chore: import upstream snapshot with attribution

python/sglang/srt/models/idefics2.py

@@ -0,0 +1,345 @@
+# Copyright 2023 The SGLang team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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 typing import Optional
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.layers.activation import get_act_fn
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.conv import Conv2dLayer
+from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.utils import add_prefix, is_npu
+
+
+class Idefics2VisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.activation_fn = get_act_fn(config.hidden_act)
+        self.fc1 = ColumnParallelLinear(
+            config.hidden_size,
+            config.intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=add_prefix("fc1", prefix),
+        )
+        self.fc2 = RowParallelLinear(
+            config.intermediate_size,
+            config.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=add_prefix("fc2", prefix),
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        return hidden_states
+
+
+class Idefics2EncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.self_attn = VisionAttention(
+            embed_dim=config.hidden_size,
+            num_heads=self.num_heads,
+            projection_size=config.intermediate_size,
+            use_qkv_parallel=True,
+            quant_config=quant_config,
+            dropout=config.attention_dropout,
+            softmax_in_single_precision=True,
+            flatten_batch=False,
+            prefix=add_prefix("self_attn", prefix),
+        )
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = Idefics2VisionMLP(
+            config,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+        )
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                Input to the layer of shape `(batch, seq_len, embed_dim)`.
+
+        """
+        residual = hidden_states
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.self_attn(hidden_states, cu_seqlens=cu_seqlens)
+
+        hidden_states = residual + hidden_states
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class Idefics2Encoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention
+    layers. Each layer is a
+    [`Idefics2EncoderLayer`].
+
+    Args:
+        config: Idefics2Config
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.layers = nn.ModuleList(
+            [
+                Idefics2EncoderLayer(
+                    config,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"layers.{i}", prefix),
+                )
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            inputs_embeds (torch.Tensor):
+                Optionally, instead of passing `input_ids` you can choose to
+                directly pass an embedded representation.
+                This is useful if you want more control over how to convert
+                `input_ids` indices into associated vectorsthan the model's
+                internal embedding lookup matrix.
+        """
+        # cu_seqlens must be on cpu because of npu_flash_attention_unpad operator restriction
+        if is_npu():
+            cu_seqlens = cu_seqlens.to("cpu")
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            layer_outputs = encoder_layer(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+            )
+            hidden_states = layer_outputs
+        return hidden_states
+
+
+class Idefics2VisionEmbeddings(nn.Module):
+    """
+    This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings
+    ` to enable images of variable
+    resolution.
+
+    The modifications are adapted from [Patch n' Pack: NaViT, a Vision
+    Transformer for any Aspect Ratio and Resolution](https://arxiv.org/abs/2307.06304)
+    which allows treating images in their native aspect ratio and without the
+    need to resize them to the same fixed size. In particular, we start from the
+    original pre-trained SigLIP model(which uses images of fixed-size square
+    images) and adapt it by training on images of variable resolutions.
+    """
+
+    def __init__(self, config: PretrainedConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+        self.patch_embedding = Conv2dLayer(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+        self.num_patches_per_side = self.image_size // self.patch_size
+        self.num_patches = self.num_patches_per_side**2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+
+    def get_position_ids(
+        self,
+        pixel_values: torch.FloatTensor,
+        patch_attention_mask: torch.BoolTensor,
+        tgt_sizes: Optional[torch.IntTensor] = None,
+    ):
+        batch_size, _, max_im_h, max_im_w = pixel_values.shape
+
+        max_nb_patches_h, max_nb_patches_w = (
+            max_im_h // self.patch_size,
+            max_im_w // self.patch_size,
+        )
+        boundaries = torch.arange(
+            1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side
+        )
+        position_ids = torch.full(
+            size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0
+        )
+
+        for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
+
+            if tgt_sizes is not None:
+                nb_patches_h = tgt_sizes[batch_idx][0]
+                nb_patches_w = tgt_sizes[batch_idx][1]
+            else:
+                nb_patches_h = p_attn_mask[:, 0].sum()
+                nb_patches_w = p_attn_mask[0].sum()
+            fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
+            fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
+            bucket_coords_h = torch.bucketize(
+                fractional_coords_h, boundaries, right=True
+            )
+            bucket_coords_w = torch.bucketize(
+                fractional_coords_w, boundaries, right=True
+            )
+            pos_ids = (
+                bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w
+            ).flatten()
+            position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
+        position_ids = position_ids.to(self.position_embedding.weight.device)
+        return position_ids
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor,
+        patch_attention_mask: torch.BoolTensor,
+        tgt_sizes: Optional[torch.IntTensor] = None,
+    ) -> torch.Tensor:
+        target_dtype = self.patch_embedding.weight.dtype
+        pixel_values = pixel_values.to(
+            device=self.patch_embedding.weight.device, dtype=target_dtype
+        )
+        patch_embeds = self.patch_embedding(pixel_values)
+        embeddings = patch_embeds.flatten(2).transpose(1, 2)
+        position_ids = self.get_position_ids(
+            pixel_values, patch_attention_mask, tgt_sizes
+        )
+
+        embeddings = embeddings + self.position_embedding(position_ids)
+        return embeddings
+
+
+class Idefics2VisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        require_post_norm: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        embed_dim = config.hidden_size
+        self.config = config
+        self.embeddings = Idefics2VisionEmbeddings(config)
+        self.encoder = Idefics2Encoder(
+            config=config,
+            quant_config=quant_config,
+            prefix=add_prefix("encoder", prefix),
+        )
+        self.post_layernorm = (
+            nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+            if require_post_norm
+            else nn.Identity()
+        )
+
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.embeddings
+
+    def compute_cu_seqlens(
+        self,
+        tgt_sizes: Optional[torch.Tensor] = None,
+        input_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        # shape: (batch_size,)
+        if tgt_sizes is not None:
+            seqlen = tgt_sizes[:, 0] * tgt_sizes[:, 1]
+        elif input_embeds is not None:
+            seqlen = torch.full(
+                size=(input_embeds.shape[0],),
+                fill_value=input_embeds.shape[1],
+                dtype=torch.int32,
+                device=input_embeds.device,
+            )
+        else:
+            raise ValueError(
+                "Either `tgt_sizes` or `input_embeds` must be provided to compute cu_seqlens."
+            )
+
+        cu_seqlens = torch.cat(
+            [
+                torch.tensor([0], device=seqlen.device, dtype=torch.int32),
+                torch.cumsum(seqlen, dim=0, dtype=torch.int32),
+            ],
+            dim=0,
+        ).to(seqlen.device)
+        return cu_seqlens
+
+    def forward(
+        self,
+        pixel_values,
+        patch_attention_mask: Optional[torch.BoolTensor] = None,
+        tgt_sizes: Optional[torch.IntTensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.embeddings(
+            pixel_values=pixel_values,
+            patch_attention_mask=patch_attention_mask,
+            tgt_sizes=tgt_sizes,
+        )
+        cu_seqlens = self.compute_cu_seqlens(tgt_sizes, hidden_states)
+        encoder_outputs = self.encoder(
+            hidden_states,
+            cu_seqlens=cu_seqlens,
+        )
+        last_hidden_state = self.post_layernorm(encoder_outputs)
+        return last_hidden_state