Create optimization.py

optimization.py

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+
+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=1500)
+    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="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.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)