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https://github.com/meta-llama/llama-stack.git
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9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
210 lines
7 KiB
Python
210 lines
7 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 math
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from collections.abc import Callable
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from typing import Any
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear
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from ..args import VisionArgs
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from .encoder import VisionEncoder
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class PixelShuffle(nn.Module):
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def __init__(self, ps_ratio):
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super().__init__()
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self.ps_ratio = ps_ratio
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def forward(self, x):
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# x: [B, N, C], N = number of patches
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assert self.ps_ratio is not None, "ps_ratio is required for pixel shuffle"
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assert x.dim() == 3, "pixel shuffle requires encoded patches [B, N, C]"
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hh = ww = int(math.sqrt(x.shape[1]))
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x = x.reshape(x.shape[0], hh, ww, -1)
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x = pixel_shuffle_op(x, ps_ratio=self.ps_ratio)
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pixel_shuffle_patches = x.reshape(x.shape[0], -1, x.shape[-1])
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return pixel_shuffle_patches
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def pixel_shuffle_op(input_x, ps_ratio):
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n, w, h, c = input_x.size()
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input_x = input_x.view(n, w, int(h * ps_ratio), int(c / ps_ratio))
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input_x = input_x.permute(0, 2, 1, 3).contiguous()
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input_x = input_x.view(
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n,
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int(h * ps_ratio),
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int(w * ps_ratio),
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int(c / (ps_ratio * ps_ratio)),
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)
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input_x = input_x.permute(0, 2, 1, 3).contiguous()
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return input_x
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class SimpleMLP(torch.nn.Module):
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def __init__(
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self,
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dim: int,
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hidden_dim: int,
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bias: bool = True,
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dropout: float = 0.0,
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act_layer: Callable = nn.GELU,
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):
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super().__init__()
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# layers
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self.c_fc = ColumnParallelLinear(
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dim,
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hidden_dim,
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bias=bias,
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gather_output=False,
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)
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self.c_proj = RowParallelLinear(
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hidden_dim,
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hidden_dim,
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bias=bias,
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input_is_parallel=True,
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)
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self.non_linearity = act_layer()
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self.dropout = dropout
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def forward(self, x):
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hidden = self.c_fc(x)
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hidden = self.non_linearity(hidden)
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hidden = F.dropout(hidden, p=self.dropout, training=self.training)
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return self.non_linearity(self.c_proj(hidden))
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class PixelShuffleMLP(torch.nn.Module):
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def __init__(
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self,
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ps_ratio: float,
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input_dim: int,
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output_dim: int = 4096,
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add_fc: bool = False,
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):
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super().__init__()
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self.pixel_shuffle = PixelShuffle(ps_ratio)
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self.mlp = SimpleMLP(
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int(input_dim // (ps_ratio**2)),
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output_dim,
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bias=False,
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dropout=0.0,
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act_layer=nn.GELU,
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)
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self.fc = nn.Identity()
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if add_fc:
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self.fc = ColumnParallelLinear(
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output_dim,
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output_dim,
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bias=False,
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)
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def forward(self, encoded_patches: torch.Tensor) -> torch.Tensor:
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encoded_patches = self.pixel_shuffle(encoded_patches)
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return self.fc(self.mlp(encoded_patches))
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class VisionEmbeddings(torch.nn.Module):
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def __init__(self, args: VisionArgs):
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super().__init__()
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self.args = args
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image_size = args.image_size
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patch_size = args.patch_size
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self.vision_encoder = VisionEncoder(
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image_size=(image_size.height, image_size.width),
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patch_size=(patch_size.height, patch_size.width),
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dim=args.dim,
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layers=args.n_layers,
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heads=args.n_heads,
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mlp_ratio=args.mlp_ratio,
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)
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self.vision_encoder = self.vision_encoder.to(torch.bfloat16)
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self.vision_adapter = PixelShuffleMLP(
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ps_ratio=args.pixel_shuffle_ratio,
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input_dim=args.dim,
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output_dim=args.output_dim,
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)
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self.output_dim = args.output_dim
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self._register_load_state_dict_pre_hook(self.load_hook)
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def load_hook(
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self,
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state_dict: dict[str, Any],
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prefix: str,
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local_metadata: dict[str, Any],
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strict: bool = True,
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missing_keys: list[str] = None,
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unexpected_keys: list[str] = None,
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error_msgs: list[str] = None,
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return_state_dict: bool = False,
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) -> None:
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original_sd = self.state_dict()
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for k in state_dict:
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if k.startswith(prefix) and len(state_dict[k].shape) == 1 and state_dict[k].shape[0] == 0:
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state_dict[k] = state_dict[k].reshape(original_sd[k[len(prefix) :]].shape)
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def _get_empty_sequence(self, h):
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return torch.zeros(
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h.shape[0],
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h.shape[1],
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self.output_dim,
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device=h.device,
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dtype=h.dtype,
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)
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# x_images is batched; each batch sample contains a list of images. so this is List[List[torch.Tensor]]
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# each image is a tensor of shape [num_tiles, C, H, W]
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def forward(
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self,
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image_batch: list[list[torch.Tensor]],
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image_mask: torch.Tensor,
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h_ref: torch.Tensor,
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) -> torch.Tensor:
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images_flattened = [image for sample in image_batch for image in sample]
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images_flattened = torch.vstack(images_flattened).unsqueeze(1).to(h_ref.dtype).to(h_ref.device)
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embedding = self.vision_encoder(images_flattened)
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projected_embedding = self.vision_adapter(embedding)
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h_image = self._get_empty_sequence(h_ref)
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return scatter_embeddings(image_batch, image_mask, h_image, projected_embedding)
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def scatter_embeddings(image_batch, image_mask, h_image, encoded_patches_proj):
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# If dynamic transform is used and the batch contains 2 images (where image_1 has 2 chunks and image_2 has 3 chunks),
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# `num_images_per_sequence` now records the number of chunks per image as `[2, 3]`.
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# `encoded_patches_proj.split` will then split the image chunks into 2 groups: `[image_1_chunks, image_2_chunks]`.
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num_images_per_sequence = [sum(image.size(0) for image in sample_images) for sample_images in image_batch]
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assert not torch.isnan(encoded_patches_proj).any()
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assert sum(num_images_per_sequence) == encoded_patches_proj.size(0), (
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f"{sum(num_images_per_sequence)=} != {encoded_patches_proj.shape=}"
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)
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encoded_patches_list = encoded_patches_proj.split(num_images_per_sequence, dim=0)
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for index in range(h_image.size(0)):
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encoded_patches_per_sample = encoded_patches_list[index]
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sample_image_mask = image_mask[index]
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if encoded_patches_per_sample.numel() == 0:
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continue
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encoded_patches_per_sample = encoded_patches_per_sample.contiguous().view(
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-1, encoded_patches_per_sample.size(-1)
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)
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n_tokens_to_fill = sample_image_mask.sum()
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assert n_tokens_to_fill <= encoded_patches_per_sample.size(0)
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h_image[index].masked_scatter_(
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sample_image_mask.expand(-1, h_image.size(-1)),
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encoded_patches_per_sample[:n_tokens_to_fill],
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)
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return h_image
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