Optimize Trained Models for Inference

Graph Transform Tool

Great Blog Post by Pete Warden from Google

Types of Optimizations

Remove training-only operations (checkpoint saving, drop out)
Strip out unused nodes
Remove debug operations
Fold batch normalization ops into weights (super cool)
Quantize weights

Original Model (CPU)

File Size

WARNING: If this directory doesn't exist, you need to train the model in an earlier notebook!!



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%%bash

ls -l /root/models/optimize_me/linear/cpu/

Graph



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%%bash

summarize_graph --in_graph=/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb



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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from google.protobuf import text_format
from tensorflow.core.framework import graph_pb2

def convert_graph_to_dot(input_graph, output_dot, is_input_graph_binary):
    graph = graph_pb2.GraphDef()
    with open(input_graph, "rb") as fh:
        if is_input_graph_binary:
            graph.ParseFromString(fh.read())
        else:
            text_format.Merge(fh.read(), graph)
    with open(output_dot, "wt") as fh:
        print("digraph graphname {", file=fh)
        for node in graph.node:
            output_name = node.name
            print("  \"" + output_name + "\" [label=\"" + node.op + "\"];", file=fh)
            for input_full_name in node.input:
                parts = input_full_name.split(":")
                input_name = re.sub(r"^\^", "", parts[0])
                print("  \"" + input_name + "\" -> \"" + output_name + "\";", file=fh)
        print("}", file=fh)
        print("Created dot file '%s' for graph '%s'." % (output_dot, input_graph))



In [ ]:

    
input_graph='/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb'
output_dot='/root/notebooks/unoptimized_cpu.dot'
convert_graph_to_dot(input_graph=input_graph, output_dot=output_dot, is_input_graph_binary=True)



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%%bash

dot -T png /root/notebooks/unoptimized_cpu.dot \
    -o /root/notebooks/unoptimized_cpu.png > /tmp/a.out



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from IPython.display import Image

Image('/root/notebooks/unoptimized_cpu.png', width=1024, height=768)

Strip Unused Nodes



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%%bash

transform_graph \
    --in_graph=/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb \
    --out_graph=/root/models/optimize_me/linear/cpu/strip_unused_optimized_cpu.pb \
    --inputs='x_observed' \
    --outputs='add' \
    --transforms='
strip_unused_nodes'



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%%bash

ls -l /root/models/optimize_me/linear/cpu

Graph



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%%bash

summarize_graph --in_graph=/root/models/optimize_me/linear/cpu/strip_unused_optimized_cpu.pb



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input_graph='/root/models/optimize_me/linear/cpu/strip_unused_optimized_cpu.pb'
output_dot='/root/notebooks/strip_unused_optimized_cpu.dot'
convert_graph_to_dot(input_graph=input_graph, output_dot=output_dot, is_input_graph_binary=True)



In [ ]:

    
%%bash

dot -T png /root/notebooks/strip_unused_optimized_cpu.dot \
    -o /root/notebooks/strip_unused_optimized_cpu.png > /tmp/a.out



In [ ]:

    
from IPython.display import Image

Image('/root/notebooks/strip_unused_optimized_cpu.png')

Remove Nodes

Remove pesky Identity and CheckNumerics



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%%bash

transform_graph \
    --in_graph=/root/models/optimize_me/linear/cpu/strip_unused_optimized_cpu.pb \
    --out_graph=/root/models/optimize_me/linear/cpu/remove_nodes_optimized_cpu.pb \
    --inputs='x_observed' \
    --outputs='add' \
    --transforms='
strip_unused_nodes
remove_nodes(op=Identity, op=CheckNumerics)'



In [ ]:

    
%%bash

ls -l /root/models/optimize_me/linear/cpu

Graph



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%%bash

summarize_graph --in_graph=/root/models/optimize_me/linear/cpu/remove_nodes_optimized_cpu.pb



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input_graph='/root/models/optimize_me/linear/cpu/remove_nodes_optimized_cpu.pb'
output_dot='/root/notebooks/remove_nodes_optimized_cpu.dot'
convert_graph_to_dot(input_graph=input_graph, output_dot=output_dot, is_input_graph_binary=True)



In [ ]:

    
%%bash

dot -T png /root/notebooks/remove_nodes_optimized_cpu.dot \
    -o /root/notebooks/remove_nodes_optimized_cpu.png > /tmp/a.out



In [ ]:

    
from IPython.display import Image

Image('/root/notebooks/remove_nodes_optimized_cpu.png')

Fold Constants



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%%bash

transform_graph \
--in_graph=/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb \
--out_graph=/root/models/optimize_me/linear/cpu/fold_constants_optimized_cpu.pb \
--inputs='x_observed' \
--outputs='add' \
--transforms='
strip_unused_nodes
remove_nodes(op=Identity, op=CheckNumerics)
fold_constants(ignore_errors=true)'

File Size



In [ ]:

    
%%bash

ls -l /root/models/optimize_me/linear/cpu

Graph



In [ ]:

    
%%bash

summarize_graph --in_graph=/root/models/optimize_me/linear/cpu/fold_constants_optimized_cpu.pb



In [ ]:

    
input_graph='/root/models/optimize_me/linear/cpu/fold_constants_optimized_cpu.pb'
output_dot='/root/notebooks/fold_constants_optimized_cpu.dot'
convert_graph_to_dot(input_graph=input_graph, output_dot=output_dot, is_input_graph_binary=True)



In [ ]:

    
%%bash

dot -T png /root/notebooks/fold_constants_optimized_cpu.dot \
    -o /root/notebooks/fold_constants_optimized_cpu.png > /tmp/a.out



In [ ]:

    
from IPython.display import Image

Image('/root/notebooks/fold_constants_optimized_cpu.png')

Fold Batch Normalizations

Prereq: fold_constants



In [ ]:

    
%%bash

transform_graph \
--in_graph=/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb \
--out_graph=/root/models/optimize_me/linear/cpu/fold_batch_norms_optimized_cpu.pb \
--inputs='x_observed' \
--outputs='add' \
--transforms='
strip_unused_nodes
remove_nodes(op=Identity, op=CheckNumerics)
fold_constants(ignore_errors=true)
fold_batch_norms
fold_old_batch_norms'

File Size



In [ ]:

    
%%bash

ls -l /root/models/optimize_me/linear/cpu

Graph



In [ ]:

    
%%bash

summarize_graph --in_graph=/root/models/optimize_me/linear/cpu/fold_batch_norms_optimized_cpu.pb



In [ ]:

    
input_graph='/root/models/optimize_me/linear/cpu/fold_batch_norms_optimized_cpu.pb'
output_dot='/root/notebooks/fold_batch_norms_optimized_cpu.dot'
convert_graph_to_dot(input_graph=input_graph, output_dot=output_dot, is_input_graph_binary=True)



In [ ]:

    
%%bash

dot -T png /root/notebooks/fold_batch_norms_optimized_cpu.dot \
    -o /root/notebooks/fold_batch_norms_optimized_cpu.png > /tmp/a.out



In [ ]:

    
from IPython.display import Image

Image('/root/notebooks/fold_batch_norms_optimized_cpu.png')

Quantize Weights

Prereq: fold_batch_norms



In [ ]:

    
%%bash

transform_graph \
--in_graph=/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb \
--out_graph=/root/models/optimize_me/linear/cpu/quantize_weights_optimized_cpu.pb \
--inputs='x_observed' \
--outputs='add' \
--transforms='
strip_unused_nodes
remove_nodes(op=Identity, op=CheckNumerics)
fold_constants(ignore_errors=true)
fold_batch_norms
fold_old_batch_norms
quantize_weights'

File Size



In [ ]:

    
%%bash

ls -l /root/models/optimize_me/linear/cpu/

Graph



In [ ]:

    
%%bash

summarize_graph --in_graph=/root/models/optimize_me/linear/cpu/quantize_weights_optimized_cpu.pb



In [ ]:

    
input_graph='/root/models/optimize_me/linear/cpu/quantize_weights_optimized_cpu.pb'
output_dot='/root/notebooks/quantize_weights_optimized_cpu.dot'
convert_graph_to_dot(input_graph=input_graph, output_dot=output_dot, is_input_graph_binary=True)



In [ ]:

    
%%bash

dot -T png /root/notebooks/quantize_weights_optimized_cpu.dot \
    -o /root/notebooks/quantize_weights_optimized_cpu.png > /tmp/a.out



In [ ]:

    
from IPython.display import Image

Image('/root/notebooks/quantize_weights_optimized_cpu.png')

Combine All Optimizations



In [ ]:

    
%%bash

transform_graph \
--in_graph=/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb \
--out_graph=/root/models/optimize_me/linear/cpu/fully_optimized_cpu.pb \
--inputs='x_observed' \
--outputs='add' \
--transforms='
strip_unused_nodes
remove_nodes(op=Identity, op=CheckNumerics)
fold_constants(ignore_errors=true)
fold_batch_norms
fold_old_batch_norms
quantize_weights'

File Size



In [ ]:

    
%%bash

ls -l /root/models/optimize_me/linear/cpu/

Graph



In [ ]:

    
%%bash

summarize_graph --in_graph=/root/models/optimize_me/linear/cpu/fully_optimized_cpu.pb



In [ ]:

    
input_graph='/root/models/optimize_me/linear/cpu/fully_optimized_cpu.pb'
output_dot='/root/notebooks/fully_optimized_cpu.dot'
convert_graph_to_dot(input_graph=input_graph, output_dot=output_dot, is_input_graph_binary=True)



In [ ]:

    
%%bash

dot -T png /root/notebooks/fully_optimized_cpu.dot \
    -o /root/notebooks/fully_optimized_cpu.png > /tmp/a.out



In [ ]:

    
from IPython.display import Image

Image('/root/notebooks/fully_optimized_cpu.png')