78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from transformers.configuration_utils import PretrainedConfig
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class FlexOlmoConfig(PretrainedConfig):
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model_type = "flex_olmo"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=100352,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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hidden_act="silu",
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max_position_embeddings=4096,
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initializer_range=0.02,
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rms_norm_eps=1e-06,
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use_cache=True,
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pad_token_id=100277,
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bos_token_id=None,
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eos_token_id=100257,
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tie_word_embeddings=False,
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rope_theta=500000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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num_experts_per_tok=5,
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num_experts=7,
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output_router_logits=False,
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router_aux_loss_coef=0.01,
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norm_topk_prob=False,
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**kwargs,
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):
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if "architectures" not in kwargs:
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kwargs["architectures"] = ["FlexOlmoForCausalLM"]
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.num_experts_per_tok = num_experts_per_tok
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self.num_experts = num_experts
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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self.norm_topk_prob = norm_topk_prob
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# Validate the correctness of rotary position embeddings parameters
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# BC: if there is a 'type' field, move it to 'rope_type'.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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