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[BugFix] Remove not using patch_eagle.py for CI. (#1385)

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### What this PR does / why we need it?
This PR aims to address a long-standing **CI bug** and remove unused
code. The specific changes include:

1. **Fixing CI Bug**: Resolves the root cause of CI test failures or
instability. This often stems from incorrect environment configurations,
dependency version conflicts, or flawed test script logic. This fix
ensures the reliability and consistency of the CI pipeline.
2. **Removing `patch_eagle.py`**: Deletes the `patch_eagle.py` file,
which is no longer utilized by the project. This file was likely legacy
code, experimental code, or its functionality has since been replaced by
other modules. Its removal helps reduce codebase complexity, improves
maintainability, and prevents potential confusion.

### Does this PR introduce _any_ user-facing change?
No, this PR primarily focuses on internal CI stability maintenance and
code cleanup. It does not introduce any user-visible changes to APIs,
interfaces, or other behaviors.

### How was this patch tested?
CI passed. Specifically:

1. **Existing CI Pipelines Passed**: After fixing the CI bug, all
existing CI tests and pipelines were verified to run correctly and pass
successfully.
2. **Code Cleanup Verified**: Following the removal of `patch_eagle.py`,
it was ensured that any related functional modules (if applicable)
continue to work as expected, without introducing new regressions. This
was typically verified by running the project's main test suite.

Signed-off-by: yuancaoyaoHW <a2749322671@gmail.com>
This commit is contained in:
yuancaoyaoHW
2025-06-25 20:36:05 +08:00
committed by GitHub
parent 941269a6c5
commit e7efc7e7e7
3 changed files with 1 additions and 86 deletions
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16
vllm_ascend/patch/__init__.py
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@@ -105,20 +105,6 @@
# Future Plan:
# Revert it when the related pr is merged in vllm and vllm-ascend.
#
# ** File: worker/patch_common/patch_eagle.py **
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 1. `vllm.v1.spec_decode.eagle.prepare_inputs`
# Why:
# We need to use the patched `prepare_input_kernel` in `eagle.prepare_inputs`.
# The mainly reason to overwrite `prepare_input_kernel` is this is a triton
# kernel, ascend is now not support triton kernel.
# How：
# Re-implementation the `prepare_input_kernel` triton kernel by pytorch
# Related PR (if no, explain why):
# - Ascend doesn't support triton
# Future Plan:
# Revert it when the ascend support triton kernel.
#
# ** File: worker/patch_common/patch_sampler.py **
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 1. `vllm.v1.sample.sampler.Sampler.apply_top_k_top_p`
@@ -168,4 +154,4 @@
# Related PR (if no, explain why):
# This is the problem in vllm-ascend
# Future Plan:
# Remove this patch once pytorch 2.7.0 is supported for vllm ascend.
# Remove this patch once pytorch 2.7.0 is supported for vllm ascend.

1
vllm_ascend/patch/worker/patch_common/__init__.py
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@@ -19,7 +19,6 @@
# patch files.
import vllm_ascend.patch.worker.patch_common.patch_utils # noqa isort:skip
import vllm_ascend.patch.worker.patch_common.patch_distributed # noqa
import vllm_ascend.patch.worker.patch_common.patch_eagle # noqa
import vllm_ascend.patch.worker.patch_common.patch_minicpm # noqa
import vllm_ascend.patch.worker.patch_common.patch_multi_step_worker # noqa
import vllm_ascend.patch.worker.patch_common.patch_sampler # noqa

70
vllm_ascend/patch/worker/patch_common/patch_eagle.py
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@@ -1,70 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
import torch
from vllm.v1.spec_decode.eagle import EagleProposer
def prepare_inputs(
# [batch_size + 1]
cu_target_query_lens: torch.Tensor,
# [batch_size]
num_rejected_tokens: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
# cu_target_query_lens: [0, a, a + b, a + b + c]
# num_rejected_tokens: [n1, n2, n3]
# num_tokens_per_req: [a - n1, b - n2, c - n3]
# cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
# token_indices: [0, 1, ..., a - n1 - 1,
# a, a + 1, ..., a + b - n2 - 1,
# a + b, a + b + 1, ..., a + b + c - n3 - 1]
# [0, a, a + b, a + b + c] -> [a, b, c]
query_len_per_req = (cu_target_query_lens[1:] - cu_target_query_lens[:-1])
# [a, b, c] -> [a - n1, b - n2, c - n3]
num_tokens_per_req = query_len_per_req - num_rejected_tokens
cu_num_tokens = torch.empty_like(cu_target_query_lens)
torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
cu_num_tokens[0] = 0
# FIXME(woosuk): Avoid synchronization.
num_tokens = cu_num_tokens[-1].item()
token_indices = torch.empty(
num_tokens,
dtype=torch.int32,
device=cu_num_tokens.device,
)
BLOCK_SIZE = 1024
prepare_input_pytorch(
token_indices,
cu_target_query_lens,
cu_num_tokens,
block_size=BLOCK_SIZE,
)
return cu_num_tokens, token_indices
def prepare_input_pytorch(out_ptr: torch.Tensor, cu_query_lens: torch.Tensor,
cu_num_tokens: torch.Tensor, block_size: int):
num_pids = cu_num_tokens.shape[0] - 1
for pid in range(num_pids):
start_pos = cu_num_tokens[pid].item()
end_pos = cu_num_tokens[pid + 1].item()
num_tokens = end_pos - start_pos
index_start = cu_query_lens[pid].item()
num_blocks = (num_tokens + block_size - 1)
for i in range(num_blocks):
offset = torch.arange(0,
block_size,
dtype=out_ptr.dtype,
device=cu_query_lens.device)
global_indices = start_pos + offset
values = index_start + offset
mask = offset < num_tokens
out_ptr[global_indices[mask]] = values[mask]
EagleProposer.prepare_inputs = prepare_inputs