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

tests/lora/test_punica_sizes.py

@@ -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,

tests/lora/test_punica_variation.py

@@ -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,

vllm/lora/ops/bgmv_expand.py

@@ -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]

vllm/lora/ops/bgmv_expand_slice.py

@@ -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.

vllm/lora/ops/sgmv_expand.py

@@ -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

vllm/lora/ops/sgmv_expand_slice.py

@@ -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

vllm/lora/ops/sgmv_shrink.py

@@ -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

vllm/lora/punica.py

@@ -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