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Results 31 - 40 of 46 for 1x2x4x5xf32 (0.17 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_drq_per_channel.mlir
module { func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32> %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32> func.return %1: tensor<*xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_xla.mlir
func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} { %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32> %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 11.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/push-tpose-through-ewise.mlir
// CHECK: %0 = tfl.sub %arg0, %cst_0 {fused_activation_function = "NONE"} : tensor<2x3x4x5xf32> // CHECK: %1 = "tfl.transpose"(%0, %cst) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32> // CHECK: return %1 : tensor<5x2x3x4xf32> // ----- // CHECK-LABEL: permNotConstNoChange func.func @permNotConstNoChange(%arg0: tensor<2x3x4x5xf32>, %perm: tensor<4xi32>) -> tensor<5x2x3x4xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 8.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/fallback_to_flex_ops_legacy.mlir
func.func @depth_to_space(%arg0: tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> { %0 = "tf.DepthToSpace"(%arg0) {block_size = 2: i64, data_format = "NHWC"}: (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> func.return %0 : tensor<1x2x2x1xf32> // CHECK: %[[CUSTOM_0:.*]] = "tfl.custom"(%arg0) <{custom_code = "FlexDepthToSpace", custom_option = #tfl<const_bytes : "{{.*}}">}> : (tensor<1x1x1x4xf32>) -> tensor<1x2x2x1xf32> // CHECK: return %[[CUSTOM_0]] : tensor<1x2x2x1xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Nov 06 01:23:21 UTC 2023 - 15.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/canonicalize.mlir
func.func @broadcast_to_to_reshape(%arg0: tensor<4x4x4xf32>, %arg1 : tensor<4xi32>) -> tensor<1x4x4x4xf32> { %0 = "tfl.broadcast_to"(%arg0, %arg1) : (tensor<4x4x4xf32>, tensor<4xi32>) -> tensor<1x4x4x4xf32> // CHECK: "tfl.reshape" // CHECK-SAME: (tensor<4x4x4xf32>, tensor<4xi32>) -> tensor<1x4x4x4xf32> func.return %0 : tensor<1x4x4x4xf32> } // Converts tfl.broadcast_to to tfl.reshape if input and output have the same
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/fold_constant_transpose.mlir
// ----- // CHECK-LABEL: transpose_simple_4d func.func @transpose_simple_4d() -> tensor<5x2x3x4xf32> { %0 = stablehlo.constant dense<1.000000e+0> : tensor<2x3x4x5xf32> %1 = stablehlo.transpose %0, dims = [3, 0, 1, 2] : (tensor<2x3x4x5xf32>) -> tensor<5x2x3x4xf32> return %1 : tensor<5x2x3x4xf32> } // CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant dense<1.000000e+00> : tensor<5x2x3x4xf32> // CHECK-NOT: transpose
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/quantization/tensorflow/tests/propagate_quantize_type.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-propagate-quantize-type | FileCheck %s module { func.func @not_propagate_matmul(%arg0: tensor<1x2x2x2xf32>) -> tensor<*xf32> { %cst = "tf.Const"() {value = dense<127> : tensor<2x1024xi8>} : () -> tensor<2x1024xi8> %cst_0 = "tf.Const"() {value = dense<0.0157480314> : tensor<f32>} : () -> tensor<f32> %0 = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-composite-functions-tf.mlir
%15 = "tf.Identity"(%14) {device = ""} : (tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32> func.return %15 : tensor<1x2x4x1xf32> } // CHECK-LABEL: func @max_unpooling_2d( // CHECK-SAME: %[[VAL_0:.*]]: tensor<1x1x2x1xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<1x1x2x1xi32>) -> tensor<1x2x4x1xf32> attributes {tf._implements = "MaxUnpooling2D"} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 122.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
%0 = "tf.Div"(%arg0, %cst_3) {device = ""} : (tensor<2x6x4x5xf32>, tensor<f32>) -> tensor<2x6x4x5xf32> %1 = "tf.AddV2"(%0, %cst_1) {device = ""} : (tensor<2x6x4x5xf32>, tensor<f32>) -> tensor<2x6x4x5xf32> %2 = "tf.Maximum"(%1, %cst_1) {device = ""} : (tensor<2x6x4x5xf32>, tensor<f32>) -> tensor<2x6x4x5xf32> %3 = "tf.Minimum"(%2, %cst_4) {device = ""} : (tensor<2x6x4x5xf32>, tensor<f32>) -> tensor<2x6x4x5xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0)