Add dist_utils.py

dist_utils.py

@@ -0,0 +1,138 @@
+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