add altclip & ast model (#944)

README.md

@@ -100,6 +100,9 @@ The table below represents the current support in the library for each of those
 | Model                         | Pynative support    | Graph Support |
 |-------------------------------|---------------------|---------------|
 | ALBERT                        | ✅                | ✅             |
+| ALIGN	                        | ✅                | ❌             |
+| AltCLIP	                    | ✅                | ❌             |
+| Audio Spectrogram Transformer	| ✅                | ❌             |
 | Autoformer                    | ✅ (Inference only)| ❌             |
 | BaiChuan                      | ✅                | ❌             |
 | Bark                          | ✅                | ❌             |

mindnlp/transformers/models/__init__.py

@@ -18,7 +18,10 @@
 from . import (
     albert,
     align,
+    altclip,
+    audio_spectrogram_transformer,
     auto,
+    autoformer,
     bark,
     bart,
     bert,
@@ -83,7 +86,10 @@
 
 from .albert import *
 from .align import *
+from .altclip import *
+from .audio_spectrogram_transformer import *
 from .auto import *
+from .autoformer import *
 from .bark import *
 from .bart import *
 from .bert import *
@@ -148,7 +154,10 @@
 __all__ = []
 __all__.extend(albert.__all__)
 __all__.extend(align.__all__)
+__all__.extend(altclip.__all__)
+__all__.extend(audio_spectrogram_transformer.__all__)
 __all__.extend(auto.__all__)
+__all__.extend(autoformer.__all__)
 __all__.extend(bart.__all__)
 __all__.extend(bark.__all__)
 __all__.extend(bert.__all__)

mindnlp/transformers/models/align/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 Huawei Technologies Co., Ltd
+# Copyright 2024 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

mindnlp/transformers/models/altclip/__init__.py

@@ -0,0 +1,24 @@
+# Copyright 2024 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Align model."""
+from . import configuration_altclip, modeling_altclip, processing_altclip
+from .configuration_altclip import *
+from .modeling_altclip import *
+from .processing_altclip import *
+
+__all__ = []
+__all__.extend(configuration_altclip.__all__)
+__all__.extend(modeling_altclip.__all__)
+__all__.extend(processing_altclip.__all__)

mindnlp/transformers/models/altclip/processing_altclip.py

@@ -0,0 +1,134 @@
+# coding=utf-8
+# Copyright 2022 WenXiang ZhongzhiCheng LedellWu LiuGuang BoWenZhang The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+Image/Text processor class for AltCLIP
+"""
+import warnings
+
+from ...processing_utils import ProcessorMixin
+from ...tokenization_utils_base import BatchEncoding
+
+
+class AltCLIPProcessor(ProcessorMixin):
+    r"""
+    Constructs a AltCLIP processor which wraps a CLIP image processor and a XLM-Roberta tokenizer into a single
+    processor.
+
+    [`AltCLIPProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`XLMRobertaTokenizerFast`]. See
+    the [`~AltCLIPProcessor.__call__`] and [`~AltCLIPProcessor.decode`] for more information.
+
+    Args:
+        image_processor ([`CLIPImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`XLMRobertaTokenizerFast`], *optional*):
+            The tokenizer is a required input.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "CLIPImageProcessor"
+    tokenizer_class = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
+
+    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
+        feature_extractor = None
+        if "feature_extractor" in kwargs:
+            warnings.warn(
+                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
+                " instead.",
+                FutureWarning,
+            )
+            feature_extractor = kwargs.pop("feature_extractor")
+
+        image_processor = image_processor if image_processor is not None else feature_extractor
+        if image_processor is None:
+            raise ValueError("You need to specify an `image_processor`.")
+        if tokenizer is None:
+            raise ValueError("You need to specify a `tokenizer`.")
+
+        super().__init__(image_processor, tokenizer)
+
+    def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to XLMRobertaTokenizerFast's [`~XLMRobertaTokenizerFast.__call__`] if `text` is not
+        `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+        CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
+        of the above two methods for more information.
+
+        Args:
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
+                number of channels, H and W are image height and width.
+
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+        Returns:
+            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+        """
+
+        if text is None and images is None:
+            raise ValueError("You have to specify either text or images. Both cannot be none.")
+
+        if text is not None:
+            encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
+
+        if images is not None:
+            image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs)
+
+        if text is not None and images is not None:
+            encoding["pixel_values"] = image_features.pixel_values
+            return encoding
+        if text is not None:
+            return encoding
+        return BatchEncoding(data={**image_features}, tensor_type=return_tensors)
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to XLMRobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
+        Please refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to XLMRobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+__all__ = ["AltCLIPProcessor"]

mindnlp/transformers/models/audio_spectrogram_transformer/__init__.py

@@ -0,0 +1,24 @@
+# Copyright 2024 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Audio Spectrgram Transformer model."""
+from . import configuration_audio_spectrogram_transformer, feature_extraction_audio_spectrogram_transformer, modeling_audio_spectrogram_transformer
+from .configuration_audio_spectrogram_transformer import *
+from .feature_extraction_audio_spectrogram_transformer import *
+from .modeling_audio_spectrogram_transformer import *
+
+__all__ = []
+__all__.extend(configuration_audio_spectrogram_transformer.__all__)
+__all__.extend(feature_extraction_audio_spectrogram_transformer.__all__)
+__all__.extend(modeling_audio_spectrogram_transformer.__all__)

mindnlp/transformers/models/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py

@@ -0,0 +1,133 @@
+# coding=utf-8
+# Copyright 2022 Google AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" Audio Spectogram Transformer (AST) model configuration"""
+
+
+from mindnlp.utils import logging
+from ...configuration_utils import PretrainedConfig
+
+
+logger = logging.get_logger(__name__)
+
+AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "MIT/ast-finetuned-audioset-10-10-0.4593": (
+        "https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
+    ),
+}
+
+
+class ASTConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ASTModel`]. It is used to instantiate an AST
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the AST
+    [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+    architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        qkv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to add a bias to the queries, keys and values.
+        frequency_stride (`int`, *optional*, defaults to 10):
+            Frequency stride to use when patchifying the spectrograms.
+        time_stride (`int`, *optional*, defaults to 10):
+            Temporal stride to use when patchifying the spectrograms.
+        max_length (`int`, *optional*, defaults to 1024):
+            Temporal dimension of the spectrograms.
+        num_mel_bins (`int`, *optional*, defaults to 128):
+            Frequency dimension of the spectrograms (number of Mel-frequency bins).
+
+    Example:
+
+    ```python
+    >>> from transformers import ASTConfig, ASTModel
+
+    >>> # Initializing a AST MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
+    >>> configuration = ASTConfig()
+
+    >>> # Initializing a model (with random weights) from the MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
+    >>> model = ASTModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "audio-spectrogram-transformer"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        patch_size=16,
+        qkv_bias=True,
+        frequency_stride=10,
+        time_stride=10,
+        max_length=1024,
+        num_mel_bins=128,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.patch_size = patch_size
+        self.qkv_bias = qkv_bias
+        self.frequency_stride = frequency_stride
+        self.time_stride = time_stride
+        self.max_length = max_length
+        self.num_mel_bins = num_mel_bins
+
+__all__ = [
+    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
+    "ASTConfig",
+]