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Results 21 - 26 of 26 for 4x5xf32 (0.21 sec)
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tensorflow/compiler/mlir/lite/stablehlo/tests/legalize-stablehlo-vhlo.mlir
%1 = "tfl.abs"(%arg2) {fused_activation_function = "NONE"} : (tensor<5xf32>) -> tensor<5xf32> %2 = stablehlo.add %1, %arg2 : tensor<5xf32> %3 = "tfl.abs"(%2) {fused_activation_function = "NONE"} : (tensor<5xf32>) -> tensor<5xf32> "stablehlo.return"(%3) : (tensor<5xf32>) -> () }) {dimensions = array<i64: 0>} : (tensor<7x5xf32>, tensor<5xf32>) -> tensor<5xf32> func.return %0: tensor<5xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 07 22:39:35 UTC 2024 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize_jax_random.mlir
func.func @tfl_wrapped_jax_random_normal(%arg0: tensor<2xui32>) -> tuple<tensor<3x4xf32>> { // This is a fake jax random normal body. %0 = stablehlo.constant dense<0.0> : tensor<12xf32> %1 = "stablehlo.reshape"(%0) : (tensor<12xf32>) -> tensor<3x4xf32> %2 = "stablehlo.tuple"(%1) : (tensor<3x4xf32>) -> tuple<tensor<3x4xf32>> func.return %2 : tuple<tensor<3x4xf32>> } // CHECK-LABEL: func @tfl_wrapped_jax_random_uniform(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/cc/pre_calibration_test.cc
module attributes {} { func.func @main(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> attributes {} { %0 = stablehlo.constant dense<1.0> : tensor<4x3xf32> %1 = stablehlo.dot_general %arg0, %0, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<1x4xf32>, tensor<4x3xf32>) -> tensor<1x3xf32> return %1 : tensor<1x3xf32> } } )mlir"); ASSERT_TRUE(module_op);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 28 21:41:08 UTC 2024 - 6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/attributes.mlir
func.return } // CHECK-LABEL: func @basic func.func @basic( %arg0: tensor<3x1xf32>, %arg1: tensor<!tf_type.resource<tensor<1x3xf32>>>) -> (tensor<3x3xf32>) { %1 = "tf.ReadVariableOp"(%arg1) {_output_shapes = ["tfshape$dim { size: 1 } dim { size: 3 }"], device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 4.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize_batch_matmul.mlir
%2 = "tfl.batch_matmul"(%1, %arg2) {adj_x = true, adj_y = false, asymmetric_quantize_inputs = false} : (tensor<4x8xf32>, tensor<4x256xf32>) -> tensor<8x256xf32> func.return %2 : tensor<8x256xf32> // CHECK: return %[[RES1]] : tensor<8x256xf32> } // CHECK-LABEL: Batchmatmul2Fullyconnected // CHECK-NOT: "tfl.batch_matmul"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/group_by_dialect.mlir
%one = "glue.constant"() { value = 1: i32 } : () -> i32 %done = "glue.compare" (%one, %one) { predicate = #glue<"compare LTE"> } : (i32, i32) -> i1 %2 = mhlo.constant dense<[[1.1]]> : tensor<1x1xf32> %3 = mhlo.multiply %2, %2 : tensor<1x1xf32> %cst = "tf.Const"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32> %0 = "tf.AddV2"(%arg0, %cst) {device = "/device:CPU:0"} : (tensor<f32>, tensor<f32>) -> tensor<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)