Merge branch 'dev' of https://github.com/JadenFiotto-Kaufman/nnsight …

…into dev

src/nnsight/models/Mamba.py

@@ -0,0 +1,161 @@
+from __future__ import annotations
+
+from typing import Any, Callable, Dict, List, Tuple, Union
+
+import causal_conv1d_cuda
+import mamba_ssm
+import selective_scan_cuda
+import torch
+from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf
+from mamba_ssm import MambaLMHeadModel
+from torch.utils.hooks import RemovableHandle
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BatchEncoding,
+    PretrainedConfig,
+    PreTrainedModel,
+    PreTrainedTokenizer,
+)
+from mamba_ssm.models.config_mamba import MambaConfig
+
+from ..patching import Patch, Patcher
+from .AbstractModel import AbstractModel
+
+
+class Mamba(AbstractModel):
+    def _register_increment_hook(self, hook: Callable) -> RemovableHandle:
+        return self.local_model.register_forward_hook(hook)
+
+    def _load_meta(self, repoid_or_path, *args, device=None, **kwargs) -> PreTrainedModel:
+        self.tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
+        self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
+        config_data = load_config_hf(repoid_or_path)
+        self.config = MambaConfig(**config_data)
+        return MambaLMHeadModel(self.config, device='meta', dtype=None, **kwargs)
+
+    def _load_local(self, repoid_or_path, *args, **kwargs) -> PreTrainedModel:
+        model = MambaLMHeadModel(self.config, **kwargs)
+        model.load_state_dict(load_state_dict_hf(repoid_or_path, **kwargs))
+        return model
+
+    def _tokenize(
+        self,
+        inputs: Union[
+            str,
+            List[str],
+            List[List[str]],
+            List[int],
+            List[List[int]],
+            torch.Tensor,
+            Dict[str, Any],
+        ],
+    ):
+        if isinstance(inputs, BatchEncoding):
+            return inputs
+
+        if isinstance(inputs, str) or (
+            isinstance(inputs, list) and isinstance(inputs[0], int)
+        ):
+            inputs = [inputs]
+
+        if isinstance(inputs, torch.Tensor) and inputs.ndim == 1:
+            inputs = inputs.unsqueeze(0)
+
+        if not isinstance(inputs[0], str):
+            inputs = [{"input_ids": ids} for ids in inputs]
+
+            return self.tokenizer.pad(inputs, return_tensors="pt")
+
+        return self.tokenizer(inputs, return_tensors="pt", padding=True)
+
+    def _prepare_inputs(
+        self,
+        inputs: Union[
+            str,
+            List[str],
+            List[List[str]],
+            List[int],
+            List[List[int]],
+            torch.Tensor,
+            Dict[str, Any],
+            BatchEncoding,
+        ],
+        labels: Any = None,
+        **kwargs,
+    ) -> BatchEncoding:
+        if isinstance(inputs, dict):
+            _inputs = self._tokenize(inputs["input_ids"])
+
+            _inputs = self._tokenize(_inputs)
+
+            if "labels" in inputs:
+                labels = self._tokenize(inputs["labels"])
+                labels = self._tokenize(labels)
+                _inputs["labels"] = labels["input_ids"]
+
+            return _inputs
+
+        inputs = self._tokenize(inputs)
+
+        if labels is not None:
+            labels = self._tokenize(labels)
+
+            inputs["labels"] = labels["input_ids"]
+
+        return inputs
+
+    def _batch_inputs(
+        self, prepared_inputs: BatchEncoding, batched_inputs: Dict
+    ) -> torch.Tensor:
+        if batched_inputs is None:
+            batched_inputs = {"input_ids": prepared_inputs["input_ids"]}
+
+        else:
+            batched_inputs["input_ids"] = torch.concatenate([batched_inputs["input_ids"], prepared_inputs["input_ids"]])
+
+        return batched_inputs, len(prepared_inputs["input_ids"])
+
+    def _example_input(self) -> Dict[str, torch.Tensor]:
+        return BatchEncoding({"input_ids": torch.tensor([[0]])})
+
+    def _scan(self, prepared_inputs, *args, **kwargs) -> None:
+        def blah(hs, *args, residual=None, **kwargs):
+            return hs, residual
+
+        def blah1(hs, *args, **kwargs):
+            return hs
+
+        def blah2(hs, *args, **kwargs):
+            return hs
+
+        def blah3(conv1d_out, delta, A, B, C, D, z, delta_bias, delta_softplus):
+            return (
+                conv1d_out,
+                torch.zeros((*conv1d_out.shape, A.shape[1] * 2), device="meta"),
+                conv1d_out,
+            )
+
+        with Patcher() as patcher:
+            patcher.add(Patch(mamba_ssm.modules.mamba_simple, blah, "rms_norm_fn"))
+            patcher.add(Patch(mamba_ssm.models.mixer_seq_simple, blah1, "rms_norm_fn"))
+            patcher.add(Patch(causal_conv1d_cuda, blah2, "causal_conv1d_fwd"))
+            patcher.add(Patch(selective_scan_cuda, blah3, "fwd"))
+
+            self.meta_model(prepared_inputs.copy()["input_ids"].to("meta"))
+
+    def _forward(self, prepared_inputs, *args, **kwargs) -> Any:
+        return self.local_model(
+            prepared_inputs["input_ids"].to(next(self.local_model.parameters()).device),
+            *args,
+            **kwargs,
+        )
+
+    def _generation(self, prepared_inputs, *args, max_length: int = 1, **kwargs) -> Any:
+        return self.local_model.generate(
+            prepared_inputs["input_ids"].to(next(self.local_model.parameters()).device),
+            *args,
+            max_length=max_length,
+            **kwargs,
+        )