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dtype fix

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This commit is contained in:
Dan Saunders
2025-09-17 18:36:22 -04:00
parent d2b49b2670
commit 7935dc0911
1 changed files with 21 additions and 15 deletions
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36
src/axolotl/kernels/moe/torch_grouped.py
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@@ -93,18 +93,16 @@ def moe_ffn_forward_grouped(
device = hidden_states.device
routing_dtype = gate_linear.weight.dtype
expert_dtype = hidden_states.dtype
x_flat = hidden_states.view(tokens, hdim)
router_logits = gate_linear(x_flat.to(routing_dtype))
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)
sample = getattr(
sample_mod = getattr(
experts_module[0], "mlp", getattr(experts_module[0], "ffn", experts_module[0])
)
if hasattr(sample, "w1") and hasattr(sample, "w3") and hasattr(sample, "w2"):
if (
hasattr(sample_mod, "w1")
and hasattr(sample_mod, "w3")
and hasattr(sample_mod, "w2")
):
expert_dtype = sample_mod.w1.weight.dtype
w13 = _stack_weights(
experts_module, ("w1", "w3"), key="w13", dtype=expert_dtype, device=device
)
@@ -112,11 +110,12 @@ def moe_ffn_forward_grouped(
experts_module, ("w2",), key="w2", dtype=expert_dtype, device=device
)
else:
names13 = (
("gate_up_proj",)
if hasattr(sample, "gate_up_proj")
else ("up_proj", "gate_proj")
)
if hasattr(sample_mod, "gate_up_proj"):
expert_dtype = sample_mod.gate_up_proj.weight.dtype
names13: Tuple[str, ...] = ("gate_up_proj",)
else:
expert_dtype = sample_mod.up_proj.weight.dtype
names13 = ("up_proj", "gate_proj")
w13 = _stack_weights(
experts_module, names13, key="w13", dtype=expert_dtype, device=device
)
@@ -124,8 +123,15 @@ def moe_ffn_forward_grouped(
experts_module, ("down_proj",), key="w2", dtype=expert_dtype, device=device
)
x_flat = hidden_states.view(tokens, hdim).to(expert_dtype)
router_logits = gate_linear(x_flat.to(routing_dtype))
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)
flat_idx = topk_idx.view(-1)
x_rep = x_flat.to(expert_dtype).repeat_interleave(top_k, dim=0)
x_rep = x_flat.repeat_interleave(top_k, dim=0)
as_list: List[torch.Tensor] = []
bs_list: List[torch.Tensor] = []