Add Python backend based PyTorch runtime

Author: kthui

CMakeLists.txt

@@ -504,6 +504,13 @@ install(
     ${INSTALL_CONFIGDIR}
 )
 
+install(
+  FILES
+    src/model.py
+  DESTINATION
+    ${CMAKE_INSTALL_PREFIX}/backends/pytorch
+)
+
 include(CMakePackageConfigHelpers)
 configure_package_config_file(
   ${CMAKE_CURRENT_LIST_DIR}/cmake/TritonPyTorchBackendConfig.cmake.in

src/model.py

@@ -0,0 +1,318 @@
+#!/usr/bin/env python3
+
+# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import importlib
+import json
+import os
+
+try:
+    import torch
+except ModuleNotFoundError as error:
+    raise RuntimeError("Missing/Incomplete PyTorch package installation") from error
+
+# triton_python_backend_utils is available in every Triton Python model. You
+# need to use this module to create inference requests and responses. It also
+# contains some utility functions for extracting information from model_config
+# and converting Triton input/output types to numpy types.
+import triton_python_backend_utils as pb_utils
+
+
+def _get_model_path(config):
+    filenames = ["model.py", "model.pt"]
+    if config["default_model_filename"]:
+        filenames.insert(0, config["default_model_filename"])
+    for filename in filenames:
+        model_path = os.path.join(pb_utils.get_model_dir(), filename)
+        if os.path.exists(model_path):
+            return model_path
+    raise pb_utils.TritonModelException(
+        "No model found in " + pb_utils.get_model_dir() + "/" + str(filenames)
+    )
+
+
+def _get_model_data_path(model_path):
+    data_path_extensions = [".pt"]
+    model_path_no_extension = model_path[: -(len(model_path.split(".")[-1]) + 1)]
+    for extension in data_path_extensions:
+        data_path = model_path_no_extension + extension
+        if os.path.exists(data_path):
+            return data_path
+    # data file not provided
+    return ""
+
+
+def _is_py_class_model(model_path):
+    return model_path[-3:] == ".py"
+
+
+def _import_module_from_path(module_name, file_path):
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _get_model_class_from_module(module):
+    names = dir(module)
+    for name in names:
+        attr = getattr(module, name)
+        try:
+            if issubclass(attr, torch.nn.Module):
+                return attr
+        except TypeError:
+            # attr may not be a class
+            pass
+    raise pb_utils.TritonModelException("Cannot find a subclass of torch.nn.Module")
+
+
+def _parse_io_config(io_config):
+    io = []
+    for conf in io_config:
+        io.append({"name": conf["name"]})
+    return io
+
+
+def _get_device_name(kind, device_id):
+    if kind == "GPU":
+        return "cuda:" + device_id
+    if kind == "CPU":
+        return "cpu"
+    # unspecified device
+    return ""
+
+
+def _get_device(kind, device_id, model):
+    device_name = _get_device_name(kind, device_id)
+    if device_name == "":
+        for param in model.parameters():
+            return param.device
+        raise pb_utils.TritonModelException("Cannot determine model device")
+    return torch.device(device_name)
+
+
+def _set_torch_parallelism(config):
+    log_msg = ""
+    parallelism_settings = ["NUM_THREADS", "NUM_INTEROP_THREADS"]
+    for setting in parallelism_settings:
+        val = "1"
+        if setting in config["parameters"]:
+            val = config["parameters"][setting]["string_value"]
+        getattr(torch, "set_" + setting.lower())(int(val))
+        log_msg += setting + " = " + val + "; "
+    return log_msg
+
+
+def _get_torch_compile_params(config):
+    params = {}
+    if "TORCH_COMPILE_OPTIONAL_PARAMETERS" in config["parameters"]:
+        val = config["parameters"]["TORCH_COMPILE_OPTIONAL_PARAMETERS"]["string_value"]
+        params = json.loads(val)
+        if "model" in params:
+            raise pb_utils.TritonModelException(
+                "'model' is not an optional parameter for 'torch.compile'"
+            )
+    return params
+
+
+def _gather_torch_tensors(scatter_tensors):
+    gather_tensors = []
+    sections = []
+    for i in range(len(scatter_tensors)):
+        tensors = scatter_tensors[i]
+        for j in range(len(tensors)):
+            tensor = tensors[j]
+            if j < len(gather_tensors):
+                # add to existing tensor
+                gather_tensors[j] = torch.cat((gather_tensors[j], tensor), 0)
+            else:
+                # start a new tensor
+                gather_tensors.append(tensor)
+        # record section
+        section_length = tensors[0].size()[0]
+        sections.append(section_length)
+    return gather_tensors, sections
+
+
+def _scatter_torch_tensors(gather_tensors, sections):
+    scatter_tensors = []
+    for j in range(len(gather_tensors)):
+        scatter_tensor = torch.split(gather_tensors[j], sections)
+        for i in range(len(scatter_tensor)):
+            tensor = scatter_tensor[i]
+            if i < len(scatter_tensors):
+                # add to existing response
+                scatter_tensors[i].append(tensor)
+            else:
+                # start a new response
+                scatter_tensors.append([tensor])
+    return scatter_tensors
+
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to initialize any state associated with this model.
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        self._model_name = args["model_name"]
+        for_model = "for '" + self._model_name + "'"
+        self._logger = pb_utils.Logger
+        self._logger.log_info("Initializing model instance " + for_model)
+
+        self._model_config = json.loads(args["model_config"])
+        self._kind = args["model_instance_kind"]
+        self._device_id = args["model_instance_device_id"]
+        self._support_batching = self._model_config["max_batch_size"] > 0
+        self._inputs = _parse_io_config(self._model_config["input"])
+        self._outputs = _parse_io_config(self._model_config["output"])
+
+        setting_msg = _set_torch_parallelism(self._model_config)
+        self._logger.log_verbose(
+            "Torch parallelism settings " + for_model + ": " + setting_msg
+        )
+
+        self._infer_mode = torch.inference_mode(mode=True)
+        self._infer_mode.__enter__()
+
+        params = _get_torch_compile_params(self._model_config)
+        self._logger.log_verbose(
+            "'torch.compile' optional parameter(s) " + for_model + ": " + str(params)
+        )
+        if self._support_batching:
+            self._gather = torch.compile(_gather_torch_tensors, **params)
+            self._scatter = torch.compile(_scatter_torch_tensors, **params)
+
+        model_path = _get_model_path(self._model_config)
+        if not _is_py_class_model(model_path):
+            self._logger.log_info("Loading '" + self._model_name + "' as TorchScript")
+            self._model = torch.jit.load(model_path)
+            self._device = _get_device(self._kind, self._device_id, self._model)
+            self._model.to(self._device)
+            self._model.eval()
+            return
+
+        self._model_module = _import_module_from_path(self._model_name, model_path)
+        self._model_class = _get_model_class_from_module(self._model_module)
+        self._raw_model = self._model_class()
+        self._device = _get_device(self._kind, self._device_id, self._raw_model)
+        data_path = _get_model_data_path(model_path)
+        if data_path != "":
+            self._raw_model.load_state_dict(
+                torch.load(data_path, map_location=self._device)
+            )
+        else:
+            self._logger.log_info("Model parameter file not found " + for_model)
+        self._raw_model.to(self._device)
+        self._raw_model.eval()
+        self._model = torch.compile(self._raw_model, **params)
+
+    def execute(self, requests):
+        """`execute` MUST be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference request is made
+        for this model. Depending on the batching configuration (e.g. Dynamic
+        Batching) used, `requests` may contain multiple requests. Every
+        Python model, must create one pb_utils.InferenceResponse for every
+        pb_utils.InferenceRequest in `requests`. If there is an error, you can
+        set the error argument when creating a pb_utils.InferenceResponse
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+
+        responses = []
+
+        requests_tensors = []
+        for request in requests:
+            tensors = []
+            for io in self._inputs:
+                tensor = pb_utils.get_input_tensor_by_name(
+                    request, io["name"]
+                ).to_dlpack()
+                tensor = torch.from_dlpack(tensor).to(self._device)
+                tensors.append(tensor)
+            requests_tensors.append(tensors)
+
+        sections = None
+        if self._support_batching:
+            requests_tensors, sections = self._gather(requests_tensors)
+            requests_tensors = [requests_tensors]
+
+        responses_tensors = []
+        for input_tensors in requests_tensors:
+            output_tensors = self._model(*input_tensors)
+            if not isinstance(output_tensors, tuple) and not isinstance(
+                output_tensors, list
+            ):
+                output_tensors = [output_tensors]
+            responses_tensors.append(output_tensors)
+
+        if self._support_batching:
+            responses_tensors = self._scatter(responses_tensors[0], sections)
+
+        for response_tensors in responses_tensors:
+            output_tensors = []
+            for i in range(len(self._outputs)):
+                io = self._outputs[i]
+                tensor = response_tensors[i].detach()
+                tensor = pb_utils.Tensor.from_dlpack(io["name"], tensor)
+                output_tensors.append(tensor)
+            inference_response = pb_utils.InferenceResponse(
+                output_tensors=output_tensors
+            )
+            responses.append(inference_response)
+
+        return responses
+
+    def finalize(self):
+        """`finalize` is called only once when the model is being unloaded.
+        Implementing `finalize` function is OPTIONAL. This function allows
+        the model to perform any necessary clean ups before exit.
+        """
+        self._logger.log_info("Removing model instance for '" + self._model_name + "'")
+        self._infer_mode.__exit__(exc_type=None, exc_value=None, traceback=None)