From 9dfe073677e127d918aaabfad7ac5bf44727f72b Mon Sep 17 00:00:00 2001 From: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> Date: Tue, 23 Dec 2025 08:37:04 +0000 Subject: [PATCH] [TRTLLM-8577][feat] Clean the Qwen3-next code by removing Qwen3NextConfig. Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> --- .../_torch/models/modeling_qwen3_next.py | 251 +----------------- 1 file changed, 1 insertion(+), 250 deletions(-) diff --git a/tensorrt_llm/_torch/models/modeling_qwen3_next.py b/tensorrt_llm/_torch/models/modeling_qwen3_next.py index 95f642b3e50..13318b1e4f2 100644 --- a/tensorrt_llm/_torch/models/modeling_qwen3_next.py +++ b/tensorrt_llm/_torch/models/modeling_qwen3_next.py @@ -23,8 +23,7 @@ import triton import triton.language as tl from torch import nn -from transformers.configuration_utils import PretrainedConfig -from transformers.modeling_rope_utils import rope_config_validation +from transformers import Qwen3NextConfig from tensorrt_llm._torch.models.checkpoints.base_weight_mapper import \ BaseWeightMapper @@ -71,254 +70,6 @@ def divide(numerator, denominator): return numerator // denominator -class Qwen3NextConfig(PretrainedConfig): - r""" - This is the configuration class to store the configuration of a [`Qwen3NextModel`]. It is used to instantiate a - Qwen3-Next model according to the specified arguments, defining the model architecture. - Instantiating a configuration with the defaults will yield a similar configuration to that of - Qwen3-Next-80B-A3B-Instruct [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct). - - Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the - documentation from [`PretrainedConfig`] for more information. - - - Args: - vocab_size (`int`, *optional*, defaults to 151936): - Vocabulary size of the model. Defines the number of different tokens that can be represented by the - `inputs_ids`. - hidden_size (`int`, *optional*, defaults to 2048): - Dimension of the hidden representations. - intermediate_size (`int`, *optional*, defaults to 5632): - Dimension of the MLP representations. - num_hidden_layers (`int`, *optional*, defaults to 48): - Number of hidden layers in the Transformer encoder. - num_attention_heads (`int`, *optional*, defaults to 16): - Number of attention heads for each attention layer in the Transformer encoder. - num_key_value_heads (`int`, *optional*, defaults to 2): - This is the number of key_value heads that should be used to implement Grouped Query Attention. If - `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if - `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When - converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed - by meanpooling all the original heads within that group. For more details checkout [this - paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`. - hidden_act (`str`, *optional*, defaults to `"silu"`): - The non-linear activation function in the decoder. - max_position_embeddings (`int`, *optional*, defaults to 32768): - The maximum sequence length that this model might ever be used with. - initializer_range (`float`, *optional*, defaults to 0.02): - The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - rms_norm_eps (`float`, *optional*, defaults to 1e-06): - The epsilon used by the rms normalization layers. - use_cache (`bool`, *optional*, defaults to `True`): - Whether or not the model should return the last key/values attentions (not used by all models). Only - relevant if `config.is_decoder=True`. - tie_word_embeddings (`bool`, *optional*, defaults to `False`): - Whether the model's input and output word embeddings should be tied. - rope_theta (`float`, *optional*, defaults to 10000.0): - The base period of the RoPE embeddings. - rope_scaling (`Dict`, *optional*): - Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type - and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value - accordingly. - Expected contents: - `rope_type` (`str`): - The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', - 'llama3'], with 'default' being the original RoPE implementation. - `factor` (`float`, *optional*): - Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In - most scaling types, a `factor` of x will enable the model to handle sequences of length x * - original maximum pre-trained length. - `original_max_position_embeddings` (`int`, *optional*): - Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during - pretraining. - `attention_factor` (`float`, *optional*): - Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention - computation. If unspecified, it defaults to value recommended by the implementation, using the - `factor` field to infer the suggested value. - `beta_fast` (`float`, *optional*): - Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear - ramp function. If unspecified, it defaults to 32. - `beta_slow` (`float`, *optional*): - Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear - ramp function. If unspecified, it defaults to 1. - `short_factor` (`List[float]`, *optional*): - Only used with 'longrope'. The scaling factor to be applied to short contexts (< - `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden - size divided by the number of attention heads divided by 2 - `long_factor` (`List[float]`, *optional*): - Only used with 'longrope'. The scaling factor to be applied to long contexts (< - `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden - size divided by the number of attention heads divided by 2 - `low_freq_factor` (`float`, *optional*): - Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE - `high_freq_factor` (`float`, *optional*): - Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE - partial_rotary_factor (`float`, *optional*, defaults to 0.25): - Percentage of the query and keys which will have rotary embedding. - attention_bias (`bool`, *optional*, defaults to `False`): - Whether to use a bias in the query, key, value and output projection layers during self-attention. - attention_dropout (`float`, *optional*, defaults to 0.0): - The dropout ratio for the attention probabilities. - head_dim (`int`, *optional*, defaults to 256): - Projection weights dimension in multi-head attention. - linear_conv_kernel_dim (`int`, *optional*, defaults to 4): - Kernel size of the convolution used in linear attention layers. - linear_key_head_dim (`int`, *optional*, defaults to 128): - Dimension of each key head in linear attention. - linear_value_head_dim (`int`, *optional*, defaults to 128): - Dimension of each value head in linear attention. - linear_num_key_heads (`int`, *optional*, defaults to 16): - Number of key heads used in linear attention layers. - linear_num_value_heads (`int`, *optional*, defaults to 32): - Number of value heads used in linear attention layers. - decoder_sparse_step (`int`, *optional*, defaults to 1): - The frequency of the MoE layer. - moe_intermediate_size (`int`, *optional*, defaults to 512): - Intermediate size of the routed expert. - shared_expert_intermediate_size (`int`, *optional*, defaults to 512): - Intermediate size of the shared expert. - num_experts_per_tok (`int`, *optional*, defaults to 10): - Number of selected experts. - num_experts (`int`, *optional*, defaults to 512): - Number of routed experts. - norm_topk_prob (`bool`, *optional*, defaults to `True`): - Whether to normalize the topk probabilities. - output_router_logits (`bool`, *optional*, defaults to `False`): - Whether or not the router logits should be returned by the model. Enabling this will also - allow the model to output the auxiliary loss, including load balancing loss and router z-loss. - router_aux_loss_coef (`float`, *optional*, defaults to 0.001): - The aux loss factor for the total loss. - mlp_only_layers (`list[int]`, *optional*, defaults to `[]`): - Indicate which layers use Qwen3NextMLP rather than Qwen3NextSparseMoeBlock - The list contains layer index, from 0 to num_layers-1 if we have num_layers layers - If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity. - layer_types (`list[str]`, *optional*): - Types of each layer (attention or linear). - - ```python - >>> from transformers import Qwen3NextModel, Qwen3NextConfig - - >>> # Initializing a Qwen3Next style configuration - >>> configuration = Qwen3NextConfig() - - >>> # Initializing a model from the Qwen3-Next-80B-A3B style configuration - >>> model = Qwen3NextModel(configuration) - - >>> # Accessing the model configuration - >>> configuration = model.config - ``` - """ - - model_type = "qwen3_next" - keys_to_ignore_at_inference = ["past_key_values"] - - base_model_tp_plan = { - "layers.*.self_attn.q_proj": "colwise", - "layers.*.self_attn.k_proj": "colwise", - "layers.*.self_attn.v_proj": "colwise", - "layers.*.self_attn.o_proj": "rowwise", - "layers.*.mlp.experts.*.gate_proj": "colwise", - "layers.*.mlp.experts.*.up_proj": "colwise", - "layers.*.mlp.experts.*.down_proj": "rowwise", - "layers.*.mlp.shared_experts.gate_proj": "colwise", - "layers.*.mlp.shared_experts.up_proj": "colwise", - "layers.*.mlp.shared_experts.down_proj": "rowwise", - "layers.*.mlp.gate_proj": "colwise", - "layers.*.mlp.up_proj": "colwise", - "layers.*.mlp.down_proj": "rowwise", - } - base_model_pp_plan = { - "embed_tokens": (["input_ids"], ["inputs_embeds"]), - "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), - "norm": (["hidden_states"], ["hidden_states"]), - } - - def __init__( - self, - vocab_size=151936, - hidden_size=2048, - intermediate_size=5632, - num_hidden_layers=48, - num_attention_heads=16, - num_key_value_heads=2, - hidden_act="silu", - max_position_embeddings=32768, - initializer_range=0.02, - rms_norm_eps=1e-6, - use_cache=True, - tie_word_embeddings=False, - rope_theta=10000.0, - rope_scaling=None, - partial_rotary_factor=0.25, - attention_bias=False, - attention_dropout=0.0, - head_dim=256, - linear_conv_kernel_dim=4, - linear_key_head_dim=128, - linear_value_head_dim=128, - linear_num_key_heads=16, - linear_num_value_heads=32, - decoder_sparse_step=1, - moe_intermediate_size=512, - shared_expert_intermediate_size=512, - num_experts_per_tok=10, - num_experts=512, - norm_topk_prob=True, - output_router_logits=False, - router_aux_loss_coef=0.001, - mlp_only_layers=[], - layer_types=None, - **kwargs, - ): - super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) - self.vocab_size = vocab_size - self.max_position_embeddings = max_position_embeddings - self.hidden_size = hidden_size - self.intermediate_size = intermediate_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.num_key_value_heads = num_key_value_heads - self.hidden_act = hidden_act - self.initializer_range = initializer_range - self.rms_norm_eps = rms_norm_eps - self.use_cache = use_cache - self.rope_theta = rope_theta - self.rope_scaling = rope_scaling - self.partial_rotary_factor = partial_rotary_factor - self.attention_bias = attention_bias - self.attention_dropout = attention_dropout - self.head_dim = head_dim - rope_config_validation(self) - - self.layer_types = layer_types - if self.layer_types is None: - interval_pattern = kwargs.get("full_attention_interval", 4) - self.layer_types = [ - "linear_attention" if bool( - (i + 1) % interval_pattern) else "full_attention" - for i in range(self.num_hidden_layers) - ] - # layer_type_validation(self.layer_types, self.num_hidden_layers) - - # linear attention part - self.linear_conv_kernel_dim = linear_conv_kernel_dim - self.linear_key_head_dim = linear_key_head_dim - self.linear_value_head_dim = linear_value_head_dim - self.linear_num_key_heads = linear_num_key_heads - self.linear_num_value_heads = linear_num_value_heads - - # MoE arguments - self.decoder_sparse_step = decoder_sparse_step - self.moe_intermediate_size = moe_intermediate_size - self.shared_expert_intermediate_size = shared_expert_intermediate_size - self.num_experts_per_tok = num_experts_per_tok - self.num_experts = num_experts - self.norm_topk_prob = norm_topk_prob - self.output_router_logits = output_router_logits - self.router_aux_loss_coef = router_aux_loss_coef - self.mlp_only_layers = mlp_only_layers - - class Qwen3NextGate(nn.Module): def __init__(