From 18224f2d0bd5a8738c485a77976aab5b0584dcb7 Mon Sep 17 00:00:00 2001
From: Elizabeth <hashim.sharif91@gmail.com>
Date: Sat, 16 Nov 2019 11:50:17 -0600
Subject: [PATCH] Added quantization logic to fp16 baseline
---
.../src/driver_new_config_fp16_repl.py | 40 +++++++++++--------
1 file changed, 24 insertions(+), 16 deletions(-)
diff --git a/llvm/projects/soc_simulator/src/driver_new_config_fp16_repl.py b/llvm/projects/soc_simulator/src/driver_new_config_fp16_repl.py
index 28a883d160..a7fca62564 100644
--- a/llvm/projects/soc_simulator/src/driver_new_config_fp16_repl.py
+++ b/llvm/projects/soc_simulator/src/driver_new_config_fp16_repl.py
@@ -220,7 +220,7 @@ class Driver:
op_type = layer_as_lst[i]
precision_type = layer_as_lst[i + 1]
op_number = layer_as_lst[i + 2]
- print(' '.join(layer_as_lst[i : i + 3]))
+ #print(' '.join(layer_as_lst[i : i + 3]))
approx_type = None
if line.find("fp16") != -1:
@@ -250,29 +250,37 @@ class Driver:
prev_layer = curr_layer
curr_conf_results.append((layer_as_lst[1], layer_results))
- # artificially generate the fp16 baseline
if not self.__conf_results: # we're appending the baseline
-
- # need to generate an artificial fp16 baseline
+ # need to find the fp16 baseline
self.fp16_baseline = []
+
+ prev_layer = Driver.PrecisionTypes.FP32
+ curr_layer = None
+
+ has_quantized = False
for layer_ind, (hardware, layer) in enumerate(curr_conf_results):
if len(layer) == 1 and layer[0][2].find("softmax") != -1: continue
fp16_layer = []
- print(layer_ind, hardware, layer)
+ #print(layer_ind, hardware, layer)
layer_table_data = self.__tensor_layers[layer_ind]
layer_name = layer_table_data["Name"]
for tensor_ind, (op_time, op_energy, tensor_op) in enumerate(layer):
- quant_time, quant_energy = 0,0
- if layer_ind == 0: # force quantization
- quant_time, quant_energy = self.__quantize("fp16", "1", Driver.PrecisionTypes.FP16, None, 0, layer_table_data)
- print("FP16 QUANT: ", quant_time, quant_energy)
+ curr_layer = Driver.PrecisionTypes.FP16 # always
+
+ quant_time, quant_energy = self.__quantize("fp16", "1", curr_layer, prev_layer, tensor_ind, layer_table_data)
+ if quant_time != 0:
+ assert not has_quantized
+ has_quantized = True
tensor_info = self.__tensor_table[layer_name][tensor_ind]
fp16_time = tensor_info["fp16_time"] + quant_time
fp16_energy = tensor_info["fp16_energy"] + quant_energy
fp16_layer.append((fp16_time, fp16_energy, tensor_op.replace("fp32", "fp16")))
+ prev_layer = curr_layer
+
+ prev_layer = curr_layer
self.fp16_baseline.append((hardware, fp16_layer))
- print(self.fp16_baseline)
+ #print(self.fp16_baseline)
self.__conf_results.append( (first_line, curr_conf_results) )
line = config_file.readline().strip()
config_file.close()
@@ -306,7 +314,8 @@ class Driver:
energy_key = "f2h%senergy" % lookup_key
time = tensor_op_row[time_key]
energy = tensor_op_row[energy_key]
- print("Quantization: (%f, %f)" % (time, energy))
+ #print(time_key, energy_key)
+ #print("Quantization: (%f, %f)" % (time, energy))
return (time, energy)
@@ -372,7 +381,7 @@ class Driver:
elif curr_layer == Driver.PrecisionTypes.FP16:
time_key = "fp16_time"
energy_key = "fp16_energy"
- print(time_key, energy_key)
+ #print(time_key, energy_key)
conversion_time = tensor_info[time_key]
conversion_energy = tensor_info[energy_key]
#print("GPU: (%f, %f)\n" % (conversion_time, conversion_energy))
@@ -420,7 +429,6 @@ class Driver:
return curr_time, curr_energy
def get_final_times_energies_conf(curr_conf, curr_conf_name):
- #print("_____________ NEW CONFIGURATION ___________")
final_time = final_energy = 0
final_conf = [] # List (conf) of lists (layers) of tuples (operation data)
@@ -444,8 +452,8 @@ class Driver:
if op_time > baseline_time:
print("**************** BIGGER ******************")
print(curr_conf_name)
- print(baseline_time, baseline_energy, baseline_op)
- print(op_time, tensor_op)
+ print(baseline_time, baseline_energy, baseline_op, layer_ind)
+ print(op_time, tensor_op, layer_ind)
final_time += baseline_time
final_energy += baseline_energy
final_tensor_op = baseline_op
@@ -454,7 +462,7 @@ class Driver:
final_energy += op_energy
final_conf_layer.append((None, None, final_tensor_op)) # Don't care about the times and energies when writing
final_conf.append((hardware, final_conf_layer))
- print("\n")
+ #print("\n")
return final_time, final_energy, (curr_conf[0], final_conf)
conf_index = 0
--
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