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Update developer overview, fix doc CMakeLists (#1140)
* Fix and change doc CMakeLists 1. Fix include directory location with hange from #1088 2. Create a DoxygenWarningLog.txt file in <build_dir>/doc/doxygen 3. Move compiled html or pdf files to <build_dir>/doc/[pdf, html]
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| MIGraphX Fundamentals | ||
| ====================== | ||
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| MIGraphX provides an optimized execution engine for deep learning neural networks. | ||
| We will cover some simple operations in the MIGraphX framework here. | ||
| For a quick start guide to using MIGraphX, look in the examples directory: ``https://github.com/ROCmSoftwarePlatform/AMDMIGraphX/tree/develop/examples/migraphx``. | ||
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| Location of the Examples | ||
| ------------------------- | ||
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| The ``ref_dev_examples.cpp`` can be found in the test directory (``/test``). | ||
| The executable file ``test_ref_dev_examples`` based on this file will be created in the ``bin/`` of the build directory after running ``make -j$(nproc) test_ref_dev_examples``. | ||
| The executable will also be created when running ``make -j$(nproc) check``, alongside with all the other tests. | ||
| Directions for building MIGraphX from source can be found in the main README file: ``https://github.com/ROCmSoftwarePlatform/AMDMIGraphX#readme``. | ||
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| Adding Two Literals | ||
| -------------------- | ||
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| A program is a collection of modules, which are collections of instructions to be executed when calling `eval <migraphx::program::eval>`. | ||
| Each instruction has an associated `operation <migraphx::operation>` which represents the computation to be performed by the instruction. | ||
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| We start with a snippet of the simple ``add_two_literals()`` function:: | ||
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| // create the program and get a pointer to the main module | ||
| migraphx::program p; | ||
| auto* mm = p.get_main_module(); | ||
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| // add two literals to the program | ||
| auto one = mm->add_literal(1); | ||
| auto two = mm->add_literal(2); | ||
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| // make the add operation between the two literals and add it to the program | ||
| mm->add_instruction(migraphx::make_op("add"), one, two); | ||
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| // compile the program on the reference device | ||
| p.compile(migraphx::ref::target{}); | ||
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| // evaulate the program and retreive the result | ||
| auto result = p.eval({}).back(); | ||
| std::cout << "add_two_literals: 1 + 2 = " << result << "\n"; | ||
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| We start by creating a simple ``migraphx::program`` object and then getting a pointer to the main module of it. | ||
| The program is a collection of ``modules`` that start executing from the main module, so instructions are added to the modules rather than directly onto the program object. | ||
| We then use the `add_literal <migraphx::program::add_literal>` function to add an instruction that stores the literal number ``1`` while returning an `instruction_ref <migraphx::instruction_ref>`. | ||
| The returned `instruction_ref <migraphx::instruction_ref>` can be used in another instruction as an input. | ||
| We use the same `add_literal <migraphx::program::add_literal>` function to add a ``2`` to the program. | ||
| After creating the literals, we then create the instruction to add the numbers together. | ||
| This is done by using the `add_instruction <migraphx::program::add_instruction>` function with the ``"add"`` `operation <migraphx::program::operation>` created by `make_op <migraphx::program::make_op>` along with the previous `add_literal` `instruction_ref <migraphx::instruction_ref>` for the input arguments of the instruction. | ||
| Finally, we can run this `program <migraphx::program>` by compiling it for the reference target (CPU) and then running it with `eval <migraphx::program::eval>` | ||
| The result is then retreived and printed to the console. | ||
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| We can compile the program for the GPU as well, but the file will have to be moved to the ``test/gpu/`` directory and the correct target must be included:: | ||
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| #include <migraphx/gpu/target.hpp> | ||
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| Using Parameters | ||
| ----------------- | ||
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| The previous program will always produce the same value of adding ``1`` and ``2``. | ||
| In the next program we want to pass an input to a program and compute a value based on the input. | ||
| We can modify the program to take an input parameter ``x``, as seen in the ``add_parameter()`` function:: | ||
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| migraphx::program p; | ||
| auto* mm = p.get_main_module(); | ||
| migraphx::shape s{migraphx::shape::int32_type, {1}}; | ||
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| // add a "x" parameter with the shape s | ||
| auto x = mm->add_parameter("x", s); | ||
| auto two = mm->add_literal(2); | ||
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| // add the "add" instruction between the "x" parameter and "two" to the module | ||
| mm->add_instruction(migraphx::make_op("add"), x, two); | ||
| p.compile(migraphx::ref::target{}); | ||
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| This adds a parameter of type ``int32``, and compiles it for the CPU. | ||
| To run the program, we need to pass the parameter as a ``parameter_map`` when we call `eval <migraphx::program::eval>`. | ||
| We create the ``parameter_map`` by setting the ``x`` key to an `argument <migraphx::argument>` object with an ``int`` data type:: | ||
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| // create a parameter_map object for passing a value to the "x" parameter | ||
| std::vector<int> data = {4}; | ||
| migraphx::parameter_map params; | ||
| params["x"] = migraphx::argument(s, data.data()); | ||
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| auto result = p.eval(params).back(); | ||
| std::cout << "add_parameters: 4 + 2 = " << result << "\n"; | ||
| EXPECT(result.at<int>() == 6); | ||
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| Handling Tensor Data | ||
| --------------------- | ||
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| In the previous examples we have only been dealing with scalars, but the `shape <migraphx::shape>` class can describe multi-dimensional tensors. | ||
| For example, we can compute a simple convolution:: | ||
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| migraphx::program p; | ||
| auto* mm = p.get_main_module(); | ||
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| // create shape objects for the input tensor and weights | ||
| migraphx::shape input_shape{migraphx::shape::float_type, {2, 3, 4, 4}}; | ||
| migraphx::shape weights_shape{migraphx::shape::float_type, {3, 3, 3, 3}}; | ||
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| // create the parameters and add the "convolution" operation to the module | ||
| auto input = mm->add_parameter("X", input_shape); | ||
| auto weights = mm->add_parameter("W", weights_shape); | ||
| mm->add_instruction(migraphx::make_op("convolution", {{"padding", {1, 1}}, {"stride", {2, 2}}}), input, weights); | ||
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| Here we create two parameters for both the ``input`` and ``weights``. | ||
| In the previous examples, we created simple literals, however, most programs will take data from allocated buffers (usually on the GPU). | ||
| In this case, we can create `argument <migraphx::argument>` objects directly from the pointers to the buffers:: | ||
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| // Compile the program | ||
| p.compile(migraphx::ref::target{}); | ||
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| // Allocated buffers by the user | ||
| std::vector<float> a = ...; | ||
| std::vector<float> c = ...; | ||
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| // Solution vector | ||
| std::vector<float> sol = ...; | ||
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| // Create the arguments in a parameter_map | ||
| migraphx::parameter_map params; | ||
| params["X"] = migraphx::argument(input_shape, a.data()); | ||
| params["W"] = migraphx::argument(weights_shape, c.data()); | ||
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| // Evaluate and confirm the result | ||
| auto result = p.eval(params).back(); | ||
| std::vector<float> results_vector(64); | ||
| result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); }); | ||
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| EXPECT(migraphx::verify_range(results_vector, sol)); | ||
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| An `argument <migraphx::argument>` can handle memory buffers from either the GPU or the CPU. | ||
| By default when running the `program <migraphx::program>`, buffers are allocated on the corresponding target. | ||
| When compiling for the CPU, the buffers by default will be allocated on the CPU. | ||
| When compiling for the GPU, the buffers by default will be allocated on the GPU. | ||
| With the option ``offloaf_copy=true`` set while compiling for the GPU, the buffers will be located on the CPU. | ||
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| Importing From ONNX | ||
| -------------------- | ||
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| A `program <migraphx::program>` can be built directly from an onnx file using the MIGraphX ONNX parser. | ||
| This makes it easier to use neural networks directly from other frameworks. | ||
| In this case, there is an ``parse_onnx`` function:: | ||
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| program p = migraphx::parse_onnx("model.onnx"); | ||
| p.compile(migraphx::gpu::target{}); | ||
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