From 7e3fc3c1aa62e2724acde294c43ae13fa65b4829 Mon Sep 17 00:00:00 2001 From: Jintao Date: Tue, 26 Sep 2023 19:37:51 +0800 Subject: [PATCH 01/36] fix hf bug (#567) --- modelscope/utils/hf_util.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/modelscope/utils/hf_util.py b/modelscope/utils/hf_util.py index 3abcce6d..5722928a 100644 --- a/modelscope/utils/hf_util.py +++ b/modelscope/utils/hf_util.py @@ -91,12 +91,13 @@ def check_hf_code(model_dir: str, auto_class: type, raise FileNotFoundError(f'{config_path} is not found') config_dict = PretrainedConfig.get_config_dict(config_path)[0] auto_class_name = auto_class.__name__ + if auto_class is AutoTokenizerHF: + tokenizer_config = get_tokenizer_config(model_dir) # load from repo if trust_remote_code: has_remote_code = False if auto_class is AutoTokenizerHF: - tokenizer_config_dict = get_tokenizer_config(model_dir) - auto_map = tokenizer_config_dict.get('auto_map', None) + auto_map = tokenizer_config.get('auto_map', None) if auto_map is not None: module_name = auto_map.get(auto_class_name, None) if module_name is not None: @@ -129,7 +130,10 @@ def check_hf_code(model_dir: str, auto_class: type, f'{model_type} not found in HF `CONFIG_MAPPING`{trust_remote_code_info}' ) elif auto_class is AutoTokenizerHF: - if model_type not in TOKENIZER_MAPPING_NAMES: + tokenizer_class = tokenizer_config.get('tokenizer_class') + if tokenizer_class is not None: + return + if model_type in TOKENIZER_MAPPING_NAMES: raise ValueError( f'{model_type} not found in HF `TOKENIZER_MAPPING_NAMES`{trust_remote_code_info}' ) From 29f4445dc270b552d8dcc2cd37d611fdced59a4c Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Tue, 26 Sep 2023 21:23:38 +0800 Subject: [PATCH 02/36] modify docker file --- docker/Dockerfile.ubuntu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/Dockerfile.ubuntu b/docker/Dockerfile.ubuntu index e5247774..2af8994b 100644 --- a/docker/Dockerfile.ubuntu +++ b/docker/Dockerfile.ubuntu @@ -48,7 +48,7 @@ ENV SETUPTOOLS_USE_DISTUTILS=stdlib RUN CUDA_HOME=/usr/local/cuda TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6" pip install --no-cache-dir 'git+https://github.com/facebookresearch/detectron2.git' # torchmetrics==0.11.4 for ofa -RUN pip install --no-cache-dir tiktoken torchmetrics==0.11.4 https://modelscope.oss-cn-beijing.aliyuncs.com/releases/v/ms_swift-1.1.0-py3-none-any.whl transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr +RUN pip install --no-cache-dir tiktoken torchmetrics==0.11.4 transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr COPY docker/scripts/install_flash_attension.sh /tmp/install_flash_attension.sh RUN if [ "$USE_GPU" = "True" ] ; then \ bash /tmp/install_flash_attension.sh; \ From 01defd40e89cb0c612431c81141fb372585c449e Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Tue, 26 Sep 2023 21:24:59 +0800 Subject: [PATCH 03/36] version to 1.9.2 --- modelscope/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modelscope/version.py b/modelscope/version.py index 7f3298ab..97e90b1d 100644 --- a/modelscope/version.py +++ b/modelscope/version.py @@ -1,5 +1,5 @@ # Make sure to modify __release_datetime__ to release time when making official release. -__version__ = '1.9.1' +__version__ = '1.9.2' # default release datetime for branches under active development is set # to be a time far-far-away-into-the-future __release_datetime__ = '2023-09-06 00:00:00' From 00e822d73ecafd472816b78ce8df30732fd4c446 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Tue, 26 Sep 2023 22:07:04 +0800 Subject: [PATCH 04/36] version to 1.9.2 --- modelscope/version.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modelscope/version.py b/modelscope/version.py index 23ef0243..97e90b1d 100644 --- a/modelscope/version.py +++ b/modelscope/version.py @@ -1,5 +1,5 @@ # Make sure to modify __release_datetime__ to release time when making official release. -__version__ = '1.9.1' +__version__ = '1.9.2' # default release datetime for branches under active development is set # to be a time far-far-away-into-the-future -__release_datetime__ = '2099-09-06 00:00:00' +__release_datetime__ = '2023-09-06 00:00:00' From 749cb37d0a5ac8bbc577aa3b97d60ecf398ef2a1 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Wed, 27 Sep 2023 09:33:32 +0800 Subject: [PATCH 05/36] fix hf bug (#567) Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/14181647 * fix hf bug (#567) --- modelscope/utils/hf_util.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/modelscope/utils/hf_util.py b/modelscope/utils/hf_util.py index 3abcce6d..5722928a 100644 --- a/modelscope/utils/hf_util.py +++ b/modelscope/utils/hf_util.py @@ -91,12 +91,13 @@ def check_hf_code(model_dir: str, auto_class: type, raise FileNotFoundError(f'{config_path} is not found') config_dict = PretrainedConfig.get_config_dict(config_path)[0] auto_class_name = auto_class.__name__ + if auto_class is AutoTokenizerHF: + tokenizer_config = get_tokenizer_config(model_dir) # load from repo if trust_remote_code: has_remote_code = False if auto_class is AutoTokenizerHF: - tokenizer_config_dict = get_tokenizer_config(model_dir) - auto_map = tokenizer_config_dict.get('auto_map', None) + auto_map = tokenizer_config.get('auto_map', None) if auto_map is not None: module_name = auto_map.get(auto_class_name, None) if module_name is not None: @@ -129,7 +130,10 @@ def check_hf_code(model_dir: str, auto_class: type, f'{model_type} not found in HF `CONFIG_MAPPING`{trust_remote_code_info}' ) elif auto_class is AutoTokenizerHF: - if model_type not in TOKENIZER_MAPPING_NAMES: + tokenizer_class = tokenizer_config.get('tokenizer_class') + if tokenizer_class is not None: + return + if model_type in TOKENIZER_MAPPING_NAMES: raise ValueError( f'{model_type} not found in HF `TOKENIZER_MAPPING_NAMES`{trust_remote_code_info}' ) From 6944d4173452d6ec79ef7ee6608350efe07d7c03 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Wed, 27 Sep 2023 11:47:23 +0800 Subject: [PATCH 06/36] skip install detection2 in cpu --- docker/Dockerfile.ubuntu | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docker/Dockerfile.ubuntu b/docker/Dockerfile.ubuntu index 2af8994b..c6c01a47 100644 --- a/docker/Dockerfile.ubuntu +++ b/docker/Dockerfile.ubuntu @@ -45,7 +45,11 @@ COPY examples /modelscope/examples # for pai-easycv setup compatiblity issue ENV SETUPTOOLS_USE_DISTUTILS=stdlib -RUN CUDA_HOME=/usr/local/cuda TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6" pip install --no-cache-dir 'git+https://github.com/facebookresearch/detectron2.git' +RUN if [ "$USE_GPU" = "True" ] ; then \ + CUDA_HOME=/usr/local/cuda TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6" pip install --no-cache-dir 'git+https://github.com/facebookresearch/detectron2.git'; \ + else \ + echo 'cpu unsupport detectron2'; \ + fi # torchmetrics==0.11.4 for ofa RUN pip install --no-cache-dir tiktoken torchmetrics==0.11.4 transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr From 007568469205fde86790042ffc436fcbbbddacc8 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Wed, 27 Sep 2023 13:51:11 +0800 Subject: [PATCH 07/36] add use gpu arg --- docker/Dockerfile.ubuntu | 1 + 1 file changed, 1 insertion(+) diff --git a/docker/Dockerfile.ubuntu b/docker/Dockerfile.ubuntu index c6c01a47..f0e28075 100644 --- a/docker/Dockerfile.ubuntu +++ b/docker/Dockerfile.ubuntu @@ -32,6 +32,7 @@ RUN pip install --no-cache-dir mpi4py paint_ldm \ mmcls>=0.21.0 mmdet>=2.25.0 decord>=0.6.0 pai-easycv ms_swift \ ipykernel fasttext fairseq deepspeed -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html +ARG USE_GPU # for cpu install cpu version faiss, faiss depends on blas lib, we install libopenblas TODO rename gpu or cpu version faiss RUN if [ "$USE_GPU" = "True" ] ; then \ pip install --no-cache-dir funtextprocessing kwsbp==0.0.6 faiss==1.7.2 safetensors typeguard==2.13.3 scikit-learn librosa==0.9.2 funasr -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html; \ From 4a5be4bc0949167ada042a74a038b11fc6f58200 Mon Sep 17 00:00:00 2001 From: Jintao Date: Wed, 27 Sep 2023 17:12:09 +0800 Subject: [PATCH 08/36] fix hf bug (#569) --- modelscope/utils/hf_util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modelscope/utils/hf_util.py b/modelscope/utils/hf_util.py index 5722928a..6ef98ccf 100644 --- a/modelscope/utils/hf_util.py +++ b/modelscope/utils/hf_util.py @@ -133,7 +133,7 @@ def check_hf_code(model_dir: str, auto_class: type, tokenizer_class = tokenizer_config.get('tokenizer_class') if tokenizer_class is not None: return - if model_type in TOKENIZER_MAPPING_NAMES: + if model_type not in TOKENIZER_MAPPING_NAMES: raise ValueError( f'{model_type} not found in HF `TOKENIZER_MAPPING_NAMES`{trust_remote_code_info}' ) From c72c95cce6c40926f844c295823b24a6293b9505 Mon Sep 17 00:00:00 2001 From: "lingcai.wl" Date: Wed, 27 Sep 2023 19:42:54 +0800 Subject: [PATCH 09/36] [to #51336898] fix minor problems in deploying Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/14190317 --- modelscope/outputs/outputs.py | 1 + modelscope/pipeline_inputs.py | 36 ++++++++++++++++---------------- modelscope/utils/input_output.py | 6 ++---- 3 files changed, 21 insertions(+), 22 deletions(-) diff --git a/modelscope/outputs/outputs.py b/modelscope/outputs/outputs.py index 82c5ce10..368abad6 100644 --- a/modelscope/outputs/outputs.py +++ b/modelscope/outputs/outputs.py @@ -562,6 +562,7 @@ TASK_OUTPUTS = { # } Tasks.facial_expression_recognition: [OutputKeys.SCORES, OutputKeys.LABELS], + Tasks.general_recognition: [OutputKeys.SCORES, OutputKeys.LABELS], # face processing base result for single img # { diff --git a/modelscope/pipeline_inputs.py b/modelscope/pipeline_inputs.py index f465a722..bffbebbd 100644 --- a/modelscope/pipeline_inputs.py +++ b/modelscope/pipeline_inputs.py @@ -82,14 +82,28 @@ def check_input_type(input_type, input): TASK_INPUTS = { + + # if task input is single var, value is InputType + # if task input is a tuple, value is tuple of InputType + # if task input is a dict, value is a dict of InputType, where key + # equals the one needed in pipeline input dict + # if task input is a list, value is a set of input format, in which + # each element corresponds to one input format as described above and + # must include a dict format. + + + Tasks.task_template: { + 'image': InputType.IMAGE, + 'text': InputType.TEXT + }, + # ============ vision tasks =================== + Tasks.image_text_retrieval: { InputKeys.IMAGE: InputType.IMAGE, InputKeys.TEXT: InputType.TEXT }, - Tasks.general_recognition: { - InputKeys.IMAGE: InputType.IMAGE, - InputKeys.TEXT: InputType.TEXT - }, + Tasks.general_recognition: + InputType.IMAGE, Tasks.video_depth_estimation: { InputKeys.IMAGE: InputType.IMAGE, InputKeys.TEXT: InputType.TEXT @@ -110,20 +124,6 @@ TASK_INPUTS = { InputType.VIDEO, - - Tasks.task_template: { - 'image': InputType.IMAGE, - 'text': InputType.TEXT - }, - # if task input is single var, value is InputType - # if task input is a tuple, value is tuple of InputType - # if task input is a dict, value is a dict of InputType, where key - # equals the one needed in pipeline input dict - # if task input is a list, value is a set of input format, in which - # each element corresponds to one input format as described above and - # must include a dict format. - - # ============ vision tasks =================== Tasks.ocr_detection: InputType.IMAGE, Tasks.ocr_recognition: diff --git a/modelscope/utils/input_output.py b/modelscope/utils/input_output.py index dbe5861d..d8e32cce 100644 --- a/modelscope/utils/input_output.py +++ b/modelscope/utils/input_output.py @@ -656,7 +656,7 @@ def service_base64_input_to_pipeline_input(task_name, body): if isinstance(service_input, (str, int, float)): return service_input, parameters - task_input_info = TASK_INPUTS[task_name] + task_input_info = TASK_INPUTS.get(task_name, None) if isinstance(task_input_info, str): # no input key default if isinstance(service_input, dict): return base64_decoder_map[task_input_info](list( @@ -767,9 +767,7 @@ def pipeline_output_to_service_base64_output(task_name, pipeline_output): pipeline_output (object): The pipeline output. """ json_serializable_output = {} - task_outputs = [] - if task_name in TASK_OUTPUTS: - task_outputs = TASK_OUTPUTS[task_name] + task_outputs = TASK_OUTPUTS.get(task_name, []) # TODO: for batch if isinstance(pipeline_output, list): pipeline_output = pipeline_output[0] From cc057378160cfa639aecf8fddeb8c897e5024d28 Mon Sep 17 00:00:00 2001 From: Jintao Date: Wed, 27 Sep 2023 17:12:09 +0800 Subject: [PATCH 10/36] fix hf bug (#569) --- modelscope/utils/hf_util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modelscope/utils/hf_util.py b/modelscope/utils/hf_util.py index 5722928a..6ef98ccf 100644 --- a/modelscope/utils/hf_util.py +++ b/modelscope/utils/hf_util.py @@ -133,7 +133,7 @@ def check_hf_code(model_dir: str, auto_class: type, tokenizer_class = tokenizer_config.get('tokenizer_class') if tokenizer_class is not None: return - if model_type in TOKENIZER_MAPPING_NAMES: + if model_type not in TOKENIZER_MAPPING_NAMES: raise ValueError( f'{model_type} not found in HF `TOKENIZER_MAPPING_NAMES`{trust_remote_code_info}' ) From 61bd1570e7b362952ae8b77e4db85589b9f2e06a Mon Sep 17 00:00:00 2001 From: Jintao Huang Date: Wed, 27 Sep 2023 21:15:55 +0800 Subject: [PATCH 11/36] remove ignore_file_pattern --- modelscope/utils/hf_util.py | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/modelscope/utils/hf_util.py b/modelscope/utils/hf_util.py index 6ef98ccf..e3e8cac8 100644 --- a/modelscope/utils/hf_util.py +++ b/modelscope/utils/hf_util.py @@ -190,18 +190,13 @@ def get_wrapped_class(module_class, ignore_file_pattern=[], **kwargs): return ClassWrapper -AutoModel = get_wrapped_class( - AutoModelHF, ignore_file_pattern=[r'\w+\.safetensors']) -AutoModelForCausalLM = get_wrapped_class( - AutoModelForCausalLMHF, ignore_file_pattern=[r'\w+\.safetensors']) -AutoModelForSeq2SeqLM = get_wrapped_class( - AutoModelForSeq2SeqLMHF, ignore_file_pattern=[r'\w+\.safetensors']) +AutoModel = get_wrapped_class(AutoModelHF) +AutoModelForCausalLM = get_wrapped_class(AutoModelForCausalLMHF) +AutoModelForSeq2SeqLM = get_wrapped_class(AutoModelForSeq2SeqLMHF) AutoModelForSequenceClassification = get_wrapped_class( - AutoModelForSequenceClassificationHF, - ignore_file_pattern=[r'\w+\.safetensors']) + AutoModelForSequenceClassificationHF) AutoModelForTokenClassification = get_wrapped_class( - AutoModelForTokenClassificationHF, - ignore_file_pattern=[r'\w+\.safetensors']) + AutoModelForTokenClassificationHF) AutoTokenizer = get_wrapped_class( AutoTokenizerHF, ignore_file_pattern=[r'\w+\.bin', r'\w+\.safetensors']) From 8d0ffa47b3cfad462c8700060c3c64643f6f6e65 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8B=84=E5=92=96?= Date: Fri, 25 Aug 2023 17:17:37 +0800 Subject: [PATCH 12/36] [feat]chat pipeline --- modelscope/models/base/base_model.py | 15 +- modelscope/pipelines/nlp/llm_pipeline.py | 405 +++++++++++++++++++++++ modelscope/pipelines/util.py | 11 +- tests/pipelines/test_llm_pipeline.py | 312 +++++++++++++++++ 4 files changed, 736 insertions(+), 7 deletions(-) create mode 100644 modelscope/pipelines/nlp/llm_pipeline.py create mode 100644 tests/pipelines/test_llm_pipeline.py diff --git a/modelscope/models/base/base_model.py b/modelscope/models/base/base_model.py index 788d5c43..9f225383 100644 --- a/modelscope/models/base/base_model.py +++ b/modelscope/models/base/base_model.py @@ -126,7 +126,7 @@ class Model(ABC): ) invoked_by = '%s/%s' % (Invoke.KEY, invoked_by) - ignore_file_pattern = kwargs.get('ignore_file_pattern', None) + ignore_file_pattern = kwargs.pop('ignore_file_pattern', None) local_model_dir = snapshot_download( model_name_or_path, revision, @@ -142,10 +142,15 @@ class Model(ABC): task_name = cfg.task if 'task' in kwargs: task_name = kwargs.pop('task') - model_cfg = cfg.model - if hasattr(model_cfg, 'model_type') and not hasattr(model_cfg, 'type'): - model_cfg.type = model_cfg.model_type - model_type = model_cfg.type + try: + model_cfg = cfg.model + if hasattr(model_cfg, + 'model_type') and not hasattr(model_cfg, 'type'): + model_cfg.type = model_cfg.model_type + model_type = model_cfg.type + except Exception: + model_cfg = {} + model_type = '' if isinstance(device, str) and device.startswith('gpu'): device = 'cuda' + device[3:] use_hf = kwargs.pop('use_hf', None) diff --git a/modelscope/pipelines/nlp/llm_pipeline.py b/modelscope/pipelines/nlp/llm_pipeline.py new file mode 100644 index 00000000..91f26812 --- /dev/null +++ b/modelscope/pipelines/nlp/llm_pipeline.py @@ -0,0 +1,405 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. +from typing import Any, Callable, Dict, Iterator, List, Tuple, Union + +import torch +from transformers import PreTrainedTokenizer + +from modelscope import AutoTokenizer, Pipeline +from modelscope.models.base import Model +from modelscope.models.nlp import ChatGLM2Tokenizer, Llama2Tokenizer +from modelscope.pipelines.builder import PIPELINES +from modelscope.pipelines.util import is_model, is_official_hub_path +from modelscope.utils.constant import Invoke, Tasks +from modelscope.utils.logger import get_logger + +logger = get_logger() + + +@PIPELINES.register_module(Tasks.chat, module_name='llm-pipeline') +class LLMPipeline(Pipeline): + + def initiate_single_model(self, model): + if isinstance(model, str): + logger.info(f'initiate model from {model}') + if isinstance(model, str) and is_official_hub_path(model): + logger.info(f'initiate model from location {model}.') + return Model.from_pretrained( + model, + invoked_by=Invoke.PIPELINE, + device_map=self.device_map, + torch_dtype=self.torch_dtype, + ignore_file_pattern=self.ignore_file_pattern) if is_model( + model) else model + else: + return model + + def __init__(self, + format_messages: Union[Callable, str] = None, + format_output: Callable = None, + tokenizer: PreTrainedTokenizer = None, + *args, + **kwargs): + self.torch_dtype = kwargs.pop('torch_dtype', None) + self.ignore_file_pattern = kwargs.pop('ignore_file_pattern', None) + super().__init__(*args, **kwargs) + + tokenizer_class = None + if isinstance(format_messages, str): + assert format_messages in LLM_FORMAT_MAP, \ + f'Can not find function for `{format_messages}`!' + format_messages, format_output, tokenizer_class = LLM_FORMAT_MAP[ + format_messages] + + if format_messages is None: + model_type = self.cfg.safe_get('model.type', + '').lower().split('-')[0] + + if model_type in LLM_FORMAT_MAP: + format_messages, format_output, tokenizer_class = LLM_FORMAT_MAP[ + model_type] + else: + raise KeyError( + f'model type `{model_type}` is not supported for LLM pipeline!' + ) + + if format_messages is not None: + self.format_messages = format_messages + if format_output is not None: + self.format_output = format_output + self.tokenizer = self._get_tokenizer( + tokenizer_class) if tokenizer is None else tokenizer + + def _process_single(self, inputs, *args, **kwargs) -> Dict[str, Any]: + preprocess_params = kwargs.get('preprocess_params', {}) + forward_params = kwargs.get('forward_params', {}) + postprocess_params = kwargs.get('postprocess_params', {}) + + is_messages = isinstance(inputs, dict) and 'messages' in inputs + tokens = self.preprocess(inputs, is_messages, **preprocess_params) + + if hasattr(self.model, 'generate'): + outputs = self.model.generate(**tokens, **forward_params) + elif hasattr(self.model, 'model') and hasattr(self.model.model, + 'generate'): + outputs = self.model.model.generate(**tokens, **forward_params) + else: + raise ValueError('model does not support `generate`!') + + outputs = outputs.tolist()[0][len(tokens['inputs'][0]):] + response = self.postprocess(outputs, is_messages, **postprocess_params) + return response + + def preprocess(self, inputs: Union[str, Dict], is_messages: bool, + **kwargs): + if is_messages: + tokens = self.format_messages(inputs, self.tokenizer, **kwargs) + else: + tokens = self.tokenizer(inputs, return_tensors='pt', **kwargs) + + tokens['inputs'] = tokens.pop('input_ids') + + if hasattr(self.model, 'device'): + device = self.model.device + elif hasattr(self.model, 'model') and hasattr(self.model.model, + 'device'): + device = self.model.model.device + else: + raise ValueError('model does not have `device` attribute!') + return {k: v.to(device) for k, v in tokens.items()} + + def postprocess(self, outputs, is_messages: bool, **kwargs): + + response = self.tokenizer.decode( + outputs, skip_special_tokens=True, **kwargs) + if is_messages: + response = self.format_output(response, **kwargs) + + return response + + def _sanitize_parameters(self, **generate_parameter): + """ + this method should sanitize the keyword args to preprocessor params, + forward params and postprocess params on '__call__' or '_process_single' method + considered to be a normal classmethod with default implementation / output + + Default Returns: + Dict[str, str]: preprocess_params = {} + Dict[str, str]: forward_params = {} + Dict[str, str]: postprocess_params = pipeline_parameters + """ + return {}, generate_parameter, {} + + def _get_tokenizer(self, tokenizer_class=None): + if isinstance(self.model, str): + model_dir = self.model + else: + model_dir = self.model.model_dir + if tokenizer_class is None: + tokenizer_class = AutoTokenizer + return tokenizer_class.from_pretrained( + model_dir, trust_remote_code=True) + + @staticmethod + def format_messages(messages: Dict[str, List[Dict[str, str]]], + tokenizer: PreTrainedTokenizer, + **kwargs) -> Dict[str, torch.Tensor]: + # {"messages":[{"role": "system", "content": "You are a helpful assistant."}...]} + tokens = [] + for role, content in LLMPipeline._message_iter(messages): + tokens = LLMPipeline._concat_with_special_tokens( + tokens, role, content, tokenizer) + return {'input_ids': torch.tensor([tokens], dtype=torch.int64)} + + @staticmethod + def format_output(response: str, **kwargs): + response = response.strip() + message = {'message': {'role': 'assistant', 'content': response}} + return message + + @staticmethod + def _message_iter( + data: Dict[str, List[Dict[str, + str]]]) -> Iterator[Tuple[str, str]]: + for pair in data['messages']: + yield pair['role'], pair['content'] + + @staticmethod + def _concat_with_special_tokens( + ids: List[int], role: str, content: Union[str, List[Dict[str, + str]]], + tokenizer: PreTrainedTokenizer) -> List[int]: + im_start = tokenizer.im_start_id + im_end = tokenizer.im_end_id + nl_token = tokenizer.encode('\n') + role = tokenizer.encode(role.strip()) + content = LLMPipeline._encode(tokenizer, content) + return LLMPipeline._concat(ids, im_start, role, nl_token, content, + im_end, nl_token) + + @staticmethod + def _encode(tokenizer: PreTrainedTokenizer, + content: Union[str, List[Dict[str, str]]]): + if isinstance(content, str): + return tokenizer.encode(content.rstrip()) + encoded = [] + for pair in content: + (modal, value), = pair.items() + if modal == 'image': + img_token_span = getattr(tokenizer, 'img_token_span', 256) + img_start_id = tokenizer.img_start_id + img_end_id = img_start_id + 1 + img_pad_id = img_start_id + 2 + list_int_url = list(bytes(value, encoding='utf-8')) + assert len( + list_int_url) <= img_token_span, 'Image url is too long.' + pad_ids = [img_pad_id] * (img_token_span - len(list_int_url)) + encoded = LLMPipeline._concat(encoded, img_start_id, + list_int_url, pad_ids, + img_end_id) + else: # text + encoded.extend(tokenizer.encode(value)) + return encoded + + @staticmethod + def _concat(ids: List[int], *args: Union[int, List[int]]) -> List[int]: + for item in args: + if isinstance(item, list): + ids.extend(item) + else: + ids.append(item) + return ids + + +def chatglm2_format_messages(messages, tokenizer, **kwargs): + + def build_chatglm2_prompt(messages, **kwargs): + prompt = '' + messages = messages['messages'] + # chatglm2 does not have system messages + assert messages[0][ + 'role'] == 'user', 'chatglm2 does not have system messages' + + for i in range(0, len(messages) - 1, 2): + prompt += '[Round {}]\n\n问：{}\n\n答：{}\n\n'.format( + i // 2 + 1, messages[i]['content'], messages[i + 1]['content']) + prompt += '[Round {}]\n\n问：{}\n\n答：'.format( + len(messages) // 2 + 1, messages[-1]['content']) + return prompt + + prompt = build_chatglm2_prompt(messages, **kwargs) + return tokenizer(prompt, return_tensors='pt') + + +def chatglm2_format_output(response, **kwargs): + response = response.strip() + response = response.replace('[[训练时间]]', '2023年') + messages = {'role': 'assistant', 'content': response} + outputs = { + 'messages': messages, + } + return outputs + + +def llama2_format_messages(messages, tokenizer, **kwargs): + from transformers import BatchEncoding + + def build_llama2_prompt(messages, tokenizer, **kwargs): + max_length = kwargs.get('max_length', 2048) + default_system_message = 'you are a helpful assistant!' + + messages = messages['messages'] + # llama2 have system messages + if messages[0]['role'] != 'system': + messages = [{ + 'role': 'system', + 'content': default_system_message + }] + messages + + system = messages[0]['content'] + system_prompt = f'[INST] <>\n{system}\n<>\n\n' + system_ids = tokenizer(system_prompt, return_tensors='pt').input_ids + + text = messages[-1]['content'] + text_prompt = f'{text.strip()} [/INST]' + text_ids = tokenizer(text_prompt, return_tensors='pt').input_ids + prompt_length = system_ids.shape[-1] + text_ids.shape[-1] + if prompt_length > max_length: + raise RuntimeError( + f'prepend prompt length {prompt_length} is bigger than max_length {max_length}' + ) + + # history items + history_prompt = '' + history_ids_list = [] + for i in range(len(messages) - 2, 0, -2): + user, assistant = messages[i]['content'], messages[i + + 1]['content'] + round_prompt = f'{user.strip()} [/INST] {assistant.strip()} [INST] ' + round_ids = tokenizer(round_prompt, return_tensors='pt').input_ids + if prompt_length + round_ids.shape[-1] > max_length: + # excess history should not be appended to the prompt + break + else: + history_prompt = round_prompt + history_prompt + history_ids_list = [round_ids] + history_ids_list + prompt_length += round_ids.shape[-1] + prompt_list = [system_prompt, history_prompt, text_prompt] + prompt_ids_list = [system_ids] + history_ids_list + [text_ids] + return ''.join(prompt_list), torch.cat(prompt_ids_list, dim=-1) + + prompt, tokens = build_llama2_prompt(messages, tokenizer, **kwargs) + return BatchEncoding({'input_ids': tokens}) + + +def baichuan_format_messages(messages, tokenizer, **kwargs): + from transformers import BatchEncoding + + def _parse_messages(messages, split_role='user'): + system, rounds = '', [] + round = [] + for i, message in enumerate(messages): + if message['role'] == 'system': + assert i == 0, 'first message should be system message.' + system = message['content'] + continue + if message['role'] == split_role and round: + rounds.append(round) + round = [] + round.append(message) + if round: + rounds.append(round) + return system, rounds + + messages = messages['messages'] + assistant_token_id = 196 + user_token_id = 195 + max_new_tokens = kwargs.get('max_new_tokens', None) or 2048 + model_max_length = 4096 + max_input_tokens = model_max_length - max_new_tokens + system, rounds = _parse_messages(messages, split_role='user') + system_tokens = tokenizer.encode(system) + max_history_tokens = max_input_tokens - len(system_tokens) + + history_tokens = [] + for round in rounds[::-1]: + round_tokens = [] + for message in round: + if message['role'] == 'user': + round_tokens.append(user_token_id) + else: + round_tokens.append(assistant_token_id) + round_tokens.extend(tokenizer.encode(message['content'])) + if len(history_tokens) == 0 or len(history_tokens) + len( + round_tokens) <= max_history_tokens: + history_tokens = round_tokens + history_tokens # concat left + if len(history_tokens) < max_history_tokens: + continue + break + + input_tokens = system_tokens + history_tokens + if messages[-1]['role'] != 'assistant': + input_tokens.append(assistant_token_id) + input_tokens = input_tokens[-max_input_tokens:] # truncate left + input_tokens = torch.LongTensor([input_tokens]) + return BatchEncoding({'input_ids': input_tokens}) + + +def wizardlm_format_messages(messages, tokenizer, **kwargs): + + def build_wizardlm_prompt(messages, tokenizer, **kwargs): + default_system_message = 'A chat between a curious user and an artificial intelligence assistant.' + 'The assistant gives helpful, detailed, and polite answers to the user\'s questions.' + + messages = messages['messages'] + # llama2 have system messages + if messages[0]['role'] != 'system': + messages = [{ + 'role': 'system', + 'content': default_system_message + }] + messages + + system_prompt = messages[0]['content'] + prompt_list = [system_prompt] + for i, message in enumerate(messages[1:]): + if message['role'] == 'user': + user_prompt = message['content'] + prompt_list.append(f'USER: {user_prompt}') + elif message['role'] == 'assistant': + user_prompt = message['content'] + prompt_list.append(f'ASSISTANT: {user_prompt}') + prompts = ' '.join(prompt_list) + return prompts + + prompts = build_wizardlm_prompt(messages, tokenizer, **kwargs) + return tokenizer(prompts, return_tensors='pt') + + +def wizardcode_format_messages(messages, tokenizer, **kwargs): + messages = messages['messages'] + assert len(messages) == 2, 'wizard code only support two messages.' + system, user = '', '' + for i, message in enumerate(messages): + if message['role'] == 'system': + assert i == 0, 'first message should be system message.' + system = message['content'] + if message['role'] == 'user': + assert i == 1, 'second message should be user message.' + user = message['content'] + + prompt = system + '\n\n### Instruction:\n' + user + '\n\n### Response:' + inputs = tokenizer( + prompt, padding=False, add_special_tokens=False, return_tensors='pt') + return inputs + + +LLM_FORMAT_MAP = { + 'chatglm2': + (chatglm2_format_messages, chatglm2_format_output, ChatGLM2Tokenizer), + 'qwen': (LLMPipeline.format_messages, LLMPipeline.format_output, None), + 'llama2': (llama2_format_messages, None, Llama2Tokenizer), + 'llama': (llama2_format_messages, None, Llama2Tokenizer), + 'baichuan': (baichuan_format_messages, None, None), + 'baichuan2': (baichuan_format_messages, None, None), + 'wizardlm': (wizardlm_format_messages, None, None), + 'wizardcode': (wizardcode_format_messages, None, None) +} diff --git a/modelscope/pipelines/util.py b/modelscope/pipelines/util.py index a2a3ed2b..9788d7d6 100644 --- a/modelscope/pipelines/util.py +++ b/modelscope/pipelines/util.py @@ -14,7 +14,7 @@ logger = get_logger() def is_config_has_model(cfg_file): try: cfg = Config.from_file(cfg_file) - return hasattr(cfg, 'model') + return hasattr(cfg, 'model') or hasattr(cfg, 'model_type') except Exception as e: logger.error(f'parse config file {cfg_file} failed: {e}') return False @@ -58,14 +58,21 @@ def is_model(path: Union[str, List]): def is_modelhub_path_impl(path): if osp.exists(path): cfg_file = osp.join(path, ModelFile.CONFIGURATION) + hf_cfg_file = osp.join(path, ModelFile.CONFIG) if osp.exists(cfg_file): return is_config_has_model(cfg_file) + elif osp.exists(hf_cfg_file): + return is_config_has_model(hf_cfg_file) else: return False else: try: cfg_file = model_file_download(path, ModelFile.CONFIGURATION) - return is_config_has_model(cfg_file) + if is_config_has_model(cfg_file): + return True + else: + hf_cfg_file = model_file_download(path, ModelFile.CONFIG) + return is_config_has_model(hf_cfg_file) except Exception: return False diff --git a/tests/pipelines/test_llm_pipeline.py b/tests/pipelines/test_llm_pipeline.py new file mode 100644 index 00000000..bbebb25e --- /dev/null +++ b/tests/pipelines/test_llm_pipeline.py @@ -0,0 +1,312 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. +import unittest + +import torch + +from modelscope import (AutoConfig, AutoModelForCausalLM, Model, + snapshot_download) +from modelscope.pipelines import pipeline +from modelscope.pipelines.nlp.llm_pipeline import LLMPipeline +from modelscope.utils.constant import Tasks +from modelscope.utils.test_utils import test_level + + +class LLMPipelineTest(unittest.TestCase): + + def setUp(self) -> None: + self.messages_zh = { + 'messages': [{ + 'role': 'user', + 'content': 'Hello! 你是谁？' + }, { + 'role': 'assistant', + 'content': '我是你的助手。' + }, { + 'role': 'user', + 'content': '你叫什么名字？' + }] + } + self.messages_zh_with_system = { + 'messages': [{ + 'role': 'system', + 'content': '你是达摩院的生活助手机器人。' + }, { + 'role': 'user', + 'content': '今天天气好吗？' + }] + } + self.prompt_zh = '请介绍一下你自己' + self.messages_en = { + 'messages': [{ + 'role': 'system', + 'content': 'You are a helpful assistant.' + }, { + 'role': 'user', + 'content': 'Hello! Where is the capital of Zhejiang?' + }, { + 'role': 'assistant', + 'content': 'Hangzhou is the capital of Zhejiang.' + }, { + 'role': 'user', + 'content': 'Tell me something about HangZhou?' + }] + } + self.prompt_en = 'Tell me the capital of Zhejiang. ' + self.messages_code = { + 'messages': [{ + 'role': + 'system', + 'content': + 'You are a helpful, respectful and honest assistant ' + 'with a deep knowledge of code and software design. ' + 'Always answer as helpfully as possible, while being safe. ' + 'Your answers should not include any harmful, unethical, racist, ' + 'sexist, toxic, dangerous, or illegal content. Please ensure that ' + 'your responses are socially unbiased and positive in nature.\n\n' + 'If a question does not make any sense, or is not factually coherent, ' + 'explain why instead of answering something not correct. ' + 'If you don\'t know the answer to a question, ' + 'please don\'t share false information.' + }, { + 'role': + 'user', + 'content': + 'write a program to implement the quicksort in java' + }] + } + self.prompt_code = 'import socket\n\ndef ping_exponential_backoff(host: str):' + + self.message_wizard_math = { + 'messages': [{ + 'role': + 'system', + 'content': + 'Below is an instruction that describes a task. ' + 'Write a response that appropriately completes the request.' + }, { + 'role': + 'user', + 'content': + 'James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint.' + 'How many total meters does he run a week?' + }] + } + self.prompt_wizard_math = """"Below is an instruction that describes a task. + Write a response that appropriately completes the request.\n\n + ### Instruction:\nJames decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. + How many total meters does he run a week?\n\n + ### Response:""" + + self.message_wizard_code = { + 'messages': [{ + 'role': + 'system', + 'content': + 'Below is an instruction that describes a task.' + 'Write a response that appropriately completes the request.' + }, { + 'role': 'user', + 'content': 'Write a Jave code to sum 1 to 10' + }] + } + self.prompt_wizard_code = """"Below is an instruction that describes a task. + Write a response that appropriately completes the request.\n\n + ### Instruction:\nWrite a Jave code to sum 1 to 10\n\n + ### Response:""" + + self.messages_mm = { + 'messages': [{ + 'role': 'system', + 'content': '你是达摩院的生活助手机器人。' + }, { + 'role': + 'user', + 'content': [ + { + 'image': + 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg' + }, + { + 'text': '这是什么?' + }, + ] + }] + } + self.gen_cfg = {'do_sample': True, 'max_length': 512} + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_chatglm2(self): + pipe = LLMPipeline(model='ZhipuAI/chatglm2-6b', device_map='auto') + print('messages: ', pipe(self.messages_zh, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_chatglm2int4(self): + pipe = LLMPipeline(model='ZhipuAI/chatglm2-6b-int4') + print('messages: ', pipe(self.messages_zh, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_chatglm232k(self): + pipe = LLMPipeline(model='ZhipuAI/chatglm2-6b-32k', device_map='auto') + print('messages: ', pipe(self.messages_zh, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_llama2(self): + pipe = LLMPipeline( + model='modelscope/Llama-2-7b-ms', + torch_dtype=torch.float16, + device_map='auto', + ignore_file_pattern=[r'.+\.bin$']) + print('messages: ', pipe(self.messages_en, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_en, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_llama2chat(self): + pipe = LLMPipeline( + model='modelscope/Llama-2-7b-chat-ms', + revision='v1.0.2', + torch_dtype=torch.float16, + device_map='auto', + ignore_file_pattern=[r'.+\.bin$']) + print('messages: ', pipe(self.messages_en, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_en, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_codellama(self): + pipe = LLMPipeline( + model='AI-ModelScope/CodeLlama-7b-Instruct-hf', + torch_dtype=torch.float16, + device_map='auto', + ignore_file_pattern=[r'.+\.bin$']) + print('messages: ', pipe(self.messages_code, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_code, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_baichuan_7b(self): + pipe = LLMPipeline( + model='baichuan-inc/baichuan-7B', + device_map='auto', + torch_dtype=torch.float16) + print('messages: ', pipe(self.messages_zh, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_baichuan_13b(self): + pipe = LLMPipeline( + model='baichuan-inc/Baichuan-13B-Base', + device_map='auto', + torch_dtype=torch.float16) + print('messages: ', pipe(self.messages_zh, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_baichuan_13bchat(self): + pipe = LLMPipeline( + model='baichuan-inc/Baichuan-13B-Chat', + device_map='auto', + torch_dtype=torch.float16) + print('messages: ', pipe(self.messages_zh, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_baichuan2_7b(self): + pipe = LLMPipeline( + model='baichuan-inc/Baichuan2-7B-Base', + device_map='auto', + torch_dtype=torch.float16) + print('messages: ', pipe(self.messages_zh, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_baichuan2_7bchat(self): + pipe = LLMPipeline( + model='baichuan-inc/Baichuan2-7B-Chat', + device_map='auto', + torch_dtype=torch.float16) + print('messages: ', pipe(self.messages_zh, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_wizardlm_13b(self): + pipe = LLMPipeline( + model='AI-ModelScope/WizardLM-13B-V1.2', + device_map='auto', + torch_dtype=torch.float16, + format_messages='wizardlm') + print('messages: ', pipe(self.messages_en, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_en, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_wizardmath(self): + pipe = LLMPipeline( + model='AI-ModelScope/WizardMath-7B-V1.0', + device_map='auto', + torch_dtype=torch.float16, + format_messages='wizardcode') + print('messages: ', pipe(self.message_wizard_math, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_wizard_math, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_wizardcode_13b(self): + pipe = LLMPipeline( + model='AI-ModelScope/WizardCoder-Python-13B-V1.0', + device_map='auto', + torch_dtype=torch.float16, + format_messages='wizardcode') + print('messages: ', pipe(self.message_wizard_code, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_wizard_code, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_wizardcode_1b(self): + pipe = LLMPipeline( + model='AI-ModelScope/WizardCoder-1B-V1.0', + device_map='auto', + torch_dtype=torch.float16, + format_messages='wizardcode') + print('messages: ', pipe(self.message_wizard_code, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_wizard_code, **self.gen_cfg)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_qwen(self): + pipe = LLMPipeline( + model='ccyh123/Qwen-7B-Chat', + device_map='auto', + format_messages='qwen') + print('messages: ', pipe(self.messages_zh_with_system, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skip('Need AutoGPTQ') + def test_qwen_int4(self): + from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig + model_dir = snapshot_download('ccyh123/Qwen-7B-Chat-Int4') + quantize_config = BaseQuantizeConfig( + bits=4, # quantize model to 4-bit + group_size=128, # it is recommended to set the value to 128 + desc_act= + False, # set to False can significantly speed up inference but the perplexity may slightly bad + ) + model = AutoGPTQForCausalLM.from_pretrained( + model_dir, + quantize_config, + device_map='auto', + trust_remote_code=True, + use_safetensors=True) + model.model_dir = model_dir + pipe = LLMPipeline(model=model, format_messages='qwen') + print('messages: ', pipe(self.messages_zh_with_system, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + @unittest.skip('File does not exists configuration.json') + def test_qwen_vl(self): + pipe = LLMPipeline( + model='ccyh123/Qwen-VL-Chat', + device_map='auto', + format_messages='qwen') + print('messages: ', pipe(self.messages_mm, **self.gen_cfg)) + print('prompt: ', pipe(self.prompt_zh, **self.gen_cfg)) + + +if __name__ == '__main__': + unittest.main() From 31d04f45b7de43f491e02c4d526d56f4aae81f03 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Thu, 28 Sep 2023 01:06:32 +0800 Subject: [PATCH 13/36] force reinstall 1.9.2 --- docker/Dockerfile.ubuntu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/Dockerfile.ubuntu b/docker/Dockerfile.ubuntu index f0e28075..e74baa29 100644 --- a/docker/Dockerfile.ubuntu +++ b/docker/Dockerfile.ubuntu @@ -53,7 +53,7 @@ RUN if [ "$USE_GPU" = "True" ] ; then \ fi # torchmetrics==0.11.4 for ofa -RUN pip install --no-cache-dir tiktoken torchmetrics==0.11.4 transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr +RUN pip install --no-cache-dir torchmetrics==0.11.4 tiktoken transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr COPY docker/scripts/install_flash_attension.sh /tmp/install_flash_attension.sh RUN if [ "$USE_GPU" = "True" ] ; then \ bash /tmp/install_flash_attension.sh; \ From f6d4107b0ab8e583f5e726fb6a3e9291aeaa776f Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Thu, 28 Sep 2023 10:49:51 +0800 Subject: [PATCH 14/36] fix onnxruntime issue --- docker/Dockerfile.ubuntu | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docker/Dockerfile.ubuntu b/docker/Dockerfile.ubuntu index e74baa29..ff2d3886 100644 --- a/docker/Dockerfile.ubuntu +++ b/docker/Dockerfile.ubuntu @@ -53,7 +53,8 @@ RUN if [ "$USE_GPU" = "True" ] ; then \ fi # torchmetrics==0.11.4 for ofa -RUN pip install --no-cache-dir torchmetrics==0.11.4 tiktoken transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr +# onnxruntime<1.16.0 for Since ORT 1.9, you are required to explicitly set the providers parameter when instantiating InferenceSession. For example, onnxruntime.InferenceSession(..., providers=['AzureExecutionProvider', 'CPUExecutionProvider'], ...) +RUN pip install --no-cache-dir 'onnxruntime<1.16.0' torchmetrics==0.11.4 tiktoken transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr COPY docker/scripts/install_flash_attension.sh /tmp/install_flash_attension.sh RUN if [ "$USE_GPU" = "True" ] ; then \ bash /tmp/install_flash_attension.sh; \ From e7ecb55e00cff618f2f4f3075a599f6bd7131325 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Thu, 28 Sep 2023 14:30:55 +0800 Subject: [PATCH 15/36] remove onnxruntime version --- docker/Dockerfile.ubuntu | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docker/Dockerfile.ubuntu b/docker/Dockerfile.ubuntu index ff2d3886..e74baa29 100644 --- a/docker/Dockerfile.ubuntu +++ b/docker/Dockerfile.ubuntu @@ -53,8 +53,7 @@ RUN if [ "$USE_GPU" = "True" ] ; then \ fi # torchmetrics==0.11.4 for ofa -# onnxruntime<1.16.0 for Since ORT 1.9, you are required to explicitly set the providers parameter when instantiating InferenceSession. For example, onnxruntime.InferenceSession(..., providers=['AzureExecutionProvider', 'CPUExecutionProvider'], ...) -RUN pip install --no-cache-dir 'onnxruntime<1.16.0' torchmetrics==0.11.4 tiktoken transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr +RUN pip install --no-cache-dir torchmetrics==0.11.4 tiktoken transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr COPY docker/scripts/install_flash_attension.sh /tmp/install_flash_attension.sh RUN if [ "$USE_GPU" = "True" ] ; then \ bash /tmp/install_flash_attension.sh; \ From 7f4fef0c3ad760b4f38a3dcfe51ef9906b87acf9 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Thu, 28 Sep 2023 17:14:42 +0800 Subject: [PATCH 16/36] fix provider bug --- tests/export/test_export_speech_signal_process.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/export/test_export_speech_signal_process.py b/tests/export/test_export_speech_signal_process.py index d3f6fe14..d4579936 100644 --- a/tests/export/test_export_speech_signal_process.py +++ b/tests/export/test_export_speech_signal_process.py @@ -60,7 +60,9 @@ class ExportSpeechSignalProcessTest(unittest.TestCase): return onnx_model = onnx.load(output) onnx.checker.check_model(onnx_model) - ort_session = ort.InferenceSession(output) + ort_session = ort.InferenceSession( + output, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) with torch.no_grad(): model.eval() outputs_origin = model.forward(dummy_inputs) From c12b22ae3d9e6fd4db28d7983792bcc3e985ce5c Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Thu, 28 Sep 2023 14:19:36 +0800 Subject: [PATCH 17/36] fix onnxruntime providers parameter compatible issue --- .../exporters/nlp/model_for_token_classification_exporter.py | 4 +++- modelscope/exporters/tf_model_exporter.py | 4 +++- modelscope/exporters/torch_model_exporter.py | 4 +++- .../models/cv/product_retrieval_embedding/item_detection.py | 4 +++- modelscope/pipelines/cv/face_liveness_ir_pipeline.py | 4 +++- modelscope/pipelines/cv/face_liveness_xc_pipeline.py | 4 +++- modelscope/pipelines/cv/face_quality_assessment_pipeline.py | 4 +++- modelscope/pipelines/cv/face_recognition_onnx_fm_pipeline.py | 4 +++- modelscope/pipelines/cv/face_recognition_onnx_ir_pipeline.py | 4 +++- tests/export/test_export_speech_signal_process.py | 4 +++- 10 files changed, 30 insertions(+), 10 deletions(-) diff --git a/modelscope/exporters/nlp/model_for_token_classification_exporter.py b/modelscope/exporters/nlp/model_for_token_classification_exporter.py index daa33ea9..159fcee4 100644 --- a/modelscope/exporters/nlp/model_for_token_classification_exporter.py +++ b/modelscope/exporters/nlp/model_for_token_classification_exporter.py @@ -77,7 +77,9 @@ class ModelForSequenceClassificationExporter(TorchModelExporter): return onnx_model = onnx.load(output) onnx.checker.check_model(onnx_model) - ort_session = ort.InferenceSession(output) + ort_session = ort.InferenceSession( + output, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) with torch.no_grad(): model.eval() outputs_origin = model.forward( diff --git a/modelscope/exporters/tf_model_exporter.py b/modelscope/exporters/tf_model_exporter.py index a2e69847..dbf4640e 100644 --- a/modelscope/exporters/tf_model_exporter.py +++ b/modelscope/exporters/tf_model_exporter.py @@ -102,7 +102,9 @@ class TfModelExporter(Exporter): onnx_model = onnx.load(output) onnx.checker.check_model(onnx_model, full_check=True) - ort_session = ort.InferenceSession(output) + ort_session = ort.InferenceSession( + output, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) outputs_origin = call_func( dummy_inputs) if call_func is not None else model(dummy_inputs) if isinstance(outputs_origin, (Mapping, ModelOutputBase)): diff --git a/modelscope/exporters/torch_model_exporter.py b/modelscope/exporters/torch_model_exporter.py index 473b9705..1dd1b459 100644 --- a/modelscope/exporters/torch_model_exporter.py +++ b/modelscope/exporters/torch_model_exporter.py @@ -233,7 +233,9 @@ class TorchModelExporter(Exporter): return onnx_model = onnx.load(output) onnx.checker.check_model(onnx_model) - ort_session = ort.InferenceSession(output) + ort_session = ort.InferenceSession( + output, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) with torch.no_grad(): model.eval() outputs_origin = model.forward( diff --git a/modelscope/models/cv/product_retrieval_embedding/item_detection.py b/modelscope/models/cv/product_retrieval_embedding/item_detection.py index 2002c6cb..b5024368 100644 --- a/modelscope/models/cv/product_retrieval_embedding/item_detection.py +++ b/modelscope/models/cv/product_retrieval_embedding/item_detection.py @@ -26,7 +26,9 @@ class YOLOXONNX(object): options.intra_op_num_threads = 1 options.inter_op_num_threads = 1 self.ort_session = ort.InferenceSession( - self.onnx_path, sess_options=options) + self.onnx_path, + sess_options=options, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) self.with_p6 = False self.multi_detect = multi_detect diff --git a/modelscope/pipelines/cv/face_liveness_ir_pipeline.py b/modelscope/pipelines/cv/face_liveness_ir_pipeline.py index efc9d9d5..f440c027 100644 --- a/modelscope/pipelines/cv/face_liveness_ir_pipeline.py +++ b/modelscope/pipelines/cv/face_liveness_ir_pipeline.py @@ -43,7 +43,9 @@ class FaceLivenessIrPipeline(FaceProcessingBasePipeline): logger.info('load model done') def load_onnx_model(self, onnx_path): - sess = onnxruntime.InferenceSession(onnx_path) + sess = onnxruntime.InferenceSession( + onnx_path, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) out_node_name = [] input_node_name = [] for node in sess.get_outputs(): diff --git a/modelscope/pipelines/cv/face_liveness_xc_pipeline.py b/modelscope/pipelines/cv/face_liveness_xc_pipeline.py index 3a50d91f..316ce778 100644 --- a/modelscope/pipelines/cv/face_liveness_xc_pipeline.py +++ b/modelscope/pipelines/cv/face_liveness_xc_pipeline.py @@ -51,7 +51,9 @@ class FaceLivenessXcPipeline(FaceProcessingBasePipeline): logger.info('load model done') def load_onnx_model(self, onnx_path): - sess = onnxruntime.InferenceSession(onnx_path) + sess = onnxruntime.InferenceSession( + onnx_path, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) out_node_name = [] input_node_name = [] for node in sess.get_outputs(): diff --git a/modelscope/pipelines/cv/face_quality_assessment_pipeline.py b/modelscope/pipelines/cv/face_quality_assessment_pipeline.py index 58fab659..1cf9ba59 100644 --- a/modelscope/pipelines/cv/face_quality_assessment_pipeline.py +++ b/modelscope/pipelines/cv/face_quality_assessment_pipeline.py @@ -60,7 +60,9 @@ class FaceQualityAssessmentPipeline(FaceProcessingBasePipeline): return batch_process(self.model, data) def load_onnx_model(self, onnx_path): - sess = onnxruntime.InferenceSession(onnx_path) + sess = onnxruntime.InferenceSession( + onnx_path, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) out_node_name = [] input_node_name = [] for node in sess.get_outputs(): diff --git a/modelscope/pipelines/cv/face_recognition_onnx_fm_pipeline.py b/modelscope/pipelines/cv/face_recognition_onnx_fm_pipeline.py index 7577c82d..e36d9351 100644 --- a/modelscope/pipelines/cv/face_recognition_onnx_fm_pipeline.py +++ b/modelscope/pipelines/cv/face_recognition_onnx_fm_pipeline.py @@ -53,7 +53,9 @@ class FaceRecognitionOnnxFmPipeline(FaceProcessingBasePipeline): logger.info('load model done') def load_onnx_model(self, onnx_path): - sess = onnxruntime.InferenceSession(onnx_path) + sess = onnxruntime.InferenceSession( + onnx_path, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) out_node_name = [] input_node_name = [] for node in sess.get_outputs(): diff --git a/modelscope/pipelines/cv/face_recognition_onnx_ir_pipeline.py b/modelscope/pipelines/cv/face_recognition_onnx_ir_pipeline.py index ced9b2c6..9c005d90 100644 --- a/modelscope/pipelines/cv/face_recognition_onnx_ir_pipeline.py +++ b/modelscope/pipelines/cv/face_recognition_onnx_ir_pipeline.py @@ -50,7 +50,9 @@ class FaceRecognitionOnnxIrPipeline(FaceProcessingBasePipeline): logger.info('load model done') def load_onnx_model(self, onnx_path): - sess = onnxruntime.InferenceSession(onnx_path) + sess = onnxruntime.InferenceSession( + onnx_path, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) out_node_name = [] input_node_name = [] for node in sess.get_outputs(): diff --git a/tests/export/test_export_speech_signal_process.py b/tests/export/test_export_speech_signal_process.py index d4579936..faba59a5 100644 --- a/tests/export/test_export_speech_signal_process.py +++ b/tests/export/test_export_speech_signal_process.py @@ -66,7 +66,9 @@ class ExportSpeechSignalProcessTest(unittest.TestCase): with torch.no_grad(): model.eval() outputs_origin = model.forward(dummy_inputs) - outputs_origin = numpify_tensor_nested(outputs_origin) + outputs_origin = numpify_tensor_nested( + outputs_origin, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) input_feed = {INPUT_NAME: dummy_inputs.numpy()} outputs = ort_session.run( From 74532181d02fe0cfb87779ac40beaee8aeb20bfb Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Thu, 28 Sep 2023 17:30:38 +0800 Subject: [PATCH 18/36] add jupyterlab install to docker --- docker/Dockerfile.ubuntu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/Dockerfile.ubuntu b/docker/Dockerfile.ubuntu index e74baa29..4ac4fd53 100644 --- a/docker/Dockerfile.ubuntu +++ b/docker/Dockerfile.ubuntu @@ -53,7 +53,7 @@ RUN if [ "$USE_GPU" = "True" ] ; then \ fi # torchmetrics==0.11.4 for ofa -RUN pip install --no-cache-dir torchmetrics==0.11.4 tiktoken transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr +RUN pip install --no-cache-dir jupyterlab torchmetrics==0.11.4 tiktoken transformers_stream_generator 'protobuf<=3.20.0' bitsandbytes basicsr COPY docker/scripts/install_flash_attension.sh /tmp/install_flash_attension.sh RUN if [ "$USE_GPU" = "True" ] ; then \ bash /tmp/install_flash_attension.sh; \ From d88898e4772acc2fb45f72b0183612bc4829eb5a Mon Sep 17 00:00:00 2001 From: tastelikefeet <58414341+tastelikefeet@users.noreply.github.com> Date: Sun, 1 Oct 2023 23:09:09 +0800 Subject: [PATCH 19/36] fix work_dir not set in trainer(#573) * fix work_dir * fix running --- modelscope/trainers/trainer.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/modelscope/trainers/trainer.py b/modelscope/trainers/trainer.py index a3707918..25f948bc 100644 --- a/modelscope/trainers/trainer.py +++ b/modelscope/trainers/trainer.py @@ -181,8 +181,20 @@ class EpochBasedTrainer(BaseTrainer): compile_options = {} self.model = compile_model(self.model, **compile_options) - if 'work_dir' in kwargs: + if kwargs.get('work_dir', None) is not None: self.work_dir = kwargs['work_dir'] + if 'train' not in self.cfg: + self.cfg['train'] = ConfigDict() + self.cfg['train']['work_dir'] = self.work_dir + if 'checkpoint' in self.cfg['train']: + if 'period' in self.cfg['train']['checkpoint']: + self.cfg['train']['checkpoint']['period'][ + 'save_dir'] = self.work_dir + if 'best' in self.cfg['train']['checkpoint']: + self.cfg['train']['checkpoint']['best'][ + 'save_dir'] = self.work_dir + if 'logging' in self.cfg['train']: + self.cfg['train']['logging']['out_dir'] = self.work_dir else: self.work_dir = self.cfg.train.get('work_dir', './work_dir') From 43046a719bf676837b05fd7cf80106f41fc49425 Mon Sep 17 00:00:00 2001 From: Zhicheng Zhang Date: Sun, 8 Oct 2023 13:36:34 +0800 Subject: [PATCH 20/36] =?UTF-8?q?move=20venv=20import=20from=20file=20leve?= =?UTF-8?q?l=20to=20class=20level=20to=20avoid=20import=20error=E2=80=A6?= =?UTF-8?q?=20(#575)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * move venv import from file level to class level to avoid import error on windows * pass lint check --------- Co-authored-by: Zhicheng Zhang --- modelscope/utils/plugins.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modelscope/utils/plugins.py b/modelscope/utils/plugins.py index 3d39514a..b4485830 100644 --- a/modelscope/utils/plugins.py +++ b/modelscope/utils/plugins.py @@ -9,7 +9,6 @@ import os import pkgutil import shutil import sys -import venv from contextlib import contextmanager from fnmatch import fnmatch from pathlib import Path @@ -1144,6 +1143,7 @@ class EnvsManager(object): cfg = read_config(model_dir) self.plugins = cfg.get('plugins', []) self.allow_remote = cfg.get('allow_remote', False) + import venv self.env_builder = venv.EnvBuilder( system_site_packages=True, clear=False, From e60d56a564d496fda9306bda653549ccb75bf34d Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Sun, 8 Oct 2023 15:18:31 +0800 Subject: [PATCH 21/36] fix test_export_speech_signal_process.py issue --- tests/export/test_export_speech_signal_process.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tests/export/test_export_speech_signal_process.py b/tests/export/test_export_speech_signal_process.py index faba59a5..d4579936 100644 --- a/tests/export/test_export_speech_signal_process.py +++ b/tests/export/test_export_speech_signal_process.py @@ -66,9 +66,7 @@ class ExportSpeechSignalProcessTest(unittest.TestCase): with torch.no_grad(): model.eval() outputs_origin = model.forward(dummy_inputs) - outputs_origin = numpify_tensor_nested( - outputs_origin, - providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + outputs_origin = numpify_tensor_nested(outputs_origin) input_feed = {INPUT_NAME: dummy_inputs.numpy()} outputs = ort_session.run( From 610cf981b1028f48054db3dcc1e1a5798b2e0435 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Sun, 8 Oct 2023 15:21:26 +0800 Subject: [PATCH 22/36] fix test_export_speech_signal_process.py issue --- tests/export/test_export_speech_signal_process.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tests/export/test_export_speech_signal_process.py b/tests/export/test_export_speech_signal_process.py index faba59a5..d4579936 100644 --- a/tests/export/test_export_speech_signal_process.py +++ b/tests/export/test_export_speech_signal_process.py @@ -66,9 +66,7 @@ class ExportSpeechSignalProcessTest(unittest.TestCase): with torch.no_grad(): model.eval() outputs_origin = model.forward(dummy_inputs) - outputs_origin = numpify_tensor_nested( - outputs_origin, - providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + outputs_origin = numpify_tensor_nested(outputs_origin) input_feed = {INPUT_NAME: dummy_inputs.numpy()} outputs = ort_session.run( From 57e2647a4a61b39d4fdffbceb854565bbfaba6b2 Mon Sep 17 00:00:00 2001 From: Jintao Date: Tue, 10 Oct 2023 10:43:16 +0800 Subject: [PATCH 23/36] remove configuration.json dependency (#579) --- modelscope/models/base/base_model.py | 18 ++++--- modelscope/utils/automodel_utils.py | 59 +++++++++++++---------- modelscope/utils/hf_util.py | 70 +--------------------------- 3 files changed, 47 insertions(+), 100 deletions(-) diff --git a/modelscope/models/base/base_model.py b/modelscope/models/base/base_model.py index 788d5c43..a63859cc 100644 --- a/modelscope/models/base/base_model.py +++ b/modelscope/models/base/base_model.py @@ -126,7 +126,7 @@ class Model(ABC): ) invoked_by = '%s/%s' % (Invoke.KEY, invoked_by) - ignore_file_pattern = kwargs.get('ignore_file_pattern', None) + ignore_file_pattern = kwargs.pop('ignore_file_pattern', None) local_model_dir = snapshot_download( model_name_or_path, revision, @@ -134,18 +134,19 @@ class Model(ABC): ignore_file_pattern=ignore_file_pattern) logger.info(f'initialize model from {local_model_dir}') + configuration_path = osp.join(local_model_dir, ModelFile.CONFIGURATION) + cfg = None if cfg_dict is not None: cfg = cfg_dict - else: - cfg = Config.from_file( - osp.join(local_model_dir, ModelFile.CONFIGURATION)) - task_name = cfg.task + elif os.path.exists(configuration_path): + cfg = Config.from_file(configuration_path) + task_name = getattr(cfg, 'task', None) if 'task' in kwargs: task_name = kwargs.pop('task') - model_cfg = cfg.model + model_cfg = getattr(cfg, 'model', None) if hasattr(model_cfg, 'model_type') and not hasattr(model_cfg, 'type'): model_cfg.type = model_cfg.model_type - model_type = model_cfg.type + model_type = getattr(model_cfg, 'type', None) if isinstance(device, str) and device.startswith('gpu'): device = 'cuda' + device[3:] use_hf = kwargs.pop('use_hf', None) @@ -162,6 +163,9 @@ class Model(ABC): model = model.to(device) return model # use ms + if cfg is None: + raise FileNotFoundError( + f'`{ModelFile.CONFIGURATION}` file not found.') model_cfg.model_dir = local_model_dir # install and import remote repos before build diff --git a/modelscope/utils/automodel_utils.py b/modelscope/utils/automodel_utils.py index afd83817..1f5de3b6 100644 --- a/modelscope/utils/automodel_utils.py +++ b/modelscope/utils/automodel_utils.py @@ -6,8 +6,11 @@ from modelscope.utils.ast_utils import INDEX_KEY from modelscope.utils.import_utils import LazyImportModule -def can_load_by_ms(model_dir: str, tast_name: str, model_type: str) -> bool: - if ('MODELS', tast_name, +def can_load_by_ms(model_dir: str, task_name: Optional[str], + model_type: Optional[str]) -> bool: + if model_type is None or task_name is None: + return False + if ('MODELS', task_name, model_type) in LazyImportModule.AST_INDEX[INDEX_KEY]: return True ms_wrapper_path = os.path.join(model_dir, 'ms_wrapper.py') @@ -25,11 +28,27 @@ def _can_load_by_hf_automodel(automodel_class: type, config) -> bool: return False -def get_hf_automodel_class(model_dir: str, task_name: str) -> Optional[type]: - from modelscope import (AutoConfig, AutoModel, AutoModelForCausalLM, - AutoModelForSeq2SeqLM, - AutoModelForTokenClassification, - AutoModelForSequenceClassification) +def get_default_automodel(config) -> Optional[type]: + import modelscope.utils.hf_util as hf_util + if not hasattr(config, 'auto_map'): + return None + auto_map = config.auto_map + automodel_list = [k for k in auto_map.keys() if k.startswith('AutoModel')] + if len(automodel_list) == 1: + return getattr(hf_util, automodel_list[0]) + if len(automodel_list) > 1 and len( + set([auto_map[k] for k in automodel_list])) == 1: + return getattr(hf_util, automodel_list[0]) + return None + + +def get_hf_automodel_class(model_dir: str, + task_name: Optional[str]) -> Optional[type]: + from modelscope.utils.hf_util import (AutoConfig, AutoModel, + AutoModelForCausalLM, + AutoModelForSeq2SeqLM, + AutoModelForTokenClassification, + AutoModelForSequenceClassification) automodel_mapping = { Tasks.backbone: AutoModel, Tasks.chat: AutoModelForCausalLM, @@ -37,19 +56,18 @@ def get_hf_automodel_class(model_dir: str, task_name: str) -> Optional[type]: Tasks.text_classification: AutoModelForSequenceClassification, Tasks.token_classification: AutoModelForTokenClassification, } - automodel_class = automodel_mapping.get(task_name, None) - if automodel_class is None: - return None config_path = os.path.join(model_dir, 'config.json') if not os.path.exists(config_path): return None try: - try: - config = AutoConfig.from_pretrained( - model_dir, trust_remote_code=True) - except (FileNotFoundError, ValueError): - return None + config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True) + if task_name is None: + automodel_class = get_default_automodel(config) + else: + automodel_class = automodel_mapping.get(task_name, None) + if automodel_class is None: + return None if _can_load_by_hf_automodel(automodel_class, config): return automodel_class if (automodel_class is AutoModelForCausalLM @@ -71,14 +89,5 @@ def try_to_load_hf_model(model_dir: str, task_name: str, model = None if automodel_class is not None: # use hf - device_map = kwargs.get('device_map', None) - torch_dtype = kwargs.get('torch_dtype', None) - config = kwargs.get('config', None) - - model = automodel_class.from_pretrained( - model_dir, - device_map=device_map, - torch_dtype=torch_dtype, - config=config, - trust_remote_code=True) + model = automodel_class.from_pretrained(model_dir, **kwargs) return model diff --git a/modelscope/utils/hf_util.py b/modelscope/utils/hf_util.py index 6ef98ccf..97b4ea3d 100644 --- a/modelscope/utils/hf_util.py +++ b/modelscope/utils/hf_util.py @@ -21,7 +21,7 @@ from transformers.models.auto.tokenization_auto import ( TOKENIZER_MAPPING_NAMES, get_tokenizer_config) from modelscope import snapshot_download -from modelscope.utils.constant import Invoke +from modelscope.utils.constant import DEFAULT_MODEL_REVISION, Invoke try: from transformers import GPTQConfig as GPTQConfigHF @@ -84,69 +84,6 @@ patch_tokenizer_base() patch_model_base() -def check_hf_code(model_dir: str, auto_class: type, - trust_remote_code: bool) -> None: - config_path = os.path.join(model_dir, 'config.json') - if not os.path.exists(config_path): - raise FileNotFoundError(f'{config_path} is not found') - config_dict = PretrainedConfig.get_config_dict(config_path)[0] - auto_class_name = auto_class.__name__ - if auto_class is AutoTokenizerHF: - tokenizer_config = get_tokenizer_config(model_dir) - # load from repo - if trust_remote_code: - has_remote_code = False - if auto_class is AutoTokenizerHF: - auto_map = tokenizer_config.get('auto_map', None) - if auto_map is not None: - module_name = auto_map.get(auto_class_name, None) - if module_name is not None: - module_name = module_name[0] - has_remote_code = True - else: - auto_map = config_dict.get('auto_map', None) - if auto_map is not None: - module_name = auto_map.get(auto_class_name, None) - has_remote_code = module_name is not None - - if has_remote_code: - module_path = os.path.join(model_dir, - module_name.split('.')[0] + '.py') - if not os.path.exists(module_path): - raise FileNotFoundError(f'{module_path} is not found') - return - - # trust_remote_code is False or has_remote_code is False - model_type = config_dict.get('model_type', None) - if model_type is None: - raise ValueError(f'`model_type` key is not found in {config_path}.') - - trust_remote_code_info = '.' - if not trust_remote_code: - trust_remote_code_info = ', You can try passing `trust_remote_code=True`.' - if auto_class is AutoConfigHF: - if model_type not in CONFIG_MAPPING: - raise ValueError( - f'{model_type} not found in HF `CONFIG_MAPPING`{trust_remote_code_info}' - ) - elif auto_class is AutoTokenizerHF: - tokenizer_class = tokenizer_config.get('tokenizer_class') - if tokenizer_class is not None: - return - if model_type not in TOKENIZER_MAPPING_NAMES: - raise ValueError( - f'{model_type} not found in HF `TOKENIZER_MAPPING_NAMES`{trust_remote_code_info}' - ) - else: - mapping_names = [ - m.model_type for m in auto_class._model_mapping.keys() - ] - if model_type not in mapping_names: - raise ValueError( - f'{model_type} not found in HF `auto_class._model_mapping`{trust_remote_code_info}' - ) - - def get_wrapped_class(module_class, ignore_file_pattern=[], **kwargs): """Get a custom wrapper class for auto classes to download the models from the ModelScope hub Args: @@ -166,7 +103,7 @@ def get_wrapped_class(module_class, ignore_file_pattern=[], **kwargs): ignore_file_pattern = kwargs.pop('ignore_file_pattern', default_ignore_file_pattern) if not os.path.exists(pretrained_model_name_or_path): - revision = kwargs.pop('revision', None) + revision = kwargs.pop('revision', DEFAULT_MODEL_REVISION) model_dir = snapshot_download( pretrained_model_name_or_path, revision=revision, @@ -175,9 +112,6 @@ def get_wrapped_class(module_class, ignore_file_pattern=[], **kwargs): else: model_dir = pretrained_model_name_or_path - if module_class is not GenerationConfigHF: - trust_remote_code = kwargs.get('trust_remote_code', False) - check_hf_code(model_dir, module_class, trust_remote_code) module_obj = module_class.from_pretrained(model_dir, *model_args, **kwargs) From 362872d6bb7e8beb8c02d8a27ec386a5fb023973 Mon Sep 17 00:00:00 2001 From: tastelikefeet <58414341+tastelikefeet@users.noreply.github.com> Date: Tue, 10 Oct 2023 10:43:30 +0800 Subject: [PATCH 24/36] fix onnx backend (#580) --- modelscope/exporters/torch_model_exporter.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/modelscope/exporters/torch_model_exporter.py b/modelscope/exporters/torch_model_exporter.py index 473b9705..1dd1b459 100644 --- a/modelscope/exporters/torch_model_exporter.py +++ b/modelscope/exporters/torch_model_exporter.py @@ -233,7 +233,9 @@ class TorchModelExporter(Exporter): return onnx_model = onnx.load(output) onnx.checker.check_model(onnx_model) - ort_session = ort.InferenceSession(output) + ort_session = ort.InferenceSession( + output, + providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) with torch.no_grad(): model.eval() outputs_origin = model.forward( From 4d099aa603092ebceac591d2b0b405638952a366 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Wed, 11 Oct 2023 10:07:50 +0800 Subject: [PATCH 25/36] release date to 2099 --- modelscope/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modelscope/version.py b/modelscope/version.py index 97e90b1d..5ea46f3f 100644 --- a/modelscope/version.py +++ b/modelscope/version.py @@ -2,4 +2,4 @@ __version__ = '1.9.2' # default release datetime for branches under active development is set # to be a time far-far-away-into-the-future -__release_datetime__ = '2023-09-06 00:00:00' +__release_datetime__ = '2099-09-06 00:00:00' From 35f31b011f25357dfbfa601497eebaa2384dedda Mon Sep 17 00:00:00 2001 From: Jintao Date: Thu, 12 Oct 2023 10:27:31 +0800 Subject: [PATCH 26/36] fix merge error (#582) --- modelscope/models/base/base_model.py | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/modelscope/models/base/base_model.py b/modelscope/models/base/base_model.py index 69669650..eb905e12 100644 --- a/modelscope/models/base/base_model.py +++ b/modelscope/models/base/base_model.py @@ -9,7 +9,7 @@ from modelscope.metainfo import Tasks from modelscope.models.builder import build_backbone, build_model from modelscope.utils.automodel_utils import (can_load_by_ms, try_to_load_hf_model) -from modelscope.utils.config import Config +from modelscope.utils.config import Config, ConfigDict from modelscope.utils.constant import DEFAULT_MODEL_REVISION, Invoke, ModelFile from modelscope.utils.device import verify_device from modelscope.utils.logger import get_logger @@ -143,15 +143,10 @@ class Model(ABC): task_name = getattr(cfg, 'task', None) if 'task' in kwargs: task_name = kwargs.pop('task') - try: - model_cfg = cfg.model - if hasattr(model_cfg, - 'model_type') and not hasattr(model_cfg, 'type'): - model_cfg.type = model_cfg.model_type - model_type = model_cfg.type - except Exception: - model_cfg = {} - model_type = '' + model_cfg = getattr(cfg, 'model', ConfigDict()) + if hasattr(model_cfg, 'model_type') and not hasattr(model_cfg, 'type'): + model_cfg.type = model_cfg.model_type + model_type = getattr(model_cfg, 'type', None) if isinstance(device, str) and device.startswith('gpu'): device = 'cuda' + device[3:] use_hf = kwargs.pop('use_hf', None) @@ -163,7 +158,7 @@ class Model(ABC): model = try_to_load_hf_model(local_model_dir, task_name, use_hf, **kwargs) if model is not None: - device_map = kwargs.get('device_map', None) + device_map = kwargs.pop('device_map', None) if device_map is None and device is not None: model = model.to(device) return model From e75f5b4bc41be484e2e2e4dc3dd94d9495c4746f Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Fri, 13 Oct 2023 22:42:08 +0800 Subject: [PATCH 27/36] version to 1.9.3 --- modelscope/version.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modelscope/version.py b/modelscope/version.py index 97e90b1d..f7f006e7 100644 --- a/modelscope/version.py +++ b/modelscope/version.py @@ -1,5 +1,5 @@ # Make sure to modify __release_datetime__ to release time when making official release. -__version__ = '1.9.2' +__version__ = '1.9.3' # default release datetime for branches under active development is set # to be a time far-far-away-into-the-future -__release_datetime__ = '2023-09-06 00:00:00' +__release_datetime__ = '2023-10-17 00:00:00' From 087cb4e463dc5dc022954796df484f3bfe65c9ba Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Mon, 16 Oct 2023 16:13:28 +0800 Subject: [PATCH 28/36] upgrade flash attention to 2.32.2 --- docker/scripts/install_flash_attension.sh | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docker/scripts/install_flash_attension.sh b/docker/scripts/install_flash_attension.sh index 6a3301c2..f37e567d 100644 --- a/docker/scripts/install_flash_attension.sh +++ b/docker/scripts/install_flash_attension.sh @@ -1,6 +1,4 @@ - git clone -b v1.0.8 https://github.com/Dao-AILab/flash-attention && \ - cd flash-attention && pip install . && \ - pip install csrc/layer_norm && \ - pip install csrc/rotary && \ + git clone -b v2.3.2 https://github.com/Dao-AILab/flash-attention && \ + cd flash-attention && python setup.py install && \ cd .. && \ rm -rf flash-attention From 19e7c1c80700ca5f9544fe826b40930d01923e69 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Mon, 16 Oct 2023 22:12:31 +0800 Subject: [PATCH 29/36] fix ofa new transformers compatible issue Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/14317517 * fix ofa new transformers compatible issue * fix timm.layers to timm.models.layers compatible issue --- modelscope/models/cv/shop_segmentation/head_fpn.py | 4 ++-- modelscope/models/cv/shop_segmentation/models.py | 4 ++-- modelscope/models/cv/shop_segmentation/neck_fpn.py | 4 ++-- .../models/multi_modal/ofa/tokenization_ofa.py | 12 ++++++------ 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/modelscope/models/cv/shop_segmentation/head_fpn.py b/modelscope/models/cv/shop_segmentation/head_fpn.py index dfa284d4..0d4027cb 100644 --- a/modelscope/models/cv/shop_segmentation/head_fpn.py +++ b/modelscope/models/cv/shop_segmentation/head_fpn.py @@ -9,8 +9,8 @@ import numpy as np import torch import torch.nn as nn from mmcv.cnn import ConvModule -from timm.layers.drop import drop_path -from timm.layers.weight_init import trunc_normal_ +from timm.models.layers.drop import drop_path +from timm.models.layers.weight_init import trunc_normal_ from .common import Upsample, resize diff --git a/modelscope/models/cv/shop_segmentation/models.py b/modelscope/models/cv/shop_segmentation/models.py index 1b07a08c..a206e9f1 100644 --- a/modelscope/models/cv/shop_segmentation/models.py +++ b/modelscope/models/cv/shop_segmentation/models.py @@ -11,8 +11,8 @@ from collections import OrderedDict import torch import torch.nn.functional as F import torch.utils.checkpoint as checkpoint -from timm.layers.drop import drop_path -from timm.layers.weight_init import trunc_normal_ +from timm.models.layers.drop import drop_path +from timm.models.layers.weight_init import trunc_normal_ from torch import nn diff --git a/modelscope/models/cv/shop_segmentation/neck_fpn.py b/modelscope/models/cv/shop_segmentation/neck_fpn.py index 12c11d76..d344de71 100644 --- a/modelscope/models/cv/shop_segmentation/neck_fpn.py +++ b/modelscope/models/cv/shop_segmentation/neck_fpn.py @@ -8,8 +8,8 @@ import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import ConvModule -from timm.layers.drop import drop_path -from timm.layers.weight_init import trunc_normal_ +from timm.models.layers.drop import drop_path +from timm.models.layers.weight_init import trunc_normal_ from .common import resize diff --git a/modelscope/models/multi_modal/ofa/tokenization_ofa.py b/modelscope/models/multi_modal/ofa/tokenization_ofa.py index 77de7a1d..ea79a327 100644 --- a/modelscope/models/multi_modal/ofa/tokenization_ofa.py +++ b/modelscope/models/multi_modal/ofa/tokenization_ofa.py @@ -183,6 +183,12 @@ class OFATokenizerZH(PreTrainedTokenizer): tokenize_chinese_chars=True, strip_accents=None, **kwargs): + if not os.path.isfile(vocab_file): + raise ValueError( + f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained " + 'model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`' + ) + self.vocab = load_vocab(vocab_file) super().__init__( do_lower_case=do_lower_case, do_basic_tokenize=do_basic_tokenize, @@ -199,12 +205,6 @@ class OFATokenizerZH(PreTrainedTokenizer): **kwargs, ) - if not os.path.isfile(vocab_file): - raise ValueError( - f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained " - 'model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`' - ) - self.vocab = load_vocab(vocab_file) self.ids_to_tokens = collections.OrderedDict([ (ids, tok) for tok, ids in self.vocab.items() ]) From 66430171ae3618dc8e86fc39910aa75da54f781f Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Tue, 17 Oct 2023 22:15:54 +0800 Subject: [PATCH 30/36] fix chatglm2 can't find tokenizer issue Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/14335080 * fix chatglm2 can't find tokenizer issue --- modelscope/models/nlp/chatglm2/tokenization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modelscope/models/nlp/chatglm2/tokenization.py b/modelscope/models/nlp/chatglm2/tokenization.py index 7014dc9c..4523dcdd 100644 --- a/modelscope/models/nlp/chatglm2/tokenization.py +++ b/modelscope/models/nlp/chatglm2/tokenization.py @@ -72,7 +72,6 @@ class ChatGLM2Tokenizer(PreTrainedTokenizer): model_input_names = ['input_ids', 'attention_mask', 'position_ids'] def __init__(self, vocab_file, padding_side='left', **kwargs): - super().__init__(padding_side=padding_side, **kwargs) self.name = 'GLMTokenizer' self.vocab_file = vocab_file @@ -82,6 +81,7 @@ class ChatGLM2Tokenizer(PreTrainedTokenizer): '': self.tokenizer.eos_id, '': self.tokenizer.pad_id } + super().__init__(padding_side=padding_side, **kwargs) def get_command(self, token): if token in self.special_tokens: From 0908e20da2756ad9434d019550d43e8f7e8e1608 Mon Sep 17 00:00:00 2001 From: Jintao Date: Thu, 12 Oct 2023 10:27:31 +0800 Subject: [PATCH 31/36] fix merge error (#582) --- modelscope/models/base/base_model.py | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/modelscope/models/base/base_model.py b/modelscope/models/base/base_model.py index a3b65812..8e6d4ae6 100644 --- a/modelscope/models/base/base_model.py +++ b/modelscope/models/base/base_model.py @@ -9,7 +9,7 @@ from modelscope.metainfo import Tasks from modelscope.models.builder import build_backbone, build_model from modelscope.utils.automodel_utils import (can_load_by_ms, try_to_load_hf_model) -from modelscope.utils.config import Config +from modelscope.utils.config import Config, ConfigDict from modelscope.utils.constant import DEFAULT_MODEL_REVISION, Invoke, ModelFile from modelscope.utils.device import verify_device from modelscope.utils.logger import get_logger @@ -142,15 +142,10 @@ class Model(ABC): task_name = cfg.task if 'task' in kwargs: task_name = kwargs.pop('task') - try: - model_cfg = cfg.model - if hasattr(model_cfg, - 'model_type') and not hasattr(model_cfg, 'type'): - model_cfg.type = model_cfg.model_type - model_type = model_cfg.type - except Exception: - model_cfg = {} - model_type = '' + model_cfg = getattr(cfg, 'model', ConfigDict()) + if hasattr(model_cfg, 'model_type') and not hasattr(model_cfg, 'type'): + model_cfg.type = model_cfg.model_type + model_type = getattr(model_cfg, 'type', None) if isinstance(device, str) and device.startswith('gpu'): device = 'cuda' + device[3:] use_hf = kwargs.pop('use_hf', None) @@ -162,7 +157,7 @@ class Model(ABC): model = try_to_load_hf_model(local_model_dir, task_name, use_hf, **kwargs) if model is not None: - device_map = kwargs.get('device_map', None) + device_map = kwargs.pop('device_map', None) if device_map is None and device is not None: model = model.to(device) return model From 6201d1bfbc29cce4e57a74f675fcb013a4020ddf Mon Sep 17 00:00:00 2001 From: "suluyan.sly" Date: Wed, 18 Oct 2023 16:29:13 +0800 Subject: [PATCH 32/36] [swingdeploy] oss examples Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/14342415 * oss_examples --- modelscope/utils/pipeline_inputs.json | 66 +++++++++++++-------------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/modelscope/utils/pipeline_inputs.json b/modelscope/utils/pipeline_inputs.json index 0cb9c1b1..03a00636 100644 --- a/modelscope/utils/pipeline_inputs.json +++ b/modelscope/utils/pipeline_inputs.json @@ -1,17 +1,17 @@ { "action-detection":{ "input":{ - "video":"data/test/videos/action_detection_test_video.mp4" + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/action_detection_test_video.mp4" } }, "action-recognition":{ "input":{ - "video":"data/test/videos/action_recognition_test_video.mp4" + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/action_recognition_test_video.mp4" } }, "animal-recognition":{ "input":{ - "image":"data/test/images/dogs.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/dogs.jpg" } }, "chat":{ @@ -34,52 +34,52 @@ }, "domain-specific-object-detection":{ "input":{ - "image":"data/test/images/image_traffic_sign.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_traffic_sign.jpg" } }, "face-2d-keypoints":{ "input":{ - "image":"data/test/images/face_detection.png" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/face_detection.png" } }, "face-attribute-recognition":{ "input":{ - "image":"data/test/images/face_recognition_1.png" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/face_recognition_1.png" } }, "facial-expression-recognition":{ "input":{ - "image":"data/test/images/facial_expression_recognition.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/facial_expression_recognition.jpg" } }, "general-recognition":{ "input":{ - "image":"data/test/images/dogs.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/dogs.jpg" } }, "human-detection":{ "input":{ - "image":"data/test/images/image_detection.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_detection.jpg" } }, "image-captioning":{ "input":{ - "image":"data/test/images/image_captioning.png" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_captioning.png" } }, "image-classification":{ "input":{ - "image":"data/test/images/content_check.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/content_check.jpg" } }, "image-demoireing":{ "input":{ - "image":"data/test/images/shop_segmentation.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/shop_segmentation.jpg" } }, "image-object-detection":{ "input":{ - "image":"data/test/images/image_detection.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_detection.jpg" } }, "image-portrait-stylization":{ @@ -89,7 +89,7 @@ }, "image-segmentation":{ "input":{ - "image":"data/test/images/image_semantic_segmentation.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_semantic_segmentation.jpg" }, "parameters":{ @@ -97,18 +97,18 @@ }, "image-text-retrieval":{ "input":{ - "image":"data/test/images/image_mplug_vqa.jpg", + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_mplug_vqa.jpg", "text":"What is the woman doing?" } }, "indoor-layout-estimation":{ "input":{ - "image":"data/test/images/image_traffic_sign.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_traffic_sign.jpg" } }, "live-category":{ "input":{ - "video":"data/test/videos/live_category_test_video.mp4" + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/live_category_test_video.mp4" } }, "motion-generation":{ @@ -132,22 +132,22 @@ }, "ocr-recognition":{ "input":{ - "image":"data/test/images/image_ocr_recognition.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_ocr_recognition.jpg" } }, "panorama-depth-estimation":{ "input":{ - "image":"data/test/images/panorama_depth_estimation.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/panorama_depth_estimation.jpg" } }, "semantic-segmentation":{ "input":{ - "image":"data/test/images/image_salient_detection.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_salient_detection.jpg" } }, "shop-segmentation":{ "input":{ - "image":"data/test/images/shop_segmentation.jpg" + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/shop_segmentation.jpg" } }, "text-classification":{ @@ -160,7 +160,7 @@ }, "text-driven-segmentation":{ "input":{ - "image":"data/test/images/text_driven_segmentation.jpg", + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/text_driven_segmentation.jpg", "text":"bear" } }, @@ -201,60 +201,60 @@ }, "video-captioning":{ "input":{ - "video":"data/test/videos/video_caption_and_qa_test.mp4" + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/video_caption_and_qa_test.mp4" } }, "video-category":{ "input":{ - "video":"data/test/videos/video_category_test_video.mp4" + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/video_category_test_video.mp4" } }, "video-depth-estimation":{ "input":{ - "video":"data/test/videos/video_depth_estimation.mp4" + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/video_depth_estimation.mp4" } }, "video-embedding":{ "input":{ - "video":"data/test/videos/action_recognition_test_video.mp4" + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/action_recognition_test_video.mp4" } }, "video-multi-object-tracking":{ "input":{ - "video":"data/test/videos/MOT17-03-partial.mp4" + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/MOT17-03-partial.mp4" } }, "video-panoptic-segmentation":{ "input":{ - "video":"data/test/videos/kitti-step_testing_image_02_0000.mp4" + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/kitti-step_testing_image_02_0000.mp4" } }, "video-question-answering":{ "input":{ - "video":"data/test/videos/video_caption_and_qa_test.mp4", + "video":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/video_caption_and_qa_test.mp4", "text":"How many people are there?" } }, "video-summarization":{ "input":{ - "text":"data/test/videos/video_category_test_video.mp4" + "text":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/videos/video_category_test_video.mp4" } }, "visual-entailment":{ "input":{ - "image":"data/test/images/dogs.jpg", + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/dogs.jpg", "text":"there are two birds." } }, "visual-grounding":{ "input":{ - "image":"data/test/images/visual_grounding.png", + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/visual_grounding.png", "text":"a blue turtle-like pokemon with round head" } }, "visual-question-answering":{ "input":{ - "image":"data/test/images/image_mplug_vqa.jpg", + "image":"http://modelscope.oss-cn-beijing.aliyuncs.com/demo/images/image_mplug_vqa.jpg", "text":"What is the woman doing?" } }, From e10237074e4129dbd17457e08bf21d59e496f785 Mon Sep 17 00:00:00 2001 From: "wenmeng.zwm" Date: Wed, 18 Oct 2023 20:24:42 +0800 Subject: [PATCH 33/36] fix chatglm sp_tokenizer error Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/14352495 --- modelscope/models/nlp/chatglm/tokenization.py | 7 ++++--- modelscope/models/nlp/llama/text_generation.py | 2 ++ 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/modelscope/models/nlp/chatglm/tokenization.py b/modelscope/models/nlp/chatglm/tokenization.py index f5f8cd0c..6ce1b90d 100644 --- a/modelscope/models/nlp/chatglm/tokenization.py +++ b/modelscope/models/nlp/chatglm/tokenization.py @@ -199,6 +199,10 @@ class ChatGLMTokenizer(PreTrainedTokenizer): padding_side='left', num_image_tokens=20000, **kwargs) -> None: + + self.sp_tokenizer = SPTokenizer( + vocab_file, num_image_tokens=num_image_tokens) + super().__init__( do_lower_case=do_lower_case, remove_space=remove_space, @@ -220,9 +224,6 @@ class ChatGLMTokenizer(PreTrainedTokenizer): self.end_token = end_token self.mask_token = mask_token self.gmask_token = gmask_token - - self.sp_tokenizer = SPTokenizer( - vocab_file, num_image_tokens=num_image_tokens) """ Initialisation """ @property diff --git a/modelscope/models/nlp/llama/text_generation.py b/modelscope/models/nlp/llama/text_generation.py index b9cc8032..d95cae34 100644 --- a/modelscope/models/nlp/llama/text_generation.py +++ b/modelscope/models/nlp/llama/text_generation.py @@ -71,6 +71,8 @@ def get_chat_prompt(system: str, text: str, history: List[Tuple[str, str]], # This file is mainly copied from the llama code of transformers +@MODELS.register_module(Tasks.chat, module_name=Models.llama2) +@MODELS.register_module(Tasks.chat, module_name=Models.llama) @MODELS.register_module(Tasks.text_generation, module_name=Models.llama2) @MODELS.register_module(Tasks.chat, module_name=Models.llama2) @MODELS.register_module(Tasks.text_generation, module_name=Models.llama) From f493ed007ce66ae61262a5ac7db5b0f8346e0fc1 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Wed, 18 Oct 2023 21:17:51 +0800 Subject: [PATCH 34/36] force rebuid image --- .dev_scripts/build_image.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.dev_scripts/build_image.sh b/.dev_scripts/build_image.sh index 9ce2a4a8..386e5aad 100644 --- a/.dev_scripts/build_image.sh +++ b/.dev_scripts/build_image.sh @@ -150,7 +150,7 @@ echo -e "Building image with:\npython$python_version\npytorch$torch_version\nten docker_file_content=`cat docker/Dockerfile.ubuntu` if [ "$is_ci_test" != "True" ]; then echo "Building ModelScope lib, will install ModelScope lib to image" - docker_file_content="${docker_file_content} \nRUN pip install --no-cache-dir -U transformers && pip install --no-cache-dir https://modelscope.oss-cn-beijing.aliyuncs.com/releases/build/modelscope-$modelscope_version-py3-none-any.whl " + docker_file_content="${docker_file_content} \nRUN pip install --no-cache-dir https://modelscope.oss-cn-beijing.aliyuncs.com/releases/build/modelscope-$modelscope_version-py3-none-any.whl && pip install --no-cache-dir -U transformers" fi echo "$is_dsw" if [ "$is_dsw" == "False" ]; then From 8e187bdb962b7671a3d9384f80100cfc0cf4094c Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Thu, 19 Oct 2023 12:12:35 +0800 Subject: [PATCH 35/36] remove llama2 dup in chat task Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/14359280 * remove llama2 dup in chat task --- modelscope/models/nlp/llama/text_generation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/modelscope/models/nlp/llama/text_generation.py b/modelscope/models/nlp/llama/text_generation.py index d95cae34..45b9d5f0 100644 --- a/modelscope/models/nlp/llama/text_generation.py +++ b/modelscope/models/nlp/llama/text_generation.py @@ -74,7 +74,6 @@ def get_chat_prompt(system: str, text: str, history: List[Tuple[str, str]], @MODELS.register_module(Tasks.chat, module_name=Models.llama2) @MODELS.register_module(Tasks.chat, module_name=Models.llama) @MODELS.register_module(Tasks.text_generation, module_name=Models.llama2) -@MODELS.register_module(Tasks.chat, module_name=Models.llama2) @MODELS.register_module(Tasks.text_generation, module_name=Models.llama) class LlamaForTextGeneration(MsModelMixin, LlamaForCausalLM, TorchModel): From b14f3464e59fce10e1eec665bfafd4cbaa252df8 Mon Sep 17 00:00:00 2001 From: "mulin.lyh" Date: Thu, 19 Oct 2023 12:30:10 +0800 Subject: [PATCH 36/36] force rebuild image --- .dev_scripts/build_image.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.dev_scripts/build_image.sh b/.dev_scripts/build_image.sh index 386e5aad..c1e61890 100644 --- a/.dev_scripts/build_image.sh +++ b/.dev_scripts/build_image.sh @@ -150,7 +150,7 @@ echo -e "Building image with:\npython$python_version\npytorch$torch_version\nten docker_file_content=`cat docker/Dockerfile.ubuntu` if [ "$is_ci_test" != "True" ]; then echo "Building ModelScope lib, will install ModelScope lib to image" - docker_file_content="${docker_file_content} \nRUN pip install --no-cache-dir https://modelscope.oss-cn-beijing.aliyuncs.com/releases/build/modelscope-$modelscope_version-py3-none-any.whl && pip install --no-cache-dir -U transformers" + docker_file_content="${docker_file_content} \nRUN pip install --no-cache-dir numpy https://modelscope.oss-cn-beijing.aliyuncs.com/releases/build/modelscope-$modelscope_version-py3-none-any.whl && pip install --no-cache-dir -U transformers" fi echo "$is_dsw" if [ "$is_dsw" == "False" ]; then