[gpt-oss] Add gpt-oss bf16 support

vllm/attention/backends/triton_mla.py

@@ -0,0 +1,181 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Type
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.attention.backends.mla.common import (MLACommonBackend,
+                                                MLACommonImpl,
+                                                MLACommonMetadata)
+from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
+
+
+import json
+import os
+
+# TODO: Configure environment variables temporarily. New versions do not need to be configured
+os.environ['TRITON_ENABLE_MACA_OPT_MOVE_DOT_OPERANDS_OUT_LOOP'] = '1'
+os.environ['TRITON_ENABLE_MACA_CHAIN_DOT_OPT'] = '1'
+
+def load_config():
+    # Load JSON data from the file
+    json_path = config_file_path = os.path.join(
+        os.path.dirname(os.path.realpath(__file__)), "configs", "tp8_merge.json")
+    with open(json_path, 'r') as file:
+        data = json.load(file)
+    return data
+
+JSON_DATA = load_config()
+
+def find_best_mla_para(json_data, batch_size, input_len, tp_size):
+    best_match = None
+    best_batch_size_diff = float('inf')
+    best_input_len_diff = float('inf')
+    
+    for entry in json_data:
+        if entry["BS"] == batch_size and entry["L"] == input_len:
+            return entry["num_kv_splits"], entry['num_stages']
+        batch_size_diff = abs(entry["BS"] - batch_size)
+        input_len_diff = abs(entry["L"] - input_len)
+        
+        # Check if this is a better match than the current best match
+        if batch_size_diff < best_batch_size_diff or (batch_size_diff == best_batch_size_diff and input_len_diff < best_input_len_diff):
+            best_match = entry
+            best_batch_size_diff = batch_size_diff
+            best_input_len_diff = input_len_diff
+    
+    # If a match was found, return the best_kv_splits, otherwise return None
+    return best_match["num_kv_splits"],best_match["num_stages"]
+
+
+class TritonMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "TRITON_MLA"
+
+    @staticmethod
+    def get_impl_cls() -> Type["TritonMLAImpl"]:
+        return TritonMLAImpl
+
+    @staticmethod
+    def get_metadata_cls() -> Type["TritonMLAMetadata"]:
+        return TritonMLAMetadata
+
+@dataclass
+class TritonMLAMetadata(MLACommonMetadata):
+    num_kv_splits: int = 4  # TODO: heuristic
+    num_stages: int = 1
+
+    @property
+    def decode_metadata(self):
+        if self.num_decode_tokens == 0:
+            return None
+        if self._cached_decode_metadata is not None:
+            return self._cached_decode_metadata
+        
+        decode_metadata = super().decode_metadata
+        
+        if decode_metadata is not None:
+            if decode_metadata.seq_lens_tensor is not None:
+                batch = decode_metadata.seq_lens_tensor.shape[0]
+                max_seq_len = int(decode_metadata.seq_lens_tensor.max())
+                num_kv_splits, num_stages = find_best_mla_para(JSON_DATA, batch, max_seq_len, 8)
+            else:
+                num_kv_splits = self.num_kv_splits
+                num_stages = self.num_stages
+            decode_metadata.num_kv_splits = num_kv_splits
+            decode_metadata.num_stages = num_stages
+        return decode_metadata
+
+class TritonMLAImpl(MLACommonImpl[TritonMLAMetadata]):
+
+    def __init__(
+            self,
+            num_heads: int,
+            head_size: int,
+            scale: float,
+            num_kv_heads: int,
+            alibi_slopes: Optional[List[float]],
+            sliding_window: Optional[int],
+            kv_cache_dtype: str,
+            blocksparse_params: Optional[Dict[str, Any]],
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            kv_sharing_target_layer_name: Optional[str],
+            # MLA Specific Arguments
+            **mla_args) -> None:
+        super().__init__(num_heads, head_size, scale, num_kv_heads,
+                         alibi_slopes, sliding_window, kv_cache_dtype,
+                         blocksparse_params, logits_soft_cap, attn_type,
+                         kv_sharing_target_layer_name, **mla_args)
+
+        unsupported_features = [
+            alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
+        ]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "TritonMLAImpl does not support one of the following: "
+                "alibi_slopes, sliding_window, blocksparse_params, "
+                "logits_soft_cap")
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "TritonMLAImpl")
+
+        if is_quantized_kv_cache(self.kv_cache_dtype):
+            raise NotImplementedError(
+                "TritonMLA with FP8 KV cache not yet supported")
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: TritonMLAMetadata,
+    ) -> torch.Tensor:
+        assert kv_c_and_k_pe_cache.numel() > 0
+
+        decode_meta = attn_metadata.decode_metadata
+        assert decode_meta is not None
+        B = q_nope.shape[0]
+
+        q = torch.cat([q_nope, q_pe], dim=-1)
+        o = torch.zeros(B,
+                        self.num_heads,
+                        self.kv_lora_rank,
+                        dtype=q.dtype,
+                        device=q.device)
+
+        # TODO(lucas) Allocate ahead of time
+        attn_logits = torch.empty(
+            (
+                B,
+                self.num_heads,
+                decode_meta.num_kv_splits,
+                # NOTE(lucas) idk why the +1 is here but sglang has it so we
+                # just mirror that
+                self.kv_lora_rank + 1,
+            ),
+            dtype=torch.float32,
+            device=q.device,
+        )
+
+        # Add a head dim of 1
+        kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
+        kv_c_cache = kv_c_and_k_pe_cache[..., :self.kv_lora_rank]
+        PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
+
+        # Run MQA
+        decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o,
+                             decode_meta.block_tables,
+                             decode_meta.seq_lens_tensor, attn_logits,
+                             decode_meta.num_kv_splits, decode_meta.num_stages, self.scale, PAGE_SIZE)
+
+        return self._v_up_proj(o)