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