refine tutorial doc for FP8 (#4066) (#4156)

Author: xiaolil1

docs/tutorials/features/float8.md

@@ -7,8 +7,6 @@ Float8 (FP8) is a 8-bit floating point data type, which is used to reduce memory
 
 Two formats are used in FP8 training and inference, in order to meet the required value range and precision of activation, weight and gradient in Deep Neural Network (DNN). One is E4M3 (sign-exponent-mantissa) for activation and weight, the other is E5M2 for gradients. These two formats are defined in [FP8 FORMATS FOR DEEP LEARNING](https://arxiv.org/pdf/2209.05433.pdf).
 
-FP8 data type is used for memory storage only in current stage. It will be converted to the BFloat16 data type for computation.
-
 ## FP8 Quantization
 
 On GPU, online Dynamic Quantization is used for FP8 data compression and decompression. Delayed Scaling algorithm is used for accelerating the quantizaiton process.
@@ -30,15 +28,13 @@ from intel_extension_for_pytorch.xpu.fp8.fp8 import fp8_autocast
 from intel_extension_for_pytorch.xpu.fp8.recipe import DelayedScaling
 from intel_extension_for_pytorch.nn.utils._fp8_convert import convert_fp8_model
 
-## AMP is optionally to be used for FP8
-with torch.xpu.amp.autocast(enabled=True, dtype=optimize_dtype):
-    ## 'fp8_autocase' is the handler of FP8
-    with fp8_autocast(enabled=True, fp8_recipe=DelayedScaling()):
-        ## The original model will be automatically converted to a new model with FP8 operators with 'convert_fp8_model'
-        convert_fp8_model(model)
-        outputs = model(input_ids=input_ids,
-                        token_type_ids=segment_ids,
-                        attention_mask=input_mask,
-                        labels=masked_lm_labels,
-                        next_sentence_label=next_sentence_labels)
+## 'fp8_autocase' is the handler of FP8
+with fp8_autocast(enabled=True, fp8_recipe=DelayedScaling()):
+    ## The original model will be automatically converted to a new model with FP8 operators with 'convert_fp8_model'
+    convert_fp8_model(model)
+    outputs = model(input_ids=input_ids,
+                    token_type_ids=segment_ids,
+                    attention_mask=input_mask,
+                    labels=masked_lm_labels,
+                    next_sentence_label=next_sentence_labels)
 ```