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Attention scaling fixes #1349

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Attention scaling fixes #1349

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11 changes: 11 additions & 0 deletions MaxText/configs/base.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -215,6 +215,17 @@ final_logits_soft_cap: 0.0
use_post_attn_norm: False
use_post_ffw_norm: False

# T5 does not explicitly rescale the attention logits by 1/sqrt(d). This is
# folded into the initializers of the linear transformations, which is
# equivalent under Adafactor. Under Adam, these are not equivalent, so scaling
# down the query activations may be more stable. Supported options:
#
# init: scale initialization of query weights by 1/sqrt(d)
# query: scale query activations by 1/sqrt(d) (equivalent to scaling qk product)
# both: scale both init and query
# none: don't scale either
query_scaling: init

# MLA parameters
q_lora_rank: 0
kv_lora_rank: 512
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25 changes: 18 additions & 7 deletions MaxText/layers/attentions.py
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Expand Up @@ -16,7 +16,6 @@

import enum
import functools
import math
from typing import Any, Optional, Tuple

from flax import linen as nn
Expand Down Expand Up @@ -506,7 +505,7 @@ def cudnn_flash_attention(
dtype=self.dtype,
float32_logits=self.float32_logits,
qkv_layout="BSHD_BSHD_BSHD", # 'BS3HD', 'BSHD_BS2HD' or 'BSHD_BSHD_BSHD'
scale_factor=1.0 / math.sqrt(head_dim),
scale_factor=1.0, # handled elsewhere; see config.query_scaling
transpose_batch_sequence=False,
window_size=sliding_window_size,
)
Expand Down Expand Up @@ -1347,14 +1346,14 @@ def setup(self):
def query_projection(self, inputs_q: Array) -> Array:
"""Query projection."""

# 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):
# pylint: disable=no-value-for-parameter
return self.kernel_init(*args) / depth_scaling
if self.config.query_scaling in ["init", "both"]:
return self.kernel_init(*args) / depth_scaling
else:
return self.kernel_init(*args)

query_proj = DenseGeneral(
features=(self.num_query_heads, self.head_dim),
Expand Down Expand Up @@ -1404,10 +1403,19 @@ def kv_projection(self, inputs_kv: Array, proj_name: str) -> Array:
def qkv_projection(self, inputs: Array, proj_name: str):
"""Fused QKV projection"""

depth_scaling = jnp.sqrt(self.head_dim).astype(self.dtype)

def qkv_init(*args):
# pylint: disable=no-value-for-parameter
if self.config.query_scaling in ["init", "both"]:
return self.kernel_init(*args).at[:, 0, :, :].divide(depth_scaling)
else:
return self.kernel_init(*args)

qkv_proj = DenseGeneral(
features=(3, self.num_query_heads, self.head_dim),
axis=-1,
kernel_init=self.kernel_init,
kernel_init=qkv_init,
kernel_axes=("embed", "qkv", "heads", "kv"),
dtype=self.dtype,
weight_dtype=self.weight_dtype,
Expand Down Expand Up @@ -1546,6 +1554,9 @@ def __call__(

assert not self.config.quantize_kvcache or self.kv_quant

if self.config.query_scaling in ["query", "both"]:
query /= jnp.sqrt(query.shape[-1])

if self.config.attention == "paged" and model_mode != common_types.MODEL_MODE_TRAIN:
unnormalized_out, _, exp_sum = self.paged_attention_op(
query, key, value, decoder_segment_ids, model_mode, previous_chunk, page_state=page_state
Expand Down