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Results 11 - 20 of 42 for 5x1xf32 (0.11 sec)
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tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef/function-resource-args-handle-info.mlir
func.func @main(%arg0: tensor<*x!tf_type.resource<tensor<8x1xf32>>>) -> tensor<8x1xf32> { %0 = tf_executor.graph { %outputs, %control = tf_executor.island wraps "tf.ReadVariableOp"(%arg0) : (tensor<*x!tf_type.resource<tensor<8x1xf32>>>) -> tensor<8x1xf32> tf_executor.fetch %outputs : tensor<8x1xf32> } func.return %0 : tensor<8x1xf32> } // Check that we generate _handle_dtypes and _handle_shapes for the resource
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 25 12:28:56 UTC 2022 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/ifrt/tf_restore_pruning.mlir
%0 = "tf.RestoreV2"(%cst, %cst_1, %cst_0): (tensor<!tf_type.string>, tensor<1x!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<3x1xf32> %1 = "tf.VarHandleOp"() <{container = "", shared_name = "y"}> : () -> tensor<!tf_type.resource<tensor<3x1xf32>>> "tf.AssignVariableOp"(%1, %0) : (tensor<!tf_type.resource<tensor<3x1xf32>>>, tensor<3x1xf32>) -> () return
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 25 22:02:06 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
module { func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} { %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> %1 = "tfl.mul"(%0, %arg2) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/default_quant_params.mlir
// CHECK-LABEL: hardcode_all func.func @hardcode_all(%arg0: tensor<2x2xf32>, %arg1: tensor<2x1xf32>) -> tensor<2x2xf32> { %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function="NONE"}: (tensor<2x2xf32>, tensor<2x1xf32>) -> tensor<2x2xf32> func.return %0 : tensor<2x2xf32> // CHECK: %[[q0:.*]] = "tfl.quantize"(%arg1) <{qtype = tensor<2x1x!quant.uniform<u8:f32, 0.0078431372549019607:128>>}>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 8.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tfrt_ops.mlir
%result = "tf.IfrtCall"(%arg0, %arg1) <{program_id = 1234 : i64, variable_arg_indices = [0 : i32, 1 : i32], variable_names = ["a", "b"]}> : (tensor<?xf32>, tensor<?xf32>) -> (tensor<1x1xf32>) func.return } // ----- func.func @test_ifrt_call_fail_unsorted_variable_arg_indices(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) { // expected-error@below {{variable_arg_indices must be sorted in ascending order}}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 15 06:13:11 UTC 2024 - 1.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize_batch_matmul.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tensorlist.mlir
} // ----- // CHECK-LABEL: listPushBack func.func @listPushBack(%arg0: tensor<!tf_type.variant<tensor<?x1xf32>>>, %arg1: tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>> { %0 = "tf.TensorListPushBack"(%arg0, %arg1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 9.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/fallback.mlir
%1 = "tf.MatMul"(%arg0, %0) {T = f32, device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32> func.return %1 : tensor<3x3xf32> } // CHECK-LABEL: func @gpu_device func.func @gpu_device(%arg0: tensor<3x1xf32>, %arg1: tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<3x3xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/device-transform-nnapi.mlir
} // CHECK-LABEL: pack func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> { %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return %0 : tensor<2x1xf32> // CHECK: %[[VAL_0:.*]] = arith.constant dense<[2, 1]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-BatchMatMulV2.mlir
// CHECK-SAME: rhs_contracting_dimensions = [1] %0 = "tf.BatchMatMulV2"(%arg0, %arg1) {adj_x = true, adj_y = true, device = ""} : (tensor<2x5xf32>, tensor<4x2xf32>) -> tensor<5x4xf32> func.return %0 : tensor<5x4xf32> } func.func @batchmatmulv2_adj_complex(%arg0: tensor<2x5xcomplex<f32>>, %arg1: tensor<4x2xcomplex<f32>>) -> tensor<5x4xcomplex<f32>> { // CHECK-LABEL: func @batchmatmulv2_adj_complex(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 5.5K bytes - Viewed (0)