wehub-resource-sync
83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from paddle import _C_ops
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from paddle.framework import LayerHelper, in_dynamic_or_pir_mode
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if TYPE_CHECKING:
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from paddle import Tensor
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def blha_get_max_len(
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seq_lens_encoder: Tensor, seq_lens_decoder: Tensor, batch_size: Tensor
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) -> tuple[Tensor, Tensor]:
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"""
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Apply Fused BlhaGetMaxLen kernel. Typically used before the block_multihead_attention operator.
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Args:
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seq_lens_encoder (Tensor): Sentence length of the encoder.
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seq_lens_decoder (Tensor): Sentence length of the decoder.
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batch_size (Tensor): the batch size.
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Returns:
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Tensor|(max_enc_len_this_time, max_dec_len_this_time)
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Examples:
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.. code-block:: pycon
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>>> # doctest: +REQUIRES(env:GPU)
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>>> import paddle
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>>> paddle.device.set_device('gpu')
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>>> seq_lens_encoder = paddle.cast(paddle.randn(shape=[10]), dtype=paddle.int32)
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>>> seq_lens_decoder = paddle.cast(paddle.randn(shape=[10]), dtype=paddle.int32)
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>>> bsz = 10
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>>> batch_size = paddle.ones(shape=[bsz])
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>>> max_enc_len_this_time, max_dec_len_this_time = paddle.incubate.nn.functional.blha_get_max_len(
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... seq_lens_encoder, seq_lens_decoder, batch_size
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... )
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"""
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if in_dynamic_or_pir_mode():
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return _C_ops.blha_get_max_len(
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seq_lens_encoder, seq_lens_decoder, batch_size
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)
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helper = LayerHelper('blha_get_max_len', **locals())
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max_enc_len_this_time = helper.create_variable_for_type_inference(
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dtype="int32"
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)
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max_dec_len_this_time = helper.create_variable_for_type_inference(
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dtype="int32"
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)
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inputs = {}
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inputs['seq_lens_encoder'] = seq_lens_encoder
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inputs['seq_lens_decoder'] = seq_lens_decoder
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inputs['batch_size'] = batch_size
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outputs = {
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'max_enc_len_this_time': max_enc_len_this_time,
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'max_dec_len_this_time': max_dec_len_this_time,
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}
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helper.append_op(
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type='blha_get_max_len',
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inputs=inputs,
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outputs=outputs,
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)
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return max_enc_len_this_time, max_dec_len_this_time
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