…ariant

The sharding-IR ``modeling_gpt_oss_ir.py`` already covered every feature of
the non-IR legacy file (same RMSNorm / RoPE / attention with sinks / MoE
router / experts), differing only in:

* attention Linears go through ``torch.ops.auto_deploy.torch_linear_simple``
  with sharding hint kwargs so TP > 1 attention sharding works without an
  external graph rewrite;
* view ops on q/k/v/attn_out use ``torch.ops.auto_deploy.view`` with
  ``tp_scaled_dim=2`` so the head-count dim scales with TP;
* the post-attention all-reduce is expressed as a
  ``torch.ops.auto_deploy.all_reduce`` placeholder.

Consolidate: rename ``modeling_gpt_oss_ir.py`` into the default
``modeling_gpt_oss.py`` (the legacy non-IR variant is removed) and drop
the ``AD_USE_IR_MODELS`` opt-in entry for gpt-oss in
``models/custom/__init__.py``. This matches the trajectory in upstream
PR NVIDIA#13478 (other models being migrated to sharding-IR as default).

GSM8K full 1319-sample validation, gpt-oss-120b @ TP=2 (post-rebase,
W4A8 mxfp8 activations):

  Pre-IR (legacy modeling):  88.55 % (±0.88), 992 s
  Post-IR (sharding-IR):     88.55 % (±0.88), 902 s
  Reference (PT):            90.30 %

Accuracy is identical to the post-rebase TP=2 baseline; total run-time
is ~9 % faster. The existing ``quantize_mxfp4_moe_trtllm_gen`` post-load
transform continues to handle MXFP4 weight prep and op retargeting on
top of the IR modeling.

Signed-off-by: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com>

…harding-IR canonical models (NVIDIA#13478)

Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com>
Co-authored-by: Suyog Gupta <41447211+suyoggupta@users.noreply.github.com>

…harding-IR canonical models (NVIDIA#13478)

Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com>
Co-authored-by: Suyog Gupta <41447211+suyoggupta@users.noreply.github.com>

…ariant

The sharding-IR ``modeling_gpt_oss_ir.py`` already covered every feature of
the non-IR legacy file (same RMSNorm / RoPE / attention with sinks / MoE
router / experts), differing only in:

* attention Linears go through ``torch.ops.auto_deploy.torch_linear_simple``
  with sharding hint kwargs so TP > 1 attention sharding works without an
  external graph rewrite;
* view ops on q/k/v/attn_out use ``torch.ops.auto_deploy.view`` with
  ``tp_scaled_dim=2`` so the head-count dim scales with TP;
* the post-attention all-reduce is expressed as a
  ``torch.ops.auto_deploy.all_reduce`` placeholder.

Consolidate: rename ``modeling_gpt_oss_ir.py`` into the default
``modeling_gpt_oss.py`` (the legacy non-IR variant is removed) and drop
the ``AD_USE_IR_MODELS`` opt-in entry for gpt-oss in
``models/custom/__init__.py``. This matches the trajectory in upstream
PR NVIDIA#13478 (other models being migrated to sharding-IR as default).

GSM8K full 1319-sample validation, gpt-oss-120b @ TP=2 (post-rebase,
W4A8 mxfp8 activations):

  Pre-IR (legacy modeling):  88.55 % (±0.88), 992 s
  Post-IR (sharding-IR):     88.55 % (±0.88), 902 s
  Reference (PT):            90.30 %

Accuracy is identical to the post-rebase TP=2 baseline; total run-time
is ~9 % faster. The existing ``quantize_mxfp4_moe_trtllm_gen`` post-load
transform continues to handle MXFP4 weight prep and op retargeting on
top of the IR modeling.

Signed-off-by: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com>

…orted model families

Add 'enable_sharder_ir.yaml' to the yaml_extra of every model_registry/models.yaml entry whose config class maps to one of the 13 modeling files ported in this PR (llama3, mistral, granite, hunyuan_dense, seed_oss, gemma2, cohere, exaone, gemma, deepseek_v2, glm4_moe, qwen3_moe, llama4).

Without this, those entries continue to run through the legacy detect_sharding pipeline. detect_sharding does recognize torch_linear_simple via is_any_lin_op, so it still shards correctly, but the explicit sharding-hint kwargs on the IR-ported ops are dormant metadata under that path. This commit makes the registry actually exercise the apply_sharding_hints transform on those models, matching the opt-in pattern PR NVIDIA#13478 used for the first four families (qwen3, qwen3_5_moe, deepseek, nemotron_h).

70 entries updated. Excluded by design (still on legacy via no override):

- 'multimodal.yaml' entries: vision/audio variants take a separate path.

- 'simple_shard_only.yaml' entries: Llama-3_1-Nemotron-51B, -Ultra-253B (and -FP8), Qwen3-30B-A3B, Qwen3-235B-A22B - explicit opt-outs of the regular sharding pass.

Refs NVIDIA#14642

Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com>

…orted model families

Add 'enable_sharder_ir.yaml' to the yaml_extra of every model_registry/models.yaml entry whose config class maps to one of the 13 modeling files ported in this PR (llama3, mistral, granite, hunyuan_dense, seed_oss, gemma2, cohere, exaone, gemma, deepseek_v2, glm4_moe, qwen3_moe, llama4).

Without this, those entries continue to run through the legacy detect_sharding pipeline. detect_sharding does recognize torch_linear_simple via is_any_lin_op, so it still shards correctly, but the explicit sharding-hint kwargs on the IR-ported ops are dormant metadata under that path. This commit makes the registry actually exercise the apply_sharding_hints transform on those models, matching the opt-in pattern PR NVIDIA#13478 used for the first four families (qwen3, qwen3_5_moe, deepseek, nemotron_h).

70 entries updated. Excluded by design (still on legacy via no override):

- 'multimodal.yaml' entries: vision/audio variants take a separate path.

- 'simple_shard_only.yaml' entries: Llama-3_1-Nemotron-51B, -Ultra-253B (and -FP8), Qwen3-30B-A3B, Qwen3-235B-A22B - explicit opt-outs of the regular sharding pass.

Refs NVIDIA#14642

Signed-off-by: greg-kwasniewski1 <213329731+greg-kwasniewski1@users.noreply.github.com>

…ariant

The sharding-IR ``modeling_gpt_oss_ir.py`` already covered every feature of
the non-IR legacy file (same RMSNorm / RoPE / attention with sinks / MoE
router / experts), differing only in:

* attention Linears go through ``torch.ops.auto_deploy.torch_linear_simple``
  with sharding hint kwargs so TP > 1 attention sharding works without an
  external graph rewrite;
* view ops on q/k/v/attn_out use ``torch.ops.auto_deploy.view`` with
  ``tp_scaled_dim=2`` so the head-count dim scales with TP;
* the post-attention all-reduce is expressed as a
  ``torch.ops.auto_deploy.all_reduce`` placeholder.

Consolidate: rename ``modeling_gpt_oss_ir.py`` into the default
``modeling_gpt_oss.py`` (the legacy non-IR variant is removed) and drop
the ``AD_USE_IR_MODELS`` opt-in entry for gpt-oss in
``models/custom/__init__.py``. This matches the trajectory in upstream
PR NVIDIA#13478 (other models being migrated to sharding-IR as default).

GSM8K full 1319-sample validation, gpt-oss-120b @ TP=2 (post-rebase,
W4A8 mxfp8 activations):

  Pre-IR (legacy modeling):  88.55 % (±0.88), 992 s
  Post-IR (sharding-IR):     88.55 % (±0.88), 902 s
  Reference (PT):            90.30 %

Accuracy is identical to the post-rebase TP=2 baseline; total run-time
is ~9 % faster. The existing ``quantize_mxfp4_moe_trtllm_gen`` post-load
transform continues to handle MXFP4 weight prep and op retargeting on
top of the IR modeling.

Signed-off-by: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com>

…ariant

The sharding-IR ``modeling_gpt_oss_ir.py`` already covered every feature of
the non-IR legacy file (same RMSNorm / RoPE / attention with sinks / MoE
router / experts), differing only in:

* attention Linears go through ``torch.ops.auto_deploy.torch_linear_simple``
  with sharding hint kwargs so TP > 1 attention sharding works without an
  external graph rewrite;
* view ops on q/k/v/attn_out use ``torch.ops.auto_deploy.view`` with
  ``tp_scaled_dim=2`` so the head-count dim scales with TP;
* the post-attention all-reduce is expressed as a
  ``torch.ops.auto_deploy.all_reduce`` placeholder.

Consolidate: rename ``modeling_gpt_oss_ir.py`` into the default
``modeling_gpt_oss.py`` (the legacy non-IR variant is removed) and drop
the ``AD_USE_IR_MODELS`` opt-in entry for gpt-oss in
``models/custom/__init__.py``. This matches the trajectory in upstream
PR NVIDIA#13478 (other models being migrated to sharding-IR as default).

GSM8K full 1319-sample validation, gpt-oss-120b @ TP=2 (post-rebase,
W4A8 mxfp8 activations):

  Pre-IR (legacy modeling):  88.55 % (±0.88), 992 s
  Post-IR (sharding-IR):     88.55 % (±0.88), 902 s
  Reference (PT):            90.30 %

Accuracy is identical to the post-rebase TP=2 baseline; total run-time
is ~9 % faster. The existing ``quantize_mxfp4_moe_trtllm_gen`` post-load
transform continues to handle MXFP4 weight prep and op retargeting on
top of the IR modeling.

Signed-off-by: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com>

…ariant

The sharding-IR ``modeling_gpt_oss_ir.py`` already covered every feature of
the non-IR legacy file (same RMSNorm / RoPE / attention with sinks / MoE
router / experts), differing only in:

* attention Linears go through ``torch.ops.auto_deploy.torch_linear_simple``
  with sharding hint kwargs so TP > 1 attention sharding works without an
  external graph rewrite;
* view ops on q/k/v/attn_out use ``torch.ops.auto_deploy.view`` with
  ``tp_scaled_dim=2`` so the head-count dim scales with TP;
* the post-attention all-reduce is expressed as a
  ``torch.ops.auto_deploy.all_reduce`` placeholder.

Consolidate: rename ``modeling_gpt_oss_ir.py`` into the default
``modeling_gpt_oss.py`` (the legacy non-IR variant is removed) and drop
the ``AD_USE_IR_MODELS`` opt-in entry for gpt-oss in
``models/custom/__init__.py``. This matches the trajectory in upstream
PR NVIDIA#13478 (other models being migrated to sharding-IR as default).

GSM8K full 1319-sample validation, gpt-oss-120b @ TP=2 (post-rebase,
W4A8 mxfp8 activations):

  Pre-IR (legacy modeling):  88.55 % (±0.88), 992 s
  Post-IR (sharding-IR):     88.55 % (±0.88), 902 s
  Reference (PT):            90.30 %

Accuracy is identical to the post-rebase TP=2 baseline; total run-time
is ~9 % faster. The existing ``quantize_mxfp4_moe_trtllm_gen`` post-load
transform continues to handle MXFP4 weight prep and op retargeting on
top of the IR modeling.

Signed-off-by: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com>

…ariant

The sharding-IR ``modeling_gpt_oss_ir.py`` already covered every feature of
the non-IR legacy file (same RMSNorm / RoPE / attention with sinks / MoE
router / experts), differing only in:

* attention Linears go through ``torch.ops.auto_deploy.torch_linear_simple``
  with sharding hint kwargs so TP > 1 attention sharding works without an
  external graph rewrite;
* view ops on q/k/v/attn_out use ``torch.ops.auto_deploy.view`` with
  ``tp_scaled_dim=2`` so the head-count dim scales with TP;
* the post-attention all-reduce is expressed as a
  ``torch.ops.auto_deploy.all_reduce`` placeholder.

Consolidate: rename ``modeling_gpt_oss_ir.py`` into the default
``modeling_gpt_oss.py`` (the legacy non-IR variant is removed) and drop
the ``AD_USE_IR_MODELS`` opt-in entry for gpt-oss in
``models/custom/__init__.py``. This matches the trajectory in upstream
PR NVIDIA#13478 (other models being migrated to sharding-IR as default).

GSM8K full 1319-sample validation, gpt-oss-120b @ TP=2 (post-rebase,
W4A8 mxfp8 activations):

  Pre-IR (legacy modeling):  88.55 % (±0.88), 992 s
  Post-IR (sharding-IR):     88.55 % (±0.88), 902 s
  Reference (PT):            90.30 %

Accuracy is identical to the post-rebase TP=2 baseline; total run-time
is ~9 % faster. The existing ``quantize_mxfp4_moe_trtllm_gen`` post-load
transform continues to handle MXFP4 weight prep and op retargeting on
top of the IR modeling.

Signed-off-by: Taylor Yeonbok Lee <249374542+taylor-yb-lee@users.noreply.github.com>