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shapeless compile in docs and partially shapeless reshape (#1742)
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@ -421,3 +421,73 @@ the most opportunity to optimize the computation graph:
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# Compiling the outer function is good to do as it will likely
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# be faster even though the inner functions are compiled
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fun = mx.compile(outer)
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Shapeless Compilation
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---------------------
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When the shape of an input to a compiled function changes, the function is
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recompiled. You can compile a function once and run it on inputs with
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variable shapes by specifying ``shapeless=True`` to :func:`compile`. In this
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case changes to the shapes of the inputs do not cause the function to be
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recompiled.
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.. code-block:: python
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def fun(x, y):
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return mx.abs(x + y)
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compiled_fun = mx.compile(fun, shapeless=True)
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x = mx.array(1.0)
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y = mx.array(-2.0)
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# Firt call compiles the function
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print(compiled_fun(x, y))
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# Second call with different shapes
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# does not recompile the function
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x = mx.array([1.0, -6.0])
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y = mx.array([-2.0, 3.0])
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print(compiled_fun(x, y))
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Use shapeless compilations carefully. Since compilation is not triggered when
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shapes change, any graphs which are conditional on the input shapes will not
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work as expected. Shape-dependent computations are common and sometimes subtle
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to detect. For example:
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.. code-block:: python
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def fun(x):
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return x.reshape(x.shape[0] * x.shape[1], -1)
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compiled_fun = mx.compile(fun, shapeless=True)
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x = mx.random.uniform(shape=(2, 3, 4))
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out = compiled_fun(x)
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x = mx.random.uniform(shape=(5, 5, 3))
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# Error, can't reshape (5, 5, 3) to (6, -1)
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out = compiled_fun(x)
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The second call to the ``compiled_fun`` fails because of the call to
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:func:`reshape` which uses the static shape of ``x`` in the first call. We can
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fix this by using :func:`flatten` to avoid hardcoding the shape of ``x``:
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.. code-block:: python
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def fun(x):
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return x.flatten(0, 1)
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compiled_fun = mx.compile(fun, shapeless=True)
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x = mx.random.uniform(shape=(2, 3, 4))
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out = compiled_fun(x)
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x = mx.random.uniform(shape=(5, 5, 3))
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# Ok
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out = compiled_fun(x)
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41
mlx/ops.cpp
41
mlx/ops.cpp
@ -363,41 +363,12 @@ array reshape(const array& a, Shape shape, StreamOrDevice s /* = {} */) {
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if (a.shape() == shape) {
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return a;
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}
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size_t size = 1;
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int infer_idx = -1;
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for (int i = 0; i < shape.size(); ++i) {
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if (shape[i] == -1) {
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if (infer_idx >= 0) {
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throw std::invalid_argument(
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"[reshape] Reshape can only infer one dimension.");
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}
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infer_idx = i;
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} else {
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size *= shape[i];
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}
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}
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// Infer the shape
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if (size > 0) {
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if (infer_idx >= 0) {
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shape[infer_idx] = a.size() / size;
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size *= shape[infer_idx];
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}
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} else if (infer_idx >= 0) {
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throw std::invalid_argument(
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"[reshape] Cannot infer the shape of an empty array");
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}
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// Check that the reshaping is valid
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if (a.size() != size) {
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std::ostringstream msg;
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msg << "[reshape] Cannot reshape array of size " << a.size()
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<< " into shape " << shape << ".";
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throw std::invalid_argument(msg.str());
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}
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auto p = std::make_shared<Reshape>(to_stream(s), shape);
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return array(std::move(shape), a.dtype(), std::move(p), {a});
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auto out_shape = Reshape::output_shape(a, shape);
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return array(
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std::move(out_shape),
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a.dtype(),
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std::make_shared<Reshape>(to_stream(s), std::move(shape)),
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{a});
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}
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array unflatten(
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@ -3021,6 +3021,44 @@ bool Reshape::is_equivalent(const Primitive& other) const {
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return shape_ == r_other.shape_;
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}
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Shape Reshape::output_shape(const array& input, Shape shape) {
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size_t size = 1;
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int infer_idx = -1;
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for (int i = 0; i < shape.size(); ++i) {
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if (shape[i] == -1) {
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if (infer_idx >= 0) {
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throw std::invalid_argument(
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"[reshape] Reshape can only infer one dimension.");
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}
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infer_idx = i;
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} else {
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size *= shape[i];
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}
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}
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// Infer the shape
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if (size > 0 && infer_idx >= 0) {
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shape[infer_idx] = input.size() / size;
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size *= shape[infer_idx];
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} else if (infer_idx >= 0) {
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throw std::invalid_argument(
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"[reshape] Cannot infer the shape of an empty array");
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}
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// Check that the reshaping is valid
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if (input.size() != size) {
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std::ostringstream msg;
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msg << "[reshape] Cannot reshape array of size " << input.size()
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<< " into shape " << shape << ".";
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throw std::invalid_argument(msg.str());
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}
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return shape;
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}
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std::vector<Shape> Reshape::output_shapes(const std::vector<array>& inputs) {
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return {output_shape(inputs[0], shape_)};
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}
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std::vector<array> Reduce::vjp(
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const std::vector<array>& primals,
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const std::vector<array>& cotangents,
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@ -1746,6 +1746,8 @@ class Reshape : public UnaryPrimitive {
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std::vector<int> state() const {
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return shape_;
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};
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static Shape output_shape(const array& input, Shape shape);
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std::vector<Shape> output_shapes(const std::vector<array>& inputs) override;
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private:
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Shape shape_;
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@ -830,6 +830,25 @@ class TestCompile(mlx_tests.MLXTestCase):
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a = mx.array([0.0, 1.0, 2.0, 3.0, 4.0])
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self.assertTrue(mx.allclose(cfun(a), fun(a)))
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def test_shapeless_compile_with_reshape(self):
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def fun(x):
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return x.reshape(x.shape[0] * x.shape[1], -1)
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compiled_fun = mx.compile(fun, shapeless=True)
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x = mx.zeros(shape=(2, 3, 4))
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out = compiled_fun(x)
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self.assertEqual(out.shape, (6, 4))
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x = mx.zeros(shape=(2, 3, 8))
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out = compiled_fun(x)
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self.assertEqual(out.shape, (6, 8))
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x = mx.zeros(shape=(5, 5, 5))
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with self.assertRaises(ValueError):
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compiled_fun(x)
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if __name__ == "__main__":
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unittest.main()
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@ -685,7 +685,8 @@ auto compile_shapeless_ok(const std::vector<array>& inputs) {
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TEST_CASE("test shapeless compile") {
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{
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auto cfun = compile(compile_shapeless_not_ok, /* shapeless */ true);
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CHECK_THROWS(cfun({array({1, 2, 3, 4})}));
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cfun({array({1, 2, 3, 4})});
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CHECK_THROWS(cfun({array({1, 2, 3, 4, 5})}));
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}
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{
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