From 4a6f3baacfc5fa254aa54e91193cb8fe4aaa90be Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 12 Dec 2018 01:18:57 +0800 Subject: [PATCH 01/70] Simple BFS Graph Matcher - permutation aware - compatible ops aware - MetaOpInfo --- utensor_cgen/backend/__init__.py | 3 +- utensor_cgen/backend/operators.py | 13 +- utensor_cgen/ir/base.py | 104 +++++++++--- utensor_cgen/matcher/__init__.py | 1 + utensor_cgen/matcher/_matcher_impl.py | 223 ++++++++++++++++++++++++++ utensor_cgen/matcher/_morphism.py | 52 ++++++ utensor_cgen/utils.py | 25 +++ 7 files changed, 392 insertions(+), 29 deletions(-) create mode 100644 utensor_cgen/matcher/__init__.py create mode 100644 utensor_cgen/matcher/_matcher_impl.py create mode 100644 utensor_cgen/matcher/_morphism.py diff --git a/utensor_cgen/backend/__init__.py b/utensor_cgen/backend/__init__.py index b55d32d4..a6429329 100644 --- a/utensor_cgen/backend/__init__.py +++ b/utensor_cgen/backend/__init__.py @@ -1 +1,2 @@ -from .code_generator import CodeGenerator \ No newline at end of file +from .code_generator import CodeGenerator +from .operators import OperatorFactory \ No newline at end of file diff --git a/utensor_cgen/backend/operators.py b/utensor_cgen/backend/operators.py index 0d1e7082..4ef46dd1 100644 --- a/utensor_cgen/backend/operators.py +++ b/utensor_cgen/backend/operators.py @@ -7,9 +7,12 @@ from utensor_cgen.logger import logger from utensor_cgen.transformer.optimizer import RefCntOptimizer from utensor_cgen.utils import NamescopedKWArgsParser +from utensor_cgen.matcher import OpEqualityDelegate, _morphism from .snippets import * # pylint: disable=W0401,W0614 +__all__ = ['OperatorFactory'] + class OperatorFactory(): # Can easily do something smarter @@ -46,6 +49,9 @@ def snippet(self): @OperatorFactory.register +@OpEqualityDelegate.is_associative( + permutations=((0, 1), (1, 0)) +) class _AddOperator(_Operator): op_type = "Add" # tf op type @@ -400,9 +406,10 @@ def __init__(self, op_info, **kwargs): self._snippet = QuantRangeForMultiplicationSnippet(inputs, outputs, output_type, ref_counts, to_eval) @OperatorFactory.register +@OpEqualityDelegate.is_compatible_with("Const", _morphism.Inline2ConstMorphism) class _InlineOperator(_Operator): - op_type="Inline" + op_type = "Inline" def __init__(self, op_info, **kwargs): out_tensor_info = op_info.output_tensors[0] @@ -438,6 +445,7 @@ def _prepare_inline_array_name(self, tensor_name): return preapred @OperatorFactory.register +@OpEqualityDelegate.is_compatible_with("Inline", _morphism.Const2InlineMorphism) class _ConstOperator(_Operator): op_type = "Const" @@ -496,6 +504,7 @@ def __init__(self, op_info, **kwargs): tf_dtype=out_dtype, sptr_name=pre_tname, ref_count=ref_count) + def _prepare_tensor_name(self, tensor_name): prepared = tensor_name.replace(":", "_").replace("/", "_") - return prepared \ No newline at end of file + return prepared diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 9646657e..93b15692 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -1,6 +1,5 @@ # -*- coding: utf8 -*- import re -from collections import defaultdict from copy import deepcopy import attr @@ -20,7 +19,7 @@ from .converter import AttrValueConverter, ConverterFactory -__all__ = ['TensorInfo', 'OperationInfo', 'uTensorGraph'] +__all__ = ['TensorInfo', 'OperationInfo', 'MetaOperationInfo', 'uTensorGraph'] class _NoShallowCopyMixin(object): @@ -46,6 +45,7 @@ class TensorInfo(IRBase, _NoShallowCopyMixin): name = attr.ib(validator=instance_of(six.text_type)) op_name = attr.ib(validator=instance_of(six.text_type)) dtype = attr.ib(validator=instance_of(np.dtype)) + shape = attr.ib(validator=instance_of((list, type(None)))) @shape.validator def check(self, attrib, shape_values): @@ -53,15 +53,24 @@ def check(self, attrib, shape_values): for v in shape_values: assert isinstance(v, (int, type(None))), \ "shape should be a list of integers" - ugraph = attr.ib() - @ugraph.validator + + _ugraph = attr.ib(repr=False) + @_ugraph.validator def check(self, attrib, value): if not isinstance(value, uTensorGraph): raise ValueError('Expecting a uTensorGraph, get {}'.format(type(value))) + @property + def ugraph(self): + return self._ugraph + @property def op(self): - return self.ugraph.ops_info.get(self.op_name, None) + return self._ugraph.ops_info.get(self.op_name, None) + + @property + def backend(self): + return self._ugraph.backend @property def is_dangling(self): @@ -69,6 +78,16 @@ def is_dangling(self): if not op: return True return op.is_dangling + + @property + def n_th_output(self): + if self.is_dangling: + raise ValueError( + "dangling tensor: {}".format(self.name) + ) + op = self.op + out_tnames = [t_info.name for t_info in op.output_tensors] + return out_tnames.index(self.name) def __deepcopy__(self, memo): new_tensor = TensorInfo(name=self.name, @@ -99,12 +118,15 @@ class OperationInfo(IRBase, _NoShallowCopyMixin): values of such keys will be saved as-is without any type conversion. """ name = attr.ib(type=str) - ugraph = attr.ib(repr=False) - @ugraph.validator + _backend = attr.ib(type=str) + _ugraph = attr.ib(repr=False) + @_ugraph.validator def check(self, attrib, value): if not isinstance(value, uTensorGraph): - raise ValueError(('Expecting a uTensorGraph, ' - 'get {}'.format(type(value)))) + raise ValueError( + 'Expecting a uTensorGraph, ' + 'get {}'.format(type(value)) + ) input_tensors = attr.ib(validator=instance_of(list)) @input_tensors.validator @@ -119,32 +141,33 @@ def check(self, attribute, value): raise ValueError('Expecting a list of TensorInfo for output_tensors') op_type = attr.ib(type=str) - - backend = attr.ib(type=str) - @backend.validator - def check(self, attribute, value): - if value not in ['tensorflow']: - raise ValueError('Unsupported backend: {}'.format(value)) - op_attr = attr.ib(factory=dict, converter=dict) + @property + def ugraph(self): + return self._ugraph + + @property + def backend(self): + return self._backend + @property def input_nodes(self): in_ops = [] for tensor in self.input_tensors: if tensor.op_name not in in_ops: in_ops.append(tensor.op_name) - return [self.ugraph.ops_info.get(name, None) for name in in_ops] - + return [self._ugraph.ops_info.get(name, None) for name in in_ops] + @property def output_nodes(self): out_ops = [] - for op in self.ugraph.ops: + for op in self._ugraph.ops: for in_tensor in op.input_tensors: if in_tensor.op_name == self.name and op.name not in out_ops: out_ops.append(op.name) break - return [self.ugraph.ops_info[name] for name in out_ops] + return [self._ugraph.ops_info[name] for name in out_ops] @property def is_dangling(self): @@ -157,7 +180,7 @@ def is_dangling(self): @property def n_inputs(self): return len(self.input_tensors) - + @property def n_outputs(self): return len(self.output_tensors) @@ -173,7 +196,7 @@ def __attrs_post_init__(self): else: op_attr[k] = ConverterFactory.get_generic_value(v) self.op_attr = op_attr - self.ugraph.ops_info[self.name] = self + self._ugraph.ops_info[self.name] = self def __deepcopy__(self, memo): op_info = OperationInfo(name=self.name, @@ -189,6 +212,15 @@ def copy_into_graph(self, ugraph): return deepcopy(self, {'ugraph': ugraph}) +class MetaOperationInfo(OperationInfo): + + def __init__(self, op_info, morphism): + self._op_info = op_info + self.morphism = morphism + + def __getattr__(self, name): + return getattr(self._op_info, name) + @attr.s class uTensorGraph(IRBase, _NoShallowCopyMixin): """ @@ -204,12 +236,31 @@ class uTensorGraph(IRBase, _NoShallowCopyMixin): output_nodes = attr.ib(type=list) _backend = attr.ib(default='', type=str) ops_info = attr.ib(factory=dict) + # non-init topo_order = attr.ib(factory=list, init=False) + _type_to_op_map = attr.ib(factory=dict, init=False, repr=False) def __attrs_post_init__(self): if not self.output_nodes: raise ValueError('No output_nodes given') + def get_ops_by_type(self, op_type): + if not self._type_to_op_map: + for op_info in self.ops_info.values(): + op_type = op_info.op_type + ops = self._type_to_op_map.get( + op_type, + [] + ) + [op_info] + self._type_to_op_map.update( + [(op_type, ops),] + ) + return self._type_to_op_map.get(op_type, []) + + @property + def output_ops(self): + return [self.ops_info[name] for name in self.output_nodes] + @property def backend(self): return self._backend @@ -289,10 +340,11 @@ def drop_op(self, op_name): def __deepcopy__(self, memo): new_graph = uTensorGraph(output_nodes=self.output_nodes) memo['ugraph'] = new_graph - new_ops_info = dict((k, deepcopy(v, memo)) for k, v in self.ops_info.items()) - new_topo_order = [name for name in self.topo_order] - new_graph.ops_info = new_ops_info - new_graph.topo_order = new_topo_order + new_graph.ops_info = { + k: deepcopy(v, memo) + for k, v in self.ops_info.items() + } new_graph._backend = self._backend + topologic_order_graph(new_graph) return new_graph diff --git a/utensor_cgen/matcher/__init__.py b/utensor_cgen/matcher/__init__.py new file mode 100644 index 00000000..537b406d --- /dev/null +++ b/utensor_cgen/matcher/__init__.py @@ -0,0 +1 @@ +from ._matcher_impl import uTensorGraphMatcher, OpEqualityDelegate \ No newline at end of file diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py new file mode 100644 index 00000000..958f41fc --- /dev/null +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -0,0 +1,223 @@ +from collections import deque +from itertools import product + +import attr +from attr.validators import instance_of + +from utensor_cgen.ir import MetaOperationInfo, OperationInfo, uTensorGraph +from utensor_cgen.matcher._morphism import IdentityMorphism, Morphism +from utensor_cgen.utils import ops_bfs_queue + +__all__ = ["uTensorGraphMatcher"] + +@attr.s(frozen=True, slots=True) +class OpEqualityDelegate(object): + + # op_type -> list[tuple] (permutations) + _association_map = {} + # op_type -> dict[op_type] -> morphism + _compatibility_map = {} + + @classmethod + def is_associative(cls, permutations): + def deco(op): + if op.op_type in cls._association_map: + raise ValueError( + "duplicate associativity definition found for {}".format(op.op_type) + ) + assert ( + isinstance(permutations, tuple) and + all([isinstance(perm, tuple) for perm in permutations]) + ), "`permutations` should be tuple of int tuples" + cls._association_map[op.op_type] = permutations + return op + return deco + + @classmethod + def is_compatible_with(cls, other_op_type, morphism_type, **kwargs): + if not issubclass(morphism_type, Morphism): + raise ValueError( + 'expecting Morphism for `morphism`, get {}'.format(morphism_type) + ) + def deco(op): + if op.op_type not in cls._compatibility_map: + cls._compatibility_map[op.op_type] = {} + if other_op_type in cls._compatibility_map[op.op_type]: + raise RuntimeError( + "Multiple morphisms from {} to {} detected".format(op.op_type, other_op_type) + ) + if not other_op_type in cls._compatibility_map[op.op_type]: + cls._compatibility_map[op.op_type][other_op_type] = morphism_type(**kwargs) + return op + return deco + + @classmethod + def query(cls, sub_op, patrn_op): + """ + Parameters + ---------- + sub_op : OperationInfo + the op in the subject ugraph + patrn_op : OperationInfo + the op in the pattern ugraph to match with + + Return + ------ + is_eq : bool + these two ops are equivalent if True, False o.w. + equivalent_ops : List[OperationInfo] + a list of equivalent ops derieved from `sub_op` + """ + # to activate all configurations + import utensor_cgen.backend.operators as _ + + is_eq = False + equivalent_ops = [] + if sub_op.op_type == patrn_op.op_type and sub_op.op_type not in cls._association_map: + is_eq = True + equivalent_ops = [patrn_op] + elif sub_op.op_type == patrn_op.op_type: + is_eq = True + equivalent_ops = [] + for perm in cls._association_map[sub_op.op_type]: + equivalent_ops.append( + OperationInfo( + name=sub_op.name, + backend=sub_op.backend, + ugraph=sub_op.ugraph, + input_tensors=[sub_op.input_tensors[j] for j in perm], + output_tensors=sub_op.output_tensors, + op_type=sub_op.op_type, + op_attr=sub_op.op_attr, + ) + ) + elif patrn_op.op_type in cls._compatibility_map.get(sub_op.op_type, []): + is_eq = True + morphism = cls._compatibility_map[sub_op.op_type][patrn_op.op_type] + equivalent_ops = [MetaOperationInfo(op_info=sub_op, morphism=morphism)] + elif not patrn_op.input_tensors and \ + patrn_op.op_attr.get("utensor_matcher__force_match_input", False): + # input node + is_eq = True + equivalent_ops = [sub_op] + + return is_eq, equivalent_ops + +@attr.s +class uTensorGraphMatcher(object): + + pattern_ugraph = attr.ib(validator=instance_of(uTensorGraph)) + + def _match(self, other_ugraph): + outputs_pool = [] + for op in self.pattern_ugraph.output_ops: + same_ops = other_ugraph.get_ops_by_type(op.op_type) + if not same_ops: + # there are missing output(s) + # no way to match, return empty list + return [] + outputs_pool.append(same_ops) + output_candidates = product(*outputs_pool) + for output in output_candidates: + states = [ + _MatchState( + match=uTensorGraphMatch( + pattern_ugraph=self.pattern_ugraph, + subject_ugraph=other_ugraph + ), + sub_bfs_queue=ops_bfs_queue(other_ugraph, init_nodes=output), + patrn_bfs_queue=ops_bfs_queue(self.pattern_ugraph), + ) + ] + while True: + states = self._visit(states) + if not states: + break + for state in states: + if state.is_done: + yield state.match + + def match(self, other_ugraph, n=1): + match_gen = self._match(other_ugraph) + matches = [] + try: + for _ in range(n): + matches.append(next(match_gen)) + except StopIteration: + pass + return matches + + def match_all(self, other_ugraph): + return list(self.match(other_ugraph)) + + def _visit(self, states): + # visit the state with a top-down bfs fashion + # return the states that are still matched + new_states = [] + for state in states: + match = state.match + sub_op = state.sub_bfs_queue.popleft() + patrn_op = state.patrn_bfs_queue.popleft() + is_eq, eq_ops = OpEqualityDelegate.query(sub_op, patrn_op) + if is_eq: + for eq_op in eq_ops: + new_state = _MatchState( + match=uTensorGraphMatch( + pattern_ugraph=match.pattern_ugraph, + subject_ugraph=match.subject_ugraph, + patrn2subj_op_map={k: v for k, v in match.patrn2subj_op_map.items()}, + subj2patrn_op_map={k: v for k, v in match.subj2patrn_op_map.items()}, + patrn2subj_tensor_map={k: v for k, v in match.patrn2subj_tensor_map.items()}, + subj2patrn_tensor_map={k: v for k, v in match.subj2patrn_tensor_map.items()} + ), + sub_bfs_queue=deque(state.sub_bfs_queue), + patrn_bfs_queue=deque(state.patrn_bfs_queue), + ) + new_state.match.update_op_map(patrn_op, eq_op) + new_states.append(new_state) + return new_states + +@attr.s +class uTensorGraphMatch(object): + + pattern_ugraph = attr.ib(type=uTensorGraph) + subject_ugraph = attr.ib(type=uTensorGraph) + + # map from op_name to op_info + patrn2subj_op_map = attr.ib(factory=dict) + subj2patrn_op_map = attr.ib(factory=dict) + # tensor in pattern -> tensor in target + patrn2subj_tensor_map = attr.ib(factory=dict) + # tensor in target -> tensor in pattern + subj2patrn_tensor_map = attr.ib(factory=dict) + + def update_op_map(self, pattern_op, subj_op): + self.patrn2subj_op_map[pattern_op.name] = subj_op + self.subj2patrn_op_map[subj_op.name] = pattern_op + for pattern_tensor, target_tensor in zip(pattern_op.input_tensors, subj_op.input_tensors): + self.patrn2subj_tensor_map[pattern_tensor.name] = target_tensor + self.subj2patrn_tensor_map[target_tensor.name] = pattern_tensor + +@attr.s +class _MatchState(object): + match = attr.ib() + @match.validator + def check(self, attrib, value): + if not isinstance(value, uTensorGraphMatch): + raise ValueError( + 'expecting a uTensorGraphMatch, get {}'.format(type(value)) + ) + # sub_bfs_queue is a queue for BFS of the subject ugraph + sub_bfs_queue = attr.ib(validator=instance_of(deque)) + # consume_queue is a queue defines the matching order of pattern ugraph + patrn_bfs_queue = attr.ib(validator=instance_of(deque)) + visited = attr.ib(init=False, factory=set) + + @property + def is_done(self): + """ + a state is done, if + 1. the patrn_bfs_queue is empty + 2. the sub_bfs_queue is empty + """ + return not self.patrn_bfs_queue diff --git a/utensor_cgen/matcher/_morphism.py b/utensor_cgen/matcher/_morphism.py new file mode 100644 index 00000000..4013e7f1 --- /dev/null +++ b/utensor_cgen/matcher/_morphism.py @@ -0,0 +1,52 @@ +from abc import ABCMeta, abstractmethod +from copy import deepcopy + +import attr + +from utensor_cgen.utils import MUST_OVERWRITEN + + +class Morphism(object): + __metaclass__ = ABCMeta + + @abstractmethod + def apply(self, from_op): + raise RuntimeError('abstract transform invoked') + + +@attr.s +class TypedMorphism(Morphism): + from_op_type = MUST_OVERWRITEN + + def __attrs_post_init__(self): + if self.from_op_type is MUST_OVERWRITEN: + raise ValueError( + "must overwrite {}.from_op_type".format(type(self).__name__) + ) + +class GenericMorphism(Morphism): + pass + +class IdentityMorphism(GenericMorphism): + + def apply(self, from_op): + return from_op + +@attr.s +class Const2InlineMorphism(TypedMorphism): + from_op_type = 'Const' + + def apply(self, from_op): + new_op = deepcopy(from_op, memo={'ugraph': from_op.ugraph}) + new_op.op_type = 'Inline' + return new_op + + +@attr.s +class Inline2ConstMorphism(TypedMorphism): + from_op_type = 'Inline' + + def apply(self, from_op): + new_op = deepcopy(from_op, memo={'ugraph': from_op.ugraph}) + new_op.op_type = 'Const' + return new_op diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 8a054da5..7191dbcc 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -1,6 +1,7 @@ # -*- coding: utf8 -*- import os import re +from collections import deque from copy import deepcopy import idx2numpy as idx2np @@ -116,6 +117,8 @@ def parse_tensor_name(tname): class NamescopedKWArgsParser: + """FIXME: this parser is badly designed, replace it with sth else + """ def __init__(self, name_space, kwargs): ns_pattern = re.compile(r'^{}__([^\d\W][\w\d_]*)'.format(name_space)) @@ -191,6 +194,10 @@ def __new__(cls, *args, **kwargs): def topologic_order_graph(ugraph): + if ugraph.backend != "tensorflow": + raise ValueError( + "topologic_order_graph works only on tensorflow graph" + ) # https://en.wikipedia.org/wiki/Topological_sorting queue = deepcopy(ugraph.output_nodes) visited = set() # temporary mark @@ -217,3 +224,21 @@ def visit(node_name): node_name = queue.pop(0) visit(node_name) ugraph.topo_order = ops_torder[::-1] + +def ops_bfs_queue(ugraph, init_nodes=None): + if init_nodes is None: + init_nodes = [ + ugraph.ops_info[name] for name in ugraph.output_nodes + ] + queue = deque(init_nodes) + visited = set() + bfs_deck = deque([]) + + while queue: + op = queue.popleft() + if op.name in visited: + continue + visited.add(op.name) + queue.extend(op.input_nodes) + bfs_deck.append(op) + return bfs_deck From 6daeb734c3575b6a2adc28c183b194f85cd153bd Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 13 Apr 2019 14:59:09 +0800 Subject: [PATCH 02/70] Add tests (failed) --- tests/matcher/imgs/test_case1.jpg | Bin 0 -> 635390 bytes .../test_matcher/test_permutation/__init__.py | 0 .../test_matcher/test_permutation/conftest.py | 56 ++++++++++++++++++ .../test_permutation/test_match_patrn1.py | 41 +++++++++++++ utensor_cgen/ir/base.py | 4 +- utensor_cgen/matcher/_matcher_impl.py | 13 ++-- 6 files changed, 106 insertions(+), 8 deletions(-) create mode 100644 tests/matcher/imgs/test_case1.jpg create mode 100644 tests/test_matcher/test_permutation/__init__.py create mode 100644 tests/test_matcher/test_permutation/conftest.py create mode 100644 tests/test_matcher/test_permutation/test_match_patrn1.py diff --git a/tests/matcher/imgs/test_case1.jpg b/tests/matcher/imgs/test_case1.jpg new file mode 100644 index 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z?hJp~=nC71*m6&_JyGwMUGqYbvz8aYX+O-cVjj63To?iP`Uct#*J|e$iWACX zqPSETiRjxrn>%_>HlA^S=AcJ5_^RRWG_D@m1JWeKe_A)Dh zfG#3!bh)E|ERMPWC8MpT_*i24LkF_fsQOFl^#}2{NA`I543?uF{Xv`kvLt9hqL1Bu z`)@O*V@}|xWka&r1Bbc{#TwGT7N_2@LF+Oaz> zOrZB+G3cE8wI<8#RWAhz#n*egUMqkv)VUm1?~rg?pAaRMx{^Em!}?7(LWh4gJr|#T z(QD~ln1LG^F4lWq`r%m43je5Bs zFuXzLp9?4UgMsXKb634>r~&X+qJi_wEeC`{pae|lIs8-GPVXOXO2ge&b9bjEV8y)|CCU`^1cSSofmXK{AoFQ+bFBEH$a^y(DeVIX-EyNj(6i<{>EQlB5E8Kj@5 WQfOPaceq#KYd3$XSqqB%{r*3eRE}5x literal 0 HcmV?d00001 diff --git a/tests/test_matcher/test_permutation/__init__.py b/tests/test_matcher/test_permutation/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_matcher/test_permutation/conftest.py b/tests/test_matcher/test_permutation/conftest.py new file mode 100644 index 00000000..ada90a54 --- /dev/null +++ b/tests/test_matcher/test_permutation/conftest.py @@ -0,0 +1,56 @@ +import tensorflow as tf +import pytest + +from utensor_cgen.frontend.tensorflow import GraphDefParser + +@pytest.fixture(scope='function', name='patrn_ugraph') +def patrn_ugraph(): + graph = tf.Graph() + with graph.as_default(): + ptrn_input0 = tf.placeholder(dtype=tf.float32, name='input0') + ptrn_input1 = tf.placeholder(dtype=tf.float32, name='input1') + ptrn_add0 = tf.add(ptrn_input0, ptrn_input1, name='add0') + ptrn_out = tf.add(ptrn_add0, ptrn_input1, name='output') + ugraph = GraphDefParser.parse(graph.as_graph_def(), [ptrn_out.op.name]) + return ugraph + +@pytest.fixture(scope='function', name='subject_ugraph1') +def subject_ugraph1(): + graph = tf.Graph() + with graph.as_default(): + sub_input0 = tf.constant([i for i in range(10)], name='sub_input0') + sub_input1 = tf.constant([i for i in range(10)], name='sub_input1') + sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') + sub_add0 = tf.add(sub_input0, sub_input1, name='sub_add0') + sub_add1 = tf.add(sub_add0, sub_input1, name='sub_add1') + sub_output = tf.add(sub_add1, sub_input2, name='sub_output') + ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) + return ugraph + +@pytest.fixture(scope='function', name='subject_ugraph1_1') +def subject_ugraph1_1(): + graph = tf.Graph() + with graph.as_default(): + sub_input0 = tf.constant([i for i in range(10)], name='sub_input0') + sub_input1 = tf.constant([i for i in range(10)], name='sub_input1') + sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') + # permute + sub_add0 = tf.add(sub_input1, sub_input0, name='sub_add0') + sub_add1 = tf.add(sub_add0, sub_input1, name='sub_add1') + sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') + ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) + return ugraph + +@pytest.fixture(scope='function', name='subject_ugraph1_2') +def subject_ugraph1_2(): + graph = tf.Graph() + with graph.as_default(): + sub_input0 = tf.constant([i for i in range(10)], name='sub_input0') + sub_input1 = tf.constant([i for i in range(10)], name='sub_input1') + sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') + sub_add0 = tf.add(sub_input0, sub_input1, name='sub_add0') + # permute + sub_add1 = tf.add(sub_input1, sub_add0, name='sub_add1') + sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') + ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) + return ugraph diff --git a/tests/test_matcher/test_permutation/test_match_patrn1.py b/tests/test_matcher/test_permutation/test_match_patrn1.py new file mode 100644 index 00000000..715494fa --- /dev/null +++ b/tests/test_matcher/test_permutation/test_match_patrn1.py @@ -0,0 +1,41 @@ +from utensor_cgen.matcher import uTensorGraphMatcher + +def test_id_match(patrn_ugraph): + matcher = uTensorGraphMatcher(patrn_ugraph) + matches = matcher.match(patrn_ugraph) + assert matches, 'expecting matches, get {} matches'.format(len(matches)) + match = matches[0] + for name, op in match.patrn2subj_op_map.items(): + assert name == op.name, '{} <--> {}'.format(name, op.name) + for name, op in match.subj2patrn_op_map.items(): + assert name == op.name, '{} <--> {}'.format(name, op.name) + +def test_match_sub1(patrn_ugraph, subject_ugraph1): + matcher = uTensorGraphMatcher(patrn_ugraph) + matches = matcher.match(subject_ugraph1) + assert matches, 'expecting matches, get {} matches'.format(len(matches)) + match = matches[0] + assert match.patrn2subj_op_map['input0'].name == 'sub_input0' + assert match.patrn2subj_op_map['input1'].name == 'sub_input1' + assert match.patrn2subj_op_map['add0'].name == 'sub_add0' + assert match.patrn2subj_op_map['output'].name == 'sub_add1' + +def test_match_sub1_1(patrn_ugraph, subject_ugraph1_1): + matcher = uTensorGraphMatcher(patrn_ugraph) + matches = matcher.match(subject_ugraph1_1) + assert matches, 'expecting matches, get {} matches'.format(len(matches)) + match = matches[0] + assert match.patrn2subj_op_map['input0'].name == 'sub_input0' + assert match.patrn2subj_op_map['input1'].name == 'sub_input1' + assert match.patrn2subj_op_map['add0'].name == 'sub_add0' + assert match.patrn2subj_op_map['output'].name == 'sub_add1' + +def test_match_sub1_2(patrn_ugraph, subject_ugraph1_2): + matcher = uTensorGraphMatcher(patrn_ugraph) + matches = matcher.match(subject_ugraph1_2) + assert matches, 'expecting matches, get {} matches'.format(len(matches)) + match = matches[0] + assert match.patrn2subj_op_map['input0'].name == 'sub_input0' + assert match.patrn2subj_op_map['input1'].name == 'sub_input1' + assert match.patrn2subj_op_map['add0'].name == 'sub_add0' + assert match.patrn2subj_op_map['output'].name == 'sub_add1' \ No newline at end of file diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 93b15692..f6e0f36d 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -244,7 +244,7 @@ def __attrs_post_init__(self): if not self.output_nodes: raise ValueError('No output_nodes given') - def get_ops_by_type(self, op_type): + def get_ops_by_type(self, given_op_type): if not self._type_to_op_map: for op_info in self.ops_info.values(): op_type = op_info.op_type @@ -255,7 +255,7 @@ def get_ops_by_type(self, op_type): self._type_to_op_map.update( [(op_type, ops),] ) - return self._type_to_op_map.get(op_type, []) + return self._type_to_op_map.get(given_op_type, []) @property def output_ops(self): diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index 958f41fc..617daa9f 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -96,8 +96,8 @@ def query(cls, sub_op, patrn_op): morphism = cls._compatibility_map[sub_op.op_type][patrn_op.op_type] equivalent_ops = [MetaOperationInfo(op_info=sub_op, morphism=morphism)] elif not patrn_op.input_tensors and \ - patrn_op.op_attr.get("utensor_matcher__force_match_input", False): - # input node + patrn_op.op_type == 'Placeholder': + # match input node which is a placeholder anyway is_eq = True equivalent_ops = [sub_op] @@ -118,17 +118,18 @@ def _match(self, other_ugraph): return [] outputs_pool.append(same_ops) output_candidates = product(*outputs_pool) - for output in output_candidates: + for outputs in output_candidates: states = [ _MatchState( match=uTensorGraphMatch( pattern_ugraph=self.pattern_ugraph, subject_ugraph=other_ugraph ), - sub_bfs_queue=ops_bfs_queue(other_ugraph, init_nodes=output), + sub_bfs_queue=ops_bfs_queue(other_ugraph, init_nodes=outputs), patrn_bfs_queue=ops_bfs_queue(self.pattern_ugraph), ) ] + import pdb; pdb.set_trace() while True: states = self._visit(states) if not states: @@ -146,9 +147,9 @@ def match(self, other_ugraph, n=1): except StopIteration: pass return matches - + def match_all(self, other_ugraph): - return list(self.match(other_ugraph)) + return list(self._match(other_ugraph)) def _visit(self, states): # visit the state with a top-down bfs fashion From 2d9d193dd0452b84d048772aaeb56984b2784ee2 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 13 Apr 2019 15:36:14 +0800 Subject: [PATCH 03/70] Fix bug --- tests/test_matcher/test_permutation/conftest.py | 1 - .../test_permutation/test_match_patrn1.py | 12 ++++++------ utensor_cgen/matcher/_matcher_impl.py | 10 +++++++--- 3 files changed, 13 insertions(+), 10 deletions(-) diff --git a/tests/test_matcher/test_permutation/conftest.py b/tests/test_matcher/test_permutation/conftest.py index ada90a54..303c69c0 100644 --- a/tests/test_matcher/test_permutation/conftest.py +++ b/tests/test_matcher/test_permutation/conftest.py @@ -49,7 +49,6 @@ def subject_ugraph1_2(): sub_input1 = tf.constant([i for i in range(10)], name='sub_input1') sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') sub_add0 = tf.add(sub_input0, sub_input1, name='sub_add0') - # permute sub_add1 = tf.add(sub_input1, sub_add0, name='sub_add1') sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) diff --git a/tests/test_matcher/test_permutation/test_match_patrn1.py b/tests/test_matcher/test_permutation/test_match_patrn1.py index 715494fa..c9c17c7f 100644 --- a/tests/test_matcher/test_permutation/test_match_patrn1.py +++ b/tests/test_matcher/test_permutation/test_match_patrn1.py @@ -15,8 +15,8 @@ def test_match_sub1(patrn_ugraph, subject_ugraph1): matches = matcher.match(subject_ugraph1) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert match.patrn2subj_op_map['input0'].name == 'sub_input0' - assert match.patrn2subj_op_map['input1'].name == 'sub_input1' + assert match.patrn2subj_op_map['input0'].name in ['sub_input0', 'sub_input1'] + assert match.patrn2subj_op_map['input1'].name in ['sub_input0', 'sub_input1'] assert match.patrn2subj_op_map['add0'].name == 'sub_add0' assert match.patrn2subj_op_map['output'].name == 'sub_add1' @@ -25,8 +25,8 @@ def test_match_sub1_1(patrn_ugraph, subject_ugraph1_1): matches = matcher.match(subject_ugraph1_1) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert match.patrn2subj_op_map['input0'].name == 'sub_input0' - assert match.patrn2subj_op_map['input1'].name == 'sub_input1' + assert match.patrn2subj_op_map['input0'].name in ['sub_input0', 'sub_input1'] + assert match.patrn2subj_op_map['input1'].name in ['sub_input0', 'sub_input1'] assert match.patrn2subj_op_map['add0'].name == 'sub_add0' assert match.patrn2subj_op_map['output'].name == 'sub_add1' @@ -35,7 +35,7 @@ def test_match_sub1_2(patrn_ugraph, subject_ugraph1_2): matches = matcher.match(subject_ugraph1_2) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert match.patrn2subj_op_map['input0'].name == 'sub_input0' - assert match.patrn2subj_op_map['input1'].name == 'sub_input1' + assert match.patrn2subj_op_map['input0'].name in ['sub_input0', 'sub_input1'] + assert match.patrn2subj_op_map['input1'].name in ['sub_input0', 'sub_input1'] assert match.patrn2subj_op_map['add0'].name == 'sub_add0' assert match.patrn2subj_op_map['output'].name == 'sub_add1' \ No newline at end of file diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index 617daa9f..a947313e 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -129,7 +129,6 @@ def _match(self, other_ugraph): patrn_bfs_queue=ops_bfs_queue(self.pattern_ugraph), ) ] - import pdb; pdb.set_trace() while True: states = self._visit(states) if not states: @@ -154,6 +153,7 @@ def match_all(self, other_ugraph): def _visit(self, states): # visit the state with a top-down bfs fashion # return the states that are still matched + # import pdb; pdb.set_trace() new_states = [] for state in states: match = state.match @@ -162,6 +162,11 @@ def _visit(self, states): is_eq, eq_ops = OpEqualityDelegate.query(sub_op, patrn_op) if is_eq: for eq_op in eq_ops: + new_sub_bfs_queue = deque(state.sub_bfs_queue) + for _ in eq_op.input_nodes: + new_sub_bfs_queue.popleft() + for node in eq_op.input_nodes[::-1]: + new_sub_bfs_queue.insert(0, node) new_state = _MatchState( match=uTensorGraphMatch( pattern_ugraph=match.pattern_ugraph, @@ -171,7 +176,7 @@ def _visit(self, states): patrn2subj_tensor_map={k: v for k, v in match.patrn2subj_tensor_map.items()}, subj2patrn_tensor_map={k: v for k, v in match.subj2patrn_tensor_map.items()} ), - sub_bfs_queue=deque(state.sub_bfs_queue), + sub_bfs_queue=new_sub_bfs_queue, patrn_bfs_queue=deque(state.patrn_bfs_queue), ) new_state.match.update_op_map(patrn_op, eq_op) @@ -219,6 +224,5 @@ def is_done(self): """ a state is done, if 1. the patrn_bfs_queue is empty - 2. the sub_bfs_queue is empty """ return not self.patrn_bfs_queue From 725e9d942833bb3014b20f31fa4bf26d96800bbd Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 1 May 2019 17:47:27 +0800 Subject: [PATCH 04/70] Add replace util methods --- utensor_cgen/ir/base.py | 2 +- utensor_cgen/matcher/_matcher_impl.py | 47 ++++++++++++++++++++++++++- 2 files changed, 47 insertions(+), 2 deletions(-) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index f9072631..ec36e9d7 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -58,7 +58,7 @@ def check(self, attrib, shape_values): @_ugraph.validator def check(self, attrib, value): if not isinstance(value, uTensorGraph): - raise ValueError('Expecting a uTensorGraph, get {}'.format(type(value))) + raise ValueError('Expecting a uTensorGraph, get {}'.format(type(value))) @property def ugraph(self): diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index a947313e..0523bb32 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -1,11 +1,14 @@ from collections import deque from itertools import product +from string import ascii_letters, digits +from random import choices +from copy import deepcopy import attr from attr.validators import instance_of from utensor_cgen.ir import MetaOperationInfo, OperationInfo, uTensorGraph -from utensor_cgen.matcher._morphism import IdentityMorphism, Morphism +from utensor_cgen.matcher._morphism import Morphism from utensor_cgen.utils import ops_bfs_queue __all__ = ["uTensorGraphMatcher"] @@ -203,6 +206,48 @@ def update_op_map(self, pattern_op, subj_op): for pattern_tensor, target_tensor in zip(pattern_op.input_tensors, subj_op.input_tensors): self.patrn2subj_tensor_map[pattern_tensor.name] = target_tensor self.subj2patrn_tensor_map[target_tensor.name] = pattern_tensor + + def replace_with(self, other_ugraph): + """ + Replace matched subgraph with a given ugraph + """ + pass + + _CHARSET = ascii_letters + digits + + def _random_surfix(self, length=8): + chars = choices(self._CHARSET, k=length) + return ''.join(chars) + + def _new_ugraph_with_surfix(self, ugraph, surfix=None): + if surfix is None: + surfix = self._random_surfix() + new_ugraph = deepcopy(ugraph) + new_ugraph.output_nodes = [ + '{}_{}'.format(name, surfix) for name in ugraph.output_nodes + ] + new_ugraph.topo_order = ['{}_{}'.format(name, surfix) for name in ugraph.topo_order] + ops_to_remove = set([]) + new_ops_info = {} + for ori_op_name, op in new_ugraph.ops_info.items(): + new_op_name = '{}_{}'.format(ori_op_name, surfix) + op.name = new_op_name + for tensor in op.output_tensors: + tensor_idx = tensor.name.split(':')[1] + tensor.name = '{}_{}:{}'.format(tensor.op_name, surfix, tensor_idx) + tensor.op_name = new_op_name + for tensor in op.input_tensors: + in_op_name, tensor_idx = tensor.name.split(':') + new_in_op_name = '{}_{}'.format(in_op_name, surfix) + tensor.name = '{}:{}'.format(new_in_op_name, tensor_idx) + tensor.op_name = new_in_op_name + ops_to_remove.add(ori_op_name) + new_ops_info[new_op_name] = op + for op_name in ops_to_remove: + new_ugraph.ops_info.pop(op_name) + new_ugraph.ops_info = new_ops_info + return new_ugraph + @attr.s class _MatchState(object): From ee54a0b6b3a6caad87e5a7ff6fe788d5ccd31805 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 1 May 2019 17:56:38 +0800 Subject: [PATCH 05/70] update tests --- tests/test_transformer/test_pipeline/conftest.py | 8 ++++---- .../test_pipeline/test_pipeline_transformer.py | 12 ++++++------ 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/tests/test_transformer/test_pipeline/conftest.py b/tests/test_transformer/test_pipeline/conftest.py index 8345c044..9e0c2d41 100644 --- a/tests/test_transformer/test_pipeline/conftest.py +++ b/tests/test_transformer/test_pipeline/conftest.py @@ -8,9 +8,9 @@ @pytest.fixture(scope='function', name='methods') def pipeline_methods(): - all_methods = [BatchNormTransformer.METHOD_NAME, - DropoutTransformer.METHOD_NAME, - QuantizeTransformer.METHOD_NAME, - RefCntOptimizer.METHOD_NAME] + all_methods = [(BatchNormTransformer.METHOD_NAME, {}), + (DropoutTransformer.METHOD_NAME, {}), + (QuantizeTransformer.METHOD_NAME, {}), + (RefCntOptimizer.METHOD_NAME, {})] shuffle(all_methods) return all_methods diff --git a/tests/test_transformer/test_pipeline/test_pipeline_transformer.py b/tests/test_transformer/test_pipeline/test_pipeline_transformer.py index ce5a3834..cba6e033 100644 --- a/tests/test_transformer/test_pipeline/test_pipeline_transformer.py +++ b/tests/test_transformer/test_pipeline/test_pipeline_transformer.py @@ -3,19 +3,19 @@ # three random tests def test_pipeline_1(methods): - pipeline = TransformerPipeline(methods, {}) + pipeline = TransformerPipeline(methods) assert len(pipeline.pipeline) == len(methods) - for transformer, method_name in zip(pipeline.pipeline, methods): + for transformer, (method_name, _) in zip(pipeline.pipeline, methods): assert isinstance(transformer, pipeline._TRANSFORMER_MAP[method_name]) def test_pipeline_2(methods): - pipeline = TransformerPipeline(methods, {}) + pipeline = TransformerPipeline(methods) assert len(pipeline.pipeline) == len(methods) - for transformer, method_name in zip(pipeline.pipeline, methods): + for transformer, (method_name, _) in zip(pipeline.pipeline, methods): assert isinstance(transformer, pipeline._TRANSFORMER_MAP[method_name]) def test_pipeline_3(methods): - pipeline = TransformerPipeline(methods, {}) + pipeline = TransformerPipeline(methods) assert len(pipeline.pipeline) == len(methods) - for transformer, method_name in zip(pipeline.pipeline, methods): + for transformer, (method_name, _) in zip(pipeline.pipeline, methods): assert isinstance(transformer, pipeline._TRANSFORMER_MAP[method_name]) From 53065fd6101c289675da6075340817843bd8cad3 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sat, 4 May 2019 23:41:24 +0800 Subject: [PATCH 06/70] linear_reorder: wip fails at quantization: missing attribute --- utensor_cgen/backend/code_generator.py | 2 + utensor_cgen/cli.py | 2 +- utensor_cgen/experimental/ugraph_builder.py | 66 ++++++++++++++++-- utensor_cgen/experimental/ugraph_matcher.py | 12 ++++ utensor_cgen/ir/base.py | 8 ++- utensor_cgen/transformer/linear_reoder.py | 77 +++++++++++++++++++++ utensor_cgen/transformer/pipline.py | 2 + 7 files changed, 159 insertions(+), 10 deletions(-) create mode 100644 utensor_cgen/transformer/linear_reoder.py diff --git a/utensor_cgen/backend/code_generator.py b/utensor_cgen/backend/code_generator.py index f01be1d2..dc0fc5ab 100644 --- a/utensor_cgen/backend/code_generator.py +++ b/utensor_cgen/backend/code_generator.py @@ -81,6 +81,8 @@ def formatter(name, kwargs): ) quant_ugraph = self._transform_graph(ugraph, self.trans_methods) + from utensor_cgen.ir.misc.graph_viz import viz_graph + viz_graph('out_graph', True, quant_ugraph) _logger.info('Graph transormation done') if self.save_graph: diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index a03b7ce4..7a2e948e 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -46,7 +46,7 @@ def cli(): help="list of output nodes") @click.option("--transform-methods", type=NArgsKwargsParam(sep='|>'), - default='dropout|>quantize|>inline|>biasAdd|>remove_id_op|>refcnt', + default='dropout|>linear_reoder|>quantize|>inline|>biasAdd|>remove_id_op|>refcnt', help='optimization pipeline', metavar='METHOD[|>METHOD|>...]', show_default=True) diff --git a/utensor_cgen/experimental/ugraph_builder.py b/utensor_cgen/experimental/ugraph_builder.py index 09f5389e..a5545b95 100644 --- a/utensor_cgen/experimental/ugraph_builder.py +++ b/utensor_cgen/experimental/ugraph_builder.py @@ -13,7 +13,8 @@ from utensor_cgen.experimental.ugraph_util_functions import * -__all__ = ["transpose_offline", "Const_Op", "Ram_Op", "Const_Reshape", "Uint8Q7Origin_Op", "CMSIS_FC_Op", "QuantRangeForMultiplicationu8u8int32_Op"] +__all__ = ["transpose_offline", "Const_Op", "Ram_Op", "Const_Reshape", "Uint8Q7Origin_Op", "CMSIS_FC_Op", "QuantRangeForMultiplicationu8u8int32_Op", + "conv2d_op", "relu_op", "maxpool_op"] # Let us get unique names for custom injected nodes def static_vars(**kwargs): @@ -67,7 +68,7 @@ def Const_Op(name, np_array, ugraph): ugraph=tmp_graph, op_attr=bs_ops_attr(np_array) ) - ugraph.add_op(const_op_info) + ugraph.add_op(const_op_info, False) return const_op_info.output_tensors @@ -99,7 +100,7 @@ def Reshape_Op(name, input_tensor, shape_tensor, ugraph): ugraph=tmp_ugraph ) - ugraph.add_op(reshape_opInfo) + ugraph.add_op(reshape_opInfo, False) return reshape_opInfo.output_tensors @@ -130,7 +131,7 @@ def Uint8Q7Origin_Op(name, inputs, ugraph): # if(name == 'convert_uint8_q7_Relu/eightbit_transpose_0_q7'): # import pdb; pdb.set_trace() - ugraph.add_op(q7_op_info) + ugraph.add_op(q7_op_info, False) return q7_op_info.output_tensors @@ -159,7 +160,7 @@ def CMSIS_FC_Op(name, in0, in1, bias, bShift, oShift, scratch, ugraph): ugraph=tmp_ugraph ) - ugraph.add_op(fc_op_info) + ugraph.add_op(fc_op_info, False) return fc_op_info.output_tensors def QuantRangeForMultiplicationu8u8int32_Op(name, a_range, b_range, ugraph): @@ -186,6 +187,57 @@ def QuantRangeForMultiplicationu8u8int32_Op(name, a_range, b_range, ugraph): ugraph=tmp_ugraph ) - ugraph.add_op(new_range_op_info) + ugraph.add_op(new_range_op_info, False) - return new_range_op_info.output_tensors \ No newline at end of file + return new_range_op_info.output_tensors + +def conv2d_op(name, inputs, ugraph): + tmp_ugraph = uTensorGraph(output_nodes=[name]) + conv_out = TensorInfo(name=name + ":0", + op_name=name, + dtype=np.dtype('float32'), + shape=inputs[0].shape, + ugraph=tmp_ugraph + ) + conv2d_op_info = OperationInfo(name=name, + input_tensors=inputs, + output_tensors=[conv_out], + op_type="Conv2D", + backend="tensorflow", + ugraph=tmp_ugraph) + + ugraph.add_op(conv2d_op_info, False) + return conv2d_op_info.output_tensors +def relu_op(name, inputs, ugraph): + tmp_ugraph = uTensorGraph(output_nodes=[name]) + relu_out = TensorInfo(name=name + ":0", + op_name=name, + dtype=np.dtype('float32'), + shape=inputs[0].shape, + ugraph=tmp_ugraph + ) + relu_op_info = OperationInfo(name=name, + input_tensors=inputs, + output_tensors=[relu_out], + op_type="Relu", + backend="tensorflow", + ugraph=tmp_ugraph) + + ugraph.add_op(relu_op_info, False) + return relu_op_info.output_tensors +def maxpool_op(name, inputs, ugraph): + tmp_ugraph = uTensorGraph(output_nodes=[name]) + max_out = TensorInfo(name=name + ":0", + op_name=name, + dtype=np.dtype('float32'), + shape=inputs[0].shape, + ugraph=tmp_ugraph + ) + max_op_info = OperationInfo(name=name, + input_tensors=inputs, + output_tensors=[max_out], + op_type="MaxPool", + backend="tensorflow", + ugraph=tmp_ugraph) + ugraph.add_op(max_op_info, False) + return max_op_info.output_tensors \ No newline at end of file diff --git a/utensor_cgen/experimental/ugraph_matcher.py b/utensor_cgen/experimental/ugraph_matcher.py index 2a06218b..4bc49142 100644 --- a/utensor_cgen/experimental/ugraph_matcher.py +++ b/utensor_cgen/experimental/ugraph_matcher.py @@ -40,6 +40,9 @@ def get_ops_io_info(self, op_type): ops_io_table["Const"] = [None, [0]] ops_io_table["Placeholder"] = [None, [0]] ops_io_table["Inline"] = [None, [0]] + ops_io_table["MaxPool"] = [[0],[0]] + ops_io_table["Conv2D"] = [[0, 1],[0]] + ops_io_table["Relu"] = [[0],[0]] return ops_io_table[op_type] @@ -245,6 +248,15 @@ def isomorphic_match(self, subject_graph, matcher_graph, meta): matcher_to_subject_nodes.update(partial_matcher_to_subject_nodes) matcher_to_subject_edges.update(partial_matcher_to_subject_edges) + + #including output tensors from the output node + sgraph_out_node = subject_graph.ops_info[matcher_to_subject_nodes[next(iter(matcher_output_node_names))]] + mgraph_out_node = matcher_graph.ops_info[next(iter(matcher_output_node_names))] + for mgraph_out_edge, sgraph_out_edge in zip(mgraph_out_node.output_tensors, sgraph_out_node.output_tensors): + partial_matcher_to_subject_edges[mgraph_out_edge.name] = sgraph_out_edge.name + + matcher_to_subject_edges.update(partial_matcher_to_subject_edges) + self.translator = [matcher_to_subject_nodes, matcher_to_subject_edges] return self.translator diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index e5a8599e..77d66863 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -240,7 +240,7 @@ def graph_def(self): def ops(self): return [self.ops_info[name] for name in self.topo_order] - def add_op(self, op): + def add_op(self, op, sort=True): if not isinstance(op, OperationInfo): raise ValueError('expecting OperationInfo, get {}'.format(type(op))) if op.name in self.ops_info: @@ -250,7 +250,11 @@ def add_op(self, op): # if(op.name == 'convert_uint8_q7_Relu/eightbit_transpose_0_q7'): # import pdb; pdb.set_trace() self.ops_info[op.name] = op - topologic_order_graph(self) + + # FIXME: forcing a topo-order here prevent us from dynamic-graph-construction + # The temporary fix is to disable this as an option + if sort: + topologic_order_graph(self) def drop_op(self, op_name): if op_name not in self.ops_info: diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py new file mode 100644 index 00000000..3f3cec82 --- /dev/null +++ b/utensor_cgen/transformer/linear_reoder.py @@ -0,0 +1,77 @@ +# -*- coding:utf8 -*- +r"""CMSIS-NN Transformer + +Node fusion and replacement for CMSIS-NN + +""" +import re +from collections import defaultdict +from copy import deepcopy + +import numpy as np +import tensorflow as tf +from tensorflow.tools.graph_transforms import TransformGraph + +from utensor_cgen.experimental.ugraph_builder import * +from utensor_cgen.experimental.ugraph_matcher import * +from utensor_cgen.experimental.ugraph_util_functions import * +from utensor_cgen.frontend.tensorflow import GraphDefParser +from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph +from utensor_cgen.ir.converter import AttrValueConverter # hue hue hue hue hue +from utensor_cgen.ir.converter import GenericTensorConverterMixin +from utensor_cgen.ir.utils import graph_check +from utensor_cgen.utils import parse_tensor_name, topologic_order_graph +from utensor_cgen.ir.misc.graph_viz import viz_graph + +from .base import Transformer + +__all__ = ["CMSIS_NN_Transformer"] + +class Linear_Reorder_Transformer(Transformer): + METHOD_NAME = 'linear_reoder' + KWARGS_NAMESCOPE = '_linear_reoder' + + def get_matcher_graph(self): + ugraph = uTensorGraph(output_nodes=['maxpool']) + + dummpy_input0 = Const_Op('dummy_input0', np.zeros([16,16]), ugraph) + dummpy_input1 = Const_Op('dummy_input1', np.zeros([4,4]), ugraph) + conv_out = conv2d_op('convolution2d', [dummpy_input0[0], dummpy_input1[0]], ugraph) + relu_out = relu_op('relu', conv_out, ugraph) + out_tensor = maxpool_op('maxpool', relu_out, ugraph) + topologic_order_graph(ugraph) + + #viz_graph('matcher', True, ugraph) + + meta = dict() + meta["dummy_input0"] = ["End", "Any"] + meta["dummy_input1"] = ["End", "Any"] + + return (ugraph, meta) + + def transform(self, ugraph): + [matcher_ugraph, metaData] = self.get_matcher_graph() + while True: + matcher = uGraphMatcher() + result = matcher.isomorphic_match(ugraph, matcher_ugraph, metaData) + if result == False: + break + #import pdb; pdb.set_trace() + relu_name = matcher['relu'].name + '_' + maxpool_name = matcher['maxpool'].name + '_' + + new_relu_out = relu_op(relu_name, [matcher['relu:0']], ugraph) + new_maxpool_out = maxpool_op(maxpool_name, [matcher['convolution2d:0']], ugraph) + matcher['relu:0'] = new_maxpool_out[0] + matcher['maxpool:0'] = new_relu_out[0] + matcher['relu'] = None + matcher['maxpool'] = None + + topologic_order_graph(ugraph) + graph_validate(ugraph) + + viz_graph('matcher', True, ugraph) + return ugraph ##remove me + + # graph_check(ugraph) + # return ugraph diff --git a/utensor_cgen/transformer/pipline.py b/utensor_cgen/transformer/pipline.py index 3cff8c6b..4600ff0d 100644 --- a/utensor_cgen/transformer/pipline.py +++ b/utensor_cgen/transformer/pipline.py @@ -7,6 +7,7 @@ from .optimizer import IdOpRemoveOptimizer, RefCntOptimizer from .quantize import QuantizeTransformer from .graph_viz import GraphVizTransformer +from .linear_reoder import Linear_Reorder_Transformer class TransformerPipeline(object): @@ -20,6 +21,7 @@ class TransformerPipeline(object): CMSIS_NN_Transformer.METHOD_NAME: CMSIS_NN_Transformer, IdOpRemoveOptimizer.METHOD_NAME: IdOpRemoveOptimizer, GraphVizTransformer.METHOD_NAME: GraphVizTransformer, + Linear_Reorder_Transformer.METHOD_NAME: Linear_Reorder_Transformer, } def __init__(self, methods): From 0f802fbe1c009bc4bce814e00d51e61411596e8d Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 5 May 2019 01:07:34 +0800 Subject: [PATCH 07/70] ugraph seems correct, but tf quantization complains --- .../experimental/ugraph_util_functions.py | 21 +++++++++++++++-- utensor_cgen/transformer/linear_reoder.py | 23 +++++++++++-------- utensor_cgen/utils.py | 5 +++- 3 files changed, 36 insertions(+), 13 deletions(-) diff --git a/utensor_cgen/experimental/ugraph_util_functions.py b/utensor_cgen/experimental/ugraph_util_functions.py index b2428e2e..e0cb7ba8 100644 --- a/utensor_cgen/experimental/ugraph_util_functions.py +++ b/utensor_cgen/experimental/ugraph_util_functions.py @@ -9,7 +9,8 @@ __all__ = ["is_connected", "get_input_tensor_names", "get_output_tensor_names", "tensorInfo_from_name", "get_tensor_node_names", "replace_tensors_op", "replace_tensor_op_by_name", - "graph_validate", "get_input_node_names", "get_output_node_names", "replace_tensor"] + "graph_validate", "get_input_node_names", "get_output_node_names", "replace_tensor", + "update_tensor_op_names", "print_graph"] def is_connected(graph, node0, node1): @@ -94,6 +95,10 @@ def graph_validate(graph): if input_tensor_info.op_name not in graph.topo_order: print("In %r: input tensor %r points to an op (%r) that does not exist in graph.topo_order" % (op_name, input_tensor_info.name, input_tensor_info.op_name)) conflicts.append((input_tensor_info.name, input_tensor_info.op_name)) + for out_tensor in op_info.output_tensors: + if out_tensor.op_name != op_info.name: + print("In %r: output tensor %r does not point to its origin, it points to (%r)" % (op_info.name, out_tensor.name, out_tensor.op_name)) + conflicts.append((op_info.name, out_tensor.name, out_tensor.op_name)) def get_input_node_names(graph, node_name): input_op_infos = graph.ops_info[node_name].input_nodes @@ -116,4 +121,16 @@ def replace_tensor(name, new_tensorInfo, ugraph): #outputs for i, t_info in enumerate(op_info.output_tensors): if(t_info.name == name): - op_info.output_tensors[i] = new_tensorInfo \ No newline at end of file + op_info.output_tensors[i] = new_tensorInfo + +def update_tensor_op_names(graph): + for key, op_info in graph.ops_info.items(): + for out_tensor in op_info.output_tensors: + out_tensor.op_name = op_info.name + graph.ops_info[key] = op_info + +def print_graph(graph): + for key, op_info in graph.ops_info.items(): + print(key, " :\r\n") + print(" In: ", [tensor.name for tensor in op_info.input_tensors]) + print(" Out: ", [tensor.name for tensor in op_info.output_tensors]) \ No newline at end of file diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index 3f3cec82..751fe5a5 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -56,19 +56,22 @@ def transform(self, ugraph): result = matcher.isomorphic_match(ugraph, matcher_ugraph, metaData) if result == False: break - #import pdb; pdb.set_trace() - relu_name = matcher['relu'].name + '_' - maxpool_name = matcher['maxpool'].name + '_' - new_relu_out = relu_op(relu_name, [matcher['relu:0']], ugraph) - new_maxpool_out = maxpool_op(maxpool_name, [matcher['convolution2d:0']], ugraph) - matcher['relu:0'] = new_maxpool_out[0] - matcher['maxpool:0'] = new_relu_out[0] - matcher['relu'] = None - matcher['maxpool'] = None + max_pool_op = matcher['maxpool'] + relu_op = matcher['relu'] - topologic_order_graph(ugraph) + max_pool_op.input_tensors[0] = matcher['convolution2d:0'] + max_pool_op.output_tensors[0] = matcher['relu:0'] + relu_op.input_tensors[0] = matcher['relu:0'] + relu_op.output_tensors[0] = matcher['maxpool:0'] + + matcher['maxpool'] = max_pool_op + matcher['relu'] = relu_op + + update_tensor_op_names(ugraph) graph_validate(ugraph) + topologic_order_graph(ugraph) + #import pdb; pdb.set_trace() viz_graph('matcher', True, ugraph) return ugraph ##remove me diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 7b066052..b37fd1fc 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -240,7 +240,10 @@ def visit(node_name): op_info = ugraph.ops_info[node_name] for t_info in op_info.input_tensors: - op_name = parse_tensor_name(t_info.name)[0] + # NT: we should not rely on tensor-name conventions for back-tracing + # op_name = parse_tensor_name(t_info.name)[0] + # It would be nice to rely on something similar to get_tensor_node_names(), but based on ops_info instead of topo_order + op_name = t_info.op_name visit(op_name) perm_visit.add(node_name) From 574f4d7423f45337321733e710200893559495bb Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 5 May 2019 01:56:39 +0800 Subject: [PATCH 08/70] quantization passed, good until: unsupported op type in uTensor: QuantizedMul --- .../experimental/ugraph_util_functions.py | 15 +++++++++++++-- utensor_cgen/transformer/linear_reoder.py | 9 +++++++++ 2 files changed, 22 insertions(+), 2 deletions(-) diff --git a/utensor_cgen/experimental/ugraph_util_functions.py b/utensor_cgen/experimental/ugraph_util_functions.py index e0cb7ba8..5068e2b5 100644 --- a/utensor_cgen/experimental/ugraph_util_functions.py +++ b/utensor_cgen/experimental/ugraph_util_functions.py @@ -10,7 +10,7 @@ __all__ = ["is_connected", "get_input_tensor_names", "get_output_tensor_names", "tensorInfo_from_name", "get_tensor_node_names", "replace_tensors_op", "replace_tensor_op_by_name", "graph_validate", "get_input_node_names", "get_output_node_names", "replace_tensor", - "update_tensor_op_names", "print_graph"] + "update_tensor_op_names", "print_graph", "rename_tensor"] def is_connected(graph, node0, node1): @@ -133,4 +133,15 @@ def print_graph(graph): for key, op_info in graph.ops_info.items(): print(key, " :\r\n") print(" In: ", [tensor.name for tensor in op_info.input_tensors]) - print(" Out: ", [tensor.name for tensor in op_info.output_tensors]) \ No newline at end of file + print(" Out: ", [tensor.name for tensor in op_info.output_tensors]) + +def rename_tensor(name, new_name, graph): + for key, op_info in graph.ops_info.items(): + for i, tensor in enumerate(op_info.input_tensors): + if tensor.name == name: + op_info.input_tensors[i].name = new_name + for key, op_info in graph.ops_info.items(): + for i, tensor in enumerate(op_info.output_tensors): + if tensor.name == name: + op_info.output_tensors[i].name = new_name + graph.ops_info[key] = op_info \ No newline at end of file diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index 751fe5a5..80418ddc 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -57,6 +57,7 @@ def transform(self, ugraph): if result == False: break + #swapping the ops max_pool_op = matcher['maxpool'] relu_op = matcher['relu'] @@ -68,6 +69,14 @@ def transform(self, ugraph): matcher['maxpool'] = max_pool_op matcher['relu'] = relu_op + #swapping the tensor names + relu_tensor_name = matcher['relu:0'].name + maxpool_tensor_name = matcher['maxpool:0'].name + + rename_tensor(relu_tensor_name, 'tmp_relu_name', ugraph) + rename_tensor(maxpool_tensor_name, relu_tensor_name, ugraph) + rename_tensor('tmp_relu_name', maxpool_tensor_name, ugraph) + update_tensor_op_names(ugraph) graph_validate(ugraph) topologic_order_graph(ugraph) From b046b82ba7d9cdfeb2ac82cc6eae8ac2a3b03b04 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 5 May 2019 16:22:48 +0800 Subject: [PATCH 09/70] CONV_POOL_Transformer WIP: matcher graph completed --- utensor_cgen/cli.py | 2 +- utensor_cgen/experimental/ugraph_builder.py | 132 +++++++++++++++++++- utensor_cgen/transformer/conv_pool.py | 106 ++++++++++++++++ utensor_cgen/transformer/linear_reoder.py | 9 +- 4 files changed, 238 insertions(+), 11 deletions(-) create mode 100644 utensor_cgen/transformer/conv_pool.py diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 7a2e948e..c0bf4210 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -46,7 +46,7 @@ def cli(): help="list of output nodes") @click.option("--transform-methods", type=NArgsKwargsParam(sep='|>'), - default='dropout|>linear_reoder|>quantize|>inline|>biasAdd|>remove_id_op|>refcnt', + default='dropout|>linear_reoder|>quantize|>conv_pool|>inline|>biasAdd|>remove_id_op|>refcnt', help='optimization pipeline', metavar='METHOD[|>METHOD|>...]', show_default=True) diff --git a/utensor_cgen/experimental/ugraph_builder.py b/utensor_cgen/experimental/ugraph_builder.py index a5545b95..d7603046 100644 --- a/utensor_cgen/experimental/ugraph_builder.py +++ b/utensor_cgen/experimental/ugraph_builder.py @@ -13,8 +13,9 @@ from utensor_cgen.experimental.ugraph_util_functions import * -__all__ = ["transpose_offline", "Const_Op", "Ram_Op", "Const_Reshape", "Uint8Q7Origin_Op", "CMSIS_FC_Op", "QuantRangeForMultiplicationu8u8int32_Op", - "conv2d_op", "relu_op", "maxpool_op"] +__all__ = ["transpose_offline", "Const_Op", "Ram_Op", "Const_Reshape", "Uint8Q7Origin_Op", + "CMSIS_FC_Op", "QuantRangeForMultiplicationu8u8int32_Op", "conv2d_op", "quantized_conv2d_op", + "relu_op", "maxpool_op", "requantize_op", "requantization_range_op", "quantized_maxpool_op"] # Let us get unique names for custom injected nodes def static_vars(**kwargs): @@ -196,7 +197,7 @@ def conv2d_op(name, inputs, ugraph): conv_out = TensorInfo(name=name + ":0", op_name=name, dtype=np.dtype('float32'), - shape=inputs[0].shape, + shape=inputs[0].shape, #FIXME: wrong shape most likely ugraph=tmp_ugraph ) conv2d_op_info = OperationInfo(name=name, @@ -208,6 +209,38 @@ def conv2d_op(name, inputs, ugraph): ugraph.add_op(conv2d_op_info, False) return conv2d_op_info.output_tensors + +def quantized_conv2d_op(name, inputs, ugraph): + tmp_ugraph = uTensorGraph(output_nodes=[name]) + conv_out = TensorInfo(name=name + ":0", + op_name=name, + dtype=np.dtype('uint8'), + shape=inputs[0].shape, #FIXME: wrong shape most likely + ugraph=tmp_ugraph + ) + min_out = TensorInfo(name=name + ":1", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + max_out = TensorInfo(name=name + ":2", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + + quantized_conv2d_op_info = OperationInfo(name=name, + input_tensors=inputs, + output_tensors=[conv_out, min_out, max_out], + op_type="QuantizedConv2D", + backend="tensorflow", + ugraph=tmp_ugraph) + + ugraph.add_op(quantized_conv2d_op_info, False) + return quantized_conv2d_op_info.output_tensors + def relu_op(name, inputs, ugraph): tmp_ugraph = uTensorGraph(output_nodes=[name]) relu_out = TensorInfo(name=name + ":0", @@ -225,6 +258,7 @@ def relu_op(name, inputs, ugraph): ugraph.add_op(relu_op_info, False) return relu_op_info.output_tensors + def maxpool_op(name, inputs, ugraph): tmp_ugraph = uTensorGraph(output_nodes=[name]) max_out = TensorInfo(name=name + ":0", @@ -240,4 +274,94 @@ def maxpool_op(name, inputs, ugraph): backend="tensorflow", ugraph=tmp_ugraph) ugraph.add_op(max_op_info, False) - return max_op_info.output_tensors \ No newline at end of file + return max_op_info.output_tensors + +def quantized_maxpool_op(name, inputs, ugraph): + tmp_ugraph = uTensorGraph(output_nodes=[name]) + max_value_out = TensorInfo(name=name + ":0", + op_name=name, + dtype=np.dtype('float32'), + shape=inputs[0].shape, + ugraph=tmp_ugraph + ) + min_out = TensorInfo(name=name + ":1", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + max_out = TensorInfo(name=name + ":2", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + + qnt_max_op_info = OperationInfo(name=name, + input_tensors=inputs, + output_tensors=[max_value_out, min_out, max_out], + op_type="QuantizedMaxPool", + backend="tensorflow", + ugraph=tmp_ugraph) + ugraph.add_op(qnt_max_op_info, False) + return qnt_max_op_info.output_tensors + +def requantization_range_op(name, inputs, ugraph): + tmp_ugraph = uTensorGraph(output_nodes=[name]) + + min_out = TensorInfo(name=name + ":0", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + max_out = TensorInfo(name=name + ":1", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + + rqntr_op_info = OperationInfo(name=name, + input_tensors=inputs, + output_tensors=[min_out, max_out], + op_type="RequantizationRange", + backend="tensorflow", + ugraph=tmp_ugraph) + + ugraph.add_op(rqntr_op_info, False) + return rqntr_op_info.output_tensors + + +def requantize_op(name, inputs, ugraph): + tmp_ugraph = uTensorGraph(output_nodes=[name]) + + value_out = TensorInfo(name=name + ":0", + op_name=name, + dtype=np.dtype('uint8'), + shape=inputs[0].shape, + ugraph=tmp_ugraph + ) + + min_out = TensorInfo(name=name + ":1", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + max_out = TensorInfo(name=name + ":2", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + + rqnt_op_info = OperationInfo(name=name, + input_tensors=inputs, + output_tensors=[value_out, min_out, max_out], + op_type="Requantize", + backend="tensorflow", + ugraph=tmp_ugraph) + + ugraph.add_op(rqnt_op_info, False) + return [value_out, min_out, max_out] \ No newline at end of file diff --git a/utensor_cgen/transformer/conv_pool.py b/utensor_cgen/transformer/conv_pool.py new file mode 100644 index 00000000..49669b34 --- /dev/null +++ b/utensor_cgen/transformer/conv_pool.py @@ -0,0 +1,106 @@ +# -*- coding:utf8 -*- +r"""Convolution Maxpool Fusion Transformer + +Node fusion for QuantConv2d QuantMaxPool operators + +""" +import re +from collections import defaultdict +from copy import deepcopy + +import numpy as np +import tensorflow as tf +from tensorflow.tools.graph_transforms import TransformGraph + +from utensor_cgen.experimental.ugraph_builder import * +from utensor_cgen.experimental.ugraph_matcher import * +from utensor_cgen.experimental.ugraph_util_functions import * +from utensor_cgen.frontend.tensorflow import GraphDefParser +from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph +from utensor_cgen.ir.converter import AttrValueConverter # hue hue hue hue hue +from utensor_cgen.ir.converter import GenericTensorConverterMixin +from utensor_cgen.ir.utils import graph_check +from utensor_cgen.utils import parse_tensor_name, topologic_order_graph +from utensor_cgen.ir.misc.graph_viz import viz_graph + +from .base import Transformer + +__all__ = ["CONV_POOL_Transformer"] + +class CONV_POOL_Transformer(Transformer): + METHOD_NAME = 'conv_pool' + KWARGS_NAMESCOPE = '_conv_pool' + + def get_matcher_graph(self): + ugraph = uTensorGraph(output_nodes=['quantized_maxpool'], backend="tensorflow") + + dummpy_input0 = Const_Op('dummy_input0', np.zeros([16,16], dtype=np.uint8), ugraph) + dummpy_input0_min = Const_Op('dummy_input0_min', np.zeros([1]), ugraph) + dummpy_input0_max = Const_Op('dummy_input0_max', np.zeros([1]), ugraph) + + dummpy_input1 = Const_Op('dummy_input1', np.zeros([4,4]), ugraph) + dummpy_input1_min = Const_Op('dummy_input1_min', np.zeros([1]), ugraph) + dummpy_input1_max = Const_Op('dummy_input1_max', np.zeros([1]), ugraph) + + conv_out = quantized_conv2d_op('convolution2d', [dummpy_input0[0], + dummpy_input1[0], dummpy_input0_min[0], dummpy_input0_max[0], + dummpy_input1_min[0], dummpy_input1_max[0]], ugraph) + + requantization_range_out = requantization_range_op('requantization_range', conv_out, ugraph) + + requantize_out = requantize_op('requantize', [conv_out[0], conv_out[1], conv_out[2], + requantization_range_out[0], requantization_range_out[1]], ugraph) #FIXME: check the tensor ordering here + + quantized_maxpool_op('quantized_maxpool', requantize_out, ugraph) + + topologic_order_graph(ugraph) + + #viz_graph('matcher_quant', True, ugraph) + #import pdb; pdb.set_trace() + + meta = dict() + meta["convolution2d"] = ["End"] + + return (ugraph, meta) + + def transform(self, ugraph): + [matcher_ugraph, metaData] = self.get_matcher_graph() + while True: + matcher = uGraphMatcher() + result = matcher.isomorphic_match(ugraph, matcher_ugraph, metaData) + if result == False: + break + + import pdb; pdb.set_trace() #remove me + return ugraph #remove me + + #swapping the ops + max_pool_op = matcher['maxpool'] + relu_op = matcher['relu'] + + max_pool_op.input_tensors[0] = matcher['convolution2d:0'] + max_pool_op.output_tensors[0] = matcher['relu:0'] + relu_op.input_tensors[0] = matcher['relu:0'] + relu_op.output_tensors[0] = matcher['maxpool:0'] + + matcher['maxpool'] = max_pool_op + matcher['relu'] = relu_op + + #swapping the tensor names + relu_tensor_name = matcher['relu:0'].name + maxpool_tensor_name = matcher['maxpool:0'].name + + rename_tensor(relu_tensor_name, 'tmp_relu_name', ugraph) + rename_tensor(maxpool_tensor_name, relu_tensor_name, ugraph) + rename_tensor('tmp_relu_name', maxpool_tensor_name, ugraph) + + update_tensor_op_names(ugraph) + graph_validate(ugraph) + topologic_order_graph(ugraph) + #import pdb; pdb.set_trace() + + viz_graph('matcher', True, ugraph) + return ugraph ##remove me + + # graph_check(ugraph) + # return ugraph diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index 80418ddc..248e836f 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -1,7 +1,7 @@ # -*- coding:utf8 -*- -r"""CMSIS-NN Transformer +r"""Linear Re-ordering Transformer -Node fusion and replacement for CMSIS-NN +Linear Operation Legalizations """ import re @@ -83,7 +83,4 @@ def transform(self, ugraph): #import pdb; pdb.set_trace() viz_graph('matcher', True, ugraph) - return ugraph ##remove me - - # graph_check(ugraph) - # return ugraph + return ugraph \ No newline at end of file From 78702069982a7503b5b6416ccba125c6ba5a1b7f Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 5 May 2019 16:42:53 +0800 Subject: [PATCH 10/70] fused, but with graph integrity problem --- utensor_cgen/experimental/ugraph_builder.py | 36 +++++++++++++++- utensor_cgen/transformer/conv_pool.py | 48 +++++++-------------- utensor_cgen/transformer/linear_reoder.py | 6 +-- 3 files changed, 51 insertions(+), 39 deletions(-) diff --git a/utensor_cgen/experimental/ugraph_builder.py b/utensor_cgen/experimental/ugraph_builder.py index d7603046..e457d214 100644 --- a/utensor_cgen/experimental/ugraph_builder.py +++ b/utensor_cgen/experimental/ugraph_builder.py @@ -15,7 +15,8 @@ __all__ = ["transpose_offline", "Const_Op", "Ram_Op", "Const_Reshape", "Uint8Q7Origin_Op", "CMSIS_FC_Op", "QuantRangeForMultiplicationu8u8int32_Op", "conv2d_op", "quantized_conv2d_op", - "relu_op", "maxpool_op", "requantize_op", "requantization_range_op", "quantized_maxpool_op"] + "relu_op", "maxpool_op", "requantize_op", "requantization_range_op", "quantized_maxpool_op", + "quantized_conv2d_pool_op"] # Let us get unique names for custom injected nodes def static_vars(**kwargs): @@ -364,4 +365,35 @@ def requantize_op(name, inputs, ugraph): ugraph=tmp_ugraph) ugraph.add_op(rqnt_op_info, False) - return [value_out, min_out, max_out] \ No newline at end of file + return [value_out, min_out, max_out] + +def quantized_conv2d_pool_op(name, inputs, ugraph): + tmp_ugraph = uTensorGraph(output_nodes=[name]) + conv_out = TensorInfo(name=name + ":0", + op_name=name, + dtype=np.dtype('uint8'), + shape=inputs[0].shape, #FIXME: wrong shape most likely + ugraph=tmp_ugraph + ) + min_out = TensorInfo(name=name + ":1", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + max_out = TensorInfo(name=name + ":2", + op_name=name, + dtype=np.dtype('float32'), + shape=[1], + ugraph=tmp_ugraph + ) + + quantized_conv2d_op_info = OperationInfo(name=name, + input_tensors=inputs, + output_tensors=[conv_out, min_out, max_out], + op_type="FusedConv2DMaxpool", + backend="tensorflow", + ugraph=tmp_ugraph) + + ugraph.add_op(quantized_conv2d_op_info, False) + return quantized_conv2d_op_info.output_tensors diff --git a/utensor_cgen/transformer/conv_pool.py b/utensor_cgen/transformer/conv_pool.py index 49669b34..286e7392 100644 --- a/utensor_cgen/transformer/conv_pool.py +++ b/utensor_cgen/transformer/conv_pool.py @@ -42,7 +42,7 @@ def get_matcher_graph(self): dummpy_input1_min = Const_Op('dummy_input1_min', np.zeros([1]), ugraph) dummpy_input1_max = Const_Op('dummy_input1_max', np.zeros([1]), ugraph) - conv_out = quantized_conv2d_op('convolution2d', [dummpy_input0[0], + conv_out = quantized_conv2d_op('quant_convolution2d', [dummpy_input0[0], dummpy_input1[0], dummpy_input0_min[0], dummpy_input0_max[0], dummpy_input1_min[0], dummpy_input1_max[0]], ugraph) @@ -55,11 +55,9 @@ def get_matcher_graph(self): topologic_order_graph(ugraph) - #viz_graph('matcher_quant', True, ugraph) - #import pdb; pdb.set_trace() meta = dict() - meta["convolution2d"] = ["End"] + meta["quant_convolution2d"] = ["End"] return (ugraph, meta) @@ -70,37 +68,23 @@ def transform(self, ugraph): result = matcher.isomorphic_match(ugraph, matcher_ugraph, metaData) if result == False: break - - import pdb; pdb.set_trace() #remove me - return ugraph #remove me - - #swapping the ops - max_pool_op = matcher['maxpool'] - relu_op = matcher['relu'] - - max_pool_op.input_tensors[0] = matcher['convolution2d:0'] - max_pool_op.output_tensors[0] = matcher['relu:0'] - relu_op.input_tensors[0] = matcher['relu:0'] - relu_op.output_tensors[0] = matcher['maxpool:0'] - - matcher['maxpool'] = max_pool_op - matcher['relu'] = relu_op - - #swapping the tensor names - relu_tensor_name = matcher['relu:0'].name - maxpool_tensor_name = matcher['maxpool:0'].name - rename_tensor(relu_tensor_name, 'tmp_relu_name', ugraph) - rename_tensor(maxpool_tensor_name, relu_tensor_name, ugraph) - rename_tensor('tmp_relu_name', maxpool_tensor_name, ugraph) + fused_op_name = matcher['quant_convolution2d'].name + "_" + matcher['quantized_maxpool'].name + fused_op_out = quantized_conv2d_pool_op(fused_op_name, matcher['quant_convolution2d'].input_tensors, ugraph) + matcher['quantized_maxpool:0'] = fused_op_out[0] + matcher['quantized_maxpool:1'] = fused_op_out[1] + matcher['quantized_maxpool:2'] = fused_op_out[2] + matcher['quant_convolution2d'] = None + matcher['requantization_range'] = None + matcher['requantize'] = None + matcher['quantized_maxpool'] = None update_tensor_op_names(ugraph) - graph_validate(ugraph) topologic_order_graph(ugraph) - #import pdb; pdb.set_trace() + graph_validate(ugraph) - viz_graph('matcher', True, ugraph) - return ugraph ##remove me + + viz_graph('matcher', True, ugraph) + import pdb; pdb.set_trace() - # graph_check(ugraph) - # return ugraph + return ugraph ##remove me diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index 248e836f..f78683f6 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -40,8 +40,6 @@ def get_matcher_graph(self): relu_out = relu_op('relu', conv_out, ugraph) out_tensor = maxpool_op('maxpool', relu_out, ugraph) topologic_order_graph(ugraph) - - #viz_graph('matcher', True, ugraph) meta = dict() meta["dummy_input0"] = ["End", "Any"] @@ -78,9 +76,7 @@ def transform(self, ugraph): rename_tensor('tmp_relu_name', maxpool_tensor_name, ugraph) update_tensor_op_names(ugraph) - graph_validate(ugraph) topologic_order_graph(ugraph) - #import pdb; pdb.set_trace() + graph_validate(ugraph) - viz_graph('matcher', True, ugraph) return ugraph \ No newline at end of file From 55fbf775a5b09e79d47ab3954b7550b89077bb90 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 5 May 2019 21:17:22 +0800 Subject: [PATCH 11/70] output graph topologically correct TODO: copy attributes to the fused node --- utensor_cgen/experimental/ugraph_matcher.py | 2 +- utensor_cgen/experimental/ugraph_util_functions.py | 2 +- utensor_cgen/transformer/conv_pool.py | 2 -- utensor_cgen/transformer/pipline.py | 2 ++ 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/utensor_cgen/experimental/ugraph_matcher.py b/utensor_cgen/experimental/ugraph_matcher.py index 4bc49142..d5f9bb59 100644 --- a/utensor_cgen/experimental/ugraph_matcher.py +++ b/utensor_cgen/experimental/ugraph_matcher.py @@ -333,7 +333,7 @@ def __setitem__(self, name, info): if info == None: #TODO: tensor dependency checking here - if name in self.subject_graph.topo_order: + if self.translator[0][name] in self.subject_graph.topo_order: #FIXME: name here reference matcher name, should be subject name self.subject_graph.drop_op(self.translator[0][name]) del self.translator[0][name] else: diff --git a/utensor_cgen/experimental/ugraph_util_functions.py b/utensor_cgen/experimental/ugraph_util_functions.py index 5068e2b5..635665a3 100644 --- a/utensor_cgen/experimental/ugraph_util_functions.py +++ b/utensor_cgen/experimental/ugraph_util_functions.py @@ -44,7 +44,7 @@ def get_tensor_node_names(graph, t_name): start_nodes = list() end_nodes = list() - for it_node in graph.topo_order: + for it_node, info in graph.ops_info.items(): for t in graph.ops_info[it_node].input_tensors: if t.name == t_name: end_nodes.append(it_node) diff --git a/utensor_cgen/transformer/conv_pool.py b/utensor_cgen/transformer/conv_pool.py index 286e7392..931d9111 100644 --- a/utensor_cgen/transformer/conv_pool.py +++ b/utensor_cgen/transformer/conv_pool.py @@ -82,9 +82,7 @@ def transform(self, ugraph): update_tensor_op_names(ugraph) topologic_order_graph(ugraph) graph_validate(ugraph) - viz_graph('matcher', True, ugraph) - import pdb; pdb.set_trace() return ugraph ##remove me diff --git a/utensor_cgen/transformer/pipline.py b/utensor_cgen/transformer/pipline.py index 4600ff0d..c305dfbe 100644 --- a/utensor_cgen/transformer/pipline.py +++ b/utensor_cgen/transformer/pipline.py @@ -8,6 +8,7 @@ from .quantize import QuantizeTransformer from .graph_viz import GraphVizTransformer from .linear_reoder import Linear_Reorder_Transformer +from .conv_pool import CONV_POOL_Transformer class TransformerPipeline(object): @@ -22,6 +23,7 @@ class TransformerPipeline(object): IdOpRemoveOptimizer.METHOD_NAME: IdOpRemoveOptimizer, GraphVizTransformer.METHOD_NAME: GraphVizTransformer, Linear_Reorder_Transformer.METHOD_NAME: Linear_Reorder_Transformer, + CONV_POOL_Transformer.METHOD_NAME: CONV_POOL_Transformer, } def __init__(self, methods): From eb1a510fe5f92f40b39e1a970d681b45d5580de3 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Sun, 5 May 2019 21:35:58 +0800 Subject: [PATCH 12/70] attaching op_attribs to the fused-node --- utensor_cgen/experimental/ugraph_builder.py | 5 ++++- utensor_cgen/transformer/conv_pool.py | 10 ++++++---- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/utensor_cgen/experimental/ugraph_builder.py b/utensor_cgen/experimental/ugraph_builder.py index e457d214..f93b4980 100644 --- a/utensor_cgen/experimental/ugraph_builder.py +++ b/utensor_cgen/experimental/ugraph_builder.py @@ -367,7 +367,9 @@ def requantize_op(name, inputs, ugraph): ugraph.add_op(rqnt_op_info, False) return [value_out, min_out, max_out] -def quantized_conv2d_pool_op(name, inputs, ugraph): + +#FIXME: The content in op_attr should be passed in as proper parameters +def quantized_conv2d_pool_op(name, inputs, op_attr, ugraph): tmp_ugraph = uTensorGraph(output_nodes=[name]) conv_out = TensorInfo(name=name + ":0", op_name=name, @@ -393,6 +395,7 @@ def quantized_conv2d_pool_op(name, inputs, ugraph): output_tensors=[conv_out, min_out, max_out], op_type="FusedConv2DMaxpool", backend="tensorflow", + op_attr=op_attr, ugraph=tmp_ugraph) ugraph.add_op(quantized_conv2d_op_info, False) diff --git a/utensor_cgen/transformer/conv_pool.py b/utensor_cgen/transformer/conv_pool.py index 931d9111..d989ed50 100644 --- a/utensor_cgen/transformer/conv_pool.py +++ b/utensor_cgen/transformer/conv_pool.py @@ -70,7 +70,10 @@ def transform(self, ugraph): break fused_op_name = matcher['quant_convolution2d'].name + "_" + matcher['quantized_maxpool'].name - fused_op_out = quantized_conv2d_pool_op(fused_op_name, matcher['quant_convolution2d'].input_tensors, ugraph) + op_attr = dict() + op_attr['_utensor_conv'] = matcher['quant_convolution2d'].op_attr + op_attr['_utensor_pool'] = matcher['quantized_maxpool'].op_attr + fused_op_out = quantized_conv2d_pool_op(fused_op_name, matcher['quant_convolution2d'].input_tensors, op_attr, ugraph) matcher['quantized_maxpool:0'] = fused_op_out[0] matcher['quantized_maxpool:1'] = fused_op_out[1] matcher['quantized_maxpool:2'] = fused_op_out[2] @@ -83,6 +86,5 @@ def transform(self, ugraph): topologic_order_graph(ugraph) graph_validate(ugraph) - viz_graph('matcher', True, ugraph) - - return ugraph ##remove me + #viz_graph('matcher', True, ugraph) + return ugraph From d232e02b111f79301348bd090e144bd78d193d88 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 8 May 2019 00:32:58 +0800 Subject: [PATCH 13/70] fix up --- utensor_cgen/backend/operators.py | 33 ++++++++++++++++++--- utensor_cgen/backend/snippets/_snippets.py | 4 +-- utensor_cgen/experimental/ugraph_builder.py | 4 +-- 3 files changed, 33 insertions(+), 8 deletions(-) diff --git a/utensor_cgen/backend/operators.py b/utensor_cgen/backend/operators.py index a657c0e2..a5661ed5 100644 --- a/utensor_cgen/backend/operators.py +++ b/utensor_cgen/backend/operators.py @@ -460,9 +460,9 @@ def __init__(self, op_info, **kwargs): in_dtype, filter_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype) out_dtype = op_info.output_tensors[0].dtype - strides = op_info.op_attr["strides"].value.ints_value - ksize = op_info.op_attr["ksize"].value.ints_value - padding = op_info.op_attr["padding"].value.decode('utf8') + strides = op_info.op_attr['_utensor_conv']["strides"].value.ints_value + ksize = op_info.op_attr['_utensor_pool']["ksize"].value.ints_value + padding = op_info.op_attr['_utensor_conv']["padding"].value.decode('utf8') parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] @@ -471,7 +471,7 @@ def __init__(self, op_info, **kwargs): in_dtype=in_dtype, filter_dtype=filter_dtype, out_dtype=out_dtype, ref_count=ref_count, to_eval=to_eval) -@OperatorFactory.registder +@OperatorFactory.register class _Conv2DQuantOperator(_Operator): op_type = "QuantizedConv2D" @@ -717,3 +717,28 @@ def __init__(self, op_info, **kwargs): ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = GatherOpSnippet(inputs, output, tf_dtype, ref_count, to_eval) + + +@OperatorFactory.register +class _QuantizedMulOperator(_Operator): + + op_type = "QuantizedMul" + + def __init__(self, op_info, **kwargs): + _Operator.__init__(self) + inputs = [tensor_info.name for tensor_info in op_info.input_tensors] + outputs = [tensor_info.name for tensor_info in op_info.output_tensors] + in_dtype, qout_dtype = (op_info.input_tensors[0].dtype, + op_info.output_tensors[0].dtype) #NT: why separate this out? + #DB: I don't know, it's in the uTensor C code + out_dtypes = [tensor_info.dtype for tensor_info in op_info.output_tensors[1:]] + parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, + op_info.op_attr) + ref_counts = parser.get('ref_counts', []) + to_eval = parser.get('to_eval', False) + self._snippet = QuantizedReluOpSnippet(inputs, outputs, in_dtype, + out_dtypes, qout_dtype, + ref_counts, to_eval) + print("FIXME: _QuantizedMulOperator not implemented") + print("FIXME: _QuantizedMulOperator not implemented") + print("FIXME: _QuantizedMulOperator not implemented") \ No newline at end of file diff --git a/utensor_cgen/backend/snippets/_snippets.py b/utensor_cgen/backend/snippets/_snippets.py index 794c0cc7..fe098f4d 100644 --- a/utensor_cgen/backend/snippets/_snippets.py +++ b/utensor_cgen/backend/snippets/_snippets.py @@ -19,7 +19,7 @@ "ContextSnippetsContainer", "QuantizedAddOpSnippet", "CreateTensorBinarySnippet", "WeightSnippet", "ContextGlobalArrayContainer", "QuantRangeForMultiplicationSnippet", - "CreateTensorRamSnippet", "Uint8Q7OriginSnippet"] + "CreateTensorRamSnippet", "Uint8Q7OriginSnippet", "QuantizedFusedConv2DMaxpoolOpSnippet"] # TODO: Better abstraction, i.e a better backend for code generation class CreateTensorIdxSnippet(Snippet): @@ -637,7 +637,7 @@ def __init__(self, inputs, output, strides, ksize, padding, self.template_vars["padding"] = padding self.template_vars["to_eval"] = to_eval -class QuantizedFusedConv2DOpMaxpoolSnippet(Snippet): +class QuantizedFusedConv2DMaxpoolOpSnippet(Snippet): __template_name__ = "snippets/fused_conv2d_maxpool_op.cpp" __headers__ = set(['"uTensor/ops/MatrixOps.hpp"']) diff --git a/utensor_cgen/experimental/ugraph_builder.py b/utensor_cgen/experimental/ugraph_builder.py index f93b4980..c112a6ad 100644 --- a/utensor_cgen/experimental/ugraph_builder.py +++ b/utensor_cgen/experimental/ugraph_builder.py @@ -373,7 +373,7 @@ def quantized_conv2d_pool_op(name, inputs, op_attr, ugraph): tmp_ugraph = uTensorGraph(output_nodes=[name]) conv_out = TensorInfo(name=name + ":0", op_name=name, - dtype=np.dtype('uint8'), + dtype=inputs[0].dtype, shape=inputs[0].shape, #FIXME: wrong shape most likely ugraph=tmp_ugraph ) @@ -393,7 +393,7 @@ def quantized_conv2d_pool_op(name, inputs, op_attr, ugraph): quantized_conv2d_op_info = OperationInfo(name=name, input_tensors=inputs, output_tensors=[conv_out, min_out, max_out], - op_type="FusedConv2DMaxpool", + op_type="QuantizedFusedConv2DMaxpool", backend="tensorflow", op_attr=op_attr, ugraph=tmp_ugraph) From c7687e236542becd588b151230906c7c07e0b7e0 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 8 May 2019 00:39:35 +0800 Subject: [PATCH 14/70] fixing the types --- utensor_cgen/experimental/ugraph_builder.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utensor_cgen/experimental/ugraph_builder.py b/utensor_cgen/experimental/ugraph_builder.py index c112a6ad..91b523f7 100644 --- a/utensor_cgen/experimental/ugraph_builder.py +++ b/utensor_cgen/experimental/ugraph_builder.py @@ -281,7 +281,7 @@ def quantized_maxpool_op(name, inputs, ugraph): tmp_ugraph = uTensorGraph(output_nodes=[name]) max_value_out = TensorInfo(name=name + ":0", op_name=name, - dtype=np.dtype('float32'), + dtype=inputs[0].dtype, shape=inputs[0].shape, ugraph=tmp_ugraph ) From 82f5e96d5660b1376c8dd48baabd9170fec14fb3 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 8 May 2019 23:04:34 +0800 Subject: [PATCH 15/70] generates some source files, weights file missing --- utensor_cgen/backend/operators.py | 25 ---------------------- utensor_cgen/backend/snippets/_snippets.py | 2 +- utensor_cgen/transformer/ns_transformer.py | 16 ++++++++++++++ utensor_cgen/transformer/pipline.py | 4 +++- 4 files changed, 20 insertions(+), 27 deletions(-) diff --git a/utensor_cgen/backend/operators.py b/utensor_cgen/backend/operators.py index e4c9b713..a5d0209d 100644 --- a/utensor_cgen/backend/operators.py +++ b/utensor_cgen/backend/operators.py @@ -738,28 +738,3 @@ def __init__(self, op_info, **kwargs): ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = GatherOpSnippet(inputs, output, tf_dtype, ref_count, to_eval) - - -@OperatorFactory.register -class _QuantizedMulOperator(_Operator): - - op_type = "QuantizedMul" - - def __init__(self, op_info, **kwargs): - _Operator.__init__(self) - inputs = [tensor_info.name for tensor_info in op_info.input_tensors] - outputs = [tensor_info.name for tensor_info in op_info.output_tensors] - in_dtype, qout_dtype = (op_info.input_tensors[0].dtype, - op_info.output_tensors[0].dtype) #NT: why separate this out? - #DB: I don't know, it's in the uTensor C code - out_dtypes = [tensor_info.dtype for tensor_info in op_info.output_tensors[1:]] - parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, - op_info.op_attr) - ref_counts = parser.get('ref_counts', []) - to_eval = parser.get('to_eval', False) - self._snippet = QuantizedReluOpSnippet(inputs, outputs, in_dtype, - out_dtypes, qout_dtype, - ref_counts, to_eval) - print("FIXME: _QuantizedMulOperator not implemented") - print("FIXME: _QuantizedMulOperator not implemented") - print("FIXME: _QuantizedMulOperator not implemented") \ No newline at end of file diff --git a/utensor_cgen/backend/snippets/_snippets.py b/utensor_cgen/backend/snippets/_snippets.py index 35af7bef..2314a7a6 100644 --- a/utensor_cgen/backend/snippets/_snippets.py +++ b/utensor_cgen/backend/snippets/_snippets.py @@ -20,7 +20,7 @@ "QuantizedMulOpSnippet", "CreateTensorBinarySnippet", "WeightSnippet", "ContextGlobalArrayContainer", "QuantRangeForMultiplicationSnippet", - "FusedConv2DOpMaxpoolSnippet", "QuantizedFusedConv2DOpMaxpoolSnippet", + "FusedConv2DOpMaxpoolSnippet", "QuantizedFusedConv2DMaxpoolOpSnippet", "GatherOpSnippet", "CreateTensorRamSnippet", "Uint8Q7OriginSnippet"] diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 90dbf1a1..2e7cb459 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -137,3 +137,19 @@ class BatchNormTransformer(Transformer): def transform(self, ugraph): # TODO: implement this! pass + + +class FakeGatherV2Transformer(Transformer): + """Force converting GatherV2 op to Gather op + """ + METHOD_NAME = 'FakeGatherV2' + KWARGS_NAMESCOPE = '_fake_gatherv2' + TARGET_NODENAME_PATTERN = re.compile(r'(GatherV2[_\w\d]*)/.*') + + def transform(self, ugraph): + print("warning: force replacing GatherV2 with Gather") + for key, op in ugraph.ops_info.items(): + if op.op_type == "GatherV2": + op.op_type = "Gather" + ugraph.ops_info[key] = op + return ugraph \ No newline at end of file diff --git a/utensor_cgen/transformer/pipline.py b/utensor_cgen/transformer/pipline.py index c305dfbe..b97ec8d3 100644 --- a/utensor_cgen/transformer/pipline.py +++ b/utensor_cgen/transformer/pipline.py @@ -3,7 +3,8 @@ from .base import Transformer from .cmsis_nn import CMSIS_NN_Transformer from .ns_transformer import (BatchNormTransformer, DropoutTransformer, - InlineTransformer, BiasAddTransformer) + InlineTransformer, BiasAddTransformer, + FakeGatherV2Transformer) from .optimizer import IdOpRemoveOptimizer, RefCntOptimizer from .quantize import QuantizeTransformer from .graph_viz import GraphVizTransformer @@ -24,6 +25,7 @@ class TransformerPipeline(object): GraphVizTransformer.METHOD_NAME: GraphVizTransformer, Linear_Reorder_Transformer.METHOD_NAME: Linear_Reorder_Transformer, CONV_POOL_Transformer.METHOD_NAME: CONV_POOL_Transformer, + FakeGatherV2Transformer.METHOD_NAME: FakeGatherV2Transformer } def __init__(self, methods): From 51641b848edc36dff142d737205809c7d11451e8 Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Wed, 8 May 2019 23:46:15 +0800 Subject: [PATCH 16/70] inline transformer enabler hotfix --- utensor_cgen/backend/code_generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utensor_cgen/backend/code_generator.py b/utensor_cgen/backend/code_generator.py index dc0fc5ab..2fa22ff3 100644 --- a/utensor_cgen/backend/code_generator.py +++ b/utensor_cgen/backend/code_generator.py @@ -120,7 +120,7 @@ def formatter(name, kwargs): container.add_snippet(cmt_snippet) composer.add_snippet(container) - if 'inline' in self.trans_methods: + if ('inline', {}) in self.trans_methods: _logger.info("Generate weight file: %s", weightheader_fname) with open(weightheader_fname, "w") as wf: wf.write('// Auto generated by utensor-cli\n\n') From 428f59c4d7017672187469f78cc9f8e7caced18b Mon Sep 17 00:00:00 2001 From: Neil Tan Date: Thu, 9 May 2019 15:04:12 +0800 Subject: [PATCH 17/70] fixing GatherOpSnippet --- utensor_cgen/backend/snippets/templates/snippets/gather_op.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utensor_cgen/backend/snippets/templates/snippets/gather_op.cpp b/utensor_cgen/backend/snippets/templates/snippets/gather_op.cpp index fbb763e1..57bcf4ac 100644 --- a/utensor_cgen/backend/snippets/templates/snippets/gather_op.cpp +++ b/utensor_cgen/backend/snippets/templates/snippets/gather_op.cpp @@ -7,7 +7,7 @@ S_TENSOR {{sptr_name}}; {% else %} ctx.add(new RamTensor<{{out_dtype}}>(), "{{output}}"); {% endif %} - ctx.push(new GatherOp<{{in_dtype}}, {{out_dtype}}>(), + ctx.push(new GatherOp<{{in_dtype}}>(), { {% for tname in inputs[:-1]%}"{{tname}}", {%endfor%}"{{inputs[-1]}}" }, { "{{output}}" }); {% if to_eval %} From 4a2cb98e15f4f1676dcd461e0a1d6974e1b8a469 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 1 May 2019 20:47:54 +0800 Subject: [PATCH 18/70] minor bug fix matcher replace in progress --- .../test_permutation/test_match_patrn1.py | 4 +- utensor_cgen/ir/base.py | 23 ++++ utensor_cgen/matcher/_matcher_impl.py | 110 +++++++++++++++--- utensor_cgen/transformer/base.py | 47 +------- utensor_cgen/utils.py | 47 +++++++- 5 files changed, 164 insertions(+), 67 deletions(-) diff --git a/tests/test_matcher/test_permutation/test_match_patrn1.py b/tests/test_matcher/test_permutation/test_match_patrn1.py index c9c17c7f..f93ae101 100644 --- a/tests/test_matcher/test_permutation/test_match_patrn1.py +++ b/tests/test_matcher/test_permutation/test_match_patrn1.py @@ -32,10 +32,10 @@ def test_match_sub1_1(patrn_ugraph, subject_ugraph1_1): def test_match_sub1_2(patrn_ugraph, subject_ugraph1_2): matcher = uTensorGraphMatcher(patrn_ugraph) - matches = matcher.match(subject_ugraph1_2) + matches = matcher.match_all(subject_ugraph1_2) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] assert match.patrn2subj_op_map['input0'].name in ['sub_input0', 'sub_input1'] assert match.patrn2subj_op_map['input1'].name in ['sub_input0', 'sub_input1'] assert match.patrn2subj_op_map['add0'].name == 'sub_add0' - assert match.patrn2subj_op_map['output'].name == 'sub_add1' \ No newline at end of file + assert match.patrn2subj_op_map['output'].name == 'sub_add1' diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index ec36e9d7..cb24b1ce 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -119,6 +119,7 @@ class OperationInfo(IRBase, _NoShallowCopyMixin): """ name = attr.ib(type=str) _backend = attr.ib(type=str) + # FIXME: it's better to make OperationInfo to be instantiate without ugraph _ugraph = attr.ib(repr=False) @_ugraph.validator def check(self, attrib, value): @@ -230,6 +231,18 @@ class uTensorGraph(IRBase, _NoShallowCopyMixin): topo_order : list output_nodes : list backend : str {"tensorflow", 'pytorch'(future work)} + + How to Build a uTensorGraph + =========================== + 1. create a empty graph + - give a list of names of output nodes (required) + - (optional) give backend string + - leave ops_info empty + 2. setup the ops_info + - when you set the value of ops_info, which is an OperationInfo instance, + make sure its ugraph attribute is the ugraph you just created at step 1 + 3. pass the ugraph to topologic_order_graph to setup the order + of the ops """ KWPARSER_PATTERN = re.compile(r'^([^\d\W][\w\d_]*)__([^\d\W][\w\d_]*)') @@ -260,6 +273,14 @@ def get_ops_by_type(self, given_op_type): @property def output_ops(self): return [self.ops_info[name] for name in self.output_nodes] + + @property + def input_ops(self): + ops = [] + for op in self.ops_info.values(): + if not op.input_tensors: + ops.append(op) + return ops @property def backend(self): @@ -289,6 +310,8 @@ def graph_def(self): @property def ops(self): + if not self.topo_order: + topologic_order_graph(self) return [self.ops_info[name] for name in self.topo_order] def add_op(self, op): diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index 0523bb32..38cfe9f7 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -9,7 +9,7 @@ from utensor_cgen.ir import MetaOperationInfo, OperationInfo, uTensorGraph from utensor_cgen.matcher._morphism import Morphism -from utensor_cgen.utils import ops_bfs_queue +from utensor_cgen.utils import ops_bfs_queue, topologic_order_graph __all__ = ["uTensorGraphMatcher"] @@ -133,12 +133,15 @@ def _match(self, other_ugraph): ) ] while True: - states = self._visit(states) - if not states: + visited_states = self._visit(states) + if not visited_states: break - for state in states: + states = [] + for state in visited_states: if state.is_done: yield state.match + else: + states.append(state) def match(self, other_ugraph, n=1): match_gen = self._match(other_ugraph) @@ -207,38 +210,107 @@ def update_op_map(self, pattern_op, subj_op): self.patrn2subj_tensor_map[pattern_tensor.name] = target_tensor self.subj2patrn_tensor_map[target_tensor.name] = pattern_tensor - def replace_with(self, other_ugraph): + def replace_with(self, callback, suffix=None): """ - Replace matched subgraph with a given ugraph + Replace matched subgraph with a given ugraph given by the callback, *not in place* + + Arguments + --------- + callback : callable + a callable which takes the match object and return a new ugraph to be replaced with + and a dict which maps the name of input nodes of pattern ugraph to the names of input + nodes of replacing ugraph + + Return + ------ + new_ugraph : uTensorGraph + a *new* graph with matched subgraph replaced with the graph given by the callback """ - pass + # build a matched subgraph and pass it to callback + replace_ugraph, input_map = callback(self) + if not self._is_replacible(replace_ugraph): + raise ValueError( + 'matched subgraph can not be replaced with the ugraph given' + ) + replace_ugraph, input_map = self.new_ugraph_with_suffix( + replace_ugraph, input_map, suffix + ) + new_ugraph = deepcopy(self.subject_ugraph) - _CHARSET = ascii_letters + digits - def _random_surfix(self, length=8): - chars = choices(self._CHARSET, k=length) + def _is_replacible(self, replace_ugraph): + sub_ugraph = self._build_subgraph() + replacible = True + if len(replace_ugraph.ouptut_nodes) != len(sub_ugraph.output_nodes): + replacible = False + if len(replace_ugraph.input_ops) != len(sub_ugraph.input_ops): + replacible = False + input_node_names = set([op.name for op in sub_ugraph.input_ops]) + output_node_names = set(sub_ugraph.output_nodes) + for subj_op in self.patrn2subj_op_map.values(): + if subj_op.name in input_node_names: + continue + for in_op in subj_op.input_nodes: + if in_op.name not in sub_ugraph.ops_info: + replacible = False + if subj_op.name in output_node_names: + continue + for out_op in subj_op.output_nodes: + if out_op.name not in sub_ugraph.ops_info: + replacible = False + return replacible + + def _build_subgraph(self): + output_nodes = [ + self.get_op_in_subject_ugraph(name) + for name in self.pattern_ugraph.output_nodes + ] + ugraph = uTensorGraph( + output_nodes=[op.name for op in output_nodes], + backend=self.subject_ugraph.backend + ) + ugraph.output_nodes = [op.name for op in output_nodes] + for op_name in self.pattern_ugraph.ops_info: + op = self.get_op_in_subject_ugraph(op_name) + ugraph.ops_info[op.name] = deepcopy(op, {'ugraph': ugraph}) + topologic_order_graph(ugraph) + return ugraph + + def get_op_in_subject_ugraph(self, patrn_op_name): + return self.patrn2subj_op_map[patrn_op_name] + + def get_op_in_pattern_ugraph(self, subj_op_name): + return self.subj2patrn_op_map[subj_op_name] + + CHARSET = ascii_letters + digits + + @classmethod + def _random_suffix(cls, length=8): + chars = choices(cls.CHARSET, k=length) return ''.join(chars) - def _new_ugraph_with_surfix(self, ugraph, surfix=None): - if surfix is None: - surfix = self._random_surfix() + @classmethod + def new_ugraph_with_suffix(cls, ugraph, input_map, suffix=None): + if suffix is None: + suffix = cls._random_suffix() + new_input_map = {k: '{}_{}'.format(v, suffix) for k, v in input_map.items()} new_ugraph = deepcopy(ugraph) new_ugraph.output_nodes = [ - '{}_{}'.format(name, surfix) for name in ugraph.output_nodes + '{}_{}'.format(name, suffix) for name in ugraph.output_nodes ] - new_ugraph.topo_order = ['{}_{}'.format(name, surfix) for name in ugraph.topo_order] + new_ugraph.topo_order = ['{}_{}'.format(name, suffix) for name in ugraph.topo_order] ops_to_remove = set([]) new_ops_info = {} for ori_op_name, op in new_ugraph.ops_info.items(): - new_op_name = '{}_{}'.format(ori_op_name, surfix) + new_op_name = '{}_{}'.format(ori_op_name, suffix) op.name = new_op_name for tensor in op.output_tensors: tensor_idx = tensor.name.split(':')[1] - tensor.name = '{}_{}:{}'.format(tensor.op_name, surfix, tensor_idx) + tensor.name = '{}_{}:{}'.format(tensor.op_name, suffix, tensor_idx) tensor.op_name = new_op_name for tensor in op.input_tensors: in_op_name, tensor_idx = tensor.name.split(':') - new_in_op_name = '{}_{}'.format(in_op_name, surfix) + new_in_op_name = '{}_{}'.format(in_op_name, suffix) tensor.name = '{}:{}'.format(new_in_op_name, tensor_idx) tensor.op_name = new_in_op_name ops_to_remove.add(ori_op_name) @@ -246,7 +318,7 @@ def _new_ugraph_with_surfix(self, ugraph, surfix=None): for op_name in ops_to_remove: new_ugraph.ops_info.pop(op_name) new_ugraph.ops_info = new_ops_info - return new_ugraph + return new_ugraph, new_input_map @attr.s diff --git a/utensor_cgen/transformer/base.py b/utensor_cgen/transformer/base.py index 678992f6..21af8185 100644 --- a/utensor_cgen/transformer/base.py +++ b/utensor_cgen/transformer/base.py @@ -1,8 +1,7 @@ from abc import ABCMeta, abstractmethod -from copy import deepcopy from functools import wraps -from utensor_cgen.utils import parse_tensor_name, topologic_order_graph +from utensor_cgen.utils import topologic_order_graph, prune_graph as _prune_graph class Transformer(object): @@ -30,7 +29,7 @@ def transform(ugraph): new_ugraph = ori_transform(ugraph) topologic_order_graph(new_ugraph) if self.prune_graph: - return self._prune_graph(new_ugraph) + return _prune_graph(new_ugraph) return new_ugraph self.transform = transform @@ -43,45 +42,3 @@ def __init__(self, prune_graph=True, **kwargs): @abstractmethod def transform(self, ugraph): raise NotImplementedError('You should overwrite transform method for all transformer') - - @classmethod - def _prune_graph(cls, ugraph): - """Remove nodes that is no longer needed - """ - new_ugraph = deepcopy(ugraph) - # BFS to find all ops you need - ops_in_need = set(ugraph.output_nodes) - queue = [name for name in ugraph.output_nodes] - visited = set([]) - while queue: - op_name = queue.pop(0) - #FIXME: names are framework and graph dependent - # temporary fix is included aftert the commented code - #op_info = new_ugraph.ops_info[op_name] - # in_ops = [parse_tensor_name(t_info.name)[0] - # for t_info in op_info.input_tensors] - - #TODO: move the code below to a standalone function. Consider using a more extensive data structure: - # Or, use this: in_ops = [node.name for node in ugraph.ops_info[op_name].input_nodes] - tensors_in = set([t.name for t in ugraph.ops_info[op_name].input_tensors]) - in_ops = set() - for it_node in ugraph.topo_order: - if(it_node == op_name): - continue - it_tensors_out = [t.name for t in ugraph.ops_info[it_node].output_tensors] - if not tensors_in.isdisjoint(it_tensors_out): - in_ops.add(it_node) - - queue.extend([name for name in in_ops if name not in visited]) - visited.update(in_ops) - ops_in_need.update(in_ops) - #END - - ops_to_remove = set([]) - for op_name in new_ugraph.ops_info.keys(): - if op_name not in ops_in_need: - # remove ops not needed from ops_info - ops_to_remove.add(op_name) - for op_name in ops_to_remove: - new_ugraph.ops_info.pop(op_name) - return new_ugraph diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 57b6b498..b2839ae1 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -227,11 +227,15 @@ def __new__(cls, *args, **kwargs): def topologic_order_graph(ugraph): + """ + topological sort a given ugraph *in place* + + - https://en.wikipedia.org/wiki/Topological_sorting + """ if ugraph.backend != "tensorflow": raise ValueError( "topologic_order_graph works only on tensorflow graph" ) - # https://en.wikipedia.org/wiki/Topological_sorting queue = deepcopy(ugraph.output_nodes) visited = set() # temporary mark perm_visit = set() # Permanent mark @@ -275,3 +279,44 @@ def ops_bfs_queue(ugraph, init_nodes=None): queue.extend(op.input_nodes) bfs_deck.append(op) return bfs_deck + +def prune_graph(ugraph): + """Remove nodes that is no longer needed + """ + new_ugraph = deepcopy(ugraph) + # BFS to find all ops you need + ops_in_need = set(ugraph.output_nodes) + queue = [name for name in ugraph.output_nodes] + visited = set([]) + while queue: + op_name = queue.pop(0) + #FIXME: names are framework and graph dependent + # temporary fix is included aftert the commented code + #op_info = new_ugraph.ops_info[op_name] + # in_ops = [parse_tensor_name(t_info.name)[0] + # for t_info in op_info.input_tensors] + + #TODO: move the code below to a standalone function. Consider using a more extensive data structure: + # Or, use this: in_ops = [node.name for node in ugraph.ops_info[op_name].input_nodes] + tensors_in = set([t.name for t in ugraph.ops_info[op_name].input_tensors]) + in_ops = set() + for it_node in ugraph.topo_order: + if(it_node == op_name): + continue + it_tensors_out = [t.name for t in ugraph.ops_info[it_node].output_tensors] + if not tensors_in.isdisjoint(it_tensors_out): + in_ops.add(it_node) + + queue.extend([name for name in in_ops if name not in visited]) + visited.update(in_ops) + ops_in_need.update(in_ops) + #END + + ops_to_remove = set([]) + for op_name in new_ugraph.ops_info.keys(): + if op_name not in ops_in_need: + # remove ops not needed from ops_info + ops_to_remove.add(op_name) + for op_name in ops_to_remove: + new_ugraph.ops_info.pop(op_name) + return new_ugraph \ No newline at end of file From 4fc9b1412797b730ac85e86f5f9a9a656fdaa8c6 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 7 May 2019 00:31:45 +0800 Subject: [PATCH 19/70] subgraph view --- Makefile | 2 +- tests/test_ir/test_uTensorGraph/test_graph.py | 6 +- .../test_permutation/test_match_patrn1.py | 1 + utensor_cgen/frontend/tensorflow.py | 13 +- utensor_cgen/ir/base.py | 110 ++++++++++-- utensor_cgen/ir/misc/graph_viz.py | 57 +++--- utensor_cgen/ir/utils.py | 44 +---- utensor_cgen/matcher/_matcher_impl.py | 168 ++++++++++++------ utensor_cgen/transformer/ns_transformer.py | 2 + utensor_cgen/utils.py | 4 +- 10 files changed, 257 insertions(+), 150 deletions(-) diff --git a/Makefile b/Makefile index b20aace8..6e842f42 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,7 @@ tests: rm -f tests_log.txt - make test_utils test_ir test_transformer test_frontend + make test_utils test_ir test_transformer test_frontend test_matcher test_%: @if [ -d .venv ]; then \ diff --git a/tests/test_ir/test_uTensorGraph/test_graph.py b/tests/test_ir/test_uTensorGraph/test_graph.py index 733ae88a..ecb6a2c0 100644 --- a/tests/test_ir/test_uTensorGraph/test_graph.py +++ b/tests/test_ir/test_uTensorGraph/test_graph.py @@ -1,11 +1,11 @@ from copy import deepcopy import numpy as np -import tensorflow as tf +import tensorflow as tf +from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, uTensorGraph from utensor_cgen.ir.converter import TensorProtoConverter -from utensor_cgen.frontend.tensorflow import GraphDefParser def test_ugraph_topo_order(graph_tuple): @@ -32,7 +32,9 @@ def test_op_info(): ugraph = uTensorGraph(output_nodes=['dummy']) op_info = OperationInfo(name='testing_op', input_tensors=[], + n_inputs=0, output_tensors=[], + n_outputs=0, op_type='no_op', backend='tensorflow', op_attr={ diff --git a/tests/test_matcher/test_permutation/test_match_patrn1.py b/tests/test_matcher/test_permutation/test_match_patrn1.py index f93ae101..b00402e3 100644 --- a/tests/test_matcher/test_permutation/test_match_patrn1.py +++ b/tests/test_matcher/test_permutation/test_match_patrn1.py @@ -1,5 +1,6 @@ from utensor_cgen.matcher import uTensorGraphMatcher + def test_id_match(patrn_ugraph): matcher = uTensorGraphMatcher(patrn_ugraph) matches = matcher.match(patrn_ugraph) diff --git a/utensor_cgen/frontend/tensorflow.py b/utensor_cgen/frontend/tensorflow.py index 51617dcf..4bae1467 100644 --- a/utensor_cgen/frontend/tensorflow.py +++ b/utensor_cgen/frontend/tensorflow.py @@ -1,14 +1,15 @@ from __future__ import absolute_import + import os + +import numpy as np import six import tensorflow as tf -import numpy as np from google.protobuf import text_format - -from utensor_cgen.frontend.base import Parser from utensor_cgen.frontend import FrontendSelector -from utensor_cgen.ir.base import TensorInfo, OperationInfo, uTensorGraph +from utensor_cgen.frontend.base import Parser +from utensor_cgen.ir.base import OperationInfo, TensorInfo, uTensorGraph from utensor_cgen.utils import topologic_order_graph @@ -47,7 +48,9 @@ def parse(cls, pb_file, output_nodes=None): op_attr = node.attr op_info = OperationInfo(name=node.name, input_tensors=in_tensors, + n_inputs=len(in_tensors), output_tensors=out_tensors, + n_outputs=len(out_tensors), op_type=op_type, backend='tensorflow', op_attr=op_attr, @@ -85,4 +88,4 @@ def _tf_parse_tshape(t_shape): @classmethod def _tf_is_freeze_graph(self, graph_def): is_frozen = all(node.op not in ['VariableV2'] for node in graph_def.node) - return is_frozen \ No newline at end of file + return is_frozen diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index cb24b1ce..7ea222cc 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -5,21 +5,25 @@ import attr import numpy as np import six -import tensorflow as tf from attr.validators import instance_of + +import tensorflow as tf from tensorflow.core.framework.attr_value_pb2 import AttrValue as _AttrValue -from tensorflow.core.framework.attr_value_pb2 import ( - NameAttrList as _NameAttrList) +from tensorflow.core.framework.attr_value_pb2 import \ + NameAttrList as _NameAttrList from tensorflow.core.framework.tensor_pb2 import TensorProto as _TensorProto -from tensorflow.core.framework.tensor_shape_pb2 import ( - TensorShapeProto as _TensorShapeProto) +from tensorflow.core.framework.tensor_shape_pb2 import \ + TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType - from utensor_cgen.utils import topologic_order_graph from .converter import AttrValueConverter, ConverterFactory -__all__ = ['TensorInfo', 'OperationInfo', 'MetaOperationInfo', 'uTensorGraph'] +__all__ = [ + 'TensorInfo', 'OperationInfo', + 'MetaOperationInfo', 'uTensorGraph', + 'uTensorGraphView' +] class _NoShallowCopyMixin(object): @@ -132,8 +136,12 @@ def check(self, attrib, value): input_tensors = attr.ib(validator=instance_of(list)) @input_tensors.validator def check(self, attribute, value): - if not all([isinstance(v, TensorInfo) for v in value]): + # FIXME: we need a refactor of IR, allowing None here is just temporary + # especially for graph rewrite + if not all([isinstance(v, (TensorInfo, type(None))) for v in value]): raise ValueError('Expecting a list of TensorInfo for input_tensors') + + n_inputs = attr.ib(validator=instance_of(int)) output_tensors = attr.ib(validator=instance_of(list)) @output_tensors.validator @@ -141,6 +149,8 @@ def check(self, attribute, value): if not all([isinstance(v, TensorInfo) for v in value]): raise ValueError('Expecting a list of TensorInfo for output_tensors') + n_outputs = attr.ib(validator=instance_of(int)) + op_type = attr.ib(type=str) op_attr = attr.ib(factory=dict, converter=dict) @@ -178,14 +188,6 @@ def is_dangling(self): """ return None in self.input_nodes - @property - def n_inputs(self): - return len(self.input_tensors) - - @property - def n_outputs(self): - return len(self.output_tensors) - def __attrs_post_init__(self): skip_pattern = re.compile(r'_utensor_[^_]*') if self.op_attr: @@ -198,11 +200,17 @@ def __attrs_post_init__(self): op_attr[k] = ConverterFactory.get_generic_value(v) self.op_attr = op_attr self._ugraph.ops_info[self.name] = self + if not self.n_inputs == len(self.input_tensors): + raise ValueError('n_inputs is not equal to the length of input_tensors') + if not self.n_outputs == len(self.output_tensors): + raise ValueError('n_outputs is not equal to the length of output_tensors') def __deepcopy__(self, memo): op_info = OperationInfo(name=self.name, input_tensors=deepcopy(self.input_tensors, memo), + n_inputs=self.n_inputs, output_tensors=deepcopy(self.output_tensors, memo), + n_outputs=self.n_outputs, op_type=self.op_type, backend=self.backend, op_attr=deepcopy(self.op_attr, memo), @@ -222,6 +230,7 @@ def __init__(self, op_info, morphism): def __getattr__(self, name): return getattr(self._op_info, name) + @attr.s class uTensorGraph(IRBase, _NoShallowCopyMixin): """ @@ -273,6 +282,14 @@ def get_ops_by_type(self, given_op_type): @property def output_ops(self): return [self.ops_info[name] for name in self.output_nodes] + + @property + def output_tensors(self): + out_tensors = [] + for op in self.output_ops: + for tensor in op.output_tensors: + out_tensors.append(tensor) + return out_tensors @property def input_ops(self): @@ -282,6 +299,14 @@ def input_ops(self): ops.append(op) return ops + @property + def input_tensors(self): + in_tensors = [] + for op in self.input_ops: + for tensor in op.input_tensors: + in_tensors.append(tensor) + return in_tensors + @property def backend(self): return self._backend @@ -343,3 +368,56 @@ def __deepcopy__(self, memo): new_graph._backend = self._backend topologic_order_graph(new_graph) return new_graph + + +@attr.s +class uTensorGraphView(IRBase, _NoShallowCopyMixin): + + _ugraph = attr.ib(type=uTensorGraph) + _op_names = attr.ib(type=list) + output_nodes = attr.ib(type=list) + + topo_order = attr.ib(init=False, factory=list) + ops_info = attr.ib(init=False, factory=dict) + + def __attrs_post_init__(self): + for name in self._op_names: + self.ops_info[name] = self._ugraph.ops_info[name] + topologic_order_graph(self) + + @property + def backend(self): + return self._ugraph.backend + + @property + def input_ops(self): + ops = [] + for name in self.topo_order: + op = self.ops_info[name] + input_tensors = op.input_tensors + if all([ + tensor.op.name not in self.ops_info + for tensor in input_tensors + ]): + ops.append(op) + return ops + + @property + def input_tensors(self): + in_tensors = [] + for op in self.input_ops: + for tensor in op.input_tensors: + in_tensors.append(tensor) + return in_tensors + + @property + def output_ops(self): + return [self.ops_info[name] for name in self.output_nodes] + + @property + def output_tensors(self): + out_tensors = [] + for op in self.output_ops: + for tensor in op.output_tensors: + out_tensors.append(tensor) + return out_tensors diff --git a/utensor_cgen/ir/misc/graph_viz.py b/utensor_cgen/ir/misc/graph_viz.py index 41abdc5a..e3685dbd 100644 --- a/utensor_cgen/ir/misc/graph_viz.py +++ b/utensor_cgen/ir/misc/graph_viz.py @@ -1,31 +1,32 @@ -from utensor_cgen.ir import OperationInfo, uTensorGraph -from utensor_cgen.logger import logger from graphviz import Digraph +from utensor_cgen.logger import logger + + def viz_graph(out_fname, view, ugraph): - dot = Digraph() - nodes = {} - i = 0 - for node in ugraph.ops: - nodes[node.name] = chr(ord('a') + i) - dot.node(nodes[node.name], "%s: %s" % (node.name, node.op_type)) - i += 1 - for n in node.input_tensors: - if n.name in nodes: - continue - nodes[n.name] = chr(ord('a') + i) - dot.node(nodes[n.name], "%s: Tensor" % n.name) - i += 1 - for n in node.output_tensors: - if n.name in nodes: - continue - nodes[n.name] = chr(ord('a') + i) - dot.node(nodes[n.name], "%s: Tensor" % n.name) - i += 1 - for node in ugraph.ops: - for n in node.input_tensors: - dot.edge(nodes[n.name], nodes[node.name]) - for n in node.output_tensors: - dot.edge(nodes[node.name], nodes[n.name]) - dot.render(out_fname, view=view) - logger.info('graph visualization file generated: %s', out_fname) \ No newline at end of file + dot = Digraph() + nodes = {} + i = 0 + for node in ugraph.ops: + nodes[node.name] = chr(ord('a') + i) + dot.node(nodes[node.name], "%s: %s" % (node.name, node.op_type)) + i += 1 + for n in node.input_tensors: + if n.name in nodes: + continue + nodes[n.name] = chr(ord('a') + i) + dot.node(nodes[n.name], "%s: Tensor" % n.name) + i += 1 + for n in node.output_tensors: + if n.name in nodes: + continue + nodes[n.name] = chr(ord('a') + i) + dot.node(nodes[n.name], "%s: Tensor" % n.name) + i += 1 + for node in ugraph.ops: + for n in node.input_tensors: + dot.edge(nodes[n.name], nodes[node.name]) + for n in node.output_tensors: + dot.edge(nodes[node.name], nodes[n.name]) + dot.render(out_fname, view=view) + logger.info('graph visualization file generated: %s', out_fname) diff --git a/utensor_cgen/ir/utils.py b/utensor_cgen/ir/utils.py index 482a5d50..998ff6c3 100644 --- a/utensor_cgen/ir/utils.py +++ b/utensor_cgen/ir/utils.py @@ -5,48 +5,6 @@ import tensorflow as tf -def clusters_by_name_scopes(op_infos, name_scope_prefix=None): - """ - Arguements - ---------- - op_infos : list[OperationInfo] - list of parser.OperationInfo - name_scope_prefix : str - the target name scope prefix, e.g `dropout`. - - Return - ------ - clusters : dict - a dictionary of found name_scopes as key and list of - operation names as value - """ - op_infos = deepcopy(op_infos) - if name_scope_prefix is not None: - op_infos = [op_info for op_info in op_infos - if op_info.node_name.startswith(name_scope_prefix)] - - name_scope_map = defaultdict(lambda: []) - visited = set([]) - for op_info in op_infos: - current_name_scope = op_infos[0].node_name.split('/')[0] - if op_info.node_name in visited: - continue - queue = [parse_tensor_name(tensor[0])[0] - for tensor in op_info.input_tensor + op_info.output_tensor] - cluster = set([]) - while len(queue) > 0: - op_name = queue.pop(0) - cluster.add(op_name) - input_tensors = op_info[op_name].input_tensor - output_tensors = op_info[op_name].output_tensor - all_ops = [parse_tensor_name(tensor[0])[0] - for tensor in input_tensors + output_tensors] - queue.extend(all_ops) - name_scope_map[current_name_scope] = cluster - visited.update(cluster) - return name_scope_map - - def is_list_of(vtype): def check(inst, attrib, value): is_valid = True @@ -66,4 +24,4 @@ def graph_check(graph): assert input_tensor_info.op_name in graph.ops_info, "In %r: input tensor %r points to non-existing op %r" % (op_name, input_tensor_info.name, input_tensor_info.op_name) assert input_tensor_info.op_name in graph.topo_order, "In %r: input tensor %r points to an op (%r) that does not exist in graph.topo_order" % (op_name, input_tensor_info.name, input_tensor_info.op_name) - #assert len(graph.ops_info) == len(graph.topo_order) \ No newline at end of file + #assert len(graph.ops_info) == len(graph.topo_order) diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index 38cfe9f7..ff25139b 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -1,15 +1,17 @@ from collections import deque +from copy import deepcopy from itertools import product -from string import ascii_letters, digits from random import choices -from copy import deepcopy +from string import ascii_letters, digits import attr from attr.validators import instance_of -from utensor_cgen.ir import MetaOperationInfo, OperationInfo, uTensorGraph +from utensor_cgen.ir import (MetaOperationInfo, OperationInfo, uTensorGraph, + uTensorGraphView) from utensor_cgen.matcher._morphism import Morphism -from utensor_cgen.utils import ops_bfs_queue, topologic_order_graph +from utensor_cgen.utils import (ops_bfs_queue, prune_graph, + topologic_order_graph) __all__ = ["uTensorGraphMatcher"] @@ -89,7 +91,9 @@ def query(cls, sub_op, patrn_op): backend=sub_op.backend, ugraph=sub_op.ugraph, input_tensors=[sub_op.input_tensors[j] for j in perm], + n_inputs=sub_op.n_inputs, output_tensors=sub_op.output_tensors, + n_outputs=sub_op.n_outputs, op_type=sub_op.op_type, op_attr=sub_op.op_attr, ) @@ -209,6 +213,41 @@ def update_op_map(self, pattern_op, subj_op): for pattern_tensor, target_tensor in zip(pattern_op.input_tensors, subj_op.input_tensors): self.patrn2subj_tensor_map[pattern_tensor.name] = target_tensor self.subj2patrn_tensor_map[target_tensor.name] = pattern_tensor + + @property + def is_valid(self): + """Check if the match is valid + 1. only input/output ops of the subgraph view are allowed to have external linkage + 2. input ops have only external linkage for its inputs + 3. output ops have only external linkage for its outputs + + If any of above fail, there is no trivial way to determine how to replace the matched + subgraph other than very hard code way. + """ + subj_view = self.subject_graph_view + valid = True + checked_ops = set() + for in_op in subj_view.input_ops: + for op in in_op.output_nodes: + if op.name not in subj_view.ops_info: + valid = False + checked_ops.add(in_op.name) + for out_op in subj_view.output_ops: + for op in out_op.input_nodes: + if op.name not in subj_view.ops_info: + valid = False + checked_ops.add(out_op.name) + + for name, op in subj_view.ops_info.items(): + if name in checked_ops: + continue + for in_op in op.input_nodes: + if in_op.name not in subj_view.ops_info: + valid = False + for out_op in op.output_nodes: + if out_op.name not in subj_view.ops_info: + valid = Falses + return valid def replace_with(self, callback, suffix=None): """ @@ -217,9 +256,6 @@ def replace_with(self, callback, suffix=None): Arguments --------- callback : callable - a callable which takes the match object and return a new ugraph to be replaced with - and a dict which maps the name of input nodes of pattern ugraph to the names of input - nodes of replacing ugraph Return ------ @@ -227,60 +263,66 @@ def replace_with(self, callback, suffix=None): a *new* graph with matched subgraph replaced with the graph given by the callback """ # build a matched subgraph and pass it to callback - replace_ugraph, input_map = callback(self) - if not self._is_replacible(replace_ugraph): + # input_map (dict): + # { + # (input op name of pattern graph, index) : (op name of replacing graph, index) + # } + # output_map (dict): + # { + # (output op name of pattern graph, index) : (op name of replacing graph, index) + # } + replace_ugraph, input_map, output_map = callback(self) + replaceible, reasons = self._is_replacible(replace_ugraph, input_map, output_map) + if not replaceible: raise ValueError( - 'matched subgraph can not be replaced with the ugraph given' + 'matched subgraph can not be replaced with the ugraph given: {}'.format(reasons) ) - replace_ugraph, input_map = self.new_ugraph_with_suffix( - replace_ugraph, input_map, suffix + replace_ugraph, input_map, output_map = self.new_ugraph_with_suffix( + replace_ugraph, input_map, output_map, suffix ) new_ugraph = deepcopy(self.subject_ugraph) + subj_graph_view = self.subject_graph_view + # replacing input tensors + for (patrn_op_name, patrn_idx), (repl_op_name, repl_idx) in input_map.items(): + subj_op = self.patrn2subj_op_map[patrn_op_name] + subj_in_tensor = subj_op.input_tensors[patrn_idx] + repl_op = replace_ugraph.ops_info[repl_op_name] + repl_op.input_tensors[repl_idx] = subj_in_tensor + # replacing output tensors + new_ugraph = prune_graph(new_ugraph) + return new_ugraph - def _is_replacible(self, replace_ugraph): - sub_ugraph = self._build_subgraph() + def _is_replacible(self, replace_ugraph, input_map, output_map): + """Given a ugraph to replace with, check if it's replacible with + the matched sub ugraph + """ replacible = True - if len(replace_ugraph.ouptut_nodes) != len(sub_ugraph.output_nodes): + reasons = [] + if not self.is_valid: + replaceible = False + reasons.append('the match is not valid') + subj_graph_view = self.subject_graph_view + if len(input_map) != len(subj_graph_view.input_tensors): replacible = False - if len(replace_ugraph.input_ops) != len(sub_ugraph.input_ops): + reasons.append('the number of input tensors does not match') + if len(output_map) != len(subj_graph_view.output_tensors): replacible = False - input_node_names = set([op.name for op in sub_ugraph.input_ops]) - output_node_names = set(sub_ugraph.output_nodes) - for subj_op in self.patrn2subj_op_map.values(): - if subj_op.name in input_node_names: - continue - for in_op in subj_op.input_nodes: - if in_op.name not in sub_ugraph.ops_info: - replacible = False - if subj_op.name in output_node_names: + reasons.append('the number of output tensors does not match') + for in_patrn_op_name, _ in input_map: + if not in_patrn_op_name in self.pattern_ugraph.ops_info: + replacible = False + reasons.append( + '{} is not found in the pattern graph'.format(in_patrn_op_name) + ) continue - for out_op in subj_op.output_nodes: - if out_op.name not in sub_ugraph.ops_info: - replacible = False - return replacible - - def _build_subgraph(self): - output_nodes = [ - self.get_op_in_subject_ugraph(name) - for name in self.pattern_ugraph.output_nodes - ] - ugraph = uTensorGraph( - output_nodes=[op.name for op in output_nodes], - backend=self.subject_ugraph.backend - ) - ugraph.output_nodes = [op.name for op in output_nodes] - for op_name in self.pattern_ugraph.ops_info: - op = self.get_op_in_subject_ugraph(op_name) - ugraph.ops_info[op.name] = deepcopy(op, {'ugraph': ugraph}) - topologic_order_graph(ugraph) - return ugraph - - def get_op_in_subject_ugraph(self, patrn_op_name): - return self.patrn2subj_op_map[patrn_op_name] - - def get_op_in_pattern_ugraph(self, subj_op_name): - return self.subj2patrn_op_map[subj_op_name] + for out_patrn_op_name, _ in output_map: + if not out_patrn_op_name in self.pattern_ugraph.ops_info: + replacible = False + reasons.append( + '{} is not found in the pattern graph'.format(out_patrn_op_name) + ) + return replacible, reasons CHARSET = ascii_letters + digits @@ -290,10 +332,15 @@ def _random_suffix(cls, length=8): return ''.join(chars) @classmethod - def new_ugraph_with_suffix(cls, ugraph, input_map, suffix=None): + def new_ugraph_with_suffix(cls, ugraph, input_map, output_map, suffix=None): if suffix is None: suffix = cls._random_suffix() - new_input_map = {k: '{}_{}'.format(v, suffix) for k, v in input_map.items()} + new_input_map = {} + for (op_name, idx), v in input_map.items(): + new_input_map[('{}_{}'.format(op_name, suffix), idx)] = v + new_output_map = {} + for (op_name, idx), v in output_map.items(): + new_output_map[('{}_{}'.format(op_name, suffix), idx)] = v new_ugraph = deepcopy(ugraph) new_ugraph.output_nodes = [ '{}_{}'.format(name, suffix) for name in ugraph.output_nodes @@ -318,7 +365,20 @@ def new_ugraph_with_suffix(cls, ugraph, input_map, suffix=None): for op_name in ops_to_remove: new_ugraph.ops_info.pop(op_name) new_ugraph.ops_info = new_ops_info - return new_ugraph, new_input_map + return new_ugraph, new_input_map, new_output_map + + @property + def subject_graph_view(self): + output_nodes = [ + self.patrn2subj_op_map[name].name + for name in self.pattern_ugraph.output_nodes + ] + op_names = list(self.subj2patrn_op_map.keys()) + return uTensorGraphView( + ugraph=self.subject_ugraph, + op_names=op_names, + output_nodes=output_nodes, + ) @attr.s diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 90dbf1a1..bbf80394 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -84,7 +84,9 @@ def transform(self, ugraph): in_t_infos.insert(i, dropout_in_tensor) new_op_info = OperationInfo(name=op_info.name, input_tensors=in_t_infos, + n_inputs=len(in_t_infos), output_tensors=out_t_infos, + n_outputs=len(out_t_infos), op_type=op_info.op_type, backend=op_info.backend, op_attr=op_attr, diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index b2839ae1..a524c725 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -262,6 +262,7 @@ def visit(node_name): visit(node_name) ugraph.topo_order = ops_torder[::-1] + def ops_bfs_queue(ugraph, init_nodes=None): if init_nodes is None: init_nodes = [ @@ -280,6 +281,7 @@ def ops_bfs_queue(ugraph, init_nodes=None): bfs_deck.append(op) return bfs_deck + def prune_graph(ugraph): """Remove nodes that is no longer needed """ @@ -319,4 +321,4 @@ def prune_graph(ugraph): ops_to_remove.add(op_name) for op_name in ops_to_remove: new_ugraph.ops_info.pop(op_name) - return new_ugraph \ No newline at end of file + return new_ugraph From 1bf33d5bd5921da540b62dc836a93e8932d15aee Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 9 May 2019 22:56:22 +0800 Subject: [PATCH 20/70] fix bug --- utensor_cgen/backend/code_generator.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/utensor_cgen/backend/code_generator.py b/utensor_cgen/backend/code_generator.py index f01be1d2..dee352ce 100644 --- a/utensor_cgen/backend/code_generator.py +++ b/utensor_cgen/backend/code_generator.py @@ -5,12 +5,12 @@ from tempfile import NamedTemporaryFile import numpy as np + import tensorflow as tf from tensorflow.core.framework.graph_pb2 import GraphDef from tensorflow.tools.graph_transforms import TransformGraph - -from utensor_cgen.ir import uTensorGraph from utensor_cgen.frontend import FrontendSelector +from utensor_cgen.ir import uTensorGraph from utensor_cgen.transformer.optimizer import RefCntOptimizer from utensor_cgen.transformer.pipline import TransformerPipeline from utensor_cgen.utils import NamescopedKWArgsParser @@ -118,7 +118,7 @@ def formatter(name, kwargs): container.add_snippet(cmt_snippet) composer.add_snippet(container) - if 'inline' in self.trans_methods: + if ('inline', {}) in self.trans_methods: _logger.info("Generate weight file: %s", weightheader_fname) with open(weightheader_fname, "w") as wf: wf.write('// Auto generated by utensor-cli\n\n') From ad00ee1f77861feb7a9e8397cffa0d0c33b91ba4 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 11 May 2019 01:58:14 +0800 Subject: [PATCH 21/70] null tensor match support --- .../test_matcher/test_permutation/conftest.py | 7 +- .../test_permutation/test_match_patrn1.py | 18 ++-- utensor_cgen/ir/base.py | 73 ++++++++++++--- utensor_cgen/matcher/_matcher_impl.py | 92 ++++++++++++------- utensor_cgen/utils.py | 11 ++- 5 files changed, 150 insertions(+), 51 deletions(-) diff --git a/tests/test_matcher/test_permutation/conftest.py b/tests/test_matcher/test_permutation/conftest.py index 303c69c0..ce2d5ecd 100644 --- a/tests/test_matcher/test_permutation/conftest.py +++ b/tests/test_matcher/test_permutation/conftest.py @@ -1,8 +1,9 @@ -import tensorflow as tf import pytest +import tensorflow as tf from utensor_cgen.frontend.tensorflow import GraphDefParser + @pytest.fixture(scope='function', name='patrn_ugraph') def patrn_ugraph(): graph = tf.Graph() @@ -12,6 +13,7 @@ def patrn_ugraph(): ptrn_add0 = tf.add(ptrn_input0, ptrn_input1, name='add0') ptrn_out = tf.add(ptrn_add0, ptrn_input1, name='output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [ptrn_out.op.name]) + ugraph.ops_info[ptrn_input0.op.name].add_null_input_tensor() return ugraph @pytest.fixture(scope='function', name='subject_ugraph1') @@ -25,6 +27,7 @@ def subject_ugraph1(): sub_add1 = tf.add(sub_add0, sub_input1, name='sub_add1') sub_output = tf.add(sub_add1, sub_input2, name='sub_output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) + ugraph.ops_info[sub_input0.op.name].add_null_input_tensor() return ugraph @pytest.fixture(scope='function', name='subject_ugraph1_1') @@ -39,6 +42,7 @@ def subject_ugraph1_1(): sub_add1 = tf.add(sub_add0, sub_input1, name='sub_add1') sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) + ugraph.ops_info[sub_input0.op.name].add_null_input_tensor() return ugraph @pytest.fixture(scope='function', name='subject_ugraph1_2') @@ -52,4 +56,5 @@ def subject_ugraph1_2(): sub_add1 = tf.add(sub_input1, sub_add0, name='sub_add1') sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) + ugraph.ops_info[sub_input0.op.name].add_null_input_tensor() return ugraph diff --git a/tests/test_matcher/test_permutation/test_match_patrn1.py b/tests/test_matcher/test_permutation/test_match_patrn1.py index b00402e3..f6dedafe 100644 --- a/tests/test_matcher/test_permutation/test_match_patrn1.py +++ b/tests/test_matcher/test_permutation/test_match_patrn1.py @@ -10,14 +10,20 @@ def test_id_match(patrn_ugraph): assert name == op.name, '{} <--> {}'.format(name, op.name) for name, op in match.subj2patrn_op_map.items(): assert name == op.name, '{} <--> {}'.format(name, op.name) + for tensor in patrn_ugraph.input_tensors: + assert tensor.name in match.patrn2subj_tensor_map, \ + '{} is missing'.format(tensor.name) + for tensor in patrn_ugraph.output_tensors: + assert tensor.name in match.subj2patrn_tensor_map, \ + '{} is missing'.format(tensor.name) def test_match_sub1(patrn_ugraph, subject_ugraph1): matcher = uTensorGraphMatcher(patrn_ugraph) matches = matcher.match(subject_ugraph1) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert match.patrn2subj_op_map['input0'].name in ['sub_input0', 'sub_input1'] - assert match.patrn2subj_op_map['input1'].name in ['sub_input0', 'sub_input1'] + assert match.patrn2subj_op_map['input0'].name == 'sub_input0' + assert match.patrn2subj_op_map['input1'].name == 'sub_input1' assert match.patrn2subj_op_map['add0'].name == 'sub_add0' assert match.patrn2subj_op_map['output'].name == 'sub_add1' @@ -26,8 +32,8 @@ def test_match_sub1_1(patrn_ugraph, subject_ugraph1_1): matches = matcher.match(subject_ugraph1_1) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert match.patrn2subj_op_map['input0'].name in ['sub_input0', 'sub_input1'] - assert match.patrn2subj_op_map['input1'].name in ['sub_input0', 'sub_input1'] + assert match.patrn2subj_op_map['input0'].name == 'sub_input0' + assert match.patrn2subj_op_map['input1'].name == 'sub_input1' assert match.patrn2subj_op_map['add0'].name == 'sub_add0' assert match.patrn2subj_op_map['output'].name == 'sub_add1' @@ -36,7 +42,7 @@ def test_match_sub1_2(patrn_ugraph, subject_ugraph1_2): matches = matcher.match_all(subject_ugraph1_2) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert match.patrn2subj_op_map['input0'].name in ['sub_input0', 'sub_input1'] - assert match.patrn2subj_op_map['input1'].name in ['sub_input0', 'sub_input1'] + assert match.patrn2subj_op_map['input0'].name == 'sub_input0' + assert match.patrn2subj_op_map['input1'].name == 'sub_input1' assert match.patrn2subj_op_map['add0'].name == 'sub_add0' assert match.patrn2subj_op_map['output'].name == 'sub_add1' diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 7ea222cc..c24a2124 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -15,7 +15,7 @@ from tensorflow.core.framework.tensor_shape_pb2 import \ TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType -from utensor_cgen.utils import topologic_order_graph +from utensor_cgen.utils import random_str, topologic_order_graph from .converter import AttrValueConverter, ConverterFactory @@ -38,13 +38,19 @@ class IRBase(object): def all_supported_backends(self): return ['tensorflow'] + def __hash__(self): + return id(self) -@attr.s + +@attr.s(cmp=False) class TensorInfo(IRBase, _NoShallowCopyMixin): """ name : str dtype : numpy.dtype shape : list + + TODO: the need for null tensor info, that is, + a tensor which may not be attached to an op """ name = attr.ib(validator=instance_of(six.text_type)) op_name = attr.ib(validator=instance_of(six.text_type)) @@ -92,6 +98,24 @@ def n_th_output(self): op = self.op out_tnames = [t_info.name for t_info in op.output_tensors] return out_tnames.index(self.name) + + _NULL_PREFIX = 'utensor_null' + + @classmethod + def make_null_tensor(cls, ugraph): + op_name = '{}_{}'.format(cls._NULL_PREFIX, random_str()) + name = '{}:0'.format(op_name) + return cls( + name=name, + op_name=op_name, + dtype=np.dtype('float'), + shape=None, + ugraph=ugraph + ) + + @property + def is_null_tensor(self): + return self.op_name.startswith(self._NULL_PREFIX) def __deepcopy__(self, memo): new_tensor = TensorInfo(name=self.name, @@ -102,7 +126,7 @@ def __deepcopy__(self, memo): return new_tensor -@attr.s +@attr.s(cmp=False) class OperationInfo(IRBase, _NoShallowCopyMixin): """ name : str @@ -166,6 +190,8 @@ def backend(self): def input_nodes(self): in_ops = [] for tensor in self.input_tensors: + if tensor.op is None: + continue if tensor.op_name not in in_ops: in_ops.append(tensor.op_name) return [self._ugraph.ops_info.get(name, None) for name in in_ops] @@ -187,6 +213,25 @@ def is_dangling(self): False: otherwise """ return None in self.input_nodes + + @property + def is_input_op(self): + return all( + [tensor.op_name not in self._ugraph.ops_info + for tensor in self.input_tensors] + ) + + def add_null_input_tensor(self, idx=-1): + if idx > len(self.input_tensors): + raise ValueError( + "can't insert null tensor at {} as {} input tensors present".format( + idx, len(self,input_tensors) + ) + ) + null_tensor = TensorInfo.make_null_tensor(ugraph=self._ugraph) + self.input_tensors.insert(idx, null_tensor) + self.n_inputs += 1 + return null_tensor def __attrs_post_init__(self): skip_pattern = re.compile(r'_utensor_[^_]*') @@ -218,6 +263,8 @@ def __deepcopy__(self, memo): return op_info def copy_into_graph(self, ugraph): + """experimental, don't use + """ return deepcopy(self, {'ugraph': ugraph}) @@ -231,7 +278,7 @@ def __getattr__(self, name): return getattr(self._op_info, name) -@attr.s +@attr.s(cmp=False) class uTensorGraph(IRBase, _NoShallowCopyMixin): """ Attributes @@ -285,26 +332,28 @@ def output_ops(self): @property def output_tensors(self): - out_tensors = [] + out_tensors = set([]) for op in self.output_ops: for tensor in op.output_tensors: - out_tensors.append(tensor) + out_tensors.add(tensor) return out_tensors @property def input_ops(self): ops = [] for op in self.ops_info.values(): - if not op.input_tensors: + if not op.input_tensors or all( + [tensor.is_null_tensor for tensor in op.input_tensors] + ): ops.append(op) return ops @property def input_tensors(self): - in_tensors = [] + in_tensors = set([]) for op in self.input_ops: for tensor in op.input_tensors: - in_tensors.append(tensor) + in_tensors.add(tensor) return in_tensors @property @@ -370,7 +419,7 @@ def __deepcopy__(self, memo): return new_graph -@attr.s +@attr.s(cmp=False) class uTensorGraphView(IRBase, _NoShallowCopyMixin): _ugraph = attr.ib(type=uTensorGraph) @@ -391,7 +440,7 @@ def backend(self): @property def input_ops(self): - ops = [] + ops = set([]) for name in self.topo_order: op = self.ops_info[name] input_tensors = op.input_tensors @@ -399,7 +448,7 @@ def input_ops(self): tensor.op.name not in self.ops_info for tensor in input_tensors ]): - ops.append(op) + ops.add(op) return ops @property diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index ff25139b..e4855123 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -1,8 +1,6 @@ from collections import deque from copy import deepcopy from itertools import product -from random import choices -from string import ascii_letters, digits import attr from attr.validators import instance_of @@ -10,7 +8,7 @@ from utensor_cgen.ir import (MetaOperationInfo, OperationInfo, uTensorGraph, uTensorGraphView) from utensor_cgen.matcher._morphism import Morphism -from utensor_cgen.utils import (ops_bfs_queue, prune_graph, +from utensor_cgen.utils import (ops_bfs_queue, prune_graph, random_str, topologic_order_graph) __all__ = ["uTensorGraphMatcher"] @@ -78,7 +76,9 @@ def query(cls, sub_op, patrn_op): is_eq = False equivalent_ops = [] - if sub_op.op_type == patrn_op.op_type and sub_op.op_type not in cls._association_map: + if sub_op is None or patrn_op is None: + pass + elif sub_op.op_type == patrn_op.op_type and sub_op.op_type not in cls._association_map: is_eq = True equivalent_ops = [patrn_op] elif sub_op.op_type == patrn_op.op_type: @@ -102,9 +102,8 @@ def query(cls, sub_op, patrn_op): is_eq = True morphism = cls._compatibility_map[sub_op.op_type][patrn_op.op_type] equivalent_ops = [MetaOperationInfo(op_info=sub_op, morphism=morphism)] - elif not patrn_op.input_tensors and \ - patrn_op.op_type == 'Placeholder': - # match input node which is a placeholder anyway + elif patrn_op.is_input_op: + # match input node anyway is_eq = True equivalent_ops = [sub_op] @@ -143,7 +142,17 @@ def _match(self, other_ugraph): states = [] for state in visited_states: if state.is_done: - yield state.match + input_checked = True + for patrn_op in self.pattern_ugraph.input_ops: + subj_op = state.match.patrn2subj_op_map[patrn_op.name] + if len(subj_op.input_tensors) != len(patrn_op.input_tensors): + input_checked = False + break + for (patrn_tensor, subj_tensor) in zip(patrn_op.input_tensors, subj_op.input_tensors): + state.match.subj2patrn_tensor_map[subj_tensor.name] = patrn_tensor + state.match.patrn2subj_tensor_map[patrn_tensor.name] = subj_tensor + if input_checked: + yield state.match else: states.append(state) @@ -210,7 +219,7 @@ class uTensorGraphMatch(object): def update_op_map(self, pattern_op, subj_op): self.patrn2subj_op_map[pattern_op.name] = subj_op self.subj2patrn_op_map[subj_op.name] = pattern_op - for pattern_tensor, target_tensor in zip(pattern_op.input_tensors, subj_op.input_tensors): + for pattern_tensor, target_tensor in zip(pattern_op.output_tensors, subj_op.output_tensors): self.patrn2subj_tensor_map[pattern_tensor.name] = target_tensor self.subj2patrn_tensor_map[target_tensor.name] = pattern_tensor @@ -263,13 +272,9 @@ def replace_with(self, callback, suffix=None): a *new* graph with matched subgraph replaced with the graph given by the callback """ # build a matched subgraph and pass it to callback - # input_map (dict): - # { - # (input op name of pattern graph, index) : (op name of replacing graph, index) - # } - # output_map (dict): + # input/output_map (dict): # { - # (output op name of pattern graph, index) : (op name of replacing graph, index) + # tensor in pattern graph : tensor in replacing graph # } replace_ugraph, input_map, output_map = callback(self) replaceible, reasons = self._is_replacible(replace_ugraph, input_map, output_map) @@ -283,13 +288,19 @@ def replace_with(self, callback, suffix=None): new_ugraph = deepcopy(self.subject_ugraph) subj_graph_view = self.subject_graph_view # replacing input tensors - for (patrn_op_name, patrn_idx), (repl_op_name, repl_idx) in input_map.items(): - subj_op = self.patrn2subj_op_map[patrn_op_name] - subj_in_tensor = subj_op.input_tensors[patrn_idx] - repl_op = replace_ugraph.ops_info[repl_op_name] - repl_op.input_tensors[repl_idx] = subj_in_tensor + for patrn_in_tensor, repl_in_tensor in input_map.items(): + subj_tensor = self.patrn2subj_tensor_map[patrn_in_tensor.name] + for op in repl_in_tensor.output_nodes: + for i, in_tensor in enumerate(op.input_tensors): + if in_tensor.name == repl_in_tensor.name: + op.input_tensors[i] = subj_tensor # replacing output tensors - + for patrn_out_tensor, repl_out_tensor in output_map.items(): + subj_tensor = self.patrn2subj_tensor_map[patrn_out_tensor.name] + for op in subj_tensor.output_nodes: + for i, in_tensor in enumerate(op.input_tensors): + if in_tensor.name == subj_tensor.name: + op.input_tensors[i] = repl_out_tensor new_ugraph = prune_graph(new_ugraph) return new_ugraph @@ -324,23 +335,15 @@ def _is_replacible(self, replace_ugraph, input_map, output_map): ) return replacible, reasons - CHARSET = ascii_letters + digits - @classmethod def _random_suffix(cls, length=8): - chars = choices(cls.CHARSET, k=length) - return ''.join(chars) + return random_str(length) @classmethod def new_ugraph_with_suffix(cls, ugraph, input_map, output_map, suffix=None): if suffix is None: suffix = cls._random_suffix() - new_input_map = {} - for (op_name, idx), v in input_map.items(): - new_input_map[('{}_{}'.format(op_name, suffix), idx)] = v - new_output_map = {} - for (op_name, idx), v in output_map.items(): - new_output_map[('{}_{}'.format(op_name, suffix), idx)] = v + new_ugraph = deepcopy(ugraph) new_ugraph.output_nodes = [ '{}_{}'.format(name, suffix) for name in ugraph.output_nodes @@ -365,6 +368,33 @@ def new_ugraph_with_suffix(cls, ugraph, input_map, output_map, suffix=None): for op_name in ops_to_remove: new_ugraph.ops_info.pop(op_name) new_ugraph.ops_info = new_ops_info + new_input_map = {} + for op, v in input_map.items(): + new_op = deepcopy(op, {'ugraph': new_ugraph}) + new_op.name = '{}_{}'.format(op.name, suffix) + for in_tensor in new_op.input_tensors: + op_name, idx = in_tensor.name.split(':') + in_tensor.op_name = '{}_{}'.format(op_name, suffix) + in_tensor.name = '{}:{}'.format(in_tensor.op_name, idx) + for out_tensor in new_op.output_tensors: + op_name, idx = out_tensor.name.split(':') + out_tensor.op_name = '{}_{}'.format(op_name, suffix) + out_tensor.name = '{}:{}'.format(out_tensor.op_name, idx) + new_input_map[new_op] = v + new_output_map = {} + for op, v in output_map.items(): + new_op = deepcopy(op, {'ugraph': new_ugraph}) + new_op.name = '{}_{}'.format(op.name, suffix) + for in_tensor in new_op.input_tensors: + op_name, idx = in_tensor.name.split(':') + in_tensor.op_name = '{}_{}'.format(op_name, suffix) + in_tensor.name = '{}:{}'.format(in_tensor.op_name, idx) + for out_tensor in new_op.output_tensors: + op_name, idx = out_tensor.name.split(':') + out_tensor.op_name = '{}_{}'.format(op_name, suffix) + out_tensor.name = '{}:{}'.format(out_tensor.op_name, idx) + new_output_map[new_op] = v + return new_ugraph, new_input_map, new_output_map @property diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index cef82729..50defde3 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -4,6 +4,8 @@ from ast import literal_eval from collections import deque from copy import deepcopy +from random import choices +from string import ascii_letters, digits import numpy as np from click.types import ParamType @@ -274,7 +276,9 @@ def ops_bfs_queue(ugraph, init_nodes=None): while queue: op = queue.popleft() - if op.name in visited: + if op is None or op.name in visited: + # op is None => traversal via a null tensor + # or been visited before continue visited.add(op.name) queue.extend(op.input_nodes) @@ -322,3 +326,8 @@ def prune_graph(ugraph): for op_name in ops_to_remove: new_ugraph.ops_info.pop(op_name) return new_ugraph + + +def random_str(length=8): + chars = choices(ascii_letters+digits, k=length) + return ''.join(chars) From 74a2140a6cea299ae1a24e016eacbc584c9def8c Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 11 May 2019 14:31:58 +0800 Subject: [PATCH 22/70] update tests --- tests/test_matcher/test_permutation/conftest.py | 2 +- tests/test_matcher/test_permutation/test_match_patrn1.py | 9 +++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/tests/test_matcher/test_permutation/conftest.py b/tests/test_matcher/test_permutation/conftest.py index ce2d5ecd..1f1bfadc 100644 --- a/tests/test_matcher/test_permutation/conftest.py +++ b/tests/test_matcher/test_permutation/conftest.py @@ -56,5 +56,5 @@ def subject_ugraph1_2(): sub_add1 = tf.add(sub_input1, sub_add0, name='sub_add1') sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) - ugraph.ops_info[sub_input0.op.name].add_null_input_tensor() + ugraph.ops_info[sub_input1.op.name].add_null_input_tensor() return ugraph diff --git a/tests/test_matcher/test_permutation/test_match_patrn1.py b/tests/test_matcher/test_permutation/test_match_patrn1.py index f6dedafe..48c51d60 100644 --- a/tests/test_matcher/test_permutation/test_match_patrn1.py +++ b/tests/test_matcher/test_permutation/test_match_patrn1.py @@ -19,9 +19,10 @@ def test_id_match(patrn_ugraph): def test_match_sub1(patrn_ugraph, subject_ugraph1): matcher = uTensorGraphMatcher(patrn_ugraph) - matches = matcher.match(subject_ugraph1) + matches = matcher.match_all(subject_ugraph1) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] + assert len(matches) == 1, 'should be exactly one match, get {}'.format(len(matches)) assert match.patrn2subj_op_map['input0'].name == 'sub_input0' assert match.patrn2subj_op_map['input1'].name == 'sub_input1' assert match.patrn2subj_op_map['add0'].name == 'sub_add0' @@ -39,10 +40,10 @@ def test_match_sub1_1(patrn_ugraph, subject_ugraph1_1): def test_match_sub1_2(patrn_ugraph, subject_ugraph1_2): matcher = uTensorGraphMatcher(patrn_ugraph) - matches = matcher.match_all(subject_ugraph1_2) + matches = matcher.match(subject_ugraph1_2) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert match.patrn2subj_op_map['input0'].name == 'sub_input0' - assert match.patrn2subj_op_map['input1'].name == 'sub_input1' + assert match.patrn2subj_op_map['input0'].name == 'sub_input1' + assert match.patrn2subj_op_map['input1'].name == 'sub_input0' assert match.patrn2subj_op_map['add0'].name == 'sub_add0' assert match.patrn2subj_op_map['output'].name == 'sub_add1' From 654a42f15e7ebdb4fd43023c3a61bd88d0874823 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 11 May 2019 14:51:33 +0800 Subject: [PATCH 23/70] showing unsupported ops with cli: show subcmd --- utensor_cgen/backend/code_generator.py | 2 +- utensor_cgen/backend/operators.py | 10 ++++++++-- utensor_cgen/cli.py | 16 +++++++++++++++- 3 files changed, 24 insertions(+), 4 deletions(-) diff --git a/utensor_cgen/backend/code_generator.py b/utensor_cgen/backend/code_generator.py index dee352ce..790a6d1a 100644 --- a/utensor_cgen/backend/code_generator.py +++ b/utensor_cgen/backend/code_generator.py @@ -118,7 +118,7 @@ def formatter(name, kwargs): container.add_snippet(cmt_snippet) composer.add_snippet(container) - if ('inline', {}) in self.trans_methods: + if 'inline' in [name for name, _ in self.trans_methods]: _logger.info("Generate weight file: %s", weightheader_fname) with open(weightheader_fname, "w") as wf: wf.write('// Auto generated by utensor-cli\n\n') diff --git a/utensor_cgen/backend/operators.py b/utensor_cgen/backend/operators.py index c0ed5d02..0949cf65 100644 --- a/utensor_cgen/backend/operators.py +++ b/utensor_cgen/backend/operators.py @@ -33,9 +33,15 @@ def register(cls, op_cls): @classmethod def support_op_types(cls): - """Return the list of all supported ops + """Return the set of all supported ops """ - return list(cls._operators.keys()) + return set(cls._operators.keys()) + + @classmethod + def is_supported(cls, op_type): + if op_type != 'Placeholder' and op_type not in cls._operators: + return False + return True class _Operator(object): diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index a03b7ce4..22f5e946 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -5,7 +5,8 @@ import click import pkg_resources -from .utils import NArgsParam, NArgsKwargsParam +from utensor_cgen.backend.operators import OperatorFactory +from utensor_cgen.utils import NArgsKwargsParam, NArgsParam def _get_pb_model_name(path): @@ -111,6 +112,7 @@ def _show_pb_file(pb_file, **kwargs): def _show_ugraph(ugraph, oneline=False): import textwrap + unknown_ops = set([]) if oneline: tmpl = click.style("{op_name} ", fg='yellow', bold=True) + \ "op_type: {op_type}, inputs: {inputs}, outputs: {outputs}" @@ -120,6 +122,8 @@ def _show_ugraph(ugraph, oneline=False): inputs=[tensor.name for tensor in op_info.input_tensors], outputs=[tensor.name for tensor in op_info.output_tensors]) click.echo(msg) + if not OperatorFactory.is_supported(op_info.op_type): + unknown_ops.add(op_info) else: tmpl = click.style('op_name: {op_name}\n', fg='yellow', bold=True) + \ '''\ @@ -139,7 +143,17 @@ def _show_ugraph(ugraph, oneline=False): inputs=op_info.input_tensors, outputs=op_info.output_tensors) paragraphs.append(op_str) + if not OperatorFactory.is_supported(op_info.op_type): + unknown_ops.add(op_info) click.echo('\n'.join(paragraphs)) + if unknown_ops: + click.echo( + click.style('Unknown Ops Detected', fg='red', bold=True) + ) + for op_info in unknown_ops: + click.echo( + click.style(' {}: {}'.format(op_info.name, op_info.op_type), fg='red') + ) return 0 if __name__ == '__main__': From 4be2c6aab6c236204a7e9dc17e66f51321a8a344 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 19 May 2019 16:29:45 +0800 Subject: [PATCH 24/70] Fix bug and update tests --- .../test_matcher/test_permutation/conftest.py | 6 +++--- utensor_cgen/ir/base.py | 11 ++++++++++ utensor_cgen/matcher/_matcher_impl.py | 20 +++++++++++++------ 3 files changed, 28 insertions(+), 9 deletions(-) diff --git a/tests/test_matcher/test_permutation/conftest.py b/tests/test_matcher/test_permutation/conftest.py index 1f1bfadc..c27c2e36 100644 --- a/tests/test_matcher/test_permutation/conftest.py +++ b/tests/test_matcher/test_permutation/conftest.py @@ -20,7 +20,7 @@ def patrn_ugraph(): def subject_ugraph1(): graph = tf.Graph() with graph.as_default(): - sub_input0 = tf.constant([i for i in range(10)], name='sub_input0') + sub_input0 = tf.placeholder(name='sub_input0', dtype=tf.int32) sub_input1 = tf.constant([i for i in range(10)], name='sub_input1') sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') sub_add0 = tf.add(sub_input0, sub_input1, name='sub_add0') @@ -34,7 +34,7 @@ def subject_ugraph1(): def subject_ugraph1_1(): graph = tf.Graph() with graph.as_default(): - sub_input0 = tf.constant([i for i in range(10)], name='sub_input0') + sub_input0 = tf.placeholder(name='sub_input0', dtype=tf.int32) sub_input1 = tf.constant([i for i in range(10)], name='sub_input1') sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') # permute @@ -50,7 +50,7 @@ def subject_ugraph1_2(): graph = tf.Graph() with graph.as_default(): sub_input0 = tf.constant([i for i in range(10)], name='sub_input0') - sub_input1 = tf.constant([i for i in range(10)], name='sub_input1') + sub_input1 = tf.placeholder(name='sub_input1', dtype=tf.int32) sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') sub_add0 = tf.add(sub_input0, sub_input1, name='sub_add0') sub_add1 = tf.add(sub_input1, sub_add0, name='sub_add1') diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index c24a2124..f2c0c6c1 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -222,6 +222,10 @@ def is_input_op(self): ) def add_null_input_tensor(self, idx=-1): + if self.op_type != 'Placeholder': + raise ValueError( + 'can only add null tensor to op of type Placeholder: %s' % self.op_type + ) if idx > len(self.input_tensors): raise ValueError( "can't insert null tensor at {} as {} input tensors present".format( @@ -232,6 +236,13 @@ def add_null_input_tensor(self, idx=-1): self.input_tensors.insert(idx, null_tensor) self.n_inputs += 1 return null_tensor + + def replace_with_null_input_tensor(self, idx): + if idx >= len(self.input_tensors): + raise ValueError( + 'index out of bound: %s' % idx + ) + self.input_tensors[idx] = TensorInfo.make_null_tensor(ugraph=self._ugraph) def __attrs_post_init__(self): skip_pattern = re.compile(r'_utensor_[^_]*') diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index e4855123..4ddc3a55 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -80,7 +80,7 @@ def query(cls, sub_op, patrn_op): pass elif sub_op.op_type == patrn_op.op_type and sub_op.op_type not in cls._association_map: is_eq = True - equivalent_ops = [patrn_op] + equivalent_ops = [sub_op] elif sub_op.op_type == patrn_op.op_type: is_eq = True equivalent_ops = [] @@ -172,7 +172,6 @@ def match_all(self, other_ugraph): def _visit(self, states): # visit the state with a top-down bfs fashion # return the states that are still matched - # import pdb; pdb.set_trace() new_states = [] for state in states: match = state.match @@ -182,10 +181,19 @@ def _visit(self, states): if is_eq: for eq_op in eq_ops: new_sub_bfs_queue = deque(state.sub_bfs_queue) - for _ in eq_op.input_nodes: - new_sub_bfs_queue.popleft() - for node in eq_op.input_nodes[::-1]: - new_sub_bfs_queue.insert(0, node) + in_nodes = eq_op.input_nodes + in_names = set([op.name for op in in_nodes]) + in_idx = 0 + for i, op in enumerate(new_sub_bfs_queue): + if op.name in in_names: + new_sub_bfs_queue[i] = in_nodes[in_idx] + in_idx += 1 + if in_idx >= len(in_nodes): + break + # for _ in eq_op.input_nodes: + # new_sub_bfs_queue.popleft() + # for node in eq_op.input_nodes[::-1]: + # new_sub_bfs_queue.insert(0, node) new_state = _MatchState( match=uTensorGraphMatch( pattern_ugraph=match.pattern_ugraph, From 4aea27ec0d78b09c6f1a0762851aa5a89bec3492 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 19 May 2019 17:14:28 +0800 Subject: [PATCH 25/70] Add replace method and bugs fixed Ready for tests --- utensor_cgen/ir/base.py | 33 +++++--- utensor_cgen/matcher/_matcher_impl.py | 108 +++++++++++++------------- utensor_cgen/utils.py | 12 +-- 3 files changed, 84 insertions(+), 69 deletions(-) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index f2c0c6c1..cd72abd1 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -5,9 +5,8 @@ import attr import numpy as np import six -from attr.validators import instance_of - import tensorflow as tf +from attr.validators import instance_of from tensorflow.core.framework.attr_value_pb2 import AttrValue as _AttrValue from tensorflow.core.framework.attr_value_pb2 import \ NameAttrList as _NameAttrList @@ -15,6 +14,7 @@ from tensorflow.core.framework.tensor_shape_pb2 import \ TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType + from utensor_cgen.utils import random_str, topologic_order_graph from .converter import AttrValueConverter, ConverterFactory @@ -38,9 +38,6 @@ class IRBase(object): def all_supported_backends(self): return ['tensorflow'] - def __hash__(self): - return id(self) - @attr.s(cmp=False) class TensorInfo(IRBase, _NoShallowCopyMixin): @@ -124,6 +121,14 @@ def __deepcopy__(self, memo): dtype=self.dtype, shape=deepcopy(self.shape, memo)) return new_tensor + + def __hash__(self): + return hash(self.name) + + def __eq__(self, other): + if not isinstance(other, type(self)): + return False + return self.name == other.name @attr.s(cmp=False) @@ -273,6 +278,14 @@ def __deepcopy__(self, memo): ugraph=memo['ugraph']) return op_info + def __hash__(self): + return hash(self.name) + + def __eq__(self, other): + if not isinstance(other, type(self)): + return False + return self.name == other.name + def copy_into_graph(self, ugraph): """experimental, don't use """ @@ -404,10 +417,8 @@ def add_op(self, op): raise ValueError('expecting OperationInfo, get {}'.format(type(op))) if op.name in self.ops_info: raise ValueError('duplicate op detected, {}'.format(op.name)) - op.ugraph = self + op._ugraph = self - # if(op.name == 'convert_uint8_q7_Relu/eightbit_transpose_0_q7'): - # import pdb; pdb.set_trace() self.ops_info[op.name] = op topologic_order_graph(self) @@ -418,14 +429,16 @@ def drop_op(self, op_name): self.topo_order.remove(op_name) def __deepcopy__(self, memo): - new_graph = uTensorGraph(output_nodes=self.output_nodes) + new_graph = uTensorGraph( + output_nodes=self.output_nodes, + backend=self._backend + ) memo['ugraph'] = new_graph new_graph.ops_info = { k: deepcopy(v, memo) for k, v in self.ops_info.items() } - new_graph._backend = self._backend topologic_order_graph(new_graph) return new_graph diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index 4ddc3a55..f1daadf7 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -190,10 +190,6 @@ def _visit(self, states): in_idx += 1 if in_idx >= len(in_nodes): break - # for _ in eq_op.input_nodes: - # new_sub_bfs_queue.popleft() - # for node in eq_op.input_nodes[::-1]: - # new_sub_bfs_queue.insert(0, node) new_state = _MatchState( match=uTensorGraphMatch( pattern_ugraph=match.pattern_ugraph, @@ -294,21 +290,36 @@ def replace_with(self, callback, suffix=None): replace_ugraph, input_map, output_map, suffix ) new_ugraph = deepcopy(self.subject_ugraph) + for op in replace_ugraph.ops_info.values(): + op._ugraph = new_ugraph + for tensor in op.input_tensors: + tensor._ugraph = new_ugraph + for tensor in op.output_tensors: + tensor._ugraph = new_ugraph + for tensor in input_map.values(): + tensor._ugraph = new_ugraph + for tensor in output_map.values(): + tensor._ugraph = new_ugraph subj_graph_view = self.subject_graph_view - # replacing input tensors - for patrn_in_tensor, repl_in_tensor in input_map.items(): - subj_tensor = self.patrn2subj_tensor_map[patrn_in_tensor.name] - for op in repl_in_tensor.output_nodes: - for i, in_tensor in enumerate(op.input_tensors): - if in_tensor.name == repl_in_tensor.name: - op.input_tensors[i] = subj_tensor # replacing output tensors - for patrn_out_tensor, repl_out_tensor in output_map.items(): - subj_tensor = self.patrn2subj_tensor_map[patrn_out_tensor.name] - for op in subj_tensor.output_nodes: - for i, in_tensor in enumerate(op.input_tensors): - if in_tensor.name == subj_tensor.name: + for out_tensor in subj_graph_view.output_tensors: + repl_out_tensor = output_map[self.subj2patrn_tensor_map[out_tensor.name]] + out_ops = [new_ugraph.ops_info[op.name] for op in out_tensor.op.output_nodes] + for op in out_ops: + for i, tensor in enumerate(op.input_tensors): + if tensor.name == out_tensor.name: op.input_tensors[i] = repl_out_tensor + # replacing input tensors + inv_input_map = dict([(v, k) for k, v in input_map.items()]) + for op in replace_ugraph.input_ops: + for i, repl_in_tensor in enumerate(op.input_tensors): + patrn_in_tensor = inv_input_map[repl_in_tensor] + subj_in_tensor = self.patrn2subj_tensor_map[patrn_in_tensor.name] + op.input_tensors[i] = subj_in_tensor + new_ugraph.ops_info.update( + replace_ugraph.ops_info + ) + topologic_order_graph(new_ugraph) new_ugraph = prune_graph(new_ugraph) return new_ugraph @@ -328,15 +339,17 @@ def _is_replacible(self, replace_ugraph, input_map, output_map): if len(output_map) != len(subj_graph_view.output_tensors): replacible = False reasons.append('the number of output tensors does not match') - for in_patrn_op_name, _ in input_map: - if not in_patrn_op_name in self.pattern_ugraph.ops_info: + for in_patrn_tensor, _ in input_map.items(): + in_tensor_names = set([t.name for t in self.pattern_ugraph.input_tensors]) + if not in_patrn_tensor.name in in_tensor_names: replacible = False reasons.append( '{} is not found in the pattern graph'.format(in_patrn_op_name) ) continue - for out_patrn_op_name, _ in output_map: - if not out_patrn_op_name in self.pattern_ugraph.ops_info: + for out_patrn_tensor, _ in output_map.items(): + out_tensor_names = set([t.name for t in self.pattern_ugraph.output_tensors]) + if not out_patrn_tensor.name in out_tensor_names: replacible = False reasons.append( '{} is not found in the pattern graph'.format(out_patrn_op_name) @@ -357,52 +370,39 @@ def new_ugraph_with_suffix(cls, ugraph, input_map, output_map, suffix=None): '{}_{}'.format(name, suffix) for name in ugraph.output_nodes ] new_ugraph.topo_order = ['{}_{}'.format(name, suffix) for name in ugraph.topo_order] - ops_to_remove = set([]) new_ops_info = {} for ori_op_name, op in new_ugraph.ops_info.items(): + new_op = deepcopy(op, {'ugraph': new_ugraph}) new_op_name = '{}_{}'.format(ori_op_name, suffix) - op.name = new_op_name - for tensor in op.output_tensors: + new_op.name = new_op_name + for tensor in new_op.output_tensors: tensor_idx = tensor.name.split(':')[1] - tensor.name = '{}_{}:{}'.format(tensor.op_name, suffix, tensor_idx) tensor.op_name = new_op_name - for tensor in op.input_tensors: + tensor.name = '{}:{}'.format(new_op_name, tensor_idx) + for tensor in new_op.input_tensors: in_op_name, tensor_idx = tensor.name.split(':') new_in_op_name = '{}_{}'.format(in_op_name, suffix) tensor.name = '{}:{}'.format(new_in_op_name, tensor_idx) tensor.op_name = new_in_op_name - ops_to_remove.add(ori_op_name) - new_ops_info[new_op_name] = op - for op_name in ops_to_remove: - new_ugraph.ops_info.pop(op_name) + new_ops_info[new_op_name] = new_op new_ugraph.ops_info = new_ops_info new_input_map = {} - for op, v in input_map.items(): - new_op = deepcopy(op, {'ugraph': new_ugraph}) - new_op.name = '{}_{}'.format(op.name, suffix) - for in_tensor in new_op.input_tensors: - op_name, idx = in_tensor.name.split(':') - in_tensor.op_name = '{}_{}'.format(op_name, suffix) - in_tensor.name = '{}:{}'.format(in_tensor.op_name, idx) - for out_tensor in new_op.output_tensors: - op_name, idx = out_tensor.name.split(':') - out_tensor.op_name = '{}_{}'.format(op_name, suffix) - out_tensor.name = '{}:{}'.format(out_tensor.op_name, idx) - new_input_map[new_op] = v + for k, in_tensor in input_map.items(): + new_in_tensor = deepcopy(in_tensor, {'ugraph': new_ugraph}) + op_name, idx = in_tensor.name.split(':') + new_op_name = '{}_{}'.format(op_name, suffix) + new_in_tensor.op_name = new_op_name + new_in_tensor.name = '{}:{}'.format(new_op_name, idx) + new_input_map[k] = new_in_tensor new_output_map = {} - for op, v in output_map.items(): - new_op = deepcopy(op, {'ugraph': new_ugraph}) - new_op.name = '{}_{}'.format(op.name, suffix) - for in_tensor in new_op.input_tensors: - op_name, idx = in_tensor.name.split(':') - in_tensor.op_name = '{}_{}'.format(op_name, suffix) - in_tensor.name = '{}:{}'.format(in_tensor.op_name, idx) - for out_tensor in new_op.output_tensors: - op_name, idx = out_tensor.name.split(':') - out_tensor.op_name = '{}_{}'.format(op_name, suffix) - out_tensor.name = '{}:{}'.format(out_tensor.op_name, idx) - new_output_map[new_op] = v - + for k, out_tensor in output_map.items(): + new_out_tensor = deepcopy(out_tensor, {'ugraph': new_ugraph}) + op_name, idx = out_tensor.name.split(':') + new_op_name = '{}_{}'.format(op_name, suffix) + new_out_tensor.op_name = new_op_name + new_out_tensor.name = '{}:{}'.format(new_op_name, idx) + new_output_map[k] = new_out_tensor + return new_ugraph, new_input_map, new_output_map @property diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 50defde3..d4a12fdb 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -7,13 +7,13 @@ from random import choices from string import ascii_letters, digits -import numpy as np -from click.types import ParamType - import idx2numpy as idx2np +import numpy as np import tensorflow as tf +from click.types import ParamType from tensorflow.python.framework import graph_util from tensorflow.tools.graph_transforms import TransformGraph + from utensor_cgen.logger import logger __all__ = ["save_idx", "save_consts", "save_graph", "log_graph", @@ -250,7 +250,9 @@ def visit(node_name): raise ValueError("Input graph is not a DAG") visited.add(node_name) - op_info = ugraph.ops_info[node_name] + op_info = ugraph.ops_info.get(node_name, None) + if not op_info: + return for t_info in op_info.input_tensors: op_name = parse_tensor_name(t_info.name)[0] @@ -307,7 +309,7 @@ def prune_graph(ugraph): tensors_in = set([t.name for t in ugraph.ops_info[op_name].input_tensors]) in_ops = set() for it_node in ugraph.topo_order: - if(it_node == op_name): + if it_node == op_name: continue it_tensors_out = [t.name for t in ugraph.ops_info[it_node].output_tensors] if not tensors_in.isdisjoint(it_tensors_out): From 351c061cd6a950c56ff79b736853fec038fdd74f Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 23 May 2019 22:50:16 +0800 Subject: [PATCH 26/70] Add move synmatic to ugraph, op and tensor --- utensor_cgen/ir/base.py | 123 +++++++++++++++----------- utensor_cgen/matcher/_matcher_impl.py | 19 ++-- 2 files changed, 83 insertions(+), 59 deletions(-) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index cd72abd1..20a5721b 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -5,8 +5,9 @@ import attr import numpy as np import six -import tensorflow as tf from attr.validators import instance_of + +import tensorflow as tf from tensorflow.core.framework.attr_value_pb2 import AttrValue as _AttrValue from tensorflow.core.framework.attr_value_pb2 import \ NameAttrList as _NameAttrList @@ -14,7 +15,6 @@ from tensorflow.core.framework.tensor_shape_pb2 import \ TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType - from utensor_cgen.utils import random_str, topologic_order_graph from .converter import AttrValueConverter, ConverterFactory @@ -78,23 +78,6 @@ def op(self): @property def backend(self): return self._ugraph.backend - - @property - def is_dangling(self): - op = self.op - if not op: - return True - return op.is_dangling - - @property - def n_th_output(self): - if self.is_dangling: - raise ValueError( - "dangling tensor: {}".format(self.name) - ) - op = self.op - out_tnames = [t_info.name for t_info in op.output_tensors] - return out_tnames.index(self.name) _NULL_PREFIX = 'utensor_null' @@ -128,8 +111,13 @@ def __hash__(self): def __eq__(self, other): if not isinstance(other, type(self)): return False - return self.name == other.name + return (self.name == other.name) and (self._ugraph is other._ugraph) + def move_into(self, ugraph): + """ + Move Synmatic of the OperationInfo objects + """ + self._ugraph = ugraph @attr.s(cmp=False) class OperationInfo(IRBase, _NoShallowCopyMixin): @@ -183,6 +171,23 @@ def check(self, attribute, value): op_type = attr.ib(type=str) op_attr = attr.ib(factory=dict, converter=dict) + def __attrs_post_init__(self): + skip_pattern = re.compile(r'_utensor_[^_]*') + if self.op_attr: + op_attr = {} + for k, v in self.op_attr.items(): + match = skip_pattern.match(k) + if match: + op_attr[k] = v + else: + op_attr[k] = ConverterFactory.get_generic_value(v) + self.op_attr = op_attr + self._ugraph.ops_info[self.name] = self + if not self.n_inputs == len(self.input_tensors): + raise ValueError('n_inputs is not equal to the length of input_tensors') + if not self.n_outputs == len(self.output_tensors): + raise ValueError('n_outputs is not equal to the length of output_tensors') + @property def ugraph(self): return self._ugraph @@ -211,14 +216,6 @@ def output_nodes(self): break return [self._ugraph.ops_info[name] for name in out_ops] - @property - def is_dangling(self): - """ - True: the op is dangling in the graph - False: otherwise - """ - return None in self.input_nodes - @property def is_input_op(self): return all( @@ -249,23 +246,16 @@ def replace_with_null_input_tensor(self, idx): ) self.input_tensors[idx] = TensorInfo.make_null_tensor(ugraph=self._ugraph) - def __attrs_post_init__(self): - skip_pattern = re.compile(r'_utensor_[^_]*') - if self.op_attr: - op_attr = {} - for k, v in self.op_attr.items(): - match = skip_pattern.match(k) - if match: - op_attr[k] = v - else: - op_attr[k] = ConverterFactory.get_generic_value(v) - self.op_attr = op_attr - self._ugraph.ops_info[self.name] = self - if not self.n_inputs == len(self.input_tensors): - raise ValueError('n_inputs is not equal to the length of input_tensors') - if not self.n_outputs == len(self.output_tensors): - raise ValueError('n_outputs is not equal to the length of output_tensors') - + def move_into(self, ugraph): + """ + Move Synmatic of the OperationInfo objects + """ + self._ugraph = ugraph + for tensor in self.input_tensors: + tensor.move_into(ugraph) + for tensor in self.output_tensors: + tensor.move_into(ugraph) + def __deepcopy__(self, memo): op_info = OperationInfo(name=self.name, input_tensors=deepcopy(self.input_tensors, memo), @@ -284,12 +274,15 @@ def __hash__(self): def __eq__(self, other): if not isinstance(other, type(self)): return False - return self.name == other.name + return (self.name == other.name) and (self._ugraph is other._ugraph) - def copy_into_graph(self, ugraph): - """experimental, don't use - """ - return deepcopy(self, {'ugraph': ugraph}) + def __getitem__(self, tensor_idx): + num_out_tensors = len(self.output_tensors) + if tensor_idx > num_out_tensors: + raise IndexError( + 'index out of bound: {} out of {}'.format(tensor_idx, num_out_tensors) + ) + return self.output_tensors[tensor_idx] class MetaOperationInfo(OperationInfo): @@ -427,6 +420,26 @@ def drop_op(self, op_name): raise ValueError('op not found in the graph: {}'.format(op_name)) del self.ops_info[op_name] self.topo_order.remove(op_name) + + def merge_into(self, other_ugraph): + """ + NOTE:(IMPORTANT) after this method call, you should + consider both the graph and the other ugraph is at a + dangling state, which means the output nodes may not + be correct, the topological ordering may not be correct + ...etc. After all modifications to the graph are done, + you may need to call following functions in order to + maintain the state of the graph: + + 1. prune_graph (from `utensor_cgen.utils`) + 2. topologic_order_graph (from `utensor_cgen.utils`) + """ + for op in self.ops_info.values(): + op._ugraph = other_ugraph + for input_tensor in op.input_tensors: + input_tensor.move_into(other_ugraph) + for output_tensor in op.output_tensors: + output_tensor.move_into(other_ugraph) def __deepcopy__(self, memo): new_graph = uTensorGraph( @@ -442,6 +455,11 @@ def __deepcopy__(self, memo): topologic_order_graph(new_graph) return new_graph + def __getitem__(self, op_name): + if op_name not in self.ops_info: + raise KeyError('{} not found in the graph'.format(op_name)) + return self.ops_info[op_name] + @attr.s(cmp=False) class uTensorGraphView(IRBase, _NoShallowCopyMixin): @@ -494,3 +512,8 @@ def output_tensors(self): for tensor in op.output_tensors: out_tensors.append(tensor) return out_tensors + + def __getitem__(self, op_name): + if op_name not in self.ops_info: + raise KeyError('{} not found in the graph view'.format(op_name)) + return self.ops_info[op_name] diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index f1daadf7..a27eac7c 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -109,6 +109,7 @@ def query(cls, sub_op, patrn_op): return is_eq, equivalent_ops + @attr.s class uTensorGraphMatcher(object): @@ -206,6 +207,7 @@ def _visit(self, states): new_states.append(new_state) return new_states + @attr.s class uTensorGraphMatch(object): @@ -290,25 +292,24 @@ def replace_with(self, callback, suffix=None): replace_ugraph, input_map, output_map, suffix ) new_ugraph = deepcopy(self.subject_ugraph) - for op in replace_ugraph.ops_info.values(): - op._ugraph = new_ugraph - for tensor in op.input_tensors: - tensor._ugraph = new_ugraph - for tensor in op.output_tensors: - tensor._ugraph = new_ugraph + # make replace_ugraph be a subgraph in the new_ugraph + replace_ugraph.merge_into(new_ugraph) for tensor in input_map.values(): - tensor._ugraph = new_ugraph + tensor.move_into(new_ugraph) for tensor in output_map.values(): - tensor._ugraph = new_ugraph + tensor.move_into(new_ugraph) subj_graph_view = self.subject_graph_view # replacing output tensors for out_tensor in subj_graph_view.output_tensors: repl_out_tensor = output_map[self.subj2patrn_tensor_map[out_tensor.name]] - out_ops = [new_ugraph.ops_info[op.name] for op in out_tensor.op.output_nodes] + out_ops = [new_ugraph[op.name] for op in out_tensor.op.output_nodes] for op in out_ops: for i, tensor in enumerate(op.input_tensors): if tensor.name == out_tensor.name: op.input_tensors[i] = repl_out_tensor + for i, node_name in enumerate(new_ugraph.output_nodes): + if node_name == out_tensor.op.name: + new_ugraph.output_nodes[i] = repl_out_tensor.op.name # replacing input tensors inv_input_map = dict([(v, k) for k, v in input_map.items()]) for op in replace_ugraph.input_ops: From 8abc65fac8e4938a37736e32173902bcaa3aaa63 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 23 May 2019 23:10:39 +0800 Subject: [PATCH 27/70] Add first simple replace test --- tests/test_matcher/test_replace/__init__.py | 0 tests/test_matcher/test_replace/conftest.py | 34 +++++++++++++++ .../test_matcher/test_replace/test_replace.py | 43 +++++++++++++++++++ 3 files changed, 77 insertions(+) create mode 100644 tests/test_matcher/test_replace/__init__.py create mode 100644 tests/test_matcher/test_replace/conftest.py create mode 100644 tests/test_matcher/test_replace/test_replace.py diff --git a/tests/test_matcher/test_replace/__init__.py b/tests/test_matcher/test_replace/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_matcher/test_replace/conftest.py b/tests/test_matcher/test_replace/conftest.py new file mode 100644 index 00000000..221c5a1b --- /dev/null +++ b/tests/test_matcher/test_replace/conftest.py @@ -0,0 +1,34 @@ +import numpy as np +from pytest import fixture + +import tensorflow as tf +from utensor_cgen.frontend.tensorflow import GraphDefParser + + +@fixture(name='patrn_fc_1') +def fully_connect_pattern1(): + patrn_graph = tf.Graph() + with patrn_graph.as_default(): + z_prime = tf.placeholder(name='z_prime', dtype=tf.float32) + w_prime = tf.constant(np.random.rand(3, 3), name='w_prime', dtype=tf.float32) + a_prime = tf.matmul(z_prime, w_prime, name='a_prime') + r_prime = tf.nn.relu(a_prime, name='r_prime') + patrn_ugraph = GraphDefParser.parse(patrn_graph.as_graph_def(), output_nodes=[r_prime.op.name]) + for _ in range(2): + patrn_ugraph.ops_info['z_prime'].add_null_input_tensor() + return patrn_ugraph + + +@fixture(name='subj_graph_1') +def subject_ugraph_1(): + subj_graph = tf.Graph() + with subj_graph.as_default(): + x = tf.constant(np.random.rand(3, 3), name='x', dtype=tf.float32) + y = tf.constant(np.random.rand(3, 3), name='y', dtype=tf.float32) + z = tf.add(x, y, name='z') + w = tf.constant(np.random.rand(3, 3), name='w', dtype=tf.float32) + a = tf.matmul(z, w, name='a') + r = tf.nn.relu(a, name='r') + out = tf.add(x, r, name='out') + subj_ugraph = GraphDefParser.parse(subj_graph.as_graph_def(), output_nodes=[out.op.name]) + return subj_ugraph diff --git a/tests/test_matcher/test_replace/test_replace.py b/tests/test_matcher/test_replace/test_replace.py new file mode 100644 index 00000000..ddc31ea8 --- /dev/null +++ b/tests/test_matcher/test_replace/test_replace.py @@ -0,0 +1,43 @@ +import numpy as np + +import tensorflow as tf +from utensor_cgen.frontend.tensorflow import GraphDefParser +from utensor_cgen.matcher import uTensorGraphMatcher +from utensor_cgen.utils import prune_graph, topologic_order_graph + + +def test_replace_fc_with_add(subj_graph_1, patrn_fc_1): + def callback(match): + graph = tf.Graph() + with graph.as_default(): + a = tf.placeholder(dtype=tf.float32, name='a') + b = tf.placeholder(dtype=tf.float32, name='b') + out = tf.add(a, b, name='fused_node') + ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[out.op.name]) + ugraph.ops_info['fused_node'].replace_with_null_input_tensor(0) + ugraph.ops_info['fused_node'].replace_with_null_input_tensor(1) + topologic_order_graph(ugraph) + ugraph = prune_graph(ugraph) + patrn_ugraph = match.pattern_ugraph + + input_map = { + patrn_ugraph.ops_info['z_prime'].input_tensors[0]: ugraph.ops_info['fused_node'].input_tensors[0], + patrn_ugraph.ops_info['z_prime'].input_tensors[1]: ugraph.ops_info['fused_node'].input_tensors[1] + } + output_map = { + patrn_ugraph.ops_info['r_prime'].output_tensors[0]: ugraph.ops_info['fused_node'].output_tensors[0] + } + return ugraph, input_map, output_map + matcher = uTensorGraphMatcher(patrn_fc_1) + matches = matcher.match(subj_graph_1) + assert matches, 'no match found' + match = matches[0] + new_ugraph = match.replace_with(callback) + test_pass = True + missed_op_names = [] + for op_name in match.subj2patrn_op_map: + if op_name in new_ugraph.ops_info: + test_pass = False + missed_op_names.append(op_name) + assert test_pass, \ + 'these ops should not be found in the new ugrah: {}'.format(missed_op_names) From fd525d90ede9635b2552bfe39bd715bfe8e3f867 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 23 May 2019 23:30:59 +0800 Subject: [PATCH 28/70] update tests --- tests/test_transformer/test_dropout/conftest.py | 3 ++- tests/test_transformer/test_dropout/test_dropout_transormer.py | 2 -- tests/test_transformer/test_inline/test_inline_optimizer.py | 2 -- 3 files changed, 2 insertions(+), 5 deletions(-) diff --git a/tests/test_transformer/test_dropout/conftest.py b/tests/test_transformer/test_dropout/conftest.py index 720a8726..6d00d6a3 100644 --- a/tests/test_transformer/test_dropout/conftest.py +++ b/tests/test_transformer/test_dropout/conftest.py @@ -1,5 +1,6 @@ import numpy as np import pytest + import tensorflow as tf @@ -11,7 +12,7 @@ def dropout_graph_tuple(): name='x', dtype=tf.float32) keep_prob = tf.placeholder(dtype=tf.float32, name='keep_prob') - dropout_x = tf.nn.dropout(x, keep_prob, name='dropout_x') + dropout_x = tf.nn.dropout(x, rate=1-keep_prob, name='dropout_x') bias = tf.constant(0.5, name='bias', dtype=tf.float32) y = tf.add(dropout_x, bias, name='y') return (graph.as_graph_def(), diff --git a/tests/test_transformer/test_dropout/test_dropout_transormer.py b/tests/test_transformer/test_dropout/test_dropout_transormer.py index 22665aa0..5d3c9a90 100644 --- a/tests/test_transformer/test_dropout/test_dropout_transormer.py +++ b/tests/test_transformer/test_dropout/test_dropout_transormer.py @@ -1,5 +1,4 @@ import tensorflow as tf - from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.transformer.ns_transformer import DropoutTransformer @@ -13,7 +12,6 @@ def test_dropout_trans(droput_graph_tuple): new_ugraph = transformer.transform(ugraph) for op in new_ugraph.ops_info.values(): assert op.ugraph - assert not op.is_dangling out_op = new_ugraph.ops_info[output_nodes[0]] assert set([str(op.name) for op in out_op.input_nodes]) == set(['x', 'bias']) # all dropout nodes should be gone diff --git a/tests/test_transformer/test_inline/test_inline_optimizer.py b/tests/test_transformer/test_inline/test_inline_optimizer.py index 059eec6f..48b82dfd 100644 --- a/tests/test_transformer/test_inline/test_inline_optimizer.py +++ b/tests/test_transformer/test_inline/test_inline_optimizer.py @@ -7,8 +7,6 @@ def test_inline_optimizer(inlinegraph_tuple): ugraph = GraphDefParser.parse(graph_def, output_nodes) transformer = InlineTransformer() ugraph = transformer.transform(ugraph) - for op in ugraph.ops_info.values(): - assert not op.is_dangling for node_name in ugraph.topo_order: if node_name in inline_ans: op_type = ugraph.ops_info[node_name].op_type From c73e9fa5c1a0154387e5181a4f5aaf1d18bba99a Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 26 May 2019 16:16:58 +0800 Subject: [PATCH 29/70] minor refmt --- utensor_cgen/ir/base.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index c6f46e77..e530d603 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -5,9 +5,8 @@ import attr import numpy as np import six -from attr.validators import instance_of - import tensorflow as tf +from attr.validators import instance_of from tensorflow.core.framework.attr_value_pb2 import AttrValue as _AttrValue from tensorflow.core.framework.attr_value_pb2 import \ NameAttrList as _NameAttrList @@ -15,6 +14,7 @@ from tensorflow.core.framework.tensor_shape_pb2 import \ TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType + from utensor_cgen.utils import random_str, topologic_order_graph from .converter import AttrValueConverter, ConverterFactory @@ -404,7 +404,7 @@ def ops(self): if not self.topo_order: topologic_order_graph(self) return [self.ops_info[name] for name in self.topo_order] - + def add_op(self, op): if not isinstance(op, OperationInfo): raise ValueError('expecting OperationInfo, get {}'.format(type(op))) From f0b938e0107013650a99dca15b83beefe2b1f984 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 26 May 2019 16:21:21 +0800 Subject: [PATCH 30/70] Fix merge graph bug --- utensor_cgen/ir/base.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index e530d603..f35c6ccc 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -406,6 +406,8 @@ def ops(self): return [self.ops_info[name] for name in self.topo_order] def add_op(self, op): + """Experimental, don't use. + """ if not isinstance(op, OperationInfo): raise ValueError('expecting OperationInfo, get {}'.format(type(op))) if op.name in self.ops_info: @@ -416,6 +418,8 @@ def add_op(self, op): topologic_order_graph(self) def drop_op(self, op_name): + """Experimental, don't use. + """ if op_name not in self.ops_info: raise ValueError('op not found in the graph: {}'.format(op_name)) del self.ops_info[op_name] @@ -440,6 +444,9 @@ def merge_into(self, other_ugraph): input_tensor.move_into(other_ugraph) for output_tensor in op.output_tensors: output_tensor.move_into(other_ugraph) + if op.op_type not in self._type_to_op_map: + self._type_to_op_map[op.op_type] = [] + self._type_to_op_map[op.op_type].append(op) def __deepcopy__(self, memo): new_graph = uTensorGraph( From c5f87660a242d074e818f3a3d4ddf743a75ad815 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 27 May 2019 23:57:03 +0800 Subject: [PATCH 31/70] linear reorder transformer impl. with matcher --- utensor_cgen/transformer/base.py | 5 +- utensor_cgen/transformer/graph_viz.py | 7 ++- utensor_cgen/transformer/linear_reoder.py | 64 ++++++++++++++++++++-- utensor_cgen/transformer/ns_transformer.py | 3 +- 4 files changed, 67 insertions(+), 12 deletions(-) diff --git a/utensor_cgen/transformer/base.py b/utensor_cgen/transformer/base.py index 21af8185..145a2fb0 100644 --- a/utensor_cgen/transformer/base.py +++ b/utensor_cgen/transformer/base.py @@ -1,7 +1,8 @@ from abc import ABCMeta, abstractmethod from functools import wraps -from utensor_cgen.utils import topologic_order_graph, prune_graph as _prune_graph +from utensor_cgen.utils import prune_graph as _prune_graph +from utensor_cgen.utils import topologic_order_graph class Transformer(object): @@ -36,7 +37,7 @@ def transform(ugraph): return self def __init__(self, prune_graph=True, **kwargs): - # just for define the __init__ signature + # just for defining the __init__ signature pass @abstractmethod diff --git a/utensor_cgen/transformer/graph_viz.py b/utensor_cgen/transformer/graph_viz.py index 961aeed5..f94f4cf3 100644 --- a/utensor_cgen/transformer/graph_viz.py +++ b/utensor_cgen/transformer/graph_viz.py @@ -8,9 +8,9 @@ from copy import deepcopy from utensor_cgen.ir import OperationInfo, uTensorGraph -from utensor_cgen.utils import parse_tensor_name -from utensor_cgen.logger import logger from utensor_cgen.ir.misc.graph_viz import viz_graph +from utensor_cgen.logger import logger +from utensor_cgen.utils import parse_tensor_name from .base import Transformer @@ -24,8 +24,9 @@ class GraphVizTransformer(Transformer): def __init__(self, out_fname="graph.gv", view=False): self.out_fname = out_fname self.view = view + self.prune_graph = False def transform(self, ugraph): viz_graph(self.out_fname, self.view , ugraph) - return ugraph \ No newline at end of file + return ugraph diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index f78683f6..f52cb0a4 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -9,9 +9,9 @@ from copy import deepcopy import numpy as np + import tensorflow as tf from tensorflow.tools.graph_transforms import TransformGraph - from utensor_cgen.experimental.ugraph_builder import * from utensor_cgen.experimental.ugraph_matcher import * from utensor_cgen.experimental.ugraph_util_functions import * @@ -20,12 +20,13 @@ from utensor_cgen.ir.converter import AttrValueConverter # hue hue hue hue hue from utensor_cgen.ir.converter import GenericTensorConverterMixin from utensor_cgen.ir.utils import graph_check -from utensor_cgen.utils import parse_tensor_name, topologic_order_graph -from utensor_cgen.ir.misc.graph_viz import viz_graph +from utensor_cgen.matcher import uTensorGraphMatcher +from utensor_cgen.utils import (parse_tensor_name, prune_graph, + topologic_order_graph) from .base import Transformer -__all__ = ["CMSIS_NN_Transformer"] +__all__ = ["Linear_Reorder_Transformer"] class Linear_Reorder_Transformer(Transformer): METHOD_NAME = 'linear_reoder' @@ -79,4 +80,57 @@ def transform(self, ugraph): topologic_order_graph(ugraph) graph_validate(ugraph) - return ugraph \ No newline at end of file + return ugraph + + +class LinearReorderTransformerV2(Transformer): + METHOD_NAME = 'linear_reorder_v2' + KWARGS_NAMESCOPE = '_linear_reorder_v2' + + def __init__(self): + self.prune_graph = False + + @property + def pattern_ugraph(self): + graph = tf.Graph() + with graph.as_default(): + dummy_input = tf.placeholder(dtype=tf.float32, shape=[None, 128, 128, 3]) + relu = tf.nn.relu(dummy_input, name='relu') + tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='max_pool') + pattern_ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=['max_pool']) + pattern_ugraph['relu'].replace_with_null_input_tensor(0) + pattern_ugraph = prune_graph(pattern_ugraph) + topologic_order_graph(pattern_ugraph) + return pattern_ugraph + + def transform(self, ugraph): + matcher = uTensorGraphMatcher(pattern_ugraph=self.pattern_ugraph) + matches = matcher.match(ugraph, 1) + while matches: + match = matches[0] + ugraph = match.replace_with(callback=self) + matches = matcher.match(ugraph, 1) + return ugraph + + def __call__(self, match): + graph = tf.Graph() + subj_pool_name = match.patrn2subj_op_map['max_pool'].name + subj_pool_op = match.subject_ugraph[subj_pool_name] + ksize = subj_pool_op.op_attr['ksize'].value.ints_value[:] + strides = subj_pool_op.op_attr['strides'].value.ints_value[:] + padding = subj_pool_op.op_attr['padding'].value + with graph.as_default(): + dummy_input = tf.placeholder(dtype=tf.float32, shape=[None, 128, 128, 3]) + max_pool = tf.nn.max_pool(dummy_input, ksize=ksize, strides=strides, padding=padding, name='max_pool') + tf.nn.relu(max_pool, name='relu') + ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=['relu']) + ugraph['max_pool'].replace_with_null_input_tensor(0) + ugraph = prune_graph(ugraph) + topologic_order_graph(ugraph) + input_map = { + match.pattern_ugraph['relu'].input_tensors[0]:ugraph['max_pool'].input_tensors[0] + } + output_map = { + match.pattern_ugraph['max_pool'].output_tensors[0]:ugraph['relu'].output_tensors[0] + } + return ugraph, input_map, output_map diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index f3e77e78..a01c3ff6 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -40,7 +40,6 @@ class InlineTransformer(Transformer): KWARGS_NAMESCOPE = '_utensor_inline' TARGET_NODENAME_PATTERN = re.compile(r'(const[_\w\d]*)/.*') - def transform(self, ugraph): for node_name in ugraph.topo_order: op_type = ugraph.ops_info[node_name].op_type @@ -154,4 +153,4 @@ def transform(self, ugraph): if op.op_type == "GatherV2": op.op_type = "Gather" ugraph.ops_info[key] = op - return ugraph \ No newline at end of file + return ugraph From e5c665e6989e88d37320f0156d5d61f553638d91 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 27 May 2019 23:57:20 +0800 Subject: [PATCH 32/70] Add tests for linear reorder transformer --- .../test_linear_reorder/__init__.py | 0 .../test_linear_reorder/conftest.py | 19 +++++++++++++++++++ .../test_linear_reorder/test_transformer.py | 12 ++++++++++++ 3 files changed, 31 insertions(+) create mode 100644 tests/test_transformer/test_linear_reorder/__init__.py create mode 100644 tests/test_transformer/test_linear_reorder/conftest.py create mode 100644 tests/test_transformer/test_linear_reorder/test_transformer.py diff --git a/tests/test_transformer/test_linear_reorder/__init__.py b/tests/test_transformer/test_linear_reorder/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_transformer/test_linear_reorder/conftest.py b/tests/test_transformer/test_linear_reorder/conftest.py new file mode 100644 index 00000000..e962b992 --- /dev/null +++ b/tests/test_transformer/test_linear_reorder/conftest.py @@ -0,0 +1,19 @@ +from pytest import fixture + +import tensorflow as tf +from utensor_cgen.frontend.tensorflow import GraphDefParser + + +@fixture(name='subj_ugraph_1') +def subject_ugraph_1(): + graph = tf.Graph() + with graph.as_default(): + input_1 = tf.placeholder(dtype=tf.float32, shape=[None, 512, 512, 3], name='input_1') + relu_1 = tf.nn.relu(input_1, name='relu_1') + max_pool_1 = tf.nn.max_pool(relu_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool_1') + input_2 = tf.placeholder(dtype=tf.float32, shape=[None, 512, 512, 3], name='input_2') + relu_2 = tf.nn.relu(input_1, name='relu_2') + max_pool_2 = tf.nn.max_pool(relu_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool_2') + output = tf.add(max_pool_1, max_pool_2, name='output') + subj_ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=['output']) + return subj_ugraph diff --git a/tests/test_transformer/test_linear_reorder/test_transformer.py b/tests/test_transformer/test_linear_reorder/test_transformer.py new file mode 100644 index 00000000..ccf45333 --- /dev/null +++ b/tests/test_transformer/test_linear_reorder/test_transformer.py @@ -0,0 +1,12 @@ +def test_linear_reorder_1(subj_ugraph_1): + from utensor_cgen.transformer.linear_reoder import LinearReorderTransformerV2 + + transformer = LinearReorderTransformerV2() + new_ugraph = transformer.transform(subj_ugraph_1) + for op in new_ugraph['output'].input_nodes: + assert op.op_type == 'Relu', 'expecting Relu, get {}'.format(op.op_type) + pool_op = op.input_nodes[0] + assert pool_op.op_type == 'MaxPool', \ + 'expecting MaxPool as input of Relu, get {}'.format(pool_op.op_type) + assert pool_op.op_attr['ksize'].value.ints_value == [1, 3, 3, 1] + assert pool_op.op_attr['strides'].value.ints_value == [1, 2, 2, 1] From 654b20d0990225c424b1a80f7e49c38da46d5f84 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 28 May 2019 00:18:28 +0800 Subject: [PATCH 33/70] minor fix --- tests/test_transformer/test_linear_reorder/conftest.py | 2 +- tests/test_transformer/test_linear_reorder/test_transformer.py | 1 + utensor_cgen/transformer/linear_reoder.py | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/test_transformer/test_linear_reorder/conftest.py b/tests/test_transformer/test_linear_reorder/conftest.py index e962b992..94944398 100644 --- a/tests/test_transformer/test_linear_reorder/conftest.py +++ b/tests/test_transformer/test_linear_reorder/conftest.py @@ -8,7 +8,7 @@ def subject_ugraph_1(): graph = tf.Graph() with graph.as_default(): - input_1 = tf.placeholder(dtype=tf.float32, shape=[None, 512, 512, 3], name='input_1') + input_1 = tf.placeholder(dtype=tf.float32, shape=[None, 512, 512, 10], name='input_1') relu_1 = tf.nn.relu(input_1, name='relu_1') max_pool_1 = tf.nn.max_pool(relu_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool_1') input_2 = tf.placeholder(dtype=tf.float32, shape=[None, 512, 512, 3], name='input_2') diff --git a/tests/test_transformer/test_linear_reorder/test_transformer.py b/tests/test_transformer/test_linear_reorder/test_transformer.py index ccf45333..405b3c94 100644 --- a/tests/test_transformer/test_linear_reorder/test_transformer.py +++ b/tests/test_transformer/test_linear_reorder/test_transformer.py @@ -10,3 +10,4 @@ def test_linear_reorder_1(subj_ugraph_1): 'expecting MaxPool as input of Relu, get {}'.format(pool_op.op_type) assert pool_op.op_attr['ksize'].value.ints_value == [1, 3, 3, 1] assert pool_op.op_attr['strides'].value.ints_value == [1, 2, 2, 1] + assert pool_op.input_tensors[0].shape == [None, 512, 512, 10] diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index f52cb0a4..f21c730c 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -26,7 +26,7 @@ from .base import Transformer -__all__ = ["Linear_Reorder_Transformer"] +__all__ = ["Linear_Reorder_Transformer", "LinearReorderTransformerV2"] class Linear_Reorder_Transformer(Transformer): METHOD_NAME = 'linear_reoder' From 22185faac0259b0c81be3f598d1d6c1ba1611a80 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 31 May 2019 11:38:23 +0800 Subject: [PATCH 34/70] remove hardcode graph viz --- utensor_cgen/backend/code_generator.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/utensor_cgen/backend/code_generator.py b/utensor_cgen/backend/code_generator.py index 461b12aa..e67c4148 100644 --- a/utensor_cgen/backend/code_generator.py +++ b/utensor_cgen/backend/code_generator.py @@ -5,10 +5,10 @@ from tempfile import NamedTemporaryFile import numpy as np + import tensorflow as tf from tensorflow.core.framework.graph_pb2 import GraphDef from tensorflow.tools.graph_transforms import TransformGraph - from utensor_cgen.frontend import FrontendSelector from utensor_cgen.ir import uTensorGraph from utensor_cgen.transformer.optimizer import RefCntOptimizer @@ -81,8 +81,6 @@ def formatter(name, kwargs): ) quant_ugraph = self._transform_graph(ugraph, self.trans_methods) - from utensor_cgen.ir.misc.graph_viz import viz_graph - viz_graph('out_graph', True, quant_ugraph) _logger.info('Graph transormation done') if self.save_graph: From 5b773e1ba0c3c3b8395bbc1e13fe2b63cf25c33d Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 5 Jun 2019 00:43:47 +0800 Subject: [PATCH 35/70] Add ConvPool transformer, impl. with matcher api --- utensor_cgen/ir/base.py | 9 +-- utensor_cgen/ir/misc/graph_viz.py | 8 +- utensor_cgen/transformer/conv_pool.py | 103 ++++++++++++++++++++++++-- utensor_cgen/transformer/graph_viz.py | 7 +- utensor_cgen/transformer/pipeline.py | 20 ++--- utensor_cgen/utils.py | 7 +- 6 files changed, 122 insertions(+), 32 deletions(-) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index b4ee656b..a7d2d29a 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -5,8 +5,9 @@ import attr import numpy as np import six -import tensorflow as tf from attr.validators import instance_of + +import tensorflow as tf from tensorflow.core.framework.attr_value_pb2 import AttrValue as _AttrValue from tensorflow.core.framework.attr_value_pb2 import \ NameAttrList as _NameAttrList @@ -14,7 +15,6 @@ from tensorflow.core.framework.tensor_shape_pb2 import \ TensorShapeProto as _TensorShapeProto from tensorflow.core.framework.types_pb2 import DataType as _DataType - from utensor_cgen.utils import random_str, topologic_order_graph from .converter import AttrValueConverter, ConverterFactory @@ -476,14 +476,11 @@ class uTensorGraphView(IRBase, _NoShallowCopyMixin): _ugraph = attr.ib(type=uTensorGraph) _op_names = attr.ib(type=list) output_nodes = attr.ib(type=list) - - topo_order = attr.ib(init=False, factory=list) ops_info = attr.ib(init=False, factory=dict) def __attrs_post_init__(self): for name in self._op_names: self.ops_info[name] = self._ugraph.ops_info[name] - topologic_order_graph(self) @property def backend(self): @@ -492,7 +489,7 @@ def backend(self): @property def input_ops(self): ops = set([]) - for name in self.topo_order: + for name in self.ops_info: op = self.ops_info[name] input_tensors = op.input_tensors if all([ diff --git a/utensor_cgen/ir/misc/graph_viz.py b/utensor_cgen/ir/misc/graph_viz.py index e3685dbd..8c1a52bb 100644 --- a/utensor_cgen/ir/misc/graph_viz.py +++ b/utensor_cgen/ir/misc/graph_viz.py @@ -3,7 +3,7 @@ from utensor_cgen.logger import logger -def viz_graph(out_fname, view, ugraph): +def viz_graph(ugraph, out_fname=None, view=False): dot = Digraph() nodes = {} i = 0 @@ -28,5 +28,7 @@ def viz_graph(out_fname, view, ugraph): dot.edge(nodes[n.name], nodes[node.name]) for n in node.output_tensors: dot.edge(nodes[node.name], nodes[n.name]) - dot.render(out_fname, view=view) - logger.info('graph visualization file generated: %s', out_fname) + if out_fname: + dot.render(out_fname, view=view) + logger.info('graph visualization file generated: %s', out_fname) + return dot diff --git a/utensor_cgen/transformer/conv_pool.py b/utensor_cgen/transformer/conv_pool.py index d989ed50..9798d03f 100644 --- a/utensor_cgen/transformer/conv_pool.py +++ b/utensor_cgen/transformer/conv_pool.py @@ -9,23 +9,25 @@ from copy import deepcopy import numpy as np + import tensorflow as tf from tensorflow.tools.graph_transforms import TransformGraph - from utensor_cgen.experimental.ugraph_builder import * from utensor_cgen.experimental.ugraph_matcher import * from utensor_cgen.experimental.ugraph_util_functions import * from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph -from utensor_cgen.ir.converter import AttrValueConverter # hue hue hue hue hue -from utensor_cgen.ir.converter import GenericTensorConverterMixin +from utensor_cgen.ir.converter import (AttrValueConverter, + GenericTensorConverterMixin) from utensor_cgen.ir.utils import graph_check -from utensor_cgen.utils import parse_tensor_name, topologic_order_graph -from utensor_cgen.ir.misc.graph_viz import viz_graph +from utensor_cgen.matcher import uTensorGraphMatcher +from utensor_cgen.utils import (parse_tensor_name, prune_graph, + topologic_order_graph) from .base import Transformer +from .quantize import QuantizeTransformer -__all__ = ["CONV_POOL_Transformer"] +__all__ = ["CONV_POOL_Transformer", "ConvPoolTransformer"] class CONV_POOL_Transformer(Transformer): METHOD_NAME = 'conv_pool' @@ -85,6 +87,91 @@ def transform(self, ugraph): update_tensor_op_names(ugraph) topologic_order_graph(ugraph) graph_validate(ugraph) - - #viz_graph('matcher', True, ugraph) return ugraph + +class ConvPoolTransformer(Transformer): + METHOD_NAME = 'conv_pool_v2' + KWARGS_NAMESCOPE = '_conv_pool_v2' + + def __init__(self): + super(ConvPoolTransformer, self).__init__(prune_graph=False) + + @property + def pattern_ugraph(self): + graph = tf.Graph() + with graph.as_default(): + dummy_input = tf.placeholder(dtype=tf.float32, shape=[None, 128, 128, 3], name='dummy_input') + dummy_weight = tf.zeros([32, 32, 3, 10], dtype=tf.float32, name='dummy_weight') + conv = tf.nn.conv2d(dummy_input, dummy_weight, strides=[1, 2, 2, 1], padding='VALID', name='conv') + maxpool = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', name='maxpool') + ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[maxpool.op.name]) + quant_ugraph = QuantizeTransformer().transform(ugraph) + patrn_ugraph = deepcopy(quant_ugraph) + quant_conv_op = patrn_ugraph['conv/eightbit'] + for i, _ in enumerate(quant_conv_op.input_tensors): + quant_conv_op.replace_with_null_input_tensor(i) + patrn_ugraph.output_nodes = ['maxpool/eightbit'] + patrn_ugraph = prune_graph(patrn_ugraph) + topologic_order_graph(patrn_ugraph) + return patrn_ugraph + + def transform(self, ugraph): + matcher = uTensorGraphMatcher(pattern_ugraph=self.pattern_ugraph) + matches = matcher.match(ugraph, n=1) + while matches: + match = matches[0] + ugraph = match.replace_with(callback=self) + matches = matcher.match(ugraph, n=1) + return ugraph + + def __call__(self, match): + op_name = 'quant_conv_pool' + repl_ugraph = uTensorGraph( + output_nodes=[op_name], + backend=match.subject_ugraph.backend + ) + subj_conv_op = match.patrn2subj_op_map['conv/eightbit'] + subj_pool_op = match.patrn2subj_op_map['maxpool/eightbit'] + output_tensors = [ + TensorInfo( + name='{}:{}'.format(op_name, i), + op_name=op_name, + dtype=subj_tensor.dtype, + shape=subj_tensor.shape, + ugraph=repl_ugraph + ) + for i, subj_tensor in enumerate(subj_pool_op.output_tensors) + ] + input_tensors = [ + TensorInfo.make_null_tensor(ugraph=repl_ugraph) + for _ in subj_conv_op.input_tensors + ] + quant_conv2d_pool_op = OperationInfo( + name=op_name, + input_tensors=input_tensors, + n_inputs=len(input_tensors), + output_tensors=output_tensors, + n_outputs=len(output_tensors), + op_type='QuantizedFusedConv2DMaxpool', + backend=subj_conv_op.backend, + op_attr={ + '_utensor_conv': subj_conv_op.op_attr, + '_utensor_pool': subj_pool_op.op_attr, + }, + ugraph=repl_ugraph + ) + topologic_order_graph(repl_ugraph) + input_map = { + match.pattern_ugraph['conv/eightbit'].input_tensors[0]: quant_conv2d_pool_op.input_tensors[0], + match.pattern_ugraph['conv/eightbit'].input_tensors[1]: quant_conv2d_pool_op.input_tensors[1], + match.pattern_ugraph['conv/eightbit'].input_tensors[2]: quant_conv2d_pool_op.input_tensors[2], + match.pattern_ugraph['conv/eightbit'].input_tensors[3]: quant_conv2d_pool_op.input_tensors[3], + match.pattern_ugraph['conv/eightbit'].input_tensors[4]: quant_conv2d_pool_op.input_tensors[4], + match.pattern_ugraph['conv/eightbit'].input_tensors[5]: quant_conv2d_pool_op.input_tensors[5], + } + output_map = { + match.pattern_ugraph['maxpool/eightbit'].output_tensors[0]: output_tensors[0], + match.pattern_ugraph['maxpool/eightbit'].output_tensors[1]: output_tensors[1], + match.pattern_ugraph['maxpool/eightbit'].output_tensors[2]: output_tensors[2], + } + return repl_ugraph, input_map, output_map diff --git a/utensor_cgen/transformer/graph_viz.py b/utensor_cgen/transformer/graph_viz.py index f94f4cf3..5658c4b0 100644 --- a/utensor_cgen/transformer/graph_viz.py +++ b/utensor_cgen/transformer/graph_viz.py @@ -27,6 +27,9 @@ def __init__(self, out_fname="graph.gv", view=False): self.prune_graph = False def transform(self, ugraph): - viz_graph(self.out_fname, self.view , ugraph) - + viz_graph( + ugraph=ugraph, + out_fname=self.out_fname, + view=self.view + ) return ugraph diff --git a/utensor_cgen/transformer/pipeline.py b/utensor_cgen/transformer/pipeline.py index b97ec8d3..9eea45a0 100644 --- a/utensor_cgen/transformer/pipeline.py +++ b/utensor_cgen/transformer/pipeline.py @@ -1,15 +1,15 @@ -from utensor_cgen.utils import NamescopedKWArgsParser - from .base import Transformer from .cmsis_nn import CMSIS_NN_Transformer -from .ns_transformer import (BatchNormTransformer, DropoutTransformer, - InlineTransformer, BiasAddTransformer, - FakeGatherV2Transformer) +from .conv_pool import CONV_POOL_Transformer, ConvPoolTransformer +from .graph_viz import GraphVizTransformer +from .linear_reoder import (Linear_Reorder_Transformer, + LinearReorderTransformerV2) +from .ns_transformer import (BatchNormTransformer, BiasAddTransformer, + DropoutTransformer, FakeGatherV2Transformer, + InlineTransformer) from .optimizer import IdOpRemoveOptimizer, RefCntOptimizer from .quantize import QuantizeTransformer -from .graph_viz import GraphVizTransformer -from .linear_reoder import Linear_Reorder_Transformer -from .conv_pool import CONV_POOL_Transformer + class TransformerPipeline(object): @@ -25,7 +25,9 @@ class TransformerPipeline(object): GraphVizTransformer.METHOD_NAME: GraphVizTransformer, Linear_Reorder_Transformer.METHOD_NAME: Linear_Reorder_Transformer, CONV_POOL_Transformer.METHOD_NAME: CONV_POOL_Transformer, - FakeGatherV2Transformer.METHOD_NAME: FakeGatherV2Transformer + FakeGatherV2Transformer.METHOD_NAME: FakeGatherV2Transformer, + ConvPoolTransformer.METHOD_NAME: ConvPoolTransformer, + LinearReorderTransformerV2.METHOD_NAME: LinearReorderTransformerV2 } def __init__(self, methods): diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index a2d13e8e..d910a31a 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -7,13 +7,13 @@ from random import choices from string import ascii_letters, digits -import idx2numpy as idx2np import numpy as np -import tensorflow as tf from click.types import ParamType + +import idx2numpy as idx2np +import tensorflow as tf from tensorflow.python.framework import graph_util from tensorflow.tools.graph_transforms import TransformGraph - from utensor_cgen.logger import logger __all__ = ["save_idx", "save_consts", "save_graph", "log_graph", @@ -248,7 +248,6 @@ def visit(node_name): return if node_name in visited: raise ValueError("Input graph is not a DAG") - visited.add(node_name) op_info = ugraph.ops_info.get(node_name, None) if not op_info: From 3736a425408b06da923d73e6280317a2dad76f95 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 5 Jun 2019 00:45:26 +0800 Subject: [PATCH 36/70] make cli default pipeline using trans impl. with matcher --- utensor_cgen/cli.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index e2069f0e..15e44add 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -47,7 +47,7 @@ def cli(): help="list of output nodes") @click.option("--transform-methods", type=NArgsKwargsParam(sep='|>'), - default='dropout|>linear_reoder|>quantize|>conv_pool|>inline|>biasAdd|>remove_id_op|>refcnt', + default='dropout|>linear_reorder_v2|>quantize|>conv_pool_v2|>inline|>biasAdd|>remove_id_op|>refcnt', help='optimization pipeline', metavar='METHOD[|>METHOD|>...]', show_default=True) From 390bccf91a429a40c84546d73fdb2b6ede7ace44 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 6 Jun 2019 00:15:20 +0800 Subject: [PATCH 37/70] remove incorrect match logic and update tests --- tests/test_matcher/test_permutation/conftest.py | 12 +++++------- .../test_permutation/test_match_patrn1.py | 10 +++++----- tests/test_matcher/test_replace/conftest.py | 7 +++++-- tests/test_matcher/test_replace/test_replace.py | 4 ++-- utensor_cgen/ir/base.py | 7 ------- utensor_cgen/matcher/_matcher_impl.py | 13 ++++++++----- 6 files changed, 25 insertions(+), 28 deletions(-) diff --git a/tests/test_matcher/test_permutation/conftest.py b/tests/test_matcher/test_permutation/conftest.py index c27c2e36..ddc6a422 100644 --- a/tests/test_matcher/test_permutation/conftest.py +++ b/tests/test_matcher/test_permutation/conftest.py @@ -13,7 +13,7 @@ def patrn_ugraph(): ptrn_add0 = tf.add(ptrn_input0, ptrn_input1, name='add0') ptrn_out = tf.add(ptrn_add0, ptrn_input1, name='output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [ptrn_out.op.name]) - ugraph.ops_info[ptrn_input0.op.name].add_null_input_tensor() + # ugraph.ops_info[ptrn_input0.op.name].add_null_input_tensor() return ugraph @pytest.fixture(scope='function', name='subject_ugraph1') @@ -21,13 +21,12 @@ def subject_ugraph1(): graph = tf.Graph() with graph.as_default(): sub_input0 = tf.placeholder(name='sub_input0', dtype=tf.int32) - sub_input1 = tf.constant([i for i in range(10)], name='sub_input1') + sub_input1 = tf.placeholder(name='sub_input1', dtype=tf.int32) sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') sub_add0 = tf.add(sub_input0, sub_input1, name='sub_add0') sub_add1 = tf.add(sub_add0, sub_input1, name='sub_add1') sub_output = tf.add(sub_add1, sub_input2, name='sub_output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) - ugraph.ops_info[sub_input0.op.name].add_null_input_tensor() return ugraph @pytest.fixture(scope='function', name='subject_ugraph1_1') @@ -35,26 +34,25 @@ def subject_ugraph1_1(): graph = tf.Graph() with graph.as_default(): sub_input0 = tf.placeholder(name='sub_input0', dtype=tf.int32) - sub_input1 = tf.constant([i for i in range(10)], name='sub_input1') + sub_input1 = tf.placeholder(name='sub_input1', dtype=tf.int32) sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') # permute sub_add0 = tf.add(sub_input1, sub_input0, name='sub_add0') sub_add1 = tf.add(sub_add0, sub_input1, name='sub_add1') sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) - ugraph.ops_info[sub_input0.op.name].add_null_input_tensor() return ugraph @pytest.fixture(scope='function', name='subject_ugraph1_2') def subject_ugraph1_2(): graph = tf.Graph() with graph.as_default(): - sub_input0 = tf.constant([i for i in range(10)], name='sub_input0') + sub_input0 = tf.placeholder(name='sub_input0', dtype=tf.int32) sub_input1 = tf.placeholder(name='sub_input1', dtype=tf.int32) sub_input2 = tf.constant([i for i in range(10)], name='sub_input2') sub_add0 = tf.add(sub_input0, sub_input1, name='sub_add0') sub_add1 = tf.add(sub_input1, sub_add0, name='sub_add1') sub_output = tf.multiply(sub_add1, sub_input2, name='sub_output') ugraph = GraphDefParser.parse(graph.as_graph_def(), [sub_output.op.name]) - ugraph.ops_info[sub_input1.op.name].add_null_input_tensor() + # ugraph.ops_info[sub_input1.op.name].add_null_input_tensor() return ugraph diff --git a/tests/test_matcher/test_permutation/test_match_patrn1.py b/tests/test_matcher/test_permutation/test_match_patrn1.py index 48c51d60..bf98ab22 100644 --- a/tests/test_matcher/test_permutation/test_match_patrn1.py +++ b/tests/test_matcher/test_permutation/test_match_patrn1.py @@ -22,7 +22,7 @@ def test_match_sub1(patrn_ugraph, subject_ugraph1): matches = matcher.match_all(subject_ugraph1) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert len(matches) == 1, 'should be exactly one match, get {}'.format(len(matches)) + assert len(matches) == 2, 'should be exactly two match, get {}'.format(len(matches)) assert match.patrn2subj_op_map['input0'].name == 'sub_input0' assert match.patrn2subj_op_map['input1'].name == 'sub_input1' assert match.patrn2subj_op_map['add0'].name == 'sub_add0' @@ -33,8 +33,8 @@ def test_match_sub1_1(patrn_ugraph, subject_ugraph1_1): matches = matcher.match(subject_ugraph1_1) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert match.patrn2subj_op_map['input0'].name == 'sub_input0' - assert match.patrn2subj_op_map['input1'].name == 'sub_input1' + assert match.patrn2subj_op_map['input0'].name in ['sub_input0', 'sub_input1'] + assert match.patrn2subj_op_map['input1'].name in ['sub_input0', 'sub_input1'] assert match.patrn2subj_op_map['add0'].name == 'sub_add0' assert match.patrn2subj_op_map['output'].name == 'sub_add1' @@ -43,7 +43,7 @@ def test_match_sub1_2(patrn_ugraph, subject_ugraph1_2): matches = matcher.match(subject_ugraph1_2) assert matches, 'expecting matches, get {} matches'.format(len(matches)) match = matches[0] - assert match.patrn2subj_op_map['input0'].name == 'sub_input1' - assert match.patrn2subj_op_map['input1'].name == 'sub_input0' + assert match.patrn2subj_op_map['input0'].name in ['sub_input0', 'sub_input1'] + assert match.patrn2subj_op_map['input1'].name in ['sub_input0', 'sub_input1'] assert match.patrn2subj_op_map['add0'].name == 'sub_add0' assert match.patrn2subj_op_map['output'].name == 'sub_add1' diff --git a/tests/test_matcher/test_replace/conftest.py b/tests/test_matcher/test_replace/conftest.py index 221c5a1b..64d8a6e3 100644 --- a/tests/test_matcher/test_replace/conftest.py +++ b/tests/test_matcher/test_replace/conftest.py @@ -3,6 +3,7 @@ import tensorflow as tf from utensor_cgen.frontend.tensorflow import GraphDefParser +from utensor_cgen.utils import prune_graph, topologic_order_graph @fixture(name='patrn_fc_1') @@ -14,8 +15,10 @@ def fully_connect_pattern1(): a_prime = tf.matmul(z_prime, w_prime, name='a_prime') r_prime = tf.nn.relu(a_prime, name='r_prime') patrn_ugraph = GraphDefParser.parse(patrn_graph.as_graph_def(), output_nodes=[r_prime.op.name]) - for _ in range(2): - patrn_ugraph.ops_info['z_prime'].add_null_input_tensor() + for i in range(2): + patrn_ugraph.ops_info['a_prime'].replace_with_null_input_tensor(i) + patrn_ugraph = prune_graph(patrn_ugraph) + topologic_order_graph(patrn_ugraph) return patrn_ugraph diff --git a/tests/test_matcher/test_replace/test_replace.py b/tests/test_matcher/test_replace/test_replace.py index ddc31ea8..9615b0f2 100644 --- a/tests/test_matcher/test_replace/test_replace.py +++ b/tests/test_matcher/test_replace/test_replace.py @@ -21,8 +21,8 @@ def callback(match): patrn_ugraph = match.pattern_ugraph input_map = { - patrn_ugraph.ops_info['z_prime'].input_tensors[0]: ugraph.ops_info['fused_node'].input_tensors[0], - patrn_ugraph.ops_info['z_prime'].input_tensors[1]: ugraph.ops_info['fused_node'].input_tensors[1] + patrn_ugraph.ops_info['a_prime'].input_tensors[0]: ugraph.ops_info['fused_node'].input_tensors[0], + patrn_ugraph.ops_info['a_prime'].input_tensors[1]: ugraph.ops_info['fused_node'].input_tensors[1] } output_map = { patrn_ugraph.ops_info['r_prime'].output_tensors[0]: ugraph.ops_info['fused_node'].output_tensors[0] diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index a7d2d29a..cb51d006 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -215,13 +215,6 @@ def output_nodes(self): out_ops.append(op.name) break return [self._ugraph.ops_info[name] for name in out_ops] - - @property - def is_input_op(self): - return all( - [tensor.op_name not in self._ugraph.ops_info - for tensor in self.input_tensors] - ) def add_null_input_tensor(self, idx=-1): if self.op_type != 'Placeholder': diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index a27eac7c..1e9865dc 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -77,6 +77,7 @@ def query(cls, sub_op, patrn_op): is_eq = False equivalent_ops = [] if sub_op is None or patrn_op is None: + # null tensor, do nothing pass elif sub_op.op_type == patrn_op.op_type and sub_op.op_type not in cls._association_map: is_eq = True @@ -102,10 +103,6 @@ def query(cls, sub_op, patrn_op): is_eq = True morphism = cls._compatibility_map[sub_op.op_type][patrn_op.op_type] equivalent_ops = [MetaOperationInfo(op_info=sub_op, morphism=morphism)] - elif patrn_op.is_input_op: - # match input node anyway - is_eq = True - equivalent_ops = [sub_op] return is_eq, equivalent_ops @@ -208,7 +205,7 @@ def _visit(self, states): return new_states -@attr.s +@attr.s(repr=False) class uTensorGraphMatch(object): pattern_ugraph = attr.ib(type=uTensorGraph) @@ -419,6 +416,12 @@ def subject_graph_view(self): output_nodes=output_nodes, ) + def __repr__(self): + repr_str = self.__class__.__name__ + '(\n' + for patrn_op_name, subj_op in self.patrn2subj_op_map.items(): + repr_str += ' {} -> {}\n'.format(patrn_op_name, subj_op.name) + repr_str += ')' + return repr_str @attr.s class _MatchState(object): From 5f9c22cad55fd2b286ee987af47b9186da925329 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 6 Jun 2019 00:43:31 +0800 Subject: [PATCH 38/70] minor fix --- utensor_cgen/ir/base.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index cb51d006..9807129d 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -352,8 +352,9 @@ def output_tensors(self): def input_ops(self): ops = [] for op in self.ops_info.values(): - if not op.input_tensors or all( - [tensor.is_null_tensor for tensor in op.input_tensors] + if ( + not op.input_tensors + or any([tensor.is_null_tensor for tensor in op.input_tensors]) ): ops.append(op) return ops From 6997508cb95db329a58892bb88f9d54f2fecf398 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 7 Jun 2019 13:11:05 +0800 Subject: [PATCH 39/70] fix bug --- utensor_cgen/ir/base.py | 7 ++----- utensor_cgen/ir/misc/graph_viz.py | 4 ++-- utensor_cgen/transformer/graph_viz.py | 6 ++++-- 3 files changed, 8 insertions(+), 9 deletions(-) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 9807129d..b144f7d1 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -248,6 +248,7 @@ def move_into(self, ugraph): tensor.move_into(ugraph) for tensor in self.output_tensors: tensor.move_into(ugraph) + ugraph.ops_info[self.name] = self def __deepcopy__(self, memo): op_info = OperationInfo(name=self.name, @@ -435,11 +436,7 @@ def merge_into(self, other_ugraph): 2. topologic_order_graph (from `utensor_cgen.utils`) """ for op in self.ops_info.values(): - op._ugraph = other_ugraph - for input_tensor in op.input_tensors: - input_tensor.move_into(other_ugraph) - for output_tensor in op.output_tensors: - output_tensor.move_into(other_ugraph) + op.move_into(other_ugraph) if op.op_type not in self._type_to_op_map: self._type_to_op_map[op.op_type] = [] self._type_to_op_map[op.op_type].append(op) diff --git a/utensor_cgen/ir/misc/graph_viz.py b/utensor_cgen/ir/misc/graph_viz.py index 8c1a52bb..556b0ae5 100644 --- a/utensor_cgen/ir/misc/graph_viz.py +++ b/utensor_cgen/ir/misc/graph_viz.py @@ -3,7 +3,7 @@ from utensor_cgen.logger import logger -def viz_graph(ugraph, out_fname=None, view=False): +def viz_graph(ugraph, out_fname=None, view=False, cleanup=True): dot = Digraph() nodes = {} i = 0 @@ -29,6 +29,6 @@ def viz_graph(ugraph, out_fname=None, view=False): for n in node.output_tensors: dot.edge(nodes[node.name], nodes[n.name]) if out_fname: - dot.render(out_fname, view=view) + dot.render(out_fname, view=view, cleanup=cleanup) logger.info('graph visualization file generated: %s', out_fname) return dot diff --git a/utensor_cgen/transformer/graph_viz.py b/utensor_cgen/transformer/graph_viz.py index 5658c4b0..e60fa444 100644 --- a/utensor_cgen/transformer/graph_viz.py +++ b/utensor_cgen/transformer/graph_viz.py @@ -21,15 +21,17 @@ class GraphVizTransformer(Transformer): METHOD_NAME = 'graph_viz' KWARGS_NAMESCOPE = '_utensor_graph_viz' - def __init__(self, out_fname="graph.gv", view=False): + def __init__(self, out_fname="graph.gv", view=False, cleanup=True): self.out_fname = out_fname self.view = view self.prune_graph = False + self.cleanup = cleanup def transform(self, ugraph): viz_graph( ugraph=ugraph, out_fname=self.out_fname, - view=self.view + view=self.view, + cleanup=self.cleanup ) return ugraph From 89a9c8d62a2eb293e7c65a38b790c55c501f83e2 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 8 Jun 2019 00:10:53 +0800 Subject: [PATCH 40/70] Add tests for convpool transformer (testing against vgg-like graphs) --- .../test_convpool/__init__.py | 0 .../test_convpool/conftest.py | 43 +++++++++++++++++++ .../test_convpool/test_vgg.py | 38 ++++++++++++++++ 3 files changed, 81 insertions(+) create mode 100644 tests/test_transformer/test_convpool/__init__.py create mode 100644 tests/test_transformer/test_convpool/conftest.py create mode 100644 tests/test_transformer/test_convpool/test_vgg.py diff --git a/tests/test_transformer/test_convpool/__init__.py b/tests/test_transformer/test_convpool/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_transformer/test_convpool/conftest.py b/tests/test_transformer/test_convpool/conftest.py new file mode 100644 index 00000000..e9125cbb --- /dev/null +++ b/tests/test_transformer/test_convpool/conftest.py @@ -0,0 +1,43 @@ +from random import sample + +import numpy as np +from pytest import fixture + +import tensorflow as tf +from utensor_cgen.frontend.tensorflow import GraphDefParser + + +@fixture(name='vgg_ugraph') +def gen_vgg_graph(): + graph = tf.Graph() + with graph.as_default(): + x = tf.placeholder(dtype=tf.float32, shape=[None, 2048, 2048, 3], name='input_x') + in_feat = x + num_layers = sample([3, 4, 5], 1)[0] + for i in range(1, num_layers+1): + ksize = sample([2, 3, 5], 1)[0] + in_channel = in_feat.shape.as_list()[-1] + out_channel = sample([3, 5, 10], 1)[0] + stride = sample([1, 2], 1)[0] + kernel = tf.constant( + np.random.rand(ksize, ksize, in_channel, out_channel), + dtype=tf.float32, + name='kernel_{}'.format(i) + ) + in_feat = tf.nn.conv2d( + in_feat, + kernel, + strides=[1, stride, stride, 1], + padding='VALID', + name='feat_map_{}'.format(i) + ) + in_feat = tf.nn.relu(in_feat, name='relu_{}'.format(i)) + in_feat = tf.nn.max_pool( + in_feat, + ksize=[1, ksize, ksize, 1], + strides=[1, stride, stride, 1], + name='pool_{}'.format(i), + padding='SAME', + ) + ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[in_feat.op.name]) + return ugraph diff --git a/tests/test_transformer/test_convpool/test_vgg.py b/tests/test_transformer/test_convpool/test_vgg.py new file mode 100644 index 00000000..0fe84e7e --- /dev/null +++ b/tests/test_transformer/test_convpool/test_vgg.py @@ -0,0 +1,38 @@ +from utensor_cgen.transformer.pipeline import TransformerPipeline + + +def test_vgg1(vgg_ugraph): + trans = TransformerPipeline([ + ('linear_reorder_v2', {}), + ('quantize', {}), + ('conv_pool_v2', {}) + ]) + new_ugraph = trans.transform(vgg_ugraph) + num_conv = len(vgg_ugraph.get_ops_by_type('Conv2D')) + num_convpool = len(new_ugraph.get_ops_by_type('QuantizedFusedConv2DMaxpool')) + assert not new_ugraph.get_ops_by_type('Conv2D') + assert num_conv == num_convpool + +def test_vgg2(vgg_ugraph): + trans = TransformerPipeline([ + ('linear_reorder_v2', {}), + ('quantize', {}), + ('conv_pool_v2', {}) + ]) + new_ugraph = trans.transform(vgg_ugraph) + num_conv = len(vgg_ugraph.get_ops_by_type('Conv2D')) + num_convpool = len(new_ugraph.get_ops_by_type('QuantizedFusedConv2DMaxpool')) + assert not new_ugraph.get_ops_by_type('Conv2D') + assert num_conv == num_convpool + +def test_vgg3(vgg_ugraph): + trans = TransformerPipeline([ + ('linear_reorder_v2', {}), + ('quantize', {}), + ('conv_pool_v2', {}) + ]) + new_ugraph = trans.transform(vgg_ugraph) + num_conv = len(vgg_ugraph.get_ops_by_type('Conv2D')) + num_convpool = len(new_ugraph.get_ops_by_type('QuantizedFusedConv2DMaxpool')) + assert not new_ugraph.get_ops_by_type('Conv2D') + assert num_conv == num_convpool From a0c6ae6d7a5e97732ff035200e1ef042b9a13ed9 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 8 Jun 2019 00:31:10 +0800 Subject: [PATCH 41/70] refactor test script a bit --- .../test_convpool/test_vgg.py | 53 +++++++------------ 1 file changed, 19 insertions(+), 34 deletions(-) diff --git a/tests/test_transformer/test_convpool/test_vgg.py b/tests/test_transformer/test_convpool/test_vgg.py index 0fe84e7e..35012074 100644 --- a/tests/test_transformer/test_convpool/test_vgg.py +++ b/tests/test_transformer/test_convpool/test_vgg.py @@ -1,38 +1,23 @@ from utensor_cgen.transformer.pipeline import TransformerPipeline -def test_vgg1(vgg_ugraph): - trans = TransformerPipeline([ - ('linear_reorder_v2', {}), - ('quantize', {}), - ('conv_pool_v2', {}) - ]) - new_ugraph = trans.transform(vgg_ugraph) - num_conv = len(vgg_ugraph.get_ops_by_type('Conv2D')) - num_convpool = len(new_ugraph.get_ops_by_type('QuantizedFusedConv2DMaxpool')) - assert not new_ugraph.get_ops_by_type('Conv2D') - assert num_conv == num_convpool +def factory(): + def test(vgg_ugraph): + trans = TransformerPipeline([ + ('linear_reorder_v2', {}), + ('quantize', {}), + ('conv_pool_v2', {}) + ]) + new_ugraph = trans.transform(vgg_ugraph) + num_conv = len(vgg_ugraph.get_ops_by_type('Conv2D')) + num_convpool = len(new_ugraph.get_ops_by_type('QuantizedFusedConv2DMaxpool')) + assert not new_ugraph.get_ops_by_type('Conv2D') + assert num_conv == num_convpool + return test -def test_vgg2(vgg_ugraph): - trans = TransformerPipeline([ - ('linear_reorder_v2', {}), - ('quantize', {}), - ('conv_pool_v2', {}) - ]) - new_ugraph = trans.transform(vgg_ugraph) - num_conv = len(vgg_ugraph.get_ops_by_type('Conv2D')) - num_convpool = len(new_ugraph.get_ops_by_type('QuantizedFusedConv2DMaxpool')) - assert not new_ugraph.get_ops_by_type('Conv2D') - assert num_conv == num_convpool - -def test_vgg3(vgg_ugraph): - trans = TransformerPipeline([ - ('linear_reorder_v2', {}), - ('quantize', {}), - ('conv_pool_v2', {}) - ]) - new_ugraph = trans.transform(vgg_ugraph) - num_conv = len(vgg_ugraph.get_ops_by_type('Conv2D')) - num_convpool = len(new_ugraph.get_ops_by_type('QuantizedFusedConv2DMaxpool')) - assert not new_ugraph.get_ops_by_type('Conv2D') - assert num_conv == num_convpool +# 5 random tests +test_vgg1 = factory() +test_vgg2 = factory() +test_vgg3 = factory() +test_vgg4 = factory() +test_vgg5 = factory() From 587eadbf93d3b8385b97b879ab876231be3ef787 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sat, 8 Jun 2019 23:58:57 +0800 Subject: [PATCH 42/70] minor fix and update Pipfile --- Pipfile | 3 ++- utensor_cgen/utils.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/Pipfile b/Pipfile index 95e51bba..664704a9 100644 --- a/Pipfile +++ b/Pipfile @@ -4,7 +4,7 @@ verify_ssl = true name = "pypi" [packages] -"e1839a8" = {path = ".", editable = true} +e1839a8 = {path = ".",editable = true} [dev-packages] pylint = "*" @@ -14,3 +14,4 @@ rope = "*" pillow = "*" scipy = "*" graphviz = "*" +sphinx = "*" diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index d910a31a..8bcd24cf 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -310,7 +310,7 @@ def prune_graph(ugraph): # Or, use this: in_ops = [node.name for node in ugraph.ops_info[op_name].input_nodes] tensors_in = set([t.name for t in ugraph.ops_info[op_name].input_tensors]) in_ops = set() - for it_node in ugraph.topo_order: + for it_node in ugraph.ops_info: if it_node == op_name: continue it_tensors_out = [t.name for t in ugraph.ops_info[it_node].output_tensors] From 0bd9bd8ce1f21ff70046603ccba4ef13f455154e Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 9 Jun 2019 01:19:57 +0800 Subject: [PATCH 43/70] minor refactor --- utensor_cgen/utils.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/utensor_cgen/utils.py b/utensor_cgen/utils.py index 8bcd24cf..cff5c045 100644 --- a/utensor_cgen/utils.py +++ b/utensor_cgen/utils.py @@ -232,13 +232,22 @@ def topologic_order_graph(ugraph): """ topological sort a given ugraph *in place* + - https://en.wikipedia.org/wiki/Topological_sorting + """ + ugraph.topo_order = get_topologic_order(ugraph, ugraph.output_nodes)[::-1] + + +def get_topologic_order(ugraph, init_nodes): + """ + return list of op names in topological order + - https://en.wikipedia.org/wiki/Topological_sorting """ if ugraph.backend != "tensorflow": raise ValueError( "topologic_order_graph works only on tensorflow graph" ) - queue = deepcopy(ugraph.output_nodes) + queue = deepcopy(init_nodes) visited = set() # temporary mark perm_visit = set() # Permanent mark ops_torder = [] # L @@ -266,7 +275,7 @@ def visit(node_name): while queue: node_name = queue.pop(0) visit(node_name) - ugraph.topo_order = ops_torder[::-1] + return ops_torder def ops_bfs_queue(ugraph, init_nodes=None): From 7aed32247b162f3cc258ec2319e9d5fe341912bd Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 9 Jun 2019 14:34:01 +0800 Subject: [PATCH 44/70] matcher corner case fixed: null tensors --- utensor_cgen/ir/base.py | 15 ++- utensor_cgen/matcher/_matcher_impl.py | 18 +++ utensor_cgen/transformer/ns_transformer.py | 127 +++++++++------------ 3 files changed, 85 insertions(+), 75 deletions(-) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index b144f7d1..cbc5dd40 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -73,7 +73,10 @@ def ugraph(self): @property def op(self): - return self._ugraph.ops_info.get(self.op_name, None) + op = self._ugraph.ops_info.get(self.op_name, None) + if not op and not self.is_null_tensor: + raise ValueError('Unknown op name: {}'.format(self.op_name)) + return op @property def backend(self): @@ -363,9 +366,14 @@ def input_ops(self): @property def input_tensors(self): in_tensors = set([]) + in_op_names = set(op.name for op in self.input_ops) for op in self.input_ops: - for tensor in op.input_tensors: - in_tensors.add(tensor) + in_tensors.update( + [ + tensor for tensor in op.input_tensors + if tensor.op_name not in in_op_names + ] + ) return in_tensors @property @@ -401,6 +409,7 @@ def ops(self): return [self.ops_info[name] for name in self.topo_order] def add_op(self, op, sort=True): + # experimental, don't use if not isinstance(op, OperationInfo): raise ValueError('expecting OperationInfo, get {}'.format(type(op))) if op.name in self.ops_info: diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index 1e9865dc..a908c2f2 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -4,6 +4,7 @@ import attr from attr.validators import instance_of +from click import style from utensor_cgen.ir import (MetaOperationInfo, OperationInfo, uTensorGraph, uTensorGraphView) @@ -200,9 +201,25 @@ def _visit(self, states): sub_bfs_queue=new_sub_bfs_queue, patrn_bfs_queue=deque(state.patrn_bfs_queue), ) + new_state = self._handle_null_tensors(new_state, eq_op, patrn_op) new_state.match.update_op_map(patrn_op, eq_op) new_states.append(new_state) return new_states + + def _handle_null_tensors(self, state, subj_op, patrn_op): + subj_ugraph = state.match.subject_ugraph + for idx, patrn_tensor in enumerate(patrn_op.input_tensors): + if patrn_tensor.is_null_tensor: + subj_tensor = subj_op.input_tensors[idx] + subj_ops_to_remove = set([subj_tensor.op.name]) + all_subgraph_ops = set(op.name for op in ops_bfs_queue(subj_ugraph, [subj_tensor.op])) + for op_name in all_subgraph_ops: + if all(out_op.name in all_subgraph_ops for out_op in subj_ugraph[op_name].output_nodes): + subj_ops_to_remove.add(op_name) + state.sub_bfs_queue = deque( + [op for op in state.sub_bfs_queue if op.name not in subj_ops_to_remove] + ) + return state @attr.s(repr=False) @@ -418,6 +435,7 @@ def subject_graph_view(self): def __repr__(self): repr_str = self.__class__.__name__ + '(\n' + repr_str += style(' -> ', bold=True) + '\n' for patrn_op_name, subj_op in self.patrn2subj_op_map.items(): repr_str += ' {} -> {}\n'.format(patrn_op_name, subj_op.name) repr_str += ')' diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index a01c3ff6..72b13a84 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -5,11 +5,14 @@ for inference """ import re -from collections import defaultdict from copy import deepcopy -from utensor_cgen.ir import OperationInfo, uTensorGraph -from utensor_cgen.utils import parse_tensor_name +import numpy as np + +import tensorflow as tf +from utensor_cgen.frontend.tensorflow import GraphDefParser +from utensor_cgen.matcher import uTensorGraphMatcher +from utensor_cgen.utils import prune_graph, topologic_order_graph from .base import Transformer @@ -56,76 +59,56 @@ class DropoutTransformer(Transformer): KWARGS_NAMESCOPE = '_utensor_dropout' TARGET_NODENAME_PATTERN = re.compile(r'(dropout[_\w\d]*)/.*') + @property + def pattern_ugraph(self): + graph = tf.Graph() + with graph.as_default(): + dummy_x = tf.constant(np.random.rand(10), dtype=tf.float32, name='dummy_x') + dummy_rate = tf.constant(0.5, dtype=tf.float32, name='dummy_rate') + dropout = tf.nn.dropout(dummy_x, rate=dummy_rate, name='dropout') + patrn_ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[dropout.op.name]) + patrn_ugraph['dropout/truediv'].replace_with_null_input_tensor(0) # replace dummy_x + patrn_ugraph['dropout/sub'].replace_with_null_input_tensor(1) # replce dummy_rate + patrn_ugraph = prune_graph(patrn_ugraph) + topologic_order_graph(patrn_ugraph) + return patrn_ugraph + def transform(self, ugraph): - new_graph = uTensorGraph(output_nodes=ugraph.output_nodes) - dropout_input_map = self._find_input(ugraph) - new_ops_info = {} - for node_name in ugraph.topo_order: - match = self.TARGET_NODENAME_PATTERN.match(node_name) - if match: - # ignore all dropout nodes - continue - # replace inputs with dropout inputs - op_info = ugraph.ops_info[node_name] - in_t_infos = [deepcopy(t_info, {'ugraph': new_graph}) - for t_info in op_info.input_tensors] - out_t_infos = [deepcopy(t_info, {'ugraph': new_graph}) - for t_info in op_info.output_tensors] - op_attr = deepcopy(op_info.op_attr) - for i, t_info in enumerate(in_t_infos): - op_name = parse_tensor_name(t_info.name)[0] - match = self.TARGET_NODENAME_PATTERN.match(op_name) - if match: - name_scope = match.group(1) - # assume there should be only on input except keep_prob - dropout_in_tensor = dropout_input_map[name_scope] - in_t_infos.pop(i) - in_t_infos.insert(i, dropout_in_tensor) - new_op_info = OperationInfo(name=op_info.name, - input_tensors=in_t_infos, - n_inputs=len(in_t_infos), - output_tensors=out_t_infos, - n_outputs=len(out_t_infos), - op_type=op_info.op_type, - backend=op_info.backend, - op_attr=op_attr, - ugraph=new_graph) - new_ops_info[node_name] = new_op_info - new_graph.ops_info = new_ops_info - new_graph._backend = ugraph._backend - return new_graph - - def _find_dropout_clusters(self, ugraph): - clusters = defaultdict(lambda: []) - for node_name in ugraph.topo_order: - match = self.TARGET_NODENAME_PATTERN.match(node_name) - if match: - name_scope = match.group(1) - clusters[name_scope].append(node_name) - return dict(clusters) - - def _find_input(self, ugraph): - """dropout_name --> input_tensor_info - - input_tensor_info := the tensor info of a tensor which is not generated - in the dropout namescope but is consumed by ops in - dropout namescope with name not starts with 'keep_prob' - """ - clusters = self._find_dropout_clusters(ugraph) - input_map = {} - for node_name in ugraph.topo_order: - match = self.TARGET_NODENAME_PATTERN.match(node_name) - if match: - name_scope = match.group(1) - cluster = clusters[name_scope] - op_info = ugraph.ops_info[node_name] - for in_tensor_info in op_info.input_tensors: - in_op_name = parse_tensor_name(in_tensor_info.name)[0] - if in_op_name not in cluster and not in_op_name.startswith('keep_prob'): - input_map[name_scope] = in_tensor_info - # assuming there is only one input for dropout - break - return input_map + new_ugraph = deepcopy(ugraph) + if new_ugraph.backend == 'tensorflow': + new_ugraph = self._transform_tf(new_ugraph) + else: + raise ValueError( + 'only support dropout transformer for tensorflow: get {}'.format(new_ugraph.backend) + ) + return new_ugraph + + def _transform_tf(self, ugraph): + matcher = uTensorGraphMatcher(pattern_ugraph=self.pattern_ugraph) + matches = matcher.match(ugraph, n=1) + while matches: + match = matches[0] + ugraph = self._handle_match_tf(match) + matches = matcher.match(ugraph) + return ugraph + + def _handle_match_tf(self, match): + subj_ugraph = match.subject_ugraph + subj_in_tensor = ( + match.patrn2subj_op_map['dropout/truediv'] + .input_tensors[0] + .op + .output_tensors[0] + ) + subj_out_op = match.patrn2subj_op_map['dropout/mul'] + subj_out_tensor = subj_out_op.output_tensors[0] + for op in subj_out_op.output_nodes: + for idx, tensor in enumerate(op.input_tensors): + if tensor.name == subj_out_tensor.name: + op.input_tensors[idx] = subj_in_tensor + match.subject_ugraph = prune_graph(subj_ugraph) + topologic_order_graph(match.subject_ugraph) + return match.subject_ugraph class BatchNormTransformer(Transformer): From 3c03ab026306529499090ad690e25f116789645d Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 10 Jun 2019 22:43:31 +0800 Subject: [PATCH 45/70] update README.md --- README.md | 6 ------ 1 file changed, 6 deletions(-) diff --git a/README.md b/README.md index 4fab5730..4f2a3dcc 100644 --- a/README.md +++ b/README.md @@ -100,12 +100,6 @@ Following steps are a general guild for user how to porting a `TensorFlow` proto 2. run `make tests` - Or you can use `pipenv run pytest tests` instead -# Known Limitations - -- If you want to use dropout with placeholders for the `keep_prob`, you have to name the `keep_prob` placeholder by any name that starts with "keep_prob". - - You can still use any input tensor with name starts with "keep_prob" as long as it's not the output tensor of a placeholder node. - - You can't wrap `dropout` in any `namescope` - # Philosophy - [12 Factor CLI App](https://medium.com/@jdxcode/12-factor-cli-apps-dd3c227a0e46?fbclid=IwAR1Gfq0D7oh3b-mXaIMV3RwYu39TAPrPXfz5sBKC4Rz1t-cckvC8WjBVl_w) From bfb3a1236edae4fa0934cf479a57156d462c7ea9 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 10 Jun 2019 23:00:44 +0800 Subject: [PATCH 46/70] minor fix and update tests for dropout transformer --- tests/test_transformer/test_dropout/conftest.py | 10 ++++++++++ .../test_dropout/test_dropout_transormer.py | 14 +++++++++++--- utensor_cgen/transformer/ns_transformer.py | 3 +++ 3 files changed, 24 insertions(+), 3 deletions(-) diff --git a/tests/test_transformer/test_dropout/conftest.py b/tests/test_transformer/test_dropout/conftest.py index 6d00d6a3..63cfae0b 100644 --- a/tests/test_transformer/test_dropout/conftest.py +++ b/tests/test_transformer/test_dropout/conftest.py @@ -2,6 +2,7 @@ import pytest import tensorflow as tf +from utensor_cgen.utils import random_str @pytest.fixture(scope='session', name='droput_graph_tuple') @@ -18,3 +19,12 @@ def dropout_graph_tuple(): return (graph.as_graph_def(), [keep_prob.name, dropout_x.name], [y.op.name]) + +@pytest.fixture(name='dropout_graph_tuple2') +def dropout_graph_tuple2(): + graph = tf.Graph() + with graph.as_default(): + x = tf.constant(np.random.rand(10), dtype=tf.float32, name='x') + rate = tf.placeholder(dtype=tf.float32, name='rate') + drop = tf.nn.dropout(x, rate=rate, name=random_str(10)) + return graph.as_graph_def(), [drop.op.name] diff --git a/tests/test_transformer/test_dropout/test_dropout_transormer.py b/tests/test_transformer/test_dropout/test_dropout_transormer.py index 5d3c9a90..bb96e8f9 100644 --- a/tests/test_transformer/test_dropout/test_dropout_transormer.py +++ b/tests/test_transformer/test_dropout/test_dropout_transormer.py @@ -3,7 +3,7 @@ from utensor_cgen.transformer.ns_transformer import DropoutTransformer -def test_dropout_trans(droput_graph_tuple): +def test_dropout_trans_1(droput_graph_tuple): (graph_def, (keep_prob_name, dropout_output_name), output_nodes) = droput_graph_tuple @@ -21,7 +21,7 @@ def test_dropout_trans(droput_graph_tuple): tf.import_graph_def(ugraph.graph_def, name='') with graph_2.as_default(): tf.import_graph_def(new_ugraph.graph_def, name='') - with tf.Session(graph=graph_1) as sess: + with tf.Session(graph=graph_1): keep_prob = graph_1.get_tensor_by_name(keep_prob_name) dropout_output = graph_1.get_tensor_by_name(dropout_output_name) output = graph_1.get_tensor_by_name(output_nodes[0]+":0") @@ -29,8 +29,16 @@ def test_dropout_trans(droput_graph_tuple): assert keep_prob.op.name not in new_ugraph.ops_info assert dropout_output.op.name not in new_ugraph.ops_info output_1 = output.eval({keep_prob:1.0}) - with tf.Session(graph=graph_2) as sess: + with tf.Session(graph=graph_2): output = graph_2.get_tensor_by_name(output_nodes[0]+":0") output_2 = output.eval() # expecting the same outputs with keep_prob == 1.0 assert (output_1 == output_2).all() + +def test_dropout_trans_2(dropout_graph_tuple2): + graph_def, output_nodes = dropout_graph_tuple2 + ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + trans = DropoutTransformer() + new_ugraph = trans.transform(ugraph) + assert len(new_ugraph.ops_info) == 1 + assert 'x' in new_ugraph.ops_info diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 72b13a84..0b99cae3 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -106,6 +106,9 @@ def _handle_match_tf(self, match): for idx, tensor in enumerate(op.input_tensors): if tensor.name == subj_out_tensor.name: op.input_tensors[idx] = subj_in_tensor + for idx, op_name in enumerate(subj_ugraph.output_nodes): + if op_name == subj_out_op.name: + subj_ugraph.output_nodes[idx] = subj_in_tensor.op_name match.subject_ugraph = prune_graph(subj_ugraph) topologic_order_graph(match.subject_ugraph) return match.subject_ugraph From 7dbd455881de1075937982bfb58edfa6fcf1a93b Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 10 Jun 2019 23:05:16 +0800 Subject: [PATCH 47/70] clean code --- utensor_cgen/transformer/ns_transformer.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 0b99cae3..df480576 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -4,7 +4,6 @@ Transformers that get rid of namescope/nodes which are not needed for inference """ -import re from copy import deepcopy import numpy as np @@ -22,7 +21,6 @@ class BiasAddTransformer(Transformer): METHOD_NAME = 'biasAdd' KWARGS_NAMESCOPE = '_utensor_biasAdd' - TARGET_NODENAME_PATTERN = re.compile(r'(BiasAdd[_\w\d]*)/.*') def transform(self, ugraph): for node_name in ugraph.topo_order: @@ -41,7 +39,6 @@ def transform(self, ugraph): class InlineTransformer(Transformer): METHOD_NAME = 'inline' KWARGS_NAMESCOPE = '_utensor_inline' - TARGET_NODENAME_PATTERN = re.compile(r'(const[_\w\d]*)/.*') def transform(self, ugraph): for node_name in ugraph.topo_order: @@ -57,7 +54,6 @@ class DropoutTransformer(Transformer): """ METHOD_NAME = 'dropout' KWARGS_NAMESCOPE = '_utensor_dropout' - TARGET_NODENAME_PATTERN = re.compile(r'(dropout[_\w\d]*)/.*') @property def pattern_ugraph(self): @@ -119,7 +115,6 @@ class BatchNormTransformer(Transformer): """ METHOD_NAME = 'batch_norm' KWARGS_NAMESCOPE = '_batch_norm' - TARGET_NODENAME_PATTERN = re.compile(r'(BatchNorm[_\w\d]*)/.*') def transform(self, ugraph): # TODO: implement this! @@ -131,7 +126,6 @@ class FakeGatherV2Transformer(Transformer): """ METHOD_NAME = 'FakeGatherV2' KWARGS_NAMESCOPE = '_fake_gatherv2' - TARGET_NODENAME_PATTERN = re.compile(r'(GatherV2[_\w\d]*)/.*') def transform(self, ugraph): print("warning: force replacing GatherV2 with Gather") From 115bfa59e3c2b47ee1fee427d47f61eb17f955ba Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 11 Jun 2019 00:17:29 +0800 Subject: [PATCH 48/70] enhance graph viz --- utensor_cgen/ir/misc/graph_viz.py | 47 ++++++++++++++++++++++++++----- 1 file changed, 40 insertions(+), 7 deletions(-) diff --git a/utensor_cgen/ir/misc/graph_viz.py b/utensor_cgen/ir/misc/graph_viz.py index 556b0ae5..befe3780 100644 --- a/utensor_cgen/ir/misc/graph_viz.py +++ b/utensor_cgen/ir/misc/graph_viz.py @@ -3,25 +3,58 @@ from utensor_cgen.logger import logger -def viz_graph(ugraph, out_fname=None, view=False, cleanup=True): +def viz_graph(ugraph, out_fname=None, view=False, cleanup=True, colored_nodes=None, suffix=''): + if colored_nodes is None: + colored_nodes = set() + else: + colored_nodes = set(colored_nodes) dot = Digraph() nodes = {} i = 0 for node in ugraph.ops: - nodes[node.name] = chr(ord('a') + i) - dot.node(nodes[node.name], "%s: %s" % (node.name, node.op_type)) + nodes[node.name] = '{}_{}'.format(chr(ord('a') + i), suffix) + colored = node.name in colored_nodes + if colored: + dot.node( + nodes[node.name], + "%s: %s" % (node.name, node.op_type), + color='lightskyblue1', + style='filled' + ) + else: + dot.node(nodes[node.name], "%s: %s" % (node.name, node.op_type)) i += 1 for n in node.input_tensors: if n.name in nodes: continue - nodes[n.name] = chr(ord('a') + i) - dot.node(nodes[n.name], "%s: Tensor" % n.name) + nodes[n.name] = '{}_{}'.format(chr(ord('a') + i), suffix) + colored = n.name in colored_nodes + if colored: + dot.node( + nodes[n.name], + "%s: Tensor" % n.name, + color='olivedrab2', + style='filled', + shape='box', + ) + else: + dot.node(nodes[n.name], "%s: Tensor" % n.name, shape='box') i += 1 for n in node.output_tensors: if n.name in nodes: continue - nodes[n.name] = chr(ord('a') + i) - dot.node(nodes[n.name], "%s: Tensor" % n.name) + nodes[n.name] = '{}_{}'.format(chr(ord('a') + i), suffix) + colored = n.name in colored_nodes + if colored: + dot.node( + nodes[n.name], + "%s: Tensor" % n.name, + color='olivedrab2', + style='filled', + shape='box', + ) + else: + dot.node(nodes[n.name], "%s: Tensor" % n.name, shape='box') i += 1 for node in ugraph.ops: for n in node.input_tensors: From 7b6502e576f28d6da7799fdbd9342538fc6a0d98 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 11 Jun 2019 00:47:24 +0800 Subject: [PATCH 49/70] Add node fuse image to README.md --- README.md | 8 ++++++++ images/conv_pool_fuse.png | Bin 0 -> 279551 bytes 2 files changed, 8 insertions(+) create mode 100644 images/conv_pool_fuse.png diff --git a/README.md b/README.md index 4f2a3dcc..9cdbe04d 100644 --- a/README.md +++ b/README.md @@ -79,6 +79,14 @@ In graph theory terminology, they are `leaf` nodes of your graph. 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zsy6)uTe`yWRo_&~`XzU&PdP%h?wk_oBXmfe`zw5yy^kKarG)(~Ez`kS32jkdQZ6uSg1~gp`wKJc=f`(0U_=m~3p9k{>WDL6;SWT{ve2b>MMUvZtSzsMj z&Ny()gUiY>d{*N|x-F;i8rca+`wR!K_x=IfIXDydhxf%_F-m4jDdV=Pe1MbLUDTl( z2~_o7Msmdg-1vGT)YWxEL3`%b8$rH4845P{Q7v~?OBlU^Zgo6DnY_nEF&R} zRRZijszFYN3UPUs6X}0NV?{?a?JF2@7wii}ZNhY)u0-lzA}HvlL844Pn=Wt24muODg1Ic?0t7 diff --git a/tests/test_transformer/test_dropout/conftest.py b/tests/test_transformer/test_dropout/conftest.py index 63cfae0b..d4937bff 100644 --- a/tests/test_transformer/test_dropout/conftest.py +++ b/tests/test_transformer/test_dropout/conftest.py @@ -1,7 +1,10 @@ +from random import sample + import numpy as np import pytest import tensorflow as tf +from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.utils import random_str @@ -28,3 +31,41 @@ def dropout_graph_tuple2(): rate = tf.placeholder(dtype=tf.float32, name='rate') drop = tf.nn.dropout(x, rate=rate, name=random_str(10)) return graph.as_graph_def(), [drop.op.name] + + +@pytest.fixture(name='vgg_ugraph') +def gen_vgg_graph(): + graph = tf.Graph() + with graph.as_default(): + x = tf.placeholder(dtype=tf.float32, shape=[None, 2048, 2048, 3], name='input_x') + rate = tf.placeholder(dtype=tf.float32, name='rate') + in_feat = x + num_layers = sample([3, 4, 5], 1)[0] + for i in range(1, num_layers+1): + ksize = sample([2, 3, 5], 1)[0] + in_channel = in_feat.shape.as_list()[-1] + out_channel = sample([3, 5, 10], 1)[0] + stride = sample([1, 2], 1)[0] + kernel = tf.constant( + np.random.rand(ksize, ksize, in_channel, out_channel), + dtype=tf.float32, + name='kernel_{}'.format(i) + ) + in_feat = tf.nn.conv2d( + in_feat, + kernel, + strides=[1, stride, stride, 1], + padding='VALID', + name='feat_map_{}'.format(i) + ) + in_feat = tf.nn.relu(in_feat, name='relu_{}'.format(i)) + in_feat = tf.nn.max_pool( + in_feat, + ksize=[1, ksize, ksize, 1], + strides=[1, stride, stride, 1], + name='pool_{}'.format(i), + padding='SAME', + ) + if i != num_layers: + in_feat = tf.nn.dropout(in_feat, rate=rate, name='dropout_{}'.format(i)) + return GraphDefParser.parse(graph.as_graph_def(), output_nodes=[in_feat.op.name]) diff --git a/tests/test_transformer/test_dropout/test_dropout_transormer.py b/tests/test_transformer/test_dropout/test_dropout_transormer.py index bb96e8f9..3dab3435 100644 --- a/tests/test_transformer/test_dropout/test_dropout_transormer.py +++ b/tests/test_transformer/test_dropout/test_dropout_transormer.py @@ -42,3 +42,12 @@ def test_dropout_trans_2(dropout_graph_tuple2): new_ugraph = trans.transform(ugraph) assert len(new_ugraph.ops_info) == 1 assert 'x' in new_ugraph.ops_info + + +def test_dropout_vgg(vgg_ugraph): + trans = DropoutTransformer() + from utensor_cgen.ir.misc.graph_viz import viz_graph + viz_graph(trans.pattern_ugraph, 'dropout_pattern') + new_ugraph = trans.transform(vgg_ugraph) + for op_name in new_ugraph.ops_info: + assert not op_name.startswith('dropout') diff --git a/utensor_cgen/backend/operators.py b/utensor_cgen/backend/operators.py index 12691de1..1241d3cd 100644 --- a/utensor_cgen/backend/operators.py +++ b/utensor_cgen/backend/operators.py @@ -21,7 +21,7 @@ class OperatorFactory(): def createOperatorSnippet(self, op_info, **kwargs): op_type = op_info.op_type if op_type not in self._operators: - err_msg = "unsupported op type in uTensor: {}".format(op_type) + err_msg = "unsupported op type in uTensor: {op.name}, {op.op_type}".format(op=op_info) raise ValueError(err_msg) op = self._operators[op_type](op_info, **kwargs) # Create desired object diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index cbc5dd40..b6a5f787 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -122,7 +122,7 @@ def move_into(self, ugraph): """ self._ugraph = ugraph -@attr.s(cmp=False) +@attr.s(cmp=False, repr=False) class OperationInfo(IRBase, _NoShallowCopyMixin): """ name : str @@ -281,6 +281,8 @@ def __getitem__(self, tensor_idx): ) return self.output_tensors[tensor_idx] + def __repr__(self): + return str((self.name, self.op_type)) class MetaOperationInfo(OperationInfo): diff --git a/utensor_cgen/logger.py b/utensor_cgen/logger.py index 2369d2cd..3a3eade4 100644 --- a/utensor_cgen/logger.py +++ b/utensor_cgen/logger.py @@ -1,12 +1,13 @@ #-*- coding: utf8 -*- import logging +import os import sys __all__ = ['logger'] logger = logging.getLogger(name='utensor-cli') -logger.setLevel(logging.INFO) +logger.setLevel(os.environ.get('UTENSOR_LOG_LEVEL', logging.INFO)) _fmt = logging.Formatter(fmt='[%(levelname)s %(filename)s %(funcName)s @ %(lineno)s] %(message)s') _handler = logging.StreamHandler(sys.stdout) _handler.formatter = _fmt diff --git a/utensor_cgen/matcher/_matcher_impl.py b/utensor_cgen/matcher/_matcher_impl.py index a908c2f2..5665f731 100644 --- a/utensor_cgen/matcher/_matcher_impl.py +++ b/utensor_cgen/matcher/_matcher_impl.py @@ -8,6 +8,7 @@ from utensor_cgen.ir import (MetaOperationInfo, OperationInfo, uTensorGraph, uTensorGraphView) +from utensor_cgen.logger import logger from utensor_cgen.matcher._morphism import Morphism from utensor_cgen.utils import (ops_bfs_queue, prune_graph, random_str, topologic_order_graph) @@ -462,4 +463,4 @@ def is_done(self): a state is done, if 1. the patrn_bfs_queue is empty """ - return not self.patrn_bfs_queue + return not (self.patrn_bfs_queue and self.sub_bfs_queue) diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index df480576..94469f8e 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -31,8 +31,6 @@ def transform(self, ugraph): elif op_type == 'BiasAdd': op_info = ugraph.ops_info[node_name] op_info.op_type = 'Add' - - return ugraph @@ -59,12 +57,16 @@ class DropoutTransformer(Transformer): def pattern_ugraph(self): graph = tf.Graph() with graph.as_default(): - dummy_x = tf.constant(np.random.rand(10), dtype=tf.float32, name='dummy_x') - dummy_rate = tf.constant(0.5, dtype=tf.float32, name='dummy_rate') + dummy_x = tf.constant(np.random.rand(10, 10), dtype=tf.float32, name='dummy_x') + dummy_rate = tf.placeholder(dtype=tf.float32, name='dummy_rate') dropout = tf.nn.dropout(dummy_x, rate=dummy_rate, name='dropout') patrn_ugraph = GraphDefParser.parse(graph.as_graph_def(), output_nodes=[dropout.op.name]) - patrn_ugraph['dropout/truediv'].replace_with_null_input_tensor(0) # replace dummy_x - patrn_ugraph['dropout/sub'].replace_with_null_input_tensor(1) # replce dummy_rate + # replace dummy_x + patrn_ugraph['dropout/truediv'].replace_with_null_input_tensor(0) + # # replace dummy_rate + patrn_ugraph['dropout/sub'].replace_with_null_input_tensor(1) + # # replace Shape Op + patrn_ugraph['dropout/random_uniform/RandomUniform'].replace_with_null_input_tensor(0) patrn_ugraph = prune_graph(patrn_ugraph) topologic_order_graph(patrn_ugraph) return patrn_ugraph From fae11293f4dc1c1f67d46479036eb0de3d46a788 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 11 Jun 2019 23:37:33 +0800 Subject: [PATCH 52/70] remove out-dated notebooks --- tests/deep_cnn/cifar/__init__.py | 4 - tests/deep_cnn/cifar/_cifar.py | 101 ----- tests/deep_cnn/cifar/cs231n/__init__.py | 0 tests/deep_cnn/cifar/cs231n/data_utils.py | 42 -- tests/deep_cnn/cifar/dataset.py | 128 ------ tests/deep_cnn/deep_cnn.ipynb | 500 ---------------------- 6 files changed, 775 deletions(-) delete mode 100644 tests/deep_cnn/cifar/__init__.py delete mode 100644 tests/deep_cnn/cifar/_cifar.py delete mode 100644 tests/deep_cnn/cifar/cs231n/__init__.py delete mode 100644 tests/deep_cnn/cifar/cs231n/data_utils.py delete mode 100644 tests/deep_cnn/cifar/dataset.py delete mode 100644 tests/deep_cnn/deep_cnn.ipynb diff --git a/tests/deep_cnn/cifar/__init__.py b/tests/deep_cnn/cifar/__init__.py deleted file mode 100644 index 68c5c52d..00000000 --- a/tests/deep_cnn/cifar/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# -*- coding: utf8 -*- -from __future__ import absolute_import - -from ._cifar import * diff --git a/tests/deep_cnn/cifar/_cifar.py b/tests/deep_cnn/cifar/_cifar.py deleted file mode 100644 index 4d706994..00000000 --- a/tests/deep_cnn/cifar/_cifar.py +++ /dev/null @@ -1,101 +0,0 @@ -# -*- coding: utf8 -*- -from __future__ import absolute_import, print_function - -import os -import tarfile - -import numpy as np -from tensorflow.contrib.learn.python.learn.datasets import base -from tensorflow.python.framework import dtypes -from tensorflow.python.platform import gfile - -from .cs231n.data_utils import load_CIFAR10 -from .dataset import DataSet, dense_to_one_hot - -__all__ = ["read_data_sets", "get_class_names", "onehot_to_names"] - -_SOURCE_URL = "http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz" -_LABELS_MAP = {0: 'plane', 1: 'car', 2: 'bird', - 3: 'cat', 4: 'deer', 5: 'dog', - 6: 'frog', 7: 'horse', 8: 'ship', - 9: 'truck'} - - -def read_data_sets(work_dir, - fake_data=False, - one_hot=False, - dtype=dtypes.float32, - reshape=True, - validation_size=None, - seed=None): - if fake_data: - def fake(): - return DataSet([], [], - fake_data=True, - image_dims=32*32*3, - num_class=10, - one_hot=one_hot, - dtype=dtype, - seed=seed) - - train = fake() - validation = fake() - test = fake() - return base.Datasets(train=train, validation=validation, test=test) - - root_data_dir = os.path.join(work_dir, "cifar-10-batches-py") - if not os.path.exists(root_data_dir): - # no data directory found - # download gz file - print("Trying to download cifar data (if the tar.gz file is not available)") - gz_fpath = base.maybe_download("cifar-10-python.tar.gz", - work_dir, - _SOURCE_URL) - print("Extracting data in {}".format(root_data_dir)) - with tarfile.open(gz_fpath) as tar: - tar.extractall(work_dir) - else: - print("cifar data directory found {}".format(root_data_dir)) - print("loading data...") - X_train, Y_train, X_test, Y_test = load_CIFAR10(root_data_dir) - if one_hot: - num_class_train = len(np.unique(Y_train)) - num_class_test = len(np.unique(Y_test)) - assert num_class_test == num_class_train, \ - "number of classes mismatch: {} and {}".format(num_class_train, num_class_test) - Y_train = dense_to_one_hot(Y_train, num_class_train) - Y_test = dense_to_one_hot(Y_test, num_class_test) - if validation_size is None: - validation_size = int(X_train.shape[0]/10) - valid_idx = np.random.choice(range(X_train.shape[0]), validation_size) - mask = np.array([True if row_idx in valid_idx else False for row_idx in range(X_train.shape[0])]) - X_train, X_valid = X_train[~mask], X_train[mask] - Y_train, Y_valid = Y_train[~mask], Y_train[mask] - - train_dataset = DataSet(X_train, Y_train, - one_hot=one_hot, - dtype=dtype, - reshape=reshape, - seed=seed) - valid_dataset = DataSet(X_valid, Y_valid, - one_hot=one_hot, - dtype=dtype, - reshape=reshape, - seed=seed) - test_dataset = DataSet(X_test, Y_test, - one_hot=one_hot, - dtype=dtype, - reshape=reshape, - seed=seed) - return base.Datasets(train=train_dataset, - validation=valid_dataset, - test=test_dataset) - - -def get_class_names(labels): - return np.vectorize(_LABELS_MAP.get)(labels) - - -def onehot_to_names(one_hot): - labels = np.argmax(one_hot, axis=1) - return get_class_names(labels) diff --git a/tests/deep_cnn/cifar/cs231n/__init__.py b/tests/deep_cnn/cifar/cs231n/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/deep_cnn/cifar/cs231n/data_utils.py b/tests/deep_cnn/cifar/cs231n/data_utils.py deleted file mode 100644 index 5915f1ca..00000000 --- a/tests/deep_cnn/cifar/cs231n/data_utils.py +++ /dev/null @@ -1,42 +0,0 @@ -from __future__ import print_function - -import os -import platform - -import numpy as np -from scipy.misc import imread -from six.moves import cPickle as pickle - - -def load_pickle(f): - version = platform.python_version_tuple() - if version[0] == '2': - return pickle.load(f) - elif version[0] == '3': - return pickle.load(f, encoding='latin1') - raise ValueError("invalid python version: {}".format(version)) - -def load_CIFAR_batch(filename): - """ load single batch of cifar """ - with open(filename, 'rb') as f: - datadict = load_pickle(f) - X = datadict['data'] - Y = datadict['labels'] - X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float") - Y = np.array(Y) - return X, Y - -def load_CIFAR10(ROOT): - """ load all of cifar """ - xs = [] - ys = [] - for b in range(1,6): - f = os.path.join(ROOT, 'data_batch_%d' % (b, )) - X, Y = load_CIFAR_batch(f) - xs.append(X) - ys.append(Y) - Xtr = np.concatenate(xs) - Ytr = np.concatenate(ys) - del X, Y - Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch')) - return Xtr, Ytr, Xte, Yte diff --git a/tests/deep_cnn/cifar/dataset.py b/tests/deep_cnn/cifar/dataset.py deleted file mode 100644 index c6d94d48..00000000 --- a/tests/deep_cnn/cifar/dataset.py +++ /dev/null @@ -1,128 +0,0 @@ -# -*- coding: utf8 -*- -# this file is (mostly) adapt from Tensorflow source code -from __future__ import print_function - -from functools import reduce - -import numpy -from tensorflow.python.framework import dtypes, random_seed - - -def dense_to_one_hot(labels_dense, num_classes): - """Convert class labels from scalars to one-hot vectors.""" - num_labels = labels_dense.shape[0] - index_offset = numpy.arange(num_labels) * num_classes - labels_one_hot = numpy.zeros((num_labels, num_classes)) - labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 - return labels_one_hot - -class DataSet(object): - - def __init__(self, - images, - labels, - fake_data=False, - image_dims = None, - num_class = None, - one_hot=False, - dtype=dtypes.float32, - reshape=True, - seed=None): - """Construct a DataSet. - one_hot arg is used only if fake_data is true. `dtype` can be either - `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into - `[0, 1]`. Seed arg provides for convenient deterministic testing. - """ - seed1, seed2 = random_seed.get_seed(seed) - # If op level seed is not set, use whatever graph level seed is returned - numpy.random.seed(seed1 if seed is None else seed2) - dtype = dtypes.as_dtype(dtype).base_dtype - if dtype not in (dtypes.uint8, dtypes.float32): - raise TypeError('Invalid image dtype %r, expected uint8 or float32' % - dtype) - if fake_data: - self._num_examples = 10000 - self.one_hot = one_hot - assert image_dims is not None, \ - "must give image_dims if fake_data is True: get {}".format(image_dims) - self._image_dims = image_dims - assert num_class is not None, \ - "must give num_class if fake_data is True: get {}".format(num_class) - self._num_class = num_class - else: - assert images.shape[0] == labels.shape[0], ( - 'images.shape: %s labels.shape: %s' % (images.shape, labels.shape)) - self._num_examples = images.shape[0] - - # Convert shape from [num examples, rows, columns, depth] - # to [num examples, rows*columns*depth] - if reshape: - images = images.reshape(images.shape[0], -1) - if dtype == dtypes.float32: - # Convert from [0, 255] -> [0.0, 1.0]. - images = images.astype(numpy.float32) - images = numpy.multiply(images, 1.0 / 255.0) - self._images = images - self._labels = labels - self._epochs_completed = 0 - self._index_in_epoch = 0 - - @property - def images(self): - return self._images - - @property - def labels(self): - return self._labels - - @property - def num_examples(self): - return self._num_examples - - @property - def epochs_completed(self): - return self._epochs_completed - - def next_batch(self, batch_size, fake_data=False, shuffle=True): - """Return the next `batch_size` examples from this data set.""" - if fake_data: - fake_image = [1] * self._image_dims - if self.one_hot: - fake_label = [1] + [0] * (self._num_class-1) - else: - fake_label = 0 - return [fake_image for _ in xrange(batch_size)], [ - fake_label for _ in xrange(batch_size) - ] - start = self._index_in_epoch - # Shuffle for the first epoch - if self._epochs_completed == 0 and start == 0 and shuffle: - perm0 = numpy.arange(self._num_examples) - numpy.random.shuffle(perm0) - self._images = self.images[perm0] - self._labels = self.labels[perm0] - # Go to the next epoch - if start + batch_size > self._num_examples: - # Finished epoch - self._epochs_completed += 1 - # Get the rest examples in this epoch - rest_num_examples = self._num_examples - start - images_rest_part = self._images[start:self._num_examples] - labels_rest_part = self._labels[start:self._num_examples] - # Shuffle the data - if shuffle: - perm = numpy.arange(self._num_examples) - numpy.random.shuffle(perm) - self._images = self.images[perm] - self._labels = self.labels[perm] - # Start next epoch - start = 0 - self._index_in_epoch = batch_size - rest_num_examples - end = self._index_in_epoch - images_new_part = self._images[start:end] - labels_new_part = self._labels[start:end] - return numpy.concatenate((images_rest_part, images_new_part), axis=0) , numpy.concatenate((labels_rest_part, labels_new_part), axis=0) - else: - self._index_in_epoch += batch_size - end = self._index_in_epoch - return self._images[start:end], self._labels[start:end] diff --git a/tests/deep_cnn/deep_cnn.ipynb b/tests/deep_cnn/deep_cnn.ipynb deleted file mode 100644 index 045b4698..00000000 --- a/tests/deep_cnn/deep_cnn.ipynb +++ /dev/null @@ -1,500 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:18:18.722370Z", - "start_time": "2018-08-11T14:18:18.713958Z" - } - }, - "outputs": [], - "source": [ - "import pickle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:18:23.481655Z", - "start_time": "2018-08-11T14:18:19.346213Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import tensorflow as tf\n", - "from tensorflow.tools.graph_transforms import TransformGraph\n", - "\n", - "from utensor_cgen.utils import prepare_meta_graph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:18:23.495387Z", - "start_time": "2018-08-11T14:18:23.484875Z" - } - }, - "outputs": [], - "source": [ - "print(tf.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define Graph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:18:23.543090Z", - "start_time": "2018-08-11T14:18:23.499102Z" - } - }, - "outputs": [], - "source": [ - "with open('cnn_weights.pkl', 'rb') as fid:\n", - " pretrain_weights = pickle.load(fid)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:18:23.774598Z", - "start_time": "2018-08-11T14:18:23.763396Z" - } - }, - "outputs": [], - "source": [ - "from functools import reduce" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:18:29.166289Z", - "start_time": "2018-08-11T14:18:29.149684Z" - } - }, - "outputs": [], - "source": [ - "def get_conv_filter(width, height, in_channels, out_channels,\n", - " dtype=tf.float32, initializer=None, seed=None, name=None):\n", - " \"\"\"\n", - " arguments\n", - " =========\n", - " - width: int, filter width\n", - " - height: int, filter height\n", - " - in_channels: int, input channel\n", - " - out_channels: int, output channel\n", - " - dtype: data type\n", - " - initializer: filter initializer\n", - " - seed: random seed of the initializer\n", - " \"\"\"\n", - " if initializer is None:\n", - " initializer = tf.glorot_uniform_initializer(seed=seed, dtype=dtype)\n", - " filter_shape = [width, height, in_channels, out_channels]\n", - " return tf.Variable(initializer(shape=filter_shape), name=name, dtype=dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:18:37.930133Z", - "start_time": "2018-08-11T14:18:37.913825Z" - } - }, - "outputs": [], - "source": [ - "def get_bias(shape, dtype=tf.float32, name=None, initializer=None, seed=None):\n", - " if initializer is None:\n", - " initializer = tf.glorot_uniform_initializer(seed=seed, dtype=dtype)\n", - " return tf.Variable(initializer(shape=shape), name=name, dtype=dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:25:40.900334Z", - "start_time": "2018-08-11T14:25:40.878602Z" - } - }, - "outputs": [], - "source": [ - "def conv_layer(in_fmap, w_shape, padding='SAME', stride=1, act_fun=None, name=None):\n", - " width, height, in_channel, out_channel = w_shape\n", - " strides = [1, stride, stride, 1]\n", - " with tf.name_scope(name, 'conv'):\n", - " w_filter = get_conv_filter(width, height, in_channel, out_channel)\n", - " out_fmap = tf.nn.conv2d(in_fmap, w_filter, \n", - " padding=padding, \n", - " strides=strides,\n", - " name='feature_map')\n", - " bias = get_bias(w_filter.shape.as_list()[-1:],\n", - " dtype=in_fmap.dtype,\n", - " name='bias')\n", - " act = tf.add(out_fmap, bias, name='logits')\n", - " if act_fun:\n", - " act = act_fun(act, name='activation')\n", - " return act" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:39:13.408404Z", - "start_time": "2018-08-11T14:39:13.391506Z" - } - }, - "outputs": [], - "source": [ - "def fc_layer(in_tensor, out_dim, act_fun=None, initializer=None, name=None):\n", - " \"\"\"Fully conneted layer\n", - " \"\"\"\n", - " if initializer is None:\n", - " initializer = tf.glorot_normal_initializer(dtype=in_tensor.dtype)\n", - " w_shape = [in_tensor.shape.as_list()[-1], out_dim]\n", - " with tf.name_scope(name, 'fully_connect'):\n", - " w_fc = tf.Variable(initializer(shape=w_shape, dtype=in_tensor.dtype), name='weight')\n", - " act = tf.matmul(in_tensor, w_fc, name='logits')\n", - " if act_fun:\n", - " act = act_fun(act, name='activation')\n", - " return act" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T14:25:41.568871Z", - "start_time": "2018-08-11T14:25:41.550930Z" - } - }, - "outputs": [], - "source": [ - "def cross_entropy_loss(logits, labels, name=None, axis=-1):\n", - " '''https://github.com/keras-team/keras/blob/master/keras/backend/tensorflow_backend.py#L3171\n", - " '''\n", - " with tf.name_scope(name, 'cross_entropy'):\n", - " prob = tf.nn.softmax(logits=logits, axis=axis)\n", - " prob = tf.clip_by_value(prob, 1e-7, 1-1e-7)\n", - " loss = tf.reduce_sum(-labels * tf.log(prob), name='total_loss')\n", - " return loss" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T15:36:48.189400Z", - "start_time": "2018-08-11T15:36:46.688526Z" - } - }, - "outputs": [], - "source": [ - "graph = tf.Graph()\n", - "\n", - "with graph.as_default():\n", - " tf_image_batch = tf.placeholder(tf.float32, shape=[None, 32, 32, 3])\n", - " tf_labels = tf.placeholder(tf.float32, shape=[None, 10])\n", - " tf_keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n", - " \n", - " conv1 = conv_layer(tf_image_batch, [2, 2, 3, 16],\n", - " padding='VALID')\n", - " conv2 = conv_layer(conv1,\n", - " [3, 3, 16, 32],\n", - " padding='VALID',\n", - " act_fun=tf.nn.relu)\n", - " pool1 = tf.nn.max_pool(conv2,\n", - " ksize=[1, 2, 2, 1],\n", - " strides=[1, 2, 2, 1],\n", - " padding='VALID')\n", - " conv3 = conv_layer(pool1,\n", - " [3, 3, 32, 32],\n", - " stride=2,\n", - " padding='VALID')\n", - " conv4 = conv_layer(conv3,\n", - " [3, 3, 32, 32],\n", - " padding='VALID',\n", - " stride=2,\n", - " act_fun=tf.nn.relu)\n", - " drop1 = tf.nn.dropout(conv4, keep_prob=tf_keep_prob)\n", - " pool2 = tf.nn.max_pool(drop1,\n", - " ksize=[1, 2, 2, 1],\n", - " strides=[1, 2, 2, 1],\n", - " padding='VALID')\n", - " conv5 = conv_layer(pool2,\n", - " [1, 1, 32, 64],\n", - " padding='VALID',\n", - " act_fun=tf.nn.relu)\n", - " conv6 = conv_layer(conv5,\n", - " [1, 1, 64, 128],\n", - " act_fun=tf.nn.relu)\n", - " flat_conv6 = tf.reshape(conv6, shape=[-1, reduce(lambda x, y: x*y, conv6.shape.as_list()[1:], 1)])\n", - " fc1 = fc_layer(flat_conv6, 128, act_fun=tf.nn.relu)\n", - " drop_2 = tf.nn.dropout(fc1, keep_prob=tf_keep_prob)\n", - " fc2 = fc_layer(drop_2, 64, act_fun=tf.nn.relu)\n", - " logits = fc_layer(fc2, 10)\n", - " tf_pred = tf.argmax(logits, axis=-1, name='pred')\n", - " total_loss = cross_entropy_loss(logits=logits, labels=tf_labels)\n", - " \n", - " train_op = tf.train.AdadeltaOptimizer(learning_rate=1.0, epsilon=1e-7).minimize(total_loss)\n", - " saver = tf.train.Saver(max_to_keep=None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T15:36:49.480776Z", - "start_time": "2018-08-11T15:36:49.466199Z" - } - }, - "outputs": [], - "source": [ - "from cifar import read_data_sets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T15:36:49.945291Z", - "start_time": "2018-08-11T15:36:49.939798Z" - } - }, - "outputs": [], - "source": [ - "from tensorflow.keras.preprocessing.image import ImageDataGenerator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T15:36:50.346772Z", - "start_time": "2018-08-11T15:36:50.337700Z" - } - }, - "outputs": [], - "source": [ - "import sys" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T15:53:39.127171Z", - "start_time": "2018-08-11T15:53:39.115280Z" - } - }, - "outputs": [], - "source": [ - "batch_size = 50\n", - "num_iter_per_epoch = 1500\n", - "num_epoch = 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T17:24:26.761950Z", - "start_time": "2018-08-11T16:02:43.432175Z" - } - }, - "outputs": [], - "source": [ - "!rm -rf ckpt && mkdir -p ckpt/cnn\n", - "\n", - "# this will takes long to complete if running on CPU\n", - "cifar = read_data_sets('./data', one_hot=True, reshape=False)\n", - "img_gen = ImageDataGenerator(width_shift_range=0.1,\n", - " height_shift_range=0.1,\n", - " horizontal_flip=True)\n", - "img_gen.fit(cifar.train.images)\n", - "batch_gen = img_gen.flow(cifar.train.images,\n", - " cifar.train.labels,\n", - " batch_size=batch_size)\n", - "\n", - "with tf.Session(graph=graph) as sess:\n", - " tf.global_variables_initializer().run()\n", - " # compute original loss\n", - " l, p_labels = sess.run([total_loss, tf_pred],\n", - " feed_dict={tf_image_batch: cifar.test.images,\n", - " tf_labels: cifar.test.labels,\n", - " tf_keep_prob: 1.0})\n", - " l /= cifar.test.images.shape[0]\n", - " acc = (p_labels == np.argmax(cifar.test.labels, axis=-1)).mean()\n", - " print(f'original loss: {l}')\n", - " print(f'acc on test set: {acc*100:.2f}%')\n", - " \n", - " best_loss = float('inf')\n", - " for epoch in range(num_epoch):\n", - " print(f'epoch {epoch} start')\n", - " for _ in range(num_iter_per_epoch):\n", - " images_batch, labels_batch = next(batch_gen)\n", - " _ = sess.run(train_op,\n", - " feed_dict={tf_image_batch: images_batch,\n", - " tf_labels: labels_batch,\n", - " tf_keep_prob: 0.9})\n", - " test_loss, p_labels = sess.run([total_loss, tf_pred],\n", - " feed_dict={tf_image_batch: cifar.test.images,\n", - " tf_labels: cifar.test.labels,\n", - " tf_keep_prob: 1.0})\n", - " test_loss /= cifar.test.images.shape[0]\n", - " acc = (p_labels == np.argmax(cifar.test.labels, axis=-1)).mean()\n", - " print(f'test loss: {test_loss}, {acc*100:0.2f}%')\n", - " ckpt = saver.save(sess, 'ckpt/cnn/model', global_step=epoch)\n", - " if test_loss < best_loss:\n", - " best_loss = test_loss\n", - " best_ckpt = ckpt\n", - " print(f'epoch saved {ckpt}')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T17:24:26.816351Z", - "start_time": "2018-08-11T17:24:26.801871Z" - } - }, - "outputs": [], - "source": [ - "best_ckpt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T17:24:27.081511Z", - "start_time": "2018-08-11T17:24:26.820178Z" - } - }, - "outputs": [], - "source": [ - "!tree ckpt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T17:28:47.469952Z", - "start_time": "2018-08-11T17:28:46.383686Z" - } - }, - "outputs": [], - "source": [ - "graph_def = prepare_meta_graph(best_ckpt+'.meta', output_nodes=[tf_pred.op.name])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2018-08-11T17:28:48.054678Z", - "start_time": "2018-08-11T17:28:48.047511Z" - } - }, - "outputs": [], - "source": [ - "with open('cifar10_cnn.pb', 'wb') as fid:\n", - " fid.write(graph_def.SerializeToString())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "utensor", - "language": "python", - "name": "utensor" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - }, - "toc": { - "colors": { - "hover_highlight": "#DAA520", - "navigate_num": "#000000", - "navigate_text": "#333333", - "running_highlight": "#FF0000", - "selected_highlight": "#FFD700", - "sidebar_border": "#EEEEEE", - "wrapper_background": "#FFFFFF" - }, - "moveMenuLeft": true, - "nav_menu": {}, - "navigate_menu": true, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "threshold": 4, - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": false, - "widenNotebook": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From e90771a0975154fd8271b8c6062dd6e715759b1a Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 12 Jun 2019 00:10:50 +0800 Subject: [PATCH 53/70] Update README and images --- README.md | 24 ++++++++++++++++++------ images/cnn_dropout.png | Bin 0 -> 1655155 bytes 2 files changed, 18 insertions(+), 6 deletions(-) create mode 100644 images/cnn_dropout.png diff --git a/README.md b/README.md index ab8e3135..6c23dadf 100644 --- a/README.md +++ b/README.md @@ -83,17 +83,29 @@ Run `utensor-cli convert --help` for detailed information. ## Subgraph Isomorphic Matcher and Nodes Fusion -With `uTensorGraphMatcher`, performing a subgraph isomorphic match, fuse or replace the match with another graph is just a few line of code! +With `uTensorGraphMatcher`, performing a subgraph isomorphic match, replace or manipulate the matched subgraph is just a few line of code! + +### Node Fusion ![conv-pool-fuce](images/conv_pool_fuse.png) -- Left: Original graph -- Middle: Matched convolution layer -- Right: Replace the matched layer with specialized `QuantConvPool` node +- Left: original graph +- Middle: matched convolution layer +- Right: replace the matched layer with specialized `QuantConvPool` node + +### Dropout Layer Removal + +![cnn-dropout](images/cnn_dropout.png) + +- Left: original graph with dropout Layers +- Middle: matched dropout layers +- Right: graph with dropout layers removed + +# Examples -# Example +- [Deep Multilayer Perceptron](https://github.com/uTensor/utensor_cgen/tree/develop/tests/deep_mlp) +- [End-to-End Convolution NN](https://github.com/uTensor/simple_cnn_tutorial) -Please refer to [tests/deep_mlp](https://github.com/uTensor/utensor_cgen/tree/develop/tests/deep_mlp) for detailed example # User Guide diff --git a/images/cnn_dropout.png b/images/cnn_dropout.png new file mode 100644 index 0000000000000000000000000000000000000000..ab66344a51dfd9e350776ff2ab1c95f55215d533 GIT binary patch literal 1655155 zcmeFZ_dnHtA3xr^R78=HQC7$v$IOWA897#Vb?gx#^Ta!mGIJa=BjY4wlk5t|&Pq1f zk$I5K=i&P4bM?LcfbS0qG^9_Q6vZPkkx7%rSTb?TzJniBHVDRTQ$ zr>;a2pN8-77%f?!`sdUsbtQRy@8qR1Yj5gi=JRVqLt)h+w^^PMc~$&0xn?pNT7J)7 zvp7z{zW9oU>4#6%ivLcntT3o4jGC`{&L2O#f2>NTcKYhq?TtWN7f-K064madZ$c)% zk%S{o{>cCM3T7~n{vY2y`Ih(UYw1h>^JjN=SiP?O&!4GQUn_fE4%m&75bbq6K5ona^l}8{U8ccQ&U}Pem?$)h;TlB z{?3jLVwGG4{`P1dRs`aPlnG{bFd<=Q$2&z^mH1Ue#O%o0_(K~ToBQ|gJKEYhJbZ|9 zbhNvFTL}4d*o`T0ii#56O2n&Z>%?u`(hC-am*o)=#l^*yiHX6%XTMPBzqgho;^5?L 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z9U=wM)QaxZAlA5Dxc6zZ?z|BjMXAEet0Y_EDT|on<9MS_1dXSxovqpfQFqE<-5gCu zXPdfl{rM#R33jY1zdXu#Cn4Bv87~>O`gWj8OQFJgOZbpQS*K6ori11GSQIbt1ben#nzfM`33$l3^`0Tt`ZbNzE#5g=1daQC(D0<)FqPLL)v_y<^Wg{bR*e zl#g}tg(v=d Date: Tue, 11 Jun 2019 00:56:01 +0800 Subject: [PATCH 50/70] Add notes to README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 9cdbe04d..ab8e3135 100644 --- a/README.md +++ b/README.md @@ -87,6 +87,10 @@ With `uTensorGraphMatcher`, performing a subgraph isomorphic match, fuse or repl ![conv-pool-fuce](images/conv_pool_fuse.png) +- Left: Original graph +- Middle: Matched convolution layer +- Right: Replace the matched layer with specialized `QuantConvPool` node + # Example Please refer to [tests/deep_mlp](https://github.com/uTensor/utensor_cgen/tree/develop/tests/deep_mlp) for detailed example From b797413122a06a90b386802a28d0875d9836a2de Mon Sep 17 00:00:00 2001 From: dboyliao Date: Tue, 11 Jun 2019 22:42:42 +0800 Subject: [PATCH 51/70] update cnn pb file (new dropout api) --- setup.py | 2 +- tests/deep_cnn/cifar10_cnn.pb | Bin 241735 -> 243254 bytes .../test_transformer/test_dropout/conftest.py | 41 ++++++++++++++++++ .../test_dropout/test_dropout_transormer.py | 9 ++++ utensor_cgen/backend/operators.py | 2 +- utensor_cgen/ir/base.py | 4 +- utensor_cgen/logger.py | 3 +- utensor_cgen/matcher/_matcher_impl.py | 3 +- utensor_cgen/transformer/ns_transformer.py | 14 +++--- 9 files changed, 67 insertions(+), 11 deletions(-) diff --git a/setup.py b/setup.py index f0da22c9..e54e0e08 100644 --- a/setup.py +++ b/setup.py @@ -29,7 +29,7 @@ ]}, install_requires=[ 'Jinja2', - 'tensorflow', + 'tensorflow==1.13.1', 'idx2numpy', 'attrs', 'click', diff --git a/tests/deep_cnn/cifar10_cnn.pb b/tests/deep_cnn/cifar10_cnn.pb index 144de2035a782a317dc5ea459a817a49b822a073..464077e84298db25f31c3a0cf19746ad81b065a5 100644 GIT binary patch literal 243254 zcmaI72{cwwxIS(kGe?;V4d#-BZ@(u>bE#Asq*5}421AodB4eh^3CUEHgv7VsV+a*R zNJ^6;Q&Kc){D1eZd;ho* 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ziNkyt6;#mwYvcGH2TzQPwYm+}>St`mDFDvD3h=Hu82a;M*e*GYXQzahxz`S(VAG9F@XpJRPiD@K)IR(i9Cv=vWzdQ0G?$mo? zEO)b5$UeX=y{M>YLs{z?gbI!)uhe9;0P#z8W+US1-s9g7Z#s-~Q;j|+>iMXsX4Yh* zFa<;a-^ofYj%jjhb0Z=#5ZM4KOP_l?Sp;!^__I4oo{c@?%gZ@#KypBnawtFm;iSSo z;g4nztuMhkz+s3(m{yENeW^6E#;495Q{LG2s_f|@k;yeO6?#h|&qGri&eIdZbha}K zyN}BQAQ`H$nxXjWZVuo##Uh~|KRyLZE_i3p+3Sy3C&`Z^ ziQ1z2#(BQ5Svkq@v(0|*h<$coY`4v)d&T_&LcYoF literal 0 HcmV?d00001 From 2671be1ccad270f22996bed282bee72b940b21ae Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 12 Jun 2019 23:19:03 +0800 Subject: [PATCH 54/70] Add old dropout trans back, update tests and add subcmd --- Makefile | 2 +- .../test_dropout/test_dropout_transormer.py | 44 +++++-- .../test_pipeline_transformer.py | 6 +- utensor_cgen/cli.py | 17 ++- utensor_cgen/transformer/ns_transformer.py | 111 +++++++++++++++++- utensor_cgen/transformer/pipeline.py | 15 +-- 6 files changed, 174 insertions(+), 21 deletions(-) diff --git a/Makefile b/Makefile index 6e842f42..06c6eb34 100644 --- a/Makefile +++ b/Makefile @@ -12,4 +12,4 @@ test_%: fi; clean: - rm -f tests_log.txt + rm -f tests_log.txt *.pdf diff --git a/tests/test_transformer/test_dropout/test_dropout_transormer.py b/tests/test_transformer/test_dropout/test_dropout_transormer.py index 3dab3435..98593527 100644 --- a/tests/test_transformer/test_dropout/test_dropout_transormer.py +++ b/tests/test_transformer/test_dropout/test_dropout_transormer.py @@ -1,9 +1,10 @@ import tensorflow as tf from utensor_cgen.frontend.tensorflow import GraphDefParser -from utensor_cgen.transformer.ns_transformer import DropoutTransformer +from utensor_cgen.transformer.ns_transformer import (DropoutTransformer, + DropoutTransformerV2) -def test_dropout_trans_1(droput_graph_tuple): +def test_dropout_trans_1_1(droput_graph_tuple): (graph_def, (keep_prob_name, dropout_output_name), output_nodes) = droput_graph_tuple @@ -35,19 +36,48 @@ def test_dropout_trans_1(droput_graph_tuple): # expecting the same outputs with keep_prob == 1.0 assert (output_1 == output_2).all() +def test_dropout_trans_1_2(droput_graph_tuple): + (graph_def, + (keep_prob_name, dropout_output_name), + output_nodes) = droput_graph_tuple + ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) + transformer = DropoutTransformerV2() + new_ugraph = transformer.transform(ugraph) + for op in new_ugraph.ops_info.values(): + assert op.ugraph + out_op = new_ugraph.ops_info[output_nodes[0]] + assert set([str(op.name) for op in out_op.input_nodes]) == set(['x', 'bias']) + # all dropout nodes should be gone + graph_1 = tf.Graph() + graph_2 = tf.Graph() + with graph_1.as_default(): + tf.import_graph_def(ugraph.graph_def, name='') + with graph_2.as_default(): + tf.import_graph_def(new_ugraph.graph_def, name='') + with tf.Session(graph=graph_1): + keep_prob = graph_1.get_tensor_by_name(keep_prob_name) + dropout_output = graph_1.get_tensor_by_name(dropout_output_name) + output = graph_1.get_tensor_by_name(output_nodes[0]+":0") + # test the dropout ops are gone + assert keep_prob.op.name not in new_ugraph.ops_info + assert dropout_output.op.name not in new_ugraph.ops_info + output_1 = output.eval({keep_prob:1.0}) + with tf.Session(graph=graph_2): + output = graph_2.get_tensor_by_name(output_nodes[0]+":0") + output_2 = output.eval() + # expecting the same outputs with keep_prob == 1.0 + assert (output_1 == output_2).all() + def test_dropout_trans_2(dropout_graph_tuple2): graph_def, output_nodes = dropout_graph_tuple2 ugraph = GraphDefParser.parse(graph_def, output_nodes=output_nodes) - trans = DropoutTransformer() + trans = DropoutTransformerV2() new_ugraph = trans.transform(ugraph) assert len(new_ugraph.ops_info) == 1 assert 'x' in new_ugraph.ops_info - def test_dropout_vgg(vgg_ugraph): - trans = DropoutTransformer() - from utensor_cgen.ir.misc.graph_viz import viz_graph - viz_graph(trans.pattern_ugraph, 'dropout_pattern') + trans = DropoutTransformerV2() new_ugraph = trans.transform(vgg_ugraph) for op_name in new_ugraph.ops_info: assert not op_name.startswith('dropout') diff --git a/tests/test_transformer/test_pipeline/test_pipeline_transformer.py b/tests/test_transformer/test_pipeline/test_pipeline_transformer.py index cba6e033..1e1213f7 100644 --- a/tests/test_transformer/test_pipeline/test_pipeline_transformer.py +++ b/tests/test_transformer/test_pipeline/test_pipeline_transformer.py @@ -6,16 +6,16 @@ def test_pipeline_1(methods): pipeline = TransformerPipeline(methods) assert len(pipeline.pipeline) == len(methods) for transformer, (method_name, _) in zip(pipeline.pipeline, methods): - assert isinstance(transformer, pipeline._TRANSFORMER_MAP[method_name]) + assert isinstance(transformer, pipeline.TRANSFORMER_MAP[method_name]) def test_pipeline_2(methods): pipeline = TransformerPipeline(methods) assert len(pipeline.pipeline) == len(methods) for transformer, (method_name, _) in zip(pipeline.pipeline, methods): - assert isinstance(transformer, pipeline._TRANSFORMER_MAP[method_name]) + assert isinstance(transformer, pipeline.TRANSFORMER_MAP[method_name]) def test_pipeline_3(methods): pipeline = TransformerPipeline(methods) assert len(pipeline.pipeline) == len(methods) for transformer, (method_name, _) in zip(pipeline.pipeline, methods): - assert isinstance(transformer, pipeline._TRANSFORMER_MAP[method_name]) + assert isinstance(transformer, pipeline.TRANSFORMER_MAP[method_name]) diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 15e44add..429a312e 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -21,7 +21,6 @@ def _get_pb_model_name(path): def cli(): pass - @cli.command(name='convert', help='convert graph to cpp/hpp files') @click.help_option('-h', '--help') @click.argument('pb_file', required=True, metavar='MODEL.pb') @@ -84,6 +83,22 @@ def convert_graph(pb_file, output, data_dir, embed_data_dir, save_graph, save_graph, debug_comment) generator.generate(model_path) +@cli.command(name='list-trans-methods', help='list all available graph transformation') +@click.help_option('-h', '--help') +@click.option('--verbose', is_flag=True) +def list_trans_methods(verbose): + from utensor_cgen.transformer import TransformerPipeline + + if verbose: + for name, trans_cls in TransformerPipeline.TRANSFORMER_MAP.items(): + click.secho(name, fg='white', bold=True) + click.secho(trans_cls.__doc__, fg='yellow', bold=True) + else: + click.secho( + str(TransformerPipeline.all_transform_methods()), + fg='white', bold=True + ) + return 0 @cli.command(name='show', help='show node names in the pb file') @click.help_option('-h', '--help') diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 94469f8e..bacfe863 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -4,14 +4,18 @@ Transformers that get rid of namescope/nodes which are not needed for inference """ +import re +from collections import defaultdict from copy import deepcopy import numpy as np import tensorflow as tf from utensor_cgen.frontend.tensorflow import GraphDefParser +from utensor_cgen.ir import OperationInfo, uTensorGraph from utensor_cgen.matcher import uTensorGraphMatcher -from utensor_cgen.utils import prune_graph, topologic_order_graph +from utensor_cgen.utils import (parse_tensor_name, prune_graph, + topologic_order_graph) from .base import Transformer @@ -47,11 +51,114 @@ def transform(self, ugraph): return ugraph + class DropoutTransformer(Transformer): - """Remove Dropout Op + """Dropout removal transformer + + Pros + ==== + - Insensitive to the dropout layer pattern so it works across different + versions of tensorflow + + Cons + ==== + - naming constrains on the dropout layers, layer name must starts with 'dropout' + and the keep_prob op must be with name starts with 'keep_prop' """ METHOD_NAME = 'dropout' KWARGS_NAMESCOPE = '_utensor_dropout' + TARGET_NODENAME_PATTERN = re.compile(r'(dropout[_\w\d]*)/.*') + + def transform(self, ugraph): + new_graph = uTensorGraph(output_nodes=ugraph.output_nodes) + dropout_input_map = self._find_input(ugraph) + new_ops_info = {} + for node_name in ugraph.ops_info: + match = self.TARGET_NODENAME_PATTERN.match(node_name) + if match: + # ignore all dropout nodes + continue + # replace inputs with dropout inputs + op_info = ugraph.ops_info[node_name] + in_t_infos = [deepcopy(t_info, {'ugraph': new_graph}) + for t_info in op_info.input_tensors] + out_t_infos = [deepcopy(t_info, {'ugraph': new_graph}) + for t_info in op_info.output_tensors] + op_attr = deepcopy(op_info.op_attr) + for i, t_info in enumerate(in_t_infos): + op_name = parse_tensor_name(t_info.name)[0] + match = self.TARGET_NODENAME_PATTERN.match(op_name) + if match: + name_scope = match.group(1) + # assume there should be only on input except keep_prob + dropout_in_tensor = dropout_input_map[name_scope] + in_t_infos.pop(i) + in_t_infos.insert(i, dropout_in_tensor) + new_op_info = OperationInfo(name=op_info.name, + input_tensors=in_t_infos, + n_inputs=len(in_t_infos), + output_tensors=out_t_infos, + n_outputs=len(out_t_infos), + op_type=op_info.op_type, + backend=op_info.backend, + op_attr=op_attr, + ugraph=new_graph) + new_ops_info[node_name] = new_op_info + new_graph.ops_info = new_ops_info + new_graph._backend = ugraph._backend + return new_graph + + def _find_dropout_clusters(self, ugraph): + clusters = defaultdict(lambda: []) + for node_name in ugraph.topo_order: + match = self.TARGET_NODENAME_PATTERN.match(node_name) + if match: + name_scope = match.group(1) + clusters[name_scope].append(node_name) + return dict(clusters) + + def _find_input(self, ugraph): + """dropout_name --> input_tensor_info + + input_tensor_info := the tensor info of a tensor which is not generated + in the dropout namescope but is consumed by ops in + dropout namescope with name not starts with 'keep_prob' + """ + clusters = self._find_dropout_clusters(ugraph) + input_map = {} + for node_name in ugraph.topo_order: + match = self.TARGET_NODENAME_PATTERN.match(node_name) + if match: + name_scope = match.group(1) + cluster = clusters[name_scope] + op_info = ugraph.ops_info[node_name] + for in_tensor_info in op_info.input_tensors: + in_op_name = parse_tensor_name(in_tensor_info.name)[0] + if in_op_name not in cluster and not in_op_name.startswith('keep_prob'): + input_map[name_scope] = in_tensor_info + # assuming there is only one input for dropout + break + return input_map + + +class DropoutTransformerV2(Transformer): + """Dropout removal transformer version 2 + + Implemented with subgraph matcher + + Pros + ==== + - no naming requirements on the dropout layer and keep prob op + + Cons + ==== + - sensitive to the dropout layer pattern. The pattern of dropout + layer may differ across different version of tensorflow so this + transformer may fail to match the dropout layer if the given graph + is not using the same version + """ + METHOD_NAME = 'dropout_v2' + KWARGS_NAMESCOPE = '_utensor_dropout_v2' @property def pattern_ugraph(self): diff --git a/utensor_cgen/transformer/pipeline.py b/utensor_cgen/transformer/pipeline.py index 9eea45a0..6683ca44 100644 --- a/utensor_cgen/transformer/pipeline.py +++ b/utensor_cgen/transformer/pipeline.py @@ -5,17 +5,18 @@ from .linear_reoder import (Linear_Reorder_Transformer, LinearReorderTransformerV2) from .ns_transformer import (BatchNormTransformer, BiasAddTransformer, - DropoutTransformer, FakeGatherV2Transformer, - InlineTransformer) + DropoutTransformer, DropoutTransformerV2, + FakeGatherV2Transformer, InlineTransformer) from .optimizer import IdOpRemoveOptimizer, RefCntOptimizer from .quantize import QuantizeTransformer class TransformerPipeline(object): - _TRANSFORMER_MAP = { + TRANSFORMER_MAP = { RefCntOptimizer.METHOD_NAME: RefCntOptimizer, DropoutTransformer.METHOD_NAME: DropoutTransformer, + DropoutTransformerV2.METHOD_NAME: DropoutTransformerV2, BatchNormTransformer.METHOD_NAME: BatchNormTransformer, QuantizeTransformer.METHOD_NAME: QuantizeTransformer, InlineTransformer.METHOD_NAME: InlineTransformer, @@ -37,7 +38,7 @@ def __init__(self, methods): """ self._pipeline = [] for method, kwargs in methods: - trans_cls = self._TRANSFORMER_MAP.get(method, None) + trans_cls = self.TRANSFORMER_MAP.get(method, None) if trans_cls is None: raise ValueError("Unknown transformation method: {}".format(method)) transformer = trans_cls(**kwargs) @@ -54,12 +55,12 @@ def pipeline(self): @classmethod def all_transform_methods(cls): - return list(cls._TRANSFORMER_MAP.keys()) + return list(cls.TRANSFORMER_MAP.keys()) @classmethod def register_transformer(cls, trans_cls, overwrite=False): assert issubclass(trans_cls, Transformer), \ "expecting Transformer type, get %s" % trans_cls - assert trans_cls.METHOD_NAME not in cls._TRANSFORMER_MAP or overwrite, \ + assert trans_cls.METHOD_NAME not in cls.TRANSFORMER_MAP or overwrite, \ "Registering existing transformer without overwriting" - cls._TRANSFORMER_MAP[trans_cls.METHOD_NAME] = trans_cls + cls.TRANSFORMER_MAP[trans_cls.METHOD_NAME] = trans_cls From 1121d72a7b3d58d4cfdcac7e64dff54e9df74a16 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Wed, 12 Jun 2019 23:47:28 +0800 Subject: [PATCH 55/70] minor fix --- utensor_cgen/ir/base.py | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index b6a5f787..38a5171c 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -160,8 +160,6 @@ def check(self, attribute, value): # especially for graph rewrite if not all([isinstance(v, (TensorInfo, type(None))) for v in value]): raise ValueError('Expecting a list of TensorInfo for input_tensors') - - n_inputs = attr.ib(validator=instance_of(int)) output_tensors = attr.ib(validator=instance_of(list)) @output_tensors.validator @@ -169,11 +167,20 @@ def check(self, attribute, value): if not all([isinstance(v, TensorInfo) for v in value]): raise ValueError('Expecting a list of TensorInfo for output_tensors') - n_outputs = attr.ib(validator=instance_of(int)) - op_type = attr.ib(type=str) + op_attr = attr.ib(factory=dict, converter=dict) + n_inputs = attr.ib() + @n_inputs.default + def default_n_inputs(self): + return len(self.input_tensors) + + n_outputs = attr.ib() + @n_outputs.default + def default_n_outputs(self): + return len(self.output_tensors) + def __attrs_post_init__(self): skip_pattern = re.compile(r'_utensor_[^_]*') if self.op_attr: @@ -187,9 +194,13 @@ def __attrs_post_init__(self): self.op_attr = op_attr self._ugraph.ops_info[self.name] = self if not self.n_inputs == len(self.input_tensors): - raise ValueError('n_inputs is not equal to the length of input_tensors') + raise ValueError( + 'n_inputs is not equal to the length of input_tensors: {}'.format(self.name) + ) if not self.n_outputs == len(self.output_tensors): - raise ValueError('n_outputs is not equal to the length of output_tensors') + raise ValueError( + 'n_outputs is not equal to the length of output_tensors: {}'.format(self.name) + ) @property def ugraph(self): From 0e46059bb32d8d2712c1b71828af5b66653c7c66 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 13 Jun 2019 01:12:47 +0800 Subject: [PATCH 56/70] Add lockfile --- Pipfile.lock | 901 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 901 insertions(+) create mode 100644 Pipfile.lock diff --git a/Pipfile.lock b/Pipfile.lock new file mode 100644 index 00000000..b91233f6 --- /dev/null +++ b/Pipfile.lock @@ -0,0 +1,901 @@ +{ + "_meta": { + "hash": { + "sha256": "6ec57b484a000954f5f896542e6aba2574def8f9bd7cbdfe7468133826cd1c7f" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": 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"sha256:ca943a7e809cc12257001ccfb99e3563da9af99d52f261725e96dfe0f9275bc3" + ], + "version": "==0.5.1" + } + } +} From 9d524640e7f9b6b38bc1c3ab49a7a9c9cde7fa50 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 16 Jun 2019 09:25:21 +0800 Subject: [PATCH 57/70] fix bug and make op name pattern as kwarg to dropout tranformer --- utensor_cgen/backend/snippets/_snippets.py | 6 +++--- utensor_cgen/cli.py | 5 ++++- utensor_cgen/transformer/ns_transformer.py | 11 +++++++---- 3 files changed, 14 insertions(+), 8 deletions(-) diff --git a/utensor_cgen/backend/snippets/_snippets.py b/utensor_cgen/backend/snippets/_snippets.py index 631427c6..ee5c1721 100644 --- a/utensor_cgen/backend/snippets/_snippets.py +++ b/utensor_cgen/backend/snippets/_snippets.py @@ -20,7 +20,7 @@ "QuantizedMulOpSnippet", "CreateTensorBinarySnippet", "WeightSnippet", "ContextGlobalArrayContainer", "QuantRangeForMultiplicationSnippet", - "FusedConv2DOpMaxpoolSnippet", "QuantizedFusedConv2DMaxpoolOpSnippet", + "FusedConv2DMaxpoolOpSnippet", "QuantizedFusedConv2DMaxpoolOpSnippet", "GatherOpSnippet", "CreateTensorRamSnippet", "Uint8Q7OriginSnippet"] @@ -644,7 +644,7 @@ def __init__(self, inputs, output, strides, padding, self.template_vars["padding"] = padding self.template_vars["to_eval"] = to_eval -class FusedConv2DOpMaxpoolSnippet(Snippet): +class FusedConv2DMaxpoolOpSnippet(Snippet): __template_name__ = "snippets/fused_conv2d_maxpool_op.cpp" __headers__ = set(['"uTensor/ops/MatrixOps.hpp"']) @@ -665,7 +665,7 @@ def __init__(self, inputs, output, strides, ksize, padding, self.template_vars["padding"] = padding self.template_vars["to_eval"] = to_eval -class QuantizedFusedConv2DOpMaxpoolSnippet(Snippet): +class QuantizedFusedConv2DMaxpoolOpSnippet(Snippet): __template_name__ = "snippets/quantized_fused_conv2d_maxpool_op.cpp" __headers__ = set(['"uTensor/ops/MatrixOps.hpp"']) diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 429a312e..0d38813c 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -46,7 +46,10 @@ def cli(): help="list of output nodes") @click.option("--transform-methods", type=NArgsKwargsParam(sep='|>'), - default='dropout|>linear_reorder_v2|>quantize|>conv_pool_v2|>inline|>biasAdd|>remove_id_op|>refcnt', + default=( + 'dropout(name_pattern=r"(dropout[_\w\d]*)/.*")|>linear_reorder_v2' + '|>quantize|>conv_pool_v2|>inline|>biasAdd|>remove_id_op|>refcnt' + ), help='optimization pipeline', metavar='METHOD[|>METHOD|>...]', show_default=True) diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index bacfe863..8589afd5 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -69,12 +69,15 @@ class DropoutTransformer(Transformer): KWARGS_NAMESCOPE = '_utensor_dropout' TARGET_NODENAME_PATTERN = re.compile(r'(dropout[_\w\d]*)/.*') + def __init__(self, name_pattern=r'(dropout[_\w\d]*)/.*'): + self._op_name_pattern = re.compile(name_pattern) + def transform(self, ugraph): new_graph = uTensorGraph(output_nodes=ugraph.output_nodes) dropout_input_map = self._find_input(ugraph) new_ops_info = {} for node_name in ugraph.ops_info: - match = self.TARGET_NODENAME_PATTERN.match(node_name) + match = self._op_name_pattern.match(node_name) if match: # ignore all dropout nodes continue @@ -87,7 +90,7 @@ def transform(self, ugraph): op_attr = deepcopy(op_info.op_attr) for i, t_info in enumerate(in_t_infos): op_name = parse_tensor_name(t_info.name)[0] - match = self.TARGET_NODENAME_PATTERN.match(op_name) + match = self._op_name_pattern.match(op_name) if match: name_scope = match.group(1) # assume there should be only on input except keep_prob @@ -111,7 +114,7 @@ def transform(self, ugraph): def _find_dropout_clusters(self, ugraph): clusters = defaultdict(lambda: []) for node_name in ugraph.topo_order: - match = self.TARGET_NODENAME_PATTERN.match(node_name) + match = self._op_name_pattern.match(node_name) if match: name_scope = match.group(1) clusters[name_scope].append(node_name) @@ -127,7 +130,7 @@ def _find_input(self, ugraph): clusters = self._find_dropout_clusters(ugraph) input_map = {} for node_name in ugraph.topo_order: - match = self.TARGET_NODENAME_PATTERN.match(node_name) + match = self._op_name_pattern.match(node_name) if match: name_scope = match.group(1) cluster = clusters[name_scope] From 85c1758304ad6551cbdcf6a4a9e568fffe48d647 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 16 Jun 2019 09:37:01 +0800 Subject: [PATCH 58/70] update doc str --- utensor_cgen/transformer/ns_transformer.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index 8589afd5..ec8c946a 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -62,8 +62,9 @@ class DropoutTransformer(Transformer): Cons ==== - - naming constrains on the dropout layers, layer name must starts with 'dropout' - and the keep_prob op must be with name starts with 'keep_prop' + - naming constrains on the dropout layers, layer name must matched to the + given `name_pattern` (default to r'(dropout[_\w\d]*)/.*') and the keep_prob + op must be with name starts with 'keep_prop' """ METHOD_NAME = 'dropout' KWARGS_NAMESCOPE = '_utensor_dropout' @@ -136,7 +137,7 @@ def _find_input(self, ugraph): cluster = clusters[name_scope] op_info = ugraph.ops_info[node_name] for in_tensor_info in op_info.input_tensors: - in_op_name = parse_tensor_name(in_tensor_info.name)[0] + in_op_name = in_tensor_info.op.name if in_op_name not in cluster and not in_op_name.startswith('keep_prob'): input_map[name_scope] = in_tensor_info # assuming there is only one input for dropout From 3648a2773b80d0e10e1447e8d373a15343d68f14 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 21 Jun 2019 02:36:54 +0800 Subject: [PATCH 59/70] add flags to show subcmd --- utensor_cgen/cli.py | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 0d38813c..3c49f17b 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -107,6 +107,12 @@ def list_trans_methods(verbose): @click.help_option('-h', '--help') @click.option('--oneline', is_flag=True, help='show in oneline format (no detail information)') +@click.option('--ignore-unknown-op', is_flag=True, + help='ignore unknown/unsupported ops') +@click.option("--output-nodes", + type=NArgsParam(), + metavar="NODE_NAME,NODE_NAME,...", + help="list of output nodes") @click.argument('model_file', required=True, metavar='MODEL.{pb,pkl}') def show_graph(model_file, **kwargs): _, ext = os.path.splitext(model_file) @@ -124,11 +130,11 @@ def show_graph(model_file, **kwargs): def _show_pb_file(pb_file, **kwargs): import tensorflow as tf from utensor_cgen.frontend.tensorflow import GraphDefParser - - ugraph = GraphDefParser.parse(pb_file) + output_nodes = kwargs.pop('output_nodes') + ugraph = GraphDefParser.parse(pb_file, output_nodes=output_nodes) _show_ugraph(ugraph, **kwargs) -def _show_ugraph(ugraph, oneline=False): +def _show_ugraph(ugraph, oneline=False, ignore_unknown_op=False): import textwrap unknown_ops = set([]) if oneline: @@ -148,8 +154,10 @@ def _show_ugraph(ugraph, oneline=False): op_type: {op_type} input(s): {inputs} + {input_shapes} ouptut(s): {outputs} + {output_shapes} ''' tmpl = textwrap.dedent(tmpl) paragraphs = [] @@ -159,12 +167,14 @@ def _show_ugraph(ugraph, oneline=False): op_name=op_name, op_type=op_info.op_type, inputs=op_info.input_tensors, - outputs=op_info.output_tensors) + outputs=op_info.output_tensors, + input_shapes=[tensor.shape for tensor in op_info.input_tensors], + output_shapes=[tensor.shape for tensor in op_info.output_tensors]) paragraphs.append(op_str) if not OperatorFactory.is_supported(op_info.op_type): unknown_ops.add(op_info) click.echo('\n'.join(paragraphs)) - if unknown_ops: + if unknown_ops and not ignore_unknown_op: click.echo( click.style('Unknown Ops Detected', fg='red', bold=True) ) From 5ae22f7131b504674cd5b91ffc2bd318c911f373 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 23 Jun 2019 15:31:39 +0800 Subject: [PATCH 60/70] Few bug fixs --- utensor_cgen/backend/operators.py | 12 +++++++----- utensor_cgen/backend/snippets/_snippets.py | 11 +++++++---- utensor_cgen/cli.py | 11 ++++++----- utensor_cgen/transformer/ns_transformer.py | 9 ++++++--- 4 files changed, 26 insertions(+), 17 deletions(-) diff --git a/utensor_cgen/backend/operators.py b/utensor_cgen/backend/operators.py index e076229a..a26ca3e3 100644 --- a/utensor_cgen/backend/operators.py +++ b/utensor_cgen/backend/operators.py @@ -495,10 +495,10 @@ class _QuantizedFusedConv2DMaxpoolOperator(_Operator): def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] - output = op_info.output_tensors[0].name + outputs = [tensor_info.name for tensor_info in op_info.output_tensors] in_dtype, filter_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype) - out_dtype = op_info.output_tensors[0].dtype + out_dtypes = [tensor_info.dtype for tensor_info in op_info.output_tensors] strides = op_info.op_attr['_utensor_conv']["strides"].value.ints_value ksize = op_info.op_attr['_utensor_pool']["ksize"].value.ints_value padding = op_info.op_attr['_utensor_conv']["padding"].value.decode('utf8') @@ -506,9 +506,11 @@ def __init__(self, op_info, **kwargs): op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) - self._snippet = QuantizedFusedConv2DMaxpoolOpSnippet(inputs, output, strides, ksize, padding, - in_dtype=in_dtype, filter_dtype=filter_dtype, out_dtype=out_dtype, - ref_count=ref_count, to_eval=to_eval) + self._snippet = QuantizedFusedConv2DMaxpoolOpSnippet( + inputs, outputs, strides, ksize, padding, + in_dtype=in_dtype, filter_dtype=filter_dtype, out_dtypes=out_dtypes, + ref_count=ref_count, to_eval=to_eval + ) @OperatorFactory.register diff --git a/utensor_cgen/backend/snippets/_snippets.py b/utensor_cgen/backend/snippets/_snippets.py index ee5c1721..049248a9 100644 --- a/utensor_cgen/backend/snippets/_snippets.py +++ b/utensor_cgen/backend/snippets/_snippets.py @@ -669,18 +669,21 @@ class QuantizedFusedConv2DMaxpoolOpSnippet(Snippet): __template_name__ = "snippets/quantized_fused_conv2d_maxpool_op.cpp" __headers__ = set(['"uTensor/ops/MatrixOps.hpp"']) - def __init__(self, inputs, output, strides, ksize, padding, - in_dtype, filter_dtype, out_dtype, + def __init__(self, inputs, outputs, strides, ksize, padding, + in_dtype, filter_dtype, out_dtypes, ref_count=0, to_eval=False): Snippet.__init__(self) if ref_count: self.template_vars["ref_count"] = ref_count self.template_vars["inputs"] = inputs - self.template_vars["output"] = output + self.template_vars["outputs"] = outputs self.template_vars["in_dtype"] = NP_TYPES_MAP[in_dtype].tensor_type_str self.template_vars["filter_dtype"] = NP_TYPES_MAP[filter_dtype].tensor_type_str - self.template_vars["out_dtype"] = NP_TYPES_MAP[out_dtype].tensor_type_str + self.template_vars["out_dtypes"] = [ + NP_TYPES_MAP[out_dtype].tensor_type_str + for out_dtype in out_dtypes + ] self.template_vars["strides"] = strides self.template_vars["ksize"] = ksize self.template_vars["padding"] = padding diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 3c49f17b..adc32ed3 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -48,7 +48,8 @@ def cli(): type=NArgsKwargsParam(sep='|>'), default=( 'dropout(name_pattern=r"(dropout[_\w\d]*)/.*")|>linear_reorder_v2' - '|>quantize|>conv_pool_v2|>inline|>biasAdd|>remove_id_op|>refcnt' + '|>quantize|>conv_pool_v2|>inline|>biasAdd|>remove_id_op' + '|>fake_gather_v2|>refcnt' ), help='optimization pipeline', metavar='METHOD[|>METHOD|>...]', @@ -116,8 +117,9 @@ def list_trans_methods(verbose): @click.argument('model_file', required=True, metavar='MODEL.{pb,pkl}') def show_graph(model_file, **kwargs): _, ext = os.path.splitext(model_file) + output_nodes = kwargs.pop('output_nodes') if ext == '.pb' or ext == '.pbtxt': - _show_pb_file(model_file, **kwargs) + _show_pb_file(model_file, output_nodes=output_nodes, **kwargs) elif ext == '.pkl': import pickle with open(model_file, 'rb') as fid: @@ -127,10 +129,9 @@ def show_graph(model_file, **kwargs): msg = click.style('unknown file extension: {}'.format(ext), fg='red', bold=True) click.echo(msg, file=sys.stderr) -def _show_pb_file(pb_file, **kwargs): +def _show_pb_file(pb_file, output_nodes, **kwargs): import tensorflow as tf - from utensor_cgen.frontend.tensorflow import GraphDefParser - output_nodes = kwargs.pop('output_nodes') + from utensor_cgen.frontend.tensorflow import GraphDefParse ugraph = GraphDefParser.parse(pb_file, output_nodes=output_nodes) _show_ugraph(ugraph, **kwargs) diff --git a/utensor_cgen/transformer/ns_transformer.py b/utensor_cgen/transformer/ns_transformer.py index ec8c946a..bfe29641 100644 --- a/utensor_cgen/transformer/ns_transformer.py +++ b/utensor_cgen/transformer/ns_transformer.py @@ -13,6 +13,7 @@ import tensorflow as tf from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, uTensorGraph +from utensor_cgen.logger import logger from utensor_cgen.matcher import uTensorGraphMatcher from utensor_cgen.utils import (parse_tensor_name, prune_graph, topologic_order_graph) @@ -231,17 +232,19 @@ class BatchNormTransformer(Transformer): def transform(self, ugraph): # TODO: implement this! - pass + raise RuntimeError('bach norm transformer is not yet implemented') class FakeGatherV2Transformer(Transformer): """Force converting GatherV2 op to Gather op """ - METHOD_NAME = 'FakeGatherV2' + METHOD_NAME = 'fake_gather_v2' KWARGS_NAMESCOPE = '_fake_gatherv2' def transform(self, ugraph): - print("warning: force replacing GatherV2 with Gather") + logger.warning( + "enabling {} will force replacing GatherV2 with Gather".format(self.METHOD_NAME) + ) for key, op in ugraph.ops_info.items(): if op.op_type == "GatherV2": op.op_type = "Gather" From 8744bb1b9a248b17477e350b8393a07421879921 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 4 Jul 2019 00:46:57 +0800 Subject: [PATCH 61/70] Fix bug and update REAME.md --- README.md | 4 +++- utensor_cgen/cli.py | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6c23dadf..4d6724cb 100644 --- a/README.md +++ b/README.md @@ -83,7 +83,9 @@ Run `utensor-cli convert --help` for detailed information. ## Subgraph Isomorphic Matcher and Nodes Fusion -With `uTensorGraphMatcher`, performing a subgraph isomorphic match, replace or manipulate the matched subgraph is just a few line of code! +With `uTensorGraphMatcher`, performing common subgraph tasks such as isomorphic matching along with replacing or manipulating the matched subgraph(s) takes just a few line of code! + +- (**Future Work**) High-level api for graph pattern declaration. Currently relies on `TensorFlow` api for building graph and converting to `utensor_cgen` IR graph. ### Node Fusion diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index adc32ed3..8e609e3c 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -131,7 +131,7 @@ def show_graph(model_file, **kwargs): def _show_pb_file(pb_file, output_nodes, **kwargs): import tensorflow as tf - from utensor_cgen.frontend.tensorflow import GraphDefParse + from utensor_cgen.frontend.tensorflow import GraphDefParser ugraph = GraphDefParser.parse(pb_file, output_nodes=output_nodes) _show_ugraph(ugraph, **kwargs) From 665ecfdae41db36fcaa805714f6697955299851f Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 7 Jul 2019 13:21:10 +0800 Subject: [PATCH 62/70] Add explanation to matcher show cases in README.md --- README.md | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 4d6724cb..7409896c 100644 --- a/README.md +++ b/README.md @@ -89,20 +89,23 @@ With `uTensorGraphMatcher`, performing common subgraph tasks such as isomorphic ### Node Fusion -![conv-pool-fuce](images/conv_pool_fuse.png) +Note: we'll use operation/node/layer interchangeably in the documentation + +- It's commonly seen pattern in convolution neural network (`CNN`), `conv -> relu -> pooling`. That is, a 2D convolution followed by a relu layer and then a pooliing down sampling layer. With our `uTensorGraphMatcher`, you can locate such pattern in your `CNN` model and fuse/replace matched nodes into one optimized `QuantConvPool` node. + - Left: original graph + - Middle: matched convolution layer + - Right: replace the matched layer with specialized `QuantConvPool` node -- Left: original graph -- Middle: matched convolution layer -- Right: replace the matched layer with specialized `QuantConvPool` node +![conv-pool-fuce](images/conv_pool_fuse.png) ### Dropout Layer Removal +- Though `dropout` is an effective technique to improve training performance of your model, it's not necessary during inference phrase. In the mainstream frameworks such as `Tensorflow` or `PyTorch`, an `dropout` layer is typically implemented with other elementary operations/nodes. As a result, finding and removing those nodes for interence optimization (both in model size and prediciton time) is not trivial and error prone. With our `uTensorGraphMatcher`, you can find and remove the dropout nodes as illustrated in the following picture. + - Left: original graph with dropout Layers + - Middle: matched dropout layers + - Right: graph with dropout layers removed ![cnn-dropout](images/cnn_dropout.png) -- Left: original graph with dropout Layers -- Middle: matched dropout layers -- Right: graph with dropout layers removed - # Examples - [Deep Multilayer Perceptron](https://github.com/uTensor/utensor_cgen/tree/develop/tests/deep_mlp) From f56b40be27f35a3863920d20c85d0263f6e5c133 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 7 Jul 2019 13:45:34 +0800 Subject: [PATCH 63/70] Fix typos --- README.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 7409896c..e19c608f 100644 --- a/README.md +++ b/README.md @@ -91,16 +91,19 @@ With `uTensorGraphMatcher`, performing common subgraph tasks such as isomorphic Note: we'll use operation/node/layer interchangeably in the documentation -- It's commonly seen pattern in convolution neural network (`CNN`), `conv -> relu -> pooling`. That is, a 2D convolution followed by a relu layer and then a pooliing down sampling layer. With our `uTensorGraphMatcher`, you can locate such pattern in your `CNN` model and fuse/replace matched nodes into one optimized `QuantConvPool` node. +- It's commonly seen pattern in convolution neural network (`CNN`), `conv -> relu -> pooling`. That is, a 2D convolution followed by a relu layer and then a pooling down sampling layer. +- With our `uTensorGraphMatcher`, you can locate such pattern in your `CNN` model and fuse/replace matched nodes into one optimized `QuantizedFusedConv2DMaxpool` node. - Left: original graph - Middle: matched convolution layer - - Right: replace the matched layer with specialized `QuantConvPool` node + - Right: replace the matched layer with specialized `QuantizedFusedConv2DMaxpool` node ![conv-pool-fuce](images/conv_pool_fuse.png) ### Dropout Layer Removal -- Though `dropout` is an effective technique to improve training performance of your model, it's not necessary during inference phrase. In the mainstream frameworks such as `Tensorflow` or `PyTorch`, an `dropout` layer is typically implemented with other elementary operations/nodes. As a result, finding and removing those nodes for interence optimization (both in model size and prediciton time) is not trivial and error prone. With our `uTensorGraphMatcher`, you can find and remove the dropout nodes as illustrated in the following picture. +- Though `dropout` is an effective technique to improve training performance of your model, it's not necessary during inference phrase. +- In the mainstream frameworks such as `Tensorflow` or `PyTorch`, an `dropout` layer is typically implemented with other elementary operations/nodes. As a result, finding and removing those nodes for interence optimization (both in model size and prediciton time) is not trivial and error prone. +- With our `uTensorGraphMatcher`, you can find and remove the dropout nodes as illustrated in the following picture. - Left: original graph with dropout Layers - Middle: matched dropout layers - Right: graph with dropout layers removed From 40f846f6feeac46ce3e10847b27402b82629b943 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 11 Jul 2019 21:29:01 +0800 Subject: [PATCH 64/70] Fix quant fused conv op snippet bug --- utensor_cgen/backend/operators.py | 4 ++-- utensor_cgen/backend/snippets/_snippets.py | 11 ++++++----- .../snippets/quantized_fused_conv2d_maxpool_op.cpp | 2 +- 3 files changed, 9 insertions(+), 8 deletions(-) diff --git a/utensor_cgen/backend/operators.py b/utensor_cgen/backend/operators.py index a26ca3e3..e3e62b10 100644 --- a/utensor_cgen/backend/operators.py +++ b/utensor_cgen/backend/operators.py @@ -504,12 +504,12 @@ def __init__(self, op_info, **kwargs): padding = op_info.op_attr['_utensor_conv']["padding"].value.decode('utf8') parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) - ref_count = parser.get('ref_counts', [0])[0] + ref_counts = parser.get('ref_counts', None) to_eval = parser.get('to_eval', False) self._snippet = QuantizedFusedConv2DMaxpoolOpSnippet( inputs, outputs, strides, ksize, padding, in_dtype=in_dtype, filter_dtype=filter_dtype, out_dtypes=out_dtypes, - ref_count=ref_count, to_eval=to_eval + ref_counts=ref_counts, to_eval=to_eval ) diff --git a/utensor_cgen/backend/snippets/_snippets.py b/utensor_cgen/backend/snippets/_snippets.py index 049248a9..92e7b09a 100644 --- a/utensor_cgen/backend/snippets/_snippets.py +++ b/utensor_cgen/backend/snippets/_snippets.py @@ -671,18 +671,19 @@ class QuantizedFusedConv2DMaxpoolOpSnippet(Snippet): def __init__(self, inputs, outputs, strides, ksize, padding, in_dtype, filter_dtype, out_dtypes, - ref_count=0, + ref_counts=None, to_eval=False): + # import pdb; pdb.set_trace() Snippet.__init__(self) - if ref_count: - self.template_vars["ref_count"] = ref_count + if ref_counts: + self.template_vars["ref_counts"] = ref_counts self.template_vars["inputs"] = inputs self.template_vars["outputs"] = outputs self.template_vars["in_dtype"] = NP_TYPES_MAP[in_dtype].tensor_type_str self.template_vars["filter_dtype"] = NP_TYPES_MAP[filter_dtype].tensor_type_str self.template_vars["out_dtypes"] = [ - NP_TYPES_MAP[out_dtype].tensor_type_str - for out_dtype in out_dtypes + NP_TYPES_MAP[dtype].tensor_type_str + for dtype in out_dtypes ] self.template_vars["strides"] = strides self.template_vars["ksize"] = ksize diff --git a/utensor_cgen/backend/snippets/templates/snippets/quantized_fused_conv2d_maxpool_op.cpp b/utensor_cgen/backend/snippets/templates/snippets/quantized_fused_conv2d_maxpool_op.cpp index 7aa4f07a..1161e3df 100644 --- a/utensor_cgen/backend/snippets/templates/snippets/quantized_fused_conv2d_maxpool_op.cpp +++ b/utensor_cgen/backend/snippets/templates/snippets/quantized_fused_conv2d_maxpool_op.cpp @@ -8,7 +8,7 @@ ctx.add(new RamTensor<{{out_dtypes[1]}}>({1}), "{{outputs[1]}}"); ctx.add(new RamTensor<{{out_dtypes[2]}}>({1}), "{{outputs[2]}}"); {% endif %} - ctx.push(new QuantizedFusedConvMaxpoolOp<{{in_dtype}}, {{filter_dtype}}, {{out_dtype}}>({ {% for s in strides[:-1]%}{{s}}, {%endfor%}{{strides[-1]}} }, { {% for s in ksize[:-1]%}{{s}}, {%endfor%}{{ksize[-1]}} },{{padding}}), + ctx.push(new QuantizedFusedConvMaxpoolOp<{{in_dtype}}, {{filter_dtype}}, {{out_dtypes[0]}}>({ {% for s in strides[:-1]%}{{s}}, {%endfor%}{{strides[-1]}} }, { {% for s in ksize[:-1]%}{{s}}, {%endfor%}{{ksize[-1]}} },{{padding}}), { {% for tname in inputs[:-1]%}"{{tname}}", {%endfor%}"{{inputs[-1]}}" }, { {% for tname in outputs[:-1]%}"{{tname}}", {%endfor%}"{{outputs[-1]}}" }); {% if to_eval %} From 9d4153ce23a4f41b6f1b043415d288e452f91d85 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Mon, 15 Jul 2019 01:22:06 +0800 Subject: [PATCH 65/70] remove old experimental matcher and cmsis nn transformer --- .../test_cmsis_nn/__init__.py | 0 .../test_cmsis_nn/conftest.py | 56 --- .../test_cmsis_nn/test_cmsisnn_transformer.py | 13 - .../test_convpool/test_vgg.py | 4 +- utensor_cgen/cli.py | 4 +- utensor_cgen/experimental/__init__.py | 0 utensor_cgen/experimental/ugraph_builder.py | 402 ------------------ utensor_cgen/experimental/ugraph_matcher.py | 343 --------------- .../experimental/ugraph_util_functions.py | 147 ------- utensor_cgen/transformer/__init__.py | 3 +- utensor_cgen/transformer/cmsis_nn.py | 160 ------- utensor_cgen/transformer/conv_pool.py | 79 +--- utensor_cgen/transformer/linear_reoder.py | 77 +--- utensor_cgen/transformer/pipeline.py | 9 +- 14 files changed, 15 insertions(+), 1282 deletions(-) delete mode 100644 tests/test_transformer/test_cmsis_nn/__init__.py delete mode 100644 tests/test_transformer/test_cmsis_nn/conftest.py delete mode 100644 tests/test_transformer/test_cmsis_nn/test_cmsisnn_transformer.py delete mode 100644 utensor_cgen/experimental/__init__.py delete mode 100644 utensor_cgen/experimental/ugraph_builder.py delete mode 100644 utensor_cgen/experimental/ugraph_matcher.py delete mode 100644 utensor_cgen/experimental/ugraph_util_functions.py delete mode 100644 utensor_cgen/transformer/cmsis_nn.py diff --git a/tests/test_transformer/test_cmsis_nn/__init__.py b/tests/test_transformer/test_cmsis_nn/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/test_transformer/test_cmsis_nn/conftest.py b/tests/test_transformer/test_cmsis_nn/conftest.py deleted file mode 100644 index 4b7d2cf9..00000000 --- a/tests/test_transformer/test_cmsis_nn/conftest.py +++ /dev/null @@ -1,56 +0,0 @@ -import numpy as np -import pytest -import tensorflow as tf - -from utensor_cgen.frontend.tensorflow import GraphDefParser -from utensor_cgen.ir import uTensorGraph - -#provide ((graph), (subgraph), (replacement), (expected fused graph)) -#graph: (graph, input nodes, output nodes) -#subgraph: (graph, input nodes, output nodes) -#replacement: (graph, input nodes, output nodes) -#expected fused graph: (graph, input nodes, output nodes) - -# helper functions -def make_rand_const(shape, name): - val = np.random.random(shape) - #return tf.constant(val, name) - return tf.convert_to_tensor(val, name=name, dtype=tf.float32) - -#tensorboard --logdir=./logs_graph0 -def test_graph0(): - graph = tf.Graph() - with graph.as_default(): - x = tf.placeholder(tf.float32, [None, 784], name="x") - add_op = tf.add(x, x, name="inputAdd") - W_fc1 = make_rand_const([784, 128], name='W_fc1') - b_fc1 = make_rand_const([128], name='b_fc1') - a_fc1 = tf.add(tf.matmul(add_op, W_fc1), b_fc1, name="zscore") - h_fc1 = tf.nn.relu(a_fc1, name="act1") - - ugraph = GraphDefParser.parse(graph.as_graph_def(), ["act1"]) - return ugraph - -#tensorboard --logdir=./logs_graph1 --port=8008 -def test_graph1(): - graph1 = tf.Graph() - with graph1.as_default(): - x = tf.placeholder(tf.float32, [None, 784], name="x") - W_fc1 = make_rand_const([784, 128], name='W_fc1') - b_fc1 = make_rand_const([128], name='b_fc1') - a_fc1 = tf.add(tf.matmul(x, W_fc1), b_fc1, name="zscore1") - h_fc1 = tf.nn.relu(a_fc1, "act1") - - W_fc2 = make_rand_const([128, 64], name='W_fc2') - b_fc2 = make_rand_const([64], name='b_fc2') - a_fc2 = tf.add(tf.matmul(h_fc1, W_fc2), b_fc2, name="zscore2") - h_fc2 = tf.nn.relu(a_fc2, "act2") - - ugraph1 = GraphDefParser.parse(graph1.as_graph_def(), ["act2"]) - return ugraph1 - -@pytest.fixture(scope='session', name='fusion_graph_tuple') -def fusion_graph_tuple(): - graph0 = test_graph0() - graph1 = test_graph1() - return (graph0, graph1) diff --git a/tests/test_transformer/test_cmsis_nn/test_cmsisnn_transformer.py b/tests/test_transformer/test_cmsis_nn/test_cmsisnn_transformer.py deleted file mode 100644 index 7ac00606..00000000 --- a/tests/test_transformer/test_cmsis_nn/test_cmsisnn_transformer.py +++ /dev/null @@ -1,13 +0,0 @@ -from __future__ import print_function - -from utensor_cgen.ir import uTensorGraph -from utensor_cgen.ir.utils import graph_check -from utensor_cgen.transformer import CMSIS_NN_Transformer - - -def test_cmsisnn_trnasformer(fusion_graph_tuple): - (ugraph, ugraph1) = fusion_graph_tuple - transformer = CMSIS_NN_Transformer() - test_graph = transformer.transform(ugraph1) - graph_check(test_graph) - print('cmsisnn test topo order: %s' % test_graph.topo_order) diff --git a/tests/test_transformer/test_convpool/test_vgg.py b/tests/test_transformer/test_convpool/test_vgg.py index 35012074..8660ab2e 100644 --- a/tests/test_transformer/test_convpool/test_vgg.py +++ b/tests/test_transformer/test_convpool/test_vgg.py @@ -4,9 +4,9 @@ def factory(): def test(vgg_ugraph): trans = TransformerPipeline([ - ('linear_reorder_v2', {}), + ('linear_reorder', {}), ('quantize', {}), - ('conv_pool_v2', {}) + ('conv_pool', {}) ]) new_ugraph = trans.transform(vgg_ugraph) num_conv = len(vgg_ugraph.get_ops_by_type('Conv2D')) diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 8e609e3c..18d7de32 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -47,8 +47,8 @@ def cli(): @click.option("--transform-methods", type=NArgsKwargsParam(sep='|>'), default=( - 'dropout(name_pattern=r"(dropout[_\w\d]*)/.*")|>linear_reorder_v2' - '|>quantize|>conv_pool_v2|>inline|>biasAdd|>remove_id_op' + 'dropout(name_pattern=r"(dropout[_\w\d]*)/.*")|>linear_reorder' + '|>quantize|>conv_pool|>inline|>biasAdd|>remove_id_op' '|>fake_gather_v2|>refcnt' ), help='optimization pipeline', diff --git a/utensor_cgen/experimental/__init__.py b/utensor_cgen/experimental/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/utensor_cgen/experimental/ugraph_builder.py b/utensor_cgen/experimental/ugraph_builder.py deleted file mode 100644 index 91b523f7..00000000 --- a/utensor_cgen/experimental/ugraph_builder.py +++ /dev/null @@ -1,402 +0,0 @@ -import re -from collections import defaultdict -from copy import deepcopy - -import tensorflow as tf -import numpy as np - -from utensor_cgen.ir import OperationInfo, uTensorGraph, TensorInfo -from utensor_cgen.ir.converter import AttrValueConverter, GenericTensorConverterMixin -from utensor_cgen.utils import parse_tensor_name -from utensor_cgen.ir.utils import graph_check - -from utensor_cgen.experimental.ugraph_util_functions import * - - -__all__ = ["transpose_offline", "Const_Op", "Ram_Op", "Const_Reshape", "Uint8Q7Origin_Op", - "CMSIS_FC_Op", "QuantRangeForMultiplicationu8u8int32_Op", "conv2d_op", "quantized_conv2d_op", - "relu_op", "maxpool_op", "requantize_op", "requantization_range_op", "quantized_maxpool_op", - "quantized_conv2d_pool_op"] - -# Let us get unique names for custom injected nodes -def static_vars(**kwargs): - def decorate(func): - for k in kwargs: - setattr(func, k, kwargs[k]) - return func - return decorate - -@static_vars(counters=defaultdict(int)) -def get_unique_number(target): - get_unique_number.counters[target] += 1 - return get_unique_number.counters[target] - -def bs_ops_attr(np_array): - """ Simplify creating the op_attr bullshit boilerplate from an numpy array """ - op_attr = {} - op_attr["tensorflow_device"] = '' - op_attr["dtype"] = AttrValueConverter.GenericType(value_name='type', value=3) # The hell are these values? - op_attr["value"] = AttrValueConverter.GenericType(value_name='tensor', - value=GenericTensorConverterMixin.GenericType(np_array=np_array, - dtype=np_array.dtype - ) - ) - return op_attr - -def transpose_offline(op_info): - #FIXME: might need to use deep copy on this one - out_tensor_info = op_info.output_tensors[0] - out_tensor_info.shape.reverse() - transposed_value = op_info.op_attr['value'].value.np_array.transpose() - op_info.op_attr['value'].value.np_array = transposed_value - op_info.output_tensors[0] = out_tensor_info - - return op_info - - -def Const_Op(name, np_array, ugraph): - tmp_graph = uTensorGraph(output_nodes=[name]) - const_tensor = TensorInfo(name=name + ":0", - op_name=name, - dtype=np_array.dtype, - shape=list(np_array.shape), - ugraph=tmp_graph - ) - const_op_info = OperationInfo(name=name, - input_tensors=list(), - output_tensors=[const_tensor], - op_type="Const", - backend="tensorflow", - ugraph=tmp_graph, - op_attr=bs_ops_attr(np_array) - ) - ugraph.add_op(const_op_info, False) - - return const_op_info.output_tensors - -def Ram_Op(name, np_array, ugraph): - out = Const_Op(name, np_array, ugraph) - ugraph.ops_info[name].op_type = "Ram" - - return out - -def Reshape_Op(name, input_tensor, shape_tensor, ugraph): - #FIXME: ValueError: duplicate op detected - #if ugraph == None: - tmp_ugraph = uTensorGraph(output_nodes=[name]) - - shape_const_op = ugraph.ops_info[shape_tensor[0].op_name] - - reshape_out_tensor = TensorInfo(name=name + ":0", - op_name=name, - dtype=input_tensor[0].dtype, - shape=shape_const_op.op_attr['value'].value.np_array.tolist(), - ugraph=tmp_ugraph - ) - - reshape_opInfo = OperationInfo(name=name, - input_tensors=[input_tensor[0], shape_tensor[0]], - output_tensors=[reshape_out_tensor], - op_type="Reshape", - backend="tensorflow", - ugraph=tmp_ugraph - ) - - ugraph.add_op(reshape_opInfo, False) - - return reshape_opInfo.output_tensors - -def Const_Reshape(name, input_tensor, shape, ugraph): - const_name = name + "_const" - - for i, v in enumerate(shape): - if v == None: - shape[i] = 1 - - reshape_const_tensor = Const_Op(const_name, np.array(shape), ugraph) - return Reshape_Op(name, input_tensor, reshape_const_tensor, ugraph) - -def Uint8Q7Origin_Op(name, inputs, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - q7_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=np.dtype('int8'), - shape=inputs[0].shape, - ugraph=tmp_ugraph - ) - q7_op_info = OperationInfo(name=name, - input_tensors=inputs, - output_tensors=[q7_out], - op_type="Uint8Q7OriginOp", - backend="tensorflow", - ugraph=tmp_ugraph) - - # if(name == 'convert_uint8_q7_Relu/eightbit_transpose_0_q7'): - # import pdb; pdb.set_trace() - ugraph.add_op(q7_op_info, False) - - return q7_op_info.output_tensors - -def CMSIS_FC_Op(name, in0, in1, bias, bShift, oShift, scratch, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - - #in reality, CMSIS_FC is doing in1 * in0 - out_shape = [in1[0].shape[0], in0[0].shape[1]] - # for i, v in enumerate(out_shape): - # if v == None: - # out_shape[i] = 1 - # break - - fc_out_tensor = TensorInfo(name=name + ":0", - op_name=name, - dtype=np.dtype('int32'), #hard coding to int32 - shape=out_shape, - ugraph=tmp_ugraph - ) - - fc_op_info = OperationInfo(name=name, - input_tensors=[in0[0], in1[0], bias[0], bShift[0], oShift[0], scratch[0]], - output_tensors=[fc_out_tensor], - op_type="CMSIS_NN_FC", - backend="tensorflow", - ugraph=tmp_ugraph - ) - - ugraph.add_op(fc_op_info, False) - return fc_op_info.output_tensors - -def QuantRangeForMultiplicationu8u8int32_Op(name, a_range, b_range, ugraph): - - tmp_ugraph = uTensorGraph(output_nodes=[name]) - min_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=np.dtype('float'), - shape=[1], - ugraph=tmp_ugraph - ) - max_out = TensorInfo(name=name + ":1", - op_name=name, - dtype=np.dtype('float'), - shape=[1], - ugraph=tmp_ugraph - ) - - new_range_op_info = OperationInfo(name=name, - input_tensors=[a_range[0], a_range[1], b_range[0], b_range[1]], - output_tensors=[min_out, max_out], - op_type="QuantRangeForMultiplicationu8u8int32Op", - backend="tensorflow", - ugraph=tmp_ugraph - ) - - ugraph.add_op(new_range_op_info, False) - - return new_range_op_info.output_tensors - -def conv2d_op(name, inputs, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - conv_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=np.dtype('float32'), - shape=inputs[0].shape, #FIXME: wrong shape most likely - ugraph=tmp_ugraph - ) - conv2d_op_info = OperationInfo(name=name, - input_tensors=inputs, - output_tensors=[conv_out], - op_type="Conv2D", - backend="tensorflow", - ugraph=tmp_ugraph) - - ugraph.add_op(conv2d_op_info, False) - return conv2d_op_info.output_tensors - -def quantized_conv2d_op(name, inputs, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - conv_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=np.dtype('uint8'), - shape=inputs[0].shape, #FIXME: wrong shape most likely - ugraph=tmp_ugraph - ) - min_out = TensorInfo(name=name + ":1", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - max_out = TensorInfo(name=name + ":2", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - - quantized_conv2d_op_info = OperationInfo(name=name, - input_tensors=inputs, - output_tensors=[conv_out, min_out, max_out], - op_type="QuantizedConv2D", - backend="tensorflow", - ugraph=tmp_ugraph) - - ugraph.add_op(quantized_conv2d_op_info, False) - return quantized_conv2d_op_info.output_tensors - -def relu_op(name, inputs, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - relu_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=np.dtype('float32'), - shape=inputs[0].shape, - ugraph=tmp_ugraph - ) - relu_op_info = OperationInfo(name=name, - input_tensors=inputs, - output_tensors=[relu_out], - op_type="Relu", - backend="tensorflow", - ugraph=tmp_ugraph) - - ugraph.add_op(relu_op_info, False) - return relu_op_info.output_tensors - -def maxpool_op(name, inputs, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - max_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=np.dtype('float32'), - shape=inputs[0].shape, - ugraph=tmp_ugraph - ) - max_op_info = OperationInfo(name=name, - input_tensors=inputs, - output_tensors=[max_out], - op_type="MaxPool", - backend="tensorflow", - ugraph=tmp_ugraph) - ugraph.add_op(max_op_info, False) - return max_op_info.output_tensors - -def quantized_maxpool_op(name, inputs, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - max_value_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=inputs[0].dtype, - shape=inputs[0].shape, - ugraph=tmp_ugraph - ) - min_out = TensorInfo(name=name + ":1", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - max_out = TensorInfo(name=name + ":2", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - - qnt_max_op_info = OperationInfo(name=name, - input_tensors=inputs, - output_tensors=[max_value_out, min_out, max_out], - op_type="QuantizedMaxPool", - backend="tensorflow", - ugraph=tmp_ugraph) - ugraph.add_op(qnt_max_op_info, False) - return qnt_max_op_info.output_tensors - -def requantization_range_op(name, inputs, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - - min_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - max_out = TensorInfo(name=name + ":1", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - - rqntr_op_info = OperationInfo(name=name, - input_tensors=inputs, - output_tensors=[min_out, max_out], - op_type="RequantizationRange", - backend="tensorflow", - ugraph=tmp_ugraph) - - ugraph.add_op(rqntr_op_info, False) - return rqntr_op_info.output_tensors - - -def requantize_op(name, inputs, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - - value_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=np.dtype('uint8'), - shape=inputs[0].shape, - ugraph=tmp_ugraph - ) - - min_out = TensorInfo(name=name + ":1", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - max_out = TensorInfo(name=name + ":2", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - - rqnt_op_info = OperationInfo(name=name, - input_tensors=inputs, - output_tensors=[value_out, min_out, max_out], - op_type="Requantize", - backend="tensorflow", - ugraph=tmp_ugraph) - - ugraph.add_op(rqnt_op_info, False) - return [value_out, min_out, max_out] - - -#FIXME: The content in op_attr should be passed in as proper parameters -def quantized_conv2d_pool_op(name, inputs, op_attr, ugraph): - tmp_ugraph = uTensorGraph(output_nodes=[name]) - conv_out = TensorInfo(name=name + ":0", - op_name=name, - dtype=inputs[0].dtype, - shape=inputs[0].shape, #FIXME: wrong shape most likely - ugraph=tmp_ugraph - ) - min_out = TensorInfo(name=name + ":1", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - max_out = TensorInfo(name=name + ":2", - op_name=name, - dtype=np.dtype('float32'), - shape=[1], - ugraph=tmp_ugraph - ) - - quantized_conv2d_op_info = OperationInfo(name=name, - input_tensors=inputs, - output_tensors=[conv_out, min_out, max_out], - op_type="QuantizedFusedConv2DMaxpool", - backend="tensorflow", - op_attr=op_attr, - ugraph=tmp_ugraph) - - ugraph.add_op(quantized_conv2d_op_info, False) - return quantized_conv2d_op_info.output_tensors diff --git a/utensor_cgen/experimental/ugraph_matcher.py b/utensor_cgen/experimental/ugraph_matcher.py deleted file mode 100644 index d5f9bb59..00000000 --- a/utensor_cgen/experimental/ugraph_matcher.py +++ /dev/null @@ -1,343 +0,0 @@ -import re -from collections import defaultdict -from copy import deepcopy - -from utensor_cgen.ir import OperationInfo, uTensorGraph, TensorInfo -from utensor_cgen.ir.converter import AttrValueConverter, GenericTensorConverterMixin # hue hue hue hue hue -from utensor_cgen.utils import parse_tensor_name -from tensorflow.tools.graph_transforms import TransformGraph -from utensor_cgen.ir.utils import graph_check - -from utensor_cgen.experimental.ugraph_util_functions import * - -__all__ = ["uGraphMatcher"] - -class uGraphMatcher(object): - - translator = None - subject_graph = None - matcher_graph = None - - def get_ops_io_info(self, op_type): - #please refer to OperatorFactory() in operators.py - ops_io_table = dict() - ops_io_table["Add"] = [[0, 0], [0]] - ops_io_table["ArgMax"] = [[0, 1], [0]] - ops_io_table["Dequantize"] = [[0, 1, 2], [0]] - ops_io_table["Max"] = [[0, 1], [0]] - ops_io_table["QuantizedMaxPool"] = [[0, 1, 2], [0, 1, 2]] - ops_io_table["Min"] = [[0, 1], [0]] - ops_io_table["QuantizeV2"] = [[0, 1, 2], [0, 1, 2]] - ops_io_table["QuantizedMatMul"] = [[0, 1, 2, 3, 4, 5], [0, 1, 2]] - ops_io_table["MatMul"] = [[0, 1], [0]] - ops_io_table["QuantizedRelu"] = [[0, 1, 2], [0, 1, 2]] - ops_io_table["QuantizedAdd"] = [[0, 0, 1, 2, 4, 5], [0, 1, 2]] - ops_io_table["RequantizationRange"] = [[0, 1, 2], [0, 1]] - ops_io_table["Requantize"] = [[0, 1, 2, 3, 4], [0, 1, 2]] - ops_io_table["Reshape"] = [[0, 1], [0]] - ops_io_table["QuantizedReshape"] = [[0, 1, 2, 3], [0, 1, 2]] - ops_io_table["QuantizedConv2D"] = [[0, 1, 2, 3, 4, 5], [0, 1, 2]] - ops_io_table["Const"] = [None, [0]] - ops_io_table["Placeholder"] = [None, [0]] - ops_io_table["Inline"] = [None, [0]] - ops_io_table["MaxPool"] = [[0],[0]] - ops_io_table["Conv2D"] = [[0, 1],[0]] - ops_io_table["Relu"] = [[0],[0]] - - return ops_io_table[op_type] - - def subgraph_trace_exposed_edges(self, graph, start_index=0, end_index=None): - if end_index == None: - end_index = len(graph.topo_order) - - sub_topo = graph.topo_order[start_index:end_index] - - subgraph_tensors_in = set() - subgraph_tensors_out = set() - - for node in sub_topo: - subgraph_tensors_in.update(get_input_tensor_names(graph, node)) - subgraph_tensors_out.update(get_output_tensor_names(graph, node)) - - input_edges = subgraph_tensors_in.difference(subgraph_tensors_out) - output_edges = subgraph_tensors_out.difference(subgraph_tensors_in) - - input_edges_list = list() - output_edges_list = list() - - #ensure this follows topological order - for node in sub_topo: - for t_name in get_input_tensor_names(graph, node): - if t_name in input_edges: - input_edges_list.append(t_name) - for t_name in get_output_tensor_names(graph, node): - if t_name in output_edges: - output_edges_list.append(t_name) - - return [input_edges_list, output_edges_list] - - - def subgraph_trace_internal_edges(self, graph, start_index=0, end_index=None): - if end_index == None: - end_index = len(graph.topo_order) - - sub_topo = graph.topo_order[start_index:end_index] - - subgraph_tensors_in = set() - subgraph_tensors_out = set() - - for node in sub_topo: - subgraph_tensors_in.update(get_input_tensor_names(graph, node)) - subgraph_tensors_out.update(get_output_tensor_names(graph, node)) - - internal_edges = subgraph_tensors_in.intersection(subgraph_tensors_out) - - internal_edges_list = list() - - #ensure this follows topological order - for node in sub_topo: - for t_name in get_input_tensor_names(graph, node): - if t_name in internal_edges and not t_name in internal_edges_list: - internal_edges_list.append(t_name) - for t_name in get_output_tensor_names(graph, node): - if t_name in internal_edges and not t_name in internal_edges_list: - internal_edges_list.append(t_name) - - return internal_edges_list - - #named sequence - def forward_path_tracer(self, graph, start_node_name, end_node_name, depth=-1): - # start_node = graph.ops_info[start_node_name] - # end_node = graph.ops_info[end_node_name] - output_node_names = set() - path_list = list() - - if start_node_name == end_node_name: - tmp = list() - tmp.append(start_node_name) - path_list.append(tmp) - return path_list - - if depth == -1: - depth = len(graph.topo_order) - 1 - - output_node_names = get_output_node_names(graph, start_node_name) - if len(output_node_names) == 0 or depth <= 0: - return None - - for output_node_name in output_node_names: - forward_path_list = self.forward_path_tracer(graph, output_node_name, end_node_name, depth-1) - if forward_path_list == None: - continue - for forward_path in forward_path_list: - forward_path.insert(0, start_node_name) #list - path_list.append(forward_path) #list of list - - if len(path_list) == 0: - return None - - return path_list - - - - #recursive helper - #returns False when mismatch - #returns node-name-relation if a match exist - def isomorphic_associativity_helper(self, subject_node_name, matcher_node_name, subject_graph, matcher_graph, matcher_meta=None, depth=0, subject_trace=None, matcher_trace=None): - #compare the current nodes - subject_node = subject_graph.ops_info[subject_node_name] - matcher_node = matcher_graph.ops_info[matcher_node_name] - - if subject_node.op_type != matcher_node.op_type and not "Any" in self.get_node_meta(matcher_node_name, matcher_meta) : - return False - - #create and concate the named-relations - matcher_to_subject_nodes = dict() - matcher_to_subject_edges = dict() - matcher_to_subject_nodes[matcher_node_name] = subject_node_name - - matcher_node = matcher_graph.ops_info[matcher_node_name] - [input_groups, _] = self.get_ops_io_info(matcher_node.op_type) - #dealing with end of terminations - - if depth <= 0 or len(matcher_node.output_tensors) == 0 or input_groups == None or "End" in self.get_node_meta(matcher_node_name, matcher_meta): - if subject_trace != None and matcher_trace != None: - subject_start_node_name = subject_trace[0] - subject_path_group = self.forward_path_tracer(subject_graph, subject_node_name, subject_start_node_name, len(matcher_graph.topo_order)-1) - - matcher_start_node_name = matcher_trace[0] - matcher_path_group = self.forward_path_tracer(matcher_graph, matcher_node_name, matcher_start_node_name, len(matcher_graph.topo_order)-1) - - if not self.compare_paths(subject_path_group, matcher_path_group, subject_graph, matcher_graph, None, matcher_meta): - return False - - return [matcher_to_subject_nodes, matcher_to_subject_edges] - - used_input_id = set() - - for group in list(range(0,max(input_groups)+1)): - #this loop refer to the matcher - for input_id, group_id in enumerate(input_groups): - if group_id != group: - continue - #currently iterating over the same group - - #sweeping all inputs with the current group_id - #mark used when successful - for sweeping_input_id, sweeping_group_id in enumerate(input_groups): - if sweeping_group_id != group_id or sweeping_input_id in used_input_id: - continue - #sweep the subject input nodes - sweeping_subject_input_tensor_names = get_input_tensor_names(subject_graph, subject_node_name) - sweeping_subject_input_tensor_name = sweeping_subject_input_tensor_names[sweeping_input_id] - [sweeping_subject_input_node_name, _] = get_tensor_node_names(subject_graph, sweeping_subject_input_tensor_name) - - matcher_input_tensor_names = get_input_tensor_names(matcher_graph, matcher_node_name) - matcher_input_tensor_name = matcher_input_tensor_names[input_id] - [matcher_input_node_name, _] = get_tensor_node_names(matcher_graph, matcher_input_tensor_name) - - if subject_trace == None: - subject_trace = list() - if matcher_trace == None: - matcher_trace = list() - - subject_trace.append(subject_node_name) - matcher_trace.append(matcher_node_name) - - probe = self.isomorphic_associativity_helper(sweeping_subject_input_node_name[0], matcher_input_node_name[0], subject_graph, matcher_graph, matcher_meta, depth-1, subject_trace, matcher_trace) - if probe == False: - continue - #a match is found here: - used_input_id.add(sweeping_input_id) - matcher_to_subject_nodes.update(probe[0]) - matcher_to_subject_edges[matcher_input_tensor_name] = sweeping_subject_input_tensor_name - matcher_to_subject_edges.update(probe[1]) - - if len(used_input_id) == len(input_groups): - return [matcher_to_subject_nodes, matcher_to_subject_edges] - - return False - - def isomorphic_match(self, subject_graph, matcher_graph, meta): - matcher_to_subject_nodes = dict() - matcher_to_subject_edges = dict() - matcher_output_node_names = set() - - self.subject_graph = subject_graph - self.matcher_graph = matcher_graph - - #identify matcher output nodes - [_, matcher_output_edges] = self.subgraph_trace_exposed_edges(matcher_graph) - - for matcher_output_edge in matcher_output_edges: - [tensor_origin, _] = get_tensor_node_names(matcher_graph, matcher_output_edge) - matcher_output_node_names.add(tensor_origin[0]) - max_search_depth = len(matcher_graph.topo_order) - partial_matcher_to_subject_nodes = None - partial_matcher_to_subject_edges = None - #support only signle matcher output node for now - for subject_node_name in subject_graph.topo_order: - probe = self.isomorphic_associativity_helper(subject_node_name, next(iter(matcher_output_node_names)), subject_graph, matcher_graph, meta, max_search_depth) - if probe == False: - continue - [partial_matcher_to_subject_nodes, partial_matcher_to_subject_edges] = probe - - if partial_matcher_to_subject_nodes == None and partial_matcher_to_subject_edges == None: - return False - - matcher_to_subject_nodes.update(partial_matcher_to_subject_nodes) - matcher_to_subject_edges.update(partial_matcher_to_subject_edges) - - - #including output tensors from the output node - sgraph_out_node = subject_graph.ops_info[matcher_to_subject_nodes[next(iter(matcher_output_node_names))]] - mgraph_out_node = matcher_graph.ops_info[next(iter(matcher_output_node_names))] - for mgraph_out_edge, sgraph_out_edge in zip(mgraph_out_node.output_tensors, sgraph_out_node.output_tensors): - partial_matcher_to_subject_edges[mgraph_out_edge.name] = sgraph_out_edge.name - - matcher_to_subject_edges.update(partial_matcher_to_subject_edges) - - self.translator = [matcher_to_subject_nodes, matcher_to_subject_edges] - - return self.translator - - def get_node_meta(self, node_name, meta): - if meta == None: - return ["None"] - if not node_name in meta: - return ["None"] - return meta[node_name] - - #TODO: FIXME: - #this function only has heurstic comparator - def compare_paths(self, path_group0, path_group1, graph0, graph1, meta0=None, meta1=None): - path_potential_matches = dict() #path0 to path1 - #pass alternator - if len(path_group0) != len(path_group1): - return False - - for i0, path0 in enumerate(path_group0): - for i1, path1 in enumerate(path_group1): - #length comparison - if len(path0) != len(path1): - continue - - type_compare_success = True - #type comparison - for node0_name, node1_name in zip(path0, path1): - node0 = graph0.ops_info[node0_name] - node1 = graph1.ops_info[node1_name] - - if node0.op_type != node1.op_type and (not "Any" in self.get_node_meta(node0_name, meta0) and not "Any" in self.get_node_meta(node1_name, meta1)): - type_compare_success = False - break - if type_compare_success: - if not i0 in path_potential_matches: - path_potential_matches[i0] = list() - path_potential_matches[i0].append(i1) - - #TODO: add more checks here - if len(path_potential_matches) != len(path_group0): - return False - - for i in path_potential_matches: - if len(path_potential_matches[i]) < 1: - return False - return True - - def tensor_info(self, name): - return tensorInfo_from_name(self.subject_graph, self.translator[1][name]) - - def op_info(self, name): - return self.subject_graph.ops_info[self.translator[0][name]] - - def __getitem__(self, name): - if name in self.translator[0]: - return self.op_info(name) - if name in self.translator[1]: - return self.tensor_info(name) - - assert "% not found\r\n", name - - def __setitem__(self, name, info): - if isinstance(info, TensorInfo): - replace_tensor(self.translator[1][name], info, self.subject_graph) - self.translator[1][name] = info.name - return - - if isinstance(info, OperationInfo): - replace_tensors_op(self.translator[0][name], info.name, self.subject_graph) - self.subject_graph.ops_info[self.translator[0][name]] = info - self.translator[0][name] = info.name - return - - if info == None: - #TODO: tensor dependency checking here - if self.translator[0][name] in self.subject_graph.topo_order: #FIXME: name here reference matcher name, should be subject name - self.subject_graph.drop_op(self.translator[0][name]) - del self.translator[0][name] - else: - assert "% not found\r\n", name - return - - assert False \ No newline at end of file diff --git a/utensor_cgen/experimental/ugraph_util_functions.py b/utensor_cgen/experimental/ugraph_util_functions.py deleted file mode 100644 index 635665a3..00000000 --- a/utensor_cgen/experimental/ugraph_util_functions.py +++ /dev/null @@ -1,147 +0,0 @@ -import re -from collections import defaultdict -from copy import deepcopy - -from utensor_cgen.ir import OperationInfo, uTensorGraph, TensorInfo -from utensor_cgen.ir.converter import AttrValueConverter, GenericTensorConverterMixin # hue hue hue hue hue -from utensor_cgen.utils import parse_tensor_name -from utensor_cgen.ir.utils import graph_check - -__all__ = ["is_connected", "get_input_tensor_names", "get_output_tensor_names", "tensorInfo_from_name", - "get_tensor_node_names", "replace_tensors_op", "replace_tensor_op_by_name", - "graph_validate", "get_input_node_names", "get_output_node_names", "replace_tensor", - "update_tensor_op_names", "print_graph", "rename_tensor"] - - -def is_connected(graph, node0, node1): - input_nodes = get_input_node_names(graph, node0) - output_nodes = get_output_node_names(graph, node0) - node_list = input_nodes.union(output_nodes) - - return node1 in node_list - -def get_input_tensor_names(graph, node_name): - return [t.name for t in graph.ops_info[node_name].input_tensors] - -def get_output_tensor_names(graph, node_name): - return [t.name for t in graph.ops_info[node_name].output_tensors] - - - -def tensorInfo_from_name(graph, edge_name, assertive=True): - for op_name, op_info in graph.ops_info.items(): - for t in op_info.input_tensors: - if t.name == edge_name: - return t - for t in op_info.output_tensors: - if t.name == edge_name: - return t - assert not assertive, "tensor not %s found" % edge_name - return None - -#find the origin and destinations of a tensor -def get_tensor_node_names(graph, t_name): - start_nodes = list() - end_nodes = list() - - for it_node, info in graph.ops_info.items(): - for t in graph.ops_info[it_node].input_tensors: - if t.name == t_name: - end_nodes.append(it_node) - for t in graph.ops_info[it_node].output_tensors: - if t.name == t_name: - start_nodes.append(it_node) - - return [start_nodes, end_nodes] - -def replace_tensors_op(node_name, new_node_name, graph): - for op_name, op_info in graph.ops_info.items(): - for i, output_tensor_info in enumerate(op_info.output_tensors): - if output_tensor_info.op_name == node_name: - output_tensor_info.op_name = new_node_name - op_info.output_tensors[i] = output_tensor_info - graph.ops_info[op_name] = op_info - - for i, input_tensor_info in enumerate(op_info.input_tensors): - if input_tensor_info.op_name == node_name: - input_tensor_info.op_name = new_node_name - op_info.input_tensors[i] = input_tensor_info - graph.ops_info[op_name] = op_info - - return graph - -def replace_tensor_op_by_name(tensor_name, new_node_name, graph): - for op_name, op_info in graph.ops_info.items(): - for i, output_tensor_info in enumerate(op_info.output_tensors): - if output_tensor_info.name == tensor_name: - output_tensor_info.op_name = new_node_name - op_info.output_tensors[i] = output_tensor_info - graph.ops_info[op_name] = op_info - - for i, input_tensor_info in enumerate(op_info.input_tensors): - if input_tensor_info.name == tensor_name: - input_tensor_info.op_name = new_node_name - op_info.input_tensors[i] = input_tensor_info - graph.ops_info[op_name] = op_info - return graph - -def graph_validate(graph): - conflicts = [] - for op_name, op_info in graph.ops_info.items(): - for input_tensor_info in op_info.input_tensors: - if input_tensor_info.op_name not in graph.ops_info: - print("In %r: input tensor %r points to non-existing op %r" % (op_name, input_tensor_info.name, input_tensor_info.op_name)) - conflicts.append((input_tensor_info.name, input_tensor_info.op_name)) - if input_tensor_info.op_name not in graph.topo_order: - print("In %r: input tensor %r points to an op (%r) that does not exist in graph.topo_order" % (op_name, input_tensor_info.name, input_tensor_info.op_name)) - conflicts.append((input_tensor_info.name, input_tensor_info.op_name)) - for out_tensor in op_info.output_tensors: - if out_tensor.op_name != op_info.name: - print("In %r: output tensor %r does not point to its origin, it points to (%r)" % (op_info.name, out_tensor.name, out_tensor.op_name)) - conflicts.append((op_info.name, out_tensor.name, out_tensor.op_name)) - -def get_input_node_names(graph, node_name): - input_op_infos = graph.ops_info[node_name].input_nodes - input_op_names = [op.name for op in input_op_infos] - - return input_op_names - -def get_output_node_names(graph, node_name): - output_op_infos = graph.ops_info[node_name].output_nodes - output_op_names = [op.name for op in output_op_infos] - - return output_op_names - -def replace_tensor(name, new_tensorInfo, ugraph): - for key, op_info in ugraph.ops_info.items(): - #inputs - for i, t_info in enumerate(op_info.input_tensors): - if(t_info.name == name): - op_info.input_tensors[i] = new_tensorInfo - #outputs - for i, t_info in enumerate(op_info.output_tensors): - if(t_info.name == name): - op_info.output_tensors[i] = new_tensorInfo - -def update_tensor_op_names(graph): - for key, op_info in graph.ops_info.items(): - for out_tensor in op_info.output_tensors: - out_tensor.op_name = op_info.name - graph.ops_info[key] = op_info - -def print_graph(graph): - for key, op_info in graph.ops_info.items(): - print(key, " :\r\n") - print(" In: ", [tensor.name for tensor in op_info.input_tensors]) - print(" Out: ", [tensor.name for tensor in op_info.output_tensors]) - -def rename_tensor(name, new_name, graph): - for key, op_info in graph.ops_info.items(): - for i, tensor in enumerate(op_info.input_tensors): - if tensor.name == name: - op_info.input_tensors[i].name = new_name - for key, op_info in graph.ops_info.items(): - for i, tensor in enumerate(op_info.output_tensors): - if tensor.name == name: - op_info.output_tensors[i].name = new_name - graph.ops_info[key] = op_info \ No newline at end of file diff --git a/utensor_cgen/transformer/__init__.py b/utensor_cgen/transformer/__init__.py index 1b5676fe..536b04fa 100644 --- a/utensor_cgen/transformer/__init__.py +++ b/utensor_cgen/transformer/__init__.py @@ -1,6 +1,5 @@ # -*- coding:utf8 -*- from .ns_transformer import * from .optimizer import * -from .quantize import * -from .cmsis_nn import * from .pipeline import TransformerPipeline +from .quantize import * diff --git a/utensor_cgen/transformer/cmsis_nn.py b/utensor_cgen/transformer/cmsis_nn.py deleted file mode 100644 index fed96187..00000000 --- a/utensor_cgen/transformer/cmsis_nn.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- coding:utf8 -*- -r"""CMSIS-NN Transformer - -Node fusion and replacement for CMSIS-NN - -""" -import re -from collections import defaultdict -from copy import deepcopy - -import numpy as np -import tensorflow as tf -from tensorflow.tools.graph_transforms import TransformGraph - -from utensor_cgen.experimental.ugraph_builder import * -from utensor_cgen.experimental.ugraph_matcher import * -from utensor_cgen.experimental.ugraph_util_functions import * -from utensor_cgen.frontend.tensorflow import GraphDefParser -from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph -from utensor_cgen.ir.converter import AttrValueConverter # hue hue hue hue hue -from utensor_cgen.ir.converter import GenericTensorConverterMixin -from utensor_cgen.ir.utils import graph_check -from utensor_cgen.utils import parse_tensor_name, topologic_order_graph - -from .base import Transformer - -__all__ = ["CMSIS_NN_Transformer"] - -## MatMul Only -class CMSIS_NN_Transformer(Transformer): - METHOD_NAME = 'cmsisnn' - KWARGS_NAMESCOPE = '_utensor_cmsisnn' - - def make_rand_const(self, shape, name): - val = np.random.random(shape) - return tf.convert_to_tensor(val, name=name, dtype=tf.float32) - - def get_matcher_graph(self): - graph = tf.Graph() - tf.reset_default_graph() #remove me - with graph.as_default(): - - x = tf.placeholder(dtype=tf.float32, name='input') - #x = self.make_rand_const([1,784], name='input') - W_fc1 = self.make_rand_const([784, 128], name='weight') - b_fc1 = self.make_rand_const([128], name='bias') - matmal = tf.matmul(x, W_fc1, name='matmal') - a_fc1 = tf.add(matmal, b_fc1, name="zscore") - - meta = dict() - meta["matmal_eightbit/input/quantize"] = ["End", "Any"] - - quant_graph_def = TransformGraph(input_graph_def=graph.as_graph_def(), - inputs=['input'], - outputs=['zscore'], - transforms=["quantize_weights", "quantize_nodes"]) - mgraph = GraphDefParser.parse(quant_graph_def, output_nodes=['zscore/eightbit']) - - #mgraph.viz_graph(fname="matcher.gv") - return (mgraph, meta) - - def transform(self, ugraph): - [matcher_ugraph, metaData] = self.get_matcher_graph() - #ugraph.viz_graph(fname="subject.gv") - - while True: - matcher = uGraphMatcher() - result = matcher.isomorphic_match(ugraph, matcher_ugraph, metaData) - if result == False: - break - - #turn v * M into M * v - #pM = transpose_offline(matcher["weight_quantized_const"]) - pM = transpose_offline(matcher["weight_quantized_const"]) - matcher["weight_quantized_const"] = pM - matcher["weight_quantized_const:0"] = pM.output_tensors[0] - - #turn matmal_eightbit/input/quantize:0 from [1 n] to [n 1] - pV = matcher["matmal_eightbit/input/quantize"] - act_reshape_shape = pV.output_tensors[0].shape[::-1] - -### reshape - act_transpose_op_name = pV.name + "_transpose" - act_transposed_tensors = Const_Reshape(act_transpose_op_name, [pV.output_tensors[0]], act_reshape_shape, ugraph) - - ## convert the inputs Uint8Q7OriginOp - new_input0_op_name = "convert_uint8_q7_" + act_transposed_tensors[0].name.replace(":", "_") #pV - - input0_q7_out = Uint8Q7Origin_Op(new_input0_op_name, - [act_transposed_tensors[0], - matcher["matmal_eightbit/input/quantize:1"], - matcher["matmal_eightbit/input/quantize:2"]], - ugraph) - - new_input1_op_name = "convert_uint8_q7_" + matcher["weight_quantized_const"].name #pM - - input1_q7_out = Uint8Q7Origin_Op(new_input1_op_name, - [matcher["weight_quantized_const:0"], - matcher["weight_quantized_min:0"], - matcher["weight_quantized_max:0"]], - ugraph) - - #using CMSIS-NN FC as MatMul only, for now - #generate new op name - new_op_name = "cmsis_fc_" + matcher["matmal/eightbit"].name - - #bias - bias_name = new_op_name + "_bias" - #FIXME: for debugging purpose, temporarily fixing the bias values to 0 - bias_values = np.full(act_reshape_shape, 0) - - bias_out_tensors = Const_Op(bias_name + "_bias", bias_values, ugraph) - - #bias shift - bShift_tensors = Const_Op(matcher["matmal/eightbit"].name + "_bShift", np.array([0], dtype=np.uint16), ugraph) - - oShift_tensors = Const_Op(matcher["matmal/eightbit"].name + "_oShift", np.array([0], dtype=np.uint16), ugraph) - - scratch_space = "cmsis_scratch_" + matcher["matmal/eightbit"].name - scratch_shape = list(map(lambda x: x if x else 1, matcher['matmal_eightbit/input/quantize:0'].shape)) - scratch_tensors = Ram_Op(scratch_space, np.zeros(tuple(scratch_shape), dtype=np.uint16), ugraph) - - new_op_name = "cmsis_fc_" + matcher["matmal/eightbit"].name - - cmsis_fc_out = CMSIS_FC_Op(new_op_name, input0_q7_out, input1_q7_out, - bias_out_tensors, bShift_tensors, oShift_tensors, - scratch_tensors, ugraph) - - # ugraph.drop_op(result[0]['matmal/eightbit/requant_range']) - # ugraph.drop_op(result[0]['matmal/eightbit/requantize']) - # ugraph.drop_op(result[0]['zscore/eightbit']) - # ugraph.drop_op(result[0]['zscore/eightbit/requant_range']) - # ugraph.drop_op(result[0]['zscore/eightbit/requantize']) - #ugraph.add_op(fused_op_info) - - #output reshape - act_reshape_op_name = new_op_name + "_newshape" - matmul_output_shape = list(cmsis_fc_out[0].shape) - matmul_output_shape.reverse() - reshape_out = Const_Reshape(act_reshape_op_name, cmsis_fc_out, matmul_output_shape, ugraph) - matcher["matmal/eightbit:0"] = reshape_out[0] - - #range op - new_range_op_name = new_op_name + "_range" - - range_out = QuantRangeForMultiplicationu8u8int32_Op(new_range_op_name, - [matcher["matmal_eightbit/input/quantize:1"], matcher["matmal_eightbit/input/quantize:2"]], - [matcher["weight_quantized_min:0"], matcher["weight_quantized_max:0"]], - ugraph) - - matcher["matmal/eightbit:1"] = range_out[0] - matcher["matmal/eightbit:2"] = range_out[1] - - matcher['matmal/eightbit'] = None - - topologic_order_graph(ugraph) - graph_validate(ugraph) - - graph_check(ugraph) - return ugraph diff --git a/utensor_cgen/transformer/conv_pool.py b/utensor_cgen/transformer/conv_pool.py index 9798d03f..09bd1614 100644 --- a/utensor_cgen/transformer/conv_pool.py +++ b/utensor_cgen/transformer/conv_pool.py @@ -4,94 +4,23 @@ Node fusion for QuantConv2d QuantMaxPool operators """ -import re -from collections import defaultdict from copy import deepcopy -import numpy as np - import tensorflow as tf -from tensorflow.tools.graph_transforms import TransformGraph -from utensor_cgen.experimental.ugraph_builder import * -from utensor_cgen.experimental.ugraph_matcher import * -from utensor_cgen.experimental.ugraph_util_functions import * from utensor_cgen.frontend.tensorflow import GraphDefParser from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph -from utensor_cgen.ir.converter import (AttrValueConverter, - GenericTensorConverterMixin) -from utensor_cgen.ir.utils import graph_check from utensor_cgen.matcher import uTensorGraphMatcher -from utensor_cgen.utils import (parse_tensor_name, prune_graph, - topologic_order_graph) +from utensor_cgen.utils import prune_graph, topologic_order_graph from .base import Transformer from .quantize import QuantizeTransformer -__all__ = ["CONV_POOL_Transformer", "ConvPoolTransformer"] - -class CONV_POOL_Transformer(Transformer): - METHOD_NAME = 'conv_pool' - KWARGS_NAMESCOPE = '_conv_pool' - - def get_matcher_graph(self): - ugraph = uTensorGraph(output_nodes=['quantized_maxpool'], backend="tensorflow") - - dummpy_input0 = Const_Op('dummy_input0', np.zeros([16,16], dtype=np.uint8), ugraph) - dummpy_input0_min = Const_Op('dummy_input0_min', np.zeros([1]), ugraph) - dummpy_input0_max = Const_Op('dummy_input0_max', np.zeros([1]), ugraph) - - dummpy_input1 = Const_Op('dummy_input1', np.zeros([4,4]), ugraph) - dummpy_input1_min = Const_Op('dummy_input1_min', np.zeros([1]), ugraph) - dummpy_input1_max = Const_Op('dummy_input1_max', np.zeros([1]), ugraph) - - conv_out = quantized_conv2d_op('quant_convolution2d', [dummpy_input0[0], - dummpy_input1[0], dummpy_input0_min[0], dummpy_input0_max[0], - dummpy_input1_min[0], dummpy_input1_max[0]], ugraph) - - requantization_range_out = requantization_range_op('requantization_range', conv_out, ugraph) +__all__ = ["ConvPoolTransformer"] - requantize_out = requantize_op('requantize', [conv_out[0], conv_out[1], conv_out[2], - requantization_range_out[0], requantization_range_out[1]], ugraph) #FIXME: check the tensor ordering here - - quantized_maxpool_op('quantized_maxpool', requantize_out, ugraph) - - topologic_order_graph(ugraph) - - - meta = dict() - meta["quant_convolution2d"] = ["End"] - - return (ugraph, meta) - - def transform(self, ugraph): - [matcher_ugraph, metaData] = self.get_matcher_graph() - while True: - matcher = uGraphMatcher() - result = matcher.isomorphic_match(ugraph, matcher_ugraph, metaData) - if result == False: - break - - fused_op_name = matcher['quant_convolution2d'].name + "_" + matcher['quantized_maxpool'].name - op_attr = dict() - op_attr['_utensor_conv'] = matcher['quant_convolution2d'].op_attr - op_attr['_utensor_pool'] = matcher['quantized_maxpool'].op_attr - fused_op_out = quantized_conv2d_pool_op(fused_op_name, matcher['quant_convolution2d'].input_tensors, op_attr, ugraph) - matcher['quantized_maxpool:0'] = fused_op_out[0] - matcher['quantized_maxpool:1'] = fused_op_out[1] - matcher['quantized_maxpool:2'] = fused_op_out[2] - matcher['quant_convolution2d'] = None - matcher['requantization_range'] = None - matcher['requantize'] = None - matcher['quantized_maxpool'] = None - - update_tensor_op_names(ugraph) - topologic_order_graph(ugraph) - graph_validate(ugraph) - return ugraph class ConvPoolTransformer(Transformer): - METHOD_NAME = 'conv_pool_v2' - KWARGS_NAMESCOPE = '_conv_pool_v2' + METHOD_NAME = 'conv_pool' + KWARGS_NAMESCOPE = '_conv_pool' def __init__(self): super(ConvPoolTransformer, self).__init__(prune_graph=False) diff --git a/utensor_cgen/transformer/linear_reoder.py b/utensor_cgen/transformer/linear_reoder.py index f21c730c..abe5a22e 100644 --- a/utensor_cgen/transformer/linear_reoder.py +++ b/utensor_cgen/transformer/linear_reoder.py @@ -4,88 +4,19 @@ Linear Operation Legalizations """ -import re -from collections import defaultdict -from copy import deepcopy - -import numpy as np - import tensorflow as tf -from tensorflow.tools.graph_transforms import TransformGraph -from utensor_cgen.experimental.ugraph_builder import * -from utensor_cgen.experimental.ugraph_matcher import * -from utensor_cgen.experimental.ugraph_util_functions import * from utensor_cgen.frontend.tensorflow import GraphDefParser -from utensor_cgen.ir import OperationInfo, TensorInfo, uTensorGraph -from utensor_cgen.ir.converter import AttrValueConverter # hue hue hue hue hue -from utensor_cgen.ir.converter import GenericTensorConverterMixin -from utensor_cgen.ir.utils import graph_check from utensor_cgen.matcher import uTensorGraphMatcher -from utensor_cgen.utils import (parse_tensor_name, prune_graph, - topologic_order_graph) +from utensor_cgen.utils import prune_graph, topologic_order_graph from .base import Transformer -__all__ = ["Linear_Reorder_Transformer", "LinearReorderTransformerV2"] - -class Linear_Reorder_Transformer(Transformer): - METHOD_NAME = 'linear_reoder' - KWARGS_NAMESCOPE = '_linear_reoder' - - def get_matcher_graph(self): - ugraph = uTensorGraph(output_nodes=['maxpool']) - - dummpy_input0 = Const_Op('dummy_input0', np.zeros([16,16]), ugraph) - dummpy_input1 = Const_Op('dummy_input1', np.zeros([4,4]), ugraph) - conv_out = conv2d_op('convolution2d', [dummpy_input0[0], dummpy_input1[0]], ugraph) - relu_out = relu_op('relu', conv_out, ugraph) - out_tensor = maxpool_op('maxpool', relu_out, ugraph) - topologic_order_graph(ugraph) - - meta = dict() - meta["dummy_input0"] = ["End", "Any"] - meta["dummy_input1"] = ["End", "Any"] - - return (ugraph, meta) - - def transform(self, ugraph): - [matcher_ugraph, metaData] = self.get_matcher_graph() - while True: - matcher = uGraphMatcher() - result = matcher.isomorphic_match(ugraph, matcher_ugraph, metaData) - if result == False: - break - - #swapping the ops - max_pool_op = matcher['maxpool'] - relu_op = matcher['relu'] - - max_pool_op.input_tensors[0] = matcher['convolution2d:0'] - max_pool_op.output_tensors[0] = matcher['relu:0'] - relu_op.input_tensors[0] = matcher['relu:0'] - relu_op.output_tensors[0] = matcher['maxpool:0'] - - matcher['maxpool'] = max_pool_op - matcher['relu'] = relu_op - - #swapping the tensor names - relu_tensor_name = matcher['relu:0'].name - maxpool_tensor_name = matcher['maxpool:0'].name - - rename_tensor(relu_tensor_name, 'tmp_relu_name', ugraph) - rename_tensor(maxpool_tensor_name, relu_tensor_name, ugraph) - rename_tensor('tmp_relu_name', maxpool_tensor_name, ugraph) - - update_tensor_op_names(ugraph) - topologic_order_graph(ugraph) - graph_validate(ugraph) - - return ugraph +__all__ = ["LinearReorderTransformerV2"] class LinearReorderTransformerV2(Transformer): - METHOD_NAME = 'linear_reorder_v2' - KWARGS_NAMESCOPE = '_linear_reorder_v2' + METHOD_NAME = 'linear_reorder' + KWARGS_NAMESCOPE = '_linear_reorder' def __init__(self): self.prune_graph = False diff --git a/utensor_cgen/transformer/pipeline.py b/utensor_cgen/transformer/pipeline.py index 6683ca44..97e031b3 100644 --- a/utensor_cgen/transformer/pipeline.py +++ b/utensor_cgen/transformer/pipeline.py @@ -1,9 +1,7 @@ from .base import Transformer -from .cmsis_nn import CMSIS_NN_Transformer -from .conv_pool import CONV_POOL_Transformer, ConvPoolTransformer +from .conv_pool import ConvPoolTransformer from .graph_viz import GraphVizTransformer -from .linear_reoder import (Linear_Reorder_Transformer, - LinearReorderTransformerV2) +from .linear_reoder import LinearReorderTransformerV2 from .ns_transformer import (BatchNormTransformer, BiasAddTransformer, DropoutTransformer, DropoutTransformerV2, FakeGatherV2Transformer, InlineTransformer) @@ -21,11 +19,8 @@ class TransformerPipeline(object): QuantizeTransformer.METHOD_NAME: QuantizeTransformer, InlineTransformer.METHOD_NAME: InlineTransformer, BiasAddTransformer.METHOD_NAME: BiasAddTransformer, - CMSIS_NN_Transformer.METHOD_NAME: CMSIS_NN_Transformer, IdOpRemoveOptimizer.METHOD_NAME: IdOpRemoveOptimizer, GraphVizTransformer.METHOD_NAME: GraphVizTransformer, - Linear_Reorder_Transformer.METHOD_NAME: Linear_Reorder_Transformer, - CONV_POOL_Transformer.METHOD_NAME: CONV_POOL_Transformer, FakeGatherV2Transformer.METHOD_NAME: FakeGatherV2Transformer, ConvPoolTransformer.METHOD_NAME: ConvPoolTransformer, LinearReorderTransformerV2.METHOD_NAME: LinearReorderTransformerV2 From ca0d37bfa53050e7e82b0fcfb2e812eb8089b564 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Thu, 18 Jul 2019 00:37:59 +0800 Subject: [PATCH 66/70] update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e19c608f..a1ecffca 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ Run `utensor-cli convert --help` for detailed information. With `uTensorGraphMatcher`, performing common subgraph tasks such as isomorphic matching along with replacing or manipulating the matched subgraph(s) takes just a few line of code! -- (**Future Work**) High-level api for graph pattern declaration. Currently relies on `TensorFlow` api for building graph and converting to `utensor_cgen` IR graph. +- (**Future Work**) High-level api for graph pattern declaration. Currently `utensor_cgen` uses `TensorFlow` api for building IR graph, `uTensorGraph`. ### Node Fusion From ea99036c200d73c4db0c5ed69d257a56356d06d7 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Sun, 23 Jun 2019 17:23:01 +0800 Subject: [PATCH 67/70] - setup basic doc files - README.md -> README.rst --- .gitignore | 3 + README.md | 150 --------------------------- README.rst | 223 ++++++++++++++++++++++++++++++++++++++++ doc/Makefile | 20 ++++ doc/make.bat | 35 +++++++ doc/source/conf.py | 56 ++++++++++ doc/source/index.rst | 26 +++++ setup.py | 2 +- utensor_cgen/cli.py | 9 +- utensor_cgen/ir/base.py | 97 +++++++++++++---- 10 files changed, 448 insertions(+), 173 deletions(-) delete mode 100644 README.md create mode 100644 README.rst create mode 100644 doc/Makefile create mode 100644 doc/make.bat create mode 100644 doc/source/conf.py create mode 100644 doc/source/index.rst diff --git a/.gitignore b/.gitignore index 6d40de30..1864679d 100644 --- a/.gitignore +++ b/.gitignore @@ -15,3 +15,6 @@ tests/*/cpp */*/models tests/deep_mlp/data .vscode +# docs +doc/build +doc/source/_* diff --git a/README.md b/README.md deleted file mode 100644 index a1ecffca..00000000 --- a/README.md +++ /dev/null @@ -1,150 +0,0 @@ -# Installation (Python 2 & 3) - -## For Users - -- with `setup.py` -``` -python setup.py install -``` - -- with `pip` -``` -pip install utensor_cgen -``` - -## For Developers: - -- with `pip` -``` -pip install -e .[dev] -``` - -- with `pipenv` - 1. `$ PIPENV_VENV_IN_PROJECT=1 pipenv install -d` - 2. `$ pipenv shell` - - this will spawn a subshell and activate the virtual environment for you - - You should be able to use the cli now - -You can go to this [repo](https://github.com/pypa/pipenv) for detail information about `pipenv`. - -### Troubleshooting with `pipenv` - -- If you have trouble with installation using `pipenv`, try `PIPENV_VENV_IN_PROJECT=1 pipenv install -d --skip-lock` -- there is known issue of `pip` and `pipenv`, plz refer to this [issue](https://github.com/pypa/pipenv/issues/2924) for detail - - short answer: downgrade to `pip==18.0` may help :) -- Tensorflow requires `setuptools<=39.1.0` (the latest is `40.4.3` by the time this README is writen) - - plz downgrade to `setuptools==39.1.0` - - my recommendation is to use `virtualenv` - -# Overall Architecture - -``` - ============ +-----------------+ =================== -|| model file || --> | frontend Parser | --> || uTensorGraph (IR) || - ============ +-----------------+ =================== - | - +-------------------------------+ | - | graph transformer | | - | (legalization & optimization) | <------‘ - +-------------------------------+ - | - v - =========================== - || uTensorGraph || - || (legalized and optimized) || - =========================== - | -+--------------------------+ | -| backend (code generator) | <----‘ -+--------------------------+ - | - `---> (target files, ex: model.cpp, model.hpp, weights.idx) -``` - -# Basic Usage - -## `utensor-cli show ` - -Show all nodes and detailed information of given pb file. - -Run `utensor-cli show --help` for detailed information. - -## `utensor-cli convert --output-nodes=[,,...] ` - -Convert given pb file into cpp/hpp files. - -Note that `--output-nodes` is required options. It's the names of nodes you want to output, seperated by comma if there are many. - -In graph theory terminology, they are `leaf` nodes of your graph. - -Run `utensor-cli convert --help` for detailed information. - -# Use `utensor_cgen` as Library - -## Subgraph Isomorphic Matcher and Nodes Fusion - -With `uTensorGraphMatcher`, performing common subgraph tasks such as isomorphic matching along with replacing or manipulating the matched subgraph(s) takes just a few line of code! - -- (**Future Work**) High-level api for graph pattern declaration. Currently `utensor_cgen` uses `TensorFlow` api for building IR graph, `uTensorGraph`. - -### Node Fusion - -Note: we'll use operation/node/layer interchangeably in the documentation - -- It's commonly seen pattern in convolution neural network (`CNN`), `conv -> relu -> pooling`. That is, a 2D convolution followed by a relu layer and then a pooling down sampling layer. -- With our `uTensorGraphMatcher`, you can locate such pattern in your `CNN` model and fuse/replace matched nodes into one optimized `QuantizedFusedConv2DMaxpool` node. - - Left: original graph - - Middle: matched convolution layer - - Right: replace the matched layer with specialized `QuantizedFusedConv2DMaxpool` node - -![conv-pool-fuce](images/conv_pool_fuse.png) - -### Dropout Layer Removal - -- Though `dropout` is an effective technique to improve training performance of your model, it's not necessary during inference phrase. -- In the mainstream frameworks such as `Tensorflow` or `PyTorch`, an `dropout` layer is typically implemented with other elementary operations/nodes. As a result, finding and removing those nodes for interence optimization (both in model size and prediciton time) is not trivial and error prone. -- With our `uTensorGraphMatcher`, you can find and remove the dropout nodes as illustrated in the following picture. - - Left: original graph with dropout Layers - - Middle: matched dropout layers - - Right: graph with dropout layers removed -![cnn-dropout](images/cnn_dropout.png) - -# Examples - -- [Deep Multilayer Perceptron](https://github.com/uTensor/utensor_cgen/tree/develop/tests/deep_mlp) -- [End-to-End Convolution NN](https://github.com/uTensor/simple_cnn_tutorial) - - -# User Guide - -Following steps are a general guild for user how to porting a `TensorFlow` protobuf file into a `uTensor` implementation: - -1. install `utensor_cgent` - - run `python3 setupt.py install` -2. run `utensor-cli convert --output-nodes='NODE,NODE,...' graph.pb` - - run `utensor-cli -h` for help - - the `graph.pb` is the pb file of *original* graph (not quantized) -3. If you want to see what ops/nodes are in the pb file, you can run `utensor-cli show ` - -# How to test (for Developer) - -1. follow the steps in [setup](#setup-with-pipenv) section -2. run `make tests` - - Or you can use `pipenv run pytest tests` instead - -# Philosophy - -- [12 Factor CLI App](https://medium.com/@jdxcode/12-factor-cli-apps-dd3c227a0e46?fbclid=IwAR1Gfq0D7oh3b-mXaIMV3RwYu39TAPrPXfz5sBKC4Rz1t-cckvC8WjBVl_w) - -# TODOs -1. (done?) core code generator implementation - - We need some refactoring, PRs are welcomed! -2. type alias in C/C++ - - ex: use `uint8_t` or `unsigned char`? - - a lot more about this.... -3. Relation among snippets/containers - - shared template variables? (headers, shared placeholders...etc) -4. Better configuration schema - - json - - yaml - - or ? diff --git a/README.rst b/README.rst new file mode 100644 index 00000000..ef9f110c --- /dev/null +++ b/README.rst @@ -0,0 +1,223 @@ +Installation (Python 2 & 3) +=========================== + +For Users +--------- + +- with ``setup.py`` + +.. code:: console + + $ python setup.py install + +- with `pip `_ + +.. code:: console + + $ pip install utensor_cgen + +For Developers: +--------------- + +- with `pip `_ + +.. code:: console + + $ pip install -e .[dev] + +- with `pipenv `_ + +.. code:: console + + # install `utensor_cgen` (develop mode) + $ PIPENV_VENV_IN_PROJECT=1 pipenv install -d + + # spawn a subshell and activate virtualenv + $ pipenv shell + + # get help message of `utensor-cli` + (utenosr_cgen)$ utensor-cli -h + +Troubleshooting with ``pipenv`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- If you have troubles with installation using ``pipenv``, try + ``PIPENV_VENV_IN_PROJECT=1 pipenv install -d --skip-lock`` +- there is known issue of ``pip`` and ``pipenv``, plz refer to this + `issue `__ for detail + + - short answer: downgrade to ``pip==18.0`` may help :) + +- Tensorflow requires ``setuptools<=39.1.0`` (the latest is ``40.4.3`` + by the time this README is writen) + + - plz downgrade to ``setuptools==39.1.0`` + - my recommendation is to use ``virtualenv`` + +Overall Architecture +==================== + +:: + + ============ +-----------------+ =================== + || model file || --> | frontend Parser | --> || uTensorGraph (IR) || + ============ +-----------------+ =================== + | + +-------------------------------+ | + | graph transformer | | + | (legalization & optimization) | <------‘ + +-------------------------------+ + | + v + =========================== + || uTensorGraph || + || (legalized and optimized) || + =========================== + | + +--------------------------+ | + | backend (code generator) | <----‘ + +--------------------------+ + | + `---> (target files, ex: model.cpp, model.hpp, weights.idx) + +Basic Usage +=========== + +- ``utensor-cli show `` + +Show all nodes and detailed information of given pb file. + +Run ``utensor-cli show --help`` for detailed information. + +- ``utensor-cli convert \ + --output-nodes=[,,...] \ + `` + +Convert given pb file into cpp/hpp files. + +Note that ``--output-nodes`` is required options. It's the names of +nodes you want to output, seperated by comma if there are many. + +In graph theory terminology, they are ``leaf`` nodes of your graph. + +Run ``utensor-cli convert --help`` for detailed information. + +``utensor_cgen`` as Library +=========================== + +Subgraph Isomorphic Matcher and Nodes Fusion +-------------------------------------------- + +With ``uTensorGraphMatcher``, performing common subgraph tasks such as +isomorphic matching along with replacing or manipulating the matched +subgraph(s) takes just a few line of code! + +Node Fusion +~~~~~~~~~~~ + +Note: we'll use operation/node/layer interchangeably in the +documentation + +- It's commonly seen pattern in convolution neural network (``CNN``), + ``conv -> relu -> pooling``. That is, a 2D convolution followed by a + relu layer and then a pooling down sampling layer. +- With our ``uTensorGraphMatcher``, you can locate such pattern in your + ``CNN`` model and fuse/replace matched nodes into one optimized + ``QuantizedFusedConv2DMaxpool`` node. + + - Left: original graph + - Middle: matched convolution layer + - Right: replace the matched layer with specialized + ``QuantizedFusedConv2DMaxpool`` node + +.. image:: .images/conv_pool_fuse.png + :alt: conv-pool-fuce + +Dropout Layer Removal +~~~~~~~~~~~~~~~~~~~~~ + +- Though ``dropout`` is an effective technique to improve training + performance of your model, it's not necessary during inference + phrase. +- In the mainstream frameworks such as ``Tensorflow`` or ``PyTorch``, + an ``dropout`` layer is typically implemented with other elementary + operations/nodes. As a result, finding and removing those nodes for + interence optimization (both in model size and prediciton time) is + not trivial and error prone. +- With our ``uTensorGraphMatcher``, you can find and remove the dropout + nodes as illustrated in the following picture. + + - Left: original graph with dropout Layers + - Middle: matched dropout layers + - Right: graph with dropout layers removed |cnn-dropout| + +Examples +======== + +- `Deep Multilayer + Perceptron `__ +- `End-to-End Convolution + NN `__ + +User Guide +========== + +Following steps are a general guild for user how to porting a +``TensorFlow`` protobuf file into a ``uTensor`` implementation: + +1. install ``utensor_cgent`` + + - run ``python3 setupt.py install`` + +2. run ``utensor-cli convert --output-nodes='NODE,NODE,...' graph.pb`` + + - run ``utensor-cli -h`` for help + - the ``graph.pb`` is the pb file of *original* graph (not + quantized) + +3. If you want to see what ops/nodes are in the pb file, you can run + ``utensor-cli show `` + +Testing +======= + +1. follow the steps in `setup <#setup-with-pipenv>`_ section +2. run ``make tests`` + + - Or you can use ``pipenv run pytest tests`` instead + +.. design philosophy +.. `12 Factor CLI App `_ + + +Future Works +============ + +- High-level api for graph pattern declaration. + Currently ``utensor_cgen`` uses ``TensorFlow`` + api for building IR graph, ``uTensorGraph``. + + +.. TODOs +.. ===== + +.. 1. (done?) core code generator implementation + +.. - We need some refactoring, PRs are welcomed! + +.. 2. type alias in C/C++ + +.. - ex: use ``uint8_t`` or ``unsigned char``? +.. - a lot more about this.... + +.. 3. Relation among snippets/containers + +.. - shared template variables? (headers, shared placeholders...etc) + +.. 4. Better configuration schema + +.. - json +.. - yaml +.. - or ? + +.. |cnn-dropout| image:: ./images/cnn_dropout.png diff --git a/doc/Makefile b/doc/Makefile new file mode 100644 index 00000000..d0c3cbf1 --- /dev/null +++ b/doc/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/doc/make.bat b/doc/make.bat new file mode 100644 index 00000000..9534b018 --- /dev/null +++ b/doc/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/doc/source/conf.py b/doc/source/conf.py new file mode 100644 index 00000000..02796886 --- /dev/null +++ b/doc/source/conf.py @@ -0,0 +1,56 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys + +from utensor_cgen.cli import _version as version + +sys.path.insert(0, os.path.abspath('../../utensor_cgen')) + + +# -- Project information ----------------------------------------------------- + +project = 'utensor_cgen' +copyright = '2019, uTensor Team' +author = 'dboyliao, Neil Tan, kazami, Michael Bartling' + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', +] +autodoc_typehints = 'none' + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'alabaster' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] diff --git a/doc/source/index.rst b/doc/source/index.rst new file mode 100644 index 00000000..6c888679 --- /dev/null +++ b/doc/source/index.rst @@ -0,0 +1,26 @@ +``utensor_cgen``: C/C++ code generator for ``uTensor`` +====================================================== + +Version: v\ |version| + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + +.. include:: ../../README.rst + +.. Intermediate Representation (IR) +.. ================================ + +.. .. automodule:: utensor_cgen.ir.base +.. :members: + + +.. Indices and tables +.. ================== + +---- + +* :ref:`genindex` +* :ref:`search` +.. * :ref:`modindex` diff --git a/setup.py b/setup.py index e54e0e08..e5f97820 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ from setuptools import find_packages, setup root_dir = os.path.abspath(os.path.dirname(__file__)) -with open(os.path.join(root_dir, "README.md")) as rf: +with open(os.path.join(root_dir, "README.rst")) as rf: long_desc = rf.read() with open(os.path.join(root_dir, "LICENSE")) as rf: license = rf.read() diff --git a/utensor_cgen/cli.py b/utensor_cgen/cli.py index 18d7de32..2af4addb 100644 --- a/utensor_cgen/cli.py +++ b/utensor_cgen/cli.py @@ -8,15 +8,18 @@ from utensor_cgen.backend.operators import OperatorFactory from utensor_cgen.utils import NArgsKwargsParam, NArgsParam +_version = ( + pkg_resources + .get_distribution('utensor_cgen') + .version +) def _get_pb_model_name(path): return os.path.basename(os.path.splitext(path)[0]) @click.group(name='utensor-cli') @click.help_option('-h', '--help') -@click.version_option((pkg_resources - .get_distribution('utensor_cgen') - .version), +@click.version_option(_version, '-V', '--version') def cli(): pass diff --git a/utensor_cgen/ir/base.py b/utensor_cgen/ir/base.py index 38a5171c..ebc05b7f 100644 --- a/utensor_cgen/ir/base.py +++ b/utensor_cgen/ir/base.py @@ -1,4 +1,17 @@ # -*- coding: utf8 -*- +''' +The ``base`` module of intermediate representation + +Concept +------- + +Conceptually, `uTensorGraph` works like a container of `TensorInfo` and `OperationInfo`. +That is, as long as the `TensorInfo` and `OperationInfo` is not dangling, they always +belong to some `uTensorGraph` which should be accessible via `ugraph` property of both +`TensorInfo` and `OperationInfo`. Otherwise, the graph, tensor or op is considered as dangling. + +For short, we'll refer an instance of `OperationInfo` as an **op** or a **node**. +''' import re from copy import deepcopy @@ -42,12 +55,26 @@ def all_supported_backends(self): @attr.s(cmp=False) class TensorInfo(IRBase, _NoShallowCopyMixin): """ - name : str - dtype : numpy.dtype - shape : list + Parameters + ---------- + :param name: the name of the tensor + :type name: six.string_types + + :param op_name: the name of the operator which generate + this tensor + :type op_name: six.string_types + + :param dtype: the data type of the elements. + :type dtype: numpy.dtype + + :param shape: the shape of the tensor. Should be a list + of integers or ``None``. + :type shape: list + + :param ugraph: a `uTensorGraph`, which this tensor belongs to + :type utensor_cgen.ir.uTensorGraph: - TODO: the need for null tensor info, that is, - a tensor which may not be attached to an op + `TensorInfo` defines an tensor in an computational graph """ name = attr.ib(validator=instance_of(six.string_types)) op_name = attr.ib(validator=instance_of(six.string_types)) @@ -69,6 +96,12 @@ def check(self, attrib, value): @property def ugraph(self): + """ + The `uTensorGraph` which the tensor belongs to + + :getter: return the `uTensorGraph` + :type: utensor_cgen.ir.uTensorGraph + """ return self._ugraph @property @@ -118,27 +151,49 @@ def __eq__(self, other): def move_into(self, ugraph): """ - Move Synmatic of the OperationInfo objects + Move Synmatic of the `TensorInfo` objects + + ``move_info(ugraph)`` will move the tensor to the given graph, + that is, transferring ownership of the tensor from original graph + to other graph + + Very useful for implementing an optimization pass (`Tranformer`) """ self._ugraph = ugraph @attr.s(cmp=False, repr=False) class OperationInfo(IRBase, _NoShallowCopyMixin): """ - name : str - input_tensors : List[TensorInfo] - output_tensors : List[TensorInfo] - input_nodes : Set[OperationInfo] - output_nodes : Set[OperationInfo] - op_type : str - backend : str {"tensorflow", 'pytorch'(future work)} - op_attr : dict + Parameters + ---------- + :param name: the name of the node + :type name: str + + :param input_tensors: the input tensors of the node + :type input_tensors: List[TensorInfo] + + :param output_tensors: the output tensors of the node + :type output_tensors: List[TensorInfo] + + :param input_nodes: the nodes which generate the tensors in ``input_tensors`` + :type input_nodes: Set[OperationInfo] + + :param output_nodes: the nodes which consume the ``output_tensors`` in the graph + :type output_nodes: Set[OperationInfo] + + :param op_type: the type of the node (ex: ``Add``) + :type op_type: str - Note - ==== - - `op_attr` will be a dictionary with key as str and value as generic - types defined in `converter.ConverterFactor.all_generic_types`. The - only exception is the key which match regex pattern r'_[^_]*'. The + :param backend: the name of the backend, the library/framework for the training phase + {"tensorflow", 'pytorch'} + :type backend: str + + :param op_attr: a dict containing extra information of this op + :type op_attr: dict + + - `op_attr` will be a dictionary with key as str and value as generic \ + types defined in ``converter.ConverterFactor.all_generic_types``. The \ + only exception is the key which match regex pattern ``r'_[^_]*'``. The \ values of such keys will be saved as-is without any type conversion. """ name = attr.ib(type=str) @@ -318,12 +373,16 @@ class uTensorGraph(IRBase, _NoShallowCopyMixin): How to Build a uTensorGraph =========================== 1. create a empty graph + - give a list of names of output nodes (required) - (optional) give backend string - leave ops_info empty + 2. setup the ops_info + - when you set the value of ops_info, which is an OperationInfo instance, make sure its ugraph attribute is the ugraph you just created at step 1 + 3. pass the ugraph to topologic_order_graph to setup the order of the ops """ From 5677dd486a8738861bb17491750ba382d67e3f10 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 26 Jul 2019 00:11:55 +0800 Subject: [PATCH 68/70] update README --- README.rst | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index ef9f110c..72d37410 100644 --- a/README.rst +++ b/README.rst @@ -151,6 +151,10 @@ Dropout Layer Removal - Middle: matched dropout layers - Right: graph with dropout layers removed |cnn-dropout| +We use mainly ``Tensorflow`` for declaring the pattern graph for matcher now. + +High-level graph builder is on its way, see `Future Works <#future-works>`_ for detail. + Examples ======== @@ -193,9 +197,12 @@ Testing Future Works ============ -- High-level api for graph pattern declaration. - Currently ``utensor_cgen`` uses ``TensorFlow`` - api for building IR graph, ``uTensorGraph``. +1. High-level graph builder api for building `uTensorGraph`. + + - Currently ``utensor_cgen`` uses ``TensorFlow`` api for building IR graph, ``uTensorGraph``. + - With high-level graph builder, users can build their ``uTensorGraph`` easily and do not need + to take care of the integrity of the graph. + The builder will take care of it automatically. .. TODOs From c3af9d31415eb39cb74dfdbcc673ba87e3b5a34f Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 26 Jul 2019 00:39:48 +0800 Subject: [PATCH 69/70] Fix imgs path --- README.rst | 11 ++++++++--- doc/Makefile | 4 ++++ 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index 72d37410..76a5cd92 100644 --- a/README.rst +++ b/README.rst @@ -130,8 +130,8 @@ documentation - Right: replace the matched layer with specialized ``QuantizedFusedConv2DMaxpool`` node -.. image:: .images/conv_pool_fuse.png - :alt: conv-pool-fuce +\ |conv-pool-fuse| + Dropout Layer Removal ~~~~~~~~~~~~~~~~~~~~~ @@ -149,7 +149,10 @@ Dropout Layer Removal - Left: original graph with dropout Layers - Middle: matched dropout layers - - Right: graph with dropout layers removed |cnn-dropout| + - Right: graph with dropout layers removed + +\ |cnn-dropout| + We use mainly ``Tensorflow`` for declaring the pattern graph for matcher now. @@ -228,3 +231,5 @@ Future Works .. - or ? .. |cnn-dropout| image:: ./images/cnn_dropout.png +.. |conv-pool-fuse| image:: images/conv_pool_fuse.png + :alt: conv-pool-fuse \ No newline at end of file diff --git a/doc/Makefile b/doc/Makefile index d0c3cbf1..90e3eb38 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -14,6 +14,10 @@ help: .PHONY: help Makefile +html: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + @mkdir "$(BUILDDIR)/$@/images" && cp ../images/* "$(BUILDDIR)/$@/images" + # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile From 5024724a3295fe2e1de1d8c874b96837e3d5fe73 Mon Sep 17 00:00:00 2001 From: dboyliao Date: Fri, 26 Jul 2019 00:56:25 +0800 Subject: [PATCH 70/70] Add ir chapter --- doc/Makefile | 2 +- doc/source/index.rst | 16 ++++++++-------- doc/source/ir.rst | 5 +++++ 3 files changed, 14 insertions(+), 9 deletions(-) create mode 100644 doc/source/ir.rst diff --git a/doc/Makefile b/doc/Makefile index 90e3eb38..a203d279 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -16,7 +16,7 @@ help: html: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - @mkdir "$(BUILDDIR)/$@/images" && cp ../images/* "$(BUILDDIR)/$@/images" + @mkdir -p "$(BUILDDIR)/$@/images" && cp ../images/* "$(BUILDDIR)/$@/images" # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). diff --git a/doc/source/index.rst b/doc/source/index.rst index 6c888679..25d646f4 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -4,20 +4,20 @@ Version: v\ |version| .. toctree:: - :maxdepth: 2 - :caption: Contents: + :maxdepth: 2 + :caption: Contents: .. include:: ../../README.rst -.. Intermediate Representation (IR) -.. ================================ -.. .. automodule:: utensor_cgen.ir.base -.. :members: +Indices and tables +================== +.. toctree:: + :maxdepth: 1 + + ir -.. Indices and tables -.. ================== ---- diff --git a/doc/source/ir.rst b/doc/source/ir.rst new file mode 100644 index 00000000..232dc197 --- /dev/null +++ b/doc/source/ir.rst @@ -0,0 +1,5 @@ +Intermediate Representation (IR) +================================ + +.. automodule:: utensor_cgen.ir.base + :members: \ No newline at end of file
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