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tensorflow/compiler/mlir/lite/tests/default_quant_params.mlir
// CHECK-NEXT: "tf.LayerNorm"(%arg4, %arg5, %arg6, %arg7) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32> // CHECK-NEXT: "tfl.yield" // CHECK-NEXT: }) {_tfl_quant_trait = "fully_quantizable", device = ""} :
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/quantization/tensorflow/tests/preprocess_op_weight_only.mlir
%1 = "tf.BiasAdd"(%0, %cst_0) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<6xf32>) -> tensor<*xf32> func.return %1: tensor<*xf32> } func.func private @composite_depthwise_conv2d_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 4.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/optimize.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/fold_constant_transpose.mlir
func.func @transpose_simple_1d() -> tensor<2xf32> { %0 = stablehlo.constant dense<[0.000000e+0, 1.000000e+0]> : tensor<2xf32> %1 = stablehlo.transpose %0, dims = [0] : (tensor<2xf32>) -> tensor<2xf32> return %1 : tensor<2xf32> } // CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant dense<[0.000000e+00, 1.000000e+00]> : tensor<2xf32> // CHECK-NOT: transpose // CHECK: return %[[CONST_0]] : tensor<2xf32> // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 12 08:06:02 UTC 2024 - 2.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize-after-quantization.mlir
// CHECK-LABEL: fuseMulIntoPerTensorConv2dWithQDQs func.func @fuseMulIntoPerTensorConv2dWithQDQs(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x8x7x3xf32> { %cst = arith.constant dense<1.5> : tensor<3xf32> %cst_0 = arith.constant dense<[1.0, 2.0, 3.0]> : tensor<3xf32> %w = arith.constant dense<2.0> : tensor<3x3x3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 1.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/lower_globals_to_ml_program.mlir
^bb1(%0: tensor<!tf_type.resource<tensor<?xf32>>>, %1: tensor<!tf_type.resource<tensor<?xf32>>>, %2: tensor<!tf_type.resource<tensor<?xf32>>>): "tf.AssignVariableOp"(%0, %arg0) : (tensor<!tf_type.resource<tensor<?xf32>>>, tensor<?xf32>) -> () cf.br ^bb1(%1, %2, %0 : tensor<!tf_type.resource<tensor<?xf32>>>, tensor<!tf_type.resource<tensor<?xf32>>>, tensor<!tf_type.resource<tensor<?xf32>>>) }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 24 21:57:26 UTC 2022 - 3.6K 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/quantization/tensorflow/tests/preprocess_op.mlir
%1 = "tf.BiasAdd"(%0, %cst_0) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<6xf32>) -> tensor<*xf32> func.return %1: tensor<*xf32> } func.func private @composite_depthwise_conv2d_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/odml_converter/tests/shlo_simplify.mlir
%1 = stablehlo.constant dense<[4.0, 6.0]> : tensor<2xf32> %2 = stablehlo.divide %0, %1 : tensor<2xf32> return %2 : tensor<2xf32> } // CHECK-LABEL: foldDivLHSSplat // CHECK: stablehlo.constant dense<[5.000000e-01, 0.333333343]> : tensor<2xf32> // ----- func.func @foldDivRHSSplat() -> tensor<2xf32> { %0 = stablehlo.constant dense<[4.0, 6.0]> : tensor<2xf32> %1 = stablehlo.constant dense<2.0> : tensor<2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 03:05:20 UTC 2024 - 2.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/control_edges.mlir
%tmp0, %ctl0 = tfl.control_node controls "tfl.neg"(%arg0): (tensor<1xf32>) -> tensor<1xf32> %tmp1, %ctl1 = tfl.control_node controls "tfl.neg"(%tmp0): (tensor<1xf32>) -> tensor<1xf32> %tmp2, %ctl2 = tfl.control_node controls "tfl.neg"(%tmp1): (tensor<1xf32>) -> tensor<1xf32> %tmp3, %ctl3 = tfl.control_node controls "tfl.neg"(%tmp2): (tensor<1xf32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Oct 14 21:40:53 UTC 2022 - 3.6K bytes - Viewed (0)