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scatter_max vjp + bindings + tests (#431)
Co-authored-by: DjamelMesbah <djamel.mesbah@adservio.fr>
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@ -2129,6 +2129,7 @@ std::vector<array> Scatter::vjp(
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switch (reduce_type_) {
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case Scatter::None:
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case Scatter::Sum:
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case Scatter::Max:
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break;
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default:
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throw std::runtime_error(
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@ -2139,6 +2140,17 @@ std::vector<array> Scatter::vjp(
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const array& updates = primals.back();
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const std::vector<array> indices(primals.begin() + 1, primals.end() - 1);
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// Store result of scatter if needed for reuse in vjp
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auto get_result = [&]() {
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switch (reduce_type_) {
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case Scatter::Max:
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return scatter_max(values, indices, updates, axes_, stream());
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default:
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return array({});
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}
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};
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array result = get_result();
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std::vector<array> vjps;
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for (auto num : argnums) {
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// Gradient wrt to the input array
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@ -2157,6 +2169,11 @@ std::vector<array> Scatter::vjp(
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// The input array values are kept so they all get gradients
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vjps.push_back(cotangents[0]);
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break;
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case Scatter::Max: {
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auto mask = where(result == values, array({1}), array({0}));
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vjps.push_back(multiply(cotangents[0], mask));
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break;
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}
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default:
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// Should never reach here
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throw std::invalid_argument("");
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@ -2174,6 +2191,19 @@ std::vector<array> Scatter::vjp(
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gather(cotangents[0], indices, axes_, slice_sizes, stream()));
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break;
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}
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case Scatter::Max: {
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auto slice_sizes = cotangents[0].shape();
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for (auto ax : axes_) {
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slice_sizes[ax] = 1;
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}
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auto gathered_cotan =
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gather(cotangents[0], indices, axes_, slice_sizes, stream());
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auto gathered_result =
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gather(result, indices, axes_, slice_sizes, stream());
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vjps.push_back(
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multiply(gathered_cotan, gathered_result == updates, stream()));
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break;
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}
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default: {
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// Should never reach here
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throw std::invalid_argument("");
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@ -293,6 +293,29 @@ class TestAutograd(mlx_tests.MLXTestCase):
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self.assertTrue(mx.array_equal(dfdx, mx.array([1.0])))
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self.assertEqual(dfdx.dtype, mx.float32)
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def test_scatter_max_vjp(self):
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def fun(src, updates):
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x = src.at[1].maximum(updates)
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return x
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cotan = mx.array([4.0, 5.0, 6.0])
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_, vjps = mx.vjp(fun, [mx.array([1.0, 2.0, 3.0]), mx.array([[3.0]])], [cotan])
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mx.eval(vjps)
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# Update larger than value
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self.assertTrue(mx.allclose(vjps[0], mx.array([4.0, 0.0, 6.0])))
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self.assertTrue(mx.allclose(vjps[1], mx.array([5.0])))
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cotan = mx.array([[4.0], [5.0], [6.0]])
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_, vjps = mx.vjp(
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fun, [mx.array([[1.0], [2.0], [3.0]]), mx.array([[[2.0]]])], [cotan]
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)
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mx.eval(vjps)
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# Update and value are equal
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self.assertTrue(mx.allclose(vjps[0], mx.array([[4.0], [5.0], [6.0]])))
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self.assertTrue(mx.allclose(vjps[1], mx.array([[[5.0]]])))
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def test_vjp_types(self):
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def fun(x):
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return x
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