MLIR

Multi-Level IR Compiler Framework

GPU Dialect

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.

Memory attribution

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.

Operations

gpu.block_dim

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)

gpu.block_id

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)

gpu.grid_dim

Returns the number of thread blocks in the grid along the x, y, or z dimension.

Example:

  %gDimZ = "gpu.grid_dim"() {dimension = "z"} : () -> (index)

gpu.thread_id

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)

gpu.yield

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, f32

gpu.all_reduce

The “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.

gpu.barrier

The “barrier” op synchronizes all work items of a workgroup. It is used to coordinate communication between the work items of the workgroup.

gpu.barrier

waits 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.