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[RUNTIME] Support module based interface runtime #5753

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3fbf640
Support Module based interface runtime
FrozenGene Jun 9, 2020
3fbd368
remove unnecessary comment
FrozenGene Jun 19, 2020
cb101ac
support rpc (except params issue)
FrozenGene Jun 19, 2020
95803d0
solve rpc issue
FrozenGene Jun 19, 2020
166e009
support package params
FrozenGene Jun 22, 2020
52adf79
[Complete all the functionality] Support multi models of package params
FrozenGene Jun 22, 2020
081af5f
refactor graph runtime module list
FrozenGene Jun 23, 2020
4b34dc2
header reorder
FrozenGene Jun 23, 2020
1acf52d
graph runtime debug
FrozenGene Jun 23, 2020
b3f2873
function signature
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46ff4e1
rebase to master and solve lint / clang-format error
FrozenGene Jun 23, 2020
ba5b0c5
remove export_graph_mod
FrozenGene Jun 23, 2020
bae954c
address comments
FrozenGene Jul 6, 2020
1e69f4b
refactor
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ef18312
refactor
FrozenGene Jul 9, 2020
6f7ceee
clang-format
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893c531
comment
FrozenGene Jul 9, 2020
6c515ef
fix GetLib() CHECK issue
FrozenGene Jul 9, 2020
926acec
refactor
FrozenGene Jul 9, 2020
edb60a3
add missing device api check
FrozenGene Jul 9, 2020
5e08ea9
Solve tvm::Map odr
FrozenGene Jul 9, 2020
c8f505c
skip debug graph runtime test if not enable
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Trigger notification
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e796d4b
address comments
FrozenGene Jul 13, 2020
03793b3
update doc comments
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d7f44a9
add get_json for debug graph runtime
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13 changes: 5 additions & 8 deletions python/tvm/contrib/debugger/debug_runtime.py
Original file line number Diff line number Diff line change
Expand Up @@ -35,10 +35,10 @@ def create(graph_json_str, libmod, ctx, dump_root=None):

Parameters
----------
graph_json_str : str or graph class
graph_json_str : str
The graph to be deployed in json format output by graph compiler.
The graph can only contain one operator(tvm_op) that
points to the name of PackedFunc in the libmod.
The graph can contain operator(tvm_op) that points to the name
of PackedFunc in the libmod.

libmod : tvm.Module
The module of the corresponding function.
Expand All @@ -54,11 +54,8 @@ def create(graph_json_str, libmod, ctx, dump_root=None):
graph_module : GraphModuleDebug
Debug Runtime graph module that can be used to execute the graph.
"""
if not isinstance(graph_json_str, string_types):
try:
graph_json_str = graph_json_str._tvm_graph_json()
except AttributeError:
raise ValueError("Type %s is not supported" % type(graph_json_str))
assert isinstance(graph_json_str, string_types)

try:
ctx, num_rpc_ctx, device_type_id = graph_runtime.get_device_ctx(libmod, ctx)
if num_rpc_ctx == len(ctx):
Expand Down
12 changes: 4 additions & 8 deletions python/tvm/contrib/graph_runtime.py
Original file line number Diff line number Diff line change
Expand Up @@ -29,10 +29,10 @@ def create(graph_json_str, libmod, ctx):

Parameters
----------
graph_json_str : str or graph class
graph_json_str : str
The graph to be deployed in json format output by json graph.
The graph can only contain one operator(tvm_op) that
points to the name of PackedFunc in the libmod.
The graph can contain operator(tvm_op) that points to the name
of PackedFunc in the libmod.

libmod : tvm.runtime.Module
The module of the corresponding function
Expand All @@ -48,11 +48,7 @@ def create(graph_json_str, libmod, ctx):
graph_module : GraphModule
Runtime graph module that can be used to execute the graph.
"""
if not isinstance(graph_json_str, string_types):
try:
graph_json_str = graph_json_str._tvm_graph_json()
except AttributeError:
raise ValueError("Type %s is not supported" % type(graph_json_str))
assert isinstance(graph_json_str, string_types)

ctx, num_rpc_ctx, device_type_id = get_device_ctx(libmod, ctx)

Expand Down
84 changes: 84 additions & 0 deletions python/tvm/relay/backend/graph_runtime_factory.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,84 @@
# 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.
"""Graph runtime factory."""
import warnings
from tvm._ffi.base import string_types
from tvm._ffi.registry import get_global_func
from tvm.runtime import ndarray

class GraphRuntimeFactoryModule(object):
"""Graph runtime factory module.
This is a module of graph runtime factory

Parameters
----------
graph_json_str : str
The graph to be deployed in json format output by graph compiler.
The graph can contain operator(tvm_op) that points to the name of
PackedFunc in the libmod.
libmod : tvm.Module
The module of the corresponding function
libmod_name: str
The name of module
params : dict of str to NDArray
The parameters of module
"""

def __init__(self, graph_json_str, libmod, libmod_name, params):
assert isinstance(graph_json_str, string_types)
fcreate = get_global_func("tvm.graph_runtime_factory.create")
args = []
for k, v in params.items():
args.append(k)
args.append(ndarray.array(v))
self.module = fcreate(graph_json_str, libmod, libmod_name, *args)
self.graph_json = graph_json_str
self.lib = libmod
self.libmod_name = libmod_name
self.params = params
self.iter_cnt = 0

def export_library(self, file_name, fcompile=None, addons=None, **kwargs):
return self.module.export_library(file_name, fcompile, addons, **kwargs)

# Sometimes we want to get params explicitly.
# For example, we want to save its params value to
# an independent file.
def get_params(self):
return self.params

def get_json(self):
return self.graph_json

def __getitem__(self, item):
return self.module.__getitem__(item)

def __iter__(self):
warnings.warn(
"legacy graph runtime behaviour of producing json / lib / params will be "
"removed in the next release ",
DeprecationWarning, 2)
return self

def __next__(self):
if self.iter_cnt > 2:
raise StopIteration

objs = [self.graph_json, self.lib, self.params]
obj = objs[self.iter_cnt]
self.iter_cnt += 1
return obj
9 changes: 7 additions & 2 deletions python/tvm/relay/build_module.py
Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@
from . import ty as _ty
from . import expr as _expr
from . import function as _function
from .backend import graph_runtime_factory as _graph_runtime_factory
from .backend import interpreter as _interpreter
from .backend.vm import VMExecutor

Expand Down Expand Up @@ -181,7 +182,7 @@ def get_params(self):
return ret


def build(mod, target=None, target_host=None, params=None):
def build(mod, target=None, target_host=None, params=None, mod_name='default'):
"""Helper function that builds a Relay function to run on TVM graph
runtime.

Expand All @@ -208,6 +209,9 @@ def build(mod, target=None, target_host=None, params=None):
Input parameters to the graph that do not change
during inference time. Used for constant folding.

mod_name: Optional[str]
The module name we will build

Returns
-------
graph_json : str
Expand Down Expand Up @@ -249,7 +253,8 @@ def build(mod, target=None, target_host=None, params=None):
with tophub_context:
bld_mod = BuildModule()
graph_json, mod, params = bld_mod.build(mod, target, target_host, params)
return graph_json, mod, params
mod = _graph_runtime_factory.GraphRuntimeFactoryModule(graph_json, mod, mod_name, params)
return mod


def optimize(mod, target=None, params=None):
Expand Down
175 changes: 175 additions & 0 deletions src/runtime/graph/graph_runtime_factory.cc
Original file line number Diff line number Diff line change
@@ -0,0 +1,175 @@
/*
* 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.
*/

/*!
* \file graph_runtime_factory.cc
* \brief Graph runtime factory implementations
*/

#include "./graph_runtime_factory.h"

#include <tvm/node/container.h>
#include <tvm/runtime/device_api.h>
#include <tvm/runtime/registry.h>

#include <iterator>
#include <vector>

namespace tvm {
namespace runtime {

GraphRuntimeFactory::GraphRuntimeFactory(
const std::string& graph_json,
const std::unordered_map<std::string, tvm::runtime::NDArray>& params,
const std::string& module_name) {
graph_json_ = graph_json;
params_ = params;
module_name_ = module_name;
}

PackedFunc GraphRuntimeFactory::GetFunction(
const std::string& name, const tvm::runtime::ObjectPtr<tvm::runtime::Object>& sptr_to_self) {
if (name == module_name_) {
return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
std::vector<TVMContext> contexts;
for (int i = 0; i < args.num_args; ++i) {
contexts.emplace_back(args[i].operator TVMContext());
}
*rv = this->RuntimeCreate(contexts);
});
} else if (name == "debug_create") {
return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
CHECK_GE(args.size(), 2);
std::string module_name = args[0].operator String();
CHECK(module_name == module_name_) << "Currently we only support single model for now.";
std::vector<TVMContext> contexts;
for (int i = 1; i < args.num_args; ++i) {
contexts.emplace_back(args[i].operator TVMContext());
}
*rv = this->DebugRuntimeCreate(contexts);
});
} else if (name == "remove_params") {
return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
std::unordered_map<std::string, tvm::runtime::NDArray> empty_params{};
auto exec =
make_object<GraphRuntimeFactory>(this->graph_json_, empty_params, this->module_name_);
exec->Import(this->imports_[0]);
*rv = Module(exec);
});
} else {
return PackedFunc();
}
}

void GraphRuntimeFactory::SaveToBinary(dmlc::Stream* stream) {
stream->Write(graph_json_);
std::vector<std::string> names;
std::vector<DLTensor*> arrays;
for (const auto& v : params_) {
names.emplace_back(v.first);
arrays.emplace_back(const_cast<DLTensor*>(v.second.operator->()));
}
uint64_t sz = arrays.size();
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With the MetadataModule, we should be able to remove the serialization and deserialization of params for GraphRuntime and the factory. That may affect downstream users. I can take a stab on it later.

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Could you give me a link about this MetadataModule?

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It was introduced in #5770. We should not do it in this pr. This just makes you aware of it.

CHECK(sz == names.size());
stream->Write(sz);
stream->Write(names);
for (size_t i = 0; i < sz; ++i) {
tvm::runtime::SaveDLTensor(stream, arrays[i]);
}
stream->Write(module_name_);
}

Module GraphRuntimeFactory::RuntimeCreate(const std::vector<TVMContext>& ctxs) {
auto exec = make_object<GraphRuntime>();
exec->Init(this->graph_json_, this->imports_[0], ctxs);
// set params
SetParams(exec.get(), this->params_);
return Module(exec);
}

Module GraphRuntimeFactory::DebugRuntimeCreate(const std::vector<TVMContext>& ctxs) {
const PackedFunc* pf = tvm::runtime::Registry::Get("tvm.graph_runtime_debug.create");
CHECK(pf != nullptr) << "Cannot find function tvm.graph_runtime_debug.create in registry. "
"Do you enable debug graph runtime build?";
// Debug runtime create packed function will call GetAllContexs, so we unpack the ctxs.
std::vector<int> unpacked_ctxs;
for (const auto& ctx : ctxs) {
unpacked_ctxs.emplace_back(ctx.device_type);
unpacked_ctxs.emplace_back(ctx.device_id);
}
size_t args_size = unpacked_ctxs.size() + 2;
std::vector<TVMValue> values(args_size);
std::vector<int> codes(args_size);
runtime::TVMArgsSetter setter(values.data(), codes.data());
setter(0, this->graph_json_);
setter(1, this->imports_[0]);
for (size_t i = 0; i < unpacked_ctxs.size(); ++i) {
setter(i + 2, unpacked_ctxs[i]);
}
TVMRetValue rv;
pf->CallPacked(TVMArgs(values.data(), codes.data(), args_size), &rv);
Module mod = rv.operator Module();
// debug graph runtime is one child class of graph runtime.
SetParams(const_cast<GraphRuntime*>(mod.as<GraphRuntime>()), this->params_);
return mod;
}

Module GraphRuntimeFactoryModuleLoadBinary(void* strm) {
dmlc::Stream* stream = static_cast<dmlc::Stream*>(strm);
std::string graph_json;
std::unordered_map<std::string, tvm::runtime::NDArray> params;
std::string module_name;
CHECK(stream->Read(&graph_json));
uint64_t sz;
CHECK(stream->Read(&sz));
std::vector<std::string> names;
CHECK(stream->Read(&names));
CHECK(sz == names.size());
for (size_t i = 0; i < sz; ++i) {
tvm::runtime::NDArray temp;
temp.Load(stream);
params[names[i]] = temp;
}
CHECK(stream->Read(&module_name));
auto exec = make_object<GraphRuntimeFactory>(graph_json, params, module_name);
return Module(exec);
}

TVM_REGISTER_GLOBAL("tvm.graph_runtime_factory.create").set_body([](TVMArgs args, TVMRetValue* rv) {
CHECK_GE(args.num_args, 3) << "The expected number of arguments for "
"graph_runtime_factory.create needs at least 3, "
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"but it has "
<< args.num_args;
// The argument order is graph_json, module, module_name, params.
CHECK_EQ((args.size() - 3) % 2, 0);
std::unordered_map<std::string, tvm::runtime::NDArray> params;
for (size_t i = 3; i < static_cast<size_t>(args.size()); i += 2) {
std::string name = args[i].operator String();
params[name] = args[i + 1].operator tvm::runtime::NDArray();
}
auto exec = make_object<GraphRuntimeFactory>(args[0], params, args[2]);
exec->Import(args[1]);
*rv = Module(exec);
});

TVM_REGISTER_GLOBAL("runtime.module.loadbinary_GraphRuntimeFactory")
.set_body_typed(GraphRuntimeFactoryModuleLoadBinary);

} // namespace runtime
} // namespace tvm
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