EO-1-3B / modeling_eo1.py

Upload folder using huggingface_hub

b7b76ba verified 9 months ago

19.7 kB

# Copyright 2025 EO-Robotics 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 math
from dataclasses import dataclass
from typing import Any

import torch
import torch.nn as nn
import torch.nn.functional as F  # noqa: N812
from torch import Tensor
from transformers.activations import ACT2FN
from transformers.generation import GenerationMixin
from transformers.modeling_outputs import ModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging

from .configuration_eo1 import EO1VisionFlowMatchingConfig
from .modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration

logger = logging.get_logger(__name__)


def create_sinusoidal_pos_embedding(
    time: torch.tensor,
    dimension: int,
    min_period: float = 4e-3,
    max_period: float = 4.0,
    device="cpu",
) -> Tensor:
    """Computes sine-cosine positional embedding vectors for scalar positions."""
    if dimension % 2 != 0:
        raise ValueError(f"dimension ({dimension}) must be divisible by 2")

    if time.ndim != 1:
        raise ValueError("The time tensor is expected to be of shape `(batch_size, )`.")

    fraction = torch.linspace(0.0, 1.0, dimension // 2, device=device)
    period = min_period * (max_period / min_period) ** fraction

    scaling_factor = 1.0 / period * 2 * math.pi
    sin_input = scaling_factor[None, :] * time[:, None]
    pos_emb = torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1)
    return pos_emb


@dataclass
class EO1VisionFlowMatchingOutputWithPast(ModelOutput):
    loss: torch.FloatTensor | None = None
    fm_loss: torch.FloatTensor | None = None
    ar_loss: torch.FloatTensor | None = None

    actions: torch.FloatTensor | None = None
    logits: torch.FloatTensor | None = None

    past_key_values: list[torch.FloatTensor] | None = None
    hidden_states: tuple[torch.FloatTensor] | None = None
    attentions: tuple[torch.FloatTensor] | None = None
    rope_deltas: torch.LongTensor | None = None


class EO1VisionActionProjector(torch.nn.Sequential):
    """This block implements the multi-layer perceptron (MLP) module."""

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        num_layers: int = 2,
        activation_layer: str = "linear",
        bias: bool = True,
        device: Any = None,
        dtype: torch.dtype = torch.float32,
    ):
        layers = []
        in_dim = in_channels
        hidden_channels = [in_dim] * (num_layers - 1) + [out_channels]
        for hidden_dim in hidden_channels[:-1]:
            layers.append(torch.nn.Linear(in_dim, hidden_dim, bias=bias, dtype=dtype, device=device))
            layers.append(ACT2FN[activation_layer])
            in_dim = hidden_dim
        layers.append(torch.nn.Linear(in_dim, hidden_channels[-1], bias=bias, dtype=dtype, device=device))
        super().__init__(*layers)

    @property
    def dtype(self):
        return self[0].weight.dtype


class EO1VisionFlowMatchingModel(PreTrainedModel, GenerationMixin):
    config_class = EO1VisionFlowMatchingConfig
    supports_gradient_checkpointing = True

    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_attention_backend = True
    _can_compile_fullgraph = True
    _skip_keys_device_placement = "past_key_values"

    def __init__(
        self,
        config: EO1VisionFlowMatchingConfig,
        vlm_backbone: Qwen2_5_VLForConditionalGeneration = None,
    ):
        super().__init__(config)

        hidden_size = self.config.text_config.hidden_size
        max_action_dim = self.config.max_action_dim
        self.vlm_backbone = vlm_backbone or Qwen2_5_VLForConditionalGeneration(self.config)
        self.state_proj = nn.Linear(max_action_dim, hidden_size)
        self.action_in_proj = nn.Linear(max_action_dim, hidden_size)
        self.action_out_proj = EO1VisionActionProjector(
            hidden_size,
            max_action_dim,
            self.config.num_action_layers,
            self.config.action_act,
        )
        self.action_time_mlp_in = nn.Linear(hidden_size * 2, hidden_size)
        self.action_time_mlp_out = nn.Linear(hidden_size, hidden_size)

        self.post_init()
        self.to_float32_flow_matching_head()

    def get_input_embeddings(self):
        return self.vlm_backbone.get_input_embeddings()

    def to_float32_flow_matching_head(self):
        self.action_out_proj = self.action_out_proj.to(dtype=torch.float32)
        self.action_time_mlp_in = self.action_time_mlp_in.to(dtype=torch.float32)
        self.action_time_mlp_out = self.action_time_mlp_out.to(dtype=torch.float32)
        self.state_proj = self.state_proj.to(dtype=torch.float32)
        self.action_in_proj = self.action_in_proj.to(dtype=torch.float32)

    def sample_noise(self, shape, device):
        noise = torch.normal(
            mean=0.0,
            std=1.0,
            size=shape,
            dtype=torch.float32,
            device=device,
        )
        return noise

    def sample_time(self, bsize, device):
        beta_dist = torch.distributions.Beta(concentration1=1.5, concentration0=1.0)
        time_beta = beta_dist.sample((bsize,)).to(device=device, dtype=torch.float32)
        time = time_beta * 0.999 + 0.001
        return time

    def replace_special_embeddings(
        self,
        input_ids: torch.LongTensor,
        inputs_embeds: torch.FloatTensor,
        special_features: torch.FloatTensor = None,
        special_token_ids: torch.LongTensor = None,
    ) -> torch.LongTensor:
        """Replace the special embeddings with the special features."""
        if special_features is not None and special_token_ids is not None:
            n_special_tokens = (input_ids == special_token_ids).sum().item()
            n_special_features = special_features.shape[0]
            assert n_special_tokens == n_special_features, (
                f"Special features and special tokens {special_token_ids} do not match: \
                tokens: {n_special_tokens}, features {n_special_features}"
            )
            mask = input_ids == special_token_ids
            mask_unsqueezed = mask.unsqueeze(-1)
            mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
            special_mask = mask_expanded.to(inputs_embeds.device)
            special_features = special_features.to(inputs_embeds.device, inputs_embeds.dtype)
            inputs_embeds = inputs_embeds.masked_scatter(special_mask, special_features)
        return inputs_embeds, None

    def embed_prefix(
        self,
        input_ids: torch.LongTensor,
        inputs_embeds: torch.FloatTensor | None = None,
        pixel_values: torch.Tensor | None = None,
        pixel_values_videos: torch.FloatTensor | None = None,
        image_grid_thw: torch.LongTensor | None = None,
        video_grid_thw: torch.LongTensor | None = None,
        states: torch.Tensor | None = None,
    ) -> tuple[torch.FloatTensor, torch.Tensor, torch.Tensor]:
        """Embed the suffix"""
        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)

        if pixel_values is not None:
            image_embeds = self.vlm_backbone.get_image_features(pixel_values, image_grid_thw)
            image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
            image_mask, _ = self.vlm_backbone.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
            )
            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)

        if pixel_values_videos is not None:
            video_embeds = self.vlm_backbone.get_video_features(pixel_values_videos, video_grid_thw)
            video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
            _, video_mask = self.vlm_backbone.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
            )
            inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)

        if states is not None:
            states = states.type(self.state_proj.weight.dtype)
            state_embs = self.state_proj(states)
            inputs_embeds, _ = self.replace_special_embeddings(
                input_ids, inputs_embeds, state_embs, self.config.state_token_id
            )
        return inputs_embeds

    def embed_suffix(
        self,
        timestep: torch.Tensor,
        noisy_actions: torch.Tensor,
    ) -> torch.FloatTensor:
        """Embed the suffix"""
        time_embs = create_sinusoidal_pos_embedding(
            timestep,
            self.config.text_config.hidden_size,
            device=noisy_actions.device,
        )
        time_embs = time_embs.type(noisy_actions.dtype)
        noisy_actions = noisy_actions.type(self.action_in_proj.weight.dtype)
        action_embs = self.action_in_proj(noisy_actions)
        time_embs = time_embs[:, None, :].expand_as(action_embs)

        action_time_embs = torch.cat([action_embs, time_embs], dim=2)
        action_time_embs = self.action_time_mlp_in(action_time_embs)
        action_time_embs = F.silu(action_time_embs)
        action_time_embs = self.action_time_mlp_out(action_time_embs)
        return action_time_embs

    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: list[torch.FloatTensor] | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        pixel_values: torch.Tensor | None = None,
        pixel_values_videos: torch.FloatTensor | None = None,
        image_grid_thw: torch.LongTensor | None = None,
        video_grid_thw: torch.LongTensor | None = None,
        rope_deltas: torch.LongTensor | None = None,
        cache_position: torch.LongTensor | None = None,
        second_per_grid_ts: torch.Tensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        states: torch.Tensor | None = None,
        actions: torch.Tensor | None = None,
        action_is_pad: torch.Tensor | None = None,
        **kwargs,
    ) -> EO1VisionFlowMatchingOutputWithPast:
        """multi-modal forward pass, including image, video, state, action, and language."""
        inputs_embeds = self.embed_prefix(
            input_ids,
            inputs_embeds,
            pixel_values,
            pixel_values_videos,
            image_grid_thw,
            video_grid_thw,
            states,
        )

        if actions is not None:
            noise_mask = input_ids == self.config.action_token_id
            pass_mask = input_ids == self.config.action_pass_id
            mask = noise_mask | pass_mask  # (b s)

            pass_mask_in_action = pass_mask[mask]  # (n, )
            pass_mask_in_action = pass_mask_in_action.reshape(*actions.shape[:2], 1)  # (b, h, 1)

            time = self.sample_time(actions.shape[0], inputs_embeds.device)  # (n,)
            time_expanded = time[:, None, None].repeat(1, actions.shape[1], 1)  # (b, h, 1)
            time_expanded[pass_mask_in_action] = 0.0

            noise = self.sample_noise(actions.shape, inputs_embeds.device)
            x_t = time_expanded * noise + (1 - time_expanded) * actions
            u_t = noise - actions

            action_time_embs = self.embed_suffix(time, x_t)
            mask_unsqueezed = mask.unsqueeze(-1)
            mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
            action_mask = mask_expanded.to(inputs_embeds.device)

            action_time_embs = action_time_embs.to(inputs_embeds.device, inputs_embeds.dtype)
            inputs_embeds = inputs_embeds.masked_scatter(action_mask, action_time_embs)

        if attention_mask is not None:
            attention_mask = attention_mask.to(inputs_embeds.device)

        if position_ids is None:
            prefill_noncompiled_stage = (cache_position is not None and cache_position[0] == 0) or (
                past_key_values is None or past_key_values.get_seq_length() == 0
            )
            if prefill_noncompiled_stage or self.vlm_backbone.rope_deltas is None:
                position_ids, rope_deltas = self.vlm_backbone.get_rope_index(
                    input_ids,
                    image_grid_thw,
                    video_grid_thw,
                    second_per_grid_ts=second_per_grid_ts,
                    attention_mask=attention_mask,
                )
                self.vlm_backbone.rope_deltas = rope_deltas
            else:
                batch_size, seq_length, _ = inputs_embeds.shape
                position_ids = torch.arange(seq_length, device=inputs_embeds.device)
                position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1)
                if cache_position is not None:
                    delta = (cache_position[0] + self.vlm_backbone.rope_deltas).to(inputs_embeds.device)
                else:
                    delta = torch.zeros((batch_size, seq_length), device=inputs_embeds.device)
                delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=1)
                position_ids += delta.to(position_ids.device)

        # generation
        output_actions = None
        if not (self.training or states is None):
            output_actions, outputs = self.sample_actions(
                input_ids=input_ids,
                position_ids=position_ids,
                attention_mask=attention_mask,
                past_key_values=past_key_values,
                inputs_embeds=inputs_embeds,
                cache_position=cache_position,
                states=states,
            )
        else:
            outputs = self.vlm_backbone.model(
                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,
            )

        hidden_states = outputs[0]

        # only compute necessary logits, do not upcast to float if not computing loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.vlm_backbone.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        fm_loss = None
        v_t = None
        if actions is not None:
            action_time_embs = hidden_states[action_mask[..., 0]]
            action_time_embs = action_time_embs.type(self.action_out_proj.dtype)

            v_t = self.action_out_proj(action_time_embs)
            u_t = u_t.reshape(v_t.shape)
            v_t = v_t.type(u_t.dtype)

            losses = F.mse_loss(u_t, v_t, reduction="none")
            if action_is_pad is not None:
                in_episode_bound = (~action_is_pad).reshape(-1, 1)
                losses = losses * in_episode_bound

            in_denoise_bound = (~pass_mask_in_action).reshape(-1, 1)
            losses = losses * in_denoise_bound

            fm_loss = losses.mean()
            loss = fm_loss

        ar_loss = None
        if labels is not None:
            ar_loss = self.vlm_backbone.loss_function(
                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
            )
            loss = loss + ar_loss if loss is not None else ar_loss

        return EO1VisionFlowMatchingOutputWithPast(
            loss=loss,
            fm_loss=fm_loss,
            ar_loss=ar_loss,
            actions=output_actions,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            rope_deltas=self.vlm_backbone.rope_deltas,
        )

    @torch.no_grad()
    def sample_actions(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        pixel_values: torch.Tensor | None = None,
        image_grid_thw: torch.LongTensor | None = None,
        states: torch.Tensor | None = None,
        **kwargs,
    ) -> Tensor:
        """Sample actions from the model."""

        # prepare position_ids and kv_cache
        position_ids, _ = self.vlm_backbone.get_rope_index(
            input_ids,
            image_grid_thw=image_grid_thw,
            attention_mask=attention_mask,
        )

        # embed prefix
        inputs_embeds = self.embed_prefix(
            input_ids,
            pixel_values=pixel_values,
            image_grid_thw=image_grid_thw,
            states=states,
        )

        # pass prefix, update kvcache
        seq_len = input_ids.shape[-1]
        chunk_size = self.config.action_chunk_size
        suffix_len = -1  # <|im_end|>
        prefix_len = seq_len - chunk_size - 1

        outputs = self.vlm_backbone.model(
            position_ids=position_ids[..., :prefix_len],
            attention_mask=attention_mask[:, :prefix_len],
            inputs_embeds=inputs_embeds[:, :prefix_len],
            use_cache=True,
        )

        # denoising
        device = states.device
        actions_shape = (states.shape[0], chunk_size, self.config.max_action_dim)
        noise = self.sample_noise(actions_shape, device)

        x_t = noise.type(self.action_in_proj.weight.dtype)
        dt = torch.tensor(-1.0 / self.config.num_denoise_steps, device=device)
        time = torch.ones(inputs_embeds.shape[0], device=device)
        past_key_values = outputs.past_key_values

        action_mask = input_ids == self.config.action_token_id
        while time >= -dt / 2:
            action_time_embs = self.embed_suffix(time, x_t)
            inputs_embeds[action_mask] = action_time_embs.to(inputs_embeds.dtype)

            past_key_values.crop(prefix_len)
            outputs = self.vlm_backbone.model(
                position_ids=position_ids[..., prefix_len:suffix_len],
                attention_mask=attention_mask[:, :suffix_len],
                past_key_values=past_key_values,
                inputs_embeds=inputs_embeds[:, prefix_len:suffix_len],
                use_cache=True,
            )
            action_time_embs = outputs.last_hidden_state[:, :chunk_size]
            action_time_embs = action_time_embs.type(self.action_out_proj.dtype)
            v_t = self.action_out_proj(action_time_embs)

            x_t += dt * v_t.reshape(x_t.shape)
            time += dt
        return x_t

    def prepare_inputs_for_generation(self, *args, **kwargs):
        return self.vlm_backbone.prepare_inputs_for_generation(*args, **kwargs)

    def _expand_inputs_for_generation(self, *args, **kwargs):
        return self.vlm_backbone._expand_inputs_for_generation(*args, **kwargs)


EO1VisionFlowMatchingModel.register_for_auto_class()

	# Copyright 2025 EO-Robotics 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 math
	from dataclasses import dataclass
	from typing import Any

	import torch
	import torch.nn as nn
	import torch.nn.functional as F # noqa: N812
	from torch import Tensor
	from transformers.activations import ACT2FN
	from transformers.generation import GenerationMixin
	from transformers.modeling_outputs import ModelOutput
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import logging

	from .configuration_eo1 import EO1VisionFlowMatchingConfig
	from .modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration

	logger = logging.get_logger(__name__)


	def create_sinusoidal_pos_embedding(
	time: torch.tensor,
	dimension: int,
	min_period: float = 4e-3,
	max_period: float = 4.0,
	device="cpu",
	) -> Tensor:
	"""Computes sine-cosine positional embedding vectors for scalar positions."""
	if dimension % 2 != 0:
	raise ValueError(f"dimension ({dimension}) must be divisible by 2")

	if time.ndim != 1:
	raise ValueError("The time tensor is expected to be of shape `(batch_size, )`.")

	fraction = torch.linspace(0.0, 1.0, dimension // 2, device=device)
	period = min_period * (max_period / min_period) ** fraction

	scaling_factor = 1.0 / period * 2 * math.pi
	sin_input = scaling_factor[None, :] * time[:, None]
	pos_emb = torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1)
	return pos_emb


	@dataclass
	class EO1VisionFlowMatchingOutputWithPast(ModelOutput):
	loss: torch.FloatTensor \| None = None
	fm_loss: torch.FloatTensor \| None = None
	ar_loss: torch.FloatTensor \| None = None

	actions: torch.FloatTensor \| None = None
	logits: torch.FloatTensor \| None = None

	past_key_values: list[torch.FloatTensor] \| None = None
	hidden_states: tuple[torch.FloatTensor] \| None = None
	attentions: tuple[torch.FloatTensor] \| None = None
	rope_deltas: torch.LongTensor \| None = None


	class EO1VisionActionProjector(torch.nn.Sequential):
	"""This block implements the multi-layer perceptron (MLP) module."""

	def __init__(
	self,
	in_channels: int,
	out_channels: int,
	num_layers: int = 2,
	activation_layer: str = "linear",
	bias: bool = True,
	device: Any = None,
	dtype: torch.dtype = torch.float32,
	):
	layers = []
	in_dim = in_channels
	hidden_channels = [in_dim] * (num_layers - 1) + [out_channels]
	for hidden_dim in hidden_channels[:-1]:
	layers.append(torch.nn.Linear(in_dim, hidden_dim, bias=bias, dtype=dtype, device=device))
	layers.append(ACT2FN[activation_layer])
	in_dim = hidden_dim
	layers.append(torch.nn.Linear(in_dim, hidden_channels[-1], bias=bias, dtype=dtype, device=device))
	super().__init__(*layers)

	@property
	def dtype(self):
	return self[0].weight.dtype


	class EO1VisionFlowMatchingModel(PreTrainedModel, GenerationMixin):
	config_class = EO1VisionFlowMatchingConfig
	supports_gradient_checkpointing = True

	_supports_flash_attn = True
	_supports_sdpa = True
	_supports_attention_backend = True
	_can_compile_fullgraph = True
	_skip_keys_device_placement = "past_key_values"

	def __init__(
	self,
	config: EO1VisionFlowMatchingConfig,
	vlm_backbone: Qwen2_5_VLForConditionalGeneration = None,
	):
	super().__init__(config)

	hidden_size = self.config.text_config.hidden_size
	max_action_dim = self.config.max_action_dim
	self.vlm_backbone = vlm_backbone or Qwen2_5_VLForConditionalGeneration(self.config)
	self.state_proj = nn.Linear(max_action_dim, hidden_size)
	self.action_in_proj = nn.Linear(max_action_dim, hidden_size)
	self.action_out_proj = EO1VisionActionProjector(
	hidden_size,
	max_action_dim,
	self.config.num_action_layers,
	self.config.action_act,
	)
	self.action_time_mlp_in = nn.Linear(hidden_size * 2, hidden_size)
	self.action_time_mlp_out = nn.Linear(hidden_size, hidden_size)

	self.post_init()
	self.to_float32_flow_matching_head()

	def get_input_embeddings(self):
	return self.vlm_backbone.get_input_embeddings()

	def to_float32_flow_matching_head(self):
	self.action_out_proj = self.action_out_proj.to(dtype=torch.float32)
	self.action_time_mlp_in = self.action_time_mlp_in.to(dtype=torch.float32)
	self.action_time_mlp_out = self.action_time_mlp_out.to(dtype=torch.float32)
	self.state_proj = self.state_proj.to(dtype=torch.float32)
	self.action_in_proj = self.action_in_proj.to(dtype=torch.float32)

	def sample_noise(self, shape, device):
	noise = torch.normal(
	mean=0.0,
	std=1.0,
	size=shape,
	dtype=torch.float32,
	device=device,
	)
	return noise

	def sample_time(self, bsize, device):
	beta_dist = torch.distributions.Beta(concentration1=1.5, concentration0=1.0)
	time_beta = beta_dist.sample((bsize,)).to(device=device, dtype=torch.float32)
	time = time_beta * 0.999 + 0.001
	return time

	def replace_special_embeddings(
	self,
	input_ids: torch.LongTensor,
	inputs_embeds: torch.FloatTensor,
	special_features: torch.FloatTensor = None,
	special_token_ids: torch.LongTensor = None,
	) -> torch.LongTensor:
	"""Replace the special embeddings with the special features."""
	if special_features is not None and special_token_ids is not None:
	n_special_tokens = (input_ids == special_token_ids).sum().item()
	n_special_features = special_features.shape[0]
	assert n_special_tokens == n_special_features, (
	f"Special features and special tokens {special_token_ids} do not match: \
	tokens: {n_special_tokens}, features {n_special_features}"
	)
	mask = input_ids == special_token_ids
	mask_unsqueezed = mask.unsqueeze(-1)
	mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
	special_mask = mask_expanded.to(inputs_embeds.device)
	special_features = special_features.to(inputs_embeds.device, inputs_embeds.dtype)
	inputs_embeds = inputs_embeds.masked_scatter(special_mask, special_features)
	return inputs_embeds, None

	def embed_prefix(
	self,
	input_ids: torch.LongTensor,
	inputs_embeds: torch.FloatTensor \| None = None,
	pixel_values: torch.Tensor \| None = None,
	pixel_values_videos: torch.FloatTensor \| None = None,
	image_grid_thw: torch.LongTensor \| None = None,
	video_grid_thw: torch.LongTensor \| None = None,
	states: torch.Tensor \| None = None,
	) -> tuple[torch.FloatTensor, torch.Tensor, torch.Tensor]:
	"""Embed the suffix"""
	if inputs_embeds is None:
	inputs_embeds = self.get_input_embeddings()(input_ids)

	if pixel_values is not None:
	image_embeds = self.vlm_backbone.get_image_features(pixel_values, image_grid_thw)
	image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
	image_mask, _ = self.vlm_backbone.get_placeholder_mask(
	input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
	)
	inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)

	if pixel_values_videos is not None:
	video_embeds = self.vlm_backbone.get_video_features(pixel_values_videos, video_grid_thw)
	video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
	_, video_mask = self.vlm_backbone.get_placeholder_mask(
	input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
	)
	inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)

	if states is not None:
	states = states.type(self.state_proj.weight.dtype)
	state_embs = self.state_proj(states)
	inputs_embeds, _ = self.replace_special_embeddings(
	input_ids, inputs_embeds, state_embs, self.config.state_token_id
	)
	return inputs_embeds

	def embed_suffix(
	self,
	timestep: torch.Tensor,
	noisy_actions: torch.Tensor,
	) -> torch.FloatTensor:
	"""Embed the suffix"""
	time_embs = create_sinusoidal_pos_embedding(
	timestep,
	self.config.text_config.hidden_size,
	device=noisy_actions.device,
	)
	time_embs = time_embs.type(noisy_actions.dtype)
	noisy_actions = noisy_actions.type(self.action_in_proj.weight.dtype)
	action_embs = self.action_in_proj(noisy_actions)
	time_embs = time_embs[:, None, :].expand_as(action_embs)

	action_time_embs = torch.cat([action_embs, time_embs], dim=2)
	action_time_embs = self.action_time_mlp_in(action_time_embs)
	action_time_embs = F.silu(action_time_embs)
	action_time_embs = self.action_time_mlp_out(action_time_embs)
	return action_time_embs

	def forward(
	self,
	input_ids: torch.LongTensor \| None = None,
	attention_mask: torch.Tensor \| None = None,
	position_ids: torch.LongTensor \| None = None,
	past_key_values: list[torch.FloatTensor] \| None = None,
	inputs_embeds: torch.FloatTensor \| None = None,
	labels: torch.LongTensor \| None = None,
	use_cache: bool \| None = None,
	output_attentions: bool \| None = None,
	output_hidden_states: bool \| None = None,
	pixel_values: torch.Tensor \| None = None,
	pixel_values_videos: torch.FloatTensor \| None = None,
	image_grid_thw: torch.LongTensor \| None = None,
	video_grid_thw: torch.LongTensor \| None = None,
	rope_deltas: torch.LongTensor \| None = None,
	cache_position: torch.LongTensor \| None = None,
	second_per_grid_ts: torch.Tensor \| None = None,
	logits_to_keep: int \| torch.Tensor = 0,
	states: torch.Tensor \| None = None,
	actions: torch.Tensor \| None = None,
	action_is_pad: torch.Tensor \| None = None,
	**kwargs,
	) -> EO1VisionFlowMatchingOutputWithPast:
	"""multi-modal forward pass, including image, video, state, action, and language."""
	inputs_embeds = self.embed_prefix(
	input_ids,
	inputs_embeds,
	pixel_values,
	pixel_values_videos,
	image_grid_thw,
	video_grid_thw,
	states,
	)

	if actions is not None:
	noise_mask = input_ids == self.config.action_token_id
	pass_mask = input_ids == self.config.action_pass_id
	mask = noise_mask \| pass_mask # (b s)

	pass_mask_in_action = pass_mask[mask] # (n, )
	pass_mask_in_action = pass_mask_in_action.reshape(*actions.shape[:2], 1) # (b, h, 1)

	time = self.sample_time(actions.shape[0], inputs_embeds.device) # (n,)
	time_expanded = time[:, None, None].repeat(1, actions.shape[1], 1) # (b, h, 1)
	time_expanded[pass_mask_in_action] = 0.0

	noise = self.sample_noise(actions.shape, inputs_embeds.device)
	x_t = time_expanded * noise + (1 - time_expanded) * actions
	u_t = noise - actions

	action_time_embs = self.embed_suffix(time, x_t)
	mask_unsqueezed = mask.unsqueeze(-1)
	mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
	action_mask = mask_expanded.to(inputs_embeds.device)

	action_time_embs = action_time_embs.to(inputs_embeds.device, inputs_embeds.dtype)
	inputs_embeds = inputs_embeds.masked_scatter(action_mask, action_time_embs)

	if attention_mask is not None:
	attention_mask = attention_mask.to(inputs_embeds.device)

	if position_ids is None:
	prefill_noncompiled_stage = (cache_position is not None and cache_position[0] == 0) or (
	past_key_values is None or past_key_values.get_seq_length() == 0
	)
	if prefill_noncompiled_stage or self.vlm_backbone.rope_deltas is None:
	position_ids, rope_deltas = self.vlm_backbone.get_rope_index(
	input_ids,
	image_grid_thw,
	video_grid_thw,
	second_per_grid_ts=second_per_grid_ts,
	attention_mask=attention_mask,
	)
	self.vlm_backbone.rope_deltas = rope_deltas
	else:
	batch_size, seq_length, _ = inputs_embeds.shape
	position_ids = torch.arange(seq_length, device=inputs_embeds.device)
	position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1)
	if cache_position is not None:
	delta = (cache_position[0] + self.vlm_backbone.rope_deltas).to(inputs_embeds.device)
	else:
	delta = torch.zeros((batch_size, seq_length), device=inputs_embeds.device)
	delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=1)
	position_ids += delta.to(position_ids.device)

	# generation
	output_actions = None
	if not (self.training or states is None):
	output_actions, outputs = self.sample_actions(
	input_ids=input_ids,
	position_ids=position_ids,
	attention_mask=attention_mask,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	cache_position=cache_position,
	states=states,
	)
	else:
	outputs = self.vlm_backbone.model(
	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,
	)

	hidden_states = outputs[0]

	# only compute necessary logits, do not upcast to float if not computing loss
	slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
	logits = self.vlm_backbone.lm_head(hidden_states[:, slice_indices, :])

	loss = None
	fm_loss = None
	v_t = None
	if actions is not None:
	action_time_embs = hidden_states[action_mask[..., 0]]
	action_time_embs = action_time_embs.type(self.action_out_proj.dtype)

	v_t = self.action_out_proj(action_time_embs)
	u_t = u_t.reshape(v_t.shape)
	v_t = v_t.type(u_t.dtype)

	losses = F.mse_loss(u_t, v_t, reduction="none")
	if action_is_pad is not None:
	in_episode_bound = (~action_is_pad).reshape(-1, 1)
	losses = losses * in_episode_bound

	in_denoise_bound = (~pass_mask_in_action).reshape(-1, 1)
	losses = losses * in_denoise_bound

	fm_loss = losses.mean()
	loss = fm_loss

	ar_loss = None
	if labels is not None:
	ar_loss = self.vlm_backbone.loss_function(
	logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
	)
	loss = loss + ar_loss if loss is not None else ar_loss

	return EO1VisionFlowMatchingOutputWithPast(
	loss=loss,
	fm_loss=fm_loss,
	ar_loss=ar_loss,
	actions=output_actions,
	logits=logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	rope_deltas=self.vlm_backbone.rope_deltas,
	)

	@torch.no_grad()
	def sample_actions(
	self,
	input_ids: torch.LongTensor \| None = None,
	attention_mask: torch.Tensor \| None = None,
	pixel_values: torch.Tensor \| None = None,
	image_grid_thw: torch.LongTensor \| None = None,
	states: torch.Tensor \| None = None,
	**kwargs,
	) -> Tensor:
	"""Sample actions from the model."""

	# prepare position_ids and kv_cache
	position_ids, _ = self.vlm_backbone.get_rope_index(
	input_ids,
	image_grid_thw=image_grid_thw,
	attention_mask=attention_mask,
	)

	# embed prefix
	inputs_embeds = self.embed_prefix(
	input_ids,
	pixel_values=pixel_values,
	image_grid_thw=image_grid_thw,
	states=states,
	)

	# pass prefix, update kvcache
	seq_len = input_ids.shape[-1]
	chunk_size = self.config.action_chunk_size
	suffix_len = -1 # <\|im_end\|>
	prefix_len = seq_len - chunk_size - 1

	outputs = self.vlm_backbone.model(
	position_ids=position_ids[..., :prefix_len],
	attention_mask=attention_mask[:, :prefix_len],
	inputs_embeds=inputs_embeds[:, :prefix_len],
	use_cache=True,
	)

	# denoising
	device = states.device
	actions_shape = (states.shape[0], chunk_size, self.config.max_action_dim)
	noise = self.sample_noise(actions_shape, device)

	x_t = noise.type(self.action_in_proj.weight.dtype)
	dt = torch.tensor(-1.0 / self.config.num_denoise_steps, device=device)
	time = torch.ones(inputs_embeds.shape[0], device=device)
	past_key_values = outputs.past_key_values

	action_mask = input_ids == self.config.action_token_id
	while time >= -dt / 2:
	action_time_embs = self.embed_suffix(time, x_t)
	inputs_embeds[action_mask] = action_time_embs.to(inputs_embeds.dtype)

	past_key_values.crop(prefix_len)
	outputs = self.vlm_backbone.model(
	position_ids=position_ids[..., prefix_len:suffix_len],
	attention_mask=attention_mask[:, :suffix_len],
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds[:, prefix_len:suffix_len],
	use_cache=True,
	)
	action_time_embs = outputs.last_hidden_state[:, :chunk_size]
	action_time_embs = action_time_embs.type(self.action_out_proj.dtype)
	v_t = self.action_out_proj(action_time_embs)

	x_t += dt * v_t.reshape(x_t.shape)
	time += dt
	return x_t

	def prepare_inputs_for_generation(self, args, *kwargs):
	return self.vlm_backbone.prepare_inputs_for_generation(args, *kwargs)

	def _expand_inputs_for_generation(self, args, *kwargs):
	return self.vlm_backbone._expand_inputs_for_generation(args, *kwargs)


	EO1VisionFlowMatchingModel.register_for_auto_class()