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October 3, 2022 21:42
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ElementwiseWhereSelfModulemlir
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| #loc0 = loc(unknown) | |
| module attributes {torch.debug_module_name = "ElementwiseWhereSelfModule"} { | |
| func.func @forward(%arg0: tensor<1x1x1xf32> loc(unknown), %arg1: tensor<1x1xf32> loc(unknown), %arg2: tensor<1xf32> loc(unknown)) -> tensor<1x1x1xf32> { | |
| %cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32> loc(#loc1) | |
| %0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1> loc(#loc1) | |
| %1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32> loc(#loc2) | |
| return %1 : tensor<1x1x1xf32> loc(#loc0) | |
| } loc(#loc0) | |
| } loc(#loc0) | |
| #loc1 = loc("/home/chi/src/ubuntu20/shark/torch-mlir/build/tools/torch-mlir/python_packages/torch_mlir/torch_mlir_e2e_test/test_suite/elementwise.py":150:27) | |
| #loc2 = loc("/home/chi/src/ubuntu20/shark/torch-mlir/build/tools/torch-mlir/python_packages/torch_mlir/torch_mlir_e2e_test/test_suite/elementwise.py":150:15) |
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(mlir_venv) nod% torch-mlir-opt -pass-pipeline='torch-backend-to-tosa-backend-pipeline' /tmp/ElementwiseWhereSelfModule.mlir -mlir-print-ir-after-all -mlir-pretty-debuginfo -mlir-disable-threading
// -----// IR Dump After ConvertTorchToTosa (convert-torch-to-tosa) //----- //
func.func @forward(%arg0: tensor<1x1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1xf32>) -> tensor<1x1x1xf32> {
%cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32>
%0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1>
%1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
// -----// IR Dump After TosaMakeBroadcastable (tosa-make-broadcastable) //----- //
func.func @forward(%arg0: tensor<1x1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1xf32>) -> tensor<1x1x1xf32> {
%cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32>
%0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1>
%1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
// -----// IR Dump After Canonicalizer (canonicalize) //----- //
func.func @forward(%arg0: tensor<1x1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1xf32>) -> tensor<1x1x1xf32> {
%cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32>
%0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1>
%1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
// -----// IR Dump After CSE (cse) //----- //
func.func @forward(%arg0: tensor<1x1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1xf32>) -> tensor<1x1x1xf32> {
%cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32>
%0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1>
%1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
// -----// IR Dump After FuncBackendTypeConversion (torch-func-backend-type-conversion) //----- //
module attributes {torch.debug_module_name = "ElementwiseWhereSelfModule"} {
func.func @forward(%arg0: tensor<1x1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1xf32>) -> tensor<1x1x1xf32> {
%cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32>
%0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1>
%1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
}
// -----// IR Dump After Canonicalizer (canonicalize) //----- //
func.func @forward(%arg0: tensor<1x1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1xf32>) -> tensor<1x1x1xf32> {
%cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32>
%0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1>
%1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
// -----// IR Dump After FinalizingBackendTypeConversion (torch-finalizing-backend-type-conversion) //----- //
func.func @forward(%arg0: tensor<1x1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1xf32>) -> tensor<1x1x1xf32> {
%cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32>
%0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1>
%1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
// -----// IR Dump After VerifyTosaBackendContract (torch-verify-tosa-backend-contract) //----- //
module attributes {torch.debug_module_name = "ElementwiseWhereSelfModule"} {
func.func @forward(%arg0: tensor<1x1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1xf32>) -> tensor<1x1x1xf32> {
%cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32>
%0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1>
%1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
}
module attributes {torch.debug_module_name = "ElementwiseWhereSelfModule"} {
func.func @forward(%arg0: tensor<1x1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1xf32>) -> tensor<1x1x1xf32> {
%cst = arith.constant dense<5.000000e-01> : tensor<1x1x1xf32>
%0 = "tosa.greater"(%arg0, %cst) : (tensor<1x1x1xf32>, tensor<1x1x1xf32>) -> tensor<1x1x1xi1>
%1 = "tosa.select"(%0, %arg1, %arg2) : (tensor<1x1x1xi1>, tensor<1x1xf32>, tensor<1xf32>) -> tensor<1x1x1xf32>
return %1 : tensor<1x1x1xf32>
}
}
(mlir_venv) nod%