[Kernel] implement 1st version of data-movement friendly MLA kernel with no kv update fused (#1022)

Signed-off-by: Chengji Yao <chengjiyao@google.com>

Author: yaochengji

tests/kernels/mla_v1_test.py

@@ -0,0 +1,396 @@
+import jax
+import jax.numpy as jnp
+import numpy as np
+from absl.testing import absltest, parameterized
+from jax._src import test_util as jtu
+
+import tpu_inference.kernels.mla.v1.kernel as mla
+from tpu_inference.kernels.ragged_paged_attention.v3.util import (
+    align_to, cdiv, get_dtype_packing)
+
+jax.config.parse_flags_with_absl()
+
+
+@jtu.with_config(jax_numpy_dtype_promotion="standard")
+class MlaRaggedPagedAttentionKernelTest(jtu.JaxTestCase):
+
+    def _test_mla_ragged_paged_attention(
+        self,
+        seq_lens,  # List[(q_len, kv_len)]
+        num_heads,
+        lkv_dim,
+        r_dim,
+        page_size,
+        q_dtype,
+        kv_dtype,
+        num_pages,
+        *,
+        num_kv_pages_per_block=8,
+        num_queries_per_block=8,
+        vmem_limit_bytes=100 * 1024 * 1024,
+        sm_scale=1.0,
+        sliding_window: int | None = None,
+        soft_cap: float | None = None,
+    ):
+        if not jtu.is_device_tpu_at_least(version=4):
+            self.skipTest("Expect TPUv4+")
+        rng = np.random.default_rng(1234)
+
+        def gen_random(shape, dtype):
+            return jnp.array(rng.random(size=shape,
+                                        dtype=np.float32)).astype(dtype)
+
+        padded_r_dim = align_to(r_dim, 128)
+        padded_lkv_dim = align_to(lkv_dim, 128)
+        packing = get_dtype_packing(kv_dtype)
+        q_lens = [s[0] for s in seq_lens]
+        kv_lens_list = [s[1] for s in seq_lens]
+        total_q_len = sum(q_lens)
+        cu_q_lens_list = [0]
+        for q_len in q_lens:
+            cu_q_lens_list.append(cu_q_lens_list[-1] + q_len)
+
+        max_kv_len = max(kv_lens_list) if kv_lens_list else 0
+        pages_per_seq = cdiv(max_kv_len, page_size)
+
+        page_indices_list = []
+        page_count = 0
+        for kv_len in kv_lens_list:
+            num_seq_pages = cdiv(kv_len, page_size)
+            indices = list(range(page_count, page_count + num_seq_pages))
+            page_indices_list.extend(indices + [-1] *
+                                     (pages_per_seq - num_seq_pages))
+            page_count += num_seq_pages
+
+        total_num_pages = max(num_pages, page_count)
+
+        ql_nope = gen_random((total_q_len, num_heads, lkv_dim), q_dtype)
+        q_pe = gen_random((total_q_len, num_heads, r_dim), q_dtype)
+        new_kv_c = gen_random((total_q_len, lkv_dim), kv_dtype)
+        new_k_pe = gen_random((total_q_len, r_dim), kv_dtype)
+
+        cache_kv_c = gen_random(
+            (total_num_pages, page_size // packing, packing, padded_lkv_dim),
+            kv_dtype,
+        )
+        cache_k_pe = gen_random(
+            (total_num_pages, page_size // packing, packing, padded_r_dim),
+            kv_dtype)
+        kv_lens = jnp.array(kv_lens_list, dtype=jnp.int32)
+        page_indices = jnp.array(page_indices_list, dtype=jnp.int32)
+        cu_q_lens = jnp.array(cu_q_lens_list, dtype=jnp.int32)
+        distribution = jnp.array([0, 0, len(seq_lens)], dtype=jnp.int32)
+
+        ql_nope_for_kernel = ql_nope.copy()
+        q_pe_for_kernel = q_pe.copy()
+
+        expected_out, expected_updated_kv_c, expeceted_updated_k_pe = (
+            mla.ref_mla_ragged_paged_attention(
+                ql_nope,
+                q_pe,
+                new_kv_c,
+                new_k_pe,
+                cache_kv_c.copy(),
+                cache_k_pe.copy(),
+                kv_lens,
+                page_indices,
+                cu_q_lens,
+                distribution,
+                sm_scale=sm_scale,
+                sliding_window=sliding_window,
+                soft_cap=soft_cap,
+            ))
+
+        kernel_out, kernel_updated_kv_c, kernel_updated_k_pe = (
+            mla.mla_ragged_paged_attention(
+                ql_nope_for_kernel,
+                q_pe_for_kernel,
+                new_kv_c,
+                new_k_pe,
+                cache_kv_c.copy(),
+                cache_k_pe.copy(),
+                kv_lens,
+                page_indices,
+                cu_q_lens,
+                distribution,
+                sm_scale=sm_scale,
+                sliding_window=sliding_window,
+                soft_cap=soft_cap,
+                num_kv_pages_per_block=num_kv_pages_per_block,
+                num_queries_per_block=num_queries_per_block,
+                vmem_limit_bytes=vmem_limit_bytes,
+            ))
+
+        self.assertEqual(expected_out.shape,
+                         (total_q_len, num_heads, padded_lkv_dim))
+        self.assertEqual(
+            expected_updated_kv_c.shape,
+            (total_num_pages, page_size // packing, packing, padded_lkv_dim),
+        )
+        self.assertEqual(
+            expeceted_updated_k_pe.shape,
+            (total_num_pages, page_size // packing, packing, padded_r_dim),
+        )
+        self.assertEqual(expected_out.dtype, kv_dtype)
+        self.assertEqual(expected_updated_kv_c.dtype, kv_dtype)
+        self.assertEqual(expeceted_updated_k_pe.dtype, kv_dtype)
+
+        self.assertAllClose(expected_out, kernel_out, atol=0.2, rtol=0.2)
+        self.assertAllClose(expected_updated_kv_c,
+                            kernel_updated_kv_c,
+                            atol=0.2,
+                            rtol=0.2)
+        self.assertAllClose(expeceted_updated_k_pe,
+                            kernel_updated_k_pe,
+                            atol=0.2,
+                            rtol=0.2)
+
+    def test_ragged_paged_attention_basic(self):
+        dtype = jnp.bfloat16
+        seq_lens = [(192, 328), (128, 180), (64, 255)]
+        num_heads = 128
+        lkv_dim = 512
+        r_dim = 64
+        page_size = 16
+        num_pages = 1000
+
+        self._test_mla_ragged_paged_attention(
+            seq_lens,
+            num_heads,
+            lkv_dim,
+            r_dim,
+            page_size,
+            dtype,
+            dtype,
+            num_pages,
+        )
+
+    @parameterized.product(dtype=[jnp.bfloat16], )
+    def test_ragged_paged_attention_decode_only(self, dtype):
+        seq_lens = [
+            (1, 18),
+            (1, 129),
+            (1, 597),
+            (1, 122),
+            (1, 64),
+            (1, 322),
+            (1, 463),
+            (1, 181),
+            (1, 1107),
+            (1, 123),
+            (1, 31),
+            (1, 18),
+            (1, 1229),
+            (1, 229),
+            (1, 87),
+            (1, 1328),
+        ]
+        num_heads = 128
+        lkv_dim = 512
+        r_dim = 64
+        page_size = 16
+        num_pages = 1000
+
+        self._test_mla_ragged_paged_attention(
+            seq_lens,
+            num_heads,
+            lkv_dim,
+            r_dim,
+            page_size,
+            dtype,
+            dtype,
+            num_pages,
+        )
+
+    @parameterized.product(dtype=[jnp.bfloat16], )
+    def test_ragged_paged_attention_prefill_only(self, dtype):
+        seq_lens = [
+            (5, 18),
+            (15, 129),
+            (120, 597),
+            (100, 122),
+            (21, 64),
+            (32, 322),
+            (251, 463),
+            (40, 181),
+            (64, 1107),
+            (99, 123),
+            (10, 31),
+            (5, 18),
+            (3, 1229),
+            (120, 229),
+            (9, 87),
+            (2, 1328),
+        ]
+        num_heads = 128
+        lkv_dim = 512
+        r_dim = 64
+        page_size = 16
+        num_pages = 1000
+
+        self._test_mla_ragged_paged_attention(
+            seq_lens,
+            num_heads,
+            lkv_dim,
+            r_dim,
+            page_size,
+            dtype,
+            dtype,
+            num_pages,
+        )
+
+    @parameterized.product(dtype=[jnp.bfloat16], )
+    def test_ragged_paged_attention_mixed(self, dtype):
+        seq_lens = [
+            (5, 18),
+            (1, 129),
+            (120, 597),
+            (1, 122),
+            (1, 64),
+            (32, 322),
+            (251, 463),
+            (1, 181),
+            (1, 1107),
+            (99, 123),
+            (1, 31),
+            (5, 18),
+            (3, 1229),
+            (117, 229),
+            (1, 87),
+            (1, 1328),
+        ]
+        num_heads = 128
+        lkv_dim = 512
+        r_dim = 64
+        page_size = 16
+        num_pages = 1000
+
+        self._test_mla_ragged_paged_attention(
+            seq_lens,
+            num_heads,
+            lkv_dim,
+            r_dim,
+            page_size,
+            dtype,
+            dtype,
+            num_pages,
+        )
+
+    @parameterized.product(sliding_window=[None, 5, 128], )
+    def test_ragged_paged_attention_sliding_window(
+        self,
+        sliding_window: int | None,
+    ):
+        num_seqs = 5
+        num_heads = 128
+        lkv_dim = 512
+        r_dim = 64
+        dtype = jnp.float32
+        rng = np.random.default_rng(1234)
+        q_lens = rng.integers(1, 100, num_seqs)
+        kv_lens = q_lens + rng.integers(0, 50, num_seqs)
+        seq_lens = list(zip(q_lens.tolist(), kv_lens.tolist()))
+        page_size = 16
+        num_pages = 1000
+
+        self._test_mla_ragged_paged_attention(
+            seq_lens,
+            num_heads,
+            lkv_dim,
+            r_dim,
+            page_size,
+            dtype,
+            dtype,
+            num_pages,
+            sliding_window=sliding_window,
+        )
+
+    @parameterized.product(soft_cap=[None, 50.0], )
+    def test_ragged_paged_attention_logit_soft_capping(
+        self,
+        soft_cap: float | None,
+    ):
+        num_heads = 128
+        num_seqs = 2
+        dtype = jnp.float32
+        rng = np.random.default_rng(1234)
+        q_lens = rng.integers(1, 100, num_seqs)
+        kv_lens = q_lens + rng.integers(0, 50, num_seqs)
+        seq_lens = list(zip(q_lens.tolist(), kv_lens.tolist()))
+        lkv_dim = 512
+        r_dim = 64
+        page_size = 16
+        num_pages = 1000
+
+        self._test_mla_ragged_paged_attention(
+            seq_lens,
+            num_heads,
+            lkv_dim,
+            r_dim,
+            page_size,
+            dtype,
+            dtype,
+            num_pages,
+            soft_cap=soft_cap,
+        )
+
+    def test_ragged_paged_attention_sliding_window_should_be_positive(self):
+        dtype = jnp.float32
+        seq_lens = [(192, 328), (128, 180), (64, 255)]
+        num_heads = 128
+        lkv_dim = 512
+        r_dim = 64
+        page_size = 16
+        num_pages = 1000
+
+        with self.assertRaisesRegex(ValueError, "must be positive"):
+            self._test_mla_ragged_paged_attention(
+                seq_lens,
+                num_heads,
+                lkv_dim,
+                r_dim,
+                page_size,
+                dtype,
+                dtype,
+                num_pages,
+                sliding_window=0,
+            )
+
+        with self.assertRaisesRegex(ValueError, "must be positive"):
+            self._test_mla_ragged_paged_attention(
+                seq_lens,
+                num_heads,
+                lkv_dim,
+                r_dim,
+                page_size,
+                dtype,
+                dtype,
+                num_pages,
+                sliding_window=-1,
+            )
+
+    def test_ragged_paged_attention_soft_cap_cannot_be_zero(self):
+        dtype = jnp.float32
+        seq_lens = [(192, 328), (128, 180), (64, 255)]
+        num_heads = 128
+        lkv_dim = 512
+        r_dim = 64
+        page_size = 16
+        num_pages = 1000
+
+        with self.assertRaisesRegex(ValueError, "must not be 0.0"):
+            self._test_mla_ragged_paged_attention(
+                seq_lens,
+                num_heads,
+                lkv_dim,
+                r_dim,
+                page_size,
+                dtype,
+                dtype,
+                num_pages,
+                soft_cap=0.0,
+            )
+
+
+if __name__ == "__main__":
+    absltest.main(testLoader=jtu.JaxTestLoader())