[None][feat] AutoDeploy: per graph or whole module transform infrastructure #8157
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lucaslie
commented
Oct 6, 2025
lucaslie
commented
Oct 6, 2025
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/bot run |
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PR_Github #20677 [ run ] triggered by Bot |
📝 WalkthroughWalkthroughRefactors transform pipeline to operate on full nn.Module instead of GraphModule, adds TransformInfo composition, and consolidates cleanup logic. Updates configs to control per-graph execution and shape propagation. Standardizes prepare_metadata APIs to remove input_ids and rely on position_ids across custom ops. Adjusts optimizer and tests accordingly. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor User
participant Optimizer
participant Transform
participant Model as nn.Module
participant GraphUtils as _graph utils
User->>Optimizer: __call__(cm, mod)
alt run_per_gm = false (full-model)
Optimizer->>Transform: __call__(mod, cm, factory, shared_config)
Transform->>Transform: _apply_to_full_model(mod, ...)
Transform->>GraphUtils: _run_cleanup(mod, clean_graph/shape)
Transform-->>Optimizer: (mod', TransformInfo)
else run_per_gm = true
Optimizer->>Transform: __call__(mod, cm, ...)
Transform->>Transform: _apply_per_gm_or_whole_model(mod, ...)
note right of Transform: Iterates sub GraphModules
Transform-->>Optimizer: (mod', TransformInfo)
end
Optimizer-->>User: mod'
sequenceDiagram
autonumber
participant Caller as Attn/CausalConv/MHA caller
participant Prepare as prepare_metadata op
participant SeqInfo as SequenceInfo
Caller->>Prepare: position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
Prepare->>SeqInfo: _get_sanitized_seq_len/num_sequences(position_ids, seq_len)
SeqInfo-->>Prepare: sanitized seq_len / num_seq
Prepare-->>Caller: metadata tensors (seq_len, input_pos, cache_loc, seq_start, ...)
note over Prepare,Caller: input_ids no longer required
Estimated code review effort🎯 4 (Complex) | ⏱️ ~75 minutes Suggested reviewers
Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
✅ Passed checks (1 passed)
✨ Finishing touches
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⚠️ Outside diff range comments (15)
tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_attention.py (3)
1-1: Add NVIDIA Apache-2.0 header (compliance).Prepend the required NVIDIA Apache-2.0 header at file top.
+ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + # 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.
250-271: Declare mutated cache args in custom op (fix alias analysis).This op updates k_cache/v_cache but mutates_args=() claims purity. Mark them as mutated.
-@torch.library.custom_op("auto_deploy::torch_cached_attention_with_cache", mutates_args=()) +@torch.library.custom_op("auto_deploy::torch_cached_attention_with_cache", mutates_args=(7, 8))
368-376: Ensure num_seq is Python int to avoid slice errors.convert 0‑d tensor to int for slicing.
- num_seq = SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len) + num_seq = int(SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len))tensorrt_llm/_torch/auto_deploy/custom_ops/triton_attention.py (3)
1-1: Add NVIDIA Apache-2.0 header (compliance).+ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + # 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.
185-206: Declare mutated cache args in custom op (fix alias analysis).update_kv_cache mutates k_cache/v_cache; annotate mutates_args accordingly.
-@torch.library.custom_op("auto_deploy::triton_attention_flattened_mha_with_cache", mutates_args=()) +@torch.library.custom_op( + "auto_deploy::triton_attention_flattened_mha_with_cache", mutates_args=(7, 8) +)
296-304: Cast num_seq to int to avoid slicing issues.- num_seq = SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len) + num_seq = int(SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len))tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_mamba.py (2)
1-1: Add NVIDIA Apache-2.0 header (compliance).+ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + # 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.
156-175: Fix mutates_args declaration (type and index).The op mutates ssm_state_cache (arg index 10). Current mutates_args={} is invalid and misses side effects.
-@torch.library.custom_op("auto_deploy::torch_cached_ssm_transform", mutates_args={}) +@torch.library.custom_op("auto_deploy::torch_cached_ssm_transform", mutates_args=(10,))tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (2)
1-1: Add NVIDIA Apache-2.0 header (compliance).+ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + # 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.
516-531: Return Python int from _get_sanitized_num_sequences.Prevents downstream slicing/type issues in prepare_metadata ops.
@staticmethod def _get_sanitized_num_sequences( input_or_position_ids: torch.Tensor, seq_len: torch.Tensor ) -> int: @@ - b, s = input_or_position_ids.shape[:2] - if s > 1: - num_seq = torch.sum(seq_len > 0) + b, s = input_or_position_ids.shape[:2] + if s > 1: + num_seq = int(torch.sum(seq_len > 0).item()) assert seq_len[num_seq:].sum() == 0, "seq_len should be zero-padded" - else: - num_seq = b - return num_seq + else: + num_seq = int(b) + return num_seqtests/unittest/_torch/auto_deploy/unit/singlegpu/transformations/library/test_kv_cache.py (1)
1-1: Add NVIDIA Apache-2.0 header (tests).Apply the standard header for consistency/compliance.
+ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + # 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.tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_cuda_causal_conv_cached_op.py (1)
1-1: Add NVIDIA Apache-2.0 header (policy requirement)Per coding guidelines, prepend the standard NVIDIA Apache-2.0 header to all .py files.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# 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.As per coding guidelines
tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_torch_causal_conv_cached_op.py (1)
1-1: Add NVIDIA Apache-2.0 header (policy requirement)Please prepend the standard NVIDIA Apache-2.0 header to this test file as well.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# 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.As per coding guidelines
tensorrt_llm/_torch/auto_deploy/transform/library/export_to_gm.py (1)
1-1: Add NVIDIA Apache-2.0 header (policy requirement)Please add the standard header to comply with repository guidelines.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# 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.As per coding guidelines
tensorrt_llm/_torch/auto_deploy/transform/optimizer.py (1)
1-1: Add NVIDIA Apache-2.0 header (policy requirement)Please prepend the standard header.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# 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.As per coding guidelines
🧹 Nitpick comments (5)
tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_attention.py (1)
381-389: Fake variant: fix unused args and num_seq type.Prefix unused args; cast num_seq to int. Silences Ruff ARG001 and prevents slicing issues.
-@torch_backend_prepare_metadata.register_fake -def torch_backend_prepare_metadata_fake( - position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size -): - num_seq = SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len) +@torch_backend_prepare_metadata.register_fake +def torch_backend_prepare_metadata_fake( + position_ids, seq_len, input_pos, cache_loc, _pages_per_seq, _slot_idx, _page_size +): + num_seq = int(SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len))tensorrt_llm/_torch/auto_deploy/custom_ops/triton_attention.py (1)
311-320: Fake variant: fix unused args and num_seq type.-@prepare_fused_mha_metadata.register_fake -def prepare_fused_mha_metadata_fake( - position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size -): - num_seq = SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len) +@prepare_fused_mha_metadata.register_fake +def prepare_fused_mha_metadata_fake( + position_ids, seq_len, input_pos, cache_loc, _pages_per_seq, _slot_idx, _page_size +): + num_seq = int(SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len))tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_mamba.py (1)
144-154: Fake variant: prefix unused args.Silences Ruff ARG001; no behavior change.
-@_torch_ssm_prepare_metadata.register_fake -def _torch_ssm_prepare_metadata_fake( - position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size -): +@_torch_ssm_prepare_metadata.register_fake +def _torch_ssm_prepare_metadata_fake( + position_ids, seq_len, _input_pos, _cache_loc, _pages_per_seq, slot_idx, _page_size +):tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (1)
328-333: Minor: use tuple splat instead of concatenation.Cleaner and meets Ruff RUF005.
- return ("position_ids",) + self._cached_arg_names + return ("position_ids", *self._cached_arg_names)tensorrt_llm/_torch/auto_deploy/transform/optimizer.py (1)
60-63: Fail fast if no build/load transform is configuredStarting with an empty nn.Module when mod is None can lead to opaque failures if the config lacks a builder/load transform. Add an early guard.
- # start with an empty model if not provided - if mod is None: - mod = nn.Module() + # start with an empty model if not provided + if mod is None: + # Require a build/load step in the pipeline if no model was passed in + has_builder = any( + name in self.config + for name in ("build_model", "build_and_load_factory_model", "load_weights") + ) + if not has_builder: + raise ValueError( + "No model provided and no build/load transform configured. " + "Pass an initialized nn.Module or include a build/load transform." + ) + mod = nn.Module()
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📒 Files selected for processing (24)
tensorrt_llm/_torch/auto_deploy/config/default.yaml(5 hunks)tensorrt_llm/_torch/auto_deploy/config/transformers.yaml(1 hunks)tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py(3 hunks)tensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.py(2 hunks)tensorrt_llm/_torch/auto_deploy/custom_ops/flashinfer_attention.py(2 hunks)tensorrt_llm/_torch/auto_deploy/custom_ops/mla.py(2 hunks)tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_attention.py(2 hunks)tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_causal_conv.py(2 hunks)tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_mamba.py(2 hunks)tensorrt_llm/_torch/auto_deploy/custom_ops/triton_attention.py(2 hunks)tensorrt_llm/_torch/auto_deploy/transform/interface.py(9 hunks)tensorrt_llm/_torch/auto_deploy/transform/library/build_model.py(4 hunks)tensorrt_llm/_torch/auto_deploy/transform/library/compile_model.py(3 hunks)tensorrt_llm/_torch/auto_deploy/transform/library/export_to_gm.py(2 hunks)tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py(5 hunks)tensorrt_llm/_torch/auto_deploy/transform/library/kvcache_transformers.py(6 hunks)tensorrt_llm/_torch/auto_deploy/transform/library/load_weights.py(3 hunks)tensorrt_llm/_torch/auto_deploy/transform/optimizer.py(1 hunks)tensorrt_llm/_torch/auto_deploy/transformations/_graph.py(6 hunks)tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_cuda_causal_conv_cached_op.py(1 hunks)tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_torch_attention_op.py(2 hunks)tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_torch_causal_conv_cached_op.py(1 hunks)tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_torch_mamba_cached_op.py(1 hunks)tests/unittest/_torch/auto_deploy/unit/singlegpu/transformations/library/test_kv_cache.py(1 hunks)
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tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_causal_conv.pytests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_torch_attention_op.pytests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_torch_mamba_cached_op.pytensorrt_llm/_torch/auto_deploy/transform/library/kvcache.pytests/unittest/_torch/auto_deploy/unit/singlegpu/transformations/library/test_kv_cache.pytests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_torch_causal_conv_cached_op.pytensorrt_llm/_torch/auto_deploy/transform/library/compile_model.pytensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_mamba.pytensorrt_llm/_torch/auto_deploy/transform/library/load_weights.pytensorrt_llm/_torch/auto_deploy/custom_ops/flashinfer_attention.pytensorrt_llm/_torch/auto_deploy/custom_ops/triton_attention.pytensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_attention.pytensorrt_llm/_torch/auto_deploy/transform/optimizer.pytensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.pytensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.pytensorrt_llm/_torch/auto_deploy/transformations/_graph.pytensorrt_llm/_torch/auto_deploy/transform/library/build_model.pytensorrt_llm/_torch/auto_deploy/transform/library/export_to_gm.pytensorrt_llm/_torch/auto_deploy/transform/library/kvcache_transformers.pytensorrt_llm/_torch/auto_deploy/transform/interface.pytensorrt_llm/_torch/auto_deploy/custom_ops/mla.pytests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_cuda_causal_conv_cached_op.py
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🧬 Code graph analysis (17)
tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_causal_conv.py (1)
tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (2)
_get_sanitized_seq_len(473-513)seq_len(381-382)
tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_torch_attention_op.py (1)
tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (4)
seq_len(381-382)input_pos(385-386)cache_loc(389-390)pages_per_seq(393-394)
tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py (2)
tensorrt_llm/_torch/auto_deploy/transform/interface.py (4)
_apply_to_full_model(490-500)SharedConfig(60-66)TransformInfo(121-174)BaseTransform(213-500)tensorrt_llm/_torch/auto_deploy/shim/interface.py (3)
CachedSequenceInterface(11-88)named_args(28-30)initialize_caches(59-66)
tensorrt_llm/_torch/auto_deploy/transform/library/compile_model.py (6)
tensorrt_llm/_torch/auto_deploy/transform/interface.py (4)
_apply_to_full_model(490-500)SharedConfig(60-66)TransformInfo(121-174)get(519-521)tensorrt_llm/_torch/auto_deploy/transform/library/build_model.py (2)
_apply_to_full_model(39-52)_apply_to_full_model(68-92)tensorrt_llm/_torch/auto_deploy/transform/library/export_to_gm.py (1)
_apply_to_full_model(52-76)tensorrt_llm/_torch/auto_deploy/transform/library/kvcache_transformers.py (2)
_apply_to_full_model(110-136)_apply_to_full_model(247-277)tensorrt_llm/_torch/auto_deploy/shim/interface.py (1)
CachedSequenceInterface(11-88)tensorrt_llm/_torch/auto_deploy/compile/compiler.py (2)
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tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_mamba.py (1)
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TransformRegistry(503-531)get(519-521)
tensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.py (1)
tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (2)
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tensorrt_llm/_torch/auto_deploy/transformations/_graph.py (1)
tensorrt_llm/module.py (1)
Module(33-226)
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tensorrt_llm/_torch/auto_deploy/transform/interface.py (3)
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ModelFactory(23-266)model(54-56)build_model(63-102)
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tensorrt_llm/_torch/auto_deploy/transform/interface.py (3)
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tensorrt_llm/_torch/auto_deploy/shim/interface.py (2)
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tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (5)
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page_size(185-189)
🪛 Ruff (0.13.3)
tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_causal_conv.py
169-169: Unused function argument: input_pos
(ARG001)
169-169: Unused function argument: cache_loc
(ARG001)
169-169: Unused function argument: pages_per_seq
(ARG001)
169-169: Unused function argument: page_size
(ARG001)
tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py
246-246: Unused method argument: factory
(ARG002)
247-247: Unused method argument: shared_config
(ARG002)
323-323: Unused method argument: factory
(ARG002)
324-324: Unused method argument: shared_config
(ARG002)
tensorrt_llm/_torch/auto_deploy/transform/library/compile_model.py
46-46: Unused method argument: factory
(ARG002)
47-47: Unused method argument: shared_config
(ARG002)
tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_mamba.py
144-144: Unused function argument: input_pos
(ARG001)
144-144: Unused function argument: cache_loc
(ARG001)
144-144: Unused function argument: pages_per_seq
(ARG001)
144-144: Unused function argument: page_size
(ARG001)
tensorrt_llm/_torch/auto_deploy/transform/library/load_weights.py
42-42: Unused method argument: cm
(ARG002)
44-44: Unused method argument: shared_config
(ARG002)
71-71: Unused method argument: factory
(ARG002)
72-72: Unused method argument: shared_config
(ARG002)
tensorrt_llm/_torch/auto_deploy/custom_ops/flashinfer_attention.py
216-216: Unused function argument: input_pos
(ARG001)
216-216: Unused function argument: pages_per_seq
(ARG001)
216-216: Unused function argument: slot_idx
(ARG001)
216-216: Unused function argument: page_size
(ARG001)
tensorrt_llm/_torch/auto_deploy/custom_ops/triton_attention.py
311-311: Unused function argument: pages_per_seq
(ARG001)
311-311: Unused function argument: slot_idx
(ARG001)
311-311: Unused function argument: page_size
(ARG001)
tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_attention.py
381-381: Unused function argument: pages_per_seq
(ARG001)
381-381: Unused function argument: slot_idx
(ARG001)
381-381: Unused function argument: page_size
(ARG001)
tensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.py
83-83: Unused function argument: input_pos
(ARG001)
83-83: Unused function argument: cache_loc
(ARG001)
83-83: Unused function argument: pages_per_seq
(ARG001)
83-83: Unused function argument: page_size
(ARG001)
tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py
332-332: Consider ("position_ids", *self._cached_arg_names) instead of concatenation
Replace with ("position_ids", *self._cached_arg_names)
(RUF005)
tensorrt_llm/_torch/auto_deploy/transform/library/build_model.py
41-41: Unused method argument: mod
(ARG002)
42-42: Unused method argument: cm
(ARG002)
44-44: Unused method argument: shared_config
(ARG002)
70-70: Unused method argument: mod
(ARG002)
73-73: Unused method argument: shared_config
(ARG002)
tensorrt_llm/_torch/auto_deploy/transform/library/export_to_gm.py
57-57: Unused method argument: shared_config
(ARG002)
tensorrt_llm/_torch/auto_deploy/transform/library/kvcache_transformers.py
114-114: Unused method argument: factory
(ARG002)
115-115: Unused method argument: shared_config
(ARG002)
tensorrt_llm/_torch/auto_deploy/transform/interface.py
346-346: Do not catch blind exception: Exception
(BLE001)
tensorrt_llm/_torch/auto_deploy/custom_ops/mla.py
199-199: Unused function argument: position_ids
(ARG001)
199-199: Unused function argument: pages_per_seq
(ARG001)
199-199: Unused function argument: slot_idx
(ARG001)
199-199: Unused function argument: page_size
(ARG001)
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🔇 Additional comments (27)
tests/unittest/_torch/auto_deploy/unit/singlegpu/transformations/library/test_kv_cache.py (1)
166-176: LGTM: per‑module execution toggles added.run_per_gm=False in factory/export aligns with module‑level transform flow.
Also applies to: 172-176
tensorrt_llm/_torch/auto_deploy/config/transformers.yaml (1)
9-9: ```bash
#!/bin/bash
set -euo pipefailtransforms=(
build_and_load_factory_model
move_inputs_to_device
detect_hf_attn_layers
transformers_replace_cached_attn
initialize_cache
resize_kv_cache
)for t in "${transforms[@]}"; do
echo "Checking transform: $t"
files=$(rg -l -t py "@TransformRegistry\.register\("$t"\)")
if [[ -z "$files" ]]; then
echo " ✗ no registration found for $t"
continue
fi
for f in $files; do
echo " File: $f"
if grep -q -n "def _apply_to_full_model" "$f"; then
grep -n "def _apply_to_full_model" "$f"
else
echo " ✗ _apply_to_full_model not implemented"
fi
done
done</blockquote></details> <details> <summary>tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_cuda_causal_conv_cached_op.py (1)</summary><blockquote> `179-179`: **API alignment confirmed**: All causal_conv_prepare_metadata calls now use only position_ids and seq_len; no input_ids remain. </blockquote></details> <details> <summary>tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_torch_causal_conv_cached_op.py (1)</summary><blockquote> `171-171`: **API alignment confirmed: input_ids removal complete** All torch_causal_conv_prepare_metadata call sites and tests updated to the new signature without input_ids. </blockquote></details> <details> <summary>tensorrt_llm/_torch/auto_deploy/transform/library/export_to_gm.py (1)</summary><blockquote> `63-71`: **Full-model export path LGTM; manually verify example-input keys match forward signature** Ensure `factory.get_example_inputs()` returns keys that align with the model’s `forward` signature, since `kwargs=cm.named_args` will be used. </blockquote></details> <details> <summary>tensorrt_llm/_torch/auto_deploy/transform/library/compile_model.py (1)</summary><blockquote> `42-65`: **LGTM! Clean refactor to full-model transform.** The method signature and implementation correctly shift from GraphModule-based to nn.Module-based transformation, aligning with the PR's objective to support per-full-model execution. </blockquote></details> <details> <summary>tensorrt_llm/_torch/auto_deploy/config/default.yaml (6)</summary><blockquote> `9-9`: **LGTM! Configuration aligns with full-model transform.** Setting `run_per_gm: false` correctly configures the build_model transform to operate on the full model instead of per-GraphModule, consistent with the implementation changes. --- `18-19`: **LGTM! Cleanup enabled after export.** Enabling `run_graph_cleanup: true` ensures the exported graph is canonicalized, which is appropriate for a newly exported GraphModule. --- `39-39`: **LGTM! Shape propagation requirement added.** Setting `requires_shape_prop: true` ensures shape information is available before matching eager attention patterns, which is likely needed for pattern recognition. --- `92-96`: **LGTM! Weight loading transforms configured for full-model execution.** Both load_weights and move_inputs_to_device are correctly configured with `run_per_gm: false` to operate on the full model. --- `145-148`: **LGTM! Cache initialization transforms configured for full-model execution.** Both initialize_cache and resize_kv_cache are correctly configured with `run_per_gm: false`, consistent with their implementations that operate on the full model. --- `154-154`: **LGTM! Compilation transform configured for full-model execution.** Setting `run_per_gm: false` for compile_model aligns with the implementation that compiles the entire model. </blockquote></details> <details> <summary>tensorrt_llm/_torch/auto_deploy/custom_ops/flashinfer_attention.py (2)</summary><blockquote> `156-209`: **LGTM! Public API correctly updated to use position_ids.** The function signature and implementation now use `position_ids` as the primary sequence reference instead of `input_ids`, aligning with the broader refactor to standardize metadata preparation across custom ops. The sequence length sanitization logic remains correct. --- `215-227`: **LGTM! Fake variant updated consistently.** The fake registration correctly mirrors the real variant's signature and sanitization logic using `position_ids`. </blockquote></details> <details> <summary>tensorrt_llm/_torch/auto_deploy/transform/library/build_model.py (2)</summary><blockquote> `39-52`: **LGTM! Transform correctly operates on full model.** The signature change from `_apply` to `_apply_to_full_model` with `nn.Module` parameter type aligns with the per-full-model execution strategy. The logic correctly builds the model via factory and returns appropriate metadata. --- `68-92`: **LGTM! Consistent refactor for build-and-load variant.** The signature changes mirror those in BuildModel, maintaining consistency across the transform hierarchy. </blockquote></details> <details> <summary>tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py (2)</summary><blockquote> `242-314`: **LGTM! Cache resize transform correctly refactored.** The method signature and implementation updated to operate on the full model (`nn.Module`) instead of per-GraphModule. The forward pass invocation (line 278) and all return statements correctly use `mod`. --- `319-333`: **LGTM! Cache initialization transform correctly refactored.** The signature changes are consistent with the ResizeKVCache transform, maintaining uniform interfaces across cache-related transforms. </blockquote></details> <details> <summary>tensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.py (2)</summary><blockquote> `56-78`: **LGTM! Metadata preparation updated to use position_ids.** The function signature correctly removes `input_ids` and uses `position_ids` as the primary sequence reference. The sanitization logic (line 69) is updated consistently. --- `82-91`: **LGTM! Fake variant updated consistently.** The fake registration mirrors the real variant's signature and sanitization approach using `position_ids`. </blockquote></details> <details> <summary>tensorrt_llm/_torch/auto_deploy/transform/library/kvcache_transformers.py (2)</summary><blockquote> `110-136`: **LGTM! Transform correctly profiles attention layers on full model.** The refactor to `_apply_to_full_model` is correct. The approach of attaching a fake GraphModule (`mod._gm`) to the model for profiling is appropriate for HuggingFace transformers, where we need to track attention nodes without full graph export. --- `247-277`: **LGTM! Cached attention replacement correctly integrated.** The transform properly: 1. Switches to cached attention inputs 2. Runs the parent transform on the fake GraphModule (`mod._gm`) 3. Registers the cached attention operator 4. Patches the forward method to inject metadata preparation 5. Updates config only for submodules with attention nodes This hybrid approach (full model + fake GraphModule) is appropriate for transformers models. </blockquote></details> <details> <summary>tensorrt_llm/_torch/auto_deploy/transform/interface.py (5)</summary><blockquote> `121-175`: **LGTM! TransformInfo composition operators enhance maintainability.** The addition of: - `from_last_info` class method for initializing from previous transform state - `__or__` operator for OR-merging (is_clean=True if either is clean) - `__and__` operator for AND-merging (is_clean=True only if both are clean) These operators enable cleaner composition of transform metadata throughout the pipeline, replacing in-place dict updates with functional composition. --- `277-417`: **LGTM! Transform execution flow correctly refactored.** The changes successfully: 1. Replace GraphModule-centric types with nn.Module throughout 2. Use TransformInfo operators for cleaner metadata composition 3. Introduce `_apply_per_gm_or_whole_model` to dispatch between execution modes 4. Update all internal references from `gm` to `mod` The logic is preserved and the new structure supports both per-GraphModule and full-model transform execution. --- `439-473`: **LGTM! Cleanup logic consolidated effectively.** The new `_run_cleanup` method consolidates the previous pre/post cleanup logic into a single, well-structured method. The conditional logic correctly handles: - Shape propagation requirements (clean_shape → canonicalize + run_shape_prop) - Graph cleanup requirements (clean_graph → canonicalize only) - Conditional execution based on current state (is_clean, has_valid_shapes) This reduces code duplication and improves maintainability. --- `475-500`: **LGTM! Dual execution mode support added.** The addition of `_apply_to_full_model` alongside the existing `_apply` method enables transforms to support either per-GraphModule or full-model execution. The NotImplementedError messages clearly indicate which mode each transform supports, guiding implementation. --- `85-85`: **run_per_gm default change verified safe** All transforms explicitly set run_per_gm in their configs; changing the default to True will not alter existing behavior. </blockquote></details> </blockquote></details> </details> <!-- This is an auto-generated comment by CodeRabbit for review status -->
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PR_Github #20677 [ run ] completed with state |
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PR_Github #20819 [ run ] triggered by Bot |
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PR_Github #20819 [ run ] completed with state |
Signed-off-by: Lucas Liebenwein <[email protected]>
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lucaslie
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tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_build_small_single.py
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will be merged via #8203 |
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