[Major Change][Undecided yet] Move to FlashDecoding instead of PagedAttention kernel. (#1940)

* Using flash decoding

Conditional flashdecoding.

Fix max_q.

Working kvcache

Working version with flash decoding.

Make it work for mistral.

Fix after rebase..

Less intrusive.

REvert changes in modeling.

Speedup flashdecoding.

HHachweew
Hack to make other models work.

Fixing non flash decoding llama path.

Router logic knows about page size.

Missing 2 models.

Missing cohere.

Fixing cohere flash decoding.

Revamped all this architecture.

Fix cohere.

Fixing falcon.

Enabling custom block size schedule.

Update router/src/infer.rs

Not sending preallocated output.

* Making it work on non flash decoding.

* Fix Cohere.

* Fix non decoding paths.

* Rebased.

* No need for cache_manager anymore.

* Update?

* "ipex" -> "cpu"

* These do not belong.

* Factoring cu_seqlen_qk for better abstracting over every model.

* Fixing non flash tests/imports.

* Changing return everywhere.

* Update mistral past.

* Fixing Mi{s,x}tral (non functional in Flash Decoding mode though).

* Fixup mistral clamping (had issues with cuda graphs).

* No need to recreate anything actually.

router/src/infer/v2/scheduler.rs

@@ -39,7 +39,14 @@ impl SchedulerV2 {
         speculate: u32,
         generation_health: Arc<AtomicBool>,
     ) -> Self {
-        let queue = Queue::new(requires_padding, 16, window_size, speculate);
+        // Infer shared state
+        let flashdecoding = if let Ok(flashdecoding) = std::env::var("FLASH_DECODING") {
+            matches!(flashdecoding.to_lowercase().as_str(), "1" | "true")
+        } else {
+            false
+        };
+        let block_size = if flashdecoding { 256 } else { 16 };
+        let queue = Queue::new(requires_padding, block_size, window_size, speculate);
         let batching_task_notifier = Arc::new(Notify::new());
 
         // Spawn batching background task that contains all the inference logic

router/src/infer/v3/scheduler.rs

@@ -39,9 +39,15 @@ impl SchedulerV3 {
         speculate: u32,
         generation_health: Arc<AtomicBool>,
     ) -> Self {
+        let flashdecoding = if let Ok(flashdecoding) = std::env::var("FLASH_DECODING") {
+            matches!(flashdecoding.to_lowercase().as_str(), "1" | "true")
+        } else {
+            false
+        };
+        let block_size = if flashdecoding { 256 } else { 16 };
         let queue = Queue::new(
             requires_padding,
-            16,
+            block_size,
             window_size,
             speculate,
             max_batch_total_tokens,

server/text_generation_server/layers/attention/__init__.py

 from text_generation_server.utils.import_utils import SYSTEM
 import os
 
+from .common import Seqlen
+
 if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
     raise ImportError("`USE_FLASH_ATTENTION` is false.")
 if SYSTEM == "cuda":

server/text_generation_server/layers/attention/common.py

+from dataclasses import dataclass
+from text_generation_server.models.globals import FLASH_DECODING
+import torch
+from typing import Optional
+
+
+if FLASH_DECODING:
+
+    @dataclass
+    class Seqlen:
+        input_lengths: torch.Tensor
+        cu_seqlen_q: Optional[torch.Tensor]
+        cu_seqlen_k: Optional[torch.Tensor]
+
+        def __init__(self, input_lengths):
+            self.input_lengths = input_lengths
+            device = self.input_lengths.device
+            shape = self.input_lengths.shape
+            cu_seqlen_q = torch.arange(
+                shape[0] + 1,
+                device=device,
+                dtype=torch.int32,
+            )
+            cu_seqlen_k = torch.zeros(shape[-1] + 1, device=device, dtype=torch.int32)
+            # cuda graphs don't like this and this is necessary to clamp within mistral
+            # Although FA2 might not want the clamping
+            # cu_seqlen_k[0] = 0
+            torch.cumsum(self.input_lengths, -1, out=cu_seqlen_k[1:])
+
+            self.cu_seqlen_q = cu_seqlen_q
+            self.cu_seqlen_k = cu_seqlen_k
+
+        def clamp(self, max):
+            # Flash decoding doesn't need to clamp
+            return self
+
+else:
+
+    @dataclass
+    class Seqlen:
+        input_lengths: torch.Tensor
+
+        def clamp(self, max):
+            return Seqlen(torch.clamp(self.input_lengths, max=max))

server/text_generation_server/layers/attention/cuda.py

 import torch
 from text_generation_server.utils.import_utils import SYSTEM
+from text_generation_server.models.globals import FLASH_DECODING, BLOCK_SIZE
+from text_generation_server.layers.attention import Seqlen
 
 major, minor = torch.cuda.get_device_capability()
 is_sm75 = major == 7 and minor == 5
@@ -21,7 +23,14 @@ def reshape_and_cache(
     value_cache: torch.Tensor,
     slots: torch.Tensor,
 ):
-    cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots, "auto", 1.0)
+    if FLASH_DECODING:
+        shape = key_cache.shape
+        key_cache.view(-1, shape[-2], shape[-1])[slots] = key
+        value_cache.view(-1, shape[-2], shape[-1])[slots] = value
+    else:
+        cache_ops.reshape_and_cache(
+            key, value, key_cache, value_cache, slots, "auto", 1.0
+        )
 
 
 def paged_attention(
@@ -32,7 +41,7 @@ def paged_attention(
     kv_head_mapping: torch.Tensor,
     softmax_scale: float,
     block_tables: torch.Tensor,
-    input_lengths: torch.Tensor,
+    seqlen: Seqlen,
     max_s: int,
 ):
     # Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
@@ -53,7 +62,8 @@ def paged_attention(
     #
 
     # value_cache => [num_blocks, num_heads, head_size, block_size]
-    block_size = value_cache.shape[3]
+    # block_size = value_cache.shape[3]
+    block_size = BLOCK_SIZE
     num_seqs, num_heads, head_size = query.shape
     max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
 
@@ -62,58 +72,95 @@ def paged_attention(
     # V1 to avoid the overhead of reduction. Also, if the number of
     # sequences or heads is large, we use V1 since there is enough work
     # to parallelize.
-    from vllm._C import ops
+    if FLASH_DECODING:
+        max_q = 1
+        max_k = max_s
+        import flash_attn_2_cuda
 
-    use_v1 = max_s <= 8192 and (max_num_partitions == 1 or num_seqs * num_heads > 512)
-    if use_v1:
-        ops.paged_attention_v1(
-            out,
+        # TODO fixme when flash contains the fix.
+        # Number of splits is not correctly handled
+        # by the current path
+        # https://github.com/Dao-AILab/flash-attention/blob/320fb59487658f033f56711efd3d61b7c7a6f8f3/csrc/flash_attn/flash_api.cpp#L577
+        # This fails becuase we're using causal, therefore window_right is set to 0 and the split logic is never applied.
+        out2 = flash_attn_2_cuda.varlen_fwd(
             query,
             key_cache,
             value_cache,
-            kv_head_mapping,
-            softmax_scale,
+            None,
+            seqlen.cu_seqlen_q,
+            seqlen.cu_seqlen_k,
+            None,
             block_tables,
-            input_lengths,
-            block_size,
-            max_s,
             None,
-            "auto",
-            1.0,
+            max_q,
+            max_k,
+            0.0,  # dropout
+            softmax_scale,
+            False,  # zero_tensors
+            True,  # causal
+            -1,  # Window_left
+            -1,  # Window right
+            False,  # return softmax
+            None,  # generator
         )
+        return out2[0]
     else:
-        # Run PagedAttention V2.
-        assert _PARTITION_SIZE % block_size == 0
-        tmp_output = torch.empty(
-            size=(num_seqs, num_heads, max_num_partitions, head_size),
-            dtype=out.dtype,
-            device=out.device,
-        )
-        exp_sums = torch.empty(
-            size=(num_seqs, num_heads, max_num_partitions),
-            dtype=torch.float32,
-            device=out.device,
-        )
-        max_logits = torch.empty_like(exp_sums)
+        input_lengths = seqlen.input_lengths
+        from vllm._C import ops
 
-        ops.paged_attention_v2(
-            out,
-            exp_sums,
-            max_logits,
-            tmp_output,
-            query,
-            key_cache,
-            value_cache,
-            kv_head_mapping,
-            softmax_scale,
-            block_tables,
-            input_lengths,
-            block_size,
-            max_s,
-            None,
-            "auto",
-            1.0,
+        use_v1 = max_s <= 8192 and (
+            max_num_partitions == 1 or num_seqs * num_heads > 512
         )
+        if use_v1:
+            ops.paged_attention_v1(
+                out,
+                query,
+                key_cache,
+                value_cache,
+                kv_head_mapping,
+                softmax_scale,
+                block_tables,
+                input_lengths,
+                block_size,
+                max_s,
+                None,
+                "auto",
+                1.0,
+            )
+        else:
+            # Run PagedAttention V2.
+            assert _PARTITION_SIZE % block_size == 0
+            tmp_output = torch.empty(
+                size=(num_seqs, num_heads, max_num_partitions, head_size),
+                dtype=out.dtype,
+                device=out.device,
+            )
+            exp_sums = torch.empty(
+                size=(num_seqs, num_heads, max_num_partitions),
+                dtype=torch.float32,
+                device=out.device,
+            )
+            max_logits = torch.empty_like(exp_sums)
+
+            ops.paged_attention_v2(
+                out,
+                exp_sums,
+                max_logits,
+                tmp_output,
+                query,
+                key_cache,
+                value_cache,
+                kv_head_mapping,
+                softmax_scale,
+                block_tables,
+                input_lengths,
+                block_size,
+                max_s,
+                None,
+                "auto",
+                1.0,
+            )
+    return out
 
 
 try:

server/text_generation_server/layers/attention/ipex.py

@@ -55,7 +55,8 @@ def paged_attention(
     kv_head_mapping: torch.Tensor,
     softmax_scale: float,
     block_tables: torch.Tensor,
-    input_lengths: torch.Tensor,
+    cu_seqlen_q: torch.Tensor,
+    cu_seqlen_k: torch.Tensor,
     max_s: int,
 ):
     return ipex.llm.modules.PagedAttention.single_query_cached_kv_attention(
@@ -66,7 +67,7 @@ def paged_attention(
         kv_head_mapping,
         softmax_scale,
         block_tables,
-        input_lengths,
+        cu_seqlen_q,
         BLOCK_SIZE,
         max_s,
         None,

server/text_generation_server/layers/attention/rocm.py

 import os
 import torch
 from text_generation_server.utils.import_utils import SYSTEM
+from text_generation_server.models.globals import FLASH_DECODING
 from loguru import logger
 
 major, minor = torch.cuda.get_device_capability()
@@ -26,7 +27,14 @@ def reshape_and_cache(
     value_cache: torch.Tensor,
     slots: torch.Tensor,
 ):
-    cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots, "auto", 1.0)
+    if FLASH_DECODING:
+        shape = key_cache.shape
+        key_cache.view(-1, shape[-2], shape[-1])[slots] = key
+        value_cache.view(-1, shape[-2], shape[-1])[slots] = value
+    else:
+        cache_ops.reshape_and_cache(
+            key, value, key_cache, value_cache, slots, "auto", 1.0
+        )
 
 
 def paged_attention(
@@ -37,7 +45,8 @@ def paged_attention(
     kv_head_mapping: torch.Tensor,
     softmax_scale: float,
     block_tables: torch.Tensor,
-    input_lengths: torch.Tensor,
+    cu_seqlen_q: torch.Tensor,
+    cu_seqlen_k: torch.Tensor,
     max_s: int,
 ):
     # Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
@@ -61,6 +70,7 @@ def paged_attention(
     block_size = value_cache.shape[3]
     num_seqs, num_heads, head_size = query.shape
     max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
+    input_lengths = cu_seqlen_k
 
     # NOTE(woosuk): We use a simple heuristic to decide whether to use
     # PagedAttention V1 or V2. If the number of partitions is 1, we use
@@ -119,6 +129,7 @@ def paged_attention(
             "auto",
             1.0,
         )
+    return out
 
 
 if ENGINE != "triton":

server/text_generation_server/models/__init__.py

@@ -12,7 +12,6 @@ from pathlib import Path
 from text_generation_server.utils.speculate import get_speculate, set_speculate
 from text_generation_server.models.model import Model
 from text_generation_server.models.causal_lm import CausalLM
-from text_generation_server.models.flash_causal_lm import FlashCausalLM
 from text_generation_server.models.bloom import BLOOMSharded
 from text_generation_server.models.mpt import MPTSharded
 from text_generation_server.models.seq2seq_lm import Seq2SeqLM
@@ -53,6 +52,7 @@ FLASH_ATT_ERROR_MESSAGE = "{} requires Flash Attention enabled models."
 FLASH_ATTENTION = True
 
 try:
+    from text_generation_server.models.flash_causal_lm import FlashCausalLM
     from text_generation_server.models.flash_rw import FlashRWSharded
     from text_generation_server.models.flash_gpt2 import FlashGPT2
     from text_generation_server.models.flash_neox import FlashNeoXSharded
@@ -92,6 +92,7 @@ except ImportError as e:
     FLASH_ATTENTION = False
 
 if FLASH_ATTENTION:
+    __all__.append(FlashCausalLM)
     __all__.append(FlashGPT2)
     __all__.append(FlashNeoXSharded)
     __all__.append(FlashRWSharded)

server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py

@@ -30,6 +30,7 @@ from text_generation_server.layers.attention import (
     attention,
     reshape_and_cache,
 )
+from text_generation_server.models.globals import FLASH_DECODING
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers import (
     TensorParallelRowLinear,
@@ -259,8 +260,8 @@ class FlashCohereAttention(torch.nn.Module):
         cu_seqlen_prefill,
         kv_cache,
         block_tables,
-        slots,
         input_lengths,
+        slots,
         max_s,
     ):
         qkv = self.query_key_value(hidden_states)
@@ -304,7 +305,7 @@ class FlashCohereAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],
@@ -464,6 +465,7 @@ class FlashCohereModel(torch.nn.Module):
         )
 
         residual = None
+
         for i, layer in enumerate(self.layers):
             hidden_states, residual = layer(
                 hidden_states,

server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py

@@ -336,7 +336,7 @@ class DbrxAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],

server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py

@@ -251,7 +251,7 @@ class FlashGemma2Attention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],

server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py

@@ -245,7 +245,7 @@ class FlashGemmaAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],

server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py

@@ -245,7 +245,7 @@ class FlashGPT2Attention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -33,6 +33,7 @@ from text_generation_server.layers.attention import (
     attention,
     reshape_and_cache,
 )
+from text_generation_server.models.globals import FLASH_DECODING
 from text_generation_server.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
@@ -213,7 +214,7 @@ class FlashLlamaAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],

server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py

@@ -28,6 +28,7 @@ from typing import Optional, List, Tuple
 
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import (
+    Seqlen,
     paged_attention,
     attention,
     reshape_and_cache,
@@ -229,7 +230,7 @@ class MistralAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],
@@ -512,7 +513,7 @@ class FlashMistralForCausalLM(torch.nn.Module):
         elif self.max_past is not None:
             # Clamp in decode mode as paged attention requires clamped values whereas the flash attention
             # kernel requires the true values
-            input_lengths = torch.clamp(input_lengths, max=self.max_past_tensor)
+            input_lengths = input_lengths.clamp(max=self.max_past_tensor)
 
         inputs_embeds = self.embed_tokens(input_ids)
         hidden_states = self.model(

server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py

@@ -291,7 +291,7 @@ class MixtralAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],
@@ -647,7 +647,7 @@ class FlashMixtralForCausalLM(torch.nn.Module):
         elif self.max_past is not None:
             # Clamp in decode mode as paged attention requires clamped values whereas the flash attention
             # kernel requires the true values
-            input_lengths = torch.clamp(input_lengths, max=self.max_past_tensor)
+            input_lengths = input_lengths.clamp(max=self.max_past_tensor)
 
         hidden_states = self.model(
             input_ids,

server/text_generation_server/models/custom_modeling/flash_neox_modeling.py

@@ -168,7 +168,7 @@ class FlashNeoxAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 qkv[:, 0],
                 kv_cache[0],

server/text_generation_server/models/custom_modeling/flash_phi_modeling.py

@@ -207,7 +207,7 @@ class FlashPhiAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],

server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py

@@ -149,7 +149,7 @@ class Qwen2Attention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],

server/text_generation_server/models/custom_modeling/flash_rw_modeling.py

@@ -217,7 +217,7 @@ class FlashRWAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],
@@ -340,7 +340,7 @@ class FlashRWLargeAttention(torch.nn.Module):
             )
         # Decode
         else:
-            paged_attention(
+            attn_output = paged_attention(
                 attn_output,
                 query,
                 kv_cache[0],