Implementation of mlx.random.multivariate_normal (#502) (#877)

* Implementation of mlx.random.multivariate_normal (#502) * Update python/src/random.cpp Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update python/src/random.cpp Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update python/src/random.cpp Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Updated typo in docstring * Restricted multivariate_normal to float32 * Generic mean and variance shapes * Review edits * Update mlx/random.cpp Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update python/src/random.cpp Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update python/src/random.cpp Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update python/src/random.cpp Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Test for ndim of mean and cov * nits * smaller size for test * fix broadcasted sampling --------- Co-authored-by: Awni Hannun <awni.hannun@gmail.com> Co-authored-by: Awni Hannun <awni@apple.com>
2025-06-24 09:21:16 +08:00 · 2024-04-09 22:50:12 +02:00 · 2024-04-09 22:50:12 +02:00 · fffe072028
commit fffe072028
parent a1a31eed27
6 changed files with 270 additions and 1 deletions
--- a/docs/src/python/random.rst
+++ b/docs/src/python/random.rst
@ -38,6 +38,7 @@ we use a splittable version of Threefry, which is a counter-based PRNG.
   gumbel
   key
   normal
+   multivariate_normal
   randint
   seed
   split
--- a/mlx/random.cpp
+++ b/mlx/random.cpp
@ -1,8 +1,9 @@
-// Copyright © 2023 Apple Inc.
+// Copyright © 2023-2024 Apple Inc.

 #include <cmath>
 #include <sstream>

+#include "mlx/linalg.h"
 #include "mlx/ops.h"
 #include "mlx/primitives.h"
 #include "mlx/random.h"
@ -192,6 +193,74 @@ array normal(
  return samples;
 }

+array multivariate_normal(
+    const array& mean,
+    const array& cov,
+    const std::vector<int>& shape,
+    Dtype dtype,
+    const std::optional<array>& key /* = nullopt */,
+    StreamOrDevice s) {
+  auto stream = to_stream(s);
+
+  if (dtype != float32) {
+    throw std::invalid_argument("[multivariate_normal] dtype must be float32.");
+  }
+
+  if (mean.ndim() < 1) {
+    throw std::invalid_argument(
+        "[multivariate_normal] mean must have at least one dimension.");
+  }
+
+  if (cov.ndim() < 2) {
+    throw std::invalid_argument(
+        "[multivariate_normal] cov must have at least two dimensions.");
+  }
+
+  auto n = mean.shape(-1);
+
+  // Check shapes comatibility of mean and cov
+  if (cov.shape(-1) != cov.shape(-2)) {
+    throw std::invalid_argument(
+        "[multivariate_normal] last two dimensions of cov must be equal.");
+  }
+  if (n != cov.shape(-1)) {
+    throw std::invalid_argument(
+        "[multivariate_normal] mean and cov must have compatible shapes.");
+  }
+
+  // Compute output shape
+  std::vector<int> truncated_output_shape;
+
+  auto truncated_mean_shape =
+      std::vector<int>(mean.shape().begin(), mean.shape().end() - 1);
+  auto truncated_cov_shape =
+      std::vector<int>(cov.shape().begin(), cov.shape().end() - 2);
+  auto output_shape =
+      broadcast_shapes(truncated_cov_shape, truncated_mean_shape);
+  output_shape = broadcast_shapes(output_shape, shape);
+  output_shape.push_back(n);
+
+  // Compute the square-root of the covariance matrix, using the SVD
+  auto covariance = astype(cov, float32, stream);
+  auto SVD = linalg::svd(covariance, stream);
+  auto std = astype(
+      matmul(
+          multiply(
+              SVD[0], expand_dims(sqrt(SVD[1], stream), -2, stream), stream),
+          SVD[2],
+          stream),
+      dtype,
+      stream);
+
+  // Generate standard the samples
+  auto standard_normal = normal(output_shape, dtype, 0.0, 1.0, key, stream);
+  auto scaled_out = squeeze(
+      matmul(expand_dims(standard_normal, -2, stream), std, stream),
+      -2,
+      stream);
+  return add(mean, scaled_out, stream);
+}
+
 array randint(
    const array& low,
    const array& high,
--- a/mlx/random.h
+++ b/mlx/random.h
@ -121,6 +121,15 @@ inline array normal(
  return normal(shape, float32, 0.0, 1.0, key, s);
 }

+/** Generate samples from a multivariate normal distribution. **/
+array multivariate_normal(
+    const array& mean,
+    const array& cov,
+    const std::vector<int>& shape,
+    Dtype dtype,
+    const std::optional<array>& key = std::nullopt,
+    StreamOrDevice s = {});
+
 /** Generate integer samples uniformly at random */
 array randint(
    const array& low,
--- a/python/src/random.cpp
+++ b/python/src/random.cpp
@ -179,6 +179,48 @@ void init_random(nb::module_& parent_module) {
            array: The output array of random values.
      )pbdoc");
  m.def(
+      "multivariate_normal",
+      [](const array& mean,
+         const array& cov,
+         const std::vector<int>& shape,
+         std::optional<Dtype> type,
+         const std::optional<array>& key_,
+         StreamOrDevice s) {
+        auto key = key_ ? key_.value() : default_key().next();
+        return multivariate_normal(
+            mean, cov, shape, type.value_or(float32), key, s);
+      },
+      "mean"_a,
+      "cov"_a,
+      "shape"_a = std::vector<int>{},
+      "dtype"_a.none() = float32,
+      "key"_a = nb::none(),
+      "stream"_a = nb::none(),
+      nb::sig(
+          "def multivariate_normal(mean: array, cov: array, shape: Sequence[int] = [], dtype: Optional[Dtype] = float32, key: Optional[array] = None, stream: Union[None, Stream, Device] = None) -> array"),
+      R"pbdoc(
+        Generate jointly-normal random samples given a mean and covariance.
+
+        The matrix ``cov`` must be positive semi-definite. The behavior is
+        undefined if it is not.  The only supported ``dtype`` is ``float32``.
+
+        Args:
+            mean (array): array of shape ``(..., n)``, the mean of the
+              distribution.
+            cov (array): array  of shape ``(..., n, n)``, the covariance
+              matrix of the distribution. The batch shape ``...`` must be
+              broadcast-compatible with that of ``mean``.
+            shape (list(int), optional): The output shape must be
+              broadcast-compatible with ``mean.shape[:-1]`` and ``cov.shape[:-2]``.
+              If empty, the result shape is determined by broadcasting the batch
+              shapes of ``mean`` and ``cov``. Default: ``[]``.
+            dtype (Dtype, optional): The output type. Default: ``float32``.
+            key (array, optional): A PRNG key. Default: ``None``.
+
+        Returns:
+            array: The output array of random values.
+      )pbdoc");
+  m.def(
      "randint",
      [](const ScalarOrArray& low,
         const ScalarOrArray& high,
--- a/python/tests/test_random.py
+++ b/python/tests/test_random.py
@ -1,5 +1,6 @@
 # Copyright © 2023 Apple Inc.

+import math
 import unittest

 import mlx.core as mx
@ -101,6 +102,96 @@ class TestRandom(mlx_tests.MLXTestCase):
            a = abs(mx.random.normal(shape=(10000,), loc=0, scale=1, dtype=hp))
            self.assertTrue(mx.all(a < mx.inf))

+    def test_multivariate_normal(self):
+        key = mx.random.key(0)
+        mean = mx.array([0, 0])
+        cov = mx.array([[1, 0], [0, 1]])
+
+        a = mx.random.multivariate_normal(mean, cov, key=key, stream=mx.cpu)
+        self.assertEqual(a.shape, (2,))
+
+        ## Check dtypes
+        for t in [mx.float32]:
+            a = mx.random.multivariate_normal(
+                mean, cov, dtype=t, key=key, stream=mx.cpu
+            )
+            self.assertEqual(a.dtype, t)
+        for t in [
+            mx.int8,
+            mx.int32,
+            mx.int64,
+            mx.uint8,
+            mx.uint32,
+            mx.uint64,
+            mx.float16,
+            mx.bfloat16,
+        ]:
+            with self.assertRaises(ValueError):
+                mx.random.multivariate_normal(
+                    mean, cov, dtype=t, key=key, stream=mx.cpu
+                )
+
+        ## Check incompatible shapes
+        with self.assertRaises(ValueError):
+            mean = mx.zeros((2, 2))
+            cov = mx.zeros((2, 2))
+            mx.random.multivariate_normal(mean, cov, shape=(3,), key=key, stream=mx.cpu)
+
+        with self.assertRaises(ValueError):
+            mean = mx.zeros((2))
+            cov = mx.zeros((2, 2, 2))
+            mx.random.multivariate_normal(mean, cov, shape=(3,), key=key, stream=mx.cpu)
+
+        with self.assertRaises(ValueError):
+            mean = mx.zeros((3,))
+            cov = mx.zeros((2, 2))
+            mx.random.multivariate_normal(mean, cov, key=key, stream=mx.cpu)
+
+        with self.assertRaises(ValueError):
+            mean = mx.zeros((2,))
+            cov = mx.zeros((2, 3))
+            mx.random.multivariate_normal(mean, cov, key=key, stream=mx.cpu)
+
+        ## Different shape of mean and cov
+        mean = mx.array([[0, 7], [1, 2], [3, 4]])
+        cov = mx.array([[1, 0.5], [0.5, 1]])
+        a = mx.random.multivariate_normal(mean, cov, shape=(4, 3), stream=mx.cpu)
+        self.assertEqual(a.shape, (4, 3, 2))
+
+        ## Check correcteness of the mean and covariance
+        n_test = int(1e5)
+
+        def check_jointly_gaussian(data, mean, cov):
+            empirical_mean = mx.mean(data, axis=0)
+            empirical_cov = (
+                (data - empirical_mean).T @ (data - empirical_mean) / data.shape[0]
+            )
+            N = data.shape[1]
+            self.assertTrue(
+                mx.allclose(
+                    empirical_mean, mean, rtol=0.0, atol=10 * N**2 / math.sqrt(n_test)
+                )
+            )
+            self.assertTrue(
+                mx.allclose(
+                    empirical_cov, cov, rtol=0.0, atol=10 * N**2 / math.sqrt(n_test)
+                )
+            )
+
+        mean = mx.array([4.0, 7.0])
+        cov = mx.array([[2, 0.5], [0.5, 1]])
+        data = mx.random.multivariate_normal(
+            mean, cov, shape=(n_test,), key=key, stream=mx.cpu
+        )
+        check_jointly_gaussian(data, mean, cov)
+
+        mean = mx.arange(3)
+        cov = mx.array([[1, -1, 0.5], [-1, 1, -0.5], [0.5, -0.5, 1]])
+        data = mx.random.multivariate_normal(
+            mean, cov, shape=(n_test,), key=key, stream=mx.cpu
+        )
+        check_jointly_gaussian(data, mean, cov)
+
    def test_randint(self):
        a = mx.random.randint(0, 1, [])
        self.assertEqual(a.shape, ())
--- a/tests/random_tests.cpp
+++ b/tests/random_tests.cpp
@ -420,6 +420,63 @@ TEST_CASE("test random normal") {
  }
 }

+TEST_CASE("test random multivariate_normal") {
+  {
+    auto mean = zeros({3});
+    auto cov = eye(3);
+    auto x = random::multivariate_normal(mean, cov, {1000}, float32);
+    CHECK_EQ(x.shape(), std::vector<int>({1000, 3}));
+    CHECK_EQ(x.dtype(), float32);
+  }
+
+  // Limit case
+  {
+    auto mean = array({0, 0});
+    auto cov = array({1., -1, -.1, 1.});
+    cov = reshape(cov, {2, 2});
+    auto x = random::multivariate_normal(mean, cov, {1}, float32);
+    CHECK_EQ(x.shape(), std::vector<int>({1, 2}));
+    CHECK_EQ(x.dtype(), float32);
+  }
+
+  // Check wrong shapes
+  {
+    auto mean = zeros({3, 1});
+    auto cov = eye(3);
+    CHECK_THROWS_AS(
+        random::multivariate_normal(
+            mean,
+            cov,
+            {
+                1000,
+            },
+            float32),
+        std::invalid_argument);
+  }
+  {
+    auto mean = zeros({3});
+    auto cov = zeros({1, 2, 3, 3});
+    auto x = random::multivariate_normal(mean, cov, {1000, 2}, float32);
+    CHECK_EQ(x.shape(), std::vector<int>({1000, 2, 3}));
+  }
+  {
+    auto mean = zeros({3});
+    auto cov = eye(4);
+    CHECK_THROWS_AS(
+        random::multivariate_normal(mean, cov, {1000, 3}, float32),
+        std::invalid_argument);
+  }
+
+  // Check wrong type
+  {
+    auto mean = zeros({3});
+    auto cov = eye(3);
+    CHECK_THROWS_AS(
+        random::multivariate_normal(mean, cov, {1000, 3}, float16),
+        std::invalid_argument);
+  }
+}
+
 TEST_CASE("test random randint") {
  CHECK_THROWS_AS(
      random::randint(array(3), array(5), {1}, float32), std::invalid_argument);