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from __future__ import annotations
import torch
from ._ops import add_op_namespace_prefix, ops
def sfa_size_bytes(rows: int, dim: int) -> int:
if rows <= 0 or dim <= 0 or dim % 16 != 0:
raise ValueError("rows must be positive and dim must be positive/divisible by 16")
n_blocks = dim // 16
n_row_super = (rows + 127) // 128
n_col_super = (n_blocks + 3) // 4
return n_row_super * n_col_super * 512
def _alloc_fp4(rows: int, dim: int, device: torch.device | str):
return (
torch.empty((rows, dim // 2), device=device, dtype=torch.uint8),
torch.empty((sfa_size_bytes(rows, dim),), device=device, dtype=torch.uint8),
)
@torch.library.register_fake(add_op_namespace_prefix("nvfp4_gemm_bf16"))
def _linear_fake(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
out: torch.Tensor,
alpha: float = 1.0,
variant: int = -1,
) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("fp4_w4a4_gemv_warpsplit_bf16"))
def _gemv_warpsplit_fake(a_packed, b_packed, sfa, sfb, out, alpha: float = 1.0, warps: int = 4, stages: int = 4) -> None:
if a_packed.shape[0] != 1:
raise RuntimeError("warp-split GEMV serves M=1 only")
if out.shape != (1, b_packed.shape[0]):
raise RuntimeError("out must have shape (1, N)")
return None
@torch.library.register_fake(add_op_namespace_prefix("fp4_w4a16_linear_bf16"))
def _legacy_linear_fake(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
out: torch.Tensor,
alpha: float = 1.0,
variant: int = -1,
) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("nvfp4_gemm_bias_bf16"))
def _bias_fake(a, b, sfa, sfb, bias, out) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("nvfp4_gemm_bias_residual_bf16"))
def _bias_residual_fake(a, b, sfa, sfb, bias, residual, out) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("quantize_fp4_sfa_fp16"))
def _quant_fake(x: torch.Tensor, packed: torch.Tensor, sfa: torch.Tensor, is_sfb: bool = False) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("quantize_fp4_sfa_bf16"))
def _quant_bf16_fake(x: torch.Tensor, packed: torch.Tensor, sfa: torch.Tensor, is_sfb: bool = False) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("dequantize_fp4_sfa_fp16"))
def _dequant_fake(packed: torch.Tensor, sfa: torch.Tensor, out: torch.Tensor, is_sfb: bool = False) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("nvfp4_gemm_residual_bf16"))
def _residual_fake(a, b, sfa, sfb, residual, out, alpha: float = 1.0) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("nvfp4_gemm_bias_gelu_bf16"))
def _bias_gelu_fake(a, b, sfa, sfb, bias, out, alpha: float = 1.0) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("nvfp4_gemm_bias_gelu_nvfp4"))
def _bias_gelu_nvfp4_fake(
a, b, sfa, sfb, bias, out_packed, out_sfa, alpha: float = 1.0
) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("nvfp4_gemm_streamk_bf16"))
def _streamk_fake(a, b, sfa, sfb, out, alpha: float = 1.0) -> None:
return None
@torch.library.register_fake(add_op_namespace_prefix("nvfp4_gemm_streamk_bias_bf16"))
def _streamk_bias_fake(a, b, sfa, sfb, bias, out, alpha: float = 1.0) -> None:
return None
def quantize_fp4_sfa_fp16(
x: torch.Tensor,
packed: torch.Tensor | None = None,
sfa: torch.Tensor | None = None,
is_sfb: bool = False,
):
if packed is None or sfa is None:
packed, sfa = _alloc_fp4(x.shape[0], x.shape[1], x.device)
ops.quantize_fp4_sfa_fp16(x, packed, sfa, bool(is_sfb))
return packed, sfa
def quantize_fp4_sfa_bf16(
x: torch.Tensor,
packed: torch.Tensor | None = None,
sfa: torch.Tensor | None = None,
is_sfb: bool = False,
):
"""Quantize BF16 directly to packed E2M1 and CUTLASS SFA/SFB."""
if packed is None or sfa is None:
packed, sfa = _alloc_fp4(x.shape[0], x.shape[1], x.device)
ops.quantize_fp4_sfa_bf16(x, packed, sfa, bool(is_sfb))
return packed, sfa
def dequantize_fp4_sfa_fp16(
packed: torch.Tensor,
sfa: torch.Tensor,
out: torch.Tensor | None = None,
is_sfb: bool = False,
) -> torch.Tensor:
if out is None:
out = torch.empty((packed.shape[0], packed.shape[1] * 2), device=packed.device, dtype=torch.float16)
ops.dequantize_fp4_sfa_fp16(packed, sfa, out, bool(is_sfb))
return out
def nvfp4_gemm_bf16(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
alpha: float = 1.0,
out: torch.Tensor | None = None,
variant: int = -1,
) -> torch.Tensor:
if out is None:
out = torch.empty((a_packed.shape[0], b_packed.shape[0]), device=a_packed.device, dtype=torch.bfloat16)
ops.nvfp4_gemm_bf16(a_packed, b_packed, sfa, sfb, out, float(alpha), int(variant))
return out
def fp4_w4a4_gemv_warpsplit_bf16(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
*,
alpha: float = 1.0,
warps: int = 4,
stages: int = 4,
out: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Warp-split-K NVFP4 W4A4 GEMV for the M=1 decode row (SM120).
Splits K across warps inside one block with a shared-memory reduce -
no cross-block intermediate, so it stays safe under CUDA-graph
replay - and fills the SMs the tiled GEMM underfills at long-K
small-M decode shapes. Same packed/scale layouts as the linear
entry points."""
if out is None:
out = torch.empty((1, b_packed.shape[0]), device=a_packed.device, dtype=torch.bfloat16)
ops.fp4_w4a4_gemv_warpsplit_bf16(a_packed, b_packed, sfa, sfb, out, float(alpha), int(warps), int(stages))
return out
def fp4_w4a16_linear_bf16(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
alpha: float = 1.0,
out: torch.Tensor | None = None,
variant: int = -1,
) -> torch.Tensor:
"""Compatibility alias for :func:`nvfp4_gemm_bf16`."""
return nvfp4_gemm_bf16(
a_packed, b_packed, sfa, sfb, alpha=alpha, out=out, variant=variant
)
def nvfp4_gemm_bias_bf16(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
bias: torch.Tensor,
*,
out: torch.Tensor | None = None,
) -> torch.Tensor:
"""SM110 NVFP4 GEMM with a fused per-column BF16 bias."""
if out is None:
out = torch.empty(
(a_packed.shape[0], b_packed.shape[0]),
device=a_packed.device,
dtype=torch.bfloat16,
)
ops.nvfp4_gemm_bias_bf16(a_packed, b_packed, sfa, sfb, bias, out)
return out
def nvfp4_gemm_bias_residual_bf16(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
bias: torch.Tensor,
residual: torch.Tensor,
*,
out: torch.Tensor | None = None,
) -> torch.Tensor:
"""SM110 NVFP4 GEMM with fused BF16 bias and residual add."""
if out is None:
out = torch.empty_like(residual)
ops.nvfp4_gemm_bias_residual_bf16(
a_packed, b_packed, sfa, sfb, bias, residual, out
)
return out
def nvfp4_gemm_residual_bf16(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
residual: torch.Tensor,
alpha: float = 1.0,
out: torch.Tensor | None = None,
) -> torch.Tensor:
if out is None:
out = torch.empty_like(residual)
ops.nvfp4_gemm_residual_bf16(
a_packed, b_packed, sfa, sfb, residual, out, float(alpha)
)
return out
def nvfp4_gemm_bias_gelu_bf16(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
bias: torch.Tensor,
alpha: float = 1.0,
out: torch.Tensor | None = None,
) -> torch.Tensor:
if out is None:
out = torch.empty(
(a_packed.shape[0], b_packed.shape[0]),
device=a_packed.device,
dtype=torch.bfloat16,
)
ops.nvfp4_gemm_bias_gelu_bf16(
a_packed, b_packed, sfa, sfb, bias, out, float(alpha)
)
return out
def nvfp4_gemm_bias_gelu_nvfp4(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
bias: torch.Tensor,
alpha: float = 1.0,
out_packed: torch.Tensor | None = None,
out_sfa: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
m, n = a_packed.shape[0], b_packed.shape[0]
if out_packed is None:
out_packed = torch.empty((m, n // 2), device=a_packed.device, dtype=torch.uint8)
if out_sfa is None:
out_sfa = torch.empty((sfa_size_bytes(m, n),), device=a_packed.device, dtype=torch.uint8)
ops.nvfp4_gemm_bias_gelu_nvfp4(
a_packed, b_packed, sfa, sfb, bias, out_packed, out_sfa, float(alpha)
)
return out_packed, out_sfa
def nvfp4_gemm_streamk_bf16(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
alpha: float = 1.0,
out: torch.Tensor | None = None,
) -> torch.Tensor:
if out is None:
out = torch.empty(
(a_packed.shape[0], b_packed.shape[0]),
device=a_packed.device,
dtype=torch.bfloat16,
)
ops.nvfp4_gemm_streamk_bf16(
a_packed, b_packed, sfa, sfb, out, float(alpha)
)
return out
def nvfp4_gemm_streamk_bias_bf16(
a_packed: torch.Tensor,
b_packed: torch.Tensor,
sfa: torch.Tensor,
sfb: torch.Tensor,
bias: torch.Tensor,
alpha: float = 1.0,
out: torch.Tensor | None = None,
) -> torch.Tensor:
if out is None:
out = torch.empty(
(a_packed.shape[0], b_packed.shape[0]),
device=a_packed.device,
dtype=torch.bfloat16,
)
ops.nvfp4_gemm_streamk_bias_bf16(
a_packed, b_packed, sfa, sfb, bias, out, float(alpha)
)
return out
__all__ = [
"dequantize_fp4_sfa_fp16",
"fp4_w4a16_linear_bf16",
"fp4_w4a4_gemv_warpsplit_bf16",
"nvfp4_gemm_bf16",
"nvfp4_gemm_bias_bf16",
"nvfp4_gemm_bias_gelu_bf16",
"nvfp4_gemm_bias_gelu_nvfp4",
"nvfp4_gemm_bias_residual_bf16",
"nvfp4_gemm_residual_bf16",
"nvfp4_gemm_streamk_bf16",
"nvfp4_gemm_streamk_bias_bf16",
"quantize_fp4_sfa_fp16",
"quantize_fp4_sfa_bf16",
"sfa_size_bytes",
]
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