[TRTLLM-8946][feat] Improved heuristics to detect shardable regions

tensorrt_llm/_torch/auto_deploy/models/patches/nemotron_h.py

@@ -7,7 +7,7 @@
 import torch
 import torch.nn.functional as F
 from einops import rearrange
-from transformers import AutoConfig, AutoModelForCausalLM
+from transformers import AutoModelForCausalLM
 
 from tensorrt_llm._torch.auto_deploy.models.patches.bamba import _bamba_mixer_torch_forward
 
@@ -190,41 +190,41 @@ 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
+# _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