wehub-resource-sync
357 lines
11 KiB
Python
357 lines
11 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""Tensor class for computation declaration."""
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# pylint: disable=invalid-name
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import tvm_ffi
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from tvm.runtime import Object, ObjectConvertible, const
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from tvm.tirx import DataProducer
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from tvm.tirx import expr as _expr
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from . import _ffi_api, _te_tensor_overload
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def _as_scalar_operand(value):
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return value.asobject() if isinstance(value, TensorSlice) else value
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class TensorSlice(ObjectConvertible):
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"""Auxiliary data structure for enable slicing syntax from tensor."""
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def __init__(self, tensor, indices):
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if not isinstance(indices, tuple):
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indices = (indices,)
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self.tensor = tensor
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self.indices = indices
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def __getitem__(self, indices):
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if not isinstance(indices, tuple):
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indices = (indices,)
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return TensorSlice(self.tensor, self.indices + indices)
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def asobject(self):
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"""Convert slice to object."""
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return self.tensor.__call__(*self.indices)
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@property
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def dtype(self):
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"""Data content of the tensor."""
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return self.tensor.dtype
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def expr_ty(self):
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"""Compile-time element type of the tensor."""
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return self.tensor.expr_ty()
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def __add__(self, other):
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result = _te_tensor_overload.__add__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__add__(self.asobject(), _as_scalar_operand(other))
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def __radd__(self, other):
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result = _te_tensor_overload.__radd__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__radd__(self.asobject(), _as_scalar_operand(other))
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def __sub__(self, other):
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result = _te_tensor_overload.__sub__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__sub__(self.asobject(), _as_scalar_operand(other))
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def __rsub__(self, other):
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result = _te_tensor_overload.__rsub__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__rsub__(self.asobject(), _as_scalar_operand(other))
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def __mul__(self, other):
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result = _te_tensor_overload.__mul__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__mul__(self.asobject(), _as_scalar_operand(other))
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def __rmul__(self, other):
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result = _te_tensor_overload.__rmul__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__rmul__(self.asobject(), _as_scalar_operand(other))
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def __div__(self, other):
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result = _te_tensor_overload.__div__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__div__(self.asobject(), _as_scalar_operand(other))
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def __rdiv__(self, other):
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result = _te_tensor_overload.__rdiv__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__rdiv__(self.asobject(), _as_scalar_operand(other))
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def __truediv__(self, other):
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result = _te_tensor_overload.__truediv__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__truediv__(self.asobject(), _as_scalar_operand(other))
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def __rtruediv__(self, other):
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result = _te_tensor_overload.__rtruediv__(self, other)
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if result is not NotImplemented:
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return result
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return _expr.ExprOp.__rtruediv__(self.asobject(), _as_scalar_operand(other))
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def __floordiv__(self, other):
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return _expr.ExprOp.__floordiv__(self.asobject(), _as_scalar_operand(other))
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def __rfloordiv__(self, other):
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return _expr.ExprOp.__rfloordiv__(self.asobject(), _as_scalar_operand(other))
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def __mod__(self, other):
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return _expr.ExprOp.__mod__(self.asobject(), _as_scalar_operand(other))
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def __rmod__(self, other):
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return _expr.ExprOp.__rmod__(self.asobject(), _as_scalar_operand(other))
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def __neg__(self):
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return _expr.ExprOp.__neg__(self.asobject())
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def __lshift__(self, other):
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return _expr.ExprOp.__lshift__(self.asobject(), _as_scalar_operand(other))
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def __rlshift__(self, other):
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return _expr.ExprOp.__rlshift__(self.asobject(), _as_scalar_operand(other))
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def __rshift__(self, other):
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return _expr.ExprOp.__rshift__(self.asobject(), _as_scalar_operand(other))
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def __rrshift__(self, other):
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return _expr.ExprOp.__rrshift__(self.asobject(), _as_scalar_operand(other))
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def __and__(self, other):
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return _expr.ExprOp.__and__(self.asobject(), _as_scalar_operand(other))
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def __rand__(self, other):
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return _expr.ExprOp.__rand__(self.asobject(), _as_scalar_operand(other))
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def __or__(self, other):
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return _expr.ExprOp.__or__(self.asobject(), _as_scalar_operand(other))
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def __ror__(self, other):
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return _expr.ExprOp.__ror__(self.asobject(), _as_scalar_operand(other))
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def __xor__(self, other):
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return _expr.ExprOp.__xor__(self.asobject(), _as_scalar_operand(other))
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def __rxor__(self, other):
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return _expr.ExprOp.__rxor__(self.asobject(), _as_scalar_operand(other))
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def __invert__(self):
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return _expr.ExprOp.__invert__(self.asobject())
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def __lt__(self, other):
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return _expr.ExprOp.__lt__(self.asobject(), _as_scalar_operand(other))
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def __le__(self, other):
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return _expr.ExprOp.__le__(self.asobject(), _as_scalar_operand(other))
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def __eq__(self, other):
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return _expr.ExprOp.__eq__(self.asobject(), _as_scalar_operand(other))
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def __ne__(self, other):
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return _expr.ExprOp.__ne__(self.asobject(), _as_scalar_operand(other))
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def __gt__(self, other):
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return _expr.ExprOp.__gt__(self.asobject(), _as_scalar_operand(other))
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def __ge__(self, other):
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return _expr.ExprOp.__ge__(self.asobject(), _as_scalar_operand(other))
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def __nonzero__(self):
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return _expr.ExprOp.__nonzero__(self.asobject())
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def __bool__(self):
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return self.__nonzero__()
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def equal(self, other, span=None):
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return _expr.ExprOp.equal(self.asobject(), _as_scalar_operand(other), span)
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def astype(self, dtype, span=None):
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return _expr.ExprOp.astype(self.asobject(), dtype, span)
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class TensorOpBase:
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"""Operator overloads for whole TE Tensor values."""
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def __add__(self, other):
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return _te_tensor_overload.__add__(self, other)
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def __radd__(self, other):
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return _te_tensor_overload.__radd__(self, other)
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def __sub__(self, other):
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return _te_tensor_overload.__sub__(self, other)
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def __rsub__(self, other):
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return _te_tensor_overload.__rsub__(self, other)
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def __mul__(self, other):
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return _te_tensor_overload.__mul__(self, other)
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def __rmul__(self, other):
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return _te_tensor_overload.__rmul__(self, other)
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def __div__(self, other):
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return _te_tensor_overload.__div__(self, other)
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def __rdiv__(self, other):
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return _te_tensor_overload.__rdiv__(self, other)
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def __truediv__(self, other):
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return _te_tensor_overload.__truediv__(self, other)
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def __rtruediv__(self, other):
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return _te_tensor_overload.__rtruediv__(self, other)
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def __neg__(self):
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return self.__mul__(const(-1, self.expr_ty()))
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def __nonzero__(self):
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return _expr.ExprOp.__nonzero__(self)
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def __bool__(self):
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return self.__nonzero__()
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def equal(self, other, span=None):
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return _expr.ExprOp.equal(self, other, span)
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def astype(self, dtype, span=None):
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result = _te_tensor_overload.astype(self, dtype, span)
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if result is NotImplemented:
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raise TypeError("TE Tensor overload astype is not registered")
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return result
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@tvm_ffi.register_object("te.Tensor")
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class Tensor(DataProducer, TensorOpBase):
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"""Tensor object, to construct, see function.Tensor"""
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def __call__(self, *indices):
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ndim = self.ndim
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if len(indices) != ndim:
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raise ValueError(
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f"Need to provide {ndim} index in tensor but {len(indices)} was provided"
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)
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return _expr.ProducerLoad(self, indices)
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def __getitem__(self, indices):
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return TensorSlice(self, indices)
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def __hash__(self):
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return _ffi_api.TensorHash(self)
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def __eq__(self, other):
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if not isinstance(other, Tensor):
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if isinstance(other, _expr.ExprOp):
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return _expr.EqualOp(self, other)
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return False
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if self.ndim == 0 and other.ndim == 0:
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raise ValueError(
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"Equal == comparison among rank-0 tensor is ambiguous, "
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"use Tensor.equal for content expression equvalence, "
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"use Tensor.same_as for exact reference comparison"
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)
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return _ffi_api.TensorEqual(self, other)
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@property
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def ndim(self):
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"""Dimension of the tensor."""
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return len(self.shape)
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@property
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def dtype(self):
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"""Data content of the tensor."""
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return _ffi_api.TensorDType(self)
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def expr_ty(self):
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"""Compile-time element type of the tensor."""
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return self.dtype
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@property
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def name(self):
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op = self.op
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if op.num_outputs == 1:
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return op.name
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return f"{op.name}.v{self.value_index}"
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@tvm_ffi.register_object("te.Operation")
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class Operation(Object):
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"""Represent an operation that generates a tensor"""
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def output(self, index):
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"""Get the index-th output of the operation
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Parameters
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----------
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index : int
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The index size.
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Returns
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-------
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out : Tensor
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The i-th output.
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"""
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return _ffi_api.OpGetOutput(self, index)
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@property
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def num_outputs(self):
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"""Number of outputs from this op."""
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return _ffi_api.OpNumOutputs(self)
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@property
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def input_tensors(self):
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"""List of input tensors to this op."""
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return _ffi_api.OpInputTensors(self)
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@tvm_ffi.register_object("te.PlaceholderOp")
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class PlaceholderOp(Operation):
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"""Placeholder operation."""
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@tvm_ffi.register_object("te.BaseComputeOp")
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class BaseComputeOp(Operation):
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"""Compute operation."""
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@tvm_ffi.register_object("te.ComputeOp")
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class ComputeOp(BaseComputeOp):
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"""Scalar operation."""
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@tvm_ffi.register_object("te.ScanOp")
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class ScanOp(Operation):
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"""Scan operation."""
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@tvm_ffi.register_object("te.ExternOp")
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class ExternOp(Operation):
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"""External operation."""
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