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Fix Time_series_LSTM tuning issue #163

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Merged
lvliang-intel merged 2 commits into from
Nov 25, 2022
Merged

Fix Time_series_LSTM tuning issue #163

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226cb43
Fix Time_series_LSTM tuning issue
lvliang-intel Nov 24, 2022
d290983
fix pylint issue
lvliang-intel Nov 24, 2022
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14 changes: 10 additions & 4 deletions neural_compressor/adaptor/tf_utils/graph_rewriter/int8/fuse_matmul_requantize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -588,10 +588,16 @@ def do_transformation(self):
min_filter_node = None
# The Min and Max of non-const weight node are from QuantizeV2's output, not valid nodes.
# Add check here for excluding this case.
if ":2" not in new_node.input[6]:
max_filter_node = self.graph_info[new_node.input[6]].node
if ":1" not in new_node.input[5]:
min_filter_node = self.graph_info[new_node.input[5]].node
if len(attr_fused_ops) == 0: # single matmul case
if ":2" not in new_node.input[5]:
max_filter_node = self.graph_info[new_node.input[5]].node
if ":1" not in new_node.input[4]:
min_filter_node = self.graph_info[new_node.input[4]].node
else:
if ":2" not in new_node.input[6]:
max_filter_node = self.graph_info[new_node.input[6]].node
if ":1" not in new_node.input[5]:
min_filter_node = self.graph_info[new_node.input[5]].node
last_node = self.graph_info[new_node.input[0]].node
is_min_first = bool(quantized_node.attr['input_quant_mode'].s == b'MIN_FIRST')
weight_node = self.graph_info[new_node.input[1]].node
Expand Down
81 changes: 32 additions & 49 deletions neural_compressor/adaptor/tf_utils/graph_rewriter/qdq/insert_qdq_pattern.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,6 +81,8 @@ def do_transformation(self):
self.g.graph = copy.deepcopy(self.model)
self.graph_info = self.g.parse_graph()

self.g.get_frame_info()

# insert QDQ pattern for op's input
for op_name in quantizable_op_names:
if self._ignore_insert_qdq_pattern(op_name):
Expand Down Expand Up @@ -115,20 +117,16 @@ def do_transformation(self):

computational_node = self.graph_info[computational_node_name].node
weight_name = computational_node.input[1]
weight_node = self.graph_info[weight_name].node
if re.search(r"\w+:\d+", weight_name):
weight_node = self.graph_info[weight_name.rsplit(':', 1)[0]].node
else:
weight_node = self.graph_info[weight_name].node
enter_node = None
if weight_node.op == 'Enter':
if self.itex_mode:
parent_node = self.graph_info[Helper.node_name_from_input(weight_node.input[0])].node
if not parent_node.op == 'Const':
continue
else:
enter_node = weight_node
weight_node = parent_node
weight_node = parent_node
else:
continue

Expand All @@ -139,10 +137,10 @@ def do_transformation(self):
else:
per_channel = False
weight_bit = 7

self._insert_qdq_pattern_for_weight_node(computational_node,
weight_node,
enter_node,
weight_name,
min_max_values,
per_channel,
weight_bit,
Expand Down Expand Up @@ -414,7 +412,7 @@ def _insert_qdq_pattern_for_each_input(self, op_name, namespace_prefix,
def _insert_qdq_pattern_for_weight_node(self,
computational_node,
weight_node,
enter_node,
weight_name,
min_max_values,
per_channel,
weight_bit=7.0,
Expand Down Expand Up @@ -504,41 +502,27 @@ def _insert_qdq_pattern_for_weight_node(self,
max_node = Helper.create_constant_node(max_name, max_value,
dtypes.float32, device="cpu")
if "BatchMatMul" in host_op_type and "BatchMatMul" not in weight_node.op:
min_node.input.append("^" + weight_node.name)
max_node.input.append("^" + weight_node.name)
min_node.input.append("^" + weight_name)
max_node.input.append("^" + weight_name)

quant_const_enter_node = None
min_enter_node = None
max_enter_node = None
if enter_node:
quant_const_enter_node = Helper.create_node('Enter', \
qint8_const_name + '_enter', [weight_node.name])
Helper.set_attr_string(quant_const_enter_node,
'frame_name', enter_node.attr['frame_name'].s)
Helper.set_attr_dtype(quant_const_enter_node, 'T', dtypes.float32)
Helper.set_attr_bool(quant_const_enter_node, 'is_constant', True)
Helper.set_attr_int(quant_const_enter_node, \
'parallel_iterations', enter_node.attr['parallel_iterations'].i)
if insert_reshape:
reshape_dims_4to3_name = qint8_const_name + "_reshape_dims_4to3_"
reshape_dims_4to3_node = Helper.create_constant_node(
reshape_dims_4to3_name, shape_convert, dtypes.int32)
reshape_4to3_name = qint8_const_name + "_reshape_4to3_"
reshape_4to3_node = Helper.create_node("Reshape", reshape_4to3_name,
[weight_node.name, reshape_dims_4to3_name])
reshape_4to3_node.attr["T"].CopyFrom(
attr_value_pb2.AttrValue(type=dtypes.float32.as_datatype_enum))
quant_node = Helper.create_node(
"QuantizeV2", qint8_const_name + '_quant',
[quant_const_enter_node.name, min_name, max_name])
[reshape_4to3_name, min_name, max_name])
else:
if insert_reshape:
reshape_dims_4to3_name = qint8_const_name + "_reshape_dims_4to3_"
reshape_dims_4to3_node = Helper.create_constant_node(
reshape_dims_4to3_name, shape_convert, dtypes.int32)
reshape_4to3_name = qint8_const_name + "_reshape_4to3_"
reshape_4to3_node = Helper.create_node("Reshape", reshape_4to3_name,
[weight_node.name, reshape_dims_4to3_name])
reshape_4to3_node.attr["T"].CopyFrom(
attr_value_pb2.AttrValue(type=dtypes.float32.as_datatype_enum))
quant_node = Helper.create_node(
"QuantizeV2", qint8_const_name + '_quant',
[reshape_4to3_name, min_name, max_name])
else:
quant_node = Helper.create_node(
"QuantizeV2", qint8_const_name + '_quant',
[weight_node.name, min_name, max_name])
quant_node = Helper.create_node(
"QuantizeV2", qint8_const_name + '_quant',
[weight_node.name, min_name, max_name])

dequant_node = Helper.create_node(
"Dequantize", base_name + '_dequant',
Expand All @@ -549,10 +533,10 @@ def _insert_qdq_pattern_for_weight_node(self,
Helper.set_attr_dtype(dequant_node, "T", dtypes.qint8)
Helper.set_attr_string(dequant_node, "mode", b"SCALED")
if per_channel:
if host_op_type == 'Conv2D' or host_op_type == 'Conv2DBackpropInput':
if host_op_type in ('Conv2D', 'Conv2DBackpropInput'):
Helper.set_attr_int(quant_node, 'axis', 3)
Helper.set_attr_int(dequant_node, 'axis', 3)
elif host_op_type == 'Conv3D' or host_op_type == 'Conv3DBackpropInputV2':
elif host_op_type in ('Conv3D', 'Conv3DBackpropInputV2'):
Helper.set_attr_int(quant_node, 'axis', 4)
Helper.set_attr_int(dequant_node, 'axis', 4)
elif host_op_type == 'MatMul':
Expand Down Expand Up @@ -584,33 +568,32 @@ def _insert_qdq_pattern_for_weight_node(self,
self.g_weight.add_node(reshape_3to4_node, dequant_node.name, [computational_node.name])
computational_node.input[1] = reshape_3to4_node.name
else:
if enter_node:
if weight_node.name in self.g.parent_frame_details and self.g.parent_frame_details[weight_node.name]:
min_enter_node = Helper.create_node('Enter', min_name + '_enter', [min_name])
Helper.set_attr_string(min_enter_node,
'frame_name', enter_node.attr['frame_name'].s)
Helper.set_attr_string(min_enter_node, 'frame_name',
self.g.parent_frame_details[weight_node.name].attr['frame_name'].s)
Helper.set_attr_dtype(min_enter_node, 'T', dtypes.float32)
Helper.set_attr_bool(min_enter_node, 'is_constant', True)
Helper.set_attr_int(min_enter_node, 'parallel_iterations', \
enter_node.attr['parallel_iterations'].i)
self.g.parent_frame_details[weight_node.name].attr['parallel_iterations'].i)

max_enter_node = Helper.create_node('Enter', max_name + '_enter', [max_name])
Helper.set_attr_string(max_enter_node,
'frame_name', enter_node.attr['frame_name'].s)
Helper.set_attr_string(max_enter_node, 'frame_name',
self.g.parent_frame_details[weight_node.name].attr['frame_name'].s)
Helper.set_attr_dtype(max_enter_node, 'T', dtypes.float32)
Helper.set_attr_bool(max_enter_node, 'is_constant', True)
Helper.set_attr_int(max_enter_node, 'parallel_iterations',\
enter_node.attr['parallel_iterations'].i)
self.g.parent_frame_details[weight_node.name].attr['parallel_iterations'].i)

self.g_weight.add_node(quant_const_enter_node, weight_node.name, [quant_node.name])
self.g_weight.add_node(quant_node, quant_const_enter_node.name, [])
self.g_weight.add_node(quant_node, weight_name, [])
self.g_weight.add_node(min_node, None, [min_enter_node.name])
self.g_weight.add_node(max_node, None, [max_enter_node.name])
self.g_weight.add_node(min_enter_node, min_node.name, [quant_node.name])
self.g_weight.add_node(max_enter_node, max_node.name, [quant_node.name])
self.g_weight.add_node(dequant_node, quant_node.name, [computational_node.name])
computational_node.input[1] = dequant_node.name
else:
self.g_weight.add_node(quant_node, weight_node.name, [])
self.g_weight.add_node(quant_node, weight_name, [])
self.g_weight.add_node(min_node, None, [quant_node.name])
self.g_weight.add_node(max_node, None, [quant_node.name])
self.g_weight.add_node(dequant_node, quant_node.name, [computational_node.name])
Expand Down