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vllm_br/model_executor/models/supa_module/attention.py

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+################################################################################
+# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+################################################################################
+
+from typing import Any, Optional, Tuple
+
+import torch
+import torch_br
+from torch import nn
+from torch_br.supa.profiler_kineto import record_function
+
+from vllm.attention import Attention, AttentionType
+from vllm.config import CacheConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.forward_context import ForwardContext, get_forward_context
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.rotary_embedding import (MRotaryEmbedding,
+                                                         get_rope)
+from vllm.model_executor.models.utils import extract_layer_index
+
+
+class AttentionSplit(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position: int = 4096 * 32,
+        rope_theta: int = 10000,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        rope_scaling: Optional[Tuple] = None,
+        attn_type: str = AttentionType.DECODER,
+        prefix: str = "",
+        dual_chunk_attention_config: Optional[dict[str, Any]] = None,
+        bias: bool = False,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        qconfig = None
+        if quant_config is not None and quant_config.qkv_quantized:
+            qconfig = quant_config
+
+        self.q_proj = ColumnParallelLinear(input_size=hidden_size,
+                                           output_size=self.q_size * tp_size,
+                                           bias=bias,
+                                           quant_config=qconfig,
+                                           prefix=f"{prefix}.q_proj")
+        self.k_proj = ColumnParallelLinear(input_size=hidden_size,
+                                           output_size=self.kv_size * tp_size,
+                                           bias=bias,
+                                           quant_config=qconfig,
+                                           prefix=f"{prefix}.k_proj")
+        self.v_proj = ColumnParallelLinear(input_size=hidden_size,
+                                           output_size=self.kv_size * tp_size,
+                                           bias=bias,
+                                           quant_config=qconfig,
+                                           prefix=f"{prefix}.v_proj")
+
+        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
+                                        hidden_size,
+                                        bias=False,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.o_proj")
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=self.rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            attn_type=attn_type,
+            prefix=f"{prefix}.attn",
+            **{
+                "layer_idx": extract_layer_index(prefix),
+                "dual_chunk_attention_config": dual_chunk_attention_config,
+            } if dual_chunk_attention_config else {})
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        forward_context: ForwardContext = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        if attn_metadata is None:
+            ## for dummy run
+            return hidden_states
+
+        seq_len = hidden_states.shape[-2]
+        decode_seql = 512
+
+        # numa weight and not use mrope (qwen-vl)
+        if ((hasattr(self.q_proj, "qweight")
+             and len(self.q_proj.qweight.shape) == 3) or
+            (hasattr(self.q_proj, "weight")
+             and len(self.q_proj.weight.shape) == 3)) and not isinstance(
+                 self.rotary_emb, MRotaryEmbedding) and seq_len <= decode_seql:
+            if isinstance(attn_metadata, dict):
+                attn_metadata = attn_metadata[self.attn.layer_name]
+            kv_cache = self.attn.kv_cache[forward_context.virtual_engine]
+            if kv_cache is not None:
+                with record_function('attention qkv_rope'):
+                    # int8 weight version
+                    q_weight = self.q_proj.qweight if hasattr(
+                        self.q_proj, "qweight") else self.q_proj.weight
+                    k_weight = self.k_proj.qweight if hasattr(
+                        self.k_proj, "qweight") else self.k_proj.weight
+                    v_weight = self.v_proj.qweight if hasattr(
+                        self.v_proj, "qweight") else self.v_proj.weight
+                    q_scale = self.q_proj.scales if hasattr(
+                        self.q_proj, "scales") else None
+                    k_scale = self.k_proj.scales if hasattr(
+                        self.k_proj, "scales") else None
+                    v_scale = self.v_proj.scales if hasattr(
+                        self.v_proj, "scales") else None
+                    q_bias = self.q_proj.bias if hasattr(self.q_proj,
+                                                         "bias") else None
+                    k_bias = self.k_proj.bias if hasattr(self.k_proj,
+                                                         "bias") else None
+                    v_bias = self.v_proj.bias if hasattr(self.v_proj,
+                                                         "bias") else None
+                    q, k, v = torch_br.supa_qkv_rope_decode_infer(
+                        hidden_states,
+                        q_weight,
+                        k_weight,
+                        v_weight,
+                        self.rotary_emb.sin_cache,
+                        self.rotary_emb.cos_cache,
+                        kv_cache,
+                        positions,
+                        attn_metadata.slot_mapping,
+                        self.rotary_emb.head_size,
+                        self.q_size,
+                        self.kv_size,
+                        q_scale=q_scale,
+                        k_scale=k_scale,
+                        v_scale=v_scale,
+                        q_bias=q_bias,
+                        k_bias=k_bias,
+                        v_bias=v_bias)
+
+                if hasattr(attn_metadata, 'do_cache'):
+                    attn_metadata.do_cache = False
+                with record_function('attention'):
+                    attn_output = self.attn(q, k, v)
+                with record_function('attention o_proj'):
+                    output, _ = self.o_proj(attn_output)
+
+                return output
+            else:
+                return hidden_states
+        else:
+            # uma weight or use mrope (qwen-vl)
+            q, _ = self.q_proj(hidden_states)
+            k, _ = self.k_proj(hidden_states)
+            v, _ = self.v_proj(hidden_states)
+            q, k = self.rotary_emb(positions, q, k)
+            if hasattr(attn_metadata, 'do_cache'):
+                attn_metadata.do_cache = True
+            attn_output = self.attn(q, k, v)
+            output, _ = self.o_proj(attn_output)
+            return output