[npu] add Qwen3.5 support with Partial RoPE and Hybrid Attention (#10421)

Co-authored-by: Curnane <mingliangfu@users.noreply.github.com>

examples/accelerate/fsdp2_config_qwen35.yaml

@@ -0,0 +1,20 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+fsdp_config:
+  fsdp_version: 2
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: Qwen3_5DecoderLayer,Qwen3_5VisionBlock
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_offload_params: false
+  fsdp_reshard_after_forward: true
+  fsdp_state_dict_type: FULL_STATE_DICT
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8  # Change to match your NPU count (e.g., 8 for A2, 16 for A3)
+rdzv_backend: static
+same_network: true
+use_cpu: false

examples/ascend/qwen3_5_full_sft_fsdp2.yaml

@@ -0,0 +1,47 @@
+# Start FSDP2 full fine-tuning on Ascend NPU
+# Usage:
+#   accelerate launch \
+#       --config_file examples/accelerate/fsdp2_config_qwen35.yaml \
+#       src/train.py examples/ascend/qwen3_5_full_sft_fsdp2.yaml
+#
+# Note: Change `num_processes` in fsdp2_config_qwen35.yaml to match your NPU count
+
+### model
+model_name_or_path: Qwen/Qwen3.5-4B
+trust_remote_code: true
+use_v1_kernels: true
+flash_attn: fa2
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+
+### dataset
+dataset: alpaca_en_demo
+template: qwen3_5_nothink
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/Qwen3.5-4B/full/sft
+logging_steps: 1
+save_steps: 500
+max_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+save_only_model: false
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
+
+### train
+per_device_train_batch_size: 8
+gradient_accumulation_steps: 1
+learning_rate: 1.0e-5
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 1800
+resume_from_checkpoint: null

src/llamafactory/v1/plugins/model_plugins/kernels/ops/rms_norm/npu_rms_norm.py

@@ -23,25 +23,104 @@ Init Phase:
 import re
 import types
 
+import torch
+import torch.nn.functional as F
+
 from ......accelerator.helper import DeviceType
 from ......utils.types import HFModel
 from ...base import BaseKernel
 from ...registry import register_kernel
 
 
-def npu_rms_norm_forward(self, hidden_states):
-    """NPU forward implementation for RMSNorm.
+try:
+    import torch_npu
+except ImportError:
+    pass
+
+
+def _should_use_residual_rmsnorm(module):
+    """Detect whether the module uses residual RMSNorm parameterization.
+
+    Residual RMSNorm uses ``scale = 1.0 + weight`` where weight is initialized to 0,
+    while standard RMSNorm uses ``scale = weight`` where weight is initialized to 1.
 
     Args:
-        self: RMSNorm module instance with `weight` and `variance_epsilon`.
-        hidden_states (Tensor): Input hidden states tensor, same shape as the baseline.
+        module (nn.Module): The RMSNorm module to check.
+
+    Returns:
+        bool: ``True`` if the module uses residual parameterization, ``False`` otherwise.
+
+    .. note::
+        This detection ensures compatibility with future model versions (e.g., Qwen3.6, Qwen4.0)
+        without hardcoding version numbers. Two methods are used: weight value inspection
+        (most reliable) and class name pattern matching (backward compatibility).
+    """
+    if hasattr(module, "weight") and module.weight is not None:
+        weight_mean = module.weight.data.mean().item()
+        if abs(weight_mean) < 0.3:
+            return True
+
+    class_name = module.__class__.__name__
+    residual_patterns = ["Qwen3_5", "Qwen3_6", "Qwen4"]
+    for pattern in residual_patterns:
+        if pattern in class_name:
+            return True
+
+    return False
+
+
+def npu_rms_norm_forward(self, hidden_states):
+    """NPU forward implementation for standard RMSNorm.
+
+    Args:
+        self (nn.Module): The RMSNorm module instance with ``weight`` and ``variance_epsilon``.
+        hidden_states (Tensor): Input hidden states tensor.
 
     Returns:
         Tensor: Normalized tensor consistent with the baseline RMSNorm behavior.
     """
-    import torch_npu
+    _eps = getattr(self, "variance_epsilon", None) or getattr(self, "eps", 1e-6)
 
-    return torch_npu.npu_rms_norm(hidden_states, self.weight, epsilon=self.variance_epsilon)[0]
+    if hasattr(self, "weight") and self.weight is not None:
+        if _should_use_residual_rmsnorm(self):
+            effective_weight = 1.0 + self.weight.float()
+        else:
+            effective_weight = self.weight.float()
+    else:
+        effective_weight = None
+
+    if effective_weight is not None:
+        return torch_npu.npu_rms_norm(hidden_states, effective_weight.to(hidden_states.dtype), epsilon=_eps)[0]
+    else:
+        return torch_npu.npu_rms_norm(hidden_states, self.weight, epsilon=_eps)[0]
+
+
+def npu_gated_rms_norm_forward(self, hidden_states, gate=None):
+    """NPU forward implementation for Gated RMSNorm with high-precision FP32 computation.
+
+    This function performs RMSNorm and gated SiLU multiplication in FP32 for numerical
+    stability. Unlike standard RMSNorm, Gated RMSNorm in Qwen3.5 uses standard
+    parameterization (``scale = weight`` where weight is initialized to 1), so the
+    residual weight adjustment (``1.0 + weight``) is not applied here.
+
+    Args:
+        self (nn.Module): The Gated RMSNorm module instance.
+        hidden_states (Tensor): Input hidden states tensor.
+        gate (Tensor, optional): Gate tensor for SiLU activation. Defaults to ``None``.
+
+    Returns:
+        Tensor: Output tensor cast back to the original input dtype.
+    """
+    input_dtype = hidden_states.dtype
+    hidden_states = hidden_states.to(torch.float32)
+    _eps = getattr(self, "variance_epsilon", None) or getattr(self, "eps", 1e-6)
+
+    hidden_states = torch_npu.npu_rms_norm(hidden_states, self.weight.float(), epsilon=_eps)[0]
+
+    if gate is not None:
+        hidden_states = hidden_states * F.silu(gate.to(torch.float32))
+
+    return hidden_states.to(input_dtype)
 
 
 @register_kernel
@@ -55,12 +134,9 @@ class NpuRMSNormKernel(BaseKernel):
     def apply(cls, **kwargs) -> "HFModel":
         """Iterate the model and apply NPU-optimized forward to matched RMSNorm modules.
 
-        Key points:
-        - Match modules whose class name contains "RMSNorm" (case-insensitive).
-        - Bind `_npu_rms_forward` as an instance method via `types.MethodType` to
-          replace the original `forward`.
-        - Do not modify weights, hyperparameters, or module structure to ensure
-          numerical behavior and interface consistency.
+        Matches modules whose class name contains "RMSNorm" (case-insensitive) and binds
+        the appropriate NPU-optimized forward function as an instance method via
+        ``types.MethodType`` to replace the original ``forward``.
 
         Args:
             **kwargs: Keyword arguments containing the model.
@@ -69,7 +145,7 @@ class NpuRMSNormKernel(BaseKernel):
             HFModel: The model with NPU fused RMSNorm.
 
         Raises:
-            RuntimeError: If torch_npu is not available.
+            RuntimeError: If ``torch_npu`` is not available.
             ValueError: If the model is not provided.
         """
         model = kwargs.get("model")
@@ -81,11 +157,11 @@ class NpuRMSNormKernel(BaseKernel):
 
         rms_norm_pattern = re.compile("RMSNorm", re.IGNORECASE)
 
-        for name, module in model.named_modules():
-            # Match any module whose class name contains "RMSNorm"
+        for _, module in model.named_modules():
             if re.search(rms_norm_pattern, module.__class__.__name__):
-                # Bind function as an instance method to preserve `self` semantics
-                # and replace the original forward
-                module.forward = types.MethodType(npu_rms_norm_forward, module)
+                if "Gated" in module.__class__.__name__:
+                    module.forward = types.MethodType(npu_gated_rms_norm_forward, module)
+                else:
+                    module.forward = types.MethodType(npu_rms_norm_forward, module)
 
         return model

src/llamafactory/v1/plugins/model_plugins/kernels/ops/rope/npu_rope.py

@@ -39,40 +39,80 @@ except ImportError:
     pass
 
 
-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 using NPU optimization.
+def _apply_npu_rotary_emb(q, k, cos, sin):
+    """Apply NPU-accelerated rotary embedding with automatic Partial RoPE detection.
+
+    This function automatically detects whether to use Partial RoPE or Full RoPE
+    based on the dimension ratio between ``cos/sin`` and ``q/k`` tensors, ensuring
+    compatibility with future model versions without hardcoding.
 
     Args:
         q (Tensor): Query tensor.
         k (Tensor): Key tensor.
-        cos (Tensor): Cosine part of embedding.
-        sin (Tensor): Sine part of embedding.
-        position_ids (Tensor, optional): Position IDs. Default: ``None``.
-        unsqueeze_dim (int): Dimension to unsqueeze cos and sin. Default: 1.
+        cos (Tensor): Cosine part of rotary embedding (already unsqueezed).
+        sin (Tensor): Sine part of rotary embedding (already unsqueezed).
 
     Returns:
-        tuple: (q_embed, k_embed) The embedded query and key tensors.
+        tuple[Tensor, Tensor]: The embedded query and key tensors ``(q_embed, k_embed)``.
     """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = torch_npu.npu_rotary_mul(q, cos, sin)
-    k_embed = torch_npu.npu_rotary_mul(k, cos, sin)
+    rotary_dim = cos.shape[-1]
+    query_dim = q.shape[-1]
+
+    if rotary_dim < query_dim:
+        q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
+        k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
+
+        q_embed = torch_npu.npu_rotary_mul(q_rot, cos, sin).to(q.dtype)
+        k_embed = torch_npu.npu_rotary_mul(k_rot, cos, sin).to(k.dtype)
+
+        q_embed = torch.cat([q_embed, q_pass], dim=-1)
+        k_embed = torch.cat([k_embed, k_pass], dim=-1)
+    else:
+        q_embed = torch_npu.npu_rotary_mul(q, cos, sin).to(q.dtype)
+        k_embed = torch_npu.npu_rotary_mul(k, cos, sin).to(k.dtype)
+
     return q_embed, k_embed
 
 
-def _apply_multimodal_rotary_pos_emb_qwen25_vl(q, k, cos, sin, mrope_section, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding with multimodal sections (Qwen2-VL) on NPU.
+def _apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Apply Rotary Position Embedding to query and key tensors using NPU optimization.
+
+    This function automatically supports both Full RoPE and Partial RoPE based on
+    the dimension ratio between ``cos/sin`` and ``q/k`` tensors.
 
     Args:
         q (Tensor): Query tensor.
         k (Tensor): Key tensor.
         cos (Tensor): Cosine part of embedding.
         sin (Tensor): Sine part of embedding.
-        mrope_section (Tensor): Multimodal RoPE section.
-        unsqueeze_dim (int): Dimension to unsqueeze cos and sin. Default: 1.
+        position_ids (Tensor, optional): Position IDs. Defaults to ``None``.
+        unsqueeze_dim (int): Dimension to unsqueeze cos and sin. Defaults to 1.
 
     Returns:
-        tuple: (q_embed, k_embed) The embedded query and key tensors.
+        tuple[Tensor, Tensor]: The embedded query and key tensors ``(q_embed, k_embed)``.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+
+    return _apply_npu_rotary_emb(q, k, cos, sin)
+
+
+def _apply_multimodal_rotary_pos_emb_qwen25_vl(q, k, cos, sin, mrope_section, unsqueeze_dim=1):
+    """Apply Rotary Position Embedding with multimodal sections (Qwen2-VL) on NPU.
+
+    This function supports Partial RoPE for multimodal inputs with automatic dimension
+    detection, ensuring compatibility with future model versions.
+
+    Args:
+        q (Tensor): Query tensor.
+        k (Tensor): Key tensor.
+        cos (Tensor): Cosine part of embedding.
+        sin (Tensor): Sine part of embedding.
+        mrope_section (list[int]): Multimodal RoPE section sizes.
+        unsqueeze_dim (int): Dimension to unsqueeze cos and sin. Defaults to 1.
+
+    Returns:
+        tuple[Tensor, Tensor]: The embedded query and key tensors ``(q_embed, k_embed)``.
     """
     mrope_section = mrope_section * 2
     cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
@@ -82,9 +122,7 @@ def _apply_multimodal_rotary_pos_emb_qwen25_vl(q, k, cos, sin, mrope_section, un
         unsqueeze_dim
     )
 
-    q_embed = torch_npu.npu_rotary_mul(q, cos, sin)
-    k_embed = torch_npu.npu_rotary_mul(k, cos, sin)
-    return q_embed, k_embed
+    return _apply_npu_rotary_emb(q, k, cos, sin)
 
 
 @register_kernel
@@ -96,12 +134,12 @@ class NpuRoPEKernel(BaseKernel):
 
     @classmethod
     def apply(cls, **kwargs) -> "HFModel":
-        """Apply RoPE acceleration by monkey-patching `apply_rotary_pos_emb`.
+        """Apply RoPE acceleration by monkey-patching ``apply_rotary_pos_emb``.
 
-        This function iterates through the model's modules to find attention layers,
-        identifies the module where they are defined, and replaces the original
-        `apply_rotary_pos_emb` function in that module's namespace with the
-        NPU-accelerated version from this file.
+        Iterates through the model's modules to find attention layers, identifies
+        the module where they are defined, and replaces the original
+        ``apply_rotary_pos_emb`` function in that module's namespace with the
+        NPU-accelerated version.
 
         Args:
             **kwargs: Keyword arguments containing the model.
@@ -110,7 +148,7 @@ class NpuRoPEKernel(BaseKernel):
             HFModel: The model with patched RoPE functions.
 
         Raises:
-            RuntimeError: If dependencies are not met.
+            RuntimeError: If ``torch_npu`` is not available.
             ValueError: If the model is not provided.
         """
         if not cls.check_deps():