forked from tinygrad/tinygrad
-
Notifications
You must be signed in to change notification settings - Fork 3
/
onnx.py
165 lines (155 loc) · 8.11 KB
/
onnx.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
import os
import numpy as np
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
from tinygrad.tensor import Tensor
from tinygrad.helpers import prod
from tinygrad.nn import batch_normalize
MAX_CONVS = int(os.getenv("MAX_CONVS", -1))
def get_run_onnx(onnx_model):
def shape_to_tuple(s): return tuple(x.dim_value for x in s.dim)
def buffer_parse(inp):
if inp.data_type in (1,10,7):
ret = Tensor(np.frombuffer(inp.raw_data, dtype=TENSOR_TYPE_TO_NP_TYPE[inp.data_type]).reshape(inp.dims).astype(np.float32).copy(), requires_grad=False)
else:
raise Exception(f"bad data type {inp.name} {inp.dims} {inp.data_type}")
return ret
def attribute_parse(a):
if a.type == 7: return tuple([int(x) for x in a.ints])
elif a.type == 4: return buffer_parse(a.t) # TENSOR
elif a.type == 2: return int(a.i)
elif a.type == 1: return float(a.f)
else: raise Exception(f"can't parse {a.type} {a}")
def attribute_to_dict(a): return {x.name:attribute_parse(x) for x in a}
tensors = {}
# get weights and biases
for inp in onnx_model.graph.initializer:
if len(inp.raw_data) > 0:
tensors[inp.name] = buffer_parse(inp)
elif len(inp.float_data) > 0:
tensors[inp.name] = Tensor(np.array(inp.float_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
elif len(inp.int64_data) > 0:
tensors[inp.name] = Tensor(np.array(inp.int64_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
else:
print(inp.name, inp.dims, inp.data_type, len(inp.raw_data))
print(inp)
raise Exception("no data")
tensors[inp.name].realize()
def run_onnx(inputs={}, debug=False):
input_tensors = {}
intermediate_tensors = {}
# get inputs
for inp in onnx_model.graph.input:
if inp.name in tensors: continue
shape = shape_to_tuple(inp.type.tensor_type.shape)
if shape[0] == 0: shape = tuple([1]+list(shape[1:])) # 1 batch size
if inp.name in inputs:
input_shape = inputs[inp.name].shape
assert input_shape == shape, f"wrong shape for input {inp.name}, {input_shape} isn't {shape}"
if isinstance(inputs[inp.name], Tensor):
input_tensors[inp.name] = inputs[inp.name]
else:
input_tensors[inp.name] = Tensor(inputs[inp.name], requires_grad=False)
for _,v in input_tensors.items(): v.realize()
else:
raise Exception(f"no data for {inp.name} with shape {shape}")
conv_count = 0
for num,n in enumerate(onnx_model.graph.node):
if debug: print(f"{num}: op {n.op_type}")
inp = [tensors[x] if x in tensors else (intermediate_tensors[x] if x in intermediate_tensors else input_tensors[x]) for x in n.input]
opt = attribute_to_dict(n.attribute)
# free ones
if n.op_type == "Relu": ret = inp[0].relu()
elif n.op_type == "Sigmoid": ret = inp[0].sigmoid()
elif n.op_type == "Tanh": ret = inp[0].tanh()
elif n.op_type == "Softmax": ret = inp[0].softmax()
elif n.op_type == "MatMul": ret = inp[0].matmul(inp[1])
# one liners
elif n.op_type == "Elu": ret = inp[0].elu(alpha=opt['alpha'])
elif n.op_type == "Clip": ret = inp[0].clip(*(inp[1:] if len(inp) > 1 else (opt.get('min', -3.4e38), opt.get('max', 3.4e38))))
elif n.op_type == "Concat": ret = inp[0].cat(*inp[1:], dim=opt['axis'])
elif n.op_type == "Flatten": ret = inp[0].flatten(opt['axis'] if 'axis' in opt else 0)
elif n.op_type == "Transpose": ret = inp[0].permute(order=opt['perm'])
elif n.op_type == "Squeeze": ret = inp[0].reshape([s for i,s in enumerate(inp[0].shape) if i not in opt['axes']])
elif n.op_type == "Unsqueeze": ret = inp[0].reshape(np.insert(inp[0].shape, opt['axes'][0], 1).tolist())
elif n.op_type == "ReduceL2": ret = inp[0].pow(2).sum(axis=opt['axes'], keepdim=opt['keepdims']).sqrt()
elif n.op_type == "GlobalAveragePool": ret = inp[0].mean(axis=tuple(range(2, len(inp[0].shape))), keepdim=True)
elif n.op_type == "Shape": ret = inp[0].shape
elif n.op_type == "Expand": ret = inp[0].reshape([1]*(max(len(inp[0].shape), len(inp[1]))-len(inp[0].shape)) + list(inp[0].shape)) # just broadcast
elif n.op_type == "Div": ret = inp[0].div(inp[1])
elif n.op_type == "Constant": ret = opt['value']
elif n.op_type == "Reshape": ret = inp[0].reshape([int(x) for x in inp[1].numpy()])
elif n.op_type == "Gather":
# TODO: is this correct? seems to work for simple gather ops
axis = opt['axis']
shape = list(inp[0].shape)
indices = [shape[axis]+int(x) if x<0 else int(x) for x in inp[1].numpy()]
args = [[(0,x) if j != axis else (i,i+1) for j, x in enumerate(shape)] for i in indices]
ret = inp[0].slice(arg=args[0]).cat(*[inp[0].slice(arg=arg) for arg in args[1:]], dim=axis)
ret = ret.reshape([s for i,s in enumerate(shape) if i != axis]) if len(indices) == 1 else ret # squeeze if needed
elif n.op_type == "BatchNormalization":
invstd = inp[4].add(opt.get('epsilon', 1e-5))**-0.5
ret = batch_normalize(inp[0], inp[1], inp[2], inp[3], invstd)
elif n.op_type == "Gemm": ret = inp[0].linear(inp[1].transpose() if opt.get('transB', 0) == 1 else inp[1], inp[2])
elif n.op_type == "Conv":
x,w,b = inp if len(inp) == 3 else (inp[0], inp[1], None)
assert 'dilations' not in opt or opt['dilations'] == (1,1)
if opt['pads'][0] == opt['pads'][2] and opt['pads'][1] == opt['pads'][3]:
# symmetric padding
# TODO: is this backward?
ret = x.conv2d(w, b, stride=opt['strides'], groups=opt.get('group', 1), padding=opt['pads'][0:2])
else:
x = x.pad2d((opt['pads'][0], opt['pads'][2], opt['pads'][1], opt['pads'][3]))
ret = x.conv2d(w, b, stride=opt['strides'], groups=opt.get('group', 1))
conv_count += 1
if conv_count == MAX_CONVS:
ret.numpy()
break
elif n.op_type in ["Add", "Sub", "Mul"]:
# TODO: add this to tinygrad? i don't think it's in torch
if len(inp[0].shape) != len(inp[1].shape) and prod(inp[0].shape) == prod(inp[1].shape):
inp[1] = inp[1].reshape(inp[0].shape)
# TODO: is this right?
if 'broadcast' in opt: inp[1] = inp[1].reshape([-1 if i == opt['broadcast'] else 1 for i in range(len(inp[0].shape))])
if n.op_type == "Add": ret = inp[0] + inp[1]
if n.op_type == "Sub": ret = inp[0] - inp[1]
if n.op_type == "Mul": ret = inp[0] * inp[1]
elif n.op_type == "Split":
i = 0
arg = [(0,x) for x in inp[0].shape]
for o,s in zip(n.output, opt['split']):
arg[opt['axis']] = (i,i+s)
intermediate_tensors[o] = inp[0].slice(arg=arg)
i = i+s
continue
elif n.op_type == "AveragePool":
assert opt['kernel_shape'] == opt['strides'] or opt['strides'] == (1,1)
ret = inp[0].avg_pool2d(opt['kernel_shape'])
elif n.op_type == "MaxPool":
assert opt['kernel_shape'] == opt['strides']
#opt['kernel_shape'] = opt['strides']
# TODO: this is untested and probably wrong
ret = inp[0].pad2d(opt['pads'])
ret = ret.max_pool2d(opt['kernel_shape'])
# strides aren't supported in max_pool
#chan = ret.shape[1]
#w = Tensor.eye(chan).reshape((chan, chan, 1, 1))
#ret = ret.conv2d(w, stride=opt['strides'])
elif n.op_type == "Slice":
assert onnx_model.opset_import[0].version == 10
arg = [(0,x) for x in inp[0].shape]
starts, ends, axes = inp[1:4]
assert axes.shape == (1,)
axis, starts, ends = int(axes.numpy()[0]), int(starts.numpy()[0]), int(ends.numpy()[0])
ends = min(ends, inp[0].shape[axis])
starts = starts + inp[0].shape[axis] if starts < 0 else starts
arg[axis] = (starts, ends)
ret = inp[0].slice(arg=arg)
else:
print("UNSUPPORTED", n.op_type, n.input, n.output)
raise Exception(f"op_type {n.op_type} not supported")
assert len(n.output) == 1
if debug: print(ret.shape)
intermediate_tensors[n.output[0]] = ret
#print(ret.numpy().mean())
return {outp.name:intermediate_tensors[outp.name] for outp in onnx_model.graph.output}
return run_onnx