rain1011

/

LaVIT-7B-v2

like 19

Thanks for sharing with amazing model , here is some issues i am getting running the model:
```

NotImplementedError Traceback (most recent call last)
Cell In[4], line 17
12 device = torch.device('cuda')
14 # For Multi-modal Image Generation, must set load_tokenizer=True to load the tokenizer to tokenize input image.
15 # If you have already install xformers, set use_xformers=True to save the GPU memory (Xformers is not supported on V100 GPU)
16 # If you have already download the checkpoint, set `local_files_only=True`` to avoid auto-downloading from remote
---> 17 model = build_model(model_path=model_path, model_dtype=model_dtype, check_safety=False,
18 device_id=device_id, use_xformers=True, understanding=False, load_tokenizer=True)
19 model = model.to(device)
20 print("Building Model Finsished")

File /workspace/LaVIT/models/init.py:47, in build_model(model_path, model_dtype, device_id, image_size, use_xformers, understanding, load_tokenizer, pixel_decoding, check_safety, local_files_only)
44 lavit = LaVITforUnderstanding(model_path=model_path, model_dtype=model_dtype,
45 device_id=device_id, use_xformers=use_xformers)
46 else:
---> 47 lavit = LaVITforGeneration(model_path=model_path, model_dtype=model_dtype, device_id=device_id,
48 use_xformers=use_xformers, check_safety=check_safety, load_tokenizer=load_tokenizer, pixel_decoding=pixel_decoding)
50 # Convert the model parameters to the defined precision
51 if model_dtype == 'bf16':

File /workspace/LaVIT/models/lavit_for_generation.py:121, in LaVITforGeneration.init(self, model_path, model_dtype, device_id, use_xformers, check_safety, load_tokenizer, pixel_decoding, **kwargs)
117 if xformers_version == version.parse("0.0.16"):
118 print(
119 "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
120 )
--> 121 self.unet.enable_xformers_memory_efficient_attention()
122 else:
123 raise ValueError("xformers is not available. Make sure it is installed correctly or set use_xformers=False")

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:287, in ModelMixin.enable_xformers_memory_efficient_attention(self, attention_op)
253 def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
254 r"""
255 Enable memory efficient attention from xFormers.
256
(...)
285 ```
286 """
--> 287 self.set_use_memory_efficient_attention_xformers(True, attention_op)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:251, in ModelMixin.set_use_memory_efficient_attention_xformers(self, valid, attention_op)
249 for module in self.children():
250 if isinstance(module, torch.nn.Module):
--> 251 fn_recursive_set_mem_eff(module)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:247, in ModelMixin.set_use_memory_efficient_attention_xformers..fn_recursive_set_mem_eff(module)
244 module.set_use_memory_efficient_attention_xformers(valid, attention_op)
246 for child in module.children():
--> 247 fn_recursive_set_mem_eff(child)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:247, in ModelMixin.set_use_memory_efficient_attention_xformers..fn_recursive_set_mem_eff(module)
244 module.set_use_memory_efficient_attention_xformers(valid, attention_op)
246 for child in module.children():
--> 247 fn_recursive_set_mem_eff(child)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:247, in ModelMixin.set_use_memory_efficient_attention_xformers..fn_recursive_set_mem_eff(module)
244 module.set_use_memory_efficient_attention_xformers(valid, attention_op)
246 for child in module.children():
--> 247 fn_recursive_set_mem_eff(child)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:244, in ModelMixin.set_use_memory_efficient_attention_xformers..fn_recursive_set_mem_eff(module)
242 def fn_recursive_set_mem_eff(module: torch.nn.Module):
243 if hasattr(module, "set_use_memory_efficient_attention_xformers"):
--> 244 module.set_use_memory_efficient_attention_xformers(valid, attention_op)
246 for child in module.children():
247 fn_recursive_set_mem_eff(child)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:251, in ModelMixin.set_use_memory_efficient_attention_xformers(self, valid, attention_op)
249 for module in self.children():
250 if isinstance(module, torch.nn.Module):
--> 251 fn_recursive_set_mem_eff(module)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:247, in ModelMixin.set_use_memory_efficient_attention_xformers..fn_recursive_set_mem_eff(module)
244 module.set_use_memory_efficient_attention_xformers(valid, attention_op)
246 for child in module.children():
--> 247 fn_recursive_set_mem_eff(child)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:247, in ModelMixin.set_use_memory_efficient_attention_xformers..fn_recursive_set_mem_eff(module)
244 module.set_use_memory_efficient_attention_xformers(valid, attention_op)
246 for child in module.children():
--> 247 fn_recursive_set_mem_eff(child)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py:244, in ModelMixin.set_use_memory_efficient_attention_xformers..fn_recursive_set_mem_eff(module)
242 def fn_recursive_set_mem_eff(module: torch.nn.Module):
243 if hasattr(module, "set_use_memory_efficient_attention_xformers"):
--> 244 module.set_use_memory_efficient_attention_xformers(valid, attention_op)
246 for child in module.children():
247 fn_recursive_set_mem_eff(child)

File /usr/local/lib/python3.10/dist-packages/diffusers/models/attention_processor.py:216, in Attention.set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers, attention_op)
210 _ = xformers.ops.memory_efficient_attention(
211 torch.randn((1, 2, 40), device="cuda"),
212 torch.randn((1, 2, 40), device="cuda"),
213 torch.randn((1, 2, 40), device="cuda"),
214 )
215 except Exception as e:
--> 216 raise e
218 if is_lora:
219 # TODO (sayakpaul): should we throw a warning if someone wants to use the xformers
220 # variant when using PT 2.0 now that we have LoRAAttnProcessor2_0?
221 processor = LoRAXFormersAttnProcessor(
222 hidden_size=self.processor.hidden_size,
223 cross_attention_dim=self.processor.cross_attention_dim,
224 rank=self.processor.rank,
225 attention_op=attention_op,
226 )

File /usr/local/lib/python3.10/dist-packages/diffusers/models/attention_processor.py:210, in Attention.set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers, attention_op)
207 else:
208 try:
209 # Make sure we can run the memory efficient attention
--> 210 _ = xformers.ops.memory_efficient_attention(
211 torch.randn((1, 2, 40), device="cuda"),
212 torch.randn((1, 2, 40), device="cuda"),
213 torch.randn((1, 2, 40), device="cuda"),
214 )
215 except Exception as e:
216 raise e

File /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/init.py:223, in memory_efficient_attention(query, key, value, attn_bias, p, scale, op)
116 def memory_efficient_attention(
117 query: torch.Tensor,
118 key: torch.Tensor,
(...)
124 op: Optional[AttentionOp] = None,
125 ) -> torch.Tensor:
126 """Implements the memory-efficient attention mechanism following
127 "Self-Attention Does Not Need O(n^2) Memory" <http://arxiv.org/abs/2112.05682>_.
128
(...)
221 :return: multi-head attention Tensor with shape [B, Mq, H, Kv]
222 """
--> 223 return _memory_efficient_attention(
224 Inputs(
225 query=query, key=key, value=value, p=p, attn_bias=attn_bias, scale=scale
226 ),
227 op=op,
228 )

File /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/init.py:321, in _memory_efficient_attention(inp, op)
316 def _memory_efficient_attention(
317 inp: Inputs, op: Optional[AttentionOp] = None
318 ) -> torch.Tensor:
319 # fast-path that doesn't require computing the logsumexp for backward computation
320 if all(x.requires_grad is False for x in [inp.query, inp.key, inp.value]):
--> 321 return _memory_efficient_attention_forward(
322 inp, op=op[0] if op is not None else None
323 )
325 output_shape = inp.normalize_bmhk()
326 return _fMHA.apply(
327 op, inp.query, inp.key, inp.value, inp.attn_bias, inp.p, inp.scale
328 ).reshape(output_shape)

File /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/init.py:337, in _memory_efficient_attention_forward(inp, op)
335 output_shape = inp.normalize_bmhk()
336 if op is None:
--> 337 op = _dispatch_fw(inp, False)
338 else:
339 _ensure_op_supports_or_raise(ValueError, "memory_efficient_attention", op, inp)

File /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py:120, in _dispatch_fw(inp, needs_gradient)
111 def _dispatch_fw(inp: Inputs, needs_gradient: bool) -> Type[AttentionFwOpBase]:
112 """Computes the best operator for forward
113
114 Raises:
(...)
118 AttentionOp: The best operator for the configuration
119 """
--> 120 return _run_priority_list(
121 "memory_efficient_attention_forward",
122 _dispatch_fw_priority_list(inp, needs_gradient),
123 inp,
124 )

File /usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py:63, in _run_priority_list(name, priority_list, inp)
61 for op, not_supported in zip(priority_list, not_supported_reasons):
62 msg += "\n" + _format_not_supported_reasons(op, not_supported)
---> 63 raise NotImplementedError(msg)

NotImplementedError: No operator found for memory_efficient_attention_forward with inputs:
query : shape=(1, 2, 1, 40) (torch.float32)
key : shape=(1, 2, 1, 40) (torch.float32)
value : shape=(1, 2, 1, 40) (torch.float32)
attn_bias : <class 'NoneType'>
p : 0.0
decoderF is not supported because:
xFormers wasn't build with CUDA support
attn_bias type is <class 'NoneType'>
operator wasn't built - see python -m xformers.info for more info
[email protected] is not supported because:
xFormers wasn't build with CUDA support
dtype=torch.float32 (supported: {torch.bfloat16, torch.float16})
operator wasn't built - see python -m xformers.info for more info
tritonflashattF is not supported because:
xFormers wasn't build with CUDA support
dtype=torch.float32 (supported: {torch.bfloat16, torch.float16})
operator wasn't built - see python -m xformers.info for more info
triton is not available
Only work on pre-MLIR triton for now
cutlassF is not supported because:
xFormers wasn't build with CUDA support
operator wasn't built - see python -m xformers.info for more info
smallkF is not supported because:
max(query.shape[-1] != value.shape[-1]) > 32
xFormers wasn't build with CUDA support
operator wasn't built - see python -m xformers.info for more info
unsupported embed per head: 40

Any help to resolve it would be appreciated.
Thanks,
Andy