[None][feat] AutoDeploy: refactor memory usage logging #8505
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📝 WalkthroughWalkthroughThe changes introduce a utility function Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes The changes span three files with consistent patterns but introduce meaningful additions: the new utility is straightforward, but Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
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⚠️ Outside diff range comments (3)
tensorrt_llm/_torch/auto_deploy/models/factory.py (1)
1-1: Missing NVIDIA Apache-2.0 header (2025).Add the standard header at file top.
+# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """The model factory interface used by auto-deploy to build custom models."""As per coding guidelines.
tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py (2)
1-1: Missing NVIDIA Apache-2.0 header (2025).Add required header.
+# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + """Graph transformation to automatically add kv cache into fused MHA op."""As per coding guidelines.
297-305: Guard against divide-by-zero when deriving pages; keep units consistent.If the cache is empty or
current_num_pages == 0, this can crash. Also compute bytes/page via float to avoid premature truncation.- new_cache_size = free_mem_post * 1024 * 1024 * free_mem_ratio + current_cache_size - new_num_pages = int(new_cache_size // (current_cache_size // current_num_pages)) + new_cache_size = (free_mem_post * 1024 * 1024 * free_mem_ratio) + current_cache_size + # Derive bytes per page safely + if current_num_pages <= 0: + self._log_info("Current KV cache has zero pages; skipping resize.") + return mod, TransformInfo(skipped=True, num_matches=0, is_clean=True, has_valid_shapes=True) + bytes_per_page = current_cache_size / current_num_pages + if bytes_per_page <= 0: + self._log_info("KV cache reports 0 bytes/page; skipping resize.") + return mod, TransformInfo(skipped=True, num_matches=0, is_clean=True, has_valid_shapes=True) + new_num_pages = max(current_num_pages, int(new_cache_size / bytes_per_page))
🧹 Nitpick comments (3)
tensorrt_llm/_torch/auto_deploy/utils/cuda_mem_tracker.py (1)
30-36: Add CUDA-availability guard and docstring; clarify MiB units.Make the function robust on non‑CUDA hosts and document units (1024‑based = MiB).
-def get_mem_info_in_mb(empty_cache: bool = True) -> Tuple[int, int]: - if empty_cache: - # Clear the memory cache to get the exact free memory - torch.cuda.empty_cache() - free_mem, total_mem = torch.cuda.mem_get_info() - MB = 1024**2 - return free_mem // MB, total_mem // MB +def get_mem_info_in_mb(empty_cache: bool = True) -> Tuple[int, int]: + """Return (free_mib, total_mib) for the current CUDA device. + + Args: + empty_cache: If True, call torch.cuda.empty_cache() before measuring. + Raises: + RuntimeError: If CUDA is unavailable. + """ + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is not available; cannot query CUDA memory.") + if empty_cache: + torch.cuda.empty_cache() + free_mem, total_mem = torch.cuda.mem_get_info() + MIB = 1024**2 + return free_mem // MIB, total_mem // MIBBased on coding guidelines.
tensorrt_llm/_torch/auto_deploy/models/factory.py (2)
281-283: Unit label nit: use GiB for 1024-based units.You divide by 1024**3, so log GiB for clarity/consistency with other logs.
- total_size_GB = params_size / (1024**3) - ad_logger.info(f"Estimated parameters memory: {total_size_GB:.2f} GB") + total_size_gib = params_size / (1024**3) + ad_logger.info(f"Estimated parameters memory: {total_size_gib:.2f} GiB")
277-279: Consider measuring without emptying cache to reflect steady-state free memory.Default emptying the cache before every measurement may inflate “free memory” and hide allocator fragmentation. If the goal is a diagnostic snapshot, consider
get_mem_info_in_mb(empty_cache=False)here.- free_mem_pre, _ = get_mem_info_in_mb() + free_mem_pre, _ = get_mem_info_in_mb(empty_cache=False) ... - free_mem_post, _ = get_mem_info_in_mb() + free_mem_post, _ = get_mem_info_in_mb(empty_cache=False)Also applies to: 289-290
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tensorrt_llm/_torch/auto_deploy/models/factory.py(2 hunks)tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py(3 hunks)tensorrt_llm/_torch/auto_deploy/utils/cuda_mem_tracker.py(2 hunks)
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tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.pytensorrt_llm/_torch/auto_deploy/utils/cuda_mem_tracker.pytensorrt_llm/_torch/auto_deploy/models/factory.py
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🧬 Code graph analysis (2)
tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py (5)
tensorrt_llm/_torch/auto_deploy/utils/cuda_mem_tracker.py (1)
get_mem_info_in_mb(30-36)tensorrt_llm/_torch/auto_deploy/transform/interface.py (1)
_log_info(420-422)tensorrt_llm/_torch/auto_deploy/shim/interface.py (2)
current_cache_size_bytes(61-68)named_args(33-35)tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (3)
num_pages(321-322)num_pages(325-329)named_args(228-237)tensorrt_llm/logger.py (1)
error(126-127)
tensorrt_llm/_torch/auto_deploy/models/factory.py (2)
tensorrt_llm/_torch/auto_deploy/utils/cuda_mem_tracker.py (1)
get_mem_info_in_mb(30-36)tensorrt_llm/_torch/auto_deploy/models/hf.py (1)
_load_checkpoint(394-421)
🪛 Ruff (0.14.0)
tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py
278-280: Use logging.exception instead of logging.error
Replace with exception
(TRY400)
281-281: Use raise without specifying exception name
Remove exception name
(TRY201)
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1. Log model size 2. Fix the logging of memory used during forward, when reconfiguring the kv-cache 3. Catch cache-resize OOM to give users a gentler experience Signed-off-by: Neta Zmora <[email protected]>
Signed-off-by: Neta Zmora <[email protected]>
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