Initial project scaffold

kernels/triton/flash_attention_fwd.py

@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+import torch
+
+try:
+    import triton
+    import triton.language as tl
+except ImportError:  # pragma: no cover - depends on local environment
+    triton = None
+    tl = None
+
+
+TRITON_AVAILABLE = triton is not None
+
+
+if TRITON_AVAILABLE:
+
+    @triton.jit
+    def flash_attention_fwd_kernel(
+        q_ptr,
+        k_ptr,
+        v_ptr,
+        out_ptr,
+        seq_len,
+        head_dim,
+        stride_q_batch,
+        stride_q_head,
+        stride_q_seq,
+        stride_q_dim,
+        stride_k_batch,
+        stride_k_head,
+        stride_k_seq,
+        stride_k_dim,
+        stride_v_batch,
+        stride_v_head,
+        stride_v_seq,
+        stride_v_dim,
+        stride_out_batch,
+        stride_out_head,
+        stride_out_seq,
+        stride_out_dim,
+        causal,
+        block_q: tl.constexpr,
+        block_k: tl.constexpr,
+        block_d: tl.constexpr,
+    ):
+        pid_q = tl.program_id(axis=0)
+        pid_bh = tl.program_id(axis=1)
+        # TODO(student): map pid_q and pid_bh to a batch/head/query tile.
+        # TODO(student): load Q, K, and V blocks.
+        # TODO(student): compute scores for the current block pair.
+        # TODO(student): apply optional causal masking.
+        # TODO(student): update online softmax state and accumulate the output block.
+        # TODO(student): store the final output tile.
+        pass
+
+
+def triton_flash_attention_fwd(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    causal: bool = False,
+    block_q: int = 64,
+    block_k: int = 64,
+) -> torch.Tensor:
+    if not TRITON_AVAILABLE:
+        raise RuntimeError("Triton is not installed in this environment.")
+    if q.shape != k.shape or q.shape != v.shape:
+        raise ValueError(f"q, k, v must match; got {q.shape}, {k.shape}, {v.shape}")
+    if q.ndim != 4:
+        raise ValueError("expected [batch, heads, seq, dim] inputs")
+    if not q.is_cuda or not k.is_cuda or not v.is_cuda:
+        raise ValueError("Triton kernels in this lab expect CUDA tensors.")
+    raise NotImplementedError("TODO(student): implement the FlashAttention forward launch.")
+