"""GPT-3 QKV projection benchmark: sharded across PEs via pe_accel_v1.

GPT-3 architecture:
  d_model  = 12288   (hidden dimension)
  n_heads  = 96      (attention heads)
  d_head   = 128     (dimension per head)

Sharding strategy (column-wise across all PEs):
  X        : (seq_len, d_model)       -- replicated to all PEs
  W_Q/K/V  : (d_model, d_model)       -- column-wise sharded across cubes × PEs
  out_Q/K/V: (seq_len, d_model)       -- column-wise sharded across cubes × PEs

Each PE computes:
  Q_slice = X @ W_Q_slice :  (seq_len, d_model) @ (d_model, cols_per_pe) -> (seq_len, cols_per_pe)
  K_slice, V_slice: same

PE count is configurable via N_CUBES × N_PE_PER_CUBE (DPPolicy override).
topology.yaml is unchanged.

Run:
    kernbench run gpt3_qkv
"""
from kernbench.policy.placement.dp import DPPolicy

# -- PE configuration (DPPolicy overrides — does not change topology.yaml) -----
N_SIPS        = 1
N_CUBES       = 16    # cubes per SIP
N_PE_PER_CUBE = 8     # PEs per cube
N_PES         = N_CUBES * N_PE_PER_CUBE  # 128 total

# -- GPT-3 architecture -------------------------------------------------------
GPT3_D_MODEL = 12288
SEQ_LEN      = 32
COLS_PER_PE  = GPT3_D_MODEL // N_PES  # 12288 / 128 = 96
DTYPE         = "f16"


def _gpt3_qkv_kernel(x_ptr, wq_ptr, wk_ptr, wv_ptr,
                      out_q_ptr, out_k_ptr, out_v_ptr,
                      seq_len, d_model, cols_per_pe, tl, DTYPE="f16"):
    """GPT-3 QKV sharded: each PE uses program_id to index its VA slice."""
    pid = tl.program_id(0)
    bpe = 2  # f16

    M = int(seq_len)
    K = int(d_model)
    N = int(cols_per_pe)

    w_slice = K * N * bpe
    out_slice = M * N * bpe

    x  = tl.load(int(x_ptr), shape=(M, K), dtype=DTYPE)
    wq = tl.ref(int(wq_ptr) + pid * w_slice, shape=(K, N), dtype=DTYPE)
    wk = tl.ref(int(wk_ptr) + pid * w_slice, shape=(K, N), dtype=DTYPE)
    wv = tl.ref(int(wv_ptr) + pid * w_slice, shape=(K, N), dtype=DTYPE)

    hq = tl.composite(op="gemm", a=x, b=wq,
                       out_ptr=int(out_q_ptr) + pid * out_slice)
    hk = tl.composite(op="gemm", a=x, b=wk,
                       out_ptr=int(out_k_ptr) + pid * out_slice)
    hv = tl.composite(op="gemm", a=x, b=wv,
                       out_ptr=int(out_v_ptr) + pid * out_slice)

    tl.wait(hq)
    tl.wait(hk)
    tl.wait(hv)


def run(torch):
    """Run the GPT-3 QKV benchmark."""
    M = SEQ_LEN
    K = GPT3_D_MODEL
    N = COLS_PER_PE

    # ADR-0026: DPPolicy is intra-device only. For multi-SIP execution the
    # ADR-0024 launcher calls this bench once per SIP (each worker via
    # torch.ahbm.set_device(rank)); here the policy describes only the
    # cube × PE layout within a single SIP.
    # X: replicated across all PEs within the SIP
    dp_replicate = DPPolicy(cube="replicate", pe="replicate",
                            num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)
    # W_Q/K/V, out_Q/K/V: column-wise sharded across all PEs within the SIP
    dp_sharded = DPPolicy(cube="column_wise", pe="column_wise",
                          num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)

    x     = torch.empty((M, K), dtype=DTYPE, dp=dp_replicate, name="x")
    wq    = torch.empty((K, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="wq")
    wk    = torch.empty((K, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="wk")
    wv    = torch.empty((K, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="wv")
    out_q = torch.empty((M, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="out_q")
    out_k = torch.empty((M, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="out_k")
    out_v = torch.empty((M, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="out_v")

    torch.launch("gpt3_qkv", _gpt3_qkv_kernel,
                 x, wq, wk, wv, out_q, out_k, out_v,
                 M, K, N)