[MoE] Add conditional expert calibration

Change Purpose:
- Improve MoE calibration support by adding configuration-based expert execution

Change Details:
- Create class `CalibrationConfig` to standalone llmcompressor/modeling/config.py
- Add conditional expert execution based on:
     - `moe_calibrate_all_experts`: If True, all experts run for every
       token; If False, only routed experts are run
     - `moe_calibrate_gated_acts`: If True, routed experts contribute final
       output; If False, expert activations are computed but excluded
       from the final output
- Add unit test to verify all experts are triggered during MoE calibration

Author: dichn

src/llmcompressor/modeling/config.py

@@ -0,0 +1,17 @@
+from pydantic import BaseModel, model_validator
+
+
+class CalibrationConfig(BaseModel):
+    moe_calibrate_all_experts: bool
+    moe_calibrate_gated_acts: bool
+
+    @model_validator(mode="after")
+    def validate_config(self):
+
+        if not self.moe_calibrate_gated_acts and not self.moe_calibrate_all_experts:
+            raise NotImplementedError(
+                "Using all experts for activations without calibrating all experts is not supported. "
+                "Please set moe_calibrate_gated_acts=True or moe_calibrate_all_experts=True."
+            )
+
+        return self

src/llmcompressor/modeling/deepseek_v3.py

@@ -4,19 +4,31 @@
     DeepseekV3MoE as OriginalDeepseekV3MoE,
 )
 
+from llmcompressor.modeling.config import CalibrationConfig
+
 
 class DeepseekV3MoECalibrate(torch.nn.Module):
     """
     Patched DeepseekV3MoE which sends all tokens to all experts for calibration
     """
 
-    def __init__(self, config: DeepseekV3Config, original: OriginalDeepseekV3MoE):
+    def __init__(
+        self,
+        config: DeepseekV3Config,
+        original: OriginalDeepseekV3MoE,
+        calib_config: CalibrationConfig,
+    ):
         super().__init__()
         self.config = config
         self.experts = original.experts
         self.gate = original.gate
         self.shared_experts = original.shared_experts
 
+        self.calib_config = calib_config
+
+        if not calib_config.moe_calibrate_gated_acts:
+            self.gate.top_k = self.gate.n_routed_experts  # ungate experts
+
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         residuals = hidden_states
         orig_shape = hidden_states.shape
@@ -35,19 +47,30 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             mask = expert_mask[expert_idx]
             token_indices, weight_indices = torch.where(mask)
 
-            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)
+            has_tokens = token_indices.numel() > 0
+
+            if self.calib_config.moe_calibrate_all_experts or has_tokens:
+                # calibrate one expert
+                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)
 
-            if token_indices.numel() > 0:
-                final_hidden_states.index_add_(0, token_indices, weighted_output)
+                if has_tokens:
+                    # expert contributes to output activations
+                    final_hidden_states.index_add_(0, token_indices, weighted_output)
         # End MoE
 
         hidden_states = final_hidden_states.type(hidden_states.dtype).view(*orig_shape)
         hidden_states = hidden_states + self.shared_experts(residuals)
         return hidden_states
 
 
-def replace(config: DeepseekV3Config, module: OriginalDeepseekV3MoE):
-    return DeepseekV3MoECalibrate(config=config, original=module)
+def replace(
+    config: DeepseekV3Config,
+    module: OriginalDeepseekV3MoE,
+    calib_config: CalibrationConfig,
+):
+    return DeepseekV3MoECalibrate(
+        config=config, original=module, calib_config=calib_config
+    )

src/llmcompressor/modeling/llama4.py

@@ -11,11 +11,17 @@
     Llama4TextMoe,
 )
 
+from llmcompressor.modeling.config import CalibrationConfig
 from llmcompressor.utils.dev import skip_weights_initialize
 
 
 class SequentialLlama4TextMoe(torch.nn.Module):
-    def __init__(self, config: Llama4TextConfig, original: Llama4TextMoe):
+    def __init__(
+        self,
+        config: Llama4TextConfig,
+        original: Llama4TextMoe,
+        calib_config: CalibrationConfig,
+    ):
         super().__init__()
         self.top_k = config.num_experts_per_tok
         self.hidden_dim = config.hidden_size
@@ -24,6 +30,8 @@ def __init__(self, config: Llama4TextConfig, original: Llama4TextMoe):
         self.router = original.router
         self.shared_expert = original.shared_expert
 
+        self.calib_config = calib_config
+
     def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.tensor]:
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = self.router(hidden_states)
@@ -39,7 +47,15 @@ def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.tens
 
         out = self.shared_expert(hidden_states)
         for i in range(self.num_experts):
-            out += self.experts[i](hidden_states) * router_scores[i].reshape(-1, 1)
+            score = router_scores[i]
+            has_tokens = torch.any(score > 0)
+
+            if self.calib_config.moe_calibrate_all_experts or has_tokens:
+                expert_output = self.experts[i](hidden_states)
+
+                if has_tokens:
+                    # expert contributes to output activations
+                    out += expert_output * score.unsqueeze(-1)
 
         return out, router_scores
 
@@ -64,5 +80,9 @@ def __init__(self, config: Llama4TextConfig, original: Llama4TextExperts):
             self[i].down_proj.weight.data = down.t().clone().contiguous()
 
 
-def replace(config: Llama4Config, module: Llama4TextMoe):
-    return SequentialLlama4TextMoe(config=config.get_text_config(), original=module)
+def replace(
+    config: Llama4Config, module: Llama4TextMoe, calib_config: CalibrationConfig
+):
+    return SequentialLlama4TextMoe(
+        config=config.get_text_config(), original=module, calib_config=calib_config
+    )

src/llmcompressor/modeling/prepare.py

@@ -1,25 +1,39 @@
 from compressed_tensors.utils import replace_module
 from transformers import PreTrainedModel
 
+from llmcompressor.modeling.config import CalibrationConfig
 from llmcompressor.modeling.deepseek_v3 import replace as replace_deepseekv3
 from llmcompressor.modeling.llama4 import replace as replace_llama4
 from llmcompressor.modeling.qwen3_moe import replace as replace_Qwen3MoE
 from llmcompressor.utils.helpers import patch_attr
 
 __all__ = ["replace_modules_for_calibration"]
 
+
 # ---------------------- module replacements; permanent -------------------------
 replacements = {
     "DeepseekV3MoE": replace_deepseekv3,
     "Llama4TextMoe": replace_llama4,
 }
 
 
-def replace_modules_for_calibration(model: PreTrainedModel) -> PreTrainedModel:
+def replace_modules_for_calibration(
+    model: PreTrainedModel,
+    moe_calibrate_all_experts: bool = True,
+    moe_calibrate_gated_acts: bool = True,
+) -> PreTrainedModel:
+
+    calib_config = CalibrationConfig(
+        moe_calibrate_all_experts=moe_calibrate_all_experts,
+        moe_calibrate_gated_acts=moe_calibrate_gated_acts,
+    )
+
     for name, module in model.named_modules():
         cls_name = module.__class__.__name__
         if cls_name in replacements:
-            new_module = replacements[cls_name](config=model.config, module=module)
+            new_module = replacements[cls_name](
+                config=model.config, module=module, calib_config=calib_config
+            )
             replace_module(model, name, new_module)
 
     return model
@@ -28,7 +42,7 @@ def replace_modules_for_calibration(model: PreTrainedModel) -> PreTrainedModel:
 # ------------------- module replacements; during calibration --------------------
 
 
-def update_qwen3_moe(model, stack):
+def update_qwen3_moe(model, stack, calib_config):
     for module in model.modules():
         cls_name = module.__class__.__name__
         if cls_name == "Qwen3MoeDecoderLayer":
@@ -37,7 +51,11 @@ def update_qwen3_moe(model, stack):
                 patch_attr(
                     module,
                     "mlp",
-                    replace_Qwen3MoE(config=model.config, module=module.mlp),
+                    replace_Qwen3MoE(
+                        config=model.config,
+                        module=module.mlp,
+                        calib_config=calib_config,
+                    ),
                 )
             )
 
@@ -47,9 +65,19 @@ def update_qwen3_moe(model, stack):
 }
 
 
-def moe_calibration_context(model: PreTrainedModel, stack):
+def moe_calibration_context(
+    model: PreTrainedModel,
+    stack,
+    moe_calibrate_all_experts: bool = True,
+    moe_calibrate_gated_acts: bool = True,
+):
+    calib_config = CalibrationConfig(
+        moe_calibrate_all_experts=moe_calibrate_all_experts,
+        moe_calibrate_gated_acts=moe_calibrate_gated_acts,
+    )
+
     # Temporarily updates the MoE modules within the context
     # Once the context exists, parameter updates persist
     cls_name = model.__class__.__name__
     if cls_name in moe_context:
-        moe_context.get(cls_name)(model, stack)
+        moe_context.get(cls_name)(model, stack, calib_config)

src/llmcompressor/modeling/qwen3_moe.py

@@ -20,36 +20,61 @@
     Qwen3MoeSparseMoeBlock as OriginalQwen3MoeSparseMoeBlock,
 )
 
+from llmcompressor.modeling.config import CalibrationConfig
+
 
 class Qwen3MoeSparseMoeBlock(torch.nn.Module):
     def __init__(
-        self, config: Qwen3MoeConfig, original: OriginalQwen3MoeSparseMoeBlock
+        self,
+        config: Qwen3MoeConfig,
+        original: OriginalQwen3MoeSparseMoeBlock,
+        calib_config: CalibrationConfig,
     ):
         super().__init__()
         self.num_experts = config.num_experts
         self.top_k = config.top_k
         self.norm_topk_prob = config.norm_topk_prob
+        self.calib_config = calib_config
 
         # gating
         self.gate = original.gate
         self.experts = original.experts
 
+        if not self.calib_config.moe_calibrate_gated_acts:
+            self.gate.top_k = self.num_experts  # ungate experts
+
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         batch_size, sequence_length, hidden_dim = hidden_states.shape
         hidden_states = hidden_states.view(-1, hidden_dim)
         # router_logits: (batch * sequence_length, n_experts)
         router_logits = self.gate(hidden_states)
 
-        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:  # only diff with mixtral sparse moe block!
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
+        if self.calib_config.moe_calibrate_gated_acts:
+            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
+            )
+            # only diff with mixtral sparse moe block!
+            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)
+
+        else:
+            # ungate experts
+            selected_experts = torch.arange(
+                self.num_experts, device=hidden_states.device
+            )
+            selected_experts = selected_experts.unsqueeze(0).expand(
+                hidden_states.shape[0], -1
+            )
+            routing_weights = (
+                torch.ones_like(selected_experts, dtype=hidden_states.dtype)
+                / self.num_experts
+            )
+
         final_hidden_states = torch.zeros(
             (batch_size * sequence_length, hidden_dim),
             dtype=hidden_states.dtype,
@@ -65,23 +90,37 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         for expert_idx in range(len(self.experts)):
             expert_layer = self.experts[expert_idx]
             idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-            expert_output = expert_layer(current_state)
-            current_hidden_states = expert_output * routing_weights[top_x, idx, None]
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(
-                0, top_x, current_hidden_states.to(hidden_states.dtype)
-            )
+
+            has_tokens = idx.numel() > 0
+
+            if self.calib_config.moe_calibrate_all_experts or has_tokens:
+                # Index the correct hidden states and compute the expert hidden state for
+                # the current expert. We need to make sure to multiply the output hidden
+                # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+                current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+                expert_output = expert_layer(current_state)
+                current_hidden_states = (
+                    expert_output * routing_weights[top_x, idx, None]
+                )
+
+                # However `index_add_` only support torch tensors for indexing so we'll use
+                # the `top_x` tensor here.
+                if has_tokens:
+                    final_hidden_states.index_add_(
+                        0, top_x, current_hidden_states.to(hidden_states.dtype)
+                    )
 
         final_hidden_states = final_hidden_states.reshape(
             batch_size, sequence_length, hidden_dim
         )
         return final_hidden_states, router_logits
 
 
-def replace(config: Qwen3MoeConfig, module: OriginalQwen3MoeSparseMoeBlock):
-    return Qwen3MoeSparseMoeBlock(config=config, original=module)
+def replace(
+    config: Qwen3MoeConfig,
+    module: OriginalQwen3MoeSparseMoeBlock,
+    calib_config: CalibrationConfig,
+):
+    return Qwen3MoeSparseMoeBlock(
+        config=config, original=module, calib_config=calib_config
+    )