Note: this dialect is more likely to change than others in the near future; use with caution.
This dialect provides middle-level abstractions for launching GPU kernels
following a programming model similar to that of CUDA or OpenCL. It provides
abstractions for kernel invocations (and may eventually provide those for device
management) that are not present at the lower level (e.g., as LLVM IR intrinsics
for GPUs). Its goal is to abstract away device- and driver-specific
manipulations to launch a GPU kernel and provide a simple path towards GPU
execution from MLIR. It may be targeted, for example, by DSLs using MLIR. The
dialect uses gpu as its canonical prefix.
Returns the number of threads in the thread block (aka the block size) along the
x, y, or z dimension.
Example:
%bDimX = "gpu.block_dim"() {dimension = "x"} : () -> (index)Returns the block id, i.e. the index of the current block within the grid along
the x, y, or z dimension.
Example:
%bIdY = "gpu.block_id"() {dimension = "y"} : () -> (index)Returns the number of thread blocks in the grid along the x, y, or z
dimension.
Example:
%gDimZ = "gpu.grid_dim"() {dimension = "z"} : () -> (index)Defines a function that can be executed on a GPU. This supports memory attribution and its body has a particular execution model.
GPU functions are either kernels (as indicated by the kernel attribute) or
regular functions. The former can be launched from the host side, while the
latter are device side only.
The memory attribution defines SSA values that correspond to memory buffers allocated in the memory hierarchy of the GPU (see below).
The operation has one attached region that corresponds to the body of the function. The region arguments consist of the function arguments without modification, followed by buffers defined in memory annotations. The body of a GPU function, when launched, is executed by multiple work items. There are no guarantees on the order in which work items execute, or on the connection between them. In particular, work items are not necessarily executed in lock-step. Synchronization ops such as "gpu.barrier" should be used to coordinate work items. Declarations of GPU functions, i.e. not having the body region, are not supported.
Memory buffers are defined at the function level, either in "gpu.launch" or in "gpu.func" ops. This encoding makes it clear where the memory belongs and makes the lifetime of the memory visible. The memory is only accessible while the kernel is launched/the function is currently invoked. The latter is more strict than actual GPU implementations but using static memory at the function level is just for convenience. It is also always possible to pass pointers to the workgroup memory into other functions, provided they expect the correct memory space.
The buffers are considered live throughout the execution of the GPU function
body. The absence of memory attribution syntax means that the function does not
require special buffers. Rationale: although the underlying models declare
memory buffers at the module level, we chose to do it at the function level to
provide some structuring for the lifetime of those buffers; this avoids the
incentive to use the buffers for communicating between different kernels or
launches of the same kernel, which should be done through function arguments
instead; we chose not to use alloca-style approach that would require more
complex lifetime analysis following the principles of MLIR that promote
structure and representing analysis results in the IR.
Syntax:
op ::= `gpu.func` symbol-ref-id `(` argument-list `)` (`->`
function-result-list)?
memory-attribution `kernel`? function-attributes? region
memory-attribution ::= (`workgroup` `(` ssa-id-and-type-list `)`)?
(`private` `(` ssa-id-and-type-list `)`)?
Example:
gpu.func @foo(%arg0: index)
workgroup(%workgroup: memref<32xf32, 3>)
private(%private: memref<1xf32, 5>)
kernel
attributes {qux: "quux"} {
gpu.return
}The generic form illustrates the concept
"gpu.func"(%arg: index) {sym_name: "foo", kernel, qux: "quux"} ({
^bb0(%arg0: index, %workgroup: memref<32xf32, 3>, %private: memref<1xf32, 5>):
"gpu.return"() : () -> ()
}) : (index) -> ()Note the non-default memory spaces used in memref types in memory-attribution.
Launch a kernel on the specified grid of thread blocks. The body of the kernel
is defined by the single region that this operation contains. The operation
takes at least six operands, with first three operands being grid sizes along
x,y,z dimensions, the following three arguments being block sizes along x,y,z
dimension, and the remaining operands are arguments of the kernel. When a
lower-dimensional kernel is required, unused sizes must be explicitly set to
1.
The body region has at least twelve arguments, grouped as follows:
- three arguments that contain block identifiers along x,y,z dimensions;
- three arguments that contain thread identifiers along x,y,z dimensions;
- operands of the
gpu.launchoperation as is, including six leading operands for grid and block sizes.
Operations inside the body region, and any operations in the nested regions, are not allowed to use values defined outside the body region, as if this region was a function. If necessary, values must be passed as kernel arguments into the body region. Nested regions inside the kernel body are allowed to use values defined in their ancestor regions as long as they don't cross the kernel body region boundary.
Syntax:
operation ::= `gpu.launch` `block` `(` ssa-id-list `)` `in` ssa-reassignment
`threads` `(` ssa-id-list `)` `in` ssa-reassignment
(`args` ssa-reassignment `:` type-list)?
region attr-dict?
ssa-reassignment ::= `(` ssa-id `=` ssa-use (`,` ssa-id `=` ssa-use)* `)`
Example:
gpu.launch blocks(%bx, %by, %bz) in (%sz_bx = %0, %sz_by = %1, %sz_bz = %2)
threads(%tx, %ty, %tz) in (%sz_tx = %3, %sz_ty = %4, %sz_tz = %5)
args(%arg0 = %6, %arg1 = 7) : f32, memref<?xf32, 1> {
// Block and thread identifiers, as well as block/grid sizes are
// immediately usable inside body region.
"some_op"(%bx, %tx) : (index, index) -> ()
%42 = load %arg1[%bx] : memref<?xf32, 1>
}
// Generic syntax explains how the pretty syntax maps to the IR structure.
"gpu.launch"(%cst, %cst, %c1, // Grid sizes.
%cst, %c1, %c1, // Block sizes.
%arg0, %arg1) // Actual arguments.
{/*attributes*/}
// All sizes and identifiers have "index" size.
: (index, index, index, index, index, index, f32, memref<?xf32, 1>) -> () {
// The operation passes block and thread identifiers, followed by grid and block
// sizes, followed by actual arguments to the entry block of the region.
^bb0(%bx : index, %by : index, %bz : index,
%tx : index, %ty : index, %tz : index,
%num_bx : index, %num_by : index, %num_bz : index,
%num_tx : index, %num_ty : index, %num_tz : index,
%arg0 : f32, %arg1 : memref<?xf32, 1>):
"some_op"(%bx, %tx) : (index, index) -> ()
%3 = "std.load"(%arg1, %bx) : (memref<?xf32, 1>, index) -> f32
}Rationale: using operation/block arguments gives analyses a clear way of understanding that a value has additional semantics (e.g., we will need to know what value corresponds to threadIdx.x for coalescing). We can recover these properties by analyzing the operations producing values, but it is easier just to have that information by construction.
Launch a kernel function on the specified grid of thread blocks. gpu.launch
operations are lowered to gpu.launch_func operations by outlining the kernel
body into a function in a dedicated module, which reflects the separate
compilation process. The kernel function is required to have the gpu.kernel
attribute. The module containing the kernel function is required to have the
gpu.kernel_module attribute and must be named. And finally, the module
containing the kernel module (which thus cannot be the top-level module) is
required to have the gpu.container_module attribute. The gpu.launch_func
operation has a string attribute named kernel to specify the name of the
kernel function to launch and an attribute named kernel_module to specify the
name of the module containing that kernel function.
The operation takes at least six operands, with the first three operands being
grid sizes along x,y,z dimensions and the following three being block sizes
along x,y,z dimensions. When a lower-dimensional kernel is required, unused
sizes must be explicitly set to 1. The remaining operands are passed as
arguments to the kernel function.
A custom syntax for this operation is currently not available.
Example:
module attributes {gpu.container_module} {
// This module creates a separate compilation unit for the GPU compiler.
module @kernels attributes {gpu.kernel_module} {
func @kernel_1(%arg0 : f32, %arg1 : !llvm<"float*">)
attributes { nvvm.kernel = true } {
// Operations that produce block/thread IDs and dimensions are injected when
// outlining the `gpu.launch` body to a function called by `gpu.launch_func`.
%tIdX = "gpu.thread_id"() {dimension = "x"} : () -> (index)
%tIdY = "gpu.thread_id"() {dimension = "y"} : () -> (index)
%tIdZ = "gpu.thread_id"() {dimension = "z"} : () -> (index)
%bDimX = "gpu.block_dim"() {dimension = "x"} : () -> (index)
%bDimY = "gpu.block_dim"() {dimension = "y"} : () -> (index)
%bDimZ = "gpu.block_dim"() {dimension = "z"} : () -> (index)
%bIdX = "gpu.block_id"() {dimension = "x"} : () -> (index)
%bIdY = "gpu.block_id"() {dimension = "y"} : () -> (index)
%bIdZ = "gpu.block_id"() {dimension = "z"} : () -> (index)
%gDimX = "gpu.grid_dim"() {dimension = "x"} : () -> (index)
%gDimY = "gpu.grid_dim"() {dimension = "y"} : () -> (index)
%gDimZ = "gpu.grid_dim"() {dimension = "z"} : () -> (index)
"some_op"(%bx, %tx) : (index, index) -> ()
%42 = load %arg1[%bx] : memref<?xf32, 1>
}
}
"gpu.launch_func"(%cst, %cst, %cst, // Grid sizes.
%cst, %cst, %cst, // Block sizes.
%arg0, %arg1) // Arguments passed to the kernel function.
{ kernel_module = @kernels, // Module containing the kernel function.
kernel = "kernel_1" } // Kernel function.
: (index, index, index, index, index, index, f32, !llvm<"float*">) -> ()
}Returns the thread id, i.e. the index of the current thread within the block
along the x, y, or z dimension.
Example:
%tIdX = "gpu.thread_id"() {dimension = "x"} : () -> (index)Is a special terminator operation for blocks inside regions in gpu ops. It returns values to the immediately enclosing gpu op.
Example:
gpu.yield %f0, %f1 : f32, f32The "all_reduce" op reduces the value of every work item across a local workgroup. The result is equal for all work items of a workgroup.
For example, both
%1 = "gpu.all_reduce"(%0) ({}) { op = "add" } : (f32) -> (f32)
%2 = "gpu.all_reduce"(%0) ({
^bb(%lhs : f32, %rhs : f32):
%sum = addf %lhs, %rhs : f32
"gpu.yield"(%sum) : (f32) -> ()
}) : (f32) -> (f32)compute the sum of each work item's %0 value. The first version specifies
the accumulation as operation, whereas the second version specifies the
accumulation as code region. The accumulation operation must either be
add or mul.
Either none or all work items of a workgroup need to execute this op in convergence.
The "barrier" op synchronizes all work items of a workgroup. It is used to coordinate communication between the work items of the workgroup.
gpu.barrierwaits until all work items in the workgroup have reached this point and all memory accesses made by these work items prior to the op are visible to all work items in the workgroup. Data hazards between work items accessing the same memory can be avoided by synchronizing work items in-between these accesses.
Either none or all work items of a workgroup need to execute this op in convergence.