mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-08-29 13:56:06 +00:00
George HotzandGitHub
57ae1bc7a7
* rename MULTI to UNSHARD * comment updates (glm) * rename method to unshard
308 lines
18 KiB
Python
308 lines
18 KiB
Python
import math, pathlib, functools, struct
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from tinygrad import Device, Tensor
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from tinygrad.dtype import DTypeLike, dtypes
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from tinygrad.helpers import DEBUG
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from tinygrad.renderer import Estimates
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from tinygrad.runtime.support.compiler_amd import HIPCCCompiler
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from tinygrad.runtime.support.elf import elf_loader
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from tinygrad.uop.ops import UOp, Ops, KernelInfo
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def _sharded_empty(shape:Tensor, ref:Tensor, axis:int|None, dtype:DTypeLike|None=None) -> Tensor:
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dtype = dtype or ref.dtype
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if not isinstance(ref.device, tuple): return Tensor.invalids(*shape, dtype=dtype, device=ref.device)
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shard_axis = ref.uop.axis if axis is None else axis
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shape = tuple(s // len(ref.device) if i == shard_axis else s for i, s in enumerate(shape))
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axis = ref.uop.axis if axis is None else axis
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return Tensor(Tensor.invalids(*shape, dtype=dtype, device=ref.device).uop.unshard(axis), dtype=dtype, device=ref.device)
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@functools.cache
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def custom_fused_qkv_rope_forward(q:UOp, k:UOp, v:UOp, xqkv:UOp, freqs_cis:UOp,
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device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int):
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code = (pathlib.Path(__file__).parent / "fused_qkv_rope.cpp").read_text()
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threads = 256
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thread_idx = UOp.special(threads, "lidx0")
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block_idx_x, block_idx_y = UOp.special(B, "gidx0"), UOp.special(N, "gidx1")
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sink = UOp.sink(q.base, k.base, v.base, xqkv.base, freqs_cis.base, thread_idx, block_idx_x, block_idx_y,
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arg=KernelInfo(name="fused_qkv_rope_forward"))
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compile_args = ["-std=c++20", "-ffast-math", f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}",
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f"-DATTN_H_KV={H_KV}", f"-DATTN_D={D}", f"-DTHREADS_PER_BLOCK={threads}"]
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lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
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return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
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@functools.cache
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def custom_fused_qkv_rope_backward(dxqkv:UOp, dq:UOp, dk:UOp, dv:UOp, freqs_cis:UOp,
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device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int):
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assert (B, N, H, H_KV, D) == (2, 8192, 32, 8, 128)
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code = (pathlib.Path(__file__).parent / "fused_qkv_rope_bwd.cpp").read_text()
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threads = 256
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thread_idx = UOp.special(threads, "lidx0")
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gsz = (B, N // 64, H + 2 * H_KV)
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block_idx_x, block_idx_y, block_idx_z = (UOp.special(x, f"gidx{i}") for i, x in enumerate(gsz))
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sink = UOp.sink(dxqkv.base, dq.base, dk.base, dv.base, freqs_cis.base, thread_idx, block_idx_x, block_idx_y, block_idx_z,
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arg=KernelInfo(name="fused_qkv_rope_backward"))
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compile_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "-DKITTENS_CDNA4", "-DHIP_ENABLE_WARP_SYNC_BUILTINS", "-ffast-math", f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}",
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f"-DATTN_H_KV={H_KV}", f"-DATTN_D={D}", f"-DTHREADS_PER_BLOCK={threads}"]
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lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
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return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
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def _fa_native_grads(dq:UOp, dk:UOp, dv:UOp) -> tuple[UOp, UOp, UOp]|None:
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def unwrap_partial(x:UOp) -> UOp|None:
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expected = (Ops.CAST, Ops.REDUCE, Ops.PERMUTE, Ops.CAST, Ops.RESHAPE, Ops.AFTER)
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for op in expected:
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if x.op is not op: return None
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if op is not Ops.AFTER: x = x.src[0]
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return x
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dq_native, dk_partial, dv_partial = dq.base, unwrap_partial(dk), unwrap_partial(dv)
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if dq_native.op is not Ops.AFTER or dk_partial is None or dv_partial is None: return None
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B, N, H, D, H_KV = dq.shape[0], dq.shape[1], dq.shape[2], dq.shape[3], dk.shape[2]
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heads_per_wg = 2 if D == 128 and (H // H_KV) % 2 == 0 else 1
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partials = (H // H_KV) // heads_per_wg
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if dq_native.shape != (B, H, N, D) or dk_partial.shape != (B * partials, N, H_KV, D) or dv_partial.shape != dk_partial.shape: return None
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return dq_native, dk_partial, dv_partial
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def _fused_qkv_rope_grad(dq_u:UOp, dk_u:UOp, dv_u:UOp, call:UOp) -> tuple[None, None, None, UOp, None]:
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dq, dk, dv = Tensor(dq_u, device=dq_u.device), Tensor(dk_u, device=dk_u.device), Tensor(dv_u, device=dv_u.device)
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xqkv_u, freqs_u = call.src[4], call.src[5]
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xqkv, freqs_cis = Tensor(xqkv_u, device=xqkv_u.device), Tensor(freqs_u, device=freqs_u.device)
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B, N, _ = xqkv.shape
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H, H_KV, D = dq.shape[2], dk.shape[2], dq.shape[3]
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num_devices = len(xqkv.device) if isinstance(xqkv.device, tuple) else 1
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is_dp, is_mp = xqkv.uop.axis == 0, xqkv.uop.axis == 2
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B_local = B // num_devices if is_dp else B
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H_local = H // num_devices if is_mp else H
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H_KV_local = H_KV // num_devices if is_mp else H_KV
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single_device = xqkv.device[0] if isinstance(xqkv.device, tuple) else xqkv.device
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arch = Device[single_device].renderer.target.arch
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fa_native = _fa_native_grads(dq_u, dk_u, dv_u)
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assert fa_native is not None, "fused QKV RoPE backward requires native Flash Attention gradients"
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dq, dk, dv = (Tensor(x, device=x.device) for x in fa_native)
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dxqkv = _sharded_empty_like(xqkv, axis=xqkv.uop.axis if isinstance(xqkv.device, tuple) else None)
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fxn = functools.partial(custom_fused_qkv_rope_backward, device=single_device, arch=arch,
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B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D)
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dxqkv = Tensor.custom_kernel(dxqkv, dq, dk, dv, freqs_cis, fxn=fxn)[0]
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return None, None, None, dxqkv.uop, None
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def fused_qkv_rope(xqkv:Tensor, freqs_cis:Tensor, n_heads:int, n_kv_heads:int, head_dim:int) -> tuple[Tensor, Tensor, Tensor]:
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B, N, packed_dim = xqkv.shape
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assert packed_dim == n_kv_heads * (n_heads // n_kv_heads + 2) * head_dim
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assert freqs_cis.dtype == dtypes.bfloat16, f"fused QKV RoPE requires bfloat16 frequencies, got {freqs_cis.dtype}"
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assert freqs_cis.shape == (1, freqs_cis.shape[1], 1, head_dim // 2, 2) and freqs_cis.shape[1] >= N, \
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f"invalid RoPE frequency shape {freqs_cis.shape} for sequence length {N} and head dimension {head_dim}"
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num_devices = len(xqkv.device) if isinstance(xqkv.device, tuple) else 1
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is_dp, is_mp = xqkv.uop.axis == 0, xqkv.uop.axis == 2
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B_local = B // num_devices if is_dp else B
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H_local = n_heads // num_devices if is_mp else n_heads
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H_KV_local = n_kv_heads // num_devices if is_mp else n_kv_heads
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assert H_local % H_KV_local == 0 and head_dim % 2 == 0 and head_dim <= 512
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single_device = xqkv.device[0] if isinstance(xqkv.device, tuple) else xqkv.device
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arch = Device[single_device].renderer.target.arch
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axis = 0 if is_dp else 2 if is_mp else None
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q = _sharded_empty((B, N, n_heads, head_dim), xqkv, axis=axis, dtype=dtypes.bfloat16)
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k = _sharded_empty((B, N, n_kv_heads, head_dim), xqkv, axis=axis, dtype=dtypes.bfloat16)
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v = _sharded_empty((B, N, n_kv_heads, head_dim), xqkv, axis=axis, dtype=dtypes.bfloat16)
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fxn = functools.partial(custom_fused_qkv_rope_forward, device=single_device, arch=arch,
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B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=head_dim)
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q, k, v, *_ = Tensor.custom_kernel(q, k, v, xqkv, freqs_cis, fxn=fxn, grad_fxn=_fused_qkv_rope_grad)
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return q, k, v
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def _sharded_empty_like(ref:Tensor, axis:int|None=None) -> Tensor:
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return _sharded_empty(ref.shape, ref, axis)
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@functools.cache
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def _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, shard_axis_t, single_device, arch, has_sink):
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def grad(dou:UOp, ker:UOp) -> tuple:
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do = Tensor(dou, device=dou.device)
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attn = Tensor(ker.src[1].after(ker), device=ker.src[1].device)
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l_vec = Tensor(ker.src[2].after(ker), device=ker.src[2].device)
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xq = Tensor(ker.src[3], device=ker.src[3].device)
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xk = Tensor(ker.src[4], device=ker.src[4].device)
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xv = Tensor(ker.src[5], device=ker.src[5].device)
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dq = _sharded_empty((B, H, N, D), xq, axis=shard_axis_t)
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GROUP_SIZE = H_local // H_KV_local
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HEADS_PER_WG = 2 if D == 128 and GROUP_SIZE % 2 == 0 else 1
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dk_partial = _sharded_empty((B * GROUP_SIZE // HEADS_PER_WG, N, H_KV, D), xk, axis=shard_axis)
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dv_partial = _sharded_empty((B * GROUP_SIZE // HEADS_PER_WG, N, H_KV, D), xv, axis=shard_axis)
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# delta_vec = (do * attn).sum(-1, dtype=dtypes.float32).transpose(1, 2).unsqueeze(-2).detach()
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delta_vec = _sharded_empty((B, H, 1, N), xq, dtype=dtypes.float32, axis=shard_axis_t)
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delta_vec, dq = Tensor.custom_kernel(delta_vec, dq, attn, do, fxn=functools.partial(custom_fa_backward_pre, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D))[:2]
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dq, dk_partial, dv_partial = Tensor.custom_kernel(dq, dk_partial, dv_partial, do, xq, xk, xv, l_vec, delta_vec, fxn=functools.partial(custom_fa_backward, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D))[:3]
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if D == 64:
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dq = dq.reshape(B, H, N//16, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2).permute(0, 1, 2, 8, 9, 10, 11, 3, 4, 6, 7, 5, 12).reshape(B, H, N, D).transpose(1, 2)
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else:
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dq = dq.reshape(B, H, N//16, 4, 2, 2, D//32, 4, 4, 2).permute(0, 1, 2, 7, 8, 3, 4, 6, 5, 9).reshape(B, H, N, D).transpose(1, 2)
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# reduce partial dK/dV across GROUP_SIZE query heads
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dk = dk_partial.reshape(B, GROUP_SIZE // HEADS_PER_WG, N, H_KV, D).sum(1)
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dv = dv_partial.reshape(B, GROUP_SIZE // HEADS_PER_WG, N, H_KV, D).sum(1)
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if not has_sink: return None, None, dq.uop, dk.uop, dv.uop
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sinks = Tensor(ker.src[6], device=ker.src[6].device)
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p_sink = (sinks.reshape(1, H, 1, 1) - l_vec).exp()
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dsink = -(delta_vec.float() * p_sink).sum(axis=(0, 2, 3))
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return None, None, dq.uop, dk.uop, dv.uop, dsink.uop
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return grad
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# TODO: remove write_flat once scheduler can remove reshapes between custom_kernel. TestCustomKernel.test_simple_reshape
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def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False, write_flat:bool=False, sinks:Tensor|None=None):
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assert attn_mask is None, "attn_mask not supported"
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assert is_causal, "only causal attention supported"
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B, N, H, D = xq.shape
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H_KV = xk.shape[2]
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assert D in (64, 128), "only D=64 or D=128 supported"
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has_sink = sinks is not None
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if has_sink: sinks = sinks.float()
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num_devices = len(xq.device) if isinstance(xq.device, tuple) else 1
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is_dp = xq.uop.axis == 0
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is_mp = xq.uop.axis == 2
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B_local = B // num_devices if is_dp else B
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H_local = H // num_devices if is_mp else H
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H_KV_local = H_KV // num_devices if is_mp else H_KV
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shard_axis = 0 if is_dp else 2 if is_mp else None
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shard_axis_t = 0 if is_dp else 1 if is_mp else None
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if DEBUG >= 2: print(f"Flash Attention {B=} {B_local=} {N=} {H=} {H_local=} {H_KV=} {H_KV_local=} {D=} on {num_devices} devices, {'DP' if is_dp else 'MP' if is_mp else 'no sharding'}")
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single_device = xq.device[0] if isinstance(xq.device, tuple) else xq.device
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arch = Device[single_device].renderer.target.arch
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attn = _sharded_empty_like(xq, axis=shard_axis)
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attn = _sharded_empty((B, N, H * D), xq, axis=shard_axis) if write_flat else _sharded_empty_like(xq, axis=shard_axis)
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l_vec = _sharded_empty((B, H, 1, N), xq, dtype=dtypes.float32, axis=shard_axis_t)
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grad = _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, shard_axis_t, single_device, arch, has_sink)
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fwd_inputs = (attn, l_vec, xq, xk, xv) + ((sinks,) if has_sink else ())
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attn, l_vec = Tensor.custom_kernel(*fwd_inputs, fxn=functools.partial(custom_fa_forward, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D, has_sink=has_sink), grad_fxn=grad)[:2]
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return attn, attn, l_vec
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@functools.cache
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def custom_fa_forward(o:UOp, l_vec:UOp, q:UOp, k:UOp, v:UOp, sinks:UOp|None=None, *, device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int, has_sink:bool=True):
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code = (pathlib.Path(__file__).parent / "fa_fwd_causal.cpp").read_text()
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compile_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "-DKITTENS_CDNA4", "-DHIP_ENABLE_WARP_SYNC_BUILTINS", "-ffast-math",
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f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}", f"-DATTN_H_KV={H_KV}", f"-DATTN_D={D}", f"-DATTN_SINK={int(has_sink)}"]
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Q_BLOCK_SIZE = 32
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NUM_WARPS = 8
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NUM_THREADS = 64 * NUM_WARPS
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gsz = (H, (math.ceil((N // Q_BLOCK_SIZE) / NUM_WARPS)), B)
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lsz = (NUM_THREADS, 1, 1)
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threadIdx_x = UOp.special(lsz[0], "lidx0")
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blockIdx_x, blockIdx_y, blockIdx_z = UOp.special(gsz[0], "gidx0"), UOp.special(gsz[1], "gidx1"), UOp.special(gsz[2], "gidx2")
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el = q.dtype.itemsize
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mem = (2*B*N*H*D + 2*B*N*H_KV*D) * el + B*H*N * l_vec.dtype.itemsize
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estimates = Estimates(ops=2*B*H*N*N*D, lds=mem, mem=mem)
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buf_inputs = (o.base, l_vec.base, q.base, k.base, v.base) + ((sinks.base,) if has_sink else ())
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sink = UOp.sink(*buf_inputs,
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threadIdx_x, blockIdx_x, blockIdx_y, blockIdx_z,
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arg=KernelInfo(name="custom_fa_forward", estimates=estimates))
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lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
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lib = bytearray(lib)
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rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
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struct.pack_into('<I', lib, rodata_off, 160000)
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lib = bytes(lib)
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return UOp(Ops.PROGRAM,
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src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
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@functools.cache
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def custom_fa_backward_pre(delta_vec:UOp, dq:UOp, o:UOp, do:UOp, device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int):
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code = (pathlib.Path(__file__).parent / "fa_bwd_pre.cpp").read_text()
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compile_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "-DKITTENS_CDNA4", "-DHIP_ENABLE_WARP_SYNC_BUILTINS", "-ffast-math",
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f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}", f"-DATTN_D={D}"]
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DOT_SLICE_QO = 16
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NUM_WARPS = 4
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NUM_THREADS = 64 * NUM_WARPS
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gsz = (B, H, N // (DOT_SLICE_QO * NUM_WARPS))
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lsz = (NUM_THREADS, 1, 1)
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threadIdx_x = UOp.special(lsz[0], "lidx0")
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blockIdx_x, blockIdx_y, blockIdx_z = UOp.special(gsz[0], "gidx0"), UOp.special(gsz[1], "gidx1"), UOp.special(gsz[2], "gidx2")
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el = o.dtype.itemsize
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mem = 3*B*H*N*D * el + B*H*N * delta_vec.dtype.itemsize
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estimates = Estimates(ops=2*B*H*N*D, lds=mem, mem=mem)
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sink = UOp.sink(delta_vec.base, dq.base, o.base, do.base,
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threadIdx_x, blockIdx_x, blockIdx_y, blockIdx_z,
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arg=KernelInfo(name="custom_fa_backward_pre", estimates=estimates))
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lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
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lib = bytearray(lib)
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rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
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struct.pack_into('<I', lib, rodata_off, 160000)
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lib = bytes(lib)
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return UOp(Ops.PROGRAM,
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src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
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@functools.cache
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def custom_fa_backward(dq:UOp, dk:UOp, dv:UOp, do:UOp, q:UOp, k:UOp, v:UOp, l_vec:UOp, delta_vec:UOp, device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int):
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code = (pathlib.Path(__file__).parent / "fa_bwd_causal.cpp").read_text()
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compile_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "-DKITTENS_CDNA4", "-DHIP_ENABLE_WARP_SYNC_BUILTINS", "-ffast-math",
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f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}", f"-DATTN_H_KV={H_KV}", f"-DATTN_D={D}"]
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|
|
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BLOCK_SIZE_KV = 256
|
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GROUP_SIZE = H // H_KV
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HEADS_PER_WG = 2 if D == 128 and GROUP_SIZE % 2 == 0 else 1
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NUM_WARPS = 4
|
|
NUM_THREADS = 64 * NUM_WARPS
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gsz = (H // HEADS_PER_WG, N // BLOCK_SIZE_KV, B)
|
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lsz = (NUM_THREADS, 1, 1)
|
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threadIdx_x = UOp.special(lsz[0], "lidx0")
|
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blockIdx_x, blockIdx_y, blockIdx_z = UOp.special(gsz[0], "gidx0"), UOp.special(gsz[1], "gidx1"), UOp.special(gsz[2], "gidx2")
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|
|
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el = q.dtype.itemsize
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mem = (3*B*H*N*D + 4*B*H_KV*N*D) * el + 2*B*H*N * l_vec.dtype.itemsize
|
|
estimates = Estimates(ops=5*B*H*N*N*D, lds=mem, mem=mem)
|
|
sink = UOp.sink(dq.base, dk.base, dv.base, do.base, q.base, k.base, v.base, l_vec.base, delta_vec.base,
|
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threadIdx_x, blockIdx_x, blockIdx_y, blockIdx_z,
|
|
arg=KernelInfo(name="custom_fa_backward", estimates=estimates))
|
|
|
|
lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
|
|
lib = bytearray(lib)
|
|
rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
|
|
struct.pack_into('<I', lib, rodata_off, 160000)
|
|
lib = bytes(lib)
|
|
|
|
return UOp(Ops.PROGRAM,
|
|
src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
|
|
|
|
@functools.cache
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def custom_fa_backward_post(dq_out:UOp, dq_in:UOp, device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int):
|
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code = (pathlib.Path(__file__).parent / "fa_bwd_post.cpp").read_text()
|
|
compile_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "-DKITTENS_CDNA4", "-DHIP_ENABLE_WARP_SYNC_BUILTINS", "-ffast-math",
|
|
f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}", f"-DATTN_D={D}"]
|
|
|
|
DOT_SLICE_QO = 16
|
|
NUM_WARPS = 4
|
|
NUM_THREADS = 64 * NUM_WARPS
|
|
gsz = (B, H, N // (DOT_SLICE_QO * NUM_WARPS))
|
|
lsz = (NUM_THREADS, 1, 1)
|
|
threadIdx_x = UOp.special(lsz[0], "lidx0")
|
|
blockIdx_x, blockIdx_y, blockIdx_z = UOp.special(gsz[0], "gidx0"), UOp.special(gsz[1], "gidx1"), UOp.special(gsz[2], "gidx2")
|
|
|
|
el = dq_out.dtype.itemsize
|
|
mem = 2*B*H*N*D * el
|
|
estimates = Estimates(lds=mem, mem=mem)
|
|
sink = UOp.sink(dq_out.base, dq_in.base,
|
|
threadIdx_x, blockIdx_x, blockIdx_y, blockIdx_z,
|
|
arg=KernelInfo(name="custom_fa_backward_post", estimates=estimates))
|
|
|
|
lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
|
|
lib = bytearray(lib)
|
|
rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
|
|
struct.pack_into('<I', lib, rodata_off, 160000)
|
|
lib = bytes(lib)
|
|
|
|
return UOp(Ops.PROGRAM,
|
|
src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
|