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Results 111 - 119 of 119 for 1x6xf32 (0.11 sec)
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tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir
// CHECK-LABEL: func @testLeakyRelu(%arg0: tensor<16xf32>) func.func @testLeakyRelu(tensor<16xf32>) -> tensor<16xf32> { ^bb0(%arg0: tensor<16xf32>): %0 = "tf.LeakyRelu"(%arg0) {alpha = 0.2 : f32} : (tensor<16xf32>) -> tensor<16xf32> func.return %0 : tensor<16xf32> } // ----- func.func @testLeakyWrongAlphaType(tensor<16xf32>) -> tensor<16xf32> { ^bb0(%arg0: tensor<16xf32>):
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 23 14:40:35 UTC 2023 - 236.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/canonicalize.mlir
func.func @RemoveRedundantUnpackPack(%arg0: tensor<2x5xf32>) -> tensor<2x5xf32> { %0:2 = "tfl.unpack"(%arg0) {axis = 0 : i32, num = 2 : i32} : (tensor<2x5xf32>) -> (tensor<5xf32>, tensor<5xf32>) %1 = "tfl.pack"(%0#0, %0#1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<5xf32>, tensor<5xf32>) -> (tensor<2x5xf32>) func.return %1: tensor<2x5xf32> // CHECK-NOT: pack // CHECK: return %arg0 : tensor<2x5xf32> } // -----
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/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
%10 = "tf.Cast"(%9) {Truncate = false, device = ""} : (tensor<1x3xi32>) -> tensor<1x3xf32> %11 = "tf.Mul"(%10, %cst) {device = ""} : (tensor<1x3xf32>, tensor<f32>) -> tensor<1x3xf32> %12 = "tf.Relu"(%11) {device = ""} : (tensor<1x3xf32>) -> tensor<1x3xf32> return %12 : tensor<1x3xf32> } // CHECK-LABEL: func @matmul_with_relu
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0) -
tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
For example, if we have the code ```mlir %0 = "tf.Const"() {value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %1 = "tf.Const"() {device = "", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %2 = "tf.Const"() {device = "baz", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> ``` then running this pass with 'default-device=foobar', we get: ```mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/shape_inference.mlir
func.func @simple_chain(%arg0: tensor<1xf32>) -> tensor<*xf32> { // CHECK: %[[MUL:.*]] = "tf.Mul"{{.*}} (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: %[[ADD:.*]] = "tf.Add"(%[[MUL]], %[[MUL]]) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: return %[[ADD]] : tensor<1xf32> %0 = "tf.Mul"(%arg0, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 17:24:10 UTC 2024 - 167.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/constant-fold.mlir
%0 = "tf.Div"(%arg0, %cst) : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32> func.return %0 : tensor<2x2xf32> // CHECK-LABEL: RemoveTrivialDiv // CHECK-NEXT: return %arg0 : tensor<2x2xf32> } func.func @RemoveTrivialRealDiv(%arg0: tensor<2x2xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x2xf32> { %cst = arith.constant dense<1.0> : tensor<2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jan 31 23:22:24 UTC 2024 - 36.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (1) -
tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc
// -> tensor<5x2xf32> // // is lowered to // // %shape = "tf.Const"() {value = dense<[-1, 2]> : tensor<2xi64>} // %inp0 = "tf.Reshape"(%arg0, %shape) // : (tensor<2xf32>, tensor<2xi64>) -> tensor<1x2xf32> // %inp1 = "tf.Reshape"(%arg1, %shape) // : (tensor<2x2x2xf32>, tensor<2xi64>) -> tensor<4x2xf32> // %items0 = "tf.Unpack"(%[[INP0]]) {axis = 0 : i64}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tensor_list_ops_decomposition.mlir
// CHECK-NEXT: %[[ADDN:.*]] = "tf.AddN"(%[[UPDATE]], %[[BROADCAST]]) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32> %addn = "tf.AddN"(%set, %tl) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> // CHECK-NEXT: %[[ZEROS_LIKE:.*]] = "tf.ZerosLike"(%[[ADDN]]) : (tensor<10xf32>) -> tensor<10xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 38.6K bytes - Viewed (0)