add quantized layer norm implementation (#35329)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35329

Adds a quantized implementation of LayerNorm for server.

A future PR will add the Python wrapper.

Test Plan:
numerics match the floating point implementation

benchmarks by input size:
v1 (mean+var non-vectorized): https://gist.github.com/vkuzo/f6d72c04742608112f4c2e612c74bd13
v2 (mean+var vectorized in float): https://gist.github.com/vkuzo/4dd95657c5b5f3654e0965db00eff8d2
v3 (mean+var vectorized in int, current): https://gist.github.com/vkuzo/57a75f75629da9f23b64b38ca0e3d34b

Imported from OSS

Differential Revision: D20768930

fbshipit-source-id: ddf8727e9840c65ead3b890220af0638c5637028

benchmarks/operator_benchmark/pt/qlayernorm_test.py

@@ -0,0 +1,50 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+
+import operator_benchmark as op_bench
+import torch
+
+
+"""Microbenchmarks for quantized layernorm operator."""
+
+layernorm_configs_short = op_bench.cross_product_configs(
+    dims=(
+        (1, 8, 16),
+        (8, 8, 16),
+        (32, 8, 16),
+        (64, 128, 56, 56),
+    ),
+    dtype=(torch.qint8,),
+    tags=["short"],
+)
+
+
+class QLayerNormBenchmark(op_bench.TorchBenchmarkBase):
+
+    def init(self, dims, dtype):
+        X = (torch.rand(*dims) - 0.5) * 256
+        scale = 1.0
+        zero_point = 0
+        self.qX = torch.quantize_per_tensor(
+            X, scale=scale, zero_point=zero_point, dtype=dtype)
+        self.weight = torch.rand(*self.qX.size()[1:], dtype=torch.float)
+        self.bias = torch.rand(*self.qX.size()[1:], dtype=torch.float)
+        self.eps = 1e-5
+        self.Y_scale = 0.1
+        self.Y_zero_point = 0
+
+    def forward(self):
+        return torch.ops.quantized.layer_norm(
+            self.qX, self.qX.size()[1:], weight=self.weight, bias=self.bias,
+            eps=self.eps, output_scale=self.Y_scale,
+            output_zero_point=self.Y_zero_point)
+
+
+op_bench.generate_pt_test(layernorm_configs_short, QLayerNormBenchmark)
+
+
+if __name__ == "__main__":
+    op_bench.benchmark_runner.main()