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Keep fp32 routing weights in the fp8 block-scale and bf16 trtllm MoE (#33631)

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Co-authored-by: Brayden Zhong <brayden@radixark.ai>
This commit is contained in:
Brayden Zhong
2026-09-08 08:56:40 -07:00
committed by GitHub
co-authored by Brayden Zhong
parent 8a0863c728
commit 30e7a3072d
5 changed files with 51 additions and 138 deletions
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python/sglang/kernels/ops/moe/__init__.py
-10
View File
@@ -175,16 +175,6 @@ for _mod, _fn in _PHASE25_TRITON_KERNELS:
)
del _mod, _fn
# Packed (topk_id << 16 | bf16-weight) kernel migrated from
# srt/layers/quantization/mxfp4_flashinfer_trtllm_moe (RFC #29630, Phase 2.5).
register_kernel(
KernelSpec(
op="moe.pack_topk_ids",
backend=KernelBackend.TRITON,
target="sglang.kernels.ops.moe.pack_topk_ids:PackTopkIds.triton",
)
)
# Single-CTA align for tiny batches: covers the corner the AOT/JIT
# moe_align_block_size small-batch path leaves out (num_experts > 64), and is
# selected by the moe_runner call site on numel <= SMALL_NUMEL_LIMIT.
python/sglang/kernels/ops/moe/pack_topk_ids.py
-101
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@@ -1,101 +0,0 @@
"""Pack ``(topk_id, topk_weight)`` pairs into one int32 per entry.
Migrated from ``sglang.srt.layers.quantization.mxfp4_flashinfer_trtllm_moe``
(RFC #29630, Phase 2.5). Used by the FlashInfer TRT-LLM routed-MoE path, which
consumes routing ids and bf16 weights packed as ``(id << 16) | weight_bits``.
Routing ids may be int32 or the int64 dtype produced by ``torch.topk``; they are
converted to int32 by the Triton kernel before packing.
"""
import torch
import triton
import triton.language as tl
from sglang.kernels.jit.utils import is_arch_support_pdl
class PackTopkIds:
@classmethod
def execute(
cls, topk_ids: torch.Tensor, topk_weights: torch.Tensor
) -> torch.Tensor:
return cls.triton(topk_ids, topk_weights)
@classmethod
def vanilla(
cls, topk_ids: torch.Tensor, topk_weights: torch.Tensor
) -> torch.Tensor:
weight_bits = (
topk_weights.to(torch.bfloat16).view(torch.int16).to(torch.int32) & 0xFFFF
)
return (topk_ids.to(torch.int32) << 16) | weight_bits
@classmethod
def triton(cls, topk_ids: torch.Tensor, topk_weights: torch.Tensor) -> torch.Tensor:
assert topk_ids.shape == topk_weights.shape, (
f"shape mismatch: {topk_ids.shape=} vs {topk_weights.shape=}"
)
assert topk_ids.ndim >= 1, f"expected >=1D, got {topk_ids.shape=}"
assert topk_ids.dtype in (
torch.int32,
torch.int64,
), f"topk_ids must be int32 or int64, got {topk_ids.dtype}"
assert topk_weights.dtype == torch.float32, (
f"topk_weights must be float32, got {topk_weights.dtype}"
)
assert topk_ids.is_contiguous(), "topk_ids must be contiguous"
assert topk_weights.is_contiguous(), "topk_weights must be contiguous"
out = torch.empty_like(topk_ids, dtype=torch.int32)
numel = out.numel()
if numel == 0:
return out
BLOCK_SIZE = 1024
grid = (triton.cdiv(numel, BLOCK_SIZE),)
pdl_kwargs = (
{"USE_PDL": True, "launch_pdl": True} if is_arch_support_pdl() else {}
)
_pack_topk_ids_triton_kernel[grid](
topk_ids,
topk_weights,
out,
numel,
BLOCK_SIZE=BLOCK_SIZE,
**pdl_kwargs,
)
return out
@triton.jit
def _pack_topk_ids_triton_kernel(
topk_ids_ptr,
topk_weights_ptr,
out_ptr,
numel,
BLOCK_SIZE: tl.constexpr,
USE_PDL: tl.constexpr = False,
):
pid = tl.program_id(0)
offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
mask = offsets < numel
if USE_PDL:
tl.extra.cuda.gdc_wait()
ids = tl.load(topk_ids_ptr + offsets, mask=mask, other=0)
w = tl.load(topk_weights_ptr + offsets, mask=mask, other=0.0)
if USE_PDL:
tl.extra.cuda.gdc_launch_dependents()
w_bf16 = w.to(tl.bfloat16)
w_i16 = w_bf16.to(tl.int16, bitcast=True)
w_i32 = w_i16.to(tl.int32) & 0xFFFF
ids_i32 = ids.to(tl.int32)
packed = (ids_i32 << 16) | w_i32
tl.store(out_ptr + offsets, packed, mask=mask)
python/sglang/srt/layers/moe/flashinfer_trtllm_moe.py
+3 -1
View File
@@ -120,6 +120,7 @@ def trtllm_fp8_block_scale_moe_out_wrapper(
def _fake_fp8_block_scale_routed_moe_out(
topk_ids: torch.Tensor,
topk_weights: Optional[torch.Tensor],
routing_bias: Optional[torch.Tensor],
hidden_states: torch.Tensor,
hidden_states_scale: torch.Tensor,
@@ -156,6 +157,7 @@ def _fake_fp8_block_scale_routed_moe_out(
)
def trtllm_fp8_block_scale_routed_moe_out_wrapper(
topk_ids: torch.Tensor,
topk_weights: Optional[torch.Tensor],
routing_bias: Optional[torch.Tensor],
hidden_states: torch.Tensor,
hidden_states_scale: torch.Tensor,
@@ -192,7 +194,7 @@ def trtllm_fp8_block_scale_routed_moe_out_wrapper(
) from e
kwargs = {
"topk_ids": topk_ids,
"topk_ids": topk_ids if topk_weights is None else (topk_ids, topk_weights),
"routing_bias": routing_bias,
"hidden_states": hidden_states,
"hidden_states_scale": hidden_states_scale,
python/sglang/srt/layers/moe/moe_runner/flashinfer_trtllm.py
+35 -24
View File
@@ -9,7 +9,6 @@ import torch
from torch.nn import Module
from torch.nn.parameter import Parameter
from sglang.kernels.ops.moe.pack_topk_ids import PackTopkIds
from sglang.kernels.ops.quantization.fp8_kernel import (
per_token_group_quant_fp8,
scaled_fp8_quant,
@@ -168,20 +167,29 @@ def _is_gated(layer: Module) -> bool:
return True if is_gated is None else is_gated
def _get_packed_topk_ids_for_flashinfer_routed(topk_output) -> torch.Tensor:
"""Return FlashInfer routed packed top-k ids, using prepacked output if present."""
packed_topk_ids = getattr(topk_output, "packed_topk_ids", None)
if packed_topk_ids is not None:
return packed_topk_ids
FlashInferRouting = torch.Tensor | tuple[torch.Tensor, torch.Tensor]
def _get_routing_for_flashinfer_routed(topk_output) -> FlashInferRouting:
"""Return the `topk_ids` kernel argument for the trtllm routed MoEs."""
from sglang.srt.layers.moe.topk import TopKOutputChecker
if TopKOutputChecker.format_is_packed(topk_output):
return topk_output.packed_topk_ids
assert TopKOutputChecker.format_is_standard(topk_output)
return PackTopkIds.execute(
topk_output.topk_ids.contiguous(), topk_output.topk_weights.contiguous()
return (
topk_output.topk_ids.contiguous(),
topk_output.topk_weights.contiguous(),
)
def _routing_top_k(routing: FlashInferRouting) -> int:
"""Both routing forms carry top_k as the last dim of their ids tensor."""
ids = routing[0] if isinstance(routing, tuple) else routing
return ids.shape[1]
def _align_fp8_moe_weights(
w13: torch.Tensor,
w2: torch.Tensor,
@@ -802,10 +810,14 @@ def fused_experts_none_to_flashinfer_trtllm_fp8(
assert runner_config.top_k is not None, (
"runner_config.top_k is required for flashinfer_trtllm_routed."
)
packed_topk_ids = _get_packed_topk_ids_for_flashinfer_routed(topk_output)
routing = _get_routing_for_flashinfer_routed(topk_output)
topk_ids, topk_weights = (
routing if isinstance(routing, tuple) else (routing, None)
)
trtllm_fp8_block_scale_routed_moe_out_wrapper(
topk_ids=packed_topk_ids,
topk_ids=topk_ids,
topk_weights=topk_weights,
routing_bias=None,
hidden_states=a_q,
hidden_states_scale=a_sf_t,
@@ -1063,16 +1075,16 @@ def _fused_experts_flashinfer_mxfp4_sm100_trtllm_gen(
from flashinfer.tllm_enums import ActivationType, RoutingMethodType
if is_standard:
if prepared_packed_topk is not None:
packed_topk = prepared_packed_topk
else:
packed_topk = PackTopkIds.execute(
topk_output.topk_ids, topk_output.topk_weights
)
routing = (
prepared_packed_topk
if prepared_packed_topk is not None
else _get_routing_for_flashinfer_routed(topk_output)
)
routed_top_k = _routing_top_k(routing)
defer_finalize = _deferred_finalize_enabled.get()
result = trtllm_fp4_block_scale_routed_moe(
topk_ids=packed_topk,
topk_ids=routing,
routing_bias=None,
hidden_states=x_quant,
hidden_states_scale=x_scale,
@@ -1089,7 +1101,7 @@ def _fused_experts_flashinfer_mxfp4_sm100_trtllm_gen(
output1_scale_gate_scalar=None,
output2_scale_scalar=None,
num_experts=quant_info.global_num_experts,
top_k=packed_topk.shape[1],
top_k=routed_top_k,
n_group=None,
topk_group=None,
intermediate_size=quant_info.intermediate_size_per_partition,
@@ -1109,7 +1121,7 @@ def _fused_experts_flashinfer_mxfp4_sm100_trtllm_gen(
gemm2_out=gemm2_out,
expert_weights=topk_weights,
expanded_idx_to_permuted_idx=expanded_idx,
top_k=packed_topk.shape[1],
top_k=routed_top_k,
)
return StandardCombineInput(hidden_states=result)
# The finalized kernel writes to its explicit output argument. Do
@@ -1375,9 +1387,9 @@ def fused_experts_none_to_flashinfer_trtllm_fp4(
)
if use_routed_topk:
packed_topk_ids = _get_packed_topk_ids_for_flashinfer_routed(topk_output)
routing = _get_routing_for_flashinfer_routed(topk_output)
result = trtllm_fp4_block_scale_routed_moe(
topk_ids=packed_topk_ids,
topk_ids=routing,
routing_bias=None,
hidden_states=hs_fp4,
hidden_states_scale=hs_scale,
@@ -1395,7 +1407,7 @@ def fused_experts_none_to_flashinfer_trtllm_fp4(
output2_scale_scalar=quant_info.g2_alphas,
per_token_scale=per_token_scale,
num_experts=quant_info.global_num_experts,
top_k=packed_topk_ids.shape[1],
top_k=_routing_top_k(routing),
n_group=0,
topk_group=0,
intermediate_size=quant_info.intermediate_size_per_partition,
@@ -1543,9 +1555,8 @@ def fused_experts_none_to_flashinfer_trtllm_bf16(
elif routing_method_type == RoutingMethodType.DeepSeekV3:
routing_method_type = RoutingMethodType.TopK
packed_topk_ids = _get_packed_topk_ids_for_flashinfer_routed(topk_output)
final_hidden_states = trtllm_bf16_routed_moe(
topk_ids=packed_topk_ids,
topk_ids=_get_routing_for_flashinfer_routed(topk_output),
hidden_states=hidden_states,
gemm1_weights=quant_info.gemm1_weights,
gemm2_weights=quant_info.gemm2_weights,