[Enhancement] Add fuse select assign pass

configs/_base_/onnx_config.py

@@ -6,4 +6,5 @@
  save_file='end2end.onnx',
  input_names=['input'],
  output_names=['output'],
- input_shape=None)
+ input_shape=None,
+ optimize=True)

csrc/mmdeploy/backend_ops/torchscript/optimizer/CMakeLists.txt

@@ -3,14 +3,15 @@
 project(ts_optimizer)
 
 find_package(Torch REQUIRED)
+find_library(TORCH_PYTHON_LIBRARY torch_python PATHS "${TORCH_INSTALL_PREFIX}/lib")
 if (NOT TARGET pybind11)
  add_subdirectory(${CMAKE_SOURCE_DIR}/third_party/pybind11 pybind11)
 endif ()
 
 file(GLOB_RECURSE OPTIMIZER_SRCS *.cpp)
 
 pybind11_add_module(${PROJECT_NAME} ${OPTIMIZER_SRCS})
-target_link_libraries(${PROJECT_NAME} PRIVATE ${TORCH_LIBRARIES})
+target_link_libraries(${PROJECT_NAME} PRIVATE ${TORCH_LIBRARIES} ${TORCH_PYTHON_LIBRARY})
 target_link_directories(${PROJECT_NAME} PRIVATE mmdeploy::torchscript_ops)
 set_target_properties(
  ${PROJECT_NAME} PROPERTIES LIBRARY_OUTPUT_DIRECTORY

csrc/mmdeploy/backend_ops/torchscript/optimizer/bind.cpp

@@ -1,10 +1,13 @@
 // Copyright (c) OpenMMLab. All rights reserved.
 #include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <torch/extension.h>
 
 #include <string>
 
 #include "optimizer.h"
 #include "passes/onnx/flatten_cls_head.h"
+#include "passes/onnx/fuse_select_assign.h"
 #include "passes/onnx/merge_shape_concate.h"
 #include "passes/onnx/onnx_peephole.h"
 
@@ -33,6 +36,8 @@ PYBIND11_MODULE(ts_optimizer, m) {
  onnx_module.def("_jit_pass_merge_shape_concate", MergeShapeConcate, py::arg("graph"));
  onnx_module.def("_jit_pass_onnx_peephole", ONNXPeephole, py::arg("graph"));
  onnx_module.def("_jit_pass_flatten_cls_head", FlattenClsHead, py::arg("graph"));
+ onnx_module.def("_jit_pass_fuse_select_assign", FuseSelectAssign, py::arg("graph"),
+ py::arg("params"));
 }
 
 } // namespace torch_jit

csrc/mmdeploy/backend_ops/torchscript/optimizer/ir/subgraph_matcher.cpp

@@ -295,6 +295,8 @@ bool SubgraphMatcher::SubgraphMatcherImpl::matchesSubgraphFromAnchorNode(Node* a
 SubgraphMatcher::SubgraphMatcher(const Graph& pattern, MatchAttribute match_attribute)
  : impl_(new SubgraphMatcher::SubgraphMatcherImpl(pattern, match_attribute)) {}
 
+SubgraphMatcher::~SubgraphMatcher() = default;
+
 bool SubgraphMatcher::matchesSubgraphFromAnchorNode(Node* anchor) {
  return impl_->matchesSubgraphFromAnchorNode(anchor);
 }

csrc/mmdeploy/backend_ops/torchscript/optimizer/ir/subgraph_matcher.h

@@ -17,6 +17,8 @@ class SubgraphMatcher {
  public:
  explicit SubgraphMatcher(const Graph& pattern, MatchAttribute match_attribute = TRY_MATCH);
 
+ ~SubgraphMatcher();
+
  bool matchesSubgraphFromAnchorNode(Node* anchor);
 
  /** \brief Return match map for nodes. */
@@ -27,7 +29,7 @@ class SubgraphMatcher {
 
  private:
  class SubgraphMatcherImpl;
- std::unique_ptr<SubgraphMatcherImpl> impl_ = nullptr;
+ std::unique_ptr<SubgraphMatcherImpl> impl_;
 };
 
 } // namespace torch_jit

csrc/mmdeploy/backend_ops/torchscript/optimizer/passes/onnx/fuse_select_assign.cpp

@@ -0,0 +1,148 @@
+#include "fuse_select_assign.h"
+
+#include <torch/csrc/jit/passes/dead_code_elimination.h>
+
+#include "../../ir/subgraph_matcher.h"
+#include "torch/csrc/jit/ir/irparser.h"
+
+namespace mmdeploy {
+namespace torch_jit {
+
+using c10::Symbol;
+using torch::jit::Block;
+using torch::jit::IValue;
+using torch::jit::Node;
+
+bool FuseSelectAssign(Node* node, std::unordered_map<std::string, Tensor>& params,
+ std::unordered_map<std::string, Value*>& vmap, SubgraphMatcher& matcher) {
+ auto values_map = matcher.values_map();
+
+ auto cmp1 = values_map[vmap["cmp_1"]]->node();
+ auto cmp2 = values_map[vmap["cmp_2"]]->node();
+ if (cmp1 != cmp2) {
+ // cmp_1 == cmp_2, cmp in (Great, Less)
+ if (cmp1->kind() != cmp2->kind()) return false;
+ if (!(cmp1->kind() == Symbol::onnx("Greater") || cmp1->kind() == Symbol::onnx("Less")))
+ return false;
+
+ // check threshold
+ Node* cmps[] = {cmp1, cmp2};
+ float thres = 0.0f;
+ Node* x = nullptr;
+ for (int i = 0; i < 2; ++i) {
+ auto cmp = cmps[i];
+ auto threshold = cmp->inputs()[1]->node();
+ if (threshold->kind() != Symbol::onnx("Constant")) return false;
+ auto thres_val = threshold->t(Symbol::attr("value"));
+ if (i == 0) {
+ thres = thres_val.data_ptr<float>()[0];
+ x = cmp->inputs()[0]->node();
+ } else {
+ float tmp_val = thres_val.data_ptr<float>()[0];
+ if (fabs(thres - tmp_val) > 1e-10) {
+ return false;
+ }
+ if (x != cmp->inputs()[0]->node()) {
+ return false;
+ }
+ }
+ }
+ }
+
+ {
+ // check shape of reshape
+ Node* shape = values_map[vmap["reshape_1_shape"]]->node();
+ auto shape_val = shape->t(Symbol::attr("value"));
+ if (shape_val.dim() != 1) return false;
+ if (shape_val.data_ptr<long>()[0] != -1) return false;
+ }
+
+ {
+ // check transpose
+ Node* trans[] = {values_map[vmap["trans_1"]]->node(), values_map[vmap["trans_2"]]->node()};
+ for (auto tran : trans) {
+ auto tran_perm = tran->is(Symbol::attr("perm"));
+ if (tran_perm.size() != 2) return false;
+ if (tran_perm[0] != 1 || tran_perm[1] != 0) return false;
+ }
+ }
+
+ {
+ // check gather indice
+ Node* gather_inds = values_map[vmap["gather_inds_2"]]->node();
+ auto inds_val = gather_inds->t(Symbol::attr("value"));
+ if (inds_val.dim() != 0) return false;
+ if (inds_val.data_ptr<long>()[0] != 0) return false;
+ }
+
+ {
+ // check slice start
+ Node* slice = values_map[vmap["slice_2"]]->node();
+ auto start_name = slice->inputs()[1]->debugName();
+ auto start_val = params[start_name];
+ if (start_val.dim() != 1) return false;
+ if (start_val.data_ptr<long>()[0] != 0) return false;
+ }
+
+ // create new node
+ auto graph = node->owningGraph();
+ auto z = values_map[vmap["z"]];
+ auto y = values_map[vmap["y"]];
+ auto where_node = graph->create(Symbol::onnx("Where"), {cmp1->output(), z, y});
+ where_node->insertBefore(node);
+ where_node->output()->copyMetadata(node->output());
+ node->output()->replaceAllUsesWith(where_node->output());
+ return true;
+}
+
+void FuseSelectAssign(Block* block, std::unordered_map<std::string, Tensor>& params,
+ std::unordered_map<std::string, Value*>& vmap, SubgraphMatcher& matcher) {
+ auto graph = block->owningGraph();
+ auto it = block->nodes().begin();
+ while (it != block->nodes().end()) {
+ auto node = *it;
+ ++it;
+ for (auto block : node->blocks()) {
+ FuseSelectAssign(block, params, vmap, matcher);
+ }
+
+ if (matcher.matchesSubgraphFromAnchorNode(node)) {
+ FuseSelectAssign(node, params, vmap, matcher);
+ }
+ }
+}
+
+void FuseSelectAssign(std::shared_ptr<Graph>& graph,
+ std::unordered_map<std::string, Tensor>& params) {
+ std::string pattern_str = R"IR(
+ graph(%y, %z, %cmp_1, %cmp_2, %start, %axes):
+ %nz_1 = onnx::NonZero(%cmp_1)
+ %trans_1 = onnx::Transpose(%nz_1)
+ %gather_1 = onnx::GatherND(%z, %trans_1)
+ %reshape_1_shape = onnx::Constant()
+ %reshape_1 = onnx::Reshape(%gather_1, %reshape_1_shape)
+ %shape_2 = onnx::Shape(%y)
+ %expand_2 = onnx::Expand(%cmp_2, %shape_2)
+ %nz_2 = onnx::NonZero(%expand_2)
+ %trans_2 = onnx::Transpose(%nz_2)
+ %trans_shape_2 = onnx::Shape(%trans_2)
+ %gather_inds_2 = onnx::Constant()
+ %gather_2 = onnx::Gather(%trans_shape_2, %gather_inds_2)
+ %unsqueeze_2 = onnx::Unsqueeze(%gather_2)
+ %slice_2 = onnx::Slice(%reshape_1, %start, %unsqueeze_2, %axes)
+ %scatter_2 = onnx::ScatterND(%y, %trans_2, %slice_2)
+ return (%scatter_2)
+ )IR";
+
+ Graph pattern;
+ std::unordered_map<std::string, Value*> vmap;
+ torch::jit::parseIR(pattern_str, &pattern, vmap);
+
+ SubgraphMatcher matcher(pattern, MatchAttribute::NO_MATCH);
+ FuseSelectAssign(graph->block(), params, vmap, matcher);
+ torch::jit::EliminateDeadCode(
+ graph->block(), true,
+ torch::jit::DCESideEffectPolicy::ALLOW_DELETING_NODES_WITH_SIDE_EFFECTS);
+}
+} // namespace torch_jit
+} // namespace mmdeploy

csrc/mmdeploy/backend_ops/torchscript/optimizer/passes/onnx/fuse_select_assign.h

@@ -0,0 +1,17 @@
+// Copyright (c) OpenMMLab. All rights reserved.
+#ifndef _FUSE_SELECT_ASSIGN_H_
+#define _FUSE_SELECT_ASSIGN_H_
+
+#include <torch/script.h>
+namespace mmdeploy {
+namespace torch_jit {
+using torch::Tensor;
+using torch::jit::Graph;
+
+// this pass is used to fuse y[x>thres] = z[x>thres]
+void FuseSelectAssign(std::shared_ptr<Graph>& graph,
+ std::unordered_map<std::string, Tensor>& params);
+} // namespace torch_jit
+} // namespace mmdeploy
+
+#endif

mmdeploy/apis/core/pipeline_manager.py

@@ -76,8 +76,10 @@ def pop_mp_output(self, call_id: int = None) -> Any:
  """pop multiprocess output."""
  assert self._mp_dict is not None, 'mp_dict is None.'
  call_id = self._call_id if call_id is None else call_id
- assert call_id in self._mp_dict, \
- f'`{self._func_name}` with Call id: {call_id} failed.'
+ if call_id not in self._mp_dict:
+ get_root_logger().error(
+ f'`{self._func_name}` with Call id: {call_id} failed. exit.')
+ exit()
  ret = self._mp_dict[call_id]
  self._mp_dict.pop(call_id)
  return ret

mmdeploy/apis/onnx/passes/optimize_onnx.py

@@ -10,7 +10,12 @@ def optimize_onnx(graph, params_dict, torch_out):
  ts_optimizer.onnx._jit_pass_merge_shape_concate(graph)
  ts_optimizer.onnx._jit_pass_onnx_peephole(graph)
  ts_optimizer.onnx._jit_pass_flatten_cls_head(graph)
+ ts_optimizer.onnx._jit_pass_fuse_select_assign(graph, params_dict)
  except Exception:
- pass
+ logger.warning(
+ 'Can not optimize model, please build torchscipt extension.\n'
+ 'More details: '
+ 'https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/experimental/onnx_optimizer.md' # noqa
+ )
 
  return graph, params_dict, torch_out

mmdeploy/apis/pytorch2onnx.py

@@ -82,6 +82,7 @@ def torch2onnx(img: Any,
  'verbose', False)
  keep_initializers_as_inputs = onnx_cfg.get('keep_initializers_as_inputs',
  True)
+ optimize = onnx_cfg.get('optimize', False)
  with no_mp():
  export(
  torch_model,
@@ -94,4 +95,5 @@ def torch2onnx(img: Any,
  opset_version=opset_version,
  dynamic_axes=dynamic_axes,
  verbose=verbose,
- keep_initializers_as_inputs=keep_initializers_as_inputs)
+ keep_initializers_as_inputs=keep_initializers_as_inputs,
+ optimize=optimize)

tests/test_apis/test_onnx_passes.py

@@ -188,3 +188,51 @@ def forward(self, x):
 
  node, idx = _find_next_node(idx + 1, nodes, 'Flatten')
  assert node is not None
+
+
+def test_fuse_select_assign():
+ pytest.importorskip('mmdeploy.backend.torchscript.ts_optimizer.onnx')
+
+ try:
+ from mmdeploy.backend.torchscript import ts_optimizer
+ opt_pass = ts_optimizer.onnx._jit_pass_fuse_select_assign
+ except ImportError:
+ pytest.skip('pass not found.')
+
+ def _optimize_onnx(graph, params_dict, torch_out):
+ opt_pass(graph, params_dict)
+ return graph, params_dict, torch_out
+
+ class TestModel(torch.nn.Module):
+
+ def __init__(self) -> None:
+ super().__init__()
+
+ def forward(self, x):
+ z = x / 2
+ y = torch.zeros_like(x)
+ y[x < 0.5] = z[x < 0.5]
+ return y
+
+ model = TestModel()
+ x = torch.rand(1, 4, 8, 8)
+
+ with RewriterContext({}, onnx_custom_passes=_optimize_onnx):
+ torch.onnx.export(
+ model,
+ x,
+ onnx_file,
+ input_names=['input'],
+ output_names=['output'],
+ dynamic_axes=dict(input={
+ 2: 'h',
+ 3: 'w'
+ }),
+ opset_version=11)
+
+ onnx_model = onnx.load(onnx_file)
+ graph = onnx_model.graph
+ nodes = graph.node
+
+ node, _ = _find_next_node(0, nodes, 'Where')
+ assert node is not None