fused_attn.py

# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
"""JAX multi-head attention modules"""

from enum import Enum
from functools import partial
from jax.ad_checkpoint import checkpoint_name
import jax
import jax.numpy as jnp

from transformer_engine_jax import NVTE_Bias_Type
from transformer_engine_jax import NVTE_Mask_Type
from transformer_engine_jax import NVTE_QKV_Layout

from .cpp_extensions import FusedAttnHelper
from .cpp_extensions import fused_attn_fwd_kvpacked, fused_attn_bwd_kvpacked
from .cpp_extensions import fused_attn_fwd_qkvpacked, fused_attn_bwd_qkvpacked
from .cpp_extensions import fused_attn_fwd, fused_attn_bwd


class AttnBiasType(Enum):
    """
    NO_BIAS: Softmax is performed as softmax(scale * qk)
    PRE_SCALE_BIAS: Softmax is performed as softmax(scale * (qk + bias))
    POST_SCALE_BIAS: Softmax is performed as softmax(scale * qk + bias)
    """
    NO_BIAS = NVTE_Bias_Type.NVTE_NO_BIAS
    PRE_SCALE_BIAS = NVTE_Bias_Type.NVTE_PRE_SCALE_BIAS
    POST_SCALE_BIAS = NVTE_Bias_Type.NVTE_POST_SCALE_BIAS


class AttnMaskType(Enum):
    """
    NO_MASK: No attention mask is applied.
    PADDING_MASK: Indicates the presence of paddings at the end of each sequence.
    CAUSAL_MASK: An upper triangular mask is applied to the softmax inputs.
    PADDING_CAUSAL_MASK: A combination of both causal and padding masks.
    """
    NO_MASK = NVTE_Mask_Type.NVTE_NO_MASK
    PADDING_MASK = NVTE_Mask_Type.NVTE_PADDING_MASK
    CAUSAL_MASK = NVTE_Mask_Type.NVTE_CAUSAL_MASK
    PADDING_CAUSAL_MASK = NVTE_Mask_Type.NVTE_PADDING_CAUSAL_MASK


class QKVLayout(Enum):
    """QKV layout"""
    BS3HD = NVTE_QKV_Layout.NVTE_BS3HD
    BSHD_BS2HD = NVTE_QKV_Layout.NVTE_BSHD_BS2HD
    BSHD_BSHD_BSHD = NVTE_QKV_Layout.NVTE_BSHD_BSHD_BSHD


def canonicalize_attn_mask_type(attn_mask_type: str):
    """Convert string attn_mask_type to AttnMaskType
    TE-JAX currently fall back to the padding version kernels for the libraries integration.
    The overhead between padding and non-padding version should be small.
    However, we will lease this limitation in the near feature.
    """
    match attn_mask_type:
        case 'no_mask':
            return AttnMaskType.NO_MASK
        case 'padding':
            return AttnMaskType.PADDING_MASK
        case 'causal':
            return AttnMaskType.CAUSAL_MASK
        case 'padding_causal' | 'causal_padding':
            return AttnMaskType.PADDING_CAUSAL_MASK
    raise ValueError(f"Unsupported {attn_mask_type=}, supported attn_mask_type="
                     "{'no_mask', 'padding', 'causal', 'padding_causal', 'causal_padding'}")


def is_fused_attn_kernel_available(q_dtype, kv_dtype, qkv_layout, attn_bias_type, attn_mask_type,
                                   dropout_probability, q_num_heads, kv_num_heads, q_max_seqlen,
                                   kv_max_seqlen, head_dim):
    """
    To check whether the fused attention kernel is supported
    """
    return FusedAttnHelper(q_dtype, kv_dtype, qkv_layout.value, attn_bias_type.value,
                           attn_mask_type.value, dropout_probability, q_num_heads, kv_num_heads,
                           q_max_seqlen, kv_max_seqlen, head_dim).is_fused_attn_kernel_available()


def fused_attn_qkvpacked(qkv: jnp.ndarray, bias: jnp.ndarray | None, mask: jnp.ndarray,
                         seed: jnp.ndarray | None, attn_bias_type: AttnBiasType,
                         attn_mask_type: AttnMaskType, scaling_factor: float,
                         dropout_probability: float, is_training: bool):
    """
    Fused attention with the qkvpacked inputs
    """
    output = _fused_attn_qkvpacked(qkv,
                                   bias,
                                   mask,
                                   seed,
                                   attn_bias_type=attn_bias_type,
                                   attn_mask_type=attn_mask_type,
                                   scaling_factor=scaling_factor,
                                   dropout_probability=dropout_probability,
                                   is_training=is_training)

    return output


@partial(jax.custom_vjp, nondiff_argnums=(4, 5, 6, 7, 8))
def _fused_attn_qkvpacked(qkv: jnp.ndarray, bias: jnp.ndarray | None, mask: jnp.ndarray,
                          seed: jnp.ndarray | None, attn_bias_type: AttnBiasType,
                          attn_mask_type: AttnMaskType, scaling_factor: float,
                          dropout_probability: float, is_training: bool):

    output, _ = _fused_attn_fwd_qkvpacked_rule(qkv, bias, mask, seed, attn_bias_type,
                                               attn_mask_type, scaling_factor, dropout_probability,
                                               is_training)
    return output


def _fused_attn_fwd_qkvpacked_rule(qkv: jnp.ndarray, bias: jnp.ndarray | None, mask: jnp.ndarray,
                                   seed: jnp.ndarray | None, attn_bias_type: AttnBiasType,
                                   attn_mask_type: AttnMaskType, scaling_factor: float,
                                   dropout_probability: float, is_training: bool):
    if attn_mask_type in [AttnMaskType.NO_MASK, AttnMaskType.CAUSAL_MASK]:
        batch, seqlen, *_ = qkv.shape
        actual_seqlen = jnp.full((batch,), seqlen, dtype=jnp.int32)
    else:
        assert mask is not None
        mask = jnp.logical_not(mask)
        actual_seqlen = jnp.sum(mask, axis=-2, dtype=jnp.int32)[..., 0, 0]    # shape = (b,)
    output, softmax_aux, rng_state = fused_attn_fwd_qkvpacked(
        qkv,
        bias,
        actual_seqlen,
        seed,
        attn_bias_type=attn_bias_type.value,
        attn_mask_type=attn_mask_type.value,
        scaling_factor=scaling_factor,
        dropout_probability=dropout_probability,
        is_training=is_training)
    output = checkpoint_name(output, 'context')
    softmax_aux = checkpoint_name(softmax_aux, 'context')
    rng_state = checkpoint_name(rng_state, 'context')
    return output, (qkv, bias, softmax_aux, rng_state, output, actual_seqlen)


def _fused_attn_bwd_qkvpacked_rule(attn_bias_type, attn_mask_type, scaling_factor,
                                   dropout_probability, is_training, ctx, dz):
    qkv, bias, softmax_aux, rng_state, output, actual_seqlen = ctx

    grad_qkv, grad_bias = fused_attn_bwd_qkvpacked(qkv,
                                                   bias,
                                                   softmax_aux,
                                                   rng_state,
                                                   output,
                                                   dz,
                                                   actual_seqlen,
                                                   attn_bias_type=attn_bias_type.value,
                                                   attn_mask_type=attn_mask_type.value,
                                                   scaling_factor=scaling_factor,
                                                   dropout_probability=dropout_probability,
                                                   is_training=is_training)

    if attn_bias_type == AttnBiasType.NO_BIAS:
        grad_bias = None

    return grad_qkv, grad_bias, None, None


_fused_attn_qkvpacked.defvjp(_fused_attn_fwd_qkvpacked_rule, _fused_attn_bwd_qkvpacked_rule)


def fused_attn_kvpacked(q: jnp.ndarray, kv: jnp.ndarray, bias: jnp.ndarray, mask: jnp.ndarray,
                        seed: jnp.ndarray, attn_bias_type: AttnBiasType,
                        attn_mask_type: AttnMaskType, scaling_factor: float,
                        dropout_probability: float, is_training: bool):
    """
    Fused attention with the kvpacked inputs
    """

    output = _fused_attn_kvpacked(q,
                                  kv,
                                  bias,
                                  mask,
                                  seed,
                                  attn_bias_type=attn_bias_type,
                                  attn_mask_type=attn_mask_type,
                                  scaling_factor=scaling_factor,
                                  dropout_probability=dropout_probability,
                                  is_training=is_training)

    return output


@partial(jax.custom_vjp, nondiff_argnums=(5, 6, 7, 8, 9))
def _fused_attn_kvpacked(q: jnp.ndarray, kv: jnp.ndarray, bias: jnp.ndarray, mask: jnp.ndarray,
                         seed: jnp.ndarray, attn_bias_type: AttnBiasType,
                         attn_mask_type: AttnMaskType, scaling_factor: float,
                         dropout_probability: float, is_training: bool):

    output, _ = _fused_attn_fwd_kvpacked_rule(q, kv, bias, mask, seed, attn_bias_type,
                                              attn_mask_type, scaling_factor, dropout_probability,
                                              is_training)
    return output


def _fused_attn_fwd_kvpacked_rule(q, kv, bias, mask, seed, attn_bias_type, attn_mask_type,
                                  scaling_factor, dropout_probability, is_training):
    if attn_mask_type in [AttnMaskType.NO_MASK, AttnMaskType.CAUSAL_MASK]:
        batch, s_q, *_ = q.shape
        s_kv = kv.shape[1]
        q_actual_seqlen = jnp.full((batch,), s_q, dtype=jnp.int32)
        kv_actual_seqlen = jnp.full((batch,), s_kv, dtype=jnp.int32)
    else:
        assert mask is not None
        mask = jnp.logical_not(mask)
        q_actual_seqlen = jnp.sum(mask, axis=-2, dtype=jnp.int32)[..., 0, 0]    # shape = (b,)
        if attn_mask_type == AttnMaskType.PADDING_MASK:
            kv_actual_seqlen = jnp.sum(mask, axis=-1, dtype=jnp.int32)[..., 0, 0]    # shape = (b,)
        else:
            # When mask is causal, the actual seqlen is not the last row, use max to find it
            kv_actual_seqlen = jnp.max(jnp.sum(mask, axis=-1, dtype=jnp.int32), axis=(-1, -2))

    output, softmax_aux, rng_state = fused_attn_fwd_kvpacked(
        q,
        kv,
        bias,
        q_actual_seqlen,
        kv_actual_seqlen,
        seed,
        attn_bias_type=attn_bias_type.value,
        attn_mask_type=attn_mask_type.value,
        scaling_factor=scaling_factor,
        dropout_probability=dropout_probability,
        is_training=is_training)
    output = checkpoint_name(output, 'context')
    softmax_aux = checkpoint_name(softmax_aux, 'context')
    rng_state = checkpoint_name(rng_state, 'context')
    return output, (q, kv, bias, softmax_aux, rng_state, output, q_actual_seqlen, kv_actual_seqlen)


def _fused_attn_bwd_kvpacked_rule(attn_bias_type, attn_mask_type, scaling_factor,
                                  dropout_probability, is_training, ctx, dz):
    q, kv, bias, softmax_aux, rng_state, output, q_actual_seqlen, kv_actual_seqlen = ctx

    grad_q, grad_kv, grad_bias = fused_attn_bwd_kvpacked(q,
                                                         kv,
                                                         bias,
                                                         softmax_aux,
                                                         rng_state,
                                                         output,
                                                         dz,
                                                         q_actual_seqlen,
                                                         kv_actual_seqlen,
                                                         attn_bias_type=attn_bias_type.value,
                                                         attn_mask_type=attn_mask_type.value,
                                                         scaling_factor=scaling_factor,
                                                         dropout_probability=dropout_probability,
                                                         is_training=is_training)

    if attn_bias_type == AttnBiasType.NO_BIAS:
        grad_bias = None

    return grad_q, grad_kv, grad_bias, None, None


_fused_attn_kvpacked.defvjp(_fused_attn_fwd_kvpacked_rule, _fused_attn_bwd_kvpacked_rule)


def fused_attn(q: jnp.ndarray, k: jnp.ndarray, v: jnp.ndarray, bias: jnp.ndarray, mask: jnp.ndarray,
               seed: jnp.ndarray, attn_bias_type: AttnBiasType, attn_mask_type: AttnMaskType,
               scaling_factor: float, dropout_probability: float, is_training: bool):
    """
    Dot product attention with the seperated query, key, value
    """

    output = _fused_attn(q,
                         k,
                         v,
                         bias,
                         mask,
                         seed,
                         attn_bias_type=attn_bias_type,
                         attn_mask_type=attn_mask_type,
                         scaling_factor=scaling_factor,
                         dropout_probability=dropout_probability,
                         is_training=is_training)

    return output


@partial(jax.custom_vjp, nondiff_argnums=(6, 7, 8, 9, 10))
def _fused_attn(q: jnp.ndarray, k: jnp.ndarray, v: jnp.ndarray, bias: jnp.ndarray,
                mask: jnp.ndarray, seed: jnp.ndarray, attn_bias_type: AttnBiasType,
                attn_mask_type: AttnMaskType, scaling_factor: float, dropout_probability: float,
                is_training: bool):

    output, _ = _fused_attn_fwd_rule(q, k, v, bias, mask, seed, attn_bias_type, attn_mask_type,
                                     scaling_factor, dropout_probability, is_training)
    return output


def _fused_attn_fwd_rule(q, k, v, bias, mask, seed, attn_bias_type, attn_mask_type, scaling_factor,
                         dropout_probability, is_training):
    if attn_mask_type in [AttnMaskType.NO_MASK, AttnMaskType.CAUSAL_MASK]:
        batch, s_q, *_ = q.shape
        s_kv = k.shape[1]
        q_actual_seqlen = jnp.full((batch,), s_q, dtype=jnp.int32)
        kv_actual_seqlen = jnp.full((batch,), s_kv, dtype=jnp.int32)
    else:
        assert mask is not None
        mask = jnp.logical_not(mask)
        q_actual_seqlen = jnp.sum(mask, axis=-2, dtype=jnp.int32)[..., 0, 0]    # shape = (b,)
        if attn_mask_type == AttnMaskType.PADDING_MASK:
            kv_actual_seqlen = jnp.sum(mask, axis=-1, dtype=jnp.int32)[..., 0, 0]    # shape = (b,)
        else:
            # When mask is causal, the actual seqlen is not the last row, use max to find it
            kv_actual_seqlen = jnp.max(jnp.sum(mask, axis=-1, dtype=jnp.int32), axis=(-1, -2))

    output, softmax_aux, rng_state = fused_attn_fwd(q,
                                                    k,
                                                    v,
                                                    bias,
                                                    q_actual_seqlen,
                                                    kv_actual_seqlen,
                                                    seed,
                                                    attn_bias_type=attn_bias_type.value,
                                                    attn_mask_type=attn_mask_type.value,
                                                    scaling_factor=scaling_factor,
                                                    dropout_probability=dropout_probability,
                                                    is_training=is_training)
    output = checkpoint_name(output, 'context')
    softmax_aux = checkpoint_name(softmax_aux, 'context')
    rng_state = checkpoint_name(rng_state, 'context')
    return output, (q, k, v, bias, softmax_aux, rng_state, output, q_actual_seqlen,
                    kv_actual_seqlen)


def _fused_attn_bwd_rule(attn_bias_type, attn_mask_type, scaling_factor, dropout_probability,
                         is_training, ctx, dz):
    q, k, v, bias, softmax_aux, rng_state, output, q_actual_seqlen, kv_actual_seqlen = ctx

    grad_q, grad_k, grad_v, grad_bias = fused_attn_bwd(q,
                                                       k,
                                                       v,
                                                       bias,
                                                       softmax_aux,
                                                       rng_state,
                                                       output,
                                                       dz,
                                                       q_actual_seqlen,
                                                       kv_actual_seqlen,
                                                       attn_bias_type=attn_bias_type.value,
                                                       attn_mask_type=attn_mask_type.value,
                                                       scaling_factor=scaling_factor,
                                                       dropout_probability=dropout_probability,
                                                       is_training=is_training)

    if attn_bias_type == AttnBiasType.NO_BIAS:
        grad_bias = None

    return grad_q, grad_k, grad_v, grad_bias, None, None


_fused_attn.defvjp(_fused_attn_fwd_rule, _fused_attn_bwd_rule)