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//! Element-wise multiplication (Hadamard product) for COO matrices
use super::super::CooData;
use crate::dtype::DType;
use crate::error::{Error, Result};
use crate::runtime::Runtime;
use crate::sparse::{SparseOps, SparseStorage};
impl<R: Runtime<DType = DType>> CooData<R> {
/// Element-wise multiplication (Hadamard product): C = A .* B
///
/// Computes the element-wise product of two sparse matrices with the same shape.
/// Only positions where BOTH matrices have non-zero values will be non-zero
/// in the result.
///
/// # Arguments
///
/// * `other` - Another COO matrix with the same shape and dtype
///
/// # Returns
///
/// A new COO matrix containing the element-wise product (sorted by row, then column)
///
/// # Errors
///
/// Returns error if:
/// - Shapes don't match
/// - Dtypes don't match
///
/// # Algorithm
///
/// Sorts both matrices, then performs linear merge to find matching positions.
/// GPU-accelerated when CUDA runtime is used.
///
/// # Performance
///
/// - CPU: O(nnz_a log nnz_a + nnz_b log nnz_b + nnz_a + nnz_b)
/// - GPU: Parallel sort-merge on device
/// - Result has at most min(nnz_a, nnz_b) non-zeros
///
/// # Example
///
/// ```
/// # use numr::prelude::*;
/// # #[cfg(feature = "sparse")]
/// # {
/// # use numr::sparse::SparseTensor;
/// # let device = CpuDevice::new();
/// // A: B: C = A .* B:
/// // [2, 3] [4, 0] [8, 0]
/// // [0, 5] .* [6, 7] = [0, 35]
/// # let a_sp = SparseTensor::<CpuRuntime>::from_coo_slices(&[0, 0, 1], &[0, 1, 1], &[2.0f32, 3.0, 5.0], [2, 2], &device)?;
/// # let b_sp = SparseTensor::<CpuRuntime>::from_coo_slices(&[0, 1], &[0, 1], &[4.0f32, 7.0], [2, 2], &device)?;
/// # if let numr::sparse::SparseTensor::Coo(a) = a_sp { if let numr::sparse::SparseTensor::Coo(b) = b_sp {
/// let c = a.mul(&b)?;
/// # } }
/// # }
/// # Ok::<(), numr::error::Error>(())
/// ```
pub fn mul(&self, other: &Self) -> Result<Self>
where
R::Client: SparseOps<R>,
{
// Validate shapes match
if self.shape != other.shape {
return Err(Error::ShapeMismatch {
expected: vec![self.shape[0], self.shape[1]],
got: vec![other.shape[0], other.shape[1]],
});
}
// Validate dtypes match
if self.dtype() != other.dtype() {
return Err(Error::DTypeMismatch {
lhs: self.dtype(),
rhs: other.dtype(),
});
}
let dtype = self.dtype();
let device = self.values.device();
// Get client for runtime dispatch
let client = R::default_client(device);
// Dispatch to runtime-specific implementation
crate::dispatch_dtype!(dtype, T => {
let (out_row_indices, out_col_indices, out_values) = client.mul_coo::<T>(
&self.row_indices,
&self.col_indices,
&self.values,
&other.row_indices,
&other.col_indices,
&other.values,
self.shape,
)?;
Ok(Self {
row_indices: out_row_indices,
col_indices: out_col_indices,
values: out_values,
shape: self.shape,
sorted: true, // Backend guarantees sorted output
})
}, "coo_mul")
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::dtype::DType;
use crate::runtime::cpu::CpuRuntime;
#[test]
fn test_coo_mul_overlapping() {
let device = <CpuRuntime as Runtime>::Device::default();
// A: B:
// [2, 3] [4, 0]
// [0, 5] [6, 7]
let a = CooData::<CpuRuntime>::from_slices(
&[0i64, 0, 1],
&[0i64, 1, 1],
&[2.0f32, 3.0, 5.0],
[2, 2],
&device,
)
.unwrap();
let b = CooData::<CpuRuntime>::from_slices(
&[0i64, 1, 1],
&[0i64, 0, 1],
&[4.0f32, 6.0, 7.0],
[2, 2],
&device,
)
.unwrap();
let c = a.mul(&b).unwrap();
// C = A .* B:
// [8, 0] (2*4=8 at (0,0), 3*0=0, 0*6=0)
// [0, 35] (0*6=0, 5*7=35)
assert_eq!(c.shape(), [2, 2]);
assert_eq!(c.nnz(), 2); // Only positions where both have values
let rows: Vec<i64> = c.row_indices().to_vec();
let cols: Vec<i64> = c.col_indices().to_vec();
let vals: Vec<f32> = c.values().to_vec();
assert_eq!(rows, vec![0, 1]);
assert_eq!(cols, vec![0, 1]);
assert_eq!(vals, vec![8.0, 35.0]);
}
#[test]
fn test_coo_mul_disjoint() {
let device = <CpuRuntime as Runtime>::Device::default();
// A: B:
// [1, 0] [0, 2]
// [0, 3] [4, 0]
// Completely disjoint - no overlapping positions
let a = CooData::<CpuRuntime>::from_slices(
&[0i64, 1],
&[0i64, 1],
&[1.0f32, 3.0],
[2, 2],
&device,
)
.unwrap();
let b = CooData::<CpuRuntime>::from_slices(
&[0i64, 1],
&[1i64, 0],
&[2.0f32, 4.0],
[2, 2],
&device,
)
.unwrap();
let c = a.mul(&b).unwrap();
// Result is empty since no positions overlap
assert_eq!(c.nnz(), 0);
}
#[test]
fn test_coo_mul_empty() {
let device = <CpuRuntime as Runtime>::Device::default();
let a = CooData::<CpuRuntime>::from_slices(
&[0i64, 1],
&[0i64, 1],
&[1.0f32, 2.0],
[2, 2],
&device,
)
.unwrap();
let b = CooData::<CpuRuntime>::empty([2, 2], DType::F32, &device);
// Multiply with empty matrix gives empty result
let c = a.mul(&b).unwrap();
assert_eq!(c.nnz(), 0);
let c2 = b.mul(&a).unwrap();
assert_eq!(c2.nnz(), 0);
}
#[test]
fn test_coo_mul_shape_mismatch() {
let device = <CpuRuntime as Runtime>::Device::default();
let a = CooData::<CpuRuntime>::empty([2, 3], DType::F32, &device);
let b = CooData::<CpuRuntime>::empty([3, 2], DType::F32, &device);
let result = a.mul(&b);
assert!(result.is_err());
}
#[test]
fn test_coo_mul_dtype_mismatch() {
let device = <CpuRuntime as Runtime>::Device::default();
let a = CooData::<CpuRuntime>::empty([2, 2], DType::F32, &device);
let b = CooData::<CpuRuntime>::empty([2, 2], DType::F64, &device);
let result = a.mul(&b);
assert!(result.is_err());
}
#[test]
fn test_coo_mul_same_positions() {
let device = <CpuRuntime as Runtime>::Device::default();
// Both have values at exactly the same positions
let a = CooData::<CpuRuntime>::from_slices(
&[0i64, 1, 1],
&[0i64, 0, 1],
&[2.0f32, 3.0, 4.0],
[2, 2],
&device,
)
.unwrap();
let b = CooData::<CpuRuntime>::from_slices(
&[0i64, 1, 1],
&[0i64, 0, 1],
&[5.0f32, 6.0, 7.0],
[2, 2],
&device,
)
.unwrap();
let c = a.mul(&b).unwrap();
assert_eq!(c.nnz(), 3);
let vals: Vec<f32> = c.values().to_vec();
assert_eq!(vals, vec![10.0, 18.0, 28.0]); // 2*5, 3*6, 4*7
}
}