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Results 11 - 20 of 150 for 1xf32 (0.04 sec)
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tensorflow/compiler/mlir/lite/tests/default_quant_params.mlir
%4 = "tf.LayerNorm"(%a1, %a2, %a3, %a4) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32> "tfl.yield"(%4) : (tensor<128x128xf32>) -> () }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>
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/lite/tests/flatbuffer2mlir/if_op.mlir
func.return %1 : tensor<1xf32> } func.func @cond_true(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> { %0 = tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<*xf32> func.return %0 : tensor<*xf32> } func.func @cond_false(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 1.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/convert_tpu_model_to_cpu.mlir
// CHECK-NOT: tf.BatchFunction // CHECK: %[[ADD0:.*]] = "tf.AddV2"(%[[ARG0]], %[[ARG1]]) // CHECK: return %[[ADD0]] : tensor<1xf32> func.func private @batched_func(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> { %0 = "tf.Identity"(%arg0) : (tensor<1xf32>) -> tensor<1xf32> %1 = "tf.Identity"(%arg1) : (tensor<1xf32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/pin-ops-with-side-effects.mlir
//body ^bb0(%arg_body: tensor<1xf32>): %result_body = "tfl.add"(%arg_body, %arg_body) { fused_activation_function = "NONE" } : (tensor<1xf32>, tensor<1xf32>) -> (tensor<1xf32>) "tfl.yield"(%result_body) : (tensor<1xf32>) -> () }) : (tensor<1xf32>) -> (tensor<1xf32>) %tmp5 = "tfl.add"(%tmp4, %tmp2) { fused_activation_function = "NONE" } : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 17 10:45:19 UTC 2022 - 5.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir
// ----- func.func @testAddReluPack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) { // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT" %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT" %1 = "tfl.add"(%arg0, %0) {fused_activation_function = "RELU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 19:32:06 UTC 2023 - 6.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tpu-resource-read-for-write.mlir
%fill = "tf.Fill"(%cst_0, %cst) : (tensor<1xi64>, tensor<f32>) -> tensor<1xf32> tf_device.replicate([%0, %fill] as %arg_r0: tensor<1xf32>) {n = 2 : i32} { %1 = "tf_device.launch"() <{device = "TPU_REPLICATED_HOST_0"}> ({ %2 = "tf.Identity"(%arg_r0) : (tensor<1xf32>) -> tensor<1xf32> tf_device.return %2 : tensor<1xf32> }) : () -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 16:54:40 UTC 2024 - 5.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/tac-filter.mlir
func.func @testFunctionSkiped(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) { // CHECK: tfl.add // CHECK-SAME: tac.skip_target_annotation %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: tfl.add // CHECK-SAME: tac.skip_target_annotation %1 = "tfl.add"(%arg0, %0) {fused_activation_function = "RELU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 24 01:08:29 UTC 2023 - 3.5K bytes - Viewed (0) -
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/tensorflow/tests/tf_saved_model/structured_output.py
# Note that semantically in Python, multiple return values are equivalent # to returning a tuple/list. # # CHECK: func {{@[a-zA-Z_0-9]+}}() -> ( # CHECK-SAME: tensor<1xf32> {tf_saved_model.index_path = [0]}, # CHECK-SAME: tensor<2xf32> {tf_saved_model.index_path = [1]}) # CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["f0001_multiple_results_no_punctuation"] @tf.function(input_signature=[])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 5.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/optimize-arg-operand-constraint.mlir
// CHECK-LABEL: func @main func.func @main(%arg0: tensor<1xf32>) -> tensor<1xf32> attributes {tf.entry_function = {inputs = "input", outputs = "output_node"}} { %0 = arith.constant dense<2.000000e+00> : tensor<f32> %1 = arith.constant dense<1.000000e+00> : tensor<f32> %2 = "tf.AddV2"(%arg0, %1) {T = "tfdtype$DT_FLOAT", device = "", name = "StatefulPartitionedCall/add"} : (tensor<1xf32>, tensor<f32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 05:47:26 UTC 2022 - 719 bytes - Viewed (0)