defmodule BatchServing do @moduledoc """ BatchServing encapsulates client and server work to perform batched requests. Servings can be executed on the fly, without starting a server, but most often they are used to run servers that batch requests until a given size or timeout is reached. More specifically, servings are a mechanism to apply a computation on a `BatchServing.Batch`, with hooks for preprocessing input from and postprocessing output for the client. Thus we can think of an instance of `t:Serving.t/0` (a serving) as something that encapsulates batches of Nx computations. ## Inline/serverless workflow We can use `new/1` to create a serving that will return a JIT or AOT compiled function to execute on batches of tensors: iex> serving = BatchServing.new(fn _opts -> fn a -> Enum.map(a.stack, &(&1 * &1)) end end) iex> batch = BatchServing.Batch.stack([1, 2, 3, 4]) iex> BatchServing.run(serving, batch) [1, 4, 9, 16] When defining a `Serving`, we can also customize how the data is batched by using the `client_preprocessing` as well as the result by using `client_postprocessing` hooks. iex> serving = ( ...> BatchServing.new(fn _opts -> fn a -> Enum.map(a.stack, &(&1 * &1)) end end) ...> |> BatchServing.client_preprocessing(fn input -> {input, :client_info} end) ...> |> BatchServing.client_postprocessing(&{&1, &2}) ...> ) iex> batch = BatchServing.Batch.stack([1, 2, 3, 4]) iex> BatchServing.run(serving, batch) {{[1, 4, 9, 16], :server_info}, :client_info} You can see the results are a bit different now. First of all, notice that we were able to run the serving passing a list of tensors. Our custom `client_preprocessing` function stacks those tensors into a batch of two entries and returns a tuple with a `BatchServing.Batch` struct and additional client information which we represent as the atom `:client_info`. The default client preprocessing simply enforces a batch (or a stream of batches) was given and returns no client information. Then the result is a triplet tuple, returned by the client postprocessing function, containing the result, the server information (which we will later learn how to customize), and the client information. From this, we can infer the default implementation of `client_postprocessing` simply returns the result, discarding the server and client information. So far, `Serving` has not given us much. It has simply encapsulated the execution of a function. Its full power comes when we start running our own `Serving` process. That's when we will also learn why we have a `client_` prefix in some of the function names. ## Stateful/process workflow `Serving` allows us to define an Elixir process to handle requests. This process provides several features, such as batching up to a given size or time, partitioning, and distribution over a group of nodes. To do so, we need to start a `BatchServing` process with a serving inside a supervision tree: children = [ {BatchServing, serving: BatchServing.new(fn _opts -> fn a -> Enum.map(IO.inspect(a.stack), &(&1 * &1)) end end), name: MyServing, batch_size: 10, batch_timeout: 100} ] Supervisor.start_child(children, strategy: :one_for_one) > Note: in your actual application, you want to make sure > `Serving` comes early in your supervision tree, for example > before your web application endpoint or your data processing > pipelines, as those processes may end-up hitting BatchServing. Now you can send batched runs to said process: iex> batch = BatchServing.Batch.stack([[1, 2, 3], [4, 5, 6]]) iex> BatchServing.batched_run(MyServing, batch) [ [2, 4, 6], [8, 10, 12] ] In the example, we pushed a batch of 2 and eventually got a reply. The process will wait for requests from other processes, for up to 100 milliseconds or until it gets 10 entries. Then it merges all batches together and once the result is computed, it slices and distributes those responses to each caller. If there is any `client_preprocessing` function, it will be executed before the batch is sent to the server. If there is any `client_postprocessing` function, it will be executed after getting the response from the server. ### Partitioning You can start several partitions under the same serving by passing `partitions: true` when starting the serving. The number of partitions will be determined according your compiler and for which host it is compiling. For example, when creating the serving, you may pass the following `defn_options`: BatchServing.new(computation, compiler: EXLA, client: :cuda) Now when booting up the serving: children = [ {BatchServing, serving: serving, name: MyServing, batch_size: 10, batch_timeout: 100, partitions: true} ] ### Distribution All `Serving`s are distributed by default. If the current machine does not have an instance of `Serving` running, `batched_run/3` will automatically look for one in the cluster. The nodes do not need to run the same code and applications. It is only required that they run the same `Nx` version. The load balancing between servings is done randomly, however, the number of partitions are considered if the `partitions: true` option is also given. The servings are dispatched using Erlang Distribution. You can use `Node.connect/1` to manually connect nodes. In a production setup, this is often done with the help of libraries like [`libcluster`](https://github.com/bitwalker/libcluster). ## Advanced notes ### Module-based serving In the examples so far, we have been using the default version of `Serving`, which executes the given function for each batch. However, we can also use `new/2` to start a module-based version of `Serving` which gives us more control over both inline and process workflows. A simple module implementation of a `Serving` could look like this: defmodule MyServing do @behaviour Serving @impl true def init(_inline_or_process, :unused_arg, [defn_options]) do {:ok, fn _opts -> fn a -> Enum.map(IO.inspect(a.stack), &(&1 * &1)) end end)} end @impl true def handle_batch(batch, 0, function) do {:execute, fn -> {function.(batch), :server_info} end, function} end end It has two functions. The first, `c:init/3`, receives the type of serving (`:inline` or `:process`) and the serving argument. The second function is called `c:handle_batch/3`. This function receives a `BatchServing.Batch` and returns a function to execute. The function itself must return a two element-tuple: the batched results and some server information. The server information can be any value and we set it to the atom `:server_info`. Now let's give it a try by defining a serving with our module and then running it on a batch: iex> serving = BatchServing.new(MyServing, :unused_arg) iex> batch = BatchServing.Batch.stack([[1, 2, 3]]) iex> BatchServing.run(serving, batch) [[1, 4, 9]] From here on, you use `start_link/1` to start this serving in your supervision and even customize `client_preprocessing/1` and `client_postprocessing/1` callbacks to this serving, as seen in the previous sections. Note in our implementation above assumes it won't run partitioned. In partitioned mode, `c:init/3` may receive multiple `defn_options` as the third argument and `c:handle_batch/3` may receive another partition besides 0. ### Streaming `Serving` allows both inputs and outputs to be streamed. In order to stream inputs, you only need to return a stream of `BatchServing.Batch` from the `client_preprocessing` callback. BatchServing will automatically take care of streaming the inputs in, regardless if using `run/2` or `batched_run/2`. It is recommended that the streaming batches have the same size as `batch_size`, to avoid triggering `batch_timeout` on every iteration (except for the last one which may be incomplete). To stream outputs, you must invoke `streaming/2` with any additional streaming configuration. When this is invoked, the `client_postprocessing` will receive a stream which you can further manipulate lazily using the functions in the `Stream` module. `streaming/2` also allows you to configure hooks and stream values directly from `Nx.Defn` hooks. However, when hook streaming is enabled, certain capabilities are removed: you cannot stream inputs nor have batches larger than the configured `batch_size`. You can enable both input and output streaming at once. ### Batch keys Sometimes it may be necessary to execute different functions under the same serving. Batch keys provide a mechanism to accumulate different batches, based on their key, which execute independently. As an example, we will do a serving which performs different operations based on the batch key, but it could also be used to perform the same operation for different templates: iex> serving = BatchServing.new(fn ...> :double, opts -> &Enum.map(&1.stack, fn v -> v * 2 end) ...> :half, opts -> &Enum.map(&1.stack, fn v -> v / 2 end) ...> end) iex> double_batch = BatchServing.Batch.concatenate([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) |> BatchServing.Batch.key(:double) iex> BatchServing.run(serving, double_batch) [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] iex> half_batch = BatchServing.Batch.concatenate([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) |> BatchServing.Batch.key(:half) iex> BatchServing.run(serving, half_batch) [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5] When using a process-based serving, you must specify the supported `:batch_keys` when the process is started. The batch keys will be available inside the `defn_options` passed as the third argument of the `c:init/3` callback. The batch keys will also be verified when the batch is returned from the client-preprocessing callback. """ alias __MODULE__ @doc false @enforce_keys [:module, :arg] defstruct [ :module, :arg, :client_preprocessing, :client_postprocessing, :streaming, :batch_size, distributed_postprocessing: &Function.identity/1, process_options: [], defn_options: [] ] @type metadata() :: term() @type client_info() :: term() @type client_preprocessing() :: (term() -> {BatchServing.Batch.t() | Enumerable.t(BatchServing.Batch.t()), client_info()}) @type client_postprocessing() :: ({list(), metadata()}, client_info() -> term()) @type distributed_preprocessing() :: (term() -> term()) @type distributed_postprocessing() :: (term() -> term()) @type t :: %__MODULE__{ module: atom(), arg: term(), client_preprocessing: client_preprocessing(), client_postprocessing: client_postprocessing(), distributed_postprocessing: distributed_postprocessing(), process_options: keyword(), defn_options: keyword(), streaming: nil | %{hooks: [atom()]}, batch_size: nil | pos_integer() } @process_keys [ :batch_size, :batch_timeout, :batch_keys, :partitions, :shutdown, :hibernate_after, :spawn_opt ] @doc """ The callback used to initialize the serving. The first argument reveals if the serving is executed inline, such as by calling `run/2`, by started with the process. The second argument is the serving argument given to `new/2`. The third argument option is a list of compiler options to be used to compile each partition the serving will run. It must return `{:ok, state}`, where the `state` can be any term. """ @callback init(type :: :inline | :process, arg :: term(), [defn_options :: keyword]) :: {:ok, state :: term()} @doc """ Receives a batch, a partition, and returns a function to execute the batch. In case of serving processes, the function is executed is an separate process. """ @callback handle_batch(BatchServing.Batch.t(), partition :: non_neg_integer(), state) :: {:execute, (-> {list(), metadata()}), state} when state: term() def create_serving_process_group_spec() do %{id: BatchServing.PG, start: {:pg, :start_link, [Serving.PG]}} end @doc """ Creates a new function serving. It expects a single- or double-arity function. If a single-arity function is given, it receives the compiler options and must return a one-arity function. If a double-arity function is given, it receives the batch key as first argument and the compiler options as second argument. It must return a one-arity function. The batch keys can be given on `start_link/1`. The function will be called with the arguments returned by the `client_preprocessing` callback. """ def new(function, defn_options \\ []) def new(function, defn_options) when (is_function(function, 1) or is_function(function, 2)) and is_list(defn_options) do new(BatchServing.Default, function, defn_options) end def new(function, process_options) when is_function(function, 0) and is_list(process_options) do IO.warn( "passing a zero-arity function to BatchServing.new is deprecated, " <> "please pass a single arity function that will receive the compiler options" ) new(Serving.Default, fn _ -> function.() end, []) |> process_options(process_options) end def new(module, arg) when is_atom(module) do new(module, arg, []) end @doc """ Sets the batch size for this serving. This batch size is used to split batches given to both `run/2` and `batched_run/2`, enforcing that the batch size never goes over a limit. If you only want to batch within the serving process, you must set `:batch_size` via `process_options/2` (or on `start_link/1`). > #### Why batch on `run/2`? {: .info} > > By default, `run/2` does not place a limit on its input size. It always > processes inputs directly within the current process. On the other hand, > `batched_run/2` always sends your input to a separate process, which > will batch and execute the serving only once the batch is full or a > timeout has elapsed. > > However, in some situations, an input given to `run/2` needs to be > broken into several batches. If we were to very large batches to our > computation, the computation could require too much memory. In such > cases, setting a batch size even on `run/2` is beneficial, because > BatchServing takes care of splitting a large batch into smaller ones > that do not exceed the `batch_size` value. """ def batch_size(%BatchServing{} = serving, batch_size) when batch_size > 0 do %{serving | batch_size: batch_size} end @doc """ Creates a new module-based serving. It expects a module and an argument that is given to its `init` callback. A third optional argument called `defn_options` are additional compiler options which will be given to the module. Those options will be merged into `Serving.default_options/0`. """ def new(module, arg, defn_options) when is_atom(module) and is_list(defn_options) do defn_options = Keyword.merge(BatchServing.default_options(), defn_options) %BatchServing{module: module, arg: arg, defn_options: defn_options} end @doc """ Sets the client preprocessing function. The default implementation expects a `BatchServing.Batch` or a stream of BatchServing.Batch to be given as input and return them as is. """ def client_preprocessing(%BatchServing{} = serving, function) when is_function(function, 1) or is_nil(function) do %{serving | client_preprocessing: function} end @doc """ Sets the client postprocessing function. The client postprocessing receives a tuple with the `{output, metadata}` or a stream as first argument. The second argument is always the additional information returned by the client preprocessing. The default implementation returns either the output or the stream. """ def client_postprocessing(%BatchServing{} = serving, function) when is_function(function, 2) or is_nil(function) do %{serving | client_postprocessing: function} end def client_postprocessing(%BatchServing{} = serving, function) when is_function(function, 3) do IO.warn( "Passing a 3-arity function to client_postprocessing is deprecated, " <> "instead a two-arity function that receives the output and metadata must be given" ) %{ serving | client_postprocessing: fn {output, metadata}, info -> function.(output, metadata, info) end } end @doc """ Sets the distributed postprocessing function. The default implementation is `Function.identity/1`. """ def distributed_postprocessing(%BatchServing{} = serving, function) when is_function(function, 1) do %{serving | distributed_postprocessing: function} end @doc """ Configure the serving to stream its results. Once `run/2` or `batched_run/2` are invoked, it will then return a stream. The stream must be consumed in the same process that calls `run/2` or `batched_run/2`. Batches will be streamed as they arrive. You may also opt-in to stream `Nx.Defn` hooks. ## Options * `:hooks` - a list of hook names that will become streaming events ## Implementation details ### Client postprocessing Once streaming is enabled, the client postprocessing callback will receive a stream which will emit events for each hook in the shape of: {hook_name, term()} The stream will also receive events in the shape of `{:batch, output, metadata}` as batches are processed by the serving. The client postprocessing is often expected to call `Stream.transform/3` to process those events into something usable by callers. If the `:hooks` option is given, only a single `:batch` event is emitted, at the end, as detailed next. ### Batch limits If you are streaming hooks, the serving server can no longer break batch and you are unable to push a payload bigger than `:batch_size`. For example, imagine you have a `batch_size` of 3 and you push three batches of two elements (AA, BB, and CC). Without hooks, the batches will be consumed as: AAB -> BCC With streaming, we can't break the batch `BB`, as above, so we will consistently pad with zeroes: AA0 -> BB0 -> CC0 In practice, this should not be a major problem, as you should generally avoid having a batch size that is not a multiple of the most common batches. """ def streaming(%BatchServing{} = serving, opts \\ []) do hooks = Keyword.get(opts, :hooks, []) if serving.streaming do raise ArgumentError, "serving is already marked as streaming" end %{serving | streaming: %{hooks: hooks}} end @doc """ Sets the process options of this serving. These are the same options as supported on `start_link/1`, except `:name` and `:serving` itself. """ def process_options(%BatchServing{} = serving, opts) when is_list(opts) do %{serving | process_options: Keyword.validate!(opts, @process_keys)} end @doc """ Sets the defn options of this serving. These are the options supported by `Serving.default_options/1`. """ def defn_options(%BatchServing{} = serving, defn_options) when is_list(defn_options) do %{serving | defn_options: defn_options} end def default_options() do [] end @doc """ Runs `serving` with the given `input` inline with the current process. The `serving` is executed immediately, without waiting or batching inputs from other processes. If a `batch_size/2` is specified, then the input may be split or padded, but they are still executed immediately inline. """ def run(%BatchServing{} = serving, input) do %{ module: module, arg: arg, client_preprocessing: preprocessing, client_postprocessing: postprocessing, defn_options: defn_options, streaming: streaming, batch_size: limit } = serving {batch_or_stream, info} = handle_preprocessing(preprocessing, input) {pid_ref, defn_options} = run_streaming(streaming, defn_options, batch_or_stream, limit) stream = run_batch_or_stream(batch_or_stream, limit) execution_result = case pid_ref do {pid, ref} -> send(pid, {ref, module, arg, defn_options, stream}) receive_stream("run/2", ref, :unknown) nil -> stream |> Enum.map_reduce(nil, fn %BatchServing.Batch{key: key, size: size} = batch, cache -> {:ok, state} = cache || handle_init(module, :inline, arg, [[batch_keys: [key]] ++ defn_options]) {{run_execute(batch, module, state), size}, {:ok, state}} end) |> elem(0) |> case do [{{output, metadata}, _size}] -> {output, metadata} [{{_output, metadata}, _size} | _] = all -> {all, metadata} end end handle_postprocessing(postprocessing, execution_result, info) end defp run_streaming(nil, defn_options, _batch_or_stream, _limit), do: {nil, defn_options} defp run_streaming(%{hooks: []}, defn_options, _batch_or_stream, _limit), do: {run_streaming(), defn_options} defp run_streaming(%{hooks: hooks}, defn_options, batch_or_stream, limit) do size = case batch_or_stream do %BatchServing.Batch{size: size} -> if limit == nil or size <= limit do size else raise ArgumentError, "batch size (#{size}) cannot exceed BatchServing server batch size of #{limit} when streaming hooks" end _ -> raise ArgumentError, "streaming hooks do not support input streaming, input must be a BatchServing.Batch" end {pid, ref} = run_streaming() defn_options = update_in(defn_options[:hooks], fn acc -> Enum.reduce(hooks, acc || %{}, fn hook, acc -> Map.put(acc, hook, &run_hook(ref, size, &1, hook)) end) end) {{pid, ref}, defn_options} end defp run_streaming do pid = spawn_link(fn -> receive do {ref, module, arg, defn_options, stream} -> Enum.reduce(stream, {0, nil}, fn %BatchServing.Batch{key: key, size: size} = batch, {start, cache} -> {:ok, state} = cache || handle_init(module, :inline, arg, [[batch_keys: [key]] ++ defn_options]) {output, metadata} = run_execute(batch, module, state) send(ref, {ref, {:batch, {0, size, output, metadata}}}) {start + size, {:ok, state}} end) end end) {pid, Process.monitor(pid, alias: :demonitor)} end defp run_hook(ref, size, result, hook) do send(ref, {ref, {:hook, {0, size, result, hook}}}) end defp run_batch_or_stream(%BatchServing.Batch{size: size} = batch, limit) when is_nil(limit) or size < limit do [batch] end defp run_batch_or_stream(%BatchServing.Batch{} = batch, limit) do Stream.unfold(batch, fn %BatchServing.Batch{size: size} = batch when size > limit -> BatchServing.Batch.split(batch, limit) %BatchServing.Batch{} = batch -> {batch, :done} :done -> nil end) end defp run_batch_or_stream(stream, limit) do Stream.each(stream, fn %BatchServing.Batch{size: size} when is_nil(limit) or size <= limit -> :ok other -> raise "client_preprocessing must return a stream of BatchServing.Batch" <> if(limit, do: " of maximum size #{limit}", else: "") <> ", got: #{inspect(other)}" end) end defp run_execute(batch, module, state) do {:execute, function, _} = handle_batch(module, batch, 0, state) :telemetry.span([:nx, :serving, :execute], %{module: module}, fn -> {output, metadata} = handle_executed(module, function.()) {{output, metadata}, %{module: module, metadata: metadata}} end) end ## Process API @doc false def child_spec(opts) when is_list(opts) do name = opts[:name] if name == nil or not is_atom(name) do raise ArgumentError, ":name option is expected when starting BatchServing and must be an atom" end opts[:serving] || raise ArgumentError, ":serving option is expected when starting Serving" %{ id: name, start: {__MODULE__, :start_link, [opts]}, type: :supervisor } end @doc """ Starts a `Serving` process to batch requests to a given serving. ## Options All options, except `:name` and `:serving`, can also be set via `process_options/2`. * `:name` - an atom with the name of the process * `:serving` - a `Serving` struct with the serving configuration * `:batch_keys` - all available batch keys. Batch keys allows Serving to accumulate different batches with different properties. Defaults to `[:default]` * `:batch_size` - the maximum batch size. A default value can be set with `batch_size/2`, which applies to both `run/2` and `batched_run/2`. Setting this option only affects `batched_run/2` and it defaults to `1` if none is set. * `:batch_timeout` - the maximum time to wait, in milliseconds, before executing the batch (defaults to `100`ms) * `:partitions` - when `true`, starts several partitions under this serving. The number of partitions will be determined according to your compiler and for which host it is compiling. See the module docs for more information * `:shutdown` - the maximum time for the serving to shutdown. This will block until the existing computation finishes (defaults to `30_000`ms) * `:hibernate_after` and `:spawn_opt` - configure the underlying serving workers (see `GenServer.start_link/3`) """ def start_link(opts) do opts = Keyword.validate!(opts, [:name, :serving] ++ @process_keys) name = Keyword.fetch!(opts, :name) serving = Keyword.fetch!(opts, :serving) opts = Keyword.merge(serving.process_options, opts) serving_batch_size = serving.batch_size opts_batch_size = opts[:batch_size] batch_size = if serving_batch_size && opts_batch_size && serving_batch_size != opts_batch_size do raise ArgumentError, "the batch size set via BatchServing.batch_size/2 (#{serving_batch_size}) " <> "does not match the batch size given to the serving process (#{opts_batch_size})" else serving_batch_size || opts_batch_size || 1 end shutdown = Keyword.get(opts, :shutdown, 30_000) partitions = Keyword.get(opts, :partitions, false) batch_keys = Keyword.get(opts, :batch_keys, [:default]) batch_timeout = Keyword.get(opts, :batch_timeout, 100) process_options = Keyword.take(opts, [:name, :hibernate_after, :spawn_opt]) supervisor = Module.concat(name, "Supervisor") task_supervisor = Module.concat(name, "TaskSupervisor") arg = {name, serving, partitions, batch_keys, batch_size, batch_timeout, task_supervisor} children = [ {Task.Supervisor, name: task_supervisor}, %{ id: __MODULE__, start: {GenServer, :start_link, [__MODULE__, arg, process_options]}, shutdown: shutdown } ] Supervisor.start_link(children, strategy: :one_for_all, max_restarts: 0, name: supervisor) end @doc """ Runs the given `input` on the serving process given by `name`. `name` is either an atom representing a local or distributed serving process. First it will attempt to dispatch locally, then it falls back to the distributed serving. You may specify `{:local, name}` to force a local lookup or `{:distributed, name}` to force a distributed one. The `client_preprocessing` callback will be invoked on the `input` which is then sent to the server. The server will batch requests and send a response either when the batch is full or on timeout. Then `client_postprocessing` is invoked on the response. See the module documentation for more information. In the distributed case, the callbacks are invoked in the distributed node, but still outside of the serving process. Note that you cannot batch an `input` larger than the configured `:batch_size` in the server. ## Distributed mode To run in distributed mode, the nodes do not need to run the same code and applications. It is only required that they run the same `Nx` version. If the current node is running a serving given by `name` locally and `{:distributed, name}` is used, the request will use the same distribution mechanisms instead of being handled locally, which is useful for testing locally without a need to spawn nodes. This function receives an optional `distributed_preprocessing` callback as third argument for preprocessing the input for distributed requests. When using libraries like EXLA or Torchx, the tensor is often allocated in memory inside a third-party library so it may be necessary to either transfer or copy the tensor to the binary backend before sending it to another node. This can be done by passing either `Nx.backend_transfer/1` or `Nx.backend_copy/1` as third argument: BatchServing.batched_run(MyDistributedServing, input, &Nx.backend_copy/1) Use `backend_transfer/1` if you know the input will no longer be used. Similarly, the serving has a `distributed_postprocessing` callback which can do equivalent before sending the reply to the caller. """ def batched_run(name, input, distributed_preprocessing \\ &Function.identity/1) def batched_run(name, input, distributed_preprocessing) when is_atom(name) do if pid = Process.whereis(name) do local_batched_run!(pid, name, input) else distributed_batched_run!(name, input, distributed_preprocessing) end end def batched_run({:local, name}, input, _distributed_preprocessing) when is_atom(name) do pid = Process.whereis(name) || exit({:noproc, {__MODULE__, :local_batched_run, [name, input]}}) local_batched_run!(pid, name, input) end def batched_run({:distributed, name}, input, distributed_preprocessing) when is_atom(name) do distributed_batched_run!(name, input, distributed_preprocessing) end defp local_batched_run!(pid, name, input) do case local_batched_run(pid, name, input) do {:ok, result} -> result {:DOWN, reason} -> exit({reason, {__MODULE__, :local_batched_run, [name, input]}}) end end defp local_batched_run(pid, name, input) do %{ preprocessing: preprocessing, postprocessing: postprocessing, limit: limit, mode: mode, batch_keys: batch_keys } = :persistent_term.get(persistent_key(name), nil) || raise( ArgumentError, "could not find BatchServing with name #{inspect(name)}. " <> "Make sure your BatchServing is running and/or started as part of your supervision tree" ) {preprocessed, info} = handle_preprocessing(preprocessing, input) ref = Process.monitor(pid, alias: :demonitor) size_or_unknown = case preprocessed do %BatchServing.Batch{size: size} = batch -> if mode == :hooks and batch.size > limit do raise ArgumentError, "batch size (#{batch.size}) cannot exceed BatchServing server batch size of #{limit} when streaming hooks" end validate_batch_key!(batch, batch_keys) Process.send(pid, {__MODULE__, :batched_run, [ref], batch}, [:noconnect]) size stream -> if mode == :hooks do raise ArgumentError, "streaming hooks do not support input streaming, input must be a BatchServing.Batch" end spawn_link(fn -> # We also need to monitor the streaming process. To avoid leaking # messages in the parent inbox, we ask the serving to do it. Process.send(pid, {__MODULE__, :proxy_monitor, self(), ref}, [:noconnect]) monitor_ref = Process.monitor(pid) acc = Enum.reduce(stream, 0, fn %BatchServing.Batch{size: size} = batch, acc when size <= limit -> receive_size(monitor_ref, ref, acc) validate_batch_key!(batch, batch_keys) refs = [ref, self()] Process.send(pid, {__MODULE__, :batched_run, refs, batch}, [:noconnect]) size other, _acc -> raise "client_preprocessing must return a stream of BatchServing.Batch " <> "of maximum size #{limit}, got: #{inspect(other)}" end) receive_size(monitor_ref, ref, acc) end) :unknown end case mode do :execute -> case receive_execute(ref, size_or_unknown) do {:ok, tensor, metadata} -> {:ok, handle_postprocessing(postprocessing, {tensor, metadata}, info)} {:DOWN, reason} -> {:DOWN, reason} end _ -> stream = receive_stream("batched_run/2", ref, size_or_unknown) {:ok, handle_postprocessing(postprocessing, stream, info)} end end defp validate_batch_key!(batch, batch_keys) do unless is_map_key(batch_keys, batch.key) do raise ArgumentError, "unknown batch key: #{inspect(batch.key)} (expected one of #{inspect(Map.keys(batch_keys))})" end end defp distributed_batched_run!(name, input, distributed_callback) do distributed_batched_run_with_retries!(name, distributed_callback.(input), 3) end defp distributed_batched_run_with_retries!(name, input, 0) do exit({:noproc, {__MODULE__, :distributed_batched_run, [name, input, [retries: 0]]}}) end defp distributed_batched_run_with_retries!(name, input, retries) do case :pg.get_members(Serving.PG, __MODULE__) do [] -> exit({:noproc, {__MODULE__, :distributed_batched_run, [name, input, [retries: retries]]}}) entries -> pid = Enum.random(entries) ref = make_ref() args = [self(), ref, name, input] {_, monitor_ref} = Node.spawn_monitor(node(pid), __MODULE__, :__distributed_batched_run__, args) receive do {^ref, :streaming} -> owner = self() Stream.resource( fn -> if self() != owner do raise "the stream returned from BatchServing.batched_run/2 must be consumed in the same process" end :ok end, fn :ok -> receive do {^ref, event} -> {[event], :ok} {:DOWN, ^monitor_ref, _, _, {^ref, :streaming}} -> {:halt, :ok} {:DOWN, ^monitor_ref, _, _, reason} -> exit({reason, {BatchServing, :streaming, []}}) end end, fn _ -> :ok end ) {:DOWN, ^monitor_ref, _, _, {^ref, result}} -> result {:DOWN, ^monitor_ref, _, _, :noproc} -> distributed_batched_run_with_retries!(name, input, retries - 1) {:DOWN, ^monitor_ref, _, _, reason} -> exit_args = [name, input, [retries: retries]] exit({reason, {__MODULE__, :distributed_batched_run, exit_args}}) end end end @doc false def __distributed_batched_run__(client_pid, ref, name, input) do pid = Process.whereis(name) || exit(:noproc) case local_batched_run(pid, name, input) do {:ok, result} -> %{mode: mode, distributed_postprocessing: dist_post} = :persistent_term.get(persistent_key(name)) if mode == :execute do exit({ref, dist_post.(result)}) else send(client_pid, {ref, :streaming}) Enum.each(dist_post.(result), &send(client_pid, {ref, &1})) exit({ref, :streaming}) end {:DOWN, reason} -> exit(reason) end end ## Client message receiving defp receive_size(_monitor, _ref, 0), do: :ok defp receive_size(monitor_ref, ref, pending) do receive do {^ref, size} -> receive_size(monitor_ref, ref, pending - size) {:DOWN, ^monitor_ref, _, _, reason} -> exit(reason) end end defp receive_stream(fun, ref, size) when is_integer(size) or size == :unknown do owner = self() Stream.resource( fn -> if self() != owner do raise "the stream returned from BatchServing.#{fun} must be consumed in the same process" end 0 end, fn ^size -> {:halt, :done} index -> case receive_each(ref, size, index) do :done -> {:halt, :done} {:hook, {hook_start, hook_size, output, hook}} -> value = Enum.slice(output, hook_start, hook_size) {[{hook, value}], index} {:batch, {output_start, output_size, output, metadata}} -> value = Enum.slice(output, output_start, output_size) {[{:batch, value, metadata}], index + output_size} {:DOWN, reason} -> exit({reason, {BatchServing, :streaming, []}}) end end, fn _ -> :ok end ) end defp receive_execute(ref, size) when is_integer(size) or size == :unknown do receive_execute(ref, size, 0, [], nil) end defp receive_execute(ref, size, index, acc, template_metadata) do case receive_each(ref, size, index) do :done -> {_template, metadata} = template_metadata || raise "unexpected error: streaming finished before it started" {:ok, acc, metadata} {:batch, {output_start, output_size, output, metadata}} -> # If we have a single response, slice and return immediately. # Otherwise we collect their contents and build the concatenated result later. if acc == [] and output_size + index == size do {:ok, Enum.slice(output, output_start, output_size), metadata} else receive_execute( ref, size, index + output_size, acc ++ Enum.slice(output, output_start, output_size), {output, metadata} ) end {:DOWN, reason} -> {:DOWN, reason} end end defp receive_each(_ref, size, size) do :done end defp receive_each(ref, size, index) do receive do {^ref, {:hook, _} = reply} -> reply {^ref, {:batch, {_output_start, output_size, _output, _metadata}} = reply} -> if output_size + index == size do Process.demonitor(ref, [:flush]) end reply # The serving itself never finishes with normal reason, # but the streaming process does to signal it is concluded # and its messages are proxied here. {:DOWN, ^ref, _, _, :normal} -> Process.demonitor(ref, [:flush]) :done {:DOWN, ^ref, _, _, reason} -> # We fake monitor messages, so still demonitor and flush. Process.demonitor(ref, [:flush]) {:DOWN, reason} end end ## Process callbacks require Logger @behaviour GenServer @empty_stack {[], 0, :none} @empty_queue :queue.new() @timeout_message {__MODULE__, :timeout} @impl true def init({name, serving, partitions?, batch_keys, batch_size, batch_timeout, task_supervisor}) do Process.flag(:trap_exit, true) partitions_opts = serving_partitions(serving, partitions?) partitions_count = length(partitions_opts) {mode, partitions_opts, hooks_table} = serving_streaming(serving, partitions_opts) partitions_opts = Enum.map(partitions_opts, &Keyword.put(&1, :batch_keys, batch_keys)) {:ok, module_state} = handle_init(serving.module, :process, serving.arg, partitions_opts) :persistent_term.put( persistent_key(name), %{ limit: batch_size, preprocessing: serving.client_preprocessing, postprocessing: serving.client_postprocessing, distributed_postprocessing: serving.distributed_postprocessing, mode: mode, batch_keys: Map.from_keys(batch_keys, []) } ) :pg.join(Serving.PG, __MODULE__, List.duplicate(self(), partitions_count)) for batch_key <- batch_keys do stack_init(batch_key) end # We keep batches in a stack. Once the stack is full # or it times out, we either execute or enqueue it. state = %{ module: serving.module, module_state: module_state, limit: batch_size, timeout: batch_timeout, in_queue: @empty_queue, out_queue: Enum.reduce(0..(partitions_count - 1), :queue.new(), &:queue.in/2), tasks: [], pending_batches: Map.from_keys(batch_keys, @empty_queue), task_supervisor: task_supervisor, hooks_table: hooks_table } {:ok, state} end defp serving_partitions(%BatchServing{defn_options: defn_options}, true) do [defn_options] end defp serving_partitions(%BatchServing{defn_options: defn_options}, false) do [defn_options] end defp serving_streaming(%BatchServing{streaming: nil}, partitions) do {:execute, partitions, nil} end defp serving_streaming(%BatchServing{streaming: %{hooks: []}}, partitions) do {:batches, partitions, nil} end defp serving_streaming(%BatchServing{streaming: %{hooks: hooks}}, partitions) do ets = :ets.new(__MODULE__, [:public, :set, read_concurrency: true]) partitions = Enum.with_index(partitions, fn defn_options, index -> update_in(defn_options[:hooks], fn acc -> Enum.reduce(hooks, acc || %{}, fn hook, acc -> Map.put(acc, hook, &server_hook(ets, index, hook, &1)) end) end) end) {:hooks, partitions, ets} end defp server_hook(ets, index, hook, result) do for {[ref | _pids], start, size} <- :ets.lookup_element(ets, index, 2) do send(ref, {ref, {:hook, {start, size, result, hook}}}) end end @impl true def handle_info({__MODULE__, :proxy_monitor, pid, ref}, state) do Process.monitor(pid, tag: {:proxy, ref}) {:noreply, state} end def handle_info({__MODULE__, :batched_run, refs, %BatchServing.Batch{key: key} = batch}, state) do %{limit: limit} = state count = stack_count(key) state = cond do # Single entry takes the whole batch. # Execute what we have (if any) and execute a new one. batch.size == limit -> state |> server_execute(key) |> server_stack(key, refs, batch, :skip_timer) |> server_execute(key) # We go over the limit, but if using hooks, we can't split. batch.size + count > limit and state.hooks_table != nil -> state |> server_execute(key) |> server_stack(key, refs, batch, :set_timer) # Split as necessary. true -> server_stack_and_execute_loop(state, batch, count, key, refs) end {:noreply, state} end def handle_info({@timeout_message, key, ref}, %{out_queue: out_queue} = state) do case stack_timer(key) do # We have processing power, so execute it immediately. {^ref, _timer_ref} when out_queue != @empty_queue -> {:noreply, server_execute(state, key)} # Otherwise we will queue it but keep on increasing the batch. {^ref, _timer_ref} -> stack_update(key, fn {[_ | _] = stack, count, _timer} -> {stack, count, :done} end) {:noreply, update_in(state.in_queue, &:queue.in(key, &1))} # Otherwise this is an old timer message, just ignore it. _ -> {:noreply, state} end end def handle_info({ref, :done}, %{tasks: tasks} = state) do case Enum.split_with(tasks, &(elem(&1, 0).ref == ref)) do {[{_task, partition, _ref_sizes}], tasks} -> Process.demonitor(ref, [:flush]) noreply_task_done_and_continue(state, tasks, partition) _ -> {:noreply, state} end end def handle_info({{:proxy, ref}, _ref, type, info, reason}, state) do send(ref, {:DOWN, ref, type, info, reason}) {:noreply, state} end def handle_info({:DOWN, ref, :process, _process, reason}, %{tasks: tasks} = state) do case Enum.split_with(tasks, &(elem(&1, 0).ref == ref)) do {[{_task, partition, ref_sizes}], tasks} -> server_reply_down(reason, ref_sizes) noreply_task_done_and_continue(state, tasks, partition) _ -> {:noreply, state} end end def handle_info(msg, state) do Logger.warning("Unknown message in Serving: #{inspect(msg)}") {:noreply, state} end @impl true def handle_continue(:maybe_task, state) do {:noreply, server_maybe_task(state)} end @impl true def terminate(_reason, %{tasks: tasks, pending_batches: pending_batches}) do for {batch_key, queue} <- pending_batches do # Emulate the process is gone for entries in the queue for {_batch, ref_sizes} <- :queue.to_list(queue) do server_reply_down(:noproc, ref_sizes) end # As well as for entries in the stack for {[ref | _pids], _batch} <- stack_entries(batch_key) do send(ref, {:DOWN, ref, :process, self(), :noproc}) end end # And wait until all current tasks are processed for {%Task{ref: ref}, _partition, ref_sizes} <- tasks do receive do {^ref, :done} -> Process.demonitor(ref, [:flush]) {:DOWN, ^ref, :process, _, reason} -> server_reply_down(reason, ref_sizes) end end :ok end # We don't spawn the task here because, if it crashes, # we want a checked-in version of the state that knows # the current task has finished. defp noreply_task_done_and_continue(%{out_queue: out_queue} = state, tasks, partition) do out_queue = :queue.in(partition, out_queue) {:noreply, %{state | tasks: tasks, out_queue: out_queue}, {:continue, :maybe_task}} end defp server_reply_down(reason, ref_sizes) do for {[ref | _refs], _start, _size} <- ref_sizes do send(ref, {:DOWN, ref, :process, self(), reason}) end end defp server_stack_and_execute_loop(state, batch, count, key, refs) do %{limit: limit} = state %{size: size} = batch cond do size + count < limit -> server_stack(state, key, refs, batch, :set_timer) size + count > limit -> {current, batch} = BatchServing.Batch.split(batch, limit - count) state |> server_stack(key, refs, current, :skip_timer) |> server_execute(key) |> server_stack_and_execute_loop(batch, 0, key, refs) true -> state |> server_stack(key, refs, batch, :skip_timer) |> server_execute(key) end end defp server_stack(%{limit: limit} = state, key, refs, batch, timer_mode) do stack_update(key, fn {stack, count, timer} when batch.size + count <= limit -> timer = if timer == :none and timer_mode == :set_timer do ref = make_ref() {ref, Process.send_after(self(), {@timeout_message, key, ref}, state.timeout)} else timer end {[{refs, batch} | stack], count + batch.size, timer} end) state end defp server_execute(state, key) do if stack_count(key) == 0 do state else {batch_refs, timer} = stack_to_batch_refs(key) state = update_in(state.pending_batches[key], &:queue.in(batch_refs, &1)) state = if timer == :done do state else update_in(state.in_queue, &:queue.in(key, &1)) end server_maybe_task(state) end end defp server_maybe_task(state) do %{out_queue: out_queue, in_queue: in_queue, pending_batches: pending_batches} = state with {{:value, partition}, out_queue} <- :queue.out(out_queue), {{:value, key}, in_queue} <- :queue.out(in_queue) do {{batch, ref_sizes}, pending_batches} = case :queue.out(pending_batches[key]) do {:empty, _pending_batches} -> # If there is no entry pending, then we have a timed-out in-construction batch. {batch_refs, :done} = stack_to_batch_refs(key) {batch_refs, pending_batches} {{:value, batch_refs}, queue} -> {batch_refs, Map.put(pending_batches, key, queue)} end %{module: module, module_state: module_state, hooks_table: hooks_table} = state {:execute, function, module_state} = handle_batch(module, batch, partition, module_state) wrapped_function = fn -> :telemetry.span([:nx, :serving, :execute], %{module: module}, fn -> if hooks_table do :ets.insert(hooks_table, {partition, ref_sizes}) end {output, metadata} = function.() for {[ref | pids], start, size} <- ref_sizes do send(ref, {ref, {:batch, {start, size, output, metadata}}}) for pid <- pids do send(pid, {ref, size}) end end {:done, %{metadata: metadata, module: module}} end) end task = Task.Supervisor.async_nolink(state.task_supervisor, wrapped_function) tasks = [{task, partition, ref_sizes} | state.tasks] %{ state | module_state: module_state, tasks: tasks, out_queue: out_queue, in_queue: in_queue, pending_batches: pending_batches } else _ -> state end end ## Stack management # # The stack is stored in the process dictionary for performance # since the common case does not use any batch key. defp stack_init(key) do Process.put({__MODULE__, key}, @empty_stack) :ok end defp stack_count(key) do {_stack, count, _timer} = Process.get({__MODULE__, key}) count end defp stack_timer(key) do {_stack, _count, timer} = Process.get({__MODULE__, key}) timer end defp stack_entries(key) do {stack, _count, _timer} = Process.get({__MODULE__, key}) stack end defp stack_update(key, fun) do Process.put({__MODULE__, key}, fun.(Process.get({__MODULE__, key}))) :ok end defp stack_to_batch_refs(key) do {[_ | _] = stack, count, timer} = Process.get({__MODULE__, key}) :ok = stack_init(key) with {ref, timer_ref} <- timer do Process.cancel_timer(timer_ref) receive do {@timeout_message, ^key, ^ref} -> :ok after 0 -> :ok end end {ref_sizes, batches, _} = Enum.reduce(stack, {[], [], count}, fn {refs, batch}, {ref_sizes, batches, ending} -> size = batch.size {[{refs, ending - size, size} | ref_sizes], [batch | batches], ending - size} end) {{BatchServing.Batch.merge(batches), ref_sizes}, timer} end ## Shared helpers defp persistent_key(name) when is_atom(name) do {__MODULE__, name} end defp handle_init(module, type, arg, [_ | _] = partitions) do case module.init(type, arg, partitions) do {:ok, _} = pair -> pair other -> raise "#{inspect(module)}.init/3 must return {:ok, state}. Got: #{inspect(other)}" end end defp handle_batch(module, batch, partition, state) do case module.handle_batch(batch, partition, state) do {:execute, function, _} = pair when is_function(function, 0) -> pair other -> raise "#{inspect(module)}.handle_batch/3 must return {:execute, function, state}, " <> "where function is a function that receives no arguments and returns a tuple. " <> "Got: #{inspect(other)}" end end defp handle_executed(module, result) do case result do {output, metadata} -> {output, metadata} other -> raise "the function returned by #{inspect(module)}.handle_batch/3 must return {output, metadata}. " <> "Got: #{inspect(other)}" end end defp handle_preprocessing(nil, input) do batch_or_stream = validate_batch_or_stream(input) || raise( ArgumentError, "the default client_preprocessing expects a BatchServing.Batch or a stream of BatchServing.Batch as input. " <> "Give a batch or use a custom preprocessing" ) {batch_or_stream, :client_info} end defp handle_preprocessing(preprocessing, input) do meta = %{input: input} :telemetry.span([:nx, :serving, :preprocessing], meta, fn -> result = preprocessing.(input) case result do {batch_or_stream, info} -> batch_or_stream = validate_batch_or_stream(batch_or_stream) || raise_bad_client_preprocessing!(preprocessing, result) {{batch_or_stream, info}, Map.put(meta, :info, info)} _ -> raise_bad_client_preprocessing!(preprocessing, result) end end) end defp raise_bad_client_preprocessing!(preprocessing, result) do raise "client_preprocessing function #{inspect(preprocessing)} must return a two element tuple " <> "where the first element is a BatchServing.Batch or a stream of batches and the second is any value. Got: #{inspect(result)}" end defp validate_batch_or_stream(%BatchServing.Batch{size: 0}), do: raise(ArgumentError, "cannot run with empty BatchServing.Batch") defp validate_batch_or_stream(%BatchServing.Batch{} = batch), do: batch defp validate_batch_or_stream(stream) do if Enumerable.impl_for(stream) do stream end end defp handle_postprocessing(nil, {output, _metadata}, _info), do: output defp handle_postprocessing(nil, stream, _info), do: stream defp handle_postprocessing(postprocessing, result, info) do meta = %{info: info} :telemetry.span([:nx, :serving, :postprocessing], meta, fn -> {postprocessing.(result, info), meta} end) end end defmodule BatchServing.Default do @moduledoc false @behaviour BatchServing @impl true def init(_type, fun, partitions) do batch_funs = Enum.with_index(partitions, fn defn_options, index -> value = cond do is_function(fun, 1) -> validate_batch_fun!(fun.(defn_options)) is_function(fun, 2) -> {batch_keys, defn_options} = Keyword.pop!(defn_options, :batch_keys) for batch_key <- batch_keys, into: %{}, do: {batch_key, validate_batch_fun!(fun.(batch_key, defn_options))} end {index, value} end) {:ok, Map.new(batch_funs)} end defp validate_batch_fun!(batch_fun) when is_function(batch_fun, 1), do: batch_fun defp validate_batch_fun!(other) do raise "anonymous function given to BatchServing.new/2 should return an AOT or " <> "JIT compiled function that expects one argument. Got: #{inspect(other)}" end @impl true def handle_batch(batch, partition, batch_funs) do batch_fun = case batch_funs do %{^partition => batch_keys} when is_map(batch_keys) -> Map.fetch!(batch_keys, batch.key) %{^partition => fun} -> fun end {:execute, fn -> {batch_fun.(batch), :server_info} end, batch_funs} end end