Add Flash decoding kernel ROCm (#2855)

* (vllm) updated vllm rocm kernels

* revert silu

* update partition size

* remove grouped_topk

* (nit) remove log

* add flash decoding

server/Makefile-flash-att-v2

@@ -1,5 +1,5 @@
 flash_att_v2_commit_cuda := v2.6.1
-flash_att_v2_commit_rocm := 2092111b9f975b3347c652ff7fabd431130256c4
+flash_att_v2_commit_rocm := 47bd46e0204a95762ae48712fd1a3978827c77fd
 
 build-flash-attention-v2-cuda:
 	pip install -U packaging wheel

server/text_generation_server/layers/attention/rocm.py

@@ -5,6 +5,10 @@ from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import Seqlen
 from text_generation_server.utils.log import log_master
+from text_generation_server.models.globals import (
+    ATTENTION,
+    BLOCK_SIZE,
+)
 from loguru import logger
 import vllm._custom_ops as ops
 
@@ -73,11 +77,44 @@ def paged_attention(
     # limitations under the License.
     #
 
+    if ATTENTION == "flashdecoding":
+        max_q = 1
+        max_k = max_s
+        import flash_attn_2_cuda
+
+        if softcap is None:
+            softcap = 0.0
+        out = flash_attn_2_cuda.varlen_fwd(
+            query,
+            kv_cache.key,
+            kv_cache.value,
+            None,
+            seqlen.cu_seqlen_q,
+            seqlen.cu_seqlen_k,
+            None,  # pad_k
+            None,
+            block_tables,
+            None,
+            max_q,
+            max_k,
+            0.0,  # dropout
+            softmax_scale,
+            False,  # zero_tensors
+            True,  # causal
+            -1,  # Window_left
+            -1,  # Window right
+            softcap,
+            False,  # return softmax
+            None,  # generator
+        )
+        return out[0]
+
     if softcap is not None:
         raise RuntimeError("Paged attention doesn't support softcapping")
 
     # value_cache => [num_blocks, num_heads, head_size, block_size]
-    block_size = kv_cache.value.shape[3]
+    # block_size = kv_cache.value.shape[3]
+    block_size = BLOCK_SIZE
     num_seqs, num_heads, head_size = query.shape
 
     num_kv_heads = kv_cache.key.shape[1]
@@ -247,14 +284,15 @@ def attention(
         # We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
         return flash_attn_2_cuda.varlen_fwd(
             query,
-            key,
+            # flashdecoding: pass the KV caches, paged: pass the KV.
-            value,
+            kv_cache.key if ATTENTION == "flashdecoding" else key,
+            kv_cache.value if ATTENTION == "flashdecoding" else value,
             out,
             seqlen.cu_seqlen_q,
-            seqlen.cu_seqlen_q,
+            seqlen.cu_seqlen_k,
-            None,
             None,
             None,
+            block_tables if ATTENTION == "flashdecoding" else None,
             None,
             seqlen.max_q,
             seqlen.max_k,

server/text_generation_server/models/flash_causal_lm.py

@@ -1663,7 +1663,7 @@ class FlashCausalLM(Model):
 
                 for seqlen in tuning_sequences:
                     log_master(logger.info, f"Warming up TunableOp for seqlen={seqlen}")
-                    self.tunableop_warmup(seqlen)
+                    self.tunableop_warmup(seqlen, max_total_tokens)
                     torch.cuda.tunable.write_file(tunableop_filepath)
                 if os.environ.get("PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP") != "1":
                     torch.cuda.tunable.tuning_enable(False)
@@ -1710,7 +1710,7 @@ class FlashCausalLM(Model):
         assert max_total_tokens is not None
         return int(num_blocks * BLOCK_SIZE), max_input_tokens, max_total_tokens
 
-    def tunableop_warmup(self, seqlen: int):
+    def tunableop_warmup(self, seqlen: int, max_bt: int):
         input_ids = torch.zeros(seqlen, dtype=torch.int64, device=self.device)
         position_ids = torch.zeros(seqlen, dtype=torch.int32, device=self.device)
         slots = torch.arange(seqlen, dtype=torch.int64, device=self.device)
@@ -1724,11 +1724,15 @@ class FlashCausalLM(Model):
             [0, seqlen], device=self.device, dtype=torch.int32
         )
         max_s = seqlen
+
+        block_tables = torch.arange(
+            max_bt, dtype=torch.int32, device=self.device
+        ).repeat(seqlen)
+        block_tables = block_tables.reshape((seqlen, max_bt))
+
         seqlen = Seqlen(
             input_lengths=input_lengths,
             cache_lengths=cache_lengths_tensor,
-            cu_seqlen_q=cu_seqlen_prefill,
-            max_q=1,
             max_k=seqlen,
         )
 
@@ -1738,7 +1742,7 @@ class FlashCausalLM(Model):
             position_ids=position_ids,
             cu_seqlen_prefill=cu_seqlen_prefill,
             kv_cache=self.kv_cache,
-            block_tables=None,
+            block_tables=block_tables,
             seqlen=seqlen,
             slots=slots,
             max_s=max_s,