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Running on Zero
| import torch | |
| class Attention(torch.nn.Module): | |
| def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False): | |
| super().__init__() | |
| dim_inner = head_dim * num_heads | |
| kv_dim = kv_dim if kv_dim is not None else q_dim | |
| self.num_heads = num_heads | |
| self.head_dim = head_dim | |
| self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q) | |
| self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) | |
| self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv) | |
| self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out) | |
| def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None): | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| batch_size = encoder_hidden_states.shape[0] | |
| q = self.to_q(hidden_states) | |
| k = self.to_k(encoder_hidden_states) | |
| v = self.to_v(encoder_hidden_states) | |
| q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) | |
| k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) | |
| v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) | |
| hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) | |
| hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) | |
| hidden_states = hidden_states.to(q.dtype) | |
| hidden_states = self.to_out(hidden_states) | |
| return hidden_states | |
| class CLIPEncoderLayer(torch.nn.Module): | |
| def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True): | |
| super().__init__() | |
| self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True) | |
| self.layer_norm1 = torch.nn.LayerNorm(embed_dim) | |
| self.layer_norm2 = torch.nn.LayerNorm(embed_dim) | |
| self.fc1 = torch.nn.Linear(embed_dim, intermediate_size) | |
| self.fc2 = torch.nn.Linear(intermediate_size, embed_dim) | |
| self.use_quick_gelu = use_quick_gelu | |
| def quickGELU(self, x): | |
| return x * torch.sigmoid(1.702 * x) | |
| def forward(self, hidden_states, attn_mask=None): | |
| residual = hidden_states | |
| hidden_states = self.layer_norm1(hidden_states) | |
| hidden_states = self.attn(hidden_states, attn_mask=attn_mask) | |
| hidden_states = residual + hidden_states | |
| residual = hidden_states | |
| hidden_states = self.layer_norm2(hidden_states) | |
| hidden_states = self.fc1(hidden_states) | |
| if self.use_quick_gelu: | |
| hidden_states = self.quickGELU(hidden_states) | |
| else: | |
| hidden_states = torch.nn.functional.gelu(hidden_states) | |
| hidden_states = self.fc2(hidden_states) | |
| hidden_states = residual + hidden_states | |
| return hidden_states | |
| class FluxTextEncoderClip(torch.nn.Module): | |
| def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072): | |
| super().__init__() | |
| # token_embedding | |
| self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim) | |
| # position_embeds (This is a fixed tensor) | |
| self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim)) | |
| # encoders | |
| self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)]) | |
| # attn_mask | |
| self.attn_mask = self.attention_mask(max_position_embeddings) | |
| # final_layer_norm | |
| self.final_layer_norm = torch.nn.LayerNorm(embed_dim) | |
| def attention_mask(self, length): | |
| mask = torch.empty(length, length) | |
| mask.fill_(float("-inf")) | |
| mask.triu_(1) | |
| return mask | |
| def forward(self, input_ids, clip_skip=2, extra_mask=None): | |
| embeds = self.token_embedding(input_ids) | |
| embeds = embeds + self.position_embeds.to(dtype=embeds.dtype, device=input_ids.device) | |
| attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype) | |
| if extra_mask is not None: | |
| attn_mask[:, extra_mask[0]==0] = float("-inf") | |
| for encoder_id, encoder in enumerate(self.encoders): | |
| embeds = encoder(embeds, attn_mask=attn_mask) | |
| if encoder_id + clip_skip == len(self.encoders): | |
| hidden_states = embeds | |
| embeds = self.final_layer_norm(embeds) | |
| pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)] | |
| return pooled_embeds, hidden_states | |