fix code
Browse files- config.json +80 -80
- modeling_minimax_text_01.py +3 -3
config.json
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"attention_dropout": 0.0,
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"layer_types": [
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],
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"auto_map": {
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"AutoConfig": "configuration_minimax_text_01.MiniMaxText01Config",
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],
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"attention_dropout": 0.0,
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"layer_types": [
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"full_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"linear_attention",
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"full_attention"
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],
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"auto_map": {
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"AutoConfig": "configuration_minimax_text_01.MiniMaxText01Config",
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modeling_minimax_text_01.py
CHANGED
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@@ -1200,13 +1200,13 @@ class MiniMaxText01Model(MiniMaxText01PreTrainedModel):
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
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self.
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config_copy = copy.deepcopy(config)
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self.layers = nn.ModuleList([])
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for i in range(config.num_hidden_layers):
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_config = copy.deepcopy(config)
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if self.
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_config._attn_implementation = 'linear_attention'
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_config.attention_type = 0
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else:
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@@ -1305,7 +1305,7 @@ class MiniMaxText01Model(MiniMaxText01PreTrainedModel):
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seq_length_with_past = seq_length
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if past_key_values is not None:
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for idx in range(len(past_key_values)):
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if self.
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past_key_values_length = past_key_values[idx][0].shape[-3]
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seq_length_with_past = seq_length_with_past + past_key_values_length
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break
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
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self.layer_types = config.layer_types
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config_copy = copy.deepcopy(config)
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self.layers = nn.ModuleList([])
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for i in range(config.num_hidden_layers):
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_config = copy.deepcopy(config)
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if self.layer_types[i] == "linear_attention":
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_config._attn_implementation = 'linear_attention'
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_config.attention_type = 0
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else:
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seq_length_with_past = seq_length
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if past_key_values is not None:
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for idx in range(len(past_key_values)):
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if self.layer_types[idx] == "full_attention":
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past_key_values_length = past_key_values[idx][0].shape[-3]
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seq_length_with_past = seq_length_with_past + past_key_values_length
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break
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