From 63ab05818f9415f2ed29faf72e91df73b64c0cc6 Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@tyler.cs.illinois.edu>
Date: Sun, 14 Jul 2019 00:51:42 -0500
Subject: [PATCH] Handling BatchNorm in PromiseBackend
---
.../keras/frontend/approxhpvm_translator.py | 30 +++-------
.../keras/frontend/promise_translator.py | 59 ++++++++++++++++++-
2 files changed, 66 insertions(+), 23 deletions(-)
diff --git a/llvm/projects/keras/frontend/approxhpvm_translator.py b/llvm/projects/keras/frontend/approxhpvm_translator.py
index 603431cdaa..87f53ca1be 100644
--- a/llvm/projects/keras/frontend/approxhpvm_translator.py
+++ b/llvm/projects/keras/frontend/approxhpvm_translator.py
@@ -354,23 +354,6 @@ class TensorRtTranslator:
return False
- #def genActivationCall(self, input_var, output_var, activation_type):
-
- # func_name = ""
- # if activation_type == "tanh":
- # func_name = "Tanh"
-
- # if activation_type == "relu":
- # func_name = "Relu"
-
- # if activation_type == "softmax":
- # func_name = "Softmax"
-
- # inst_str = "void* " + output_var + " = "
- # inst_str += "tensor" + func_name + "(" + input_var + "); \n"
- # print ("***** inst_str = ", inst_str, "\n")
-
- # return inst_str
@@ -460,7 +443,14 @@ class TensorRtTranslator:
input_var_name = self.getSingleInputName(cur_node)
inst_str = "void* " + out_var_name1 + " = "
- inst_str += "tensorBatchNormalization(" + input_var_name + "); \n"
+ inst_str += "tensorBatchNorm(" + input_var_name + ", "
+ inst_str += cur_node.layer_name + "_gamma, "
+ inst_str += cur_node.layer_name + "_beta, "
+ inst_str += cur_node.layer_name + "_mean, "
+ inst_str += cur_node.layer_name + "_variance, "
+ inst_str += str(cur_node.epsilon)
+ inst_str += "); \n"
+
self.program_str += inst_str
@@ -546,7 +536,6 @@ class TensorRtTranslator:
H = weights.shape[1]
W = weights.shape[0]
- #unique_file_name = "conv" + str(layer_count) + ".bin"
unique_file_name = w_name + ".bin"
dumpConvWeights(prefix + unique_file_name, weights, N, C, H, W)
@@ -574,7 +563,6 @@ class TensorRtTranslator:
self.filter_names[b_name] = 1
print (bias_weights.shape, b_name)
- #unique_file_name = "conv_bias" + str(layer_count) + ".bin"
unique_file_name = b_name + ".bin"
dumpFcBias(prefix + unique_file_name, bias_weights, bias_weights.shape[0])
@@ -599,7 +587,6 @@ class TensorRtTranslator:
H = weights.shape[0]
W = weights.shape[1]
- #unique_file_name = "fc" + str(layer_count) + ".bin"
unique_file_name = w_name + ".bin"
dumpFcWeights(prefix + unique_file_name, weights, H, W)
@@ -760,7 +747,6 @@ class TensorRtTranslator:
self.input_str += file_path + ".c_str(), 0," + str(N) + "," + str(C) + ","
self.input_str += str(H) + "," + str(W) + "); \n"
- #self.weight_str += self.input_str
# Adding input to the filter map
self.filter_names["input"] = 1
diff --git a/llvm/projects/keras/frontend/promise_translator.py b/llvm/projects/keras/frontend/promise_translator.py
index f5a269f1af..670d6918f9 100644
--- a/llvm/projects/keras/frontend/promise_translator.py
+++ b/llvm/projects/keras/frontend/promise_translator.py
@@ -102,6 +102,12 @@ class State:
return True
return False
+
+ def isBatchNorm(self):
+ if "batchnorm" in self.op_string:
+ return True
+ return False
+
def isPool(self):
if "pool" in self.op_string and self.num_ops == 1:
@@ -627,6 +633,31 @@ class PromiseRtTranslator:
state.clear()
+
+ def genBatchNormLayer(self, state):
+
+ first_op = state.getFirstOp()
+ last_op = state.getFirstOp()
+
+ input_var = self.getSingleInputName(first_op)
+ output_var = self.getVariableName(last_op)
+
+ promise_layer_str = "void* " + output_var + " = "
+ promise_layer_str += "tensorBatchNorm(" + input_var + ", "
+ promise_layer_str += first_op.layer_name + "_gamma, "
+ promise_layer_str += first_op.layer_name + "_beta, "
+ promise_layer_str += first_op.layer_name + "_mean, "
+ promise_layer_str += first_op.layer_name + "_variance, "
+ promise_layer_str += str(first_op.epsilon)
+ promise_layer_str += "); \n"
+
+ self.program_str += promise_layer_str
+
+ self.appendLayerString("BatchNorm", state)
+
+ state.clear()
+
+
def genSoftmaxLayer(self, state):
@@ -744,7 +775,10 @@ class PromiseRtTranslator:
elif state.isDepthwiseConv():
self.genDepthwiseConvLayer(state)
-
+
+ elif state.isBatchNorm():
+ self.genBatchNormLayer(state)
+
elif state.isPool():
self.genPoolLayer(state)
@@ -812,6 +846,26 @@ class PromiseRtTranslator:
self.traverseSuccessors(cur_node, state)
+
+
+ def handle_batchnorm(self, cur_node, state):
+ if not self.shouldVisit(cur_node):
+ return
+
+ layer_name = cur_node.layer_name
+ print ("handle_batchnorm", layer_name)
+ self.visited_nodes[layer_name] = True
+
+ self.genPreviousLayer(state)
+
+ state.add(cur_node, "batchnorm")
+
+ self.genBatchNormLayer(state)
+
+ self.traverseSuccessors(cur_node, state)
+
+
+
def handle_add(self, cur_node, state):
@@ -907,6 +961,9 @@ class PromiseRtTranslator:
if layer_type == "DepthwiseConv2D":
self.handle_depthwise_conv(output_node, state)
+ if layer_type == "BatchNormalization":
+ self.handle_batchnorm(output_node, state)
+
if layer_type == "Dense":
self.handle_dense(output_node, state)
--
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