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Results 11 - 20 of 45 for 3x1xi32 (0.13 sec)
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tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/bucketize.mlir
func.func @main(%arg0: tensor<3x2xf32>) -> tensor<3x2xi32> { // CHECK-LABEL: @main // CHECK: "tfl.bucketize"(%arg0) <{boundaries = [0.000000e+00 : f32, 1.000000e+01 : f32, 1.000000e+02 : f32]}> : (tensor<3x2xf32>) -> tensor<3x2xi32> %0 = "tfl.bucketize"(%arg0) {boundaries = [0.0 : f32, 10.0 : f32, 100.0 : f32]} : (tensor<3x2xf32>) -> tensor<3x2xi32> func.return %0 : tensor<3x2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 571 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
%2 = "tfl.pseudo_const"() {value = dense<[[0.2]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %3 = "tfl.pseudo_const"() {value = dense<[[0.3]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %4 = "tfl.pseudo_const"() {value = dense<[[0.4]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %5 = "tfl.pseudo_const"() {value = dense<[[0.5]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
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/const-fold.mlir
// CHECK: %[[CST:.*]] = arith.constant dense<2> : tensor<1xi32> // CHECK: return %[[CST]] %0 = "tfl.rank"(%arg0) : (tensor<2x1xi32>) -> tensor<1xi32> func.return %0 : tensor<1xi32> } // CHECK-LABEL: @reshape func.func @reshape() -> tensor<4xi32> { %input = arith.constant dense<[[1, 2], [3, 4]]> : tensor<2x2xi32> %shape = arith.constant dense<[4]> : tensor<1xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 45.8K 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> { // CHECK: {{%.*}} = corert.get_op_handler %arg0 "/device:GPU:0"
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/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/lite/tests/shape-inference.mlir
module attributes {tf.versions = {producer = 888 : i32}} { func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> { %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32> // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32> %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32> func.return %0 : tensor<*xi32> } } // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 11.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize_same_scale.mlir
%5 = "quantfork.qcast"(%4) {volatile} : (tensor<3x4xf32>) -> tensor<3x4x!quant.uniform<i8:f32, 0.13170163023705575:-1>> %6 = "quantfork.dcast"(%5) : (tensor<3x4x!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<3x4xf32> %7 = stablehlo.slice %6 [1:3, 2:4] : (tensor<3x4xf32>) -> tensor<2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 35.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training-16bits.mlir
%5 = "tfl.pseudo_const"() {value = dense<[[0.5]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %6 = "tfl.pseudo_const"() {value = dense<[[0.6]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %7 = "tfl.pseudo_const"() {value = dense<[[0.7]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %8 = "tfl.pseudo_const"() {value = dense<[[0.8]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32> %9 = "tfl.no_value"() {value} : () -> none
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/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)