291 lines
9.5 KiB
Python
291 lines
9.5 KiB
Python
import triton # type: ignore[import]
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import triton.language as tl # type: ignore[import]
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def matmul_get_configs():
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return [
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triton.Config(
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{
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"BLOCK_SIZE_M": BM,
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"BLOCK_SIZE_N": BN,
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"BLOCK_SIZE_K": BK,
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"GROUP_SIZE_M": 8,
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},
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num_stages=s,
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num_warps=w,
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)
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for BM in [128]
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for BN in [128]
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for BK in [64]
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for s in ([3])
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for w in [4]
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]
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def _matmul_launch_metadata(grid, kernel, args):
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ret = {}
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M, N, K = args["M"], args["N"], args["K"]
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ret["name"] = f"{kernel.name} [M={M}, N={N}, K={K}]"
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if "c_ptr" in args:
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bytes_per_elem = args["c_ptr"].element_size()
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else:
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bytes_per_elem = 1 if args["FP8_OUTPUT"] else 2
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ret[f"flops{bytes_per_elem * 8}"] = 2.0 * M * N * K
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ret["bytes"] = bytes_per_elem * (M * K + N * K + M * N)
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return ret
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@triton.jit
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def _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS):
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group_id = tile_id // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + (tile_id % group_size_m)
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pid_n = (tile_id % num_pid_in_group) // group_size_m
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return pid_m, pid_n
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@triton.autotune(
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configs=matmul_get_configs(),
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key=["M", "N", "K"],
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)
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@triton.jit(launch_metadata=_matmul_launch_metadata)
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def gemm_kernel_persistent(
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a_ptr,
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b_ptr,
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c_ptr,
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M,
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N,
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K,
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stride_am,
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stride_ak,
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stride_bk,
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stride_bn,
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stride_cm,
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stride_cn,
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alpha,
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beta,
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BLOCK_SIZE_M: tl.constexpr,
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BLOCK_SIZE_N: tl.constexpr,
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BLOCK_SIZE_K: tl.constexpr,
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GROUP_SIZE_M: tl.constexpr,
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NUM_SMS: tl.constexpr,
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):
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start_pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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k_tiles = tl.cdiv(K, BLOCK_SIZE_K)
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num_tiles = num_pid_m * num_pid_n
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# NOTE: There is currently a bug in blackwell pipelining that means it can't handle a value being
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# used in both the prologue and epilogue, so we duplicate the counters as a work-around.
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tile_id_c = start_pid - NUM_SMS
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offs_k_for_mask = tl.arange(0, BLOCK_SIZE_K)
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num_pid_in_group = GROUP_SIZE_M * num_pid_n
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for tile_id in tl.range(start_pid, num_tiles, NUM_SMS, flatten=True):
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pid_m, pid_n = _compute_pid(
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tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
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)
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start_m = pid_m * BLOCK_SIZE_M
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start_n = pid_n * BLOCK_SIZE_N
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offs_am = start_m + tl.arange(0, BLOCK_SIZE_M)
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offs_bn = start_n + tl.arange(0, BLOCK_SIZE_N)
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offs_am = tl.where(offs_am < M, offs_am, 0)
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offs_bn = tl.where(offs_bn < N, offs_bn, 0)
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offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M)
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offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), BLOCK_SIZE_N)
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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for ki in range(k_tiles):
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offs_k = ki * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
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a_ptrs = a_ptr + (
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offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
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)
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b_ptrs = b_ptr + (
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offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn
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)
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a = tl.load(
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a_ptrs, mask=offs_k_for_mask[None, :] < K - ki * BLOCK_SIZE_K, other=0.0
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)
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b = tl.load(
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b_ptrs, mask=offs_k_for_mask[:, None] < K - ki * BLOCK_SIZE_K, other=0.0
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)
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accumulator = tl.dot(a, b, accumulator)
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tile_id_c += NUM_SMS
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pid_m, pid_n = _compute_pid(
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tile_id_c, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
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)
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offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
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c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
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c = accumulator.to(c_ptr.dtype.element_ty)
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c = tl.fma(c, alpha, beta * tl.load(c_ptrs, mask=c_mask))
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tl.store(c_ptrs, c, mask=c_mask)
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@triton.jit(launch_metadata=_matmul_launch_metadata)
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def gemm_kernel_descriptor_persistent(
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a_ptr,
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b_ptr,
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c_ptr, #
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M,
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N,
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K, #
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alpha,
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beta,
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BLOCK_SIZE_M: tl.constexpr, #
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BLOCK_SIZE_N: tl.constexpr, #
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BLOCK_SIZE_K: tl.constexpr, #
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GROUP_SIZE_M: tl.constexpr, #
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EPILOGUE_SUBTILE: tl.constexpr, #
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NUM_SMS: tl.constexpr,
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): #
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dtype = c_ptr.dtype.element_ty
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start_pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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k_tiles = tl.cdiv(K, BLOCK_SIZE_K)
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num_tiles = num_pid_m * num_pid_n
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a_desc = tl.make_tensor_descriptor(
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a_ptr,
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shape=[M, K],
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strides=[K, 1],
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block_shape=[BLOCK_SIZE_M, BLOCK_SIZE_K],
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)
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b_desc = tl.make_tensor_descriptor(
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b_ptr,
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shape=[N, K],
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strides=[K, 1],
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block_shape=[BLOCK_SIZE_N, BLOCK_SIZE_K],
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)
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c_desc = tl.make_tensor_descriptor(
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c_ptr,
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shape=[M, N],
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strides=[N, 1],
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block_shape=[
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BLOCK_SIZE_M,
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BLOCK_SIZE_N if not EPILOGUE_SUBTILE else BLOCK_SIZE_N // 2,
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],
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)
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# tile_id_c is used in the epilogue to break the dependency between
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# the prologue and the epilogue
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tile_id_c = start_pid - NUM_SMS
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num_pid_in_group = GROUP_SIZE_M * num_pid_n
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for tile_id in tl.range(start_pid, num_tiles, NUM_SMS, flatten=True):
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pid_m, pid_n = _compute_pid(
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tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
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)
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offs_am = pid_m * BLOCK_SIZE_M
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offs_bn = pid_n * BLOCK_SIZE_N
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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for ki in range(k_tiles):
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offs_k = ki * BLOCK_SIZE_K
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a = a_desc.load([offs_am, offs_k])
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b = b_desc.load([offs_bn, offs_k])
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accumulator = tl.dot(a, b.T, accumulator)
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tile_id_c += NUM_SMS
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pid_m, pid_n = _compute_pid(
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tile_id_c, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS
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)
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offs_cm = pid_m * BLOCK_SIZE_M
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offs_cn = pid_n * BLOCK_SIZE_N
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if EPILOGUE_SUBTILE:
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acc = tl.reshape(accumulator, (BLOCK_SIZE_M, 2, BLOCK_SIZE_N // 2))
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acc = tl.permute(acc, (0, 2, 1))
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acc0, acc1 = tl.split(acc)
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acc0 = tl.fma(acc0, alpha, beta * c_desc.load([offs_cm, offs_cn]))
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acc1 = tl.fma(
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acc1, alpha, beta * c_desc.load([offs_cm, offs_cn + BLOCK_SIZE_N // 2])
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)
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c0 = acc0.to(dtype)
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c_desc.store([offs_cm, offs_cn], c0)
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c1 = acc1.to(dtype)
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c_desc.store([offs_cm, offs_cn + BLOCK_SIZE_N // 2], c1)
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else:
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accumulator = tl.fma(
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accumulator, alpha, beta * c_desc.load([offs_cm, offs_cn])
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)
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c = accumulator.to(dtype)
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c_desc.store([offs_cm, offs_cn], c)
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# only for testing
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@triton.autotune(
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configs=matmul_get_configs(),
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key=["M", "N", "K"],
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)
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@triton.jit(launch_metadata=_matmul_launch_metadata)
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def gemm_kernel(
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a_ptr,
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b_ptr,
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c_ptr, #
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M,
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N,
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K, #
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stride_am,
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stride_ak, #
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stride_bk,
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stride_bn, #
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stride_cm,
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stride_cn, #
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alpha,
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beta,
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BLOCK_SIZE_M: tl.constexpr, #
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BLOCK_SIZE_N: tl.constexpr, #
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BLOCK_SIZE_K: tl.constexpr, #
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GROUP_SIZE_M: tl.constexpr, #
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):
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pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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num_pid_in_group = GROUP_SIZE_M * num_pid_n
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group_id = pid // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + (pid % group_size_m)
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pid_n = (pid % num_pid_in_group) // group_size_m
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start_m = pid_m * BLOCK_SIZE_M
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start_n = pid_n * BLOCK_SIZE_N
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offs_am = start_m + tl.arange(0, BLOCK_SIZE_M)
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offs_bn = start_n + tl.arange(0, BLOCK_SIZE_N)
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offs_am = tl.where(offs_am < M, offs_am, 0)
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offs_bn = tl.where(offs_bn < N, offs_bn, 0)
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offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M)
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offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), BLOCK_SIZE_N)
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offs_k = tl.arange(0, BLOCK_SIZE_K)
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a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
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b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
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a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
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b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
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accumulator = tl.dot(a, b, accumulator)
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a_ptrs += BLOCK_SIZE_K * stride_ak
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b_ptrs += BLOCK_SIZE_K * stride_bk
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c = accumulator.to(c_ptr.dtype.element_ty)
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offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
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c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
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c = tl.fma(c, alpha, beta * tl.load(c_ptrs, mask=c_mask))
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tl.store(c_ptrs, c, mask=c_mask)
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