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Results 31 - 40 of 92 for 5x3xi32 (0.11 sec)
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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/quantization/stablehlo/tests/passes/restore_function_name.mlir
// CHECK-SAME: %[[ARG3:[^:[:space:]]+]] func.func private @main(%arg0: tensor<1x4xf32>, %arg1: tensor<4x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} { %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0] : (tensor<1x4xf32>, tensor<4x3xf32>) -> tensor<1x3xf32> return %0 : tensor<1x3xf32> // CHECK: %[[DOT:.+]] = stablehlo.dot_general %[[ARG2]], %[[ARG3]] // CHECK: return %[[DOT]] } }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 08 22:40:14 UTC 2024 - 3.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/ifrt/rewrite_cluster_to_ifrt_call.mlir
return %0 : tensor<1x3xf32> } // CHECK-LABEL: @_func func.func private @_func(%arg0: tensor<1x3xf32>) -> (tensor<1x3xf32>) { return %arg0 : tensor<1x3xf32> } } // ----- // SPMD: one input and no return // // CHECK-LABEL: func.func @serving_default(%arg0: tensor<1x3xf32>) { // CHECK-NEXT: "tf.IfrtCall"(%arg0)
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/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/tests/passes/insert_calibration_statistics_saver.mlir
return %3 : tensor<1x3xf32> } func.func private @composite_matmul_with_bias_fn_2(%arg0: tensor<1x4xf32>, %arg1: tensor<4x3xf32>, %arg2: tensor<3xf32>) -> tensor<1x3xf32> attributes {tf_quant.composite_function} { %0 = "tf.MatMul"(%arg0, %arg1) <{grad_a = false, grad_b = false, transpose_a = false, transpose_b = false}> {attr_map = "0:transpose_a,1:transpose_b", device = ""} : (tensor<1x4xf32>, tensor<4x3xf32>) -> tensor<1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 01:09:50 UTC 2024 - 24.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/push-tpose-through-ewise.mlir
func.func @pushTposeAfterAddSimpleWithFold(%arg0: tensor<2x3xi32>) -> tensor<3x2xi32> { %perm = arith.constant dense<[1, 0]> : tensor<2xi32> %0 = "tfl.transpose"(%arg0, %perm) : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32> %cst = arith.constant dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32> %1 = tfl.add %0, %cst { fused_activation_function = "NONE" } : tensor<3x2xi32> func.return %1 : tensor<3x2xi32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 8.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range-float16.mlir
%recurrent_input = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32> %cell_input = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32> %16 = "tfl.unidirectional_sequence_lstm"( %arg0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %9, %9, %10, %11, %10, %10,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training-16bits.mlir
%recurrent_input = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32> %recurrent_stats = "quantfork.stats"(%recurrent_input) {layerStats = dense<[0.0, 1.0]> : tensor<2xf32>} : (tensor<1x3xf32>) -> tensor<1x3xf32> %cell_input = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 26.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/mlrt/rewrite_ifrt_load_variable.mlir
// CHECK-NEXT: "tf.IfrtCall"(%arg0, [[ARRAYKEY]]) <{program_id = 6515870160938153680 : i64, variable_arg_indices = [1 : i32]}> {__tpu_compile_metadata_text = "retvals { sharding { } }"} : (tensor<1x3xf32>, tensor<!tf_type.string>) -> tensor<1x1xf32> // CHECK-NEXT: return // func.func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 21:35:32 UTC 2024 - 1.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/components/pre_calibration_component.mlir
func.func @main(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> { %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> } // CHECK: @main(%[[ARG_0:.+]]: tensor<1x4xf32>) -> tensor<1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 5.1K bytes - Viewed (0)