Restore compatibility with models handling fewer tokens

.github/workflows/build-and-test.yml

@@ -34,6 +34,12 @@ jobs:
           cache-environment: false
           post-cleanup: 'none'
 
+      - name: Check for TODOs
+        shell: bash -l {0}
+        run: |
+          cd main
+          make checktodo
+
       - name: Check format
         shell: bash -l {0}
         run: |

Makefile

@@ -15,6 +15,9 @@ checkformat:
 	docformatter --check --black --recursive netam tests
 	black --check netam tests
 
+checktodo:
+	grep -rq --include=\*.{py,Snakemake} "TODO" . && echo "TODOs found" && exit 1 || echo "No TODOs found" && exit 0
+
 lint:
 	flake8 . --max-complexity=30 --ignore=E731,W503,E402,F541,E501,E203,E266 --statistics --exclude=_ignore
 
@@ -27,4 +30,4 @@ notebooks:
 		jupyter nbconvert --to notebook --execute "$$nb" --output _ignore/"$$(basename $$nb)"; \
 	done
 
-.PHONY: install test notebooks format lint docs
+.PHONY: install test notebooks format lint docs checktodo

netam/common.py

@@ -18,7 +18,7 @@
     apply_aa_mask_to_nt_sequence,
     RESERVED_TOKEN_TRANSLATIONS,
     BASES,
-    AA_TOKEN_STR_SORTED,
+    TOKEN_STR_SORTED,
 )
 
 BIG = 1e9
@@ -66,7 +66,7 @@ def aa_idx_tensor_of_str_ambig(aa_str):
     character."""
     try:
         return torch.tensor(
-            [AA_TOKEN_STR_SORTED.index(aa) for aa in aa_str], dtype=torch.int
+            [TOKEN_STR_SORTED.index(aa) for aa in aa_str], dtype=torch.int
         )
     except ValueError:
         print(f"Found an invalid amino acid in the string: {aa_str}")
@@ -126,7 +126,7 @@ def aa_strs_from_idx_tensor(idx_tensor):
 
     Args:
         idx_tensor (Tensor): A 2D tensor of shape (batch_size, seq_len) containing
-                             indices into AA_TOKEN_STR_SORTED.
+                             indices into TOKEN_STR_SORTED.
 
     Returns:
         List[str]: A list of amino acid strings with trailing 'X's removed.
@@ -135,7 +135,7 @@ def aa_strs_from_idx_tensor(idx_tensor):
 
     aa_str_list = []
     for row in idx_tensor:
-        aa_str = "".join(AA_TOKEN_STR_SORTED[idx] for idx in row.tolist())
+        aa_str = "".join(TOKEN_STR_SORTED[idx] for idx in row.tolist())
         aa_str_list.append(aa_str.rstrip("X"))
 
     return aa_str_list

netam/dnsm.py

@@ -163,9 +163,7 @@ def build_selection_matrix_from_parent(self, parent: str):
         """
         parent = sequences.translate_sequence(parent)
         selection_factors = self.model.selection_factors_of_aa_str(parent)
-        selection_matrix = torch.zeros(
-            (len(selection_factors), sequences.MAX_AA_TOKEN_IDX + 1), dtype=torch.float
-        )
+        selection_matrix = torch.zeros((len(selection_factors), 20), dtype=torch.float)
         # Every "off-diagonal" entry of the selection matrix is set to the selection
         # factor, where "diagonal" means keeping the same amino acid.
         selection_matrix[:, :] = selection_factors[:, None]

netam/dxsm.py

@@ -29,6 +29,7 @@
     nt_mutation_frequency,
     MAX_AA_TOKEN_IDX,
     RESERVED_TOKEN_REGEX,
+    AA_AMBIG_IDX,
 )
 
 
@@ -70,7 +71,7 @@ def __init__(
         # to the ambiguous amino acid, and then will fill in the actual values
         # below.
         self.aa_parents_idxss = torch.full(
-            (pcp_count, self.max_aa_seq_len), MAX_AA_TOKEN_IDX
+            (pcp_count, self.max_aa_seq_len), AA_AMBIG_IDX
         )
         self.aa_children_idxss = self.aa_parents_idxss.clone()
         self.aa_subs_indicators = torch.zeros((pcp_count, self.max_aa_seq_len))
@@ -303,7 +304,7 @@ def serial_find_optimal_branch_lengths(self, dataset, **optimization_kwargs):
 
     def find_optimal_branch_lengths(self, dataset, **optimization_kwargs):
         worker_count = min(mp.cpu_count() // 2, 10)
-        # The following can be used when one wants a better traceback.
+        # # The following can be used when one wants a better traceback.
         # burrito = self.__class__(None, dataset, copy.deepcopy(self.model))
         # return burrito.serial_find_optimal_branch_lengths(
         #     dataset, **optimization_kwargs

netam/framework.py

@@ -321,6 +321,11 @@ def load_crepe(prefix, device=None):
             f"Model class '{model_class_name}' not found in 'models' module."
         )
 
+    if issubclass(model_class, models.TransformerBinarySelectionModelLinAct):
+        if "embedding_dim" not in config["model_hyperparameters"]:
+            # Assume the model is from before any new tokens were added, so 21
+            config["model_hyperparameters"]["embedding_dim"] = 21
+
     model = model_class(**config["model_hyperparameters"])
 
     model_state_path = f"{prefix}.pth"

netam/models.py

@@ -597,11 +597,29 @@ def selection_factors_of_aa_str(self, aa_str: str) -> Tensor:
         mask = aa_mask_tensor_of(aa_str)
         mask = mask.to(self.device)
 
+        # Here we're ignoring sites containing tokens that have index greater
+        # than the embedding dimension. If extra tokens have been added since
+        # this model was defined, they are stripped out before feeding the
+        # sequence to the model, and the returned selection factors will be NaN
+        # at sites containing those unrecognized tokens.
+        model_valid_sites = aa_idxs < self.hyperparameters["embedding_dim"]
+        if self.hyperparameters["output_dim"] == 1:
+            result = torch.full((len(aa_str),), float("nan"), device=self.device)
+        else:
+            result = torch.full(
+                (len(aa_str), self.hyperparameters["output_dim"]),
+                float("nan"),
+                device=self.device,
+            )
+
         with torch.no_grad():
-            model_out = self(aa_idxs.unsqueeze(0), mask.unsqueeze(0)).squeeze(0)
-            final_out = torch.exp(model_out)
+            model_out = self(
+                aa_idxs[model_valid_sites].unsqueeze(0),
+                mask[model_valid_sites].unsqueeze(0),
+            ).squeeze(0)
+            result[model_valid_sites] = torch.exp(model_out)[: model_valid_sites.sum()]
 
-        return final_out[: len(aa_str)]
+        return result
 
 
 class TransformerBinarySelectionModelLinAct(AbstractBinarySelectionModel):
@@ -613,16 +631,18 @@ def __init__(
         layer_count: int,
         dropout_prob: float = 0.5,
         output_dim: int = 1,
+        embedding_dim: int = MAX_AA_TOKEN_IDX + 1,
     ):
         super().__init__()
         # Note that d_model has to be divisible by nhead, so we make that
         # automatic here.
         self.d_model_per_head = d_model_per_head
         self.d_model = d_model_per_head * nhead
         self.nhead = nhead
+        self.embedding_dim = embedding_dim
         self.dim_feedforward = dim_feedforward
         self.pos_encoder = PositionalEncoding(self.d_model, dropout_prob)
-        self.amino_acid_embedding = nn.Embedding(MAX_AA_TOKEN_IDX + 1, self.d_model)
+        self.amino_acid_embedding = nn.Embedding(self.embedding_dim, self.d_model)
         self.encoder_layer = nn.TransformerEncoderLayer(
             d_model=self.d_model,
             nhead=nhead,
@@ -642,6 +662,7 @@ def hyperparameters(self):
             "layer_count": self.encoder.num_layers,
             "dropout_prob": self.pos_encoder.dropout.p,
             "output_dim": self.linear.out_features,
+            "embedding_dim": self.embedding_dim,
         }
 
     def init_weights(self) -> None:
@@ -748,14 +769,15 @@ def predict(self, representation: Tensor):
 class SingleValueBinarySelectionModel(AbstractBinarySelectionModel):
     """A one parameter selection model as a baseline."""
 
-    def __init__(self, output_dim: int = 1):
+    def __init__(self, output_dim: int = 1, embedding_dim: int = MAX_AA_TOKEN_IDX + 1):
         super().__init__()
         self.single_value = nn.Parameter(torch.tensor(0.0))
         self.output_dim = output_dim
+        self.embedding_dim = embedding_dim
 
     @property
     def hyperparameters(self):
-        return {"output_dim": self.output_dim}
+        return {"output_dim": self.output_dim, "embedding_dim": self.embedding_dim}
 
     def forward(self, amino_acid_indices: Tensor, mask: Tensor) -> Tensor:
         """Build a binary log selection matrix from an index-encoded parent sequence."""

netam/molevol.py

@@ -9,7 +9,7 @@
 import torch
 from torch import Tensor, optim
 
-from netam.sequences import CODON_AA_INDICATOR_MATRIX, MAX_AA_TOKEN_IDX
+from netam.sequences import CODON_AA_INDICATOR_MATRIX
 
 import netam.sequences as sequences
 
@@ -444,7 +444,7 @@ def mutsel_log_pcp_probability_of(
     """
 
     assert len(parent) % 3 == 0
-    assert sel_matrix.shape == (len(parent) // 3, MAX_AA_TOKEN_IDX + 1)
+    assert sel_matrix.shape == (len(parent) // 3, 20)
 
     parent_idxs = sequences.nt_idx_tensor_of_str(parent)
     child_idxs = sequences.nt_idx_tensor_of_str(child)

netam/sequences.py

@@ -17,15 +17,15 @@
 
 NT_STR_SORTED = "".join(BASES)
 BASES_AND_N_TO_INDEX = {base: idx for idx, base in enumerate(NT_STR_SORTED + "N")}
-# ambiguous must remain last. It is assumed elsewhere that the max index
-# denotes the ambiguous base
-AA_TOKEN_STR_SORTED = AA_STR_SORTED + RESERVED_TOKENS + "X"
+# Must add new tokens to the end of this string.
+TOKEN_STR_SORTED = AA_STR_SORTED + "X" + RESERVED_TOKENS
+AA_AMBIG_IDX = len(AA_STR_SORTED)
 
 RESERVED_TOKEN_AA_BOUNDS = (
-    min(AA_TOKEN_STR_SORTED.index(token) for token in RESERVED_TOKENS),
-    max(AA_TOKEN_STR_SORTED.index(token) for token in RESERVED_TOKENS),
+    min(TOKEN_STR_SORTED.index(token) for token in RESERVED_TOKENS),
+    max(TOKEN_STR_SORTED.index(token) for token in RESERVED_TOKENS),
 )
-MAX_AA_TOKEN_IDX = len(AA_TOKEN_STR_SORTED) - 1
+MAX_AA_TOKEN_IDX = len(TOKEN_STR_SORTED) - 1
 CODONS = ["".join(codon_list) for codon_list in itertools.product(BASES, repeat=3)]
 STOP_CODONS = ["TAA", "TAG", "TGA"]
 # Each token in RESERVED_TOKENS will appear once in aa strings, and three times
@@ -48,7 +48,7 @@ def nt_idx_array_of_str(nt_str):
 def aa_idx_array_of_str(aa_str):
     """Return the indices of the amino acids in a string."""
     try:
-        return np.array([AA_TOKEN_STR_SORTED.index(aa) for aa in aa_str])
+        return np.array([TOKEN_STR_SORTED.index(aa) for aa in aa_str])
     except ValueError:
         print(f"Found an invalid amino acid in the string: {aa_str}")
         raise
@@ -66,7 +66,7 @@ def nt_idx_tensor_of_str(nt_str):
 def aa_idx_tensor_of_str(aa_str):
     """Return the indices of the amino acids in a string."""
     try:
-        return torch.tensor([AA_TOKEN_STR_SORTED.index(aa) for aa in aa_str])
+        return torch.tensor([TOKEN_STR_SORTED.index(aa) for aa in aa_str])
     except ValueError:
         print(f"Found an invalid amino acid in the string: {aa_str}")
         raise
@@ -135,7 +135,7 @@ def translate_sequences(nt_sequences):
 def aa_index_of_codon(codon):
     """Return the index of the amino acid encoded by a codon."""
     aa = translate_sequence(codon)
-    return AA_TOKEN_STR_SORTED.index(aa)
+    return TOKEN_STR_SORTED.index(aa)
 
 
 def generic_mutation_frequency(ambig_symb, parent, child):
@@ -185,15 +185,16 @@ def pcp_criteria_check(parent, child, max_mut_freq=0.3):
 def generate_codon_aa_indicator_matrix():
     """Generate a matrix that maps codons (rows) to amino acids (columns)."""
 
-    matrix = np.zeros((len(CODONS), len(AA_TOKEN_STR_SORTED)))
+    matrix = np.zeros((len(CODONS), len(AA_STR_SORTED)))
 
     for i, codon in enumerate(CODONS):
         try:
             aa = translate_sequences([codon])[0]
-            aa_idx = AA_TOKEN_STR_SORTED.index(aa)
-            matrix[i, aa_idx] = 1
         except ValueError:  # Handle STOP codon
             pass
+        else:
+            aa_idx = AA_STR_SORTED.index(aa)
+            matrix[i, aa_idx] = 1
 
     return matrix
 

tests/old_models/dasm_13k-v1jaffe+v1tang-joint.yml

@@ -0,0 +1,17 @@
+encoder_class: PlaceholderEncoder
+encoder_parameters: {}
+model_class: TransformerBinarySelectionModelWiggleAct
+model_hyperparameters:
+  d_model_per_head: 4
+  dim_feedforward: 64
+  dropout_prob: 0.1
+  layer_count: 3
+  nhead: 4
+  output_dim: 20
+serialization_version: 0
+training_hyperparameters:
+  batch_size: 1024
+  learning_rate: 0.001
+  min_learning_rate: 1.0e-06
+  optimizer_name: RMSprop
+  weight_decay: 1.0e-06

tests/old_models/dnsm_13k-v1jaffe+v1tang-joint.yml

@@ -0,0 +1,17 @@
+encoder_class: PlaceholderEncoder
+encoder_parameters: {}
+model_class: TransformerBinarySelectionModelWiggleAct
+model_hyperparameters:
+  d_model_per_head: 4
+  dim_feedforward: 64
+  dropout_prob: 0.1
+  layer_count: 3
+  nhead: 4
+  output_dim: 1
+serialization_version: 0
+training_hyperparameters:
+  batch_size: 1024
+  learning_rate: 0.001
+  min_learning_rate: 1.0e-06
+  optimizer_name: RMSprop
+  weight_decay: 1.0e-06

tests/test_ambiguous.py

@@ -11,7 +11,6 @@
     load_pcp_df,
     add_shm_model_outputs_to_pcp_df,
 )
-from netam.sequences import MAX_AA_TOKEN_IDX
 from netam import pretrained
 import random
 
@@ -170,7 +169,7 @@ def dasm_model():
         d_model_per_head=4,
         dim_feedforward=256,
         layer_count=2,
-        output_dim=MAX_AA_TOKEN_IDX + 1,
+        output_dim=20,
     )
 
 

tests/test_backward_compat.py

@@ -0,0 +1,28 @@
+import torch
+
+from netam.framework import load_crepe
+from netam.sequences import set_wt_to_nan
+
+
+# The outputs used for this test are produced by running
+# `test_backward_compat_copy.py` on the wd-old-model-runner branch.
+# This is to ensure that we can still load older crepes, even if we change the
+# dimensions of model layers, as we did with the Embedding layer in
+# https://github.com/matsengrp/netam/pull/92.
+def test_old_model_outputs():
+    example_seq = "QVQLVESGGGVVQPGRSLRLSCAASGFTFSSSGMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVKGRFTISRDNSKNTVYLQMNSLRAEDTAVYYCAREGHSNYPYYYYYMDVWGKGTTVTVSS"
+    dasm_crepe = load_crepe("tests/old_models/dasm_13k-v1jaffe+v1tang-joint")
+    dnsm_crepe = load_crepe("tests/old_models/dnsm_13k-v1jaffe+v1tang-joint")
+
+    dasm_vals = torch.nan_to_num(
+        set_wt_to_nan(
+            torch.load("tests/old_models/dasm_output", weights_only=True), example_seq
+        ),
+        0.0,
+    )
+    dnsm_vals = torch.load("tests/old_models/dnsm_output", weights_only=True)
+
+    dasm_result = torch.nan_to_num(dasm_crepe([example_seq])[0], 0.0)
+    dnsm_result = dnsm_crepe([example_seq])[0]
+    assert torch.allclose(dasm_result, dasm_vals)
+    assert torch.allclose(dnsm_result, dnsm_vals)

tests/test_common.py

@@ -33,6 +33,6 @@ def test_codon_mask_tensor_of():
 
 
 def test_aa_strs_from_idx_tensor():
-    aa_idx_tensor = torch.tensor([[0, 1, 2, 3, 20, 21], [4, 5, 19, 21, 21, 21]])
+    aa_idx_tensor = torch.tensor([[0, 1, 2, 3, 20, 21], [4, 5, 19, 21, 20, 20]])
     aa_strings = aa_strs_from_idx_tensor(aa_idx_tensor)
-    assert aa_strings == ["ACDE^", "FGY"]
+    assert aa_strings == ["ACDEX^", "FGY^"]

tests/test_dasm.py

@@ -8,7 +8,6 @@
     crepe_exists,
     load_crepe,
 )
-from netam.sequences import MAX_AA_TOKEN_IDX
 from netam.models import TransformerBinarySelectionModelWiggleAct
 from netam.dasm import (
     DASMBurrito,
@@ -30,7 +29,7 @@ def dasm_burrito(pcp_df):
         d_model_per_head=4,
         dim_feedforward=256,
         layer_count=2,
-        output_dim=MAX_AA_TOKEN_IDX + 1,
+        output_dim=20,
     )
 
     burrito = DASMBurrito(