add step-3.7-flash bf16 model libs

modeling_step3p7.py

@@ -0,0 +1,1395 @@
+# Copyright 2025 The LLAMA4 and HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import copy
+import inspect
+from dataclasses import dataclass
+from typing import Callable, Literal, Optional, Tuple, TypedDict, Union
+
+from PIL import Image
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.masking_utils import (
+    create_causal_mask,
+    create_sliding_window_causal_mask,
+)
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, can_return_tuple, logging
+from .configuration_step3p7 import Step3p7Config, Step3p7TextConfig
+from .vision_encoder import StepRoboticsVisionEncoder
+
+
+logger = logging.get_logger(__name__)
+_MASK_INPUT_EMBEDS_ARG = (
+    "inputs_embeds"
+    if "inputs_embeds" in inspect.signature(create_causal_mask).parameters
+    else "input_embeds"
+)
+
+__all__ = [
+    "Step3p7Model",
+]
+
+
+class StepVLImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values: torch.Tensor
+    patch_pixel_values: Optional[torch.Tensor]
+    num_patches: list[int]
+
+
+class StepVLImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    image_embeds: torch.Tensor
+
+
+StepVLImageInputs = Union[StepVLImagePixelInputs, StepVLImageEmbeddingInputs]
+
+
+def _flatten_embeddings(embeddings) -> torch.Tensor:
+    """
+    Recursively flattens and concatenates NestedTensors on all but the last
+    dimension.
+    """
+
+    if isinstance(embeddings, torch.Tensor):
+        # Flatten all but the last dimension.
+        return embeddings.flatten(0, -2)
+
+    return torch.cat(tuple(_flatten_embeddings(t) for t in embeddings))
+
+def _embedding_count_expression(embeddings) -> str:
+    """
+    Constructs a debugging representation of the number of embeddings in the
+    NestedTensors.
+    """
+
+    if isinstance(embeddings, torch.Tensor):
+        return " x ".join([str(dim) for dim in embeddings.shape[:-1]])
+
+    return " + ".join(_embedding_count_expression(inner) for inner in embeddings)
+
+
+def _merge_multimodal_embeddings(
+    inputs_embeds: torch.Tensor,
+    is_multimodal: torch.Tensor,
+    multimodal_embeddings,
+) -> torch.Tensor:
+    """
+    Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the
+    positions in ``inputs_embeds`` corresponding to placeholder tokens in
+    ``input_ids``.
+    Note:
+        This updates ``inputs_embeds`` in place.
+    """
+    num_expected_tokens = is_multimodal.sum().item()
+    assert isinstance(num_expected_tokens, int)
+
+    flattened = _flatten_embeddings(multimodal_embeddings)
+    if flattened.shape[0] != num_expected_tokens:
+        expr = _embedding_count_expression(multimodal_embeddings)
+        raise ValueError(
+            f"Attempted to assign {expr} = {flattened.shape[0]} "
+            f"multimodal tokens to {num_expected_tokens} placeholders"
+        )
+
+    is_multimodal = is_multimodal.to(inputs_embeds.device)
+    flattened = flattened.to(inputs_embeds.device)
+    inputs_embeds[is_multimodal] = flattened
+    return inputs_embeds
+
+def merge_multimodal_embeddings(
+    input_ids: torch.Tensor,
+    inputs_embeds: torch.Tensor,
+    multimodal_embeddings,
+    placeholder_token_id: Union[int, list[int]],
+) -> torch.Tensor:
+    """
+    Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the
+    positions in ``inputs_embeds`` corresponding to placeholder tokens in
+    ``input_ids``.
+    
+    ``placeholder_token_id`` can be a list of token ids (e.g, token ids 
+    of img_start, img_break, and img_end tokens) when needed: This means 
+    the order of these tokens in the ``input_ids`` MUST MATCH the order of 
+    their embeddings in ``multimodal_embeddings`` since we need to 
+    slice-merge instead of individually scattering.
+    For example, if input_ids is "TTTTTSIIIBIIIBIIIETTT", where
+    - T is text token
+    - S is image start token
+    - I is image embedding token
+    - B is image break token
+    - E is image end token.
+    
+    Then the image embeddings (that correspond to I's) from vision encoder 
+    must be padded with embeddings of S, B, and E in the same order of 
+    input_ids for a correct embedding merge.
+    Note:
+        This updates ``inputs_embeds`` in place.
+    """
+    if isinstance(placeholder_token_id, list):
+        placeholder_token_id = torch.tensor(
+            placeholder_token_id, device=input_ids.device
+        )
+        return _merge_multimodal_embeddings(
+            inputs_embeds,
+            torch.isin(input_ids, placeholder_token_id),
+            multimodal_embeddings,
+        )
+
+    return _merge_multimodal_embeddings(
+        inputs_embeds,
+        (input_ids == placeholder_token_id),
+        multimodal_embeddings,
+    )
+
+
+class Step3p7PreTrainedModel(PreTrainedModel):
+    # Link this model family to its configuration class so PreTrainedModel.from_pretrained
+    # can load the config instead of failing with a NoneType error.
+    config_class = Step3p7Config
+    supports_gradient_checkpointing = True
+    _skip_keys_device_placement = ["past_key_values"]
+    _keys_to_ignore_on_load_unexpected = [
+        r"model\.layers\.45\.*",
+        r"model\.layers\.46\.*",
+        r"model\.layers\.47\.*",
+    ]
+    _supports_flash_attn = False
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_static_cache = True
+    _supports_attention_backend = True
+
+    @classmethod
+    def from_pretrained(
+        cls, pretrained_model_name_or_path, *model_args, **kwargs
+    ):
+        key_mapping = getattr(cls, "_checkpoint_conversion_mapping", None)
+        if key_mapping is not None and kwargs.get("key_mapping") is None:
+            # Transformers only applies checkpoint renaming when key_mapping is
+            # passed explicitly; inheriting the class attribute alone is not enough.
+            kwargs["key_mapping"] = copy.deepcopy(key_mapping)
+        return super().from_pretrained(
+            pretrained_model_name_or_path, *model_args, **kwargs
+        )
+
+
+class Step3p7RotaryEmbedding(nn.Module):
+    def __init__(self, config: Step3p7TextConfig, device=None, layer_idx=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        self.layer_idx = layer_idx
+        self.original_rope_parameters = None
+        if config.rope_parameters is not None:
+            self.original_rope_parameters = config.rope_parameters
+            config.rope_parameters = dict(config.rope_parameters)
+            self.rope_type = config.rope_parameters.get(
+                "rope_type", config.rope_parameters.get("type")
+            )
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        partial_rotary_factors = getattr(
+            config, "partial_rotary_factors", None
+        )
+        if partial_rotary_factors is not None:
+            config.partial_rotary_factor = partial_rotary_factors[self.layer_idx]
+        else:
+            config.partial_rotary_factor = 1.0
+
+        self.rope_theta = config.rope_theta
+        if isinstance(config.rope_theta, list):
+            self.rope_theta = config.rope_theta.copy()
+            config.rope_theta = self.rope_theta[self.layer_idx]
+
+        self.config = copy.copy(config)
+        if config.rope_parameters is not None:
+            self.config.rope_parameters = dict(config.rope_parameters)
+        self.rope_init_fn = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = self.rope_init_fn(
+            self.config, device
+        )
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+        config.rope_theta = self.rope_theta
+        config.rope_parameters = self.original_rope_parameters
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = (
+            self.inv_freq[None, :, None]
+            .float()
+            .expand(position_ids.shape[0], -1, 1)
+            .to(x.device)
+        )
+        position_ids_expanded = position_ids[:, None, :].float().to(x.device)
+
+        device_type = (
+            x.device.type
+            if isinstance(x.device.type, str) and x.device.type != "mps"
+            else "cpu"
+        )
+        with torch.autocast(
+            device_type=device_type, enabled=False
+        ):  # Force float32
+            freqs = (
+                inv_freq_expanded.float() @ position_ids_expanded.float()
+            ).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: Step3p7TextConfig | None = None,
+        device: Optional["torch.device"] = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_theta
+        dim = (
+            getattr(config, "head_dim", None)
+            or config.hidden_size // config.num_attention_heads
+        )
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base
+            ** (
+                torch.arange(0, dim, 2, dtype=torch.int64).to(
+                    device=device, dtype=torch.float
+                )
+                / dim
+            )
+        )
+        return inv_freq, attention_factor
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., :x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    rotary_dim = cos.shape[-1]
+    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
+    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
+
+    # Apply rotary embeddings on the first half or full tensor
+    q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
+    k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
+
+    # Concatenate back to full shape
+    q_embed = torch.cat([q_embed, q_pass], dim=-1)
+    k_embed = torch.cat([k_embed, k_pass], dim=-1)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(
+        batch, num_key_value_heads * n_rep, slen, head_dim
+    )
+
+
+# Adapted from transformers.models.llama.modeling_llama.eager_attention_forward.
+# Llama4 does not cast attention weights to fp32 here.
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+    # breakpoint()
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, :key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+    attn_weights = nn.functional.dropout(
+        attn_weights, p=dropout, training=module.training
+    )
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+@dataclass
+class Step3p7CausalLMOutputWithPast(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+        Language modeling loss (for next-token prediction).
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+        `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    last_hidden_state: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[list[torch.FloatTensor]] = None
+    hidden_states: Optional[tuple[torch.FloatTensor]] = None
+    attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+class Step3p7MLP(nn.Module):
+    def __init__(self, config, intermediate_size=None, swiglu_limit=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = (
+            intermediate_size
+            if intermediate_size is not None
+            else config.intermediate_size
+        )
+        self.gate_proj = nn.Linear(self.hidden_size,
+                                   self.intermediate_size,
+                                   bias=False)
+        self.up_proj = nn.Linear(self.hidden_size,
+                                 self.intermediate_size,
+                                 bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size,
+                                   self.hidden_size,
+                                   bias=False)
+        self.act_fn = ACT2FN["silu"]
+        self.limit = swiglu_limit
+
+    def forward(self, x):
+        up = self.up_proj(x)
+        gate = self.act_fn(self.gate_proj(x))
+        if self.limit is not None:
+            gate = gate.clamp(min=None, max=self.limit)
+            up = up.clamp(min=-self.limit, max=self.limit)
+
+        return self.down_proj(gate * up)
+
+
+def sigmoid_routing_function(gating_output: torch.Tensor, topk: int,
+                             renormalize: bool):
+    gating_output = gating_output.float()
+    gate_prob = torch.sigmoid(gating_output)
+    gate_prob = gate_prob / gate_prob.sum(dim=-1, keepdim=True)
+    topk_prob, indices = torch.topk(gate_prob, k=topk, dim=1)
+    expert_topk_weight = topk_prob
+    if renormalize:
+        expert_topk_weight = expert_topk_weight / torch.sum(
+            expert_topk_weight, dim=-1, keepdim=True)
+    return expert_topk_weight, indices
+
+
+def softmax_routing_function(gating_output: torch.Tensor, top_k: int,
+                             renormalize: bool):
+    gating_output = gating_output.float()
+    gate_prob = torch.softmax(gating_output, dim=-1)
+    gate_prob = gate_prob / gate_prob.sum(dim=-1, keepdim=True)
+    topk_prob, indices = torch.topk(gate_prob, k=top_k, dim=1)
+    expert_topk_weight = topk_prob
+    if renormalize:
+        expert_topk_weight = expert_topk_weight / torch.sum(
+            expert_topk_weight, dim=-1, keepdim=True)
+    return expert_topk_weight, indices.to(torch.int32)
+
+
+class MoELinear(nn.Module):
+
+    def __init__(self, num_experts, in_features, out_features):
+        super().__init__()
+        self.num_experts = num_experts
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = nn.Parameter(
+            torch.empty(num_experts, out_features, in_features))
+
+    def forward(self, x, expert_id):
+        x = F.linear(x.float(), self.weight[expert_id].float())
+        return x
+
+
+class Step3p7MoEMLP(nn.Module):
+
+    def __init__(self, config, swiglu_limit=None):
+        super().__init__()
+        self.num_experts = config.moe_num_experts
+        self.top_k = config.moe_top_k
+        self.hidden_size = config.hidden_size
+        self.moe_intermediate_size = config.moe_intermediate_size
+
+        self.use_moe_router_bias = config.use_moe_router_bias
+        if self.use_moe_router_bias:
+            self.router_bias = nn.Parameter(torch.zeros(config.moe_num_experts,
+                                                        dtype=torch.float32),
+                                            requires_grad=False)
+            self.custom_routing_function = self.router_bias_func
+        elif config.moe_router_activation == "sigmoid":
+            self.custom_routing_function = sigmoid_routing_function
+        else:
+            self.custom_routing_function = None
+        self.need_fp32_gate = config.need_fp32_gate
+        self.routed_scaling_factor = getattr(config,
+                                             "moe_router_scaling_factor", 1.0)
+        
+        # gating
+        self.gate = nn.Linear(self.hidden_size, self.num_experts, bias=False)
+            
+        self.act_fn = ACT2FN["silu"]
+        self.limit = swiglu_limit
+
+        self.up_proj = MoELinear(self.num_experts, self.hidden_size,
+                                 self.moe_intermediate_size)
+        self.gate_proj = MoELinear(self.num_experts, self.hidden_size,
+                                   self.moe_intermediate_size)
+        self.down_proj = MoELinear(self.num_experts,
+                                   self.moe_intermediate_size,
+                                   self.hidden_size)
+
+    def router_bias_func(self, gating_output: torch.Tensor, topk: int,
+                         renormalize: bool):
+        gate_prob = torch.sigmoid(gating_output.float())
+        gate_prob_with_bias = gate_prob + self.router_bias.unsqueeze(0)
+        _, indices = torch.topk(gate_prob_with_bias, k=topk, dim=1)
+        topk_prob = torch.gather(gate_prob, 1, indices)
+        expert_topk_weight = topk_prob
+        if renormalize:
+            expert_topk_weight = expert_topk_weight / (
+                torch.sum(expert_topk_weight, dim=-1, keepdim=True) + 1e-20)
+        return expert_topk_weight, indices
+
+    def get_expert_output(self, inputs: torch.Tensor, expert_id):
+        #if self.limit is None:
+        up = self.up_proj(inputs, expert_id)
+        gate = self.act_fn(self.gate_proj(inputs, expert_id))
+        if self.limit is not None:
+            gate = gate.clamp(min=None, max=self.limit)
+            up = up.clamp(min=-self.limit, max=self.limit)
+
+        return self.down_proj(gate * up, expert_id)
+
+    def forward(self, hidden_states):
+        """ """
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        if self.need_fp32_gate:
+            router_logits = torch.matmul(
+                hidden_states.to(torch.float32),
+                self.gate.weight.t().to(torch.float32),
+            )
+        else:
+            # router_logits: (batch * sequence_length, n_experts)
+            router_logits = self.gate(hidden_states)
+
+        if self.custom_routing_function:
+            routing_weights, selected_experts = self.custom_routing_function(
+                router_logits, self.top_k, renormalize=True)
+        else:
+            routing_weights = F.softmax(router_logits,
+                                        dim=1,
+                                        dtype=torch.float)
+            routing_weights, selected_experts = torch.topk(routing_weights,
+                                                           self.top_k,
+                                                           dim=-1)
+
+        routing_weights = routing_weights * self.routed_scaling_factor
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device)
+
+        # One hot encode the selected experts to create an expert mask
+        # this will be used to easily index which expert is going to be sollicitated
+        expert_mask = torch.nn.functional.one_hot(
+            selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+        # Loop over all available experts in the model and perform the computation on each expert
+        for expert_idx in range(self.num_experts):
+            idx, top_x = torch.where(expert_mask[expert_idx])
+
+            # Index the correct hidden states and compute the expert hidden state for
+            # the current expert. We need to make sure to multiply the output hidden
+            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+            current_hidden_states = (
+                self.get_expert_output(current_state, expert_idx) *
+                routing_weights[top_x, idx, None])
+
+            # However `index_add_` only support torch tensors for indexing so we'll use
+            # the `top_x` tensor here.
+            final_hidden_states.index_add_(
+                0, top_x, current_hidden_states.to(hidden_states.dtype))
+        final_hidden_states = final_hidden_states.reshape(
+            batch_size, sequence_length, hidden_dim)
+        return final_hidden_states
+
+
+class Step3p7RMSNorm(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        eps: float = 1e-5,
+    ) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        dtype = x.dtype
+        x = x.float()
+        variance = x.pow(2).mean(dim=-1, keepdim=True)
+        normed = x * torch.rsqrt(variance + self.variance_epsilon)
+        normed = normed * (self.weight.float() + 1)
+        return normed.to(dtype)
+class Step3p7Attention(nn.Module):
+
+    def __init__(self, config: Step3p7TextConfig, layer_idx):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.num_attention_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_attention_groups
+
+        layer_types = getattr(config, "layer_types", [])
+        if layer_types:
+            enable_sliding_window = layer_types[
+                self.layer_idx] == "sliding_attention"
+        else:
+            enable_sliding_window = self.layer_idx % 2 == 0
+        
+        yarn_only_types = getattr(config, "yarn_only_types", None)
+        if yarn_only_types and layer_types[
+                self.layer_idx] not in yarn_only_types:
+            config.rope_parameters = None
+        else:
+            config.rope_parameters = getattr(config, "rope_scaling", None)
+
+        self.sliding_window = config.sliding_window
+        if enable_sliding_window:
+            self.num_attention_heads = config.attention_other_setting[
+                "num_attention_heads"]
+            self.num_key_value_heads = config.attention_other_setting[
+                "num_attention_groups"]
+
+        if self.sliding_window is not None and enable_sliding_window:
+            self.sliding_window = (self.sliding_window)
+        else:
+            self.sliding_window = None
+        self.head_dim = getattr(config, "head_dim",
+                        config.hidden_size // self.num_attention_heads)
+        self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads
+
+        self.rotary_emb = Step3p7RotaryEmbedding(config, layer_idx=layer_idx)
+
+        self.q_size = self.num_attention_heads * self.head_dim
+        self.kv_size = self.num_key_value_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+
+        self.q_proj = nn.Linear(config.hidden_size, self.q_size, bias=False)
+        self.k_proj = nn.Linear(config.hidden_size, self.kv_size, bias=False)
+        self.v_proj = nn.Linear(config.hidden_size, self.kv_size, bias=False)
+        self.o_proj = nn.Linear(self.q_size, config.hidden_size, bias=False)
+        self.attention_dropout = getattr(config, "attention_dropout", 0.0)
+        self.q_norm = Step3p7RMSNorm(self.head_dim,
+                                    eps=config.rms_norm_eps)
+        self.k_norm = Step3p7RMSNorm(self.head_dim,
+                                    eps=config.rms_norm_eps)
+
+        self.use_head_wise_attn_gate = config.use_head_wise_attn_gate
+        if self.use_head_wise_attn_gate:
+            self.g_proj = nn.Linear(config.hidden_size,
+                                    self.num_attention_heads,
+                                    bias=False)
+
+        self.use_rope = True
+        use_rope_layers = getattr(config, "use_rope_layers", None)
+        if use_rope_layers:
+            self.use_rope = use_rope_layers[self.layer_idx]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
+               Optional[Tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_norm(
+            self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        key_states = self.k_norm(
+            self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(
+            1, 2)
+        if self.use_head_wise_attn_gate:
+            gate_states = self.g_proj(hidden_states)
+        cos, sin = self.rotary_emb(hidden_states, position_ids)
+
+        # cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin)
+
+        # query_states, key_states = apply_rotary_pos_emb(query_norm_states, key_norm_states, cos, sin)
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "cache_position": cache_position
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        # TODO: considering FP8；
+        # RuntimeError: Expected attn_mask dtype to be bool or float or to match query dtype,
+        # but got attn_mask.dtype: long int and  query.dtype: c10::BFloat16 instead.
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[
+                self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,  # main diff with Llama
+            **kwargs,
+        )
+        attn_output = attn_output.reshape(*input_shape, -1)
+        if self.use_head_wise_attn_gate:
+            output = attn_output.view(
+                *attn_output.shape[:-1], self.num_attention_heads,
+                self.head_dim) * gate_states.unsqueeze(-1).sigmoid()
+            attn_output = output.view(*attn_output.shape)
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class Step3p7DecoderLayer(GradientCheckpointingLayer):
+
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.layer_idx = layer_idx
+        self.self_attn = Step3p7Attention(config, layer_idx)
+        layer_types = getattr(config, "layer_types", None) or []
+        if layer_types:
+            self.attention_type = layer_types[layer_idx]
+        else:
+            self.attention_type = (
+                "sliding_attention" if layer_idx % 2 == 0 else "full_attention"
+            )
+
+        moe_layers_enum = getattr(config, "moe_layers_enum", None)
+        if moe_layers_enum is not None:
+            if isinstance(moe_layers_enum, str):
+                moe_layers_idx = [
+                    int(i) for i in moe_layers_enum.split(',') if i.strip()
+                ]
+            else:
+                moe_layers_idx = [int(i) for i in moe_layers_enum]
+        else:
+            moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
+        self.is_moe_layer = layer_idx in moe_layers_idx
+        self.use_moe = False
+
+        if config.swiglu_limits_shared and config.swiglu_limits_shared[
+                layer_idx] is not None and config.swiglu_limits_shared[
+                    layer_idx] != 0:
+            swiglu_limit_shared = config.swiglu_limits_shared[layer_idx]
+        else:
+            swiglu_limit_shared = None
+        if config.swiglu_limits and config.swiglu_limits[
+                layer_idx] is not None and config.swiglu_limits[layer_idx] != 0:
+            swiglu_limit = config.swiglu_limits[layer_idx]
+        else:
+            swiglu_limit = None
+        if self.is_moe_layer:
+            self.moe = Step3p7MoEMLP(config, swiglu_limit=swiglu_limit)  #
+            self.share_expert = Step3p7MLP(
+                config,
+                intermediate_size=config.share_expert_dim,
+                swiglu_limit=swiglu_limit_shared)
+            self.use_moe = True
+        else:
+            self.mlp = Step3p7MLP(config,
+                                 intermediate_size=config.intermediate_size,
+                                 swiglu_limit=swiglu_limit_shared)
+
+        self.input_layernorm = Step3p7RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Step3p7RMSNorm(
+            config.hidden_size,
+            eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[tuple[torch.Tensor]] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+        
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        if self.use_moe:
+            share_output = self.share_expert(hidden_states)
+            moe_output = self.moe(hidden_states)
+            ffn_output = moe_output + share_output
+        else:
+            ffn_output = self.mlp(hidden_states)
+        if isinstance(ffn_output, tuple):
+            hidden_states, _ = ffn_output
+        else:
+            hidden_states = ffn_output
+
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class Step3p7TextPreTrainedModel(PreTrainedModel):
+    # Link this model family to its configuration class so PreTrainedModel.from_pretrained
+    # can load the config instead of failing with a NoneType error.
+    config_class = Step3p7TextConfig
+    supports_gradient_checkpointing = True
+    _skip_keys_device_placement = ["past_key_values"]
+    _keys_to_ignore_on_load_unexpected = [
+        r"model\.layers\.45\.*",
+        r"model\.layers\.46\.*",
+        r"model\.layers\.47\.*",
+    ]
+    _supports_flash_attn = False
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_static_cache = True
+    _supports_attention_backend = True
+
+
+class Step3p7TextModel(Step3p7TextPreTrainedModel, GenerationMixin):
+    _no_split_modules = ["Step3p7DecoderLayer"]
+    base_model_prefix = "model"
+    _tied_weights_keys = ["lm_head.weight"]
+    config: Step3p7TextConfig
+
+    def __init__(self, config: Step3p7TextConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size,
+                                         self.padding_idx)
+        self.layers = nn.ModuleList([
+            Step3p7DecoderLayer(config, layer_idx)
+            for layer_idx in range(config.num_hidden_layers)
+        ])
+        self.norm = Step3p7RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+        layer_types = self.config.layer_types or []
+        self.has_sliding_layers = (not layer_types or
+                                   "sliding_attention" in layer_types)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    
+    def get_input_embeddings(self, input_ids):
+        return self.embed_tokens(input_ids)
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> Union[tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = (
+            return_dict
+            if return_dict is not None
+            else getattr(self.config, "return_dict", True)
+        )
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(
+                input_ids.to(self.embed_tokens.weight.device))
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length(
+            ) if past_key_values is not None else 0
+            cache_position = torch.arange(past_seen_tokens,
+                                          past_seen_tokens +
+                                          inputs_embeds.shape[1],
+                                          device=inputs_embeds.device)
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        hidden_states = inputs_embeds
+
+        # It may already have been prepared by e.g. `generate`
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            # Prepare mask arguments
+            mask_kwargs = {
+                "config": self.config,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+            mask_kwargs[_MASK_INPUT_EMBEDS_ARG] = inputs_embeds
+            # Create the masks
+            causal_mask_mapping = {
+                "full_attention": create_causal_mask(**mask_kwargs),
+            }
+
+            # The sliding window alternating layers are not always activated depending on the config
+            if self.has_sliding_layers:
+                causal_mask_mapping[
+                    "sliding_attention"] = create_sliding_window_causal_mask(
+                        **mask_kwargs)
+
+        # # create position embeddings to be shared across the decoder layers
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        for decoder_layer in self.layers[:self.config.num_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states, )
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask_mapping[
+                    decoder_layer.attention_type],
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                **kwargs,
+            )
+
+            hidden_states = layer_outputs
+
+        hidden_states = self.norm(hidden_states)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class Step3p7Model(Step3p7PreTrainedModel, GenerationMixin):
+    config: Step3p7Config
+    _tied_weights_keys = ["lm_head.weight"]
+    base_model_prefix = ""
+
+    def __init__(self, config: Step3p7Config):
+        super().__init__(config)
+        self.vision_model = StepRoboticsVisionEncoder(config.vision_config)
+        self.language_model = Step3p7TextModel(config.text_config)
+        self.vocab_size = config.text_config.vocab_size
+        self.vit_large_projector = nn.Linear(
+                config.vision_config.width * 4,
+                config.text_config.hidden_size,                
+                bias=config.projector_bias) 
+        self.image_placeholder_token_id = config.image_token_id
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings  = None,
+    ) -> torch.Tensor:
+        # breakpoint()
+        input_ids = input_ids.squeeze(0)
+        if multimodal_embeddings is None:
+            inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        else:
+            is_text = input_ids != self.config.image_token_id
+            text_ids = input_ids[is_text]
+            text_embeds = self.language_model.get_input_embeddings(text_ids)
+                     
+            inputs_embeds = torch.empty(input_ids.shape[0],
+                                        text_embeds.shape[-1],
+                                        dtype=text_embeds.dtype,
+                                        device=text_embeds.device)
+            inputs_embeds[is_text] = text_embeds
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.config.image_token_id)
+        inputs_embeds = inputs_embeds.unsqueeze(0)
+        return inputs_embeds
+       
+
+    def set_input_embeddings(self, value):
+        return self.language_model.set_input_embeddings(value)
+
+    def set_decoder(self, decoder):
+        self.language_model = decoder
+
+    def get_decoder(self):
+        return self.language_model
+    
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[StepVLImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        patch_pixel_values = kwargs.pop("patch_pixel_values", None)
+        num_patches = kwargs.pop("num_patches", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            # pixel_values = flatten_bn(pixel_values, concat=True)
+            if pixel_values.dim() >= 3:
+                pixel_values = pixel_values.view(-1, *pixel_values.shape[-3:])
+            if patch_pixel_values is not None:
+                # patch_pixel_values = flatten_bn(patch_pixel_values,
+                #                                 concat=True)
+                patch_pixel_values = patch_pixel_values.view(
+                    -1, *patch_pixel_values.shape[-3:])
+                # Handle empty patch_pixel_values by setting to None
+                if patch_pixel_values.shape[0] == 0:
+                    patch_pixel_values = None
+
+            return StepVLImagePixelInputs(
+                type="pixel_values",
+                pixel_values=pixel_values.to(self.dtype).to(self.device),
+                patch_pixel_values=patch_pixel_values.to(self.dtype).to(
+                    self.device) if patch_pixel_values is not None else None,
+                num_patches=num_patches,
+            )
+
+        if image_embeds is not None:
+            if image_embeds.dim() == 2 or image_embeds.dim() >= 3:
+                image_embeds = image_embeds.view(-1, image_embeds.shape[-1])
+            else:
+                raise ValueError(
+                    f"Unexpected shape for image_embeds: {image_embeds.shape}")
+
+            return StepVLImageEmbeddingInputs(
+                type="image_embeds",
+                image_embeds=image_embeds.to(self.dtype).to(self.device),
+            )
+        return None
+    
+    def _process_image_features(self,
+                                image_features: torch.Tensor) -> torch.Tensor:
+        B, P = image_features.shape[:2]
+        HW = int(P ** 0.5)
+        image_features = image_features.permute(0, 2, 1).view(B, -1, HW, HW)
+        image_features = self.vision_model.vit_downsampler1(image_features)
+        image_features = self.vision_model.vit_downsampler2(image_features)
+
+        B, C, HW, HW = image_features.shape
+        image_features = image_features.view(B, -1, HW * HW).permute(0, 2, 1)
+        image_features = self.vit_large_projector(image_features)
+        return image_features
+
+    def _get_vision_model_output(self,
+                                 input_tensor: torch.Tensor) -> torch.Tensor:
+        return self.vision_model(input_tensor)
+
+    def _process_image_input(
+            self, image_input: StepVLImageInputs) -> tuple[torch.Tensor, ...]:
+
+        if image_input["type"] == "image_embeds":
+            image_features = image_input["image_embeds"]
+        else:
+            image_features = self._get_vision_model_output(
+                image_input["pixel_values"])
+            patch_image_features = self._get_vision_model_output(
+                image_input["patch_pixel_values"]
+            ) if image_input["patch_pixel_values"] is not None else None
+            num_patches = image_input["num_patches"]
+
+        image_features = self._process_image_features(image_features)
+        patch_image_features = self._process_image_features(
+            patch_image_features) if patch_image_features is not None else None
+
+        merged_image_features = []
+        cur_patch_idx = 0
+        for i, num_patch in enumerate(num_patches):
+            cur_feature = []
+            if num_patch > 0:
+                patch_slice = patch_image_features[
+                    cur_patch_idx:cur_patch_idx + num_patch]
+                cur_feature.append(patch_slice.view(-1, patch_slice.shape[-1]))
+            cur_feature.append(image_features[i].view(
+                -1, image_features.shape[-1]))
+            cur_patch_idx += num_patch
+            merged_image_features.append(
+                torch.cat(cur_feature) if len(cur_feature) >
+                1 else cur_feature[0])
+    
+        return merged_image_features
+    
+    def get_multimodal_embeddings(self, **kwargs):
+        # breakpoint()
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        images: Optional[list[Image.Image]] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> Union[tuple, Step3p7CausalLMOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        Example:
+        ```python
+        >>> from transformers import AutoTokenizer, Llama4ForCausalLM
+        >>> model = Llama4ForCausalLM.from_pretrained("meta-llama4/Llama4-2-7b-hf")
+        >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama4/Llama4-2-7b-hf")
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if inputs_embeds is None:
+            input_ids = input_ids
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.language_model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        output = Step3p7CausalLMOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            attentions=outputs.attentions,
+        )
+        return output if return_dict else output.to_tuple()
+
+
+class Step3p7ForConditionalGeneration(Step3p7PreTrainedModel, GenerationMixin):
+    _checkpoint_conversion_mapping = {
+        "^vision_model": "model.vision_model",
+        r"^model(?!\.(language_model|vision_model))": "model.language_model",
+        "^vit_large_projector": "model.vit_large_projector",
+    }
+    _tied_weights_keys = ["lm_head.weight"]
+    config: Step3p7Config
+
+    def __init__(self, config: Step3p7Config):
+        super().__init__(config)
+        self.model = Step3p7Model(config)
+        self.lm_head = nn.Linear(config.hidden_size,
+                                config.text_config.vocab_size,
+                                bias=False)
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.model.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings):
+        self.model.set_output_embeddings(new_embeddings)
+
+    def set_decoder(self, decoder):
+        self.model.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.model.get_decoder()
+
+    @property
+    def language_model(self):
+        return self.model.language_model
+
+    @property
+    def visual(self):
+        return self.model.vision_model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        num_patches=None,
+        patch_pixel_values=None,
+        patch_newline_mask=None,
+        image_embeds: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> Union[tuple, Step3p7CausalLMOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+
+        outputs = self.model(
+            input_ids=input_ids,
+            num_patches=num_patches,
+            patch_pixel_values=patch_pixel_values,
+            patch_newline_mask=patch_newline_mask,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        logits = self.lm_head(hidden_states)
+
+        los = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.vocab_size
+            )
+
+        return Step3p7CausalLMOutputWithPast(
+            logits=logits,
+        )
+
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        patch_pixel_values=None,
+        num_patches=None,
+        image_embeds=None,
+        attention_mask=None,
+        cache_position=None,
+        logits_to_keep=None,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            logits_to_keep=logits_to_keep,
+            **kwargs,
+        )
+
+        if cache_position[0] == 0:
+            # During cached decoding, input ids no longer contain image tokens,
+            # so pixel values should only be passed at the first step.
+            model_inputs["pixel_values"] = pixel_values
+
+        return model_inputs
+    
+    def _fix_state_dict_key_on_load(self, key: str) -> tuple[str, bool]:
+        if key.startswith("language_model."):
+            return key[len("language_model.") :], True
+        
+        return key, False