nvfp4-megamoe-kernel/cutedsl/nvfp4_linear.py

"""CuTeDSL NVFP4 Linear (single GEMM)

Generic NVFP4 GEMM runner for attention projections and any single
linear layer. Uses ScaledGroupedGemmKernel with num_groups=1.

CUDA-graph-compatible: all buffers pre-allocated, no CPU-GPU syncs.
"""

import torch

from cutedsl.bridge import (
    quantize_activation_nvfp4,
    quantize_to_nvfp4,
    make_b_k_major,
    assemble_scales_3d_side,
    run_nvfp4_grouped_gemm,
)
from cutedsl.kernel.moe.torch_scaled_grouped_mm import (
    ceil_div as cutedsl_ceil_div,
    pad_and_swizzle_single,
)


class _Nvfp4LinearApply(torch.autograd.Function):
    """Custom autograd function to make CuTeDSL runner opaque to torch.compile.
    
    torch.compile (fullgraph mode) can't trace through CuTeDSL internals
    (JIT compilation, Path.cwd, etc.). By routing through a custom autograd
    function, torch.compile treats it as an opaque op and doesn't try to
    inline it.
    """
    @staticmethod
    def forward(ctx, runner, hidden_states):
        return runner._run_impl(hidden_states)


class CuTeDSLNvfp4Linear:
    """Single NVFP4 GEMM using CuTeDSL (num_groups=1).

    Handles any (K, N) weight matrix in NVFP4 format.
    Simple: quantize activation → GEMM → BF16 output.
    No SiLU, no fusion, no routing.

    CUDA-graph-compatible: all buffers pre-allocated, no CPU-GPU syncs.
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        max_num_tokens: int = 8192,
        device: str = "cuda",
    ):
        self.in_features = in_features
        self.out_features = out_features
        self.max_num_tokens = max_num_tokens
        self.device = device

        # Weights (set after construction, then call finalize_weights)
        self.fp4 = None  # list of 1 tensor
        self.sf = None   # list of 1 tensor
        self.gs = None   # list of 1 float

        # Processed weights
        self._mat_b = None
        self._scale_b = None
        self._gsb = None

        # Activation global scale
        self._activation_global_scale = 1.0 / (6.0 * 448.0)

        # Pre-allocated buffers
        self._padded_x_fp4_buf = None
        self._expert_offsets_buf = None
        self._gsa_buf = None
        self._buffers_allocated = False

    def finalize_weights(self):
        """Process weights for CuTeDSL GEMM."""
        self._mat_b = make_b_k_major(torch.stack(self.fp4))  # (1, K_packed, N_packed)
        self._scale_b = assemble_scales_3d_side(self.sf)
        self._gsb = torch.tensor(self.gs, dtype=torch.float32, device=self.device)

        # Free raw weights
        self.fp4 = None
        self.sf = None
        self.gs = None

    def _allocate_buffers(self):
        """Pre-allocate buffers at max size for cudagraph compatibility."""
        max_rows = cutedsl_ceil_div(self.max_num_tokens, 128) * 128

        self._padded_x_fp4_buf = torch.zeros(
            max_rows, self.in_features // 2, dtype=torch.uint8, device=self.device
        ).view(torch.float4_e2m1fn_x2)

        self._expert_offsets_buf = torch.zeros(1, dtype=torch.int32, device=self.device)
        self._gsa_buf = torch.zeros(1, dtype=torch.float32, device=self.device)
        self._buffers_allocated = True

    def _ensure_initialized(self):
        if self._mat_b is None:
            self.finalize_weights()
        if not self._buffers_allocated:
            self._allocate_buffers()

    def _assemble_scales_single_group(self, x_sf):
        """Assemble 2D-side activation scales for num_groups=1."""
        num_rows, num_cols = x_sf.shape
        padded_rows = cutedsl_ceil_div(num_rows, 128) * 128
        padded_cols = cutedsl_ceil_div(num_cols, 4) * 4

        buf = torch.zeros(padded_rows, padded_cols, dtype=torch.float16, device=x_sf.device).to(torch.float8_e4m3fn)
        buf[:num_rows, :num_cols] = x_sf
        swizzled_flat = pad_and_swizzle_single(buf)
        return swizzled_flat.reshape(padded_rows, padded_cols)

    def compute_activation_global_scale(self, hidden_states_sample):
        """Compute activation global scale from a warmup forward."""
        self._ensure_initialized()
        with torch.no_grad():
            _, _, gs = quantize_to_nvfp4(hidden_states_sample)
            self._activation_global_scale = gs


    def run(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """Forward: BF16 input → NVFP4 GEMM → BF16 output."""
        return _Nvfp4LinearApply.apply(self, hidden_states)

    def _run_impl(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """Actual implementation — called via custom autograd to be torch.compile-safe."""
        self._ensure_initialized()

        num_tokens = hidden_states.shape[0]
        padded_rows = cutedsl_ceil_div(num_tokens, 128) * 128

        # Quantize activation
        x_fp4, x_sf = quantize_activation_nvfp4(
            hidden_states, self._activation_global_scale
        )

        # Scatter x_fp4 into padded buffer
        padded_x_fp4 = self._padded_x_fp4_buf
        padded_x_fp4.view(torch.uint8).zero_()
        padded_x_fp4.view(torch.uint8)[:num_tokens] = x_fp4.view(torch.uint8)

        # Assemble A-side scales
        scale_a = self._assemble_scales_single_group(x_sf)

        # Expert offsets: [padded_rows] for 1 group
        expert_offsets = self._expert_offsets_buf
        expert_offsets.fill_(padded_rows)

        # Global scales
        gsa = self._gsa_buf.fill_(self._activation_global_scale)

        # Run GEMM
        out = run_nvfp4_grouped_gemm(
            mat_a=padded_x_fp4,
            mat_b=self._mat_b,
            scale_a=scale_a,
            scale_b=self._scale_b,
            expert_offsets=expert_offsets,
            global_scale_a=gsa,
            global_scale_b=self._gsb,
        )

        return out[:num_tokens]

    def __call__(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return self.run(hidden_states)