refactor : align config field names with Hugging Face
- dim -> hidden_size, n_layers -> num_hidden_layers - dim_ffn -> intermediate_size, n_heads -> num_attention_heads - n_kv_heads -> num_key_value_heads, max_len -> max_position_embeddings - norm_eps -> rms_norm_eps, tie_weight -> tie_word_embeddings - update model, inference, training, scripts, tests, docs
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@@ -32,26 +32,26 @@ class InferenceScheduler:
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if max_seq_len is not None:
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self.max_seq_len = max_seq_len
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elif config.max_len is not None:
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self.max_seq_len = config.max_len
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elif config.max_position_embeddings is not None:
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self.max_seq_len = config.max_position_embeddings
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else:
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raise ValueError(
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"max_seq_len must be provided either as argument "
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"or in model config (config.max_len)"
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"or in model config (config.max_position_embeddings)"
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)
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self.device = device or next(model.parameters()).device
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self.dtype = dtype or next(model.parameters()).dtype
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head_dim = config.dim // config.n_heads
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head_dim = config.hidden_size // config.num_attention_heads
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if cache is not None:
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self._cache = cache
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else:
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self._cache = ContiguousCache(
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config.n_layers,
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config.num_hidden_layers,
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max_batch_size,
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self.max_seq_len,
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config.n_kv_heads,
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config.num_key_value_heads,
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head_dim,
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self.device,
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self.dtype,
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