v0.10.1rc1

tests/ut/ops/test_rotary_embedding.py

@@ -0,0 +1,318 @@
+import math
+import unittest
+from unittest.mock import MagicMock, PropertyMock, patch
+
+import torch
+from vllm.model_executor.layers.rotary_embedding import (
+    DeepseekScalingRotaryEmbedding, RotaryEmbedding)
+
+from tests.ut.base import TestBase
+from vllm_ascend.ops.rotary_embedding import _custom_rotary_embedding_enabled
+
+
+class TestCustomRotaryEmbeddingEnabled(unittest.TestCase):
+
+    def setUp(self):
+        # Common setup for tests
+        self.positions = torch.tensor([1, 2, 3])
+        self.query = torch.randn(3, 4, dtype=torch.float16)
+        self.key = torch.randn(3, 4, dtype=torch.float16)
+        self.head_size = 32
+        self.cos_sin_cache = torch.randn(3, 4)
+
+        # Mock self object for rope_forward_oot
+        self.mock_self = MagicMock()
+        self.mock_self.head_size = self.head_size
+        self.mock_self.cos_sin_cache = self.cos_sin_cache
+        self.mock_self.is_neox_style = True
+        self.mock_self.forward_native.return_value = (self.query, self.key)
+
+    def test_custom_rotary_embedding_enabled(self):
+        # Test when all conditions are True
+        with patch('vllm_ascend.ops.rotary_embedding.enable_custom_op',
+                   return_value=True):
+            result = _custom_rotary_embedding_enabled(self.query, True,
+                                                      self.head_size)
+            self.assertTrue(result)
+
+        # Test when dtype is not float16
+        with patch('vllm_ascend.ops.rotary_embedding.enable_custom_op',
+                   return_value=True):
+            query = self.query.to(torch.float32)
+            result = _custom_rotary_embedding_enabled(query, True,
+                                                      self.head_size)
+            self.assertFalse(result)
+
+        # Test when neox_style is False
+        with patch('vllm_ascend.ops.rotary_embedding.enable_custom_op',
+                   return_value=True):
+            result = _custom_rotary_embedding_enabled(self.query, False,
+                                                      self.head_size)
+            self.assertFalse(result)
+
+        # Test when head_size is not divisible by 32
+        with patch('vllm_ascend.ops.rotary_embedding.enable_custom_op',
+                   return_value=True):
+            result = _custom_rotary_embedding_enabled(self.query, True,
+                                                      self.head_size + 1)
+            self.assertFalse(result)
+
+        # Test when custom op is disabled
+        with patch('vllm_ascend.ops.rotary_embedding.enable_custom_op',
+                   return_value=False):
+            result = _custom_rotary_embedding_enabled(self.query, True,
+                                                      self.head_size)
+            self.assertFalse(result)
+
+
+class TestAscendRotaryEmbedding(unittest.TestCase):
+
+    def setUp(self):
+        # Common setup for tests
+        self.positions = torch.tensor([1, 2, 3])
+        self.query = torch.randn(3, 1, 32, dtype=torch.float16)
+        self.key = torch.randn(3, 1, 32, dtype=torch.float16)
+        self.head_size = 32
+        self.rotary_dim = self.head_size
+        self.max_position = 16
+        self.rope_theta = 10000
+        self.is_neox_style = True
+        self.cos_sin_cache = torch.randn(3, 1, 32)
+        self.layer = RotaryEmbedding(self.head_size, self.rotary_dim,
+                                     self.max_position, self.rope_theta,
+                                     self.is_neox_style, torch.float16)
+
+        # Mock self object for rope_forward_oot
+        self.mock_self = MagicMock()
+        self.mock_self.head_size = self.head_size
+        self.mock_self.cos_sin_cache = self.cos_sin_cache
+        self.mock_self.is_neox_style = self.is_neox_style
+
+    @patch('torch.ops._C')
+    @patch('vllm_ascend.ops.rotary_embedding.is_310p', return_value=False)
+    @patch('vllm_ascend.ops.rotary_embedding._custom_rotary_embedding_enabled',
+           return_value=True)
+    @patch('torch.ops._npu_rotary_embedding')
+    def test_rope_forward_oot_custom_kernel(self, mock_rotary_embedding,
+                                            mock_custom_enabled, mock_is_310p,
+                                            mock__c):
+        mock_config = MagicMock()
+        mock_config.torchair_graph_config.enabled = False
+
+        # Setup mock for custom kernel path
+
+        mock__c.rotary_embedding.return_value = self.query, self.key
+
+        result_q, result_k = self.layer.forward(self.positions, self.query,
+                                                self.key)
+
+        mock__c.rotary_embedding.assert_called_once()
+        self.assertEqual(result_q.shape, self.query.shape)
+        self.assertEqual(result_k.shape, self.key.shape)
+
+    @patch('vllm_ascend.ops.rotary_embedding._custom_rotary_embedding_enabled',
+           return_value=False)
+    @patch('torch_npu._npu_rotary_embedding')
+    def test_rope_forward_oot_contiguous(self, mock_npu_rotary,
+                                         mock_custom_enabled):
+        mock_config = MagicMock()
+        mock_config.torchair_graph_config.enabled = False
+
+        # Test contiguous path when custom is disabled
+        non_contig_query = self.query.transpose(0, 1)
+        non_contig_key = self.key.transpose(0, 1)
+
+        result_q, result_k = self.layer.forward(self.positions,
+                                                non_contig_query,
+                                                non_contig_key)
+
+        mock_npu_rotary.assert_called_once()
+        self.assertEqual(result_q.shape, non_contig_query.shape)
+        self.assertEqual(result_k.shape, non_contig_key.shape)
+
+    def test_rope_forward_oot_with_offsets(self):
+        mock_config = MagicMock()
+        mock_config.torchair_graph_config.enabled = False
+
+        # Test that NotImplementedError is raised when offsets is provided
+        offsets = torch.tensor([1, 2, 3])
+        with self.assertRaises(NotImplementedError):
+            self.layer.forward(self.positions, self.query, self.key, offsets)
+
+    @patch('vllm_ascend.ops.rotary_embedding._custom_rotary_embedding_enabled',
+           return_value=False)
+    @patch('torch_npu._npu_rotary_embedding')
+    def test_rope_forward_oot_neox_style_override(self, mock_npu_rotary,
+                                                  mock_custom_enabled):
+        mock_config = MagicMock()
+        mock_config.torchair_graph_config.enabled = False
+
+        # Test neox_style override
+        result_q, result_k = self.layer.forward(self.positions,
+                                                self.query,
+                                                self.key,
+                                                is_neox_style_override=False)
+
+        # Check that neox_style=False was passed to the NPU function
+        args, kwargs = mock_npu_rotary.call_args
+        self.assertFalse(args[-1])
+
+
+class MockRopeModule:
+
+    def __init__(self, max_seq_len=2048, is_neox_style=True):
+        self.max_seq_len = max_seq_len
+        self.is_neox_style = is_neox_style
+        self.cos_cached = None
+        self.sin_cached = None
+        self.rotary_dim = 1
+        self.base = 1
+
+
+class TestAscendDeepseekScalingRotaryEmbedding(TestBase):
+
+    def setUp(self):
+        # Common setup for tests
+        self.positions = torch.tensor([1, 2, 3])
+        self.query = torch.randn(3, 1, 32, dtype=torch.float16)
+        self.key = torch.randn(3, 1, 32, dtype=torch.float16)
+        self.head_size = 32
+        self.rotary_dim = self.head_size
+        self.max_position = 16
+        self.rope_theta = 10000
+        self.is_neox_style = True
+        self.scaling_factor = 1
+        self.layer = None
+
+    def _create_layer(self):
+        self.layer = DeepseekScalingRotaryEmbedding(
+            self.head_size, self.rotary_dim, self.max_position,
+            self.rope_theta, self.is_neox_style, self.scaling_factor,
+            torch.float16)
+        return self.layer
+
+    @patch("vllm.platforms.current_platform.device_type",
+           new=torch.device("cpu"))
+    @patch("vllm_ascend.ops.rotary_embedding.NPUPlatform",
+           new_callable=PropertyMock)
+    def test_native_rope_deepseek_forward_base(self, mock_npuplatform):
+        mock_npuplatform.device_type = torch.device("cpu")
+        self.layer = self._create_layer()
+        with patch("vllm_ascend.ops.rotary_embedding._rope_forward_oot",
+                   return_value=(self.query,
+                                 self.key)) as mock_rope_forward_oot:
+            q_pe, k_pe = self.layer.forward(self.positions, self.query,
+                                            self.key)
+        mock_rope_forward_oot.assert_called_once()
+        assert q_pe.shape == self.query.shape
+        assert k_pe.shape == self.key.shape
+
+    @patch('vllm_ascend.ops.rotary_embedding._rope_forward_oot')
+    @patch("vllm.platforms.current_platform.device_type",
+           new=torch.device("cpu"))
+    @patch("vllm_ascend.ops.rotary_embedding.NPUPlatform",
+           new_callable=PropertyMock)
+    def test_native_rope_deepseek_forward_cache_handling(
+            self, mock_npuplatform, mock_rope_forward_oot):
+        mock_npuplatform.device_type = torch.device("cpu")
+        self.layer = self._create_layer()
+        self.layer.max_seq_len = 1024
+        # Test cache situation is true
+        with patch.object(self.layer, "_set_cos_sin_cache") as mock_set_cache:
+            mock_rope_forward_oot.return_value = (self.query, self.key)
+
+            q_pe, k_pe = self.layer.forward(self.positions,
+                                            self.query,
+                                            self.key,
+                                            max_seq_len=2048)
+        mock_set_cache.assert_called_once()
+        assert q_pe.shape == self.query.shape
+        assert k_pe.shape == self.key.shape
+
+    @patch('vllm_ascend.ops.rotary_embedding._rope_forward_oot')
+    @patch("vllm.platforms.current_platform.device_type",
+           new=torch.device("cpu"))
+    @patch("vllm_ascend.ops.rotary_embedding.NPUPlatform",
+           new_callable=PropertyMock)
+    def test_native_rope_deepseek_forward_key_reshaping(
+            self, mock_npuplatform, mock_rope_forward_oot):
+        mock_npuplatform.device_type = torch.device("cpu")
+        self.layer = self._create_layer()
+
+        key = torch.randn(1, 32)
+
+        mock_rope_forward_oot.return_value = (self.query, key)
+
+        q_pe, k_pe = self.layer.forward(self.positions, self.query, key)
+        mock_rope_forward_oot.assert_called_once()
+        assert q_pe.shape == self.query.shape
+        assert k_pe.shape == key.shape
+
+    @patch('vllm_ascend.ops.rotary_embedding._rope_forward_oot')
+    @patch("vllm.platforms.current_platform.device_type",
+           new=torch.device("cpu"))
+    @patch("vllm_ascend.ops.rotary_embedding.NPUPlatform",
+           new_callable=PropertyMock)
+    def test_native_rope_deepseek_forward_non_neox_style(
+            self, mock_npuplatform, mock_rope_forward_oot):
+        mock_npuplatform.device_type = torch.device("cpu")
+        self.layer = self._create_layer()
+
+        mock_rope_forward_oot.return_value = (self.query, self.key)
+
+        q_pe, k_pe = self.layer.forward(self.positions, self.query, self.key)
+
+        mock_rope_forward_oot.assert_called_once()
+        assert q_pe.shape == self.query.shape
+        assert k_pe.shape == self.key.shape
+
+    @patch("vllm.platforms.current_platform.device_type",
+           new=torch.device("cpu"))
+    @patch("vllm_ascend.ops.rotary_embedding.NPUPlatform",
+           new_callable=PropertyMock)
+    def test_basic_case(self, mock_npuplatform):
+        # Test with standard values
+        mock_npuplatform.device_type = torch.device("cpu")
+        self.layer = self._create_layer()
+        num_rotations = 100
+        dim = 512
+        base = 10000
+        max_position_embeddings = 2048
+
+        result = self.layer._yarn_find_correction_dim(num_rotations, dim, base,
+                                                      max_position_embeddings)
+
+        # Calculate expected value manually
+        expected = (dim * torch.log(
+            torch.tensor(max_position_embeddings) /
+            (num_rotations * 2 * torch.pi))) / (2 *
+                                                torch.log(torch.tensor(base)))
+
+        self.assertTrue(torch.allclose(result, expected))
+
+    @patch("vllm.platforms.current_platform.device_type",
+           new=torch.device("cpu"))
+    @patch("vllm_ascend.ops.rotary_embedding.NPUPlatform",
+           new_callable=PropertyMock)
+    def test_yarn_get_mscale(self, mock_npuplatform):
+        mock_npuplatform.device_type = torch.device("cpu")
+        self.layer = self._create_layer()
+
+        # test_scale_less_than_or_equal_1
+        self.assertEqual(self.layer._yarn_get_mscale(scale=0.5), 1.0)
+        self.assertEqual(self.layer._yarn_get_mscale(scale=1.0), 1.0)
+        self.assertEqual(self.layer._yarn_get_mscale(scale=0.999), 1.0)
+
+        # test_scale_greater_than_1:
+        test_cases = [(2.0, 1.0, 1.0 + 0.1 * math.log(2.0)),
+                      (10.0, 1.0, 1.0 + 0.1 * math.log(10.0)),
+                      (5.0, 2.0, 1.0 + 0.2 * math.log(5.0)),
+                      (math.e, 1.0, 1.0 + 0.1)]
+
+        for scale, mscale, expected in test_cases:
+            result = self.layer._yarn_get_mscale(scale, mscale)
+            self.assertAlmostEqual(
+                result,
+                expected,
+                places=6,
+                msg=f"Failed for scale={scale}, mscale={mscale}")