Tests - Add test cases for pre-process modules in GPT-like models to numerical tests

tests/numerical_tests/modules/test_embedding.py

@@ -0,0 +1,77 @@
+import pytest
+import torch
+
+from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding
+from megatron.core.transformer import TransformerConfig
+from megatron.core.transformer.spec_utils import ModuleSpec, build_module
+from tests.numerical_tests.modules.test_module import TestModule
+
+
+class TestLanguageModelEmbedding(TestModule):
+    """
+    Test LanguageModelEmbedding
+    """
+
+    @pytest.mark.parametrize('inputs_kv', [
+        {
+            'seq_length': 4096,
+            'micro_batch_size': 1,
+        },
+    ])
+    @pytest.mark.parametrize('config_kv', [
+        {
+            'use_cpu_initialization': True,
+            'bf16': True,
+            'num_layers': 1,
+            'hidden_size': 5120,
+            'num_attention_heads': 1,
+            'hidden_dropout': 0.0,
+            'attention_dropout': 0.0,
+        },
+    ])
+    @pytest.mark.parametrize('embedding_kv', [
+        {
+            'vocab_size': 200064,
+            'position_embedding_type': 'rope',
+        }
+    ])
+    @pytest.mark.parametrize('steps', [10])
+    def test_language_model_embedding(self, inputs_kv, config_kv, embedding_kv, steps, request):
+        config = TransformerConfig(**config_kv)
+
+        module_spec = ModuleSpec(
+            module=LanguageModelEmbedding,
+        )
+        model = build_module(
+            module_spec,
+            config=config,
+            vocab_size=embedding_kv['vocab_size'],
+            max_sequence_length=inputs_kv['seq_length'],
+            position_embedding_type=embedding_kv['position_embedding_type'],
+        )
+        model, optimizer = self.setup_model_and_optimizer(config, model)
+
+        for step in range(steps):
+            inputs = (
+                torch.randint(
+                    low=0,
+                    high=embedding_kv['vocab_size'],
+                    size=(inputs_kv['seq_length'], inputs_kv['micro_batch_size']),
+                    dtype=torch.int64
+                ).cuda(),
+                None,
+            )
+            output = model(*inputs)
+            loss = output.mean()
+            loss.backward()
+
+            self.save_output(
+                [inputs[0]],
+                [output],
+                optimizer.get_parameters(),
+                optimizer.get_main_grads_for_grad_norm(),
+                step,
+                request,
+            )
+
+            optimizer.step()