Merge triton-mlir branch - Complete rewrite of the backend from scratch (#1004)
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>
This commit is contained in:
Philippe Tillet
GitHub
132
test/Conversion/triton_ops.mlir
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132
test/Conversion/triton_ops.mlir
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// RUN: triton-opt %s | FileCheck %s
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func @cast_ops(%scalar_ptr: !tt.ptr<f32>, %scalar_f32: f32, %scalar_i64: i64) {
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// scalar -> scalar
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// CHECK: i64 -> !tt.ptr<f32>
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%0 = tt.int_to_ptr %scalar_i64 : i64 -> !tt.ptr<f32>
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// CHECK: !tt.ptr<f32> -> i64
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%1 = tt.ptr_to_int %scalar_ptr : !tt.ptr<f32> -> i64
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// CHECK: f32 to f16
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%2 = arith.truncf %scalar_f32 : f32 to f16
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// 0D tensor -> 0D tensor
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%tensor_ptr_0d = tt.splat %scalar_ptr : (!tt.ptr<f32>) -> tensor<!tt.ptr<f32>>
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%tensor_f32_0d = tt.splat %scalar_f32 : (f32) -> tensor<f32>
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%tensor_i64_0d = tt.splat %scalar_i64 : (i64) -> tensor<i64>
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// CHECK: tensor<i64> -> tensor<!tt.ptr<f32>>
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%3 = tt.int_to_ptr %tensor_i64_0d : tensor<i64> -> tensor<!tt.ptr<f32>>
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// CHECK: tensor<!tt.ptr<f32>> -> tensor<i64>
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%4 = tt.ptr_to_int %tensor_ptr_0d : tensor<!tt.ptr<f32>> -> tensor<i64>
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// CHECK: tensor<f32> to tensor<f16>
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%5 = arith.truncf %tensor_f32_0d : tensor<f32> to tensor<f16>
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// 1D tensor -> 1D tensor
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%tensor_ptr_1d = tt.splat %scalar_ptr : (!tt.ptr<f32>) -> tensor<16x!tt.ptr<f32>>
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%tensor_f32_1d = tt.splat %scalar_f32 : (f32) -> tensor<16xf32>
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%tensor_i64_1d = tt.splat %scalar_i64 : (i64) -> tensor<16xi64>
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// CHECK: tensor<16xi64> -> tensor<16x!tt.ptr<f32>>
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%6 = tt.int_to_ptr %tensor_i64_1d : tensor<16xi64> -> tensor<16x!tt.ptr<f32>>
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// CHECK: tensor<16x!tt.ptr<f32>> -> tensor<16xi64>
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%7 = tt.ptr_to_int %tensor_ptr_1d : tensor<16x!tt.ptr<f32>> -> tensor<16xi64>
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// CHECK: tensor<16xf32> to tensor<16xf16>
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%8 = arith.truncf %tensor_f32_1d : tensor<16xf32> to tensor<16xf16>
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return
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}
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func @addptr_ops(%scalar_ptr: !tt.ptr<f32>, %scalar_i32: i32) {
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// scalar -> scalar
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// CHECK: !tt.ptr<f32>
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%0 = tt.addptr %scalar_ptr, %scalar_i32 : !tt.ptr<f32>, i32
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// 0D tensor -> 0D tensor
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%tensor_ptr_0d = tt.splat %scalar_ptr : (!tt.ptr<f32>) -> tensor<!tt.ptr<f32>>
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%tensor_i32_0d = tt.splat %scalar_i32 : (i32) -> tensor<i32>
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// CHECK: tensor<!tt.ptr<f32>>
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%1 = tt.addptr %tensor_ptr_0d, %tensor_i32_0d : tensor<!tt.ptr<f32>>, tensor<i32>
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// 1D tensor -> 1D tensor
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%tensor_ptr_1d = tt.splat %scalar_ptr : (!tt.ptr<f32>) -> tensor<16x!tt.ptr<f32>>
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%tensor_i32_1d = tt.splat %scalar_i32 : (i32) -> tensor<16xi32>
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// CHECK: tensor<16x!tt.ptr<f32>>
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%2 = tt.addptr %tensor_ptr_1d, %tensor_i32_1d : tensor<16x!tt.ptr<f32>>, tensor<16xi32>
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return
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}
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func @load_store_ops_scalar(%ptr: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %mask : i1) {
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// Test if Load/Store ops can handle scalar values
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%other = arith.constant 0.0e+0 : f32
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// load scalar
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// CHECK: %[[L0:.*]] = tt.load %{{.*}} {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : f32
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%a = tt.load %ptr {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : f32
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// CHECK: %[[L1:.*]] = tt.load %{{.*}}, %{{.*}} {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : f32
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%b = tt.load %ptr, %mask {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : f32
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// CHECK: %[[L2:.*]] = tt.load %{{.*}}, %{{.*}}, %{{.*}} {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : f32
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%c = tt.load %ptr, %mask, %other {cache = 1 : i32, evict = 1 : i32, isVolatile = true} : f32
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// store scalar
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// CHECK: tt.store %{{.*}}, %[[L0]] : f32
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tt.store %ptr, %a : f32
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// CHECK: tt.store %{{.*}}, %[[L1]], %{{.*}} : f32
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tt.store %ptr, %b, %mask : f32
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// CHECK: tt.store %{{.*}}, %[[L2]], %{{.*}} : f32
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tt.store %ptr, %c, %mask : f32
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return
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}
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func @reduce_ops_infer(%ptr: !tt.ptr<f32>, %v : tensor<1x2x4xf32>) {
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// Test if reduce ops infer types correctly
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// CHECK: %{{.*}} = tt.reduce %{{.*}} -> tensor<2x4xf32>
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%a = tt.reduce %v {redOp = 1 : i32, axis = 0 : i32} : tensor<1x2x4xf32> -> tensor<2x4xf32>
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// CHECK: %{{.*}} = tt.reduce %{{.*}} -> tensor<1x4xf32>
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%b = tt.reduce %v {redOp = 1 : i32, axis = 1 : i32} : tensor<1x2x4xf32> -> tensor<1x4xf32>
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// CHECK: %{{.*}} = tt.reduce %{{.*}} -> tensor<1x2xf32>
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%c = tt.reduce %v {redOp = 1 : i32, axis = 2 : i32} : tensor<1x2x4xf32> -> tensor<1x2xf32>
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// CHECK: %{{.*}} = tt.reduce %{{.*}} -> tensor<1xf32>
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%e = tt.reduce %b {redOp = 1 : i32, axis = 1 : i32} : tensor<1x4xf32> -> tensor<1xf32>
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// CHECK: %{{.*}} = tt.reduce %{{.*}} -> tensor<4xf32>
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%f = tt.reduce %a {redOp = 1 : i32, axis = 0 : i32} : tensor<2x4xf32> -> tensor<4xf32>
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// CHECK: %{{.*}} = tt.reduce %{{.*}} -> f32
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%g = tt.reduce %f {redOp = 1 : i32, axis = 0 : i32} : tensor<4xf32> -> f32
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// Avoid optimizations for c, e, and g
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%ptr1x2 = tt.splat %ptr : (!tt.ptr<f32>) -> tensor<1x2x!tt.ptr<f32>>
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%ptr1 = tt.splat %ptr : (!tt.ptr<f32>) -> tensor<1x!tt.ptr<f32>>
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tt.store %ptr1x2, %c : tensor<1x2xf32>
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tt.store %ptr1, %e : tensor<1xf32>
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tt.store %ptr, %g : f32
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return
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}
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func @dot_ops_infer(%ptr: !tt.ptr<f32>, %v : f32) {
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// Test if reduce ops infer types correctly
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%v128x32 = tt.splat %v : (f32) -> tensor<128x32xf32>
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%v32x128 = tt.splat %v : (f32) -> tensor<32x128xf32>
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%v128x1 = tt.splat %v : (f32) -> tensor<128x1xf32>
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%v1x128 = tt.splat %v : (f32) -> tensor<1x128xf32>
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%zero128x128 = arith.constant dense<0.00e+00> : tensor<128x128xf32>
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%zero32x32 = arith.constant dense<0.00e+00> : tensor<32x32xf32>
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%zero1x1 = arith.constant dense<0.00e+00> : tensor<1x1xf32>
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// CHECK: %{{.*}} = tt.dot %{{.*}} -> tensor<128x128xf32>
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%r1 = tt.dot %v128x32, %v32x128, %zero128x128 {allowTF32 = true, transA = false, transB = false} : tensor<128x32xf32> * tensor<32x128xf32> -> tensor<128x128xf32>
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// CHECK: %{{.*}} = tt.dot %{{.*}} -> tensor<32x32xf32>
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%r2 = tt.dot %v32x128, %v128x32, %zero32x32 {allowTF32 = true, transA = false, transB = false} : tensor<32x128xf32> * tensor<128x32xf32> -> tensor<32x32xf32>
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// CHECK: %{{.*}} = tt.dot %{{.*}} -> tensor<128x128xf32>
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%r3 = tt.dot %v128x1, %v1x128, %zero128x128 {allowTF32 = true, transA = false, transB = false} : tensor<128x1xf32> * tensor<1x128xf32> -> tensor<128x128xf32>
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// CHECK: %{{.*}} = tt.dot %{{.*}} -> tensor<1x1xf32>
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%r4 = tt.dot %v1x128, %v128x1, %zero1x1 {allowTF32 = true, transA = false, transB = false} : tensor<1x128xf32> * tensor<128x1xf32> -> tensor<1x1xf32>
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%ptr128x128 = tt.splat %ptr : (!tt.ptr<f32>) -> tensor<128x128x!tt.ptr<f32>>
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%ptr32x32 = tt.splat %ptr : (!tt.ptr<f32>) -> tensor<32x32x!tt.ptr<f32>>
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%ptr1x1 = tt.splat %ptr : (!tt.ptr<f32>) -> tensor<1x1x!tt.ptr<f32>>
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tt.store %ptr128x128, %r1 : tensor<128x128xf32>
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tt.store %ptr32x32, %r2 : tensor<32x32xf32>
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tt.store %ptr128x128, %r3 : tensor<128x128xf32>
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tt.store %ptr1x1, %r4 : tensor<1x1xf32>
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return
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}
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53
test/Conversion/triton_to_tritongpu.mlir
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53
test/Conversion/triton_to_tritongpu.mlir
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// 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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1016
test/Conversion/tritongpu_to_llvm.mlir
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1016
test/Conversion/tritongpu_to_llvm.mlir
Normal file
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