888cb175a6
Co-authored-by: Chunyuan WU <chunyuan.wu@intel.com> Co-authored-by: Thien Tran <gau.nernst@yahoo.com.sg>
129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING
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import torch
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from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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if TYPE_CHECKING:
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.model_executor.model_runner import ModelRunner
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class IntelAMXAttnBackend(AttentionBackend):
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def __init__(self, model_runner: ModelRunner):
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import sgl_kernel
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super().__init__()
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self.forward_metadata = None
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self.device = model_runner.device
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self.num_head = (
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model_runner.model_config.num_attention_heads // model_runner.tp_size
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)
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self.v_head_dim = model_runner.token_to_kv_pool.get_value_buffer(0).shape[-1]
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self.decode_attention_fwd = torch.ops.sgl_kernel.decode_attention_cpu
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self.extend_attention_fwd = torch.ops.sgl_kernel.extend_attention_cpu
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def init_forward_metadata(self, forward_batch: ForwardBatch):
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"""Init the metadata for a forward pass."""
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bs = forward_batch.batch_size
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attn_logits = torch.zeros(
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(
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bs,
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self.num_head,
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8, # self.num_kv_splits,
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self.v_head_dim + 1,
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),
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dtype=torch.float32,
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device=self.device,
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)
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if forward_batch.forward_mode.is_decode_or_idle():
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max_extend_len = None
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else:
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max_extend_len = torch.max(forward_batch.extend_seq_lens).item()
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self.forward_metadata = (attn_logits, max_extend_len)
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def forward_extend(
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self,
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q,
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k,
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v,
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layer: RadixAttention,
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forward_batch: ForwardBatch,
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save_kv_cache=True,
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):
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if layer.qk_head_dim != layer.v_head_dim:
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o = q.new_empty((q.shape[0], layer.tp_q_head_num * layer.v_head_dim))
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else:
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o = torch.empty_like(q)
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if save_kv_cache:
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forward_batch.token_to_kv_pool.set_kv_buffer(
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layer, forward_batch.out_cache_loc, k, v
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)
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_, max_extend_len = self.forward_metadata
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self.extend_attention_fwd(
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q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
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k,
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v,
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o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
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forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
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forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
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forward_batch.req_to_token_pool.req_to_token,
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forward_batch.req_pool_indices,
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forward_batch.seq_lens,
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forward_batch.extend_seq_lens,
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forward_batch.extend_start_loc,
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max_extend_len,
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layer.scaling,
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layer.logit_cap,
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)
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return o
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def forward_decode(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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layer: RadixAttention,
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forward_batch: ForwardBatch,
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save_kv_cache=True,
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):
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attn_logits, _ = self.forward_metadata
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q = q.reshape(-1, layer.tp_q_head_num * layer.qk_head_dim)
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if layer.qk_head_dim != layer.v_head_dim:
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o = q.new_empty((q.shape[0], layer.tp_q_head_num * layer.v_head_dim))
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else:
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o = torch.empty_like(q)
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self.decode_attention_fwd(
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q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
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forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
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forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
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o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
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k,
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v,
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forward_batch.out_cache_loc,
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attn_logits,
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forward_batch.req_to_token_pool.req_to_token,
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forward_batch.req_pool_indices,
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forward_batch.seq_lens,
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layer.scaling,
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layer.logit_cap,
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)
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return o
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def support_triton(self):
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return False
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