tvm.tir¶

Namespace for Tensor-level IR

Classes:

`Buffer`	Symbolic data buffer in TVM.
`DataProducer`
`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.
`BijectiveLayout`	Bijective mapping for two layouts (src-layout and dst-layout).
`Var`(name, dtype[, span])	Symbolic variable.
`SizeVar`(name, dtype[, span])	Symbolic variable to represent a tensor index size
`Reduce`(combiner, src, rdom, condition, ...)	Reduce node.
`FloatImm`(dtype, value[, span])	Float constant.
`IntImm`(dtype, value[, span])	Int constant.
`StringImm`(value[, span])	String constant.
`Cast`(dtype, value[, span])	Cast expression.
`Add`(a, b[, span])	Add node.
`Sub`(a, b[, span])	Sub node.
`Mul`(a, b[, span])	Mul node.
`Div`(a, b[, span])	Div node.
`Mod`(a, b[, span])	Mod node.
`FloorDiv`(a, b[, span])	FloorDiv node.
`FloorMod`(a, b[, span])	FloorMod node.
`Min`(a, b[, span])	Min node.
`Max`(a, b[, span])	Max node.
`EQ`(a, b[, span])	EQ node.
`NE`(a, b[, span])	NE node.
`LT`(a, b[, span])	LT node.
`LE`(a, b[, span])	LE node.
`GT`(a, b[, span])	GT node.
`GE`(a, b[, span])	GE node.
`And`(a, b[, span])	And node.
`Or`(a, b[, span])	Or node.
`Not`(a[, span])	Not node.
`Select`(condition, true_value, false_value[, ...])	Select node.
`BufferLoad`(buffer, indices[, span])	Buffer load node.
`ProducerLoad`(producer, indices[, span])	Producer load node.
`Load`(dtype, buffer_var, index[, predicate, span])	Load node.
`Ramp`(base, stride, lanes[, span])	Ramp node.
`Broadcast`(value, lanes[, span])	Broadcast node.
`Shuffle`(vectors, indices[, span])	Shuffle node.
`Call`(dtype, op, args[, span])	Call node.
`CallEffectKind`()	Possible kinds of Call effects.
`Let`(var, value, body[, span])	Let node.
`IterVar`(dom, var, iter_type[, thread_tag, span])	Represent iteration variable.
`CommReducer`(lhs, rhs, result, identity_element)	Commutative reduce operator
`Any`([span])	Any node.
`Stmt`	Base class of all the statements.
`LetStmt`(var, value, body[, span])	LetStmt node.
`AssertStmt`(condition, message, body[, span])	AssertStmt node.
`ForKind`(value)	The kind of the for loop.
`For`(loop_var, min_val, extent, kind, body[, ...])	For node.
`While`(condition, body[, span])	While node.
`BufferStore`(buffer, value, indices[, span])	Buffer store node.
`BufferRealize`(buffer, bounds, condition, body)	Buffer realize node.
`Store`(buffer_var, value, index[, predicate, ...])	Store node.
`ProducerStore`(producer, value, indices[, span])	ProducerStore node.
`Allocate`(buffer_var, dtype, extents, ...[, ...])	Allocate node.
`AllocateConst`(buffer_var, dtype, extents, ...)	Allocate constant node.
`AttrStmt`(node, attr_key, value, body[, span])	AttrStmt node.
`DeclBuffer`(buffer, body[, span])	DeclBuffer node.
`ProducerRealize`(producer, bounds, condition, ...)	ProducerRealize node.
`SeqStmt`(seq[, span])	Sequence of statements.
`IfThenElse`(condition, then_case, else_case)	IfThenElse node.
`Evaluate`(value[, span])	Evaluate node.
`Prefetch`(buffer, bounds[, span])	Prefetch node.
`BufferRegion`(buffer, region)	BufferRegion node.
`MatchBufferRegion`(buffer, source)	MatchBufferRegion node.
`Block`(iter_vars, reads, writes, name_hint, body)	Block node.
`BlockRealize`(iter_values, predicate, block)	BlockRealize node.
`PrimFunc`(params, body[, ret_type, ...])	A function declaration expression.
`TensorIntrin`(desc, impl)	A tensor intrinsic.
`IndexMap`(initial_indices, final_indices, ...)	A mapping from multi-dimensional indices to another set of multi-dimensional indices
`StmtSRef`	An object that refers to schedulable elements in the TensorIR, aka "sref".
`BlockScope`	An object corresponds to each block sref in the sref tree, which tracks the producer-consumer dependency between blocks.
`ScheduleState`(mod, *[, debug_mask])	The state of scheduling, which exposes a Replace method as the primary resort for all the scheduling primitives to manipulate the TensorIR.
`Schedule`(mod, *[, seed, debug_mask, ...])	The user-facing schedule class

Functions:

`decl_buffer`(shape[, dtype, name, data, ...])	Declare a new symbolic buffer.
`bijective_layout`(src_layout, dst_layout)	Create a bijective layout mapping.
`layout`(layout_str)	Create a layout node from a string.
`stmt_seq`(*args)	Make sequence of statements
`stmt_list`(stmt)	Make list of stmt from blocks.
`call_packed_lowered`(*args[, span])	Lowered version of call packed.
`call_cpacked_lowered`(*args[, span])	Lowered version of call c-packed.
`call_packed`(*args[, span])	Build expression by call an external packed function.
`call_cpacked`(*args[, span])	Build expression by call an external packed function.
`call_intrin`(dtype, func_name, *args[, span])	Build expression by calling an intrinsic function.
`call_pure_extern`(dtype, func_name, *args[, span])	Build expression by calling a pure extern function.
`call_extern`(dtype, func_name, *args[, span])	Build expression by calling a extern function.
`call_llvm_intrin`(dtype, name, *args[, span])	Build expression by calling a llvm intrinsic function
`call_llvm_pure_intrin`(dtype, name, *args[, span])	Build expression by calling a pure llvm intrinsic function
`ret`(val)	Create a tir return expression
`all`(*args[, span])	Create a new expression of the intersection of all conditions in the
`any`(*args[, span])	Create a new experssion of the union of all conditions in the arguments
`min_value`(dtype[, span])	minimum value of dtype
`max_value`(dtype[, span])	maximum value of dtype
`trace`(args[, trace_action])	Trace tensor data at the runtime.
`tvm_check_return`(expected, ...)	Return new on stack dtype[num] :param expected: The expected return code.
`tvm_stack_alloca`(dtype_str, num)	Return new on stack dtype[num]
`tvm_stack_make_shape`(*args)	Allocate a shape tuple on stack, return the handle
`tvm_stack_make_array`(data, shape, strides, ...)	Allocate a NDArray(DLTensor) on stack, return the handle
`tvm_tuple`(*value)	Create a tuple structure in value field of AttrStmt
`tvm_struct_get`(arr, index, field, dtype)	Get struct field value in array
`tvm_struct_set`(arr, index, field, value)	Set value in struct field in array
`address_of`(buffer_load[, span])	Returns the address of an element in the buffer
`lookup_param`(param_name[, span])	Returns the param by name
`assume`([cond])	Provide a true statement that can be used for simplifications
`undef`()	Returns an initialized but arbitrary value
`tvm_thread_allreduce`(*freduce_args)	param freduce_args The args.
`type_annotation`(dtype)	Create a type annotation expression
`tvm_access_ptr`(ptype, data, offset, extent, ...)	Get head access address with memory access pattern info
`tvm_throw_last_error`()	Throw TVMGetLastError()
`tvm_load_matrix_sync`(fragment, m, n, k, ...)	TVM intrinsic for tensor core load operators
`tvm_store_matrix_sync`(fragment, m, n, k, ...)	TVM intrinsic for tensor core store operators
`tvm_mma_sync`(fragment_d, index_d, ...)	TVM intrinsic for tensor core mma_sync operators
`tvm_bmma_sync`(fragment_d, index_d, ...)	TVM intrinsic for tensor core bmma_sync operators
`tvm_fill_fragment`(fragment, m, n, k, index, ...)	TVM intrinsic for tensor core fill_fragment operators
`ptx_mma`(dtype, shape, A_layout, B_layout, ...)	TVM intrinsic for ptx tensor core mma instructions https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-for-mma
`ptx_mma_sp`(dtype, shape, A_layout, B_layout, ...)	TVM intrinsic for sparse tensor core ptx instructions https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-for-sparse-mma
`mma_store`(dtype, m, n, dst_ptr, src_ptr, ...)	TVM intrinsic for storing the result of PTX MMA into a destination pointer
`mma_fill`(dtype, local_size, local_ptr, offset)	TVM intrinsic for zero-initalizing an MMA accumulation registor
`ptx_ldmatrix`(dtype, trans, num, type, ...)	TVM intrinsic for ptx load matrix from shared memory https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-ldmatrix
`ptx_cp_async`(dtype, shared_ptr, ...)	TVM intrinsic for ptx async copy from global to shared memory https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async
`ptx_commit_group`()	TVM intrinsic for ptx async copy commit https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-commit-group
`ptx_wait_group`(num)	TVM intrinsic for ptx async copy wait https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-wait-group
`vectorlow`(dtype, vec)	Get the low level half of the vector
`vectorhigh`(dtype, vec)	Get the high level half of the vector
`vectorcombine`(dtype, vec1, vec2)	Concat two vectors
`infinity`(dtype[, span])	infinity value of dtype
`reinterpret`(dtype, value)	infinity value of dtype
`exp`(x)	Take exponential of input x.
`exp2`(x)	Calculate 2**x
`exp10`(x)	Calculate 10**x
`log`(x)	Take log of input x.
`log2`(x)	Take log2 of input x.
`log10`(x)	Take log10 of input x.
`log1p`(x)	Take log(x + 1) with respect to input x.
`ldexp`(x1, x2)	Returns x1 * (2 ** x2).
`clz`(x)	Count leading zero bits of an integer x.
`sin`(x)	Take sin of input x.
`sinh`(x)	Take sinh of input x.
`asin`(x)	Take asin of input x.
`asinh`(x)	Take asinh of input x.
`cos`(x)	Take cos of input x.
`cosh`(x)	Take cosh of input x.
`acos`(x)	Take acos of input x.
`acosh`(x)	Take acos of input x.
`tan`(x)	Take tan of input x.
`tanh`(x)	Take hyperbolic tanh of input x.
`atan`(x)	Take atan of input x.
`atan2`(x1, x2)	Take arctan2(x1, x2).
`atanh`(x)	Take atanh of input x.
`erf`(x)	Take gauss error function of the input x.
`sigmoid`(x)	Quick function to get sigmoid
`sqrt`(x)	Take square root of input x.
`rsqrt`(x)	Take reciprocal of square root of input x.
`floor`(x[, span])	Take floor of float input x.
`ceil`(x[, span])	Take ceil of float input x.
`hypot`(x1, x2)	Equivalent to sqrt(x12 + x22), element-wise.
`trunc`(x[, span])	Get truncated value of the input.
`abs`(x[, span])	Get absolute value of the input element-wise.
`round`(x[, span])	Round elements of the array to the nearest integer.
`nextafter`(x1, x2)	Return the next floating-point value after x1 towards x2.
`nearbyint`(x[, span])	Round elements of the array to the nearest integer.
`power`(x, y[, span])	x power y
`popcount`(x)	Count the number of set bits in input x.
`fmod`(x, y)	Return the remainder of x divided by y with the same sign as x.
`if_then_else`(cond, t, f[, span])	Conditional selection expression.
`likely`(cond[, span])	Mark condition as likely.
`isnan`(x[, span])	Check if input value is Nan.
`isnullptr`(x[, span])	Check if input value is nullptr.
`isfinite`(x[, span])	Check if input value is finite.
`isinf`(x[, span])	Check if input value is infinite.
`copysign`(x1, x2)	Change the sign of x1 to that of x2, element-wise.
`div`(a, b[, span])	Compute a / b as in C/C++ semantics.
`indexdiv`(a, b[, span])	Compute floor(a / b) where a and b are non-negative.
`indexmod`(a, b[, span])	Compute the remainder of indexdiv.
`truncdiv`(a, b[, span])	Compute the truncdiv of two expressions.
`truncmod`(a, b[, span])	Compute the truncmod of two expressions.
`floordiv`(a, b[, span])	Compute the floordiv of two expressions.
`floormod`(a, b[, span])	Compute the floormod of two expressions.
`ceildiv`(lhs, rhs[, span])	Generic ceildiv operator.
`comm_reducer`(fcombine, fidentity[, name])	Create a commutative reducer for reduction.
`min`(expr, axis[, where, init])	Create a min expression over axis.
`max`(expr, axis[, where, init])	Create a max expression over axis.
`sum`(expr, axis[, where, init])	Create a sum expression over axis.
`q_multiply_shift`(x, y, q, s)	Execute a multiplication between two Q-numbers x and y followed by a right shift s.
`q_multiply_shift_per_axis`(x, y, ls, rs, q, ...)	Execute a multiplication between two Q-numbers x and y
`shift_left`(x, y[, span])	Return the result of x left shifted by y bits.
`shift_right`(x, y[, span])	Return the result of x right shifted by y bits.
`TVMBackendAllocWorkspace`(device_type, ...)	Backend function to allocate temporal workspace
`TVMBackendFreeWorkspace`(device_type, ...)	Backend function to free temporal workspace.
`start_profile_intrinsic`(id)	Start profile intrinsic.
`end_profile_intrinsic`(id)	End profile intrinsic.
`add`(lhs, rhs[, span])	Generic add operator.
`subtract`(lhs, rhs[, span])	Generic subtract operator.
`multiply`(lhs, rhs[, span])	Generic multiply operator.

Exceptions:

ScheduleError

Error that happens during TensorIR scheduling.

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.
`scope`()	Return the storage scope associated with this buffer.
`get_flattened_buffer`()	Generate a Buffer that is a flattened version of this buffer.
`offset_of`(indices)	Determine the offset of the provided indices in the flattened buffer.

access_ptr(access_mask, ptr_type='handle', content_lanes=1, offset=0, extent=None)¶

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.
extent (Expr, optional) – The extent of pointer.

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)
# Get access ptr for read with extent
buffer.access_ptr("r", extent = 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

scope()¶: Return the storage scope associated with this buffer. :returns: scope – The storage scope associated with this buffer. :rtype: str

get_flattened_buffer()¶

Generate a Buffer that is a flattened version of this buffer.

Returns: flattened – The corresponding flat buffer.
Return type: Buffer

offset_of(indices)¶

Determine the offset of the provided indices in the flattened buffer.

Parameters: indices (Union[PrimExpr, List[PrimExpr]]) – The indices of the element in the original buffer.
Returns: flattened_indices – The offset indices of the element in the flattened buffer.
Return type: List[PrimExpr]

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

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.
axis_separators (list of int, optional) – If passed, a list of separators between groups of axes, each of which is flattened to an output axis. For flat memory spaces, should either be None, or an empty list.
span (Optional[Span]) – The location of the decl_buffer creation in the source.

Returns

buffer – The created buffer

Return type

tvm.tir.Buffer

Example

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

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

Note

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

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

class tvm.tir.DataProducer¶

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:

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

See also

bijective_layout: Declare a layout

Methods:

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

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: Union[str, tvm.tir.data_layout.Layout], dst_layout: Union[str, tvm.tir.data_layout.Layout]) → tvm.tir.data_layout.BijectiveLayout¶

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: str) → tvm.tir.data_layout.Layout¶

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: str, dtype: Union[str, tvm.ir.type.Type], span: Optional[tvm.ir.base.Span] = None)¶

Symbolic variable.

Parameters

name (str) – The name
dtype (Union[str, tvm.irType]) – The data type
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.SizeVar(name, dtype, span=None)¶

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

class tvm.tir.Reduce(combiner, src, rdom, condition, value_index, init=None, span=None)¶

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.
init (list of Expr) – The initial value for output. This can be an int, float or ProducerLoad
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.FloatImm(dtype, value, span=None)¶

Float constant.

Parameters

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

class tvm.tir.IntImm(dtype, value, span=None)¶

Int constant.

Parameters

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

class tvm.tir.StringImm(value, span=None)¶

String constant.

Parameters

value (str) – The value of the function.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Cast(dtype, value, span=None)¶

Cast expression.

Parameters

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

class tvm.tir.Add(a, b, span=None)¶

Add node.

Parameters

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

class tvm.tir.Sub(a, b, span=None)¶

Sub node.

Parameters

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

class tvm.tir.Mul(a, b, span=None)¶

Mul node.

Parameters

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

class tvm.tir.Div(a, b, span=None)¶

Div node.

Parameters

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

class tvm.tir.Mod(a, b, span=None)¶

Mod node.

Parameters

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

class tvm.tir.FloorDiv(a, b, span=None)¶

FloorDiv node.

Parameters

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

class tvm.tir.FloorMod(a, b, span=None)¶

FloorMod node.

Parameters

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

class tvm.tir.Min(a, b, span=None)¶

Min node.

Parameters

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

class tvm.tir.Max(a, b, span=None)¶

Max node.

Parameters

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

class tvm.tir.EQ(a, b, span=None)¶

EQ node.

Parameters

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

class tvm.tir.NE(a, b, span=None)¶

NE node.

Parameters

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

class tvm.tir.LT(a, b, span=None)¶

LT node.

Parameters

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

class tvm.tir.LE(a, b, span=None)¶

LE node.

Parameters

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

class tvm.tir.GT(a, b, span=None)¶

GT node.

Parameters

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

class tvm.tir.GE(a, b, span=None)¶

GE node.

Parameters

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

class tvm.tir.And(a, b, span=None)¶

And node.

Parameters

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

class tvm.tir.Or(a, b, span=None)¶

Or node.

Parameters

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

class tvm.tir.Not(a, span=None)¶

Not node.

Parameters

a (PrimExpr) – The input value
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Select(condition, true_value, false_value, span=None)¶

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

class tvm.tir.BufferLoad(buffer, indices, span=None)¶

Buffer load node.

Parameters

buffer (Buffer) – The buffer to be loaded.
indices (List[PrimExpr]) – The buffer indices.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.ProducerLoad(producer, indices, span=None)¶

Producer load node.

Parameters

producer (DataProducer) – The buffer to be loaded.
indices (List[PrimExpr]) – The buffer indices.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Load(dtype, buffer_var, index, predicate=None, span=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.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Ramp(base, stride, lanes, span=None)¶

Ramp node.

Parameters

base (PrimExpr) – The base expression.
stride (ramp stride) – The stride of the ramp.
lanes (int) – The lanes of the expression.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Broadcast(value, lanes, span=None)¶

Broadcast node.

Parameters

value (PrimExpr) – The value of the expression.
lanes (int) – The lanes of the expression.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Shuffle(vectors, indices, span=None)¶

Shuffle node.

Parameters

vectors (Array of Expr) – The vectors
indices (Array of indices) – The indices
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Call(dtype, op, args, span=None)¶

Call node.

Parameters

dtype (str) – The return data type
op (Union[RelayExpr, str]) – The function to be called, or the name to the global tvm.Op
args (list of Expr) – The input arguments to the call
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.CallEffectKind¶: Possible kinds of Call effects.

class tvm.tir.Let(var, value, body, span=None)¶

Let node.

Parameters

var (Var) – The variable in the binding.
value (PrimExpr) – The value in to be binded.
body (PrimExpr) – The body expression.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.IterVar(dom, var, iter_type, thread_tag='', span=None)¶

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

See also

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

class tvm.tir.CommReducer(lhs, rhs, result, identity_element, span=None)¶

Commutative reduce operator

Parameters

lhs (List[Var]) – The left arguments of the reducer.
rhs (List[Var]) – The right arguments of the reducer.
result (List[PrimExpr]) – The reduction results.
identity_element (List[PrimExpr]) – The identity elements.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Any(span=None)¶

Any node.

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

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

class tvm.tir.LetStmt(var, value, body, span=None)¶

LetStmt node.

Parameters

var (Var) – The variable in the binding.
value (PrimExpr) – The value in to be binded.
body (Stmt) – The body statement.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.AssertStmt(condition, message, body, span=None)¶

AssertStmt node.

Parameters

condition (PrimExpr) – The assert condition.
message (PrimExpr) – The error message.
body (tvm.tir.Stmt) – The body statement.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.ForKind(value)¶: The kind of the for loop.

Note

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

class tvm.tir.For(loop_var, min_val, extent, kind, body, thread_binding=None, annotations=None, span=None)¶

For node.

Parameters

loop_var (Var) – The loop variable.
min_val (PrimExpr) – The beginning value.
extent (PrimExpr) – The length of the loop.
kind (ForKind) – The type of the for.
body (Stmt) – The body statement.
thread_binding (Optional[tir.IterVar]) – The thread this loop binds to. Only valid if kind is ThreadBinding
annotations (tvm.ir.Map) – Additional annotation hints.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.While(condition, body, span=None)¶

While node.

Parameters

condition (PrimExpr) – The termination condition.
body (Stmt) – The body statement.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.BufferStore(buffer, value, indices, span=None)¶

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

class tvm.tir.BufferRealize(buffer, bounds, condition, body, span=None)¶

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

class tvm.tir.Store(buffer_var, value, index, predicate=None, span=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.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.ProducerStore(producer, value, indices, span=None)¶

ProducerStore node.

Parameters

producer (DataProducer) – The data producer.
value (PrimExpr) – The value to be stored.
indices (list of Expr) – The index arguments of the store.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Allocate(buffer_var, dtype, extents, condition, body, annotations=None, span=None)¶

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.
annotations (Optional[Mapping[str, Object]]) – Additional annotation hints
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.AllocateConst(buffer_var, dtype, extents, data_or_idx, body, annotations=None, span=None)¶

Allocate constant 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
data_or_idx (Union[NDArray, int]) – If an NDArray, this is the const data associated with the constant. If an integer, this is the index into the “constants” attribute of the IRModule that contains the AllocateConst.
body (Stmt) – The body statement.
annotations (Optional[Map]) – Additional annotations about the allocation.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.AttrStmt(node, attr_key, value, body, span=None)¶

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

class tvm.tir.DeclBuffer(buffer, body, span=None)¶

DeclBuffer node.

Parameters

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

class tvm.tir.ProducerRealize(producer, bounds, condition, body, storage_scope='', span=None)¶

ProducerRealize node.

Parameters

producer (DataProducer) – The data producer.
bounds (list of range) – The bound of realize
condition (PrimExpr) – The realize condition.
body (Stmt) – The realize body
storage_scope (str) – The storage scope associated with this realization
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.SeqStmt(seq, span=None)¶

Sequence of statements.

Parameters

seq (List[Stmt]) – The statements
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.IfThenElse(condition, then_case, else_case, span=None)¶

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

class tvm.tir.Evaluate(value, span=None)¶

Evaluate node.

Parameters

value (PrimExpr) – The expression to be evalued.
span (Optional[Span]) – The location of this itervar in the source code.

class tvm.tir.Prefetch(buffer, bounds, span=None)¶

Prefetch node.

Parameters

buffer (Buffer) – The buffer to be prefetched.
bounds (list of Range) – The bounds to be prefetched.
span (Optional[Span]) – The location of this itervar in the source code.

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.BufferRegion(buffer: tvm.tir.buffer.Buffer, region: List[tvm.ir.expr.Range])¶

BufferRegion node.

Parameters

buffer (Buffer) – The buffer of the buffer region
region (List[Range]) – The region array of the buffer region

class tvm.tir.MatchBufferRegion(buffer: tvm.tir.buffer.Buffer, source: tvm.tir.stmt.BufferRegion)¶

MatchBufferRegion node.

Parameters

buffer (Buffer) – The target buffer
source (BufferRegion) – The region of source buffer

class tvm.tir.Block(iter_vars: List[tvm.tir.expr.IterVar], reads: List[tvm.tir.stmt.BufferRegion], writes: List[tvm.tir.stmt.BufferRegion], name_hint: str, body: tvm.tir.stmt.Stmt, init: Optional[tvm.tir.stmt.Stmt] = None, alloc_buffers: Optional[List[tvm.tir.buffer.Buffer]] = None, match_buffers: Optional[List[tvm.tir.stmt.MatchBufferRegion]] = None, annotations: Optional[Mapping[str, tvm.runtime.object.Object]] = None, span: Optional[tvm.ir.base.Span] = None)¶

Block node.

Parameters

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

class tvm.tir.BlockRealize(iter_values: List[tvm.ir.expr.PrimExpr], predicate: Union[tvm.ir.expr.PrimExpr, bool], block: tvm.tir.stmt.Block, span: Optional[tvm.ir.base.Span] = None)¶

BlockRealize node.

Parameters

iter_values (List[PrimExpr]) – The binding values of the block var.
predicate (Union[PrimExpr, bool]) – The predicate of the block.
block (Block) – The block to realize
span (Optional[Span]) – The location of this block_realize in the source code.

class tvm.tir.PrimFunc(params, body, ret_type=None, buffer_map=None, attrs=None, span=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
span (Optional[Span]) – The location of this itervar in the source code.

Methods:

`with_body`(new_body[, span])	Create a new PrimFunc with the same set signatures but a new body.
`specialize`(param_map)	Specialize parameters of PrimFunc
`script`([tir_prefix, show_meta])	Print IRModule into TVMScript
`show`([style, black_format])	A sugar for print highlighted TVM script.

with_body(new_body, span=None)¶

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

Parameters

new_body (Stmt) – The new body.
span (Optional[Span]) – The location of this itervar in the source code.

Returns

new_func – The created new function.

Return type

PrimFunc

specialize(param_map: Mapping[tvm.tir.expr.Var, Union[tvm.ir.expr.PrimExpr, tvm.tir.buffer.Buffer]])¶

Specialize parameters of PrimFunc

Parameters: param_map (Mapping[Var, Union[PrimExpr, Buffer]]) – The mapping from function params to the instance

Examples

We can define a Meta TIR function with symbolic shape:

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

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

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

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

The specialized function:

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

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

Returns: func – The new function with parameter specialized
Return type: PrimFunc

script(tir_prefix: str = 'T', show_meta: bool = False) → str¶

Print IRModule into TVMScript

Parameters

tir_prefix (str) – The tir namespace prefix
show_meta (bool) – Whether to show meta information

Returns

script – The TVM Script of the PrimFunc

Return type

str

show(style: Optional[str] = None, black_format: bool = True) → None¶

A sugar for print highlighted TVM script.

Parameters

style (str, optional) – Pygmentize printing style, auto-detected if None. See tvm.script.highlight.cprint for more details.
black_format (bool) – If true (default), use the formatter Black to format the TVMScript

class tvm.tir.TensorIntrin(desc, impl)¶

A tensor intrinsic.

Parameters

desc (PrimFunc) – The function to describe the computation.
impl (PrimFunc) – The function of the implementation for the execution.

Methods:

`register`(name, desc, impl[, override])	Register a tensor intrinsic with its name.
`get`(name[, allow_missing])	Look up a tensor intrinsic by its name.

static register(name: str, desc: tvm.tir.function.PrimFunc, impl: tvm.tir.function.PrimFunc, override: bool = False)¶

Register a tensor intrinsic with its name.

Parameters

name (str) – The name of the TensorIntrin to register.
desc (PrimFunc) – The function to describe the computation.
impl (PrimFunc) – The function of the implementation for the execution.
override (bool) – Whether override existing intrinsic.

static get(name: str, allow_missing: bool = False) → Optional[tvm.tir.function.TensorIntrin]¶

Look up a tensor intrinsic by its name.

Parameters

name (str) – The name of the TensorIntrin to look up.
allow_missing (bool) – Whether to allow missing tensor intrin. If False, raise an error if the tensor intrin
exist. (doesn't) –

Returns

result – The TensorIntrin with the specified name, or None if not found.

Return type

Optional[TensorIntrin]

class tvm.tir.IndexMap(initial_indices, final_indices, inverse_index_map)¶

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

Parameters

initial_indices (List[Var]) – Variables representing the indices prior to remapping.
final_indices (List[PrimExpr]) – Expressions defining the indices after remapping.
inverse_index_map (Union[Callable, Optional[IndexMap]]) – The optional pre-defined inverse index map. When this is defined, IndexMap::Inverse will return the pre-defined inverse index map. Otherwise, the inverse index map will be computed on the fly. It is the user’s responsibility to ensure the correctness of the pre-defined inverse index map.

Methods:

`from_func`(mapping_function[, ndim, ...])	Create an index map from a function
`from_func_with_separators`(mapping_function)	Create an index map from a function
`is_equivalent_to`(other_map)	Return if the index maps are equivalent.
`map_indices`(indices)	Apply the index map to a set of indices
`map_shape`(shape)	Apply the index map to a buffer shape
`map_ndarray`(arr_src)	Apply thie index map to transform the layout of the input NDArray
`inverse`(shape)	Return the inverse of the map
`non_surjective_inverse`(shape)	Return the inverse of the map

static from_func(mapping_function: Callable, ndim: Optional[int] = None, inverse_index_map: Optional[Union[Callable, tvm.tir.function.IndexMap]] = None)¶

Create an index map from a function

Parameters

mapping_function (Callable) – The function to map from source indices to target indices. The function should accept tir.Var parameters and return a either a tir.PrimExpr, or a list of tir.PrimExpr. Returning a tir.PrimExpr is equivalent to returning a list of length 1 containing that tir.PrimExpr.
ndim (Optional[int]) – The dimensionality of the buffer to which this transformation should be applied. If mapping_function uses variadic argument *args, ndim must be specified. If mapping_function does not use variadic arguments, ndim is optional.
inverse_index_map (Union[Callable, Optional[IndexMap]]) – The optional pre-defined inverse index map. When this is defined, IndexMap::Inverse will return the pre-defined inverse index map. Otherwise, the inverse index map will be computed on the fly. It is the user’s responsibility to ensure the correctness of the pre-defined inverse index map.

Returns

index_map – Returns an IndexMap representing the mapping_function.

Return type

IndexMap

static from_func_with_separators(mapping_function: Callable, ndim: Optional[int] = None, inverse_index_map: Optional[Union[Callable, tvm.tir.function.IndexMap]] = None)¶

Create an index map from a function

Parameters

mapping_function (Callable) – The function to map from source indices to target indices. The function should accept tir.Var parameters and return either a tir.PrimExpr or a list. Each element of the returned list should be either a tir.PrimExpr or the object IndexMap.AXIS_SEPARATOR. Returning a tir.PrimExpr is equivalent to returning a list of length 1 containing that tir.PrimExpr.
ndim (Optional[int]) – The dimensionality of the buffer to which this transformation should be applied. If mapping_function uses variadic argument *args, ndim must be specified. If mapping_function does not use variadic arguments, ndim is optional.
inverse_index_map (Union[Callable, Optional[IndexMap]]) – The optional pre-defined inverse index map. When this is defined, IndexMap::Inverse will return the pre-defined inverse index map. Otherwise, the inverse index map will be computed on the fly. It is the user’s responsibility to ensure the correctness of the pre-defined inverse index map.

Returns

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

Return type

Tuple[IndexMap, List[int]]

is_equivalent_to(other_map: tvm.tir.function.IndexMap) → bool¶

Return if the index maps are equivalent.

Parameters: other_map (IndexMap) – The IndexMap to which the comparison should be made.
Returns: is_equivalent – True if the two mappings represent the same transformation, otherwise False
Return type: bool

map_indices(indices: List[tvm.ir.expr.PrimExpr]) → List[tvm.ir.expr.PrimExpr]¶

Apply the index map to a set of indices

Parameters: indices (List[PrimExpr]) – The indices to be mapped
Returns: result – The mapped indices
Return type: List[PrimExpr]

map_shape(shape: List[tvm.ir.expr.PrimExpr]) → List[tvm.ir.expr.PrimExpr]¶

Apply the index map to a buffer shape

Parameters: shape (List[PrimExpr]) – The buffer shape to be mapped
Returns: result – The mapped shape
Return type: List[PrimExpr]

map_ndarray(arr_src: tvm.runtime.ndarray.NDArray) → tvm.runtime.ndarray.NDArray¶

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

Parameters: arr_src (runtime.NDArray) – The NDArray to be transformed
Returns: arr_dst – The transformed NDArray
Return type: runtime.NDArray

inverse(shape: List[Union[tvm.ir.expr.Range, tvm.ir.expr.PrimExpr]]) → tvm.tir.function.IndexMap¶

Return the inverse of the map

Throws an error if the function is not bijective.

Parameters: shape (List[Union[Range,PrimExpr]]) – The region over which the inverse should be determined. Used for validating that the mapping is bijective over this range.
Returns: inverse – The inverse
Return type: IndexMap

non_surjective_inverse(shape: List[Union[tvm.ir.expr.Range, tvm.ir.expr.PrimExpr]]) → Tuple[tvm.tir.function.IndexMap, tvm.ir.expr.PrimExpr]¶

Return the inverse of the map

Can be applied to transformations that introduce padding.

Parameters: shape (List[Union[Range,PrimExpr]]) – The region over which the inverse should be determined. Used for determining the predicate.
Returns: result – The inverse, and a predicate for which the inverse maps to a valid index in the input range.
Return type: Tuple[IndexMap, PrimExpr]

Examples

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

tvm.tir.call_packed_lowered(*args, span=None)¶

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

Returns

call – The call expression.

Return type

PrimExpr

See also

te.extern: Create tensor with extern function call.

tvm.tir.call_cpacked_lowered(*args, span=None)¶

Lowered version of call c-packed. Same as call_packed, except that the first argument is the function name (as in call_extern), and the last argument is the resource handle.

Parameters

args (list of Expr or Buffer.) – Positional arguments.
span (Optional[Span]) – The location of this operator in the source code.

Returns

call – The call expression.

Return type

PrimExpr

See also

te.extern: Create tensor with extern function call.

tvm.tir.call_packed(*args, span=None)¶

Build expression by call an external packed function.

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

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

Parameters

args (list of Expr or Buffer.) – Positional arguments.
span (Optional[Span]) – The location of this operator in the source code.

Returns

call – The call expression.

Return type

PrimExpr

See also

te.extern: Create tensor with extern function call.

tvm.tir.call_cpacked(*args, span=None)¶

Build expression by call an external packed function.

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

Parameters

args (list of Expr or Buffer.) – Positional arguments.
span (Optional[Span]) – The location of this operator in the source code.

Returns

call – The call expression.

Return type

PrimExpr

See also

te.extern: Create tensor with extern function call.

tvm.tir.call_intrin(dtype, func_name, *args, span=None)¶

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

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.call_pure_extern(dtype, func_name, *args, span=None)¶

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

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.call_extern(dtype, func_name, *args, span=None)¶

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

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.call_llvm_intrin(dtype, name, *args, span=None)¶

Build expression by calling a 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.
span (Optional[Span]) – The location of this operator in the source code.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.call_llvm_pure_intrin(dtype, name, *args, span=None)¶

Build expression by calling a pure 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.
span (Optional[Span]) – The location of this operator in the source code.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.ret(val)¶

Create a tir return expression

Parameters: val (Expr) – The returned tir expression, whose data type is int, float or void pointer.
Returns: ret – The return expression
Return type: PrimExpr

tvm.tir.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.tir.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.tir.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.tir.max_value(dtype: str, span: Optional[tvm.ir.base.Span] = 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.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.tvm_check_return(expected, return_unexpected, nested_call)¶

Return new on stack dtype[num] :param expected: The expected return code. :type expected: int :param return_unexpected: The unexpected return code. :type return_unexpected: int :param nested_call: The call expression to check return. :type nested_call: PrimExpr

Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.tvm_stack_alloca(dtype_str, num)¶

Return new on stack dtype[num]

Parameters

dtype_str (str) – The data type of array.
num (int) – The size of array.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.tvm_stack_make_shape(*args)¶

Allocate a shape tuple on stack, return the handle

Parameters: args (int) – The tuple shape.
Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.tvm_stack_make_array(data, shape, strides, ndim, arr_dtype, elem_offset)¶

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

Parameters

data (Expr) – The data of array.
shape (Expr) – The shape of array.
strides (Expr) – The strides of array.
ndim (Expr) – The dimensions of array.
arr_dtype (Expr) – The data type of array.
elem_offse (Expr) – The element offset of array.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.tvm_tuple(*value)¶

Create a tuple structure in value field of AttrStmt

Parameters: value (Expr) – The value in tuple.
Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.tvm_struct_get(arr, index, field, dtype)¶

Get struct field value in array

Parameters

dtype (str) – The date type of the result.
arr (StructType*) – The array of struct.
index (int) – The index of struct.
field (int) – The field of struct.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.tvm_struct_set(arr, index, field, value)¶

Set value in struct field in array

Parameters

arr (StructType*) – The array of struct.
index (int) – The index of struct.
field (int) – The field of struct.
value (Expr) – The value to be set in field.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.address_of(buffer_load, span=None)¶

Returns the address of an element in the buffer

Parameters

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

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.lookup_param(param_name, span=None)¶

Returns the param by name

Parameters

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

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.assume(cond=None)¶

Provide a true statement that can be used for simplifications

Parameters: cond (Expr) – The constraint condition.
Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.undef()¶

Returns an initialized but arbitrary value

Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.tvm_thread_allreduce(*freduce_args)¶

Parameters: freduce_args (Expr) – The args.
Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.type_annotation(dtype)¶

Create a type annotation expression

Parameters: dtype (Expr) – The data type.
Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.tvm_access_ptr(ptype, data, offset, extent, rw_mask)¶

Get head access address with memory access pattern info

Parameters

ptype (Expr) – The data type of pointer.
data (DType*) – The data of pointer.
offset (int) – The offset of pointer.
extent (int) – The extent of pointer.
rw_mask (int) – The read write mask.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.tvm_throw_last_error()¶

Throw TVMGetLastError()

Returns: ret – The return expression
Return type: PrimExpr

tvm.tir.tvm_load_matrix_sync(fragment, m, n, k, index, buffer_ptr, stride, layout)¶

TVM intrinsic for tensor core load operators

Parameters

fragment (Var) – The wmma fragment.
m (UIntImm) – The shape of wmma fragment.
n (UIntImm) – The shape of wmma fragment.
k (UIntImm) – The shape of wmma fragment.
index (Expr) – The fragment index.
buffer_ptr (Expr) – The fragment buffer pointer.
stride (Expr) – The fragment stride.
layout (Literal["row_major", "column_major"]) – The fragment layout.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.tvm_store_matrix_sync(fragment, m, n, k, index, buffer_ptr, stride, layout)¶

TVM intrinsic for tensor core store operators

Parameters

fragment (Var) – The wmma fragment.
m (UIntImm) – The shape of wmma fragment.
n (UIntImm) – The shape of wmma fragment.
k (UIntImm) – The shape of wmma fragment.
index (Expr) – The fragment index.
buffer_ptr (Expr) – The fragment buffer pointer.
stride (Expr) – The fragment stride.
layout (Literal["row_major", "column_major"]) – The fragment layout.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.tvm_mma_sync(fragment_d, index_d, fragment_a, index_a, fragment_b, index_b, fragment_c, index_c)¶

TVM intrinsic for tensor core mma_sync operators

Parameters

fragment_d (Var) – The wmma fragment_d.
index_d (Expr) – The fragment_d index.
fragment_a (Var) – The wmma fragment_a.
index_a (Expr) – The fragment_a index.
fragment_b (Var) – The wmma fragment_b.
index_b (Expr) – The fragment_b index.
fragment_c (Var) – The wmma fragment_c.
index_c (Expr) – The fragment_c index.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.tvm_bmma_sync(fragment_d, index_d, fragment_a, index_a, fragment_b, index_b, fragment_c, index_c)¶

TVM intrinsic for tensor core bmma_sync operators

Parameters

fragment_d (Var) – The bwmma fragment_d.
index_d (Expr) – The fragment_d index.
fragment_a (Var) – The bwmma fragment_a.
index_a (Expr) – The fragment_a index.
fragment_b (Var) – The bwmma fragment_b.
index_b (Expr) – The fragment_b index.
fragment_c (Var) – The bwmma fragment_c.
index_c (Expr) – The fragment_c index.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.tvm_fill_fragment(fragment, m, n, k, index, value)¶

TVM intrinsic for tensor core fill_fragment operators

Parameters

fragment (Var) – The wmma fragment
m (UIntImm) – The shape of wmma fragment.
n (UIntImm) – The shape of wmma fragment.
k (UIntImm) – The shape of wmma fragment.
index (Expr) – The fragment index.
value (Expr) – The value to be filled in fragment.

Returns

call – The call expression.

Return type

PrimExpr

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

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

Parameters

dtype (str) – The data type of the result.
shape (str) – The shape of mma fragment.
A_layout (Literal["row", "col"]) – The layout of multiplicand fragment A.
B_layout (Literal["row", "col"]) – The layout of multiplicand fragment B.
A_dtype (str) – The data type of multiplicand fragment A.
B_dtype (str) – The data type of multiplicand fragment B.
C_dtype (str) – The data type of accumulator fragment C.
multiplicand_a (Var) – The multiplicand fragment A variable.
a_index (Expr) – The index of multiplicand fragment A.
multiplicand_b (Var) – The multiplicand fragment B variable.
b_index (Expr) – The index of multiplicand fragment A.
accumulator (Var) – The accumulator fragment C variable.
c_index (Expr) – The index of accumulator fragment C.
saturate (bool) – The optional saturation at the output.

operatorOptional[Literal[“xor”, “and”]]: The 1-bit operator.

Returns: call – The call expression.
Return type: PrimExpr

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

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

Parameters

dtype (str) – The data type of the result.
shape (str) – The shape of mma fragment.
A_layout (Literal["row", "col"]) – The layout of multiplicand fragment A.
B_layout (Literal["row", "col"]) – The layout of multiplicand fragment B.
A_dtype (str) – The data type of multiplicand fragment A.
B_dtype (str) – The data type of multiplicand fragment B.
C_dtype (str) – The data type of multiplicand fragment C.
multiplicand_a (Var) – The multiplicand fragment A variable.
a_index (Expr) – The index of multiplicand fragment A.
multiplicand_b (Var) – The multiplicand fragment B variable.
b_index (Expr) – The index of multiplicand fragment B.
accumulator (Var) – The accumulator fragment C variable.
c_index (Expr) – The index of accumulator fragment C.
metadata (Expr) – The metadata of operand.
meta_index (Expr) – The metadata index of operand.
sparse_selector (Expr) – The sparse selector indicating the thread that stores the metadata.
saturate (bool) – The optional saturation at the output.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.mma_store(dtype, m, n, dst_ptr, src_ptr, src_offset, dst_stride)¶

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

Parameters

dtype (str) – The data type of the result.
m (IntImm) – The shape of mma fragment.
n (IntImm) – The shape of mma fragment.
dst_ptr (Var) – The destination pointer variable.
src_ptr (Var) – The source pointer variable.
src_offset (Expr) – The source offset.
dst_stride (Var) – The destination stride.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.mma_fill(dtype, local_size, local_ptr, offset)¶

TVM intrinsic for zero-initalizing an MMA accumulation registor

Parameters

dtype (str) – The data type of the result.
local_size (IntImm) – The number of elements.
local_ptr (Var) – The destination pointer variable.
offset (Expr) – The destination offset.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.ptx_ldmatrix(dtype, trans, num, type, local_ptr, local_offset, smem_ptr, smem_offset)¶

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

Parameters

dtype (str) – The data type of the result.
trans (bool) – The matrix is loaded in column-major format.
num (IntImm) – The number of matrices.
type (Literal[".b16"]) – The data type of the matrices.
local_ptr (Var) – The local pointer variable.
local_offset (Expr) – The offset of local pointer.
smem_ptr (Var) – The shared memory pointer variable.
smem_offset (Expr) – The offset of shared memort pointer.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.ptx_cp_async(dtype, shared_ptr, shared_offset, global_ptr, global_offset, bytes)¶

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

Parameters

dtype (str) – The data type of the result.
shared_ptr (Var) – The shared memory pointer variable.
shared_offset (Expr) – The offset of shared memory pointer.
global_ptr (Var) – The global memory pointer variable.
global_offset (Expr) – The offset of global memory pointer.
bytes (int) – The data size to copy.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.ptx_commit_group()¶

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

Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.ptx_wait_group(num)¶

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

Parameters: num (int) – The number of the most recent uncommitted pending cp.async groups to wait.
Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.vectorlow(dtype, vec)¶

Get the low level half of the vector

Parameters

dtype (str) – The data type of the result.
vec (list) – The input vector.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.vectorhigh(dtype, vec)¶

Get the high level half of the vector

Parameters

dtype (str) – The data type of the result.
vec (list) – The input vector.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.vectorcombine(dtype, vec1, vec2)¶

Concat two vectors

Parameters

vec1 (list) – The input vector.
vec2 (list) – The input vector.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.infinity(dtype: str, span: Optional[tvm.ir.base.Span] = None) → Any¶

infinity value of dtype

Parameters

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

Returns

value – The infinity value of dtype.

Return type

tvm.Expr

tvm.tir.reinterpret(dtype, value) → Any¶

infinity value of dtype

Parameters

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

Returns

value – The reinterpret cast value of dtype.

Return type

tvm.Expr

tvm.tir.exp(x)¶

Take exponential 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.clz(x)¶

Count leading zero bits of an integer x.

Parameters: x (PrimExpr) – Input 32 or 64 bit integer. The result is undefined if the input is 0.
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: tvm.tir.expr.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.tir.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.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, span=None)¶

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

Returns

y – The result.

Return type

PrimExpr

tvm.tir.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.tir.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.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, 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.tir.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.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, 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.tir.likely(cond, span=None)¶

Mark condition as likely.

Parameters

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

Returns

y – The marked expression.

Return type

PrimExpr

tvm.tir.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.tir.isnullptr(x, span=None)¶

Check if input value is nullptr.

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.tir.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.tir.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.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, 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.tir.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.tir.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.tir.truncdiv(a, b, span=None)¶

Compute the truncdiv 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

Note

This is the default integer division behavior in C.

tvm.tir.truncmod(a, b, span=None)¶

Compute the truncmod 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

Note

This is the default integer division behavior in C.

tvm.tir.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.tir.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.tir.ceildiv(lhs, rhs, span=None)¶

Generic ceildiv 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 ceildiv operaton.

Return type

tvm.Expr

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, 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.tir.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.tir.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.tir.q_multiply_shift(x, y, q, s)¶

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

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

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

Parameters

x (PrimExpr) – First Q-number
y (PrimExpr) – Second Q-number
q (PrimExpr) – Number of fractional bits in x and y. Needs to be > 0
s (PrimExpr) – Integer shift

Returns

y – The result.

Return type

PrimExpr

tvm.tir.q_multiply_shift_per_axis(x: tvm.ir.expr.PrimExpr, y: tvm.ir.expr.PrimExpr, ls: tvm.ir.expr.PrimExpr, rs: tvm.ir.expr.PrimExpr, q: tvm.tir.expr.IntImm, is_lshift_required: tvm.tir.expr.IntImm, is_rshift_required: tvm.tir.expr.IntImm)¶

Execute a multiplication between two Q-numbers x and y

Parameters

x (PrimExpr) – First Q-number.
y (PrimExpr) – Second Q-number.
ls (PrimExpr) – Integer left shift.
rs (PrimExpr) – Integer right shift.
q (IntImm) – Number of fractional bits in x and y. Needs to be > 0.
is_lshift_required (IntImm) – Whether we need to do left shift or not.
is_rshift_required (IntImm) – Whether we need to do right shift or not.

Returns

z – The result.

Return type

PrimExpr

tvm.tir.shift_left(x, y, span=None)¶

Return the result of x left shifted by y bits.

Parameters

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

Returns

z – The result.

Return type

PrimExpr

tvm.tir.shift_right(x, y, span=None)¶

Return the result of x right shifted by y bits.

Parameters

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

Returns

z – The result.

Return type

PrimExpr

tvm.tir.TVMBackendAllocWorkspace(device_type, device_id, nbytes, dtype_code_hint, dtype_bits_hint)¶

Backend function to allocate temporal workspace

Parameters

device_type (int) – The device type which the space will be allocated.
device_id (int) – The device id which the space will be allocated.
nbytes (int) – The size of the space requested.
dtype_code_hint (int) – The type code of the array elements. Only used in certain backends such as OpenGL.
dtype_bits_hint (int) – The type bits of the array elements. Only used in certain backends such as OpenGL.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.TVMBackendFreeWorkspace(device_type, device_id, ptr)¶

Backend function to free temporal workspace.

Parameters

device_type (int) – The device type which the space will be allocated.
device_id (int) – The device id which the space will be allocated.
ptr (Var) – The result allocated space pointer.

Returns

call – The call expression.

Return type

PrimExpr

tvm.tir.start_profile_intrinsic(id)¶

Start profile intrinsic. :param id: The intrinsic id. :type id: int

Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.end_profile_intrinsic(id)¶

End profile intrinsic. :param id: The intrinsic id. :type id: int

Returns: call – The call expression.
Return type: PrimExpr

tvm.tir.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.tir.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.tir.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

class tvm.tir.StmtSRef¶

An object that refers to schedulable elements in the TensorIR, aka “sref”.

Glossary - Block sref: An StmtSref that points to a TensorIR block. - Loop sref: An StmtSRef that points to a TensorIR for loop. - Parent sref: The parent sref of an sref is the block/loop sref that points to its closest schedulable statement of its ancestors on the TensorIR AST. - Root sref: Sref to the root block. Every sref has exactly one parent sref except for root sref. - Sref tree: The parent-children-relationship of srefs that forms a tree, uniquely determined by the TensorIR AST.

Attributes:

`stmt`	The block/for stmt the object refers to
`parent`	The parent sref

Methods:

`inline_mark`()	A special StmtSRef, which doesn't point to any stmt in the AST, only serving as a "mark" to hint compute-at to do the work of compute-inline
`root_mark`()	A special StmtSRef, which doesn't point to any stmt in the AST, only serving as a "mark" to hint compute-at to do nothing

property stmt: Optional[Union[tvm.tir.stmt.Block, tvm.tir.stmt.For]]¶: The block/for stmt the object refers to

property parent: Optional[tvm.tir.schedule.block_scope.StmtSRef]¶: The parent sref

static inline_mark() → tvm.tir.schedule.block_scope.StmtSRef¶: A special StmtSRef, which doesn’t point to any stmt in the AST, only serving as a “mark” to hint compute-at to do the work of compute-inline

static root_mark() → tvm.tir.schedule.block_scope.StmtSRef¶: A special StmtSRef, which doesn’t point to any stmt in the AST, only serving as a “mark” to hint compute-at to do nothing

class tvm.tir.BlockScope¶

An object corresponds to each block sref in the sref tree, which tracks the producer-consumer dependency between blocks.

Glossary:

Block scope: A contiguous subtree of the sref tree, rooted at each block sref, whose components are:
- scope root: a block sref
- internal srefs: loop srefs
- scope leaves: block srefs
Child block: The scope leaf blocks under the scope root or a specific internal sref

Methods:

`get_deps_by_src`(block)	Get all dependencies whose src is the target`block`.
`get_deps_by_dst`(block)	Get all dependencies whose dst is the target block.

get_deps_by_src(block: tvm.tir.schedule.block_scope.StmtSRef) → List[tvm.tir.schedule.block_scope.Dependency]¶

Get all dependencies whose src is the target`block`.

Parameters: block (StmtSRef) – The queried block
Returns: blocks – The dependencies
Return type: List[Dependency]

get_deps_by_dst(block: tvm.tir.schedule.block_scope.StmtSRef) → List[tvm.tir.schedule.block_scope.Dependency]¶

Get all dependencies whose dst is the target block.

Parameters: block (StmtSRef) – The queried block
Returns: blocks – The dependencies
Return type: List[Dependency]

class tvm.tir.ScheduleState(mod: Union[tvm.tir.function.PrimFunc, tvm.ir.module.IRModule], *, debug_mask: Union[str, int] = 'none')¶

The state of scheduling, which exposes a Replace method as the primary resort for all the scheduling primitives to manipulate the TensorIR.

The data structure contains the following information 1) The AST being scheduled (mod) 2) The sref tree of schedulable statements (indicated by the srefs) 3) The dependency information of each block scope (block_info) 4) A reverse mapping from the AST nodes to that in the sref tree (get_sref) 5) A debug flag, if set, extra checking is enabled (debug_mask)

Parameters

mod (IRModule) – The AST of the module being scheduled
debug_mask (int) – Do extra correctness checking after the object construction and each time after calling the Replace method.

Methods:

`get_sref`(stmt)	Return the corresponding sref that points to the stmt
`get_block_scope`(block_sref)	Get the BlockScope correpsonding to the block sref
`replace`(src_sref, tgt_stmt[, block_sref_reuse])	Replace the part of the AST, as being pointed to by src_sref, with a specific statement tgt_stmt, and maintain the sref tree accordingly.

get_sref(stmt: Union[tvm.tir.stmt.Block, tvm.tir.stmt.For]) → Optional[tvm.tir.schedule.block_scope.StmtSRef]¶

Return the corresponding sref that points to the stmt

Parameters: stmt (Union[Block, For]) – The schedulable statement in the TensorIR to be retrieved for its sref
Returns: sref – The corresponding sref
Return type: StmtSRef

get_block_scope(block_sref: tvm.tir.schedule.block_scope.StmtSRef) → tvm.tir.schedule.block_scope.BlockScope¶

Get the BlockScope correpsonding to the block sref

Parameters: block_sref (StmtSRef) – The block sref to be retrieved
Returns: sref – The corresponding sref
Return type: StmtSRef

replace(src_sref: tvm.tir.schedule.block_scope.StmtSRef, tgt_stmt: Union[tvm.tir.stmt.Block, tvm.tir.stmt.For, tvm.tir.stmt.BlockRealize], block_sref_reuse: Optional[Dict[tvm.tir.stmt.Block, tvm.tir.stmt.Block]] = None) → None¶

Replace the part of the AST, as being pointed to by src_sref, with a specific statement tgt_stmt, and maintain the sref tree accordingly. Replace will try to perform copy on write as much as possible when the ScheduleState holds the only copy to the IRModule and IR nodes.

Only 3 types of replacements are allowed: from src_sref->stmt to tgt_stmt. 1) Block -> Block 2) Loop -> Loop 3) Loop -> BlockRealize

Parameters

src_sref (StmtSRef) – The sref to the statement to be replaced in the TensorIR AST
tgt_stmt (Union[Block, For, BlockRealize]) – The statement to be replaced to
block_sref_reuse (Optional[Dict[Block, Block]] = None) – Maps an old block (to be replaced in the subtree under src_sref->stmt) to a new block (replaced to, in the subtree under tgt_stmt), and enforces reuse of srefs between them (rather than create new srefs) i.e. after being replaced, the sref that points to the old block will point to the new one

Note

The reuse of loop srefs are detected automatically according to the reuse of loop vars.

class tvm.tir.Schedule(mod: Union[tvm.tir.function.PrimFunc, tvm.ir.module.IRModule], *, seed: Optional[int] = None, debug_mask: Union[str, int] = 'none', error_render_level: str = 'detail')¶

The user-facing schedule class

A schedule is a set of transformations that change the order of computation but preserve the semantics of computation. Some example of schedules: 1) Split a loop into two; 2) Reorder two loops; 3) Inline the computation of a specific buffer into its consumer

The schedule class stores auxiliary information to schedule correctly and efficiently.

Link to tutorial: https://tvm.apache.org/docs/tutorials/language/schedule_primitives.html

Attributes:

`mod`	Returns the AST of the module being scheduled
`state`	Returns the ScheduleState in the current schedule class
`trace`	Returns the internally maintained trace of scheduling program execution

Methods:

`work_on`(func_name)	Instruct the schedule to work on a function in the IRModule.
`copy`()	Returns a copy of the schedule, including both the state and the symbol table, * guaranteeing that * 1) SRef tree is completely reconstructed; * 2) The IRModule being scheduled is untouched; * 3) All the random variables are valid in the copy, pointing to the corresponding sref * reconstructed
`seed`(seed)	Seed the randomness
`fork_seed`()	Returns a forked random state as seed for new schedules
`show`(rand_var)	Returns a string representation of the value that the random variable evaluates to
`get`(rand_var_or_sref)	Returns: - the corresponding Block that a BlockRV evaluates to; - the corresponding For that a LoopRV evaluates to; - the corresponding integer that a ExprRV evaluates to; - the corresponding Block that a block sref points to; - the corresponding For that a loop sref points to;
`get_sref`(rand_var_or_stmt)	Returns the corresponding sref to the given 1) LoopRV 2) BlockRV 3) Block 4) For
`remove_rv`(rand_var)	Remove a random variable from the symbol table
`sample_categorical`(candidates, probs[, decision])	Sample an integer given the probability distribution
`sample_perfect_tile`(loop, n[, ...])	Sample the factors to perfect tile a specific loop
`sample_compute_location`(block[, decision])	Sample a compute-at location of the given block
`get_block`(name[, func_name])	Retrieve a block in a specific function with its name
`get_loops`(block)	Get the parent loops of the block in its scope, from outer to inner
`get_child_blocks`(block_or_loop)	Get the leaf blocks of a specific block/loop
`get_producers`(block)	Get the producers of a specific block
`get_consumers`(block)	Get the consumers of a specific block
`fuse`(*loops[, preserve_unit_iters])	Fuse a list of consecutive loops into one.
`split`(loop, factors[, preserve_unit_iters])	Split a loop into a list of consecutive loops.
`reorder`(*ordered_loops)	Reorder a list of loops.
`add_unit_loop`(block_or_loop)	Create a new unit loop on top of the specific block or loop.
`parallel`(loop)	Parallelize the input loop.
`vectorize`(loop)	Vectorize the input loop.
`bind`(loop, thread_axis)	Bind the input loop to the given thread axis.
`unroll`(loop)	Unroll the input loop.
`cache_read`(block, read_buffer_index, ...[, ...])	Create a block that reads a buffer region into a read cache.
`cache_write`(block, write_buffer_index, ...)	Create a block that reads a buffer region into a write cache.
`cache_inplace`(block, read_buffer_index, ...)	Create blocks that reads & write a buffer region into a cache block.
`cache_index`(block, buffer_index)	Create a block to cache precomputed index for later use.
`reindex`(block, buffer)	Create a block that read/write a buffer region into a read/write cache with reindexing.
`compute_at`(block, loop[, ...])	Compute-At.
`reverse_compute_at`(block, loop[, ...])	Reverse-Compute-At.
`compute_inline`(block)	Inline a block into its consumer(s).
`reverse_compute_inline`(block)	Inline a block into its only producer.
`decompose_reduction`(block, loop)	Decompose a reduction block into two separate blocks.
`rfactor`(loop, factor_axis)	Factorize an associative reduction block by the specified loop.
`storage_align`(block, buffer_index, axis, ...)	Set alignment requirement for specific dimension such that stride[axis] == k * factor + offset for some k.
`set_scope`(block, buffer_index, storage_scope)	Set the storage scope of a buffer, where the buffer is specified by the a block and a write-index
`blockize`(loop[, preserve_unit_iters])	Convert the subtree rooted at a specific loop into a block.
`tensorize`(block_or_loop, tensor_intrin[, ...])	Tensorize the computation enclosed by loop with the tensor intrinsic.
`annotate`(block_or_loop, ann_key, ann_val)	Annotate a block/loop with a key value pair
`unannotate`(block_or_loop, ann_key)	Unannotate a block/loop's annotation with key ann_key
`transform_layout`(block, buffer, index_map[, ...])	Apply a transformation represented by IndexMap to buffer
`transform_block_layout`(block, index_map)	Apply a transformation represented by IndexMap to block
`set_axis_separator`(block, buffer, ...)	Set the axis separator of a buffer, where the buffer is specified by a block and a read or write index.
`decompose_padding`(block, loop)	Decompose a block of padding computation pattern into two separate blocks.
`can_decompose_padding`(block, loop)	Check whether the block match padding pattern and can be decomposed.
`pad_einsum`(block, padding)	Pad the computation of Einsum.
`rolling_buffer`(block, write_buffer_index)	Compute the target buffer via rolling buffering, select the outermost rollable axis with a positive bound overlap that appears in the block's ancestor loops as rolling axis, fold and circularize the buffer along the rolling dimension, append block predicate to avoid recomputing overlapping elements.
`enter_postproc`()	A no-op that marks the start of postprocessing phase of scheduling

property mod: tvm.ir.module.IRModule¶: Returns the AST of the module being scheduled

property state: tvm.tir.schedule.state.ScheduleState¶: Returns the ScheduleState in the current schedule class

property trace: Optional[tvm.tir.schedule.trace.Trace]¶: Returns the internally maintained trace of scheduling program execution

work_on(func_name: str) → None¶

Instruct the schedule to work on a function in the IRModule.

By default, the schedule works on the function with the name “main”, or the only function in the IRModule if there is only one. If there is multiple functions in the IRModule, and none of their names are “main”, users will have to call this method to explicitly specify which function to work on.

This sugar function will guide the GetBlock method if its func_name is not specified.

Parameters: func_name (str) – The name of the function to work on.

copy() → tvm.tir.schedule.schedule.Schedule¶

Returns a copy of the schedule, including both the state and the symbol table, * guaranteeing that * 1) SRef tree is completely reconstructed; * 2) The IRModule being scheduled is untouched; * 3) All the random variables are valid in the copy, pointing to the corresponding sref * reconstructed

Returns: copy – A new copy of the schedule
Return type: Schedule

seed(seed: int) → None¶

Seed the randomness

Parameters: seed (int) – The new random seed, -1 if use device random, otherwise non-negative

fork_seed() → int¶

Returns a forked random state as seed for new schedules

Returns: seed – The forked random state, not the same as the current random state
Return type: int

show(rand_var: Union[tvm.ir.expr.PrimExpr, tvm.tir.schedule.schedule.BlockRV, tvm.tir.schedule.schedule.LoopRV]) → str¶

Returns a string representation of the value that the random variable evaluates to

Parameters: rand_var (Union[ExprRV, BlockRV, LoopRV]) – The random variable to be evaluated
Returns: str_repr – The string representation
Return type: str

get(rand_var_or_sref: Union[tvm.ir.expr.PrimExpr, tvm.tir.schedule.schedule.BlockRV, tvm.tir.schedule.schedule.LoopRV, tvm.tir.schedule.block_scope.StmtSRef]) → Optional[Union[int, tvm.tir.stmt.Block, tvm.tir.stmt.For]]¶

Returns: - the corresponding Block that a BlockRV evaluates to; - the corresponding For that a LoopRV evaluates to; - the corresponding integer that a ExprRV evaluates to; - the corresponding Block that a block sref points to; - the corresponding For that a loop sref points to;

Parameters: rand_var_or_sref (Union[ExprRV, BlockRV, LoopRV, StmtSRef]) – The random variable / sref to be evaluated
Returns: result – The corresponding result
Return type: Optional[Union[int, Block, For]]

get_sref(rand_var_or_stmt: Union[tvm.tir.schedule.schedule.BlockRV, tvm.tir.schedule.schedule.LoopRV, tvm.tir.stmt.Block, tvm.tir.stmt.For]) → Optional[tvm.tir.schedule.block_scope.StmtSRef]¶

Returns the corresponding sref to the given 1) LoopRV 2) BlockRV 3) Block 4) For

Parameters: rand_var_or_stmt (Union[BlockRV, LoopRV, Block, For]) – The random variable / sref to be evaluated
Returns: result – The corresponding result
Return type: Optional[StmtSRef]

remove_rv(rand_var: Union[tvm.ir.expr.PrimExpr, tvm.tir.schedule.schedule.BlockRV, tvm.tir.schedule.schedule.LoopRV]) → None¶

Remove a random variable from the symbol table

Parameters: rand_var (Union[BlockRV, LoopRV, ExprRV]) – The random variable to be removed

sample_categorical(candidates: List[int], probs: List[float], decision: Optional[int] = None) → tvm.ir.expr.PrimExpr¶

Sample an integer given the probability distribution

Parameters

candidates (List[int]) – The candidates to be sampled from
probs (List[float]) – The probability of each candidate
decision (Optional[int]) – The sampling decision, if any

Returns

result – The random variable sampled from candidates

Return type

ExprRV

sample_perfect_tile(loop: tvm.tir.schedule.schedule.LoopRV, n: int, max_innermost_factor: int = 16, decision: Optional[List[int]] = None) → List[tvm.ir.expr.PrimExpr]¶

Sample the factors to perfect tile a specific loop

Parameters

loop (LoopRV) – The loop to be tiled
n (int) – The number of tiles to be sampled
max_innermost_factor (int) – The maximum tile size allowed to be sampled in the innermost loop
decision (Optional[List[int]]) – The sampling decision, if any

Returns

result – A list of length n, the random perfect tile sizes sampled

Return type

List[ExprRV]

sample_compute_location(block: Union[tvm.tir.schedule.schedule.BlockRV, str], decision: Optional[int] = None) → tvm.tir.schedule.schedule.LoopRV¶

Sample a compute-at location of the given block

Parameters

block (Union[BlockRV, str]) – The block whose compute-at location is to be sampled
decision (Optional[int]) – The sampling decision

Returns

result – The sampled loop where the input block is to be computed at

Return type

LoopRV

get_block(name: str, func_name: Optional[str] = None) → tvm.tir.schedule.schedule.BlockRV¶

Retrieve a block in a specific function with its name

By default, if func_name is not specified, the schedule will search for the block in the function that is currently being “worked on”. To switch the function to be worked on, use work_on before calling this method.

Parameters

name (str) – The name of the block
func_name (Optional[str] = None) – The name of the function

Returns

block – The block retrieved IndexError is raised if 0 or multiple blocks exist with the specific name.

Return type

BlockRV

get_loops(block: Union[tvm.tir.schedule.schedule.BlockRV, str]) → List[tvm.tir.schedule.schedule.LoopRV]¶

Get the parent loops of the block in its scope, from outer to inner

Parameters: block (Union[BlockRV, str]) – The query block
Returns: loops – A list of loops above the given block in its scope, from outer to inner
Return type: List[LoopRV]

get_child_blocks(block_or_loop: Union[tvm.tir.schedule.schedule.BlockRV, tvm.tir.schedule.schedule.LoopRV]) → List[tvm.tir.schedule.schedule.BlockRV]¶

Get the leaf blocks of a specific block/loop

Parameters: block_or_loop (Union[BlockRV, LoopRV]) – The query block/loop
Returns: blocks – A list of leaf blocks inside a specific block/loop
Return type: List[LoopRV]

get_producers(block: Union[tvm.tir.schedule.schedule.BlockRV, str]) → List[tvm.tir.schedule.schedule.BlockRV]¶

Get the producers of a specific block

Parameters: block (Union[BlockRV, str]) – The block in the query
Returns: producers – A list of producers of the given block
Return type: List[BlockRV]

get_consumers(block: Union[tvm.tir.schedule.schedule.BlockRV, str]) → List[tvm.tir.schedule.schedule.BlockRV]¶

Get the consumers of a specific block

Parameters: block (Union[BlockRV, str]) – The block in the query
Returns: consumers – A list of consumers of the given block
Return type: List[BlockRV]

fuse(*loops: List[tvm.tir.schedule.schedule.LoopRV], preserve_unit_iters: bool = True) → tvm.tir.schedule.schedule.LoopRV¶

Fuse a list of consecutive loops into one. It requires: 1) The loops can’t have annotations or thread bindings. 2) The (i+1)-th loop must be the only child of the i-th loop. 3) All loops must start with 0. 4) The domain of a loop to be fused cannot depend on another loop to be fused.

Parameters: *loops (List[LoopRV]) – The loops to be fused
Returns: fused_loop – The new loop after fusion
Return type: LoopRV

Examples

Before applying fuse, in TensorIR, the IR is:

@T.prim_func
def before_fuse(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

Create the schedule and do fuse:

sch = tir.Schedule(before_fuse)
i, j = sch.get_loops(sch.get_block("B"))
sch.fuse(i, j)
print(sch.mod["main"].script())

After applying fuse, the IR becomes:

@T.prim_func
def after_fuse(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.match_buffer(b, (128, 128))
    # the 2 loops are fused into 1
    for i_j_fused in T.serial(0, 16384):
        with T.block("B"):
            vi = T.axis.S(128, T.floordiv(i_j_fused, 128))
            vj = T.axis.S(128, T.floormod(i_j_fused, 128))
            B[vi, vj] = A[vi, vj] * 2.0

split(loop: tvm.tir.schedule.schedule.LoopRV, factors: List[Optional[Union[int, tvm.ir.expr.PrimExpr]]], preserve_unit_iters: bool = True) → List[tvm.tir.schedule.schedule.LoopRV]¶

Split a loop into a list of consecutive loops. It requires: 1) The loop can’t have annotation or thread binding. 2) The loop must start with 0. Predicates may be added to ensure the total loop numbers keeps unchanged. In factors, at most one of the factors can be None, which will be automatically inferred.

Parameters

loop (LoopRV) – The loop to be split
factors (List[Union[int, ExprRV, None]]) – The splitting factors Potential inputs are: - None - ExprRV - Positive constant integers
preserve_unit_iters (bool) – Whether or not to preserve unit iterators in block bindings

Returns

split_loops – The new loops after split

Return type

List[LoopRV]

Examples

Before split, in TensorIR, the IR is:

@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

Create the schedule and do split:

sch = tir.Schedule(before_split)
i, j = sch.get_loops(sch.get_block("B"))
sch.split(i, factors=[2, 64])
print(sch.mod["main"].script())

After applying split, the IR becomes:

@T.prim_func
def after_split(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.match_buffer(b, (128, 128))
    # the original loop is split into 2 loops
    for i0, i1, j in T.grid(2, 64, 128):
        with T.block("B"):
            vi = T.axis.S(128, i0 * 64 + i1)
            vj = T.axis.S(128, j)
            B[vi, vj] = A[vi, vj] * 2.0

reorder(*ordered_loops: List[tvm.tir.schedule.schedule.LoopRV]) → None¶

Reorder a list of loops. It doesn’t require the loops to be consecutive. It requires: 1) The loops are in the same chain. That means: the loops can be ordered to [l_1, l_2, … , l_n] where l_i is an ancestor of l_{i+1} and there are only single-branch loops between l_1 and l_n (which also indicates they are under the same scope). 2) After reordering, the domain of an outer loop cannot depend on any of the inner loops. 3) For every block under the loop nests, its block binding must be affine, and the block variables must be either data parallel or reduction. 4) No duplicated loops are allowed in the arguments.

Parameters: *ordered_loops (List[LoopRV]) – The loops in the new order

Examples

Before reorder, in TensorIR, the IR is:

@T.prim_func
def before_reorder(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

Create the schedule and do reorder:

sch = tir.Schedule(before_reorder)
i, j = sch.get_loops(sch.get_block("B"))
sch.reorder(j, i)
print(sch.mod["main"].script())

After applying reorder, the IR becomes:

@T.prim_func
def after_reorder(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.match_buffer(b, (128, 128))
    # Here j and i are reordered
    for j, i 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

add_unit_loop(block_or_loop: Union[tvm.tir.schedule.schedule.LoopRV, tvm.tir.schedule.schedule.BlockRV]) → tvm.tir.schedule.schedule.LoopRV¶

Create a new unit loop on top of the specific block or loop.

Parameters: block_or_loop (Union[LoopRV, BlockRV]) – The block above which the new loop is created
Returns: new_loop – The new unit loop
Return type: LoopRV

Examples

Before add_unit_loop, in TensorIR, the IR is:

@T.prim_func
def before_add_unit_loop(
    A: T.Buffer[(), "int32"],
    B: T.Buffer[(), "int32"],
    C: T.Buffer[(), "int32"],
) -> None:
    with T.block("C"):
        vi = T.axis.spatial(1, 0)
        C[()] = A[()] + B[()]

Create the schedule and do add-unit-loop:

sch = tir.Schedule(before_add_unit_loop)
sch.add_unit_loop(sch.get_block("C"))
print(sch.mod["main"].script())

After applying add-unit-loop, the IR becomes:

@T.prim_func
def after_add_unit_loop(
    A: T.Buffer[(), "int32"],
    B: T.Buffer[(), "int32"],
    C: T.Buffer[(), "int32"],
) -> None:
    for u in T.serial(1):
        with T.block("C"):
            vi = T.axis.spatial(1, 0)
            C[()] = A[()] + B[()]

parallel(loop: tvm.tir.schedule.schedule.LoopRV) → None¶

Parallelize the input loop. It requires: 1) The scope block that the loop is in should have stage-pipeline property 2) All the blocks under the loop are complete blocks or reduction blocks, and have affine bindings 3) For each block under the loop, the loop can only be contained in data-parallel block iters’ bindings

Parameters: loop (LoopRV) – The loop to be parallelized

Examples

Before parallel, in TensorIR, the IR is:

@T.prim_func
def before_parallel(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

Create the schedule and do parallel:

sch = tir.Schedule(before_parallel)
i, j = sch.get_loops(sch.get_block("B"))
sch.parallel(i)

After applying parallel, the IR becomes:

@T.prim_func
def after_parallel(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.match_buffer(b, (128, 128))
    for i in T.parallel(0, 128):
        for j in T.serial(0, 128):
            with T.block("B"):
                vi, vj = T.axis.remap("SS", [i, j])
                B[vi, vj] = A[vi, vj] * 2.0

vectorize(loop: tvm.tir.schedule.schedule.LoopRV) → None¶

Vectorize the input loop. It requires: 1) The scope block that the loop is in should have stage-pipeline property 2) All the blocks under the loop are complete blocks or reduction blocks, and have affine bindings 3) For each block under the loop, the loop can only be contained in data-parallel block iters’ bindings

Parameters: loop (LoopRV) – The loop to be vectorized

Examples

Before vectorize, in TensorIR, the IR is:

@T.prim_func
def before_vectorize(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

Create the schedule and do vectorize:

sch = tir.Schedule(before_vectorize)
i, j = sch.get_loops(sch.get_block("B"))
sch.vectorize(j)

After applying vectorize, the IR becomes:

@T.prim_func
def after_vectorize(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.match_buffer(b, (128, 128))
    for i in T.serial(0, 128):
        for j in T.vectorized(0, 128):
            with T.block("B"):
                vi, vj = T.axis.remap("SS", [i, j])
                B[vi, vj] = A[vi, vj] * 2.0

bind(loop: tvm.tir.schedule.schedule.LoopRV, thread_axis: str) → None¶

Bind the input loop to the given thread axis. It requires: 1) The scope block that the loop is in should have stage-pipeline property 2) All the blocks under the loop are complete blocks or reduction blocks, and have affine bindings 3) For each block under the loop, if the thread axis starts with “threadIdx`, the loop can only be contained in data-parallel block iter and reduction block iters’ bindings. Otherwise the loop can only be contained in data-parallel block iters’ bindings

Parameters

loop (LoopRV) – The loop to be bound to the thread axis
thread_axis (str) – The thread axis to be bound to the loop. Possible candidates: - blockIdx.x/y/z - threadIdx.x/y/z - vthread.x/y/z - vthread (It is a legacy behavior that will be deprecated. Please use vthread.x/y/z instead.)

Examples

Before bind, in TensorIR, the IR is:

@T.prim_func
def before_bind(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

Create the schedule and do bind:

sch = tir.Schedule(before_bind)
i, j = sch.get_loops(sch.get_block("B"))
sch.bind(i, "blockIdx.x")
sch.bind(j, "threadIdx.x")

After applying bind, the IR becomes:

@T.prim_func
def after_bind(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.match_buffer(b, (128, 128))
    for i in T.thread_binding(0, 128, thread = "blockIdx.x"):
        for j in T.thread_binding(0, 128, thread = "threadIdx.x"):
            with T.block("B"):
                vi, vj = T.axis.remap("SS", [i, j])
                B[vi, vj] = A[vi, vj] * 2.0

unroll(loop: tvm.tir.schedule.schedule.LoopRV) → None¶

Unroll the input loop. It requires nothing

Parameters: loop (LoopRV) – The loop to be unrolled

Examples

Before unroll, in TensorIR, the IR is:

@T.prim_func
def before_unroll(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

Create the schedule and do unroll:

sch = tir.Schedule(before_unroll)
i, j = sch.get_loops(sch.get_block("B"))
sch.unroll(i)

After applying unroll, the IR becomes:

@T.prim_func
def after_unroll(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.match_buffer(b, (128, 128))
    for i in T.unroll(0, 128):
        for j in T.serial(0, 128):
            with T.block("B"):
                vi, vj = T.axis.remap("SS", [i, j])
                B[vi, vj] = A[vi, vj] * 2.0

cache_read(block: Union[tvm.tir.schedule.schedule.BlockRV, str], read_buffer_index: Union[int, str, tvm.tir.buffer.Buffer], storage_scope: str, consumer_blocks: Optional[List[Union[tvm.tir.schedule.schedule.BlockRV, str]]] = None) → tvm.tir.schedule.schedule.BlockRV¶

Create a block that reads a buffer region into a read cache. It requires:

There is at most one block who write the buffer in the scope.
The scope block have stage-pipeline property.

Parameters

block (Union[BlockRV, str]) – The consumer block of the target buffer.
buffer (Union[int, str, Buffer]) – The index of the buffer in block’s read region, the unique name of a read buffer in the block, or a Buffer object that is within the blocks read region.
storage_scope (str) – The target storage scope.
consumer_blocks (Optional[List[Union[BlockRV, str]]]) – An optional list of consumers that should read from the cache. If not specified, all consumers will use the cache.

Returns

cached_block – The block of the cache stage

Return type

BlockRV

Examples

Before cache_read, in TensorIR, the IR is:

@T.prim_func
def before_cache_read(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

Create the schedule and cache_read:

sch = tir.Schedule(before_cache_read)
block_b = sch.get_block("B")
sch.cache_read(block_b, 0, "local")
print(sch.mod["main"].script())

After applying cache_read, the IR becomes:

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

cache_write(block: Union[tvm.tir.schedule.schedule.BlockRV, str], write_buffer_index: Union[int, str, tvm.tir.buffer.Buffer], storage_scope: str, consumer_blocks=None) → tvm.tir.schedule.schedule.BlockRV¶

Create a block that reads a buffer region into a write cache. It requires:

There is only one block who write the buffer in the scope.
The scope block have stage-pipeline property.

Parameters

block (Union[BlockRV, str]) – The producer block of the target buffer.
write_buffer_index (int) – The index of the buffer in block’s write region, the unique name of a write buffer in the block, or a Buffer object that is within the blocks write region.
storage_scope (str) – The target storage scope.
consumer_blocks (Optional[List[Union[BlockRV, str]]]) – An optional list of consumers that should read directly from the cache. If not specified, all consumers will read from the original buffer.

Returns

cached_block – The block of the cache stage

Return type

BlockRV

Examples

Before cache_write, in TensorIR, the IR is:

@T.prim_func
def before_cache_write(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

Create the schedule and cache_write:

sch = tir.Schedule(before_cache_write)
block_b = sch.get_block("B")
sch.cache_write(block_b, 0, "local")
print(sch.mod["main"].script())

After applying cache_write, the IR becomes:

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

cache_inplace(block: Union[tvm.tir.schedule.schedule.BlockRV, str], read_buffer_index: Union[int, str, tvm.tir.buffer.Buffer], storage_scope: str) → List[tvm.tir.schedule.schedule.BlockRV]¶

Create blocks that reads & write a buffer region into a cache block. It requires the the target block both read & write the target buffer. Mainly for inplace operation.

Parameters

block (Union[BlockRV, str]) – The target block operates on the target buffer.
read_buffer_index (int) – The index of the buffer in block’s read region, the unique name of a read buffer in the block, or a Buffer object that is within the blocks read region.
storage_scope (str) – The target storage scope.

Returns

cached_blocks – The blocks of the cache stage, read cache first, write cache second

Return type

List[BlockRV]

Examples

Before cache_inplace, in TensorIR, the IR is:

@T.prim_func
def before_cache_inplace(data_io: T.Buffer[(64), "int32"]):
    for i0 in T.serial(1):
        with T.block("A"):
            T.reads(data_io[:64])
            T.writes(data_io[:64])
            T.evaluate(T.call_extern("call_impl", data_io.data, dtype=""))

Create the schedule and cache_inplace:

sch = tir.Schedule(before_cache_inplace)
block_a = sch.get_block("A")
sch.cache_inplace(block_a, 0, "local")
print(sch.mod["main"].script())

After applying cache_inplace, the IR becomes:

@T.prim_func
def cache_inplace(data_io: T.Buffer[64, "int32"]) -> None:
    data_io_local = T.alloc_buffer([64], dtype="int32", scope="local")
    for i0 in T.serial(1):
        for ax0 in T.serial(64):
            with T.block("data_io_local"):
                v0 = T.axis.spatial(64, ax0)
                T.reads(data_io[v0])
                T.writes(data_io_local[v0])
                data_io_local[v0] = data_io[v0]
        with T.block("A"):
            T.reads(data_io_local[0 : 64])
            T.writes(data_io_local[0 : 64])
            T.evaluate(T.call_extern("call_impl", data_io_local.data, dtype=""))
        for ax0 in T.serial(64):
            with T.block("data_io_local"):
                v0 = T.axis.spatial(64, ax0)
                T.reads(data_io_local[v0])
                T.writes(data_io[v0])
                data_io[v0] = data_io_local[v0]

cache_index(block: Union[tvm.tir.schedule.schedule.BlockRV, str], buffer_index: Union[int, str, tvm.tir.buffer.Buffer]) → List[tvm.tir.schedule.schedule.BlockRV]¶

Create a block to cache precomputed index for later use. if there is no index computation, keep unchanged.

Parameters

block (Union[BlockRV, str]) – The target block operates on the target buffer.
buffer_index (int) – The index of the target buffer in block’s read region

Returns

cached_blocks – The blocks of the stage writing the cache buffers

Return type

List[BlockRV]

Examples

Before cache_inplace, in TensorIR, the IR is:

@T.prim_func
def resize(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (1, 3, 40, 40))
    B = T.match_buffer(b, (1, 3, 80, 80))
    for i0, i1, i2, i3 in T.grid(1, 3, 80, 80):
        with T.block("A"):
            n, c, vi, vj = T.axis.remap("SSSS", [i0, i1, i2, i3])
            B[n, c, vi, vj] = A[n, c, vi//4 + vj//4, vj//2]

Create the schedule and cache_index:

sch = tir.Schedule(resize)
block_a = sch.get_block("A")
sch.cache_index(block_a, 0)
print(sch.mod["main"].script())

After applying cache_index, the IR becomes:

@T.prim_func
def resize_cache_index(
    A: T.Buffer[(1, 3, 40, 40), "float32"], B: T.Buffer[(1, 3, 80, 80), "float32"]
) -> None:
    index_var_0 = T.alloc_buffer([80, 80], dtype="int32", strides=[1])
    index_var_1 = T.alloc_buffer([80], dtype="int32", strides=[1])
    for ax0, ax1 in T.grid(80, 80):
        with T.block("index_0"):
            v0 = T.axis.spatial(80, ax0)
            v1 = T.axis.spatial(80, ax1)
            T.reads()
            T.writes(index_var_0[v0, v1])
            index_var_0[v0, v1] = v0 // 4 + v1 // 4
    for ax0 in T.serial(80):
        with T.block("index_1"):
            v0 = T.axis.spatial(80, ax0)
            T.reads()
            T.writes(index_var_1[v0])
            index_var_1[v0] = v0 // 2
    for i0, i1, i2, i3 in T.grid(1, 3, 80, 80):
        with T.block("A"):
            n, c, vi, vj = T.axis.remap("SSSS", [i0, i1, i2, i3])
            T.reads(A[n, c, vi // 4 + vj // 4, vj // 2])
            T.writes(B[n, c, vi, vj])
            B[n, c, vi, vj] = A[n, c, index_var_0[vi, vj], index_var_1[vj]]

reindex(block: Union[tvm.tir.schedule.schedule.BlockRV, str], buffer: Union[Tuple[str, int], str, tvm.tir.buffer.Buffer]) → tvm.tir.schedule.schedule.BlockRV¶

Create a block that read/write a buffer region into a read/write cache with reindexing. The layout of the cache will be the same as by the iterators of the block that reads/writes the buffer. It requires: 1) There is only one block who reads/writes the target buffer 2) There is only one buffer load/store of this buffer in the block

Parameters

block (Union[BlockRV, str]) – The block that accesses the target buffer. If a string, this must uniquely identify a block.
buffer (Union[Tuple[str,int], Buffer, str]) –
The buffer to be transformed, or a specification of how to identify the buffer to be transformed.

If buffer if a tuple of (str,int), the first item should be either “read” or “write”, and the second item is an index into the block’s read or write regions.

If buffer is a string, it is the name of the buffer, which must exist within the reads/writes of the block. In addition, the reads/writes of the block may not contain more than one buffer with this name.

If buffer is a Buffer object, it must exist within the reads/writes of the block.

Returns

reindex_block – The block of the reindex stage

Return type

BlockRV

Examples

Before transform_layout, in TensorIR, the IR is:

@T.prim_func
def before_reindex(
    A: T.Buffer[(128, 128), "float32"],
    B: T.Buffer[(128, 128), "float32"]
) -> None:
    for i, j in T.grid(128, 128):
        with T.block("B"):
            vi, vj = T.axis.remap("SS", [i, j])
            B[vi, vj] = A[vj, vi] * 2.0

Create the schedule and do transform_layout:

sch = tir.Schedule(before_reindex)
block = sch.get_block("B")
sch.reindex(block, ("read", 0))

After applying reindex, the IR becomes:

@T.prim_func
def after_reindex(
    A: T.Buffer[(128, 128), "float32"],
    B: T.Buffer[(128, 128), "float32"]
) -> None:
    A_reindex = T.alloc_buffer((128, 128), "float32")
    for i, j in T.grid(128, 128):
        with T.block("A_reindex"):
            vi, vj = T.axis.remap("SS", [i, j])
            A_reindex[vi, vj] = A[vj, vi]
    for i, j in T.grid(128, 128):
        with T.block("B"):
            vi, vj = T.axis.remap("SS", [i, j])
            B[vi, vj] = A_reindex[vi, vj] * 2.0

compute_at(block: Union[tvm.tir.schedule.schedule.BlockRV, str], loop: tvm.tir.schedule.schedule.LoopRV, preserve_unit_loops: bool = False, index: int = - 1) → None¶

Compute-At. Move a producer block under the specific loop, and regenerate the loops induced by the block so that the buffer region produced by the producer block could cover those regions consumed by its consumer blocks under the given loop. It requires:

block and loop are under the same scope, loop is not the ancestor of block
The scope block has stage-pipeline property

3) The subtree of the scope block, where the given block is in, satisfies the compact dataflow condition. i.e. all the blocks in the scope block’s subtree must be either complete block or reduction block

4) The block is not an output block with regard to the scope block, i.e. the buffers written by the block are allocated under the scope block

All the consumers of the block are under the given loop

Parameters

block (Union[BlockRV, str]) – The block to be moved
loop (LoopRV) – The loop where the block to be moved under
preserve_unit_loops (bool) – Whether to keep the trivial loops whose extents are 1
index (int) – The block index of the loop body subtree blocks: - index = -1 means inserted into the last possible insertion point; - index = -2 means inserted into the first possible insertion point; - Otherwise, index is a nonnegative number that indicates the insertion point

Examples

Before compute-at, in TensorIR, the IR is:

@T.prim_func
def before_compute_at(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128), "float32")
    B = T.alloc_buffer((128, 128), "float32")
    C = T.match_buffer(c, (128, 128), "float32")
    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
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + 1.0

Create the schedule and do compute-at:

sch = tir.Schedule(before_compute_at)
block = sch.get_block("B")
loop, _ = sch.get_loops(sch.get_block("C"))
sch.compute_at(block, loop, preserve_unit_loops=False)
print(sch.mod["main"].script())

After applying compute-at, the IR becomes:

@T.prim_func
def after_compute_at(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128), "float32")
    B = T.alloc_buffer((128, 128), "float32")
    C = T.match_buffer(c, (128, 128), "float32")
    for i in T.serial(0, 128):
        for j in T.serial(0, 128):
            with T.block("B"):
                vi, vj = T.axis.remap("SS", [i, j])
                B[vi, vj] = A[vi, vj] * 2.0
        for j in T.serial(0, 128):
            with T.block("C"):
                vi, vj = T.axis.remap("SS", [i, j])
                C[vi, vj] = B[vi, vj] + 1.0

reverse_compute_at(block: Union[tvm.tir.schedule.schedule.BlockRV, str], loop: tvm.tir.schedule.schedule.LoopRV, preserve_unit_loops: bool = False, index: int = - 1) → None¶

Reverse-Compute-At. Move a consumer block under the specific loop, and regenerate the loops induced by the block so that the buffer region consumed by the consumer block could cover those regions produced by its producer blocks under the given loop. It requires:

block and loop are under the same scope, loop is not the ancestor of block
The scope block has stage-pipeline property

3) The subtree of the scope block, where the given block is in, satisfies the compact dataflow condition. i.e. all the blocks in the scope block’s subtree must be either complete block or reduction block

All the producers of the block are under the given loop

Parameters

block (Union[BlockRV, str]) – The block to be moved
loop (LoopRV) – The loop where the block to be moved under
preserve_unit_loops (bool) – Whether to keep the trivial loops whose extents are 1
index (int) – The block index of the loop body subtree blocks: - index = -1 means inserted into the last possible insertion point; - index = -2 means inserted into the first possible insertion point; - Otherwise, index is a nonnegative number that indicates the insertion point

Examples

Before reverse-compute-at, in TensorIR, the IR is:

@T.prim_func
def before_reverse_compute_at(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128), "float32")
    B = T.alloc_buffer((128, 128), "float32")
    C = T.match_buffer(c, (128, 128), "float32")
    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
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + 1.0

Create the schedule and do reverse-compute-at:

sch = tir.Schedule(before_reverse_compute_at)
block = sch.get_block("C")
loop, _ = sch.get_loops(sch.get_block("B"))
sch.reverse_compute_at(block, loop, preserve_unit_loops=False)
print(sch.mod["main"].script())

After applying reverse-compute-at, the IR becomes:

@T.prim_func
def after_reverse_compute_at(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128), "float32")
    B = T.alloc_buffer((128, 128), "float32")
    C = T.match_buffer(c, (128, 128), "float32")
    for i in T.serial(0, 128):
        for j in T.serial(0, 128):
            with T.block("B"):
                vi, vj = T.axis.remap("SS", [i, j])
                B[vi, vj] = A[vi, vj] * 2.0
        for j in T.serial(0, 128):
            with T.block("C"):
                vi, vj = T.axis.remap("SS", [i, j])
                C[vi, vj] = B[vi, vj] + 1.0

compute_inline(block: Union[tvm.tir.schedule.schedule.BlockRV, str]) → None¶

Inline a block into its consumer(s). It requires:

The block is a complete non-root block, which only produces one buffer
The block must not be the only leaf in the scope.
The body of the block must be a BufferStore statement in the form of, A[i, j, k, ...] = ... where the indices of the LHS are all distinct atomic variables, and no variables other than those indexing variables are allowed in the statement.

Parameters: block (Union[BlockRV, str]) – The block to be inlined to its consumer(s)

Examples

Before compute-inline, in TensorIR, the IR is:

@T.prim_func
def before_inline(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.alloc_buffer((128, 128))
    C = T.match_buffer(c, (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
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + 1.0

Create the schedule and do compute-inline:

sch = tir.Schedule(before_inline)
sch.compute_inline(sch.get_block("B"))
print(sch.mod["main"].script())

After applying compute-inline, the IR becomes:

@T.prim_func
def after_inline(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    C = T.match_buffer(c, (128, 128))
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = A[vi, vj] * 2.0 + 1.0

reverse_compute_inline(block: Union[tvm.tir.schedule.schedule.BlockRV, str]) → None¶

Inline a block into its only producer. It requires:

The block is a complete non-root block, which only produces and consumes one buffer
The block must not be the only leaf in the scope.
The only producer of the block is a read-after-write producer and a complete non-root block
The body of the block must be a BufferStore statement in the form of, B[f(i, j, k, ...)] = g(i, j, k, A[i, j, k, ...] ...) where the indices of each BufferLoad on the RHS are all distinct atomic variables, and no variables other than those indexing variables are allowed in the statement.

Parameters: block (Union[BlockRV, str]) – The block to be inlined to its producer

Examples

Before reverse-compute-inline, in TensorIR, the IR is:

@T.prim_func
def before_inline(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.alloc_buffer((128, 128))
    C = T.match_buffer(c, (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
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + 1.0

Create the schedule and do reverse-compute-inline:

sch = tir.Schedule(before_inline)
sch.reverse_compute_inline(sch.get_block("C"))
print(sch.mod["main"].script())

After applying reverse-compute-inline, the IR becomes:

@T.prim_func
def after_inline(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    C = T.match_buffer(c, (128, 128))
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = A[vi, vj] * 2.0 + 1.0

decompose_reduction(block: Union[tvm.tir.schedule.schedule.BlockRV, str], loop: tvm.tir.schedule.schedule.LoopRV) → tvm.tir.schedule.schedule.BlockRV¶

Decompose a reduction block into two separate blocks.

The init block, which is translated from the init statement of the reduction block;
The update block, which is the original block without init statement.

The init block is inserted right before the given loop.

The schedule primitive requires:

The input block is a reduction block.
The input loop is the ancestor of the block.
The input loop is not lower than all the loops related to reduce block var.

Parameters

block (Union[BlockRV, str]) – The reduction block to be decomposed
loop (LoopRV) – The loop above which the init block is inserted before.

Returns

init_block – The init block

Return type

BlockRV

Examples

Before decompose-reduction, in TensorIR, the IR is:

@T.prim_func
def before_decompose(a: ty.handle, c: ty.handle) -> None:
    A = tir.match_buffer(a, [128, 128])
    B = tir.match_buffer(b, [128, 128])
    C = tir.match_buffer(c, [128, 128])
    for i, j, k in tir.grid(128, 128, 128):
        with tir.block([128, 128, tir.reduce_axis(0, 128)], "C") as [vi, vj, vk]:
            with tir.init():
                C[vi, vj] = 0.0
            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]

Create the schedule and do decompose-reduction with specified loop:

sch = tir.Schedule(before_decompose)
C = sch.get_block("C")
i, j, k = sch.get_loops(C)
sch.decompose_reduction(C, i)
print(sch.mod["main"].script())

After applying decompose-reduction, the IR becomes:

@T.prim_func
def after_decompose(a: ty.handle, c: ty.handle) -> None:
    A = tir.match_buffer(a, [128, 128])
    B = tir.match_buffer(b, [128, 128])
    C = tir.match_buffer(c, [128, 128])
    for i in tir.serial(128):
        for j in tir.serial(128):
            with tir.block([128, 128]) as [vi, vj]:
                C[vi, vj] = 0.0
    for i, j, k in tir.grid(128, 128, 128):
        with tir.block([128, 128, tir.reduce_axis(0, 128)], "C") as [vi, vj, vk]:
            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]

rfactor(loop: tvm.tir.schedule.schedule.LoopRV, factor_axis: int) → tvm.tir.schedule.schedule.BlockRV¶

Factorize an associative reduction block by the specified loop.

An associative reduction cannot be parallelized directly, because it leads to potential race condition during accumulation. Alternatively, the reduction could be factorized on a loop with the following steps: - Step 1: evenly slice the reduction into n separate chunks, where n is the loop extent - Step 2: compute the chunks separately and write the result into n intermediate buffers; - Step 3: accumulate the n separate buffer into the result buffer. Note that the Step 2 above introduces opportunities for parallelization.

RFactor is a schedule primitive that implements the transformation described above: Given a block that writes to buffer B, it factorizes a loop of extent n.

For example, the pseudocode below accumulates B[i] = sum(A[i, : , : ]):

for i in range(128):                    # loop i is a data parallel loop
    for j in range(128):                # loop j is a reduction loop
        for k in range(128):            # loop k is a reduction loop
            B[i] = B[i] + A[i, j, k]

Suppose RFactor is applied on the innermost loop k and factor_axis = 1. RFactor then creates an intermediate buffer and two blocks.

1. The intermediate buffer, or “rf-buffer” is a buffer of rank ndim(B) + 1 and size size(B) * n, whose shape expands from shape(B) by adding an axis of n at the position specified by factor_axis. For example,

shape(B) = [1, 2, 3], factor_axis = 0 => shape(B_rf) = [n, 1, 2, 3]

shape(B) = [1, 2, 3], factor_axis = 1 => shape(B_rf) = [1, n, 2, 3]

shape(B) = [1, 2, 3], factor_axis = 2 => shape(B_rf) = [1, 2, n, 3]

shape(B) = [1, 2, 3], factor_axis = 3 => shape(B_rf) = [1, 2, 3, n]

2. The rfactor block, or “rf-block”, is a block that writes to the rf-buffer without accumulating over the loop k, i.e. the loop k is converted from a reduction loop to a data parallel loop. In our example, the rf-block is:

B_rf = np.zeros((128, 128))     # the rf-buffer
for k in range(128):            # loop k is converted to a data parallel loop
    for i in range(128):        # loop i is a data parallel loop (unchanged)
        for j in range(128):    # loop j is a reduction loop (unchanged)
            B_rf[i, k] = B_rf[i, k] + A[i, j, k]

3. The write-back block, or wb-block, is a block that accumulates the rf-buffer into the result buffer. All the reduction loops are removed except the loop k for accumulation. In our example, the wb-block is:

for i in range(128):            # loop i is a data parallel loop (unchanged)
                                # loop j is removed because it is a reduction loop
    for k in range(128):        # loop k is a reduction loop (unchanged)
        B[i] = B[i] + B_rf[i, k]

Parameters

loop (LoopRV) – The loop outside block for which we want to do rfactor
factor_axis (int) – The position where the new dimension is placed in the new introduced rfactor buffer

Returns

rf_block – The block which computes partial results over each slices (i.e., the first block as described in the above illustration)

Return type

BlockRV

Examples

Before rfactor, in TensorIR, the IR is:

@T.prim_func
def before_rfactor(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, (128, 128, 128))
    B = T.match_buffer(b, (128,))
    for ii, i, j in T.grid(128, 128, 128):
    with T.block("B"):
        vii, vi, vj = T.axis.remap("SRR", [ii, i, j])
        with T.init():
            B[vii] = 0.0
        B[vii] = B[vii] + A[vii, vi, vj]

Create the schedule and do rfactor:

sch = tir.Schedule(before_rfactor)
_, _, k = sch.get_loops(sch.get_block("B"))
sch.rfactor(k, 0)
print(sch.mod["main"].script())

After applying rfactor, the IR becomes:

@T.prim_func
def after_rfactor(a: T.handle, b: T.handle) -> None:
    A = T.match_buffer(a, [128, 128, 128])
    B = T.match_buffer(b, [128])
    B_rf = T.alloc_buffer([128, 128])
    for i2, ii, i in T.grid(128, 128, 128):
        with T.block("B_rf"):
            vi2, vii, vi = T.axis.remap("SSR", [i2, ii, i])
            with T.init():
                B_rf[vi2, vii] = 0.0
            B_rf[vi2, vii] = (B_rf[vi2, vii] + A[vii, vi, vi2])
    for ii, i2 in T.grid(128, 128):
        with T.block("B"):
            vii, vi2 = T.axis.remap("SR", [ii, i2])
            with T.init():
                B[vii] = 0.0
            B[vii] = B[vii] + B_rf[vi2, vii]

Note

Rfactor requires: 1) loop has only one child block, and it is a reduction block; 2) loop is a reduction loop, i.e. the loop variable is bound to only reduction variables in the block binding; 3) loop is not parallelized, vectorized, unrolled or bound to any thread axis; 4) The block scope that loop is in is a staged-pipeline; 5) The outermost loop outside the reduction block should has the reduction block as its first child block; 6) The outermost reduction loop should have only one child block; 7) An unary extent loop that is not bound to any reduction or data parallel variables in the block binding should not appear under some reduction loop; 8) The reduction block should write to only one buffer, and its init and body are both simple BufferStore`s, and the pattern is registered as an associative reducer. The pre-defined patterns include: plus, multiplication, min and max; 9) Each of the loops on top of the block cannot be bound to a data parallel and a reduction block binding at the same time; 10) `factor_axis should be in range [-ndim(B) - 1, ndim(B)], where B is the buffer that the reduction block writes to. Negative indexing is normalized according to numpy convention.

storage_align(block: Union[tvm.tir.schedule.schedule.BlockRV, str], buffer_index: int, axis: int, factor: int, offset: int) → None¶

Set alignment requirement for specific dimension such that stride[axis] == k * factor + offset for some k. This is useful to set memory layout 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

block (Union[BlockRV, str]) – The producer block of the buffer.
buffer_index (int) – The index of the buffer in block’s write region.
axis (int) – The dimension to be specified for alignment.
factor (int) – The factor multiple of alignment.
offset (int) – The required offset factor.

Examples

Before storage_align, in TensorIR, the IR is:

@T.prim_func
def before_storage_align(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.alloc_buffer((128, 128))
    C = T.match_buffer(c, (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
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + 1.0

Create the schedule and do storage_align:

sch = tir.Schedule(before_storage_align)
sch.storage_align(sch.get_block("B"), buffer_index=0, axis=0, factor=128, offset=1)
print(sch.mod["main"].script())

After applying storage_align, the IR becomes:

@T.prim_func
def after_storage_align(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128))
    B = T.alloc_buffer((128, 128))
    C = T.match_buffer(c, (128, 128))
    for i, j in T.grid(128, 128):
        with T.block("B"):
            T.block_attr({"buffer_dim_align": [[[0, 128, 1]]]})
            vi, vj = T.axis.remap("SS", [i, j])
            B[vi, vj] = A[vi, vj] * 2.0
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + 1.0

After lowering passes, buffer B will have strides as [129, 1].

Note

Storage_align requires the buffer to be an intermediate buffer defined via alloc_buffer.

set_scope(block: Union[tvm.tir.schedule.schedule.BlockRV, str], buffer_index: int, storage_scope: str) → None¶

Set the storage scope of a buffer, where the buffer is specified by the a block and a write-index

Parameters

block (Union[BlockRV, str]) – The producer block of the buffer
buffer_index (int) – The index of the buffer in block’s write region
storage_scope (str) – The storage scope to be set

Examples

Before set_scope, in TensorIR, the IR is:

@T.prim_func
def before_set_scope(
    A: T.Buffer[(128, 128), "float32"], C: T.Buffer[(128, 128), "float32"]
) -> None:
    B = T.alloc_buffer((128, 128), dtype="float32")

    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
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + 1.0

Create the schedule and do set_scope:

sch = tir.Schedule(before_set_scope)
sch.set_scope(sch.get_block("B"), buffer_index=0, storage_scope="shared")
print(sch.mod["main"].script())

After applying set_scope, the IR becomes:

@T.prim_func
def after_set_scope(
    A: T.Buffer[(128, 128), "float32"], C: T.Buffer[(128, 128), "float32"]
) -> None:
    B_shared = T.alloc_buffer([128, 128], dtype="float32", scope="shared")

    for i, j in T.grid(128, 128):
        with T.block("B"):
            vi, vj = T.axis.remap("SS", [i, j])
            B_shared[vi, vj] = A[vi, vj] * T.float32(2)
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B_shared[vi, vj] + T.float32(1)

Note

Set_scope requires the buffer to be an intermediate buffer defined via alloc_buffer.

blockize(loop: tvm.tir.schedule.schedule.LoopRV, preserve_unit_iters: bool = True) → tvm.tir.schedule.schedule.BlockRV¶

Convert the subtree rooted at a specific loop into a block.

Parameters

loop (LoopRV) – The root of the subtree.
preserve_unit_iters (bool) – Whether or not to preserve unit iterators in block bindings

Returns

result – The new block.

Return type

BlockRV

Examples

Before blockize, in TensorIR, the IR is:

@T.prim_func
def before_blockize(
    A: T.Buffer[(128, 128), "float32"],
    B: T.Buffer[(128, 128), "float32"]
) -> None:
    for i_0, j_0, i_1, j_1 in T.grid(8, 8, 16, 16):
        with T.block("B"):
            vi = T.axis.spatial(128, i_0 * 16 + i_1)
            vj = T.axis.spatial(128, j_0 * 16 + j_1)
            T.reads(A[vi, vj])
            T.writes(B[vi, vj])
            B[vi, vj] = A[vi, vj] * T.float32(2)

Create the schedule and do set_scope:

sch = tir.Schedule(before_blockize)
B = sch.get_block("B")
_, _, i1, _ = sch.get_loops(B)
sch.blockize(i1)
print(sch.mod["main"].script())

After applying blockize, the IR becomes:

@T.prim_func
def after_blockize(
    A: T.Buffer[(128, 128), "float32"],
    B: T.Buffer[(128, 128), "float32"]
)-> None:
    for i_0, j_0 in T.grid(8, 8):
        with T.block("B_o"):
            vio, vjo = T.axis.remap("SS", [i_0, j_0])
            T.reads(A[vio * 16 : vio * 16 + 16, vjo * 16 : vjo * 16 + 16])
            T.writes(B[vio * 16 : vio * 16 + 16, vjo * 16 : vjo * 16 + 16])
            for i_1, j_1 in T.grid(16, 16):
                with T.block("B"):
                    vi, vj = T.axis.remap("SS", [i_1, j_1])
                    T.reads(A[vio * 16 + vi, vjo * 16 + vj])
                    T.writes(B[vio * 16 + vi, vjo * 16 + vj])
                    B[vio * 16 + vi, vjo * 16 + vj] = A[vio * 16 + vi, vjo * 16 + vj]                                                                   * T.float32(2)

Note

blockize requires there is exactly one block under the given loop and the bindings of the block are divisible by the subspace represented by the loops starting at the given loop.

tensorize(block_or_loop: Union[tvm.tir.schedule.schedule.BlockRV, tvm.tir.schedule.schedule.LoopRV], tensor_intrin: str, preserve_unit_iters: bool = True) → None¶

Tensorize the computation enclosed by loop with the tensor intrinsic.

Parameters

block_or_loop (Union[BlockRV, LoopRV]) – The loop to be tensorized.
tensor_intrin (str) – The tensor intrin or the name of the tensor intrin.
preserve_unit_iters (bool) – Whether or not to preserve unit iterators in block bindings

Examples

Before tensorize, in TensorIR, the IR is:

@T.prim_func
def before_tensorize(
    A: T.Buffer[(128, 128), "float32"],
    B: T.Buffer[(128, 128), "float32"],
    C: T.Buffer[(128, 128), "float32"],
) -> None:
    # body
    # with T.block("root")
    for i_0, j_0, k_0, i_1, j_1, k_1 in T.grid(8, 8, 8, 16, 16, 16):
        with T.block("update"):
            vi = T.axis.spatial(128, i_0 * 16 + i_1)
            vj = T.axis.spatial(128, j_0 * 16 + j_1)
            vk = T.axis.reduce(128, k_0 * 16 + k_1)
            T.reads(C[vi, vj], A[vi, vk], B[vj, vk])
            T.writes(C[vi, vj])
            C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]

Declare and register the tensor intrinsic:

@T.prim_func
def mma_desc(a: T.handle, b: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (16, 16), align=128, offset_factor=1)
    B = T.match_buffer(b, (16, 16), align=128, offset_factor=1)
    C = T.match_buffer(c, (16, 16), align=128, offset_factor=1)

    with T.block("root"):
        T.reads(C[0 : 16, 0 : 16], A[0 : 16, 0 : 16], B[0 : 16, 0 : 16])
        T.writes(C[0 : 16, 0 : 16])
        for i, j, k in T.grid(16, 16, 16):
            with T.block("update"):
                vi, vj, vk = T.axis.remap("SSR", [i, j, k])
                C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]

@T.prim_func
def mma_intrin(a: T.handle, b: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (16, 16), align=128, offset_factor=1)
    B = T.match_buffer(b, (16, 16), align=128, offset_factor=1)
    C = T.match_buffer(c, (16, 16), align=128, offset_factor=1)

    with T.block("root"):
        T.reads(C[0 : 16, 0 : 16], A[0 : 16, 0 : 16], B[0 : 16, 0 : 16])
        T.writes(C[0 : 16, 0 : 16])
        T.evaluate(
            T.tvm_mma_sync(
                C.data,
                C.elem_offset // 256,
                A.data,
                A.elem_offset // 256,
                B.data,
                B.elem_offset // 256,
                C.data,
                C.elem_offset // 256,
                dtype="handle",
            )
        )

tir.TensorIntrin.register("test_mma_intrin", mma_desc, mma_intrin)

Create the schedule and do tensorize:

sch = tir.Schedule(before_tensorize)
update = sch.get_block("update")
_, _, _, i1, _, _ = sch.get_loops(update)
sch.tensorize(i1, "test_mma_intrin")
print(sch.mod["main"].script())

After applying tensorize, the IR becomes:

@T.prim_func
def after_tensorize(
    A: T.Buffer[(128, 128), "float32"],
    B: T.Buffer[(128, 128), "float32"],
    C: T.Buffer[(128, 128), "float32"],
) -> None:
    # body
    # with T.block("root")
    for i_0, j_0, k_0 in T.grid(8, 8, 8):
        with T.block("update_o"):
            vio, vjo, vko = T.axis.remap("SSR", [i_0, j_0, k_0])
            T.reads(
                C[vio * 16 : vio * 16 + 16, vjo * 16 : vjo * 16 + 16],
                A[vio * 16 : vio * 16 + 16, vko * 16 : vko * 16 + 16],
                B[vjo * 16 : vjo * 16 + 16, vko * 16 : vko * 16 + 16],
            )
            T.writes(C[vio * 16 : vio * 16 + 16, vjo * 16 : vjo * 16 + 16])
            A_1 = T.match_buffer(
                A[vio * 16 : vio * 16 + 16, vko * 16 : vko * 16 + 16],
                [16, 16],
                dtype="float32",
                offset_factor=1,
            )
            B_1 = T.match_buffer(
                B[vjo * 16 : vjo * 16 + 16, vko * 16 : vko * 16 + 16],
                [16, 16],
                dtype="float32",
                offset_factor=1,
            )
            C_1 = T.match_buffer(
                C[vio * 16 : vio * 16 + 16, vjo * 16 : vjo * 16 + 16],
                [16, 16],
                dtype="float32",
                offset_factor=1,
            )
            T.evaluate(
                T.tvm_mma_sync(
                    C_1.data,
                    C_1.elem_offset // 256,
                    A_1.data,
                    A_1.elem_offset // 256,
                    B_1.data,
                    B_1.elem_offset // 256,
                    C_1.data,
                    C_1.elem_offset // 256,
                    dtype="handle",
                )
            )

annotate(block_or_loop: Union[tvm.tir.schedule.schedule.BlockRV, tvm.tir.schedule.schedule.LoopRV], ann_key: str, ann_val: Union[str, int, float, tvm.ir.expr.PrimExpr, List[Union[str, int, float, tvm.ir.expr.PrimExpr]], Dict[str, Union[str, int, float, tvm.ir.expr.PrimExpr, List[Union[str, int, float, tvm.ir.expr.PrimExpr]]]]]) → None¶

Annotate a block/loop with a key value pair

Parameters

block_or_loop (Union[BlockRV, LoopRV]) – The block/loop to be annotated
ann_key (str) – The annotation key
ann_val (AnnotationValueT) – The annotation value

Examples

Before annotate, in TensorIR, the IR is:

@T.prim_func
def before_annotate(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

Create the schedule and do annotate:

sch = tir.Schedule(before_annotate)
sch.annotate(sch.get_block("B"), "ann_key", "ann_value")
print(sch.mod["main"].script())

After applying annotate, the IR becomes:

@T.prim_func
def after_annotate(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])
            T.block_attr({"ann_key", "ann_value"})
            B[vi, vj] = A[vi, vj] * 2.0

unannotate(block_or_loop: Union[tvm.tir.schedule.schedule.BlockRV, tvm.tir.schedule.schedule.LoopRV], ann_key: str) → None¶

Unannotate a block/loop’s annotation with key ann_key

Parameters

block_or_loop (Union[BlockRV, LoopRV]) – The block/loop to be unannotated
ann_key (str) – The annotation key

Examples

Before unannotate, in TensorIR, the IR is:

@T.prim_func
def before_unannotate(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])
            T.block_attr({"ann_key", "ann_value"})
            B[vi, vj] = A[vi, vj] * 2.0

Create the schedule and do annotate:

sch = tir.Schedule(before_unannotate)
sch.unannotate(sch.get_block("B"), "ann_key")
print(sch.mod["main"].script())

After applying unannotate, the IR becomes:

@T.prim_func
def after_unannotate(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

transform_layout(block: Union[tvm.tir.schedule.schedule.BlockRV, str], buffer: Union[Tuple[str, int], str, tvm.tir.buffer.Buffer], index_map: Union[tvm.tir.function.IndexMap, Callable], pad_value: Optional[Union[int, float, tvm.ir.expr.PrimExpr, tvm.tir.function.IndexMap, Callable]] = None) → None¶

Apply a transformation represented by IndexMap to buffer

Parameters

block (Union[BlockRV, str]) – The block that accesses the target buffer. If a string, this must uniquely identify a block.
buffer (Union[Tuple[str,int], Buffer, str]) –
The buffer to be transformed, or a specification of how to identify the buffer to be transformed.

If buffer if a tuple of (str,int), the first item should be either “read” or “write”, and the second item is an index into the block’s read or write regions.

If buffer is a string, it is the name of the buffer, which must exist within the reads/writes of the block. In addition, the reads/writes of the block may not contain more than one buffer with this name.

If buffer is a Buffer object, it must exist within the reads/writes of the block.
index_map (Union[IndexMap, Callable]) –
The transformation to apply.

If index_map is a callable, and the returned list contains IndexMap.AXIS_SEPARATOR, the SetAxisSeparators primitive will be called in addition to the TransformLayout primitive.
pad_value (Optional[Union[int, float, PrimExpr, IndexMap, Callable]]) –
The value to be used for any padding introduced by the transformation. If the schedule contains a producer block for the specified buffer, the pad value will be written as part of the producer block if possible, or after the producer block otherwise. Otherwise, if the buffer is an input, will insert an annotation block to state that the padding contains the known value.

The pad value may not contain instances of BufferLoad, except where it loads a value from the buffer being transformed (e.g. to create a circular buffer with padding that consists of repeated elements).

Note: If applied to an input buffer, the calling scope is responsible for ensuring that the pad_value is present. Algebraic symplifications, branch elimination, and other optimizations may assume that this precondition is met, and may result in incorrect results being returned.

If None, the transformation may not introduce padding.

If an int, float or PrimExpr, the transformation is the specific value to be present in the padding.

If an IndexMap or Callable, the transformation is the value to be present in the padding in terms of the transformed index.

Examples

Before transform_layout, in TensorIR, the IR is:

@T.prim_func
def before_transform_layout(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128), "float32")
    B = T.alloc_buffer((128, 128), "float32")
    C = T.match_buffer(c, (128, 128), "float32")
    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
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + 1.0

Create the schedule and do transform_layout:

sch = tir.Schedule(before_storage_align)
sch.transform_layout(sch.get_block("B"), buffer=("write",0),
                     index_map=lambda m, n: (m // 16, n // 16, m % 16, n % 16))
print(sch.mod["main"].script())

After applying transform_layout, the IR becomes:

@T.prim_func
def two_elementwise_transformed_intermediate_buffer(a: T.handle, c: T.handle) -> None:
    A = T.match_buffer(a, (128, 128), "float32")
    B = T.alloc_buffer((8, 8, 16, 16), "float32")
    C = T.match_buffer(c, (128, 128), "float32")
    for i, j in T.grid(128, 128):
        with T.block("B"):
            vi, vj = T.axis.remap("SS", [i, j])
            B[vi // 16, vj // 16, vi % 16, vj % 16] = A[vi, vj] * 2.0
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi // 16, vj // 16, vi % 16, vj % 16] + 1.0

transform_block_layout(block: Union[tvm.tir.schedule.schedule.BlockRV, str], index_map: Union[tvm.tir.function.IndexMap, Callable]) → None¶

Apply a transformation represented by IndexMap to block

Parameters

block (Union[BlockRV, str]) – The block to be transformed
index_map (Union[IndexMap, Callable]) – The transformation to apply.

Examples

Before transform_block_layout, in TensorIR, the IR is:

@T.prim_func
def before_transform_block_layout(
    A: T.Buffer[(16, 16), "float32"],
    B: T.Buffer[(16, 16), "float32"]
) -> None:
    for i, j in T.grid(16, 16):
        with T.block("B"):
            vi, vj = T.axis.remap("SS", [i, j])
            B[vi, vj] = A[vi, vj] * 2.0

Create the schedule and do transform_block_layout:

sch = tir.Schedule(before_transform_block_layout)
sch.transform_block_layout(sch.get_block("B"), lambda i, j: (i * 16 + j,))
print(sch.mod["main"].script())

After applying transform_block_layout, the IR becomes:

@T.prim_func
def after_transform_block_layout(
    A: T.Buffer[(16, 16), "float32"],
    B: T.Buffer[(16, 16), "float32"]
) -> None:
    for i in range(256):
        with T.block("B"):
            vi, = T.axis.remap("S", [i])
            B[vi // 16, vi % 16] = A[vi // 16, vi % 16] * 2.0

set_axis_separator(block: Union[tvm.tir.schedule.schedule.BlockRV, str], buffer: Union[Tuple[str, int], str, tvm.tir.buffer.Buffer], axis_separators: Optional[List[int]]) → None¶

Set the axis separator of a buffer, where the buffer is specified by a block and a read or write index.

Parameters

block (Union[BlockRV, str]) – The block that accesses the target buffer. If a string, this must uniquely identify a block.
buffer (Union[Tuple[str,int], Buffer, str]) –
The buffer to be transformed, or a specification of how to identify the buffer to be transformed.

If buffer if a tuple of (str,int), the first item should be either “read” or “write”, and the second item is an index into the block’s read or write regions.

If buffer is a string, it is the name of the buffer, which must exist within the reads/writes of the block. In addition, the reads/writes of the block may not contain more than one buffer with this name.

If buffer is a Buffer object, it must exist within the reads/writes of the block.
axis_separators (Optional[List[int]]) – The axis separators.

Examples

Before set_axis_separator, in TensorIR, the IR is:

@T.prim_func
def before_set_axis_separator(
    A: T.Buffer[(128, 128), "float32"], C: T.Buffer[(128, 128), "float32"]
) -> None:
    B = T.alloc_buffer((128, 128), dtype="float32")

    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
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + 1.0

Create the schedule and do set_axis_separator:

sch = tir.Schedule(before_set_axis_separator)
sch.set_axis_separators(sch.get_block("B"), buffer_index=0, buffer_index_type="write",
                        axis_separators=[1])
print(sch.mod["main"].script())

After applying set_axis_separator, the IR becomes:

@T.prim_func
def after_set_axis_separators(
    A: T.Buffer[(128, 128), "float32"], C: T.Buffer[(128, 128), "float32"]
) -> None:
    B = T.alloc_buffer([128, 128], dtype="float32", axis_separators=[1])

    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] * T.float32(2)
    for i, j in T.grid(128, 128):
        with T.block("C"):
            vi, vj = T.axis.remap("SS", [i, j])
            C[vi, vj] = B[vi, vj] + T.float32(1)

decompose_padding(block: Union[tvm.tir.schedule.schedule.BlockRV, str], loop: tvm.tir.schedule.schedule.LoopRV) → tvm.tir.schedule.schedule.BlockRV¶

Decompose a block of padding computation pattern into two separate blocks.

The block which fill const pad values into full write region;
The block which fill in-bound values into region where pad predicate is true.

The pad value filling block is inserted right before the given loop.

The schedule primitive requires:

The input block is a complete block.
The input loop is the ancestor of the block.
The input block is a block which match padding pattern.

Parameters

block (Union[BlockRV, str]) – The padding block to be decomposed.
loop (LoopRV) – The loop above which the pad value filling block is inserted before.

Returns

pad_value_block – The block filling const pad values.

Return type

BlockRV

Examples

Before decompose-padding, in TensorIR, the IR is:

@T.prim_func
def before_decompose(x: T.Buffer[128, "int32"], y: T.Buffer[140, "int32"]):
    for i in range(140):
        with T.block("block"):
            vi = T.axis.remap("S", [i])
            y[vi] = T.if_then_else(vi >= 6 and vi < 134, x[vi - 6], 0, dtype="int32")

Create the schedule and do decompose-padding with specified loop:

sch = tir.Schedule(before_decompose, debug_mask="all")
block = sch.get_block("block")
sch.decompose_padding(block, sch.get_loops(block)[0])
print(sch.mod["main].script())

After applying decompose-padding, the IR becomes:

@T.prim_func
def after_decompose(x: T.Buffer[128, "int32"], y: T.Buffer[140, "int32"]):
    for i in T.serial(140):
        with T.block("block_pad_const"):
            vi = T.axis.spatial(140, i)
            y[vi] = 0
    for i in T.serial(128):
        with T.block("block"):
            vi = T.axis.spatial(128, i)
            y[vi + 6] = x[vi]

can_decompose_padding(block: Union[tvm.tir.schedule.schedule.BlockRV, str], loop: tvm.tir.schedule.schedule.LoopRV) → bool¶: Check whether the block match padding pattern and can be decomposed.

pad_einsum(block: Union[tvm.tir.schedule.schedule.BlockRV, str], padding: List[int]) → None¶

Pad the computation of Einsum.

This schedule primitives identifies the Einsum pattern in the block body, and find its producer blocks. It then pads the computation of the Einsum pattern and its producer blocks. The output buffer and the producer buffer is resized according to the padding size. It requires the output buffer and the producer buffer to be allocated inside the PrimFunc.

The padding is a list of non-negative integers, each element corresponds to the padding for each block iter in the order of block iters. The block and it’s producer blocks should have trivial bindings, i.e. each block iter is bound to a single loop variable. After padding, thblock iter extent and the corresponding outer loop is extended by the padding size.

The size of the producer buffers are infered from the padding size of the Einsum computation. The producer buffers are padded by the initial value of the corresponding reduction.

Parameters

block (Union[BlockRV, str]) – The block that matches the Einsum pattern.
padding (List[int]) – The padding for each block iter.

Examples

Before applying pad-einsum, in TensorIR, the IR is:

@T.prim_func
def before_pad_einsum(
    A: T.Buffer[(128, 127), "float32"],
    B: T.Buffer[(127, 127), "float32"],
    C: T.Buffer[(128, 127), "float32"],
) -> None:
    A_shared = T.alloc_buffer((128, 127), "float32", scope="shared")
    B_shared = T.alloc_buffer((127, 127), "float32", scope="shared")
    C_shared = T.alloc_buffer((128, 127), "float32", scope="shared")
    for i0, i1 in T.grid(128, 127):
        with T.block("A"):
            i, j = T.axis.remap("SS", [i0, i1])
            A_shared[i, j] = A[i, j]
    for i0, i1 in T.grid(127, 127):
        with T.block("B"):
            i, j = T.axis.remap("SS", [i0, i1])
            B_shared[i, j] = B[i, j]
    for i0, i1, i2 in T.grid(128, 127, 127):
        with T.block("C_shared"):
            i, j, k = T.axis.remap("SSR", [i0, i1, i2])
            with T.init():
                C_shared[i, j] = T.float32(0)
            C_shared[i, j] = C_shared[i, j] + A_shared[i, k] * B_shared[k, j]
    for i0, i1 in T.grid(128, 127):
        with T.block("C"):
            i, j = T.axis.remap("SS", [i0, i1])
            C[i, j] = C_shared[i, j]

Create the schedule and do pad-einsum with specified block:

sch = tir.Schedule(before_pad_einsum, debug_mask="all")
block = sch.get_block("C_shared")
sch.pad_einsum(block, [0, 1, 1])
print(sch.mod["main"].script())

After applying decompose-padding, the IR becomes:

@T.prim_func
def after_pad_einsum(
    A: T.Buffer[(128, 127), "float32"],
    B: T.Buffer[(127, 127), "float32"],
    C: T.Buffer[(128, 127), "float32"],
) -> None:
    A_shared_padded = T.alloc_buffer([128, 128], dtype="float32", scope="shared")
    B_shared_padded = T.alloc_buffer([128, 128], dtype="float32", scope="shared")
    C_shared_padded = T.alloc_buffer([128, 128], dtype="float32", scope="shared")
    for i0, i1 in T.grid(128, 128):
        with T.block("A"):
            i, j = T.axis.remap("SS", [i0, i1])
            T.reads(A[i, j])
            T.writes(A_shared_padded[i, j])
            A_shared_padded[i, j] = T.if_then_else(
                j < 127, A[i, j], T.float32(0), dtype="float32"
            )
    for i0, i1 in T.grid(128, 128):
        with T.block("B"):
            i, j = T.axis.remap("SS", [i0, i1])
            T.reads(B[i, j])
            T.writes(B_shared_padded[i, j])
            B_shared_padded[i, j] = T.if_then_else(
                i < 127 and j < 127, B[i, j], T.float32(0), dtype="float32"
            )
    for i0, i1, i2 in T.grid(128, 128, 128):
        with T.block("C_shared"):
            i, j, k = T.axis.remap("SSR", [i0, i1, i2])
            T.reads(A_shared_padded[i, k], B_shared_padded[k, j])
            T.writes(C_shared_padded[i, j])
            with T.init():
                C_shared_padded[i, j] = T.float32(0)
            C_shared_padded[i, j] = (
                C_shared_padded[i, j] + A_shared_padded[i, k] * B_shared_padded[k, j]
            )
    for i0, i1 in T.grid(128, 127):
        with T.block("C"):
            i, j = T.axis.remap("SS", [i0, i1])
            T.reads(C_shared_padded[i, j])
            T.writes(C[i, j])
            C[i, j] = C_shared_padded[i, j]

rolling_buffer(block: Union[tvm.tir.schedule.schedule.BlockRV, str], write_buffer_index: int) → None¶

Compute the target buffer via rolling buffering, select the outermost rollable axis with a positive bound overlap that appears in the block’s ancestor loops as rolling axis, fold and circularize the buffer along the rolling dimension, append block predicate to avoid recomputing overlapping elements. It requires:

The block is not an output block and has only RAW dependencies.
The buffer to be an intermediate buffer defined via alloc_buffer.

3) The LCA of the producer and consumer of the buffer is a for loop, typically, the producer and consumer of the buffer are cascaded through compute_at.

4) The access region of the buffer has at least one dimension that contains a positive bound overlap.

Parameters

block (Union[BlockRV, str]) – The producer block of the buffer.
write_buffer_index (int) – The index of the buffer in block’s write region.

Examples

Before rolling_buffer, in TensorIR, the IR is:

@T.prim_func
def before_rolling_buffer(
    A: T.Buffer[(12, 12), "int8"], C: T.Buffer[(8, 8), "int8"]
) -> None:
    # body
    # with T.block("root")
    B = T.alloc_buffer([10, 10], dtype="int8")
    for i0, i1 in T.grid(2, 2):
        for ax0, ax1, ax2, ax3 in T.grid(6, 6, 3, 3):
            with T.block("B"):
                ax0_1 = T.axis.spatial(10, i0 * 4 + ax0)
                ax1_1 = T.axis.spatial(10, i1 * 4 + ax1)
                rv0, rv1 = T.axis.remap("RR", [ax2, ax3])
                B[ax0_1, ax1_1] = T.max(
                    B[ax0_1, ax1_1], A[ax0_1 + rv0, ax1_1 + rv1]
                )
        for ax0, ax1, ax2, ax3 in T.grid(4, 4, 3, 3):
            with T.block("C"):
                ax0_1 = T.axis.spatial(8, i0 * 4 + ax0)
                ax1_1 = T.axis.spatial(8, i1 * 4 + ax1)
                rv0, rv1 = T.axis.remap("RR", [ax2, ax3])
                C[ax0_1, ax1_1] = T.max(
                    C[ax0_1, ax1_1], B[ax0_1 + rv0, ax1_1 + rv1]
                )

Create the schedule and do rolling_buffer:

sch = tir.Schedule(before_rolling_buffer)
sch.rolling_buffer(sch.get_block("B"), write_buffer_index=0)
print(sch.mod["main"].script())

After applying rolling_buffer, the IR becomes:

@T.prim_func
def after_rolling_buffer(
    A: T.Buffer[(12, 12), "int8"],
    C: T.Buffer[(8, 8), "int8"]
) -> None:
    # body
    # with T.block("root")
    B = T.alloc_buffer([6, 10], dtype="int8")
    for i0, i1 in T.grid(2, 2):
        for ax0, ax1, ax2, ax3 in T.grid(6, 6, 3, 3):
            with T.block("B"):
                T.where((i0 < 1 or 2 <= ax0) and (i1 < 1 or 2 <= ax1))
                ax0_1 = T.axis.spatial(10, i0 * 4 + ax0)
                ax1_1 = T.axis.spatial(10, i1 * 4 + ax1)
                rv0, rv1 = T.axis.remap("RR", [ax2, ax3])
                B[ax0_1 % 6, ax1_1] = T.max(
                    B[ax0_1 % 6, ax1_1], A[ax0_1 + rv0, ax1_1 + rv1]
                )
        for ax0, ax1, ax2, ax3 in T.grid(4, 4, 3, 3):
            with T.block("C"):
                ax0_1 = T.axis.spatial(8, i0 * 4 + ax0)
                ax1_1 = T.axis.spatial(8, i1 * 4 + ax1)
                rv0, rv1 = T.axis.remap("RR", [ax2, ax3])
                C[ax0_1, ax1_1] = T.max(
                    C[ax0_1, ax1_1], B[ax0_1 % 6 + rv0, ax1_1 + rv1]
                )

Note

The region_cover property of the consumer block of the target buffer will become false.

enter_postproc() → None¶: A no-op that marks the start of postprocessing phase of scheduling

exception tvm.tir.ScheduleError¶: Error that happens during TensorIR scheduling.

tvm.tir.transform¶

Namespace of all TIR transformations

Functions:

`prim_func_pass`([pass_func, opt_level, name, ...])	Decorate a function pass.
`AnnotateEntryFunc`()	Set a PrimFunc as the entry point if it is only function in IRModule.
`Apply`(ftransform)	Apply ftransform to each function in the Module.
`ApplyLayoutTransforms`()	Reshape buffers that appear in the "layout_transform_map" fucntion attribute.
`BF16CastElimination`()	Eliminate verbose casting between fp32 and bf16 Checks if the AST has the pattern: castto32(castto16(some_fp32_op(...))) The verbose casting is generated by BF16Promote for multiple bf16 Ops in a row.
`BF16Legalize`()	Legalize bf16 typed Ops.
`BF16Promote`()	Promote bf16 to fp32.
`BF16TypeLowering`()	Replace all bf16 type with uint16.
`BindTarget`(target)	Annotate a PrimFunc with a given target.
`CoProcSync`()	Detect and insert sync points to co-processor.
`CombineContextCall`()	Combine context calls in the host function.
`CommonSubexprElimTIR`([enable_cse_tir, ...])	Replace redundant computations by new variables.
`CompactBufferAllocation`()	Compact the buffer access region.
`ConvertBlocksToOpaque`()	Substitute all the block vars with the PrimExprs they are bound to, indicated by the corresponding iter_values in BlockRealize, and then convert the blocks into opaque ones by removing all the iter_values in BlockRealize and iter_vars in Block.
`ConvertForLoopsToSerial`()	Convert Parallel For Loops to Serial For Loops.
`DecorateDeviceScope`()	Decorate all the function's body as device function.
`ExtractPrimFuncConstants`()	Collects and unificates tir non-scalar constants to module's attr 'Constants' array.
`Filter`(fcond)	Filter out PrimFuncs that does not satisfy the given condition.
`FlattenBuffer`()	Flatten the multi-dimensional BufferLoad and BufferStore to single dimensional BufferLoad/BufferStore for the TIR not contains opaque block.
`HoistExpression`()	Generalized verison of HoistIfThenElse.
`HoistIfThenElse`([variant])	Hoist loop-invariant IfThenElse nodes to outside the eligible loops.
`InferFragment`()	Infer the TensorCore fragment infomation using tensor intrinsics.
`InjectCopyIntrin`(pragma_key, fintrin)	Inject virtual thread loops.
`InjectDoubleBuffer`()	Inject double buffer statements.
`InjectPTXAsyncCopy`()	Rewrite global to shared memory copy on CUDA with asyncronous copy.
`InjectPrefetch`()	Inject prefetch instructions into stmt.
`InjectRollingBuffer`()	Inject rolling buffer statements.
`InjectSoftwarePipeline`()	Transform annotated loops into pipelined one that parallelize producers and consumers
`InjectVirtualThread`()	Inject virtual thread loops.
`InstrumentBoundCheckers`()	Instruments bound checkers.
`InstrumentProfileIntrinsics`()	Insert intrinsic calls to instrument function and loop level profiling.
`LegalizePackedCalls`()	Legalize packed calls to have its arguments wrapped in TVMValues
`LiftAttrScope`(attr_key)	Lift common attrs with attr_key to outer scope.
`LoopPartition`()	Inject virtual thread loops.
`LowerCrossThreadReduction`()	Lower cross-thread reduction from thread bindings to intrinsic function calls.
`LowerCustomDatatypes`()	Lower custom datatypes.
`LowerDeviceStorageAccessInfo`()	Lower attached storage access information on device.
`LowerInitBlock`()	Lower block init stmt into IfThenElse statements.
`LowerIntrin`()	Lower target specific intrinsic calls.
`LowerMatchBuffer`()	Remove match buffers inside the block.
`LowerOpaqueBlock`()	Remove the block to ensure that the TIR can not be scheduled again.
`LowerTVMBuiltin`()	Lower tvm builtin intrinsics.
`LowerThreadAllreduce`()	Lower cross thread alleduce.
`LowerWarpMemory`()	Lower warp memory access to low-level device related function calls.
`MakePackedAPI`()	Transform the PrimFuncs in the module to a packed func API.
`MakeUnpackedAPI`()	Transform the PrimFuncs in the module to a C API compatible with internal calls.
`ManifestSharedMemoryLocalStage`()	Add the explicit local stage for the shared memory access on GPU.
`MergeDynamicSharedMemoryAllocations`()	This pass merges multiple TIR-level dynamic shared memory allocations into one allocation.
`NarrowDataType`(target_bits)	Narrow down PrimExpr datatype in stmt to target_bits.
`PlanAndUpdateBufferAllocationLocation`()	Locate the buffer allocation to the exact position (usually is the lca of buffer access).
`ReduceBranchingThroughOvercompute`()	Reduce branching by introducing overcompute
`RemoveAssume`()	Remove all instances of builtin::assume
`RemoveNoOp`()	Remove No Op from the Stmt.
`RemoveStoreUndef`()	Remove stores of undefined values from the Stmt.
`RemoveWeightLayoutRewriteBlock`([...])	Remove weight layout rewrite block before benchmarking during tuning stage.
`RenormalizeSplitPattern`()	Renormalize the split pattern from floordiv(floormod()) to floormod(floordiv())
`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.
`TextureFlatten`()	Flatten the multi-dimensional read/write to 2D.
`ThreadSync`(storage_scope)	Insert sync between parallel read/write of shared buffers.
`UnifyThreadBinding`()	Unify all the thread bindings for "blockIdx.x/y/z", "threadIdx.x/y/z", and "vthread.x/y/z".
`UnrollLoop`()	Unroll the constant loop marked by unroll.
`VectorizeLoop`([enable_vectorize])	Lower vectorization loops.
`VerifyMemory`()	Verify if func contains illegal host side direct memory access.
`VerifyVTCMLimit`(limit)	Verify if the size of the allocated vtcm memory satisfies the limit.

Classes:

`PrimFuncPass`	A pass that works on each `tvm.tir.PrimFunc()` in a module.
`HoistedConditionals`(value)	Flags for use in HoistExpressionConfig.conditional_types
`HoistedLetBindings`(value)	Flags for use in HoistExpressionConfig.let_binding_types

tvm.tir.transform.prim_func_pass(pass_func=None, opt_level: Optional[int] = None, name: Optional[str] = None, required: Optional[List[str]] = None) → Union[Callable, tvm.tir.transform.function_pass.PrimFuncPass]¶

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[(tvm.tir.PrimFunc, IRModule, PassContext) -> tvm.tir.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 following:
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.AnnotateEntryFunc()¶

Set a PrimFunc as the entry point if it is only function in IRModule.

Returns: fpass – The result pass
Return type: tvm.transform.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.ApplyLayoutTransforms()¶

Reshape buffers that appear in the “layout_transform_map” fucntion attribute.

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

tvm.tir.transform.BF16CastElimination()¶

Eliminate verbose casting between fp32 and bf16 Checks if the AST has the pattern: castto32(castto16(some_fp32_op(…))) The verbose casting is generated by BF16Promote for multiple bf16 Ops in a row. e.g.: X[i] + Y[i] + T[i] => bf16((float32(bf16((float32(X[i]) + float32(Y[i])))) + float32(T[i]))) After this pass: bf16(float32(X[i]) + float32(Y[i]) + float32(T[i]))

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

tvm.tir.transform.BF16Legalize()¶

Legalize bf16 typed Ops. Runs BF16Promote, BF16CastElimination and BF16TypeLowering

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

tvm.tir.transform.BF16Promote()¶

Promote bf16 to fp32. Add a cast to fp32 before Ops, then add a cast back to bf16.

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

tvm.tir.transform.BF16TypeLowering()¶

Replace all bf16 type with uint16. Also lower the casting between fp32 and bf16

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

tvm.tir.transform.BindTarget(target)¶

Annotate a PrimFunc with a given target. :param target: target :type target: tvm.target.Target

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.CommonSubexprElimTIR(enable_cse_tir: bool = True, identify_equiv_terms: bool = False)¶

Replace redundant computations by new variables.

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

tvm.tir.transform.CompactBufferAllocation()¶

Compact the buffer access region. by removing the buffer regions that are not accessed, i.e. narrowing the buffer shape and adjust the access region if necessary.

Example

Before narrowing, B is a [16, 16] buffer, but only a skinny vector B[i, 0:16] is accessed.

for i in range(0, 16):
    with T.block():
        B = T.alloc_buffer(16, 16)
        for j in range(0, 16):
            B[i, j] = A[i, j] + 1
        for j in range(0, 16):
            C[i, j] = B[i, j] + 1

This pass narrows the buffer shape and adjust its accessed region accordingly. In this particular case, because only a 1 * 16 vector of B is accessed, the pass narrows B to shape [1, 16], and changes the access to B[i, j] to B[0, j].

for i in range(0, 16):
    with T.block():
        B = T.alloc_buffer(1, 16)
        for j in range(0, 16):
            B[0, j] = A[i, j] + 1
        for j in range(0, 16):
            C[i, j] = B[0, j] + 1

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

tvm.tir.transform.ConvertBlocksToOpaque()¶

Substitute all the block vars with the PrimExprs they are bound to, indicated by the corresponding iter_values in BlockRealize, and then convert the blocks into opaque ones by removing all the iter_values in BlockRealize and iter_vars in Block.

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

tvm.tir.transform.ConvertForLoopsToSerial()¶

Convert Parallel For Loops to Serial For Loops.

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.ExtractPrimFuncConstants()¶

Collects and unificates tir non-scalar constants to module’s attr ‘Constants’ array.

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

tvm.tir.transform.Filter(fcond: Callable)¶

Filter out PrimFuncs that does not satisfy the given condition. fcond should be a function that takes a primfunc and returns boolean.

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

tvm.tir.transform.FlattenBuffer()¶

Flatten the multi-dimensional BufferLoad and BufferStore to single dimensional BufferLoad/BufferStore for the TIR not contains opaque block.

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

tvm.tir.transform.HoistExpression()¶

Generalized verison of HoistIfThenElse.

Hoist loop-invariant expressions to outside the eligible loops. Searches for expressions in:

LetStmt bindings
IfThenElse conditions
Boolean operators

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

tvm.tir.transform.HoistIfThenElse(variant: Optional[str] = None)¶

Hoist loop-invariant IfThenElse nodes to outside the eligible loops.

Parameters

variant (Optional[String]) –

The variant of the pass. variant can have any one of following values [“basic”, None(Default)].

The basic variant supports basic hoisting scenarios where it expects the For & If Nodes are in place consecutively and does not involve global scope variables or more advanced scenarios.

Default variant supports all hoisting scenarios,i.e., {“Basic” + “Advanced”} supported with control with PassContext configs like below:

config={“tir.HoistIfThenElse”: {“support_block_scope_hosting”: True}}

Returns

fpass – The result pass

Return type

tvm.transform.Pass

class tvm.tir.transform.HoistedConditionals(value)¶

Flags for use in HoistExpressionConfig.conditional_types

Each bitflag represents a type of expression that should be hoisted to the outermost loop possible.

Attributes:

`Never`	No hoisting of conditionals
`IfElseStmt`	If set, look for hoist candidates in IfElseStmt
`IfElseExpr`	If set, look for hoist candidates in tir.if_then_else
`BooleanExpression`	If set, look for hoist candidates in all boolean expressions
`UsingBlockVar`	If set, allow hoisting of conditionals that use a block variable (e.g.
`All`	Enable all hoisting of conditionals

Never = 0¶: No hoisting of conditionals

IfElseStmt = 1¶: If set, look for hoist candidates in IfElseStmt

IfElseExpr = 2¶: If set, look for hoist candidates in tir.if_then_else

BooleanExpression = 4¶: If set, look for hoist candidates in all boolean expressions

UsingBlockVar = 8¶: If set, allow hoisting of conditionals that use a block variable (e.g. threadIdx.x)

All = 15¶: Enable all hoisting of conditionals

class tvm.tir.transform.HoistedLetBindings(value)¶

Flags for use in HoistExpressionConfig.let_binding_types

Each bitflag represents a type of let binding expression that should be hoisted to the outermost loop possible.

Attributes:

`Never`	No hoisting of let bindings
`RequiredByConditional`	Bindings that are used by a hoisted conditional
`LetStmt`	Bindings occuring in LetStmt
`LetExpr`	Bindings occuring in Let expressions
`All`	Enable all hoisting of let bindings

Never = 0¶: No hoisting of let bindings

RequiredByConditional = 1¶: Bindings that are used by a hoisted conditional

LetStmt = 2¶: Bindings occuring in LetStmt

LetExpr = 4¶: Bindings occuring in Let expressions

All = 7¶: Enable all hoisting of let bindings

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: str, 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()¶

Inject double buffer statements.

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

tvm.tir.transform.InjectPTXAsyncCopy()¶

Rewrite global to shared memory copy on CUDA with asyncronous copy.

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.InjectRollingBuffer()¶

Inject rolling buffer statements.

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

tvm.tir.transform.InjectSoftwarePipeline()¶

Transform annotated loops into pipelined one that parallelize producers and consumers

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.InstrumentProfileIntrinsics()¶

Insert intrinsic calls to instrument function and loop level profiling.

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

tvm.tir.transform.LegalizePackedCalls()¶

Legalize packed calls to have its arguments wrapped in TVMValues

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

tvm.tir.transform.LiftAttrScope(attr_key: str)¶

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()¶

Inject virtual thread loops.

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

tvm.tir.transform.LowerCrossThreadReduction()¶

Lower cross-thread reduction from thread bindings to intrinsic function calls.

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.LowerInitBlock()¶

Lower block init stmt into IfThenElse statements.

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

tvm.tir.transform.LowerIntrin()¶

Lower target specific intrinsic calls.

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

tvm.tir.transform.LowerMatchBuffer()¶

Remove match buffers inside the block. Also, it will validate the binding.

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

tvm.tir.transform.LowerOpaqueBlock()¶

Remove the block to ensure that the TIR can not be scheduled again.

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()¶

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

Prior to this pass, the PrimFunc may have Buffer arguments defined in the PrimFuncNode::buffer_map. This pass consumes the buffer_map, using it to generate TVMArgs and TVMRetValue* arguments that implement the PackedFunc API.

For static shapes, the BufferNode::shape, BufferNode::strides, and BufferNode::elem_offset member variables are used to generate runtime checks on the corresponding member variables in the user-provided DLTensor* or tvm.nd.array argument. (e.g. A PrimFunc that accepts a buffer of shape [16,32] validates that the DLTensor::shape array is [16,32].)

For dynamic Buffers, in which one or more of these BufferNode member variables use tir.Var that are not defined by other PrimFunc parameters, these are instead used to define the variables based on the corresponding DLTensor members. (e.g. A PrimFunc that accepts a buffer of shape [tir.Var(“n”), tir.Var(“m”)], when passed a DLTensor of shape [16,32], will define n = 16 and n=32, based on the argument’s shape.

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

tvm.tir.transform.MakeUnpackedAPI()¶

Transform the PrimFuncs in the module to a C API compatible with internal calls.

Prior to this pass, the PrimFunc may have Buffer arguments defined in the PrimFuncNode::buffer_map. This pass consumes the buffer_map, using it to generate T* arguments (e.g. float32*) that can be directly called by a C API.

For static shapes, no runtime validation is performed to confirm that the argument buffer’s shape matches the expected shape. For dynamic shapes, MakeUnpackedAPI requires that the dynamic parameters be passed as separate tir.Var parameters.

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

tvm.tir.transform.ManifestSharedMemoryLocalStage()¶

Add the explicit local stage for the shared memory access on GPU.

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

tvm.tir.transform.MergeDynamicSharedMemoryAllocations()¶

This pass merges multiple TIR-level dynamic shared memory allocations into one allocation.

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

tvm.tir.transform.NarrowDataType(target_bits: int)¶

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.PlanAndUpdateBufferAllocationLocation()¶

Locate the buffer allocation to the exact position (usually is the lca of buffer access). This pass will inject opaque block with alloc_buffers at the allocation site.

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

tvm.tir.transform.ReduceBranchingThroughOvercompute()¶

Reduce branching by introducing overcompute

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

tvm.tir.transform.RemoveAssume()¶

Remove all instances of builtin::assume

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

tvm.tir.transform.RemoveNoOp()¶

Remove No Op from the Stmt.

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

tvm.tir.transform.RemoveStoreUndef()¶

Remove stores of undefined values from the Stmt.

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

tvm.tir.transform.RemoveWeightLayoutRewriteBlock(skip_ndarray_rewrite=False)¶

Remove weight layout rewrite block before benchmarking during tuning stage.

Parameters

skip_ndarray_rewrite (bool) –

If True, exact rewrite of NDArray, according to the given index map, will be skipped. Only the shape of the NDArray is transformed correctly, and the content of the destination array will be filled with random values.

When this pass is called many times during MetaSchedule tuning, the raw data of NDArray, before and after rewrite, does not matter. Since NDArray layout rewrite, using IndexMap’s MapNDArray, is currently slow, skipping the exact rewrite is sometimes necessary.

Returns

fpass – The result pass

Return type

tvm.transform.Pass

tvm.tir.transform.RenormalizeSplitPattern()¶

Renormalize the split pattern from floordiv(floormod()) to floormod(floordiv())

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: bool = 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.TextureFlatten()¶

Flatten the multi-dimensional read/write to 2D.

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

tvm.tir.transform.ThreadSync(storage_scope: str)¶

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.UnifyThreadBinding()¶

Unify all the thread bindings for “blockIdx.x/y/z”, “threadIdx.x/y/z”, and “vthread.x/y/z”. Before the unification, two vars that are bound to a thread axis (e.g., “threadIdx.x”) use different IterVars and variables in their AttrStmts. After the unification, we use a consolidated IterVar and a variable for them.

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

Note

vthread is a legacy behavior that will be deprecated, though thread bindings of vthread are still also unified in this pass. Please use vthread.x, vthread.y and vthread.z instead.

tvm.tir.transform.UnrollLoop()¶

Unroll the constant loop marked by unroll.

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

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

tvm.tir.transform.VectorizeLoop(enable_vectorize: bool = 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.transform.VerifyVTCMLimit(limit: int)¶

Verify if the size of the allocated vtcm memory satisfies the limit.

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

tvm.tir.analysis¶

Namespace of all TIR analysis utils.

Classes:

`Block`(iter_vars, reads, writes, name_hint, body)	Block node.
`Buffer`	Symbolic data buffer in TVM.
`BufferRegion`(buffer, region)	BufferRegion node.
`IRModule`([functions, type_definitions])	IRModule that holds functions and type definitions.
`Object`	Base class for all tvm's runtime objects.
`PrimExpr`	Base class of all primitive expressions.
`PrimFunc`(params, body[, ret_type, ...])	A function declaration expression.
`Stmt`	Base class of all the statements.
`Var`(name, dtype[, span])	Symbolic variable.

Functions:

`OOBChecker`()	Detect out of bounds memory access in arrays.
`apply_prim_func_arg_and_result_memory_constraints`(...)	Returns func written to capture the memory (aka storage) scope constraints for each of the func's parameters given by arg_and_result_memory_scopes.
`calculate_allocated_bytes`(func)	Calculate allocated memory per memory scope required by TIR PrimFuncs.
`calculate_constant_bytes`(func, ...)	Calculate the constant size in bytes needed by the TIR allocates inside the TIR PrimFunc.
`calculate_workspace_bytes`(func, ...)	Calculate the workspace size in bytes needed by the TIR allocates inside the TIR PrimFunc.
`detect_buffer_access_lca`(func)	Detect the lowest common ancestor(LCA) of buffer access, including both high-level access(BufferLoad, BufferStore) and low-level access(Load, Store and opaque access).
`estimate_tir_flops`(stmt_or_mod)	Estimate the FLOPs of a TIR fragment.
`expr_deep_equal`(lhs, rhs)	Deeply compare two nested expressions.
`find_anchor_block`(mod)	Find the "anchor block" of the given module.
`get_block_access_region`(block, buffer_var_map)	Detect which regions of tensors in this block are read or written to.
`get_block_read_write_region`(block, ...)	Auto detect the block read/write region according to its body stmt.
`get_prim_func_arg_and_result_memory_constraints`(...)	Returns the memory (aka storage) scope constraints for all the arguments and result of func.
`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.
`verify_well_formed`(func[, assert_mode])	Verify if the given TIR is well-formed. The verification includes:

class tvm.tir.analysis.Block(iter_vars: List[tvm.tir.expr.IterVar], reads: List[tvm.tir.stmt.BufferRegion], writes: List[tvm.tir.stmt.BufferRegion], name_hint: str, body: tvm.tir.stmt.Stmt, init: Optional[tvm.tir.stmt.Stmt] = None, alloc_buffers: Optional[List[tvm.tir.buffer.Buffer]] = None, match_buffers: Optional[List[tvm.tir.stmt.MatchBufferRegion]] = None, annotations: Optional[Mapping[str, tvm.runtime.object.Object]] = None, span: Optional[tvm.ir.base.Span] = None)

Block node.

Parameters

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

class tvm.tir.analysis.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.
`scope`()	Return the storage scope associated with this buffer.
`get_flattened_buffer`()	Generate a Buffer that is a flattened version of this buffer.
`offset_of`(indices)	Determine the offset of the provided indices in the flattened buffer.

access_ptr(access_mask, ptr_type='handle', content_lanes=1, offset=0, extent=None)

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.
extent (Expr, optional) – The extent of pointer.

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)
# Get access ptr for read with extent
buffer.access_ptr("r", extent = 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

scope(): Return the storage scope associated with this buffer. :returns: scope – The storage scope associated with this buffer. :rtype: str

get_flattened_buffer()

Generate a Buffer that is a flattened version of this buffer.

Returns: flattened – The corresponding flat buffer.
Return type: Buffer

offset_of(indices)

Determine the offset of the provided indices in the flattened buffer.

Parameters: indices (Union[PrimExpr, List[PrimExpr]]) – The indices of the element in the original buffer.
Returns: flattened_indices – The offset indices of the element in the flattened buffer.
Return type: List[PrimExpr]

class tvm.tir.analysis.BufferRegion(buffer: tvm.tir.buffer.Buffer, region: List[tvm.ir.expr.Range])

BufferRegion node.

Parameters

buffer (Buffer) – The buffer of the buffer region
region (List[Range]) – The region array of the buffer region

class tvm.tir.analysis.IRModule(functions=None, type_definitions=None)

IRModule that holds functions and type definitions.

IRModule is the basic unit for all IR transformations across the stack.

Parameters: functions (Optional[dict]) – Map of global var to BaseFunc

Methods:

`update`(other)	Insert functions in another Module to current one.
`update_func`(var, func)	Update the function corresponding to a global variable in the module.
`get_global_var`(name)	Get a global variable in the function by name.
`get_global_vars`()	Collect all global vars defined in this module.
`get_global_type_vars`()	Collect all global type vars defined in this module.
`get_global_type_var`(name)	Get a global type variable in the function by name.
`get_constructor`(tag)	Look up an ADT constructor by tag.
`from_expr`(expr[, functions, type_defs])	Construct a module from a standalone expression.
`script`([tir_prefix, show_meta])	Print IRModule into TVMScript
`show`([style, black_format])	A sugar for print highlighted TVM script.
`get_attr`(attr_key)	Get the IRModule attribute.
`with_attr`(attr_key, attr_value)	Copy the IRModule and add an attribute to it.

update(other)

Insert functions in another Module to current one.

Parameters: other (IRModule) – The module to merge into the current Module.

update_func(var, func)

Update the function corresponding to a global variable in the module.

Parameters

var (GlobalVar) – The global variable.
func (tvm.relay.Function) – The function to be inserted.

get_global_var(name)

Get a global variable in the function by name.

Parameters: name (str) – The name of the global variable.
Returns: global_var – The global variable mapped to name.
Return type: GlobalVar
Raises: tvm.error.TVMError if we cannot find corresponding global var. –

get_global_vars()

Collect all global vars defined in this module.

Returns: global_vars – An array of global vars.
Return type: Array[GlobalVar]

get_global_type_vars()

Collect all global type vars defined in this module.

Returns: global_type_vars – An array of global type vars.
Return type: Array[GlobalTypeVar]

get_global_type_var(name)

Get a global type variable in the function by name.

Parameters: name (str) – The name of the global type variable.
Returns: global_type_var – The global variable mapped to name.
Return type: GlobalTypeVar
Raises: tvm.error.TVMError if we cannot find corresponding global type var. –

get_constructor(tag)

Look up an ADT constructor by tag.

Parameters: tag (int) – The tag for a constructor.
Returns: constructor – The constructor associated with the given tag,
Return type: Constructor
Raises: tvm.error.TVMError if the corresponding constructor cannot be found. –

static from_expr(expr, functions=None, type_defs=None)

Construct a module from a standalone expression.

Parameters

expr (RelayExpr) – The starting expression
global_funcs (Optional[dict]) – Map of global vars to function definitions
type_defs (Optional[dict]) – Map of global type vars to type definitions

Returns

mod – A module containing the passed definitions, where expr is set as the entry point (wrapped in a function if necessary)

Return type

Module

script(tir_prefix: str = 'T', show_meta: bool = False) → str

Print IRModule into TVMScript

Parameters

tir_prefix (str) – The tir namespace prefix
show_meta (bool) – Whether to show meta information

Returns

script – The TVM Script of the IRModule

Return type

str

show(style: Optional[str] = None, black_format: bool = True) → None

A sugar for print highlighted TVM script.

Parameters

style (str, optional) – Pygmentize printing style, auto-detected if None. See tvm.script.highlight.cprint for more details.
black_format (bool) – If true (default), use the formatter Black to format the TVMScript

get_attr(attr_key)

Get the IRModule attribute.

Parameters: attr_key (str) – The attribute key.
Returns: attr_value – Attribute value
Return type: Any

with_attr(attr_key, attr_value)

Copy the IRModule and add an attribute to it.

Parameters

attr_key (str) – The attribute key.
attr_value (Object) – The new attribute value.

Returns

mod – A new copy of the IRModule with the attribute

Return type

IRModule

tvm.tir.analysis.OOBChecker()

Detect out of bounds memory access in arrays.

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

class tvm.tir.analysis.Object: Base class for all tvm’s runtime objects.

class tvm.tir.analysis.PrimExpr

Base class of all primitive expressions.

PrimExpr is used in the low-level code optimizations and integer analysis.

class tvm.tir.analysis.PrimFunc(params, body, ret_type=None, buffer_map=None, attrs=None, span=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
span (Optional[Span]) – The location of this itervar in the source code.

Methods:

`with_body`(new_body[, span])	Create a new PrimFunc with the same set signatures but a new body.
`specialize`(param_map)	Specialize parameters of PrimFunc
`script`([tir_prefix, show_meta])	Print IRModule into TVMScript
`show`([style, black_format])	A sugar for print highlighted TVM script.

with_body(new_body, span=None)

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

Parameters

new_body (Stmt) – The new body.
span (Optional[Span]) – The location of this itervar in the source code.

Returns

new_func – The created new function.

Return type

PrimFunc

specialize(param_map: Mapping[tvm.tir.expr.Var, Union[tvm.ir.expr.PrimExpr, tvm.tir.buffer.Buffer]])

Specialize parameters of PrimFunc

Parameters: param_map (Mapping[Var, Union[PrimExpr, Buffer]]) – The mapping from function params to the instance

Examples

We can define a Meta TIR function with symbolic shape:

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

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

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

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

The specialized function:

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

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

Returns: func – The new function with parameter specialized
Return type: PrimFunc

script(tir_prefix: str = 'T', show_meta: bool = False) → str

Print IRModule into TVMScript

Parameters

tir_prefix (str) – The tir namespace prefix
show_meta (bool) – Whether to show meta information

Returns

script – The TVM Script of the PrimFunc

Return type

str

show(style: Optional[str] = None, black_format: bool = True) → None

A sugar for print highlighted TVM script.

Parameters

style (str, optional) – Pygmentize printing style, auto-detected if None. See tvm.script.highlight.cprint for more details.
black_format (bool) – If true (default), use the formatter Black to format the TVMScript

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

class tvm.tir.analysis.Var(name: str, dtype: Union[str, tvm.ir.type.Type], span: Optional[tvm.ir.base.Span] = None)

Symbolic variable.

Parameters

name (str) – The name
dtype (Union[str, tvm.irType]) – The data type
span (Optional[Span]) – The location of this itervar in the source code.

tvm.tir.analysis.apply_prim_func_arg_and_result_memory_constraints(func: tvm.tir.function.PrimFunc, relay_func_type: tvm.runtime.object.Object, arg_and_result_memory_scopes: List[str]) → tvm.tir.function.PrimFunc

Returns func written to capture the memory (aka storage) scope constraints for each of the func’s parameters given by arg_and_result_memory_scopes. However, arg_and_result_memory_scopes should be w.r.t. the func’s representation as a Relay Function of relay_func_type before lowering and conversion to DPS.

Visible for testing.

CAUTION: This is experimental. The resulting PrimFunc may not have fully accounted for all new memory scopes.

Parameters

func (tvm.tir.PrimFunc) – The function to retrieve constraints from.
relay_func_type (tvm.relay.FuncType) – The type of the Relay Function from which the func was derived.
arg_and_result_memory_scopes (Array[AnyStr]) – Memory constraints for funcs args and result in Relay form. The empty string denotes ‘no constraint’.

Returns

result – The rewritten func.

Return type

tvm.tir.PrimFunc

tvm.tir.analysis.calculate_allocated_bytes(func: tvm.tir.function.PrimFunc) → Dict[str, int]

Calculate allocated memory per memory scope required by TIR PrimFuncs.

Parameters: func (tvm.tir.PrimFunc) – The function to be detected.
Returns: result – Allocated memory size per scope in bytes.
Return type: Dict[String, int]

tvm.tir.analysis.calculate_constant_bytes(func: tvm.tir.function.PrimFunc, constant_byte_alignment: int) → int

Calculate the constant size in bytes needed by the TIR allocates inside the TIR PrimFunc.

Parameters

func (tvm.tir.PrimFunc) – The function to be detected.
constant_byte_alignment (int) – The byte alignment required for each tensor

Returns

result – Workspace size in bytes.

Return type

int

tvm.tir.analysis.calculate_workspace_bytes(func: tvm.tir.function.PrimFunc, workspace_byte_alignment: int) → int

Calculate the workspace size in bytes needed by the TIR allocates inside the TIR PrimFunc.

Parameters

func (tvm.tir.PrimFunc) – The function to be detected.
workspace_byte_alignment (int) – The byte alignment required for each tensor

Returns

result – Workspace size in bytes.

Return type

int

tvm.tir.analysis.detect_buffer_access_lca(func: tvm.tir.function.PrimFunc) → Dict[tvm.tir.buffer.Buffer, tvm.tir.stmt.Stmt]

Detect the lowest common ancestor(LCA) of buffer access, including both high-level access(BufferLoad, BufferStore) and low-level access(Load, Store and opaque access). The LCA may be a For loop or a Block.

Parameters: func (tvm.tir.PrimFunc) – The function to be detected.
Returns: result – Map from buffer to the LCA of all access to it.
Return type: Dict[Buffer, Stmt]

tvm.tir.analysis.estimate_tir_flops(stmt_or_mod: Union[tvm.tir.stmt.Stmt, tvm.ir.module.IRModule]) → float

Estimate the FLOPs of a TIR fragment.

Parameters: stmt_or_mod (Union[Stmt, IRModule]) – The TIR fragment or IRModule to be estimated.
Returns: flops – The estimated FLOPs.
Return type: float

tvm.tir.analysis.expr_deep_equal(lhs: tvm.ir.expr.PrimExpr, rhs: tvm.ir.expr.PrimExpr) → bool

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.find_anchor_block(mod: tvm.ir.module.IRModule) → tvm.tir.stmt.Block

Find the “anchor block” of the given module.

We define the anchor block to be the block with (1) an init statement and (2) having the biggest flops count. The latter condition is only used when there are multiple blocks with an init statement.

For example, if the input module is conv2d + fused spatial blocks, conv2d is the anchor block. The input module may not contain more than one such block. For example, a module having two conv2d is not allowed as an input.

However, a module created from winograd convolution has multiple blocks with an init statement (input transform, batched GEMM, and output transform). We use the second condition, the flops count, to determine that the batched GEMM block is the anchor block.

Parameters: mod (tvm.ir.IRModule) – The input TIR module.
Returns: anchor_block – The anchor block if found, None otherwise.
Return type: Block

tvm.tir.analysis.get_block_access_region(block: tvm.tir.stmt.Block, buffer_var_map: Dict[tvm.tir.expr.Var, tvm.tir.buffer.Buffer]) → List[List[tvm.tir.stmt.BufferRegion]]

Detect which regions of tensors in this block are read or written to.: Regions are sorted by order of appearance in the AST.

Parameters

block (tvm.tir.Block) – The block in which we are detecting read/write regions.
buffer_var_map (Dict[Var, Buffer]) – The outside buffers which may access the block. Mapping from buffer var to the buffer

Returns

result –

Array of access regions. There are three arrays of BufferRegion:

first: read regions
second: write regions
third: opaque regions

Return type

List[List[BufferRegion]]

tvm.tir.analysis.get_block_read_write_region(block: tvm.tir.stmt.Block, buffer_var_map: Dict[tvm.tir.expr.Var, tvm.tir.buffer.Buffer]) → List[List[tvm.tir.stmt.BufferRegion]]

Auto detect the block read/write region according to its body stmt.: An opaque access will be counted as both a read and a write access

Parameters

block (tvm.tir.Block) – The block in which we are detecting read/write regions.
buffer_var_map (Dict[Var, Buffer]) – The outside buffers which may access the block. Mapping from buffer var to the buffer

Returns

result – An array only consisting of the read regions and write regions of the input block

Return type

List[List[BufferRegion]]

tvm.tir.analysis.get_prim_func_arg_and_result_memory_constraints(func: tvm.tir.function.PrimFunc, relay_func_type: tvm.runtime.object.Object) → List[str]

Returns the memory (aka storage) scope constraints for all the arguments and result of func. However the result will be w.r.t. the func’s representation as a Relay Function of relay_func_type before lowering and conversion to DPS.

Visible for testing.

Parameters

func (tvm.tir.PrimFunc) – The function to retrieve constraints from.
relay_func_type (tvm.relay.FuncType) – The type of the Relay Function from which the func was derived.

Returns

result – Memory scope constraints for funcs args and result in Relay form. The empty string denotes ‘no constraint’.

Return type

List[AnyStr]

tvm.tir.analysis.verify_gpu_code(func: tvm.tir.function.PrimFunc, constraints: Dict[str, int]) → None

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: tvm.tir.function.PrimFunc) → bool

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: tvm.tir.function.PrimFunc) → bool

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.analysis.verify_well_formed(func: tvm.tir.function.PrimFunc, assert_mode: bool = True) → bool

Verify if the given TIR is well-formed. The verification includes:

Check if expressions not contain vars that is defined outside the block.

Parameters

func (tvm.tir.PrimFunc) – The function to be verified.
assert_mode (bool) – The indicator if it raises an error when the function is not well-formed.

Returns

result – Whether it is a well-formed TIR function.

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
`pre_order_visit`(stmt, fvisit)	Recursive pre-order visit on stmt AST, applying fvisit on each node.
`substitute`(node, vmap)	Substitute the var specified by vmap.
`renew_defs`(func)	Re-generate the definition nodes for a TIR, including VarDef, BufferDef.

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

Recursively visit and transform ir nodes in post DFS order.

Parameters

stmt (tvm.tir.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 tvm.tir.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

tvm.tir.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.pre_order_visit(stmt, fvisit)¶

Recursive pre-order visit on stmt AST, applying fvisit on each node.: If fvisit returns False, it won’t visit the children of the node.

Parameters: fvisit (function of the signature Object -> bool) – 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

tvm.tir.Stmt

tvm.tir.stmt_functor.renew_defs(func: tvm.tir.function.PrimFunc)¶

Re-generate the definition nodes for a TIR, including VarDef, BufferDef. This pass works as a simple DeepCopy to duplicate a function with different Vars and Buffers but the same behavior

Parameters: func (PrimFunc) – The input function
Returns: result – The new generated func.
Return type: PrimFunc