Python class
Dim
Dim
class max.graph.Dim(value)
Bases: object
A tensor dimension.
Dims describe the shape of tensors in a Graph. In most cases, you don’t
need to construct a Dim directly. Instead, you pass dimension values
directly to TensorType or BufferType constructors:
from max.graph import Dim, TensorType, DeviceRef
tensor_type = TensorType(DType.int64, ("batch", 10), device=DeviceRef.CPU())A tensor dimension can be one of three types:
- Static: A known size. See
StaticDim. - Symbolic: An unknown size identified by name. See
SymbolicDim. - Algebraic: An expression derived from symbolic dimensions. See
AlgebraicDim.
Static dimensions let the graph compiler resolve shapes at compile time. This enables more aggressive optimizations than symbolic or algebraic dimensions allow. That said, when tensors share a named symbolic dimension, the compiler can leverage the implied shape equality to optimize some operations.
Converts valid input values to Dim.
-
Parameters:
-
value (DimLike)
from_mlir()
static from_mlir(attr)
Constructs a dimension from an mlir.Attribute.
-
Parameters:
-
attr (TypedAttr) – The MLIR Attribute to parse into a dimension.
-
Returns:
-
The dimension represented by the MLIR Attr value.
-
Return type:
parameters
property parameters: Iterable[SymbolicDim]
Lists the symbolic dimension names on which this dim depends.
to_mlir()
to_mlir()
Creates an mlir.Attribute representing this dimension.
This is used internally when constructing tensor MLIR types.
-
Returns:
-
An
mlir.Attributein the context representing the dimension. -
Return type:
-
TypedAttr
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