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[alt] Fix a broken import + modify multi_conv tests (#301)
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@zjgarvey
zjgarvey authored Jul 25, 2024
1 parent 3a2dff8 commit ebb3f5a
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307 changes: 219 additions & 88 deletions alt_e2eshark/onnx_tests/combinations/multi_conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,113 +5,221 @@
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
from onnx import TensorProto
import onnx
from onnx.helper import make_model, make_node, make_graph, make_tensor_value_info
from onnx.helper import (
make_model,
make_node,
make_graph,
make_tensor_value_info,
make_tensor,
)
from typing import Optional

from e2e_testing.framework import OnnxModelInfo
from e2e_testing.registry import register_test


class MultipleConvModel(OnnxModelInfo):
class MultipleConvBase(OnnxModelInfo):
def __init__(self, n, omp, cd, ov=None, use_bias=False, use_clips=False):
self.use_bias = use_bias
self.use_clips = use_clips
super().__init__(n, omp, cd, ov)

def construct_model(self):
AX0 = make_tensor_value_info("AX0", TensorProto.FLOAT, [1,3,513,513])
AK0 = make_tensor_value_info("AK0", TensorProto.FLOAT, [32, 3, 3, 3])
AK1 = make_tensor_value_info("AK1", TensorProto.FLOAT, [32, 1, 3, 3])
AK2 = make_tensor_value_info("AK2", TensorProto.FLOAT, [16,32,1,1])
X3 = make_tensor_value_info("X3", TensorProto.FLOAT, [1,16,257,257])
# float input tensor:
AX0 = make_tensor_value_info("AX0", TensorProto.FLOAT, [1, 3, 513, 513])
# quantized weight tensor inputs
BK0 = make_tensor_value_info("BK0", TensorProto.INT8, [32, 3, 3, 3])
BK1 = make_tensor_value_info("BK1", TensorProto.INT8, [32, 1, 3, 3])
BK2 = make_tensor_value_info("BK2", TensorProto.INT8, [16, 32, 1, 1])
# output tensor
X3 = make_tensor_value_info("X3", TensorProto.FLOAT, [1, 16, 257, 257])

# scale for K1
KS1T = make_tensor("KS1T", TensorProto.FLOAT, [], [5.000000e-01])
# ZP is a trivial zeropoint.
ZPT = make_tensor("ZPT", TensorProto.INT8, [], [0])
# quantized bias values and corresponding scales
if self.use_bias:
B0T = make_tensor(
"B0T",
TensorProto.INT8,
[32],
[
8,
-58,
-45,
82,
10,
-40,
52,
71,
45,
81,
2,
14,
96,
49,
-42,
-34,
116,
-32,
11,
85,
95,
-51,
-82,
38,
7,
60,
25,
9,
80,
59,
92,
94,
],
)
B1T = make_tensor(
"B1T",
TensorProto.INT8,
[32],
[
28,
-14,
-5,
54,
0,
8,
44,
36,
5,
65,
39,
-1,
51,
36,
-14,
4,
39,
-17,
32,
30,
43,
-8,
2,
9,
22,
20,
43,
84,
28,
58,
72,
51,
],
)
B2T = make_tensor(
"B2T",
TensorProto.INT8,
[16],
[
48,
123,
-30,
-68,
14,
102,
-20,
-18,
-58,
65,
-19,
-12,
-82,
-58,
-38,
-59,
],
)
BS2T = make_tensor("BS2T", TensorProto.FLOAT, [], [2.500000e-01])
BS0T = make_tensor("BS0T", TensorProto.FLOAT, [], [3.125000e-02])
BS1T = make_tensor(
"BS1T", TensorProto.FLOAT, [], [6.250000e-02]
) # also used to quantize/dequantize input between convs
if self.use_clips:
LT = make_tensor("LT", TensorProto.FLOAT, [], [0.0])
UT = make_tensor("UT", TensorProto.FLOAT, [], [6.0])

node_list = []
node_list.append(make_node(
op_type="DynamicQuantizeLinear",
inputs=["AX0"],
outputs=["BX0","SX0","ZX0"],
))
node_list.append(make_node(
op_type="DynamicQuantizeLinear",
inputs=["AK0"],
outputs=["BK0","SK0","ZK0"],
))
node_list.append(make_node(
op_type="DequantizeLinear",
inputs=["BX0","SX0","ZX0"],
outputs=["X0"],
))
node_list.append(make_node(
op_type="DequantizeLinear",
inputs=["BK0","SK0","ZK0"],
outputs=["K0"],
))
node_list.append(make_node(
app_node = lambda op_ty, inputs, outputs, **kwargs: node_list.append(
make_node(op_ty, inputs, outputs, **kwargs)
)
# Quantization Scheme Constants
app_node("Constant", [], ["ZP"], value=ZPT)
app_node("Constant", [], ["BS0"], value=BS0T)
app_node("Constant", [], ["KS1"], value=KS1T)
app_node("Constant", [], ["BS1"], value=BS1T)
if self.use_bias:
app_node("Constant", [], ["BS2"], value=BS2T)
# bias constants and conversions
app_node("Constant", [], ["AB0"], value=B0T)
app_node("Constant", [], ["AB1"], value=B1T)
app_node("Constant", [], ["AB2"], value=B2T)
app_node("DequantizeLinear", ["AB0", "BS0", "ZP"], ["B0"])
app_node("DequantizeLinear", ["AB1", "BS1", "ZP"], ["B1"])
app_node("DequantizeLinear", ["AB2", "BS2", "ZP"], ["B2"])
if self.use_clips:
app_node("Constant", [], ["L"], value=LT)
app_node("Constant", [], ["U"], value=UT)
app_node("QuantizeLinear", ["AX0", "BS1", "ZP"], ["BX0"])
app_node("DequantizeLinear", ["BX0", "BS1", "ZP"], ["X0"])
app_node("DequantizeLinear", ["BK0", "BS0", "ZP"], ["K0"])
app_node(
op_type="Conv",
inputs=["X0","K0"],
inputs=["X0", "K0", "B0"] if self.use_bias else ["X0", "K0"],
outputs=["AX1"],
group=1,
kernel_shape=[3,3],
pads=[1,1,1,1],
strides=[2,2],
))
node_list.append(make_node(
op_type="DynamicQuantizeLinear",
inputs=["AX1"],
outputs=["BX1","SX1","ZX1"],
))
node_list.append(make_node(
op_type="DynamicQuantizeLinear",
inputs=["AK1"],
outputs=["BK1","SK1","ZK1"],
))
node_list.append(make_node(
op_type="DequantizeLinear",
inputs=["BX1","SX1","ZX1"],
outputs=["X1"],
))
node_list.append(make_node(
op_type="DequantizeLinear",
inputs=["BK1","SK1","ZK1"],
outputs=["K1"],
))
node_list.append(make_node(
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[2, 2],
)
name1 = "AX1"
if self.use_clips:
name1 = "CX1"
app_node("Clip", ["AX1", "L", "U"], [name1])
app_node("QuantizeLinear", [name1, "BS1", "ZP"], ["BX1"])
app_node("DequantizeLinear", ["BX1", "BS1", "ZP"], ["X1"])
app_node("DequantizeLinear", ["BK1", "KS1", "ZP"], ["K1"])
app_node(
op_type="Conv",
inputs=["X1","K1"],
inputs=["X1", "K1", "B1"] if self.use_bias else ["X1", "K1"],
outputs=["AX2"],
group=32,
kernel_shape=[3,3],
pads=[1,1,1,1],
strides=[1,1],
))
node_list.append(make_node(
op_type="DynamicQuantizeLinear",
inputs=["AX2"],
outputs=["BX2","SX2","ZX2"],
))
node_list.append(make_node(
op_type="DynamicQuantizeLinear",
inputs=["AK2"],
outputs=["BK2","SK2","ZK2"],
))
node_list.append(make_node(
op_type="DequantizeLinear",
inputs=["BX2","SX2","ZX2"],
outputs=["X2"],
))
node_list.append(make_node(
op_type="DequantizeLinear",
inputs=["BK2","SK2","ZK2"],
outputs=["K2"],
))
node_list.append(make_node(
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[1, 1],
)
name2 = "AX2"
if self.use_clips:
name2 = "CX2"
app_node("Clip", ["AX2", "L", "U"], [name2])
app_node("QuantizeLinear", [name2, "BS1", "ZP"], ["BX2"])
app_node("DequantizeLinear", ["BX2", "BS1", "ZP"], ["X2"])
app_node("DequantizeLinear", ["BK2", "BS1", "ZP"], ["K2"])
app_node(
op_type="Conv",
inputs=["X2","K2"],
inputs=["X2", "K2", "B2"] if self.use_bias else ["X2", "K2"],
outputs=["X3"],
group=1,
kernel_shape=[1,1],
pads=[0,0,0,0],
strides=[1,1],
))
kernel_shape=[1, 1],
pads=[0, 0, 0, 0],
strides=[1, 1],
)

graph=make_graph(
graph = make_graph(
node_list,
"main",
[AX0, AK0, AK1, AK2],
[AX0, BK0, BK1, BK2],
[X3],
)

Expand All @@ -120,5 +228,28 @@ def construct_model(self):

onnx.save(onnx_model, self.model)


class MultipleConvModel(MultipleConvBase):
def __init__(self, n, omp, cd, ov=None):
super().__init__(n, omp, cd, ov, False, False)


class MultipleConvModelBias(MultipleConvBase):
def __init__(self, n, omp, cd, ov=None):
super().__init__(n, omp, cd, ov, True, False)


class MultipleConvModelBiasClips(MultipleConvBase):
def __init__(self, n, omp, cd, ov=None):
super().__init__(n, omp, cd, ov, True, True)


class MultipleConvModelClips(MultipleConvBase):
def __init__(self, n, omp, cd, ov=None):
super().__init__(n, omp, cd, ov, False, True)


register_test(MultipleConvModel, "multi_conv")

register_test(MultipleConvModelBias, "multi_conv_bias")
register_test(MultipleConvModelBiasClips, "multi_conv_bias_clips")
register_test(MultipleConvModelClips, "multi_conv_clips")
1 change: 0 additions & 1 deletion alt_e2eshark/onnx_tests/models/azure_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,6 @@
from e2e_testing import azutils
from e2e_testing.framework import OnnxModelInfo
from e2e_testing.registry import register_test
from .protected_list import protected_models

class AzureDownloadableModel(OnnxModelInfo):
def __init__(self, name: str, onnx_model_path: str, cache_dir: str):
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