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[TRTLLM-8577][feat] Clean the Qwen3-next code by removing Qwen3NextCo… (#10228)
Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com>
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Guoming Zhang
GitHub
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93ac0bc1dc
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1865020b6f
@ -23,8 +23,7 @@ import torch.nn.functional as F
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import triton
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import triton.language as tl
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from torch import nn
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from transformers.configuration_utils import PretrainedConfig
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from transformers.modeling_rope_utils import rope_config_validation
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from transformers import Qwen3NextConfig
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from tensorrt_llm._torch.models.checkpoints.base_weight_mapper import \
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BaseWeightMapper
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@ -71,254 +70,6 @@ def divide(numerator, denominator):
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return numerator // denominator
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class Qwen3NextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Qwen3NextModel`]. It is used to instantiate a
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Qwen3-Next model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of
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Qwen3-Next-80B-A3B-Instruct [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct).
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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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Args:
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vocab_size (`int`, *optional*, defaults to 151936):
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Vocabulary size of the model. Defines the number of different tokens that can be represented by the
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`inputs_ids`.
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 5632):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 48):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 2):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
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hidden_act (`str`, *optional*, defaults to `"silu"`):
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The non-linear activation function in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 32768):
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The maximum sequence length that this model might ever be used with.
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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 initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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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 attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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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 tied.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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partial_rotary_factor (`float`, *optional*, defaults to 0.25):
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Percentage of the query and keys which will have rotary embedding.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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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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head_dim (`int`, *optional*, defaults to 256):
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Projection weights dimension in multi-head attention.
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linear_conv_kernel_dim (`int`, *optional*, defaults to 4):
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Kernel size of the convolution used in linear attention layers.
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linear_key_head_dim (`int`, *optional*, defaults to 128):
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Dimension of each key head in linear attention.
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linear_value_head_dim (`int`, *optional*, defaults to 128):
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Dimension of each value head in linear attention.
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linear_num_key_heads (`int`, *optional*, defaults to 16):
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Number of key heads used in linear attention layers.
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linear_num_value_heads (`int`, *optional*, defaults to 32):
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Number of value heads used in linear attention layers.
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decoder_sparse_step (`int`, *optional*, defaults to 1):
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The frequency of the MoE layer.
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moe_intermediate_size (`int`, *optional*, defaults to 512):
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Intermediate size of the routed expert.
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shared_expert_intermediate_size (`int`, *optional*, defaults to 512):
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Intermediate size of the shared expert.
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num_experts_per_tok (`int`, *optional*, defaults to 10):
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Number of selected experts.
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num_experts (`int`, *optional*, defaults to 512):
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Number of routed experts.
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norm_topk_prob (`bool`, *optional*, defaults to `True`):
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Whether to normalize the topk probabilities.
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output_router_logits (`bool`, *optional*, defaults to `False`):
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Whether or not the router logits should be returned by the model. Enabling this will also
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allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
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router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
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The aux loss factor for the total loss.
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mlp_only_layers (`list[int]`, *optional*, defaults to `[]`):
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Indicate which layers use Qwen3NextMLP rather than Qwen3NextSparseMoeBlock
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The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
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If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
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layer_types (`list[str]`, *optional*):
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Types of each layer (attention or linear).
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```python
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>>> from transformers import Qwen3NextModel, Qwen3NextConfig
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>>> # Initializing a Qwen3Next style configuration
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>>> configuration = Qwen3NextConfig()
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>>> # Initializing a model from the Qwen3-Next-80B-A3B style configuration
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>>> model = Qwen3NextModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "qwen3_next"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.experts.*.gate_proj": "colwise",
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"layers.*.mlp.experts.*.up_proj": "colwise",
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"layers.*.mlp.experts.*.down_proj": "rowwise",
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"layers.*.mlp.shared_experts.gate_proj": "colwise",
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"layers.*.mlp.shared_experts.up_proj": "colwise",
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"layers.*.mlp.shared_experts.down_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size=151936,
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hidden_size=2048,
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intermediate_size=5632,
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num_hidden_layers=48,
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num_attention_heads=16,
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num_key_value_heads=2,
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hidden_act="silu",
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max_position_embeddings=32768,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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partial_rotary_factor=0.25,
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attention_bias=False,
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attention_dropout=0.0,
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head_dim=256,
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linear_conv_kernel_dim=4,
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linear_key_head_dim=128,
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linear_value_head_dim=128,
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linear_num_key_heads=16,
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linear_num_value_heads=32,
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decoder_sparse_step=1,
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moe_intermediate_size=512,
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shared_expert_intermediate_size=512,
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num_experts_per_tok=10,
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num_experts=512,
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norm_topk_prob=True,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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mlp_only_layers=[],
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layer_types=None,
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**kwargs,
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):
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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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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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.partial_rotary_factor = partial_rotary_factor
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.head_dim = head_dim
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rope_config_validation(self)
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self.layer_types = layer_types
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if self.layer_types is None:
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interval_pattern = kwargs.get("full_attention_interval", 4)
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self.layer_types = [
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"linear_attention" if bool(
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(i + 1) % interval_pattern) else "full_attention"
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for i in range(self.num_hidden_layers)
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]
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# layer_type_validation(self.layer_types, self.num_hidden_layers)
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# linear attention part
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self.linear_conv_kernel_dim = linear_conv_kernel_dim
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self.linear_key_head_dim = linear_key_head_dim
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self.linear_value_head_dim = linear_value_head_dim
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self.linear_num_key_heads = linear_num_key_heads
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self.linear_num_value_heads = linear_num_value_heads
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# MoE arguments
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self.decoder_sparse_step = decoder_sparse_step
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self.moe_intermediate_size = moe_intermediate_size
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self.shared_expert_intermediate_size = shared_expert_intermediate_size
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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.norm_topk_prob = norm_topk_prob
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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.mlp_only_layers = mlp_only_layers
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class Qwen3NextGate(nn.Module):
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def __init__(
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