Source code for flax.linen.attention

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"""Attention core modules for Flax."""

import functools
from typing import (Any, Callable, Optional, Tuple)
from flax.linen.dtypes import promote_dtype

from flax.linen.initializers import zeros
from flax.linen.linear import default_kernel_init
from flax.linen.linear import DenseGeneral
from flax.linen.linear import PrecisionLike
from flax.linen.module import compact
from flax.linen.module import merge_param
from flax.linen.module import Module

import jax
from jax import lax
from jax import random
import jax.numpy as jnp

PRNGKey = Any
Shape = Tuple[int, ...]
Dtype = Any
Array = Any


[docs]def dot_product_attention_weights(query: Array, key: Array, bias: Optional[Array] = None, mask: Optional[Array] = None, broadcast_dropout: bool = True, dropout_rng: Optional[PRNGKey] = None, dropout_rate: float = 0., deterministic: bool = False, dtype: Optional[Dtype] = None, precision: PrecisionLike = None): """Computes dot-product attention weights given query and key. Used by :func:`dot_product_attention`, which is what you'll most likely use. But if you want access to the attention weights for introspection, then you can directly call this function and call einsum yourself. Args: query: queries for calculating attention with shape of `[batch..., q_length, num_heads, qk_depth_per_head]`. key: keys for calculating attention with shape of `[batch..., kv_length, num_heads, qk_depth_per_head]`. bias: bias for the attention weights. This should be broadcastable to the shape `[batch..., num_heads, q_length, kv_length]`. This can be used for incorporating causal masks, padding masks, proximity bias, etc. mask: mask for the attention weights. This should be broadcastable to the shape `[batch..., num_heads, q_length, kv_length]`. This can be used for incorporating causal masks. Attention weights are masked out if their corresponding mask value is `False`. broadcast_dropout: bool: use a broadcasted dropout along batch dims. dropout_rng: JAX PRNGKey: to be used for dropout dropout_rate: dropout rate deterministic: bool, deterministic or not (to apply dropout) dtype: the dtype of the computation (default: infer from inputs and params) precision: numerical precision of the computation see `jax.lax.Precision` for details. Returns: Output of shape `[batch..., num_heads, q_length, kv_length]`. """ query, key = promote_dtype(query, key, dtype=dtype) dtype = query.dtype assert query.ndim == key.ndim, 'q, k must have same rank.' assert query.shape[:-3] == key.shape[:-3], ( 'q, k batch dims must match.') assert query.shape[-2] == key.shape[-2], ( 'q, k num_heads must match.') assert query.shape[-1] == key.shape[-1], 'q, k depths must match.' # calculate attention matrix depth = query.shape[-1] query = query / jnp.sqrt(depth).astype(dtype) # attn weight shape is (batch..., num_heads, q_length, kv_length) attn_weights = jnp.einsum('...qhd,...khd->...hqk', query, key, precision=precision) # apply attention bias: masking, dropout, proximity bias, etc. if bias is not None: attn_weights = attn_weights + bias # apply attention mask if mask is not None: big_neg = jnp.finfo(dtype).min attn_weights = jnp.where(mask, attn_weights, big_neg) # normalize the attention weights attn_weights = jax.nn.softmax(attn_weights).astype(dtype) # apply attention dropout if not deterministic and dropout_rate > 0.: keep_prob = 1.0 - dropout_rate if broadcast_dropout: # dropout is broadcast across the batch + head dimensions dropout_shape = tuple([1] * (key.ndim - 2)) + attn_weights.shape[-2:] keep = random.bernoulli(dropout_rng, keep_prob, dropout_shape) # type: ignore else: keep = random.bernoulli(dropout_rng, keep_prob, attn_weights.shape) # type: ignore multiplier = (keep.astype(dtype) / jnp.asarray(keep_prob, dtype=dtype)) attn_weights = attn_weights * multiplier return attn_weights
[docs]def dot_product_attention(query: Array, key: Array, value: Array, bias: Optional[Array] = None, mask: Optional[Array] = None, broadcast_dropout: bool = True, dropout_rng: Optional[PRNGKey] = None, dropout_rate: float = 0., deterministic: bool = False, dtype: Optional[Dtype] = None, precision: PrecisionLike = None): """Computes dot-product attention given query, key, and value. This is the core function for applying attention based on https://arxiv.org/abs/1706.03762. It calculates the attention weights given query and key and combines the values using the attention weights. Note: query, key, value needn't have any batch dimensions. Args: query: queries for calculating attention with shape of `[batch..., q_length, num_heads, qk_depth_per_head]`. key: keys for calculating attention with shape of `[batch..., kv_length, num_heads, qk_depth_per_head]`. value: values to be used in attention with shape of `[batch..., kv_length, num_heads, v_depth_per_head]`. bias: bias for the attention weights. This should be broadcastable to the shape `[batch..., num_heads, q_length, kv_length]`. This can be used for incorporating causal masks, padding masks, proximity bias, etc. mask: mask for the attention weights. This should be broadcastable to the shape `[batch..., num_heads, q_length, kv_length]`. This can be used for incorporating causal masks. Attention weights are masked out if their corresponding mask value is `False`. broadcast_dropout: bool: use a broadcasted dropout along batch dims. dropout_rng: JAX PRNGKey: to be used for dropout dropout_rate: dropout rate deterministic: bool, deterministic or not (to apply dropout) dtype: the dtype of the computation (default: infer from inputs) precision: numerical precision of the computation see `jax.lax.Precision` for details. Returns: Output of shape `[batch..., q_length, num_heads, v_depth_per_head]`. """ query, key, value = promote_dtype(query, key, value, dtype=dtype) dtype = query.dtype assert key.ndim == query.ndim == value.ndim, 'q, k, v must have same rank.' assert query.shape[:-3] == key.shape[:-3] == value.shape[:-3], ( 'q, k, v batch dims must match.') assert query.shape[-2] == key.shape[-2] == value.shape[-2], ( 'q, k, v num_heads must match.') assert key.shape[-3] == value.shape[-3], 'k, v lengths must match.' # compute attention weights attn_weights = dot_product_attention_weights( query, key, bias, mask, broadcast_dropout, dropout_rng, dropout_rate, deterministic, dtype, precision) # return weighted sum over values for each query position return jnp.einsum('...hqk,...khd->...qhd', attn_weights, value, precision=precision)
[docs]class MultiHeadDotProductAttention(Module): """Multi-head dot-product attention. Attributes: num_heads: number of attention heads. Features (i.e. inputs_q.shape[-1]) should be divisible by the number of heads. dtype: the dtype of the computation (default: infer from inputs and params) param_dtype: the dtype passed to parameter initializers (default: float32) qkv_features: dimension of the key, query, and value. out_features: dimension of the last projection broadcast_dropout: bool: use a broadcasted dropout along batch dims. dropout_rate: dropout rate deterministic: if false, the attention weight is masked randomly using dropout, whereas if true, the attention weights are deterministic. precision: numerical precision of the computation see `jax.lax.Precision` for details. kernel_init: initializer for the kernel of the Dense layers. bias_init: initializer for the bias of the Dense layers. use_bias: bool: whether pointwise QKVO dense transforms use bias. attention_fn: dot_product_attention or compatible function. Accepts query, key, value, and returns output of shape `[bs, dim1, dim2, ..., dimN,, num_heads, value_channels]`` decode: whether to prepare and use an autoregressive cache. """ num_heads: int dtype: Optional[Dtype] = None param_dtype: Dtype = jnp.float32 qkv_features: Optional[int] = None out_features: Optional[int] = None broadcast_dropout: bool = True dropout_rate: float = 0. deterministic: Optional[bool] = None precision: PrecisionLike = None kernel_init: Callable[[PRNGKey, Shape, Dtype], Array] = default_kernel_init bias_init: Callable[[PRNGKey, Shape, Dtype], Array] = zeros use_bias: bool = True attention_fn: Callable[..., Array] = dot_product_attention decode: bool = False
[docs] @compact def __call__(self, inputs_q: Array, inputs_kv: Array, mask: Optional[Array] = None, deterministic: Optional[bool] = None): """Applies multi-head dot product attention on the input data. Projects the inputs into multi-headed query, key, and value vectors, applies dot-product attention and project the results to an output vector. Args: inputs_q: input queries of shape `[batch_sizes..., length, features]`. inputs_kv: key/values of shape `[batch_sizes..., length, features]`. mask: attention mask of shape `[batch_sizes..., num_heads, query_length, key/value_length]`. Attention weights are masked out if their corresponding mask value is `False`. deterministic: if false, the attention weight is masked randomly using dropout, whereas if true, the attention weights are deterministic. Returns: output of shape `[batch_sizes..., length, features]`. """ features = self.out_features or inputs_q.shape[-1] qkv_features = self.qkv_features or inputs_q.shape[-1] assert qkv_features % self.num_heads == 0, ( 'Memory dimension must be divisible by number of heads.') head_dim = qkv_features // self.num_heads dense = functools.partial(DenseGeneral, axis=-1, dtype=self.dtype, param_dtype=self.param_dtype, features=(self.num_heads, head_dim), kernel_init=self.kernel_init, bias_init=self.bias_init, use_bias=self.use_bias, precision=self.precision) # project inputs_q to multi-headed q/k/v # dimensions are then [batch..., length, n_heads, n_features_per_head] query, key, value = (dense(name='query')(inputs_q), dense(name='key')(inputs_kv), dense(name='value')(inputs_kv)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.decode: # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable('cache', 'cached_key') cached_key = self.variable('cache', 'cached_key', jnp.zeros, key.shape, key.dtype) cached_value = self.variable('cache', 'cached_value', jnp.zeros, value.shape, value.dtype) cache_index = self.variable('cache', 'cache_index', lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = ( cached_key.value.shape) # shape check of cached keys against query input expected_shape = tuple(batch_dims) + (1, num_heads, depth_per_head) if expected_shape != query.shape: raise ValueError('Autoregressive cache shape error, ' 'expected query shape %s instead got %s.' % (expected_shape, query.shape)) # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value cache_index.value = cache_index.value + 1 # causal mask for cached decoder self-attention: # our single query position should only attend to those key # positions that have already been generated and cached, # not the remaining zero elements. mask = combine_masks( mask, jnp.broadcast_to(jnp.arange(max_length) <= cur_index, tuple(batch_dims) + (1, 1, max_length))) dropout_rng = None if self.dropout_rate > 0.: # Require `deterministic` only if using dropout. m_deterministic = merge_param('deterministic', self.deterministic, deterministic) if not m_deterministic: dropout_rng = self.make_rng('dropout') else: m_deterministic = True # apply attention x = self.attention_fn( query, key, value, mask=mask, dropout_rng=dropout_rng, dropout_rate=self.dropout_rate, broadcast_dropout=self.broadcast_dropout, deterministic=m_deterministic, dtype=self.dtype, precision=self.precision) # pytype: disable=wrong-keyword-args # back to the original inputs dimensions out = DenseGeneral(features=features, axis=(-2, -1), kernel_init=self.kernel_init, bias_init=self.bias_init, use_bias=self.use_bias, dtype=self.dtype, param_dtype=self.param_dtype, precision=self.precision, name='out')(x) return out
[docs]class SelfAttention(MultiHeadDotProductAttention): """Self-attention special case of multi-head dot-product attention."""
[docs] @compact def __call__(self, inputs_q: Array, mask: Optional[Array] = None, # type: ignore deterministic: Optional[bool] = None): """Applies multi-head dot product self-attention on the input data. Projects the inputs into multi-headed query, key, and value vectors, applies dot-product attention and project the results to an output vector. Args: inputs_q: input queries of shape `[batch_sizes..., length, features]`. mask: attention mask of shape `[batch_sizes..., num_heads, query_length, key/value_length]`. Attention weights are masked out if their corresponding mask value is `False`. deterministic: if false, the attention weight is masked randomly using dropout, whereas if true, the attention weights are deterministic. Returns: output of shape `[batch_sizes..., length, features]`. """ return super().__call__(inputs_q, inputs_q, mask, deterministic=deterministic)
# mask-making utility functions
[docs]def make_attention_mask(query_input: Array, key_input: Array, pairwise_fn: Callable[..., Any] = jnp.multiply, extra_batch_dims: int = 0, dtype: Dtype = jnp.float32): """Mask-making helper for attention weights. In case of 1d inputs (i.e., `[batch..., len_q]`, `[batch..., len_kv]`, the attention weights will be `[batch..., heads, len_q, len_kv]` and this function will produce `[batch..., 1, len_q, len_kv]`. Args: query_input: a batched, flat input of query_length size key_input: a batched, flat input of key_length size pairwise_fn: broadcasting elementwise comparison function extra_batch_dims: number of extra batch dims to add singleton axes for, none by default dtype: mask return dtype Returns: A `[batch..., 1, len_q, len_kv]` shaped mask for 1d attention. """ mask = pairwise_fn(jnp.expand_dims(query_input, axis=-1), jnp.expand_dims(key_input, axis=-2)) mask = jnp.expand_dims(mask, axis=-3) mask = jnp.expand_dims(mask, axis=tuple(range(extra_batch_dims))) return mask.astype(dtype)
[docs]def make_causal_mask(x: Array, extra_batch_dims: int = 0, dtype: Dtype = jnp.float32) -> Array: """Make a causal mask for self-attention. In case of 1d inputs (i.e., `[batch..., len]`, the self-attention weights will be `[batch..., heads, len, len]` and this function will produce a causal mask of shape `[batch..., 1, len, len]`. Args: x: input array of shape `[batch..., len]` extra_batch_dims: number of batch dims to add singleton axes for, none by default dtype: mask return dtype Returns: A `[batch..., 1, len, len]` shaped causal mask for 1d attention. """ idxs = jnp.broadcast_to(jnp.arange(x.shape[-1], dtype=jnp.int32), x.shape) return make_attention_mask(idxs, idxs, jnp.greater_equal, extra_batch_dims=extra_batch_dims, dtype=dtype)
def combine_masks(*masks: Optional[Array], dtype: Dtype = jnp.float32) -> Array: """Combine attention masks. Args: *masks: set of attention mask arguments to combine, some can be None. dtype: dtype for the returned mask. Returns: Combined mask, reduced by logical and, returns None if no masks given. """ masks_list = [m for m in masks if m is not None] if not masks_list: return None assert all(map(lambda x: x.ndim == masks_list[0].ndim, masks_list)), ( f'masks must have same rank: {tuple(map(lambda x: x.ndim, masks_list))}') mask, *other_masks = masks_list for other_mask in other_masks: mask = jnp.logical_and(mask, other_mask) return mask.astype(dtype)