Skip to content

[#9643][fix] AutoDeploy: fix nano sharding config #9668

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
lucaslie merged 1 commit into from
Dec 3, 2025
Merged

[#9643][fix] AutoDeploy: fix nano sharding config #9668

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
21 changes: 20 additions & 1 deletion examples/auto_deploy/nano_v3.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,27 @@ kv_cache_config:
enable_block_reuse: false
transforms:
detect_sharding:
sharding_source: ['factory', 'heuristic']
sharding_dims: ['ep', 'bmm']
manual_config:
head_dim: 128
tp_plan:
# mamba SSM layer
"in_proj": "mamba"
"out_proj": "rowwise"
# attention layer
"q_proj": "colwise"
"k_proj": "colwise"
"v_proj": "colwise"
"o_proj": "rowwise"
# NOTE: consider not sharding shared experts and/or
# latent projections at all, keeping them replicated.
# To do so, comment out the corresponding entries.
# moe layer: SHARED experts
"up_proj": "colwise"
"down_proj": "rowwise"
# MoLE: latent projections: simple shard
"fc1_latent_proj": "gather"
"fc2_latent_proj": "gather"
multi_stream_moe:
stage: compile
enabled: true
Expand Down
36 changes: 0 additions & 36 deletions tensorrt_llm/_torch/auto_deploy/models/patches/nemotron_h.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -190,42 +190,6 @@ def get_model_from_config_patched(config, **kwargs):
# TODO: figure out how this can be incorporated into the export patch system
AutoModelForCausalLM.from_config = get_model_from_config_patched

# _config_from_pretrained_original = AutoConfig.from_pretrained
# _nemotron_h_base_model_tp_plan = {
# # mamba SSM layer
# "in_proj": "mamba",
# "out_proj": "rowwise",
# # attention layer
# "q_proj": "colwise",
# "k_proj": "colwise",
# "v_proj": "colwise",
# "o_proj": "rowwise",
# # NOTE: consider not sharding shared experts and/or
# # latent projections at all, keeping them replicated.
# # To do so, comment out the corresponding entries.
# # moe layer: SHARED experts
# "up_proj": "colwise",
# "down_proj": "rowwise",
# # MoLE: latent projections: simple shard
# "fc1_latent_proj": "gather",
# "fc2_latent_proj": "gather",
# }


# def get_config_from_pretrained_patched(*args, **kwargs):
# ret = _config_from_pretrained_original(*args, **kwargs)
# config = ret[0] if isinstance(ret, tuple) else ret
# # heuristic to check if it's a NemotronH MoE Model
# model_type = getattr(config, "model_type", None)
# num_moe_layers = getattr(config, "layers_block_type", []).count("moe")
# if model_type == "nemotron_h" and num_moe_layers > 0:
# config.base_model_tp_plan = _nemotron_h_base_model_tp_plan
# return (config, *ret[1:]) if isinstance(ret, tuple) else config


# # TODO: figure out how this can be incorporated into the export patch system
# AutoConfig.from_pretrained = get_config_from_pretrained_patched

# TODO: figure out how this can be incorporated into the export patch system
# Only patch if the module isn't available
_mamba_ssm_module = "mamba_ssm"
Expand Down
Loading