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tensorflow/compiler/mlir/lite/tests/fuse-tftext.mlir
%21 = "tf.Cast"(%20) {Truncate = false, device = ""} : (tensor<1x1xi32>) -> tensor<1x1xi64> %22 = "tf.Reshape"(%21, %12) {device = ""} : (tensor<1x1xi64>, tensor<1xi64>) -> tensor<1xi64> %23 = "tf.Reshape"(%arg0, %5) {device = ""} : (tensor<1x!tf_type.string>, tensor<1xi32>) -> tensor<1x!tf_type.string>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 460.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize.mlir
%3 = "quantfork.qcast"(%2) {volatile} : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>> %4 = "quantfork.dcast"(%3) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32> %5 = "tf.Reshape"(%4, %cst) {device = ""} : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32> %6 = "quantfork.qcast"(%5) {volatile} : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 6.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/hlo_matchers.cc
// // %iota_r1 = "mhlo.iota"(){iota_dimension = 0 : i32} : () -> tensor<44xi32> // %iota = "mhlo.reshape"(%iota_r1): (tensor<44xi32>) -> tensor<1x1x44xi32> // // Where $dimensions is of size 1 and $dimensions[0] = 2. // // In general matches a 1-D Iota with multiple dimensions of size 1 added // through a reshape. bool MatchReshapedIota(DenseIntElementsAttr dimensions, Value iota) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/pick-subgraphs.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 24.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/collection_ops_util.cc
buffer_type.getShape().drop_front(), buffer_type.getElementType()); auto reshape = builder.create<TF::ReshapeOp>( loc, ArrayRef<Type>{element_type}, ArrayRef<Value>{slice, GetR1Const(element_type.getShape(), builder, loc)}); return reshape.getOutput(); } Value SetElement(Value index, Value buffer, Value element, OpBuilder builder,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 14.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
// masks will complicate the strided_slice computation logic, we can simplify // the logic by inserting a reshape op to pad the inputs so strided_slice can // be easier to handle. // // So the graph may looks like below: // original_input -> strided_slice -> output // (transforms) // original_input -> reshape -> strided_slice -> output // // And the new shape is computed based on the masks. //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.cc
// -> tensor<5x2xf32> // // is lowered to // // %shape = "tf.Const"() {value = dense<[-1, 2]> : tensor<2xi64>} // %inp0 = "tf.Reshape"(%arg0, %shape) // : (tensor<2xf32>, tensor<2xi64>) -> tensor<1x2xf32> // %inp1 = "tf.Reshape"(%arg1, %shape) // : (tensor<2x2x2xf32>, tensor<2xi64>) -> tensor<4x2xf32> // %items0 = "tf.Unpack"(%[[INP0]]) {axis = 0 : i64}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
Output reshape_input = ops::Placeholder(root.WithOpName("reshape_input"), DT_FLOAT, ops::Placeholder::Shape(TensorShape({500, 500}))); Output reshape = ops::Reshape(root.WithOpName("reshape"), reshape_input, shape); std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global())); TF_ASSERT_OK(root.ToGraph(graph.get()));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 10:11:10 UTC 2024 - 79.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
): n = 5 x_shape = [v if v is not None else n for v in shapes[0]] y_shape = [v if v is not None else n for v in shapes[1]] class MatmulModel(module.Module): def __init__(self, bias: Optional[core.Tensor]): self._bias = bias self._kernel = np.random.uniform(size=y_shape).astype('f4') self._min = (-0.8, -0.8, -0.9) self._max = (0.9, 0.9, 1.0)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0)