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import copy
import torch
import torch.distributed as dist
from diffusers import LTXVideoTransformer3DModel
from torch._utils import _get_device_module
from torch.distributed.tensor import DTensor, Replicate
from torch.distributed.tensor.debug import CommDebugMode
from torch.distributed.tensor.device_mesh import DeviceMesh
from torch.distributed.tensor.parallel.api import parallelize_module
from torch.distributed.tensor.parallel.style import (
ColwiseParallel,
RowwiseParallel,
)
# from torch.utils._python_dispatch import TorchDispatchMode
DEVICE_TYPE = "cuda"
PG_BACKEND = "nccl"
DEVICE_COUNT = _get_device_module(DEVICE_TYPE).device_count()
def main(world_size: int, rank: int):
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats(rank)
CHANNELS = 128
CROSS_ATTENTION_DIM = 2048
CAPTION_CHANNELS = 4096
NUM_LAYERS = 28
NUM_ATTENTION_HEADS = 32
ATTENTION_HEAD_DIM = 64
# CHANNELS = 4
# CROSS_ATTENTION_DIM = 32
# CAPTION_CHANNELS = 64
# NUM_LAYERS = 1
# NUM_ATTENTION_HEADS = 4
# ATTENTION_HEAD_DIM = 8
config = {
"in_channels": CHANNELS,
"out_channels": CHANNELS,
"patch_size": 1,
"patch_size_t": 1,
"num_attention_heads": NUM_ATTENTION_HEADS,
"attention_head_dim": ATTENTION_HEAD_DIM,
"cross_attention_dim": CROSS_ATTENTION_DIM,
"num_layers": NUM_LAYERS,
"activation_fn": "gelu-approximate",
"qk_norm": "rms_norm_across_heads",
"norm_elementwise_affine": False,
"norm_eps": 1e-6,
"caption_channels": CAPTION_CHANNELS,
"attention_bias": True,
"attention_out_bias": True,
}
# Normal model
torch.manual_seed(0)
model = LTXVideoTransformer3DModel(**config).to(DEVICE_TYPE)
# TP model
model_tp = copy.deepcopy(model)
device_mesh = DeviceMesh(DEVICE_TYPE, torch.arange(world_size))
print(f"Device mesh: {device_mesh}")
transformer_tp_plan = {
# ===== Condition embeddings =====
# "time_embed.emb.timestep_embedder.linear_1": ColwiseParallel(),
# "time_embed.emb.timestep_embedder.linear_2": RowwiseParallel(output_layouts=Shard(-1)),
# "time_embed.linear": ColwiseParallel(input_layouts=Shard(-1), output_layouts=Replicate()),
# "time_embed": PrepareModuleOutput(output_layouts=(Replicate(), Shard(-1)), desired_output_layouts=(Replicate(), Replicate())),
# "caption_projection.linear_1": ColwiseParallel(),
# "caption_projection.linear_2": RowwiseParallel(),
# "rope": PrepareModuleOutput(output_layouts=(Replicate(), Replicate()), desired_output_layouts=(Shard(1), Shard(1)), use_local_output=False),
# ===== =====
}
for block in model_tp.transformer_blocks:
block_tp_plan = {}
# ===== Attention =====
# 8 all-to-all, 3 all-reduce
# block_tp_plan["attn1.to_q"] = ColwiseParallel(use_local_output=False)
# block_tp_plan["attn1.to_k"] = ColwiseParallel(use_local_output=False)
# block_tp_plan["attn1.to_v"] = ColwiseParallel(use_local_output=False)
# block_tp_plan["attn1.norm_q"] = SequenceParallel()
# block_tp_plan["attn1.norm_k"] = SequenceParallel()
# block_tp_plan["attn1.to_out.0"] = RowwiseParallel(input_layouts=Shard(1))
# block_tp_plan["attn2.to_q"] = ColwiseParallel(use_local_output=False)
# block_tp_plan["attn2.to_k"] = ColwiseParallel(use_local_output=False)
# block_tp_plan["attn2.to_v"] = ColwiseParallel(use_local_output=False)
# block_tp_plan["attn2.norm_q"] = SequenceParallel()
# block_tp_plan["attn2.norm_k"] = SequenceParallel()
# block_tp_plan["attn2.to_out.0"] = RowwiseParallel(input_layouts=Shard(1))
# ===== =====
block_tp_plan["ff.net.0.proj"] = ColwiseParallel()
block_tp_plan["ff.net.2"] = RowwiseParallel()
parallelize_module(block, device_mesh, block_tp_plan)
parallelize_module(model_tp, device_mesh, transformer_tp_plan)
comm_mode = CommDebugMode()
batch_size = 2
num_frames, height, width = 49, 512, 512
temporal_compression_ratio, spatial_compression_ratio = 8, 32
latent_num_frames, latent_height, latent_width = (
(num_frames - 1) // temporal_compression_ratio + 1,
height // spatial_compression_ratio,
width // spatial_compression_ratio,
)
video_sequence_length = latent_num_frames * latent_height * latent_width
caption_sequence_length = 64
hidden_states = torch.randn(batch_size, video_sequence_length, CHANNELS, device=DEVICE_TYPE)
encoder_hidden_states = torch.randn(batch_size, caption_sequence_length, CAPTION_CHANNELS, device=DEVICE_TYPE)
encoder_attention_mask = None
timestep = torch.randint(0, 1000, (batch_size, 1), device=DEVICE_TYPE)
inputs = {
"hidden_states": hidden_states,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
"timestep": timestep,
"num_frames": latent_num_frames,
"height": latent_height,
"width": latent_width,
"rope_interpolation_scale": [1 / (8 / 25), 8, 8],
"return_dict": False,
}
output = model(**inputs)[0]
with comm_mode:
output_tp = model_tp(**inputs)[0]
output_tp = (
output_tp.redistribute(output_tp.device_mesh, [Replicate()]).to_local()
if isinstance(output_tp, DTensor)
else output_tp
)
print("Output shapes:", output.shape, output_tp.shape)
print(
"Comparing output:",
rank,
torch.allclose(output, output_tp, atol=1e-5, rtol=1e-5),
(output - output_tp).abs().max(),
)
print(f"Max memory reserved ({rank=}): {torch.cuda.max_memory_reserved(rank) / 1024**3:.2f} GB")
if rank == 0:
print()
print("get_comm_counts:", comm_mode.get_comm_counts())
# print()
# print("get_parameter_info:", comm_mode.get_parameter_info()) # Too much noise
print()
print("Sharding info:\n" + "".join(f"{k} - {v}\n" for k, v in comm_mode.get_sharding_info().items()))
print()
print("get_total_counts:", comm_mode.get_total_counts())
comm_mode.generate_json_dump("dump_comm_mode_log.json", noise_level=1)
comm_mode.log_comm_debug_tracing_table_to_file("dump_comm_mode_tracing_table.txt", noise_level=1)
dist.init_process_group(PG_BACKEND)
WORLD_SIZE = dist.get_world_size()
RANK = dist.get_rank()
torch.cuda.set_device(RANK)
if RANK == 0:
print(f"World size: {WORLD_SIZE}")
print(f"Device count: {DEVICE_COUNT}")
try:
with torch.no_grad():
main(WORLD_SIZE, RANK)
finally:
dist.destroy_process_group()
# LTXVideoTransformer3DModel(
# (proj_in): Linear(in_features=128, out_features=2048, bias=True)
# (time_embed): AdaLayerNormSingle(
# (emb): PixArtAlphaCombinedTimestepSizeEmbeddings(
# (time_proj): Timesteps()
# (timestep_embedder): TimestepEmbedding(
# (linear_1): Linear(in_features=256, out_features=2048, bias=True)
# (act): SiLU()
# (linear_2): Linear(in_features=2048, out_features=2048, bias=True)
# )
# )
# (silu): SiLU()
# (linear): Linear(in_features=2048, out_features=12288, bias=True)
# )
# (caption_projection): PixArtAlphaTextProjection(
# (linear_1): Linear(in_features=4096, out_features=2048, bias=True)
# (act_1): GELU(approximate='tanh')
# (linear_2): Linear(in_features=2048, out_features=2048, bias=True)
# )
# (rope): LTXVideoRotaryPosEmbed()
# (transformer_blocks): ModuleList(
# (0-27): 28 x LTXVideoTransformerBlock(
# (norm1): RMSNorm()
# (attn1): Attention(
# (norm_q): RMSNorm()
# (norm_k): RMSNorm()
# (to_q): Linear(in_features=2048, out_features=2048, bias=True)
# (to_k): Linear(in_features=2048, out_features=2048, bias=True)
# (to_v): Linear(in_features=2048, out_features=2048, bias=True)
# (to_out): ModuleList(
# (0): Linear(in_features=2048, out_features=2048, bias=True)
# (1): Dropout(p=0.0, inplace=False)
# )
# )
# (norm2): RMSNorm()
# (attn2): Attention(
# (norm_q): RMSNorm()
# (norm_k): RMSNorm()
# (to_q): Linear(in_features=2048, out_features=2048, bias=True)
# (to_k): Linear(in_features=2048, out_features=2048, bias=True)
# (to_v): Linear(in_features=2048, out_features=2048, bias=True)
# (to_out): ModuleList(
# (0): Linear(in_features=2048, out_features=2048, bias=True)
# (1): Dropout(p=0.0, inplace=False)
# )
# )
# (ff): FeedForward(
# (net): ModuleList(
# (0): GELU(
# (proj): Linear(in_features=2048, out_features=8192, bias=True)
# )
# (1): Dropout(p=0.0, inplace=False)
# (2): Linear(in_features=8192, out_features=2048, bias=True)
# )
# )
# )
# )
# (norm_out): LayerNorm((2048,), eps=1e-06, elementwise_affine=False)
# (proj_out): Linear(in_features=2048, out_features=128, bias=True)
# )
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