[Relay][Op] Multinomial #12284
[Relay][Op] Multinomial #12284
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@sfvaroglu can you take a look at this PR? |
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LGTM, thanks @jwfromm! Would be nice to have this in the onnx importer, too :) |
| assert not ( | ||
| replacement is False and num_samples > 1 | ||
| ), "Multinomial without replacement is not yet supported." | ||
| seed = np.random.randint(1e6) |
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Ideally there would be one seed that we pass through the entire graph that is set or initialized at runtime. But I don't think we have the infrastructure for that yet. This is fine for now but maybe you could add a comment about how to improve this in the future?
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Yeah thats a good point, we'd have to use a global dictionary or something for that. I'll add a note. For now, this approach matches how we handle other rng importer functions.
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| @tvm.testing.parametrize_targets | ||
| def test_multinomial(target, dev): | ||
| def _verify_multinomial(size, num_samples): |
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Could you do some rough checking of expected value and variance of the distribution. It's always hard to tell if these random things are implemented correctly, but I think this would help.
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Is there a good way to do this without potentially introducing flakiness? I guess we could use a fixed seed. Would that be satisfactory?
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You could generate a "large" sample of at least 10,000 values and then use a chi-squared test (scipy.stats.chisquare). You'd look at the p-value from that chi-squared test and compare it to an acceptably low threshold for flakiness -- for example, have this unit test fail if the p-value is smaller than 1e-6, which should only happen by chance in one run per million.
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Thanks for this tip. I added a chisquared test which confirms that the behavior of this function is expected.
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@tkonolige can you give this another look. I think its all set to merge. |
* Add multinomial operator. * Implemented Pytorch integration with multinomial. * Fixed test paramatrization and added onnx integration. * Add statistical testing. * Make get_type more flexible.
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This PR introduces the
multinomialrandom operator. It's a neat adaptation ofrandom.uniformthat allows weighted selection of indices from a probability tensor. This op is used in new Dalle-like architectures to generate random images. The PR provides a topi implemenation and tests, relay integration, and an initial pytorch integration. I did not implement sampling without replacement at this time as it seems complicated to do as a tensor operation.