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Set encoder TP size to 1 by default (#569)

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This commit is contained in:
Wenxuan Tan
2025-07-09 17:44:24 -05:00
committed by GitHub
parent 14adfe2edc
commit 65ed588570
18 changed files with 27 additions and 53 deletions
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docs/source/inference/cli.md
+3 -3
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@@ -27,7 +27,7 @@ fastvideo generate --help
### Hardware Configuration
- `--num-gpus {NUM_GPUS}`: Number of GPUs to use
- `--tp-size {TP_SIZE}`: Tensor parallelism size (Typically should match the number of GPUs)
- `--tp-size {TP_SIZE}`: Tensor parallelism size (only for the encoder, should not be larger than 1 if text encoder offload is enabled, as layerwise offload + prefetch is faster)
- `--sp-size {SP_SIZE}`: Sequence parallelism size (Typically should match the number of GPUs)
#### Video Configuration
@@ -68,7 +68,7 @@ Example configuration file (config.json):
"output_path": "outputs/",
"num_gpus": 2,
"sp_size": 2,
"tp_size": 2,
"tp_size": 1,
"num_frames": 45,
"height": 720,
"width": 1280,
@@ -102,7 +102,7 @@ prompt: "A beautiful woman in a red dress walking down a street"
output_path: "outputs/"
num_gpus: 2
sp_size: 2
tp_size: 2
tp_size: 1
num_frames: 45
height: 720
width: 1280
examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh
+1 -1
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@@ -30,7 +30,7 @@ training_args=(
parallel_args=(
--num_gpus $NUM_GPUS
--sp_size 8
--tp_size 8
--tp_size 1
--hsdp_replicate_dim 1
--hsdp_shard_dim 8
)
examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.slurm
+1 -1
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@@ -66,7 +66,7 @@ training_args=(
parallel_args=(
--num_gpus $NUM_GPUS
--sp_size $NUM_GPUS
--tp_size $NUM_GPUS
--tp_size 1
--hsdp_replicate_dim $SLURM_JOB_NUM_NODES
--hsdp_shard_dim $NUM_GPUS
)
examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.sh
+1 -1
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@@ -30,7 +30,7 @@ training_args=(
parallel_args=(
--num_gpus $NUM_GPUS
--sp_size $NUM_GPUS
--tp_size $NUM_GPUS
--tp_size 1
--hsdp_replicate_dim 1
--hsdp_shard_dim $NUM_GPUS
)
examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.slurm
+1 -1
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@@ -63,7 +63,7 @@ training_args=(
parallel_args=(
--num_gpus $NUM_GPUS
--sp_size 4
--tp_size 4
--tp_size 1
--hsdp_replicate_dim 2
--hsdp_shard_dim 4
)
fastvideo/v1/fastvideo_args.py
+1 -6
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@@ -292,7 +292,7 @@ class FastVideoArgs:
assert self.sp_size != -1, "sp_size must be set for training"
if self.tp_size == -1:
self.tp_size = self.num_gpus
self.tp_size = 1
if self.sp_size == -1:
self.sp_size = self.num_gpus
if self.hsdp_shard_dim == -1:
@@ -305,11 +305,6 @@ class FastVideoArgs:
if self.num_gpus < max(self.tp_size, self.sp_size):
self.num_gpus = max(self.tp_size, self.sp_size)
if self.tp_size != self.sp_size:
raise ValueError(
f"tp_size ({self.tp_size}) must be equal to sp_size ({self.sp_size})"
)
if self.enable_torch_compile and self.num_gpus > 1:
logger.warning(
"Currently torch compile does not work with multi-gpu. Setting enable_torch_compile to False"
fastvideo/v1/tests/nightly/test_e2e_i2v_overfit_single_sample.py
+1 -1
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@@ -105,7 +105,7 @@ def run_training():
"--num_latent_t", "8",
"--num_gpus", NUM_GPUS_PER_NODE_TRAINING,
"--sp_size", NUM_GPUS_PER_NODE_TRAINING,
"--tp_size", NUM_GPUS_PER_NODE_TRAINING,
"--tp_size", 1,
"--hsdp_replicate_dim", "1",
"--hsdp_shard_dim", NUM_GPUS_PER_NODE_TRAINING,
"--num_gpus", NUM_GPUS_PER_NODE_TRAINING,
fastvideo/v1/tests/ssim/README.md
+3 -3
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@@ -24,7 +24,7 @@ FastHunyuan-diffusers: {
"flow_shift": 17,
"seed": 1024,
"sp_size": 2,
"tp_size": 2,
"tp_size": 1,
"vae_sp": true,
"fps": 24
}
@@ -41,7 +41,7 @@ Wan2.1-T2V-1.3B-Diffusers: {
"flow_shift": 7.0,
"seed": 1024,
"sp_size": 2,
"tp_size": 2,
"tp_size": 1,
"vae_sp": True,
"fps": 24,
"neg_prompt": "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards",
@@ -60,7 +60,7 @@ Wan2.1-I2V-14B-480P-Diffusers: {
"flow_shift": 7.0,
"seed": 1024,
"sp_size": 2,
"tp_size": 2,
"tp_size": 1,
"vae_sp": True,
"fps": 24,
"neg_prompt": "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards",
fastvideo/v1/tests/ssim/test_inference_similarity.py
+6 -6
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@@ -33,7 +33,7 @@ HUNYUAN_PARAMS = {
"flow_shift": 17,
"seed": 1024,
"sp_size": 2,
"tp_size": 2,
"tp_size": 1,
"vae_sp": True,
"fps": 24,
}
@@ -50,7 +50,7 @@ WAN_T2V_PARAMS = {
"flow_shift": 7.0,
"seed": 1024,
"sp_size": 2,
"tp_size": 2,
"tp_size": 1,
"vae_sp": True,
"fps": 24,
"neg_prompt": "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards",
@@ -69,7 +69,7 @@ WAN_I2V_PARAMS = {
"flow_shift": 7.0,
"seed": 1024,
"sp_size": 2,
"tp_size": 2,
"tp_size": 1,
"vae_sp": True,
"fps": 24,
"neg_prompt": "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards",
@@ -238,7 +238,7 @@ def test_i2v_inference_similarity(prompt, ATTENTION_BACKEND, model_id):
logger.error("Failed to write SSIM results to file")
min_acceptable_ssim = 0.97
assert mean_ssim >= min_acceptable_ssim, f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim}"
assert mean_ssim >= min_acceptable_ssim, f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim} for {model_id} with backend {ATTENTION_BACKEND}"
@pytest.mark.parametrize("prompt", TEST_PROMPTS)
@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN", "TORCH_SDPA"])
@@ -337,5 +337,5 @@ def test_inference_similarity(prompt, ATTENTION_BACKEND, model_id):
if not success:
logger.error("Failed to write SSIM results to file")
min_acceptable_ssim = 0.95
assert mean_ssim >= min_acceptable_ssim, f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim}"
min_acceptable_ssim = 0.93
assert mean_ssim >= min_acceptable_ssim, f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim} for {model_id} with backend {ATTENTION_BACKEND}"
fastvideo/v1/tests/training/Vanilla/test_training_loss.py
+1 -1
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@@ -43,7 +43,7 @@ def run_worker():
"--num_latent_t", "4",
"--num_gpus", "4",
"--sp_size", "4",
"--tp_size", "4",
"--tp_size", "1",
"--hsdp_replicate_dim", "1",
"--hsdp_shard_dim", "4",
"--train_sp_batch_size", "1",
scripts/finetune/finetune_v1.sh
+1 -1
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@@ -20,7 +20,7 @@ torchrun --nnodes 1 --nproc_per_node $NUM_GPUS\
--train_batch_size=4 \
--num_latent_t 20 \
--sp_size 4 \
--tp_size 4 \
--tp_size 1 \
--hsdp_replicate_dim 1 \
--hsdp_shard_dim 4 \
--num_gpus $NUM_GPUS \
scripts/inference/v1_inference_fasthunyuan.sh
+1 -4
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@@ -3,13 +3,10 @@
num_gpus=4
export MODEL_BASE=FastVideo/FastHunyuan-Diffusers
export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
# Note that the tp_size and sp_size should be the same and equal to the number
# of GPUs. They are used for different parallel groups. sp_size is used for
# dit model and tp_size is used for encoder models.
fastvideo generate \
--model-path $MODEL_BASE \
--sp-size $num_gpus \
--tp-size $num_gpus \
--tp-size 1 \
--num-gpus $num_gpus \
--height 720 \
--width 1280 \
scripts/inference/v1_inference_hunyuan.sh
+1 -4
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@@ -4,13 +4,10 @@ num_gpus=4
export MODEL_BASE=hunyuanvideo-community/HunyuanVideo
export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
# export MODEL_BASE=hunyuanvideo-community/HunyuanVideo
# Note that the tp_size and sp_size should be the same and equal to the number
# of GPUs. They are used for different parallel groups. sp_size is used for
# dit model and tp_size is used for encoder models.
fastvideo generate \
--model-path $MODEL_BASE \
--sp-size $num_gpus \
--tp-size $num_gpus \
--tp-size 1 \
--num-gpus $num_gpus \
--height 720 \
--width 1280 \
scripts/inference/v1_inference_hunyuan_STA.sh
+1 -4
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@@ -5,13 +5,10 @@ export FASTVIDEO_ATTENTION_CONFIG=assets/mask_strategy_hunyuan.json
export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
export MODEL_BASE=hunyuanvideo-community/HunyuanVideo
# export MODEL_BASE=hunyuanvideo-community/HunyuanVideo
# Note that the tp_size and sp_size should be the same and equal to the number
# of GPUs. They are used for different parallel groups. sp_size is used for
# dit model and tp_size is used for encoder models.
fastvideo generate \
--model-path $MODEL_BASE \
--sp-size ${num_gpus} \
--tp-size ${num_gpus} \
--tp-size 1 \
--height 768 \
--width 1280 \
--num-frames 117 \
scripts/inference/v1_inference_wan.sh
+1 -4
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@@ -4,13 +4,10 @@ num_gpus=2
export FASTVIDEO_ATTENTION_BACKEND=
export MODEL_BASE=Wan-AI/Wan2.1-T2V-1.3B-Diffusers
# export MODEL_BASE=hunyuanvideo-community/HunyuanVideo
# Note that the tp_size and sp_size should be the same and equal to the number
# of GPUs. They are used for different parallel groups. sp_size is used for
# dit model and tp_size is used for encoder models.
fastvideo generate \
--model-path $MODEL_BASE \
--sp-size $num_gpus \
--tp-size $num_gpus \
--tp-size 1 \
--num-gpus $num_gpus \
--height 480 \
--width 832 \
scripts/inference/v1_inference_wan_STA.sh
+1 -4
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@@ -4,13 +4,10 @@ num_gpus=2
export FASTVIDEO_ATTENTION_CONFIG=assets/mask_strategy_wan.json
export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
export MODEL_BASE=Wan-AI/Wan2.1-T2V-14B-Diffusers
# Note that the tp_size and sp_size should be the same and equal to the number
# of GPUs. They are used for different parallel groups. sp_size is used for
# dit model and tp_size is used for encoder models.
fastvideo generate \
--model-path $MODEL_BASE \
--sp-size $num_gpus \
--tp-size $num_gpus \
--tp-size 1 \
--num-gpus $num_gpus \
--height 768 \
--width 1280 \
scripts/inference/v1_inference_wan_VSA.sh
+2 -5
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@@ -4,14 +4,11 @@ num_gpus=1
export FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN
# change model path to local dir if you want to inference using your checkpoint
export MODEL_BASE=Wan-AI/Wan2.1-T2V-1.3B-Diffusers
# export MODEL_BASE=hunyuanvideo-community/HunyuanVideo
# Note that the tp_size and sp_size should be the same and equal to the number
# of GPUs. They are used for different parallel groups. sp_size is used for
# dit model and tp_size is used for encoder models.
# export MODEL_BASE=hunyuanvideo-community/HunyuanVideo
fastvideo generate \
--model-path $MODEL_BASE \
--sp-size $num_gpus \
--tp-size $num_gpus \
--tp-size 1 \
--num-gpus $num_gpus \
--height 448 \
--width 832 \
scripts/inference/v1_inference_wan_i2v.sh
-3
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@@ -4,9 +4,6 @@ num_gpus=2
export FASTVIDEO_ATTENTION_BACKEND=
export MODEL_BASE=Wan-AI/Wan2.1-I2V-14B-480P-Diffusers
# export MODEL_BASE=hunyuanvideo-community/HunyuanVideo
# Note that the tp_size and sp_size should be the same and equal to the number
# of GPUs. They are used for different parallel groups. sp_size is used for
# dit model and tp_size is used for encoder models.
fastvideo generate \
--model-path $MODEL_BASE \
--sp-size $num_gpus \