moe kernels init scaffold

docs/moe_backends.md

@@ -0,0 +1,18 @@
+MoE Backends in Axolotl
+
+Axolotl supports selecting a Mixture-of-Experts (MoE) compute backend via an environment variable:
+
+- AXOLOTL_MOE_BACKEND=auto|hf_triton|torch_grouped|naive
+
+Behavior
+- auto (default): prefers PyTorch 2.8+ grouped GEMM, then Hugging Face kernels hub, otherwise naive.
+- hf_triton: uses the Hugging Face kernels hub (kernels-community/triton_kernels) when available.
+- torch_grouped: targets PyTorch 2.8+ grouped GEMM.
+- naive: keeps the reference per-expert loop.
+
+Notes
+- Current implementation wires the backend selector and routes Mixtral MoE through it. The hf_triton path is initially a stub: it uses kernels hub for routing but still falls back to per-expert computation until grouped GEMM is fully integrated.
+- No changes to training scripts are required; Axolotl wraps Transformers Trainer; selection happens inside the model forward.
+
+Example
+AXOLOTL_MOE_BACKEND=hf_triton accelerate launch -m axolotl.cli.train path/to/config.yaml

scripts/bench_moe.py

@@ -0,0 +1,161 @@
+#!/usr/bin/env python
+import argparse
+import os
+import time
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SwiGLUMlp(nn.Module):
+    def __init__(self, hidden_size: int, intermediate_size: int):
+        super().__init__()
+        self.w1 = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.w3 = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.w2 = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.act_fn = F.silu
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.w2(self.act_fn(self.w1(x)) * self.w3(x))
+
+
+class Experts(nn.Module):
+    def __init__(self, num_experts: int, hidden_size: int, intermediate_size: int):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            SwiGLUMlp(hidden_size, intermediate_size) for _ in range(num_experts)
+        )
+        self.num_experts = num_experts
+
+    def __getitem__(self, idx):
+        return self.layers[idx]
+
+
+def forward_naive(
+    hidden_states: torch.Tensor, gate: nn.Linear, experts: Experts, top_k: int
+):
+    bsz, seqlen, hdim = hidden_states.shape
+    x = hidden_states.view(-1, hdim)
+    router_logits = gate(x)
+    routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+    topk_weight, topk_idx = torch.topk(routing_weights, top_k, dim=-1, sorted=False)
+    topk_weight = (topk_weight / topk_weight.sum(dim=-1, keepdim=True)).to(x.dtype)
+    x_rep = x.repeat_interleave(top_k, dim=0)
+    y = torch.empty_like(x_rep)
+    flat_idx = topk_idx.view(-1)
+    for i in range(experts.num_experts):
+        sel = flat_idx == i
+        if sel.any():
+            y[sel] = experts[i](x_rep[sel])
+    y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+    return y.view(bsz, seqlen, hdim)
+
+
+def forward_hf_triton(
+    hidden_states: torch.Tensor, gate: nn.Linear, experts: Experts, top_k: int
+):
+    try:
+        from axolotl.kernels.moe import hf_triton as _hf
+    except Exception:
+        return None
+    try:
+        y, _ = _hf.moe_ffn_forward_stub(hidden_states, gate, experts, top_k)
+        return y
+    except Exception:
+        return None
+
+
+def bench(fn, *args, iters=50, warmup=10, sync=True):
+    # warmup
+    for _ in range(warmup):
+        out = fn(*args)
+        if sync and torch.cuda.is_available():
+            torch.cuda.synchronize()
+    # measure
+    times = []
+    for _ in range(iters):
+        if sync and torch.cuda.is_available():
+            torch.cuda.synchronize()
+        t0 = time.perf_counter()
+        out = fn(*args)
+        if sync and torch.cuda.is_available():
+            torch.cuda.synchronize()
+        dt = (time.perf_counter() - t0) * 1000.0
+        times.append(dt)
+    return sum(times) / len(times)
+
+
+def main():
+    p = argparse.ArgumentParser(description="MoE microbenchmark")
+    p.add_argument("--bsz", type=int, default=8)
+    p.add_argument("--seq", type=int, default=1024)
+    p.add_argument("--hidden", type=int, default=4096)
+    p.add_argument("--inter", type=int, default=14336)
+    p.add_argument("--experts", type=int, default=8)
+    p.add_argument("--top_k", type=int, default=2)
+    p.add_argument(
+        "--dtype", type=str, default="bf16", choices=["bf16", "fp16", "fp32"]
+    )
+    p.add_argument("--iters", type=int, default=50)
+    p.add_argument("--warmup", type=int, default=10)
+    args = p.parse_args()
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    dtype = {
+        "bf16": torch.bfloat16,
+        "fp16": torch.float16,
+        "fp32": torch.float32,
+    }[args.dtype]
+
+    torch.manual_seed(0)
+    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,
+    )
+    print(f"naive	{t_naive:.2f} ms	{tokens / (t_naive / 1000):.1f} tok/s")
+
+    # HF Triton (routing + stub compute for now)
+    os.environ.setdefault("AXOLOTL_MOE_BACKEND", "hf_triton")
+    t_hf = forward_hf_triton
+    y = t_hf(x, gate, experts, args.top_k)
+    if y is not None:
+        t_ms = bench(
+            t_hf, x, gate, experts, args.top_k, iters=args.iters, warmup=args.warmup
+        )
+        print(f"hf_triton	{t_ms:.2f} ms	{tokens / (t_ms / 1000):.1f} tok/s")
+    else:
+        print("hf_triton	N/A (kernels hub not available)")
+
+    # torch_grouped placeholder — not yet implemented
+    print("torch_grouped	N/A (pending implementation)")
+
+
+if __name__ == "__main__":
+    main()

src/axolotl/kernels/moe/__init__.py

@@ -0,0 +1,3 @@
+from .backends import MOEBackend, get_moe_backend_name
+
+__all__ = ["get_moe_backend_name", "MOEBackend"]

src/axolotl/kernels/moe/backends.py

@@ -0,0 +1,65 @@
+import os
+import warnings
+from enum import Enum
+
+
+class MOEBackend(str, Enum):
+    AUTO = "auto"
+    HF_TRITON = "hf_triton"
+    TORCH_GROUPED = "torch_grouped"
+    NAIVE = "naive"
+
+
+def _probe_torch_grouped() -> bool:
+    try:
+        import torch  # noqa: F401
+
+        # Prefer a simple version check; exact APIs may vary across 2.8+.
+        ver = tuple(int(x) for x in torch.__version__.split("+")[0].split(".")[:2])
+        return ver >= (2, 8)
+    except Exception:
+        return False
+
+
+def _probe_hf_triton() -> bool:
+    try:
+        # The hub loads kernels lazily; this import is a light probe.
+        import importlib
+
+        importlib.import_module("kernels")
+        return True
+    except Exception:
+        return False
+
+
+def get_moe_backend_name(preferred: str | None = None) -> MOEBackend:
+    """
+    Resolve the desired MoE backend using, in order of precedence:
+    - explicit preferred argument
+    - environment variable AXOLOTL_MOE_BACKEND
+    - auto detection
+    """
+    choice = (preferred or os.getenv("AXOLOTL_MOE_BACKEND") or "auto").lower()
+    try:
+        selected = MOEBackend(choice)
+    except ValueError:
+        warnings.warn(f"Unknown moe backend '{choice}', falling back to auto")
+        selected = MOEBackend.AUTO
+
+    if selected == MOEBackend.AUTO:
+        if _probe_torch_grouped():
+            return MOEBackend.TORCH_GROUPED
+        if _probe_hf_triton():
+            return MOEBackend.HF_TRITON
+        return MOEBackend.NAIVE
+    if selected == MOEBackend.TORCH_GROUPED and not _probe_torch_grouped():
+        warnings.warn(
+            "torch_grouped requested but torch>=2.8 not detected; falling back to hf_triton/naive"
+        )
+        return MOEBackend.HF_TRITON if _probe_hf_triton() else MOEBackend.NAIVE
+    if selected == MOEBackend.HF_TRITON and not _probe_hf_triton():
+        warnings.warn(
+            "hf_triton requested but kernels hub not available; falling back to torch_grouped/naive"
+        )
+        return MOEBackend.TORCH_GROUPED if _probe_torch_grouped() else MOEBackend.NAIVE
+    return selected

src/axolotl/kernels/moe/hf_triton.py

@@ -0,0 +1,104 @@
+"""
+Adapter for Hugging Face kernels hub (kernels-community/triton_kernels).
+This file provides light probes and placeholders for future integration.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Optional, Tuple
+
+
+@dataclass
+class HFTritonHandles:
+    routing: Any
+    matmul_ogs: Any
+    swiglu: Any
+
+
+def available() -> bool:
+    try:
+        import kernels  # noqa: F401
+
+        return True
+    except Exception:
+        return False
+
+
+def load() -> Optional[HFTritonHandles]:
+    try:
+        from kernels import get_kernel
+
+        tk = get_kernel("kernels-community/triton_kernels")
+        return HFTritonHandles(
+            routing=tk.routing, matmul_ogs=tk.matmul_ogs, swiglu=tk.swiglu
+        )
+    except Exception:
+        return None
+
+
+def route_topk(logits, top_k: int):
+    handles = load()
+    if handles is None:
+        return None
+    return handles.routing.routing_torch(logits, n_expts_act=top_k)
+
+
+def swiglu(x, alpha, limit=1.0, routing_data=None):
+    handles = load()
+    if handles is None:
+        return None
+    pc = handles.swiglu.PrecisionConfig(limit=limit)
+    return handles.swiglu.swiglu(x, alpha, pc, routing_data)
+
+
+def moe_ffn_forward_stub(
+    hidden_states, gate_linear, experts_module, top_k: int
+) -> Tuple[object, object]:
+    """
+    Temporary stub that uses kernels hub routing, but falls back to per-expert compute.
+    Returns (final_hidden_states, router_logits).
+    """
+    import torch
+    import torch.nn.functional as F
+
+    bsz, seqlen, hdim = hidden_states.shape
+    flat = hidden_states.view(-1, hdim)
+    router_logits = gate_linear(flat)
+    # use hub routing if available; otherwise fallback to softmax+topk
+    routed = None
+    if available():
+        try:
+            routed = route_topk(router_logits, top_k)
+        except Exception:
+            routed = None
+    if routed is None:
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        topk_weight, topk_idx = torch.topk(routing_weights, top_k, dim=-1, sorted=False)
+        topk_weight /= topk_weight.sum(dim=-1, keepdim=True)
+        topk_weight = topk_weight.to(flat.dtype)
+        x_rep = flat.repeat_interleave(top_k, dim=0)
+        y = torch.empty_like(x_rep)
+        flat_idx = topk_idx.view(-1)
+        for i in range(experts_module.num_experts):
+            expert = experts_module[i]
+            y[flat_idx == i] = expert(x_rep[flat_idx == i])
+        y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+        return y.reshape(bsz, seqlen, hdim), router_logits
+    # If routed via hub, still fallback to per-expert compute until grouped GEMM path is wired.
+    ex_routing_data, gather_idx, scatter_idx = routed
+    # Convert to naive per-expert compute on packed tokens (future: call matmul_ogs + swiglu)
+    # For now, reconstruct the same result as naive path (no speedup but validates routing).
+    # We map the selected experts from gather_idx back to expert ids via router_logits argmax among top-k.
+    # Simpler: reuse naive computation for correctness; detailed integration will follow.
+    routing_weights = torch.softmax(router_logits, dim=1, dtype=torch.float)
+    topk_weight, topk_idx = torch.topk(routing_weights, top_k, dim=-1, sorted=False)
+    topk_weight = (topk_weight / topk_weight.sum(dim=-1, keepdim=True)).to(flat.dtype)
+    x_rep = flat.repeat_interleave(top_k, dim=0)
+    y = torch.empty_like(x_rep)
+    flat_idx = topk_idx.view(-1)
+    for i in range(experts_module.num_experts):
+        expert = experts_module[i]
+        y[flat_idx == i] = expert(x_rep[flat_idx == i])
+    y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+    return y.reshape(bsz, seqlen, hdim), router_logits

src/axolotl/kernels/moe/torch_grouped.py

@@ -0,0 +1,16 @@
+"""
+Placeholder for PyTorch 2.8+ grouped GEMM MoE path.
+Currently probes availability; full integration to be implemented.
+"""
+
+from __future__ import annotations
+
+
+def available() -> bool:
+    try:
+        import torch  # noqa: F401
+
+        ver = tuple(int(x) for x in torch.__version__.split("+")[0].split(".")[:2])
+        return ver >= (2, 8)
+    except Exception:
+        return False

src/axolotl/monkeypatch/mixtral/__init__.py

@@ -6,8 +6,13 @@ import torch
 
 
 def patch_mixtral_moe_forward_zero3() -> None:
+    import warnings
+
     import torch.nn.functional as F
 
+    from axolotl.kernels.moe import backends as _moe_backends, hf_triton as _hf_triton
+    from axolotl.kernels.moe.backends import MOEBackend, get_moe_backend_name
+
     def mlp_forward(self, hidden_states):
         current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(
             hidden_states
@@ -21,21 +26,42 @@ def patch_mixtral_moe_forward_zero3() -> None:
         hidden_states = hidden_states.view(-1, hidden_dim)
         # router_logits: (batch * sequence_length, n_experts)
         router_logits = self.gate(hidden_states)
+        backend = get_moe_backend_name()
+        if backend == MOEBackend.HF_TRITON and _hf_triton.available():
+            # Stub path: use kernels hub routing and fallback per-expert compute
+            try:
+                final_hidden_states, router_logits = _hf_triton.moe_ffn_forward_stub(
+                    hidden_states.view(batch_size, sequence_length, hidden_dim),
+                    self.gate,
+                    self.experts,
+                    self.top_k,
+                )
+                return final_hidden_states, router_logits
+            except Exception as e:
+                warnings.warn(f"hf_triton backend failed, falling back to naive: {e}")
+        elif (
+            backend == MOEBackend.TORCH_GROUPED
+            and not _moe_backends._probe_torch_grouped()
+        ):
+            warnings.warn(
+                "torch_grouped selected but not available; falling back to naive"
+            )
 
         routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
         topk_weight, topk_idx = torch.topk(
             routing_weights, self.top_k, dim=-1, sorted=False
         )
         topk_weight /= topk_weight.sum(dim=-1, keepdim=True)
-        # we cast back to the input dtype
         topk_weight = topk_weight.to(hidden_states.dtype)
 
-        hidden_states = hidden_states.repeat_interleave(self.top_k, dim=0)
-        y = torch.empty_like(hidden_states)
+        hidden_states_rep = hidden_states.repeat_interleave(self.top_k, dim=0)
+        y = torch.empty_like(hidden_states_rep)
         flat_topk_idx = topk_idx.view(-1)
         for i in range(self.num_experts):
             expert = self.experts[i]
-            y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
+            sel = flat_topk_idx == i
+            if sel.any():
+                y[sel] = expert(hidden_states_rep[sel])
         y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
         final_hidden_states = y.reshape(batch_size, sequence_length, hidden_dim)
         return final_hidden_states, router_logits