From e40760d5af575109cc7d102bbcd9895df43db153 Mon Sep 17 00:00:00 2001
From: Elizabeth <hashim.sharif91@gmail.com>
Date: Wed, 9 Oct 2019 01:26:38 -0500
Subject: [PATCH] Fixed popen bug when running promise timing model
---
llvm/projects/soc_simulator/src/driver.py | 76 ++++++++++-------------
1 file changed, 33 insertions(+), 43 deletions(-)
diff --git a/llvm/projects/soc_simulator/src/driver.py b/llvm/projects/soc_simulator/src/driver.py
index b56f60ac68..50f80ba15d 100644
--- a/llvm/projects/soc_simulator/src/driver.py
+++ b/llvm/projects/soc_simulator/src/driver.py
@@ -35,21 +35,21 @@ def parse_tensor_layer_file(layer_filename):
tensor_layer["Name"] = layer_name
if is_conv(layer_name):
- tensor_layer["N"] = layer_data[1]
- tensor_layer["Cin"] = layer_data[2]
- tensor_layer["H"] = layer_data[3]
- tensor_layer["W"] = layer_data[4]
- tensor_layer["Cout"] = layer_data[5]
- tensor_layer["Kh"] = layer_data[6]
- tensor_layer["Kw"] = layer_data[7]
- tensor_layer["Sh"] = layer_data[8]
- tensor_layer["Sw"] = layer_data[9]
+ tensor_layer["N"] = int(layer_data[1])
+ tensor_layer["Cin"] = int(layer_data[2])
+ tensor_layer["H"] = int(layer_data[3])
+ tensor_layer["W"] = int(layer_data[4])
+ tensor_layer["Cout"] = int(layer_data[5])
+ tensor_layer["Kh"] = int(layer_data[6])
+ tensor_layer["Kw"] = int(layer_data[7])
+ tensor_layer["Sh"] = int(layer_data[8])
+ tensor_layer["Sw"] = int(layer_data[9])
elif is_fc(layer_name):
- tensor_layer["RA"] = layer_data[1]
- tensor_layer["CA"] = layer_data[2]
- tensor_layer["RB"] = layer_data[3]
- tensor_layer["CB"] = layer_data[4]
+ tensor_layer["RA"] = int(layer_data[1])
+ tensor_layer["CA"] = int(layer_data[2])
+ tensor_layer["RB"] = int(layer_data[3])
+ tensor_layer["CB"] = int(layer_data[4])
elif not is_nml(layer_name): # TODO should we store data for NMLs?
print("ERROR: Invalid layer name %s" % layer_name)
@@ -113,7 +113,7 @@ class ApproxTypes:
def is_promise(config_layer):
# TODO overhead in call to split?
- return config_layer.split(' ')[0] < fp16_swing
+ return int(config_layer.split(' ')[0]) < fp16_swing
# NOTE smart_dma is always true
def quantize(curr_layer, prev_layer, h2f_f2h_operation_ind, layer_data):
@@ -167,17 +167,20 @@ def run_promise_simulation(swing, layer_data):
exit(1)
# Run promise simulator
- # TODO need to print time and energy so we can pipe it
- # get output --> total time and energy
- ptm_process = subprocess.Popen(["./ptm", str(rows_a), str(cols_a), str(rows_b), \
- str(cols_b), str(patch_factor), str(swing)])
- output, _ = ptm_process.communicate()
- total_time_energy = output.strip().split(' ')
+ # TODO need to print time and energy in the ptm runner so we can pipe it
+ output = subprocess.Popen(["./ptm", str(rows_a), str(cols_a), str(rows_b), \
+ str(cols_b), str(patch_factor), str(swing)], \
+ stdout = subprocess.PIPE, stderr = subprocess.PIPE).communicate()[0]
+ total_time_energy = output.strip().split(',')
- assert(len(total_time_energy) == 2)
- return total_time_energy[0], total_time_energy[1]
+ assert(len(total_time_energy) == 2)
+ return total_time_energy[0], total_time_energy[1]
+# Default dict of default dicts
+# [Time/Energy][number corresponding to order the layer config was read in] = time/energy
+aggregate_results = defaultdict(lambda: defaultdict(float))
+
def run_simulations(config_filename, results_filename):
config_file = open(config_filename, "r")
results_file = open(results_filename, "w")
@@ -186,41 +189,27 @@ def run_simulations(config_filename, results_filename):
# layers are separated by commas
# tensor ops are separated by spaces
- for config in config_file:
+ for config_ind, config in enumerate(config_file):
config_layers = config.strip().split(',')
prev_layer = ApproxTypes.FP32
curr_layer = None
-
+
for layer_ind, config_layer in enumerate(config_layers): # level
layer_data = tensor_layers[layer_ind] # layer
-
if is_promise(config_layer):
+ print("IS PROMISE")
print("Running layer %s on PROMISE" % layer_data["Name"])
curr_layer = ApproxTypes.PROMISE
quant_time, quant_energy = quantize(curr_layer, prev_layer, 0, layer_data)
# Compute
+ time, energy = run_promise_simulation(config_layer, layer_data)
+ aggregate_results["Time"][config_ind] = time
+ aggregate_results["Energy"][config_ind] = energy
else:
+ print("Not promise")
pass
- # for each config file line --> parse the comma separated voltage swing levels
- # recall: each line = a configuration that works
- # for each level
- # if promise --> promise runs an entire layer
- # quantize, no patching and unpatching
- # run on promise
- # output the total time and energy
- # else
- # for each sublevel (separated by spaces)
- # quantize
- # run
- # keep track of total time and energy --> update as needed
- # output the total time and energy
-
-# quantization: we always have smart dma
-# need to search stuff up
-# $layer = a map of elements
-# stores the layer name, then
if __name__ == "__main__":
'''
@@ -230,3 +219,4 @@ if __name__ == "__main__":
'''
parse_tensor_layer_file("/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/build_mobilenet/mobilenet_layers.txt")
parse_tensor_table("/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/build_pldi/mobilenet_results/mobilenet_tensors.txt")
+ run_simulations("/home/nvidia/Gitlab/hpvm/llvm/projects/soc_simulator/pipeline_GEMO/pipeline_GEMO_promise_confs1.txt", "blah")
--
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