ywkang
114510d4b9
- Add cycle-accurate PE accelerator scheduler (SchedulerV2) with tiled GEMM/Math pipelines (DMA_IN → GEMM → MATH → DMA_WB) - Add DPPolicy num_pes/num_cubes/num_sips overrides for single-PE testing - Support tuple target_pe for targeting specific PE subsets - Add gemm_single_pe and gpt3_qkv benchmarks - Switch default topology to pe_scheduler_v2 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
93 lines
3.5 KiB
Python
93 lines
3.5 KiB
Python
"""GPT-3 QKV projection benchmark: sharded across PEs via pe_accel_v1.
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GPT-3 architecture:
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d_model = 12288 (hidden dimension)
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n_heads = 96 (attention heads)
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d_head = 128 (dimension per head)
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Sharding strategy (column-wise across all PEs):
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X : (seq_len, d_model) -- replicated to all PEs
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W_Q/K/V : (d_model, d_model) -- column-wise sharded across cubes × PEs
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out_Q/K/V: (seq_len, d_model) -- column-wise sharded across cubes × PEs
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Each PE computes:
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Q_slice = X @ W_Q_slice : (seq_len, d_model) @ (d_model, cols_per_pe) -> (seq_len, cols_per_pe)
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K_slice, V_slice: same
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PE count is configurable via N_CUBES × N_PE_PER_CUBE (DPPolicy override).
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topology.yaml is unchanged.
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Run:
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kernbench run gpt3_qkv
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"""
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from kernbench.policy.placement.dp import DPPolicy
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# -- PE configuration (DPPolicy overrides — does not change topology.yaml) -----
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N_SIPS = 1
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N_CUBES = 16 # cubes per SIP
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N_PE_PER_CUBE = 8 # PEs per cube
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N_PES = N_CUBES * N_PE_PER_CUBE # 128 total
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# -- GPT-3 architecture -------------------------------------------------------
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GPT3_D_MODEL = 12288
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SEQ_LEN = 32
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COLS_PER_PE = GPT3_D_MODEL // N_PES # 12288 / 128 = 96
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DTYPE = "f16"
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def _gpt3_qkv_kernel(x_ptr, wq_ptr, wk_ptr, wv_ptr,
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out_q_ptr, out_k_ptr, out_v_ptr,
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seq_len, d_model, cols_per_pe, tl, DTYPE="f16"):
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"""GPT-3 QKV sharded: each PE uses program_id to index its VA slice."""
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pid = tl.program_id(0)
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bpe = 2 # f16
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M = int(seq_len)
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K = int(d_model)
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N = int(cols_per_pe)
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w_slice = K * N * bpe
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out_slice = M * N * bpe
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x = tl.load(int(x_ptr), shape=(M, K), dtype=DTYPE)
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wq = tl.ref(int(wq_ptr) + pid * w_slice, shape=(K, N), dtype=DTYPE)
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wk = tl.ref(int(wk_ptr) + pid * w_slice, shape=(K, N), dtype=DTYPE)
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wv = tl.ref(int(wv_ptr) + pid * w_slice, shape=(K, N), dtype=DTYPE)
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hq = tl.composite(op="gemm", a=x, b=wq,
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out_ptr=int(out_q_ptr) + pid * out_slice)
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hk = tl.composite(op="gemm", a=x, b=wk,
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out_ptr=int(out_k_ptr) + pid * out_slice)
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hv = tl.composite(op="gemm", a=x, b=wv,
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out_ptr=int(out_v_ptr) + pid * out_slice)
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tl.wait(hq)
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tl.wait(hk)
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tl.wait(hv)
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def run(torch):
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"""Run the GPT-3 QKV benchmark."""
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M = SEQ_LEN
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K = GPT3_D_MODEL
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N = COLS_PER_PE
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# X: replicated across all PEs
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dp_replicate = DPPolicy(cube="replicate", pe="replicate",
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num_sips=N_SIPS, num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)
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# W_Q/K/V, out_Q/K/V: column-wise sharded across all PEs
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dp_sharded = DPPolicy(cube="column_wise", pe="column_wise",
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num_sips=N_SIPS, num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)
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x = torch.empty((M, K), dtype=DTYPE, dp=dp_replicate, name="x")
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wq = torch.empty((K, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="wq")
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wk = torch.empty((K, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="wk")
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wv = torch.empty((K, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="wv")
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out_q = torch.empty((M, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="out_q")
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out_k = torch.empty((M, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="out_k")
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out_v = torch.empty((M, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="out_v")
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torch.launch("gpt3_qkv", _gpt3_qkv_kernel,
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x, wq, wk, wv, out_q, out_k, out_v,
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M, K, N)
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