Add fx train quantization

ci/test.sh

@@ -4,4 +4,5 @@ python3 -m pip install --user --upgrade pip
 if [ -f requirements.txt ]; then python3 -m pip install -r requirements.txt --user; fi
 python3 -m pip install oneflow --user -U -f https://staging.oneflow.info/branch/master/cu110
 python3 setup.py install
-python3 -m pytest examples/oneflow2onnx
+python3 -m pytest examples/oneflow2onnx/nodes
+python3 -m pytest examples/oneflow2onnx/models

examples/oneflow2onnx/models/test_inceptionv3.py

@@ -120,16 +120,16 @@ def __init__(
         self.dropout = nn.Dropout()
         self.fc = nn.Linear(2048, num_classes)
 
-        # if init_weights:
-        #     for m in self.modules():
-        #         if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
-        #             stddev = float(m.stddev) if hasattr(m, "stddev") else 0.1  # type: ignore
-        #             flow.nn.init.trunc_normal_(
-        #                 m.weight, mean=0.0, std=stddev, a=-2, b=2
-        #             )
-        #         elif isinstance(m, nn.BatchNorm2d):
-        #             nn.init.constant_(m.weight, 1)
-        #             nn.init.constant_(m.bias, 0)
+        if init_weights:
+            for m in self.modules():
+                if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
+                    stddev = float(m.stddev) if hasattr(m, "stddev") else 0.1  # type: ignore
+                    flow.nn.init.trunc_normal_(
+                        m.weight, mean=0.0, std=stddev, a=-2, b=2
+                    )
+                elif isinstance(m, nn.BatchNorm2d):
+                    nn.init.constant_(m.weight, 1)
+                    nn.init.constant_(m.bias, 0)
 
     def _transform_input(self, x: Tensor) -> Tensor:
         if self.transform_input:

examples/oneflow2onnx/quantization/common.py

@@ -0,0 +1,326 @@
+#
+# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from itertools import chain
+import argparse
+import os
+
+import pycuda.driver as cuda
+import pycuda.autoinit
+import numpy as np
+
+import tensorrt as trt
+
+try:
+    # Sometimes python does not understand FileNotFoundError
+    FileNotFoundError
+except NameError:
+    FileNotFoundError = IOError
+
+EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+
+
+def GiB(val):
+    return val * 1 << 30
+
+
+def add_help(description):
+    parser = argparse.ArgumentParser(
+        description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    args, _ = parser.parse_known_args()
+
+
+def find_sample_data(
+    description="Runs a TensorRT Python sample", subfolder="", find_files=[], err_msg=""
+):
+    """
+    Parses sample arguments.
+    Args:
+        description (str): Description of the sample.
+        subfolder (str): The subfolder containing data relevant to this sample
+        find_files (str): A list of filenames to find. Each filename will be replaced with an absolute path.
+    Returns:
+        str: Path of data directory.
+    """
+
+    # Standard command-line arguments for all samples.
+    kDEFAULT_DATA_ROOT = os.path.join(os.sep, "usr", "src", "tensorrt", "data")
+    parser = argparse.ArgumentParser(
+        description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        "-d",
+        "--datadir",
+        help="Location of the TensorRT sample data directory, and any additional data directories.",
+        action="append",
+        default=[kDEFAULT_DATA_ROOT],
+    )
+    args, _ = parser.parse_known_args()
+
+    def get_data_path(data_dir):
+        # If the subfolder exists, append it to the path, otherwise use the provided path as-is.
+        data_path = os.path.join(data_dir, subfolder)
+        if not os.path.exists(data_path):
+            if data_dir != kDEFAULT_DATA_ROOT:
+                print(
+                    "WARNING: "
+                    + data_path
+                    + " does not exist. Trying "
+                    + data_dir
+                    + " instead."
+                )
+            data_path = data_dir
+        # Make sure data directory exists.
+        if not (os.path.exists(data_path)) and data_dir != kDEFAULT_DATA_ROOT:
+            print(
+                "WARNING: {:} does not exist. Please provide the correct data path with the -d option.".format(
+                    data_path
+                )
+            )
+        return data_path
+
+    data_paths = [get_data_path(data_dir) for data_dir in args.datadir]
+    return data_paths, locate_files(data_paths, find_files, err_msg)
+
+
+def locate_files(data_paths, filenames, err_msg=""):
+    """
+    Locates the specified files in the specified data directories.
+    If a file exists in multiple data directories, the first directory is used.
+    Args:
+        data_paths (List[str]): The data directories.
+        filename (List[str]): The names of the files to find.
+    Returns:
+        List[str]: The absolute paths of the files.
+    Raises:
+        FileNotFoundError if a file could not be located.
+    """
+    found_files = [None] * len(filenames)
+    for data_path in data_paths:
+        # Find all requested files.
+        for index, (found, filename) in enumerate(zip(found_files, filenames)):
+            if not found:
+                file_path = os.path.abspath(os.path.join(data_path, filename))
+                if os.path.exists(file_path):
+                    found_files[index] = file_path
+
+    # Check that all files were found
+    for f, filename in zip(found_files, filenames):
+        if not f or not os.path.exists(f):
+            raise FileNotFoundError(
+                "Could not find {:}. Searched in data paths: {:}\n{:}".format(
+                    filename, data_paths, err_msg
+                )
+            )
+    return found_files
+
+
+# Simple helper data class that's a little nicer to use than a 2-tuple.
+class HostDeviceMem(object):
+    def __init__(self, host_mem, device_mem):
+        self.host = host_mem
+        self.device = device_mem
+
+    def __str__(self):
+        return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
+
+    def __repr__(self):
+        return self.__str__()
+
+
+# Allocates all buffers required for an engine, i.e. host/device inputs/outputs.
+def allocate_buffers(engine):
+    inputs = []
+    outputs = []
+    bindings = []
+    stream = cuda.Stream()
+    for binding in engine:
+        size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
+        dtype = trt.nptype(engine.get_binding_dtype(binding))
+        # Allocate host and device buffers
+        host_mem = cuda.pagelocked_empty(size, dtype)
+        device_mem = cuda.mem_alloc(host_mem.nbytes)
+        # Append the device buffer to device bindings.
+        bindings.append(int(device_mem))
+        # Append to the appropriate list.
+        if engine.binding_is_input(binding):
+            inputs.append(HostDeviceMem(host_mem, device_mem))
+        else:
+            outputs.append(HostDeviceMem(host_mem, device_mem))
+    return inputs, outputs, bindings, stream
+
+
+# This function is generalized for multiple inputs/outputs.
+# inputs and outputs are expected to be lists of HostDeviceMem objects.
+def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
+    # Transfer input data to the GPU.
+    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
+    # Run inference.
+    context.execute_async(
+        batch_size=batch_size, bindings=bindings, stream_handle=stream.handle
+    )
+    # Transfer predictions back from the GPU.
+    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
+    # Synchronize the stream
+    stream.synchronize()
+    # Return only the host outputs.
+    return [out.host for out in outputs]
+
+
+# This function is generalized for multiple inputs/outputs for full dimension networks.
+# inputs and outputs are expected to be lists of HostDeviceMem objects.
+def do_inference_v2(context, bindings, inputs, outputs, stream):
+    # Transfer input data to the GPU.
+    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
+    # Run inference.
+    context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
+    # Transfer predictions back from the GPU.
+    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
+    # Synchronize the stream
+    stream.synchronize()
+    # Return only the host outputs.
+    return [out.host for out in outputs]
+
+
+def generate_md5_checksum(local_path):
+    """Returns the MD5 checksum of a local file.
+    Keyword argument:
+    local_path -- path of the file whose checksum shall be generated
+    """
+    with open(local_path, "rb") as local_file:
+        data = local_file.read()
+        import hashlib
+
+        return hashlib.md5(data).hexdigest()
+
+
+def download_file(local_path, link, checksum_reference=None):
+    """Checks if a local file is present and downloads it from the specified path otherwise.
+    If checksum_reference is specified, the file's md5 checksum is compared against the
+    expected value.
+    Keyword arguments:
+    local_path -- path of the file whose checksum shall be generated
+    link -- link where the file shall be downloaded from if it is not found locally
+    checksum_reference -- expected MD5 checksum of the file
+    """
+    if not os.path.exists(local_path):
+        print("Downloading from %s, this may take a while..." % link)
+        import wget
+
+        wget.download(link, local_path)
+        print()
+    if checksum_reference is not None:
+        checksum = generate_md5_checksum(local_path)
+        if checksum != checksum_reference:
+            raise ValueError(
+                "The MD5 checksum of local file %s differs from %s, please manually remove \
+                 the file and try again."
+                % (local_path, checksum_reference)
+            )
+    return local_path
+
+
+# `retry_call` and `retry` are used to wrap the function we want to try multiple times
+def retry_call(func, args=[], kwargs={}, n_retries=3):
+    """Wrap a function to retry it several times.
+    Args:
+        func: function to call
+        args (List): args parsed to func
+        kwargs (Dict): kwargs parsed to func
+        n_retries (int): maximum times of tries
+    """
+    for i_try in range(n_retries):
+        try:
+            func(*args, **kwargs)
+            break
+        except:
+            if i_try == n_retries - 1:
+                raise
+            print("retry...")
+
+
+# Usage: @retry(n_retries)
+def retry(n_retries=3):
+    """Wrap a function to retry it several times. Decorator version of `retry_call`.
+    Args:
+        n_retries (int): maximum times of tries
+    Usage:
+        @retry(n_retries)
+        def func(...):
+            pass
+    """
+
+    def wrapper(func):
+        def _wrapper(*args, **kwargs):
+            retry_call(func, args, kwargs, n_retries)
+
+        return _wrapper
+
+    return wrapper
+
+
+def build_qat_engine_from_onnx(model_file, verbose=False):
+    if verbose:
+        TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)
+    else:
+        TRT_LOGGER = trt.Logger(trt.Logger.INFO)
+
+    network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+    network_flags = network_flags | (
+        1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_PRECISION)
+    )
+
+    with trt.Builder(TRT_LOGGER) as builder, builder.create_network(
+        flags=network_flags
+    ) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
+        with open(model_file, "rb") as model:
+            if not parser.parse(model.read()):
+                print("ERROR: Failed to parse the ONNX file.")
+                for error in range(parser.num_errors):
+                    print(parser.get_error(error))
+                return None
+        config = builder.create_builder_config()
+        config.max_workspace_size = 1 << 30
+        config.flags = config.flags | 1 << int(trt.BuilderFlag.INT8)
+        return builder.build_engine(network, config)
+
+
+def run_tensorrt(onnx_path, test_case):
+    with build_qat_engine_from_onnx(onnx_path) as engine:
+        inputs, outputs, bindings, stream = allocate_buffers(engine)
+        with engine.create_execution_context() as context:
+            batch_size = test_case.shape[0]
+            test_case = test_case.reshape(-1)
+            np.copyto(inputs[0].host, test_case)
+            trt_outputs = do_inference_v2(
+                context,
+                bindings=bindings,
+                inputs=inputs,
+                outputs=outputs,
+                stream=stream,
+            )
+            data = trt_outputs[0]
+            return data.reshape(batch_size, -1)
+
+
+def get_onnx_provider(ctx: str = "cpu"):
+    if ctx == "gpu":
+        return ["CUDAExecutionProvider"]
+    elif ctx == "cpu":
+        return ["CPUExecutionProvider"]
+    else:
+        raise NotImplementedError("Not supported device type. ")