[GitHub] [tvm] AndrewZhaoLuo commented on a diff in pull request #12666: [FQ2I] Quantized constant bias

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

AndrewZhaoLuo commented on code in PR #12666:
URL: https://github.com/apache/tvm/pull/12666#discussion_r960212897


##########
python/tvm/relay/transform/fake_quantization_to_integer.py:
##########
@@ -158,23 +157,29 @@ def bias_add(expr, type_map):
     """Rewrite a bias_add op"""
     x, b = expr.args
     x_t = type_map[x]
-    b_t = type_map[b]
-    in_scale = fold_constant(x_t.scale)
-    in_zero_point = fold_constant(x_t.zero_point)
-    if not (
-        approx_equal(x_t.scale, b_t.scale)
-        and approx_equal(x_t.zero_point, b_t.zero_point)
-        and tvm.ir.structural_equal(x_t.dtype, b_t.dtype)
-    ):
-        b = relay.qnn.op.requantize(
-            b,
-            b_t.scale,
-            b_t.zero_point,
-            in_scale,
-            in_zero_point,
-            out_dtype=x_t.dtype,
-            axis=0,
-        )
+    if b in type_map:
+        # Ensure bias matches the previous op
+        b_t = type_map[b]
+        in_scale = fold_constant(x_t.scale)
+        in_zero_point = fold_constant(x_t.zero_point)
+        if not (
+            approx_equal(x_t.scale, b_t.scale)
+            and approx_equal(x_t.zero_point, b_t.zero_point)
+            and tvm.ir.structural_equal(x_t.dtype, b_t.dtype)
+        ):
+            b = relay.qnn.op.requantize(
+                b,
+                b_t.scale,
+                b_t.zero_point,
+                in_scale,
+                in_zero_point,
+                out_dtype=x_t.dtype,
+                axis=0,
+            )
+    else:

Review Comment:
   Can we just do something like
   
   type_map.get(b, x_t) # assume is x_t's type if b is not fake quantized



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