petuca opened a new issue, #11691: URL: https://github.com/apache/tvm/issues/11691
Thanks for participating in the TVM community! We use https://discuss.tvm.ai for any general usage questions and discussions. The issue tracker is used for actionable items such as feature proposals discussion, roadmaps, and bug tracking. You are always welcomed to post on the forum first :smile_cat: Issues that are inactive for a period of time may get closed. We adopt this policy so that we won't lose track of actionable issues that may fall at the bottom of the pile. Feel free to reopen a new one if you feel there is an additional problem that needs attention when an old one gets closed. Here we have a dot operation problem established in MXNet model with non 2D tensors. For example here we want to do dot product of two tensors: - data tensor: [3] - weight tensor: [3,] ``` import numpy as np import mxnet as mx from mxnet import gluon from mxnet.gluon import nn import tvm from tvm import relay, transform from tvm.contrib import graph_executor shape_myx = (3,) shape_params = (3,1) transpose_b = False class MyNetHybrid(gluon.HybridBlock): def __init__(self, **kwargs): super(MyNetHybrid, self).__init__(**kwargs) with self.name_scope(): self.mat_weights = self.params.get('mat_weights', shape=shape_params) def hybrid_forward(self, F, x, mat_weights): x = F.dot(x, mat_weights, transpose_b=transpose_b) return x mynet = MyNetHybrid() mynet.initialize() myx = mx.nd.uniform(shape=shape_myx) shape_dict = {'data' : myx.shape} mod, params = relay.frontend.from_mxnet(mynet, shape_dict) dev = tvm.cpu() with tvm.transform.PassContext(opt_level=3): lib = relay.build(mod, target='llvm', params=params) ``` Looks like this bug is very similar to those reported in #10651 and in PR #11174 for ONNX and PyTorch models. Similar error is obtained using any shape different from 2D for any of the data and weight tensors. ### Expected behavior Should be compiled by TVM, as it follows correct MXNet specification and can be executed by MXNet. ### Actual behavior ``` Traceback (most recent call last): File "/home/syrmia/anaconda3/envs/tvmenv/lib/python3.7/site-packages/spyder_kernels/py3compat.py", line 356, in compat_exec exec(code, globals, locals) File "/home/syrmia/Desktop/tvm_tutorial/my_scripts/untitled1.py", line 40, in <module> mod, params = relay.frontend.from_mxnet(mynet, shape_dict) File "/home/syrmia/tvm/python/tvm/relay/frontend/mxnet.py", line 2975, in from_mxnet func = _from_mxnet_impl(sym, shape, dtype, params, mod) File "/home/syrmia/tvm/python/tvm/relay/frontend/mxnet.py", line 2884, in _from_mxnet_impl res = _convert_map[op_name](*op_params) File "/home/syrmia/tvm/python/tvm/relay/frontend/mxnet.py", line 802, in _mx_dot raise tvm.error.OpAttributeUnimplemented("Only 2-D arrays are supported.") AttributeError: module 'tvm.error' has no attribute 'OpAttributeUnimplemented' ``` When I comment the lines for checking ranks in from_mxnet.py file I got this error: ``` ... File "/home/syrmia/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 81, in cfun rv = local_pyfunc(*pyargs) File "/home/syrmia/tvm/python/tvm/relay/op/nn/_nn.py", line 112, in alter_op_layout_dense return topi.nn.dense_alter_layout(attrs, inputs, tinfos, out_type) File "/home/syrmia/anaconda3/envs/tvmenv/lib/python3.7/site-packages/decorator.py", line 232, in fun return caller(func, *(extras + args), **kw) File "/home/syrmia/tvm/python/tvm/target/generic_func.py", line 286, in dispatch_func return dispatch_dict[k](*args, **kwargs) File "/home/syrmia/tvm/python/tvm/topi/x86/dense_alter_op.py", line 48, in _alter_dense_layout M, K = get_const_tuple(data_tensor.shape) ValueError: not enough values to unpack (expected 2, got 1) ``` ### Steps to reproduce The code above successfully reproduce this problem. ### Potential solution Changing the _mx_dot function in from_mxnet.py with: ``` def _mx_dot(inputs, attrs): assert len(inputs) == 2 a = inputs[0] b = inputs[1] rank_a = len(_infer_type(a).checked_type.shape) rank_b = len(_infer_type(b).checked_type.shape) if rank_a < 1 or rank_b < 1: raise tvm.error.OpAttributeInvalid("Unsupported shape of input tensors.") transpose_a = attrs.get_bool("transpose_a", False) transpose_b = attrs.get_bool("transpose_b", False) if transpose_a is True: msg = 'Value {} in attribute "transpose_a" of operator dot ' "is not valid." raise tvm.error.OpAttributeInvalid(msg.format(transpose_a)) # When performing dot product we need to properly handle shape of result -> out_shape if rank_a == 1: out_shape = list() a = _op.expand_dims(a, axis=0) else: shape_a = list(_infer_type(a).checked_type.shape) out_shape = shape_a[:-1] a = _op.reshape(a, newshape=(-1, shape_a[-1])) if rank_b == 1: if not out_shape: out_shape = [1,] b = _op.expand_dims(b, axis=0) else: # Transpose matrix b if needed trans_axes = list(range(rank_b)) if transpose_b: trans_axes = trans_axes[-1:] + trans_axes[:-1] b = _op.transpose(b, axes=trans_axes) shape_b = list(_infer_type(b).checked_type.shape) out_shape += shape_b[1:] # Additional transpose is mandatory since _op.nn.dense function transposes second tensor by default b = _op.transpose(_op.reshape(b, newshape=(shape_b[0], -1)), axes=[1, 0]) out = _op.reshape(_op.nn.dense(a, b), newshape=out_shape) return out ``` cc: @masahi @junrushao1994 @kevinthesun @ganler -- This is an automated message from the Apache Git Service. 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