diff --git a/cosmos_framework/model/generator/mot/maskless_attention.py b/cosmos_framework/model/generator/mot/maskless_attention.py index 2e4d267b..f4eb89c2 100644 --- a/cosmos_framework/model/generator/mot/maskless_attention.py +++ b/cosmos_framework/model/generator/mot/maskless_attention.py @@ -60,8 +60,8 @@ import torch import triton -from cosmos_framework.model.attention.utils.environment import is_torch_compiling from cosmos_framework.configs.base.defaults.multiview_attention import temporal_window_bounds +from cosmos_framework.model.attention.utils.environment import is_torch_compiling from cosmos_framework.model.generator.mot import maskless_kernels as kernels from cosmos_framework.model.generator.mot.causal_flex_attention import ( _ROLE_PADDING, @@ -432,10 +432,10 @@ def gapped_replay_unavailable_reason(device: torch.device) -> str | None: if torch.version.hip is not None: return "it needs NVIDIA CUDA, not ROCm" major, minor = torch.cuda.get_device_capability(device) - if (major, minor) != (9, 0) and major != 10: - # Hopper and Blackwell use different FA4 kernels; the replay GPU tests cover - # the segment isolation and untouched gap rows required by both paths. - return f"it needs FlashAttention-4 on Hopper (sm90) or Blackwell (sm100/sm103), got sm{major}{minor}" + if (major, minor) not in ((9, 0), (12, 0)) and major != 10: + # FA4 >= 4.0.0b30 guards invalid varlen tiles on SM120; keep the + # existing gapped replay path rather than introducing a second backend. + return f"it needs FlashAttention-4 on Hopper (sm90) or Blackwell (sm100/sm103/sm120), got sm{major}{minor}" try: _flash_attn_interface() except ImportError as error: @@ -912,6 +912,8 @@ def _gapped_replay_backward( Every launch uses the same folded query layout and segment arguments as forward. Rows nothing reaches are zero. """ + if torch.cuda.get_device_capability(q.device) == (12, 0): + raise NotImplementedError("SM120 replay is inference-only; backward is not qualified.") flash_attn = _flash_attn_interface() runtime = _runtime(handle) q, k, v = (tensor.contiguous() for tensor in (q, k, v)) # [1,Q,H,D], [1,KV,H_KV,D], [1,KV,H_KV,Dv] diff --git a/cosmos_framework/model/generator/mot/maskless_attention_test.py b/cosmos_framework/model/generator/mot/maskless_attention_test.py new file mode 100644 index 00000000..1ce127d1 --- /dev/null +++ b/cosmos_framework/model/generator/mot/maskless_attention_test.py @@ -0,0 +1,131 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: OpenMDW-1.1 + +"""SM120 replay compatibility against independent dense attention references.""" + +import math +from types import ModuleType + +import pytest +import torch + +from cosmos_framework.model.generator.mot import maskless_attention as replay + + +@pytest.mark.L0 +@pytest.mark.gpus(0) +@pytest.mark.parametrize("capability", [(9, 0), (10, 0), (10, 3), (12, 0)]) +def test_replay_supported_architectures(monkeypatch: pytest.MonkeyPatch, capability: tuple[int, int]) -> None: + monkeypatch.setattr(torch.version, "hip", None) + monkeypatch.setattr(torch.cuda, "get_device_capability", lambda device: capability) + monkeypatch.setattr(replay, "_flash_attn_interface", lambda: ModuleType("flash_attn")) + assert replay.gapped_replay_unavailable_reason(torch.device("cuda")) is None + + +@pytest.mark.L0 +@pytest.mark.gpus(0) +@pytest.mark.parametrize("capability", [(8, 0), (8, 9), (9, 1), (11, 0), (12, 1)]) +def test_replay_rejects_unqualified_architectures(monkeypatch: pytest.MonkeyPatch, capability: tuple[int, int]) -> None: + monkeypatch.setattr(torch.version, "hip", None) + monkeypatch.setattr(torch.cuda, "get_device_capability", lambda device: capability) + reason = replay.gapped_replay_unavailable_reason(torch.device("cuda")) + assert reason is not None + assert f"sm{capability[0]}{capability[1]}" in reason + + +@pytest.mark.L0 +@pytest.mark.gpus(0) +@pytest.mark.parametrize(("device", "hip", "message"), [("cpu", None, "CUDA device"), ("cuda", "6.0", "not ROCm")]) +def test_replay_rejects_non_nvidia_devices( + monkeypatch: pytest.MonkeyPatch, device: str, hip: str | None, message: str +) -> None: + monkeypatch.setattr(torch.version, "hip", hip) + + def unexpected_capability_query(device): + pytest.fail("Non-NVIDIA devices must be rejected before querying CUDA capability") + + monkeypatch.setattr(torch.cuda, "get_device_capability", unexpected_capability_query) + reason = replay.gapped_replay_unavailable_reason(torch.device(device)) + assert reason is not None + assert message in reason + + +@pytest.mark.L0 +@pytest.mark.gpus(0) +def test_sm120_replay_requires_flash_attention(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr(torch.version, "hip", None) + monkeypatch.setattr(torch.cuda, "get_device_capability", lambda device: (12, 0)) + + def missing_backend(): + raise ImportError("FA4 is not installed") + + monkeypatch.setattr(replay, "_flash_attn_interface", missing_backend) + with pytest.raises(RuntimeError, match="could not be imported: FA4 is not installed"): + replay.require_gapped_replay(torch.device("cuda")) + + +def _require_sm120() -> None: + if not torch.cuda.is_available() or torch.cuda.get_device_capability() != (12, 0): + pytest.skip("Requires an SM120 GPU") + pytest.importorskip("flash_attn.cute.interface") + + +@pytest.mark.L0 +@pytest.mark.gpus(1) +@pytest.mark.parametrize("dim", [64, 128]) +@pytest.mark.parametrize(("heads", "kv_heads"), [(8, 2), (8, 8), (16, 1)]) +def test_sm120_replay_matches_dense_partition_reference(dim: int, heads: int, kv_heads: int) -> None: + _require_sm120() + torch.manual_seed(20261026) + q = torch.randn(1, 257, heads, dim, device="cuda", dtype=torch.bfloat16) + k = torch.randn(1, 389, kv_heads, dim, device="cuda", dtype=torch.bfloat16) + v = torch.randn_like(k) + segments = [ + replay.GappedSegment(0, 31, 10, 71), + replay.GappedSegment(55, 74, 100, 101), + replay.GappedSegment(55, 74, 240, 100), + replay.GappedSegment(199, 41, 3, 93), + replay.GappedSegment(140, 31, 310, 71), + ] + caption = replay.ReplayMasklessPass( + "caption", + torch.tensor([0, 5, 55, 100, 199, 225], device="cuda"), + torch.tensor([1, 8, 12, 190, 388], device="cuda"), + (0, 6), + (0, 5), + ) + plan = replay.build_gapped_replay_plan(segments, [caption], torch.device("cuda")) + out, lse = replay._gapped_replay_forward(q, k, v, plan.handle) + + # Count each partition's edges independently, including duplicated keys. + count = torch.zeros(257, 389, device="cuda") + for segment in segments: + count[ + segment.q_row : segment.q_row + segment.q_len, + segment.k_row : segment.k_row + segment.k_len, + ] += 1 + count[caption.q_gather[:, None], caption.kv_gather[None, :]] += 1 + keys = k.float().repeat_interleave(heads // kv_heads, dim=2) + values = v.float().repeat_interleave(heads // kv_heads, dim=2) + scores = torch.einsum("bqhd,bkhd->bhqk", q.float(), keys) / math.sqrt(dim) + count.log()[None, None] + valid = count.sum(-1) > 0 + weights = torch.nan_to_num(scores.softmax(-1)) + expected = torch.einsum("bhqk,bkhd->bqhd", weights, values) + expected_lse = scores.logsumexp(-1).transpose(1, 2) + assert torch.isfinite(out).all() + torch.testing.assert_close(out.float(), expected, atol=0.025, rtol=0) + torch.testing.assert_close(lse[:, valid], expected_lse[:, valid], atol=0.01, rtol=0) + assert torch.count_nonzero(out[:, ~valid]) == 0 + assert torch.isneginf(lse[:, ~valid]).all() + + +@pytest.mark.L0 +@pytest.mark.gpus(1) +def test_sm120_replay_backward_is_explicitly_unsupported() -> None: + _require_sm120() + q = torch.zeros(1, 1, 1, 64, device="cuda", dtype=torch.bfloat16) + lse = torch.zeros(1, 1, 1, device="cuda") + # An unregistered handle proves the guard runs before plan lookup or a kernel launch. + handle = torch.tensor([-1], dtype=torch.int64) + with pytest.raises(NotImplementedError, match="inference-only"): + replay._gapped_replay_backward(q, q, q, q, lse, q, handle) diff --git a/docs/inference_sim_auto.md b/docs/inference_sim_auto.md index 6a3149f2..7e29e1f3 100644 --- a/docs/inference_sim_auto.md +++ b/docs/inference_sim_auto.md @@ -40,6 +40,14 @@ Rolling replay runs its same-camera attention through the FlashAttention-4 kernels. The `cu130-train` group installed there includes FlashAttention-4, so no separate installation is needed. +On SM120 GPUs such as RTX 6000D, replay inference uses the same native +FlashAttention-4 gapped-varlen path as the other supported architectures. +The pinned FlashAttention-4 4.0.0b30 release includes the SM80/SM120 invalid-tile +fix required by this workload. Upgrade the environment through the setup command +above; admitting SM120 with the older 4.0.0b24 pin can cause illegal memory access. +SM120 replay backward is not qualified and is explicitly rejected; SM120 support +is inference only. + ## Checkpoint Download the published snapshot and pass its local directory. A bare Hugging diff --git a/pyproject.toml b/pyproject.toml index c12b2c87..1944836e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -195,7 +195,7 @@ vllm = [ # Match nvcr.io/nvidia/pytorch:25.11-py3: https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-26-01.html cu128 = [ # FA4 CuTeDSL kernels; the cu13 extra is only needed by CUDA 13 environments. - "flash-attn-4==4.0.0b24", + "flash-attn-4==4.0.0b30", "flash-attn-3-nv==1.0.3+cu128.torch210; platform_machine == 'x86_64'", "flash-attn==2.7.4.post1+cu128.torch210; platform_machine == 'x86_64'", "natten==0.21.6.dev6+cu128.torch210; platform_machine == 'x86_64'", @@ -230,7 +230,7 @@ cu128-train = [ # Match nvcr.io/nvidia/pytorch:25.11-py3: https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-11.html cu130 = [ # Match the upstream FA4 installation on both x86_64 and aarch64. - "flash-attn-4[cu13]==4.0.0b24", + "flash-attn-4[cu13]==4.0.0b30", "flash-attn-3-nv==1.0.3+cu130.torch210; platform_machine == 'x86_64'", "flash-attn==2.7.4.post1+cu130.torch210; platform_machine == 'x86_64'", # GB300/B300 (sm_103a): use the custom build that includes sm_103a kernels (both arches). @@ -266,7 +266,7 @@ cu130-train = [ ] # torch 2.13 on CUDA 13.0. Same layout as cu130 but pinned to torch 2.13. cu130-torch213 = [ - "flash-attn-4[cu13]==4.0.0b24", + "flash-attn-4[cu13]==4.0.0b30", #"flash-attn-3-nv==1.0.3+cu130.torch210; platform_machine == 'x86_64'", # TODO: torch213 build (cu130.torch210 wheels are cp313-only + torch210 ABI) #"flash-attn==2.7.4.post1+cu130.torch210; platform_machine == 'x86_64'", # TODO: torch213 build (cu130.torch210 wheels are cp313-only + torch210 ABI) # torch2.13 build (natten 0.21.6, sm 80/86/89/90/100/103/110/120/121) — matches this group's torch/ABI. diff --git a/uv.lock b/uv.lock index 733fd19c..bb32a945 100644 --- a/uv.lock +++ b/uv.lock @@ -2818,7 +2818,7 @@ provides-extras = ["automl", "automl-prompts", "reasoner-eval", "guardrail", "in cu128 = [ { name = "flash-attn", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==2.7.4.post1+cu128.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "flash-attn-3-nv", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==1.0.3+cu128.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, - { name = "flash-attn-4", marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b24" }, + { name = "flash-attn-4", marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b30" }, { name = "natten", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==0.21.6.dev6+cu128.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "nvidia-cublas-cu12", marker = "python_full_version == '3.13.*'", specifier = "==12.8.4.1" }, { name = "nvidia-cuda-cupti-cu12", marker = "python_full_version == '3.13.*'", specifier = "==12.8.90" }, @@ -2843,7 +2843,7 @@ cu128 = [ cu128-train = [ { name = "flash-attn", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==2.7.4.post1+cu128.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "flash-attn-3-nv", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==1.0.3+cu128.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, - { name = "flash-attn-4", marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b24" }, + { name = "flash-attn-4", marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b30" }, { name = "natten", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==0.21.6.dev6+cu128.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "nvidia-cublas-cu12", marker = "python_full_version == '3.13.*'", specifier = "==12.8.4.1" }, { name = "nvidia-cuda-cupti-cu12", marker = "python_full_version == '3.13.*'", specifier = "==12.8.90" }, @@ -2871,7 +2871,7 @@ cu128-train = [ cu130 = [ { name = "flash-attn", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==2.7.4.post1+cu130.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "flash-attn-3-nv", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==1.0.3+cu130.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, - { name = "flash-attn-4", extras = ["cu13"], marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b24" }, + { name = "flash-attn-4", extras = ["cu13"], marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b30" }, { name = "natten", marker = "python_full_version == '3.13.*' and platform_machine == 'aarch64'", specifier = "==0.21.6.dev6+cu130.torch210.gb300", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "natten", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==0.21.6.dev6+cu130.torch210.gb300", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "nvidia-cublas", marker = "python_full_version == '3.13.*'", specifier = "==13.1.0.3" }, @@ -2895,7 +2895,7 @@ cu130 = [ { name = "torchvision", marker = "python_full_version == '3.13.*'", specifier = "==0.25.0+cu130", index = "https://download.pytorch.org/whl" }, ] cu130-torch213 = [ - { name = "flash-attn-4", extras = ["cu13"], marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b24" }, + { name = "flash-attn-4", extras = ["cu13"], marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b30" }, { name = "natten", marker = "python_full_version == '3.13.*'", specifier = "==0.21.6+cu130.torch213", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "nvidia-cublas", marker = "python_full_version == '3.13.*'", specifier = "==13.1.1.3" }, { name = "nvidia-cuda-cupti", marker = "python_full_version == '3.13.*'", specifier = "==13.0.85" }, @@ -2918,7 +2918,7 @@ cu130-torch213 = [ { name = "torchvision", marker = "python_full_version == '3.13.*'", specifier = "==0.28.0+cu130", index = "https://download.pytorch.org/whl" }, ] cu130-torch213-train = [ - { name = "flash-attn-4", extras = ["cu13"], marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b24" }, + { name = "flash-attn-4", extras = ["cu13"], marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b30" }, { name = "natten", marker = "python_full_version == '3.13.*'", specifier = "==0.21.6+cu130.torch213", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "nvidia-cublas", marker = "python_full_version == '3.13.*'", specifier = "==13.1.1.3" }, { name = "nvidia-cuda-cupti", marker = "python_full_version == '3.13.*'", specifier = "==13.0.85" }, @@ -2946,7 +2946,7 @@ cu130-torch213-train = [ cu130-train = [ { name = "flash-attn", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==2.7.4.post1+cu130.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "flash-attn-3-nv", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==1.0.3+cu130.torch210", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, - { name = "flash-attn-4", extras = ["cu13"], marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b24" }, + { name = "flash-attn-4", extras = ["cu13"], marker = "python_full_version == '3.13.*'", specifier = "==4.0.0b30" }, { name = "natten", marker = "python_full_version == '3.13.*' and platform_machine == 'aarch64'", specifier = "==0.21.6.dev6+cu130.torch210.gb300", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "natten", marker = "python_full_version == '3.13.*' and platform_machine == 'x86_64'", specifier = "==0.21.6.dev6+cu130.torch210.gb300", index = "https://nvidia-cosmos.github.io/cosmos-dependencies/v1.5.0" }, { name = "nvidia-cublas", marker = "python_full_version == '3.13.*'", specifier = "==13.1.0.3" }, @@ -3492,221 +3492,13 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ff/60/d8f1dbfb7f06b94c662e98c95189e6f39b817da638bc8fcea0d003f89e5d/cuda_pathfinder-1.4.0-py3-none-any.whl", hash = "sha256:437079ca59e7b61ae439ecc501d69ed87b3accc34d58153ef1e54815e2c2e118", size = 38406, upload-time = "2026-02-25T22:13:00.807Z" }, ] -[[package]] -name = "cuda-python" -version = "12.9.4" -source = { registry = "https://pypi.org/simple" } -resolution-markers = [ - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-16-cosmos-framework-train' and extra != 'group-16-cosmos-framework-cu128' and extra == 'group-16-cosmos-framework-cu128-train' and extra != 'group-16-cosmos-framework-cu130' and extra != 'group-16-cosmos-framework-cu130-torch213' and extra != 'group-16-cosmos-framework-cu130-torch213-train' and extra != 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