simplify

src/axolotl/kernels/moe/torch_grouped.py

@@ -56,35 +56,24 @@ def _stack_weights(
 
 def _call_grouped_mm(
     As: List[torch.Tensor], Bs: List[torch.Tensor], dtype: torch.dtype
-) -> Tuple[Optional[List[torch.Tensor]], Optional[str]]:
+) -> List[torch.Tensor]:
     if not As:
-        return [], None
+        return []
     if dtype not in (torch.bfloat16, torch.float16):
-        msg = f"unsupported dtype {dtype}"
-        _LOGGER.debug("torch_grouped: %s", msg)
-        return None, msg
+        raise RuntimeError(f"unsupported dtype {dtype} for grouped_mm")
 
-    try:
-        As2 = [a.to(dtype).contiguous().view(a.shape[0], a.shape[1]) for a in As]
-        Bs2 = [b.to(dtype).contiguous().view(b.shape[0], b.shape[1]) for b in Bs]
-        device = As2[0].device
-        lengths = torch.tensor(
-            [a.shape[0] for a in As2], device=device, dtype=torch.int32
-        )
-        offsets = torch.cumsum(lengths, dim=0).to(torch.int32)
-        Y_cat = torch._grouped_mm(
-            torch.cat(As2, dim=0), torch.stack(Bs2, dim=0), offsets
-        )
-        outs: List[torch.Tensor] = []
-        start = 0
-        for size in lengths.tolist():
-            outs.append(Y_cat[start : start + size])
-            start += size
-        return outs, None
-    except RuntimeError as err:
-        message = f"_grouped_mm failed ({err})"
-        _LOGGER.warning("torch_grouped: %s", message)
-        return None, message
+    As2 = [a.to(dtype).contiguous().view(a.shape[0], a.shape[1]) for a in As]
+    Bs2 = [b.to(dtype).contiguous().view(b.shape[0], b.shape[1]) for b in Bs]
+    device = As2[0].device
+    lengths = torch.tensor([a.shape[0] for a in As2], device=device, dtype=torch.int32)
+    offsets = torch.cumsum(lengths, dim=0)
+    Y_cat = torch._grouped_mm(torch.cat(As2, dim=0), torch.stack(Bs2, dim=0), offsets)
+    outs: List[torch.Tensor] = []
+    start = 0
+    for size in lengths.tolist():
+        outs.append(Y_cat[start : start + size])
+        start += size
+    return outs
 
 
 def moe_ffn_forward_grouped(
@@ -159,9 +148,7 @@ def moe_ffn_forward_grouped(
     if not as_list:
         return torch.zeros_like(x_flat).view(bsz, seqlen, hdim), router_logits
 
-    up_out, reason = _call_grouped_mm(as_list, bs_list, expert_dtype)
-    if up_out is None:
-        return None, None
+    up_out = _call_grouped_mm(as_list, bs_list, expert_dtype)
 
     down_inputs: List[torch.Tensor] = []
     down_weights: List[torch.Tensor] = []
@@ -172,9 +159,7 @@ def moe_ffn_forward_grouped(
         down_inputs.append(hidden)
         down_weights.append(w2[i])
 
-    down_out, reason = _call_grouped_mm(down_inputs, down_weights, expert_dtype)
-    if down_out is None:
-        return None, None
+    down_out = _call_grouped_mm(down_inputs, down_weights, expert_dtype)
 
     for (_i, sel), tensor in zip(slices, down_out, strict=False):
         buf[sel] = tensor