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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/01/09 08:00:12 UTC
[GitHub] [incubator-tvm] masahi commented on a change in pull request #4497:
[WIP] [Relay] Add a PyTorch to Relay Parser
masahi commented on a change in pull request #4497: [WIP] [Relay] Add a PyTorch to Relay Parser
URL: https://github.com/apache/incubator-tvm/pull/4497#discussion_r364599526
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File path: python/tvm/relay/frontend/pytorch.py
##########
@@ -0,0 +1,1104 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks
+# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except
+"""PT: PyTorch frontend."""
+import numpy as np
+
+import tvm
+
+from .. import analysis as _analysis
+from .. import expr as _expr
+from .. import module as _module
+from .. import op as _op
+from .common import get_relay_op
+from .common import infer_shape as _infer_shape
+
+__all__ = ['from_pytorch']
+
+# operator implementation
+def _elemwise(name):
+ def _impl(inputs, input_types):
+ data0 = convert_input(inputs[0])
+ data1 = convert_input(inputs[1])
+
+ if not isinstance(data0, (_expr.Call, _expr.TupleGetItem, _expr.Var)):
+ temp = data0
+ data0 = data1
+ data1 = temp
+
+ return get_relay_op(name)(data0, data1)
+ return _impl
+
+def _unsqueeze():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ axis = inputs[1]
+
+ return _op.transform.expand_dims(data, int(axis), 1)
+ return _impl
+
+def _concatenate():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ axis = inputs[1]
+
+ if isinstance(data, (_expr.Call, _expr.TupleGetItem, _expr.Var)):
+ data = [data]
+
+ return _op.tensor.concatenate(data, int(axis))
+ return _impl
+
+def _slice():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ strides = []
+
+ inferred_shape = _infer_shape(data)
+ end = []
+ for infer in inferred_shape:
+ end.append(int(infer))
+ if isinstance(data, _expr.Var):
+ end = _infer_shape(data)
+ end = list(end)
+
+ begin = [0]*len(end)
+ dim = int(inputs[1])
+ begin[dim] = int(inputs[2])
+
+ if inputs[3].isdigit():
+ end[dim] = min(end[dim], int(inputs[3]))
+
+ strides.append(int(inputs[4]))
+ return _op.transform.strided_slice(data, begin, end, strides)
+ return _impl
+
+def _select():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ inferred_shape = _infer_shape(data)
+ end = []
+
+ for infer in inferred_shape:
+ end.append(int(infer))
+
+ begin = [0]*len(end)
+ dim = int(inputs[1])
+ index = int(inputs[2])
+
+ end[dim] = index+1
+ begin[dim] = index
+
+ strides = [1]*len(end)
+
+ sym = _op.transform.strided_slice(data, begin, end, strides)
+ axis = [dim]
+
+ return _op.transform.squeeze(sym, axis)
+ return _impl
+
+def _ones():
+ def _impl(inputs, input_types):
+ if isinstance(inputs[0], _expr.Var):
+ shape = _infer_shape(inputs[0])
+ elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)):
+ shape = _infer_shape(inputs[0])
+ else:
+ shape = inputs[0].shape
+
+ fill_value = _get_fill_value(input_types)
+
+ return get_relay_op('full')(fill_value, shape)
+ return _impl
+
+def _zeros():
+ def _impl(inputs, input_types):
+ if isinstance(inputs[0], _expr.Var):
+ shape = _infer_shape(inputs[0])
+ elif isinstance(inputs[0], (_expr.Call, _expr.TupleGetItem)):
+ shape = _infer_shape(inputs[0])
+ else:
+ shape = inputs[0].shape
+
+ fill_value = _get_fill_value(input_types)
+
+ return _op.full(fill_value, shape)
+ return _impl
+
+def _get_fill_value(input_types):
+ if input_types[0] == 'int':
+ fill_value = _expr.const(1)
+ elif input_types[0] == 'float':
+ fill_value = _expr.const(1.0)
+ else:
+ fill_value = _expr.const(1)
+
+ return fill_value
+
+def _relu():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.nn.relu(data)
+ return _impl
+
+def _adaptive_avg_2d():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ output_size = _infer_shape(inputs[1])
+
+ return _op.contrib.contrib.adaptive_avg_pool2d(
+ data,
+ output_size=output_size)
+ return _impl
+
+def _adaptive_max_2d():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ output_size = _infer_shape(inputs[1])
+
+ return _op.contrib.contrib.adaptive_max_pool2d(
+ data,
+ output_size=output_size)
+ return _impl
+
+def _maxpool_2d():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+
+ pool_size = _infer_shape(inputs[1])
+ strides = _infer_shape(inputs[2])
+ padding = _infer_shape(inputs[3])
+
+ ceil_mode = int(inputs[5])
+
+ return _op.nn.max_pool2d(data, pool_size, strides, padding, "NCHW", ceil_mode)
+ return _impl
+
+def _hardtanh():
+ def _impl(inputs, input_types):
+ a = inputs[0]
+ tanh_min = float(inputs[1])
+ tanh_max = float(inputs[2])
+ return _op.tensor.clip(a, tanh_min, tanh_max)
+ return _impl
+
+def _convolution():
+ def _impl(inputs, input_types):
+ # Use transpose or normal
+ use_transpose = False
+ if inputs[6] == '1':
+ use_transpose = True
+
+ use_bias = False
+ if isinstance(inputs[2], _expr.Var):
+ use_bias = True
+
+ data = inputs[0]
+ weight = inputs[1]
+ bias = inputs[2]
+
+ if isinstance(weight, (_expr.Call, _expr.Var, _expr.TupleGetItem)):
+ inferred_shape = _infer_shape(weight)
+ weight_shape = []
+ for infer in inferred_shape:
+ weight_shape.append(infer)
+ else:
+ weight_shape = weight.shape
+ channels = weight_shape[0]
+
+ strides = inputs[3]
+ padding = inputs[4]
+ dilation = inputs[5]
+
+ kernel_size = weight_shape[2:]
+
+ else:
+ data = inputs[0]
+ weight = inputs[1]
+ bias = inputs[2]
+
+ if isinstance(weight, (_expr.Call, _expr.Var, _expr.TupleGetItem)):
+ inferred_shape = _infer_shape(weight)
+ weight_shape = []
+ for infer in inferred_shape:
+ weight_shape.append(infer)
+ else:
+ weight_shape = weight.shape
+ channels = weight_shape[0]
+
+ strides = inputs[3]
+ padding = inputs[4]
+ dilation = inputs[5]
+
+ kernel_size = weight_shape[2:]
+
+ if isinstance(strides, _expr.Var):
+ strides = _infer_shape(strides)
+
+ if isinstance(padding, _expr.Var):
+ padding = _infer_shape(padding)
+
+ if isinstance(dilation, _expr.Var):
+ dilation = _infer_shape(dilation)
+
+ groups = int(inputs[8])
+
+ if use_transpose:
+ conv_out = _op.nn.conv2d_transpose(data,
+ weight,
+ strides=strides,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ channels=channels,
+ kernel_size=kernel_size,
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ out_layout="",
+ out_dtype="")
+ else:
+ conv_out = _op.nn.conv2d(data,
+ weight,
+ strides=strides,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ channels=channels,
+ kernel_size=kernel_size,
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ out_layout="",
+ out_dtype="")
+
+ if use_bias:
+ return _op.nn.bias_add(conv_out, bias)
+ else:
+ return conv_out
+ return _impl
+
+def _softmax():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ axis = inputs[1]
+ if isinstance(axis, str):
+ axis = int(axis)
+
+ return _op.nn.softmax(data, axis=axis)
+ return _impl
+
+def _threshold():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.nn.relu(data)
+ return _impl
+
+def _contiguous():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.tensor.copy(data)
+ return _impl
+
+def _batch_norm():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ data_type = input_types[0]
+
+ channels = _infer_shape(data)
+
+ if isinstance(inputs[1], _expr.Var) and isinstance(inputs[2], _expr.Var):
+ scale = center = True
+ weight = inputs[1]
+ beta = inputs[2]
+ else:
+ scale = center = False
+
+ if scale:
+ gamma = weight
+ else:
+ if data_type == 'double':
+ gamma = _expr.const(np.ones([int(channels[1])]).astype('float64'))
+ elif data_type == 'float':
+ gamma = _expr.const(np.ones([int(channels[1])]).astype('float32'))
+ elif data_type == 'half':
+ gamma = _expr.const(np.ones([int(channels[1])]).astype('float16'))
+ elif data_type == 'long':
+ gamma = _expr.const(np.ones([int(channels[1])]).astype('int64'))
+ elif data_type == 'int':
+ gamma = _expr.const(np.ones([int(channels[1])]).astype('int32'))
+ elif data_type == 'short':
+ gamma = _expr.const(np.ones([int(channels[1])]).astype('int16'))
+ elif data_type == 'char':
+ gamma = _expr.const(np.ones([int(channels[1])]).astype('int8'))
+ elif data_type == 'byte':
+ gamma = _expr.const(np.ones([int(channels[1])]).astype('uint8'))
+
+ if center:
+ beta = beta
+ else:
+ if data_type == 'double':
+ beta = _expr.const(np.zeros([int(channels[1])]).astype('float64'))
+ elif data_type == 'float':
+ beta = _expr.const(np.zeros([int(channels[1])]).astype('float32'))
+ elif data_type == 'half':
+ beta = _expr.const(np.zeros([int(channels[1])]).astype('float16'))
+ elif data_type == 'long':
+ beta = _expr.const(np.zeros([int(channels[1])]).astype('int64'))
+ elif data_type == 'int':
+ beta = _expr.const(np.zeros([int(channels[1])]).astype('int32'))
+ elif data_type == 'short':
+ beta = _expr.const(np.zeros([int(channels[1])]).astype('int16'))
+ elif data_type == 'char':
+ beta = _expr.const(np.zeros([int(channels[1])]).astype('int8'))
+ elif data_type == 'byte':
+ beta = _expr.const(np.zeros([int(channels[1])]).astype('uint8'))
+
+ moving_mean = inputs[3]
+ moving_var = inputs[4]
+ epsilon = float(inputs[7])
+
+ center = center
+ scale = scale
+
+ return _op.nn.batch_norm(data,
+ gamma,
+ beta,
+ moving_mean,
+ moving_var,
+ axis=1,
+ epsilon=epsilon,
+ center=center,
+ scale=scale)[0]
+ return _impl
+
+def _transpose():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+
+ if isinstance(data, _expr.Var):
+ ndims = len(_infer_shape(data))
+ elif isinstance(data, (_expr.Call, _expr.TupleGetItem)):
+ ndims = _infer_shape(data)
+ else:
+ ndims = data.shape
+
+ if isinstance(data, tvm.ndarray.NDArray):
+ ndims = len(data.shape)
+ axes = list(range(ndims))
+
+ num_inputs = len(inputs)
+
+ if num_inputs == 1:
+ if ndims >= 2:
+ axes[-1] = ndims - 2
+ axes[-2] = ndims - 1
+ if not isinstance(data, _expr.Var):
+ data = _expr.const(data)
+
+ elif num_inputs == 3:
+ parse = lambda i: ndims * (i < 0) + i
+ src, dst = [parse(int(inputs[i])) for i in [1, 2]]
+ axes[src] = dst
+ axes[dst] = src
+ else:
+ axes = inputs[1]
+ return _op.transform.transpose(data, axes)
+ return _impl
+
+def _flatten():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.nn.batch_flatten(data)
+ return _impl
+
+def _dense():
+ def _impl(inputs, input_types):
+ use_bias = False
+
+ if isinstance(inputs[0], _expr.Var):
+ use_bias = True
+
+ data = inputs[1]
+ data_type = input_types[1]
+ weight = inputs[2]
+
+ beta = inputs[3]
+ alpha = inputs[4]
+
+ if not isinstance(alpha, (_expr.Var, _expr.Call, _expr.TupleGetItem)):
+ if data_type == 'double':
+ alpha = _expr.const(np.float64(alpha), dtype='float64')
+ elif data_type == 'float':
+ alpha = _expr.const(np.float32(alpha), dtype='float32')
+ elif data_type == 'half':
+ alpha = _expr.const(np.float16(alpha), dtype='float16')
+ elif data_type == 'long':
+ alpha = _expr.const(np.int64(alpha), dtype='int64')
+ elif data_type == 'int':
+ alpha = _expr.const(np.int32(alpha), dtype='int32')
+ elif data_type == 'short':
+ alpha = _expr.const(np.int16(alpha), dtype='int16')
+ elif data_type == 'char':
+ alpha = _expr.const(np.int8(alpha), dtype='int8')
+ elif data_type == 'byte':
+ alpha = _expr.const(np.uint8(alpha), dtype='uint8')
+ data *= alpha
+
+ if not isinstance(beta, (_expr.Var, _expr.Call, _expr.TupleGetItem)):
+ if data_type == 'double':
+ beta = _expr.const(np.foat64(beta), dtype='float64')
+ elif data_type == 'float':
+ beta = _expr.const(np.float32(beta), dtype='float32')
+ elif data_type == 'half':
+ beta = _expr.const(np.float16(beta), dtype='float16')
+ elif data_type == 'long':
+ beta = _expr.const(np.int64(beta), dtype='int64')
+ elif data_type == 'int':
+ beta = _expr.const(np.int32(beta), dtype='int32')
+ elif data_type == 'short':
+ beta = _expr.const(np.int16(beta), dtype='int16')
+ elif data_type == 'char':
+ beta = _expr.const(np.int8(beta), dtype='int8')
+ elif data_type == 'byte':
+ beta = _expr.const(np.uint8(beta), dtype='uint8')
+ weight *= beta
+
+ weight_out = _op.transform.transpose(weight, axes=[1, 0])
+
+ units = _infer_shape(weight_out)[0]
+ dense_out = _op.nn.dense(data, weight_out, units=units)
+
+ if use_bias:
+ bias = inputs[0]
+ return _op.nn.bias_add(dense_out, bias)
+ else:
+ return dense_out
+ return _impl
+
+def _size():
+ def _impl(inputs, input_types):
+ axis = int(inputs[1])
+ if isinstance(inputs[0], _expr.Var):
+ shape = _infer_shape(inputs[0])
+ else:
+ shape = _infer_shape(inputs[0])
+ return shape[axis]
+ return _impl
+
+def _numtotensor():
+ def _impl(inputs, input_types):
+ val = inputs[0]
+ dtype = type(val)
+
+ if isinstance(val, tvm.expr.IntImm):
+ val = val.__int__()
+ dtype = int
+
+ arr = val * np.ones([]).astype(dtype)
+ return arr
+ return _impl
+
+def _view():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+
+ if len(inputs) == 3:
+ new_shape = [inputs[1], _infer_shape(inputs[2])[0]]
+ else:
+ if isinstance(inputs[1], list):
+ new_shape = inputs[1]
+ else:
+ new_shape = _infer_shape(inputs[1])
+
+ return _op.transform.reshape(data, new_shape)
+ return _impl
+
+def _clone():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.tensor.copy(data)
+ return _impl
+
+def _log_softmax():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ axis = int(inputs[1])
+ return _op.nn.log_softmax(data, axis)
+ return _impl
+
+def _sigmoid():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.tensor.sigmoid(data)
+ return _impl
+
+def _avg_pool2d():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+
+ pool_size = _infer_shape(inputs[1])
+ strides = _infer_shape(inputs[2])
+ padding = _infer_shape(inputs[3])
+
+ ceil_mode = int(inputs[4])
+ count_include_pad = int(inputs[5])
+
+ return _op.nn.avg_pool2d(data,
+ pool_size=pool_size,
+ strides=strides,
+ padding=padding,
+ ceil_mode=ceil_mode,
+ count_include_pad=count_include_pad)
+ return _impl
+
+def _dropout():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ rate = float(inputs[1])
+
+ return _op.nn.dropout(data, rate)
+ return _impl
+
+def _reduce(name):
+ def _impl(inputs, attrs, params):
+ data = inputs[0]
+ return get_relay_op(name)(data)
+ return _impl
+
+def _mean():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ axis = _infer_shape(inputs[1])
+
+ keepdims = int(inputs[2])
+ exclude = int(inputs[3])
+
+ return _op.mean(data, axis, keepdims, exclude)
+ return _impl
+
+def _chunk():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+
+ num_chunks = int(inputs[1])
+ axis = int(inputs[2])
+
+ if isinstance(data, _expr.Var):
+ inferred_shape = _infer_shape(data)
+ elif isinstance(data, (_expr.Call, _expr.TupleGetItem)):
+ inferred_shape = _infer_shape(data)
+
+ shape = []
+ for infer in inferred_shape:
+ shape.append(infer)
+
+ dim = int(shape[axis])
+
+ if dim % num_chunks:
+ unif_size = int(dim / (num_chunks - 1))
+ else:
+ unif_size = int(dim / num_chunks)
+
+ chunks = []
+ for i in range(0, dim, unif_size):
+ begin = [0] * len(shape)
+ end = shape[:]
+ begin[axis] = i
+ end[axis] = i + unif_size
+ stride = [1] * len(shape)
+
+ chunk_out = _op.transform.strided_slice(data, begin, end, stride)
+ chunks.append(chunk_out)
+
+
+ if dim % num_chunks:
+ begin = [0] * len(shape)
+ end = shape[:]
+ begin[axis] = unif_size * (num_chunks - 1)
+ end[axis] = dim
+ stride = [1] * len(shape)
+
+ chunk_out = _op.transform.strided_slice(data, begin, end, stride)
+ chunks.append(chunk_out)
+
+ return chunks
+ return _impl
+
+def _matmul():
+ def _impl(inputs, input_types):
+ data0 = inputs[0]
+ data1 = inputs[1]
+ data1_t = _op.transpose(data1, axes=(1, 0))
+
+ return _op.nn.dense(data0, data1_t)
+ return _impl
+
+def _expand():
+ def _impl(inputs, input_types):
+ data_in = inputs[0]
+ if isinstance(data_in, _expr.Var):
+ shape = _infer_shape(data_in)
+ elif isinstance(data_in, (_expr.Call, _expr.TupleGetItem)):
+ shape = _infer_shape(data_in)
+
+ ndims = len(shape)
+ sizes = _infer_shape(inputs[1])
+ out = inputs[0]
+
+ for i in range(ndims):
+ if sizes[i] in {-1, shape[i]}:
+ continue
+ data = list()
+ for temp in range(sizes[i]):
+ data.append(out)
+ call = _op.tensor.concatenate(data, i)
+
+ return call
+ return _impl
+
+def _int():
+ def _impl(inputs, input_types):
+ if isinstance(inputs[0], _expr.Call):
+ return inputs[0]
+ return int(inputs[0])
+ return _impl
+
+def _listunpack():
+ def _impl(inputs, input_types):
+ return inputs[0]
+ return _impl
+
+def _to():
Review comment:
rename it to identity since aten::detach (and possibly others) is also identity.
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