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Refactored stockham
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be57a16a80
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@ -51,6 +51,35 @@ std::vector<int> prime_factors(int n) {
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return factors;
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}
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int next_fast_n(int n) {
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return next_power_of_2(n);
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}
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std::vector<int> stockham_decompose(int n) {
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auto radices = supported_radices();
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std::vector<int> steps(radices.size(), 0);
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int orig_n = n;
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for (int i = 0; i < radices.size(); i++) {
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int radix = radices[i];
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// Manually tuned radices for powers of 2
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if (is_power_of_2(orig_n) && orig_n < 512 && radix > 4) {
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continue;
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}
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while (n % radix == 0) {
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steps[i] += 1;
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n /= radix;
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if (n == 1) {
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return steps;
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}
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}
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}
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return {};
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}
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struct FourStepParams {
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bool required = false;
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bool first_step = true;
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@ -70,7 +99,7 @@ void fft_op(
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metal::Device& d,
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const Stream& s);
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struct FFTPlan {
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struct OldFFTPlan {
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int n = 0;
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// Number of steps for each radix in the Stockham decomposition
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std::vector<int> stockham;
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@ -85,9 +114,71 @@ struct FFTPlan {
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int n2 = 0;
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};
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int next_fast_n(int n) {
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return next_power_of_2(n);
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}
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class FFTPlan {
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public:
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enum FFTType {
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NOOP,
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STOCKHAM,
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RADER,
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BLUESTEIN,
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SMALL_FOUR_STEP,
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LARGE_FOUR_STEP
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};
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FFTPlan(int n) : n_(n) {
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// NOOP
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if (n == 1) {
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type_ = NOOP;
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}
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// Four step fft
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else if (n > MAX_STOCKHAM_FFT_SIZE && is_power_of_2(n)) {
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if (n <= 1 << 20) {
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type_ = SMALL_FOUR_STEP;
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n2_ = n > 65536 ? 1024 : 64;
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n1_ = n / n2_;
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} else {
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type_ = LARGE_FOUR_STEP;
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}
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}
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// Bluestein fft
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else if (n > MAX_STOCKHAM_FFT_SIZE) {
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type_ = BLUESTEIN;
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bluestein_n_ = next_fast_n(2 * n - 1);
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}
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// Stockham fft
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else if (auto steps = stockham_decompose(n); steps.size() > 0) {
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type_ = STOCKHAM;
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steps_ = steps;
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}
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// throw for now but we have rader and bluestein to do
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else {
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}
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}
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FFTType type() const {
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return type_;
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}
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int size() const {
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return n_;
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}
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const std::vector<int>& steps() const {
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return steps_;
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}
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private:
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int n_;
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FFTType type_;
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std::vector<int> steps_;
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int n1_;
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int n2_;
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int bluestein_n_;
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};
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std::vector<int> plan_stockham_fft(int n) {
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auto radices = supported_radices();
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@ -113,10 +204,10 @@ std::vector<int> plan_stockham_fft(int n) {
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throw std::runtime_error("Unplannable");
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}
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FFTPlan plan_fft(int n) {
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OldFFTPlan plan_fft(int n) {
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auto radices = supported_radices();
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FFTPlan plan;
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OldFFTPlan plan;
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plan.n = n;
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plan.rader = std::vector<int>(radices.size(), 0);
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@ -176,7 +267,7 @@ FFTPlan plan_fft(int n) {
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return plan;
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}
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int compute_elems_per_thread(FFTPlan plan) {
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int compute_elems_per_thread(OldFFTPlan plan) {
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// Heuristics for selecting an efficient number
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// of threads to use for a particular mixed-radix FFT.
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auto n = plan.n;
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@ -359,7 +450,7 @@ void multi_upload_bluestein_fft(
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size_t axis,
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bool inverse,
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bool real,
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FFTPlan& plan,
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OldFFTPlan& plan,
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std::vector<array>& copies,
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const Stream& s) {
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auto& d = metal::device(s.device);
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@ -488,7 +579,7 @@ void four_step_fft(
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size_t axis,
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bool inverse,
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bool real,
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FFTPlan& plan,
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OldFFTPlan& plan,
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std::vector<array>& copies,
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const Stream& s,
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bool in_place) {
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@ -771,6 +862,51 @@ void fft_op(
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d.add_temporaries(std::move(copies), s.index);
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}
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inline int compute_elems_per_thread(int n, const std::vector<int>& steps) {
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auto radices = supported_radices();
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std::set<int> used_radices;
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for (int i = 0; i < steps.size(); i++) {
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if (steps[i] > 0) {
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used_radices.insert(radices[i % radices.size()]);
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}
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}
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// Manual tuning for 7/11/13
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if (used_radices.find(7) != used_radices.end() &&
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(used_radices.find(11) != used_radices.end() ||
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used_radices.find(13) != used_radices.end())) {
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return 7;
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} else if (
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used_radices.find(11) != used_radices.end() &&
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used_radices.find(13) != used_radices.end()) {
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return 11;
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}
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// TODO(alexbarron) Some really weird stuff is going on
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// for certain `elems_per_thread` on large composite n.
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// Possibly a compiler issue?
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if (n == 3159)
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return 13;
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if (n == 3645)
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return 5;
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if (n == 3969)
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return 7;
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if (n == 1982)
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return 5;
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if (used_radices.size() == 1) {
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return *(used_radices.begin());
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}
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if (used_radices.size() == 2 &&
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(used_radices.find(11) != used_radices.end() ||
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used_radices.find(13) != used_radices.end())) {
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return std::accumulate(used_radices.begin(), used_radices.end(), 0) / 2;
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}
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// In all other cases use the second smallest radix.
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return *(++used_radices.begin());
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}
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inline array ensure_fastest_moving_axis(
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const array& x,
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int axis,
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@ -840,6 +976,7 @@ inline void prepare_output_array(const array& in, array& out, int axis) {
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}
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void fft_stockham_inplace(
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const FFTPlan& plan,
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const array& in_,
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array& out,
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size_t axis,
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@ -847,8 +984,56 @@ void fft_stockham_inplace(
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bool real,
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metal::Device& d,
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const Stream& s) {
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// Prepare the input and output arrays such that `axis` has stride 1.
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// Possibly copy the input but never the output as it doesn't have anything
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// useful in it yet.
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array in = ensure_fastest_moving_axis(in_, axis, d, s);
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prepare_output_array(in, out, axis);
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// Prepare the arguments for stockham fft
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int n = plan.size();
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bool power_of_2 = is_power_of_2(n);
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int total_batch_size =
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out.dtype() == float32 ? out.size() / n : in.size() / n;
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auto& steps = plan.steps();
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int elems_per_thread = compute_elems_per_thread(n, steps);
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int threads_per_fft = ceildiv(plan.size(), elems_per_thread);
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int tg_batch_size = std::max(MIN_THREADGROUP_MEM_SIZE / plan.size(), 1);
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int tg_mem_size = next_power_of_2(tg_batch_size * plan.size());
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int batch_size = ceildiv(total_batch_size, tg_batch_size);
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batch_size = real ? ceildiv(batch_size, 2) : batch_size;
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std::vector<MTLFC> func_consts = {
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{&inverse, MTL::DataType::DataTypeBool, 0},
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{&power_of_2, MTL::DataType::DataTypeBool, 1},
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{&elems_per_thread, MTL::DataType::DataTypeInt, 2}};
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for (int i = 0; i < steps.size(); i++) {
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func_consts.emplace_back(&steps[i], MTL::DataType::DataTypeInt, 4 + i);
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}
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// Get the kernel
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auto in_type = in.dtype() == float32 ? "float" : "float2";
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auto out_type = out.dtype() == float32 ? "float" : "float2";
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std::string hash_name;
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std::string kname;
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kname.reserve(64);
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hash_name.reserve(64);
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concatenate(kname, "fft_mem_", tg_mem_size, "_", in_type, "_", out_type);
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concatenate(hash_name, kname, "_n", n, "_inv_", inverse);
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auto template_def =
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get_template_definition(kname, "fft", tg_mem_size, in_type, out_type);
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auto kernel = get_fft_kernel(d, kname, hash_name, func_consts, template_def);
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// Launch it
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auto& compute_encoder = d.get_command_encoder(s.index);
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compute_encoder.set_compute_pipeline_state(kernel);
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compute_encoder.set_input_array(in, 0);
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compute_encoder.set_output_array(out, 1);
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compute_encoder.set_bytes(n, 2);
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compute_encoder.set_bytes(batch_size, 3);
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MTL::Size group_dims(1, tg_batch_size, threads_per_fft);
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MTL::Size grid_dims(batch_size, tg_batch_size, threads_per_fft);
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compute_encoder.dispatch_threads(grid_dims, group_dims);
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}
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void fft_op_inplace(
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@ -860,15 +1045,18 @@ void fft_op_inplace(
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metal::Device& d,
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const Stream& s) {
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// Get the FFT size and plan it
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size_t n = out.dtype() == float32 ? out.shape(axis) : in.shape(axis);
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auto plan = plan_fft(n);
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if (n == 1) {
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std::cout << "--------------> 1-size FFT <-----------------" << std::endl;
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}
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auto plan =
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FFTPlan(out.dtype() == float32 ? out.shape(axis) : in.shape(axis));
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if (plan.four_step && plan.bluestein_n < 0) {
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// four_step_fft(in, out, axis, inverse, real, plan, inplace, d, s);
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return;
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switch (plan.type()) {
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case FFTPlan::NOOP:
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std::cout << "--------------> 1-size FFT <-----------------" << std::endl;
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break;
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case FFTPlan::STOCKHAM:
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fft_stockham_inplace(plan, in, out, axis, inverse, real, d, s);
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break;
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default:
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std::cout << "----- NYI ----" << std::endl;
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}
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}
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