[1/N][Feat] Implement primal full graph with limited scenario #1503
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Should this part also be changed when full graph is enabled? Does it still need to divide by vllm-ascend/vllm_ascend/utils.py Lines 316 to 317 in c58accc |
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Jul 7, 2025
Signed-off-by: Yizhou Liu <[email protected]>
Signed-off-by: Yizhou Liu <[email protected]>
Signed-off-by: Yizhou Liu <[email protected]>
Signed-off-by: Yizhou Liu <[email protected]>
Signed-off-by: Yizhou Liu <[email protected]>
Signed-off-by: Yizhou Liu <[email protected]>
…izes Signed-off-by: Yizhou Liu <[email protected]>
Signed-off-by: Yizhou Liu <[email protected]>
… common metadata Signed-off-by: Yizhou Liu <[email protected]>
Signed-off-by: Yizhou Liu <[email protected]>
…ature both enabled and disabled Signed-off-by: Yizhou Liu <[email protected]>
… mode Signed-off-by: Yizhou Liu <[email protected]>
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Jul 7, 2025
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Jul 31, 2025
…t#1503) This pull request introduces full-graph capture, replacing the previous piecewise-graph approach. Key improvements include: * **Reduced dispatch latency:** By capturing the entire model execution graph at once, we minimize overhead compared to multiple smaller captures. * **Stabilized multi-GPU performance:** Eliminates throughput fluctuations during the `MODEL_EXECUTE` phase across multiple cards. * **Stream resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured concurrently. **Known issues:** 1. Capturing larger or more numerous graphs increases GPU memory usage, which can lead to OOM errors or inference hangs. 2. The new paged-attention implementation relies on the FIA operator, which in certain workloads is slower than the previous approach—resulting in a regression in end-to-end throughput. There may be other undiscovered corner cases. This PR is the first in a planned series; we will continue to iterate on and address any remaining issues in subsequent submissions. ```python compilation_config={ "full_cuda_graph": True, }, ``` --------- Signed-off-by: Yizhou Liu <[email protected]>
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…t#1503) This pull request introduces full-graph capture, replacing the previous piecewise-graph approach. Key improvements include: * **Reduced dispatch latency:** By capturing the entire model execution graph at once, we minimize overhead compared to multiple smaller captures. * **Stabilized multi-GPU performance:** Eliminates throughput fluctuations during the `MODEL_EXECUTE` phase across multiple cards. * **Stream resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured concurrently. **Known issues:** 1. Capturing larger or more numerous graphs increases GPU memory usage, which can lead to OOM errors or inference hangs. 2. The new paged-attention implementation relies on the FIA operator, which in certain workloads is slower than the previous approach—resulting in a regression in end-to-end throughput. There may be other undiscovered corner cases. This PR is the first in a planned series; we will continue to iterate on and address any remaining issues in subsequent submissions. ```python compilation_config={ "full_cuda_graph": True, }, ``` --------- Signed-off-by: Yizhou Liu <[email protected]>
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Aug 11, 2025
…t#1503) This pull request introduces full-graph capture, replacing the previous piecewise-graph approach. Key improvements include: * **Reduced dispatch latency:** By capturing the entire model execution graph at once, we minimize overhead compared to multiple smaller captures. * **Stabilized multi-GPU performance:** Eliminates throughput fluctuations during the `MODEL_EXECUTE` phase across multiple cards. * **Stream resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured concurrently. **Known issues:** 1. Capturing larger or more numerous graphs increases GPU memory usage, which can lead to OOM errors or inference hangs. 2. The new paged-attention implementation relies on the FIA operator, which in certain workloads is slower than the previous approach—resulting in a regression in end-to-end throughput. There may be other undiscovered corner cases. This PR is the first in a planned series; we will continue to iterate on and address any remaining issues in subsequent submissions. ```python compilation_config={ "full_cuda_graph": True, }, ``` --------- Signed-off-by: Yizhou Liu <[email protected]>
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Aug 11, 2025
…t#1503) This pull request introduces full-graph capture, replacing the previous piecewise-graph approach. Key improvements include: * **Reduced dispatch latency:** By capturing the entire model execution graph at once, we minimize overhead compared to multiple smaller captures. * **Stabilized multi-GPU performance:** Eliminates throughput fluctuations during the `MODEL_EXECUTE` phase across multiple cards. * **Stream resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured concurrently. **Known issues:** 1. Capturing larger or more numerous graphs increases GPU memory usage, which can lead to OOM errors or inference hangs. 2. The new paged-attention implementation relies on the FIA operator, which in certain workloads is slower than the previous approach—resulting in a regression in end-to-end throughput. There may be other undiscovered corner cases. This PR is the first in a planned series; we will continue to iterate on and address any remaining issues in subsequent submissions. ```python compilation_config={ "full_cuda_graph": True, }, ``` --------- Signed-off-by: Yizhou Liu <[email protected]>
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Aug 12, 2025
…t#1503) This pull request introduces full-graph capture, replacing the previous piecewise-graph approach. Key improvements include: * **Reduced dispatch latency:** By capturing the entire model execution graph at once, we minimize overhead compared to multiple smaller captures. * **Stabilized multi-GPU performance:** Eliminates throughput fluctuations during the `MODEL_EXECUTE` phase across multiple cards. * **Stream resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured concurrently. **Known issues:** 1. Capturing larger or more numerous graphs increases GPU memory usage, which can lead to OOM errors or inference hangs. 2. The new paged-attention implementation relies on the FIA operator, which in certain workloads is slower than the previous approach—resulting in a regression in end-to-end throughput. There may be other undiscovered corner cases. This PR is the first in a planned series; we will continue to iterate on and address any remaining issues in subsequent submissions. ```python compilation_config={ "full_cuda_graph": True, }, ``` --------- Signed-off-by: Yizhou Liu <[email protected]>
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Aug 12, 2025
…t#1503) This pull request introduces full-graph capture, replacing the previous piecewise-graph approach. Key improvements include: * **Reduced dispatch latency:** By capturing the entire model execution graph at once, we minimize overhead compared to multiple smaller captures. * **Stabilized multi-GPU performance:** Eliminates throughput fluctuations during the `MODEL_EXECUTE` phase across multiple cards. * **Stream resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured concurrently. **Known issues:** 1. Capturing larger or more numerous graphs increases GPU memory usage, which can lead to OOM errors or inference hangs. 2. The new paged-attention implementation relies on the FIA operator, which in certain workloads is slower than the previous approach—resulting in a regression in end-to-end throughput. There may be other undiscovered corner cases. This PR is the first in a planned series; we will continue to iterate on and address any remaining issues in subsequent submissions. ```python compilation_config={ "full_cuda_graph": True, }, ``` --------- Signed-off-by: Yizhou Liu <[email protected]>
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Aug 13, 2025
…t#1503) This pull request introduces full-graph capture, replacing the previous piecewise-graph approach. Key improvements include: * **Reduced dispatch latency:** By capturing the entire model execution graph at once, we minimize overhead compared to multiple smaller captures. * **Stabilized multi-GPU performance:** Eliminates throughput fluctuations during the `MODEL_EXECUTE` phase across multiple cards. * **Stream resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured concurrently. **Known issues:** 1. Capturing larger or more numerous graphs increases GPU memory usage, which can lead to OOM errors or inference hangs. 2. The new paged-attention implementation relies on the FIA operator, which in certain workloads is slower than the previous approach—resulting in a regression in end-to-end throughput. There may be other undiscovered corner cases. This PR is the first in a planned series; we will continue to iterate on and address any remaining issues in subsequent submissions. ```python compilation_config={ "full_cuda_graph": True, }, ``` --------- Signed-off-by: Yizhou Liu <[email protected]>
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Aug 15, 2025
…t#1503) This pull request introduces full-graph capture, replacing the previous piecewise-graph approach. Key improvements include: * **Reduced dispatch latency:** By capturing the entire model execution graph at once, we minimize overhead compared to multiple smaller captures. * **Stabilized multi-GPU performance:** Eliminates throughput fluctuations during the `MODEL_EXECUTE` phase across multiple cards. * **Stream resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured concurrently. **Known issues:** 1. Capturing larger or more numerous graphs increases GPU memory usage, which can lead to OOM errors or inference hangs. 2. The new paged-attention implementation relies on the FIA operator, which in certain workloads is slower than the previous approach—resulting in a regression in end-to-end throughput. There may be other undiscovered corner cases. This PR is the first in a planned series; we will continue to iterate on and address any remaining issues in subsequent submissions. ```python compilation_config={ "full_cuda_graph": True, }, ``` --------- Signed-off-by: Yizhou Liu <[email protected]>
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Sep 17, 2025
…th the latest design Revert "[Feat] Implement primal full graph with limited scenario (vllm-project#1503)" This reverts commit 14660be. Signed-off-by: Yizhou Liu <[email protected]>
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Sep 22, 2025
Note: This depends on [vLLM #25161](vllm-project/vllm#25161) and the torch\_npu release from September 30. ### What this PR does / why we need it? This pull request adds `FULL_DECODE_ONLY` mode for GQA/MHA models (MLA models like DeepSeek V3/R1 are not included). Key improvements include: * **Reduced dispatch latency:** By replaying the entire model execution graph at once, we cut overhead compared with multiple smaller replays. * **Stabilized multi-device performance:** Captureing the whole model as one static graph also mitigates the dispatch fluctuations across devices. * **Stream/resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured. **Known issues:** 1. `_npu_paged_attention` currently manages its own workspace in `torch_npu`, which can deadlock when synchronizing during graph replay — we’re working on a fix. There may be other corner cases. This PR is the first in a planned series; we’ll continue to iterate and address remaining issues in follow-ups. This is essentially a port of #1503 and #1677, but includes two major changes: 1. Let `graph_dispatcher` decide the graph mode instead of hard-coding it in the backend, which decouples Full Graph and Piecewise Graph and could make it possible to remove dynamo. 2. Adapt to the new `attn_group` logic, but leave a small hack in `update_graph_params`; multi-attention models may or may not be fully supported yet. ### Does this PR introduce _any_ user-facing change? ```python compilation_config={ "cudagraph_mode": "FULL_DECODE_ONLY", }, ``` ### How was this patch tested? Tests included. - vLLM version: v0.10.2 - vLLM main: vllm-project/vllm@9607d5e --------- Signed-off-by: Yizhou Liu <[email protected]>
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Sep 22, 2025
…m-project#2128) Note: This depends on [vLLM #25161](vllm-project/vllm#25161) and the torch\_npu release from September 30. ### What this PR does / why we need it? This pull request adds `FULL_DECODE_ONLY` mode for GQA/MHA models (MLA models like DeepSeek V3/R1 are not included). Key improvements include: * **Reduced dispatch latency:** By replaying the entire model execution graph at once, we cut overhead compared with multiple smaller replays. * **Stabilized multi-device performance:** Captureing the whole model as one static graph also mitigates the dispatch fluctuations across devices. * **Stream/resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured. **Known issues:** 1. `_npu_paged_attention` currently manages its own workspace in `torch_npu`, which can deadlock when synchronizing during graph replay — we’re working on a fix. There may be other corner cases. This PR is the first in a planned series; we’ll continue to iterate and address remaining issues in follow-ups. This is essentially a port of vllm-project#1503 and vllm-project#1677, but includes two major changes: 1. Let `graph_dispatcher` decide the graph mode instead of hard-coding it in the backend, which decouples Full Graph and Piecewise Graph and could make it possible to remove dynamo. 2. Adapt to the new `attn_group` logic, but leave a small hack in `update_graph_params`; multi-attention models may or may not be fully supported yet. ### Does this PR introduce _any_ user-facing change? ```python compilation_config={ "cudagraph_mode": "FULL_DECODE_ONLY", }, ``` ### How was this patch tested? Tests included. - vLLM version: v0.10.2 - vLLM main: vllm-project/vllm@9607d5e --------- Signed-off-by: Yizhou Liu <[email protected]> Signed-off-by: Che Ruan <[email protected]>
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Sep 22, 2025
…m-project#2128) Note: This depends on [vLLM #25161](vllm-project/vllm#25161) and the torch\_npu release from September 30. ### What this PR does / why we need it? This pull request adds `FULL_DECODE_ONLY` mode for GQA/MHA models (MLA models like DeepSeek V3/R1 are not included). Key improvements include: * **Reduced dispatch latency:** By replaying the entire model execution graph at once, we cut overhead compared with multiple smaller replays. * **Stabilized multi-device performance:** Captureing the whole model as one static graph also mitigates the dispatch fluctuations across devices. * **Stream/resource savings:** Consolidating graph captures frees up streams, allowing more graphs to be captured. **Known issues:** 1. `_npu_paged_attention` currently manages its own workspace in `torch_npu`, which can deadlock when synchronizing during graph replay — we’re working on a fix. There may be other corner cases. This PR is the first in a planned series; we’ll continue to iterate and address remaining issues in follow-ups. This is essentially a port of vllm-project#1503 and vllm-project#1677, but includes two major changes: 1. Let `graph_dispatcher` decide the graph mode instead of hard-coding it in the backend, which decouples Full Graph and Piecewise Graph and could make it possible to remove dynamo. 2. Adapt to the new `attn_group` logic, but leave a small hack in `update_graph_params`; multi-attention models may or may not be fully supported yet. ### Does this PR introduce _any_ user-facing change? ```python compilation_config={ "cudagraph_mode": "FULL_DECODE_ONLY", }, ``` ### How was this patch tested? Tests included. - vLLM version: v0.10.2 - vLLM main: vllm-project/vllm@9607d5e --------- Signed-off-by: Yizhou Liu <[email protected]> Signed-off-by: Che Ruan <[email protected]>
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What this PR does / why we need it?
This pull request introduces full-graph capture, replacing the previous piecewise-graph approach. Key improvements include:
MODEL_EXECUTEphase across multiple cards.Known issues:
There may be other undiscovered corner cases. This PR is the first in a planned series; we will continue to iterate on and address any remaining issues in subsequent submissions.
Does this PR introduce any user-facing change?
How was this patch tested?