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2976b5d992
Test plan: python tests/verifications/generate_report.py --providers fireworks,together,llama_meta_ref,openai Co-authored-by: Eric Huang <erichuang@fb.com>
318 lines
12 KiB
Python
318 lines
12 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import io
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import json
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import uuid
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from dataclasses import dataclass
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from typing import Dict, List, Optional, Tuple
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import torch
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from PIL import Image as PIL_Image
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# TODO: either fork these or move them to the common package
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from ..datatypes import (
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BuiltinTool,
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RawContent,
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RawMediaItem,
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RawMessage,
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RawTextItem,
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Role,
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StopReason,
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ToolCall,
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ToolPromptFormat,
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)
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from ..llama3.tool_utils import ToolUtils
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from .args import VisionArgs
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from .datatypes import LLMInput
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from .preprocess import ResizeNormalizeImageTransform, VariableSizeImageTransform
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from .tokenizer import Tokenizer
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def role_str(role: Role) -> str:
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role_strs = {
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Role.user: "user",
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Role.system: "system",
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Role.tool: "ipython", # special
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Role.assistant: "assistant",
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}
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return role_strs[role]
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@dataclass
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class TransformedImage:
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image_tiles: torch.Tensor
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# is the aspect ratio needed anywhere?
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aspect_ratio: Tuple[int, int]
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def convert_image_to_rgb(image: PIL_Image.Image, bg: Tuple[int, int, int] = (255, 255, 255)) -> PIL_Image.Image:
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if image.mode == "RGBA":
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image.load() # for png.split()
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new_img = PIL_Image.new("RGB", image.size, bg)
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new_img.paste(image, mask=image.split()[3]) # 3 is the alpha channel
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return new_img
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return image.convert("RGB")
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class ChatFormat:
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possible_headers: Dict[Role, str]
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def __init__(
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self,
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tokenizer: Tokenizer,
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vision_args: Optional[VisionArgs] = None,
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max_num_chunks: int = 16,
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):
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self.tokenizer = tokenizer
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self.vision_args = vision_args
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self.max_num_chunks = max_num_chunks
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self.possible_headers = {role: f"<|header_start|>{role_str(role)}<|header_end|>\n\n" for role in Role}
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self.image_transform = None
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self.dynamic_image_transform = None
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if vision_args:
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self.dynamic_image_transform = VariableSizeImageTransform(vision_args.image_size.width)
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self.image_transform = ResizeNormalizeImageTransform(
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vision_args.image_size.width, vision_args.image_size.height
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)
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def _encode_header(self, role: str) -> List[int]:
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tokens = []
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tokens.append(self.tokenizer.special_tokens["<|header_start|>"])
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# TODO: need to check if this is correct
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tokens.extend(self.tokenizer.encode("ipython" if role == "tool" else role, bos=False, eos=False))
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tokens.append(self.tokenizer.special_tokens["<|header_end|>"])
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tokens.extend(self.tokenizer.encode("\n\n", bos=False, eos=False))
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return tokens
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def encode_content(self, content: RawContent) -> LLMInput:
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tokens, images = self._encode_content(content, bos=True)
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return self._model_input_from_tokens_images(tokens, images)
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def _encode_image(
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self,
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transformed_image: TransformedImage,
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) -> List[int]:
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assert self.vision_args is not None, "The model is not vision-enabled"
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image_tensor = transformed_image.image_tiles
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image_channels = image_tensor.shape[-3]
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image_height = image_tensor.shape[-2]
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image_width = image_tensor.shape[-1]
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image_chunks = image_tensor.view(-1, image_channels, image_height, image_width).shape[0]
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patch_height = self.vision_args.patch_size.height
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patch_width = self.vision_args.patch_size.width
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if image_height % patch_height != 0:
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raise ValueError(f"{image_height=} not divisible by {patch_height=}")
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if image_width % patch_width != 0:
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raise ValueError(f"{image_width=} not divisible by {patch_width=}")
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ds_ratio = int(round(1.0 / (self.vision_args.pixel_shuffle_ratio**2)))
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n_patches_per_chunk = int((image_height // patch_height) * (image_width // patch_width) // ds_ratio)
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image_ar = transformed_image.aspect_ratio
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tokens = [self.tokenizer.special_tokens["<|image_start|>"]]
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if image_chunks == 1:
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tokens += [self.tokenizer.special_tokens["<|image|>"]]
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tokens += [self.tokenizer.special_tokens["<|patch|>"]] * n_patches_per_chunk
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tokens += [self.tokenizer.special_tokens["<|image_end|>"]]
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else:
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ratio_h, ratio_w = image_ar
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for _ in range(ratio_h):
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for xx in range(ratio_w):
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tokens += [self.tokenizer.special_tokens["<|patch|>"]] * n_patches_per_chunk
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if xx < ratio_w - 1:
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tokens.append(self.tokenizer.special_tokens["<|tile_x_separator|>"])
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tokens.append(self.tokenizer.special_tokens["<|tile_y_separator|>"])
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tokens += [self.tokenizer.special_tokens["<|image|>"]]
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tokens += [self.tokenizer.special_tokens["<|patch|>"]] * n_patches_per_chunk
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tokens += [self.tokenizer.special_tokens["<|image_end|>"]]
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return tokens
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def _encode_content(self, content: RawContent, bos: bool = False) -> Tuple[List[int], List[TransformedImage]]:
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tokens = []
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tranformed_images = []
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added_bos = False
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def _process(c):
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nonlocal added_bos, bos
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if isinstance(c, str) or isinstance(c, RawTextItem):
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if isinstance(c, RawTextItem):
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c = c.text
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tokens.extend(self.tokenizer.encode(c, bos=False if added_bos else bos, eos=False))
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added_bos = True
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elif isinstance(c, RawMediaItem):
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if not self.vision_args:
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raise ValueError("The model is not vision-enabled, but a media item was found")
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bos = False if added_bos else bos
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if bos:
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tokens.append(self.tokenizer.special_tokens["<|begin_of_text|>"])
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added_bos = True
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bytes_io = io.BytesIO(c.data) if isinstance(c.data, bytes) else c.data
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image = PIL_Image.open(bytes_io)
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image = convert_image_to_rgb(image)
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image_tiles, ar = self.dynamic_image_transform(image, max_num_chunks=self.max_num_chunks)
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if image_tiles.shape[0] > 1:
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image_global = self.image_transform(image)
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image_global = image_global.unsqueeze(0)
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image_combine = torch.cat((image_tiles, image_global), dim=0)
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image_tiles = image_combine
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transformed_image = TransformedImage(image_tiles=image_tiles, aspect_ratio=ar)
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tokens.extend(self._encode_image(transformed_image))
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tranformed_images.append(transformed_image)
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if isinstance(content, list):
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for c in content:
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_process(c)
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else:
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_process(content)
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return tokens, tranformed_images
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def encode_message(
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self, message: RawMessage, tool_prompt_format: ToolPromptFormat
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) -> Tuple[List[int], List[TransformedImage]]:
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tokens = self._encode_header(message.role)
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images = []
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def _process_content(c):
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toks, imgs = self._encode_content(c)
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tokens.extend(toks)
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images.extend(imgs)
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_process_content(message.content)
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if message.role == "user" and message.context is not None:
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# This is RAG context; why is it here in the chat format? I don't think
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# this is needed and can be moved upwards
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_process_content("\n\n")
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_process_content(message.context)
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if message.role == "assistant":
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for t in message.tool_calls:
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content = ToolUtils.encode_tool_call(t, tool_prompt_format)
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_process_content(content)
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# Tool calls and Tool Response messages should be eom
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eom = False
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if message.role == "assistant":
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eom = message.stop_reason == StopReason.end_of_message or message.tool_calls
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elif message.role == "tool":
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eom = True
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tokens.append(self.tokenizer.special_tokens["<|eom|>" if eom else "<|eot|>"])
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return tokens, images
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def encode_dialog_prompt(
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self,
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messages: List[RawMessage],
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tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json,
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) -> LLMInput:
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tokens = []
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images = []
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tokens.append(self.tokenizer.special_tokens["<|begin_of_text|>"])
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for message in messages:
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toks, imgs = self.encode_message(message, tool_prompt_format)
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tokens.extend(toks)
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images.extend(imgs)
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# Add the start of an assistant message for the model to complete.
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tokens.extend(self._encode_header("assistant"))
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return self._model_input_from_tokens_images(tokens, images)
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# TODO(this should be generic, not only for assistant messages)
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def decode_assistant_message(self, tokens: List[int], stop_reason: StopReason) -> RawMessage:
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content = self.tokenizer.decode(tokens)
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return self.decode_assistant_message_from_content(content, stop_reason)
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def decode_assistant_message_from_content(self, content: str, stop_reason: StopReason) -> RawMessage:
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content = content.strip(" ")
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header_str = self.possible_headers[Role.assistant]
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if content.startswith(header_str):
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content = content[len(header_str) :]
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ipython = content.startswith("<|python_start|>")
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if ipython:
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content = content[len("<|python_start|>") :]
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content = content.replace("<|python_end|>", "")
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if content.endswith("<|eot|>"):
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content = content[: -len("<|eot|>")]
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stop_reason = StopReason.end_of_turn
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elif content.endswith("<|eom|>"):
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content = content[: -len("<|eom|>")]
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stop_reason = StopReason.end_of_message
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tool_name = None
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tool_arguments = {}
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custom_tool_info = ToolUtils.maybe_extract_custom_tool_call(content)
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if custom_tool_info is not None:
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tool_name, tool_arguments = custom_tool_info
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# Sometimes when agent has custom tools alongside builin tools
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# Agent responds for builtin tool calls in the format of the custom tools
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# This code tries to handle that case
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if tool_name in BuiltinTool.__members__:
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tool_name = BuiltinTool[tool_name]
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tool_arguments = {
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"query": list(tool_arguments.values())[0],
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}
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else:
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builtin_tool_info = ToolUtils.maybe_extract_builtin_tool_call(content)
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if builtin_tool_info is not None:
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tool_name, query = builtin_tool_info
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tool_arguments = {
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"query": query,
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}
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if tool_name in BuiltinTool.__members__:
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tool_name = BuiltinTool[tool_name]
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elif ipython:
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tool_name = BuiltinTool.code_interpreter
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tool_arguments = {
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"code": content,
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}
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tool_calls = []
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if tool_name is not None and tool_arguments is not None:
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call_id = str(uuid.uuid4())
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tool_calls.append(
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ToolCall(
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call_id=call_id,
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tool_name=tool_name,
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arguments=tool_arguments,
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arguments_json=json.dumps(tool_arguments),
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)
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)
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return RawMessage(
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role="assistant",
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content=content,
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stop_reason=stop_reason,
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tool_calls=tool_calls,
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)
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def _model_input_from_tokens_images(self, tokens: List[int], images: List[TransformedImage]) -> LLMInput:
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return LLMInput(
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tokens=tokens,
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images=[x.image_tiles for x in images] if len(images) > 0 else None,
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)
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