diff --git a/triton_test.py b/triton_test.py deleted file mode 100644 index eeb77a9..0000000 --- a/triton_test.py +++ /dev/null @@ -1,154 +0,0 @@ -import torch - -import triton -import triton.language as tl - -# % -# :code:`triton.jit`'ed functions can be auto-tuned by using the `triton.autotune` -# decorator, which consumes: -# - A list of :code:`triton.Config` objects that define different configurations of -# meta-parameters (e.g., BLOCK_SIZE_M) and compilation options (e.g., num_warps) to try -# - An autotuning *key* whose change in values will trigger evaluation of all the -# provided configs - - -@triton.autotune( - configs=[ - triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8), - triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8), - triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), - triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), - triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), - triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), - triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), - triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4), - triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2), - triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2), - ], - key=['M', 'N', 'K'], -) -@triton.jit -def matmul_kernel( - # Pointers to matrices - a_ptr, b_ptr, c_ptr, - # Matrix dimensions - M, N, K, - # The stride variables represent how much to increase the ptr by when moving by 1 - # element in a particular dimension. E.g. stride_am is how much to increase a_ptr - # by to get the element one row down (A has M rows) - stride_am, stride_ak, - stride_bk, stride_bn, - stride_cm, stride_cn, - # Meta-parameters - BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, - GROUP_SIZE_M: tl.constexpr, - ACTIVATION: tl.constexpr, -): - """Kernel for computing the matmul C = A x B. - A has shape (M, K), B has shape (K, N) and C has shape (M, N) - """ - # ----------------------------------------------------------- - # Map program ids `pid` to the block of C it should compute. - # This is done in a grouped ordering to promote L2 data reuse - # See above `L2 Cache Optimizations` section for details - pid = tl.program_id(axis=0) - num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) - num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) - num_pid_in_group = GROUP_SIZE_M * num_pid_n - group_id = pid // num_pid_in_group - first_pid_m = group_id * GROUP_SIZE_M - group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) - pid_m = first_pid_m + (pid % group_size_m) - pid_n = (pid % num_pid_in_group) // group_size_m - - # ---------------------------------------------------------- - # Create pointers for the first blocks of A and B. - # We will advance this pointer as we move in the K direction - # and accumulate - # a_ptrs is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers - # b_ptrs is a block of [BLOCK_SIZE_K, BLOCK_SIZE_n] pointers - # see above `Pointer Arithmetics` section for details - offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) - offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - offs_k = tl.arange(0, BLOCK_SIZE_K) - a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) - b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) - - # ----------------------------------------------------------- - # Iterate to compute a block of the C matrix - # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block - # of fp32 values for higher accuracy. - # `accumulator` will be converted back to fp16 after the loop - accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) - for k in range(0, K, BLOCK_SIZE_K): - # Note that for simplicity, we don't apply a mask here. - # This means that if K is not a multiple of BLOCK_SIZE_K, - # this will access out-of-bounds memory and produce an - # error or (worse!) incorrect results. - a = tl.load(a_ptrs) - b = tl.load(b_ptrs) - # We accumulate along the K dimension - accumulator += tl.dot(a, b) - # Advance the ptrs to the next K block - a_ptrs += BLOCK_SIZE_K * stride_ak - b_ptrs += BLOCK_SIZE_K * stride_bk - # you can fuse arbitrary activation functions here - # while the accumulator is still in FP32! - if ACTIVATION == "leaky_relu": - accumulator = leaky_relu(accumulator) - c = accumulator.to(tl.float16) - - # ----------------------------------------------------------- - # Write back the block of the output matrix C - offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) - offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] - c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) - tl.store(c_ptrs, c, mask=c_mask) - - -# we can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul` -@triton.jit -def leaky_relu(x): - x = x + 1 - return tl.where(x >= 0, x, 0.01 * x) - -def matmul(a, b, activation=""): - # checks constraints - assert a.shape[1] == b.shape[0], "incompatible dimensions" - assert a.is_contiguous(), "matrix A must be contiguous" - assert b.is_contiguous(), "matrix B must be contiguous" - M, K = a.shape - K, N = b.shape - assert ( - K % 32 == 0 - ), "We don't check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K" - # allocates output - c = torch.empty((M, N), device=a.device, dtype=a.dtype) - # 1D launch kernel where each block gets its own program. - grid = lambda META: ( - triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), - ) - matmul_kernel[grid]( - a, b, c, - M, N, K, - a.stride(0), a.stride(1), - b.stride(0), b.stride(1), - c.stride(0), c.stride(1), - ACTIVATION=activation, - ) - return c - - - -torch.manual_seed(0) -a = torch.randn((512, 512), device='cuda', dtype=torch.float16) -b = torch.randn((512, 512), device='cuda', dtype=torch.float16) -triton_output = matmul(a, b) -torch_output = torch.matmul(a, b) -print(f"triton_output={triton_output}") -print(f"torch_output={torch_output}") -if triton.testing.allclose(triton_output, torch_output): - print("✅ Triton and Torch match") -else: - print("❌ Triton and Torch differ")