add missing files

server/Makefile-triton

@@ -0,0 +1,8 @@
+triton-rocm:
+	pip uninstall -y triton
+	pip install -U ninja cmake wheel packaging --no-cache-dir
+	git clone https://github.com/ROCm/triton.git triton
+
+build-triton-rocm: triton-rocm
+	cd triton && git fetch && git checkout b9e5290de8bf3a79c4e91ceed7e61b3c8d041b30
+	cd triton/python && python setup.py build

server/text_generation_server/utils/flash_attn_triton.py

@@ -0,0 +1,809 @@
+#!/usr/bin/env python
+"""
+Fused Attention
+===============
+
+This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao
+(https://tridao.me/publications/flash2/flash2.pdf)
+Credits: OpenAI kernel team, AMD ML Frameworks Triton team
+
+Features supported:
+
+1) Fwd with causal masking
+2) Any sequence lengths without padding (currently fwd kernel only)
+3) Support for different sequence lengths for q and k
+4) Nested tensor API currently does not support dropout or bias.
+
+Not currently supported:
+
+1) Non power of two head dims
+
+"""
+
+import torch
+import triton
+import triton.language as tl
+
+torch_dtype: tl.constexpr = torch.float16
+
+
+@triton.jit
+def cdiv_fn(x, y):
+    return (x + y - 1) // y
+
+
+@triton.jit
+def max_fn(x, y):
+    return tl.math.max(x, y)
+
+
+@triton.jit
+def dropout_offsets(philox_seed, philox_offset, dropout_p, m, n, stride):
+    ms = tl.arange(0, m)
+    ns = tl.arange(0, n)
+    return philox_offset + ms[:, None] * stride + ns[None, :]
+
+
+@triton.jit
+def dropout_rng(philox_seed, philox_offset, dropout_p, m, n, stride):
+    rng_offsets = dropout_offsets(philox_seed, philox_offset, dropout_p, m, n,
+                                  stride).to(tl.uint32)
+    # TODO: use tl.randint for better performance
+    return tl.rand(philox_seed, rng_offsets)
+
+
+@triton.jit
+def dropout_mask(philox_seed, philox_offset, dropout_p, m, n, stride):
+    rng_output = dropout_rng(philox_seed, philox_offset, dropout_p, m, n,
+                             stride)
+    rng_keep = rng_output > dropout_p
+    return rng_keep
+
+
+@triton.jit
+def load_fn(block_ptr, first, second, pad):
+    if first and second:
+        tensor = tl.load(block_ptr, boundary_check=(0, 1), padding_option=pad)
+    elif first:
+        tensor = tl.load(block_ptr, boundary_check=(0, ), padding_option=pad)
+    elif second:
+        tensor = tl.load(block_ptr, boundary_check=(1, ), padding_option=pad)
+    else:
+        tensor = tl.load(block_ptr)
+    return tensor
+
+
+@triton.jit
+def _attn_fwd_inner(
+    acc,
+    l_i,
+    m_i,
+    q,
+    K_block_ptr,
+    V_block_ptr,
+    start_m,
+    actual_seqlen_k,
+    dropout_p,
+    philox_seed,
+    batch_philox_offset,
+    encoded_softmax_block_ptr,
+    block_min,
+    block_max,
+    offs_n_causal,
+    masked_blocks,
+    n_extra_tokens,
+    bias_ptr,
+    IS_CAUSAL: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    OFFS_M: tl.constexpr,
+    OFFS_N: tl.constexpr,
+    PRE_LOAD_V: tl.constexpr,
+    MASK_STEPS: tl.constexpr,
+    ENABLE_DROPOUT: tl.constexpr,
+    RETURN_ENCODED_SOFTMAX: tl.constexpr,
+    PADDED_HEAD: tl.constexpr,
+):
+    # loop over k, v, and update accumulator
+    for start_n in range(block_min, block_max, BLOCK_N):
+        # For padded blocks, we will overrun the tensor size if
+        # we load all BLOCK_N. For others, the blocks are all within range.
+        k = load_fn(
+            K_block_ptr,
+            PADDED_HEAD,
+            MASK_STEPS and (n_extra_tokens != 0),
+            "zero",
+        )
+        if PRE_LOAD_V:
+            v = load_fn(
+                V_block_ptr,
+                MASK_STEPS and (n_extra_tokens != 0),
+                PADDED_HEAD,
+                "zero",
+            )
+        qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        # We start from end of seqlen_k so only the first iteration would need
+        # to be checked for padding if it is not a multiple of block_n
+        # TODO: This can be optimized to only be true for the padded block.
+        if MASK_STEPS:  # noqa: SIM102
+            # If this is the last block / iteration, we want to
+            # mask if the sequence length is not a multiple of block size
+            # a solution is to always do BLOCK_M // BLOCK_N + 1 steps
+            # if not is_modulo_mn. last step might get wasted but that is okay.
+            # check if this masking works for that case.
+            if (start_n + BLOCK_N == block_max) and (n_extra_tokens != 0):
+                boundary_m = tl.full([BLOCK_M],
+                                     actual_seqlen_k,
+                                     dtype=tl.int32)
+                size_n = start_n + OFFS_N[None, :]
+                mask = size_n < boundary_m[:, None]
+                qk = tl.where(mask, qk, float("-inf"))
+        if IS_CAUSAL:
+            causal_boundary = start_n + offs_n_causal
+            causal_mask = OFFS_M[:, None] >= causal_boundary[None, :]
+            qk = tl.where(causal_mask, qk, float("-inf"))
+        # -- compute qk ----
+        qk += tl.dot(q, k)
+        if bias_ptr is not None:
+            bias = load_fn(bias_ptr, False, MASK_STEPS
+                           and (n_extra_tokens != 0), "zero")
+            # While bias is added after multiplying qk with sm_scale, our
+            # optimization to use 2^x instead of e^x results in an additional
+            # scale factor of log2(e) which we must also multiply the bias with.
+            qk += bias * 1.44269504089
+        m_ij = tl.maximum(m_i, tl.max(qk, 1))
+        qk = qk - m_ij[:, None]
+        p = tl.math.exp2(qk)
+
+        # CAVEAT: Must update l_ij before applying dropout
+        l_ij = tl.sum(p, 1)
+        if ENABLE_DROPOUT:
+            philox_offset = (batch_philox_offset +
+                             start_m * BLOCK_M * actual_seqlen_k + start_n -
+                             BLOCK_N)
+            keep = dropout_mask(
+                philox_seed,
+                philox_offset,
+                dropout_p,
+                BLOCK_M,
+                BLOCK_N,
+                actual_seqlen_k,
+            )
+            if RETURN_ENCODED_SOFTMAX:
+                tl.store(
+                    encoded_softmax_block_ptr,
+                    tl.where(keep, p,
+                             -p).to(encoded_softmax_block_ptr.type.element_ty),
+                )
+            p = tl.where(keep, p, 0.0)
+        elif RETURN_ENCODED_SOFTMAX:
+            tl.store(
+                encoded_softmax_block_ptr,
+                p.to(encoded_softmax_block_ptr.type.element_ty),
+            )
+        # -- update output accumulator --
+        alpha = tl.math.exp2(m_i - m_ij)
+        acc = acc * alpha[:, None]
+        if not PRE_LOAD_V:
+            v = load_fn(
+                V_block_ptr,
+                MASK_STEPS and (n_extra_tokens != 0),
+                PADDED_HEAD,
+                "zero",
+            )
+        # -- update m_i and l_i
+        l_i = l_i * alpha + l_ij
+        # update m_i and l_i
+        m_i = m_ij
+        acc += tl.dot(p.to(V_block_ptr.type.element_ty), v)
+        V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0))
+        K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N))
+        if bias_ptr is not None:
+            bias_ptr = tl.advance(bias_ptr, (0, BLOCK_N))
+        if RETURN_ENCODED_SOFTMAX:
+            encoded_softmax_block_ptr = tl.advance(encoded_softmax_block_ptr,
+                                                   (0, BLOCK_N))
+    return acc, l_i, m_i
+
+
+@triton.autotune(
+    configs=[
+        triton.Config(
+            {
+                "BLOCK_M": 256,
+                "BLOCK_N": 64,
+                "waves_per_eu": 2,
+                "PRE_LOAD_V": False,
+            },
+            num_stages=1,
+            num_warps=8,
+        ),
+        triton.Config(
+            {
+                "BLOCK_M": 128,
+                "BLOCK_N": 128,
+                "waves_per_eu": 2,
+                "PRE_LOAD_V": False,
+            },
+            num_stages=1,
+            num_warps=4,
+        ),
+        triton.Config(
+            {
+                "BLOCK_M": 256,
+                "BLOCK_N": 128,
+                "waves_per_eu": 2,
+                "PRE_LOAD_V": False,
+            },
+            num_stages=1,
+            num_warps=8,
+        ),
+        triton.Config(
+            {
+                "BLOCK_M": 128,
+                "BLOCK_N": 64,
+                "waves_per_eu": 3,
+                "PRE_LOAD_V": True,
+            },
+            num_stages=1,
+            num_warps=4,
+        ),
+        triton.Config(
+            {
+                "BLOCK_M": 128,
+                "BLOCK_N": 64,
+                "waves_per_eu": 3,
+                "PRE_LOAD_V": False,
+            },
+            num_stages=1,
+            num_warps=4,
+        ),
+        triton.Config(
+            {
+                "BLOCK_M": 64,
+                "BLOCK_N": 64,
+                "waves_per_eu": 4,
+                "PRE_LOAD_V": False,
+            },
+            num_stages=1,
+            num_warps=8,
+        ),
+        triton.Config(
+            {
+                "BLOCK_M": 32,
+                "BLOCK_N": 32,
+                "waves_per_eu": 4,
+                "PRE_LOAD_V": False,
+            },
+            num_stages=1,
+            num_warps=8,
+        ),
+        # TODO: This config fails with head_size not pow2 with data mismatches.
+        #    triton.Config({'BLOCK_M': 32, 'BLOCK_N': 16, 'waves_per_eu': 1,
+        #                   'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
+        triton.Config(
+            {
+                "BLOCK_M": 16,
+                "BLOCK_N": 16,
+                "waves_per_eu": 1,
+                "PRE_LOAD_V": False,
+            },
+            num_stages=1,
+            num_warps=4,
+        ),
+    ],
+    key=["hq", "hk", "IS_CAUSAL", "dropout_p", "BLOCK_DMODEL"],
+)
+@triton.jit
+def attn_fwd(
+    Q,
+    K,
+    V,
+    bias,
+    sm_scale,
+    L,
+    Out,
+    stride_qz,
+    stride_qh,
+    stride_qm,
+    stride_qk,
+    stride_kz,
+    stride_kh,
+    stride_kn,
+    stride_kk,
+    stride_vz,
+    stride_vh,
+    stride_vk,
+    stride_vn,
+    stride_oz,
+    stride_oh,
+    stride_om,
+    stride_on,
+    stride_bz,
+    stride_bh,
+    stride_bm,
+    stride_bn,
+    cu_seqlens_q,
+    cu_seqlens_k,
+    dropout_p,
+    philox_seed,
+    philox_offset_base,
+    encoded_softmax,
+    hq,
+    hk,
+    ACTUAL_BLOCK_DMODEL: tl.constexpr,
+    MAX_SEQLENS_Q: tl.constexpr,
+    MAX_SEQLENS_K: tl.constexpr,
+    VARLEN: tl.constexpr,
+    IS_CAUSAL: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    PRE_LOAD_V: tl.constexpr,
+    BIAS_TYPE: tl.constexpr,
+    ENABLE_DROPOUT: tl.constexpr,
+    RETURN_ENCODED_SOFTMAX: tl.constexpr,
+):
+    start_m = tl.program_id(0)
+    off_h_q = tl.program_id(1)
+    off_z = tl.program_id(2)
+    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = tl.arange(0, BLOCK_N)
+    if VARLEN:
+        cu_seqlens_q_start = tl.load(cu_seqlens_q + off_z)
+        cu_seqlens_q_end = tl.load(cu_seqlens_q + off_z + 1)
+        seqlen_q = cu_seqlens_q_end - cu_seqlens_q_start
+        # We have a one-size-fits-all grid in id(0). Some seqlens might be too
+        # small for all start_m so for those we return early.
+        if start_m * BLOCK_M > seqlen_q:
+            return
+        cu_seqlens_k_start = tl.load(cu_seqlens_k + off_z)
+        cu_seqlens_k_end = tl.load(cu_seqlens_k + off_z + 1)
+        seqlen_k = cu_seqlens_k_end - cu_seqlens_k_start
+    else:
+        cu_seqlens_q_start = 0
+        cu_seqlens_k_start = 0
+        seqlen_q = MAX_SEQLENS_Q
+        seqlen_k = MAX_SEQLENS_K
+
+    # Now we compute whether we need to exit early due to causal masking.
+    # This is because for seqlen_q > seqlen_k, M rows of the attn scores
+    # are completely masked, resulting in 0s written to the output, and
+    # inf written to LSE. We don't need to do any GEMMs in this case.
+    # This block of code determines what N is, and if this WG is operating
+    # on those M rows.
+    n_blocks = cdiv_fn(seqlen_k, BLOCK_N)
+    if IS_CAUSAL:
+        # If seqlen_q == seqlen_k, the attn scores are a square matrix.
+        # If seqlen_q != seqlen_k, attn scores are rectangular which means
+        # the causal mask boundary is bottom right aligned, and ends at either
+        # the top edge (seqlen_q < seqlen_k) or left edge.
+        # This captures the decrease in n_blocks if we have a rectangular attn
+        # matrix
+        n_blocks_seqlen = cdiv_fn(
+            (start_m + 1) * BLOCK_M + seqlen_k - seqlen_q, BLOCK_N)
+        # This is what adjusts the block_max for the current WG, only
+        # if IS_CAUSAL. Otherwise we want to always iterate through all n_blocks
+        n_blocks = min(n_blocks, n_blocks_seqlen)
+        # If we have no blocks after adjusting for seqlen deltas, this WG is
+        # part of the blocks that are all 0. We exit early.
+        if n_blocks <= 0:
+            o_offset = (off_z * stride_oz + cu_seqlens_q_start * stride_om +
+                        off_h_q * stride_oh)
+            O_block_ptr = tl.make_block_ptr(
+                base=Out + o_offset,
+                shape=(seqlen_q, BLOCK_DMODEL),
+                strides=(stride_om, stride_on),
+                offsets=(start_m * BLOCK_M, 0),
+                block_shape=(BLOCK_M, BLOCK_DMODEL),
+                order=(1, 0),
+            )
+            acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=Out.type.element_ty)
+            # We still need to write 0s to the result
+            # tl.store(O_block_ptr,
+            # acc.to(Out.type.element_ty), boundary_check=(0,1))
+            # l_ptrs = L + off_z * hq * MAX_SEQLENS_Q + off_h_q * MAX_SEQLENS_Q
+            #          + offs_m
+            # We store inf to LSE, not -inf because in the bwd pass,
+            # we subtract this
+            # from qk which makes it -inf, such that exp(qk - inf) = 0
+            # for these masked blocks.
+            # l = tl.full([BLOCK_M], value=float("inf"), dtype=tl.float32)
+            # tl.store(l_ptrs, l)
+            # TODO: Should dropout and return encoded softmax be handled here?
+            return
+
+    is_mqa = hq != hk
+    off_h_k = off_h_q % hk if is_mqa else off_h_q
+    n_extra_tokens = 0
+    if seqlen_k < BLOCK_N:
+        n_extra_tokens = BLOCK_N - seqlen_k
+    elif seqlen_k % BLOCK_N:
+        n_extra_tokens = seqlen_k % BLOCK_N
+    padded_head = ACTUAL_BLOCK_DMODEL != BLOCK_DMODEL
+
+    # Compute pointers for all the tensors used in this kernel.
+    q_offset = (off_z * stride_qz + off_h_q * stride_qh +
+                cu_seqlens_q_start * stride_qm)
+    Q_block_ptr = tl.make_block_ptr(
+        base=Q + q_offset,
+        shape=(seqlen_q, ACTUAL_BLOCK_DMODEL),
+        strides=(stride_qm, stride_qk),
+        offsets=(start_m * BLOCK_M, 0),
+        block_shape=(BLOCK_M, BLOCK_DMODEL),
+        order=(1, 0),
+    )
+    k_offset = (off_z * stride_kz + off_h_k * stride_kh +
+                cu_seqlens_k_start * stride_kn)
+    K_block_ptr = tl.make_block_ptr(
+        base=K + k_offset,
+        shape=(ACTUAL_BLOCK_DMODEL, seqlen_k),
+        strides=(stride_kk, stride_kn),
+        offsets=(0, 0),
+        block_shape=(BLOCK_DMODEL, BLOCK_N),
+        order=(0, 1),
+    )
+    v_offset = (off_z * stride_vz + off_h_k * stride_vh +
+                cu_seqlens_k_start * stride_vk)
+    V_block_ptr = tl.make_block_ptr(
+        base=V + v_offset,
+        shape=(seqlen_k, ACTUAL_BLOCK_DMODEL),
+        strides=(stride_vk, stride_vn),
+        offsets=(0, 0),
+        block_shape=(BLOCK_N, BLOCK_DMODEL),
+        order=(1, 0),
+    )
+    if BIAS_TYPE != 0:
+        bias_ptr = tl.make_block_ptr(
+            base=bias + off_h_q * stride_bh,
+            shape=(seqlen_q, seqlen_k),
+            strides=(stride_bm, stride_bn),
+            offsets=(start_m * BLOCK_M, 0),
+            block_shape=(BLOCK_M, BLOCK_N),
+            order=(1, 0),
+        )
+    else:
+        bias_ptr = None
+    if ENABLE_DROPOUT:
+        batch_philox_offset = philox_offset_base \
+                              + (off_z * hq + off_h_q) \
+                              * seqlen_q * seqlen_k
+    else:
+        batch_philox_offset = 0
+    # We can ask to return the dropout mask without actually doing any dropout.
+    # In this case, we return an invalid pointer so indicate the mask is not i
+    # valid.
+    # TODO: Fix encoded softmax. It currently uses just h_q in the base offset.
+    if RETURN_ENCODED_SOFTMAX:
+        encoded_softmax_block_ptr = tl.make_block_ptr(
+            base=encoded_softmax + off_h_q * seqlen_q * seqlen_k,
+            shape=(seqlen_q, seqlen_k),
+            strides=(seqlen_k, 1),
+            offsets=(start_m * BLOCK_M, 0),
+            block_shape=(BLOCK_M, BLOCK_N),
+            order=(1, 0),
+        )
+    else:
+        encoded_softmax_block_ptr = 0
+    # initialize pointer to m and l
+    m_i = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
+    l_i = tl.full([BLOCK_M], 1.0, dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
+    # scale sm_scale by log_2(e) and use 2^x in the loop as we do not
+    # have native e^x support in HW.
+    qk_scale = sm_scale * 1.44269504089
+    # Q is loaded once at the beginning and shared by all N blocks.
+    q = load_fn(Q_block_ptr, True, padded_head, "zero")
+    q = (q * qk_scale).to(Q_block_ptr.type.element_ty)
+
+    # Here we compute how many full and masked blocks we have.
+    padded_block_k = n_extra_tokens != 0
+    is_modulo_mn = not padded_block_k and (seqlen_q % BLOCK_M == 0)
+    if IS_CAUSAL:
+        # There are always at least BLOCK_M // BLOCK_N masked blocks.
+        # Additionally there might be one more due to dissimilar seqlens.
+        masked_blocks = BLOCK_M // BLOCK_N + (not is_modulo_mn)
+    else:
+        # Padding on Q does not need to be masked in the FA loop.
+        masked_blocks = padded_block_k
+    # if IS_CAUSAL, not is_modulo_mn does not always result in an additional
+    # block. In this case we might exceed n_blocks so pick the min.
+    masked_blocks = min(masked_blocks, n_blocks)
+    n_full_blocks = n_blocks - masked_blocks
+    block_min = 0
+    block_max = n_blocks * BLOCK_N
+    # Compute for full blocks. Here we set causal to false regardless of its
+    # value because there is no masking. Similarly we do not need padding.
+    if n_full_blocks > 0:
+        block_max = (n_blocks - masked_blocks) * BLOCK_N
+        acc, l_i, m_i = _attn_fwd_inner(
+            acc,
+            l_i,
+            m_i,
+            q,
+            K_block_ptr,
+            V_block_ptr,
+            start_m,
+            seqlen_k,
+            dropout_p,
+            philox_seed,
+            batch_philox_offset,
+            encoded_softmax_block_ptr,
+            # _, _, offs_n_causal, masked_blocks, n_extra_tokens, _
+            block_min,
+            block_max,
+            0,
+            0,
+            0,
+            bias_ptr,
+            # IS_CAUSAL, ....
+            False,
+            BLOCK_M,
+            BLOCK_DMODEL,
+            BLOCK_N,
+            offs_m,
+            offs_n,
+            # _, MASK_STEPS, ...
+            PRE_LOAD_V,
+            False,
+            ENABLE_DROPOUT,
+            RETURN_ENCODED_SOFTMAX,
+            padded_head,
+        )
+        block_min = block_max
+        block_max = n_blocks * BLOCK_N
+
+    tl.debug_barrier()
+    # Remaining blocks, if any, are full / not masked.
+    if masked_blocks > 0:
+        offs_n_causal = offs_n + (seqlen_q - seqlen_k) if IS_CAUSAL else 0
+        K_block_ptr = tl.advance(K_block_ptr, (0, n_full_blocks * BLOCK_N))
+        V_block_ptr = tl.advance(V_block_ptr, (n_full_blocks * BLOCK_N, 0))
+        if bias_ptr is not None:
+            bias_ptr = tl.advance(bias_ptr, (0, n_full_blocks * BLOCK_N))
+        if RETURN_ENCODED_SOFTMAX:
+            encoded_softmax_block_ptr = tl.advance(encoded_softmax_block_ptr,
+                                                   (0, n_full_blocks))
+        acc, l_i, m_i = _attn_fwd_inner(
+            acc,
+            l_i,
+            m_i,
+            q,
+            K_block_ptr,
+            V_block_ptr,
+            start_m,
+            seqlen_k,
+            dropout_p,
+            philox_seed,
+            batch_philox_offset,
+            encoded_softmax_block_ptr,
+            block_min,
+            block_max,
+            offs_n_causal,
+            masked_blocks,
+            n_extra_tokens,
+            bias_ptr,
+            IS_CAUSAL,
+            BLOCK_M,
+            BLOCK_DMODEL,
+            BLOCK_N,
+            offs_m,
+            offs_n,
+            # _, MASK_STEPS, ...
+            PRE_LOAD_V,
+            True,
+            ENABLE_DROPOUT,
+            RETURN_ENCODED_SOFTMAX,
+            padded_head,
+        )
+    # epilogue
+    acc = acc / l_i[:, None]
+    if ENABLE_DROPOUT:
+        acc = acc / (1 - dropout_p)
+    # If seqlen_q > seqlen_k but the delta is not a multiple of BLOCK_M,
+    # then we have one block with a row of all NaNs which come from computing
+    # softmax over a row of all -infs (-inf - inf = NaN). We check for that here
+    # and store 0s where there are NaNs as these rows should've been zeroed out.
+    end_m_idx = (start_m + 1) * BLOCK_M
+    start_m_idx = start_m * BLOCK_M
+    causal_start_idx = seqlen_q - seqlen_k
+    acc = acc.to(Out.type.element_ty)
+    if IS_CAUSAL:  # noqa: SIM102
+        if causal_start_idx > start_m_idx and causal_start_idx < end_m_idx:
+            out_mask_boundary = tl.full((BLOCK_DMODEL, ),
+                                        causal_start_idx,
+                                        dtype=tl.int32)
+            mask_m_offsets = start_m_idx + tl.arange(0, BLOCK_M)
+            out_ptrs_mask = (mask_m_offsets[:, None] >=
+                             out_mask_boundary[None, :])
+            z = 0.0
+            acc = tl.where(out_ptrs_mask, acc, z.to(acc.type.element_ty))
+    # write back LSE
+    # l_ptrs = L + off_z * hq * MAX_SEQLENS_Q + off_h_q * MAX_SEQLENS_Q + offs_m
+    # If seqlen_q not multiple of BLOCK_M, we need to mask out the last
+    # few rows. This is only true for the last M block. For others,
+    # overflow_size will be -ve
+    # overflow_size = end_m_idx - seqlen_q
+    # if overflow_size > 0:
+    #    boundary = tl.full((BLOCK_M,), BLOCK_M - overflow_size, dtype=tl.int32)
+    #    # This is a > check because mask being 0 blocks the store.
+    #    l_ptrs_mask = boundary > tl.arange(0, BLOCK_M)
+    #    tl.store(l_ptrs, m_i + tl.math.log2(l_i), mask=l_ptrs_mask)
+    # else:
+    #    tl.store(l_ptrs, m_i + tl.math.log2(l_i))
+
+    # write back O
+    o_offset = (off_z * stride_oz + cu_seqlens_q_start * stride_om +
+                off_h_q * stride_oh)
+    O_block_ptr = tl.make_block_ptr(
+        base=Out + o_offset,
+        shape=(seqlen_q, ACTUAL_BLOCK_DMODEL),
+        strides=(stride_om, stride_on),
+        offsets=(start_m * BLOCK_M, 0),
+        block_shape=(BLOCK_M, BLOCK_DMODEL),
+        order=(1, 0),
+    )
+    # Need boundary check on this to make sure the padding from the
+    # Q and KV tensors in both dims are not part of what we store back.
+    # TODO: Do the boundary check optionally.
+    tl.store(O_block_ptr, acc, boundary_check=(0, 1))
+
+
+def check_args(
+    q,
+    k,
+    v,
+    o,
+    varlen=True,
+    max_seqlens=None,
+    cu_seqlens_q=None,
+    cu_seqlens_k=None,
+):
+    assert q.dim() == k.dim() and q.dim() == v.dim()
+    if varlen:
+        assert q.dim() == 3
+        total_q, nheads_q, head_size = q.shape
+        total_k, nheads_k, _ = k.shape
+        assert cu_seqlens_q is not None
+        assert cu_seqlens_k is not None
+        assert len(cu_seqlens_q) == len(cu_seqlens_k)
+    else:
+        assert q.dim() == 4
+        batch, nheads_q, seqlen_q, head_size = q.shape
+        _, nheads_k, seqlen_k, _ = k.shape
+        assert max_seqlens > 0
+    assert k.shape == v.shape
+    assert q.shape[-1] == k.shape[-1] and q.shape[-1] == v.shape[-1]
+    # TODO: Change assert if we support qkl f8 and v f16
+    assert q.dtype == k.dtype and q.dtype == v.dtype
+    # TODO: Fix assert to check head size <=256 once supported
+    assert head_size <= 128
+    assert o.shape == q.shape
+    assert (nheads_q % nheads_k) == 0
+
+
+class _attention(torch.autograd.Function):
+
+    @staticmethod
+    def forward(
+        ctx,
+        q,
+        k,
+        v,
+        o,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        max_seqlens_q,
+        max_seqlens_k,
+        causal=False,
+        sm_scale=1.0,
+        bias=None,
+    ):
+        if o is None:
+            o = torch.empty_like(q, dtype=v.dtype)
+
+        check_args(
+            q,
+            k,
+            v,
+            o,
+            varlen=True,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+        )
+        if True:  # varlen
+            total_q, nheads_q, head_size = q.shape
+            total_k, nheads_k, _ = k.shape
+            batch = len(cu_seqlens_q) - 1
+            q_strides = (0, q.stride(1), q.stride(0), q.stride(2))
+            k_strides = (0, k.stride(1), k.stride(0), k.stride(2))
+            v_strides = (0, v.stride(1), v.stride(0), v.stride(2))
+            o_strides = (0, o.stride(1), o.stride(0), o.stride(2))
+        else:
+            batch, seqlen_q, nheads_q, head_size = q.shape
+            _, seqlen_k, nheads_k, _ = k.shape
+            q_strides = (q.stride(0), q.stride(2), q.stride(1), q.stride(3))
+            k_strides = (k.stride(0), k.stride(2), k.stride(1), k.stride(3))
+            v_strides = (v.stride(0), v.stride(2), v.stride(1), v.stride(3))
+            o_strides = (o.stride(0), o.stride(2), o.stride(1), o.stride(3))
+
+        # Get closest power of 2 over or equal to 32.
+        unpadded_head_dims = {32, 64, 128}
+        if head_size not in unpadded_head_dims:
+            padded_d_model = None
+            for i in unpadded_head_dims:
+                if i > head_size:
+                    padded_d_model = i
+                    break
+            assert padded_d_model is not None
+        else:
+            padded_d_model = head_size
+
+        grid = lambda META: (
+            triton.cdiv(max_seqlens_q, META["BLOCK_M"]),
+            nheads_q,
+            batch,
+        )
+
+        encoded_softmax = None
+
+        # Seed the RNG so we get reproducible results for testing.
+        philox_seed = 0x1BF52
+        philox_offset = 0x1D4B42
+
+        if bias is not None:
+            bias_strides = (
+                bias.stride(0),
+                bias.stride(1),
+                bias.stride(2),
+                bias.stride(3),
+            )
+        else:
+            bias_strides = (0, 0, 0, 0)
+
+        attn_fwd[grid](
+            q,
+            k,
+            v,
+            bias,
+            sm_scale,
+            None,
+            o,
+            *q_strides,
+            *k_strides,
+            *v_strides,
+            *o_strides,
+            *bias_strides,
+            cu_seqlens_q,
+            cu_seqlens_k,
+            dropout_p=0.0,
+            philox_seed=philox_seed,
+            philox_offset_base=philox_offset,
+            encoded_softmax=encoded_softmax,
+            hq=nheads_q,
+            hk=nheads_k,
+            ACTUAL_BLOCK_DMODEL=head_size,
+            MAX_SEQLENS_Q=max_seqlens_q,
+            MAX_SEQLENS_K=max_seqlens_k,
+            IS_CAUSAL=causal,
+            VARLEN=True,
+            BLOCK_DMODEL=padded_d_model,
+            BIAS_TYPE=0 if bias is None else 1,
+            ENABLE_DROPOUT=False,
+            RETURN_ENCODED_SOFTMAX=False,
+        )
+
+        ctx.grid = grid
+        ctx.sm_scale = sm_scale
+        ctx.BLOCK_DMODEL = head_size
+        ctx.causal = causal
+        ctx.dropout_p = 0.0
+        ctx.philox_seed = philox_seed
+        ctx.philox_offset = philox_offset
+        ctx.encoded_softmax = encoded_softmax
+        ctx.return_encoded_softmax = False
+        return o, encoded_softmax
+
+
+triton_attention = _attention.apply