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onnxruntime/onnxruntime/python/tools/symbolic_shape_infer.py
Tianlei Wu a6c5ba0185
Stable Diffusion CUDA Optimizations (#14428) 
### Description

Add stable diffusion CUDA kernel optimizations.

The following are included:
(1) GroupNorm operator. This kernel is from TensorRT 8.5.
(2) BiasSplitGelu operator. This kernel is modified from SplitGelu of
TensorRT 8.5. We added bias to the SplitGelu.
(3) NhwcConv operator. This adds support of NHWC format (ONNX Conv
operator uses NCHW format).
(3) Update MultiHeadAttention (packed kv and no bias) for cross
attention. This could avoid transpose of kv for TRT fused cross
attention kernel.
(4) Optimization and benchmark script

Not included:
(1) Script to convert Conv to NhwcConv in onnx graph.
(2) Update symbolic shape inference for NhwcConv.
(3) Add SeqLen2Spatial operator
(4) Documents

Limitations: GroupNorm, BiasSplitGelu and NhwcConv kernels are
implemented based on stable diffusion usage. They might not be
applicable to any input size or dimensions. For example, BiasSplitGelu
requires hidden size to be 2560 | 5120 | 10240, and NhwcConv assumes 4D
input/weight.

There is minor increasement of binary size. For SM=75 only, python
package wheel size adds (33757K - 33640K) = 117 KB. It is possible to
move NHWC from template parameter to constructor to reduce binary size
(with slight cost of performance).

Note: for RTX 4090/4080/4070 Ti, need build with CUDA 11.8 and latest
cuDNN to get best performance.
2023-02-02 23:43:51 -08:00

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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# -*- coding: UTF-8 -*-
import argparse
import logging
import numpy as np
import onnx
import sympy
from onnx import helper, numpy_helper, shape_inference
from packaging import version
assert version.parse(onnx.__version__) >= version.parse("1.8.0")
logger = logging.getLogger(__name__)
def get_attribute(node, attr_name, default_value=None):
found = [attr for attr in node.attribute if attr.name == attr_name]
if found:
return helper.get_attribute_value(found[0])
return default_value
def get_dim_from_proto(dim):
return getattr(dim, dim.WhichOneof("value")) if type(dim.WhichOneof("value")) == str else None
def is_sequence(type_proto):
cls_type = type_proto.WhichOneof("value")
assert cls_type in ["tensor_type", "sequence_type"]
return cls_type == "sequence_type"
def get_shape_from_type_proto(type_proto):
assert not is_sequence(type_proto)
if type_proto.tensor_type.HasField("shape"):
return [get_dim_from_proto(d) for d in type_proto.tensor_type.shape.dim]
else:
return None # note no shape is different from shape without dim (scalar)
def get_elem_type_from_type_proto(type_proto):
if is_sequence(type_proto):
return type_proto.sequence_type.elem_type.tensor_type.elem_type
else:
return type_proto.tensor_type.elem_type
def get_shape_from_value_info(vi):
cls_type = vi.type.WhichOneof("value")
if cls_type is None:
return None
if is_sequence(vi.type):
if "tensor_type" == vi.type.sequence_type.elem_type.WhichOneof("value"):
return get_shape_from_type_proto(vi.type.sequence_type.elem_type)
else:
return None
else:
return get_shape_from_type_proto(vi.type)
def make_named_value_info(name):
vi = onnx.ValueInfoProto()
vi.name = name
return vi
def get_shape_from_sympy_shape(sympy_shape):
return [None if i is None else (int(i) if is_literal(i) else str(i)) for i in sympy_shape]
def is_literal(dim):
return type(dim) in [int, np.int64, np.int32, sympy.Integer] or (hasattr(dim, "is_number") and dim.is_number)
def handle_negative_axis(axis, rank):
assert axis < rank and axis >= -rank
return axis if axis >= 0 else rank + axis
def get_opset(mp, domain=None):
domain = domain or ["", "onnx", "ai.onnx"]
if type(domain) != list:
domain = [domain]
for opset in mp.opset_import:
if opset.domain in domain:
return opset.version
return None
def as_scalar(x):
if type(x) == list:
assert len(x) == 1
return x[0]
elif type(x) == np.ndarray:
return x.item()
else:
return x
def as_list(x, keep_none):
if type(x) == list:
return x
elif type(x) == np.ndarray:
return list(x)
elif keep_none and x is None:
return None
else:
return [x]
def sympy_reduce_product(x):
if type(x) == list:
value = sympy.Integer(1)
for v in x:
value = value * v
else:
value = x
return value
class SymbolicShapeInference:
def __init__(self, int_max, auto_merge, guess_output_rank, verbose, prefix=""):
self.dispatcher_ = {
"Add": self._infer_symbolic_compute_ops,
"ArrayFeatureExtractor": self._infer_ArrayFeatureExtractor,
"AveragePool": self._infer_Pool,
"BatchNormalization": self._infer_BatchNormalization,
"Cast": self._infer_Cast,
"CategoryMapper": self._infer_CategoryMapper,
"Compress": self._infer_Compress,
"Concat": self._infer_Concat,
"ConcatFromSequence": self._infer_ConcatFromSequence,
"Constant": self._infer_Constant,
"ConstantOfShape": self._infer_ConstantOfShape,
"Conv": self._infer_Conv,
"CumSum": self._pass_on_shape_and_type,
"Div": self._infer_symbolic_compute_ops,
"Einsum": self._infer_Einsum,
"Expand": self._infer_Expand,
"Equal": self._infer_symbolic_compute_ops,
"Floor": self._infer_symbolic_compute_ops,
"Gather": self._infer_Gather,
"GatherElements": self._infer_GatherElements,
"GatherND": self._infer_GatherND,
"Identity": self._pass_on_shape_and_type,
"If": self._infer_If,
"Loop": self._infer_Loop,
"MatMul": self._infer_MatMul,
"MatMulInteger16": self._infer_MatMulInteger,
"MaxPool": self._infer_Pool,
"Max": self._infer_symbolic_compute_ops,
"Min": self._infer_symbolic_compute_ops,
"Mul": self._infer_symbolic_compute_ops,
"NonMaxSuppression": self._infer_NonMaxSuppression,
"NonZero": self._infer_NonZero,
"OneHot": self._infer_OneHot,
"Pad": self._infer_Pad,
"Range": self._infer_Range,
"Reciprocal": self._pass_on_shape_and_type,
"ReduceSum": self._infer_ReduceSum,
"ReduceProd": self._infer_ReduceProd,
"Reshape": self._infer_Reshape,
"Resize": self._infer_Resize,
"Round": self._pass_on_shape_and_type,
"Scan": self._infer_Scan,
"ScatterElements": self._infer_ScatterElements,
"SequenceAt": self._infer_SequenceAt,
"SequenceInsert": self._infer_SequenceInsert,
"Shape": self._infer_Shape,
"Size": self._infer_Size,
"Slice": self._infer_Slice,
"SoftmaxCrossEntropyLoss": self._infer_SoftmaxCrossEntropyLoss,
"SoftmaxCrossEntropyLossInternal": self._infer_SoftmaxCrossEntropyLoss,
"NegativeLogLikelihoodLossInternal": self._infer_SoftmaxCrossEntropyLoss,
"Split": self._infer_Split,
"SplitToSequence": self._infer_SplitToSequence,
"Squeeze": self._infer_Squeeze,
"Sub": self._infer_symbolic_compute_ops,
"Tile": self._infer_Tile,
"TopK": self._infer_TopK,
"Transpose": self._infer_Transpose,
"Unsqueeze": self._infer_Unsqueeze,
"Where": self._infer_symbolic_compute_ops,
"ZipMap": self._infer_ZipMap,
"Neg": self._infer_symbolic_compute_ops,
# contrib ops:
"Attention": self._infer_Attention,
"BiasGelu": self._infer_BiasGelu,
"MultiHeadAttention": self._infer_MultiHeadAttention,
"EmbedLayerNormalization": self._infer_EmbedLayerNormalization,
"FastGelu": self._infer_FastGelu,
"Gelu": self._infer_Gelu,
"GemmFastGelu": self._infer_GemmFastGelu,
"LayerNormalization": self._infer_LayerNormalization,
"LongformerAttention": self._infer_LongformerAttention,
"PythonOp": self._infer_PythonOp,
"SkipLayerNormalization": self._infer_SkipLayerNormalization,
"SkipSimplifiedLayerNormalization": self._infer_SkipLayerNormalization,
"GroupNorm": self._infer_GroupNorm,
"BiasSplitGelu": self._infer_BiasSplitGelu,
}
self.aten_op_dispatcher_ = {
"embedding": self._infer_Gather,
"bitwise_or": self._infer_aten_bitwise_or,
"diagonal": self._infer_aten_diagonal,
"max_pool2d_with_indices": self._infer_aten_pool2d,
"max": self._infer_aten_minmax,
"min": self._infer_aten_minmax,
"multinomial": self._infer_aten_multinomial,
"unfold": self._infer_aten_unfold,
"argmax": self._infer_aten_argmax,
"avg_pool2d": self._infer_aten_pool2d,
"_adaptive_avg_pool2d": self._infer_aten_pool2d,
"numpy_T": self._infer_Transpose,
"native_group_norm": self._infer_aten_group_norm,
"upsample_nearest1d": self._infer_aten_upsample_nearest,
"upsample_nearest2d": self._infer_aten_upsample_nearest,
"upsample_nearest3d": self._infer_aten_upsample_nearest,
}
self.run_ = True
self.suggested_merge_ = {}
self.symbolic_dims_ = {}
self.input_symbols_ = {}
self.auto_merge_ = auto_merge
self.guess_output_rank_ = guess_output_rank
self.verbose_ = verbose
self.int_max_ = int_max
self.subgraph_id_ = 0
self.prefix_ = prefix
def _add_suggested_merge(self, symbols, apply=False):
assert all([(type(s) == str and s in self.symbolic_dims_) or is_literal(s) for s in symbols])
symbols = set(symbols)
for k, v in self.suggested_merge_.items():
if k in symbols:
symbols.remove(k)
symbols.add(v)
map_to = None
# if there is literal, map to it first
for s in symbols:
if is_literal(s):
map_to = s
break
# when no literals, map to input symbolic dims, then existing symbolic dims
if map_to is None:
for s in symbols:
if s in self.input_symbols_:
map_to = s
break
if map_to is None:
for s in symbols:
if type(self.symbolic_dims_[s]) == sympy.Symbol:
map_to = s
break
# when nothing to map to, use the shorter one
if map_to is None:
if self.verbose_ > 0:
logger.warning("Potential unsafe merge between symbolic expressions: ({})".format(",".join(symbols)))
symbols_list = list(symbols)
lens = [len(s) for s in symbols_list]
map_to = symbols_list[lens.index(min(lens))]
symbols.remove(map_to)
for s in symbols:
if s == map_to:
continue
if is_literal(map_to) and is_literal(s):
assert int(map_to) == int(s)
self.suggested_merge_[s] = int(map_to) if is_literal(map_to) else map_to
for k, v in self.suggested_merge_.items():
if v == s:
self.suggested_merge_[k] = map_to
if apply and self.auto_merge_:
self._apply_suggested_merge()
def _apply_suggested_merge(self, graph_input_only=False):
if not self.suggested_merge_:
return
for i in list(self.out_mp_.graph.input) + ([] if graph_input_only else list(self.out_mp_.graph.value_info)):
for d in i.type.tensor_type.shape.dim:
if d.dim_param in self.suggested_merge_:
v = self.suggested_merge_[d.dim_param]
if is_literal(v):
d.dim_value = int(v)
else:
d.dim_param = v
def _preprocess(self, in_mp):
self.out_mp_ = onnx.ModelProto()
self.out_mp_.CopyFrom(in_mp)
self.graph_inputs_ = dict([(i.name, i) for i in list(self.out_mp_.graph.input)])
self.initializers_ = dict([(i.name, i) for i in self.out_mp_.graph.initializer])
self.known_vi_ = dict([(i.name, i) for i in list(self.out_mp_.graph.input)])
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
]
)
)
def _merge_symbols(self, dims):
if not all([type(d) == str for d in dims]):
if self.auto_merge_:
unique_dims = list(set(dims))
is_int = [is_literal(d) for d in unique_dims]
assert sum(is_int) <= 1 # if there are more than 1 unique ints, something is wrong
if sum(is_int) == 1:
int_dim = is_int.index(1)
if self.verbose_ > 0:
logger.debug(
"dim {} has been merged with value {}".format(
unique_dims[:int_dim] + unique_dims[int_dim + 1 :],
unique_dims[int_dim],
)
)
self._check_merged_dims(unique_dims, allow_broadcast=False)
return unique_dims[int_dim]
else:
if self.verbose_ > 0:
logger.debug("dim {} has been mergd with dim {}".format(unique_dims[1:], unique_dims[0]))
return dims[0]
else:
return None
if all([d == dims[0] for d in dims]):
return dims[0]
merged = [self.suggested_merge_[d] if d in self.suggested_merge_ else d for d in dims]
if all([d == merged[0] for d in merged]):
assert merged[0] in self.symbolic_dims_
return merged[0]
else:
return None
# broadcast from right to left, and merge symbolic dims if needed
def _broadcast_shapes(self, shape1, shape2):
new_shape = []
rank1 = len(shape1)
rank2 = len(shape2)
new_rank = max(rank1, rank2)
for i in range(new_rank):
dim1 = shape1[rank1 - 1 - i] if i < rank1 else 1
dim2 = shape2[rank2 - 1 - i] if i < rank2 else 1
if dim1 == 1 or dim1 == dim2:
new_dim = dim2
elif dim2 == 1:
new_dim = dim1
else:
new_dim = self._merge_symbols([dim1, dim2])
if not new_dim:
# warning about unsupported broadcast when not auto merge
# note that auto merge has the risk of incorrectly merge symbols while one of them being 1
# for example, 'a' = 1, 'b' = 5 at runtime is valid broadcasting, but with auto merge 'a' == 'b'
if self.auto_merge_:
self._add_suggested_merge([dim1, dim2], apply=True)
else:
logger.warning("unsupported broadcast between " + str(dim1) + " " + str(dim2))
new_shape = [new_dim] + new_shape
return new_shape
def _get_shape(self, node, idx):
name = node.input[idx]
if name in self.known_vi_:
vi = self.known_vi_[name]
return get_shape_from_value_info(vi)
else:
assert name in self.initializers_
return list(self.initializers_[name].dims)
def _get_shape_rank(self, node, idx):
return len(self._get_shape(node, idx))
def _get_sympy_shape(self, node, idx):
sympy_shape = []
for d in self._get_shape(node, idx):
if type(d) == str:
sympy_shape.append(
self.symbolic_dims_[d]
if d in self.symbolic_dims_
else sympy.Symbol(d, integer=True, nonnegative=True)
)
else:
assert None != d
sympy_shape.append(d)
return sympy_shape
def _get_value(self, node, idx):
name = node.input[idx]
assert name in self.sympy_data_ or name in self.initializers_
return self.sympy_data_[name] if name in self.sympy_data_ else numpy_helper.to_array(self.initializers_[name])
def _try_get_value(self, node, idx):
if idx >= len(node.input):
return None
name = node.input[idx]
if name in self.sympy_data_ or name in self.initializers_:
return self._get_value(node, idx)
return None
def _update_computed_dims(self, new_sympy_shape):
for i, new_dim in enumerate(new_sympy_shape):
if not is_literal(new_dim) and not type(new_dim) == str:
str_dim = str(new_dim)
if str_dim in self.suggested_merge_:
if is_literal(self.suggested_merge_[str_dim]):
continue # no need to create dim for literals
new_sympy_shape[i] = self.symbolic_dims_[self.suggested_merge_[str_dim]]
else:
# add new_dim if it's a computational expression
if not str(new_dim) in self.symbolic_dims_:
self.symbolic_dims_[str(new_dim)] = new_dim
def _onnx_infer_single_node(self, node):
# skip onnx shape inference for some ops, as they are handled in _infer_*
skip_infer = node.op_type in [
"If",
"Loop",
"Scan",
"SplitToSequence",
"ZipMap", # contrib ops
"Attention",
"BiasGelu",
"EmbedLayerNormalization",
"FastGelu",
"Gelu",
"GemmFastGelu",
"LayerNormalization",
"LongformerAttention",
"SkipLayerNormalization",
"PythonOp",
"MultiHeadAttention",
"GroupNorm",
"BiasSplitGelu",
]
if not skip_infer:
# Only pass initializers that satisfy the following condition:
# (1) Operator need value of some input for shape inference.
# For example, Unsqueeze in opset 13 uses the axes input to calculate shape of output.
# (2) opset version >= 9. In older version, initializer is required in graph input by onnx spec.
# (3) The initializer is not in graph input. The means the node input is "constant" in inference.
initializers = []
if (get_opset(self.out_mp_) >= 9) and node.op_type in ["Unsqueeze"]:
initializers = [
self.initializers_[name]
for name in node.input
if (name in self.initializers_ and name not in self.graph_inputs_)
]
# run single node inference with self.known_vi_ shapes
tmp_graph = helper.make_graph(
[node],
"tmp",
[self.known_vi_[i] for i in node.input if i],
[make_named_value_info(i) for i in node.output],
initializers,
)
self.tmp_mp_.graph.CopyFrom(tmp_graph)
self.tmp_mp_ = shape_inference.infer_shapes(self.tmp_mp_)
for i_o in range(len(node.output)):
o = node.output[i_o]
vi = self.out_mp_.graph.value_info.add()
if not skip_infer:
vi.CopyFrom(self.tmp_mp_.graph.output[i_o])
else:
vi.name = o
self.known_vi_[o] = vi
def _onnx_infer_subgraph(self, node, subgraph, use_node_input=True, inc_subgraph_id=True):
if self.verbose_ > 2:
logger.debug(
"Inferencing subgraph of node {} with output({}...): {}".format(node.name, node.output[0], node.op_type)
)
# node inputs are not passed directly to the subgraph
# it's up to the node dispatcher to prepare subgraph input
# for example, with Scan/Loop, subgraph input shape would be trimmed from node input shape
# besides, inputs in subgraph could shadow implicit inputs
subgraph_inputs = set([i.name for i in list(subgraph.initializer) + list(subgraph.input)])
subgraph_implicit_input = set([name for name in self.known_vi_.keys() if not name in subgraph_inputs])
tmp_graph = helper.make_graph(
list(subgraph.node),
"tmp",
list(subgraph.input) + [self.known_vi_[i] for i in subgraph_implicit_input],
[make_named_value_info(i.name) for i in subgraph.output],
)
tmp_graph.initializer.extend([i for i in self.out_mp_.graph.initializer if i.name in subgraph_implicit_input])
tmp_graph.initializer.extend(subgraph.initializer)
self.tmp_mp_.graph.CopyFrom(tmp_graph)
symbolic_shape_inference = SymbolicShapeInference(
self.int_max_,
self.auto_merge_,
self.guess_output_rank_,
self.verbose_,
prefix=self.prefix_ + "_" + str(self.subgraph_id_),
)
if inc_subgraph_id:
self.subgraph_id_ += 1
all_shapes_inferred = False
symbolic_shape_inference._preprocess(self.tmp_mp_)
symbolic_shape_inference.suggested_merge_ = self.suggested_merge_.copy()
while symbolic_shape_inference.run_:
all_shapes_inferred = symbolic_shape_inference._infer_impl(self.sympy_data_.copy())
symbolic_shape_inference._update_output_from_vi()
if use_node_input:
# if subgraph uses node input, it needs to update to merged dims
subgraph.ClearField("input")
subgraph.input.extend(symbolic_shape_inference.out_mp_.graph.input[: len(node.input)])
subgraph.ClearField("output")
subgraph.output.extend(symbolic_shape_inference.out_mp_.graph.output)
subgraph.ClearField("value_info")
subgraph.value_info.extend(symbolic_shape_inference.out_mp_.graph.value_info)
subgraph.ClearField("node")
subgraph.node.extend(symbolic_shape_inference.out_mp_.graph.node)
# for new symbolic dims from subgraph output, add to main graph symbolic dims
subgraph_shapes = [get_shape_from_value_info(o) for o in symbolic_shape_inference.out_mp_.graph.output]
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_]
)
new_dims = {}
for d in subgraph_new_symbolic_dims:
assert d in symbolic_shape_inference.symbolic_dims_
new_dims[d] = symbolic_shape_inference.symbolic_dims_[d]
self.symbolic_dims_.update(new_dims)
return symbolic_shape_inference
def _get_int_values(self, node, broadcast=False):
values = [self._try_get_value(node, i) for i in range(len(node.input))]
if all([v is not None for v in values]):
# some shape compute is in floating point, cast to int for sympy
for i, v in enumerate(values):
if type(v) != np.ndarray:
continue
if len(v.shape) > 1:
new_v = None # ignore value for rank > 1
elif len(v.shape) == 0:
new_v = int(v.item())
else:
assert len(v.shape) == 1
new_v = [int(vv) for vv in v]
values[i] = new_v
values_len = [len(v) if type(v) == list else 0 for v in values]
max_len = max(values_len)
if max_len >= 1 and broadcast:
# broadcast
for i, v in enumerate(values):
if v is None:
continue # don't broadcast if value is unknown
if type(v) == list:
if len(v) < max_len:
values[i] = v * max_len
else:
assert len(v) == max_len
else:
values[i] = [v] * max_len
return values
def _compute_on_sympy_data(self, node, op_func):
assert len(node.output) == 1
values = self._get_int_values(node, broadcast=True)
if all([v is not None for v in values]):
is_list = [type(v) == list for v in values]
as_list = any(is_list)
if as_list:
self.sympy_data_[node.output[0]] = [op_func(vs) for vs in zip(*values)]
else:
self.sympy_data_[node.output[0]] = op_func(values)
def _pass_on_sympy_data(self, node):
assert len(node.input) == 1 or node.op_type in [
"Reshape",
"Unsqueeze",
"Squeeze",
]
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],
get_elem_type_from_type_proto(self.known_vi_[node.input[0]].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_symbolic_dim = sympy.Integer(int(v)) if is_literal(v) else v
else:
new_symbolic_dim = sympy.Symbol(new_dim, integer=True, nonnegative=True)
self.symbolic_dims_[new_dim] = new_symbolic_dim
return new_symbolic_dim
def _new_symbolic_dim_from_output(self, node, out_idx=0, dim=0):
return self._new_symbolic_dim(
"{}{}_{}_o{}_".format(
node.op_type,
self.prefix_,
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_value_info(self.known_vi_[node.output[0]])
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 _fuse_tensor_type(self, node, out_idx, dst_type, src_type):
"""
update dst_tensor_type to be compatible with src_tensor_type when dimension mismatches
"""
dst_tensor_type = (
dst_type.sequence_type.elem_type.tensor_type if is_sequence(dst_type) else dst_type.tensor_type
)
src_tensor_type = (
src_type.sequence_type.elem_type.tensor_type if is_sequence(src_type) else src_type.tensor_type
)
if dst_tensor_type.elem_type != src_tensor_type.elem_type:
node_id = node.name if node.name else node.op_type
raise ValueError(
f"For node {node_id}, dst_tensor_type.elem_type != src_tensor_type.elem_type: "
f"{onnx.onnx_pb.TensorProto.DataType.Name(dst_tensor_type.elem_type)} vs "
f"{onnx.onnx_pb.TensorProto.DataType.Name(src_tensor_type.elem_type)}"
)
if dst_tensor_type.HasField("shape"):
for di, ds in enumerate(zip(dst_tensor_type.shape.dim, src_tensor_type.shape.dim)):
if ds[0] != ds[1]:
# create a new symbolic dimension for node/out_idx/mismatch dim id in dst_tensor_type for tensor_type
# for sequence_type, clear the dimension
new_dim = onnx.TensorShapeProto.Dimension()
if not is_sequence(dst_type):
new_dim.dim_param = str(self._new_symbolic_dim_from_output(node, out_idx, di))
dst_tensor_type.shape.dim[di].CopyFrom(new_dim)
else:
dst_tensor_type.CopyFrom(src_tensor_type)
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],
"Neg": lambda l: -l[0],
}
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 = str(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_ or i in self.initializers_ 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_ConcatFromSequence(self, node):
seq_shape = self._get_shape(node, 0)
new_axis = 1 if get_attribute(node, "new_axis") else 0
axis = handle_negative_axis(get_attribute(node, "axis"), len(seq_shape) + new_axis)
concat_dim = str(self._new_symbolic_dim_from_output(node, 0, axis))
new_shape = seq_shape
if new_axis:
new_shape = seq_shape[:axis] + [concat_dim] + seq_shape[axis:]
else:
new_shape[axis] = concat_dim
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0],
self.known_vi_[node.input[0]].type.sequence_type.elem_type.tensor_type.elem_type,
new_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
# note input0 is a 1D vector of shape, the new symbolic shape has the rank of the shape vector length
sympy_shape = self._new_symbolic_shape(self._get_shape(node, 0)[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_Einsum(self, node):
# ref:https://github.com/onnx/onnx/blob/623dfaa0151b2e4ce49779c3ec31cbd78c592b80/onnx/defs/math/defs.cc#L3275
equation = get_attribute(node, "equation")
equation = equation.replace(b" ", b"")
mid_index = equation.find(b"->")
left_equation = equation[:mid_index] if mid_index != -1 else equation
num_operands = 0
num_ellipsis = 0
num_ellipsis_indices = 0
letter_to_dim = {}
terms = left_equation.split(b",")
for term in terms:
ellipsis_index = term.find(b"...")
shape = self._get_shape(node, num_operands)
rank = len(shape)
if ellipsis_index != -1:
if num_ellipsis == 0:
num_ellipsis_indices = rank - len(term) + 3
num_ellipsis = num_ellipsis + 1
for i in range(1, rank + 1):
letter = term[-i]
if letter != 46: # letter != b'.'
dim = shape[-i]
if letter not in letter_to_dim.keys():
letter_to_dim[letter] = dim
elif type(dim) != sympy.Symbol:
letter_to_dim[letter] = dim
num_operands = num_operands + 1
new_sympy_shape = []
from collections import OrderedDict
num_letter_occurrences = OrderedDict()
if mid_index != -1:
right_equation = equation[mid_index + 2 :]
right_ellipsis_index = right_equation.find(b"...")
if right_ellipsis_index != -1:
for i in range(num_ellipsis_indices):
new_sympy_shape.append(shape[i])
for c in right_equation:
if c != 46: # c != b'.'
new_sympy_shape.append(letter_to_dim[c])
else:
for i in range(num_ellipsis_indices):
new_sympy_shape.append(shape[i])
for c in left_equation:
if c != 44 and c != 46: # c != b',' and c != b'.':
if c in num_letter_occurrences:
num_letter_occurrences[c] = num_letter_occurrences[c] + 1
else:
num_letter_occurrences[c] = 1
for key, value in num_letter_occurrences.items():
if value == 1:
new_sympy_shape.append(letter_to_dim[key])
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_sympy_shape))
def _infer_Expand(self, node):
expand_to_shape = as_list(self._try_get_value(node, 1), keep_none=True)
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],
self.known_vi_[node.input[0]].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._try_get_value(node, 1)
if idx is not None:
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 or idx == -1
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 as_scalar(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:
self._fuse_tensor_type(node, i_out, vi.type, subgraph.output[i_out].type)
# pass on sympy data from subgraph, if cond is constant
if cond is not None and i_sub == (0 if as_scalar(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)
num_loop_carried = len(node.input) - 2 # minus the length and initial loop condition
# when sequence_type is used as loop carried input
# needs to run subgraph infer twice if the tensor shape in sequence contains None
for i, si in enumerate(subgraph.input):
si_name = si.name
si.CopyFrom(self.known_vi_[node.input[i]])
si.name = si_name
self._onnx_infer_subgraph(node, subgraph)
# check subgraph input/output for shape changes in loop carried variables
# for tensor_type, create new symbolic dim when changing, i.e., output = Concat(input, a)
# for sequence_type, propagate from output to input
need_second_infer = False
for i_out in range(1, num_loop_carried + 1):
so = subgraph.output[i_out]
so_shape = get_shape_from_value_info(so)
if is_sequence(so.type):
if so_shape and None in so_shape:
# copy shape from output to input
# note that loop input is [loop_len, cond, input_0, input_1, ...]
# while loop output is [cond, output_0, output_1, ...]
subgraph.input[i_out + 1].type.sequence_type.elem_type.CopyFrom(so.type.sequence_type.elem_type)
need_second_infer = True
else:
si = subgraph.input[i_out + 1]
si_shape = get_shape_from_value_info(si)
for di, dims in enumerate(zip(si_shape, so_shape)):
if dims[0] != dims[1]:
new_dim = onnx.TensorShapeProto.Dimension()
new_dim.dim_param = str(self._new_symbolic_dim_from_output(node, i_out, di))
si.type.tensor_type.shape.dim[di].CopyFrom(new_dim)
so.type.tensor_type.shape.dim[di].CopyFrom(new_dim)
need_second_infer = True
if need_second_infer:
if self.verbose_ > 2:
logger.debug(
"Rerun Loop: {}({}...), because of sequence in loop carried variables".format(
node.name, node.output[0]
)
)
self._onnx_infer_subgraph(node, subgraph, inc_subgraph_id=False)
# create a new symbolic dimension for iteration dependent dimension
loop_iter_dim = str(self._new_symbolic_dim_from_output(node))
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:
assert not is_sequence(vi.type) # TODO: handle loop accumulation in sequence_type
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 = str(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 = str(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)
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 = self.known_vi_[node.output[0]]
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_aten_bitwise_or(self, node):
shape0 = self._get_shape(node, 0)
shape1 = self._get_shape(node, 1)
new_shape = self._broadcast_shapes(shape0, shape1)
t0 = self.known_vi_[node.input[0]]
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], t0.type.tensor_type.elem_type, new_shape))
def _infer_aten_diagonal(self, node):
sympy_shape = self._get_sympy_shape(node, 0)
rank = len(sympy_shape)
offset = self._try_get_value(node, 1)
dim1 = self._try_get_value(node, 2)
dim2 = self._try_get_value(node, 3)
assert offset is not None and dim1 is not None and dim2 is not None
dim1 = handle_negative_axis(dim1, rank)
dim2 = handle_negative_axis(dim2, rank)
new_shape = []
for dim, val in enumerate(sympy_shape):
if dim not in [dim1, dim2]:
new_shape.append(val)
shape1 = sympy_shape[dim1]
shape2 = sympy_shape[dim2]
if offset >= 0:
diag_shape = sympy.Max(0, sympy.Min(shape1, shape2 - offset))
else:
diag_shape = sympy.Max(0, sympy.Min(shape1 + offset, shape2))
new_shape.append(diag_shape)
if node.output[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,
get_shape_from_sympy_shape(new_shape),
)
)
def _infer_aten_multinomial(self, node):
sympy_shape = self._get_sympy_shape(node, 0)
rank = len(sympy_shape)
assert rank in [1, 2]
num_samples = self._try_get_value(node, 1)
di = rank - 1
last_dim = num_samples if num_samples else str(self._new_symbolic_dim_from_output(node, 0, di))
output_shape = sympy_shape[:-1] + [last_dim]
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0],
onnx.TensorProto.INT64,
get_shape_from_sympy_shape(output_shape),
)
)
def _infer_aten_pool2d(self, node):
sympy_shape = self._get_sympy_shape(node, 0)
assert len(sympy_shape) == 4
sympy_shape[-2:] = [self._new_symbolic_dim_from_output(node, 0, i) for i in [2, 3]]
self._update_computed_dims(sympy_shape)
for i, o in enumerate(node.output):
if not o:
continue
vi = self.known_vi_[o]
elem_type = onnx.TensorProto.INT64 if i == 1 else self.known_vi_[node.input[0]].type.tensor_type.elem_type
vi.CopyFrom(helper.make_tensor_value_info(o, elem_type, get_shape_from_sympy_shape(sympy_shape)))
def _infer_aten_minmax(self, node):
vi = self.known_vi_[node.output[0]]
if len(node.input) == 1:
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], self.known_vi_[node.input[0]].type.tensor_type.elem_type, []
)
)
else:
assert len(node.input) == 3
keepdim = self._try_get_value(node, 2)
assert keepdim is not None # can only handle known keepdim case.
dim = self._try_get_value(node, 1)
if dim is None:
rank = self._get_shape_rank(node, 0)
output_shape = self._new_symbolic_shape(rank if keepdim else rank - 1, node)
else:
shape = self._get_sympy_shape(node, 0)
dim = handle_negative_axis(dim, len(shape))
output_shape = shape[:dim]
if keepdim:
output_shape += [1]
output_shape += shape[dim + 1 :]
output_shape = get_shape_from_sympy_shape(output_shape)
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[0], self.known_vi_[node.input[0]].type.tensor_type.elem_type, output_shape
)
)
vi1 = self.known_vi_[node.output[1]]
vi1.CopyFrom(helper.make_tensor_value_info(node.output[1], onnx.TensorProto.INT64, output_shape))
def _infer_aten_unfold(self, node):
sympy_shape = self._get_sympy_shape(node, 0)
dimension = self._try_get_value(node, 1)
size = self._try_get_value(node, 2)
step = self._try_get_value(node, 3)
if dimension is not None and size is not None and step is not None:
assert dimension < len(sympy_shape)
sympy_shape[dimension] = (sympy_shape[dimension] - size) // step + 1
sympy_shape.append(size)
else:
rank = len(sympy_shape)
sympy_shape = self._new_symbolic_shape(rank + 1, node)
self._update_computed_dims(sympy_shape)
if node.output[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,
get_shape_from_sympy_shape(sympy_shape),
)
)
def _infer_aten_argmax(self, node):
new_shape = None
if node.input[1] == "":
# The argmax of the flattened input is returned.
new_shape = []
else:
dim = self._try_get_value(node, 1)
keepdim = self._try_get_value(node, 2)
if keepdim is not None:
sympy_shape = self._get_sympy_shape(node, 0)
if dim is not None:
dim = handle_negative_axis(dim, len(sympy_shape))
if keepdim:
sympy_shape[dim] = 1
else:
del sympy_shape[dim]
else:
rank = len(sympy_shape)
sympy_shape = self._new_symbolic_shape(rank if keepdim else rank - 1, node)
self._update_computed_dims(sympy_shape)
new_shape = get_shape_from_sympy_shape(sympy_shape)
if node.output[0] and new_shape is not None:
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], onnx.TensorProto.INT64, new_shape))
def _infer_aten_group_norm(self, node):
self._propagate_shape_and_type(node)
input_shape = self._get_shape(node, 0)
N = input_shape[0] if input_shape is not None and len(input_shape) != 0 else None
group = self._try_get_value(node, 6)
output_dtype = self.known_vi_[node.input[0]].type.tensor_type.elem_type
for i in [1, 2]:
if node.output[i]:
vi = self.known_vi_[node.output[i]]
vi.CopyFrom(
helper.make_tensor_value_info(
node.output[i],
output_dtype,
[
N if N is not None else str(self._new_symbolic_dim_from_output(node, i, 0)),
as_scalar(group)
if group is not None
else str(self._new_symbolic_dim_from_output(node, i, 1)),
],
)
)
def _infer_aten_upsample_nearest(self, node):
new_shape = None
input_shape = self._get_shape(node, 0)
if input_shape is not None:
new_shape = input_shape[:2]
output_size = self._try_get_value(node, 1)
if output_size is not None:
new_shape += [dim_size.item() for dim_size in output_size]
else:
rank = len(input_shape)
new_shape += [str(self._new_symbolic_dim_from_output(node, 0, i)) for i in range(2, rank)]
if node.output[0] and new_shape is not None:
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_BatchNormalization(self, node):
self._propagate_shape_and_type(node)
# this works for opsets < 14 and 14 since we check i < len(node.output) in the loop
for i in [1, 2, 3, 4]:
if i < len(node.output) and node.output[i] != "":
# all of these parameters have the same shape as the 1st input
self._propagate_shape_and_type(node, input_index=1, output_index=i)
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_sympy_shape = [self._new_symbolic_dim_from_output(node)]
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_ReduceSum(self, node):
keep_dims = get_attribute(node, "keepdims", 1)
if get_opset(self.out_mp_) >= 13 and len(node.input) > 1:
# ReduceSum changes axes to input[1] in opset 13
axes = self._try_get_value(node, 1)
vi = self.known_vi_[node.output[0]]
if axes is None:
assert keep_dims # can only handle keep_dims==True when axes is unknown, by generating new ranks
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(self._new_symbolic_shape(self._get_shape_rank(node, 0), node)),
)
)
else:
shape = self._get_shape(node, 0)
output_shape = []
axes = [handle_negative_axis(a, len(shape)) for a in axes]
for i, d in enumerate(shape):
if i in axes:
if keep_dims:
output_shape.append(1)
else:
output_shape.append(d)
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_ReduceProd(self, node):
axes = get_attribute(node, "axes")
keep_dims = get_attribute(node, "keepdims", 1)
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_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 optional 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_SequenceAt(self, node):
# need to create new symbolic dimension if sequence shape has None:
seq_shape = self._get_shape(node, 0)
vi = self.known_vi_[node.output[0]]
if seq_shape is not None:
for di, d in enumerate(seq_shape):
if d is not None:
continue
new_dim = onnx.TensorShapeProto.Dimension()
new_dim.dim_param = str(self._new_symbolic_dim_from_output(node, 0, di))
vi.type.tensor_type.shape.dim[di].CopyFrom(new_dim)
def _infer_SequenceInsert(self, node):
# workaround bug in onnx's shape inference
vi_seq = self.known_vi_[node.input[0]]
vi_tensor = self.known_vi_[node.input[1]]
vi_out_seq = self.known_vi_[node.output[0]]
vi_out_seq.CopyFrom(vi_seq)
vi_out_seq.name = node.output[0]
self._fuse_tensor_type(node, 0, vi_out_seq.type, vi_tensor.type)
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):
def less_equal(x, y):
try:
return bool(x <= y)
except TypeError:
pass
try:
return bool(y >= x)
except TypeError:
pass
try:
return bool(-x >= -y)
except TypeError:
pass
try:
return bool(-y <= -x)
except TypeError:
# the last attempt; this may raise TypeError
return bool(y - x >= 0)
def handle_negative_index(index, bound):
"""normalizes a negative index to be in [0, bound)"""
try:
if not less_equal(0, index):
if is_literal(index) and index <= -self.int_max_:
# this case is handled separately
return index
return bound + index
except TypeError:
logger.warning("Cannot determine if {} < 0".format(index))
return index
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):
e = handle_negative_index(e, new_sympy_shape[i])
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:
if is_literal(new_sympy_shape[i]):
e = sympy.Min(e, new_sympy_shape[i])
else:
try:
if not less_equal(e, new_sympy_shape[i]):
e = new_sympy_shape[i]
except Exception:
logger.warning(
"Unable to determine if {} <= {}, treat as equal".format(e, new_sympy_shape[i])
)
e = new_sympy_shape[i]
s = handle_negative_index(s, new_sympy_shape[i])
if is_literal(new_sympy_shape[i]) and is_literal(s):
s = max(0, min(s, new_sympy_shape[i]))
new_sympy_shape[i] = sympy.simplify((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
and len(steps) == 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] : steps[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
vi.type.tensor_type.shape.CopyFrom(onnx.TensorShapeProto())
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):
input_shape = self._get_shape(node, 0)
op_set = get_opset(self.out_mp_)
# Depending on op-version 'axes' are provided as attribute or via 2nd input
if op_set < 13:
axes = get_attribute(node, "axes")
assert self._try_get_value(node, 1) is None
else:
axes = self._try_get_value(node, 1)
assert get_attribute(node, "axes") is None
if axes is None:
# No axes have been provided (neither via attribute nor via input).
# In this case the 'Shape' op should remove all axis with dimension 1.
# For symbolic dimensions we guess they are !=1.
output_shape = [s for s in input_shape if s != 1]
if self.verbose_ > 0:
symbolic_dimensions = [s for s in input_shape if type(s) != int]
if len(symbolic_dimensions) > 0:
logger.debug(
f"Symbolic dimensions in input shape of op: '{node.op_type}' node: '{node.name}'. "
+ f"Assuming the following dimensions are never equal to 1: {symbolic_dimensions}"
)
else:
axes = [handle_negative_axis(a, len(input_shape)) for a in axes]
output_shape = []
for i in range(len(input_shape)):
if i not in axes:
output_shape.append(input_shape[i])
else:
assert input_shape[i] == 1 or type(input_shape[i]) != int
if self.verbose_ > 0 and type(input_shape[i]) != int:
logger.debug(
f"Symbolic dimensions in input shape of op: '{node.op_type}' node: '{node.name}'. "
+ f"Assuming the dimension '{input_shape[i]}' at index {i} of the input to be equal to 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,
output_shape,
)
)
self._pass_on_sympy_data(node)
def _infer_Tile(self, node):
repeats_value = self._try_get_value(node, 1)
new_sympy_shape = []
if repeats_value is not None:
input_sympy_shape = self._get_sympy_shape(node, 0)
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)
else:
new_sympy_shape = self._new_symbolic_shape(self._get_shape_rank(node, 0), node)
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_Transpose(self, node):
if node.input[0] in self.sympy_data_:
data_shape = self._get_shape(node, 0)
perm = get_attribute(node, "perm", reversed(list(range(len(data_shape)))))
input_data = self.sympy_data_[node.input[0]]
self.sympy_data_[node.output[0]] = (
np.transpose(np.array(input_data).reshape(*data_shape), axes=tuple(perm)).flatten().tolist()
)
def _infer_Unsqueeze(self, node):
input_shape = self._get_shape(node, 0)
op_set = get_opset(self.out_mp_)
# Depending on op-version 'axes' are provided as attribute or via 2nd input
if op_set < 13:
axes = get_attribute(node, "axes")
assert self._try_get_value(node, 1) is None
else:
axes = self._try_get_value(node, 1)
assert get_attribute(node, "axes") is None
output_rank = len(input_shape) + len(axes)
axes = [handle_negative_axis(a, output_rank) for a in axes]
input_axis = 0
output_shape = []
for i in range(output_rank):
if i in axes:
output_shape.append(1)
else:
output_shape.append(input_shape[input_axis])
input_axis += 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,
output_shape,
)
)
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_Attention(self, node):
shape = self._get_shape(node, 0)
shape_bias = self._get_shape(node, 2)
if shape and len(shape) == 3 and shape_bias and len(shape_bias) == 1:
qkv_hidden_sizes_attr = get_attribute(node, "qkv_hidden_sizes")
if qkv_hidden_sizes_attr is not None:
assert len(qkv_hidden_sizes_attr) == 3
shape[2] = int(qkv_hidden_sizes_attr[2])
elif isinstance(shape_bias[0], int):
shape[2] = int(shape_bias[0] / 3)
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, shape))
if len(node.output) > 1:
# input shape: (batch_size, sequence_length, hidden_size)
# past shape: (2, batch_size, num_heads, past_sequence_length, head_size)
# mask shape: (batch_size, total_sequence_length) or (batch_size, sequence_length, total_sequence_length) or (batch_size, 1, max_seq_len, max_seq_len)
# present shape: (2, batch_size, num_heads, total_sequence_length, head_size), where total_sequence_length=sequence_length+past_sequence_length
input_shape = self._get_shape(node, 0)
past_shape = self._get_shape(node, 4)
mask_shape = self._get_shape(node, 3)
if past_shape and len(past_shape) == 5:
if mask_shape and len(mask_shape) in [2, 3]:
past_shape[3] = mask_shape[-1]
elif input_shape and len(input_shape) == 3:
if isinstance(input_shape[1], int) and isinstance(past_shape[3], int):
past_shape[3] = input_shape[1] + past_shape[3]
else:
past_shape[3] = f"{past_shape[3]}+{input_shape[1]}"
vi = self.known_vi_[node.output[1]]
vi.CopyFrom(helper.make_tensor_value_info(vi.name, output_dtype, past_shape))
def _infer_BiasGelu(self, node):
self._propagate_shape_and_type(node)
def _infer_MultiHeadAttention(self, node):
# Input 0 (query) has shape (batch_size, sequence_length, hidden_size)
# Without packed KV:
# Input 1 (key) has shape (batch_size, kv_sequence_length, hidden_size)
# Input 2 (value) has shape (batch_size, kv_sequence_length, v_hidden_size)
# With packed KV:
# Input 1 (key) has shape (batch_size, kv_sequence_length, num_heads, 2, head_size)
# Input 2 (value) is nullptr
# Output 0 has shape (batch_size, sequence_length, v_hidden_size)
query_shape = self._get_shape(node, 0)
key_shape = self._get_shape(node, 1)
if query_shape is not None and len(query_shape) == 3:
# By default, hidden size is same for Q/K/V. Only need check v_hidden_size when value is provided.
output_shape = query_shape
if key_shape and len(key_shape) == 3:
value_shape = self._get_shape(node, 2)
if value_shape and len(value_shape) == 3:
output_shape[2] = value_shape[2]
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, output_shape))
def _infer_FastGelu(self, node):
self._propagate_shape_and_type(node)
def _infer_Gelu(self, node):
self._propagate_shape_and_type(node)
def _infer_GemmFastGelu(self, node):
self._compute_matmul_shape(node)
def _infer_LayerNormalization(self, node):
self._propagate_shape_and_type(node)
def _infer_LongformerAttention(self, node):
self._propagate_shape_and_type(node)
def _infer_EmbedLayerNormalization(self, node):
input_ids_shape = self._get_shape(node, 0)
word_embedding_shape = self._get_shape(node, 2)
assert len(input_ids_shape) == 2 and len(word_embedding_shape) == 2
output_shape = input_ids_shape + [word_embedding_shape[1]]
word_embedding_dtype = self.known_vi_[node.input[2]].type.tensor_type.elem_type
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], word_embedding_dtype, output_shape))
mask_index_shape = [input_ids_shape[0]]
vi = self.known_vi_[node.output[1]]
vi.CopyFrom(helper.make_tensor_value_info(node.output[1], onnx.TensorProto.INT32, mask_index_shape))
if len(node.output) > 2:
# Optional output of add before layer nomalization is done
# shape is same as the output
vi = self.known_vi_[node.output[2]]
vi.CopyFrom(helper.make_tensor_value_info(node.output[2], word_embedding_dtype, output_shape))
def _infer_SkipLayerNormalization(self, node):
self._propagate_shape_and_type(node)
if len(node.output) > 3:
self._propagate_shape_and_type(node, 0, 3)
# If the SkipLayerNormalization node contains the optional
# output for inference, infer the shape and type for it too
if len(node.output) > 3:
self._propagate_shape_and_type(node, 0, 3)
def _infer_GroupNorm(self, node):
self._propagate_shape_and_type(node)
def _infer_BiasSplitGelu(self, node):
input_shape = self._get_shape(node, 0)
bias_shape = self._get_shape(node, 1)
if input_shape and bias_shape and isinstance(bias_shape[0], int):
output_shape = input_shape
output_shape[2] = int(bias_shape[0] / 2)
vi = self.known_vi_[node.output[0]]
output_dtype = self.known_vi_[node.input[0]].type.tensor_type.elem_type
vi.CopyFrom(helper.make_tensor_value_info(vi.name, output_dtype, output_shape))
def _infer_PythonOp(self, node):
output_tensor_types = get_attribute(node, "output_tensor_types")
assert output_tensor_types
output_tensor_ranks = get_attribute(node, "output_tensor_ranks")
assert output_tensor_ranks
# set the context output seperately.
# The first output is autograd's context.
vi = self.known_vi_[node.output[0]]
vi.CopyFrom(helper.make_tensor_value_info(node.output[0], onnx.TensorProto.INT64, []))
# Outputs after autograd's context are tensors.
# We assume their ranks are fixed for different model inputs.
for i in range(len(node.output) - 1):
# Process the i-th tensor outputs.
vi = self.known_vi_[node.output[i + 1]]
sympy_shape = self._new_symbolic_shape(output_tensor_ranks[i], node)
shape = get_shape_from_sympy_shape(sympy_shape)
value_info = helper.make_tensor_value_info(node.output[i + 1], output_tensor_types[i], shape)
vi.CopyFrom(value_info)
def _propagate_shape_and_type(self, node, input_index=0, output_index=0):
shape = self._get_shape(node, input_index)
output_dtype = self.known_vi_[node.input[input_index]].type.tensor_type.elem_type
vi = self.known_vi_[node.output[output_index]]
vi.CopyFrom(helper.make_tensor_value_info(node.output[output_index], output_dtype, shape))
def _is_none_dim(self, dim_value):
if type(dim_value) != str:
return False
if "unk__" not in dim_value:
return False
if dim_value in self.symbolic_dims_.keys():
return False
return True
def _is_shape_contains_none_dim(self, out_shape):
for out in out_shape:
if self._is_none_dim(out):
return out
return None
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_shape = get_shape_from_value_info(i)
if input_shape is None:
continue
if is_sequence(i.type):
input_dims = i.type.sequence_type.elem_type.tensor_type.shape.dim
else:
input_dims = i.type.tensor_type.shape.dim
for i_dim, dim in enumerate(input_shape):
if dim is None:
# some models use None for symbolic dim in input, replace it with a string
input_dims[i_dim].dim_param = str(self._new_symbolic_dim(i.name, i_dim))
self.input_symbols_.update([d for d in input_shape 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:
# Since inputs are not produced by other ops, we can assume positivity
self.symbolic_dims_[s] = sympy.Symbol(s, integer=True, positive=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")
# compute prerequesite for node for topological sort
# node with subgraphs may have dependency on implicit inputs, which will affect topological sort
prereq_for_node = {} # map from node to all its inputs, including implicit ones in subgraph
def get_prereq(node):
names = set(i for i in node.input if i)
subgraphs = []
if "If" == node.op_type:
subgraphs = [
get_attribute(node, "then_branch"),
get_attribute(node, "else_branch"),
]
elif node.op_type in ["Loop", "Scan"]:
subgraphs = [get_attribute(node, "body")]
for g in subgraphs:
g_outputs_and_initializers = {i.name for i in g.initializer}
g_prereq = set()
for n in g.node:
g_outputs_and_initializers.update(n.output)
for n in g.node:
g_prereq.update([i for i in get_prereq(n) if i not in g_outputs_and_initializers])
names.update(g_prereq)
# remove subgraph inputs from g_prereq since those are local-only
for i in g.input:
if i.name in names:
names.remove(i.name)
return names
for n in self.tmp_mp_.graph.node:
prereq_for_node[n.output[0]] = get_prereq(n)
# 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 any([o.name in sorted_known_vi for o in self.out_mp_.graph.output]):
# Loop/Scan will have some 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 prereq_for_node[node.output[0]] 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)
known_aten_op = False
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
elif node.op_type == "ATen" and node.domain == "org.pytorch.aten":
for attr in node.attribute:
# TODO: Is overload_name needed?
if attr.name == "operator":
aten_op_name = attr.s.decode("utf-8") if isinstance(attr.s, bytes) else attr.s
if aten_op_name in self.aten_op_dispatcher_:
known_aten_op = True
self.aten_op_dispatcher_[aten_op_name](node)
break
if self.verbose_ > 2:
logger.debug(node.op_type + ": " + node.name)
for i, name in enumerate(node.input):
logger.debug(
" 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)):
# Special case: We do not care about the training related
# outputs of SkipLayerNormalization
if node.op_type == "SkipLayerNormalization" and i_o in [1, 2]:
continue
vi = self.known_vi_[node.output[i_o]]
out_type = vi.type
out_type_kind = out_type.WhichOneof("value")
# do not process shape for non-tensors
if out_type_kind not in ["tensor_type", "sparse_tensor_type", None]:
if self.verbose_ > 2:
if out_type_kind == "sequence_type":
seq_cls_type = out_type.sequence_type.elem_type.WhichOneof("value")
if "tensor_type" == seq_cls_type:
logger.debug(
" {}: sequence of {} {}".format(
node.output[i_o],
str(get_shape_from_value_info(vi)),
onnx.TensorProto.DataType.Name(
vi.type.sequence_type.elem_type.tensor_type.elem_type
),
)
)
else:
logger.debug(" {}: sequence of {}".format(node.output[i_o], seq_cls_type))
else:
logger.debug(" {}: {}".format(node.output[i_o], out_type_kind))
continue
out_shape = get_shape_from_value_info(vi)
out_type_undefined = out_type.tensor_type.elem_type == onnx.TensorProto.UNDEFINED
if self.verbose_ > 2:
logger.debug(
" {}: {} {}".format(
node.output[i_o],
str(out_shape),
onnx.TensorProto.DataType.Name(vi.type.tensor_type.elem_type),
)
)
if node.output[i_o] in self.sympy_data_:
logger.debug(" Sympy Data: " + str(self.sympy_data_[node.output[i_o]]))
# onnx >= 1.11.0, use unk__#index instead of None when the shape dim is uncertain
if (
out_shape is not None and (None in out_shape or self._is_shape_contains_none_dim(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",
"Equal",
"Less",
"Greater",
"LessOrEqual",
"GreaterOrEqual",
"Min",
"Max",
]:
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 or self._is_shape_contains_none_dim(out_shape):
if None in out_shape:
idx = out_shape.index(None)
else:
idx = out_shape.index(self._is_shape_contains_none_dim(out_shape))
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 and not self._is_none_dim(out_shape[idx]):
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_ and not known_aten_op:
is_unknown_op = out_type_undefined and (out_shape is None or 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:
logger.debug(
"Possible unknown op: {} node: {}, guessing {} shape".format(
node.op_type, node.name, vi.name
)
)
if self.verbose_ > 2:
logger.debug(
" {}: {} {}".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:
logger.debug("Stopping at incomplete shape inference at " + node.op_type + ": " + node.name)
logger.debug("node inputs:")
for i in node.input:
logger.debug(self.known_vi_[i])
logger.debug("node outputs:")
for o in node.output:
logger.debug(self.known_vi_[o])
if self.auto_merge_ and not out_type_undefined:
logger.debug("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:
logger.warning("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,
)
parser.add_argument(
"--save_as_external_data",
help="Saving an ONNX model to external data",
action="store_true",
default=False,
)
parser.add_argument(
"--all_tensors_to_one_file",
help="Saving all the external data to one file",
action="store_true",
default=False,
)
parser.add_argument(
"--external_data_location",
help="The file location to save the external file",
default="./",
)
parser.add_argument(
"--external_data_size_threshold",
help="The size threshold for external data",
type=int,
default=1024,
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_arguments()
logger.info("input model: " + args.input)
if args.output:
logger.info("output model " + args.output)
logger.info("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:
if args.save_as_external_data:
onnx.save_model(
out_mp,
args.output,
save_as_external_data=True,
all_tensors_to_one_file=args.all_tensors_to_one_file,
location=args.external_data_location,
size_threshold=args.external_data_size_threshold,
convert_attribute=False,
)
else:
onnx.save(out_mp, args.output)
logger.info("Done!")
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