bpt.py

import flax.linen as nn
import jax
import jax.lax as lax
import jax.numpy as jnp
from einops import rearrange
from functools import partial

def permute_tokens(attention_type, tokens, num_devices):
    if attention_type == "striped":
        return rearrange(tokens, "b (n d) -> b (d n)", d=num_devices)
    else:
        return tokens

def unpermute_outputs(attention_type, outputs, num_devices):
    if attention_type == "striped":
        return rearrange(outputs, "b (d n) x -> b (n d) x", d=num_devices)
    else:
        return outputs

"""Ring Attention Implementation
Ring Attention with Blockwise Transformers for Near-Infinite Context https://arxiv.org/abs/2310.01889 Liu et al. 2023.

Ring Attention generalizes blockwise attention and distributes the attention computation across multiple devices, allowing up to number of devices times longer sequences than BPT.

Extended with support for Striped Attention, which permutes the input sequence to ensure every device gets a roughly equal portion of the causal mask, thereby reducing workload imbalances.
"""
def _ring_attention_fwd(q, k, v, attn_bias, axis_name, float32_logits, blockwise_kwargs):
    if float32_logits:
        q, k = q.astype(jnp.float32), k.astype(jnp.float32)
    batch, q_len, num_heads, dim_per_head = q.shape
    batch, kv_len, num_heads, dim_per_head = k.shape
    numerator = jnp.zeros((batch, q_len, num_heads, dim_per_head)).astype(q.dtype)
    denominator = jnp.zeros((batch, num_heads, q_len)).astype(q.dtype)
    axis_size = lax.psum(1, axis_name)
    block_size = q_len # assumes this function is pre-sharded inside shard_map
    query_chunk_size = blockwise_kwargs["query_chunk_size"]
    key_chunk_size = blockwise_kwargs["key_chunk_size"]
    def scan_kv_block(carry, idx):
        prev_max_score, numerator, denominator, k, v = carry
        ''' einsum version
        mask = lax.dynamic_slice_in_dim(attn_mask,
            (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1)
        attn_weights = jnp.einsum("bqhd,bkd->bhqk", q, k) / scale
        attn_weights = jnp.where(mask, -jnp.inf, attn_weights)
        max_score = jnp.maximum(prev_max_score, jnp.max(attn_weights, axis=-1))
        exp_weights = jnp.exp(attn_weights - max_score[..., None])
        correction = rearrange(jnp.exp(prev_max_score - max_score), 'b h q -> b q h')[..., None]
        numerator = numerator * correction + jnp.einsum("bhqk,bkd->bqhd", exp_weights, v)
        denominator = denominator * jnp.exp(prev_max_score - max_score) + jnp.sum(exp_weights, axis=-1)
        '''
        # blockwise version
        attn_bias_slice = lax.dynamic_slice_in_dim(attn_bias,
            (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1
        )
        q_block_idx = lax.axis_index(axis_name)
        k_block_idx = (lax.axis_index(axis_name) - idx) % axis_size
        q_chunk_idx_start = q_block_idx * (block_size // query_chunk_size)
        k_chunk_idx_start = k_block_idx * (block_size // key_chunk_size)
        numerator, denominator, max_score = _blockwise_attention_fwd(q, k, v, (numerator, denominator, prev_max_score), q_chunk_idx_start, k_chunk_idx_start, bias=attn_bias_slice, **blockwise_kwargs)
        k, v = map(lambda x: lax.ppermute(x, axis_name, perm=[(i, (i + 1) % axis_size) for i in range(axis_size)]), (k, v))
        return (max_score, numerator, denominator, k, v), None
    prev_max_score = jnp.full((batch, num_heads, q_len), -jnp.inf).astype(q.dtype)
    (max_score, numerator, denominator, _, _), _ = lax.scan(scan_kv_block,
        init=(prev_max_score, numerator, denominator, k, v), xs=jnp.arange(0, axis_size))
    output = numerator / rearrange(denominator, 'b h q -> b q h')[..., None]
    return output.astype(v.dtype), (output, q, k, v, attn_bias, denominator, max_score)

def _ring_attention_bwd(axis_name, float32_logits, blockwise_kwargs, res, g):
    del float32_logits
    output, q, k, v, attn_bias, denominator, max_score = res
    batch, q_len, num_heads, dim_per_head = q.shape
    batch, kv_len, num_heads, dim_per_head = k.shape
    axis_size = lax.psum(1, axis_name)
    dq = jnp.zeros_like(q, dtype=jnp.float32)
    dk = jnp.zeros_like(k, dtype=jnp.float32)
    dv = jnp.zeros_like(v, dtype=jnp.float32)
    query_chunk_size = blockwise_kwargs["query_chunk_size"]
    key_chunk_size = blockwise_kwargs["key_chunk_size"]
    block_size = q.shape[1] # assumes this function is pre-sharded inside shard_map
    def scan_kv_block(carry, idx):
        dq, dk, dv, k, v = carry
        ''' einsum version
        # mask = lax.dynamic_slice_in_dim(attn_mask,
        #     (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1)
        # attn_weights = jnp.einsum("bqhd,bkd->bhqk", q, k) / scale
        # attn_weights = jnp.where(mask, -jnp.inf, attn_weights)
        # exp_weights = jnp.exp(attn_weights - max_score[..., None]) / denominator[..., None]
        # ds = jnp.einsum("bqhd,bkd->bhqk", g, v)
        # dl = (ds - jnp.einsum("bqhd,bqhd->bhs", g, output)[..., None]) * exp_weights
        # dq = dq + jnp.einsum("bhqk,bkd->bqhd", dl, k) / scale
        # dk = dk + jnp.einsum("bqhd,bhqk->bkd", q, dl) / scale
        # dv = dv + jnp.einsum("bhqk,bqhd->bkd", exp_weights, g)
        '''
        # blockwise version
        attn_bias_slice = lax.dynamic_slice_in_dim(attn_bias,
            (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1
        )
        q_block_idx = lax.axis_index(axis_name)
        k_block_idx = (lax.axis_index(axis_name) - idx) % axis_size
        q_chunk_idx_start = q_block_idx * (block_size // query_chunk_size)
        k_chunk_idx_start = k_block_idx * (block_size // key_chunk_size)
        dq, dk, dv = _blockwise_attention_bwd(q, k, v, g, (dq, dk, dv, output, denominator, max_score), q_chunk_idx_start, k_chunk_idx_start, bias=attn_bias_slice, **blockwise_kwargs)
        k, v, dk, dv = map(lambda x: lax.ppermute(x, axis_name, perm=[(i,
            (i + 1) % axis_size) for i in range(axis_size)]), (k, v, dk, dv))
        return (dq, dk, dv, k, v), None
    (dq, dk, dv, k, v), _ = lax.scan(scan_kv_block, init=(dq, dk, dv, k, v), xs=jnp.arange(0, axis_size))
    dq, dk, dv = dq.astype(q.dtype), dk.astype(k.dtype), dv.astype(v.dtype)
    return dq, dk, dv, None

@partial(jax.custom_vjp, nondiff_argnums=[4, 5, 6])
def ring_attention(q, k, v, attn_bias, axis_name, float32_logits, blockwise_kwargs):
    y, _ = _ring_attention_fwd(q, k, v, attn_bias, axis_name, float32_logits, blockwise_kwargs)
    return y

ring_attention.defvjp(_ring_attention_fwd, _ring_attention_bwd)

def _blockwise_attention_fwd(q, k, v, carry, q_chunk_idx_start, k_chunk_idx_start, bias, causal_layout, query_chunk_size,
                             key_chunk_size, block_size, deterministic, dropout_rng, attn_pdrop, dtype, policy, precision, prevent_cse):
    batch, q_len, num_heads, dim_per_head = q.shape
    batch, kv_len, num_heads, dim_per_head = k.shape
    batch, kv_len, num_heads, dim_per_head = v.shape
    num_q = q_len // query_chunk_size
    num_kv = kv_len // key_chunk_size
    q = q.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
    k = k.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
    v = v.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
    q, k, v = map(lambda x: jnp.moveaxis(x, 1, 0), (q, k, v))

    numerator, denominator, max_score = carry
    numerator = numerator.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
    numerator = jnp.moveaxis(numerator, 1, 0)
    denominator = denominator.reshape((batch, num_heads, num_q, query_chunk_size))
    max_score = max_score.reshape((batch, num_heads, num_q, query_chunk_size))
    denominator, max_score = map(lambda x: rearrange(x, 'b h n c -> n b h c'), (denominator, max_score))

    scale = jnp.sqrt(q.shape[-1])
    if bias is not None:
        for bias_dim, broadcast_dim in zip(bias.shape, (batch, num_heads, q_len, kv_len)):
            assert bias_dim == 1 or bias_dim == broadcast_dim
    if not deterministic and attn_pdrop > 0.0:
        attn_dropout_rng, dropout_rng = jax.random.split(dropout_rng)
        attn_dropout = jax.random.bernoulli(attn_dropout_rng, attn_pdrop, (batch, num_heads, q_len, kv_len))
    else:
        attn_dropout = None
    _chunk_bias_fn = partial(
        _chunk_attention_bias,
        query_chunk_size, key_chunk_size, block_size, bias, deterministic,
        attn_dropout, attn_pdrop, causal_layout, dtype)
    def scan_attention(_, scan):
        q_chunk, numerator_chunk, denominator_chunk, max_score_chunk, q_chunk_idx = scan
        @partial(jax.checkpoint, prevent_cse=prevent_cse, policy=policy)
        def scan_kv_block(carry, scan):
            k_chunk, value_chunk, k_chunk_idx = scan
            numerator_chunk, denominator_chunk, prev_max_score_chunk = carry
            attn_weights = jnp.einsum('bqhd,bkhd->bhqk', q_chunk, k_chunk, precision=precision) / scale
            bias_chunk = _chunk_bias_fn(q_chunk_idx_start + q_chunk_idx, k_chunk_idx_start + k_chunk_idx)
            attn_weights = attn_weights + bias_chunk

            max_score_chunk = jnp.maximum(prev_max_score_chunk, jnp.max(attn_weights, axis=-1))
            max_score_chunk = lax.stop_gradient(max_score_chunk)
            exp_weights = jnp.exp(attn_weights - max_score_chunk[..., None])
            exp_values = jnp.einsum('bhqk,bkhd->bqhd', exp_weights, value_chunk, precision=precision)
            correction = rearrange(jnp.exp(prev_max_score_chunk - max_score_chunk), 'b h q -> b q h')[..., None]
            numerator_chunk = numerator_chunk * correction + exp_values
            denominator_chunk = denominator_chunk * jnp.exp(prev_max_score_chunk - max_score_chunk) + exp_weights.sum(axis=-1)
            return (numerator_chunk, denominator_chunk, max_score_chunk), None

        def skip_upper_half(carry, args):
            key_chunk, value_chunk, k_chunk_idx = args
            if causal_layout is None:
                skip_block = jnp.array(False)
            else:
                q_lo = (q_chunk_idx_start + q_chunk_idx) * query_chunk_size
                k_lo = (k_chunk_idx_start + k_chunk_idx) * key_chunk_size
                if causal_layout == "striped":
                    q_lo %= block_size
                    k_lo %= block_size
                else:
                    assert causal_layout == "normal"
                q_hi = q_lo + query_chunk_size
                skip_block = q_hi <= k_lo
            return jax.lax.cond(
                skip_block,
                lambda carry, args: (carry, None),
                scan_kv_block,
                carry,
                args
            )

        (numerator_chunk, denominator_chunk, max_score_chunk), _ = lax.scan(
            skip_upper_half, init=(numerator_chunk, denominator_chunk, max_score_chunk), xs=(k, v, jnp.arange(0, num_kv))
        )
        output_chunk = numerator_chunk / rearrange(denominator_chunk, 'b h q -> b q h')[..., None].astype(dtype)
        return (), (output_chunk, numerator_chunk, denominator_chunk, max_score_chunk)
    _, (_, numerator, denominator, max_score) = lax.scan(scan_attention, init=(), xs=(q, numerator, denominator, max_score, jnp.arange(0, num_q)))

    numerator = jnp.moveaxis(numerator, 1, 0)
    numerator = numerator.reshape((batch, q_len, num_heads, dim_per_head))
    denominator, max_score = map(lambda x: rearrange(x, 'n b h c -> b h n c'), (denominator, max_score))
    denominator = denominator.reshape((batch, num_heads, q_len))
    max_score = max_score.reshape((batch, num_heads, q_len))

    return numerator, denominator, max_score

def _blockwise_attention_bwd(q, k, v, g, carry, q_chunk_idx_start, k_chunk_idx_start, bias, causal_layout, query_chunk_size, key_chunk_size, block_size, deterministic, dropout_rng, attn_pdrop, dtype, policy, precision, prevent_cse):
    batch, q_len, num_heads, dim_per_head = q.shape
    batch, kv_len, num_heads, dim_per_head = k.shape
    batch, kv_len, num_heads, dim_per_head = v.shape
    num_q = q_len // query_chunk_size
    num_kv = kv_len // key_chunk_size
    dq, dk, dv, output, denominator, max_score = carry

    g = g.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
    dq = dq.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
    dk = dk.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
    dv = dv.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
    output = output.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
    g, dq, dk, dv, output = map(lambda x: jnp.moveaxis(x, 1, 0), (g, dq, dk, dv, output))

    denominator = denominator.reshape((batch, num_heads, num_q, query_chunk_size))
    max_score = max_score.reshape((batch, num_heads, num_q, query_chunk_size))
    denominator, max_score = map(lambda x: rearrange(x, 'b h n c -> n b h c'), (denominator, max_score))

    q = q.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
    k = k.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
    v = v.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
    q, k, v = map(lambda x: jnp.moveaxis(x, 1, 0), (q, k, v))

    scale = jnp.sqrt(q.shape[-1])
    if bias is not None:
        for bias_dim, broadcast_dim in zip(bias.shape, (batch, num_heads, q_len, kv_len)):
            assert bias_dim == 1 or bias_dim == broadcast_dim
    if not deterministic and attn_pdrop > 0.0:
        attn_dropout_rng, dropout_rng = jax.random.split(dropout_rng)
        attn_dropout = jax.random.bernoulli(attn_dropout_rng, attn_pdrop, (batch, num_heads, q_len, kv_len))
    else:
        attn_dropout = None
    _chunk_bias_fn = partial(
        _chunk_attention_bias,
        query_chunk_size, key_chunk_size, block_size, bias, deterministic,
        attn_dropout, attn_pdrop, causal_layout, dtype)
    def scan_attention(carry, scan):
        dk, dv = carry
        q_chunk, dq_chunk, g_chunk, output_chunk, denominator_chunk, max_score_chunk, q_chunk_idx = scan
        dl_part = jnp.einsum("bqhd,bqhd->bhq", g_chunk, output_chunk)[..., None]
        @partial(jax.checkpoint, prevent_cse=prevent_cse, policy=policy)
        def scan_kv_block(carry, scan):
            k_chunk, value_chunk, k_chunk_idx = scan
            dq_chunk = carry
            attn_weights = jnp.einsum('bqhd,bkhd->bhqk', q_chunk, k_chunk, precision=precision) / scale
            bias_chunk = _chunk_bias_fn(q_chunk_idx_start + q_chunk_idx, k_chunk_idx_start + k_chunk_idx)
            # bias_chunk = jnp.moveaxis(bias_chunk, 1, 2)
            attn_weights = attn_weights + bias_chunk
            exp_weights = jnp.exp(attn_weights - max_score_chunk[..., None]) / denominator_chunk[..., None]

            ds = jnp.einsum("bqhd,bkhd->bhqk", g_chunk, value_chunk)
            dl = (ds - dl_part) * exp_weights
            dq_chunk = dq_chunk + jnp.einsum("bhqk,bkhd->bqhd", dl, k_chunk) / scale
            dk_chunk = jnp.einsum("bqhd,bhqk->bkhd", q_chunk, dl) / scale
            dv_chunk = jnp.einsum("bhqk,bqhd->bkhd", exp_weights, g_chunk)
            return dq_chunk, (dk_chunk, dv_chunk)

        def skip_upper_half(carry, args):
            key_chunk, value_chunk, k_chunk_idx = args
            if causal_layout is None:
                skip_block = jnp.array(False)
            else:
                q_lo = (q_chunk_idx_start + q_chunk_idx) * query_chunk_size
                k_lo = (k_chunk_idx_start + k_chunk_idx) * key_chunk_size
                if causal_layout == "striped":
                    q_lo %= block_size
                    k_lo %= block_size
                else:
                    assert causal_layout == "normal"
                q_hi = q_lo + query_chunk_size
                skip_block = q_hi <= k_lo
            return lax.cond(
                skip_block,
                lambda carry, args: (
                    carry, (
                        jnp.zeros((batch, key_chunk_size, num_heads, dim_per_head), dtype=dk.dtype),
                        jnp.zeros((batch, key_chunk_size, num_heads, dim_per_head), dtype=dk.dtype),
                    )
                ),
                scan_kv_block, carry, args)

        dq_chunk, (dk_part, dv_part) = lax.scan(
            skip_upper_half, init=dq_chunk, xs=(k, v, jnp.arange(0, num_kv))
        )
        return (dk + dk_part, dv + dv_part), dq_chunk
    (dk, dv), dq = lax.scan(scan_attention, init=(dk, dv), xs=(q, dq, g, output, denominator, max_score, jnp.arange(0, num_q)))

    dq, dk, dv = map(lambda x: jnp.moveaxis(x, 1, 0), (dq, dk, dv))
    dq = dq.reshape((batch, q_len, num_heads, dim_per_head))
    dk = dk.reshape((batch, kv_len, num_heads, dim_per_head))
    dv = dv.reshape((batch, kv_len, num_heads, dim_per_head))

    return dq, dk, dv

'''
Computing ffn blockwise without materializing the large hidden tensor, training 4x longer sequences than the memory-efficient transformer.
Blockwise parallel transformer https://arxiv.org/abs/2305.19370 Liu et al. 2023
'''
def blockwise_ffn(remat_ffn, inputs, chunk_size, deterministic):
    # remat_ffn: a rematerialized ffn with policy jax.checkpoint_policies.nothing_saveable()
    # inputs: (batch, seq_len, dim)
    # chunk_size: the chunk size to split the sequence
    inputs = rearrange(inputs, 'b (c n) d -> b c n d', c=chunk_size)
    def scan_ffn(remat_ffn, carry, hidden_states):
        outputs = remat_ffn(hidden_states, deterministic=deterministic)
        return carry, outputs
    scan_axis = inputs.ndim - 2
    _, output = nn.scan(
        scan_ffn,
        variable_broadcast="params",
        split_rngs={"params": False, "dropout": True},
        in_axes=scan_axis,
        out_axes=scan_axis,
    )(remat_ffn, None, inputs)
    output = rearrange(output, 'b c n d -> b (c n) d')
    return output


def blockwise_attn(query, key, value, bias, deterministic,
        dropout_rng, attn_pdrop, causal, query_chunk_size,
        key_chunk_size, dtype, policy, precision, float32_logits,
        prevent_cse):
    # query, key, value: (batch, seq_len, num_heads, dim_per_head)
    # bias: (batch, seq_len) can be used to mask out attention (e.g. padding)
    # causal: whether to use causal mask
    # policy: one of jax.checkpoint_policies
    query = query / jnp.sqrt(query.shape[-1]).astype(dtype)
    if float32_logits:
        query = query.astype(jnp.float32)
        key = key.astype(jnp.float32)

    batch, q_len, num_heads, dim_per_head = query.shape
    batch, kv_len, num_heads, dim_per_head = key.shape
    batch, kv_len, num_heads, dim_per_head = value.shape

    num_q = q_len // query_chunk_size
    num_kv = kv_len // key_chunk_size
    query = query.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
    key = key.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
    value = value.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))

    query = jnp.moveaxis(query, 1, 0)
    key = jnp.moveaxis(key, 1, 0)
    value = jnp.moveaxis(value, 1, 0)

    if bias is not None:
        for bias_dim, broadcast_dim in zip(bias.shape, (batch, num_heads, q_len, kv_len)):
            assert bias_dim == 1 or bias_dim == broadcast_dim
    if not deterministic and attn_pdrop > 0.0:
        attn_dropout_rng, dropout_rng = jax.random.split(dropout_rng)
        attn_dropout = jax.random.bernoulli(attn_dropout_rng, attn_pdrop, (batch, num_heads, q_len, kv_len))
    else:
        attn_dropout = None

    _chunk_bias_fn = partial(
        _chunk_attention_bias,
        query_chunk_size, key_chunk_size, None, bias, deterministic,
        attn_dropout, attn_pdrop, "normal" if causal else None, dtype)

    def scan_attention(carry, args):
        del carry
        query_chunk, query_chunk_idx = args

        @partial(jax.checkpoint, prevent_cse=prevent_cse, policy=policy)
        def scan_kv_block(carry, args):
            key_chunk, value_chunk, key_chunk_idx = args
            (numerator, denominator, prev_max_score) = carry
            attn_weights = jnp.einsum('bqhd,bkhd->bqhk', query_chunk, key_chunk, precision=precision)
            bias_chunk = _chunk_bias_fn(query_chunk_idx, key_chunk_idx)
            bias_chunk = jnp.moveaxis(bias_chunk, 1, 2)
            attn_weights = attn_weights + bias_chunk

            max_score = jnp.max(attn_weights, axis=-1, keepdims=True)
            max_score = jnp.maximum(prev_max_score, max_score)
            max_score = lax.stop_gradient(max_score)
            exp_weights = jnp.exp(attn_weights - max_score)
            exp_values = jnp.einsum(
                'bqhv,bvhd->bqhd', exp_weights, value_chunk, precision=precision
            )
            correction = jnp.exp(prev_max_score - max_score)
            numerator = numerator * correction + exp_values
            denominator = denominator * correction + exp_weights.sum(axis=-1, keepdims=True)
            return (numerator, denominator, max_score), None

        def skip_upper_half(carry, args):
            key_chunk, value_chunk, key_chunk_idx = args
            skip_block = jnp.array(False)
            if causal:
                skip_block = query_chunk_idx < key_chunk_idx
            return lax.cond(
                skip_block,
                lambda carry, args: (carry, None),
                scan_kv_block,
                carry,
                args,
            )

        init_carry = (
            jnp.zeros((batch, query_chunk_size, num_heads, dim_per_head), dtype=query.dtype),
            jnp.zeros((batch, query_chunk_size, num_heads, dim_per_head), dtype=query.dtype),
            (-jnp.inf) * jnp.ones((batch, query_chunk_size, num_heads, 1), dtype=query.dtype),
        )
        (numerator, denominator, max_score), _ = lax.scan(
            skip_upper_half, init_carry, xs=(key, value, jnp.arange(0, num_kv))
        )
        output = (numerator / denominator).astype(dtype)
        return (), output

    _, output = lax.scan(scan_attention, (), xs=(query, jnp.arange(0, num_q)))
    output = rearrange(output, 'n b c h d -> b (n c) h d')
    return output

def _chunk_attention_bias(query_chunk_size, key_chunk_size, block_size,
            bias, deterministic, attn_dropout, attn_pdrop, causal_layout,
            dtype, query_chunk_idx, key_chunk_idx):
    query_offset = query_chunk_idx * query_chunk_size
    key_offset = key_chunk_idx * key_chunk_size
    chunk_bias = jnp.zeros((1, 1, 1, 1), dtype=dtype)
    if bias is not None:
        chunk_bias = lax.dynamic_slice(
            bias,
            start_indices=(0, 0, query_offset, key_offset),
            slice_sizes=(*bias.shape[:2], min(bias.shape[-2], query_chunk_size), min(bias.shape[-1], key_chunk_size)),
        )

    if causal_layout == "normal":
        query_idx = lax.broadcasted_iota(dtype=jnp.int32, shape=(query_chunk_size, 1), dimension=0)
        key_idx = lax.broadcasted_iota(dtype=jnp.int32, shape=(1, key_chunk_size), dimension=1)
        offset = query_offset - key_offset
        query_idx += offset
        causal_mask_value = (query_idx < key_idx) * jnp.finfo(dtype).min
        chunk_bias += causal_mask_value.reshape(1, 1, *causal_mask_value.shape)
    elif causal_layout == "striped":
        assert block_size is not None
        assert block_size % query_chunk_size == 0
        assert block_size % key_chunk_size == 0
        query_block_idx = query_offset // block_size
        key_block_idx = key_offset // block_size
        query_offset_in_block = query_offset % block_size
        key_offset_in_block = key_offset % block_size
        query_idx = lax.broadcasted_iota(dtype=jnp.int32, shape=(query_chunk_size, 1), dimension=0)
        key_idx = lax.broadcasted_iota(dtype=jnp.int32, shape=(1, key_chunk_size), dimension=1)
        exclude_diagonal = query_block_idx < key_block_idx
        offset = query_offset_in_block - key_offset_in_block - exclude_diagonal.astype(jnp.int32)
        query_idx += offset
        causal_mask_value = (query_idx < key_idx) * jnp.finfo(dtype).min
        chunk_bias += causal_mask_value.reshape(1, 1, *causal_mask_value.shape)
    else:
        assert causal_layout is None

    if not deterministic and attn_pdrop > 0.0:
        attn_dropout_slice = lax.dynamic_slice(
            attn_dropout,
            start_indices=(0, 0, query_offset, key_offset),
            slice_sizes=(
                *attn_dropout.shape[:2],
                min(attn_dropout.shape[-2], query_chunk_size),
                min(attn_dropout.shape[-1], key_chunk_size),
            ),
        )
        chunk_bias += attn_dropout_slice * jnp.finfo(dtype).min
    return chunk_bias.astype(dtype)