| from transformers import PretrainedConfig |
| import torch |
|
|
|
|
| class ImpressoConfig(PretrainedConfig): |
| model_type = "stacked_bert" |
|
|
| def __init__( |
| self, |
| vocab_size=30522, |
| hidden_size=768, |
| num_hidden_layers=12, |
| num_attention_heads=12, |
| intermediate_size=3072, |
| hidden_act="gelu", |
| hidden_dropout_prob=0.1, |
| attention_probs_dropout_prob=0.1, |
| max_position_embeddings=512, |
| type_vocab_size=2, |
| initializer_range=0.02, |
| layer_norm_eps=1e-12, |
| pad_token_id=0, |
| position_embedding_type="absolute", |
| use_cache=True, |
| classifier_dropout=None, |
| pretrained_config=None, |
| values_override=None, |
| label_map=None, |
| **kwargs, |
| ): |
| super().__init__(pad_token_id=pad_token_id, **kwargs) |
|
|
| self.vocab_size = vocab_size |
| self.hidden_size = hidden_size |
| self.num_hidden_layers = num_hidden_layers |
| self.num_attention_heads = num_attention_heads |
| self.hidden_act = hidden_act |
| self.intermediate_size = intermediate_size |
| self.hidden_dropout_prob = hidden_dropout_prob |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob |
| self.max_position_embeddings = max_position_embeddings |
| self.type_vocab_size = type_vocab_size |
| self.initializer_range = initializer_range |
| self.layer_norm_eps = layer_norm_eps |
| self.position_embedding_type = position_embedding_type |
| self.use_cache = use_cache |
| self.classifier_dropout = classifier_dropout |
| self.pretrained_config = pretrained_config |
| self.label_map = label_map |
|
|
| self.values_override = values_override or {} |
| self.outputs = { |
| "logits": {"shape": [None, None, self.hidden_size], "dtype": "float32"} |
| } |
|
|
| @classmethod |
| def is_torch_support_available(cls): |
| """ |
| Indicate whether Torch support is available for this configuration. |
| Required for compatibility with certain parts of the Transformers library. |
| """ |
| return True |
|
|
| @classmethod |
| def patch_ops(self): |
| """ |
| A method required by some Hugging Face utilities to modify operator mappings. |
| Currently, it performs no operation and is included for compatibility. |
| Args: |
| ops: A dictionary of operations to potentially patch. |
| Returns: |
| The (unmodified) ops dictionary. |
| """ |
| return None |
|
|
| def generate_dummy_inputs(self, tokenizer, batch_size=1, seq_length=8, framework="pt"): |
| """ |
| Generate dummy inputs for testing or export. |
| Args: |
| tokenizer: The tokenizer used to tokenize inputs. |
| batch_size: Number of input samples in the batch. |
| seq_length: Length of each sequence. |
| framework: Framework ("pt" for PyTorch, "tf" for TensorFlow). |
| Returns: |
| Dummy inputs as a dictionary. |
| """ |
| if framework == "pt": |
| input_ids = torch.randint( |
| low=0, |
| high=self.vocab_size, |
| size=(batch_size, seq_length), |
| dtype=torch.long |
| ) |
| attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long) |
| return {"input_ids": input_ids, "attention_mask": attention_mask} |
| else: |
| raise ValueError("Framework '{}' not supported.".format(framework)) |
|
|
|
|
| |
| ImpressoConfig.register_for_auto_class() |
|
|
