Create configuration_manta.py

configuration_manta.py

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+# coding=utf-8
+# Copyright 2020, The Manta Authors and HuggingFace Inc.
+#
+# 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.
+""" Manta model configuration"""
+from typing import Mapping
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.onnx import OnnxSeq2SeqConfigWithPast
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MANTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "nthngdy/manta-base": "https://huggingface.co/nthngdy/manta-base/resolve/main/config.json",
+}
+
+
+class MantaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MantaModel`] or a [`TFMantaModel`]. It is used to
+    instantiate a Manta 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 Manta-base architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Arguments:
+        vocab_size (`int`, *optional*, defaults to 32128):
+            Vocabulary size of the Manta model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MantaModel`] or [`TFMantaModel`].
+        byte_embedding_dim (`int`, *optional*, defaults to 64):
+            Size of the input byte embeddings fed to the MANTa tokenization module.
+        frontier_predictor_num_layers (`int`, *optional*, defaults to 1):
+            Number of sliding window attention layers in the frontier predictor of the tokenization module.
+        frontier_predictor_num_attention_heads (`int`, *optional*, defaults to 8):
+            Number of attention heads in the frontier predictor of the tokenization module.
+        frontier_predictor_attention_window (`int`, *optional*, defaults to 16):
+            Size of the sliding attention window along the byte sequence.
+        pooling_variance_regularization (`float`, *optional*, defaults to 1.0e-6):
+            Variance regularization term used in the computation of the byte-block assignment map.
+        pooling_kernel_size (`int` or `List[List[int]]`, *optional*, defaults to 3):
+            Size(s) of the 1D-convolution kernel(s) used for the byte pooling operation in the tokenization module. Providing an integer
+            will imply using a convolution filter of `(pooling_kernel_size, byte_embedding_dim)`. Several kernel sizes can be provided
+            in the form `[(kernel_size, num_channels), ...]`. These will be concatenated in the style of [Character BERT](https://arxiv.org/pdf/2010.10392.pdf).
+        pooling_depthwise_convolution (`bool`, *optional*, defaults to `True`):
+            Activates depthwise convolution in the pooling operation of the tokenization module. Depthwise convolution will be faster, but might be
+            less powerful than normal convolution, and impedes using different number of channels.
+        pooling_mean_pool (`bool`, *optional*, defaults to `False`):
+            Activates mean-pooling instead of default max-pooling as the reduction operation for each block.
+        max_length_inputs (`int`, *optional*, defaults to 256):
+            Maximum sequence length of the byte input sequences. Can be greater than max_length_encoder_decoder.
+        max_length_encoder_decoder (`int`, *optional*, defaults to 256):
+            Maximum output sequence length of the tokenization module. This allows to control the length of the sequences that the encoder-decoder model receives.
+        d_model (`int`, *optional*, defaults to 512):
+            Size of the encoder layers and the pooler layer.
+        d_kv (`int`, *optional*, defaults to 64):
+            Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
+            num_heads`.
+        d_ff (`int`, *optional*, defaults to 2048):
+            Size of the intermediate feed forward layer in each `MantaBlock`.
+        num_layers (`int`, *optional*, defaults to 6):
+            Number of hidden layers in the Transformer encoder.
+        num_decoder_layers (`int`, *optional*):
+            Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
+        num_heads (`int`, *optional*, defaults to 8):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        relative_attention_num_buckets (`int`, *optional*, defaults to 32):
+            The number of buckets to use for each attention layer.
+        relative_attention_max_distance (`int`, *optional*, defaults to 128):
+            The maximum distance of the longer sequences for the bucket separation.
+        dropout_rate (`float`, *optional*, defaults to 0.1):
+            The ratio for all dropout layers.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        initializer_factor (`float`, *optional*, defaults to 1):
+            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+            testing).
+        feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
+            Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. Mantav1.1 uses the
+            `"gated-gelu"` feed forward projection. Original Manta uses `"relu"`.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+    """
+    model_type = "manta"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
+
+    def __init__(
+        self,
+        vocab_size=384,
+        byte_embedding_dim=64,
+        frontier_predictor_num_layers=1,
+        frontier_predictor_num_attention_heads=8,
+        frontier_predictor_attention_window=16,
+        pooling_variance_regularization=1.0e-6,
+        pooling_kernel_size=3,
+        pooling_depthwise_convolution=True,
+        pooling_mean_pool=False,
+        max_length_inputs=256,
+        max_length_encoder_decoder=256,
+        d_model=512,
+        d_kv=64,
+        d_ff=2048,
+        num_layers=6,
+        num_decoder_layers=None,
+        num_heads=8,
+        relative_attention_num_buckets=32,
+        relative_attention_max_distance=128,
+        dropout_rate=0.1,
+        layer_norm_epsilon=1e-6,
+        initializer_factor=1.0,
+        feed_forward_proj="relu",
+        is_encoder_decoder=True,
+        use_cache=True,
+        pad_token_id=0,
+        eos_token_id=1,
+        **kwargs
+    ):
+        self.vocab_size = vocab_size
+        self.byte_embedding_dim = byte_embedding_dim
+        self.frontier_predictor_num_layers = frontier_predictor_num_layers
+        self.frontier_predictor_num_attention_heads = frontier_predictor_num_attention_heads
+        self.frontier_predictor_attention_window = frontier_predictor_attention_window
+        self.pooling_variance_regularization = pooling_variance_regularization
+        self.pooling_kernel_size = pooling_kernel_size
+        self.pooling_depthwise_convolution = pooling_depthwise_convolution
+        self.pooling_mean_pool = pooling_mean_pool
+        self.max_length_inputs = max_length_inputs
+        self.max_length_encoder_decoder = max_length_encoder_decoder
+        self.d_model = d_model
+        self.d_kv = d_kv
+        self.d_ff = d_ff
+        self.num_layers = num_layers
+        self.num_decoder_layers = (
+            num_decoder_layers if num_decoder_layers is not None else self.num_layers
+        )  # default = symmetry
+        self.num_heads = num_heads
+        self.relative_attention_num_buckets = relative_attention_num_buckets
+        self.relative_attention_max_distance = relative_attention_max_distance
+        self.dropout_rate = dropout_rate
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_factor = initializer_factor
+        self.feed_forward_proj = feed_forward_proj
+        self.use_cache = use_cache
+
+        act_info = self.feed_forward_proj.split("-")
+        self.dense_act_fn = act_info[-1]
+        self.is_gated_act = act_info[0] == "gated"
+
+        if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
+            raise ValueError(
+                f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
+                "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
+                "'gated-gelu' or 'relu'"
+            )
+
+        if (
+            pooling_depthwise_convolution
+            and isinstance(pooling_kernel_size, list)
+            and any(size != byte_embedding_dim for _, size in pooling_kernel_size)
+        ):
+            raise ValueError(
+                f"`pooling_kernel_size`: {pooling_kernel_size} is not a valid list of kernels when "
+                f"`pooling_depthwise_convolution` is True. Please set all kernel dimensions to {byte_embedding_dim}"
+                f"(=`byte_embedding_dim`) or `pooling_depthwise_convolution“ to False."
+            )
+
+        tie_word_embeddings = kwargs.pop("tie_word_embeddings", False)
+        if tie_word_embeddings and byte_embedding_dim != d_model:
+            raise ValueError(
+                f"The input embedding dimension (`byte_embedding_dim={byte_embedding_dim}`) is not the same as the "
+                f"model hidden dimension (`d_model={d_model}`), making it impossible to tie input and output weights."
+            )
+
+        # for backwards compatibility
+        if feed_forward_proj == "gated-gelu":
+            self.dense_act_fn = "gelu_new"
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            is_encoder_decoder=is_encoder_decoder,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )