Feat (gptq): optimizing CPU to GPU memory transfer (#1009)

src/brevitas/graph/gptq.py

@@ -132,7 +132,12 @@ def __init__(
         # Initialize Hessian matrix and counter. We need it in float32 to compute the inverse
         self.H = torch.zeros((self.groups, self.columns, self.columns),
                              device='cpu',
-                             dtype=torch.float32)
+                             dtype=torch.float32,
+                             pin_memory=torch.cuda.is_available())
+        self.B = torch.zeros((self.groups, self.columns, self.columns),
+                             device='cpu',
+                             dtype=torch.float32,
+                             pin_memory=torch.cuda.is_available())
         self.nsamples = 0
 
         assert torch_version >= version.parse('1.10'), "GPTQ requires torch 1.10 or higher"
@@ -184,7 +189,9 @@ def update_batch(self, module, input, current_layer):
         self.H *= self.nsamples / (self.nsamples + batch_size)
         self.nsamples += batch_size
         inp_processed = math.sqrt(2 / self.nsamples) * inp_processed.to(torch.float32)
-        self.H += (inp_processed.bmm(inp_processed.transpose(2, 1))).to(self.H.device)
+        # optimizing CPU to GPU transfer using in-place copy to pinned memory
+        self.B.copy_(inp_processed.bmm(inp_processed.transpose(2, 1)))
+        self.H += self.B
         # If we are executing GPTQ with group of parallel layers, we keep track of how many forward
         # we executed. Once we executed as many as the number of parallel_layers, we raise
         # StopFwdException
@@ -255,7 +262,7 @@ def single_layer_update(self, percdamp=.01):
                 f'Increasing the number of samples might fix this issue')
             return
         finally:
-            del self.H
+            del self.H, self.B
 
         for i1 in range(0, self.columns, self.blocksize):
             i2 = min(i1 + self.blocksize, self.columns)