IQ.Pilot Release Commit @ 0798119

tinygrad_repo/extra/gemm/amd_copy_matmul.py

@@ -0,0 +1,118 @@
+from tinygrad import Device, UOp, getenv
+from tinygrad.uop.ops import AxisType, KernelInfo, Ops
+from tinygrad.dtype import AddrSpace, dtypes
+
+N = getenv("N", 4096)
+M = getenv("M", N)
+K = getenv("K", N)
+
+WARP_SIZE = 32
+BLOCK_M, BLOCK_N = 128, 128
+BLOCK_K = getenv("BK", 16)
+assert N % BLOCK_N == 0 and M % BLOCK_M == 0 and K % BLOCK_K == 0
+
+use_wmma = getenv("WMMA")
+if use_wmma:
+  is_rdna4 = Device[Device.DEFAULT].renderer.target.arch.startswith("gfx12")
+
+  WAVES_M, WAVES_N = 2, 2
+  LANES_PER_WAVE_M, LANES_PER_WAVE_N = 2, 16
+
+  # wmma params
+  WMMA_M, WMMA_N, WMMA_K = 16, 16, 16
+  WMMA_ACC = WMMA_M // LANES_PER_WAVE_M
+  UNROLL_M, UNROLL_N = (WMMA_ACC, 1) if is_rdna4 else (1, 1)
+else:
+  WAVES_M, WAVES_N = 4, 1
+  LANES_PER_WAVE_M, LANES_PER_WAVE_N = 4, 8
+  UNROLL_M, UNROLL_N = 4, 4
+
+# total lanes must be the warp size
+assert LANES_PER_WAVE_M*LANES_PER_WAVE_N == WARP_SIZE
+
+# WARP_SIZE * total waves
+THREADS_PER_BLOCK = WARP_SIZE * WAVES_M * WAVES_N
+
+# accumulator size
+TM = BLOCK_M // (WAVES_M * LANES_PER_WAVE_M)
+TN = BLOCK_N // (WAVES_N * LANES_PER_WAVE_N)
+
+def block_128x128_gemm(c:UOp, a:UOp, b:UOp) -> UOp:
+  wave_m = UOp.range(WAVES_M, 2, AxisType.LOCAL)
+  wave_n = UOp.range(WAVES_N, 3, AxisType.LOCAL)
+  lane = UOp.range(WARP_SIZE, -1, AxisType.WARP)
+  tid = (wave_m * WAVES_N + wave_n) * WARP_SIZE + lane
+
+  # -- GLOBAL -> LOCAL --
+  # wmma: spatial outer, k inner (k contiguous for vectorized WMMA tile loads)
+  # gemm: k outer, spatial inner
+  A_local = UOp.placeholder((BLOCK_M, BLOCK_K) if use_wmma else (BLOCK_K, BLOCK_M), a.dtype.base, slot=0, addrspace=AddrSpace.LOCAL)
+  B_local = UOp.placeholder((BLOCK_N, BLOCK_K) if use_wmma else (BLOCK_K, BLOCK_N), b.dtype.base, slot=1, addrspace=AddrSpace.LOCAL)
+
+  a = a.reshape(K // BLOCK_K, BLOCK_K, BLOCK_M)
+  b = b.reshape(K // BLOCK_K, BLOCK_K, BLOCK_N)
+  k_tile = UOp.range(K // BLOCK_K, 100, AxisType.REDUCE)
+
+  # copy with transpose for wmma (input is k×spatial, LDS is spatial×k)
+  A_copy = A_local.permute((1,0)) if use_wmma else A_local
+  B_copy = B_local.permute((1,0)) if use_wmma else B_local
+  A_store = A_copy.reshape(-1, THREADS_PER_BLOCK)[:, tid].store(a[k_tile].reshape(-1, THREADS_PER_BLOCK)[:, tid])
+  B_store = B_copy.reshape(-1, THREADS_PER_BLOCK)[:, tid].store(b[k_tile].reshape(-1, THREADS_PER_BLOCK)[:, tid])
+  barrier = UOp.barrier(A_store, B_store)
+  A_local, B_local = A_local.after(barrier), B_local.after(barrier)
+
+  # -- COMPUTE --
+  lane_m, lane_n = lane // LANES_PER_WAVE_N, lane % LANES_PER_WAVE_N
+
+  # accumulator (unified: both paths use (TM, TN) with scalar dtypes.float)
+  acc = UOp.placeholder((TM, TN), dtypes.float, slot=2, addrspace=AddrSpace.REG)
+  acc = acc.after(acc.store(acc.zeros_like()))
+
+  if use_wmma:
+    k = UOp.range(BLOCK_K // WMMA_K, 101, AxisType.REDUCE)
+    tile_m = UOp.range(TM // WMMA_ACC, 200, AxisType.LOOP)
+    tile_n = UOp.range(TN, 201, AxisType.LOOP)
+
+    acc_frag = acc.reshape(TM // WMMA_ACC, WMMA_ACC, TN).permute(0,2,1)[tile_m, tile_n]
+    a_frag = A_local.reshape(WAVES_M, TM // WMMA_ACC, WMMA_M, BLOCK_K // WMMA_K, WMMA_K)[wave_m, tile_m, lane_n, k]
+    b_frag = B_local.reshape(WAVES_N, TN, WMMA_N, BLOCK_K // WMMA_K, WMMA_K)[wave_n, tile_n, lane_n, k]
+    if is_rdna4:
+      # NOTE: since this is part of K, these 2 can be anywhere in the frags and long as a and b match
+      a_frag = a_frag.reshape(2, 8)[lane_m, :]
+      b_frag = b_frag.reshape(2, 8)[lane_m, :]
+    wmma = UOp(Ops.SHAPED_WMMA, dtypes.float, (a_frag, b_frag, acc_frag.after(k)), arg=((16, 16, 16), 'AMD', 32))
+    acc_store = acc_frag.store(wmma).end(tile_m, tile_n)
+  else:
+    # registers for LOCAL -> REG
+    a_frag = UOp.placeholder((TM//UNROLL_M, UNROLL_M), dtypes.float, slot=0, addrspace=AddrSpace.REG)
+    b_frag = UOp.placeholder((TN//UNROLL_N, UNROLL_N), dtypes.float, slot=1, addrspace=AddrSpace.REG)
+
+    k = UOp.range(BLOCK_K, 101, AxisType.REDUCE)
+    a_frag = a_frag.after(a_frag.store(A_local[k].reshape(WAVES_M, TM//UNROLL_M, LANES_PER_WAVE_M, UNROLL_M)[wave_m, :, lane_m, :]))
+    b_frag = b_frag.after(b_frag.store(B_local[k].reshape(WAVES_N, TN//UNROLL_N, LANES_PER_WAVE_N, UNROLL_N)[wave_n, :, lane_n, :]))
+
+    # FMA
+    a_frag = a_frag.reshape(TM, 1).expand(TM, TN)
+    b_frag = b_frag.reshape(1, TN).expand(TM, TN)
+    acc_store = acc.store(acc.after(k) + (a_frag * b_frag))
+
+  # store accumulator and loop
+  acc = acc.after(acc_store.end(k).barrier().end(k_tile))
+
+  # store accumulator to output (unified)
+  c = c.reshape(WAVES_M, TM//UNROLL_M, LANES_PER_WAVE_M, UNROLL_M,
+                WAVES_N, TN//UNROLL_N, LANES_PER_WAVE_N, UNROLL_N)
+  c = c.permute((0,4,2,6, 1,3,5,7)).reshape(THREADS_PER_BLOCK, TM, TN)
+  return c[tid].store(acc).end(wave_m, wave_n, lane)
+
+def amd_copy_matmul(c:UOp, a:UOp, b:UOp) -> UOp:
+  block_id_m = UOp.range(M // BLOCK_M, 0, AxisType.GLOBAL)
+  block_id_n = UOp.range(N // BLOCK_N, 1, AxisType.GLOBAL)
+  c = c.reshape(M // BLOCK_M, BLOCK_M, N // BLOCK_N, BLOCK_N)[block_id_m, :, block_id_n, :]
+  a = a.T.reshape(K, M // BLOCK_M, BLOCK_M)[:, block_id_m, :]
+  b = b.reshape(K, N // BLOCK_N, BLOCK_N)[:, block_id_n, :]
+  return block_128x128_gemm(c, a, b).end(block_id_n, block_id_m).sink(arg=KernelInfo(opts_to_apply=()))
+
+if __name__ == "__main__":
+  from amd_uop_matmul import eval_custom_matmul
+  eval_custom_matmul(amd_copy_matmul, dtypes.half if use_wmma else dtypes.float)