tvm.tir¶

Namespace for Tensor-level IR

Classes

`Add`(a, b)	Add node.
`Allocate`(buffer_var, dtype, extents, …)	Allocate node.
`And`(a, b)	And node.
`Any`()	Any node.
`AssertStmt`(condition, message, body)	AssertStmt node.
`AttrStmt`(node, attr_key, value, body)	AttrStmt node.
`BijectiveLayout`	Bijective mapping for two layouts (src-layout and dst-layout).
`Broadcast`(value, lanes)	Broadcast node.
`Buffer`	Symbolic data buffer in TVM.
`BufferLoad`(buffer, indices)	Buffer load node.
`BufferRealize`(buffer, bounds, condition, body)	Buffer realize node.
`BufferStore`(buffer, value, indices)	Buffer store node.
`Call`(dtype, name, args, call_type, func, …)	Call node.
`Cast`(dtype, value)	Cast expression.
`Div`(a, b)	Div node.
`EQ`(a, b)	EQ node.
`Evaluate`(value)	Evaluate node.
`FloatImm`(dtype, value)	Float constant.
`FloorDiv`(a, b)	FloorDiv node.
`FloorMod`(a, b)	FloorMod node.
`For`(loop_var, min_val, extent, for_type, …)	For node.
`Free`(buffer_var)	Free node.
`GE`(a, b)	GE node.
`GT`(a, b)	GT node.
`IfThenElse`(condition, then_case, else_case)	IfThenElse node.
`IntImm`(dtype, value)	Int constant.
`IterVar`(dom, var, iter_type[, thread_tag])	Represent iteration variable.
`LE`(a, b)	LE node.
`LT`(a, b)	LT node.
`Layout`	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.
`Let`(var, value, body)	Let node.
`LetStmt`(var, value, body)	LetStmt node.
`Load`(dtype, buffer_var, index[, predicate])	Load node.
`Max`(a, b)	Max node.
`Min`(a, b)	Min node.
`Mod`(a, b)	Mod node.
`Mul`(a, b)	Mul node.
`NE`(a, b)	NE node.
`Not`(a)	Not node.
`Or`(a, b)	Or node.
`Prefetch`(buffer, bounds)	Prefetch node.
`PrimFunc`(params, body[, ret_type, …])	A function declaration expression.
`Provide`(func, value_index, value, args)	Provide node.
`Ramp`(base, stride, lanes)	Ramp node.
`Realize`(func, value_index, dtype, bounds, …)	Realize node.
`Reduce`(combiner, src, rdom, condition, …)	Reduce node.
`Select`(condition, true_value, false_value)	Select node.
`SeqStmt`(seq)	Sequence of statements.
`Shuffle`(vectors, indices)	Shuffle node.
`SizeVar`(name, dtype)	Symbolic variable to represent a tensor index size which is greater or equal to zero.
`Stmt`	Base class of all the statements.
`Store`(buffer_var, value, index[, predicate])	Store node.
`StringImm`(value)	String constant.
`Sub`(a, b)	Sub node.
`Var`(name, dtype)	Symbolic variable.

Functions

`abs`(x)	Get absolute value of the input element-wise.
`acos`(x)	Take acos of input x.
`acosh`(x)	Take acos of input x.
`all`(*args)	Create a new experssion of the intersection of all conditions in the
`any`(*args)	Create a new experssion of the union of all conditions in the arguments
`asin`(x)	Take asin of input x.
`asinh`(x)	Take asinh of input x.
`atan`(x)	Take atan of input x.
`atan2`(x1, x2)	Take arctan2(x1, x2).
`atanh`(x)	Take atanh of input x.
`bijective_layout`(src_layout, dst_layout)	Create a bijective layout mapping.
`call_extern`(dtype, func_name, *args)	Build expression by calling a extern function.
`call_intrin`(dtype, func_name, *args)	Build expression by calling an intrinsic function.
`call_llvm_intrin`(dtype, name, *args)	Build expression by calling an llvm intrinsic function
`call_packed`(*args)	Build expression by call an external packed function.
`call_pure_extern`(dtype, func_name, *args)	Build expression by calling a pure extern function.
`call_pure_intrin`(dtype, func_name, *args)	Build expression by calling a pure intrinsic function.
`ceil`(x)	Take ceil of float input x.
`comm_reducer`(fcombine, fidentity[, name])	Create a commutative reducer for reduction.
`copysign`(x1, x2)	Change the sign of x1 to that of x2, element-wise.
`cos`(x)	Take cos of input x.
`cosh`(x)	Take cosh of input x.
`decl_buffer`(shape[, dtype, name, data, …])	Declare a new symbolic buffer.
`div`(a, b)	Compute a / b as in C/C++ semantics.
`erf`(x)	Take gauss error function of the input x.
`exp`(x)	Take exponetial of input x.
`exp10`(x)	Calculate 10**x
`exp2`(x)	Calculate 2**x
`floor`(x)	Take floor of float input x.
`floordiv`(a, b)	Compute the floordiv of two expressions.
`floormod`(a, b)	Compute the floormod of two expressions.
`fmod`(x, y)	Return the remainder of x divided by y with the same sign as x.
`hypot`(x1, x2)	Equivalent to sqrt(x12 + x22), element-wise.
`if_then_else`(cond, t, f)	Conditional selection expression.
`indexdiv`(a, b)	Compute floor(a / b) where a and b are non-negative.
`indexmod`(a, b)	Compute the remainder of indexdiv.
`isfinite`(x)	Check if input value is finite.
`isinf`(x)	Check if input value is infinite.
`isnan`(x)	Check if input value is Nan.
`layout`(layout_str)	Create a layout node from a string.
`ldexp`(x1, x2)	Returns x1 * (2 ** x2).
`log`(x)	Take log of input x.
`log10`(x)	Take log10 of input x.
`log1p`(x)	Take log(x + 1) with respect to input x.
`log2`(x)	Take log2 of input x.
`max`(expr, axis[, where])	Create a max expression over axis.
`max_value`(dtype)	maximum value of dtype
`min`(expr, axis[, where])	Create a min expression over axis.
`min_value`(dtype)	minimum value of dtype
`nearbyint`(x)	Round elements of the array to the nearest integer.
`nextafter`(x1, x2)	Return the next floating-point value after x1 towards x2.
`popcount`(x)	Count the number of set bits in input x.
`power`(x, y)	x power y
`round`(x)	Round elements of the array to the nearest integer.
`rsqrt`(x)	Take reciprocal of square root of input x.
`sigmoid`(x)	Quick function to get sigmoid
`sin`(x)	Take sin of input x.
`sinh`(x)	Take sinh of input x.
`sqrt`(x)	Take square root of input x.
`stmt_list`(stmt)	Make list of stmt from blocks.
`stmt_seq`(*args)	Make sequence of statements
`sum`(expr, axis[, where])	Create a sum expression over axis.
`tan`(x)	Take tan of input x.
`tanh`(x)	Take hyperbolic tanh of input x.
`trace`(args[, trace_action])	Trace tensor data at the runtime.
`trunc`(x)	Get truncated value of the input.
`truncdiv`(a, b)	Compute the truncdiv of two expressions.
`truncmod`(a, b)	Compute the truncmod of two expressions.

class tvm.tir.Buffer¶

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.

See also

decl_buffer: Declare a buffer

Methods

`access_ptr`(access_mask[, ptr_type, …])	Get an access pointer to the head of buffer.
`vload`(begin[, dtype])	Generate an Expr that loads dtype from begin index.
`vstore`(begin, value)	Generate a Stmt that store value into begin index.

access_ptr(access_mask, ptr_type='handle', content_lanes=1, offset=0)¶

Get an access pointer to the head of buffer.

This is the recommended method to get buffer data ptress when interacting with external functions.

Parameters

access_mask (int) – The access pattern MASK. Indicate whether the access will read or write to the data content.
ptr_type (str, optional) – The data type of the result pointer. Do not specify unless we want to cast pointer to specific type.
content_lanes (int, optional) – The number of lanes for the data type. This value is greater than one for vector types.
offset (Expr, optional) – The offset of pointer. We can use it to offset by the number of elements from the address of ptr.

Examples

# Get access ptr for read
buffer.access_ptr("r")
# Get access ptr for read/write with bitmask
buffer.access_ptr(Buffer.READ | Buffer.WRITE)
# Get access ptr for read/write with str flag
buffer.access_ptr("rw")
# Get access ptr for read with offset
buffer.access_ptr("r", offset = 100)

vload(begin, dtype=None)¶

Generate an Expr that loads dtype from begin index.

Parameters

begin (Array of Expr) – The beginning index in unit of Buffer.dtype
dtype (str) – The data type to be loaded, can be vector type which have lanes that is multiple of Buffer.dtype

Returns

load – The corresponding load expression.

Return type

Expr

vstore(begin, value)¶

Generate a Stmt that store value into begin index.

Parameters

begin (Array of Expr) – The beginning index in unit of Buffer.dtype
value (Expr) – The value to be stored.

Returns

store – The corresponding store stmt.

Return type

Stmt

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='')¶

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

shape (tuple of Expr) – The shape of the buffer.
dtype (str, optional) – The data type of the buffer.
name (str, optional) – The name of the buffer.
data (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.

Returns

buffer – The created buffer

Return type

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})
ctx = tvm.cpu(0)
a = tvm.nd.array(np.random.uniform(size=(2, 4, 3)).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=(2, 1, 3)).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros((2, 4, 3), dtype=C.dtype), ctx)
fadd(a, b, c)
tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())

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.

class tvm.tir.Layout¶

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).

See also

layout: Declare a layout

Methods

`factor_of`(axis)	Get the factor size of the subordinate axis.
`index_of`(axis)	Get the index of an axis

index_of(axis)¶

Get the index of an axis

Parameters: axis (str) – The axis name, need to be [a-z,A-Z]
Returns: index – The index of the axis, -1 if not found.
Return type: int

factor_of(axis)¶

Get the factor size of the subordinate axis.

Parameters: axis (str) – The axis name, need to be [a-z,A-Z]
Returns: factor – the size of the subordinate-axis of axis (if axis is a primal-axis), or the size of axis itself (if axis is a subordinate-axis). Return -1 if axis is not in the layout.
Return type: int

class tvm.tir.BijectiveLayout¶

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_layout (str or Layout) – source layout.
dst_layout (str or Layout) – destination layout.

Methods

`backward_index`(index)	Given the indices of the dst-layout, infer the src index.
`backward_shape`(shape)	Given the shape of the dst-layout, infer the src shape.
`forward_index`(index)	Given the indices of the src-layout, infer the dst index.
`forward_shape`(shape)	Given the shape of the src-layout, infer the dst shape.

See also

bijective_layout: Declare a layout

forward_index(index)¶

Given the indices of the src-layout, infer the dst index.

Parameters: index (Array of Expr) – The indices in src-layout.
Returns: dst_index – The inferred indices in dst-layout.
Return type: Array of Expr

backward_index(index)¶

Given the indices of the dst-layout, infer the src index.

Parameters: index (Array of Expr) – The indices in dst-layout.
Returns: src_index – The inferred indices in src-layout.
Return type: Array of Expr

forward_shape(shape)¶

Given the shape of the src-layout, infer the dst shape.

Parameters: shape (Array of Expr) – The shape in src-layout.
Returns: dst_shape – The inferred shape in dst-layout.
Return type: Array of Expr

backward_shape(shape)¶

Given the shape of the dst-layout, infer the src shape.

Parameters: shape (Array of Expr) – The shape in dst-layout.
Returns: src_shape – The inferred shape in src-layout.
Return type: Array of Expr

tvm.tir.bijective_layout(src_layout, dst_layout)¶

Create a bijective layout mapping.

Parameters

src_layout (str or Layout) – source layout.
dst_layout (str or Layout) – destination layout.

Returns

bijective_layout – The created bijective layout

Return type

BijectiveLayout

tvm.tir.layout(layout_str)¶

Create a layout node from a string.

Parameters: layout_str (str) – 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).
Returns: layout – The created layout
Return type: Layout

class tvm.tir.Var(name, dtype)¶

Symbolic variable.

Parameters

name (str) – The name
dtype (Union[str, tvm.irType]) – The data type

class tvm.tir.SizeVar(name, dtype)¶

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

Parameters

name (str) – The name
dtype (int) – The data type

class tvm.tir.Reduce(combiner, src, rdom, condition, value_index)¶

Reduce node.

Parameters

combiner (CommReducer) – The combiner.
src (list of Expr) – The source expression.
rdom (list of IterVar) – The iteration domain
condition (PrimExpr) – The reduce condition.
value_index (int) – The value index.

class tvm.tir.FloatImm(dtype, value)¶

Float constant.

Parameters

dtype (str) – The data type
value (float) – The constant value.

class tvm.tir.IntImm(dtype, value)¶

Int constant.

Parameters

dtype (str) – The data type
value (int) – The constant value.

class tvm.tir.StringImm(value)¶

String constant.

Parameters: value (str) – The value of the function.

class tvm.tir.Cast(dtype, value)¶

Cast expression.

Parameters

dtype (str) – The data type
value (PrimExpr) – The value of the function.

class tvm.tir.Add(a, b)¶

Add node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.Sub(a, b)¶

Sub node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.Mul(a, b)¶

Mul node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.Div(a, b)¶

Div node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.Mod(a, b)¶

Mod node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.FloorDiv(a, b)¶

FloorDiv node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.FloorMod(a, b)¶

FloorMod node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.Min(a, b)¶

Min node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.Max(a, b)¶

Max node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.EQ(a, b)¶

EQ node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.NE(a, b)¶

NE node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.LT(a, b)¶

LT node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.LE(a, b)¶

LE node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.GT(a, b)¶

GT node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.GE(a, b)¶

GE node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.And(a, b)¶

And node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.Or(a, b)¶

Or node.

Parameters

a (PrimExpr) – The left hand operand.
b (PrimExpr) – The right hand operand.

class tvm.tir.Not(a)¶

Not node.

Parameters: a (PrimExpr) – The input value

class tvm.tir.Select(condition, true_value, false_value)¶

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

condition (PrimExpr) – The condition expression.
true_value (PrimExpr) – The value to take when condition is true.
false_value (PrimExpr) – The value to take when condition is false.

class tvm.tir.BufferLoad(buffer, indices)¶

Buffer load node.

Parameters

buffer (Buffer) – The buffer to be loaded.
indices (List[PrimExpr]) – The buffer indices.

class tvm.tir.Load(dtype, buffer_var, index, predicate=None)¶

Load node.

Parameters

dtype (str) – The data type.
buffer_var (Var) – The buffer variable in the load expression.
index (PrimExpr) – The index in the load.
predicate (PrimExpr) – The load predicate.

class tvm.tir.Ramp(base, stride, lanes)¶

Ramp node.

Parameters

base (PrimExpr) – The base expression.
stride (ramp stride) – The stride of the ramp.
lanes (int) – The lanes of the expression.

class tvm.tir.Broadcast(value, lanes)¶

Broadcast node.

Parameters

value (PrimExpr) – The value of the expression.
lanes (int) – The lanes of the expression.

class tvm.tir.Shuffle(vectors, indices)¶

Shuffle node.

Parameters

vectors (Array of Expr) – The vectors
indices (Array of indices) – The indices

class tvm.tir.Call(dtype, name, args, call_type, func, value_index)¶

Call node.

Parameters

dtype (str) – The return data type
name (str) – The name of the function
args (list of Expr) – The input arguments to the call
call_type (int) – The type of the call
func (Operation, optional) – Operation if call_type is Halide
value_index (int) – The output value index

class tvm.tir.Let(var, value, body)¶

Let node.

Parameters

var (Var) – The variable in the binding.
value (PrimExpr) – The value in to be binded.
body (PrimExpr) – The body expression.

class tvm.tir.IterVar(dom, var, iter_type, thread_tag='')¶

Represent iteration variable.

IterVar represents axis iterations in the computation.

Parameters

dom (Range) – The domain of the iteration.
var (Union[Var, str]) – The internal variable that is used for iteration.
iter_type (int) – The iteration type.
thread_tag (str) – The thread type tag.

See also

te.thread_axis: Create thread axis IterVar.
te.reduce_axis: Create reduce axis IterVar.

class tvm.tir.Any¶: Any node.

class tvm.tir.Stmt¶: Base class of all the statements.

class tvm.tir.LetStmt(var, value, body)¶

LetStmt node.

Parameters

var (Var) – The variable in the binding.
value (PrimExpr) – The value in to be binded.
body (Stmt) – The body statement.

class tvm.tir.AssertStmt(condition, message, body)¶

AssertStmt node.

Parameters

condition (PrimExpr) – The assert condition.
message (PrimExpr) – The error message.
body (Stmt) – The body statement.

class tvm.tir.For(loop_var, min_val, extent, for_type, device_api, body)¶

For node.

Parameters

loop_var (Var) – The loop variable.
min_val (PrimExpr) – The begining value.
extent (PrimExpr) – The length of the loop.
for_type (int) – The for type.
device_api (int) – The device api type.
body (Stmt) – The body statement.

class tvm.tir.BufferStore(buffer, value, indices)¶

Buffer store node.

Parameters

buffer (Buffer) – The buffer.
value (PrimExpr) – The value we to be stored.
indices (List[PrimExpr]) – The indices location to be stored.

class tvm.tir.BufferRealize(buffer, bounds, condition, body)¶

Buffer realize node.

Parameters

buffer (Buffer) – The buffer.
bounds (List[Range]) – The value we to be stored.
condition (PrimExpr) – The realize condition.
body (Stmt) – The body of the statement.

class tvm.tir.Store(buffer_var, value, index, predicate=None)¶

Store node.

Parameters

buffer_var (Var) – The buffer Variable.
value (PrimExpr) – The value we want to store.
index (PrimExpr) – The index in the store expression.
predicate (PrimExpr) – The store predicate.

class tvm.tir.Provide(func, value_index, value, args)¶

Provide node.

Parameters

func (Operation) – The operation to create the function.
value_index (int) – The output value index
value (PrimExpr) – The value to be stored.
args (list of Expr) – The index arguments of the Provide.

class tvm.tir.Allocate(buffer_var, dtype, extents, condition, body)¶

Allocate node.

Parameters

buffer_var (Var) – The buffer variable.
dtype (str) – The data type of the buffer.
extents (list of Expr) – The extents of the allocate
condition (PrimExpr) – The condition.
body (Stmt) – The body statement.

class tvm.tir.AttrStmt(node, attr_key, value, body)¶

AttrStmt node.

Parameters

node (Node) – The node to annotate the attribute
attr_key (str) – Attribute type key.
value (PrimExpr) – The value of the attribute
body (Stmt) – The body statement.

class tvm.tir.Free(buffer_var)¶

Free node.

Parameters: buffer_var (Var) – The buffer variable.

class tvm.tir.Realize(func, value_index, dtype, bounds, condition, body)¶

Realize node.

Parameters

func (Operation) – The operation to create the function.
value_index (int) – The output value index
dtype (str) – The data type of the operation.
bounds (list of range) – The bound of realize
condition (PrimExpr) – The realize condition.
body (Stmt) – The realize body

class tvm.tir.SeqStmt(seq)¶

Sequence of statements.

Parameters: seq (List[Stmt]) – The statements

class tvm.tir.IfThenElse(condition, then_case, else_case)¶

IfThenElse node.

Parameters

condition (PrimExpr) – The expression
then_case (Stmt) – The statement to execute if condition is true.
else_case (Stmt) – The statement to execute if condition is false.

class tvm.tir.Evaluate(value)¶

Evaluate node.

Parameters: value (PrimExpr) – The expression to be evalued.

class tvm.tir.Prefetch(buffer, bounds)¶

Prefetch node.

Parameters

buffer (Buffer) – The buffer to be prefetched.
bounds (list of Range) – The bounds to be prefetched.

tvm.tir.stmt_seq(*args)¶

Make sequence of statements

Parameters: args (list of Expr or Var) – List of statements to be combined as sequence.
Returns: stmt – The combined statement.
Return type: Stmt

tvm.tir.stmt_list(stmt)¶

Make list of stmt from blocks.

Parameters: stmt (A block statement) –
Returns: stmt_list – The unpacked list of statements
Return type: list of Stmt

class tvm.tir.PrimFunc(params, body, ret_type=None, buffer_map=None, attrs=None)¶

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_map (Map[tvm.tir.Var, tvm.tir.Buffer]) – The buffer binding map.
attrs (Optional[tvm.Attrs]) – Attributes of the function, can be None

Methods

with_body(new_body)

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

with_body(new_body)¶

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

Parameters: new_body (Stmt) – The new body.
Returns: new_func – The created new function.
Return type: PrimFunc

tvm.tir.call_packed(*args)¶

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

Parameters: args (list of Expr or Buffer.) – Positional arguments.
Returns: call – The call expression.
Return type: PrimExpr

See also

te.extern(): Create tensor with extern function call.

tvm.tir.call_pure_intrin(dtype, func_name, *args)¶

Build expression by calling a pure intrinsic function.

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

Parameters

dtype (str) – The data type of the result.
func_name (str) – The intrinsic function name.
args (list) – Positional arguments.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.call_intrin(dtype, func_name, *args)¶

Build expression by calling an intrinsic function.

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

Parameters

dtype (str) – The data type of the result.
func_name (str) – The intrinsic function name.
args (list) – Positional arguments.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.call_pure_extern(dtype, func_name, *args)¶

Build expression by calling a pure extern function.

Parameters

dtype (str) – The data type of the result.
func_name (str) – The extern function name.
args (list) – Positional arguments.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.call_extern(dtype, func_name, *args)¶

Build expression by calling a extern function.

Parameters

dtype (str) – The data type of the result.
func_name (str) – The extern function name.
args (list) – Positional arguments.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.call_llvm_intrin(dtype, name, *args)¶

Build expression by calling an llvm intrinsic function

Parameters

dtype (str) – The data type of the result.
name (str) – The name of the llvm intrinsic function.
args (list) – Poistional arguments.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.all(*args)¶

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

Parameters: args (list) – List of symbolic boolean expressions
Returns: expr – Expression
Return type: Expr

tvm.tir.any(*args)¶

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

Parameters: args (list) – List of symbolic boolean expressions
Returns: expr – Expression
Return type: Expr

tvm.tir.min_value(dtype)¶

minimum value of dtype

Parameters: dtype (str) – The data type.
Returns: value – The minimum value of dtype.
Return type: tvm.Expr

tvm.tir.max_value(dtype)¶

maximum value of dtype

Parameters: dtype (str) – The data type.
Returns: value – The maximum value of dtype.
Return type: tvm.Expr

tvm.tir.trace(args, trace_action='tvm.default_trace_action')¶

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

args (list of Expr or Buffers.) – Positional arguments.
trace_action (str.) – The name of the trace action.

Returns

call – The call expression.

Return type

PrimExpr

See also

tvm.tir.call_packed(): Creates packed function.

tvm.tir.exp(x)¶

Take exponetial of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.exp2(x)¶

Calculate 2**x

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.exp10(x)¶

Calculate 10**x

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.log(x)¶

Take log of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.log2(x)¶

Take log2 of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.log10(x)¶

Take log10 of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.log1p(x)¶

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

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.ldexp(x1, x2)¶

Returns x1 * (2 ** x2).

Parameters

x1 (PrimExpr) – Input argument.
x2 (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

tvm.tir.sin(x)¶

Take sin of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.sinh(x)¶

Take sinh of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.asin(x)¶

Take asin of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.asinh(x)¶

Take asinh of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.cos(x)¶

Take cos of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.cosh(x)¶

Take cosh of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.acos(x)¶

Take acos of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.acosh(x)¶

Take acos of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.tan(x)¶

Take tan of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.tanh(x)¶

Take hyperbolic tanh of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.atan(x)¶

Take atan of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.atan2(x1, x2)¶

Take arctan2(x1, x2).

Parameters

x1 (PrimExpr) – Input argument.
x2 (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

tvm.tir.atanh(x)¶

Take atanh of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.erf(x)¶

Take gauss error function of the input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.sigmoid(x)¶

Quick function to get sigmoid

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.sqrt(x)¶

Take square root of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.rsqrt(x)¶

Take reciprocal of square root of input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.floor(x)¶

Take floor of float input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.ceil(x)¶

Take ceil of float input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.hypot(x1, x2)¶

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

Parameters

x1 (PrimExpr) – Input argument.
x2 (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

tvm.tir.trunc(x)¶

Get truncated value of the input.

The truncated value of the scalar x is the nearest integer i which is closer to zero than x is.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.abs(x)¶

Get absolute value of the input element-wise.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.round(x)¶

Round elements of the array to the nearest integer.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.nextafter(x1, x2)¶

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

Parameters

x1 (PrimExpr) – Input argument.
x2 (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

tvm.tir.nearbyint(x)¶

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: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.power(x, y)¶

x power y

Parameters

x (PrimExpr) – Input argument.
y (PrimExpr) – The exponent

Returns

z – The result.

Return type

PrimExpr

tvm.tir.popcount(x)¶

Count the number of set bits in input x.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.fmod(x, y)¶

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

Parameters

x (PrimExpr) – Input argument.
y (PrimExpr) – Input argument.

Returns

z – The result.

Return type

PrimExpr

tvm.tir.if_then_else(cond, t, f)¶

Conditional selection expression.

Parameters

cond (PrimExpr) – The condition
t (PrimExpr) – The result expression if cond is true.
f (PrimExpr) – The result expression if cond is false.

Returns

result – The result of conditional expression.

Return type

Node

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.isnan(x)¶

Check if input value is Nan.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.isfinite(x)¶

Check if input value is finite.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.isinf(x)¶

Check if input value is infinite.

Parameters: x (PrimExpr) – Input argument.
Returns: y – The result.
Return type: PrimExpr

tvm.tir.copysign(x1, x2)¶

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

Parameters

x1 (PrimExpr) – Input argument.
x2 (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

tvm.tir.div(a, b)¶

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

Parameters

a (PrimExpr) – The left hand operand, known to be non-negative.
b (PrimExpr) – The right hand operand, known to be non-negative.

Returns

res – The result expression.

Return type

PrimExpr

Note

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

tvm.tir.indexdiv(a, b)¶

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

Parameters

a (PrimExpr) – The left hand operand, known to be non-negative.
b (PrimExpr) – The right hand operand, known to be non-negative.

Returns

res – The result expression.

Return type

PrimExpr

Note

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

tvm.tir.indexmod(a, b)¶

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

Parameters

a (PrimExpr) – The left hand operand, known to be non-negative.
b (PrimExpr) – The right hand operand, known to be non-negative.

Returns

res – The result expression.

Return type

PrimExpr

Note

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

tvm.tir.truncdiv(a, b)¶

Compute the truncdiv of two expressions.

Parameters

a (PrimExpr) – The left hand operand
b (PrimExpr) – The right hand operand

Returns

res – The result expression.

Return type

PrimExpr

Note

This is the default integer division behavior in C.

tvm.tir.truncmod(a, b)¶

Compute the truncmod of two expressions.

Parameters

a (PrimExpr) – The left hand operand
b (PrimExpr) – The right hand operand

Returns

res – The result expression.

Return type

PrimExpr

Note

This is the default integer division behavior in C.

tvm.tir.floordiv(a, b)¶

Compute the floordiv of two expressions.

Parameters

a (PrimExpr) – The left hand operand
b (PrimExpr) – The right hand operand

Returns

res – The result expression.

Return type

PrimExpr

tvm.tir.floormod(a, b)¶

Compute the floormod of two expressions.

Parameters

a (PrimExpr) – The left hand operand
b (PrimExpr) – The right hand operand

Returns

res – The result expression.

Return type

PrimExpr

tvm.tir.comm_reducer(fcombine, fidentity, name='reduce')¶

Create a commutative reducer for reduction.

Parameters

fcombine (function(Expr -> Expr -> Expr)) – A binary function which takes two Expr as input to return a Expr.
fidentity (function(str -> Expr)) – A function which takes a type string as input to return a const Expr.

Returns

reducer – 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.

Return type

function

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.min(expr, axis, where=None, *args)¶

Create a min expression over axis.

Parameters

expr (PrimExpr) – The source expression.
axis (IterVar) – The reduction IterVar axis
where (optional, Expr) – Filtering predicate of the reduction.

Returns

value – The result value.

Return type

PrimExpr

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.max(expr, axis, where=None, *args)¶

Create a max expression over axis.

Parameters

expr (PrimExpr) – The source expression.
axis (IterVar) – The reduction IterVar axis
where (optional, Expr) – Filtering predicate of the reduction.

Returns

value – The result value.

Return type

PrimExpr

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.sum(expr, axis, where=None, *args)¶

Create a sum expression over axis.

Parameters

expr (PrimExpr) – The source expression.
axis (IterVar) – The reduction IterVar axis
where (optional, Expr) – Filtering predicate of the reduction.

Returns

value – The result value.

Return type

PrimExpr

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.transform¶

Namespace of all TIR transformations

Functions

`Apply`(ftransform)	Apply ftransform to each function in the Module.
`CoProcSync`()	Detect and insert sync points to co-processor.
`CombineContextCall`()	Combine context calls in the host function.
`DecorateDeviceScope`()	Decorate all the function’s body as device function.
`Filter`(fcond)	Filter functions by the calling convention attribute.
`InferFragment`()	Infer the TensorCore fragment infomation using tensor intrinsics.
`InjectCopyIntrin`(pragma_key, fintrin)	Inject virtual thread loops.
`InjectDoubleBuffer`(split_loop_factor)	Inject double buffer statements.
`InjectPrefetch`()	Inject prefetch instructions into stmt.
`InjectVirtualThread`()	Inject virtual thread loops.
`InstrumentBoundCheckers`()	Instruments bound checkers.
`LiftAttrScope`(attr_key)	Lift common attrs with attr_key to outer scope.
`LoopPartition`(split_const_loop)	Inject virtual thread loops.
`LowerCustomDatatypes`()	Lower custom datatypes.
`LowerDeviceStorageAccessInfo`()	Lower attached storage access information on device.
`LowerIntrin`()	Lower target specific intrinsic calls.
`LowerTVMBuiltin`()	Lower tvm builtin intrinsics.
`LowerThreadAllreduce`()	Lower cross thread alleduce.
`LowerWarpMemory`()	Lower warp memory access to low-level device related function calls.
`MakePackedAPI`([num_unpacked_params])	Transform the PrimFuncs in the module to a packed func API.
`NarrowDataType`(target_bits)	Narrow down PrimExpr datatype in stmt to target_bits.
`RemoveNoOp`()	Remove No Op from the Stmt.
`RewriteUnsafeSelect`()	Detect and rewrite unsafe select that contains memory access.
`Simplify`()	Run arithmetic simplifications on the statements and expressions.
`SkipAssert`()	Skip assert stmt.
`SplitHostDevice`()	Split the function into a host function and device functions.
`StorageFlatten`(cache_line_size[, …])	Flatten the multi-dimensional read/write to 1D.
`StorageRewrite`()	Rewrite storage allocation pattern.
`ThreadSync`(storage_scope)	Insert sync between parallel read/write of shared buffers.
`UnrollLoop`(auto_max_step, auto_max_depth, …)	Unroll the constant loop marked by unroll.
`VectorizeLoop`([enable_vectorize])	Lower vectorization loops.
`VerifyMemory`()	Verify if func contains illegal host side direct memory access.
`prim_func_pass`([pass_func, opt_level, name, …])	Decorate a function pass.

Classes

PrimFuncPass

A pass that works on each tvm.tir.PrimFunc() in a module.

tvm.tir.transform.prim_func_pass(pass_func=None, opt_level=None, name=None, required=None)¶

Decorate a function pass.

This function returns a callback when pass_func is provided. Otherwise, it returns the created function pass using the given optimization function.

Parameters

pass_func (Optional[Callable[(PrimFunc, IRModule, PassContext) -> PrimFunc]]) – The transformation function or class.
opt_level (int) – The optimization level of this module pass.
name (Optional[str]) – The name of the function pass. The name could be empty. In this case, the name of the optimization function will be used as the pass name.
required (Optional[List[str]]) – The list of passes that the function pass is dependent on.

Returns

create_function_pass – A decorator will be returned if pass_func is not provided, otherwise return the decorated result. The returned decorator has two behaviors depending on the input: A new FunctionPass will be returned when we decorate a pass function. A new FunctionPass class will be returned when we decorate a class type.

Return type

Union[Callable, FunctionPass]

Examples

The following code block decorates a function pass class.

@tvm.tir.transform.prim_func_pass(opt_level=1)
class TestReplaceFunc:
    def __init__(self, new_func):
        self.new_func = new_func

    def transform_function(self, func, mod, ctx):
        # just for demo purposes
        # transform func to new_func
        return self.new_func

The following code creates a function pass by decorating a user defined transform function.

@tvm.tir.transform.prim_func_pass(opt_level=2)
def transform(func, mod, ctx):
    # my transformations here.
    return func

function_pass = transform
assert isinstance(function_pass, transform.FunctionPass)
assert function_pass.info.opt_level == 2

# Given a module m, the optimization could be invoked as the follwoing:
updated_mod = function_pass(m)
# Now constant folding should have been applied to every function in
# the provided module m. And the updated module will be returned.

class tvm.tir.transform.PrimFuncPass¶: A pass that works on each tvm.tir.PrimFunc() in a module. A function pass class should be created through py:func:tvm.tir.transform.function_pass.

tvm.tir.transform.Apply(ftransform)¶

Apply ftransform to each function in the Module.

This function is a thin wrapper around tvm.tir.transform.prim_func_pass

Parameters: ftransform (tvm.tir.PrimFunc -> tvm.tir.PrimFunc) – The transformation pass.
Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.CoProcSync()¶

Detect and insert sync points to co-processor.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.CombineContextCall()¶

Combine context calls in the host function.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.DecorateDeviceScope()¶

Decorate all the function’s body as device function.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.Filter(fcond)¶

Filter functions by the calling convention attribute.

Parameters: fcond (tvm.tir.PrimFunc -> bool) – The condition of the filtering.
Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.InferFragment()¶

Infer the TensorCore fragment infomation using tensor intrinsics.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.InjectCopyIntrin(pragma_key, fintrin)¶

Inject virtual thread loops.

Parameters

pragma_key (str) – The pragma key for hint of copy.
fintrin (function) – The function with signature copyintrin(src, dst, pad_before, pad_after, pad_value)

Returns

fpass – The result pass

Return type

tvm.transform.Pass

tvm.tir.transform.InjectDoubleBuffer(split_loop_factor)¶

Inject double buffer statements.

Parameters: split_loop_factor (int) – Loop splitting factor.
Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.InjectPrefetch()¶

Inject prefetch instructions into stmt.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.InjectVirtualThread()¶

Inject virtual thread loops.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.InstrumentBoundCheckers()¶

Instruments bound checkers.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.LiftAttrScope(attr_key)¶

Lift common attrs with attr_key to outer scope.

Parameters: attr_key (str) – The attribute key to be checked.
Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.LoopPartition(split_const_loop)¶

Inject virtual thread loops.

Parameters: split_const_loop (bool) – Flag to enable partition for const loop.
Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.LowerCustomDatatypes()¶

Lower custom datatypes.

See tvm::datatypes::Registry for more information on adding custom datatypes.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.LowerDeviceStorageAccessInfo()¶

Lower attached storage access information on device.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

Note

Run this pass after all storage access analysis finish.

tvm.tir.transform.LowerIntrin()¶

Lower target specific intrinsic calls.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.LowerTVMBuiltin()¶

Lower tvm builtin intrinsics.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.LowerThreadAllreduce()¶

Lower cross thread alleduce.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.LowerWarpMemory()¶

Lower warp memory access to low-level device related function calls.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.MakePackedAPI(num_unpacked_params=0)¶

Transform the PrimFuncs in the module to a packed func API.

Parameters: num_unpacked_params (int) – Number of parameters that we hope to directly pass via normal arguments following the PackedFunc input signature.
Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.NarrowDataType(target_bits)¶

Narrow down PrimExpr datatype in stmt to target_bits.

Parameters: target_bits (int) – The target bit configuration.
Returns: fpass – The result pass
Return type: tvm.transform.Pass

Note

Run this pass after StorageFlatten.

tvm.tir.transform.RemoveNoOp()¶

Remove No Op from the Stmt.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.RewriteUnsafeSelect()¶

Detect and rewrite unsafe select that contains memory access.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.Simplify()¶

Run arithmetic simplifications on the statements and expressions.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.SkipAssert()¶

Skip assert stmt.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.SplitHostDevice()¶

Split the function into a host function and device functions.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.StorageFlatten(cache_line_size, create_bound_attribute=False)¶

Flatten the multi-dimensional read/write to 1D.

Parameters

cache_line_size (int) – The size of CPU cache line.
create_bound_attribute – Whether to create bound attributes.

Returns

fpass – The result pass

Return type

tvm.transform.Pass

tvm.tir.transform.StorageRewrite()¶

Rewrite storage allocation pattern.

Moves the allocation to outer most possible scope. Trying to share space between allocations to make a static allocation plan when possible.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.ThreadSync(storage_scope)¶

Insert sync between parallel read/write of shared buffers.

Parameters: storage_scope (str) – The target storage scope.
Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.UnrollLoop(auto_max_step, auto_max_depth, auto_max_extent, explicit_unroll)¶

Unroll the constant loop marked by unroll.

This pass also automatically attach pragma unroll tag to loops which meets the standard.

Parameters

auto_max_step (int) – The maximum step before stop attach automatic unroll
auto_max_depth (int) –
The maximum depth before stop attach automatic unroll

auto_max_extentint
The maximum extent of the loop we can unroll. This is an legacy option that do not take the loop total steps into account.
explicit_unroll (bool) – Whether explicitly unroll the loop, or leave unroll annotation to codegen.

Returns

fpass – The result pass

Return type

tvm.transform.Pass

tvm.tir.transform.VectorizeLoop(enable_vectorize=True)¶

Lower vectorization loops.

Parameters: enable_vectorize (bool) – Whether vectorization is enabled. Will lower to scalar loop when it is turned off.
Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.transform.VerifyMemory()¶

Verify if func contains illegal host side direct memory access.

Returns: fpass – The result pass
Return type: tvm.transform.Pass

tvm.tir.analysis¶

Namespace of all TIR analysis utils.

Functions

`expr_deep_equal`(lhs, rhs)	Deeply compare two nested expressions.
`verify_gpu_code`(func, constraints)	Verify if module contains illegal host side direct memory access.
`verify_memory`(func)	Verify if func contains illegal host side direct memory access.
`verify_ssa`(func)	Verify if the func is in SSA form.

tvm.tir.analysis.expr_deep_equal(lhs, rhs)¶

Deeply compare two nested expressions.

Parameters

lhs (PrimExpr) – The left operand.
rhs (PrimExpr) – The right operand.

Returns

result – The comparison result

Return type

bool

Note

This function does not remap variable bindings, it will not return true for (let x = 1 in x + 1) vs (let y = 1 in y + 1), unless x.same_as(y). Use py:func:tvm.ir.structural_equal to handle structural variable remapping.

Due to the restriction of not remapping variables, this function can run faster than StructuralEqual and can be used as a utility function during arithmetic simplifications.

Always consider py:func:tvm.ir.structural_equal first, which handles the structural remapping.

See also

tvm.ir.structural_equal()

tvm.tir.analysis.verify_gpu_code(func, constraints)¶

Verify if module contains illegal host side direct memory access.

Parameters

func (tvm.tir.PrimFunc) – The module to be verified.
constraints (Dict[str, int]) – The attribute constraints.

Returns

result – The result of verification.

Return type

bool

tvm.tir.analysis.verify_memory(func)¶

Verify if func contains illegal host side direct memory access.

Parameters: func (tvm.tir.PrimFunc) – The module to be verified.
Returns: result – The result of verification.
Return type: bool

tvm.tir.analysis.verify_ssa(func)¶

Verify if the func is in SSA form.

Parameters: func (tvm.tir.PrimFunc) – The module to be verified.
Returns: result – The result of verification.
Return type: bool

tvm.tir.stmt_functor¶

Statement functor utilities for IR transformations

Functions

`ir_transform`(stmt, preorder, postorder[, …])	Recursively visit and transform ir nodes in post DFS order.
`post_order_visit`(stmt, fvisit)	Recursively visit the ir in post DFS order node, apply fvisit Each node is guaranteed to be visited only once.
`substitute`(node, vmap)	Substitute the var specified by vmap.

tvm.tir.stmt_functor.ir_transform(stmt, preorder, postorder, only_enable=None)¶

Recursively visit and transform ir nodes in post DFS order.

Parameters

stmt (Stmt) – The input to be transformed.
preorder (function) – The function called in before recursive mutation If preorder returns None, then the transform will proceed to recursive call. If preorder returns a not None Stmt/Expr, the transformer will simply return it and won’t do further recursion.
postorder (function) – The function called after recursive mutation.
only_enable (Optional[List[str]]) – List of types that we only enable.

Returns

result – The result.

Return type

Stmt

tvm.tir.stmt_functor.post_order_visit(stmt, fvisit)¶

Recursively visit the ir in post DFS order node, apply fvisit: Each node is guaranteed to be visited only once.

Parameters: fvisit (function) – The visitor function.

tvm.tir.stmt_functor.substitute(node, vmap)¶

Substitute the var specified by vmap.

Parameters

node (ObjectRef) – The input.
vmap (Dict[Var, PrimExpr]) – The variable mapping.

Returns

result – The result.

Return type

Stmt