Add intel_amx backend for Radix Attention for CPU (#6408)

Co-authored-by: Chunyuan WU <chunyuan.wu@intel.com>
Co-authored-by: Thien Tran <gau.nernst@yahoo.com.sg>

python/sglang/srt/layers/attention/base_attn_backend.py

@@ -109,3 +109,7 @@ class AttentionBackend(ABC):
     ):
         """Run a forward for extend."""
         raise NotImplementedError()
+
+    def support_triton(self):
+        """Check if the current backend supports triton."""
+        return True

python/sglang/srt/layers/attention/intel_amx_backend.py

@@ -0,0 +1,128 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+
+from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+
+if TYPE_CHECKING:
+    from sglang.srt.layers.radix_attention import RadixAttention
+    from sglang.srt.model_executor.model_runner import ModelRunner
+
+
+class IntelAMXAttnBackend(AttentionBackend):
+    def __init__(self, model_runner: ModelRunner):
+        import sgl_kernel
+
+        super().__init__()
+        self.forward_metadata = None
+        self.device = model_runner.device
+
+        self.num_head = (
+            model_runner.model_config.num_attention_heads // model_runner.tp_size
+        )
+
+        self.v_head_dim = model_runner.token_to_kv_pool.get_value_buffer(0).shape[-1]
+
+        self.decode_attention_fwd = torch.ops.sgl_kernel.decode_attention_cpu
+        self.extend_attention_fwd = torch.ops.sgl_kernel.extend_attention_cpu
+
+    def init_forward_metadata(self, forward_batch: ForwardBatch):
+        """Init the metadata for a forward pass."""
+
+        bs = forward_batch.batch_size
+        attn_logits = torch.zeros(
+            (
+                bs,
+                self.num_head,
+                8,  # self.num_kv_splits,
+                self.v_head_dim + 1,
+            ),
+            dtype=torch.float32,
+            device=self.device,
+        )
+        if forward_batch.forward_mode.is_decode_or_idle():
+            max_extend_len = None
+        else:
+            max_extend_len = torch.max(forward_batch.extend_seq_lens).item()
+        self.forward_metadata = (attn_logits, max_extend_len)
+
+    def forward_extend(
+        self,
+        q,
+        k,
+        v,
+        layer: RadixAttention,
+        forward_batch: ForwardBatch,
+        save_kv_cache=True,
+    ):
+        if layer.qk_head_dim != layer.v_head_dim:
+            o = q.new_empty((q.shape[0], layer.tp_q_head_num * layer.v_head_dim))
+        else:
+            o = torch.empty_like(q)
+
+        if save_kv_cache:
+            forward_batch.token_to_kv_pool.set_kv_buffer(
+                layer, forward_batch.out_cache_loc, k, v
+            )
+
+        _, max_extend_len = self.forward_metadata
+
+        self.extend_attention_fwd(
+            q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
+            k,
+            v,
+            o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
+            forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
+            forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
+            forward_batch.req_to_token_pool.req_to_token,
+            forward_batch.req_pool_indices,
+            forward_batch.seq_lens,
+            forward_batch.extend_seq_lens,
+            forward_batch.extend_start_loc,
+            max_extend_len,
+            layer.scaling,
+            layer.logit_cap,
+        )
+        return o
+
+    def forward_decode(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        layer: RadixAttention,
+        forward_batch: ForwardBatch,
+        save_kv_cache=True,
+    ):
+        attn_logits, _ = self.forward_metadata
+
+        q = q.reshape(-1, layer.tp_q_head_num * layer.qk_head_dim)
+
+        if layer.qk_head_dim != layer.v_head_dim:
+            o = q.new_empty((q.shape[0], layer.tp_q_head_num * layer.v_head_dim))
+        else:
+            o = torch.empty_like(q)
+
+        self.decode_attention_fwd(
+            q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
+            forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
+            forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
+            o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
+            k,
+            v,
+            forward_batch.out_cache_loc,
+            attn_logits,
+            forward_batch.req_to_token_pool.req_to_token,
+            forward_batch.req_pool_indices,
+            forward_batch.seq_lens,
+            layer.scaling,
+            layer.logit_cap,
+        )
+
+        return o
+
+    def support_triton(self):
+        return False

python/sglang/srt/layers/attention/torch_native_backend.py

@@ -265,3 +265,6 @@ class TorchNativeAttnBackend(AttentionBackend):
         )
 
         return o
+
+    def support_triton(self):
+        return False