Use internal quantizer for input to the modules (#1551)

Internal quantizer for input to the modules
Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

transformer_engine/pytorch/module/layernorm_linear.py

@@ -1358,7 +1358,7 @@ class LayerNormLinear(TransformerEngineBaseModule):
         grad_output_quantizer = None
         output_quantizer = None
         input_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_INPUT]
-        input_quantizer.internal = False
+        input_quantizer.internal = True
         weight_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_WEIGHT]
         weight_quantizer.internal = True
         if fp8_output:

transformer_engine/pytorch/module/layernorm_mlp.py

@@ -1528,7 +1528,7 @@ class LayerNormMLP(TransformerEngineBaseModule):
         ) = [None] * 8
         if self.fp8:
             fc1_input_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_INPUT]
-            fc1_input_quantizer.internal = False  # temporary
+            fc1_input_quantizer.internal = True
             fc1_weight_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_WEIGHT]
             fc1_weight_quantizer.internal = True
             fc2_input_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM2_INPUT]

transformer_engine/pytorch/module/linear.py

@@ -1136,7 +1136,7 @@ class Linear(TransformerEngineBaseModule):
         grad_output_quantizer = None
         output_quantizer = None
         input_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_INPUT]
-        input_quantizer.internal = False
+        input_quantizer.internal = True
         weight_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_WEIGHT]
         weight_quantizer.internal = True
         if fp8_output: