mirror of
https://github.com/ml-explore/mlx.git
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c26208f67d
* remove hazard tracking * with fence map * no hazard tracking with fences * nits * fix fence retain * cleanup * fix quantized rebase
421 lines
15 KiB
C++
421 lines
15 KiB
C++
// Copyright © 2024 Apple Inc.
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#include <algorithm>
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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/kernels/defines.h"
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#include "mlx/backend/metal/reduce.h"
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#include "mlx/backend/metal/utils.h"
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#include "mlx/fast_primitives.h"
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namespace mlx::core::fast {
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void RMSNorm::eval_gpu(
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const std::vector<array>& inputs,
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std::vector<array>& outputs) {
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auto& s = stream();
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auto& d = metal::device(s.device);
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auto& out = outputs[0];
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// Make sure that the last dimension is contiguous
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std::vector<array> copies;
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auto check_input = [&copies, &s](const array& x) -> const array& {
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bool no_copy = x.flags().contiguous && x.strides()[x.ndim() - 1] == 1;
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if (no_copy && x.ndim() > 1) {
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auto s = x.strides()[x.ndim() - 2];
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no_copy &= (s == 0 || s == x.shape().back());
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}
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if (no_copy) {
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return x;
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} else {
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copies.push_back(array(x.shape(), x.dtype(), nullptr, {}));
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copy_gpu(x, copies.back(), CopyType::General, s);
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return copies.back();
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}
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};
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const array& x = check_input(inputs[0]);
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const array& w = inputs[1];
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if (x.is_donatable()) {
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out.move_shared_buffer(x);
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} else {
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out.set_data(
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allocator::malloc_or_wait(x.data_size() * x.itemsize()),
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x.data_size(),
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x.strides(),
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x.flags());
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}
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auto axis_size = static_cast<uint32_t>(x.shape().back());
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int n_rows = x.data_size() / axis_size;
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const int simd_size = 32;
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const int n_reads = RMS_N_READS;
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const int looped_limit = RMS_LOOPED_LIMIT;
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std::string op_name = "rms";
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if (axis_size > looped_limit) {
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op_name += "_looped";
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}
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op_name += type_to_name(out);
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auto& compute_encoder = d.get_command_encoder(s.index);
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{
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auto kernel = d.get_kernel(op_name);
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MTL::Size grid_dims, group_dims;
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if (axis_size <= looped_limit) {
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size_t threadgroup_needed = (axis_size + n_reads - 1) / n_reads;
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size_t simds_needed = (threadgroup_needed + simd_size - 1) / simd_size;
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size_t threadgroup_size = simd_size * simds_needed;
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assert(threadgroup_size <= kernel->maxTotalThreadsPerThreadgroup());
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size_t n_threads = n_rows * threadgroup_size;
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grid_dims = MTL::Size(n_threads, 1, 1);
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group_dims = MTL::Size(threadgroup_size, 1, 1);
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} else {
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size_t threadgroup_size = kernel->maxTotalThreadsPerThreadgroup();
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size_t n_threads = n_rows * threadgroup_size;
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grid_dims = MTL::Size(n_threads, 1, 1);
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group_dims = MTL::Size(threadgroup_size, 1, 1);
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}
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uint32_t w_stride = w.strides()[0];
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compute_encoder->setComputePipelineState(kernel);
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compute_encoder.set_input_array(
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x.data_shared_ptr() == nullptr ? out : x, 0);
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compute_encoder.set_input_array(w, 1);
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compute_encoder.set_output_array(out, 2);
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compute_encoder->setBytes(&eps_, sizeof(float), 3);
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compute_encoder->setBytes(&axis_size, sizeof(int), 4);
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compute_encoder->setBytes(&w_stride, sizeof(uint32_t), 5);
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compute_encoder->setThreadgroupMemoryLength(
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16 * 8, 0); // minimum of 16 bytes
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compute_encoder->setThreadgroupMemoryLength(simd_size * sizeof(float), 1);
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compute_encoder.dispatchThreads(grid_dims, group_dims);
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}
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d.add_temporaries(std::move(copies), s.index);
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}
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void RMSNormVJP::eval_gpu(
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const std::vector<array>& inputs,
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std::vector<array>& outputs) {
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auto& s = stream();
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auto& d = metal::device(s.device);
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// Ensure row contiguity. We could relax this step by checking that the array
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// is contiguous (no broadcasts or holes) and that the input strides are the
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// same as the cotangent strides but for now this is simpler.
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std::vector<array> copies;
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auto check_input = [&copies, &s](const array& x) -> const array& {
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if (x.flags().row_contiguous) {
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return x;
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}
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// Make sure we 'll only ever allocate once. The point of that goes beyond
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// the minor optimization. We need to ensure that there will be no
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// reallocation such that the references won't change when we
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// push_back(...). So tl;dr 3 possible copies x, g and gw_temp.
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copies.reserve(3);
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copies.push_back(array(x.shape(), x.dtype(), nullptr, {}));
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copy_gpu(x, copies.back(), CopyType::General, s);
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return copies.back();
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};
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const array& x = check_input(inputs[0]);
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const array& w = inputs[1];
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const array& g = check_input(inputs[2]);
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array& gx = outputs[0];
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array& gw = outputs[1];
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// Allocate space for the outputs
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bool x_in_gx = false;
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bool g_in_gx = false;
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if (x.is_donatable()) {
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gx.move_shared_buffer(x);
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x_in_gx = true;
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} else if (g.is_donatable()) {
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gx.move_shared_buffer(g);
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g_in_gx = true;
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} else {
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gx.set_data(allocator::malloc_or_wait(gx.nbytes()));
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}
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auto axis_size = static_cast<uint32_t>(x.shape().back());
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int n_rows = x.data_size() / axis_size;
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// Allocate the gradient accumulator gw and a temporary to store the
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// gradients before they are accumulated.
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array gw_temp({n_rows, x.shape().back()}, gw.dtype(), nullptr, {});
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bool g_in_gw = false;
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if (!g_in_gx && g.is_donatable()) {
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gw_temp.move_shared_buffer(g);
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g_in_gw = true;
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} else {
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gw_temp.set_data(allocator::malloc_or_wait(gw_temp.nbytes()));
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}
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copies.push_back(gw_temp);
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gw.set_data(allocator::malloc_or_wait(gw.nbytes()));
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const int simd_size = 32;
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const int n_reads = RMS_N_READS;
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const int looped_limit = RMS_LOOPED_LIMIT;
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std::string op_name = "vjp_rms";
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if (axis_size > looped_limit) {
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op_name += "_looped";
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}
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op_name += type_to_name(gx);
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auto& compute_encoder = d.get_command_encoder(s.index);
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{
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auto kernel = d.get_kernel(op_name);
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MTL::Size grid_dims, group_dims;
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if (axis_size <= looped_limit) {
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size_t threadgroup_needed = (axis_size + n_reads - 1) / n_reads;
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size_t simds_needed = (threadgroup_needed + simd_size - 1) / simd_size;
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size_t threadgroup_size = simd_size * simds_needed;
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assert(threadgroup_size <= kernel->maxTotalThreadsPerThreadgroup());
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size_t n_threads = n_rows * threadgroup_size;
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grid_dims = MTL::Size(n_threads, 1, 1);
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group_dims = MTL::Size(threadgroup_size, 1, 1);
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} else {
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size_t threadgroup_size = kernel->maxTotalThreadsPerThreadgroup();
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size_t n_threads = n_rows * threadgroup_size;
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grid_dims = MTL::Size(n_threads, 1, 1);
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group_dims = MTL::Size(threadgroup_size, 1, 1);
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}
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uint32_t w_stride = w.strides()[0];
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compute_encoder->setComputePipelineState(kernel);
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compute_encoder.set_input_array(x_in_gx ? gx : x, 0);
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compute_encoder.set_input_array(w, 1);
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compute_encoder.set_input_array(g_in_gx ? gx : (g_in_gw ? gw_temp : g), 2);
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compute_encoder.set_output_array(gx, 3);
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compute_encoder.set_output_array(gw_temp, 4);
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compute_encoder->setBytes(&eps_, sizeof(float), 5);
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compute_encoder->setBytes(&axis_size, sizeof(int), 6);
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compute_encoder->setBytes(&w_stride, sizeof(uint32_t), 7);
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compute_encoder.dispatchThreads(grid_dims, group_dims);
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}
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ReductionPlan plan(
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ReductionOpType::ContiguousStridedReduce, {n_rows}, {axis_size});
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strided_reduce_general_dispatch(
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gw_temp, gw, "sum", plan, {0}, compute_encoder, d, s);
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d.add_temporaries(std::move(copies), s.index);
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}
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void LayerNorm::eval_gpu(
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const std::vector<array>& inputs,
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std::vector<array>& outputs) {
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auto& s = stream();
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auto& d = metal::device(s.device);
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auto& out = outputs[0];
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// Make sure that the last dimension is contiguous
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std::vector<array> copies;
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auto check_input = [&copies, &s](const array& x) -> const array& {
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bool no_copy = x.flags().contiguous && x.strides()[x.ndim() - 1] == 1;
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if (no_copy && x.ndim() > 1) {
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auto s = x.strides()[x.ndim() - 2];
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no_copy &= (s == 0 || s == x.shape().back());
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}
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if (no_copy) {
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return x;
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} else {
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copies.push_back(array(x.shape(), x.dtype(), nullptr, {}));
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copy_gpu(x, copies.back(), CopyType::General, s);
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return copies.back();
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}
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};
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const array& x = check_input(inputs[0]);
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const array& w = inputs[1];
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const array& b = inputs[2];
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if (x.is_donatable()) {
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out.move_shared_buffer(x);
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} else {
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out.set_data(
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allocator::malloc_or_wait(x.data_size() * x.itemsize()),
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x.data_size(),
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x.strides(),
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x.flags());
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}
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auto axis_size = static_cast<uint32_t>(x.shape().back());
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int n_rows = x.data_size() / axis_size;
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const int simd_size = 32;
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const int n_reads = RMS_N_READS;
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const int looped_limit = RMS_LOOPED_LIMIT;
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std::string op_name = "layer_norm";
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if (axis_size > looped_limit) {
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op_name += "_looped";
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}
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op_name += type_to_name(out);
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auto& compute_encoder = d.get_command_encoder(s.index);
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{
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auto kernel = d.get_kernel(op_name);
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MTL::Size grid_dims, group_dims;
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if (axis_size <= looped_limit) {
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size_t threadgroup_needed = (axis_size + n_reads - 1) / n_reads;
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size_t simds_needed = (threadgroup_needed + simd_size - 1) / simd_size;
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size_t threadgroup_size = simd_size * simds_needed;
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assert(threadgroup_size <= kernel->maxTotalThreadsPerThreadgroup());
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size_t n_threads = n_rows * threadgroup_size;
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grid_dims = MTL::Size(n_threads, 1, 1);
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group_dims = MTL::Size(threadgroup_size, 1, 1);
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} else {
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size_t threadgroup_size = kernel->maxTotalThreadsPerThreadgroup();
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size_t n_threads = n_rows * threadgroup_size;
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grid_dims = MTL::Size(n_threads, 1, 1);
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group_dims = MTL::Size(threadgroup_size, 1, 1);
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}
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uint32_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
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uint32_t b_stride = (b.ndim() == 1) ? b.strides()[0] : 0;
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compute_encoder->setComputePipelineState(kernel);
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compute_encoder.set_input_array(
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x.data_shared_ptr() == nullptr ? out : x, 0);
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compute_encoder.set_input_array(w, 1);
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compute_encoder.set_input_array(b, 2);
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compute_encoder.set_output_array(out, 3);
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compute_encoder->setBytes(&eps_, sizeof(float), 4);
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compute_encoder->setBytes(&axis_size, sizeof(int), 5);
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compute_encoder->setBytes(&w_stride, sizeof(uint32_t), 6);
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compute_encoder->setBytes(&b_stride, sizeof(uint32_t), 7);
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compute_encoder.dispatchThreads(grid_dims, group_dims);
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}
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d.add_temporaries(std::move(copies), s.index);
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}
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void LayerNormVJP::eval_gpu(
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const std::vector<array>& inputs,
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std::vector<array>& outputs) {
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auto& s = stream();
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auto& d = metal::device(s.device);
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// Ensure row contiguity. We could relax this step by checking that the array
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// is contiguous (no broadcasts or holes) and that the input strides are the
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// same as the cotangent strides but for now this is simpler.
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std::vector<array> copies;
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auto check_input = [&copies, &s](const array& x) -> const array& {
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if (x.flags().row_contiguous) {
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return x;
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}
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// Make sure we 'll only ever allocate once. The point of that goes beyond
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// the minor optimization. We need to ensure that there will be no
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// reallocation such that the references won't change when we
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// push_back(...). So tl;dr 3 possible copies x, g and gw_temp.
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copies.reserve(3);
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copies.push_back(array(x.shape(), x.dtype(), nullptr, {}));
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copy_gpu(x, copies.back(), CopyType::General, s);
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return copies.back();
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};
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const array& x = check_input(inputs[0]);
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const array& w = inputs[1];
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const array& b = inputs[2];
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const array& g = check_input(inputs[3]);
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array& gx = outputs[0];
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array& gw = outputs[1];
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array& gb = outputs[2];
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// Allocate space for the outputs
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bool x_in_gx = false;
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bool g_in_gx = false;
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if (x.is_donatable()) {
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gx.move_shared_buffer(x);
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x_in_gx = true;
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} else if (g.is_donatable()) {
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gx.move_shared_buffer(g);
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g_in_gx = true;
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} else {
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gx.set_data(allocator::malloc_or_wait(gx.nbytes()));
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}
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auto axis_size = static_cast<uint32_t>(x.shape().back());
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int n_rows = x.data_size() / axis_size;
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// Allocate a temporary to store the gradients for w and allocate the output
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// gradient accumulators.
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array gw_temp({n_rows, x.shape().back()}, gw.dtype(), nullptr, {});
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bool g_in_gw = false;
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if (!g_in_gx && g.is_donatable()) {
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gw_temp.move_shared_buffer(g);
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g_in_gw = true;
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} else {
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gw_temp.set_data(allocator::malloc_or_wait(gw_temp.nbytes()));
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}
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copies.push_back(gw_temp);
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gw.set_data(allocator::malloc_or_wait(gw.nbytes()));
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gb.set_data(allocator::malloc_or_wait(gb.nbytes()));
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// Finish with the gradient for b in case we had a b
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auto& compute_encoder = d.get_command_encoder(s.index);
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if (gb.ndim() == 1 && gb.size() == axis_size) {
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ReductionPlan plan(
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ReductionOpType::ContiguousStridedReduce, {n_rows}, {axis_size});
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strided_reduce_general_dispatch(
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g_in_gx ? gx : (g_in_gw ? gw_temp : g),
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gb,
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"sum",
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plan,
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{0},
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compute_encoder,
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d,
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s);
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}
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const int simd_size = 32;
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const int n_reads = RMS_N_READS;
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const int looped_limit = RMS_LOOPED_LIMIT;
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std::string op_name = "vjp_layer_norm";
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if (axis_size > looped_limit) {
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op_name += "_looped";
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}
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op_name += type_to_name(gx);
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{
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auto kernel = d.get_kernel(op_name);
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MTL::Size grid_dims, group_dims;
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if (axis_size <= looped_limit) {
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size_t threadgroup_needed = (axis_size + n_reads - 1) / n_reads;
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size_t simds_needed = (threadgroup_needed + simd_size - 1) / simd_size;
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size_t threadgroup_size = simd_size * simds_needed;
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assert(threadgroup_size <= kernel->maxTotalThreadsPerThreadgroup());
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size_t n_threads = n_rows * threadgroup_size;
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grid_dims = MTL::Size(n_threads, 1, 1);
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group_dims = MTL::Size(threadgroup_size, 1, 1);
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} else {
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size_t threadgroup_size = kernel->maxTotalThreadsPerThreadgroup();
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size_t n_threads = n_rows * threadgroup_size;
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grid_dims = MTL::Size(n_threads, 1, 1);
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group_dims = MTL::Size(threadgroup_size, 1, 1);
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}
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uint32_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
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compute_encoder->setComputePipelineState(kernel);
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compute_encoder.set_input_array(x_in_gx ? gx : x, 0);
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compute_encoder.set_input_array(w, 1);
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compute_encoder.set_input_array(g_in_gx ? gx : (g_in_gw ? gw_temp : g), 2);
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compute_encoder.set_output_array(gx, 3);
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compute_encoder.set_output_array(gw_temp, 4);
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compute_encoder->setBytes(&eps_, sizeof(float), 5);
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compute_encoder->setBytes(&axis_size, sizeof(int), 6);
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compute_encoder->setBytes(&w_stride, sizeof(uint32_t), 7);
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compute_encoder.dispatchThreads(grid_dims, group_dims);
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}
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if (gw.ndim() == 1 && gw.size() == axis_size) {
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ReductionPlan plan(
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ReductionOpType::ContiguousStridedReduce, {n_rows}, {axis_size});
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strided_reduce_general_dispatch(
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gw_temp, gw, "sum", plan, {0}, compute_encoder, d, s);
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}
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d.add_temporaries(std::move(copies), s.index);
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}
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} // namespace mlx::core::fast
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