forked from ccf-ai-infra/Intro-ops
62 lines
2.4 KiB
Python
62 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_binary, 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, a: torch.Tensor, b: torch.Tensor) -> None:
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if not out.is_cuda or not a.is_cuda or not b.is_cuda:
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raise ValueError("vector_add expects CUDA tensors")
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if out.shape != a.shape or out.shape != b.shape:
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raise ValueError("vector_add v1 expects matching shapes")
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if out.dtype != a.dtype or out.dtype != b.dtype:
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raise TypeError("vector_add expects matching dtypes")
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if not out.is_contiguous() or not a.is_contiguous() or not b.is_contiguous():
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raise ValueError("vector_add v1 supports contiguous tensors only")
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def prepare_vector_add(
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out: torch.Tensor,
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a: torch.Tensor,
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b: torch.Tensor,
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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, a, b)
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if backend is Backend.TILELANG:
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from ops.vector_add.tilelang.vector_add_tl import prepare_vector_add_tl
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return prepare_vector_add_tl(out, a, b)
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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_binary("vector_add")
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desc = Descriptor()
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out_view = tensor_view(out)
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a_view = tensor_view(a)
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b_view = tensor_view(b)
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check_status(funcs.create(ctypes.byref(desc), ctypes.byref(out_view), ctypes.byref(a_view), ctypes.byref(b_view)))
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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(a.data_ptr()), ctypes.c_void_p(b.data_ptr()))
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return PreparedOp(funcs, desc, workspace, args, out)
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def vector_add_(out: torch.Tensor, a: torch.Tensor, b: torch.Tensor, backend: str | Backend = Backend.NVIDIA) -> torch.Tensor:
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with prepare_vector_add(out, a, b, backend) as prepared:
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prepared.run()
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return out
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def vector_add(a: torch.Tensor, b: torch.Tensor, backend: str | Backend = Backend.NVIDIA) -> torch.Tensor:
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out = torch.empty_like(a)
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return vector_add_(out, a, b, backend)
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