mirror of
https://github.com/ml-explore/mlx.git
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226748b3e7
* try cpp 20 for compile * unary, binary, ternary in jit * nits * fix gather/scatter * fix rebase * reorg compile * add ternary to compile * jit copy * jit compile flag * fix build * use linked function for ternary * some nits * docs + circle min size build * docs + circle min size build * fix extension * fix no cpu build * improve includes
380 lines
11 KiB
C++
380 lines
11 KiB
C++
// Copyright © 2023-2024 Apple Inc.
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#include <fmt/format.h>
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#include "mlx/backend/common/compiled.h"
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#include "mlx/backend/metal/copy.h"
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#include "mlx/backend/metal/device.h"
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#include "mlx/backend/metal/jit/includes.h"
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#include "mlx/backend/metal/jit/indexing.h"
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#include "mlx/backend/metal/utils.h"
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#include "mlx/primitives.h"
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#include "mlx/utils.h"
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namespace mlx::core {
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constexpr int METAL_MAX_INDEX_ARRAYS = 20;
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std::pair<std::string, std::string> make_index_args(
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const std::string& idx_type,
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int nidx) {
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std::ostringstream idx_args;
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std::ostringstream idx_arr;
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for (int i = 0; i < nidx; ++i) {
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idx_args << fmt::format(
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"const device {0} *idx{1} [[buffer({2})]],", idx_type, i, 20 + i);
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idx_arr << fmt::format("idx{0}", i);
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if (i < nidx - 1) {
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idx_args << "\n";
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idx_arr << ",";
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}
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}
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return {idx_args.str(), idx_arr.str()};
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}
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void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
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auto& src = inputs[0];
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int nidx = inputs.size() - 1;
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if (nidx > METAL_MAX_INDEX_ARRAYS) {
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std::ostringstream msg;
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msg << "[Gather::eval_gpu] Gathering with more than "
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<< METAL_MAX_INDEX_ARRAYS << " index arrays not yet supported.";
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throw std::runtime_error(msg.str());
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}
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out.set_data(allocator::malloc_or_wait(out.nbytes()));
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if (out.size() == 0) {
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return;
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}
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auto& s = stream();
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auto& d = metal::device(s.device);
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int idx_ndim = nidx ? inputs[1].ndim() : 0;
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size_t ndim = src.ndim();
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std::string lib_name;
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std::string kernel_name;
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std::string idx_type_name = nidx ? type_to_name(inputs[1]) : "";
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{
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std::ostringstream kname;
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kname << "gather" << type_to_name(out) << idx_type_name << "_" << nidx
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<< "_" << idx_ndim;
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lib_name = kname.str();
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kernel_name = lib_name;
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}
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auto lib = d.get_library(lib_name);
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if (lib == nullptr) {
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std::ostringstream kernel_source;
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kernel_source << metal::utils() << metal::gather();
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std::string out_type_str = get_type_string(out.dtype());
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std::string idx_type_str =
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nidx ? get_type_string(inputs[1].dtype()) : "bool";
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auto [idx_args, idx_arr] = make_index_args(idx_type_str, nidx);
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// Index dimension specializations
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kernel_source << fmt::format(
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gather_kernels,
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type_to_name(out) + idx_type_name,
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out_type_str,
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idx_type_str,
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nidx,
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idx_args,
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idx_arr,
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idx_ndim);
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lib = d.get_library(lib_name, kernel_source.str());
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}
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auto& compute_encoder = d.get_command_encoder(s.index);
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auto kernel = d.get_kernel(kernel_name, lib);
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compute_encoder->setComputePipelineState(kernel);
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size_t slice_size = 1;
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for (auto s : slice_sizes_) {
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slice_size *= s;
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}
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// Launch 2D grid of threads: indices x slice
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size_t dim0 = out.size() / slice_size;
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size_t dim1 = slice_size;
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auto group_dims = get_block_dims(dim0, dim1, 1);
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MTL::Size grid_dims = MTL::Size(dim0, dim1, 1);
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// Collect all idx shapes and strides into one place
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std::vector<int> idx_shapes;
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std::vector<size_t> idx_strides;
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for (int i = 0; i < nidx; ++i) {
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idx_shapes.insert(
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idx_shapes.end(),
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inputs[i + 1].shape().begin(),
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inputs[i + 1].shape().end());
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idx_strides.insert(
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idx_strides.end(),
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inputs[i + 1].strides().begin(),
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inputs[i + 1].strides().end());
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}
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// Set all the buffers
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compute_encoder.set_input_array(src, 0);
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compute_encoder.set_output_array(out, 1);
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// Set source info
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compute_encoder->setBytes(src.shape().data(), ndim * sizeof(int), 2);
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compute_encoder->setBytes(src.strides().data(), ndim * sizeof(size_t), 3);
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compute_encoder->setBytes(&ndim, sizeof(size_t), 4);
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compute_encoder->setBytes(slice_sizes_.data(), ndim * sizeof(int), 5);
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compute_encoder->setBytes(axes_.data(), nidx * sizeof(int), 6);
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// Set index info
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//
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// We don't need to check for empty idx_shapes because gather has a
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// idx_ndim == 0 specialization
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compute_encoder->setBytes(
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idx_shapes.data(), idx_shapes.size() * sizeof(int), 7);
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compute_encoder->setBytes(
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idx_strides.data(), idx_strides.size() * sizeof(size_t), 8);
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compute_encoder->setBytes(&idx_ndim, sizeof(int), 9);
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// Set index buffers
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for (int i = 0; i < nidx; ++i) {
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compute_encoder.set_input_array(inputs[i + 1], 20 + i);
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}
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// Launch grid
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compute_encoder.dispatchThreads(grid_dims, group_dims);
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}
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void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
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if (size_of(out.dtype()) == 8) {
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std::ostringstream msg;
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msg << "[Scatter::eval_gpu] Does not support " << out.dtype();
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throw std::invalid_argument(msg.str());
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}
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int nidx = axes_.size();
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if (nidx > METAL_MAX_INDEX_ARRAYS) {
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std::ostringstream msg;
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msg << "[Scatter::eval_gpu] Gathering with more than "
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<< METAL_MAX_INDEX_ARRAYS << " index arrays not yet supported.";
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throw std::runtime_error(msg.str());
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}
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// Copy src into out
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auto copy_type =
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inputs[0].data_size() == 1 ? CopyType::Scalar : CopyType::General;
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copy_gpu(inputs[0], out, copy_type);
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// Empty update
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if (inputs.back().size() == 0) {
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return;
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}
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// Get stream
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auto& s = stream();
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auto& d = metal::device(s.device);
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int idx_ndim = nidx ? inputs[1].ndim() : 0;
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bool index_nd1_specialization = (idx_ndim == 1);
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// Bail from fast path (1d index specialization) if scatter dims aren't
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// the outermost dims and contiguous since update access won't be raster
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// order.
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for (auto i = 0; i < axes_.size() && index_nd1_specialization; i++) {
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index_nd1_specialization &= (axes_[i] == i);
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}
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// Bail from fast path (1d index specialization) if any of the dims are
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// broadcasted, since we can't rely on linear indexing in that case.
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for (int i = 1; i < inputs.size() && index_nd1_specialization; i++) {
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index_nd1_specialization &= inputs[i].flags().row_contiguous;
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}
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std::string lib_name;
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std::string kernel_name;
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std::string idx_type_name = nidx ? type_to_name(inputs[1]) : "";
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std::string op_name;
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switch (reduce_type_) {
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case Scatter::None:
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op_name = "none";
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break;
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case Scatter::Sum:
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op_name = "sum";
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break;
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case Scatter::Prod:
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op_name = "prod";
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break;
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case Scatter::Max:
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op_name = "max";
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break;
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case Scatter::Min:
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op_name = "min";
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break;
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}
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{
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std::ostringstream kname;
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if (index_nd1_specialization) {
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kname << "scatter_1d_index" << type_to_name(out) << idx_type_name;
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} else {
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kname << "scatter" << type_to_name(out) << idx_type_name;
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}
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kname << "_" << op_name << "_" << nidx;
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lib_name = kname.str();
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kernel_name = kname.str();
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}
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auto lib = d.get_library(lib_name);
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if (lib == nullptr) {
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std::ostringstream kernel_source;
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kernel_source << metal::utils() << metal::reduction() << metal::scatter();
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std::string out_type_str = get_type_string(out.dtype());
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std::string idx_type_str =
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nidx ? get_type_string(inputs[1].dtype()) : "bool";
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std::string op_type;
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switch (reduce_type_) {
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case Scatter::None:
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op_type = "None";
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break;
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case Scatter::Sum:
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op_type = "Sum<{0}>";
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break;
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case Scatter::Prod:
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op_type = "Prod<{0}>";
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break;
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case Scatter::Max:
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op_type = "Max<{0}>";
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break;
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case Scatter::Min:
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op_type = "Min<{0}>";
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break;
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}
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if (reduce_type_ != Scatter::None) {
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op_type = fmt::format(op_type, out_type_str);
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}
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auto [idx_args, idx_arr] = make_index_args(idx_type_str, nidx);
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kernel_source << fmt::format(
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scatter_kernels,
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type_to_name(out) + idx_type_name + "_" + op_name,
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out_type_str,
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idx_type_str,
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op_type,
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nidx,
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idx_args,
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idx_arr);
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lib = d.get_library(lib_name, kernel_source.str());
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}
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auto& compute_encoder = d.get_command_encoder(s.index);
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auto kernel = d.get_kernel(kernel_name, lib);
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auto& upd = inputs.back();
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size_t nthreads = upd.size();
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compute_encoder->setComputePipelineState(kernel);
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// Set all the buffers
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compute_encoder.set_input_array(upd, 1);
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compute_encoder.set_output_array(out, 2);
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// Set update info
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uint upd_ndim = upd.ndim();
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size_t upd_size = 1;
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for (int i = idx_ndim; i < upd.ndim(); ++i) {
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upd_size *= upd.shape(i);
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}
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if (index_nd1_specialization) {
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compute_encoder->setBytes(
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out.shape().data(), out.shape().size() * sizeof(int), 3);
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compute_encoder->setBytes(
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out.strides().data(), out.strides().size() * sizeof(size_t), 4);
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compute_encoder->setBytes(&upd_size, sizeof(size_t), 5);
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// Set index buffers
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for (int i = 0; i < nidx; ++i) {
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compute_encoder.set_input_array(inputs[i + 1], 20 + i);
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}
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// Launch grid
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MTL::Size grid_dims = MTL::Size(upd_size, nthreads / upd_size, 1);
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MTL::Size group_dims = get_block_dims(upd_size, nthreads / upd_size, 1);
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compute_encoder.dispatchThreads(grid_dims, group_dims);
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} else {
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// Collect all idx shapes and strides into one place
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std::vector<int> idx_shapes;
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std::vector<size_t> idx_strides;
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for (int i = 0; i < nidx; ++i) {
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idx_shapes.insert(
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idx_shapes.end(),
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inputs[i + 1].shape().begin(),
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inputs[i + 1].shape().end());
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idx_strides.insert(
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idx_strides.end(),
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inputs[i + 1].strides().begin(),
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inputs[i + 1].strides().end());
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}
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if (upd_ndim == 0) {
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// Need placeholders so Metal doesn't compalain
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int shape_ = 0;
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size_t stride_ = 0;
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compute_encoder->setBytes(&shape_, sizeof(int), 3);
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compute_encoder->setBytes(&stride_, sizeof(size_t), 4);
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} else {
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compute_encoder->setBytes(upd.shape().data(), upd_ndim * sizeof(int), 3);
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compute_encoder->setBytes(
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upd.strides().data(), upd_ndim * sizeof(size_t), 4);
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}
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compute_encoder->setBytes(&upd_ndim, sizeof(size_t), 5);
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compute_encoder->setBytes(&upd_size, sizeof(size_t), 6);
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// Set output info
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size_t out_ndim = out.ndim();
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if (out_ndim == 0) {
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// Need placeholders so Metal doesn't compalain
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int shape_ = 0;
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size_t stride_ = 0;
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compute_encoder->setBytes(&shape_, sizeof(int), 7);
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compute_encoder->setBytes(&stride_, sizeof(size_t), 8);
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} else {
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compute_encoder->setBytes(out.shape().data(), out_ndim * sizeof(int), 7);
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compute_encoder->setBytes(
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out.strides().data(), out_ndim * sizeof(size_t), 8);
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}
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compute_encoder->setBytes(&out_ndim, sizeof(size_t), 9);
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compute_encoder->setBytes(axes_.data(), axes_.size() * sizeof(int), 10);
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// Set index info
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if (idx_ndim == 0) {
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// Add a 0 in idx_shapes and strides to avoid the missing buffer binding
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// error in the metal API.
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idx_shapes.push_back(0);
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idx_strides.push_back(0);
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}
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compute_encoder->setBytes(
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idx_shapes.data(), idx_shapes.size() * sizeof(int), 11);
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compute_encoder->setBytes(
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idx_strides.data(), idx_strides.size() * sizeof(size_t), 12);
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compute_encoder->setBytes(&idx_ndim, sizeof(int), 13);
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// Set index buffers
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for (int i = 0; i < nidx; ++i) {
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compute_encoder.set_input_array(inputs[i + 1], 20 + i);
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}
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// Launch grid
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MTL::Size grid_dims = MTL::Size(upd_size, nthreads / upd_size, 1);
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MTL::Size group_dims = get_block_dims(upd_size, nthreads / upd_size, 1);
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compute_encoder.dispatchThreads(grid_dims, group_dims);
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}
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}
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} // namespace mlx::core
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