3.2.2.post2

cookbook/tutorials/2_embed.ipynb

@@ -49,18 +49,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Grab a token from [the Forge console](https://forge.evolutionaryscale.ai/console) and add it below. Note that your token is like a password for your account and you should take care to protect it. For this reason it is recommended to frequently create a new token and delete old, unused ones. It is also recommended to paste the token directly into an environment variable or use a utility like `getpass` as shown below so tokens are not accidentally shared or checked into code repositories."
+    "Grab a token from [Forge](https://forge.evolutionaryscale.ai/) and add it below. Note that your token is like a password for your account and you should take care to protect it. For this reason it is recommended to frequently create a new token and delete old, unused ones. It is also recommended to paste the token directly into an environment variable or use a utility like `getpass` as shown below so tokens are not accidentally shared or checked into code repositories."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "from getpass import getpass\n",
     "\n",
-    "token = getpass(\"Token from Forge console: \")"
+    "token = getpass(\"Token from Forge: \")"
    ]
   },
   {

cookbook/tutorials/3_gfp_design.ipynb

@@ -80,18 +80,18 @@
     "\n",
     "The largest ESM3 (98 billion parameters) was trained with 1.07e24 FLOPs on 2.78 billion proteins and 771 billion unique tokens. To create esmGFP we used the 7 billion parameter variant of ESM3. We'll use this model via the [EvolutionaryScale Forge](https://forge.evolutionaryscale.ai) API.\n",
     "\n",
-    "Grab a token from [the Forge console](https://forge.evolutionaryscale.ai/console) and add it below. Note that your token is like a password for your account and you should take care to protect it. For this reason it is recommended to frequently create a new token and delete old, unused ones. It is also recommended to paste the token directly into an environment variable or use a utility like `getpass` as shown below so tokens are not accidentally shared or checked into code repositories.\n"
+    "Grab a token from [Forge](https://forge.evolutionaryscale.ai/) and add it below. Note that your token is like a password for your account and you should take care to protect it. For this reason it is recommended to frequently create a new token and delete old, unused ones. It is also recommended to paste the token directly into an environment variable or use a utility like `getpass` as shown below so tokens are not accidentally shared or checked into code repositories.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "id": "zNrU9Q2SYonX"
    },
    "outputs": [],
    "source": [
-    "token = getpass(\"Token from Forge console: \")"
+    "token = getpass(\"Token from Forge: \")"
    ]
   },
   {

cookbook/tutorials/4_forge_generate.ipynb

@@ -53,7 +53,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Grab a token from [the Forge console](https://forge.evolutionaryscale.ai/console) and add it below. Note that your token is like a password for your account and you should take care to protect it. For this reason it is recommended to frequently create a new token and delete old, unused ones. It is also recommended to paste the token directly into an environment variable or use a utility like `getpass` as shown below so tokens are not accidentally shared or checked into code repositories."
+    "Grab a token from [Forge](https://forge.evolutionaryscale.ai/) and add it below. Note that your token is like a password for your account and you should take care to protect it. For this reason it is recommended to frequently create a new token and delete old, unused ones. It is also recommended to paste the token directly into an environment variable or use a utility like `getpass` as shown below so tokens are not accidentally shared or checked into code repositories."
    ]
   },
   {
@@ -64,7 +64,7 @@
    "source": [
     "from getpass import getpass\n",
     "\n",
-    "token = getpass(\"Token from Forge console: \")\n",
+    "token = getpass(\"Token from Forge: \")\n",
     "model = client(model=\"esm3-open\", url=\"https://forge.evolutionaryscale.ai\", token=token)"
    ]
   },

cookbook/tutorials/5_guided_generation.ipynb

@@ -120,7 +120,7 @@
     "\n",
     "from esm.sdk import client\n",
     "\n",
-    "token = getpass(\"Token from Forge console: \")\n",
+    "token = getpass(\"Token from Forge: \")\n",
     "model = client(\n",
     "    model=\"esm3-medium-2024-08\", url=\"https://forge.evolutionaryscale.ai\", token=token\n",
     ")"

esm/__init__.py

@@ -1 +1 @@
-__version__ = "3.2.2"
+__version__ = "3.2.2.post2"

esm/sdk/api.py

@@ -148,20 +148,43 @@ def to_protein_complex(
             gt_chains = list(copy_annotations_from_ground_truth.chain_iter())
         else:
             gt_chains = None
+
+        # Expand pLDDT to match sequence length if needed, inserting NaN at chain breaks
+        # This handles the case where the server doesn't include chain breaks in pLDDT
+        # We should fix this in the server side.
+        if self.plddt is not None and len(self.plddt) != len(self.sequence):
+            # Only expand if there's a mismatch (likely due to chain breaks)
+            if "|" in self.sequence:
+                # Create expanded pLDDT with NaN at chain break positions
+                expanded_plddt = torch.full((len(self.sequence),), float("nan"))
+                plddt_idx = 0
+                for i, aa in enumerate(self.sequence):
+                    if aa != "|":
+                        if plddt_idx < len(self.plddt):
+                            expanded_plddt[i] = self.plddt[plddt_idx]
+                        plddt_idx += 1
+                plddt = expanded_plddt
+            else:
+                # Mismatch but no chain breaks - shouldn't happen but preserve original
+                plddt = self.plddt
+        else:
+            plddt = self.plddt
+
         pred_chains = []
         for i, (start, end) in enumerate(chain_boundaries):
             if i >= len(SINGLE_LETTER_CHAIN_IDS):
                 raise ValueError(
                     f"Too many chains to convert to ProteinComplex. The maximum number of chains is {len(SINGLE_LETTER_CHAIN_IDS)}"
                 )
+
             pred_chain = ProteinChain.from_atom37(
                 atom37_positions=coords[start:end],
                 sequence=self.sequence[start:end],
                 chain_id=gt_chains[i].chain_id
                 if gt_chains is not None
                 else SINGLE_LETTER_CHAIN_IDS[i],
                 entity_id=gt_chains[i].entity_id if gt_chains is not None else None,
-                confidence=self.plddt[start:end] if self.plddt is not None else None,
+                confidence=plddt[start:end] if plddt is not None else None,
             )
             pred_chains.append(pred_chain)
         return ProteinComplex.from_chains(pred_chains)
@@ -298,13 +321,6 @@ def use_generative_unmasking_strategy(self):
         self.temperature_annealing = True
 
 
-@define
-class MSA:
-    # Paired MSA sequences.
-    # One would typically compute these using, for example, ColabFold.
-    sequences: list[str]
-
-
 @define
 class InverseFoldingConfig:
     invalid_ids: Sequence[int] = []

esm/sdk/forge.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import asyncio
 import base64
 import pickle
@@ -7,7 +9,6 @@
 import torch
 
 from esm.sdk.api import (
-    MSA,
     ESM3InferenceClient,
     ESMCInferenceClient,
     ESMProtein,
@@ -27,6 +28,15 @@
 from esm.sdk.retry import retry_decorator
 from esm.utils.constants.api import MIMETYPE_ES_PICKLE
 from esm.utils.misc import deserialize_tensors, maybe_list, maybe_tensor
+from esm.utils.msa import MSA
+from esm.utils.structure.input_builder import (
+    StructurePredictionInput,
+    serialize_structure_prediction_input,
+)
+from esm.utils.structure.molecular_complex import (
+    MolecularComplex,
+    MolecularComplexResult,
+)
 from esm.utils.types import FunctionAnnotation
 
 
@@ -36,10 +46,8 @@ def _list_to_function_annotations(l) -> list[FunctionAnnotation] | None:
     return [FunctionAnnotation(*t) for t in l]
 
 
-def _maybe_logits(data: dict[str, Any], track: str, return_bytes: bool = False):
-    ret = data.get("logits", {}).get(track, None)
-    # TODO(s22chan): just return this when removing return_bytes
-    return ret if ret is None or not return_bytes else maybe_tensor(ret)
+def _maybe_logits(data: dict[str, Any], track: str):
+    return maybe_tensor(data.get("logits", {}).get(track, None))
 
 
 def _maybe_b64_decode(obj, return_bytes: bool):
@@ -137,7 +145,7 @@ async def _async_fetch_msa(self, sequence: str) -> MSA:
         data = await self._async_post(
             "msa", request={}, params={"sequence": sequence, "use_env": False}
         )
-        return MSA(sequences=data["msa"])
+        return MSA.from_sequences(sequences=data["msa"])
 
     def _fetch_msa(self, sequence: str) -> MSA:
         print("Fetching MSA ... this may take a few minutes")
@@ -146,7 +154,7 @@ def _fetch_msa(self, sequence: str) -> MSA:
         data = self._post(
             "msa", request={}, params={"sequence": sequence, "use_env": False}
         )
-        return MSA(sequences=data["msa"])
+        return MSA.from_sequences(sequences=data["msa"])
 
     @retry_decorator
     async def async_fold(
@@ -209,6 +217,70 @@ def fold(
 
         return self._process_fold_response(data, sequence)
 
+    @retry_decorator
+    async def async_fold_all_atom(
+        self, all_atom_input: StructurePredictionInput, model_name: str | None = None
+    ) -> MolecularComplexResult | list[MolecularComplexResult] | ESMProteinError:
+        """Fold a molecular complex containing proteins, nucleic acids, and/or ligands.
+
+        Args:
+            all_atom_input: StructurePredictionInput containing sequences for different molecule types
+            model_name: Override the client level model name if needed
+        """
+        request = self._process_fold_all_atom_request(
+            all_atom_input, model_name if model_name is not None else self.model
+        )
+
+        try:
+            data = await self._async_post("fold_all_atom", request)
+        except ESMProteinError as e:
+            return e
+
+        return self._process_fold_all_atom_response(data)
+
+    @retry_decorator
+    def fold_all_atom(
+        self, all_atom_input: StructurePredictionInput, model_name: str | None = None
+    ) -> MolecularComplexResult | list[MolecularComplexResult] | ESMProteinError:
+        """Predict coordinates for a molecular complex containing proteins, dna, rna, and/or ligands.
+
+        Args:
+            all_atom_input: StructurePredictionInput containing sequences for different molecule types
+            model_name: Override the client level model name if needed
+        """
+        request = self._process_fold_all_atom_request(
+            all_atom_input, model_name if model_name is not None else self.model
+        )
+
+        try:
+            data = self._post("fold_all_atom", request)
+        except ESMProteinError as e:
+            return e
+
+        return self._process_fold_all_atom_response(data)
+
+    @staticmethod
+    def _process_fold_all_atom_request(
+        all_atom_input: StructurePredictionInput, model_name: str | None = None
+    ) -> dict[str, Any]:
+        request: dict[str, Any] = {
+            "all_atom_input": serialize_structure_prediction_input(all_atom_input),
+            "model": model_name,
+        }
+
+        return request
+
+    @staticmethod
+    def _process_fold_all_atom_response(data: dict[str, Any]) -> MolecularComplexResult:
+        complex_data = data.get("complex")
+        molecular_complex = MolecularComplex.from_state_dict(complex_data)
+        return MolecularComplexResult(
+            complex=molecular_complex,
+            plddt=maybe_tensor(data.get("plddt"), convert_none_to_nan=True),
+            ptm=data.get("ptm", None),
+            distogram=maybe_tensor(data.get("distogram"), convert_none_to_nan=True),
+        )
+
     @retry_decorator
     async def async_inverse_fold(
         self,
@@ -602,19 +674,15 @@ def _process_logits_response(
 
         return LogitsOutput(
             logits=ForwardTrackData(
-                sequence=_maybe_logits(data, "sequence", return_bytes),
-                structure=_maybe_logits(data, "structure", return_bytes),
-                secondary_structure=_maybe_logits(
-                    data, "secondary_structure", return_bytes
-                ),
-                sasa=_maybe_logits(data, "sasa", return_bytes),
-                function=_maybe_logits(data, "function", return_bytes),
+                sequence=_maybe_logits(data, "sequence"),
+                structure=_maybe_logits(data, "structure"),
+                secondary_structure=_maybe_logits(data, "secondary_structure"),
+                sasa=_maybe_logits(data, "sasa"),
+                function=_maybe_logits(data, "function"),
             ),
             embeddings=maybe_tensor(data["embeddings"]),
             mean_embedding=data["mean_embedding"],
-            residue_annotation_logits=_maybe_logits(
-                data, "residue_annotation", return_bytes
-            ),
+            residue_annotation_logits=_maybe_logits(data, "residue_annotation"),
             hidden_states=maybe_tensor(data["hidden_states"]),
             mean_hidden_state=maybe_tensor(data["mean_hidden_state"]),
         )
@@ -965,6 +1033,7 @@ def _process_logits_request(
             "sequence": config.sequence,
             "return_embeddings": config.return_embeddings,
             "return_mean_embedding": config.return_mean_embedding,
+            "return_mean_hidden_states": config.return_mean_hidden_states,
             "return_hidden_states": config.return_hidden_states,
             "ith_hidden_layer": config.ith_hidden_layer,
         }
@@ -981,12 +1050,11 @@ def _process_logits_response(
         data["hidden_states"] = _maybe_b64_decode(data["hidden_states"], return_bytes)
 
         output = LogitsOutput(
-            logits=ForwardTrackData(
-                sequence=_maybe_logits(data, "sequence", return_bytes)
-            ),
+            logits=ForwardTrackData(sequence=_maybe_logits(data, "sequence")),
             embeddings=maybe_tensor(data["embeddings"]),
             mean_embedding=data["mean_embedding"],
             hidden_states=maybe_tensor(data["hidden_states"]),
+            mean_hidden_state=maybe_tensor(data["mean_hidden_state"]),
         )
         return output
 

esm/sdk/retry.py

@@ -2,10 +2,9 @@
 from contextvars import ContextVar
 from functools import wraps
 
-import httpx
 from tenacity import (
     retry,
-    retry_if_exception_type,
+    retry_if_exception,
     retry_if_result,
     stop_after_attempt,
     wait_incrementing,
@@ -30,8 +29,12 @@ def retry_if_specific_error(exception):
 
 
 def log_retry_attempt(retry_state):
+    try:
+        outcome = retry_state.outcome.result()
+    except Exception:
+        outcome = retry_state.outcome.exception()
     print(
-        f"Retrying... Attempt {retry_state.attempt_number} after {retry_state.next_action.sleep}s due to: {retry_state.outcome.result()}"
+        f"Retrying... Attempt {retry_state.attempt_number} after {retry_state.next_action.sleep}s due to: {outcome}"
     )
 
 
@@ -41,13 +44,18 @@ def retry_decorator(func):
     instance's retry settings.
     """
 
+    def return_last_value(retry_state):
+        """Return the result of the last call attempt."""
+        return retry_state.outcome.result()
+
     @wraps(func)
     async def async_wrapper(instance, *args, **kwargs):
         if skip_retries_var.get():
             return await func(instance, *args, **kwargs)
         retry_decorator = retry(
+            retry_error_callback=return_last_value,
             retry=retry_if_result(retry_if_specific_error)
-            | retry_if_exception_type(httpx.ConnectTimeout),  # ADDED
+            | retry_if_exception(retry_if_specific_error),
             wait=wait_incrementing(
                 increment=1, start=instance.min_retry_wait, max=instance.max_retry_wait
             ),
@@ -62,8 +70,9 @@ def wrapper(instance, *args, **kwargs):
         if skip_retries_var.get():
             return func(instance, *args, **kwargs)
         retry_decorator = retry(
+            retry_error_callback=return_last_value,
             retry=retry_if_result(retry_if_specific_error)
-            | retry_if_exception_type(httpx.ConnectTimeout),  # ADDED
+            | retry_if_exception(retry_if_specific_error),
             wait=wait_incrementing(
                 increment=1, start=instance.min_retry_wait, max=instance.max_retry_wait
             ),

esm/utils/generation.py

@@ -43,7 +43,9 @@ def _trim_sequence_tensor_dataclass(o: Any, sequence_len: int):
 
     sliced = {}
     for k, v in attr.asdict(o, recurse=False).items():
-        if v is None:
+        if k in ["mean_hidden_state", "mean_embedding"]:
+            sliced[k] = v
+        elif v is None:
             sliced[k] = None
         elif isinstance(v, torch.Tensor):
             # Trim padding.