[TBE] Add a test module for table batch embedding

This commit adds a test module specifically for table batch embedding
algorithm. This test case is in reference to the FBGEMM table batch
embedding:
https://github.com/pytorch/FBGEMM/blob/main/fbgemm_gpu/bench/split_table_batched_embeddings_benchmark.py#L270

Signed-Off-by: Gaurav Shukla <[email protected]>

Author: Gaurav Shukla

e2e_testing/torchscript/main.py

@@ -49,6 +49,7 @@
 from . import constant_alloc
 from . import threshold
 from . import histogram_binning_calibration
+from . import table_batch_embedding
 
 def _get_argparse():
     config_choices = ['native_torch', 'torchscript', 'refbackend', 'tosa', 'external']

e2e_testing/torchscript/table_batch_embedding.py

@@ -0,0 +1,61 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+import torch
+
+from torch_mlir_e2e_test.torchscript.framework import TestUtils
+from torch_mlir_e2e_test.torchscript.registry import register_test_case
+from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
+
+# ==============================================================================
+
+# Reference: https://github.com/pytorch/FBGEMM/blob/main/fbgemm_gpu/bench/split_table_batched_embeddings_benchmark.py#L270
+
+# Global parameters.
+NUM_TABLES = 2
+NUM_EMBEDDINGS = 10
+EMBEDDING_DIM = 4
+BATCH_SIZE = 4
+BAG_SIZE = 3
+
+
+class TableBatchEmbeddingModule(torch.nn.Module):
+    def __init__(self):
+        super(TableBatchEmbeddingModule, self).__init__()
+        self.num_tables = NUM_TABLES
+        self.num_embeddings = NUM_EMBEDDINGS
+        self.embedding_dim = EMBEDDING_DIM
+        self.batch_size = BATCH_SIZE
+        self.bag_size = BAG_SIZE
+        # Currently, pooling_mode is fixed to 'sum'.
+        self.nn_embedding_list = torch.nn.ModuleList([
+            torch.nn.EmbeddingBag(
+                self.num_embeddings, self.embedding_dim, mode="sum", sparse=False)
+            for i in range(self.num_tables)
+        ])
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.int64, True),
+        ([-1], torch.int64, True),
+    ])
+    def forward(self, indices, offsets):
+        indices_list = indices.view(self.num_tables, self.batch_size, self.bag_size)
+        final_output = torch.tensor([])
+        for i, nn_embedding in enumerate(self.nn_embedding_list):
+            indices = indices_list[i].view(-1)
+            output = nn_embedding(indices, offsets).view(self.batch_size, -1)
+            final_output = torch.cat((final_output, output), dim=1)
+        return final_output
+
+
+@register_test_case(module_factory=lambda: TableBatchEmbeddingModule())
+def TableBatchEmbeddingModule_basic(module, tu: TestUtils):
+    indices = torch.randint(0, NUM_EMBEDDINGS, (NUM_TABLES * BATCH_SIZE * BAG_SIZE,))
+    offsets = torch.cumsum(
+        torch.tensor([0] + [BAG_SIZE for _ in range(BATCH_SIZE - 1)], dtype=torch.int64), 0)
+    module.forward(indices, offsets)
+

e2e_testing/torchscript/xfail_sets.py

@@ -16,6 +16,7 @@
 COMMON_TORCH_MLIR_LOWERING_XFAILS = {
     "QuantizedMLP_basic",
     "IouOfModule_basic",
+    "TableBatchEmbeddingModule_basic",
 }
 REFBACKEND_XFAIL_SET = COMMON_TORCH_MLIR_LOWERING_XFAILS