208 lines
10 KiB
MLIR
208 lines
10 KiB
MLIR
// RUN: mlir-hlo-opt %s -verify-diagnostics -split-input-file | mlir-hlo-opt | FileCheck %s
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// CHECK-LABEL: func @batch_norm_grad_memrefs
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func @batch_norm_grad_memrefs(%arg0: memref<8x8x8x8xf32>, %arg1: memref<8xf32>, %arg2: memref<8xf32>,
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%arg3: memref<8xf32>, %arg4: memref<8x8x8x8xf32>,
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%grad_operand: memref<8x8x8x8xf32>, %grad_scale: memref<8xf32>,
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%grad_offset: memref<8xf32>) -> () {
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"lmhlo_gpu.batch_norm_grad"(%arg0, %arg1, %arg2, %arg3, %arg4, %grad_operand, %grad_scale, %grad_offset) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64}
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: (memref<8x8x8x8xf32>, memref<8xf32>, memref<8xf32>, memref<8xf32>, memref<8x8x8x8xf32>,
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memref<8x8x8x8xf32>, memref<8xf32>, memref<8xf32>) -> ()
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return
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}
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// CHECK-LABEL: func @batch_norm_inference_memrefs
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func @batch_norm_inference_memrefs(%arg0: memref<8x8x8x8xf32>, %arg1: memref<8xf32>, %arg2: memref<8xf32>,
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%arg3: memref<8xf32>, %arg4: memref<8xf32>, %arg_out: memref<8x8x8x8xf32>) -> () {
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"lmhlo_gpu.batch_norm_inference"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg_out) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64}
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: (memref<8x8x8x8xf32>, memref<8xf32>, memref<8xf32>, memref<8xf32>, memref<8xf32>, memref<8x8x8x8xf32>) -> ()
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return
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}
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// CHECK-LABEL: func @batch_norm_training_memrefs
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func @batch_norm_training_memrefs(%arg0: memref<8x8x8x8xf32>, %arg1: memref<8xf32>, %arg2: memref<8xf32>,
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%output: memref<8x8x8x8xf32>, %batch_mean: memref<8xf32>,
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%batch_var: memref<8xf32>) -> () {
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"lmhlo_gpu.batch_norm_training"(%arg0, %arg1, %arg2, %output, %batch_mean, %batch_var) {epsilon = 1.000000e-03 : f32, feature_index = 3 : i64}
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: (memref<8x8x8x8xf32>, memref<8xf32>, memref<8xf32>, memref<8x8x8x8xf32>, memref<8xf32>, memref<8xf32>) -> ()
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return
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}
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// CHECK-LABEL: func @conv_forward
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func @conv_forward(%input : memref<1x1x8x8xf16>, %filter: memref<1x1x2x2xf16>, %output: memref<1x1x7x7xf16>) {
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%scratch = alloc() : memref<32xi8>
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// This defined a 2D convolution over a 8x8 single channel input using a 2x2
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// filter and with an output of 7x7xf16. The 1x1x8x8 is (N, C, H, W)
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"lmhlo_gpu.conv_forward"(%input, %filter, %output, %scratch)
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{ dimension_numbers = {input_batch_dimension = 0 : i64,
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input_feature_dimension = 1 : i64,
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input_spatial_dimensions = dense<[2,3]> : tensor<2xi64>,
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kernel_input_feature_dimension = 0 : i64,
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kernel_output_feature_dimension = 1 : i64,
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kernel_spatial_dimensions = dense<[2,3]> : tensor<2xi64>,
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output_batch_dimension = 0 : i64,
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output_feature_dimension = 1 : i64,
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output_spatial_dimensions = dense<[2,3]> : tensor<2xi64>},
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window_strides = dense<[1, 1]> : tensor<2xi64>,
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padding = dense<[0,0]> : tensor<2xi64>,
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lhs_dilation = dense<[1,1]> : tensor<2xi64>,
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rhs_dilation = dense<[1,1]> : tensor<2xi64>,
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feature_group_count = 1,
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batch_group_count = 1,
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result_scale = 1.0,
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backend_config = {algorithm=0, tensor_ops_enabled = true }
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}
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: (memref<1x1x8x8xf16>, memref<1x1x2x2xf16>, memref<1x1x7x7xf16>, memref<32xi8>) -> ()
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return
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}
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// CHECK-LABEL: func @conv_backfilter
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func @conv_backfilter(%input : memref<3x56x56x16xf64>, %filter: memref<3x3x3x64xf64>, %output: memref<54x54x16x64xf64>) {
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%scratch = alloc() : memref<23328xui8>
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"lmhlo_gpu.conv_backwardfilter"(%input, %filter, %output, %scratch)
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{ backend_config = {algorithm = 1 : i64, tensor_ops_enabled = false},
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batch_group_count = 1 : i64,
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dimension_numbers = {input_batch_dimension = 0 : i64,
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input_feature_dimension = 3 : i64,
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input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>,
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kernel_input_feature_dimension = 2 : i64,
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kernel_output_feature_dimension = 3 : i64,
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kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>,
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output_batch_dimension = 0 : i64,
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output_feature_dimension = 3 : i64,
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output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>},
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feature_group_count = 1 : i64,
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lhs_dilation = dense<1> : tensor<2xi64>,
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padding = dense<0> : tensor<2xi64>,
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precision_config = [],
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result_scale = 1.000000e+00 : f64,
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rhs_dilation = dense<1> : tensor<2xi64>,
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window_strides = dense<1> : tensor<2xi64>}
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: (memref<3x56x56x16xf64>, memref<3x3x3x64xf64>, memref<54x54x16x64xf64>, memref<23328xui8>) -> ()
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return
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}
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// CHECK-LABEL: func @conv_backinput
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func @conv_backinput(%input : memref<4x5x16x16xf64>, %filter : memref<5x3x7x7xf64>, %output : memref<4x3x16x16xf64>) {
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%scratch = alloc() : memref<32xui8>
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"lmhlo_gpu.conv_backwardinput"(%input, %filter, %output, %scratch)
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{ backend_config = {algorithm = 1 : i64, tensor_ops_enabled = false},
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batch_group_count = 1 : i64,
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dimension_numbers = {input_batch_dimension = 0 : i64,
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input_feature_dimension = 1 : i64,
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input_spatial_dimensions = dense<[2, 3]> : tensor<2xi64>,
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kernel_input_feature_dimension = 1 : i64,
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kernel_output_feature_dimension = 0 : i64,
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kernel_spatial_dimensions = dense<[2, 3]> : tensor<2xi64>,
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output_batch_dimension = 0 : i64,
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output_feature_dimension = 1 : i64,
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output_spatial_dimensions = dense<[2, 3]> : tensor<2xi64>},
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feature_group_count = 1 : i64,
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lhs_dilation = dense<1> : tensor<2xi64>,
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padding = dense<3> : tensor<2xi64>,
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precision_config = [],
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result_scale = 1.000000e+00 : f64,
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rhs_dilation = dense<1> : tensor<2xi64>,
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window_strides = dense<1> : tensor<2xi64>,
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window_reversal = dense<true>: tensor<2xi1>}
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: (memref<4x5x16x16xf64>, memref<5x3x7x7xf64>, memref<4x3x16x16xf64>, memref<32xui8>) -> ()
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return
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}
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// CHECK-LABEL: func @conv_fused
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func @conv_fused(%input : memref<1x17x9x9xf16>, %filter : memref<3x3x17x32xf16>, %bias : memref<32xf16>, %output : memref<1x32x9x9xf16>) {
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%scratch = alloc() : memref<32xui8>
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"lmhlo_gpu.conv_forward_fused"(%input, %filter, %bias, %output, %scratch)
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{activation_mode = "Relu",
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backend_config = {algorithm = 0 : i64, tensor_ops_enabled = false},
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batch_group_count = 1 : i64,
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dimension_numbers = {input_batch_dimension = 0 : i64,
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input_feature_dimension = 1 : i64,
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input_spatial_dimensions = dense<[2, 3]> : tensor<2xi64>,
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kernel_input_feature_dimension = 2 : i64,
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kernel_output_feature_dimension = 3 : i64,
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kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>,
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output_batch_dimension = 0 : i64,
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output_feature_dimension = 1 : i64,
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output_spatial_dimensions = dense<[2, 3]> : tensor<2xi64>},
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feature_group_count = 1 : i64,
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lhs_dilation = dense<1> : tensor<2xi64>,
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padding = dense<1> : tensor<2xi64>,
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precision_config = ["DEFAULT", "DEFAULT", "DEFAULT"],
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result_scale = 1.000000e+00 : f64,
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rhs_dilation = dense<1> : tensor<2xi64>,
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window_strides = dense<1> : tensor<2xi64>}
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: (memref<1x17x9x9xf16>, memref<3x3x17x32xf16>, memref<32xf16>, memref<1x32x9x9xf16>, memref<32xui8>) -> ()
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return
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}
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// CHECK-LABEL: func @conv_fused_side_input
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func @conv_fused_side_input(%input : memref<1x17x9x9xf16>, %filter : memref<3x3x17x32xf16>, %bias : memref<32xf16>, %side_input: memref<32xf16>, %output : memref<1x32x9x9xf16>) {
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%scratch = alloc() : memref<0xui8>
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"lmhlo_gpu.conv_forward_fused_with_side_input"(%input, %filter, %bias, %side_input, %output, %scratch)
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{activation_mode = "Relu",
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backend_config = {algorithm = 0 : i64, tensor_ops_enabled = false},
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batch_group_count = 1 : i64,
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dimension_numbers = {input_batch_dimension = 0 : i64,
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input_feature_dimension = 1 : i64,
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input_spatial_dimensions = dense<[2, 3]> : tensor<2xi64>,
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kernel_input_feature_dimension = 2 : i64,
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kernel_output_feature_dimension = 3 : i64,
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kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>,
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output_batch_dimension = 0 : i64,
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output_feature_dimension = 1 : i64,
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output_spatial_dimensions = dense<[2, 3]> : tensor<2xi64>},
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feature_group_count = 1 : i64,
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lhs_dilation = dense<1> : tensor<2xi64>,
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padding = dense<1> : tensor<2xi64>,
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precision_config = ["DEFAULT", "DEFAULT", "DEFAULT"],
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result_scale = 1.000000e+00 : f64,
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rhs_dilation = dense<1> : tensor<2xi64>,
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side_input_scale = 1.000000e+00 : f64,
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window_strides = dense<1> : tensor<2xi64>}
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: (memref<1x17x9x9xf16>, memref<3x3x17x32xf16>, memref<32xf16>, memref<32xf16>, memref<1x32x9x9xf16>, memref<0xui8>) -> ()
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return
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}
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// CHECK-LABEL: func @gemm
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func @gemm(%lhs: memref<5x4xf32>, %rhs: memref<4x5xf32>, %output:memref<5x5xf32>) {
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"lmhlo_gpu.gemm"(%lhs, %rhs, %output) { dot_dimension_numbers = {
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lhs_batching_dimensions = dense<[1,1]> : tensor<2xi64>,
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rhs_batching_dimensions = dense<[1,1]> : tensor<2xi64>,
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lhs_contracting_dimensions = dense<[1,1]> : tensor<2xi64>,
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rhs_contracting_dimensions = dense<[1,1]> : tensor<2xi64>},
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alpha_real = 0.5,
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alpha_imag = 0.0,
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batch_size = 1,
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algorithm = 0}
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: (memref<5x4xf32>, memref<4x5xf32>, memref<5x5xf32>) -> ()
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return
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}
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// CHECK-LABEL: func @gemm_bias
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func @gemm_bias(%lhs: memref<5x4xf32>, %rhs: memref<4x5xf32>,
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%bias: memref<5x5xf32>, %output:memref<5x5xf32>) {
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"lmhlo_gpu.gemm_bias"(%lhs, %rhs, %bias, %output) { dot_dimension_numbers = {
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lhs_batching_dimensions = dense<[1,1]> : tensor<2xi64>,
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rhs_batching_dimensions = dense<[1,1]> : tensor<2xi64>,
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lhs_contracting_dimensions = dense<[1,1]> : tensor<2xi64>,
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rhs_contracting_dimensions = dense<[1,1]> : tensor<2xi64>},
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alpha_real = 0.5,
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alpha_imag = 0.0,
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beta = 1.0,
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batch_size = 1,
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algorithm = 0}
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: (memref<5x4xf32>, memref<4x5xf32>, memref<5x5xf32>, memref<5x5xf32>) -> ()
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return
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}
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// CHECK-LABEL: func @cholesky
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func @cholesky(%arg : memref<10x10xf32>, %out: memref<10x10xf32>) {
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%scratch = alloc() : memref<32xi8>
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%info = alloc() : memref<32xi32>
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"lmhlo_gpu.cholesky"(%arg, %out, %scratch, %info) { is_lower = true }
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: (memref<10x10xf32>, memref<10x10xf32>, memref<32xi8>, memref<32xi32>) -> ()
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return
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}
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