Loop - 13 vs 16

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1. Loop13 → Loop16 +8 -8

Loop13 → Loop16 RENAMED Viewed

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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	- Operator inputs defined as (max_trip_count, condition_var).
12	+ Operator inputs defined as (max_trip_count, condition_var).
13	- input ("", ""):
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	- input ("", cond) // Note this is analogous to a while loop
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	- input ("", 1) // Note this is analogous to a do-while loop
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	- input (trip_count, "") // Note this is analogous to a for loop
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	- input (trip_count, cond)
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; 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; 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).
112 112	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.
113 113	### Attributes
114 114	* **body - GRAPH** (required) :
115 115	The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.
116 116	### Inputs
117 117	Between 2 and 2147483647 inputs.
118 118	- **M** (optional, heterogeneous) - **I**:
119 119	A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.
120 120	- **cond** (optional, heterogeneous) - **B**:
121 121	A boolean termination condition. Optional. Pass empty string to skip.
122 122	- **v_initial** (variadic) - **V**:
123 123	The initial values of any loop-carried dependencies (values that change across loop iterations)
124 124	### Outputs
125 125	Between 1 and 2147483647 outputs.
126 126	- **v_final_and_scan_outputs** (variadic) - **V**:
127 127	Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.
128 128	### Type Constraints
129	- * **V** in ( seq(tensor(bool)), seq(tensor(complex128)), seq(tensor(complex64)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
129	+ * **V** in ( optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(complex128))), optional(seq(tensor(complex64))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(complex128)), optional(tensor(complex64)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(complex128)), seq(tensor(complex64)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8)), tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
130	- All Tensor and Sequence types
130	+ All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.
131 131	* **I** in ( tensor(int64) ):
132 132	tensor of int64, which should be a scalar.
133 133	* **B** in ( tensor(bool) ):
134 134	tensor of bool, which should be a scalar.