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Optimizing Complex Matrix Multiplication using Karatsuba’s Algorithm (#2220)

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* Implementing Complex Matmul using Karatsuba Algorithm

* Implemented Karatsuba's Algorithm for complex matmul and pre-commit them

* fix

---------

Co-authored-by: Awni Hannun <awni@apple.com>
This commit is contained in:
Suryash Malviya 2025-06-02 18:58:46 -04:00 committed by GitHub
parent cbad6c3093
commit 0408ba0a76
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GPG Key ID: B5690EEEBB952194
2 changed files with 24 additions and 15 deletions
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25
mlx/ops.cpp
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@ -2862,21 +2862,30 @@ array matmul(
<< " second input with shape " << b.shape() << "."; << " second input with shape " << b.shape() << ".";
throw std::invalid_argument(msg.str()); throw std::invalid_argument(msg.str());
} }
// Type promotion
auto out_type = promote_types(a.dtype(), b.dtype()); // complex matmul using Karatsuba's Algorithm
// Complex matmul in terms of real matmuls if (a.dtype() == complex64 || b.dtype() == complex64) {
if (out_type == complex64) { // Extract real and imaginary parts
auto a_real = real(a, s); auto a_real = real(a, s);
auto b_real = real(b, s);
auto a_imag = imag(a, s); auto a_imag = imag(a, s);
auto b_real = real(b, s);
auto b_imag = imag(b, s); auto b_imag = imag(b, s);
auto c_real =
subtract(matmul(a_real, b_real, s), matmul(a_imag, b_imag, s), s); // Compute real and imaginary components of the result
auto c_imag = add(matmul(a_real, b_imag, s), matmul(a_imag, b_real, s), s); auto m1 = matmul(a_real, b_real, s);
auto m2 = matmul(a_imag, b_imag, s);
auto m3 = matmul(add(a_real, a_imag, s), add(b_real, b_imag, s), s);
auto c_real = subtract(m1, m2, s);
auto c_imag = subtract(m3, add(m1, m2, s), s);
return add( return add(
c_real, multiply(array(complex64_t{0, 1}, complex64), c_imag, s), s); c_real, multiply(array(complex64_t{0, 1}, complex64), c_imag, s), s);
} }
// Type promotion
auto out_type = promote_types(a.dtype(), b.dtype());
if (!issubdtype(out_type, floating)) { if (!issubdtype(out_type, floating)) {
std::ostringstream msg; std::ostringstream msg;
msg << "[matmul] Only real floating point types are supported but " msg << "[matmul] Only real floating point types are supported but "

14
python/tests/test_blas.py
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@ -1210,13 +1210,6 @@ class TestBlas(mlx_tests.MLXTestCase):
self.assertTrue(np.allclose(c, c_np)) self.assertTrue(np.allclose(c, c_np))
# Test addmm # Test addmm
M = 16
K = 50
N = 32
def rand(shape):
return mx.random.uniform(shape=shape) + 1j * mx.random.uniform(shape=shape)
a = rand((M, K)) a = rand((M, K))
b = rand((K, N)) b = rand((K, N))
c = rand((M, N)) c = rand((M, N))
@ -1224,6 +1217,13 @@ class TestBlas(mlx_tests.MLXTestCase):
out_np = 2.0 * np.matmul(a, b) + 2.0 * c out_np = 2.0 * np.matmul(a, b) + 2.0 * c
self.assertTrue(np.allclose(out, out_np)) self.assertTrue(np.allclose(out, out_np))
# complex with real
a = rand((M, K)).real
b = rand((K, N))
c = mx.matmul(a, b)
c_np = np.matmul(a, b)
self.assertTrue(np.allclose(out, out_np))
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()