feat(agent): add triton kernels targeted for KernelBench

dlblas/kernels/kernelagent/level1/100_HingeLoss

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+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+@triton.jit
+def hinge_loss_kernel(
+    predictions_ptr,
+    targets_ptr,
+    output_ptr,
+    n_elements,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_elements
+
+    p = tl.load(predictions_ptr + offsets, mask=mask)
+    t = tl.load(targets_ptr + offsets, mask=mask)
+
+    element = 1.0 - p * t
+    clamped = tl.where(element > 0, element, 0.0)
+
+    total = tl.sum(clamped, axis=0)
+    if pid == 0:
+        mean_val = total / n_elements
+        tl.store(output_ptr, mean_val)
+
+class ModelNew(nn.Module):
+    def __init__(self):
+        super(ModelNew, self).__init__()
+
+    def forward(self, predictions, targets):
+        total_elements = predictions.numel()
+        if total_elements == 0:
+            return torch.tensor(0.0, device=predictions.device)
+
+        predictions_flat = predictions.view(-1)
+        targets_flat = targets.view(-1)
+        output = torch.empty(1, device=predictions.device)
+
+        grid = (1,)
+        BLOCK_SIZE = triton.next_power_of_2(total_elements)
+        hinge_loss_kernel[grid](
+            predictions_flat, 
+            targets_flat, 
+            output,
+            total_elements,
+            BLOCK_SIZE=BLOCK_SIZE,
+        )
+        return output.squeeze(0)
+
+batch_size = 128
+input_shape = (1,)
+dim = 1
+
+def get_inputs():
+    return [torch.randn(batch_size, *input_shape), torch.randint(0, 2, (batch_size, 1)).float() * 2 - 1]
+
+def get_init_inputs():
+    return []

dlblas/kernels/kernelagent/level1/10_3D_tensor_matrix_multiplication

@@ -0,0 +1,107 @@
+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+@triton.autotune(
+    configs=[
+        triton.Config({'BLOCK_M': 64, 'BLOCK_L': 64, 'BLOCK_K': 64}, num_warps=4, num_stages=4),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_L': 128, 'BLOCK_K': 64}, num_warps=4, num_stages=3),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_L': 64, 'BLOCK_K': 64}, num_warps=4, num_stages=3),
+        triton.Config({'BLOCK_M': 128, 'BLOCK_L': 128, 'BLOCK_K': 64}, num_warps=8, num_stages=3),
+        triton.Config({'BLOCK_M': 64, 'BLOCK_L': 64, 'BLOCK_K': 128}, num_warps=4, num_stages=3),
+    ],
+    key=['M', 'K', 'L'],
+)
+@triton.jit
+def _matmul_kernel(
+    A_ptr, B_ptr, C_ptr,
+    N, M, K, L,
+    stride_An, stride_Am, stride_Ak,
+    stride_Bk, stride_Bl,
+    stride_Cn, stride_Cm, stride_Cl,
+    BLOCK_M: tl.constexpr, BLOCK_L: tl.constexpr, BLOCK_K: tl.constexpr,
+):
+    pid_n = tl.program_id(0)
+    pid_m = tl.program_id(1)
+    pid_l = tl.program_id(2)
+
+    if pid_n >= N:
+        return
+
+    # Create block offsets with proper masking
+    m_offsets = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    l_offsets = pid_l * BLOCK_L + tl.arange(0, BLOCK_L)
+    k_offsets = tl.arange(0, BLOCK_K)
+
+    # Initialize accumulator
+    acc = tl.zeros((BLOCK_M, BLOCK_L), dtype=tl.float32)
+
+    # Compute pointer bases
+    a_base = A_ptr + pid_n * stride_An
+    b_base = B_ptr
+    c_base = C_ptr + pid_n * stride_Cn
+
+    # Blocked matrix multiplication
+    for k in range(0, tl.cdiv(K, BLOCK_K)):
+        # Compute current K block
+        k_start = k * BLOCK_K
+
+        # Load A block with coalesced access
+        a_ptrs = a_base + m_offsets[:, None] * stride_Am + (k_start + k_offsets[None, :]) * stride_Ak
+        a_mask = (m_offsets[:, None] < M) & ((k_start + k_offsets[None, :]) < K)
+        a = tl.load(a_ptrs, mask=a_mask, other=0.0)
+
+        # Load B block with coalesced access
+        b_ptrs = b_base + (k_start + k_offsets[:, None]) * stride_Bk + l_offsets[None, :] * stride_Bl
+        b_mask = ((k_start + k_offsets[:, None]) < K) & (l_offsets[None, :] < L)
+        b = tl.load(b_ptrs, mask=b_mask, other=0.0)
+
+        # Accumulate matrix product with full FP32 precision
+        acc += tl.dot(a, b, allow_tf32=False)
+
+    # Store result with masking
+    c_ptrs = c_base + m_offsets[:, None] * stride_Cm + l_offsets[None, :] * stride_Cl
+    c_mask = (m_offsets[:, None] < M) & (l_offsets[None, :] < L)
+    tl.store(c_ptrs, acc, mask=c_mask)
+
+class ModelNew(nn.Module):
+    def __init__(self):
+        super(ModelNew, self).__init__()
+
+    def forward(self, A, B):
+        N, M, K = A.shape
+        L = B.shape[1]
+        A = A.contiguous()
+        B = B.contiguous()
+        C = torch.empty((N, M, L), device=A.device, dtype=A.dtype)
+
+        # Dynamic grid using autotuner meta-parameters
+        grid = lambda meta: (
+            N,
+            triton.cdiv(M, meta['BLOCK_M']),
+            triton.cdiv(L, meta['BLOCK_L'])
+        )
+
+        # Launch kernel without overriding autotuned parameters
+        _matmul_kernel[grid](
+            A, B, C,
+            N, M, K, L,
+            A.stride(0), A.stride(1), A.stride(2),
+            B.stride(0), B.stride(1),
+            C.stride(0), C.stride(1), C.stride(2),
+        )
+        return C
+
+N = 16
+M = 1024
+K = 2048
+L = 768
+
+def get_inputs():
+    A = torch.randn(N, M, K, device='cuda')
+    B = torch.randn(K, L, device='cuda')
+    return [A, B]
+
+def get_init_inputs():
+    return []

dlblas/kernels/kernelagent/level1/11_4D_tensor_matrix_multiplication

@@ -0,0 +1,92 @@
+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+@triton.autotune(
+    configs=[
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32}, num_warps=4, num_stages=4),
+        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32}, num_warps=4, num_stages=4),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32}, num_warps=8, num_stages=3),
+        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32}, num_warps=4, num_stages=4),
+    ],
+    key=['M', 'N', 'K'],
+)
+@triton.jit
+def _triton_matmul(
+    a_ptr, b_ptr, c_ptr,
+    M, N, K,
+    stride_am, stride_ak,
+    stride_bk, stride_bn,
+    stride_cm, stride_cn,
+    BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
+):
+    pid_m = tl.program_id(0)
+    pid_n = tl.program_id(1)
+
+    rm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    rn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+
+    acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+
+    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
+        rk = k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
+        a_mask = (rm[:, None] < M) & (rk[None, :] < K)
+        b_mask = (rk[:, None] < K) & (rn[None, :] < N)
+
+        a = tl.load(a_ptr + rm[:, None] * stride_am + rk[None, :] * stride_ak, 
+                    mask=a_mask, other=0.0)
+        b = tl.load(b_ptr + rk[:, None] * stride_bk + rn[None, :] * stride_bn, 
+                    mask=b_mask, other=0.0)
+        acc += tl.dot(a, b, allow_tf32=False, out_dtype=tl.float32)
+
+    c_ptrs = c_ptr + rm[:, None] * stride_cm + rn[None, :] * stride_cn
+    c_mask = (rm[:, None] < M) & (rn[None, :] < N)
+    tl.store(c_ptrs, acc, mask=c_mask)
+
+class ModelNew(nn.Module):
+    def __init__(self):
+        super(ModelNew, self).__init__()
+
+    def forward(self, A, B):
+        b, i, j, l = A.shape
+        k = B.shape[1]
+
+        # Ensure contiguous memory layout
+        A_flat = A.reshape(-1, l).contiguous()
+        B = B.contiguous()
+
+        M, K = A_flat.shape
+        N = k
+
+        C_flat = torch.empty((M, N), device=A.device, dtype=A.dtype)
+
+        grid = lambda META: (
+            triton.cdiv(M, META['BLOCK_SIZE_M']), 
+            triton.cdiv(N, META['BLOCK_SIZE_N']),
+        )
+
+        _triton_matmul[grid](
+            A_flat, B, C_flat,
+            M, N, K,
+            A_flat.stride(0), A_flat.stride(1),
+            B.stride(0), B.stride(1),
+            C_flat.stride(0), C_flat.stride(1),
+        )
+
+        return C_flat.reshape(b, i, j, k)
+
+# Test code
+b = 16
+i = 256
+j = 512
+l = 256
+k = 768
+
+def get_inputs():
+    A = torch.randn(b, i, j, l)
+    B = torch.randn(l, k)
+    return [A, B]
+
+def get_init_inputs():
+    return []  # No special initialization inputs needed

dlblas/kernels/kernelagent/level1/12_Matmul_with_diagonal_matrices_

@@ -0,0 +1,72 @@
+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+@triton.jit
+def diag_matmul_kernel(
+    A_ptr,
+    B_ptr,
+    C_ptr,
+    N, M,
+    stride_B0, stride_B1,
+    stride_C0, stride_C1,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    if pid >= N:
+        return
+
+    a_val = tl.load(A_ptr + pid)
+    row_start_B = pid * stride_B0
+    row_start_C = pid * stride_C0
+
+    for col_block in range(0, tl.cdiv(M, BLOCK_SIZE)):
+        col_offset = col_block * BLOCK_SIZE
+        col_indices = col_offset + tl.arange(0, BLOCK_SIZE)
+        mask = col_indices < M
+
+        b_vals = tl.load(
+            B_ptr + row_start_B + col_indices * stride_B1,
+            mask=mask,
+            other=0.0
+        )
+        c_vals = a_val * b_vals
+        tl.store(
+            C_ptr + row_start_C + col_indices * stride_C1,
+            c_vals,
+            mask=mask
+        )
+
+class ModelNew(nn.Module):
+    def __init__(self):
+        super(ModelNew, self).__init__()
+
+    def forward(self, A, B):
+        N, M = B.shape
+        C = torch.empty_like(B)
+
+        if B.numel() == 0:
+            return C
+
+        BLOCK_SIZE = 1024
+        grid = (N,)
+        diag_matmul_kernel[grid](
+            A, B, C,
+            N, M,
+            B.stride(0), B.stride(1),
+            C.stride(0), C.stride(1),
+            BLOCK_SIZE=BLOCK_SIZE
+        )
+        return C
+
+M = 4096
+N = 4096
+
+def get_inputs():
+    A = torch.randn(N)
+    B = torch.randn(N, M)
+    return [A, B]
+
+def get_init_inputs():
+    return []  # No special initialization inputs needed