From b7270862e353b73531cd8bff4735d537c905a312 Mon Sep 17 00:00:00 2001 From: Han Xiao Date: Mon, 25 Apr 2022 17:07:15 +0200 Subject: [PATCH] feat(client): add rank endpoint (#695) --- .github/README-img/rerank-chart.svg | 1 + .github/README-img/rerank.png | Bin 0 -> 165078 bytes README.md | 40 +++++++++- client/clip_client/__init__.py | 2 +- client/clip_client/client.py | 83 ++++++++++++++++++++- docs/user-guides/client.md | 68 +++++++++++++++++ server/clip_server/__init__.py | 2 +- server/clip_server/executors/clip_onnx.py | 9 +-- server/clip_server/executors/clip_torch.py | 47 ++++++++---- tests/__init__.py | 3 + tests/conftest.py | 9 +++ tests/test_ranker.py | 51 ++++++++++--- 12 files changed, 279 insertions(+), 36 deletions(-) create mode 100644 .github/README-img/rerank-chart.svg create mode 100644 .github/README-img/rerank.png diff --git a/.github/README-img/rerank-chart.svg b/.github/README-img/rerank-chart.svg new file mode 100644 index 000000000..3ade84257 --- /dev/null +++ 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z)!=fri)M{vdS1%Zmz)+(0vy_cH?Q_y>q~*(d*W6wovboTv@(7_Z3&07YBtPw(2kx7+HiuZT-xo9_EybJrQ zT2gUBow&B^N1wm{(UZS&$NBjFl`LD5L}52}5d;Fo(jv1TRRA^D&1h6SmAV)>G+Cc6 zoM%P?(X(XVw-mLZP3R(wh2;hJoQt~Ien3~g2fs2pVr~dm7TF z^BD2)m^Twy9_s*q^(bG@)MIY@ocn5QbFHcX`kHerkFZp#6Tyg{v2{Zimx;w=#fJfT zLe|!ujK_Hr61eReBIw#OQiLW&hS~VO?C%UvC{_$dfoGs^=Y-@u6k>7{INjg<_Ajco z=%ghwScpMh!-<(cI7O~xooG30ZO6VvHd<>?PaCs~t$%HPRNaLE4|HH3-CyLSQg7k@ z^*4QZrH9z^;T-xF<>|vGPk!{n^2M{e%U$xxd4`szczD(YpQz^Xbb0FA%x!vJE5UoN z0agL2{;1gJa<)4BH+l@Lm@>5u^_jg_|8smPtYW2k1g^nKc17uD(yt9B;y6A=a&(h& zFEJbT**L@vi2AKmIfxL%vDInmO&yA~C2D4lGToUVst18|W$!)3E!Rf3Op#WeQg*d~ zYY(%QDl~bwZyUoWrZ5Lc%P;JE8h^ToF1RXd!Fk&T)qSbMIXd4Z-ry>AU2SWket6Xs zM850#rh6zwJMhC8KE3Q0W5v~i?V(p!ID#{;ak_nCNv!2LfaDEdKcj0Ice0LK#~q_C z^;~u{@m2%bqK6TQ%oW`$y|rWU&Wjhu%Sw5MntM-a;f@Vt0W6IIAqdalSfV;Z zX*OeW2pPF93};*eV7e*zLpidK)J+`B#_ -Intrigued? That's only scratching the surface of what CLIP-as-service is capable of. [Read our docs to learn more](https://clip-as-service.jina.ai). +### Rerank image-text matches via CLIP model + +From `0.3.0` CLIP-as-service adds a new `/rerank` endpoint that re-ranks cross-modal matches according to their joint likelihood in CLIP model. For example, given an image Document with some predefined sentence matches as below: + +```python +from clip_client import Client +from docarray import Document + +c = Client(server='grpc://demo-cas.jina.ai:51000') +r = c.rerank( + [ + Document( + uri='.github/README-img/rerank.png', + matches=[ + Document(text=f'a photo of a {p}') + for p in ( + 'control room', + 'lecture room', + 'conference room', + 'podium indoor', + 'television studio', + ) + ], + ) + ] +) + +print(r['@m', ['text', 'scores__clip_score__value']]) +``` + +```text +[['a photo of a television studio', 'a photo of a conference room', 'a photo of a lecture room', 'a photo of a control room', 'a photo of a podium indoor'], +[0.9920725226402283, 0.006038925610482693, 0.0009973491542041302, 0.00078492151806131, 0.00010626466246321797]] +``` +One can see now `a photo of a television studio` is ranked to the top with `clip_score` score at `0.992`. In practice, one can use this endpoint to re-rank the matching result from another search system, for improving the cross-modal search quality. + +Rerank endpoint image inputRerank endpoint output + +Intrigued? That's only scratching the surface of what CLIP-as-service is capable of. [Read our docs to learn more](https://clip-as-service.jina.ai). ## Support diff --git a/client/clip_client/__init__.py b/client/clip_client/__init__.py index 304f5aeb7..74e0c756c 100644 --- a/client/clip_client/__init__.py +++ b/client/clip_client/__init__.py @@ -1,3 +1,3 @@ -__version__ = '0.2.4' +__version__ = '0.3.0' from .client import Client diff --git a/client/clip_client/client.py b/client/clip_client/client.py index df9f2aa87..ebcde945c 100644 --- a/client/clip_client/client.py +++ b/client/clip_client/client.py @@ -14,7 +14,6 @@ ) from urllib.parse import urlparse - if TYPE_CHECKING: import numpy as np from docarray import DocumentArray, Document @@ -316,3 +315,85 @@ def _prepare_streaming(self, disable, total): from docarray import DocumentArray self._results = DocumentArray() + + @staticmethod + def _prepare_single_doc(d: 'Document'): + if d.content_type in ('text', 'blob'): + return d + elif not d.blob and d.uri: + d.load_uri_to_blob() + return d + elif d.tensor is not None: + return d + else: + raise TypeError(f'unsupported input type {d!r} {d.content_type}') + + @staticmethod + def _prepare_rank_doc(d: 'Document', _source: str = 'matches'): + _get = lambda d: getattr(d, _source) + if not _get(d): + raise ValueError(f'`.rerank()` requires every doc to have `.{_source}`') + d = Client._prepare_single_doc(d) + setattr(d, _source, [Client._prepare_single_doc(c) for c in _get(d)]) + return d + + def _iter_rank_docs( + self, content, _source='matches' + ) -> Generator['Document', None, None]: + from rich import filesize + from docarray import Document + + self._return_plain = True + + if hasattr(self, '_pbar'): + self._pbar.start_task(self._s_task) + + for c in content: + if isinstance(c, Document): + yield self._prepare_rank_doc(c, _source) + else: + raise TypeError(f'unsupported input type {c!r}') + + if hasattr(self, '_pbar'): + self._pbar.update( + self._s_task, + advance=1, + total_size=str( + filesize.decimal( + int(os.environ.get('JINA_GRPC_SEND_BYTES', '0')) + ) + ), + ) + + def _get_rank_payload(self, content, kwargs): + return dict( + on='/rerank', + inputs=self._iter_rank_docs( + content, _source=kwargs.get('source', 'matches') + ), + request_size=kwargs.get('batch_size', 8), + total_docs=len(content) if hasattr(content, '__len__') else None, + ) + + def rerank(self, docs: Iterable['Document'], **kwargs) -> 'DocumentArray': + """Rerank image-text matches according to the server CLIP model. + + Given a Document with nested matches, where the root is image/text and the matches is in another modality, i.e. + text/image; this method reranks the matches according to the CLIP model. + + Each match now has a new score inside ``clip_score`` and matches are sorted descendingly according to this score. + More details can be found in: https://github.com/openai/CLIP#usage + + :param docs: the input Documents + :return: the reranked Documents in a DocumentArray. + + """ + self._prepare_streaming( + not kwargs.get('show_progress'), + total=len(docs), + ) + with self._pbar: + self._client.post( + **self._get_rank_payload(docs, kwargs), on_done=self._gather_result + ) + return self._results diff --git a/docs/user-guides/client.md b/docs/user-guides/client.md index ea5aebbd1..17df43398 100644 --- a/docs/user-guides/client.md +++ b/docs/user-guides/client.md @@ -255,6 +255,74 @@ asyncio.run(main()) The final time cost will be less than `3s + time(t2)`. +## Reranking + +```{tip} +This feature is only available with `clip_server>=0.3.0` and the server is running with PyTorch backend. +``` + +One can also rerank cross-modal matches via {meth}`~clip_client.client.Client.rerank`. First construct a cross-modal Document where the root contains an image and `.matches` contain sentences to rerank. One can also construct text-to-image rerank as below: + +````{tab} Given image, rerank sentences + +```python +from docarray import Document + +d = Document( + uri='.github/README-img/rerank.png', + matches=[ + Document(text=f'a photo of a {p}') + for p in ( + 'control room', + 'lecture room', + 'conference room', + 'podium indoor', + 'television studio', + ) + ], +) +``` + +```` + +````{tab} Given sentence, rerank images + +```python +from docarray import Document + +d = Document( + text='a photo of conference room', + matches=[ + Document(uri='.github/README-img/4.png'), + Document(uri='.github/README-img/9.png'), + Document(uri='https://clip-as-service.jina.ai/_static/favicon.png'), + ], +) +``` + +```` + + + +Then call `rerank`, you can feed it with multiple Documents as a list: + +```python +from clip_client import Client + +c = Client(server='grpc://demo-cas.jina.ai:51000') +r = c.rerank([d]) + +print(r['@m', ['text', 'scores__clip_score__value']]) +``` + +Finally, in the return you can observe the matches are re-ranked according to `.scores['clip_score']`: + +```text +[['a photo of a television studio', 'a photo of a conference room', 'a photo of a lecture room', 'a photo of a control room', 'a photo of a podium indoor'], +[0.9920725226402283, 0.006038925610482693, 0.0009973491542041302, 0.00078492151806131, 0.00010626466246321797]] +``` + + (profiling)= ## Profiling diff --git a/server/clip_server/__init__.py b/server/clip_server/__init__.py index a6587aeab..0404d8103 100644 --- a/server/clip_server/__init__.py +++ b/server/clip_server/__init__.py @@ -1 +1 @@ -__version__ = '0.2.4' +__version__ = '0.3.0' diff --git a/server/clip_server/executors/clip_onnx.py b/server/clip_server/executors/clip_onnx.py index 774585b7c..172cc8265 100644 --- a/server/clip_server/executors/clip_onnx.py +++ b/server/clip_server/executors/clip_onnx.py @@ -1,11 +1,11 @@ import os +import warnings from multiprocessing.pool import ThreadPool, Pool from typing import List, Tuple, Optional import numpy as np import onnxruntime as ort from jina import Executor, requests, DocumentArray -from jina.logging.logger import JinaLogger from clip_server.model import clip from clip_server.model.clip_onnx import CLIPOnnxModel @@ -33,7 +33,6 @@ def __init__( **kwargs, ): super().__init__(**kwargs) - self.logger = JinaLogger(self.__class__.__name__) self._preprocess_blob = clip._transform_blob(_SIZE[name]) self._preprocess_tensor = clip._transform_ndarray(_SIZE[name]) @@ -67,13 +66,13 @@ def __init__( ) if not self._device.startswith('cuda') and ( - not os.environ.get('OMP_NUM_THREADS') + 'OMP_NUM_THREADS' not in os.environ and hasattr(self.runtime_args, 'replicas') ): replicas = getattr(self.runtime_args, 'replicas', 1) num_threads = max(1, torch.get_num_threads() // replicas) if num_threads < 2: - self.logger.warning( + warnings.warn( f'Too many replicas ({replicas}) vs too few threads {num_threads} may result in ' f'sub-optimal performance.' ) @@ -117,7 +116,7 @@ async def encode(self, docs: 'DocumentArray', **kwargs): elif d.uri: _img_da.append(d) else: - self.logger.warning( + warnings.warn( f'The content of document {d.id} is empty, cannot be processed' ) diff --git a/server/clip_server/executors/clip_torch.py b/server/clip_server/executors/clip_torch.py index 9a4fd6fc5..f373d6f03 100644 --- a/server/clip_server/executors/clip_torch.py +++ b/server/clip_server/executors/clip_torch.py @@ -1,10 +1,10 @@ import os +import warnings from multiprocessing.pool import ThreadPool -from typing import Optional, List, Tuple +from typing import Optional, List, Tuple, Dict import numpy as np -from jina import Executor, requests, DocumentArray -from jina.logging.logger import JinaLogger +from jina import Executor, requests, DocumentArray, Document from clip_server.model import clip @@ -20,7 +20,6 @@ def __init__( **kwargs, ): super().__init__(**kwargs) - self.logger = JinaLogger(self.__class__.__name__) import torch @@ -30,13 +29,13 @@ def __init__( self._device = device if not self._device.startswith('cuda') and ( - not os.environ.get('OMP_NUM_THREADS') + 'OMP_NUM_THREADS' not in os.environ and hasattr(self.runtime_args, 'replicas') ): replicas = getattr(self.runtime_args, 'replicas', 1) num_threads = max(1, torch.get_num_threads() // replicas) if num_threads < 2: - self.logger.warning( + warnings.warn( f'Too many replicas ({replicas}) vs too few threads {num_threads} may result in ' f'sub-optimal performance.' ) @@ -82,26 +81,31 @@ def _split_img_txt_da(d, _img_da, _txt_da): elif d.uri: _img_da.append(d) - @requests(on='/rank') - async def rank(self, docs: 'DocumentArray', **kwargs): + @requests(on='/rerank') + async def rerank(self, docs: 'DocumentArray', parameters: Dict, **kwargs): + _source = parameters.get('source', 'matches') + _get = lambda d: getattr(d, _source) + for d in docs: _img_da = DocumentArray() _txt_da = DocumentArray() self._split_img_txt_da(d, _img_da, _txt_da) - for c in d.chunks: + for c in _get(d): self._split_img_txt_da(c, _img_da, _txt_da) if len(_img_da) != 1 and len(_txt_da) != 1: raise ValueError( - 'chunks must be all in same modality, either all images or all text' + f'`d.{_source}` must be all in same modality, either all images or all text' ) elif not _img_da or not _txt_da: raise ValueError( - 'root and chunks must be in different modality, one is image one is text' + f'`d` and `d.{_source}` must be in different modality, one is image one is text' + ) + elif len(_get(d)) <= 1: + raise ValueError( + f'`d.{_source}` must have more than one Documents to do ranking' ) - elif len(d.chunks) <= 1: - raise ValueError('must have more than one chunks to rank over chunks') else: _img_da = self._preproc_image(_img_da) _txt_da, texts = self._preproc_text(_txt_da) @@ -120,10 +124,21 @@ async def rank(self, docs: 'DocumentArray', **kwargs): elif len(_txt_da) == 1: probs = probs_text - for c, v in zip(d.chunks, probs): - c.scores['clip-rank'].value = v - _txt_da.texts = texts + _img_da.tensors = None + _txt_da.tensors = None + + for c, v in zip(_get(d), probs): + c.scores['clip_score'].value = v + setattr( + d, + _source, + sorted( + _get(d), + key=lambda _m: _m.scores['clip_score'].value, + reverse=True, + ), + ) @requests async def encode(self, docs: 'DocumentArray', **kwargs): diff --git a/tests/__init__.py b/tests/__init__.py index e69de29bb..fad87c7b2 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -0,0 +1,3 @@ +import os + +os.environ['OMP_NUM_THREADS'] = '1' diff --git a/tests/conftest.py b/tests/conftest.py index d038030d7..b7023b8a8 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -26,3 +26,12 @@ def make_flow(port_generator, request): f = Flow(port=port_generator()).add(name=request.param, uses=CLIPEncoder) with f: yield f + + +@pytest.fixture(scope='session', params=['torch']) +def make_torch_flow(port_generator, request): + from clip_server.executors.clip_torch import CLIPEncoder + + f = Flow(port=port_generator()).add(name=request.param, uses=CLIPEncoder) + with f: + yield f diff --git a/tests/test_ranker.py b/tests/test_ranker.py index 0b7d5b92c..f40791c5b 100644 --- a/tests/test_ranker.py +++ b/tests/test_ranker.py @@ -1,6 +1,7 @@ import os import pytest +from clip_client import Client from clip_server.executors.clip_torch import CLIPEncoder from docarray import DocumentArray, Document @@ -13,14 +14,14 @@ async def test_torch_executor_rank_img2texts(): f'{os.path.dirname(os.path.abspath(__file__))}/**/*.jpg' ) for d in da: - d.chunks.append(Document(text='hello, world!')) - d.chunks.append(Document(text='goodbye, world!')) + d.matches.append(Document(text='hello, world!')) + d.matches.append(Document(text='goodbye, world!')) - await ce.rank(da) - print(da['@c', 'scores__clip-rank__value']) + await ce.rerank(da, {}) + print(da['@m', 'scores__clip_score__value']) for d in da: - for c in d.chunks: - assert c.scores['clip-rank'].value is not None + for c in d.matches: + assert c.scores['clip_score'].value is not None @pytest.mark.asyncio @@ -30,13 +31,41 @@ async def test_torch_executor_rank_text2imgs(): [Document(text='hello, world!'), Document(text='goodbye, world!')] ) for d in db: - d.chunks.extend( + d.matches.extend( DocumentArray.from_files( f'{os.path.dirname(os.path.abspath(__file__))}/**/*.jpg' ) ) - await ce.rank(db) - print(db['@c', 'scores__clip-rank__value']) + await ce.rerank(db, {}) + print(db['@m', 'scores__clip_score__value']) for d in db: - for c in d.chunks: - assert c.scores['clip-rank'].value is not None + for c in d.matches: + assert c.scores['clip_score'].value is not None + + +@pytest.mark.parametrize( + 'd', + [ + Document( + uri='https://docarray.jina.ai/_static/favicon.png', + matches=[Document(text='hello, world'), Document(text='goodbye, world')], + ), + Document( + text='hello, world', + matches=[ + Document(uri='https://docarray.jina.ai/_static/favicon.png'), + Document( + uri=f'{os.path.dirname(os.path.abspath(__file__))}/img/00000.jpg' + ), + ], + ), + ], +) +def test_docarray_inputs(make_torch_flow, d): + c = Client(server=f'grpc://0.0.0.0:{make_torch_flow.port}') + r = c.rerank([d]) + assert isinstance(r, DocumentArray) + rv = r['@m', 'scores__clip_score__value'] + for v in rv: + assert v is not None + assert v > 0