manupa-arm commented on a change in pull request #7785:
URL: https://github.com/apache/tvm/pull/7785#discussion_r616604456
##########
File path: src/relay/backend/build_module.cc
##########
@@ -473,23 +492,35 @@ class RelayBuildModule : public runtime::ModuleNode {
// Relay IRModule -> IRModule optimizations.
relay_module = Optimize(relay_module, targets_, params);
+
// Get the updated function.
auto func = Downcast<Function>(relay_module->Lookup("main"));
// Generate code for the updated function.
- graph_codegen_ = std::unique_ptr<GraphCodegen>(new GraphCodegen());
- graph_codegen_->Init(nullptr, targets_);
- graph_codegen_->Codegen(func);
+ const String executor_str =
+ target_host->GetAttr<String>("executor").value_or(kTvmExecutorGraph);
+ if (executor_str == kTvmExecutorGraph) {
Review comment:
nit : how about using factory method here : e.g.,
ExecutorCodegen::MakeCodegen(const String& exec_str)
##########
File path: tests/python/relay/aot/test_crt_aot.py
##########
@@ -0,0 +1,247 @@
+# 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 tflite
+import os
+import io
+import struct
+import numpy as np
+import pathlib
+import shutil
+import subprocess
+import tempfile
+import tarfile
+import pytest
+
+import tvm
+from tvm import relay
+from tvm.relay import transform
+from tvm.relay.op.contrib import get_pattern_table
+from tvm.contrib import utils
+from tvm.relay.backend import compile_engine
+from tvm.contrib import utils
+from tvm.contrib import graph_executor
+from tvm.micro import export_model_library_format
+from tvm.relay import testing
+
+from aot_test_utils import *
+
+
+def test_conv_with_params():
+ RELAY_MODEL = """
+#[version = "0.0.5"]
+def @main(%data : Tensor[(1, 3, 64, 64), uint8], %weight : Tensor[(8, 3, 5,
5), int8]) {
+ %1 = nn.conv2d(
+ %data,
+ %weight,
+ padding=[2, 2],
+ channels=8,
+ kernel_size=[5, 5],
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ out_dtype="int32");
+ %1
+}
+"""
+ mod = tvm.parser.fromtext(RELAY_MODEL)
+ main_func = mod["main"]
+ shape_dict = {p.name_hint: p.checked_type.concrete_shape for p in
main_func.params}
+ type_dict = {p.name_hint: p.checked_type.dtype for p in main_func.params}
+
+ weight_data = np.ones(shape_dict["weight"]).astype(type_dict["weight"])
+ input_data = np.ones(shape_dict["data"]).astype(type_dict["data"])
+
+ params = {"weight": weight_data}
+ inputs = {"data": input_data}
+ output_list = generate_ref_data(mod, inputs, params)
+
+ input_list = [input_data]
+ compile_and_run(mod, input_list, output_list, params)
+
+
+def test_add_with_params():
+ x = relay.var("x", shape=(1, 10))
+ y = relay.var("y", shape=(1, 10))
+ z = relay.add(x, y)
+ func = relay.Function([x, y], z)
+
+ x_in = np.ones((1, 10)).astype("float32")
+ y_in = np.random.uniform(size=(1, 10)).astype("float32")
+
+ params = {"x": x_in}
+ inputs = {"y": y_in}
+ output_list = generate_ref_data(func, inputs, params)
+
+ input_list = [y_in]
+ compile_and_run(func, input_list, output_list, params)
+
+
+def test_conv2d():
+ """Test a subgraph with a single conv2d operator."""
+
+ def conv2d_direct():
+ dtype = "float32"
+ ishape = (1, 32, 14, 14)
+ w1shape = (32, 32, 3, 3)
+
+ data0 = relay.var("data", shape=ishape, dtype=dtype)
+ weight0 = relay.var("weight", shape=w1shape, dtype=dtype)
+ out = relay.nn.conv2d(data0, weight0, kernel_size=(3, 3), padding=(1,
1))
+ main_f = relay.Function([data0, weight0], out)
+ mod = tvm.IRModule()
+ mod["main"] = main_f
+ mod = transform.InferType()(mod)
+
+ i_data = np.random.uniform(0, 1, ishape).astype(dtype)
+ w1_data = np.random.uniform(0, 1, w1shape).astype(dtype)
+
+ return mod, {"data": i_data, "weight": w1_data}, (1, 32, 14, 14)
+
+ def group_conv2d():
+ dtype = "float32"
+ ishape = (1, 32, 14, 14)
+ w2shape = (32, 1, 3, 3)
+
+ data0 = relay.var("data", shape=(ishape), dtype=dtype)
+ weight0 = relay.var("weight", shape=(w2shape), dtype=dtype)
+ out = relay.nn.conv2d(data0, weight0, kernel_size=(3, 3), padding=(1,
1), groups=32)
+ main_f = relay.Function([data0, weight0], out)
+ mod = tvm.IRModule()
+ mod["main"] = main_f
+ mod = transform.InferType()(mod)
+
+ i_data = np.random.uniform(0, 1, ishape).astype(dtype)
+ w_data = np.random.uniform(0, 1, w2shape).astype(dtype)
+
+ return mod, {"data": i_data, "weight": w_data}, (1, 32, 14, 14)
+
+ for mod, inputs, out_shape in [conv2d_direct(), group_conv2d()]:
+ output_list = generate_ref_data(mod, inputs)
+ input_list = [inputs["data"], inputs["weight"]]
+ compile_and_run(mod, input_list, output_list)
+
+
+def test_concatenate():
+ dtype = "float32"
+ x = relay.var("x", shape=(10, 5), dtype=dtype)
+ y = relay.var("y", shape=(10, 5), dtype=dtype)
+ t = relay.var("z", shape=(), dtype=dtype)
+ z = relay.concatenate((x, y), axis=1)
+ z = relay.add(z, t)
+ # Check result.
+ func = relay.Function([x, y, t], z)
+ x_data = np.random.rand(10, 5).astype(dtype)
+ y_data = np.random.rand(10, 5).astype(dtype)
+ t_data = np.random.uniform(size=()).astype(dtype)
+ inputs = {"x": x_data, "y": y_data, "z": t_data}
+
+ output_list = generate_ref_data(func, inputs)
+ input_list = [inputs["x"], inputs["y"], inputs["z"]]
+ compile_and_run(func, input_list, output_list)
+
+
+def test_nested_tuples():
+ x = relay.var("x", shape=(10,))
+ x1 = x + relay.const(1.0)
+ x2 = x1 + relay.const(1.0)
+ x3 = x2 + relay.const(1.0)
+ x4 = x3 + relay.const(1.0)
+ out = relay.Tuple([x1, relay.Tuple([relay.Tuple([x2, x3]), x4])])
+ func = relay.Function([x], out)
+
+ x_data = np.random.uniform(size=(10,)).astype(np.float32)
+ inputs = {"x": x_data}
+ output_list = generate_ref_data(func, inputs)
+ input_list = [x_data]
+ compile_and_run(func, input_list, output_list)
+
+
+def test_tuple_getitem():
+ func = relay.Function([], relay.TupleGetItem(relay.Tuple([relay.const(1),
relay.const(2)]), 0))
+ output_list = generate_ref_data(func, {})
+ input_list = []
+ compile_and_run(func, input_list, output_list)
+
+
+def test_id():
+ x = relay.var("x", "float32")
+ ident = relay.Function([x], x)
+ one = np.array(1.0, "float32")
+ inputs = {"x": one}
+ output_list = generate_ref_data(ident, inputs)
+ input_list = [one]
+ compile_and_run(ident, input_list, output_list)
+
+
+def test_add_const():
+ two = relay.add(relay.const(1), relay.const(1))
+ func = relay.Function([], two)
+ output_list = generate_ref_data(func, {})
+ input_list = []
+ compile_and_run(func, input_list, output_list)
+
+
+def test_mul_param():
+ x = relay.var("x", shape=(10, 10))
+ y = relay.var("y", shape=(1, 10))
+ func = relay.Function([x, y], relay.multiply(x, y))
+ x_data = np.random.rand(10, 10).astype("float32")
+ y_data = np.random.rand(1, 10).astype("float32")
+ inputs = {"x": x_data, "y": y_data}
+ output_list = generate_ref_data(func, inputs)
+ input_list = [inputs["x"], inputs["y"]]
+ compile_and_run(func, input_list, output_list)
+
+
+def test_subtract():
+ i = relay.var("i", shape=[], dtype="int32")
+ sub = relay.subtract(i, relay.const(1, dtype="int32"))
+ func = relay.Function([i], sub, ret_type=relay.TensorType([], "int32"))
+ i_data = np.array(1, dtype="int32")
+ inputs = {"i": i_data}
+ output_list = generate_ref_data(func, inputs)
+ input_list = [inputs["i"]]
+ compile_and_run(func, input_list, output_list)
+
+
+def test_tuple_output():
+ x = relay.var("x", shape=(6, 9))
+ y = relay.split(x, 3).astuple()
+ a = relay.TupleGetItem(y, 0)
+ b = relay.TupleGetItem(y, 1)
+ c = relay.TupleGetItem(y, 2)
+ out = relay.Tuple([a, b])
+ func = relay.Function([x], out)
+ x_data = np.random.rand(6, 9).astype("float32")
+ inputs = {"x": x_data}
+ output_list = generate_ref_data(func, inputs)
+ input_list = [inputs["x"]]
+ compile_and_run(func, input_list, output_list)
+
+
+def test_mobilenet():
+ mod, params = testing.mobilenet.get_workload(batch_size=1)
+ data_shape = [int(x) for x in mod["main"].checked_type.arg_types[0].shape]
+ data = np.random.uniform(size=data_shape).astype("float32")
+ inputs = {"data": data}
+ output_list = generate_ref_data(mod, inputs, params)
+ input_list = [inputs["data"]]
+ compile_and_run(mod, input_list, output_list, params)
+
Review comment:
Can we have the BYOC infra test : test_byoc_utvm that is in
tests/micro/zephyr/test_zephyr.py ?
##########
File path: src/relay/backend/build_module.cc
##########
@@ -473,23 +492,35 @@ class RelayBuildModule : public runtime::ModuleNode {
// Relay IRModule -> IRModule optimizations.
relay_module = Optimize(relay_module, targets_, params);
+
// Get the updated function.
auto func = Downcast<Function>(relay_module->Lookup("main"));
// Generate code for the updated function.
- graph_codegen_ = std::unique_ptr<GraphCodegen>(new GraphCodegen());
- graph_codegen_->Init(nullptr, targets_);
- graph_codegen_->Codegen(func);
+ const String executor_str =
+ target_host->GetAttr<String>("executor").value_or(kTvmExecutorGraph);
+ if (executor_str == kTvmExecutorGraph) {
+ executor_codegen_ = std::unique_ptr<ExecutorCodegen>(new GraphCodegen());
+ } else {
+ executor_codegen_ = std::unique_ptr<ExecutorCodegen>(new AOTCodegen());
+ }
- ret_.graph_json = graph_codegen_->GetJSON();
- ret_.params = graph_codegen_->GetParams();
+ executor_codegen_->Init(nullptr, targets_);
+ executor_codegen_->Codegen(func);
+
+ if (executor_str == kTvmExecutorGraph) {
+ ret_.graph_json =
reinterpret_cast<GraphCodegen*>(executor_codegen_.get())->GetJSON();
Review comment:
nit : I think we can have a virtual method
ExecutorCodegen::UpdateOutput(const BuildOutput& ret) that implements the
update as required by specialized classes.
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