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tensorflow/compiler/mlir/lite/experimental/tac/tests/raise-target-subgraphs.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
func.func @batch_matmul_two_tensors_with_dynamic_shape(%arg0: tensor<2x?x?xf32>, %arg1: tensor<2x?x?xf32>) -> (tensor<2x?x?xf32>) { %cst = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32> %cst_0 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32> %cst_1 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32> %cst_2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/lower_tf.mlir
// return %[[SUM2]] %0 = "tf.AddN"(%arg0, %arg1, %arg2, %arg3) : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32> func.return %0 : tensor<*xf32> } // CHECK-LABEL: func @addN_5 func.func @addN_5(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>, %arg2: tensor<*xf32>, %arg3: tensor<*xf32>, %arg4: tensor<*xf32>) -> tensor<*xf32> { // CHECK: %[[SUM0:.*]] = "tf.AddV2"(%arg0, %arg1)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 92K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
// Use other output %3:6 = "tf.FusedBatchNormV3"( %2#0, %arg1, %arg2, %arg3, %arg4) {T = "tfdtype$DT_FLOAT", U = "tfdtype$DT_FLOAT", data_format = "NHWC", epsilon = 0.001 : f32, is_training = false} : (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>) -> (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/decompose_resource_ops.mlir
// CHECK-DAG: %[[SIGN:.*]] = "tf.Sign"(%[[PROX]]) : (tensor<4xf32>) -> tensor<4xf32> // CHECK-DAG: %[[ABS:.*]] = "tf.Abs"(%[[PROX]]) : (tensor<4xf32>) -> tensor<4xf32> // CHECK-DAG: %[[SCALED_L1:.*]] = "tf.Mul"(%[[ADAGRAD_LR]], %[[L1]]) : (tensor<4xf32>, tensor<f32>) -> tensor<4xf32> // CHECK-DAG: %[[PROX_NEW:.*]] = "tf.Sub"(%[[ABS]], %[[SCALED_L1]]) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 19:47:48 UTC 2024 - 51.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize_composite_functions.mlir
%1 = stablehlo.get_dimension_size %0, dim = 0 : (tensor<?x3x4x2xf32>) -> tensor<i32> %2 = stablehlo.reshape %1 : (tensor<i32>) -> tensor<1xi32> %3 = stablehlo.concatenate %2, %cst_0, %cst_1, %cst_2, dim = 0 : (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<4xi32> %4 = stablehlo.dynamic_broadcast_in_dim %arg2, %3, dims = [0, 1, 2, 3] : (tensor<1x1x1x2xf32>, tensor<4xi32>) -> tensor<?x3x4x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 91.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tensor_array_ops_decomposition.mlir
// CHECK-DAG: %[[IND_SLICE1_START:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32> // CHECK-DAG: %[[IND_SLICE1_SIZE:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32> // CHECK: %[[IND_SLICE1:.*]] = "tf.Slice"(%[[INDS]], %[[IND_SLICE1_START]], %[[IND_SLICE1_SIZE]]) : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: %[[SLICE1_START:.*]] = "tf.ConcatV2"(%[[IND_SLICE1]],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 49K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/lift_quantizable_spots_as_functions.mlir
%1 = stablehlo.constant dense<2.000000e+00> : tensor<4xf32> %2 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x3x4xf32>, tensor<3x3x4x4xf32>) -> tensor<1x3x3x4xf32> %3 = stablehlo.broadcast_in_dim %1, dims = [3] : (tensor<4xf32>) -> tensor<1x3x3x4xf32> %4 = stablehlo.add %2, %3 : tensor<1x3x3x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 49.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training.mlir
tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<4x2xf32>, tensor<4xf32>, tensor<1x4xf32>, tensor<1x2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> tensor<*xf32> %24 = "quantfork.stats"(%23) {layerStats = dense<[-1.0, 2.0]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 52.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/mlrt/while_to_map_fn.mlir
%outputs_50 = "tf.Mul"(%outputs_30, %outputs_48) {device = ""} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32> %outputs_52 = "tf.Reshape"(%outputs_50, %outputs_0) {device = ""} : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32> %outputs_54 = "tf.MatMul"(%outputs_40, %outputs_52) {device = "", transpose_a = false, transpose_b = false} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 23 06:40:22 UTC 2024 - 68.6K bytes - Viewed (0)