mirror of
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123 lines
4.4 KiB
Python
123 lines
4.4 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 draft_model_update
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def run_paddle_test(device="cpu"):
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np.random.seed(42)
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paddle.seed(42)
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if device == "cpu":
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paddle.set_device(device)
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elif device == "xpu":
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paddle.set_device(device)
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else:
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raise ValueError(f"Invalid device: {device}")
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# 设置参数
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max_bsz = 128
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max_draft_token = 3
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pre_id_length = 3
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max_seq_len = 100
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max_base_model_draft_token = 4
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substep = 2
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# 创建随机张量
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inter_next_tokens = paddle.randint(1, 100, shape=(max_bsz, max_seq_len), dtype="int64")
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draft_tokens = paddle.randint(1, 100, shape=(max_bsz, max_draft_token), dtype="int64")
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pre_ids = paddle.randint(1, 100, shape=(max_bsz, pre_id_length), dtype="int64")
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seq_lens_this_time = paddle.randint(1, 2, shape=(max_bsz,), dtype="int32")
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seq_lens_encoder = paddle.randint(1, 10, shape=(max_bsz,), dtype="int32")
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seq_lens_decoder = paddle.randint(1, 10, shape=(max_bsz,), dtype="int32")
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step_idx = paddle.randint(1, 10, shape=(max_bsz,), dtype="int64")
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output_cum_offsets = paddle.randint(0, 2, shape=(max_bsz,), dtype="int32")
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output_cum_offsets[0] = 0 # 确保第一个偏移量为0
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stop_flags = paddle.zeros([max_bsz], dtype="bool")
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not_need_stop = paddle.zeros([1], dtype="bool")
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max_dec_len = paddle.randint(100, 102, shape=(max_bsz,), dtype="int64")
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end_ids = paddle.to_tensor([2], dtype="int64")
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base_model_draft_tokens = paddle.randint(1, 10, shape=(max_bsz, max_base_model_draft_token), dtype="int64")
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# 打印张量信息
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# print("inter_next_tokens shape:", inter_next_tokens.shape)
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# print("draft_tokens shape:", draft_tokens.shape)
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# print("pre_ids shape:", pre_ids.shape)
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# print("seq_lens_this_time shape:", seq_lens_this_time.shape)
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# print("seq_lens_encoder shape:", seq_lens_encoder.shape)
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# print("seq_lens_decoder shape:", seq_lens_decoder.shape)
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# print("step_idx shape:", step_idx.shape)
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# print("output_cum_offsets shape:", output_cum_offsets.shape)
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# print("stop_flags shape:", stop_flags.shape)
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# print("not_need_stop shape:", not_need_stop.shape)
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# print("max_dec_len shape:", max_dec_len.shape)
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# print("end_ids shape:", end_ids.shape)
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# print("base_model_draft_tokens shape:", base_model_draft_tokens.shape)
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# print("draft_tokens before update:", draft_tokens)
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# print("pre_ids before update:", pre_ids)
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draft_model_update(
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inter_next_tokens,
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draft_tokens,
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pre_ids,
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seq_lens_this_time,
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seq_lens_encoder,
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seq_lens_decoder,
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step_idx,
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output_cum_offsets,
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stop_flags,
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not_need_stop,
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max_dec_len,
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end_ids,
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base_model_draft_tokens,
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max_seq_len,
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substep,
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)
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# print("draft_tokens after update:", draft_tokens)
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# print("pre_ids after update:", pre_ids)
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return (
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draft_tokens,
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pre_ids,
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seq_lens_this_time,
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seq_lens_encoder,
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seq_lens_decoder,
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step_idx,
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stop_flags,
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not_need_stop,
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base_model_draft_tokens,
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)
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if __name__ == "__main__":
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res_xpu = run_paddle_test("xpu")
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res_cpu = run_paddle_test()
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for idx in range(len(res_cpu)):
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# 将结果转换为numpy数组
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cpu_arr = res_cpu[idx].numpy()
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xpu_arr = res_xpu[idx].numpy()
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# 检查是否为布尔类型
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if cpu_arr.dtype == bool:
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assert np.array_equal(cpu_arr, xpu_arr), f"布尔结果在索引 {idx} 处不匹配"
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else:
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# 对于数值类型，使用更宽松的比较条件
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assert np.allclose(
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cpu_arr, xpu_arr, rtol=1e-4, atol=1e-5
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), f"数值结果在索引 {idx} 处不匹配，最大差异: {np.max(np.abs(cpu_arr - xpu_arr))}"
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print(f"结果 {idx} 验证通过")
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