Loop - 11 vs 13

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.

Files changed (1) hide show

1. Loop11 → Loop13 +2 -20

Loop11 → Loop13 RENAMED Viewed

	@@ -1 +1 @@
1 1	Generic Looping construct. This loop has multiple termination conditions:
2 2	1) Trip count. Iteration count specified at runtime. Set by
3 3	specifying the input M. Optional. Set to empty string to omit.
4 4	Note that a static trip count (specified at graph construction time) can be
5 5	specified by passing in a constant node for input M.
6 6	2) Loop termination condition. This is an input to the op that determines
7 7	whether to run the first iteration and also a loop-carried dependency for
8 8	the body graph. The body graph must yield a value for the condition variable,
9 9	whether this input is provided or not.
10 10	This table summarizes the operating modes of this operator with equivalent
11 11	C-style code:
12 12	Operator inputs defined as (max_trip_count, condition_var).
13 13	input ("", ""):
14 14	for (int i=0; ; ++i) {
15 15	cond = ... // Note this value is ignored, but is required in the body
16 16	}
17 17	input ("", cond) // Note this is analogous to a while loop
18 18	bool cond = ...;
19 19	for (int i=0; cond; ++i) {
20 20	cond = ...;
21 21	}
22 22	input ("", 1) // Note this is analogous to a do-while loop
23 23	bool cond = true
24 24	for (int i=0; cond; ++i) {
25 25	cond = ...;
26 26	}
27 27	input (trip_count, "") // Note this is analogous to a for loop
28 28	int trip_count = ...
29 29	for (int i=0; i < trip_count; ++i) {
30 30	cond = ...; // ignored
31 31	}
32 32	input (trip_count, cond)
33 33	int trip_count = ...;
34 34	bool cond = ...;
35 35	for (int i=0; i < trip_count &amp;amp;&amp;amp; cond; ++i) {
36 36	cond = ...;
37 37	}
38 38	*Sample usage - cond as well as trip count*
39 39	graph predict-net {
40 40	%a = Constant[value = <Scalar Tensor [3]>]()
41 41	%b = Constant[value = <Scalar Tensor [6]>]()
42 42	%keepgoing = Constant[value = <Scalar Tensor [1]>]()
43 43	%max_trip_count = Constant[value = <Scalar Tensor [10]>]()
44 44	%keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
45 45	return
46 46	}
47 47	graph body-net (
48 48	%i[INT32, scalar] // iteration number
49 49	%keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
50 50	%b_in[INT32, scalar] // incoming value of loop-carried-dependency b
51 51	) {
52 52	%my_local = Add(%a, %b_in)
53 53	%b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
54 54	%keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
55 55	%user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
56 56	return %keepgoing_out, %b_out, %user_defined_val
57 57	}
58 58	*Sample equivalent C code*
59 59	{
60 60	/* User-defined code (enclosing scope) */
61 61	int a = 3, b = 6;
62 62	bool keepgoing = true; // Analogous to input cond
63 63	/* End user-defined code */
64 64	/* Implicitly-defined code */
65 65	const int max_trip_count = 10; // Analogous to input M
66 66	int user_defined_vals[]; // Imagine this is resizable
67 67	/* End implicitly-defined code */
68 68	/* initialize loop-carried variables and scan-output variables */
69 69	bool keepgoing_out = keepgoing
70 70	int b_out = b
71 71	for (int i=0; i < max_trip_count &amp;amp;&amp;amp; keepgoing_out; ++i) {
72 72	/* Implicitly-defined code: bind actual parameter values
73 73	to formal parameter variables of loop-body */
74 74	bool keepgoing_in = keepgoing_out;
75 75	bool b_in = b_out;
76 76	/* User-defined code (loop body) */
77 77	int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine
78 78	b_out = a - b_in;
79 79	keepgoing_out = my_local > b_out;
80 80	user_defined_val = b_in + b_in; // b_in and b_out are different variables
81 81	/* End user-defined code */
82 82	/* Implicitly defined-code */
83 83	user_defined_vals[i] = user_defined_val // accumulate scan-output values
84 84	}
85 85	// int t = my_local; // Can't do this. my_local is not accessible here.
86 86	// The values below are bound to the output variables of the loop and therefore accessible
87 87	// b_out; user_defined_vals; keepgoing_out;
88 88	}
89 89	There are several things of note in this code snippet:
90 90	1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can
91 91	be referenced in the inputs of the loop.
92 92	2) Any values computed in the loop body that needs to be used in a subsequent
93 93	iteration or after the loop are modelled using a pair of variables in the loop-body,
94 94	consisting of an input variable (eg., b_in) and an output variable (eg., b_out).
95 95	These are referred to as loop-carried dependences. The loop operation node
96 96	supplies the input value of the input variable for the first iteration, and
97 97	returns the output value of the output variable produced by the final
98 98	iteration.
99 99	3) Scan_output variables are used to implicitly concatenate values computed across
100 100	all the iterations. In the above example, the value of user_defined_val computed
101 101	over all iterations are concatenated and returned as the value of user_defined_vals
102 102	after the loop.
103 103	4) Values created in the body cannot be accessed in the enclosing scope,
104 104	except using the mechanism described above.
105 105	Note that the semantics of this op support "diagonal" or "wavefront" execution.
106 106	(See Step 3 here for an example:
107 107	https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).
108 108	Frontends should emit multi-layer RNNs as a series of While operators (with
109 109	time being the inner looping dimension), with each successive layer consuming
110 110	the scan_outputs from the previous layer, possibly going through several
111 111	point-wise operators (e.g. dropout, residual connections, linear layer).
112	- The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.
113	-
114 112	**Attributes**
115 113	* **body** (required):
116 114	The graph run each iteration. It has 2+N inputs: (iteration_num,
117 115	condition, loop carried dependencies...). It has 1+N+K outputs:
118 116	(condition, loop carried dependencies..., scan_outputs...). Each
119 117	scan_output is created by concatenating the value of the specified
120 118	output value at the end of each iteration of the loop. It is an
121 119	error if the dimensions or data type of these scan_outputs change
122 120	across loop iterations.
123 121	**Inputs**
124 122	Between 2 and 2147483647 inputs.
125 123	* **M** (optional, heterogeneous) - **I**:
126 124	A maximum trip-count for the loop specified at runtime. Optional.
127 125	Pass empty string to skip.
128 126	* **cond** (optional, heterogeneous) - **B**:
129 127	A boolean termination condition. Optional. Pass empty string to
130 128	skip.
131 129	* **v_initial** (variadic) - **V**:
132 130	The initial values of any loop-carried dependencies (values that
133 131	change across loop iterations)
134 132	**Outputs**
135 133	Between 1 and 2147483647 outputs.
136 134	* **v_final_and_scan_outputs** (variadic) - **V**:
137	- Final N loop carried dependency values then K scan_outputs. Scan
135	+ Final N loop carried dependency values then K scan_outputs
138	- outputs must be Tensors.
139 136	**Type Constraints**
140 137	* **V** in (
141	- seq(tensor(bool)),
142	- seq(tensor(complex128)),
143	- seq(tensor(complex64)),
144	- seq(tensor(double)),
145	- seq(tensor(float)),
146	- seq(tensor(float16)),
147	- seq(tensor(int16)),
148	- seq(tensor(int32)),
149	- seq(tensor(int64)),
150	- seq(tensor(int8)),
151	- seq(tensor(string)),
152	- seq(tensor(uint16)),
153	- seq(tensor(uint32)),
154	- seq(tensor(uint64)),
155	- seq(tensor(uint8)),
156 138	tensor(bool),
157 139	tensor(complex128),
158 140	tensor(complex64),
159 141	tensor(double),
160 142	tensor(float),
161 143	tensor(float16),
162 144	tensor(int16),
163 145	tensor(int32),
164 146	tensor(int64),
165 147	tensor(int8),
166 148	tensor(string),
167 149	tensor(uint16),
168 150	tensor(uint32),
169 151	tensor(uint64),
170 152	tensor(uint8)
171 153	):
172	- All Tensor and Sequence types
154	+ All Tensor types
173 155	* **I** in (
174 156	tensor(int64)
175 157	):
176 158	tensor of int64, which should be a scalar.
177 159	* **B** in (
178 160	tensor(bool)
179 161	):
180 162	tensor of bool, which should be a scalar.