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@ -9,27 +9,28 @@ import numpy as np
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from transformers import WhisperForConditionalGeneration
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from transformers import WhisperForConditionalGeneration
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conv_map = {'self_attn_layer_norm': 'attn_ln',
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conv_map = {
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'encoder_attn.k_proj': 'attn.key',
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'self_attn.k_proj' : 'attn.key',
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'self_attn.out_proj': 'attn.out',
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'self_attn.q_proj' : 'attn.query',
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'encoder_attn.out_proj': 'cross_attn.out',
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'self_attn.v_proj' : 'attn.value',
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'self_attn.q_proj': 'attn.query',
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'self_attn.out_proj' : 'attn.out',
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'encoder_attn.q_proj': 'cross_attn.query',
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'self_attn_layer_norm' : 'attn_ln',
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'self_attn.v_proj': 'attn.value',
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'encoder_attn.q_proj' : 'cross_attn.query',
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'encoder_attn.v_proj': 'cross_attn.value',
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'encoder_attn.v_proj' : 'cross_attn.value',
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'encoder_attn_layer_norm': 'cross_attn_ln',
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'encoder_attn.out_proj' : 'cross_attn.out',
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'fc1': 'mlp.0',
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'encoder_attn_layer_norm' : 'cross_attn_ln',
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'fc2': 'mlp.2',
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'fc1' : 'mlp.0',
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'final_layer_norm': 'mlp_ln',
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'fc2' : 'mlp.2',
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'encoder.layer_norm.bias': 'encoder.ln_post.bias',
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'final_layer_norm' : 'mlp_ln',
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'encoder.layer_norm.weight': 'encoder.ln_post.weight',
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'encoder.layer_norm.bias' : 'encoder.ln_post.bias',
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'encoder.layer_norm.weight' : 'encoder.ln_post.weight',
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'encoder.embed_positions.weight': 'encoder.positional_embedding',
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'encoder.embed_positions.weight': 'encoder.positional_embedding',
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'decoder.layer_norm.bias': 'decoder.ln.bias',
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'decoder.layer_norm.bias' : 'decoder.ln.bias',
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'decoder.layer_norm.weight': 'decoder.ln.weight',
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'decoder.layer_norm.weight' : 'decoder.ln.weight',
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'decoder.embed_positions.weight': 'decoder.positional_embedding',
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'decoder.embed_positions.weight': 'decoder.positional_embedding',
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'decoder.embed_tokens.weight': 'decoder.token_embedding.weight',
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'decoder.embed_tokens.weight' : 'decoder.token_embedding.weight',
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'proj_out.weight': 'decoder.proj.weight',
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'proj_out.weight' : 'decoder.proj.weight',
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}
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}
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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def bytes_to_unicode():
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def bytes_to_unicode():
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@ -95,12 +96,12 @@ fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
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fout.write(struct.pack("i", hparams["vocab_size"]))
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fout.write(struct.pack("i", hparams["vocab_size"]))
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fout.write(struct.pack("i", hparams["max_source_positions"]))
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fout.write(struct.pack("i", hparams["max_source_positions"]))
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fout.write(struct.pack("i", hparams["d_model"]))
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fout.write(struct.pack("i", hparams["d_model"]))
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fout.write(struct.pack("i", hparams["decoder_attention_heads"]))
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fout.write(struct.pack("i", hparams["decoder_layers"]))
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fout.write(struct.pack("i", hparams["max_length"]))
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fout.write(struct.pack("i", hparams["d_model"]))
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fout.write(struct.pack("i", hparams["encoder_attention_heads"]))
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fout.write(struct.pack("i", hparams["encoder_attention_heads"]))
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fout.write(struct.pack("i", hparams["encoder_layers"]))
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fout.write(struct.pack("i", hparams["encoder_layers"]))
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fout.write(struct.pack("i", hparams["max_length"]))
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fout.write(struct.pack("i", hparams["d_model"]))
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fout.write(struct.pack("i", hparams["decoder_attention_heads"]))
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fout.write(struct.pack("i", hparams["decoder_layers"]))
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fout.write(struct.pack("i", hparams["num_mel_bins"]))
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fout.write(struct.pack("i", hparams["num_mel_bins"]))
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fout.write(struct.pack("i", use_f16))
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fout.write(struct.pack("i", use_f16))
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@ -139,7 +140,7 @@ for name in list_vars.keys():
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if nn[1] == "layers":
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if nn[1] == "layers":
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nn[1] = "blocks"
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nn[1] = "blocks"
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if ".".join(nn[3:-1]) == "self_attn.k_proj":
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if ".".join(nn[3:-1]) == "encoder_attn.k_proj":
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mapped = "attn.key" if nn[0] == "encoder" else "cross_attn.key"
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mapped = "attn.key" if nn[0] == "encoder" else "cross_attn.key"
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else:
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else:
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mapped = conv_map[".".join(nn[3:-1])]
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mapped = conv_map[".".join(nn[3:-1])]
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