add model splitting support (open-mmlab#1)

* add function marker and model extractor

* add fsaf split & partial mask rcnn split, import extract.py

* 1. add value renaming  2. add apply_marks in config to turn on/off marks

* rewind changes on pytorch2onnx.py

Co-authored-by: q.yao <streetyao@live.com>

configs/mmdet/split.py

@@ -0,0 +1,4 @@
+_base_ = ['./base.py', '../_base_/backends/tensorrt.py']
+
+backend = 'default'
+apply_marks = True

mmdeploy/apis/pytorch2onnx.py

@@ -62,4 +62,4 @@ def torch2onnx(img: Any,
             keep_initializers_as_inputs=pytorch2onnx_cfg[
                 'keep_initializers_as_inputs'])
 
-    ret_value.value = 0
+    ret_value.value = 0

mmdeploy/mmdet/models/dense_heads/__init__.py

@@ -1,3 +1,5 @@
 from .anchor_head import AnchorHead
+from .rpn_head import rpn_head_forward
+from .fsaf_head import fsaf_head_forward
 
-__all__ = ['AnchorHead']
+__all__ = ['AnchorHead', 'rpn_head_forward', 'fsaf_head_forward']

mmdeploy/mmdet/models/dense_heads/fsaf_head.py

@@ -0,0 +1,7 @@
+from mmdeploy.utils import FUNCTION_REWRITERS, mark
+
+
+@FUNCTION_REWRITERS.register_rewriter('mmdet.models.FSAFHead.forward')
+@mark('rpn_forward')
+def fsaf_head_forward(rewriter, *args):
+    return rewriter.origin_func(*args)

mmdeploy/mmdet/models/dense_heads/rpn_head.py

@@ -0,0 +1,7 @@
+from mmdeploy.utils import FUNCTION_REWRITERS, mark
+
+
+@FUNCTION_REWRITERS.register_rewriter('mmdet.models.RPNHead.forward')
+@mark('rpn_forward')
+def rpn_head_forward(rewriter, self, feats):
+    return rewriter.origin_func(self, feats)

mmdeploy/mmdet/models/detectors/__init__.py

@@ -1,3 +1,4 @@
 from .single_stage import SingleStageDetector
+from .two_stage import extract_feat
 
-__all__ = ['SingleStageDetector']
+__all__ = ['SingleStageDetector', 'extract_feat']

mmdeploy/mmdet/models/detectors/two_stage.py

@@ -0,0 +1,19 @@
+from mmdeploy.utils import FUNCTION_REWRITERS, mark
+from mmdeploy.utils import SYMBOLICS_REGISTER
+from mmcv.onnx.symbolic import grid_sampler
+
+
+@FUNCTION_REWRITERS.register_rewriter('mmdet.models.TwoStageDetector.extract_feat')
+@mark('extract_feat')
+def extract_feat(rewriter, self, img):
+    return rewriter.origin_func(self, img)
+
+
+@FUNCTION_REWRITERS.register_rewriter('mmdet.models.TwoStageDetector.forward')
+def two_stage_forward(rewriter, self, img, *args):
+    return rewriter.origin_func(self, [img], img_metas=[[{}]], return_loss=False, *args)
+
+
+@SYMBOLICS_REGISTER.register_symbolic('grid_sampler', is_pytorch=True)
+def symbolic_grid_sample(symbolic_wrapper, *args):
+    return grid_sampler(*args)

mmdeploy/utils/__init__.py

@@ -1,8 +1,9 @@
 from .function_rewriter import FUNCTION_REWRITERS, RewriterContext
 from .module_rewriter import MODULE_REWRITERS, patch_model
 from .symbolic_register import SYMBOLICS_REGISTER, register_extra_symbolics
+from .function_marker import mark
 
 __all__ = [
     'RewriterContext', 'FUNCTION_REWRITERS', 'MODULE_REWRITERS', 'patch_model',
-    'SYMBOLICS_REGISTER', 'register_extra_symbolics'
+    'SYMBOLICS_REGISTER', 'register_extra_symbolics', 'mark'
 ]

mmdeploy/utils/function_marker.py

@@ -0,0 +1,61 @@
+import inspect
+import torch
+from .function_rewriter import FUNCTION_REWRITERS
+
+
+class Mark(torch.autograd.Function):
+    @staticmethod
+    def symbolic(g, x, type, name, id, attrs):
+        n = g.op("mmcv::Mark", x, type_s=type, name_s=name, id_i=id, **attrs)
+        return n
+
+    @staticmethod
+    def forward(ctx, x, *args):
+        return x
+
+
+@FUNCTION_REWRITERS.register_rewriter("mmdeploy.utils.function_marker.Mark.symbolic")
+def mark_symbolic(rewriter, g, x, *args):
+    if rewriter.cfg.get("apply_marks", False):
+        return rewriter.origin_func(g, x, *args)
+    return x
+
+
+def mark_tensors(xs, type, name, attrs):
+    index = 0
+    visit = set()
+
+    def impl(ys, prefix):
+        nonlocal index
+        if isinstance(ys, torch.Tensor):
+            if ys not in visit:
+                visit.add(ys)
+                index += 1
+                return Mark.apply(ys, type, prefix, index - 1, attrs)
+            return ys
+        elif isinstance(ys, list):
+            return [impl(y, f'{prefix}/{i}') for i, y in enumerate(ys)]
+        elif isinstance(ys, tuple):
+            return tuple(impl(y, f'{prefix}/{i}') for i, y in enumerate(ys))
+        elif isinstance(ys, dict):
+            return {k: impl(v, f'{prefix}/{k}') for k, v in ys.items()}
+        return ys
+    return impl(xs, name)
+
+
+def mark(func, **attrs):
+    attrs['func_s'] = func
+
+    def decorator(f):
+        params = inspect.signature(f).parameters.keys()
+        def g(*args, **kwargs):
+            if torch.onnx.is_in_onnx_export():
+                args = [mark_tensors(arg, 'input', name, attrs)
+                        for name, arg in zip(params, args)]
+                rets = f(*args, **kwargs)
+                # TODO: maybe we can traverse the AST to get the retval names?
+                return mark_tensors(rets, 'output', func, attrs)
+            else:
+                return f(*args, **kwargs)
+        return g
+    return decorator

tools/extract.py

@@ -0,0 +1,234 @@
+import argparse
+import os.path as osp
+import onnx
+import onnx.utils
+import onnx.helper
+from onnx import AttributeProto
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Extract model based on markers.')
+    parser.add_argument('input_model', help='Input ONNX model')
+    parser.add_argument('output_model', help='Output ONNX model')
+    parser.add_argument(
+        '--start', help='Start markers, format: func:type, e.g. backbone:input')
+    parser.add_argument('--end', help='End markers')
+
+    args = parser.parse_args()
+
+    args.start = args.start.split(',') if args.start else []
+    args.end = args.end.split(',') if args.end else []
+
+    return args
+
+
+def remove_markers(model):
+    shortcut = []
+    success = True
+    while success:
+        success = False
+        for i, node in enumerate(model.graph.node):
+            if node.op_type == 'Mark':
+                for input in node.input:
+                    shortcut.append((input, node.output))
+                del model.graph.node[i]
+                success = True
+                break
+    for src, dsts in shortcut:
+        for curr in model.graph.node:
+            for k, input in enumerate(curr.input):
+                if input in dsts:
+                    curr.input[k] = src
+        # TODO: handle duplicated case?
+        for k, output in enumerate(model.graph.output):
+            print(output.name, dsts)
+            if output.name in dsts:
+                output.name = src
+    return model
+
+
+def attribute_to_dict(attribute):
+    ret = {}
+    for a in attribute:
+        name = a.name
+        if a.type == AttributeProto.AttributeType.STRING:
+            ret[name] = str(a.s, 'utf-8')
+        elif a.type == AttributeProto.AttributeType.INT:
+            ret[name] = a.i
+    return ret
+
+
+def _dfs_search_reacable_nodes_fast(self, node_output_name, graph_input_nodes, reachable_nodes):
+    outputs = {}
+    for index, node in enumerate(self.graph.node):
+        for name in node.output:
+            if name not in outputs:
+                outputs[name] = set()
+            outputs[name].add(index)
+
+    def impl(node_output_name, graph_input_nodes, reachable_nodes):
+        if node_output_name in graph_input_nodes:
+            return
+        if node_output_name not in outputs:
+            return
+        for index in outputs[node_output_name]:
+            node = self.graph.node[index]
+            if node in reachable_nodes:
+                continue
+            reachable_nodes.append(node)
+            for name in node.input:
+                impl(name, graph_input_nodes, reachable_nodes)
+    impl(node_output_name, graph_input_nodes, reachable_nodes)
+
+
+def get_new_name(attrs):
+    if 'name' in attrs:
+        return attrs['name']
+    return '_'.join((attrs['func'], attrs['type'], str(attrs['id'])))
+
+
+def rename_value(model, old_name, new_name):
+    for n in model.graph.node:
+        for i, output in enumerate(n.output):
+            if output == old_name:
+                n.output[i] = new_name
+        for i, input in enumerate(n.input):
+            if input == old_name:
+                n.input[i] = new_name
+    for v in model.graph.value_info:
+        if v.name == old_name:
+            v.name = new_name
+    for i, name in enumerate(model.graph.input):
+        if name == old_name:
+            model.graph.input[i] = new_name
+    for i, name in enumerate(model.graph.output):
+        if name == old_name:
+            model.graph.output[i] = new_name
+
+
+def extract_model(model, start, end):
+    inputs = []
+    outputs = []
+    if not isinstance(start, (list, tuple)):
+        start = [start]
+    for s in start:
+        start_name, start_type = s.split(':')
+        assert start_type in ['input', 'output']
+        for node in model.graph.node:
+            if node.op_type == 'Mark':
+                attr = attribute_to_dict(node.attribute)
+                if attr['func'] == start_name and attr['type'] == start_type:
+                    name = node.output[0] if start_type == 'input' else node.input[0]
+                    if name not in inputs:
+                        new_name = get_new_name(attr)
+                        rename_value(model, name, new_name)
+                        inputs.append(new_name)
+
+    print(f'inputs: {inputs}')
+
+    # collect outputs
+    # outputs = []
+    if not isinstance(end, (list, tuple)):
+        end = [end]
+    for e in end:
+        end_name, end_type = e.split(':')
+        assert end_type in ['input', 'output']
+        for node in model.graph.node:
+            if node.op_type == 'Mark':
+                attr = attribute_to_dict(node.attribute)
+                if attr['func'] == end_name and attr['type'] == end_type:
+                    name = node.input[0] if end_type == 'input' else node.output[0]
+                    if name not in outputs:
+                        new_name = get_new_name(attr)
+                        rename_value(model, name, new_name)
+                        outputs.append(new_name)
+
+    print(f'outputs: {outputs}')
+
+    # replace Mark with Identity
+    for node in model.graph.node:
+        if node.op_type == 'Mark':
+            del node.attribute[:]
+            node.domain = ''
+            node.op_type = 'Identity'
+
+    # patch extractor
+    onnx.utils.Extractor._dfs_search_reachable_nodes = _dfs_search_reacable_nodes_fast
+
+    extractor = onnx.utils.Extractor(model)
+    extracted_model = extractor.extract_model(inputs, outputs)
+
+    # collect all used inputs
+    used = set()
+    for node in extracted_model.graph.node:
+        for input in node.input:
+            used.add(input)
+
+    for output in extracted_model.graph.output:
+        used.add(output.name)
+
+    # delete unused inputs
+    success = True
+    while success:
+        success = False
+        for i, input in enumerate(extracted_model.graph.input):
+            if input.name not in used:
+                del extracted_model.graph.input[i]
+                success = True
+                break
+
+    # eliminate output without shape
+    for xs in [extracted_model.graph.output]:
+        for x in xs:
+            if not x.type.tensor_type.shape.dim:
+                print(f'fixing output shape: {x.name}')
+                x.CopyFrom(onnx.helper.make_tensor_value_info(
+                    x.name, x.type.tensor_type.elem_type, []))
+
+    # eliminate 0-batch dimension, dirty workaround for two-stage detectors
+    for input in extracted_model.graph.input:
+        if input.name in inputs:
+            if input.type.tensor_type.shape.dim[0].dim_value == 0:
+                input.type.tensor_type.shape.dim[0].dim_value = 1
+
+    # eliminate duplicated value_info for inputs
+    success = True
+    while success:
+        success = False
+        for i, x in enumerate(extracted_model.graph.value_info):
+            if x.name in inputs:
+                del extracted_model.graph.value_info[i]
+                success = True
+                break
+
+    return extracted_model
+
+
+def collect_avaiable_marks(model):
+    marks = []
+    for node in model.graph.node:
+        if node.op_type == 'Mark':
+            attr = attribute_to_dict(node.attribute)
+            func = attr['func']
+            if func not in marks:
+                marks.append(func)
+    return marks
+
+
+def main():
+    args = parse_args()
+
+    model = onnx.load(args.input_model)
+    marks = collect_avaiable_marks(model)
+    print("Available marks:\n    {}".format('\n    '.join(marks)))
+
+    extracted_model = extract_model(model, args.start, args.end)
+
+    if osp.splitext(args.output_model)[-1] != '.onnx':
+        args.output_model += '.onnx'
+    onnx.save(extracted_model, args.output_model)
+
+
+if __name__ == '__main__':
+    main()