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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/07/17 00:36:57 UTC
[GitHub] [incubator-tvm] comaniac commented on a change in pull request #6073: [Ansor][AutoTVM v2.0] Part 1: Add annotation/compute_at/compute_root/compute_inline steps
comaniac commented on a change in pull request #6073:
URL: https://github.com/apache/incubator-tvm/pull/6073#discussion_r456141719
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -90,12 +90,69 @@ Stage::Stage(te::Operation op, StageKind op_type, const Array<Iterator>& iters,
data_ = std::move(node);
}
+/********** AttachMap **********/
+void AttachMap::SetComputeAtIter(int stage_id, int target_stage_id, int target_iter_id) {
+ AttachMapNode* pnode = CopyOnWrite();
+
+ // Delete the current entry of this stage
+ DeleteStageEntry(pnode, stage_id);
+
+ // Store the new relations to map
+ IterKey iter_key(target_stage_id, target_iter_id);
+ pnode->stage_to_attach_iter[stage_id] = iter_key;
+ pnode->iter_to_attached_stages[iter_key].push_back(stage_id);
+}
+
+void AttachMap::DeleteStage(int stage_id) {
+ AttachMapNode* pnode = CopyOnWrite();
+ // Delete the original stage entry
+ DeleteStageEntry(pnode, stage_id);
+}
+
+void AttachMap::UpdateIters(const std::vector<IterKey>& old_iters,
+ const std::vector<IterKey>& new_iters) {
+ AttachMapNode* pnode = CopyOnWrite();
+
+ CHECK_EQ(old_iters.size(), new_iters.size());
+ for (size_t i = 0; i < old_iters.size(); ++i) {
+ auto entry = pnode->iter_to_attached_stages.find(old_iters[i]);
+ if (entry == pnode->iter_to_attached_stages.end()) {
+ continue;
+ }
+
+ // Replace iter in the value of `stage_to_attach_iter`
+ for (const auto& s : entry->second) {
+ pnode->stage_to_attach_iter[s] = new_iters[i];
+ }
+
+ // Replace iter in the key of `iter_to_attached_stages`
Review comment:
Maybe "Remove the old iter from iter_to_attached_stages and add the new iter" might be better.
##########
File path: src/auto_scheduler/measure_record.cc
##########
@@ -169,6 +206,18 @@ struct Handler<::tvm::Array<::tvm::auto_scheduler::Step>> {
fused_ids.push_back(i);
}
data->push_back(::tvm::auto_scheduler::FuseStep(stage_id, fused_ids));
+ } else if (name == "AN") {
Review comment:
I feel the current approach of (de)serializing records is not scalable and hard to be maintained. Specifically, we put all deserialization rules to `Read` and all serialization rules to `Write`. The key to connect the serialization logic to the corresponding deserialization logic is a two character short name (e.g., AN). It seems to me that it would be better to define a short name and (de)serialization logic in each step. In this case, we can use `ps->serialize(writer)` for serialization, and build a step factory to deserialize them.
##########
File path: python/tvm/auto_scheduler/loop_state.py
##########
@@ -161,16 +202,116 @@ def fuse(self, stage, iters):
The Stage to be fused, can be a Stage order index, Stage operation or stage
output tensor.
iters : List[Iterator]
- The iterators to be fused
+ The iterators to be fused.
+
+ Returns
+ -------
+ res_it : Iterator
+ The fused Iterator.
+ """
+ self.state_object, res = _ffi_api.StateFuse(self.state_object,
+ self._resolve_stage_id(stage), iters)
+ return res
+
+ def vectorize(self, stage, iterator):
+ """ Schedule primitive corresponds to te.vectorize.
+
+ Parameters
+ ----------
+ stage : Union[int, Operation, Tensor]
+ The Stage to be vectorized, can be a Stage order index, Stage operation or stage
+ output tensor.
+ iterator : Iterator
+ The iterator to be vectorized.
Returns
-------
res_it : Iterator
- The fused Iterator
+ The vectorized Iterator.
"""
- stage_id = self._resolve_stage_id(stage)
+ self.state_object, res = _ffi_api.StateVectorize(self.state_object,
+ self._resolve_stage_id(stage), iterator)
+ return res
+
+ def parallel(self, stage, iterator):
+ """ Schedule primitive corresponds to te.parallel.
- self.state_object, res = _ffi_api.StateFuse(self.state_object, stage_id, iters)
+ Parameters
+ ----------
+ stage : Union[int, Operation, Tensor]
+ The Stage to be paralleled, can be a Stage order index, Stage operation or stage
+ output tensor.
+ iterator : Iterator
+ The iterator to be paralleled.
+
+ Returns
+ -------
+ res_it : Iterator
+ The paralleled Iterator.
+ """
+ self.state_object, res = _ffi_api.StateParallel(self.state_object,
+ self._resolve_stage_id(stage), iterator)
+ return res
+
+ def unroll(self, stage, iterator, max_unroll=None):
+ """ Schedule primitive corresponds to te.unroll.
+
+ Parameters
+ ----------
+ stage : Union[int, Operation, Tensor]
+ The Stage to be unrolled, can be a Stage order index, Stage operation or stage
+ output tensor.
+ iterator : Iterator
+ The iterator to be unrolled.
+ max_unroll : Optional[int]
+ The max unroll limit. Iterator with extent larger than this limit will be skipped.
+
+ Returns
+ -------
+ res_it : Iterator
+ The unrolled Iterator.
+ """
+ self.state_object, res = _ffi_api.StateUnroll(self.state_object,
+ self._resolve_stage_id(stage), iterator,
+ max_unroll if max_unroll else -1)
+ return res
+
+ def bind(self, stage, iterator, thread_name):
+ """ Schedule primitive corresponds to te.bind.
+
+ Parameters
+ ----------
+ stage : Union[int, Operation, Tensor]
+ The Stage to be binded, can be a Stage order index, Stage operation or stage
+ output tensor.
+ iterator : Iterator
+ The iterator to be binded.
+ thread_name : str
+ The thread type to be binded. Currently support:
+ - vthread
+ - blockIdx.x
+ - threadIdx.x
+ - blockIdx.y
+ - threadIdx.y
+
+ Returns
+ -------
+ res_it : Iterator
+ The binded Iterator.
+ """
+ trans_table = {
+ "vthread": 4,
+ "blockIdx.x": 5,
+ "threadIdx.x": 6,
+ "blockIdx.y": 7,
+ "threadIdx.y": 8,
+ }
Review comment:
We should make this dict static.
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -143,12 +265,67 @@ void State::DoReorderStep(const ReorderStep& step) {
Stage(stage->op, stage->op_type, iters, stage->compute_at, stage->attrs));
}
+void State::DoComputeAtStep(const ComputeAtStep& step) {
+ const Stage& stage = operator->()->stages[step->stage_id];
+
+ // After compute_at, we don't know the accurate length information any more
+ // If we do want to know the accurate lengths, we can call ComputeDAG::InferBound
Review comment:
This comment should be in the `compute_at()` function definition in `loop_state.h` because this is more like a side effect of this function.
In addition, I think it's fine to say we intentionally removed the loop sizes of that state when doing this step since it is not accurate anymore after compute_at. Run ComputeDAG::InferBound again to recover the loop sizes.
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -264,6 +462,38 @@ Iterator State::DoFuseStep(const FuseStep& step) {
pstate->stages.Set(stage_id,
Stage(stage->op, stage->op_type, new_iters, stage->compute_at, stage->attrs));
+ // We have to update the iterator relations in attach map, these two vectors keep the replacement
+ // mapping
Review comment:
ditto.
##########
File path: tests/python/unittest/test_auto_scheduler_loop_state.py
##########
@@ -61,5 +61,79 @@ def test_split_fuse_reorder():
assert s1[C].iters[4].range.extent == 8
assert s1[C].iters[5].range.extent == 2
+ s1.parallel(C, j1)
+ s1.unroll(C, j2)
+ s1.vectorize(C, j3)
+ s1.bind(C, i1, "blockIdx.x")
+ s1.bind(C, i2, "vthread")
+ s1.bind(C, i3, "threadIdx.y")
+
+
+def test_compute_at_root_inline():
+ dag = auto_scheduler.ComputeDAG(conv2d_nchw_bn_relu(1, 224, 224, 3, 64, 7, 2, 3))
+ s0 = dag.get_init_state()
+
+ # data, padding, kernel = 0, 1, 2
+ conv = s0.stage_ops[3]
+ # bias = 4
+ bias_add = s0.stage_ops[5]
+ # bn_scale = 6
+ bn_mul = s0.stage_ops[7]
+ # bn_offset = 8
+ bn_add = s0.stage_ops[9]
+ relu = s0.stage_ops[10]
+
+ s0.compute_inline(bn_add)
+ s0.compute_inline(bn_mul)
+ s0.compute_inline(bias_add)
+ s0.compute_at(conv, relu, s0[relu].iters[2])
+ print(s0)
Review comment:
Remove this line.
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -90,12 +90,69 @@ Stage::Stage(te::Operation op, StageKind op_type, const Array<Iterator>& iters,
data_ = std::move(node);
}
+/********** AttachMap **********/
+void AttachMap::SetComputeAtIter(int stage_id, int target_stage_id, int target_iter_id) {
+ AttachMapNode* pnode = CopyOnWrite();
+
+ // Delete the current entry of this stage
+ DeleteStageEntry(pnode, stage_id);
+
+ // Store the new relations to map
+ IterKey iter_key(target_stage_id, target_iter_id);
+ pnode->stage_to_attach_iter[stage_id] = iter_key;
+ pnode->iter_to_attached_stages[iter_key].push_back(stage_id);
+}
+
+void AttachMap::DeleteStage(int stage_id) {
+ AttachMapNode* pnode = CopyOnWrite();
+ // Delete the original stage entry
+ DeleteStageEntry(pnode, stage_id);
+}
+
+void AttachMap::UpdateIters(const std::vector<IterKey>& old_iters,
+ const std::vector<IterKey>& new_iters) {
+ AttachMapNode* pnode = CopyOnWrite();
+
+ CHECK_EQ(old_iters.size(), new_iters.size());
+ for (size_t i = 0; i < old_iters.size(); ++i) {
+ auto entry = pnode->iter_to_attached_stages.find(old_iters[i]);
+ if (entry == pnode->iter_to_attached_stages.end()) {
+ continue;
+ }
+
+ // Replace iter in the value of `stage_to_attach_iter`
Review comment:
Maybe "Update the attaching target of an old iter to the new iter" might be clearer.
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -90,12 +90,69 @@ Stage::Stage(te::Operation op, StageKind op_type, const Array<Iterator>& iters,
data_ = std::move(node);
}
+/********** AttachMap **********/
+void AttachMap::SetComputeAtIter(int stage_id, int target_stage_id, int target_iter_id) {
+ AttachMapNode* pnode = CopyOnWrite();
+
+ // Delete the current entry of this stage
+ DeleteStageEntry(pnode, stage_id);
+
+ // Store the new relations to map
+ IterKey iter_key(target_stage_id, target_iter_id);
+ pnode->stage_to_attach_iter[stage_id] = iter_key;
+ pnode->iter_to_attached_stages[iter_key].push_back(stage_id);
+}
+
+void AttachMap::DeleteStage(int stage_id) {
+ AttachMapNode* pnode = CopyOnWrite();
+ // Delete the original stage entry
+ DeleteStageEntry(pnode, stage_id);
+}
+
+void AttachMap::UpdateIters(const std::vector<IterKey>& old_iters,
+ const std::vector<IterKey>& new_iters) {
Review comment:
I guess you need to guarantee the length of both vectors are the same?
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -143,12 +265,67 @@ void State::DoReorderStep(const ReorderStep& step) {
Stage(stage->op, stage->op_type, iters, stage->compute_at, stage->attrs));
}
+void State::DoComputeAtStep(const ComputeAtStep& step) {
+ const Stage& stage = operator->()->stages[step->stage_id];
+
+ // After compute_at, we don't know the accurate length information any more
+ // If we do want to know the accurate lengths, we can call ComputeDAG::InferBound
+ Array<Iterator> new_iters;
+ for (const Iterator& it : stage->iters) {
+ new_iters.push_back(Iterator(it->name, Range(), it->iter_kind, it->annotation));
+ }
+
+ StateNode* pstate = CopyOnWrite();
+ pstate->stages.Set(step->stage_id, Stage(stage->op, stage->op_type, std::move(new_iters),
+ ComputeAtKind::kIter, stage->attrs));
+ // Update attach map
+ pstate->attach_map.SetComputeAtIter(step->stage_id, step->target_stage_id, step->target_iter_id);
+}
+
+void State::DoComputeRootStep(const ComputeRootStep& step) {
+ const Stage& stage = operator->()->stages[step->stage_id];
+
+ // After compute_at, we don't know the accurate length information any more
+ // If we do want to know the accurate lengths, we can call ComputeDAG::InferBound
Review comment:
ditto.
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -90,12 +90,69 @@ Stage::Stage(te::Operation op, StageKind op_type, const Array<Iterator>& iters,
data_ = std::move(node);
}
+/********** AttachMap **********/
+void AttachMap::SetComputeAtIter(int stage_id, int target_stage_id, int target_iter_id) {
+ AttachMapNode* pnode = CopyOnWrite();
+
+ // Delete the current entry of this stage
+ DeleteStageEntry(pnode, stage_id);
+
+ // Store the new relations to map
+ IterKey iter_key(target_stage_id, target_iter_id);
+ pnode->stage_to_attach_iter[stage_id] = iter_key;
+ pnode->iter_to_attached_stages[iter_key].push_back(stage_id);
+}
+
+void AttachMap::DeleteStage(int stage_id) {
+ AttachMapNode* pnode = CopyOnWrite();
+ // Delete the original stage entry
+ DeleteStageEntry(pnode, stage_id);
+}
+
+void AttachMap::UpdateIters(const std::vector<IterKey>& old_iters,
+ const std::vector<IterKey>& new_iters) {
+ AttachMapNode* pnode = CopyOnWrite();
+
+ CHECK_EQ(old_iters.size(), new_iters.size());
+ for (size_t i = 0; i < old_iters.size(); ++i) {
+ auto entry = pnode->iter_to_attached_stages.find(old_iters[i]);
+ if (entry == pnode->iter_to_attached_stages.end()) {
+ continue;
+ }
Review comment:
Add a comment saying that we are skpping the old iterators that have no stage attached.
##########
File path: src/auto_scheduler/loop_state.h
##########
@@ -217,6 +196,68 @@ class Stage : public ObjectRef {
TVM_DEFINE_OBJECT_REF_COW_METHOD(StageNode);
};
+/*! \brief Use stage_id to represent a stage. */
+using StageKey = int;
+/*! \brief Use stage_id and iter_id to represent a iterator. */
+using IterKey = std::pair<int, int>;
+
+/*!
+ * \brief stores the compute_at relation between stages
+ * This stores a bi-directional mapping from stages and iter:
+ * 1. Stage to its attached iterator
+ * 2. Iterator to the stage attached to it
+ * You can use AttachMapNode::stage_to_attach_iter and AttachMapNode::iter_to_attached_stages
+ * to query the relations
+ */
+class AttachMapNode : public Object {
+ public:
+ /*! \brief A Map to store the mapping of stage to its attached iterator. */
+ std::unordered_map<StageKey, IterKey> stage_to_attach_iter;
+ /*! \brief A Map to store the mapping of iterator to the stage attached to it. */
+ std::unordered_map<IterKey, std::vector<StageKey>> iter_to_attached_stages;
+
+ static constexpr const char* _type_key = "auto_scheduler.AttachMap";
+ TVM_DECLARE_FINAL_OBJECT_INFO(AttachMapNode, Object);
+};
+
+/*!
+ * \brief Managed reference to AttachMapNode.
+ * \sa AttachMapNode
+ */
+class AttachMap : public ObjectRef {
+ public:
+ /*!
+ * \brief Process the stage/iterator mapping after compute at.
+ * \param stage_id The index of the stage to be compute at.
+ * \param target_stage_id The index of stage that this step will compute at to.
+ * \param target_iter_id The index of iterator in target stage that this step will compute at to.
+ */
+ void SetComputeAtIter(int stage_id, int target_stage_id, int target_iter_id);
+ /*!
+ * \brief This is a public wrapper of `DeleteStageEntry`. To delete the entry of a specific stage.
+ * \param stage_id The index of the stage to be compute at.
+ */
+ void DeleteStage(int stage_id);
+ /*!
+ * \brief Update the iterator relations in AttachMap.
Review comment:
To my understanding, this function attempts to update from (stage -> old_iter) to (stage -> new_iter). It needs a clearer description.
##########
File path: tests/python/unittest/test_auto_scheduler_loop_state.py
##########
@@ -61,5 +61,79 @@ def test_split_fuse_reorder():
assert s1[C].iters[4].range.extent == 8
assert s1[C].iters[5].range.extent == 2
+ s1.parallel(C, j1)
+ s1.unroll(C, j2)
+ s1.vectorize(C, j3)
+ s1.bind(C, i1, "blockIdx.x")
+ s1.bind(C, i2, "vthread")
+ s1.bind(C, i3, "threadIdx.y")
Review comment:
Should check their annotations?
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -143,12 +265,67 @@ void State::DoReorderStep(const ReorderStep& step) {
Stage(stage->op, stage->op_type, iters, stage->compute_at, stage->attrs));
}
+void State::DoComputeAtStep(const ComputeAtStep& step) {
+ const Stage& stage = operator->()->stages[step->stage_id];
+
+ // After compute_at, we don't know the accurate length information any more
+ // If we do want to know the accurate lengths, we can call ComputeDAG::InferBound
+ Array<Iterator> new_iters;
+ for (const Iterator& it : stage->iters) {
+ new_iters.push_back(Iterator(it->name, Range(), it->iter_kind, it->annotation));
+ }
+
+ StateNode* pstate = CopyOnWrite();
+ pstate->stages.Set(step->stage_id, Stage(stage->op, stage->op_type, std::move(new_iters),
+ ComputeAtKind::kIter, stage->attrs));
+ // Update attach map
+ pstate->attach_map.SetComputeAtIter(step->stage_id, step->target_stage_id, step->target_iter_id);
+}
+
+void State::DoComputeRootStep(const ComputeRootStep& step) {
+ const Stage& stage = operator->()->stages[step->stage_id];
+
+ // After compute_at, we don't know the accurate length information any more
+ // If we do want to know the accurate lengths, we can call ComputeDAG::InferBound
+ Array<Iterator> new_iters;
+ for (const Iterator& it : stage->iters) {
+ new_iters.push_back(Iterator(it->name, Range(), it->iter_kind, it->annotation));
+ }
+
+ StateNode* pstate = CopyOnWrite();
+ pstate->stages.Set(step->stage_id, Stage(stage->op, stage->op_type, std::move(new_iters),
+ ComputeAtKind::kRoot, stage->attrs));
+ // Update attach map
+ pstate->attach_map.DeleteStage(step->stage_id);
+}
+
+void State::DoComputeInlineStep(const ComputeInlineStep& step) {
+ const Stage& stage = operator->()->stages[step->stage_id];
+
+ // CHECK the validity of compute_inline
+ for (size_t i = 0; i < stage->iters.size(); ++i) {
+ CHECK_EQ(operator->()->attach_map->iter_to_attached_stages.count(
+ std::make_pair(step->stage_id, i)),
+ 0)
+ << "Invalid compute_inline: Because there are some other stages "
Review comment:
Remove "Because"
##########
File path: tests/python/unittest/test_auto_scheduler_loop_state.py
##########
@@ -61,5 +61,79 @@ def test_split_fuse_reorder():
assert s1[C].iters[4].range.extent == 8
assert s1[C].iters[5].range.extent == 2
+ s1.parallel(C, j1)
+ s1.unroll(C, j2)
+ s1.vectorize(C, j3)
+ s1.bind(C, i1, "blockIdx.x")
+ s1.bind(C, i2, "vthread")
+ s1.bind(C, i3, "threadIdx.y")
+
+
+def test_compute_at_root_inline():
+ dag = auto_scheduler.ComputeDAG(conv2d_nchw_bn_relu(1, 224, 224, 3, 64, 7, 2, 3))
+ s0 = dag.get_init_state()
+
+ # data, padding, kernel = 0, 1, 2
+ conv = s0.stage_ops[3]
+ # bias = 4
+ bias_add = s0.stage_ops[5]
+ # bn_scale = 6
+ bn_mul = s0.stage_ops[7]
+ # bn_offset = 8
+ bn_add = s0.stage_ops[9]
+ relu = s0.stage_ops[10]
+
+ s0.compute_inline(bn_add)
+ s0.compute_inline(bn_mul)
+ s0.compute_inline(bias_add)
+ s0.compute_at(conv, relu, s0[relu].iters[2])
+ print(s0)
+ assert str(s0) == \
Review comment:
I feel that it's not a good idea to check the correctness using printed strings...
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -143,12 +265,67 @@ void State::DoReorderStep(const ReorderStep& step) {
Stage(stage->op, stage->op_type, iters, stage->compute_at, stage->attrs));
}
+void State::DoComputeAtStep(const ComputeAtStep& step) {
+ const Stage& stage = operator->()->stages[step->stage_id];
+
+ // After compute_at, we don't know the accurate length information any more
+ // If we do want to know the accurate lengths, we can call ComputeDAG::InferBound
+ Array<Iterator> new_iters;
+ for (const Iterator& it : stage->iters) {
+ new_iters.push_back(Iterator(it->name, Range(), it->iter_kind, it->annotation));
+ }
+
+ StateNode* pstate = CopyOnWrite();
+ pstate->stages.Set(step->stage_id, Stage(stage->op, stage->op_type, std::move(new_iters),
+ ComputeAtKind::kIter, stage->attrs));
+ // Update attach map
+ pstate->attach_map.SetComputeAtIter(step->stage_id, step->target_stage_id, step->target_iter_id);
+}
+
+void State::DoComputeRootStep(const ComputeRootStep& step) {
+ const Stage& stage = operator->()->stages[step->stage_id];
+
+ // After compute_at, we don't know the accurate length information any more
+ // If we do want to know the accurate lengths, we can call ComputeDAG::InferBound
+ Array<Iterator> new_iters;
+ for (const Iterator& it : stage->iters) {
+ new_iters.push_back(Iterator(it->name, Range(), it->iter_kind, it->annotation));
+ }
+
+ StateNode* pstate = CopyOnWrite();
+ pstate->stages.Set(step->stage_id, Stage(stage->op, stage->op_type, std::move(new_iters),
+ ComputeAtKind::kRoot, stage->attrs));
+ // Update attach map
+ pstate->attach_map.DeleteStage(step->stage_id);
+}
+
+void State::DoComputeInlineStep(const ComputeInlineStep& step) {
+ const Stage& stage = operator->()->stages[step->stage_id];
+
+ // CHECK the validity of compute_inline
Review comment:
s/CHECK/Check
##########
File path: src/auto_scheduler/loop_state.cc
##########
@@ -210,6 +387,16 @@ Array<Iterator> State::DoSplitStepCommon(int stage_id, int iter_id,
Stage(stage->op, stage->op_type, new_iters, stage->compute_at, stage->attrs));
pstate->concrete &= concrete;
+ // We have to update the iterator relations in attach map, these two vectors keep the replacement
+ // mapping
Review comment:
```suggestion
// Use two vectors to represent the iterator relation before and after the split
// in order to update the attach_map
```
We may also need to mention in the split function defintion in `loop_state.h` that if the attached target iterator is split, then the innermost split iterator would be the new attached target.
##########
File path: tests/python/unittest/test_auto_scheduler_measure.py
##########
@@ -18,14 +18,51 @@
""" Test measurement and log serialization. """
import tvm
-from tvm import auto_scheduler
+import topi
+from tvm import te, auto_scheduler
import tempfile
from test_auto_scheduler_common import get_tiled_matmul
def test_record():
- dag, s = get_tiled_matmul()
+ A = te.placeholder((512, 512), name='A')
Review comment:
Move the checker in L67 to here. If LLVM is disabled, we don't have to even build this DAG.
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