tvm.te¶

Namespace for Tensor Expression Language

class tvm.te.ComputeOp¶: Scalar operation.

class tvm.te.ExternOp¶: External operation.

class tvm.te.HybridOp¶

Hybrid operation.

property axis¶: Represent the IterVar axis, also defined when it is a HybridOp

class tvm.te.PlaceholderOp¶: Placeholder operation.

class tvm.te.ScanOp¶

Scan operation.

property scan_axis¶: Represent the scan axis, only defined when it is a ScanOp

class tvm.te.SpecializedCondition(conditions)¶

Specialized condition to enable op specialization.

static current()¶: Returns the current specialized condition

class tvm.te.Tensor¶

Tensor object, to construct, see function.Tensor

property axis¶: Axis of the tensor.

property ndim¶: Dimension of the tensor.

property op¶: The corressponding Operation.

property shape¶: The output shape of the tensor.

property value_index¶: The output value index the tensor corresponds to.

class tvm.te.TensorComputeOp¶: Tensor operation.

class tvm.te.TensorSlice(tensor, indices)¶

Auxiliary data structure for enable slicing syntax from tensor.

asobject()¶: Convert slice to object.

property dtype¶: Data content of the tensor.

tvm.te.abs(x, span=None)¶

Get absolute value of the input element-wise.

Parameters:

x (PrimExpr) – Input argument.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

y – The result.

Return type:

PrimExpr

tvm.te.acos(x)¶

Take acos of input x.

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

tvm.te.acosh(x)¶

Take acos of input x.

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

tvm.te.add(lhs, rhs, span=None)¶

Generic add operator.

Parameters:

lhs (object) – The left operand.
rhs (object) – The right operand.
span (Optional[Span]) – The location of this operator in the source.

Returns:

op – The result Expr of add operaton.

Return type:

tvm.Expr

tvm.te.all(*args, span=None)¶

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

Parameters:

args (list) – List of symbolic boolean expressions
span (Optional[Span]) – The location of this operator in the source code.

Returns:

expr – Expression

Return type:

Expr

tvm.te.any(*args, span=None)¶

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

Parameters:

args (list) – List of symbolic boolean expressions
span (Optional[Span]) – The location of this operator in the source code.

Returns:

expr – Expression

Return type:

Expr

tvm.te.asin(x)¶

Take asin of input x.

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

tvm.te.asinh(x)¶

Take asinh of input x.

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

tvm.te.atan(x)¶

Take atan of input x.

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

tvm.te.atanh(x)¶

Take atanh of input x.

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

tvm.te.ceil(x, span=None)¶

Take ceil of float input x.

Parameters:

x (PrimExpr) – Input argument.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

y – The result.

Return type:

PrimExpr

tvm.te.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.te.compute(shape, fcompute, name='compute', tag='', attrs=None, varargs_names=None)¶

Construct a new tensor by computing over the shape domain.

The compute rule is result[axis] = fcompute(axis)

Parameters:

shape (Tuple of Expr) – The shape of the tensor
fcompute (lambda function of indices-> value) – Specifies the input source expression
name (str, optional) – The name hint of the tensor
tag (str, optional) – Additional tag information about the compute.
attrs (dict, optional) – The additional auxiliary attributes about the compute.
varargs_names (list, optional) – The names to use for each of the varargs. If not supplied, the varargs will be called i1, i2, …

Returns:

tensor – The created tensor

Return type:

Tensor

tvm.te.const(value, dtype='int32', span=None)¶

Create a new constant with specified value and dtype

Parameters:

value (Union[bool, int, float, numpy.ndarray, tvm.nd.NDArray]) – The constant value.
dtype (str) – The data type
span (Optional[Span]) – The location of this variable in the source.

Returns:

const – The result constant expr.

Return type:

PrimExpr

tvm.te.cos(x)¶

Take cos of input x.

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

tvm.te.cosh(x)¶

Take cosh of input x.

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

tvm.te.create_prim_func(ops: List[Tensor | Var], index_dtype_override: str | None = None) → PrimFunc¶

Create a TensorIR PrimFunc from tensor expression

Parameters:: ops (List[Union[_tensor.Tensor, tvm.tir.Var]]) – The source expression.

Example

We define a matmul kernel using following code:

import tvm
from tvm import te
from tvm.te import create_prim_func
import tvm.script

A = te.placeholder((128, 128), name="A")
B = te.placeholder((128, 128), name="B")
k = te.reduce_axis((0, 128), "k")
C = te.compute((128, 128), lambda x, y: te.sum(A[x, k] * B[y, k], axis=k), name="C")
func = create_prim_func([A, B, C])
print(func.script())

If we want to use TensorIR schedule to do transformations on such kernel, we need to use create_prim_func([A, B, C]) to create a schedulable PrimFunc. The generated function looks like:

@T.prim_func
def tir_matmul(a: T.handle, b: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.match_buffer(b, (128, 128))
    C = T.match_buffer(c, (128, 128))

    for i, j, k in T.grid(128, 128, 128):
        with T.block():
            vi, vj, vk = T.axis.remap("SSR", [i, j, k])
            with T.init():
                C[vi, vj] = 0.0
            C[vi, vj] += A[vi, vk] * B[vj, vk]

Returns:: func – The created function.
Return type:: tir.PrimFunc

tvm.te.create_schedule(ops)¶

Create a schedule for list of ops

Parameters:: ops (list of Operations) – The source expression.
Returns:: sch – The created schedule.
Return type:: schedule.Schedule

tvm.te.decl_tensor_intrin(op, fcompute, name='tensor_intrin', binds=None, scalar_params=None, default_buffer_params=None)¶

Declare a tensor intrinsic function.

Parameters:

op (Operation) – The symbolic description of the intrinsic operation
fcompute (lambda function of inputs, outputs-> stmt) –
Specifies the IR statement to do the computation. See the following note for function signature of fcompute
Note

Parameters
- ins (list of tvm.tir.Buffer) - Placeholder for each inputs
- outs (list of tvm.tir.Buffer) - Placeholder for each outputs
Returns
- stmt (tvm.tir.Stmt, or tuple of three stmts)
- If a single stmt is returned, it represents the body
- If tuple of three stmts are returned they corresponds to body, reduce_init, reduce_update
name (str, optional) – The name of the intrinsic.
binds (dict of Tensor to tvm.tir.Buffer, optional) – Dictionary that maps the Tensor to Buffer which specified the data layout requirement of the function. By default, a new compact buffer is created for each tensor in the argument.
scalar_params (a list of variables used by op, whose values will be passed) – as scalar_inputs when the tensor intrinsic is called.
default_buffer_params (Optional[dict]) – Dictionary of buffer arguments to be passed when constructing a buffer.

Returns:

intrin – A TensorIntrin that can be used in tensorize schedule.

Return type:

TensorIntrin

tvm.te.div(a, b, span=None)¶

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.
span (Optional[Span]) – The location of this operator in the source.

Returns:

res – The result expression.

Return type:

PrimExpr

Note

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

tvm.te.erf(x)¶

Take gauss error function of the input x.

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

tvm.te.exp(x)¶

Take exponential of input x.

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

tvm.te.extern(shape, inputs, fcompute, name='extern', dtype=None, in_buffers=None, out_buffers=None, tag='', attrs=None)¶

Compute several tensors via an extern function.

Parameters:

shape (tuple or list of tuples.) – The shape of the outputs.
inputs (list of Tensor) – The inputs
fcompute (lambda function of inputs, outputs-> stmt) –
Specifies the IR statement to do the computation. See the following note for function signature of fcompute
Note

Parameters
- ins (list of tvm.tir.Buffer) - Placeholder for each inputs
- outs (list of tvm.tir.Buffer) - Placeholder for each outputs
Returns
- stmt (tvm.tir.Stmt) - The statement that carries out array computation.
name (str, optional) – The name hint of the tensor
dtype (str or list of str, optional) – The data types of outputs, by default dtype will be same as inputs.
in_buffers (tvm.tir.Buffer or list of tvm.tir.Buffer, optional) – Input buffers.
out_buffers (tvm.tir.Buffer or list of tvm.tir.Buffer, optional) – Output buffers.

tag: str, optional: Additonal tag information about the compute.
attrs: dict, optional: The additional auxiliary attributes about the compute.

Returns:: tensor – The created tensor or tuple of tensors contains multiple outputs.
Return type:: Tensor or list of Tensors

Example

In the code below, C is generated by calling external PackedFunc tvm.contrib.cblas.matmul

A = te.placeholder((n, l), name="A")
B = te.placeholder((l, m), name="B")
C = te.extern((n, m), [A, B],
               lambda ins, outs: tvm.tir.call_packed(
                  "tvm.contrib.cblas.matmul",
                    ins[0], ins[1], outs[0], 0, 0), name="C")

tvm.te.extern_primfunc(input_tensors: List[Tensor], primfunc: PrimFunc, **kwargs)¶

Compute tensors via a schedulable TIR PrimFunc

Parameters:

input_tensors (list of Tensor) – Input tensors that map to the corresponding primfunc input params.
primfunc (PrimFunc) – The TIR PrimFunc

Returns:

tensor – The created tensor or tuple of tensors if it contains multiple outputs.

Return type:

Tensor or list of Tensors

Example

In the code below, a TVMScript defined TIR PrimFunc is inlined into a TE ExternOp. Applying te.create_prim_func on this

A = te.placeholder((128, 128), name="A")
B = te.placeholder((128, 128), name="B")

@T.prim_func
def before_split(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.match_buffer(b, (128, 128))
    for i, j in T.grid(128, 128):
        with T.block("B"):
            vi, vj = T.axis.remap("SS", [i, j])
            B[vi, vj] = A[vi, vj] * 2.0

C = te.extern_primfunc([A, B], func)

tvm.te.floor(x: PrimExprWithOp, span=None)¶

Take floor of float input x.

Parameters:

x (PrimExpr) – Input argument.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

y – The result.

Return type:

PrimExpr

tvm.te.floordiv(a, b, span=None)¶

Compute the floordiv of two expressions.

Parameters:

a (PrimExpr) – The left hand operand
b (PrimExpr) – The right hand operand
span (Optional[Span]) – The location of this operator in the source.

Returns:

res – The result expression.

Return type:

PrimExpr

tvm.te.floormod(a, b, span=None)¶

Compute the floormod of two expressions.

Parameters:

a (PrimExpr) – The left hand operand
b (PrimExpr) – The right hand operand
span (Optional[Span]) – The location of this operator in the source.

Returns:

res – The result expression.

Return type:

PrimExpr

tvm.te.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.te.gradient(output, inputs, head=None)¶

Perform reverse-mode automatic differentiation.

Parameters:

output (Tensor) – The tensor to differentiate.
inputs (List[Tensor]) – The list of input tensors to be differentiated wrt.
head (Tensor) – The adjoint of the output, in other words, some tensor, by which the Jacobians will be multiplied. Its shape must be of the form prefix + output.shape. If None is passed, the identity tensor of shape output.shape + output.shape will be used.

Returns:

tensors – The result gradient, in the same order as the inputs

Return type:

List[Tensor]

Example

x = tvm.placeholder((32, 3, 28, 28), name='x')
w1 = tvm.placeholder((10, 3, 3, 3), name='w1')
w2 = tvm.placeholder((10, 10, 3, 3), name='w2')
z1 = topi.nn.conv2d(x, w1, 1, 1, 1)
z2 = topi.nn.conv2d(z1, w2, 1, 1, 1)
y = topi.sum(z2)

# produce gradients
[dw1, dw2] = tvm.gradient(y, [w1, w2])

# produce Jacobians
[jw1, jw2] = tvm.gradient(z2, [w1, w2])

# produce gradients, the head adjoint for z2 is provided manually
[dw1, dw2] = tvm.gradient(z2, [w1, w2], topi.full_like(z2, 1.0))

tvm.te.if_then_else(cond, t, f, span=None)¶

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.
span (Optional[Span]) – The location of this operator in the source.

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.te.indexdiv(a, b, span=None)¶

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.
span (Optional[Span]) – The location of this operator in the source.

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.te.indexmod(a, b, span=None)¶

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.
span (Optional[Span]) – The location of this operator in the source.

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.te.isfinite(x, span=None)¶

Check if input value is finite.

Parameters:

x (PrimExpr) – Input argument.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

y – The result.

Return type:

PrimExpr

tvm.te.isinf(x, span=None)¶

Check if input value is infinite.

Parameters:

x (PrimExpr) – Input argument.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

y – The result.

Return type:

PrimExpr

tvm.te.isnan(x, span=None)¶

Check if input value is Nan.

Parameters:

x (PrimExpr) – Input argument.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

y – The result.

Return type:

PrimExpr

tvm.te.log(x)¶

Take log of input x.

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

tvm.te.log10(x)¶

Take log10 of input x.

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

tvm.te.log2(x)¶

Take log2 of input x.

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

tvm.te.max(expr, axis, where=None, init=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.te.max_value(dtype: str, span: Span | None = None) → Any¶

maximum value of dtype

Parameters:

dtype (str) – The data type.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

value – The maximum value of dtype.

Return type:

tvm.Expr

tvm.te.min(expr, axis, where=None, init=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.te.min_value(dtype, span=None)¶

minimum value of dtype

Parameters:

dtype (str) – The data type.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

value – The minimum value of dtype.

Return type:

tvm.Expr

tvm.te.multiply(lhs, rhs, span=None)¶

Generic multiply operator.

Parameters:

lhs (object) – The left operand.
rhs (object) – The right operand.
span (Optional[Span]) – The location of this operator in the source.

Returns:

op – The result Expr of multiply operaton.

Return type:

tvm.Expr

tvm.te.nearbyint(x, span=None)¶

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.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

y – The result.

Return type:

PrimExpr

tvm.te.placeholder(shape, dtype=None, name='placeholder')¶

Construct an empty tensor object.

Parameters:

shape (Tuple of Expr) – The shape of the tensor
dtype (str, optional) – The data type of the tensor
name (str, optional) – The name hint of the tensor

Returns:

tensor – The created tensor

Return type:

Tensor

tvm.te.popcount(x)¶

Count the number of set bits in input x.

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

tvm.te.power(x, y, span=None)¶

x power y

Parameters:

x (PrimExpr) – Input argument.
y (PrimExpr) – The exponent
span (Optional[Span]) – The location of this operator in the source code.

Returns:

z – The result.

Return type:

PrimExpr

tvm.te.reduce_axis(dom, name='rv', thread_tag='', span=None)¶

Create a new IterVar for reduction.

Parameters:

dom (Range) – The domain of iteration.
name (str) – The name of the variable.
thread_tag (Optional[str]) – The name of the thread_tag.
span (Optional[Span]) – The location of this variable in the source.

Returns:

axis – An iteration variable representing the value.

Return type:

IterVar

tvm.te.round(x, span=None)¶

Round elements of the array to the nearest integer.

Parameters:

x (PrimExpr) – Input argument.
span (Optional[Span]) – The location of this operator in the source code.

Returns:

y – The result.

Return type:

PrimExpr

tvm.te.rsqrt(x)¶

Take reciprocal of square root of input x.

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

tvm.te.scan(init, update, state_placeholder, inputs=None, name='scan', tag='', attrs=None)¶

Construct new tensors by scanning over axis.

Parameters:

init (Tensor or list of Tensor) – The initial condition of first init.shape[0] timestamps
update (Tensor or list of Tensor) – The update rule of the scan given by symbolic tensor.
state_placeholder (Tensor or list of Tensor) – The placeholder variables used by update.
inputs (Tensor or list of Tensor, optional) – The list of inputs to the scan. This is not required, but can be useful for the compiler to detect scan body faster.
name (str, optional) – The name hint of the tensor
tag (str, optional) – Additonal tag information about the compute.
attrs (dict, optional) – The additional auxiliary attributes about the compute.

Returns:

tensor – The created tensor or tuple of tensors contains multiple outputs.

Return type:

Tensor or list of Tensors

Example

# The following code is equivalent to numpy.cumsum
m = te.var("m")
n = te.var("n")
X = te.placeholder((m, n), name="X")
s_state = te.placeholder((m, n))
s_init = te.compute((1, n), lambda _, i: X[0, i])
s_update = te.compute((m, n), lambda t, i: s_state[t-1, i] + X[t, i])
res = tvm.te.scan(s_init, s_update, s_state, X)

tvm.te.sigmoid(x)¶

Quick function to get sigmoid

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

tvm.te.sin(x)¶

Take sin of input x.

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

tvm.te.sinh(x)¶

Take sinh of input x.

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

tvm.te.size_var(name='size', dtype='int32', span=None)¶

Create a new variable represents a tensor shape size, which is non-negative.

Parameters:

name (str) – The name
dtype (str) – The data type
span (Optional[Span]) – The location of this variable in the source.

Returns:

var – The result symbolic shape variable.

Return type:

SizeVar

tvm.te.sqrt(x)¶

Take square root of input x.

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

tvm.te.subtract(lhs, rhs, span=None)¶

Generic subtract operator.

Parameters:

lhs (object) – The left operand.
rhs (object) – The right operand.
span (Optional[Span]) – The location of this operator in the source.

Returns:

op – The result Expr of subtract operaton.

Return type:

tvm.Expr

tvm.te.sum(expr, axis, where=None, init=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.te.tag_scope(tag)¶

The operator tag scope.

Parameters:: tag (str) – The tag name.
Returns:: tag_scope – The tag scope object, which can be used as decorator or context manger.
Return type:: TagScope

Example

n = te.var('n')
m = te.var('m')
l = te.var('l')
A = te.placeholder((n, l), name='A')
B = te.placeholder((m, l), name='B')
k = te.reduce_axis((0, l), name='k')

with tvm.te.tag_scope(tag='matmul'):
    C = te.compute((n, m), lambda i, j: te.sum(A[i, k] * B[j, k], axis=k))

# or use tag_scope as decorator
@tvm.te.tag_scope(tag="conv")
def compute_relu(data):
    return te.compute(data.shape, lambda *i: tvm.tir.Select(data(*i) < 0, 0.0, data(*i)))

tvm.te.tan(x)¶

Take tan of input x.

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

tvm.te.tanh(x)¶

Take hyperbolic tanh of input x.

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

tvm.te.thread_axis(dom=None, tag='', name='', span=None)¶

Create a new IterVar to represent thread index.

Parameters:

dom (Range or str) – The domain of iteration When str is passed, dom is set to None and str is used as tag
tag (str, optional) – The thread tag
name (str, optional) – The name of the var.
span (Optional[Span]) – The location of this variable in the source.

Returns:

axis – The thread itervar.

Return type:

IterVar

tvm.te.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