Dont quantize the current token for attention

examples/models/llama/source_transformation/quantized_kv_cache.py

@@ -188,6 +188,26 @@ def update(self, input_pos, k_val, v_val):
             self.quantized_cache_dtype,
             self.cache_fp_type,
         )
+
+        if self.is_transposed:
+            if self.enable_dynamic_shape:
+                start_pos = input_pos[0].item()
+                torch._check_is_size(start_pos)
+                dim_to_slice = 2 if self.is_transposed else 1
+                torch._check(start_pos < self.k_cache.size(dim_to_slice))
+                seq_length = k_val.size(dim_to_slice)
+                narrowed_k = k_out.narrow(dim_to_slice, start_pos, seq_length)
+                narrowed_k.copy_(k_val)
+                narrowed_v = v_out.narrow(dim_to_slice, start_pos, seq_length)
+                narrowed_v.copy_(v_val)
+            else:
+                k_out[:, :, input_pos] = k_val
+                v_out[:, :, input_pos] = v_val
+        else:
+            start_pos = input_pos[0].item()
+            _ = torch.ops.llama.update_quantized_cache(k_val, k_out, start_pos)
+            _ = torch.ops.llama.update_quantized_cache(v_val, v_out, start_pos)
+
         return k_out, v_out
 
     @classmethod

examples/models/llama/source_transformation/test_sdpa_with_quantized_kv_cache.py

@@ -66,12 +66,6 @@ def test_simple(self, is_dynamic_shape=False):
         torch.testing.assert_close(
             float_out,
             quantized_out,
-            # had to adjust rtol because switching to using custom_sdpa means we
-            # will use dequantized k and v instead of original k and v
-            # this leads to larger differences in the output.
-            # subsequent diff in the stack will address this issue.
-            rtol=1e-01,
-            atol=1e-03,
         )
 
         input_pos = torch.tensor([3], dtype=torch.int64)