Create image_embedding_phi3_v.py

image_embedding_phi3_v.py

@@ -0,0 +1,322 @@
+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. 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 torch
+from torch import nn
+from transformers import CLIPVisionConfig, CLIPVisionModel, PretrainedConfig
+from transformers.models.clip.modeling_clip import CLIPAttention
+from transformers.utils import logging
+
+try:
+    from flash_attn import flash_attn_func
+except ImportError:
+    pass
+
+logger = logging.get_logger(__name__)
+
+
+MAX_INPUT_ID = int(1e9)
+
+CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(
+  attention_dropout=0.0,
+  dropout=0.0,
+  hidden_act="quick_gelu",
+  hidden_size=1024,
+  image_size=336,
+  initializer_factor=1.0,
+  initializer_range=0.02,
+  intermediate_size=4096,
+  layer_norm_eps=1e-05,
+  num_attention_heads=16,
+  num_channels=3,
+  num_hidden_layers=24,
+  patch_size=14,
+  projection_dim=768
+)
+
+class CLIPAttentionFA2(CLIPAttention):
+    """Add flash attention 2 to CLIPAttention. (This is only used in the vision encoder)"""
+
+    def forward(self,
+        hidden_states,
+        attention_mask=None,
+        causal_attention_mask=None,
+        output_attentions=False,
+    ):
+        """Input shape: Batch x Time x Channel"""
+
+        assert attention_mask is None, "CLIPAttentionFA2 does not support attention_mask"
+        assert causal_attention_mask is None, "CLIPAttentionFA2 does not support causal_attention_mask"
+        assert output_attentions is False, "CLIPAttentionFA2 does not support output_attentions"
+
+        bsz, tgt_len, embed_dim = hidden_states.size()
+        query_states = self.q_proj(hidden_states).reshape(bsz, tgt_len, self.num_heads, self.head_dim)
+        key_states = self.k_proj(hidden_states).reshape(bsz, tgt_len, self.num_heads, self.head_dim)
+        value_states = self.v_proj(hidden_states).reshape(bsz, tgt_len, self.num_heads, self.head_dim)
+
+        attn_output = flash_attn_func(
+            query_states,
+            key_states,
+            value_states,
+            dropout_p=self.dropout if self.training else 0.0,
+            softmax_scale=self.scale,
+            causal=False,
+        ).reshape(bsz, tgt_len, embed_dim)
+
+        attn_output = self.out_proj(attn_output)
+        return attn_output, None
+
+
+class Phi3ImageEmbedding(nn.Module):
+    """Phi3 Image embedding."""
+
+    def __init__(self, config: PretrainedConfig, wte=None, **kwargs) -> None:
+        super().__init__()
+
+        # n_embed or hidden_size
+        hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size
+        if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'):
+            embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop
+            self.drop = nn.Dropout(embd_drop)
+        else:
+            self.drop = None
+
+        self.wte = wte
+
+        if isinstance(config.img_processor, dict) and config.img_processor.get('name', None) == 'clip_vision_model':
+            assert 'model_name' in config.img_processor, 'model_name must be provided for CLIPVisionModel'
+            assert 'image_dim_out' in config.img_processor, 'image_dim_out must be provided for CLIPVisionModel'
+            assert 'num_img_tokens' in config.img_processor, 'num_img_tokens must be provided for CLIPVisionModel'
+            assert config.img_processor['model_name'] == 'openai/clip-vit-large-patch14-336'
+            clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG
+            self.img_processor = CLIPVisionModel(clip_config)
+            image_dim_out = config.img_processor['image_dim_out']
+            self.num_img_tokens = config.img_processor['num_img_tokens']
+
+            # FA2 in CLIP
+            if config._attn_implementation == 'flash_attention_2':
+                for layer in self.img_processor.vision_model.encoder.layers:
+                    clip_fa2 = CLIPAttentionFA2(clip_config)
+                    del layer.self_attn
+                    layer.self_attn = clip_fa2
+        else:
+            raise NotImplementedError(f'img_processor = {config.img_processor}, not implemented')
+
+        self.image_dim_out = image_dim_out
+        self.img_sizes = None
+
+        # global_gn and sub_gn for hd transform, serves as line separator
+        self.use_hd_transform = kwargs.get('use_hd_transform', False)
+        self.with_learnable_separator = kwargs.get('with_learnable_separator', False)
+        self.hd_transform_order = kwargs.get('hd_transform_order', 'glb_sub')
+        # with_hd_transform and with_learnable_separator should have same value
+        assert self.use_hd_transform == self.with_learnable_separator, 'use_hd_transform and with_learnable_separator should have same value'
+        if self.with_learnable_separator:
+            assert self.use_hd_transform, 'learnable separator is only for hd transform'
+            # 1024 * 4, merge spatial to channel dimension
+            self.glb_GN = nn.Parameter(torch.zeros([1, 1, self.image_dim_out * 4]))
+            self.sub_GN = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out * 4]))
+            logger.info(f'learnable separator enabled for hd transform, hd_transform_order = {self.hd_transform_order}')
+
+        projection_cls = kwargs.get('projection_cls', 'linear')
+        if projection_cls == 'linear':
+            self.img_projection = nn.Linear(image_dim_out, hidden_size)
+        elif projection_cls == 'mlp' and self.use_hd_transform:
+            dim_projection = hidden_size
+            depth = 2
+            layers = [nn.Linear(image_dim_out * 4, dim_projection)]
+            for _ in range(1, depth):
+                layers.extend([nn.GELU(),
+                                nn.Linear(dim_projection, dim_projection)])
+            self.img_projection = nn.Sequential(*layers)
+        elif projection_cls == 'mlp':
+            dim_projection = hidden_size
+            depth = 2
+            layers = [nn.Linear(image_dim_out, dim_projection)]
+            for _ in range(1, depth):
+                layers.extend([nn.GELU(),
+                                nn.Linear(dim_projection, dim_projection)])
+            self.img_projection = nn.Sequential(*layers)
+        else:
+            raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented')
+
+        self.vocab_size = config.vocab_size
+        self.img_features = None
+
+        if isinstance(config.img_processor, dict):
+            self.layer_idx = config.img_processor.get('layer_idx', -2)
+            self.type_feature = config.img_processor.get('type_feature', 'patch')
+        else:
+            self.layer_idx = -2
+            self.type_feature = 'patch'
+
+
+    def set_img_features(self, img_features: torch.FloatTensor) -> None:
+        self.img_features = img_features
+
+    def set_img_sizes(self, img_sizes: torch.LongTensor) -> None:
+        self.img_sizes = img_sizes
+
+    def get_img_features(self, img_embeds: torch.FloatTensor) -> torch.FloatTensor:
+        LAYER_IDX = self.layer_idx
+        TYPE_FEATURE = self.type_feature
+
+        img_processor_output = self.img_processor(img_embeds, output_hidden_states=True)
+        img_feature = img_processor_output.hidden_states[LAYER_IDX]
+
+        if TYPE_FEATURE == "patch":
+            patch_feature = img_feature[:, 1:]
+            return patch_feature
+
+        raise NotImplementedError
+
+    def forward(
+        self, input_ids: torch.LongTensor, pixel_values: torch.FloatTensor, image_sizes=None
+    ) -> torch.FloatTensor:
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+
+        # positions for image tokens
+        positions = torch.nonzero((input_ids < 0) & (input_ids > -MAX_INPUT_ID), as_tuple=True)
+        has_image = len(positions[0].tolist()) > 0
+        input_ids = input_ids.clamp_min(0).clamp_max(self.vocab_size).detach()
+        hidden_states = self.wte(input_ids)
+
+        if has_image:
+            assert self.use_hd_transform
+            num_images, num_crops, c, h, w = pixel_values.shape
+            assert c == 3 and h == w == 336
+            img_features = self.get_img_features(pixel_values.flatten(0, 1)).reshape(
+                num_images, num_crops, -1, self.image_dim_out
+            )
+            image_features_proj = self.hd_feature_transform(img_features, image_sizes)
+            hidden_states = hidden_states.index_put(
+                positions, image_features_proj, accumulate=False
+            )
+
+        if self.drop is not None:
+            hidden_states = self.drop(hidden_states)
+
+        return hidden_states
+
+    def hd_feature_transform(self, image_features, image_sizes):
+        """
+        image_features: (num_images, num_crops+1, 24*24, 1024)
+        """
+        assert (
+            self.hd_transform_order == 'sub_glb'
+        ), f'hd_transform_order `{self.hd_transform_order}` not implemented'
+        if isinstance(self.img_projection, nn.Sequential):
+            target_device = self.img_projection[0].bias.device
+            target_dtype = self.img_projection[0].bias.dtype
+        else:  # It's a single nn.Linear layer
+            target_device = self.img_projection.bias.device
+            target_dtype = self.img_projection.bias.dtype
+
+        global_image_features = image_features[:, 0]  # (num_images, 24*24, 1024)
+        # global feature can be viewed as a special HD case with num_crops 1x1
+        global_image_features_hd = self.reshape_hd_patches_2x2merge(global_image_features, 1, 1)
+        global_image_features_hd_newline = self.add_image_newline(global_image_features_hd)
+
+        all_image_embeddings = []
+        # need a for loop to process each image because of different image sizes
+        # (patch arrangement is different for each image)
+        for i, img_size in enumerate(image_sizes):
+            h, w = img_size
+            h_crop = h // 336
+            w_crop = w // 336
+            num_crops = h_crop * w_crop
+
+            # NOTE: real num_crops is padded
+            # (num_crops, 24*24, 1024)
+            sub_image_features = image_features[i, 1 : 1 + num_crops]
+            sub_image_features_hd = self.reshape_hd_patches_2x2merge(
+                sub_image_features, h_crop, w_crop
+            )
+            sub_image_features_hd_newline = self.add_image_newline(sub_image_features_hd)
+
+            # [sub features, separator, global features]
+            all_image_embeddings.extend(
+                [
+                    sub_image_features_hd_newline.squeeze(0),  # (h_crop*12*(w_crop*12+1), 4096)
+                    self.glb_GN.squeeze(0),
+                    global_image_features_hd_newline[i],
+                ]
+            )
+
+        image_features_proj = self.img_projection(
+            torch.cat(all_image_embeddings, dim=0).to(target_device).to(target_dtype)
+        )
+
+        return image_features_proj
+
+    def reshape_hd_patches_2x2merge(self, image_features, h_crop, w_crop):
+        """
+        image_features: (num_images*num_crops, 24*24, 1024)
+        output: (num_images, h_crop*12, w_crop*12, 4096), h_crop*w_crop == num_crops
+        """
+        N, L, C = image_features.shape
+        assert L == 24 * 24 and C == 1024 and N % (h_crop * w_crop) == 0
+        num_images = N // (h_crop * w_crop)
+        H = int(L**0.5)
+        image_features_hd = (
+            image_features.reshape(N, H, H, C)  # N, 24, 24, 1024
+            .reshape(N, H // 2, 2, H // 2, 2, C)  # N, 12, 2, 12, 2, 1024
+            .permute(0, 1, 3, 2, 4, 5)  # N, 12, 12, 2, 2, 1024
+            .reshape(N, -1, 4 * C)  # N, 144, 4096
+            .reshape(
+                num_images, h_crop, w_crop, H // 2, H // 2, -1
+            )  # n_img, h_crop, w_crop, 12, 12, 4096
+            .permute(0, 1, 3, 2, 4, 5)  # n_img, h_crop, 12, w_crop, 12, 4096
+            .reshape(
+                num_images, h_crop * H // 2, w_crop * H // 2, 4 * C
+            )  # n_img, h_crop*12, w_crop*12, 4096
+        )
+
+        # alternative implementation using einops
+        # from einops import rearrange
+        # image_features_nhwc = rearrange(
+        #     image_features,
+        #     'N (H W) c -> N H W c',
+        #     H=H,
+        #     W=H,
+        # )
+        # image_features_2x2merge = rearrange(
+        #     image_features_nhwc,
+        #     'N (h h_pool) (w w_pool) c -> N h w (h_pool w_pool c)',
+        #     h_pool=2,
+        #     w_pool=2,
+        # )
+        # image_features_hd = rearrange(
+        #     image_features_2x2merge,
+        #     '(n_img h_crop w_crop) h w C -> n_img (h_crop h) (w_crop w) C',
+        #     h_crop=h_crop,
+        #     w_crop=w_crop,
+        # )
+
+        return image_features_hd
+
+    def add_image_newline(self, image_features_hd):
+        """
+        image_features_hd: (num_images, h_crop*12, w_crop*12, 4096)
+        output: (num_images, (h_crop*12) * (w_crop*12+1), 4096)
+        """
+        num_images, h, w, hid_dim = image_features_hd.shape
+        # add the newline token to the HD image feature patches
+        newline_embeddings = self.sub_GN.expand(num_images, h, -1, -1)  # (n_img, h, 1, hid_dim)
+        image_features_hd_newline = torch.cat(
+            [image_features_hd, newline_embeddings], dim=2
+        ).reshape(num_images, -1, hid_dim)
+        return image_features_hd_newline