# tvm.te¶

Namespace for Tensor Expression Language

Classes

 ComputeOp Scalar operation. ExternOp External operation. HybridOp Hybrid operation. PlaceholderOp Placeholder operation. ScanOp Scan operation. Schedule Schedule for all the stages. SpecializedCondition(conditions) Specialized condition to enable op specialization. Stage A Stage represents schedule for one operation. Tensor Tensor object, to construct, see function.Tensor TensorComputeOp Tensor operation. TensorSlice(tensor, indices) Auxiliary data structure for enable slicing syntax from tensor.

Functions

 Get absolute value of the input element-wise. 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 Take atan of input x. Take ceil of float input x. comm_reducer(fcombine, fidentity[, name]) Create a commutative reducer for reduction. compute(shape, fcompute[, name, tag, attrs]) Construct a new tensor by computing over the shape domain. Take cos of input x. Create a schedule for list of ops decl_tensor_intrin(op, fcompute[, name, …]) Declare a tensor intrinsic function. div(a, b) Compute a / b as in C/C++ semantics. Take gauss error function of the input x. Take exponetial of input x. extern(shape, inputs, fcompute[, name, …]) Compute several tensor via extern function. 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. gradient(output, inputs[, head]) Perform reverse-mode automatic differentiation. 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. Check if input value is finite. Check if input value is infinite. Check if input value is Nan. Take log 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 Round elements of the array to the nearest integer. placeholder(shape[, dtype, name]) Construct an empty tensor object. Count the number of set bits in input x. power(x, y) x power y reduce_axis(dom[, name]) Create a new IterVar for reduction. Round elements of the array to the nearest integer. Take reciprocal of square root of input x. scan(init, update, state_placeholder[, …]) Construct new tensors by scanning over axis. Quick function to get sigmoid Take sin of input x. size_var([name, dtype]) Create a new variable represents a tensor shape size, which is non-negative. Take square root of input x. sum(expr, axis[, where]) Create a sum expression over axis. tag_scope(tag) The operator tag scope. Take tan of input x. Take hyperbolic tanh of input x. thread_axis([dom, tag, name]) Create a new IterVar to represent thread index. trace(args[, trace_action]) Trace tensor data at the runtime. Get truncated value of the input. truncdiv(a, b) Compute the truncdiv of two expressions. truncmod(a, b) Compute the truncmod of two expressions. var([name, dtype]) Create a new variable with specified name and dtype
tvm.te.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

Alias of tvm.tir.any()

tvm.te.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

Alias of tvm.tir.all()

tvm.te.min_value(dtype)

minimum value of dtype

Parameters

dtype (str) – The data type.

Returns

value – The minimum value of dtype.

Return type

tvm.Expr

Alias of tvm.tir.min_value()

tvm.te.max_value(dtype)

maximum value of dtype

Parameters

dtype (str) – The data type.

Returns

value – The maximum value of dtype.

Return type

tvm.Expr

Alias of tvm.tir.max_value()

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

tvm.tir.call_packed()

Creates packed function.

Alias of tvm.tir.trace()

tvm.te.exp(x)

Take exponetial of input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.exp()

tvm.te.erf(x)

Take gauss error function of the input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.erf()

tvm.te.tanh(x)

Take hyperbolic tanh of input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.tanh()

tvm.te.sigmoid(x)

Quick function to get sigmoid

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.sigmoid()

tvm.te.log(x)

Take log of input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.log()

tvm.te.tan(x)

Take tan of input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.tan()

tvm.te.cos(x)

Take cos of input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.cos()

tvm.te.sin(x)

Take sin of input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.sin()

tvm.te.atan(x)

Take atan of input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.atan()

tvm.te.sqrt(x)

Take square root of input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.sqrt()

tvm.te.rsqrt(x)

Take reciprocal of square root of input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.rsqrt()

tvm.te.floor(x)

Take floor of float input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.floor()

tvm.te.ceil(x)

Take ceil of float input x.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.ceil()

tvm.te.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

Alias of tvm.tir.trunc()

tvm.te.abs(x)

Get absolute value of the input element-wise.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.abs()

tvm.te.round(x)

Round elements of the array to the nearest integer.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.round()

tvm.te.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

Alias of tvm.tir.nearbyint()

tvm.te.power(x, y)

x power y

Parameters
Returns

z – The result.

Return type

PrimExpr

Alias of tvm.tir.power()

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

Alias of tvm.tir.popcount()

tvm.te.fmod(x, y)

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

Parameters
Returns

z – The result.

Return type

PrimExpr

Alias of tvm.tir.fmod()

tvm.te.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.

Alias of tvm.tir.if_then_else()

tvm.te.isnan(x)

Check if input value is Nan.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.isnan()

tvm.te.isfinite(x)

Check if input value is finite.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.isfinite()

tvm.te.isinf(x)

Check if input value is infinite.

Parameters

x (PrimExpr) – Input argument.

Returns

y – The result.

Return type

PrimExpr

Alias of tvm.tir.isinf()

tvm.te.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).

Alias of tvm.tir.div()

tvm.te.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.

Alias of tvm.tir.indexdiv()

tvm.te.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.

Alias of tvm.tir.indexmod()

tvm.te.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.

Alias of tvm.tir.truncdiv()

tvm.te.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.

Alias of tvm.tir.truncmod()

tvm.te.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

Alias of tvm.tir.floordiv()

tvm.te.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

Alias of tvm.tir.floormod()

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:

1. accept (expr, axis, where) to produce an Reduce Expr on specified axis;

2. 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")


Alias of tvm.tir.comm_reducer()

tvm.te.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)


Alias of tvm.tir.min()

tvm.te.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)


Alias of tvm.tir.max()

tvm.te.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)


Alias of tvm.tir.sum()

class tvm.te.Schedule

Schedule for all the stages.

Methods

 cache_read(tensor, scope, readers) Create a cache read of original tensor for readers. cache_write(tensor, scope) Create a cache write of original tensor, before storing into tensor. create_group(outputs, inputs[, include_inputs]) Create stage group by giving output and input boundary. Build a normalized schedule from the current schedule. rfactor(tensor, axis[, factor_axis]) Factor a reduction axis in tensor’s schedule to be an explicit axis.
normalize()

Build a normalized schedule from the current schedule.

Insert necessary rebase to make certain iter var to start from 0. This is needed before bound inference and followup step.

Returns

sch – The normalized schedule.

Return type

Schedule

create_group(outputs, inputs, include_inputs=False)

Create stage group by giving output and input boundary.

The operators between outputs and inputs are placed as member of group. outputs are include in the group, while inputs are not included.

Parameters
• outputs (list of Tensors) – The outputs of the group.

• inputs (list of Tensors) – The inputs of the group.

• include_inputs (boolean, optional) – Whether include input operations in the group if they are used by outputs.

Returns

group – A virtual stage represents the group, user can use compute_at to move the attachment point of the group.

Return type

Stage

cache_read(tensor, scope, readers)

This will mutate the body of the readers. A new cache stage will be created for the tensor. Call this before doing any split/fuse schedule.

Parameters
• tensor (Tensor) – The tensor to be cached.

• scope (str) – The scope of cached

Returns

cache – The created cache tensor.

Return type

Tensor

cache_write(tensor, scope)

Create a cache write of original tensor, before storing into tensor.

This will mutate the body of the tensor. A new cache stage will created before feed into the tensor.

This function can be used to support data layout transformation. If there is a split/fuse/reorder on the data parallel axis of tensor before cache_write is called. The intermediate cache stores the data in the layout as the iteration order of leave axis. The data will be transformed back to the original layout in the original tensor. User can further call compute_inline to inline the original layout and keep the data stored in the transformed layout.

Parameters
• tensor (Tensor, list or tuple) – The tensors to be feed to. All the tensors must be produced by one computeOp

• scope (str) – The scope of cached

Returns

cache – The created cache tensor.

Return type

Tensor

rfactor(tensor, axis, factor_axis=0)

Factor a reduction axis in tensor’s schedule to be an explicit axis.

This will create a new stage that generated the new tensor with axis as the first dimension. The tensor’s body will be rewritten as a reduction over the factored tensor.

Parameters
• tensor (Tensor) – The tensor to be factored.

• axis (IterVar) – The reduction axis in the schedule to be factored.

• factor_axis (int) – The position where the new axis is placed.

Returns

tfactor – The created factored tensor.

Return type

Tensor or Array of Tensor

class tvm.te.Stage

A Stage represents schedule for one operation.

Methods

 bind(ivar, thread_ivar) Bind ivar to thread index thread_ivar compute_at(parent, scope) Attach the stage at parent’s scope Mark stage as inline Attach the stage at parent, and mark it as root Compute the current stage via double buffering. env_threads(threads) Mark threads to be launched at the outer scope of composed op. fuse(*args) Fuse multiple consecutive iteration variables into a single iteration variable. The special OpenGL schedule parallel(var) Parallelize the iteration. pragma(var, pragma_type[, pragma_value]) Annotate the iteration with pragma prefetch(tensor, var, offset) Prefetch the specified variable reorder(*args) reorder the arguments in the specified order. set_scope(scope) Set the thread scope of this stage set_store_predicate(predicate) Set predicate under which store to the array can be performed. split(parent[, factor, nparts]) Split the stage either by factor providing outer scope, or both storage_align(axis, factor, offset) Set alignment requirement for specific axis tensorize(var, tensor_intrin) Tensorize the computation enclosed by var with tensor_intrin tile(x_parent, y_parent, x_factor, y_factor) Perform tiling on two dimensions unroll(var) Unroll the iteration. vectorize(var) Vectorize the iteration.
split(parent, factor=None, nparts=None)

Split the stage either by factor providing outer scope, or both

Parameters
• parent (IterVar) – The parent iter var.

• factor (Expr, optional) – The splitting factor

• nparts (Expr, optional) – The number of outer parts.

Returns

• outer (IterVar) – The outer variable of iteration.

• inner (IterVar) – The inner variable of iteration.

fuse(*args)

Fuse multiple consecutive iteration variables into a single iteration variable.

fused = fuse(…fuse(fuse(args[0], args[1]), args[2]),…, args[-1]) The order is from outer to inner.

Parameters

args (list of IterVars) – Itervars that proceeds each other

Returns

fused – The fused variable of iteration.

Return type

IterVar

set_scope(scope)

Set the thread scope of this stage

Parameters

scope (str) – The thread scope of this stage

bind(ivar, thread_ivar)

Parameters
• ivar (IterVar) – The iteration to be binded to thread.

env_threads(threads)

Mark threads to be launched at the outer scope of composed op.

Parameters

set_store_predicate(predicate)

Set predicate under which store to the array can be performed.

Use this when there are duplicated threads doing the same store and we only need one of them to do the store.

Parameters

predicate (Expr) – The guard condition fo store.

compute_at(parent, scope)

Attach the stage at parent’s scope

Parameters
• parent (Stage) – The parent stage

• scope (IterVar) – The loop scope t be attached to.

compute_inline()

Mark stage as inline

Parameters

parent (Stage) – The parent stage

compute_root()

Attach the stage at parent, and mark it as root

Parameters

parent (Stage) – The parent stage

reorder(*args)

reorder the arguments in the specified order.

Parameters

args (list of IterVar) – The order to be ordered

tile(x_parent, y_parent, x_factor, y_factor)

Perform tiling on two dimensions

The final loop order from outmost to inner most are [x_outer, y_outer, x_inner, y_inner]

Parameters
• x_parent (IterVar) – The original x dimension

• y_parent (IterVar) – The original y dimension

• x_factor (Expr) – The stride factor on x axis

• y_factor (Expr) – The stride factor on y axis

Returns

• x_outer (IterVar) – Outer axis of x dimension

• y_outer (IterVar) – Outer axis of y dimension

• x_inner (IterVar) – Inner axis of x dimension

• p_y_inner (IterVar) – Inner axis of y dimension

vectorize(var)

Vectorize the iteration.

Parameters

var (IterVar) – The iteration to be vectorize

tensorize(var, tensor_intrin)

Tensorize the computation enclosed by var with tensor_intrin

Parameters
• var (IterVar) – The iteration boundary of tensorization.

• tensor_intrin (TensorIntrin) – The tensor intrinsic used for computation.

unroll(var)

Unroll the iteration.

Parameters

var (IterVar) – The iteration to be unrolled.

parallel(var)

Parallelize the iteration.

Parameters

var (IterVar) – The iteration to be parallelized.

pragma(var, pragma_type, pragma_value=None)

Annotate the iteration with pragma

This will translate to a pragma_scope surrounding the corresponding loop generated. Useful to support experimental features and extensions.

Parameters
• var (IterVar) – The iteration to be anotated

• pragma_type (str) – The pragma string to be annotated

• pragma_value (Expr, optional) – The pragma value to pass along the pragma

Note

Most pragmas are advanced/experimental features and may subject to change. List of supported pragmas:

• debug_skip_region

Force skip the region marked by the axis and turn it into no-op. This is useful for debug purposes.

• parallel_launch_point

Specify to launch parallel threads outside the specified iteration loop. By default the threads launch at the point of parallel construct. This pragma moves the launching point to even outer scope. The threads are launched once and reused across multiple parallel constructs as BSP style program.

• parallel_barrier_when_finish

Insert a synchronization barrier between working threads after the specified loop iteration finishes.

• parallel_stride_pattern

Hint parallel loop to execute in strided pattern. for (int i = task_id; i < end; i += num_task)

prefetch(tensor, var, offset)

Prefetch the specified variable

Parameters
• tensor (Tensor) – The tensor to be prefetched

• var (IterVar) – The loop point at which the prefetching is applied

• offset (Expr) – The number of iterations to be prefetched before actual execution

storage_align(axis, factor, offset)

Set alignment requirement for specific axis

This ensures that stride[axis] == k * factor + offset for some k. This is useful to set memory layout to for more friendly memory access pattern. For example, we can set alignment to be factor=2, offset=1 to avoid bank conflict for thread access on higher dimension in GPU shared memory.

Parameters
• axis (IterVar) – The axis dimension to be aligned.

• factor (int) – The factor in alignment specification.

• offset (int) – The offset in the alignment specification.

double_buffer()

Compute the current stage via double buffering.

This can only be applied to intermediate stage. This will double the storage cost of the current stage. Can be useful to hide load latency.

opengl()

The special OpenGL schedule

Maps each output element to a pixel.

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

class tvm.te.SpecializedCondition(conditions)

Specialized condition to enable op specialization.

Methods

 Returns the current specialized condition
static current()

Returns the current specialized condition

class tvm.te.TensorSlice(tensor, indices)

Auxiliary data structure for enable slicing syntax from tensor.

Methods

 Convert slice to object.

Attributes

 dtype Data content of the tensor.
asobject()

Convert slice to object.

property dtype

Data content of the tensor.

class tvm.te.Tensor

Tensor object, to construct, see function.Tensor

Attributes

 axis Axis of the tensor. ndim Dimension of the tensor. op The corressponding Operation. shape The output shape of the tensor. value_index The output value index the tensor corresponds to.
property ndim

Dimension of the tensor.

property axis

Axis of the tensor.

property op

The corressponding Operation.

property value_index

The output value index the tensor corresponds to.

property shape

The output shape of the tensor.

tvm.te.decl_tensor_intrin(op, fcompute, name='tensor_intrin', binds=None, scalar_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

Returns

• stmt (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 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.

Returns

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

Return type

TensorIntrin

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.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.compute(shape, fcompute, name='compute', tag='', attrs=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

• attrs (dict, optional) – The additional auxiliary attributes about the compute.

Returns

tensor – The created tensor

Return type

Tensor

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

• attrs (dict, optional) – The additional auxiliary attributes about the compute.

Returns

tensor – The created tensor or tuple of tensors it it 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.extern(shape, inputs, fcompute, name='extern', dtype=None, in_buffers=None, out_buffers=None, tag='', attrs=None)

Compute several tensor via 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

Returns

• 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 (Buffer or list of Buffer, optional) – Input buffers.

• out_buffers (Buffer or list of Buffers, optional) – Output buffers.

tag: str, optional

attrs: dict, optional

Returns

tensor – The created tensor or tuple of tensors it it 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.var(name='tindex', dtype='int32')

Create a new variable with specified name and dtype

Parameters
• name (str) – The name

• dtype (str) – The data type

Returns

var – The result symbolic variable.

Return type

Var

tvm.te.size_var(name='size', dtype='int32')

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

Parameters
• name (str) – The name

• dtype (str) – The data type

Returns

var – The result symbolic shape variable.

Return type

SizeVar

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

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.

Returns

Return type

IterVar

tvm.te.reduce_axis(dom, name='rv')

Create a new IterVar for reduction.

Parameters
• dom (Range) – The domain of iteration.

• name (str) – The name of the variable.

Returns

axis – An iteration variable representing the value.

Return type

IterVar

class tvm.te.PlaceholderOp

Placeholder operation.

class tvm.te.ComputeOp

Scalar operation.

class tvm.te.TensorComputeOp

Tensor operation.

class tvm.te.ScanOp

Scan operation.

Attributes

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

Represent the scan axis, only defined when it is a ScanOp

class tvm.te.ExternOp

External operation.

class tvm.te.HybridOp

Hybrid operation.

Attributes

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

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

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)

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

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

[dw1, dw2] = tvm.gradient(z2, [w1, w2], topi.full_like(z2, 1.0))


# tvm.te.hybrid¶

Hybrid Programming APIs of TVM Python Package.

This package maps a subset of python to HalideIR so that: 1. Users can write some preliminary versions of the computation patterns have not been supported yet and verify it across the real execution and python semantic emulation. 2. So far, it is a text format dedicated to HalideIR Phase 0. Refer tvm.lower for more details. A larger ambition of this module is to support all levels of HalideIR.

Classes

 HybridModule([src, name]) The usage of Hybrid Module is very similar to conventional TVM module, but conventional TVM module requires a function body which is already fully lowered.

Functions

 build(sch, inputs, outputs[, name]) Dump the corrent schedule to hybrid module decorate(func, fwrapped) A wrapper call of decorator package, differs to call time form_body(sch) According to the given schedule, form the raw body script(pyfunc) Decorate a python function function as hybrid script. source_to_op(src, args, symbols, closure_vars) Another level of wrapper
tvm.te.hybrid.form_body(sch)

According to the given schedule, form the raw body :param sch: :type sch: tvm.te.schedule.Schedule :param The given scheduler to form the raw body:

Returns

Return type

The body formed according to the given schedule

tvm.te.hybrid.decorate(func, fwrapped)

A wrapper call of decorator package, differs to call time

Parameters
• func (function) – The original function

• fwrapped (function) – The wrapped function

class tvm.te.hybrid.HybridModule(src=None, name=None)

The usage of Hybrid Module is very similar to conventional TVM module, but conventional TVM module requires a function body which is already fully lowered. This contradicts to the fact that Hybrid Module is originally a text format for Phase 0 HalideIR. Thus, a totally separated module is defined.

Methods

 load(path) Load the module from a python file
load(path)

Load the module from a python file

Parameters

path (str) – Path to the given python file

tvm.te.hybrid.source_to_op(src, args, symbols, closure_vars)

Another level of wrapper

Parameters
• src (ast.node or str) – If an ast.node, then directly lower it. If a str, then parse it to ast and lower it.

• args (list of Tensors or Vars) – The argument lists to the function. It is NOT encouraged to write a function without arguments. It is NOT encouraged to write a function with side effect.

• symbols (list of str) – The symbol list of the global context of the function.

• closure_vars (dict) – A dict of external name reference captured by this function.

Returns

res – The result of output tensors of the formed OpNode.

Return type

list of output tensors

tvm.te.hybrid.script(pyfunc)

Decorate a python function function as hybrid script.

The hybrid function support emulation mode and parsing to the internal language IR.

Returns

hybrid_func – A decorated hybrid script function.

Return type

function

tvm.te.hybrid.build(sch, inputs, outputs, name='hybrid_func')

Dump the corrent schedule to hybrid module

Parameters
• sch (Schedule) – The schedule to be dumped

• inputs (An array of Tensors or Vars) – The inputs of the function body

• outputs (An array of Tensors) – The outputs of the function body

Returns

module – The built results is wrapped in a HybridModule. The usage of HybridModule is roughly the same as normal TVM-built modules.

Return type

HybridModule