areusch commented on a change in pull request #7518:
URL: https://github.com/apache/tvm/pull/7518#discussion_r603671640
##########
File path: src/relay/backend/graph_runtime_codegen.cc
##########
@@ -181,23 +186,56 @@ class GraphOpNode : public GraphNode {
const std::string op_type_name_{"tvm_op"};
};
-/*! \brief Code generator for graph runtime */
+/*! \brief Code generator for the graph runtime, produces a module containing
the graph JSON,
+ * module, and parameters.
+ */
class GraphRuntimeCodegen : public
backend::MemoizedExprTranslator<std::vector<GraphNodeRef>> {
public:
GraphRuntimeCodegen(runtime::Module* mod, const TargetsMap& targets) :
mod_(mod) {
- compile_engine_ = CompileEngine::Global();
targets_ = targets;
}
LoweredOutput Codegen(relay::Function func) {
- auto pf = GetPackedFunc("relay.backend.GraphPlanMemory");
- storage_device_map_ = (*pf)(func);
+ // Jared: why do we do this? just call C++ API.
Review comment:
at some point we should support overriding this. it seems like you'd
need to build a C++ impl right now to do that, so I don't know that we need
this hook now. but, we should plan to put some customization back in the future?
##########
File path: src/relay/backend/graph_plan_memory.cc
##########
@@ -252,9 +265,22 @@ class StorageAllocator : public StorageAllocaBaseVisitor {
}
// The call map
void VisitExpr_(const CallNode* op) final {
+ // temporary hack for change of style.
Review comment:
same
##########
File path: src/driver/driver_api.cc
##########
@@ -244,14 +244,17 @@ std::pair<IRModule, IRModule> SplitDevHostFuncs(IRModule
mod_mixed, const Target
}
if (target->kind->device_type == kDLCPU && target_host == target) {
- ICHECK(mdevice->functions.empty()) << "No device code should be generated
when target "
- << "and host_target are both llvm
target."
- << "\n";
+ // We need to relax this check for just TIR functions.
Review comment:
+1
##########
File path: src/relay/backend/te_compiler_cache.cc
##########
@@ -0,0 +1,681 @@
+/*
+ * 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 "./te_compiler_cache.h"
+
+#include <tvm/driver/driver_api.h>
+#include <tvm/ir/type_functor.h>
+#include <tvm/relay/analysis.h>
+#include <tvm/relay/attrs/device_copy.h>
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/op.h>
+#include <tvm/relay/op_attr_types.h>
+#include <tvm/runtime/container.h>
+#include <tvm/runtime/device_api.h>
+#include <tvm/runtime/registry.h>
+#include <tvm/te/operation.h>
+#include <tvm/te/schedule.h>
+#include <tvm/te/schedule_pass.h>
+#include <tvm/topi/tags.h>
+
+#include <functional>
+#include <limits>
+#include <mutex>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include "../transforms/pass_utils.h"
+#include "utils.h"
+
+namespace tvm {
+namespace relay {
+namespace tec {
+
+TVM_REGISTER_NODE_TYPE(LoweredOutputNode);
+TVM_REGISTER_NODE_TYPE(CachedFuncNode);
+TVM_REGISTER_NODE_TYPE(CCacheKeyNode);
+TVM_REGISTER_NODE_TYPE(CCacheValueNode);
+
+LoweredOutput::LoweredOutput(tvm::Array<te::Tensor> outputs, OpImplementation
impl) {
+ auto n = make_object<LoweredOutputNode>();
+ n->outputs = std::move(outputs);
+ n->implementation = std::move(impl);
+ data_ = std::move(n);
+}
+
+CCacheKey::CCacheKey(Function source_func, Target target) {
+ auto n = make_object<CCacheKeyNode>();
+ n->source_func = std::move(source_func);
+ n->target = std::move(target);
+ data_ = std::move(n);
+}
+
+CachedFunc::CachedFunc(tvm::Target target, GlobalVar prim_fn_var,
tvm::Array<te::Tensor> inputs,
+ tvm::Array<te::Tensor> outputs, te::Schedule schedule,
+ tvm::Array<Integer> shape_func_param_states, IRModule
funcs) {
+ auto n = make_object<CachedFuncNode>();
+ n->target = target;
+ n->prim_fn_var = prim_fn_var;
+ n->inputs = inputs;
+ n->outputs = outputs;
+ n->schedule = schedule;
+ n->shape_func_param_states = shape_func_param_states;
+ n->funcs = funcs;
+ data_ = std::move(n);
+}
+
+Array<IndexExpr> GetShape(const Array<IndexExpr>& shape) {
+ // for now, we always use int32 shape when possible
+ // even if the result of shape inference becomes int64.
+ Array<IndexExpr> res;
+ for (IndexExpr val : shape) {
+ const int64_t* pval = tir::as_const_int(val);
+ if (pval != nullptr) {
+#ifndef TVM_INDEX_DEFAULT_I64
+ ICHECK_LE(pval[0], std::numeric_limits<int32_t>::max());
+ ICHECK_GE(pval[0], std::numeric_limits<int32_t>::min());
+ res.push_back(IntImm(DataType::Int(32), *pval));
+#else
+ res.push_back(val);
+#endif // TVM_INDEX_DEFAULT_I64
+ } else if (val->IsInstance<tir::AnyNode>()) {
+ res.push_back(val.as<tir::AnyNode>()->ToVar());
+ } else {
+ res.push_back(val);
+ }
+ }
+ return res;
+}
+
+// The getter to get schedule from compile engine.
Review comment:
this isn't really a Getter is it? What does it translate from and to?
##########
File path: tests/python/relay/test_backend_graph_runtime.py
##########
@@ -142,7 +143,7 @@ def test_plan_memory():
# Current rule requires vars have unique storage id
# because we don't do inplace, we will need another
# two alternating temporary space.
- assert len(storage_ids) == 4
+ assert len(storage_ids) == 4, f"found storaged_ids: {storage_ids}"
Review comment:
nit: storage_ids
##########
File path: tests/python/relay/test_backend_graph_runtime.py
##########
@@ -126,6 +126,7 @@ def test_plan_memory():
func = relay.Function([x, y], z)
mod = tvm.IRModule.from_expr(func)
mod = relay.transform.InferType()(mod)
+ print(mod)
Review comment:
remove?
##########
File path: tests/python/relay/test_backend_graph_runtime.py
##########
@@ -231,10 +232,12 @@ def test_graph_executor_nested_tuples():
if __name__ == "__main__":
- test_plan_memory()
- test_with_params()
- test_add_op_scalar()
test_add_op_tensor()
- test_add_op_broadcast()
- test_gru_like()
- test_compile_nested_tuples()
+ # test_plan_memory()
+ # test_with_params()
+ # test_add_op_scalar()
+ # test_add_op_tensor()
+ # test_add_op_broadcast()
+ # test_gru_like()
+ # test_compile_nested_tuples()
+ # test_add_op_tensor()
Review comment:
sys.exit(pytest.main([__file__] + sys.argv[1:]))
##########
File path: src/relay/backend/graph_plan_memory.cc
##########
@@ -166,10 +167,22 @@ class StorageAllocaInit : protected
StorageAllocaBaseVisitor {
}
void VisitExpr_(const CallNode* op) final {
+ // temporary hack for change of style.
Review comment:
can you state which arg is being dropped here?
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