Add SchedulerV2 (pe_accel), DPPolicy overrides, and new benchmarks

- 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>
2026-03-26 23:18:49 -07:00
parent 63669f82cb
commit 114510d4b9
22 changed files with 1822 additions and 15 deletions
@@ -0,0 +1,92 @@
+"""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
+
+    # X: replicated across all PEs
+    dp_replicate = DPPolicy(cube="replicate", pe="replicate",
+                            num_sips=N_SIPS, num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)
+    # W_Q/K/V, out_Q/K/V: column-wise sharded across all PEs
+    dp_sharded = DPPolicy(cube="column_wise", pe="column_wise",
+                          num_sips=N_SIPS, 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)