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remove hashes, add lst, overflow, ebuf

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stefan committed 2024-04-05 23:24:00 +09:00
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commit f59e97afa9
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+78 -65
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src/kyupy/wave_sim.py
+78 -65
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@@ -99,26 +99,13 @@ class WaveSim(sim.SimOps):
self.simctl_int[0] = range(sims) # unique seed for each sim by default, zero this to pick same delays for all sims.
self.simctl_int[1] = 2 # random picking by default.
# flat array for line use information
line_use = defaultdict(list)
for lidx in range(len(self.circuit.lines)):
if self.line_use_start[lidx] < 0: continue
if self.line_use_stop[lidx] < 0:
log.warn(f'line {lidx} never read?')
for i in range(self.line_use_start[lidx], self.line_use_stop[lidx]):
line_use[i].append(lidx)
self.line_use_counts = np.array([len(line_use[i]) for i in range(len(self.levels))], dtype=np.int32)
self.line_use_offsets = np.zeros_like(self.line_use_counts)
self.line_use_offsets[1:] = self.line_use_counts.cumsum()[:-1]
self.line_use = np.hstack([line_use[i] for i in range(len(self.levels))])
self.h = np.zeros((self.c_locs_len, sims), dtype=np.float32) # hashes of generated waveforms
self.h_base = np.zeros_like(self.h) # base hashes to compare to
self.e = np.zeros((self.c_locs_len, sims), dtype=np.uint8) # aux data for each line and sim
self.error_counts = np.zeros(self.s_len, dtype=np.uint32) # number of capture errors by PPO
self.lsts = np.zeros(self.s_len, dtype=np.float32) # LST by PPO
self.overflows = np.zeros(self.s_len, dtype=np.uint32) # Overflows by PPO
self.nbytes = sum([a.nbytes for a in (self.c, self.s, self.h, self.c_locs, self.c_caps, self.ops, self.simctl_int)])
self.nbytes = sum([a.nbytes for a in (self.c, self.s, self.e, self.c_locs, self.c_caps, self.ops, self.simctl_int)])
def __repr__(self):
dev = 'GPU' if hasattr(self.c, 'copy_to_host') else 'CPU'
@@ -144,7 +131,7 @@ class WaveSim(sim.SimOps):
"""
sims = min(sims or self.sims, self.sims)
for op_start, op_stop in zip(self.level_starts, self.level_stops):
level_eval_cpu(self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.h, self.abuf, 0, sims, self.delays, self.simctl_int, seed)
level_eval_cpu(self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.e, self.abuf, 0, sims, self.delays, self.simctl_int, seed)
def c_to_s(self, time=TMAX, sd=0.0, seed=1):
"""Simulates a capture operation at all sequential elements and primary outputs.
@@ -172,7 +159,7 @@ class WaveSim(sim.SimOps):
self.s[2, self.ppio_s_locs] = self.s[8, self.ppio_s_locs]
def _wave_eval(op, cbuf, c_locs, c_caps, hbuf, sim, delays, simctl_int, seed):
def _wave_eval(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int, seed):
overflows = int(0)
lut = op[0]
@@ -202,8 +189,6 @@ def _wave_eval(op, cbuf, c_locs, c_caps, hbuf, sim, delays, simctl_int, seed):
z_mem = c_locs[z_idx]
z_cap = c_caps[z_idx]
h = np.float32(0)
a_cur = int(0)
b_cur = int(0)
c_cur = int(0)
@@ -251,7 +236,6 @@ def _wave_eval(op, cbuf, c_locs, c_caps, hbuf, sim, delays, simctl_int, seed):
next_t = cbuf[d_mem + d_cur, sim] + delays[d_idx, (d_cur & 1) ^ 1, z_val ^ 1]
if (z_cur & 1) != ((lut >> inputs) & 1):
h += h*3 + max(current_t, -10) # hash based on generated transitions before filtering
# we generate an edge in z_mem, if ...
if (z_cur == 0 # it is the first edge in z_mem ...
or next_t < current_t # -OR- the next edge on SAME input is EARLIER (need current edge to filter BOTH in next iteration) ...
@@ -281,11 +265,15 @@ def _wave_eval(op, cbuf, c_locs, c_caps, hbuf, sim, delays, simctl_int, seed):
# generate or propagate overflow flag
cbuf[z_mem + z_cur, sim] = TMAX_OVL if overflows > 0 else max(a, b, c, d)
hbuf[z_idx, sim] = h
nrise = max(0, (z_cur+1) // 2 - (cbuf[z_mem, sim] == TMIN))
nfall = z_cur // 2
e = int(((cbuf[z_mem, sim] == TMIN) << 1) & 2) # initial value
e |= z_val # final value
e |= (nrise + nfall)<<2 # number of transitions
ebuf[z_idx, sim] = e
return nrise, nfall
@@ -293,11 +281,11 @@ wave_eval_cpu = numba.njit(_wave_eval)
@numba.njit
def level_eval_cpu(ops, op_start, op_stop, c, c_locs, c_caps, hbuf, abuf, sim_start, sim_stop, delays, simctl_int, seed):
def level_eval_cpu(ops, op_start, op_stop, c, c_locs, c_caps, ebuf, abuf, sim_start, sim_stop, delays, simctl_int, seed):
for op_idx in range(op_start, op_stop):
op = ops[op_idx]
for sim in range(sim_start, sim_stop):
nrise, nfall = wave_eval_cpu(op, c, c_locs, c_caps, hbuf, sim, delays, simctl_int[:, sim], seed)
nrise, nfall = wave_eval_cpu(op, c, c_locs, c_caps, ebuf, sim, delays, simctl_int[:, sim], seed)
a_loc = op[6]
a_wr = op[7]
a_wf = op[8]
@@ -370,10 +358,10 @@ class WaveSimCuda(WaveSim):
self.delays = cuda.to_device(self.delays)
self.simctl_int = cuda.to_device(self.simctl_int)
self.abuf = cuda.to_device(self.abuf)
self.h = cuda.to_device(self.h)
self.h_base = cuda.to_device(self.h_base)
self.line_use = cuda.to_device(self.line_use)
self.e = cuda.to_device(self.e)
self.error_counts = cuda.to_device(self.error_counts)
self.lsts = cuda.to_device(self.lsts)
self.overflows = cuda.to_device(self.overflows)
self.retval_int = cuda.to_device(np.array([0], dtype=np.int32))
@@ -389,10 +377,10 @@ class WaveSimCuda(WaveSim):
state['delays'] = np.array(self.delays)
state['simctl_int'] = np.array(self.simctl_int)
state['abuf'] = np.array(self.abuf)
state['h'] = np.array(self.h)
state['h_base'] = np.array(self.h_base)
state['line_use'] = np.array(self.line_use)
state['e'] = np.array(self.e)
state['error_counts'] = np.array(self.error_counts)
state['lsts'] = np.array(self.lsts)
state['overflows'] = np.array(self.overflows)
state['retval_int'] = np.array(self.retval_int)
return state
@@ -406,10 +394,10 @@ class WaveSimCuda(WaveSim):
self.delays = cuda.to_device(self.delays)
self.simctl_int = cuda.to_device(self.simctl_int)
self.abuf = cuda.to_device(self.abuf)
self.h = cuda.to_device(self.h)
self.h_base = cuda.to_device(self.h_base)
self.line_use = cuda.to_device(self.line_use)
self.e = cuda.to_device(self.e)
self.error_counts = cuda.to_device(self.error_counts)
self.lsts = cuda.to_device(self.lsts)
self.overflows = cuda.to_device(self.overflows)
self.retval_int = cuda.to_device(self.retval_int)
def s_to_c(self):
@@ -424,7 +412,7 @@ class WaveSimCuda(WaveSim):
if op_from > op_start: continue
if op_to is not None and op_to <= op_start: break
grid_dim = self._grid_dim(sims, op_stop - op_start)
wave_eval_gpu[grid_dim, self._block_dim](self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.h, self.abuf, int(0),
wave_eval_gpu[grid_dim, self._block_dim](self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.e, self.abuf, int(0),
sims, self.delays, self.simctl_int, seed)
cuda.synchronize()
@@ -433,7 +421,7 @@ class WaveSimCuda(WaveSim):
op_start = self.level_starts[level]
op_stop = self.level_stops[level]
grid_dim = self._grid_dim(sims, op_stop - op_start)
wave_eval_gpu[grid_dim, self._block_dim](self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.h, self.abuf, int(0),
wave_eval_gpu[grid_dim, self._block_dim](self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.e, self.abuf, int(0),
sims, self.delays, self.simctl_int, seed)
def c_to_s(self, time=TMAX, sd=0.0, seed=1):
@@ -445,24 +433,39 @@ class WaveSimCuda(WaveSim):
grid_dim = self._grid_dim(self.sims, self.s_len)
ppo_to_ppi_gpu[grid_dim, self._block_dim](self.s, self.c_locs, time, self.ppi_offset, self.ppo_offset)
def set_base_hashes(self):
nitems = self.h_base.shape[0] * self.h_base.shape[1]
grid_dim = cdiv(nitems, 256)
memcpy_gpu[grid_dim, 256](self.h, self.h_base, nitems)
def compare_hashes_level(self, lv):
self.retval_int[0] = 0
grid_dim = self._grid_dim(self.sims, self.line_use_counts[lv])
diff_hash_gpu[grid_dim, self._block_dim](self.h, self.h_base, self.line_use, self.line_use_offsets[lv],
self.line_use_counts[lv], self.retval_int)
return self.retval_int[0]
def calc_error_counts(self, sims=None):
def acc_error_counts(self, sims=None):
sims = min(sims or self.sims, self.sims)
grid_dim = cdiv(self.s_len, 256)
calc_error_counts_gpu[grid_dim, 256](self.s, sims, self.error_counts)
acc_error_counts_gpu[grid_dim, 256](self.s, sims, self.error_counts)
def reset_error_counts(self):
self.error_counts[:] = 0
def get_error_counts(self):
return np.array(self.error_counts)
def acc_overflows(self, sims=None):
sims = min(sims or self.sims, self.sims)
grid_dim = cdiv(self.s_len, 256)
acc_overflows_gpu[grid_dim, 256](self.s, sims, self.overflows)
def reset_overflows(self):
self.overflows[:] = 0
def get_overflows(self):
return np.array(self.overflows)
def acc_lsts(self, sims=None):
sims = min(sims or self.sims, self.sims)
grid_dim = cdiv(self.s_len, 256)
acc_lsts_gpu[grid_dim, 256](self.s, sims, self.lsts)
def reset_lsts(self):
self.lsts[:] = 0.0
def get_lsts(self):
return np.array(self.lsts)
@cuda.jit()
def memcpy_gpu (src, dst, nitems):
@@ -473,23 +476,33 @@ def memcpy_gpu (src, dst, nitems):
@cuda.jit()
def diff_hash_gpu(hbuf1, hbuf2, h_locs, h_locs_offset, h_locs_cnt, differs):
x, y = cuda.grid(2)
if x >= hbuf1.shape[1]: return
if y >= h_locs_cnt: return
h_loc = h_locs[h_locs_offset+y]
if hbuf1[h_loc, x] != hbuf2[h_loc, x]:
differs[0] = 1
@cuda.jit()
def calc_error_counts_gpu(s, sims, error_counts):
def acc_error_counts_gpu(s, sims, error_counts):
x = cuda.grid(1)
if x >= s.shape[1]: return
cnt = 0
for i in range(sims):
cnt += (s[6,x,i] != s[8,x,i])
error_counts[x] = cnt
error_counts[x] += cnt
@cuda.jit()
def acc_overflows_gpu(s, sims, overflows):
x = cuda.grid(1)
if x >= s.shape[1]: return
cnt = 0
for i in range(sims):
cnt += s[10,x,i]
overflows[x] += cnt
@cuda.jit()
def acc_lsts_gpu(s, sims, lsts):
x = cuda.grid(1)
if x >= s.shape[1]: return
lst = 0
for i in range(sims):
lst = max(lst, s[5,x,i])
lsts[x] = max(lsts[x], lst)
@cuda.jit()
@@ -520,7 +533,7 @@ _wave_eval_gpu = cuda.jit(_wave_eval, device=True)
@cuda.jit()
def wave_eval_gpu(ops, op_start, op_stop, cbuf, c_locs, c_caps, hbuf, abuf, sim_start, sim_stop, delays, simctl_int, seed):
def wave_eval_gpu(ops, op_start, op_stop, cbuf, c_locs, c_caps, ebuf, abuf, sim_start, sim_stop, delays, simctl_int, seed):
x, y = cuda.grid(2)
sim = sim_start + x
op_idx = op_start + y
@@ -532,7 +545,7 @@ def wave_eval_gpu(ops, op_start, op_stop, cbuf, c_locs, c_caps, hbuf, abuf, sim_
a_wr = op[7]
a_wf = op[8]
nrise, nfall = _wave_eval_gpu(op, cbuf, c_locs, c_caps, hbuf, sim, delays, simctl_int[:, sim], seed)
nrise, nfall = _wave_eval_gpu(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int[:, sim], seed)
# accumulate WSA into abuf
if a_loc >= 0: