From 0d98a56898accc01451d0cbb671088acfb6898dc Mon Sep 17 00:00:00 2001
From: Francesco Conti <FrancescoConti@users.noreply.github.com>
Date: Wed, 5 May 2021 12:33:21 +0200
Subject: [PATCH] Ensure that requantization factor D is always power-of-two

---
 nemo/quant/pact.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nemo/quant/pact.py b/nemo/quant/pact.py
index 5a121f7..0597271 100644
--- a/nemo/quant/pact.py
+++ b/nemo/quant/pact.py
@@ -52,7 +52,7 @@ def pact_quantized_requantize(t, eps_in, eps_out, D=1, exclude_requant_rounding=
 def pact_integer_requantize(t, eps_in, eps_out, D=1):
     eps_ratio = (D*eps_in/eps_out).round()
     device = t.device
-    return torch.as_tensor((torch.as_tensor(t, dtype=torch.int64) * torch.as_tensor(eps_ratio, dtype=torch.int64) // torch.as_tensor(D, dtype=torch.int64)), dtype=torch.float32, device=device)
+    return torch.as_tensor((torch.as_tensor(t, dtype=torch.int64) * torch.as_tensor(eps_ratio, dtype=torch.int64) // D), dtype=torch.float32, device=device)
 
 # re-quantize from a lower precision (larger eps_in) to a higher precision (lower eps_out)
 def pact_integer_requantize_add(*t, eps_in_list, eps_out, D=1):
@@ -480,7 +480,7 @@ def get_output_eps(self, eps_in_list):
         self.eps_out = max(self.eps_in_list)
         self.alpha_out = 2.0**(self.precision.get_bits())-1
         # D is selected as a power-of-two
-        self.D = 2.0**torch.ceil(torch.log2(self.requantization_factor * self.eps_out / min(self.eps_in_list)))
+        self.D = 2**torch.as_tensor(torch.ceil(torch.log2(self.requantization_factor * self.eps_out / min(self.eps_in_list))), dtype=torch.int64)
         return self.eps_out
 
     def forward(self, *x):
@@ -604,11 +604,11 @@ def set_output_eps(self, limit_at_32_bits=True, **kwargs):
         # self.eps_out   = self.alpha.item()/(2.0**(self.precision.get_bits())-1)
         self.alpha_out = 2.0**(self.precision.get_bits())-1
         # D is selected as a power-of-two
-        D = 2.0**torch.ceil(torch.log2(self.requantization_factor * self.eps_out / self.eps_in))
+        D = 2**torch.as_tensor(torch.ceil(torch.log2(self.requantization_factor * self.eps_out / self.eps_in)), dtype=torch.int64)
         if not limit_at_32_bits:
             self.D = D
         else:
-            self.D = min(D, 2.0**(32-(self.precision.get_bits())))
+            self.D = min(D, 2**(32-(self.precision.get_bits())))
 
     def get_output_eps(self, eps_in):
         r"""Get the output quantum (:math:`\varepsilon`) given the input one.