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Results 41 - 50 of 86 for output_shapes (0.31 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/passes/convert_tf_xla_op_to_tf_op.cc
// dimensions. SmallVector<int64_t> output_shape(output_tensor_rank); for (int i = 0; i < output_tensor_rank; i++) { if (collapsed_dims.contains(i)) { // The collapsed dimension's size should have been 1, so it restores the // dimension with size 1. output_shape[i] = 1; } else { output_shape[i] = *shape_itr; shape_itr++; } }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 13.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/bridge/convert_tf_quant_ops_to_mhlo.mlir
%bias_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32> %bias_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32> %output_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32> %output_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 7.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef/tf_tpu_embedding_ops.mlir
tf_executor.fetch } func.return } // CHECK: name: "RecvTPUEmbedding" // CHECK-NEXT: op: "RecvTPUEmbeddingActivations" // CHECK-NEXT: attr { // CHECK-NEXT: key: "_output_shapes" // CHECK-NEXT: value { // CHECK-NEXT: list { // CHECK-NEXT: shape { // CHECK-NEXT: dim { // CHECK-NEXT: size: 512 // CHECK-NEXT: } // CHECK-NEXT: dim {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Aug 14 15:35:49 UTC 2023 - 1.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/utils/tf_to_xla_attribute_utils.cc
SmallVector<int64_t> output_shape(input_shape.getShape().begin(), input_shape.getShape().end()); for (int i : spatial_dims) { output_shape[i] += padding_values[2 * i] + padding_values[2 * i + 1]; } return builder.create<TF::PadV2Op>( loc, RankedTensorType::get(output_shape, builder.getI8Type()), input, temp_padding,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 13.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/device_conversion.mlir
%arg1: tensor<1x3xf32> {tf_saved_model.index_path = [0]}) -> (tensor<3x3xf32> {tf_saved_model.index_path = []}) { // CHECK: {{%.*}} = corert.get_op_handler %arg0 "/device:GPU:0" %2 = "tf.MatMul"(%arg0, %arg1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:GPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32> 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/tensorflow/utils/xla_sharding_util.cc
const mlir::TensorType cluster_func_output_type, const xla::OpSharding& output_sharding, mlir::Type* tiled_logical_computation_type) { const auto output_shape = cluster_func_output_type.getShape(); auto new_output_shape = llvm::to_vector<4>(output_shape); auto dimension_to_splits_map = GetDimensionIndicesAndNumSplitsFromSharding(output_sharding); if (!dimension_to_splits_map.ok()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 21:28:13 UTC 2024 - 34K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.cc
int64_t out_row_dim = output_shape[output_shape.size() - 2]; int64_t out_col_dim = output_shape[output_shape.size() - 1]; int64_t expected_out_row_dim = op.getAdjX() ? x_col_dim : x_row_dim; int64_t expected_out_col_dim = op.getAdjY() ? y_row_dim : y_col_dim; if (expected_out_row_dim != ShapedType::kDynamic && out_row_dim != ShapedType::kDynamic && out_row_dim != expected_out_row_dim)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 169.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf-ops.mlir
%weight_scales: tensor<f32>, %weight_zps: tensor<i32>, %output_scales: tensor<f32>, %output_zps: tensor<i32>) -> () { // expected-error @below {{'tf.UniformQuantizedDot' op quantization_axis is -1, scales must have 0 rank.}} %1 = "tf.UniformQuantizedDot"( %input, %weight, %input_scales, %input_zps, %weight_scales, %weight_zps, %output_scales, %output_zps) { lhs_quantization_axis = -1 : i64,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 23 14:40:35 UTC 2023 - 236.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/convert_tensor.h
// Converts a shape from MLIR to a TensorFlow tensor shape proto. void ConvertToTensorShapeProto(llvm::ArrayRef<int64_t> shape, TensorShapeProto* output_shape); // Converts an MLIR type to a TensorFlow tensor shape. PartialTensorShape ConvertTypeToTensorShape(const mlir::Type& type); // Converts an MLIR shaped type to a TensorFlow shape attribute.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Apr 26 09:37:10 UTC 2024 - 2.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.cc
} // Gather shapes for output. for (auto v : ddn.lhs_batch_dimensions()) { output_shape.push_back(lhs_shape[v]); } // Batch dimension is gathered from the right side. if (output_shape.empty()) { for (auto v : ddn.rhs_batch_dimensions()) { output_shape.push_back(rhs_shape[v]); } } // Gather remaining dimensions.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 47.1K bytes - Viewed (0)