298 lines
10 KiB
Python
298 lines
10 KiB
Python
# Adapted from
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# https://github.com/THUDM/GLM-4
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"""Inference-only GLM-4v model visual encoder compatible with THUDM weights."""
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from argparse import Namespace
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from typing import Optional
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import torch
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from torch import nn
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from torch.nn import LayerNorm
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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class PatchEmbedding(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.proj = nn.Conv2d(config.in_channels,
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config.hidden_size,
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kernel_size=config.patch_size,
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stride=config.patch_size)
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self.cls_embedding = nn.Parameter(torch.zeros(1, config.hidden_size))
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self.position_embedding = nn.Embedding(config.num_positions,
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config.hidden_size)
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def forward(self, images: torch.Tensor) -> torch.Tensor:
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"""
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Parameters:
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images : torch.Tensor
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Input image tensor with shape (B, C, H, W)
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Returns:
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torch.Tensor
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Transformed tensor with shape (B, L, D)
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"""
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images = images.to(self.proj.weight.device)
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x = self.proj(images)
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x = x.flatten(2).transpose(1, 2)
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cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)
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x = torch.cat((cls_token, x), dim=1)
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x += self.position_embedding.weight.unsqueeze(0)
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return x
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class Attention(nn.Module):
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def __init__(
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self,
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config,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.tp_size = get_tensor_model_parallel_world_size()
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self.num_heads_per_rank = config.num_heads // self.tp_size
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self.head_dim = config.hidden_size // config.num_heads
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self.scale = self.head_dim**-0.5
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self.query_key_value = QKVParallelLinear(
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config.hidden_size,
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self.head_dim,
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config.num_heads,
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quant_config=quant_config,
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)
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self.dense = RowParallelLinear(
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config.hidden_size,
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config.hidden_size,
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quant_config=quant_config,
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)
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self.output_dropout = torch.nn.Dropout(config.dropout_prob)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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B, L, _ = x.shape
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qkv, _ = self.query_key_value(x) # B, L, 3 * H * D
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q, k, v = qkv.chunk(3, dim=-1)
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q = q.reshape(B, L, self.num_heads_per_rank,
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self.head_dim).permute(0, 2, 1, 3) # B, H, L, D
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k = k.reshape(B, L, self.num_heads_per_rank,
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self.head_dim).permute(0, 2, 1, 3) # B, H, L, D
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v = v.reshape(B, L, self.num_heads_per_rank,
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self.head_dim).permute(0, 2, 1, 3) # B, H, L, D
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out = torch.nn.functional.scaled_dot_product_attention(q,
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k,
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v,
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attn_mask=None,
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dropout_p=0.,
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is_causal=False)
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output, _ = self.dense(out.transpose(1, 2).view(B, L, -1))
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output = self.output_dropout(output)
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return output
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class MLP(nn.Module):
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def __init__(
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self,
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config,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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self.activation_fn = get_act_fn(config.hidden_act)
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self.fc1 = ColumnParallelLinear(
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config.hidden_size,
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config.intermediate_size,
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quant_config=quant_config,
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)
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self.fc2 = RowParallelLinear(
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config.intermediate_size,
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config.hidden_size,
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quant_config=quant_config,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, _ = self.fc1(x)
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x = self.activation_fn(x)
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x, _ = self.fc2(x)
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return x
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class TransformerLayer(nn.Module):
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def __init__(
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self,
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config,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.input_layernorm = LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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self.attention = Attention(config, quant_config=quant_config)
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self.mlp = MLP(config, quant_config=quant_config)
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self.post_attention_layernorm = LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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def forward(self, hidden_states):
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attention_input = hidden_states
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attention_output = self.input_layernorm(
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self.attention(attention_input))
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hidden_states = attention_input + attention_output
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mlp_input = hidden_states
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mlp_output = self.post_attention_layernorm(self.mlp(mlp_input))
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output = mlp_input + mlp_output
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return output
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class Transformer(nn.Module):
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def __init__(
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self,
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config,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.layers = nn.ModuleList([
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TransformerLayer(config, quant_config=quant_config)
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for _ in range(config.num_hidden_layers)
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])
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def forward(self, hidden_states):
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for layer_module in self.layers:
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hidden_states = layer_module(hidden_states)
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return hidden_states
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class GLU(nn.Module):
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def __init__(
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self,
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config,
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in_features,
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quant_config: Optional[QuantizationConfig] = None,
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):
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"""
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The original implementation is the same as:
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```python
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self.dense_h_to_4h = ColumnParallelLinear(
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config.hidden_size,
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config.ffn_hidden_size,
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bias=False,
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quant_config=quant_config
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)
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self.gate_proj = ColumnParallelLinear(
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config.hidden_size,
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config.ffn_hidden_size,
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bias=False,
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quant_config=quant_config
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)
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```
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```
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gate_proj_output, _ = self.gate_proj(x)
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dense_h_to_4h_output, _ = self.dense_h_to_4h(x)
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x = torch.cat([gate_proj_output, dense_h_to_4h_output], dim=-1)
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```
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We merge two ColumnParallelLinear into one MergedColumnParallelLinear:
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```
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self.merged_proj = MergedColumnParallelLinear(
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config.hidden_size,
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[config.ffn_hidden_size] * 2,
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bias=False,
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quant_config=quant_config
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)
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```
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```
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x, _ = self.merged_proj(x)
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```
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"""
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super().__init__()
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self.linear_proj = ReplicatedLinear(in_features,
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config.hidden_size,
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bias=False,
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quant_config=quant_config)
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self.norm1 = nn.LayerNorm(config.hidden_size)
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self.act1 = nn.GELU()
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self.act2 = SiluAndMul()
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self.merged_proj = MergedColumnParallelLinear(
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config.hidden_size, [config.ffn_hidden_size] * 2,
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bias=False,
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quant_config=quant_config)
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self.dense_4h_to_h = RowParallelLinear(config.ffn_hidden_size,
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config.hidden_size,
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bias=False,
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quant_config=quant_config)
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def forward(self, x):
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x, _ = self.linear_proj(x)
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x = self.act1(self.norm1(x))
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x, _ = self.merged_proj(x)
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x = self.act2(x)
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x, _ = self.dense_4h_to_h(x)
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return x
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class EVA2CLIPModel(nn.Module):
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def __init__(
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self,
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config,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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vision_config = Namespace(**config.vision_config)
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self.patch_embedding = PatchEmbedding(vision_config)
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self.transformer = Transformer(vision_config,
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quant_config=quant_config)
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self.linear_proj = GLU(config,
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in_features=config.hidden_size,
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quant_config=quant_config)
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self.conv = nn.Conv2d(in_channels=vision_config.hidden_size,
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out_channels=config.hidden_size,
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kernel_size=2,
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stride=2)
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self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.eoi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
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self.scaling_factor = vision_config.scaling_factor
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def forward(self, images: torch.Tensor) -> torch.Tensor:
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"""
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Parameters:
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images : torch.Tensor
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Input image tensor with shape (B, C, H, W)
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Returns:
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torch.Tensor
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Transformed tensor with shape (B, L, D)
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"""
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x = self.patch_embedding(images)
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x = self.transformer(x)
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x = x[:, 1:]
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b, s, h = x.shape
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grid_size = int(s**0.5)
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x = x.view(b, grid_size, grid_size, h).permute(0, 3, 1, 2)
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x = self.conv(x)
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x = x.flatten(2).transpose(1, 2)
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x = self.linear_proj(x)
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boi = self.boi.expand(x.shape[0], -1, -1)
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eoi = self.eoi.expand(x.shape[0], -1, -1)
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x = torch.cat((boi, x, eoi), dim=1)
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x = x / self.scaling_factor
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return x
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