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tensorflow/compiler/mlir/tensorflow/tests/decompose_reduce_dataset.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 18 17:16:34 UTC 2022 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/fake_quant_e2e_flow.mlir
%2 = "tf.Conv2D"(%1, %0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32> %3 = "tf.BiasAdd"(%2, %cst) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 3.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/order_by_dialect.mlir
%0 = mhlo.constant dense<0.000000e+00> : tensor<32x10xf32> %1 = mhlo.constant dense<0.000000e+00> : tensor<32x28x28x5xf32> %2 = "tf.ReadVariableOp"(%arg4) : (tensor<!tf_type.resource<tensor<10xf32>>>) -> tensor<10xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 7.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/lstm.json
// CHECK-DAG: %[[input_18:.*]] = "quantfork.stats"({{.*}}) <{layerStats = dense<[-8.000000e-01, 1.600000e+00]> : tensor<2xf32>}> : (tensor<1x4xf32>) -> tensor<1x4xf32> // CHECK-DAG: %[[input_19:.*]] = "quantfork.stats"({{.*}}) <{layerStats = dense<[-2.000000e+00, 4.000000e+00]> : tensor<2xf32>}> : (tensor<1x2xf32>) -> tensor<1x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 06:25:50 UTC 2024 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/partially_shaped_variables.py
# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, {{.*}} type = tensor<*xf32>, value = dense<0.000000e+00> : tensor<1xf32>}> {tf_saved_model.exported_names = ["v0"]} : () -> () # CHECK: "tf_saved_model.global_tensor"() <{is_mutable, {{.*}} type = tensor<?xf32>, value = dense<[0.000000e+00, 1.000000e+00]> : tensor<2xf32>}> {tf_saved_model.exported_names = ["v1"]} : () -> () self.v0 = tf.Variable([0.], shape=tf.TensorShape(None))
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 31 08:49:35 UTC 2023 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/convert_custom_aggregation_op_to_quant_stats.mlir
func.return %0#0, %1#0 : tensor<8x8x8x8xf32>, tensor<8x8x8x8xf32> } // CHECK: func @customAggregator // CHECK-NEXT: %[[stats:.*]] = "quantfork.stats"(%arg0) <{layerStats = dense<[-1.000000e-01, 2.000000e-01]> : tensor<2xf32>}> : (tensor<8x8x8x8xf32>) -> tensor<8x8x8x8xf32> // CHECK-NEXT: return %[[stats]], %arg0 func.func @doNotHandleNoMinMaxCases(%arg0: tensor<8x8x8x8xf32>) -> (tensor<8x8x8x8xf32>, tensor<8x8x8x8xf32>, tensor<8x8x8x8xf32>) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 06:25:50 UTC 2024 - 2.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_optimize.mlir
%cst2 = arith.constant dense<[1.0, 2.0]> : tensor<2xf32> %0 = "tf.Conv2D"(%arg0, %cst0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<1x112x112x3xf32>, tensor<1x3x3x2xf32>) -> tensor<1x28x23x2xf32> %1 = "tf.Mul"(%0, %cst2) : (tensor<1x28x23x2xf32>, tensor<2xf32>) -> tensor<1x28x23x2xf32> func.return %1 : tensor<1x28x23x2xf32>
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/lite/tests/quantize-dynamic-range-float16.mlir
none, none, none, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, none, none, tensor<1x3xf32>, tensor<1x3xf32>, none, none, none, none) -> tensor<1x2x3xf32> %17 = "quantfork.stats"(%16) {layerStats = dense<[-0.1, 0.1]> : tensor<2xf32>} : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32> func.return %17 : tensor<1x2x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/group_by_dialect.mlir
%4 = mhlo.constant dense<0.0> : tensor<200x10xf32> %13 = mhlo.constant dense<-0.0> : tensor<f32> %29 = mhlo.reduce(%4 init: %13) applies mhlo.add across dimensions = [1] : (tensor<200x10xf32>, tensor<f32>) -> tensor<200xf32> return } // CHECK: func @handles_mhlo_regions // CHECK: call [[name:@[^(]*]]( // CHECK: func [[name]]() // ----- func.func @handles_regions_that_use_arguments(%arg0: f32, %arg1: f32) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 28 23:43:21 UTC 2022 - 5.7K bytes - Viewed (0)