fix

scripts/bench_moe.py

@@ -1,11 +1,18 @@
 #!/usr/bin/env python
 import argparse
+import sys
 import time
+from pathlib import Path
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+try:
+    from axolotl.kernels.moe import torch_grouped as tg
+except Exception:  # pragma: no cover - fallback when torch_grouped unavailable
+    tg = None
+
 
 class SwiGLUMlp(nn.Module):
     def __init__(self, hidden_size: int, intermediate_size: int):
@@ -51,10 +58,10 @@ def forward_naive(
     return y.view(bsz, seqlen, hdim)
 
 
-def bench(fn, *args, iters=50, warmup=10, sync=True):
+def bench(fn, iters=50, warmup=10, sync=True):
     # warmup
     for _ in range(warmup):
-        fn(*args)
+        fn()
         if sync and torch.cuda.is_available():
             torch.cuda.synchronize()
     # measure
@@ -63,7 +70,7 @@ def bench(fn, *args, iters=50, warmup=10, sync=True):
         if sync and torch.cuda.is_available():
             torch.cuda.synchronize()
         t0 = time.perf_counter()
-        fn(*args)
+        fn()
         if sync and torch.cuda.is_available():
             torch.cuda.synchronize()
         dt = (time.perf_counter() - t0) * 1000.0
@@ -94,6 +101,18 @@ def main():
     )
     p.add_argument("--iters", type=int, default=50)
     p.add_argument("--warmup", type=int, default=10)
+    p.add_argument(
+        "--hf-block",
+        type=str,
+        default="none",
+        choices=["none", "qwen2_moe"],
+        help="Use a Hugging Face MoE block for benchmarking instead of the toy SwiGLU layer.",
+    )
+    p.add_argument(
+        "--profile",
+        action="store_true",
+        help="Capture CUDA profiler tables for naive and grouped runs.",
+    )
     args = p.parse_args()
 
     device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -107,101 +126,136 @@ def main():
     if device == "cuda":
         torch.cuda.manual_seed(0)
 
-    # Model
-    experts = Experts(args.experts, args.hidden, args.inter).to(
-        device=device, dtype=dtype
-    )
-    gate = nn.Linear(args.hidden, args.experts, bias=False).to(
-        device=device, dtype=dtype
-    )
-
     # data
     x = torch.randn(args.bsz, args.seq, args.hidden, device=device, dtype=dtype)
 
-    # Report config
     tokens = args.bsz * args.seq
-    print(
-        f"Device={device} dtype={dtype} tokens={tokens} hidden={args.hidden} inter={args.inter} experts={args.experts} top_k={args.top_k}"
-    )
 
-    # Naive baseline
-    t_naive = bench(
-        forward_naive,
-        x,
-        gate,
-        experts,
-        args.top_k,
-        iters=args.iters,
-        warmup=args.warmup,
-    )
-    flops_total = estimate_moe_flops(tokens, args.hidden, args.inter, args.top_k)
+    use_hf = args.hf_block != "none"
+
+    if use_hf:
+        project_root = Path(__file__).resolve().parents[2]
+        transformers_src = project_root / "transformers" / "src"
+        if transformers_src.exists() and str(transformers_src) not in sys.path:
+            sys.path.append(str(transformers_src))
+
+        if args.hf_block == "qwen2_moe":
+            from transformers.models.qwen2_moe.configuration_qwen2_moe import (
+                Qwen2MoeConfig,
+            )
+            from transformers.models.qwen2_moe.modeling_qwen2_moe import (
+                Qwen2MoeSparseMoeBlock,
+            )
+
+            cfg = Qwen2MoeConfig(
+                hidden_size=args.hidden,
+                moe_intermediate_size=args.inter,
+                shared_expert_intermediate_size=args.inter,
+                num_experts=args.experts,
+                num_experts_per_tok=args.top_k,
+                norm_topk_prob=True,
+                qkv_bias=True,
+            )
+
+            block_naive = Qwen2MoeSparseMoeBlock(cfg).to(device=device, dtype=dtype)
+            block_grouped = Qwen2MoeSparseMoeBlock(cfg).to(device=device, dtype=dtype)
+            block_grouped.load_state_dict(block_naive.state_dict())
+
+        def run_naive_model(inp: torch.Tensor) -> torch.Tensor:
+            out, _ = block_naive(inp)
+            return out
+
+        def run_grouped_model(inp: torch.Tensor) -> torch.Tensor:
+            if tg is None or not tg.available():
+                return torch.empty(0)
+            block_grouped.experts._ax_parent_block = block_grouped
+            y, _ = tg.moe_ffn_forward_grouped(
+                inp, block_grouped.gate, block_grouped.experts, block_grouped.top_k
+            )
+            return y if y is not None else torch.empty(0)
+
+        flops_total = estimate_moe_flops(tokens, args.hidden, args.inter, args.top_k)
+        print(
+            f"Device={device} dtype={dtype} tokens={tokens} hidden={args.hidden} inter={args.inter} "
+            f"experts={args.experts} top_k={args.top_k} hf_block={args.hf_block}"
+        )
+
+    else:
+        experts = Experts(args.experts, args.hidden, args.inter).to(
+            device=device, dtype=dtype
+        )
+        gate = nn.Linear(args.hidden, args.experts, bias=False).to(
+            device=device, dtype=dtype
+        )
+
+        def run_naive_model(inp: torch.Tensor) -> torch.Tensor:
+            return forward_naive(inp, gate, experts, args.top_k)
+
+        def run_grouped_model(inp: torch.Tensor) -> torch.Tensor:
+            if tg is None or not tg.available():
+                return torch.empty(0)
+            y, _ = tg.moe_ffn_forward_grouped(inp, gate, experts, args.top_k)
+            return y if y is not None else torch.empty(0)
+
+        flops_total = estimate_moe_flops(tokens, args.hidden, args.inter, args.top_k)
+        print(
+            f"Device={device} dtype={dtype} tokens={tokens} hidden={args.hidden} inter={args.inter} "
+            f"experts={args.experts} top_k={args.top_k}"
+        )
+
+    # Benchmark naive
+    t_naive = bench(lambda: run_naive_model(x), iters=args.iters, warmup=args.warmup)
     tflops_naive = flops_total / ((t_naive / 1000.0) * 1e12)
     print(
         f"naive\t{t_naive:.2f} ms\t{tokens / (t_naive / 1000):.1f} tok/s\t{tflops_naive:.2f} TFLOP/s"
     )
 
-    # Prepare reference output once for checks
     with torch.no_grad():
-        y_ref = forward_naive(x, gate, experts, args.top_k)
+        y_ref = run_naive_model(x)
 
-    # torch_grouped backend (PyTorch 2.8+)
-    try:
-        from axolotl.kernels.moe import torch_grouped as tg
-    except Exception:
-        tg = None
+    # Benchmark grouped
     if tg is not None and tg.available():
-
-        def forward_tg(a, g, ex, topk):
-            y, _ = tg.moe_ffn_forward_grouped(a, g, ex, topk)
-            return y
-
-        y_tg = forward_tg(x, gate, experts, args.top_k)
-        if y_tg is not None:
-            t_ms = bench(
-                forward_tg,
-                x,
-                gate,
-                experts,
-                args.top_k,
+        y_grouped = run_grouped_model(x)
+        if y_grouped.numel() == 0:
+            print("torch_grouped\tN/A (op not callable)")
+        else:
+            t_grouped = bench(
+                lambda: run_grouped_model(x),
                 iters=args.iters,
                 warmup=args.warmup,
             )
-            tflops = flops_total / ((t_ms / 1000.0) * 1e12)
-            speedup = t_naive / t_ms
+            tflops_grouped = flops_total / ((t_grouped / 1000.0) * 1e12)
+            speedup = t_naive / t_grouped
             print(
-                f"torch_grouped\t{t_ms:.2f} ms\t{tokens / (t_ms / 1000):.1f} tok/s\t{tflops:.2f} TFLOP/s\t{speedup:.2f}×"
+                f"torch_grouped\t{t_grouped:.2f} ms\t{tokens / (t_grouped / 1000):.1f} tok/s\t"
+                f"{tflops_grouped:.2f} TFLOP/s\t{speedup:.2f}×"
+            )
+            diff = (y_ref.float() - y_grouped.float()).abs()
+            max_abs = diff.max().item()
+            mean_abs = diff.mean().item()
+            rel_l2 = (
+                (diff.pow(2).sum() / (y_ref.float().pow(2).sum() + 1e-12)).sqrt().item()
+            )
+            print(
+                f"torch_grouped_check: max_abs={max_abs:.3e} mean_abs={mean_abs:.3e} rel_l2={rel_l2:.3e}"
             )
-            with torch.no_grad():
-                y_fast = y_tg
-                y_ref32 = y_ref.float()
-                y_fast32 = y_fast.float()
-                diff = (y_ref32 - y_fast32).abs()
-                max_abs = diff.max().item()
-                mean_abs = diff.mean().item()
-                rel_l2 = (
-                    (diff.pow(2).sum() / (y_ref32.pow(2).sum() + 1e-12)).sqrt().item()
-                )
-                print(
-                    f"torch_grouped_check: max_abs={max_abs:.3e} mean_abs={mean_abs:.3e} rel_l2={rel_l2:.3e}"
-                )
-        else:
-            print("torch_grouped\tN/A (op not callable)")
     else:
         print("torch_grouped\tN/A (unavailable)")
 
-    with torch.profiler.profile(
-        activities=[torch.profiler.ProfilerActivity.CUDA], record_shapes=True
-    ) as prof:
-        forward_naive(x, gate, experts, args.top_k)
-    print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
+    if args.profile and tg is not None and tg.available():
+        with torch.profiler.profile(
+            activities=[torch.profiler.ProfilerActivity.CUDA], record_shapes=True
+        ) as prof:
+            run_naive_model(x)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
 
-    with torch.profiler.profile(
-        activities=[torch.profiler.ProfilerActivity.CUDA],
-        record_shapes=True,
-        with_stack=False,
-    ) as prof:
-        forward_tg(x, gate, experts, args.top_k)
-    print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
+        with torch.profiler.profile(
+            activities=[torch.profiler.ProfilerActivity.CUDA],
+            record_shapes=True,
+            with_stack=False,
+        ) as prof:
+            run_grouped_model(x)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
 
 
 if __name__ == "__main__":

src/axolotl/kernels/moe/torch_grouped.py

@@ -267,9 +267,12 @@ def moe_ffn_forward_grouped(
         )
         return None, None
 
-    expert_impls = _iter_expert_impls(experts_module)
+    parent_block = getattr(experts_module, "_ax_parent_block", None)
+    expert_container = getattr(experts_module, "experts", experts_module)
+
+    expert_impls = _iter_expert_impls(expert_container)
     sample_mod = expert_impls[0]
-    storage = _ensure_grouped_weights(experts_module, expert_impls, sample_mod)
+    storage = _ensure_grouped_weights(expert_container, expert_impls, sample_mod)
     w_gate = storage.gate
     w_up = storage.up
     w2 = storage.down
@@ -278,11 +281,12 @@ def moe_ffn_forward_grouped(
     router_logits = gate_linear(x_flat.to(routing_dtype))
 
     shared_out_flat: Optional[torch.Tensor] = None
-    if hasattr(experts_module, "shared_expert"):
-        shared_expert = experts_module.shared_expert
+    shared_owner = parent_block if parent_block is not None else experts_module
+    if hasattr(shared_owner, "shared_expert"):
+        shared_expert = shared_owner.shared_expert
         shared_out_flat = shared_expert(x_flat)
         shared_out_flat = shared_out_flat.to(expert_dtype)
-        shared_gate = getattr(experts_module, "shared_expert_gate", None)
+        shared_gate = getattr(shared_owner, "shared_expert_gate", None)
         if shared_gate is not None:
             gate_input = shared_gate(x_flat.to(shared_gate.weight.dtype))
             gate_vals = torch.sigmoid(gate_input)

src/axolotl/monkeypatch/moe_grouped.py

@@ -76,6 +76,11 @@ def apply_grouped_to_moe_blocks(cfg=None) -> None:
         @wraps(orig_forward)
         def _grouped_forward(self, hidden_states: torch.Tensor, *args, **kwargs):
             bsz, seqlen, hdim = hidden_states.shape
+            # expose parent block so grouped backend can access shared expert context
+            try:
+                self.experts._ax_parent_block = self
+            except Exception:
+                pass
             y, router_logits = _tg.moe_ffn_forward_grouped(
                 hidden_states, self.gate, self.experts, self.top_k
             )