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[mlir][sparse] sampled matrix multiplication fusion test
This integration tests runs a fused and non-fused version of sampled matrix multiplication. Both should eventually have the same performance! NOTE: relies on pending tensor.init fix! Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D110444
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mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_mm_fusion.mlir
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| // RUN: mlir-opt %s \ | ||
| // RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ | ||
| // RUN: --sparsification --sparse-tensor-conversion \ | ||
| // RUN: --linalg-bufferize --convert-linalg-to-loops \ | ||
| // RUN: --convert-vector-to-scf --convert-scf-to-std \ | ||
| // RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \ | ||
| // RUN: --std-bufferize --finalizing-bufferize --lower-affine \ | ||
| // RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-math-to-llvm \ | ||
| // RUN: --convert-std-to-llvm --reconcile-unrealized-casts | \ | ||
| // RUN: mlir-cpu-runner \ | ||
| // RUN: -e entry -entry-point-result=void \ | ||
| // RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ | ||
| // RUN: FileCheck %s | ||
| // | ||
| // Do the same run, but now with SIMDization as well. | ||
| // This should not change the outcome. | ||
| // | ||
| // RUN: mlir-opt %s \ | ||
| // RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ | ||
| // RUN: --sparsification="vectorization-strategy=2 vl=8" --sparse-tensor-conversion \ | ||
| // RUN: --linalg-bufferize --convert-linalg-to-loops \ | ||
| // RUN: --convert-vector-to-scf --convert-scf-to-std \ | ||
| // RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \ | ||
| // RUN: --std-bufferize --finalizing-bufferize --lower-affine \ | ||
| // RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-math-to-llvm \ | ||
| // RUN: --convert-std-to-llvm --reconcile-unrealized-casts | \ | ||
| // RUN: mlir-cpu-runner \ | ||
| // RUN: -e entry -entry-point-result=void \ | ||
| // RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ | ||
| // RUN: FileCheck %s | ||
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| #SM = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }> | ||
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| #trait_sampled_dense_dense = { | ||
| indexing_maps = [ | ||
| affine_map<(i,j,k) -> (i,j)>, // S | ||
| affine_map<(i,j,k) -> (i,k)>, // A | ||
| affine_map<(i,j,k) -> (k,j)>, // B | ||
| affine_map<(i,j,k) -> (i,j)> // X (out) | ||
| ], | ||
| iterator_types = ["parallel", "parallel", "reduction"], | ||
| doc = "X(i,j) += S(i,j) SUM_k A(i,k) B(k,j)" | ||
| } | ||
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| #trait_matmul = { | ||
| indexing_maps = [ | ||
| affine_map<(d0, d1, d2) -> (d1, d0)>, | ||
| affine_map<(d0, d1, d2) -> (d0, d2)>, | ||
| affine_map<(d0, d1, d2) -> (d1, d2)> | ||
| ], | ||
| iterator_types = ["reduction", "parallel", "parallel"] | ||
| } | ||
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| #trait_scale = { | ||
| indexing_maps = [ | ||
| affine_map<(d0, d1) -> (d0, d1)>, | ||
| affine_map<(d0, d1) -> (d0, d1)>, | ||
| affine_map<(d0, d1) -> (d0, d1)> | ||
| ], | ||
| iterator_types = ["parallel", "parallel"] | ||
| } | ||
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| // | ||
| // Integration test for sampled dense dense matmul fusion. | ||
| // | ||
| module { | ||
| // | ||
| // A kernel that computes a direct sampled matrix matrix multiplication. | ||
| // | ||
| func @sampled_dd(%args: tensor<8x8xf64, #SM>, | ||
| %arga: tensor<8x8xf64>, | ||
| %argb: tensor<8x8xf64>) -> tensor<8x8xf64> { | ||
| %d = constant 0.0 : f64 | ||
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| %0 = linalg.init_tensor [8, 8] : tensor<8x8xf64> | ||
| %1 = linalg.fill(%d, %0) : f64, tensor<8x8xf64> -> tensor<8x8xf64> | ||
| %2 = linalg.generic #trait_sampled_dense_dense | ||
| ins(%args, %arga, %argb: tensor<8x8xf64, #SM>, | ||
| tensor<8x8xf64>, tensor<8x8xf64>) | ||
| outs(%1: tensor<8x8xf64>) { | ||
| ^bb(%s: f64, %a: f64, %b: f64, %x: f64): | ||
| %p = mulf %a, %b : f64 | ||
| %q = mulf %s, %p : f64 | ||
| %r = addf %x, %q : f64 | ||
| linalg.yield %r : f64 | ||
| } -> tensor<8x8xf64> | ||
| return %2 : tensor<8x8xf64> | ||
| } | ||
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| // | ||
| // A kernel that computes an unfused sampled matrix matrix multiplication. | ||
| // | ||
| func @sampled_dd_unfused(%args: tensor<8x8xf64, #SM>, | ||
| %arga: tensor<8x8xf64>, | ||
| %argb: tensor<8x8xf64>) -> tensor<8x8xf64> { | ||
| %d = constant 0.0 : f64 | ||
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| %0 = linalg.init_tensor [8, 8] : tensor<8x8xf64> | ||
| %1 = linalg.fill(%d, %0) : f64, tensor<8x8xf64> -> tensor<8x8xf64> | ||
| %2 = linalg.generic #trait_matmul | ||
| ins(%arga, %argb : tensor<8x8xf64>, tensor<8x8xf64>) | ||
| outs(%1 : tensor<8x8xf64>) { | ||
| ^bb0(%a: f64, %b: f64, %x: f64): | ||
| %p = mulf %a, %b : f64 | ||
| %q = addf %x, %p : f64 | ||
| linalg.yield %q : f64 | ||
| } -> tensor<8x8xf64> | ||
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| %3 = linalg.init_tensor [8, 8] : tensor<8x8xf64> | ||
| %4 = linalg.fill(%d, %3) : f64, tensor<8x8xf64> -> tensor<8x8xf64> | ||
| %5 = linalg.generic #trait_scale | ||
| ins(%2, %args : tensor<8x8xf64>, tensor<8x8xf64, #SM>) | ||
| outs(%4 : tensor<8x8xf64>) { | ||
| ^bb0(%t: f64, %s: f64, %x: f64): | ||
| %r = mulf %t, %s : f64 | ||
| linalg.yield %r : f64 | ||
| } -> tensor<8x8xf64> | ||
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| return %5 : tensor<8x8xf64> | ||
| } | ||
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| // | ||
| // Main driver. | ||
| // | ||
| func @entry() { | ||
| %d0 = constant 0.0 : f64 | ||
| %c0 = constant 0 : index | ||
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| %t = constant sparse<[[0, 0], [7,7]], [1.0, 2.0]> | ||
| : tensor<8x8xf64> | ||
| %s = sparse_tensor.convert %t | ||
| : tensor<8x8xf64> to tensor<8x8xf64, #SM> | ||
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| %a = constant dense<3.0> : tensor<8x8xf64> | ||
| %b = constant dense<4.0> : tensor<8x8xf64> | ||
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| // Call the kernels. | ||
| %0 = call @sampled_dd(%s, %a, %b) | ||
| : (tensor<8x8xf64, #SM>, | ||
| tensor<8x8xf64>, tensor<8x8xf64>) -> tensor<8x8xf64> | ||
| %1 = call @sampled_dd_unfused(%s, %a, %b) | ||
| : (tensor<8x8xf64, #SM>, | ||
| tensor<8x8xf64>, tensor<8x8xf64>) -> tensor<8x8xf64> | ||
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| // Verify the outputs. | ||
| // | ||
| // CHECK: ( ( 96, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), | ||
| // CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), | ||
| // CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), | ||
| // CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 192 ) ) | ||
| // | ||
| // CHECK: ( ( 96, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), | ||
| // CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), | ||
| // CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 0 ), | ||
| // CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 192 ) ) | ||
| // | ||
| %m0 = memref.buffer_cast %0 : memref<8x8xf64> | ||
| %m1 = memref.buffer_cast %1 : memref<8x8xf64> | ||
| %v0 = vector.transfer_read %m0[%c0, %c0], %d0 | ||
| : memref<8x8xf64>, vector<8x8xf64> | ||
| %v1 = vector.transfer_read %m1[%c0, %c0], %d0 | ||
| : memref<8x8xf64>, vector<8x8xf64> | ||
| vector.print %v0 : vector<8x8xf64> | ||
| vector.print %v1 : vector<8x8xf64> | ||
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| return | ||
| } | ||
| } |