Update the API of registering fake kernels to new standard (#5190)

Summary:
Pull Request resolved: #5190

Pull Request resolved: #5084

Update the decorator functions in registering operator abstract implementations/fake tensors to newer API

Reviewed By: zonglinpeng, hsharma35

Differential Revision: D62206602

Author: Yi Li

backends/cadence/aot/TARGETS

@@ -66,9 +66,8 @@ python_library(
         "ops_registrations.py",
     ],
     deps = [
-        ":utils",
-        "//caffe2:torch",
-        "//executorch/exir:scalar_type",
+        "fbcode//caffe2:torch",
+        "fbcode//executorch/backends/cadence/aot:utils",
     ],
 )
 

backends/cadence/aot/ops_registrations.py

@@ -10,7 +10,7 @@
 from typing import Optional, Tuple
 
 import torch
-from torch.library import impl, Library
+from torch.library import Library, register_fake
 
 from .utils import get_conv1d_output_size, get_conv2d_output_size
 
@@ -68,7 +68,7 @@
 m = Library("cadence", "IMPL", "Meta")
 
 
-@impl(m, "quantize_per_tensor")
+@register_fake("cadence::quantize_per_tensor")
 def quantize_per_tensor_meta(
     input: torch.Tensor,
     scale: float,
@@ -80,7 +80,7 @@ def quantize_per_tensor_meta(
     return input.new_empty(input.size(), dtype=dtype)
 
 
-@impl(m, "dequantize_per_tensor")
+@register_fake("cadence::dequantize_per_tensor")
 def dequantize_per_tensor_meta(
     input: torch.Tensor,
     scale: float,
@@ -92,7 +92,7 @@ def dequantize_per_tensor_meta(
     return input.new_empty(input.size(), dtype=torch.float)
 
 
-@impl(m, "quantized_linear")
+@register_fake("cadence::quantized_linear")
 def quantized_linear_meta(
     src: torch.Tensor,
     weight: torch.Tensor,
@@ -114,7 +114,7 @@ def quantized_linear_meta(
     return src.new_empty(out_size, dtype=torch.uint8)
 
 
-@impl(m, "quantized_conv")
+@register_fake("cadence::quantized_conv")
 def quantized_conv_meta(
     input: torch.Tensor,
     weight: torch.Tensor,
@@ -152,7 +152,7 @@ def quantized_conv_meta(
     return input.new_empty(output_size, dtype=input.dtype)
 
 
-@impl(m, "quantized_layer_norm")
+@register_fake("cadence::quantized_layer_norm")
 def quantized_layer_norm_meta(
     input: torch.Tensor,
     X_scale: torch.Tensor,
@@ -167,7 +167,7 @@ def quantized_layer_norm_meta(
     return input.new_empty(input.size(), dtype=torch.uint8)
 
 
-@impl(m, "quantized_relu")
+@register_fake("cadence::quantized_relu")
 def quantized_relu_meta(
     X: torch.Tensor,
     X_zero_point: torch.Tensor,
@@ -178,7 +178,7 @@ def quantized_relu_meta(
     return X.new_empty(X.size(), dtype=torch.uint8)
 
 
-@impl(m, "quantized_matmul")
+@register_fake("cadence::quantized_matmul")
 def quantized_matmul_meta(
     X: torch.Tensor,
     X_zero_point: int,