Prefix caching WIP

backends/v3/src/radix.rs

@@ -205,6 +205,7 @@ pub struct RadixTrie {
     /// call that a real time lookup would require.
     time: u64,
 }
+
 impl Default for RadixTrie {
     fn default() -> Self {
         Self::new()

server/text_generation_server/layers/attention/__init__.py

@@ -6,7 +6,12 @@ from .common import Seqlen
 if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
     raise ImportError("`USE_FLASH_ATTENTION` is false.")
 if SYSTEM == "cuda":
-    from .cuda import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING
+    from .cuda import (
+        attention,
+        paged_attention,
+        reshape_and_cache,
+        SUPPORTS_WINDOWING,
+    )
 elif SYSTEM == "rocm":
     from .rocm import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING
 elif SYSTEM == "ipex":

server/text_generation_server/layers/attention/cuda.py

@@ -221,9 +221,11 @@ SUPPORTS_WINDOWING = V2
 if ATTENTION == "flashinfer":
 
     def attention(
-        q,
+        q: torch.Tensor,
-        k,
+        k: torch.Tensor,
-        v,
+        v: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
         cu_seqlens,
         max_s,
         softmax_scale,
@@ -231,14 +233,15 @@ if ATTENTION == "flashinfer":
         causal=True,
         softcap=0.0,
     ):
-        from text_generation_server.layers.attention.flash_infer import prefill_state
+        assert window_size_left == -1, "Windowing is not supported with flash infer"
+        from text_generation_server.layers.attention.flash_infer import (
+            prefill_with_paged_kv_state,
+        )
 
-        return prefill_state.get().forward(
+        return prefill_with_paged_kv_state.get().forward(
-            q,
+            q.contiguous(),
-            k,
-            v,
             causal=causal,
-            window_left=window_size_left,
+            paged_kv_cache=(key_cache, value_cache),
             logits_soft_cap=softcap,
             sm_scale=softmax_scale,
         )
@@ -249,6 +252,8 @@ elif V2:
         q,
         k,
         v,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
         cu_seqlens,
         max_s,
         softmax_scale,
@@ -289,6 +294,8 @@ else:
         q,
         k,
         v,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
         cu_seqlens,
         max_s,
         softmax_scale,

server/text_generation_server/layers/attention/flash_infer.py

@@ -9,6 +9,10 @@ prefill_state: ContextVar[flashinfer.BatchPrefillWithRaggedKVCacheWrapper] = Con
     "prefill_state"
 )
 
+prefill_with_paged_kv_state: ContextVar[
+    flashinfer.BatchPrefillWithPagedKVCacheWrapper
+] = ContextVar("prefill_with_paged_kv_state")
+
 decode_state: ContextVar[flashinfer.BatchDecodeWithPagedKVCacheWrapper] = ContextVar(
     "decode_state"
 )
@@ -24,6 +28,78 @@ def get_workspace(device):
     return workspace
 
 
+def create_prefill_with_paged_kv_state(
+    *,
+    device: torch.device,
+):
+    """Create a prefill state that uses the KV cache."""
+    workspace_buffer = get_workspace(device)
+    return flashinfer.BatchPrefillWithPagedKVCacheWrapper(
+        workspace_buffer, kv_layout="NHD", use_cuda_graph=False
+    )
+
+
+@contextmanager
+def use_prefill_with_paged_kv_state(
+    *,
+    state: flashinfer.BatchPrefillWithPagedKVCacheWrapper,
+    block_tables: torch.Tensor,
+    cu_seqlens: torch.Tensor,
+    input_lengths: torch.Tensor,
+    num_heads: int,
+    num_kv_heads: int,
+    head_size: int,
+    page_size: int,
+    query_dtype: str = "float16",
+):
+    """
+    Context manager to set the active flashinfer prefill state to the given
+    `state` and parameters. This state will be used by all calls to the
+    `attention` function while the context manager is active.
+    """
+
+    indptr = torch.zeros(
+        input_lengths.shape[0] + 1, device=input_lengths.device, dtype=torch.int32
+    )
+    # Round up to page size and then calculate the cumulative sum to get
+    # the indices into the block table.
+    torch.add(input_lengths, page_size - 1, out=indptr[1:])
+    indptr[1:].div_(page_size, rounding_mode="floor")
+    indptr[1:].cumsum_(-1)
+
+    # Get the lengths of the last page in a block.
+    if page_size == 1:
+        last_page_len = torch.ones(
+            input_lengths.shape[0], dtype=torch.int32, device=input_lengths.device
+        )
+    else:
+        last_page_len = torch.empty(
+            input_lengths.shape[0], dtype=torch.int32, device=input_lengths.device
+        )
+        torch.sub(input_lengths, 1, out=last_page_len)
+        last_page_len.remainder_(page_size)
+        last_page_len += 1
+
+    token = prefill_with_paged_kv_state.set(state)
+    try:
+        state.begin_forward(
+            qo_indptr=cu_seqlens,
+            paged_kv_indptr=indptr,
+            paged_kv_indices=block_tables,
+            paged_kv_last_page_len=last_page_len,
+            num_qo_heads=num_heads,
+            num_kv_heads=num_kv_heads,
+            head_dim=head_size,
+            q_data_type=query_dtype,
+            page_size=page_size,
+        )
+        yield
+    finally:
+        state.end_forward()
+        if token is not None:
+            prefill_with_paged_kv_state.reset(token)
+
+
 def create_prefill_state(
     *,
     device: torch.device,

server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py

@@ -298,6 +298,8 @@ class FlashCohereAttention(torch.nn.Module):
                 query,
                 key,
                 value,
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py

@@ -337,6 +337,8 @@ class DbrxAttention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py

@@ -365,6 +365,8 @@ class DeepseekV2Attention(torch.nn.Module):
                 query,
                 key,
                 value,
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py

@@ -238,6 +238,8 @@ class FlashGemma2Attention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py

@@ -232,6 +232,8 @@ class FlashGemmaAttention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py

@@ -232,6 +232,8 @@ class FlashGPT2Attention(torch.nn.Module):
                 query,
                 key,
                 value,
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -21,6 +21,7 @@
 from contextlib import contextmanager
 from typing import List, Optional, Tuple
 
+from loguru import logger
 import torch
 import torch.distributed
 
@@ -220,6 +221,8 @@ class FlashLlamaAttention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py

@@ -219,6 +219,8 @@ class MistralAttention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py

@@ -276,6 +276,8 @@ class MixtralAttention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_neox_modeling.py

@@ -173,6 +173,8 @@ class FlashNeoxAttention(torch.nn.Module):
                 qkv[:, 0],
                 qkv[:, 1],
                 qkv[:, 2],
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_phi_modeling.py

@@ -194,6 +194,8 @@ class FlashPhiAttention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py

@@ -137,6 +137,8 @@ class Qwen2Attention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_rw_modeling.py

@@ -208,6 +208,8 @@ class FlashRWAttention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,
@@ -326,6 +328,8 @@ class FlashRWLargeAttention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=2, index=0),
                 torch.select(kv, dim=2, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -293,6 +293,8 @@ class FlashMQAttention(torch.nn.Module):
                 query,
                 torch.select(key_value, dim=1, index=0),
                 torch.select(key_value, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py

@@ -242,6 +242,8 @@ class Starcoder2Attention(torch.nn.Module):
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
+                kv_cache[0],
+                kv_cache[1],
                 cu_seqlen_prefill,
                 max_s,
                 self.softmax_scale,

server/text_generation_server/models/flash_causal_lm.py

@@ -20,6 +20,9 @@ from typing import Any, ContextManager, Iterable, Optional, Tuple, List, Type, D
 
 from text_generation_server.adapters import AdapterBatchData, AdapterBatchMetadata
 from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
+from text_generation_server.layers.attention.flash_infer import (
+    create_prefill_with_paged_kv_state,
+)
 from text_generation_server.utils.chunks import concat_text_chunks
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.models import Model
@@ -43,6 +46,7 @@ from text_generation_server.models.globals import (
     ATTENTION,
     BLOCK_SIZE,
     CUDA_GRAPHS,
+    PREFIX_CACHING,
     get_adapter_to_index,
 )
 from text_generation_server.layers.attention import Seqlen
@@ -138,6 +142,9 @@ class FlashCausalLMBatch(Batch):
     block_tables_tensor: torch.Tensor
     # tensor of length \sum_{i=0}^{b} max_s_i  holding the paged attention slots for all sequences
     slots: torch.Tensor
+    # size [b], containing the number of blocks that can be retrieved from the cache
+    prefix_lens: List[int]
+    prefix_lens_tensor: torch.Tensor
 
     max_seqlen: int
 
@@ -146,6 +153,9 @@ class FlashCausalLMBatch(Batch):
     prefill_next_token_indices: Optional[torch.tensor]
     prefill_cu_outlens: Optional[List[int]]
 
+    # Prefixes
+    prefix_ids: List[List[int]]
+
     # All tokens
     all_input_ids: List[List[int]]
     all_input_ids_tensor: torch.Tensor
@@ -213,6 +223,7 @@ class FlashCausalLMBatch(Batch):
         prefix_offsets = []
         read_offsets = []
         all_input_ids = []
+        prefix_ids = []
         requests_idx_mapping = {}
 
         all_prefill_logprobs = True
@@ -230,7 +241,7 @@ class FlashCausalLMBatch(Batch):
 
         # Cumulative length
         cumulative_length = 0
-        cumulative_max_length = 0
+        cumulative_slot_tokens = 0
         prefill_out_cumulative_length = 0
 
         num_blocks = 0
@@ -240,6 +251,7 @@ class FlashCausalLMBatch(Batch):
 
         block_tables = []
         slots = []
+        prefix_lens = []
 
         # Parse batch
         for i, (r, tokenized_input) in enumerate(
@@ -255,6 +267,19 @@ class FlashCausalLMBatch(Batch):
             ):
                 tokenized_input = tokenized_input[1:]
 
+            orig_input_length = len(tokenized_input)
+
+            if PREFIX_CACHING:
+                prefix_len = r.prefix_len
+                if prefix_len == orig_input_length:
+                    assert prefix_len > 0
+                    prefix_len -= 1
+            else:
+                prefix_len = 0
+
+            prefix_ids.append(tokenized_input[:prefix_len])
+            tokenized_input = tokenized_input[prefix_len:]
+
             input_length = len(tokenized_input)
             input_lengths.append(input_length)
 
@@ -264,7 +289,9 @@ class FlashCausalLMBatch(Batch):
             all_input_ids.append(tokenized_input)
 
             # Position ids
-            request_position_ids = torch.arange(0, input_length, dtype=torch.int32)
+            request_position_ids = torch.arange(
+                prefix_len, orig_input_length, dtype=torch.int32
+            )
             position_ids.append(request_position_ids)
 
             # Add cumulative lengths of all previous inputs
@@ -288,11 +315,17 @@ class FlashCausalLMBatch(Batch):
             # Remove one as the first token des not have a past
             speculative_length = get_speculate()
             speculative_length = 0 if speculative_length is None else speculative_length
-            total_tokens = input_length + max_new_tokens - 1 + speculative_length
+
+            # Tokens that need to be mapped to blocks.
+            block_tokens = orig_input_length + max_new_tokens - 1 + speculative_length
+
+            # Tokens that need to be mapped to slots. We don't need slots for the
+            # cached prefix (if present).
+            slot_tokens = input_length + max_new_tokens - 1 + speculative_length
 
             # blocks and slots can be empty (for example in warmup)
             if not r.blocks:
-                needed_blocks = math.ceil(total_tokens / BLOCK_SIZE)
+                needed_blocks = math.ceil(block_tokens / BLOCK_SIZE)
                 request_blocks = [
                     b for b in range(num_blocks, num_blocks + needed_blocks)
                 ]
@@ -303,16 +336,20 @@ class FlashCausalLMBatch(Batch):
                 ]
             else:
                 request_blocks = r.blocks
-                request_slots = r.slots
+                request_slots = r.slots[
+                    prefix_len:  #: orig_input_length + max_new_tokens + speculative_length
+                ]
 
             block_tables.append(request_blocks)
-            slots.extend(request_slots[:total_tokens])
+
+            slots.extend(request_slots)
+            prefix_lens.append(prefix_len)
             num_blocks += len(request_blocks)
-            start_slots.append(cumulative_max_length)
+            start_slots.append(cumulative_slot_tokens)
 
             request_slot_indices = torch.arange(
-                cumulative_max_length,
+                cumulative_slot_tokens,
-                cumulative_max_length + input_length,
+                cumulative_slot_tokens + input_length,
                 dtype=torch.int64,
             )
             slot_indices.append(request_slot_indices)
@@ -348,7 +385,7 @@ class FlashCausalLMBatch(Batch):
 
             # Update
             cumulative_length += input_length
-            cumulative_max_length += total_tokens
+            cumulative_slot_tokens += slot_tokens
             max_seqlen = max(max_seqlen, input_length)
             max_blocks = max(max_blocks, len(request_blocks))
             max_length = max(
@@ -425,12 +462,14 @@ class FlashCausalLMBatch(Batch):
         )
 
         slots = torch.tensor(slots, dtype=torch.int64, device=device)
+
         block_tables_tensor = torch.zeros(
             (len(block_tables), max_blocks), dtype=torch.int32, device="cpu"
         )
         for i, request_blocks in enumerate(block_tables):
             block_tables_tensor[i, : len(request_blocks)] = torch.tensor(request_blocks)
         block_tables_tensor = block_tables_tensor.to(device)
+        prefix_lens_tensor = torch.tensor(prefix_lens, dtype=torch.int32, device=device)
 
         return cls(
             batch_id=pb.id,
@@ -445,6 +484,8 @@ class FlashCausalLMBatch(Batch):
             block_tables=block_tables,
             block_tables_tensor=block_tables_tensor,
             slots=slots,
+            prefix_lens=prefix_lens,
+            prefix_lens_tensor=prefix_lens_tensor,
             max_seqlen=max_seqlen,
             prefill_head_indices=prefill_head_indices,
             prefill_next_token_indices=prefill_next_token_indices,
@@ -455,6 +496,7 @@ class FlashCausalLMBatch(Batch):
             read_offsets=read_offsets,
             all_input_ids=all_input_ids,
             all_input_ids_tensor=all_input_ids_tensor,
+            prefix_ids=prefix_ids,
             next_token_chooser=next_token_chooser,
             stopping_criterias=stopping_criterias,
             top_n_tokens=top_n_tokens,
@@ -510,6 +552,7 @@ class FlashCausalLMBatch(Batch):
         start_slots = []
         block_tables = []
         all_input_ids = []
+        prefix_ids = []
 
         input_lengths = []
         prefix_offsets = []
@@ -536,6 +579,7 @@ class FlashCausalLMBatch(Batch):
             max_seqlen = max(max_seqlen, request_input_length)
 
             all_input_ids.append(self.all_input_ids[idx])
+            prefix_ids.append(self.prefix_ids[idx])
 
             input_lengths.append(request_input_length)
             prefix_offsets.append(self.prefix_offsets[idx])
@@ -621,6 +665,7 @@ class FlashCausalLMBatch(Batch):
             read_offsets=read_offsets,
             all_input_ids=all_input_ids,
             all_input_ids_tensor=all_input_ids_tensor,
+            prefix_ids=prefix_ids,
             next_token_chooser=next_token_chooser,
             stopping_criterias=stopping_criterias,
             top_n_tokens=top_n_tokens,
@@ -681,6 +726,7 @@ class FlashCausalLMBatch(Batch):
         block_tables_tensor = batches[0].block_tables_tensor.new_zeros(
             (total_batch_size, max_blocks)
         )
+        prefix_lens_tensor = batches[0].prefix_lens_tensor.new_empty(total_batch_size)
         all_input_ids_tensor = batches[0].all_input_ids_tensor.new_zeros(
             (total_batch_size, max_length)
         )
@@ -698,7 +744,9 @@ class FlashCausalLMBatch(Batch):
 
         start_slots = []
         block_tables = []
+        prefix_lens = []
         all_input_ids = []
+        prefix_ids = []
 
         input_lengths = []
         prefix_offsets = []
@@ -760,10 +808,14 @@ class FlashCausalLMBatch(Batch):
                 start_index:end_index, : batch.block_tables_tensor.shape[1]
             ] = batch.block_tables_tensor[:, :max_blocks]
 
+            prefix_lens_tensor[start_index:end_index] = batch.prefix_lens_tensor
+
             start_slots.append(batch.start_slots + cumulative_slots)
 
             block_tables.extend(batch.block_tables)
+            prefix_lens.extend(batch.prefix_lens)
             all_input_ids.extend(batch.all_input_ids)
+            prefix_ids.extend(batch.prefix_ids)
 
             input_lengths.extend(batch.input_lengths)
             prefix_offsets.extend(batch.prefix_offsets)
@@ -809,6 +861,8 @@ class FlashCausalLMBatch(Batch):
             slot_indices=slot_indices,
             block_tables=block_tables,
             block_tables_tensor=block_tables_tensor,
+            prefix_lens=prefix_lens,
+            prefix_lens_tensor=prefix_lens_tensor,
             slots=slots,
             max_seqlen=max_seqlen,
             prefill_head_indices=None,
@@ -820,6 +874,7 @@ class FlashCausalLMBatch(Batch):
             read_offsets=read_offsets,
             all_input_ids=all_input_ids,
             all_input_ids_tensor=all_input_ids_tensor,
+            prefix_ids=prefix_ids,
             next_token_chooser=next_token_chooser,
             stopping_criterias=stopping_criterias,
             top_n_tokens=top_n_tokens,
@@ -976,6 +1031,9 @@ class FlashCausalLM(Model):
             )
 
             self.prefill_state = create_prefill_state(device=device)
+            self.prefill_with_paged_kv_state = create_prefill_with_paged_kv_state(
+                device=device
+            )
 
             if not CUDA_GRAPHS:
                 self.decode_state = create_decode_state(
@@ -1074,12 +1132,23 @@ class FlashCausalLM(Model):
         input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device)
         position_ids = torch.zeros(bs, dtype=torch.int32, device=self.device)
         slots = torch.arange(bs, dtype=torch.int64, device=self.device)
-        input_lengths = torch.ones(bs, dtype=torch.int32, device=self.device) * max_s
+        input_lengths = [max_s] * bs
-        block_tables = (
+        prefix_lengths = [0] * bs
-            torch.arange(max_bt, dtype=torch.int32, device=self.device)
+        input_lengths_tensor = (
-            .repeat(bs)
+            torch.ones(bs, dtype=torch.int32, device=self.device) * max_s
-            .reshape((bs, max_bt))
         )
+        prefix_lengths_tensor = torch.zeros(bs, dtype=torch.int32, device=self.device)
+        block_tables = torch.arange(
+            max_bt, dtype=torch.int32, device=self.device
+        ).repeat(bs)
+        block_tables = block_tables.reshape((bs, max_bt))
+
+        if ATTENTION == "flashinfer":
+            block_tables = block_tables_to_ragged(
+                block_tables=block_tables,
+                input_lengths=input_lengths,
+                prefix_lens=prefix_lengths,
+            )
 
         self.cuda_graphs[bs] = {
             "input_ids": input_ids,
@@ -1087,9 +1156,9 @@ class FlashCausalLM(Model):
             "kv_cache": self.kv_cache,
             "block_tables": block_tables,
             "slots": slots,
-            "input_lengths": input_lengths,
+            "input_lengths": input_lengths_tensor,
         }
-        input_lengths_ = Seqlen(input_lengths=input_lengths)
+        input_lengths_ = Seqlen(input_lengths=input_lengths_tensor)
         graph = torch.cuda.CUDAGraph()
         self.cuda_graphs[bs]["graph"] = graph
 
@@ -1104,7 +1173,7 @@ class FlashCausalLM(Model):
             last_page_len = torch.ones(bs, dtype=torch.int32, device=self.device)
             state = create_decode_state_cuda_graphs(
                 device=input_ids.device,
-                block_tables=block_tables.view(-1),
+                block_tables=block_tables,
                 block_tables_ptr=block_tables_ptr,
                 last_page_len=last_page_len,
                 num_heads=self.num_heads,
@@ -1120,7 +1189,10 @@ class FlashCausalLM(Model):
             block_tables=block_tables,
             cu_seqlen_prefill=None,
             input_lengths=input_lengths,
+            input_lengths_tensor=input_lengths_tensor,
             state=state,
+            prefix_lens=prefix_lengths,
+            prefix_lens_tensor=prefix_lengths_tensor,
         ):
             self.model.forward(
                 input_ids=input_ids,
@@ -1138,7 +1210,7 @@ class FlashCausalLM(Model):
             torch.cuda.synchronize()
 
             with torch.cuda.graph(graph, pool=MEM_POOL):
-                input_lengths = Seqlen(input_lengths=input_lengths)
+                input_lengths_tensor = Seqlen(input_lengths=input_lengths_tensor)
                 logits, speculative_logits = self.model.forward(
                     input_ids=input_ids,
                     position_ids=position_ids,
@@ -1146,7 +1218,7 @@ class FlashCausalLM(Model):
                     kv_cache=self.kv_cache,
                     block_tables=block_tables,
                     slots=slots,
-                    input_lengths=input_lengths,
+                    input_lengths=input_lengths_tensor,
                     max_s=max_s,
                     prefill_cache_indices=None,
                     lm_head_indices=None,
@@ -1375,7 +1447,10 @@ class FlashCausalLM(Model):
             with self._forward_context(
                 block_tables=block_tables,
                 cu_seqlen_prefill=cu_seqlen_prefill,
-                input_lengths=input_lengths,
+                input_lengths=batch.input_lengths,
+                input_lengths_tensor=input_lengths,
+                prefix_lens=batch.prefix_lens,
+                prefix_lens_tensor=batch.prefix_lens_tensor,
             ):
                 input_lengths = Seqlen(input_lengths=input_lengths)
                 logits, speculative_logits = self.model.forward(
@@ -1399,19 +1474,32 @@ class FlashCausalLM(Model):
         # Static inputs are potentially padded
         cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids
         cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids
-        cuda_graph["block_tables"][
+        if ATTENTION == "flashinfer":
-            : block_tables.shape[0], : block_tables.shape[1]
+            block_tables = block_tables_to_ragged(
-        ] = block_tables
+                block_tables=block_tables,
+                input_lengths=batch.input_lengths,
+                prefix_lens=batch.prefix_lens,
+            )
+            cuda_graph["block_tables"][: block_tables.shape[0]] = block_tables
+        else:
+            cuda_graph["block_tables"][
+                : block_tables.shape[0], : block_tables.shape[1]
+            ] = block_tables
         cuda_graph["slots"].fill_(-1)
         cuda_graph["slots"][: slots.shape[0]] = slots
         cuda_graph["input_lengths"].zero_()
-        cuda_graph["input_lengths"][: input_lengths.shape[0]] = input_lengths
+        cuda_graph["input_lengths"][: input_lengths.shape[0]] = (
+            input_lengths + batch.prefix_lens_tensor
+        )
 
         state = cuda_graph.get("state")
         with self._forward_context(
             block_tables=block_tables,
             cu_seqlen_prefill=None,
-            input_lengths=input_lengths,
+            input_lengths=batch.input_lengths,
+            input_lengths_tensor=input_lengths,
+            prefix_lens=batch.prefix_lens,
+            prefix_lens_tensor=batch.prefix_lens_tensor,
             state=state,
         ):
             # Replay the graph
@@ -1610,6 +1698,7 @@ class FlashCausalLM(Model):
             batch.read_offsets,
             batch.stopping_criterias,
             batch.all_input_ids,
+            batch.prefix_ids,
             batch.next_token_chooser.do_sample,
             batch.next_token_chooser.seeds,
             batch.top_n_tokens,
@@ -1627,6 +1716,7 @@ class FlashCausalLM(Model):
             read_offset,
             stopping_criteria,
             all_input_ids,
+            prefix_ids,
             do_sample,
             seed,
             top_n_tokens,
@@ -1701,18 +1791,18 @@ class FlashCausalLM(Model):
                     out_end_index = batch.prefill_cu_outlens[i + 1]
 
                     # Remove generated token to only have prefill and add nan for first prompt token
-                    request_prefill_logprobs = [float("nan")] + prefill_logprobs[
+                    request_prefill_logprobs = (
-                        out_start_index : out_end_index - 1
+                        [float("nan")] * (len(prefix_ids) + 1)
-                    ]
+                    ) + prefill_logprobs[out_start_index : out_end_index - 1]
                     prefill_token_ids = all_input_ids[:-1]
                     prefill_texts = self.tokenizer.batch_decode(
-                        prefill_token_ids,
+                        prefix_ids + prefill_token_ids,
                         clean_up_tokenization_spaces=False,
                         skip_special_tokens=False,
                     )
 
                     prefill_tokens = Tokens(
-                        prefill_token_ids,
+                        prefix_ids + prefill_token_ids,
                         request_prefill_logprobs,
                         prefill_texts,
                         is_special=[],
@@ -1794,7 +1884,10 @@ class FlashCausalLM(Model):
         *,
         block_tables: torch.Tensor,
         cu_seqlen_prefill: Optional[torch.Tensor],
-        input_lengths: torch.Tensor,
+        input_lengths: List[int],
+        input_lengths_tensor: torch.Tensor,
+        prefix_lens: List[int],
+        prefix_lens_tensor: torch.Tensor,
         state: Optional[Any] = None,
     ) -> ContextManager:
         if ATTENTION != "flashinfer":
@@ -1803,24 +1896,65 @@ class FlashCausalLM(Model):
         from text_generation_server.layers.attention.flash_infer import (
             use_decode_state,
             use_prefill_state,
+            use_prefill_with_paged_kv_state,
         )
 
+        # has_prefix_lens = any(prefix_len > 0 for prefix_len in prefix_lens)
+
         if cu_seqlen_prefill is not None:
-            return use_prefill_state(
+            if True:  # has_prefix_lens:
-                state=state if state is not None else self.prefill_state,
+                return use_prefill_with_paged_kv_state(
-                cu_seqlens=cu_seqlen_prefill,
+                    state=(
-                num_heads=self.num_heads,
+                        state if state is not None else self.prefill_with_paged_kv_state
-                num_kv_heads=self.num_kv_heads,
+                    ),
-                head_size=self.head_size,
+                    block_tables=block_tables_to_ragged(
-            )
+                        block_tables=block_tables,
+                        input_lengths=input_lengths,
+                        prefix_lens=prefix_lens,
+                    ),
+                    cu_seqlens=cu_seqlen_prefill,
+                    input_lengths=input_lengths_tensor + prefix_lens_tensor,
+                    num_heads=self.num_heads,
+                    num_kv_heads=self.num_kv_heads,
+                    head_size=self.head_size,
+                    page_size=BLOCK_SIZE,
+                )
+            else:
+                return use_prefill_state(
+                    state=state if state is not None else self.prefill_state,
+                    cu_seqlens=cu_seqlen_prefill,
+                    num_heads=self.num_heads,
+                    num_kv_heads=self.num_kv_heads,
+                    head_size=self.head_size,
+                )
         else:
-            assert input_lengths is not None
+            assert input_lengths_tensor is not None
             return use_decode_state(
                 state=state if state is not None else self.decode_state,
-                input_lengths=input_lengths,
+                input_lengths=input_lengths_tensor + prefix_lens_tensor,
-                block_tables=block_tables.view(-1),
+                block_tables=block_tables,
                 num_heads=self.num_heads,
                 num_kv_heads=self.num_kv_heads,
                 head_size=self.head_size,
                 page_size=BLOCK_SIZE,
             )
+
+
+def block_tables_to_ragged(
+    *, block_tables: torch.Tensor, input_lengths: List[int], prefix_lens: List[int]
+) -> torch.Tensor:
+    """Convert block table to ragged format compatible with FlashInfer."""
+    assert len(input_lengths) == len(prefix_lens)
+
+    total_len = sum(input_lengths) + sum(prefix_lens)
+    block_tables_ragged = torch.empty(
+        total_len, dtype=torch.int32, device=block_tables.device
+    )
+
+    offset = 0
+    for i, (input_length, prefix_len) in enumerate(zip(input_lengths, prefix_lens)):
+        seq_len = prefix_len + input_length
+        block_tables_ragged[offset : offset + seq_len] = block_tables[i][:seq_len]
+        offset += seq_len
+
+    return block_tables_ragged

server/text_generation_server/models/globals.py

@@ -29,7 +29,6 @@ elif ATTENTION == "flashinfer":
 else:
     BLOCK_SIZE = 16
 
-
 cuda_graphs = os.getenv("CUDA_GRAPHS")
 if cuda_graphs is not None:
     try: