biondizzle
bbf9a5f46a
- transform_nvfp4_weights_for_mega_moe now accepts weight_scale_2 - Folds global scale into block scales: UE4M3 * FP32 -> UE4M3 - Dequantize to f32, multiply by global scale, clamp [0,448], re-quantize - This is needed because the kernel only applies one level of block scaling
234 lines
9.6 KiB
Python
234 lines
9.6 KiB
Python
import torch
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from typing import Tuple, Optional
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from ..utils.math import align
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# noinspection PyBroadException
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try:
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# noinspection PyProtectedMember
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import torch.distributed._symmetric_memory as symm_mem
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import torch.distributed as dist
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except Exception as exception:
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print(f'Failed to load mega kernels, please check your PyTorch version: {exception}')
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from .. import _C
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class SymmBuffer:
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def __init__(self, group: dist.ProcessGroup,
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# MoE arguments
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num_experts: int,
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num_max_tokens_per_rank: int, num_topk: int,
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hidden: int, intermediate_hidden: int,
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use_fp8_dispatch: bool = True,
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activation: str = 'swiglu'):
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self.group = group
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self.num_experts = num_experts
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self.num_max_tokens_per_rank = num_max_tokens_per_rank
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self.num_topk = num_topk
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self.hidden = hidden
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self.intermediate_hidden = intermediate_hidden
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# Allocate a symmetric buffer
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num_bytes, slice_input_buffers = _C.get_symm_buffer_size_for_mega_moe(
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group.size(), num_experts,
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num_max_tokens_per_rank, num_topk,
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hidden, intermediate_hidden,
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use_fp8_dispatch, activation
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)
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self.buffer = symm_mem.empty(num_bytes, dtype=torch.int8, device='cuda')
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self.handle = symm_mem.rendezvous(self.buffer, group=group)
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self.buffer.zero_()
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self.group.barrier()
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torch.cuda.synchronize()
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# Create input buffer views
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(self.x, self.x_sf,
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self.topk_idx, self.topk_weights,
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self.l1_acts, self.l1_acts_sf,
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self.l2_acts, self.l2_acts_sf) = slice_input_buffers(self.buffer)
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def destroy(self):
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self.handle = None
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self.buffer = None
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self.group = None
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self.x = None
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self.x_sf = None
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def get_symm_buffer_for_mega_moe(group: dist.ProcessGroup,
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num_experts: int,
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num_max_tokens_per_rank: int, num_topk: int,
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hidden: int, intermediate_hidden: int,
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use_fp8_dispatch: bool = True,
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activation: str = 'swiglu') -> SymmBuffer:
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# Token count must be aligned to block sizes
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num_max_tokens_per_rank = align(num_max_tokens_per_rank, _C.get_token_alignment_for_mega_moe())
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return SymmBuffer(
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group, num_experts,
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num_max_tokens_per_rank, num_topk,
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hidden, intermediate_hidden,
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use_fp8_dispatch, activation
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)
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def _interleave_l1_weights(l1_weights: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
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# [gate: 0..7, up: 0..7, gate: 8..15, up: 8..15, ...] instead of [gate | up]
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def interleave(t, gran: int = 8) -> torch.Tensor:
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g, n, *rest = t.shape
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half = n // 2
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gate = t[:, :half].reshape(g, half // gran, gran, *rest)
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up = t[:, half:].reshape(g, half // gran, gran, *rest)
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return torch.empty_like(t).copy_(torch.stack([gate, up], dim=2).reshape(g, n, *rest))
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return interleave(l1_weights[0]), interleave(l1_weights[1])
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def _transpose_sf_for_utccp(sf: torch.Tensor) -> torch.Tensor:
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num_groups, mn, packed_sf_k = sf.shape
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assert sf.dtype == torch.int and mn % 128 == 0
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result = (sf.reshape(num_groups, -1, 4, 32, packed_sf_k)
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.transpose(2, 3)
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.reshape(num_groups, mn, packed_sf_k))
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return torch.empty_like(sf).copy_(result)
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def transform_weights_for_mega_moe(
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l1_weights: Tuple[torch.Tensor, torch.Tensor],
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l2_weights: Tuple[torch.Tensor, torch.Tensor]
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) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
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# L1: interleave gate/up, then transpose SF for UTCCP
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l1_interleaved = _interleave_l1_weights(l1_weights)
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l1_weights = (l1_interleaved[0], _transpose_sf_for_utccp(l1_interleaved[1]))
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# L2: only transpose SF for UTCCP
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l2_weights = (l2_weights[0], _transpose_sf_for_utccp(l2_weights[1]))
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return l1_weights, l2_weights
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def _pack_nvfp4_sf_for_utccp(sf: torch.Tensor) -> torch.Tensor:
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"""Pack NVFP4 UE4M3 block scales (float8_e4m3fn) into int32 UTCCP layout.
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NVFP4 uses UE4M3 scales with group_size=16 (scale_vec::4X).
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The UTCCP layout packs 4 consecutive scale bytes into each int32,
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then applies the 4x32 transpose for TMA consumption.
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Input: (num_experts, mn, K//16) float8_e4m3fn scales
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Output: (num_experts, mn, K//64) int32 packed UTCCP-transposed scales
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"""
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num_groups, mn, sf_k = sf.shape
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assert sf_k % 4 == 0, f"NVFP4 SF K dim must be divisible by 4, got {sf_k}"
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assert mn % 128 == 0, f"MN dim must be divisible by 128, got {mn}"
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# View as uint8 and pack 4 consecutive bytes into int32
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sf_uint8 = sf.view(torch.uint8) # (num_groups, mn, sf_k)
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# Pack: every 4 uint8 → 1 int32
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packed = (sf_uint8[..., 0::4].to(torch.int32) |
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(sf_uint8[..., 1::4].to(torch.int32) << 8) |
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(sf_uint8[..., 2::4].to(torch.int32) << 16) |
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(sf_uint8[..., 3::4].to(torch.int32) << 24)) # (num_groups, mn, sf_k//4)
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# Apply UTCCP 4x32 transpose (same as MXFP4 — the transpose is determined
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# by the 128-element alignment, not the scale vector size)
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packed_sf_k = sf_k // 4
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result = (packed.reshape(num_groups, -1, 4, 32, packed_sf_k)
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.transpose(2, 3)
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.reshape(num_groups, mn, packed_sf_k))
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return torch.empty_like(packed).copy_(result)
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def transform_nvfp4_weights_for_mega_moe(
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l1_weights: Tuple[torch.Tensor, torch.Tensor],
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l2_weights: Tuple[torch.Tensor, torch.Tensor],
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l1_weight_scale_2: Optional[torch.Tensor] = None,
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l2_weight_scale_2: Optional[torch.Tensor] = None
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) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
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"""Transform NVFP4 expert weights for the mega_moe kernel.
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NVFP4 weights come as (weight, scale) where:
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- weight: uint8 E2M1 packed, shape (num_experts, N, K//2)
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- scale: float8_e4m3fn UE4M3 block scales, shape (num_experts, N, K//16)
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The kernel expects (weight, packed_sf) where packed_sf is int32 UTCCP layout.
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If weight_scale_2 (float32 global scale) is provided, it is folded into the
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block scales: effective_scale = block_scale * global_scale → re-quantized to UE4M3.
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This is needed because the kernel only applies one level of scaling.
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"""
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def fold_global_scale(sf: torch.Tensor, scale_2: Optional[torch.Tensor]) -> torch.Tensor:
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"""Fold weight_scale_2 into block scales: UE4M3 * FP32 → UE4M3"""
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if scale_2 is None:
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return sf
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# Dequantize UE4M3 → float32
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sf_f32 = sf.to(torch.float32)
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# Multiply by global scale
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# scale_2 shape: (num_experts, 1, 1) or (num_experts,) — broadcast over mn and K
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if scale_2.dim() == 1:
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scale_2 = scale_2.view(-1, 1, 1)
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sf_f32 = sf_f32 * scale_2
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# Re-quantize to UE4M3 (clamp to [0, 448])
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sf_f32 = sf_f32.clamp(0.0, 448.0)
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return sf_f32.to(torch.float8_e4m3fn)
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# Fold global scales into block scales
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l1_sf = fold_global_scale(l1_weights[1], l1_weight_scale_2)
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l2_sf = fold_global_scale(l2_weights[1], l2_weight_scale_2)
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# L1: interleave gate/up, then pack + transpose SF for UTCCP
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l1_interleaved = _interleave_l1_weights((l1_weights[0], l1_sf))
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l1_out = (l1_interleaved[0], _pack_nvfp4_sf_for_utccp(l1_interleaved[1]))
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# L2: only pack + transpose SF for UTCCP
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l2_out = (l2_weights[0], _pack_nvfp4_sf_for_utccp(l2_sf))
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return l1_out, l2_out
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def fp8_fp4_mega_moe(y: torch.Tensor,
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l1_weights: Tuple[torch.Tensor, torch.Tensor],
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l2_weights: Tuple[torch.Tensor, torch.Tensor],
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sym_buffer: SymmBuffer,
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cumulative_local_expert_recv_stats: Optional[torch.Tensor] = None,
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recipe: Tuple[int, int, int] = (1, 1, 32),
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activation: str = 'swiglu',
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activation_clamp: Optional[float] = None,
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fast_math: bool = True):
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_C.fp8_fp4_mega_moe(
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y,
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l1_weights, l2_weights,
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cumulative_local_expert_recv_stats,
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sym_buffer.buffer,
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sym_buffer.handle.buffer_ptrs, sym_buffer.group.rank(),
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sym_buffer.num_max_tokens_per_rank,
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sym_buffer.num_experts, sym_buffer.num_topk,
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recipe,
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activation, activation_clamp,
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fast_math
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)
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def fp8_nvfp4_mega_moe(y: torch.Tensor,
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l1_weights: Tuple[torch.Tensor, torch.Tensor],
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l2_weights: Tuple[torch.Tensor, torch.Tensor],
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sym_buffer: SymmBuffer,
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cumulative_local_expert_recv_stats: Optional[torch.Tensor] = None,
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recipe: Tuple[int, int, int] = (1, 1, 16),
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activation: str = 'swiglu',
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activation_clamp: Optional[float] = None,
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fast_math: bool = True):
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"""NVFP4 mega MoE: uses kind::mxf4nvf4.block_scale.scale_vec::4X
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with UE4M3 block scales (group_size=16).
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Weight format: (uint8 E2M1 packed, int32 packed UTCCP UE4M3 scales)
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Recipe: (1, 1, 16) — kGranK=16 for NVFP4 group_size=16.
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"""
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_C.fp8_nvfp4_mega_moe(
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y,
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l1_weights, l2_weights,
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cumulative_local_expert_recv_stats,
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sym_buffer.buffer,
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sym_buffer.handle.buffer_ptrs, sym_buffer.group.rank(),
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sym_buffer.num_max_tokens_per_rank,
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sym_buffer.num_experts, sym_buffer.num_topk,
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recipe,
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activation, activation_clamp,
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fast_math
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)
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