DeepGEMM/deep_gemm/mega/__init__.py

import torch
from typing import Tuple, Optional
from ..utils.math import align

# noinspection PyBroadException
try:
    # noinspection PyProtectedMember
    import torch.distributed._symmetric_memory as symm_mem
    import torch.distributed as dist
except Exception as exception:
    print(f'Failed to load mega kernels, please check your PyTorch version: {exception}')

from .. import _C


class SymmBuffer:
    def __init__(self, group: dist.ProcessGroup,
                 # MoE arguments
                 num_experts: int,
                 num_max_tokens_per_rank: int, num_topk: int,
                 hidden: int, intermediate_hidden: int,
                 use_fp8_dispatch: bool = True,
                 activation: str = 'swiglu'):
        self.group = group
        self.num_experts = num_experts
        self.num_max_tokens_per_rank = num_max_tokens_per_rank
        self.num_topk = num_topk
        self.hidden = hidden
        self.intermediate_hidden = intermediate_hidden

        # Allocate a symmetric buffer
        num_bytes, slice_input_buffers = _C.get_symm_buffer_size_for_mega_moe(
            group.size(), num_experts,
            num_max_tokens_per_rank, num_topk,
            hidden, intermediate_hidden,
            use_fp8_dispatch, activation
        )
        self.buffer = symm_mem.empty(num_bytes, dtype=torch.int8, device='cuda')
        self.handle = symm_mem.rendezvous(self.buffer, group=group)
        self.buffer.zero_()
        self.group.barrier()
        torch.cuda.synchronize()

        # Create input buffer views
        (self.x, self.x_sf,
         self.topk_idx, self.topk_weights,
         self.l1_acts, self.l1_acts_sf,
         self.l2_acts, self.l2_acts_sf) = slice_input_buffers(self.buffer)

    def destroy(self):
        self.handle = None
        self.buffer = None
        self.group = None
        self.x = None
        self.x_sf = None


def get_symm_buffer_for_mega_moe(group: dist.ProcessGroup,
                                 num_experts: int,
                                 num_max_tokens_per_rank: int, num_topk: int,
                                 hidden: int, intermediate_hidden: int,
                                 use_fp8_dispatch: bool = True,
                                 activation: str = 'swiglu') -> SymmBuffer:
    # Token count must be aligned to block sizes
    num_max_tokens_per_rank = align(num_max_tokens_per_rank, _C.get_token_alignment_for_mega_moe())

    return SymmBuffer(
        group, num_experts,
        num_max_tokens_per_rank, num_topk,
        hidden, intermediate_hidden,
        use_fp8_dispatch, activation
    )


class NVFP4SymmBuffer:
    """Symmetric buffer for NVFP4 mega_moe kernel.
    
    Same structure as SymmBuffer but with NVFP4-specific SF layout:
    group_size=16 means 2x the SF data compared to MXFP4 (group_size=32).
    x_sf is packed int32 (4 UE4M3 per int32) with shape (M, K//64).
    """
    def __init__(self, group: dist.ProcessGroup,
                 num_experts: int,
                 num_max_tokens_per_rank: int, num_topk: int,
                 hidden: int, intermediate_hidden: int,
                 use_fp8_dispatch: bool = True,
                 activation: str = 'swiglu'):
        self.group = group
        self.num_experts = num_experts
        self.num_max_tokens_per_rank = num_max_tokens_per_rank
        self.num_topk = num_topk
        self.hidden = hidden
        self.intermediate_hidden = intermediate_hidden

        # Allocate a symmetric buffer (NVFP4 variant)
        num_bytes, slice_input_buffers = _C.get_symm_buffer_size_for_nvfp4_mega_moe(
            group.size(), num_experts,
            num_max_tokens_per_rank, num_topk,
            hidden, intermediate_hidden,
            use_fp8_dispatch, activation
        )
        self.buffer = symm_mem.empty(num_bytes, dtype=torch.int8, device='cuda')
        self.handle = symm_mem.rendezvous(self.buffer, group=group)
        self.buffer.zero_()
        self.group.barrier()
        torch.cuda.synchronize()

        # Create input buffer views
        (self.x, self.x_sf,
         self.topk_idx, self.topk_weights,
         self.l1_acts, self.l1_acts_sf,
         self.l2_acts, self.l2_acts_sf) = slice_input_buffers(self.buffer)

    def destroy(self):
        self.handle = None
        self.buffer = None
        self.group = None
        self.x = None
        self.x_sf = None


def get_symm_buffer_for_nvfp4_mega_moe(group: dist.ProcessGroup,
                                       num_experts: int,
                                       num_max_tokens_per_rank: int, num_topk: int,
                                       hidden: int, intermediate_hidden: int,
                                       use_fp8_dispatch: bool = True,
                                       activation: str = 'swiglu') -> NVFP4SymmBuffer:
    # Token count must be aligned to block sizes
    num_max_tokens_per_rank = align(num_max_tokens_per_rank, _C.get_token_alignment_for_nvfp4_mega_moe())

    return NVFP4SymmBuffer(
        group, num_experts,
        num_max_tokens_per_rank, num_topk,
        hidden, intermediate_hidden,
        use_fp8_dispatch, activation
    )


def _interleave_l1_weights(l1_weights: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
    # [gate: 0..7, up: 0..7, gate: 8..15, up: 8..15, ...] instead of [gate | up]
    def interleave(t, gran: int = 8) -> torch.Tensor:
        g, n, *rest = t.shape
        half = n // 2
        gate = t[:, :half].reshape(g, half // gran, gran, *rest)
        up = t[:, half:].reshape(g, half // gran, gran, *rest)
        return torch.empty_like(t).copy_(torch.stack([gate, up], dim=2).reshape(g, n, *rest))

    return interleave(l1_weights[0]), interleave(l1_weights[1])


def _transpose_sf_for_utccp(sf: torch.Tensor) -> torch.Tensor:
    num_groups, mn, packed_sf_k = sf.shape
    assert sf.dtype == torch.int and mn % 128 == 0
    result = (sf.reshape(num_groups, -1, 4, 32, packed_sf_k)
                .transpose(2, 3)
                .reshape(num_groups, mn, packed_sf_k))
    return torch.empty_like(sf).copy_(result)


def transform_weights_for_mega_moe(
    l1_weights: Tuple[torch.Tensor, torch.Tensor],
    l2_weights: Tuple[torch.Tensor, torch.Tensor]
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
    # L1: interleave gate/up, then transpose SF for UTCCP
    l1_interleaved = _interleave_l1_weights(l1_weights)
    l1_weights = (l1_interleaved[0], _transpose_sf_for_utccp(l1_interleaved[1]))
    # L2: only transpose SF for UTCCP
    l2_weights = (l2_weights[0], _transpose_sf_for_utccp(l2_weights[1]))
    return l1_weights, l2_weights


def _pack_nvfp4_sf_for_utccp(sf: torch.Tensor) -> torch.Tensor:
    """Pack NVFP4 UE4M3 block scales (float8_e4m3fn) into int32 UTCCP layout.
    
    NVFP4 uses UE4M3 scales with group_size=16 (scale_vec::4X).
    The UTCCP layout packs 4 consecutive scale bytes into each int32,
    then applies the 4x32 transpose for TMA consumption.
    
    Input: (num_experts, mn, K//16) float8_e4m3fn scales
    Output: (num_experts, mn, K//64) int32 packed UTCCP-transposed scales
    """
    num_groups, mn, sf_k = sf.shape
    assert sf_k % 4 == 0, f"NVFP4 SF K dim must be divisible by 4, got {sf_k}"
    assert mn % 128 == 0, f"MN dim must be divisible by 128, got {mn}"
    
    # View as uint8 and pack 4 consecutive bytes into int32
    sf_uint8 = sf.view(torch.uint8)  # (num_groups, mn, sf_k)
    # Pack: every 4 uint8 → 1 int32
    packed = (sf_uint8[..., 0::4].to(torch.int32) |
              (sf_uint8[..., 1::4].to(torch.int32) << 8) |
              (sf_uint8[..., 2::4].to(torch.int32) << 16) |
              (sf_uint8[..., 3::4].to(torch.int32) << 24))  # (num_groups, mn, sf_k//4)
    
    # Apply UTCCP 4x32 transpose (same as MXFP4 — the transpose is determined
    # by the 128-element alignment, not the scale vector size)
    packed_sf_k = sf_k // 4
    result = (packed.reshape(num_groups, -1, 4, 32, packed_sf_k)
                .transpose(2, 3)
                .reshape(num_groups, mn, packed_sf_k))
    return torch.empty_like(packed).copy_(result)


def transform_nvfp4_weights_for_mega_moe(
    l1_weights: Tuple[torch.Tensor, torch.Tensor],
    l2_weights: Tuple[torch.Tensor, torch.Tensor],
    l1_weight_scale_2: Optional[torch.Tensor] = None,
    l2_weight_scale_2: Optional[torch.Tensor] = None
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
    """Transform NVFP4 expert weights for the mega_moe kernel.
    
    NVFP4 weights come as (weight, scale) where:
    - weight: uint8 E2M1 packed, shape (num_experts, N, K//2)
    - scale: float8_e4m3fn UE4M3 block scales, shape (num_experts, N, K//16)
    
    The kernel expects (weight, packed_sf) where packed_sf is int32 TMA-aligned
    UTCCP layout with gran_k=16.
    
    If weight_scale_2 (float32 global scale) is provided, it is folded into the
    block scales: effective_scale = block_scale * global_scale → re-quantized to UE4M3.
    """
    from deep_gemm import transform_sf_into_required_layout
    
    def fold_global_scale(sf: torch.Tensor, scale_2: Optional[torch.Tensor]) -> torch.Tensor:
        """Fold weight_scale_2 into block scales: UE4M3 * FP32 → UE4M3"""
        if scale_2 is None:
            return sf
        # UE8M0 → float32: must reinterpret raw uint8 bits as IEEE 754 exponent,
        # NOT cast float8_e4m3fn → float32 (Bug #7: E8M0 bytes misinterpreted as E4M3)
        sf_f32 = (sf.view(torch.uint8).to(torch.int32) << 23).view(torch.float32)
        if scale_2.dim() == 1:
            scale_2 = scale_2.view(-1, 1, 1)
        sf_f32 = sf_f32 * scale_2
        sf_f32 = sf_f32.clamp(0.0, 448.0)
        return sf_f32.to(torch.float8_e4m3fn)
    
    # Fold global scales into block scales
    l1_sf = fold_global_scale(l1_weights[1], l1_weight_scale_2)
    l2_sf = fold_global_scale(l2_weights[1], l2_weight_scale_2)
    
    num_experts = l1_weights[0].shape[0]
    l1_n = l1_weights[0].shape[1]
    l1_k = l1_weights[0].shape[2] * 2  # K (weight is K//2 uint8)
    l2_n = l2_weights[0].shape[1]
    l2_k = l2_weights[0].shape[2] * 2
    
    # Pack UE4M3 (float8_e4m3fn) into int32 for DeepGEMM TMA consumption
    # 4 UE4M3 bytes → 1 int32
    def pack_ue4m3_to_int32(sf):
        sf_u8 = sf.view(torch.uint8)
        assert sf_u8.shape[-1] % 4 == 0
        packed = (sf_u8[..., 0::4].to(torch.int32) |
                  (sf_u8[..., 1::4].to(torch.int32) << 8) |
                  (sf_u8[..., 2::4].to(torch.int32) << 16) |
                  (sf_u8[..., 3::4].to(torch.int32) << 24))
        return packed.contiguous()
    
    l1_sf_packed = pack_ue4m3_to_int32(l1_sf)
    l2_sf_packed = pack_ue4m3_to_int32(l2_sf)
    
    # Transpose to MN-major layout (stride(-2)=1) and make contiguous
    # transform_sf_into_required_layout expects MN-major input for TMA stride checks
    l1_sf_mn = l1_sf_packed.transpose(-2, -1).contiguous().transpose(-2, -1)
    l2_sf_mn = l2_sf_packed.transpose(-2, -1).contiguous().transpose(-2, -1)
    
    # Transform SF into TMA-aligned UTCCP layout using DeepGEMM's C++ function
    # recipe (1, 16): gran_mn=1, gran_k=16 (NVFP4 native block16)
    l1_sf_transformed = transform_sf_into_required_layout(
        l1_sf_mn, l1_n, l1_k, (1, 16), num_experts)
    l2_sf_transformed = transform_sf_into_required_layout(
        l2_sf_mn, l2_n, l2_k, (1, 16), num_experts)
    
    # L1: interleave gate/up
    l1_interleaved = _interleave_l1_weights((l1_weights[0], l1_sf_transformed))
    # DeepGEMM expects int8 (kPackedFP4 = torch.kInt8)
    l1_out = (l1_interleaved[0].view(torch.int8), l1_interleaved[1])
    l2_out = (l2_weights[0].view(torch.int8), l2_sf_transformed)
    return l1_out, l2_out


def fp8_fp4_mega_moe(y: torch.Tensor,
                     l1_weights: Tuple[torch.Tensor, torch.Tensor],
                     l2_weights: Tuple[torch.Tensor, torch.Tensor],
                     sym_buffer: SymmBuffer,
                     cumulative_local_expert_recv_stats: Optional[torch.Tensor] = None,
                     recipe: Tuple[int, int, int] = (1, 1, 32),
                     activation: str = 'swiglu',
                     activation_clamp: Optional[float] = None,
                     fast_math: bool = True):
    _C.fp8_fp4_mega_moe(
        y,
        l1_weights, l2_weights,
        cumulative_local_expert_recv_stats,
        sym_buffer.buffer,
        sym_buffer.handle.buffer_ptrs, sym_buffer.group.rank(),
        sym_buffer.num_max_tokens_per_rank,
        sym_buffer.num_experts, sym_buffer.num_topk,
        recipe,
        activation, activation_clamp,
        fast_math
    )


def fp8_nvfp4_mega_moe(y: torch.Tensor,
                       l1_weights: Tuple[torch.Tensor, torch.Tensor],
                       l2_weights: Tuple[torch.Tensor, torch.Tensor],
                       sym_buffer: SymmBuffer,
                       cumulative_local_expert_recv_stats: Optional[torch.Tensor] = None,
                       recipe: Tuple[int, int, int] = (1, 1, 16),
                       activation: str = 'swiglu',
                       activation_clamp: Optional[float] = None,
                       fast_math: bool = True):
    """NVFP4 mega MoE: uses kind::mxf4nvf4.block_scale.scale_vec::4X
    with UE4M3 block scales (group_size=16).
    
    Both activations AND weights are E2M1 packed (FP4×FP4).
    Weight format: (uint8 E2M1 packed, int32 packed UTCCP UE4M3 scales)
    Activation format: E2M1 packed uint8 + UE4M3 scales (computed by staging kernel)
    Recipe: (1, 1, 16) — kGranK=16 for NVFP4 group_size=16.
    """
    _C.fp8_nvfp4_mega_moe(
        y,
        l1_weights, l2_weights,
        cumulative_local_expert_recv_stats,
        sym_buffer.buffer,
        sym_buffer.handle.buffer_ptrs, sym_buffer.group.rank(),
        sym_buffer.num_max_tokens_per_rank,
        sym_buffer.num_experts, sym_buffer.num_topk,
        recipe,
        activation, activation_clamp,
        fast_math
    )