Upload config

config.json

@@ -23,6 +23,9 @@
     "d"
   ],
   "attn_sparse_layout_seq": null,
+  "auto_map": {
+    "AutoConfig": "configuration_codify.CodifyConfig"
+  },
   "backcheck_pw": "none",
   "backcheck_sa": "none",
   "bos_token_id": 1,

configuration_codify.py

@@ -0,0 +1,152 @@
+from collections import OrderedDict
+from typing import TYPE_CHECKING, Any, List, Mapping, Optional
+
+from packaging import version
+
+from transformers import is_torch_available
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer, TensorType
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.onnx import OnnxConfigWithPast, PatchingSpec
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+CODIFY_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "smallcloudai/codify_medium_multi": "https://huggingface.co/smallcloudai/codify_medium_multi/blob/main/config.json",
+    "smallcloudai/codify_3b_multi": "https://huggingface.co/smallcloudai/codify_3b_multi/blob/main/config.json",
+}
+
+
+class CodifyConfig(PretrainedConfig):
+    model_type = "codify"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "num_hidden_layers": "L",
+        "num_attention_heads": "attn_heads",
+        "hidden_size": "E",
+    }
+
+    def __init__(
+            self,
+            vocab_size=51305,
+            layer_norm_epsilon=1e-5,
+            initializer_range=0.02,
+            use_cache=True,
+            bos_token_id=1,
+            eos_token_id=2,
+            mlp_mult=4,
+            tie_word_embeddings=False,
+            **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.mlp_mult = mlp_mult
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id,
+                         tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class CodifyOnnxConfig(OnnxConfigWithPast):
+    torch_onnx_minimum_version = version.parse("1.12")
+
+    def __init__(
+            self,
+            config: PretrainedConfig,
+            task: str = "default",
+            patching_specs: List[PatchingSpec] = None,
+            use_past: bool = False,
+    ):
+        super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
+        if not getattr(self._config, "pad_token_id", None):
+            # TODO: how to do that better?
+            self._config.pad_token_id = 0
+
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
+        if self.use_past:
+            # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
+            self.fill_with_past_key_values_(common_inputs, direction="inputs", inverted_values_shape=True)
+            common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
+        else:
+            common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
+
+        return common_inputs
+
+    @property
+    def num_layers(self) -> int:
+        return self._config.num_hidden_layers
+
+    @property
+    def num_attention_heads(self) -> int:
+        return self._config.n_head
+
+    @property
+    def atol_for_validation(self) -> float:
+        return 1e-3
+
+    def generate_dummy_inputs(
+            self,
+            tokenizer: "PreTrainedTokenizer",
+            batch_size: int = -1,
+            seq_length: int = -1,
+            is_pair: bool = False,
+            framework: Optional["TensorType"] = None,
+    ) -> Mapping[str, Any]:
+        common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
+            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+        )
+
+        # We need to order the input in the way they appears in the forward()
+        ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
+
+        # Need to add the past_keys
+        if self.use_past:
+            if not is_torch_available():
+                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+            else:
+                import torch
+
+                batch, seqlen = common_inputs["input_ids"].shape
+                # Not using the same length for past_key_values
+                past_key_values_length = seqlen + 2
+                head_dim = self._config.hidden_size // self.num_attention_heads
+                past_key_shape = (
+                    batch * self.num_attention_heads,
+                    head_dim,
+                    past_key_values_length,
+                )
+                past_value_shape = (
+                    batch * self.num_attention_heads,
+                    past_key_values_length,
+                    head_dim,
+                )
+                ordered_inputs["past_key_values"] = [
+                    (torch.zeros(past_key_shape), torch.zeros(past_value_shape)) for _ in range(self.num_layers)
+                ]
+
+        ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
+        if self.use_past:
+            mask_dtype = ordered_inputs["attention_mask"].dtype
+            ordered_inputs["attention_mask"] = torch.cat(
+                [ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
+            )
+
+        return ordered_inputs
+
+    @property
+    def default_onnx_opset(self) -> int:
+        return 13
+
+
+from transformers import AutoConfig
+
+AutoConfig.register(CodifyConfig.model_type, CodifyConfig)