From 392651c5ab6aeb20bb1b12926c31e397e3a93da0 Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@tyler.cs.illinois.edu>
Date: Tue, 17 Sep 2019 01:39:19 -0500
Subject: [PATCH] Adding better diagnostic messages and knob for smart Quant
---
.../keras/frontend/approxhpvm_translator.py | 33 +++++--
.../projects/keras/frontend/quantize_utils.py | 99 +++++++++++++++++++
2 files changed, 122 insertions(+), 10 deletions(-)
create mode 100644 llvm/projects/keras/frontend/quantize_utils.py
diff --git a/llvm/projects/keras/frontend/approxhpvm_translator.py b/llvm/projects/keras/frontend/approxhpvm_translator.py
index 87f53ca1be..21e109d9ed 100644
--- a/llvm/projects/keras/frontend/approxhpvm_translator.py
+++ b/llvm/projects/keras/frontend/approxhpvm_translator.py
@@ -172,7 +172,7 @@ class DFGNode:
print("\t strides = ", self.strides)
- if nodeHasActivation(self):
+ if layerHasActivationAttr(self):
self.activation_type = layer.activation.__name__
print ("\t Activation = ", self.activation_type)
@@ -397,7 +397,14 @@ class TensorRtTranslator:
self.program_str += inst_str
- if layer_type == "Dense":
+ if strides[0] > 1 and cur_node.padding.strip() == "same":
+ print ("!ERROR: Same Padding not supported for Conv with Stride > 1")
+ print ("Use: ZeroPadding2D(padding=(" + str(padding) + "," + str(padding) + "));\n");
+ sys.exit(0)
+
+
+
+ elif layer_type == "Dense":
input_var_name = self.getSingleInputName(cur_node)
weights = cur_node.weights
@@ -409,7 +416,7 @@ class TensorRtTranslator:
self.program_str += inst_str
- if self.hasBiasAdd(cur_node):
+ elif self.hasBiasAdd(cur_node):
out_var_name2 = self.getVariableName(cur_node)
inst_str = "void* " + out_var_name2 + " = "
@@ -423,23 +430,26 @@ class TensorRtTranslator:
out_var_name1 = out_var_name2
- if layer_type == "Activation":
+ elif layer_type == "Activation":
input_var_name = self.getSingleInputName(cur_node)
inst_str = genActivationCallStr(input_var_name, out_var_name1, cur_node.activation_type)
self.program_str += inst_str
- if self.hasActivation(cur_node) and layer_type != "Activation":
+ elif self.hasActivation(cur_node) and layer_type != "Activation":
activation_type = cur_node.activation_type
out_var_name3 = self.getVariableName(cur_node)
inst_str = genActivationCallStr(out_var_name1, out_var_name3, activation_type)
self.program_str += inst_str
+ if activation_type == "softmax":
+ print ("Softmax canNOT be part of Dense/Conv Op. Insert: Activation('softmax');")
+ sys.exit(0)
- if layer_type == "BatchNormalization":
+ elif layer_type == "BatchNormalization":
input_var_name = self.getSingleInputName(cur_node)
inst_str = "void* " + out_var_name1 + " = "
@@ -454,7 +464,7 @@ class TensorRtTranslator:
self.program_str += inst_str
- if layer_type == "Add":
+ elif layer_type == "Add":
input_vars = self.getMultipleInputNames(cur_node)
inst_str = "void* " + out_var_name1 + " = "
@@ -462,7 +472,7 @@ class TensorRtTranslator:
self.program_str += inst_str
- if layer_type == "MaxPooling2D" or layer_type == "AveragePooling2D":
+ elif layer_type == "MaxPooling2D" or layer_type == "AveragePooling2D":
input_var_name = self.getSingleInputName(cur_node)
pool_size = cur_node.pool_size
@@ -481,8 +491,12 @@ class TensorRtTranslator:
inst_str += "," + str(padding) + "," + str(padding) + "," + str(strides[0]) + "," + str(strides[1])
inst_str += "); \n"
self.program_str += inst_str
+ #else:
+ # print ("ERROR: Operator = ", layer_type, " is NOT currently supported")
-
+
+
+
@@ -622,7 +636,6 @@ class TensorRtTranslator:
if layer_type == "BatchNormalization":
weights = layer.get_weights()
- print ("\n\n ************ len(weights = ", len(weights), "\n\n")
gamma_w = weights[0]
gamma_id = layer_name + "_gamma"
diff --git a/llvm/projects/keras/frontend/quantize_utils.py b/llvm/projects/keras/frontend/quantize_utils.py
new file mode 100644
index 0000000000..998bd31ace
--- /dev/null
+++ b/llvm/projects/keras/frontend/quantize_utils.py
@@ -0,0 +1,99 @@
+
+
+from scipy import stats
+from numpy import linalg
+import numpy as np
+import sys
+
+
+# NOTE: enable/disable smart quantization of weights and activations
+smart_quantization = False
+
+
+def quantize_arr(input_arr, min_val, max_val):
+
+ quantize_range = 256.0
+ input_range = max_val - min_val
+ mul_factor = input_range / quantize_range
+
+ v1 = np.subtract(input_arr, min_val)
+ v2 = np.divide(v1, mul_factor)
+ v3 = v2.astype(int)
+ v4 = np.multiply(v3, mul_factor)
+ v5 = np.add(v4, min_val)
+ v6 = np.clip(v5, min_val, max_val)
+
+ return v6
+
+
+def compute_norm(a1, a2):
+
+ norm_inp = np.subtract(a1, a2)
+ #norm = linalg.norm(norm_inp, ord = 1)
+ norm = np.sum(np.abs(norm_inp))
+ print ("*** norm = ", norm)
+
+ return norm
+
+
+def get_best_quant_range(input_arr):
+
+ # For disabled smart quantization, skip expensive quant range computation
+ if smart_quantization == False:
+ min_val = np.percentile(input_arr, 0.1)
+ max_val = np.percentile(input_arr, 99.9)
+ return (min_val, max_val)
+
+
+ # Trying different threshold values for INT8 quantization
+ min_percentiles = [0, 0.1, 0.2, 0.3]
+ max_percentiles = [100, 99.9, 99.8, 99.7]
+
+
+ min_norm = 100000000
+ min_pair = (0, 100)
+ range_vals = (0, 0)
+ for i in min_percentiles:
+ for j in max_percentiles:
+ print (" i = ", i, " j = ", j, " \n")
+ min_val = np.percentile(input_arr, i)
+ max_val = np.percentile(input_arr, j)
+
+ res = quantize_arr(input_arr, min_val, max_val)
+ norm = compute_norm(res, input_arr)
+
+ if norm < min_norm:
+ min_norm = norm
+ min_pair = (i, j)
+ range_vals = (min_val, max_val)
+
+ print ("--- min_norm = ", min_norm, " , min_pair = ", min_pair , " range_vals = ", range_vals)
+
+
+ return range_vals
+
+
+
+
+
+
+if __name__ == "__main__":
+
+
+ vals = np.zeros((2,3))
+ vals[0][0] = 1.2
+ vals[0][1] = 0.48
+ vals[0][2] = 0.5
+ vals[1][0] = -0.3
+ vals[1][1] = 0.25
+ vals[1][2] = 0.46
+
+ input_arr = np.array(vals)
+
+ res = quantize_arr(input_arr, -0.3, 1.4)
+
+ print (res, "\n")
+
+ divergence = compute_norm(res, input_arr)
+
+ print ("divergence = ", divergence, "\n")
--
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