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README.md

@@ -1,8 +1,12 @@
----
+______________________________________________________________________
+
+library_name: transformers
 license: mit
 widget:
- - text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
----
+
+- text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
+
+______________________________________________________________________
 
 ## ESM-2 (TransformerEngine-optimized)
 
@@ -23,11 +27,11 @@ which demonstrate how to fine-tune ESM-2 models on your tasks of interest.
 Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have
 somewhat better accuracy, but require much more memory and time to train:
 
-| Checkpoint name | Num layers | Num parameters |
-|------------------------------|----|----------|
-| [esm2_t48_15B_UR50D](https://huggingface.co/nvidia/esm2_t48_15B_UR50D) | 48 | 15B     |
-| [esm2_t36_3B_UR50D](https://huggingface.co/nvidia/esm2_t36_3B_UR50D) | 36 | 3B      |
-| [esm2_t33_650M_UR50D](https://huggingface.co/nvidia/esm2_t33_650M_UR50D) | 33 | 650M    |
-| [esm2_t30_150M_UR50D](https://huggingface.co/nvidia/esm2_t30_150M_UR50D) | 30 | 150M    |
-| [esm2_t12_35M_UR50D](https://huggingface.co/nvidia/esm2_t12_35M_UR50D) | 12 | 35M     |
-| [esm2_t6_8M_UR50D](https://huggingface.co/nvidia/esm2_t6_8M_UR50D)  | 6  | 8M      |
+| Checkpoint name                                                          | Num layers | Num parameters |
+| ------------------------------------------------------------------------ | ---------- | -------------- |
+| [esm2_t48_15B_UR50D](https://huggingface.co/nvidia/esm2_t48_15B_UR50D)   | 48         | 15B            |
+| [esm2_t36_3B_UR50D](https://huggingface.co/nvidia/esm2_t36_3B_UR50D)     | 36         | 3B             |
+| [esm2_t33_650M_UR50D](https://huggingface.co/nvidia/esm2_t33_650M_UR50D) | 33         | 650M           |
+| [esm2_t30_150M_UR50D](https://huggingface.co/nvidia/esm2_t30_150M_UR50D) | 30         | 150M           |
+| [esm2_t12_35M_UR50D](https://huggingface.co/nvidia/esm2_t12_35M_UR50D)   | 12         | 35M            |
+| [esm2_t6_8M_UR50D](https://huggingface.co/nvidia/esm2_t6_8M_UR50D)       | 6          | 8M             |

config.json

@@ -23,6 +23,7 @@
   "layer_norm_eps": 1e-05,
   "mask_token_id": 32,
   "max_position_embeddings": 1026,
+  "max_seq_length": null,
   "micro_batch_size": null,
   "model_type": "nv_esm",
   "num_attention_heads": 20,
@@ -32,8 +33,8 @@
   "qkv_weight_interleaved": true,
   "token_dropout": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.53.0.dev0",
+  "transformers_version": "4.55.0.dev0",
   "use_cache": true,
   "vocab_list": null,
   "vocab_size": 33
-}
+}

esm_nv.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: LicenseRef-Apache2
 # Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2025 NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,13 +16,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""TransformerEngine-optimized ESM model. Adapted from `modeling_esm.py` in
-huggingface/transformers."""
+
+"""TransformerEngine-optimized ESM model.
+
+Adapted from `modeling_esm.py` in huggingface/transformers.
+"""
 
 from typing import Optional, Tuple, Union
 
+# TODO: put import guard around transformer_engine here, with an informative error message around
+# installation and the nvidia docker container.
 import torch
-import torch.utils.checkpoint
 import transformer_engine.pytorch
 from torch import nn
 from torch.nn import CrossEntropyLoss
@@ -37,10 +42,13 @@ from transformers.models.esm.configuration_esm import EsmConfig
 from transformers.models.esm.modeling_esm import EsmEmbeddings, EsmPooler
 from transformers.utils import logging
 
+
 logger = logging.get_logger(__name__)
 
 
 class NVEsmConfig(EsmConfig):
+    """NVEsmConfig is a configuration for the NVEsm model."""
+
     model_type: str = "nv_esm"
 
     def __init__(
@@ -50,6 +58,7 @@ class NVEsmConfig(EsmConfig):
         attn_input_format: str = "bshd",
         fuse_qkv_params: bool = True,
         micro_batch_size: Optional[int] = None,
+        max_seq_length: Optional[int] = None,
         **kwargs,
     ):
         """Initialize the NVEsmConfig with additional TE-related config options.
@@ -74,9 +83,11 @@ class NVEsmConfig(EsmConfig):
             micro_batch_size: The micro batch size to use for the attention. This is needed for
                 JIT Warmup, a technique where jit fused functions are warmed up before training to
                 ensure same kernels are used for forward propogation and activation recompute phase.
+            max_seq_length: The maximum sequence length to use for the attention. This is needed for
+                JIT Warmup, a technique where jit fused functions are warmed up before training to
+                ensure same kernels are used for forward propogation and activation recompute phase.
             **kwargs: Additional config options to pass to EsmConfig.
         """
-
         super().__init__(**kwargs)
         # Additional TE-related config options.
         self.qkv_weight_interleaved = qkv_weight_interleaved
@@ -84,10 +95,18 @@ class NVEsmConfig(EsmConfig):
         self.attn_input_format = attn_input_format
         self.fuse_qkv_params = fuse_qkv_params
         self.micro_batch_size = micro_batch_size
+        self.max_seq_length = max_seq_length
 
 
 class NVEsmEncoder(nn.Module):
-    def __init__(self, config):
+    """NVEsmEncoder is a TransformerEngine-optimized ESM encoder."""
+
+    def __init__(self, config: NVEsmConfig):
+        """Initialize a NVEsmEncoder.
+
+        Args:
+            config (NVEsmConfig): The configuration of the model.
+        """
         super().__init__()
         self.config = config
         self.layers = nn.ModuleList(
@@ -105,39 +124,41 @@ class NVEsmEncoder(nn.Module):
                     self_attn_mask_type="padding",
                     activation=config.encoder_activation,
                     attn_input_format=config.attn_input_format,
-                    seq_length=config.max_length,
+                    seq_length=config.max_seq_length,
                     micro_batch_size=config.micro_batch_size,
                     num_gqa_groups=config.num_attention_heads,
                     fuse_qkv_params=config.fuse_qkv_params,
                     params_dtype=config.torch_dtype,
+                    window_size=(-1, -1),
                 )
                 for i in range(config.num_hidden_layers)
             ]
         )
-        self.emb_layer_norm_after = transformer_engine.pytorch.LayerNorm(
-            config.hidden_size, eps=config.layer_norm_eps
-        )
+        self.emb_layer_norm_after = transformer_engine.pytorch.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         if config.position_embedding_type == "rotary":
-            self.rotary_embeddings = RotaryPositionEmbedding(
-                config.hidden_size // config.num_attention_heads
-            )
-            self.te_rope_emb = self.rotary_embeddings(
-                max_seq_len=config.max_position_embeddings
-            ).cuda()
+            self.rotary_embeddings = RotaryPositionEmbedding(config.hidden_size // config.num_attention_heads)
+            self.te_rope_emb = self.rotary_embeddings(max_seq_len=config.max_position_embeddings).cuda()
         else:
             self.te_rope_emb = None
 
     def forward(
         self,
-        hidden_states,
-        attention_mask=None,
-        output_hidden_states=False,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: bool = False,
     ):
+        """Forward pass of the NVEsmEncoder.
+
+        Args:
+            hidden_states (torch.Tensor): The hidden states.
+            attention_mask (torch.Tensor): The attention mask.
+            output_hidden_states (bool): Whether to output the hidden states.
+        """
         all_hidden_states = () if output_hidden_states else None
 
         for layer_module in self.layers:
             if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
+                all_hidden_states = (*all_hidden_states, hidden_states)
 
             hidden_states = layer_module(
                 hidden_states,
@@ -148,7 +169,7 @@ class NVEsmEncoder(nn.Module):
         hidden_states = self.emb_layer_norm_after(hidden_states)
 
         if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
+            all_hidden_states = (*all_hidden_states, hidden_states)
 
         return BaseModelOutput(
             last_hidden_state=hidden_states,
@@ -157,18 +178,42 @@ class NVEsmEncoder(nn.Module):
 
 
 class NVEsmPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
+    """An abstract class to handle weights initialization and pretrained model loading."""
 
     config_class = NVEsmConfig
     base_model_prefix = "esm"
     supports_gradient_checkpointing = False
-    _no_split_modules = [
+    _no_split_modules = (
         "TransformerLayer",
         "EsmEmbeddings",
-    ]
+    )
+
+    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights.
+
+        Args:
+            module (nn.Module): The module to initialize the weights for.
+        """
+        if isinstance(
+            module, (nn.Linear, transformer_engine.pytorch.Linear, transformer_engine.pytorch.LayerNormLinear)
+        ):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        if isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        if isinstance(module, (nn.LayerNorm, transformer_engine.pytorch.LayerNorm)):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, transformer_engine.pytorch.LayerNormLinear):
+            module.layer_norm_weight.data.fill_(1.0)
+            if module.layer_norm_bias is not None:
+                module.layer_norm_bias.data.zero_()
 
 
 class NVEsmModel(NVEsmPreTrainedModel):
@@ -177,7 +222,13 @@ class NVEsmModel(NVEsmPreTrainedModel):
     This model uses NVDIA's TransformerEngine to optimize attention layer training and inference.
     """
 
-    def __init__(self, config, add_pooling_layer=True):
+    def __init__(self, config: NVEsmConfig, add_pooling_layer: bool = True):
+        """Initialize a NVEsmModel.
+
+        Args:
+            config (NVEsmConfig): The configuration of the model.
+            add_pooling_layer (bool): Whether to add a pooling layer.
+        """
         super().__init__(config)
         self.config = config
 
@@ -189,9 +240,15 @@ class NVEsmModel(NVEsmPreTrainedModel):
         self.post_init()
 
     def get_input_embeddings(self):
+        """Get the input embeddings of the model."""
         return self.embeddings.word_embeddings
 
-    def set_input_embeddings(self, value):
+    def set_input_embeddings(self, value: torch.Tensor):
+        """Set the input embeddings of the model.
+
+        Args:
+            value (torch.Tensor): The input embeddings.
+        """
         self.embeddings.word_embeddings = value
 
     def forward(
@@ -203,6 +260,19 @@ class NVEsmModel(NVEsmPreTrainedModel):
         inputs_embeds: Optional[torch.Tensor] = None,
         output_hidden_states: Optional[bool] = None,
     ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        """Forward pass of the NVEsmModel.
+
+        Args:
+            input_ids (torch.Tensor): The input ids.
+            attention_mask (torch.Tensor): The attention mask.
+            position_ids (torch.Tensor): The position ids.
+            head_mask (torch.Tensor): The head mask.
+            inputs_embeds (torch.Tensor): The input embeddings.
+            output_hidden_states (bool): Whether to output the hidden states.
+
+        Returns:
+            BaseModelOutputWithPooling: The output of the model.
+        """
         r"""
         encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
             Sequence of hidden-states at the output of the last layer of the encoder. Used in the
@@ -219,15 +289,11 @@ class NVEsmModel(NVEsmPreTrainedModel):
             boolean mask where 1s are masked and 0s are not masked.
         """
         output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
 
         if input_ids is not None and inputs_embeds is not None:
-            raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time"
-            )
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
             self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
             input_shape = input_ids.size()
@@ -244,9 +310,7 @@ class NVEsmModel(NVEsmPreTrainedModel):
 
         # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
         # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
-            attention_mask, input_shape
-        )
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
 
         # TE expects a boolean attention mask, where 1s are masked and 0s are not masked
         extended_attention_mask = extended_attention_mask < -1
@@ -270,9 +334,7 @@ class NVEsmModel(NVEsmPreTrainedModel):
             output_hidden_states=output_hidden_states,
         )
         sequence_output = encoder_outputs[0]
-        pooled_output = (
-            self.pooler(sequence_output) if self.pooler is not None else None
-        )
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
 
         return BaseModelOutputWithPooling(
             last_hidden_state=sequence_output,
@@ -282,9 +344,16 @@ class NVEsmModel(NVEsmPreTrainedModel):
 
 
 class NVEsmForMaskedLM(NVEsmPreTrainedModel):
-    _tied_weights_keys = ["lm_head.decoder.weight"]
+    """NVEsmForMaskedLM is a TransformerEngine-optimized ESM model for masked language modeling."""
+
+    _tied_weights_keys = ("lm_head.decoder.weight",)
 
-    def __init__(self, config):
+    def __init__(self, config: NVEsmConfig):
+        """Initialize a NVEsmForMaskedLM.
+
+        Args:
+            config (NVEsmConfig): The configuration of the model.
+        """
         super().__init__(config)
 
         if config.is_decoder:
@@ -300,9 +369,11 @@ class NVEsmForMaskedLM(NVEsmPreTrainedModel):
         self.post_init()
 
     def get_output_embeddings(self):
+        """Get the output embeddings of the model."""
         return self.lm_head.decoder
 
     def set_output_embeddings(self, new_embeddings):
+        """Set the output embeddings of the model."""
         self.lm_head.decoder = new_embeddings
 
     def forward(
@@ -314,6 +385,19 @@ class NVEsmForMaskedLM(NVEsmPreTrainedModel):
         labels: Optional[torch.LongTensor] = None,
         output_hidden_states: Optional[bool] = None,
     ) -> Union[Tuple, MaskedLMOutput]:
+        """Forward pass of the NVEsmForMaskedLM.
+
+        Args:
+            input_ids (torch.LongTensor): The input ids.
+            attention_mask (torch.Tensor): The attention mask.
+            position_ids (torch.LongTensor): The position ids.
+            inputs_embeds (torch.FloatTensor): The input embeddings.
+            labels (torch.LongTensor): The labels.
+            output_hidden_states (bool): Whether to output the hidden states.
+
+        Returns:
+            MaskedLMOutput: The output of the model.
+        """
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
@@ -337,9 +421,7 @@ class NVEsmForMaskedLM(NVEsmPreTrainedModel):
             loss_fct = CrossEntropyLoss()
 
             labels = labels.to(prediction_scores.device)
-            masked_lm_loss = loss_fct(
-                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
-            )
+            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
 
         return MaskedLMOutput(
             loss=masked_lm_loss,
@@ -347,18 +429,30 @@ class NVEsmForMaskedLM(NVEsmPreTrainedModel):
             hidden_states=outputs.hidden_states,
         )
 
-    def predict_contacts(self, tokens, attention_mask):
+    def predict_contacts(self, tokens: torch.Tensor, attention_mask: torch.Tensor):
+        """Predict the contacts of the model.
+
+        Args:
+            tokens (torch.Tensor): The tokens.
+            attention_mask (torch.Tensor): The attention mask.
+
+        Returns:
+            torch.Tensor: The predicted contacts.
+        """
         return self.esm.predict_contacts(tokens, attention_mask=attention_mask)
 
 
 class NVEsmLMHead(nn.Module):
     """ESM Head for masked language modeling using TransformerEngine."""
 
-    def __init__(self, config):
+    def __init__(self, config: NVEsmConfig):
+        """Initialize a NVEsmLMHead.
+
+        Args:
+            config (NVEsmConfig): The configuration of the model.
+        """
         super().__init__()
-        self.dense = transformer_engine.pytorch.Linear(
-            config.hidden_size, config.hidden_size
-        )
+        self.dense = transformer_engine.pytorch.Linear(config.hidden_size, config.hidden_size)
 
         self.decoder = transformer_engine.pytorch.LayerNormLinear(
             config.hidden_size,
@@ -368,6 +462,12 @@ class NVEsmLMHead(nn.Module):
         )
 
     def forward(self, features, **kwargs):
+        """Forward pass of the NVEsmLMHead.
+
+        Args:
+            features (torch.Tensor): The features.
+            **kwargs: Additional arguments.
+        """
         x = self.dense(features)
         x = torch.nn.functional.gelu(x)
         x = self.decoder(x)