ESM-2 to NeMo checkpoint conversion (#537)

Adds a conversion script to convert from huggingface to ESM-2 checkpoints --------- Signed-off-by: Peter St. John <[email protected]>
NVIDIA · Jan 22, 2025 · 257e918 · 257e918
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diff --git a/sub-packages/bionemo-core/src/bionemo/core/data/resources/esm2.yaml b/sub-packages/bionemo-core/src/bionemo/core/data/resources/esm2.yaml
@@ -7,23 +7,32 @@
   description: >
     A pretrained 650M parameter ESM2 model. See https://ngc.nvidia.com/catalog/models/nvidia:clara:esm2nv650m.
 
-- tag: nv_3b:2.1
-  ngc: "nvidia/clara/esm2nv3b:2.1"
+- tag: 8m:2.0
+  ngc: nvidia/clara/esm2nv8m:2.0
   ngc_registry: model
-  pbss: "s3://general-purpose/esm2/checkpoints/3b/esm2_3b_checkpoint.tar.gz"
-  sha256: a79327a4054bf8d1d7075e1b3c961dbc503da02d72ed15f707d9cbbd49d181b6 # pragma: allowlist secret
+  pbss: s3://general-purpose/esm2/checkpoints/converted/8m/esm2_hf_converted_8m_checkpoint.tar.gz
+  sha256: 2957b2c36d5978d0f595d6f1b72104b312621cf0329209086537b613c1c96d16 # pragma: allowlist secret
   owner: Peter St John <[email protected]>
   description: >
-    An ESM-2 3B model pre-trained on NVIDIA's train/test data split.
+    A NeMo2 compatible checkpoint converted from the huggingface facebook/esm2_t6_8M_UR50D model.
 
-- tag: nv_650m:2.1
-  ngc: "nvidia/clara/esm2nv650m:2.1"
+- tag: 650m:2.0
+  ngc: nvidia/clara/esm2nv650m:2.0
   ngc_registry: model
-  pbss: "s3://general-purpose/esm2/checkpoints/650m/esm2_650m_checkpoint.tar.gz"
-  sha256: b83e9b5d62f1499b443817c5cd0facd3bdd4013a51a897e05e17228bf650befe # pragma: allowlist secret
-  owner: Peter St John <pstjohn@nvidia.com>
+  pbss: "s3://bionemo-ci/models/esm2_650M_nemo2.tar.gz"
+  sha256: 0798767e843e3d54315aef91934d28ae7d8e93c2849d5fcfbdf5fac242013997 # pragma: allowlist secret
+  owner: Farhad Ramezanghorbani <farhadr@nvidia.com>
   description: >
-    An ESM-2 650M model pre-trained on NVIDIA's train/test data split.
+    A NeMo2 compatible checkpoint converted from the huggingface facebook/esm2_t33_650M_UR50D model.
+
+- tag: 3b:2.0
+  ngc: nvidia/clara/esm2nv3b:2.0
+  ngc_registry: model
+  pbss: "s3://bionemo-ci/models/esm2_3B_nemo2.tar.gz"
+  sha256: a2248cfed1ef39f83bd32a0e08b84c0a8f39325d383e2d92767022ff7f5260ed # pragma: allowlist secret
+  owner: Farhad Ramezanghorbani <[email protected]>
+  description: >
+    A NeMo2 compatible checkpoint converted from the huggingface facebook/esm2_t36_3B_UR50D model.
 
 # - tag: nv_8m:2.1
 #   ngc: "nvidia/clara/esm2nv8m:2.1"
@@ -34,53 +43,44 @@
 #   description: >
 #     An ESM-2 8M model pre-trained on NVIDIA's train/test data split.
 
-- tag: 8m:2.0
-  ngc: "nvidia/clara/esm2nv8m:2.0"
+- tag: nv_650m:2.1
+  ngc: "nvidia/clara/esm2nv650m:2.1"
   ngc_registry: model
-  pbss: "s3://general-purpose/esm2/checkpoints/converted/8m/esm2_hf_converted_8m_checkpoint.tar.gz"
-  sha256: 2957b2c36d5978d0f595d6f1b72104b312621cf0329209086537b613c1c96d16 # pragma: allowlist secret
+  pbss: "s3://general-purpose/esm2/checkpoints/650m/esm2_650m_checkpoint.tar.gz"
+  sha256: b83e9b5d62f1499b443817c5cd0facd3bdd4013a51a897e05e17228bf650befe # pragma: allowlist secret
   owner: Peter St John <[email protected]>
   description: >
-    The original 8M parameter ESM2 model weights converted to the NeMo2 checkpoint format.
-
-- tag: 650m:2.0
-  ngc: nvidia/clara/esm2nv650m:2.0
-  ngc_registry: model
-  pbss: "s3://bionemo-ci/models/esm2_650M_nemo2.tar.gz"
-  sha256: 0798767e843e3d54315aef91934d28ae7d8e93c2849d5fcfbdf5fac242013997 # pragma: allowlist secret
-  owner: Farhad Ramezanghorbani <[email protected]>
-  description: >
-    The original 650M parameter ESM2 model weights converted to the NeMo2 checkpoint format.
+    An ESM-2 650M model pre-trained on NVIDIA's train/test data split.
 
-- tag: 3b:2.0
-  ngc: nvidia/clara/esm2nv3b:2.0
+- tag: nv_3b:2.1
+  ngc: "nvidia/clara/esm2nv3b:2.1"
   ngc_registry: model
-  pbss: "s3://bionemo-ci/models/esm2_3B_nemo2.tar.gz"
-  sha256: a2248cfed1ef39f83bd32a0e08b84c0a8f39325d383e2d92767022ff7f5260ed # pragma: allowlist secret
-  owner: Farhad Ramezanghorbani <farhadr@nvidia.com>
+  pbss: "s3://general-purpose/esm2/checkpoints/3b/esm2_3b_checkpoint.tar.gz"
+  sha256: a79327a4054bf8d1d7075e1b3c961dbc503da02d72ed15f707d9cbbd49d181b6 # pragma: allowlist secret
+  owner: Peter St John <pstjohn@nvidia.com>
   description: >
-    The original 3B parameter ESM2 model c converted to the NeMo2 checkpoint format.
+    An ESM-2 3B model pre-trained on NVIDIA's train/test data split.
 
 - tag: fulldata_esm2_pretrain:2.0
   ngc: nvidia/clara/esm2_pretrain_nemo2_data:1.0
   ngc_registry: resource
   pbss: "s3://general-purpose/esm2/pretrain/2024_03.tar.gz"
-  sha256: 404d0ad8de58fa8aae96f8d9f54263a088bc7e4f7d668215afbe04c28416151b  # pragma: allowlist secret
+  sha256: 404d0ad8de58fa8aae96f8d9f54263a088bc7e4f7d668215afbe04c28416151b # pragma: allowlist secret
   owner: Peter St John <[email protected]>
   description: Full data for ESM2 pretraining.
 
 - tag: testdata_esm2_pretrain:2.0
   ngc: nvidia/clara/esm2_pretrain_nemo2_testdata:1.0
   ngc_registry: resource
   pbss: "s3://general-purpose/esm2/pretrain/2024_03_sanity.tar.gz"
-  sha256: 006911f92bbc0ded7ea302bbdbfab4c694b409e699c32fd49de1c527a99dba3e  # pragma: allowlist secret
+  sha256: 006911f92bbc0ded7ea302bbdbfab4c694b409e699c32fd49de1c527a99dba3e # pragma: allowlist secret
   owner: Peter St John <[email protected]>
   description: Test data for ESM2 pretraining.
 
 - tag: esm2_inference_testdata:2.0
-  ngc: nvidia/clara/esm2_inference_testdata:2.0  # TODO: upload to NGC
+  ngc: nvidia/clara/esm2_inference_testdata:2.0 # TODO: upload to NGC
   ngc_registry: resource
   pbss: "s3://bionemo-ci/test_data/esm2/artificial_protein_sequences.csv"
-  sha256: 14ae3acfbf82218bc9e3e53d21a5b0594ba7c0369e169c9f1034e3fe4378d175  # pragma: allowlist secret
+  sha256: 14ae3acfbf82218bc9e3e53d21a5b0594ba7c0369e169c9f1034e3fe4378d175 # pragma: allowlist secret
   owner: Farhad Ramezanghorbani <[email protected]>
   description: Test data for ESM2 inference.
diff --git a/sub-packages/bionemo-esm2/src/bionemo/esm2/model/convert.py b/sub-packages/bionemo-esm2/src/bionemo/esm2/model/convert.py
@@ -0,0 +1,179 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-Apache2
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from pathlib import Path
+
+import torch
+from nemo.lightning import io, teardown
+from nemo.lightning.pytorch.utils import dtype_from_hf
+from transformers import AutoConfig as HFAutoConfig
+from transformers import AutoModelForMaskedLM
+
+from bionemo.esm2.data.tokenizer import BioNeMoESMTokenizer, get_tokenizer
+from bionemo.esm2.model.model import ESM2Config
+from bionemo.llm.lightning import BionemoLightningModule
+from bionemo.llm.model.biobert.lightning import biobert_lightning_module
+
+
+@io.model_importer(BionemoLightningModule, "hf")
+class HFESM2Importer(io.ModelConnector[AutoModelForMaskedLM, BionemoLightningModule]):
+    """Converts a Hugging Face ESM-2 model to a NeMo ESM-2 model."""
+
+    def init(self) -> BionemoLightningModule:
+        """Initialize the converted model."""
+        return biobert_lightning_module(self.config, tokenizer=self.tokenizer)
+
+    def apply(self, output_path: Path) -> Path:
+        """Applies the transformation.
+
+        Largely inspired by
+        https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/features/hf-integration.html
+        """
+        source = AutoModelForMaskedLM.from_pretrained(str(self), trust_remote_code=True, torch_dtype="auto")
+        target = self.init()
+        trainer = self.nemo_setup(target)
+        self.convert_state(source, target)
+        self.nemo_save(output_path, trainer)
+
+        print(f"Converted ESM-2 model to Nemo, model saved to {output_path}")
+
+        teardown(trainer, target)
+        del trainer, target
+
+        return output_path
+
+    def convert_state(self, source, target):
+        """Converting HF state dict to NeMo state dict."""
+        mapping = {
+            # "esm.encoder.layer.0.attention.self.rotary_embeddings.inv_freq": "rotary_pos_emb.inv_freq",
+            "esm.encoder.layer.*.attention.output.dense.weight": "encoder.layers.*.self_attention.linear_proj.weight",
+            "esm.encoder.layer.*.attention.output.dense.bias": "encoder.layers.*.self_attention.linear_proj.bias",
+            "esm.encoder.layer.*.attention.LayerNorm.weight": "encoder.layers.*.self_attention.linear_qkv.layer_norm_weight",
+            "esm.encoder.layer.*.attention.LayerNorm.bias": "encoder.layers.*.self_attention.linear_qkv.layer_norm_bias",
+            "esm.encoder.layer.*.intermediate.dense.weight": "encoder.layers.*.mlp.linear_fc1.weight",
+            "esm.encoder.layer.*.intermediate.dense.bias": "encoder.layers.*.mlp.linear_fc1.bias",
+            "esm.encoder.layer.*.output.dense.weight": "encoder.layers.*.mlp.linear_fc2.weight",
+            "esm.encoder.layer.*.output.dense.bias": "encoder.layers.*.mlp.linear_fc2.bias",
+            "esm.encoder.layer.*.LayerNorm.weight": "encoder.layers.*.mlp.linear_fc1.layer_norm_weight",
+            "esm.encoder.layer.*.LayerNorm.bias": "encoder.layers.*.mlp.linear_fc1.layer_norm_bias",
+            "esm.encoder.emb_layer_norm_after.weight": "encoder.final_layernorm.weight",
+            "esm.encoder.emb_layer_norm_after.bias": "encoder.final_layernorm.bias",
+            "lm_head.dense.weight": "lm_head.dense.weight",
+            "lm_head.dense.bias": "lm_head.dense.bias",
+            "lm_head.layer_norm.weight": "lm_head.layer_norm.weight",
+            "lm_head.layer_norm.bias": "lm_head.layer_norm.bias",
+        }
+
+        # lm_head.bias
+        return io.apply_transforms(
+            source,
+            target,
+            mapping=mapping,
+            transforms=[_pad_embeddings, _pad_bias, _import_qkv_weight, _import_qkv_bias],
+        )
+
+    @property
+    def tokenizer(self) -> BioNeMoESMTokenizer:
+        """We just have the one tokenizer for ESM-2."""
+        return get_tokenizer()
+
+    @property
+    def config(self) -> ESM2Config:
+        """Returns the transformed ESM-2 config given the model tag."""
+        source = HFAutoConfig.from_pretrained(str(self), trust_remote_code=True)
+        output = ESM2Config(
+            num_layers=source.num_hidden_layers,
+            hidden_size=source.hidden_size,
+            ffn_hidden_size=source.intermediate_size,
+            position_embedding_type="rope",
+            num_attention_heads=source.num_attention_heads,
+            seq_length=source.max_position_embeddings,
+            fp16=(dtype_from_hf(source) == torch.float16),
+            bf16=(dtype_from_hf(source) == torch.bfloat16),
+            params_dtype=dtype_from_hf(source),
+        )
+
+        return output
+
+
+@io.state_transform(
+    source_key="esm.embeddings.word_embeddings.weight",
+    target_key="embedding.word_embeddings.weight",
+)
+def _pad_embeddings(ctx: io.TransformCTX, source_embed):
+    """Pad the embedding layer to the new input dimension."""
+    nemo_embedding_dimension = ctx.target.config.make_vocab_size_divisible_by
+    hf_embedding_dimension = source_embed.size(0)
+    num_padding_rows = nemo_embedding_dimension - hf_embedding_dimension
+    padding_rows = torch.zeros(num_padding_rows, source_embed.size(1))
+    return torch.cat((source_embed, padding_rows), dim=0)
+
+
+@io.state_transform(
+    source_key="lm_head.bias",
+    target_key="output_layer.bias",
+)
+def _pad_bias(ctx: io.TransformCTX, source_bias):
+    """Pad the embedding layer to the new input dimension."""
+    nemo_embedding_dimension = ctx.target.config.make_vocab_size_divisible_by
+    hf_embedding_dimension = source_bias.size(0)
+    output_bias = torch.zeros(nemo_embedding_dimension, dtype=source_bias.dtype, device=source_bias.device)
+    output_bias[:hf_embedding_dimension] = source_bias
+    return output_bias
+
+
+@io.state_transform(
+    source_key=(
+        "esm.encoder.layer.*.attention.self.query.weight",
+        "esm.encoder.layer.*.attention.self.key.weight",
+        "esm.encoder.layer.*.attention.self.value.weight",
+    ),
+    target_key="encoder.layers.*.self_attention.linear_qkv.weight",
+)
+def _import_qkv_weight(ctx: io.TransformCTX, query, key, value):
+    """Pad the embedding layer to the new input dimension."""
+    concat_weights = torch.cat((query, key, value), dim=0)
+    input_shape = concat_weights.size()
+    np = ctx.target.config.num_attention_heads
+    # transpose weights
+    # [sequence length, batch size, num_splits_model_parallel * attention head size * #attention heads]
+    # --> [sequence length, batch size, attention head size * num_splits_model_parallel * #attention heads]
+    concat_weights = concat_weights.view(3, np, -1, query.size()[-1])
+    concat_weights = concat_weights.transpose(0, 1).contiguous()
+    concat_weights = concat_weights.view(*input_shape)
+    return concat_weights
+
+
+@io.state_transform(
+    source_key=(
+        "esm.encoder.layer.*.attention.self.query.bias",
+        "esm.encoder.layer.*.attention.self.key.bias",
+        "esm.encoder.layer.*.attention.self.value.bias",
+    ),
+    target_key="encoder.layers.*.self_attention.linear_qkv.bias",
+)
+def _import_qkv_bias(ctx: io.TransformCTX, query, key, value):
+    """Pad the embedding layer to the new input dimension."""
+    concat_biases = torch.cat((query, key, value), dim=0)
+    input_shape = concat_biases.size()
+    np = ctx.target.config.num_attention_heads
+    # transpose biases
+    # [num_splits_model_parallel * attention head size * #attention heads]
+    # --> [attention head size * num_splits_model_parallel * #attention heads]
+    concat_biases = concat_biases.view(3, np, -1)
+    concat_biases = concat_biases.transpose(0, 1).contiguous()
+    concat_biases = concat_biases.view(*input_shape)
+    return concat_biases
diff --git a/sub-packages/bionemo-esm2/src/bionemo/esm2/testing/__init__.py b/sub-packages/bionemo-esm2/src/bionemo/esm2/testing/__init__.py
@@ -0,0 +1,14 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-Apache2
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.