Fu DaiandClaude Opus 4.8
1a585693be
FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
from collections import OrderedDict
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from typing import Mapping
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from transformers.configuration_utils import PretrainedConfig
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from transformers.onnx import OnnxConfig
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from surya.common.s3 import S3DownloaderMixin
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ID2LABEL = {
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0: 'good',
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1: 'bad'
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}
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class DistilBertConfig(S3DownloaderMixin, PretrainedConfig):
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model_type = "distilbert"
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attribute_map = {
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"hidden_size": "dim",
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"num_attention_heads": "n_heads",
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"num_hidden_layers": "n_layers",
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}
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def __init__(
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self,
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vocab_size=30522,
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max_position_embeddings=512,
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sinusoidal_pos_embds=False,
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n_layers=6,
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n_heads=12,
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dim=768,
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hidden_dim=4 * 768,
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dropout=0.1,
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attention_dropout=0.1,
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activation="gelu",
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initializer_range=0.02,
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qa_dropout=0.1,
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seq_classif_dropout=0.2,
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pad_token_id=0,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.sinusoidal_pos_embds = sinusoidal_pos_embds
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self.n_layers = n_layers
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self.n_heads = n_heads
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self.dim = dim
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self.hidden_dim = hidden_dim
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation = activation
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self.initializer_range = initializer_range
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self.qa_dropout = qa_dropout
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self.seq_classif_dropout = seq_classif_dropout
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super().__init__(**kwargs, pad_token_id=pad_token_id)
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class DistilBertOnnxConfig(OnnxConfig):
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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if self.task == "multiple-choice":
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dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
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else:
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dynamic_axis = {0: "batch", 1: "sequence"}
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return OrderedDict(
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[
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("input_ids", dynamic_axis),
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("attention_mask", dynamic_axis),
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]
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) |