v0.10.1rc1

tests/ut/ops/test_vocab_parallel_embedding.py

@@ -0,0 +1,232 @@
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm/tests/lora/test_layers.py
+
+import unittest
+from unittest.mock import MagicMock, patch
+
+import torch
+
+from vllm_ascend.ops.vocab_parallel_embedding import (
+    AscendLogitsProcessor, AscendParallelLMHead, AscendVocabParallelEmbedding)
+
+VOCAB_PARALLEL_EMBEDDING_TEST_NUM_RANDOM_SEEDS = 128
+
+
+class TestCustomVocabParallelEmbedding(unittest.TestCase):
+
+    def setUp(self):
+        self.num_embeddings = 50
+        self.embedding_dim = 10
+        self.org_num_embeddings = 40
+        self.padding_size = 8
+
+    def _create_layer(self):
+        # Patch methods and dependencies for VocabParallelEmbedding
+        mock_group = MagicMock()
+        mock_group.world_size = 2
+        mock_group.rank_in_group = 0
+        with patch("vllm_ascend.ops.vocab_parallel_embedding.get_tp_group", return_value=mock_group), \
+            patch("vllm.model_executor.layers.vocab_parallel_embedding.get_tensor_model_parallel_rank", return_value=0), \
+            patch("vllm.model_executor.layers.vocab_parallel_embedding.get_tensor_model_parallel_world_size", return_value=2), \
+            patch("vllm.model_executor.layers.vocab_parallel_embedding.pad_vocab_size", side_effect=lambda x, y: x + y), \
+            patch("vllm.model_executor.layers.vocab_parallel_embedding.divide", side_effect=lambda x, y: x // y):
+
+            # Create an instance of VocabParallelEmbedding
+            layer = AscendVocabParallelEmbedding(
+                num_embeddings=self.num_embeddings,
+                embedding_dim=self.embedding_dim,
+                org_num_embeddings=self.org_num_embeddings,
+                padding_size=self.padding_size,
+                quant_config=None,  # Mock quantization config
+                prefix="")
+
+            layer.shard_indices = MagicMock()
+            layer.shard_indices.org_vocab_start_index = 10
+            layer.shard_indices.org_vocab_end_index = 20
+            layer.shard_indices.num_org_vocab_padding = 5
+            layer.shard_indices.added_vocab_start_index = 30
+            layer.shard_indices.added_vocab_end_index = 40
+
+            # Mock the quantization method
+            layer.quant_method.embedding = MagicMock(
+                side_effect=lambda _, x: torch.randn(x.shape[0], self.
+                                                     embedding_dim))
+            return layer
+
+    def test_get_masked_input_and_mask(self):
+        """Test the mask and offset calculation helper function."""
+        layer = self._create_layer()
+
+        input_ = torch.tensor([5, 15, 25, 35, 45])
+
+        masked_input, mask = layer._get_masked_input_and_mask(
+            input_,
+            org_vocab_start_index=10,
+            org_vocab_end_index=20,
+            num_org_vocab_padding=5,
+            added_vocab_start_index=30,
+            added_vocab_end_index=40)
+
+        expected_mask = torch.tensor([True, False, True, False, True])
+        self.assertTrue(
+            torch.equal(mask, expected_mask),
+            f"Mask mismatch. Expected {expected_mask}, got {mask}")
+
+        expected_masked = torch.tensor([0, 5, 0, 20, 0])
+        self.assertTrue(
+            torch.equal(masked_input, expected_masked),
+            f"Masked input mismatch. Expected {expected_masked}, got {masked_input}"
+        )
+
+    def test_forward_with_tp_size_1(self):
+        """Test forward pass without tensor parallelism."""
+        # Create a fresh mock embedding with tp_size=1
+        layer = self._create_layer()
+        layer.tp_size = 1
+        layer.quant_method.embedding = MagicMock(
+            return_value=torch.randn(3, layer.embedding_dim))
+
+        input_ = torch.tensor([1, 2, 3])
+
+        with patch(
+                "vllm_ascend.ops.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
+                side_effect=lambda x: x) as mock_reduce_tp1:
+            output = layer.forward(input_)
+
+        # Should just pass through without masking
+        layer.quant_method.embedding.assert_called_once_with(
+            layer, input_.long())
+        self.assertEqual(output.shape, (3, layer.embedding_dim))
+
+        # Verify all_reduce was called once
+        mock_reduce_tp1.assert_called_once()
+
+    def test_forward_with_tp(self):
+        layer = self._create_layer()
+        layer.tp_size = 2
+
+        input_ = torch.tensor([15, 35])  # one org vocab, one added vocab
+
+        with patch(
+                "vllm_ascend.ops.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
+                side_effect=lambda x: x) as mock_reduce_tp:
+            # Call the forward method
+            output = layer.forward(input_)
+
+        # Check that masking was applied correctly
+        layer.quant_method.embedding.assert_called_once()
+        called_input = layer.quant_method.embedding.call_args[0][1]
+        expected_input = torch.tensor([5, 20])  # after offset calculation
+        self.assertTrue(torch.all(called_input == expected_input))
+
+        # Check that all reduce was called
+        mock_reduce_tp.assert_called_once()
+        self.assertEqual(output.shape, (2, self.embedding_dim))
+
+    def test_forward_with_invalid_vocab(self):
+        """Test that invalid vocab indices are properly masked out."""
+        # Create a fresh embedding layer
+        layer = self._create_layer()
+        input_ = torch.tensor([5, 15, 25, 35, 45])  # includes invalid cases
+        # Create predictable mock output
+        mock_output = torch.randn(5, self.embedding_dim)
+        layer.quant_method.embedding = MagicMock(
+            return_value=mock_output.clone())
+
+        # Patch tensor_model_parallel_all_reduce to mock its behavior
+        with patch(
+                "vllm_ascend.ops.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
+                side_effect=lambda x: x):
+            # Call the forward method
+            output = layer.forward(input_)
+        # Check that invalid positions (0, 2, 4) were zeroed out
+        self.assertTrue(torch.all(output[0] == 0))
+        self.assertTrue(torch.all(output[2] == 0))
+        self.assertTrue(torch.all(output[4] == 0))
+        self.assertTrue(torch.all(output[1] == mock_output[1]))
+        self.assertTrue(torch.all(output[3] == mock_output[3]))
+        self.assertEqual(output.shape, (5, self.embedding_dim))
+
+    def test_output_shape(self):
+        """Test that output shape is correct."""
+        # Create a fresh embedding layer
+        layer = self._create_layer()
+
+        test_cases = [
+            (torch.tensor([15]), (1, self.embedding_dim)),
+            (torch.tensor([15, 35]), (2, self.embedding_dim)),
+            (torch.tensor([15, 35, 16, 36]), (4, self.embedding_dim)),
+        ]
+
+        for input_, expected_shape in test_cases:
+            with self.subTest(input=input_):
+                with patch(
+                        "vllm_ascend.ops.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
+                        side_effect=lambda x: x):
+                    # Call the forward method
+                    output = layer.forward(input_)
+                self.assertEqual(output.shape, expected_shape)
+
+
+class TestAscendLogitsProcessor(unittest.TestCase):
+
+    def setUp(self):
+        self.vocab_size = 50
+        self.num_embeddings = 50
+        self.embedding_dim = 10
+        self.org_num_embeddings = 40
+        self.padding_size = 8
+
+        self.mock_group = MagicMock()
+        self.mock_group.world_size = 2
+        self.mock_group.rank_in_group = 0
+        self.mock_ascend_config = MagicMock()
+        self.mock_quant_method = MagicMock()
+        self.mock_quant_method.apply = MagicMock(
+            return_value=torch.randn(1, self.vocab_size))
+        self.patches = [
+            patch("vllm_ascend.ascend_config.get_ascend_config",
+                  return_value=self.mock_ascend_config),
+            patch(
+                "vllm_ascend.ops.vocab_parallel_embedding.get_lmhead_tp_group",
+                return_value=self.mock_group),
+            patch("vllm_ascend.ops.vocab_parallel_embedding.lmhead_tp_enable",
+                  return_value=True),
+            patch(
+                "vllm_ascend.ops.vocab_parallel_embedding.get_lmhead_tp_group.all_to_all",
+                return_value=torch.randn(1, self.vocab_size))
+        ]
+
+        for p in self.patches:
+            p.start()
+
+    def tearDown(self):
+        for p in self.patches:
+            p.stop()
+
+    def test_create_processor(self):
+        processor = AscendLogitsProcessor(vocab_size=self.vocab_size)
+        self.assertEqual(processor.vocab_size, self.vocab_size)
+
+    def test_get_logits(self):
+        processor = AscendLogitsProcessor(vocab_size=self.vocab_size)
+        lmhead = AscendParallelLMHead(num_embeddings=self.num_embeddings,
+                                      embedding_dim=self.embedding_dim,
+                                      prefix="lm_head")
+        lmhead.quant_method = self.mock_quant_method
+        lmhead.quant_method.apply = self.mock_quant_method.apply
+        hidden_state = torch.randn(1, self.org_num_embeddings)
+        processor._get_logits(hidden_state, lmhead)
+        self.mock_quant_method.apply.assert_called_once()