Upload 2 files

Custom classes to use until merge with main

configuration_switch_transformers.py

@@ -0,0 +1,192 @@
+# coding=utf-8
+# Copyright 2022, Google 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.
+""" Switch Transformers model configuration"""
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
+}
+
+
+class SwitchTransformersConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SwitchTransformersModel`]. It is used to
+    instantiate a SwitchTransformers 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
+    SwitchTransformers [google/switch-base-8](https://huggingface.co/google/switch-base-8) 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 SwitchTransformers model. Defines the number of different tokens that can be
+            represented by the `inputs_ids` passed when calling [`SwitchTransformersModel`].
+        d_model (`int`, *optional*, defaults to 768):
+            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 `SwitchTransformersBlock`.
+        expert_capacity (`int`, *optional*, defaults to 64):
+            Number of tokens that can be stored in each expert. If set to 1, the model will behave like a regular
+            Transformer.
+        num_layers (`int`, *optional*, defaults to 12):
+            Number of dense hidden layers in the Transformer encoder layer.
+        num_sparse_encoder_layers (`int`, *optional*, defaults to 3):
+            Number of sparse (MoE) dense hidden layers in the Transformer encoder layer.
+        num_decoder_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
+        num_sparse_decoder_layers (`int`, *optional*, defaults to 3):
+            Number of sparse (MoE) dense hidden layers in the Transformer decoder layer.
+        num_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_experts (`int`, *optional*, defaults to 8):
+            Number of experts for each SwitchTransformer layer.
+        router_bias (`bool`, *optional*, defaults to `False`):
+            Whether to add a bias to the router.
+        router_jitter_noise (`float`, *optional*, defaults to 0.01):
+            Amount of noise to add to the router.
+        router_dtype (`str`, *optional*, default to `"float32"`):
+            The `dtype` used for the routers. It is preferable to keep the `dtype` to `"float32"` as specified in the
+            *selective precision* discussion in [the paper](https://arxiv.org/abs/2101.03961).
+        router_ignore_padding_tokens (`bool`, *optional*, defaults to `False`):
+            Whether to ignore padding tokens when routing.
+        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.
+        classifier_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for classifier.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        router_z_loss_coef (`float`, *optional*, defaults to 0.001):
+            The z loss factor for the total loss.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+        initializer_factor (`float`, *optional*, defaults to 1.0):
+            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+            testing).
+        dense_act_fn (`string`, *optional*, defaults to `"relu"`):
+            Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. SwitchTransformersv1.1
+            uses the `"gated-gelu"` feed forward projection. Original SwitchTransformers uses `"relu"`.
+        add_router_probs (`bool`, *optional*, defaults to `False`):
+            Whether to output router probabilities to compute router auxiliary loss.
+        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 = "switch_transformers"
+    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=32128,
+        d_model=768,
+        d_kv=64,
+        d_ff=2048,
+        expert_capacity=64,
+        num_layers=12,
+        num_sparse_encoder_layers=3,
+        num_decoder_layers=12,
+        num_sparse_decoder_layers=3,
+        num_heads=12,
+        num_experts=8,
+        router_bias=False,
+        router_jitter_noise=0.01,
+        router_dtype="float32",
+        router_ignore_padding_tokens=False,
+        relative_attention_num_buckets=32,
+        relative_attention_max_distance=128,
+        dropout_rate=0.1,
+        classifier_dropout=0.0,
+        layer_norm_epsilon=1e-6,
+        router_z_loss_coef=0.001,
+        router_aux_loss_coef=0.001,
+        initializer_factor=1.0,
+        dense_act_fn="relu",
+        is_encoder_decoder=True,
+        add_router_probs=False,
+        use_cache=True,
+        pad_token_id=0,
+        eos_token_id=1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.d_model = d_model
+        self.d_kv = d_kv
+        self.d_ff = d_ff
+
+        self.num_sparse_encoder_layers = num_sparse_encoder_layers
+
+        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_sparse_decoder_layers = num_sparse_decoder_layers
+
+        # This tells us, each how many encoder layer we'll have to set a sparse layer.
+        if self.num_sparse_encoder_layers > 0:
+            self.encoder_sparse_step = self.num_layers // self.num_sparse_encoder_layers
+        else:
+            self.encoder_sparse_step = self.num_layers  # HACK: this will create 0 sparse layers
+
+        # This tells us, each how many encoder layer we'll have to set a sparse layer.
+        if self.num_sparse_decoder_layers > 0:
+            self.decoder_sparse_step = self.num_decoder_layers // self.num_sparse_decoder_layers
+        else:
+            self.decoder_sparse_step = self.num_decoder_layers  # HACK: this will create 0 sparse layers
+
+        self.num_heads = num_heads
+        self.num_experts = num_experts
+        self.expert_capacity = expert_capacity
+        self.router_bias = router_bias
+        self.router_jitter_noise = router_jitter_noise
+        if router_dtype not in ["float32", "float16", "bfloat16"]:
+            raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
+        self.router_dtype = router_dtype
+
+        self.router_ignore_padding_tokens = router_ignore_padding_tokens
+        self.relative_attention_num_buckets = relative_attention_num_buckets
+        self.relative_attention_max_distance = relative_attention_max_distance
+
+        self.dropout_rate = dropout_rate
+        if classifier_dropout is not None:
+            self.classifier_dropout = classifier_dropout
+        else:
+            self.classifier_dropout = 0.0
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_factor = initializer_factor
+        self.use_cache = use_cache
+        self.add_router_probs = add_router_probs
+
+        self.router_z_loss_coef = router_z_loss_coef
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.dense_act_fn = dense_act_fn
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            is_encoder_decoder=is_encoder_decoder,
+            **kwargs,
+        )