[Model] Add Step3vl 10b (#32329)

Signed-off-by: luotingdan <luotingdan@stepfun.com>
Signed-off-by: ltd0924 <32387785+ltd0924@users.noreply.github.com>
Co-authored-by: luotingdan <luotingdan@stepfun.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: Roger Wang <hey@rogerw.io>

docs/models/supported_models.md

@@ -720,6 +720,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `SkyworkR1VChatModel` | Skywork-R1V-38B | T + I | `Skywork/Skywork-R1V-38B` | | ✅︎ |
 | `SmolVLMForConditionalGeneration` | SmolVLM2 | T + I | `SmolVLM2-2.2B-Instruct` | ✅︎ | |
 | `Step3VLForConditionalGeneration` | Step3-VL | T + I<sup>+</sup> | `stepfun-ai/step3` | | ✅︎ |
+| `StepVLForConditionalGeneration` | Step3-VL-10B | T + I<sup>+</sup> | `stepfun-ai/Step3-VL-10B` | | ✅︎ |
 | `TarsierForConditionalGeneration` | Tarsier | T + I<sup>E+</sup> | `omni-search/Tarsier-7b`, `omni-search/Tarsier-34b` | | ✅︎ |
 | `Tarsier2ForConditionalGeneration`<sup>^</sup> | Tarsier2 | T + I<sup>E+</sup> + V<sup>E+</sup> | `omni-research/Tarsier2-Recap-7b`, `omni-research/Tarsier2-7b-0115` | | ✅︎ |
 | `UltravoxModel` | Ultravox | T + A<sup>E+</sup> | `fixie-ai/ultravox-v0_5-llama-3_2-1b` | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -925,6 +925,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "Step3VLForConditionalGeneration": _HfExamplesInfo(
         "stepfun-ai/step3", trust_remote_code=True
     ),
+    "StepVLForConditionalGeneration": _HfExamplesInfo(
+        "stepfun-ai/Step3-VL-10B", trust_remote_code=True
+    ),
     "UltravoxModel": _HfExamplesInfo(
         "fixie-ai/ultravox-v0_5-llama-3_2-1b",
         trust_remote_code=True,

vllm/model_executor/models/registry.py

@@ -323,6 +323,7 @@ _MULTIMODAL_MODELS = {
         "hunyuan_vision",
         "HunYuanVLForConditionalGeneration",
     ),
+    "StepVLForConditionalGeneration": ("step_vl", "StepVLForConditionalGeneration"),
     "InternVLChatModel": ("internvl", "InternVLChatModel"),
     "NemotronH_Nano_VL_V2": ("nano_nemotron_vl", "NemotronH_Nano_VL_V2"),
     "OpenCUAForConditionalGeneration": (

vllm/model_executor/models/step_vl.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""This is basically a copy from perception_models/core/vision_encoder/pe.py"""
+
+from collections.abc import Callable
+from functools import partial
+
+import torch
+from einops import rearrange, repeat
+from torch import nn
+from torch.nn import functional as F
+
+from vllm.config import VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
+from vllm.model_executor.layers.conv import Conv2dLayer
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+
+from .step3_vl import Step3VLForConditionalGeneration
+from .utils import WeightsMapper, init_vllm_registered_model, maybe_prefix
+from .vision import run_dp_sharded_vision_model
+
+_DEFAULT_NORM_LAYER = partial(nn.LayerNorm, eps=1e-5)
+
+
+def rotate_half(x):
+    x = rearrange(x, "... (d r) -> ... d r", r=2)
+    x1, x2 = x.unbind(dim=-1)
+    x = torch.stack((-x2, x1), dim=-1)
+    return rearrange(x, "... d r -> ... (d r)")
+
+
+def apply_rotary_emb(freqs, t, start_index=0, scale=1.0, seq_dim=-2):
+    dtype = t.dtype
+
+    if t.ndim == 3:
+        seq_len = t.shape[seq_dim]
+        freqs = freqs[-seq_len:]
+
+    rot_dim = freqs.shape[-1]
+    end_index = start_index + rot_dim
+
+    assert rot_dim <= t.shape[-1], (
+        "feature dimension {} is not of sufficient size to rotate in all the "
+        "positions {}".format(t.shape[-1], rot_dim)
+    )
+
+    t_left, t, t_right = (
+        t[..., :start_index],
+        t[..., start_index:end_index],
+        t[..., end_index:],
+    )
+    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
+    out = torch.cat((t_left, t, t_right), dim=-1)
+
+    return out.type(dtype)
+
+
+class PerceptionEncoderRope2D(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        max_grid_height: int,
+        max_grid_width: int,
+        use_cls_token: bool = False,
+        theta=10000,
+        max_freq=10,
+        num_freqs=1,
+        theta_rescale_factor=1.0,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.max_grid_height = max_grid_height
+        self.max_grid_width = max_grid_width
+        self.use_cls_token = use_cls_token
+        self.theta = theta * theta_rescale_factor ** (dim / (dim - 2))
+        self.max_freq = max_freq
+        self.num_freqs = num_freqs
+        cache = self._compute_2d_freqs()
+        self.register_buffer("freqs_cache", cache, persistent=False)
+
+    def _compute_inv_freq(self, base: int | float, dim: int) -> torch.Tensor:
+        freqs = 1.0 / (base ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+        return freqs
+
+    def _compute_freqs(self, t: torch.Tensor, inv_freq: torch.Tensor):
+        freqs = torch.einsum("..., f -> ... f", t.type(inv_freq.dtype), inv_freq)
+        freqs = repeat(freqs, "... n -> ... (n r)", r=2)
+        return freqs
+
+    def _compute_2d_freqs(self) -> torch.Tensor:
+        grid_h_range = torch.arange(self.max_grid_height, dtype=torch.float)
+        grid_w_range = torch.arange(self.max_grid_width, dtype=torch.float)
+        if self.use_cls_token:
+            grid_h_range += 1
+            grid_w_range += 1
+        inv_freq = self._compute_inv_freq(self.theta, self.dim // 2)
+        freqs_h = self._compute_freqs(grid_h_range, inv_freq)[:, None].expand(
+            self.max_grid_height, self.max_grid_width, -1
+        )
+        freqs_w = self._compute_freqs(grid_w_range, inv_freq)[None, :].expand(
+            self.max_grid_height, self.max_grid_width, -1
+        )
+        freqs = torch.cat([freqs_w, freqs_h], dim=-1).reshape(
+            self.max_grid_height * self.max_grid_width, -1
+        )
+        if self.use_cls_token:
+            freqs = torch.cat([torch.zeros(1, freqs.shape[-1]), freqs], dim=0)
+        freqs = freqs[None, None, ...]
+        return freqs
+
+    def forward(self, q: torch.Tensor, k: torch.Tensor, grid_hw: tuple[int, int]):
+        if grid_hw[0] != self.max_grid_height or grid_hw[1] != self.max_grid_width:
+            rows = torch.arange(grid_hw[0], device=q.device).view(-1, 1)
+            cols = torch.arange(grid_hw[1], device=q.device).view(1, -1)
+            positions = (rows * self.max_grid_width + cols).reshape(-1).to(torch.long)
+            if self.use_cls_token:
+                positions = torch.cat(
+                    [torch.zeros(1, device=q.device), positions + 1], dim=0
+                )
+                positions = positions.to(torch.long)
+            freqs = self.freqs_cache.index_select(2, positions)
+        else:
+            freqs = self.freqs_cache
+        q = apply_rotary_emb(freqs, q)
+        k = apply_rotary_emb(freqs, k)
+        return q, k
+
+
+class PerceptionEncoderLayerScale(nn.Module):
+    def __init__(self, dim, init_values=1e-5, inplace=False):
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x):
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class PerceptionEncoderMLP(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        act_layer: Callable[[], nn.Module],
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.fc1 = ColumnParallelLinear(
+            input_dim,
+            hidden_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+            disable_tp=use_data_parallel,
+        )
+        self.activation = act_layer
+        self.fc2 = RowParallelLinear(
+            hidden_dim,
+            input_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+            disable_tp=use_data_parallel,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.fc1(x)
+        x = self.activation(x)
+        x, _ = self.fc2(x)
+        return x
+
+
+class PerceptionEncoderVisionAttention(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        max_grid_height: int,
+        max_grid_width: int,
+        use_cls_token: bool = False,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.total_num_heads = num_heads
+        self.head_dim = embed_dim // num_heads
+        self.scale = self.head_dim**-0.5
+
+        tp_size = 1 if use_data_parallel else get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_size == 0, (
+            "embed_dim must be divisible by num_heads"
+        )
+        self.num_heads = self.total_num_heads // tp_size
+
+        self.qkv_proj = QKVParallelLinear(
+            embed_dim,
+            self.head_dim,
+            self.total_num_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+            disable_tp=use_data_parallel,
+        )
+        self.out_proj = RowParallelLinear(
+            embed_dim,
+            embed_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+            disable_tp=use_data_parallel,
+        )
+        self.attn = MMEncoderAttention(self.num_heads, self.head_dim, self.scale)
+        self.rope = PerceptionEncoderRope2D(
+            dim=self.head_dim,
+            max_grid_height=max_grid_height,
+            max_grid_width=max_grid_width,
+            use_cls_token=use_cls_token,
+        )
+
+    def forward(self, x: torch.Tensor, grid_hw: tuple[int, int]) -> torch.Tensor:
+        bsz, seq_len, _ = x.shape
+        qkv, _ = self.qkv_proj(x)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+
+        q = q.view(bsz, seq_len, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
+        k = k.view(bsz, seq_len, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
+        q, k = self.rope(q, k, grid_hw=grid_hw)
+        q = q.permute(0, 2, 1, 3).reshape(bsz, seq_len, self.num_heads * self.head_dim)
+        k = k.permute(0, 2, 1, 3).reshape(bsz, seq_len, self.num_heads * self.head_dim)
+
+        attn_output = self.attn(q, k, v)
+        attn_output, _ = self.out_proj(attn_output)
+        return attn_output
+
+
+class PerceptionEncoderVisionBlock(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        n_head: int,
+        max_grid_height: int,
+        max_grid_width: int,
+        mlp_ratio: float = 4.0,
+        ls_init_value: float = None,
+        act_layer: Callable = nn.GELU,
+        norm_layer: Callable = nn.LayerNorm,
+        use_cls_token: bool = False,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.attn = PerceptionEncoderVisionAttention(
+            d_model,
+            n_head,
+            max_grid_height=max_grid_height,
+            max_grid_width=max_grid_width,
+            use_cls_token=use_cls_token,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+            use_data_parallel=use_data_parallel,
+        )
+        self.ls_1 = (
+            PerceptionEncoderLayerScale(d_model, ls_init_value)
+            if ls_init_value is not None
+            else nn.Identity()
+        )
+        self.ls_2 = (
+            PerceptionEncoderLayerScale(d_model, ls_init_value)
+            if ls_init_value is not None
+            else nn.Identity()
+        )
+        self.ln_1 = norm_layer(d_model)
+        self.ln_2 = norm_layer(d_model)
+        hidden_dim = int(d_model * mlp_ratio)
+        self.mlp = PerceptionEncoderMLP(
+            d_model,
+            hidden_dim,
+            act_layer,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+            use_data_parallel=use_data_parallel,
+        )
+
+    def forward(self, x: torch.Tensor, grid_hw: tuple[int, int]):
+        x = x + self.ls_1(self.attn(self.ln_1(x), grid_hw=grid_hw))
+        x = x + self.ls_2(self.mlp(self.ln_2(x)))
+        return x
+
+
+class PerceptionEncoderVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        width: int,
+        layers: int,
+        heads: int,
+        max_grid_height: int,
+        max_grid_width: int,
+        mlp_ratio: float = 4.0,
+        ls_init_value: float = None,
+        act_layer: Callable = nn.GELU,
+        norm_layer: Callable = nn.LayerNorm,
+        use_cls_token: bool = False,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.width = width
+        self.layers = layers
+        self.resblocks = nn.ModuleList(
+            [
+                PerceptionEncoderVisionBlock(
+                    d_model=width,
+                    n_head=heads,
+                    max_grid_height=max_grid_height,
+                    max_grid_width=max_grid_width,
+                    mlp_ratio=mlp_ratio,
+                    ls_init_value=ls_init_value,
+                    act_layer=act_layer,
+                    norm_layer=norm_layer,
+                    use_cls_token=use_cls_token,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.resblocks.{i}",
+                    use_data_parallel=use_data_parallel,
+                )
+                for i in range(layers)
+            ]
+        )
+
+    def forward(self, x: torch.Tensor, grid_hw: tuple[int, int]):
+        for block in self.resblocks:
+            x = block(x, grid_hw=grid_hw)
+        return x
+
+
+class PerceptionEncoder(nn.Module):
+    def __init__(
+        self,
+        config,
+        act_layer: Callable,
+        norm_layer: Callable = _DEFAULT_NORM_LAYER,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.patch_size = config.patch_size
+
+        self.output_dim = config.output_dim or config.width
+        self.heads = config.heads
+        self.width = config.width
+        self.layers = config.layers
+
+        self.use_abs_posemb = config.use_abs_posemb
+        self.use_cls_token = config.use_cls_token
+        self.use_rope2d = config.use_rope2d
+        if not self.use_rope2d:
+            raise ValueError("use_rope2d must be True")
+        self.image_size = config.image_size
+
+        self.conv1 = Conv2dLayer(
+            in_channels=3,
+            out_channels=config.width,
+            kernel_size=config.patch_size,
+            stride=config.patch_size,
+            bias=False,
+        )
+
+        self.ln_pre = norm_layer(config.width) if config.use_ln_pre else nn.Identity()
+        self.ln_post = norm_layer(self.width) if config.use_ln_post else nn.Identity()
+
+        self.transformer = PerceptionEncoderVisionTransformer(
+            config.width,
+            config.layers,
+            config.heads,
+            max_grid_height=self.image_size // self.patch_size,
+            max_grid_width=self.image_size // self.patch_size,
+            mlp_ratio=config.mlp_ratio,
+            ls_init_value=config.ls_init_value,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+            use_cls_token=self.use_cls_token,
+            quant_config=quant_config,
+            prefix=f"{prefix}.transformer",
+            use_data_parallel=use_data_parallel,
+        )
+
+        self.vit_downsampler1 = Conv2dLayer(
+            config.width, config.width * 2, kernel_size=3, stride=2, padding=1
+        )
+        self.vit_downsampler2 = Conv2dLayer(
+            config.width * 2, config.width * 4, kernel_size=3, stride=2, padding=1
+        )
+
+        if self.use_cls_token:
+            self.class_embedding = nn.Parameter(
+                (self.width**-0.5) * torch.randn(self.width)
+            )
+
+        if self.use_abs_posemb:
+            self.posemb_grid_size = self.image_size // self.patch_size
+            self.positional_embedding = nn.Parameter(
+                (self.width**-0.5)
+                * torch.randn(
+                    int(self.use_cls_token) + self.posemb_grid_size**2,
+                    self.width,
+                )
+            )
+
+    def sample_abs_posemb(self, grid_h: int, grid_w: int):
+        if self.posemb_grid_size == grid_h and self.posemb_grid_size == grid_w:
+            return self.positional_embedding[None, ...]
+
+        pos_embed = self.positional_embedding
+        if self.use_cls_token:
+            cls_token_embed, pos_embed = pos_embed[:1], pos_embed[1:]
+
+        pos_embed = (
+            pos_embed.reshape(1, self.posemb_grid_size, self.posemb_grid_size, -1)
+            .permute(0, 3, 1, 2)
+            .contiguous()
+        )
+        pos_embed = F.interpolate(
+            pos_embed, size=(grid_h, grid_w), mode="bilinear", align_corners=False
+        )
+        pos_embed = pos_embed.permute(0, 2, 3, 1).reshape(-1, self.width)
+
+        if self.use_cls_token:
+            pos_embed = torch.cat([cls_token_embed, pos_embed], dim=0)
+
+        return pos_embed[None, ...]
+
+    def forward_features(self, x: torch.Tensor):
+        batch, _, h, w = x.shape
+        grid_h, grid_w = h // self.patch_size, w // self.patch_size
+
+        x = self.conv1(x)
+        x = x.permute(0, 2, 3, 1).reshape(batch, -1, self.width)
+
+        if self.use_cls_token:
+            x = torch.cat(
+                [self.class_embedding.view(1, 1, -1).expand(batch, -1, -1), x], dim=1
+            )
+
+        if self.use_abs_posemb:
+            x = x + self.sample_abs_posemb(grid_h, grid_w)
+
+        x = self.ln_pre(x)
+        x = self.transformer(x, grid_hw=(grid_h, grid_w))
+        x = self.ln_post(x)
+
+        if self.use_cls_token:
+            x = x[:, 1:, :]
+
+        return x
+
+    def forward(self, x: torch.Tensor):
+        x = self.forward_features(x)
+        B, P, C = x.shape
+        T = int(P**0.5)
+        x = x.transpose(2, 1).contiguous()
+        x = x.view(B, C, T, T)
+
+        x = self.vit_downsampler1(x)
+        x = self.vit_downsampler2(x)
+
+        B, C, T, T = x.shape
+        return x.view(B, -1, T * T).transpose(1, 2)
+
+
+class StepVLForConditionalGeneration(Step3VLForConditionalGeneration):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "model.": "language_model.model.",
+            "lm_head.": "language_model.lm_head.",
+        },
+        orig_to_new_substr={
+            ".attn.in_proj_weight": ".attn.qkv_proj.weight",
+            ".attn.in_proj_bias": ".attn.qkv_proj.bias",
+            ".mlp.c_fc": ".mlp.fc1",
+            ".mlp.c_proj": ".mlp.fc2",
+        },
+    )
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super(Step3VLForConditionalGeneration, self).__init__()
+
+        config = vllm_config.model_config.hf_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
+        if multimodal_config.get_limit_per_prompt("image"):
+            self.vision_model = PerceptionEncoder(
+                config.vision_config,
+                get_act_fn(config.vision_config.hidden_act),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "vision_model"),
+                use_data_parallel=self.use_data_parallel,
+            )
+            self.vit_large_projector = ColumnParallelLinear(
+                config.vision_config.width * 4,
+                config.text_config.hidden_size,
+                bias=config.projector_bias,
+                gather_output=True,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "vit_large_projector"),
+                disable_tp=self.use_data_parallel,
+            )
+        else:
+            self.vision_model = None
+            self.vit_large_projector = None
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
+    def _get_vision_model_output(
+        self, input_tensor: torch.Tensor | None
+    ) -> torch.Tensor | None:
+        if input_tensor is None:
+            return None
+        if self.use_data_parallel:
+            return run_dp_sharded_vision_model(input_tensor, self.vision_model)
+        return self.vision_model(input_tensor)
+
+    def _process_image_features(self, image_features: torch.Tensor) -> torch.Tensor:
+        image_features, _ = self.vit_large_projector(image_features)
+        return image_features