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80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
import torch
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LM_CONFIGS = {
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"acestep-5Hz-lm-0.6B": {
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"hidden_size": 1024,
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"intermediate_size": 3072,
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"num_hidden_layers": 28,
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"num_attention_heads": 16,
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"layer_types": ["full_attention"] * 28,
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"max_window_layers": 28,
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},
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"acestep-5Hz-lm-1.7B": {
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"hidden_size": 2048,
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"intermediate_size": 6144,
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"num_hidden_layers": 28,
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"num_attention_heads": 16,
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"layer_types": ["full_attention"] * 28,
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"max_window_layers": 28,
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},
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"acestep-5Hz-lm-4B": {
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"hidden_size": 2560,
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"intermediate_size": 9728,
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"num_hidden_layers": 36,
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"num_attention_heads": 32,
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"layer_types": ["full_attention"] * 36,
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"max_window_layers": 36,
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},
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}
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class AceStepLM(torch.nn.Module):
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"""
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Language model for ACE-Step.
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Converts natural language prompts into structured parameters
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(caption, lyrics, bpm, keyscale, duration, timesignature, etc.)
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for ACE-Step music generation.
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Wraps a Qwen3ForCausalLM transformers model. Config is manually
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constructed based on variant type, and model weights are loaded
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via DiffSynth's standard mechanism from safetensors files.
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"""
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def __init__(
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self,
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variant: str = "acestep-5Hz-lm-1.7B",
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):
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super().__init__()
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from transformers import Qwen3Config, Qwen3ForCausalLM
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config_params = LM_CONFIGS[variant]
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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=151645,
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head_dim=128,
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hidden_act="silu",
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initializer_range=0.02,
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max_position_embeddings=40960,
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model_type="qwen3",
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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=217204,
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**config_params,
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)
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self.model = Qwen3ForCausalLM(config)
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self.config = config
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