mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 08:21:53 +08:00
Jiajun Ji
cb03958b52
* [XPU] Refactor get_padding_offset to single kernel. * add unittest. * fix codestyle. * remove cum_offsets_now. * remove max_len.
75 lines
2.7 KiB
Python
75 lines
2.7 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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import numpy as np
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import paddle
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from fastdeploy.model_executor.ops.xpu import get_padding_offset
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np.random.seed(2023)
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max_len = 10
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seq_lens = np.array([4, 3, 6], "int32").reshape(-1, 1)
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token_num = int(np.sum(seq_lens))
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bs = seq_lens.shape[0]
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input_ids = np.zeros([bs, max_len], "int64")
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for i in range(bs):
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ids_len = seq_lens[i, 0]
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input_ids[i, 0:ids_len] = np.random.randint(1, 10, seq_lens[i, 0], "int64")
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(
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x_remove_padding,
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cum_offsets_out,
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batch_id_per_token,
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cu_seqlens_q,
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cu_seqlens_k,
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) = get_padding_offset(
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paddle.to_tensor(input_ids),
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paddle.to_tensor(seq_lens.flatten()),
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token_num,
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)
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print("input_ids:\n", input_ids)
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print("seq_lens:\n", seq_lens.flatten())
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print("token_num:\n", token_num)
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print("x_remove_padding:\n", x_remove_padding)
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print("cum_offsets_out:\n", cum_offsets_out)
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print("batch_id_per_token:\n", batch_id_per_token)
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print("cu_seqlens_q:\n", cu_seqlens_q)
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print("cu_seqlens_k:\n", cu_seqlens_k)
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ref_x_remove_padding = np.array([8, 7, 8, 2, 4, 5, 5, 7, 6, 1, 7, 2, 6], "int64")
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ref_cum_offsets_out = np.array([0, 6, 13], "int32")
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ref_batch_id_per_token = np.array([0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2], "int32")
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ref_cu_seqlens_q = np.array([0, 4, 7, 13], "int32")
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ref_cu_seqlens_k = np.array([0, 4, 7, 13], "int32")
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assert (
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np.sum(np.abs(ref_x_remove_padding - x_remove_padding.numpy())) == 0
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), f"Check x_remove_padding failed.\nref: {ref_x_remove_padding}\ngot: {x_remove_padding.numpy()}"
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assert (
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np.sum(np.abs(ref_cum_offsets_out - cum_offsets_out.numpy())) == 0
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), f"Check cum_offsets_out failed.\nref: {ref_cum_offsets_out}\ngot: {cum_offsets_out.numpy()}"
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assert (
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np.sum(np.abs(ref_batch_id_per_token - batch_id_per_token.numpy())) == 0
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), f"Check batch_id_per_token failed.\nref: {ref_batch_id_per_token}\ngot: {batch_id_per_token.numpy()}"
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assert (
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np.sum(np.abs(ref_cu_seqlens_q - cu_seqlens_q.numpy())) == 0
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), f"Check cu_seqlens_q failed.\nref: {ref_cu_seqlens_q}\ngot: {cu_seqlens_q.numpy()}"
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assert (
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np.sum(np.abs(ref_cu_seqlens_k - cu_seqlens_k.numpy())) == 0
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), f"Check cu_seqlens_k failed.\nref: {ref_cu_seqlens_k}\ngot: {cu_seqlens_k.numpy()}"
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print("\nAll checks passed!")
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