diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml index 9efa03d06a..2ff16efe76 100644 --- a/.github/workflows/cicd-main.yml +++ b/.github/workflows/cicd-main.yml @@ -208,7 +208,7 @@ jobs: build-contexts: | nemo-rl=${{ github.run_id }}/ build-args: | - MAX_JOBS=32 + MAX_JOBS=4 NEMO_RL_COMMIT=${{ github.sha }} cicd-doc-tests: diff --git a/.gitmodules b/.gitmodules index fd75d5044f..796f7b17c3 100644 --- a/.gitmodules +++ b/.gitmodules @@ -11,7 +11,7 @@ [submodule "3rdparty/Automodel-workspace/Automodel"] path = 3rdparty/Automodel-workspace/Automodel url = https://github.com/NVIDIA-NeMo/Automodel.git - branch = nemo-rl-submodule + branch = yifu/bump-torch-and-hf shallow = true [submodule "3rdparty/Gym-workspace/Gym"] path = 3rdparty/Gym-workspace/Gym diff --git a/3rdparty/Automodel-workspace/Automodel b/3rdparty/Automodel-workspace/Automodel index a2db048383..910f4e0402 160000 --- a/3rdparty/Automodel-workspace/Automodel +++ b/3rdparty/Automodel-workspace/Automodel @@ -1 +1 @@ -Subproject commit a2db048383cd54b3fafc928df4c30bf7bbf7c430 +Subproject commit 910f4e0402ec3af0c3b8642639f0347732067630 diff --git a/examples/configs/recipes/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e.yaml b/examples/configs/recipes/llm/grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e.yaml similarity index 86% rename from examples/configs/recipes/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e.yaml rename to examples/configs/recipes/llm/grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e.yaml index 4ed7b47d06..6411c6fb49 100644 --- a/examples/configs/recipes/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e.yaml +++ b/examples/configs/recipes/llm/grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e.yaml @@ -6,7 +6,7 @@ grpo: loss_fn: use_importance_sampling_correction: true checkpointing: - checkpoint_dir: results/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e + checkpoint_dir: results/grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e policy: model_name: meta-llama/Llama-3.1-8B-Instruct tokenizer: @@ -48,11 +48,12 @@ policy: data: max_input_seq_length: 4096 logger: - log_dir: logs/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e + log_dir: logs/grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e wandb_enabled: true tensorboard_enabled: true wandb: project: nemo-rl - name: grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e + name: grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e cluster: + num_nodes: 2 gpus_per_node: 8 diff --git a/nemo_rl/models/generation/fp8.py b/nemo_rl/models/generation/fp8.py index 0939e3582b..4f42b79e1d 100644 --- a/nemo_rl/models/generation/fp8.py +++ b/nemo_rl/models/generation/fp8.py @@ -272,6 +272,7 @@ def init_fp8(vllm_cfg, model_name, model_parallel_size): if vllm_cfg.get("use_deep_gemm", False): os.environ["VLLM_USE_DEEP_GEMM"] = "1" + os.environ["VLLM_USE_DEEP_GEMM_E8M0"] = "0" if vllm_cfg["async_engine"]: # for async engine, vllm spawns a process for each DP, so we patch @@ -541,6 +542,39 @@ def cast_tensor_to_fp8_blockwise( return fp_data, descale_fp +# Ref: https://github.com/vllm-project/vllm/blob/275de34170654274616082721348b7edd9741d32/vllm/model_executor/layers/quantization/utils/fp8_utils.py#L1175 +# Patches this method to not create new torch.nn.Parameter for layer weights +# to maintain weight loaders. +def maybe_post_process_fp8_weight_block(layer: torch.nn.Module): + assert layer.weight_block_size is not None + + from vllm.model_executor.layers.quantization.utils.fp8_utils import ( + deepgemm_post_process_fp8_weight_block, + ) + from vllm.utils.deep_gemm import ( + is_deep_gemm_e8m0_used, + should_use_deepgemm_for_fp8_linear, + ) + + # On Blackwell or Hopper, if E8M0 for DeepGemm is used, we need to + # requantize the weight and input to the specific scale + # at the same time. + should_use_deepgemm = should_use_deepgemm_for_fp8_linear( + layer.orig_dtype, layer.weight + ) + if should_use_deepgemm: + dg_weight, dg_weight_scale = deepgemm_post_process_fp8_weight_block( + wq=layer.weight.data, + ws=layer.weight_scale.data, + quant_block_shape=tuple(layer.weight_block_size), + use_e8m0=is_deep_gemm_e8m0_used(), + ) + # This is the only part we change from the original function (https://github.com/vllm-project/vllm/blob/275de34170654274616082721348b7edd9741d32/vllm/model_executor/layers/quantization/utils/fp8_utils.py#L1196-L1197) + # Instead of creating new torch.nn.Parameter, we update the data in place. + layer.weight.data.copy_(dg_weight) + layer.weight_scale.data.copy_(dg_weight_scale) + + def process_weights_after_loading(self, layer) -> None: """This function is used to process the weights after loading for a Linear layer. @@ -548,7 +582,6 @@ def process_weights_after_loading(self, layer) -> None: new torch.nn.Parameter objects, because that removes the weight_loader attribute which we need for refit. """ from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - maybe_post_process_fp8_weight_block, process_fp8_weight_block_strategy, ) @@ -566,7 +599,7 @@ def process_weights_after_loading(self, layer) -> None: layer.weight_scale = torch.nn.Parameter(weight_scale.data, requires_grad=False) layer.update_param_tp_status() - maybe_post_process_fp8_weight_block(layer, self.cutlass_block_fp8_supported) + maybe_post_process_fp8_weight_block(layer) def process_weights_after_loading_moe(self, layer) -> None: diff --git a/nemo_rl/models/generation/vllm/vllm_worker.py b/nemo_rl/models/generation/vllm/vllm_worker.py index 6c0d3577e9..0ee3cd5d9c 100644 --- a/nemo_rl/models/generation/vllm/vllm_worker.py +++ b/nemo_rl/models/generation/vllm/vllm_worker.py @@ -16,6 +16,7 @@ import gc import os import sys +from importlib.util import find_spec from typing import Any, Optional, cast import ray @@ -156,63 +157,134 @@ def __init__( self.rank = 0 self.world_size = 1 - # Monkey patch for vLLM to ensure RAY_ADDRESS is set in Ray actors. - try: - from vllm.logger import init_logger + # Monkey patches for vLLM behavior. We avoid importing vllm modules + # here to prevent side effects during initialization and instead + # locate the files via importlib metadata. - logger = init_logger("vllm_patch") + from vllm.logger import init_logger - def _patch_vllm_init_workers_ray(): - """Patch the vLLM ray_distributed_executor.py file. + logger = init_logger("vllm_patch") - 1. Pass custom runtime_env in _init_workers_ray call. - - This allows passing custom py_executable to worker initialization. - 2. Add NCCL_CUMEM_ENABLE and NCCL_NVLS_ENABLE to vLLM ADDITIONAL_ENV_VARS. - - This is a workaround to fix async vllm in some scenarios. - - See https://github.com/NVIDIA-NeMo/RL/pull/898 for more details. - """ - try: - import vllm.executor.ray_distributed_executor as ray_executor_module + def _get_vllm_file(relative_path: str) -> str: + """Return absolute path to a vLLM file or raise if it cannot be found. + + The relative_path should be a POSIX-style path under the vllm + package root, e.g. "v1/executor/ray_executor.py" or + "attention/layer.py". + """ + spec = find_spec("vllm") + if spec is None or not spec.submodule_search_locations: + raise RuntimeError( + "vLLM package not found while attempting to patch " + f"'{relative_path}'. Ensure vLLM is installed and " + "available in this environment." + ) - file_to_patch = ray_executor_module.__file__ + base_dir = next(iter(spec.submodule_search_locations)) + file_path = os.path.join(base_dir, *relative_path.split("/")) - with open(file_to_patch, "r") as f: - content = f.read() + if not os.path.exists(file_path): + raise RuntimeError( + "Failed to locate expected vLLM file to patch. " + f"Looked for '{relative_path}' at '{file_path}'. " + "This likely indicates an unexpected vLLM installation " + "layout or version mismatch." + ) + + return file_path + + def _patch_vllm_init_workers_ray(): + """Patch the vLLM ray_distributed_executor.py file. + + 1. Pass custom runtime_env in _init_workers_ray call. + - This allows passing custom py_executable to worker initialization. + 2. Add NCCL_CUMEM_ENABLE and NCCL_NVLS_ENABLE to vLLM ADDITIONAL_ENV_VARS. + - This is a workaround to fix async vllm in some scenarios. + - See https://github.com/NVIDIA-NeMo/RL/pull/898 for more details. + """ + file_to_patch = _get_vllm_file("v1/executor/ray_executor.py") - old_lines = [ - "self._init_workers_ray(placement_group)", - 'ADDITIONAL_ENV_VARS = {"HF_TOKEN", "HUGGING_FACE_HUB_TOKEN"}', - ] + with open(file_to_patch, "r") as f: + content = f.read() - new_lines = [ - f'self._init_workers_ray(placement_group, runtime_env={{"py_executable": "{self.py_executable}"}})', - 'ADDITIONAL_ENV_VARS = {"HF_TOKEN", "HUGGING_FACE_HUB_TOKEN", "NCCL_CUMEM_ENABLE", "NCCL_NVLS_ENABLE", "RAY_ENABLE_UV_RUN_RUNTIME_ENV"}', - ] + old_lines = [ + "self._init_workers_ray(placement_group)", + 'ADDITIONAL_ENV_VARS = {"HF_TOKEN", "HUGGING_FACE_HUB_TOKEN"}', + ] - need_replace = False - for old_line, new_line in zip(old_lines, new_lines): - if new_line in content or old_line not in content: - continue - content = content.replace(old_line, new_line) - need_replace = True + new_lines = [ + f'self._init_workers_ray(placement_group, runtime_env={{"py_executable": "{self.py_executable}"}})', + 'ADDITIONAL_ENV_VARS = {"HF_TOKEN", "HUGGING_FACE_HUB_TOKEN", "NCCL_CUMEM_ENABLE", "NCCL_NVLS_ENABLE", "RAY_ENABLE_UV_RUN_RUNTIME_ENV"}', + ] + + need_replace = False + for old_line, new_line in zip(old_lines, new_lines): + if new_line in content or old_line not in content: + continue + content = content.replace(old_line, new_line) + need_replace = True + + if not need_replace: + return + + # Write back the patched content + with open(file_to_patch, "w") as f: + f.write(content) + + def _patch_vllm_vit_flash_attn_backend(): + """Patch vLLM vision attention backend selection logic. + + Modify the CUDA branch of maybe_get_vit_flash_attn_backend in + vllm.attention.layer to avoid overriding the backend when it + is already set to XFORMERS. This avoids flash attention related + errors when the ViT head dimension is not a multiple of 32. + + Related issues: + - https://github.com/vllm-project/vllm/issues/27562 + - https://github.com/vllm-project/vllm/issues/26989 + + This is properly fixed in https://github.com/vllm-project/vllm/pull/28763. We can remove this patch once we upgrade to a version of vllm that contains this fix. + """ + file_to_patch = _get_vllm_file("attention/layer.py") + with open(file_to_patch, "r") as f: + content = f.read() + + old_snippet = ( + " elif current_platform.is_cuda():\n" + " if (\n" + " attn_backend != AttentionBackendEnum.FLASH_ATTN\n" + " and check_upstream_fa_availability(torch.get_default_dtype())\n" + " ):\n" + " attn_backend = AttentionBackendEnum.FLASH_ATTN\n" + " use_upstream_fa = True\n" + ) + + new_snippet = ( + " elif current_platform.is_cuda():\n" + " if (\n" + " attn_backend != AttentionBackendEnum.FLASH_ATTN\n" + " and attn_backend != AttentionBackendEnum.XFORMERS\n" + " and check_upstream_fa_availability(torch.get_default_dtype())\n" + " ):\n" + " attn_backend = AttentionBackendEnum.FLASH_ATTN\n" + " use_upstream_fa = True\n" + ) - if not need_replace: - return + # Only patch if the file still has the old snippet and + # hasn't been patched already. + if new_snippet in content or old_snippet not in content: + return - # Write back the patched content - with open(file_to_patch, "w") as f: - f.write(content) + content = content.replace(old_snippet, new_snippet) - except (ImportError, FileNotFoundError, PermissionError): - # Allow failures gracefully - pass + with open(file_to_patch, "w") as f: + f.write(content) - _patch_vllm_init_workers_ray() - logger.info("Successfully patched vllm _init_workers_ray.") + _patch_vllm_init_workers_ray() + logger.info("Successfully patched vllm _init_workers_ray.") - except (ImportError, AttributeError): - # vllm not installed or has a different structure, skipping patch. - pass + _patch_vllm_vit_flash_attn_backend() + logger.info("Successfully patched vllm vit flash attention backend.") try: import vllm diff --git a/nemo_rl/models/generation/vllm/vllm_worker_async.py b/nemo_rl/models/generation/vllm/vllm_worker_async.py index c8ab9fcf2a..13bb32ff3f 100644 --- a/nemo_rl/models/generation/vllm/vllm_worker_async.py +++ b/nemo_rl/models/generation/vllm/vllm_worker_async.py @@ -296,7 +296,6 @@ def _setup_vllm_openai_api_server(self, app: FastAPI) -> FastAPI: openai_serving_models = OpenAIServingModels( engine_client=engine_client, - model_config=model_config, base_model_paths=base_model_paths, lora_modules=None, ) @@ -437,7 +436,6 @@ class NeMoRLOpenAIServingChat(NeMoRLOpenAIServingMixin, OpenAIServingChat): ) openai_serving_chat = NeMoRLOpenAIServingChat( engine_client, - model_config, openai_serving_models, return_tokens_as_token_ids=True, **serving_chat_kwargs, @@ -498,7 +496,6 @@ class NeMoRLOpenAIServingTokenization( openai_serving_tokenization = NeMoRLOpenAIServingTokenization( engine_client, - model_config, openai_serving_models, request_logger=serving_chat_kwargs["request_logger"], chat_template=serving_chat_kwargs["chat_template"], diff --git a/nemo_rl/models/policy/workers/megatron_policy_worker.py b/nemo_rl/models/policy/workers/megatron_policy_worker.py index d73a3e384e..55f6d4fe83 100644 --- a/nemo_rl/models/policy/workers/megatron_policy_worker.py +++ b/nemo_rl/models/policy/workers/megatron_policy_worker.py @@ -23,6 +23,67 @@ import ray import torch + + +def _apply_transformer_engine_patch(): + """Apply patch from https://github.com/NVIDIA/TransformerEngine/pull/2286/files.""" + try: + import transformer_engine + + te_path = os.path.dirname(transformer_engine.__file__) + perm_file = os.path.join(te_path, "pytorch", "triton", "permutation.py") + + if not os.path.exists(perm_file): + return + + with open(perm_file, "r") as f: + content = f.read() + + if "get_int_dtype = triton.constexpr_function(get_int_dtype)" not in content: + print(f"Applying Triton fix to {perm_file}...") + + # 1. Replace the usage + old_usage = "idtype = core.get_int_dtype(bitwidth=x.dtype.primitive_bitwidth, signed=True)" + new_usage = "idtype = get_int_dtype(bitwidth=x.dtype.primitive_bitwidth, signed=True)" + + # 2. Insert the definition before the first @triton.jit + jit_anchor = "@triton.jit" + + new_definition = ( + "\n\n" + "get_int_dtype = core.get_int_dtype\n" + "get_int_dtype = triton.constexpr_function(get_int_dtype)\n" + ) + + new_content = None + if old_usage in content: + temp_content = content.replace(old_usage, new_usage) + + if jit_anchor in temp_content: + new_content = temp_content.replace( + jit_anchor, new_definition + jit_anchor, 1 + ) + + if new_content: + try: + with open(perm_file, "w") as f: + f.write(new_content) + print("Successfully patched transformer_engine.") + except OSError as e: + print( + f"Could not write patch to transformer_engine (permission denied?): {e}" + ) + else: + print("Could not find patch locations in permutation.py") + + except (ImportError, FileNotFoundError, AttributeError): + pass + except Exception as e: + print(f"Error checking/patching transformer_engine: {e}") + + +_apply_transformer_engine_patch() + from megatron.bridge import AutoBridge from megatron.bridge.models.model_provider import get_model from megatron.bridge.training import fault_tolerance diff --git a/pyproject.toml b/pyproject.toml index a5a9881ea4..3869577a89 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,11 +19,11 @@ dependencies = [ "setuptools", "pip", # Required for frozen environments; uv venv --seed may not reliably install pip "ninja", # for flash-attn parallel build - "torch==2.8.0", + "torch==2.9.0", "triton; sys_platform == 'linux' and (platform_machine == 'x86_64' or platform_machine == 'aarch64')", "colored==2.2.3", "ray[default]==2.49.2", - "transformers>=4.55.4", + "transformers>=4.57.1", "wandb", "numpy", "datasets>=4.0.0", @@ -58,7 +58,7 @@ automodel = [ # Flash-attn version should be selected to satisfy both TE + vLLM requirements (xformers in particular) # https://github.com/NVIDIA/TransformerEngine/blob/v2.3/transformer_engine/pytorch/attention/dot_product_attention/utils.py#L108 # https://github.com/facebookresearch/xformers/blob/8354497deb2c04c67fbb2e2ad911e86530da0e90/xformers/ops/fmha/flash.py#L76 - "vllm==0.11.0", # Remove this once https://github.com/NVIDIA-NeMo/RL/issues/811 resolved + "vllm==0.11.2", # Remove this once https://github.com/NVIDIA-NeMo/RL/issues/811 resolved "flash-attn==2.8.1", "mamba-ssm", "causal-conv1d", @@ -70,7 +70,7 @@ vllm = [ # sudo apt-get update # sudo apt-get install libibverbs-dev "deep_ep @ git+https://github.com/deepseek-ai/DeepEP.git@e3908bf5bd0cc6265bcb225d15cd8c996d4759ef", - "vllm==0.11.0", + "vllm==0.11.2", "num2words>=0.5.14", # Remove this once https://github.com/NVIDIA-NeMo/RL/issues/501 resolved "flash-attn==2.8.1", @@ -93,7 +93,7 @@ mcore = [ "megatron-core", "megatron-bridge", # Remove this once https://github.com/NVIDIA-NeMo/RL/issues/501 resolved - "vllm==0.11.0", + "vllm==0.11.2", # Flash-attn version should be selected to satisfy both TE + vLLM requirements (xformers in particular) # https://github.com/NVIDIA/TransformerEngine/blob/v2.3/transformer_engine/pytorch/attention/dot_product_attention/utils.py#L108 # https://github.com/facebookresearch/xformers/blob/8354497deb2c04c67fbb2e2ad911e86530da0e90/xformers/ops/fmha/flash.py#L76 @@ -106,7 +106,7 @@ nemo_gym = ["nemo_gym"] # This is a default group so that we install these even with bare `uv sync` build = [ # Build requirement for TE - "torch==2.8.0", + "torch==2.9.0", # Build requirement for TE "setuptools", "packaging", diff --git a/tests/test_suites/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e.sh b/tests/test_suites/llm/grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e.sh similarity index 99% rename from tests/test_suites/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e.sh rename to tests/test_suites/llm/grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e.sh index de177d2c65..4a6d63473c 100755 --- a/tests/test_suites/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e.sh +++ b/tests/test_suites/llm/grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e.sh @@ -3,7 +3,7 @@ SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd) source $SCRIPT_DIR/common.env # ===== BEGIN CONFIG ===== -NUM_NODES=1 +NUM_NODES=2 STEPS_PER_RUN=100 MAX_STEPS=100 NUM_RUNS=$(( (MAX_STEPS + STEPS_PER_RUN - 1) / STEPS_PER_RUN )) # Round up diff --git a/tests/test_suites/nightly.txt b/tests/test_suites/nightly.txt index 2a8249cf5f..8df3983b09 100644 --- a/tests/test_suites/nightly.txt +++ b/tests/test_suites/nightly.txt @@ -17,7 +17,7 @@ tests/test_suites/llm/grpo-llama3.2-1b-instruct-1n8g-megatron_generation.sh # Functional 32b run tests/test_suites/llm/grpo-qwen2.5-32b-32n8g-fsdp2tp8-actckpt.v3.sh -# Functional moonlight run +# Functional moonlight run tests/test_suites/llm/grpo-moonlight-16ba3b-4n8g-megatron.sh # Functional VLM run @@ -40,7 +40,7 @@ tests/test_suites/llm/grpo-math-qwen3-30ba3b-megatron-tp4-32k.sh # FP8 tests/test_suites/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-rollouts.v3.sh -tests/test_suites/llm/grpo-llama3.1-8b-instruct-1n8g-megatron-fp8-e2e.sh +tests/test_suites/llm/grpo-llama3.1-8b-instruct-2n8g-megatron-fp8-e2e.sh tests/test_suites/llm/grpo-moonlight-16ba3b-4n8g-megatron-fp8-e2e.sh tests/test_suites/llm/grpo-qwen3-8b-base-1n8g-fp8-kvcache-megatron.sh diff --git a/tools/build-custom-vllm.sh b/tools/build-custom-vllm.sh index 260dae7295..73b47957f5 100644 --- a/tools/build-custom-vllm.sh +++ b/tools/build-custom-vllm.sh @@ -66,7 +66,7 @@ uv run --no-project use_existing_torch.py echo "Installing dependencies..." uv pip install --upgrade pip uv pip install numpy setuptools setuptools_scm -uv pip install torch==2.8.0 --torch-backend=cu129 +uv pip install torch==2.9.0 --torch-backend=cu129 # Install vLLM using precompiled wheel echo "Installing vLLM with precompiled wheel..." diff --git a/uv.lock b/uv.lock index 883b81c397..6ba9a71d74 100644 --- a/uv.lock +++ b/uv.lock @@ -74,8 +74,8 @@ dependencies = [ { name = "psutil" }, { name = "pyyaml" }, { name = "safetensors" }, - { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" }, - { name = "torch", version = "2.8.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform != 'darwin'" }, + { name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" }, + { name = "torch", version = "2.9.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform != 'darwin'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/f7/66/be171836d86dc5b8698b3a9bf4b9eb10cb53369729939f88bf650167588b/accelerate-1.10.0.tar.gz", hash = "sha256:8270568fda9036b5cccdc09703fef47872abccd56eb5f6d53b54ea5fb7581496", size = 392261, upload-time = "2025-08-07T10:54:51.664Z" } wheels = [ @@ -235,6 +235,25 @@ wheels = [ { url 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