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tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-with-tf2xla-hlo-importer.mlir
// CHECK-LABEL: @xla_svd func.func @xla_svd(%arg0: tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) { // CHECK-NOT: XlaSvd %s, %u, %v = "tf.XlaSvd"(%arg0) {max_iter = 1, epsilon = 1.0E-09 : f32, precision_config = ""} : (tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) func.return %s, %u, %v : tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32> } func.func @identity(%arg0: f32) -> f32 {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 38.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/post-quantize.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 19.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tensor_array_ops_decomposition.mlir
// CHECK: %[[IND_SLICE0:.*]] = "tf.Slice"(%[[INDS]], %[[IND_SLICE0_START]], %[[IND_SLICE0_SIZE]]) : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32> // CHECK: %[[SLICE0_START:.*]] = "tf.ConcatV2"(%[[IND_SLICE0]], // CHECK: %[[OLD_SLICE0:.*]] = "tf.Slice"(%[[READ2]], %[[SLICE0_START]], // CHECK-SAME: (tensor<5x3xf32>, tensor<2xi32>, tensor<2xi32>) -> tensor<1x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 49K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/decompose_resource_ops.mlir
// CHECK-DAG: %[[SIGN:.*]] = "tf.Sign"(%[[PROX]]) : (tensor<4xf32>) -> tensor<4xf32> // CHECK-DAG: %[[ABS:.*]] = "tf.Abs"(%[[PROX]]) : (tensor<4xf32>) -> tensor<4xf32> // CHECK-DAG: %[[SCALED_L1:.*]] = "tf.Mul"(%[[ADAGRAD_LR]], %[[L1]]) : (tensor<4xf32>, tensor<f32>) -> tensor<4xf32> // CHECK-DAG: %[[PROX_NEW:.*]] = "tf.Sub"(%[[ABS]], %[[SCALED_L1]]) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 19:47:48 UTC 2024 - 51.3K bytes - Viewed (0) -
src/hash/crc32/crc32_table_ppc64le.s
DATA ·IEEEConst+1544(SB)/8,$0x0000000090db8c44 /* x^161856 mod p(x), x^161792 mod p(x) */ DATA ·IEEEConst+1552(SB)/8,$0x0000000067a2c786 DATA ·IEEEConst+1560(SB)/8,$0x000000010010a4ce /* x^160832 mod p(x), x^160768 mod p(x) */ DATA ·IEEEConst+1568(SB)/8,$0x0000000048b9496c DATA ·IEEEConst+1576(SB)/8,$0x00000001c8f4c72c /* x^159808 mod p(x), x^159744 mod p(x) */ DATA ·IEEEConst+1584(SB)/8,$0x000000015a422de6
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Feb 19 20:44:20 UTC 2024 - 113.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
// Look for resource or variant element type and ensure we refine the subtype. // We only support a single subtype at the moment, we won't handle something // like: // tensor<!tf_type.variant<tensor<10xf32>, tensor<8xf32>> if (rhs_element_type_with_subtype && rhs_element_type_with_subtype.GetSubtypes().size() == 1) { auto lhs_element_type_with_subtype =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
from tensorflow.compiler.mlir.tensorflow.gen_mlir_passthrough_op import mlir_passthrough_op mlir_module = '''python func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> { %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32> return %ret : tensor<10x10xf32> } ''' @tf.function def foo(x, y):
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0)