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TEMP changes for matching #22856
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Signed-off-by: Luka Govedic <[email protected]>
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👋 Hi! Thank you for contributing to the vLLM project. 💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels. Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging. To run CI, PR reviewers can either: Add 🚀 |
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Code Review
This pull request refactors the FP8 quantization logic to use a QuantFP8 module, which is a good abstraction. The fusion passes are updated accordingly to match this new pattern. While the changes are generally moving in the right direction, I've identified a few critical issues, including commented-out code that disables functionality or tests, and leftover debugging statements. These should be addressed before this pull request can be merged.
| # if current_platform.has_device_capability(100): | ||
| # AllReduceFusedRMSNormStaticQuantNVFP4Pattern( | ||
| # epsilon, | ||
| # self.model_dtype, | ||
| # self.device, | ||
| # self.allreduce_params, | ||
| # ).register(self.patterns) | ||
| # AllReduceFusedAddRMSNormStaticQuantNVFP4Pattern( | ||
| # epsilon, | ||
| # self.model_dtype, | ||
| # self.device, | ||
| # self.allreduce_params, | ||
| # ).register(self.patterns) | ||
| # AllReduceRMSNormPattern( | ||
| # epsilon, | ||
| # self.model_dtype, | ||
| # self.device, | ||
| # self.allreduce_params, | ||
| # ).register(self.patterns) | ||
| # AllReduceFusedAddRMSNormPattern( | ||
| # epsilon, | ||
| # self.model_dtype, | ||
| # self.device, | ||
| # self.allreduce_params, | ||
| # ).register(self.patterns) |
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This large block of commented-out code disables several fusion patterns, including those for FP4 and non-quantized RMSNorm. This appears to be a significant functional regression. If this is a temporary change for development, it should be reverted before merging. If this is intentional, it needs a clear justification.
| torch.testing.assert_close(result, result2, atol=ATOL, rtol=RTOL) | ||
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| # In pre-nodes, fp8 quant should be there and fused kernels should not | ||
| # backend.check_before_ops(model.ops_in_model_before()) |
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| print(backend.graph_pre_pass) | ||
| print(backend.graph_post_pass) | ||
| for node in find_op_nodes( | ||
| torch.ops.vllm.flashinfer_trtllm_fused_allreduce_norm.default, | ||
| backend.graph_post_pass): | ||
| print(f"{node.args=}") | ||
| print(f"{node.kwargs=}") | ||
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Signed-off-by: Luka Govedic <[email protected]>
…d fused_add_rms_norm Signed-off-by: Luka Govedic <[email protected]>
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Signed-off-by: Luka Govedič <[email protected]>
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This pull request has merge conflicts that must be resolved before it can be |
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Superseeded by #24604 |
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.Purpose
Test Plan
Test Result
(Optional) Documentation Update