/test/registered/cpu/test_decode.py

import unittest

import torch
from torch.nn.functional import scaled_dot_product_attention

from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_utils import CustomTestCase

register_cpu_ci(est_time=10, suite="base-b-test-cpu")
register_cpu_ci(est_time=10, suite="base-b-test-cpu-arm64")

torch.manual_seed(1234)


class TestDecodeAttention(CustomTestCase):
    def _scaled_dot_product_attention(self, Q, K, V, S, scaling, sliding_window):
        # sliding_window <= 0 means no sliding window
        # Q: [n_tokens_q, n_heads, q_mult, d_head]
        # K: [n_tokens_kv, n_heads, d_head]
        # V: [n_tokens_kv, n_heads, d_head]
        n_tokens_q, n_heads, q_mult, d_head = Q.shape
        n_tokens_kv = K.shape[0]
        assert K.shape == (n_tokens_kv, n_heads, d_head)
        assert V.shape == (n_tokens_kv, n_heads, d_head)
        K = K[:, :, None, :].expand(-1, -1, q_mult, -1)
        V = V[:, :, None, :].expand(-1, -1, q_mult, -1)
        S = S.reshape(n_heads, q_mult, 1, 1).expand(-1, -1, n_tokens_q, -1)
        if n_tokens_q == n_tokens_kv:  # Prefill
            mask = torch.triu(
                Q.new_full((n_tokens_q, n_tokens_kv), -float("inf")), diagonal=1
            )
        else:  # Decode
            mask = Q.new_zeros((n_tokens_q, n_tokens_kv))
        if sliding_window is not None and sliding_window > 0:
            mask += torch.tril(
                mask.new_full((n_tokens_q, n_tokens_kv), -float("inf")),
                diagonal=n_tokens_kv - n_tokens_q - sliding_window,
            )
        QK = torch.einsum("qhmd,khmd->hmqk", Q, K)
        QK *= scaling
        QK += mask[None, None, :, :]
        QK = torch.cat([QK, S], dim=-1)
        W = torch.softmax(QK, dim=-1)
        W = W[..., :-1]
        attn = torch.einsum("hmqk,khmd->qhmd", W, V)
        return attn.reshape(n_tokens_q, -1)

    def _run_sdpa_forward_decode_sink(
        self,
        query: torch.Tensor,
        output: torch.Tensor,
        k_cache: torch.Tensor,
        v_cache: torch.Tensor,
        req_to_token: torch.Tensor,
        req_pool_indices: torch.Tensor,
        seq_lens: torch.Tensor,
        num_kv_heads: int,
        q_mult: int,
        scaling=None,
        sliding_window=None,
        attention_sinks=None,
        enable_gqa=False,
        causal=False,
    ):
        # [num_tokens, num_heads, head_size] -> [num_heads, num_tokens, head_size]
        query = query.movedim(0, query.dim() - 2)
        start_q, start_kv = 0, 0
        for seq_idx in range(seq_lens.shape[0]):
            # TODO: this loop process a sequence per iter, this is inefficient.
            # Need optimize the performance later.
            seq_len_q = 1
            seq_len_kv = seq_lens[seq_idx]
            end_q = start_q + seq_len_q
            end_kv = start_kv + seq_len_kv
            per_req_query = query[:, start_q:end_q, :]
            # get key and value from cache. per_req_tokens contains the kv cache
            # index for each token in the sequence.
            req_pool_idx = req_pool_indices[seq_idx]
            per_req_tokens = req_to_token[req_pool_idx, :seq_len_kv]

            per_req_query = per_req_query.permute(1, 0, 2).reshape(
                seq_len_q, num_kv_heads, q_mult, per_req_query.shape[-1]
            )
            per_req_key = k_cache[per_req_tokens].movedim(0, query.dim() - 2)
            per_req_value = v_cache[per_req_tokens].movedim(0, query.dim() - 2)
            per_req_key = per_req_key.permute(1, 0, 2)
            per_req_value = per_req_value.permute(1, 0, 2)

            per_req_out = self._scaled_dot_product_attention(
                per_req_query,
                per_req_key,
                per_req_value,
                attention_sinks,
                scaling=scaling,
                sliding_window=sliding_window,
            ).reshape(seq_len_q, -1, per_req_value.shape[-1])
            output[start_q:end_q, :, :] = per_req_out
            start_q, start_kv = end_q, end_kv
        return output

    def _run_sdpa_forward_decode(
        self,
        query: torch.Tensor,
        output: torch.Tensor,
        k_cache: torch.Tensor,
        v_cache: torch.Tensor,
        req_to_token: torch.Tensor,
        req_pool_indices: torch.Tensor,
        seq_lens: torch.Tensor,
        encoder_lens=None,
        scaling=None,
        enable_gqa=False,
        causal=False,
        is_cross_attn=False,
    ):
        # [num_tokens, num_heads, head_size] -> [num_heads, num_tokens, head_size]
        query = query.movedim(0, query.dim() - 2)

        start_q, start_kv = 0, 0
        for seq_idx in range(seq_lens.shape[0]):
            seq_len_q = 1
            seq_len_kv = seq_lens[seq_idx]
            end_q = start_q + seq_len_q
            if encoder_lens is not None:
                start_kv = 0 if is_cross_attn else encoder_lens[seq_idx]
                end_kv = (
                    encoder_lens[seq_idx] if is_cross_attn else start_kv + seq_len_kv
                )
            else:
                start_kv = 0
                end_kv = start_kv + seq_len_kv

            per_req_query = query[:, start_q:end_q, :]

            # get key and value from cache. per_req_tokens contains the kv cache
            # index for each token in the sequence.
            req_pool_idx = req_pool_indices[seq_idx]
            per_req_tokens = req_to_token[req_pool_idx, start_kv:end_kv]
            per_req_key = k_cache[per_req_tokens].movedim(0, query.dim() - 2)
            per_req_value = v_cache[per_req_tokens].movedim(0, query.dim() - 2)

            per_req_out = (
                scaled_dot_product_attention(
                    per_req_query.unsqueeze(0),
                    per_req_key.unsqueeze(0),
                    per_req_value.unsqueeze(0),
                    enable_gqa=enable_gqa,
                    scale=scaling,
                    is_causal=causal,
                )
                .squeeze(0)
                .movedim(query.dim() - 2, 0)
            )
            output[start_q:end_q, :, :] = per_req_out
            start_q, start_kv = end_q, end_kv

        return output

    def _test_grouped_decode_attention_once(
        self, B, H_Q, H_KV, D, D_V, sliding_window, sink, is_cross_attn, dtype, device
    ):
        # This represents the number of tokens already in the sequence
        seq_len = 1024
        encoder_len = 0 if sink else 10
        total_tokens = B * (seq_len + encoder_len)
        sm_scale = 1.0 / (D**0.5)
        logit_cap = 0.0
        num_kv_splits = 8
        enable_gqa = H_Q != H_KV

        # q represents the new token being generated, one per batch
        q = torch.randn(B, H_Q, D, dtype=dtype, device=device)
        sinks = torch.rand(H_Q, dtype=dtype, device=device) * 10

        # k_buffer and v_buffer represent all previous tokens
        k_buffer = torch.randn(total_tokens, H_KV, D, dtype=dtype, device=device)
        v_buffer = torch.randn(total_tokens, H_KV, D_V, dtype=dtype, device=device)

        key = torch.randn(B, H_KV, D, dtype=dtype)
        value = torch.randn(B, H_KV, D_V, dtype=dtype)
        loc = torch.randint(0, 10, (B,)).to(torch.int64)

        # set kv cache
        k_buffer[loc] = key
        v_buffer[loc] = value

        # o will have the same shape as q
        o = torch.zeros(B, H_Q, D_V, dtype=dtype, device=device)
        o_grouped = torch.zeros(B, H_Q, D_V, dtype=dtype, device=device)

        req_to_token = (
            torch.arange(total_tokens, device=device)
            .reshape(B, seq_len + encoder_len)
            .to(torch.int32)
        )
        b_req_idx = torch.arange(B, device=device).to(torch.int64)
        b_seq_len = torch.full((B,), seq_len, device=device).to(torch.int64)
        encoder_lens = torch.full((B,), encoder_len, device=device).to(torch.int64)

        attn_logits = torch.empty(
            (B, H_Q, num_kv_splits, D_V + 1),
            dtype=torch.float32,
            device=device,
        )

        # k_buffer, v_buffer, query, key and value supports non-contiguous tensors
        k_buffer = k_buffer.transpose(0, 1).contiguous().transpose(0, 1)
        v_buffer = v_buffer.transpose(0, 1).contiguous().transpose(0, 1)
        q = q.transpose(0, 1).contiguous().transpose(0, 1)
        key = key.transpose(0, 1).contiguous().transpose(0, 1)
        value = value.transpose(0, 1).contiguous().transpose(0, 1)
        torch.ops.sgl_kernel.decode_attention_cpu(
            q,
            k_buffer,
            v_buffer,
            o,
            key if not is_cross_attn else None,
            value if not is_cross_attn else None,
            loc,
            attn_logits,
            req_to_token,
            b_req_idx,
            b_seq_len,
            sm_scale,
            logit_cap,
            is_cross_attn,
            sliding_window if sliding_window is not None else 0,
            encoder_lens,
            sinks if sink else None,
        )

        if sink:
            self._run_sdpa_forward_decode_sink(
                q,
                o_grouped,
                k_buffer,
                v_buffer,
                req_to_token,
                b_req_idx,
                b_seq_len,
                num_kv_heads=H_KV,
                q_mult=H_Q // H_KV if enable_gqa else 1,
                scaling=sm_scale,
                sliding_window=sliding_window if sliding_window is not None else None,
                attention_sinks=sinks,
                enable_gqa=enable_gqa,
            )
        else:
            self._run_sdpa_forward_decode(
                q,
                o_grouped,
                k_buffer,
                v_buffer,
                req_to_token,
                b_req_idx,
                b_seq_len,
                scaling=sm_scale,
                enable_gqa=enable_gqa,
                encoder_lens=encoder_lens,
                is_cross_attn=is_cross_attn,
            )
        cos_sim = torch.nn.functional.cosine_similarity(
            o.flatten(), o_grouped.flatten(), dim=0
        )
        self.assertGreater(cos_sim.item(), 0.99)
        torch.testing.assert_close(o, o_grouped, atol=3e-2, rtol=1e-6)

    def _test_grouped_decode_attention(self, device="cuda"):
        configs = [
            (2, 16, 16, 64, 64),
            (2, 16, 1, 16, 16),
            (2, 32, 8, 33, 55),
            (2, 16, 1, 64, 64),
            (2, 64, 1, 13, 13),
            (2, 128, 1, 80, 80),
            (2, 128, 2, 512, 512),
            (1, 16, 1, 576, 512),
            (1, 16, 16, 576, 512),
            (1, 22, 1, 576, 512),
            (1, 40, 8, 128, 128),
        ]

        for B, H_Q, H_KV, D, D_V in configs:
            for dtype in [torch.bfloat16, torch.float16]:
                for sink in [True, False]:
                    if D != D_V and sink:
                        continue
                    for sliding_window in [None, 10]:
                        if sliding_window is not None and not sink:
                            continue
                        self._test_grouped_decode_attention_once(
                            B,
                            H_Q,
                            H_KV,
                            D,
                            D_V,
                            sliding_window,
                            sink,
                            False,
                            dtype=dtype,
                            device=device,
                        )
                self._test_grouped_decode_attention_once(
                    B, H_Q, H_KV, D, D_V, None, False, True, dtype=dtype, device=device
                )

    def test_grouped_decode_attention(self):
        self._test_grouped_decode_attention("cpu")


if __name__ == "__main__":
    unittest.main()