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54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
import torch
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class AceStepTextEncoder(torch.nn.Module):
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def __init__(
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self,
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):
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super().__init__()
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from transformers import Qwen3Config, Qwen3Model
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config = Qwen3Config(
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attention_bias=False,
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attention_dropout=0.0,
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bos_token_id=151643,
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dtype="bfloat16",
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eos_token_id=151643,
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head_dim=128,
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hidden_act="silu",
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hidden_size=1024,
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initializer_range=0.02,
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intermediate_size=3072,
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layer_types=["full_attention"] * 28,
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max_position_embeddings=32768,
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max_window_layers=28,
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model_type="qwen3",
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num_attention_heads=16,
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num_hidden_layers=28,
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num_key_value_heads=8,
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pad_token_id=151643,
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rms_norm_eps=1e-06,
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rope_scaling=None,
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rope_theta=1000000,
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sliding_window=None,
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tie_word_embeddings=True,
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use_cache=True,
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use_sliding_window=False,
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vocab_size=151669,
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)
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self.model = Qwen3Model(config)
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.LongTensor,
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attention_mask: torch.Tensor,
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):
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outputs = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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return_dict=True,
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)
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return outputs.last_hidden_state
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