[TRTLLM-8577][feat] Clean the Qwen3-next code by removing Qwen3NextCo… (NVIDIA#10228)

Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com>
Signed-off-by: Daniil Kulko <kulkodaniil@gmail.com>

Author: nv-guomingz

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__(