-affine-loop-opt

Affine loop transformations

Options

-affine-opt-tile-sizes      : Affine loop tiling sizes
-affine-opt-copy-depths     : Affine loop data copy loop depths
-affine-opt-copy-fast-space : Fast memory space to use for affine data copy
-affine-opt-copy-slow-space : slow memory space to use for affine data copy
-affine-opt-tile-separate   : Affine loop tiling separates full and partial tiles
-aaffine-opt-label          : Transform loops with the given label
-affine-opt-post-label      : Label to apply to transformed loop nest

-air-annotate-front-and-back-ops-in-for-pattern

Annotates ops in for loop body which are at the front and back of the body’s dependency graph

This pass analyzes the loop body of an asynchronous for loop and annotates the asynchronous operations which are located at the front and back of the loop body’s dependency tree. This pass is used in the ping-pong pattern transformation to detect the insertion and exit points in data producer and consumer sub-trees.

-air-automatic-tiling

Tile loop nests manually or automatically with prime factorization

This pass performs multi-dimensional tiling of loop nests. If the tile sizes are specified in the command line, all loops in loops nests will be tiled along with the input factors. If no input tiling sizes are received, the pass tiles all loops in loop bands with prime factors of the original loop tripcounts. This pass assumes that loops are in normalized form and the loop spaces are hyper-rectangular.

Example 1: Manual tiling

-air-automatic-tiling="loop-tile-sizes=64,2,2" -affine-simplify-structures -cse

Input:

module  {
  func.func @task(%arg0: tensor<4096xi32>, %arg1: tensor<4096xi32>) -> tensor<4096xi32> {
    %0 = memref.alloc() : memref<4096xi32>
    %1 = "aten.type_cast"(%arg0) : (tensor<4096xi32>) -> memref<4096xi32>
    %2 = "aten.type_cast"(%arg1) : (tensor<4096xi32>) -> memref<4096xi32>
    %c0 = constant 0 : index
    %c4096 = constant 4096 : index
    %c0_0 = constant 0 : index
    affine.for %arg2 = 0 to 4096 {
      %4 = affine.load %1[%arg2] : memref<4096xi32>
      %5 = affine.load %2[%arg2] : memref<4096xi32>
      %6 = muli %4, %5 : i32
      affine.store %6, %0[%arg2] : memref<4096xi32>
    } {affine_opt_label = "air.binary_op"}
    %3 = "aten.type_cast"(%0) : (memref<4096xi32>) -> tensor<4096xi32>
    return %3 : tensor<4096xi32>
  }
}

Output:

#map = affine_map<(d0, d1, d2, d3) -> (d0 + d1 * 64 + d2 * 128 + d3 * 256)>
module  {
  func.func @task(%arg0: tensor<4096xi32>, %arg1: tensor<4096xi32>) -> tensor<4096xi32> {
    %0 = memref.alloc() : memref<4096xi32>
    %1 = "aten.type_cast"(%arg0) : (tensor<4096xi32>) -> memref<4096xi32>
    %2 = "aten.type_cast"(%arg1) : (tensor<4096xi32>) -> memref<4096xi32>
    affine.for %arg2 = 0 to 16 {
      affine.for %arg3 = 0 to 2 {
        affine.for %arg4 = 0 to 2 {
          affine.for %arg5 = 0 to 64 {
            %4 = affine.apply #map(%arg5, %arg4, %arg3, %arg2)
            %5 = affine.load %1[%4] : memref<4096xi32>
            %6 = affine.load %2[%4] : memref<4096xi32>
            %7 = muli %5, %6 : i32
            affine.store %7, %0[%4] : memref<4096xi32>
          }
        }
      }
    } {affine_opt_label = ""}
    %3 = "aten.type_cast"(%0) : (memref<4096xi32>) -> tensor<4096xi32>
    return %3 : tensor<4096xi32>
  }
}

Example 2: Automatic tiling

-air-automatic-tiling -affine-simplify-structures -cse

Input:

module  {
  func.func @task(%arg0: tensor<28x10xf32>, %arg1: tensor<28x10xf32>) -> tensor<28x10xf32> {
    %0 = memref.alloc() : memref<28x10xf32>
    %1 = "aten.type_cast"(%arg0) : (tensor<28x10xf32>) -> memref<28x10xf32>
    %2 = "aten.type_cast"(%arg1) : (tensor<28x10xf32>) -> memref<28x10xf32>
    affine.for %arg2 = 0 to 28 {
      affine.for %arg3 = 0 to 10 {
        %4 = affine.load %1[%arg2, %arg3] : memref<28x10xf32>
        %5 = affine.load %2[%arg2, %arg3] : memref<28x10xf32>
        %6 = mulf %4, %5 : f32
        affine.store %6, %0[%arg2, %arg3] : memref<28x10xf32>
      }
    } {affine_opt_label = "air.binary_op"}
    %3 = "aten.type_cast"(%0) : (memref<28x10xf32>) -> tensor<28x10xf32>
    return %3 : tensor<28x10xf32>
  }
}

Output:

#map0 = affine_map<(d0, d1, d2) -> (d0 + d1 * 7 + d2 * 14)>
#map1 = affine_map<(d0, d1) -> (d0 + d1 * 5)>
module  {
  func.func @task(%arg0: tensor<28x10xf32>, %arg1: tensor<28x10xf32>) -> tensor<28x10xf32> {
    %0 = memref.alloc() : memref<28x10xf32>
    %1 = "aten.type_cast"(%arg0) : (tensor<28x10xf32>) -> memref<28x10xf32>
    %2 = "aten.type_cast"(%arg1) : (tensor<28x10xf32>) -> memref<28x10xf32>
    affine.for %arg2 = 0 to 2 {
      affine.for %arg3 = 0 to 2 {
        affine.for %arg4 = 0 to 7 {
          %4 = affine.apply #map0(%arg4, %arg3, %arg2)
          affine.for %arg5 = 0 to 2 {
            affine.for %arg6 = 0 to 5 {
              %5 = affine.apply #map1(%arg6, %arg5)
              %6 = affine.load %1[%4, %5] : memref<28x10xf32>
              %7 = affine.load %2[%4, %5] : memref<28x10xf32>
              %8 = mulf %6, %7 : f32
              affine.store %8, %0[%4, %5] : memref<28x10xf32>
            }
          }
        }
      }
    }
    %3 = "aten.type_cast"(%0) : (memref<28x10xf32>) -> tensor<28x10xf32>
    return %3 : tensor<28x10xf32>
  }
}

Options

-loop-tile-sizes : A list of multi-dimensional loop tile sizes
-tile-separate   : AIR loop tiling separates full and partial tiles
-air-label       : Transform loops with the given label
-air-post-label  : Label to apply to transformed loop nest

-air-broadcast-detection

Detect DMA broadcast opportunities

This pass detects DMA broadcast opportunities by tracing the source indices’ dependence to the induction variables of any parent spatial loop space. Upon successful detection, the DMA shall be annotated by an affine set attribute named ‘broadcast_pattern’.

-air-collapse-herd

Collapse a multi-dimensional air.herd into a single column.

The pass attempts to collapse the air.herd to the left, attempting to occupy complete columns of AIE tiles. The attempt will stop if the number of tiles in air.herd exceeds the user provided max-col-size option.

Options

-max-col-size : The maximum column size after collapse, before collapse is cancelled. Disabled by default.

-air-construct-ping-pong-dependency-pattern

Transform an scf.for loop into ping-pong pattern

This pass transform an scf.for loop into ping-pong pattern, by constructing dependency edges to connect explicitly unrolled and annotated data producer and consumer processes for ping and pong buffers, respectively. The dependency edges, being yielded across loop iterations, directly represent a compute scheduling scheme which leads to concurrency between communication and compute in the form of ping-pong buffering.

-air-dealias-memref

De-alias a memref into multiple memrefs

This pass detects memrefs which can de-alias into multiple memrefs over time, and generates extra copies of this memref. This process can improve the stability of the memref-to-buffer mappings.

-air-dependency

AIR dependency analysis

This pass analyzes dependencies among air.async_region ops, and constructs the dependency relationship between asynchronous events for scheduling. The pass also generates a dot file which visualizes the dependency graph.

Example 1: Simple data depdendency tracing

-air-dependency

Input:

module  {
  func.func @foo(%arg0: memref<1024xi32>, %arg1: memref<1024xi32>) {
    %c1 = arith.constant 1 : index
    %0 = memref.alloc() : memref<1024xi32, 1>
    air.launch_herd tile (%arg2, %arg3) in (%arg4=%c1, %arg5=%c1) args(%arg6=%0, %arg7=%arg1) : memref<1024xi32, 1>,memref<1024xi32> {
      %c0 = arith.constant 0 : index
      %c16 = arith.constant 16 : index
      %1 = memref.alloc() : memref<16xi32, 2>
      air.dma_memcpy (%1, %arg6, [%c0], [%c16], %c16) {id = 1 : i32} : (memref<16xi32, 2>, memref<1024xi32, 1>, [index], [index], index) -> ()
      air.dma_memcpy (%arg6, %1, [%c16], [%c0], %c16) {id = 2 : i32} : (memref<1024xi32, 1>, memref<16xi32, 2>, [index], [index], index) -> ()
      air.herd_terminator
    }
    memref.dealloc %0 : memref<1024xi32, 1>
    return
  }
}

Output:

module {
  func.func @foo(%arg0: memref<1024xi32>, %arg1: memref<1024xi32>) {
    %c1 = arith.constant 1 : index
    %asyncToken, %valOut = air.execute async  {
      %1 = memref.alloc() : memref<1024xi32, 1>
      air.execute_terminator %1 : memref<1024xi32, 1>
    } {id = 1 : i32} : (memref<1024xi32, 1>)
    %0 = air.launch_herd async  tile (%arg2, %arg3) in (%arg4=%c1, %arg5=%c1) args(%arg6=%valOut, %arg7=%arg1) : memref<1024xi32, 1>, memref<1024xi32> attributes {id = 1 : i32} {
      %c0 = arith.constant 0 : index
      %c16 = arith.constant 16 : index
      %asyncToken_1, %valOut_2 = air.execute async  {
        %3 = memref.alloc() : memref<16xi32, 2>
        air.execute_terminator %3 : memref<16xi32, 2>
      } {id = 2 : i32} : (memref<16xi32, 2>)
      %1 = air.dma_memcpy async [%asyncToken_1] (%valOut_2, %arg6, [%c0], [%c16], %c16) {id = 1 : i32} : (memref<16xi32, 2>, memref<1024xi32, 1>, [index], [index], index) -> ()
      %2 = air.dma_memcpy async [%1] (%arg6, %valOut_2, [%c16], [%c0], %c16) {id = 2 : i32} : (memref<1024xi32, 1>, memref<16xi32, 2>, [index], [index], index) -> ()
      air.herd_terminator
    }
    %asyncToken_0 = air.execute async [%0, %asyncToken]  : (!air.async.token, !air.async.token) {
      memref.dealloc %valOut : memref<1024xi32, 1>
      air.execute_terminator
    } {id = 3 : i32}
    return
  }
}

Example 2: Loop-carried depdendency tracing

-air-dependency

Input:

#map = affine_map<()[s0] -> (s0 * 32)>
module attributes {torch.debug_module_name = "mmult"} {
  func.func @forward(%arg0: memref<1024x1024xf32>, %arg1: memref<1024x1024xf32>, %arg2: memref<1024x1024xf32>) {
    %c1024 = arith.constant 1024 : index
    %cst = arith.constant 0.000000e+00 : f32
    %c32 = arith.constant 32 : index
    %c128 = arith.constant 128 : index
    %c0 = arith.constant 0 : index
    %c4 = arith.constant 4 : index
    %0 = memref.alloc() {alignment = 128 : i64} : memref<1024x1024xf32>
    %1 = memref.alloc() {alignment = 128 : i64} : memref<1024x1024xf32>
    linalg.fill ins(%cst : f32) outs(%0 : memref<1024x1024xf32>)
    memref.copy %0, %1 : memref<1024x1024xf32> to memref<1024x1024xf32>
    scf.for %arg3 = %c0 to %c1024 step %c128 {
      scf.for %arg4 = %c0 to %c1024 step %c128 {
        scf.for %arg5 = %c0 to %c1024 step %c32 {
          air.launch_herd  tile (%arg6, %arg7) in (%arg8=%c4, %arg9=%c4) args(%arg10=%arg3, %arg11=%arg5, %arg12=%arg0, %arg13=%arg4, %arg14=%arg1, %arg15=%1) : index, index, memref<1024x1024xf32>, index, memref<1024x1024xf32>, memref<1024x1024xf32> attributes {sym_name = "herd_0"} {
            %c1 = arith.constant 1 : index
            %c1024_0 = arith.constant 1024 : index
            %c32_1 = arith.constant 32 : index
            %2 = affine.apply #map()[%arg6]
            %3 = affine.apply #map()[%arg7]
            %4 = arith.addi %arg10, %2 : index
            %5 = arith.addi %arg13, %3 : index
            %6 = memref.alloc() : memref<32x32xf32, 2>
            %7 = memref.alloc() : memref<32x32xf32, 2>
            %8 = memref.alloc() : memref<32x32xf32, 2>
            air.dma_memcpy_nd (%6[] [] [], %arg12[%4, %arg11] [%c32_1, %c32_1] [%c1024_0, %c1]) {id = 1 : i32} : (memref<32x32xf32, 2>, memref<1024x1024xf32>)
            air.dma_memcpy_nd (%7[] [] [], %arg14[%arg11, %5] [%c32_1, %c32_1] [%c1024_0, %c1]) {id = 2 : i32} : (memref<32x32xf32, 2>, memref<1024x1024xf32>)
            air.dma_memcpy_nd (%8[] [] [], %arg15[%4, %5] [%c32_1, %c32_1] [%c1024_0, %c1]) {id = 3 : i32} : (memref<32x32xf32, 2>, memref<1024x1024xf32>)
            linalg.matmul ins(%6, %7 : memref<32x32xf32, 2>, memref<32x32xf32, 2>) outs(%8 : memref<32x32xf32, 2>)
            air.dma_memcpy_nd (%arg15[%4, %5] [%c32_1, %c32_1] [%c1024_0, %c1], %8[] [] []) {id = 4 : i32} : (memref<1024x1024xf32>, memref<32x32xf32, 2>)
            memref.dealloc %6 : memref<32x32xf32, 2>
            memref.dealloc %7 : memref<32x32xf32, 2>
            memref.dealloc %8 : memref<32x32xf32, 2>
            air.herd_terminator
          }
        }
        scf.yield
      }
    }
    memref.copy %1, %arg2 : memref<1024x1024xf32> to memref<1024x1024xf32>
    return
  }
}

Output:

#map = affine_map<()[s0] -> (s0 * 32)>
module attributes {torch.debug_module_name = "mmult"} {
  func.func @forward(%arg0: memref<1024x1024xf32>, %arg1: memref<1024x1024xf32>, %arg2: memref<1024x1024xf32>) {
    %c1024 = arith.constant 1024 : index
    %cst = arith.constant 0.000000e+00 : f32
    %c32 = arith.constant 32 : index
    %c128 = arith.constant 128 : index
    %c0 = arith.constant 0 : index
    %c4 = arith.constant 4 : index
    %asyncToken, %valOut = air.execute async  {
      %2 = memref.alloc() {alignment = 128 : i64} : memref<1024x1024xf32>
      air.execute_terminator %2 : memref<1024x1024xf32>
    } {id = 1 : i32} : (memref<1024x1024xf32>)
    %asyncToken_0, %valOut_1 = air.execute async  {
      %2 = memref.alloc() {alignment = 128 : i64} : memref<1024x1024xf32>
      air.execute_terminator %2 : memref<1024x1024xf32>
    } {id = 2 : i32} : (memref<1024x1024xf32>)
    %asyncToken_2 = air.execute async [%asyncToken]  : (!air.async.token) {
      linalg.fill ins(%cst : f32) outs(%valOut : memref<1024x1024xf32>)
      air.execute_terminator
    } {id = 3 : i32}
    %asyncToken_3 = air.execute async [%asyncToken_0, %asyncToken_2]  : (!air.async.token, !air.async.token) {
      memref.copy %valOut, %valOut_1 : memref<1024x1024xf32> to memref<1024x1024xf32>
      air.execute_terminator
    } {id = 4 : i32}
    %0 = air.wait_all async [%asyncToken_3]  {id = 6 : i32}
    %1 = scf.for %arg3 = %c0 to %c1024 step %c128 iter_args(%arg4 = %0) -> (!air.async.token) {
      %c0_5 = arith.constant 0 : index
      %c1024_6 = arith.constant 1024 : index
      %c128_7 = arith.constant 128 : index
      %2 = air.wait_all async [%arg4]  {id = 4 : i32}
      %3 = scf.for %arg5 = %c0_5 to %c1024_6 step %c128_7 iter_args(%arg6 = %2) -> (!air.async.token) {
        %c0_8 = arith.constant 0 : index
        %c1024_9 = arith.constant 1024 : index
        %c32_10 = arith.constant 32 : index
        %5 = air.wait_all async [%arg6]  {id = 2 : i32}
        %6 = scf.for %arg7 = %c0_8 to %c1024_9 step %c32_10 iter_args(%arg8 = %5) -> (!air.async.token) {
          %c4_11 = arith.constant 4 : index
          %8 = air.launch_herd async [%arg8]  tile (%arg9, %arg10) in (%arg11=%c4_11, %arg12=%c4_11) args(%arg13=%arg3, %arg14=%arg7, %arg15=%arg0, %arg16=%arg5, %arg17=%arg1, %arg18=%valOut_1) : index, index, memref<1024x1024xf32>, index, memref<1024x1024xf32>, memref<1024x1024xf32> attributes {id = 1 : i32, sym_name = "herd_0"} {
            %c1 = arith.constant 1 : index
            %c1024_12 = arith.constant 1024 : index
            %c32_13 = arith.constant 32 : index
            %asyncToken_14, %valOut_15 = air.execute async  {
              %14 = affine.apply #map()[%arg9]
              air.execute_terminator %14 : index
            } {id = 5 : i32} : (index)
            %asyncToken_16, %valOut_17 = air.execute async  {
              %14 = affine.apply #map()[%arg10]
              air.execute_terminator %14 : index
            } {id = 6 : i32} : (index)
            %asyncToken_18, %valOut_19 = air.execute async [%asyncToken_14]  : (!air.async.token) {
              %14 = arith.addi %arg13, %valOut_15 : index
              air.execute_terminator %14 : index
            } {id = 7 : i32} : (index)
            %asyncToken_20, %valOut_21 = air.execute async [%asyncToken_16]  : (!air.async.token) {
              %14 = arith.addi %arg16, %valOut_17 : index
              air.execute_terminator %14 : index
            } {id = 8 : i32} : (index)
            %asyncToken_22, %valOut_23 = air.execute async  {
              %14 = memref.alloc() : memref<32x32xf32, 2>
              air.execute_terminator %14 : memref<32x32xf32, 2>
            } {id = 9 : i32} : (memref<32x32xf32, 2>)
            %asyncToken_24, %valOut_25 = air.execute async  {
              %14 = memref.alloc() : memref<32x32xf32, 2>
              air.execute_terminator %14 : memref<32x32xf32, 2>
            } {id = 10 : i32} : (memref<32x32xf32, 2>)
            %asyncToken_26, %valOut_27 = air.execute async  {
              %14 = memref.alloc() : memref<32x32xf32, 2>
              air.execute_terminator %14 : memref<32x32xf32, 2>
            } {id = 11 : i32} : (memref<32x32xf32, 2>)
            %10 = air.dma_memcpy_nd async [%asyncToken_22, %asyncToken_18] (%valOut_23[] [] [], %arg15[%valOut_19, %arg14] [%c32_13, %c32_13] [%c1024_12, %c1]) {id = 1 : i32} : (memref<32x32xf32, 2>, memref<1024x1024xf32>)
            %11 = air.dma_memcpy_nd async [%asyncToken_24, %asyncToken_20] (%valOut_25[] [] [], %arg17[%arg14, %valOut_21] [%c32_13, %c32_13] [%c1024_12, %c1]) {id = 2 : i32} : (memref<32x32xf32, 2>, memref<1024x1024xf32>)
            %12 = air.dma_memcpy_nd async [%asyncToken_26, %asyncToken_20, %asyncToken_18] (%valOut_27[] [] [], %arg18[%valOut_19, %valOut_21] [%c32_13, %c32_13] [%c1024_12, %c1]) {id = 3 : i32} : (memref<32x32xf32, 2>, memref<1024x1024xf32>)
            %asyncToken_28 = air.execute async [%11, %12, %10]  : (!air.async.token, !air.async.token, !air.async.token) {
              linalg.matmul ins(%valOut_23, %valOut_25 : memref<32x32xf32, 2>, memref<32x32xf32, 2>) outs(%valOut_27 : memref<32x32xf32, 2>)
              air.execute_terminator
            } {id = 12 : i32}
            %13 = air.dma_memcpy_nd async [%asyncToken_28] (%arg18[%valOut_19, %valOut_21] [%c32_13, %c32_13] [%c1024_12, %c1], %valOut_27[] [] []) {id = 4 : i32} : (memref<1024x1024xf32>, memref<32x32xf32, 2>)
            %asyncToken_29 = air.execute async [%asyncToken_28]  : (!air.async.token) {
              memref.dealloc %valOut_23 : memref<32x32xf32, 2>
              air.execute_terminator
            } {id = 13 : i32}
            %asyncToken_30 = air.execute async [%asyncToken_28]  : (!air.async.token) {
              memref.dealloc %valOut_25 : memref<32x32xf32, 2>
              air.execute_terminator
            } {id = 14 : i32}
            %asyncToken_31 = air.execute async [%13]  : (!air.async.token) {
              memref.dealloc %valOut_27 : memref<32x32xf32, 2>
              air.execute_terminator
            } {id = 15 : i32}
            air.herd_terminator
          }
          %9 = air.wait_all async [%8]  {id = 1 : i32}
          scf.yield %9 : !air.async.token
        }
        %7 = air.wait_all async [%6]  {id = 3 : i32}
        scf.yield %7 : !air.async.token
      }
      %4 = air.wait_all async [%3]  {id = 5 : i32}
      scf.yield %4 : !air.async.token
    }
    %asyncToken_4 = air.execute async [%1]  : (!air.async.token) {
      memref.copy %valOut_1, %arg2 : memref<1024x1024xf32> to memref<1024x1024xf32>
      air.execute_terminator
    } {id = 16 : i32}
    return
  }
}

-air-dependency-canonicalize

Canonicalize the dependency graph

This pass optimizes the dependency graph in air by removing non-dominant dependency edges via performing a transitive reduction on the input graph.

Options

-dump-graph : Dump post-canonicalization dot graphs.
-output-dir : Target directory to dump dot graphs.

-air-dependency-parse-graph

Parse the dependency graph and dump dot files

This pass parses the dependency graph into an internal format, and dump dot files for graph visualization.

Options

-output-dir : Target directory to dump dot graphs.
-show-cores : Show the graph of each AIE core.

-air-dependency-schedule-opt

Optimize scheduling based on air async dependency

This pass contains multiple passes which optimize the schedule based on the dependency graph generated from -air-dependency pass.

-air-dma-to-channel

Convert air.dma_memcpy_nd to air.channel

Transforms direct memory access (DMA) operations into channel-based communications, consisting of a series of channel put and get operations via shared channel constructs.

Example:

Input:

%0 = air.launch async [%async_token_0, %async_token_3, %async_token_6] (%arg0, %arg1) in (%arg2=%c4, %arg3=%c4) args(%arg4=%results_5, %arg5=%results, %arg6=%results_2) : memref<512x512xi32>, memref<512x1024xi32>, memref<1024x512xi32> attributes {id = 3 : i32} {
  %1 = air.segment @segment_0 async  args(%arg7=%arg0, %arg8=%arg1, %arg9=%arg4, %arg10=%arg5, %arg11=%arg6) : index, index, memref<512x512xi32>, memref<512x1024xi32>, memref<1024x512xi32> attributes {id = 2 : i32} {
    ...
    %3 = scf.for %arg12 = %c0_8 to %c1024 step %c256 iter_args(%arg13 = %2) -> (!air.async.token) {
      %8 = air.dma_memcpy_nd async [%arg13, %arg13] (%results_14[%c0_8, %arg12] [%c128, %c256] [%c1024, %c1], %arg10[%results_10, %arg12] [%c128, %c256] [%c1024, %c1]) {id = 1 : i32} : (memref<128x1024xi32, 1 : i32>, memref<512x1024xi32>)
      ...
    }
    %6 = air.herd @herd_0 async [%async_token_13, %async_token_15, %async_token_17]  tile (%arg12, %arg13) in (%arg14=%c4_7, %arg15=%c4_7) args(%arg16=%results_14, %arg17=%results_16, %arg18=%results_18) : memref<128x1024xi32, 1 : i32>, memref<1024x128xi32, 1 : i32>, memref<128x128xi32, 1 : i32> attributes {id = 1 : i32} {
      ...
      %9 = scf.for %arg19 = %c0_23 to %c128_26 step %c4_24 iter_args(%arg20 = %8) -> (!air.async.token) {
        ...
        %16 = air.dma_memcpy_nd async [%async_token_37, %async_token_35, %arg20] (%results_38[%c0_23] [%c1024_22] [%c1_25], %arg16[%c0_44, %c0_43, %results_36] [%c4_24, %c32, %c8] [%c8, %c1024_22, %c1_25]) {broadcast_set = affine_set<()[s0, s1] : (s0 == 0, s1 >= 0, -s1 + 3 >= 0)>, id = 3 : i32} : (memref<4x8x4x8xi32, 2 : i32>, memref<128x1024xi32, 1 : i32>)
        ...
      }
      ...
      air.herd_terminator
    }
    ...
    air.segment_terminator
  }
  air.launch_terminator
}

Output:

...
air.channel @channel_8 [1, 1]
...
air.channel @channel_0 [1, 1] {broadcast_shape = [1, 4]}
...
%0 = air.launch async [%async_token_0, %async_token_3, %async_token_6] (%arg0, %arg1) in (%arg2=%c4, %arg3=%c4) args(%arg4=%results_5, %arg5=%results, %arg6=%results_2) : memref<512x512xi32>, memref<512x1024xi32>, memref<1024x512xi32> attributes {id = 3 : i32} {
  ...
  %2 = scf.for %arg7 = %c0_7 to %c1024 step %c256 iter_args(%arg8 = %1) -> (!air.async.token) {
    ...
    %17 = air.channel.put async [%async_token_8, %arg8]  @channel_8[] (%arg5[%results_9, %arg7] [%c128, %c256] [%c1024, %c1]) : (memref<512x1024xi32>)
    ...
  }
  ...
  %16 = air.segment @segment_0 async  args(%arg7=%arg0, %arg8=%arg1, %arg9=%arg4, %arg10=%arg5, %arg11=%arg6) : index, index, memref<512x512xi32>, memref<512x1024xi32>, memref<1024x512xi32> attributes {id = 2 : i32} {
    ...
    %18 = scf.for %arg12 = %c0_32 to %c1024_33 step %c256_34 iter_args(%arg13 = %17) -> (!air.async.token) {
      %49 = air.channel.get async [%arg13, %arg13]  @channel_8[] (%results_40[%c0_32, %arg12] [%c128_30, %c256_34] [%c1024_33, %c1_29]) : (memref<128x1024xi32, 1 : i32>)
      ...
    }
    ...
    %23 = scf.for %arg12 = %c0_47 to %c128_50 step %c4_48 iter_args(%arg13 = %22) -> (!air.async.token) {
      ...
      %49 = air.channel.put async [%async_token_160, %async_token_39, %arg13]  @channel_0[] (%results_40[%c0_163, %c0_162, %results_161] [%c4_48, %c32, %c8] [%c8, %c1024_46, %c1_49]) : (memref<128x1024xi32, 1 : i32>)
      ...
    }
    ...
    %47 = air.herd @herd_0 async [%async_token_39, %async_token_41, %async_token_43]  tile (%arg12, %arg13) in (%arg14=%c4_31, %arg15=%c4_31) args(%arg16=%results_40, %arg17=%results_42, %arg18=%results_44) : memref<128x1024xi32, 1 : i32>, memref<1024x128xi32, 1 : i32>, memref<128x128xi32, 1 : i32> attributes {id = 1 : i32} {
      ...
      %50 = scf.for %arg19 = %c0_155 to %c128_159 step %c4_156 iter_args(%arg20 = %49) -> (!air.async.token) {
        ...
        %57 = air.channel.get async [%async_token_170, %async_token_168, %arg20]  @channel_0[%arg12, %arg13] (%results_171[%c0_155] [%c1024_154] [%c1_158]) : (memref<4x8x4x8xi32, 2 : i32>)
        ...
      }
      ...
      air.herd_terminator
    }
    air.segment_terminator
  }
  air.launch_terminator
}

-air-enforce-loop-carried-memref-dealloc

Enforce memref dealloc ops in loop iterations

This pass enforces memref deallocation events to happen within each loop iteration, by connecting them in the loop-carried dependency path.

-air-example-pass

Skeleton module op pass

-air-fuse-channels

Fuse multiple air.channel ops into one

This pass fuses multiple air.channel ops into one. The condition for fusing channels is such that the puts and gets of all candidate channels mush share the same control loop hierarchy, where every parent loops must have matching loop bounds. The ‘aggressive-mode’ option, when enabled, will attempt to use as few air.channels as possible by time-multiplexing air.channel.puts and air.channel.gets to share the same air.channel symbol.

Options

-aggressive-mode : List of memory spaces to enable aggressive channel fusion with. Available options include ['L1', 'L2', 'L3'].

-air-fuse-parallel-launch

Fuse parallel launch pass

-air-herd-assign

Transfor affine.for to affine.parallel

-air-hoist-alloc-in-for-pattern

Hoist pairs of alloc and dealloc ops out of for loop

This pass hoists pairs of alloc and dealloc ops out of a for loop body, to represent the static allocation of memories. This pass is used in the ping-pong pattern transformation to identify and isolate the statically allocated ping and pong buffers.

Options

-keep-memref-dealloc : Flag to keep memref dealloc ops after transformation. Memref dealloc is used in air-to-aie pass as handle to generate lock releases.

-air-hoist-dma-in-accum-pattern

Hoist pairs of DMA ops out of for loop based on dependency graph

This pass detects redundant DMA operations in scf.for loops based on AIR event dependency generated by -air-dependency pass, and optimize the for loop’s performance by hoisting them out of for loop’s body.

-air-hoist-ops-not-using-ping-pong

Hoists ops which are not direct users of the target memref

This pass isolates an scf.for loop in preparation for ping-pong transformation, by identifying child operations which are not direct consumers or producers of the memref targetted for ping-pong buffering, and hoisting said operations out of the scf.for loop.

-air-isolate-async-dma-loop-nests

Hoist dma ops into perfectly nested loop

This pass isolates loops containing dma memcpy ops into perfectly nested loops, by hoisting them out of their previous parent loop.

-air-label-broadcast-channel-with-tile

Label broadcasted channel ops with tile coordinates.

-air-label-scf-for-in-segment

Label all candidate scf.for loops within air.segment for unrolling

This pass labels all scf.for loops contained in air.segment ops, except for those which are also contained in air.herd ops, with an ‘unroll’ integer attribute. Scf.for loops labelled with this attribute shall be unrolled, with the provided factor, by any subsequent ‘air-unroll-loop-for-pipelining-pattern’ pass.

-air-label-scf-for-to-ping-pong

Label all candidate scf.for loops for ping-pong transformation

This pass labels all scf.for loops which contain air.execute event of memref.alloc, which is a direct child op of said scf.for, as candidate loop for ping-pong transformation. The label includes an attribute added to the child memref.alloc ops for subsequent hoisting, and an attribute added to the scf.for with an unroll factor.

-air-linalg-codegen

AIR codegen strategies for linalg

This pass implements some tiling strategies for linalg ops targeting AIR dialect.

Options

-herd-size           : Herd size to target
-l1-tile-size        : Tile factors to pass to L1 tiling
-l2-tile-size        : Tile factors to pass to L2 tiling
-l1-tile-permute     : Tile permute vector to pass to L1 tiling
-l2-tile-permute     : Tile permute vector to pass to L2 tiling
-l1-promote-operands : Indices of subviews to promote
-l2-promote-operands : Indices of subviews to promote
-l1-promote          : Promote tiles to L1 memory
-l2-promote          : Promote tiles to L2 memory
-l1-size             : L1 allocation limit in bytes
-l2-size             : L2 allocation limit in bytes
-input-filter        : Input filter for linalg transformations
-test-patterns       : test patterns

-air-linalg-name

Give linalg ops a LinalgTransformMarker string attribute if they don’t already have one

-air-linalg-op-stats

AIR linalg operation statistics

-air-loop-fusion

Hoist dma ops into perfectly nested loop

Optimizes the data movement around L2 memories by rearranging and potentially fusing perfect scf.for loop nests of air.channel.put and air.channel.get, which access the same L2 memref, into scf.for loop nest patterns mappable to a complex finite-state machine consisting of a multiple of AIE DMA Block Descriptors.

-air-loop-merging

Merge several nested subloops into a single loop

This pass transforms several perfectly nested subloops into a single loop. The trip count of the new single loop is the product of all trip counts in subloops. The original loop induction variables are restored using floordiv and modulo operations. Users can specify which loop levels they want to merge together.

Example: Merge subloops with -air-loop-merging="loop-merge-levels=1,2,3" -affine-simplify-structures -cse

Input:

#map0 = affine_map<(d0, d1, d2) -> (d0 + d1 * 7 + d2 * 14)>
#map1 = affine_map<(d0, d1) -> (d0 + d1 * 5)>
module  {
  func.func @task(%arg0: tensor<28x10xf32>, %arg1: tensor<28x10xf32>) -> tensor<28x10xf32> {
    %0 = memref.alloc() : memref<28x10xf32>
    %1 = "aten.type_cast"(%arg0) : (tensor<28x10xf32>) -> memref<28x10xf32>
    %2 = "aten.type_cast"(%arg1) : (tensor<28x10xf32>) -> memref<28x10xf32>
    affine.for %arg2 = 0 to 2 {
      affine.for %arg3 = 0 to 2 {
        affine.for %arg4 = 0 to 7 {
          affine.for %arg5 = 0 to 2 {
            affine.for %arg6 = 0 to 5 {
              %4 = affine.apply #map0(%arg4, %arg3, %arg2)
              %5 = affine.apply #map1(%arg6, %arg5)
              %6 = affine.load %1[%4, %5] : memref<28x10xf32>
              %7 = affine.load %2[%4, %5] : memref<28x10xf32>
              %8 = mulf %6, %7 : f32
              affine.store %8, %0[%4, %5] : memref<28x10xf32>
            }
          }
        }
      }
    } {affine_opt_label = ""}
    %3 = "aten.type_cast"(%0) : (memref<28x10xf32>) -> tensor<28x10xf32>
    return %3 : tensor<28x10xf32>
  }
}

Output:

#map0 = affine_map<(d0, d1) -> (d0 * 14 + d1 floordiv 2 - ((d1 floordiv 2) floordiv 7) * 7 + (d1 floordiv 14) * 7)>
#map1 = affine_map<(d0, d1) -> (d0 + d1 * 5 - (d1 floordiv 2) * 10)>
module  {
  func.func @task(%arg0: tensor<28x10xf32>, %arg1: tensor<28x10xf32>) -> tensor<28x10xf32> {
    %0 = memref.alloc() : memref<28x10xf32>
    %1 = "aten.type_cast"(%arg0) : (tensor<28x10xf32>) -> memref<28x10xf32>
    %2 = "aten.type_cast"(%arg1) : (tensor<28x10xf32>) -> memref<28x10xf32>
    affine.for %arg2 = 0 to 2 {
      affine.for %arg3 = 0 to 28 {
        affine.for %arg4 = 0 to 5 {
          %4 = affine.apply #map0(%arg2, %arg3)
          %5 = affine.apply #map1(%arg4, %arg3)
          %6 = affine.load %1[%4, %5] : memref<28x10xf32>
          %7 = affine.load %2[%4, %5] : memref<28x10xf32>
          %8 = mulf %6, %7 : f32
          affine.store %8, %0[%4, %5] : memref<28x10xf32>
        }
      }
    } {affine_opt_label = ""}
    %3 = "aten.type_cast"(%0) : (memref<28x10xf32>) -> tensor<28x10xf32>
    return %3 : tensor<28x10xf32>
  }
}

-air-loop-permutation

Change the loop ordering according to the input mapping

This pass performs a loop nest reordering according to the input mapping. The i-th loop will be moved from position i -> permMap[i] where the counting of i starts at the outermost loop. The pass transforms only perfect loop nests. The specified ordering starts from 0, and should be of the same length as the loop nest size. Each number is required to appear once in the input mapping.

Example: Permute a loop nest with -air-loop-permutation="loop-order=4,3,2,1,0"

Input:

#map0 = affine_map<(d0, d1, d2) -> (d0 + d1 * 7 + d2 * 14)>
#map1 = affine_map<(d0, d1) -> (d0 + d1 * 5)>
module  {
  func.func @task(%arg0: tensor<28x10xf32>, %arg1: tensor<28x10xf32>) -> tensor<28x10xf32> {
    %0 = memref.alloc() : memref<28x10xf32>
    %1 = "aten.type_cast"(%arg0) : (tensor<28x10xf32>) -> memref<28x10xf32>
    %2 = "aten.type_cast"(%arg1) : (tensor<28x10xf32>) -> memref<28x10xf32>
    affine.for %arg2 = 0 to 2 {
      affine.for %arg3 = 0 to 2 {
        affine.for %arg4 = 0 to 7 {
          affine.for %arg5 = 0 to 2 {
            affine.for %arg6 = 0 to 5 {
              %4 = affine.apply #map0(%arg4, %arg3, %arg2)
              %5 = affine.apply #map1(%arg6, %arg5)
              %6 = affine.load %1[%4, %5] : memref<28x10xf32>
              %7 = affine.load %2[%4, %5] : memref<28x10xf32>
              %8 = mulf %6, %7 : f32
              affine.store %8, %0[%4, %5] : memref<28x10xf32>
            }
          }
        }
      }
    }
    %3 = "aten.type_cast"(%0) : (memref<28x10xf32>) -> tensor<28x10xf32>
    return %3 : tensor<28x10xf32>
  }
}

Output:

#map0 = affine_map<(d0, d1, d2) -> (d0 + d1 * 7 + d2 * 14)>
#map1 = affine_map<(d0, d1) -> (d0 + d1 * 5)>
module  {
  func.func @task(%arg0: tensor<28x10xf32>, %arg1: tensor<28x10xf32>) -> tensor<28x10xf32> {
    %0 = memref.alloc() : memref<28x10xf32>
    %1 = "aten.type_cast"(%arg0) : (tensor<28x10xf32>) -> memref<28x10xf32>
    %2 = "aten.type_cast"(%arg1) : (tensor<28x10xf32>) -> memref<28x10xf32>
    affine.for %arg2 = 0 to 5 {
      affine.for %arg3 = 0 to 2 {
        affine.for %arg4 = 0 to 7 {
          affine.for %arg5 = 0 to 2 {
            affine.for %arg6 = 0 to 2 {
              %4 = affine.apply #map0(%arg4, %arg5, %arg6)
              %5 = affine.apply #map1(%arg2, %arg3)
              %6 = affine.load %1[%4, %5] : memref<28x10xf32>
              %7 = affine.load %2[%4, %5] : memref<28x10xf32>
              %8 = mulf %6, %7 : f32
              affine.store %8, %0[%4, %5] : memref<28x10xf32>
            }
          }
        }
      }
    }
    %3 = "aten.type_cast"(%0) : (memref<28x10xf32>) -> tensor<28x10xf32>
    return %3 : tensor<28x10xf32>
  }
}

Options

-loop-order     : The target loop permutation ordering
-air-label      : Transform loops with the given label
-air-post-label : Label to apply to transformed loop nest

-air-lower-herd-parallel

Remove scf.parallel from inside herds by transforming them to scf.for.

-air-lower-linalg-tensors

Lowering from linalg on tensors to loops

This pass implements a lowering pipeline from linalg on tensors to affine loops. There are three stages:

1. Bufferize with linalg::populateLinalgBufferizePatterns
2. Run some peephole optimizations to cleanup after bufferization.
3. Named ops are transformed to linalg GenericOps then all GenericOps are lowered to loops using linalg::LinalgLoweringPattern.

The transforms are biased toward aie.core regions and are intended to be run after the air-to-aie pass.

-air-ping-pong-transform

Lower to pipelining pattern

This pass lowers to pipelining pattern. This pass looks for the target ping and pong buffers, and a surrounding scf.for loop, to construct explicit dependency edges which represent a ping-pong buffering scheduling.

Options

-keep-memref-dealloc : Flag to keep memref dealloc ops after transformation. Memref dealloc is used in air-to-aie pass as handle to generate lock releases.

-air-pipeline-reduce

Turn a reduction dimension into a herd pipeline

Options

-tile-size          : Tile factors to pass to L1 tiling
-pipeline-depth     : Pipeline depth to generate
-pipeline-direction : Pipeline direction attribute to use. Can be 'vert' or 'horiz'
-promote            : Promote subviews to memory buffers and insert copies.

-air-place-herds

Places herds onto a segment.

This pass performs placement of air herds onto a segment with a specific number of rows and columns. Assumes segment size (provided with an anchor point) will fit on physical board dimensions. The placement starts at the bottom left of the segment and tries to place the largest herd as it moves to the right side of the row. If it can’t place the largest herd remaining in a given tile, it will try again with smaller and smaller herds.

Example with grid size set to 8 rows and 10 columns:

-air-place-herds"num-rows=8 num-cols=10 row-anchor=0 col-anchor=0"

Input:

  #map0 = affine_map<()[s0] -> (s0 * 64)>
  #map1 = affine_map<()[s0] -> (s0 * 512)>
  #map2 = affine_map<()[s0] -> (s0 * 32)>
  module attributes {torch.debug_module_name = "mmult"} {
    func.func @forward(%arg0: memref<24576x1024xbf16>, %arg1: memref<1024x1024xbf16>) -> memref<24576x1024xbf16> {
      %c16 = arith.constant 16 : index
      %c48 = arith.constant 48 : index
      %cst = arith.constant 0.000000e+00 : bf16
      %0 = memref.alloc() {alignment = 128 : i64} : memref<24576x1024xbf16>
      linalg.fill ins(%cst : bf16) outs(%0 : memref<24576x1024xbf16>)
      %1 = memref.alloc() {alignment = 128 : i64} : memref<24576x1024xbf16>
      memref.copy %0, %1 : memref<24576x1024xbf16> to memref<24576x1024xbf16>
      %2 = memref.alloc() {alignment = 128 : i64} : memref<24576x1024xbf16>
      air.launch @launch_0 (%arg2, %arg3) in (%arg4=%c48, %arg5=%c16) args(%arg6=%arg0, %arg7=%arg1, %arg8=%1, %arg9=%2) : memref<24576x1024xbf16>, memref<1024x1024xbf16>, memref<24576x1024xbf16>, memref<24576x1024xbf16> attributes {resource_type = "vckxyz", size_x = 6 : i64, size_y = 2 : i64} {
        air.segment @segment_0  args(%arg10=%arg2, %arg11=%arg3, %arg12=%arg4, %arg13=%arg5, %arg14=%arg6, %arg15=%arg7, %arg16=%arg8, %arg17=%arg9) : index, index, index, index, memref<24576x1024xbf16>, memref<1024x1024xbf16>, memref<24576x1024xbf16>, memref<24576x1024xbf16> attributes {resource_type = "vckxyz", size_x = 3 : i64, size_y = 2 : i64} {
          %c1 = arith.constant 1 : index
          %c2 = arith.constant 2 : index
          %c0 = arith.constant 0 : index
          %c1024 = arith.constant 1024 : index
          %c64 = arith.constant 64 : index
          %3 = affine.apply #map0()[%arg11]
          %4 = affine.apply #map1()[%arg10]
          scf.for %arg18 = %c0 to %c1024 step %c64 {
            %12 = memref.alloc() : memref<64x64xbf16, 1>
            %13 = memref.alloc() : memref<64x64xbf16, 1>
            %14 = memref.alloc() : memref<64x64xbf16, 1>
            air.dma_memcpy_nd (%12[] [] [], %arg14[%4, %arg18] [%c64, %c64] [%c1024, %c1]) {id = 1 : i32} : (memref<64x64xbf16, 1>, memref<24576x1024xbf16>)
            air.dma_memcpy_nd (%13[] [] [], %arg15[%arg18, %3] [%c64, %c64] [%c1024, %c1]) {id = 2 : i32} : (memref<64x64xbf16, 1>, memref<1024x1024xbf16>)
            air.dma_memcpy_nd (%14[] [] [], %arg16[%4, %3] [%c64, %c64] [%c1024, %c1]) {id = 3 : i32} : (memref<64x64xbf16, 1>, memref<24576x1024xbf16>)
            air.herd @matmul_herd_0  tile (%arg19, %arg20) in (%arg21=%c2, %arg22=%c2) args(%arg23=%12, %arg24=%13, %arg25=%14) : memref<64x64xbf16, 1>, memref<64x64xbf16, 1>, memref<64x64xbf16, 1> {
              %c1_0 = arith.constant 1 : index
              %c0_1 = arith.constant 0 : index
              %c64_2 = arith.constant 64 : index
              %c32 = arith.constant 32 : index
              %15 = affine.apply #map2()[%arg19]
              %16 = affine.apply #map2()[%arg20]
              scf.for %arg26 = %c0_1 to %c64_2 step %c32 {
                %17 = memref.alloc() : memref<32x32xbf16, 2>
                %18 = memref.alloc() : memref<32x32xbf16, 2>
                %19 = memref.alloc() : memref<32x32xbf16, 2>
                air.dma_memcpy_nd (%17[] [] [], %arg23[%15, %arg26] [%c32, %c32] [%c64_2, %c1_0]) {id = 4 : i32} : (memref<32x32xbf16, 2>, memref<64x64xbf16, 1>)
                air.dma_memcpy_nd (%18[] [] [], %arg24[%arg26, %16] [%c32, %c32] [%c64_2, %c1_0]) {id = 5 : i32} : (memref<32x32xbf16, 2>, memref<64x64xbf16, 1>)
                air.dma_memcpy_nd (%19[] [] [], %arg25[%15, %16] [%c32, %c32] [%c64_2, %c1_0]) {id = 6 : i32} : (memref<32x32xbf16, 2>, memref<64x64xbf16, 1>)
                linalg.matmul ins(%17, %18 : memref<32x32xbf16, 2>, memref<32x32xbf16, 2>) outs(%19 : memref<32x32xbf16, 2>)
                air.dma_memcpy_nd (%arg25[%15, %16] [%c32, %c32] [%c64_2, %c1_0], %19[] [] []) {id = 7 : i32} : (memref<64x64xbf16, 1>, memref<32x32xbf16, 2>)
                memref.dealloc %17 : memref<32x32xbf16, 2>
                memref.dealloc %18 : memref<32x32xbf16, 2>
                memref.dealloc %19 : memref<32x32xbf16, 2>
              }
              air.herd_terminator
            }
            air.dma_memcpy_nd (%arg16[%4, %3] [%c64, %c64] [%c1024, %c1], %14[] [] []) {id = 8 : i32} : (memref<24576x1024xbf16>, memref<64x64xbf16, 1>)
            memref.dealloc %12 : memref<64x64xbf16, 1>
            memref.dealloc %13 : memref<64x64xbf16, 1>
            memref.dealloc %14 : memref<64x64xbf16, 1>
          }
          air.segment_terminator
        }
        air.launch_terminator
      }
      return %2 : memref<24576x1024xbf16>
    }
  }

output:

  ....
  air.herd @matmul_herd_0  tile (%arg19, %arg20) in (%arg21=%c2, %arg22=%c2) args(%arg23=%12, %arg24=%13, %arg25=%14) : memref<64x64xbf16, 1>, memref<64x64xbf16, 1>, memref<64x64xbf16, 1> attributes {x_loc = 0 : i64, y_loc = 7: i64} {
  ...

Options

-num-rows   : Number of rows of AIE tiles in a segment
-num-cols   : Number of columns of AIE tiles in a segment
-row-anchor : Anchoring row number of segments
-col-anchor : Anchoring column number of segments

-air-prune-linalg-generic-input-dma

Detect and prune redundant DMA into linalg generic

This pass detects and prunes redundant DMA which copies into linalg generic input operands.

-air-regularize-loop

Move operations inside the innermost loop body to regularize loop nests

This pass regularizes loop nests by moving intermediate operations between subloops in a loop nest inside the innermost loop body. The pass is essentiallythe inverse of the affine loop invariant code motion pass. For each opeation that makes the loop nest non-perfect, the pass will check recursively if the content of the operation is independent of the induction variable of the inner loop. And if it is independent, the operation will be moved inside the inner loop body until the induction variable of the inner loop is dependent on the operation or there are no loops at the same level.

Example: Regularize a loop nest with -air-regularize-loop

Input:

#map0 = affine_map<(d0, d1, d2) -> (d0 + d1 * 7 + d2 * 14)>
#map1 = affine_map<(d0, d1) -> (d0 + d1 * 5)>
module  {
  func.func @task(%arg0: tensor<28x10xf32>, %arg1: tensor<28x10xf32>) -> tensor<28x10xf32> {
    %0 = memref.alloc() : memref<28x10xf32>
    %1 = "aten.type_cast"(%arg0) : (tensor<28x10xf32>) -> memref<28x10xf32>
    %2 = "aten.type_cast"(%arg1) : (tensor<28x10xf32>) -> memref<28x10xf32>
    affine.for %arg2 = 0 to 2 {
      affine.for %arg3 = 0 to 2 {
        affine.for %arg4 = 0 to 7 {
          %4 = affine.apply #map0(%arg4, %arg3, %arg2)
          affine.for %arg5 = 0 to 2 {
            affine.for %arg6 = 0 to 5 {
              %5 = affine.apply #map1(%arg6, %arg5)
              %6 = affine.load %1[%4, %5] : memref<28x10xf32>
              %7 = affine.load %2[%4, %5] : memref<28x10xf32>
              %8 = mulf %6, %7 : f32
              affine.store %8, %0[%4, %5] : memref<28x10xf32>
            }
          }
        }
      }
    }
    %3 = "aten.type_cast"(%0) : (memref<28x10xf32>) -> tensor<28x10xf32>
    return %3 : tensor<28x10xf32>
  }
}

Output:

#map0 = affine_map<(d0, d1, d2) -> (d0 + d1 * 7 + d2 * 14)>
#map1 = affine_map<(d0, d1) -> (d0 + d1 * 5)>
module  {
  func.func @task(%arg0: tensor<28x10xf32>, %arg1: tensor<28x10xf32>) -> tensor<28x10xf32> {
    %0 = memref.alloc() : memref<28x10xf32>
    %1 = "aten.type_cast"(%arg0) : (tensor<28x10xf32>) -> memref<28x10xf32>
    %2 = "aten.type_cast"(%arg1) : (tensor<28x10xf32>) -> memref<28x10xf32>
    affine.for %arg2 = 0 to 2 {
      affine.for %arg3 = 0 to 2 {
        affine.for %arg4 = 0 to 7 {
          affine.for %arg5 = 0 to 2 {
            affine.for %arg6 = 0 to 5 {
              %4 = affine.apply #map0(%arg4, %arg3, %arg2)
              %5 = affine.apply #map1(%arg6, %arg5)
              %6 = affine.load %1[%4, %5] : memref<28x10xf32>
              %7 = affine.load %2[%4, %5] : memref<28x10xf32>
              %8 = mulf %6, %7 : f32
              affine.store %8, %0[%4, %5] : memref<28x10xf32>
            }
          }
        }
      }
    }
    %3 = "aten.type_cast"(%0) : (memref<28x10xf32>) -> tensor<28x10xf32>
    return %3 : tensor<28x10xf32>
  }
}

-air-renumber-dma

Renumber air dma op ids

Options

-mode : In which hierarchy level to renumber the dma ops

-air-return-elimination

Convert functions to return their values with out parameters

-air-rm-linalg-name

Remove LinalgTransformMarker string attributes from linalg ops

-air-specialize-channel-wrap-and-stride

Specialize air.channel op in perfect loop nest with wraps and strides

This pass specialize air.channel op in perfect loop nest with wraps and strides, for efficient mapping to hardware buffer descriptors.

-air-specialize-dma-broadcast

Specialize dma operations for broadcast pattern

Specializes air.dma_memcpy_nd operations for broadcast patterns within a computation. This specialization involves transforming data movement operations into more optimized versions that are aware of the broadcast semantics.

-air-split-l2-memref

Split L2 memref into smaller buffers to better fit with the data movement harware constraints

Checks if any of the air.segment op is implicitly allocated to more than one physical L2 memory tiles. If true, then transforms them by splitting their L2 memory references (memrefs) into multiple allocations of same or smaller size, such that they can be distributed across those physical memory tiles. Such transformation can optimize the IR’s hardware mapping, given that the L2 memory tile has a finite number of DMA channels available to move data to and from compute tiles.

To check if any air.segment op can be allocated to more than one physical L2 memory tiles, the option tiles-per-l2-tile is used to specify in the target architecture how many compute tiles are in close affinity to each L2 memory tile, i.e. how many compute tiles can efficiently communicate to one L2 memory tile. If any air.segment op must be allocated to more compute tiles than this number, then that means the air.segment op can allocate L2 memrefs to multiple L2 memory tiles.

Example:

Input:

  %1 = scf.for %arg7 = %c0_7 to %c1024 step %c256 iter_args(%arg8 = %async_token_8) -> (!air.async.token) {
    %5 = air.channel.put async [%arg8]  @channel_8[] (%arg5[%results_9, %arg7] [%c128, %c256] [%c1024, %c1]) {id = 1 : i32} : (memref<512x1024xi32>)
    scf.yield %5 : !air.async.token
  }
  ...
  %4 = air.segment @segment_0 async  attributes {id = 2 : i32} {
    ...
    %async_token_22, %results_23 = air.execute -> (memref<128x1024xi32, 1 : i32>) {
      %alloc = memref.alloc() : memref<128x1024xi32, 1 : i32>
      air.execute_terminator %alloc : memref<128x1024xi32, 1 : i32>
    }
    %7 = scf.for %arg7 = %c0_19 to %c1024_20 step %c256_21 iter_args(%arg8 = %async_token_22) -> (!air.async.token) {
      %20 = air.channel.get async [%arg8]  @channel_8[] (%results_23[%c0_19, %arg7] [%c128_17, %c256_21] [%c1024_20, %c1_16]) {id = 4 : i32} : (memref<128x1024xi32, 1 : i32>)
      scf.yield %20 : !air.async.token
    }
    ...
    %9 = scf.for %arg7 = %c0_19 to %c128_17 step %c4_18 iter_args(%arg8 = %async_token_22) -> (!air.async.token) {
      %async_token_31, %results_32 = air.execute [%arg8] -> (index) {
        %21 = affine.apply affine_map<()[s0] -> (s0 * 8)>()[%arg7]
        air.execute_terminator %21 : index
      }
      %20 = air.channel.put async [%async_token_31]  @channel_0[] (%results_23[%c0_19, %c0_19, %results_32] [%c4_18, %c32, %c8] [%c8, %c1024_20, %c1_16]) {id = 6 : i32} : (memref<128x1024xi32, 1 : i32>)
      scf.yield %20 : !air.async.token
    }
    %10 = scf.for %arg7 = %c0_19 to %c128_17 step %c4_18 iter_args(%arg8 = %async_token_22) -> (!air.async.token) {
      %async_token_31, %results_32 = air.execute [%arg8] -> (index) {
        %21 = affine.apply affine_map<()[s0] -> (s0 * 8)>()[%arg7]
        air.execute_terminator %21 : index
      }
      %20 = air.channel.put async [%async_token_31]  @channel_1[] (%results_23[%c0_19, %c32, %results_32] [%c4_18, %c32, %c8] [%c8, %c1024_20, %c1_16]) {id = 7 : i32} : (memref<128x1024xi32, 1 : i32>)
      scf.yield %20 : !air.async.token
    }
    %11 = scf.for %arg7 = %c0_19 to %c128_17 step %c4_18 iter_args(%arg8 = %async_token_22) -> (!air.async.token) {
      %async_token_31, %results_32 = air.execute [%arg8] -> (index) {
        %21 = affine.apply affine_map<()[s0] -> (s0 * 8)>()[%arg7]
        air.execute_terminator %21 : index
      }
      %20 = air.channel.put async [%async_token_31]  @channel_2[] (%results_23[%c0_19, %c64, %results_32] [%c4_18, %c32, %c8] [%c8, %c1024_20, %c1_16]) {id = 8 : i32} : (memref<128x1024xi32, 1 : i32>)
      scf.yield %20 : !air.async.token
    }
    %12 = scf.for %arg7 = %c0_19 to %c128_17 step %c4_18 iter_args(%arg8 = %async_token_22) -> (!air.async.token) {
      %async_token_31, %results_32 = air.execute [%arg8] -> (index) {
        %21 = affine.apply affine_map<()[s0] -> (s0 * 8)>()[%arg7]
        air.execute_terminator %21 : index
      }
      %20 = air.channel.put async [%async_token_31]  @channel_3[] (%results_23[%c0_19, %c96, %results_32] [%c4_18, %c32, %c8] [%c8, %c1024_20, %c1_16]) {id = 9 : i32} : (memref<128x1024xi32, 1 : i32>)
      scf.yield %20 : !air.async.token
    }
    ...
    air.segment_terminator
  }
  air.launch_terminator
}

Output:

  %0 = air.launch async [%async_token_0, %async_token_3, %async_token_6] (%arg0, %arg1) in (%arg2=%c4, %arg3=%c4) args(%arg4=%results_5, %arg5=%results, %arg6=%results_2) : memref<512x512xi32>, memref<512x1024xi32>, memref<1024x512xi32> attributes {id = 1 : i32} {
    ...
    %5 = scf.for %arg7 = %c0_7 to %c1024 step %c256 iter_args(%arg8 = %async_token_8) -> (!air.async.token) {
      %21 = air.channel.put async [%arg8]  @channel_12[%c0_7, %c0_7] (%arg5[%1, %arg7] [%c32, %c256] [%c1024, %c1]) {id = 1 : i32} : (memref<512x1024xi32>)
      %22 = air.channel.put async [%arg8]  @channel_12[%c1, %c0_7] (%arg5[%2, %arg7] [%c32, %c256] [%c1024, %c1]) {id = 2 : i32} : (memref<512x1024xi32>)
      %23 = air.channel.put async [%arg8]  @channel_12[%c2, %c0_7] (%arg5[%3, %arg7] [%c32, %c256] [%c1024, %c1]) {id = 3 : i32} : (memref<512x1024xi32>)
      %24 = air.channel.put async [%arg8]  @channel_12[%c3, %c0_7] (%arg5[%4, %arg7] [%c32, %c256] [%c1024, %c1]) {id = 4 : i32} : (memref<512x1024xi32>)
      %25 = air.wait_all async [%21, %22, %23, %24] 
      scf.yield %25 : !air.async.token
    }
    ...
    %20 = air.segment @segment_0 async  attributes {id = 2 : i32} {
      ...
      %async_token_53, %results_54 = air.execute -> (memref<32x1024xi32, 1>) {
        %alloc = memref.alloc() : memref<32x1024xi32, 1>
        air.execute_terminator %alloc : memref<32x1024xi32, 1>
      }
      %async_token_55, %results_56 = air.execute -> (memref<32x1024xi32, 1>) {
        %alloc = memref.alloc() : memref<32x1024xi32, 1>
        air.execute_terminator %alloc : memref<32x1024xi32, 1>
      }
      %async_token_57, %results_58 = air.execute -> (memref<32x1024xi32, 1>) {
        %alloc = memref.alloc() : memref<32x1024xi32, 1>
        air.execute_terminator %alloc : memref<32x1024xi32, 1>
      }
      %async_token_59, %results_60 = air.execute -> (memref<32x1024xi32, 1>) {
        %alloc = memref.alloc() : memref<32x1024xi32, 1>
        air.execute_terminator %alloc : memref<32x1024xi32, 1>
      }
      ...
      %25 = scf.for %arg7 = %c0_22 to %c1024_23 step %c256_24 iter_args(%arg8 = %24) -> (!air.async.token) {
        %68 = air.channel.get async [%arg8]  @channel_12[%c0_22, %c0_22] (%results_54[%c0_50, %arg7] [%c32_18, %c256_24] [%c1024_51, %c1_52]) {id = 13 : i32} : (memref<32x1024xi32, 1>)
        %69 = air.channel.get async [%arg8]  @channel_12[%c1_19, %c0_22] (%results_56[%c0_43, %arg7] [%c32_18, %c256_24] [%c1024_44, %c1_45]) {id = 14 : i32} : (memref<32x1024xi32, 1>)
        %70 = air.channel.get async [%arg8]  @channel_12[%c2_17, %c0_22] (%results_58[%c0_36, %arg7] [%c32_18, %c256_24] [%c1024_37, %c1_38]) {id = 15 : i32} : (memref<32x1024xi32, 1>)
        %71 = air.channel.get async [%arg8]  @channel_12[%c3_16, %c0_22] (%results_60[%c0_29, %arg7] [%c32_18, %c256_24] [%c1024_30, %c1_31]) {id = 16 : i32} : (memref<32x1024xi32, 1>)
        %72 = air.wait_all async [%68, %69, %70, %71] 
        scf.yield %72 : !air.async.token
      }
      ...
      %31 = scf.for %arg7 = %c0_22 to %c128_20 step %c4_21 iter_args(%arg8 = %30) -> (!air.async.token) {
        %async_token_177, %results_178 = air.execute [%arg8] -> (index) {
          %69 = affine.apply affine_map<()[s0] -> (s0 * 8)>()[%arg7]
          air.execute_terminator %69 : index
        }
        %68 = air.channel.put async [%async_token_177]  @channel_0[] (%results_54[%c0_22, %c0_46, %results_178] [%c4_21, %c32_18, %c8] [%c8_47, %c1024_48, %c1_49]) {id = 21 : i32} : (memref<32x1024xi32, 1>)
        scf.yield %68 : !air.async.token
      }
      %32 = air.wait_all async [%async_token_55] 
      %33 = scf.for %arg7 = %c0_22 to %c128_20 step %c4_21 iter_args(%arg8 = %32) -> (!air.async.token) {
        %async_token_177, %results_178 = air.execute [%arg8] -> (index) {
          %69 = affine.apply affine_map<()[s0] -> (s0 * 8)>()[%arg7]
          air.execute_terminator %69 : index
        }
        %68 = air.channel.put async [%async_token_177]  @channel_1[] (%results_56[%c0_22, %c0_39, %results_178] [%c4_21, %c32_18, %c8] [%c8_40, %c1024_41, %c1_42]) {id = 22 : i32} : (memref<32x1024xi32, 1>)
        scf.yield %68 : !air.async.token
      }
      %34 = air.wait_all async [%async_token_57] 
      %35 = scf.for %arg7 = %c0_22 to %c128_20 step %c4_21 iter_args(%arg8 = %34) -> (!air.async.token) {
        %async_token_177, %results_178 = air.execute [%arg8] -> (index) {
          %69 = affine.apply affine_map<()[s0] -> (s0 * 8)>()[%arg7]
          air.execute_terminator %69 : index
        }
        %68 = air.channel.put async [%async_token_177]  @channel_2[] (%results_58[%c0_22, %c0_32, %results_178] [%c4_21, %c32_18, %c8] [%c8_33, %c1024_34, %c1_35]) {id = 23 : i32} : (memref<32x1024xi32, 1>)
        scf.yield %68 : !air.async.token
      }
      %36 = air.wait_all async [%async_token_59] 
      %37 = scf.for %arg7 = %c0_22 to %c128_20 step %c4_21 iter_args(%arg8 = %36) -> (!air.async.token) {
        %async_token_177, %results_178 = air.execute [%arg8] -> (index) {
          %69 = affine.apply affine_map<()[s0] -> (s0 * 8)>()[%arg7]
          air.execute_terminator %69 : index
        }
        %68 = air.channel.put async [%async_token_177]  @channel_3[] (%results_60[%c0_22, %c0_25, %results_178] [%c4_21, %c32_18, %c8] [%c8_26, %c1024_27, %c1_28]) {id = 24 : i32} : (memref<32x1024xi32, 1>)
        scf.yield %68 : !air.async.token
      }
      ...
      air.segment_terminator
    }
    air.launch_terminator
  }

Options

-tiles-per-l2-tile : Number of compute tiles per L2 memory tile. Used to estimate if an air.segment shall allocate to multiple L2 memory tiles, and therefore requires L2 memref splitting.

-air-transform

Transform IR with transform dialect

Options

-filename : Transform Dialect filename

-air-unroll-channel-by-factor

Unroll channel puts and gets by an integer factor

This pass unrolls all puts and gets to an air.channel by an integer factor, so as to represent the usage of multiple physical DMA channels in parallel for improved available bandwidth for this data movement.

Options

-channel-name  : Target channel to unroll.
-unroll-dim    : Dimension id to unroll.
-unroll-factor : Integer unroll factor.

-air-unroll-loop-for-pipelining-pattern

Unroll loop by an integer factor

This pass unrolls a loop by an integer factor. This pass is used in the ping-pong pattern transformation to unroll a scf.for loop by 2 to ensure explicit representation of ping and pong processes, respectively.

-air-unroll-outer-affine-loops

Unroll loops in a perfectly nested affine for loop nest, outer to inner.

The pass attempts to unroll the outermost dimensions in affine loop nests.

Example:

Input:

func.func @matmul_512x512_1024xi32__dispatch_0_matmul_512x512x1024_i32() {
  ...
  affine.for %arg0 = 0 to 4 step 4 {
    affine.for %arg1 = 0 to 4 step 4 {
      affine.for %arg2 = affine_map<(d0) -> (d0)>(%arg0) to affine_map<(d0) -> (d0 + 4)>(%arg0) {
        affine.for %arg3 = affine_map<(d0) -> (d0)>(%arg1) to affine_map<(d0) -> (d0 + 4)>(%arg1) {
          ...
          %25 = airrt.dma_memcpy_nd(%c17_i32, %15, %16, %0[%c0_i64, %17, %18, %19], [%c1_i64, %22, %23, %24], [%c0_i64, %20, %21]) {metadata = @airMemcpyId19} : (i32, i64, i64, memref<512x1024xi32>, [i64, i64, i64, i64], [i64, i64, i64, i64], [i64, i64, i64]) : !airrt.event
          ...
          %74 = airrt.dma_memcpy_nd(%c13_i32, %67, %68, %3[%c0_i64_13, %c0_i64_13, %69, %70], [%c1_i64_14, %c1_i64_14, %72, %73], [%c0_i64_13, %c0_i64_13, %71]) {metadata = @airMemcpyId15} : (i32, i64, i64, memref<1024x512xi32>, [i64, i64, i64, i64], [i64, i64, i64, i64], [i64, i64, i64]) : !airrt.event
          ...
          %111 = airrt.dma_memcpy_nd(%c78_i32, %104, %105, %6[%c0_i64_24, %c0_i64_24, %106, %107], [%c1_i64_25, %c1_i64_25, %109, %110], [%c0_i64_24, %c0_i64_24, %108]) {metadata = @airMemcpyId78} : (i32, i64, i64, memref<512x512xi32>, [i64, i64, i64, i64], [i64, i64, i64, i64], [i64, i64, i64]) : !airrt.event
          ...
        }
      }
    }
  } {affine_opt_label = ""}
  return
}

Output:

func.func @matmul_512x512_1024xi32__dispatch_0_matmul_512x512x1024_i32() {
  ...
  affine.for %arg0 = affine_map<(d0) -> (d0)>(%c0) to affine_map<(d0) -> (d0 + 4)>(%c0) {
    affine.for %arg1 = affine_map<(d0) -> (d0)>(%c0_0) to affine_map<(d0) -> (d0 + 4)>(%c0_0) {
      ...
      %25 = airrt.dma_memcpy_nd(%c17_i32, %15, %16, %0[%c0_i64, %17, %18, %19], [%c1_i64, %22, %23, %24], [%c0_i64, %20, %21]) {metadata = @airMemcpyId19} : (i32, i64, i64, memref<512x1024xi32>, [i64, i64, i64, i64], [i64, i64, i64, i64], [i64, i64, i64]) : !airrt.event
      ...
      %74 = airrt.dma_memcpy_nd(%c13_i32, %67, %68, %3[%c0_i64_15, %c0_i64_15, %69, %70], [%c1_i64_16, %c1_i64_16, %72, %73], [%c0_i64_15, %c0_i64_15, %71]) {metadata = @airMemcpyId15} : (i32, i64, i64, memref<1024x512xi32>, [i64, i64, i64, i64], [i64, i64, i64, i64], [i64, i64, i64]) : !airrt.event
      ...
      %111 = airrt.dma_memcpy_nd(%c78_i32, %104, %105, %6[%c0_i64_26, %c0_i64_26, %106, %107], [%c1_i64_27, %c1_i64_27, %109, %110], [%c0_i64_26, %c0_i64_26, %108]) {metadata = @airMemcpyId78} : (i32, i64, i64, memref<512x512xi32>, [i64, i64, i64, i64], [i64, i64, i64, i64], [i64, i64, i64]) : !airrt.event
      ...
    }
  }
  return
}

Options

-depth : The number of outermost loops in the loop nest to unroll