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import torch
import random
from torch.nn.functional import mse_loss
from tqdm import tqdm
from collections import defaultdict
from transformers import AutoModelForMaskedLM
from esm2.modeling_fastesm import FastEsmForMaskedLM
from esm2.load_official import load_official_model as load_official_esm2_model
from esm_plusplus.modeling_esm_plusplus import ESMplusplusForMaskedLM
from esm_plusplus.load_official import load_official_model as load_official_esmc_model
from e1_fastplms.load_official import load_official_model as load_official_e1_model
from e1_fastplms.modeling_e1 import E1ForMaskedLM
from weight_parity_utils import assert_state_dict_equal
class ComplianceChecker:
def __init__(
self,
test_number_batches: int = 25,
batch_size: int = 8,
min_sequence_length: int = 16,
max_sequence_length: int = 128,
):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.test_number_batches = test_number_batches
self.batch_size = batch_size
self.min_sequence_length = min_sequence_length
self.max_sequence_length = max_sequence_length
self.canonical_amino_acids = "ACDEFGHIKLMNPQRSTVWY"
def _load_esmc(self, from_auto_model: bool = False, force_download: bool = False):
official_model_path = "esmc-300"
fast_model_path = "Synthyra/ESMplusplus_small"
official_model, tokenizer = load_official_esmc_model(
reference_repo_id=official_model_path,
device=self.device,
dtype=torch.bfloat16,
)
load_class = AutoModelForMaskedLM if from_auto_model else ESMplusplusForMaskedLM
fast_model = load_class.from_pretrained(
fast_model_path,
dtype=torch.bfloat16,
device_map=self.device,
force_download=force_download,
trust_remote_code=True,
).eval()
return official_model, fast_model, tokenizer
def _load_esm2(self, from_auto_model: bool = False, force_download: bool = False):
official_model_path = "facebook/esm2_t6_8M_UR50D"
fast_model_path = "Synthyra/ESM2-8M"
official_model, tokenizer = load_official_esm2_model(
reference_repo_id=official_model_path,
device=self.device,
dtype=torch.bfloat16,
)
load_class = AutoModelForMaskedLM if from_auto_model else FastEsmForMaskedLM
fast_model = load_class.from_pretrained(
fast_model_path,
dtype=torch.bfloat16,
device_map=self.device,
force_download=force_download,
trust_remote_code=True,
).eval()
return official_model, fast_model, tokenizer
def _load_e1(self, from_auto_model: bool = False, force_download: bool = False):
official_model_path = "Profluent-Bio/E1-150m"
fast_model_path = "Synthyra/Profluent-E1-150M"
official_model, tokenizer = load_official_e1_model(
reference_repo_id=official_model_path,
device=self.device,
dtype=torch.bfloat16,
)
load_class = AutoModelForMaskedLM if from_auto_model else E1ForMaskedLM
fast_model = load_class.from_pretrained(
fast_model_path,
dtype=torch.bfloat16,
device_map=self.device,
force_download=force_download,
trust_remote_code=True,
).eval()
return official_model, fast_model, tokenizer
def _generate_random_sequence(self, length: int) -> str:
return 'M' + "".join(random.choices(self.canonical_amino_acids, k=length))
def _generate_random_batch(self, batch_size: int, min_length: int, max_length: int) -> list[str]:
return [self._generate_random_sequence(random.randint(min_length, max_length)) for _ in range(batch_size)]
def _weight_compliance(self, official_model, fast_model):
for (official_name, official_param), (fast_name, fast_param) in zip(official_model.model.state_dict().items(), fast_model.state_dict().items()):
if official_name == fast_name:
diff = mse_loss(official_param, fast_param).item()
if diff > 0.0:
print(f"{official_name}: {diff}")
assert diff < 1e-3, f"Parameter {official_name} has a large difference: {diff}"
else:
print(f"Name mismatch: {official_name} != {fast_name}")
@torch.inference_mode()
def _foward_compliance(self, model_type: str, official_model, fast_model, tokenizer, only_non_pad_tokens: bool = False):
cumulative_logits_mse = 0
cumulative_preds_accuracy = 0
hidden_state_diff_dict = defaultdict(int)
for _ in tqdm(range(self.test_number_batches)):
batch = self._generate_random_batch(self.batch_size, self.min_sequence_length, self.max_sequence_length)
if model_type == "E1":
tokenized = tokenizer.get_batch_kwargs(batch, device=self.device)
tokenized = {
"input_ids": tokenized["input_ids"],
"within_seq_position_ids": tokenized["within_seq_position_ids"],
"global_position_ids": tokenized["global_position_ids"],
"sequence_ids": tokenized["sequence_ids"],
"attention_mask": (tokenized["sequence_ids"] != -1).long(),
}
else:
tokenized = tokenizer(batch, return_tensors="pt", padding=True)
tokenized = {k: v.to(self.device) for k, v in tokenized.items()}
attention_mask = tokenized['attention_mask'].cpu().bool()
model_inputs = tokenized.copy()
if model_type == "ESMC":
model_inputs["sequence_id"] = model_inputs["attention_mask"].to(dtype=torch.bool)
official_output = official_model(**model_inputs, output_hidden_states=True)
official_hidden_states = official_output.hidden_states
official_logits = official_output.logits.cpu()
if only_non_pad_tokens:
official_logits = official_logits[attention_mask]
official_preds = official_logits.argmax(dim=-1)
fast_output = fast_model(**model_inputs, output_hidden_states=True)
fast_hidden_states = fast_output.hidden_states
fast_logits = fast_output.logits.cpu()
if only_non_pad_tokens:
fast_logits = fast_logits[attention_mask]
fast_preds = fast_logits.argmax(dim=-1)
cumulative_logits_mse += mse_loss(official_logits, fast_logits)
cumulative_preds_accuracy += (official_preds == fast_preds).float().mean()
for i in range(len(official_hidden_states)):
official_state, fast_state = official_hidden_states[i], fast_hidden_states[i]
if only_non_pad_tokens:
official_state, fast_state = official_state[attention_mask], fast_state[attention_mask]
hidden_state_diff_dict[i] += mse_loss(official_state, fast_state).item()
avg_logits_mse = cumulative_logits_mse / self.test_number_batches
avg_preds_accuracy = cumulative_preds_accuracy / self.test_number_batches
print(f"Average logits MSE: {avg_logits_mse}")
print(f"Average preds accuracy: {avg_preds_accuracy}")
if avg_logits_mse > 1e-3 or avg_preds_accuracy < 0.95:
print("Differences were too large, printing hidden state differences for debugging...")
for k, v in hidden_state_diff_dict.items():
print(f"Hidden state {k} Avg MSE: {v / self.test_number_batches}")
def __call__(
self,
model_type: str = "ESMC",
force_download: bool = False,
from_auto_model: bool = False,
only_non_pad_tokens: bool = False,
):
if model_type == "ESMC":
official_model, fast_model, tokenizer = self._load_esmc(from_auto_model, force_download)
elif model_type == "ESM2":
official_model, fast_model, tokenizer = self._load_esm2(from_auto_model, force_download)
elif model_type == "E1":
official_model, fast_model, tokenizer = self._load_e1(from_auto_model, force_download)
else:
raise ValueError(f"Unsupported model type: {model_type}. Supported: ESMC, ESM2, E1")
assert_state_dict_equal(
reference_state_dict=official_model.model.state_dict(),
candidate_state_dict=fast_model.state_dict(),
context=f"{model_type} weight parity",
)
self._weight_compliance(official_model, fast_model)
self._foward_compliance(model_type, official_model, fast_model, tokenizer, only_non_pad_tokens)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--hf_token", type=str, default=None)
parser.add_argument("--only_non_pad_tokens", action="store_true")
parser.add_argument("--force_download", action="store_true")
parser.add_argument("--from_auto_model", action="store_true")
parser.add_argument("--model_types", nargs="+", default=["ESMC", "ESM2", "E1"])
args = parser.parse_args()
if args.hf_token is not None:
from huggingface_hub import login
login(token=args.hf_token)
checker = ComplianceChecker()
for model_type in args.model_types:
print(f"Checking {model_type}...")
checker(
model_type=model_type,
from_auto_model=args.from_auto_model,
only_non_pad_tokens=args.only_non_pad_tokens,
force_download=args.force_download
)
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