ATOM compilation & CUDA graphs guide

Quick Reference

Concept	Key Class / Enum	Import
Compilation Levels	CompilationLevel	from atom.config import CompilationLevel
Compilation Config	CompilationConfig	from atom.config import CompilationConfig
CUDA Graph Modes	CUDAGraphMode	from atom.config import CUDAGraphMode
Token / Request Graphs	TokenGraph, ReqGraph, @eager_region	from atom.utils.region_graph import TokenGraph
Forward Context	ForwardContext	from atom.utils.forward_context import ForwardContext
Compiler Backend	VllmBackend	from atom.utils.backends import VllmBackend
Compiler Manager	CompilerManager	from atom.utils.backends import CompilerManager
Compiler Interface	CompilerInterface	from atom.utils.compiler_inferface import CompilerInterface
Inductor Adaptor	InductorAdaptor	from atom.utils.compiler_inferface import InductorAdaptor
Piecewise Backend	PiecewiseBackend	from atom.utils.cuda_piecewise_backend import PiecewiseBackend
Compile Decorator	@support_torch_compile	from atom.utils.decorators import support_torch_compile
Custom Op Registration	direct_register_custom_op	from atom.utils.custom_register import direct_register_custom_op

Compilation Levels at a Glance

Level	Name	Behavior
0	NO_COMPILATION	Pure eager execution, no torch.compile
1	DYNAMO_AS_IS	torch.compile with backend="eager"
2	DYNAMO_ONCE	torch.compile with Inductor
3	PIECEWISE	Piecewise compilation: split at splitting_ops, each piece compiled by Inductor (production default)

CUDA Graph Modes at a Glance

Mode	Value	Behavior
NONE	0	No graph capture
TR_HYBRID	1	Token graphs, breaking at eager regions (default for level 3)
FULL	2	Whole-model capture
FULL_DECODE_ONLY	(FULL, NONE)	Full for decode, none for mixed batches
FULL_AND_TR_HYBRID	(FULL, TR_HYBRID)	Recorded as TR_HYBRID by the native runner: decode replays token graphs too

Compilation levels

ATOM provides four compilation levels via the CompilationLevel class in atom/config.py. The level is set through CompilationConfig.level and controls how torch.compile is applied to the model.

Level 0 — NO_COMPILATION

No torch.compile is applied. The model runs in pure eager mode. This is the simplest mode and is useful for debugging or when using models that are incompatible with torch.compile.

When level=0, the @support_torch_compile decorator sets self.do_not_compile = True and the model’s __call__ method bypasses compilation entirely, calling self.forward() directly.

Level 1 — DYNAMO_AS_IS

Uses torch.compile with backend="eager" and fullgraph=True. This runs Dynamo’s bytecode analysis and graph capture but does not apply any compiler optimizations. It is useful as a quick check to verify that a model is compatible with Dynamo’s tracing.

Like level 0, DYNAMO_AS_IS causes the decorator to set self.do_not_compile = True, since the model runner (rather than the decorator) handles the compilation at this level.

Level 2 — DYNAMO_ONCE

Uses torch.compile with the Inductor backend. The model graph is traced by Dynamo and compiled once through Inductor for optimized GPU kernel generation. The @support_torch_compile decorator’s custom dispatcher is activated when compilation_level >= DYNAMO_ONCE, allowing compiled bytecode to be dispatched directly after the first compilation without repeated guard evaluation.

Level 3 — PIECEWISE (production default)

The most advanced level. When Config.__post_init__ detects level == PIECEWISE, it:

1. Calls CompilationConfig.set_splitting_ops_for_v1() to configure the splitting operations (default: ["aiter.unified_attention_with_output", "aiter.mla_attention"]).

2. Calls Config._set_cudagraph_sizes() to compute the graph batch sizes.

3. Sets cudagraph_mode = CUDAGraphMode.TR_HYBRID when none was chosen.

4. Calls CompilationConfig.init_with_cudagraph_sizes() to finalize compile sizes.

The VllmBackend is then used as the torch.compile backend. It splits the model graph into subgraphs at the splitting operations and compiles each subgraph independently via PiecewiseBackend.

CUDA graph modes

The CUDAGraphMode enum in atom/config.py controls how CUDA graphs are captured and replayed. CUDA graphs record a sequence of GPU operations and replay them with minimal CPU overhead, which is critical for low-latency decode steps.

NONE (value: 0)

No CUDA graph capture or replay. Every forward pass launches kernels individually. This mode is used during profiling, warmup, or when CUDA graphs are not supported.

TR_HYBRID (value: 1)

Token graphs and request graphs mixed, for every step alike. The compiled forward is recorded as a TokenGraph (atom/utils/region_graph.py) per token bucket: one bucket per bs * q the capture loop visits, which any decode step padded to that many rows replays, whatever its requests. The compiled pieces are recorded inline; the recording breaks only at the split ops declared @eager_region – the ones whose launches depend on host data (the attention / mixer ops of most models). Those run eagerly between the graph’s segments, reading the live step’s metadata, and hand their result to the next segment at the address it was recorded reading. A region whose launches a request bucket fixes is where a ReqGraph joins the mix; no model records one there yet.

A split op (mark_spliting_op) is a compile boundary only; it is a graph break only if also declared @eager_region. DeepSeek-V4’s compressor launch (v4_attn_compress_launch) is a split op recorded inline: its plan is sized by the token count. It runs ahead of the Q/KV projections and forks the compressors onto side streams, as FULL does, so the projections overlap them; _sparse_attention joins them before the first eager region, so fork and join land in one recorded segment. Its only break is the FP4 indexer’s logits (_fp4_band_logits in atom/model_ops/indexer/score.py), whose launch is planned from the step’s sequences; the bands, the FP8 launch and the top-k follow the rows alone and are recorded.

FULL (value: 2)

The entire model forward pass is captured as a single CUDA graph. This is suitable for small models or workloads with small, uniform batch sizes. Not all attention backends support full CUDA graph capture.

FULL_DECODE_ONLY (value: (FULL, NONE))

A tuple mode that applies different strategies to different batch types:

• Decode batches: Captured with full CUDA graphs.

• Mixed prefill-decode batches: Run without CUDA graphs.

This is useful for prefill/decode disaggregated (P/D) setups where decode latency matters more than prefill performance.

FULL_AND_TR_HYBRID (value: (FULL, TR_HYBRID))

A tuple mode that the native runner records exactly as TR_HYBRID (ModelRunner._tr_hybrid_active asks uses_tr_hybrid()): decode batches replay token graphs as well, and no full decode graph is captured.

Helper methods

The CUDAGraphMode enum provides several helper methods for runtime dispatch:

Method	Returns	Purpose
decode_mode()	CUDAGraphMode	Returns the mode to use for decode batches. For tuple modes, returns the first element.
mixed_mode()	CUDAGraphMode	Returns the mode to use for mixed batches. For tuple modes, returns the second element.
separate_routine()	bool	Returns True if the mode is a tuple (different strategies for decode vs. mixed).
has_full_cudagraphs()	bool	Returns True if any part of the mode uses FULL capture.
uses_tr_hybrid()	bool	Returns True if either decode or mixed mode uses TR_HYBRID.
max_cudagraph_mode()	CUDAGraphMode	Returns the highest-valued mode across both decode and mixed modes.

CUDA graph capture

CUDA graph capture is handled by ModelRunner.capture_cudagraph() in atom/model_engine/model_runner.py. This method is called at startup (under @torch.inference_mode()) to pre-capture graphs for a set of batch sizes.

Capture flow

capture_cudagraph()
  |
  +-- Determine capture_sizes list
  |     |-- If cudagraph_capture_sizes is set: use directly
  |     |-- If cuda_graph_sizes has 1 value N: [1, 2, ..., 8, 16, 32, ..., N]
  |     +-- If cuda_graph_sizes has >1 values: use the provided list
  |
  +-- Sort capture_sizes in descending order (largest batch first)
  |
  +-- Assert max batch size <= max_num_seqs
  |
  +-- Initialize graph storage: self.req_graph = ReqGraph()
  |
  +-- For each batch size bs (with progress bar on rank 0):
  |     |
  |     +-- Compute max_q_len (= mtp_k + 1 if MTP drafter, else 1)
  |     +-- Compute running_tokens = bs * max_q_len (the rows the recording runs)
  |     +-- Prepare cu_seqlens_q, positions
  |     +-- Build attn_metadata and context via attn_metadata_builder
  |     +-- Set forward context (set_forward_context) with
  |     |   running_tokens_per_dp_rank = running_tokens on every rank:
  |     |   capture is symmetric, every DP rank records the same bucket
  |     +-- Warmup run: model(input_ids[:running_tokens], positions[:running_tokens])
  |     +-- Record: self.req_graph.record(full_forward) -- one graph per
  |     |   request bucket, in the pool every recording shares
  |     +-- torch.cuda.synchronize()
  |
  +-- Sort capture_sizes back to ascending order
  +-- Return (elapsed_time, capture_sizes)

Graph keying

Decode graphs are request-bucket graphs (atom/utils/region_graph.py’s ReqGraph, held as ModelRunner.req_graph), keyed by request_bucket:

request_bucket(running_bs, max_seqlen_q) == (running_bs, draft_num)

• running_bs: The padded batch size used during capture.

• draft_num: The draft tokens each request verifies – its query rows less the one it decodes: 0 for standard decode, mtp_k for MTP speculative decoding.

Under TBO decode (--enable-tbo all), a bucket that splits (decode_bucket_splits: running_bs > 2) is recorded as a TBO graph: its two microbatches cut at the bucket (decode_ubatch_request_ranges(running_bs)), every DP rank recording the same split. A decode step that replays it lays each microbatch’s attention metadata out for that split (ForwardMode.ubatch_split_bs), whatever each rank scheduled into the bucket. A split decode no recording serves – off the ladder, under --enforce-eager, or under TR_HYBRID, whose token graphs have no split recording – runs eagerly, its microbatches cut from the scheduled requests.

Graph pool sharing

Every ReqGraph and TokenGraph (the drafter’s included) records into one pool, region_graph.graph_pool(). Recordings never overlap in use – a graph’s output is consumed before the next one replays – so the pool is the largest recording’s, not their sum.

Default capture sizes

When cuda_graph_sizes has a single value (e.g., [512], the default), the capture sizes follow this pattern:

list(range(1, 9)) + [i for i in range(16, cuda_graph_sizes[0] + 1, 16)]
# Example with default 512:
# [1, 2, 3, 4, 5, 6, 7, 8, 16, 32, 48, 64, ..., 496, 512]

Graph replay in run_model()

During inference, ModelRunner.run_model() decides whether to use eager execution or graph replay:

def run_model(self, input_ids):
    forward_context = get_forward_context()
    context = forward_context.context
    bs = context.batch_size
    is_prefill = context.is_prefill
    positions = context.positions

    if is_prefill or self.enforce_eager or bs > self.capture_sizes[-1]:
        # Eager path: prefills, enforce_eager mode, or oversized batches
        hidden_states = self.model(input_ids, positions)
    else:
        # Graph replay path: decode batches within captured range
        graph_key = request_bucket(
            context.running_bs, forward_context.attn_metadata.max_seqlen_q
        )
        logits = self._decode_graph(graph_key)()  # ReqGraph.replay
        num_tokens = context.batch_size * max_q_len
        hidden_states = self.forward_vars["outputs"][:num_tokens]

    return self.model.compute_logits(hidden_states), hidden_states

Key decisions:

• Prefill: Always eager (variable sequence lengths make CUDA graphs impractical).

• Decode with bs <= max captured size: Replay the pre-captured graph.

• Decode with bs > max captured size: Fall back to eager execution.

• enforce_eager=True: Always eager, regardless of batch size.

Piecewise compilation

Piecewise compilation splits the model’s computation graph at specified operations and compiles each subgraph independently. It is a compilation step only: which CUDA graphs a step replays – FULL request graphs or TR_HYBRID token graphs – is CUDAGraphMode’s, independent of the compilation level.

Splitting operations

The splitting_ops field in CompilationConfig defines which operations split the graph. When set_splitting_ops_for_v1() is called (automatically at level 3), the default splitting ops are:

["aiter.unified_attention_with_output", "aiter.mla_attention"]

These attention operations are split out because:

1. They require dynamic metadata (sequence lengths, block tables) that changes per step.

2. Some attention backends are not compatible with CUDA graph capture.

3. Attention kernels are already highly optimized, so Inductor compilation provides minimal additional benefit.

Compilation pipeline

The VllmBackend.__call__ method orchestrates the piecewise compilation:

1. Graph splitting: split_graph() divides the traced model graph at the splitting operations into a sequence of SplitItem objects, each containing a subgraph.

2. Submodule identification: Subgraphs that are not splitting operations are identified as candidates for compilation.

3. Dynamic-shape compilation: PiecewiseCompileInterpreter runs the split graph with fake inputs and compiles each non-splitting subgraph via CompilerManager.compile() for a general (dynamic) shape.

4. Backend creation: For each compiled subgraph, a PiecewiseBackend instance is created. It holds:

   • compiled_graph_for_general_shape: The Inductor-compiled graph for dynamic shapes.

   • concrete_size_entries: A dictionary mapping specific runtime shapes to ConcreteSizeEntry objects for shape-specialized compilation.

5. Runtime dispatch: When PiecewiseBackend.__call__ is invoked:

   • On the first run, it uses the general-shape compiled graph.

   • For subsequent runs, if the runtime shape is in compile_sizes, it lazily compiles a shape-specialized version via CompilerManager.compile() and caches it.

   • For shapes not in compile_sizes, it falls back to the general-shape compiled graph.

Cache management

The CompilerManager caches compiled graphs using a key of (runtime_shape, graph_index, backend_name). The cache is stored in a Python file (vllm_compile_cache.py) at the local cache directory (~/.cache/atom/torch_compile_cache/<hash>/rank_<i>/<prefix>/).

On subsequent runs with the same model and configuration, compiled graphs are loaded from the cache, bypassing Inductor compilation entirely.

Forward context & stateless dispatch

The ForwardContext dataclass in atom/utils/forward_context.py provides a module-level global mechanism for passing metadata to layers during the forward pass. This is critical for CUDA graphs because captured graphs cannot accept new arguments — all dynamic metadata must be accessible through a side channel.

ForwardContext fields

Field	Type	Purpose
no_compile_layers	dict[int, Any]	Layers that should skip compilation (from static_forward_context)
attn_metadata	AttentionMetaData or dict	Attention-specific metadata (sequence lengths, block tables, etc.)
kv_cache_data	dict[str, KVCacheTensor]	KV cache tensors for each layer
context	Context	Basic forward pass context (positions, is_prefill, batch_size, running_bs, running_tokens)
dp_metadata	DPMetadata	Data-parallel metadata (token counts across DP ranks)
spec_decode_metadata	SpecDecodeMetadata	Speculative decoding metadata (draft tokens, logits indices)

Lifecycle

The forward context follows a set-use-reset lifecycle:

1. Set: Before each forward pass, set_forward_context() is called with attention metadata, the ATOM config, a Context object, and optional DP/speculative decoding metadata.

2. Access: During the forward pass, any layer can call get_forward_context() to retrieve the current metadata without needing it passed as a function argument. This is used by both eager execution and CUDA graph replay paths.

3. Reset: After the forward pass, reset_forward_context() replaces the global context with an empty ForwardContext().

Context dataclass

The Context object carries the most frequently accessed per-step state:

@dataclass
class Context:
    positions: torch.Tensor    # Token position IDs
    is_prefill: bool = False   # Whether this is a prefill step
    batch_size: int = 0        # Number of sequences in the batch
    running_bs: int = 0        # Padded SEQUENCE count: graph lookup, draft width
    running_tokens: int = 0    # Padded ROW count: what MoE all_gather pads to
    is_draft: bool = False     # Whether this is a draft model forward

running_bs drives CUDA graph dispatch: it holds the padded batch size that maps to a pre-captured graph key. running_tokens is the same shape counted in hidden_states rows, which is what MoE pads to before the cross-DP all_gather – the two are not interconvertible on a prefill (ragged prompts) or a DSpark ragged decode (flat token bucket), which is why both are stored.

Integration with CUDA graphs

For FULL request graphs (ReqGraph), the forward context is set before run_model() via set_forward_context(), and run_model() reads context.running_bs and attn_metadata.max_seqlen_q to look up the correct pre-captured graph.

For TR_HYBRID token graphs, ForwardMode.decide settles whether the step runs a recorded token bucket (ForwardMode.token_graph), and set_forward_context turns that into ForwardContext.tr_graph = "replay"; the model’s recording unit then replays its TokenGraph for the step’s rows. The capture loop records with tr_graph = "record".

A model declares its recording unit with @token_graph (atom/utils/region_graph.py), independently of @support_torch_compile, so a token graph does not depend on torch.compile. The unit is the outermost module whose forward holds all the step’s per-token work: DeepSeek-V4 marks DeepseekV4ForCausalLM, whose DP-attention input_ids gather feeds the hash MoE, and V4.1 marks its three stages (_Backbone, _EarlyLayers, _LateLayers). Every other target model marks its compiled *Model.

A recording serves prefill and decode steps of its token count alike, so a decode bucket recorded at capture is not recorded again for prefill. It counts as a phase’s bucket only once that phase’s record pass asked for it, though: TokenGraph.captured_sizes("prefill" | "decode") are the buckets a step of that phase pads to, so a model that records only decode buckets (DeepSeek-V4 under TR_HYBRID) reports no prefill bucket, and its scheduler sends no dummy prefill. A unit whose forward records different work in each phase declares @token_graph(per_phase=True) and is recorded once per phase. V4.1’s stages are per phase: a decode forward computes no kv for the attention region a prefill reads. Drafters are not marked: each records its own passes as DraftGraphs (atom/spec_decode/draft_graph.py) in either graph mode.

Every recording (TokenGraph.record, and through it ReqGraph and DraftGraph) first waits out the NCCL watchdog (atom/distributed/nccl_watchdog.py:drain_nccl_works). A collective recorded into the graph joins its NCCL stream into the capture, and a watchdog poll of an eager collective still pending on that stream fails with hipErrorCapturedEvent and invalidates the capture; torch.cuda.synchronize() alone does not prevent it.

aiter’s custom collectives keep their registered (direct-read) capture path inside token graphs, as under FULL: each recorded collective reads its peers’ inputs at the addresses the recording gave them, which a segment’s pool holds fixed, and the kernels’ closing barrier keeps a rank from overwriting an input before every peer has read it. A recording’s collectives are thus sized by its bucket, so under DP attention every prefilling rank, an idle rank’s dummy prefill included, replays the bucket holding the group’s largest token count (ForwardMode._pad_to_token_graph); ranks on different buckets would hang. The copy-in path the removed PIECEWISE mode forced cost one device-to-device copy per collective (about 0.9 ms per DeepSeek-V4-Flash DP decode step).

Compiler backend

CompilerManager

CompilerManager in atom/utils/backends.py manages the full compilation lifecycle:

• Initialization: Creates a CompilerInterface via make_compiler(). Uses InductorStandaloneAdaptor for PyTorch 2.8+ or InductorAdaptor for earlier versions.

• Caching: Maintains a dictionary mapping (runtime_shape, graph_index, backend_name) to compiler-specific handles. Caches are serialized to a Python file using pprint.

• Compile-or-load: On each call to compile(), first attempts load() from the cache. On miss, delegates to the compiler and stores the result.

CompilerInterface

CompilerInterface in atom/utils/compiler_inferface.py (note the typo in the filename) defines the abstract interface that all compiler backends must implement:

Method	Purpose
initialize_cache(cache_dir, disable_cache, prefix)	Set up cache directories for the compiler
compute_hash(vllm_config)	Generate a hash of compiler-specific state for cache invalidation
compile(graph, example_inputs, compiler_config, runtime_shape, key)	Compile a graph, returning (compiled_callable, handle)
load(handle, graph, example_inputs, graph_index, runtime_shape)	Load a previously compiled graph from the handle

InductorAdaptor

The default compiler for PyTorch < 2.8. Uses torch._inductor.compile_fx.compile_fx and monkey-patches several internal functions to:

• Extract the compilation hash for caching.

• Provide a dummy shape environment (AlwaysHitShapeEnv) so Inductor cache lookups succeed outside of Dynamo’s tracing context.

• Force caching of graphs that Inductor would normally refuse to cache.

When runtime_shape is an integer (specific batch size), it enables max_autotune and coordinate_descent_tuning for Triton kernel parameter optimization.

InductorStandaloneAdaptor

The preferred compiler for PyTorch 2.8+. Uses torch._inductor.standalone_compile which provides a cleaner interface without the monkey-patching required by InductorAdaptor. Compiled artifacts are saved to disk in “unpacked” format and can be loaded directly.

VllmBackend

VllmBackend in atom/utils/backends.py serves as the torch.compile backend for level 3 (piecewise) compilation. When Dynamo calls it:

1. Computes a cache directory hash from config, traced files, and compiler state.

2. Splits the graph at splitting_ops using split_graph().

3. Runs PiecewiseCompileInterpreter to compile each non-splitting subgraph.

4. Saves the computation graph to computation_graph.py for debugging.

5. Returns the stitching graph module (split_gm) as the callable.

If cudagraph_copy_inputs is True, it wraps the callable to copy input tensors into static buffers before each call, ensuring CUDA graph input address stability.

@support_torch_compile decorator

The @support_torch_compile decorator in atom/utils/decorators.py augments a model class to support torch.compile:

1. Class modification: Adds TorchCompileWrapperWithCustomDispatcher as a base class and overrides __init__ and __call__.

2. Dynamic shape marking: On the first compilation, it inspects the forward method signature, identifies torch.Tensor arguments, and calls torch._dynamo.mark_dynamic() to mark their batch dimensions as dynamic.

3. Custom dispatch: After the first compilation, if use_custom_dispatcher is True (levels >= 2), subsequent calls bypass Dynamo’s guard mechanism and dispatch directly to the compiled bytecode via dispatch_to_code(0).

4. Safety check: The bytecode hook checks for update in the compiled code’s co_names, raising an error if the model modifies nn.Module buffers during the forward pass (which would cause silent errors with CUDA graphs).

Custom op registration

direct_register_custom_op() in atom/utils/custom_register.py registers custom operators with PyTorch’s torch.library system:

direct_register_custom_op(
    op_name="my_op",
    op_func=my_kernel,
    mutates_args=["output"],
    fake_impl=my_fake_impl,
)

This registers the op under the "aiter" library namespace (e.g., aiter.my_op), making it visible to Dynamo’s tracing. The fake_impl is used during tracing to compute output shapes without executing the real kernel. The dispatch_key defaults to "CUDA" for GPU operations.

Registered custom ops can be used as splitting_ops in piecewise compilation (e.g., "aiter.unified_attention_with_output").

Configuration options

All compilation-related configuration fields from CompilationConfig:

Field	Type	Default	Description
level	int	0	Compilation level (0-3). See Section 1.
use_cudagraph	bool	True	Whether CUDA graph capture is enabled.
cudagraph_capture_sizes	Optional[list[int]]	None	Explicit list of batch sizes to capture. Overrides cuda_graph_sizes.
cuda_graph_sizes	list[int]	[512]	Controls auto-generated capture sizes. 1 value = generate pattern; >1 values = use directly.
cudagraph_mode	Optional[CUDAGraphMode]	None	CUDA graph mode. Set to TR_HYBRID automatically at level 3.
splitting_ops	Optional[list[str]]	None	Operations that split the graph for piecewise compilation. Auto-set at level 3.
cudagraph_copy_inputs	bool	False	Copy input tensors to static buffers for CUDA graph address stability. Effective at level 3 whenever cudagraph_mode is not NONE.
use_inductor	bool	True	Whether to use the Inductor compiler backend.
compile_sizes	Optional[list[Union[int, str]]]	None	Specific sizes to compile with Inductor. Supports "cudagraph_capture_sizes" string.
inductor_compile_config	dict	{}	Additional Inductor configuration (e.g., max_autotune).
debug_dump_path	str	""	Path to dump debug information (traced graphs, decompiled code).
cache_dir	str	""	Custom cache directory. Auto-generated if empty (~/.cache/atom/torch_compile_cache/<hash>/).

Related fields on Config:

Field	Type	Default	Description
enforce_eager	bool	False	Force eager execution, skip all compilation and CUDA graphs.
capture_sizes	Optional[list[int]]	None	Final list of batch sizes for CUDA graph capture (computed from CompilationConfig).
compilation_config	CompilationConfig	CompilationConfig()	The compilation configuration dataclass.

Decision tree

Use this decision tree to select the right compilation level and CUDA graph mode for your workload:

Is the model supported by torch.compile?
|
+-- No --> Level 0 (NO_COMPILATION)
|          enforce_eager=True
|
+-- Yes
    |
    +-- Debugging / profiling?
    |   |
    |   +-- Yes --> Level 0 (NO_COMPILATION)
    |
    +-- Quick compatibility check?
    |   |
    |   +-- Yes --> Level 1 (DYNAMO_AS_IS)
    |
    +-- Want Inductor optimization without CUDA graphs?
    |   |
    |   +-- Yes --> Level 2 (DYNAMO_ONCE)
    |
    +-- Production deployment
        |
        +-- Level 3 (PIECEWISE) [recommended]
            |
            +-- Standard serving --> cudagraph_mode=TR_HYBRID (default)
            |
            +-- Small model / uniform batches --> cudagraph_mode=FULL
            |
            +-- P/D disaggregated (decode instance) --> cudagraph_mode=FULL_DECODE_ONLY

Common configurations

Default production setup (level 3, TR_HYBRID graphs):

CompilationConfig(level=3)
# Automatically sets:
#   splitting_ops = ["aiter.unified_attention_with_output", "aiter.mla_attention"]
#   cudagraph_mode = CUDAGraphMode.TR_HYBRID
#   cuda_graph_sizes = [512]
#   capture_sizes = [1, 2, ..., 8, 16, 32, ..., 512]

Custom capture sizes:

CompilationConfig(level=3, cudagraph_capture_sizes=[1, 2, 4, 8])

Debugging with full eager execution:

Config(model="...", enforce_eager=True)
# or
CompilationConfig(level=0)

Inductor with debug dump:

CompilationConfig(level=3, debug_dump_path="/tmp/atom_debug")
# Dumps traced graphs and decompiled code to /tmp/atom_debug/rank_0/

Source files

File	Description
atom/config.py	CompilationLevel, CompilationConfig, CUDAGraphMode, Config.__post_init__ (compilation setup)
atom/utils/region_graph.py	TokenGraph, ReqGraph, eager_region, token_graph, graph_pool
atom/utils/backends.py	CompilerManager, VllmBackend, SplitItem, split_graph(), PiecewiseCompileInterpreter
atom/utils/forward_context.py	ForwardContext, Context, AttentionMetaData, DPMetadata, set_forward_context(), get_forward_context()
atom/utils/compiler_inferface.py	CompilerInterface, InductorAdaptor, InductorStandaloneAdaptor, AlwaysHitShapeEnv
atom/utils/cuda_piecewise_backend.py	PiecewiseBackend, ConcreteSizeEntry
atom/utils/decorators.py	@support_torch_compile, TorchCompileWrapperWithCustomDispatcher, start_monitoring_torch_compile
atom/utils/custom_register.py	direct_register_custom_op(), aiter_lib (Library instance)
atom/model_engine/model_runner.py	ModelRunner.capture_cudagraph(), ModelRunner.run_model()