WideCompiler

Version 0.7.0

Compile-friendly batched model execution. Fuse N identical models into a single Wide model for massive speedups.

What it does

Instead of running N models sequentially:

outputs = [model(x) for model in models]  # N kernel launches

Fuse them into one:

import wide_compiler

wide = wide_compiler.compile(models, sample_input)
output = wide(packed_input)  # 1 kernel launch

Speedups: 2-173x depending on model type, N, and compilation mode (A100, compiled).

What's New in 0.7.0

24 Primitives - Added 11 new primitives: RMSNorm (21x), AdaLayerNormZero (15x), MLPEmbedder (15x), CrossAttention (18x), ConvTranspose1d/2d (5-6x), BatchNorm3d (24x), RNN (6x), PReLU (12x), Dropout (174x), AdaptiveAvgPool2d (15x)
5 Flux-Style Blocks - WideMLP, WideAttention, WideJointAttention, WideDoubleStreamBlock, WideSingleStreamBlock for transformer architectures
Registry-Based TracedWideModel - Fully dynamic primitive lookup, no more hardcoded WIDE_BUILDERS dict
RMSNorm Support - Native PyTorch RMSNorm with 21x speedup at N=32
Comprehensive Benchmarks - All 29 components (24 primitives + 5 blocks) tested and validated
I/O Shape Documentation - Clear input/output formats for every primitive and block

Installation

git clone https://github.com/AbstractEyes/pytorch-parallel-compiler
cd pytorch-parallel-compiler
pip install -e .

Quick Start

Full Model Fusion (TracedWideModel)

import torch
import wide_compiler

# Define your model
class MLP(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = torch.nn.Linear(64, 128)
        self.fc2 = torch.nn.Linear(128, 64)

    def forward(self, x):
        return self.fc2(torch.nn.functional.relu(self.fc1(x)))

# Create N models
models = [MLP().cuda() for _ in range(100)]
sample = torch.randn(1, 64).cuda()

# Compile to Wide model
wide = wide_compiler.compile(models, sample)

# Pack inputs and run
inputs = [torch.randn(32, 64).cuda() for _ in range(100)]
packed = wide_compiler.pack(inputs)
output = wide(packed)

# Unpack outputs
outputs = wide_compiler.unpack(output, n=100)

Using Wide Blocks (Flux-style)

import torch
from wide_compiler.core.blocks import WideMLP, WideAttention

# Create N separate MLP blocks
mlps = [...]  # Your N MLP modules
wide_mlp = WideMLP.from_modules(mlps, strategy='fused')

# Input: N-first format [N, B, T, D]
x = torch.randn(8, 4, 128, 256).cuda()
out = wide_mlp(x)  # [8, 4, 128, 256]

# Create N attention blocks
attns = [...]  # Your N attention modules
wide_attn = WideAttention.from_modules(attns, strategy='fused')

# With RoPE embeddings
rope = torch.randn(128, 64).cuda()  # Positional embeddings
out = wide_attn(x, rope=rope)  # [8, 4, 128, 256]

Using Wide Primitives Directly (N-first format)

import torch
from wide_compiler.core.primitives import WideRMSNorm, WideLinear

# Create N separate RMSNorm layers
norms = [torch.nn.RMSNorm(256).cuda() for _ in range(8)]
wide_norm = WideRMSNorm.from_modules(norms, strategy='batched')

# Input: N-first format [N, B, T, D]
x = torch.randn(8, 4, 128, 256).cuda()
out = wide_norm(x)  # [8, 4, 128, 256]

Note: Wide primitives use N-first format [N, B, ...]. For automatic packing/unpacking with channel-packed format, use TracedWideModel (see above).

API

Main Entry Point

import wide_compiler

# From list of models
wide = wide_compiler.compile(models, sample_input)

# From single model (creates N copies with different weights)
wide = wide_compiler.compile(MyModel(), sample_input, n=100)

# With torch.compile enabled
wide = wide_compiler.compile(models, sample_input, compile_model=True)

# With validation
wide = wide_compiler.compile(models, sample_input, validate=True)

# With config
config = wide_compiler.WideConfig.fast()
wide = wide_compiler.compile(models, sample_input, config=config)

Builder Pattern

wide = (wide_compiler.WideBuilder(models)
    .with_sample(sample_input)
    .validate()
    .compile(mode='reduce-overhead')
    .build())

Pack / Unpack

# Pack N inputs: List[Tensor] → Tensor [B, N*C, ...]
packed = wide_compiler.pack(inputs)

# Unpack output: Tensor [B, N*C, ...] → List[Tensor]
outputs = wide_compiler.unpack(output, n=100)

Configuration

from wide_compiler import WideConfig

# Presets
config = WideConfig.default()   # Basic, no compile
config = WideConfig.fast()      # Compiled, no validation
config = WideConfig.debug()     # Verbose, strict
config = WideConfig.safe()      # With validation

# Custom
config = WideConfig(
    compile=True,
    compile_mode='reduce-overhead',  # 'default', 'max-autotune'
    validate=True,
    validate_rtol=1e-3,
    debug=True,
)

CLI

Benchmark Primitives (v0.7.0 - 24 primitives)

# Benchmark specific primitive
wide_compiler benchmark rmsnorm -p quick
wide_compiler benchmark dropout -p quick
wide_compiler benchmark multiheadcrossattention -p quick

# All 24 primitives:
# linear, conv1d, conv2d, conv3d, convtranspose1d, convtranspose2d,
# batchnorm1d, batchnorm2d, batchnorm3d, layernorm, groupnorm,
# instancenorm2d, rmsnorm, ada_layer_norm_zero_single,
# embedding, mlp_embedder, attention, multiheadcrossattention,
# gru, lstm, rnn, prelu, dropout, adaptiveavgpool2d

# Benchmark blocks (5 total)
wide_compiler benchmark mlp_block -p quick
wide_compiler benchmark attention_block -p quick
wide_compiler benchmark joint_attention -p quick
wide_compiler benchmark double_stream_block -p quick
wide_compiler benchmark single_stream_block -p quick

# With presets
wide_compiler benchmark rmsnorm -p quick     # Quick (fewer configs)
wide_compiler benchmark rmsnorm -p full      # Full sweep (default)
wide_compiler benchmark rmsnorm -p ci        # CI preset (minimal)

# With torch.compile
wide_compiler benchmark rmsnorm -p quick -c

# Other options
wide_compiler benchmark rmsnorm -t 20          # Show top 20 results
wide_compiler benchmark rmsnorm -s             # Auto-save with timestamp
wide_compiler benchmark rmsnorm -o results.json  # Save to specific file

Run Test Suite

# Run all 29 components (24 primitives + 5 blocks)
python test_cases.py

# Primitives only
python test_cases.py --primitives

# Blocks only
python test_cases.py --blocks

# With different presets
python test_cases.py --preset full

Other Commands

# Run correctness tests
wide_compiler test

# Show FX trace for built-in models
wide_compiler trace -m mlp
wide_compiler trace -m resblock

# Show library info
wide_compiler info

Supported Layers (v0.7.0 - 24 primitives)

Linear & Embedding

Layer	Wide Version	I/O Format	Strategies	Best Speedup
`nn.Embedding`	`WideEmbedding`	`[N,B,T]→[N,B,T,D]`	indexed, gather, sequential	27.1x @ N=32
`MLPEmbedder`	`WideMLPEmbedder`	`[N,B,D]→[N,B,Dout]`	fused, sequential	14.8x @ N=32
`nn.Linear`	`WideLinear`	`[N,B,...,Din]→[N,B,...,Dout]`	einsum, sequential	8.8x @ N=32

Convolution Layers

Layer	Wide Version	I/O Format	Strategies	Best Speedup
`nn.Conv1d`	`WideConv1d`	`[N,B,C,L]→[N,B,Cout,Lout]`	grouped, sequential	12.1x @ N=32
`nn.Conv2d`	`WideConv2d`	`[N,B,C,H,W]→[N,B,Cout,Hout,Wout]`	grouped, channels_last, sequential	6.2x @ N=32
`nn.ConvTranspose2d`	`WideConvTranspose2d`	`[N,B,C,H,W]→[N,B,Cout,Hout,Wout]`	grouped, channels_last, sequential	5.7x @ N=32
`nn.ConvTranspose1d`	`WideConvTranspose1d`	`[N,B,C,L]→[N,B,Cout,Lout]`	grouped, sequential	5.3x @ N=32
`nn.Conv3d`	`WideConv3d`	`[N,B,C,D,H,W]→[N,B,Cout,Dout,Hout,Wout]`	grouped, sequential	4.4x @ N=16

Normalization Layers

Layer	Wide Version	I/O Format	Strategies	Best Speedup
`nn.BatchNorm1d`	`WideBatchNorm1d`	`[N,B,C]→[N,B,C]`	wide	36.7x @ N=32
`nn.BatchNorm2d`	`WideBatchNorm2d`	`[N,B,C,H,W]→[N,B,C,H,W]`	wide	35.8x @ N=32
`nn.BatchNorm3d`	`WideBatchNorm3d`	`[N,B,C,D,H,W]→[N,B,C,D,H,W]`	wide	23.5x @ N=32
`nn.InstanceNorm2d`	`WideInstanceNorm2d`	`[N,B,C,H,W]→[N,B,C,H,W]`	fused, sequential	21.3x @ N=32
`nn.RMSNorm`	`WideRMSNorm`	`[N,B,...,D]→[N,B,...,D]`	batched, sequential	20.8x @ N=32
`AdaLayerNormZeroSingle`	`WideAdaLayerNormZeroSingle`	`[N,B,D],[N,B,Demb]→[N,B,D],gate`	fused, sequential	15.2x @ N=16
`nn.GroupNorm`	`WideGroupNorm`	`[N,B,C,...]→[N,B,C,...]`	fused, sequential	12.9x @ N=32
`nn.LayerNorm`	`WideLayerNorm`	`[N,B,...,D]→[N,B,...,D]`	wide	9.8x @ N=32

Attention Layers

Layer	Wide Version	I/O Format	Strategies	Best Speedup
`MultiheadCrossAttention`	`WideMultiheadCrossAttention`	`[N,B,Tq,D],[N,B,Tkv,D]→[N,B,Tq,D]`	fused, sequential	17.8x @ N=32
`nn.MultiheadAttention`	`WideAttention`	`[N,B,T,D]→[N,B,T,D]`	fused, sequential	9.6x @ N=32

RNN Layers

Layer	Wide Version	I/O Format	Strategies	Best Speedup
`nn.RNN`	`WideRNN`	`[N,B,T,Din]→[N,B,T,H],[N,B,H]`	fused, sequential	5.6x @ N=32
`nn.LSTM`	`WideLSTM`	`[N,B,T,Din]→[N,B,T,H],[N,B,H],[N,B,H]`	fused, sequential	3.3x @ N=32
`nn.GRU`	`WideGRU`	`[N,B,T,Din]→[N,B,T,H],[N,B,H]`	fused, sequential	2.9x @ N=32

Other Layers

Layer	Wide Version	I/O Format	Strategies	Best Speedup
`nn.Dropout`	`WideDropout`	`[N,B,...]→[N,B,...]`	independent, shared, sequential	173.7x @ N=32
`nn.AdaptiveAvgPool2d`	`WideAdaptiveAvgPool2d`	`[N,B,C,Hin,Win]→[N,B,C,Hout,Wout]`	batched, sequential	15.2x @ N=32
`nn.PReLU`	`WidePReLU`	`[N,B,C,...]→[N,B,C,...]`	wide, sequential	12.0x @ N=32
`F.relu`, `F.gelu`, etc.	`FunctionalOp`	agnostic	—	—
`+`, `-`, `*`, `/`, `@`	`BinaryOp`	agnostic	—	—

All primitives operate on N-first format [N, B, ...] internally for optimal performance.

Benchmarks: A100 GPU, torch.compile (default mode), quick preset. See test_cases.py for full results.

Flux-Style Blocks (v0.7.0 - 5 blocks)

Higher-level composite blocks for transformer architectures.

Block	I/O Format	Components	Best Speedup
`WideAttention`	`[N,B,T,D]→[N,B,T,D]`	QKV proj + SDPA + out proj	10.7x @ N=16
`WideDoubleStreamBlock`	`[N,B,Ttxt,D],[N,B,Timg,D]→[N,B,Ttxt,D],[N,B,Timg,D]`	JointAttn + 2x MLP + norms	6.8x @ N=8
`WideJointAttention`	`[N,B,Ttxt,D],[N,B,Timg,D]→[N,B,Ttxt,D],[N,B,Timg,D]`	Dual-stream attention	5.5x @ N=32
`WideMLP`	`[N,B,T,D]→[N,B,T,D]`	2x Linear + activation	3.9x @ N=16
`WideSingleStreamBlock`	`[N,B,T,D],[N,B,Demb]→[N,B,T,D]`	AdaLN + Attn + MLP	3.5x @ N=8

Block Usage

from wide_compiler.core.blocks import WideDoubleStreamBlock

# Create N double-stream blocks
blocks = [...]  # Your N DoubleStreamBlock modules
wide_block = WideDoubleStreamBlock.from_modules(blocks, strategy='fused')

# Input: Two streams (text and image)
txt = torch.randn(8, 4, 64, 256).cuda()   # [N, B, Ttxt, D]
img = torch.randn(8, 4, 256, 256).cuda()  # [N, B, Timg, D]
rope = torch.randn(320, 128).cuda()       # [Ttxt+Timg, head_dim]

# Forward through block
txt_out, img_out = wide_block(txt, img, rope=rope)

Primitive Benchmarks (A100, compiled)

All benchmarks: A100 GPU, torch.compile (default mode), quick preset (N=[4,8,16,32]).

Top Performers

Primitive	Best Speedup	Strategy
Dropout	173.7x	shared
BatchNorm1d	36.7x	wide
BatchNorm2d	35.8x	wide
Embedding	27.1x	indexed/gather
BatchNorm3d	23.5x	wide
InstanceNorm2d	21.3x	fused
RMSNorm	20.8x	batched
MultiheadCrossAttention	17.8x	fused
AdaptiveAvgPool2d	15.2x	batched
AdaLayerNormZeroSingle	15.2x	fused

Linear & Embedding

Primitive	Best Speedup	Strategy
MLPEmbedder	14.8x	fused
Linear	8.8x	einsum

Convolution Layers

Primitive	Best Speedup	Strategy
Conv1d	12.1x	grouped
PReLU	12.0x	wide
Conv2d	6.2x	grouped
ConvTranspose2d	5.7x	grouped
ConvTranspose1d	5.3x	grouped
Conv3d	4.4x	grouped

Attention Layers

Primitive	Best Speedup	Strategy
Attention	9.6x	fused

Other Layers

Primitive	Best Speedup	Strategy	Notes
GroupNorm	12.9x	fused
LayerNorm	9.8x	wide
RNN	5.6x	fused	Speedup improved with compilation
LSTM	3.3x	fused	Speedup improved with compilation
GRU	2.9x	fused	Speedup improved with compilation

Key Takeaways (A100 Compiled)

Dropout achieves extreme speedups (173.7x) with shared random state
BatchNorm layers see massive gains with compilation (23-37x)
Embedding scales exceptionally well (27.1x)
RMSNorm provides 20.8x speedup, outperforms LayerNorm (9.8x) by 2.1x
CrossAttention scales to 17.8x with compilation
RNN layers benefit from compilation (GRU: 2.9x, LSTM: 3.3x, RNN: 5.6x)
Conv1d reaches 12.1x with compilation
Compilation is critical - most primitives see 2-5x additional speedup

Block Benchmarks (A100, compiled)

Block	N=4	N=8	N=16	N=32	Components
AttentionBlock	4.0x	7.6x	10.7x	8.4x	QKV proj + SDPA + norm
DoubleStreamBlock	4.0x	6.8x	—	—	JointAttn + 2xMLP + norms
JointAttention	3.0x	3.9x	5.4x	5.5x	Dual-stream QKV + concat attn
MLPBlock	3.1x	3.4x	3.9x	2.5x	2x Linear + activation
SingleStreamBlock	3.1x	3.5x	—	—	AdaLN + Attn + MLP

How it Works (v0.7.0)

FX Tracing - torch.fx.symbolic_trace captures the computation graph
Registry Lookup - Each layer dynamically resolved via global registry (24 primitives registered)
Wide Primitives - Each layer replaced with N-first fused equivalent:
- Linear → Batched einsum [N, B, I] @ [N, O, I] (transposed weight)
- Conv2d → Grouped convolution on [N*B, C, H, W] with groups=N
- Attention → Reshape N→batch, single Flash Attention call
- RMSNorm → Batched normalization [N, B, D]
- All primitives operate on N-first [N, B, ...]
Strategy Selection - Each primitive auto-selects optimal strategy based on N
Compile-Friendly - 0 graph breaks, all native PyTorch ops

Optimal Data Flow - Only 2 reshapes per forward pass:

Input [B, N*C, ...] → Unpack → [N, B, C, ...]
  ↓ All stages operate on N-first (zero intermediate conversions)
Output [N, B, C, ...] → Pack → [B, N*C, ...]

Why N-first format is optimal:

# All primitives operate on [N, B, ...] format
# Data flows through without any intermediate packing
# Only reshape at boundaries (input/output)
# Result: Maximum kernel fusion, minimum overhead

Project Structure

wide_compiler/
├── __init__.py
├── __main__.py
├── api.py                    # compile(), WideBuilder, pack(), unpack()
├── cli.py                    # CLI commands
├── test_cases.py             # Reusable test suite (NEW)
└── core/
    ├── config.py             # WideConfig
    ├── registry.py           # Dynamic primitive registration (24 primitives)
    ├── traced_wide.py        # FX tracing + TracedWideModel
    ├── ensemble_util.py      # pack_inputs(), unpack_outputs()
    ├── benchmark/            # Benchmark system
    │   ├── benchmark_api.py      # High-level API
    │   ├── benchmark_runner.py   # Execution engine
    │   ├── benchmark_schema.py   # BenchmarkJob, results
    │   └── benchmark_registry.py # Auto-discovery
    ├── blocks/               # Flux-style composite blocks (NEW)
    │   ├── wide_mlp.py
    │   ├── wide_attention.py
    │   ├── wide_joint_attention.py
    │   ├── wide_double_stream_block.py
    │   └── wide_single_stream_block.py
    └── primitives/           # 24 wide primitives
        ├── wide_linear.py
        ├── wide_conv1d.py, wide_conv2d.py, wide_conv3d.py
        ├── wide_convtranspose1d.py, wide_convtranspose2d.py (NEW)
        ├── wide_batchnorm_1d.py, wide_batchnorm_2d.py, wide_batchnorm_3d.py (NEW)
        ├── wide_layernorm.py
        ├── wide_groupnorm.py
        ├── wide_instancenorm.py
        ├── wide_rmsnorm.py (NEW)
        ├── wide_ada_layer_norm_zero_single.py (NEW)
        ├── wide_embedding.py
        ├── wide_mlp_embedder.py (NEW)
        ├── wide_attention.py
        ├── wide_cross_attention.py (NEW)
        ├── wide_gru.py, wide_lstm.py, wide_rnn.py (NEW)
        ├── wide_prelu.py (NEW)
        ├── wide_dropout.py (NEW)
        └── wide_adaptive_avgpool2d.py (NEW)

Limitations

General

Identical architecture required - All N models must have same structure
Static shapes - FX tracing requires fixed tensor shapes
No dynamic control flow - if/for based on tensor values won't trace

RNN Primitives (WideGRU, WideLSTM, WideRNN)

Single layer only - num_layers=1 currently required
Unidirectional only - bidirectional=False required
batch_first only - batch_first=True required
Compilation recommended - RNN layers see 2-6x speedup with torch.compile

Use Cases

Ensemble models - Run N ensemble members in parallel
Hyperparameter search - Evaluate N configurations simultaneously
Population-based training - Evolve N agents together
Monte Carlo dropout - N stochastic forward passes
Transformer ensembles - N attention heads across models
Flux-style diffusion - Parallel text/image streams with WideDoubleStreamBlock

License

Apache License 2.0

Author

AbstractPhil - HuggingFace | GitHub

Name		Name	Last commit message	Last commit date
Latest commit History 110 Commits
.idea		.idea
benchmarks		benchmarks
models		models
wide_compiler		wide_compiler
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
test_cases.py		test_cases.py
tst_wide_tiny_flux.py		tst_wide_tiny_flux.py

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WideCompiler

Version 0.7.0

What it does

What's New in 0.7.0

Installation

Quick Start

Full Model Fusion (TracedWideModel)

Using Wide Blocks (Flux-style)

Using Wide Primitives Directly (N-first format)

API

Main Entry Point

Builder Pattern

Pack / Unpack

Configuration

CLI

Benchmark Primitives (v0.7.0 - 24 primitives)

Run Test Suite

Other Commands

Supported Layers (v0.7.0 - 24 primitives)

Linear & Embedding

Convolution Layers

Normalization Layers

Attention Layers

RNN Layers

Other Layers

Flux-Style Blocks (v0.7.0 - 5 blocks)

Block Usage

Primitive Benchmarks (A100, compiled)

Top Performers

Linear & Embedding

Convolution Layers

Attention Layers

Other Layers

Key Takeaways (A100 Compiled)

Block Benchmarks (A100, compiled)

How it Works (v0.7.0)

Project Structure

Limitations

General

RNN Primitives (WideGRU, WideLSTM, WideRNN)

Use Cases

License

Author

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