505 lines
21 KiB
Python
505 lines
21 KiB
Python
# Copyright 2025 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/configs/nemotron_h.py
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"""NemotronH 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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from sglang.srt.configs.mamba_utils import (
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Mamba2CacheParams,
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Mamba2StateShape,
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mamba2_state_dtype,
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)
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logger = logging.get_logger(__name__)
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MAMBA = "M"
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ATTENTION = "*"
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MLP = "-"
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MOE = "E"
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DEFAULT_LAYERS_BLOCK_TYPE = ["mamba", "moe", "attention", "moe"]
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DEFAULT_MTP_LAYERS_BLOCK_TYPE = ["attention", "moe"]
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DEFAULT_MAMBA_CHUNK_SIZE = 256
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class NemotronHConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a
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[`NemotronHModel`]. It is used to instantiate a NemotronH model according
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to the specified arguments, defining the model architecture. Instantiating
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a configuration with the defaults will yield a similar configuration to
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that of the NemotronH-v0.1 model.
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Args:
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vocab_size (`int`, *optional*, defaults to 131072):
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Vocabulary size of the NemotronH model. Defines the number of
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different tokens that can be represented by the `inputs_ids`
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passed when calling [`NemotronHModel`]
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be
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tied. Note that this is only relevant if the model has an output
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word embedding layer.
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 21504):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*):
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Deprecated. Kept only for backward compatibility. The effective
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layer count is derived from `layers_block_type`.
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hybrid_override_pattern (`str`, *optional*, defaults to
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`"M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-"`):
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Deprecated compatibility field. Pattern string where each
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character represents Mamba2 (`M`), Attention (`*`), MLP (`-`),
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or MoE (`E`).
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layers_block_type (`list[str]`, *optional*):
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Canonical layer layout. Each entry is one of:
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`"mamba"`, `"attention"`, `"mlp"`, `"moe"`.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the
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Transformer encoder.
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attention_head_dim (`int`, *optional*, defaults to 128):
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Dimension of each attention head.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to
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implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use
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Multi Head Attention (MHA), if `num_key_value_heads=1` the model
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will use Multi Query Attention (MQA) otherwise GQA is used.
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mlp_hidden_act (`str`, *optional*, defaults to "relu2"):
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The non-linear activation function in the MLP layers.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in attention layers.
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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in MLP layers.
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use_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in the model.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for
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initializing all weight matrices.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
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The epsilon used by the layer normalization layers.
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residual_in_fp32 (`bool`, *optional*, defaults to `False`):
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Whether or not residuals should be in `float32`. If set to `False`
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residuals will keep the same `dtype` as the rest of the model.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values
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attentions (not used by all models). Only relevant if
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`config.is_decoder=True`.
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num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
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Number of prompt logits to calculate during generation. If `None`,
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all logits will be calculated. If an integer value, only last
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`num_logits_to_keep` logits will be calculated.
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pad_token_id (`int`, *optional*, defaults to 0):
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The id of the padding token.
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bos_token_id (`int`, *optional*, defaults to 1):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 2):
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The id of the "end-of-sequence" token.
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sliding_window (`int`, *optional*, defaults to None):
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Sliding window attention window size.
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max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model might ever be used
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with.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the hidden states.
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use_mamba_kernels (`bool`, *optional*, defaults to `True`):
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Flag indicating whether or not to use the fast mamba kernels.
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These are available only if `mamba-ssm` and `causal-conv1d`
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are installed, and the mamba modules are running on a CUDA device.
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ssm_state_size (`int`, *optional*, defaults to 128):
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The dimension of the mamba state space latents.
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mamba_num_heads (`int`, *optional*, defaults to 128):
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Number of heads in Mamba layers.
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mamba_n_groups (`int`, *optional*, defaults to 8):
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Number of groups in Mamba layers.
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mamba_head_dim (`int`, *optional*, defaults to 64):
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Dimension of each Mamba head.
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mamba_d_conv (`int`, *optional*, defaults to 4):
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The size of the mamba convolution kernel.
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mamba_expand (`int`, *optional*, defaults to 2):
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Expanding factor used to determine the mamba intermediate size.
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mamba_hidden_act (`str`, *optional*, defaults to "silu"):
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The non-linear activation function in the Mamba layers.
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mamba_dt_min (`float`, *optional*, defaults to 0.001):
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Minimum value for the time step in Mamba.
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mamba_dt_max (`float`, *optional*, defaults to 0.1):
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Maximum value for the time step in Mamba.
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mamba_dt_limit (`tuple`, *optional*, defaults to (0.0, float("inf"))):
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Limits for the time step in Mamba.
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mamba_dt_init_floor (`float`, *optional*, defaults to 1e-4):
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Floor value for time step initialization in Mamba.
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mamba_conv_bias (`bool`, *optional*, defaults to `True`):
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Whether to use bias in the convolution layer of the mamba mixer
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block.
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mamba_proj_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in the input and output projections of the
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mamba mixer block.
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mamba_chunk_size (`int`, *optional*, defaults to 256):
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Size of chunks for Mamba processing.
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rescale_prenorm_residual (`bool`, *optional*, defaults to `True`):
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Whether to rescale the pre-normalization residual connections.
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"""
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model_type = "nemotron_h"
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keys_to_ignore_at_inference = ["past_key_values"]
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@staticmethod
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def _validate_layers_block_type(
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layers_block_type, expected_length=None, param_name="layers_block_type"
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):
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"""
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Validate layers_block_type list.
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Args:
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layers_block_type: List of layer types to validate.
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expected_length: If provided, validate the list has this length.
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param_name: Parameter name for error messages.
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Raises:
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ValueError: If validation fails.
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"""
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if not isinstance(layers_block_type, list):
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raise ValueError(
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f"{param_name} must be a list of strings. Got type: {type(layers_block_type)}"
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)
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if expected_length is not None and len(layers_block_type) != expected_length:
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raise ValueError(
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f"{param_name} must have length {expected_length}. Got length {len(layers_block_type)}."
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)
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valid_types = {"mamba", "attention", "mlp", "moe"}
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if not all(block_type in valid_types for block_type in layers_block_type):
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invalid = set(layers_block_type) - valid_types
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raise ValueError(
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f"{param_name} contains invalid types: {invalid}. Must be one of: {valid_types}"
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)
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@staticmethod
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def _resolve_layers_block_type(
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layers_block_type, hybrid_override_pattern, kwargs
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) -> list[str]:
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"""Resolve canonical layers_block_type from new and legacy config fields."""
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# Prefer explicit kwargs override first (legacy HF path), otherwise use
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# the function argument value from config fields.
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pattern = kwargs.pop("hybrid_override_pattern", hybrid_override_pattern)
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if layers_block_type is None:
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if pattern is not None:
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layers_block_type = NemotronHConfig._pattern_to_list(pattern)
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else:
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# Last-resort fallback to preserve compatibility when neither
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# canonical nor legacy pattern fields are provided.
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layers_block_type = DEFAULT_LAYERS_BLOCK_TYPE
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return layers_block_type
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@staticmethod
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def _resolve_mtp_layers_block_type(mtp_layers_block_type, kwargs) -> list[str]:
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"""Resolve canonical mtp_layers_block_type from new and legacy config fields."""
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if "mtp_hybrid_override_pattern" in kwargs:
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pattern = kwargs.pop("mtp_hybrid_override_pattern")
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if mtp_layers_block_type is None or mtp_layers_block_type == [
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"attention",
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"moe",
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]:
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mtp_layers_block_type = NemotronHConfig._pattern_to_list(pattern)
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return mtp_layers_block_type
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@staticmethod
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def _resolve_mamba_chunk_size(mamba_chunk_size, kwargs) -> int:
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"""Resolve canonical mamba_chunk_size from new and legacy config fields."""
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chunk_size = kwargs.pop("chunk_size", None)
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if (
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mamba_chunk_size is not None
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and chunk_size is not None
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and mamba_chunk_size != chunk_size
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):
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logger.warning(
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"Both chunk_size=%s and mamba_chunk_size=%s were provided. "
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"Using mamba_chunk_size.",
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chunk_size,
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mamba_chunk_size,
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)
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if mamba_chunk_size is None:
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mamba_chunk_size = chunk_size
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if mamba_chunk_size is None:
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mamba_chunk_size = DEFAULT_MAMBA_CHUNK_SIZE
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return mamba_chunk_size
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def __init__(
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self,
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vocab_size=131072,
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tie_word_embeddings=False,
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hidden_size=4096,
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intermediate_size=21504,
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num_hidden_layers=None, # Deprecated, only for backward compatibility
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hybrid_override_pattern="M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M*-M-M-M-M-M-",
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layers_block_type=None,
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num_attention_heads=32,
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head_dim=128,
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num_key_value_heads=8, # nemo: num_query_groups
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mlp_hidden_act="relu2",
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attention_bias=False,
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mlp_bias=False,
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use_bias=False,
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initializer_range=0.02, # nemo: init_method_std
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layer_norm_epsilon=1e-5, # nemo: layernorm_epsilon
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residual_in_fp32=False, # Megatron Core default value
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use_cache=True,
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num_logits_to_keep=1,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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sliding_window=None,
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max_position_embeddings=4096,
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attention_dropout=0.0,
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hidden_dropout=0.0, # * ADDED
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use_mamba_kernels=True,
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ssm_state_size=128, # mamba_state_size
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mamba_num_heads=128,
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mamba_n_groups=8, # nemo: mamba_ssm_ngroups = num_heads
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mamba_head_dim=64,
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mamba_d_conv=4,
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mamba_expand=2,
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mamba_hidden_act="silu",
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mamba_dt_min=0.001,
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mamba_dt_max=0.1,
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mamba_dt_limit=(0.0, float("inf")),
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mamba_dt_init_floor=1e-4,
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mamba_conv_bias=True,
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mamba_proj_bias=False,
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mamba_chunk_size=None,
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rescale_prenorm_residual=True,
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n_routed_experts=8,
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n_shared_experts=1,
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moe_intermediate_size=7688,
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moe_shared_expert_intermediate_size=7688,
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moe_latent_size=None,
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num_experts_per_tok=2,
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routed_scaling_factor=1.0,
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n_group=1,
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topk_group=1,
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norm_topk_prob=True,
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num_nextn_predict_layers=0,
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mtp_layers_block_type=DEFAULT_MTP_LAYERS_BLOCK_TYPE,
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**kwargs,
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):
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mamba_chunk_size = self._resolve_mamba_chunk_size(mamba_chunk_size, kwargs)
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# Compatibility parsing: normalize legacy pattern fields into canonical list fields.
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layers_block_type = self._resolve_layers_block_type(
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layers_block_type, hybrid_override_pattern, kwargs
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)
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mtp_layers_block_type = self._resolve_mtp_layers_block_type(
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mtp_layers_block_type, kwargs
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)
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# num_hidden_layers is deprecated and ignored as a source of truth.
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if (
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num_hidden_layers is not None
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and len(layers_block_type) != num_hidden_layers
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):
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logger.warning(
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f"num_hidden_layers ({num_hidden_layers}) is deprecated and doesn't match "
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f"layers_block_type length ({len(layers_block_type)}). Using layers_block_type length."
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)
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# Core model attributes.
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self.vocab_size = vocab_size
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self.tie_word_embeddings = tie_word_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_attention_heads = num_attention_heads
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self.head_dim = head_dim
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self.sliding_window = sliding_window
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self.max_position_embeddings = max_position_embeddings
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self.attention_dropout = attention_dropout
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self.hidden_dropout = hidden_dropout
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self._validate_layers_block_type(
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layers_block_type, expected_length=None, param_name="layers_block_type"
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)
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self.layers_block_type = layers_block_type
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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.mlp_hidden_act = mlp_hidden_act
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self.attention_bias = attention_bias
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self.mlp_bias = mlp_bias
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self.use_bias = use_bias
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self.initializer_range = initializer_range
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self.layer_norm_epsilon = layer_norm_epsilon
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self.residual_in_fp32 = residual_in_fp32
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self.use_cache = use_cache
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self.num_logits_to_keep = num_logits_to_keep
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# Mamba attributes.
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self.use_mamba_kernels = use_mamba_kernels
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self.mamba_n_groups = mamba_n_groups
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self.mamba_head_dim = mamba_head_dim
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self.ssm_state_size = ssm_state_size
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self.mamba_num_heads = mamba_num_heads
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self.conv_kernel = mamba_d_conv
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self.expand = mamba_expand
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self.mamba_hidden_act = mamba_hidden_act
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self.time_step_min = mamba_dt_min
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self.time_step_max = mamba_dt_max
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self.time_step_limit = mamba_dt_limit
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self.time_step_floor = mamba_dt_init_floor
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self.use_conv_bias = mamba_conv_bias
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self.mamba_proj_bias = mamba_proj_bias
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self.mamba_chunk_size = mamba_chunk_size
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self.rescale_prenorm_residual = rescale_prenorm_residual
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# MoE attributes.
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self.n_routed_experts = n_routed_experts
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self.n_shared_experts = n_shared_experts
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self.moe_intermediate_size = moe_intermediate_size
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self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size
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self.moe_latent_size = moe_latent_size
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self.num_experts_per_tok = num_experts_per_tok
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self.routed_scaling_factor = routed_scaling_factor
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self.n_group = n_group
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self.topk_group = topk_group
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self.norm_topk_prob = norm_topk_prob
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# MTP attributes.
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self.num_nextn_predict_layers = num_nextn_predict_layers
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if self.num_nextn_predict_layers > 0:
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if mtp_layers_block_type is None:
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raise ValueError(
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"mtp_layers_block_type is required when num_nextn_predict_layers > 0. "
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"Please provide an explicit list of layer types for MTP layers. "
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"Example: mtp_layers_block_type=['attention', 'moe']"
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)
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self._validate_layers_block_type(
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mtp_layers_block_type, None, "mtp_layers_block_type"
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)
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self.mtp_layers_block_type = mtp_layers_block_type
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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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@property
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def mamba_layer_ids(self):
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return [
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i
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for i in range(self.num_hidden_layers)
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if self.hybrid_override_pattern[i] == MAMBA
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]
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@property
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def full_attention_layer_ids(self):
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return [
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i
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for i in range(self.num_hidden_layers)
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if self.hybrid_override_pattern[i] == ATTENTION
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]
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@property
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def mamba2_cache_params(self) -> Mamba2CacheParams:
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from sglang.srt.layers.dp_attention import get_attention_tp_size
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shape = Mamba2StateShape.create(
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tp_world_size=get_attention_tp_size(),
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intermediate_size=self.mamba_num_heads * self.mamba_head_dim,
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n_groups=self.n_groups,
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num_heads=self.mamba_num_heads,
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head_dim=self.mamba_head_dim,
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state_size=self.ssm_state_size,
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conv_kernel=self.conv_kernel,
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)
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return Mamba2CacheParams(
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shape=shape, layers=self.mamba_layer_ids, dtype=mamba2_state_dtype(self)
|
|
)
|
|
|
|
@property
|
|
def num_hidden_layers(self) -> int:
|
|
"""
|
|
Number of hidden layers derived from the length of layers_block_type.
|
|
This property replaces the deprecated num_hidden_layers parameter.
|
|
"""
|
|
return len(self.layers_block_type)
|
|
|
|
@num_hidden_layers.setter
|
|
def num_hidden_layers(self, value):
|
|
"""
|
|
Setter for backward compatibility when loading configs.
|
|
The value is ignored since num_hidden_layers is computed from layers_block_type.
|
|
"""
|
|
pass
|
|
|
|
@property
|
|
def hybrid_override_pattern(self) -> str:
|
|
"""
|
|
Backward compatibility property.
|
|
Returns the pattern string representation of layers_block_type.
|
|
"""
|
|
return self._list_to_pattern(self.layers_block_type)
|
|
|
|
@hybrid_override_pattern.setter
|
|
def hybrid_override_pattern(self, value):
|
|
"""
|
|
Setter for backward compatibility when loading configs.
|
|
"""
|
|
self.layers_block_type = self._pattern_to_list(value)
|
|
|
|
@property
|
|
def mtp_hybrid_override_pattern(self) -> str:
|
|
"""
|
|
Backward compatibility property.
|
|
Returns the pattern string representation of mtp_layers_block_type.
|
|
"""
|
|
return self._list_to_pattern(self.mtp_layers_block_type)
|
|
|
|
@mtp_hybrid_override_pattern.setter
|
|
def mtp_hybrid_override_pattern(self, value):
|
|
"""Setter for backward compatibility when loading configs."""
|
|
self.mtp_layers_block_type = self._pattern_to_list(value)
|
|
|
|
@staticmethod
|
|
def _list_to_pattern(layers_list: list[str]) -> str:
|
|
"""Convert list of layer types back to pattern string (for backward compatibility)."""
|
|
reverse_mapping = {
|
|
"mamba": MAMBA,
|
|
"moe": MOE,
|
|
"attention": ATTENTION,
|
|
"mlp": MLP,
|
|
}
|
|
return "".join(reverse_mapping[layer_type] for layer_type in layers_list)
|
|
|
|
@staticmethod
|
|
def _pattern_to_list(pattern: str) -> list[str]:
|
|
"""Convert pattern string to list of layer types (for backward compatibility)."""
|
|
if any(char not in {MAMBA, MOE, ATTENTION, MLP} for char in pattern):
|
|
raise ValueError(
|
|
"Pattern must only contain characters 'M', '*', '-' or 'E'. "
|
|
f"Got: {pattern}"
|
|
)
|
|
pattern_mapping = {
|
|
MAMBA: "mamba",
|
|
MOE: "moe",
|
|
ATTENTION: "attention",
|
|
MLP: "mlp",
|
|
}
|
|
return [pattern_mapping[char] for char in pattern]
|