mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-17 21:10:43 +00:00
32bf6390ad
* Some fixes to symbolic shape inference 1. Topological sort before iteration in graph 2. Fix a case in slice: start=100000, end=-100000, step=-1, dim=2 3. Fix Nuphar Gemm test's random seed 4. Slice opset 1 axes is optional
1322 lines
66 KiB
Python
Executable file
1322 lines
66 KiB
Python
Executable file
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# -*- coding: UTF-8 -*-
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import argparse
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import numpy as np
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import onnx
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import sys
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from onnx import helper, numpy_helper, shape_inference
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import sympy
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from packaging import version
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assert version.parse(onnx.__version__) >= version.parse("1.5.0")
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def get_attribute(node, attr_name, default_value=None):
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found = [attr for attr in node.attribute if attr.name == attr_name]
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if found:
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return helper.get_attribute_value(found[0])
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return default_value
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def get_dim_from_type_proto(dim):
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return getattr(dim, dim.WhichOneof('value')) if type(dim.WhichOneof('value')) == str else None
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def get_shape_from_type_proto(type_proto):
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return [get_dim_from_type_proto(d) for d in type_proto.tensor_type.shape.dim]
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def get_shape_from_sympy_shape(sympy_shape):
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return [None if i is None else (int(i) if is_literal(i) else str(i)) for i in sympy_shape]
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def is_literal(dim):
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return type(dim) in [int, np.int64, np.int32, sympy.Integer] or (hasattr(dim, 'is_number') and dim.is_number)
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def handle_negative_axis(axis, rank):
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assert axis < rank and axis >= -rank
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return axis if axis >= 0 else rank + axis
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def get_opset(mp, domain=None):
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domain = domain or ['', 'onnx', 'ai.onnx']
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if type(domain) != list:
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domain = [domain]
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for opset in mp.opset_import:
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if opset.domain in domain:
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return opset.version
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return None
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def as_scalar(x):
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if type(x) == list:
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assert len(x) == 1
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return x[0]
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elif type(x) == np.ndarray:
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return x.item()
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else:
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return x
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def as_list(x, keep_none):
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if type(x) == list:
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return x
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elif type(x) == np.ndarray:
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return list(x)
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elif keep_none and x is None:
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return None
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else:
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return [x]
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def sympy_reduce_product(x):
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if type(x) == list:
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value = sympy.Integer(1)
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for v in x:
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value = value * v
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else:
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value = x
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return value
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class SymbolicShapeInference:
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def __init__(self, int_max, auto_merge, guess_output_rank, verbose):
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self.dispatcher_ = {
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'Add' : self._infer_symbolic_compute_ops,
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'ArrayFeatureExtractor' : self._infer_ArrayFeatureExtractor,
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'AveragePool' : self._infer_Pool,
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'Cast' : self._infer_Cast,
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'CategoryMapper' : self._infer_CategoryMapper,
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'Compress' : self._infer_Compress,
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'Concat' : self._infer_Concat,
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'Constant' : self._infer_Constant,
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'ConstantOfShape' : self._infer_ConstantOfShape,
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'Conv' : self._infer_Conv,
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'CumSum' : self._pass_on_shape_and_type,
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'Div' : self._infer_symbolic_compute_ops,
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'Expand' : self._infer_Expand,
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'Equal' : self._infer_symbolic_compute_ops,
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'Floor' : self._infer_symbolic_compute_ops,
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'Gather' : self._infer_Gather,
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'GatherElements' : self._infer_GatherElements,
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'GatherND' : self._infer_GatherND,
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'Gelu' : self._pass_on_shape_and_type,
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'If' : self._infer_If,
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'Loop' : self._infer_Loop,
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'MatMul' : self._infer_MatMul,
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'MatMulInteger16' : self._infer_MatMulInteger,
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'MaxPool' : self._infer_Pool,
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'Max' : self._infer_symbolic_compute_ops,
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'Min' : self._infer_symbolic_compute_ops,
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'Mul' : self._infer_symbolic_compute_ops,
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'NonMaxSuppression' : self._infer_NonMaxSuppression,
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'NonZero' : self._infer_NonZero,
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'OneHot' : self._infer_OneHot,
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'Pad' : self._infer_Pad,
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'Range' : self._infer_Range,
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'ReduceProd' : self._infer_ReduceProd,
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'Reshape' : self._infer_Reshape,
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'Resize' : self._infer_Resize,
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'Round' : self._pass_on_shape_and_type,
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'Scan' : self._infer_Scan,
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'ScatterElements' : self._infer_ScatterElements,
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'Shape' : self._infer_Shape,
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'Size' : self._infer_Size,
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'Slice' : self._infer_Slice,
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'SoftmaxCrossEntropyLoss':self._infer_SoftmaxCrossEntropyLoss,
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'Split' : self._infer_Split,
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'SplitToSequence' : self._infer_SplitToSequence,
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'Squeeze' : self._infer_Squeeze,
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'Sub' : self._infer_symbolic_compute_ops,
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'Tile' : self._infer_Tile,
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'TopK' : self._infer_TopK,
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'Unsqueeze' : self._infer_Unsqueeze,
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'Where' : self._infer_symbolic_compute_ops,
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'ZipMap' : self._infer_ZipMap}
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self.run_ = True
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self.suggested_merge_ = {}
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self.symbolic_dims_ = {}
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self.input_symbols_ = {}
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self.auto_merge_ = auto_merge
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self.guess_output_rank_ = guess_output_rank
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self.verbose_ = verbose
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self.int_max_ = int_max
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def _add_suggested_merge(self, symbols, apply=False):
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assert all([(type(s) == str and s in self.symbolic_dims_) or is_literal(s) for s in symbols])
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symbols = set(symbols)
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for k,v in self.suggested_merge_.items():
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if k in symbols:
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symbols.remove(k)
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symbols.add(v)
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map_to = None
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# if there is literal, map to it first
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for s in symbols:
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if is_literal(s):
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map_to = s
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break
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# when no literals, map to input symbolic dims, then existing symbolic dims
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if map_to is None:
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for s in symbols:
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if s in self.input_symbols_:
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map_to = s
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break
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if map_to is None:
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for s in symbols:
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if type(self.symbolic_dims_[s]) == sympy.Symbol:
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map_to = s
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break
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# when nothing to map to, use the shorter one
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if map_to is None:
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if self.verbose_ > 0:
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print('Potential unsafe merge between symbolic expressions: ({})'.format(','.join(symbols)))
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symbols_list = list(symbols)
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lens = [len(s) for s in symbols_list]
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map_to = symbols_list[lens.index(min(lens))]
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symbols.remove(map_to)
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for s in symbols:
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if s == map_to:
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continue
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if is_literal(map_to) and is_literal(s):
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assert int(map_to) == int(s)
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self.suggested_merge_[s] = int(map_to) if is_literal(map_to) else map_to
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for k,v in self.suggested_merge_.items():
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if v == s:
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self.suggested_merge_[k] = map_to
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if apply and self.auto_merge_:
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self._apply_suggested_merge()
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def _apply_suggested_merge(self, graph_input_only=False):
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if not self.suggested_merge_:
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return
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for i in list(self.out_mp_.graph.input) + ([] if graph_input_only else list(self.out_mp_.graph.value_info)):
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for d in i.type.tensor_type.shape.dim:
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if d.dim_param in self.suggested_merge_:
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v = self.suggested_merge_[d.dim_param]
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if is_literal(v):
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d.dim_value = int(v)
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else:
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d.dim_param = v
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def _preprocess(self, in_mp):
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self.out_mp_ = onnx.ModelProto()
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self.out_mp_.CopyFrom(in_mp)
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self.initializers_ = dict([(i.name, i) for i in self.out_mp_.graph.initializer])
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self.known_vi_ = dict([(i.name, i) for i in list(self.out_mp_.graph.input)])
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self.known_vi_.update(dict([(i.name, helper.make_tensor_value_info(i.name, i.data_type, list(i.dims))) for i in self.out_mp_.graph.initializer]))
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def _merge_symbols(self, dims):
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if not all([type(d) == str for d in dims]):
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if self.auto_merge_:
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unique_dims = list(set(dims))
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is_int = [is_literal(d) for d in unique_dims]
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assert sum(is_int) <= 1 # if there are more than 1 unique ints, something is wrong
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if sum(is_int) == 1:
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int_dim = is_int.index(1)
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if self.verbose_ > 0:
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print('dim {} has been merged with value {}'.format(unique_dims[:int_dim] + unique_dims[int_dim+1:], unique_dims[int_dim]))
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self._check_merged_dims(unique_dims, allow_broadcast=False)
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return unique_dims[int_dim]
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else:
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if self.verbose_ > 0:
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print('dim {} has been mergd with dim {}'.format(unique_dims[1:], unique_dims[0]))
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return dims[0]
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else:
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return None
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if all([d == dims[0] for d in dims]):
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return dims[0]
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merged = [self.suggested_merge_[d] if d in self.suggested_merge_ else d for d in dims]
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if all([d == merged[0] for d in merged]):
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assert merged[0] in self.symbolic_dims_
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return merged[0]
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else:
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return None
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# broadcast from right to left, and merge symbolic dims if needed
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def _broadcast_shapes(self, shape1, shape2):
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new_shape = []
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rank1 = len(shape1)
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rank2 = len(shape2)
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new_rank = max(rank1, rank2)
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for i in range(new_rank):
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dim1 = shape1[rank1 - 1 - i] if i < rank1 else 1
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dim2 = shape2[rank2 - 1 - i] if i < rank2 else 1
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if dim1 == 1 or dim1 == dim2:
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new_dim = dim2
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elif dim2 == 1:
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new_dim = dim1
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else:
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new_dim = self._merge_symbols([dim1, dim2])
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if not new_dim:
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# warning about unsupported broadcast when not auto merge
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# note that auto merge has the risk of incorrectly merge symbols while one of them being 1
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# for example, 'a' = 1, 'b' = 5 at runtime is valid broadcasting, but with auto merge 'a' == 'b'
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if self.auto_merge_:
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self._add_suggested_merge([dim1, dim2], apply=True)
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else:
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print('unsupported broadcast between ' + str(dim1) + ' ' + str(dim2))
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new_shape = [new_dim] + new_shape
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return new_shape
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def _get_shape(self, node, idx):
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name = node.input[idx]
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if name in self.known_vi_:
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return get_shape_from_type_proto(self.known_vi_[name].type)
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else:
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assert name in self.initializers_
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return list(self.initializers_[name].dims)
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def _get_shape_rank(self, node, idx):
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return len(self._get_shape(node, idx))
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def _get_sympy_shape(self, node, idx):
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sympy_shape = []
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for d in self._get_shape(node, idx):
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if type(d) == str:
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sympy_shape.append(self.symbolic_dims_[d] if d in self.symbolic_dims_ else sympy.Symbol(d, integer=True))
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else:
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assert None != d
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sympy_shape.append(d)
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return sympy_shape
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def _get_value(self, node, idx):
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name = node.input[idx]
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assert name in self.sympy_data_ or name in self.initializers_
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return self.sympy_data_[name] if name in self.sympy_data_ else numpy_helper.to_array(self.initializers_[name])
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def _try_get_value(self, node, idx):
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if idx >= len(node.input):
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return None
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name = node.input[idx]
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if name in self.sympy_data_ or name in self.initializers_:
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return self._get_value(node, idx)
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return None
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def _update_computed_dims(self, new_sympy_shape):
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for i, new_dim in enumerate(new_sympy_shape):
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if not is_literal(new_dim) and not type(new_dim) == str:
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str_dim = str(new_dim)
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if str_dim in self.suggested_merge_:
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if is_literal(self.suggested_merge_[str_dim]):
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continue # no need to create dim for literals
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new_sympy_shape[i] = self.symbolic_dims_[self.suggested_merge_[str_dim]]
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else:
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# add new_dim if it's a computational expression
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if not str(new_dim) in self.symbolic_dims_:
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self.symbolic_dims_[str(new_dim)] = new_dim
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def _onnx_infer_single_node(self, node):
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# skip onnx shape inference for some ops, as they are handled in _infer_*
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skip_infer = node.op_type in ['If', 'Loop', 'Scan', 'SplitToSequence', 'ZipMap']
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if not skip_infer:
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# run single node inference with self.known_vi_ shapes
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# note that inference rely on initializer values is not handled
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# as we don't copy initializer weights to tmp_graph for inference speed purpose
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tmp_graph = helper.make_graph([node],
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'tmp',
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[self.known_vi_[i] for i in node.input if i],
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[helper.make_tensor_value_info(i, onnx.TensorProto.UNDEFINED, None) for i in node.output])
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self.tmp_mp_.graph.CopyFrom(tmp_graph)
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self.tmp_mp_ = shape_inference.infer_shapes(self.tmp_mp_)
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for i_o in range(len(node.output)):
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o = node.output[i_o]
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vi = self.out_mp_.graph.value_info.add()
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if not skip_infer:
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vi.CopyFrom(self.tmp_mp_.graph.output[i_o])
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else:
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vi.name = o
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self.known_vi_[o] = vi
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def _onnx_infer_subgraph(self, node, subgraph, use_node_input=True):
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if self.verbose_ > 2:
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print('Inferencing subgraph of node {} with output({}...): {}'.format(node.name, node.output[0], node.op_type))
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# node inputs are not passed directly to the subgraph
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# it's up to the node dispatcher to prepare subgraph input
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# for example, with Scan/Loop, subgraph input shape would be trimmed from node input shape
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# besides, inputs in subgraph could shadow implicit inputs
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subgraph_inputs = set([i.name for i in list(subgraph.initializer) + list(subgraph.input)])
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subgraph_implicit_input = set([name for name in self.known_vi_.keys() if not name in subgraph_inputs])
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tmp_graph = helper.make_graph(list(subgraph.node),
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'tmp',
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list(subgraph.input) + [self.known_vi_[i] for i in subgraph_implicit_input],
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[helper.make_tensor_value_info(i.name, onnx.TensorProto.UNDEFINED, None) for i in subgraph.output])
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tmp_graph.initializer.extend([i for i in self.out_mp_.graph.initializer if i.name in subgraph_implicit_input])
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tmp_graph.initializer.extend(subgraph.initializer)
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self.tmp_mp_.graph.CopyFrom(tmp_graph)
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symbolic_shape_inference = SymbolicShapeInference(self.int_max_, self.auto_merge_, self.guess_output_rank_, self.verbose_)
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all_shapes_inferred = False
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symbolic_shape_inference._preprocess(self.tmp_mp_)
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symbolic_shape_inference.suggested_merge_ = self.suggested_merge_.copy()
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while symbolic_shape_inference.run_:
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all_shapes_inferred = symbolic_shape_inference._infer_impl(self.sympy_data_.copy())
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symbolic_shape_inference._update_output_from_vi()
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if use_node_input:
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# if subgraph uses node input, it needs to update to merged dims
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subgraph.ClearField('input')
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subgraph.input.extend(symbolic_shape_inference.out_mp_.graph.input[:len(node.input)])
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subgraph.ClearField('output')
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subgraph.output.extend(symbolic_shape_inference.out_mp_.graph.output)
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subgraph.ClearField('value_info')
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subgraph.value_info.extend(symbolic_shape_inference.out_mp_.graph.value_info)
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subgraph.ClearField('node')
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subgraph.node.extend(symbolic_shape_inference.out_mp_.graph.node)
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# for new symbolic dims from subgraph output, add to main graph symbolic dims
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subgraph_shapes = [get_shape_from_type_proto(o.type) for o in symbolic_shape_inference.out_mp_.graph.output]
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subgraph_new_symbolic_dims = set([d for s in subgraph_shapes if s for d in s if type(d) == str and not d in self.symbolic_dims_])
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new_dims = {}
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for d in subgraph_new_symbolic_dims:
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assert d in symbolic_shape_inference.symbolic_dims_
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new_dims[d] = symbolic_shape_inference.symbolic_dims_[d]
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self.symbolic_dims_.update(new_dims)
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return symbolic_shape_inference
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def _get_int_values(self, node, broadcast=False):
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values = [self._try_get_value(node, i) for i in range(len(node.input))]
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if all([v is not None for v in values]):
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# some shape compute is in floating point, cast to int for sympy
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for i,v in enumerate(values):
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if type(v) != np.ndarray:
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continue
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if len(v.shape) > 1:
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new_v = None # ignore value for rank > 1
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elif len(v.shape) == 0:
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new_v = int(v.item())
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else:
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assert len(v.shape) == 1
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new_v = [int(vv) for vv in v]
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values[i] = new_v
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values_len = [len(v) if type(v) == list else 0 for v in values]
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max_len = max(values_len)
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if max_len >= 1 and broadcast:
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# broadcast
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for i,v in enumerate(values):
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if v is None:
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continue # don't broadcast if value is unknown
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if type(v) == list:
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if len(v) < max_len:
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values[i] = v*max_len
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else:
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assert len(v) == max_len
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else:
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values[i] = [v]*max_len
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return values
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def _compute_on_sympy_data(self, node, op_func):
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assert len(node.output) == 1
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values = self._get_int_values(node, broadcast=True)
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if all([v is not None for v in values]):
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is_list = [type(v) == list for v in values]
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as_list = any(is_list)
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if as_list:
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self.sympy_data_[node.output[0]] = [op_func(vs) for vs in zip(*values)]
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else:
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self.sympy_data_[node.output[0]] = op_func(values)
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def _pass_on_sympy_data(self, node):
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assert len(node.input) == 1 or node.op_type == 'Reshape'
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self._compute_on_sympy_data(node, lambda x: x[0])
|
|
|
|
def _pass_on_shape_and_type(self, node):
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
self.known_vi_[node.input[0]].type.tensor_type.elem_type,
|
|
self._get_shape(node, 0)))
|
|
|
|
def _new_symbolic_dim(self, prefix, dim):
|
|
new_dim = '{}_d{}'.format(prefix, dim)
|
|
if new_dim in self.suggested_merge_:
|
|
v = self.suggested_merge_[new_dim]
|
|
new_dim = sympy.Integer(int(v)) if is_literal(v) else v
|
|
else:
|
|
self.symbolic_dims_[new_dim] = sympy.Symbol(new_dim, integer=True)
|
|
return new_dim
|
|
|
|
def _new_symbolic_dim_from_output(self, node, out_idx=0, dim=0):
|
|
return self._new_symbolic_dim('{}{}_o{}_'.format(node.op_type, list(self.out_mp_.graph.node).index(node), out_idx), dim)
|
|
|
|
def _new_symbolic_shape(self, rank, node, out_idx=0):
|
|
return [self._new_symbolic_dim_from_output(node, out_idx, i) for i in range(rank)]
|
|
|
|
def _compute_conv_pool_shape(self, node):
|
|
sympy_shape = self._get_sympy_shape(node, 0)
|
|
if len(node.input) > 1:
|
|
W_shape = self._get_sympy_shape(node, 1)
|
|
rank = len(W_shape) - 2 # number of spatial axes
|
|
kernel_shape = W_shape[-rank:]
|
|
sympy_shape[1] = W_shape[0]
|
|
else:
|
|
W_shape = None
|
|
kernel_shape = get_attribute(node, 'kernel_shape')
|
|
rank = len(kernel_shape)
|
|
|
|
assert len(sympy_shape) == rank + 2
|
|
|
|
# only need to symbolic shape inference if input has symbolic dims in spatial axes
|
|
is_symbolic_dims = [not is_literal(i) for i in sympy_shape[-rank:]]
|
|
|
|
if not any(is_symbolic_dims):
|
|
shape = get_shape_from_type_proto(self.known_vi_[node.output[0]].type)
|
|
if len(shape) > 0:
|
|
assert len(sympy_shape) == len(shape)
|
|
sympy_shape[-rank:] = [sympy.Integer(d) for d in shape[-rank:]]
|
|
return sympy_shape
|
|
|
|
dilations = get_attribute(node, 'dilations', [1]*rank)
|
|
strides = get_attribute(node, 'strides', [1]*rank)
|
|
effective_kernel_shape = [(k - 1) * d + 1 for k, d in zip(kernel_shape, dilations)]
|
|
pads = get_attribute(node, 'pads')
|
|
if pads is None:
|
|
pads = [0]*(2*rank)
|
|
auto_pad = get_attribute(node, 'auto_pad', b'NOTSET').decode('utf-8')
|
|
if auto_pad != 'VALID' and auto_pad != 'NOTSET':
|
|
try:
|
|
residual = [sympy.Mod(d, s) for d, s in zip(sympy_shape[-rank:], strides)]
|
|
total_pads = [max(0, (k - s) if r == 0 else (k - r)) for k, s, r in zip(effective_kernel_shape, strides, residual)]
|
|
except TypeError: # sympy may throw TypeError: cannot determine truth value of Relational
|
|
total_pads = [max(0, (k - s)) for k, s in zip(effective_kernel_shape, strides)] # assuming no residual if sympy throws error
|
|
elif auto_pad == 'VALID':
|
|
total_pads = []
|
|
else:
|
|
total_pads = [0]*rank
|
|
else:
|
|
assert len(pads) == 2*rank
|
|
total_pads = [p1 + p2 for p1, p2 in zip(pads[:rank], pads[rank:])]
|
|
|
|
ceil_mode = get_attribute(node, 'ceil_mode', 0)
|
|
for i in range(rank):
|
|
effective_input_size = sympy_shape[-rank + i]
|
|
if len(total_pads) > 0:
|
|
effective_input_size = effective_input_size + total_pads[i]
|
|
if ceil_mode:
|
|
strided_kernel_positions = sympy.ceiling((effective_input_size - effective_kernel_shape[i]) / strides[i])
|
|
else:
|
|
strided_kernel_positions = (effective_input_size - effective_kernel_shape[i]) // strides[i]
|
|
sympy_shape[-rank + i] = strided_kernel_positions + 1
|
|
return sympy_shape
|
|
|
|
def _check_merged_dims(self, dims, allow_broadcast=True):
|
|
if allow_broadcast:
|
|
dims = [d for d in dims if not(is_literal(d) and int(d) <= 1)]
|
|
if not all([d == dims[0] for d in dims]):
|
|
self._add_suggested_merge(dims, apply=True)
|
|
|
|
def _compute_matmul_shape(self, node, output_dtype=None):
|
|
lhs_shape = self._get_shape(node, 0)
|
|
rhs_shape = self._get_shape(node, 1)
|
|
lhs_rank = len(lhs_shape)
|
|
rhs_rank = len(rhs_shape)
|
|
lhs_reduce_dim = 0
|
|
rhs_reduce_dim = 0
|
|
assert lhs_rank > 0 and rhs_rank > 0
|
|
if lhs_rank == 1 and rhs_rank == 1:
|
|
new_shape = []
|
|
elif lhs_rank == 1:
|
|
rhs_reduce_dim = -2
|
|
new_shape = rhs_shape[:rhs_reduce_dim] + [rhs_shape[-1]]
|
|
elif rhs_rank == 1:
|
|
lhs_reduce_dim = -1
|
|
new_shape = lhs_shape[:lhs_reduce_dim]
|
|
else:
|
|
lhs_reduce_dim = -1
|
|
rhs_reduce_dim = -2
|
|
new_shape = self._broadcast_shapes(lhs_shape[:-2], rhs_shape[:-2]) + [lhs_shape[-2]] + [rhs_shape[-1]]
|
|
# merge reduce dim
|
|
self._check_merged_dims([lhs_shape[lhs_reduce_dim], rhs_shape[rhs_reduce_dim]], allow_broadcast=False)
|
|
if output_dtype is None:
|
|
# infer output_dtype from input type when not specified
|
|
output_dtype = self.known_vi_[node.input[0]].type.tensor_type.elem_type
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], output_dtype, new_shape))
|
|
|
|
def _infer_ArrayFeatureExtractor(self, node):
|
|
data_shape = self._get_shape(node, 0)
|
|
indices_shape = self._get_shape(node, 1)
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
self.known_vi_[node.input[0]].type.tensor_type.elem_type,
|
|
data_shape[:-1] + indices_shape))
|
|
|
|
def _infer_symbolic_compute_ops(self, node):
|
|
funcs = {'Add' : lambda l: l[0] + l[1],
|
|
'Div' : lambda l: l[0] // l[1], # integer div in sympy
|
|
'Equal' : lambda l : l[0] == l[1],
|
|
'Floor' : lambda l : sympy.floor(l[0]),
|
|
'Max' : lambda l: l[1] if is_literal(l[0]) and int(l[0]) < -self.int_max_ else (l[0] if is_literal(l[1]) and int(l[1]) < -self.int_max_ else sympy.Max(l[0], l[1])),
|
|
'Min' : lambda l: l[1] if is_literal(l[0]) and int(l[0]) > self.int_max_ else (l[0] if is_literal(l[1]) and int(l[1]) > self.int_max_ else sympy.Min(l[0], l[1])),
|
|
'Mul' : lambda l: l[0] * l[1],
|
|
'Sub' : lambda l: l[0] - l[1],
|
|
'Where' : lambda l: l[1] if l[0] else l[2]}
|
|
assert node.op_type in funcs
|
|
self._compute_on_sympy_data(node, funcs[node.op_type])
|
|
|
|
def _infer_Cast(self, node):
|
|
self._pass_on_sympy_data(node)
|
|
|
|
def _infer_CategoryMapper(self, node):
|
|
input_type = self.known_vi_[node.input[0]].type.tensor_type.elem_type
|
|
if input_type == onnx.TensorProto.STRING:
|
|
output_type = onnx.TensorProto.INT64
|
|
else:
|
|
output_type = onnx.TensorProto.STRING
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
output_type,
|
|
self._get_shape(node, 0)))
|
|
|
|
def _infer_Compress(self, node):
|
|
input_shape = self._get_shape(node, 0)
|
|
# create a new symbolic dimension for Compress output
|
|
compress_len = self._new_symbolic_dim_from_output(node)
|
|
axis = get_attribute(node, 'axis')
|
|
if axis == None:
|
|
# when axis is not specified, input is flattened before compress so output is 1D
|
|
output_shape = [compress_len]
|
|
else:
|
|
output_shape = input_shape
|
|
output_shape[handle_negative_axis(axis, len(input_shape))] = compress_len
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], self.known_vi_[node.input[0]].type.tensor_type.elem_type, output_shape))
|
|
|
|
def _infer_Concat(self, node):
|
|
if any([i in self.sympy_data_ for i in node.input]):
|
|
values = self._get_int_values(node)
|
|
if all([v is not None for v in values]):
|
|
assert 0 == get_attribute(node, 'axis')
|
|
self.sympy_data_[node.output[0]] = []
|
|
for i in range(len(node.input)):
|
|
value = values[i]
|
|
if type(value) == list:
|
|
self.sympy_data_[node.output[0]].extend(value)
|
|
else:
|
|
self.sympy_data_[node.output[0]].append(value)
|
|
|
|
sympy_shape = self._get_sympy_shape(node, 0)
|
|
axis = handle_negative_axis(get_attribute(node, 'axis'), len(sympy_shape))
|
|
for i_idx in range(1, len(node.input)):
|
|
input_shape = self._get_sympy_shape(node, i_idx)
|
|
if input_shape:
|
|
sympy_shape[axis] = sympy_shape[axis] + input_shape[axis]
|
|
self._update_computed_dims(sympy_shape)
|
|
# merge symbolic dims for non-concat axes
|
|
for d in range(len(sympy_shape)):
|
|
if d == axis:
|
|
continue
|
|
dims = [self._get_shape(node, i_idx)[d] for i_idx in range(len(node.input)) if self._get_shape(node, i_idx)]
|
|
if all([d == dims[0] for d in dims]):
|
|
continue
|
|
merged = self._merge_symbols(dims)
|
|
if type(merged) == str:
|
|
sympy_shape[d] = self.symbolic_dims_[merged] if merged else None
|
|
else:
|
|
sympy_shape[d] = merged
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], self.known_vi_[node.input[0]].type.tensor_type.elem_type, get_shape_from_sympy_shape(sympy_shape)))
|
|
|
|
def _infer_Constant(self, node):
|
|
t = get_attribute(node, 'value')
|
|
self.sympy_data_[node.output[0]] = numpy_helper.to_array(t)
|
|
|
|
def _infer_ConstantOfShape(self, node):
|
|
sympy_shape = self._get_int_values(node)[0]
|
|
vi = self.known_vi_[node.output[0]]
|
|
if sympy_shape is not None:
|
|
if type(sympy_shape) != list:
|
|
sympy_shape = [sympy_shape]
|
|
self._update_computed_dims(sympy_shape)
|
|
# update sympy data if output type is int, and shape is known
|
|
if vi.type.tensor_type.elem_type == onnx.TensorProto.INT64 and all([is_literal(x) for x in sympy_shape]):
|
|
self.sympy_data_[node.output[0]] = np.ones([int(x) for x in sympy_shape], dtype=np.int64) * numpy_helper.to_array(get_attribute(node, 'value', 0))
|
|
else:
|
|
# create new dynamic shape
|
|
sympy_shape = self._new_symbolic_shape(self._get_shape_rank(node,0), node)
|
|
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
vi.type.tensor_type.elem_type,
|
|
get_shape_from_sympy_shape(sympy_shape)))
|
|
|
|
def _infer_Conv(self, node):
|
|
sympy_shape = self._compute_conv_pool_shape(node)
|
|
self._update_computed_dims(sympy_shape)
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], vi.type.tensor_type.elem_type, get_shape_from_sympy_shape(sympy_shape)))
|
|
|
|
def _infer_Expand(self, node):
|
|
expand_to_shape = self._try_get_value(node, 1)
|
|
if expand_to_shape is not None:
|
|
# new_shape's dim can come from shape value
|
|
self._update_computed_dims(expand_to_shape)
|
|
shape = self._get_shape(node, 0)
|
|
new_shape = self._broadcast_shapes(shape, get_shape_from_sympy_shape(expand_to_shape))
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], self.known_vi_[node.input[0]].type.tensor_type.elem_type, new_shape))
|
|
|
|
def _infer_Gather(self, node):
|
|
data_shape = self._get_shape(node, 0)
|
|
axis = handle_negative_axis(get_attribute(node, 'axis', 0), len(data_shape))
|
|
indices_shape = self._get_shape(node, 1)
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
vi.type.tensor_type.elem_type,
|
|
data_shape[:axis] + indices_shape + data_shape[axis+1:]))
|
|
# for 1D input, do some sympy compute
|
|
if node.input[0] in self.sympy_data_ and len(data_shape) == 1 and 0 == get_attribute(node, 'axis', 0):
|
|
idx = self._get_value(node, 1)
|
|
data = self.sympy_data_[node.input[0]]
|
|
if type(data) == list:
|
|
if type(idx) == np.ndarray and len(idx.shape) == 1:
|
|
self.sympy_data_[node.output[0]] = [data[int(i)] for i in idx]
|
|
else:
|
|
self.sympy_data_[node.output[0]] = data[int(idx)]
|
|
else:
|
|
assert idx == 0
|
|
self.sympy_data_[node.output[0]] = data
|
|
|
|
def _infer_GatherElements(self, node):
|
|
indices_shape = self._get_shape(node, 1)
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
self.known_vi_[node.input[0]].type.tensor_type.elem_type,
|
|
indices_shape))
|
|
|
|
def _infer_GatherND(self, node):
|
|
data_shape = self._get_shape(node, 0)
|
|
data_rank = len(data_shape)
|
|
indices_shape = self._get_shape(node, 1)
|
|
indices_rank = len(indices_shape)
|
|
last_index_dimension = indices_shape[-1]
|
|
assert is_literal(last_index_dimension) and last_index_dimension <= data_rank
|
|
new_shape = indices_shape[:-1] + data_shape[last_index_dimension:]
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
self.known_vi_[node.input[0]].type.tensor_type.elem_type,
|
|
new_shape))
|
|
|
|
def _infer_If(self, node):
|
|
# special case for constant condition, in case there are mismatching shape from the non-executed branch
|
|
subgraphs = [get_attribute(node, 'then_branch'), get_attribute(node, 'else_branch')]
|
|
cond = self._try_get_value(node, 0)
|
|
if cond is not None:
|
|
if cond > 0:
|
|
subgraphs[1].CopyFrom(subgraphs[0])
|
|
else:
|
|
subgraphs[0].CopyFrom(subgraphs[1])
|
|
|
|
for i_sub, subgraph in enumerate(subgraphs):
|
|
subgraph_infer = self._onnx_infer_subgraph(node, subgraph, use_node_input=False)
|
|
for i_out in range(len(node.output)):
|
|
vi = self.known_vi_[node.output[i_out]]
|
|
if i_sub == 0:
|
|
vi.CopyFrom(subgraph.output[i_out])
|
|
vi.name = node.output[i_out]
|
|
else:
|
|
assert all([d1 == d2 for d1,d2 in zip(vi.type.tensor_type.shape.dim, subgraph.output[i_out].type.tensor_type.shape.dim)])
|
|
# pass on sympy data from subgraph, if cond is constant
|
|
if cond is not None and i_sub == (0 if cond > 0 else 1):
|
|
if subgraph.output[i_out].name in subgraph_infer.sympy_data_:
|
|
self.sympy_data_[vi.name] = subgraph_infer.sympy_data_[subgraph.output[i_out].name]
|
|
|
|
def _infer_Loop(self, node):
|
|
subgraph = get_attribute(node, 'body')
|
|
assert len(subgraph.input) == len(node.input)
|
|
for i, si in enumerate(subgraph.input):
|
|
subgraph_name = si.name
|
|
si.CopyFrom(self.known_vi_[node.input[i]])
|
|
si.name = subgraph_name
|
|
self._onnx_infer_subgraph(node, subgraph)
|
|
# create a new symbolic dimension for iteration dependent dimension
|
|
loop_iter_dim = self._new_symbolic_dim_from_output(node)
|
|
num_loop_carried = len(node.input) - 2
|
|
for i in range(len(node.output)):
|
|
vi = self.known_vi_[node.output[i]]
|
|
vi.CopyFrom(subgraph.output[i + 1]) # first subgraph output is condition, not in node output
|
|
if i >= num_loop_carried:
|
|
subgraph_vi_dim = subgraph.output[i + 1].type.tensor_type.shape.dim
|
|
vi.type.tensor_type.shape.ClearField('dim')
|
|
vi_dim = vi.type.tensor_type.shape.dim
|
|
vi_dim.add().dim_param = loop_iter_dim
|
|
vi_dim.extend(list(subgraph_vi_dim))
|
|
vi.name = node.output[i]
|
|
|
|
def _infer_MatMul(self, node):
|
|
self._compute_matmul_shape(node)
|
|
|
|
def _infer_MatMulInteger(self, node):
|
|
self._compute_matmul_shape(node, onnx.TensorProto.INT32)
|
|
|
|
def _infer_NonMaxSuppression(self, node):
|
|
selected = self._new_symbolic_dim_from_output(node)
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], onnx.TensorProto.INT64, [selected, 3]))
|
|
|
|
def _infer_NonZero(self, node):
|
|
input_rank = self._get_shape_rank(node, 0)
|
|
# create a new symbolic dimension for NonZero output
|
|
nz_len = self._new_symbolic_dim_from_output(node, 0, 1)
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], vi.type.tensor_type.elem_type, [input_rank, nz_len]))
|
|
|
|
def _infer_OneHot(self, node):
|
|
sympy_shape = self._get_sympy_shape(node, 0)
|
|
depth = self._try_get_value(node, 1)
|
|
axis = get_attribute(node, 'axis', -1)
|
|
axis = handle_negative_axis(axis, len(sympy_shape) + 1)
|
|
new_shape = get_shape_from_sympy_shape(
|
|
sympy_shape[:axis] + [self._new_symbolic_dim_from_output(node) if not is_literal(depth) else depth] +
|
|
sympy_shape[axis:])
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], self.known_vi_[node.input[2]].type.tensor_type.elem_type, new_shape))
|
|
|
|
def _infer_Pad(self, node):
|
|
if get_opset(self.out_mp_) <= 10:
|
|
pads = get_attribute(node, 'pads')
|
|
else:
|
|
pads = self._try_get_value(node, 1)
|
|
|
|
vi = self.known_vi_[node.output[0]]
|
|
output_shape = get_shape_from_type_proto(vi.type)
|
|
if len(output_shape) == 0 or None in output_shape:
|
|
sympy_shape = self._get_sympy_shape(node, 0)
|
|
rank = len(sympy_shape)
|
|
if pads is not None:
|
|
assert len(pads) == 2*rank
|
|
new_sympy_shape = [d + pad_up + pad_down for d, pad_up, pad_down in zip(sympy_shape, pads[:rank], pads[rank:])]
|
|
self._update_computed_dims(new_sympy_shape)
|
|
else:
|
|
# dynamic pads, create new symbolic dimensions
|
|
new_sympy_shape = self._new_symbolic_shape(rank, node)
|
|
output_tp = self.known_vi_[node.input[0]].type.tensor_type.elem_type
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], output_tp, get_shape_from_sympy_shape(new_sympy_shape)))
|
|
|
|
def _infer_Pool(self, node):
|
|
sympy_shape = self._compute_conv_pool_shape(node)
|
|
self._update_computed_dims(sympy_shape)
|
|
for o in node.output:
|
|
if not o:
|
|
continue
|
|
vi = self.known_vi_[o]
|
|
vi.CopyFrom(helper.make_tensor_value_info(o, vi.type.tensor_type.elem_type, get_shape_from_sympy_shape(sympy_shape)))
|
|
|
|
def _infer_Range(self, node):
|
|
vi = self.known_vi_[node.output[0]]
|
|
input_data = self._get_int_values(node)
|
|
if all([i is not None for i in input_data]):
|
|
start = as_scalar(input_data[0])
|
|
limit = as_scalar(input_data[1])
|
|
delta = as_scalar(input_data[2])
|
|
new_sympy_shape = [sympy.Max(sympy.ceiling((limit - start)/delta), 0)]
|
|
else:
|
|
new_dim = self._new_symbolic_dim_from_output(node)
|
|
new_sympy_shape = [self.symbolic_dims_[new_dim]]
|
|
self._update_computed_dims(new_sympy_shape)
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], self.known_vi_[node.input[0]].type.tensor_type.elem_type, get_shape_from_sympy_shape(new_sympy_shape)))
|
|
|
|
def _infer_ReduceProd(self, node):
|
|
axes = get_attribute(node, 'axes')
|
|
keep_dims = get_attribute(node, 'keepdims')
|
|
if keep_dims == 0 and axes == [0]:
|
|
data = self._get_int_values(node)[0]
|
|
if data is not None:
|
|
self.sympy_data_[node.output[0]] = sympy_reduce_product(data)
|
|
|
|
def _infer_Reshape(self, node):
|
|
shape_value = self._try_get_value(node, 1)
|
|
vi = self.known_vi_[node.output[0]]
|
|
if shape_value is None:
|
|
shape_shape = self._get_shape(node, 1)
|
|
assert len(shape_shape) == 1
|
|
shape_rank = shape_shape[0]
|
|
assert is_literal(shape_rank)
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
vi.type.tensor_type.elem_type,
|
|
get_shape_from_sympy_shape(self._new_symbolic_shape(shape_rank, node))))
|
|
else:
|
|
input_shape = self._get_shape(node, 0)
|
|
input_sympy_shape = self._get_sympy_shape(node, 0)
|
|
total = int(1)
|
|
for d in input_sympy_shape:
|
|
total = total * d
|
|
new_sympy_shape = []
|
|
deferred_dim_idx = -1
|
|
non_deferred_size = int(1)
|
|
for i, d in enumerate(shape_value):
|
|
if type(d) == sympy.Symbol:
|
|
new_sympy_shape.append(d)
|
|
elif d == 0:
|
|
new_sympy_shape.append(input_sympy_shape[i])
|
|
non_deferred_size = non_deferred_size * input_sympy_shape[i]
|
|
else:
|
|
new_sympy_shape.append(d)
|
|
if d == -1:
|
|
deferred_dim_idx = i
|
|
elif d != 0:
|
|
non_deferred_size = non_deferred_size * d
|
|
|
|
assert new_sympy_shape.count(-1) < 2
|
|
if -1 in new_sympy_shape:
|
|
new_dim = total // non_deferred_size
|
|
new_sympy_shape[deferred_dim_idx] = new_dim
|
|
self._update_computed_dims(new_sympy_shape)
|
|
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
vi.type.tensor_type.elem_type,
|
|
get_shape_from_sympy_shape(new_sympy_shape)))
|
|
|
|
self._pass_on_sympy_data(node)
|
|
|
|
def _infer_Resize(self, node):
|
|
vi = self.known_vi_[node.output[0]]
|
|
input_sympy_shape = self._get_sympy_shape(node, 0)
|
|
if get_opset(self.out_mp_) <= 10:
|
|
scales = self._try_get_value(node, 1)
|
|
if scales is not None:
|
|
new_sympy_shape = [sympy.simplify(sympy.floor(d*s)) for d,s in zip(input_sympy_shape, scales)]
|
|
self._update_computed_dims(new_sympy_shape)
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
self.known_vi_[node.input[0]].type.tensor_type.elem_type,
|
|
get_shape_from_sympy_shape(new_sympy_shape)))
|
|
else:
|
|
roi = self._try_get_value(node, 1)
|
|
scales = self._try_get_value(node, 2)
|
|
sizes = self._try_get_value(node, 3)
|
|
if sizes is not None:
|
|
new_sympy_shape = [sympy.simplify(sympy.floor(s)) for s in sizes]
|
|
self._update_computed_dims(new_sympy_shape)
|
|
elif scales is not None:
|
|
rank = len(scales)
|
|
if get_attribute(node, 'coordinate_transformation_mode') == 'tf_crop_and_resize':
|
|
assert len(roi) == 2*rank
|
|
roi_start = list(roi)[:rank]
|
|
roi_end = list(roi)[rank:]
|
|
else:
|
|
roi_start = [0]*rank
|
|
roi_end = [1]*rank
|
|
scales = list(scales)
|
|
new_sympy_shape = [sympy.simplify(sympy.floor(d * (end - start) * scale)) for d, start, end, scale in zip(input_sympy_shape, roi_start, roi_end, scales)]
|
|
self._update_computed_dims(new_sympy_shape)
|
|
else:
|
|
new_sympy_shape = self._new_symbolic_shape(self._get_shape_rank(node, 0), node)
|
|
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
self.known_vi_[node.input[0]].type.tensor_type.elem_type,
|
|
get_shape_from_sympy_shape(new_sympy_shape)))
|
|
|
|
def _infer_Scan(self, node):
|
|
subgraph = get_attribute(node, 'body')
|
|
num_scan_inputs = get_attribute(node, 'num_scan_inputs')
|
|
scan_input_axes = get_attribute(node, 'scan_input_axes', [0]*num_scan_inputs)
|
|
num_scan_states = len(node.input) - num_scan_inputs
|
|
scan_input_axes = [handle_negative_axis(ax, self._get_shape_rank(node, i + num_scan_states)) for i, ax in enumerate(scan_input_axes)]
|
|
# We may have cases where the subgraph has optionial inputs that appear in both subgraph's input and initializer,
|
|
# but not in the node's input. In such cases, the input model might be invalid, but let's skip those optional inputs.
|
|
assert len(subgraph.input) >= len(node.input)
|
|
subgraph_inputs = subgraph.input[:len(node.input)]
|
|
for i, si in enumerate(subgraph_inputs):
|
|
subgraph_name = si.name
|
|
si.CopyFrom(self.known_vi_[node.input[i]])
|
|
if i >= num_scan_states:
|
|
scan_input_dim = si.type.tensor_type.shape.dim
|
|
scan_input_dim.remove(scan_input_dim[scan_input_axes[i - num_scan_states]])
|
|
si.name = subgraph_name
|
|
self._onnx_infer_subgraph(node, subgraph)
|
|
num_scan_outputs = len(node.output) - num_scan_states
|
|
scan_output_axes = get_attribute(node, 'scan_output_axes', [0]*num_scan_outputs)
|
|
scan_input_dim = get_shape_from_type_proto(self.known_vi_[node.input[-1]].type)[scan_input_axes[-1]]
|
|
for i, o in enumerate(node.output):
|
|
vi = self.known_vi_[o]
|
|
if i >= num_scan_states:
|
|
shape = get_shape_from_type_proto(subgraph.output[i].type)
|
|
new_dim = handle_negative_axis(scan_output_axes[i - num_scan_states], len(shape) + 1)
|
|
shape = shape[:new_dim] + [scan_input_dim] + shape[new_dim:]
|
|
vi.CopyFrom(helper.make_tensor_value_info(o, subgraph.output[i].type.tensor_type.elem_type, shape))
|
|
else:
|
|
vi.CopyFrom(subgraph.output[i])
|
|
vi.name = o
|
|
|
|
def _infer_ScatterElements(self, node):
|
|
data_shape = self._get_shape(node, 0)
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
self.known_vi_[node.input[0]].type.tensor_type.elem_type,
|
|
data_shape))
|
|
|
|
def _infer_Shape(self, node):
|
|
self.sympy_data_[node.output[0]] = self._get_sympy_shape(node, 0)
|
|
|
|
def _infer_Size(self, node):
|
|
sympy_shape = self._get_sympy_shape(node, 0)
|
|
self.sympy_data_[node.output[0]] = sympy_reduce_product(sympy_shape)
|
|
self.known_vi_[node.output[0]].CopyFrom(helper.make_tensor_value_info(node.output[0], onnx.TensorProto.INT64, []))
|
|
|
|
def _infer_Slice(self, node):
|
|
if get_opset(self.out_mp_) <= 9:
|
|
axes = get_attribute(node, 'axes')
|
|
starts = get_attribute(node, 'starts')
|
|
ends = get_attribute(node, 'ends')
|
|
if not axes:
|
|
axes = list(range(len(starts)))
|
|
steps = [1]*len(axes)
|
|
else:
|
|
starts = as_list(self._try_get_value(node, 1), keep_none=True)
|
|
ends = as_list(self._try_get_value(node, 2), keep_none=True)
|
|
axes = self._try_get_value(node, 3)
|
|
steps = self._try_get_value(node, 4)
|
|
if axes is None and not (starts is None and ends is None):
|
|
axes = list(range(0, len(starts if starts is not None else ends)))
|
|
if steps is None and not (starts is None and ends is None):
|
|
steps = [1]*len(starts if starts is not None else ends)
|
|
axes = as_list(axes, keep_none=True)
|
|
steps = as_list(steps, keep_none=True)
|
|
|
|
new_sympy_shape = self._get_sympy_shape(node, 0)
|
|
if starts is None or ends is None:
|
|
if axes is None:
|
|
for i in range(len(new_sympy_shape)):
|
|
new_sympy_shape[i] = self._new_symbolic_dim_from_output(node,0,i)
|
|
else:
|
|
new_sympy_shape = get_shape_from_sympy_shape(new_sympy_shape)
|
|
for i in axes:
|
|
new_sympy_shape[i] = self._new_symbolic_dim_from_output(node,0,i)
|
|
else:
|
|
for i,s,e,t in zip(axes, starts, ends, steps):
|
|
if is_literal(e):
|
|
if e >= self.int_max_:
|
|
e = new_sympy_shape[i]
|
|
elif e <= -self.int_max_:
|
|
e = 0 if s > 0 else -1
|
|
elif is_literal(new_sympy_shape[i]):
|
|
if e < 0:
|
|
e = max(0, e + new_sympy_shape[i])
|
|
e = min(e, new_sympy_shape[i])
|
|
else:
|
|
if e > 0:
|
|
e = sympy.Min(e, new_sympy_shape[i]) if e > 1 else e #special case for slicing first to make computation easier
|
|
else:
|
|
e = new_sympy_shape[i] + e
|
|
else:
|
|
if is_literal(new_sympy_shape[i]):
|
|
e = sympy.Min(e, new_sympy_shape[i])
|
|
else:
|
|
try:
|
|
if (e - new_sympy_shape[i]) >= 0:
|
|
e = new_sympy_shape[i]
|
|
except Exception:
|
|
print('Unable to determine if {} <= {}, treat as equal'.format(e, new_sympy_shape[i]))
|
|
e = new_sympy_shape[i]
|
|
|
|
if is_literal(s) and int(s) < 0:
|
|
s = new_sympy_shape[i] + s
|
|
if is_literal(new_sympy_shape[i]) and is_literal(s):
|
|
s = max(0, min(s, new_sympy_shape[i]))
|
|
|
|
new_sympy_shape[i] = (e - s + t + (-1 if t > 0 else 1)) // t
|
|
|
|
self._update_computed_dims(new_sympy_shape)
|
|
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
vi.type.tensor_type.elem_type,
|
|
get_shape_from_sympy_shape(new_sympy_shape)))
|
|
|
|
# handle sympy_data if needed, for slice in shape computation
|
|
if node.input[0] in self.sympy_data_ and [0] == axes and len(starts) == 1 and len(ends) == 1:
|
|
input_sympy_data = self.sympy_data_[node.input[0]]
|
|
if type(input_sympy_data) == list or (type(input_sympy_data) == np.array and len(input_sympy_data.shape) == 1):
|
|
self.sympy_data_[node.output[0]] = input_sympy_data[starts[0]:ends[0]]
|
|
|
|
def _infer_SoftmaxCrossEntropyLoss(self, node):
|
|
vi = self.known_vi_[node.output[0]]
|
|
elem_type = self.known_vi_[node.input[0]].type.tensor_type.elem_type
|
|
vi.type.tensor_type.elem_type = elem_type
|
|
|
|
if len(node.output) > 1:
|
|
data_shape = self._get_shape(node, 0)
|
|
vi = self.known_vi_[node.output[1]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(vi.name, elem_type, data_shape))
|
|
|
|
def _infer_Split_Common(self, node, make_value_info_func):
|
|
input_sympy_shape = self._get_sympy_shape(node, 0)
|
|
axis = handle_negative_axis(get_attribute(node, 'axis', 0), len(input_sympy_shape))
|
|
split = get_attribute(node, 'split')
|
|
if not split:
|
|
num_outputs = len(node.output)
|
|
split = [input_sympy_shape[axis]/sympy.Integer(num_outputs)]*num_outputs
|
|
self._update_computed_dims(split)
|
|
else:
|
|
split = [sympy.Integer(s) for s in split]
|
|
|
|
for i_o in range(len(split)):
|
|
vi = self.known_vi_[node.output[i_o]]
|
|
vi.CopyFrom(make_value_info_func(node.output[i_o],
|
|
self.known_vi_[node.input[0]].type.tensor_type.elem_type,
|
|
get_shape_from_sympy_shape(input_sympy_shape[:axis] + [split[i_o]] + input_sympy_shape[axis+1:])))
|
|
self.known_vi_[vi.name] = vi
|
|
|
|
def _infer_Split(self, node):
|
|
self._infer_Split_Common(node, helper.make_tensor_value_info)
|
|
|
|
def _infer_SplitToSequence(self, node):
|
|
self._infer_Split_Common(node, helper.make_sequence_value_info)
|
|
|
|
def _infer_Squeeze(self, node):
|
|
self._pass_on_sympy_data(node)
|
|
|
|
def _infer_Tile(self, node):
|
|
repeats_value = self._get_value(node, 1)
|
|
input_sympy_shape = self._get_sympy_shape(node, 0)
|
|
new_sympy_shape = []
|
|
for i,d in enumerate(input_sympy_shape):
|
|
new_dim = d * repeats_value[i]
|
|
new_sympy_shape.append(new_dim)
|
|
self._update_computed_dims(new_sympy_shape)
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[0],
|
|
vi.type.tensor_type.elem_type,
|
|
get_shape_from_sympy_shape(new_sympy_shape)))
|
|
|
|
def _infer_TopK(self, node):
|
|
rank = self._get_shape_rank(node, 0)
|
|
axis = handle_negative_axis(get_attribute(node, 'axis', -1), rank)
|
|
new_shape = self._get_shape(node, 0)
|
|
|
|
if get_opset(self.out_mp_) <= 9:
|
|
k = get_attribute(node, 'k')
|
|
else:
|
|
k = self._get_int_values(node)[1]
|
|
|
|
if k == None:
|
|
k = self._new_symbolic_dim_from_output(node)
|
|
else:
|
|
k = as_scalar(k)
|
|
|
|
if type(k) in [int, str]:
|
|
new_shape[axis] = k
|
|
else:
|
|
new_sympy_shape = self._get_sympy_shape(node, 0)
|
|
new_sympy_shape[axis] = k
|
|
self._update_computed_dims(new_sympy_shape) # note that TopK dim could be computed in sympy_data, so need to update computed_dims when it enters shape
|
|
new_shape = get_shape_from_sympy_shape(new_sympy_shape)
|
|
|
|
for i_o in range(len(node.output)):
|
|
vi = self.known_vi_[node.output[i_o]]
|
|
vi.CopyFrom(helper.make_tensor_value_info(node.output[i_o], vi.type.tensor_type.elem_type, new_shape))
|
|
|
|
def _infer_Unsqueeze(self, node):
|
|
self._pass_on_sympy_data(node)
|
|
|
|
def _infer_ZipMap(self, node):
|
|
map_key_type = None
|
|
if get_attribute(node, 'classlabels_int64s') is not None:
|
|
map_key_type = onnx.TensorProto.INT64
|
|
elif get_attribute(node, 'classlabels_strings') is not None:
|
|
map_key_type = onnx.TensorProto.STRING
|
|
|
|
assert map_key_type is not None
|
|
new_vi = onnx.ValueInfoProto()
|
|
new_vi.name = node.output[0]
|
|
new_vi.type.sequence_type.elem_type.map_type.value_type.tensor_type.elem_type = onnx.TensorProto.FLOAT
|
|
new_vi.type.sequence_type.elem_type.map_type.key_type = map_key_type
|
|
vi = self.known_vi_[node.output[0]]
|
|
vi.CopyFrom(new_vi)
|
|
|
|
def _infer_impl(self, start_sympy_data=None):
|
|
self.sympy_data_ = start_sympy_data or {}
|
|
self.out_mp_.graph.ClearField('value_info')
|
|
self._apply_suggested_merge(graph_input_only=True)
|
|
self.input_symbols_ = set()
|
|
for i in self.out_mp_.graph.input:
|
|
input_dims = i.type.tensor_type.shape.dim
|
|
for i_dim in range(len(input_dims)):
|
|
if get_dim_from_type_proto(input_dims[i_dim]) is None:
|
|
# some models use None for symbolic dim in input, replace it with a string
|
|
input_dims[i_dim].dim_param = self._new_symbolic_dim(i.name, i_dim)
|
|
self.input_symbols_.update([d for d in get_shape_from_type_proto(i.type) if type(d) == str])
|
|
|
|
for s in self.input_symbols_:
|
|
if s in self.suggested_merge_:
|
|
s_merge = self.suggested_merge_[s]
|
|
assert s_merge in self.symbolic_dims_
|
|
self.symbolic_dims_[s] = self.symbolic_dims_[s_merge]
|
|
else:
|
|
self.symbolic_dims_[s] = sympy.Symbol(s, integer=True)
|
|
|
|
# create a temporary ModelProto for single node inference
|
|
# note that we remove initializer to have faster inference
|
|
# for tensor ops like Reshape/Tile/Expand that read initializer, we need to do sympy computation based inference anyways
|
|
self.tmp_mp_ = onnx.ModelProto()
|
|
self.tmp_mp_.CopyFrom(self.out_mp_)
|
|
self.tmp_mp_.graph.ClearField('initializer')
|
|
|
|
# topological sort nodes, note there might be dead nodes so we check if all graph outputs are reached to terminate
|
|
sorted_nodes = []
|
|
sorted_known_vi = set([i.name for i in list(self.out_mp_.graph.input) + list(self.out_mp_.graph.initializer)])
|
|
if all([o.name in sorted_known_vi for o in self.out_mp_.graph.output]):
|
|
# Loop/Scan will have all graph output in graph inputs, so don't do topological sort
|
|
sorted_nodes = self.out_mp_.graph.node
|
|
else:
|
|
while not all([o.name in sorted_known_vi for o in self.out_mp_.graph.output]):
|
|
old_sorted_nodes_len = len(sorted_nodes)
|
|
for node in self.out_mp_.graph.node:
|
|
if (node.output[0] not in sorted_known_vi ) and all([i in sorted_known_vi for i in node.input if i]):
|
|
sorted_known_vi.update(node.output)
|
|
sorted_nodes.append(node)
|
|
if old_sorted_nodes_len == len(sorted_nodes) and not all([o.name in sorted_known_vi for o in self.out_mp_.graph.output]):
|
|
raise Exception('Invalid model with cyclic graph')
|
|
|
|
for node in sorted_nodes:
|
|
assert all([i in self.known_vi_ for i in node.input if i])
|
|
self._onnx_infer_single_node(node)
|
|
if node.op_type in self.dispatcher_:
|
|
self.dispatcher_[node.op_type](node)
|
|
elif node.op_type in ['ConvTranspose']:
|
|
# onnx shape inference ops like ConvTranspose may have empty shape for symbolic input
|
|
# before adding symbolic compute for them
|
|
# mark the output type as UNDEFINED to allow guessing of rank
|
|
vi = self.known_vi_[node.output[0]]
|
|
if len(vi.type.tensor_type.shape.dim) == 0:
|
|
vi.type.tensor_type.elem_type = onnx.TensorProto.UNDEFINED
|
|
|
|
if self.verbose_ > 2:
|
|
print(node.op_type + ': ' + node.name)
|
|
for i, name in enumerate(node.input):
|
|
print(' Input {}: {} {}'.format(i, name, 'initializer' if name in self.initializers_ else ''))
|
|
|
|
# onnx automatically merge dims with value, i.e. Mul(['aaa', 'bbb'], [1000, 1]) -> [1000, 'bbb']
|
|
# symbolic shape inference needs to apply merge of 'aaa' -> 1000 in this case
|
|
if node.op_type in ['Add', 'Sub', 'Mul', 'Div', 'MatMul', 'MatMulInteger', 'MatMulInteger16', 'Where', 'Sum']:
|
|
vi = self.known_vi_[node.output[0]]
|
|
out_rank = len(get_shape_from_type_proto(vi.type))
|
|
in_shapes = [self._get_shape(node, i) for i in range(len(node.input))]
|
|
for d in range(out_rank - (2 if node.op_type in ['MatMul', 'MatMulInteger', 'MatMulInteger16'] else 0)):
|
|
in_dims = [s[len(s) - out_rank + d] for s in in_shapes if len(s) + d >= out_rank]
|
|
if len(in_dims) > 1:
|
|
self._check_merged_dims(in_dims, allow_broadcast=True)
|
|
|
|
for i_o in range(len(node.output)):
|
|
vi = self.known_vi_[node.output[i_o]]
|
|
out_type = vi.type
|
|
out_type_kind = out_type.WhichOneof('value')
|
|
# only TensorProto and SparseTensorProto have shape
|
|
if out_type_kind != 'tensor_type' and out_type_kind != 'sparse_tensor_type':
|
|
continue
|
|
out_shape = get_shape_from_type_proto(vi.type)
|
|
out_type_undefined = out_type.tensor_type.elem_type == onnx.TensorProto.UNDEFINED
|
|
if self.verbose_ > 2:
|
|
print(' {}: {} {}'.format(node.output[i_o], str(out_shape), vi.type.tensor_type.elem_type))
|
|
if node.output[i_o] in self.sympy_data_:
|
|
print(' Sympy Data: ' + str(self.sympy_data_[node.output[i_o]]))
|
|
|
|
if None in out_shape or out_type_undefined:
|
|
if self.auto_merge_:
|
|
if node.op_type in ['Add', 'Sub', 'Mul', 'Div', 'MatMul', 'MatMulInteger', 'MatMulInteger16', 'Concat', 'Where', 'Sum']:
|
|
shapes = [self._get_shape(node, i) for i in range(len(node.input))]
|
|
if node.op_type in ['MatMul', 'MatMulInteger', 'MatMulInteger16']:
|
|
if None in out_shape:
|
|
idx = out_shape.index(None)
|
|
dim_idx = [len(s) - len(out_shape) + idx for s in shapes]
|
|
# only support auto merge for MatMul for dim < rank-2 when rank > 2
|
|
assert len(shapes[0]) > 2 and dim_idx[0] < len(shapes[0]) - 2
|
|
assert len(shapes[1]) > 2 and dim_idx[1] < len(shapes[1]) - 2
|
|
elif node.op_type == 'Expand':
|
|
# auto merge for cases like Expand([min(batch, 1), min(seq, 512)], [batch, seq])
|
|
shapes = [self._get_shape(node, 0), self._get_value(node, 1)]
|
|
else:
|
|
shapes = []
|
|
|
|
if shapes:
|
|
for idx in range(len(out_shape)):
|
|
if out_shape[idx] is not None:
|
|
continue
|
|
# note that the broadcasting rule aligns from right to left
|
|
# if a tensor has a lower rank (dim_idx[idx] < 0), it would automatically broadcast and need no merge
|
|
dim_idx = [len(s) - len(out_shape) + idx for s in shapes]
|
|
if len(dim_idx) > 0:
|
|
self._add_suggested_merge([s[i] if is_literal(s[i]) else str(s[i]) for s, i in zip(shapes, dim_idx) if i >= 0])
|
|
self.run_ = True
|
|
else:
|
|
self.run_ = False
|
|
else:
|
|
self.run_ = False
|
|
|
|
# create new dynamic dims for ops not handled by symbolic shape inference
|
|
if self.run_ == False and not node.op_type in self.dispatcher_:
|
|
is_unknown_op = (out_type_undefined and len(out_shape) == 0)
|
|
if is_unknown_op:
|
|
# unknown op to ONNX, maybe from higher opset or other domain
|
|
# only guess the output rank from input 0 when using guess_output_rank option
|
|
out_rank = self._get_shape_rank(node, 0) if self.guess_output_rank_ else -1
|
|
else:
|
|
# valid ONNX op, but not handled by symbolic shape inference, just assign dynamic shape
|
|
out_rank = len(out_shape)
|
|
|
|
if out_rank >= 0:
|
|
new_shape = self._new_symbolic_shape(out_rank, node, i_o)
|
|
if out_type_undefined:
|
|
# guess output data type from input vi if not defined
|
|
out_dtype = self.known_vi_[node.input[0]].type.tensor_type.elem_type
|
|
else:
|
|
# otherwise, use original data type
|
|
out_dtype = vi.type.tensor_type.elem_type
|
|
vi.CopyFrom(helper.make_tensor_value_info(vi.name,
|
|
out_dtype,
|
|
get_shape_from_sympy_shape(new_shape)))
|
|
|
|
if self.verbose_ > 0:
|
|
if is_unknown_op:
|
|
print("Possible unknown op: {} node: {}, guessing {} shape".format(node.op_type, node.name, vi.name))
|
|
if self.verbose_ > 2:
|
|
print(' {}: {} {}'.format(node.output[i_o], str(new_shape), vi.type.tensor_type.elem_type))
|
|
|
|
self.run_ = True
|
|
continue # continue the inference after guess, no need to stop as no merge is needed
|
|
|
|
if self.verbose_ > 0 or not self.auto_merge_ or out_type_undefined:
|
|
print('Stopping at incomplete shape inference at ' + node.op_type + ': ' + node.name)
|
|
print('node inputs:')
|
|
for i in node.input:
|
|
print(self.known_vi_[i])
|
|
print('node outputs:')
|
|
for o in node.output:
|
|
print(self.known_vi_[o])
|
|
if self.auto_merge_ and not out_type_undefined:
|
|
print('Merging: ' + str(self.suggested_merge_))
|
|
return False
|
|
|
|
self.run_ = False
|
|
return True
|
|
|
|
def _update_output_from_vi(self):
|
|
for output in self.out_mp_.graph.output:
|
|
if output.name in self.known_vi_:
|
|
output.CopyFrom(self.known_vi_[output.name])
|
|
|
|
@staticmethod
|
|
def infer_shapes(in_mp, int_max=2**31 - 1, auto_merge=False, guess_output_rank=False, verbose=0):
|
|
onnx_opset = get_opset(in_mp)
|
|
if not onnx_opset or onnx_opset < 7:
|
|
print('Only support models of onnx opset 7 and above.')
|
|
return None
|
|
symbolic_shape_inference = SymbolicShapeInference(int_max, auto_merge, guess_output_rank, verbose)
|
|
all_shapes_inferred = False
|
|
symbolic_shape_inference._preprocess(in_mp)
|
|
while symbolic_shape_inference.run_:
|
|
all_shapes_inferred = symbolic_shape_inference._infer_impl()
|
|
symbolic_shape_inference._update_output_from_vi()
|
|
if not all_shapes_inferred:
|
|
raise Exception("Incomplete symbolic shape inference")
|
|
return symbolic_shape_inference.out_mp_
|
|
|
|
def parse_arguments():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--input', required=True, help='The input model file')
|
|
parser.add_argument('--output', help='The output model file')
|
|
parser.add_argument('--auto_merge', help='Automatically merge symbolic dims when confliction happens', action='store_true', default=False)
|
|
parser.add_argument('--int_max', help='maximum value for integer to be treated as boundless for ops like slice', type=int, default=2**31 - 1)
|
|
parser.add_argument('--guess_output_rank', help='guess output rank to be the same as input 0 for unknown ops', action='store_true', default=False)
|
|
parser.add_argument('--verbose', help='Prints detailed logs of inference, 0: turn off, 1: warnings, 3: detailed', type=int, default=0)
|
|
return parser.parse_args()
|
|
|
|
if __name__ == '__main__':
|
|
args = parse_arguments()
|
|
print('input model: ' + args.input)
|
|
if args.output:
|
|
print('output model ' + args.output)
|
|
print('Doing symbolic shape inference...')
|
|
out_mp = SymbolicShapeInference.infer_shapes(onnx.load(args.input), args.int_max, args.auto_merge, args.guess_output_rank, args.verbose)
|
|
if args.output and out_mp:
|
|
onnx.save(out_mp, args.output)
|
|
print('Done!')
|