Delete grid_search_0318

grid_search_0318/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50/config.json

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-{
-  "architectures": [
-    "Qwen3NextForCausalLM"
-  ],
-  "attention_bias": true,
-  "attention_dropout": 0.0,
-  "attn_output_gate": false,
-  "auto_map": {
-    "AutoConfig": "configuration_qwen3_next.Qwen3NextConfig",
-    "AutoModel": "modeling_qwen3_next.Qwen3NextForCausalLM",
-    "AutoModelForCausalLM": "modeling_qwen3_next.Qwen3NextForCausalLM"
-  },
-  "bos_token_id": 1,
-  "decoder_sparse_step": 1,
-  "dtype": "float32",
-  "enable_qk_norm": false,
-  "eos_token_id": 2,
-  "full_attention_interval": 0,
-  "head_dim": 64,
-  "hidden_act": "silu",
-  "hidden_size": 1920,
-  "initializer_range": 0.02,
-  "intermediate_size": 4800,
-  "layer_types": ["linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "full_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "full_attention", "full_attention", "linear_attention", "full_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "full_attention", "linear_attention", "full_attention", "full_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention", "linear_attention"],
-  "linear_conv_kernel_dim": 4,
-  "linear_key_head_dim": 64,
-  "linear_num_key_heads": 8,
-  "linear_num_value_heads": 32,
-  "linear_value_head_dim": 64,
-  "max_position_embeddings": 32768,
-  "mlp_only_layers": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55],
-  "num_experts_per_tok": 2,
-  "num_experts": 0,
-  "model_type": "qwen3_next",
-  "moe_intermediate_size": 0,
-  "norm_topk_prob": true,
-  "num_attention_heads": 30,
-  "num_hidden_layers": 56,
-  "num_key_value_heads": 6,
-  "output_router_logits": false,
-  "partial_rotary_factor": 1.0,
-  "rms_norm_eps": 1e-06,
-  "rope_scaling": null,
-  "rope_theta": 490000,
-  "router_aux_loss_coef": 0.001,
-  "router_bias": false,
-  "moe_router_score_function": "softmax",
-  "shared_expert_intermediate_size": 0,
-  "use_shared_expert_gate": true,
-  "tie_word_embeddings": false,
-  "transformers_version": "4.57.1",
-  "use_cache": true,
-  "use_sliding_window": false,
-  "ffn_token_shift": null,
-  "ffn_intermediate_token_shift": null,
-  "attn_token_shift": null,
-  "attn_q_token_shift": null,
-  "attn_k_token_shift": null,
-  "attn_v_token_shift": null,
-  "token_shift_conv_size": 4,
-  "token_shift_conv_init": "default",
-  "attn_position_embedding_type": "rope",
-  "rnn_position_embedding_type": "nope",
-  "attn_logits_scaling": null,
-  "vocab_size": 99000
-}

grid_search_0318/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50/configuration_qwen3_next.py

@@ -1,329 +0,0 @@
-# coding=utf-8
-# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Qwen3-Next model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig, layer_type_validation
-from transformers.modeling_rope_utils import rope_config_validation
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-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).
-        enable_qk_norm (`bool`, *optional*, defaults to `False`):
-            Whether to apply L2 normalization to the query and key embeddings.
-        router_bias (`bool`, *optional*, defaults to `False`):
-            Whether to use a bias in the router logits.
-        moe_router_score_function (`str`, *optional*, defaults to `"softmax"`):
-            The score function used in the MoE router.
-        ffn_token_shift (`str`, *optional*): Token shift before FFN/MoE. `None`, `"cat"`, or `"conv"`.
-        ffn_intermediate_token_shift (`str`, *optional*): Token shift in MLP before down_proj. `None`, `"cat"`, or `"conv"`.
-        attn_token_shift (`str`, *optional*): Token shift before attention. `None`, `"cat"`, or `"conv"`.
-        attn_q_token_shift (`str`, *optional*): Token shift on query after projection. `None`, `"cat"`, or `"conv"`.
-        attn_k_token_shift (`str`, *optional*): Token shift on key after projection. `None`, `"cat"`, or `"conv"`.
-        attn_v_token_shift (`str`, *optional*): Token shift on value after projection. `None`, `"cat"`, or `"conv"`.
-        token_shift_conv_size (`int`, *optional*, defaults to 4): Kernel size for token-shift Conv1d.
-        token_shift_conv_init (`str`, *optional*, defaults to `"default"`): Init for token-shift Conv1d; `"identity"` for causal identity.
-    ```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,
-        enable_qk_norm=False, # @o2iginal
-        router_bias=False, # @o2iginal
-        attn_output_gate=False, # @o2iginal
-        moe_router_score_function="softmax", # @xcx
-        # Cannon layer / token shifting (align with Megatron) @o2iginal
-        ffn_token_shift=None,
-        ffn_intermediate_token_shift=None,
-        attn_token_shift=None,
-        attn_q_token_shift=None,
-        attn_k_token_shift=None,
-        attn_v_token_shift=None,
-        token_shift_conv_size=4,
-        token_shift_conv_init="default",
-        # Separate RoPE for attention vs linear/RNN (GDN): "rope" or "nope"
-        attn_position_embedding_type="rope",
-        rnn_position_embedding_type="nope",
-        # Optional logits scaling for length extrapolation (attention only): None, float, or "log" / "log <a>"
-        attn_logits_scaling=None,
-        **kwargs,
-    ):
-        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
-        assert attn_position_embedding_type in ("rope", "nope"), (
-            f"attn_position_embedding_type must be 'rope' or 'nope', got {attn_position_embedding_type}"
-        )
-        assert rnn_position_embedding_type in ("rope", "nope"), (
-            f"rnn_position_embedding_type must be 'rope' or 'nope', got {rnn_position_embedding_type}"
-        )
-        self.attn_position_embedding_type = attn_position_embedding_type
-        self.rnn_position_embedding_type = rnn_position_embedding_type
-        self.attn_logits_scaling = attn_logits_scaling
-        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)
-
-        # 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
-        self.enable_qk_norm = enable_qk_norm
-        self.router_bias = router_bias
-        self.attn_output_gate = attn_output_gate
-        self.moe_router_score_function = moe_router_score_function
-        self.enable_qk_norm = enable_qk_norm
-        self.router_bias = router_bias
-        self.attn_output_gate = attn_output_gate
-        self.moe_router_score_function = moe_router_score_function
-
-        # Token shifting (cannon layer): None | "cat" | "conv"
-        self.ffn_token_shift = ffn_token_shift
-        self.ffn_intermediate_token_shift = ffn_intermediate_token_shift
-        self.attn_token_shift = attn_token_shift
-        self.attn_q_token_shift = attn_q_token_shift
-        self.attn_k_token_shift = attn_k_token_shift
-        self.attn_v_token_shift = attn_v_token_shift
-        self.token_shift_conv_size = token_shift_conv_size
-        self.token_shift_conv_init = token_shift_conv_init
-
-__all__ = ["Qwen3NextConfig"]

grid_search_0318/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50/hf2mcore.log

@@ -1,1277 +0,0 @@
-torchrun --nproc_per_node 1 --nnodes 1 --node_rank 0 --master_addr localhost --master_port 42639 /mnt/ssd/lvzhihao/PostTrain/YuLan-Pretrain/scripts/distributed_checkpoints_convertor/impl/convert.py --tokenizer-type HuggingFaceTokenizer --tokenizer-model /mnt/ssd/cache_tmp/tmp/tmp.9kVbw0fz8t --hf-dir /mnt/ssd/cache_tmp/tmp/tmp.9kVbw0fz8t --mcore2hf --use-gpu --bf16 --normalization RMSNorm --swiglu --disable-bias-linear --seq-length 1 --max-position-embeddings 490000 --attention-backend auto --position-embedding-type rope --kv-channels 64 --group-query-attention --add-qkv-bias --num-layers 56 --hidden-size 1920 --ffn-hidden-size 4800 --num-attention-heads 30 --untie-embeddings-and-output-weights --rotary-base 490000 --rotary-percent 1.00 --num-query-groups 6 --normalization RMSNorm --norm-epsilon 1e-6 --linear-attention-type gated_delta_net --linear-attention-freq [1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0,1,1,1,1,1,1] --linear-conv-kernel-dim 4 --linear-key-head-dim 64 --linear-value-head-dim 64 --linear-num-key-heads 8 --linear-num-value-heads 32 --micro-batch-size 1 --global-batch-size 1024 --train-iters 500000 --weight-decay 0.1 --adam-beta1 0.9 --adam-beta2 0.95 --init-method-std 0.006 --clip-grad 1.0 --lr 2.0e-5 --lr-decay-style cosine --min-lr 6.0e-6 --lr-warmup-fraction .001 --lr-decay-iters 430000 --bf16 --tensor-model-parallel-size 1 --pipeline-model-parallel-size 1 --expert-tensor-parallel-size 1 --expert-model-parallel-size 1 --log-interval 100 --save-interval 10000 --eval-interval 1000 --eval-iters 10 --model-type GPT --load-dir /mnt/hdd/lvzhihao/mcore_models/grid_search_0318/checkpoint/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50 --save-dir /mnt/hdd/lvzhihao/mcore_models/grid_search_0318/checkpoint/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50/iter_953-hf --dist-ckpt-optim-fully-reshardable --skip-train --use-cpu-initialization --padded-vocab-size 99000 --no-load-optim --no-load-rng --logging-level 1 --attention-backend auto --synchronizer mcore_gdn_moe --pretrain-script mcore_gdn_moe.model_provider --debug --max-shard-size 20GB
-W0320 09:19:34.443000 3250 .venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
-W0320 09:19:34.443000 3250 .venv/lib/python3.10/site-packages/torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
-fused_indices_to_multihot has reached end of life. Please migrate to a non-experimental function.
-Current Python version 3.10 is below the recommended 3.11 version. It is recommended to upgrade to Python 3.11 or higher for the best experience.
-Warning: Pai-Megatron-Patch arguments not available, some arguments may not be recognized
-using world size: 1, data-parallel size: 1, context-parallel size: 1, hierarchical context-parallel sizes: None, tensor-model-parallel size: 1, pipeline-model-parallel size: 1
-Number of virtual stages per pipeline stage: None
-accumulate and all-reduce gradients in fp32 for bfloat16 data type.
-using torch.bfloat16 for parameters ...
------------------------- arguments ------------------------
-  account_for_embedding_in_pipeline_split ......... False
-  account_for_loss_in_pipeline_split .............. False
-  accumulate_allreduce_grads_in_fp32 .............. True
-  activation_func_clamp_value ..................... None
-  adam_beta1 ...................................... 0.9
-  adam_beta2 ...................................... 0.95
-  adam_eps ........................................ 1e-08
-  adamw_lr_mup_scaler ............................. False
-  add_bias_linear ................................. False
-  add_position_embedding .......................... True
-  add_qkv_bias .................................... True
-  adlr_autoresume ................................. False
-  adlr_autoresume_interval ........................ 1000
-  align_grad_reduce ............................... True
-  align_param_gather .............................. False
-  allow_ambiguous_pad_tokens ...................... False
-  app_tag_run_name ................................ None
-  app_tag_run_version ............................. 0.0.0
-  apply_layernorm_1p .............................. False
-  apply_query_key_layer_scaling ................... False
-  apply_residual_connection_post_layernorm ........ False
-  apply_rope_fusion ............................... True
-  async_save ...................................... None
-  async_tensor_model_parallel_allreduce ........... True
-  attention_backend ............................... AttnBackend.auto
-  attention_dropout ............................... 0.1
-  attention_output_gate ........................... False
-  attention_softmax_in_fp32 ....................... False
-  attn_k_token_shift .............................. None
-  attn_output_gate ................................ None
-  attn_output_gate_rand_init ...................... False
-  attn_q_token_shift .............................. None
-  attn_token_shift ................................ None
-  attn_v_token_shift .............................. None
-  auto_detect_ckpt_format ......................... False
-  auto_generate_cu_seqlens ........................ False
-  auto_model ...................................... AutoModelForCausalLM
-  barrier_with_L1_time ............................ True
-  benchmark_eval .................................. False
-  benchmark_global_batch .......................... None
-  benchmark_interval .............................. None
-  benchmark_micro_batch ........................... None
-  benchmark_sequence_length ....................... None
-  benchmark_tasks ................................. None
-  bert_binary_head ................................ True
-  bert_embedder_type .............................. megatron
-  bert_load ....................................... None
-  bf16 ............................................ True
-  bias_dropout_fusion ............................. True
-  bias_gelu_fusion ................................ False
-  bias_swiglu_fusion .............................. True
-  biencoder_projection_dim ........................ 0
-  biencoder_shared_query_context_model ............ False
-  block_data_path ................................. None
-  cache_mla_latents ............................... False
-  calc_ft_timeouts ................................ False
-  calculate_per_token_loss ........................ False
-  check_for_large_grads ........................... False
-  check_for_nan_in_loss_and_grad .................. True
-  check_for_spiky_loss ............................ False
-  check_weight_hash_across_dp_replicas_interval ... None
-  ckpt_assume_constant_structure .................. False
-  ckpt_convert_format ............................. None
-  ckpt_convert_save ............................... None
-  ckpt_convert_update_legacy_dist_opt_format ...... False
-  ckpt_format ..................................... torch_dist
-  ckpt_fully_parallel_load ........................ False
-  ckpt_fully_parallel_save ........................ True
-  ckpt_fully_parallel_save_deprecated ............. False
-  ckpt_step ....................................... None
-  classes_fraction ................................ 1.0
-  clip_grad ....................................... 1.0
-  clone_scatter_output_in_embedding ............... True
-  config_logger_dir ............................... 
-  consumed_train_samples .......................... 0
-  consumed_valid_samples .......................... 0
-  context_parallel_size ........................... 1
-  cp_comm_type .................................... ['p2p']
-  create_attention_mask_in_dataloader ............. True
-  cross_entropy_fusion_impl ....................... native
-  cross_entropy_loss_fusion ....................... False
-  cuda_graph_impl ................................. none
-  cuda_graph_scope ................................ []
-  cuda_graph_warmup_steps ......................... 3
-  data_args_path .................................. None
-  data_cache_path ................................. None
-  data_parallel_random_init ....................... False
-  data_parallel_sharding_strategy ................. no_shard
-  data_parallel_size .............................. 1
-  data_path ....................................... None
-  data_per_class_fraction ......................... 1.0
-  data_sharding ................................... True
-  dataloader_type ................................. single
-  ddp_average_in_collective ....................... False
-  ddp_bucket_size ................................. None
-  ddp_num_buckets ................................. None
-  ddp_pad_buckets_for_high_nccl_busbw ............. False
-  debug ........................................... True
-  decode_only_cuda_graphs ......................... False
-  decoder_first_pipeline_num_layers ............... None
-  decoder_last_pipeline_num_layers ................ None
-  decoder_num_layers .............................. None
-  decoder_seq_length .............................. None
-  decoupled_lr .................................... None
-  decoupled_min_lr ................................ None
-  decrease_batch_size_if_needed ................... False
-  defer_embedding_wgrad_compute ................... False
-  delay_wgrad_compute ............................. False
-  deprecated_use_mcore_models ..................... False
-  deterministic_mode .............................. False
-  dino_bottleneck_size ............................ 256
-  dino_freeze_last_layer .......................... 1
-  dino_head_hidden_size ........................... 2048
-  dino_local_crops_number ......................... 10
-  dino_local_img_size ............................. 96
-  dino_norm_last_layer ............................ False
-  dino_teacher_temp ............................... 0.07
-  dino_warmup_teacher_temp ........................ 0.04
-  dino_warmup_teacher_temp_epochs ................. 30
-  disable_attn_output_gate ........................ False
-  disable_bf16_reduced_precision_matmul ........... False
-  disable_chunked_prefill ......................... False
-  disable_explicit_attention_mask ................. False
-  disable_mamba_mem_eff_path ...................... False
-  disable_straggler_on_startup .................... False
-  disable_symmetric_registration .................. False
-  dist_ckpt_format_deprecated ..................... None
-  dist_ckpt_optim_fully_reshardable ............... True
-  dist_ckpt_save_pre_mcore_014 .................... False
-  dist_ckpt_strictness ............................ assume_ok_unexpected
-  distrib_optim_fully_reshardable_mem_efficient ... False
-  distribute_saved_activations .................... False
-  distributed_backend ............................. nccl
-  distributed_timeout_minutes ..................... 10
-  distributed_timeout_seconds_after_init .......... None
-  document_packing_algorithm ...................... random
-  dryrun .......................................... False
-  dump_param_to_param_group_map ................... None
-  emb_deviation_loss_coeff ........................ 0
-  emb_deviation_type .............................. None
-  embedding_init_method_std ....................... None
-  embedding_path .................................. None
-  empty_unused_memory_level ....................... 0
-  enable_cuda_graph ............................... False
-  enable_debug_logging ............................ False
-  enable_experimental ............................. False
-  enable_ft_package ............................... False
-  enable_full_sharding_in_hsdp .................... False
-  enable_gloo_process_groups ...................... True
-  enable_msc ...................................... True
-  enable_one_logger ............................... True
-  encoder_num_layers .............................. 56
-  encoder_seq_length .............................. 1
-  end_weight_decay ................................ 0.1
-  eod_mask_loss ................................... False
-  error_injection_rate ............................ 0
-  error_injection_type ............................ transient_error
-  eval_interval ................................... 1000
-  eval_iters ...................................... 10
-  evidence_data_path .............................. None
-  exit_duration_in_mins ........................... None
-  exit_interval ................................... None
-  exit_on_missing_checkpoint ...................... False
-  exit_signal_handler ............................. False
-  exp_avg_dtype ................................... torch.float32
-  exp_avg_sq_dtype ................................ torch.float32
-  expert_model_parallel_size ...................... 1
-  expert_tensor_parallel_size ..................... 1
-  external_cuda_graph ............................. False
-  ffn_hidden_size ................................. 4800
-  ffn_intermediate_token_shift .................... None
-  ffn_token_shift ................................. None
-  fine_grained_activation_offloading .............. False
-  finetune ........................................ False
-  first_last_layers_bf16 .......................... False
-  flash_decode .................................... False
-  fp16 ............................................ False
-  fp16_lm_cross_entropy ........................... False
-  fp32_residual_connection ........................ False
-  fp4 ............................................. None
-  fp4_param ....................................... False
-  fp4_recipe ...................................... nvfp4
-  fp8 ............................................. None
-  fp8_amax_compute_algo ........................... most_recent
-  fp8_amax_history_len ............................ 1
-  fp8_interval .................................... 1
-  fp8_margin ...................................... 0
-  fp8_param_gather ................................ False
-  fp8_recipe ...................................... delayed
-  fp8_wgrad ....................................... True
-  freeze_layernorm_weight ......................... False
-  freeze_non_mamba ................................ False
-  fsdp_double_buffer .............................. False
-  full_validation ................................. False
-  gdn_cp_impl ..................................... cp2hp
-  geglu ........................................... False
-  global_batch_size ............................... 1024
-  glu_linear_offset ............................... 0.0
-  grad_reduce_in_bf16 ............................. False
-  gradient_accumulation_fusion .................... True
-  gradient_reduce_div_fusion ...................... True
-  group_query_attention ........................... True
-  grpo_clamp_eps_lower ............................ 0.01
-  grpo_clamp_eps_upper ............................ 0.01
-  grpo_default_temperature ........................ 1.0
-  grpo_default_top_p .............................. 0
-  grpo_entropy_term_weight ........................ 0.0
-  grpo_filter_groups_with_same_reward ............. False
-  grpo_group_size ................................. 2
-  grpo_iterations ................................. 2
-  grpo_kl_beta .................................... 0.001
-  grpo_prompts_per_step ........................... 32
-  head_lr_mult .................................... 1.0
-  heterogeneous_layers_config_encoded_json ........ None
-  heterogeneous_layers_config_path ................ None
-  hf_dir .......................................... /mnt/ssd/cache_tmp/tmp/tmp.9kVbw0fz8t
-  hidden_dropout .................................. 0.1
-  hidden_size ..................................... 1920
-  hierarchical_context_parallel_sizes ............. None
-  high_priority_stream_groups ..................... []
-  hybrid_attention_ratio .......................... 0.0
-  hybrid_context_parallel ......................... False
-  hybrid_mlp_ratio ................................ 0.0
-  hybrid_override_pattern ......................... None
-  hysteresis ...................................... 2
-  ict_head_size ................................... None
-  ict_load ........................................ None
-  img_h ........................................... 224
-  img_w ........................................... 224
-  increase_log_level_interval ..................... 1000
-  increase_log_level_iters ........................ 5
-  indexer_batch_size .............................. 128
-  indexer_log_interval ............................ 1000
-  inference_batch_times_seqlen_threshold .......... -1
-  inference_dynamic_batching ...................... False
-  inference_dynamic_batching_block_size ........... 256
-  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
-  inference_dynamic_batching_buffer_overflow_factor  None
-  inference_dynamic_batching_buffer_size_gb ....... 40.0
-  inference_dynamic_batching_max_requests_override  None
-  inference_dynamic_batching_max_tokens_override .. None
-  inference_dynamic_batching_num_cuda_graphs ...... 16
-  inference_dynamic_batching_track_paused_request_events  False
-  inference_dynamic_batching_unified_memory_level . 0
-  inference_max_batch_size ........................ 8
-  inference_max_seq_length ........................ 2560
-  inference_rng_tracker ........................... False
-  init_method_std ................................. 0.006
-  init_method_xavier_uniform ...................... False
-  init_model_with_meta_device ..................... False
-  initial_loss_scale .............................. 4294967296
-  inprocess_active_world_size ..................... 1
-  inprocess_barrier_timeout ....................... 120
-  inprocess_completion_timeout .................... 120
-  inprocess_empty_cuda_cache ...................... False
-  inprocess_granularity ........................... node
-  inprocess_hard_timeout .......................... 90
-  inprocess_heartbeat_interval .................... 30
-  inprocess_heartbeat_timeout ..................... 60
-  inprocess_last_call_wait ........................ 1
-  inprocess_max_iterations ........................ None
-  inprocess_monitor_process_interval .............. 1.0
-  inprocess_monitor_thread_interval ............... 1.0
-  inprocess_progress_watchdog_interval ............ 1.0
-  inprocess_restart ............................... False
-  inprocess_soft_timeout .......................... 60
-  inprocess_termination_grace_time ................ 1
-  is_hybrid_model ................................. False
-  iter_per_epoch .................................. 1250
-  iterations_to_skip .............................. []
-  keep_fp8_transpose_cache ........................ False
-  kitchen_config_file ............................. None
-  kitchen_recipe_number ........................... None
-  kv_channels ..................................... 64
-  kv_lora_rank .................................... 32
-  langrl_env_config ............................... None
-  langrl_external_server .......................... False
-  langrl_inference_server_conversation_template ... None
-  langrl_inference_server_type .................... inplace_megatron
-  lazy_mpu_init ................................... None
-  legacy_tokenizer ................................ False
-  linear_attention_freq ........................... [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1]
-  linear_attention_type ........................... gated_delta_net
-  linear_conv_kernel_dim .......................... 4
-  linear_key_head_dim ............................. 64
-  linear_num_key_heads ............................ 8
-  linear_num_value_heads .......................... 32
-  linear_value_head_dim ........................... 64
-  load ............................................ None
-  load_complemental_dataset ....................... None
-  load_dir ........................................ /mnt/hdd/lvzhihao/mcore_models/grid_search_0318/checkpoint/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50
-  load_main_params_from_ckpt ...................... None
-  local_rank ...................................... 0
-  log_energy ...................................... False
-  log_hidden_states ............................... []
-  log_interval .................................... 100
-  log_loss_scale_to_tensorboard ................... True
-  log_memory_to_tensorboard ....................... False
-  log_num_zeros_in_grad ........................... False
-  log_params ...................................... []
-  log_params_norm ................................. False
-  log_per_module_grad_rms ......................... False
-  log_per_module_update_rms ....................... False
-  log_progress .................................... False
-  log_straggler ................................... False
-  log_throughput .................................. False
-  log_timers_to_tensorboard ....................... False
-  log_validation_ppl_to_tensorboard ............... False
-  log_world_size_to_tensorboard ................... False
-  logging_level ................................... 1
-  loss_scale ...................................... None
-  loss_scale_window ............................... 1000
-  lr .............................................. 2e-05
-  lr_decay_iters .................................. 430000
-  lr_decay_samples ................................ None
-  lr_decay_style .................................. cosine
-  lr_warmup_fraction .............................. 0.001
-  lr_warmup_init .................................. 0.0
-  lr_warmup_iters ................................. 0
-  lr_warmup_samples ............................... 0
-  lr_wsd_decay_iters .............................. None
-  lr_wsd_decay_samples ............................ None
-  lr_wsd_decay_style .............................. exponential
-  main_grads_dtype ................................ torch.float32
-  main_params_dtype ............................... torch.float32
-  make_vocab_size_divisible_by .................... 128
-  mamba_disable_cp ................................ False
-  mamba_expand .................................... 2
-  mamba_head_dim .................................. 64
-  mamba_num_groups ................................ 8
-  mamba_num_heads ................................. None
-  mamba_state_dim ................................. 128
-  manual_gc ....................................... False
-  manual_gc_eval .................................. True
-  manual_gc_interval .............................. 0
-  mask_factor ..................................... 1.0
-  mask_prob ....................................... 0.15
-  mask_type ....................................... random
-  masked_softmax_fusion ........................... True
-  max_position_embeddings ......................... 490000
-  max_seqlen_per_cp_rank .......................... None
-  max_shard_size .................................. 20GB
-  max_tokens_to_oom ............................... 12000
-  mcore2hf ........................................ True
-  memory_snapshot_path ............................ None
-  merge_file ...................................... None
-  micro_batch_size ................................ 1
-  microbatch_group_size_per_vp_stage .............. None
-  mid_level_dataset_surplus ....................... 0.005
-  min_loss_scale .................................. 1.0
-  min_lr .......................................... 6e-06
-  min_offloaded_tensor_size ....................... 1048576
-  mlp_chunks_for_prefill .......................... 1
-  mmap_bin_files .................................. True
-  mock_data ....................................... False
-  model_type ...................................... GPT
-  moe_apply_probs_on_input ........................ False
-  moe_aux_loss_coeff .............................. 0.0
-  moe_deepep_num_sms .............................. 20
-  moe_enable_deepep ............................... False
-  moe_expert_capacity_factor ...................... None
-  moe_extended_tp ................................. False
-  moe_ffn_hidden_size ............................. None
-  moe_flex_dispatcher_backend ..................... deepep
-  moe_grouped_gemm ................................ False
-  moe_hybridep_num_sms ............................ 16
-  moe_input_jitter_eps ............................ None
-  moe_layer_freq .................................. 1
-  moe_layer_recompute ............................. False
-  moe_pad_expert_input_to_capacity ................ False
-  moe_pad_experts_for_cuda_graph_inference ........ False
-  moe_per_layer_logging ........................... False
-  moe_permute_fusion .............................. False
-  moe_router_bias_update_method ................... sign
-  moe_router_bias_update_rate ..................... 0.001
-  moe_router_dtype ................................ None
-  moe_router_enable_expert_bias ................... False
-  moe_router_force_load_balancing ................. False
-  moe_router_fusion ............................... False
-  moe_router_group_topk ........................... None
-  moe_router_load_balancing_type .................. aux_loss
-  moe_router_num_groups ........................... None
-  moe_router_padding_for_fp8 ...................... False
-  moe_router_padding_for_quantization ............. False
-  moe_router_pre_softmax .......................... False
-  moe_router_score_function ....................... softmax
-  moe_router_topk ................................. 2
-  moe_router_topk_scaling_factor .................. None
-  moe_shared_expert_gate .......................... False
-  moe_shared_expert_intermediate_size ............. None
-  moe_shared_expert_overlap ....................... False
-  moe_token_dispatcher_type ....................... allgather
-  moe_token_drop_policy ........................... probs
-  moe_upcycling_granularity ....................... 1
-  moe_use_legacy_grouped_gemm ..................... False
-  moe_use_upcycling ............................... False
-  moe_z_loss_coeff ................................ None
-  mrope_section ................................... None
-  mscale .......................................... 1.0
-  mscale_all_dim .................................. 0.0
-  mtp_linear_attention_type ....................... None
-  mtp_loss_scaling_factor ......................... 0.1
-  mtp_num_layers .................................. None
-  multi_latent_attention .......................... False
-  multiple_validation_sets ........................ False
-  muon_ball_momentum .............................. 0.9
-  muon_ball_msign_steps ........................... 5
-  muon_ball_power_iteration_steps ................. 10
-  muon_ball_qkv_split_mode ........................ component
-  muon_ball_radius_mode ........................... spectral_mup
-  muon_ball_retract_alpha ......................... 0.05
-  muon_ball_retract_mode .......................... hard
-  muon_ball_scale_mode ............................ spectral_mup
-  muon_ball_split_fc1 ............................. True
-  muon_ball_split_moe_experts ..................... True
-  muon_ball_split_qkv ............................. True
-  muon_ball_use_nesterov .......................... True
-  muon_extra_scale_factor ......................... 1.0
-  muon_fp32_matmul_prec ........................... medium
-  muon_momentum ................................... 0.9
-  muon_num_ns_steps ............................... 5
-  muon_qkv_split_mode ............................. component
-  muon_scale_mode ................................. spectral_mup
-  muon_scale_vectorized_mode ...................... full
-  muon_split_fc1 .................................. True
-  muon_split_moe_experts .......................... True
-  muon_split_qkv .................................. True
-  muon_tp_mode .................................... blockwise
-  muon_use_nesterov ............................... False
-  muon_vectorize .................................. []
-  muon_vectorize_attn_dim ......................... hidden_size
-  nccl_all_reduce_for_prefill ..................... False
-  nccl_communicator_config_path ................... None
-  nccl_ub ......................................... False
-  no_load_optim ................................... True
-  no_load_rng ..................................... True
-  no_load_scheduler ............................... None
-  no_persist_layer_norm ........................... False
-  no_rope_freq .................................... None
-  no_save_optim ................................... None
-  no_save_rng ..................................... None
-  no_save_step_one ................................ None
-  no_weight_decay_cond_type ....................... None
-  non_persistent_ckpt_type ........................ None
-  non_persistent_global_ckpt_dir .................. None
-  non_persistent_local_ckpt_algo .................. fully_parallel
-  non_persistent_local_ckpt_dir ................... None
-  non_persistent_save_interval .................... None
-  norm_epsilon .................................... 1e-06
-  normalization ................................... RMSNorm
-  num_attention_heads ............................. 30
-  num_channels .................................... 3
-  num_classes ..................................... 1000
-  num_dataset_builder_threads ..................... 1
-  num_distributed_optimizer_instances ............. 1
-  num_experts ..................................... None
-  num_hf_saver .................................... None
-  num_layers ...................................... 56
-  num_layers_at_end_in_bf16 ....................... 1
-  num_layers_at_start_in_bf16 ..................... 1
-  num_layers_per_virtual_pipeline_stage ........... None
-  num_query_groups ................................ 6
-  num_virtual_stages_per_pipeline_rank ............ None
-  num_workers ..................................... 2
-  object_storage_cache_path ....................... None
-  offload_modules ................................. []
-  one_logger_async ................................ False
-  one_logger_project .............................. megatron-lm
-  one_logger_run_name ............................. None
-  onnx_safe ....................................... None
-  openai_gelu ..................................... False
-  optimizer ....................................... adam
-  optimizer_cpu_offload ........................... False
-  optimizer_offload_fraction ...................... 1.0
-  output_bert_embeddings .......................... False
-  overlap_cpu_optimizer_d2h_h2d ................... False
-  overlap_grad_reduce ............................. False
-  overlap_moe_expert_parallel_comm ................ False
-  overlap_p2p_comm ................................ False
-  overlap_p2p_comm_warmup_flush ................... False
-  overlap_param_gather ............................ False
-  overlap_param_gather_with_optimizer_step ........ False
-  override_hf_eod_token_id ........................ None
-  override_opt_param_scheduler .................... False
-  padded_vocab_size ............................... 99000
-  params_dtype .................................... torch.bfloat16
-  patch_dim ....................................... 16
-  per_split_data_args_path ........................ None
-  perform_initialization .......................... True
-  perform_rl_step ................................. False
-  pin_cpu_grads ................................... True
-  pin_cpu_params .................................. True
-  pipeline_model_parallel_comm_backend ............ None
-  pipeline_model_parallel_layout .................. None
-  pipeline_model_parallel_size .................... 1
-  position_embedding_type ......................... rope
-  pretrain_script ................................. mcore_gdn_moe.model_provider
-  pretrained_checkpoint ........................... None
-  profile ......................................... False
-  profile_ranks ................................... [0]
-  profile_step_end ................................ 12
-  profile_step_start .............................. 10
-  q_lora_rank ..................................... None
-  qk_head_dim ..................................... 128
-  qk_l2_norm ...................................... False
-  qk_layernorm .................................... False
-  qk_pos_emb_head_dim ............................. 64
-  query_in_block_prob ............................. 0.1
-  quick_geglu ..................................... False
-  rampup_batch_size ............................... None
-  rank ............................................ 0
-  recompute_granularity ........................... None
-  recompute_method ................................ None
-  recompute_modules ............................... None
-  recompute_num_layers ............................ None
-  record_memory_history ........................... False
-  relative_attention_max_distance ................. 128
-  relative_attention_num_buckets .................. 32
-  reparam_checkpoint .............................. None
-  reparam_fallback_value .......................... None
-  reparam_keys .................................... None
-  replication ..................................... False
-  replication_factor .............................. 2
-  replication_jump ................................ None
-  rerun_mode ...................................... validate_results
-  reset_attention_mask ............................ False
-  reset_iteration_one_to_zero ..................... False
-  reset_position_ids .............................. False
-  result_rejected_tracker_filename ................ None
-  retriever_report_topk_accuracies ................ []
-  retriever_score_scaling ......................... False
-  retriever_seq_length ............................ 256
-  retro_add_retriever ............................. False
-  retro_attention_gate ............................ 1
-  retro_cyclic_train_iters ........................ None
-  retro_encoder_attention_dropout ................. 0.1
-  retro_encoder_hidden_dropout .................... 0.1
-  retro_encoder_layers ............................ 2
-  retro_num_neighbors ............................. 2
-  retro_num_retrieved_chunks ...................... 2
-  retro_project_dir ............................... None
-  retro_verify_neighbor_count ..................... True
-  reuse_grad_buf_for_mxfp8_param_ag ............... False
-  rl_calculate_intra_group_similarity ............. False
-  rl_importance_sampling_truncation_coef .......... None
-  rl_inference_logprobs_is_correction ............. False
-  rl_offload_kv_cache_during_training ............. False
-  rl_offload_optimizer_during_inference ........... False
-  rl_partial_rollouts ............................. False
-  rl_prompts_per_eval ............................. 32
-  rl_remove_kv_cache_during_training .............. False
-  rl_reset_cuda_graphs ............................ False
-  rl_sequence_packing_algo ........................ fifo
-  rl_sequence_packing_bin_size .................... 8192
-  rl_use_sequence_packing ......................... False
-  rope_scaling_factor ............................. 8.0
-  rope_type ....................................... None
-  rotary_base ..................................... 490000
-  rotary_interleaved .............................. False
-  rotary_percent .................................. 1.0
-  rotary_scaling_factor ........................... 1.0
-  rotary_seq_len_interpolation_factor ............. None
-  run_workload_inspector_server ................... False
-  sample_rate ..................................... 1.0
-  save ............................................ None
-  save_after_load ................................. False
-  save_dir ........................................ /mnt/hdd/lvzhihao/mcore_models/grid_search_0318/checkpoint/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50/iter_953-hf
-  save_interval ................................... 10000
-  save_retain_interval ............................ None
-  scatter_gather_tensors_in_pipeline .............. True
-  seed ............................................ 1234
-  seq_length ...................................... 1
-  sequence_parallel ............................... False
-  sft ............................................. False
-  sft_tokenizer_prompt_format ..................... nemotron-h-aligned
-  sgd_momentum .................................... 0.9
-  sharp_enabled_group ............................. None
-  short_seq_prob .................................. 0.1
-  skip_train ...................................... True
-  skipped_train_samples ........................... 0
-  softmax_type .................................... vanilla
-  spec ............................................ None
-  spectral_ball_momentum .......................... 0.9
-  spectral_ball_msign_steps ....................... 8
-  spectral_ball_power_iteration_steps ............. 20
-  spectral_ball_qkv_split_mode .................... component
-  spectral_ball_radius_mode ....................... spectral_mup
-  spectral_ball_retract_alpha ..................... 0.05
-  spectral_ball_retract_mode ...................... hard
-  spectral_ball_scale_mode ........................ spectral_mup
-  spectral_ball_solver ............................ bisection
-  spectral_ball_solver_max_iterations ............. 20
-  spectral_ball_solver_tolerance_f ................ 1e-08
-  spectral_ball_split_fc1 ......................... True
-  spectral_ball_split_moe_experts ................. True
-  spectral_ball_split_qkv ......................... True
-  spectral_ball_use_nesterov ...................... True
-  spectral_mup_init ............................... False
-  split ........................................... None
-  split_expert_init ............................... True
-  split_fc1_init .................................. True
-  split_qkv_init .................................. True
-  split_qkv_init_mode ............................. group
-  sqreglu ......................................... False
-  squared_relu .................................... False
-  start_samples ................................... None
-  start_weight_decay .............................. 0.1
-  straggler_ctrlr_port ............................ 65535
-  straggler_minmax_count .......................... 1
-  strict_fsdp_dtensor_load ........................ True
-  suggested_communication_unit_size ............... None
-  swanlab_exp_name ................................ 
-  swanlab_project ................................. 
-  swanlab_save_dir ................................ 
-  swanlab_workspace ............................... 
-  swiglu .......................................... True
-  swin_backbone_type .............................. tiny
-  symmetric_ar_type ............................... None
-  synchronizer .................................... mcore_gdn_moe
-  target_ckpt_format .............................. torch_dist
-  te_rng_tracker .................................. False
-  tensor_model_parallel_size ...................... 1
-  tensorboard_dir ................................. None
-  tensorboard_log_interval ........................ 1
-  tensorboard_queue_size .......................... 1000
-  test_data_path .................................. None
-  test_mode ....................................... False
-  tiktoken_num_special_tokens ..................... 1000
-  tiktoken_pattern ................................ None
-  tiktoken_special_tokens ......................... None
-  timing_log_level ................................ 0
-  timing_log_option ............................... minmax
-  titles_data_path ................................ None
-  token_shift_conv_init ........................... default
-  token_shift_conv_size ........................... 4
-  tokenizer_metadata .............................. None
-  tokenizer_model ................................. /mnt/ssd/cache_tmp/tmp/tmp.9kVbw0fz8t
-  tokenizer_type .................................. HuggingFaceTokenizer
-  torch_fsdp2_reshard_after_forward ............... True
-  tp_comm_bootstrap_backend ....................... nccl
-  tp_comm_bulk_dgrad .............................. True
-  tp_comm_bulk_wgrad .............................. True
-  tp_comm_overlap ................................. False
-  tp_comm_overlap_ag .............................. True
-  tp_comm_overlap_cfg ............................. None
-  tp_comm_overlap_rs .............................. True
-  tp_comm_overlap_rs_dgrad ........................ False
-  tp_comm_split_ag ................................ True
-  tp_comm_split_rs ................................ True
-  train_data_path ................................. None
-  train_iters ..................................... 500000
-  train_samples ................................... None
-  train_sync_interval ............................. None
-  transformer_impl ................................ transformer_engine
-  transformer_pipeline_model_parallel_size ........ 1
-  trust_remote_code ............................... False
-  untie_embeddings_and_output_weights ............. True
-  use_checkpoint_args ............................. False
-  use_checkpoint_opt_param_scheduler .............. False
-  use_cpu_initialization .......................... True
-  use_dist_ckpt ................................... True
-  use_dist_ckpt_deprecated ........................ False
-  use_distributed_optimizer ....................... False
-  use_flash_attn .................................. False
-  use_fused_weighted_squared_relu ................. False
-  use_gpu ......................................... True
-  use_legacy_models ............................... False
-  use_megatron_fsdp ............................... False
-  use_mp_args_from_checkpoint_args ................ False
-  use_one_sent_docs ............................... False
-  use_persistent_ckpt_worker ...................... False
-  use_precision_aware_optimizer ................... False
-  use_pytorch_profiler ............................ False
-  use_ring_exchange_p2p ........................... False
-  use_rope_scaling ................................ False
-  use_rotary_position_embeddings .................. False
-  use_sharp ....................................... False
-  use_te_activation_func .......................... False
-  use_tokenizer_model_from_checkpoint_args ........ True
-  use_torch_fsdp2 ................................. False
-  use_torch_optimizer_for_cpu_offload ............. False
-  use_tp_pp_dp_mapping ............................ False
-  v_head_dim ...................................... 128
-  valid_data_path ................................. None
-  variable_seq_lengths ............................ False
-  virtual_pipeline_model_parallel_size ............ None
-  vision_backbone_type ............................ vit
-  vision_pretraining .............................. False
-  vision_pretraining_type ......................... classify
-  vocab_extra_ids ................................. 0
-  vocab_file ...................................... None
-  vocab_size ...................................... None
-  wandb_entity .................................... 
-  wandb_exp_name .................................. 
-  wandb_project ................................... 
-  wandb_save_dir .................................. 
-  weight_decay .................................... 0.1
-  weight_decay_incr_style ......................... constant
-  wgrad_deferral_limit ............................ 0
-  window_attn_skip_freq ........................... None
-  window_size ..................................... None
-  word_embedding_dropout_prob ..................... 0.0
-  world_size ...................................... 1
-  yaml_cfg ........................................ None
--------------------- end of arguments ---------------------
-INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1024
-> building HuggingFaceTokenizer tokenizer ...
-You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file you can ignore this message.
-WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
-INFO:megatron.training.initialize:Setting logging level to 1
-WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode validate_results
-> initializing torch distributed ...
-[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
-[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
-[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
-> initialized tensor model parallel with size 1
-> initialized pipeline model parallel with size 1
-> setting random seeds to 1234 ...
-> compiling dataset index builder ...
-make: Entering directory '/mnt/ssd/lvzhihao/PostTrain/YuLan-Pretrain/megatron/core/datasets'
-make: Nothing to be done for 'default'.
-make: Leaving directory '/mnt/ssd/lvzhihao/PostTrain/YuLan-Pretrain/megatron/core/datasets'
->>> done with dataset index builder. Compilation time: 0.106 seconds
-WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations.
-> compiling and loading fused kernels ...
-/mnt/ssd/lvzhihao/PostTrain/YuLan-Pretrain/.venv/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
-  warnings.warn(  # warn only once
-[rank0]:[W320 09:19:59.967589007 ProcessGroupNCCL.cpp:5023] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can specify device_id in init_process_group() to force use of a particular device.
->>> done with compiling and loading fused kernels. Compilation time: 0.404 seconds
-WORLD_SIZE: 1, RANK: 0, LOCAL_RANK: 0
-building GPT model ...
-`torch_dtype` is deprecated! Use `dtype` instead!
-`torch_dtype` is deprecated! Use `dtype` instead!
-INFO:transformers_modules.tmp_dot_9kVbw0fz8t.modeling_qwen3_next:[Qwen3Next custom] attn_position_embedding_type=rope, rnn_position_embedding_type=nope, attn_logits_scaling=None
-Qwen3NextForCausalLM(
-  (model): Qwen3NextModel(
-    (embed_tokens): Embedding(99000, 1920)
-    (layers): ModuleList(
-      (0-11): 12 x Qwen3NextDecoderLayer(
-        (linear_attn): Qwen3NextGatedDeltaNet(
-          (act): SiLUActivation()
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (in_proj_qkvz): Linear(in_features=1920, out_features=5120, bias=False)
-          (in_proj_ba): Linear(in_features=1920, out_features=64, bias=False)
-          (norm): FusedRMSNormGated(64, eps=1e-06, activation=silu)
-          (out_proj): Linear(in_features=2048, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (12): Qwen3NextDecoderLayer(
-        (self_attn): Qwen3NextAttention(
-          (q_proj): Linear(in_features=1920, out_features=1920, bias=True)
-          (k_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (v_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (o_proj): Linear(in_features=1920, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (13-19): 7 x Qwen3NextDecoderLayer(
-        (linear_attn): Qwen3NextGatedDeltaNet(
-          (act): SiLUActivation()
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (in_proj_qkvz): Linear(in_features=1920, out_features=5120, bias=False)
-          (in_proj_ba): Linear(in_features=1920, out_features=64, bias=False)
-          (norm): FusedRMSNormGated(64, eps=1e-06, activation=silu)
-          (out_proj): Linear(in_features=2048, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (20-21): 2 x Qwen3NextDecoderLayer(
-        (self_attn): Qwen3NextAttention(
-          (q_proj): Linear(in_features=1920, out_features=1920, bias=True)
-          (k_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (v_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (o_proj): Linear(in_features=1920, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (22): Qwen3NextDecoderLayer(
-        (linear_attn): Qwen3NextGatedDeltaNet(
-          (act): SiLUActivation()
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (in_proj_qkvz): Linear(in_features=1920, out_features=5120, bias=False)
-          (in_proj_ba): Linear(in_features=1920, out_features=64, bias=False)
-          (norm): FusedRMSNormGated(64, eps=1e-06, activation=silu)
-          (out_proj): Linear(in_features=2048, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (23): Qwen3NextDecoderLayer(
-        (self_attn): Qwen3NextAttention(
-          (q_proj): Linear(in_features=1920, out_features=1920, bias=True)
-          (k_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (v_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (o_proj): Linear(in_features=1920, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (24-45): 22 x Qwen3NextDecoderLayer(
-        (linear_attn): Qwen3NextGatedDeltaNet(
-          (act): SiLUActivation()
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (in_proj_qkvz): Linear(in_features=1920, out_features=5120, bias=False)
-          (in_proj_ba): Linear(in_features=1920, out_features=64, bias=False)
-          (norm): FusedRMSNormGated(64, eps=1e-06, activation=silu)
-          (out_proj): Linear(in_features=2048, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (46): Qwen3NextDecoderLayer(
-        (self_attn): Qwen3NextAttention(
-          (q_proj): Linear(in_features=1920, out_features=1920, bias=True)
-          (k_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (v_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (o_proj): Linear(in_features=1920, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (47): Qwen3NextDecoderLayer(
-        (linear_attn): Qwen3NextGatedDeltaNet(
-          (act): SiLUActivation()
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (in_proj_qkvz): Linear(in_features=1920, out_features=5120, bias=False)
-          (in_proj_ba): Linear(in_features=1920, out_features=64, bias=False)
-          (norm): FusedRMSNormGated(64, eps=1e-06, activation=silu)
-          (out_proj): Linear(in_features=2048, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (48-49): 2 x Qwen3NextDecoderLayer(
-        (self_attn): Qwen3NextAttention(
-          (q_proj): Linear(in_features=1920, out_features=1920, bias=True)
-          (k_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (v_proj): Linear(in_features=1920, out_features=384, bias=True)
-          (o_proj): Linear(in_features=1920, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-      (50-55): 6 x Qwen3NextDecoderLayer(
-        (linear_attn): Qwen3NextGatedDeltaNet(
-          (act): SiLUActivation()
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (in_proj_qkvz): Linear(in_features=1920, out_features=5120, bias=False)
-          (in_proj_ba): Linear(in_features=1920, out_features=64, bias=False)
-          (norm): FusedRMSNormGated(64, eps=1e-06, activation=silu)
-          (out_proj): Linear(in_features=2048, out_features=1920, bias=False)
-        )
-        (mlp): Qwen3NextMLP(
-          (gate_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (up_proj): Linear(in_features=1920, out_features=4800, bias=False)
-          (down_proj): Linear(in_features=4800, out_features=1920, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-    )
-    (norm): LlamaRMSNorm()
-    (rotary_emb): Qwen3NextRotaryEmbedding()
-  )
-  (lm_head): Linear(in_features=1920, out_features=99000, bias=False)
-)
-GPTModel(
-  (embedding): LanguageModelEmbedding(
-    (word_embeddings): VocabParallelEmbedding()
-    (embedding_dropout): Dropout(p=0.1, inplace=False)
-  )
-  (rotary_pos_emb): RotaryEmbedding()
-  (decoder): TransformerBlock(
-    (layers): ModuleList(
-      (0-11): 12 x TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): GatedDeltaNet(
-          (in_proj): TELayerNormColumnParallelLinear(in_features=1920, out_features=5184, bias=False, TP=1)
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (out_norm): RMSNorm()
-          (out_proj): TERowParallelLinear(in_features=2048, out_features=1920, bias=False, TP=1)
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (12): TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): SelfAttention(
-          (core_attention): TEDotProductAttention(
-            (flash_attention): FlashAttention()
-            (fused_attention): FusedAttention()
-            (unfused_attention): UnfusedDotProductAttention(
-              (scale_mask_softmax): FusedScaleMaskSoftmax()
-              (attention_dropout): Dropout(p=0.1, inplace=False)
-            )
-          )
-          (linear_proj): TERowParallelLinear(in_features=1920, out_features=1920, bias=False, TP=1)
-          (linear_qkv): TELayerNormColumnParallelLinear(in_features=1920, out_features=2688, bias=True, TP=1)
-          (q_layernorm): IdentityOp()
-          (k_layernorm): IdentityOp()
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (13-19): 7 x TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): GatedDeltaNet(
-          (in_proj): TELayerNormColumnParallelLinear(in_features=1920, out_features=5184, bias=False, TP=1)
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (out_norm): RMSNorm()
-          (out_proj): TERowParallelLinear(in_features=2048, out_features=1920, bias=False, TP=1)
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (20-21): 2 x TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): SelfAttention(
-          (core_attention): TEDotProductAttention(
-            (flash_attention): FlashAttention()
-            (fused_attention): FusedAttention()
-            (unfused_attention): UnfusedDotProductAttention(
-              (scale_mask_softmax): FusedScaleMaskSoftmax()
-              (attention_dropout): Dropout(p=0.1, inplace=False)
-            )
-          )
-          (linear_proj): TERowParallelLinear(in_features=1920, out_features=1920, bias=False, TP=1)
-          (linear_qkv): TELayerNormColumnParallelLinear(in_features=1920, out_features=2688, bias=True, TP=1)
-          (q_layernorm): IdentityOp()
-          (k_layernorm): IdentityOp()
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (22): TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): GatedDeltaNet(
-          (in_proj): TELayerNormColumnParallelLinear(in_features=1920, out_features=5184, bias=False, TP=1)
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (out_norm): RMSNorm()
-          (out_proj): TERowParallelLinear(in_features=2048, out_features=1920, bias=False, TP=1)
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (23): TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): SelfAttention(
-          (core_attention): TEDotProductAttention(
-            (flash_attention): FlashAttention()
-            (fused_attention): FusedAttention()
-            (unfused_attention): UnfusedDotProductAttention(
-              (scale_mask_softmax): FusedScaleMaskSoftmax()
-              (attention_dropout): Dropout(p=0.1, inplace=False)
-            )
-          )
-          (linear_proj): TERowParallelLinear(in_features=1920, out_features=1920, bias=False, TP=1)
-          (linear_qkv): TELayerNormColumnParallelLinear(in_features=1920, out_features=2688, bias=True, TP=1)
-          (q_layernorm): IdentityOp()
-          (k_layernorm): IdentityOp()
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (24-45): 22 x TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): GatedDeltaNet(
-          (in_proj): TELayerNormColumnParallelLinear(in_features=1920, out_features=5184, bias=False, TP=1)
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (out_norm): RMSNorm()
-          (out_proj): TERowParallelLinear(in_features=2048, out_features=1920, bias=False, TP=1)
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (46): TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): SelfAttention(
-          (core_attention): TEDotProductAttention(
-            (flash_attention): FlashAttention()
-            (fused_attention): FusedAttention()
-            (unfused_attention): UnfusedDotProductAttention(
-              (scale_mask_softmax): FusedScaleMaskSoftmax()
-              (attention_dropout): Dropout(p=0.1, inplace=False)
-            )
-          )
-          (linear_proj): TERowParallelLinear(in_features=1920, out_features=1920, bias=False, TP=1)
-          (linear_qkv): TELayerNormColumnParallelLinear(in_features=1920, out_features=2688, bias=True, TP=1)
-          (q_layernorm): IdentityOp()
-          (k_layernorm): IdentityOp()
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (47): TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): GatedDeltaNet(
-          (in_proj): TELayerNormColumnParallelLinear(in_features=1920, out_features=5184, bias=False, TP=1)
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (out_norm): RMSNorm()
-          (out_proj): TERowParallelLinear(in_features=2048, out_features=1920, bias=False, TP=1)
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (48-49): 2 x TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): SelfAttention(
-          (core_attention): TEDotProductAttention(
-            (flash_attention): FlashAttention()
-            (fused_attention): FusedAttention()
-            (unfused_attention): UnfusedDotProductAttention(
-              (scale_mask_softmax): FusedScaleMaskSoftmax()
-              (attention_dropout): Dropout(p=0.1, inplace=False)
-            )
-          )
-          (linear_proj): TERowParallelLinear(in_features=1920, out_features=1920, bias=False, TP=1)
-          (linear_qkv): TELayerNormColumnParallelLinear(in_features=1920, out_features=2688, bias=True, TP=1)
-          (q_layernorm): IdentityOp()
-          (k_layernorm): IdentityOp()
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-      (50-55): 6 x TransformerLayer(
-        (input_layernorm): IdentityOp()
-        (self_attention): GatedDeltaNet(
-          (in_proj): TELayerNormColumnParallelLinear(in_features=1920, out_features=5184, bias=False, TP=1)
-          (conv1d): Conv1d(3072, 3072, kernel_size=(4,), stride=(1,), padding=(3,), groups=3072, bias=False)
-          (out_norm): RMSNorm()
-          (out_proj): TERowParallelLinear(in_features=2048, out_features=1920, bias=False, TP=1)
-        )
-        (pre_cross_attn_layernorm): IdentityOp()
-        (cross_attention): IdentityOp()
-        (cross_attn_bda): IdentityFuncOp()
-        (pre_mlp_layernorm): IdentityOp()
-        (mlp): MLP(
-          (linear_fc1): TELayerNormColumnParallelLinear(in_features=1920, out_features=9600, bias=False, TP=1)
-          (linear_fc2): TERowParallelLinear(in_features=4800, out_features=1920, bias=False, TP=1)
-        )
-      )
-    )
-    (final_layernorm): RMSNorm()
-  )
-  (output_layer): ColumnParallelLinear(in_features=1920, out_features=99000, bias=False, TP=1)
-)
-/mnt/ssd/lvzhihao/PostTrain/YuLan-Pretrain/megatron/core/dist_checkpointing/strategies/common.py:89: UserWarning: Environment variable TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD detected, since the`weights_only` argument was not explicitly passed to `torch.load`, forcing weights_only=False.
-  return torch.load(load_path, map_location='cpu')
-(TP, PP) mismatch after resume ((1, 1) vs (2, 1) from checkpoint): RNG state will be ignored
-sharded_state_dict metadata loaded from the checkpoint: {'singleton_local_shards': True, 'distrib_optim_sharding_type': 'fully_reshardable', 'distrib_optim_fully_reshardable_mem_efficient': False, 'chained_optim_avoid_prefix': True}
-Job sharding has changed: Rerun state will be ignored
- loading distributed checkpoint from /mnt/hdd/lvzhihao/mcore_models/grid_search_0318/checkpoint/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50 at iteration 953
-/mnt/ssd/lvzhihao/PostTrain/YuLan-Pretrain/megatron/core/dist_checkpointing/strategies/torch.py:956: FutureWarning: `load_state_dict` is deprecated and will be removed in future versions. Please use `load` instead.
-  checkpoint.load_state_dict(
- checkpoint version 3.0
-  successfully loaded checkpoint from /mnt/hdd/lvzhihao/mcore_models/grid_search_0318/checkpoint/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50 [ t 1/1, p 1/1 ] at iteration 953
-INFO:root:Converting layer 0 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 1 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 2 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 3 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 4 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 5 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 6 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 7 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 8 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 9 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 10 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 11 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 12 is_gdn=False is_not_moe=True
-INFO:root:[DEBUG] Layer 12: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: attention_output_gate=False, linear_layer=TELayerNormColumnParallelLinear
-INFO:root:Converting layer 13 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 14 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 15 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 16 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 17 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 18 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 19 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 20 is_gdn=False is_not_moe=True
-INFO:root:[DEBUG] Layer 20: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: attention_output_gate=False, linear_layer=TELayerNormColumnParallelLinear
-INFO:root:Converting layer 21 is_gdn=False is_not_moe=True
-INFO:root:[DEBUG] Layer 21: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: attention_output_gate=False, linear_layer=TELayerNormColumnParallelLinear
-INFO:root:Converting layer 22 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 23 is_gdn=False is_not_moe=True
-INFO:root:[DEBUG] Layer 23: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: attention_output_gate=False, linear_layer=TELayerNormColumnParallelLinear
-INFO:root:Converting layer 24 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 25 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 26 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 27 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 28 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 29 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 30 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 31 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 32 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 33 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 34 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 35 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 36 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 37 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 38 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 39 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 40 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 41 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 42 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 43 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 44 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 45 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 46 is_gdn=False is_not_moe=True
-INFO:root:[DEBUG] Layer 46: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: attention_output_gate=False, linear_layer=TELayerNormColumnParallelLinear
-INFO:root:Converting layer 47 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 48 is_gdn=False is_not_moe=True
-INFO:root:[DEBUG] Layer 48: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: attention_output_gate=False, linear_layer=TELayerNormColumnParallelLinear
-INFO:root:Converting layer 49 is_gdn=False is_not_moe=True
-INFO:root:[DEBUG] Layer 49: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: args.attention_output_gate=False
-INFO:root:[DEBUG] set_gated_selfattn_state: attention_output_gate=False, linear_layer=TELayerNormColumnParallelLinear
-INFO:root:Converting layer 50 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 51 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 52 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 53 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 54 is_gdn=True is_not_moe=True
-INFO:root:Converting layer 55 is_gdn=True is_not_moe=True
-DEBUG:root:[RANK 0] 0 send op & 0 recv op.
-INFO:root:[Iters 0 RANK 0] starts synchronizing parameters with other ranks...
-INFO:root:[Iters 0 RANK 0] finishes synchronizing
-[Iters 0 RANK 0] model.safetensors is saved.
-DEBUG:root:[Iters 0 RANK 0] joined
-Conversion finished in 66.89246106147766 seconds.
-[rank0]:[W320 09:21:08.114512752 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

grid_search_0318/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50/model.safetensors

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-oid sha256:ce63dadb1999cb096274acde86635b06e3a6c5a3cfc27496c466c053a05b5cb6
-size 5343303296

grid_search_0318/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50/modeling_qwen3_next.py

@@ -1,1561 +0,0 @@
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           This file was automatically generated from src/transformers/models/qwen3_next/modular_qwen3_next.py.
-#               Do NOT edit this file manually as any edits will be overwritten by the generation of
-#             the file from the modular. If any change should be done, please apply the change to the
-#                          modular_qwen3_next.py file directly. One of our CI enforces this.
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-# coding=utf-8
-# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from typing import Any, Callable, Optional, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache
-from transformers.generation import GenerationMixin
-from transformers.masking_utils import create_causal_mask
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_layers import (
-    GradientCheckpointingLayer,
-)
-from transformers.modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
-from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-from transformers.processing_utils import Unpack
-from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
-from transformers.utils.deprecation import deprecate_kwarg
-from transformers.utils.generic import OutputRecorder, check_model_inputs
-from transformers.utils.import_utils import (
-    is_causal_conv1d_available,
-    is_flash_linear_attention_available,
-)
-try:
-    from configuration_qwen3_next import Qwen3NextConfig
-except ImportError:
-    from .configuration_qwen3_next import Qwen3NextConfig
-
-if is_causal_conv1d_available():
-    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
-else:
-    causal_conv1d_update, causal_conv1d_fn = None, None
-
-if is_flash_linear_attention_available():
-    from fla.modules import FusedRMSNormGated
-    from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule
-else:
-    chunk_gated_delta_rule, fused_recurrent_gated_delta_rule = None, None
-    FusedRMSNormGated = None
-
-logger = logging.get_logger(__name__)
-
-# ANSI colors for console (custom features log)
-_CG = "\033[92m"   # green
-_CY = "\033[93m"   # yellow
-_CC = "\033[96m"   # cyan
-_CR = "\033[0m"    # reset
-
-
-def _log_custom_features(config: Qwen3NextConfig) -> None:
-    """Log attn/rnn RoPE and attn_logits_scaling settings (once per model load)."""
-    attn_pe = getattr(config, "attn_position_embedding_type", "rope")
-    rnn_pe = getattr(config, "rnn_position_embedding_type", "nope")
-    logits_scaling = getattr(config, "attn_logits_scaling", None)
-    msg = (
-        f"{_CG}[Qwen3Next custom]{_CR} "
-        f"{_CC}attn_position_embedding_type{_CR}={_CY}{attn_pe}{_CR}, "
-        f"{_CC}rnn_position_embedding_type{_CR}={_CY}{rnn_pe}{_CR}, "
-        f"{_CC}attn_logits_scaling{_CR}={_CY}{logits_scaling}{_CR}"
-    )
-    logger.info(msg)
-
-
-class Qwen3NextRMSNormGated(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6, **kwargs):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states, gate=None):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        # Norm before gate
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        hidden_states = self.weight * hidden_states.to(input_dtype)
-        hidden_states = hidden_states * F.silu(gate.to(torch.float32))
-
-        return hidden_states.to(input_dtype)
-
-
-class Qwen3NextDynamicCache:
-    """
-    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the linear attention
-    cache (which has a constant shape regardless of seq_len).
-
-    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
-    and `ssm_states` for gated deltanet cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
-    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
-    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
-    For linear attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
-    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
-    and `recurrent_states` represents the recurrent state and has a shape of `(batch_size, d_inner, d_state)`.
-    """
-
-    is_compileable = False
-
-    def __init__(self, config: Qwen3NextConfig):
-        super().__init__()
-        self.layer_types = config.layer_types
-        self.transformer_layers = [
-            i for i in range(config.num_hidden_layers) if self.layer_types[i] == "full_attention"
-        ]
-        self.last_linear_layer = len(self.layer_types) - 1 - self.layer_types[::-1].index("linear_attention")
-
-        # Initialize everything to None -> will be lazy initialized to allow multi-gpu (device_map) inference
-        self.conv_states = [None for _ in range(config.num_hidden_layers)]
-        self.recurrent_states = [None for _ in range(config.num_hidden_layers)]
-        self.key_cache = [None for _ in range(config.num_hidden_layers)]
-        self.value_cache = [None for _ in range(config.num_hidden_layers)]
-
-    def __len__(self):
-        return len(self.layer_types)
-
-    def __getitem__(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def update(
-        self,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        layer_idx: int,
-        cache_kwargs: Optional[dict[str, Any]] = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if self.key_cache[layer_idx] is None:
-            self.key_cache[layer_idx] = key_states
-            self.value_cache[layer_idx] = value_states
-        else:
-            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
-            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
-
-        return self.key_cache[layer_idx], self.value_cache[layer_idx]
-
-    def reorder_cache(self, beam_idx: torch.LongTensor):
-        """Reorders the cache for beam search, given the selected beam indices."""
-        for layer_idx in range(len(self.key_cache)):
-            if self.key_cache[layer_idx] is not None:
-                device = self.key_cache[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx)
-                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx)
-
-            if self.conv_states[layer_idx] is not None:
-                device = self.conv_states[layer_idx].device
-                beam_idx = beam_idx.to(device)
-                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx)
-                self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select(0, beam_idx)
-
-    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
-        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
-        # take any layer that contains cache and not empty tensor
-        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
-        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None:
-            return 0
-        return self.key_cache[layer_idx].shape[-2]
-
-    def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
-        """
-        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
-        the given layer at `layer_idx`.
-        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer.
-        """
-        kv_offset = 0
-        query_length = cache_position.shape[0]
-        past_seen_tokens = self.get_seq_length(layer_idx)
-        kv_length = query_length + past_seen_tokens
-        return kv_length, kv_offset
-
-    @property
-    def has_previous_state(self):
-        """We have a previous state if the last linear (conv) layer was already updated."""
-        return self.conv_states[self.last_linear_layer] is not None
-
-
-class Qwen3NextRotaryEmbedding(nn.Module):
-    inv_freq: torch.Tensor  # fix linting for `register_buffer`
-
-    def __init__(self, config: Qwen3NextConfig, device=None):
-        super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
-            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-        else:
-            self.rope_type = "default"
-        self.max_seq_len_cached = config.max_position_embeddings
-        self.original_max_seq_len = config.max_position_embeddings
-
-        self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-
-        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = self.inv_freq
-
-    @torch.no_grad()
-    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
-    def forward(self, x, position_ids):
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
-        position_ids_expanded = position_ids[:, None, :].float()
-
-        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos() * self.attention_scaling
-            sin = emb.sin() * self.attention_scaling
-
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-class Qwen3NextRMSNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-6):
-        super().__init__()
-        self.eps = eps
-        self.weight = nn.Parameter(torch.zeros(dim))
-
-    def _norm(self, x):
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-
-    def forward(self, x):
-        output = self._norm(x.float())
-        # Llama does x.to(float16) * w whilst Qwen3Next is (x * w).to(float16)
-        # See https://github.com/huggingface/transformers/pull/29402
-        output = output * (1.0 + self.weight.float())
-        return output.type_as(x)
-
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}, eps={self.eps}"
-
-
-class LlamaRMSNorm(nn.Module):  # Copy from Llama
-    def __init__(self, hidden_size, eps=1e-6):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-# Adapted from transformers.models.glm.modular_glm.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Removes the interleaving of cos and sin from GLM
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`, *optional*):
-            Deprecated and unused.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-
-    # Keep half or full tensor for later concatenation
-    rotary_dim = cos.shape[-1]
-    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
-    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
-
-    # Apply rotary embeddings on the first half or full tensor
-    q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
-    k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
-
-    # Concatenate back to full shape
-    q_embed = torch.cat([q_embed, q_pass], dim=-1)
-    k_embed = torch.cat([k_embed, k_pass], dim=-1)
-    return q_embed, k_embed
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-def eager_attention_forward(
-    module: nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: Optional[torch.Tensor],
-    scaling: float,
-    dropout: float = 0.0,
-    **kwargs: Unpack[TransformersKwargs],
-):
-    key_states = repeat_kv(key, module.num_key_value_groups)
-    value_states = repeat_kv(value, module.num_key_value_groups)
-
-    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
-    if attention_mask is not None:
-        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-        attn_weights = attn_weights + causal_mask
-
-    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
-    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
-    attn_output = torch.matmul(attn_weights, value_states)
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, attn_weights
-
-
-class Qwen3NextAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: Qwen3NextConfig, layer_idx: int):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
-        self.scaling = self.head_dim**-0.5
-        self.attention_dropout = config.attention_dropout
-        self.is_causal = True
-        self.attn_output_gate = config.attn_output_gate
-        self.q_proj = nn.Linear(
-            config.hidden_size, config.num_attention_heads * self.head_dim * (1 + self.attn_output_gate), bias=config.attention_bias
-        )
-        self.k_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.v_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.o_proj = nn.Linear(
-            config.num_attention_heads * self.head_dim, config.hidden_size, bias=False
-        )
-        self.enable_qk_norm = config.enable_qk_norm
-        if self.enable_qk_norm:
-            self.q_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps)  # unlike olmo, only on the head dim!
-            self.k_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps)  # thus post q_norm does not need reshape
-        else:
-            self.q_norm = None
-            self.k_norm = None
-
-        # Separate RoPE for attention: "rope" or "nope" (no RoPE)
-        self.attn_position_embedding_type = getattr(config, "attn_position_embedding_type", "rope")
-        # Optional logits scaling for length extrapolation: None, float, or "log" / "log <a>"
-        self.attn_logits_scaling = getattr(config, "attn_logits_scaling", None)
-
-        # Token shift on Q/K/V after projection (cannon layer, conv mode); per-head conv (head_dim, 1, kernel_size)
-        kernel_size = getattr(config, "token_shift_conv_size", 4)
-        self.attn_q_token_shift = getattr(config, "attn_q_token_shift", None)
-        self.attn_k_token_shift = getattr(config, "attn_k_token_shift", None)
-        self.attn_v_token_shift = getattr(config, "attn_v_token_shift", None)
-        if self.attn_q_token_shift == "conv":
-            self.q_token_shift_conv = nn.Conv1d(
-                self.head_dim,
-                self.head_dim,
-                kernel_size=kernel_size,
-                padding=0,
-                groups=self.head_dim,
-                bias=False,
-            )
-        else:
-            self.q_token_shift_conv = None
-        if self.attn_k_token_shift == "conv":
-            self.k_token_shift_conv = nn.Conv1d(
-                self.head_dim,
-                self.head_dim,
-                kernel_size=kernel_size,
-                padding=0,
-                groups=self.head_dim,
-                bias=False,
-            )
-        else:
-            self.k_token_shift_conv = None
-        if self.attn_v_token_shift == "conv":
-            self.v_token_shift_conv = nn.Conv1d(
-                self.head_dim,
-                self.head_dim,
-                kernel_size=kernel_size,
-                padding=0,
-                groups=self.head_dim,
-                bias=False,
-            )
-        else:
-            self.v_token_shift_conv = None
-
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor],
-        past_key_values: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
-        input_shape = hidden_states.shape[:-1]
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-        if self.attn_output_gate:
-            query_states, gate = torch.chunk(
-                self.q_proj(hidden_states).view(*input_shape, -1, self.head_dim * 2), 2, dim=-1
-            )
-            gate = gate.reshape(*input_shape, -1)
-        else:
-            query_states = self.q_proj(hidden_states).view(*input_shape, -1, self.head_dim)
-            gate = None
-
-        if self.enable_qk_norm:
-            query_states = self.q_norm(query_states.view(hidden_shape)).transpose(1, 2)
-            key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
-        else:
-            query_states = query_states.view(hidden_shape).transpose(1, 2)
-            key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-
-        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-
-        # Token shift on Q/K/V (cannon layer, conv mode): [batch, heads, seq, head_dim] -> [batch*heads, head_dim, seq]
-        if self.attn_q_token_shift == "conv" and self.q_token_shift_conv is not None:
-            b, nh, sq, hn = query_states.shape
-            q_bcl = query_states.reshape(b * nh, hn, sq)
-            q_bcl = apply_causal_depthwise_conv1d_bcl(q_bcl, self.q_token_shift_conv.weight)
-            query_states = q_bcl.reshape(b, nh, hn, sq).permute(0, 1, 3, 2).contiguous()
-        if self.attn_k_token_shift == "conv" and self.k_token_shift_conv is not None:
-            b, ng, sq, hn = key_states.shape
-            k_bcl = key_states.reshape(b * ng, hn, sq)
-            k_bcl = apply_causal_depthwise_conv1d_bcl(k_bcl, self.k_token_shift_conv.weight)
-            key_states = k_bcl.reshape(b, ng, hn, sq).permute(0, 1, 3, 2).contiguous()
-        if self.attn_v_token_shift == "conv" and self.v_token_shift_conv is not None:
-            b, ng, sq, hn = value_states.shape
-            v_bcl = value_states.reshape(b * ng, hn, sq)
-            v_bcl = apply_causal_depthwise_conv1d_bcl(v_bcl, self.v_token_shift_conv.weight)
-            value_states = v_bcl.reshape(b, ng, hn, sq).permute(0, 1, 3, 2).contiguous()
-
-        cos, sin = position_embeddings
-        if self.attn_position_embedding_type == "rope":
-            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-        # when "nope", do not apply RoPE
-
-        if past_key_values is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # Optional logits scaling for length extrapolation (before attention, same as FLA)
-        if self.attn_logits_scaling is not None:
-            if isinstance(self.attn_logits_scaling, (int, float)):
-                query_states = query_states * float(self.attn_logits_scaling)
-            elif isinstance(self.attn_logits_scaling, str):
-                position_ids = kwargs.get("position_ids")
-                if position_ids is None:
-                    batch_size, num_heads, seq_len, _ = query_states.shape
-                    position_ids = torch.arange(
-                        seq_len, device=query_states.device, dtype=torch.long
-                    ).unsqueeze(0).expand(batch_size, -1)
-                    logger.warning_once(
-                        "attn_logits_scaling uses position-dependent scaling but position_ids was not passed; "
-                        "using arange(0, seq_len). Pass position_ids for correct behavior with padding."
-                    )
-                parts = self.attn_logits_scaling.split()
-                a = float(parts[1]) if len(parts) > 1 else 362.0
-                position_ids_f = position_ids.to(device=query_states.device, dtype=torch.float32)
-                scale = (torch.log(position_ids_f + a) / math.log(a)).to(query_states.dtype)
-                # query_states: (B, H, T, D); scale: (B, T) -> broadcast
-                query_states = query_states * scale.unsqueeze(1).unsqueeze(-1)
-            else:
-                raise TypeError(
-                    f"attn_logits_scaling must be float, str or None, got {type(self.attn_logits_scaling)}"
-                )
-
-        attention_interface: Callable = eager_attention_forward
-        if self.config._attn_implementation != "eager":
-            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-
-        attn_output, attn_weights = attention_interface(
-            self,
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            dropout=0.0 if not self.training else self.attention_dropout,
-            scaling=self.scaling,
-            **kwargs,
-        )
-
-        if self.attn_output_gate:
-            attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-            attn_output = attn_output * torch.sigmoid(gate)
-        else:
-            attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-def apply_mask_to_padding_states(hidden_states, attention_mask):
-    """
-    Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
-    """
-    if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
-        dtype = hidden_states.dtype
-        hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
-
-    return hidden_states
-
-
-is_fast_path_available = all(
-    (causal_conv1d_fn, causal_conv1d_update, chunk_gated_delta_rule, fused_recurrent_gated_delta_rule)
-)
-
-
-def torch_causal_conv1d_update(
-    hidden_states,
-    conv_state,
-    weight,
-    bias=None,
-    activation=None,
-):
-    _, hidden_size, seq_len = hidden_states.shape
-    state_len = conv_state.shape[-1]
-
-    hidden_states_new = torch.cat([conv_state, hidden_states], dim=-1).to(weight.dtype)
-    conv_state.copy_(hidden_states_new[:, :, -state_len:])
-    out = F.conv1d(hidden_states_new, weight.unsqueeze(1), bias, padding=0, groups=hidden_size)
-    out = F.silu(out[:, :, -seq_len:])
-    out = out.to(hidden_states.dtype)
-    return out
-
-
-def apply_causal_depthwise_conv1d_bcl(
-    x_bcl: torch.Tensor,
-    weight_c1w: torch.Tensor,
-) -> torch.Tensor:
-    """Apply causal depthwise Conv1d for token shifting. Aligns with Megatron cannon layer.
-
-    x_bcl: [B, C, L], weight_c1w: [C, 1, W]. Output: [B, C, L].
-    """
-    if causal_conv1d_fn is not None:
-        return causal_conv1d_fn(
-            x=x_bcl,
-            weight=weight_c1w.squeeze(1),
-            bias=None,
-            activation=None,
-        )
-    w = weight_c1w.shape[-1]
-    x_pad = F.pad(x_bcl, (w - 1, 0))
-    y = F.conv1d(
-        x_pad,
-        weight_c1w,
-        bias=None,
-        stride=1,
-        padding=0,
-        groups=weight_c1w.shape[0],
-    )
-    return y[..., : x_bcl.shape[-1]]
-
-
-def l2norm(x: torch.FloatTensor, dim: int = -1, eps: float = 1e-6):
-    """This function is intended to align with the l2norm implementation in the FLA library."""
-    inv_norm = 1 / torch.sqrt((x * x).sum(dim=dim, keepdim=True) + eps)
-    return x * inv_norm
-
-
-def torch_chunk_gated_delta_rule(
-    query,
-    key,
-    value,
-    g,
-    beta,
-    chunk_size=64,
-    initial_state=None,
-    output_final_state=False,
-    use_qk_l2norm_in_kernel=False,
-):
-    initial_dtype = query.dtype
-    if use_qk_l2norm_in_kernel:
-        query = l2norm(query, dim=-1, eps=1e-6)
-        key = l2norm(key, dim=-1, eps=1e-6)
-    query, key, value, beta, g = [
-        x.transpose(1, 2).contiguous().to(torch.float32) for x in (query, key, value, beta, g)
-    ]
-
-    batch_size, sequence_length, num_heads, k_head_dim = key.shape
-    v_head_dim = value.shape[-1]
-    pad_size = (chunk_size - num_heads % chunk_size) % chunk_size
-    query = F.pad(query, (0, 0, 0, pad_size))
-    key = F.pad(key, (0, 0, 0, pad_size))
-    value = F.pad(value, (0, 0, 0, pad_size))
-    beta = F.pad(beta, (0, pad_size))
-    g = F.pad(g, (0, pad_size))
-    tot_heads = num_heads + pad_size
-    scale = 1 / (query.shape[-1] ** 0.5)
-    query = query * scale
-
-    v_beta = value * beta.unsqueeze(-1)
-    k_beta = key * beta.unsqueeze(-1)
-    # reshape to chunks
-    query, key, value, k_beta, v_beta = [
-        x.reshape(x.shape[0], x.shape[1], -1, chunk_size, x.shape[-1]) for x in (query, key, value, k_beta, v_beta)
-    ]
-    g = g.reshape(g.shape[0], g.shape[1], -1, chunk_size)
-    mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=0)
-
-    # chunk decay
-    g = g.cumsum(dim=-1)
-    decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
-    attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
-    for i in range(1, chunk_size):
-        row = attn[..., i, :i].clone()
-        sub = attn[..., :i, :i].clone()
-        attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
-    attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
-    value = attn @ v_beta
-    k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
-    last_recurrent_state = (
-        torch.zeros(batch_size, sequence_length, k_head_dim, v_head_dim).to(value)
-        if initial_state is None
-        else initial_state.to(value)
-    )
-    core_attn_out = torch.zeros_like(value)
-    mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=1)
-
-    # for each chunk
-    for i in range(0, tot_heads // chunk_size):
-        q_i, k_i, v_i = query[:, :, i], key[:, :, i], value[:, :, i]
-        attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
-        v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
-        v_new = v_i - v_prime
-        attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
-        core_attn_out[:, :, i] = attn_inter + attn @ v_new
-        last_recurrent_state = (
-            last_recurrent_state * g[:, :, i, -1, None, None].exp()
-            + (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new
-        )
-
-    if not output_final_state:
-        last_recurrent_state = None
-    core_attn_out = core_attn_out.reshape(core_attn_out.shape[0], core_attn_out.shape[1], -1, core_attn_out.shape[-1])
-    core_attn_out = core_attn_out[:, :, :num_heads]
-    core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype)
-    return core_attn_out, last_recurrent_state
-
-
-def torch_recurrent_gated_delta_rule(
-    query, key, value, g, beta, initial_state, output_final_state, use_qk_l2norm_in_kernel=False
-):
-    initial_dtype = query.dtype
-    if use_qk_l2norm_in_kernel:
-        query = l2norm(query, dim=-1, eps=1e-6)
-        key = l2norm(key, dim=-1, eps=1e-6)
-    query, key, value, beta, g = [
-        x.transpose(1, 2).contiguous().to(torch.float32) for x in (query, key, value, beta, g)
-    ]
-
-    batch_size, sequence_length, num_heads, k_head_dim = key.shape
-    v_head_dim = value.shape[-1]
-    scale = 1 / (query.shape[-1] ** 0.5)
-    query = query * scale
-
-    core_attn_out = torch.zeros(batch_size, sequence_length, num_heads, v_head_dim).to(value)
-    last_recurrent_state = (
-        torch.zeros(batch_size, sequence_length, k_head_dim, v_head_dim).to(value)
-        if initial_state is None
-        else initial_state.to(value)
-    )
-
-    for i in range(num_heads):
-        q_t = query[:, :, i]
-        k_t = key[:, :, i]
-        v_t = value[:, :, i]
-        g_t = g[:, :, i].exp().unsqueeze(-1).unsqueeze(-1)
-        beta_t = beta[:, :, i].unsqueeze(-1)
-
-        last_recurrent_state = last_recurrent_state * g_t
-        kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
-        delta = (v_t - kv_mem) * beta_t
-        last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta.unsqueeze(-2)
-        core_attn_out[:, :, i] = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
-
-    if not output_final_state:
-        last_recurrent_state = None
-    core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype)
-    return core_attn_out, last_recurrent_state
-
-
-class Qwen3NextGatedDeltaNet(nn.Module):
-    def __init__(self, config: Qwen3NextConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.num_v_heads = config.linear_num_value_heads
-        self.num_k_heads = config.linear_num_key_heads
-        self.head_k_dim = config.linear_key_head_dim
-        self.head_v_dim = config.linear_value_head_dim
-        self.key_dim = self.head_k_dim * self.num_k_heads
-        self.value_dim = self.head_v_dim * self.num_v_heads
-
-        self.conv_kernel_size = config.linear_conv_kernel_dim
-        self.layer_idx = layer_idx
-
-        # Optional RoPE for linear/RNN path: "rope" or "nope" (same as FLA rnn_position_embedding_type)
-        self.rnn_position_embedding_type = getattr(config, "rnn_position_embedding_type", "nope")
-        rope_theta = getattr(config, "rope_theta", 10000.0)
-        if self.rnn_position_embedding_type == "rope":
-            inv_freq = 1.0 / (
-                rope_theta ** (torch.arange(0, self.head_k_dim, 2, dtype=torch.float32) / self.head_k_dim)
-            )
-            self.register_buffer("_inv_freq", inv_freq, persistent=False)
-        else:
-            self._inv_freq = None
-        self.activation = config.hidden_act
-        self.act = ACT2FN[config.hidden_act]
-        self.layer_norm_epsilon = config.rms_norm_eps
-
-        # QKV
-        self.conv_dim = self.key_dim * 2 + self.value_dim
-        self.conv1d = nn.Conv1d(
-            in_channels=self.conv_dim,
-            out_channels=self.conv_dim,
-            bias=False,
-            kernel_size=self.conv_kernel_size,
-            groups=self.conv_dim,
-            padding=self.conv_kernel_size - 1,
-        )
-
-        # projection of the input hidden states
-        projection_size_qkvz = self.key_dim * 2 + self.value_dim * 2
-        projection_size_ba = self.num_v_heads * 2
-        self.in_proj_qkvz = nn.Linear(self.hidden_size, projection_size_qkvz, bias=False)
-        self.in_proj_ba = nn.Linear(self.hidden_size, projection_size_ba, bias=False)
-
-        # time step projection (discretization)
-        # instantiate once and copy inv_dt in init_weights of PretrainedModel
-        self.dt_bias = nn.Parameter(torch.ones(self.num_v_heads))
-
-        A = torch.empty(self.num_v_heads).uniform_(0, 16)
-        self.A_log = nn.Parameter(torch.log(A))
-
-        # self.norm = FusedRMSNormGated(self.head_v_dim, eps=self.layer_norm_epsilon)
-        self.norm = (
-            Qwen3NextRMSNormGated(self.head_v_dim, eps=self.layer_norm_epsilon)
-            if FusedRMSNormGated is None
-            else FusedRMSNormGated(
-                self.head_v_dim,
-                eps=self.layer_norm_epsilon,
-                activation=self.activation,
-                device=torch.cuda.current_device(),
-                dtype=config.dtype if config.dtype is not None else torch.get_current_dtype(),
-            )
-        )
-
-        self.out_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False)
-
-        self.causal_conv1d_fn = causal_conv1d_fn
-        self.causal_conv1d_update = causal_conv1d_update or torch_causal_conv1d_update
-        self.chunk_gated_delta_rule = chunk_gated_delta_rule or torch_chunk_gated_delta_rule
-        self.recurrent_gated_delta_rule = fused_recurrent_gated_delta_rule or torch_recurrent_gated_delta_rule
-
-        if not is_fast_path_available:
-            logger.warning_once(
-                "The fast path is not available because one of the required library is not installed. Falling back to "
-                "torch implementation. To install follow https://github.com/fla-org/flash-linear-attention#installation and"
-                " https://github.com/Dao-AILab/causal-conv1d"
-            )
-
-    def fix_query_key_value_ordering(self, mixed_qkvz, mixed_ba):
-        """
-        Derives `query`, `key` and `value` tensors from `mixed_qkvz` and `mixed_ba`.
-        """
-        # (b, s, d_model) -> (b, s, num_k_heads, 2 * head_k_dim + 2 * head_v_dim * num_v_heads // num_k_heads)
-        new_tensor_shape_qkvz = mixed_qkvz.size()[:-1] + (
-            self.num_k_heads,
-            2 * self.head_k_dim + 2 * self.head_v_dim * self.num_v_heads // self.num_k_heads,
-        )
-        new_tensor_shape_ba = mixed_ba.size()[:-1] + (self.num_k_heads, 2 * self.num_v_heads // self.num_k_heads)
-
-        mixed_qkvz = mixed_qkvz.view(*new_tensor_shape_qkvz)
-        mixed_ba = mixed_ba.view(*new_tensor_shape_ba)
-        split_arg_list_qkvz = [
-            self.head_k_dim,
-            self.head_k_dim,
-            (self.num_v_heads // self.num_k_heads * self.head_v_dim),
-            (self.num_v_heads // self.num_k_heads * self.head_v_dim),
-        ]
-        split_arg_list_ba = [self.num_v_heads // self.num_k_heads, self.num_v_heads // self.num_k_heads]
-        query, key, value, z = torch.split(mixed_qkvz, split_arg_list_qkvz, dim=3)
-        b, a = torch.split(mixed_ba, split_arg_list_ba, dim=3)
-        # [b, sq, ng, np/ng * hn] -> [b, sq, np, hn]
-        value = value.reshape(value.size(0), value.size(1), -1, self.head_v_dim)
-        z = z.reshape(z.size(0), z.size(1), -1, self.head_v_dim)
-        b = b.reshape(b.size(0), b.size(1), self.num_v_heads)
-        a = a.reshape(a.size(0), a.size(1), self.num_v_heads)
-        return query, key, value, z, b, a
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        cache_params: Optional[Qwen3NextDynamicCache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-    ):
-        hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
-
-        # Set up dimensions for reshapes later
-        batch_size, seq_len, _ = hidden_states.shape
-
-        use_precomputed_states = (
-            cache_params is not None
-            and cache_params.has_previous_state
-            and seq_len == 1
-            and cache_position is not None
-        )
-
-        # getting projected states from cache if it exists
-        if cache_params is not None:
-            conv_state = cache_params.conv_states[self.layer_idx]
-            recurrent_state = cache_params.recurrent_states[self.layer_idx]
-
-        projected_states_qkvz = self.in_proj_qkvz(hidden_states)
-        projected_states_ba = self.in_proj_ba(hidden_states)
-        query, key, value, z, b, a = self.fix_query_key_value_ordering(projected_states_qkvz, projected_states_ba)
-        query, key, value = (x.reshape(x.shape[0], x.shape[1], -1) for x in (query, key, value))
-
-        mixed_qkv = torch.cat((query, key, value), dim=-1)
-        mixed_qkv = mixed_qkv.transpose(1, 2)
-
-        if use_precomputed_states:
-            # 2. Convolution sequence transformation
-            # NOTE: the conv state is updated in `causal_conv1d_update`
-            mixed_qkv = self.causal_conv1d_update(
-                mixed_qkv,
-                conv_state,
-                self.conv1d.weight.squeeze(1),
-                self.conv1d.bias,
-                self.activation,
-            )
-        else:
-            if cache_params is not None:
-                conv_state = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx] = conv_state
-            if self.causal_conv1d_fn is not None:
-                mixed_qkv = self.causal_conv1d_fn(
-                    x=mixed_qkv,
-                    weight=self.conv1d.weight.squeeze(1),
-                    bias=self.conv1d.bias,
-                    activation=self.activation,
-                    seq_idx=None,
-                )
-            else:
-                mixed_qkv = F.silu(self.conv1d(mixed_qkv)[:, :, :seq_len])
-
-        mixed_qkv = mixed_qkv.transpose(1, 2)
-        query, key, value = torch.split(
-            mixed_qkv,
-            [
-                self.key_dim,
-                self.key_dim,
-                self.value_dim,
-            ],
-            dim=-1,
-        )
-        query = query.reshape(query.shape[0], query.shape[1], -1, self.head_k_dim)
-        key = key.reshape(key.shape[0], key.shape[1], -1, self.head_k_dim)
-        value = value.reshape(value.shape[0], value.shape[1], -1, self.head_v_dim)
-
-        # Optional RoPE for linear/RNN (same as FLA GatedDeltaNet when rnn_position_embedding_type=="rope")
-        if self._inv_freq is not None and position_ids is not None:
-            # query, key: (batch, seq, num_k_heads, head_k_dim); need cos, sin (batch, seq, head_k_dim)
-            inv_freq = self._inv_freq.to(query.device)
-            freqs = position_ids[:, :, None].float() * inv_freq[None, None, :]
-            emb = torch.cat([freqs, freqs], dim=-1)
-            cos = emb.cos().to(query.dtype)
-            sin = emb.sin().to(query.dtype)
-            query, key = apply_rotary_pos_emb(query, key, cos, sin, unsqueeze_dim=2)
-
-        beta = b.sigmoid()
-        # If the model is loaded in fp16, without the .float() here, A might be -inf
-        g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
-        if self.num_v_heads // self.num_k_heads > 1:
-            query = query.repeat_interleave(self.num_v_heads // self.num_k_heads, dim=2)
-            key = key.repeat_interleave(self.num_v_heads // self.num_k_heads, dim=2)
-
-        if not use_precomputed_states:
-            core_attn_out, last_recurrent_state = self.chunk_gated_delta_rule(
-                query,
-                key,
-                value,
-                g=g,
-                beta=beta,
-                initial_state=None,
-                output_final_state=cache_params is not None,
-                use_qk_l2norm_in_kernel=True,
-            )
-
-        else:
-            core_attn_out, last_recurrent_state = self.recurrent_gated_delta_rule(
-                query,
-                key,
-                value,
-                g=g,
-                beta=beta,
-                initial_state=recurrent_state,
-                output_final_state=cache_params is not None,
-                use_qk_l2norm_in_kernel=True,
-            )
-        # Update cache
-        if cache_params is not None:
-            cache_params.recurrent_states[self.layer_idx] = last_recurrent_state
-
-        z_shape_og = z.shape
-        # reshape input data into 2D tensor
-        # core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
-        # z = z.reshape(-1, z.shape[-1])
-        core_attn_out = core_attn_out
-        z = z
-        core_attn_out = self.norm(core_attn_out, z)
-
-        core_attn_out = core_attn_out.reshape(z_shape_og)
-        core_attn_out = core_attn_out.reshape(core_attn_out.shape[0], core_attn_out.shape[1], -1)
-
-        output = self.out_proj(core_attn_out)
-        return output
-
-
-class Qwen3NextMLP(nn.Module):
-    def __init__(self, config, intermediate_size=None):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.act_fn = ACT2FN[config.hidden_act]
-
-        # Token shifting (cannon layer): conv mode adds depthwise Conv1d
-        self.ffn_token_shift = getattr(config, "ffn_token_shift", None)
-        self.ffn_intermediate_token_shift = getattr(config, "ffn_intermediate_token_shift", None)
-        kernel_size = getattr(config, "token_shift_conv_size", 4)
-        if self.ffn_token_shift == "conv":
-            self.token_shift_conv = nn.Conv1d(
-                self.hidden_size,
-                self.hidden_size,
-                kernel_size=kernel_size,
-                padding=0,
-                groups=self.hidden_size,
-                bias=False,
-            )
-        else:
-            self.token_shift_conv = None
-        if self.ffn_intermediate_token_shift == "conv":
-            self.intermediate_token_shift_conv = nn.Conv1d(
-                self.intermediate_size,
-                self.intermediate_size,
-                kernel_size=kernel_size,
-                padding=0,
-                groups=self.intermediate_size,
-                bias=False,
-            )
-        else:
-            self.intermediate_token_shift_conv = None
-
-    def forward(self, x, per_token_scale=None):
-        # Token shift at MLP entry (conv mode; cat mode is stateless, not implemented here for loading conv checkpoints)
-        if self.ffn_token_shift == "conv" and self.token_shift_conv is not None:
-            # x: [batch, seq, hidden] -> [batch, hidden, seq]
-            x_bcl = x.transpose(1, 2).contiguous()
-            x_bcl = apply_causal_depthwise_conv1d_bcl(x_bcl, self.token_shift_conv.weight)
-            x = x_bcl.transpose(1, 2).contiguous()
-
-        # Compute gate and up projections
-        gate = self.gate_proj(x)
-        up = self.up_proj(x)
-        # Apply activation: act_fn(gate) * up
-        intermediate = self.act_fn(gate) * up
-        # Apply per_token_scale if provided (to align with Megatron's behavior)
-        if per_token_scale is not None:
-            intermediate = intermediate * per_token_scale.unsqueeze(-1)
-
-        # Intermediate token shift before down_proj (conv mode)
-        if self.ffn_intermediate_token_shift == "conv" and self.intermediate_token_shift_conv is not None:
-            inter_bcl = intermediate.transpose(1, 2).contiguous()
-            inter_bcl = apply_causal_depthwise_conv1d_bcl(inter_bcl, self.intermediate_token_shift_conv.weight)
-            intermediate = inter_bcl.transpose(1, 2).contiguous()
-
-        # Apply down projection
-        down_proj = self.down_proj(intermediate)
-        return down_proj
-
-
-class Qwen3NextSparseMoeBlock(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.num_experts = config.num_experts
-        self.top_k = config.num_experts_per_tok
-        self.norm_topk_prob = config.norm_topk_prob
-        self.score_func = config.moe_router_score_function
-
-        # Token shift at MoE entry (cannon layer, reuses ffn_token_shift)
-        self.ffn_token_shift = getattr(config, "ffn_token_shift", None)
-        kernel_size = getattr(config, "token_shift_conv_size", 4)
-        if self.ffn_token_shift == "conv":
-            self.token_shift_conv = nn.Conv1d(
-                config.hidden_size,
-                config.hidden_size,
-                kernel_size=kernel_size,
-                padding=0,
-                groups=config.hidden_size,
-                bias=False,
-            )
-        else:
-            self.token_shift_conv = None
-
-        # gating
-        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=config.router_bias)
-        self.experts = nn.ModuleList(
-            [Qwen3NextMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(self.num_experts)]
-        )
-
-        if config.shared_expert_intermediate_size > 0:
-            self.shared_expert = Qwen3NextMLP(config, intermediate_size=config.shared_expert_intermediate_size)
-            self.shared_expert_gate = torch.nn.Linear(config.hidden_size, 1, bias=False)
-        else:
-            self.shared_expert = None
-            self.shared_expert_gate = None
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        """ """
-        batch_size, sequence_length, hidden_dim = hidden_states.shape
-
-        # Token shift at MoE entry (cannon layer, conv mode)
-        if self.ffn_token_shift == "conv" and self.token_shift_conv is not None:
-            h_bcl = hidden_states.transpose(1, 2).contiguous()
-            h_bcl = apply_causal_depthwise_conv1d_bcl(h_bcl, self.token_shift_conv.weight)
-            hidden_states = h_bcl.transpose(1, 2).contiguous()
-
-        hidden_states = hidden_states.view(-1, hidden_dim)
-        # router_logits: (batch * sequence_length, n_experts)
-        router_logits = self.gate(hidden_states)
-
-        if self.score_func == "sigmoid":
-            routing_weights = torch.sigmoid(router_logits.to(torch.float32))
-        elif self.score_func == "softmax":
-            routing_weights = F.softmax(router_logits.to(torch.float32), dim=-1)
-        else:
-            raise NotImplementedError(f"Unknown score function {self.score_func}")
-        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
-        if self.norm_topk_prob:
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
-
-        final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
-        )
-
-        # One hot encode the selected experts to create an expert mask
-        # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
-
-        # Loop over all available experts in the model and perform the computation on each expert
-        expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
-        for expert_idx in expert_hit:
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
-
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert.
-            # To align with Megatron: apply routing_weights after activation, before down_proj
-            # (instead of after the full expert output)
-            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-            current_routing_weights = routing_weights[top_x, idx]  # [M] where M is num tokens for this expert
-            # Pass routing_weights as per_token_scale to apply it after activation, before down_proj
-            current_hidden_states = expert_layer(current_state, per_token_scale=current_routing_weights)
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
-
-        if self.shared_expert is not None:
-            shared_expert_output = self.shared_expert(hidden_states)
-            if self.shared_expert_gate is not None:
-                shared_expert_output = F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output
-
-            final_hidden_states = final_hidden_states + shared_expert_output
-
-        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
-        return final_hidden_states, router_logits
-
-
-class Qwen3NextDecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: Qwen3NextConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        # token mixer
-        self.layer_type = config.layer_types[layer_idx]
-        if self.layer_type == "linear_attention":
-            self.linear_attn = Qwen3NextGatedDeltaNet(config, layer_idx)
-        elif self.layer_type == "full_attention":
-            self.self_attn = Qwen3NextAttention(config, layer_idx)
-
-        if (layer_idx not in config.mlp_only_layers) and (
-            config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
-        ):
-            self.mlp = Qwen3NextSparseMoeBlock(config)
-        else:
-            self.mlp = Qwen3NextMLP(config, intermediate_size=config.intermediate_size)
-
-        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        # Token shift before attention (cannon layer, conv mode)
-        self.attn_token_shift = getattr(config, "attn_token_shift", None)
-        kernel_size = getattr(config, "token_shift_conv_size", 4)
-        if self.attn_token_shift == "conv":
-            self.attn_token_shift_conv = nn.Conv1d(
-                config.hidden_size,
-                config.hidden_size,
-                kernel_size=kernel_size,
-                padding=0,
-                groups=config.hidden_size,
-                bias=False,
-            )
-        else:
-            self.attn_token_shift_conv = None
-
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[tuple[torch.Tensor]] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> torch.FloatTensor:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, sequence_length)` where padding elements are indicated by 0.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_router_logits (`bool`, *optional*):
-                Whether or not to return the logits of all the routers. They are useful for computing the router loss,
-                and should not be returned during inference.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-            past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-                Indices depicting the position of the input sequence tokens in the sequence.
-            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
-                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
-                with `head_dim` being the embedding dimension of each attention head.
-            kwargs (`dict`, *optional*):
-                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
-                into the model
-        """
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Token shift before attention (cannon layer, conv mode)
-        if self.attn_token_shift == "conv" and self.attn_token_shift_conv is not None:
-            # [batch, seq, hidden] -> [batch, hidden, seq]
-            h_bcl = hidden_states.transpose(1, 2).contiguous()
-            h_bcl = apply_causal_depthwise_conv1d_bcl(h_bcl, self.attn_token_shift_conv.weight)
-            hidden_states = h_bcl.transpose(1, 2).contiguous()
-
-        # Token Mixer
-        if self.layer_type == "linear_attention":
-            hidden_states = self.linear_attn(
-                hidden_states=hidden_states,
-                cache_params=past_key_values,
-                cache_position=cache_position,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-            )
-        elif self.layer_type == "full_attention":
-            # Self Attention
-            hidden_states, _ = self.self_attn(
-                hidden_states=hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                **kwargs,
-            )
-
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        # For the MoE layers, we need to unpack
-        if isinstance(hidden_states, tuple):
-            hidden_states, _ = hidden_states
-        hidden_states = residual + hidden_states
-
-        return hidden_states
-
-
-class Qwen3NextPreTrainedModel(PreTrainedModel):
-    config: Qwen3NextConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["Qwen3NextDecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _keys_to_ignore_on_load_unexpected = [r"^mtp.*"]
-    _can_record_outputs = {
-        "router_logits": OutputRecorder(Qwen3NextSparseMoeBlock, index=1),
-        "hidden_states": Qwen3NextDecoderLayer,
-        "attentions": Qwen3NextAttention,
-    }
-    _is_stateful = True
-
-    def _init_weights(self, module):
-        super()._init_weights(module)
-        if isinstance(module, Qwen3NextGatedDeltaNet):
-            module.dt_bias.data.fill_(1.0)
-            module.A_log.data.uniform_(0, 16).log_()
-
-
-class Qwen3NextModel(Qwen3NextPreTrainedModel):
-    def __init__(self, config: Qwen3NextConfig):
-        super().__init__(config)
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
-        self.layers = nn.ModuleList(
-            [Qwen3NextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = Qwen3NextRotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-        _log_custom_features(config)
-
-    # @check_model_inputs
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Unpack[TransformersKwargs],
-    ) -> MoeModelOutputWithPast:
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        if use_cache and past_key_values is None:
-            past_key_values = Qwen3NextDynamicCache(config=self.config)
-
-        if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = create_causal_mask(
-            config=self.config,
-            input_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            cache_position=cache_position,
-            past_key_values=past_key_values,
-            position_ids=position_ids,
-        )
-        linear_attn_mask = self._update_linear_attn_mask(attention_mask, cache_position)
-
-        hidden_states = inputs_embeds
-
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            layer_mask = linear_attn_mask if decoder_layer.layer_type == "linear_attention" else causal_mask
-
-            hidden_states = decoder_layer(
-                hidden_states,
-                position_embeddings=position_embeddings,
-                attention_mask=layer_mask,
-                position_ids=position_ids,
-                past_key_values=past_key_values,
-                use_cache=use_cache,
-                cache_position=cache_position,
-                **kwargs,
-            )
-
-        hidden_states = self.norm(hidden_states)
-
-        return MoeModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values,
-        )
-
-    def _update_linear_attn_mask(self, attention_mask, cache_position):
-        """
-        NOTE: Left-padding is used for linear attention mask.
-        No need for zeroing states when
-            1. Cached forward
-            2. Attending to all inputs
-        """
-        linear_attn_mask = attention_mask
-        if cache_position[0] > 0 or (attention_mask is not None and torch.all(attention_mask == 1)):
-            linear_attn_mask = None
-        return linear_attn_mask
-
-
-def load_balancing_loss_func(
-    gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
-    num_experts: Optional[int] = None,
-    top_k=2,
-    attention_mask: Optional[torch.Tensor] = None,
-) -> Union[torch.Tensor, int]:
-    r"""
-    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
-
-    See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
-    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
-    experts is too unbalanced.
-
-    Args:
-        gate_logits:
-            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
-            shape [batch_size X sequence_length, num_experts].
-        num_experts:
-            Number of experts
-        top_k:
-            The number of experts to route per-token, can be also interpreted as the `top-k` routing
-            parameter.
-        attention_mask (`torch.Tensor`, *optional*):
-            The attention_mask used in forward function
-            shape [batch_size X sequence_length] if not None.
-
-    Returns:
-        The auxiliary loss.
-    """
-    if gate_logits is None or not isinstance(gate_logits, tuple):
-        return 0
-
-    if isinstance(gate_logits, tuple):
-        compute_device = gate_logits[0].device
-        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
-
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
-
-    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
-
-    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
-
-    if attention_mask is None:
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.mean(routing_weights, dim=0)
-    else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
-        expert_attention_mask = (
-            attention_mask[None, :, :, None, None]
-            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
-            .reshape(-1, top_k, num_experts)
-            .to(compute_device)
-        )
-
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
-        )
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
-        router_per_expert_attention_mask = (
-            attention_mask[None, :, :, None]
-            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
-            .reshape(-1, num_experts)
-            .to(compute_device)
-        )
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
-            router_per_expert_attention_mask, dim=0
-        )
-
-    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
-    return overall_loss * num_experts
-
-
-@auto_docstring
-class Qwen3NextForCausalLM(Qwen3NextPreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-    _tp_plan = {"lm_head": "colwise_rep"}
-    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = Qwen3NextModel(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.router_aux_loss_coef = config.router_aux_loss_coef
-        self.num_experts = config.num_experts
-        self.num_experts_per_tok = config.num_experts_per_tok
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @can_return_tuple
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Qwen3NextDynamicCache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_router_logits: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        logits_to_keep: Union[int, torch.Tensor] = 0,
-        **kwargs: Unpack[TransformersKwargs],
-    ) -> MoeCausalLMOutputWithPast:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, Qwen3NextForCausalLM
-
-        >>> model = Qwen3NextForCausalLM.from_pretrained("Qwen/Qwen3-Next-80B-A3B-Instruct")
-        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Next-80B-A3B-Instruct")
-
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```"""
-
-        output_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
-        )
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs: MoeModelOutputWithPast = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_router_logits=output_router_logits,
-            cache_position=cache_position,
-            **kwargs,
-        )
-
-        hidden_states = outputs.last_hidden_state
-        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
-
-        aux_loss = None
-        if output_router_logits:
-            aux_loss = load_balancing_loss_func(
-                outputs.router_logits,
-                self.num_experts,
-                self.num_experts_per_tok,
-                attention_mask,
-            )
-            if labels is not None:
-                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
-
-        return MoeCausalLMOutputWithPast(
-            loss=loss,
-            aux_loss=aux_loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            router_logits=outputs.router_logits,
-        )
-
-
-__all__ = [
-    "Qwen3NextForCausalLM",
-    "Qwen3NextModel",
-    "Qwen3NextPreTrainedModel",
-]

grid_search_0318/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50/special_tokens_map.json

@@ -1,30 +0,0 @@
-{
-  "bos_token": {
-    "content": "<s>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "eos_token": {
-    "content": "</s>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "<pad>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "<unk>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}

grid_search_0318/yulan-gdn-sft-1b-sl65536-lr5e-6-gbs16-mb1-tp2-pp1-cp4-fresh-2026.03.19-18.56.50/tokenizer_config.json

@@ -1,436 +0,0 @@
-{
-  "add_bos_token": false,
-  "add_eos_token": false,
-  "add_prefix_space": null,
-  "added_tokens_decoder": {
-    "0": {
-      "content": "<unk>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": "<s>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": "<|endoftext|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "102": {
-      "content": "<pad>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "103": {
-      "content": "<reasoning_step>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "104": {
-      "content": "<|im_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "105": {
-      "content": "<|im_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "106": {
-      "content": "<|object_ref_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "107": {
-      "content": "<|object_ref_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "108": {
-      "content": "<|box_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "109": {
-      "content": "<|box_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "110": {
-      "content": "<|quad_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "111": {
-      "content": "<|quad_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "112": {
-      "content": "<|vision_start|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "113": {
-      "content": "<|vision_end|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "114": {
-      "content": "<|vision_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "115": {
-      "content": "<|image_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "116": {
-      "content": "<|video_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "117": {
-      "content": "<tool_call>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "118": {
-      "content": "</tool_call>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "119": {
-      "content": "<|fim_prefix|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "120": {
-      "content": "<|fim_middle|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "121": {
-      "content": "<|fim_suffix|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "122": {
-      "content": "<|fim_pad|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "123": {
-      "content": "<|repo_name|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "124": {
-      "content": "<|file_sep|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "125": {
-      "content": "<tool_response>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "126": {
-      "content": "</tool_response>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "127": {
-      "content": "<think>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "128": {
-      "content": "</think>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "1071": {
-      "content": "<|sequence|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1072": {
-      "content": "<|/sequence|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1073": {
-      "content": "<|identity|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1074": {
-      "content": "<|identity|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1075": {
-      "content": "<|tail0|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1076": {
-      "content": "<|tail1|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1077": {
-      "content": "<|tail2|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1078": {
-      "content": "<|tail3|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1079": {
-      "content": "<|tail4|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1080": {
-      "content": "<|head0|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1081": {
-      "content": "<|head1|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1082": {
-      "content": "<|head2|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1083": {
-      "content": "<|head3|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1084": {
-      "content": "<|head4|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1085": {
-      "content": "<|chunk_id|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1086": {
-      "content": "<|/chunk_id|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1087": {
-      "content": "<|last_chunk_id|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1088": {
-      "content": "<|/last_chunk_id|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "additional_special_tokens": [
-    "<|im_start|>",
-    "<|im_end|>",
-    "<|object_ref_start|>",
-    "<|object_ref_end|>",
-    "<|box_start|>",
-    "<|box_end|>",
-    "<|quad_start|>",
-    "<|quad_end|>",
-    "<|vision_start|>",
-    "<|vision_end|>",
-    "<|vision_pad|>",
-    "<|image_pad|>",
-    "<|video_pad|>",
-    "<|sequence|>",
-    "<|/sequence|>",
-    "<|identity|>",
-    "<|/identity|>",
-    "<|tail0|>",
-    "<|tail1|>",
-    "<|tail2|>",
-    "<|tail3|>",
-    "<|tail4|>",
-    "<|head0|>",
-    "<|head1|>",
-    "<|head2|>",
-    "<|head3|>",
-    "<|head4|>",
-    "<|chunk_id|>",
-    "<|/chunk_id|>",
-    "<|last_chunk_id|>",
-    "<|/last_chunk_id|>"
-  ],
-  "bos_token": null,
-  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n<think>\\n' }}\n{%- endif %}",
-  "clean_up_tokenization_spaces": false,
-  "eos_token": "<|im_end|>",
-  "model_max_length": 32768,
-  "pad_token": "<|endoftext|>",
-  "padding_side": "right",
-  "sp_model_kwargs": {},
-  "spaces_between_special_tokens": false,
-  "tokenizer_class": "LlamaTokenizerFast",
-  "unk_token": "<unk>",
-  "use_default_system_prompt": false
-}