CPU: map changes from developing branch in sgl-kernel (#6833)

Co-authored-by: mingfeima <mingfei.ma@intel.com>

test/srt/cpu/test_mla.py

@@ -0,0 +1,155 @@
+import itertools
+import unittest
+
+import sgl_kernel
+import torch
+from torch.nn.functional import scaled_dot_product_attention
+from utils import precision
+
+from sglang.test.test_utils import CustomTestCase
+
+
+class TestMLA(CustomTestCase):
+    def _run_sdpa_forward_decode(
+        self,
+        query: torch.Tensor,
+        output: torch.Tensor,
+        k_cache: torch.Tensor,
+        v_cache: torch.Tensor,
+        key: torch.Tensor,
+        loc: torch.Tensor,
+        req_to_token: torch.Tensor,
+        req_pool_indices: torch.Tensor,
+        seq_lens: torch.Tensor,
+        scaling=None,
+        enable_gqa=False,
+        causal=False,
+    ):
+        # set kv cache
+        k_cache[loc] = key
+
+        # [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
+            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_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, seq_len):
+        dtype = torch.bfloat16
+
+        total_tokens = B * seq_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)
+
+        # k_buffer and v_buffer represent all previous tokens
+        k_buffer = torch.randn(total_tokens, H_KV, D, dtype=dtype)
+        v_buffer = k_buffer.narrow(2, 0, D_V)
+
+        key = torch.randn(B, H_KV, D, dtype=dtype)
+        value = key.narrow(2, 0, D_V)
+        # make sure no duplicates in loc
+        loc = torch.randperm(total_tokens)[:B].to(torch.int64)
+
+        k_buffer2 = k_buffer.clone()
+        v_buffer2 = k_buffer2.narrow(2, 0, D_V)
+
+        # o will have the same shape as q
+        o = torch.zeros(B, H_Q, D_V, dtype=dtype)
+        o_grouped = torch.zeros(B, H_Q, D_V, dtype=dtype)
+
+        req_to_token = torch.arange(total_tokens).reshape(B, seq_len).to(torch.int32)
+        b_req_idx = torch.arange(B).to(torch.int64)
+        b_seq_len = torch.full((B,), seq_len).to(torch.int64)
+
+        attn_logits = torch.empty(
+            (B, H_Q, num_kv_splits, D_V + 1),
+            dtype=torch.float32,
+        )
+
+        torch.ops.sgl_kernel.decode_attention_cpu(
+            q,
+            k_buffer2,
+            v_buffer2,
+            o,
+            key,
+            value,
+            loc,
+            attn_logits,
+            req_to_token,
+            b_req_idx,
+            b_seq_len,
+            sm_scale,
+            logit_cap,
+        )
+
+        self._run_sdpa_forward_decode(
+            q,
+            o_grouped,
+            k_buffer,
+            v_buffer,
+            key,
+            loc,
+            req_to_token,
+            b_req_idx,
+            b_seq_len,
+            scaling=sm_scale,
+            enable_gqa=enable_gqa,
+        )
+
+        cos_sim = torch.nn.functional.cosine_similarity(
+            o.flatten(), o_grouped.flatten(), dim=0
+        )
+        atol = rtol = precision[q.dtype]
+        self.assertGreater(cos_sim.item(), 0.99)
+        torch.testing.assert_close(o, o_grouped, atol=atol, rtol=rtol)
+        torch.testing.assert_close(k_buffer, k_buffer2, atol=atol, rtol=rtol)
+        torch.testing.assert_close(v_buffer, v_buffer2, atol=atol, rtol=rtol)
+
+    def test_grouped_decode_attention(self):
+        configs = [
+            (1, 22, 1, 576, 512, 8 * 111),
+            (4, 22, 1, 576, 512, 8 * 128),
+            (40, 22, 1, 576, 512, 8 * 133),
+        ]
+
+        for B, H_Q, H_KV, D, D_V, seqlen in configs:
+            self._test_grouped_decode_attention_once(B, H_Q, H_KV, D, D_V, seqlen)
+
+
+if __name__ == "__main__":
+    unittest.main()