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# /// script
# requires-python = "==3.10"
# dependencies = ["torch==2.7.0", "triton", "numpy", "kernels"]
# [tool.uv.sources]
# kernels = { git = "https://github.com/huggingface/kernels.git" }
# ///
import torch
import sys
import time
from kernels import get_kernel, get_local_kernel
from pathlib import Path
load_method = 2 # 1: sym, 2: local, 3: hf
if load_method == 1:
sys.path.insert(0, "./torch-ext")
import yamoe
elif load_method == 2:
yamoe = get_local_kernel(Path("result"), "yamoe")
elif load_method == 3:
yamoe = get_kernel("drbh/yamoe", revision="v0.1.0")
torch.manual_seed(42)
def zero_grads(m):
for p in m.parameters():
if p.grad is not None:
p.grad = None
def benchmark_backward(model, x, tag: str, iters: int = 10, warmup: int = 10):
x_local = x.detach().clone().requires_grad_(True)
x_local.retain_grad()
# Warmup
for _ in range(warmup):
out = model(x_local)
out = out[0] if isinstance(out, tuple) else out
loss = out.mean()
zero_grads(model)
if x_local.grad is not None:
x_local.grad = None
loss.backward()
# Benchmark
torch.cuda.reset_peak_memory_stats()
torch.cuda.synchronize()
start = time.perf_counter()
for _ in range(iters):
out = model(x_local)
out = out[0] if isinstance(out, tuple) else out
loss = out.mean()
zero_grads(model)
if x_local.grad is not None:
x_local.grad = None
loss.backward()
torch.cuda.synchronize()
bwd_ms = (time.perf_counter() - start) * 1e3 / iters
peak_mem = torch.cuda.max_memory_allocated() / 1024**3 # Convert to GB
print(f"[{tag}] backward: {bwd_ms:.2f} ms | peak mem: {peak_mem:.2f} GB")
return bwd_ms
def print_grad_norms(m, x, tag):
print(f"\n[{tag}] Gradient norms:")
xg = x.grad.norm().item() if x.grad is not None else 0.0
print(f" input grad: {xg:.6f}")
for name, p in m.named_parameters():
if p.grad is None:
print(f" {name}: None")
else:
print(f" {name}: {p.grad.norm().item():.6f}")
def main():
ref_moe_cls = yamoe.vendored.gpt_oss_mlp.GptOssMLP
new_moe_cls = yamoe.Yamoe
batch_size, seq_len, hidden_dim = 4, 1024, 2880
num_experts, top_k = 8, 2
config = type("Config", (), {})()
config.hidden_size = hidden_dim
config.intermediate_size = hidden_dim
config.num_local_experts = num_experts
config.num_experts_per_tok = top_k
ref_moe = ref_moe_cls(config)
print(ref_moe)
for p in ref_moe.parameters():
if p.dim() > 1:
torch.nn.init.xavier_uniform_(p)
else:
torch.nn.init.zeros_(p)
x = torch.randn(batch_size, seq_len, hidden_dim, device="cuda")
ref_moe = ref_moe.cuda()
# Test reference implementation backward
print("\nReference Implementation Backward")
# Small warmup
print(" Warming up...")
x_warmup = x.detach().requires_grad_(True)
for _ in range(3):
out = ref_moe(x_warmup)
out = out[0] if isinstance(out, tuple) else out
loss = out.mean()
zero_grads(ref_moe)
if x_warmup.grad is not None:
x_warmup.grad = None
loss.backward()
torch.cuda.synchronize()
# Run once to get gradient info
x_ref = x.detach().requires_grad_(True)
x_ref.retain_grad()
ref_output = ref_moe(x_ref)
out = ref_output[0] if isinstance(ref_output, tuple) else ref_output
print(f" Input shape: {x_ref.shape}")
print(f" Output shape: {out.shape}")
print(
f" Output mean: {out.mean():.6f}, std: {out.std():.6f}, norm: {out.norm():.6f}"
)
loss = out.mean()
zero_grads(ref_moe)
loss.backward()
print_grad_norms(ref_moe, x_ref, "reference")
benchmark_backward(ref_moe, x, tag="reference", warmup=10, iters=20)
# Switch to YAMOE-backed backward
print("\nYAMOE-backed Implementation Backward")
ref_moe.forward = new_moe_cls.forward.__get__(ref_moe)
ref_moe._routing_weights_buffer = None
ref_moe._batch_indices_buffer = None
ref_moe._last_batch_seq = None
ref_moe._last_num_experts = None
ref_moe.enable_router_grads = True
ref_moe.num_experts = num_experts
ref_moe.top_k = top_k
# Small warmup
print(" Warming up...")
x_warmup = x.detach().requires_grad_(True)
for _ in range(3):
out = ref_moe(x_warmup)
out = out[0] if isinstance(out, tuple) else out
loss = out.mean()
zero_grads(ref_moe)
if x_warmup.grad is not None:
x_warmup.grad = None
loss.backward()
torch.cuda.synchronize()
# Run once to get gradient info
x_cuda = x.detach().requires_grad_(True)
x_cuda.retain_grad()
cuda_output = ref_moe(x_cuda)
out = cuda_output[0] if isinstance(cuda_output, tuple) else cuda_output
print(f" Input shape: {x_cuda.shape}")
print(f" Output shape: {out.shape}")
print(
f" Output mean: {out.mean():.6f}, std: {out.std():.6f}, norm: {out.norm():.6f}"
)
loss = out.mean()
zero_grads(ref_moe)
loss.backward()
print_grad_norms(ref_moe, x_cuda, "yamoe-backed")
benchmark_backward(ref_moe, x, tag="yamoe-backed", warmup=10, iters=20)
if __name__ == "__main__":
main()
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