# Copyright 2022 The T5X Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Dense attention classes and mask/weighting functions.""" # pylint: disable=attribute-defined-outside-init,g-bare-generic import dataclasses import functools import operator from typing import Any, Callable, Iterable, List, Optional, Sequence, Tuple, Union import jax import jax.numpy as jnp import numpy as np from flax import linen as nn from flax.linen import partitioning as nn_partitioning from flax.linen.dtypes import promote_dtype from jax import lax, random # from flax.linen.partitioning import param_with_axes, with_sharding_constraint param_with_axes = nn_partitioning.param_with_axes with_sharding_constraint = nn_partitioning.with_sharding_constraint # Type annotations Array = jnp.ndarray DType = jnp.dtype PRNGKey = jnp.ndarray Shape = Iterable[int] Activation = Callable[..., Array] PrecisionLike = Union[None, str, lax.Precision, Tuple[str, str], Tuple[lax.Precision, lax.Precision]] DotGeneralT = Callable[..., Array] ConvGeneralDilatedT = Callable[..., Array] PaddingLike = Union[str, int, Sequence[Union[int, Tuple[int, int]]]] LaxPadding = Union[str, Sequence[Tuple[int, int]]] # Parameter initializers. Initializer = Callable[[PRNGKey, Shape, DType], Array] InitializerAxis = Union[int, Tuple[int, ...]] NdInitializer = Callable[[PRNGKey, Shape, DType, InitializerAxis, InitializerAxis], Array] default_embed_init = nn.initializers.variance_scaling(1.0, "fan_in", "normal", out_axis=0) # ------------------------------------------------------------------------------ # Temporary inlined JAX N-d initializer code # TODO(levskaya): remove once new JAX release is out. # ------------------------------------------------------------------------------ def _compute_fans(shape: jax.core.NamedShape, in_axis=-2, out_axis=-1): """Inlined JAX `nn.initializer._compute_fans`.""" if isinstance(in_axis, int): in_size = shape[in_axis] else: in_size = int(np.prod([shape[i] for i in in_axis])) if isinstance(out_axis, int): out_size = shape[out_axis] else: out_size = int(np.prod([shape[i] for i in out_axis])) receptive_field_size = shape.total / in_size / out_size fan_in = in_size * receptive_field_size fan_out = out_size * receptive_field_size return fan_in, fan_out def variance_scaling(scale, mode, distribution, in_axis=-2, out_axis=-1, dtype=jnp.float_): """Inlined JAX `nn.initializer.variance_scaling`.""" def init(key, shape, dtype=dtype): return jnp.zeros(shape, dtype=dtype) dtype = jax.dtypes.canonicalize_dtype(dtype) shape = jax.core.as_named_shape(shape) fan_in, fan_out = _compute_fans(shape, in_axis, out_axis) if mode == "fan_in": denominator = fan_in elif mode == "fan_out": denominator = fan_out elif mode == "fan_avg": denominator = (fan_in + fan_out) / 2 else: raise ValueError("invalid mode for variance scaling initializer: {}".format(mode)) variance = jnp.array(scale / denominator, dtype=dtype) if distribution == "truncated_normal": # constant is stddev of standard normal truncated to (-2, 2) stddev = jnp.sqrt(variance) / jnp.array(0.87962566103423978, dtype) return random.truncated_normal(key, -2, 2, shape, dtype) * stddev elif distribution == "normal": return random.normal(key, shape, dtype) * jnp.sqrt(variance) elif distribution == "uniform": return random.uniform(key, shape, dtype, -1) * jnp.sqrt(3 * variance) else: raise ValueError("invalid distribution for variance scaling initializer: {}".format(distribution)) return init # ------------------------------------------------------------------------------ def nd_dense_init(scale, mode, distribution): """Initializer with in_axis, out_axis set at call time.""" def init_fn(key, shape, dtype, in_axis, out_axis): fn = variance_scaling(scale, mode, distribution, in_axis, out_axis) return fn(key, shape, dtype) return init_fn def dot_product_attention( query: Array, key: Array, value: Array, bias: Optional[Array] = None, dropout_rng: Optional[PRNGKey] = None, dropout_rate: float = 0.0, deterministic: bool = False, dtype: DType = jnp.float32, float32_logits: bool = False, ): """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. 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. 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: float32) float32_logits: bool, if True then compute logits in float32 to avoid numerical issues with bfloat16. Returns: Output of shape `[batch, length, num_heads, v_depth_per_head]`. """ 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." assert query.shape[-1] == key.shape[-1], "q, k depths must match." # Casting logits and softmax computation for float32 for model stability. if float32_logits: query = query.astype(jnp.float32) key = key.astype(jnp.float32) # `attn_weights`: [batch, num_heads, q_length, kv_length] attn_weights = jnp.einsum("bqhd,bkhd->bhqk", query, key) # Apply attention bias: masking, dropout, proximity bias, etc. if bias is not None: attn_weights = attn_weights + bias.astype(attn_weights.dtype) # Normalize the attention weights across `kv_length` dimension. attn_weights = jax.nn.softmax(attn_weights).astype(dtype) # Apply attention dropout. if not deterministic and dropout_rate > 0.0: keep_prob = 1.0 - dropout_rate # T5 broadcasts along the "length" dim, but unclear which one that # corresponds to in positional dimensions here, assuming query dim. dropout_shape = list(attn_weights.shape) dropout_shape[-2] = 1 keep = random.bernoulli(dropout_rng, keep_prob, dropout_shape) keep = jnp.broadcast_to(keep, attn_weights.shape) multiplier = keep.astype(attn_weights.dtype) / jnp.asarray(keep_prob, dtype=dtype) attn_weights = attn_weights * multiplier # Take the linear combination of `value`. return jnp.einsum("bhqk,bkhd->bqhd", attn_weights, value) dynamic_vector_slice_in_dim = jax.vmap(lax.dynamic_slice_in_dim, in_axes=(None, 0, None, None)) class MultiHeadDotProductAttention(nn.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. head_dim: dimension of each head. dtype: the dtype of the computation. dropout_rate: dropout rate kernel_init: initializer for the kernel of the Dense layers. float32_logits: bool, if True then compute logits in float32 to avoid numerical issues with bfloat16. """ num_heads: int head_dim: int dtype: DType = jnp.float32 dropout_rate: float = 0.0 kernel_init: NdInitializer = nd_dense_init(1.0, "fan_in", "normal") float32_logits: bool = False # computes logits in float32 for stability. @nn.compact def __call__( self, inputs_q: Array, inputs_kv: Array, mask: Optional[Array] = None, bias: Optional[Array] = None, *, decode: bool = False, deterministic: bool = False, ) -> Array: """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. There are two modes: decoding and non-decoding (e.g., training). The mode is determined by `decode` argument. For decoding, this method is called twice, first to initialize the cache and then for an actual decoding process. The two calls are differentiated by the presence of 'cached_key' in the variable dict. In the cache initialization stage, the cache variables are initialized as zeros and will be filled in the subsequent decoding process. In the cache initialization call, `inputs_q` has a shape [batch, length, q_features] and `inputs_kv`: [batch, length, kv_features]. During the incremental decoding stage, query, key and value all have the shape [batch, 1, qkv_features] corresponding to a single step. Args: inputs_q: input queries of shape `[batch, q_length, q_features]`. inputs_kv: key/values of shape `[batch, kv_length, kv_features]`. mask: attention mask of shape `[batch, num_heads, q_length, kv_length]`. bias: attention bias of shape `[batch, num_heads, q_length, kv_length]`. decode: Whether to prepare and use an autoregressive cache. deterministic: Disables dropout if set to True. Returns: output of shape `[batch, length, q_features]`. """ projection = functools.partial( DenseGeneral, axis=-1, features=(self.num_heads, self.head_dim), kernel_axes=("embed", "heads", "kv"), dtype=self.dtype, ) # NOTE: T5 does not explicitly rescale the attention logits by # 1/sqrt(depth_kq)! This is folded into the initializers of the # linear transformations, which is equivalent under Adafactor. depth_scaling = jnp.sqrt(self.head_dim).astype(self.dtype) def query_init(*args): return self.kernel_init(*args) / depth_scaling # Project inputs_q to multi-headed q/k/v # dimensions are then [batch, length, num_heads, head_dim] query = projection(kernel_init=query_init, name="query")(inputs_q) key = projection(kernel_init=self.kernel_init, name="key")(inputs_kv) value = projection(kernel_init=self.kernel_init, name="value")(inputs_kv) query = with_sharding_constraint(query, ("batch", "length", "heads", "kv")) key = with_sharding_constraint(key, ("batch", "length", "heads", "kv")) value = with_sharding_constraint(value, ("batch", "length", "heads", "kv")) if decode: # Detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") # The key and value have dimension [batch, length, num_heads, head_dim], # but we cache them as [batch, num_heads, head_dim, length] as a TPU # fusion optimization. This also enables the "scatter via one-hot # broadcast" trick, which means we do a one-hot broadcast instead of a # scatter/gather operations, resulting in a 3-4x speedup in practice. def swap_dims(x): return x[:-3] + tuple(x[i] for i in [-2, -1, -3]) cached_key = self.variable("cache", "cached_key", jnp.zeros, swap_dims(key.shape), key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, swap_dims(value.shape), value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: batch, num_heads, head_dim, length = cached_key.value.shape # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. # Sanity shape check of cached key against input query. expected_shape = (batch, 1, num_heads, head_dim) if expected_shape != query.shape: raise ValueError( "Autoregressive cache shape error, " "expected query shape %s instead got %s." % (expected_shape, query.shape) ) # Create a OHE of the current index. NOTE: the index is increased below. cur_index = cache_index.value one_hot_indices = jax.nn.one_hot(cur_index, length, dtype=key.dtype) # In order to update the key, value caches with the current key and # value, we move the length axis to the back, similar to what we did for # the cached ones above. # Note these are currently the key and value of a single position, since # we feed one position at a time. one_token_key = jnp.moveaxis(key, -3, -1) one_token_value = jnp.moveaxis(value, -3, -1) # Update key, value caches with our new 1d spatial slices. # We implement an efficient scatter into the cache via one-hot # broadcast and addition. key = cached_key.value + one_token_key * one_hot_indices value = cached_value.value + one_token_value * one_hot_indices cached_key.value = key cached_value.value = value cache_index.value = cache_index.value + 1 # Move the keys and values back to their original shapes. key = jnp.moveaxis(key, -1, -3) value = jnp.moveaxis(value, -1, -3) # 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(length) <= cur_index, # (1, 1, length) represent (head dim, query length, key length) # query length is 1 because during decoding we deal with one # index. # The same mask is applied to all batch elements and heads. (batch, 1, 1, length), ), ) # Grab the correct relative attention bias during decoding. This is # only required during single step decoding. if bias is not None: # The bias is a full attention matrix, but during decoding we only # have to take a slice of it. # This is equivalent to bias[..., cur_index:cur_index+1, :]. bias = dynamic_vector_slice_in_dim(jnp.squeeze(bias, axis=0), jnp.reshape(cur_index, (-1)), 1, -2) # Convert the boolean attention mask to an attention bias. if mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( mask > 0, jnp.full(mask.shape, 0.0).astype(self.dtype), jnp.full(mask.shape, -1e10).astype(self.dtype), ) else: attention_bias = None # Add provided bias term (e.g. relative position embedding). if bias is not None: attention_bias = combine_biases(attention_bias, bias) dropout_rng = None if not deterministic and self.dropout_rate > 0.0: dropout_rng = self.make_rng("dropout") # Apply attention. x = dot_product_attention( query, key, value, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout_rate, deterministic=deterministic, dtype=self.dtype, float32_logits=self.float32_logits, ) # Back to the original inputs dimensions. out = DenseGeneral( features=inputs_q.shape[-1], # output dim is set to the input dim. axis=(-2, -1), kernel_init=self.kernel_init, kernel_axes=("heads", "kv", "embed"), dtype=self.dtype, name="out", )(x) return out def _normalize_axes(axes: Iterable[int], ndim: int) -> Tuple[int]: # A tuple by convention. len(axes_tuple) then also gives the rank efficiently. return tuple([ax if ax >= 0 else ndim + ax for ax in axes]) def _canonicalize_tuple(x): if isinstance(x, Iterable): return tuple(x) else: return (x,) # ------------------------------------------------------------------------------ # DenseGeneral for attention layers. # ------------------------------------------------------------------------------ class DenseGeneral(nn.Module): """A linear transformation (without bias) with flexible axes. Attributes: features: tuple with numbers of output features. axis: tuple with axes to apply the transformation on. dtype: the dtype of the computation (default: float32). kernel_init: initializer function for the weight matrix. """ features: Union[Iterable[int], int] axis: Union[Iterable[int], int] = -1 dtype: DType = jnp.float32 params_dtype: DType = jnp.float32 kernel_init: NdInitializer = nd_dense_init(1.0, "fan_in", "normal") kernel_axes: Tuple[str, ...] = () use_bias: bool = True bias_init: Any = nn.initializers.zeros @nn.compact def __call__(self, inputs: Array) -> Array: """Applies a linear transformation to the inputs along multiple dimensions. Args: inputs: The nd-array to be transformed. Returns: The transformed input. """ features = _canonicalize_tuple(self.features) axis = _canonicalize_tuple(self.axis) inputs = jnp.asarray(inputs, self.dtype) axis = _normalize_axes(axis, inputs.ndim) kernel_shape = tuple([inputs.shape[ax] for ax in axis]) + features kernel_in_axis = np.arange(len(axis)) kernel_out_axis = np.arange(len(axis), len(axis) + len(features)) kernel = param_with_axes( "kernel", self.kernel_init, kernel_shape, self.params_dtype, kernel_in_axis, kernel_out_axis, axes=self.kernel_axes, ) if self.use_bias: bias = param_with_axes( "bias", self.bias_init, features, self.params_dtype, axes=(self.kernel_axes[-1],), ) kernel = jnp.asarray(kernel, self.dtype) contract_ind = tuple(range(0, len(axis))) y = lax.dot_general(inputs, kernel, ((axis, contract_ind), ((), ()))) if self.use_bias: bias = jnp.asarray(bias, self.dtype) # y += jnp.reshape(bias, (1,) * (y.ndim - 1) + (-1,)) y += jnp.reshape(bias, (1,) * (len(features) - y.ndim) + bias.shape[:]) return y def _convert_to_activation_function(fn_or_string: Union[str, Callable]) -> Callable: """Convert a string to an activation function.""" if fn_or_string == "linear": return lambda x: x elif isinstance(fn_or_string, str): return getattr(nn, fn_or_string) elif callable(fn_or_string): return fn_or_string else: raise ValueError("don't know how to convert %s to an activation function" % (fn_or_string,)) class MlpBlock(nn.Module): """Transformer MLP / feed-forward block. Attributes: intermediate_dim: Shared dimension of hidden layers. activations: Type of activations for each layer. Each element is either 'linear', a string function name in flax.linen, or a function. kernel_init: Kernel function, passed to the dense layers. deterministic: Whether the dropout layers should be deterministic. intermediate_dropout_rate: Dropout rate used after the intermediate layers. dtype: Type for the dense layer. """ intermediate_dim: int = 2048 activations: Sequence[Union[str, Callable]] = ("relu",) kernel_init: NdInitializer = nd_dense_init(1.0, "fan_in", "truncated_normal") intermediate_dropout_rate: float = 0.1 dtype: Any = jnp.float32 @nn.compact def __call__(self, inputs, decode: bool = False, deterministic: bool = False): """Applies Transformer MlpBlock module.""" # Iterate over specified MLP input activation functions. # e.g. ('relu',) or ('gelu', 'linear') for gated-gelu. activations = [] for idx, act_fn in enumerate(self.activations): dense_name = "wi" if len(self.activations) == 1 else f"wi_{idx}" x = DenseGeneral( self.intermediate_dim, dtype=self.dtype, kernel_init=self.kernel_init, kernel_axes=("embed", "mlp"), name=dense_name, )(inputs) x = _convert_to_activation_function(act_fn)(x) activations.append(x) # Take elementwise product of above intermediate activations. x = functools.reduce(operator.mul, activations) # Apply dropout and final dense output projection. x = nn.Dropout(rate=self.intermediate_dropout_rate, broadcast_dims=(-2,))( x, deterministic=deterministic ) # Broadcast along length. x = with_sharding_constraint(x, ("batch", "length", "mlp")) output = DenseGeneral( inputs.shape[-1], dtype=self.dtype, kernel_init=self.kernel_init, kernel_axes=("mlp", "embed"), name="wo", )(x) return output class Embed(nn.Module): """A parameterized function from integers [0, n) to d-dimensional vectors. Attributes: num_embeddings: number of embeddings. features: number of feature dimensions for each embedding. dtype: the dtype of the embedding vectors (default: float32). embedding_init: embedding initializer. one_hot: performs the gather with a one-hot contraction rather than a true gather. This is currently needed for SPMD partitioning. """ num_embeddings: int features: int cast_input_dtype: Optional[DType] = None dtype: DType = jnp.float32 params_dtype: DType = jnp.float32 attend_dtype: Optional[DType] = None embedding_init: Initializer = default_embed_init one_hot: bool = True embedding: Array = dataclasses.field(init=False) def setup(self): self.embedding = param_with_axes( "embedding", self.embedding_init, (self.num_embeddings, self.features), self.params_dtype, axes=("vocab", "embed"), ) def __call__(self, inputs: Array) -> Array: """Embeds the inputs along the last dimension. Args: inputs: input data, all dimensions are considered batch dimensions. Returns: Output which is embedded input data. The output shape follows the input, with an additional `features` dimension appended. """ if self.cast_input_dtype: inputs = inputs.astype(self.cast_input_dtype) if not jnp.issubdtype(inputs.dtype, jnp.integer): raise ValueError("Input type must be an integer or unsigned integer.") if self.one_hot: iota = lax.iota(jnp.int32, self.num_embeddings) one_hot = jnp.array(inputs[..., jnp.newaxis] == iota, dtype=self.dtype) output = jnp.dot(one_hot, jnp.asarray(self.embedding, self.dtype)) else: output = jnp.asarray(self.embedding, self.dtype)[inputs] output = with_sharding_constraint(output, ("batch", "length", "embed")) return output def attend(self, query: Array) -> Array: """Attend over the embedding using a query array. Args: query: array with last dimension equal the feature depth `features` of the embedding. Returns: An array with final dim `num_embeddings` corresponding to the batched inner-product of the array of query vectors against each embedding. Commonly used for weight-sharing between embeddings and logit transform in NLP models. """ dtype = self.attend_dtype if self.attend_dtype is not None else self.dtype return jnp.dot(query, jnp.asarray(self.embedding, dtype).T) class RelativePositionBiases(nn.Module): """Adds T5-style relative positional embeddings to the attention logits. Attributes: num_buckets: Number of buckets to bucket distances between key and query positions into. max_distance: Maximum distance before everything is lumped into the last distance bucket. num_heads: Number of heads in the attention layer. Each head will get a different relative position weighting. dtype: Type of arrays through this module. embedding_init: initializer for relative embedding table. """ num_buckets: int max_distance: int num_heads: int dtype: Any embedding_init: Callable[..., Array] = nn.linear.default_embed_init @staticmethod def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on. Args: relative_position: an int32 array bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ ret = 0 n = -relative_position if bidirectional: num_buckets //= 2 ret += (n < 0).astype(np.int32) * num_buckets n = np.abs(n) else: n = np.maximum(n, 0) # now n is in the range [0, inf) max_exact = num_buckets // 2 is_small = n < max_exact val_if_large = max_exact + ( np.log(n.astype(np.float32) / max_exact + np.finfo(np.float32).eps) / np.log(max_distance / max_exact) * (num_buckets - max_exact) ).astype(np.int32) val_if_large = np.minimum(val_if_large, num_buckets - 1) ret += np.where(is_small, n, val_if_large) return ret @nn.compact def __call__(self, qlen, klen, bidirectional=True): """Produce relative position embedding attention biases. Args: qlen: attention query length. klen: attention key length. bidirectional: whether to allow positive memory-query relative position embeddings. Returns: output: `(1, len, q_len, k_len)` attention bias """ # TODO(levskaya): should we be computing this w. numpy as a program # constant? context_position = np.arange(qlen, dtype=jnp.int32)[:, None] memory_position = np.arange(klen, dtype=jnp.int32)[None, :] relative_position = memory_position - context_position # shape (qlen, klen) rp_bucket = self._relative_position_bucket( relative_position, bidirectional=bidirectional, num_buckets=self.num_buckets, max_distance=self.max_distance, ) relative_attention_bias = param_with_axes( "rel_embedding", self.embedding_init, (self.num_heads, self.num_buckets), jnp.float32, axes=("heads", "relpos_buckets"), ) relative_attention_bias = jnp.asarray(relative_attention_bias, self.dtype) # Instead of using a slow gather, we create a leading-dimension one-hot # array from rp_bucket and use it to perform the gather-equivalent via a # contraction, i.e.: # (num_head, num_buckets) x (num_buckets one-hot, qlen, klen). # This is equivalent to relative_attention_bias[:, rp_bucket] bcast_iota = lax.broadcasted_iota(jnp.int32, (self.num_buckets, 1, 1), 0) rp_bucket_one_hot = jnp.array(rp_bucket[jnp.newaxis, ...] == bcast_iota, dtype=self.dtype) # --> shape (qlen, klen, num_heads) values = lax.dot_general( relative_attention_bias, rp_bucket_one_hot, (((1,), (0,)), ((), ())), # rhs, lhs contracting dims ) # no batched dims # Add a singleton batch dimension. # --> shape (1, num_heads, qlen, klen) return values[jnp.newaxis, ...] # ------------------------------------------------------------------------------ # T5 Layernorm - no subtraction of mean or bias. # ------------------------------------------------------------------------------ # class LayerNorm(nn.Module): # """T5 Layer normalization operating on the last axis of the input data.""" # epsilon: float = 1e-6 # dtype: Any = jnp.float32 # scale_init: Initializer = nn.initializers.ones # @nn.compact # def __call__(self, x: jnp.ndarray) -> jnp.ndarray: # """Applies layer normalization on the input.""" # x = jnp.asarray(x, jnp.float32) # features = x.shape[-1] # mean2 = jnp.mean(lax.square(x), axis=-1, keepdims=True) # y = jnp.asarray(x * lax.rsqrt(mean2 + self.epsilon), self.dtype) # scale = param_with_axes( # 'scale', self.scale_init, (features,), jnp.float32, axes=('embed',)) # scale = jnp.asarray(scale, self.dtype) # return y * scale class LayerNorm(nn.Module): """Layer normalization (https://arxiv.org/abs/1607.06450). Operates on the last axis of the input data. It normalizes the activations of the layer for each given example in a batch independently, rather than across a batch like Batch Normalization. i.e. applies a transformation that maintains the mean activation within each example close to 0 and the activation standard deviation close to 1. Attributes: epsilon: A small float added to variance to avoid dividing by zero. dtype: the dtype of the computation (default: float32). use_bias: If True, bias (beta) is added. use_scale: If True, multiply by scale (gamma). When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer. bias_init: Initializer for bias, by default, zero. scale_init: Initializer for scale, by default, one. """ epsilon: float = 1e-6 dtype: Any = jnp.float32 params_dtype: DType = jnp.float32 use_bias: bool = True use_scale: bool = True bias_init: Callable[[PRNGKey, Shape, Any], Array] = nn.initializers.zeros scale_init: Callable[[PRNGKey, Shape, Any], Array] = nn.initializers.ones @nn.compact def __call__(self, x): """Applies layer normalization on the input. Args: x: the inputs Returns: Normalized inputs (the same shape as inputs). """ x = jnp.asarray(x, jnp.float32) features = x.shape[-1] mean = jnp.mean(x, axis=-1, keepdims=True) mean2 = jnp.mean(lax.square(x), axis=-1, keepdims=True) var = mean2 - lax.square(mean) mul = lax.rsqrt(var + self.epsilon) if self.use_scale: scale = param_with_axes( "scale", self.scale_init, (features,), self.params_dtype, axes=("embed",), ) mul = mul * jnp.asarray(scale, self.dtype) y = (x - mean) * mul if self.use_bias: bias = param_with_axes("bias", self.bias_init, (features,), self.params_dtype, axes=("embed",)) y = y + jnp.asarray(bias, self.dtype) return jnp.asarray(y, self.dtype) # ------------------------------------------------------------------------------ # Mask-making utility functions. # ------------------------------------------------------------------------------ def make_attention_mask( query_input: Array, key_input: Array, pairwise_fn: Callable = jnp.multiply, extra_batch_dims: int = 0, dtype: DType = jnp.float32, ) -> Array: """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. """ # [batch, len_q, len_kv] mask = pairwise_fn( # [batch, len_q] -> [batch, len_q, 1] jnp.expand_dims(query_input, axis=-1), # [batch, len_q] -> [batch, 1, len_kv] jnp.expand_dims(key_input, axis=-2), ) # [batch, 1, len_q, len_kv]. This creates the head dim. mask = jnp.expand_dims(mask, axis=-3) mask = jnp.expand_dims(mask, axis=tuple(range(extra_batch_dims))) return mask.astype(dtype) 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]`. Note that a causal mask does not depend on the values of x; it only depends on the shape. If x has padding elements, they will not be treated in a special manner. 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): """Combine attention masks. Args: *masks: set of attention mask arguments to combine, some can be None. dtype: final mask dtype Returns: Combined mask, reduced by logical and, returns None if no masks given. """ masks = [m for m in masks if m is not None] if not masks: return None assert all( (x.ndim == masks[0].ndim for x in masks) ), f"masks must have same rank: {tuple((x.ndim for x in masks))}" mask, *other_masks = masks for other_mask in other_masks: mask = jnp.logical_and(mask, other_mask) return mask.astype(dtype) def combine_biases(*masks: Optional[Array]): """Combine attention biases. Args: *masks: set of attention bias arguments to combine, some can be None. Returns: Combined mask, reduced by summation, returns None if no masks given. """ masks = [m for m in masks if m is not None] if not masks: return None assert all( (x.ndim == masks[0].ndim for x in masks) ), f"masks must have same rank: {tuple((x.ndim for x in masks))}" mask, *other_masks = masks for other_mask in other_masks: mask = mask + other_mask return mask def make_decoder_mask( decoder_target_tokens: Array, dtype: DType, decoder_causal_attention: Optional[Array] = None, decoder_segment_ids: Optional[Array] = None, ) -> Array: """Compute the self-attention mask for a decoder. Decoder mask is formed by combining a causal mask, a padding mask and an optional packing mask. If decoder_causal_attention is passed, it makes the masking non-causal for positions that have value of 1. A prefix LM is applied to a dataset which has a notion of "inputs" and "targets", e.g., a machine translation task. The inputs and targets are concatenated to form a new target. `decoder_target_tokens` is the concatenated decoder output tokens. The "inputs" portion of the concatenated sequence can attend to other "inputs" tokens even for those at a later time steps. In order to control this behavior, `decoder_causal_attention` is necessary. This is a binary mask with a value of 1 indicating that the position belonged to "inputs" portion of the original dataset. Example: Suppose we have a dataset with two examples. ds = [{"inputs": [6, 7], "targets": [8]}, {"inputs": [3, 4], "targets": [5]}] After the data preprocessing with packing, the two examples are packed into one example with the following three fields (some fields are skipped for simplicity). decoder_target_tokens = [[6, 7, 8, 3, 4, 5, 0]] decoder_segment_ids = [[1, 1, 1, 2, 2, 2, 0]] decoder_causal_attention = [[1, 1, 0, 1, 1, 0, 0]] where each array has [batch, length] shape with batch size being 1. Then, this function computes the following mask. mask = [[[[1, 1, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]]]] mask[b, 1, :, :] represents the mask for the example `b` in the batch. Because mask is for a self-attention layer, the mask's shape is a square of shape [query length, key length]. mask[b, 1, i, j] = 1 means that the query token at position i can attend to the key token at position j. Args: decoder_target_tokens: decoder output tokens. [batch, length] dtype: dtype of the output mask. decoder_causal_attention: a binary mask indicating which position should only attend to earlier positions in the sequence. Others will attend bidirectionally. [batch, length] decoder_segment_ids: decoder segmentation info for packed examples. [batch, length] Returns: the combined decoder mask. """ masks = [] # The same mask is applied to all attention heads. So the head dimension is 1, # i.e., the mask will be broadcast along the heads dim. # [batch, 1, length, length] causal_mask = make_causal_mask(decoder_target_tokens, dtype=dtype) # Positions with value 1 in `decoder_causal_attneition` can attend # bidirectionally. if decoder_causal_attention is not None: # [batch, 1, length, length] inputs_mask = make_attention_mask( decoder_causal_attention, decoder_causal_attention, jnp.logical_and, dtype=dtype, ) masks.append(jnp.logical_or(causal_mask, inputs_mask).astype(dtype)) else: masks.append(causal_mask) # Padding mask. masks.append(make_attention_mask(decoder_target_tokens > 0, decoder_target_tokens > 0, dtype=dtype)) # Packing mask if decoder_segment_ids is not None: masks.append(make_attention_mask(decoder_segment_ids, decoder_segment_ids, jnp.equal, dtype=dtype)) return combine_masks(*masks, dtype=dtype) def canonicalize_padding(padding: PaddingLike, rank: int) -> LaxPadding: """ "Canonicalizes conv padding to a jax.lax supported format.""" if isinstance(padding, str): return padding if isinstance(padding, int): return [(padding, padding)] * rank if isinstance(padding, Sequence) and len(padding) == rank: new_pad = [] for p in padding: if isinstance(p, int): new_pad.append((p, p)) elif isinstance(p, tuple) and len(p) == 2: new_pad.append(p) else: break if len(new_pad) == rank: return new_pad raise ValueError( f"Invalid padding format: {padding}, should be str, int," f" or a sequence of len {rank} where each element is an" " int or pair of ints." ) def _conv_dimension_numbers(input_shape): """Computes the dimension numbers based on the input shape.""" ndim = len(input_shape) lhs_spec = (0, ndim - 1) + tuple(range(1, ndim - 1)) rhs_spec = (ndim - 1, ndim - 2) + tuple(range(0, ndim - 2)) out_spec = lhs_spec return lax.ConvDimensionNumbers(lhs_spec, rhs_spec, out_spec) class _Conv(nn.Module): """Convolution Module wrapping `lax.conv_general_dilated[_local]`. Attributes: features: number of convolution filters. kernel_size: shape of the convolutional kernel. For 1D convolution, the kernel size can be passed as an integer. For all other cases, it must be a sequence of integers. strides: an integer or a sequence of `n` integers, representing the inter-window strides (default: 1). padding: either the string `'SAME'`, the string `'VALID'`, the string `'CIRCULAR'` (periodic boundary conditions), or a sequence of `n` `(low, high)` integer pairs that give the padding to apply before and after each spatial dimension. A single int is interpeted as applying the same padding in all dims and passign a single int in a sequence causes the same padding to be used on both sides. `'CAUSAL'` padding for a 1D convolution will left-pad the convolution axis, resulting in same-sized output. input_dilation: an integer or a sequence of `n` integers, giving the dilation factor to apply in each spatial dimension of `inputs` (default: 1). Convolution with input dilation `d` is equivalent to transposed convolution with stride `d`. kernel_dilation: an integer or a sequence of `n` integers, giving the dilation factor to apply in each spatial dimension of the convolution kernel (default: 1). Convolution with kernel dilation is also known as 'atrous convolution'. feature_group_count: integer, default 1. If specified divides the input features into groups. use_bias: whether to add a bias to the output (default: True). mask: Optional mask for the weights during masked convolution. The mask must be the same shape as the convolution weight matrix. dtype: the dtype of the computation (default: infer from input and params). params_dtype: the dtype passed to parameter initializers (default: float32). precision: numerical precision of the computation see `jax.lax.Precision` for details. kernel_init: initializer for the convolutional kernel. bias_init: initializer for the bias. """ features: int kernel_size: Sequence[int] strides: Union[None, int, Sequence[int]] = 1 padding: PaddingLike = "SAME" input_dilation: Union[None, int, Sequence[int]] = 1 kernel_dilation: Union[None, int, Sequence[int]] = 1 feature_group_count: int = 1 use_bias: bool = True mask: Optional[Array] = None dtype: Optional[DType] = None params_dtype: DType = jnp.float32 precision: PrecisionLike = None kernel_init: Callable[[PRNGKey, Shape, DType], Array] = nn.initializers.lecun_normal() bias_init: Callable[[PRNGKey, Shape, DType], Array] = nn.initializers.zeros conv_general_dilated: ConvGeneralDilatedT = lax.conv_general_dilated kernel_axes: Tuple[str, ...] = () @property def shared_weights(self) -> bool: # type: ignore """Defines whether weights are shared or not between different pixels. Returns: `True` to use shared weights in convolution (regular convolution). `False` to use different weights at different pixels, a.k.a. "locally connected layer", "unshared convolution", or "local convolution". """ ... @nn.compact def __call__(self, inputs: Array) -> Array: """Applies a (potentially unshared) convolution to the inputs. Args: inputs: input data with dimensions (*batch_dims, spatial_dims..., features). This is the channels-last convention, i.e. NHWC for a 2d convolution and NDHWC for a 3D convolution. Note: this is different from the input convention used by `lax.conv_general_dilated`, which puts the spatial dimensions last. Note: If the input has more than 1 batch dimension, all batch dimensions are flattened into a single dimension for the convolution and restored before returning. In some cases directly vmap'ing the layer may yield better performance than this default flattening approach. If the input lacks a batch dimension it will be added for the convolution and removed n return, an allowance made to enable writing single-example code. Returns: The convolved data. """ if isinstance(self.kernel_size, int): raise TypeError( "Expected Conv kernel_size to be a" " tuple/list of integers (eg.: [3, 3]) but got" f" {self.kernel_size}." ) else: kernel_size = tuple(self.kernel_size) def maybe_broadcast(x: Optional[Union[int, Sequence[int]]]) -> Tuple[int, ...]: if x is None: # backward compatibility with using None as sentinel for # broadcast 1 x = 1 if isinstance(x, int): return (x,) * len(kernel_size) return tuple(x) # Combine all input batch dimensions into a single leading batch axis. num_batch_dimensions = inputs.ndim - (len(kernel_size) + 1) if num_batch_dimensions != 1: input_batch_shape = inputs.shape[:num_batch_dimensions] total_batch_size = int(np.prod(input_batch_shape)) flat_input_shape = (total_batch_size,) + inputs.shape[num_batch_dimensions:] inputs = jnp.reshape(inputs, flat_input_shape) # self.strides or (1,) * (inputs.ndim - 2) strides = maybe_broadcast(self.strides) input_dilation = maybe_broadcast(self.input_dilation) kernel_dilation = maybe_broadcast(self.kernel_dilation) padding_lax = canonicalize_padding(self.padding, len(kernel_size)) if padding_lax == "CIRCULAR": kernel_size_dilated = [(k - 1) * d + 1 for k, d in zip(kernel_size, kernel_dilation)] zero_pad: List[Tuple[int, int]] = [(0, 0)] pads = zero_pad + [((k - 1) // 2, k // 2) for k in kernel_size_dilated] + [(0, 0)] inputs = jnp.pad(inputs, pads, mode="wrap") padding_lax = "VALID" elif padding_lax == "CAUSAL": if len(kernel_size) != 1: raise ValueError("Causal padding is only implemented for 1D convolutions.") left_pad = kernel_dilation[0] * (kernel_size[0] - 1) pads = [(0, 0), (left_pad, 0), (0, 0)] inputs = jnp.pad(inputs, pads) padding_lax = "VALID" dimension_numbers = _conv_dimension_numbers(inputs.shape) in_features = jnp.shape(inputs)[-1] if self.shared_weights: # One shared convolutional kernel for all pixels in the output. assert in_features % self.feature_group_count == 0 kernel_shape = kernel_size + ( in_features // self.feature_group_count, self.features, ) else: if self.feature_group_count != 1: raise NotImplementedError( "`lax.conv_general_dilated_local` does not support " f"`feature_group_count != 1`, got `{self.feature_group_count}`." ) # Need to know the spatial output shape of a standard convolution to # create the unshared convolution kernel. conv_output_shape = jax.eval_shape( lambda lhs, rhs: self.conv_general_dilated( # pylint: disable=g-long-lambda lhs=lhs, rhs=rhs, window_strides=strides, padding=padding_lax, dimension_numbers=dimension_numbers, lhs_dilation=input_dilation, rhs_dilation=kernel_dilation, ), inputs, jax.ShapedArray(kernel_size + (in_features, self.features), inputs.dtype), ).shape # One (unshared) convolutional kernel per each pixel in the output. kernel_shape = conv_output_shape[1:-1] + ( np.prod(kernel_size) * in_features, self.features, ) if self.mask is not None and self.mask.shape != kernel_shape: raise ValueError( "Mask needs to have the same shape as weights. " f"Shapes are: {self.mask.shape}, {kernel_shape}" ) kernel = param_with_axes( "kernel", self.kernel_init, kernel_shape, self.params_dtype, axes=self.kernel_axes, ) if self.mask is not None: kernel *= self.mask if self.use_bias: if self.shared_weights: # One bias weight per output channel, shared between pixels. bias_shape = (self.features,) else: # One bias weight per output entry, unshared betwen pixels. bias_shape = conv_output_shape[1:] bias = param_with_axes( "bias", self.bias_init, bias_shape, self.params_dtype, axes=(self.kernel_axes[-1],), ) else: bias = None inputs, kernel, bias = promote_dtype(inputs, kernel, bias, dtype=self.dtype) if self.shared_weights: y = self.conv_general_dilated( inputs, kernel, strides, padding_lax, lhs_dilation=input_dilation, rhs_dilation=kernel_dilation, dimension_numbers=dimension_numbers, feature_group_count=self.feature_group_count, precision=self.precision, ) else: y = lax.conv_general_dilated_local( lhs=inputs, rhs=kernel, window_strides=strides, padding=padding_lax, filter_shape=kernel_size, lhs_dilation=input_dilation, rhs_dilation=kernel_dilation, dimension_numbers=dimension_numbers, precision=self.precision, ) if self.use_bias: bias = bias.reshape((1,) * (y.ndim - bias.ndim) + bias.shape) y += bias if num_batch_dimensions != 1: output_shape = input_batch_shape + y.shape[1:] y = jnp.reshape(y, output_shape) return y class Conv(_Conv): """Convolution Module wrapping `lax.conv_general_dilated`. Attributes: features: number of convolution filters. kernel_size: shape of the convolutional kernel. For 1D convolution, the kernel size can be passed as an integer. For all other cases, it must be a sequence of integers. strides: an integer or a sequence of `n` integers, representing the inter-window strides (default: 1). padding: either the string `'SAME'`, the string `'VALID'`, the string `'CIRCULAR'` (periodic boundary conditions), or a sequence of `n` `(low, high)` integer pairs that give the padding to apply before and after each spatial dimension. A single int is interpeted as applying the same padding in all dims and passign a single int in a sequence causes the same padding to be used on both sides. `'CAUSAL'` padding for a 1D convolution will left-pad the convolution axis, resulting in same-sized output. input_dilation: an integer or a sequence of `n` integers, giving the dilation factor to apply in each spatial dimension of `inputs` (default: 1). Convolution with input dilation `d` is equivalent to transposed convolution with stride `d`. kernel_dilation: an integer or a sequence of `n` integers, giving the dilation factor to apply in each spatial dimension of the convolution kernel (default: 1). Convolution with kernel dilation is also known as 'atrous convolution'. feature_group_count: integer, default 1. If specified divides the input features into groups. use_bias: whether to add a bias to the output (default: True). mask: Optional mask for the weights during masked convolution. The mask must be the same shape as the convolution weight matrix. dtype: the dtype of the computation (default: infer from input and params). params_dtype: the dtype passed to parameter initializers (default: float32). precision: numerical precision of the computation see `jax.lax.Precision` for details. kernel_init: initializer for the convolutional kernel. bias_init: initializer for the bias. """ @property def shared_weights(self) -> bool: return True