def evaluate(self, cur_G, node, direction, qrecs, fusion=None):
in_qs = self.get_inqtypes_down(cur_G, node)
if fusion:
out_qs = self.get_outqtypes_up_fusion(cur_G, node)
else:
out_qs = self.get_outqtypes_up(cur_G, node)
nid = NodeId(node) if fusion is None else NodeId(fusion, fnode=node)
pnid = NodeId(node) if fusion is None else NodeId(fusion)
stat = self._stats.get(nid, None)
opts = self.get_options(pnid)
scheme_priority = self.get_scheme_priority(pnid)
if isinstance(node, FusionBase) and node.quantize_internals:
try:
if direction == "up":
qrec = self.elimination_fusion_pass_up(
node, qrecs, in_qs, out_qs)
else:
qrec = self.elimination_fusion_pass_down(
node, qrecs, in_qs, out_qs)
# qrec on fusion
except CantContinueError:
# no change
qrec = self._qset[nid]
else:
qrec = self._resolve_qrec(cur_G, direction,
node, stat, opts, scheme_priority, in_qs, out_qs, fusion=fusion)
qrecs[nid] = qrec
return qrec
def match(self, G: GraphView, set_identity: bool = True):
split_nodes = [
node for node in G.nodes() if isinstance(node, SplitParameters)
]
has_modified_graph = False
for node in split_nodes:
# traverse reshapes or transposes that do nothing - check gen
# find edges connected to concats
res = self.find_split_concat(G, node)
if res is None:
continue
# TODO(martin) - group edges that have adjacent inputs and outputs
if G.quantization:
qrec = G.quantization[NodeId(node)]
for idx, bundle in enumerate(res):
if not bundle:
continue
has_modified_graph = True
copy_node = CopyParameters("%s_copy_%s" % (node.name, idx))
for edge_set in bundle:
first_edge = edge_set[0]
G.remove_edge(first_edge)
G.add_edge(
NNEdge(copy_node,
first_edge.to_node,
to_idx=first_edge.to_idx))
G.add_edge(NNEdge(node, copy_node, from_idx=idx))
if G.quantization:
G.quantization[NodeId(copy_node)] = qrec.__class__(
in_qs=deepcopy(qrec.out_qs[idx]),
out_qs=deepcopy(qrec.out_qs[idx]))
return has_modified_graph
def _resolve_qrec(self, cur_G, direction, node, stat, opts, scheme_priority,
in_qs, out_qs=None, fusion=None, set_out_qs=False):
qrec = None
set_in_qs = in_qs
while qrec is None:
handler = self._choose_quantizer(cur_G,
node, set_in_qs, out_qs, stat, opts, scheme_priority,
fusion, set_out_qs, direction)
if handler is not None:
pnid = NodeId(node) if fusion is None else NodeId(fusion)
handler_opts = self.get_options(pnid, handler)
cur_in_qs = set_in_qs
if cur_in_qs is None:
cur_in_qs = self._call_get_in_qs_from_stats(
cur_G,
direction,
handler,
node, in_qs, out_qs,
stat, handler_opts, fusion, set_out_qs=set_out_qs)
qrec = self._call_quantizer(cur_G,
handler, node, cur_in_qs, out_qs, stat, handler_opts, fusion,
direction=direction, set_out_qs=set_out_qs)
if qrec is None:
if out_qs is not None:
out_qs = None
elif set_in_qs is None:
raise ValueError(
f'No quantizer found for node {node.name} with options {opts}')
else:
set_in_qs = None
return qrec
def do_nodeoption(self, args):
""" Allows setting of autotiler generator control parameters and other code generation
options such as the location of inputs and outputs. For a complete set of the parameters that
can be set refer to the autotiler documentation."""
self._check_graph()
if args.step is None and args.parameter is None:
for nodeid, elem in self.G.node_options.items():
print("{}: {}".format(nodeid, elem))
return
nodes = self.get_node_step_or_name(args.step, allow_comma=True)[0]
if args.parameter is None:
nothing = True
for node in nodes:
node_options = self.G.node_options.get(NodeId(node))
if node_options:
nothing = False
self.poutput(f'Node: {node.name}:')
print(node_options)
if nothing:
self.poutput("no nodeoptions set")
return
if not nodes:
self.perror("No nodes selected")
return
for node in nodes:
node_options = node.at_options
if args.value is None:
val = None
else:
try:
option_type = node_options.valid_options[args.parameter]
except KeyError:
self.pwarning(
f"{args.parameter} is not a valid parameter for node {node.name}"
)
continue
val = option_type(args.value)
try:
if args.parameter in [
"RNN_STATES_AS_INPUTS", "LSTM_OUTPUT_C_STATE"
] and val:
if args.parameter == "RNN_STATES_AS_INPUTS":
node.rnn_states_as_inputs = (val, self.G)
if args.parameter == "LSTM_OUTPUT_C_STATE":
node.lstm_output_c_state = val
node.set_c_state_as_output(self.G)
else:
setattr(node_options, args.parameter, val)
self.pfeedback(
f'set option {args.parameter} on node {node.name} to {val}'
)
self.G.node_options[NodeId(node)] = node_options
except KeyError:
self.pwarning(
f"{args.parameter} is not a valid parameter for node {node.name}"
)
def common_quantize(cls, in_qtype, out_qtype, node, **kwargs):
all_nodes = kwargs['all_nodes']
opts = kwargs['opts']
G = kwargs['G']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
if cls.is_constant(x):
LOG.info("reducing %s to a constant", node.name)
if out_qtype:
val = x[0].value_as(out_qtype)
else:
val = cls.get_constant(x)
params = ConstantInputParameters(node.name,
value=val,
dims=Dim.unnamed(val.shape),
qtype=out_qtype,
constant_store=G.constant_store)
if opts.get('load_quantization'):
G.quantization[NodeId(params)] = MultQuantizationRecord(
in_qs=[out_qtype], out_qs=[out_qtype])
else:
params = QuantizeParameters(node.name, from_qtype=in_qtype, to_qtype=out_qtype)
G.add_edge(NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
if opts.get('load_quantization'):
G.quantization[NodeId(params)] = MultQuantizationRecord(
in_qs=[in_qtype], out_qs=[out_qtype])
all_nodes[node.output[0]] = (params, 0, deepcopy(x[2]))
return params
def test_conv_pool_relu_kernel_gen(mnist_unfused_8bit_state):
G = load_state(mnist_unfused_8bit_state)
conv_params = G.graph_state.steps[1]['node']
relu_params = G.graph_state.steps[2]['node']
pool_params = G.graph_state.steps[3]['node']
conv_q = G.quantization[NodeId(conv_params)]
pool_q = G.quantization[NodeId(pool_params)]
relu_q = G.quantization[NodeId(relu_params)]
code_block = gen_conv_pool_relu("Test", conv_params, conv_q, None, None,
None, None)
assert str(code_block) ==\
'CNN_ConvolutionPoolReLU("Test", 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 32, 28, 28,\n KOP_CONV, 5, 5, 1, 1, 1, 1, 0,\n KOP_NONE, 0, 0, 0, 0, 0, 0, 0, KOP_NONE);'
code_block = gen_conv_pool_relu("Test", conv_params, conv_q, pool_params,
pool_q, relu_params, relu_q)
assert str(code_block) ==\
'CNN_ConvolutionPoolReLU("Test", 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 32, 28, 28,\n KOP_CONV, 5, 5, 1, 1, 1, 1, 0,\n KOP_MAXPOOL, 2, 2, 1, 1, 2, 2, 0, KOP_RELU);'
code_block = gen_conv_pool_relu("Test", conv_params, conv_q, None, None,
relu_params, relu_q)
assert str(code_block) ==\
'CNN_ConvolutionPoolReLU("Test", 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 32, 28, 28,\n KOP_CONV, 5, 5, 1, 1, 1, 1, 0,\n KOP_NONE, 0, 0, 0, 0, 0, 0, 0, KOP_RELU);'
code_block = gen_conv_pool_relu("Test", conv_params, conv_q, pool_params,
pool_q, None, None)
assert str(code_block) ==\
'CNN_ConvolutionPoolReLU("Test", 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 32, 28, 28,\n KOP_CONV, 5, 5, 1, 1, 1, 1, 0,\n KOP_MAXPOOL, 2, 2, 1, 1, 2, 2, 0, KOP_NONE);'
code_block = gen_conv_pool_relu("Test", None, None, pool_params, pool_q,
relu_params, relu_q)
assert str(code_block) ==\
'CNN_PoolReLU("Test", 0, 1, 1, 1, 1, 32, 32, 24, 24,\n KOP_MAXPOOL, 2, 2, 1, 1, 2, 2, 0, KOP_RELU);'
code_block = gen_conv_pool_relu("Test", None, None, None, None,
relu_params, relu_q)
assert str(code_block) ==\
'CNN_PoolReLU("Test", 0, 1, 1, 1, 1, 32, 32, 24, 24,\n KOP_NONE, 0, 0, 0, 0, 0, 0, 0, KOP_RELU);'
def _import_nodes(self, G, graph, handlers, all_nodes, outputs, opts):
for node in graph.nodes:
handler = handlers.get(node.op_name, None)
if not handler:
raise ValueError("no handler found for %s" % node.op_type)
if node.is_custom and handler:
handler = handler.get(node.custom_op_name, None)
if not handler:
raise ValueError("no handler found for custom operation %s" %
node.custom_op_name)
params = handler.handle(node, all_nodes=all_nodes, G=G, opts=opts, importer=self)
if params is None:
continue
for idx, out_tensor in enumerate(node.output):
output = outputs.get(out_tensor)
if not output:
continue
G.add_edge(NNEdge(from_node=params,
to_node=output[0], from_idx=idx, to_idx=output[1]))
if opts.get('load_quantization'):
qtype = deepcopy(G.quantization[NodeId(params)].out_qs[idx])
G.quantization[NodeId(output[0])] = MultQuantizationRecord(
in_qs=[qtype],
out_qs=[qtype]
)
def find_direct_connects(self,
G,
node,
has_modified_graph,
find_output=True):
# traverse reshapes or transposes that do nothing - check gen
# find edges connected to concats
res = self.find_split_concat(G, node, find_output=find_output)
if res is None:
return has_modified_graph
if G.quantization:
qrec = G.quantization[NodeId(node)]
for idx, bundle in enumerate(res):
if not bundle:
continue
has_modified_graph = True
copy_node = CopyParameters("%s_copy_%s" % (node.name, idx))
for edge_set in bundle:
first_edge = edge_set[0]
G.remove_edge(first_edge)
LOG.info('inserting copy between %s/%s and %s/%s', node.name,
idx, first_edge.to_node.name, first_edge.to_idx)
G.add_edge(
NNEdge(copy_node,
first_edge.to_node,
to_idx=first_edge.to_idx))
G.add_edge(NNEdge(node, copy_node, from_idx=idx))
if G.quantization:
G.quantization[NodeId(copy_node)] = QRec.copy_ktype(
qrec,
in_qs=[deepcopy(qrec.out_qs[idx])],
out_qs=[deepcopy(qrec.out_qs[idx])])
return True
def format_dump_file(G, outputs, quantized):
# simplify the output since we only have one for now and add weights
foutputs = []
for idx, out in enumerate(outputs):
tensors = [out[0]]
node = G.graph_state.steps[idx]['node']
if isinstance(node, FusionParameters):
for filt in node.contained_filters():
if quantized:
qrec = G.quantization[NodeId(node, filt)]
tensors.append(qrec.weights_q.quantize(filt.weights))
tensors.append(qrec.biases_q.quantize(filt.biases))
else:
tensors.append(np.copy(filt.weights))
tensors.append(np.copy(filt.biases))
elif isinstance(node, FilterParameters):
if quantized:
qrec = G.quantization[NodeId(node, None)]
tensors.append(qrec.weights_q.quantize(node.weights))
tensors.append(qrec.biases_q.quantize(node.biases))
else:
tensors.append(np.copy(node.weights))
tensors.append(np.copy(node.biases))
else:
tensors.append(None)
tensors.append(None)
foutputs.append(tuple(tensors))
return foutputs
def move_node(G, node, edges):
nid = NodeId(node)
qrec = G.quantization[nid] if G.quantization and nid in G.quantization else None
node_in_edge = G.in_edges(node.name)[0]
node_out_edges = G.out_edges(node.name)
G.remove(node)
for node_out_edge in node_out_edges:
new_edge = NNEdge(from_node=node_in_edge.from_node, to_node=node_out_edge.to_node,
from_idx=node_in_edge.from_idx, to_idx=node_out_edge.to_idx)
G.add_edge(new_edge)
cnt = 0
original_node = node
for edge in edges:
LOG.info("Moving node %s between %s and %s",
node.name, edge.from_node.name, edge.to_node.name)
if cnt > 0:
new_node = deepcopy(node)
new_node.name = f'{original_node.name}_{cnt}'
else:
new_node = node
cnt += 1
new_node.in_dims = [edge.from_node.out_dims[edge.from_idx].clone()]
new_node.out_dims = [edge.to_node.in_dims[edge.to_idx].clone()]
G.insert_node(new_node, edge.from_node, edge.to_node,
from_idx=edge.from_idx, to_idx=edge.to_idx,
edge_class=NNEdge)
if qrec:
from_qrec = G.quantization[NodeId(edge.from_node)]
new_qrec = deepcopy(qrec)
new_qrec.in_qs[0] = deepcopy(from_qrec.out_qs[edge.from_idx])
G.quantization[NodeId(new_node)] = new_qrec
G.quantization.propagate(
G, new_node, node_in_edge.from_node, qtype=new_qrec.out_qs[0])
def match(self, G: GraphView, set_identity: bool = True):
if not G.quantization:
return
for nid in [nid for nid, qrec in G.quantization.sorted_iterator(G) if qrec is None or not (qrec.in_qs and qrec.out_qs)]:
if nid.fnode_name:
LOG.warning("can't add quantization to fused node %s", nid.fnode_name)
continue
if nid.node_name not in G:
# previous fusions may have removed nodes from the graph
continue
node = nid.get_node(G)
predecessors = [NodeId(pred) for pred in G.predecessors(node.name)]
successors = [NodeId(succ) for succs in G.successors(node.name) for succ in succs]
go_back = not successors or (predecessors and all(pred in G.quantization for pred in predecessors))
go_forward = not predecessors or (successors and all(succ in G.quantization for succ in successors))
if not (go_back or go_forward):
LOG.warning("node %s is not connected to anything and has no quantization", node.name)
continue
if go_forward:
out_qrecs = set(G.quantization[nid] for nid in successors)
if not all(isinstance(out_qrec, MultQuantizationRecord) for out_qrec in out_qrecs):
continue
out_qtypes = reduce_qtypes([(edge.from_idx, G.quantization[NodeId(edge.to_node)].in_qs[edge.to_idx])
for edge in G.out_edges(node.name)])
else:
out_qtypes = None
if go_back:
in_qrecs = set(G.quantization[nid] for nid in predecessors)
if not all(isinstance(in_qrec, MultQuantizationRecord) for in_qrec in in_qrecs):
continue
in_qtypes = reduce_qtypes([(edge.to_idx, G.quantization[NodeId(edge.from_node)].out_qs[edge.from_idx])
for edge in G.in_edges(node.name)])
else:
in_qtypes = None
if not in_qtypes:
if not predecessors:
LOG.info("setting quantization on input node %s", node.name)
qrec = MultQuantizationRecord(in_qs=deepcopy(out_qtypes), out_qs=deepcopy(out_qtypes))
else:
raise NotImplementedError("propagating qrecs not implemented")
elif not out_qtypes:
if not successors:
LOG.info("setting quantization on output node %s", node.name)
qrec = MultQuantizationRecord(in_qs=deepcopy(in_qtypes), out_qs=deepcopy(in_qtypes))
else:
raise NotImplementedError("propagating qrecs not implemented")
else:
LOG.info("setting quantization on node %s", node.name)
qrec = MultQuantizationRecord(in_qs=deepcopy(in_qtypes), out_qs=deepcopy(out_qtypes))
G.quantization[nid] = qrec
if set_identity:
self.set_identity(G)
return False
def _match(self, G: GraphView, set_identity: bool = True, **kwargs):
nodes_removed = []
modified_graph = False
for node in G.nodes(node_classes=QuantizeParameters):
if issubclass(node.from_qtype.dtype, (np.floating, bfloat16)):
if issubclass(node.to_qtype.dtype, (np.floating, bfloat16)):
LOG.warning(
'node %s quantizes from floating type to floating type and cannot directly be removed',
node.name)
continue
if self.propagate_up(G, node, node.to_qtype):
modified_graph = True
nodes_removed.append(node)
G.remove_and_reconnect(node, edge_class=NNEdge)
if G.quantization:
del G.quantization[NodeId(node)]
else:
LOG.warning('unable to remove quantize node %s', node.name)
else:
if self.propagate_down(G, node, node.from_qtype):
modified_graph = True
nodes_removed.append(node)
G.remove_and_reconnect(node, edge_class=NNEdge)
if G.quantization:
del G.quantization[NodeId(node)]
else:
LOG.warning('unable to remove quantize node %s', node.name)
if set_identity:
self.set_identity(G)
return modified_graph
def replace_function(self, G: GraphView, subgraph: GraphView):
relu_node = None
constant_node = None
mul_node = None
for node in subgraph.nodes():
if isinstance(node, ReluActivationParameters):
relu_node = node
elif isinstance(node, ConstantInputParameters):
constant_node = node
elif isinstance(node, MatrixMulParameters):
mul_node = node
activation = HSigmoidActivationParameters(mul_node.name +
"_fused_close_hsigmoid",
offset=0)
if G.quantization:
reluqrec = G.quantization[NodeId(relu_node)]
mulqrec = G.quantization[NodeId(mul_node)]
del G.quantization[NodeId(constant_node)]
if isinstance(reluqrec, (SymmetricQuantizationRecord)):
pqrec = SymmetricQuantizationRecord(in_qs=reluqrec.in_qs,
out_qs=mulqrec.out_qs)
elif isinstance(reluqrec, (MultQuantizationRecord)):
pqrec = MultQuantizationRecord(in_qs=reluqrec.in_qs,
out_qs=mulqrec.out_qs)
elif isinstance(reluqrec, (Float32QuantizationRecord)):
pqrec = Float32QuantizationRecord(in_qs=reluqrec.in_qs,
out_qs=mulqrec.out_qs)
else:
raise NotImplementedError()
G.quantization[NodeId(activation)] = pqrec
return activation, None, None
def set_stats(G, current_stats=None, current_options=None):
stats = {} if current_stats is None else current_stats.copy()
current_options = {} if current_options is None else current_options.copy()
for node in G.nodes():
nid = NodeId(node)
if nid not in stats or stats[nid] is None:
stats[nid] = build_stat(G, nid, node=node)
if isinstance(node, FusionBase) and node.quantize_internals:
for fnode in node.subgraph.nodes():
nid = NodeId(node, fnode)
if nid not in stats:
# if fusion input or output recs are not present build them
if isinstance(fnode, (FusionInputParameters, FusionOutputParameters)):
continue
else:
qrec = G.quantization.get(
nid) if G.quantization else None
stats[nid] = build_stat_from_qrec(qrec)
elif isinstance(node, ExpressionFusionParameters):
if stats[nid] is None or 'expression' not in stats[nid]:
if (G.quantization is None or nid not in G.quantization or G.quantization[nid].cache is None or
'expression' not in G.quantization[nid].cache):
raise ValueError(
f"quantized expression for {node.name} not found in current stats")
stats[nid]['expression'] = G.quantization[nid].cache['expression']
elif isinstance(node, ConstantInputParameters):
if G.quantization and nid in G.quantization:
current_options.setdefault(nid, {})['qtype_ind'] = G.quantization[nid].out_qs[0]
return stats, current_options
def replace_function(self, G: GraphView, subgraph: GraphView):
relu_node = None
constant_node = None
mul_node = None
for node in subgraph.nodes():
if isinstance(node, ReluActivationParameters):
relu_node = node
elif isinstance(node, ConstantInputParameters):
constant_node = node
elif isinstance(node, MatrixMulParameters):
mul_node = node
activation = HSigmoidActivationParameters(mul_node.name +
"_fused_close_hsigmoid",
offset=0)
if G.quantization:
reluqrec = G.quantization[NodeId(relu_node)]
mulqrec = G.quantization[NodeId(mul_node)]
del G.quantization[NodeId(constant_node)]
pqrec = QRec.copy_ktype(reluqrec,
in_qs=reluqrec.in_qs,
out_qs=mulqrec.out_qs)
G.quantization[NodeId(activation)] = pqrec
return activation, None, None
def insert_resizer(G, out_edge, resize_op, from_shape):
input_node = out_edge.from_node
net_in_dim = input_node.in_dims[0]
from_dim = deepcopy(net_in_dim)
from_dim.h = from_shape[0]
from_dim.w = from_shape[1]
if resize_op == 'bilinear':
resize_node = BilinearResizerParameters(input_node.name + "_resizer",
(net_in_dim.h, net_in_dim.w))
elif resize_op == 'nearest':
resize_node = NearestNeighborResizerParameters(
input_node.name + "_resizer", (net_in_dim.h, net_in_dim.w))
to_node = out_edge.to_node
to_idx = out_edge.to_idx
resize_node.in_dims = [from_dim]
input_node.dims.h = from_shape[0]
input_node.dims.w = from_shape[1]
# qrec updated to reflect resizer
input_qrec = G.quantization and G.quantization.get(NodeId(input_node))
if input_qrec:
resizer_qrec = deepcopy(input_qrec)
resizer_qrec.in_qs = resizer_qrec.out_qs
G.quantization[NodeId(resize_node)] = resizer_qrec
G.remove_edge(out_edge)
G.add_node(resize_node)
G.add_edge(NNEdge(input_node, resize_node))
G.add_edge(NNEdge(resize_node, to_node, to_idx=to_idx))
def quantize_forward(self, G: NNGraph, edge_recs, result=None):
if result is None:
result = QuantizationSet()
for node in [step['node'] for step in G.graph_state.steps]:
LOG.debug("quantize forward %s", node.name)
in_qs = self.get_in_qs(G, edge_recs, node)
if isinstance(node, ConvFusionParameters):
qrec, qrecs = self.quantize_fusion(G, node, in_qs)
for node_id, fqrec in qrecs.items():
result[node_id] = fqrec
elif isinstance(node, ConcatParameters):
qrec = self.quantize_backward(G, result, edge_recs, node)
else:
qrec = self.calculate_q(
node, self._activation_stats.get(NodeId(node, None)),
self._filter_stats.get(NodeId(node, None)), in_qs,
self._min_qsnr, self._force_width)
result[NodeId(node, None)] = qrec
if not qrec:
break
for edges in G.indexed_out_edges(node.name):
for edge in edges:
edge_recs[edge.params] = qrec.out_qs[edge.from_idx]
return result
def execute_uncached_step(G, in_tensors, step_idx, qrecs, qmode):
if qmode is None:
qmode = QuantizationMode.none()
ExecutionProgress.start()
node = G.graph_state.steps[step_idx]
assert not isinstance(
node, InputParameters), "executing input step is not supported"
ExecutionProgress.progress(step_idx, node.name)
output = in_tensors
nid = NodeId(node, None)
if qmode.get_quantized(node, step_idx):
qrec = qrecs[nid]
if qmode.is_step:
__quantize_input(qrec, output)
else:
qrec = None
if isinstance(node, FusionParameters):
for fusion_node in node.contained_nodes():
fnid = NodeId(node, fusion_node)
fqrec = None if not qrec else qrecs[fnid]
output, _ = Executer.execute(fusion_node, output, qrec=fqrec)
elif isinstance(node, InputParameters):
output, _ = Executer.execute(node, in_tensors, qrec=qrec)
else:
output, _ = Executer.execute(node, output, qrec=qrec)
if qmode.is_step and qmode.get_quantized(node, step_idx):
qrec = qrecs[NodeId(node, None)]
output = [
qrec.out_qs[i].dequantize(out) for i, out in enumerate(output)
]
return output
def quantize_fusion(self, G, node, in_qs, dtype):
fin_qs = in_qs
nodes = node.contained_nodes()
if node.fusion_type in ['conv_active_pool', 'conv_active']:
conv_node = nodes[0]
act_node = nodes[1]
act_astats = self._activation_stats.get(NodeId(node, act_node))
conv_qrec = self.calculate_q(G,
conv_node,
act_astats,
fin_qs,
dtype,
out_dtype=np.int8)
self.qrecs[NodeId(node, conv_node)] = conv_qrec
fin_qs = conv_qrec.out_qs
nodes = nodes[1:]
for fnode in nodes:
qrec = self.calculate_q(G,
fnode,
self._activation_stats.get(NodeId(node, fnode)),
fin_qs,
dtype)
self.qrecs[NodeId(node, fnode)] = qrec
fin_qs = qrec.out_qs
return MultQuantizationRecord(in_qs=in_qs, out_qs=fin_qs)
def insert_copy_on_common_concat_in(self, G, concat_nodes):
# in every concat nodes collect all the in edges (from_node, from_idx)
# if there are repetition of tuples, insert a copy in every repetition
# different concats cannot have the same in edge (from_node, from_idx)
concat_in_edges = []
has_modified_graph = False
for concat_node in concat_nodes:
for idx, in_edge in enumerate(G.indexed_in_edges(
concat_node.name)):
real_in_edge = find_real_in_edge(G, in_edge)
if real_in_edge in concat_in_edges:
has_modified_graph = True
copy_node = CopyParameters("%s_copy_%s" %
(concat_node.name, idx))
G.remove_edge(in_edge)
LOG.info(
'common_concat: inserting copy between %s/%s and %s/%s',
in_edge.from_node.name, idx, concat_node.name,
in_edge.to_idx)
G.add_edge(
NNEdge(in_edge.from_node,
copy_node,
from_idx=in_edge.from_idx))
G.add_edge(
NNEdge(copy_node, concat_node, to_idx=in_edge.to_idx))
if G.quantization:
qrec = G.quantization[NodeId(concat_node)]
G.quantization[NodeId(copy_node)] = QRec.copy_ktype(
qrec,
in_qs=[deepcopy(qrec.in_qs[idx])],
out_qs=[deepcopy(qrec.in_qs[idx])])
else:
concat_in_edges.append(real_in_edge)
return has_modified_graph
def _collect(self, G, input_tensors, step_idx) -> Mapping[NodeId, Mapping]:
LOG.debug("gather quantization statistics")
if G.has_quantized_parameters:
quantization = G.quantization
else:
quantization = None
executer = GraphExecuter(G, qrecs=quantization)
foutputs = self._collect_execution(executer, input_tensors, quantization)
executer = GraphExecuter(G, qrecs=G.quantization)
qoutputs = self._collect_execution(executer,
input_tensors,
G.quantization,
qmode=QuantizationMode.all_dequantize())
stats = OrderedDict()
for idx, fstat in enumerate(foutputs):
qstat = qoutputs[idx]
if fstat['fusion_outputs']:
for jdx, ffstat in enumerate(fstat['fusion_outputs']):
nid = NodeId(fstat['node'], ffstat['node'])
stats[nid] =\
self._collect_one(ffstat,
qstat['fusion_outputs'][jdx],
G.quantization[nid],
quant_compare=self._quant_compare)
nid = NodeId(fstat['node'], None)
stats[nid] = self._collect_one(fstat,
qstat,
G.quantization[nid],
quant_compare=self._quant_compare)
return stats
def bindings_generator(cls, gen, node, qrec, in_eparams, out_eparams, cname) -> bool:
step_idx = node.step_idx
cnodes = node.contained_nodes()
quants = [gen.G.quantization[NodeId(node, fnode)] for fnode in cnodes]
add_node = [node for node in cnodes if isinstance(
node, MatrixAddParameters)]
if add_node:
quants = [gen.G.quantization[NodeId(
node, fnode)] for fnode in cnodes]
set_add_in_scale(quants[1])
scaled_idx = quants[1].cache['scaled_idx']
not_scaled_idx = 0 if scaled_idx else 1
gen.bindings.append(
CommentBindingList("Node {} in1q {} in2q {} outq {}", cname,
quants[1].in_qs[scaled_idx], quants[1].in_qs[not_scaled_idx], quants[-1].out_qs[0])
)
gen.bindings.append(
NodeBindingList(cname, GNodeArgEdge(in_eparams[scaled_idx]),
GNodeArgEdge(in_eparams[not_scaled_idx]),
GNodeArgEdge(out_eparams[0], "GNA_OUT"),
GNodeArgNode(node, 'infos'),
GNodeArgNode(node.contained_nodes()[0], 'infos')
))
return True
def _match(self,
G: GraphView,
set_identity: bool = True,
**kwargs) -> bool:
has_modified_graph = False
for node in [
node for node in G.nodes(node_classes=StridedSliceParameters)
]:
if node.slice_shape != tuple(node.in_dims[0].shape):
continue
has_modified_graph = True
nid = NodeId(node)
if node.slice_shape == node.out_shape:
LOG.info(
f'removing strided slice {node.name} that does nothing')
G.remove_and_reconnect(node, edge_class=NNEdge)
if G.quantization and nid in G.quantization:
del G.quantization[nid]
else:
reshape = ReshapeParameters(
G.unique_name(f'{node.name}_reshape'),
old_shape=node.slice_shape,
shape=node.out_shape)
LOG.info(
f'replacing strided slice {node.name} with reshape {reshape.name}'
)
G.replace_node(node, reshape)
if G.quantization and nid in G.quantization:
G.quantization[NodeId(reshape)] = G.quantization[nid]
del G.quantization[nid]
if set_identity:
self.set_identity(G)
return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True):
if not G.quantization:
return
softmaxes = [
node for node in G.nodes() if isinstance(node, SoftMaxParameters)
]
qrecs = [G.quantization[NodeId(node)] for node in softmaxes]
if not all(isinstance(qrec, MultQuantizationRecord) for qrec in qrecs):
return
for softmax, qrec in zip(softmaxes, qrecs):
in_q = qrec.in_qs[0]
in_q.scale_to_pow2()
for edge in G.in_edges(softmax.name):
propagate_qtype_up(G, in_q, edge)
for edge in G.out_edges(softmax.name):
assert isinstance(
edge.to_node,
(OutputParameters, QuantizeParameters
)), "Softmax is supported only at the end of the graph"
out_qrec = G.quantization[NodeId(edge.to_node)]
out_qrec.in_qs[0] = qrec.out_qs[0]
out_qrec.out_qs[0] = qrec.out_qs[0]
if set_identity:
self.set_identity(G)
return False
def check_quantization(self, G, node, reshape, direction='in'):
if G.quantization:
qclass = self.get_output_qrec_class(G)
node_qrec = G.quantization[NodeId(node)]
qtype = getattr(node_qrec, f'{direction}_qs')[0]
G.quantization[NodeId(reshape)] = qclass(
in_qs=[deepcopy(qtype)], out_qs=[deepcopy(qtype)], ktype=node_qrec.ktype)
def replace_function(self, G: GraphView, subgraph: GraphView):
filter_node = None
constant_node = None
for node in subgraph.nodes():
if isinstance(node, FilterParameters):
filter_node = node
elif isinstance(node, ConstantInputParameters):
constant_node = node
LOG.info("fusing bias in %s into %s", constant_node.name,
filter_node.name)
flattened_constant = constant_node.value.flatten()
# shape needs to match
if flattened_constant.shape[0] == filter_node.filter.out_c:
if filter_node.has_bias:
assert filter_node.biases is not None, "can't absorb bias into filter. maybe weights are not loaded"
filter_node.biases += flattened_constant
else:
filter_node.biases = flattened_constant
else:
raise DontReplaceError()
if G.quantization:
fnid = NodeId(filter_node)
cnid = NodeId(constant_node)
if fnid in G.quantization and cnid in G.quantization:
G.quantization[fnid].biases_q = G.quantization[cnid].out_qs[0]
return filter_node, None, None
def _match(self, G: GraphView, set_identity: bool = True, **kwargs):
rnn_nodes = [
self.find_unpack(G, node) for node in G.nodes()
if isinstance(node, RNNBaseParameters) and node.n_output_cells > 1
]
rnn_nodes_by_slice = self.validate_slices(G, rnn_nodes)
rnn_nodes_by_slice = self.validate_multi_branch(G, rnn_nodes_by_slice)
if not rnn_nodes_by_slice:
return False
for unpack_node, rnn_unpacks in rnn_nodes_by_slice.items():
modified_nodes = set()
for rnn_unpack in rnn_unpacks:
self.process_path(G, rnn_unpack, modified_nodes)
# since process path will have removed all unnecessary nodes the edges will be correct here
out_edges = G.out_edges(unpack_node.name)
in_edges = G.in_edges(unpack_node.name)
assert len(in_edges
) == 1, "expecting unpack node to have only one in edge"
in_edge = in_edges[0]
changes_shape = unpack_node.changes_shape if isinstance(
unpack_node, StridedSliceParameters) else False
LOG.info("Eliminating last cell unpack: %s", unpack_node.name)
G.remove(unpack_node)
# Here the strided slice can change the output shape of the RNN
# so insert a reshape to do the shape change
if changes_shape:
reshape = ReshapeParameters(
unpack_node.name + '_reshape',
old_shape=Dim.unnamed(unpack_node.post_slice_shape),
shape=Dim.unnamed(unpack_node.out_shape))
G.add_edge(
NNEdge(from_node=in_edge.from_node,
to_node=reshape,
from_idx=in_edge.from_idx))
for out_edge in out_edges:
G.add_edge(
NNEdge(from_node=reshape,
to_node=out_edge.to_node,
to_idx=out_edge.to_idx))
if G.quantization:
G.quantization[NodeId(reshape)] = G.quantization[NodeId(
unpack)]
else:
for out_edge in out_edges:
G.add_edge(
NNEdge(from_node=in_edge.from_node,
to_node=out_edge.to_node,
from_idx=in_edge.from_idx,
to_idx=out_edge.to_idx))
if G.quantization:
del G.quantization[NodeId(unpack_node)]
if set_identity:
self.set_identity(G)
return True
def execute_triangle_iterator(G,
in_tensors: Sequence,
qrecs,
value_cache,
include_nodes: Optional[Set[Node]] = None):
LOG.info("execute triangle")
cache_entry = value_cache.get_outputs(G, in_tensors[0])
for step_idx, step in enumerate(G.graph_state.steps):
node = step['node']
if include_nodes and node not in include_nodes:
continue
# collect outputs from previous nodes
# InputNode is already set above
if isinstance(node, InputParameters):
inputs = [in_tensors]
else:
inputs = [None] * len(node.in_dims)
for edge in G.in_edges(node.name):
inputs[edge.to_idx] = edge.from_node.value
in_values = len(inputs[0])
fixed_inputs = []
for in_values_idx in range(in_values):
fixed_inputs.append([None] * len(node.in_dims))
for in_values_idx in range(in_values):
fixed_input = fixed_inputs[in_values_idx]
for input_idx in range(len(node.in_dims)):
if inputs[input_idx]:
fixed_input[input_idx] = inputs[input_idx][
in_values_idx]
inputs = fixed_inputs
# # regroup inputs [[a1, a2], [b1, b2], ...] to [[a1, b1], [a2, b2], ...]
# assert all(len(inputs[idx]) == len(inputs[0]) for idx in range(1, len(inputs)))
# # pylint: disable=unsubscriptable-object
# inputs = [[inp[idx] for inp in inputs] for idx in range(len(inputs[0]))]
outputs = []
qrec = qrecs[NodeId(node, None)]
for inp in inputs:
ExecutionProgress.progress(step_idx, node.name)
if isinstance(node, FusionParameters):
for fusion_node in node.contained_nodes():
fqrec = qrecs[NodeId(node, fusion_node)]
outputs.append(
Executer.execute(fusion_node, inp, qrec=fqrec))
else:
outputs.append(Executer.execute(node, inp, qrec=qrec)[0])
report_outputs = [qrec.out_qs[i](out) for i, out in enumerate(outputs)]
report_outputs.append(cache_entry[step_idx][0])
yield step_idx, node, report_outputs
outputs.append(qrec.out_qs[0].quantize(cache_entry[step_idx][0]))
node.value = outputs
def move_to_fusion(self, node: Parameters, new_pnode: Parameters):
nid = NodeId(node)
fnid = NodeId(new_pnode, node)
if nid in self.qset:
self.qset[fnid] = self.qset[nid]
del self.qset[nid]
if self.stats and nid in self.stats:
self.stats[fnid] = self.stats[nid]
def quantize_fusion(self,
G: NNGraph,
node: ConvFusionParameters,
in_qs,
force_out=None) -> SymmetricQuantizationRecord:
if node.fusion_type == 'conv_active':
result = OrderedDict()
nodes = node.contained_nodes()
conv_node = nodes[0]
conv_astats = self._activation_stats.get(NodeId(node, conv_node))
conv_qrec = self.calculate_filter_q(conv_node,
conv_astats,
in_q=in_qs[0],
force_width=self._force_width,
out_as_acc=True)
result[NodeId(node, conv_node)] = conv_qrec
act_node = nodes[1]
act_astats = self._activation_stats.get(NodeId(node, act_node))
if force_out and force_out.bits:
act_max_q = self.compute_activation_out_maxq(
act_node, force_out.bits)
if force_out.q is not None:
if (act_max_q is not None and force_out.q > act_max_q
) or force_out.q > conv_qrec.out_qs[0].q:
# We cannot shift left in the kernel
# TODO - This should try to increase the input q and perhaps the width
# Unlikely to happen
raise NotImplementedError()
act_o_q = QType(bits=force_out.bits,
q=force_out.q,
signed=True)
else:
if act_max_q is not None:
act_o_q.q = min(act_max_q, act_o_q.q)
else:
act_o_q = QType.from_min_max(
max_val=act_astats['range_out'][0]['max'],
min_val=act_astats['range_out'][0]['min'],
bits=self._force_width)
act_o_q.q = min(act_o_q.q, conv_qrec.out_qs[0].q)
if force_out and force_out.q:
if force_out.q > act_max_q or force_out.q > conv_qrec.out_qs[
0].q:
# We cannot shift left in the kernel
# TODO - This should try to increase the input q and perhaps the width
# Unlikely to happen
raise NotImplementedError()
act_o_q.q = force_out.q
act_qrec = SymmetricQuantizationRecord(in_qs=conv_qrec.out_qs,
out_qs=[act_o_q])
result[NodeId(node, act_node)] = act_qrec
return SymmetricQuantizationRecord(in_qs=in_qs,
out_qs=act_qrec.out_qs), result
else:
return self.default_quantize_fusion(G,
node,
in_qs,
force_out=force_out)