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>
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
"""Single-PE GEMM benchmark via scheduler_v2 (pe_accel).
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Full host-to-PE pipeline:
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Host → PCIE_EP → IO_CPU → M_CPU → PE_CPU → SchedulerV2 → PE_DMA → HBM
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Single PE: num_sips=1, num_cubes=1, num_pes=1 via DPPolicy override.
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Both operands use tl.ref (HBM-resident); scheduler_v2 tiles and streams
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per-tile DMA internally.
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Run:
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kernbench run gemm_single_pe
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"""
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from kernbench.policy.placement.dp import DPPolicy
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# GEMM dimensions: (M, K) x (K, N) → (M, N)
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M, K, N = 32, 128, 32
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DTYPE = "f16"
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def _gemm_kernel(a_ptr, b_ptr, out_ptr, M, K, N, tl, DTYPE="f16"):
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"""Single-PE GEMM: out = a @ b. Both operands streamed from HBM by scheduler."""
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M, K, N = int(M), int(K), int(N)
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a = tl.ref(int(a_ptr), shape=(M, K), dtype=DTYPE)
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b = tl.ref(int(b_ptr), shape=(K, N), dtype=DTYPE)
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h = tl.composite(op="gemm", a=a, b=b, out_ptr=int(out_ptr))
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tl.wait(h)
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def run(torch):
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"""Run the single-PE GEMM benchmark."""
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dp = DPPolicy(cube="replicate", pe="replicate",
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num_sips=1, num_cubes=1, num_pes=1)
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a = torch.empty((M, K), dtype=DTYPE, dp=dp, name="a")
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b = torch.empty((K, N), dtype=DTYPE, dp=dp, name="b")
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out = torch.empty((M, N), dtype=DTYPE, dp=dp, name="out")
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torch.launch("gemm_single_pe", _gemm_kernel, a, b, out, M, K, N)
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