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[Kernel][LoRA] Add assertion for punica sgmv kernels (#7585)

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Jee Jee Li 2024-09-24 02:57:42 +08:00 committed by GitHub
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8 changed files with 64 additions and 38 deletions
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5
tests/lora/test_punica_sizes.py
View File

@ -169,6 +169,7 @@ def test_punica_sgmv(
device,
)
max_seq_length = seq_len_tensor.max()
token_nums = seq_len_tensor.sum().item()
if isinstance(max_seq_length, tuple):
max_seq_length = max_seq_length[0].item()
else:
@ -183,6 +184,7 @@ def test_punica_sgmv(
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
scaling,
)
else:
@ -195,6 +197,7 @@ def test_punica_sgmv(
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
add_inputs=True,
)
ref_torch_groupgemm(
@ -347,6 +350,7 @@ def test_punica_expand_nslices(
device,
)
max_seq_length = seq_len_tensor.max()
token_nums = seq_len_tensor.sum().item()
if isinstance(max_seq_length, tuple):
max_seq_length = max_seq_length[0].item()
else:
@ -364,6 +368,7 @@ def test_punica_expand_nslices(
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
slice_offset,
hidden_size,
add_inputs=True,

5
tests/lora/test_punica_variation.py
View File

@ -84,6 +84,7 @@ def test_punica_sgmv(
device,
)
max_seq_length = seq_len_tensor.max()
token_nums = seq_len_tensor.sum().item()
if isinstance(max_seq_length, tuple):
max_seq_length = max_seq_length[0].item()
else:
@ -98,6 +99,7 @@ def test_punica_sgmv(
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
scaling,
)
else:
@ -110,6 +112,7 @@ def test_punica_sgmv(
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
add_inputs=True,
)
ref_torch_groupgemm(
@ -262,6 +265,7 @@ def test_punica_expand_nslices(
device,
)
max_seq_length = seq_len_tensor.max()
token_nums = seq_len_tensor.sum().item()
if isinstance(max_seq_length, tuple):
max_seq_length = max_seq_length[0].item()
else:
@ -279,6 +283,7 @@ def test_punica_expand_nslices(
lora_indices_tensor,
batches,
max_seq_length,
token_nums,
slice_offset,
hidden_size,
add_inputs=True,

2
vllm/lora/ops/bgmv_expand.py
View File

@ -100,7 +100,7 @@ def _bgmv_expand(
corresponding to each batch, An index of -1 means no lora should be
applied.
batches (int): batch size
add_inputs (bool, optional): Defaults to False. adds the final lora
add_inputs (bool, optional): Defaults to False, adds the final lora
results to the output.
"""
assert inputs.dtype in [torch.float16, torch.bfloat16, torch.float32]

2
vllm/lora/ops/bgmv_expand_slice.py
View File

@ -104,7 +104,7 @@ def _bgmv_expand_slice(
lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
corresponding to each batch, An index of -1 means no lora should be
applied.
slice_offst (int): output_tensor's offst
slice_offset (int): output_tensor's offset
slice_size (int): current output_tensor's size
batches (int): batch size
add_inputs (bool, optional): Defaults to False.

16
vllm/lora/ops/sgmv_expand.py
View File

@ -106,6 +106,7 @@ def _sgmv_expand(
lora_indices_tensor: torch.Tensor,
batches: int,
max_seq_length: int,
token_nums: int,
add_inputs: bool = False,
) -> None:
"""
@ -115,17 +116,19 @@ def _sgmv_expand(
output_tensor (torch.Tensor): output tensor
b_seq_start_loc (torch.Tensor): (batch_size,). The cumulative
sequence lengths of the sequences in the batch, used to index
into sequence. E.g.,if the sequence length is [4, 6], it is
into sequence. E.g., if the sequence length is [4, 6], it is
[0, 4, 10].
seq_len_tensor (torch.Tensor): (batch_size,). record the sequence
length of the sequences in the batch
seq_len_tensor (torch.Tensor): (batch_size,). Record the sequence
length of the sequences in the batch.
lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
corresponding to each batch. An index of -1 means no lora should be
applied.
batches (int): batch size
max_seq_length (int): The max sequence lengths of the sequences
in the batch
add_inputs (bool, optional): Defaults to False. adds the final lora
max_seq_length (int): The max sequence lengths of the sequences in the
batch.
token_nums (int): The token numbers in the batch. Used to verify if the
token numbers in the inputs matches the one in the metadata.
add_inputs (bool, optional): Defaults to False, adds the final lora
results to the output.
"""
@ -134,6 +137,7 @@ def _sgmv_expand(
torch.float16,
torch.bfloat16,
]
assert inputs.size(0) == token_nums
assert inputs.size(1) == lora_b_weights.size(-1)
assert b_seq_start_loc.size(0) == batches
assert lora_indices_tensor.size(0) == batches

18
vllm/lora/ops/sgmv_expand_slice.py
View File

@ -112,6 +112,7 @@ def _sgmv_expand_slice(
lora_indices_tensor: torch.Tensor,
batches: int,
max_seq_length: int,
token_nums: int,
slice_offset: int,
slice_size: int,
add_inputs: bool = False,
@ -124,20 +125,22 @@ def _sgmv_expand_slice(
output_tensor (torch.Tensor): output tensor
b_seq_start_loc (torch.Tensor): (batch_size,). The cumulative
sequence lengths of the sequences in the batch, used to index
into sequence. E.g.,if the sequence length is [4, 6], it is
into sequence. E.g., if the sequence length is [4, 6], it is
[0, 4, 10].
seq_len_tensor (torch.Tensor): (batch_size,). record the sequence
length of the sequences in the batch
seq_len_tensor (torch.Tensor): (batch_size,). Record the sequence
length of the sequences in the batch
lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
corresponding to each batch. An index of -1 means no lora should be
applied.
batches (int): batch size
max_seq_length (int): The max sequence lengths of the sequences
max_seq_length (int): The max sequence lengths of the sequences
in the batch
slice_offst (int): output_tensor's offst
token_nums (int): The token numbers in the batch. Used to verify if the
token numbers in the inputs matches the one in the metadata.
slice_offset (int): output_tensor's offset
slice_size (int): current output_tensor's size
add_inputs (bool, optional): Defaults to False. adds the final lora
results to the output..
add_inputs (bool, optional): Defaults to False, adds the final lora
results to the output.
"""
assert inputs.dtype in [torch.float16, torch.bfloat16, torch.float32]
@ -145,6 +148,7 @@ def _sgmv_expand_slice(
torch.float16,
torch.bfloat16,
]
assert inputs.size(0) == token_nums
assert inputs.size(1) == lora_b_weights.size(-1)
assert b_seq_start_loc.size(0) == batches
assert lora_indices_tensor.size(0) == batches

16
vllm/lora/ops/sgmv_shrink.py
View File

@ -110,6 +110,7 @@ def _sgmv_shrink(
lora_indices_tensor: torch.Tensor,
batches: int,
max_seq_length: int,
token_nums: int,
scaling: float,
) -> None:
"""
@ -120,17 +121,19 @@ def _sgmv_shrink(
output_tensor (torch.Tensor): output tensor
b_seq_start_loc (torch.Tensor): (batch_size,). The cumulative
sequence lengths of the sequences in the batch, used to index
into sequence. E.g.,if the sequence length is [4, 6], it is
into sequence. E.g., if the sequence length is [4, 6], it is
[0, 4].
seq_len_tensor (torch.Tensor): (batch_size,). record the sequence
length of the sequences in the batch
seq_len_tensor (torch.Tensor): (batch_size,). Record the sequence
length of the sequences in the batch.
lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
corresponding to each batch. An index of -1 means no lora should be
applied.
batches (int): batch size
max_seq_length (int): The max sequence lengths of the sequences
in the batch
scaling (float): Scaling factor.
max_seq_length (int): The max sequence lengths of the sequences in the
batch.
token_nums (int): The token numbers in the batch. Used to verify if the
token numbers in the inputs matches the one in the metadata.
scaling (float): Scaling factor.
"""
assert inputs.dtype == lora_a_weights.dtype
assert inputs.dtype in [torch.float16, torch.bfloat16]
@ -138,6 +141,7 @@ def _sgmv_shrink(
torch.float16,
torch.bfloat16,
]
assert inputs.size(0) == token_nums
assert inputs.size(1) == lora_a_weights.size(-1)
assert b_seq_start_loc.size(0) == batches
assert lora_indices_tensor.size(0) == batches

38
vllm/lora/punica.py
View File

@ -27,7 +27,7 @@ if TYPE_CHECKING:
def compute_meta(
token_lora_tensor: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, bool]:
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int, bool]:
"""
Get the information required for the sgmv kernel. With the features:
1. If consecutive requests in the batch use the same LoRA, this function
@ -43,7 +43,7 @@ def compute_meta(
b_seq_start_tensor = torch.zeros_like(seq_length_tensor)
b_seq_start_tensor[1:].copy_(cum_result[:-1])
max_length = seq_length_tensor.max().item()
token_nums = seq_length_tensor.sum().item()
batch_size = lora_indices_tensor.size(0)
no_lora = False
# -1 means no lora should be applied. Use `no_lora` to determine whether
@ -52,7 +52,7 @@ def compute_meta(
if batch_size == 1 and lora_indices_tensor == -1:
no_lora = True
return (b_seq_start_tensor, seq_length_tensor, lora_indices_tensor,
batch_size, max_length, no_lora)
batch_size, max_length, token_nums, no_lora)
# TODO see if this can be vectorized
@ -178,7 +178,7 @@ def convert_mapping(
class PunicaWrapper:
"""
PunicaWrapper is designed to manage and provide metadata for the punica
kernel. The main function is to maintain the state information for
kernel. The main function is to maintain the state information for
Multi-LoRA, and to provide the interface for the punica kernel.
"""
@ -216,6 +216,7 @@ class PunicaWrapper:
dtype=torch.long,
device=device)
self.max_length: int = 0
self.token_nums: int = 0
self.batch_size: int = -1
self.is_prefill = False
self.no_lora = False
@ -276,13 +277,13 @@ class PunicaWrapper:
long_lora_offsets_tensor)
else:
self._long_lora_indices.zero_()
self.indices_len[:] = indices_len
def _update_prefill_metada(self, token_lora_tensor: torch.Tensor) -> None:
(b_seq_start_tensor, seq_length_tensor, lora_indices_tensor,
batch_size, max_length, no_lora) = compute_meta(token_lora_tensor)
batch_size, max_length, token_nums,
no_lora) = compute_meta(token_lora_tensor)
self._seq_start_locs[:b_seq_start_tensor.shape[0]].copy_(
b_seq_start_tensor)
@ -291,25 +292,28 @@ class PunicaWrapper:
lora_indices_tensor)
self.batch_size = batch_size
self.max_length = max_length
self.token_nums = token_nums
self.no_lora = no_lora
@property
def prefill_metadata(
self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int]:
self
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int]:
"""
This property provides a convenient way to access the necessary
metadata for prefill-related kernel computations.
1. seq_start_locs: Tensor of sequence start positions
2. seq_lengths: Tensor of sequence lengths
1. seq_start_locs: Tensor of sequence start positions.
2. seq_lengths: Tensor of sequence lengths.
3. lora_indices_per_batch: Tensor of lora indices, and an index of
-1 means no lora should be applied.
4. batch_size: batch size after clustering identical lora indices
5. max_length: The maximum sequence length in the batch
4. batch_size: Batch size after clustering identical lora indices.
5. max_length: The maximum sequence length in the batch.
6. token_nums: The token numbers in the batch.
"""
return (self._seq_start_locs[:self.batch_size],
self._seq_lengths[:self.batch_size],
self._lora_indices_per_batch[:self.batch_size],
self.batch_size, self.max_length)
self.batch_size, self.max_length, self.token_nums)
@property
def token_lora_indices(self) -> torch.Tensor:
@ -324,7 +328,7 @@ class PunicaWrapper:
def sampler_indices(self) -> torch.Tensor:
"""
This property is used to access the lora indices specifically for
LogitsProcessorWithLoRA
LogitsProcessorWithLoRA.
"""
sampler_indices_len = self.indices_len[1]
return self._sampler_indices[:sampler_indices_len]
@ -332,7 +336,7 @@ class PunicaWrapper:
@property
def sampler_indices_padded(self) -> torch.Tensor:
"""
This property provides access to padded sampler indices
This property provides access to padded sampler indices.
"""
indices_padded_len = self.indices_len[2]
return self._sampler_indices_padded[:indices_padded_len]
@ -341,7 +345,7 @@ class PunicaWrapper:
def embeddings_indices(self) -> torch.Tensor:
"""
This property provides access to the indices used for lora embeddings,
specifically for VocabParallelEmbeddingWithLoRA
specifically for VocabParallelEmbeddingWithLoRA.
"""
embeddings_indices_len = self.indices_len[3]
return self._embeddings_indices[:, :embeddings_indices_len]
@ -350,7 +354,7 @@ class PunicaWrapper:
def long_lora_indices(self) -> torch.Tensor:
"""
This property provides access to the indices used for long context
lora, specifically for LinearScalingRotaryEmbeddingWithLora
lora, specifically for LinearScalingRotaryEmbeddingWithLora.
"""
long_lora_len = self.indices_len[4]
return self._long_lora_indices[:long_lora_len]
@ -524,7 +528,7 @@ class PunicaWrapper:
scale (float): Scaling factor.
y_offset (Optional[int], optional): Offset to apply to the starting
column of y.
y_slice_size (Optional[int], optional): Size of the y column slice..
y_slice_size (Optional[int], optional): Size of the y column slice.
buffer (Optional[torch.Tensor], optional): Defaults to None.
"""
y_org = y