tvm.tir

tvm.tir#

Namespace for Tensor-level IR

class tvm.tir.Add(a, b, span=None)[源代码]#

Add node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.Allocate(buffer_var, dtype, extents, condition, body, annotations=None, span=None)[源代码]#

Allocate node.

Parameters#

buffer_varVar: The buffer variable.
dtypestr: The data type of the buffer.
extentslist of Expr: The extents of the allocate
conditionPrimExpr: The condition.
bodyStmt: The body statement.
annotations: Optional[Mapping[str, Object]]: Additional annotation hints
spanOptional[Span]: The location of the stmt in the source code.

参数:

buffer_var (Var)
dtype (str)
extents (List[PrimExpr])
condition (PrimExpr)
body (Stmt)
annotations (Mapping[str, Object])
span (Span | None)

class tvm.tir.AllocateConst(buffer_var, dtype, extents, data_or_idx, body, annotations=None, span=None)[源代码]#

Allocate constant node.

Parameters#

buffer_varVar: The buffer variable.
dtypestr: The data type of the buffer.
extentslist of Expr: The extents of the allocate
data_or_idxUnion[NDArray, int]: If an NDArray, this is the const data associated with the constant. If an integer, this is the index into the "constants" attribute of the IRModule that contains the AllocateConst.
bodyStmt: The body statement.
annotationsOptional[Mapping[str, Object]]: Additional annotations about the allocation.
spanOptional[Span]: The location of the stmt in the source code.

参数:

buffer_var (Var)
dtype (str)
extents (List[PrimExpr])
data_or_idx (NDArray | int)
body (Stmt)
annotations (Mapping[str, Object])
span (Span | None)

class tvm.tir.And(a, b, span=None)[源代码]#

And node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.Any(span=None)[源代码]#

Any node.

spanOptional[Span]: The location of this expression in the source code.

参数:: span (Span | None)

class tvm.tir.AssertStmt(condition, message, body, span=None)[源代码]#

AssertStmt node.

Parameters#

conditionPrimExpr: The assert condition.
messagePrimExpr: The error message.
bodytvm.tir.Stmt: The body statement.
spanOptional[Span]: The location of the stmt in the source code.

参数:

condition (PrimExpr)
message (PrimExpr)
body (Stmt)
span (Span | None)

class tvm.tir.AttrStmt(node, attr_key, value, body, span=None)[源代码]#

AttrStmt node.

Parameters#

nodeObject: The node to annotate the attribute
attr_keystr: Attribute type key.
valuePrimExpr: The value of the attribute
bodyStmt: The body statement.
spanOptional[Span]: The location of the stmt in the source code.

参数:

node (Object)
attr_key (str)
value (PrimExpr)
body (Stmt)
span (Span | None)

class tvm.tir.BijectiveLayout[源代码]#

Bijective mapping for two layouts (src-layout and dst-layout). It provides shape and index conversion between each other.

Do not construct directly, use bijective_layout instead. See the documentation of bijective_layout for more details.

Parameters#

src_layoutstr or Layout: source layout.
dst_layoutstr or Layout: destination layout.

Parameters#

iter_varsList[IterVar]: The block Variable.
readsList[BufferRegion]: The read buffer regions of the block.
writes: List[BufferRegion]: The write buffer regions of the block.
name_hint: str: the name_hint of the block.
body: Stmt: The body of the block.
init: Optional[Stmt]: The init block of the reduction block
alloc_buffers: Optional[list[Buffer]]: The buffer allocations
match_buffers: Optional[List[MatchBufferRegion]]: The subregion buffer match
annotations: Optional[Mapping[str, Object]]: Additional annotation hints.
spanOptional[Span]: The location of this block in the source code.

参数:

iter_vars (List[IterVar])
reads (List[BufferRegion])
writes (List[BufferRegion])
name_hint (str)
body (Stmt)
init (Stmt | None)
alloc_buffers (List[Buffer])
match_buffers (List[MatchBufferRegion])
annotations (Mapping[str, Object])
span (Span | None)

class tvm.tir.BlockDependenceInfo(mod)[源代码]#

An object that helps build and query block level dependences using the 2 core objects BlockScope and StmtSRef

The data structures exposed are: 1) sref2scope: Mapping from the srefs to its corresponding BlockScope 2) stmt2ref: Mapping from blocks to corresponding StmtSRefs

Note that this object does not store SRefs to loops as the purpose is only to expose block level dependences. This provides the advantage that the scope block (parent block) for a given block sref can be directly accessed as sref->parent

参数:: mod (IRModule)

get_block_scope(block_sref)[源代码]#

Get the BlockScope correpsonding to the block sref

Parameters#

block_srefStmtSRef: The block sref to be retrieved

Returns#

scopeStmtSRef: The corresponding BlockScope

参数:: block_sref (StmtSRef)
返回类型:: BlockScope

get_sref(block)[源代码]#

Return the corresponding sref that points to the block

Parameters#

stmtBlock: The block for which the sref is to be retrived

Returns#

srefStmtSRef: The corresponding sref

参数:: block (Block)
返回类型:: StmtSRef | None

class tvm.tir.BlockRealize(iter_values, predicate, block, span=None)[源代码]#

BlockRealize node.

Parameters#

iter_valuesList[PrimExpr]: The binding values of the block var.
predicateUnion[PrimExpr, bool]: The predicate of the block.
blockBlock: The block to realize
spanOptional[Span]: The location of this block_realize in the source code.

参数:

iter_values (List[PrimExpr])
predicate (PrimExpr)
block (Block)
span (Span | None)

class tvm.tir.Broadcast(value, lanes, span=None)[源代码]#

Broadcast node.

Parameters#

valuePrimExpr: The value of the expression.
lanesPrimExpr: The lanes of the expression.
spanOptional[Span]: The location of this expression in the source code.

参数:

value (PrimExpr)
lanes (PrimExpr)
span (Span | None)

class tvm.tir.Buffer[源代码]#

Symbolic data buffer in TVM.

Buffer provide a way to represent data layout specialization of data structure in TVM.

Do not construct directly, use decl_buffer() instead. See the documentation of decl_buffer() for more details.

Parameters#

bufferBuffer: The buffer to be loaded.
indicesList[PrimExpr]: The buffer indices to load values from.
spanOptional[Span]: The location of this expression in the source code.
predicateOptional[PrimExpr]: A vector mask of boolean values indicating which lanes of a vector are to be loaded. The number lanes of the mask must be equal to the number of lanes being loaded.

参数:

buffer (Buffer)
indices (List[PrimExpr])
predicate (PrimExpr | None)
span (Span | None)

class tvm.tir.BufferRealize(buffer, bounds, condition, body, span=None)[源代码]#

Buffer realize node.

Parameters#

bufferBuffer: The buffer.
boundsList[Range]: The value we to be stored.
conditionPrimExpr: The realize condition.
bodyStmt: The body of the statement.
spanOptional[Span]: The location of the stmt in the source code.

参数:

buffer (Buffer)
bounds (List[Range])
condition (PrimExpr)
body (Stmt)
span (Span | None)

class tvm.tir.BufferRegion(buffer, region)[源代码]#

BufferRegion node.

Parameters#

bufferBuffer: The buffer of the buffer region
regionList[Range]: The region array of the buffer region

参数:

buffer (Buffer)
region (List[Range])

class tvm.tir.BufferStore(buffer, value, indices, predicate=None, span=None)[源代码]#

Buffer store node.

Parameters#

bufferBuffer: The buffer.
valuePrimExpr: The value we to be stored.
indicesList[PrimExpr]: The indices location to be stored.
predicateOptional[PrimExpr]: A vector mask of boolean values indicating which lanes of a vector are to be stored. The number lanes of the mask must be equal to the number of lanes in value.
spanOptional[Span]: The location of the stmt in the source code.

参数:

buffer (Buffer)
value (PrimExpr)
indices (List[PrimExpr])
predicate (PrimExpr | None)
span (Span | None)

class tvm.tir.Call(dtype, op, args, span=None)[源代码]#

Call node.

Parameters#

dtypestr: The return data type
opUnion[Op, str]: The function to be called, or the name to the global tvm.Op
argslist of Expr: The input arguments to the call
spanOptional[Span]: The location of this expression in the source code.

参数:

dtype (str)
op (Op)
args (List[PrimExpr])
span (Span | None)

class tvm.tir.CallEffectKind[源代码]#: Possible kinds of Call effects.

class tvm.tir.Cast(dtype, value, span=None)[源代码]#

Cast expression.

Parameters#

dtypestr: The data type
valuePrimExpr: The value of the function.
spanOptional[Span]: The location of this expression in the source code.

参数:

dtype (str)
value (PrimExpr)
span (Span | None)

class tvm.tir.CommReducer(lhs, rhs, result, identity_element, span=None)[源代码]#

Commutative reduce operator

Parameters#

lhsList[Var]: The left arguments of the reducer.
rhsList[Var]: The right arguments of the reducer.
resultList[PrimExpr]: The reduction results.
identity_elementList[PrimExpr]: The identity elements.
spanOptional[Span]: The location of this expression in the source code.

参数:

lhs (List[Var])
rhs (List[Var])
result (List[PrimExpr])
identity_element (List[PrimExpr])
span (Span | None)

class tvm.tir.DataProducer[源代码]#

class tvm.tir.DeclBuffer(buffer, body, span=None)[源代码]#

DeclBuffer node.

Parameters#

buffer: Buffer: The buffer being declared.
body: Stmt: The body statement to be executed.
span: Optional[Span]: The location of this DeclBuffer in the source code.

参数:

buffer (Buffer)
body (Stmt)
span (Span | None)

class tvm.tir.Div(a, b, span=None)[源代码]#

Div node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.EQ(a, b, span=None)[源代码]#

EQ node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.Evaluate(value, span=None)[源代码]#

Evaluate node.

Parameters#

valuePrimExpr: The expression to be evaluated.
spanOptional[Span]: The location of the stmt in the source code.

参数:

value (PrimExpr)
span (Span | None)

class tvm.tir.FloatImm(dtype, value, span=None)[源代码]#

Float constant.

Parameters#

dtypestr: The data type
valuefloat: The constant value.
spanOptional[Span]: The location of this expression in the source code.

参数:

dtype (str)
value (float)
span (Span | None)

class tvm.tir.FloorDiv(a, b, span=None)[源代码]#

FloorDiv node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.FloorMod(a, b, span=None)[源代码]#

FloorMod node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.For(loop_var, min, extent, kind, body, thread_binding=None, annotations=None, span=None)[源代码]#

For node.

Parameters#

loop_varVar: The loop variable.
minPrimExpr: The beginning value.
extentPrimExpr: The length of the loop.
kindForKind: The type of the for.
bodyStmt: The body statement.
thread_binding: Optional[tir.IterVar]: The thread this loop binds to. Only valid if kind is ThreadBinding
annotations: Optional[Mapping[str, Object]]: Additional annotation hints.
spanOptional[Span]: The location of the stmt in the source code.

参数:

loop_var (Var)
min (PrimExpr)
extent (PrimExpr)
kind (ForKind)
body (Stmt)
thread_binding (IterVar | None)
annotations (Mapping[str, Object])
span (Span | None)

class tvm.tir.ForKind(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[源代码]#: The kind of the for loop.

note#

ForKind can change the control flow semantics of the loop and need to be considered in all TIR passes.

class tvm.tir.GE(a, b, span=None)[源代码]#

GE node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.GT(a, b, span=None)[源代码]#

GT node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.IfThenElse(condition, then_case, else_case, span=None)[源代码]#

IfThenElse node.

Parameters#

conditionPrimExpr: The expression
then_caseStmt: The statement to execute if condition is true.
else_caseOptional[Stmt]: The statement to execute if condition is false.
spanOptional[Span]: The location of the stmt in the source code.

参数:

condition (PrimExpr)
then_case (Stmt)
else_case (Stmt | None)
span (Span | None)

class tvm.tir.IndexMap(initial_indices, final_indices, inverse_index_map)[源代码]#

A mapping from multi-dimensional indices to another set of multi-dimensional indices

Parameters#

initial_indicesList[Var]: Variables representing the indices prior to remapping.
final_indicesList[PrimExpr]: Expressions defining the indices after remapping.
inverse_index_mapUnion[Callable, Optional[IndexMap]]: The optional pre-defined inverse index map. When this is defined, IndexMap::Inverse will return the pre-defined inverse index map. Otherwise, the inverse index map will be computed on the fly. It is the user's responsibility to ensure the correctness of the pre-defined inverse index map.

static from_func(mapping_function, ndim=None, inverse_index_map=None, *, index_dtype='int64')[源代码]#

Create an index map from a function

Parameters#

mapping_function : Callable

The function to map from source indices to target indices. The function should accept tir.Var parameters and return a either a tir.PrimExpr, or a list of tir.PrimExpr. Returning a tir.PrimExpr is equivalent to returning a list of length 1 containing that tir.PrimExpr.

ndim: Optional[int]

The dimensionality of the buffer to which this transformation should be applied. If mapping_function uses variadic argument *args, ndim must be specified. If mapping_function does not use variadic arguments, ndim is optional.

inverse_index_mapUnion[Callable, Optional[IndexMap]]: The optional pre-defined inverse index map. When this is defined, IndexMap::Inverse will return the pre-defined inverse index map. Otherwise, the inverse index map will be computed on the fly. It is the user's responsibility to ensure the correctness of the pre-defined inverse index map.

Returns#

index_map: IndexMap

Returns an IndexMap representing the mapping_function.

参数:

mapping_function (Callable)
ndim (int | None)
inverse_index_map (Callable | IndexMap | None)
index_dtype (str)

static from_func_with_separators(mapping_function, ndim=None, inverse_index_map=None, *, index_dtype='int64')[源代码]#

Create an index map from a function

Parameters#

mapping_function : Callable

The function to map from source indices to target indices. The function should accept tir.Var parameters and return either a tir.PrimExpr or a list. Each element of the returned list should be either a tir.PrimExpr or the object IndexMap.AXIS_SEPARATOR. Returning a tir.PrimExpr is equivalent to returning a list of length 1 containing that tir.PrimExpr.

ndim: Optional[int]

The dimensionality of the buffer to which this transformation should be applied. If mapping_function uses variadic argument *args, ndim must be specified. If mapping_function does not use variadic arguments, ndim is optional.

inverse_index_mapUnion[Callable, Optional[IndexMap]]: The optional pre-defined inverse index map. When this is defined, IndexMap::Inverse will return the pre-defined inverse index map. Otherwise, the inverse index map will be computed on the fly. It is the user's responsibility to ensure the correctness of the pre-defined inverse index map.
index_dtypestr: The default index dtype to use for input iters in the mapping function.

Returns#

ret: Tuple[IndexMap, List[int]]

Returns a tuple whose first element is an IndexMap representing the mapping_function, and whose second index is a list of indices at which IndexMap.AXIS_SEPARATOR occurred.

参数:

mapping_function (Callable)
ndim (int | None)
inverse_index_map (Callable | IndexMap | None)
index_dtype (str)

inverse(shape)[源代码]#

Return the inverse of the map

Throws an error if the function is not bijective.

Parameters#

shape: List[Union[Range,PrimExpr]]

The region over which the inverse should be determined. Used for validating that the mapping is bijective over this range.

Returns#

inverse : IndexMap

The inverse

参数:: shape (List[Range | PrimExpr])
返回类型:: IndexMap

is_equivalent_to(other_map)[源代码]#

Return if the index maps are equivalent.

Parameters#

other_map: IndexMap

The IndexMap to which the comparison should be made.

Returns#

is_equivalent: bool

True if the two mappings represent the same transformation, otherwise False

参数:: other_map (IndexMap)
返回类型:: bool

map_indices(indices)[源代码]#

Apply the index map to a set of indices

Parameters#

indicesList[PrimExpr]: The indices to be mapped

Returns#

resultList[PrimExpr]: The mapped indices

参数:: indices (List[PrimExpr])
返回类型:: List[PrimExpr]

map_ndarray(arr_src)[源代码]#

Apply thie index map to transform the layout of the input NDArray

Parameters#

arr_srcruntime.NDArray: The NDArray to be transformed

Returns#

arr_dstruntime.NDArray: The transformed NDArray

参数:: arr_src (NDArray)
返回类型:: NDArray

map_shape(shape)[源代码]#

Apply the index map to a buffer shape

Parameters#

shapeList[PrimExpr]: The buffer shape to be mapped

Returns#

resultList[PrimExpr]: The mapped shape

参数:: shape (List[PrimExpr])
返回类型:: List[PrimExpr]

non_surjective_inverse(shape)[源代码]#

Return the inverse of the map

Can be applied to transformations that introduce padding.

Parameters#

shape: List[Union[Range,PrimExpr]]

The region over which the inverse should be determined. Used for determining the predicate.

Returns#

result : Tuple[IndexMap, PrimExpr]

The inverse, and a predicate for which the inverse maps to a valid index in the input range.

Examples#

index_map = IndexMap.from_func(lambda i: [i//4, i%4])
inverse_map, predicate = index_map.non_surjective_inverse([14])
assert inverse_map.is_equivalent_to(IndexMap.from_func(lambda j,k: [4*j + k])
print(predicate) # Prints "(axis0==3) && (axis2 >= 2)"

参数:: shape (List[Range | PrimExpr])
返回类型:: Tuple[IndexMap, PrimExpr]

参数:

initial_indices (List[Var])
final_indices (List[PrimExpr])

class tvm.tir.IntImm(dtype, value, span=None)[源代码]#

Int constant.

Parameters#

dtypestr: The data type
valueint: The constant value.
spanOptional[Span]: The location of this expression in the source code.

参数:

dtype (str)
value (int)
span (Span | None)

class tvm.tir.IterVar(dom, var, iter_type, thread_tag='', span=None)[源代码]#

Represent iteration variable.

IterVar represents axis iterations in the computation.

Parameters#

domRange: The domain of the iteration.
varUnion[Var, str]: The internal variable that is used for iteration.
iter_typeint: The iteration type.
thread_tagstr: The thread type tag.
spanOptional[Span]: The location of this expression in the source code.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.LT(a, b, span=None)[源代码]#

LT node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.Layout[源代码]#

Layout is composed of upper cases, lower cases and numbers, where upper case indicates a primal axis and the corresponding lower case with factor size indicates the subordinate axis. For example, NCHW16c can describe a 5-D tensor of [batch_size, channel, height, width, channel_block]. Here subordinate axis channel_block=16 is the factor size of the primal axis C (channel).

Parameters#

varVar: The variable in the binding.
valuePrimExpr: The value in to be bound.
bodyPrimExpr: The body expression.
spanOptional[Span]: The location of this expression in the source code.

参数:

var (Var)
value (PrimExpr)
body (PrimExpr)
span (Span | None)

class tvm.tir.LetStmt(var, value, body, span=None)[源代码]#

LetStmt node.

Parameters#

varVar: The variable in the binding.
valuePrimExpr: The value in to be bound.
bodyStmt: The body statement.
spanOptional[Span]: The location of the stmt in the source code.

参数:

var (Var)
value (PrimExpr)
body (Stmt)
span (Span | None)

class tvm.tir.MatchBufferRegion(buffer, source)[源代码]#

MatchBufferRegion node.

Parameters#

bufferBuffer: The target buffer
sourceBufferRegion: The region of source buffer

参数:

buffer (Buffer)
source (BufferRegion)

class tvm.tir.Max(a, b, span=None)[源代码]#

Max node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.Min(a, b, span=None)[源代码]#

Min node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.Mod(a, b, span=None)[源代码]#

Mod node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.Mul(a, b, span=None)[源代码]#

Mul node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.NE(a, b, span=None)[源代码]#

NE node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.Not(a, span=None)[源代码]#

Not node.

Parameters#

aPrimExpr: The input value
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
span (Span | None)

class tvm.tir.Or(a, b, span=None)[源代码]#

Or node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.Prefetch(buffer, bounds, span=None)[源代码]#

Prefetch node.

Parameters#

bufferBuffer: The buffer to be prefetched.
boundsList[Range]: The bounds to be prefetched.
spanOptional[Span]: The location of the stmt in the source code.

参数:

buffer (Buffer)
bounds (List[Range])
span (Span | None)

class tvm.tir.PrimFunc(params, body, ret_type=None, buffer_map=None, attrs=None, span=None)[源代码]#

A function declaration expression.

Parameters#

params: List[Union[tvm.tir.Var, tvm.tir.Buffer]]: List of input parameters to the function.
body: tvm.tir.Stmt: The body of the function.
ret_type: tvm.ir.Type: The return type annotation of the function.
buffer_mapMap[tvm.tir.Var, tvm.tir.Buffer]: The buffer binding map.
attrs: Optional[tvm.Attrs]: Attributes of the function, can be None
spanOptional[Span]: The location of this itervar in the source code.

specialize(param_map)[源代码]#

Specialize parameters of PrimFunc

Parameters#

param_mapMapping[Var, Union[PrimExpr, Buffer]]: The mapping from function params to the instance

Examples#

We can define a Meta TIR function with symbolic shape:

@T.prim_func
def mem_copy(a: T.handle, b: T.handle, m: T.int32, n: T.int32) -> None:
    A = T.match_buffer(a, (m, n), "float32")
    B = T.match_buffer(b, (m, n), "float32")

    for i, j in T.grid(m, n):
        with T.block():
            vi, vj = T.axis.remap("SS", [i, j])
            B[vi, vj] = A[vi, vj]

Then we can make it specialized with given shapes or buffers.

a, _, m, n = mem_copy.params
func = mem_copy.specialize({a: tir.decl_buffer((16, 16))})
# or
func = mem_copy.specialize({n: 16, m: 16})

The specialized function:

@T.prim_func
def mem_copy_16_16(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (16, 16), "float32")
    B = T.match_buffer(b, (16, 16), "float32")

    for i, j in T.grid(16, 16):
        with T.block():
            vi, vj = T.axis.remap("SS", [i, j])
            B[vi, vj] = A[vi, vj]

Returns#

funcPrimFunc: The new function with parameter specialized

参数:: param_map (Mapping[Var, PrimExpr | Buffer])

with_body(new_body, span=None)[源代码]#

Create a new PrimFunc with the same set signatures but a new body.

Parameters#

new_bodyStmt: The new body.
spanOptional[Span]: The location of this itervar in the source code.

Returns#

new_funcPrimFunc: The created new function.

参数:: span (Span | None)

class tvm.tir.ProducerLoad(producer, indices, span=None)[源代码]#

Producer load node.

Parameters#

producerDataProducer: The buffer to be loaded.
indicesList[PrimExpr]: The buffer indices.
spanOptional[Span]: The location of this expression in the source code.

参数:

producer (DataProducer)
indices (List[PrimExpr])
span (Span | None)

class tvm.tir.ProducerRealize(producer, bounds, condition, body, storage_scope='', span=None)[源代码]#

ProducerRealize node.

Parameters#

producerDataProducer: The data producer.
boundsList[Range]: The bound of realize
conditionPrimExpr: The realize condition.
bodyStmt: The realize body
storage_scopestr: The storage scope associated with this realization
spanOptional[Span]: The location of the stmt in the source code.

参数:

producer (DataProducer)
bounds (List[Range])
condition (PrimExpr)
body (Stmt)
storage_scope (str)
span (Span | None)

class tvm.tir.ProducerStore(producer, value, indices, span=None)[源代码]#

ProducerStore node.

Parameters#

producerDataProducer: The data producer.
valuePrimExpr: The value to be stored.
indiceslist of Expr: The index arguments of the store.
spanOptional[Span]: The location of the stmt in the source code.

参数:

producer (DataProducer)
value (PrimExpr)
indices (List[PrimExpr])
span (Span | None)

class tvm.tir.Ramp(base, stride, lanes, span=None)[源代码]#

Ramp node.

Parameters#

basePrimExpr: The base expression.
stridePrimExpr: The stride of the ramp.
lanesPrimExpr: The lanes of the expression.
spanOptional[Span]: The location of this expression in the source code.

参数:

base (PrimExpr)
stride (PrimExpr)
lanes (PrimExpr)
span (Span | None)

class tvm.tir.Reduce(combiner, src, rdom, condition, value_index, init=None, span=None)[源代码]#

Reduce node.

Parameters#

combinerCommReducer: The combiner.
srclist of Expr: The source expression.
rdomlist of IterVar: The iteration domain
conditionPrimExpr: The reduce condition.
value_indexint: The value index.
initlist of Expr: The initial value for output. This can be an int, float or ProducerLoad
spanOptional[Span]: The location of this expression in the source code.

参数:

combiner (CommReducer)
src (List[PrimExpr])
rdom (List[IterVar])
condition (PrimExpr)
value_index (int)
init (List[PrimExpr])
span (Span | None)

class tvm.tir.Select(condition, true_value, false_value, span=None)[源代码]#

Select node.

Note#

Select may compute both true_value and false_value. Use tvm.tir.if_then_else instead if you want to get a conditional expression that only evaluates the correct branch.

Parameters#

conditionPrimExpr: The condition expression.
true_valuePrimExpr: The value to take when condition is true.
false_valuePrimExpr: The value to take when condition is false.
spanOptional[Span]: The location of this expression in the source code.

参数:

condition (PrimExpr)
true_value (PrimExpr)
false_value (PrimExpr)
span (Span | None)

class tvm.tir.SeqStmt(seq, span=None)[源代码]#

Sequence of statements.

Parameters#

seqList[Stmt]: The statements
spanOptional[Span]: The location of the stmt in the source code.

参数:

seq (List[Stmt])
span (Span | None)

class tvm.tir.Shuffle(vectors, indices, span=None)[源代码]#

Shuffle node.

Parameters#

vectorsList[PrimExpr]: The vectors
indicesList[PrimExpr]: The indices
spanOptional[Span]: The location of this expression in the source code.

参数:

vectors (List[PrimExpr])
indices (List[PrimExpr])
span (Span | None)

class tvm.tir.SizeVar(name, dtype, span=None)[源代码]#

Symbolic variable to represent a tensor index size: which is greater or equal to zero.

Parameters#

namestr: The name
dtypeUnion[str, ir.Type]: The data type
spanOptional[Span]: The location of this expression in the source code.

参数:

name (str)
dtype (str)
span (Span | None)

class tvm.tir.Stmt[源代码]#: Base class of all the statements.

class tvm.tir.StringImm(value, span=None)[源代码]#

String constant.

Parameters#

valuestr: The value of the function.
spanOptional[Span]: The location of this expression in the source code.

参数:

value (str)
span (Span | None)

class tvm.tir.Sub(a, b, span=None)[源代码]#

Sub node.

Parameters#

aPrimExpr: The left hand operand.
bPrimExpr: The right hand operand.
spanOptional[Span]: The location of this expression in the source code.

参数:

a (PrimExpr)
b (PrimExpr)
span (Span | None)

class tvm.tir.TensorIntrin(desc, impl)[源代码]#

A tensor intrinsic.

Parameters#

descPrimFunc: The function to describe the computation.
implPrimFunc: The function of the implementation for the execution.

static get(name, allow_missing=False)[源代码]#

Look up a tensor intrinsic by its name.

Parameters#

namestr: The name of the TensorIntrin to look up.
allow_missingbool: Whether to allow missing tensor intrin. If False, raise an error if the tensor intrin

doesn't exist.

Returns#

resultOptional[TensorIntrin]: The TensorIntrin with the specified name, or None if not found.

参数:

name (str)
allow_missing (bool)

返回类型:

TensorIntrin | None

static register(name, desc, impl, override=False)[源代码]#

Register a tensor intrinsic with its name.

Parameters#

namestr: The name of the TensorIntrin to register.
descPrimFunc: The function to describe the computation.
implPrimFunc: The function of the implementation for the execution.
override: bool: Whether override existing intrinsic.

参数:

name (str)
desc (PrimFunc)
impl (PrimFunc)
override (bool)

class tvm.tir.Var(name, dtype, span=None)[源代码]#

Symbolic variable.

Parameters#

namestr: The name
dtypeUnion[str, ir.Type]: The data type
spanOptional[Span]: The location of this expression in the source code.

参数:

name (str)
dtype (str)
span (Span | None)

class tvm.tir.While(condition, body, span=None)[源代码]#

While node.

Parameters#

conditionPrimExpr: The termination condition.
bodyStmt: The body statement.
spanOptional[Span]: The location of the stmt in the source code.

参数:

condition (PrimExpr)
body (Stmt)
span (Span | None)

tvm.tir.TVMBackendAllocWorkspace(device_type, device_id, nbytes, dtype_code_hint, dtype_bits_hint)[源代码]#

Backend function to allocate temporal workspace

Parameters#

device_typeint: The device type which the space will be allocated.
device_idint: The device id which the space will be allocated.
nbytesint: The size of the space requested.
dtype_code_hintint: The type code of the array elements. Only used in certain backends such as OpenGL.
dtype_bits_hintint: The type bits of the array elements. Only used in certain backends such as OpenGL.

Returns#

callPrimExpr: The call expression.

tvm.tir.TVMBackendFreeWorkspace(device_type, device_id, ptr)[源代码]#

Backend function to free temporal workspace.

Parameters#

device_typeint: The device type which the space will be allocated.
device_idint: The device id which the space will be allocated.
ptrVar: The result allocated space pointer.

Returns#

callPrimExpr: The call expression.

tvm.tir.abs(x, span=None)[源代码]#

Get absolute value of the input element-wise.

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

tvm.tir.acos(x)[源代码]#

Take acos of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.acosh(x)[源代码]#

Take acos of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.add(lhs, rhs, span=None)[源代码]#

Generic add operator.

Parameters#

lhsobject: The left operand.
rhsobject: The right operand.
spanOptional[Span]: The location of this operator in the source.

Returns#

optvm.Expr: The result Expr of add operaton.

tvm.tir.address_of(buffer_load, span=None)[源代码]#

Returns the address of an element in the buffer

Parameters#

buffer_load: BufferLoad: The buffer load.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

tvm.tir.all(*args, span=None)[源代码]#

Create a new expression of the intersection of all conditions in the: arguments

Parameters#

argslist: List of symbolic boolean expressions
spanOptional[Span]: The location of this operator in the source code.

Returns#

expr: Expr: Expression

tvm.tir.any(*args, span=None)[源代码]#

Create a new experssion of the union of all conditions in the arguments

Parameters#

argslist: List of symbolic boolean expressions
spanOptional[Span]: The location of this operator in the source code.

Returns#

expr: Expr: Expression

tvm.tir.asin(x)[源代码]#

Take asin of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.asinh(x)[源代码]#

Take asinh of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.assume(cond=None)[源代码]#

Provide a true statement that can be used for simplifications

Parameters#

condExpr: The constraint condition.

Returns#

callPrimExpr: The call expression.

tvm.tir.atan(x)[源代码]#

Take atan of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.atan2(x1, x2)[源代码]#

Take arctan2(x1, x2).

Parameters#

x1PrimExpr: Input argument.
x2PrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.atanh(x)[源代码]#

Take atanh of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.bijective_layout(src_layout, dst_layout)[源代码]#

Create a bijective layout mapping.

Parameters#

src_layoutstr or Layout: source layout.
dst_layoutstr or Layout: destination layout.

Returns#

bijective_layoutBijectiveLayout: The created bijective layout

参数:

src_layout (str | Layout)
dst_layout (str | Layout)

返回类型:

BijectiveLayout

tvm.tir.bitwise_and(x, y, span=None)[源代码]#

Take bitwise and of two values

Parameters#

xPrimExpr: Left operand
yPrimExpr: Right operand
spanOptional[Span]: The location of this operator in the source code.

Returns#

resPrimExpr: The result.

tvm.tir.bitwise_not(x, span=None)[源代码]#

Take bitwise not of input value

Parameters#

xPrimExpr: Input operand
spanOptional[Span]: The location of this operator in the source code.

Returns#

resPrimExpr: The result.

tvm.tir.bitwise_or(x, y, span=None)[源代码]#

Take bitwise or of two values

Parameters#

xPrimExpr: Left operand
yPrimExpr: Right operand
spanOptional[Span]: The location of this operator in the source code.

Returns#

resPrimExpr: The result.

tvm.tir.bitwise_xor(x, y, span=None)[源代码]#

Take bitwise xor of two values

Parameters#

xPrimExpr: Left operand
yPrimExpr: Right operand
spanOptional[Span]: The location of this operator in the source code.

Returns#

resPrimExpr: The result.

tvm.tir.call_cpacked(*args, span=None)[源代码]#

Build expression by call an external packed function.

Same as call_packed, except that the first argument is the function name (as in call_extern), and the last argument is the resource handle.

Parameters#

argslist of Expr or Buffer.: Positional arguments.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

Parameters#

argslist of Expr or Buffer.: Positional arguments.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

Parameters#

dtypestr: The data type of the result.
func_name: str: The extern function name.
argslist: Positional arguments.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

tvm.tir.call_intrin(dtype, func_name, *args, span=None)[源代码]#

Build expression by calling an intrinsic function.

Intrinsics can be overloaded with multiple data types via the intrinsic translation rule.

Parameters#

dtypestr: The data type of the result.
func_name: str: The intrinsic function name.
argslist: Positional arguments.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

tvm.tir.call_llvm_intrin(dtype, name, *args, span=None)[源代码]#

Build expression by calling a llvm intrinsic function

Parameters#

dtypestr: The data type of the result.
namestr: The name of the llvm intrinsic function.
argslist: Positional arguments.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

tvm.tir.call_llvm_pure_intrin(dtype, name, *args, span=None)[源代码]#

Build expression by calling a pure llvm intrinsic function

Parameters#

dtypestr: The data type of the result.
namestr: The name of the llvm intrinsic function.
argslist: Positional arguments.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

tvm.tir.call_packed(*args, span=None)[源代码]#

Build expression by call an external packed function.

The argument to packed function can be Expr or Buffer. The argument is the corresponding POD type when Expr is presented.

When the argument is Buffer, the corresponding PackedFunc will receive an TVMArrayHandle whose content is valid during the callback period. If the PackedFunc is a python callback, then the corresponding argument is NDArray.

Parameters#

argslist of Expr or Buffer.: Positional arguments.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

Parameters#

argslist of Expr or Buffer.: Positional arguments.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

Parameters#

dtypestr: The data type of the result.
func_name: str: The extern function name.
argslist: Positional arguments.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

tvm.tir.call_tir(global_var, *args)[源代码]#

Performs a call into another PrimFunc in the same IRModule

Returns#

callPrimExpr: The call expression.

参数:: global_var (GlobalVar)

tvm.tir.ceil(x, span=None)[源代码]#

Take ceil of float input x.

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

tvm.tir.ceildiv(lhs, rhs, span=None)[源代码]#

Generic ceildiv operator.

Parameters#

lhsobject: The left operand.
rhsobject: The right operand.
spanOptional[Span]: The location of this operator in the source.

Returns#

optvm.Expr: The result Expr of ceildiv operaton.

tvm.tir.clz(x)[源代码]#

Count leading zero bits of an integer x.

Parameters#

xPrimExpr: Input 32 or 64 bit integer. The result is undefined if the input is 0.

Returns#

yPrimExpr: The result.

tvm.tir.comm_reducer(fcombine, fidentity, name='reduce')[源代码]#

Create a commutative reducer for reduction.

Parameters#

fcombinefunction(Expr -> Expr -> Expr): A binary function which takes two Expr as input to return a Expr.
fidentityfunction(str -> Expr): A function which takes a type string as input to return a const Expr.

Returns#

reducerfunction

A function which creates a reduce expression over axis. There are two ways to use it:

accept (expr, axis, where) to produce an Reduce Expr on specified axis;
simply use it with multiple Exprs.

Example#

n = te.var("n")
m = te.var("m")
mysum = te.comm_reducer(lambda x, y: x+y,
    lambda t: tvm.tir.const(0, dtype=t), name="mysum")
A = te.placeholder((n, m), name="A")
k = te.reduce_axis((0, m), name="k")
B = te.compute((n,), lambda i: mysum(A[i, k], axis=k), name="B")

tvm.tir.copysign(x1, x2)[源代码]#

Change the sign of x1 to that of x2, element-wise.

Parameters#

x1PrimExpr: Input argument.
x2PrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.cos(x)[源代码]#

Take cos of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.cosh(x)[源代码]#

Take cosh of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.create_barriers(barrier_count)[源代码]#

TVM intrinsic to create N barriers

Parameters#

barrier_countint: The number of barriers to create.

Returns#

callPrimExpr: The call expression.

tvm.tir.decl_buffer(shape, dtype=None, name='buffer', data=None, strides=None, elem_offset=None, scope='', data_alignment=-1, offset_factor=0, buffer_type='', axis_separators=None, span=None)[源代码]#

Declare a new symbolic buffer.

Normally buffer is created automatically during lower and build. This is only needed if user want to specify their own buffer layout.

See the note below for detailed discussion on usage of buffer.

Parameters#

shapetuple of Expr: The shape of the buffer.
dtypestr, optional: The data type of the buffer.
namestr, optional: The name of the buffer.
datatir.Var, optional: The data pointer in the buffer.
strides: array of Expr: The stride of the buffer.
elem_offset: Expr, optional: The beginning offset of the array to data. In terms of number of elements of dtype.
scope: str, optional: The storage scope of the buffer, if not global. If scope equals empty string, it means it is global memory.
data_alignment: int, optional: The alignment of data pointer in bytes. If -1 is passed, the alignment will be set to TVM's internal default.
offset_factor: int, optional: The factor of elem_offset field, when set, elem_offset is required to be multiple of offset_factor. If 0 is pssed, the alignment will be set to 1. if non-zero is passed, we will created a Var for elem_offset if elem_offset is not None.
buffer_type: str, optional, {"", "auto_broadcast"}: auto_broadcast buffer allows one to implement broadcast computation without considering whether dimension size equals to one. TVM maps buffer[i][j][k] -> buffer[i][0][k] if dimension j's shape equals 1.
axis_separatorslist of int, optional: If passed, a list of separators between groups of axes, each of which is flattened to an output axis. For flat memory spaces, should either be None, or an empty list.
span: Optional[Span]: The location of the decl_buffer creation in the source.

Returns#

buffertvm.tir.Buffer: The created buffer

Example#

Here's an example of how broadcast buffer can be used to define a symbolic broadcast operation,

m0, m1, m2 = te.var("m0"), te.var("m1"), te.var("m2")
n0, n1, n2 = te.var("n0"), te.var("n1"), te.var("n2")
o0, o1, o2 = te.var("o0"), te.var("o1"), te.var("o2")
A = te.placeholder((m0, m1, m2), name='A')
B = te.placeholder((n0, n1, n2), name='B')
C = te.compute((o0, o1, o2), lambda i, j, k: A[i, j, k] + B[i, j, k], name='C')
Ab = tvm.tir.decl_buffer(A.shape, A.dtype, name="Ab", buffer_type="auto_broadcast")
Bb = tvm.tir.decl_buffer(B.shape, B.dtype, name="Bb", buffer_type="auto_broadcast")
s = te.create_schedule(C.op)
fadd = tvm.build(s, [A, B, C], target='llvm', name='bcast_add', binds={A:Ab, B:Bb})
dev = tvm.cpu(0)
a = tvm.nd.array(np.random.uniform(size=(2, 4, 3)).astype(A.dtype), dev)
b = tvm.nd.array(np.random.uniform(size=(2, 1, 3)).astype(B.dtype), dev)
c = tvm.nd.array(np.zeros((2, 4, 3), dtype=C.dtype), dev)
fadd(a, b, c)
tvm.testing.assert_allclose(c.numpy(), a.numpy() + b.numpy())

Note#

Buffer data structure reflects the DLTensor structure in dlpack. While DLTensor data structure is very general, it is usually helpful to create function that only handles specific case of data structure and make compiled function benefit from it.

If user pass strides and elem_offset is passed as None when constructing the function, then the function will be specialized for the DLTensor that is compact and aligned. If user pass a fully generic symbolic array to the strides, then the resulting function becomes fully generic.

tvm.tir.div(a, b, span=None)[源代码]#

Compute a / b as in C/C++ semantics.

Parameters#

aPrimExpr: The left hand operand, known to be non-negative.
bPrimExpr: The right hand operand, known to be non-negative.
spanOptional[Span]: The location of this operator in the source.

Returns#

resPrimExpr: The result expression.

Note#

When operands are integers, returns truncdiv(a, b, span).

tvm.tir.dp4a(vec1, vec2, acc=0)[源代码]#

Dot product of two int8x4 vectors and add an optional accumulator

Parameters#

vec1int8x4: The input vector.
vec2int8x4: The input vector.
accint32: The accumulator.

Returns#

callPrimExpr: The call expression.

tvm.tir.end_profile_intrinsic(id)[源代码]#

End profile intrinsic. Parameters ---------- id : int

The intrinsic id.

Returns#

callPrimExpr: The call expression.

tvm.tir.erf(x)[源代码]#

Take gauss error function of the input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.exp(x)[源代码]#

Take exponential of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.exp10(x)[源代码]#

Calculate 10**x

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.exp2(x)[源代码]#

Calculate 2**x

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.floor(x, span=None)[源代码]#

Take floor of float input x.

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

参数:: x (PrimExprWithOp)

tvm.tir.floordiv(a, b, span=None)[源代码]#

Compute the floordiv of two expressions.

Parameters#

aPrimExpr: The left hand operand
bPrimExpr: The right hand operand
spanOptional[Span]: The location of this operator in the source.

Returns#

resPrimExpr: The result expression.

tvm.tir.floormod(a, b, span=None)[源代码]#

Compute the floormod of two expressions.

Parameters#

aPrimExpr: The left hand operand
bPrimExpr: The right hand operand
spanOptional[Span]: The location of this operator in the source.

Returns#

resPrimExpr: The result expression.

tvm.tir.fmod(x, y)[源代码]#

Return the remainder of x divided by y with the same sign as x.

Parameters#

xPrimExpr: Input argument.
yPrimExpr: Input argument.

Returns#

zPrimExpr: The result.

tvm.tir.get_active_lane_mask(dtype, base, limit)[源代码]#

Calculate a predicate mask given an upper bound (limit) and a current value (base).

It will be lowered to the llvm.get.active.lane.mask intrinsic. (https://llvm.org/docs/LangRef.html#llvm-get-active-lane-mask-intrinsics)

Parameters#

dtypestr: The data type of the result.
basePrimExpr: An expression reprsenting the base.
limitPrimExpr: An expression representing the limit.

tvm.tir.get_vscale_expr(dtype, min_size=128)[源代码]#

Create a datatype dependent scalable expression.

Parameters#

dtypeUnion[str, tvm.DataType]: Element data type.
min_sizeint: The minimum size of the scalable vector in bits.

参数:

dtype (str | DataType)
min_size (int)

返回类型:

PrimExpr

tvm.tir.hypot(x1, x2)[源代码]#

Equivalent to sqrt(x1**2 + x2**2), element-wise.

Parameters#

x1PrimExpr: Input argument.
x2PrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.if_then_else(cond, t, f, span=None)[源代码]#

Conditional selection expression.

Parameters#

condPrimExpr: The condition
tPrimExpr: The result expression if cond is true.
fPrimExpr: The result expression if cond is false.
spanOptional[Span]: The location of this operator in the source.

Returns#

resultNode: The result of conditional expression.

Note#

Unlike Select, if_then_else will not execute the branch that does not satisfy the condition. You can use it to guard against out of bound access. Unlike Select, if_then_else cannot be vectorized if some lanes in the vector have different conditions.

tvm.tir.indexdiv(a, b, span=None)[源代码]#

Compute floor(a / b) where a and b are non-negative.

Parameters#

aPrimExpr: The left hand operand, known to be non-negative.
bPrimExpr: The right hand operand, known to be non-negative.
spanOptional[Span]: The location of this operator in the source.

Returns#

resPrimExpr: The result expression.

Note#

Use this function to split non-negative indices. This function may take advantage of operands' non-negativeness.

tvm.tir.indexmod(a, b, span=None)[源代码]#

Compute the remainder of indexdiv. a and b are non-negative.

Parameters#

aPrimExpr: The left hand operand, known to be non-negative.
bPrimExpr: The right hand operand, known to be non-negative.
spanOptional[Span]: The location of this operator in the source.

Returns#

resPrimExpr: The result expression.

Note#

Use this function to split non-negative indices. This function may take advantage of operands' non-negativeness.

tvm.tir.infinity(dtype, span=None)[源代码]#

infinity value of dtype

Parameters#

dtypestr: The data type.
spanOptional[Span]: The location of this operator in the source code.

Returns#

valuetvm.Expr: The infinity value of dtype.

参数:

dtype (str)
span (Span | None)

返回类型:

Any

tvm.tir.isfinite(x, span=None)[源代码]#

Check if input value is finite.

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

tvm.tir.isinf(x, span=None)[源代码]#

Check if input value is infinite.

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

tvm.tir.isnan(x, span=None)[源代码]#

Check if input value is Nan.

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

tvm.tir.isnullptr(x, span=None)[源代码]#

Check if input value is nullptr.

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

tvm.tir.layout(layout_str, dtype='int32')[源代码]#

Create a layout node from a string.

Parameters#

layout_strstr: A layout representation is composed of upper cases, lower cases and numbers, where upper case indicates a primal axis and the corresponding lower case with factor size indicates the subordinate axis. For example, NCHW16c can describe a 5-D tensor of [batch_size, channel, height, width, channel_block]. Here subordinate axis channel_block=16 is the factor size of the primal axis C (channel).
dtypestr: The dtype of generated axes vars in the returned layout. It is required to be integer type.

Returns#

layoutLayout: The created layout

参数:

layout_str (str)
dtype (str)

返回类型:

Layout

tvm.tir.ldexp(x1, x2)[源代码]#

Returns x1 * (2 ** x2).

Parameters#

x1PrimExpr: Input argument.
x2PrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.likely(cond, span=None)[源代码]#

Mark condition as likely.

Parameters#

condPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The marked expression.

tvm.tir.log(x)[源代码]#

Take log of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.log10(x)[源代码]#

Take log10 of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.log1p(x)[源代码]#

Take log(x + 1) with respect to input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.log2(x)[源代码]#

Take log2 of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.lookup_param(param_name, span=None)[源代码]#

Returns the param by name

Parameters#

param_namestr: The name of param.
spanOptional[Span]: The location of this operator in the source code.

Returns#

callPrimExpr: The call expression.

tvm.tir.make_filled_simdgroup_matrix(d, index, value, col=8, row=8)[源代码]#

Create a filled SIMDGroup matrix

Parameters#

dvar: The simdgroup var
indexPrimExpr: The index of the matrix.
valuePrimExpr: The value to fill.
colint: The number of columns.
rowint: The number of rows.

Returns#

callPrimExpr: The call expression.

参数:

d (Var)
index (PrimExpr)
value (PrimExpr)
col (int)
row (int)

tvm.tir.max(expr, axis, where=None, init=None, *args)#

Create a max expression over axis.

Parameters#

exprPrimExpr: The source expression.
axisIterVar: The reduction IterVar axis
whereoptional, Expr: Filtering predicate of the reduction.

Returns#

valuePrimExpr: The result value.

Example#

m = te.var("m")
n = te.var("n")
A = te.placeholder((m, n), name="A")
k = te.reduce_axis((0, n), name="k")

# there are two way to use this max reducer:
# mode 1, accept (expr, axis, where) to produce an Reduce Expr
# tvm.max represents tvm.te.max or tvm.tir.max.
B = te.compute((m,), lambda i: tvm.max(A[i, k], axis=k), name="B")

# mode 2, simply use it with multiple Exprs:
max_res = tvm.max(m, n)

tvm.tir.max_value(dtype, span=None)[源代码]#

maximum value of dtype

Parameters#

dtypestr: The data type.
spanOptional[Span]: The location of this operator in the source code.

Returns#

valuetvm.Expr: The maximum value of dtype.

参数:

dtype (str)
span (Span | None)

返回类型:

Any

tvm.tir.min(expr, axis, where=None, init=None, *args)#

Create a min expression over axis.

Parameters#

exprPrimExpr: The source expression.
axisIterVar: The reduction IterVar axis
whereoptional, Expr: Filtering predicate of the reduction.

Returns#

valuePrimExpr: The result value.

Example#

m = te.var("m")
n = te.var("n")
A = te.placeholder((m, n), name="A")
k = te.reduce_axis((0, n), name="k")

# there are two way to use this min reducer:
# mode 1, accept (expr, axis, where) to produce an Reduce Expr
# tvm.min represents tvm.te.min or tvm.tir.min.
B = te.compute((m,), lambda i: tvm.min(A[i, k], axis=k), name="B")

# mode 2, simply use it with multiple Exprs:
min_res = tvm.min(m, n)

tvm.tir.min_value(dtype, span=None)[源代码]#

minimum value of dtype

Parameters#

dtypestr: The data type.
spanOptional[Span]: The location of this operator in the source code.

Returns#

valuetvm.Expr: The minimum value of dtype.

tvm.tir.mma_fill(dtype, local_size, local_ptr, offset)[源代码]#

TVM intrinsic for zero-initalizing an MMA accumulation registor

Parameters#

dtypestr: The data type of the result.
local_sizeIntImm: The number of elements.
local_ptrVar: The destination pointer variable.
offsetExpr: The destination offset.

Returns#

callPrimExpr: The call expression.

tvm.tir.mma_store(dtype, m, n, dst_ptr, src_ptr, src_offset, dst_stride)[源代码]#

TVM intrinsic for storing the result of PTX MMA into a destination pointer

Parameters#

dtypestr: The data type of the result.
mIntImm: The shape of mma fragment.
nIntImm: The shape of mma fragment.
dst_ptrVar: The destination pointer variable.
src_ptrVar: The source pointer variable.
src_offsetExpr: The source offset.
dst_strideVar: The destination stride.

Returns#

callPrimExpr: The call expression.

tvm.tir.multiply(lhs, rhs, span=None)[源代码]#

Generic multiply operator.

Parameters#

lhsobject: The left operand.
rhsobject: The right operand.
spanOptional[Span]: The location of this operator in the source.

Returns#

optvm.Expr: The result Expr of multiply operaton.

tvm.tir.nearbyint(x, span=None)[源代码]#

Round elements of the array to the nearest integer. This intrinsic uses llvm.nearbyint instead of llvm.round which is faster but will results different from te.round. Notably nearbyint rounds according to the rounding mode, whereas te.round (llvm.round) ignores that. For differences between the two see: https://en.cppreference.com/w/cpp/numeric/math/round https://en.cppreference.com/w/cpp/numeric/math/nearbyint

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

tvm.tir.nextafter(x1, x2)[源代码]#

Return the next floating-point value after x1 towards x2.

Parameters#

x1PrimExpr: Input argument.
x2PrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.popcount(x)[源代码]#

Count the number of set bits in input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.pow(x, y, span=None)[源代码]#

x power y

Parameters#

xPrimExpr: Input argument.
yPrimExpr: The exponent
spanOptional[Span]: The location of this operator in the source code.

Returns#

zPrimExpr: The result.

tvm.tir.power(x, y, span=None)[源代码]#

x power y

Parameters#

xPrimExpr: Input argument.
yPrimExpr: The exponent
spanOptional[Span]: The location of this operator in the source code.

Returns#

zPrimExpr: The result.

tvm.tir.ptx_arrive_barrier(barrier_id)[源代码]#

TVM intrinsic for ptx barrier arrival using mbarrier.arrive https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#parallel-synchronization-and-communication-instructions-mbarrier-arrive

Parameters#

barrier_idint: The ID of the barrier shared memory pointer.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_arrive_barrier_expect_tx(barrier_id, byte_count)[源代码]#

TVM intrinsic for ptx barrier arrival with expect tx using mbarrier.arrive.expect_tx https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#parallel-synchronization-and-communication-instructions-mbarrier-arrive https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#parallel-synchronization-and-communication-instructions-mbarrier-expect-tx-operation

Parameters#

barrier_idint: The ID of the barrier shared memory pointer.
byte_countint: Increases the tx count of the mbarrier object to track completion of addtional async transactions.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_commit_group()[源代码]#

TVM intrinsic for ptx async copy commit https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-commit-group

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_cp_async(dtype, shared_ptr, shared_offset, global_ptr, global_offset, bytes)[源代码]#

TVM intrinsic for ptx async copy from global to shared memory using cp.async https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async

Parameters#

dtypestr: The data type of the result.
shared_ptrVar: The shared memory pointer variable.
shared_offsetExpr: The offset of shared memory pointer.
global_ptrVar: The global memory pointer variable.
global_offsetExpr: The offset of global memory pointer.
bytesint: The data size to copy.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_cp_async_barrier(barrier_id)[源代码]#

TVM intrinsic for ptx async copy barrier using cp.async.mbarrier.arrive https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#parallel-synchronization-and-communication-instructions-cp-async-mbarrier-arrive

Parameters#

barrier_idint: The ID of the barrier shared memory pointer.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_cp_async_bulk(dtype, shared_ptr, shared_offset, global_ptr, global_offset, bytes, barrier_id)[源代码]#

TVM intrinsic for ptx async copy from global to shared memory using cp.async.bulk https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk

Parameters#

dtypestr: The data type of the result.
shared_ptrVar: The shared memory pointer variable.
shared_offsetExpr: The offset of shared memory pointer.
global_ptrVar: The global memory pointer variable.
global_offsetExpr: The offset of global memory pointer.
bytesint: The data size to copy.
barrier_idint: The ID of the barrier shared memory pointer.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_init_barrier_thread_count(barrier_id, thread_count)[源代码]#

TVM intrinsic for ptx barrier initialization of thread count using mbarrier.init https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#parallel-synchronization-and-communication-instructions-mbarrier-init

Parameters#

barrier_idint: The ID of the barrier shared memory pointer.
thread_countint: Number of threads expected to arrive at the barrier.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_ldmatrix(dtype, trans, num, type, local_ptr, local_offset, smem_ptr, smem_offset)[源代码]#

TVM intrinsic for ptx load matrix from shared memory https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-ldmatrix

Parameters#

dtypestr: The data type of the result.
transbool: The matrix is loaded in column-major format.
numIntImm: The number of matrices.
typeLiteral[".b16"]: The data type of the matrices.
local_ptrVar: The local pointer variable.
local_offsetExpr: The offset of local pointer.
smem_ptrVar: The shared memory pointer variable.
smem_offsetExpr: The offset of shared memort pointer.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_mma(dtype, shape, A_layout, B_layout, A_dtype, B_dtype, C_dtype, multiplicand_a, a_index, multiplicand_b, b_index, accumulator, c_index, saturate, operator=None)[源代码]#

TVM intrinsic for ptx tensor core mma instructions https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-for-mma

Parameters#

dtypestr: The data type of the result.
shapestr: The shape of mma fragment.
A_layoutLiteral["row", "col"]: The layout of multiplicand fragment A.
B_layoutLiteral["row", "col"]: The layout of multiplicand fragment B.
A_dtypestr: The data type of multiplicand fragment A.
B_dtypestr: The data type of multiplicand fragment B.
C_dtypestr: The data type of accumulator fragment C.
multiplicand_aVar: The multiplicand fragment A variable.
a_indexExpr: The index of multiplicand fragment A.
multiplicand_bVar: The multiplicand fragment B variable.
b_indexExpr: The index of multiplicand fragment A.
accumulatorVar: The accumulator fragment C variable.
c_indexExpr: The index of accumulator fragment C.
saturatebool: The optional saturation at the output.
operatorOptional[Literal["xor", "and"]]: The 1-bit operator.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_mma_sp(dtype, shape, A_layout, B_layout, A_dtype, B_dtype, C_dtype, multiplicand_a, a_index, multiplicand_b, b_index, accumulator, c_index, metadata, meta_index, sparse_selector, saturate)[源代码]#

TVM intrinsic for sparse tensor core ptx instructions https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-for-sparse-mma

Parameters#

dtypestr: The data type of the result.
shapestr: The shape of mma fragment.
A_layoutLiteral["row", "col"]: The layout of multiplicand fragment A.
B_layoutLiteral["row", "col"]: The layout of multiplicand fragment B.
A_dtypestr: The data type of multiplicand fragment A.
B_dtypestr: The data type of multiplicand fragment B.
C_dtypestr: The data type of multiplicand fragment C.
multiplicand_aVar: The multiplicand fragment A variable.
a_indexExpr: The index of multiplicand fragment A.
multiplicand_bVar: The multiplicand fragment B variable.
b_indexExpr: The index of multiplicand fragment B.
accumulatorVar: The accumulator fragment C variable.
c_indexExpr: The index of accumulator fragment C.
metadataExpr: The metadata of operand.
meta_indexExpr: The metadata index of operand.
sparse_selectorExpr: The sparse selector indicating the thread that stores the metadata.
saturatebool: The optional saturation at the output.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_wait_barrier(barrier_id)[源代码]#

TVM intrinsic for ptx barrier wait using mbarrier.try_wait https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#parallel-synchronization-and-communication-instructions-mbarrier-test-wait-mbarrier-try-wait

Parameters#

barrier_idint: The ID of the barrier shared memory pointer.

Returns#

callPrimExpr: The call expression.

tvm.tir.ptx_wait_group(num)[源代码]#

TVM intrinsic for ptx async copy wait https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-wait-group

Parameters#

numint: The number of the most recent uncommitted pending cp.async groups to wait.

Returns#

callPrimExpr: The call expression.

tvm.tir.q_multiply_shift(x, y, q, s)[源代码]#

Execute a multiplication between two Q-numbers x and y followed by a right shift s. The mathematical expression is:

out = round(x*y*2^-s)

More about Q-numbers here: https://en.wikipedia.org/wiki/Q_(number_format) The rounding rule is to the nearest value, rounding half up (i.e., round(x.1) = x and round (x.5) = x+1)

Parameters#

xPrimExpr: First Q-number
yPrimExpr: Second Q-number
qPrimExpr: Number of fractional bits in x and y. Needs to be > 0
sPrimExpr: Integer shift

Returns#

yPrimExpr: The result.

tvm.tir.q_multiply_shift_per_axis(x, y, ls, rs, q, is_lshift_required, is_rshift_required)[源代码]#

Execute a multiplication between two Q-numbers x and y

Parameters#

xPrimExpr: First Q-number.
yPrimExpr: Second Q-number.
lsPrimExpr: Integer left shift.
rsPrimExpr: Integer right shift.
qIntImm: Number of fractional bits in x and y. Needs to be > 0.
is_lshift_requiredIntImm: Whether we need to do left shift or not.
is_rshift_requiredIntImm: Whether we need to do right shift or not.

Returns#

zPrimExpr: The result.

参数:

x (PrimExpr)
y (PrimExpr)
ls (PrimExpr)
rs (PrimExpr)
q (IntImm)
is_lshift_required (IntImm)
is_rshift_required (IntImm)

tvm.tir.reinterpret(dtype, value, span=None)[源代码]#

infinity value of dtype

Parameters#

dtypestr: The data type.
valuePrimExpr: The input value.
spanOptional[Span]: The location of this operator in the source code.

Returns#

valuetvm.Expr: The reinterpret cast value of dtype.

参数:: span (Span | None)
返回类型:: Any

tvm.tir.ret(val, span=None)[源代码]#

Create a tir return expression

Parameters#

valExpr: The returned tir expression, whose data type is int, float or void pointer.
spanOptional[Span]: The location of this operator in the source code.

Returns#

retPrimExpr: The return expression

tvm.tir.round(x, span=None)[源代码]#

Round elements of the array to the nearest integer.

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

tvm.tir.rsqrt(x)[源代码]#

Take reciprocal of square root of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.shift_left(x, y, span=None)[源代码]#

Return the result of x left shifted by y bits.

Parameters#

xPrimExpr: Input argument.
yPrimExpr: Input argument.

Returns#

zPrimExpr: The result.

tvm.tir.shift_right(x, y, span=None)[源代码]#

Return the result of x right shifted by y bits.

Parameters#

xPrimExpr: Input argument.
yPrimExpr: Input argument.

Returns#

zPrimExpr: The result.

tvm.tir.sigmoid(x)[源代码]#

Quick function to get sigmoid

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.simdgroup_load(d, index, ptr, stride, col=8, row=8, transpose_matrix=False)[源代码]#

Load data from device memory or threadgroup memory to simdgroup

Parameters#

dvar: The simdgroup var
indexPrimExpr: The index of the matrix.
ptrPrimExpr: The pointer.
stridePrimExpr: The stride.
colint: The number of columns.
rowint: The number of rows.
transpose_matrixbool: Whether to transpose the matrix.

Returns#

callPrimExpr: The call expression.

参数:

d (Var)
index (PrimExpr)
ptr (PrimExpr)
stride (PrimExpr)
col (int)
row (int)
transpose_matrix (bool)

tvm.tir.simdgroup_multiply_accumulate(d, index_d, a, index_a, b, index_b, c, index_c)[源代码]#

Multiply and accumulate two matrices in simdgroup i.e. d = a * b + c

Parameters#

dVar: The destination matrix.
index_dPrimExpr: The index of the destination matrix.
aVar: The first matrix.
index_aPrimExpr: The index of the first matrix.
bVar: The second matrix.
index_bPrimExpr: The index of the second matrix.
cVar: The third matrix.
index_cPrimExpr: The index of the third matrix.

Returns#

callPrimExpr: The call expression.

参数:

d (Var)
index_d (PrimExpr)
a (Var)
index_a (PrimExpr)
b (Var)
index_b (PrimExpr)
c (Var)
index_c (PrimExpr)

tvm.tir.simdgroup_store(d, index, ptr, stride, col=8, row=8, transpose_matrix=False)[源代码]#

Store data from simdgroup to device memory or threadgroup memory

Parameters#

dPrimExpr: The SIMDGroup.
indexPrimExpr: The index of the matrix.
ptrPrimExpr: The pointer.
stridePrimExpr: The stride.
colint: The number of columns.
rowint: The number of rows.
transpose_matrixbool: Whether to transpose the matrix.

Returns#

callPrimExpr: The call expression.

参数:

d (PrimExpr)
index (PrimExpr)
ptr (PrimExpr)
stride (PrimExpr)
col (int)
row (int)
transpose_matrix (bool)

tvm.tir.sin(x)[源代码]#

Take sin of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.sinh(x)[源代码]#

Take sinh of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.sqrt(x)[源代码]#

Take square root of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.start_profile_intrinsic(id)[源代码]#

Start profile intrinsic. Parameters ---------- id : int

The intrinsic id.

Returns#

callPrimExpr: The call expression.

tvm.tir.stmt_list(stmt)[源代码]#

Make list of stmt from blocks.

Parameters#

stmtStmt: The input statement.

Returns#

stmt_listList[Stmt]: The unpacked list of statements

参数:: stmt (Stmt)
返回类型:: List[Stmt]

tvm.tir.stmt_seq(*args)[源代码]#

Make sequence of statements

Parameters#

*argsUnion[PrimExpr, Stmt]: List of statements to be combined as sequence.

Returns#

stmtStmt: The combined statement.

参数:: args (PrimExpr | Stmt)
返回类型:: SeqStmt

tvm.tir.subtract(lhs, rhs, span=None)[源代码]#

Generic subtract operator.

Parameters#

lhsobject: The left operand.
rhsobject: The right operand.
spanOptional[Span]: The location of this operator in the source.

Returns#

optvm.Expr: The result Expr of subtract operaton.

tvm.tir.sum(expr, axis, where=None, init=None, *args)#

Create a sum expression over axis.

Parameters#

exprPrimExpr: The source expression.
axisIterVar: The reduction IterVar axis
whereoptional, Expr: Filtering predicate of the reduction.

Returns#

valuePrimExpr: The result value.

Example#

m = te.var("m")
n = te.var("n")
A = te.placeholder((m, n), name="A")
k = te.reduce_axis((0, n), name="k")

# there are two way to use this sum reducer:
# mode 1, accept (expr, axis, where) to produce an Reduce Expr
# tvm.sum represents tvm.te.sum or tvm.tir.sum.
B = te.compute((m,), lambda i: tvm.sum(A[i, k], axis=k), name="B")

# mode 2, simply use it with multiple Exprs:
sum_res = tvm.sum(m, n)

tvm.tir.tan(x)[源代码]#

Take tan of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.tanh(x)[源代码]#

Take hyperbolic tanh of input x.

Parameters#

xPrimExpr: Input argument.

Returns#

yPrimExpr: The result.

tvm.tir.trace(args, trace_action='tvm.default_trace_action')[源代码]#

Trace tensor data at the runtime.

The trace function allows to trace specific tensor at the runtime. The tracing value should come as last argument. The trace action should be specified, by default tvm.default_trace_action is used.

Parameters#

argslist of Expr or Buffers.: Positional arguments.
trace_actionstr.: The name of the trace action.

Returns#

callPrimExpr: The call expression.

Parameters#

xPrimExpr: Input argument.
spanOptional[Span]: The location of this operator in the source code.

Returns#

yPrimExpr: The result.

tvm.tir.truncdiv(a, b, span=None)[源代码]#

Compute the truncdiv of two expressions.

Parameters#

aPrimExpr: The left hand operand
bPrimExpr: The right hand operand
spanOptional[Span]: The location of this operator in the source.

Returns#

resPrimExpr: The result expression.

Note#

This is the default integer division behavior in C.

tvm.tir.truncmod(a, b, span=None)[源代码]#

Compute the truncmod of two expressions.

Parameters#

aPrimExpr: The left hand operand
bPrimExpr: The right hand operand
spanOptional[Span]: The location of this operator in the source.

Returns#

resPrimExpr: The result expression.

Note#

This is the default integer division behavior in C.

tvm.tir.tvm_access_ptr(ptype, data, offset, extent, rw_mask)[源代码]#

Get head access address with memory access pattern info

Parameters#

ptypeExpr: The data type of pointer.
dataDType*: The data of pointer.
offsetint: The offset of pointer.
extentint: The extent of pointer.
rw_maskint: The read write mask.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_bmma_sync(fragment_d, index_d, fragment_a, index_a, fragment_b, index_b, fragment_c, index_c)[源代码]#

TVM intrinsic for tensor core bmma_sync operators

Parameters#

fragment_dVar: The bwmma fragment_d.
index_dExpr: The fragment_d index.
fragment_aVar: The bwmma fragment_a.
index_aExpr: The fragment_a index.
fragment_bVar: The bwmma fragment_b.
index_bExpr: The fragment_b index.
fragment_cVar: The bwmma fragment_c.
index_cExpr: The fragment_c index.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_check_return(expected, return_unexpected, nested_call)[源代码]#

Return new on stack dtype[num] Parameters ---------- expected : int

The expected return code.

return_unexpectedint: The unexpected return code.
nested_callPrimExpr: The call expression to check return.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_fill_fragment(fragment, m, n, k, index, value)[源代码]#

TVM intrinsic for tensor core fill_fragment operators

Parameters#

fragmentVar: The wmma fragment
mUIntImm: The shape of wmma fragment.
nUIntImm: The shape of wmma fragment.
kUIntImm: The shape of wmma fragment.
indexExpr: The fragment index.
valueExpr: The value to be filled in fragment.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_load_matrix_sync(fragment, m, n, k, index, buffer_ptr, stride, layout)[源代码]#

TVM intrinsic for tensor core load operators

Parameters#

fragmentVar: The wmma fragment.
mUIntImm: The shape of wmma fragment.
nUIntImm: The shape of wmma fragment.
kUIntImm: The shape of wmma fragment.
indexExpr: The fragment index.
buffer_ptrExpr: The fragment buffer pointer.
strideExpr: The fragment stride.
layoutLiteral["row_major", "column_major"]: The fragment layout.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_mma_sync(fragment_d, index_d, fragment_a, index_a, fragment_b, index_b, fragment_c, index_c)[源代码]#

TVM intrinsic for tensor core mma_sync operators

Parameters#

fragment_dVar: The wmma fragment_d.
index_dExpr: The fragment_d index.
fragment_aVar: The wmma fragment_a.
index_aExpr: The fragment_a index.
fragment_bVar: The wmma fragment_b.
index_bExpr: The fragment_b index.
fragment_cVar: The wmma fragment_c.
index_cExpr: The fragment_c index.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_stack_alloca(dtype_str, num)[源代码]#

Return new on stack dtype[num]

Parameters#

dtype_strstr: The data type of array.
numint: The size of array.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_stack_make_array(data, shape, strides, ndim, arr_dtype, elem_offset)[源代码]#

Allocate a NDArray(DLTensor) on stack, return the handle

Parameters#

dataExpr: The data of array.
shapeExpr: The shape of array.
stridesExpr: The strides of array.
ndimExpr: The dimensions of array.
arr_dtypeExpr: The data type of array.
elem_offseExpr: The element offset of array.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_stack_make_shape(*args)[源代码]#

Allocate a shape tuple on stack, return the handle

Parameters#

argsint: The tuple shape.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_store_matrix_sync(fragment, m, n, k, index, buffer_ptr, stride, layout)[源代码]#

TVM intrinsic for tensor core store operators

Parameters#

fragmentVar: The wmma fragment.
mUIntImm: The shape of wmma fragment.
nUIntImm: The shape of wmma fragment.
kUIntImm: The shape of wmma fragment.
indexExpr: The fragment index.
buffer_ptrExpr: The fragment buffer pointer.
strideExpr: The fragment stride.
layoutLiteral["row_major", "column_major"]: The fragment layout.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_struct_get(arr, index, field, dtype)[源代码]#

Get struct field value in array

Parameters#

dtypestr: The date type of the result.
arrStructType*: The array of struct.
indexint: The index of struct.
fieldint: The field of struct.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_struct_set(arr, index, field, value)[源代码]#

Set value in struct field in array

Parameters#

arrStructType*: The array of struct.
indexint: The index of struct.
fieldint: The field of struct.
valueExpr: The value to be set in field.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_thread_allreduce(*freduce_args)[源代码]#

Perform allreduce inside threadblock.

Parameters#

freduce_argsExpr: The args.

Returns#

callPrimExpr: The call expression.

tvm.tir.tvm_throw_last_error()[源代码]#

Throw TVMGetLastError()

Returns#

retPrimExpr: The return expression

tvm.tir.tvm_tuple(*value)[源代码]#

Create a tuple structure in value field of AttrStmt

Parameters#

valueExpr: The value in tuple.

Returns#

callPrimExpr: The call expression.

tvm.tir.type_annotation(dtype)[源代码]#

Create a type annotation expression

Parameters#

dtypeExpr: The data type.

Returns#

callPrimExpr: The call expression.

tvm.tir.undef()[源代码]#

Returns an initialized but arbitrary value

Returns#

callPrimExpr: The call expression.

tvm.tir.vectorcombine(dtype, vec1, vec2)[源代码]#

Concat two vectors

Parameters#

vec1list: The input vector.
vec2list: The input vector.

Returns#

callPrimExpr: The call expression.

tvm.tir.vectorhigh(dtype, vec)[源代码]#

Get the high level half of the vector

Parameters#

dtypestr: The data type of the result.
veclist: The input vector.

Returns#

callPrimExpr: The call expression.

tvm.tir.vectorlow(dtype, vec)[源代码]#

Get the low level half of the vector

Parameters#

dtypestr: The data type of the result.
veclist: The input vector.

Returns#

callPrimExpr: The call expression.

tvm.tir.vscale()[源代码]#: Get the target's vscale value. It will be lowered to llvm.vscale intrinsic (https://llvm.org/docs/LangRef.html#llvm-vscale-intrinsic) Returns ------- call : PrimExpr

Call to the vscale intrinsic

tvm.tir

目录

tvm.tir#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

See Also#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Parameters#

See Also#

Parameters#

Examples#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

note#

Parameters#

Parameters#

Parameters#

Parameters#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Parameters#

Returns#

Examples#

Parameters#

Parameters#

See Also#

Parameters#

Parameters#

See Also#

Parameters#