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# ------------------------------------------------------------------------------
# Copyright 2025 2toINF (https://github.com/2toINF)
#
# 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.
# ------------------------------------------------------------------------------
from __future__ import annotations
import logging
import traceback
from typing import Any, Dict
import numpy as np
import torch
from fastapi import FastAPI
from fastapi.responses import JSONResponse
from PIL import Image
import uvicorn
import json_numpy
import cv2
from transformers import PreTrainedModel
from .modeling_florence2 import Florence2ForConditionalGeneration
from .transformer import SoftPromptedTransformer
from .action_hub import build_action_space
from .configuration_xvla import XVLAConfig
class XVLA(PreTrainedModel):
"""
XVLA: HuggingFace-compatible Vision-Language-Action policy.
Components:
• Florence2 encoder-only backbone (vision-language)
• SoftPromptedTransformer (temporal/action head)
• Action space (pre/post-processing + loss)
"""
config_class = XVLAConfig
base_model_prefix = "xvla"
supports_gradient_checkpointing = True
def __init__(self, config: XVLAConfig, *args, **kwargs):
super().__init__(config, *args, **kwargs)
# Core settings
self.num_actions: int = config.num_actions
self.use_proprio: bool = config.use_proprio
self.action_mode: str = config.action_mode.lower()
# Action space (dimensions + hooks)
self.action_space = build_action_space(config.action_mode.lower())
dim_action = self.action_space.dim_action
dim_proprio = getattr(self.action_space, "dim_proprio", dim_action)
# Florence2 backbone (encoder only)
self.vlm = Florence2ForConditionalGeneration(config.florence_config)
if hasattr(self.vlm, "language_model"):
lm = self.vlm.language_model
if hasattr(lm, "model") and hasattr(lm.model, "decoder"):
del lm.model.decoder
if hasattr(lm, "lm_head"):
del lm.lm_head
projection_dim = getattr(self.vlm.config, "projection_dim", None)
if projection_dim is None:
raise ValueError("Florence2 config must provide `projection_dim` for multimodal fusion.")
# Temporal/action head
self.transformer = SoftPromptedTransformer(
hidden_size=config.hidden_size,
multi_modal_input_size=projection_dim,
depth=config.depth,
num_heads=config.num_heads,
mlp_ratio=config.mlp_ratio,
num_domains=config.num_domains,
dim_action=dim_action,
dim_propio=dim_proprio,
len_soft_prompts=config.len_soft_prompts,
dim_time=config.dim_time,
max_len_seq=config.max_len_seq,
use_hetero_proj=config.use_hetero_proj,
)
# Deferred FastAPI app
self.app: FastAPI | None = None
# ============================= Florence2 encoder =============================
def forward_vlm(
self,
input_ids: torch.LongTensor, # [B, L]
pixel_values: torch.FloatTensor, # [B, V, C, H, W]
image_mask: torch.Tensor, # [B, V] (bool or 0/1)
) -> Dict[str, torch.Tensor]:
"""
Encode text + multi-view images via Florence2 encoder.
Returns:
{ "vlm_features": [B, T_enc, D], "aux_visual_inputs": [B, (V-1)*N, D] }
"""
B, V = pixel_values.shape[:2]
flat_mask = image_mask.view(-1).to(torch.bool) # [B*V]
flat_images = pixel_values.flatten(0, 1) # [B*V, C, H, W]
num_valid = int(flat_mask.sum().item())
if num_valid == 0:
raise ValueError("At least one image view must be valid per batch.")
valid_images = flat_images[flat_mask] # [#valid, C, H, W]
valid_feats = self.vlm._encode_image(valid_images) # [#valid, N, D]
N, D = valid_feats.shape[1:]
image_features = valid_feats.new_zeros((B * V, N, D))
image_features[flat_mask] = valid_feats
image_features = image_features.view(B, V, N, D) # [B, V, N, D]
inputs_embeds = self.vlm.get_input_embeddings()(input_ids) # [B, L, D]
merged_embeds, attention_mask = self.vlm._merge_input_ids_with_image_features(
image_features[:, 0], # first view: [B, N, D]
inputs_embeds, # [B, L, D]
)
enc_out = self.vlm.language_model.model.encoder(
attention_mask=attention_mask,
inputs_embeds=merged_embeds,
)[0] # [B, T_enc, D]
aux_visual_inputs = image_features[:, 1:].reshape(B, -1, D) # remaining views flattened
return {"vlm_features": enc_out, "aux_visual_inputs": aux_visual_inputs}
# ================================= training =================================
def forward(
self,
input_ids: torch.LongTensor,
image_input: torch.FloatTensor,
image_mask: torch.Tensor,
domain_id: torch.LongTensor,
proprio: torch.Tensor,
action: torch.Tensor, # [B, T=num_actions, D=dim_action]
) -> Dict[str, torch.Tensor]:
"""
1) Encode multimodal inputs.
2) Diffusion-style noisy mixture of actions: x_t = t*noise + (1-t)*gt.
3) Space-specific preprocessing, prediction, and supervised loss.
"""
enc = self.forward_vlm(input_ids, image_input, image_mask)
B = input_ids.shape[0]
t = (torch.rand(1, device=input_ids.device)
+ torch.arange(B, device=input_ids.device) / B) % (1 - 1e-5)
action_noisy = torch.randn_like(action) * t.view(-1, 1, 1) + action * (1 - t).view(-1, 1, 1)
proprio_m, action_noisy_m = self.action_space.preprocess(proprio, action_noisy)
pred_action = self.transformer(
domain_id=domain_id,
action_with_noise=action_noisy_m,
t=t,
proprio=proprio_m,
**enc,
)
return self.action_space.compute_loss(pred_action, action)
# ================================= inference =================================
@torch.no_grad()
def generate_actions(
self,
input_ids: torch.LongTensor,
image_input: torch.FloatTensor,
image_mask: torch.Tensor,
domain_id: torch.LongTensor,
proprio: torch.Tensor,
steps: int = 10,
) -> torch.Tensor:
"""
Iterative denoising (linear schedule).
Applies action_space.postprocess at the end (e.g., sigmoid on gripper).
"""
self.eval()
enc = self.forward_vlm(input_ids, image_input, image_mask)
B = input_ids.shape[0]
D = self.action_space.dim_action
x1 = torch.randn(B, self.num_actions, D, device=proprio.device, dtype=proprio.dtype)
action = torch.zeros_like(x1)
steps = max(1, int(steps))
for i in range(steps, 0, -1):
t = torch.full((B,), i / steps, device=proprio.device, dtype=proprio.dtype)
x_t = x1 * t.view(-1, 1, 1) + action * (1 - t).view(-1, 1, 1)
proprio_m, x_t_m = self.action_space.preprocess(proprio, x_t)
action = self.transformer(
domain_id=domain_id,
action_with_noise=x_t_m,
proprio=proprio_m,
t=t,
**enc,
)
return self.action_space.postprocess(action)
# =============================== FastAPI service =============================
def _build_app(self, processor):
"""
Minimal FastAPI app for XVLA inference.
Args:
processor: callable(images, text) -> Dict[str, torch.Tensor]
expected keys: "input_ids", "image_input", "image_mask"
"""
if self.app is not None:
return
app = FastAPI()
@app.post("/act")
def act(payload: Dict[str, Any]):
try:
self.eval()
# Decode up to 3 image inputs
images = []
for key in ("image0", "image1", "image2"):
if key not in payload: continue
v = json_numpy.loads(payload[key])
if isinstance(v, np.ndarray):
if v.ndim == 1: # encoded bytes
v = cv2.imdecode(v, cv2.IMREAD_COLOR)
images.append(Image.fromarray(v))
elif isinstance(v, (list, tuple)):
images.append(Image.fromarray(np.array(v)))
elif isinstance(v, str):
images.append(Image.open(v))
if not images:
return JSONResponse({"error": "No valid images found."}, status_code=400)
# Multimodal preprocessing by processor
inputs = processor(images, payload["language_instruction"])
if not {"input_ids", "image_input", "image_mask"}.issubset(inputs):
return JSONResponse({"error": "Processor returned incomplete inputs."}, status_code=400)
# Build proprio/domain tensors
proprio = torch.as_tensor(np.asarray(json_numpy.loads(payload["proprio"])))
domain_id = torch.tensor([int(payload["domain_id"])], dtype=torch.long)
# Align to model's device/dtype
device = next(self.parameters()).device
dtype = next(self.parameters()).dtype
def to_model(t: torch.Tensor) -> torch.Tensor:
if not isinstance(t, torch.Tensor):
t = torch.as_tensor(t)
# cast floats to model dtype, keep integral/bool as-is
return t.to(device=device, dtype=dtype) if t.is_floating_point() else t.to(device=device)
inputs = {k: to_model(v) for k, v in inputs.items()}
inputs.update({
"proprio": to_model(proprio.unsqueeze(0)),
"domain_id": domain_id.to(device),
})
# Inference
steps = int(payload.get("steps", 10))
action = self.generate_actions(**inputs, steps=steps).squeeze(0).float().cpu().numpy()
return JSONResponse({"action": action.tolist()})
except Exception:
logging.error(traceback.format_exc())
return JSONResponse({"error": "Request failed"}, status_code=400)
self.app = app
def run(self, processor, host: str = "0.0.0.0", port: int = 8000):
"""
Launch the FastAPI service.
"""
self._build_app(processor)
assert self.app is not None
uvicorn.run(self.app, host=host, port=port)
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