20100a7254
This PR merges the `triton-mlir` branch, in which we have been quietly rewriting the Triton backend from scratch to increase maintainability, stability and ultimately performance. Changes to the runtime are minimal, and this new version aims to remain backward-compatible with the previous commit. The legacy backend is now officially deprecated, but can still be accessed via the `legacy-backend` tag. Co-authored-by: Keren Zhou <kerenzhou@openai.com> Co-authored-by: Yan Chunwei <yanchunwei@outlook.com> Co-authored-by: goostavz <109190422+goostavz@users.noreply.github.com> Co-authored-by: Shintaro Iwasaki <siwasaki@fb.com> Co-authored-by: Yan Da <dyanab@connect.ust.hk> Co-authored-by: Jun Yang <yangjunpro@gmail.com> Co-authored-by: Ian Bearman <ianb@microsoft.com> Co-authored-by: Jason Ansel <jansel@jansel.net> Co-authored-by: Qingyi Liu <qingyil@nvidia.com> Co-authored-by: ben-zhang-609 <110140741+ben-zhang-609@users.noreply.github.com> Co-authored-by: Chenggang Zhao <lyricz@yeah.net> Co-authored-by: ben-zhang-609 <benzh609@gmail.com> Co-authored-by: dongdongl <dongdongl@nvidia.com>
54 lines
3.1 KiB
MLIR
54 lines
3.1 KiB
MLIR
// RUN: triton-opt %s -split-input-file -convert-triton-to-tritongpu=num-warps=2 | FileCheck %s
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func @ops() {
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// CHECK: module attributes {"triton_gpu.num-warps" = 2 : i32} {{.*}}
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%a = arith.constant dense<1.00e+00> : tensor<128x32xf16>
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%b = arith.constant dense<2.00e+00> : tensor<32x128xf16>
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%c = arith.constant dense<3.00e+00> : tensor<128x128xf32>
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%0 = tt.dot %a, %b, %c {allowTF32 = true, transA = false, transB = false} : tensor<128x32xf16> * tensor<32x128xf16> -> tensor<128x128xf32>
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return
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}
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// -----
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func @load_ops(%ptr: !tt.ptr<f32> {tt.divisibility = 16 : i32}) {
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// Test if LoadOp is lowered properly (see #771)
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%ptrs = tt.splat %ptr : (!tt.ptr<f32>) -> tensor<128x!tt.ptr<f32>>
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%mask = arith.constant dense<true> : tensor<128xi1>
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%other = arith.constant dense<0.0e+0> : tensor<128xf32>
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// CHECK: %{{.*}} = tt.load %{{.*}} {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : {{.*}}
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%a = tt.load %ptrs {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : tensor<128xf32>
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// CHECK: %{{.*}} = tt.load %{{.*}}, %{{.*}} {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : {{.*}}
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%b = tt.load %ptrs, %mask {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : tensor<128xf32>
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// CHECK: %{{.*}} = tt.load %{{.*}}, %{{.*}}, %{{.*}} {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : {{.*}}
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%c = tt.load %ptrs, %mask, %other {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : tensor<128xf32>
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tt.store %ptrs, %a : tensor<128xf32>
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tt.store %ptrs, %b : tensor<128xf32>
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tt.store %ptrs, %c : tensor<128xf32>
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return
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}
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// -----
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func @reduce_ops(%ptr: !tt.ptr<f32> {tt.divisibility = 16 : i32}) {
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// Test if the total number of threadsPerWarp is 32
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// Test if the total number of warps is 2
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// CHECK: #blocked0 = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [4, 8], warpsPerCTA = [1, 2], order = [0, 1]}>
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// CHECK: #blocked1 = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [8, 4], warpsPerCTA = [1, 2], order = [0, 1]}>
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// CHECK: #blocked2 = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [16, 2], warpsPerCTA = [1, 2], order = [0, 1]}>
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// CHECK: module attributes {"triton_gpu.num-warps" = 2 : i32} {{.*}}
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%c0 = arith.constant dense<1.00e+00> : tensor<4x4xf32>
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%c1 = arith.constant dense<2.00e+00> : tensor<8x2xf32>
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%c2 = arith.constant dense<3.00e+00> : tensor<16x16xf32>
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// CHECK: tensor<4x4xf32, #blocked0> -> tensor<4xf32, #triton_gpu.slice<{dim = 0, parent = #blocked0}>>
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%c0_ = tt.reduce %c0 {redOp = 1 : i32, axis = 0 : i32} : tensor<4x4xf32> -> tensor<4xf32>
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// CHECK: tensor<8x2xf32, #blocked1> -> tensor<2xf32, #triton_gpu.slice<{dim = 0, parent = #blocked1}>
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%c1_ = tt.reduce %c1 {redOp = 1 : i32, axis = 0 : i32} : tensor<8x2xf32> -> tensor<2xf32>
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// CHECK: tensor<8x2xf32, #blocked1> -> tensor<8xf32, #triton_gpu.slice<{dim = 1, parent = #blocked1}>>
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%c2_ = tt.reduce %c1 {redOp = 1 : i32, axis = 1 : i32} : tensor<8x2xf32> -> tensor<8xf32>
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// CHECK: tensor<16x16xf32, #blocked2> -> tensor<16xf32, #triton_gpu.slice<{dim = 0, parent = #blocked2}>>
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%c3_ = tt.reduce %c2 {redOp = 1 : i32, axis = 0 : i32} : tensor<16x16xf32> -> tensor<16xf32>
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return
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}
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