wehub-resource-sync
45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
# Copyright (c) 2025 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 paddle import Tensor, _C_ops
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from paddle.framework import in_dynamic_or_pir_mode
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def batched_gemm(
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lhs: Tensor,
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rhs: Tensor,
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batch_sizes: list,
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trans_lhs: bool = False,
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trans_rhs: bool = False,
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) -> tuple[Tensor]:
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"""
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Cluster launched gemm into one op, which can be further fused and optimized.
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Args:
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lhs (Tensor): A tensor shaped in (total_seq_len, input_hidden_size), meant to be
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perform gemm operation according to batch range.
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rhs (Tensor): A tensor shaped in (num_batches, input_hidden_size, output_hidden_size).
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batch_sizes(list): A list of integers representing the number of rows in each batch.
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trans_lhs (bool): Whether view lhs matrix as last 2D-transposed. Default: False.
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trans_rhs (bool): Whether view rhs matrix as last 2D-transposed. Default: False.
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Returns:
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tuple:
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- out (Tensor): The result of batched gemm operation.
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"""
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if in_dynamic_or_pir_mode():
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return _C_ops.batched_gemm(lhs, rhs, batch_sizes, trans_lhs, trans_rhs)
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