forked from ccf-ai-infra/Intro-ops
63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
from __future__ import annotations
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import ctypes
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import torch
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from operator_runtime.backend import Backend, normalize_backend
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from operator_runtime.ctypes_bindings import Descriptor, bind_reduce_like, check_status
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from operator_runtime.prepared import PreparedOp
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from operator_runtime.tensor_view import tensor_view
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def _check(out: torch.Tensor, src: torch.Tensor, dim: int) -> None:
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if not out.is_cuda or not src.is_cuda:
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raise ValueError("softmax expects CUDA tensors")
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if src.dtype is not torch.float32 or out.dtype is not torch.float32:
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raise TypeError("softmax v1 supports float32 only")
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if src.ndim != 2 or out.ndim != 2 or dim != 1:
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raise ValueError("softmax v1 supports 2D row-wise dim=1")
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if out.shape != src.shape:
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raise ValueError("softmax output shape must match input")
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if not out.is_contiguous() or not src.is_contiguous():
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raise ValueError("softmax v1 supports contiguous tensors only")
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def prepare_softmax(
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out: torch.Tensor,
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src: torch.Tensor,
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dim: int = 1,
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backend: str | Backend = Backend.NVIDIA,
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) -> PreparedOp:
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backend = normalize_backend(backend)
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_check(out, src, dim)
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if backend is Backend.TILELANG:
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from ops.softmax.tilelang.softmax_tl import prepare_softmax_tl
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return prepare_softmax_tl(out, src, dim=dim)
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if backend is not Backend.NVIDIA:
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raise NotImplementedError(f"backend {backend.value} is not runnable")
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funcs = bind_reduce_like("softmax")
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desc = Descriptor()
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out_view = tensor_view(out)
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src_view = tensor_view(src)
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check_status(funcs.create(ctypes.byref(desc), ctypes.byref(out_view), ctypes.byref(src_view), ctypes.c_int64(dim)))
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workspace_size = ctypes.c_size_t()
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check_status(funcs.workspace(desc, ctypes.byref(workspace_size)))
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workspace = torch.empty(workspace_size.value, dtype=torch.uint8, device=out.device) if workspace_size.value else None
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args = (ctypes.c_void_p(out.data_ptr()), ctypes.c_void_p(src.data_ptr()))
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return PreparedOp(funcs, desc, workspace, args, out)
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def softmax_(out: torch.Tensor, src: torch.Tensor, dim: int = 1, backend: str | Backend = Backend.NVIDIA) -> torch.Tensor:
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with prepare_softmax(out, src, dim, backend) as prepared:
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prepared.run()
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return out
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def softmax(src: torch.Tensor, dim: int = 1, backend: str | Backend = Backend.NVIDIA) -> torch.Tensor:
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out = torch.empty_like(src)
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return softmax_(out, src, dim, backend)
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