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[UnitTest][MTP] add test_speculate_get_token_penalty_multi_scores.py (#3742)
* add test_speculate_get_token_penalty_multi_scores * fix
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co63oc
251
tests/operators/test_speculate_get_token_penalty_multi_scores.py
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251
tests/operators/test_speculate_get_token_penalty_multi_scores.py
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# 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 unittest
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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 speculate_get_token_penalty_multi_scores
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def min_length_logits_process(
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logits,
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cur_len,
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min_len,
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eos_token_id,
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output_padding_offset,
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output_cum_offsets,
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token_num,
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bs,
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length,
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end_length,
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max_seq_len,
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):
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for token_idx in range(token_num):
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bi = (token_idx + output_padding_offset[token_idx]) / max_seq_len
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bi = bi.astype(paddle.int32)
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if bi >= bs:
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continue
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query_start_token_idx = bi * max_seq_len - output_cum_offsets[bi]
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if cur_len[bi] < 0:
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continue
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if cur_len[bi] + (token_idx - query_start_token_idx) < min_len[bi]:
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for i in range(end_length):
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logits[token_idx][eos_token_id[i]] = -1e10
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def update_repeat_times(
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pre_ids, cur_len, repeat_times, output_padding_offset, token_num, bs, length, length_id, max_seq_len
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):
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for token_idx in range(token_num):
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bi = (token_idx + output_padding_offset[token_idx]) / max_seq_len
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bi = bi.astype(paddle.int32)
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if bi >= bs:
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continue
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if cur_len[bi] < 0:
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continue
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pre_ids_now = pre_ids[bi]
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repeat_times_now = repeat_times[token_idx]
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for i in range(length_id):
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id = pre_ids_now[i]
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if id < 0:
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break
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repeat_times_now[id] = repeat_times_now[id] + 1
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def update_value_by_repeat_times(
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repeat_times,
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penalty_scores,
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frequency_score,
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presence_score,
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temperatures,
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logits,
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output_padding_offset,
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token_num,
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bs,
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length,
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max_seq_len,
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):
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for token_idx in range(token_num):
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bi = (token_idx + output_padding_offset[token_idx]) / max_seq_len
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bi = bi.astype(paddle.int32)
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if bi >= bs:
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continue
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logits_now = logits[token_idx]
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repeat_times_now = repeat_times[token_idx]
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alpha = penalty_scores[bi]
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beta = frequency_score[bi]
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gamma = presence_score[bi]
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for i in range(length):
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times = repeat_times_now[i]
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logit_now = logits_now[i]
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if times != 0:
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logit_now = logit_now * alpha if logit_now < 0 else logit_now / alpha
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logit_now = logit_now - times * beta - gamma
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logits_now[i] = logit_now / temperatures[bi]
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def ban_bad_words(logits, bad_words_list, output_padding_offset, token_num, bs, length, bad_words_length, max_seq_len):
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for token_idx in range(token_num):
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bi = (token_idx + output_padding_offset[token_idx]) / max_seq_len
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bi = bi.astype(paddle.int32)
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if bi >= bs:
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continue
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logits_now = logits[token_idx]
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for i in range(bad_words_length):
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bad_words_token_id = bad_words_list[i]
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if bad_words_token_id >= length or bad_words_token_id < 0:
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continue
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logits_now[bad_words_token_id] = -1e10
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def speculate_get_token_penalty_multi_scores_ref(
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pre_ids,
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logits,
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penalty_scores,
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frequency_score,
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presence_score,
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temperatures,
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bad_tokens,
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cur_len,
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min_len,
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eos_token_id,
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seq_lens_this_time,
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output_padding_offset,
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output_cum_offsets,
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max_seq_len,
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):
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shape = logits.shape
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repeat_times = paddle.full(shape, 0, dtype=paddle.int32)
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bs = seq_lens_this_time.shape[0]
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token_num = shape[0]
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length = shape[1]
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length_id = pre_ids.shape[1]
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length_bad_words = bad_tokens.shape[1]
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end_length = eos_token_id.shape[0]
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min_length_logits_process(
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logits,
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cur_len,
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min_len,
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eos_token_id,
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output_padding_offset,
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output_cum_offsets,
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token_num,
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bs,
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length,
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end_length,
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max_seq_len,
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)
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update_repeat_times(
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pre_ids, cur_len, repeat_times, output_padding_offset, token_num, bs, length, length_id, max_seq_len
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)
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update_value_by_repeat_times(
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repeat_times,
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penalty_scores,
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frequency_score,
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presence_score,
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temperatures,
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logits,
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output_padding_offset,
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token_num,
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bs,
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length,
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max_seq_len,
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)
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ban_bad_words(logits, bad_tokens, output_padding_offset, token_num, bs, length, length_bad_words, max_seq_len)
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class TestSpeculateGetTokenPenaltyMultiScores(unittest.TestCase):
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def test_speculate_get_token_penalty_multi_scores(self):
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paddle.seed(2023)
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np.random.seed(2023)
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bs = 64
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max_seq_len = 1024 # 1024 #2048 #8192
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data_type = "float32"
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# prepare output_padding_offset and output_cum_offsets
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tokens = [1] * bs
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token_num = np.sum(tokens)
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output_padding_offset = []
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output_cum_offsets = [0]
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opo_offset = 0
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for bid in range(bs):
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ts = tokens[bid]
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for i in range(ts):
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output_padding_offset.append(opo_offset)
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opo_offset += max_seq_len - ts
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output_cum_offsets.append(opo_offset)
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output_cum_offsets = output_cum_offsets[:-1]
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output_padding_offset = paddle.to_tensor(output_padding_offset, "int32")
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output_cum_offsets = paddle.to_tensor(output_cum_offsets, "int32")
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# prepare pre_ids and logits
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pre_ids_len = 122
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logits_len = 110
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pre_ids = np.random.randint(1, logits_len, size=(bs, pre_ids_len))
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negative_start = np.random.randint(1, pre_ids_len + 1, size=(bs))
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for i in range(bs):
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pre_ids[:, negative_start[i] :] = -1
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pre_ids = paddle.to_tensor(pre_ids).astype("int64")
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logits = paddle.zeros([token_num, logits_len]).astype(data_type)
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# prepare other params
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penalty_scores = paddle.to_tensor(np.random.random([bs])).astype(data_type)
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frequency_scores = paddle.to_tensor(np.random.random([bs])).astype(data_type)
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presence_scores = paddle.to_tensor(np.random.random([bs])).astype(data_type)
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temperatures = paddle.to_tensor(np.random.random([bs])).astype("float32")
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bad_tokens = paddle.to_tensor(np.random.randint(1, 2, size=([bs, 1]))).astype("int64")
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cur_len = paddle.to_tensor(np.random.randint(1, 50, size=(bs))).astype("int64")
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min_len = paddle.to_tensor(np.random.randint(1, 50, size=(bs))).astype("int64")
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eos_token_id = paddle.to_tensor(np.random.randint(1, 64, size=(bs))).astype("int64")
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seq_len_this_time = paddle.to_tensor(
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np.random.randint(0, 1, size=(bs)), "int32"
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) # value of seq_len_this_time is useless
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inputs = (
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pre_ids,
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logits,
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penalty_scores,
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frequency_scores,
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presence_scores,
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temperatures,
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bad_tokens,
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cur_len,
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min_len,
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eos_token_id,
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seq_len_this_time,
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output_padding_offset,
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output_cum_offsets,
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max_seq_len,
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)
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# inplace modify, not return data
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inputs_clone = [x.clone() if isinstance(x, paddle.Tensor) else x for x in inputs]
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speculate_get_token_penalty_multi_scores(*inputs)
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speculate_get_token_penalty_multi_scores_ref(*inputs_clone)
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np.testing.assert_allclose(inputs[1].numpy(), inputs_clone[1].numpy(), atol=1e-5, rtol=1e-5)
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if __name__ == "__main__":
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unittest.main()
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