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Results 1 - 8 of 8 for 1xf32 (0.06 sec)
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tensorflow/compiler/mlir/lite/experimental/tac/execution_metadata_exporter_test.cc
%2 = "tfl.add"(%arg0, %arg3) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> %3 = "tfl.pack"(%1, %2) {axis = 0 : i32, per_device_costs = {CPU = 2.0 : f32, GPU = -1.0 : f32}, values_count = 2 : i32, tac.device = "CPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 06:11:34 UTC 2024 - 6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
} func.func @div(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> { %0 = "tf.Div"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> func.return %0: tensor<1xf32> // CHECK-LABEL: div // CHECK: tfl.div %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xf32> // CHECK: return } func.func @squaredDifferenceRelu(tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
%0 = "tfl.unidirectional_sequence_lstm"(%arg0,...
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/composite-lowering.mlir
%12 = mhlo.clamp %9, %11, %1 : tensor<2xf32> %13 = mhlo.multiply %arg0, %12 : tensor<2xf32> %14 = mhlo.divide %13, %1 : tensor<2xf32> return %14 : tensor<2xf32> } // CHECK-LABEL: func.func @hardswish( // CHECK-SAME: %[[VAL_0:.*]]: tensor<2xf32>) -> tensor<*xf32> { // CHECK: %[[VAL_1:.*]] = "tfl.hard_swish"(%[[VAL_0]]) : (tensor<2xf32>) -> tensor<2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 18:45:51 UTC 2024 - 32.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/ifrt/sink_variable_as_named_array.mlir
%cst = "tf.Const"() <{value = dense<[1]> : tensor<1xi32>}> : () -> tensor<1xi32> %0 = "tf.VarHandleOp"() <{container = "", shared_name = "Variable"}> : () -> tensor<!tf_type.resource<tensor<2x3xbf16>>> %1 = "tf.ResourceGather"(%0, %cst) <{batch_dims = 0 : i64, validate_indices = true}> : (tensor<!tf_type.resource<tensor<2x3xbf16>>>, tensor<1xi32>) -> tensor<1x3xbf16> return %1: tensor<1x3xbf16> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 15:33:17 UTC 2024 - 5.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td
%1 = call @then_branch_func(%arg1) : (tensor<*xf32>) -> tensor<*xf32> "tf.Yield"(%1) : (tensor<*xf32>) -> () }, { %1 = call @else_branch_func(%arg1) : (tensor<*xf32>) -> tensor<*xf32> "tf.Yield"(%1) : (tensor<*xf32>) -> () }) {is_stateless = false} : (tensor<i1>) -> tensor<*xf32> ``` will be transformed into this functional operation
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 99.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
// Look for resource or variant element type and ensure we refine the subtype. // We only support a single subtype at the moment, we won't handle something // like: // tensor<!tf_type.variant<tensor<10xf32>, tensor<8xf32>> if (rhs_element_type_with_subtype && rhs_element_type_with_subtype.GetSubtypes().size() == 1) { auto lhs_element_type_with_subtype =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
from tensorflow.compiler.mlir.tensorflow.gen_mlir_passthrough_op import mlir_passthrough_op mlir_module = '''python func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> { %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32> return %ret : tensor<10x10xf32> } ''' @tf.function def foo(x, y):
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0)