mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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[model] Patch GDN for NPU (#10504)
Co-authored-by: jiaqiw09 <jiaqiw960714@gmail.com>
This commit is contained in:
A1waysBeenHere
GitHub
387
src/llamafactory/third_party/triton/wy_fast.py
vendored
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387
src/llamafactory/third_party/triton/wy_fast.py
vendored
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@@ -0,0 +1,387 @@
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# Copyright 2025 the LlamaFactory team.
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# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
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# Copyright (c) 2026, Huawei Technologies Co., Ltd. All rights reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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from typing import Optional
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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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from .utils import exp, prepare_chunk_indices
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@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
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@triton.jit(do_not_specialize=["T"])
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def prepare_wy_repr_bwd_kernel(
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k,
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v,
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beta,
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g,
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A,
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dw,
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du,
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dk,
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dv,
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dbeta,
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dg,
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cu_seqlens,
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chunk_indices,
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T,
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B,
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H: tl.constexpr,
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K: tl.constexpr,
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V: tl.constexpr,
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NT: tl.constexpr,
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BT: tl.constexpr,
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BK: tl.constexpr,
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BV: tl.constexpr,
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IS_VARLEN: tl.constexpr,
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):
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core_id = tl.program_id(0)
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total_cores = tl.num_programs(0)
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T_max = T
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base_chunks_per_pid = NT // total_cores
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remainder_chunks = NT % total_cores
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if core_id < remainder_chunks:
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chunks_this_pid = base_chunks_per_pid + 1
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start_idx = core_id * chunks_this_pid
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else:
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chunks_this_pid = base_chunks_per_pid
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start_idx = core_id * chunks_this_pid + remainder_chunks
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for idx in range(start_idx, start_idx + chunks_this_pid):
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for i_b in range(B):
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if IS_VARLEN:
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i_n, i_t = (
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tl.load(chunk_indices + idx * 2).to(tl.int32),
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tl.load(chunk_indices + idx * 2 + 1).to(tl.int32),
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)
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bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
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T = eos - bos
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else:
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i_t = idx
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bos, eos = i_b * T, i_b * T + T
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o_t = i_t * BT + tl.arange(0, BT)
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m_t = o_t < T
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m_A = (o_t[:, None] > o_t[None, :]) & (m_t[:, None] & m_t)
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for i_h in range(0, H):
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if IS_VARLEN:
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offset = bos + i_h * T_max
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else:
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offset = bos * H + i_h * T_max
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p_beta = tl.make_block_ptr(beta + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
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p_g = tl.make_block_ptr(g + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
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p_A = tl.make_block_ptr(
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A + (bos * H + i_h) * BT, (BT, T), (1, H * BT), (0, i_t * BT), (BT, BT), (0, 1)
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)
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b_A = tl.load(p_A, boundary_check=(0, 1))
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b_beta = tl.load(p_beta, boundary_check=(0,))
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b_g = tl.load(p_g, boundary_check=(0,))
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b_g_exp = tl.exp(b_g)
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b_dbeta = tl.zeros([BT], dtype=tl.float32)
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b_dA = tl.zeros([BT, BT], dtype=tl.float32)
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b_dg = tl.zeros([BT], dtype=tl.float32)
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for i_k in range(tl.cdiv(K, BK)):
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p_k = tl.make_block_ptr(
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k + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)
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)
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p_dk = tl.make_block_ptr(
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dk + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)
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)
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p_dw = tl.make_block_ptr(
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dw + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)
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)
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b_k = tl.load(p_k, boundary_check=(0, 1))
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b_k_beta_g = (b_k * b_beta[:, None] * b_g_exp[:, None]).to(b_k.dtype)
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b_dw = tl.load(p_dw, boundary_check=(0, 1))
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b_dA += tl.dot(b_dw, tl.trans(b_k_beta_g))
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b_dk_beta_g = tl.dot(b_A, b_dw)
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b_dk = b_dk_beta_g * b_beta[:, None] * b_g_exp[:, None]
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b_dbeta += tl.sum(b_dk_beta_g * b_k * b_g_exp[:, None], 1)
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b_dg += tl.sum(b_dk_beta_g * b_k * b_g_exp[:, None] * b_beta[:, None], 1)
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tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1))
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for i_v in range(tl.cdiv(V, BV)):
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p_v = tl.make_block_ptr(
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v + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
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)
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p_dv = tl.make_block_ptr(
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dv + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
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)
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p_du = tl.make_block_ptr(
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du + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
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)
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b_v = tl.load(p_v, boundary_check=(0, 1))
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b_v_beta = (b_v * b_beta[:, None]).to(b_v.dtype)
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b_du = tl.load(p_du, boundary_check=(0, 1))
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b_dA += tl.dot(b_du, tl.trans(b_v_beta))
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b_dv_beta = tl.dot(b_A, b_du)
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b_dv = b_dv_beta * b_beta[:, None]
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b_dbeta += tl.sum(b_dv_beta * b_v, 1)
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tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1))
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b_dA = tl.where(m_A, b_dA, 0)
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b_dA = tl.dot(b_dA.to(b_A.dtype), b_A)
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b_dA = tl.dot(b_A, b_dA.to(b_A.dtype))
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b_dA = tl.where(m_A, -b_dA * exp(b_g[:, None] - b_g[None, :]), 0)
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b_dA = b_dA.to(k.dtype.element_ty)
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b_A = tl.zeros([BT, BT], dtype=tl.float32)
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for i_k in range(tl.cdiv(K, BK)):
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p_k = tl.make_block_ptr(
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k + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)
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)
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p_dk = tl.make_block_ptr(
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dk + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)
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)
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b_k = tl.load(p_k, boundary_check=(0, 1))
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b_dk = tl.load(p_dk, boundary_check=(0, 1))
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b_k_beta = (b_k * b_beta[:, None]).to(b_k.dtype)
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b_A += tl.dot(b_k_beta, tl.trans(b_k))
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b_dk_beta = tl.dot(b_dA, b_k)
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b_dbeta += tl.sum(b_dk_beta * b_k, 1)
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b_dk += tl.dot(tl.trans(b_dA), b_k_beta)
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b_dk += b_dk_beta * b_beta[:, None]
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tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1))
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b_dA_A = b_dA * b_A
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b_dg += tl.sum(b_dA_A, axis=1) - tl.sum(b_dA_A, axis=0)
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p_dg = tl.make_block_ptr(dg + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
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p_dbeta = tl.make_block_ptr(dbeta + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
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tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0,))
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tl.store(p_dbeta, b_dbeta.to(p_dbeta.dtype.element_ty), boundary_check=(0,))
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@triton.heuristics(
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{
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"USE_G": lambda args: args["g"] is not None,
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"USE_GK": lambda args: args["gk"] is not None,
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"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
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}
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)
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@triton.jit(do_not_specialize=["T"])
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def recompute_w_u_fwd_kernel(
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k,
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v,
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beta,
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w,
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u,
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A,
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g,
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gk,
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cu_seqlens,
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chunk_indices,
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T_tmp,
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B,
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H: tl.constexpr,
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K: tl.constexpr,
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V: tl.constexpr,
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NT: tl.constexpr,
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BT: tl.constexpr,
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BK: tl.constexpr,
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BV: tl.constexpr,
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USE_G: tl.constexpr,
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USE_GK: tl.constexpr,
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IS_VARLEN: tl.constexpr,
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):
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core_id = tl.program_id(0)
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total_cores = tl.num_programs(0)
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T_max = T_tmp
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base_chunks_per_pid = NT // total_cores
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remainder_chunks = NT % total_cores
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if core_id < remainder_chunks:
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chunks_this_pid = base_chunks_per_pid + 1
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start_idx = core_id * chunks_this_pid
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else:
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chunks_this_pid = base_chunks_per_pid
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start_idx = core_id * chunks_this_pid + remainder_chunks
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for idx in range(start_idx, start_idx + chunks_this_pid):
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for i_b in range(B):
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for i_h in range(0, H):
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if IS_VARLEN:
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i_n, i_t = (
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tl.load(chunk_indices + idx * 2).to(tl.int32),
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tl.load(chunk_indices + idx * 2 + 1).to(tl.int32),
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)
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bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
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offset = bos + i_h * T_max
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T = eos - bos
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else:
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T = T_tmp
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i_t = idx
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bos, eos = i_b * T, i_b * T + T
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offset = bos * H + i_h * T_max
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p_beta = tl.make_block_ptr(beta + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
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b_beta = tl.load(p_beta, boundary_check=(0,))
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p_A = tl.make_block_ptr(
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A + (bos * H + i_h) * BT, (T, BT), (H * BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)
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)
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b_A = tl.load(p_A, boundary_check=(0, 1))
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for i_v in range(tl.cdiv(V, BV)):
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p_v = tl.make_block_ptr(
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v + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
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)
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p_u = tl.make_block_ptr(
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u + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
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)
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b_v = tl.load(p_v, boundary_check=(0, 1))
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b_vb = (b_v * b_beta[:, None]).to(b_v.dtype)
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b_u = tl.dot(b_A, b_vb, allow_tf32=False)
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tl.store(p_u, b_u.to(p_u.dtype.element_ty), boundary_check=(0, 1))
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if USE_G:
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p_g = tl.make_block_ptr(g + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
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b_g = tl.exp(tl.load(p_g, boundary_check=(0,)))
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for i_k in range(tl.cdiv(K, BK)):
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p_k = tl.make_block_ptr(
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k + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)
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)
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p_w = tl.make_block_ptr(
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w + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)
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)
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b_k = tl.load(p_k, boundary_check=(0, 1))
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b_kb = b_k * b_beta[:, None]
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if USE_G:
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b_kb *= b_g[:, None]
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if USE_GK:
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p_gk = tl.make_block_ptr(
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gk + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)
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)
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b_kb *= tl.exp(tl.load(p_gk, boundary_check=(0, 1)))
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b_w = tl.dot(b_A, b_kb.to(b_k.dtype))
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tl.store(p_w, b_w.to(p_w.dtype.element_ty), boundary_check=(0, 1))
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def recompute_w_u_fwd(
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k: torch.Tensor,
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v: torch.Tensor,
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beta: torch.Tensor,
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A: torch.Tensor,
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g: Optional[torch.Tensor] = None,
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gk: Optional[torch.Tensor] = None,
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cu_seqlens: Optional[torch.LongTensor] = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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B, T, H, K, V = *k.shape, v.shape[-1]
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BT = A.shape[-1]
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BK = 128
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BV = 128
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chunk_indices = prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None
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NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
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g = g.transpose(1, 2).contiguous() if g is not None else None
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beta = beta.transpose(1, 2).contiguous()
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w = torch.empty_like(k)
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u = torch.empty_like(v)
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cv_kernel_num = 24
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recompute_w_u_fwd_kernel[(cv_kernel_num,)](
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k=k,
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v=v,
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beta=beta,
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w=w,
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u=u,
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A=A,
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g=g,
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gk=gk,
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cu_seqlens=cu_seqlens,
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chunk_indices=chunk_indices,
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T_tmp=T,
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B=B,
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H=H,
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K=K,
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V=V,
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NT=NT,
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BT=BT,
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BK=BK,
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BV=BV,
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)
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return w, u
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def prepare_wy_repr_bwd(
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k: torch.Tensor,
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v: torch.Tensor,
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g: torch.Tensor,
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beta: torch.Tensor,
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A: torch.Tensor,
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dw: torch.Tensor,
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du: torch.Tensor,
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cu_seqlens: Optional[torch.LongTensor],
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chunk_size: int = 64,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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B, T, H, K, V = *k.shape, v.shape[-1]
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BT = chunk_size
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chunk_indices = prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None
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NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
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BK = 128
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BV = 128
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beta = beta.transpose(1, 2).contiguous()
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g = g.transpose(1, 2).contiguous()
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dk = torch.empty_like(k)
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dv = torch.empty_like(v)
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dbeta = torch.empty_like(beta)
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dg = torch.empty_like(g)
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cv_kernel_num = 24
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prepare_wy_repr_bwd_kernel[(cv_kernel_num,)](
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k=k,
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v=v,
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beta=beta,
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g=g,
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A=A,
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dw=dw,
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du=du,
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dk=dk,
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dv=dv,
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dbeta=dbeta,
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dg=dg,
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cu_seqlens=cu_seqlens,
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chunk_indices=chunk_indices,
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T=T,
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B=B,
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H=H,
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K=K,
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V=V,
|
||||
NT=NT,
|
||||
BT=BT,
|
||||
BK=BK,
|
||||
BV=BV,
|
||||
)
|
||||
|
||||
dbeta = dbeta.transpose(1, 2).contiguous()
|
||||
dg = dg.transpose(1, 2).contiguous()
|
||||
|
||||
return dk, dv, dbeta, dg
|
||||
|
||||
|
||||
bwd_prepare_wy_repr = prepare_wy_repr_bwd
|
||||
|
||||
fwd_recompute_w_u = recompute_w_u_fwd
|
||||
Reference in New Issue
Block a user