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[kernel] [bugfix] [ci] bump v0.2.4. Fix STA output handling, TurboDiffusion CUDA norm dtypes for fastvideo-kernel unit tests. (#1020)

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Co-authored-by: William Lin <SolitaryThinker@users.noreply.github.com>
This commit is contained in:
alexzms
2026-01-19 17:42:42 -08:00
committed by GitHub
co-authored by William Lin
parent 21f3ce6577
commit 31f44110b5
10 changed files with 138 additions and 50 deletions
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fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.cu
+53 -17
View File
@@ -4,6 +4,7 @@
#include <torch/all.h>
#include <torch/python.h>
#include <cutlass/cutlass.h>
#include <cutlass/numeric_types.h>
#include "common/common.hpp"
#include "norm/layernorm.hpp"
@@ -14,10 +15,6 @@ auto layer_norm(
std::optional<at::Tensor const> const B,
std::optional<at::Tensor> Output
) {
using ElementIn = float;
using ElementOut = float;
using ElementWeight = float;
int64_t const m = Input.size(0);
int64_t const n = Input.size(1);
torch::Device const input_device = Input.device();
@@ -26,31 +23,70 @@ auto layer_norm(
Output.emplace(
torch::empty(
{m, n},
torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
)
);
}
TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
"Output dtype must match Input dtype. Got Output=",
Output.value().scalar_type(), ", Input=", Input.scalar_type());
if (W.has_value()) {
TORCH_CHECK(W.value().scalar_type() == Input.scalar_type(),
"W dtype must match Input dtype. Got W=",
W.value().scalar_type(), ", Input=", Input.scalar_type());
}
if (B.has_value()) {
TORCH_CHECK(B.value().scalar_type() == Input.scalar_type(),
"B dtype must match Input dtype. Got B=",
B.value().scalar_type(), ", Input=", Input.scalar_type());
}
void *Iptr = Input.data_ptr();
void *Wptr = W.has_value() ? W.value().data_ptr() : nullptr;
void *Bptr = B.has_value() ? B.value().data_ptr() : nullptr;
void *Optr = Output.value().data_ptr();
BOOL_SWITCH(B.has_value(), BIAS, [&]{
BOOL_SWITCH(W.has_value(), AFFINE, [&]{
CONFIG_SWITCH(n, [&]{
layernorm<
ElementIn, ElementOut, ElementWeight,
AFFINE, BIAS,
MAX_HIDDEN_SIZE, NUM_THR_PER_CTA> (
Iptr, Wptr, Bptr,
Optr, eps, m, n,
at::cuda::getCurrentCUDAStream().stream()
);
auto stream = at::cuda::getCurrentCUDAStream().stream();
if (Input.scalar_type() == at::kHalf) {
using ElementIn = cutlass::half_t;
using ElementOut = cutlass::half_t;
using ElementWeight = cutlass::half_t;
BOOL_SWITCH(B.has_value(), BIAS, [&]{
BOOL_SWITCH(W.has_value(), AFFINE, [&]{
CONFIG_SWITCH(n, [&]{
layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
});
});
});
});
} else if (Input.scalar_type() == at::kBFloat16) {
using ElementIn = cutlass::bfloat16_t;
using ElementOut = cutlass::bfloat16_t;
using ElementWeight = cutlass::bfloat16_t;
BOOL_SWITCH(B.has_value(), BIAS, [&]{
BOOL_SWITCH(W.has_value(), AFFINE, [&]{
CONFIG_SWITCH(n, [&]{
layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
});
});
});
} else if (Input.scalar_type() == at::kFloat) {
using ElementIn = float;
using ElementOut = float;
using ElementWeight = float;
BOOL_SWITCH(B.has_value(), BIAS, [&]{
BOOL_SWITCH(W.has_value(), AFFINE, [&]{
CONFIG_SWITCH(n, [&]{
layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
});
});
});
} else {
TORCH_CHECK(false, "Unsupported dtype for layer_norm_cuda: ", Input.scalar_type());
}
fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.hpp
+15 -2
View File
@@ -68,9 +68,22 @@ public:
// mean reduction
float u = _reduce_sum(x, shared_data) / params.n;
// IMPORTANT:
// Loader pads out-of-range lanes with 0. That is OK for the sum, but after
// subtracting mean, those padded lanes become -u and would incorrectly
// contribute to the variance. Mask them back to 0 before variance reduction.
// We launch exactly NumThrPerCta threads for a 1xMaxHiddenSize tile,
// so each thread is responsible for a contiguous chunk in N.
int thr_n_offset = tidx * NumElementPerThread;
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < NumElementPerThread; ++i)
x[i] -= u;
for (int i = 0; i < NumElementPerThread; ++i) {
int idx = thr_n_offset + i;
if (idx < params.n) {
x[i] -= u;
} else {
x[i] = 0.f;
}
}
__syncthreads();
// var reduction
fastvideo-kernel/csrc/turbodiffusion/norm/rmsnorm.cu
+38 -17
View File
@@ -4,6 +4,7 @@
#include <torch/all.h>
#include <torch/python.h>
#include <cutlass/cutlass.h>
#include <cutlass/numeric_types.h>
#include <pybind11/pybind11.h>
#include "common/common.hpp"
@@ -16,10 +17,6 @@ auto rms_norm(
std::optional<at::Tensor>& Output
) {
using ElementIn = float;
using ElementOut = float;
using ElementWeight = float;
int64_t const m = Input.size(0);
int64_t const n = Input.size(1);
torch::Device const input_device = Input.device();
@@ -28,27 +25,51 @@ auto rms_norm(
Output.emplace(
torch::empty(
{m, n},
torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
)
);
}
TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
"Output dtype must match Input dtype. Got Output=",
Output.value().scalar_type(), ", Input=", Input.scalar_type());
if (Weight.has_value()) {
TORCH_CHECK(Weight.value().scalar_type() == Input.scalar_type(),
"Weight dtype must match Input dtype. Got Weight=",
Weight.value().scalar_type(), ", Input=", Input.scalar_type());
}
void *Iptr = Input.data_ptr();
void *Wptr = Weight.has_value() ? Weight.value().data_ptr() : nullptr;
void *Optr = Output.value().data_ptr();
CONFIG_SWITCH(n, [&]{
rmsnorm<
ElementIn, ElementOut, ElementWeight,
MAX_HIDDEN_SIZE, NUM_THR_PER_CTA
> (
Iptr, Wptr,
Optr,
eps, m, n,
at::cuda::getCurrentCUDAStream().stream()
);
});
if (Input.scalar_type() == at::kHalf) {
using ElementIn = cutlass::half_t;
using ElementOut = cutlass::half_t;
using ElementWeight = cutlass::half_t;
CONFIG_SWITCH(n, [&]{
rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
});
} else if (Input.scalar_type() == at::kBFloat16) {
using ElementIn = cutlass::bfloat16_t;
using ElementOut = cutlass::bfloat16_t;
using ElementWeight = cutlass::bfloat16_t;
CONFIG_SWITCH(n, [&]{
rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
});
} else if (Input.scalar_type() == at::kFloat) {
using ElementIn = float;
using ElementOut = float;
using ElementWeight = float;
CONFIG_SWITCH(n, [&]{
rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
});
} else {
TORCH_CHECK(false, "Unsupported dtype for rms_norm_cuda: ", Input.scalar_type());
}
return Output;
fastvideo-kernel/pyproject.toml
+1 -1
View File
@@ -9,7 +9,7 @@ build-backend = "scikit_build_core.build"
[project]
name = "fastvideo-kernel"
version = "0.2.3"
version = "0.2.4"
description = "Unified CUDA kernels for FastVideo"
readme = "README.md"
requires-python = ">=3.10"
fastvideo-kernel/python/fastvideo_kernel/block_sparse_attn.py
+8 -1
View File
@@ -1,5 +1,6 @@
from __future__ import annotations
import os
from typing import Tuple
import torch
@@ -23,6 +24,12 @@ def _is_sm90() -> bool:
return major == 9 and minor == 0
def _force_triton() -> bool:
# Force Triton even on SM90 and even if the compiled extension is available.
# Useful for CI / debugging / parity testing.
return os.environ.get("FASTVIDEO_KERNEL_VSA_FORCE_TRITON", "0") == "1"
def _map_to_index_torch(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Pure-torch (no triton) conversion:
@@ -281,7 +288,7 @@ def block_sparse_attn(
- Otherwise: uses Triton implementation (requires q/k/v to have same padded length today).
"""
block_sparse_fwd, block_sparse_bwd = _get_sm90_ops()
if _is_sm90() and (block_sparse_fwd is not None) and (block_sparse_bwd is not None):
if (not _force_triton()) and _is_sm90() and (block_sparse_fwd is not None) and (block_sparse_bwd is not None):
return block_sparse_attn_sm90(q, k, v, block_map, variable_block_sizes)
# Triton path: generally assumes q/k/v share the same padded length
if q.shape[2] != k.shape[2] or q.shape[2] != v.shape[2]:
fastvideo-kernel/python/fastvideo_kernel/ops.py
+11 -3
View File
@@ -46,14 +46,22 @@ def sliding_tile_attention(
flag = shape_map[seq_shape]
for head_idx, (t, h, w) in enumerate(window_size):
# Per-head slices are not contiguous in the batch dimension when batch>1
# (they keep the original head-stride). The TK kernel assumes contiguous
# [B, H, S, D] layout, so we materialize a contiguous [B,1,S,D] view.
q_h = q[:, head_idx:head_idx + 1].contiguous()
k_h = k[:, head_idx:head_idx + 1].contiguous()
v_h = v[:, head_idx:head_idx + 1].contiguous()
o_h = torch.empty_like(q_h)
sta_fwd(
q[:, head_idx:head_idx + 1], k[:, head_idx:head_idx + 1],
v[:, head_idx:head_idx + 1], output[:, head_idx:head_idx + 1],
q_h, k_h,
v_h, o_h,
t, h, w, text_length, False, has_text, flag
)
output[:, head_idx:head_idx + 1] = o_h
if has_text:
sta_fwd(q, k, v, output, 3, 3, 3, text_length, True, True, flag)
sta_fwd(q.contiguous(), k.contiguous(), v.contiguous(), output, 3, 3, 3, text_length, True, True, flag)
return output[:, :, :seq_length]
fastvideo-kernel/python/fastvideo_kernel/version.py
+1 -1
View File
@@ -1 +1 @@
__version__ = "0.2.3"
__version__ = "0.2.4"
fastvideo/tests/training/VSA/test_training_loss_VSA.py
+9 -6
View File
@@ -6,6 +6,11 @@ import json
from huggingface_hub import snapshot_download
import torch
# Ensure backend selection happens during import-time initialization
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
# Force VSA to use Triton implementation even on H100 / when CUDA extension is available
# os.environ["FASTVIDEO_KERNEL_VSA_FORCE_TRITON"] = "1"
# Import the training pipeline
sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent))
from fastvideo.training.wan_training_pipeline import main
@@ -19,8 +24,6 @@ h200_reference_wandb_summary_file = "fastvideo/tests/training/VSA/h200_reference
NUM_NODES = "1"
NUM_GPUS_PER_NODE = "2"
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
def run_worker():
"""Worker function that will be run on each GPU"""
# Create and populate args
@@ -118,10 +121,10 @@ def test_distributed_training():
wandb_summary = json.load(open(summary_file))
fields_and_thresholds = {
'avg_step_time': 1.0,
'grad_norm': 0.1,
'step_time': 1.0,
'train_loss': 0.02
'avg_step_time': 3,
'grad_norm': 0.2,
'step_time': 2.5,
'train_loss': 0.04
}
failures = []
pyproject.toml
+1 -1
View File
@@ -63,7 +63,7 @@ dependencies = [
"remote-pdb",
# Kernel & Packaging
"fastvideo-kernel==0.2.3",
"fastvideo-kernel==0.2.4",
"wheel",
# Training Dependencies
pyproject_other.toml
+1 -1
View File
@@ -63,7 +63,7 @@ dependencies = [
"remote-pdb",
# Kernel & Packaging
"fastvideo-kernel==0.2.3",
"fastvideo-kernel==0.2.4",
"wheel",
# Training Dependencies