Move dist_utils.py to diffusers/

dist_utils.py

@@ -1,138 +0,0 @@
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-import torch.distributed as dist
-from diffusers.models.attention import Attention
-from diffusers.models.embeddings import apply_rotary_emb
-
-try:
-    import xfuser
-    from xfuser.core.distributed import (get_sequence_parallel_rank,
-                                         get_sequence_parallel_world_size,
-                                         get_sp_group, get_world_group,
-                                         init_distributed_environment,
-                                         initialize_model_parallel)
-    from xfuser.core.long_ctx_attention import xFuserLongContextAttention
-except Exception as ex:
-    get_sequence_parallel_world_size = None
-    get_sequence_parallel_rank = None
-    xFuserLongContextAttention = None
-    get_sp_group = None
-    get_world_group = None
-    init_distributed_environment = None
-    initialize_model_parallel = None
-
-def set_multi_gpus_devices(ulysses_degree, ring_degree):
-    if ulysses_degree > 1 or ring_degree > 1:
-        if get_sp_group is None:
-            raise RuntimeError("xfuser is not installed.")
-        dist.init_process_group("nccl")
-        print('parallel inference enabled: ulysses_degree=%d ring_degree=%d rank=%d world_size=%d' % (
-            ulysses_degree, ring_degree, dist.get_rank(),
-            dist.get_world_size()))
-        assert dist.get_world_size() == ring_degree * ulysses_degree, \
-                    "number of GPUs(%d) should be equal to ring_degree * ulysses_degree." % dist.get_world_size()
-        init_distributed_environment(rank=dist.get_rank(), world_size=dist.get_world_size())
-        initialize_model_parallel(sequence_parallel_degree=dist.get_world_size(),
-                ring_degree=ring_degree,
-                ulysses_degree=ulysses_degree)
-        # device = torch.device("cuda:%d" % dist.get_rank())
-        device = torch.device(f"cuda:{get_world_group().local_rank}")
-        print('rank=%d device=%s' % (get_world_group().rank, str(device)))
-    else:
-        device = "cuda"
-    return device
-
-class CogVideoXMultiGPUsAttnProcessor2_0:
-    r"""
-    Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
-    query and key vectors, but does not include spatial normalization.
-    """
-
-    def __init__(self):
-        if xFuserLongContextAttention is not None:
-            try:
-                self.hybrid_seq_parallel_attn = xFuserLongContextAttention()
-            except Exception:
-                self.hybrid_seq_parallel_attn = None
-        else:
-            self.hybrid_seq_parallel_attn = None
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        text_seq_length = encoder_hidden_states.size(1)
-
-        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(hidden_states)
-        value = attn.to_v(hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-
-        # Apply RoPE if needed
-        if image_rotary_emb is not None:
-            query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
-            if not attn.is_cross_attention:
-                key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
-        if self.hybrid_seq_parallel_attn is None:
-            hidden_states = F.scaled_dot_product_attention(
-                query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-            )
-            hidden_states = hidden_states
-        else:
-            img_q = query[:, :, text_seq_length:].transpose(1, 2)
-            txt_q = query[:, :, :text_seq_length].transpose(1, 2)
-            img_k = key[:, :, text_seq_length:].transpose(1, 2)
-            txt_k = key[:, :, :text_seq_length].transpose(1, 2)
-            img_v = value[:, :, text_seq_length:].transpose(1, 2)
-            txt_v = value[:, :, :text_seq_length].transpose(1, 2)
-
-            hidden_states = self.hybrid_seq_parallel_attn(
-                None,
-                img_q, img_k, img_v, dropout_p=0.0, causal=False,
-                joint_tensor_query=txt_q,
-                joint_tensor_key=txt_k,
-                joint_tensor_value=txt_v,
-                joint_strategy='front',
-            ).transpose(1, 2)
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        encoder_hidden_states, hidden_states = hidden_states.split(
-            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
-        )
-        return hidden_states, encoder_hidden_states