cross13tasks / code /model /modules /action_model /LayerwiseFM_ActionHeader.py

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# Copyright 2025 NVIDIA Corp. and affiliates. All rights reserved.
# Modified by [Junqiu YU/ Fudan University] in [2025]. 
# Modification: [rm and add some connect adapter to match with starVLA, e.g., "rm "].



from dataclasses import dataclass, field

import torch
import torch.nn.functional as F
from torch import nn
from torch.distributions import Beta
from transformers import PretrainedConfig
from transformers.feature_extraction_utils import BatchFeature

from starVLA.model.modules.action_model.flow_matching_head.action_encoder import (
    SinusoidalPositionalEncoding,
    swish,
)

from starVLA.model.modules.action_model.flow_matching_head.cross_attention_dit import DiT, SelfAttentionTransformer

# TODO try to meger DiT Modules with follow_match_head, they are just the same arch, but diff loss, use diffusers package will be simple

class CategorySpecificLinear(nn.Module):
    def __init__(self, num_categories, input_dim, hidden_dim):
        super().__init__()
        self.num_categories = num_categories
        # For each category, we have separate weights and biases.
        self.W = nn.Parameter(0.02 * torch.randn(num_categories, input_dim, hidden_dim))
        self.b = nn.Parameter(torch.zeros(num_categories, hidden_dim))

    def forward(self, x, cat_ids):
        selected_W = self.W[cat_ids]
        selected_b = self.b[cat_ids]
        # import ipdb; ipdb.set_trace()
        return torch.bmm(x, selected_W) + selected_b.unsqueeze(1)


class CategorySpecificMLP(nn.Module):
    def __init__(self, num_categories, input_dim, hidden_dim, output_dim):
        super().__init__()
        self.num_categories = num_categories
        self.layer1 = CategorySpecificLinear(num_categories, input_dim, hidden_dim)
        self.layer2 = CategorySpecificLinear(num_categories, hidden_dim, output_dim)

    def forward(self, x, cat_ids):
        hidden = F.relu(self.layer1(x, cat_ids))
        return self.layer2(hidden, cat_ids)



class MLP(nn.Module):
    def __init__(self, input_dim, hidden_dim=1024, output_dim=2048):
        super().__init__()
        self.layer1 = nn.Linear(input_dim, hidden_dim)
        self.layer2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        return self.layer2(F.relu(self.layer1(x)))


class ActionEncoder(nn.Module):
    def __init__(self, action_dim, hidden_size=1024):
        super().__init__()
        self.hidden_size = hidden_size
        self.action_dim = action_dim
        self.layer1 = nn.Linear(action_dim, hidden_size)
        self.layer2 = nn.Linear(2 * hidden_size, hidden_size)
        self.layer3 = nn.Linear(hidden_size, hidden_size)
        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)

    def forward(self, actions, timesteps):
        """
        actions:   shape (B, T, action_dim)
        timesteps: shape (B,)  -- a single scalar per batch item
        returns:   shape (B, T, hidden_size)
        """
        B, T, _ = actions.shape

        # 1) Expand each batch's single scalar time 'tau' across all T steps
        #    so that shape => (B, T)
        #    e.g. if timesteps is (B,), replicate across T
        if timesteps.dim() == 1 and timesteps.shape[0] == B:
            # shape (B,) => (B,T)
            timesteps = timesteps.unsqueeze(1).expand(-1, T)
        else:
            raise ValueError(
                "Expected `timesteps` to have shape (B,) so we can replicate across T."
            )

        # 2) Standard action MLP step for shape => (B, T, w)
        a_emb = self.layer1(actions)

        # 3) Get the sinusoidal encoding (B, T, w)
        tau_emb = self.pos_encoding(timesteps).to(dtype=a_emb.dtype)

        # 4) Concat along last dim => (B, T, 2w), then layer2 => (B, T, w), swish
        x = torch.cat([a_emb, tau_emb], dim=-1)
        x = swish(self.layer2(x))

        # 5) Finally W3 => (B, T, w)
        x = self.layer3(x)
        return x



class MultiEmbodimentActionEncoder(nn.Module):
    def __init__(self, action_dim, hidden_size=1024, num_embodiments=8):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_embodiments = num_embodiments

        # W1: R^{w x d}, W2: R^{w x 2w}, W3: R^{w x w}
        self.W1 = CategorySpecificLinear(num_embodiments, action_dim, hidden_size)  # (d -> w)
        self.W2 = CategorySpecificLinear(num_embodiments, 2 * hidden_size, hidden_size)  # (2w -> w)
        self.W3 = CategorySpecificLinear(num_embodiments, hidden_size, hidden_size)  # (w -> w)
        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)

    def forward(self, actions, timesteps, cat_ids):
        """
        actions:   shape (B, T, action_dim)
        timesteps: shape (B,)  -- a single scalar per batch item
        cat_ids:   shape (B,)
        returns:   shape (B, T, hidden_size)
        """
        B, T, _ = actions.shape

        # 1) Expand each batch's single scalar time 'tau' across all T steps
        #    so that shape => (B, T)
        #    e.g. if timesteps is (B,), replicate across T
        if timesteps.dim() == 1 and timesteps.shape[0] == B:
            # shape (B,) => (B,T)
            timesteps = timesteps.unsqueeze(1).expand(-1, T)
        else:
            raise ValueError(
                "Expected `timesteps` to have shape (B,) so we can replicate across T."
            )

        # 2) Standard action MLP step for shape => (B, T, w)
        a_emb = self.W1(actions, cat_ids)

        # 3) Get the sinusoidal encoding (B, T, w)
        tau_emb = self.pos_encoding(timesteps).to(dtype=a_emb.dtype)

        # 4) Concat along last dim => (B, T, 2w), then W2 => (B, T, w), swish
        x = torch.cat([a_emb, tau_emb], dim=-1)
        x = swish(self.W2(x, cat_ids))

        # 5) Finally W3 => (B, T, w)
        x = self.W3(x, cat_ids)
        return x


@dataclass
class FlowmatchingActionHeadConfig(PretrainedConfig):
    """NOTE: N1.5 uses XEmbFlowmatchingPolicyHeadConfig as action head"""

    add_pos_embed: bool = field(
        default=True, metadata={"help": "Whether to add positional embedding"}
    )
    diffusion_model_cfg: dict = field(
        default=None, metadata={"help": "Diffusion model configuration."}
    )
    input_embedding_dim: int = field(
        default=1536, metadata={"help": "Input embedding channel dimension."}
    )

    hidden_size: int = field(default=1024, metadata={"help": "Input embedding dimension."})
    max_seq_len: int = field(default=1024, metadata={"help": "Maxium Sequence Length"})
    action_dim: int = field(default=None, metadata={"help": "Action dimension."})
    action_horizon: int = field(default=None, metadata={"help": "Action horizon."})
    noise_beta_alpha: float = field(default=1.5, metadata={"help": ""})
    noise_beta_beta: float = field(default=1.0, metadata={"help": ""})
    noise_s: float = field(
        default=0.999, metadata={"help": "Flow matching noise Beta distribution s."}
    )
    num_timestep_buckets: int = field(
        default=1000, metadata={"help": "Number of timestep discretization buckets."}
    )
    num_inference_timesteps: int = field(
        default=None,
        metadata={"help": "Number of inference steps for noise diffusion."},
    )
    max_num_embodiments: int = field(default=32, metadata={"help": "Number of embodiments."})
    tune_projector: bool = field(default=True, metadata={"help": "Whether to tune the projector."})
    tune_diffusion_model: bool = field(
        default=True, metadata={"help": "Whether to tune the diffusion model."}
    )
    load_pretrained_det_decode_layer_path: str = field(
        default=None, metadata={"help": "Path to pretrained detection model."}
    )
    detection_coeff: float = field(default=1.0, metadata={"help": "Detection coefficient."})

    freeze_decode_layer: bool = field(default=False)
    expand_batch: int = field(default=None)
    use_vlln: bool = field(default=True)

    vl_self_attention_cfg: dict = field(default=None)
    num_target_vision_tokens: int = field(
        default=32, metadata={"help": "Number of target vision tokens."}
    )

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        for key, value in kwargs.items():
            setattr(self, key, value)




DiTConfig = {"num_layers": 36, "input_embedding_dim": 2048, "attention_head_dim": 64, "num_attention_heads": 32} # default for qwen2.5-vl


class LayerwiseFlowmatchingActionHead(nn.Module):
    def __init__(
        self,
        global_config,
        **kwargs,
    ):
        super().__init__()
        action_config = global_config.framework.action_model
        diffusion_model_cfg = action_config.diffusion_model_cfg

        # 更新 DiTConfig 到 diffusion_model_cfg
        DiTConfig["num_layers"] = global_config.framework.qwenvl.num_vl_layers
        DiTConfig["input_embedding_dim"] = global_config.framework.qwenvl.vl_hidden_dim
        DiTConfig["num_attention_heads"] = DiTConfig["input_embedding_dim"] // DiTConfig["attention_head_dim"]
        diffusion_model_cfg.update(DiTConfig)
        # diffusion_model_cfg["interleave_self_attention"] = False
        diffusion_model_cfg.cross_attention_dim = DiTConfig["input_embedding_dim"] # should match vl embedding dim, but for some case we might want to change it for cross + self attention
        self.input_embedding_dim = global_config.framework.qwenvl.vl_hidden_dim
        self.model = DiT(**diffusion_model_cfg) # TODO better way is copy LLM from VLM
        self.dit_out_hidden_size = self.input_embedding_dim
        self.action_dim = action_config.action_dim
        self.action_horizon = action_config.future_action_window_size + 1
        self.num_inference_timesteps = action_config.num_inference_timesteps

        self.state_encoder = MLP(
            input_dim=action_config.state_dim,
            output_dim=self.input_embedding_dim,
        ) if action_config.state_dim else None

        self.action_encoder = ActionEncoder(
            action_dim=action_config.action_dim,
            hidden_size=self.input_embedding_dim,
        )
        self.action_decoder = MLP(
            input_dim=self.input_embedding_dim,
            hidden_dim=1024,
            output_dim=self.action_dim,
        )
        self.future_tokens = nn.Embedding(action_config.num_target_vision_tokens, self.input_embedding_dim)
        nn.init.normal_(self.future_tokens.weight, mean=0.0, std=0.02)

        if action_config.add_pos_embed:
            self.position_embedding = nn.Embedding(action_config.max_seq_len, self.input_embedding_dim)
            nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)

        self.beta_dist = Beta(action_config.noise_beta_alpha, action_config.noise_beta_beta)
        self.num_timestep_buckets = action_config.num_timestep_buckets
        self.config = action_config

    def sample_time(self, batch_size, device, dtype):
        sample = self.beta_dist.sample([batch_size]).to(device, dtype=dtype)
        return (self.config.noise_s - sample) / self.config.noise_s

    def prepare_input(self, batch: dict) -> BatchFeature:
        return BatchFeature(data=batch)


    def forward(self, vl_embs_list: list, actions: torch.Tensor, state: torch.Tensor = None, encoder_attention_mask: torch.Tensor = None):
        """
        vl_embs: list of torch.Tensor, each shape (B, seq_length, feature_dim)
        actions: shape (B, future_action_window_size, D_action)
        encoder_attention_mask: optional (B, seq_length) mask for VLM padding tokens
        """
        device = actions.device
        num_layers = len(vl_embs_list)
        B, L, D = vl_embs_list[0].shape
        # Embed noised action trajectory.
        noise = torch.randn(actions.shape, device=actions.device, dtype=actions.dtype)
        t = self.sample_time(actions.shape[0], device=actions.device, dtype=actions.dtype)
        t = t[:, None, None]  # shape (B,1,1) for broadcast

        noisy_trajectory = (1 - t) * noise + t * actions
        velocity = actions - noise

        # Convert (continuous) t -> discrete if needed
        t_discretized = (t[:, 0, 0] * self.num_timestep_buckets).long()
        action_features = self.action_encoder(noisy_trajectory, t_discretized)

        # Embed state
        state_features = self.state_encoder(state) if state is not None else None

        # Maybe add position embedding.
        if self.config.add_pos_embed:
            pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
            pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
            action_features = action_features + pos_embs

        # state and action embedding along sequence dimension.
        future_tokens = self.future_tokens.weight.unsqueeze(0).expand(B, -1, -1)
        sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1) \
            if state_features is not None else torch.cat((future_tokens, action_features), dim=1)
        
        # Encode timesteps
        temb = self.model.timestep_encoder(t_discretized)

        # Convert encoder_attention_mask from long int (0/1) to bool so that
        # F.scaled_dot_product_attention accepts it (requires bool or float, not long).
        # Qwen attention mask: 1 = valid token (attend), 0 = padding (mask out).
        if encoder_attention_mask is not None:
            encoder_attention_mask = encoder_attention_mask.bool()

        # Layerwise cross-attention with vl_embs
        model_output = sa_embs
        for layer_idx, layer in enumerate(self.model.transformer_blocks):
            model_output = layer(
                hidden_states=model_output,
                encoder_hidden_states=vl_embs_list[layer_idx],
                temb=temb,
                encoder_attention_mask=encoder_attention_mask,
            )
        
        # TODO miss self att and _process_output, but work well
        pred = self.action_decoder(model_output)
        pred_actions = pred[:, -actions.shape[1] :]

        # Slice out only the action portion of pred and target.
        loss = ((pred_actions - velocity) ** 2).mean()
        return loss

    @torch.no_grad()
    def predict_action(self, vl_embs_list: list, state: torch.Tensor = None, encoder_attention_mask: torch.Tensor = None) -> torch.Tensor:
        # Set initial actions as the sampled noise.
        batch_size = vl_embs_list[0].shape[0]
        device = vl_embs_list[0].device
        actions = torch.randn(
            size=(batch_size, self.action_horizon, self.action_dim),
            dtype=vl_embs_list[0].dtype,
            device=device,
        )

        num_steps = self.num_inference_timesteps
        dt = 1.0 / num_steps

        state_features = self.state_encoder(state) if state is not None else None

        # Convert encoder_attention_mask dtype once before the denoising loop.
        if encoder_attention_mask is not None:
            encoder_attention_mask = encoder_attention_mask.bool()

        # Run denoising steps.
        for t in range(num_steps):
            t_cont = t / float(num_steps)
            t_discretized_int = int(t_cont * self.num_timestep_buckets)
            timesteps_tensor = torch.full(
                size=(batch_size,), fill_value=t_discretized_int, device=device, dtype=torch.long
            )

            # Embed current action trajectory with timestep
            action_features = self.action_encoder(actions, timesteps_tensor)

            # Maybe add position embedding.
            if self.config.add_pos_embed:
                pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
                pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
                action_features = action_features + pos_embs

            future_tokens = self.future_tokens.weight.unsqueeze(0).expand(batch_size, -1, -1)
            sa_embs = (
                torch.cat((state_features, future_tokens, action_features), dim=1)
                if state_features is not None
                else torch.cat((future_tokens, action_features), dim=1)
            )

            # Encode timestep
            temb = self.model.timestep_encoder(timesteps_tensor)

            # Layerwise cross-attention with vl_embs_list
            model_output = sa_embs
            for layer_idx, layer in enumerate(self.model.transformer_blocks):
                model_output = layer(
                    hidden_states=model_output,
                    encoder_hidden_states=vl_embs_list[layer_idx],
                    temb=temb,
                    encoder_attention_mask=encoder_attention_mask,
                )
            # TODO miss self att and _process_output 
            pred = self.action_decoder(model_output)
            pred_velocity = pred[:, -self.action_horizon :]

            # Euler integration
            actions = actions + dt * pred_velocity
        return actions

    @property
    def device(self):
        return next(iter(self.parameters())).device

    @property
    def dtype(self):
        return next(iter(self.parameters())).dtype



def get_action_model(config=None):
    """
    Factory: build FlowmatchingActionHead from global framework config.
    
    Args:
        config: Global config (expects config.framework.action_model namespace).
    Returns:
        FlowmatchingActionHead: Initialized FlowMatchingActionHead.
    """
    return LayerwiseFlowmatchingActionHead(
        global_config=config
    )



if __name__ == "__main__":
    # TODO make each backbone.py can be debug independently

    pass

	# Copyright 2025 NVIDIA Corp. and affiliates. All rights reserved.
	# Modified by [Junqiu YU/ Fudan University] in [2025].
	# Modification: [rm and add some connect adapter to match with starVLA, e.g., "rm "].



	from dataclasses import dataclass, field

	import torch
	import torch.nn.functional as F
	from torch import nn
	from torch.distributions import Beta
	from transformers import PretrainedConfig
	from transformers.feature_extraction_utils import BatchFeature

	from starVLA.model.modules.action_model.flow_matching_head.action_encoder import (
	SinusoidalPositionalEncoding,
	swish,
	)

	from starVLA.model.modules.action_model.flow_matching_head.cross_attention_dit import DiT, SelfAttentionTransformer

	# TODO try to meger DiT Modules with follow_match_head, they are just the same arch, but diff loss, use diffusers package will be simple

	class CategorySpecificLinear(nn.Module):
	def __init__(self, num_categories, input_dim, hidden_dim):
	super().__init__()
	self.num_categories = num_categories
	# For each category, we have separate weights and biases.
	self.W = nn.Parameter(0.02 * torch.randn(num_categories, input_dim, hidden_dim))
	self.b = nn.Parameter(torch.zeros(num_categories, hidden_dim))

	def forward(self, x, cat_ids):
	selected_W = self.W[cat_ids]
	selected_b = self.b[cat_ids]
	# import ipdb; ipdb.set_trace()
	return torch.bmm(x, selected_W) + selected_b.unsqueeze(1)


	class CategorySpecificMLP(nn.Module):
	def __init__(self, num_categories, input_dim, hidden_dim, output_dim):
	super().__init__()
	self.num_categories = num_categories
	self.layer1 = CategorySpecificLinear(num_categories, input_dim, hidden_dim)
	self.layer2 = CategorySpecificLinear(num_categories, hidden_dim, output_dim)

	def forward(self, x, cat_ids):
	hidden = F.relu(self.layer1(x, cat_ids))
	return self.layer2(hidden, cat_ids)



	class MLP(nn.Module):
	def __init__(self, input_dim, hidden_dim=1024, output_dim=2048):
	super().__init__()
	self.layer1 = nn.Linear(input_dim, hidden_dim)
	self.layer2 = nn.Linear(hidden_dim, output_dim)

	def forward(self, x):
	return self.layer2(F.relu(self.layer1(x)))


	class ActionEncoder(nn.Module):
	def __init__(self, action_dim, hidden_size=1024):
	super().__init__()
	self.hidden_size = hidden_size
	self.action_dim = action_dim
	self.layer1 = nn.Linear(action_dim, hidden_size)
	self.layer2 = nn.Linear(2 * hidden_size, hidden_size)
	self.layer3 = nn.Linear(hidden_size, hidden_size)
	self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)

	def forward(self, actions, timesteps):
	"""
	actions: shape (B, T, action_dim)
	timesteps: shape (B,) -- a single scalar per batch item
	returns: shape (B, T, hidden_size)
	"""
	B, T, _ = actions.shape

	# 1) Expand each batch's single scalar time 'tau' across all T steps
	# so that shape => (B, T)
	# e.g. if timesteps is (B,), replicate across T
	if timesteps.dim() == 1 and timesteps.shape[0] == B:
	# shape (B,) => (B,T)
	timesteps = timesteps.unsqueeze(1).expand(-1, T)
	else:
	raise ValueError(
	"Expected `timesteps` to have shape (B,) so we can replicate across T."
	)

	# 2) Standard action MLP step for shape => (B, T, w)
	a_emb = self.layer1(actions)

	# 3) Get the sinusoidal encoding (B, T, w)
	tau_emb = self.pos_encoding(timesteps).to(dtype=a_emb.dtype)

	# 4) Concat along last dim => (B, T, 2w), then layer2 => (B, T, w), swish
	x = torch.cat([a_emb, tau_emb], dim=-1)
	x = swish(self.layer2(x))

	# 5) Finally W3 => (B, T, w)
	x = self.layer3(x)
	return x



	class MultiEmbodimentActionEncoder(nn.Module):
	def __init__(self, action_dim, hidden_size=1024, num_embodiments=8):
	super().__init__()
	self.hidden_size = hidden_size
	self.num_embodiments = num_embodiments

	# W1: R^{w x d}, W2: R^{w x 2w}, W3: R^{w x w}
	self.W1 = CategorySpecificLinear(num_embodiments, action_dim, hidden_size) # (d -> w)
	self.W2 = CategorySpecificLinear(num_embodiments, 2 * hidden_size, hidden_size) # (2w -> w)
	self.W3 = CategorySpecificLinear(num_embodiments, hidden_size, hidden_size) # (w -> w)
	self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)

	def forward(self, actions, timesteps, cat_ids):
	"""
	actions: shape (B, T, action_dim)
	timesteps: shape (B,) -- a single scalar per batch item
	cat_ids: shape (B,)
	returns: shape (B, T, hidden_size)
	"""
	B, T, _ = actions.shape

	# 1) Expand each batch's single scalar time 'tau' across all T steps
	# so that shape => (B, T)
	# e.g. if timesteps is (B,), replicate across T
	if timesteps.dim() == 1 and timesteps.shape[0] == B:
	# shape (B,) => (B,T)
	timesteps = timesteps.unsqueeze(1).expand(-1, T)
	else:
	raise ValueError(
	"Expected `timesteps` to have shape (B,) so we can replicate across T."
	)

	# 2) Standard action MLP step for shape => (B, T, w)
	a_emb = self.W1(actions, cat_ids)

	# 3) Get the sinusoidal encoding (B, T, w)
	tau_emb = self.pos_encoding(timesteps).to(dtype=a_emb.dtype)

	# 4) Concat along last dim => (B, T, 2w), then W2 => (B, T, w), swish
	x = torch.cat([a_emb, tau_emb], dim=-1)
	x = swish(self.W2(x, cat_ids))

	# 5) Finally W3 => (B, T, w)
	x = self.W3(x, cat_ids)
	return x


	@dataclass
	class FlowmatchingActionHeadConfig(PretrainedConfig):
	"""NOTE: N1.5 uses XEmbFlowmatchingPolicyHeadConfig as action head"""

	add_pos_embed: bool = field(
	default=True, metadata={"help": "Whether to add positional embedding"}
	)
	diffusion_model_cfg: dict = field(
	default=None, metadata={"help": "Diffusion model configuration."}
	)
	input_embedding_dim: int = field(
	default=1536, metadata={"help": "Input embedding channel dimension."}
	)

	hidden_size: int = field(default=1024, metadata={"help": "Input embedding dimension."})
	max_seq_len: int = field(default=1024, metadata={"help": "Maxium Sequence Length"})
	action_dim: int = field(default=None, metadata={"help": "Action dimension."})
	action_horizon: int = field(default=None, metadata={"help": "Action horizon."})
	noise_beta_alpha: float = field(default=1.5, metadata={"help": ""})
	noise_beta_beta: float = field(default=1.0, metadata={"help": ""})
	noise_s: float = field(
	default=0.999, metadata={"help": "Flow matching noise Beta distribution s."}
	)
	num_timestep_buckets: int = field(
	default=1000, metadata={"help": "Number of timestep discretization buckets."}
	)
	num_inference_timesteps: int = field(
	default=None,
	metadata={"help": "Number of inference steps for noise diffusion."},
	)
	max_num_embodiments: int = field(default=32, metadata={"help": "Number of embodiments."})
	tune_projector: bool = field(default=True, metadata={"help": "Whether to tune the projector."})
	tune_diffusion_model: bool = field(
	default=True, metadata={"help": "Whether to tune the diffusion model."}
	)
	load_pretrained_det_decode_layer_path: str = field(
	default=None, metadata={"help": "Path to pretrained detection model."}
	)
	detection_coeff: float = field(default=1.0, metadata={"help": "Detection coefficient."})

	freeze_decode_layer: bool = field(default=False)
	expand_batch: int = field(default=None)
	use_vlln: bool = field(default=True)

	vl_self_attention_cfg: dict = field(default=None)
	num_target_vision_tokens: int = field(
	default=32, metadata={"help": "Number of target vision tokens."}
	)

	def __init__(self, **kwargs):
	super().__init__(**kwargs)
	for key, value in kwargs.items():
	setattr(self, key, value)




	DiTConfig = {"num_layers": 36, "input_embedding_dim": 2048, "attention_head_dim": 64, "num_attention_heads": 32} # default for qwen2.5-vl


	class LayerwiseFlowmatchingActionHead(nn.Module):
	def __init__(
	self,
	global_config,
	**kwargs,
	):
	super().__init__()
	action_config = global_config.framework.action_model
	diffusion_model_cfg = action_config.diffusion_model_cfg

	# 更新 DiTConfig 到 diffusion_model_cfg
	DiTConfig["num_layers"] = global_config.framework.qwenvl.num_vl_layers
	DiTConfig["input_embedding_dim"] = global_config.framework.qwenvl.vl_hidden_dim
	DiTConfig["num_attention_heads"] = DiTConfig["input_embedding_dim"] // DiTConfig["attention_head_dim"]
	diffusion_model_cfg.update(DiTConfig)
	# diffusion_model_cfg["interleave_self_attention"] = False
	diffusion_model_cfg.cross_attention_dim = DiTConfig["input_embedding_dim"] # should match vl embedding dim, but for some case we might want to change it for cross + self attention
	self.input_embedding_dim = global_config.framework.qwenvl.vl_hidden_dim
	self.model = DiT(**diffusion_model_cfg) # TODO better way is copy LLM from VLM
	self.dit_out_hidden_size = self.input_embedding_dim
	self.action_dim = action_config.action_dim
	self.action_horizon = action_config.future_action_window_size + 1
	self.num_inference_timesteps = action_config.num_inference_timesteps

	self.state_encoder = MLP(
	input_dim=action_config.state_dim,
	output_dim=self.input_embedding_dim,
	) if action_config.state_dim else None

	self.action_encoder = ActionEncoder(
	action_dim=action_config.action_dim,
	hidden_size=self.input_embedding_dim,
	)
	self.action_decoder = MLP(
	input_dim=self.input_embedding_dim,
	hidden_dim=1024,
	output_dim=self.action_dim,
	)
	self.future_tokens = nn.Embedding(action_config.num_target_vision_tokens, self.input_embedding_dim)
	nn.init.normal_(self.future_tokens.weight, mean=0.0, std=0.02)

	if action_config.add_pos_embed:
	self.position_embedding = nn.Embedding(action_config.max_seq_len, self.input_embedding_dim)
	nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)

	self.beta_dist = Beta(action_config.noise_beta_alpha, action_config.noise_beta_beta)
	self.num_timestep_buckets = action_config.num_timestep_buckets
	self.config = action_config

	def sample_time(self, batch_size, device, dtype):
	sample = self.beta_dist.sample([batch_size]).to(device, dtype=dtype)
	return (self.config.noise_s - sample) / self.config.noise_s

	def prepare_input(self, batch: dict) -> BatchFeature:
	return BatchFeature(data=batch)


	def forward(self, vl_embs_list: list, actions: torch.Tensor, state: torch.Tensor = None, encoder_attention_mask: torch.Tensor = None):
	"""
	vl_embs: list of torch.Tensor, each shape (B, seq_length, feature_dim)
	actions: shape (B, future_action_window_size, D_action)
	encoder_attention_mask: optional (B, seq_length) mask for VLM padding tokens
	"""
	device = actions.device
	num_layers = len(vl_embs_list)
	B, L, D = vl_embs_list[0].shape
	# Embed noised action trajectory.
	noise = torch.randn(actions.shape, device=actions.device, dtype=actions.dtype)
	t = self.sample_time(actions.shape[0], device=actions.device, dtype=actions.dtype)
	t = t[:, None, None] # shape (B,1,1) for broadcast

	noisy_trajectory = (1 - t) * noise + t * actions
	velocity = actions - noise

	# Convert (continuous) t -> discrete if needed
	t_discretized = (t[:, 0, 0] * self.num_timestep_buckets).long()
	action_features = self.action_encoder(noisy_trajectory, t_discretized)

	# Embed state
	state_features = self.state_encoder(state) if state is not None else None

	# Maybe add position embedding.
	if self.config.add_pos_embed:
	pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
	pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
	action_features = action_features + pos_embs

	# state and action embedding along sequence dimension.
	future_tokens = self.future_tokens.weight.unsqueeze(0).expand(B, -1, -1)
	sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1) \
	if state_features is not None else torch.cat((future_tokens, action_features), dim=1)

	# Encode timesteps
	temb = self.model.timestep_encoder(t_discretized)

	# Convert encoder_attention_mask from long int (0/1) to bool so that
	# F.scaled_dot_product_attention accepts it (requires bool or float, not long).
	# Qwen attention mask: 1 = valid token (attend), 0 = padding (mask out).
	if encoder_attention_mask is not None:
	encoder_attention_mask = encoder_attention_mask.bool()

	# Layerwise cross-attention with vl_embs
	model_output = sa_embs
	for layer_idx, layer in enumerate(self.model.transformer_blocks):
	model_output = layer(
	hidden_states=model_output,
	encoder_hidden_states=vl_embs_list[layer_idx],
	temb=temb,
	encoder_attention_mask=encoder_attention_mask,
	)

	# TODO miss self att and _process_output, but work well
	pred = self.action_decoder(model_output)
	pred_actions = pred[:, -actions.shape[1] :]

	# Slice out only the action portion of pred and target.
	loss = ((pred_actions - velocity) ** 2).mean()
	return loss

	@torch.no_grad()
	def predict_action(self, vl_embs_list: list, state: torch.Tensor = None, encoder_attention_mask: torch.Tensor = None) -> torch.Tensor:
	# Set initial actions as the sampled noise.
	batch_size = vl_embs_list[0].shape[0]
	device = vl_embs_list[0].device
	actions = torch.randn(
	size=(batch_size, self.action_horizon, self.action_dim),
	dtype=vl_embs_list[0].dtype,
	device=device,
	)

	num_steps = self.num_inference_timesteps
	dt = 1.0 / num_steps

	state_features = self.state_encoder(state) if state is not None else None

	# Convert encoder_attention_mask dtype once before the denoising loop.
	if encoder_attention_mask is not None:
	encoder_attention_mask = encoder_attention_mask.bool()

	# Run denoising steps.
	for t in range(num_steps):
	t_cont = t / float(num_steps)
	t_discretized_int = int(t_cont * self.num_timestep_buckets)
	timesteps_tensor = torch.full(
	size=(batch_size,), fill_value=t_discretized_int, device=device, dtype=torch.long
	)

	# Embed current action trajectory with timestep
	action_features = self.action_encoder(actions, timesteps_tensor)

	# Maybe add position embedding.
	if self.config.add_pos_embed:
	pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
	pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
	action_features = action_features + pos_embs

	future_tokens = self.future_tokens.weight.unsqueeze(0).expand(batch_size, -1, -1)
	sa_embs = (
	torch.cat((state_features, future_tokens, action_features), dim=1)
	if state_features is not None
	else torch.cat((future_tokens, action_features), dim=1)
	)

	# Encode timestep
	temb = self.model.timestep_encoder(timesteps_tensor)

	# Layerwise cross-attention with vl_embs_list
	model_output = sa_embs
	for layer_idx, layer in enumerate(self.model.transformer_blocks):
	model_output = layer(
	hidden_states=model_output,
	encoder_hidden_states=vl_embs_list[layer_idx],
	temb=temb,
	encoder_attention_mask=encoder_attention_mask,
	)
	# TODO miss self att and _process_output
	pred = self.action_decoder(model_output)
	pred_velocity = pred[:, -self.action_horizon :]

	# Euler integration
	actions = actions + dt * pred_velocity
	return actions

	@property
	def device(self):
	return next(iter(self.parameters())).device

	@property
	def dtype(self):
	return next(iter(self.parameters())).dtype



	def get_action_model(config=None):
	"""
	Factory: build FlowmatchingActionHead from global framework config.

	Args:
	config: Global config (expects config.framework.action_model namespace).

	Returns:
	FlowmatchingActionHead: Initialized FlowMatchingActionHead.
	"""
	return LayerwiseFlowmatchingActionHead(
	global_config=config
	)



	if __name__ == "__main__":
	# TODO make each backbone.py can be debug independently

	pass