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Results 1 - 10 of 22 for y_reshape (0.17 sec)
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tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/reshape.mlir
// Confirm we can extract type info from reshape func.func @main() -> tensor<2x2xf32> { // CHECK: %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32> // CHECK: %{{.*}} = "tfl.reshape"(%{{.*}}, %[[cst]]) : (tensor<4xf32>, tensor<2xi32>) -> tensor<2x2xf32> %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 730 bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/testdata/prepare_to_library.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jan 31 23:44:50 UTC 2024 - 2.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/cannonicalize_ops_outside_compilation.mlir
// due to the outside compilation attribute could be removed in // canonicalization of Reshape ops. // Reshape should not be executed on TPU as all are marked by outside // compilation. And there should be no host-device communication. // CHECK: tf._TPUCompileMlir // CHECK-NOT: tf.Reshape // CHECK-NOT: tf._XlaHostComputeMlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 13 21:23:47 UTC 2024 - 2.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/unwrap_xla_call_module_op.mlir
%0 = stablehlo.reshape %arg0 : (tensor<10x1x3xf32>) -> tensor<3x10xf32> return %0 : tensor<3x10xf32> } // CHECK: %[[RESHAPE:.*]] = stablehlo.reshape // CHECK-NEXT: return %[[RESHAPE]] // CHECK: @main_1 func.func private @main_1(%arg0: tensor<3x10xf32>) -> tensor<6x5xf32> { %0 = stablehlo.reshape %arg0 : (tensor<3x10xf32>) -> tensor<6x5xf32> return %0 : tensor<6x5xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 08 22:40:14 UTC 2024 - 3.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
func.return %3 : tensor<2x1xf32> } // CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32> // CHECK: [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32> // CHECK: [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/legacy_reshape.json
// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32> // CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32> { "version": 3, "operator_codes": [ { "builtin_code": "RESHAPE" } ], "subgraphs": [ { "tensors": [ { "shape": [1, 4], "name": "input",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 986 bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/optimize.cc
auto cst_attr = rewriter.getI64TensorAttr(values); return rewriter.create<TF::ConstOp>(location, cst_attr.getType(), cst_attr); } // Rewrites broadcast->reshape to a reshape->broadcast that reduces // the rank of the input and output of the broadcast. class SimplifyBroadcastReshape : public OpRewritePattern<BroadcastToOp> { using OpRewritePattern<BroadcastToOp>::OpRewritePattern;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize_jax_random.mlir
// CHECK: } 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/lite/utils/utils.td
def IsTransposeTrivial : Constraint<CPred< "TFL::IsTransposeTrivial($0.getType().cast<ShapedType>().getShape(), $1)">>; // Constraint that checks if the reshape op is equivalent to a transpose op. // This is true if the reshape op is a trivial reshape op, meaning no change in // the order of non-identity dimensions. def IsReshapeEquivalentToTranspose : Constraint<CPred< "TFL::IsReshapeEquivalentToTranspose("
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 4.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/device-transform-nnapi.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4.9K bytes - Viewed (0)