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Results 1 - 10 of 16 for output_types (0.2 sec)
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tensorflow/compiler/mlir/tensorflow/tests/side-effect-analysis-test.mlir
// expected-remark@above {{ID: 0}}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 20 04:39:18 UTC 2023 - 129.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/shape_inference.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 17:24:10 UTC 2024 - 167.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td
%1 = "tf.ReduceDataset"(%arg0, %arg1) { Targuments = [], Tstate = [i64], device = "", f = @__reduce_func_1, f._tf_data_function = true, output_shapes = [#tf_type.shape<>], output_types = [i64], use_inter_op_parallelism = true, _xla_compile_device_type="TPU"} : (tensor<!tf_type.variant>, tensor<i64>) -> (tensor<i64>) func.return } ```
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 99.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/translate/import_model.cc
"Placeholder node"); } DataType dtype = it->second.imported_dtype; // Uses the existing output type if it isn't specified by the user. if (dtype == DT_INVALID) { dtype = node->attr().at("output_types").list().type(0); } // Update op name, drop inputs and set attributes required by the Placeholder // op. *node->mutable_op() = "Placeholder"; node->clear_attr(); node->clear_input();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 11:17:36 UTC 2024 - 183.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
} bool changed = false; int next_op_result = 0; for (auto output_type : main_output_types) { if (tensorflow::IsTokenType(output_type)) continue; auto output_type_ranked = mlir::dyn_cast<RankedTensorType>(output_type); if (output_type_ranked == nullptr) { llvm::errs() << "Unsupported XlaCallModule result type: " << output_type << "\n"; return false; }
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/lite/ir/tfl_ops.cc
RankedTensorType output_type, int64_t axis) { const auto outer_dims = output_type.getShape().take_front(axis); const int64_t outer_size = std::accumulate( outer_dims.begin(), outer_dims.end(), 1, std::multiplies<int64_t>()); const auto base_inner_dims = output_type.getShape().drop_front(axis + 1); const int64_t base_inner_size =
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/lite/stablehlo/transforms/uniform_quantized_stablehlo_to_tfl_pass.cc
// before broadcasting. if (operand_type.getRank() < output_type.getRank()) { input = InsertExpandDimsOp(op, rewriter, input, output_type.getRank()); } SmallVector<int32_t> broadcast_shape = CastI64ArrayToI32(output_type.getShape()).value(); TensorType broadcast_shape_type = output_type.cloneWith({output_type.getRank()}, rewriter.getI32Type()); auto broadcast_shape_attr =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 09:00:19 UTC 2024 - 99.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize.cc
if (!type.hasStaticShape()) { return nullptr; } auto output_type = RankedTensorType::get({1}, builder.getI32Type()); const int num_elements = type.getNumElements(); return builder.create<ConstOp>( value.getLoc(), output_type, DenseIntElementsAttr::get(output_type, num_elements)); } Type GetEmbeddingLookupShape(Value lookup, Value value) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 102.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc
crops_values.push_back(crops_value_int); } } auto output_type = mlir::cast<TensorType>(op.getOutput().getType()); if (output_type.hasRank()) { if (output_type.getRank() != 4) return op.emitOpError() << "requires output to be a 4D tensor, but got " << output_type; auto static_dims = [](int64_t dim_a, int64_t dim_b) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 146.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo.cc
// Determine the 2-D convolution output shape. auto output_type = mlir::cast<ShapedType>(conv_op->getResult(0).getType()); SmallVector<int64_t, 4> output_2d_shape; for (int64_t dim : output_type.getShape()) { output_2d_shape.push_back(dim); } output_2d_shape.push_back(1); auto output_2d_type = RankedTensorType::get(output_2d_shape, output_type.getElementType());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 154.9K bytes - Viewed (0)