[Mlir-commits] [mlir] 10dbf23 - Revert "[mlir][sparse][gpu] end-to-end example with sparse GPU pipeline"

[Mehdi Amini]

Author: Mehdi Amini
Date: 2023-04-06T19:11:27-07:00
New Revision: 10dbf23edc0f617c25789fffcbfa916b33a8eab4

URL: https://github.com/llvm/llvm-project/commit/10dbf23edc0f617c25789fffcbfa916b33a8eab4
DIFF: https://github.com/llvm/llvm-project/commit/10dbf23edc0f617c25789fffcbfa916b33a8eab4.diff

LOG: Revert "[mlir][sparse][gpu] end-to-end example with sparse GPU pipeline"

This reverts commit bf94afa10e5101f401f191f0386a9316cf0a5cda.

The bot is broken: https://lab.llvm.org/buildbot/#/builders/61/builds/42062

Added: 
    

Modified: 
    

Removed: 
    mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-matvec.mlir


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diff  --git a/mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-matvec.mlir b/mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-matvec.mlir
deleted file mode 100644
index b5ff5e89fd82..000000000000
--- a/mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-matvec.mlir
+++ /dev/null
@@ -1,70 +0,0 @@
-// RUN: mlir-opt %s \
-// RUN:   --sparse-compiler="enable-runtime-library=false parallelization-strategy=dense-outer-loop gpu-triple=nvptx64-nvidia-cuda gpu-chip=sm_80 gpu-features=+ptx71" \
-// RUN: | mlir-cpu-runner \
-// RUN:   --shared-libs=%mlir_cuda_runtime \
-// RUN:   --shared-libs=%mlir_runner_utils \
-// RUN:   --e main --entry-point-result=void \
-// RUN: | FileCheck %s
-
-#CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
-
-module {
-  // Compute matrix vector y = Ax
-  func.func @matvec(%A: tensor<?x?xf64, #CSR>, %x: tensor<?xf64>, %y_in: tensor<?xf64>) -> tensor<?xf64> {
-    %y_out = linalg.matvec
-      ins(%A, %x: tensor<?x?xf64, #CSR>, tensor<?xf64>)
-      outs(%y_in: tensor<?xf64>) -> tensor<?xf64>
-    return %y_out : tensor<?xf64>
-  }
-
-  func.func @main() {
-    %f0 = arith.constant 0.0 : f64
-    %c0 = arith.constant 0 : index
-    %c1 = arith.constant 1 : index
-
-    // Stress test with a dense matrix DA.
-    %DA = tensor.generate {
-    ^bb0(%i: index, %j: index):
-      %k = arith.addi %i, %j : index
-      %l = arith.index_cast %k : index to i64
-      %f = arith.uitofp %l : i64 to f64
-      tensor.yield %f : f64
-    } : tensor<1024x64xf64>
-
-    // Convert to a "sparse" m x n matrix A.
-    %A = sparse_tensor.convert %DA : tensor<1024x64xf64> to tensor<?x?xf64, #CSR>
-
-    // Initialize dense vector with n elements:
-    //   (1, 2, 3, 4, ..., n)
-    %d1 = tensor.dim %A, %c1 : tensor<?x?xf64, #CSR>
-    %x = tensor.generate %d1 {
-    ^bb0(%i : index):
-      %k = arith.addi %i, %c1 : index
-      %j = arith.index_cast %k : index to i64
-      %f = arith.uitofp %j : i64 to f64
-      tensor.yield %f : f64
-    } : tensor<?xf64>
-
-    // Initialize dense vector to m zeros.
-    %d0 = tensor.dim %A, %c0 : tensor<?x?xf64, #CSR>
-    %y = tensor.generate %d0 {
-    ^bb0(%i : index):
-      tensor.yield %f0 : f64
-    } : tensor<?xf64>
-
-    // Call the kernel.
-    %0 = call @matvec(%A, %x, %y) : (tensor<?x?xf64, #CSR>, tensor<?xf64>, tensor<?xf64>) -> tensor<?xf64>
-
-    //
-    // Sanity check on results.
-    //
-    // CHECK: ( 87360, 89440, 91520, 93600, 95680, 97760, 99840, 101920, 104000, 106080, 108160, 110240, 112320, 114400, 116480, 118560, 120640, 122720, 124800, 126880, 128960, 131040, 133120, 135200, 137280, 139360, 141440, 143520, 145600, 147680, 149760, 151840, 153920, 156000, 158080, 160160, 162240, 164320, 166400, 168480, 170560, 172640, 174720, 176800, 178880, 180960, 183040, 185120, 187200, 189280, 191360, 193440, 195520, 197600, 199680, 201760, 203840, 205920, 208000, 210080, 212160, 214240, 216320, 218400 )
-    //
-    %pb0 = vector.transfer_read %0[%c0], %f0 : tensor<?xf64>, vector<64xf64>
-    vector.print %pb0 : vector<64xf64>
-
-    // Release the resources.
-    bufferization.dealloc_tensor %A : tensor<?x?xf64, #CSR>
-    return
-  }
-}