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chore: import upstream snapshot with attribution
2026-07-13 12:32:31 +08:00

88 lines
3.0 KiB
Python

# Copyright (c) 2026 LightSeek Foundation
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""Vendored flashinfer softmax with bf16/fp16/fp32 input, fp32 output."""
from __future__ import annotations
import functools
from pathlib import Path
from typing import Optional, Union
import torch
_WORKSPACE_BYTES = 1 * 1024 * 1024
@functools.cache
def _load_module():
import tvm_ffi
so_path = (
Path(__file__).parent / "objs" / "flashinfer_softmax" / "flashinfer_softmax.so"
)
if not so_path.exists():
raise RuntimeError(
f"tokenspeed_kernel flashinfer_softmax library not found at {so_path}. "
"Run: pip install -e tokenspeed-kernel/python/"
)
return tvm_ffi.load_module(str(so_path))
@functools.cache
def _get_workspace(device: torch.device) -> torch.Tensor:
return torch.empty(_WORKSPACE_BYTES, dtype=torch.uint8, device=device)
def softmax(
logits: torch.Tensor,
temperature: Optional[Union[float, torch.Tensor]] = None,
enable_pdl: bool = False,
) -> torch.Tensor:
"""softmax(logits / temperature). Returns fp32 probs."""
assert logits.is_contiguous(), "softmax expects contiguous logits"
assert logits.is_cuda, "softmax requires CUDA tensors"
assert logits.dim() == 2
assert logits.dtype in (
torch.float32,
torch.float16,
torch.bfloat16,
), f"softmax: unsupported logits dtype {logits.dtype}"
if isinstance(temperature, torch.Tensor):
assert temperature.is_contiguous() and temperature.dtype == torch.float32
temp_arr: Optional[torch.Tensor] = temperature.view(-1)
temp_val = 0.0
else:
temp_arr = None
temp_val = 1.0 if temperature is None else float(temperature)
output = torch.empty_like(logits, dtype=torch.float32)
workspace = _get_workspace(logits.device)
_load_module().softmax(
workspace,
logits,
output,
temp_arr,
float(temp_val),
bool(enable_pdl),
)
return output