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Commit 4bffa2c2 authored by Hendrik Borras's avatar Hendrik Borras
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Moved GemmToMatMul from FINN to finn-base.

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docker/Dockerfile.finn
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......@@ -86,7 +86,7 @@ RUN pip install -e git+https://github.com/fbcotter/dataset_loading.git@0.0.4#egg
# git-based Python repo dependencies
# these are installed in editable mode for easier co-development
ARG FINN_BASE_COMMIT="7f13a67ba27c4f2c979bcbca26f49a5409b4b433"
ARG FINN_BASE_COMMIT="5f4051a733494c7d0f615a4ffd626c7a9977429c"
ARG QONNX_COMMIT="02b15f56d199576cefe9aff672d5e009349e402a"
ARG FINN_EXP_COMMIT="f82c0d9868bb88ea045dfadb28508d327d287221"
ARG BREVITAS_COMMIT="462f86cdc60f9915baf13afd1676fb21da44c2ee"
......
src/finn/transformation/qonnx/convert_qonnx_to_finn.py
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......@@ -29,9 +29,9 @@
from qonnx.transformation.quant_constant_folding import FoldTransposeIntoQuantInit
from finn.transformation.base import Transformation
from finn.transformation.gemm_to_matmul import GemmToMatMul
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.qonnx.fold_quant_weights import FoldQuantWeights
from finn.transformation.qonnx.gemm_to_matmul import GemmToMatMul
from finn.transformation.qonnx.quant_act_to_multithreshold import (
ConvertQuantActToMultiThreshold,
)
......
src/finn/transformation/qonnx/gemm_to_matmul.py deleted 100644 → 0
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View file @ c4a6ccea
# Copyright (c) 2021, Xilinx
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of FINN nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import warnings
from onnx import TensorProto, helper
from finn.core.datatype import DataType
from finn.transformation.base import Transformation
from finn.util.basic import get_by_name
class GemmToMatMul(Transformation):
"""
Converts Gemm nodes into a MatMul and an Add nodes.
This transformation is built to support version 9 of the Gemm node, as
documented here: https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Gemm-9
However, earlier and later versions of the node are likely to work as well.
Explicitly not supported is the optionality of input C in versions >=11 and
the broadcast attribute of version <=6.
"""
def apply(self, model):
graph = model.graph
node_ind = 0
for n in graph.node:
node_ind += 1
if n.op_type == "Gemm":
running_node_index = node_ind
# Transpose A?
transA = get_by_name(n.attribute, "transA")
if transA is not None and transA.i:
# Insert transpose node
shape = model.get_tensor_shape(n.input[0])
if shape is not None:
shape = tuple(reversed(shape))
inp_trans_out = helper.make_tensor_value_info(
model.make_new_valueinfo_name(),
TensorProto.FLOAT,
shape,
)
graph.value_info.append(inp_trans_out)
inp_trans_node = helper.make_node(
"Transpose", [n.input[0]], [inp_trans_out.name]
)
graph.node.insert(running_node_index, inp_trans_node)
running_node_index += 1
dt = model.get_tensor_datatype(n.input[0])
if dt != DataType["FLOAT32"]:
model.set_tensor_datatype(inp_trans_out.name, dt)
n.input[0] = inp_trans_out.name
# Transpose B?
transB = get_by_name(n.attribute, "transB")
if transB is not None and transB.i:
# Insert transpose node
shape = model.get_tensor_shape(n.input[1])
if shape is not None:
shape = tuple(reversed(shape))
inp_trans_out = helper.make_tensor_value_info(
model.make_new_valueinfo_name(),
TensorProto.FLOAT,
shape,
)
graph.value_info.append(inp_trans_out)
inp_trans_node = helper.make_node(
"Transpose", [n.input[1]], [inp_trans_out.name]
)
graph.node.insert(running_node_index, inp_trans_node)
running_node_index += 1
# Copy over the datatype
dt = model.get_tensor_datatype(n.input[1])
if dt != DataType["FLOAT32"]:
model.set_tensor_datatype(inp_trans_out.name, dt)
n.input[1] = inp_trans_out.name
# Insert MatMul: A * B
matMul_node = helper.make_node(
"MatMul", [n.input[0], n.input[1]], [n.output[0]]
)
graph.node.insert(running_node_index, matMul_node)
matMul_node = graph.node[running_node_index]
running_node_index += 1
# Insert Mul: (A*B) * alpha
alpha = get_by_name(n.attribute, "alpha")
if alpha is None:
alpha = np.array(1.0)
else:
alpha = np.array(alpha.f)
mul_tensor = helper.make_tensor_value_info(
model.make_new_valueinfo_name(),
TensorProto.FLOAT,
None,
)
graph.value_info.append(mul_tensor)
model.set_initializer(mul_tensor.name, alpha)
A_shape = model.get_tensor_shape(n.input[0])
B_shape = model.get_tensor_shape(n.input[1])
if A_shape is not None and B_shape is not None:
shape = [A_shape[0], B_shape[1]]
else:
shape = None
act_mul_tensor = helper.make_tensor_value_info(
model.make_new_valueinfo_name(),
TensorProto.FLOAT,
shape,
)
graph.value_info.append(act_mul_tensor)
mul_node = helper.make_node(
"Mul",
[act_mul_tensor.name, mul_tensor.name],
[n.output[0]],
)
graph.node.insert(running_node_index, mul_node)
mul_node_main_branch = graph.node[running_node_index]
running_node_index += 1
matMul_node.output[0] = act_mul_tensor.name
# Other branch: Insert Mul: beta * C
beta = get_by_name(n.attribute, "beta")
if beta is None:
beta = np.array(1.0)
else:
beta = np.array(beta.f)
mul_tensor = helper.make_tensor_value_info(
model.make_new_valueinfo_name(),
TensorProto.FLOAT,
None,
)
graph.value_info.append(mul_tensor)
model.set_initializer(mul_tensor.name, beta)
C_shape = model.get_tensor_shape(n.input[2])
act_mul_tensor = helper.make_tensor_value_info(
model.make_new_valueinfo_name(),
TensorProto.FLOAT,
C_shape,
)
graph.value_info.append(act_mul_tensor)
mul_node = helper.make_node(
"Mul",
[n.input[2], mul_tensor.name],
[act_mul_tensor.name],
)
graph.node.insert(running_node_index, mul_node)
running_node_index += 1
dt = model.get_tensor_datatype(n.input[2])
if dt != DataType["FLOAT32"]:
model.set_tensor_datatype(act_mul_tensor.name, dt)
n.input[2] = act_mul_tensor.name
# Insert Add: ((A*B) * alpha) + (beta * C)
shape = model.get_tensor_shape(mul_node_main_branch.input[0])
act_add_tensor = helper.make_tensor_value_info(
model.make_new_valueinfo_name(),
TensorProto.FLOAT,
shape,
)
graph.value_info.append(act_add_tensor)
mul_node_main_branch.output[0] = act_add_tensor.name
add_node = helper.make_node(
"Add",
[act_add_tensor.name, n.input[2]],
[n.output[0]],
)
graph.node.insert(running_node_index, add_node)
running_node_index += 1
# Delete Gemm node
graph.node.remove(n)
# Remove potential unity multiplications from alpha and beta attributes
# model = model.transform(RemoveUnityMul())
return model, True
return model, False
class RemoveUnityMul(Transformation):
"""
Removes multiplication nodes, which have a unity initializer.
"""
def apply(self, model):
graph = model.graph
node_ind = 0
for n in graph.node:
node_ind += 1
if n.op_type == "Mul":
init = model.get_initializer(n.input[1])
if init is not None:
# Check if all multipliers are unity
init = init.flatten()
if (init == 1.0).all():
predecessors = model.find_direct_predecessors(n)
successors = model.find_direct_successors(n)
# Check if we reached the top or bottom of the graph
if predecessors is not None:
for predecessor in predecessors:
predecessor.output[0] = n.output[0]
graph.node.remove(n)
return model, True
elif successors is not None:
for successor in successors:
successor.input[0] = n.input[0]
graph.node.remove(n)
return model, True
else:
warnings.warn(
f"Can't remove empty unity multiplication node {n}, "
f"due to no available successors or predecessors."
)
return model, False
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