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Results 21 - 30 of 45 for 1x1xf32 (0.2 sec)
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tensorflow/compiler/mlir/tfrt/tests/reconfig_batch_op.mlir
// CHECK-LABEL: func private @batched_function func.func private @batched_function(%arg0: tensor<1x3xf32>) -> tensor<1x3xf32> { %2 = "tf.Identity"(%arg0) : (tensor<1x3xf32>) -> tensor<1x3xf32> func.return %2 : tensor<1x3xf32> } // CHECK-LABEL: func @main func.func @main(%arg0: tensor<1x3xf32>) -> tensor<*xf32> { // CHECK: "tf.BatchFunction" // CHECK-SAME: allowed_batch_sizes = [6]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 17:38:34 UTC 2024 - 5.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/batch_function_lowering.mlir
func.return %2 : tensor<1x3xf32> } // CHECK-LABEL: func @main func.func @main(%arg0: tensor<1x3xf32>) -> tensor<*xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "batch/BatchFunction:0"}} { %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf-variables.mlir
// CHECK: %[[ADD:.*]] = tfl.add %[[VAR_VAL]], %arg0 {fused_activation_function = "NONE"} : tensor<1x10xf32> // CHECK: "tfl.assign_variable"(%[[RESOURCE]], %[[ADD]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>, tensor<1x10xf32>) -> () // CHECK: %[[RESULT:.*]] = "tfl.read_variable"(%[[RESOURCE]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>) -> tensor<1x10xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 7.7K 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/tensorflow/tests/tf_saved_model/include_variables_in_init_v1.py
# CHECK-NEXT: %[[READ_VAR_0:.*]] = "tf.ReadVariableOp"(%[[ARG_2]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32> # CHECK-NEXT: %[[MATMUL_0:.*]] = "tf.MatMul"(%[[ARG_1]], %[[READ_VAR_0]]) <{{{.*}}}> {{{.*}}} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32> # CHECK-NEXT: return %[[MATMUL_0]] : tensor<3x3xf32> def Test(): x = tf.constant([[1.0], [1.0], [1.0]]) y = tf.compat.v1.get_variable( name='y',
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 31 08:49:35 UTC 2023 - 3.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/basic_v1.py
# CHECK-NEXT: [[R0:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32> # CHECK-NEXT: [[R1:%.*]] = "tf.MatMul"([[ARG0]], [[R0]]) <{{{.*}}}> {device = ""} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32> # CHECK-NEXT: return [[R1]] : tensor<3x3xf32> def Test(): x = tf.constant([[1.0], [1.0], [1.0]]) y = tf.compat.v1.get_variable( name='y',
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 31 08:49:35 UTC 2023 - 2.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/xla_call_module_to_call.mlir
return %2 : tensor<1x3xf32> } // CHECK-LABEL: func.func private @composite_dot_general_fn_1 // CHECK-SAME: -> tensor<1x3xf32> func.func private @composite_dot_general_fn_1(%arg0: tensor<1x1024xf32>, %arg1: tensor<1024x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 04 20:02:00 UTC 2024 - 1.4K 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/lite/experimental/tac/tests/e2e/device-transform-nnapi.mlir
} // CHECK-LABEL: pack func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> { %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return %0 : tensor<2x1xf32> // CHECK: %[[VAL_0:.*]] = arith.constant dense<[2, 1]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.2K 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)