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Merge branch 'main' into nightly

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Daniel Han 2025-05-28 06:15:34 -07:00
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23
unsloth/kernels/moe/README.md
View file

@ -43,18 +43,26 @@ sets of tests. E.g., to run forward tests with autotune turned on: `pytest -sv
- `grouped_gemm/tests/test_qwen3_moe.py`: end to end test for Qwen3 MoE block. IMPORTANT: read `tests/run_qwen3_moe_tests.sh` as well as notes in the test itself for complications when running parametrized pytest test suites and triton / autotune. TLDR: use the test script and NOT pytest to run the tests.
### Benchmarks
- `grouped_gemm/benchmark/benchmark_fused_moe.py`: benchmarks HF `Qwen3SpareMOEBlock` against the fused implementation
- `grouped_gemm/benchmark/benchmark_fused_moe.py`: benchmarks HF `Qwen3SpareMOEBlock` or `Llama4TextMoe` against the fused implementation
Running with these flags on an `H100` to bench forward pass (run with `--help` to see all available flags):
For `Qwen3-30B-A3B`:
```
python benchmark/benchmark_fused_moe.py --mode forward --seqlen 1024 --permute_x --permute_y --autotune
python benchmark/benchmark_fused_moe.py --model qwen3 --mode forward --seqlen 1024 --permute_x --permute_y --autotune
```
For the backward bench:
```
python benchmark/benchmark_fused_moe.py --mode backward --seqlen 1024 --permute_x --permute_y --autotune
python benchmark/benchmark_fused_moe.py --model qwen3 --mode backward --seqlen 1024 --permute_x --permute_y --autotune
```
On my machine and env, I get speedups > 25x and 14x respectively.
For `Llama-4-Scout-17B-16E`:
```
python benchmark/benchmark_fused_moe.py --model llama4 --autotune --mode=forward --permute_y
```
Ditto for backwards.
### Notes
- Tested and benched on `H100`, though should run on Ampere and possibly even earlier gpu generations though the autotuning configs will need to be adjusted.
@ -62,6 +70,7 @@ On my machine and env, I get speedups > 25x and 14x respectively.
- The kernels can be run either as autotuned (see `autotuning.py`) or with manually specified config (see `tuning.py`). Recommended to run using autotuner since the MoE block requires 2 configs for the forward (2 grouped gemms) and 4 for the backwards (dX and dW per grouped gemm, 2 grouped gemms).
- Running with autotuning turned off with the default manual kernel config will result is **highly** sub-optimal performance as it is only meant for testing / debugging purposes.
- I've tried to strike a balance between compilation time and autotuning search space -- can probably squeeze even more performance for specific workloads.
- The Llama4 reference layer is still highly under-optimized as there are many low-hanging opportunities for further speedups around routing and shared expert calculation.
TODO:
- TMA store: implemented but not enabled currently due to non-determinism arising from triton pipelining bug.
@ -69,4 +78,8 @@ TODO:
- Additional optimizations:
- Fused / optimized implementations of routing, token sorting, etc.
- Better software pipelining within grouped gemm
- Threadblock swizzling for better L2 caching
- Threadblock swizzling for better L2 caching
- Llama4
- Fused gather / topk weight merging
- Custom topk, gather indices kernel
- Shared expert fusion with experts calculation

422
unsloth/kernels/moe/benchmark/benchmark_fused_moe.py
View file

@ -1,7 +1,22 @@
import argparse
import time
from contextlib import nullcontext
import torch
from transformers import AutoConfig
from transformers.models.llama4 import Llama4TextConfig
from transformers.models.llama4.modeling_llama4 import Llama4TextMoe
from transformers.models.qwen3_moe import Qwen3MoeConfig
from transformers.models.qwen3_moe.modeling_qwen3_moe import Qwen3MoeSparseMoeBlock
from triton.testing import do_bench
from utils import (
create_kernel_configs,
get_autotuner,
post_process_results,
postprocess_autotune_results,
save_results,
)
from grouped_gemm.kernels.autotuning import (
DEFAULT_K_BLOCK_SIZES,
DEFAULT_M_BLOCK_SIZES,
@ -16,60 +31,50 @@ from grouped_gemm.kernels.tuning import (
KernelResult,
TritonTuningContext,
)
from grouped_gemm.reference.moe_block import Qwen3MoeFusedGroupedGEMMBlock
from transformers import AutoConfig
from transformers.models.qwen3_moe import Qwen3MoeConfig
from transformers.models.qwen3_moe.modeling_qwen3_moe import Qwen3MoeSparseMoeBlock
from triton.testing import do_bench
from utils import (
create_kernel_configs,
post_process_results,
save_results,
)
from grouped_gemm.reference.layers.llama4_moe import Llama4TritonTextMoe
from grouped_gemm.reference.layers.qwen3_moe import Qwen3MoeFusedGroupedGEMMBlock
SEED = 42
LLAMA4_ID = "meta-llama/Llama-4-Scout-17B-16E"
QWEN3_MODEL_ID = "Qwen/Qwen3-30B-A3B"
def run_benchmark_forward(
config: Qwen3MoeConfig,
ref_model: torch.nn.Module,
tt_model: torch.nn.Module,
config: AutoConfig,
seqlen: int,
dtype: torch.dtype,
permute_x: bool,
permute_y: bool,
autotune: bool,
kernel_config_fwd: KernelConfigForward = None,
kernel_config_bwd_dW: KernelConfigBackward_dW = None,
kernel_config_bwd_dX: KernelConfigBackward_dX = None,
bs: int = 1,
):
torch.manual_seed(SEED) # Should not be needed when running using pytest -- autouse fixture in conftest.py
torch.manual_seed(
SEED
) # Should not be needed when running using pytest -- autouse fixture in conftest.py
device = "cuda"
hidden_size = config.hidden_size
bs = 1
# Reference op -- HF
moe_block = Qwen3MoeSparseMoeBlock(config).to(device, dtype)
X = torch.randn(
bs, seqlen, hidden_size, dtype=dtype, device=device, requires_grad=True
)
# Triton kernel grouped gemm version of MoE Block -- this is what we're testing
fused_gemm_block = Qwen3MoeFusedGroupedGEMMBlock.from_hf(
moe_block,
permute_x=permute_x,
permute_y=permute_y,
autotune=autotune,
kernel_config_fwd=kernel_config_fwd,
kernel_config_bwd_dW=kernel_config_bwd_dW,
kernel_config_bwd_dX=kernel_config_bwd_dX,
).to(device, dtype)
X = torch.randn(bs, seqlen, hidden_size, dtype=dtype, device=device, requires_grad=True)
ref_output, _ = moe_block(X)
# Forward
bench_forward_ref = lambda: moe_block(X)
bench_forward_fused = lambda: fused_gemm_block(X)
bench_forward_ref = lambda: ref_model(X) # noqa: E731
bench_forward_fused = lambda: tt_model(X) # noqa: E731
ref_forward_time = do_bench(bench_forward_ref)
with TritonTuningContext(kernel_config_fwd) as ctx:
if not autotune:
assert kernel_config_fwd is not None
tuning_context = TritonTuningContext(kernel_config_fwd)
else:
tuning_context = nullcontext()
with tuning_context:
fused_forward_time = do_bench(bench_forward_fused)
if not ctx.success:
if (not autotune) and (not tuning_context.success):
return 0, 1
print(
@ -77,8 +82,107 @@ def run_benchmark_forward(
)
return ref_forward_time, fused_forward_time
def run_benchmark_backward(
config: Qwen3MoeConfig,
ref_model: torch.nn.Module,
tt_model: torch.nn.Module,
config: AutoConfig,
seqlen: int,
dtype: torch.dtype,
bs=1,
):
torch.manual_seed(
SEED
) # Should not be needed when running using pytest -- autouse fixture in conftest.py
device = "cuda"
hidden_size = config.hidden_size
X = torch.randn(
bs, seqlen, hidden_size, dtype=dtype, device=device, requires_grad=True
)
X_test = X.detach().clone().requires_grad_(True)
output, _ = ref_model(X)
# Prevent autotuning forward pass
from grouped_gemm.kernels.forward import _autotuned_grouped_gemm_forward_kernel
_autotuned_grouped_gemm_forward_kernel.configs = (
_autotuned_grouped_gemm_forward_kernel.configs[:20]
)
test_output, _ = tt_model(X_test)
# Bench
grad_output = torch.randn_like(output)
bench_backward_ref = lambda: output.backward(grad_output, retain_graph=True) # noqa: E731
bench_backward_fused = lambda: test_output.backward(grad_output, retain_graph=True) # noqa: E731
ref_backward_time = do_bench(
bench_backward_ref, grad_to_none=[X, *ref_model.parameters()]
)
fused_backward_time = do_bench(
bench_backward_fused, grad_to_none=[X_test, *tt_model.parameters()]
)
print(
f"Backward: ref {ref_backward_time:.4f}, fused {fused_backward_time:.4f}, speedup {ref_backward_time / fused_backward_time:.1f}x"
)
return ref_backward_time, fused_backward_time
def setup_model(
config: Qwen3MoeConfig | Llama4TextConfig,
dtype,
permute_x,
permute_y,
autotune,
kernel_config_fwd,
kernel_config_bwd_dW,
kernel_config_bwd_dX,
dX_only=False,
dW_only=False,
overlap_router_shared=False,
device="cuda",
):
if isinstance(config, Qwen3MoeConfig):
ref_model = Qwen3MoeSparseMoeBlock(config).to(device, dtype)
# Triton kernel grouped gemm version of MoE Block -- this is what we're testing
tt_model = Qwen3MoeFusedGroupedGEMMBlock.from_hf(
ref_model,
permute_x=permute_x,
permute_y=permute_y,
autotune=autotune,
kernel_config_fwd=kernel_config_fwd,
kernel_config_bwd_dW=kernel_config_bwd_dW,
kernel_config_bwd_dX=kernel_config_bwd_dX,
dX_only=dX_only,
dW_only=dW_only,
).to(device, dtype)
elif isinstance(config, Llama4TextConfig):
ref_model = Llama4TextMoe(config).to(device, dtype)
tt_model = Llama4TritonTextMoe(
config,
overlap_router_shared=overlap_router_shared,
permute_x=permute_x,
permute_y=permute_y,
autotune=autotune,
kernel_config_fwd=kernel_config_fwd,
kernel_config_bwd_dW=kernel_config_bwd_dW,
kernel_config_bwd_dX=kernel_config_bwd_dX,
dX_only=dX_only,
dW_only=dW_only,
).to(device, dtype)
else:
raise ValueError(f"Unrecognized config {type(config).__name__}")
return ref_model, tt_model
def run_benchmark(
mode: str,
model_config: Qwen3MoeConfig | Llama4TextConfig,
seqlen: int,
dtype: torch.dtype,
permute_x: bool,
@ -87,20 +191,21 @@ def run_benchmark_backward(
kernel_config_fwd: KernelConfigForward = None,
kernel_config_bwd_dW: KernelConfigBackward_dW = None,
kernel_config_bwd_dX: KernelConfigBackward_dX = None,
dX_only: bool = False,
dW_only: bool = False,
overlap_router_shared: bool = False,
results_dir: str = None,
):
torch.manual_seed(SEED) # Should not be needed when running using pytest -- autouse fixture in conftest.py
device = "cuda"
hidden_size = config.hidden_size
bs = 1
if autotune:
autotuner = get_autotuner(mode)
if mode == "dW":
dW_only = True
elif mode == "dX":
dX_only = True
else:
dW_only = dX_only = False
# Reference op -- HF
moe_block = Qwen3MoeSparseMoeBlock(config).to(device, dtype)
# Triton kernel grouped gemm version of MoE Block -- this is what we're testing
fused_gemm_block = Qwen3MoeFusedGroupedGEMMBlock.from_hf(
moe_block,
ref_model, tt_model = setup_model(
model_config,
dtype=dtype,
permute_x=permute_x,
permute_y=permute_y,
autotune=autotune,
@ -109,130 +214,109 @@ def run_benchmark_backward(
kernel_config_bwd_dX=kernel_config_bwd_dX,
dX_only=dX_only,
dW_only=dW_only,
).to(device, dtype)
X = torch.randn(bs, seqlen, hidden_size, dtype=dtype, device=device, requires_grad=True)
X_test = X.detach().clone().requires_grad_(True)
output, _ = moe_block(X)
# Prevent autotuning forward pass
from grouped_gemm.kernels.forward import _autotuned_grouped_gemm_forward_kernel
_autotuned_grouped_gemm_forward_kernel.configs = _autotuned_grouped_gemm_forward_kernel.configs[:20]
test_output, _ = fused_gemm_block(X_test)
# Bench
grad_output = torch.randn_like(output)
bench_backward_ref = lambda: output.backward(grad_output, retain_graph=True) # noqa: E731
bench_backward_fused = lambda: test_output.backward(grad_output, retain_graph=True) # noqa: E731
ref_backward_time = do_bench(bench_backward_ref, grad_to_none=[X, *moe_block.parameters()])
fused_backward_time = do_bench(bench_backward_fused, grad_to_none=[X_test, *fused_gemm_block.parameters()])
print(
f"Backward: ref {ref_backward_time:.4f}, fused {fused_backward_time:.4f}, speedup {ref_backward_time / fused_backward_time:.1f}x"
overlap_router_shared=overlap_router_shared,
)
return ref_backward_time, fused_backward_time
def run_benchmark(
mode: str,
model_config: Qwen3MoeConfig,
seqlen: int,
dtype: torch.dtype,
permute_x: bool,
permute_y: bool,
autotune: bool,
kernel_config_fwd: KernelConfigForward = None,
kernel_config_bwd_dW: KernelConfigBackward_dW = None,
kernel_config_bwd_dX: KernelConfigBackward_dX = None,
):
if mode == "forward":
ref_time, fused_time = run_benchmark_forward(
model_config,
seqlen,
dtype,
permute_x,
permute_y,
autotune,
kernel_config_fwd,
kernel_config_bwd_dW,
kernel_config_bwd_dX,
ref_model,
tt_model,
config=model_config,
seqlen=seqlen,
dtype=dtype,
autotune=autotune,
kernel_config_fwd=kernel_config_fwd,
)
elif mode == "dW":
else:
ref_time, fused_time = run_benchmark_backward(
model_config,
seqlen,
dtype,
permute_x,
permute_y,
autotune,
kernel_config_fwd,
kernel_config_bwd_dW,
kernel_config_bwd_dX,
dW_only=True,
)
elif mode == "dX":
ref_time, fused_time = run_benchmark_backward(
model_config,
seqlen,
dtype,
permute_x,
permute_y,
autotune,
kernel_config_fwd,
kernel_config_bwd_dW,
kernel_config_bwd_dX,
dX_only=True,
)
elif mode == "backward":
ref_time, fused_time = run_benchmark_backward(
model_config,
seqlen,
dtype,
permute_x,
permute_y,
autotune,
kernel_config_fwd,
kernel_config_bwd_dW,
kernel_config_bwd_dX,
dX_only=False,
dW_only=False,
ref_model, tt_model, config=model_config, seqlen=seqlen, dtype=dtype
)
if autotune:
if mode == "backward":
autotuner_dW, autotuner_dX = autotuner
postprocess_autotune_results(
autotuner_dW, "dW", ref_time, fused_time, results_dir
)
postprocess_autotune_results(
autotuner_dX, "dX", ref_time, fused_time, results_dir
)
else:
postprocess_autotune_results(
autotuner, mode, ref_time, fused_time, results_dir
)
return ref_time, fused_time
# NOTE: better to use autotuner for now, since the MoE block needs 2 different kernel configs for forward (2 grouped gemms, gate_up_proj and down_proj)
# and the backward pass needs 4 different kernel configs (2 grouped gemms each for dW and dX)
# The benchmark only supports 1 kernel config at a time so the same config will be used for both grouped gemms, which is suboptimal.
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--results_dir", type=str, default="benchmark_results")
parser.add_argument("--model", type=str, choices=["llama4", "qwen3"], required=True)
parser.add_argument("--seqlen", type=int, default=1024)
parser.add_argument("--dtype", type=str, choices=["bfloat16", "float16"], default="bfloat16")
parser.add_argument(
"--dtype", type=str, choices=["bfloat16", "float16"], default="bfloat16"
)
parser.add_argument("--permute_x", action="store_true")
parser.add_argument("--permute_y", action="store_true")
parser.add_argument("--autotune", action="store_true")
parser.add_argument("--BLOCK_SIZE_M", nargs=2, type=int, default=[DEFAULT_M_BLOCK_SIZES[0], DEFAULT_M_BLOCK_SIZES[-1]])
parser.add_argument("--BLOCK_SIZE_N", nargs=2, type=int, default=[DEFAULT_N_BLOCK_SIZES[0], DEFAULT_N_BLOCK_SIZES[-1]])
parser.add_argument("--BLOCK_SIZE_K", nargs=2, type=int, default=[DEFAULT_K_BLOCK_SIZES[0], DEFAULT_K_BLOCK_SIZES[-1]])
parser.add_argument("--num_warps", nargs=2, type=int, default=[DEFAULT_NUM_WARPS[0], DEFAULT_NUM_WARPS[-1]])
parser.add_argument("--num_stages", nargs=2, type=int, default=[DEFAULT_NUM_STAGES[0], DEFAULT_NUM_STAGES[-1]])
parser.add_argument("--use_tma_load_w", action="store_true") # No need to specify, will automatically parametrize these for each kernel config
parser.add_argument("--use_tma_load_x", action="store_true") # No need to specify, will automatically parametrize these for each kernel config
parser.add_argument("--use_tma_load_dy", action="store_true") # No need to specify, will automatically parametrize these for each kernel config
parser.add_argument("--mode", type=str, choices=["forward", "backward", "dW", "dX"], default="forward")
parser.add_argument("--overlap_router_shared", action="store_true")
parser.add_argument(
"--BLOCK_SIZE_M",
nargs=2,
type=int,
default=[DEFAULT_M_BLOCK_SIZES[0], DEFAULT_M_BLOCK_SIZES[-1]],
)
parser.add_argument(
"--BLOCK_SIZE_N",
nargs=2,
type=int,
default=[DEFAULT_N_BLOCK_SIZES[0], DEFAULT_N_BLOCK_SIZES[-1]],
)
parser.add_argument(
"--BLOCK_SIZE_K",
nargs=2,
type=int,
default=[DEFAULT_K_BLOCK_SIZES[0], DEFAULT_K_BLOCK_SIZES[-1]],
)
parser.add_argument(
"--num_warps",
nargs=2,
type=int,
default=[DEFAULT_NUM_WARPS[0], DEFAULT_NUM_WARPS[-1]],
)
parser.add_argument(
"--num_stages",
nargs=2,
type=int,
default=[DEFAULT_NUM_STAGES[0], DEFAULT_NUM_STAGES[-1]],
)
parser.add_argument(
"--use_tma_load_w", action="store_true"
) # No need to specify, will automatically parametrize these for each kernel config
parser.add_argument(
"--use_tma_load_x", action="store_true"
) # No need to specify, will automatically parametrize these for each kernel config
parser.add_argument(
"--use_tma_load_dy", action="store_true"
) # No need to specify, will automatically parametrize these for each kernel config
parser.add_argument(
"--mode",
type=str,
choices=["forward", "backward", "dW", "dX"],
default="forward",
)
args = parser.parse_args()
args.dtype = getattr(torch, args.dtype)
model_id = "Qwen/Qwen3-30B-A3B"
model_id = QWEN3_MODEL_ID if args.model == "qwen3" else LLAMA4_ID
model_config = AutoConfig.from_pretrained(model_id)
model_config = model_config.text_config if args.model == "llama4" else model_config
mode = args.mode
if args.autotune:
# logging.basicConfig(level=logging.INFO)
print(
f"Benchmarking {model_id} {mode}: seqlen={args.seqlen}, dtype={args.dtype}, permute_x={args.permute_x}, permute_y={args.permute_y}, autotune"
)
@ -245,16 +329,28 @@ if __name__ == "__main__":
permute_x=args.permute_x,
permute_y=args.permute_y,
autotune=args.autotune,
overlap_router_shared=args.overlap_router_shared,
results_dir=args.results_dir,
)
end_time = time.time()
print(f"Total time: {end_time - start_time:.4f} seconds")
# NOTE: better to use autotuner for now, since the MoE block needs 2 different kernel configs for forward (2 grouped gemms, gate_up_proj and down_proj)
# and the backward pass needs 4 different kernel configs (2 grouped gemms each for dW and dX)
# The benchmark only supports 1 kernel config at a time so the same config will be used for both grouped gemms, which is suboptimal.
else:
assert False, "Use autotune for now"
kernel_configs = create_kernel_configs(args, args.permute_x, args.permute_y)
print(f"Running {len(kernel_configs)} kernel configs")
default_kernel_config_fwd = KernelConfigForward(permute_x=args.permute_x, permute_y=args.permute_y)
default_kernel_config_bwd_dW = KernelConfigBackward_dW(permute_x=args.permute_x, permute_y=args.permute_y)
default_kernel_config_bwd_dX = KernelConfigBackward_dX(permute_x=args.permute_x, permute_y=args.permute_y)
default_kernel_config_fwd = KernelConfigForward(
permute_x=args.permute_x, permute_y=args.permute_y
)
default_kernel_config_bwd_dW = KernelConfigBackward_dW(
permute_x=args.permute_x, permute_y=args.permute_y
)
default_kernel_config_bwd_dX = KernelConfigBackward_dX(
permute_x=args.permute_x, permute_y=args.permute_y
)
results = []
for kernel_config in kernel_configs:
if args.mode == "forward":
@ -274,7 +370,7 @@ if __name__ == "__main__":
print(
f"Benchmarking {model_id} {args.mode} with seqlen={args.seqlen}, dtype={args.dtype}, permute_x={args.permute_x}, permute_y={args.permute_y}, kernel_config_fwd={kernel_config_fwd}, kernel_config_bwd_dW={kernel_config_bwd_dW}, kernel_config_bwd_dX={kernel_config_bwd_dX}"
)
ref_time, fused_time = run_benchmark(
args.mode,
model_config,
@ -287,11 +383,17 @@ if __name__ == "__main__":
kernel_config_bwd_dW=kernel_config_bwd_dW,
kernel_config_bwd_dX=kernel_config_bwd_dX,
)
results.append(KernelResult(
torch_time=ref_time,
triton_time=fused_time,
speedup=ref_time / fused_time,
kernel_config=kernel_config,
))
df = post_process_results(results, args.mode, args.seqlen, args.dtype, args.autotune)
save_results(df, args.results_dir, args.mode, args.seqlen, args.dtype, args.autotune)
results.append(
KernelResult(
torch_time=ref_time,
triton_time=fused_time,
speedup=ref_time / fused_time,
kernel_config=kernel_config,
)
)
df = post_process_results(
results, args.mode, args.seqlen, args.dtype, args.autotune
)
save_results(
df, args.results_dir, args.mode, args.seqlen, args.dtype, args.autotune
)

93
unsloth/kernels/moe/benchmark/utils.py
View file

@ -8,6 +8,7 @@ from itertools import product
import pandas as pd
import torch
from grouped_gemm.kernels.tuning import (
KernelConfigBackward_dW,
KernelConfigBackward_dX,
@ -22,7 +23,12 @@ def create_merged_results(
df: pd.DataFrame, mode: str, seqlen: int, dtype: torch.dtype, autotune: bool
):
kernel_result_cols = df.columns.to_list()
test_config_dict = {"mode": mode, "seqlen": seqlen, "dtype": dtype, "autotune": autotune}
test_config_dict = {
"mode": mode,
"seqlen": seqlen,
"dtype": dtype,
"autotune": autotune,
}
test_config_cols = list(test_config_dict.keys())
for col in test_config_cols:
df[col] = test_config_dict[col]
@ -65,12 +71,28 @@ def create_kernel_configs(args: argparse.Namespace, permute_x: bool, permute_y:
block_n_range = power_of_two_range(args.BLOCK_SIZE_N[0], args.BLOCK_SIZE_N[1])
block_k_range = power_of_two_range(args.BLOCK_SIZE_K[0], args.BLOCK_SIZE_K[1])
num_warps_range = multiples_of_range(args.num_warps[0], args.num_warps[1], step=2)
num_stages_range = multiples_of_range(args.num_stages[0], args.num_stages[1], step=1)
num_stages_range = multiples_of_range(
args.num_stages[0], args.num_stages[1], step=1
)
mode = args.mode
kernel_configs = []
for block_m, block_n, block_k, num_warps, num_stages, tma_load_a, tma_load_b in product(
block_m_range, block_n_range, block_k_range, num_warps_range, num_stages_range, [True, False], [True, False]
for (
block_m,
block_n,
block_k,
num_warps,
num_stages,
tma_load_a,
tma_load_b,
) in product(
block_m_range,
block_n_range,
block_k_range,
num_warps_range,
num_stages_range,
[True, False],
[True, False],
):
if mode == "forward":
kernel_config = KernelConfigForward(
@ -141,3 +163,66 @@ def power_of_two_range(start, end):
def multiples_of_range(start, end, step=1):
return list(range(start, end + step, step))
def map_key_to_args(key, mode):
pass
def save_autotune_results(autotune_cache, mode, ref_time, fused_time, results_dir):
device_name = torch.cuda.get_device_name().replace(" ", "_")
dt = datetime.datetime.now().strftime("%Y%m%d_%H%M")
save_dir = f"{results_dir}/{mode}/autotune/{dt}/{device_name}"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for key, config in autotune_cache.items():
key = [
str(k) if not "torch" in str(k) else str(k.split("torch.")[-1]) for k in key
]
filename = "_".join(key)
save_path = f"{save_dir}/{filename}.json"
print(f"Saving autotune results to {save_path}")
with open(save_path, "w") as f:
result = {
**config.all_kwargs(),
"ref_time": ref_time,
"fused_time": fused_time,
}
json.dump(result, f)
def get_autotuner(mode):
if mode == "forward":
from grouped_gemm.kernels.forward import _autotuned_grouped_gemm_forward_kernel
return _autotuned_grouped_gemm_forward_kernel
elif mode == "dW":
from grouped_gemm.kernels.backward import _autotuned_grouped_gemm_dW_kernel
return _autotuned_grouped_gemm_dW_kernel
elif mode == "dX":
from grouped_gemm.kernels.backward import _autotuned_grouped_gemm_dX_kernel
return _autotuned_grouped_gemm_dX_kernel
elif mode == "backward":
from grouped_gemm.kernels.backward import (
_autotuned_grouped_gemm_dW_kernel,
_autotuned_grouped_gemm_dX_kernel,
)
return _autotuned_grouped_gemm_dW_kernel, _autotuned_grouped_gemm_dX_kernel
else:
raise ValueError(f"Invalid mode: {mode}")
def postprocess_autotune_results(autotuner, mode, ref_time, fused_time, results_dir):
for key, value in autotuner.cache.items():
print(f"{mode} {key}: {value.all_kwargs()}")
save_autotune_results(
autotuner.cache,
mode=mode,
ref_time=ref_time,
fused_time=fused_time,
results_dir=results_dir,
)

661
unsloth/kernels/moe/grouped_gemm/LICENSE Normal file
View file

@ -0,0 +1,661 @@
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
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APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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17. Interpretation of Sections 15 and 16.
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above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
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from dataclasses import dataclass
from typing import Tuple
import torch
import torch.nn.functional as F
from transformers.models.llama4 import Llama4TextConfig
from transformers.models.llama4.modeling_llama4 import Llama4TextMoe
from grouped_gemm.interface import grouped_gemm
from grouped_gemm.kernels.tuning import (
KernelConfigBackward_dW,
KernelConfigBackward_dX,
KernelConfigForward,
)
from grouped_gemm.reference.moe_ops import (
get_routing_indices,
permute,
torch_grouped_gemm,
unpermute,
)
"""
Reference implementation of Llama4 MoE block using triton grouped gemm.
`Llama4GroupedGemmTextMoe` is the HF `Llama4TextMoe` block implemented with a torch-native grouped gemm.
`Llama4TritonTextMoe` is the HF `Llama4TextMoe` implemented with triton grouped gemm.
"""
@dataclass
class Llama4MoeResult:
token_counts_by_expert: torch.Tensor
gather_indices: torch.Tensor
topk_weights: torch.Tensor
hidden_states_after_weight_merge: torch.Tensor
first_gemm: torch.Tensor
intermediate: torch.Tensor
second_gemm: torch.Tensor
hidden_states_unpermute: torch.Tensor
shared_expert_out: torch.Tensor
final_out: torch.Tensor
router_logits: torch.Tensor = None
class Llama4GroupedGemmTextMoe(Llama4TextMoe):
EXPERT_WEIGHT_NAMES = ["experts.gate_up_proj", "experts.down_proj"]
def __init__(
self,
config: Llama4TextConfig,
overlap_router_shared=False,
verbose=False,
debug=False,
):
super().__init__(config)
self.overlap_router_shared = overlap_router_shared
self.verbose = verbose
self.debug = debug
# Permute in-place expert weights
E, K, N = self.num_experts, self.hidden_dim, self.experts.expert_dim
assert self.experts.gate_up_proj.shape == torch.Size([E, K, 2 * N]), (
f"{self.experts.gate_up_proj.shape} != {[E, K, 2 * N]}"
)
permuted_shape = [E, 2 * N, K]
permuted_stride = [2 * N * K, K, 1]
if verbose:
print(
f"Changing gate_up_proj from {self.experts.gate_up_proj.size()}:{self.experts.gate_up_proj.stride()} to {permuted_shape}:{permuted_stride}"
)
with torch.no_grad():
self.experts.gate_up_proj.as_strided_(permuted_shape, permuted_stride)
if verbose:
print(
f"{self.experts.gate_up_proj.shape}:{self.experts.gate_up_proj.stride()}"
)
assert self.experts.down_proj.shape == torch.Size([E, N, K]), (
f"{self.experts.down_proj.shape} != {[E, N, K]}"
)
permuted_shape = [E, K, N]
permuted_stride = [K * N, N, 1]
if verbose:
print(
f"Changing down_proj from {self.experts.down_proj.size()}:{self.experts.down_proj.stride()} to {permuted_shape}:{permuted_stride}"
)
with torch.no_grad():
self.experts.down_proj.as_strided_(permuted_shape, permuted_stride)
if verbose:
print(f"{self.experts.down_proj.shape}:{self.experts.down_proj.stride()}")
if overlap_router_shared:
self.shared_expert_stream = torch.cuda.Stream()
self.default_event = torch.cuda.Event()
self.shared_expert_end_event = torch.cuda.Event()
@torch.no_grad
def copy_weights(self, other: Llama4TextMoe):
for name, param_to_copy in other.named_parameters():
if self.verbose:
print(f"Copying {name} with shape {param_to_copy.shape}")
param = self.get_parameter(name)
if any(n in name for n in self.EXPERT_WEIGHT_NAMES):
param_to_copy = param_to_copy.permute(0, 2, 1)
assert param.shape == param_to_copy.shape, (
f"{param.shape} != {param_to_copy.shape}"
)
param.copy_(param_to_copy)
return self
def check_weights(self, other: Llama4TextMoe):
for name, other_param in other.named_parameters():
if any(n in name for n in self.EXPERT_WEIGHT_NAMES):
other_param = other_param.permute(0, 2, 1)
param = self.get_parameter(name)
assert param.equal(other_param), f"Param {name} not equal!"
assert param.is_contiguous(), f"{name} not contiguous!"
def act_and_mul(self, x: torch.Tensor) -> torch.Tensor:
assert x.shape[-1] == 2 * self.experts.expert_dim
gate_proj = x[..., : self.experts.expert_dim]
up_proj = x[..., self.experts.expert_dim :]
return self.experts.act_fn(gate_proj) * up_proj
def run_router(self, hidden_states: torch.Tensor) -> torch.Tensor:
# router_logits: (batch * sequence_length, n_experts)
hidden_states = hidden_states.view(-1, self.hidden_dim)
router_logits = self.router(hidden_states)
routing_weights, selected_experts = torch.topk(
router_logits, self.top_k, dim=-1
)
routing_weights = F.sigmoid(routing_weights.float()).to(hidden_states.dtype)
return router_logits, routing_weights, selected_experts
def get_token_counts_and_gather_indices(
self, selected_experts: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
token_counts_by_expert, gather_indices = get_routing_indices(
selected_experts, self.num_experts
)
assert not token_counts_by_expert.requires_grad
assert not gather_indices.requires_grad
return token_counts_by_expert, gather_indices
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
""" """
batch_size, sequence_length, hidden_dim = hidden_states.shape
num_tokens = batch_size * sequence_length
total_tokens = num_tokens * self.top_k
hidden_states = hidden_states.view(-1, hidden_dim)
if self.overlap_router_shared:
# Marker for all prior ops on default stream
self.default_event.record()
router_logits, routing_weights, selected_experts = self.run_router(
hidden_states
)
assert routing_weights.shape == (num_tokens, self.top_k), (
f"{routing_weights.shape} != {(num_tokens, self.top_k)}"
)
if self.overlap_router_shared:
with torch.cuda.stream(self.shared_expert_stream):
# Ensure prior kernels on default stream complete
self.default_event.wait()
shared_expert_out = self.shared_expert(hidden_states)
# Ensure hidden states remains valid on this stream
hidden_states.record_stream(self.shared_expert_stream)
self.shared_expert_end_event.record()
# Ensure shared expert still valid on default stream
shared_expert_out.record_stream(torch.cuda.current_stream())
self.shared_expert_end_event.wait()
else:
shared_expert_out = self.shared_expert(hidden_states)
hidden_states = (
hidden_states.view(num_tokens, self.top_k, hidden_dim)
* routing_weights[..., None]
)
if self.top_k > 1:
hidden_states = hidden_states.sum(dim=1)
hidden_states_after_weight_merge = hidden_states.view(-1, hidden_dim)
# 1. Compute tokens per expert and indices for gathering tokes from token order to expert order
# NOTE: these are auxiliary data structs which don't need to be recorded in autograd graph
token_counts_by_expert, gather_indices = (
self.get_token_counts_and_gather_indices(selected_experts)
)
# 2. Permute tokens from token order to expert order
hidden_states = permute(
hidden_states_after_weight_merge, gather_indices, self.top_k
)
assert hidden_states.shape == (total_tokens, hidden_dim)
# Start expert computation
first_gemm = torch_grouped_gemm(
X=hidden_states, W=self.experts.gate_up_proj, m_sizes=token_counts_by_expert
)
assert first_gemm.shape == (total_tokens, 2 * self.experts.expert_dim)
intermediate = self.act_and_mul(first_gemm)
assert intermediate.shape == (total_tokens, self.experts.expert_dim)
# See comment above
second_gemm = torch_grouped_gemm(
X=intermediate, W=self.experts.down_proj, m_sizes=token_counts_by_expert
)
assert second_gemm.shape == (total_tokens, hidden_dim)
# Post-processing
hidden_states_unpermute = unpermute(second_gemm, gather_indices)
assert hidden_states_unpermute.shape == (total_tokens, hidden_dim)
# grouped_gemm_out = hidden_states.view(batch_size, sequence_length, hidden_dim)
final_out = hidden_states_unpermute + shared_expert_out
result = (
Llama4MoeResult(
token_counts_by_expert=token_counts_by_expert,
gather_indices=gather_indices,
topk_weights=routing_weights,
hidden_states_after_weight_merge=hidden_states_after_weight_merge,
first_gemm=first_gemm,
intermediate=intermediate,
second_gemm=second_gemm,
hidden_states_unpermute=hidden_states_unpermute,
shared_expert_out=shared_expert_out,
final_out=final_out,
router_logits=router_logits,
)
if self.debug
else (final_out, routing_weights)
)
return result
class Llama4TritonTextMoe(Llama4GroupedGemmTextMoe):
def __init__(
self,
config: Llama4TextConfig,
overlap_router_shared=False,
permute_x: bool = False,
permute_y: bool = True,
autotune: bool = True,
kernel_config_fwd: KernelConfigForward = None,
kernel_config_bwd_dW: KernelConfigBackward_dW = None,
kernel_config_bwd_dX: KernelConfigBackward_dX = None,
dW_only: bool = False,
dX_only: bool = False,
verbose=False,
):
super().__init__(config, overlap_router_shared=overlap_router_shared)
assert not permute_x, (
"Llama4 triton grouped gemm does not support permute x due to pre-multiplication of router weights"
)
self.permute_x = permute_x
self.permute_y = permute_y
self.autotune = autotune
if not autotune:
assert (
kernel_config_fwd is not None
and kernel_config_bwd_dW is not None
and kernel_config_bwd_dX is not None
), "Kernel configs must be provided if autotune is False"
self.kernel_config_fwd = kernel_config_fwd
self.kernel_config_bwd_dW = kernel_config_bwd_dW
self.kernel_config_bwd_dX = kernel_config_bwd_dX
self.dW_only = dW_only
self.dX_only = dX_only
@torch.no_grad
def copy_weights(self, other: Llama4TextMoe):
for name, param_to_copy in other.named_parameters():
if self.verbose:
print(f"Copying {name} with shape {param_to_copy.shape}")
param = self.get_parameter(name)
if any(n in name for n in self.EXPERT_WEIGHT_NAMES):
param_to_copy = param_to_copy.permute(0, 2, 1)
assert param.shape == param_to_copy.shape, (
f"{param.shape} != {param_to_copy.shape}"
)
param.copy_(param_to_copy)
return self
def check_weights(self, other: Llama4TextMoe):
for name, other_param in other.named_parameters():
if any(n in name for n in self.EXPERT_WEIGHT_NAMES):
other_param = other_param.permute(0, 2, 1)
param = self.get_parameter(name)
assert param.equal(other_param), f"Param {name} not equal!"
assert param.is_contiguous(), f"{name} not contiguous!"
def act_and_mul(self, x: torch.Tensor) -> torch.Tensor:
assert x.shape[-1] == 2 * self.experts.expert_dim
gate_proj = x[..., : self.experts.expert_dim]
up_proj = x[..., self.experts.expert_dim :]
return self.experts.act_fn(gate_proj) * up_proj
def run_router(self, hidden_states: torch.Tensor) -> torch.Tensor:
# router_logits: (batch * sequence_length, n_experts)
hidden_states = hidden_states.view(-1, self.hidden_dim)
router_logits = self.router(hidden_states)
routing_weights, selected_experts = torch.topk(
router_logits, self.top_k, dim=-1
)
routing_weights = F.sigmoid(routing_weights.float()).to(hidden_states.dtype)
return router_logits, routing_weights, selected_experts
def get_token_counts_and_gather_indices(
self, selected_experts: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
token_counts_by_expert, gather_indices = get_routing_indices(
selected_experts, self.num_experts
)
assert not token_counts_by_expert.requires_grad
assert not gather_indices.requires_grad
return token_counts_by_expert, gather_indices
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
""" """
batch_size, sequence_length, hidden_dim = hidden_states.shape
num_tokens = batch_size * sequence_length
total_tokens = num_tokens * self.top_k
hidden_states = hidden_states.view(-1, hidden_dim)
if self.overlap_router_shared:
# Marker for all prior ops on default stream
self.default_event.record()
router_logits, routing_weights, selected_experts = self.run_router(
hidden_states
)
assert routing_weights.shape == (num_tokens, self.top_k), (
f"{routing_weights.shape} != {(num_tokens, self.top_k)}"
)
if self.overlap_router_shared:
with torch.cuda.stream(self.shared_expert_stream):
# Ensure prior kernels on default stream complete
self.default_event.wait()
shared_expert_out = self.shared_expert(hidden_states)
# Ensure hidden states remains valid on this stream
hidden_states.record_stream(self.shared_expert_stream)
self.shared_expert_end_event.record()
# Ensure shared expert still valid on default stream
shared_expert_out.record_stream(torch.cuda.current_stream())
self.shared_expert_end_event.wait()
else:
shared_expert_out = self.shared_expert(hidden_states)
hidden_states = (
hidden_states.view(num_tokens, self.top_k, hidden_dim)
* routing_weights[..., None]
)
if self.top_k > 1:
hidden_states = hidden_states.sum(dim=1)
hidden_states = hidden_states.view(-1, hidden_dim)
# 1. Compute tokens per expert and indices for gathering tokes from token order to expert order
# NOTE: these are auxiliary data structs which don't need to be recorded in autograd graph
token_counts_by_expert, gather_indices = (
self.get_token_counts_and_gather_indices(selected_experts)
)
# 2. Permute tokens from token order to expert order
hidden_states = permute(hidden_states, gather_indices, self.top_k)
assert hidden_states.shape == (total_tokens, hidden_dim)
# Start expert computation
hidden_states = grouped_gemm(
X=hidden_states,
W=self.experts.gate_up_proj,
m_sizes=token_counts_by_expert,
gather_indices=gather_indices,
topk=self.top_k,
permute_x=self.permute_x,
permute_y=False, # output of first grouped gemm should never be permuted
autotune=self.autotune,
kernel_config_fwd=self.kernel_config_fwd,
kernel_config_bwd_dW=self.kernel_config_bwd_dW,
kernel_config_bwd_dX=self.kernel_config_bwd_dX,
is_first_gemm=True,
dW_only=self.dW_only,
dX_only=self.dX_only,
)
hidden_states = self.act_and_mul(hidden_states)
hidden_states = grouped_gemm(
X=hidden_states,
W=self.experts.down_proj,
m_sizes=token_counts_by_expert,
gather_indices=gather_indices,
topk=self.top_k,
permute_x=False,
permute_y=self.permute_y,
autotune=self.autotune,
kernel_config_fwd=self.kernel_config_fwd,
kernel_config_bwd_dW=self.kernel_config_bwd_dW,
kernel_config_bwd_dX=self.kernel_config_bwd_dX,
is_first_gemm=False,
dW_only=self.dW_only,
dX_only=self.dX_only,
)
# Post-processing
# 1. Unpermute from expert order to token order
if not self.permute_y:
hidden_states = unpermute(hidden_states, gather_indices)
hidden_states += shared_expert_out
return hidden_states, routing_weights

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from dataclasses import dataclass
from typing import Tuple
import torch
import torch.nn.functional as F
from transformers.models.qwen3_moe.configuration_qwen3_moe import Qwen3MoeConfig
from transformers.models.qwen3_moe.modeling_qwen3_moe import (
ACT2FN,
Qwen3MoeSparseMoeBlock,
)
from grouped_gemm.interface import grouped_gemm
from grouped_gemm.kernels.tuning import (
KernelConfigBackward_dW,
KernelConfigBackward_dX,
KernelConfigForward,
)
from grouped_gemm.reference.moe_ops import (
get_routing_indices,
permute,
torch_grouped_gemm,
unpermute,
)
"""
Reference implementation of HF Qwen3 MoE block using grouped gemm.
The Qwen3MoeGroupedGEMMBlock is a reference torch-native implemention.
Qwen3MoeFusedGroupedGEMMBlock is a version using the triton grouped gemm kernel.
NOTE: This is NOT to be used for production as it contains many extra checks and saves all intermediate results for debugging.
"""
@dataclass
class GroupedGEMMResult:
token_counts_by_expert: torch.Tensor
gather_indices: torch.Tensor
topk_weights: torch.Tensor
first_gemm: torch.Tensor
intermediate: torch.Tensor
second_gemm: torch.Tensor
hidden_states_unpermute: torch.Tensor
hidden_states: torch.Tensor # final output
class Qwen3MoeGroupedGEMMBlock(torch.nn.Module):
def __init__(
self,
config,
gate: torch.Tensor,
gate_up_proj: torch.Tensor,
down_proj: torch.Tensor,
):
super().__init__()
self.num_experts = config.num_experts
self.top_k = config.num_experts_per_tok
self.norm_topk_prob = config.norm_topk_prob
self.hidden_size = config.hidden_size
self.moe_intermediate_size = config.moe_intermediate_size
assert gate.shape == (config.num_experts, config.hidden_size)
assert gate_up_proj.shape == (
config.num_experts,
2 * config.moe_intermediate_size,
config.hidden_size,
)
assert down_proj.shape == (
config.num_experts,
config.hidden_size,
config.moe_intermediate_size,
)
# gating
self.gate = torch.nn.Parameter(gate)
# experts
self.gate_up_proj = torch.nn.Parameter(gate_up_proj, requires_grad=True)
self.down_proj = torch.nn.Parameter(down_proj, requires_grad=True)
self.act_fn = ACT2FN[config.hidden_act]
@staticmethod
def extract_hf_weights(moe_block: Qwen3MoeSparseMoeBlock):
config: Qwen3MoeConfig = moe_block.experts[0].config
num_experts = config.num_experts
gate = moe_block.gate.weight.data
gate_proj = torch.stack(
[moe_block.experts[i].gate_proj.weight.data for i in range(num_experts)],
dim=0,
)
up_proj = torch.stack(
[moe_block.experts[i].up_proj.weight.data for i in range(num_experts)],
dim=0,
)
down_proj = torch.stack(
[moe_block.experts[i].down_proj.weight.data for i in range(num_experts)],
dim=0,
)
gate_up_proj = torch.cat([gate_proj, up_proj], dim=1)
return gate, gate_up_proj, down_proj
@classmethod
def from_hf(cls, moe_block: Qwen3MoeSparseMoeBlock):
config: Qwen3MoeConfig = moe_block.experts[0].config
gate, gate_up_proj, down_proj = cls.extract_hf_weights(moe_block)
return cls(config, gate, gate_up_proj, down_proj)
def check_weights(self, moe_block: Qwen3MoeSparseMoeBlock):
for i in range(self.num_experts):
assert self.gate_up_proj[i].equal(
torch.cat(
[
moe_block.experts[i].gate_proj.weight.data,
moe_block.experts[i].up_proj.weight.data,
],
dim=0,
)
)
assert self.down_proj[i].equal(moe_block.experts[i].down_proj.weight.data)
def act_and_mul(self, x: torch.Tensor) -> torch.Tensor:
assert x.shape[-1] == 2 * self.moe_intermediate_size
gate_proj = x[..., : self.moe_intermediate_size]
up_proj = x[..., self.moe_intermediate_size :]
return self.act_fn(gate_proj) * up_proj
def run_router(self, hidden_states: torch.Tensor) -> torch.Tensor:
# router_logits: (batch * sequence_length, n_experts)
router_logits = torch.nn.functional.linear(hidden_states, self.gate)
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
routing_weights, selected_experts = torch.topk(
routing_weights, self.top_k, dim=-1
)
if self.norm_topk_prob: # only diff with mixtral sparse moe block!
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
# we cast back to the input dtype
routing_weights = routing_weights.to(hidden_states.dtype)
return router_logits, routing_weights, selected_experts
def get_token_counts_and_gather_indices(
self, selected_experts: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
token_counts_by_expert, gather_indices = get_routing_indices(
selected_experts, self.num_experts
)
assert not token_counts_by_expert.requires_grad
assert not gather_indices.requires_grad
return token_counts_by_expert, gather_indices
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
""" """
batch_size, sequence_length, hidden_dim = hidden_states.shape
num_tokens = batch_size * sequence_length
total_tokens = num_tokens * self.top_k
hidden_states = hidden_states.view(-1, hidden_dim)
router_logits, routing_weights, selected_experts = self.run_router(
hidden_states
)
# 1. Compute tokens per expert and indices for gathering tokes from token order to expert order
# NOTE: these are auxiliary data structs which don't need to be recorded in autograd graph
token_counts_by_expert, gather_indices = (
self.get_token_counts_and_gather_indices(selected_experts)
)
# 2. Permute tokens from token order to expert order
hidden_states = permute(hidden_states, gather_indices, self.top_k)
assert hidden_states.shape == (total_tokens, hidden_dim)
# Start expert computation
first_gemm = torch_grouped_gemm(
X=hidden_states, W=self.gate_up_proj, m_sizes=token_counts_by_expert
)
assert first_gemm.shape == (total_tokens, 2 * self.moe_intermediate_size)
intermediate = self.act_and_mul(first_gemm)
assert intermediate.shape == (total_tokens, self.moe_intermediate_size)
second_gemm = torch_grouped_gemm(
X=intermediate, W=self.down_proj, m_sizes=token_counts_by_expert
)
assert second_gemm.shape == (total_tokens, hidden_dim)
# Post-processing
# 1. Unpermute from expert order to token order
hidden_states_unpermute = unpermute(second_gemm, gather_indices)
assert hidden_states_unpermute.shape == (total_tokens, hidden_dim)
# 2. Merge topk weights
hidden_states = (
hidden_states_unpermute.view(num_tokens, self.top_k, hidden_dim)
* routing_weights[..., None]
)
hidden_states = hidden_states.sum(dim=1)
assert hidden_states.shape == (num_tokens, hidden_dim)
hidden_states = hidden_states.view(batch_size, sequence_length, hidden_dim)
return GroupedGEMMResult(
token_counts_by_expert=token_counts_by_expert,
gather_indices=gather_indices,
topk_weights=routing_weights,
first_gemm=first_gemm,
intermediate=intermediate,
second_gemm=second_gemm,
hidden_states_unpermute=hidden_states_unpermute,
hidden_states=hidden_states,
), router_logits
class Qwen3MoeFusedGroupedGEMMBlock(Qwen3MoeGroupedGEMMBlock):
def __init__(
self,
config: Qwen3MoeConfig,
gate: torch.Tensor,
gate_up_proj: torch.Tensor,
down_proj: torch.Tensor,
permute_x: bool = True,
permute_y: bool = True,
autotune: bool = True,
kernel_config_fwd: KernelConfigForward = None,
kernel_config_bwd_dW: KernelConfigBackward_dW = None,
kernel_config_bwd_dX: KernelConfigBackward_dX = None,
dW_only: bool = False,
dX_only: bool = False,
):
super().__init__(config, gate, gate_up_proj, down_proj)
self.permute_x = permute_x
self.permute_y = permute_y
self.autotune = autotune
if not autotune:
assert (
kernel_config_fwd is not None
and kernel_config_bwd_dW is not None
and kernel_config_bwd_dX is not None
), "Kernel configs must be provided if autotune is False"
self.kernel_config_fwd = kernel_config_fwd
self.kernel_config_bwd_dW = kernel_config_bwd_dW
self.kernel_config_bwd_dX = kernel_config_bwd_dX
self.dW_only = dW_only
self.dX_only = dX_only
@classmethod
def from_hf(
cls,
moe_block: Qwen3MoeSparseMoeBlock,
permute_x: bool = True,
permute_y: bool = True,
autotune: bool = True,
kernel_config_fwd: KernelConfigForward = None,
kernel_config_bwd_dW: KernelConfigBackward_dW = None,
kernel_config_bwd_dX: KernelConfigBackward_dX = None,
dW_only: bool = False,
dX_only: bool = False,
):
config: Qwen3MoeConfig = moe_block.experts[0].config
gate, gate_up_proj, down_proj = Qwen3MoeGroupedGEMMBlock.extract_hf_weights(
moe_block
)
return cls(
config,
gate,
gate_up_proj,
down_proj,
permute_x=permute_x,
permute_y=permute_y,
autotune=autotune,
kernel_config_fwd=kernel_config_fwd,
kernel_config_bwd_dW=kernel_config_bwd_dW,
kernel_config_bwd_dX=kernel_config_bwd_dX,
dW_only=dW_only,
dX_only=dX_only,
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
batch_size, sequence_length, hidden_dim = hidden_states.shape
num_tokens = batch_size * sequence_length
total_tokens = num_tokens * self.top_k
hidden_states = hidden_states.view(-1, hidden_dim)
router_logits, routing_weights, selected_experts = self.run_router(
hidden_states
)
# Pre-processing
# 1. Compute tokens per expert and indices for gathering tokes from token order to expert order
# NOTE: these are auxiliary data structs which don't need to be recorded in autograd graph
token_counts_by_expert, gather_indices = (
self.get_token_counts_and_gather_indices(selected_experts)
)
# 2. permute_x -> permutation will be fused in prologue of first grouped gemm
if not self.permute_x:
hidden_states = permute(hidden_states, gather_indices, self.top_k)
# Start expert computation
hidden_states = grouped_gemm(
X=hidden_states,
W=self.gate_up_proj,
m_sizes=token_counts_by_expert,
gather_indices=gather_indices,
topk=self.top_k,
permute_x=self.permute_x,
permute_y=False, # output of first grouped gemm should never be permuted
autotune=self.autotune,
kernel_config_fwd=self.kernel_config_fwd,
kernel_config_bwd_dW=self.kernel_config_bwd_dW,
kernel_config_bwd_dX=self.kernel_config_bwd_dX,
is_first_gemm=True,
dW_only=self.dW_only,
dX_only=self.dX_only,
)
hidden_states = self.act_and_mul(hidden_states)
hidden_states = grouped_gemm(
X=hidden_states,
W=self.down_proj,
m_sizes=token_counts_by_expert,
gather_indices=gather_indices,
topk=self.top_k,
permute_x=False,
permute_y=self.permute_y,
autotune=self.autotune,
kernel_config_fwd=self.kernel_config_fwd,
kernel_config_bwd_dW=self.kernel_config_bwd_dW,
kernel_config_bwd_dX=self.kernel_config_bwd_dX,
is_first_gemm=False,
dW_only=self.dW_only,
dX_only=self.dX_only,
)
# Post-processing
# 1. Unpermute from expert order to token order
if not self.permute_y:
hidden_states = unpermute(hidden_states, gather_indices)
# 2. Merge topk weights
hidden_states = (
hidden_states.view(num_tokens, self.top_k, hidden_dim)
* routing_weights[..., None]
)
hidden_states = hidden_states.sum(dim=1)
hidden_states = hidden_states.view(batch_size, sequence_length, hidden_dim)
return hidden_states, router_logits

202
unsloth/kernels/moe/grouped_gemm/reference/moe_ops.py
View file

@ -1,14 +1,6 @@
from dataclasses import dataclass
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.qwen3_moe import Qwen3MoeConfig
from transformers.models.qwen3_moe.modeling_qwen3_moe import (
ACT2FN,
Qwen3MoeSparseMoeBlock,
)
def permute(X: torch.Tensor, gather_indices: torch.Tensor, topk: int):
@ -58,13 +50,9 @@ def calculate_topk(
def _activation(gating_output: torch.Tensor):
if use_sigmoid:
scores = torch.sigmoid(gating_output.to(torch.float32)).to(
gating_output.dtype
)
scores = torch.sigmoid(gating_output.to(torch.float32)).to(gating_output.dtype)
else:
scores = F.softmax(gating_output.to(torch.float32), dim=1).to(
gating_output.dtype
)
scores = F.softmax(gating_output.to(torch.float32), dim=1).to(gating_output.dtype)
return scores
@ -79,17 +67,13 @@ def calculate_topk(
topk_weights = _activation(topk_weights)
if renormalize:
topk_weights /= torch.sum(topk_weights, dim=-1, keepdim=True).to(
gating_output.dtype
)
topk_weights /= torch.sum(topk_weights, dim=-1, keepdim=True).to(gating_output.dtype)
return topk_weights, topk_ids
@torch.no_grad()
def get_routing_indices(
selected_experts, num_experts, return_scatter_indices: bool = False
):
def get_routing_indices(selected_experts, num_experts, return_scatter_indices: bool = False):
"""
Returns:
token_counts_by_expert: [num_experts]
@ -155,181 +139,3 @@ def torch_grouped_gemm(X, W, m_sizes, transpose=True):
m_start = m_end
return result
@dataclass
class GroupedGEMMResult:
token_counts_by_expert: torch.Tensor
gather_indices: torch.Tensor
topk_weights: torch.Tensor
first_gemm: torch.Tensor
intermediate: torch.Tensor
second_gemm: torch.Tensor
hidden_states_unpermute: torch.Tensor
hidden_states: torch.Tensor # final output
class Qwen3MoeGroupedGEMMBlock(nn.Module):
def __init__(
self,
config,
gate: torch.Tensor,
gate_up_proj: torch.Tensor,
down_proj: torch.Tensor,
):
super().__init__()
self.num_experts = config.num_experts
self.top_k = config.num_experts_per_tok
self.norm_topk_prob = config.norm_topk_prob
self.hidden_size = config.hidden_size
self.moe_intermediate_size = config.moe_intermediate_size
assert gate.shape == (config.num_experts, config.hidden_size)
assert gate_up_proj.shape == (
config.num_experts,
2 * config.moe_intermediate_size,
config.hidden_size,
)
assert down_proj.shape == (
config.num_experts,
config.hidden_size,
config.moe_intermediate_size,
)
# gating
self.gate = torch.nn.Parameter(gate)
# experts
self.gate_up_proj = torch.nn.Parameter(gate_up_proj, requires_grad=True)
self.down_proj = torch.nn.Parameter(down_proj, requires_grad=True)
self.act_fn = ACT2FN[config.hidden_act]
@staticmethod
def extract_hf_weights(moe_block: Qwen3MoeSparseMoeBlock):
config: Qwen3MoeConfig = moe_block.experts[0].config
num_experts = config.num_experts
gate = moe_block.gate.weight.data
gate_proj = torch.stack(
[moe_block.experts[i].gate_proj.weight.data for i in range(num_experts)],
dim=0,
)
up_proj = torch.stack(
[moe_block.experts[i].up_proj.weight.data for i in range(num_experts)],
dim=0,
)
down_proj = torch.stack(
[moe_block.experts[i].down_proj.weight.data for i in range(num_experts)],
dim=0,
)
gate_up_proj = torch.cat([gate_proj, up_proj], dim=1)
return gate, gate_up_proj, down_proj
@classmethod
def from_hf(cls, moe_block: Qwen3MoeSparseMoeBlock):
config: Qwen3MoeConfig = moe_block.experts[0].config
gate, gate_up_proj, down_proj = cls.extract_hf_weights(moe_block)
return cls(config, gate, gate_up_proj, down_proj)
def check_weights(self, moe_block: Qwen3MoeSparseMoeBlock):
for i in range(self.num_experts):
assert self.gate_up_proj[i].equal(
torch.cat(
[
moe_block.experts[i].gate_proj.weight.data,
moe_block.experts[i].up_proj.weight.data,
],
dim=0,
)
)
assert self.down_proj[i].equal(moe_block.experts[i].down_proj.weight.data)
def act_and_mul(self, x: torch.Tensor) -> torch.Tensor:
assert x.shape[-1] == 2 * self.moe_intermediate_size
gate_proj = x[..., : self.moe_intermediate_size]
up_proj = x[..., self.moe_intermediate_size :]
return self.act_fn(gate_proj) * up_proj
def run_router(self, hidden_states: torch.Tensor) -> torch.Tensor:
# router_logits: (batch * sequence_length, n_experts)
router_logits = torch.nn.functional.linear(hidden_states, self.gate)
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
routing_weights, selected_experts = torch.topk(
routing_weights, self.top_k, dim=-1
)
if self.norm_topk_prob: # only diff with mixtral sparse moe block!
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
# we cast back to the input dtype
routing_weights = routing_weights.to(hidden_states.dtype)
return router_logits, routing_weights, selected_experts
def get_token_counts_and_gather_indices(
self, selected_experts: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
token_counts_by_expert, gather_indices = get_routing_indices(
selected_experts, self.num_experts
)
assert not token_counts_by_expert.requires_grad
assert not gather_indices.requires_grad
return token_counts_by_expert, gather_indices
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
""" """
batch_size, sequence_length, hidden_dim = hidden_states.shape
num_tokens = batch_size * sequence_length
total_tokens = num_tokens * self.top_k
hidden_states = hidden_states.view(-1, hidden_dim)
router_logits, routing_weights, selected_experts = self.run_router(
hidden_states
)
# 1. Compute tokens per expert and indices for gathering tokes from token order to expert order
# NOTE: these are auxiliary data structs which don't need to be recorded in autograd graph
token_counts_by_expert, gather_indices = (
self.get_token_counts_and_gather_indices(selected_experts)
)
# 2. Permute tokens from token order to expert order
hidden_states = permute(hidden_states, gather_indices, self.top_k)
assert hidden_states.shape == (total_tokens, hidden_dim)
# Start expert computation
first_gemm = torch_grouped_gemm(
X=hidden_states, W=self.gate_up_proj, m_sizes=token_counts_by_expert
)
assert first_gemm.shape == (total_tokens, 2 * self.moe_intermediate_size)
intermediate = self.act_and_mul(first_gemm)
assert intermediate.shape == (total_tokens, self.moe_intermediate_size)
second_gemm = torch_grouped_gemm(
X=intermediate, W=self.down_proj, m_sizes=token_counts_by_expert
)
assert second_gemm.shape == (total_tokens, hidden_dim)
# Post-processing
# 1. Unpermute from expert order to token order
hidden_states_unpermute = unpermute(second_gemm, gather_indices)
assert hidden_states_unpermute.shape == (total_tokens, hidden_dim)
# 2. Merge topk weights
hidden_states = (
hidden_states_unpermute.view(num_tokens, self.top_k, hidden_dim)
* routing_weights[..., None]
)
hidden_states = hidden_states.sum(dim=1)
assert hidden_states.shape == (num_tokens, hidden_dim)
hidden_states = hidden_states.view(batch_size, sequence_length, hidden_dim)
return GroupedGEMMResult(
token_counts_by_expert=token_counts_by_expert,
gather_indices=gather_indices,
topk_weights=routing_weights,
first_gemm=first_gemm,
intermediate=intermediate,
second_gemm=second_gemm,
hidden_states_unpermute=hidden_states_unpermute,
hidden_states=hidden_states,
), router_logits

5
unsloth/kernels/moe/tests/moe_utils.py
View file

@ -13,12 +13,11 @@ from grouped_gemm.kernels.tuning import (
KernelConfigBackward_dX,
KernelConfigForward,
)
from grouped_gemm.reference.moe_ops import (
from grouped_gemm.reference.layers.qwen3_moe import (
GroupedGEMMResult,
Qwen3MoeGroupedGEMMBlock,
permute,
unpermute,
)
from grouped_gemm.reference.moe_ops import permute, unpermute
def rebind_experts_to_shared_buffer(

259
unsloth/kernels/moe/tests/test_llama4_moe.py Normal file
View file

@ -0,0 +1,259 @@
import argparse
import sys
from contextlib import contextmanager
from functools import partial
import pytest
import torch
from transformers import AutoConfig
from transformers.models.llama4 import Llama4Config, Llama4TextConfig
from transformers.models.llama4.modeling_llama4 import Llama4TextMoe
from grouped_gemm.kernels.tuning import (
KernelConfigBackward_dW,
KernelConfigBackward_dX,
KernelConfigForward,
)
from grouped_gemm.reference.layers.llama4_moe import (
Llama4GroupedGemmTextMoe,
Llama4TritonTextMoe,
)
TOLERANCES = {
torch.bfloat16: (1e-2, 1e-2),
torch.float16: (1e-3, 1e-3),
torch.float: (1e-5, 1e-5),
}
LLAMA4_SCOUT_ID = "meta-llama/Llama-4-Scout-17B-16E"
SEED = 42
SEQ_LENS = [1024]
DTYPES = [torch.bfloat16]
# Reduce the number of autotuning configs to prevent excessive runtime
NUM_AUTOTUNE_CONFIGS = 50
@contextmanager
def annotated_context(prelude, epilogue="Passed!", char="-", num_chars=80):
print(char * num_chars)
print(prelude)
yield
print(epilogue)
print(char * num_chars)
def get_text_config(model_id):
config: Llama4Config = AutoConfig.from_pretrained(model_id)
return config.text_config
def prep_triton_kernel_traits(autotune):
if not autotune:
kernel_config_fwd = KernelConfigForward()
kernel_config_bwd_dW = KernelConfigBackward_dW()
kernel_config_bwd_dX = KernelConfigBackward_dX()
else:
from grouped_gemm.kernels.backward import (
_autotuned_grouped_gemm_dW_kernel,
_autotuned_grouped_gemm_dX_kernel,
)
from grouped_gemm.kernels.forward import _autotuned_grouped_gemm_forward_kernel
# Hack to reduce number of autotuning configs
_autotuned_grouped_gemm_forward_kernel.configs = (
_autotuned_grouped_gemm_forward_kernel.configs[:NUM_AUTOTUNE_CONFIGS]
)
_autotuned_grouped_gemm_dW_kernel.configs = (
_autotuned_grouped_gemm_dW_kernel.configs[:NUM_AUTOTUNE_CONFIGS]
)
_autotuned_grouped_gemm_dX_kernel.configs = (
_autotuned_grouped_gemm_dX_kernel.configs[:NUM_AUTOTUNE_CONFIGS]
)
kernel_config_fwd = None
kernel_config_bwd_dW = None
kernel_config_bwd_dX = None
return kernel_config_fwd, kernel_config_bwd_dW, kernel_config_bwd_dX
def sparse_to_dense(t: torch.Tensor):
t = t.sum(dim=0).view(-1)
return t
@torch.no_grad()
def _check_diff(
t1: torch.Tensor,
t2: torch.Tensor,
atol,
rtol,
precision=".6f",
verbose=False,
msg="",
):
t2 = t2.view_as(t1)
diff = t1.sub(t2).abs().max().item()
if verbose:
if msg == "":
msg = "diff"
print(f"{msg}: {diff:{precision}}")
assert torch.allclose(t1, t2, atol=atol, rtol=rtol)
def run_backwards(y: torch.Tensor, grad_output: torch.Tensor, module: torch.nn.Module):
y.backward(grad_output)
for name, param in module.named_parameters():
assert param.grad is not None, f"{name} missing grad!"
def _check_grads(
m1: torch.nn.Module,
m2: torch.nn.Module,
atol,
rtol,
precision=".6f",
verbose=False,
msg="",
):
for name, param in m1.named_parameters():
_check_diff(
param.grad,
m2.get_parameter(name).grad,
atol=atol,
rtol=rtol,
precision=precision,
verbose=verbose,
msg=f"{msg}:{name}.grad",
)
@pytest.fixture
def model_config():
return AutoConfig.from_pretrained(LLAMA4_SCOUT_ID).text_config
@pytest.mark.parametrize(
"overlap_router_shared",
[False, True],
ids=lambda x: "overlap_router_shared" if x else "no_overlap",
)
@pytest.mark.parametrize(
"permute_y", [False, True], ids=lambda x: "permute_y" if x else "no_permute_y"
)
@pytest.mark.parametrize(
"permute_x", [False], ids=lambda x: "permute_x" if x else "no_permute_x"
) # Llama4 does not support permute_x
@pytest.mark.parametrize(
"autotune", [True], ids=lambda x: "autotune" if x else "manual"
)
@pytest.mark.parametrize("seqlen", SEQ_LENS, ids=lambda x: f"seqlen={x}")
@pytest.mark.parametrize("dtype", DTYPES, ids=str)
def test_llama4_ref(
dtype: torch.dtype,
seqlen,
autotune: bool,
permute_x: bool,
permute_y: bool,
overlap_router_shared: bool,
model_config: Llama4TextConfig, # test fixture
bs: int = 1,
device="cuda",
precision=".6f",
verbose=False,
):
torch.manual_seed(
SEED
) # Should not be needed when running using pytest -- autouse fixture in conftest.py
device = "cuda"
hidden_dim = model_config.hidden_size
atol, rtol = TOLERANCES[dtype]
check_diff = partial(
_check_diff, atol=atol, rtol=rtol, precision=precision, verbose=verbose
)
check_grads = partial(
_check_grads, atol=atol, rtol=rtol, precision=precision, verbose=verbose
)
# Reference op -- HF
llama4_ref = Llama4TextMoe(model_config).to(dtype=dtype, device=device)
# Torch grouped gemm impl
llama4_gg_ref = Llama4GroupedGemmTextMoe(
model_config, overlap_router_shared=overlap_router_shared
).to(dtype=dtype, device=device)
llama4_gg_ref.copy_weights(llama4_ref)
llama4_gg_ref.check_weights(llama4_ref)
x_ref = torch.randn(
bs, seqlen, hidden_dim, dtype=dtype, device=device, requires_grad=True
)
x_torch_gg = x_ref.detach().clone().requires_grad_()
x_triton = x_ref.detach().clone().requires_grad_()
y_ref, routing_ref = llama4_ref(x_ref)
y_torch_gg, routing_torch_gg = llama4_gg_ref(x_torch_gg)
assert y_ref.shape == y_torch_gg.shape, f"{y_ref.shape} != {y_torch_gg.shape}"
with annotated_context("Testing torch grouped gemm Llama4TextMoe"):
check_diff(y_ref, y_torch_gg, msg="y_torch_gg")
check_diff(
sparse_to_dense(routing_ref), routing_torch_gg, msg="routing_torch_gg"
)
kernel_config_fwd, kernel_config_bwd_dW, kernel_config_bwd_dX = (
prep_triton_kernel_traits(autotune)
)
llama4_triton = Llama4TritonTextMoe(
model_config,
overlap_router_shared=overlap_router_shared,
permute_x=permute_x,
permute_y=permute_y,
autotune=autotune,
kernel_config_fwd=kernel_config_fwd,
kernel_config_bwd_dW=kernel_config_bwd_dW,
kernel_config_bwd_dX=kernel_config_bwd_dX,
).to(device=device, dtype=dtype)
llama4_triton.copy_weights(llama4_ref)
llama4_triton.check_weights(llama4_ref)
y_triton, routing_triton = llama4_triton(x_triton)
with annotated_context("Testing triton grouped gemm Llama4TextMoe forward"):
check_diff(y_ref, y_triton, msg="y_triton")
check_diff(sparse_to_dense(routing_ref), routing_triton, msg="routing_triton")
ref_grad = torch.randn_like(y_ref)
run_backwards(y_ref, ref_grad, llama4_ref)
run_backwards(y_torch_gg, ref_grad, llama4_gg_ref)
with annotated_context("Testing torch group gemm Llama4TextMoe backward"):
check_grads(llama4_ref, llama4_gg_ref, msg="torch_gg")
run_backwards(y_triton, ref_grad, llama4_triton)
with annotated_context("Testing triton group gemm Llama4TextMoe backward"):
check_grads(llama4_ref, llama4_triton, msg="triton")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--seqlen", type=int, default=1024)
parser.add_argument(
"--dtype", type=str, choices=["bfloat16", "float16"], default="bfloat16"
)
args = parser.parse_args()
args.dtype = getattr(torch, args.dtype)
args_dict = vars(args)
model_id = LLAMA4_SCOUT_ID
text_config: Llama4TextConfig = get_text_config(model_id)
for overlap in [False, True]:
test_llama4_ref(
seqlen=args.seqlen,
model_config=text_config,
dtype=args.dtype,
autotune=True,
permute_x=False,
permute_y=True,
overlap_router_shared=overlap,
verbose=True,
)

29
unsloth/kernels/moe/tests/test_qwen3_moe.py
View file

@ -12,7 +12,7 @@ from grouped_gemm.kernels.tuning import (
KernelConfigBackward_dX,
KernelConfigForward,
)
from grouped_gemm.reference.moe_ops import Qwen3MoeGroupedGEMMBlock
from grouped_gemm.reference.layers.qwen3_moe import Qwen3MoeGroupedGEMMBlock
from .moe_utils import (
Qwen3MoeFusedGroupedGEMMBlock,
@ -76,7 +76,7 @@ def config(model_id: str):
@contextmanager
def test_context(prelude, epilogue="Passed!", char="-", num_chars=80):
def annotated_context(prelude, epilogue="Passed!", char="-", num_chars=80):
print(char * num_chars)
print(prelude)
yield
@ -110,8 +110,6 @@ def test_qwen3_moe(
permute_x: bool,
permute_y: bool,
autotune: bool,
atol: float,
rtol: float,
):
torch.manual_seed(
SEED
@ -119,6 +117,7 @@ def test_qwen3_moe(
device = "cuda"
hidden_size = config.hidden_size
bs = 1
atol, rtol = TOLERANCES[dtype]
# Reference op -- HF
moe_block = Qwen3MoeSparseMoeBlock(config).to(device, dtype)
@ -173,7 +172,7 @@ def test_qwen3_moe(
grouped_result = run_forward(grouped_gemm_block, X, is_grouped_gemm=True)
fused_result = run_forward(fused_gemm_block, X, is_grouped_gemm=True)
with test_context(
with annotated_context(
"Testing forward pass",
epilogue="Passed forward tests!",
char="=",
@ -181,10 +180,12 @@ def test_qwen3_moe(
):
# Sanity checks
with test_context("Checking HF vs torch grouped gemm MoE forward outputs..."):
with annotated_context(
"Checking HF vs torch grouped gemm MoE forward outputs..."
):
check_fwd(ref_result, grouped_result, atol, rtol, verbose=False)
with test_context(
with annotated_context(
"Checking torch grouped gemm MoE vs fused grouped gemm MoE forward outputs..."
):
# We implement a custom check for grouped gemm results to test each of the intermediate results for easier debugging
@ -197,7 +198,9 @@ def test_qwen3_moe(
verbose=False,
)
# Actual test
with test_context("Checking HF vs fused grouped gemm MoE forward outputs..."):
with annotated_context(
"Checking HF vs fused grouped gemm MoE forward outputs..."
):
check_fwd(ref_result, fused_result, atol, rtol, verbose=True)
# Backward
@ -215,18 +218,18 @@ def test_qwen3_moe(
fused_gemm_block, grad_output, output=fused_result.output, X=fused_result.X
)
with test_context(
with annotated_context(
"Testing backward pass",
epilogue="Passed backward tests!",
char="=",
num_chars=100,
):
# Sanity checks
with test_context("Checking HF vs torch grouped gemm MoE grads..."):
with annotated_context("Checking HF vs torch grouped gemm MoE grads..."):
check_grads(
ref_backward_result, grouped_backward_result, atol, rtol, verbose=False
)
with test_context(
with annotated_context(
"Checking torch grouped gemm MoE vs fused grouped gemm MoE grads..."
):
check_grads(
@ -238,7 +241,7 @@ def test_qwen3_moe(
)
# Actual test
with test_context("Checking HF vs fused grouped gemm MoE grads..."):
with annotated_context("Checking HF vs fused grouped gemm MoE grads..."):
check_grads(
ref_backward_result, fused_backward_result, atol, rtol, verbose=True
)
@ -264,4 +267,4 @@ if __name__ == "__main__":
print(
f"Testing {model_id} with seqlen={args.seqlen}, dtype={args.dtype}, permute_x={args.permute_x}, permute_y={args.permute_y}, autotune={args.autotune}, atol={atol}, rtol={rtol}"
)
test_qwen3_moe(config, atol=atol, rtol=rtol, **args_dict)
test_qwen3_moe(config, **args_dict)