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https://github.com/PaddlePaddle/FastDeploy.git
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c234b995ab
* support qwen3-embedding * fix ci bug * support pooling dummy_run * fix * delete print * parallel_config.max_model_len * delete is_pooling_model in dummy_run * fix * fd_model * fix embedding load * fix * fix post_process
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
"""
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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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"""
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from dataclasses import dataclass
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from typing import Optional
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import paddle
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from fastdeploy.engine.pooling_params import PoolingParams
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@dataclass
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class PoolingCursor:
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index: list[int]
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first_token_indices_gpu: paddle.Tensor
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last_token_indices_gpu: paddle.Tensor
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prompt_lens_cpu: paddle.Tensor
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num_scheduled_tokens_cpu: paddle.Tensor
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def __getitem__(self, indices: slice):
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return PoolingCursor(
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index=self.index[indices],
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first_token_indices_gpu=self.first_token_indices_gpu[indices],
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last_token_indices_gpu=self.last_token_indices_gpu[indices],
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prompt_lens_cpu=self.prompt_lens_cpu[indices],
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num_scheduled_tokens_cpu=self.num_scheduled_tokens_cpu[indices],
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)
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def is_partial_prefill(self):
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return not paddle.all(self.prompt_lens_cpu == self.num_scheduled_tokens_cpu).item()
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@dataclass
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class PoolingMetadata:
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"""Tensors for pooling."""
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prompt_lens: paddle.Tensor # CPU Tensor
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prompt_token_ids: Optional[paddle.Tensor]
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pooling_params: list[PoolingParams]
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pooling_cursor: Optional[PoolingCursor] = None
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def __getitem__(self, indices: slice):
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return PoolingMetadata(
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prompt_lens=self.prompt_lens[indices],
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prompt_token_ids=None if self.prompt_token_ids is None else self.prompt_token_ids[indices],
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pooling_params=self.pooling_params[indices],
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pooling_cursor=None if self.pooling_cursor is None else self.pooling_cursor[indices],
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)
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def build_pooling_cursor(self, num_scheduled_tokens: list[int], device: str):
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self.pooling_cursor = build_pooling_cursor(num_scheduled_tokens, self.prompt_lens, device)
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def build_pooling_cursor(num_scheduled_tokens: list[int], prompt_lens: paddle.Tensor, device: str):
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assert len(prompt_lens) == len(num_scheduled_tokens)
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n_seq = len(num_scheduled_tokens)
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index = list(range(n_seq))
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num_scheduled_tokens = paddle.to_tensor(num_scheduled_tokens)
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cumsum = paddle.zeros([n_seq + 1], dtype="int64")
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paddle.cumsum(num_scheduled_tokens, axis=0, out=cumsum[1:])
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if device == "gpu":
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cumsum_device = cumsum.cuda()
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else:
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cumsum_device = cumsum
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return PoolingCursor(
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index=index,
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first_token_indices_gpu=cumsum_device[:n_seq],
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last_token_indices_gpu=cumsum_device[1:] - 1,
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prompt_lens_cpu=prompt_lens,
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num_scheduled_tokens_cpu=num_scheduled_tokens,
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)
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