Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,861 Bytes
dc155d4 879ee4e dc155d4 b09f6a2 dc155d4 988720a dc155d4 fb1983e dc155d4 988720a c3d6a8e 879ee4e dc155d4 6ff4937 dc155d4 82d7cc1 dc155d4 988720a dc155d4 988720a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
"""
"""
from typing import Any
from typing import Callable
from typing import ParamSpec
import spaces
import torch
from torch.utils._pytree import tree_map_only
from torchao.quantization import quantize_
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
from torchao.quantization import Int8WeightOnlyConfig
from optimization_utils import capture_component_call
from optimization_utils import aoti_compile
from optimization_utils import ZeroGPUCompiledModel
from optimization_utils import drain_module_parameters
P = ParamSpec('P')
TRANSFORMER_NUM_FRAMES_DIM = torch.export.Dim('num_frames', min=3, max=21)
TRANSFORMER_DYNAMIC_SHAPES = {
'hidden_states': {
2: TRANSFORMER_NUM_FRAMES_DIM,
},
}
INDUCTOR_CONFIGS = {
'conv_1x1_as_mm': True,
'epilogue_fusion': False,
'coordinate_descent_tuning': True,
'coordinate_descent_check_all_directions': True,
'max_autotune': True,
'triton.cudagraphs': True,
}
def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs):
@spaces.GPU(duration=1200)
def compile_transformer():
# pipeline.load_lora_weights(
# "Kijai/WanVideo_comfy",
# weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
# adapter_name="lightx2v"
# )
# kwargs_lora = {}
# kwargs_lora["load_into_transformer_2"] = True
# pipeline.load_lora_weights(
# "Kijai/WanVideo_comfy",
# weight_name="Wan22-Lightning/Wan2.2-Lightning_T2V-A14B-4steps-lora_LOW_fp16.safetensors",
# adapter_name="lightx2v_2", **kwargs_lora
# )
# pipeline.set_adapters(["lightx2v", "lightx2v_2"], adapter_weights=[1., 1.])
# pipeline.fuse_lora(adapter_names=["lightx2v"], lora_scale=3., components=["transformer"])
# pipeline.fuse_lora(adapter_names=["lightx2v_2"], lora_scale=1., components=["transformer_2"])
# pipeline.unload_lora_weights()
with capture_component_call(pipeline, 'transformer') as call:
pipeline(*args, **kwargs)
dynamic_shapes = tree_map_only((torch.Tensor, bool), lambda t: None, call.kwargs)
dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
quantize_(pipeline.transformer_2, Float8DynamicActivationFloat8WeightConfig())
hidden_states: torch.Tensor = call.kwargs['hidden_states']
hidden_states_transposed = hidden_states.transpose(-1, -2).contiguous()
if hidden_states.shape[-1] > hidden_states.shape[-2]:
hidden_states_landscape = hidden_states
hidden_states_portrait = hidden_states_transposed
else:
hidden_states_landscape = hidden_states_transposed
hidden_states_portrait = hidden_states
exported_landscape_1 = torch.export.export(
mod=pipeline.transformer,
args=call.args,
kwargs=call.kwargs | {'hidden_states': hidden_states_landscape},
dynamic_shapes=dynamic_shapes,
)
exported_portrait_2 = torch.export.export(
mod=pipeline.transformer_2,
args=call.args,
kwargs=call.kwargs | {'hidden_states': hidden_states_portrait},
dynamic_shapes=dynamic_shapes,
)
compiled_landscape_1 = aoti_compile(exported_landscape_1, INDUCTOR_CONFIGS)
compiled_portrait_2 = aoti_compile(exported_portrait_2, INDUCTOR_CONFIGS)
compiled_landscape_2 = ZeroGPUCompiledModel(compiled_landscape_1.archive_file, compiled_portrait_2.weights)
compiled_portrait_1 = ZeroGPUCompiledModel(compiled_portrait_2.archive_file, compiled_landscape_1.weights)
return (
compiled_landscape_1,
compiled_landscape_2,
compiled_portrait_1,
compiled_portrait_2,
)
quantize_(pipeline.text_encoder, Int8WeightOnlyConfig())
cl1, cl2, cp1, cp2 = compile_transformer()
def combined_transformer_1(*args, **kwargs):
hidden_states: torch.Tensor = kwargs['hidden_states']
if hidden_states.shape[-1] > hidden_states.shape[-2]:
return cl1(*args, **kwargs)
else:
return cp1(*args, **kwargs)
def combined_transformer_2(*args, **kwargs):
hidden_states: torch.Tensor = kwargs['hidden_states']
if hidden_states.shape[-1] > hidden_states.shape[-2]:
return cl2(*args, **kwargs)
else:
return cp2(*args, **kwargs)
pipeline.transformer.forward = combined_transformer_1
drain_module_parameters(pipeline.transformer)
pipeline.transformer_2.forward = combined_transformer_2
drain_module_parameters(pipeline.transformer_2) |