aimet_onnx.adascale

Top level APIs

aimet_onnx.adascale.adascale_optimizer.apply_adascale(sim, inputs, adascale_model_config, num_iterations=1500, *, topology=None, loss_fn=None)
Parameters:

  • sim (QuantizationSimModel) – Quantization Sim model

  • inputs (Collection[Dict[str, ndarray]]) – (Collection[Dict[str, np.ndarray]]): The set of input samples to use during optimization.

  • adascale_model_config (AdaScaleModelConfig) – Adascale model config. There are pre-defined configs for Llama, Qwen2, Mistral, Qwen3, Phi3. For other models use AdaScaleModelConfig

  • num_iterations (int) – Number of iterations to optimize for during AdaScale

  • topology (Optional[LlmTopology]) – Decoder-stack topology of the model held by sim, from analyze_llm_topology(). AdaScale optimizes one decoder block at a time and takes the block boundaries from it — structure discovery belongs to llm_topology, not to AdaScale. Analyze the float model before building the sim: a topology describes the model rather than the sim, so it is derived once and reused. Optional today: when omitted the topology is discovered internally from the sim’s graph and a warning is raised. Passing it explicitly is recommended, and may become required in a future release.

  • loss_fn (Optional[Callable[[Tensor, Tensor, int], Tensor]]) – Loss function with signature loss_fn(fp_out, quant_out, data_idx) returning a scalar loss tensor. data_idx is the index of the current input in inputs order. Defaults to MSE loss if None.

Example usage:
>>> model = DummyModel()
>>> inputs = ...
>>> adascale_model_config = adascale_model_config['llama']
>>> topology = analyze_llm_topology(model, "llama")   # analyze the float model
>>> sim = QuantizationSimModel(model)
>>> apply_adascale(sim, inputs, adascale_model_config, num_iterations=num_iterations,
...                topology=topology)
>>> sim.compute_encodings(...)
>>> sim.export(...)

  1. apply_adascale modifies the weights in-place in the model

  2. compute encodings should not be called before the apply_adascale call

  3. Activation quantizers will remain uninitialized throughout the feature, and so compute encodings needs to be called by the user afterwards. This is so activation encodings will be computed with updated weights taken into account.

Warning: This feature is currently considered experimental pending API changes

class aimet_onnx.adascale.adascale_optimizer.AdaScaleModelConfig(model_type, beta_gamma_lr=0.001, scales_lr=0.0005)[source]

Model topology

AdaScale optimizes one decoder block at a time and takes those block boundaries from an LlmTopology, which describes the structure of the decoder stack. Analyze the float model before creating the sim, then pass the result as topology:

topology = analyze_llm_topology(onnx_model, model_type="llama")
sim = QuantizationSimModel(onnx_model, ...)
apply_adascale(sim, inputs, adascale_model_config, topology=topology)
aimet_onnx.llm_topology.analyze_llm_topology(model, model_type, *, ir_model=None)[source]

Analyze model end-to-end and return a name-based LlmTopology.

How the decoder blocks and their layers are found depends on model_type:

  • For ACTIVE_NORM_MODEL_TYPES, by graph structure, as analyze_llm_topology_by_norm_count() does with its defaults: every active RMSNorm is detected, and the norms are grouped two per decoder block.

  • For every other supported model type, by HuggingFace module name (see hf_patterns): the per-block projections and norms, embed_tokens, the final norm and lm_head. Blocks are the decoder layers those names belong to. The remaining fields are derived from the graph around the named layers:

    • residual_input — the tensor entering the block’s input norm; residual_output — the next block’s residual_input, or for the last block the tensor entering the final norm (the final residual Add’s output for a headless backbone).

    • qk_matmul / attn_v_matmul — the dynamic MatMuls between the named q/k/v and o_proj (best-effort: empty when attention is a fused op).

    • KV-cache names, hidden_size and head_dim — as before.

    The named layers are then cross-checked against the graph, so a name that points at the wrong node is an error rather than a wrong topology: each block’s q/k/v (gate/up) must be exactly the weighted linears its input (post-attention) norm feeds, o_proj and down_proj must be the linears writing the residual stream, and the final norm must feed exactly lm_head.

Parameters:

  • model (ModelProto) – ONNX ModelProto to analyze. May be a float export or a QuantizationSimModel graph. Not mutated. A dynamo export must first go through fix_node_names_pass().

  • model_type (str) – HuggingFace PretrainedConfig.model_type of the model, e.g. "llama". For a VLM, that of its top-level config (e.g. "qwen3_vl"); the name tables of VLM backbones not analyzed by active norms are keyed by the text config’s instead (e.g. "qwen2_5_vl_text").

  • ir_model (Optional[Model]) – Pre-built analysis IR for model — as returned by build_analysis_ir(), i.e. quantizer-stripped and RMSNorm-fused. Built here when None. Pass one only to avoid a second from_proto of a large model when the caller already holds it; a faithful (unfused) IR will not detect norms and must not be passed.

Return type:

LlmTopology

Returns:

LlmTopology with every field populated. head_dim is None when the export exposes no past_value graph input to derive it from.

Raises:

ValueError – If model_type is unsupported, if the layers cannot be identified by name (NamedLayerMatchError), if the named layers disagree with the graph, or if the decoder blocks cannot be found by active norms.

class aimet_onnx.llm_topology.LlmTopology(embed_tokens=list(), lm_head=list(), blocks=list(), past_key_input_names=list(), past_key_output_names=list(), past_value_input_names=list(), past_value_output_names=list(), active_norms=None, hidden_size=None, head_dim=None)[source]

Topology of an ONNX decoder-stack model: blocks + backbone-level roles + dims.

Parameters:

  • embed_tokens (List[str]) – Names of the token-embedding ``Gather``(s) that produce the initial residual-stream activations.

  • lm_head (List[str]) – Names of the vocabulary-projection linear(s) downstream of the final norm.

  • blocks (List[Union[AttentionBlockTopology, MambaBlockTopology]]) – Per-decoder-block topology in topological order.

  • past_key_input_names (List[str]) – Raw past_key_* graph inputs in declaration order, collected tolerantly (empty for prefill-only exports without a KV-cache). Pairing these to blocks and validating that their count matches the block count are the consumer’s responsibility (e.g. R3) — R1-only and prefill-only flows do not require KV-cache inputs.

  • past_key_output_names (List[str]) – Key-cache graph outputs in declaration order.

  • past_value_input_names (List[str]) – Value-cache graph inputs in declaration order.

  • past_value_output_names (List[str]) – Value-cache graph outputs in declaration order.

  • active_norms (Optional[List[ActiveNorm]]) – Active norms in topological order used to build the topology.

  • hidden_size (Optional[int]) – Residual-stream hidden dimension (None if not inferred; analyze_llm_topology() fills it).

  • head_dim (Optional[int]) – Per-head dimension (None when it could not be derived, e.g. an export without KV-cache inputs).