[V1] [Hybrid] Some additional clean-up in Mamba2 prefix caching

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -595,21 +595,32 @@ def forward_cuda(
         if prefix_caching_enabled:
             # If prefix caching is enabled, retrieve the relevant variables
             # for prefill and decode
-            last_state_idx_d, last_state_idx_p = torch.split(
-                attn_metadata.last_state_idx, [num_decodes, num_prefills], dim=0
+            block_idx_last_computed_token_d, block_idx_last_computed_token_p = (
+                torch.split(
+                    attn_metadata.block_idx_last_computed_token,
+                    [num_decodes, num_prefills],
+                    dim=0,
+                )
             )
-            current_last_idx_d, current_last_idx_p = torch.split(
-                attn_metadata.current_last_idx, [num_decodes, num_prefills], dim=0
+            block_idx_last_scheduled_token_d, block_idx_last_scheduled_token_p = (
+                torch.split(
+                    attn_metadata.block_idx_last_scheduled_token,
+                    [num_decodes, num_prefills],
+                    dim=0,
+                )
             )
             # Prefill-only variables:
-            current_first_idx_p = attn_metadata.current_first_idx_p
-            context_lens_p = attn_metadata.context_lens_p
-            last_computed_offset_p = attn_metadata.last_computed_offset_p
+            block_idx_first_scheduled_token_p = (
+                attn_metadata.block_idx_first_scheduled_token_p
+            )
+            num_computed_tokens_p = attn_metadata.num_computed_tokens_p
         else:
-            last_state_idx_d, last_state_idx_p = None, None
-            current_last_idx_d, current_last_idx_p = None, None
-            current_first_idx_p = None
-            context_lens_p = None
+            block_idx_last_computed_token_d = None
+            block_idx_last_computed_token_p = None
+            block_idx_last_scheduled_token_d = None
+            block_idx_last_scheduled_token_p = None
+            block_idx_first_scheduled_token_p = None
+            num_computed_tokens_p = None
 
         # Preallocate output tensor to avoid memcpy cost for merging prefill
         # and decode outputs
@@ -637,7 +648,8 @@ def forward_cuda(
             #   to by "state_indices_tensor_p".
             #   In particular, it will always write the state at the
             #   sequence end.
-            #   In addition, "current_first_idx_p" and "current_last_idx_p"
+            #   In addition, "block_idx_first_scheduled_token_p" and
+            #   "block_idx_last_scheduled_token_p"
             #   are provided (which are pointers into
             #   "state_indices_tensor_p"), it will write additional cache
             #   states aligned at "block_size_to_align".
@@ -652,10 +664,10 @@ def forward_cuda(
                 conv_states=conv_state,
                 has_initial_state=has_initial_states_p,
                 cache_indices=state_indices_tensor_p,
-                current_first_idx=current_first_idx_p,
-                current_last_idx=current_last_idx_p,
-                initial_state_idx=last_state_idx_p,
-                context_lens=context_lens_p,
+                block_idx_first_scheduled_token=block_idx_first_scheduled_token_p,
+                block_idx_last_scheduled_token=block_idx_last_scheduled_token_p,
+                initial_state_idx=block_idx_last_computed_token_p,
+                num_computed_tokens=num_computed_tokens_p,
                 block_size_to_align=mamba_block_size,
                 metadata=attn_metadata,
                 query_start_loc=query_start_loc_p,
@@ -669,7 +681,7 @@ def forward_cuda(
                 kernel_ssm_indices = state_indices_tensor_p
                 if prefix_caching_enabled:
                     kernel_ssm_indices = state_indices_tensor_p.gather(
-                        1, last_state_idx_p.unsqueeze(1)
+                        1, block_idx_last_computed_token_p.unsqueeze(1)
                     ).squeeze(1)
                 initial_states = torch.where(
                     has_initial_states_p[:, None, None, None],
@@ -703,52 +715,76 @@ def forward_cuda(
             )
 
             if prefix_caching_enabled:
-                # Save states for sequences with more than just the final state:
-                n_blocks_to_fill = current_last_idx_p - current_first_idx_p
-                for seq_idx in (n_blocks_to_fill > 0).nonzero().squeeze(1):
+                # The chunk_stride is the number of chunks per mamba block
+                # e.g., if mamba_block_size = 512 and chunk_size = 256,
+                # then chunk_stride = 2
+                chunk_stride = mamba_block_size // chunk_size
+
+                # Save state for sequences with more than just final state
+                for seq_idx in range(num_prefills):
+                    # Block index for the first scheduled token
+                    block_idx_first_scheduled_token = block_idx_first_scheduled_token_p[
+                        seq_idx
+                    ]
+
+                    # Block index for the last scheduled token
+                    block_idx_last_scheduled_token = block_idx_last_scheduled_token_p[
+                        seq_idx
+                    ]
+
+                    # Number of blocks that need to be written
+                    n_blocks_to_fill = (
+                        block_idx_last_scheduled_token - block_idx_first_scheduled_token
+                    )
+
+                    # Skip sequences that don't have any blocks to fill
+                    if n_blocks_to_fill == 0:
+                        continue
+
+                    # Look up the state indices
                     cache_blocks_to_fill = state_indices_tensor_p[
                         seq_idx,
-                        current_first_idx_p[seq_idx] : current_first_idx_p[seq_idx]
-                        + n_blocks_to_fill[seq_idx],
+                        block_idx_first_scheduled_token:block_idx_last_scheduled_token,
                     ]
-                    # chunks = [0 1 2 3 4 5 6 ...]
-                    # First aligned chunk would typically be:
-                    #  mamba_block_size = 1024, chunk_size = 256
-                    #  1024 // 256 - 1 --> chunks[3]
-                    # But when last chunk wasn't block aligned:
-                    # - last_computed_offset_p[seq_idx] // chunk_size
-                    # e.g. 1000 // 256 -> 3 completed --> store chunk[0]
-                    # e.g. 513 // 256 -> 2 completed --> store chunk[1] (skip 1)
-                    # e.g. 256 // 256 -> 1 completed --> store chunk[2] (skip 2)
-                    # e.g. 10 // 256 -> 0 completed --> store chunk[3] (skip 3)
-                    chunk_stride = mamba_block_size // chunk_size
-                    first_aligned_chunk = (
-                        torch.concat(
-                            [
-                                torch.zeros(
-                                    1,
-                                    dtype=last_chunk_indices_p.dtype,
-                                    device=last_chunk_indices_p.device,
-                                ),
-                                last_chunk_indices_p + 1,
-                            ]
-                        )[seq_idx]
-                        + chunk_stride
-                        - 1
-                        - last_computed_offset_p[seq_idx] // chunk_size
+
+                    # First chunk index for this sequence
+                    if seq_idx == 0:
+                        first_chunk = 0
+                    else:
+                        first_chunk = 1 + last_chunk_indices_p[seq_idx - 1]
+
+                    # First chunk that is aligned on the mamba block boundary
+                    first_aligned_chunk = first_chunk + chunk_stride - 1
+
+                    # Calculate the number of computed tokens that were not
+                    # already cached
+                    num_unaligned_computed_tokens = (
+                        num_computed_tokens_p[seq_idx] % mamba_block_size
                     )
+
+                    if num_unaligned_computed_tokens > 0:
+                        # If the number of computed tokens is not block aligned,
+                        # then we need to shift the index accordingly
+                        first_aligned_chunk -= (
+                            num_unaligned_computed_tokens // chunk_size
+                        )
+
+                    # Get states to write
                     from_where = varlen_states[
                         first_aligned_chunk : first_aligned_chunk
-                        + n_blocks_to_fill[seq_idx] * chunk_stride : chunk_stride
+                        + n_blocks_to_fill * chunk_stride : chunk_stride
                     ]
+
+                    # Write the states
                     ssm_state[cache_blocks_to_fill] = from_where
 
-                # For all seqs, store the last state (Note: might be partial):
+                # For all seqs, store the last state (note: might be partial):
                 ssm_state[
                     state_indices_tensor_p.gather(
-                        1, current_last_idx_p.unsqueeze(1)
+                        1, block_idx_last_scheduled_token_p.unsqueeze(1)
                     ).squeeze(1)
                 ] = varlen_states[last_chunk_indices_p]
+
             else:
                 # update ssm states
                 # - varlen state is a (num_prefills, nheads, headdim, dstate)
@@ -759,14 +795,17 @@ def forward_cuda(
         if has_decode:
             if prefix_caching_enabled:
                 state_indices_tensor_d_input = state_indices_tensor_d.gather(
-                    1, last_state_idx_d.unsqueeze(1)
+                    1, block_idx_last_computed_token_d.unsqueeze(1)
                 ).squeeze(1)
                 state_indices_tensor_d_output = state_indices_tensor_d.gather(
-                    1, current_last_idx_d.unsqueeze(1)
+                    1, block_idx_last_scheduled_token_d.unsqueeze(1)
                 ).squeeze(1)
-                # Note:
-                # for decode always: current_first_idx_d == current_last_idx_d
-                # at block boundaries: current_first_idx_d > last_state_idx_d
+                # for decode:
+                #   block_idx_first_scheduled_token_d ==
+                #       block_idx_last_scheduled_token_d
+                # at block boundaries:
+                #   block_idx_first_scheduled_token_d >
+                #       block_idx_last_computed_token_d
             else:
                 # Without caching, read and write in-place to the same blocks:
                 state_indices_tensor_d_input = state_indices_tensor_d
@@ -780,8 +819,8 @@ def forward_cuda(
                 self.conv1d.bias,
                 self.activation,
                 conv_state_indices=state_indices_tensor_d,
-                current_last_idx=current_last_idx_d,
-                initial_state_idx=last_state_idx_d,
+                block_idx_last_scheduled_token=block_idx_last_scheduled_token_d,
+                initial_state_idx=block_idx_last_computed_token_d,
             )
 
             hidden_states_d, B_d, C_d = split_hidden_states_B_C_fn(hidden_states_B_C_d)

vllm/model_executor/layers/mamba/ops/causal_conv1d.py

@@ -27,10 +27,10 @@ def _causal_conv1d_fwd_kernel(  # continuous batching
     query_start_loc_ptr,
     batch_ptr,
     token_chunk_offset_ptr,
-    current_first_idx,  # (batch,)
-    current_last_idx,  # (batch,)
+    block_idx_first_scheduled_token,  # (batch,)
+    block_idx_last_scheduled_token,  # (batch,)
     initial_state_idx,  # (batch,)
-    context_lens,  # (batch,)
+    num_computed_tokens,  # (batch,)
     o_ptr,  # (dim, seqlen) - actually pointing to x_ptr
     # Matrix dimensions
     dim: tl.constexpr,
@@ -94,9 +94,9 @@ def _causal_conv1d_fwd_kernel(  # continuous batching
         # In particular, if prefix caching is enabled, the program write additional cache states to "cache_indices_ptr"
 
         # Get the length of the completed sequence so far and compute the offset.
-        current_first_index = tl.load(current_first_idx + idx_seq)
-        current_last_index = tl.load(current_last_idx + idx_seq)
-        sequence_completed_index = tl.load(context_lens + idx_seq)
+        current_first_index = tl.load(block_idx_first_scheduled_token + idx_seq)
+        current_last_index = tl.load(block_idx_last_scheduled_token + idx_seq)
+        sequence_completed_index = tl.load(num_computed_tokens + idx_seq)
 
         # Compute the offset where the first stride_block_m-aligned first full block is
         # Value in "token-space"
@@ -476,10 +476,10 @@ def causal_conv1d_fn(
     has_initial_state: Optional[torch.Tensor] = None,
     activation: Optional[str] = "silu",
     pad_slot_id: int = PAD_SLOT_ID,
-    current_first_idx: Optional[torch.Tensor] = None,
-    current_last_idx: Optional[torch.Tensor] = None,
+    block_idx_first_scheduled_token: Optional[torch.Tensor] = None,
+    block_idx_last_scheduled_token: Optional[torch.Tensor] = None,
     initial_state_idx: Optional[torch.Tensor] = None,
-    context_lens: Optional[torch.Tensor] = None,
+    num_computed_tokens: Optional[torch.Tensor] = None,
     block_size_to_align=0,
     metadata=None,
     validate_data=False,
@@ -523,13 +523,13 @@ def causal_conv1d_fn(
         for example: cache_indices = [pad_slot_id, 1, 20, pad_slot_id]
         in this case, the kernel will not process entries at
         indices 0 and 3
-    current_first_idx: (batch,), dtype int32
+    block_idx_first_scheduled_token: (batch,), dtype int32
         The pointer into cache_indices, where the first cache block to be filled is located.
-    current_last_idx: (batch,), dtype int32
+    block_idx_last_scheduled_token: (batch,), dtype int32
         The pointer into cache_indices, where the last cache block to be filled is located.
     initial_state_idx: (batch,), dtype int32
         The pointer into cache_indices, where the cache block containing the initial state is located.
-    context_lens: (batch,), dtype int32
+    num_computed_tokens: (batch,), dtype int32
         The number of tokens already completed for each sequence
     block_size_to_align: int
         The block size to align the cached states to
@@ -708,10 +708,10 @@ def grid(META):
         query_start_loc,
         batch_ptr,
         token_chunk_offset_ptr,
-        current_first_idx,
-        current_last_idx,
+        block_idx_first_scheduled_token,
+        block_idx_last_scheduled_token,
         initial_state_idx,
-        context_lens,
+        num_computed_tokens,
         out,
         # Matrix dimensions
         dim,
@@ -735,7 +735,7 @@ def grid(META):
         HAS_BIAS=bias is not None,
         KERNEL_WIDTH=width,
         SILU_ACTIVATION=activation in ["silu", "swish"],
-        IS_APC_ENABLED=current_last_idx is not None,
+        IS_APC_ENABLED=block_idx_last_scheduled_token is not None,
         USE_PAD_SLOT=pad_slot_id is not None,
         NP2_STATELEN=np2_statelen,
         # launch_cooperative_grid=True
@@ -756,7 +756,7 @@ def _causal_conv1d_update_kernel(
     conv_state_indices_ptr,
     num_accepted_tokens_ptr,
     query_start_loc_ptr,  # (batch + 1)
-    current_last_idx,  # (batch,)
+    block_idx_last_scheduled_token,  # (batch,)
     initial_state_idx,  # (batch,)
     o_ptr,  # (batch, dim, seqlen)
     # Matrix dimensions
@@ -802,7 +802,7 @@ def _causal_conv1d_update_kernel(
     if IS_APC_ENABLED:
         # Get the state from the initial_state_idx
         conv_state_init = tl.load(initial_state_idx + idx_seq)
-        current_last_index = tl.load(current_last_idx + idx_seq)
+        current_last_index = tl.load(block_idx_last_scheduled_token + idx_seq)
     else:
         conv_state_init = 0
         current_last_index = 0
@@ -1078,7 +1078,7 @@ def causal_conv1d_update(
     query_start_loc: Optional[torch.Tensor] = None,
     max_query_len: int = -1,
     pad_slot_id: int = PAD_SLOT_ID,
-    current_last_idx: Optional[torch.Tensor] = None,
+    block_idx_last_scheduled_token: Optional[torch.Tensor] = None,
     initial_state_idx: Optional[torch.Tensor] = None,
     validate_data=False,
 ):
@@ -1097,7 +1097,7 @@ def causal_conv1d_update(
         If not None, the conv_state is a larger tensor along the batch dim,
         and we are selecting the batch coords specified by conv_state_indices.
         Useful for a continuous batching scenario.
-    current_last_idx: (batch,), dtype int32
+    block_idx_last_scheduled_token: (batch,), dtype int32
         The pointer into conv_state_indices, where the last cache block to be filled is located.
     initial_state_idx: (batch,), dtype int32
         The pointer into conv_state_indices, where the cache block containing the initial state is located.
@@ -1201,7 +1201,7 @@ def grid(META):
         conv_state_indices,
         num_accepted_tokens,
         query_start_loc,
-        current_last_idx,
+        block_idx_last_scheduled_token,
         initial_state_idx,
         out,
         # Matrix dimensions
@@ -1230,7 +1230,7 @@ def grid(META):
         KERNEL_WIDTH=width,
         SILU_ACTIVATION=activation in ["silu", "swish"],
         IS_VARLEN=query_start_loc is not None,
-        IS_APC_ENABLED=current_last_idx is not None,
+        IS_APC_ENABLED=block_idx_last_scheduled_token is not None,
         IS_SPEC_DECODING=num_accepted_tokens is not None,
         NP2_STATELEN=np2_statelen,
         USE_PAD_SLOT=pad_slot_id is not None,