wanchaol’s gists

wanchaol / repro.py

Created May 10, 2019 00:36

	import torch


	@torch.jit.script
	def test_ad(a, b, c, d, e, f):
	# type: (List[int], Tensor, List[int], Tensor, Tensor, Tensor)
	v19 = f > 0
	v17 = v19.type_as(f)
	v14 = e.mul(v17)
	grad_input = v14.mm(d)

wanchaol / gist:bcd988809a192c56f55319fc6a305637

Created May 8, 2019 09:25

LSTM graph compare

	LSTM forward graph: after the PR #20039:

	graph(%input : Float(, ),
	%input0 : Float(, ),
	%cx : Float(, ),
	%weight : Float(, ),
	%weight0 : Float(, ),
	%bias : Float(*),
	%bias0 : Float(*)):
	%9 : Float(, ) = aten::t(%weight)

wanchaol / gist:7118f18d2210570aa3a8c2021edee608

Created May 8, 2019 04:25

LSTM graph before and after

	LSTM forward graph: before the PR #20039

	graph(%input : Float(, ),
	%input0 : Float(, ),
	%cx : Float(, ),
	%weight : Float(, ),
	%weight0 : Float(, ),
	%bias : Float(*),
	%bias0 : Float(*)):
	%9 : Float(, ) = aten::t(%weight)

wanchaol / gist:aceb0a1e8d3a93c8853689730b0f709f

Created May 2, 2019 05:46

	loss value: tensor(7.5122, grad_fn=<_TransducerBackward>)
	caffe2/aten/src/ATen/native/Normalization.cpp:311:18: runtime error: 6.17887e+39 is outside the range of representable values of type 'float'
	#0 0x7f877131e37a in std::tuple<at::Tensor, at::Tensor, at::Tensor> at::native::batch_norm_backward_cpu_template<float>(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, std::array<bool, 3ul>)::'lambda'(long, long)::operator()(long, long) const::'lambda0'(float&, float const&)::operator()(float&, float const&) const caffe2/aten/src/ATen/native/Normalization.cpp:311
	#1 0x7f877131ddc3 in void at::apply_op<std::tuple<at::Tensor, at::Tensor, at::Tensor> at::native::batch_norm_backward_cpu_template<float>(at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, bool, double, std::array<bool, 3ul>)::'lambda'(long, long)::operator()(long, l

wanchaol / gist:c50875569d4266aff20133b6d0f387c3

Last active April 29, 2019 05:00

	import torch
	import torch.nn.functional as F
	import torch.jit as jit
	from torch import Tensor


	@jit.script
	def test_mm_back(input1, input2, normalized_shape):
	# type: (Tensor, Tensor, List[int]) -> Tensor
	return F.layer_norm(torch.mm(input1, input2), normalized_shape)

wanchaol / gist:bb44846d4fcbf6447152a79a6b666c0c

Created April 26, 2019 18:16

	@torch.jit.script
	def test_if_refinement(weight, bias):
	# type: (Optional[Tensor], Optional[Tensor]) -> Tuple[Optional[int], Optional[int]]
	if weight is not None and bias is not None:
	grad_weight = 1
	grad_bias = 2
	elif weight is not None:
	grad_weight = 2
	grad_bias = None
	elif bias is not None:

wanchaol / gist:0e02974f23f2bb58ae20e0293bed830e

Created April 25, 2019 17:54

	======================================================================
	ERROR: test_max_pool2d (__main__.TestOperators)
	----------------------------------------------------------------------
	Traceback (most recent call last):
	File "test/test_operators.py", line 148, in test_max_pool2d
	max_pool2d(X)
	File "/data/users/wanchaol/pytorch/torch/nn/modules/module.py", line 493, in __call__
	result = self.forward(input, *kwargs)
	RuntimeError: [10:54:24] /home/wanchaol/local/tvm/src/relay/ir/error.cc:131:
	Error(s) have occurred. We have annotated the program with them:

wanchaol / gist:ded928ba77c4703e41fa1e681172bec5

Created April 12, 2019 18:27

lstmcell_forward_unoptimized

	graph(%x : Float(, ),
	%hx : Float(, ),
	%cx : Float(, ),
	%w_ih : Float(, ),
	%w_hh : Float(, ),
	%b_ih : Float(*),
	%b_hh : Float(*)):
	%hy : Float(, ), %cy : Float(, ) = prim::DifferentiableGraph_0(%cx, %b_hh, %b_ih, %hx, %w_hh, %x, %w_ih)
	%30 : (Float(, ), Float(, )) = prim::TupleConstruct(%hy, %cy)
	return (%30)

wanchaol / gist:1afcbbef8b9efad10bbda8d2802868ff

Created April 12, 2019 18:22

lstmcelll_forward

	graph(%x : Float(, ),
	%hx : Float(, ),
	%cx : Float(, ),
	%w_ih : Float(, ),
	%w_hh : Float(, ),
	%b_ih : Float(*),
	%b_hh : Float(*)):
	%hy : Float(, ), %cy : Float(, ) = prim::DifferentiableGraph_0(%cx, %b_hh, %b_ih, %hx, %w_hh, %x, %w_ih)
	%30 : (Float(, ), Float(, )) = prim::TupleConstruct(%hy, %cy)
	return (%30)

wanchaol / gist:1501e4b225d2c82226cdb981cabd7f65

Created April 1, 2019 06:25

	LSTM forward graph: after the PR #20039:

	graph(%input : Float(, ),
	%input0 : Float(, ),
	%cx : Float(, ),
	%weight : Float(, ),
	%weight0 : Float(, ),
	%bias : Float(*),
	%bias0 : Float(*)):
	%9 : Float(, ) = aten::t(%weight)

	LSTM forward graph: before the PR #20039

	graph(%input : Float(, ),
	%input0 : Float(, ),
	%cx : Float(, ),
	%weight : Float(, ),
	%weight0 : Float(, ),
	%bias : Float(*),
	%bias0 : Float(*)):
	%9 : Float(, ) = aten::t(%weight)

	graph(%x : Float(, ),
	%hx : Float(, ),
	%cx : Float(, ),
	%w_ih : Float(, ),
	%w_hh : Float(, ),
	%b_ih : Float(*),
	%b_hh : Float(*)):
	%hy : Float(, ), %cy : Float(, ) = prim::DifferentiableGraph_0(%cx, %b_hh, %b_ih, %hx, %w_hh, %x, %w_ih)
	%30 : (Float(, ), Float(, )) = prim::TupleConstruct(%hy, %cy)
	return (%30)

	graph(%x : Float(, ),
	%hx : Float(, ),
	%cx : Float(, ),
	%w_ih : Float(, ),
	%w_hh : Float(, ),
	%b_ih : Float(*),
	%b_hh : Float(*)):
	%hy : Float(, ), %cy : Float(, ) = prim::DifferentiableGraph_0(%cx, %b_hh, %b_ih, %hx, %w_hh, %x, %w_ih)
	%30 : (Float(, ), Float(, )) = prim::TupleConstruct(%hy, %cy)
	return (%30)

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