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3a6058e445
* add stable ci * fix * update * fix * rename tests dir;fix stable ci bug * add timeout limit * update
53 lines
1.9 KiB
Python
53 lines
1.9 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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"""UT for get_token_penalty"""
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import numpy as np
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import paddle
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from fastdeploy.model_executor.ops.gpu import get_token_penalty_once
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paddle.seed(2023)
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pre_ids = paddle.randint(0, 10000, (8, 1000))
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pre_ids[:, -1] = pre_ids[:, -2]
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print(pre_ids)
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logits = paddle.rand(shape=[8, 10000], dtype="float16")
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penalty_scores = np.array([1.2] * 8).astype(np.float16).reshape(-1, 1)
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penalty_scores = paddle.to_tensor(penalty_scores)
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print("logits[0][pre_ids[0]]: ", logits[0][pre_ids[0]])
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res = get_token_penalty_once(pre_ids, logits, penalty_scores)
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for i in range(8):
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print(f"res[{i}]:{res[i][pre_ids[i]]}")
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input_ids = pre_ids
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score = paddle.index_sample(logits, input_ids)
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score = paddle.where(score < 0, score * penalty_scores, score / penalty_scores)
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bsz = paddle.shape(logits)[0] # TODO: Bsz as input for inference with dynamic batch_size
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bsz_range = paddle.arange(start=bsz * 0, end=bsz, step=bsz / bsz, name="bsz_range", dtype="int64").unsqueeze(-1)
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input_ids = input_ids + bsz_range * logits.shape[-1]
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res2 = paddle.scatter(logits.flatten(), input_ids.flatten(), score.flatten()).reshape(logits.shape)
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print("-------------------------------------------")
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for i in range(8):
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print(res2[i][pre_ids[i]])
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print("res_sub:")
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for i in range(8):
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print(res2[i][pre_ids[i]] - res[i][pre_ids[i]])
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print((res.numpy() - res2.numpy()).sum())
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