[FEATURE SUPPORT] add gemm python kernel

README.md

@@ -24,7 +24,7 @@ The following common BLAS kernels have been implemented in multiple frameworks.
 | [dot](./docs/dot.md) | dot product | $z = x^\top y$ | $2n$ | $2n$ | [✅](./kernel_course/python_ops/dot.py) | [✅](./kernel_course/pytorch_ops/dot.py) | [✅](./kernel_course/triton_ops/dot.py) | ❌ | [✅](./tests/test_dot.py) |
 | [gemv](./docs/gemv.md) | general matrix-vector multiply | $y = \alpha A x + \beta y$ | $2mn$ | $mn + n + 2m$ | [✅](./kernel_course/python_ops/gemv.py) | [✅](./kernel_course/pytorch_ops/gemv.py) | [✅](./kernel_course/triton_ops/gemv.py) | ❌ | [✅](./tests/test_gemv.py) |
 | [geru](./docs/geru.md) | general rank-1 update | $A = A + \alpha x y^\top$ | $2mn$ | $2mn + m + n$ | [✅](./kernel_course/python_ops/geru.py) | [✅](./kernel_course/pytorch_ops/geru.py) | [✅](./kernel_course/triton_ops/geru.py) | ❌ | [✅](./tests/test_geru.py) |
-| gemm | general matrix-matrix multiply | $C = \alpha A B + \beta C$ | $2mnk$ | $mk + nk + 2mn$ | ❌ | ❌ | ❌ | ❌ | ❌ |
+| [gemm](./docs/gemm.md) | general matrix-matrix multiply | $C = \alpha A B + \beta C$ | $2mnk$ | $mk + nk + 2mn$ | [✅](./kernel_course/python_ops/gemm.py) | ❌ | ❌ | ❌ | ❌ |
 
 
 ## Transformer Modules

docs/gemm.md

@@ -0,0 +1,35 @@
+# GEMM Kernel
+
+The `gemm` operator computes the matrix-matrix product of two matrices.
+
+## Mathematical Definition
+
+Given input matrices `A` and `B`, along with an output matrix `C` and scalars `α` and `β`, the kernel evaluates
+
+$$
+C = \alpha A B + \beta C
+$$
+
+The matrix-matrix product is computed by multiplying the matrix `A` with the matrix `B`, scaling the result by `α`, scaling the matrix `C` by `β`, and then adding the two scaled results together to produce the updated matrix `C`.
+
+## Kernel Implementations
+
+- [Python Implementation](../kernel_course/python_ops/gemm.py)
+- [PyTorch Implementation](../kernel_course/pytorch_ops/gemm.py)
+- [Triton Implementation](../kernel_course/triton_ops/gemm.py)
+- [CuTe Implementation](../kernel_course/cute_ops/gemm.py)
+
+All backends share the interface:
+
+```python
+def gemm(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor, alpha: float, beta: float) -> torch.Tensor:
+    ...
+```
+
+## Testing
+
+See the [test suite](../tests/test_gemm.py) for the validation harness that exercises every backend.
+
+```bash
+pytest tests/test_gemm.py -s
+```

kernel_course/python_ops/gemm.py

@@ -0,0 +1,28 @@
+import torch
+
+
+def gemm(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    C: torch.Tensor,
+    alpha: float,
+    beta: float,
+) -> torch.Tensor:
+    """
+    Updates tensor `C` by adding the product of matrices `A` and `B`
+    scaled by `alpha`, and `C` scaled by `beta`.
+
+    Args:
+        A (torch.Tensor): First matrix tensor.
+        B (torch.Tensor): Second matrix tensor to be multiplied with `A`.
+        C (torch.Tensor): Matrix tensor to be updated.
+        alpha (float): Scaling factor for the product of `A` and `B`.
+        beta (float): Scaling factor for `C`.
+
+    Returns:
+        torch.Tensor: The updated tensor `C`.
+    """
+
+    C = alpha * A @ B + beta * C
+
+    return C