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[CustomOp] Register VocabParallelEmbedding instead of overwrite forward (#2515)

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### What this PR does / why we need it?
Register VocabParallelEmbedding instead of overwrite forward

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: v0.10.1.1
- vLLM main:
644d57d531

---------

Signed-off-by: Icey <1790571317@qq.com>
This commit is contained in:
Icey
2025-08-28 08:57:34 +08:00
committed by GitHub
parent 516e14ae6a
commit c578f817ca
5 changed files with 122 additions and 241 deletions
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259
tests/ut/ops/test_vocab_parallel_embedding.py
View File

@@ -13,187 +13,61 @@
# 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.model_executor.layers.vocab_parallel_embedding import \
VocabParallelEmbedding
from tests.ut.base import TestBase
from vllm_ascend.ops.vocab_parallel_embedding import (
get_masked_input_and_mask, vocab_parallel_embedding_forward)
from vllm_ascend.ops.vocab_parallel_embedding import \
AscendVocabParallelEmbedding
VOCAB_PARALLEL_EMBEDDING_TEST_NUM_RANDOM_SEEDS = 128
class TestGetMaskedInputAndMask(TestBase):
class TestCustomVocabParallelEmbedding(unittest.TestCase):
def setUp(self):
self.input_ = torch.arange(12)
def test_get_masked_input_and_mask(self):
# tp 1 no padding
input_modified, _ = get_masked_input_and_mask(
self.input_,
org_vocab_start_index=0,
org_vocab_end_index=8,
added_vocab_start_index=8,
added_vocab_end_index=12,
num_org_vocab_padding=0)
assert torch.equal(self.input_, input_modified)
# tp 2 no padding
input_rank_0, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=0,
org_vocab_end_index=4,
added_vocab_start_index=8,
added_vocab_end_index=10,
num_org_vocab_padding=0)
input_rank_1, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=4,
org_vocab_end_index=8,
added_vocab_start_index=10,
added_vocab_end_index=12,
num_org_vocab_padding=0)
assert torch.equal(input_rank_0,
torch.tensor([0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 0, 0]))
assert torch.equal(input_rank_1,
torch.tensor([0, 0, 0, 0, 0, 1, 2, 3, 0, 0, 4, 5]))
# tp 4 no padding
input_rank_0, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=0,
org_vocab_end_index=2,
added_vocab_start_index=8,
added_vocab_end_index=9,
num_org_vocab_padding=0)
input_rank_1, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=2,
org_vocab_end_index=4,
added_vocab_start_index=9,
added_vocab_end_index=10,
num_org_vocab_padding=0)
input_rank_2, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=4,
org_vocab_end_index=6,
added_vocab_start_index=10,
added_vocab_end_index=11,
num_org_vocab_padding=0)
input_rank_3, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=6,
org_vocab_end_index=8,
added_vocab_start_index=11,
added_vocab_end_index=12,
num_org_vocab_padding=0)
assert torch.equal(input_rank_0,
torch.tensor([0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0]))
assert torch.equal(input_rank_1,
torch.tensor([0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0]))
assert torch.equal(input_rank_2,
torch.tensor([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0]))
assert torch.equal(input_rank_3,
torch.tensor([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2]))
# tp 1 with padding
input_modified, _ = get_masked_input_and_mask(
self.input_,
org_vocab_start_index=0,
org_vocab_end_index=8,
added_vocab_start_index=8,
added_vocab_end_index=12,
num_org_vocab_padding=2)
assert torch.equal(
input_modified,
torch.tensor([0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13]))
# tp 2 with padding
input_rank_0, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=0,
org_vocab_end_index=4,
added_vocab_start_index=8,
added_vocab_end_index=10,
num_org_vocab_padding=2)
input_rank_1, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=4,
org_vocab_end_index=8,
added_vocab_start_index=10,
added_vocab_end_index=12,
num_org_vocab_padding=2)
assert torch.equal(input_rank_0,
torch.tensor([0, 1, 2, 3, 0, 0, 0, 0, 6, 7, 0, 0]))
assert torch.equal(input_rank_1,
torch.tensor([0, 0, 0, 0, 0, 1, 2, 3, 0, 0, 6, 7]))
# tp 4 with padding
input_rank_0, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=0,
org_vocab_end_index=2,
added_vocab_start_index=8,
added_vocab_end_index=9,
num_org_vocab_padding=2)
input_rank_1, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=2,
org_vocab_end_index=4,
added_vocab_start_index=9,
added_vocab_end_index=10,
num_org_vocab_padding=2)
input_rank_2, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=4,
org_vocab_end_index=6,
added_vocab_start_index=10,
added_vocab_end_index=11,
num_org_vocab_padding=2)
input_rank_3, _ = get_masked_input_and_mask(self.input_,
org_vocab_start_index=6,
org_vocab_end_index=8,
added_vocab_start_index=11,
added_vocab_end_index=12,
num_org_vocab_padding=2)
assert torch.equal(input_rank_0,
torch.tensor([0, 1, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0]))
assert torch.equal(input_rank_1,
torch.tensor([0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 0, 0]))
assert torch.equal(input_rank_2,
torch.tensor([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 4, 0]))
assert torch.equal(input_rank_3,
torch.tensor([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4]))
class TestVocabParallelEmbedding(TestBase):
def setUp(self):
# Create a mock VocabParallelEmbedding instance
self.mock_embedding = MagicMock(spec=VocabParallelEmbedding)
self.mock_embedding.tp_size = 2 # Test with tensor parallelism
self.mock_embedding.shard_indices = MagicMock()
self.mock_embedding.shard_indices.org_vocab_start_index = 10
self.mock_embedding.shard_indices.org_vocab_end_index = 20
self.mock_embedding.shard_indices.num_org_vocab_padding = 5
self.mock_embedding.shard_indices.added_vocab_start_index = 30
self.mock_embedding.shard_indices.added_vocab_end_index = 40
self.mock_embedding.quant_method = MagicMock()
# Set consistent embedding dimension for all tests
self.num_embeddings = 50
self.embedding_dim = 10
# Mock embedding returns tensor with shape (input_length, embedding_dim)
self.mock_embedding.quant_method.embedding = MagicMock(
side_effect=lambda _, x: torch.randn(x.shape[0], self.embedding_dim
))
self.org_num_embeddings = 40
self.padding_size = 8
def _create_layer(self):
# Patch methods and dependencies for VocabParallelEmbedding
with 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."""
input_ = torch.tensor([5, 15, 25, 35, 45]) # includes all cases
layer = self._create_layer()
masked_input, mask = get_masked_input_and_mask(
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,
@@ -201,13 +75,11 @@ class TestVocabParallelEmbedding(TestBase):
added_vocab_start_index=30,
added_vocab_end_index=40)
# The mask should be True for INVALID tokens (ones we want to mask out)
expected_mask = torch.tensor([True, False, True, False, True])
self.assertTrue(
torch.equal(mask, expected_mask),
f"Mask mismatch. Expected {expected_mask}, got {mask}")
# Check masked input values
expected_masked = torch.tensor([0, 5, 0, 20, 0])
self.assertTrue(
torch.equal(masked_input, expected_masked),
@@ -217,62 +89,64 @@ class TestVocabParallelEmbedding(TestBase):
def test_forward_with_tp_size_1(self):
"""Test forward pass without tensor parallelism."""
# Create a fresh mock embedding with tp_size=1
mock_embedding = MagicMock(spec=VocabParallelEmbedding)
mock_embedding.tp_size = 1
mock_embedding.quant_method = MagicMock()
mock_embedding.quant_method.embedding = MagicMock(
return_value=torch.randn(3, self.embedding_dim))
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 = vocab_parallel_embedding_forward(mock_embedding, input_)
output = layer.forward(input_)
# Should just pass through without masking
mock_embedding.quant_method.embedding.assert_called_once_with(
mock_embedding, input_.long())
self.assertEqual(output.shape, (3, self.embedding_dim))
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):
"""Test forward pass with tensor parallelism."""
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:
output = vocab_parallel_embedding_forward(self.mock_embedding,
input_)
# Call the forward method
output = layer.forward(input_)
# Check that masking was applied correctly
self.mock_embedding.quant_method.embedding.assert_called_once()
called_input = self.mock_embedding.quant_method.embedding.call_args[0][
1]
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
# self.dist_mock.tensor_model_parallel_all_reduce.assert_called_once()
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)
self.mock_embedding.quant_method.embedding = MagicMock(
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):
output = vocab_parallel_embedding_forward(self.mock_embedding,
input_)
# 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))
@@ -283,6 +157,9 @@ class TestVocabParallelEmbedding(TestBase):
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)),
@@ -294,6 +171,6 @@ class TestVocabParallelEmbedding(TestBase):
with patch(
"vllm_ascend.ops.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
side_effect=lambda x: x):
output = vocab_parallel_embedding_forward(
self.mock_embedding, input_)
# Call the forward method
output = layer.forward(input_)
self.assertEqual(output.shape, expected_shape)

4
tests/ut/test_utils.py
View File

@@ -289,13 +289,13 @@ class TestUtils(TestBase):
# ascend custom op is not registered
utils.register_ascend_customop()
# should call register_oot three
self.assertEqual(mock_customop.register_oot.call_count, 9)
self.assertEqual(mock_customop.register_oot.call_count, 10)
self.assertTrue(utils._ASCEND_CUSTOMOP_IS_REIGISTERED)
# ascend custom op is already registered
utils.register_ascend_customop()
# should not register_oot again, thus only called three in this ut
self.assertEqual(mock_customop.register_oot.call_count, 9)
self.assertEqual(mock_customop.register_oot.call_count, 10)
class TestProfileExecuteDuration(TestBase):

93
vllm_ascend/ops/vocab_parallel_embedding.py
View File

@@ -23,52 +23,51 @@ from vllm.model_executor.layers.vocab_parallel_embedding import \
VocabParallelEmbedding
def get_masked_input_and_mask(
input_: torch.Tensor, org_vocab_start_index: int,
org_vocab_end_index: int, num_org_vocab_padding: int,
added_vocab_start_index: int,
added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]:
# torch.compile will fuse all of the pointwise ops below
# into a single kernel, making it very fast
org_vocab_mask = (input_ >= org_vocab_start_index) & (
input_ < org_vocab_end_index)
# Adapt: avoid create added_vocab_mask when added_vocab_start_index == added_vocab_end_index.
if added_vocab_start_index == added_vocab_end_index:
valid_offset = (org_vocab_start_index * org_vocab_mask)
vocab_mask = org_vocab_mask
else:
added_vocab_mask = (input_ >= added_vocab_start_index) & (
input_ < added_vocab_end_index)
added_offset = added_vocab_start_index - (
org_vocab_end_index -
org_vocab_start_index) - num_org_vocab_padding
valid_offset = (org_vocab_start_index *
org_vocab_mask) + (added_offset * added_vocab_mask)
vocab_mask = org_vocab_mask | added_vocab_mask
# Adapt end.
input_ = vocab_mask * (input_ - valid_offset)
return input_, ~vocab_mask
class AscendVocabParallelEmbedding(VocabParallelEmbedding):
def _get_masked_input_and_mask(
self, input_: torch.Tensor, org_vocab_start_index: int,
org_vocab_end_index: int, num_org_vocab_padding: int,
added_vocab_start_index: int,
added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]:
# torch.compile will fuse all of the pointwise ops below
# into a single kernel, making it very fast
org_vocab_mask = (input_ >= org_vocab_start_index) & (
input_ < org_vocab_end_index)
# Adapt: avoid create added_vocab_mask when added_vocab_start_index == added_vocab_end_index.
if added_vocab_start_index == added_vocab_end_index:
valid_offset = (org_vocab_start_index * org_vocab_mask)
vocab_mask = org_vocab_mask
else:
added_vocab_mask = (input_ >= added_vocab_start_index) & (
input_ < added_vocab_end_index)
added_offset = added_vocab_start_index - (
org_vocab_end_index -
org_vocab_start_index) - num_org_vocab_padding
valid_offset = (org_vocab_start_index *
org_vocab_mask) + (added_offset * added_vocab_mask)
vocab_mask = org_vocab_mask | added_vocab_mask
# Adapt end.
input_ = vocab_mask * (input_ - valid_offset)
return input_, ~vocab_mask
def vocab_parallel_embedding_forward(self, input_):
if self.tp_size > 1:
# Build the mask.
masked_input, input_mask = get_masked_input_and_mask(
input_, self.shard_indices.org_vocab_start_index,
self.shard_indices.org_vocab_end_index,
self.shard_indices.num_org_vocab_padding,
self.shard_indices.added_vocab_start_index,
self.shard_indices.added_vocab_end_index)
else:
masked_input = input_
# Get the embeddings.
output_parallel = self.quant_method.embedding(self, masked_input.long())
# Mask the output embedding.
if self.tp_size > 1:
output_parallel.masked_fill_(input_mask.unsqueeze(-1), 0)
# Reduce across all the model parallel GPUs.
output = tensor_model_parallel_all_reduce(output_parallel)
return output
VocabParallelEmbedding.forward = vocab_parallel_embedding_forward
def forward(self, input_):
if self.tp_size > 1:
# Build the mask.
masked_input, input_mask = self._get_masked_input_and_mask(
input_, self.shard_indices.org_vocab_start_index,
self.shard_indices.org_vocab_end_index,
self.shard_indices.num_org_vocab_padding,
self.shard_indices.added_vocab_start_index,
self.shard_indices.added_vocab_end_index)
else:
masked_input = input_
# Get the embeddings.
output_parallel = self.quant_method.embedding(self,
masked_input.long())
# Mask the output embedding.
if self.tp_size > 1:
output_parallel.masked_fill_(input_mask.unsqueeze(-1), 0)
# Reduce across all the model parallel GPUs.
output = tensor_model_parallel_all_reduce(output_parallel)
return output

2
vllm_ascend/quantization/quantizer.py
View File

@@ -78,7 +78,7 @@ class VLLMAscendQuantizer:
"vllm_ascend.ops.layernorm.AscendRMSNorm", "forward_oot",
[wrapper_rmsnorm_forward_oot])
VLLMAscendQuantizer.apply_patch(
"vllm.model_executor.layers.vocab_parallel_embedding.VocabParallelEmbedding",
"vllm_ascend.ops.vocab_parallel_embedding.AscendVocabParallelEmbedding",
"__init__", [wrapper_vocab_parallel_embedding_init])
break
VLLMAscendQuantizer.patched = True

5
vllm_ascend/utils.py
View File

@@ -512,6 +512,11 @@ def register_ascend_customop():
from vllm_ascend.ops.common_fused_moe import AscendFusedMoE
CustomOp.register_oot(_decorated_op_cls=AscendFusedMoE, name="FusedMoE")
from vllm_ascend.ops.vocab_parallel_embedding import \
AscendVocabParallelEmbedding
CustomOp.register_oot(_decorated_op_cls=AscendVocabParallelEmbedding,
name="VocabParallelEmbedding")
# NOTE: Keep this at last to ensure all custom actions are registered
_ASCEND_CUSTOMOP_IS_REIGISTERED = True