support smt and glm4

.gitignore

@@ -27,3 +27,7 @@ mmlu_result*
 ktransformers/ktransformers_ext/cuda_musa/
 test_prompt.txt
 csrc/demo
+build*
+CMakeFiles/
+kvc2/
+sched/

ktransformers/ktransformers

@@ -0,0 +1 @@
+/home/djw/py311_717/ktransformers/ktransformers

ktransformers/models/configuration_glm4_moe.py

@@ -0,0 +1,242 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/glm4_moe/modular_glm4_moe.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_glm4_moe.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The ZhipuAI Inc. team and 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.
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_rope_utils import rope_config_validation
+
+
+class Glm4MoeConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Glm4MoeModel`]. It is used to instantiate a
+    Glm4Moe model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of [THUDM/GLM-4-100B-A10B](https://huggingface.co/THUDM/GLM-4-100B-A10B).
+
+    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 151552):
+            Vocabulary size of the Glm4Moe model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Glm4MoeModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 10944):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 46):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 96):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        partial_rotary_factor (`float`, *optional*, defaults to 0.5):
+            The factor of the partial rotary position.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            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, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
+
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 131072):
+            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-05):
+            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
+        attention_bias (`bool`, defaults to `False`, *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.
+        moe_intermediate_size (`int`, *optional*, defaults to 1408):
+            Intermediate size of the routed expert.
+        num_experts_per_tok (`int`, *optional*, defaults to 8):
+            number of experts per token.
+        n_shared_experts (`int`, *optional*, defaults to 1):
+            Number of shared experts.
+        n_routed_experts (`int`, *optional*, defaults to 128):
+            Number of routed experts.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor or routed experts.
+        n_group (`int`, *optional*, defaults to 1):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to 1):
+            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+        first_k_dense_replace (`int`, *optional*, defaults to 1):
+            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+                                                            \--k dense layers--/
+        norm_topk_prob (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the topk probabilities.
+        use_qk_norm (`bool`, *optional*, defaults to `False`):
+            Whether to use query-key normalization in the attention
+    ```python
+    >>> from transformers import Glm4MoeModel, Glm4MoeConfig
+
+    >>> # Initializing a Glm4Moe style configuration
+    >>> configuration = Glm4MoeConfig()
+
+    >>> # Initializing a model from the GLM-4-MOE-100B-A10B style configuration
+    >>> model = Glm4MoeModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "glm4_moe"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Default tensor parallel plan for base model `Glm4Moe`
+    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.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=151552,
+        hidden_size=4096,
+        intermediate_size=10944,
+        num_hidden_layers=46,
+        num_attention_heads=96,
+        partial_rotary_factor=0.5,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=131072,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        moe_intermediate_size=1408,
+        num_experts_per_tok=8,
+        n_shared_experts=1,
+        n_routed_experts=128,
+        routed_scaling_factor=1.0,
+        n_group=1,
+        topk_group=1,
+        first_k_dense_replace=1,
+        norm_topk_prob=True,
+        use_qk_norm=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.partial_rotary_factor = partial_rotary_factor
+
+        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.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)
+
+        # MoE arguments
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.routed_scaling_factor = routed_scaling_factor
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.use_qk_norm = use_qk_norm
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["Glm4MoeConfig"]

ktransformers/models/configuration_smallthinker.py

@@ -0,0 +1,177 @@
+# coding=utf-8
+from transformers.configuration_utils import PretrainedConfig
+
+class SmallthinkerConfig(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`SmallthinkerModel`]. 
+    It is used to instantiate a Smallthinker model according to the specified arguments, defining the model architecture. 
+    The default values for each of the parameters are the same as the ones used in the original Smallthinker 4B model.
+
+    General configs:
+    - model_type: "smallthinker"
+    - model_name
+    - num_hidden_layers
+    - hidden_size
+
+    Tokenizer configs:
+    - pad_token_id
+    - bos_token_id
+    - eos_token_id
+
+    Embedding configs:
+    - vocab_size
+
+    RMSNorm configs:
+    - rms_norm_eps
+
+    Attention configs:
+    - num_attention_heads
+    - num_key_value_heads
+    - head_dim
+    - use_cache
+    - use_qk_norm
+    - rope_layout: array of 0 or 1s, 0 for nope, 1 for rope
+    - rope_theta
+    - max_position_embeddings
+    - sliding_window_layout: array of 0 or 1s, 0 for normal attention, 1 for SWA
+    - sliding_window_size
+
+    General FFN configs:
+    - moe_layer_layout: array of 0 or 1s, 0 for dense layer, 1 for MoE layer
+    
+    Dense FFN configs:
+    - dense_ffn_hidden_size
+
+    MoE FFN configs:
+    - moe_num_primary_experts
+    - moe_shared_primary_experts
+    - moe_ffn_hidden_size
+    - moe_enable_early_router: Use attention output as router input if true
+    - moe_primary_router_use_sigmoid: Use normalized sigmoid 
+    - moe_num_active_primary_experts
+    - moe_enable_secondary_experts
+    - moe_num_secondary_experts
+    - moe_secondary_expert_size
+
+    LM Head configs:
+    - tie_word_embeddings
+
+    Visibility configs:
+    - profile_sparsity
+
+    Other configs:
+    - initializer_range
+    """
+    def __init__(self,
+        model_type = "smallthinker",
+        model_name="smallthinker_4b_base",
+        num_hidden_layers=32,
+        hidden_size=1536,
+        pad_token_id=None,
+        bos_token_id=151643,
+        eos_token_id=[151643,151645],
+        vocab_size=151936,
+        rms_norm_eps=1e-6,
+        num_attention_heads=12,
+        num_key_value_heads=2,
+        head_dim=128,
+        use_cache=True,
+        use_qk_norm=False,
+        rope_layout=[1]*32,
+        rope_theta=1e6,
+        max_position_embeddings=4096 * 32,
+        sliding_window_layout=[0]*32,
+        sliding_window_size=4096,
+        moe_layer_layout=[1]*32,
+        dense_ffn_hidden_size=4096,
+        moe_num_primary_experts=32,
+        moe_shared_primary_experts=0,
+        moe_ffn_hidden_size=768,
+        moe_enable_early_router=True,
+        moe_primary_router_apply_softmax=False,
+        moe_num_active_primary_experts=4,
+        moe_enable_secondary_experts=False,
+        moe_num_secondary_experts=0,
+        moe_secondary_expert_size=0,
+        tie_word_embeddings=True,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        # Configuration sanitizers
+        assert num_attention_heads % num_key_value_heads == 0,      "[Smallthinker config sanitizer] num_attention_heads must be divisible by num_key_value_heads"
+        assert len(rope_layout) == num_hidden_layers,               "[Smallthinker config sanitizer] rope_layout must have the same length as num_hidden_layers"
+        assert len(sliding_window_layout) == num_hidden_layers,     "[Smallthinker config sanitizer] sliding_window_layout must have the same length as num_hidden_layers"
+        assert len(moe_layer_layout) == num_hidden_layers,          "[Smallthinker config sanitizer] moe_layer_layout must have the same length as num_hidden_layers"
+
+        if any(moe_layer_layout):
+            assert moe_num_primary_experts != 0,                    "[Smallthinker config sanitizer] moe_num_primary_experts must be set non-zero if there is any MoE layer"
+            assert moe_ffn_hidden_size != 0,                        "[Smallthinker config sanitizer] moe_ffn_hidden_size must be set non-zero if there is any MoE layer"
+            assert moe_num_active_primary_experts != 0,             "[Smallthinker config sanitizer] moe_num_active_primary_experts must be set non-zero if there is any MoE layer"
+            if moe_enable_secondary_experts:
+                assert moe_num_secondary_experts != 0,              "[Smallthinker config sanitizer] moe_num_secondary_experts must be set non-zero if moe_enable_secondary_experts is True"
+                assert moe_secondary_expert_size != 0,              "[Smallthinker config sanitizer] moe_secondary_expert_size must be set non-zero if moe_enable_secondary_experts is True"
+                assert moe_num_secondary_experts * moe_secondary_expert_size == moe_ffn_hidden_size, "[Smallthinker config sanitizer] moe_num_secondary_experts * moe_secondary_expert_size must equal moe_ffn_hidden_size"
+
+        if not all(moe_layer_layout):
+            assert dense_ffn_hidden_size != 0,                      "[Smallthinker config sanitizer] dense_ffn_hidden_size must be set non-zero if there is any dense FFN layer"
+
+        # General configs
+        self.model_type = model_type
+        self.model_name = model_name
+        self.num_hidden_layers = num_hidden_layers
+        self.hidden_size = hidden_size
+
+        # Tokenizer configs
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        # Embedding configs
+        self.vocab_size = vocab_size
+
+        # RMSNorm configs
+        self.rms_norm_eps = rms_norm_eps
+
+        # Attention configs
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.use_cache = use_cache
+        self.use_qk_norm = use_qk_norm
+        self.rope_layout = rope_layout
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.sliding_window_layout = sliding_window_layout
+        self.sliding_window_size = sliding_window_size
+
+        # General FFN configs
+        self.moe_layer_layout = moe_layer_layout
+
+        # Dense FFN configs
+        self.dense_ffn_hidden_size = dense_ffn_hidden_size
+
+        # MoE FFN configs
+        self.moe_num_primary_experts = moe_num_primary_experts
+        self.moe_shared_primary_experts = moe_shared_primary_experts
+        self.moe_ffn_hidden_size = moe_ffn_hidden_size
+        self.moe_enable_early_router = moe_enable_early_router
+        self.moe_primary_router_apply_softmax = moe_primary_router_apply_softmax
+        self.moe_num_active_primary_experts = moe_num_active_primary_experts
+        self.moe_enable_secondary_experts = moe_enable_secondary_experts
+        self.moe_num_secondary_experts = moe_num_secondary_experts
+        self.moe_secondary_expert_size = moe_secondary_expert_size
+
+        # Logging configs
+        # self.output_router_logits = False
+
+        # Other configs
+        self.initializer_range = initializer_range
+
+        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+        self._attn_implementation = "eager" # SDPA is not allowed for now
+
+        # if self._attn_implementation != "flash_attention_2":
+        #     raise NotImplementedError("SDPA impl is buggy for now. NEVER TRY TO USE IT.")
+        
+__all__ = ["SmallthinkerConfig"]

ktransformers/models/custom_modeling_glm4_moe.py

@@ -0,0 +1,140 @@
+"""
+Date: 2024-11-06 10:05:11
+LastEditors: djw
+LastEditTime: 2024-11-13 07:50:51
+"""
+
+import math
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import math
+from typing import List, Optional, Tuple, Union
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from ktransformers.server.balance_serve.inference.forward_batch import ForwardBatchInput, ForwardBatchOutput
+from ktransformers.models.custom_cache import KGQACache
+from ktransformers.models.modeling_glm4_moe import Glm4MoeModel,  Glm4MoePreTrainedModel
+from ktransformers.models.configuration_glm4_moe import Glm4MoeConfig
+from ktransformers.operators.flashinfer_batch_prefill_wrapper import flashInferAttn
+
+torch.set_grad_enabled(False)
+torch.set_default_dtype(torch.bfloat16)
+import flashinfer
+
+class KGlm4MoeForCausalLM(Glm4MoePreTrainedModel):
+
+    cache: KGQACache
+    use_cuda_graph = False
+    def __init__(
+        self,
+        config: Glm4MoeConfig,
+        cache,
+    ):
+
+        super().__init__(config)
+        self.model = Glm4MoeModel(config)
+        self.config = config
+        self.cache = cache
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.attn = [None] * 100
+        
+    def init_wrapper(self, use_cuda_graph, device, max_batch_token, max_batch_size, max_pages, cuda_graph_idx = 0):
+        self.attn[cuda_graph_idx] = flashInferAttn(use_cuda_graph=use_cuda_graph, max_batch_token=max_batch_token, max_batch_size=max_batch_size, max_pages=max_pages, device=device)
+
+
+    def batch_embeddings(self, batch: ForwardBatchInput, device="cuda:0"):
+        features = []
+        for i in range(batch.batch_size):
+            tokens = batch.minibatch.tokens.contiguous()
+            feature = (
+                self.model.embed_tokens(tokens.to(torch.device('cpu')))
+                .to(torch.bfloat16)
+                .to(device=device)
+            )
+            features.append(feature)
+
+        return features
+
+
+    def forward(
+        self,
+        batch: ForwardBatchInput | None = None,
+        features: List[torch.Tensor] | None = None,
+        bsz_tensors: torch.Tensor | None = None,
+        num_tokens_tensors: torch.Tensor | None = None,
+        page_idx: torch.Tensor | None = None,
+        page_offset: torch.Tensor | None = None,
+        cuda_graph_idx: int | None = 0
+    ) -> ForwardBatchOutput:
+        current_stream = torch.cuda.current_stream()
+
+        forward_batch_output = ForwardBatchOutput()
+
+        
+        hidden_states = features[0]
+        self.attn[cuda_graph_idx].calc_batch_indices(hidden_states.shape[0])
+
+        freqs_cis = self.model.rotary_emb(hidden_states.unsqueeze(0), batch.minibatch.position_ids.unsqueeze(0))
+
+        with torch.cuda.stream(current_stream):
+            residual = torch.zeros_like(hidden_states)
+            for i, decode_layer in enumerate(self.model.layers):
+                if self.model.transfer_map is not None and i in self.model.transfer_map:
+                    prev_stream = torch.cuda.current_stream()
+                    cur_device = self.model.transfer_map[i]
+                    if cur_device not in self.model.stream_device_map:
+                        self.model.stream_device_map[cur_device] = torch.cuda.Stream(cur_device)
+                    torch.cuda.set_device(cur_device)
+                    self.model.stream_device_map[cur_device].wait_stream(prev_stream)
+                    torch.cuda.set_stream(self.model.stream_device_map[cur_device])
+                    hidden_states = hidden_states.to(
+                        self.model.transfer_map[i], non_blocking=True
+                    )
+
+                    batch.minibatch.position_ids = (
+                        batch.minibatch.position_ids.to(self.model.transfer_map[i], non_blocking=True)
+                        if batch.minibatch.position_ids is not None
+                        else None
+                    )
+                router_input = hidden_states.clone()
+                hidden_states, residual = decode_layer.input_layernorm(hidden_states, num_tokens_tensors, residual)
+                hidden_states = decode_layer.self_attn(hidden_states, self.cache, 
+                                                       freqs_cis,
+                                                       wrapper=self.attn[cuda_graph_idx], bsz_tensors=num_tokens_tensors, 
+                                                       position_ids=batch.minibatch.position_ids
+                                                       )
+
+                hidden_states, residual = decode_layer.post_attention_layernorm(hidden_states, num_tokens_tensors, residual)
+                if i < self.model.config.first_k_dense_replace:
+                    hidden_states = decode_layer.feed_forward(router_input, hidden_states, num_tokens_tensors)
+                else:
+                    hidden_states = decode_layer.feed_forward(hidden_states, num_tokens_tensors, cuda_graph_idx)
+                    # hidden_states = hidden_states.squeeze(0)
+
+        forward_batch_output = ForwardBatchOutput()
+        with torch.cuda.stream(current_stream):
+            local_logit = self.lm_head(self.model.norm(hidden_states, num_tokens_tensors, residual)[0], num_tokens_tensors)
+            forward_batch_output.logits.append(local_logit)
+
+        return forward_batch_output
+    
+
+               
+    def flash_infer_attn_plan(self, batch: ForwardBatchInput, bsz_tensors, num_tokens_tensors,
+        num_q_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        page_size: int,
+        causal: bool,
+        q_data_type: torch.dtype,
+        kv_data_type: torch.dtype,
+        cuda_graph_idx: int = 0
+        ):
+        minibatch = batch.minibatch
+        self.attn[cuda_graph_idx].plan(minibatch.q_indptr, minibatch.kv_indptr, minibatch.kv_indices, 
+                          minibatch.kv_last_page_len, bsz_tensors, num_tokens_tensors, num_q_heads, num_kv_heads, head_dim, page_size, causal=causal, q_data_type=q_data_type, kv_data_type=kv_data_type)
+        

ktransformers/models/custom_modeling_smallthinker.py

@@ -0,0 +1,140 @@
+"""
+Date: 2024-11-06 10:05:11
+LastEditors: djw
+LastEditTime: 2024-11-13 07:50:51
+"""
+
+import math
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import math
+from typing import List, Optional, Tuple, Union
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from ktransformers.server.balance_serve.inference.forward_batch import ForwardBatchInput, ForwardBatchOutput
+from ktransformers.models.custom_cache import KGQACache
+from ktransformers.models.modeling_smallthinker import SmallthinkerModel,  SmallthinkerPreTrainedModel
+from ktransformers.models.configuration_smallthinker import SmallthinkerConfig
+from ktransformers.operators.flashinfer_batch_prefill_wrapper import flashInferAttn
+
+torch.set_grad_enabled(False)
+torch.set_default_dtype(torch.bfloat16)
+import flashinfer
+
+class KSmallthinkerForCausalLM(SmallthinkerPreTrainedModel):
+
+    cache: KGQACache
+    use_cuda_graph = False
+    def __init__(
+        self,
+        config: SmallthinkerConfig,
+        cache,
+    ):
+
+        super().__init__(config)
+        self.model = SmallthinkerModel(config)
+        self.config = config
+        self.cache = cache
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.attn = [None] * 100
+        
+    def init_wrapper(self, use_cuda_graph, device, max_batch_token, max_batch_size, max_pages, cuda_graph_idx = 0):
+        self.attn[cuda_graph_idx] = flashInferAttn(use_cuda_graph=use_cuda_graph, max_batch_token=max_batch_token, max_batch_size=max_batch_size, max_pages=max_pages, device=device)
+
+
+    def batch_embeddings(self, batch: ForwardBatchInput, device="cuda:0"):
+        features = []
+        for i in range(batch.batch_size):
+            tokens = batch.minibatch.tokens.contiguous()
+            feature = (
+                self.model.embed_tokens(tokens.to(torch.device('cpu')))
+                .to(torch.bfloat16)
+                .to(device=device)
+            )
+            features.append(feature)
+
+        return features
+
+
+    def forward(
+        self,
+        batch: ForwardBatchInput | None = None,
+        features: List[torch.Tensor] | None = None,
+        bsz_tensors: torch.Tensor | None = None,
+        num_tokens_tensors: torch.Tensor | None = None,
+        page_idx: torch.Tensor | None = None,
+        page_offset: torch.Tensor | None = None,
+        cuda_graph_idx: int | None = 0
+    ) -> ForwardBatchOutput:
+        current_stream = torch.cuda.current_stream()
+
+        forward_batch_output = ForwardBatchOutput()
+
+        
+        hidden_states = features[0]
+        self.attn[cuda_graph_idx].calc_batch_indices(hidden_states.shape[0])
+
+        freqs_cis = self.model.rotary_emb(hidden_states.unsqueeze(0), batch.minibatch.position_ids.unsqueeze(0))
+
+        with torch.cuda.stream(current_stream):
+            residual = torch.zeros_like(hidden_states)
+            for i, decode_layer in enumerate(self.model.layers):
+                if self.model.transfer_map is not None and i in self.model.transfer_map:
+                    prev_stream = torch.cuda.current_stream()
+                    cur_device = self.model.transfer_map[i]
+                    if cur_device not in self.model.stream_device_map:
+                        self.model.stream_device_map[cur_device] = torch.cuda.Stream(cur_device)
+                    torch.cuda.set_device(cur_device)
+                    self.model.stream_device_map[cur_device].wait_stream(prev_stream)
+                    torch.cuda.set_stream(self.model.stream_device_map[cur_device])
+                    hidden_states = hidden_states.to(
+                        self.model.transfer_map[i], non_blocking=True
+                    )
+
+                    batch.minibatch.position_ids = (
+                        batch.minibatch.position_ids.to(self.model.transfer_map[i], non_blocking=True)
+                        if batch.minibatch.position_ids is not None
+                        else None
+                    )
+                router_input = hidden_states.clone()
+                hidden_states, residual = decode_layer.input_layernorm(hidden_states, num_tokens_tensors, residual)
+                hidden_states = decode_layer.self_attn(hidden_states, self.cache, 
+                                                       freqs_cis if self.model.rope_layout[i] else None, 
+                                                       wrapper=self.attn[cuda_graph_idx], bsz_tensors=num_tokens_tensors, 
+                                                       position_ids=batch.minibatch.position_ids
+                                                       )
+
+                hidden_states, residual = decode_layer.post_attention_layernorm(hidden_states, num_tokens_tensors, residual)
+                if not self.config.moe_layer_layout[i]:
+                    hidden_states = decode_layer.feed_forward(router_input, hidden_states, num_tokens_tensors)
+                else:
+                    hidden_states = decode_layer.feed_forward(hidden_states, num_tokens_tensors, cuda_graph_idx)
+                    # hidden_states = hidden_states.squeeze(0)
+
+        forward_batch_output = ForwardBatchOutput()
+        with torch.cuda.stream(current_stream):
+            local_logit = self.lm_head(self.model.norm(hidden_states, num_tokens_tensors, residual)[0], num_tokens_tensors)
+            forward_batch_output.logits.append(local_logit)
+
+        return forward_batch_output
+    
+
+               
+    def flash_infer_attn_plan(self, batch: ForwardBatchInput, bsz_tensors, num_tokens_tensors,
+        num_q_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        page_size: int,
+        causal: bool,
+        q_data_type: torch.dtype,
+        kv_data_type: torch.dtype,
+        cuda_graph_idx: int = 0
+        ):
+        minibatch = batch.minibatch
+        self.attn[cuda_graph_idx].plan(minibatch.q_indptr, minibatch.kv_indptr, minibatch.kv_indices, 
+                          minibatch.kv_last_page_len, bsz_tensors, num_tokens_tensors, num_q_heads, num_kv_heads, head_dim, page_size, causal=causal, q_data_type=q_data_type, kv_data_type=kv_data_type)
+        

ktransformers/models/modeling_glm4_moe.py

@@ -0,0 +1,650 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/glm4_moe/modular_glm4_moe.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_glm4_moe.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The ZhipuAI Inc. team and 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.
+
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.generic import check_model_inputs
+from .configuration_glm4_moe import Glm4MoeConfig
+
+
+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
+
+
+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)
+
+
+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.
+
+    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
+
+
+class Glm4MoeAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Glm4MoeConfig, layer_idx: Optional[int] = None):
+        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.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, 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.use_qk_norm = config.use_qk_norm
+        if self.use_qk_norm:
+            self.q_norm = Glm4MoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.k_norm = Glm4MoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape)
+        key_states = self.k_proj(hidden_states).view(hidden_shape)
+        value_states = self.v_proj(hidden_states).view(hidden_shape)
+
+        if self.use_qk_norm:  # main diff from Llama
+            query_states = self.q_norm(query_states)
+            key_states = self.k_norm(key_states)
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        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,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class Glm4MoeMLP(nn.Module):
+    def __init__(self, config, hidden_size=None, intermediate_size=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
+        self.intermediate_size = config.intermediate_size if intermediate_size is None else 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]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class Glm4MoeTopkRouter(nn.Module):
+    def __init__(self, config: Glm4MoeConfig):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.n_group = config.n_group
+        self.topk_group = config.topk_group
+        self.norm_topk_prob = config.norm_topk_prob
+
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
+        self.register_buffer("e_score_correction_bias", torch.zeros((self.n_routed_experts), dtype=torch.float32))
+
+    @torch.no_grad()
+    def get_topk_indices(self, scores):
+        scores_for_choice = scores.view(-1, self.n_routed_experts) + self.e_score_correction_bias.unsqueeze(0)
+        group_scores = (
+            scores_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group)
+            .topk(2, dim=-1)[0]
+            .sum(dim=-1)
+        )
+        group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
+        group_mask = torch.zeros_like(group_scores)
+        group_mask.scatter_(1, group_idx, 1)
+        score_mask = (
+            group_mask.unsqueeze(-1)
+            .expand(-1, self.n_group, self.n_routed_experts // self.n_group)
+            .reshape(-1, self.n_routed_experts)
+        )
+        scores_for_choice = scores_for_choice.masked_fill(~score_mask.bool(), 0.0)
+        topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1]
+        return topk_indices
+
+    def forward(self, hidden_states):
+        hidden_states = hidden_states.view(-1, self.config.hidden_size)
+        router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
+        scores = router_logits.sigmoid()
+        topk_indices = self.get_topk_indices(scores)
+        topk_weights = scores.gather(1, topk_indices)
+        if self.norm_topk_prob:
+            denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weights /= denominator
+        topk_weights = topk_weights * self.routed_scaling_factor
+        return topk_indices, topk_weights
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Glm4MoeRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Glm4MoeRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.hidden_size = hidden_size
+        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 extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class Glm4MoeMoE(nn.Module):
+    """
+    A mixed expert module containing shared experts.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList(
+            [
+                Glm4MoeMLP(config, intermediate_size=config.moe_intermediate_size)
+                for _ in range(config.n_routed_experts)
+            ]
+        )
+        self.gate = Glm4MoeTopkRouter(config)
+        self.shared_experts = Glm4MoeMLP(
+            config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts
+        )
+
+    def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor):
+        r"""
+        CALL FOR CONTRIBUTION! I don't have time to optimise this right now, but expert weights need to be fused
+        to not have to do a loop here (deepseek has 256 experts soooo yeah).
+        """
+        final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype)
+        expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=len(self.experts))
+        expert_mask = expert_mask.permute(2, 0, 1)
+
+        for expert_idx in range(len(self.experts)):
+            expert = self.experts[expert_idx]
+            mask = expert_mask[expert_idx]
+            token_indices, weight_indices = torch.where(mask)
+
+            if token_indices.numel() > 0:
+                expert_weights = topk_weights[token_indices, weight_indices]
+                expert_input = hidden_states[token_indices]
+                expert_output = expert(expert_input)
+                weighted_output = expert_output * expert_weights.unsqueeze(-1)
+                final_hidden_states.index_add_(0, token_indices, weighted_output)
+
+        # in original deepseek, the output of the experts are gathered once we leave this module
+        # thus the moe module is itelsf an IsolatedParallel module
+        # and all expert are "local" meaning we shard but we don't gather
+        return final_hidden_states.type(hidden_states.dtype)
+
+    def forward(self, hidden_states):
+        residuals = hidden_states
+        orig_shape = hidden_states.shape
+        topk_indices, topk_weights = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape)
+        hidden_states = hidden_states + self.shared_experts(residuals)
+        return hidden_states
+
+
+class Glm4MoeDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: Glm4MoeConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = Glm4MoeAttention(config=config, layer_idx=layer_idx)
+
+        if layer_idx >= config.first_k_dense_replace:
+            self.mlp = Glm4MoeMoE(config)
+        else:
+            self.mlp = Glm4MoeMLP(config)
+
+        self.input_layernorm = Glm4MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Glm4MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            use_cache=use_cache,
+            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)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@auto_docstring
+class Glm4MoePreTrainedModel(PreTrainedModel):
+    config: Glm4MoeConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Glm4MoeDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_static_cache = False
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": Glm4MoeDecoderLayer,
+        "attentions": Glm4MoeAttention,
+    }
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, Glm4MoeRMSNorm):
+            module.weight.data.fill_(1.0)
+        elif isinstance(module, Glm4MoeTopkRouter):
+            module.weight.data.normal_(mean=0.0, std=std)
+
+
+class Glm4MoeRotaryEmbedding(nn.Module):
+    def __init__(self, config: Glm4MoeConfig, 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)
+
+
+@auto_docstring
+class Glm4MoeModel(Glm4MoePreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.92.*", r"model\.layers\.46.*"]
+
+    def __init__(self, config: Glm4MoeConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [Glm4MoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = Glm4MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Glm4MoeRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @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,
+        cache_position: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        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: torch.Tensor = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        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.Tensor = 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,
+        )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+@auto_docstring
+class Glm4MoeForCausalLM(Glm4MoePreTrainedModel, 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 = Glm4MoeModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @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[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CausalLMOutputWithPast:
+        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, Glm4MoeForCausalLM
+
+        >>> model = Glm4MoeForCausalLM.from_pretrained("meta-glm4_moe/Glm4Moe-2-7b-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("meta-glm4_moe/Glm4Moe-2-7b-hf")
+
+        >>> 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."
+        ```"""
+        outputs: BaseModelOutputWithPast = 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,
+            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=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = ["Glm4MoePreTrainedModel", "Glm4MoeModel", "Glm4MoeForCausalLM"]

ktransformers/operators/RoPE.py

@@ -26,6 +26,8 @@ from ktransformers.operators.base_operator import BaseInjectedModule
 from ktransformers.util.custom_loader import GGUFLoader
 from ktransformers.util.utils import InferenceState
 from transformers.configuration_utils import PretrainedConfig
+from ktransformers.models.modeling_smallthinker import SmallthinkerRotaryEmbedding
+from ktransformers.models.modeling_glm4_moe import Glm4MoeRotaryEmbedding
 import torch
 
 # Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2Moe
@@ -438,3 +440,92 @@ class KQwen3MoeRotaryEmbedding(BaseInjectedModule, DeepseekV2RotaryEmbedding):
         self.orig_module.__init__(
             self.orig_module.config
         )
+    
+
+class KSmallthinkerRotaryEmbedding(BaseInjectedModule, SmallthinkerRotaryEmbedding):
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module,
+        #  device: str = "cuda",
+        generate_device: str = "cuda",
+        prefill_device: str = "cuda",
+        **kwargs,
+    ):
+        BaseInjectedModule.__init__(
+            self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
+        )
+        self.orig_module.__init__(
+            config
+        )
+        self.generate_device = generate_device
+        self.prefill_device = prefill_device
+
+    def load(self):
+        self.orig_module.__init__(
+            self.orig_module.config,
+            device = self.generate_device,
+        )
+        
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            self._dynamic_frequency_update(position_ids, device=x.device)
+        # Core RoPE block
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        # print(inv_freq_expanded.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.to(x.device) @ position_ids_expanded).transpose(1, 2)
+            freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+        freqs_cis = freqs_cis * self.attention_scaling
+        return freqs_cis
+
+class KGlm4MoeRotaryEmbedding(BaseInjectedModule, Glm4MoeRotaryEmbedding):
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module,
+        #  device: str = "cuda",
+        generate_device: str = "cuda",
+        prefill_device: str = "cuda",
+        **kwargs,
+    ):
+        BaseInjectedModule.__init__(
+            self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
+        )
+        self.orig_module.__init__(
+            config
+        )
+        self.generate_device = generate_device
+        self.prefill_device = prefill_device
+
+    def load(self):
+        self.orig_module.__init__(
+            self.orig_module.config,
+            device = self.generate_device,
+        )
+        
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            self._dynamic_frequency_update(position_ids, device=x.device)
+        # Core RoPE block
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        # print(inv_freq_expanded.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.to(x.device) @ position_ids_expanded).transpose(1, 2)
+            freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+        freqs_cis = freqs_cis * self.attention_scaling
+        return freqs_cis

ktransformers/operators/balance_serve_attention.py

@@ -9,6 +9,8 @@ from torch import nn
 from ktransformers.models.modeling_deepseek import DeepseekV2Attention, apply_rotary_pos_emb
 from ktransformers.models.modeling_qwen2_moe import Qwen2MoeAttention
 from ktransformers.models.modeling_qwen3_moe import Qwen3MoeAttention
+from ktransformers.models.modeling_smallthinker import SmallthinkerAttention
+from ktransformers.models.modeling_glm4_moe import Glm4MoeAttention
 from typing import Optional, Tuple
 from ktransformers.operators.base_operator import BaseInjectedModule
 from ktransformers.util.custom_loader import GGUFLoader
@@ -455,3 +457,230 @@ class deepseek_torch_attn(BaseInjectedModule, DeepseekV2Attention):
             attn_output = self.o_proj(attn_output, batch_num_tokens_tensors)
             final_attention_output = torch.cat((final_attention_output, attn_output), dim=0)
         return final_attention_output
+
+class KSmallthinkerAttention(BaseInjectedModule, SmallthinkerAttention):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 prefill_device: str = "cuda",
+                 generate_device: str = "cuda",
+                 chunck_size: int = 1000,
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        self.orig_module.__init__(orig_module.config,
+            orig_module.layer_idx)
+        self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
+
+    def apply_rotary_pos_emb(
+        self,
+        xq: torch.Tensor,
+        xk: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+        xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+        xq_out = torch.view_as_real(xq_ * freqs_cis[:, :, None, :]).flatten(3)
+        xk_out = torch.view_as_real(xk_ * freqs_cis[:, :, None, :]).flatten(3)
+        return xq_out.type_as(xq), xk_out.type_as(xk)
+
+    def forward(self,
+                hidden_states: torch.Tensor,
+                kv_cache: KGQACache,
+                freqs_cis: torch.Tensor,
+                wrapper: flashInferAttn,
+                bsz_tensors: torch.Tensor,
+                position_ids: torch.Tensor = None,
+                ):
+
+        if self.use_qk_norm:
+            raise NotImplementedError("use_qk_norm is not implemented yet")
+
+        q_len, _ = hidden_states.size()
+        query_states = self.q_proj(hidden_states, bsz_tensors)
+        key_states = self.k_proj(hidden_states, bsz_tensors)
+        value_states = self.v_proj(hidden_states, bsz_tensors)
+
+        query_states = query_states.view(q_len, self.num_attention_heads, self.head_dim)
+        key_states = key_states.view(q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(q_len, self.num_key_value_heads, self.head_dim)
+        
+        # cos, sin = freqs_cis
+        """
+        print(query_states.shape)
+        print(key_states.shape)
+        print(cos.shape)
+        print(sin.shape)
+        """
+        if freqs_cis:  
+            query_states, key_states = self.apply_rotary_pos_emb(query_states.unsqueeze(0), key_states.unsqueeze(0), freqs_cis)
+
+
+
+        query_states = query_states.view(q_len, self.num_attention_heads, self.head_dim)
+        key_states = key_states.view(q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(q_len, self.num_key_value_heads, self.head_dim)
+
+        k_cache = kv_cache.get_k_cache(self.layer_idx)
+        v_cache = kv_cache.get_v_cache(self.layer_idx)
+
+
+        attn_output = wrapper.forward(query_states, k_cache, v_cache, key_states, value_states)
+  
+
+        attn_output = self.o_proj(attn_output.view(q_len, self.num_attention_heads * self.head_dim), bsz_tensors)
+
+        return attn_output
+
+class KSmallthinkerAttention(BaseInjectedModule, SmallthinkerAttention):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 prefill_device: str = "cuda",
+                 generate_device: str = "cuda",
+                 chunck_size: int = 1000,
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        self.orig_module.__init__(orig_module.config,
+            orig_module.layer_idx)
+        self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
+
+    def apply_rotary_pos_emb(
+        self,
+        xq: torch.Tensor,
+        xk: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+        xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+        xq_out = torch.view_as_real(xq_ * freqs_cis[:, :, None, :]).flatten(3)
+        xk_out = torch.view_as_real(xk_ * freqs_cis[:, :, None, :]).flatten(3)
+        return xq_out.type_as(xq), xk_out.type_as(xk)
+
+    def forward(self,
+                hidden_states: torch.Tensor,
+                kv_cache: KGQACache,
+                freqs_cis: torch.Tensor,
+                wrapper: flashInferAttn,
+                bsz_tensors: torch.Tensor,
+                position_ids: torch.Tensor = None,
+                ):
+
+        if self.use_qk_norm:
+            raise NotImplementedError("use_qk_norm is not implemented yet")
+
+        q_len, _ = hidden_states.size()
+        query_states = self.q_proj(hidden_states, bsz_tensors)
+        key_states = self.k_proj(hidden_states, bsz_tensors)
+        value_states = self.v_proj(hidden_states, bsz_tensors)
+
+        query_states = query_states.view(q_len, self.num_attention_heads, self.head_dim)
+        key_states = key_states.view(q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(q_len, self.num_key_value_heads, self.head_dim)
+        
+        # cos, sin = freqs_cis
+        """
+        print(query_states.shape)
+        print(key_states.shape)
+        print(cos.shape)
+        print(sin.shape)
+        """
+        if freqs_cis:  
+            query_states, key_states = self.apply_rotary_pos_emb(query_states.unsqueeze(0), key_states.unsqueeze(0), freqs_cis)
+
+
+
+        query_states = query_states.view(q_len, self.num_attention_heads, self.head_dim)
+        key_states = key_states.view(q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(q_len, self.num_key_value_heads, self.head_dim)
+
+        k_cache = kv_cache.get_k_cache(self.layer_idx)
+        v_cache = kv_cache.get_v_cache(self.layer_idx)
+
+
+        attn_output = wrapper.forward(query_states, k_cache, v_cache, key_states, value_states)
+  
+
+        attn_output = self.o_proj(attn_output.view(q_len, self.num_attention_heads * self.head_dim), bsz_tensors)
+
+        return attn_output
+    
+
+class KGlm4MoeAttention(BaseInjectedModule, Glm4MoeAttention):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 prefill_device: str = "cuda",
+                 generate_device: str = "cuda",
+                 chunck_size: int = 1000,
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        self.orig_module.__init__(orig_module.config,
+            orig_module.layer_idx)
+        self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
+
+    def apply_rotary_pos_emb(
+        self,
+        xq: torch.Tensor,
+        xk: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+        xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+        xq_out = torch.view_as_real(xq_ * freqs_cis[:, :, None, :]).flatten(3)
+        xk_out = torch.view_as_real(xk_ * freqs_cis[:, :, None, :]).flatten(3)
+        return xq_out.type_as(xq), xk_out.type_as(xk)
+
+    def forward(self,
+                hidden_states: torch.Tensor,
+                kv_cache: KGQACache,
+                freqs_cis: torch.Tensor,
+                wrapper: flashInferAttn,
+                bsz_tensors: torch.Tensor,
+                position_ids: torch.Tensor = None,
+                ):
+
+        if self.use_qk_norm:
+            query_states = self.q_norm(query_states)
+            key_states = self.k_norm(key_states)
+
+        q_len, _ = hidden_states.size()
+        query_states = self.q_proj(hidden_states, bsz_tensors)
+        key_states = self.k_proj(hidden_states, bsz_tensors)
+        value_states = self.v_proj(hidden_states, bsz_tensors)
+
+        query_states = query_states.view(q_len, self.num_attention_heads, self.head_dim)
+        key_states = key_states.view(q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(q_len, self.num_key_value_heads, self.head_dim)
+        
+        # cos, sin = freqs_cis
+        """
+        print(query_states.shape)
+        print(key_states.shape)
+        print(cos.shape)
+        print(sin.shape)
+        """
+        if freqs_cis:  
+            query_states, key_states = self.apply_rotary_pos_emb(query_states.unsqueeze(0), key_states.unsqueeze(0), freqs_cis)
+
+
+
+        query_states = query_states.view(q_len, self.num_attention_heads, self.head_dim)
+        key_states = key_states.view(q_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(q_len, self.num_key_value_heads, self.head_dim)
+
+        k_cache = kv_cache.get_k_cache(self.layer_idx)
+        v_cache = kv_cache.get_v_cache(self.layer_idx)
+
+
+        attn_output = wrapper.forward(query_states, k_cache, v_cache, key_states, value_states)
+  
+
+        attn_output = self.o_proj(attn_output.view(q_len, self.num_attention_heads * self.head_dim), bsz_tensors)
+
+        return attn_output

ktransformers/operators/experts.py

@@ -729,6 +729,8 @@ from ktransformers.models.modeling_deepseek_v3 import DeepseekV3MoE
 from ktransformers.models.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
 from ktransformers.models.modeling_qwen3_moe import Qwen3MoeSparseMoeBlock
 from ktransformers.models.modeling_mixtral import MixtralSparseMoeBlock
+from ktransformers.models.modeling_smallthinker import SmallthinkerMoeBlock
+from ktransformers.models.modeling_glm4_moe import Glm4MoeMoE
 
 
 class KQwen2MoeSparseMoeBlock(BaseInjectedModule, Qwen2MoeSparseMoeBlock):
@@ -1248,6 +1250,12 @@ class KTransformersExpertsV2(BaseInjectedModule, KExpertsBase):
                  **kwargs):
         BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
         KExpertsBase.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
+
+        if prefill_op == 'None':
+            prefill_op = None
+        if generate_op == 'None':
+            generate_op = None
+
         if generate_op is not None:
             self.generate_experts = EXPERTS_MAP[generate_op](key, gguf_loader, config, len(orig_module), device=generate_device, **kwargs)
         else:
@@ -1464,6 +1472,264 @@ class KQwen3MoeSparseMoeBlockV2(BaseInjectedModule, Qwen3MoeSparseMoeBlock):
         # )
 
 
+        if isinstance(self.experts, KExpertsBase):
+            y = self.moe_on_cpuinfer(hidden_states, selected_experts, routing_weights, bsz_tensor, cuda_graph_idx).view(*orig_shape).to(device=hidden_states.device)
+        elif hidden_states.size(0) > 10:
+            # TODO may bugs here
+            y = (
+                self.moe_infer(hidden_states, selected_experts, routing_weights)
+                .view(*orig_shape)
+                .to(device=hidden_states.device)
+            )
+        else:
+            # TODO may bugs here
+            y = (
+                self.moe_infer_simple(hidden_states, selected_experts, routing_weights)
+                .view(*orig_shape)
+                .to(device=hidden_states.device)
+            ) 
+        # y += y_
+        return y
+
+    @torch.no_grad()
+    def moe_on_cpuinfer(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor, bsz_tensor, cuda_graph_idx=0) -> torch.Tensor:
+        outs = torch.empty_like(x)
+        outs = self.experts(x, topk_ids, topk_weight, bsz_tensor, cuda_graph_idx)
+        return outs
+
+    @torch.no_grad()
+    # TODO may bugs here
+    def moe_infer_simple(
+        self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        x: [num_tokens, hidden_size]
+        topk_ids, topk_weight: [num_tokens, num_selected_experts]
+        """
+        outs = torch.zeros_like(x)
+        for token_idx in range(topk_ids.size(0)):
+            for expert_idx in range(topk_ids.size(1)):
+                expert = self.experts[topk_ids[token_idx, expert_idx]]
+                outs[token_idx] += (
+                    expert.forward(x[token_idx]) * topk_weight[token_idx, expert_idx]
+                )
+        return outs
+
+    @torch.no_grad()
+    # TODO may bugs here
+    def moe_infer(self, x, topk_ids, topk_weight):
+        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
+        cnts.scatter_(1, topk_ids, 1)
+        tokens_per_expert = cnts.sum(dim=0)
+        idxs = topk_ids.view(-1).argsort()
+        sorted_tokens = x[idxs // topk_ids.shape[1]]
+        tokens_per_expert = tokens_per_expert.cpu().numpy()
+
+        outputs = []
+        start_idx = 0
+        for i, num_tokens in enumerate(tokens_per_expert):
+            end_idx = start_idx + num_tokens
+            if num_tokens == 0:
+                continue
+            expert = self.experts[i + self.ep_rank * self.experts_per_rank]
+            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+            expert_out = expert.forward(tokens_for_this_expert)
+            outputs.append(expert_out)
+            start_idx = end_idx
+
+        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+
+        new_x = torch.empty_like(outs)
+        new_x[idxs] = outs
+        final_out = (
+            new_x.view(*topk_ids.shape, -1)
+            .type(topk_weight.dtype)
+            .mul_(topk_weight.unsqueeze(dim=-1))
+            .sum(dim=1)
+            .type(new_x.dtype)
+        )
+        return final_out
+
+
+class KSmallthinkerMoeBlock(BaseInjectedModule, SmallthinkerMoeBlock):
+    def forward(self, router_input: torch.Tensor, hidden_states: torch.Tensor, bsz_tensor=None, cuda_graph_idx=0):
+
+        orig_shape = hidden_states.shape
+        sequence_length = orig_shape[1]
+
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+
+        if bsz_tensor is None:
+            if self.enable_early_router:
+                router_logits = self.primary_router(router_input)
+            else:
+                router_logits = self.primary_router(hidden_states)
+        else:
+            if self.enable_early_router:
+                router_logits = self.primary_router(router_input, bsz_tensor)
+            else:
+                router_logits = self.primary_router(hidden_states, bsz_tensor)
+
+        router_logits, selected_experts = torch.topk(router_logits, self.num_active_primary_experts, dim=-1)
+
+
+        if router_logits.device.type == "xpu":
+            # TODO: support self.moe_primary_router_apply_softmax False case
+            from ipex_llm.transformers.models.common import moe_softmax_topk
+            selected_experts, routing_weights = moe_softmax_topk(
+                router_logits.half(), self.top_k, self.norm_topk_prob
+            )
+        else:
+            if self.moe_primary_router_apply_softmax:
+                routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+            else:
+                routing_weights = F.sigmoid(router_logits)
+                routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        # only for generate phase
+        if hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_available() and torch.cuda.is_current_stream_capturing(): # TODO: this branch cause jit bug
+            self.experts.generate_experts.submit_for_one_decode(hidden_states, selected_experts, routing_weights, bsz_tensor, cuda_graph_idx)
+            # y_ = self.shared_expert(hidden_states, bsz_tensor).squeeze(0)
+            # y_ = F.sigmoid(self.shared_expert_gate(hidden_states)) * y_    
+
+            y = self.experts.generate_experts.sync_for_one_decode(cuda_graph_idx).unsqueeze(0)
+            
+            # y += y_
+            y.resize_(*orig_shape)
+            return y
+
+        # y_ = self.shared_expert(hidden_states, bsz_tensor).squeeze(0)
+        # y_ = (
+        #     F.sigmoid(self.shared_expert_gate(hidden_states)) * y_
+        # )
+
+
+        if isinstance(self.experts, KExpertsBase):
+            y = self.moe_on_cpuinfer(hidden_states, selected_experts, routing_weights, bsz_tensor, cuda_graph_idx).view(*orig_shape).to(device=hidden_states.device)
+        elif hidden_states.size(0) > 10:
+            # TODO may bugs here
+            y = (
+                self.moe_infer(hidden_states, selected_experts, routing_weights)
+                .view(*orig_shape)
+                .to(device=hidden_states.device)
+            )
+        else:
+            # TODO may bugs here
+            y = (
+                self.moe_infer_simple(hidden_states, selected_experts, routing_weights)
+                .view(*orig_shape)
+                .to(device=hidden_states.device)
+            ) 
+        # y += y_
+        return y
+
+    @torch.no_grad()
+    def moe_on_cpuinfer(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor, bsz_tensor, cuda_graph_idx=0) -> torch.Tensor:
+        outs = torch.empty_like(x)
+        outs = self.experts(x, topk_ids, topk_weight, bsz_tensor, cuda_graph_idx)
+        return outs
+
+    @torch.no_grad()
+    # TODO may bugs here
+    def moe_infer_simple(
+        self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        x: [num_tokens, hidden_size]
+        topk_ids, topk_weight: [num_tokens, num_selected_experts]
+        """
+        outs = torch.zeros_like(x)
+        for token_idx in range(topk_ids.size(0)):
+            for expert_idx in range(topk_ids.size(1)):
+                expert = self.experts[topk_ids[token_idx, expert_idx]]
+                outs[token_idx] += (
+                    expert.forward(x[token_idx]) * topk_weight[token_idx, expert_idx]
+                )
+        return outs
+
+    @torch.no_grad()
+    # TODO may bugs here
+    def moe_infer(self, x, topk_ids, topk_weight):
+        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
+        cnts.scatter_(1, topk_ids, 1)
+        tokens_per_expert = cnts.sum(dim=0)
+        idxs = topk_ids.view(-1).argsort()
+        sorted_tokens = x[idxs // topk_ids.shape[1]]
+        tokens_per_expert = tokens_per_expert.cpu().numpy()
+
+        outputs = []
+        start_idx = 0
+        for i, num_tokens in enumerate(tokens_per_expert):
+            end_idx = start_idx + num_tokens
+            if num_tokens == 0:
+                continue
+            expert = self.experts[i + self.ep_rank * self.experts_per_rank]
+            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+            expert_out = expert.forward(tokens_for_this_expert)
+            outputs.append(expert_out)
+            start_idx = end_idx
+
+        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+
+        new_x = torch.empty_like(outs)
+        new_x[idxs] = outs
+        final_out = (
+            new_x.view(*topk_ids.shape, -1)
+            .type(topk_weight.dtype)
+            .mul_(topk_weight.unsqueeze(dim=-1))
+            .sum(dim=1)
+            .type(new_x.dtype)
+        )
+        return final_out
+
+
+class KGlm4MoeMoE(BaseInjectedModule, Glm4MoeMoE):
+    def forward(self, hidden_states, bsz_tensor=None, cuda_graph_idx=0):
+
+        orig_shape = hidden_states.shape
+        sequence_length = orig_shape[1]
+
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+
+        if bsz_tensor is None:
+            router_logits = self.gate(hidden_states)
+        else:
+            router_logits = self.gate(hidden_states, bsz_tensor)
+
+        if router_logits.device.type == "xpu":
+            # TODO: support self.moe_primary_router_apply_softmax False case
+            from ipex_llm.transformers.models.common import moe_softmax_topk
+            selected_experts, routing_weights = moe_softmax_topk(
+                router_logits.half(), self.top_k, self.norm_topk_prob
+            )
+        else:
+            routing_weights = torch.nn.functional.softmax(router_logits, dim=1, dtype=torch.float)
+            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)
+
+        # only for generate phase
+        if hasattr(self.experts.generate_experts, "submit_for_one_decode") and torch.cuda.is_available() and torch.cuda.is_current_stream_capturing(): # TODO: this branch cause jit bug
+            self.experts.generate_experts.submit_for_one_decode(hidden_states, selected_experts, routing_weights, bsz_tensor, cuda_graph_idx)
+            y_ = self.shared_expert(hidden_states, bsz_tensor).squeeze(0)
+            # y_ = F.sigmoid(self.shared_expert_gate(hidden_states)) * y_    
+
+            y = self.experts.generate_experts.sync_for_one_decode(cuda_graph_idx).unsqueeze(0)
+            
+            y += y_
+            y.resize_(*orig_shape)
+            return y
+
+        # y_ = self.shared_expert(hidden_states, bsz_tensor).squeeze(0)
+        # y_ = (
+        #     F.sigmoid(self.shared_expert_gate(hidden_states)) * y_
+        # )
+
+
         if isinstance(self.experts, KExpertsBase):
             y = self.moe_on_cpuinfer(hidden_states, selected_experts, routing_weights, bsz_tensor, cuda_graph_idx).view(*orig_shape).to(device=hidden_states.device)
         elif hidden_states.size(0) > 10:

ktransformers/operators/gate.py

@@ -213,3 +213,4 @@ class KMoEGateIPEXLLM(KMoEGate):
                                                self.n_group, self.topk_group, self.top_k,
                                                self.norm_topk_prob, self.routed_scaling_factor)
         return topk_idx, topk_weight.to(x.dtype)
+

ktransformers/operators/layernorm.py

@@ -28,6 +28,8 @@ import torch.nn as nn
 from ktransformers.models.modeling_deepseek_v3 import DeepseekV3RMSNorm
 from ktransformers.models.modeling_qwen2_moe import Qwen2MoeRMSNorm
 from ktransformers.models.modeling_qwen3_moe import Qwen3MoeRMSNorm
+from ktransformers.models.modeling_smallthinker import SmallthinkerRMSNorm
+from ktransformers.models.modeling_glm4_moe import Glm4MoeRMSNorm
 from ktransformers.operators.base_operator import BaseInjectedModule
 from ktransformers.util.custom_loader import GGUFLoader
 if not torch.xpu.is_available():
@@ -165,6 +167,94 @@ class KQwen3MoeRMSNorm(Qwen3MoeRMSNorm, BaseInjectedModule):
         hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
         return self.weight * hidden_states.to(input_dtype)
     
+class KSmallthinkerRMSNorm(SmallthinkerRMSNorm, BaseInjectedModule):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 prefill_device: str = "cuda",
+                 generate_device: str = "cuda",
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        self.orig_module.__init__(orig_module.hidden_size,
+            orig_module.variance_epsilon)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        batch_size_tensor: torch.Tensor = None,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        #return self.forward_native(x, residual)
+        bsz, hidden_size = x.shape
+        x = x.view(-1, self.orig_module.hidden_size)
+        if batch_size_tensor is None:
+            return self.forward_native(x)
+        if residual is not None:
+            fused_add_rmsnorm(x, residual, self.weight.data, batch_size_tensor, self.variance_epsilon)
+            #residual = x + residual
+            #out = rmsnorm(residual, self.weight.data, batch_size_tensor, self.variance_epsilon)
+            return x, residual
+        # print(x.shape, self.weight.data.shape, self.variance_epsilon, x.dtype, self.weight.data.dtype, x.device, self.weight.device, x.is_contiguous(), self.weight.data.is_contiguous())
+        out = rmsnorm(x, self.weight.data, batch_size_tensor,self.variance_epsilon)
+        out = out.view(bsz, hidden_size)
+        return out
+
+    def forward_native(
+        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)
+
+class KGlm4MoeRMSNorm(Glm4MoeRMSNorm, BaseInjectedModule):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 prefill_device: str = "cuda",
+                 generate_device: str = "cuda",
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        self.orig_module.__init__(orig_module.hidden_size,
+            orig_module.variance_epsilon)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        batch_size_tensor: torch.Tensor = None,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        #return self.forward_native(x, residual)
+        bsz, hidden_size = x.shape
+        x = x.view(-1, self.orig_module.hidden_size)
+        if batch_size_tensor is None:
+            return self.forward_native(x)
+        if residual is not None:
+            fused_add_rmsnorm(x, residual, self.weight.data, batch_size_tensor, self.variance_epsilon)
+            #residual = x + residual
+            #out = rmsnorm(residual, self.weight.data, batch_size_tensor, self.variance_epsilon)
+            return x, residual
+        # print(x.shape, self.weight.data.shape, self.variance_epsilon, x.dtype, self.weight.data.dtype, x.device, self.weight.device, x.is_contiguous(), self.weight.data.is_contiguous())
+        out = rmsnorm(x, self.weight.data, batch_size_tensor,self.variance_epsilon)
+        out = out.view(bsz, hidden_size)
+        return out
+
+    def forward_native(
+        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)
+
+
+
 class DeepseekV3RMSNormTorch(DeepseekV3RMSNorm, BaseInjectedModule):
     def __init__(self,
                 key: str,

ktransformers/operators/mlp.py

@@ -5,6 +5,8 @@ from transformers import PretrainedConfig
 import torch.nn as nn
 from ktransformers.models.modeling_deepseek_v3 import DeepseekV3MLP
 from ktransformers.models.modeling_qwen2_moe import Qwen2MoeMLP
+from ktransformers.models.modeling_smallthinker import SmallthinkerDenseMlpBlock
+from ktransformers.models.modeling_glm4_moe import Glm4MoeMLP
 class kDeepseekV3MLP(DeepseekV3MLP, BaseInjectedModule):
     def __init__(self,
                  key: str,
@@ -35,3 +37,34 @@ class KQwen2MoeMLP(Qwen2MoeMLP, BaseInjectedModule):
     def forward(self, x, bsz_tensor):
         down_proj = self.down_proj(self.act_fn(self.gate_proj(x, bsz_tensor)) * self.up_proj(x, bsz_tensor), bsz_tensor)
         return down_proj
+
+
+class KSmallthinkerDenseMlpBlock(SmallthinkerDenseMlpBlock, BaseInjectedModule):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 prefill_device: str = "cuda",
+                 generate_device: str = "cuda",
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        self.orig_module.__init__(orig_module.config)
+    def forward(self, x, bsz_tensor):
+        down_proj = self.down(nn.functional.relu(self.gate(x, bsz_tensor)) * self.up(x, bsz_tensor), bsz_tensor)
+        return down_proj
+
+class KGlm4MoeMLP(Glm4MoeMLP, BaseInjectedModule):
+    def __init__(self,
+                 key: str,
+                 gguf_loader : GGUFLoader,
+                 config: PretrainedConfig,
+                 orig_module: nn.Module,
+                 prefill_device: str = "cuda",
+                 generate_device: str = "cuda",
+                 **kwargs):
+        BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+        self.orig_module.__init__(orig_module.config)
+    def forward(self, x, bsz_tensor):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x, bsz_tensor)) * self.up_proj(x, bsz_tensor), bsz_tensor)
+        return down_proj

ktransformers/optimize/optimize_rules/Glm4Moe-serve.yaml

@@ -0,0 +1,90 @@
+- match:
+    class: ktransformers.models.modeling_glm4_moe.Glm4MoeRotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.KGlm4MoeRotaryEmbedding
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    name: "^lm_head$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "VLinearMarlin"
+      prefill_op: "KLinearTorch"
+
+# - match:
+#     name: "^model\\.layers\\..*$"  # regular expression 
+#     class: torch.nn.Linear  # only match modules matching name and class simultaneously
+#   replace:
+#     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+#     kwargs:
+#       generate_device: "cuda"
+#       prefill_device: "cuda"
+#       generate_op: "VLinearMarlin"
+#       prefill_op: "KLinearTorch"
+- match:
+    name: "^model\\.layers\\.(?!.*mlp\\.shared_expert_gate).*$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: ktransformers.models.modeling_glm4_moe.Glm4MoeMoE
+  replace:
+    class: ktransformers.operators.experts.KGlm4MoeMoE
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExpertsV2     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cuda"
+      prefill_op: None
+      generate_device: "cpu"
+      generate_op: "KExpertsCPU"
+      out_device: "cuda"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.balance_serve_attention.KSmallthinkerAttention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
+
+- match:
+    class: ktransformers.models.modeling_glm4_moe.Glm4MoeRMSNorm
+  replace:
+    class: ktransformers.operators.layernorm.KGlm4MoeRMSNorm
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    class: ktransformers.models.modeling_glm4_moe.Glm4MoeMLP
+  replace:
+    class:  ktransformers.operators.mlp.KGlm4MoeMLP
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"

ktransformers/optimize/optimize_rules/Smallthinker-serve.yaml

@@ -0,0 +1,90 @@
+- match:
+    class: ktransformers.models.modeling_smallthinker.SmallthinkerRotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.KSmallthinkerRotaryEmbedding
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    name: "^lm_head$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "VLinearMarlin"
+      prefill_op: "KLinearTorch"
+
+# - match:
+#     name: "^model\\.layers\\..*$"  # regular expression 
+#     class: torch.nn.Linear  # only match modules matching name and class simultaneously
+#   replace:
+#     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+#     kwargs:
+#       generate_device: "cuda"
+#       prefill_device: "cuda"
+#       generate_op: "VLinearMarlin"
+#       prefill_op: "KLinearTorch"
+- match:
+    name: "^model\\.layers\\.(?!.*feed_forward\\.shared_expert_gate).*$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearMarlin"
+      prefill_op: "KLinearTorch"
+- match:
+    name: "^model\\.layers\\..*\\.block_sparse_moe$"
+    class: ktransformers.models.modeling_smallthinker.SmallthinkerMoeBlock
+  replace:
+    class: ktransformers.operators.experts.KSmallthinkerMoeBlock
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    name: "^model\\.layers\\..*\\.block_sparse_moe\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExpertsV2     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cuda"
+      prefill_op: None
+      generate_device: "cpu"
+      generate_op: "KExpertsCPU"
+      out_device: "cuda"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.balance_serve_attention.KSmallthinkerAttention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
+
+- match:
+    class: ktransformers.models.modeling_smallthinker.SmallthinkerRMSNorm
+  replace:
+    class: ktransformers.operators.layernorm.KSmallthinkerRMSNorm
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+
+- match:
+    class: ktransformers.models.modeling_smallthinker.SmallthinkerDenseMlpBlock
+  replace:
+    class:  ktransformers.operators.mlp.KSmallthinkerDenseMlpBlock
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"

ktransformers/server/backend/interfaces/balance_serve.py

@@ -24,7 +24,11 @@ from ktransformers.models.custom_modeling_deepseek_v3 import KDeepseekV3ForCausa
 from ktransformers.models.custom_modeling_deepseek_v2 import KDeepseekV2ForCausalLM
 from ktransformers.models.custom_modeling_qwen2_moe import KQwen2MoeForCausalLM
 from ktransformers.models.custom_modeling_qwen3_moe import KQwen3MoeForCausalLM
+from ktransformers.models.custom_modeling_smallthinker import KSmallthinkerForCausalLM
+from ktransformers.models.custom_modeling_glm4_moe import KGlm4MoeForCausalLM
 from ktransformers.models.configuration_qwen3_moe import Qwen3MoeConfig
+from ktransformers.models.configuration_smallthinker import SmallthinkerConfig
+from ktransformers.models.configuration_glm4_moe import Glm4MoeConfig
 from ktransformers.server.balance_serve.inference.model_runner import ModelRunner 
 from ktransformers.server.balance_serve.inference.sampling.sampler import Sampler, SamplingOptions
 from ktransformers.server.balance_serve.inference.query_manager import QueryManager
@@ -60,6 +64,8 @@ default_optimize_rules = {
     "DeepseekV3ForCausalLM": ktransformer_rules_dir + "DeepSeek-V3-Chat-serve.yaml",
     "Qwen2MoeForCausalLM": ktransformer_rules_dir + "Qwen2-serve.yaml",
     "Qwen3MoeForCausalLM": ktransformer_rules_dir + "Qwen3Moe-serve.yaml",
+    "SmallthinkerForCausalLM": ktransformer_rules_dir + "Smallthinker-serve.yaml",
+    "Glm4MoeForCausalLM": ktransformer_rules_dir + "Glm4Moe-serve.yaml",
 }
 
 
@@ -123,13 +129,22 @@ class Engine:
         self.sched_client = SchedulerClient(args.sched_port)
         self.updates = []
 
+        print(f"args.model_name: {args.model_name}")
+
+        if args.model_name == "Qwen3MoeForCausalLM": 
+            config = Qwen3MoeConfig.from_pretrained(args.model_dir, trust_remote_code=True)
+        elif args.model_name == "Glm4MoeForCausalLM":
+            config = Glm4MoeConfig.from_pretrained(args.model_dir, trust_remote_code=True)
+        elif args.model_name == "SmallthinkerForCausalLM":
+            config = SmallthinkerConfig.from_pretrained(args.model_dir, trust_remote_code=True)
+            config._attn_implementation = "eager"  
+        else:
             try:
                 config = AutoConfig.from_pretrained(args.model_dir, trust_remote_code=True) 
             except:
-            if args.model_name == "Qwen3Moe": 
-                config = Qwen3MoeConfig.from_pretrained(args.model_dir, trust_remote_code=True)
-            else:
-                assert False, f"model {args.model_name} not supported" 
+                raise ValueError(f"Model {args.model_name} not supported. Please check your model directory or model name.")
+
+
          
 
         self.gen_queue = generated_token_queue
@@ -147,6 +162,13 @@ class Engine:
                     self.model = KQwen2MoeForCausalLM(config, self.cache)
                 else:
                     self.model = KQwen3MoeForCausalLM(config, self.cache)
+            elif config.architectures[0] == "SmallthinkerForCausalLM":
+                self.cache = KGQACache(config, self.args.page_size)
+                self.model = KSmallthinkerForCausalLM(config, self.cache)
+            elif config.architectures[0] == "Glm4MoeForCausalLM":
+                self.cache = KGQACache(config, self.args.page_size)
+                self.model = KGlm4MoeForCausalLM(config, self.cache)
+
 
 
         context = zmq.Context()
@@ -197,7 +219,7 @@ class Engine:
         self.block_num = inference_context.k_cache[0].size(1)
         self.model_runner = ModelRunner(self.model, self.device, self.args.use_cuda_graph, page_size = args.page_size, block_num=self.block_num)
         #@TODO add config
-        if config.architectures[0] == "Qwen2MoeForCausalLM" or config.architectures[0] == "Qwen3MoeForCausalLM":
+        if config.architectures[0] == "Qwen2MoeForCausalLM" or config.architectures[0] == "Qwen3MoeForCausalLM" or config.architectures[0] == "Glm4MoeForCausalLM" or config.architectures[0] == "SmallthinkerForCausalLM":
             self.model.init_wrapper(self.args.use_cuda_graph, self.device, max(self.model_runner.cuda_graphs), args.max_batch_size, self.block_num) 
         else:
             self.model.init_wrapper(self.args.use_cuda_graph, self.device, args.max_batch_size, self.block_num)

ktransformers/server/balance_serve/inference/model_runner.py

@@ -29,6 +29,8 @@ from ktransformers.models.custom_modeling_deepseek_v3 import KDeepseekV3ForCausa
 from ktransformers.models.custom_modeling_deepseek_v2 import KDeepseekV2ForCausalLM
 from ktransformers.models.custom_modeling_qwen2_moe import KQwen2MoeForCausalLM
 from ktransformers.models.custom_modeling_qwen3_moe import KQwen3MoeForCausalLM
+from ktransformers.models.custom_modeling_smallthinker import KSmallthinkerForCausalLM
+from ktransformers.models.custom_modeling_glm4_moe import KGlm4MoeForCausalLM
 from ktransformers.server.balance_serve.inference.query_manager import QueryManager
 from ktransformers.server.balance_serve.settings import sched_ext
 
@@ -53,7 +55,7 @@ def generate_cuda_graphs(chunk_size: int) -> list:
 class ModelRunner:
     """A CudaGraphRunner runs the forward pass of a model with CUDA graph and torch.compile."""
 
-    model: KDeepseekV3ForCausalLM  | KQwen2MoeForCausalLM | KQwen3MoeForCausalLM 
+    model: KDeepseekV3ForCausalLM  | KQwen2MoeForCausalLM | KQwen3MoeForCausalLM | KSmallthinkerForCausalLM | KGlm4MoeForCausalLM
     input: ForwardBatchInput | list[ForwardBatchInput]
     output: ForwardBatchOutput
     
@@ -93,7 +95,7 @@ class ModelRunner:
                                              num_heads=self.model.config.num_attention_heads, head_dim_ckv=self.model.config.kv_lora_rank, 
                                              head_dim_kpe=self.model.config.qk_rope_head_dim, page_size=self.model.cache.page_size, causal=True,
                                              sm_scale=self.model.model.layers[0].self_attn.softmax_scale, q_data_type=torch.bfloat16, kv_data_type=torch.bfloat16)
-        elif isinstance(self.model, KQwen2MoeForCausalLM) or isinstance(self.model, KQwen3MoeForCausalLM):
+        elif isinstance(self.model, KQwen2MoeForCausalLM) or isinstance(self.model, KQwen3MoeForCausalLM) or isinstance(self.model, KSmallthinkerForCausalLM) or isinstance(self.model, KGlm4MoeForCausalLM):
             self.model.flash_infer_attn_plan(batch, self.bsz_tensor_buf, self.num_tokens_tensor_buf,
                                              num_q_heads=self.model.config.num_attention_heads, num_kv_heads=self.model.config.num_key_value_heads,
                                              head_dim=self.model.config.head_dim if hasattr(self.model.config, 'head_dim') else self.model.config.hidden_size // self.model.config.num_attention_heads, 
@@ -124,7 +126,7 @@ class ModelRunner:
             num_tokens = self.features_buf[i][0].size(0)
             print("capturing cuda graph", batch_size, num_tokens)
 
-            if isinstance(self.model, KQwen2MoeForCausalLM) or isinstance(self.model, KQwen3MoeForCausalLM):
+            if isinstance(self.model, KQwen2MoeForCausalLM) or isinstance(self.model, KQwen3MoeForCausalLM) or isinstance(self.model, KSmallthinkerForCausalLM) or isinstance(self.model, KGlm4MoeForCausalLM):
                 self.model.init_wrapper(self.use_cuda_graph, self.device, num_tokens ,batch_size, self.block_num, i) # TODO: 1024 is a magic number(max_batch_tokens)
 
             self.bsz_tensor_buf[0] = batch_size