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Results 1 - 10 of 149 for 16xf32 (0.1 sec)
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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/lite/experimental/tac/tests/get-op-cost.mlir
func.return %1 : tensor<10x10x10xf32> } // ----- func.func @pack_CPU(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<2x100xf32> attributes {tac.device = "CPU", tac.interface_name = "func_2"} { // CHECK: tac.cost = 1.000000e+02 %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, tac.device = "CPU", values_count = 2 : i32} : (tensor<100xf32>, tensor<100xf32>) -> tensor<2x100xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 05:29:10 UTC 2022 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/sink_constant.mlir
%3 = "tf.Mul"(%arg0, %0) : (tensor<16xf32>, tensor<f32>) -> tensor<16xf32> %4 = "tf.Mul"(%3, %0) : (tensor<16xf32>, tensor<f32>) -> tensor<16xf32> %5 = "tf.Mul"(%4, %1) : (tensor<16xf32>, tensor<f32>) -> tensor<16xf32> %6 = "tf.Mul"(%5, %2) : (tensor<16xf32>, tensor<f32>) -> tensor<16xf32> tf_device.return %6 : tensor<16xf32> }) {} : () -> tensor<16xf32> tf_executor.yield %3 : tensor<16xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 05:47:26 UTC 2022 - 1.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant.mlir
%min = arith.constant dense<0.0> : tensor<16xf32> %max = arith.constant dense<255.0> : tensor<16xf32> %mini = "tf.Identity"(%min) : (tensor<16xf32>) -> tensor<16xf32> %maxi = "tf.Identity"(%max) : (tensor<16xf32>) -> tensor<16xf32> %fq = "tf.FakeQuantWithMinMaxVarsPerChannel"(%in, %mini, %maxi) {num_bits = 5, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<16xf32>, tensor<16xf32>) -> tensor<3x3x3x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/optimize.mlir
%2 = "tf.Mul"(%1, %value) {T = "tfdtype$DT_FLOAT"} : (tensor<256x8x7x16xf32>, tensor<16xf32>) -> tensor<256x8x7x16xf32> func.return %2 : tensor<256x8x7x16xf32> // CHECK-DAG: %[[cst:.*]] = "tf.Const{{.*}} dense<8.000000e+00> : tensor<3x3x3x16xf32> // CHECK-DAG: %[[cst_0:.*]] = "tf.Const{{.*}} dense<1.200000e+01> : tensor<16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 3.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef/type_attr.mlir
// CHECK: Placeholder // CHECK: key: "type" // CHECK: type: DT_INT8 func.func @main(%arg0 : tensor<16xf32>) { tf_executor.graph { %1:2 = tf_executor.island wraps "tf.MlirPassthroughOp"(%arg0) {extra_type_attr = [tensor<5xi32>, tensor<16xf32>], Tinputs = [tensor<16xf32>], Toutputs = [tensor<16xf32>], mlir_module = ""} : (tensor<16xf32>) -> tensor<16xf32> tf_executor.fetch } func.return } func.func @plain() { tf_executor.graph {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 28 12:06:33 UTC 2022 - 1.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/get-arithmetic-count.mlir
func.return %0 : tensor<256x32x32x16xf32> } func.func @testConv2DDynamicShape(tensor<?x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<?x32x32x16xf32> { ^bb0(%arg0: tensor<?x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>): // CHECK: _arithmetic_count = -1 : i64
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 14 04:58:17 UTC 2022 - 7.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant_4bit.mlir
%min = arith.constant dense<0.0> : tensor<16xf32> %max = arith.constant dense<15.0> : tensor<16xf32> %mini = "tf.Identity"(%min) : (tensor<16xf32>) -> tensor<16xf32> %maxi = "tf.Identity"(%max) : (tensor<16xf32>) -> tensor<16xf32> %fq = "tf.FakeQuantWithMinMaxVarsPerChannel"(%in, %mini, %maxi) {num_bits = 3, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<16xf32>, tensor<16xf32>) -> tensor<3x3x3x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/fold-constants-to-subgraph.mlir
%2 = func.call @fold_all_test(%arg0, %0, %1) : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x30x30x16xf32> func.return %2 : tensor<256x30x30x16xf32> } // ALL-LABEL: @fold_all_test func.func @fold_all_test(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>) -> tensor<256x30x30x16xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 10.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/decompose-hybrid-quantization.mlir
%1 = "tfl.pseudo_const"() { value = dense<1.0> : tensor<16xf32>} : () -> tensor<16xf32> %2 = "tfl.conv_3d"(%arg0, %0, %1) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, dilation_d_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32, stride_d = 1 : i32} : (tensor<1x32x32x32x8xf32>, tensor<1x1x1x8x16x!quant.uniform<i8:f32, 1.0>>, tensor<16xf32>) -> tensor<1x32x32x32x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.1K bytes - Viewed (0)