[vLLM IR] Port activations (gelu) to IR op#40135
[vLLM IR] Port activations (gelu) to IR op#40135Alex-ai-future wants to merge 8 commits intovllm-project:mainfrom
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This pull request introduces IR operations for GELU variants (gelu_new, gelu_fast, and quick_gelu), including their registration, vllm_c kernel implementations, and configuration for op priority. It refactors existing activation layers to utilize these IR ops and adds comprehensive tests for lowering and kernel correctness. The review feedback correctly identifies that forward_native methods in the activation layers should explicitly invoke the native IR implementation to ensure they remain valid baselines for correctness testing, rather than relying on the default IR dispatch which might select optimized kernels.
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| def forward_native(self, x: torch.Tensor) -> torch.Tensor: | ||
| """PyTorch-native implementation equivalent to forward().""" | ||
| c = math.sqrt(2.0 / math.pi) | ||
| return 0.5 * x * (1.0 + torch.tanh(c * (x + 0.044715 * torch.pow(x, 3.0)))) | ||
| return ir.ops.gelu_new(x) |
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The forward_native method is intended to be a reference implementation using standard PyTorch operations. By calling ir.ops.gelu_new(x), it now uses the IR dispatch mechanism, which may select an optimized kernel (like vllm_c) depending on the environment and priority settings. This makes correctness tests (such as those in tests/kernels/core/test_activation.py) tautological, as they compare the optimized output against itself. To maintain the integrity of these tests, forward_native should explicitly call the native implementation.
| return ir.ops.gelu_new(x) | |
| return ir.ops.gelu_new.impls["native"].impl_fn(x) |
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| def forward_native(self, x: torch.Tensor) -> torch.Tensor: | ||
| """PyTorch-native implementation equivalent to forward().""" | ||
| return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x))) | ||
| return ir.ops.gelu_fast(x) |
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Similar to NewGELU, FastGELU.forward_native should bypass the IR dispatch logic and call the native implementation directly to ensure it remains a valid baseline for correctness verification.
| return ir.ops.gelu_fast(x) | |
| return ir.ops.gelu_fast.impls["native"].impl_fn(x) |
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| def forward_native(self, x: torch.Tensor) -> torch.Tensor: | ||
| """PyTorch-native implementation equivalent to forward().""" | ||
| return x * torch.sigmoid(1.702 * x) | ||
| return ir.ops.quick_gelu(x) |
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To ensure QuickGELU.forward_native remains a reliable reference for testing, it should explicitly invoke the native implementation of the IR op rather than relying on the default dispatch.
| return ir.ops.quick_gelu(x) | |
| return ir.ops.quick_gelu.impls["native"].impl_fn(x) |
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Documentation preview: https://vllm--40135.org.readthedocs.build/en/40135/ |
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This commit adds vLLM IR support for GELU activation functions: - gelu_new: GPT-2 style GELU approximation - gelu_fast: Fast GELU approximation - quick_gelu: Quick GELU approximation Changes: 1. vllm/ir/ops/activation.py: Define IR ops with native torch semantics 2. vllm/kernels/vllm_c.py: Register vllm_c kernel implementations for CUDA platforms 3. vllm/ir/ops/__init__.py: Export new GELU IR ops 4. tests/ir/ops/test_activation.py: Add comprehensive tests for GELU IR ops 5. tests/compile/passes/ir/test_lowering.py: Add lowering tests for GELU ops 6. tests/kernels/core/test_activation.py: Update to test IR ops directly The implementation follows the vLLM IR design from the torch.compile SIG, providing: - Platform-aware dispatching (vllm_c on CUDA, native on CPU) - torch.compile integration via VllmIRLoweringPass - Priority-based kernel selection for autotuning support Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com> Signed-off-by: Alex <alex.tech.lab@outlook.com>
Replace platform-specific custom ops and manual PyTorch formulas in NewGELU, FastGELU, and QuickGELU with centralized ir.ops calls. This removes redundant platform checks, simplifies the activation logic, and standardizes execution across all hardware backends. Signed-off-by: Alex <alex.tech.lab@outlook.com>
Consolidate separate test classes for gelu_new, gelu_fast, and quick_gelu into a unified, parameterized TestGeluOps class. Add coverage for multiple dtypes (float16, bfloat16, float32) and tensor shapes to reduce code duplication and improve test maintainability. Signed-off-by: Alex <alex.tech.lab@outlook.com>
Adds gelu_new, gelu_fast, and quick_gelu fields to IrOpPriorityConfig. This enables users to specify kernel selection priorities for these GELU activation functions within the IR pipeline. Signed-off-by: Alex <alex.tech.lab@outlook.com>
Remove GeluModel and basic GELU lowering test cases to streamline the test suite. These tests will be replaced by a unified, parameterized testing framework to eliminate duplication across IR operations. A detailed TODO is added to document the planned refactoring strategy. Signed-off-by: Alex <alex.tech.lab@outlook.com>
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GELU Algorithm Porting & Integration
Step 1: Port GELU Algorithm Implementation
Notes
vllm_ckernel is implemented; other kernels may contain duplicate code (corrections are appreciated).Step 2: Integrate New Features
Notes
Step 3: Merge & Adapt to Unified Test Standards
Related
General
Purpose
Test Plan
Test Result
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.