mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-09-01 12:49:50 +08:00
LlaVA in MLX (#461)
* add: llava mlx first draft * add: weights comparision * add forward pass skeleton * update: now imports weights correctly * delete base * latest * adding config * fix: use config * add mlx config * feat: add image processor for llava processor * wip * feat: llava working example * chore: refactor generate script * chore: clean up * add: warning to user if no <image> token despite using one * add: __call__ to LlavaModel * add: call to LlavaModel * update fp * clean up var names * update: native GeLU * Cleanup * update generate and readme * remove todo comment * rearrange tests * fix example code * nits in README * update readme * nit in readme * nits in README * chore(llava): refactor image embedding merging logic * min mlx version * nits in readmes * fix cli prompt, some nits * updates, slight simplify --------- Co-authored-by: anchen <li.anchen.au@gmail.com> Co-authored-by: Awni Hannun <awni@apple.com>
This commit is contained in:
Noah Kasmanoff
223
llava/vision.py
Normal file
223
llava/vision.py
Normal file
@@ -0,0 +1,223 @@
|
||||
# Copyright © 2024 Apple Inc.
|
||||
|
||||
import inspect
|
||||
import math
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
|
||||
import mlx.core as mx
|
||||
import mlx.nn as nn
|
||||
|
||||
|
||||
@dataclass
|
||||
class VisionConfig:
|
||||
model_type: str
|
||||
num_hidden_layers: int = 24
|
||||
hidden_size: int = 1024
|
||||
intermediate_size: int = 4096
|
||||
num_attention_heads: int = 16
|
||||
image_size: int = 336
|
||||
patch_size: int = 14
|
||||
projection_dim: int = 768
|
||||
vocab_size: int = 32000
|
||||
num_channels: int = 3
|
||||
layer_norm_eps: float = 1e-5
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, params):
|
||||
return cls(
|
||||
**{
|
||||
k: v
|
||||
for k, v in params.items()
|
||||
if k in inspect.signature(cls).parameters
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dims: int,
|
||||
num_heads: int,
|
||||
query_input_dims: Optional[int] = None,
|
||||
key_input_dims: Optional[int] = None,
|
||||
value_input_dims: Optional[int] = None,
|
||||
value_dims: Optional[int] = None,
|
||||
value_output_dims: Optional[int] = None,
|
||||
bias: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if (dims % num_heads) != 0:
|
||||
raise ValueError(
|
||||
"The input feature dimensions should be divisible by the "
|
||||
f"number of heads ({dims} % {num_heads}) != 0"
|
||||
)
|
||||
|
||||
query_input_dims = query_input_dims or dims
|
||||
key_input_dims = key_input_dims or dims
|
||||
value_input_dims = value_input_dims or key_input_dims
|
||||
value_dims = value_dims or dims
|
||||
value_output_dims = value_output_dims or dims
|
||||
|
||||
self.num_heads = num_heads
|
||||
self.q_proj = nn.Linear(query_input_dims, dims, bias=bias)
|
||||
self.k_proj = nn.Linear(key_input_dims, dims, bias=bias)
|
||||
self.v_proj = nn.Linear(value_input_dims, value_dims, bias=bias)
|
||||
self.out_proj = nn.Linear(value_dims, value_output_dims, bias=bias)
|
||||
|
||||
def __call__(self, queries, keys, values, mask=None):
|
||||
queries = self.q_proj(queries)
|
||||
keys = self.k_proj(keys)
|
||||
values = self.v_proj(values)
|
||||
|
||||
num_heads = self.num_heads
|
||||
B, L, D = queries.shape
|
||||
_, S, _ = keys.shape
|
||||
queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
|
||||
keys = keys.reshape(B, S, num_heads, -1).transpose(0, 2, 3, 1)
|
||||
values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
|
||||
|
||||
scale = math.sqrt(1 / queries.shape[-1])
|
||||
scores = (queries * scale) @ keys
|
||||
if mask is not None:
|
||||
scores = scores + mask.astype(scores.dtype)
|
||||
scores = mx.softmax(scores, axis=-1)
|
||||
values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
|
||||
|
||||
return self.out_proj(values_hat)
|
||||
|
||||
|
||||
class MLP(nn.Module):
|
||||
def __init__(self, config: VisionConfig):
|
||||
super().__init__()
|
||||
self.activation_fn = nn.GELU(approx="fast")
|
||||
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
||||
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
||||
|
||||
def __call__(self, x: mx.array) -> mx.array:
|
||||
x = self.activation_fn(self.fc1(x))
|
||||
x = self.fc2(x)
|
||||
return x
|
||||
|
||||
|
||||
class EncoderLayer(nn.Module):
|
||||
def __init__(self, config: VisionConfig):
|
||||
super().__init__()
|
||||
self.embed_dim = config.hidden_size
|
||||
self.self_attn = Attention(
|
||||
config.hidden_size, config.num_attention_heads, bias=True
|
||||
)
|
||||
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
||||
self.mlp = MLP(config)
|
||||
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
||||
|
||||
def __call__(self, x: mx.array, mask: Optional[mx.array] = None) -> mx.array:
|
||||
y = self.layer_norm1(x)
|
||||
y = self.self_attn(y, y, y, mask)
|
||||
x = x + y
|
||||
y = self.layer_norm2(x)
|
||||
y = self.mlp(y)
|
||||
return x + y
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self, config: VisionConfig):
|
||||
super().__init__()
|
||||
self.layers = [EncoderLayer(config) for _ in range(config.num_hidden_layers)]
|
||||
|
||||
|
||||
class VisionEmbeddings(nn.Module):
|
||||
def __init__(self, config: VisionConfig):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.embed_dim = config.hidden_size
|
||||
self.image_size = config.image_size
|
||||
self.patch_size = config.patch_size
|
||||
|
||||
self.class_embedding = mx.zeros((config.hidden_size,))
|
||||
|
||||
self.patch_embedding = nn.Conv2d(
|
||||
in_channels=config.num_channels,
|
||||
out_channels=self.embed_dim,
|
||||
kernel_size=self.patch_size,
|
||||
stride=self.patch_size,
|
||||
bias=False,
|
||||
)
|
||||
|
||||
self.num_patches = (self.image_size // self.patch_size) ** 2
|
||||
self.num_positions = self.num_patches + 1
|
||||
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
|
||||
|
||||
def __call__(self, x: mx.array) -> mx.array:
|
||||
batch_size = x.shape[0]
|
||||
patch_embeddings = self.patch_embedding(x)
|
||||
patch_embeddings = mx.flatten(patch_embeddings, start_axis=1, end_axis=2)
|
||||
embed_dim = patch_embeddings.shape[-1]
|
||||
cls_embeddings = mx.broadcast_to(
|
||||
self.class_embedding, (batch_size, 1, embed_dim)
|
||||
)
|
||||
embeddings = mx.concatenate((cls_embeddings, patch_embeddings), axis=1)
|
||||
embeddings += self.position_embedding.weight
|
||||
return embeddings
|
||||
|
||||
|
||||
class ClipVisionModel(nn.Module):
|
||||
def __init__(self, config: VisionConfig):
|
||||
super().__init__()
|
||||
self.embeddings = VisionEmbeddings(config)
|
||||
self.pre_layrnorm = nn.LayerNorm(config.hidden_size)
|
||||
self.encoder = Encoder(config)
|
||||
self.post_layernorm = nn.LayerNorm(config.hidden_size)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
x: mx.array,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
) -> mx.array:
|
||||
x = self.embeddings(x)
|
||||
x = self.pre_layrnorm(x)
|
||||
|
||||
encoder_states = (x,) if output_hidden_states else None
|
||||
|
||||
for l in self.encoder.layers:
|
||||
x = l(x, mask=None)
|
||||
if output_hidden_states:
|
||||
encoder_states = encoder_states + (x,)
|
||||
|
||||
pooler_output = self.post_layernorm(x[:, 0, :])
|
||||
return pooler_output, x, encoder_states
|
||||
|
||||
|
||||
class VisionModel(nn.Module):
|
||||
def __init__(self, config: VisionConfig):
|
||||
super().__init__()
|
||||
|
||||
self.model_type = config.model_type
|
||||
if self.model_type != "clip_vision_model":
|
||||
raise ValueError(f"Unsupported model type: {self.model_type}")
|
||||
|
||||
self.vision_model = ClipVisionModel(config)
|
||||
|
||||
def __call__(
|
||||
self, x: mx.array, output_hidden_states: Optional[bool] = None
|
||||
) -> mx.array:
|
||||
return self.vision_model(x, output_hidden_states)
|
||||
|
||||
@staticmethod
|
||||
def sanitize(weights):
|
||||
sanitized_weights = {}
|
||||
for k, v in weights.items():
|
||||
if "position_ids" in k:
|
||||
# Remove unused position_ids
|
||||
continue
|
||||
elif "patch_embedding.weight" in k:
|
||||
# PyTorch conv2d weight tensors have shape:
|
||||
# [out_channels, in_channels, kH, KW]
|
||||
# MLX conv2d expects the weight be of shape:
|
||||
# [out_channels, kH, KW, in_channels]
|
||||
sanitized_weights[k] = v.transpose(0, 2, 3, 1)
|
||||
else:
|
||||
sanitized_weights[k] = v
|
||||
|
||||
return sanitized_weights
|
||||
Reference in New Issue
Block a user