Support dynamic ntk rope
Browse files
configuration_internlm.py
CHANGED
@@ -19,9 +19,8 @@
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# limitations under the License.
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""" InternLM model configuration"""
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from transformers.utils import logging
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from transformers.configuration_utils import PretrainedConfig
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logger = logging.get_logger(__name__)
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@@ -30,9 +29,9 @@ INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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class InternLMConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate
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model according to the specified arguments, defining the model architecture. Instantiating a
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defaults will yield a similar configuration to that of the InternLM-7B.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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@@ -81,7 +80,7 @@ class InternLMConfig(PretrainedConfig):
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model_type = "internlm"
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_auto_class = "AutoConfig"
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def __init__(
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self,
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vocab_size=103168,
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hidden_size=4096,
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@@ -98,6 +97,7 @@ class InternLMConfig(PretrainedConfig):
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eos_token_id=2,
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tie_word_embeddings=False,
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bias=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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@@ -111,10 +111,11 @@ class InternLMConfig(PretrainedConfig):
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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.bias = bias
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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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# limitations under the License.
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""" InternLM model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class InternLMConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate
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an InternLM model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the InternLM-7B.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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model_type = "internlm"
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_auto_class = "AutoConfig"
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def __init__( # pylint: disable=W0102
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self,
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vocab_size=103168,
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hidden_size=4096,
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eos_token_id=2,
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tie_word_embeddings=False,
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bias=True,
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rotary={"base": 10000, "type": "dynamic"}, # pylint: disable=W0102
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**kwargs,
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):
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self.vocab_size = vocab_size
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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.bias = bias
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self.rotary = rotary
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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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