[CI][ROCm] Add Cosmos3 nightly coverage (#7933)
Signed-off-by: haic0 <149741444+haic0@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com>
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@@ -0,0 +1,78 @@
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#!/usr/bin/env python3
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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"""Prebuild the BF16 AITER MHA modules used by Cosmos3 on ROCm."""
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from __future__ import annotations
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import os
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from pathlib import Path
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import torch
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from vllm_omni.diffusion.attention.backends.utils import fa
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from vllm_omni.platforms import current_omni_platform
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ATTENTION_VARIANTS = (
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(True, "mha_fwd_bf16_nbias_mask_nlse_ndropout_nqscale"),
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(False, "mha_fwd_bf16_nbias_nmask_nlse_ndropout_nqscale"),
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)
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def main() -> None:
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if not current_omni_platform.is_rocm():
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raise RuntimeError("The Cosmos3 AITER prewarm must run on ROCm")
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if fa.flash_attn_func is None:
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raise RuntimeError("ROCm FlashAttention/AITER is unavailable")
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cache_dir_value = os.environ.get("AITER_JIT_DIR")
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if not cache_dir_value:
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raise RuntimeError("AITER_JIT_DIR must identify the job-local cache")
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cache_dir = Path(cache_dir_value)
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device = torch.device(current_omni_platform.device_type)
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dtype = torch.bfloat16
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batch_size = 1
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sequence_length = 64
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num_heads = 8
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head_dim = 64
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torch.manual_seed(42)
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query = torch.randn(
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batch_size,
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sequence_length,
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num_heads,
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head_dim,
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device=device,
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dtype=dtype,
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)
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key = torch.randn_like(query)
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value = torch.randn_like(query)
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for causal, expected_module in ATTENTION_VARIANTS:
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output = fa.flash_attn_func(
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query,
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key,
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value,
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dropout_p=0.0,
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softmax_scale=head_dim**-0.5,
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causal=causal,
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)
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if isinstance(output, tuple):
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output = output[0]
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current_omni_platform.synchronize()
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if output.shape != query.shape:
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raise RuntimeError(f"Unexpected AITER output shape: {output.shape}")
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if not torch.isfinite(output).all():
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raise RuntimeError("AITER prewarm produced non-finite output")
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module_path = cache_dir / f"{expected_module}.so"
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if not module_path.is_file():
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raise RuntimeError(f"Expected AITER module was not built: {module_path}")
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print(f"Prebuilt Cosmos3 AITER module: {module_path}", flush=True)
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if __name__ == "__main__":
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main()
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@@ -99,3 +99,36 @@ steps:
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artifact_dir="$${BUILDKITE_BUILD_CHECKOUT_PATH:?}/artifacts/rocm-lingbot-nightly"
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mkdir -p "$$artifact_dir"
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cp -v /tmp/lingbot-pytest.xml "$$artifact_dir/"
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- group: ":full_moon: Cosmos3 MI300 Nightly"
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steps:
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- label: "Cosmos3 T2I Function"
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agent_pool: mi300_1
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mirror_hardwares: [amdproduction]
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grade: NonBlocking
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timeout_in_minutes: 90
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artifact_paths:
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- artifacts/rocm-cosmos3-nightly/**/*
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commands:
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# Cosmos3 uses both masked and unmasked BF16 AITER MHA modules. Build
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# them before server startup, then reuse the same job-local cache
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# during the smoke test.
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- export VLLM_ROCM_USE_AITER=1
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- export AITER_JIT_DIR="/tmp/vllm-omni-cosmos3-aiter-$$BUILDKITE_JOB_ID"
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- export TORCH_EXTENSIONS_DIR="/tmp/vllm-omni-cosmos3-torch-extensions-$$BUILDKITE_JOB_ID"
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- mkdir -p "$$AITER_JIT_DIR" "$$TORCH_EXTENSIONS_DIR"
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- pytest --collect-only -q tests/e2e/online_serving/test_cosmos3.py::test_text_to_image_001 -m "core_model and rocm and MI325 and cards_1" --run-level full_model
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- >-
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timeout --signal=TERM --kill-after=2m 55m
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python .buildkite/amd/scripts/prewarm_cosmos3_aiter.py
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- >-
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timeout --signal=TERM --kill-after=2m 25m
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pytest -s -v
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tests/e2e/online_serving/test_cosmos3.py::test_text_to_image_001
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-m "core_model and rocm and MI325 and cards_1"
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--run-level full_model
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--junitxml=/tmp/cosmos3-pytest.xml
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- |
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artifact_dir="$${BUILDKITE_BUILD_CHECKOUT_PATH:?}/artifacts/rocm-cosmos3-nightly"
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mkdir -p "$$artifact_dir"
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cp -v /tmp/cosmos3-pytest.xml "$$artifact_dir/"
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@@ -40,7 +40,7 @@ SERVER_ARGS = [
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"Cosmos3OmniDiffusersPipeline",
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"--no-guardrails",
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]
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SINGLE_CARD_MARKS = hardware_marks(res={"cuda": "H100"})
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SINGLE_CARD_MARKS = hardware_marks(res={"cuda": "H100", "rocm": "MI325"})
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def _get_diffusion_feature_cases(model: str):
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