除了KV缓存之外：在AWS Trainium2上使用原生PyTorch框架，对i14的Laplace 3B线性注意力模型进行训练和部署

Most large language models (LLMs) use transformer architectures with softmax attention. During inference, their key-value (KV) cache grows with the conversation or document context. This can reduce concurrency, increase serving cost, and force agentic applications to discard useful history.

i14 designed Laplace 3B as a linear attention alternative. Its recurrent state stays at 29.5 MB per sequence, independent of context length. Working with AWS, i14 used native PyTorch support through TorchNeuron Native to bring this custom architecture to AWS Trainium2. This preserved its Gated Linear Attention (GLA) design and Muon optimizer.

On one trn2.48xlarge instance, Laplace 3B trained at about 120,000 tokens per second. The model served 11,544 output tokens per second across 1,280 concurrent sequences. This post explains the model design, the changes i14 made for Trainium2, and the training and inference results.

About i14

i14 is an AI research and technology company that develops model architectures, components, and runtimes. Its work draws on AI, computational cognitive science, and research into how biological systems learn over time. i14 applies these ideas to models that can retain long-range context without allowing serving state to grow with every token.

Why linear attention models?

Transformer softmax attention compares tokens across the context. During training, this computation grows quadratically with sequence length. During inference, the KV cache grows as the model processes more tokens.

Linear attention reorganizes the computation around a recurrent state that is updated for each token. Laplace 3B uses GLA to compress prior context into a fixed-size matrix state. This changes training attention from O(T2) to O(T) and keeps inference state fixed at 29.5 MB per sequence.

Consideration	Transformer softmax attention	Laplace 3B with GLA
Training attention	Builds pairwise token scores with O(T2) scaling	Updates chunked recurrent state with O(T) scaling
Inference memory	KV cache grows with context	Recurrent state remains fixed in size
Longer contexts	Each sequence consumes more accelerator memory	Context length does not increase recurrent-state allocation
Implementation	Uses widely optimized attention kernels	Requires architecture-specific kernels and compiler tuning

Linear attention compresses history rather than retaining every key and value. You must therefore evaluate retrieval quality for your workloads. The fixed state can benefit long-running agents, large-document analysis, and applications that need persistent memory with predictable serving requirements.

How i14 adapted Laplace 3B for AWS Trainium2

The original implementation of Laplace 3B used GPU-optimized Triton kernels. The AWS Generative AI Innovation Center (GenAIIC) team first rewrote the GLA kernel with standard PyTorch operators for Trainium2. After comparing both approaches, the team chose the beta version of TorchNeuron Native, the native PyTorch backend for AWS Trainium, because it performed better.

The implementation required three changes:

1. Using native PyTorch for the GLA attention kernel. The team replaced the GPU-specific fused_chunk_simple_gla Triton kernel with standard PyTorch operators, using 16 chunks of 128 tokens to match the 128 partitions of the NeuronCore on-chip buffer.
2. Redesigning how Muon communicates. Two all_to_all collectives in MuonFSDP2 cut per-rank traffic by 24 times and raised throughput by 1.63 times.
3. Compiling the sequential backward pass. The team statically unrolled the 16 chunk steps of the GLA recurrence to fit the graph-size and on-chip memory limits of the compiler.

The engineering challenges section describes each change in detail.

Why AWS Trainium accelerators?

AWS Trainium is a family of AWS machine learning (ML) accelerators designed for model training and inference. A trn2.48xlarge instance combines 16 Trainium2 chips, 64 logical NeuronCores, high-bandwidth memory, and NeuronLink-v3 connectivity. These resources support the distributed training and inference configurations described in this post.

Three properties of Trainium2 shaped the implementation: software-managed on-chip memory (Challenge 1), a fast chip-to-chip interconnect (Challenge 2), and a static compiler (Challenge 3). The compiler traces the GLA recurrence as a fixed graph.

Software-managed on-chip memory. Each NeuronCore has a 28 MiB State Buffer (SBUF) with 128 partitions and a 2 MiB partial-sum buffer (PSUM). The PSUM is wired to a 128×128 systolic Tensor Engine, alongside separate vector and scalar units. The SBUF is not a cache: the compiler schedules every HBM-to-SBUF transfer ahead of time, so the data is in place with no evictions or misses (Figure 1).

i14 and the Laplace 3B architecture

Laplace 3B is the 24-layer i14 decoder, where every layer replaces softmax attention with Simple GLA. Figure 2 follows one layer from the input x_t at the top to the output x_t + 1 at the bottom. This section explains each module: normalization and convolution, the QKVG projection, the GLA mixer, and the feed-forward channel mixer.

Normalization and convolution. Each token’s residual-stream vector x_t ∈ ℝ^D (D = 3, 072) passes through a LayerNorm and a depth-wise causal Conv1d of width k = 4 with SiLU. This gives every position a 4-gram summary before the mixer. The GLA mixer then does content-based retrieval over the whole history through its decaying state.

QKVG projection. The post-conv output goes through one fused matrix W_qkvg ∈ ℝ^{9, 216 × 3, 072}, where 9, 216 = H × (2d_k + 2d_v). It splits into four per-token streams: queries q_t, keys k_t, values v_t, and a gate g_t (H = 16, d_k = 96, d_v = 192). The gate is used after the mixer.

GLA mixer. Each head h keeps a matrix state S_t^(h) of size d_k × d_v (96 × 192), a compressed, decaying record of past key-value pairs. At each token, it updates as:

Here λ_h is the fixed per-head decay, and k_tv_t^⊤ is the rank-1 outer product that writes the current key-value pair into the state. The readout queries the state:

Next, a self-augmentation step adds a gated copy of the value to each head’s output:

Here σ is the sigmoid and ⊙ is the element-wise product. The gate is the query dotted with a learned per-channel rescaling of the key, where a is the learned scale (aug_balance). At initialization, a = 0, which gives a half-strength skip of the value. The per-head outputs pass through a Head RMSNorm within each decay tier, are concatenated, gated by SiLU(g_t), and projected by W_out ∈ ℝ^D × D back to the residual stream.

Decay schedule. The per-head decay rate λ_h controls how fast a head forgets past tokens. It is fixed at initialization (not learned) and set by a geometric schedule. The schedule gives the 16 heads a spread of memory lengths and defines each head’s log-decay rate γ_h:

Decay schedule’s reach. For this model, T_min = 1 and T_max = 2, 048. Head 0 therefore has λ₀ = 0.5 (half-life of one token), and head 15 has λ₁₅ ≈ 0.9997 (half-life of 2,048 tokens). Heads are grouped into tiers, each sharing a Head RMSNorm.

Channel mixer (FFN). After the attention block and its residual, a second LayerNorm feeds a GLU (SiLU) FFN with inner dimension 8,192. Its three matrices are two up-projections W₁, W₂ ∈ ℝ^{8, 192 × D} and one down-projection W₃ ∈ ℝ^{D × 8, 192}. A second residual follows.

The state S^(h) is fixed at d_k × d_v = 96 × 192 per head regardless of sequence length. As a result, inference is O(1) per token, and training is O(T) rather than O(T²).

Parameter	Value
Parameters	3.028B total (2.718B Muon + 309.6M AdamW)
Layers	24 decoder layers, D = 3, 072 hidden dim
Heads	16 heads, dk = 96 (half-width Q/K), dv = 192 (full-width V)
Mixer	Simple GLA with fixed geometric decay (Tmin = 1, Tmax = 2, 048)
Conv	Depth-wise causal Conv1d, kernel width 4, SiLU
FFN	GLU (SiLU), inner dim 8,192
Norm	LayerNorm pre-attn + Head RMSNorm (per tier) + LayerNorm pre-FFN
Vocab	50,304 (padded to multiple of 64)
Sequence	2,048
Training data	100B tokens, FineWeb-Edu
Optimizer	Muon for 2D weight matrices; AdamW for embeddings, norms
Batch size	524,288 tokens/step, cosine LR, 3,815-step warmup

Engineering challenges

Each of the three changes raised its own engineering challenge.

Challenge 1: Using native PyTorch on Trainium for GLA

On GPU, GLA runs as the fused Triton kernel fused_chunk_simple_gla from the flash-linear-attention library. The GenAIIC team re-expressed it with standard PyTorch operators running through TorchNeuron Native, as explicit loops over fixed-size chunks. The chunk count is fixed at compile time and sets the backward-unroll depth. The team uses fla_num_chunks = 16, so each chunk holds C = 128 tokens.

Within each chunk c, the intra-chunk output is masked linear attention, and the running state S adds earlier context:

The state then advances to the next chunk:

Here λ^C is the per-head decay compounded over the chunk. The 16 intra-chunk computations run in parallel, and only the state update is sequential (Figure 3).

Because the SBUF has 128 partitions, each 128-token chunk maps one token per partition, with no padding or cross-partition movement. This alignment increased measured throughput by 14 percent (Figure 4).