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test_transformers.py
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test_transformers.py
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# Owner(s): ["module: nn"]
import torch
import unittest
from torch.testing._internal.common_nn import NNTestCase
from torch.testing._internal.common_utils import TEST_FAIRSEQ, parametrize, instantiate_parametrized_tests
from torch.testing._internal.common_cuda import TEST_CUDA
if TEST_FAIRSEQ:
import fairseq.models.transformer as fairseq_transformer
class TestTransformers(NNTestCase):
_do_cuda_memory_leak_check = True
_do_cuda_non_default_stream = True
@parametrize("use_torchscript", [True, False])
@parametrize("with_no_grad", [True, False])
@parametrize("training", [True, False])
def test_transformerencoder_fastpath_torchscript(self, use_torchscript, with_no_grad, training):
"""
Test TransformerEncoder does not crash
"""
model = torch.nn.TransformerEncoder(
torch.nn.TransformerEncoderLayer(d_model=2, nhead=2, dim_feedforward=8, batch_first=True),
num_layers=2,
enable_nested_tensor=True
)
if training:
model = model.train()
else:
model = model.eval()
if use_torchscript:
model = torch.jit.script(model)
x = torch.Tensor([[[1, 2], [3, 4]]]).to(torch.float)
mask = torch.Tensor([[0, 1]]).to(torch.bool)
if with_no_grad:
with torch.no_grad():
model(x, src_key_padding_mask=mask)
else:
model(x, src_key_padding_mask=mask)
@unittest.skipIf(not TEST_FAIRSEQ, "numpy not found")
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
def test_decoder_only_layer(self):
DEFAULT_PADDING_IDX = 0
class FairseqDecoder(torch.nn.Module):
def __init__(
self,
embed_dim,
attention_heads,
ffn_embed_dim,
num_layers,
embedding_layer, # torch.nn.Embedding. Must have a padding_idx field
dropout=0,
normalize_before=False,
torch_encoder=None, # torch encoder that you can map weights from
activation="relu",
):
super().__init__()
cfg = fairseq_transformer.TransformerConfig()
cfg.decoder.embed_dim = embed_dim
cfg.decoder.output_dim = embed_dim
cfg.decoder.attention_heads = attention_heads
cfg.decoder.ffn_embed_dim = ffn_embed_dim
cfg.dropout = dropout
cfg.decoder.normalize_before = normalize_before
cfg.decoder.layers = num_layers
# make embedding behavior same as other encoders
cfg.no_token_positional_embeddings = True
cfg.no_scale_embedding = True
cfg.activation_fn = activation
dictionary = {} # TODO: verify what this is
self.decoder = fairseq_transformer.TransformerDecoder(
cfg,
dictionary,
embedding_layer,
no_encoder_attn=True,
output_projection=None,
)
if torch_encoder is not None:
self.decoder = torch_to_fairseq(torch_encoder, self.decoder)
self.decoder = self.decoder.eval().cuda().half()
def forward(
self,
tokens,
src_lengths=None,
with_triangle_mask=False,
incremental_state=None,
):
return self.decoder(
prev_output_tokens=tokens,
encoder_out=None,
incremental_state=incremental_state,
features_only=True,
full_context_alignment=not with_triangle_mask,
alignment_layer=None,
alignment_heads=None,
src_lengths=src_lengths,
return_all_hiddens=False,
)[0]
class BetterDecoder(torch.nn.Module):
"""
Only incremental decoder for now
"""
def __init__(self, transformer, embedding, pad_idx):
super().__init__()
self.transformer = transformer
self.embedding = embedding
self.padding_idx = pad_idx
def forward(
self,
x,
src_mask=None,
include_padding_mask=True,
incr_key_lst=None,
incr_value_lst=None,
is_incremental_decoding=False,
):
padding_mask = None
if not x.is_nested and include_padding_mask:
padding_mask = x.eq(self.padding_idx)
if(is_incremental_decoding):
x = x[:, -1:] # only take the last token
x = self.embedding(x)
one_encoder_layer = self.transformer.layers[0]
self_attn = one_encoder_layer.self_attn
embed_dim = self_attn.embed_dim
num_heads = self_attn.num_heads
use_gelu = (
one_encoder_layer.activation_relu_or_gelu == 2
) # see torch/nn/modules/activation attention impl. 1 == relu, 2 == gelu
assert (
one_encoder_layer.activation_relu_or_gelu != 0
) # 0 == not relu or gelu
norm_first = one_encoder_layer.norm_first
# TODO: make this a bit less janky. but for now we initialize with an empty tensor.
if(not is_incremental_decoding):
assert len(incr_key_lst) == 0 or incr_key_lst[0] is None
assert len(incr_value_lst) == 0 or incr_value_lst[0] is None
while len(incr_key_lst) <= len(self.transformer.layers):
if(is_incremental_decoding):
incr_key_lst.append(torch.Tensor([]).cuda().half())
incr_value_lst.append(torch.Tensor([]).cuda().half())
else:
incr_key_lst.append(None)
incr_value_lst.append(None)
for i, layer in enumerate(self.transformer.layers):
incr_key = incr_key_lst[i]
incr_value = incr_value_lst[i]
x, incr_key, incr_value = torch._transformer_decoder_only_layer_fwd(
src=x,
embed_dim=embed_dim,
num_heads=num_heads,
qkv_weight=layer.self_attn.in_proj_weight,
qkv_bias=layer.self_attn.in_proj_bias,
proj_weight=layer.self_attn.out_proj.weight,
proj_bias=layer.self_attn.out_proj.bias,
use_gelu=use_gelu,
norm_first=norm_first,
# TODO: layer_norm_eps hardcoded to be same as nn.TransformerEncoder default.
# fix by pulling from self_attn.norm1
eps=1e-5,
norm_weight_1=layer.norm1.weight,
norm_bias_1=layer.norm1.bias,
norm_weight_2=layer.norm2.weight,
norm_bias_2=layer.norm2.bias,
ffn_weight_1=layer.linear1.weight,
ffn_bias_1=layer.linear1.bias,
ffn_weight_2=layer.linear2.weight,
ffn_bias_2=layer.linear2.bias,
mask=src_mask,
incr_key=incr_key, # altered in place
incr_value=incr_value,
)
# not in-place
if(not is_incremental_decoding):
incr_key = None
incr_value = None
incr_key_lst[i] = incr_key
incr_value_lst[i] = incr_value
return x, incr_key_lst, incr_value_lst
def torch_to_fairseq(torch_encoder, fairseq_encoder):
for src_layer, dst_layer in zip(torch_encoder.layers, fairseq_encoder.layers):
w_q, w_k, w_v = src_layer.self_attn.in_proj_weight.chunk(3, dim=0)
b_q, b_k, b_v = src_layer.self_attn.in_proj_bias.chunk(3, dim=0)
dst_layer.self_attn.q_proj.weight = torch.nn.Parameter(w_q)
dst_layer.self_attn.q_proj.bias = torch.nn.Parameter(b_q)
dst_layer.self_attn.k_proj.weight = torch.nn.Parameter(w_k)
dst_layer.self_attn.k_proj.bias = torch.nn.Parameter(b_k)
dst_layer.self_attn.v_proj.weight = torch.nn.Parameter(w_v)
dst_layer.self_attn.v_proj.bias = torch.nn.Parameter(b_v)
dst_layer.self_attn.out_proj.weight = src_layer.self_attn.out_proj.weight
dst_layer.self_attn.out_proj.bias = src_layer.self_attn.out_proj.bias
dst_layer.fc1.weight = src_layer.linear1.weight
dst_layer.fc1.bias = src_layer.linear1.bias
# fairseq may use fusedlayernorm from nvidia apex - diff properties
dst_layer.self_attn_layer_norm.load_state_dict(src_layer.norm1.state_dict())
dst_layer.fc2.weight = src_layer.linear2.weight
dst_layer.fc2.bias = src_layer.linear2.bias
dst_layer.final_layer_norm.load_state_dict(src_layer.norm2.state_dict())
return fairseq_encoder
def set_weights_deterministic(model):
for idx, p in enumerate(model.parameters()):
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
x = torch.cos(torch.arange(0, sz).float().view(shape))
p.data.copy_(x)
D = 4 # d_model
H = 2 # nhead
FD = 16 # dim_feedforward
V = 100 # vocab size
L = 2 # num layers
embedding_layer = torch.nn.Embedding(V, D, DEFAULT_PADDING_IDX)
layer = torch.nn.TransformerEncoderLayer(
d_model=D,
nhead=H,
dim_feedforward=FD,
batch_first=True,
activation="gelu",
)
transformer = torch.nn.TransformerEncoder(
layer,
num_layers=L,
).eval().cuda().half()
set_weights_deterministic(embedding_layer)
set_weights_deterministic(transformer)
better_decoder = (
BetterDecoder(transformer, embedding_layer, DEFAULT_PADDING_IDX)
.eval()
.cuda()
.half()
)
fairseq_decoder = (
FairseqDecoder(
D,
H,
FD,
L,
embedding_layer,
dropout=0,
normalize_before=False,
torch_encoder=transformer,
activation="gelu",
)
.eval()
.cuda()
.half()
)
tokens = torch.Tensor([
[5, 6, 7, 8],
[9, 10, 11, 12]
]).to(torch.int).cuda()
lengths_tensor = torch.Tensor([2, 2]).to(torch.int).cuda()
# bs = 2, seqlen = 4
bs, seqlen = tokens.shape
upper_triangle = torch.zeros(seqlen, seqlen)
upper_triangle.fill_(-100000000)
upper_triangle = torch.triu(upper_triangle, 1)
upper_triangle = upper_triangle.cuda().half()
upper_triangle_expanded = upper_triangle.unsqueeze(0).unsqueeze(0)
upper_triangle_expanded = upper_triangle_expanded.expand(
bs, H, -1, -1
)
# test forced decoding
with torch.no_grad():
result, _, _ = better_decoder(
tokens,
src_mask=upper_triangle_expanded,
include_padding_mask=False,
incr_key_lst=[],
incr_value_lst=[],
is_incremental_decoding=False,
)
ref_output = fairseq_decoder(tokens, lengths_tensor, with_triangle_mask=True)
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=1e-3, rtol=1e-2)
# test incremental decoding
bs, seqlen = tokens.shape
incr_state = {}
ref_outputs = [fairseq_decoder(
tokens[:, :i],
src_lengths=None,
with_triangle_mask=False,
incremental_state=incr_state,
) for i in range(1, seqlen + 1)]
ref_output = torch.stack(ref_outputs)
incr_key_lst = []
incr_value_lst = []
results = []
for i in range(1, seqlen + 1):
res, incr_key_lst, incr_value_lst = better_decoder(
tokens[:, :i],
src_mask=None,
include_padding_mask=False,
incr_key_lst=incr_key_lst,
incr_value_lst=incr_value_lst,
is_incremental_decoding=True,
)
results.append(res)
result = torch.stack(results)
self.assertEqual(result.shape, ref_output.shape)
torch.testing.assert_close(result, ref_output, atol=1e-3, rtol=1e-2)
instantiate_parametrized_tests(TestTransformers)