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Fix norm nd grad #5306
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Fix norm nd grad #5306
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@matt-gardner I'd love to get your input on this if you have time, specifically with regard to the changes on how the gradients are summed (#5298). |
matt-gardner
suggested changes
Jul 26, 2021
| @@ -48,9 +46,9 @@ def saliency_interpret_from_json(self, inputs: JsonDict) -> JsonDict: | |||
| # gradient and its respective embedding. | |||
| input_idx = int(key[-1]) - 1 | |||
| # The [0] here is undo-ing the batching that happens in get_gradients. | |||
| emb_grad = numpy.sum(grad[0] * embeddings_list[input_idx][0], axis=1) | |||
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This line is computing a dot product between the gradient vector and the embedding vector. The original implementation is correct. The proposed change is not a dot product anymore.
| @@ -30,9 +29,9 @@ def saliency_interpret_from_json(self, inputs: JsonDict) -> JsonDict: | |||
| # Normalize results | |||
| for key, grad in grads.items(): | |||
| # The [0] here is undo-ing the batching that happens in get_gradients. | |||
| embedding_grad = numpy.sum(grad[0], axis=1) | |||
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It's not obvious, but this line is also part of a dot product. I don't remember why it's implemented this way, but it might be for consistency across the different interpreters, or there might be some efficiency considerations that I'm not remembering. If you look at line 116 you'll see the first half of the dot product computation.
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Fixes #5298 , #5300 .
Changes proposed in this pull request:
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