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[ROCM] enable aiter fused moe kernel for llama4 bf16 checkpoints #16674

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Merged
merged 4 commits into from
Apr 17, 2025
Merged

[ROCM] enable aiter fused moe kernel for llama4 bf16 checkpoints #16674

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c51662d
enable aiter fused moe bf16 for llama4
sijiac Apr 15, 2025
5f057ca
fix lint
sijiac Apr 15, 2025
8dbbb11
add assert
sijiac Apr 16, 2025
2ee3ec7
lint
sijiac Apr 16, 2025
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13 changes: 13 additions & 0 deletions vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@ def rocm_aiter_fused_experts(
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
use_fp8_w8a8: bool = False,
apply_router_weight_on_input: bool = False,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
block_shape: Optional[List[int]] = None,
Expand All @@ -39,6 +40,18 @@ def rocm_aiter_fused_experts(
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
per_token_group_quant_fp8)

if apply_router_weight_on_input:
assert (topk_weights.dim() == 2
), "`topk_weights` should be in shape (num_tokens, topk)"
_, topk = topk_weights.shape
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can we assert topk_weights.dim() == 2?

assert (
topk == 1
), "Only support topk=1 when `apply_router_weight_on_input` is True"

hidden_states = hidden_states * topk_weights.to(hidden_states.dtype)
topk_ids = topk_ids.to(torch.int32)
topk_weights = torch.ones_like(topk_weights, dtype=torch.float32)
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Does AITER require fp32 weight?

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yes, the passed-in topk_weights must in fp32 dtype, otherwise, it will have numeric issues


if envs.VLLM_ROCM_USE_AITER_FP8_BLOCK_SCALED_MOE and use_fp8_w8a8:
assert w1_scale is not None
assert w2_scale is not None
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