[LLM] First commit the llm deployment code

test/operators/test_split_fuse.py

@@ -0,0 +1,84 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" UT for set_stop_value """
+import paddle
+from fastdeploy.model_executor.ops.gpu import get_mm_split_fuse
+
+
+input_ids = []
+image_type_ids = []
+grid_thw = []
+
+
+def split_grid(origin_grid_thw):
+    # 划分grid_thw，该函数用于视频场景
+    # origin_grid_thw = [6, 10, 12] ---> [2, 10, 12, 2, 10, 12, 2, 10, 12]
+    grid_thw = []
+    for t, h, w in origin_grid_thw:
+        if t > 2:
+            num_groups = t // 2
+            remainder = t % 2
+            for _ in range(num_groups):
+                grid_thw.extend([2, h, w])
+            if remainder > 0:
+                grid_thw.extend([remainder, h, w])
+        else:
+            grid_thw.extend([t, h, w])
+    return grid_thw
+
+
+if __name__ == "__main__":
+    grid_thw = [[6, 20, 20], [6, 40, 20]]
+    grid_thw = split_grid(grid_thw)
+    image_bs = len(grid_thw) // 3
+    image_type_ids = [0] * image_bs
+    # 随机拼接input_ids: [txt0+img1+tx1+img2]
+    input_ids = [2] * 19
+    img1 = [100295] * 100 * 3
+    txt1 = [3] * 19
+    img2 = [100295] * 200 * 3
+    input_ids.extend(img1)
+    input_ids.extend(txt1)
+    input_ids.extend(img2)
+
+    split_fuse_img_size = 16
+    split_fuse_text_size = 384  # 1024
+
+    seq_len = len(input_ids)
+    input_ids_tensor = paddle.to_tensor(input_ids, dtype="int64")
+    image_type_ids_tensor = paddle.to_tensor(image_type_ids, dtype="int32")
+    is_image_token = paddle.where(input_ids_tensor == 100295, 1, 0)
+    image_token_sum = paddle.cumsum(is_image_token)  # 前缀和
+    image_token_sum = paddle.concat([paddle.zeros([1], dtype="int64"), image_token_sum])
+
+    grid_thw_tensor = paddle.to_tensor(grid_thw, dtype="int64")
+    image_chunk_selections, split_fuse_cur_seq_lens = get_mm_split_fuse(
+        input_ids_tensor.cpu(),
+        image_type_ids_tensor.cast("int32").cpu(),
+        image_token_sum.cast("int32").cpu(),
+        grid_thw_tensor.cpu(),
+        100295,
+        image_bs,
+        0,
+        seq_len,
+        split_fuse_img_size,
+        split_fuse_text_size,
+        2048,
+    )
+
+    print("seq_len: ", seq_len)
+    print("grid_thw", grid_thw_tensor)
+    print("image_chunk_selections: ", image_chunk_selections)
+    print("split_fuse_cur_seq_lens: ", split_fuse_cur_seq_lens)