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85 lines
2.7 KiB
Python
85 lines
2.7 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 set_stop_value"""
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import paddle
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from fastdeploy.model_executor.ops.gpu import get_mm_split_fuse
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input_ids = []
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image_type_ids = []
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grid_thw = []
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def split_grid(origin_grid_thw):
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# 划分grid_thw，该函数用于视频场景
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# origin_grid_thw = [6, 10, 12] ---> [2, 10, 12, 2, 10, 12, 2, 10, 12]
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grid_thw = []
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for t, h, w in origin_grid_thw:
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if t > 2:
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num_groups = t // 2
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remainder = t % 2
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for _ in range(num_groups):
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grid_thw.extend([2, h, w])
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if remainder > 0:
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grid_thw.extend([remainder, h, w])
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else:
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grid_thw.extend([t, h, w])
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return grid_thw
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if __name__ == "__main__":
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grid_thw = [[6, 20, 20], [6, 40, 20]]
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grid_thw = split_grid(grid_thw)
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image_bs = len(grid_thw) // 3
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image_type_ids = [0] * image_bs
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# 随机拼接input_ids: [txt0+img1+tx1+img2]
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input_ids = [2] * 19
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img1 = [100295] * 100 * 3
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txt1 = [3] * 19
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img2 = [100295] * 200 * 3
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input_ids.extend(img1)
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input_ids.extend(txt1)
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input_ids.extend(img2)
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split_fuse_img_size = 16
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split_fuse_text_size = 384 # 1024
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seq_len = len(input_ids)
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input_ids_tensor = paddle.to_tensor(input_ids, dtype="int64")
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image_type_ids_tensor = paddle.to_tensor(image_type_ids, dtype="int32")
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is_image_token = paddle.where(input_ids_tensor == 100295, 1, 0)
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image_token_sum = paddle.cumsum(is_image_token) # 前缀和
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image_token_sum = paddle.concat([paddle.zeros([1], dtype="int64"), image_token_sum])
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grid_thw_tensor = paddle.to_tensor(grid_thw, dtype="int64")
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image_chunk_selections, split_fuse_cur_seq_lens = get_mm_split_fuse(
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input_ids_tensor.cpu(),
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image_type_ids_tensor.cast("int32").cpu(),
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image_token_sum.cast("int32").cpu(),
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grid_thw_tensor.cpu(),
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100295,
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image_bs,
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0,
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seq_len,
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split_fuse_img_size,
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split_fuse_text_size,
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2048,
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)
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print("seq_len: ", seq_len)
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print("grid_thw", grid_thw_tensor)
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print("image_chunk_selections: ", image_chunk_selections)
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print("split_fuse_cur_seq_lens: ", split_fuse_cur_seq_lens)
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