Layernorm one_plus support in fast_layer_norm #1557
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mikolajblaz
reviewed
Dec 13, 2022
| @@ -80,7 +80,8 @@ layer_norm::BwdFunction & get_bwd_launcher(torch::Dtype wtype, torch::Dtype ityp | |||
| std::vector<at::Tensor> ln_fwd(const at::Tensor &x, // BxSxhidden_size | |||
| const at::Tensor &gamma, // hidden_size | |||
| const at::Tensor &beta, // hidden_size | |||
| const float epsilon | |||
| const float epsilon, | |||
| const float one_plus | |||
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Since this is a float anyway, we could call it sth like gamma_shift and allow values different than 0 and 1.
This is not needed so this is just a side note.
| for is_1p in use_1p: | ||
| for h in hidden_sizes: | ||
| with self.subTest(f"hidden_size={h}, use_1p={is_1p}"): | ||
| self.assertAll(_test_impl(256, 2, h, fp32, fp32, is_1p)) |
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Is there an existing test (already in Apex) that checks that custom LayerNorm (like FastLayerNormFN) behaves exactly the same as e.g. PyT LayerNorm? It would be nice to double check that our modification with adding +1 is mathematically correct. It seems that _test_impl uses backward_ to check correctness, which is using the same +1 logic as the kernels.
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In this implementation, gamma tensor will be initialized to 0 (instead of 1) and the +1 is numerically handled. It is enabled by setting use_1p=True (default: False)