[Unity] Relay -> Relax translator

apps/relax_examples/e2e_auto_tir.py

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+# 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.
+import datetime
+import os
+import csv
+import json
+import argparse
+import logging
+from typing import Dict
+import numpy as np  # type: ignore
+
+import tvm
+from tvm import relay, relax, runtime, transform
+from tvm.ir.module import IRModule
+from tvm import meta_schedule as ms
+from tvm.meta_schedule.testing.relay_workload import get_network
+from tvm.meta_schedule.testing.custom_builder_runner import run_module_via_rpc
+from tvm.relax.testing import relay_translator
+from tvm.target.target import Target
+
+
+def _parse_args():
+    args = argparse.ArgumentParser()
+    args.add_argument(
+        "--workload",
+        type=str,
+        required=True,
+    )
+    args.add_argument(
+        "--input-shape",
+        type=str,
+        required=True,
+    )
+    args.add_argument(
+        "--target",
+        type=str,
+        required=True,
+    )
+    args.add_argument(
+        "--num-trials",
+        type=int,
+        required=True,
+    )
+    args.add_argument(
+        "--rpc-host",
+        type=str,
+        default=None,
+    )
+    args.add_argument(
+        "--rpc-port",
+        type=int,
+        default=None,
+    )
+    args.add_argument(
+        "--rpc-key",
+        type=str,
+        default=None,
+    )
+    args.add_argument(
+        "--work-dir",
+        type=str,
+        required=True,
+    )
+    args.add_argument(
+        "--cache-dir",
+        type=str,
+        default=None,
+    )
+    args.add_argument(
+        "--rpc-timeout-sec",
+        type=int,
+        default=180,
+    )
+    args.add_argument("--num-measurement-repeats", type=int, default=5)
+    args.add_argument("--num-measurements", type=int, default=10)
+    args.add_argument("--results-file", type=str, required=False, default=None)
+    parsed = args.parse_args()
+    parsed.target = tvm.target.Target(parsed.target)
+    parsed.input_shape = json.loads(parsed.input_shape)
+    if parsed.target.attrs.get("mtriple", None) == "aarch64-linux-gnu":
+        parsed.alloc_repeat = 3
+    else:
+        parsed.alloc_repeat = 1
+    if parsed.rpc_host and parsed.rpc_port and parsed.rpc_key:
+        parsed.rpc_config = ms.runner.RPCConfig(
+            tracker_host=parsed.rpc_host,
+            tracker_port=parsed.rpc_port,
+            tracker_key=parsed.rpc_key,
+            session_timeout_sec=parsed.rpc_timeout_sec,
+        )
+        parsed.workers = parsed.rpc_config.count_num_servers(allow_missing=False)
+    else:
+        # check all rpc configs are None
+        assert (
+            (parsed.rpc_host is None) and (parsed.rpc_port is None) and (parsed.rpc_key is None)
+        ), "Please set all 'rpc_host', 'rpc_port' and 'rpc_key' to use PRC server"
+        parsed.rpc_config = None
+        parsed.workers = 1
+    return parsed
+
+
+logging.basicConfig()
+logging.getLogger("tvm.meta_schedule").setLevel(logging.DEBUG)
+ARGS = _parse_args()
+
+
+def apply_opt_before_tuning(
+    relay_mod: IRModule, params: Dict[str, runtime.NDArray], target: Target
+):
+    with transform.PassContext(opt_level=3):
+        main_func = relay_mod["main"]
+        bind_main_func = relay.build_module.bind_params_by_name(main_func, params)
+        relay_mod = IRModule.from_expr(bind_main_func)
+        relay_mod = relay.transform.SimplifyInference()(relay_mod)
+        relay_mod = relay.transform.FoldConstant()(relay_mod)
+        relay_mod = relay.transform.FoldScaleAxis()(relay_mod)
+        relay_mod = relay.transform.CanonicalizeOps()(relay_mod)
+        relay_mod = relay.transform.AlterOpLayout()(relay_mod)
+        relay_mod = relay.transform.FoldConstant()(relay_mod)
+
+        relax_mod = relay_translator.from_relay(relay_mod["main"], target=target)
+        relax_mod = relax.transform.AnnotateTIROpPattern()(relax_mod)
+        relax_mod = relax.transform.FuseOps()(relax_mod)
+        relax_mod = relax.transform.FuseTIR()(relax_mod)
+    return relax_mod
+
+
+def f_measurement(
+    rt_mod: runtime.Module, device: runtime.ndarray.Device, input_data: Dict[str, runtime.NDArray]
+):
+    vm = relax.vm.VirtualMachine(exec=rt_mod, device=device)
+    vm.save_function("main", "measure_func", **input_data, include_return=False)
+    evaluator = vm.time_evaluator(
+        func_name="measure_func",
+        dev=device,
+        repeat=ARGS.num_measurement_repeats,
+        number=ARGS.num_measurements,
+        min_repeat_ms=500,
+    )
+    return evaluator()
+
+
+def get_runner():
+    runner_config = {
+        "evaluator_config": ms.runner.EvaluatorConfig(
+            number=3,
+            repeat=1,
+            min_repeat_ms=100,
+            enable_cpu_cache_flush=False,
+        ),
+        "alloc_repeat": ARGS.alloc_repeat,
+    }
+    if ARGS.rpc_config:
+        runner = ms.runner.RPCRunner(
+            rpc_config=ARGS.rpc_config, max_workers=ARGS.workers, **runner_config
+        )
+    else:
+        runner = ms.runner.LocalRunner(**runner_config)
+
+    return runner
+
+
+def main():
+    relay_mod, params, (input_name, input_shape, input_dtype) = get_network(
+        ARGS.workload,
+        ARGS.input_shape,
+        cache_dir=ARGS.cache_dir,
+    )
+    input_info = {input_name: input_shape}
+    input_data = {}
+    for input_name, input_shape in input_info.items():
+        print(f"  input_name: {input_name}")
+        print(f"  input_shape: {input_shape}")
+        print(f"  input_dtype: {input_dtype}")
+
+    # translate the ResNet model from Relay to Relax
+    relax_mod = apply_opt_before_tuning(relay_mod, params, target=ARGS.target)
+    assert isinstance(relax_mod, tvm.IRModule)
+
+    db = ms.relax_integration.tune_relax(
+        mod=relax_mod,
+        target=ARGS.target,
+        params=params,
+        num_trials_per_iter=64,
+        max_trials_per_task=ARGS.num_trials,
+        max_trials_global=ARGS.num_trials,
+        runner=get_runner(),
+        work_dir=ARGS.work_dir,
+    )
+    executable = ms.relax_integration.compile_relax(
+        db,
+        mod=relax_mod,
+        target=ARGS.target,
+        params=params,
+    )
+
+    for input_name, input_shape in input_info.items():
+        if input_dtype.startswith("float"):
+            input_data[input_name] = np.random.uniform(size=input_shape).astype(input_dtype)
+        else:
+            input_data[input_name] = np.random.randint(
+                low=0, high=10000, size=input_shape, dtype=input_dtype
+            )
+
+    # for documentation purposes
+    start_time = datetime.datetime.now()
+
+    if ARGS.rpc_config:
+        result = run_module_via_rpc(
+            rpc_config=ARGS.rpc_config,
+            lib=executable.mod,
+            dev_type=ARGS.target.kind.name,
+            args=input_data,
+            continuation=f_measurement,
+        )
+    else:
+        dev = tvm.device(ARGS.target.kind.name)
+        result = f_measurement(executable.mod, dev, input_data)
+
+    print(result)
+
+    if not ARGS.results_file:
+        return
+
+    out_path = os.path.abspath(os.path.expanduser(ARGS.results_file))
+    with open(out_path, "w") as out_file:
+        writer = csv.writer(out_file)
+        # write experiment parameters at the top as a record
+        writer.writerow(["start", str(start_time)])
+        writer.writerow(["workload", ARGS.workload])
+        writer.writerow(["input_shape", ARGS.input_shape])
+        writer.writerow(["target", ARGS.target])
+        writer.writerow(["num_measurement_repeats", ARGS.num_measurement_repeats])
+        for res in result.results:
+            writer.writerow([str(res)])
+
+
+if __name__ == "__main__":
+    main()

apps/relax_examples/mlp.py

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+# 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.
+
+# Example code on creating, compiling, and running an MLP model in relax
+
+
+import tvm
+from tvm import relax, tir, topi
+import numpy as np
+
+
+def build_mlp(data, weight):
+    bb = relax.BlockBuilder()
+
+    with bb.function("mlp", [data, weight]):
+        gv0 = bb.emit_te(tvm.contrib.cblas.matmul, data, weight, transa=False, transb=False)
+        gv1 = bb.emit_te(topi.nn.relu, gv0)
+        bb.emit_func_output(gv1)
+
+    mod = bb.get()
+    return mod
+
+
+if __name__ == "__main__":
+    # symbolic dimensions
+    n, m = tir.Var("n", "int64"), tir.Var("m", "int64")
+    # create data and weight variables
+    data = relax.Var("data", relax.TensorStructInfo([n, m], "float32"))
+    weight = relax.Var("weight", relax.TensorStructInfo([m, n], "float32"))
+
+    # construct a mlp model
+    mod = build_mlp(data, weight)
+
+    # build and create vm executor
+    target = tvm.target.Target("llvm", host="llvm")
+    ex = relax.vm.build(mod, target)
+    vm = relax.VirtualMachine(ex, tvm.cpu())
+
+    # run the mlp model on relax vm
+    data = tvm.nd.array(np.random.rand(16, 32).astype(np.float32))
+    weight = tvm.nd.array(np.random.rand(32, 16).astype(np.float32))
+    res = vm["mlp"](data, weight)
+    print(res)

apps/relax_examples/nn_module.py

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+# 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.
+
+# Example code on creating, compiling, and running a neural network with pytorch-like API
+
+
+import tvm
+from tvm.relay import Call
+from tvm import relax, tir
+from tvm.relax.testing import nn
+from tvm.script import relax as R
+import numpy as np
+
+
+if __name__ == "__main__":
+    builder = relax.BlockBuilder()
+
+    # a symbolic variable to represent minibatch size
+    n = tir.Var("n", "int64")
+    input_size = 784
+    hidden_sizes = [128, 32]
+    output_size = 10
+
+    # build a three linear-layer neural network for a classification task
+    with builder.function("main"):
+        model = nn.Sequential(
+            nn.Linear(input_size, hidden_sizes[0]),
+            nn.ReLU(),
+            nn.Linear(hidden_sizes[0], hidden_sizes[1]),
+            nn.ReLU(),
+            nn.Linear(hidden_sizes[1], output_size),
+            nn.LogSoftmax(),
+        )
+        data = nn.Placeholder((n, input_size), name="data")
+        output = model(data)
+        params = [data] + model.parameters()
+        builder.emit_func_output(output, params=params)
+
+    # get and print the IRmodule being built
+    mod = builder.get()
+    mod.show()
+
+    # build the IRModule and create relax vm
+    target = tvm.target.Target("llvm", host="llvm")
+    ex = relax.vm.build(mod, target)
+    vm = relax.VirtualMachine(ex, tvm.cpu())
+
+    # init parameters
+    params = nn.init_params(mod)
+
+    # run the model on relax vm
+    # the input data has a minibatch size of 3
+    data = tvm.nd.array(np.random.rand(3, input_size).astype(np.float32))
+    res = vm["main"](data, *params)
+    print(res)