Integrate byoc preprocess in collage and benchmark (apache#26)

Integrate implicit call of BYOC preprocessing module into collage tunning module
and enable benchmark script for adreno targets.

Benchmark results:

**Networks                     | OpenCL texture | OpenCLML  | Collage**
resnet-18-float32        | 0.010584622      | 0.00720695 | 0.007289728
resnet-18-float16        | 0.007052029      | 0.0045642   | 0.004857585
resnet-34-float32        | 0.016259185      | 0.01242092 | 0.013071063
resnet-34-float16        | 0.011350326      | 0.0073473   | 0.00796802
resnet-50-float32        | 0.019188419      | 0.02085548 | 0.018910226
resnet-50-float16        | 0.01338978        | 0.01199576 | 0.011089206
densenet-121-float32 | 0.025430062      | 0.01798478 | 0.013212844
densenet-121-float16 | 0.012384599      | 0.01101491 | 0.008722716
inception_v3-float32   | 0.040408253      | 0.02229727 | 0.022636675
inception_v3-float16   | 0.029910533      | 0.01368941 | 0.014519823
mobilenet-float32       | 0.004093148      | 0.00367917 | 0.003189258
mobilenet-float16       | 0.00280268        | 0.00244494 | 0.002101514

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Co-authored-by: krishnaraj36 <[email protected]>

apps/benchmark/adreno/adreno_gpu_bench_collage.py

@@ -0,0 +1,384 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+"""Compares Collage with various other baselines."""
+import argparse
+import tvm
+from tvm import relay
+import logging
+import os
+import sys
+import numpy as np
+from tvm.relay import testing
+from tvm.contrib.utils import tempdir
+from tvm import rpc
+from tvm.relay.build_module import bind_params_by_name
+from tvm import autotvm
+from tvm.runtime.vm import VirtualMachine
+import tvm.contrib.graph_executor as runtime
+from tvm.contrib import utils, ndk
+from tvm.relay.collage.collage import *
+from tvm.relay.op.contrib import clml
+
+logging.basicConfig(level=logging.INFO)
+
+
+###
+### How aggressively to look for candidates?
+###
+TVM_MAX_DEPTH = 8
+BYOC_MAX_DEPTH = 8
+
+##
+## Default config definition
+##
+HOST = tvm.target.Target("llvm -mtriple=arm64-linux-android")
+OPENCL = tvm.target.Target("opencl -device=adreno", HOST)
+NDK_CC = os.getenv("TVM_NDK_CC", "aarch64-linux-android-g++")
+
+
+def print_progress(msg):
+    """print progress message
+
+    Parameters
+    ----------
+    msg: str
+        The message to print
+    """
+    sys.stdout.write(msg + "\r")
+    sys.stdout.flush()
+
+
+def tune_tasks(
+    tasks,
+    measure_option,
+    tuner="xgb",
+    n_trial=1024,
+    early_stopping=None,
+    log_filename="tuning.log",
+):
+    from tvm.autotvm.tuner import XGBTuner
+
+    tmp_log_file = log_filename + ".tmp"
+
+    for i, tsk in enumerate(reversed(tasks)):
+        print("Task: ", tsk)
+        prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
+
+        # create tuner
+        if tuner == "xgb":
+            tuner_obj = XGBTuner(tsk, loss_type="reg")
+        elif tuner == "xgb_knob":
+            tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="knob")
+        elif tuner == "xgb_itervar":
+            tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="itervar")
+        elif tuner == "xgb_curve":
+            tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="curve")
+        elif tuner == "xgb_rank":
+            tuner_obj = XGBTuner(tsk, loss_type="rank")
+        elif tuner == "xgb_rank_knob":
+            tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
+        elif tuner == "xgb_rank_itervar":
+            tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="itervar")
+        elif tuner == "xgb_rank_curve":
+            tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="curve")
+        elif tuner == "xgb_rank_binary":
+            tuner_obj = XGBTuner(tsk, loss_type="rank-binary")
+        elif tuner == "xgb_rank_binary_knob":
+            tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="knob")
+        elif tuner == "xgb_rank_binary_itervar":
+            tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="itervar")
+        elif tuner == "xgb_rank_binary_curve":
+            tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="curve")
+        elif tuner == "ga":
+            tuner_obj = GATuner(tsk, pop_size=50)
+        elif tuner == "random":
+            tuner_obj = RandomTuner(tsk)
+        elif tuner == "gridsearch":
+            tuner_obj = GridSearchTuner(tsk)
+        else:
+            raise ValueError("Invalid tuner: " + tuner)
+
+        tsk_trial = min(n_trial, len(tsk.config_space))
+        tuner_obj.tune(
+            n_trial=tsk_trial,
+            early_stopping=early_stopping,
+            measure_option=measure_option,
+            callbacks=[
+                autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
+                autotvm.callback.log_to_file(tmp_log_file),
+            ],
+        )
+
+        autotvm.record.pick_best(tmp_log_file, log_filename)
+
+
+########### Collage Drivers ###########
+
+
+def compile_and_run(label, model, targets, inputs):
+    """Compile model for target and run it with profiling."""
+    logging.info(f"Compiling {model['name']} using {label} with {targets}...")
+    mod = model["mod"]
+    exe = tvm.relay.vm.compile(mod, target=targets, params=model["params"])
+    lib = exe.mod
+    temp = utils.tempdir()
+    dso_binary = "dev_lib_cl.so"
+    dso_binary_path = temp.relpath(dso_binary)
+    logging.info(f"Exporting library to {dso_binary_path}...")
+    lib.export_library(dso_binary_path, cc=NDK_CC)
+    tracker = rpc.connect_tracker(args.host, args.port)
+    remote = tracker.request(args.rpc_key, priority=0, session_timeout=600)
+    ctx = remote.cl(0)
+    remote.upload(dso_binary_path)
+    rlib = remote.load_module(dso_binary)
+    vm_factory = tvm.runtime.vm.VirtualMachine(rlib, ctx, "naive")
+    func_name = "main"
+    main_args = {v.name_hint: arg_for(v.checked_type, ctx) for v in mod[func_name].params}
+    profile = vm_factory.benchmark(
+        ctx, repeat=5, number=20, min_repeat_ms=0, func_name=func_name, **main_args
+    )
+    return profile.mean
+
+
+def collage(model, input_data, tune_log=""):
+    """Run the Collage partitioner for a set of Opencl Adreno related targets and profile the result"""
+    logging.info(f"collage | {model['name']}")
+    logging.info("-------------- BEGIN ORIGINAL --------------")
+    logging.info(model["mod"])
+    logging.info("-------------- END ORIGINAL ----------------")
+    with autotvm.apply_history_best(tune_log):
+        targets = []
+        targets.append(OPENCL)
+        use_fp16 = model["main_dtype"] == "float16"
+        targets.append(tvm.target.Target("clml", HOST))
+
+        # Register byoc fusion style for compiler with available
+        # options [compiler.NoFusion | compiler.TVMFusion | compiler.MaxDepthFusion]
+        config = {
+            "relay.collage.tvm_max_depth": TVM_MAX_DEPTH,
+            "relay.collage.byoc_max_depth": BYOC_MAX_DEPTH,
+            "relay.collage.byoc_fusion_style": ["clml.NoFusion"],
+        }
+        logging.info(f"Using PassContext(config={config}")
+        ctxt = tvm.transform.PassContext(config=config)
+        config = tvm.target.make_compilation_config(ctxt, targets)
+        with ctxt:
+            mod = model["mod"]
+            """Collage partition with tvm opencl and clml target on rpc device"""
+            mod = tvm.relay.transform.CollagePartition(
+                config,
+                cost_estimator=CostEstimator(
+                    host=args.host, port=args.port, rpc_key=args.rpc_key, ndk_cc=NDK_CC
+                ),
+            )(mod)
+            partitioned_model = model.copy()
+            partitioned_model["mod"] = mod
+            logging.info("-------------- BEGIN PARTITIONED --------------")
+            logging.info(partitioned_model["mod"])
+            logging.info("-------------- END PARTITIONED ----------------")
+            return compile_and_run("collage", partitioned_model, targets, input_data)
+
+
+def just_clml(model, input_data, tune_log=""):
+    """Run partition_for_clml, complete the compilation with TVM, and profile the result."""
+    logging.info(f"just_clml | {model['name']}")
+    logging.info("-------------- BEGIN ORIGINAL --------------")
+    logging.info(model["mod"])
+    logging.info("-------------- END ORIGINAL ----------------")
+    with autotvm.apply_history_best(tune_log):
+        with tvm.transform.PassContext(opt_level=3):
+            logging.info("Partitioning for CLML...")
+            mod = tvm.relay.op.contrib.clml.partition_for_clml(model["mod"], model["params"])
+            partitioned_model = model.copy()
+            partitioned_model["mod"] = mod
+            logging.info("-------------- BEGIN PARTITIONED --------------")
+            logging.info(partitioned_model["mod"])
+            logging.info("-------------- END PARTITIONED ----------------")
+            targets = []
+            targets.append(OPENCL)
+            targets.append(tvm.target.Target("clml", HOST))
+            return compile_and_run("just_clml", partitioned_model, OPENCL, input_data)
+
+
+def just_tvm(model, input_data, tune_log=""):
+    """Compile and profile using vanilla TVM."""
+    logging.info(f"just_tvm | {model['name']}")
+    logging.info("-------------- BEGIN ORIGINAL --------------")
+    logging.info(model["mod"])
+    logging.info("-------------- END ORIGINAL ----------------")
+    with autotvm.apply_history_best(tune_log):
+        with tvm.transform.PassContext(opt_level=3):
+            return compile_and_run("just_tvm", model, OPENCL, input_data)
+
+
+def get_model(model_name, dtype):
+
+    if "mobilenet" in model_name:
+        mod, params = testing.mobilenet.get_workload(batch_size=1, dtype=dtype)
+    elif "resnet" in model_name:
+        n_layer = int(model_name.split("-")[1])
+        mod, params = testing.resnet.get_workload(num_layers=n_layer, batch_size=1, dtype=dtype)
+    elif model_name == "inception_v3":
+        input_shape = (1, 3, 299, 299)
+        mod, params = testing.inception_v3.get_workload(batch_size=1, dtype=dtype)
+    elif "vgg" in model_name:
+        n_layer = int(model_name.split("-")[1])
+        mod, params = testing.vgg.get_workload(num_layers=n_layer, batch_size=1, dtype=dtype)
+    elif "densenet" in model_name:
+        n_layer = int(model_name.split("-")[1])
+        mod, params = testing.densenet.get_workload(
+            densenet_size=n_layer, batch_size=1, dtype=dtype
+        )
+    elif "squeezenet" in model_name:
+        version = model_name.split("_v")[1]
+        mod, params = testing.squeezenet.get_workload(batch_size=1, version=version, dtype=dtype)
+
+    initializer = tvm.relay.testing.init.Xavier()
+    for param_name in list(params.keys()):
+        filter_data = np.zeros(params[param_name].shape).astype(params[param_name].dtype)
+        if len(filter_data.shape) > 1:
+            initializer("weight", filter_data)
+        else:
+            initializer("bias", filter_data)
+        params[param_name] = tvm.nd.array(filter_data)
+
+    if params:
+        mod["main"] = bind_params_by_name(mod["main"], params)
+        mod = tvm.relay.transform.FoldConstant()(mod)
+    return {
+        "name": model_name,
+        "input_shapes": {"data": [1, 3, 224, 224]},
+        "input_dtypes": {"data": dtype},
+        "mod": mod,
+        "params": params,
+        "main_dtype": dtype,
+    }
+
+
+########### Runners ###########
+def evaluate_network(model_name, dtype):
+    print("Network evaluating .. " + model_name + " " + dtype)
+    np.random.seed(0)
+    model = get_model(model_name, dtype)
+    tune_log = "adreno_v0.01.log"
+    if args.tune:
+        # Auto Tuning
+        tune_log = "adreno-" + model_name + "-" + dtype + ".log"
+        tuning_options = {
+            "log_filename": tune_log,
+            "early_stopping": None,
+            "measure_option": autotvm.measure_option(
+                builder=autotvm.LocalBuilder(build_func=ndk.create_shared, timeout=15),
+                runner=autotvm.RPCRunner(
+                    args.rpc_key,
+                    host=args.host,
+                    port=args.port,
+                    number=3,
+                    timeout=600,
+                ),
+            ),
+        }
+        tasks = autotvm.task.extract_from_program(
+            net, target=OPENCL, target_host=HOST, params=params
+        )
+        tune_tasks(tasks, **tuning_options)
+
+    print_progress("%-20s building..." % network)
+    input_data = {}
+    for name, shape in model["input_shapes"].items():
+        input_data[name] = np.random.uniform(-1.0, 1.0, shape).astype(model["input_dtypes"][name])
+    clml_time = just_clml(model, input_data, tune_log)
+    tvm_time = just_tvm(model, input_data, tune_log)
+
+    """Run Collage for tvm and clml compiler target."""
+    collage_time = collage(model, input_data, tune_log)
+    return (tvm_time, clml_time, collage_time)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--network",
+        type=str,
+        choices=[
+            "resnet-18",
+            "resnet-34",
+            "resnet-50",
+            "vgg-16",
+            "vgg-19",
+            "densenet-121",
+            "inception_v3",
+            "mobilenet",
+            "squeezenet_v1.0",
+            "squeezenet_v1.1",
+        ],
+        help="The name of neural network",
+    )
+    parser.add_argument("--host", type=str, default="127.0.0.1")
+    parser.add_argument("--port", type=int, default=9190)
+    parser.add_argument("--rpc-key", type=str, default="android")
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        choices=["float32", "float16"],
+        help="The data type of neural network",
+    )
+    parser.add_argument("--tune", type=bool, default=False)
+    args = parser.parse_args()
+
+    if args.network is None:
+        networks = [
+            "resnet-18",
+            "resnet-34",
+            "resnet-50",
+            # "vgg-16",
+            # "vgg-19",
+            "densenet-121",
+            "inception_v3",
+            "mobilenet",
+            "squeezenet_v1.0",
+            "squeezenet_v1.1",
+        ]
+    else:
+        networks = [args.network]
+
+    if args.dtype is None:
+        dtypes = ["float32", "float16"]
+    else:
+        dtypes = [args.dtype]
+
+    results = {}
+    net_results = []
+    for network in networks:
+        for dtype in dtypes:
+            ftime = evaluate_network(network, dtype)
+            results[network + "-" + dtype] = ftime
+            # net_results.append([network + "-" + dtype] + list(ftime))
+            # np.savetxt("results.txt", np.array(net_results), fmt="%s")
+
+    print("----------------------------------------------------------------------")
+    print(
+        "%-30s %-20s %-20s %-20s"
+        % ("Network Name", "TVM Opencl Time", "CLML Time", "Collage - TVM/CLML Time")
+    )
+    print("----------------------------------------------------------------------")
+    for key, val in results.items():
+        print(
+            "%-30s %-20s %-20s %-20s"
+            % (key, "%.2f ms" % val[0], "%.2f ms" % val[1], "%.2f ms" % val[2])
+        )