feat: add quick start and readme example

README.md

@@ -7,4 +7,56 @@ triton-kernels is a set of kernels that enable fast moe on different architectur
 
 Original code here https://github.com/triton-lang/triton/tree/main/python/triton_kernels
 
-The current version is the following commit 7d0efaa7231661299284a603512fce4fa255e62c
+The current version is the following commit 7d0efaa7231661299284a603512fce4fa255e62c
+
+
+## Quickstart
+
+```bash
+uv run https://huggingface.co/kernels-community/triton_kernels/raw/main/readme_example.py
+```
+
+```python
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "torch",
+#     "triton",
+#     "numpy",
+#     "kernels",
+# ]
+# ///
+
+import torch
+import sys
+from kernels import get_kernel
+
+torch.manual_seed(42)
+torch.cuda.manual_seed(42)
+
+# Load triton_kernels module via kernels library
+triton_kernels = get_kernel("kernels-community/triton_kernels")
+
+# Access modules directly from the loaded kernel
+swiglu = triton_kernels.swiglu
+routing = triton_kernels.routing
+
+# Setup
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# SwiGLU example
+x = torch.randn(512, 1024, device=device, dtype=torch.bfloat16)
+y = swiglu.swiglu_torch(x, 0.5, swiglu.PrecisionConfig(limit=1.0))
+print(f"SwiGLU: {x.shape} -> {y.shape}")
+
+# Routing example
+logits = torch.randn(128, 8, device=device, dtype=torch.float16)
+routing_data, gather_idx, scatter_idx = routing.routing_torch(logits, n_expts_act=2)
+print(f"Routing: {routing_data.expt_hist.sum()} tokens routed")
+
+# MoE integrated
+n_tokens = routing_data.expt_hist.sum().item()
+x_moe = torch.randn(n_tokens, 512, device=device, dtype=torch.bfloat16)
+y_moe = swiglu.swiglu_torch(x_moe, 0.5, swiglu.PrecisionConfig(limit=1.0))
+print(f"MoE SwiGLU: {x_moe.shape} -> {y_moe.shape}")
+```

readme_example.py

@@ -0,0 +1,42 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "torch",
+#     "triton",
+#     "numpy",
+#     "kernels",
+# ]
+# ///
+
+import torch
+import sys
+from kernels import get_kernel
+
+torch.manual_seed(42)
+torch.cuda.manual_seed(42)
+
+# Load triton_kernels module via kernels library
+triton_kernels = get_kernel("kernels-community/triton_kernels")
+
+# Access modules directly from the loaded kernel
+swiglu = triton_kernels.swiglu
+routing = triton_kernels.routing
+
+# Setup
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# SwiGLU example
+x = torch.randn(512, 1024, device=device, dtype=torch.bfloat16)
+y = swiglu.swiglu_torch(x, 0.5, swiglu.PrecisionConfig(limit=1.0))
+print(f"SwiGLU: {x.shape} -> {y.shape}")
+
+# Routing example
+logits = torch.randn(128, 8, device=device, dtype=torch.float16)
+routing_data, gather_idx, scatter_idx = routing.routing_torch(logits, n_expts_act=2)
+print(f"Routing: {routing_data.expt_hist.sum()} tokens routed")
+
+# MoE integrated
+n_tokens = routing_data.expt_hist.sum().item()
+x_moe = torch.randn(n_tokens, 512, device=device, dtype=torch.bfloat16)
+y_moe = swiglu.swiglu_torch(x_moe, 0.5, swiglu.PrecisionConfig(limit=1.0))
+print(f"MoE SwiGLU: {x_moe.shape} -> {y_moe.shape}")