[quant] Add benchmakrs for embedding_bag coversion ops (pytorch#43291)

Summary:
Pull Request resolved: pytorch#43291

Test Float2Fused and Fused2Float conversion operators for embedding_bag byte and 4-bit ops

Test Plan:
```
python -m pt.qembedding_pack_tes
```

Imported from OSS

Reviewed By: radkris-git

Differential Revision: D23231641

fbshipit-source-id: a2afe51bba52980d2e96dfd7dbc183327e9349fd

Author: supriyar

benchmarks/operator_benchmark/benchmark_all_quantized_test.py

@@ -22,6 +22,7 @@
     qtensor_method_test,
     quantization_test,
     qunary_test,
+    qembedding_pack_test,
 )
 
 

benchmarks/operator_benchmark/pt/qembedding_pack_test.py

@@ -0,0 +1,64 @@
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import operator_benchmark as op_bench
+import torch
+import numpy as np
+
+embeddingbag_conversion_short_configs = op_bench.cross_product_configs(
+    num_embeddings=(80,),
+    embedding_dim=(128, 256, 512),
+    tags=('short',)
+)
+
+embeddingbag_conversion_long_configs = op_bench.cross_product_configs(
+    num_embeddings=(100, 120, 1000),
+    embedding_dim=(16, 64, 128, 256, 512, 1024, 2048),
+    tags=('long',)
+)
+
+conversion_ops = op_bench.op_list(
+    attrs=(
+        ('qembeddingbag_byte_prepack', torch.ops.quantized.embedding_bag_byte_prepack),
+        ('qembeddingbag_4bit_prepack', torch.ops.quantized.embedding_bag_4bit_prepack),
+        ('qembeddingbag_2bit_prepack', torch.ops.quantized.embedding_bag_2bit_prepack),
+    ),
+    attr_names=('op_name', 'op_func'),
+)
+
+unpack_ops = op_bench.op_list(
+    attrs=(
+        ('qembeddingbag_byte_unpack', torch.ops.quantized.embedding_bag_byte_unpack),
+        ('qembeddingbag_4bit_unpack', torch.ops.quantized.embedding_bag_4bit_unpack),
+        ('qembeddingbag_2bit_unpack', torch.ops.quantized.embedding_bag_2bit_unpack),
+    ),
+    attr_names=('op_name', 'op_func'),
+)
+
+class EmbeddingBagFloatToFusedBase(op_bench.TorchBenchmarkBase):
+    def init(self, num_embeddings, embedding_dim, op_func):
+        self.weight = torch.from_numpy((np.random.random_sample((
+            num_embeddings, embedding_dim)) + 1).astype(np.float32))
+        self.op_func = op_func
+
+    def forward(self):
+        return self.op_func(self.weight)
+
+class EmbeddingBagFusedToFloatBase(op_bench.TorchBenchmarkBase):
+    def init(self, num_embeddings, embedding_dim, op_func):
+        weight = torch.randn(num_embeddings, embedding_dim + 8, dtype=torch.float)
+        self.packed_weight = weight.to(torch.uint8)
+        self.op_func = op_func
+
+    def forward(self):
+        return self.op_func(self.packed_weight)
+
+
+op_bench.generate_pt_tests_from_op_list(conversion_ops,
+                                        embeddingbag_conversion_short_configs + embeddingbag_conversion_long_configs,
+                                        EmbeddingBagFloatToFusedBase)
+op_bench.generate_pt_tests_from_op_list(unpack_ops,
+                                        embeddingbag_conversion_short_configs + embeddingbag_conversion_long_configs,
+                                        EmbeddingBagFusedToFloatBase)
+
+if __name__ == "__main__":
+    op_bench.benchmark_runner.main()