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
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
|
# File 'lib/tensor_stream/evaluator/ruby/math_ops.rb', line 3
def MathOps.included(klass)
klass.class_eval do
register_op :tanh, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.tanh(t) }
end
register_op :tan, no_eval: true do |context, tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.tan(t) }
end
register_op :atan, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.atan(t) }
end
register_op :sin, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.sin(t) }
end
register_op :add, no_eval: true do |context, tensor, inputs|
a, b = inputs
call_vector_op(tensor, :add, a, b, context) { |t, u| t + u }
end
register_op :add_n, no_eval: true do |context, tensor, inputs|
if inputs.size == 1
complete_eval(inputs[0], context)
elsif inputs.size > 1
a = inputs.pop
until inputs.empty?
b = inputs.pop
a = call_vector_op(tensor, :add, a, b, context) { |t, u| t + u }
end
a
end
end
register_op :sub, no_eval: true do |context, tensor, inputs|
a, b = inputs
call_vector_op(tensor, :sub, a, b, context) { |t, u| t - u }
end
register_op %i[floor_mod mod], no_eval: true do |context, tensor, inputs|
a, b = inputs
call_vector_op(tensor, :mod, a, b, context) { |t, u| t % u }
end
register_op %i[floor_div], no_eval: true do |context, tensor, inputs|
a, b = inputs
if fp_type?(tensor.data_type)
call_vector_op(tensor, :div, a, b, context) { |t, u| (t / u).to_i.to_f }
else
call_vector_op(tensor, :div, a, b, context) { |t, u| t / u }
end
end
register_op :mul, no_eval: true do |context, tensor, inputs|
a, b = inputs
call_vector_op(tensor, :mul, a, b, context) { |t, u| t * u }
end
register_op :pow, no_eval: true do |context, tensor, inputs|
a, b = inputs
call_vector_op(tensor, :pow, a, b, context) { |t, u| t**u }
end
register_op :squared_difference, no_eval: true do |context, tensor, inputs|
a, b = inputs
call_vector_op(tensor, :squared_difference, a, b, context) { |t, u| (t - u) * (t - u) }
end
register_op :round, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| t.round }
end
register_op :abs, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| t.abs }
end
register_op :asin, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.asin(t) }
end
register_op :acos, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.acos(t) }
end
register_op :cos, no_eval: true do |context, tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.cos(t) }
end
register_op :log1p, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.log(1 + t) }
end
register_op :log, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| t < 0 ? Float::NAN : Math.log(t) }
end
register_op :exp, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.exp(t) }
end
register_op :sigmoid, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| sigmoid(t) }
end
register_op :sqrt, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| Math.sqrt(t) }
end
register_op :floor, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| t.floor }
end
register_op :ceil, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| t.ceil }
end
register_op :square, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| t * t }
end
register_op :reciprocal, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| 1 / t }
end
register_op %i[neg negate], no_eval: true do |context, tensor, inputs|
call_vector_op(tensor, :negate, inputs[0], nil, context) { |t, _u| -t }
end
register_op :tanh_grad, no_eval: true do |context, _tensor, inputs|
call_op(inputs[0], context) { |t, _b| 1 - Math.tanh(t) * Math.tanh(t) }
end
register_op(%i[argmax arg_max]) do |_context, tensor, inputs|
axis = inputs[1] || 0
rank = get_rank(inputs[0])
raise TensorStream::InvalidArgumentError, "Expected dimension in the range [#{-rank},#{rank}) but got #{axis}" if axis < -rank || axis >= rank
new_shape = shape_eval(inputs[0])
ns = new_shape.each_with_index.collect do |shape, index|
next nil if index == axis
shape
end.compact
Tensor.cast_dtype(TensorShape.reshape(get_op_with_axis(inputs[0], axis, 0, :max), ns), tensor.data_type)
end
register_op(%i[argmin arg_min]) do |_context, tensor, inputs|
axis = inputs[1] || 0
rank = get_rank(inputs[0])
raise TensorStream::InvalidArgumentError, "Expected dimension in the range [#{-rank},#{rank}) but got #{axis}" if axis < -rank || axis >= rank
new_shape = shape_eval(inputs[0])
ns = new_shape.each_with_index.collect do |shape, index|
next nil if index == axis
shape
end.compact
Tensor.cast_dtype(TensorShape.reshape(get_op_with_axis(inputs[0], axis, 0, :min), ns), tensor.data_type)
end
register_op :cumprod do |context, tensor, inputs|
c = fp_type?(tensor.data_type) ? 1.0 : 1
reverse_option = tensor.options[:reverse]
exclusive = tensor.options[:exclusive]
reduction(context, tensor) do |arr|
if arr.nil?
c
else
count = arr.size
arr = arr.reverse if reverse_option
arr = [1] + arr if exclusive
start_prod = arr[0]
mapped = arr[1...count].map do |v|
start_prod = vector_op(start_prod, v) { |a, b| a * b }
end
arr = [arr[0]] + mapped
reverse_option ? arr.reverse : arr
end
end
end
register_op :sum, noop: true do |context, tensor, _inputs|
reduction(context, tensor) do |arr|
reduced_val = arr[0]
arr[1..arr.size].each do |v|
reduced_val = vector_op(reduced_val, v) { |t, u| t + u }
end
reduced_val
end
end
register_op :prod, noop: true do |context, tensor, _inputs|
c = fp_type?(tensor.data_type) ? 1.0 : 1
reduction(context, tensor) do |arr|
if arr.nil?
c
else
reduced_val = arr[0]
arr[1..arr.size].each do |v|
reduced_val = vector_op(reduced_val, v) { |a, b| a * b }
end
reduced_val
end
end
end
register_op :sigmoid_grad, no_eval: true do |context, tensor, inputs|
a, b = inputs
call_vector_op(tensor, :sigmoid_grad, a, b, context) { |t, u| u * sigmoid(t) * (1 - sigmoid(t)) }
end
register_op :mean, noop: true do |context, tensor, _inputs|
c = fp_type?(tensor.data_type) ? 0.0 : 0
reduction(context, tensor) do |arr|
return c if arr.nil?
reduced_val = arr[0]
arr[1..arr.size].each do |v|
reduced_val = vector_op(reduced_val, v) { |a, b| a + b }
end
vector_op(reduced_val, nil) { |a, _b| a / arr.size }
end
end
register_op :mat_mul do |_context, tensor, inputs|
matrix_a, matrix_b = inputs
rank_a = get_rank(matrix_a)
rank_b = get_rank(matrix_b)
raise "#{tensor.inputs[0].name} rank must be greater than 1" if rank_a < 2
raise "#{tensor.inputs[1].name} rank must be greater than 1" if rank_b < 2
matrix_a = matrix_a.transpose if tensor.options[:transpose_a]
matrix_b = matrix_b.transpose if tensor.options[:transpose_b]
raise TensorStream::ValueError, "incompatible shape sizes for matrix multiplication (#{matrix_a[0].size} != #{matrix_b.size}) #{shape_eval(matrix_a)} vs #{shape_eval(matrix_b)}" if matrix_a[0].size != matrix_b.size
(Matrix[*matrix_a] * Matrix[*matrix_b]).to_a
end
register_op %i[max maximum], noop: true do |context, tensor, inputs|
call_vector_op(tensor, :max, inputs[0], inputs[1], context) { |t, u| [t, u].max }
end
register_op %i[min minimum], noop: true do |context, tensor, inputs|
call_vector_op(tensor, :min, inputs[0], inputs[1], context) { |t, u| [t, u].min }
end
def reduction(child_context, tensor, &block)
val = global_eval(tensor, tensor.inputs[0], child_context)
axis = global_eval(tensor, tensor.inputs[1], child_context)
keep_dims = global_eval(tensor, tensor.options[:keepdims], child_context)
reduce(val, axis, keep_dims, &block)
end
end
end
|