Executor API
TensorRT-LLM includes a high-level C++ API called the Executor API which allows you to execute requests asynchronously, with in-flight batching, and without the need to define callbacks.
A software component (referred to as “the client” in the text that follows) can interact
with the executor using the API defined in the executor.h
file.
For details about the API, refer to the _cpp_gen/executor.rst.
The following sections provide an overview of the main classes defined in the Executor API.
The Executor Class
The Executor
class is responsible for receiving requests from the client, and providing responses for those requests. The executor is constructed by providing a path to a directory containing the TensorRT-LLM engine or buffers containing the engine and the model JSON configuration. The client can create requests and enqueue those requests for execution using the enqueueRequest
or enqueueRequests
methods of the Executor
class. Enqueued requests will be scheduled for execution by the executor, and multiple independent requests can be batched together at every iteration of the main execution loop (a process often referred to as continuous batching or iteration-level batching). Responses for a particular request can be awaited for by calling the awaitResponses
method, and by providing the request id. Alternatively, responses for any requests can be awaited for by omitting to provide the request id when calling awaitResponses
. The Executor
class also allows to cancel requests using the cancelRequest
method and to obtain per-iteration and per-request statistics using the getLatestIterationStats
.
The Request Class
The Request
class is used to define properties of the request, such as the input token ids and the maximum number of tokens to generate. The streaming
parameter can be used to indicate if the request should generate a response for each new generated tokens (streaming = true
) or only after all tokens have been generated (streaming = false
). Other mandatory parameters of the request include the sampling configuration (defined by the SamplingConfig
class) which contains parameters controlling the decoding process and the output configuration (defined by the OutputConfig
class) which controls what information should be included in the Result
for a particular response.
Optional parameters can also be provided when constructing a request such as a list of bad words, a list of stop words, a client id, or configurations objects for prompt tuning, LoRA, or speculative decoding, or a number of sequences to generate for example.
The Response Class
The awaitResponses
method of the Executor
class returns a vector of responses. Each response contains the request id associated with this response, and also contains either an error or a Result
. Check if the response has an error by using the hasError
method before trying to obtain the Result
associated with this response using the getResult
method.
The Result Class
The Result
class holds the result for a given request. It contains a Boolean parameter called isFinal
that indicates if this is the last Result
that will be returned for the given request id. It also contains the generated tokens. If the request is configured with streaming = false
and numReturnSequences = 1
, a single response will be returned, the isFinal
Boolean will be set to true
and all generated tokens will be included in the outputTokenIds
. If streaming = true
and numReturnSequences = 1
is used, a Result
will include one or more tokens (depending on the request returnAllGeneratedTokens
parameter) except the last result and the isFinal
flag will be set to true
for the last result associated with this request.
The request numReturnSequences
parameter controls the number of output sequences to generate for each prompt. When this option is used, the Executor will return at least numReturnSequences
responses for each request, each containing one Result. The sequenceIndex
attribute of the Result
class indicates the index of the generated sequence in the result (0 <= sequenceIndex < numReturnSequences
). It contains a Boolean parameter called isSequenceFinal
that indicates if this is the last result for the sequence and also contains a Boolean parameter isFinal
that indicates when all sequences for the request have been generated. When numReturnSequences = 1
, isFinal
is identical to isSequenceFinal
.
Here is an example that shows how a subset of 3 responses might look like for numReturnSequences = 3
:
Response 1: requestId = 1, Result with sequenceIndex = 0, isSequenceFinal = false, isFinal = false
Response 2: requestId = 1, Result with sequenceIndex = 1, isSequenceFinal = true, isFinal = false
Response 3: requestId = 1, Result with sequenceIndex = 2, isSequenceFinal = false, isFinal = false
In this example, each response contains one result for different sequences. The isSequenceFinal
flag of the second Result is set to true, indicating that it is the last result for sequenceIndex = 1
, however, the isFinal flag of each Response is set to false because sequences 0 and 2 are not completed.
Sending Requests with Different Beam Widths
The executor can process requests with different beam widths if the following conditions are met:
The model was built with a
max_beam_width > 1
.The executor is configured with a
maxBeamWidth > 1
(the configuredmaxBeamWidth
must be less than or equal to the model’smax_beam_width
).The requested beam widths are less than or equal to the configured
maxBeamWidth
.For requests with two different beam widths,
x
andy
, requests with beam widthy
are not enqueued until all responses for requests with beam widthx
have been awaited.
The request queue of the executor must be empty to allow it to reconfigure itself for a new beam width. This reconfiguration will happen automatically when requests with a new beam width are enqueued. If requests with different beam widths are enqueued at the same time, the executor will encounter an error and terminate all requests prematurely.
Controlling output with Logits Post-Processor
Optionally, you can alter the logits produced by the network by providing an instance of Executor::LogitsPostProcessorConfig
. For instance, this feature can be used to generate JSON formatted output. Executor::LogitsPostProcessorConfig
specifies a map of named callbacks in the following form
std::unordered_map<std::string, function<Tensor(IdType, Tensor&, BeamTokens const&, StreamPtr const&, std::optional<IdType>)>>
The map key is the name associated with that logits post-processing callback. Each request can then specify the name of the logits post-processor to use for that particular request, if any.
The first argument to the callback is the request id, second is the logits tensor, third are the tokens produced by the request so far, fourth is the operation stream used by the logits tensor, and last one is an optional client id. The callback returns a modified tensor of logits. Multiple requests can share same client id and callback can use different logic based on client id.
You must use the stream to access the logits tensor. For example, to perform an addition with a bias tensor, the addition operation is enqueued on that stream. Alternatively, you can call stream->synchronize()
, however, that will slow down the entire execution pipeline.
The executor also includes a LogitsPostProcessorBatched
method that enables altering logits of multiple requests in a batch. The batched method allows further optimizations and reduces callback overheads.
std::function<void(std::vector<IdType> const&, std::vector<Tensor>&, std::vector<std::reference_wrapper<BeamTokens const>> const&, StreamPtr const&, std::vector<std::optional<IdType>> const&)>
A single batched callback can be specified in LogitsPostProcessorConfig
. Each request can opt to apply this callback by specifying the name of the logits post-processor as Request::kBatchedPostProcessorName
.
Note: Neither callback variant is supported with the STATIC
batching type for the moment.
In a multi-GPU run, the callback is invoked on all ranks in the first tensor-parallel group, by default. To ensure correct execution, replicate the client-side state that is accessed by the callback on these ranks. If replication is expensive or infeasible, use LogitsPostProcessorConfig::setReplicate(false)
to invoke the callback only on rank 0. The executor broadcasts the sampled tokens internally to ensure correct execution.
C++ Executor API Example
Two C++ examples are provided that shows how to use the Executor API and can be found in the examples/cpp/executor
folder.
Python Bindings for the Executor API
Python bindings for the Executor API are also available to use the Executor API from Python. The Python bindings are defined in bindings.cpp and once built, are available in package tensorrt_llm.bindings.executor
. Running 'help('tensorrt_llm.bindings.executor')
in a Python interpreter will provide an overview of the classes available.
In addition, three Python examples are provided to demonstrate how to use the Python bindings to the Executor API for single and multi-GPU models. They can be found in examples/bindings
.