Shape error when using torchtune.modules.RotaryPositionalEmbeddings

[Leo-Lifeblood]

🐛 Describe the bug

When using the position encoding layer strange shape errors occur I dont have the time or insight to resolve

import torch import torchtune

max_value = max(tokenizer_causal.vocab.values()) + 1

max_value = 50

class causallm(torch.nn.Module): def init(self, d_model, num_heads, d_ff, num_layers): super().init()

    self.embeddings = torch.nn.Embedding(max_value, d_model)
    self.pos_embeddings = torchtune.modules.RotaryPositionalEmbeddings(d_model)

    self.encoder_layer = torch.nn.TransformerEncoderLayer(d_model, num_heads, d_ff, activation=torch.nn.GELU(), batch_first=True)

    self.transformer_encoder = torch.nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)

    self.output = torch.nn.Linear(d_model, max_value)

    self.num_heads = num_heads
    self.head_dim = d_model // num_heads

def forward(self, x, attention_mask=None):
    x = self.embeddings(x)

    #seq_len = x.shape[1]
    #batch_size = x.shape[0]
    #x = x.view(batch_size, seq_len, self.num_heads, self.head_dim)
    x = self.pos_embeddings(x)

    x = self.transformer_encoder(x, src_mask=attention_mask, is_causal=True)

    x = self.output(x)

    return x

model = causallm(d_model=512, num_heads=8, d_ff=2048, num_layers=2)

input_ids = torch.randint(0, max_value, (32, 128)) # input tensor with batch size 32 and sequence length 128 attention_mask = torch.ones((32, 128), dtype=torch.bool) # attention mask

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = model.to(device) input_ids = input_ids.to(device) attention_mask = attention_mask.to(device)

outputs = model(input_ids, attention_mask=attention_mask)

---

RuntimeError Traceback (most recent call last) in <cell line: 11>() 9 10 # Forward pass ---> 11 outputs = model(input_ids, attention_mask=attention_mask)

5 frames /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, kwargs) 1530 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1531 else: -> 1532 return self._call_impl(args, kwargs) 1533 1534 def _call_impl(self, *args, **kwargs):

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, *kwargs) 1539 or _global_backward_pre_hooks or _global_backward_hooks 1540 or _global_forward_hooks or _global_forward_pre_hooks): -> 1541 return forward_call(args, **kwargs) 1542 1543 try:

in forward(self, x, attention_mask) 23 #batch_size = x.shape[0] 24 #x = x.view(batch_size, seq_len, self.num_heads, self.head_dim) ---> 25 x = self.pos_embeddings(x) 26 27

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, kwargs) 1530 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1531 else: -> 1532 return self._call_impl(args, kwargs) 1533 1534 def _call_impl(self, *args, **kwargs):

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, *kwargs) 1539 or _global_backward_pre_hooks or _global_backward_hooks 1540 or _global_forward_hooks or _global_forward_pre_hooks): -> 1541 return forward_call(args, **kwargs) 1542 1543 try:

/usr/local/lib/python3.10/dist-packages/torchtune/modules/position_embeddings.py in forward(self, x, input_pos) 109 # reshape the cache for broadcasting 110 # tensor has shape [1, s, 1, n_d // 2, 2] --> 111 rope_cache = rope_cache.view(1, xshaped.size(1), 1, xshaped.size(3), 2) 112 113 # tensor has shape [b, s, n_h, n_d // 2, 2]

RuntimeError: shape '[1, 128, 1, 2, 2]' is invalid for input of size 65536

Versions

PyTorch version: 2.3.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: 14.0.0-1ubuntu1.1 CMake version: version 3.27.9 Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-6.1.85+-x86_64-with-glibc2.35 Is CUDA available: False CUDA runtime version: 12.2.140 CUDA_MODULE_LOADING set to: N/A GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 2 On-line CPU(s) list: 0,1 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) CPU @ 2.20GHz CPU family: 6 Model: 79 Thread(s) per core: 2 Core(s) per socket: 1 Socket(s): 1 Stepping: 0 BogoMIPS: 4399.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities Hypervisor vendor: KVM Virtualization type: full L1d cache: 32 KiB (1 instance) L1i cache: 32 KiB (1 instance) L2 cache: 256 KiB (1 instance) L3 cache: 55 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0,1 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Mitigation; PTE Inversion Vulnerability Mds: Vulnerable; SMT Host state unknown Vulnerability Meltdown: Vulnerable Vulnerability Mmio stale data: Vulnerable Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Vulnerable Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers Vulnerability Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable (Syscall hardening enabled) Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Vulnerable

Versions of relevant libraries: [pip3] numpy==1.25.2 [pip3] torch==2.3.0+cu121 [pip3] torchao==0.1 [pip3] torchaudio==2.3.0+cu121 [pip3] torchsummary==1.5.1 [pip3] torchtext==0.18.0 [pip3] torchtune==0.1.1 [pip3] torchvision==0.18.0+cu121 [pip3] triton==2.3.0 [conda] Could not collect

[awgu]

This might be better filed in the torchtune repo?

[ebsmothers]

Hi @Leo-Lifeblood thanks for creating the issue. I believe you are seeing this error because you're using the RotaryPositionalEmbeddings class with an input tensor shape that doesn't line up with what's expected. Typically these are used inside of self-attention, where the tensor shape is (batch_size, seq_len, num_heads, head_dim) (ref). But here it seems like you are using it on the outputs of self.embeddings, which should be a 2D tensor based on your inputs. Depending on what you're trying to do, you can consider directly importing one of our model builder functions, e.g. llama3 will allow you to pass a basic set of params and will give you back an nn.Module that's somewhat similar to what you have here. If you're looking to do something a bit more custom, happy to provide some pointers on how you can achieve that as well.

[iankur]

@ebsmothers why do we apply rope on the expanded key tensor? it seems wasteful as rope is applied on head_dim anyway.