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comfy/customzluda/sa/attn_qk_int8_per_block.py
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164
comfy/customzluda/sa/attn_qk_int8_per_block.py
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@ -0,0 +1,164 @@
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import torch, math
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import triton
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import triton.language as tl
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# Autotune Here
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configs = [
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triton.Config({'BLOCK_M': BM, 'BLOCK_N': BN, 'STAGE':S, 'waves_per_eu':wpe}, num_warps=nw, num_stages=ns) \
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for BM in [32]\
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for BN in [16]\
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for nw in[2, 4]\
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for ns in [1]\
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for S in [1]\
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for wpe in [3,4]
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]
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def keep(conf):
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BLOCK_M = conf.kwargs["BLOCK_M"]
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BLOCK_N = conf.kwargs["BLOCK_N"]
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BLOCK_AREA = BLOCK_M * BLOCK_N
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# do not keep too high block area, any higher doesnt seem to help for navi21
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if (BLOCK_AREA > 1024):
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return False
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# do not keep 'mirror image' configs (ie keep [64,32] and discard [32,64])
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if (BLOCK_M < BLOCK_N):
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return False
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# do not keep skinny sizes for now
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if (BLOCK_M//BLOCK_N >= 8):
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return False
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# do not keep configs where num_warps is too high or low
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if (BLOCK_AREA >= 1024 and conf.num_warps != 2):
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return False
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if (BLOCK_AREA >= 2048 and conf.num_warps != 4):
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return False
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return True
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@triton.jit
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def _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len,
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K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn,
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start_m,
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BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr,
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STAGE: tl.constexpr, offs_m: tl.constexpr, offs_n: tl.constexpr,
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):
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lo, hi = 0, kv_len
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for start_n in range(lo, hi, BLOCK_N):
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start_n = tl.multiple_of(start_n, BLOCK_N)
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k_mask = offs_n[None, :] < (kv_len - start_n)
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k = tl.load(K_ptrs, mask = k_mask)
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k_scale = tl.load(K_scale_ptr)
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qk = tl.dot(q, k).to(tl.float32) * q_scale * k_scale
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m_ij = tl.maximum(m_i, tl.max(qk, 1))
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qk = qk - m_ij[:, None]
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p = tl.math.exp2(qk)
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l_ij = tl.sum(p, 1)
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alpha = tl.math.exp2(m_i - m_ij)
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l_i = l_i * alpha + l_ij
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acc = acc * alpha[:, None]
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v = tl.load(V_ptrs, mask = offs_n[:, None] < (kv_len - start_n))
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p = p.to(tl.float16)
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acc += tl.dot(p, v, out_dtype=tl.float32)
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m_i = m_ij
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K_ptrs += BLOCK_N * stride_kn
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K_scale_ptr += 1
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V_ptrs += BLOCK_N * stride_vn
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return acc, l_i
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@triton.autotune(
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list(filter(keep, configs)),
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key=['qo_len', 'kv_len', 'h_qo']
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)
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@triton.jit
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def _attn_fwd(Q, K, V, Q_scale, K_scale, Out,
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stride_qz, stride_qh, stride_qn,
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stride_kz, stride_kh, stride_kn,
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stride_vz, stride_vh, stride_vn,
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stride_oz, stride_oh, stride_on,
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qo_len, kv_len, H: tl.constexpr, num_kv_groups: tl.constexpr,
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HEAD_DIM: tl.constexpr,
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BLOCK_M: tl.constexpr,
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BLOCK_N: tl.constexpr,
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STAGE: tl.constexpr
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):
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start_m = tl.program_id(0)
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off_z = tl.program_id(2).to(tl.int64)
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off_h = tl.program_id(1).to(tl.int64)
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q_scale_offset = (off_z * H + off_h) * tl.cdiv(qo_len, BLOCK_M)
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k_scale_offset = (off_z * (H // num_kv_groups) + off_h // num_kv_groups) * tl.cdiv(kv_len, BLOCK_N)
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offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
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offs_n = tl.arange(0, BLOCK_N)
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offs_k = tl.arange(0, HEAD_DIM)
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Q_ptrs = Q + (off_z * stride_qz + off_h * stride_qh) + offs_m[:, None] * stride_qn + offs_k[None, :]
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Q_scale_ptr = Q_scale + q_scale_offset + start_m
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K_ptrs = K + (off_z * stride_kz + (off_h // num_kv_groups) * stride_kh) + offs_n[None, :] * stride_kn + offs_k[:, None]
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K_scale_ptr = K_scale + k_scale_offset
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V_ptrs = V + (off_z * stride_vz + (off_h // num_kv_groups) * stride_vh) + offs_n[:, None] * stride_vn + offs_k[None, :]
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O_block_ptr = Out + (off_z * stride_oz + off_h * stride_oh) + offs_m[:, None] * stride_on + offs_k[None, :]
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m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
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l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
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acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
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q = tl.load(Q_ptrs, mask = offs_m[:, None] < qo_len)
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q_scale = tl.load(Q_scale_ptr)
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acc, l_i = _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len, K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn,
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start_m,
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BLOCK_M, HEAD_DIM, BLOCK_N,
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4 - STAGE, offs_m, offs_n
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)
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acc = acc / l_i[:, None]
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tl.store(O_block_ptr, acc.to(Out.type.element_ty), mask = (offs_m[:, None] < qo_len))
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def forward(q, k, v, q_scale, k_scale, tensor_layout="HND", output_dtype=torch.float16):
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BLOCK_M = 128
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BLOCK_N = 64
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stage = 1
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o = torch.empty(q.shape, dtype=output_dtype, device=q.device)
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if tensor_layout == "HND":
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b, h_qo, qo_len, head_dim = q.shape
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_, h_kv, kv_len, _ = k.shape
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stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(1), q.stride(2)
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stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(1), k.stride(2)
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stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(1), v.stride(2)
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stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(1), o.stride(2)
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elif tensor_layout == "NHD":
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b, qo_len, h_qo, head_dim = q.shape
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_, kv_len, h_kv, _ = k.shape
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stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(2), q.stride(1)
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stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(2), k.stride(1)
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stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(2), v.stride(1)
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stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(2), o.stride(1)
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else:
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raise ValueError(f"tensor_layout {tensor_layout} not supported")
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HEAD_DIM_K = head_dim
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num_kv_groups = h_qo // h_kv
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grid = lambda META: (triton.cdiv(qo_len, META['BLOCK_M']), h_qo, b)
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_attn_fwd[grid](
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q, k, v, q_scale, k_scale, o,
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stride_bz_q, stride_h_q, stride_seq_q,
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stride_bz_k, stride_h_k, stride_seq_k,
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stride_bz_v, stride_h_v, stride_seq_v,
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stride_bz_o, stride_h_o, stride_seq_o,
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qo_len, kv_len,
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h_qo, num_kv_groups,
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HEAD_DIM=HEAD_DIM_K,
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)
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return o
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148
comfy/customzluda/sa/attn_qk_int8_per_block_causal.py
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148
comfy/customzluda/sa/attn_qk_int8_per_block_causal.py
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@ -0,0 +1,148 @@
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import torch, math
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import triton
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import triton.language as tl
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@triton.jit
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def _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len,
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K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn,
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start_m,
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BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr,
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STAGE: tl.constexpr, offs_m: tl.constexpr, offs_n: tl.constexpr,
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):
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if STAGE == 1:
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lo, hi = 0, start_m * BLOCK_M
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elif STAGE == 2:
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lo, hi = start_m * BLOCK_M, (start_m + 1) * BLOCK_M
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lo = tl.multiple_of(lo, BLOCK_M)
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K_scale_ptr += lo // BLOCK_N
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K_ptrs += stride_kn * lo
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V_ptrs += stride_vn * lo
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for start_n in range(lo, hi, BLOCK_N):
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start_n = tl.multiple_of(start_n, BLOCK_N)
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k_mask = offs_n[None, :] < (kv_len - start_n)
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k = tl.load(K_ptrs, mask = k_mask)
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k_scale = tl.load(K_scale_ptr)
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qk = tl.dot(q, k).to(tl.float32) * q_scale * k_scale
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if STAGE == 2:
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mask = offs_m[:, None] >= (start_n + offs_n[None, :])
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qk = qk + tl.where(mask, 0, -1.0e6)
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m_ij = tl.maximum(m_i, tl.max(qk, 1))
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qk -= m_ij[:, None]
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else:
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m_ij = tl.maximum(m_i, tl.max(qk, 1))
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qk = qk - m_ij[:, None]
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p = tl.math.exp2(qk)
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l_ij = tl.sum(p, 1)
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alpha = tl.math.exp2(m_i - m_ij)
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l_i = l_i * alpha + l_ij
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acc = acc * alpha[:, None]
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v = tl.load(V_ptrs, mask = offs_n[:, None] < (kv_len - start_n))
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p = p.to(tl.float16)
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acc += tl.dot(p, v, out_dtype=tl.float32) # zlp
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m_i = m_ij
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K_ptrs += BLOCK_N * stride_kn
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K_scale_ptr += 1
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V_ptrs += BLOCK_N * stride_vn
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return acc, l_i, m_i
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@triton.jit
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def _attn_fwd(Q, K, V, Q_scale, K_scale, Out,
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stride_qz, stride_qh, stride_qn,
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stride_kz, stride_kh, stride_kn,
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stride_vz, stride_vh, stride_vn,
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stride_oz, stride_oh, stride_on,
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qo_len, kv_len, H:tl.constexpr, num_kv_groups:tl.constexpr,
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HEAD_DIM: tl.constexpr,
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BLOCK_M: tl.constexpr,
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BLOCK_N: tl.constexpr,
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STAGE: tl.constexpr
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):
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start_m = tl.program_id(0)
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off_z = tl.program_id(2).to(tl.int64)
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off_h = tl.program_id(1).to(tl.int64)
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q_scale_offset = (off_z * H + off_h) * tl.cdiv(qo_len, BLOCK_M)
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k_scale_offset = (off_z * (H // num_kv_groups) + off_h // num_kv_groups) * tl.cdiv(kv_len, BLOCK_N)
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offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
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offs_n = tl.arange(0, BLOCK_N)
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offs_k = tl.arange(0, HEAD_DIM)
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Q_ptrs = Q + (off_z * stride_qz + off_h * stride_qh) + offs_m[:, None] * stride_qn + offs_k[None, :]
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Q_scale_ptr = Q_scale + q_scale_offset + start_m
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K_ptrs = K + (off_z * stride_kz + (off_h // num_kv_groups) * stride_kh) + offs_n[None, :] * stride_kn + offs_k[:, None]
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K_scale_ptr = K_scale + k_scale_offset
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V_ptrs = V + (off_z * stride_vz + (off_h // num_kv_groups) * stride_vh) + offs_n[:, None] * stride_vn + offs_k[None, :]
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O_block_ptr = Out + (off_z * stride_oz + off_h * stride_oh) + offs_m[:, None] * stride_on + offs_k[None, :]
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m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
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l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
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acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
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q = tl.load(Q_ptrs, mask = offs_m[:, None] < qo_len)
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q_scale = tl.load(Q_scale_ptr)
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acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len, K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn,
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start_m,
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BLOCK_M, HEAD_DIM, BLOCK_N,
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4 - STAGE, offs_m, offs_n
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)
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acc, l_i, _ = _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len, K_ptrs, K_scale_ptr, V_ptrs, stride_kn, stride_vn,
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start_m,
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BLOCK_M, HEAD_DIM, BLOCK_N,
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2, offs_m, offs_n
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)
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acc = acc / l_i[:, None]
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tl.store(O_block_ptr, acc.to(Out.type.element_ty), mask = (offs_m[:, None] < qo_len))
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def forward(q, k, v, q_scale, k_scale, tensor_layout="HND", output_dtype=torch.float16):
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BLOCK_M = 32 #zlp
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BLOCK_N = 16 #zlp
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stage = 3
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o = torch.empty(q.shape, dtype=output_dtype, device=q.device)
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if tensor_layout == "HND":
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b, h_qo, qo_len, head_dim = q.shape
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_, h_kv, kv_len, _ = k.shape
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stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(1), q.stride(2)
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stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(1), k.stride(2)
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stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(1), v.stride(2)
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stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(1), o.stride(2)
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elif tensor_layout == "NHD":
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b, qo_len, h_qo, head_dim = q.shape
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_, kv_len, h_kv, _ = k.shape
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stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(2), q.stride(1)
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stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(2), k.stride(1)
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stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(2), v.stride(1)
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stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(2), o.stride(1)
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else:
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raise ValueError(f"tensor_layout {tensor_layout} not supported")
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assert qo_len == kv_len, "qo_len and kv_len must be equal for causal attention"
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HEAD_DIM_K = head_dim
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num_kv_groups = h_qo // h_kv
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grid = (triton.cdiv(qo_len, BLOCK_M), h_qo, b )
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_attn_fwd[grid](
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q, k, v, q_scale, k_scale, o,
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stride_bz_q, stride_h_q, stride_seq_q,
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stride_bz_k, stride_h_k, stride_seq_k,
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stride_bz_v, stride_h_v, stride_seq_v,
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stride_bz_o, stride_h_o, stride_seq_o,
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qo_len, kv_len,
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h_qo, num_kv_groups,
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BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, HEAD_DIM=HEAD_DIM_K,
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STAGE=stage,
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num_warps=4 if head_dim == 64 else 8,
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num_stages=4)
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return o
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