| """
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| Copyright (c) 2024 by SageAttention team.
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|
|
| Licensed under the Apache License, Version 2.0 (the "License");
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| you may not use this file except in compliance with the License.
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| You may obtain a copy of the License at
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|
|
| http://www.apache.org/licenses/LICENSE-2.0
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|
|
| Unless required by applicable law or agreed to in writing, software
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| distributed under the License is distributed on an "AS IS" BASIS,
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| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| See the License for the specific language governing permissions and
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| limitations under the License.
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| """
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|
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| import torch
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| import triton
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| import triton.language as tl
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|
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| @triton.jit
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| def quant_per_block_int8_kernel(Input, Output, Scale, L,
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| stride_iz, stride_ih, stride_in,
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| stride_oz, stride_oh, stride_on,
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| stride_sz, stride_sh,
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| sm_scale,
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| C: tl.constexpr, BLK: tl.constexpr):
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| off_blk = tl.program_id(0)
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| off_h = tl.program_id(1)
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| off_b = tl.program_id(2)
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|
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| offs_n = off_blk * BLK + tl.arange(0, BLK)
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| offs_k = tl.arange(0, C)
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|
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| input_ptrs = Input + off_b * stride_iz + off_h * stride_ih + offs_n[:, None] * stride_in + offs_k[None, :]
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| output_ptrs = Output + off_b * stride_oz + off_h * stride_oh + offs_n[:, None] * stride_on + offs_k[None, :]
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| scale_ptrs = Scale + off_b * stride_sz + off_h * stride_sh + off_blk
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|
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| x = tl.load(input_ptrs, mask=offs_n[:, None] < L)
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| x = x.to(tl.float32)
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| x *= sm_scale
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| scale = tl.max(tl.abs(x)) / 127.
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| x_int8 = x / scale
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| x_int8 += 0.5 * tl.where(x_int8 >= 0, 1, -1)
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| x_int8 = x_int8.to(tl.int8)
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| tl.store(output_ptrs, x_int8, mask=offs_n[:, None] < L)
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| tl.store(scale_ptrs, scale)
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|
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| def per_block_int8(q, k, km=None, BLKQ=128, BLKK=64, sm_scale=None, tensor_layout="HND"):
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| q_int8 = torch.empty(q.shape, dtype=torch.int8, device=q.device)
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| k_int8 = torch.empty(k.shape, dtype=torch.int8, device=k.device)
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| if km is not None:
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| k = k - km
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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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|
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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_qo, stride_h_qo, stride_seq_qo = q_int8.stride(0), q_int8.stride(1), q_int8.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_ko, stride_h_ko, stride_seq_ko = k_int8.stride(0), k_int8.stride(1), k_int8.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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|
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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_qo, stride_h_qo, stride_seq_qo = q_int8.stride(0), q_int8.stride(2), q_int8.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_ko, stride_h_ko, stride_seq_ko = k_int8.stride(0), k_int8.stride(2), k_int8.stride(1)
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| else:
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| raise ValueError(f"Unknown tensor layout: {tensor_layout}")
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|
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| q_scale = torch.empty((b, h_qo, (qo_len + BLKQ - 1) // BLKQ), device=q.device, dtype=torch.float32)
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| k_scale = torch.empty((b, h_kv, (kv_len + BLKK - 1) // BLKK), device=q.device, dtype=torch.float32)
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|
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| if sm_scale is None:
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| sm_scale = head_dim**-0.5
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|
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| grid = ((qo_len + BLKQ - 1) // BLKQ, h_qo, b)
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| quant_per_block_int8_kernel[grid](
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| q, q_int8, q_scale, qo_len,
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| stride_bz_q, stride_h_q, stride_seq_q,
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| stride_bz_qo, stride_h_qo, stride_seq_qo,
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| q_scale.stride(0), q_scale.stride(1),
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| sm_scale=(sm_scale * 1.44269504),
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| C=head_dim, BLK=BLKQ
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| )
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| grid = ((kv_len + BLKK - 1) // BLKK, h_kv, b)
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| quant_per_block_int8_kernel[grid](
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| k, k_int8, k_scale, kv_len,
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| stride_bz_k, stride_h_k, stride_seq_k,
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| stride_bz_ko, stride_h_ko, stride_seq_ko,
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| k_scale.stride(0), k_scale.stride(1),
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| sm_scale=1.0,
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| C=head_dim, BLK=BLKK
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| )
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|
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| return q_int8, q_scale, k_int8, k_scale
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|
|
