[GitHub] [tvm-rfcs] areusch commented on a change in pull request #22: [RFC][TIR] TIR Non-scalar Constants

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areusch commented on a change in pull request #22:
URL: https://github.com/apache/tvm-rfcs/pull/22#discussion_r703945618



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File path: rfcs/0022-tir-non-scalar-constants.md
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+- Feature Name: tir_non_scalar_constants
+- Start Date: 2021-06-01
+- RFC PR: https://github.com/apache/tvm-rfcs/pull/22
+- GitHub Issue: TBD
+
+# 1. Summary
+
+This RFC proposes how non-scalar constants could be represented in TIR and used by passes in the lowering process.
+
+# 2. Motivation 
+
+Currently, the non-scalar constants could be represented in Relay (relay.Constant) to be used by relay passes but not in TIR. Therefore, when performing lowering using TIR passes, we have to maintain a side-channel of tir::Var to constant non-scalar data mapping to perform transformations that could use the knowledge where some of the data are constants.
+
+Few example scenarios as further motivation :
+
+## Weight compression
+
+When lowering for accelerators (E.g. : [Arm(R) Ethos(TM)-U NPU](https://github.com/apache/tvm-rfcs/pull/11)), certain operations will need to get tiled to co-optimize performance and memory utilization. Such tiling patterns create slices of weights that need compressing that will end up with varying sizes. Therefore, the knowledge of some tir::Vars refer to constants are critical in the level of TIR to perform this.
+
+## Memory Planning
+
+The TIR program has the ability to express both inter and intra operator memory requirement, post-scheduling as explained further by [Unified Static Memory Planning RFC](https://github.com/apache/tvm-rfcs/pull/9). It would be better if the constants could be embedded to the TIR PrimFunc. Moreover, this allows various [target-dependent lowerings](https://github.com/apache/tvm-rfcs/pull/10), to produce TIR PrimFuncs with constants in it.
+
+## Winograd Constants
+
+The Winograd transformation (used for fast GEMMs) involves multiplication by a hard-coded constant tensor. This is currently accomplished in TE using a complicated TE compute expression with many nested selects. Being able to directly express a constant tensor here would significantly simplify this code.
+
+
+# 3. Guide-level explanation
+
+This is not particularly a user-facing feature and this will allow constants to be 'linked' to TIR. Initially, we are planning to use this with gated on '-link-params' argument for relay.build and TVMC.
+
+# 4. Reference-level explanation
+
+The proposal is quite simple and it could be explained as follows :
+
+```
+@tvm.script.tir
+def myfunc():   
+   param = tir.allocate_const([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], "int32", [10])
+```
+
+This follows closely the semantics of tir.allocate and the difference being it represent a buffer filled with constants.

Review comment:
       @junrushao1994 does this answer your questions? 
   
   it does seem like we could specify placement of the parameters in terms of memory pool/scope in `tir.allocate_const` node if we wanted to, though. the main use case i could see for this is marking weights to be loaded into e.g. accelerator memory. i do think that sort of thing is related to this proposal, but i don't think anything here stops this feature from being implemented later on. so i think we can continue with the proposal as written




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