[GitHub] [tvm] ibsidorenko commented on a diff in pull request #13660: [QNN] Change in Pass Context for lookup table calculation

[GitBox <gi...@apache.org>]

ibsidorenko commented on code in PR #13660:
URL: https://github.com/apache/tvm/pull/13660#discussion_r1057708382


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python/tvm/relay/qnn/op/canonicalizations.py:
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@@ -23,10 +23,25 @@
 
 
 def run_const_expr(expr: "relay.Expr") -> np.ndarray:
-    """Evaluate a const expression, receiving result as np array."""
-    mod = tvm.IRModule.from_expr(expr)
-    vm_exe = relay.create_executor("vm", mod=mod)
-    return vm_exe.evaluate()().asnumpy()
+    """Evaluate a const expression, receiving result as np array.
+
+    If a number of passes are disabled in the current Pass Context, then there is no need to disable
+    these passes for const expression evaluation as well. That's why we use empty list
+    "disabled_pass=[]", all other arguments are inherited from the current Pass Context.

Review Comment:
   I put here `disabled_pass=[]` to ignore the original `disabled_pass` list from the current context. Otherwise VM inherits non-empty `disabled_pass` list and fails.
   Note, we call this code only for several qnn ops: tanh, sqrt, erf, exp + some other ops. It does not affect global constant folding.
   
   For clarification, here is my example to illustrate this issue:
   I am trying to compile subgraph with `qnn.tanh` operation for Hexagon target **without** QNN canonicalization but **with** QNN legalization (this is acceptable for Hexagon). For `qnn.tanh` we compose lookup table by means of Virtual Machine and compute it on the host. For most of users/developers host is x86 cpu. But for x86 QNN canonicalization is mandatory pass, otherwise it fails.
   Looks like the simplest way to fix this issue is to create new Pass Context and ignore the original `disabled_pass` list for lookup table calculation and constant evaluation. Other arguments (opt_level, instruments, config ...) I inherit from the current context.
   
   P.S. Corresponding unit test will be added in the next PR.



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