Upload configuration_step3p7.py with huggingface_hub

configuration_step3p7.py

@@ -0,0 +1,207 @@
+from typing import Any, Optional, Sequence, Union
+
+from transformers.configuration_utils import PretrainedConfig
+
+class StepRoboticsVisionEncoderConfig(PretrainedConfig):
+    model_type = "perception_encoder"
+
+    def __init__(
+        self,
+        width=1536,
+        layers=47,
+        heads=16,
+        num_channels=3,
+        image_size=728,
+        mlp_ratio = 8960/1536,
+        patch_size=14,
+        hidden_act="quick_gelu",
+        layer_norm_eps=1e-5,
+        ues_cls_token=False,
+        use_cls_token: Optional[bool] = None,
+        use_ln_pre=True,
+        use_ln_post=False,
+        use_abs_posemb=True,
+        use_rope2d=True,
+        ls_init_value=0.1,
+        **kwargs,
+    ):
+        self.width = width
+        self.layers = layers
+        self.heads = heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.mlp_ratio = mlp_ratio
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        if use_cls_token is None:
+            use_cls_token = ues_cls_token
+        self.ues_cls_token = use_cls_token
+        self.use_cls_token = use_cls_token
+        self.use_ln_pre = use_ln_pre
+        self.ls_init_value = ls_init_value
+        self.use_ln_post = use_ln_post
+        self.use_abs_posemb = use_abs_posemb
+        self.use_rope2d = use_rope2d
+        super().__init__(**kwargs)
+
+
+class Step3p7TextConfig(PretrainedConfig):
+    model_type = "step3p5"
+    architectures = ["Step3p5ForCausalLM"]
+
+    def __init__(
+        self,
+        hidden_size: int = 4096,
+        intermediate_size: int = 11264,
+        num_attention_heads: int = 64,
+        num_attention_groups: int = 8,
+        num_hidden_layers: int = 45,
+        max_seq_len: int = 128000,
+        vocab_size: int = 128815,
+        rms_norm_eps: float = 1e-5,
+        moe_intermediate_size: int = 1280,
+        moe_num_experts: int = 288,
+        moe_top_k: int = 8,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 128000,
+        share_expert_dims: int = 1280,
+        share_expert_dim: Optional[int] = None,
+        head_dim: int = 128,
+        norm_expert_weight: bool = True,
+        layer_types: list[str] = None,
+        sliding_window: Optional[int] = None,
+        pad_token_id: int = 1,
+        attention_dropout: float = 0.0,
+        use_head_wise_attn_gate: bool = False,
+        use_moe_router_bias: bool = False,
+        moe_router_activation: str = "softmax",
+        moe_router_scaling_factor: float = 1.0,
+        need_fp32_gate: bool = False,
+        attention_other_setting: Optional[dict[str, Any]] = None,
+        swiglu_limits: Optional[list[Optional[float]]] = None,
+        swiglu_limits_shared: Optional[list[Optional[float]]] = None,
+        use_rope_layers: Optional[list[bool]] = None,
+        yarn_only_types: Optional[list[str]] = None,
+        moe_layers_enum: tuple[int] = (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+                                       15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
+                                       25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
+                                       35, 36, 37, 38, 39, 40, 41, 42, 43, 44),
+        **kwargs,
+    ) -> None:
+        torch_dtype = kwargs.get("torch_dtype")
+        trim_layer_types = _normalize_per_layer_values(layer_types,
+                                                  num_hidden_layers)
+        if isinstance(rope_scaling, dict):
+            rope_scaling = dict(rope_scaling)
+        if share_expert_dim is None:
+            share_expert_dim = share_expert_dims
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_attention_groups = num_attention_groups
+        self.num_hidden_layers = num_hidden_layers
+        self.max_seq_len = max_seq_len
+        self.vocab_size = vocab_size
+        self.rms_norm_eps = rms_norm_eps
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_num_experts = moe_num_experts
+        self.moe_top_k = moe_top_k
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.max_position_embeddings = max_position_embeddings
+        self.share_expert_dim = share_expert_dim
+        self.head_dim = head_dim
+        self.norm_expert_weight = norm_expert_weight
+        self.moe_layers_enum = moe_layers_enum
+        self.layer_types = trim_layer_types
+        self.sliding_window = sliding_window
+        self.pad_token_id = pad_token_id
+        self.attention_dropout = attention_dropout
+        self.use_head_wise_attn_gate = use_head_wise_attn_gate
+        self.use_moe_router_bias = use_moe_router_bias
+        self.moe_router_activation = moe_router_activation
+        self.moe_router_scaling_factor = moe_router_scaling_factor
+        self.need_fp32_gate = need_fp32_gate
+        self.attention_other_setting = attention_other_setting
+        self.swiglu_limits = swiglu_limits
+        self.swiglu_limits_shared = swiglu_limits_shared
+        self.use_rope_layers = use_rope_layers
+        self.yarn_only_types = yarn_only_types
+        super().__init__(**kwargs)
+        if torch_dtype is not None:
+            self.torch_dtype = torch_dtype
+        self.layer_types = layer_types
+
+    def to_dict(self):
+        output = super().to_dict()
+        torch_dtype = getattr(self, "torch_dtype", None)
+        if torch_dtype is not None:
+            output["torch_dtype"] = torch_dtype
+        return output
+
+
+def _normalize_per_layer_values(
+    values: Optional[Sequence[Any]],
+    num_hidden_layers: int,
+) -> Optional[list[Any]]:
+    if values is None:
+        return None
+    normalized = list(values)
+    if not normalized:
+        return normalized
+    if len(normalized) < num_hidden_layers:
+        normalized.extend([normalized[-1]] *
+                          (num_hidden_layers - len(normalized)))
+    # Some checkpoints keep MTP/spec layer entries after the decoder layers.
+    # This config only builds num_hidden_layers decoder layers, and HF strict
+    # validation requires per-layer fields to match that decoder count.
+    return normalized[:num_hidden_layers]
+
+class Step3p7Config(PretrainedConfig):
+    # This loader is a compatibility shim for original Step VL checkpoints
+    # whose top-level config model_type is `step3p7`.
+    model_type = "step3p7"
+
+    def __init__(
+        self,
+        vision_config: Optional[Union[dict, StepRoboticsVisionEncoderConfig]] = None,
+        text_config: Optional[Union[dict, Step3p7TextConfig]] = None,
+        understand_projector_stride: int = 2,
+        projector_bias: bool = False,
+        image_token_id: int = 151679,
+        **kwargs,
+    ) -> None:
+        shared_rope_scaling = kwargs.get("rope_scaling")
+        if isinstance(shared_rope_scaling, dict):
+            shared_rope_scaling = dict(shared_rope_scaling)
+
+        if vision_config is None:
+            vision_config = StepRoboticsVisionEncoderConfig()
+        elif isinstance(vision_config, dict):
+            vision_config = StepRoboticsVisionEncoderConfig(**vision_config)
+        self.vision_config = vision_config
+
+        if text_config is None:
+            text_config = Step3p7TextConfig(rope_scaling=shared_rope_scaling)
+        elif isinstance(text_config, dict):
+            text_config = dict(text_config)
+            if shared_rope_scaling is not None and "rope_scaling" not in text_config:
+                text_config["rope_scaling"] = shared_rope_scaling
+            text_config = Step3p7TextConfig(**text_config)
+        elif shared_rope_scaling is not None and text_config.rope_scaling is None:
+            text_config.rope_scaling = dict(shared_rope_scaling)
+        self.text_config = text_config
+
+        rope_scaling = kwargs.get("rope_scaling")
+        if isinstance(rope_scaling, dict):
+            kwargs["rope_scaling"] = dict(rope_scaling)
+
+        self.understand_projector_stride = understand_projector_stride
+        self.projector_bias = projector_bias
+        self.hidden_size = text_config.hidden_size
+        self.max_position_embeddings = text_config.max_position_embeddings
+        self.image_token_id = image_token_id
+        # Help Auto classes find the correct implementation when saving/loading.
+        super().__init__(**kwargs)