Update configs & Using sgl-fp8 as default

configs/seko_talk/seko_talk_02_fp8.json

@@ -15,7 +15,7 @@
     "cpu_offload": false,
     "use_31_block": false,
     "mm_config": {
-        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Vllm"
+        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Sgl"
     },
     "adapter_quantized": true,
     "adapter_quant_scheme": "fp8",

configs/seko_talk/seko_talk_04_fp8_dist.json

@@ -19,7 +19,7 @@
         "seq_p_attn_type": "ulysses"
     },
     "mm_config": {
-        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Vllm"
+        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Sgl"
     },
     "adapter_quantized": true,
     "adapter_quant_scheme": "fp8",

configs/seko_talk/seko_talk_06_offload_fp8_H100.json

@@ -27,6 +27,6 @@
     "vae_cpu_offload": false,
     "use_tiling_vae": true,
     "mm_config": {
-            "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Vllm"
+            "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Sgl"
     }
 }

configs/seko_talk/seko_talk_10_fp8_dist_fixed_min_area.json

@@ -19,7 +19,7 @@
         "seq_p_attn_type": "ulysses"
     },
     "mm_config": {
-        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Vllm"
+        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Sgl"
     },
     "adapter_quantized": true,
     "adapter_quant_scheme": "fp8",

configs/seko_talk/seko_talk_11_fp8_dist_fixed_shape.json

@@ -23,7 +23,7 @@
         "seq_p_attn_type": "ulysses"
     },
     "mm_config": {
-        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Vllm"
+        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Sgl"
     },
     "adapter_quantized": true,
     "adapter_quant_scheme": "fp8",

configs/seko_talk/seko_talk_12_fp8_dist_fixed_shape_8gpus_1s.json

@@ -24,7 +24,7 @@
         "seq_p_attn_type": "ulysses"
     },
     "mm_config": {
-        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Vllm"
+        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Sgl"
     },
     "adapter_quantized": true,
     "adapter_quant_scheme": "fp8",

lightx2v/models/input_encoders/hf/t5/model.py

@@ -8,7 +8,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from loguru import logger
 
-from lightx2v.models.input_encoders.hf.q_linear import Q8FQuantLinearFp8, Q8FQuantLinearInt8, TorchaoQuantLinearInt8, VllmQuantLinearFp8, VllmQuantLinearInt8
+from lightx2v.models.input_encoders.hf.q_linear import Q8FQuantLinearFp8, Q8FQuantLinearInt8, SglQuantLinearFp8, TorchaoQuantLinearInt8, VllmQuantLinearInt8
 from lightx2v.utils.envs import *
 from lightx2v.utils.utils import load_weights
 
@@ -87,7 +87,7 @@ class T5Attention(nn.Module):
             if quant_scheme == "int8":
                 linear_cls = VllmQuantLinearInt8
             elif quant_scheme == "fp8":
-                linear_cls = VllmQuantLinearFp8
+                linear_cls = SglQuantLinearFp8
             elif quant_scheme == "int8-torchao":
                 linear_cls = TorchaoQuantLinearInt8
             elif quant_scheme == "int8-q8f":
@@ -154,7 +154,7 @@ class T5FeedForward(nn.Module):
             if quant_scheme == "int8":
                 linear_cls = VllmQuantLinearInt8
             elif quant_scheme == "fp8":
-                linear_cls = VllmQuantLinearFp8
+                linear_cls = SglQuantLinearFp8
             elif quant_scheme == "int8-torchao":
                 linear_cls = TorchaoQuantLinearInt8
             elif quant_scheme == "int8-q8f":

lightx2v/models/input_encoders/hf/xlm_roberta/model.py

@@ -10,7 +10,7 @@ from loguru import logger
 
 # from lightx2v.attentions import attention
 from lightx2v.common.ops.attn import TorchSDPAWeight
-from lightx2v.models.input_encoders.hf.q_linear import Q8FQuantLinearFp8, Q8FQuantLinearInt8, TorchaoQuantLinearInt8, VllmQuantLinearFp8, VllmQuantLinearInt8
+from lightx2v.models.input_encoders.hf.q_linear import Q8FQuantLinearFp8, Q8FQuantLinearInt8, SglQuantLinearFp8, TorchaoQuantLinearInt8, VllmQuantLinearInt8
 from lightx2v.utils.utils import load_weights
 
 __all__ = [
@@ -62,7 +62,7 @@ class SelfAttention(nn.Module):
             if quant_scheme == "int8":
                 linear_cls = VllmQuantLinearInt8
             elif quant_scheme == "fp8":
-                linear_cls = VllmQuantLinearFp8
+                linear_cls = SglQuantLinearFp8
             elif quant_scheme == "int8-torchao":
                 linear_cls = TorchaoQuantLinearInt8
             elif quant_scheme == "int8-q8f":
@@ -140,7 +140,7 @@ class AttentionBlock(nn.Module):
             if quant_scheme == "int8":
                 linear_cls = VllmQuantLinearInt8
             elif quant_scheme == "fp8":
-                linear_cls = VllmQuantLinearFp8
+                linear_cls = SglQuantLinearFp8
             elif quant_scheme == "int8-torchao":
                 linear_cls = TorchaoQuantLinearInt8
             elif quant_scheme == "int8-q8f":

lightx2v/models/runners/default_runner.py

@@ -217,7 +217,8 @@ class DefaultRunner(BaseRunner):
     def run_main(self, total_steps=None):
         self.init_run()
         for segment_idx in range(self.video_segment_num):
-            with ProfilingContext4Debug(f"segment end2end {segment_idx}"):
+            logger.info(f"🔄 segment_idx: {segment_idx + 1}/{self.video_segment_num}")
+            with ProfilingContext(f"segment end2end {segment_idx}"):
                 # 1. default do nothing
                 self.init_run_segment(segment_idx)
                 # 2. main inference loop

lightx2v/models/runners/wan/wan_audio_runner.py

@@ -456,7 +456,7 @@ class WanAudioRunner(WanRunner):  # type:ignore
 
         self.config.seed = self.config.seed + segment_idx
         torch.manual_seed(self.config.seed)
-        logger.info(f"Processing segment {segment_idx + 1}/{self.video_segment_num}, seed: {self.config.seed}")
+        # logger.info(f"Processing segment {segment_idx + 1}/{self.video_segment_num}, seed: {self.config.seed}")
 
         if (self.config.get("lazy_load", False) or self.config.get("unload_modules", False)) and not hasattr(self, "audio_encoder"):
             self.audio_encoder = self.load_audio_encoder()

lightx2v/models/video_encoders/hf/wan/vae.py

@@ -893,7 +893,7 @@ class WanVAE:
     def _calculate_2d_grid(self, latent_height, latent_width, world_size):
         if (latent_height, latent_width, world_size) in self.grid_table:
             best_h, best_w = self.grid_table[(latent_height, latent_width, world_size)]
-            logger.info(f"Vae using cached 2D grid: {best_h}x{best_w} grid for {latent_height}x{latent_width} latent")
+            # logger.info(f"Vae using cached 2D grid: {best_h}x{best_w} grid for {latent_height}x{latent_width} latent")
             return best_h, best_w
 
         best_h, best_w = 1, world_size
@@ -908,7 +908,7 @@ class WanVAE:
                     if aspect_diff < min_aspect_diff:
                         min_aspect_diff = aspect_diff
                         best_h, best_w = h, w
-        logger.info(f"Vae using 2D grid & Update cache: {best_h}x{best_w} grid for {latent_height}x{latent_width} latent")
+        # logger.info(f"Vae using 2D grid & Update cache: {best_h}x{best_w} grid for {latent_height}x{latent_width} latent")
         self.grid_table[(latent_height, latent_width, world_size)] = (best_h, best_w)
         return best_h, best_w