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Results 1 - 10 of 43 for 3x7xf32 (0.16 sec)
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tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul.pbtxt
# CHECK: %[[VAL_9:.*]] = "tfl.transpose"(%[[VAL_1]], %[[VAL_2]]) : (tensor<3x7xf32>, tensor<2xi32>) -> tensor<7x3xf32> # CHECK: %[[VAL_10:.*]] = "tfl.fully_connected"(%[[VAL_7]]#0, %[[VAL_9]], %[[VAL_3]]) <{fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"}> : (tensor<1x5x3xf32>, tensor<7x3xf32>, none) -> tensor<5x7xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 2.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul_disabled.pbtxt
} # CHECK: func @main(%[[VAL_0:.*]]: tensor<2x5x3xf32>, %[[VAL_1:.*]]: tensor<3x7xf32>) -> tensor<2x5x7xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "Placeholder,Placeholder_1", outputs = "MatMul"}} { # CHECK: %[[VAL_2:.*]] = "tfl.batch_matmul"(%[[VAL_0]], %[[VAL_1]]) <{adj_x = false, adj_y = false}> : (tensor<2x5x3xf32>, tensor<3x7xf32>) -> tensor<2x5x7xf32> # CHECK: return %[[VAL_2]] : tensor<2x5x7xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.5K 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/ifrt/rewrite_cluster_to_ifrt_call.mlir
return %outputs_0 : tensor<1x1xf32> } // CHECK-LABEL: @_func func.func private @_func(%arg0: tensor<1x3xf32>, %arg1: tensor<3x1xf32>) -> (tensor<1x1xf32>) { %outputs_0 = "tf.MatMul"(%arg0, %arg1) {transpose_a = false, transpose_b = false} : (tensor<1x3xf32>, tensor<3x1xf32>) -> tensor<1x1xf32> return %outputs_0 : tensor<1x1xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Feb 17 07:28:40 UTC 2024 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/ifrt/sink_variable_as_named_array.mlir
// CHECK-NEXT: return [[RES]], [[MATRES]] : tensor<1x1xf32>, tensor<1x1xf32> // module { func.func @serving_default(%arg0: tensor<1x3xf32>) -> (tensor<1x1xf32>, tensor<1x1xf32>) { %0 = "tf.VarHandleOp"() <{container = "", shared_name = "y"}> : () -> tensor<!tf_type.resource<tensor<3x1xf32>>> %2 = "tf.ReadVariableOp"(%0) : (tensor<!tf_type.resource<tensor<3x1xf32>>>) -> tensor<3x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 15:33:17 UTC 2024 - 5.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/device_conversion.mlir
func.return %2 : tensor<3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 645 bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/basic.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 00:18:59 UTC 2024 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/shared_variable_v1.py
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/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/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)