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Results 51 - 60 of 84 for 4x6xf32 (0.49 sec)
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tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_to_nhwc.mlir
{ data_format = "NCHW", epsilon = 1.001000e-05 : f32 } : (tensor<?x256x56x56xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> (tensor<?x256x56x56xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<*xf32>) // CHECK: %[[BATCH_NORM1:[_a-z0-9]*]], {{.*}} = "tf.FusedBatchNormV3" // CHECK-SAME: %[[CONV1]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 7.3K 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/lite/tests/raise-custom-ops.mlir
// CHECK-NEXT: "tfl.yield"(%[[MY_CUSTOM]]) : (tensor<4xf32>) -> () // CHECK-NEXT: }) {fused_activation_function = "RELU", int_attr = 2 : i32} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> // CHECK-NEXT: %[[CUSTOM_2:.*]] = "tfl.custom_tf"(%[[MUL]], %[[CST]]) ({ // CHECK-NEXT: ^bb0(%arg1: tensor<4xf32>, %arg2: tensor<4xf32>):
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/cc/saved_model_import_test.cc
// MLIR @main function corresponds to the TF function "main_original". OwningOpRef<ModuleOp> module_op = ParseModuleOpString(R"mlir( func.func private @main(%arg: tensor<1x2xf32>) -> (tensor<1x2xf32>) attributes {tf._original_func_name = "main_original"} { return %arg : tensor<1x2xf32> } )mlir"); ASSERT_TRUE(module_op); absl::flat_hash_map<FunctionName, FunctionAlias> function_aliases;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 07 03:47:17 UTC 2024 - 4.6K 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/lite/tests/mlir2flatbuffer/cast_bf16.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 18 21:28:19 UTC 2024 - 2.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/fallback.mlir
%1 = "tf.MatMul"(%arg0, %0) {T = f32, device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32> func.return %1 : tensor<3x3xf32> } // CHECK-LABEL: func @gpu_device func.func @gpu_device(%arg0: tensor<3x1xf32>, %arg1: tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<3x3xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/hoist_invariant_ops.mlir
%1 = "tf.ReadVariableOp"(%0) {device = "/device:CPU:0"} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32> %2 = "tf.AddV2"(%arg0, %1) {device = "/device:CPU:0"} : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<1x3xf32> %3 = "tf.Identity"(%2) {device = "/device:CPU:0"} : (tensor<1x3xf32>) -> tensor<1x3xf32> func.return %3 : tensor<1x3xf32> } // CHECK-LABEL: func @main
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 01 23:54:14 UTC 2024 - 18.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant-4bit.mlir
func.func @fakeQuantPerChannelForActivation(%arg0: tensor<8x4xf32>) -> (tensor<8x4xf32>) { %arg1 = arith.constant dense<[0.0, -1.0, 1.0, 0.0]> : tensor<4xf32> %arg2 = arith.constant dense<[15.0, 14.0, 16.0, 1.0e-9]> : tensor<4xf32> %0 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<8x4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<8x4xf32> func.return %0 : tensor<8x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 22K bytes - Viewed (0)