grok-1/model.py
2024-03-17 11:11:31 -07:00

1399 lines
45 KiB
Python

# Copyright 2024 X.AI Corp.
#
# 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.
import functools
import logging
import re
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
import haiku as hk
import jax
import jax.experimental.maps
import jax.numpy as jnp
from jax import config, tree_util
from jax.experimental.shard_map import shard_map
from jax.lax import with_sharding_constraint as pjit_sharding_constraint
from jax.sharding import PartitionSpec
from jax.sharding import PartitionSpec as P
config.update("jax_spmd_mode", "allow_all")
logger = logging.getLogger(__name__)
rank_logger = logging.getLogger("rank")
@dataclass
class QuantizedWeight8bit:
weight: jnp.array
scales: jnp.array
@property
def shape(self):
return self.weight.shape
tree_util.register_pytree_node(
QuantizedWeight8bit,
lambda qw: ([qw.weight, qw.scales], ()),
lambda _, children: QuantizedWeight8bit(children[0], children[1]),
)
class TrainingState(NamedTuple):
"""Container for the training state."""
params: hk.Params
def _match(qs, ks):
"""Return True if regexes in qs match any window of strings in tuple ks."""
# compile regexes and force complete match
qts = tuple(map(lambda x: re.compile(x + "$"), qs))
for i in range(len(ks) - len(qs) + 1):
matches = [x.match(y) for x, y in zip(qts, ks[i:])]
if matches and all(matches):
return True
return False
def with_sharding_constraint(x, constraint):
if jax.experimental.maps.thread_resources.env.physical_mesh.empty:
return x
else:
return pjit_sharding_constraint(x, constraint)
def cast_bfloat16(x):
if x.dtype.kind == "f":
return x.astype(jnp.bfloat16)
else:
return x
def ffn_size(emb_size, widening_factor):
_ffn_size = int(widening_factor * emb_size) * 2 // 3
_ffn_size = _ffn_size + (8 - _ffn_size) % 8 # ensure it's a multiple of 8
logger.debug(f"emd_size: {emb_size} adjusted ffn_size: {_ffn_size}")
return _ffn_size
def apply_rules(rules):
def _apply_rules(path, value):
del value # Unused.
path_list = [str(i.key).split("/") for i in path if isinstance(i, jax.tree_util.DictKey)]
flattened_path = jax.tree_util.tree_flatten(path_list)[0]
for rule, replacement in rules:
if _match(rule, flattened_path):
if isinstance(replacement, PartitionSpec):
if "layer_stack" in flattened_path:
replacement = PartitionSpec(None, *replacement)
rank_logger.debug(f"Apply {replacement} to {flattened_path} with rule {rule}")
return replacement
rank_logger.info(f"{flattened_path} no matching found!")
return None
return _apply_rules
TRANSFORMER_PARTITION_RULES = [
# attention
(("multi_head_attention", "(query|key|value)", "w"), P("data", "model")),
(("multi_head_attention", "(query|key|value)", "b"), P(None)),
(("multi_head_attention", "linear", "w"), P("model", "data")),
(("multi_head_attention", "linear", "b"), P(None)),
# mlp
((r"decoder_layer_[0-9]+", "linear", "w"), P("data", "model")),
((r"decoder_layer_[0-9]+", "linear", "b"), P(None)),
((r"decoder_layer_[0-9]+", "linear_v", "w"), P("data", "model")),
((r"decoder_layer_[0-9]+", "linear_v", "b"), P(None)),
(
(r"decoder_layer_[0-9]+", "linear_1", "w"),
P(
"model",
"data",
),
),
((r"decoder_layer_[0-9]+", "linear_1", "b"), P(None)),
# layer norms
((r"decoder_layer_[0-9]+", "layer_norm", "offset"), P(None)),
((r"decoder_layer_[0-9]+", "layer_norm", "scale"), P(None)),
((r"decoder_layer_[0-9]+", "layer_norm_1", "offset"), P(None)),
((r"decoder_layer_[0-9]+", "layer_norm_1", "scale"), P(None)),
# rms norms
((r"decoder_layer_[0-9]+", "rms_norm", "scale"), P(None)),
((r"decoder_layer_[0-9]+", "rms_norm_1", "scale"), P(None)),
((r"decoder_layer_[0-9]+", "rms_norm_2", "scale"), P(None)),
((r"decoder_layer_[0-9]+", "rms_norm_3", "scale"), P(None)),
# router
(("router", "w"), P("data")),
# moe mlp
(("moe", "linear", "w"), P(None, "data", "model")),
(("moe", "linear", "b"), P(None)),
(("moe", "linear_v", "w"), P(None, "data", "model")),
(("moe", "linear_v", "b"), P(None)),
(("moe", "linear_1", "w"), P(None, "model", "data")),
(("moe", "linear_1", "b"), P(None)),
# layer norms
(("moe", "layer_norm", "offset"), P(None)),
(("moe", "layer_norm", "scale"), P(None)),
(("moe", "layer_norm_1", "offset"), P(None)),
(("moe", "layer_norm_1", "scale"), P(None)),
# rms norms
(("moe", "rms_norm", "scale"), P(None)),
(("moe", "rms_norm_1", "scale"), P(None)),
(("moe", "rms_norm_2", "scale"), P(None)),
(("moe", "rms_norm_3", "scale"), P(None)),
]
LM_PARTITION_RULES = [
# Embedding layer.
(
("language_model", "positional_embeddings"),
P(None, ("data", "model")),
),
(
("language_model", "in_out_embed", "embeddings"),
P(None, ("data", "model")),
),
# Final RMSNorm.
(("language_model", "rms_norm"), P(None)),
]
TOP_K = 8
class KVMemory(NamedTuple):
k: Optional[jax.Array]
v: Optional[jax.Array]
step: Optional[jax.Array]
def init_layer_memories(
batch_size: int,
sequence_len: int,
num_kv_heads: int,
key_size: int,
num_layers: int,
step: Optional[jax.Array] = None,
dtype=jnp.bfloat16,
):
return [
KVMemory(
k=jnp.zeros((batch_size, sequence_len, num_kv_heads, key_size), dtype=dtype),
v=jnp.zeros((batch_size, sequence_len, num_kv_heads, key_size), dtype=dtype),
step=step,
)
for _ in range(num_layers)
]
class Memory(NamedTuple):
# Self-attention key/value cache.
layers: List[KVMemory]
class Router(hk.Module):
def __init__(
self,
num_selected_experts: int,
data_axis: Union[str, Tuple[str, ...]] = "data",
model_axis: Union[str, Tuple[str, ...]] = "model",
shard_activations: bool = False,
mesh: Any = None,
name: str = "router",
):
super().__init__(name)
self.shard_activations = shard_activations
self.data_axis = data_axis
self.model_axis = model_axis
self.mesh = mesh
self.num_selected_experts = num_selected_experts
def compute_routing_prob(
self, inputs: jax.Array, padding_mask: Optional[jax.Array], num_experts: int
):
return self._compute_routing_prob(inputs, padding_mask, num_experts)
@hk.transparent
def _compute_routing_prob(
self,
inputs: jax.Array,
padding_mask: Optional[jax.Array],
num_experts: int,
):
# Using fp32 for the routing prob computation.
inputs = jax.lax.convert_element_type(inputs, jnp.float32)
# [batch_size, seq_len, num_experts]
routing_logits = self._router_weights(inputs, num_experts, sharding=P("data"))
assert routing_logits.dtype == jnp.float32
routing_probs = jax.nn.softmax(routing_logits)
if padding_mask is not None:
routing_probs *= padding_mask
return routing_probs, routing_logits, 0
@hk.transparent
def _router_weights(
self,
x: jax.Array,
num_experts: int,
sharding: Optional[P] = None,
):
fprop_dtype = x.dtype
if not x.shape:
raise ValueError("Input must not be scalar.")
input_size = self.input_size = x.shape[-1]
w = hk.get_parameter(
"w", [input_size, num_experts], jnp.float32, init=hk.initializers.Constant(0)
)
if sharding:
w = with_sharding_constraint(w, sharding)
out = jnp.dot(x, w.astype(fprop_dtype))
return out
class MoELayer(hk.Module):
def __init__(
self,
num_experts: int,
layer_fn: Callable,
router: Router,
mesh: Any = None,
shard_activations: bool = False,
data_axis: Union[str, Tuple[str, ...]] = "data",
model_axis: Union[str, Tuple[str, ...]] = "model",
name: Optional[str] = "moe",
):
super().__init__(name)
self.num_experts = num_experts
self.layer_fn = layer_fn
self.router = router
self.mesh = mesh
self.shard_activations = shard_activations
self.data_axis = data_axis
self.model_axis = model_axis
@hk.transparent
def _inference_call(self, inputs: jax.Array, padding_mask: Optional[jax.Array] = None):
routing_probs, _, _ = self.router.compute_routing_prob(
inputs, padding_mask, self.num_experts
)
expert_gate, expert_index = jax.lax.top_k(routing_probs, k=self.router.num_selected_experts)
tmp = jnp.reshape(inputs, (inputs.shape[0] * inputs.shape[1], inputs.shape[2]))
broad_inputs = jnp.tile(tmp[:, jnp.newaxis, :], (1, self.router.num_selected_experts, 1))
broad_inputs = jnp.reshape(
broad_inputs, (broad_inputs.shape[0] * broad_inputs.shape[1], broad_inputs.shape[2])
)
init_fn, _ = hk.transform(self.layer_fn)
vmapped_init_fn = jax.vmap(init_fn, in_axes=0, out_axes=0)
lifted_init_fn = hk.experimental.transparent_lift(vmapped_init_fn)
# Fetch the vmapped params of the DenseBlock.
params = lifted_init_fn(
jax.random.split(jax.random.PRNGKey(1), self.num_experts),
jnp.zeros((self.num_experts, 1, 1, inputs.shape[-1])),
)
# Index and prob are in the shape [m, 2] indicating which token assigned to which experts.
# b: num_expert
# m: token or sequence dim
# k: input embed dim
# n: output embed dim
# e: the number of experts chosen for each token
@functools.partial(
shard_map,
mesh=self.mesh,
in_specs=(
P(self.data_axis, None),
P(None, None, self.model_axis),
P(None, None, self.model_axis),
P(None),
P(None),
),
out_specs=P(self.data_axis, self.model_axis),
check_rep=False,
)
def moe_slow_matmul1(input, weight, scales, index, prob):
weight = weight * scales
one_hot_indices = jax.nn.one_hot(index.reshape(-1), 8, axis=0)
all_expert_output = jnp.einsum("mk,bkn->bmn", input, weight)
output = jnp.einsum("bm,bmn->mn", one_hot_indices, all_expert_output)
return output
@functools.partial(
shard_map,
mesh=self.mesh,
in_specs=(
P(self.data_axis, self.model_axis),
P(None, self.model_axis, None),
P(None, self.model_axis, None),
P(None),
P(None),
),
out_specs=P(self.data_axis, None),
check_rep=False,
)
def moe_slow_matmul2(input, weight, scales, index, prob):
weight = weight * scales
one_hot_indices = jax.nn.one_hot(index.reshape(-1), 8, axis=0)
all_expert_output = jnp.einsum("mk,bkn->bmn", input, weight)
output = jnp.einsum("bm,bmn->mn", one_hot_indices, all_expert_output)
return jax.lax.psum(output, axis_name="model")
if hasattr(params["linear"]["w"], "scales"):
x = moe_slow_matmul1(
broad_inputs,
params["linear_v"]["w"].weight,
params["linear_v"]["w"].scales,
expert_index,
expert_gate,
)
y = moe_slow_matmul1(
broad_inputs,
params["linear"]["w"].weight,
params["linear"]["w"].scales,
expert_index,
expert_gate,
)
y = jax.nn.gelu(y)
out = moe_slow_matmul2(
x * y,
params["linear_1"]["w"].weight,
params["linear_1"]["w"].scales,
expert_index,
expert_gate,
)
out = jnp.reshape(
out,
[
inputs.shape[0],
inputs.shape[1],
self.router.num_selected_experts,
out.shape[-1],
],
)
out = expert_gate[:, :, :, None].astype(jnp.bfloat16) * out
out = jnp.sum(out, axis=2)
out = out.astype(jnp.bfloat16)
else:
# This is only here so that we can construct a valid init_fn with this code.
return inputs
return out
def __call__(self, inputs: jax.Array, padding_mask: jax.Array):
return self._inference_call(inputs)
class MHAOutput(NamedTuple):
"""Outputs of the multi-head attention operation."""
embeddings: jax.Array
memory: Any
class DecoderOutput(NamedTuple):
embeddings: jax.Array
memory: Any
class TransformerOutput(NamedTuple):
embeddings: jax.Array
memory: Any
@dataclass
class TransformerConfig:
emb_size: int
key_size: int
num_q_heads: int
num_kv_heads: int
num_layers: int
vocab_size: int = 128 * 1024
widening_factor: float = 4.0
attn_output_multiplier: float = 1.0
name: Optional[str] = None
num_experts: int = -1
capacity_factor: float = 1.0
num_selected_experts: int = 1
init_scale: float = 1.0
shard_activations: bool = False
# Used for activation sharding.
data_axis: Union[str, Tuple[str, ...]] = "data"
model_axis: Union[str, Tuple[str, ...]] = "model"
def __post_init__(self):
if isinstance(self.data_axis, list):
self.data_axis = tuple(self.data_axis)
if isinstance(self.model_axis, list):
self.model_axis = tuple(self.model_axis)
def partition_rules(self):
return TRANSFORMER_PARTITION_RULES
def make(self, mesh=None) -> "Transformer":
data_axis = tuple(self.data_axis) if isinstance(self.data_axis, list) else self.data_axis
model_axis = (
tuple(self.model_axis) if isinstance(self.model_axis, list) else self.model_axis
)
return Transformer(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
widening_factor=self.widening_factor,
key_size=self.key_size,
init_scale=self.init_scale,
mesh=mesh,
attn_output_multiplier=self.attn_output_multiplier,
shard_activations=self.shard_activations,
num_layers=self.num_layers,
num_experts=self.num_experts,
num_selected_experts=self.num_selected_experts,
data_axis=data_axis,
model_axis=model_axis,
)
def get_memory_sharding(self):
return Memory(
layers=[
KVMemory(
k=P(self.data_axis, self.model_axis),
v=P(self.data_axis, self.model_axis),
step=P(self.data_axis),
)
for _ in range(self.num_layers)
],
)
def hk_rms_norm(
x: jax.Array,
fixed_scale=False,
sharding=P(None),
) -> jax.Array:
"""Applies a unique LayerNorm to x with default settings."""
ln = RMSNorm(axis=-1, create_scale=not fixed_scale, sharding=sharding)
return ln(x)
def make_attention_mask(
query_input: jax.Array,
key_input: jax.Array,
pairwise_fn: Callable[..., Any] = jnp.multiply,
dtype: Any = jnp.bfloat16,
):
"""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
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)
return mask.astype(dtype)
class Linear(hk.Linear):
def __init__(
self,
output_size: int,
with_bias: bool = True,
sharding: Optional[P] = None,
mesh: Any = None,
name: Optional[str] = None,
shard_axis: int = 0,
):
super().__init__(
output_size=output_size,
with_bias=with_bias,
name=name,
)
self.sharding = sharding
self.mesh = mesh
self.shard_axis = shard_axis
def __call__(
self,
inputs: jax.Array,
) -> jax.Array:
"""Computes a linear transform of the input."""
fprop_dtype = inputs.dtype
if not inputs.shape:
raise ValueError("Input must not be scalar.")
input_size = self.input_size = inputs.shape[-1]
output_size = self.output_size
w = hk.get_parameter(
"w", [input_size, output_size], jnp.float32, init=hk.initializers.Constant(0)
)
if hasattr(w, "scales"):
shape = inputs.shape
inputs = jnp.reshape(inputs, (-1, shape[-1]))
@functools.partial(
shard_map,
mesh=self.mesh,
in_specs=(self.sharding, self.sharding),
out_specs=self.sharding,
check_rep=False,
)
def mul(w, s):
return w.astype(s.dtype) * s
w = mul(w.weight, w.scales)
out = jnp.dot(inputs, w.astype(fprop_dtype))
if self.with_bias:
b = hk.get_parameter(
"b", [self.output_size], jnp.float32, init=hk.initializers.Constant(0)
)
b = jnp.broadcast_to(b, out.shape)
out = out + b.astype(fprop_dtype)
return out
class RMSNorm(hk.RMSNorm):
def __init__(
self,
axis: Union[int, Sequence[int], slice],
eps: float = 1e-5,
name: Optional[str] = None,
create_scale: bool = True,
sharding: Optional[P] = None,
):
super().__init__(axis, eps, create_scale=create_scale, name=name)
self.sharding = sharding
def __call__(self, inputs: jax.Array):
fprop_dtype = inputs.dtype
param_shape = (inputs.shape[-1],)
if self.create_scale:
scale = hk.get_parameter(
"scale",
param_shape,
dtype=jnp.float32,
init=hk.initializers.Constant(0),
)
if self.sharding:
scale = with_sharding_constraint(scale, self.sharding)
scale = jnp.broadcast_to(scale.astype(jnp.float32), inputs.shape)
else:
scale = 1.0
inputs = inputs.astype(jnp.float32)
scale = scale.astype(jnp.float32)
mean_squared = jnp.mean(jnp.square(inputs), axis=[-1], keepdims=True)
mean_squared = jnp.broadcast_to(mean_squared, inputs.shape)
normed_inputs = inputs * jax.lax.rsqrt(mean_squared + self.eps)
outputs = scale * normed_inputs
return outputs.astype(fprop_dtype)
def rotate_half(
x: jax.Array,
) -> jax.Array:
"""Obtain the rotated counterpart of each feature"""
x1, x2 = jnp.split(x, 2, axis=-1)
return jnp.concatenate((-x2, x1), axis=-1)
class RotaryEmbedding(hk.Module):
"""Applies rotary embeddings (RoPE) to the input sequence tensor,
as described in https://arxiv.org/abs/2104.09864.
Attributes:
dim (int): Dimensionality of the feature vectors
base_exponent (int): Base exponent to compute embeddings from
"""
def __init__(
self,
dim: int,
name: Optional[str] = None,
base_exponent: int = 10000,
):
super().__init__(name)
self.dim = dim
self.base_exponent = base_exponent
assert self.dim % 2 == 0
def __call__(
self,
x: jax.Array,
seq_dim: int,
offset: jax.Array,
const_position: Optional[int] = None,
t: Optional[jax.Array] = None,
) -> jax.Array:
fprop_dtype = x.dtype
# Compute the per-dimension frequencies
exponents = jnp.arange(0, self.dim, 2, dtype=jnp.float32)
inv_freq = jnp.asarray(
1.0 / (self.base_exponent ** (exponents / self.dim)), dtype=jnp.float32
)
if jnp.shape(offset) == ():
# Offset can be a scalar or one offset per batch element.
offset = jnp.expand_dims(offset, 0)
# Compute the per element phase (to pass into sin and cos)
if const_position:
t = const_position * jnp.ones(
(
1,
x.shape[seq_dim],
),
dtype=jnp.float32,
)
elif t is None:
t = jnp.arange(x.shape[seq_dim], dtype=jnp.float32) + jnp.expand_dims(offset, -1)
phase = jnp.einsum("bi,j->bij", t, inv_freq)
phase = jnp.tile(phase, reps=(1, 2))[:, :, None, :]
x = x * jnp.cos(phase) + rotate_half(x) * jnp.sin(phase)
x = x.astype(fprop_dtype)
return x
class MultiHeadAttention(hk.Module):
def __init__(
self,
num_q_heads: int,
num_kv_heads: int,
key_size: int,
*,
with_bias: bool = True,
value_size: Optional[int] = None,
model_size: Optional[int] = None,
attn_output_multiplier: 1.0,
data_axis: Union[str, Tuple[str, ...]] = "data",
model_axis: Union[str, Tuple[str, ...]] = "model",
name: Optional[str] = None,
):
super().__init__(name=name)
self.num_q_heads = num_q_heads
self.num_kv_heads = num_kv_heads
self.key_size = key_size
self.value_size = value_size or key_size
self.model_size = model_size or key_size * num_q_heads
self.data_axis = data_axis
self.model_axis = model_axis
self.attn_output_multiplier = attn_output_multiplier
self.with_bias = with_bias
def __call__(
self,
query: jax.Array,
key: Optional[jax.Array],
value: Optional[jax.Array],
mask: Optional[jax.Array] = None,
kv_memory: Optional[KVMemory] = None,
mesh: Any = None,
) -> MHAOutput:
# In shape hints below, we suppress the leading dims [...] for brevity.
# Hence e.g. [A, B] should be read in every case as [..., A, B].
sequence_length = query.shape[1]
projection = self._linear_projection
use_memory = False
if kv_memory is not None:
if kv_memory.k is None:
assert kv_memory.v is None
assert key is not None
assert value is not None
else:
assert kv_memory.v is not None
use_memory = True
else:
assert key is not None
assert value is not None
# Check that the keys and values have consistent batch size and sequence length.
if not use_memory:
assert key.shape[:2] == value.shape[:2], f"key/value shape: {key.shape}/{value.shape}"
if mask is not None:
assert mask.ndim == 4
assert mask.shape[0] in {
1,
query.shape[0],
}, f"mask/query shape: {mask.shape}/{query.shape}"
if not use_memory:
assert key.shape[0] in {
1,
query.shape[0],
}, f"key/query shape: {key.shape}/{query.shape}"
assert mask.shape[1] == 1
assert mask.shape[2] in {
1,
query.shape[1],
}, f"mask/query shape: {mask.shape}/{query.shape}"
if not use_memory:
assert mask.shape[3] in {
1,
key.shape[1],
}, f"mask/query shape: {mask.shape}/{key.shape}"
# Compute key/query/values (overload K/Q/V to denote the respective sizes).
assert self.num_q_heads % self.num_kv_heads == 0
query_heads = projection(
query,
self.key_size,
self.num_q_heads,
name="query",
sharding=P("data", "model"),
mesh=mesh,
) # [B, T', H, Q=K]
new_memory = None
key_heads = projection(
key,
self.key_size,
self.num_kv_heads,
name="key",
sharding=P("data", "model"),
mesh=mesh,
) # [B, T, H, K]
value_heads = projection(
value,
self.value_size,
self.num_kv_heads,
name="value",
sharding=P("data", "model"),
mesh=mesh,
) # [B, T, H, V]
rotate = RotaryEmbedding(dim=self.key_size, base_exponent=int(1e4))
key_heads = rotate(key_heads, seq_dim=1, offset=(kv_memory.step if kv_memory else 0))
query_heads = rotate(query_heads, seq_dim=1, offset=(kv_memory.step if kv_memory else 0))
@functools.partial(jax.vmap)
def update_into(mem, start, update):
return jax.lax.dynamic_update_slice_in_dim(mem, update, start, axis=0)
if kv_memory:
if mesh is not None:
@functools.partial(
shard_map,
mesh=mesh,
in_specs=(
P("data", None, "model"),
P("data"),
P("data", None, "model"),
),
out_specs=P("data", None, "model"),
check_rep=False,
)
def update_into_shmap(mems, starts, updates):
return update_into(mems, starts, updates)
key_heads = update_into_shmap(kv_memory.k, kv_memory.step, key_heads)
value_heads = update_into_shmap(kv_memory.v, kv_memory.step, value_heads)
else:
key_heads = update_into(kv_memory.k, kv_memory.step, key_heads)
value_heads = update_into(kv_memory.v, kv_memory.step, value_heads)
new_step = kv_memory.step + sequence_length
memory_mask = jnp.arange(kv_memory.k.shape[1]) < new_step[:, None]
memory_mask = memory_mask[:, None, None, :] # [B, H, T, T]
if mask is not None:
mask = memory_mask * mask
else:
mask = memory_mask
new_memory = KVMemory(
k=key_heads,
v=value_heads,
step=new_step,
)
# Add separate dimension for grouped query heads.
query_heads = with_sharding_constraint(query_heads, P(self.data_axis, None, "model", None))
key_heads = with_sharding_constraint(key_heads, P(self.data_axis, None, "model", None))
value_heads = with_sharding_constraint(value_heads, P(self.data_axis, None, "model", None))
b, t, h, d = query_heads.shape
_, _, kv_h, _ = key_heads.shape
assert h % kv_h == 0, f"query_heads {h} must be a multiple of kv_heads {kv_h}"
query_heads = jnp.reshape(query_heads, (b, t, kv_h, h // kv_h, d))
query_heads = with_sharding_constraint(
query_heads, P(self.data_axis, None, "model", None, None)
)
# Compute attention weights.
# Attention softmax is always carried out in fp32.
attn_logits = jnp.einsum("...thHd,...Thd->...hHtT", query_heads, key_heads).astype(
jnp.float32
)
attn_logits *= self.attn_output_multiplier
max_attn_val = jnp.array(30.0, dtype=attn_logits.dtype)
attn_logits = max_attn_val * jnp.tanh(attn_logits / max_attn_val)
mask = mask[:, :, None, :, :]
if mask is not None:
if mask.ndim != attn_logits.ndim:
raise ValueError(
f"Mask dimensionality {mask.ndim} must match logits dimensionality "
f"{attn_logits.ndim} for {mask.shape}/{attn_logits.shape}."
)
attn_logits = jnp.where(mask, attn_logits, -1e30)
attn_weights = jax.nn.softmax(attn_logits).astype(query.dtype) # [H, T', T]
# Weight the values by the attention and flatten the head vectors.
attn = jnp.einsum("...hHtT,...Thd->...thHd", attn_weights, value_heads)
attn = with_sharding_constraint(attn, P(self.data_axis, None, "model", None, None))
leading_dims = attn.shape[:2]
attn = jnp.reshape(attn, (*leading_dims, -1)) # [T', H*V]
attn = with_sharding_constraint(attn, P(self.data_axis, None, "model"))
# Apply another projection to get the final embeddings.
final_projection = Linear(
self.model_size,
with_bias=False,
sharding=P("model", "data"),
mesh=mesh,
)
return MHAOutput(final_projection(attn), new_memory)
@hk.transparent
def _linear_projection(
self,
x: jax.Array,
head_size: int,
num_heads: int,
sharding: Optional[P] = None,
name: Optional[str] = None,
mesh: Any = None,
) -> jax.Array:
y = Linear(
num_heads * head_size,
with_bias=False,
name=name,
sharding=sharding,
mesh=mesh,
)(x)
*leading_dims, _ = x.shape
return y.reshape((*leading_dims, num_heads, head_size))
@dataclass
class MHABlock(hk.Module):
"""A MHA Block"""
num_q_heads: int
num_kv_heads: int
key_size: int
attn_output_multiplier: float = 1.0
mesh: Any = None
data_axis: Union[str, Tuple[str, ...]] = "data"
model_axis: Union[str, Tuple[str, ...]] = "model"
@hk.transparent
def __call__(
self,
inputs: jax.Array, # [B, T, D]
mask: jax.Array, # [B, 1, T, T] or [B, 1, 1, T] or B[1, 1, 1, 1]
layer_memory: Optional[KVMemory],
) -> MHAOutput:
_, _, model_size = inputs.shape
assert mask.ndim == 4, f"shape: {mask.shape}"
assert mask.shape[2] in {1, inputs.shape[1]}, str(mask.shape)
assert mask.shape[3] in {1, inputs.shape[1]}, str(mask.shape)
side_input = inputs
def attn_block(query, key, value, mask, memory) -> MHAOutput:
return MultiHeadAttention(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
key_size=self.key_size,
model_size=model_size,
data_axis=self.data_axis,
model_axis=self.model_axis,
attn_output_multiplier=self.attn_output_multiplier,
)(
query,
key,
value,
mask,
memory,
mesh=self.mesh,
)
attn_output = attn_block(inputs, side_input, side_input, mask, layer_memory)
h_attn = attn_output.embeddings
return attn_output._replace(embeddings=h_attn)
@dataclass
class DenseBlock(hk.Module):
num_q_heads: int
num_kv_heads: int
key_size: int
widening_factor: float = 4.0
sharding_constraint: bool = False
mesh: Any = None
@hk.transparent
def __call__(
self,
inputs: jax.Array, # [B, T, D]
) -> jax.Array: # [B, T, D]
_, _, model_size = inputs.shape
h_v = Linear(
ffn_size(
model_size,
self.widening_factor,
),
with_bias=False,
mesh=self.mesh,
sharding=P("data", "model"),
name="linear_v",
)(inputs)
h_w1 = jax.nn.gelu(
Linear(
ffn_size(
model_size,
self.widening_factor,
),
with_bias=False,
mesh=self.mesh,
sharding=P("data", "model"),
)(inputs)
)
h_dense = Linear(
model_size,
with_bias=False,
sharding=P("model", "data"),
mesh=self.mesh,
shard_axis=1,
)(h_w1 * h_v)
return h_dense
@dataclass
class DecoderLayer(hk.Module):
"""A transformer stack."""
num_q_heads: int
num_kv_heads: int
key_size: int
num_layers: int
# MoE.
num_experts: int
layer_index: Optional[int] = None
num_selected_experts: int = 1
widening_factor: float = 4.0
name: Optional[str] = None
data_axis: Union[str, Tuple[str, ...]] = "data"
model_axis: Union[str, Tuple[str, ...]] = "model"
shard_activations: bool = False
attn_output_multiplier: float = 1.0
mesh: Any = None
def __call__(
self,
inputs: jax.Array, # [B, T, D]
mask: jax.Array, # [B, 1, T, T] or [B, 1, 1, T]
padding_mask: Optional[jax.Array],
layer_memory: Optional[KVMemory],
) -> DecoderOutput:
"""Transforms input embedding sequences to output embedding sequences."""
def layer_norm(x):
return hk_rms_norm(x)
if self.shard_activations:
sharding = P(self.data_axis, None, self.model_axis)
else:
sharding = P(self.data_axis, None)
h = with_sharding_constraint(inputs, sharding)
attn_output = MHABlock(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
key_size=self.key_size,
attn_output_multiplier=self.attn_output_multiplier,
mesh=self.mesh,
data_axis=self.data_axis,
model_axis=self.model_axis,
)(layer_norm(h), mask, layer_memory)
h_attn = attn_output.embeddings
h_attn = layer_norm(h_attn)
h += h_attn
h = with_sharding_constraint(h, sharding)
def base_dense_block(h):
h = DenseBlock(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
key_size=self.key_size,
widening_factor=self.widening_factor,
sharding_constraint=False,
mesh=self.mesh,
)(h)
return h
if self.num_experts > 1:
rank_logger.debug("Using MoE!")
router = Router(
num_selected_experts=self.num_selected_experts,
shard_activations=self.shard_activations,
data_axis=self.data_axis,
model_axis=self.model_axis,
mesh=self.mesh,
)
h_dense = MoELayer(
num_experts=self.num_experts,
mesh=self.mesh,
layer_fn=base_dense_block,
router=router,
shard_activations=self.shard_activations,
data_axis=self.data_axis,
model_axis=self.model_axis,
)(layer_norm(h), padding_mask)
else:
h_dense = base_dense_block(layer_norm(h))
h_dense = layer_norm(h_dense)
h += h_dense
h = with_sharding_constraint(h, sharding)
return DecoderOutput(
embeddings=h,
memory=attn_output.memory,
)
class LanguageModelOutput(NamedTuple):
logits: jax.Array
model_state: Any
class InOutEmbed(hk.Embed):
"""Module for embedding tokens in a low-dimensional space."""
def __init__(
self,
vocab_size: Optional[int] = None,
embed_dim: Optional[int] = None,
sharding: Optional[P] = None,
name: Optional[str] = None,
):
super().__init__(
vocab_size=vocab_size,
embed_dim=embed_dim,
name=name,
)
self.sharding = sharding
@property
def embeddings(self):
embed_mat = hk.get_parameter(
"embeddings",
[self.vocab_size, self.embed_dim],
dtype=jnp.float32,
init=hk.initializers.Constant(0),
)
if self.sharding:
embed_mat = with_sharding_constraint(embed_mat, self.sharding)
return embed_mat
def decode(
self,
inputs: jax.Array,
) -> jax.Array:
return jnp.dot(inputs, self.embeddings.T.astype(inputs.dtype))
@dataclass
class LanguageModelConfig:
"""An autoregressive transformer-based language model."""
model: Optional[TransformerConfig]
vocab_size: int
pad_token: int
eos_token: int
sequence_len: int
model_size: int = 0
embedding_init_scale: float = 1.0
embedding_multiplier_scale: float = 1.0
output_multiplier_scale: float = 1.0
name: Optional[str] = None
fprop_dtype: Any = jnp.bfloat16
model_type: Optional[str] = None
init_scale_override: Optional[float] = None
shard_embeddings: bool = True
_initialized = False
def initialize(self):
# We cannot specify [] as a default value (it is mutable), hence None.
model_config = self.model
assert self.init_scale_override is None, (
"Overriding model initialize scale is supported only for predefined models."
)
if self.model_size == 0:
self.model_size = model_config.emb_size
assert self.model is not None, "Model could not be initialized."
self._initialized = True
return self
def make(self, *args, **kwargs):
if not self._initialized:
logger.warning(
f"LanguageModel {self.name} is not initialized. Initializing for one replica."
)
self.initialize()
return LanguageModel(
model=self.model.make(*args, **kwargs),
config=self,
fprop_dtype=self.fprop_dtype,
mesh=kwargs.get("mesh", None),
)
def partition_rules(self):
return LM_PARTITION_RULES + self.model.partition_rules()
def layer_norm(x, model):
return hk_rms_norm(x)
@dataclass
class LanguageModel(hk.Module):
"""An autoregressive transformer-based language model."""
model: "Transformer"
config: LanguageModelConfig
fprop_dtype: Any = jnp.bfloat16
name: Optional[str] = None
mesh: Any = None
def __call__(
self,
tokens: jax.Array,
memory: Optional[Memory] = None,
*,
batch: Dict[str, jax.Array] = {},
last_hid_only: bool = False,
length: Optional[jax.Array] = None,
) -> LanguageModelOutput:
"""Forward pass, producing a sequence of logits."""
del batch # Unused.
config = self.config
input_mask = jnp.greater(tokens, config.pad_token)
# Embed the input tokens and positions.
in_out_embed = InOutEmbed(
self.config.vocab_size,
embed_dim=self.config.model_size,
sharding=P(None, ("data", "model")),
)
input_embeddings = in_out_embed(tokens).astype(config.fprop_dtype)
input_embeddings = with_sharding_constraint(
input_embeddings, P("data", None, self.model.model_axis)
)
input_embeddings *= config.embedding_multiplier_scale
model_output = self.model(
input_embeddings,
input_mask,
memory=memory,
) # [B, T, D]
embeddings, model_state = model_output.embeddings, model_output.memory
if self.model.shard_activations:
embeddings = with_sharding_constraint(
embeddings, P("data", None, self.model.model_axis)
)
else:
embeddings = with_sharding_constraint(embeddings, P("data", None))
rank_logger.debug(f"Final embedding shape: {embeddings.shape}")
embeddings = layer_norm(embeddings, self.model)
assert embeddings.dtype == self.fprop_dtype
if last_hid_only:
last_step = jnp.maximum(jnp.sum(input_mask.astype(jnp.int32), axis=1) - 1, 0)
last_hid = jax.vmap(lambda x, i: x[i], in_axes=0, out_axes=0)(embeddings, last_step)
return last_hid
if length is not None:
last_step = jnp.maximum(length.astype(jnp.int32) - 1, 0)
embeddings = jax.vmap(lambda x, i: x[i], in_axes=0, out_axes=0)(embeddings, last_step)
embeddings = jnp.expand_dims(embeddings, axis=1)
# Decode the embeddings (here, we use tied weights).
rank_logger.info(embeddings.shape)
out = in_out_embed.decode(embeddings)
rank_logger.info(out.shape)
out *= config.output_multiplier_scale
if self.model.shard_activations:
out = with_sharding_constraint(out, P("data", None, self.model.model_axis))
else:
out = with_sharding_constraint(out, P("data", None))
return LanguageModelOutput(
logits=out,
model_state=model_state,
)
def init_memory(self, batch_size: int, seq_len: int, dtype=jnp.bfloat16):
return self.model.init_memory(batch_size=batch_size, sequence_len=seq_len, dtype=dtype)
def prefill_memory(self, prompts, memory):
# Pad to the left and right align?
# Basically assume prompt is already padded
model_output = self(prompts, memory=memory)
return model_output.logits, model_output.model_state
@dataclass
class Transformer(hk.Module):
"""A transformer stack."""
num_q_heads: int
num_kv_heads: int
key_size: int
widening_factor: float
init_scale: float
mesh: Any
attn_output_multiplier: float
shard_activations: bool
num_layers: int
# MoE
num_experts: int
num_selected_experts: int
name: Optional[str] = None
# Used for activation sharding
data_axis: Union[str, Tuple[str, ...]] = "data"
model_axis: Union[str, Tuple[str, ...]] = "model"
def init_memory(self, batch_size: int, sequence_len: int, dtype=jnp.bfloat16):
return Memory(
layers=init_layer_memories(
batch_size,
sequence_len,
self.num_kv_heads,
self.key_size,
self.num_layers,
step=jnp.zeros(batch_size, dtype=jnp.int32),
dtype=dtype,
),
)
def __call__(
self,
embeddings: jax.Array, # [B, T, D]
mask: jax.Array, # [B, T]
memory: Optional[Memory],
) -> TransformerOutput:
"""Transforms input embedding sequences to output embedding sequences."""
fprop_dtype = embeddings.dtype
_, seq_len, model_size = embeddings.shape
padding_mask = mask.copy()
mask = mask[:, None, None, :] # [B, H=1, T'=1, T]
# Compute causal mask for autoregressive sequence modelling.
causal_mask = jnp.tril(jnp.ones((1, 1, seq_len, seq_len))).astype(
fprop_dtype
) # [B=1, H=1, T, T]
mask = mask * causal_mask # [B, H=1, T, T]
h = embeddings
kv_memories = []
def block(
h,
mask,
padding_mask,
memory,
layer_index: Optional[int] = None,
widening_factor: Optional[int] = None,
name: Optional[str] = None,
) -> DecoderOutput:
return DecoderLayer(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
key_size=self.key_size,
widening_factor=widening_factor or self.widening_factor,
num_layers=self.num_layers,
mesh=self.mesh,
data_axis=self.data_axis,
model_axis=self.model_axis,
attn_output_multiplier=self.attn_output_multiplier,
shard_activations=self.shard_activations,
# MoE.
num_experts=self.num_experts,
num_selected_experts=self.num_selected_experts,
name=name,
layer_index=layer_index,
)(
h,
mask,
padding_mask,
memory,
)
for i in range(self.num_layers):
decoder_output = block(
h,
mask,
padding_mask,
memory.layers[i] if memory else None,
layer_index=i,
name=f"decoder_layer_{i}",
)
h, new_kv_memory = (
decoder_output.embeddings,
decoder_output.memory,
)
kv_memories.append(new_kv_memory)
return TransformerOutput(
embeddings=h,
memory=Memory(layers=kv_memories),
)