support for stripping forks and memory re-use in wavesim.
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@@ -1,5 +1,7 @@
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import numpy as np
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import math
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import math
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from bisect import bisect, insort_left
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import numpy as np
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from . import numba
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from . import numba
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@@ -8,8 +10,74 @@ TMAX_OVL = np.float32(1.1 * 2 ** 127) # almost np.PINF with overflow mark
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TMIN = np.float32(-2 ** 127) # almost np.NINF for 32-bit floating point values
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TMIN = np.float32(-2 ** 127) # almost np.NINF for 32-bit floating point values
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class Heap:
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def __init__(self):
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self.chunks = dict() # map start location to chunk size
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self.released = list() # chunks that were released
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self.current_size = 0
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self.max_size = 0
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def alloc(self, size):
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for idx, loc in enumerate(self.released):
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if self.chunks[loc] == size:
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del self.released[idx]
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return loc
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elif self.chunks[loc] > size: # split chunk
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chunksize = self.chunks[loc]
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self.chunks[loc] = size
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self.chunks[loc + size] = chunksize - size
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self.released[idx] = loc + size # move released pointer: loc -> loc+size
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return loc
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# no previously released chunk; make new one
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loc = self.current_size
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self.chunks[loc] = size
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self.current_size += size
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self.max_size = max(self.max_size, self.current_size)
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return loc
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def free(self, loc):
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size = self.chunks[loc]
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if loc + size == self.current_size: # end of managed area, remove chunk
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del self.chunks[loc]
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self.current_size -= size
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# check and remove prev chunk if free
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if len(self.released) > 0:
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prev = self.released[-1]
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if prev + self.chunks[prev] == self.current_size:
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chunksize = self.chunks[prev]
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del self.chunks[prev]
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del self.released[-1]
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self.current_size -= chunksize
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return
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released_idx = bisect(self.released, loc)
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if released_idx < len(self.released) and loc + size == self.released[released_idx]: # next chunk is free, merge
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chunksize = size + self.chunks[loc + size]
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del self.chunks[loc + size]
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self.chunks[loc] = chunksize
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size = self.chunks[loc]
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self.released[released_idx] = loc
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else:
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insort_left(self.released, loc) # put in a new release
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if released_idx > 0: # check if previous chunk is free
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prev = self.released[released_idx - 1]
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if prev + self.chunks[prev] == loc: # previous chunk is adjacent to freed one, merge
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chunksize = size + self.chunks[prev]
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del self.chunks[loc]
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self.chunks[prev] = chunksize
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del self.released[released_idx]
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def __repr__(self):
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r = []
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for loc in sorted(self.chunks.keys()):
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size = self.chunks[loc]
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released_idx = bisect(self.released, loc)
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is_released = released_idx > 0 and len(self.released) > 0 and self.released[released_idx - 1] == loc
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r.append(f'{loc:5d}: {"free" if is_released else "used"} {size}')
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return "\n".join(r)
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class WaveSim:
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class WaveSim:
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def __init__(self, circuit, timing, sims=8, wavecaps=16):
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def __init__(self, circuit, timing, sims=8, wavecaps=16, strip_forks=False, keep_waveforms=True):
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self.circuit = circuit
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self.circuit = circuit
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self.sims = sims
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self.sims = sims
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self.overflows = 0
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self.overflows = 0
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@@ -24,67 +92,37 @@ class WaveSim:
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intf_wavecap = 4 # sufficient for storing only 1 transition.
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intf_wavecap = 4 # sufficient for storing only 1 transition.
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# state allocation table. maps line and interface indices to self.state memory locations
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# indices for state allocation table (sat)
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self.sat = np.zeros((len(circuit.lines) + 2 + 2 * len(self.interface), 3), dtype='int')
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self.sat[:, 0] = -1
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filled = 0
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for lidx, cap in enumerate(wavecaps):
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self.sat[lidx] = filled, cap, 0
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filled += cap
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self.zero_idx = len(circuit.lines)
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self.zero_idx = len(circuit.lines)
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self.sat[self.zero_idx] = filled, intf_wavecap, 0
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filled += intf_wavecap
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self.tmp_idx = self.zero_idx + 1
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self.tmp_idx = self.zero_idx + 1
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self.sat[self.tmp_idx] = filled, intf_wavecap, 0
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filled += intf_wavecap
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self.ppi_offset = self.tmp_idx + 1
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self.ppi_offset = self.tmp_idx + 1
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self.ppo_offset = self.ppi_offset + len(self.interface)
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self.ppo_offset = self.ppi_offset + len(self.interface)
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for i, n in enumerate(self.interface):
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self.sat_length = self.ppo_offset + len(self.interface)
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if len(n.outs) > 0:
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self.sat[self.ppi_offset + i] = filled, intf_wavecap, 0
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filled += intf_wavecap
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if len(n.ins) > 0:
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self.sat[self.ppo_offset + i] = self.sat[n.ins[0].index]
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# pad timing
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# translate circuit structure into self.ops
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self.timing = np.zeros((len(self.sat), 2, 2))
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self.timing[:len(timing)] = timing
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# allocate self.state
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self.state = np.zeros((filled, sims), dtype='float32') + TMAX
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# generate self.ops
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ops = []
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ops = []
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interface_dict = dict([(n, i) for i, n in enumerate(self.interface)])
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interface_dict = dict([(n, i) for i, n in enumerate(self.interface)])
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for n in circuit.topological_order():
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for n in circuit.topological_order():
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if n in interface_dict:
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if n in interface_dict:
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inp_idx = self.ppi_offset + interface_dict[n]
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inp_idx = self.ppi_offset + interface_dict[n]
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if len(n.outs) > 0 and n.outs[0] is not None:
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if len(n.outs) > 0 and n.outs[0] is not None: # first output of a PI/PPI
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ops.append((0b1010, n.outs[0].index, inp_idx, self.zero_idx))
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ops.append((0b1010, n.outs[0].index, inp_idx, self.zero_idx))
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if 'dff' in n.kind.lower():
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if 'dff' in n.kind.lower(): # second output of DFF is inverted
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if len(n.outs) > 1 and n.outs[1] is not None:
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if len(n.outs) > 1 and n.outs[1] is not None:
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ops.append((0b0101, n.outs[1].index, inp_idx, self.zero_idx))
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ops.append((0b0101, n.outs[1].index, inp_idx, self.zero_idx))
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else:
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else: # if not DFF, no output is inverted.
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for o_line in n.outs[1:]:
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for o_line in n.outs[1:]:
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if o_line is not None:
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if o_line is not None:
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ops.append((0b1010, o_line.index, inp_idx, self.zero_idx))
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ops.append((0b1010, o_line.index, inp_idx, self.zero_idx))
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else:
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else: # regular node, not PI/PPI or PO/PPO
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o0_idx = self.tmp_idx
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o0_idx = n.outs[0].index if len(n.outs) > 0 and n.outs[0] is not None else self.tmp_idx
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i0_idx = self.zero_idx
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i0_idx = n.ins[0].index if len(n.ins) > 0 and n.ins[0] is not None else self.zero_idx
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i1_idx = self.zero_idx
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i1_idx = n.ins[1].index if len(n.ins) > 1 and n.ins[1] is not None else self.zero_idx
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if len(n.outs) > 0 and n.outs[0] is not None:
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o0_idx = n.outs[0].index
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else:
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print(f'no outputs for {n}')
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if len(n.ins) > 0 and n.ins[0] is not None: i0_idx = n.ins[0].index
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if len(n.ins) > 1 and n.ins[1] is not None: i1_idx = n.ins[1].index
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kind = n.kind.lower()
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kind = n.kind.lower()
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if kind == '__fork__':
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if kind == '__fork__':
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for o_line in n.outs:
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if not strip_forks:
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ops.append((0b1010, o_line.index, i0_idx, i1_idx))
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for o_line in n.outs:
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ops.append((0b1010, o_line.index, i0_idx, i1_idx))
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elif kind.startswith('nand'):
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elif kind.startswith('nand'):
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ops.append((0b0111, o0_idx, i0_idx, i1_idx))
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ops.append((0b0111, o0_idx, i0_idx, i1_idx))
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elif kind.startswith('nor'):
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elif kind.startswith('nor'):
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@@ -109,18 +147,91 @@ class WaveSim:
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print('unknown gate type', kind)
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print('unknown gate type', kind)
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self.ops = np.asarray(ops, dtype='int32')
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self.ops = np.asarray(ops, dtype='int32')
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# generate level data
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# create a map from fanout lines to stem lines for fork stripping
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levels = np.zeros(len(self.sat), dtype='int32')
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stems = np.zeros(self.sat_length, dtype='int32') - 1 # default to -1: 'no fanout line'
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if strip_forks:
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for f in circuit.forks.values():
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prev_line = f.ins[0]
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while prev_line.driver.kind == '__fork__':
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prev_line = prev_line.driver.ins[0]
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stem_idx = prev_line.index
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for ol in f.outs:
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stems[ol.index] = stem_idx
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# calculate level (distance from PI/PPI) and reference count for each line
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levels = np.zeros(self.sat_length, dtype='int32')
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ref_count = np.zeros(self.sat_length, dtype='int32')
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level_starts = [0]
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level_starts = [0]
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current_level = 1
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current_level = 1
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for i, op in enumerate(self.ops):
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for i, op in enumerate(self.ops):
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if levels[op[2]] >= current_level or levels[op[3]] >= current_level:
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# if we fork-strip, always take the stems for determining fan-in level
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i0_idx = stems[op[2]] if stems[op[2]] >= 0 else op[2]
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i1_idx = stems[op[3]] if stems[op[3]] >= 0 else op[3]
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if levels[i0_idx] >= current_level or levels[i1_idx] >= current_level:
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current_level += 1
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current_level += 1
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level_starts.append(i)
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level_starts.append(i)
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levels[op[1]] = current_level
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levels[op[1]] = current_level # set level of the output line
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ref_count[i0_idx] += 1
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ref_count[i1_idx] += 1
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self.level_starts = np.asarray(level_starts, dtype='int32')
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self.level_starts = np.asarray(level_starts, dtype='int32')
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self.level_stops = np.asarray(level_starts[1:] + [len(self.ops)], dtype='int32')
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self.level_stops = np.asarray(level_starts[1:] + [len(self.ops)], dtype='int32')
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# state allocation table. maps line and interface indices to self.state memory locations
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self.sat = np.zeros((self.sat_length, 3), dtype='int')
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self.sat[:, 0] = -1
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h = Heap()
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# allocate and keep memory for special fields
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self.sat[self.zero_idx] = h.alloc(intf_wavecap), intf_wavecap, 0
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self.sat[self.tmp_idx] = h.alloc(intf_wavecap), intf_wavecap, 0
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ref_count[self.zero_idx] += 1
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ref_count[self.tmp_idx] += 1
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# allocate and keep memory for PI/PPI, keep memory for PO/PPO (allocated later)
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for i, n in enumerate(self.interface):
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if len(n.outs) > 0:
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self.sat[self.ppi_offset + i] = h.alloc(intf_wavecap), intf_wavecap, 0
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ref_count[self.ppi_offset + i] += 1
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if len(n.ins) > 0:
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i0_idx = stems[n.ins[0].index] if stems[n.ins[0].index] >= 0 else n.ins[0].index
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ref_count[i0_idx] += 1
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# allocate memory for the rest of the circuit
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for op_start, op_stop in zip(self.level_starts, self.level_stops):
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free_list = []
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for op in self.ops[op_start:op_stop]:
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# if we fork-strip, always take the stems
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i0_idx = stems[op[2]] if stems[op[2]] >= 0 else op[2]
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i1_idx = stems[op[3]] if stems[op[3]] >= 0 else op[3]
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ref_count[i0_idx] -= 1
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ref_count[i1_idx] -= 1
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if ref_count[i0_idx] <= 0: free_list.append(self.sat[i0_idx, 0])
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if ref_count[i1_idx] <= 0: free_list.append(self.sat[i1_idx, 0])
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o_idx = op[1]
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cap = wavecaps[o_idx]
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self.sat[o_idx] = h.alloc(cap), cap, 0
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if not keep_waveforms:
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for loc in free_list:
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h.free(loc)
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# copy memory location and capacity from stems to fanout lines
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for lidx, stem in enumerate(stems):
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if stem >= 0: # if at a fanout line
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self.sat[lidx] = self.sat[stem]
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# copy memory location to PO/PPO area
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for i, n in enumerate(self.interface):
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if len(n.ins) > 0:
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self.sat[self.ppo_offset + i] = self.sat[n.ins[0].index]
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# pad timing
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self.timing = np.zeros((self.sat_length, 2, 2))
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self.timing[:len(timing)] = timing
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# allocate self.state
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self.state = np.zeros((h.max_size, sims), dtype='float32') + TMAX
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m1 = np.array([2 ** x for x in range(7, -1, -1)], dtype='uint8')
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m1 = np.array([2 ** x for x in range(7, -1, -1)], dtype='uint8')
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m0 = ~m1
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m0 = ~m1
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self.mask = np.rollaxis(np.vstack((m0, m1)), 1)
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self.mask = np.rollaxis(np.vstack((m0, m1)), 1)
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@@ -9,8 +9,8 @@ TMIN = np.float32(-2 ** 127) # almost np.NINF for 32-bit floating point values
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class WaveSimCuda(WaveSim):
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class WaveSimCuda(WaveSim):
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def __init__(self, circuit, timing, sims=8, wavecaps=16):
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def __init__(self, circuit, timing, sims=8, wavecaps=16, strip_forks=False, keep_waveforms=True):
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super().__init__(circuit, timing, sims, wavecaps)
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super().__init__(circuit, timing, sims, wavecaps, strip_forks, keep_waveforms)
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self.tdata = np.zeros((len(self.interface), 3, (sims - 1) // 8 + 1), dtype='uint8')
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self.tdata = np.zeros((len(self.interface), 3, (sims - 1) // 8 + 1), dtype='uint8')
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@@ -130,6 +130,14 @@ def test_b14(mydir):
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compare_to_logic_sim(wsim)
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compare_to_logic_sim(wsim)
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def test_b14_strip_forks(mydir):
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c = verilog.parse(mydir / 'b14.v.gz', branchforks=True)
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df = sdf.parse(mydir / 'b14.sdf.gz')
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lt = df.annotation(c, pin_index)
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wsim = WaveSim(c, lt, 8, strip_forks=True)
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compare_to_logic_sim(wsim)
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def test_b14_cuda(mydir):
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def test_b14_cuda(mydir):
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c = verilog.parse(mydir / 'b14.v.gz', branchforks=True)
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c = verilog.parse(mydir / 'b14.v.gz', branchforks=True)
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df = sdf.parse(mydir / 'b14.sdf.gz')
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df = sdf.parse(mydir / 'b14.sdf.gz')
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