Enable Qwen3.5-MoE PTQ

examples/llm_ptq/hf_ptq.py

@@ -639,16 +639,19 @@ def export_quantized(
                 extra_state_dict=mtp_state_dict,
             )
 
-        # Copy custom model files (Python files and JSON configs) if trust_remote_code is used
-        copy_custom_model_files(args.pyt_ckpt_path, export_path, args.trust_remote_code)
-
         # Restore default padding and export the tokenizer as well.
         if tokenizer is not None:
             tokenizer.padding_side = default_padding_side
             if default_pad_token is not None:
                 tokenizer.pad_token = default_pad_token
             tokenizer.save_pretrained(export_path)
 
+        # Copy custom model files (Python files and JSON configs) if trust_remote_code is used.
+        # This must run AFTER tokenizer.save_pretrained() so original tokenizer files
+        # from the source checkpoint take precedence over regenerated ones (which may
+        # differ in format due to newer transformers versions).
+        copy_custom_model_files(args.pyt_ckpt_path, export_path, args.trust_remote_code)
+
         end_time = time.time()
         print(
             f"Quantized model exported to: {export_path}. Total time used {end_time - start_time}s"

modelopt/torch/export/layer_utils.py

@@ -339,6 +339,7 @@ def is_moe(module: nn.Module) -> bool:
             "Qwen2MoeSparseMoeBlock".lower(),
             "Qwen3MoeSparseMoeBlock".lower(),
             "Qwen3NextSparseMoeBlock".lower(),
+            "Qwen3_5MoeSparseMoeBlock".lower(),
         ]
     )
 
@@ -999,6 +1000,7 @@ def module_match_name_list(module, name_list):
             "Qwen2MoeSparseMoeBlock",
             "Qwen3MoeSparseMoeBlock",
             "Qwen3NextSparseMoeBlock",
+            "Qwen3_5MoeSparseMoeBlock",
             "DeepseekMoE",
         ],
     ):
@@ -1134,7 +1136,10 @@ def set_expert_quantizer_amax(
     # Apply target amax to quantizers that need it
     for module, attr_name, quantizer in all_quantizers:
         # Check if quantizer needs amax (use property for consistency)
-        needs_amax = getattr(quantizer, "amax", None) is None
+        # Also treat zero amax as needing recalibration — a zero amax is never valid
+        # and indicates the quantizer wasn't activated during calibration
+        amax = getattr(quantizer, "amax", None)
+        needs_amax = amax is None or (isinstance(amax, torch.Tensor) and torch.all(amax == 0))
 
         # Skip dynamic quantizers for input quantizers
         if "input_quantizer" in attr_name and getattr(quantizer, "_dynamic", False):
@@ -1740,7 +1745,7 @@ def _split_fused_qkv_weight_and_scaling(
 
     qkv_in = weight.shape[-1] if weight_dim > 1 else 1
 
-    num_kv_heads = num_kv_heads if num_kv_heads else num_heads
+    num_kv_heads = num_kv_heads or num_heads
     assert num_heads % num_kv_heads == 0, (
         f"num_heads({num_heads}) must be divisible by num_kv_heads({num_kv_heads}))."
     )

modelopt/torch/export/unified_export_hf.py

@@ -574,7 +574,7 @@ def _process_quantized_modules(
     """
     fsdp_module_to_reshard = None
 
-    for _, sub_module in model.named_modules():
+    for name, sub_module in model.named_modules():
         # Optimization to perform resharding only once per decoder layer to avoid extra communication overhead
         if isinstance(sub_module, FSDPModule):
             # Every time we encounter a new FSDPModule, the previous decoder layer is fully processed.
@@ -593,8 +593,13 @@ def _process_quantized_modules(
             sub_module.unpack_weight()
         if get_quantization_format(sub_module) != QUANTIZATION_NONE:
             if is_quantlinear(sub_module):
-                with fsdp2_aware_weight_update(model, sub_module, reshard=False):
-                    _export_quantized_weight(sub_module, dtype)
+                try:
+                    with fsdp2_aware_weight_update(model, sub_module, reshard=False):
+                        _export_quantized_weight(sub_module, dtype)
+                except AssertionError as e:
+                    raise AssertionError(
+                        f"Failed to export module '{name}' (type={type(sub_module).__name__}): {e}"
+                    ) from e
             elif (
                 "Llama4TextExperts" in type(sub_module).__name__
                 or "GptOssExperts" in type(sub_module).__name__
@@ -954,6 +959,45 @@ def _export_diffusers_checkpoint(
     print(f"Export complete. Saved to: {export_dir}")
 
 
+def _revert_weight_conversion_noop(model: Any, state_dict: dict) -> dict:
+    """No-op replacement for transformers' revert_weight_conversion."""
+    return state_dict
+
+
+def _try_patch_module(mod_path: str) -> tuple[Any, Any] | None:
+    """Try to patch revert_weight_conversion in a single module."""
+    import importlib
+
+    try:
+        mod = importlib.import_module(mod_path)
+        if hasattr(mod, "revert_weight_conversion"):
+            original = getattr(mod, "revert_weight_conversion")
+            setattr(mod, "revert_weight_conversion", _revert_weight_conversion_noop)
+            return (mod, original)
+    except (ImportError, AttributeError):
+        pass
+    return None
+
+
+def _patch_revert_weight_conversion() -> list[tuple[Any, Any]]:
+    """Patch revert_weight_conversion in transformers to avoid IndexError on scalar tensors."""
+    patches: list[tuple[Any, Any]] = []
+    for mod_path in [
+        "transformers.core_model_loading",
+        "transformers.modeling_utils",
+    ]:
+        result = _try_patch_module(mod_path)
+        if result is not None:
+            patches.append(result)
+    return patches
+
+
+def _unpatch_revert_weight_conversion(patches: list[tuple[Any, Any]]) -> None:
+    """Restore the original revert_weight_conversion functions."""
+    for mod, original in patches:
+        mod.revert_weight_conversion = original
+
+
 def export_hf_checkpoint(
     model: Any,
     dtype: torch.dtype | None = None,
@@ -1013,11 +1057,20 @@ def export_hf_checkpoint(
             model.hf_quantizer = None
 
         # Save model
-        model.save_pretrained(
-            export_dir,
-            state_dict={**post_state_dict, **(extra_state_dict or {})},
-            save_modelopt_state=save_modelopt_state,
-        )
+        # Temporarily disable revert_weight_conversion if available — it doesn't handle
+        # quantized state dicts (scalar scale tensors have 0 dimensions, causing IndexError).
+        # We must patch both the source module and the importing module since
+        # modeling_utils does `from core_model_loading import revert_weight_conversion`.
+        _patches = _patch_revert_weight_conversion()
+
+        try:
+            model.save_pretrained(
+                export_dir,
+                state_dict={**post_state_dict, **(extra_state_dict or {})},
+                save_modelopt_state=save_modelopt_state,
+            )
+        finally:
+            _unpatch_revert_weight_conversion(_patches)
 
         original_config = f"{export_dir}/config.json"
         config_data = {}

modelopt/torch/quantization/plugins/huggingface.py

@@ -653,6 +653,107 @@ def forward(
         return next_states
 
 
+class _Qwen35MoeExpertModule(nn.Module):
+    """Container for a single Qwen3.5 MoE expert's linear layers.
+
+    Produces the naming pattern: experts.{id}.gate_proj.weight
+    (consistent with standard Qwen3 MoE per-expert module structure).
+    """
+
+    def __init__(self, hidden_dim: int, expert_dim: int):
+        super().__init__()
+        self.gate_proj = nn.Linear(hidden_dim, expert_dim, bias=False)
+        self.up_proj = nn.Linear(hidden_dim, expert_dim, bias=False)
+        self.down_proj = nn.Linear(expert_dim, hidden_dim, bias=False)
+
+
+class _QuantQwen35MoeExperts(QuantModule):
+    def _setup(self):
+        """Modify the Qwen3_5MoeExperts by using per-expert nn.Module containers.
+
+        This produces the naming pattern: experts.{id}.gate_proj.weight
+        (consistent with standard Qwen3 MoE).
+        """
+        from accelerate import init_empty_weights
+
+        dtype, device = self.gate_up_proj.dtype, self.gate_up_proj.device
+
+        def _copy_weight(module, weight):
+            module.to_empty(device=device)
+            with torch.no_grad():
+                module.weight.data = weight.detach().data.to(dtype=dtype, device=device)
+
+        expert_dim = self.intermediate_dim
+
+        with init_empty_weights():
+            expert_modules = nn.ModuleList(
+                [
+                    _Qwen35MoeExpertModule(self.hidden_dim, expert_dim)
+                    for _ in range(self.num_experts)
+                ]
+            )
+
+        for idx in range(self.num_experts):
+            # gate_up_proj shape: (num_experts, 2*intermediate_dim, hidden_dim)
+            # Already in (out_features, in_features) format, no transpose needed
+            _copy_weight(expert_modules[idx].gate_proj, self.gate_up_proj[idx, :expert_dim, :])
+            _copy_weight(expert_modules[idx].up_proj, self.gate_up_proj[idx, expert_dim:, :])
+            # down_proj shape: (num_experts, hidden_dim, intermediate_dim)
+            # Already in (out_features, in_features) format
+            _copy_weight(expert_modules[idx].down_proj, self.down_proj[idx])
+
+        delattr(self, "gate_up_proj")
+        delattr(self, "down_proj")
+        # Register expert modules directly as numbered children (like nn.ModuleList)
+        # so the naming pattern is: experts.{id}.gate_proj.weight (no extra nesting)
+        for idx in range(self.num_experts):
+            self.add_module(str(idx), expert_modules[idx])
+
+    def __len__(self):
+        """Support len() so the module is iterable like standard MoE experts."""
+        return self.num_experts
+
+    def __iter__(self):
+        """Support iteration over expert modules."""
+        for idx in range(self.num_experts):
+            yield getattr(self, str(idx))
+
+    def __getitem__(self, idx):
+        """Support indexing to get individual expert modules."""
+        return getattr(self, str(int(idx)))
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        top_k_index: torch.Tensor,
+        top_k_weights: torch.Tensor,
+    ) -> torch.Tensor:
+        final_hidden_states = torch.zeros_like(hidden_states)
+        with torch.no_grad():
+            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
+            expert_mask = expert_mask.permute(2, 1, 0)
+            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+        for expert_idx in expert_hit:
+            expert_idx = expert_idx[0]
+            if expert_idx == self.num_experts:
+                continue
+            with torch.no_grad():
+                top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
+            current_state = hidden_states[token_idx]
+            expert = self[expert_idx]
+            gate = expert.gate_proj(current_state)
+            up = expert.up_proj(current_state)
+            current_hidden_states = self.act_fn(gate) * up
+            current_hidden_states = expert.down_proj(current_hidden_states)
+            current_hidden_states = (
+                current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
+            )
+            final_hidden_states.index_add_(
+                0, token_idx, current_hidden_states.to(final_hidden_states.dtype)
+            )
+        return final_hidden_states
+
+
 class _QuantDbrxFFN(_QuantSparseMoe):
     @property
     def num_experts(self):
@@ -797,6 +898,46 @@ def unpack_weight(self):
     pass
 
 
+class _QuantQwen35MoeSparseMoeBlock(_QuantSparseMoe):
+    """Qwen3.5 MoE stores top_k/num_experts in the router (self.gate), not as direct attributes.
+
+    We override forward instead of just bridging attributes because the router (self.gate)
+    uses its own top_k internally for routing decisions. We must modify self.gate.top_k
+    directly so all experts see calibration data.
+    """
+
+    def _setup(self):
+        self.num_experts = self.experts.num_experts
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if any(getattr(m, "_if_calib", False) for m in self.experts.modules()):
+            # Force all tokens to all experts during calibration
+            original_top_k = self.gate.top_k
+            self.gate.top_k = self.num_experts
+            super(_QuantSparseMoe, self).forward(hidden_states)
+            self.gate.top_k = original_top_k
+        return super(_QuantSparseMoe, self).forward(hidden_states)
+
+
+try:
+    from transformers.models.qwen3_5_moe.modeling_qwen3_5_moe import (
+        Qwen3_5MoeExperts,
+        Qwen3_5MoeSparseMoeBlock,
+    )
+
+    if Qwen3_5MoeSparseMoeBlock not in QuantModuleRegistry:
+        QuantModuleRegistry.register({Qwen3_5MoeSparseMoeBlock: "hf.Qwen3_5MoeSparseMoeBlock"})(
+            _QuantQwen35MoeSparseMoeBlock
+        )
+
+    if Qwen3_5MoeExperts not in QuantModuleRegistry:
+        QuantModuleRegistry.register({Qwen3_5MoeExperts: "hf.Qwen3_5MoeExperts"})(
+            _QuantQwen35MoeExperts
+        )
+except ImportError:
+    pass
+
+
 class _QuantGptOssExperts(_QuantFunctionalMixin):
     """Quantized wrapper for `transformers.GptOssExperts`.
 

modelopt/torch/utils/dataset_utils.py

@@ -224,7 +224,7 @@ def get_dataset_dataloader(
         A instance of dataloader.
     """
     assert tokenizer is not None, "Please provide a tokenizer."
-    # batch_encode_plus will modify the tokenizer in place, so we need to clone it.
+    # Tokenizer encoding may modify the tokenizer in place, so we need to clone it.
     tokenizer = copy.deepcopy(tokenizer)
 
     if tokenizer.padding_side != "left":
@@ -247,7 +247,7 @@ def get_dataset_dataloader(
         samples = get_dataset_samples(ds_name, num_sample)
         all_samples.extend(samples)
 
-    batch_encoded = tokenizer.batch_encode_plus(
+    batch_encoded = tokenizer(
         all_samples,
         return_tensors="pt",
         padding=True,