Upload modeling_hunyuan.py

modeling_hunyuan.py

@@ -1,5 +1,16 @@
-# coding=utf-8
-# Copyright 2024 Tencent Inc. All Rights Reserved.
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# 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.
 #
 """ PyTorch HunYuan model."""
 
@@ -8,7 +19,6 @@ import warnings
 from typing import List, Optional, Tuple, Union
 
 import torch
-torch.set_default_dtype(torch.float32)
 from torch import Tensor
 import torch.nn.functional as F
 import torch.utils.checkpoint
@@ -64,7 +74,8 @@ _CONFIG_FOR_DOC = "HunYuanConfig"
 def topkgating(logits: Tensor, topk: int):
     logits = logits.float()
     gates = F.softmax(logits, dim=1)
-    expert_capacity = topk * gates.shape[0]
+    # expert_capacity = topk * gates.shape[0]
+    expert_capacity = max(topk, topk * gates.shape[0] // gates.shape[1])
     num_experts = int(gates.shape[1])
     # Top-k router probability and corresponding expert indices for each token.
     # Shape: [tokens_per_group, num_selected_experts].
@@ -254,7 +265,7 @@ class HunYuanRotaryEmbedding(nn.Module):
         self.max_position_embeddings = max_position_embeddings
         self.base = base
         inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
-        inv_freq = inv_freq.bfloat16()
+        # inv_freq = inv_freq.bfloat16()
         self.register_buffer("inv_freq", inv_freq, persistent=False)
 
         # Build here to make `torch.jit.trace` work.
@@ -266,6 +277,7 @@ class HunYuanRotaryEmbedding(nn.Module):
         self.max_seq_len_cached = seq_len
         t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
 
+        self.inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
         freqs = torch.outer(t, self.inv_freq)
         # Different from paper, but it uses a different permutation in order to obtain the same calculation
         emb = torch.cat((freqs, freqs), dim=-1).float()
@@ -274,7 +286,7 @@ class HunYuanRotaryEmbedding(nn.Module):
 
     def forward(self, x, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.max_seq_len_cached:
+        if seq_len > self.max_seq_len_cached or self.inv_freq.dtype != torch.float32:
             self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
 
         return (
@@ -399,7 +411,7 @@ class HunYuanMLP(nn.Module):
         self.layer_idx = layer_idx
         self.hidden_size = config.hidden_size
         if is_shared_mlp:
-            self.intermediate_size = config.intermediate_size * config.num_shared_expert
+            self.intermediate_size = config.intermediate_size * config.num_shared_expert[0]
         else:
             self.intermediate_size = config.intermediate_size
         self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
@@ -450,142 +462,66 @@ class HunYuanTopKGate(nn.Module):
         if self.moe_topk == 1:
             gate_output = top1gating(logits, random_routing_dropped_token=self.random_routing_dropped_token)
         else:
-            gate_output = topkgating(logits, self.moe_topk)
+            gate_output = topkgating(logits, self.moe_topk[0])
 
         return gate_output
 
 
 class HunYuanMoE(nn.Module):
-    """Mixture-of-Experts block with vectorized expert execution and Straight‑Through Estimator (STE) utilities.
-
-    This implementation removes all Python‑side loops over experts. Expert parameters are **stacked** on‑the‑fly and
-    all experts are executed in a single batched matmul sequence, which allows efficient tensor‑parallel execution
-    (e.g. with DeepSpeed ZeRO‑3) while keeping the state‑dict format unchanged (each expert remains an individual
-    sub‑module for full compatibility with existing checkpoints)."""
-
     def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
         super().__init__()
         self.config = config
         self.layer_idx = layer_idx
         self.moe_topk = config.moe_topk
         self.num_experts = config.num_experts
-
-        # Optional shared MLP branch (mixed MoE + dense)
         if config.use_mixed_mlp_moe:
             self.shared_mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=True)
-
-        # Router
         self.gate = HunYuanTopKGate(config, layer_idx=layer_idx)
-
-        # Experts kept as individual sub‑modules so that load_state_dict / save_pretrained stay identical.
         self.experts = nn.ModuleList(
             [HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False) for _ in range(config.num_experts)]
         )
 
-    # ---------------------------------------------------------------------
-    # Internal helpers
-    # ---------------------------------------------------------------------
-    def _stack_weights(self):
-        """Return stacked (batched) expert weights for gate/up/down projections.
-
-        Shapes:
-            Wg : (E, I, H)
-            Wu : (E, I, H)
-            Wd : (E, H, I)  – note transposed compared to nn.Linear's weight so that
-                              torch.matmul(act, Wd.transpose(-2,‑1)) produces (E, C, H)
-        """
-        Wg = torch.stack([exp.gate_proj.weight for exp in self.experts], dim=0)
-        Wu = torch.stack([exp.up_proj.weight for exp in self.experts], dim=0)
-        Wd = torch.stack([exp.down_proj.weight for exp in self.experts], dim=0)
-        return Wg, Wu, Wd
-
-    # ---------------------------------------------------------------------
-    # Public API
-    # ---------------------------------------------------------------------
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        *,
-        return_router_logits: bool = False,
-    ):
-        """Sparse Top‑k MoE forward pass ("y_sparse") with optional router logits.
-
-        This is the route used during both training and inference for the *sparse*
-        branch. No Python loops over experts are used."
-        """
+    def forward(self, hidden_states):
         bsz, seq_len, hidden_size = hidden_states.shape
 
-        # Optional dense branch when using mixed MoE
         if self.config.use_mixed_mlp_moe:
             hidden_states_mlp = self.shared_mlp(hidden_states)
 
-        # ---------------- Routing ----------------
-        l_moe, combine_weights, dispatch_mask, _ = self.gate(hidden_states)
-
-        flat_input = hidden_states.reshape(-1, hidden_size)                        # (S, H) where S = B*T
-        # dispatch tokens → experts
-        dispatched_input = torch.einsum("sec,sm->ecm",                            # (E, C, H)
-                                        dispatch_mask.to(hidden_states.dtype),
-                                        flat_input)
-
-        # ---------------- Expert computation ----------------
-        Wg, Wu, Wd = self._stack_weights()
-        Wg = Wg.to(hidden_states.dtype)
-        Wu = Wu.to(hidden_states.dtype)
-        Wd = Wd.to(hidden_states.dtype)
-
-        gate_out = torch.einsum("ech,eih->eci", dispatched_input, Wg)             # (E, C, I)
-        up_out   = torch.einsum("ech,eih->eci", dispatched_input, Wu)             # (E, C, I)
-        act_fn   = self.experts[0].act_fn
-        interm   = act_fn(gate_out) * up_out                                      # (E, C, I)
-        expert_output = torch.matmul(interm, Wd.transpose(-2, -1))                # (E, C, H)
-
-        # ---------------- Combine ----------------
-        combined_output = torch.einsum("sec,ecm->sm",                             # (S, H)
-                                       combine_weights.to(hidden_states.dtype),
-                                       expert_output)
-        combined_output = combined_output.reshape(bsz, seq_len, hidden_size)
+        l_moe, combine_weights, dispatch_mask, exp_counts = self.gate(hidden_states)
 
-        if self.config.use_mixed_mlp_moe:
-            combined_output = hidden_states_mlp + combined_output
+        reshaped_input = hidden_states.reshape(-1, hidden_size)
 
-        if return_router_logits:
-            router_logits = self.gate.wg(flat_input).view(bsz, seq_len, self.num_experts)
-            return combined_output, router_logits
-        return combined_output
+        dispatched_input = torch.einsum("sec,sm->ecm", dispatch_mask.type_as(hidden_states), reshaped_input)
 
-    # ---------------------------------------------------------------------
-    # Dense branch – outputs *all* expert activations (no routing)
-    # ---------------------------------------------------------------------
-    @torch.no_grad()
-    def forward_all(self, hidden_states: torch.Tensor):
-        """Compute every expert on every token ("dense" MoE path).
+        chunks = dispatched_input.chunk(self.num_experts, dim=0)
+        expert_outputs = []
+        for chunk, expert in zip(chunks, self.experts):
+            expert_outputs.append(expert(chunk))
 
-        Returns:
-            Tensor of shape (B, T, E, H) – per‑expert hidden states.
-        """
-        bsz, seq_len, hidden_size = hidden_states.shape
-        flat_input = hidden_states.reshape(-1, hidden_size)                       # (S, H)
+        expert_output = torch.cat(expert_outputs, dim=0)
+        combined_output = torch.einsum("sec,ecm->sm", combine_weights.type_as(hidden_states), expert_output)
+        combined_output = combined_output.reshape(bsz, seq_len, hidden_size)
 
-        Wg, Wu, Wd = self._stack_weights()
-        Wg = Wg.to(hidden_states.dtype)
-        Wu = Wu.to(hidden_states.dtype)
-        Wd = Wd.to(hidden_states.dtype)
+        if self.config.use_mixed_mlp_moe:
+            output = hidden_states_mlp + combined_output
+        else:
+            output = combined_output
 
-        gate_out = torch.einsum("sh,eih->esi", flat_input, Wg)                    # (E, S, I)
-        up_out   = torch.einsum("sh,eih->esi", flat_input, Wu)                    # (E, S, I)
-        act_fn   = self.experts[0].act_fn
-        interm   = act_fn(gate_out) * up_out                                      # (E, S, I)
-        dense_out = torch.matmul(interm, Wd.transpose(-2, -1))                    # (E, S, H)
+        return output
 
-        dense_out = dense_out.permute(1, 0, 2).contiguous()                       # (S, E, H)
-        dense_out = dense_out.view(bsz, seq_len, self.num_experts, hidden_size)   # (B, T, E, H)
 
-        if self.config.use_mixed_mlp_moe:
-            hidden_states_mlp = self.shared_mlp(hidden_states)                    # (B, T, H)
-            dense_out = dense_out + hidden_states_mlp.unsqueeze(2)
+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)
+
 
-        return dense_out
 class HunYuanAttention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
@@ -1128,18 +1064,33 @@ class HunYuanDecoderLayer(nn.Module):
         kv_states: Optional[Tuple[torch.Tensor]] = None,
         **kwargs,
     ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """Modified to include Straight‑Through Estimator (STE) training logic."""
-    
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            kv_states (`Tuple(torch.FloatTensor)`, *optional*): Used when CLA is enabled,
+                key and value states from past attention blocks
+        """
         if "padding_mask" in kwargs:
             warnings.warn(
                 "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
                 "`attention_mask` instead.`"
             )
-    
-        # ---------------- Self‑Attention ----------------
+
         residual = hidden_states
+
         hidden_states = self.input_layernorm(hidden_states)
-    
+
+        # Self Attention
         hidden_states, self_attn_weights, present_key_value, kv_states = self.self_attn(
             hidden_states=hidden_states,
             attention_mask=attention_mask,
@@ -1151,40 +1102,47 @@ class HunYuanDecoderLayer(nn.Module):
             **kwargs,
         )
         hidden_states = residual + hidden_states
-    
-        # ---------------- MLP / MoE (+STE) ----------------
+
+        # Fully Connected
         residual = hidden_states
         hidden_states = self.post_attention_layernorm(hidden_states)
-    
-        if self.training and isinstance(self.mlp, HunYuanMoE):
-            # Sparse path + router logits
-            y_sparse, router_logits = self.mlp(hidden_states, return_router_logits=True)
-    
-            # Dense (all‑experts) path – memory‑efficient, no grad
-            with torch.no_grad():
-                y_all = self.mlp.forward_all(hidden_states)                      # (B, T, E, H)
-    
-            gate = router_logits.softmax(-1).unsqueeze(-1)                        # (B, T, E, 1)
-            y_dense = (gate * y_all).sum(-2)                                      # (B, T, H)
-    
-            mlp_out = y_dense + (y_sparse - y_dense).detach()
-        else:
-            mlp_out = self.mlp(hidden_states)
-    
-        hidden_states = residual + mlp_out
-    
-        # ---------------- Outputs ----------------
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
         outputs = (hidden_states,)
-    
+
         if output_attentions:
             outputs += (self_attn_weights,)
-    
+
         if use_cache:
             outputs += (present_key_value,)
-    
+
         outputs += (kv_states,)
-    
+
         return outputs
+
+
+HUNYUAN_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`HunYuanConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
+    HUNYUAN_START_DOCSTRING,
+)
 class HunYuanPreTrainedModel(PreTrainedModel):
     config_class = HunYuanConfig
     base_model_prefix = "model"
@@ -1277,6 +1235,10 @@ HUNYUAN_INPUTS_DOCSTRING = r"""
 """
 
 
+@add_start_docstrings(
+    "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
+    HUNYUAN_START_DOCSTRING,
+)
 class HunYuanModel(HunYuanPreTrainedModel):
     """
     Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HunYuanDecoderLayer`]
@@ -1455,7 +1417,7 @@ class HunYuanModel(HunYuanPreTrainedModel):
         )
 
 
-class HunYuanForCausalLM(HunYuanPreTrainedModel):
+class HunYuanMoEV1ForCausalLM(HunYuanPreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config: HunYuanConfig):
@@ -1585,7 +1547,7 @@ class HunYuanForCausalLM(HunYuanPreTrainedModel):
             if isinstance(past_key_values, Cache):
                 cache_length = past_key_values.get_seq_length()
                 past_length = past_key_values.seen_tokens
-                max_cache_length = past_key_values.get_max_length()
+                max_cache_length = past_key_values.get_max_cache_shape()
             else:
                 cache_length = past_length = past_key_values[0][0].shape[2]
                 max_cache_length = None
@@ -1644,6 +1606,21 @@ class HunYuanForCausalLM(HunYuanPreTrainedModel):
         return reordered_past
 
 
+@add_start_docstrings(
+    """
+    The HunYuan Model transformer with a sequence classification head on top (linear layer).
+
+    [`HunYuanForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    HUNYUAN_START_DOCSTRING,
+)
 class HunYuanForSequenceClassification(HunYuanPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
@@ -1748,4 +1725,186 @@ class HunYuanForSequenceClassification(HunYuanPreTrainedModel):
             past_key_values=transformer_outputs.past_key_values,
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
-        )
+        )
+
+
+# ================================================================
+# Dense/Sparse MoE utilities
+# These enable dense training (all experts active) with sparse inference,
+# by fusing the per‑expert parameters into single linear layers and
+# applying a straight‑through estimator (STE) – gradients flow through the
+# dense path, while the forward pass matches sparse routing behaviour.
+# ================================================================
+
+import types
+
+class HunYuanDenseMoE(nn.Module):
+    """Dense counterpart of :class:`HunYuanMoE`.
+
+    *   The per‑expert linear layers (``gate_proj``, ``up_proj``, ``down_proj``)
+        are concatenated to form three *fused* linear layers.
+    *   Forward pass:
+            1. **Dense path** – every expert contributes,
+               weighted by the softmax router probabilities.
+            2. **Sparse path** – only the Top‑K experts (identical to the
+               original sparse MoE) are evaluated.
+            3. **STE** – ``output = dense + (sparse – dense).detach()`` –
+               identical values to sparse inference, dense gradients.
+    """
+
+    def __init__(self, moe: "HunYuanMoE"):
+        super().__init__()
+        self.config = moe.config
+        self.layer_idx = moe.layer_idx
+        self.num_experts = moe.num_experts
+        # All experts share the same hidden/intermediate sizes
+        self.hidden_size = moe.experts[0].hidden_size
+        self.intermediate_size = moe.experts[0].intermediate_size
+
+        # Router is reused directly
+        self.gate = moe.gate
+
+        # ------------------------------------------------------------------
+        # Fuse per‑expert parameters
+        # ------------------------------------------------------------------
+        with torch.no_grad():
+            fused_gate_w = torch.cat([exp.gate_proj.weight for exp in moe.experts], dim=0).clone()
+            fused_up_w   = torch.cat([exp.up_proj.weight   for exp in moe.experts], dim=0).clone()
+            # down_proj weights are shaped (hidden, intermediate)
+            fused_down_w = torch.cat([exp.down_proj.weight for exp in moe.experts], dim=1).clone()
+
+        self.fused_gate_proj = nn.Linear(self.hidden_size,
+                                         self.intermediate_size * self.num_experts,
+                                         bias=False)
+        self.fused_up_proj = nn.Linear(self.hidden_size,
+                                       self.intermediate_size * self.num_experts,
+                                       bias=False)
+        self.fused_down_proj = nn.Linear(self.intermediate_size * self.num_experts,
+                                         self.hidden_size,
+                                         bias=False)
+
+        # Load weights
+        self.fused_gate_proj.weight.data.copy_(fused_gate_w)
+        self.fused_up_proj.weight.data.copy_(fused_up_w)
+        self.fused_down_proj.weight.data.copy_(fused_down_w)
+
+        self.act_fn = moe.experts[0].act_fn
+        self.topk = self.gate.moe_topk[0] if isinstance(self.gate.moe_topk, (list, tuple)) else self.gate.moe_topk
+
+    def _dense_path(self, x, probs):
+        """Compute dense mixture – every expert active."""
+        gate_out = self.fused_gate_proj(x)                           # (T, I*E)
+        up_out   = self.fused_up_proj(x)                             # (T, I*E)
+        interm   = self.act_fn(gate_out) * up_out                    # (T, I*E)
+
+        # Reshape to (T, E, I)
+        interm = interm.view(-1, self.num_experts, self.intermediate_size)
+
+        # Weight by softmax router probabilities
+        interm_weighted = interm * probs.unsqueeze(-1)               # (T, E, I)
+
+        # Collapse experts back to vector and project down
+        dense_flat = interm_weighted.reshape(-1, self.intermediate_size * self.num_experts)
+        return self.fused_down_proj(dense_flat)                      # (T, H)
+
+    def _sparse_path(self, x, probs):
+        """Compute sparse Top‑K mixture (matches original inference)."""
+        # Pre‑compute per‑expert activations to avoid repeated forward calls
+        gate_out = self.fused_gate_proj(x)                           # (T, I*E)
+        up_out   = self.fused_up_proj(x)                             # (T, I*E)
+        interm   = self.act_fn(gate_out) * up_out                    # (T, I*E)
+        interm = interm.view(-1, self.num_experts, self.intermediate_size)  # (T,E,I)
+
+        # Top‑K experts per token
+        values, indices = torch.topk(probs, self.topk, dim=1)        # (T,K)
+
+        # Gather corresponding intermediate activations
+        gathered = interm.gather(1, indices.unsqueeze(-1).expand(-1, -1, self.intermediate_size))  # (T,K,I)
+        gathered = gathered * values.unsqueeze(-1)                  # weight by router prob
+
+        # Gather matching down-proj weights
+        # fused_down_proj.weight: (H, I*E)  -> reshape to (E,I,H)
+        down_w = self.fused_down_proj.weight.view(self.hidden_size,
+                                                  self.num_experts,
+                                                  self.intermediate_size).permute(1,2,0).contiguous()  # (E,I,H)
+        selected_w = down_w.index_select(0, indices.reshape(-1)).view(indices.size(0), self.topk,
+                                                                      self.intermediate_size, self.hidden_size)
+        # (T,K,I,H)
+
+        # Compute output: batch matmul over I
+        sparse_out = torch.einsum('t k i, t k i h -> t h', gathered, selected_w)
+        return sparse_out                                             # (T, H)
+
+    def forward(self, hidden_states):
+        bsz, seq_len, _ = hidden_states.shape
+        x = hidden_states.reshape(-1, self.hidden_size)  # T x H
+        logits = self.gate.wg(x)                         # (T,E)
+        probs  = torch.softmax(logits, dim=1)            # (T,E)
+
+        dense_out  = self._dense_path(x, probs)
+        sparse_out = self._sparse_path(x, probs)
+
+        out = dense_out + (sparse_out - dense_out).detach()  # STE
+        return out.view(bsz, seq_len, self.hidden_size)
+
+# -----------------------------------------------------------------------
+# Helper for module replacement
+# -----------------------------------------------------------------------
+def _replace_submodule(root: nn.Module, target: str, new_module: nn.Module):
+    """Replace a (possibly nested) sub‑module.
+
+    ``target`` is the dotted path returned by ``model.named_modules()``.
+    """
+    parts = target.split('.')
+    parent = root
+    for p in parts[:-1]:
+        parent = getattr(parent, p)
+    setattr(parent, parts[-1], new_module)
+
+# -----------------------------------------------------------------------
+# Public APIs
+# -----------------------------------------------------------------------
+def densify(model: nn.Module):
+    """Convert all :class:`HunYuanMoE` modules under *model* to
+    :class:`HunYuanDenseMoE`. Operates **in‑place**."""
+    replacements = []
+    for name, module in model.named_modules():
+        if isinstance(module, HunYuanMoE):
+            replacements.append((name, module))
+    for name, sparse_moe in replacements:
+        dense_moe = HunYuanDenseMoE(sparse_moe).to(next(sparse_moe.parameters()).device)
+        _replace_submodule(model, name, dense_moe)
+    return model
+
+
+def sparsify(model: nn.Module):
+    """Rebuild standard sparse :class:`HunYuanMoE` modules from their
+    fused :class:`HunYuanDenseMoE` form. Operates **in‑place**."""
+    replacements = []
+    for name, module in model.named_modules():
+        if isinstance(module, HunYuanDenseMoE):
+            replacements.append((name, module))
+    for name, dense_moe in replacements:
+        cfg = dense_moe.config
+        sparse_moe = HunYuanMoE(cfg, layer_idx=dense_moe.layer_idx).to(next(dense_moe.parameters()).device)
+
+        # Copy router
+        sparse_moe.gate.load_state_dict(dense_moe.gate.state_dict())
+
+        # Slice fused weights back to per‑expert
+        for idx, expert in enumerate(sparse_moe.experts):
+            start = idx * dense_moe.intermediate_size
+            end   = (idx + 1) * dense_moe.intermediate_size
+
+            expert.gate_proj.weight.data.copy_(
+                dense_moe.fused_gate_proj.weight.data[start:end]
+            )
+            expert.up_proj.weight.data.copy_(
+                dense_moe.fused_up_proj.weight.data[start:end]
+            )
+            expert.down_proj.weight.data.copy_(
+                dense_moe.fused_down_proj.weight.data[:, start:end]
+            )
+
+        _replace_submodule(model, name, sparse_moe)
+    return model