Fallback _embedding_bag_backward and force sparse=false.
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JackCaoG
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Jun 27, 2024
| call(grad, indices_, offsets_, offset2bag, bag_size_, max_indices_, | ||
| num_weights, scale_grad_by_freq, mode, /*sparse=*/false, | ||
| per_sample_weights_opt, padding_idx); | ||
| } |
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can we add a test to test/test_operations.py, run the embedding bag fwd and bwd and make sure it doesn't crash? this way we can prevent it from regressing.
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bhavya01
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Jun 28, 2024
vanbasten23
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Jul 1, 2024
| fresh = tensor.clone() | ||
| # Make this tensor a leaf tensor by detaching and reseting its | ||
| # requires_grad property. | ||
| fresh = fresh.detach() |
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Very cool. I wonder why we need to make the tensor a leaf tensor. What would happen if we don't do it?
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It's so we can grab its tensor.grad and compare.
vanbasten23
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Fix: #6008
This PR, in addition to pytorch/pytorch#129691, add support for embedding bag calls when its
sparseparameter is true.Problem: when
sparse=true,_embedding_bag_backwardcalledat::_sparse_coo_tensor_unsafe_symintfunction which returns a sparse tensor. Since PyTorch/XLA does not support sparse tensors, this resulted in a dispatch error (see the original issue).Solution: although, ideally we should support sparse tensors, in the short-term we decided (in an offline discussion) to fallback to the dense backwards function.
cc @miladm @JackCaoG