Use new API to register custom ExecuTorch kernels into ATen #2937
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| /* | ||
| * Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| * All rights reserved. | ||
| * | ||
| * This source code is licensed under the BSD-style license found in the | ||
| * LICENSE file in the root directory of this source tree. | ||
| */ | ||
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| #include <executorch/examples/models/llama2/custom_ops/op_sdpa.h> | ||
| #include <executorch/extension/aten_util/make_aten_functor_from_et_functor.h> | ||
| #include <executorch/extension/kernel_util/make_boxed_from_unboxed_functor.h> | ||
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| #include <torch/library.h> | ||
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| namespace torch { | ||
| namespace executor { | ||
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| namespace native { | ||
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| Tensor& sdpa_with_kv_cache_out_no_context( | ||
| const Tensor& q_projected, | ||
| const Tensor& k_projected, | ||
| const Tensor& v_projected, | ||
| Tensor& key_cache, | ||
| Tensor& value_cache, | ||
| const int64_t start_pos, | ||
| const int64_t seq_len, | ||
| // @lint-ignore CLANGTIDY facebook-hte-ConstantArgumentPassByValue | ||
| // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy | ||
| const optional<Tensor> attn_mask, | ||
| const double dropout_p, | ||
| const bool is_causal, | ||
| // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy | ||
| const optional<double> scale, | ||
| Tensor& output) { | ||
| exec_aten::RuntimeContext context{}; | ||
| return torch::executor::native::sdpa_with_kv_cache_out( | ||
| context, | ||
| q_projected, | ||
| k_projected, | ||
| v_projected, | ||
| key_cache, | ||
| value_cache, | ||
| start_pos, | ||
| seq_len, | ||
| attn_mask, | ||
| dropout_p, | ||
| is_causal, | ||
| scale, | ||
| output); | ||
| } | ||
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| at::Tensor sdpa_with_kv_cache_aten( | ||
| const at::Tensor& q_projected, | ||
| const at::Tensor& k_projected, | ||
| const at::Tensor& v_projected, | ||
| at::Tensor& key_cache, | ||
| at::Tensor& value_cache, | ||
| const int64_t start_pos, | ||
| const int64_t seq_len, | ||
| // @lint-ignore CLANGTIDY facebook-hte-ConstantArgumentPassByValue | ||
| // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy | ||
| const c10::optional<at::Tensor> attn_mask, | ||
| const double dropout_p, | ||
| const bool is_causal, | ||
| // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy | ||
| const c10::optional<double> scale) { | ||
| auto output = at::empty_like(q_projected); | ||
| WRAP_TO_ATEN(sdpa_with_kv_cache_out_no_context, 11) | ||
| (q_projected, | ||
| k_projected, | ||
| v_projected, | ||
| key_cache, | ||
| value_cache, | ||
| start_pos, | ||
| seq_len, | ||
| attn_mask, | ||
| dropout_p, | ||
| is_causal, | ||
| scale, | ||
| output); | ||
| return output; | ||
| } | ||
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| } // namespace native | ||
| } // namespace executor | ||
| } // namespace torch | ||
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| TORCH_LIBRARY(llama, m) { | ||
| m.def( | ||
| "sdpa_with_kv_cache(Tensor query, Tensor key, Tensor value, Tensor(a!) key_cache, " | ||
| "Tensor(b!) value_cache, SymInt start_pos, SymInt seq_len, Tensor? attn_mask=None, " | ||
| "float drpout_p=0.0, bool is_causal=False, float? scale=None) -> Tensor"); | ||
| m.def( | ||
| "sdpa_with_kv_cache.out(Tensor query, Tensor key, Tensor value, Tensor(a!) key_cache, " | ||
| "Tensor(b!) value_cache, SymInt start_pos, SymInt seq_len, Tensor? attn_mask=None, " | ||
| "float drpout_p=0.0, bool is_causal=False, float? scale=None, *, Tensor(c!) out) -> Tensor(c!)"); | ||
| } | ||
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| TORCH_LIBRARY_IMPL(llama, CompositeExplicitAutograd, m) { | ||
| m.impl( | ||
| "sdpa_with_kv_cache", torch::executor::native::sdpa_with_kv_cache_aten); | ||
| m.impl( | ||
| "sdpa_with_kv_cache.out", | ||
| WRAP_TO_ATEN( | ||
| torch::executor::native::sdpa_with_kv_cache_out_no_context, 11)); | ||
| } |
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This name will be too vague over time: are we only going to have one library with custom ops? Or is this llama-specifc? If it is just one global library, how do we decide which kinds of custom ops get to live in it?
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yeah i agree. this custom ops were examples/models/llama specific at the time it was added. Though things have changed since.