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tensorflow/compiler/mlir/tensorflow/tests/tpu_sharding_identification.mlir
} func.func @_func(%arg0: tensor<2x4xf32>, %arg1: tensor<4x2xf32>) -> tensor<2x2xf32> { %0 = "tf.MatMul"(%arg0, %arg1) {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\01"} : (tensor<2x4xf32>, tensor<4x2xf32>) -> tensor<2x2xf32> %1 = "tf.Identity"(%0) : (tensor<2x2xf32>) -> tensor<2x2xf32> return %1 : tensor<2x2xf32> } // ----- // The following op sharding is used in the following test case:
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Feb 20 19:07:52 UTC 2024 - 47.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tpu_space_to_depth_pass.mlir
%26 = "tf.Cast"(%25) {Truncate = false} : (tensor<2xi64>) -> tensor<2xf32> %27 = "tf.Equal"(%14, %26) {incompatible_shape_error = true} : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1> %28 = "tf.Cast"(%27) {Truncate = false} : (tensor<2xi1>) -> tensor<2xf32> %29 = "tf.Sum"(%28, %6) {keep_dims = false} : (tensor<2xf32>, tensor<1xi32>) -> tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 37.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/const-fold.mlir
func.func @add_dense_dense_float_mixfng_1_n() -> tensor<2x2xf32> { %cst_0 = arith.constant dense<[[1.5, -2.5]]> : tensor<1x2xf32> %cst_1 = arith.constant dense<[[-3.], [4.]]> : tensor<2x1xf32> %0 = "tfl.add"(%cst_0, %cst_1) {fused_activation_function = "NONE"} : (tensor<1x2xf32>, tensor<2x1xf32>) -> tensor<2x2xf32> func.return %0 : tensor<2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 45.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/tests/end2end.mlir
func.func @my_add_n_single_input(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> { %0 = "tf.MyAddN"(%arg0) {N=1:i32} : (tensor<2x3xf32>) -> tensor<2x3xf32> func.return %0 : tensor<2x3xf32> // CHECK-NEXT: return %arg0 : tensor<2x3xf32> } // CHECK-LABEL: my_add_n_multiple_inputs func.func @my_add_n_multiple_inputs(%arg0: tensor<2x3xf32>, %arg1: tensor<2x3xf32>, %arg2: tensor<2x3xf32>) -> tensor<2x3xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 13.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/attrs_and_constraints_test.cc
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 22.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir
} // CHECK-LABEL: prepareAdd func.func @prepareAdd(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> { %cst = arith.constant dense<[[0.0, 1.0], [2.0, 255.0]]> : tensor<2x2xf32> %add = "tfl.add"(%arg0, %cst) {fused_activation_function="NONE"} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32> func.return %add : tensor<2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_tf_graph_test.cc
%outputs_9, %control_10 = tf_executor.island wraps "tf.XlaSharding"(%outputs_7) {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\01", sharding = "\08\03\1A\02\02\01\22\02\00\01", unspecified_dims = []} : (tensor<2x2xf32>) -> tensor<2x2xf32> tf_executor.fetch %outputs_9 : tensor<2x2xf32> } return %0 : tensor<2x2xf32> } } )";
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 08:08:57 UTC 2024 - 11.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/README.md
%3 = "tfl.reshape"(%2, %cst_0) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x1x1x2xf32>, tensor<1xi32>) -> tensor<2xf32> %4 = "tfl.reshape"(%3, %cst_1) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<2xf32>, tensor<2xi32>) -> tensor<2x1xf32> return %4 : tensor<2x1xf32> } ``` #### Compute Costs Pass In the compute cost pass, we will essentially compute the cost of each op within
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 29 18:32:13 UTC 2022 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/tfl_while_outline.mlir
%0 = "tfl.batch_matmul"(%arg0, %cst_0) {adj_x = false, adj_y = false} : (tensor<1x256xf32>, tensor<256x256xi8>) -> tensor<1x256xf32> %1 = "tfl.batch_matmul"(%0, %cst_1) {adj_x = false, adj_y = false} : (tensor<1x256xf32>, tensor<256x256x!quant.uniform<i8:f32, 1.000000e+00>>) -> tensor<1x256xf32> %2:2 = "tfl.while"(%cst_2, %1) ({ ^bb0(%arg1: tensor<i32>, %arg2: tensor<1x256xf32>): %cst_3 = arith.constant dense<10> : tensor<i32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/executor_tpuv1_island_coarsening/executor_tpuv1_island_coarsening.mlir
%identity_out1, %control_identity1 = tf_executor.island wraps "tf.Identity"(%partitioned_out#0) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32> %identity_out2, %control_identity2 = tf_executor.island wraps "tf.Identity"(%partitioned_out#1) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32> tf_executor.fetch } func.return }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Aug 02 03:15:59 UTC 2022 - 36.2K bytes - Viewed (0)