feat: Add Inductor backend config templates

[JewelRoam]

Overview

This PR introduces a flexible configuration system for PyTorch Inductor backend,
allowing users to select predefined config templates that set groups of
torch._inductor.config overrides. This provides an extension to PyTorch's
official "mode" concept while maintaining full compatibility with the existing
test_compiler.py framework.

Motivation

Previously, the InductorBackend accepted only basic config parameters through
individual inductor_config dictionary entries. Users could not easily enable
common combinations of Inductor options such as:

• CUTLASS-based GEMM kernels - For optimal GEMM performance on modern GPUs
• CUDA Graphs - To reduce kernel launch overhead for small batch inference
• Model freezing - To inline weights as constants for deployment optimization
• TMA (Tensor Memory Accelerator) - For H100+ GPUs with hardware acceleration

This PR addresses these limitations by introducing config templates - pre-defined,
well-tested combinations of torch._inductor.config options that users can select
by name.

Changes Summary

1. Inductor Backend Configuration Templates

File: graph_net_bench/torch/backend/inductor_backend.py

New Features

• _INDUCTOR_CONFIG_TEMPLATES dictionary with 8 predefined templates
• _TEMPLATE_TO_COMPILE_MODE mapping for templates that imply compile modes
• _set_nested_attr() utility function for setting nested config attributes

Supported Templates

Template	Description	Config Overrides	Compile Mode
triton	Default Triton backend with cpp_wrapper disabled	default
cpp_wrapper	Use C++ wrapper for generated kernels	default
cutlass	Enable max-autotune for CUTLASS GEMM kernels	default
aten	Enable ATen fallback for debugging	default
cudagraphs	Enable CUDA Graphs for reduced overhead	reduce-overhead
max_autotune	Comprehensive autotuning across backends	max-autotune
freezing	Enable model freezing (inline weights)	default
tma	Enable TMA persistent matmul kernels	default

TMA Graceful Fallback

The TMA template has built-in graceful fallback behavior:

• On H100+ (Hopper, CC >= 9.0): Enables TMA persistent kernels
• On A100 or other GPUs: Enables non-TMA persistent kernels as fallback
• Configuration is always accepted regardless of underlying hardware
• No runtime error occurs on GPUs without TMA support

This ensures the template works universally while still leveraging TMA
benefits when available.

Enhanced Configuration Interface

config = {
    "template": "max_autotune",     # Select predefined template
    "mode": "reduce-overhead",       # Override compile mode
    "freezing": True,                  # Top-level override
    "inductor_config": {              # Arbitrary config overrides
        "triton.cudagraphs": False,   # Nested config (dotted paths)
        "triton.enable_persistent_tma_matmul": True,  # Deeply nested
    }
}

Configuration Priority (highest to lowest)

1. inductor_config - Explicit user-specified overrides
2. freezing - Top-level convenience flag
3. template - Predefined template defaults

2. CUDA Graphs Compatibility Fix

File: graph_net_bench/torch/test_compiler.py

Problem

When CUDA Graphs is enabled (via triton.cudagraphs or mode="reduce-overhead"):

• PyTorch records output tensor pointers to CUDA Graph buffers
• Subsequent model calls overwrite the same buffer locations
• Test framework runs eager model, then compiled model sequentially
• Accessing compiled output after eager run triggers:

  RuntimeError: Error: accessing tensor output of CUDAGraphs
  that has been overwritten by a subsequent run.
  

Solution

Clone model outputs immediately after model invocation:

# Clone outputs to prevent CUDA Graphs buffer overwrite issues.
if isinstance(outs, torch.Tensor):
    outs = outs.clone()
elif isinstance(outs, tuple):
    outs = tuple(t.clone() if isinstance(t, torch.Tensor) else t for t in outs)

Impact

• ✅ Fixed in: test_compiler.py
• ✅ Not affected: eval_backend_perf.py and eval_backend_diff.py
  (they use torch.save/torch.load which creates independent copies)

3. Comprehensive Test Suite

File: test/inductor_backend_test.py (new file, 323 lines)

Test Structure

# Three test classes covering different aspects:

class TestInductorBackendTemplates(unittest.TestCase):
    # 10 tests for template configuration validation

class TestInductorBackendIntegration(unittest.TestCase):
    # 2 tests for overall backend integration

class TestInductorConfigValidation(unittest.TestCase):
    # 15 tests for PyTorch config validation
    # 1 test for TMA fallback on non-TMA GPUs

Test Coverage

Category	Tests	Coverage
Template validation	10	All 8 templates produce correct overrides and modes
Integration	2	Backend initialization and override priority
Config validation	15	All config keys exist in torch._inductor.config
TMA fallback	1	Graceful fallback on non-TMA GPUs (e.g., A100)
Total	28	Full test coverage

Validation Results

$ python -m unittest test.inductor_backend_test -v

test_all_templates_exist ... ok
test_aten_template ... ok
test_cpp_wrapper_template ... ok
test_cudagraphs_template ... ok
test_cutlass_template ... ok
test_empty_config ... ok
test_freezing_override ... ok
test_freezing_template ... ok
test_inductor_config_override ... ok
test_invalid_template_raises_error ... ok
test_max_autotune_template ... ok
test_mode_override ... ok
test_tma_fallback_on_non_tma_gpu ... ok
test_tma_template ... ok
test_template_to_mode_mapping ... ok
test_triton_template ... ok
# ... (all 28 tests pass)

Ran 28 tests in 0.002s
OK

Usage Examples

Basic Template Usage

# Use CUTLASS-based GEMM kernels
python -m graph_net_bench.torch.test_compiler \
    --compiler inductor \
    --model-path samples/torchvision/alexnet \
    --config "eyJ0ZW1wbGF0ZSI6ICJjdXRsYXNzIn0=" \
    --trials 5 --warmup 3

Template with Custom Mode

# Use max-autotune template but disable CUDA Graphs
python -c "
import json, base64
config = {
    'template': 'max_autotune',
    'mode': 'max-autotune-no-cudagraphs'
}
print(base64.b64encode(json.dumps(config).encode()).decode())
"

python -m graph_net_bench.torch.test_compiler \
    --compiler inductor \
    --model-path samples/torchvision/alexnet \
    --config "$(python -c ...)" \
    --trials 5 --warmup 3

Combined Configuration

# Combine template with additional options
python -c "
import json, base64
config = {
    'template': 'max_autotune',
    'freezing': True,
    'inductor_config': {
        'coordinate_descent_tuning': False
    }
}
print(base64.b64encode(json.dumps(config).encode()).decode())
"

All Available Templates

Template Name	Description	Base64 Config
triton	Default Triton backend	eyJ0ZW1wbGF0ZSI6ICJ0cml0b24ifQ==
cpp_wrapper	C++ wrapper for kernels	eyJ0ZW1wbGF0ZSI6ICJjcHBfd3JhcHBlciJ9
cutlass	CUTLASS GEMM kernels	eyJ0ZW1wbGF0ZSI6ICJjdXRsYXNzIn0=
aten	ATen fallback for debugging	eyJ0ZW1wbGF0ZSI6ICJhdGVuIn0=
cudagraphs	CUDA Graphs for reduced overhead	eyJ0ZW1wbGF0ZSI6ICJjdWRhZ3JhcGhzIn0=
max_autotune	Maximum autotuning	eyJ0ZW1wbGF0ZSI6ICJtYXhfYXV0b3R1bmUifQ==
freezing	Model freezing	eyJ0ZW1wbGF0ZSI6ICJmcmVlemluZyJ9
tma	Tensor Memory Accelerator	eyJ0ZW1wbGF0ZSI6ICJ0bWEifQ==

Testing

Manual Testing Results

All 8 templates have been manually tested with actual model compilation:

Template	Tested	Result	Notes
triton	✅	Works correctly
cpp_wrapper	✅	Works correctly
cutlass	✅	AUTOTUNE output visible in logs
aten	✅	Works correctly
cudagraphs	✅	Fixed - no more CUDA Graphs errors
max_autotune	✅	AUTOTUNE output visible
freezing	✅	Works correctly
tma	✅	Graceful fallback on A100

Automated Testing

$ python -m unittest test.inductor_backend_test
Ran 28 tests in 0.002s
OK

Breaking Changes

None. This is a purely additive feature with no changes to existing behavior
when the template parameter is not specified.

Migration Guide

For users wanting to use the new template system:

1. Basic Usage: Add --config with base64-encoded template
2. Combined Usage: Mix templates with additional config options
3. Custom Config: Use inductor_config for any custom settings

No code changes required - this is fully backward compatible.

Documentation References

All configuration keys have been verified against PyTorch 2.7.1 source code:

• Config File: https://github.com/pytorch/pytorch/blob/main/torch/_inductor/config.py

  • cpp_wrapper - Line 620
  • max_autotune - Line 575
  • max_autotune_gemm - Line 621
  • epilogue_fusion - Line 527
  • coordinate_descent_tuning - Line 614
  • freezing - Line 429
  • triton.cudagraphs - Line 1660
  • autotune_fallback_to_aten - Line 696
  • triton.enable_persistent_tma_matmul - Line 1916

• Compile API: https://pytorch.org/docs/stable/generated/torch.compile.html

  • mode="default" - Default mode
  • mode="reduce-overhead" - Uses CUDA Graphs
  • mode="max-autotune" - Enables comprehensive autotuning
  • mode="max-autotune-no-cudagraphs" - Autotuning without CUDA Graphs

Hardware Requirements

Feature	Minimum Requirement
TMA (Tensor Memory Accelerator)	NVIDIA H100+ (Compute Capability >= 9.0)
CUDA Graphs	Any CUDA-capable GPU
CUTLASS GEMM	Separate CUTLASS backend installation required
Triton	CUDA-capable GPU with compute capability >= 7.0

Performance Considerations

• CUDA Graphs: Best for small batch sizes, may increase memory usage
• CUTLASS: Significantly faster GEMM for large tensors
• TMA: Memory bandwidth optimization, requires H100+ hardware
• Freezing: Reduces memory footprint, weights cannot be updated after
• Max Autotune: Longer compilation time but optimal performance

Checklist

• All config keys verified against PyTorch source code
• All templates produce correct overrides
• Unit tests added (28 tests)
• Unit tests pass (28/28 OK)
• Manual testing completed for all 8 templates
• CUDA Graphs compatibility issue fixed
• Documentation updated with inline comments
• Usage examples provided
• Backward compatibility maintained
• Code follows PyTorch naming conventions

Related Issues

• Fixes CUDA Graphs output buffer overwrite issue in test framework
• Provides graceful TMA fallback for non-TMA GPUs (e.g., A100)

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