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from typing import Optional, Tuple |
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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 fla.ops.common.utils import prepare_chunk_offsets |
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from fla.ops.utils.op import exp |
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from fla.utils import check_shared_mem, is_nvidia_hopper, use_cuda_graph |
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NUM_WARPS = [2, 4] if is_nvidia_hopper else [2, 4, 8, 16] |
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@triton.heuristics({ |
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'USE_G': lambda args: args['g'] is not None, |
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'USE_INITIAL_STATE': lambda args: args['h0'] is not None, |
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'STORE_FINAL_STATE': lambda args: args['ht'] is not None, |
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'USE_OFFSETS': lambda args: args['offsets'] is not None, |
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}) |
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@triton.autotune( |
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configs=[ |
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triton.Config({}, num_warps=num_warps, num_stages=num_stages) |
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for num_warps in NUM_WARPS |
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for num_stages in [2, 3, 4] |
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], |
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key=['H', 'K', 'V', 'BT', 'BK', 'BV', 'USE_G'], |
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use_cuda_graph=use_cuda_graph, |
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) |
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@triton.jit(do_not_specialize=['T']) |
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def chunk_gated_delta_rule_fwd_kernel_h( |
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k, |
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v, |
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d, |
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v_new, |
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g, |
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h, |
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h0, |
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ht, |
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offsets, |
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chunk_offsets, |
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T, |
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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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BT: tl.constexpr, |
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BC: tl.constexpr, |
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BK: tl.constexpr, |
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BV: tl.constexpr, |
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NT: tl.constexpr, |
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USE_G: tl.constexpr, |
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USE_INITIAL_STATE: tl.constexpr, |
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STORE_FINAL_STATE: tl.constexpr, |
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USE_OFFSETS: tl.constexpr, |
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HEAD_FIRST: tl.constexpr, |
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): |
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i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
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i_n, i_h = i_nh // H, i_nh % H |
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if USE_OFFSETS: |
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bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32) |
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T = eos - bos |
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NT = tl.cdiv(T, BT) |
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boh = tl.load(chunk_offsets + i_n).to(tl.int32) |
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else: |
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bos, eos = i_n * T, i_n * T + T |
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NT = tl.cdiv(T, BT) |
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boh = i_n * NT |
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b_h = tl.zeros([BK, BV], dtype=tl.float32) |
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if USE_INITIAL_STATE: |
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p_h0 = tl.make_block_ptr(h0 + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
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b_h = tl.load(p_h0, boundary_check=(0, 1)).to(tl.float32) |
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for i_t in range(NT): |
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if HEAD_FIRST: |
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p_h = tl.make_block_ptr(h + (i_nh * NT + i_t) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
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else: |
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p_h = tl.make_block_ptr(h + ((boh + i_t) * H + i_h) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
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tl.store(p_h, b_h.to(p_h.dtype.element_ty), boundary_check=(0, 1)) |
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b_hc = tl.zeros([BK, BV], dtype=tl.float32) |
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if USE_G: |
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last_idx = min((i_t + 1) * BT, T) - 1 |
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if HEAD_FIRST: |
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b_g_last = tl.load(g + i_nh * T + last_idx) |
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else: |
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b_g_last = tl.load(g + bos * H + last_idx * H + i_h) |
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else: |
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b_g_last = None |
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last_idx = None |
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for i_c in range(tl.cdiv(min(BT, T - i_t * BT), BC)): |
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if HEAD_FIRST: |
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p_k = tl.make_block_ptr(k + i_nh * T*K, (K, T), (1, K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1)) |
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p_d = tl.make_block_ptr(d + i_nh * T*K, (T, K), (K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0)) |
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p_v = tl.make_block_ptr(v + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0)) |
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p_v_new = tl.make_block_ptr(v_new+i_nh*T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0)) |
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p_g = tl.make_block_ptr(g + i_nh * T, (T,), (1,), (i_t * BT + i_c * BC,), (BC,), (0,)) if USE_G else None |
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else: |
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p_k = tl.make_block_ptr(k+(bos*H+i_h)*K, (K, T), (1, H*K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1)) |
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p_d = tl.make_block_ptr(d+(bos*H+i_h)*K, (T, K), (H*K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0)) |
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p_v = tl.make_block_ptr(v+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0)) |
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p_v_new = tl.make_block_ptr(v_new+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT+i_c*BC, i_v * BV), (BC, BV), (1, 0)) |
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p_g = tl.make_block_ptr(g+bos*H+i_h, (T,), (H,), (i_t*BT+i_c*BC, ), (BC,), (0,)) if USE_G else None |
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b_g = tl.load(p_g, boundary_check=(0, )) if USE_G else None |
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b_k = tl.load(p_k, boundary_check=(0, 1)) |
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b_k = (b_k * exp(b_g_last - b_g)[None, :]).to(b_k.dtype) if USE_G else b_k |
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b_d = tl.load(p_d, boundary_check=(0, 1)) |
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b_d = (b_d * exp(b_g)[:, None]).to(b_d.dtype) if USE_G else b_d |
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b_v = tl.load(p_v, boundary_check=(0, 1)) |
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b_v2 = b_v - tl.dot(b_d, b_h.to(b_d.dtype)) |
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tl.store(p_v_new, b_v2.to(p_v_new.dtype.element_ty), boundary_check=(0, 1)) |
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b_hc += tl.dot(b_k, b_v2.to(b_k.dtype), allow_tf32=False) |
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b_h *= exp(b_g_last) if USE_G else 1 |
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b_h += b_hc |
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if STORE_FINAL_STATE: |
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p_ht = tl.make_block_ptr(ht + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
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tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), boundary_check=(0, 1)) |
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@triton.heuristics({ |
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'USE_G': lambda args: args['g'] is not None, |
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'USE_INITIAL_STATE': lambda args: args['dh0'] is not None, |
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'USE_FINAL_STATE_GRADIENT': lambda args: args['dht'] is not None, |
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'USE_OFFSETS': lambda args: args['offsets'] is not None, |
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}) |
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@triton.autotune( |
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configs=[ |
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triton.Config({}, num_warps=num_warps, num_stages=num_stages) |
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for num_warps in NUM_WARPS |
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for num_stages in [2, 3, 4] |
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], |
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key=['BT', 'BK', 'BV', 'USE_G'], |
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use_cuda_graph=use_cuda_graph, |
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) |
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@triton.jit(do_not_specialize=['T']) |
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def chunk_gated_delta_rule_bwd_kernel_dhu( |
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q, |
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k, |
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d, |
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g, |
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dht, |
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dh0, |
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do, |
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dh, |
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dv, |
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dv2, |
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offsets, |
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chunk_offsets, |
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scale, |
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T, |
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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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BT: tl.constexpr, |
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BC: 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_INITIAL_STATE: tl.constexpr, |
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USE_FINAL_STATE_GRADIENT: tl.constexpr, |
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USE_OFFSETS: tl.constexpr, |
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HEAD_FIRST: tl.constexpr |
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): |
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i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2) |
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i_n, i_h = i_nh // H, i_nh % H |
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if USE_OFFSETS: |
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bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32) |
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T = eos - bos |
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NT = tl.cdiv(T, BT) |
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boh = tl.load(chunk_offsets + i_n).to(tl.int32) |
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else: |
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bos, eos = i_n * T, i_n * T + T |
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NT = tl.cdiv(T, BT) |
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boh = i_n * NT |
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b_dh = tl.zeros([BK, BV], dtype=tl.float32) |
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if USE_FINAL_STATE_GRADIENT: |
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p_dht = tl.make_block_ptr(dht + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
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b_dh += tl.load(p_dht, boundary_check=(0, 1)) |
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for i_t in range(NT - 1, -1, -1): |
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if HEAD_FIRST: |
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p_dh = tl.make_block_ptr(dh + (i_nh * NT + i_t) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
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else: |
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p_dh = tl.make_block_ptr(dh + ((boh+i_t) * H + i_h) * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
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tl.store(p_dh, b_dh.to(p_dh.dtype.element_ty), boundary_check=(0, 1)) |
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b_dh_tmp = tl.zeros([BK, BV], dtype=tl.float32) |
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if USE_G: |
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last_idx = min((i_t + 1) * BT, T) - 1 |
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if HEAD_FIRST: |
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bg_last = tl.load(g + i_nh * T + last_idx) |
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else: |
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bg_last = tl.load(g + (bos + last_idx) * H + i_h) |
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else: |
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bg_last = None |
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last_idx = None |
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for i_c in range(tl.cdiv(BT, BC) - 1, -1, -1): |
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if HEAD_FIRST: |
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p_q = tl.make_block_ptr(q + i_nh * T*K, (K, T), (1, K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1)) |
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p_k = tl.make_block_ptr(k + i_nh * T*K, (T, K), (K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0)) |
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p_d = tl.make_block_ptr(d + i_nh * T*K, (K, T), (1, K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1)) |
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p_dv = tl.make_block_ptr(dv + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0)) |
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p_do = tl.make_block_ptr(do + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0)) |
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p_g = tl.make_block_ptr(g + i_nh * T, (T,), (1,), (i_t * BT + i_c * BC,), (BC,), (0,)) if USE_G else None |
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p_dv2 = tl.make_block_ptr(dv2 + i_nh * T*V, (T, V), (V, 1), (i_t * BT + i_c * BC, i_v * BV), (BC, BV), (1, 0)) |
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else: |
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p_q = tl.make_block_ptr(q+(bos*H+i_h)*K, (K, T), (1, H*K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1)) |
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p_k = tl.make_block_ptr(k+(bos*H+i_h)*K, (T, K), (H*K, 1), (i_t * BT + i_c * BC, i_k * BK), (BC, BK), (1, 0)) |
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p_d = tl.make_block_ptr(d+(bos*H+i_h)*K, (K, T), (1, H*K), (i_k * BK, i_t * BT + i_c * BC), (BK, BC), (0, 1)) |
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p_dv = tl.make_block_ptr(dv+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_c * BC, i_v * BV), (BC, BV), (1, 0)) |
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p_do = tl.make_block_ptr(do+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_c * BC, i_v * BV), (BC, BV), (1, 0)) |
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p_g = tl.make_block_ptr(g+bos*H+i_h, (T,), (H,), (i_t*BT + i_c * BC,), (BC,), (0,)) if USE_G else None |
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p_dv2 = tl.make_block_ptr(dv2+(bos*H+i_h)*V, (T, V), (H*V, 1), (i_t*BT + i_c * BC, i_v * BV), (BC, BV), (1, 0)) |
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b_g = tl.load(p_g, boundary_check=(0,)) if USE_G else None |
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b_q = tl.load(p_q, boundary_check=(0, 1)) |
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b_q = (b_q * scale * exp(b_g)[None, :]).to(b_q.dtype) if USE_G else (b_q * scale).to(b_q.dtype) |
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b_k = tl.load(p_k, boundary_check=(0, 1)) |
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b_d = tl.load(p_d, boundary_check=(0, 1)) |
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b_k = (b_k * exp(bg_last - b_g)[:, None]).to(b_k.dtype) if USE_G else b_k |
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b_d = (b_d * exp(b_g)[None, :]).to(b_d.dtype) if USE_G else b_d |
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b_do = tl.load(p_do, boundary_check=(0, 1)) |
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b_dv = tl.load(p_dv, boundary_check=(0, 1)) |
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b_dv2 = b_dv + tl.dot(b_k, b_dh.to(b_k.dtype), allow_tf32=False) |
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tl.store(p_dv2, b_dv2.to(p_dv.dtype.element_ty), boundary_check=(0, 1)) |
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b_dh_tmp += tl.dot(b_q, b_do.to(b_q.dtype), allow_tf32=False) |
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b_dh_tmp -= tl.dot(b_d, b_dv2.to(b_q.dtype), allow_tf32=False) |
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b_dh *= exp(bg_last) if USE_G else 1 |
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b_dh += b_dh_tmp |
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if USE_INITIAL_STATE: |
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p_dh0 = tl.make_block_ptr(dh0 + i_nh * K*V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)) |
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tl.store(p_dh0, b_dh.to(p_dh0.dtype.element_ty), boundary_check=(0, 1)) |
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def chunk_gated_delta_rule_fwd_h( |
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k: torch.Tensor, |
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w: torch.Tensor, |
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u: torch.Tensor, |
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g: Optional[torch.Tensor] = None, |
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initial_state: Optional[torch.Tensor] = None, |
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output_final_state: bool = False, |
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offsets: Optional[torch.LongTensor] = None, |
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indices: Optional[torch.LongTensor] = None, |
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head_first: bool = True, |
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chunk_size: int = 64 |
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) -> Tuple[torch.Tensor, torch.Tensor]: |
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if head_first: |
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B, H, T, K, V = *k.shape, u.shape[-1] |
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else: |
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B, T, H, K, V = *k.shape, u.shape[-1] |
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BT = min(chunk_size, max(triton.next_power_of_2(T), 16)) |
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if offsets is None: |
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N, NT, chunk_offsets = B, triton.cdiv(T, BT), None |
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else: |
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N, NT, chunk_offsets = len(offsets) - 1, len(indices), prepare_chunk_offsets(offsets, BT) |
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BK = triton.next_power_of_2(K) |
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assert BK <= 256, "current kernel does not support head dimension larger than 256." |
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if check_shared_mem('hopper', k.device.index): |
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BV = 64 |
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BC = 64 if K <= 128 else 32 |
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elif check_shared_mem('ampere', k.device.index): |
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BV = 32 |
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BC = 64 |
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else: |
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BV = 32 |
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BC = 32 if K <= 128 else 16 |
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BC = min(BT, BC) |
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NK = triton.cdiv(K, BK) |
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NV = triton.cdiv(V, BV) |
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assert NK == 1, 'NK > 1 is not supported because it involves time-consuming synchronization' |
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if head_first: |
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h = k.new_empty(B, H, NT, K, V) |
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else: |
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h = k.new_empty(B, NT, H, K, V) |
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final_state = k.new_empty(N, H, K, V, dtype=torch.float32) if output_final_state else None |
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v_new = torch.empty_like(u) |
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grid = (NK, NV, N * H) |
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chunk_gated_delta_rule_fwd_kernel_h[grid]( |
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k=k, |
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v=u, |
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d=w, |
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v_new=v_new, |
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g=g, |
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h=h, |
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h0=initial_state, |
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ht=final_state, |
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offsets=offsets, |
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chunk_offsets=chunk_offsets, |
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T=T, |
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H=H, |
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K=K, |
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V=V, |
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BT=BT, |
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BC=BC, |
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BK=BK, |
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BV=BV, |
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NT=NT, |
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HEAD_FIRST=head_first |
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) |
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return h, v_new, final_state |
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def chunk_gated_delta_rule_bwd_dhu( |
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q: torch.Tensor, |
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k: torch.Tensor, |
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w: torch.Tensor, |
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g: torch.Tensor, |
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|
h0: torch.Tensor, |
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|
dht: Optional[torch.Tensor], |
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|
do: torch.Tensor, |
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|
dv: torch.Tensor, |
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|
scale: float, |
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|
offsets: Optional[torch.LongTensor] = None, |
|
|
indices: Optional[torch.LongTensor] = None, |
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|
head_first: bool = True, |
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|
chunk_size: int = 64 |
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|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: |
|
|
if head_first: |
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|
B, H, T, K, V = *q.shape, do.shape[-1] |
|
|
else: |
|
|
B, T, H, K, V = *q.shape, do.shape[-1] |
|
|
BT = min(chunk_size, max(triton.next_power_of_2(T), 16)) |
|
|
|
|
|
if offsets is None: |
|
|
N, NT, chunk_offsets = B, triton.cdiv(T, BT), None |
|
|
else: |
|
|
N, NT, chunk_offsets = len(offsets) - 1, len(indices), prepare_chunk_offsets(offsets, BT) |
|
|
|
|
|
BK = triton.next_power_of_2(K) |
|
|
assert BK <= 256, "current kernel does not support head dimension being larger than 256." |
|
|
|
|
|
|
|
|
if check_shared_mem('hopper', q.device.index): |
|
|
BV = 64 |
|
|
BC = 64 if K <= 128 else 32 |
|
|
|
|
|
elif check_shared_mem('ampere', q.device.index): |
|
|
BV = 32 |
|
|
BC = 64 if K <= 128 else 32 |
|
|
else: |
|
|
BV = 32 if K <= 128 else 16 |
|
|
BC = 16 |
|
|
|
|
|
BC = min(BT, BC) |
|
|
NK, NV = triton.cdiv(K, BK), triton.cdiv(V, BV) |
|
|
assert NK == 1, 'NK > 1 is not supported because it involves time-consuming synchronization' |
|
|
|
|
|
if head_first: |
|
|
dh = q.new_empty(B, H, NT, K, V) |
|
|
else: |
|
|
dh = q.new_empty(B, NT, H, K, V) |
|
|
dh0 = torch.empty_like(h0, dtype=torch.float32) if h0 is not None else None |
|
|
dv2 = torch.empty_like(dv) |
|
|
|
|
|
grid = (NK, NV, N * H) |
|
|
chunk_gated_delta_rule_bwd_kernel_dhu[grid]( |
|
|
q=q, |
|
|
k=k, |
|
|
d=w, |
|
|
g=g, |
|
|
dht=dht, |
|
|
dh0=dh0, |
|
|
do=do, |
|
|
dh=dh, |
|
|
dv=dv, |
|
|
dv2=dv2, |
|
|
offsets=offsets, |
|
|
chunk_offsets=chunk_offsets, |
|
|
scale=scale, |
|
|
T=T, |
|
|
H=H, |
|
|
K=K, |
|
|
V=V, |
|
|
BT=BT, |
|
|
BC=BC, |
|
|
BK=BK, |
|
|
BV=BV, |
|
|
HEAD_FIRST=head_first |
|
|
) |
|
|
return dh, dh0, dv2 |
|
|
|
