use std::fmt::Debug;
use scirs2_core::numeric::Float;
use std::collections::HashMap;
use super::graph_capture::{
DataType, OperandId, OperationId, OperationType, TensorShape, XLAComputation, XLAOperation,
};
use crate::error::{OptimError, Result};
pub struct OperationLowering {
lowering_rules: HashMap<String, LoweringRule>,
primitive_mappings: HashMap<OperationType, Vec<PrimitiveOperation>>,
decomposition_patterns: Vec<DecompositionPattern>,
}
#[derive(Debug, Clone)]
pub struct LoweringRule {
pub name: String,
pub source_op: String,
pub target_primitives: Vec<String>,
pub lowering_fn: String,
pub prerequisites: Vec<String>,
}
#[derive(Debug, Clone)]
pub struct PrimitiveOperation {
pub name: String,
pub input_types: Vec<DataType>,
pub output_type: DataType,
pub constraints: Vec<OperationConstraint>,
}
#[derive(Debug, Clone)]
pub struct OperationConstraint {
pub constraint_type: ConstraintType,
pub description: String,
pub parameters: HashMap<String, String>,
}
#[derive(Debug, Clone)]
pub enum ConstraintType {
ShapeCompatibility,
DataTypeCompatibility,
MemoryAlignment,
HardwareSupport,
PerformanceRequirement,
}
#[derive(Debug, Clone)]
pub struct DecompositionPattern {
pub name: String,
pub source_pattern: OperationPattern,
pub target_sequence: Vec<OperationTemplate>,
pub conditions: Vec<String>,
}
#[derive(Debug, Clone)]
pub struct OperationPattern {
pub op_type: OperationType,
pub input_patterns: Vec<OperandPattern>,
pub attribute_patterns: HashMap<String, String>,
}
#[derive(Debug, Clone)]
pub struct OperandPattern {
pub shape_pattern: ShapePattern,
pub dtype_pattern: Option<DataType>,
pub value_constraints: Vec<String>,
}
#[derive(Debug, Clone)]
pub enum ShapePattern {
Any,
Scalar,
Vector(Option<usize>),
Matrix(Option<(usize, usize)>),
Tensor(Vec<Option<usize>>),
Broadcast,
}
#[derive(Debug, Clone)]
pub struct OperationTemplate {
pub op_type: OperationType,
pub input_mapping: Vec<OperandMapping>,
pub output_shape_calc: String,
pub attributes: HashMap<String, String>,
}
#[derive(Debug, Clone)]
pub enum OperandMapping {
Direct(usize), Constant(String), Computed(String), Intermediate(String), }
#[derive(Debug)]
pub struct LoweringContext<T: Float + Debug + Send + Sync + 'static> {
pub computation: XLAComputation<T>,
pub intermediates: HashMap<String, OperandId>,
pub options: LoweringOptions,
pub hardware_caps: HardwareCapabilities,
}
#[derive(Debug, Clone, Default)]
pub struct LoweringOptions {
pub optimize_memory: bool,
pub optimize_compute: bool,
pub enable_fusion: bool,
pub target_precision: Option<DataType>,
pub max_decomposition_depth: usize,
}
#[derive(Debug, Clone)]
pub struct HardwareCapabilities {
pub supported_dtypes: Vec<DataType>,
pub native_operations: Vec<OperationType>,
pub memory_hierarchy: MemoryHierarchy,
pub compute_units: ComputeUnits,
}
#[derive(Debug, Clone)]
pub struct MemoryHierarchy {
pub l1_cache_size: usize,
pub l2_cache_size: usize,
pub hbm_size: usize,
pub memory_bandwidth: f64,
}
#[derive(Debug, Clone)]
pub struct ComputeUnits {
pub scalar_units: usize,
pub vector_units: usize,
pub matrix_units: usize,
pub vector_width: usize,
pub matrix_dims: (usize, usize),
}
impl Default for OperationLowering {
fn default() -> Self {
Self::new()
}
}
impl OperationLowering {
pub fn new() -> Self {
let mut lowering = Self {
lowering_rules: HashMap::new(),
primitive_mappings: HashMap::new(),
decomposition_patterns: Vec::new(),
};
lowering.initialize_builtin_rules();
lowering
}
fn initialize_builtin_rules(&mut self) {
self.add_primitive_mapping(
OperationType::Add,
vec![PrimitiveOperation {
name: "add".to_string(),
input_types: vec![DataType::F32, DataType::F32],
output_type: DataType::F32,
constraints: vec![],
}],
);
self.add_primitive_mapping(
OperationType::Multiply,
vec![PrimitiveOperation {
name: "multiply".to_string(),
input_types: vec![DataType::F32, DataType::F32],
output_type: DataType::F32,
constraints: vec![],
}],
);
self.add_decomposition_pattern(DecompositionPattern {
name: "batch_norm_decomposition".to_string(),
source_pattern: OperationPattern {
op_type: OperationType::Custom(super::graph_capture::CustomOperation {
name: "batch_norm".to_string(),
custom_attributes: HashMap::new(),
backend_config: None,
}),
input_patterns: vec![],
attribute_patterns: HashMap::new(),
},
target_sequence: vec![
OperationTemplate {
op_type: OperationType::Subtract,
input_mapping: vec![OperandMapping::Direct(0), OperandMapping::Direct(1)],
output_shape_calc: "input_shape[0]".to_string(),
attributes: HashMap::new(),
},
OperationTemplate {
op_type: OperationType::Multiply,
input_mapping: vec![
OperandMapping::Intermediate("norm_sub".to_string()),
OperandMapping::Direct(2),
],
output_shape_calc: "input_shape[0]".to_string(),
attributes: HashMap::new(),
},
],
conditions: vec!["input_rank >= 2".to_string()],
});
}
pub fn lower_operations<
T: Float + Default + std::fmt::Debug + Clone + Send + Sync + 'static,
>(
computation: XLAComputation<T>,
) -> Result<XLAComputation<T>> {
let lowering = Self::new();
let options = LoweringOptions::default();
let hardware_caps = HardwareCapabilities::default();
let mut context = LoweringContext {
computation,
intermediates: HashMap::new(),
options,
hardware_caps,
};
lowering.lower_computation(&mut context)
}
fn lower_computation<T: Float + Default + std::fmt::Debug + Clone + Send + Sync + 'static>(
&self,
context: &mut LoweringContext<T>,
) -> Result<XLAComputation<T>> {
let mut new_operations = Vec::new();
for operation in &context.computation.operations {
let lowered_ops = self.lower_operation(operation, context)?;
new_operations.extend(lowered_ops);
}
let mut new_computation = context.computation.clone();
new_computation.operations = new_operations;
Ok(new_computation)
}
fn lower_operation<T: Float + Default + std::fmt::Debug + Clone + Send + Sync + 'static>(
&self,
operation: &XLAOperation<T>,
context: &LoweringContext<T>,
) -> Result<Vec<XLAOperation<T>>> {
if self.is_primitive(&operation.op_type) {
return Ok(vec![operation.clone()]);
}
if let Some(pattern) = self.find_matching_pattern(&operation.op_type) {
return self.apply_decomposition_pattern(operation, pattern, context);
}
if let Some(primitives) = self.primitive_mappings.get(&operation.op_type) {
return self.apply_primitive_mapping(operation, primitives, context);
}
Ok(vec![operation.clone()])
}
fn is_primitive(&self, op_type: &OperationType) -> bool {
matches!(
op_type,
OperationType::Add
| OperationType::Multiply
| OperationType::Subtract
| OperationType::Divide
| OperationType::Dot
| OperationType::Reshape
| OperationType::Transpose
)
}
fn find_matching_pattern(&self, op_type: &OperationType) -> Option<&DecompositionPattern> {
self.decomposition_patterns
.iter()
.find(|pattern| pattern.source_pattern.op_type == *op_type)
}
fn apply_decomposition_pattern<
T: Float + Default + std::fmt::Debug + Clone + Send + Sync + 'static,
>(
&self,
operation: &XLAOperation<T>,
pattern: &DecompositionPattern,
context: &LoweringContext<T>,
) -> Result<Vec<XLAOperation<T>>> {
let mut result_operations = Vec::new();
let current_intermediates = context.intermediates.clone();
for template in &pattern.target_sequence {
let new_op = self.instantiate_template(operation, template, ¤t_intermediates)?;
result_operations.push(new_op);
}
Ok(result_operations)
}
fn apply_primitive_mapping<
T: Float + Default + std::fmt::Debug + Clone + Send + Sync + 'static,
>(
&self,
operation: &XLAOperation<T>,
_primitives: &[PrimitiveOperation],
_context: &LoweringContext<T>,
) -> Result<Vec<XLAOperation<T>>> {
Ok(vec![operation.clone()])
}
fn instantiate_template<
T: Float + Default + std::fmt::Debug + Clone + Send + Sync + 'static,
>(
&self,
source_op: &XLAOperation<T>,
template: &OperationTemplate,
_intermediates: &HashMap<String, OperandId>,
) -> Result<XLAOperation<T>> {
let mut new_op = source_op.clone();
new_op.op_type = template.op_type.clone();
let mut new_inputs = Vec::new();
for mapping in &template.input_mapping {
match mapping {
OperandMapping::Direct(idx) => {
if *idx < source_op.inputs.len() {
new_inputs.push(source_op.inputs[*idx]);
}
}
OperandMapping::Constant(_) => {
if !source_op.inputs.is_empty() {
new_inputs.push(source_op.inputs[0]);
}
}
OperandMapping::Computed(_) => {
if !source_op.inputs.is_empty() {
new_inputs.push(source_op.inputs[0]);
}
}
OperandMapping::Intermediate(_) => {
if !source_op.inputs.is_empty() {
new_inputs.push(source_op.inputs[0]);
}
}
}
}
new_op.inputs = new_inputs;
Ok(new_op)
}
fn add_primitive_mapping(
&mut self,
op_type: OperationType,
primitives: Vec<PrimitiveOperation>,
) {
self.primitive_mappings.insert(op_type, primitives);
}
fn add_decomposition_pattern(&mut self, pattern: DecompositionPattern) {
self.decomposition_patterns.push(pattern);
}
}
impl Default for HardwareCapabilities {
fn default() -> Self {
Self {
supported_dtypes: vec![DataType::F32, DataType::BF16, DataType::S32],
native_operations: vec![
OperationType::Add,
OperationType::Multiply,
OperationType::Dot,
OperationType::Reshape,
OperationType::Transpose,
],
memory_hierarchy: MemoryHierarchy {
l1_cache_size: 1024 * 1024, l2_cache_size: 32 * 1024 * 1024, hbm_size: 32 * 1024 * 1024 * 1024, memory_bandwidth: 1600.0, },
compute_units: ComputeUnits {
scalar_units: 128,
vector_units: 64,
matrix_units: 4,
vector_width: 256,
matrix_dims: (128, 128),
},
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_operation_lowering_creation() {
let lowering = OperationLowering::new();
assert!(!lowering.primitive_mappings.is_empty());
assert!(!lowering.decomposition_patterns.is_empty());
}
#[test]
fn test_primitive_check() {
let lowering = OperationLowering::new();
assert!(lowering.is_primitive(&OperationType::Add));
assert!(lowering.is_primitive(&OperationType::Multiply));
}
#[test]
fn test_hardware_capabilities() {
let caps = HardwareCapabilities::default();
assert!(!caps.supported_dtypes.is_empty());
assert!(!caps.native_operations.is_empty());
}
}