(feat)Draft model supports Qwen3 MoE

docs/examples/qwen3-moe-eagle3-offline.md

@@ -0,0 +1,61 @@
+# Eagle3 for Llama3 - Offline
+
+## Introduction
+
+This document provides a step-by-step guide on how to train the EAGLE3 model for the Llama3.1-8B-Instruct model in an offline manner. In offline training, we generate the hidden states required by EAGLE3 draft model beforehand and store them to the disk. During training, we load them back to the GPU memory. As offline training requires a lot of disk space, we do not recommend running this on large datasets such as Perfect-Blend.
+
+## Training on ShareGPT dataset
+
+### **Step 1. Prepare ShareGPT dataset**
+
+First of all, we should download the dataset.
+
+```shell
+python ./scripts/prepare_data.py --dataset sharegpt
+```
+
+### **Step 2. Prepare Hidden States**
+
+We need to prepare the hidden states for the training.
+
+```shell
+torchrun --nproc_per_node=8 \
+    scripts/prepare_hidden_states.py \
+    --target-model-path /home/data/weights/Qwen3-32B \
+    --enable-aux-hidden-states \
+    --data-path ./cache/dataset/sharegpt_train.jsonl \
+    --chat-template qwen \
+    --max-length 2048 \
+    --tp-size 8 \
+    --batch-size 32 \
+    --num-samples 20 \
+    --output-path ./cache/hidden_states
+```
+
+The hidden states will be saved to the disk in the `output-path` directory.
+
+### **Step 3. Start Training**
+
+```shell
+torchrun \
+    --standalone \
+    --nproc_per_node $NUM_GPUS \
+    $ROOT_DIR/scripts/train_eagle3.py \
+    --target-model-path  /home/data/weights/Qwen3-30B-A3B/  \
+    --draft-model-config $ROOT_DIR/configs/qwen3-30B-A3B-eagle3_moe.json \
+    --train-data-path ./cache/dataset/sharegpt_train.jsonl \
+    --train-hidden-states-path  ./cache/hidden_states \
+    --output-dir ./outputs/qwen3-moe-8b-eagle3-sharegpt-offline \
+    --num-epochs 4 \
+    --batch-size 1 \
+    --learning-rate 1e-4 \
+    --max-length 2048 \
+    --save-interval 1984 \
+    --chat-template qwen \
+    --cache-dir $ROOT_DIR/cache \
+    --embedding-key model.embed_tokens.weight \
+    --sp-ulysses-size 4 \
+    --attention-backend "usp" \
+    --target-model-backend sglang
+
+```

specforge/core/eagle3.py

@@ -545,7 +545,7 @@ def _compute_target_p_padded(target, t2d, loss_mask, length):
         return target_p_padded, position_mask
 
 
-@torch.compile(dynamic=None)
+# @torch.compile(dynamic=None)
 def _compute_target_p(target, t2d, loss_mask):
     target_head = target
     target_max_token = target_head.argmax(-1)
@@ -559,7 +559,7 @@ def _compute_target_p(target, t2d, loss_mask):
     return target_p, position_mask
 
 
-@torch.compile(dynamic=None)
+# @torch.compile(dynamic=None)
 def _compute_metric_acc(logits, target_p, position_mask, loss_mask):
     return (
         (logits.argmax(-1) == target_p.argmax(-1)) * position_mask.squeeze(-1)

specforge/core/loss.py

@@ -12,7 +12,7 @@
 
 
 # Reference implementation
-@torch.compile(dynamic=None)
+# @torch.compile(dynamic=None)
 def _compute_loss(logits, target_p, position_mask):
     logits = logits.float()
     out_logp = nn.LogSoftmax(dim=2)(logits)
@@ -30,7 +30,7 @@ def _calculate_settings(n):
         raise RuntimeError(
             f"Cannot launch Triton kernel since n = {n} exceeds the recommended Triton blocksize = {MAX_FUSED_SIZE}."
         )
-
+    BLOCK_SIZE = 2048
     num_warps = 4
     if BLOCK_SIZE >= 32768:
         num_warps = 32

specforge/modeling/__init__.py

@@ -1,6 +1,8 @@
 # from .auto import AutoDistributedTargetModel, AutoDraftModelConfig, AutoEagle3DraftModel
 from .auto import AutoDraftModelConfig, AutoEagle3DraftModel
 from .draft.llama3_eagle import LlamaForCausalLMEagle3
+from .draft.qwen3_moe_eagle import Qwen3MoEForCausalLMEagle3
+from .draft.router_moe import Qwen3MoERouterForCausalLMEagle3
 from .target.eagle3_target_model import (
     CustomEagle3TargetModel,
     HFEagle3TargetModel,
@@ -10,6 +12,7 @@
 
 __all__ = [
     "LlamaForCausalLMEagle3",
+    "Qwen3MoERouterForCausalLMEagle3",
     "SGLangEagle3TargetModel",
     "HFEagle3TargetModel",
     "CustomEagle3TargetModel",

specforge/modeling/auto.py

@@ -19,6 +19,8 @@
 )
 
 from .draft.llama3_eagle import LlamaForCausalLMEagle3
+from .draft.qwen3_moe_eagle import Qwen3MoEForCausalLMEagle3
+from .draft.router_moe import Qwen3MoERouterForCausalLMEagle3
 from .target.custom_backend import (
     GptOssForCausalLM,
     Llama4ForCausalLM,
@@ -34,6 +36,7 @@ class AutoEagle3DraftModel(AutoModelForCausalLMBase):
     # the model mapping is currently hardcoded, we should support lazy model mapping via registry
     _model_mapping = {
         LlamaConfig: LlamaForCausalLMEagle3,
+        Qwen3MoeConfig: Qwen3MoERouterForCausalLMEagle3,
     }
 
     @classmethod
@@ -133,6 +136,7 @@ class AutoDraftModelConfig:
 
     _config_mapping = {
         "LlamaForCausalLMEagle3": LlamaConfig,
+        "Qwen3MoERouterForCausalLMEagle3": Qwen3MoeConfig,
     }
 
     @classmethod

specforge/modeling/draft/llama3_eagle.py

@@ -102,7 +102,7 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-@torch.compile(dynamic=True)
+# @torch.compile(dynamic=True)
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
     cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
@@ -272,7 +272,7 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
             "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
         )
 
-    @torch.compile(dynamic=True)
+    # @torch.compile(dynamic=True)
     def forward(self, x, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
         if seq_len and seq_len > self.max_seq_len_cached:
@@ -1347,7 +1347,7 @@ def __init__(self, hidden_size, eps=1e-6):
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
 
-    @torch.compile(dynamic=True)
+    # @torch.compile(dynamic=True)
     def forward(self, hidden_states):
         input_dtype = hidden_states.dtype
         hidden_states = hidden_states.to(torch.float32)
@@ -1474,6 +1474,7 @@ def __init__(self, config, quant_config=None, attention_backend="sdpa") -> None:
         d2t = torch.zeros(self.draft_vocab_size, dtype=torch.int64)
         self.register_buffer("t2d", t2d)
         self.register_buffer("d2t", d2t)
+        self.save_idx = 1
 
     def forward(
         self,
@@ -1528,6 +1529,12 @@ def forward(
 
         # norm
         hidden_states = self.norm(hidden_states)
+        self.save_idx += 1
+        save_path = f"./router/{self.save_idx}.pt"
+        # 2. 保存张量（转CPU避免GPU张量依赖，detach解耦计算图）
+        torch.save(hidden_states.detach().cpu(), save_path)
+        if self.save_idx >= 16:
+            exit(0)
 
         return hidden_states
 
@@ -1563,3 +1570,5 @@ def backbone(
             output_attentions=False,
             use_cache=False,
         )
+
+#