        example_before="x = linear(x); x = activation(x)",
        example_after="x = fused_linear_activation(x)"
    ),

    FusionPattern(
        name="LayerNorm + Activation",
        fusion_type=FusionType.PRODUCER_CONSUMER,
        operations=["LayerNorm", "GELU/ReLU"],
        memory_reduction=0.4,
        compute_improvement=0.25,
        description="Combine normalization statistics with activation computation",
        example_before="x = layer_norm(x); x = gelu(x)",
        example_after="x = fused_norm_activation(x)"
    ),

    FusionPattern(
        name="Element-wise Operations",
        fusion_type=FusionType.ELEMENT_WISE,
        operations=["Add", "Multiply", "Scale"],
        memory_reduction=0.6,
        compute_improvement=0.4,
        description="Combine multiple element-wise operations in single kernel",
        example_before="x = x + bias; x = x * scale; x = x + residual",
        example_after="x = fused_element_wise(x, bias, scale, residual)"
    ),

    FusionPattern(
        name="Attention QKV Projection",
        fusion_type=FusionType.PRODUCER_CONSUMER,
        operations=["Linear", "Reshape", "Transpose"],
        memory_reduction=0.7,
        compute_improvement=0.5,
        description="Combine Q, K, V projections into single matrix multiplication",
        example_before="q=proj_q(x); k=proj_k(x); v=proj_v(x)",
        example_after="qkv = proj_qkv(x); q,k,v = qkv.chunk(3, -1)"
    ),

    FusionPattern(
        name="SwiGLU Activation",
        fusion_type=FusionType.PRODUCER_CONSUMER,
        operations=["Linear", "SiLU", "Multiply"],
        memory_reduction=0.5,
        compute_improvement=0.35,
        description="Fuse gate and up projections with SwiGLU activation",
        example_before="gate=proj_gate(x); up=proj_up(x); return silu(gate)*up",
        example_after="gate_up=proj_gate_up(x); gate,up=split; return silu(gate)*up"
    )
]


def identify_fusion_opportunities(model: nn.Module, sample_input: torch.Tensor) -> list[dict[str, Any]]:
    """
    Analyze a PyTorch model to identify kernel fusion opportunities.

    This function demonstrates how to systematically analyze model architectures
    to identify optimization opportunities. It serves as a template for building
    optimization analysis tools.

    Args:
        model: PyTorch model to analyze
        sample_input: Representative input tensor for analysis

    Returns:
        List of identified fusion opportunities with optimization potential
    """
    opportunities = []

    modules = list(model.named_modules())

    for i, (name, module) in enumerate(modules[:-1]):
        current_module = module
        next_name, next_module = modules[i + 1]

        # Check for common fusion patterns
        fusion_opportunity = _analyze_module_pair(name, current_module, next_name, next_module)
        if fusion_opportunity:
            opportunities.append(fusion_opportunity)

    element_wise_opportunities = _identify_element_wise_fusion(model)
    opportunities.extend(element_wise_opportunities)

    attention_opportunities = _identify_attention_fusion(model)
    opportunities.extend(attention_opportunities)

    return opportunities


def _analyze_module_pair(name1: str, module1: nn.Module, name2: str, module2: nn.Module) -> dict[str, Any] | None:
    """Analyze a pair of sequential modules for fusion opportunities."""

    # Linear + Activation pattern
    if isinstance(module1, nn.Linear) and _is_activation_module(module2):
        return {
            "pattern": "Linear + Activation",
            "modules": [name1, name2],
            "fusion_type": FusionType.PRODUCER_CONSUMER,
            "estimated_speedup": 1.3,
            "memory_reduction": 0.4,
            "recommendation": "Consider using FusedLinearActivation or @torch.compile"
        }

    # LayerNorm + Activation pattern
    if isinstance(module1, nn.LayerNorm) and _is_activation_module(module2):
        return {
            "pattern": "LayerNorm + Activation",
            "modules": [name1, name2],
            "fusion_type": FusionType.PRODUCER_CONSUMER,
            "estimated_speedup": 1.25,
            "memory_reduction": 0.35,
            "recommendation": "Use FusedLayerNormActivation for optimal performance"
        }

    return None


def _is_activation_module(module: nn.Module) -> bool:
    """Check if module is an activation function."""
    activation_types = (nn.ReLU, nn.GELU, nn.SiLU, nn.Tanh, nn.Sigmoid)
    return isinstance(module, activation_types)


def _identify_element_wise_fusion(model: nn.Module) -> list[dict[str, Any]]:
    """Identify element-wise operations that can be fused."""
    opportunities = []

    # This would require more sophisticated analysis in practice
    # For now, provide educational template

    # Look for residual connection patterns
    for _name, module in model.named_modules():
        if hasattr(module, 'forward'):
            # In practice, would use graph analysis tools
            pass

    return opportunities


def _identify_attention_fusion(model: nn.Module) -> list[dict[str, Any]]:
    """Identify attention-specific fusion opportunities."""
    opportunities = []

    for name, module in model.named_modules():
        if 'attention' in name.lower() or 'attn' in name.lower():
            # Check for separate Q, K, V projections that could be fused
            if hasattr(module, 'q_proj') and hasattr(module, 'k_proj') and hasattr(module, 'v_proj'):
                opportunities.append({
                    "pattern": "Separate QKV Projections",
                    "module": name,
                    "fusion_type": FusionType.PRODUCER_CONSUMER,
                    "estimated_speedup": 2.1,
                    "memory_reduction": 0.6,
                    "recommendation": "Replace with single QKV projection matrix"
                })

    return opportunities


def apply_operation_fusion(
    operations: list[Callable],
    fusion_type: FusionType,
    compile_mode: str = "default"
) -> Callable:
    """
    Apply fusion to a sequence of operations.

    This demonstrates how to take a sequence of operations and create
    a fused implementation that can be optimized by torch.compile.

     FUSION IMPLEMENTATION STRATEGIES:
    - Function composition: Chain operations in single function
    - torch.compile optimization: Enable automatic kernel fusion
    - Memory reuse: Minimize intermediate tensor allocations
    - Type hint optimization: Help compiler with type information

    Args:
        operations: List of operations to fuse
        fusion_type: Type of fusion to apply
        compile_mode: torch.compile optimization mode

    Returns:
        Fused operation function optimized for GPU execution
    """

    if fusion_type == FusionType.PRODUCER_CONSUMER:
        # Create producer-consumer fusion
        @torch.compile(mode=compile_mode)
        def fused_producer_consumer(x: torch.Tensor) -> torch.Tensor:
            """
            Fused producer-consumer operations.

             FUSION OPTIMIZATION:
            - Sequential operations combined in single function
            - torch.compile can optimize entire sequence
            - Intermediate results stay in GPU registers
            - Memory bandwidth reduced through eliminated storage
            """
            result = x
            for op in operations:
                result = op(result)
            return result

        return fused_producer_consumer

    elif fusion_type == FusionType.ELEMENT_WISE:
        # Create element-wise fusion
        @torch.compile(mode=compile_mode)
        def fused_element_wise(*inputs: torch.Tensor) -> torch.Tensor:
            """
            Fused element-wise operations.

             ELEMENT-WISE FUSION:
            - All operations applied to same tensor elements
            - Perfect vectorization across GPU cores
            - Single memory access per element
            - Optimal arithmetic intensity
            """
            result = inputs[0]
            for i, op in enumerate(operations):
                if i == 0:
                    result = op(result)
                else:
                    result = op(result, inputs[min(i, len(inputs)-1)])
            return result

        return fused_element_wise

    else:
        raise ValueError(f"Unsupported fusion type: {fusion_type}")


def validate_fusion_correctness(
    original_ops: list[Callable],
    fused_op: Callable,
    test_inputs: list[torch.Tensor],
    tolerance: float = 1e-6
) -> dict[str, Any]:
    """
    Validate that fusion maintains numerical correctness.

    Critical principle: Optimization should never change correctness.
    This function demonstrates rigorous validation approaches for
    ensuring fusion maintains numerical accuracy.

     VALIDATION TECHNIQUES:
    - Numerical comparison: Check output equivalence within tolerance
    - Statistical analysis: Analyze error distribution and characteristics
    - Edge case testing: Validate behavior with extreme inputs
    - Performance measurement: Quantify optimization benefits

    Args:
        original_ops: Original sequence of operations
        fused_op: Fused operation to validate
        test_inputs: Test inputs for validation
        tolerance: Numerical tolerance for comparison

    Returns:
        Validation results including correctness and performance metrics
    """
    validation_results = {
        "correctness_passed": True,
        "max_error": 0.0,
        "mean_error": 0.0,
        "performance_improvement": 0.0,
        "test_cases_passed": 0,
        "test_cases_total": len(test_inputs)
    }

    errors = []

    for _i, test_input in enumerate(test_inputs):
        original_result = test_input
        for op in original_ops:
            original_result = op(original_result)

        fused_result = fused_op(test_input)

        if original_result.shape != fused_result.shape:
            validation_results["correctness_passed"] = False
            continue

        error = torch.abs(original_result - fused_result).max().item()
        errors.append(error)

        if error > tolerance:
            validation_results["correctness_passed"] = False
        else:
            validation_results["test_cases_passed"] += 1

    if errors:
        validation_results["max_error"] = max(errors)
        validation_results["mean_error"] = sum(errors) / len(errors)

    # In practice, would use proper benchmarking methodology
    validation_results["performance_improvement"] = 1.3  # Educational placeholder

    return validation_results


#  EDUCATIONAL: Example fusion implementations for common patterns

class FusedLinearGELU(nn.Module):
    """
    Example implementation of fused Linear + GELU pattern.

     EDUCATIONAL DEMONSTRATION:
    This shows how to implement a common fusion pattern manually.
    In practice, torch.compile can often achieve similar fusion automatically.
    """

    def __init__(self, input_dim: int, output_dim: int, bias: bool = True):
        super().__init__()
        self.linear = nn.Linear(input_dim, output_dim, bias=bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Fused Linear + GELU forward pass.

         FUSION PATTERN: Producer-consumer fusion
        - Linear layer produces intermediate result
        - GELU consumes result immediately
        - torch.compile can fuse these operations automatically
        """
        # Single operation that can be optimized by torch.compile
        return F.gelu(self.linear(x))


class FusedElementWiseOps(nn.Module):
    """
    Example implementation of fused element-wise operations.

     EDUCATIONAL DEMONSTRATION:
    Shows how to combine multiple element-wise operations that
    can be vectorized efficiently on GPU hardware.
    """

    def __init__(self, scale: float = 1.0):
        super().__init__()
        self.scale = scale

    def forward(self, x: torch.Tensor, bias: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
        """
        Fused element-wise operations.

         FUSION PATTERN: Element-wise fusion
        - All operations work on same tensor elements
        - Single pass through memory
        - Optimal GPU core utilization
        """
        # Single expression that can be optimized as one kernel
        return (x + bias) * self.scale + residual


#  OPTIMIZATION: Factory function for creating fused operations
def create_fused_operation(pattern_name: str, **kwargs) -> nn.Module:
    """
    Factory function for creating common fused operations.

    This demonstrates how to systematically apply proven optimization
    patterns to create high-performance neural network components.

    Args:
        pattern_name: Name of fusion pattern to create
        **kwargs: Configuration arguments for the specific pattern

    Returns:
        Optimized fused operation module
    """
    pattern_name = pattern_name.lower()

    if pattern_name == "linear_gelu":
        return FusedLinearGELU(**kwargs)
    elif pattern_name == "element_wise":
        return FusedElementWiseOps(**kwargs)
    else:
        raise ValueError(f"Unknown fusion pattern: {pattern_name}")


#  EDUCATIONAL: Fusion analysis utilities
def print_fusion_opportunities(opportunities: list[dict[str, Any]]) -> None:
    """
    Print fusion opportunities in a readable format.

    This demonstrates how to present optimization analysis results
    in a way that's actionable for developers.
    """
    if not opportunities:
        print(" No fusion opportunities identified - model may already be well optimized!")
        return

    print(f" Found {len(opportunities)} fusion opportunities:\n")

    for i, opp in enumerate(opportunities, 1):
        print(f"{i}. {opp.get('pattern', 'Unknown Pattern')}")
        print(f"   Modules: {', '.join(opp.get('modules', ['N/A']))}")
        print(f"   Estimated speedup: {opp.get('estimated_speedup', 'N/A')}x")
        print(f"   Memory reduction: {opp.get('memory_reduction', 0)*100:.1f}%")
        print(f"   Recommendation: {opp.get('recommendation', 'N/A')}")
        print()


def benchmark_fusion_impact(
    original_model: nn.Module,
    optimized_model: nn.Module,
    sample_input: torch.Tensor,
    num_iterations: int = 100
) -> dict[str, float]:
    """
    Benchmark the performance impact of fusion optimizations.

    Always measure optimization impact to verify that theoretical
    improvements translate to real performance gains.
    """
    # This would implement proper benchmarking in practice
    # Educational placeholder showing the methodology

    return {
        "speedup_ratio": 1.8,  # Educational placeholder
        "memory_reduction": 0.4,
        "throughput_improvement": 0.6
    }
