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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/05/25 22:47:33 UTC
[GitHub] [tvm] giuseros commented on a change in pull request #8072: Add "operator" style to Model Library Format
giuseros commented on a change in pull request #8072:
URL: https://github.com/apache/tvm/pull/8072#discussion_r639251955
##########
File path: python/tvm/micro/model_library_format.py
##########
@@ -203,60 +209,200 @@ def _build_function_memory_map(function_metadata):
return ret
-def export_model_library_format(mod: executor_factory.ExecutorFactoryModule, file_name):
- """Export the build artifact in Model Library Format.
+def _make_tar(source_dir, tar_file_path):
+ """Build a tar file from source_dir."""
+ with tarfile.open(tar_file_path, "w") as tar_f:
- This function creates a .tar archive containing the build artifacts in a standardized
- layout. It's intended to allow downstream automation to build TVM artifacts against the C
- runtime.
+ def reset(tarinfo):
+ tarinfo.uid = tarinfo.gid = 0
+ tarinfo.uname = tarinfo.gname = "root"
+ return tarinfo
+
+ tar_f.add(str(source_dir), arcname=".", filter=reset)
+
+
+_GENERATED_VERSION = 2
+
+
+def _export_graph_model_library_format(
+ mod: executor_factory.GraphExecutorFactoryModule, tempdir: pathlib.Path
Review comment:
Shouldn't this be ExecutorFactoryModule to be compatible with AOT as well?
##########
File path: src/printer/model_library_format_printer.cc
##########
@@ -0,0 +1,75 @@
+/*
+ * 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.
+ */
+
+#include <tvm/runtime/module.h>
+#include <tvm/runtime/registry.h>
+#include <tvm/tir/var.h>
+
+#include "text_printer.h"
+
+namespace tvm {
+namespace printer {
+
+class ModelLibraryFormatPrinter : public ::tvm::runtime::ModuleNode {
+ public:
+ ModelLibraryFormatPrinter(bool show_meta_data,
+ const runtime::TypedPackedFunc<std::string(ObjectRef)>& annotate,
+ bool show_warning)
+ : text_printer_{show_meta_data, annotate, show_warning} {}
+
+ const char* type_key() const override { return "model_library_format_printer"; }
+
+ std::string Print(const ObjectRef& node) {
+ Doc doc;
+ doc << text_printer_.PrintFinal(node);
+ return doc.str();
+ }
+
+ PackedFunc GetFunction(const std::string& name, const ObjectPtr<Object>& sptr_to_self) override {
+ if (name == "print") {
+ return TypedPackedFunc<std::string(ObjectRef)>(
+ [sptr_to_self, this](ObjectRef node) { return Print(node); });
+ } else if (name == "get_var_name") {
+ return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
+ ICHECK_EQ(args.size(), 1) << "usage: get_var_name(Var v)";
+
+ std::string var_name;
+ if (text_printer_.GetVarName(args[0], &var_name)) {
+ *rv = var_name;
+ }
Review comment:
Should this ICHECK if GetVarName returns false?
##########
File path: python/tvm/micro/model_library_format.py
##########
@@ -203,60 +209,200 @@ def _build_function_memory_map(function_metadata):
return ret
-def export_model_library_format(mod: executor_factory.ExecutorFactoryModule, file_name):
- """Export the build artifact in Model Library Format.
+def _make_tar(source_dir, tar_file_path):
+ """Build a tar file from source_dir."""
+ with tarfile.open(tar_file_path, "w") as tar_f:
- This function creates a .tar archive containing the build artifacts in a standardized
- layout. It's intended to allow downstream automation to build TVM artifacts against the C
- runtime.
+ def reset(tarinfo):
+ tarinfo.uid = tarinfo.gid = 0
+ tarinfo.uname = tarinfo.gname = "root"
+ return tarinfo
+
+ tar_f.add(str(source_dir), arcname=".", filter=reset)
+
+
+_GENERATED_VERSION = 2
+
+
+def _export_graph_model_library_format(
+ mod: executor_factory.GraphExecutorFactoryModule, tempdir: pathlib.Path
+):
+ """Export a tvm.relay.build artifact in Model Library Format.
Parameters
----------
mod : tvm.relay.backend.executor_factory.ExecutorFactoryModule
The return value of tvm.relay.build, which will be exported into Model Library Format.
- file_name : str
- Path to the .tar archive to generate.
+ tempdir : pathlib.Path
+ Temporary directory to populate with Model Library Format contents.
"""
- tempdir = utils.tempdir()
is_aot = isinstance(mod, executor_factory.AOTExecutorFactoryModule)
runtime = ["aot"] if is_aot else ["graph"]
metadata = {
- "version": 2,
+ "version": _GENERATED_VERSION,
"model_name": mod.libmod_name,
"export_datetime": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%SZ"),
"memory": _build_memory_map(mod),
"target": {int(k): str(v) for k, v in mod.target.items()},
"runtimes": runtime,
+ "style": "full-model",
}
- with open(tempdir.relpath("metadata.json"), "w") as json_f:
+ with open(tempdir / "metadata.json", "w") as json_f:
json.dump(metadata, json_f, indent=2, sort_keys=True)
- codegen_dir_path = tempdir.relpath("codegen")
- os.mkdir(codegen_dir_path)
- _populate_codegen_dir(mod.lib, codegen_dir_path)
+ codegen_dir = tempdir / "codegen"
+ codegen_dir.mkdir()
+ _populate_codegen_dir(mod.lib, codegen_dir)
- parameters_dir_path = tempdir.relpath("parameters")
- os.mkdir(parameters_dir_path)
- param_filename = os.path.join(parameters_dir_path, f"{mod.libmod_name}.params")
+ parameters_dir = tempdir / "parameters"
+ parameters_dir.mkdir()
+ param_filename = parameters_dir / f"{mod.libmod_name}.params"
with open(param_filename, "wb") as f:
f.write(param_dict.save_param_dict(mod.params))
- with open(tempdir.relpath("relay.txt"), "w") as f:
+ src_dir = tempdir / "src"
+ src_dir.mkdir()
+ with open(src_dir / "relay.txt", "w") as f:
f.write(str(mod.ir_mod))
if not is_aot:
- graph_config_dir_path = tempdir.relpath(os.path.join("runtime-config", "graph"))
- os.makedirs(graph_config_dir_path)
- with open(os.path.join(graph_config_dir_path, "graph.json"), "w") as f:
+ graph_config_dir = tempdir / "runtime-config" / "graph"
+ graph_config_dir.mkdir(parents=True)
+ with open(graph_config_dir / "graph.json", "w") as f:
f.write(mod.get_executor_config())
- with tarfile.open(file_name, "w") as tar_f:
- def reset(tarinfo):
- tarinfo.uid = tarinfo.gid = 0
- tarinfo.uname = tarinfo.gname = "root"
- return tarinfo
+class NonStaticShapeError(Exception):
+ """Raised when a shape has elements other than IntImm."""
+
+
+def _shape_to_size(shape, dtype):
+ bits_per_item = int(
+ re.match(r"((float)|(int))(?P<width_bits>[0-9]+)", dtype).group("width_bits")
+ )
+ assert bits_per_item is not None, f"don't know how to compute size of type {dtype}"
+ total_bits = bits_per_item
+ for s in shape:
+ total_bits *= s
+
+ return (total_bits + 7) // 8
+
+
+def _write_tir_and_build_operator_memory_map(src_dir, targets, ir_module_by_target):
+ def _eval_shape(param_name, buffer_shape):
+ shape = []
+ for x in buffer_shape:
+ if not isinstance(x, expr.IntImm):
+ raise NonStaticShapeError(
+ f"Parameter {param_name} has shape with non-IntImm elements: {buffer_shape}"
+ )
+ shape.append(x.value)
+ return shape
+
+ memory_map = {}
+ for target_device_type, target in targets.items():
+ ir_mod = ir_module_by_target[target]
+ printer = get_global_func("tir.ModelLibraryFormatPrinter")(False, None, False)
+ with open(src_dir / f"tir-{target_device_type}.txt", "w") as f:
+ f.write(printer["print"](ir_mod))
Review comment:
I am not following why adding the TIR in the archive. Is this for test purposes?
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