Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[AMD][Pipeliner] Improve clustering and add prefetch #4881

Merged
merged 9 commits into from
Nov 12, 2024
Merged

[AMD][Pipeliner] Improve clustering and add prefetch #4881

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
4815676
[AMD][Pipeliner] Add prefetch scheduling to stream-pipeline
sjw36 Oct 25, 2024
a5517b6
* updated tritonamdgpu-reorder-instructions to remove pre-ordering tt…
sjw36 Oct 25, 2024
fc51b89
* format
sjw36 Oct 25, 2024
ce333d9
* updated documentation and minor changes based on feedback
sjw36 Nov 1, 2024
243ea2e
* fixed init order
sjw36 Nov 4, 2024
1caf71f
* format
sjw36 Nov 8, 2024
4925e1b
* address feedback
sjw36 Nov 8, 2024
4694770
* removed dead code
sjw36 Nov 8, 2024
a117950
* added back ScheduleGlobalLoadLocalStore for non-matmul case
sjw36 Nov 9, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
345 changes: 0 additions & 345 deletions test/TritonGPU/amd/amd-reorder-instructions.mlir
View file
Open in desktop

Large diffs are not rendered by default.

32 changes: 16 additions & 16 deletions test/TritonGPU/amd/amd-sched-2nd-load.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -35,11 +35,11 @@ module attributes {"triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-war
%c1 = arith.constant 1 : i32
%cst = arith.constant dense<0.000000e+00> : tensor<256x256xf32, #mma>
%0:1 = scf.for %arg0 = %c0 to %c1 step %c1 iter_args(%arg1 = %cst) -> (tensor<256x256xf32, #mma>) : i32 {
%4 = tt.load %A_ptr : tensor<256x128x!tt.ptr<f16>, #blocked>
%1 = triton_gpu.local_load %A_LDS : !tt.memdesc<256x128xf16, #shared, #triton_gpu.shared_memory, mutable> -> tensor<256x128xf16, #dotOp0>
%5 = tt.load %B_ptr : tensor<128x256x!tt.ptr<f16>, #blocked1>
%2 = triton_gpu.local_load %B_LDS : !tt.memdesc<128x256xf16, #shared1, #triton_gpu.shared_memory, mutable> -> tensor<128x256xf16, #dotOp1>
%3 = tt.dot %1, %2, %arg1 : tensor<256x128xf16, #dotOp0> * tensor<128x256xf16, #dotOp1> -> tensor<256x256xf32, #mma>
%4 = tt.load %A_ptr : tensor<256x128x!tt.ptr<f16>, #blocked>
%5 = tt.load %B_ptr : tensor<128x256x!tt.ptr<f16>, #blocked1>
triton_gpu.local_store %4, %A_LDS : tensor<256x128xf16, #blocked> -> !tt.memdesc<256x128xf16, #shared, #triton_gpu.shared_memory, mutable>
triton_gpu.local_store %5, %B_LDS : tensor<128x256xf16, #blocked1> -> !tt.memdesc<128x256xf16, #shared1, #triton_gpu.shared_memory, mutable>
scf.yield %3 : tensor<256x256xf32, #mma>
Expand All @@ -64,11 +64,11 @@ module attributes {"triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-war
%c1 = arith.constant 1 : i32
%cst = arith.constant dense<0.000000e+00> : tensor<256x256xf32, #mma>
%0:1 = scf.for %arg0 = %c0 to %c1 step %c1 iter_args(%arg1 = %cst) -> (tensor<256x256xf32, #mma>) : i32 {
%4 = tt.load %A_ptr : tensor<256x64x!tt.ptr<f16>, #blocked>
%1 = triton_gpu.local_load %A_LDS : !tt.memdesc<256x64xf16, #shared, #triton_gpu.shared_memory, mutable> -> tensor<256x64xf16, #dotOp0>
%5 = tt.load %B_ptr : tensor<64x256x!tt.ptr<f16>, #blocked1>
%2 = triton_gpu.local_load %B_LDS : !tt.memdesc<64x256xf16, #shared1, #triton_gpu.shared_memory, mutable> -> tensor<64x256xf16, #dotOp1>
%3 = tt.dot %1, %2, %arg1 : tensor<256x64xf16, #dotOp0> * tensor<64x256xf16, #dotOp1> -> tensor<256x256xf32, #mma>
%4 = tt.load %A_ptr : tensor<256x64x!tt.ptr<f16>, #blocked>
%5 = tt.load %B_ptr : tensor<64x256x!tt.ptr<f16>, #blocked1>
triton_gpu.local_store %4, %A_LDS : tensor<256x64xf16, #blocked> -> !tt.memdesc<256x64xf16, #shared, #triton_gpu.shared_memory, mutable>
triton_gpu.local_store %5, %B_LDS : tensor<64x256xf16, #blocked1> -> !tt.memdesc<64x256xf16, #shared1, #triton_gpu.shared_memory, mutable>
scf.yield %3 : tensor<256x256xf32, #mma>
Expand All @@ -81,8 +81,8 @@ module attributes {"triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-war
// Should NOT apply: tile size 256x64x128 with single dot
// CHECK-LABEL: sink_2nd_load_256x64x128
// CHECK: %[[tileA:.*]] = tt.load
// CHECK-NEXT: %[[tileB:.*]] = tt.load
// CHECK-NEXT: local_load
// CHECK-NEXT: %[[tileB:.*]] = tt.load
// CHECK-NEXT: local_load
// CHECK-NEXT: tt.dot
// CHECK-NEXT: triton_gpu.local_store %[[tileA]]
Expand All @@ -93,11 +93,11 @@ module attributes {"triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-war
%c1 = arith.constant 1 : i32
%cst = arith.constant dense<0.000000e+00> : tensor<256x64xf32, #mma>
%0:1 = scf.for %arg0 = %c0 to %c1 step %c1 iter_args(%arg1 = %cst) -> (tensor<256x64xf32, #mma>) : i32 {
%4 = tt.load %A_ptr : tensor<256x128x!tt.ptr<f16>, #blocked>
%1 = triton_gpu.local_load %A_LDS : !tt.memdesc<256x128xf16, #shared, #triton_gpu.shared_memory, mutable> -> tensor<256x128xf16, #dotOp0>
%5 = tt.load %B_ptr : tensor<128x64x!tt.ptr<f16>, #blocked1>
%2 = triton_gpu.local_load %B_LDS : !tt.memdesc<128x64xf16, #shared1, #triton_gpu.shared_memory, mutable> -> tensor<128x64xf16, #dotOp1>
%3 = tt.dot %1, %2, %arg1 : tensor<256x128xf16, #dotOp0> * tensor<128x64xf16, #dotOp1> -> tensor<256x64xf32, #mma>
%4 = tt.load %A_ptr : tensor<256x128x!tt.ptr<f16>, #blocked>
%5 = tt.load %B_ptr : tensor<128x64x!tt.ptr<f16>, #blocked1>
triton_gpu.local_store %4, %A_LDS : tensor<256x128xf16, #blocked> -> !tt.memdesc<256x128xf16, #shared, #triton_gpu.shared_memory, mutable>
triton_gpu.local_store %5, %B_LDS : tensor<128x64xf16, #blocked1> -> !tt.memdesc<128x64xf16, #shared1, #triton_gpu.shared_memory, mutable>
scf.yield %3 : tensor<256x64xf32, #mma>
Expand All @@ -110,8 +110,8 @@ module attributes {"triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-war
// Should NOT apply: tile size 256x256x32 with single dot
// CHECK-LABEL: sink_2nd_load_256x256x32
// CHECK: %[[tileA:.*]] = tt.load
// CHECK-NEXT: %[[tileB:.*]] = tt.load
// CHECK-NEXT: local_load
// CHECK-NEXT: %[[tileB:.*]] = tt.load
// CHECK-NEXT: local_load
// CHECK-NEXT: tt.dot
// CHECK-NEXT: triton_gpu.local_store %[[tileA]]
Expand All @@ -122,11 +122,11 @@ module attributes {"triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-war
%c1 = arith.constant 1 : i32
%cst = arith.constant dense<0.000000e+00> : tensor<256x256xf32, #mma>
%0:1 = scf.for %arg0 = %c0 to %c1 step %c1 iter_args(%arg1 = %cst) -> (tensor<256x256xf32, #mma>) : i32 {
%4 = tt.load %A_ptr : tensor<256x32x!tt.ptr<f16>, #blocked>
%1 = triton_gpu.local_load %A_LDS : !tt.memdesc<256x32xf16, #shared, #triton_gpu.shared_memory, mutable> -> tensor<256x32xf16, #dotOp0>
%5 = tt.load %B_ptr : tensor<32x256x!tt.ptr<f16>, #blocked1>
%2 = triton_gpu.local_load %B_LDS : !tt.memdesc<32x256xf16, #shared1, #triton_gpu.shared_memory, mutable> -> tensor<32x256xf16, #dotOp1>
%3 = tt.dot %1, %2, %arg1 : tensor<256x32xf16, #dotOp0> * tensor<32x256xf16, #dotOp1> -> tensor<256x256xf32, #mma>
%4 = tt.load %A_ptr : tensor<256x32x!tt.ptr<f16>, #blocked>
%5 = tt.load %B_ptr : tensor<32x256x!tt.ptr<f16>, #blocked1>
triton_gpu.local_store %4, %A_LDS : tensor<256x32xf16, #blocked> -> !tt.memdesc<256x32xf16, #shared, #triton_gpu.shared_memory, mutable>
triton_gpu.local_store %5, %B_LDS : tensor<32x256xf16, #blocked1> -> !tt.memdesc<32x256xf16, #shared1, #triton_gpu.shared_memory, mutable>
scf.yield %3 : tensor<256x256xf32, #mma>
Expand All @@ -142,8 +142,8 @@ module attributes {"triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-war
// Should NOT apply: the 2nd load has a user before the dot
// CHECK-LABEL: sink_2nd_load_128x128x128_user_before_dot
// CHECK: %[[tileA:.*]] = tt.load
// CHECK-NEXT: %[[tileB:.*]] = tt.load
// CHECK-NEXT: local_load
// CHECK-NEXT: %[[tileB:.*]] = tt.load
// CHECK-NEXT: local_load
// CHECK-NEXT: tt.store
// CHECK-NEXT: tt.dot
Expand All @@ -154,10 +154,10 @@ module attributes {"triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-war
%c1 = arith.constant 1 : i32
%cst = arith.constant dense<0.000000e+00> : tensor<128x128xf32, #mma>
%0:1 = scf.for %arg0 = %c0 to %c1 step %c1 iter_args(%arg1 = %cst) -> (tensor<128x128xf32, #mma>) : i32 {
%1 = triton_gpu.local_load %A_LDS : !tt.memdesc<128x128xf16, #shared, #triton_gpu.shared_memory, mutable> -> tensor<128x128xf16, #dotOp0>
%2 = triton_gpu.local_load %B_LDS : !tt.memdesc<128x128xf16, #shared1, #triton_gpu.shared_memory, mutable> -> tensor<128x128xf16, #dotOp1>
%4 = tt.load %A_ptr : tensor<128x128x!tt.ptr<f16>, #blocked>
%1 = triton_gpu.local_load %A_LDS : !tt.memdesc<128x128xf16, #shared, #triton_gpu.shared_memory, mutable> -> tensor<128x128xf16, #dotOp0>
%5 = tt.load %B_ptr : tensor<128x128x!tt.ptr<i64>, #blocked>
%2 = triton_gpu.local_load %B_LDS : !tt.memdesc<128x128xf16, #shared1, #triton_gpu.shared_memory, mutable> -> tensor<128x128xf16, #dotOp1>
tt.store %B_ptr, %5 : tensor<128x128x!tt.ptr<i64>, #blocked>
%3 = tt.dot %1, %2, %arg1 : tensor<128x128xf16, #dotOp0> * tensor<128x128xf16, #dotOp1> -> tensor<128x128xf32, #mma>
triton_gpu.local_store %4, %A_LDS : tensor<128x128xf16, #blocked> -> !tt.memdesc<128x128xf16, #shared, #triton_gpu.shared_memory, mutable>
Expand All @@ -174,12 +174,12 @@ module attributes {"triton_gpu.num-warps" = 1 : i32, "triton_gpu.threads-per-war
// Category 3: two dots in the for loop. Make sure the optimization is not applied
// should NOT apply: two dots
// CHECK-LABEL: sink_2nd_load_256x256x64_two_dot
// CHECK: triton_gpu.local_load
// CHECK: tt.load
// CHECK-NEXT: tt.load
// CHECK-NEXT: triton_gpu.local_load
// CHECK-NEXT: triton_gpu.local_load
// CHECK-NEXT: tt.dot
// CHECK-NEXT: tt.dot
// CHECK-NEXT: tt.load
// CHECK-NEXT: tt.load
// CHECK-NEXT: triton_gpu.local_store
// CHECK-NEXT: triton_gpu.local_store
#blocked = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [8, 8], warpsPerCTA = [1, 1], order = [1, 0]}>
Expand Down
4 changes: 2 additions & 2 deletions test/TritonGPU/loop-pipeline-hip.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -35,9 +35,9 @@ module attributes {"triton_gpu.target" = "hip:gfx942", "triton_gpu.num-ctas" = 1
%16 = tt.addptr %14, %15 : tensor<64x16x!tt.ptr<f16>, #blocked>, tensor<64x16xi32, #blocked>
// CHECK: triton_gpu.local_store
// CHECK: scf.for
// CHECK: tt.load
// CHECK: tt.dot
// CHECK: tt.dot
// CHECK: tt.load
// CHECK: triton_gpu.local_store
// CHECK: scf.yield
%17:2 = scf.for %arg2 = %c0_i32 to %c8_i32 step %c1_i32 iter_args(%arg3 = %cst_1, %arg4 = %cst_2) -> (tensor<128x16xf32, #mma>, tensor<128x64xf32, #mma>) : i32 {
Expand Down Expand Up @@ -165,9 +165,9 @@ module attributes {"triton_gpu.target" = "hip:gfx942", "triton_gpu.num-ctas" = 1
// CHECK-LABEL: tt.func public @add_barrier_kernel
// CHECK: tt.load
// CHECK: scf.for
// CHECK: tt.load
// CHECK: gpu.barrier
// CHECK: tt.store
// CHECK: tt.load
// CHECK: scf.yield
// CHECK: gpu.barrier
// CHECK: tt.store
Expand Down
Loading
Loading