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Upload configuration_mixformer_sequential.py

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configuration_mixformer_sequential.py ADDED
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+ # Copyright (c) Microsoft Corporation.
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+ # Licensed under the MIT license.
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+
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+ import math
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+ from typing import Any, Dict, List, Optional, Union
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+
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+ from transformers import PretrainedConfig
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+
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+
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+ class MixFormerSequentialConfig(PretrainedConfig):
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+ """MixFormer (sequential for DeepSpeed) configuration."""
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+
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+ model_type = "mixformer-sequential"
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+
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+ attribute_map = {
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+ "max_position_embeddings": "n_positions",
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+ "hidden_size": "n_embd",
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+ "num_attention_heads": "n_head",
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+ "num_hidden_layers": "n_layer",
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+ "input_emb_layer": "embd_layer", # `input_emb_layer` key is for backward compatibility
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+ "blocks": "architecture", # `blocks` key is for backward compatibility
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+ }
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+
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+ def __init__(
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+ self,
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+ vocab_size: Optional[int] = 50304,
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+ n_positions: Optional[int] = 2048,
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+ n_embd: Optional[int] = 1024,
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+ n_layer: Optional[int] = 20,
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+ n_inner: Optional[int] = None,
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+ n_head: Optional[int] = 16,
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+ rotary_dim: Optional[int] = 32,
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+ activation_function: Optional[str] = "gelu_new",
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+ embd_layer: Optional[str] = "default",
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+ architecture: Union[Dict[str, Any], List[Dict[str, Any]]] = None,
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+ embd_pdrop: Optional[float] = 0.0,
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+ resid_pdrop: Optional[float] = 0.0,
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+ layer_norm_epsilon: Optional[float] = 1e-5,
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+ initializer_range: Optional[float] = 0.02,
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+ tie_word_embeddings: Optional[bool] = False,
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+ pad_vocab_size_multiple: Optional[int] = 64,
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+ **kwargs
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+ ) -> None:
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+ self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
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+ self.n_positions = n_positions
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+ self.n_embd = n_embd
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+ self.n_layer = n_layer
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+ self.n_inner = n_inner
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+ self.n_head = n_head
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+ self.rotary_dim = min(rotary_dim, n_embd // n_head)
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+ self.activation_function = activation_function
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+ self.embd_layer = embd_layer
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+ self.architecture = architecture
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+ self.embd_pdrop = embd_pdrop
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+ self.resid_pdrop = resid_pdrop
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+ self.layer_norm_epsilon = layer_norm_epsilon
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+ self.initializer_range = initializer_range
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+
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+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)