gistfile1.cpp

// AmpTest.cpp : Defines the entry point for the console application.
//

#include "stdafx.h"
#include <boost/preprocessor/repeat.hpp>
#include <boost/preprocessor/enum.hpp>
#include <boost/preprocessor/enum_params.hpp>
#include <boost/range/counting_range.hpp>
#include <boost/range/adaptor/reversed.hpp>

using namespace concurrency;

template<typename T, int N> struct fixed_array;

#define ENUM_ARRAY_INDEX_M(z, n, data) data[n]
#define ENUM_ARRAY_INDEX(count, data) BOOST_PP_ENUM(count, ENUM_ARRAY_INDEX_M, data)
#define ENUM_FIELD_INIT_M(z, n, data) m_ ## data ## n (data ## n)
#define ENUM_FIELD_INIT(count, data) BOOST_PP_ENUM(count, ENUM_FIELD_INIT_M, data)
#define ENUM_FIELD_DECL_M(z, n, data) data ## n;
#define ENUM_FIELD_DECL(count, data) BOOST_PP_REPEAT(count, ENUM_FIELD_DECL_M, data)
#define ENUM_CASE_M(z, n, data) case n: return data ## n;
#define ENUM_CASE(count, data) BOOST_PP_REPEAT(count, ENUM_CASE_M, data)

#define DEF_SPEC_ARRAY(z, n, data) \
	template<typename T> struct fixed_array<T, n> { \
	public: \
		ENUM_FIELD_DECL(n, T m_elem); \
		\
		inline fixed_array(BOOST_PP_ENUM_PARAMS(n, T const& elem)) restrict(amp, cpu) : ENUM_FIELD_INIT(n, elem) {}; \
		inline T const& operator[](int i) const restrict(amp, cpu) { \
			switch (i) { \
			ENUM_CASE(n, m_elem) \
			default: return m_elem0; \
			} \
		} \
	};

BOOST_PP_REPEAT(16, DEF_SPEC_ARRAY, ());

template<unsigned N> class range_to;
const int uniform_param = 0x80000000;

template<unsigned N, int I> class param_indexer {
public:
	template<typename T> static auto& index(T& param) restrict(amp, cpu) {
		return param[N + I];
	}
};
template<unsigned N> class param_indexer<N, uniform_param> {
public:
	template<typename T> static T& index(T& param) restrict(amp, cpu) {
		return param;
	}
};
template<typename F>
class range_invoker {
public:
	F f;
	template<int... I, unsigned N, typename... P> inline void each(range_to<N> unused, P&... p) restrict(amp, cpu) {
		each<I...>(range_to<N - 1>(), p...);
		f(N - 1, param_indexer<N - 1, I>::index(p)...);
	}
	template<int... I, unsigned N, typename... P> inline void eachRev(range_to<N> unused, P&... p) restrict(amp, cpu) {
		f(N - 1, param_indexer<N - 1, I>::index(p)...);
		eachRev<I...>(range_to<N - 1>(), p...);
	}
	template<int... I, typename... P> inline void each(range_to<0> unused, P&... p) restrict(amp, cpu) { }
	template<int... I, typename... P> inline void eachRev(range_to<0> unused, P&... p) restrict(amp, cpu) { }
};
template<unsigned N> class range_to {
public:
	template<int... I, typename F, typename... P> static void each(F f, P&... p) restrict(amp, cpu) {
		range_invoker<F> invoker{ f };
		invoker.each<I...>(range_to<N>(), p...);
	}
	template<int... I, typename F, typename... P> static void eachRev(F f, P&... p) restrict(amp, cpu) {
		range_invoker<F> invoker{ f };
		invoker.eachRev<I...>(range_to<N>(), p...);
	}
};

template<typename T, int Rank>
void fill(array<T, Rank>& arr, T initValue) {
	parallel_for_each(arr.extent, [&arr, initValue](index<Rank> idx) restrict(amp) {
		arr[idx] = initValue;
	});
}

void printArray(array_view<float, 1> view) {
	view.refresh();
	array_view<float, 1> temp(view.extent);
	view.copy_to(temp);
	for (int i = 0; i < temp.extent[0]; ++i)
		std::cout << std::setw(8) << temp[i];
	std::cout << std::endl;
}

template<int N = 1>
struct table {
	array<float, N> m_value;
	array<float, N> m_gradient;

	inline table(extent<N> size) : m_value(size), m_gradient(size) { }
	inline extent<N> extent() const { return m_value.extent; }
};

template<int N = 1>
struct table_view {
	array_view<float, N> m_value;
	array_view<float, N> m_gradient;

	inline table_view() : m_value(extent<N>()), m_gradient(extent<N>()) { }
	inline table_view(table<N>& src) : m_value(src.m_value), m_gradient(src.m_gradient) { }
	inline extent<N> extent() const { return m_value.extent; }
	template<int M>
	table_view<M> view_as(concurrency::extent<M> extent) {
		return table_view<M>{ m_value.view_as(extent), m_gradient.view_as(extent) };
	}
	table_view<N> section(concurrency::index<N> index, concurrency::extent<N> extent) {
		return table_view<M>{ m_value.section(index, extent), m_gradient.section(index, extent) };
	}
};

class network;

class module {
protected:
	network* m_network;

	inline module(network* nn) : m_network(nn) { }

public:
	virtual void updateOutput() = 0;
	virtual void updateGradInput() = 0;
};

template<int N, typename S>
class module_function : public module {
protected:
	table<> m_output;
	fixed_array<table_view<>, N> m_inputs;
public:
	template<typename... T>
	inline module_function(network* nn, T... inputs) : module(nn), m_output(S::extent({ inputs... })), m_inputs(inputs...) { }

	virtual void updateOutput() {
		auto inputs = m_inputs;
		table_view<> output = m_output;


		try {
			// Update outputs
			output.m_value.discard_data();

			parallel_for_each(
				output.extent(),
				[=](index<1> idx) restrict(amp) {
					S::forward(idx, output, inputs);
				}
			);

			if (m_network->getIsLearning()) {
				// Clear gradients
				for (int i = 0; i < N; ++i)
					inputs[i].m_gradient.discard_data();

				parallel_for_each(
					output.extent(),
					[=](index<1> idx) restrict(amp) {
						range_to<N>::each<0>([=](int i, auto& inputi) restrict(amp) {
							inputi.m_gradient[idx] = 0.0f;
						}, inputs);
					}
				);
			}

		} catch (concurrency::runtime_exception& ex) {
			OutputDebugStringA(ex.what());
			DebugBreak();
		}
	}
	virtual void updateGradInput() {
		auto inputs = m_inputs;
		table_view<> output = m_output;

		try {
			parallel_for_each(
				output.extent(),
				[=](index<1> idx) restrict(amp) {
					S::backward(idx, output, inputs);
				}
			);
		} catch (concurrency::runtime_exception& ex) {
			OutputDebugStringA(ex.what());
			DebugBreak();
		}
	}

	inline table_view<> getOutput() {
		return table_view<>(m_output);
	}
};

// Base class for modules of the form `foldl <op> [inputs...]`,
// which includes scalar arithmetic
template<int N, typename S>
class module_scalar : public module_function<N, S> {
public:
	using module_function::module_function;

	static inline extent<1> extent(std::initializer_list<table_view<>> inputs) {
		auto extent = inputs.begin()->extent();
		for (auto& input : inputs) {
			if (input.extent() != extent)
				throw "Extent mismatch";
		}
		return extent;
	}

	template<bool unused = false>
	static inline void forward(index<1> idx, table_view<> const& output, fixed_array<table_view<>, N> const& inputs) restrict(amp) {
		// Default implementation of `forward` is essentially `foldl <op> [inputs...]`

		float acc = S::identity();
		range_to<N>::each<uniform_param, 0>([=](int i, float& acc, auto& inputi) restrict(amp) {
			acc = S::op(acc, inputi.m_value[idx]);
		}, acc, inputs);
		output.m_value[idx] = acc;
	}
	template<bool unused = false>
	static inline void backward(index<1> idx, table_view<> const& output, fixed_array<table_view<>, N> const& inputs) restrict(amp) {
		// This is a fun little snippet of code which calculates all N partial
		// derivatives of `foldl <op> [inputs...]` in O(N) time

		// We can't use normal loops because AMP is funny about them...

		float x[N];
		range_to<N>::each<0, 0>([=](int i, float& xi, auto& inputi) restrict(amp) {
			xi = inputi.m_value[idx];
		}, x, inputs);

		float acc[N];
		acc[0] = S::identity();
		range_to<N - 1>::each<1, 0, 0>([=](int i, float& acci1, float& acci, auto& xi) restrict(amp) {
			acci1 = S::op(acci, xi);
		}, acc, acc, x);

		float dacc0[N];
		dacc0[N - 1] = 1.0f;
		range_to<N - 1>::eachRev<0, 1, 1>([=](int i, float& dacc0i, float& acci, auto& xi) restrict(amp) {
			dacc0i = S::dop0(acci, xi);
		}, dacc0, acc, x);

		float y[N];
		range_to<N - 1>::eachRev<0, 0, 0, 0>([=](int i, float& yi, float& acci, auto& xi, float& dacc0i) restrict(amp) {
			float dacc1 = S::dop1(acci, xi);
			yi = dacc1*dacc0i;
		}, y, acc, x, dacc0);

		float gradient = output.m_gradient[idx];
		range_to<N>::each<0, 0>([=](int i, auto& inputi, float& yi) restrict(amp) {
			inputi.m_gradient[idx] += gradient*yi;
		}, inputs, y);
	}
};

template<typename S>
class module_unary : public module_scalar<1, S> {
public:
	using module_scalar::module_scalar;

	static inline void forward(index<1> idx, table_view<> const& output, fixed_array<table_view<>, 1> const& inputs) restrict(amp) {
		output.m_value[idx] = S::op(inputs[0].m_value[idx]);
	}
	static inline void backward(index<1> idx, table_view<> const& output, fixed_array<table_view<>, 1> const& inputs) restrict(amp) {
		inputs[0].m_gradient[idx] += output.m_gradient[idx] * S::dop(output.m_value[idx], inputs[0].m_value[idx]);
	}
};

template<int N = 2>
class module_add : public module_scalar<N, module_add<N>> {
public:
	using module_scalar::module_scalar;

	static inline float identity() restrict(amp) {
		return 0.0f;
	}
	static inline float op(float a, float b) restrict(amp) {
		return a+b;
	}
	static inline float dop0(float a, float b) restrict(amp) {
		return 1.0f;
	}
	static inline float dop1(float a, float b) restrict(amp) {
		return 1.0f;
	}
};

class module_sub : public module_scalar<2, module_sub> {
public:
	using module_scalar::module_scalar;

	static inline float identity() restrict(amp) {
		return 0.0f;
	}
	static inline float op(float a, float b) restrict(amp) {
		return -(a + b);
	}
	static inline float dop0(float a, float b) restrict(amp) {
		return -1.0f;
	}
	static inline float dop1(float a, float b) restrict(amp) {
		return -1.0f;
	}
};
class module_neg : public module_unary<module_neg> {
public:
	using module_unary::module_unary;

	static inline float op(float input) restrict(amp) {
		return -input;
	}
	static inline float dop(float output, float input) restrict(amp) {
		return -1.0f;
	}
};

template<int N = 2>
class module_mul : public module_scalar<N, module_mul<N>> {
public:
	using module_scalar::module_scalar;

	static inline float identity() restrict(amp) {
		return 1.0f;
	}
	static inline float op(float a, float b) restrict(amp) {
		return a*b;
	}
	static inline float dop0(float a, float b) restrict(amp) {
		return b;
	}
	static inline float dop1(float a, float b) restrict(amp) {
		return a;
	}
};

class module_div : public module_scalar<2, module_div> {
public:
	using module_scalar::module_scalar;

	static inline float identity() restrict(amp) {
		return 1.0f;
	}
	static inline float op(float a, float b) restrict(amp) {
		return 1.0f / (a*b);
	}
	static inline float dop0(float a, float b) restrict(amp) {
		return -1.0f / (a*a*b);
	}
	static inline float dop1(float a, float b) restrict(amp) {
		return -1.0f / (a*b*b);
	}
};
class module_rcp : public module_unary<module_rcp> {
public:
	using module_unary::module_unary;

	static inline float op(float input) restrict(amp) {
		return 1.0f / input;
	}
	static inline float dop(float output, float input) restrict(amp) {
		return -1.0f / (input*input);
	}
};
class module_sigmoid : public module_unary<module_sigmoid> {
public:
	using module_unary::module_unary;

	static inline float op(float input) restrict(amp) {
		return 1.0f / (1.0f + concurrency::fast_math::exp(-input));
	}
	static inline float dop(float output, float input) restrict(amp) {
		return output*(1.0f - output);
	}
};
class module_tanh : public module_unary<module_tanh> {
public:
	using module_unary::module_unary;

	static inline float op(float input) restrict(amp) {
		return concurrency::fast_math::tanh(input);
	}
	static inline float dop(float output, float input) restrict(amp) {
		return 1.0f - output*output;
	}
};

class module_input : public module {
protected:
	table<> m_output;
public:
	inline module_input(network* nn, extent<1> extent) : module(nn), m_output(extent) { }

	virtual void updateOutput() {
	}
	virtual void updateGradInput() {
	}

	void setValue(array_view<float,1> value) {
		if (value.extent != m_output.extent())
			throw "Extent mismatch";
		value.copy_to(m_output.m_value);
	}
	inline table_view<> getOutput() {
		return table_view<>(m_output);
	}
};

template<int N, typename S>
class module_container : public module {
protected:
	fixed_array<table_view<>, N> m_outputs;
public:
	template<typename... P>
	inline module_container(network* nn, P&&... p) : module(nn), m_outputs(S::build(nn, std::forward<P>(p)...)) { }
	virtual void updateOutput() {
	}
	virtual void updateGradInput() {
	}
};

class module_lstm : public module_container<1, module_lstm> {
public:
	using module_container::module_container;

	static fixed_array<table_view<>, 1> build(network* nn) {

	}
};

enum tensor_type {
	tensor_type_transient = 0,
	tensor_type_state,
	tensor_type_weight,
	tensor_type_count
};

class tensor_base {
public:
	virtual unsigned size() = 0;
	virtual void setSource(table_view<1> source) = 0;
};
template<int N = 1> class tensor : public tensor_base {
private:
	table_view<N> m_data;
	extent<N> m_extent;

public:
	tensor(network* nn, tensor_type type, extent<N> extent) : m_data(), m_extent(extent) {
		nn->registerTensor(type, this);
	}
	virtual unsigned size() {
		return m_extent.size();
	}
	virtual void setSource(table_view<1> source) {
		m_data = source.view_as(m_extent);
	}
	inline extent<N> extent() {
		return m_extent;
	}
	inline table_view<N> view() {
		return m_data;
	}
};

class linear_tensor_allocator {
private:
	std::vector<tensor_base*> m_tensors;
	std::unique_ptr<table<1>> m_table;

public:
	void registerTensor(tensor_base* tensor) {
		m_tensors.push_back(tensor);
	}
	void allocate() {
		unsigned totalSize = 0;
		for (auto tensor : m_tensors) {
			totalSize += tensor->size();
		}
		m_table = std::make_unique<table<1>>(extent<1>(totalSize));
		table_view<1> tableView(*m_table);

		unsigned offset = 0;
		for (auto tensor : m_tensors) {
			unsigned size = tensor->size();
			tensor->setSource(tableView.section(index<1>(offset), extent<1>(size)));
			offset += size;
		}
	}
};

class network {
private:
	std::vector<std::unique_ptr<module>> m_moduleSeq;
	bool m_isLearning;
	linear_tensor_allocator m_tensorAllocators[tensor_type_count];

	template<typename T>
	inline T* addModule(std::unique_ptr<T>&& m) {
		T* result = m.get();
		m_moduleSeq.push_back(std::move(m));
		return result;
	}
	
public:
	inline network() : m_isLearning(true) {
	}
	inline bool getIsLearning() {
		return m_isLearning;
	}
	inline void setIsLearning(bool isLearning) {
		m_isLearning = isLearning;
	}

	template<typename T, typename... P> inline T* make(P&&... args) {
		return addModule(std::make_unique<T>(this, std::forward<P>(args)...));
	}
	void updateOutput() {
		for (auto& module : m_moduleSeq)
			module->updateOutput();
	}
	void updateGradInput() {
		for (auto& module : boost::adaptors::reverse(m_moduleSeq))
			module->updateGradInput();
	}
	void compile() {
		for (int i = 0; i < tensor_type_count; ++i)
			m_tensorAllocators[i].allocate();
	}
	inline void registerTensor(tensor_type type, tensor_base* tensor) {
		m_tensorAllocators[type].registerTensor(tensor);
	}
};

struct rms_config {
	float learningRate = 1e-2f;
	float alpha = 0.99f;
	float epsilon = 1e-8f;
};

float lerp(float alpha, float a, float b) restrict(amp) {
	return alpha*b + (1.0f - alpha)*a;
}

template<typename F>
class rms_prop {
private:
	rms_config m_config;
	F m_f;
	array_view<float, 1>& m_x;
	array<float, 1> m_state;
	array<float, 1> m_loss;

public:
	rms_prop(F f, array_view<float, 1>& x, rms_config const& config) : m_f(f), m_x(x), m_state(x.get_extent()), m_loss(x.get_extent()), m_config(config) {
		fill(m_state, 0.0f);
		fill(m_loss, 0.0f);
	}
	void step() {
		parallel_for_each(x.extent, [&](index<Rank> idx) restrict(amp) {
			m_state[idx] = lerp(m_config.alpha, m_state[idx], m_state[idx]);
		});
	}
};

int _tmain(int argc, _TCHAR* argv[])
{
	network nn;

	extent<1> size(8);

	auto a = nn.make<module_input>(size);
	auto b = nn.make<module_add<2>>(a->getOutput(), a->getOutput());
	auto c = nn.make<module_div>(b->getOutput(), a->getOutput());

	array<float, 1> data(size, boost::make_counting_iterator(1.0f));

	a->setValue(data);
	nn.updateOutput();
	nn.updateGradInput();

	printArray(a->getOutput().m_value);
	printArray(b->getOutput().m_value);
	printArray(c->getOutput().m_value);

	getchar();
	return 0;
}