From f2fdb01d050650b4074ed2869e311ea8021ab9aa Mon Sep 17 00:00:00 2001
From: shingjan <yjshi03@gmail.com>
Date: Sun, 7 Jun 2020 00:55:21 -0500
Subject: [PATCH] add instruction for running onnx frontend
---
hpvm/projects/onnx/frontend/README.md | 10 ++
hpvm/projects/onnx/frontend/config.py | 2 +-
hpvm/projects/onnx/frontend/graph_codegen.py | 119 -------------------
hpvm/projects/onnx/frontend/hpvm_codegen.py | 10 +-
hpvm/projects/onnx/frontend/main.py | 2 +-
hpvm/projects/onnx/frontend/utils.py | 16 +--
6 files changed, 25 insertions(+), 134 deletions(-)
create mode 100644 hpvm/projects/onnx/frontend/README.md
diff --git a/hpvm/projects/onnx/frontend/README.md b/hpvm/projects/onnx/frontend/README.md
new file mode 100644
index 0000000000..69ffe81f5a
--- /dev/null
+++ b/hpvm/projects/onnx/frontend/README.md
@@ -0,0 +1,10 @@
+### How to Run
+```
+python main.py
+```
+Set all your config, e.g. onnx model location, input size and emit directory for generated source code, in **config.py**.
+
+### Resources
+1. [ONNX overview](https://github.com/onnx/onnx/blob/master/docs/IR.md)
+2. [ONNX operator specs](https://github.com/onnx/onnx/blob/master/docs/Operators.md)
+3. [Conversion between models - available adapters](https://github.com/onnx/onnx/blob/master/onnx/version_converter.py#L21)
\ No newline at end of file
diff --git a/hpvm/projects/onnx/frontend/config.py b/hpvm/projects/onnx/frontend/config.py
index c8053b8d98..55ae0cebf4 100644
--- a/hpvm/projects/onnx/frontend/config.py
+++ b/hpvm/projects/onnx/frontend/config.py
@@ -1,6 +1,6 @@
model_name = "alexnet"
input_size = [1,2,3,4]
onnx_file_dir = "../models/keras/alexnet.onnx"
-opset_version_default = 11
+opset_version_default = 10
src_emit_dir = "./test_src"
diff --git a/hpvm/projects/onnx/frontend/graph_codegen.py b/hpvm/projects/onnx/frontend/graph_codegen.py
index f47abfefab..df02685386 100644
--- a/hpvm/projects/onnx/frontend/graph_codegen.py
+++ b/hpvm/projects/onnx/frontend/graph_codegen.py
@@ -25,118 +25,9 @@ class GraphCodeGen(object):
self.var_cnt = self.var_cnt + 1
return "var_" + str(self.var_cnt)
- def emit_node_call(self, cur_node):
- inst_str = ""
- # check if all inputs of this node is mapped
- for i in cur_node.input:
- self.tensors[i].get_mapped_name()
- # set var name for output node
- if len(cur_node.output) > 1:
- raise ValueError("Output number for a single layer larger than 1!")
- if cur_node.op_type in skip_layer:
- mapped_output_name = self.get_last_var()
- else:
- mapped_output_name = self.get_new_var()
- output_name = cur_node.output[0]
- self.tensors[output_name].set_mapped_name(mapped_output_name)
- #print(cur_node)
- if cur_node.op_type == "Conv":# or cur_node.op_type == "DepthwiseConv":
- input_var_name = self.tensors[cur_node.input[0]].get_mapped_name()
- weight = cur_node.input[1]
- strides = list()
- padding = 0
- for attr in cur_node.attribute:
- if attr.name == "pads":
- padding = attr.ints[0]
- elif attr.name == "strides":
- for stride in attr.ints:
- strides.append(stride)
-
- inst_str += "void* " + mapped_output_name + " = "
- inst_str += "tensorConvolution(" + input_var_name + ", "
- inst_str += self.tensors[cur_node.input[1]].get_mapped_name() + ", "
- inst_str += str(padding) + ", "
- inst_str += str(padding) + ", "
- inst_str += str(strides[0]) + ", "
- inst_str += str(strides[1]) + ", "
- inst_str += "1, 1); \n"
-
- # check if has bias add (Optional)
- # in ONNX it is only in Conv
- # in Keras bias add could exist in Dense
- if len(cur_node.input) == 3:
- mapped_output_name2 = self.get_new_var()
- inst_str += "void* " + mapped_output_name2 + " = "
- inst_str += "tensorAdd(" + mapped_output_name + ", "
- inst_str += self.tensors[cur_node.input[2]].get_mapped_name() + ""
- inst_str += "); \n"
- self.tensors[output_name].set_mapped_name(mapped_output_name2)
- elif cur_node.op_type == "MaxPool" or cur_node.op_type == "AveragePool":
- input_var_name = self.tensors[cur_node.input[0]].get_mapped_name()
- strides = list()
- pool_size = list()
- for attr in cur_node.attribute:
- if attr.name == "kernel_shape":
- for pool in attr.ints:
- pool_size.append(pool)
- elif attr.name == "strides":
- for stride in attr.ints:
- strides.append(stride)
- # FIXME: Non-same padding is *NOT* currently supported
- padding = 0
- pool_type = 0
- if cur_node.op_type == "MaxPool":
- pool_type = "0"
- elif cur_node.op_type == "AveragePool":
- pool_type = "1"
- # tensorPooling(input, pool_type, pool_h, pool_w, v_pad, h_pad, v_stride, h_stride)
- inst_str += "void* " + mapped_output_name + " = "
- inst_str += "tensorPooling(" + input_var_name + "," + pool_type + "," + str(pool_size[0]) + "," + str(pool_size[1])
- inst_str += "," + str(padding) + "," + str(padding) + "," + str(strides[0]) + "," + str(strides[1])
- inst_str += "); \n"
- # self.program_str += inst_str
- elif cur_node.op_type == "MatMul":
- left_input = self.tensors[cur_node.input[0]].get_mapped_name()
- right_input = self.tensors[cur_node.input[1]].get_mapped_name()
- inst_str += "void* " + mapped_output_name + " = "
- inst_str += "tensorGemmGPU(" + left_input + ", " + right_input + "); \n"
- elif cur_node.op_type == "Add":
- left_input = self.tensors[cur_node.input[0]].get_mapped_name()
- right_input = self.tensors[cur_node.input[1]].get_mapped_name()
- inst_str += "void* " + mapped_output_name + " = "
- inst_str += "tensorAdd(" + left_input + ", " + right_input + "); \n"
- elif cur_node.op_type == "Softmax":
- mapped_input_name = self.tensors[cur_node.input[0]].get_mapped_name()
- inst_str += "void* " + mapped_output_name + " = "
- inst_str += "tensorSoftmax(" + mapped_input_name + "); \n"
- elif cur_node.op_type == "Relu":
- mapped_input_name = self.tensors[cur_node.input[0]].get_mapped_name()
- inst_str += "void* " + mapped_output_name + " = "
- inst_str += "tensorRelu(" + mapped_input_name + "); \n"
- elif cur_node.op_type == "BatchNormalization":
- mapped_input_name = self.tensors[cur_node.input[0]].get_mapped_name()
- epsilon = ""
- for attr in cur_node.attribute:
- if attr.name == "epsilon":
- epsilon = str(attr.f)
- inst_str += "void* " + mapped_output_name + " = "
- inst_str += "tensorBatchNorm(" + mapped_input_name + ", "
- inst_str += self.tensors[cur_node.input[1]].get_mapped_name() + ", "
- inst_str += self.tensors[cur_node.input[2]].get_mapped_name() + ", "
- inst_str += self.tensors[cur_node.input[3]].get_mapped_name() + ", "
- inst_str += self.tensors[cur_node.input[4]].get_mapped_name() + ", "
- inst_str += str(epsilon)
- inst_str += "); \n"
- elif cur_node.op_type in skip_layer:
- pass
- else:
- raise ValueError("Not supported op type:" + cur_node.op_type + "! \n")
- return inst_str
-
################################################
# CodeGen functions
################################################
-
def emit_weights(self):
weights_str = ""
weights_str += "std::string dir_prefix = std::string(\"" + str(self.weights_dir) + "\");\n"
@@ -178,10 +69,6 @@ class GraphCodeGen(object):
self.tensors[cur_node.output[0]].set_mapped_name(mapped_output_name)
self.program_str += node.codegen(self.tensors)
- def emit_graph2(self):
- for node in self.graph.node:
- self.program_str += self.emit_node_call(node)
-
def emit_header(self):
headers = "\n#include <stdio.h> \n"
headers += "#include <stdlib.h> \n"
@@ -201,10 +88,6 @@ class GraphCodeGen(object):
self.program_str += initialization
def emit_footer(self, test_data=None):
- #if test_data is not None and self.dfg.last_node is not None:
- #last_node = self.dfg.last_node
- #output_var = self.output_map[last_node.layer_name]
-
destructors = "\nllvm_hpvm_cleanupTensorRt(); \n"
end_main = "\nreturn 0; \n\n}\n"
self.program_str += destructors
@@ -237,12 +120,10 @@ class GraphCodeGen(object):
def emit_batch_loop_end(self):
end_loop_str = ""
end_loop_str += "\nuint32_t* labels = readLabelsBatch3(labels_path.c_str(),start,end); \n"
- #last_node = self.dfg.last_node
mapped_output_var = self.tensors[self.graph.output[0].name].get_mapped_name()
accuracy_call = "\nfloat accuracy = computeAccuracy3(labels, " + \
mapped_output_var + "); \n"
end_loop_str += accuracy_call
- #end_loop_str += "float accuracy = computeAccuracy2(labels, batch_size, var_60); "
end_loop_str += "final_accuracy += accuracy; \n"
end_loop_str += "freeBatchMemory(); \n "
end_loop_str += "\n}\n\n"
diff --git a/hpvm/projects/onnx/frontend/hpvm_codegen.py b/hpvm/projects/onnx/frontend/hpvm_codegen.py
index 48cfaec36b..2446e0747f 100644
--- a/hpvm/projects/onnx/frontend/hpvm_codegen.py
+++ b/hpvm/projects/onnx/frontend/hpvm_codegen.py
@@ -1,18 +1,16 @@
from tensor import WeightTensor
-
-skip_layer = ["Identity", "Flatten", "Pad"]
+from utils import skip_layer
class HpvmCodeGen:
- def __init__(self, DFG, weights_dir, test_data=None, test_labels=None):
+ def __init__(self, DFG, weights_dir, test_data_shape=None):
self.program_str = ""
self.graph = DFG.graph
self.tensors = DFG.tensors
self.nodes = DFG.nodes
self.var_cnt = 0
self.weights_dir = weights_dir
- self.test_data = test_data
- self.test_labels = test_labels
- self.filter_names = dict() # essentially tensors
+ self.test_data_shape = test_data_shape
+ self.filter_names = dict() # essentially weight tensors
for tensor in self.tensors.values():
if isinstance(tensor, WeightTensor):
self.filter_names[tensor.get_mapped_name()] = 1
diff --git a/hpvm/projects/onnx/frontend/main.py b/hpvm/projects/onnx/frontend/main.py
index 6014569acd..b1dba8d21f 100644
--- a/hpvm/projects/onnx/frontend/main.py
+++ b/hpvm/projects/onnx/frontend/main.py
@@ -6,7 +6,7 @@ import glob
def check_version(model, new_version):
try:
- opset = model.opset_import[0].version + 1 if model.opset_import else 1
+ opset = model.opset_import[0].version if model.opset_import else 1
except AttributeError:
opset = 1 # default opset version set to 1 if not specified
print("opset version: ", opset)
diff --git a/hpvm/projects/onnx/frontend/utils.py b/hpvm/projects/onnx/frontend/utils.py
index fa0d9de28f..2a43557958 100644
--- a/hpvm/projects/onnx/frontend/utils.py
+++ b/hpvm/projects/onnx/frontend/utils.py
@@ -90,18 +90,20 @@ def dumpConvWeights(file_name, weights):
print ("*DumpConvWeights")
print("-min_val = ", np.amin(weights))
print("-max_val = ", np.amax(weights))
-
- N = weights.shape[0]
- C = weights.shape[1]
- H = weights.shape[2]
- W = weights.shape[3]
-
+ weights_keras = np.einsum('NCWH->WHCN', weights)
+ print ("Convert shape for conv weights: " + str(weights_keras.shape))
+ N = weights_keras.shape[3]
+ C = weights_keras.shape[2]
+ H = weights_keras.shape[1]
+ W = weights_keras.shape[0]
f = open(file_name, "wb")
for i in range(N):
for j in range(C):
for k in range(H):
for l in range(W):
- f.write(weights[i][j][k][l])
+ # FIXME: Legacy code from Keras frontend
+ # should actually interchange k with l
+ f.write(weights_keras[k][l][j][i])
f.close()
--
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