From f1096cae916b5d09f2f0faf93eafa79e9eaff3f3 Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@tyler.cs.illinois.edu>
Date: Fri, 12 Jul 2019 01:37:14 -0500
Subject: [PATCH] DepthwiseConv2D working with HPVM frontend translator
---
.../keras/frontend/approxhpvm_translator.py | 33 +++++-----
.../keras/frontend/hpvm_dfg_translator.py | 64 ++++++++++++++++---
2 files changed, 71 insertions(+), 26 deletions(-)
diff --git a/llvm/projects/keras/frontend/approxhpvm_translator.py b/llvm/projects/keras/frontend/approxhpvm_translator.py
index 13f6187620..d58a61921e 100644
--- a/llvm/projects/keras/frontend/approxhpvm_translator.py
+++ b/llvm/projects/keras/frontend/approxhpvm_translator.py
@@ -336,24 +336,23 @@ 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"
+ #def genActivationCall(self, input_var, output_var, activation_type):
+
+ # func_name = ""
+ # if activation_type == "tanh":
+ # func_name = "Tanh"
- if activation_type == "softmax":
- func_name = "Softmax"
+ # if activation_type == "relu":
+ # func_name = "Relu"
- inst_str = "void* " + output_var + " = "
- inst_str += "tensor" + func_name + "(" + input_var + "); \n"
+ # if activation_type == "softmax":
+ # func_name = "Softmax"
- print ("***** inst_str = ", inst_str, "\n")
+ # inst_str = "void* " + output_var + " = "
+ # inst_str += "tensor" + func_name + "(" + input_var + "); \n"
+ # print ("***** inst_str = ", inst_str, "\n")
- return inst_str
+ # return inst_str
@@ -425,8 +424,8 @@ class TensorRtTranslator:
if layer_type == "Activation":
input_var_name = self.getSingleInputName(cur_node)
-
- inst_str = self.genActivationCall(input_var_name, out_var_name1, cur_node.activation_type)
+
+ inst_str = genActivationCallStr(input_var_name, out_var_name1, cur_node.activation_type)
self.program_str += inst_str
@@ -434,7 +433,7 @@ class TensorRtTranslator:
activation_type = cur_node.activation_type
out_var_name3 = self.getVariableName(cur_node)
- inst_str = self.genActivationCall(out_var_name1, out_var_name3, activation_type)
+ inst_str = genActivationCallStr(out_var_name1, out_var_name3, activation_type)
self.program_str += inst_str
diff --git a/llvm/projects/keras/frontend/hpvm_dfg_translator.py b/llvm/projects/keras/frontend/hpvm_dfg_translator.py
index 8f33a4c5e0..3ddb675c7b 100644
--- a/llvm/projects/keras/frontend/hpvm_dfg_translator.py
+++ b/llvm/projects/keras/frontend/hpvm_dfg_translator.py
@@ -1,5 +1,6 @@
import sys
+from frontend.utils import *
class HPVMTranslator:
@@ -285,6 +286,50 @@ class HPVMTranslator:
self.root_str += self.genHpvmNodeEdges(out_var_name, input_var_name, weight_name)
+ def genDepthwiseConvNode(self, cur_node):
+ #input_var_name = self.getSingleInputName(cur_node)
+ out_var_name = self.getVariableName(cur_node)
+
+ header_str = self.genNodeHeader(out_var_name, 2)
+ inst_str = header_str
+
+ weights = cur_node.weights
+ strides = cur_node.strides
+
+ padding = 0
+ if cur_node.padding.strip() == "valid":
+ padding = 0
+ else:
+ padding = cur_node.padding
+ padding = int((weights.shape[0] - 1) / 2)
+
+ prev_padding = self.getPrevLayerPadding(cur_node)
+ if prev_padding != None:
+ # FIXME: currently only supporting symmetric padding
+ padding = prev_padding[0][0]
+
+ inst_str += " void *r = __visc__tensor_group_convolution(t1, t2, "
+ inst_str += str(padding) + ", "
+ inst_str += str(padding) + ", "
+ inst_str += str(strides[0]) + ", "
+ inst_str += str(strides[1]) + ", "
+ inst_str += "1, "
+
+ C = weights.shape[2]
+ inst_str += str(C) + "); \n"
+
+ footer_str = self.genNodeFooter(2)
+ inst_str += footer_str
+
+ self.node_str += inst_str
+
+ input_var_name = self.getSingleInputName(cur_node)
+ weight_name = cur_node.layer_name + "_w"
+
+ self.root_str += self.genHpvmNodeEdges(out_var_name, input_var_name, weight_name)
+
+
+
def genBiasNode(self, cur_node):
input_var_name = self.output_map[cur_node.layer_name]
@@ -384,13 +429,18 @@ class HPVMTranslator:
if layer_type == "Conv2D":
self.genConvNode(cur_node)
+ if layer_type == "DepthwiseConv2D":
+ self.genDepthwiseConvNode(cur_node)
+
if layer_type == "Dense":
self.genDenseNode(cur_node)
-
- if self.hasBiasAdd(cur_node):
+
+ if nodeHasBias(cur_node):
+ #if self.hasBiasAdd(cur_node):
self.genBiasNode(cur_node)
-
- if self.hasActivation(cur_node):
+
+ if nodeHasActivation(cur_node) and layer_type != "Activation":
+ #if self.hasActivation(cur_node):
self.genSubActivationNode(cur_node)
if layer_type == "Activation":
@@ -536,11 +586,7 @@ class HPVMTranslator:
def generateSourceProgram(self, dir_prefix):
- #print (self.node_str)
- #print (self.root_str)
- #print (self.root_struct_str)
- #print (self.main_func_str)
-
+
program_str = self.file_header_str + self.node_str + self.root_str
program_str += self.root_struct_str + self.main_func_str
--
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