None
Frame 0 Prompt: ['A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.', 'yellow color scheme']
Seed used: 85109229
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Input In [72], in <cell line: 173>()
172 torch.cuda.empty_cache()
173 try:
--> 174 do_run()
175 except KeyboardInterrupt:
176 pass
Input In [65], in do_run()
475 for prompt in frame_prompt:
476 txt, weight = parse_prompt(prompt)
--> 477 txt = clip_model.encode_text(clip.tokenize(prompt).to(device)).float()
479 if args.fuzzy_prompt:
480 for i in range(25):
File ~/notebook/CLIP/clip/model.py:349, in CLIP.encode_text(self, text)
347 x = x + self.positional_embedding.type(self.dtype)
348 x = x.permute(1, 0, 2) # NLD -> LND
--> 349 x = self.transformer(x)
350 x = x.permute(1, 0, 2) # LND -> NLD
351 x = self.ln_final(x).type(self.dtype)
File ~/miniconda3/envs/jupyter/lib/python3.8/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File ~/notebook/CLIP/clip/model.py:204, in Transformer.forward(self, x)
203 def forward(self, x: torch.Tensor):
--> 204 return self.resblocks(x)
File ~/miniconda3/envs/jupyter/lib/python3.8/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File ~/miniconda3/envs/jupyter/lib/python3.8/site-packages/torch/nn/modules/container.py:139, in Sequential.forward(self, input)
137 def forward(self, input):
138 for module in self:
--> 139 input = module(input)
140 return input
File ~/miniconda3/envs/jupyter/lib/python3.8/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File ~/notebook/CLIP/clip/model.py:191, in ResidualAttentionBlock.forward(self, x)
190 def forward(self, x: torch.Tensor):
--> 191 x = x + self.attention(self.ln_1(x))
192 x = x + self.mlp(self.ln_2(x))
193 return x
File ~/notebook/CLIP/clip/model.py:188, in ResidualAttentionBlock.attention(self, x)
186 def attention(self, x: torch.Tensor):
187 self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
--> 188 return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
File ~/miniconda3/envs/jupyter/lib/python3.8/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File ~/miniconda3/envs/jupyter/lib/python3.8/site-packages/torch/nn/modules/activation.py:1153, in MultiheadAttention.forward(self, query, key, value, key_padding_mask, need_weights, attn_mask, average_attn_weights)
1142 attn_output, attn_output_weights = F.multi_head_attention_forward(
1143 query, key, value, self.embed_dim, self.num_heads,
1144 self.in_proj_weight, self.in_proj_bias,
(...)
1150 q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
1151 v_proj_weight=self.v_proj_weight, average_attn_weights=average_attn_weights)
1152 else:
-> 1153 attn_output, attn_output_weights = F.multi_head_attention_forward(
1154 query, key, value, self.embed_dim, self.num_heads,
1155 self.in_proj_weight, self.in_proj_bias,
1156 self.bias_k, self.bias_v, self.add_zero_attn,
1157 self.dropout, self.out_proj.weight, self.out_proj.bias,
1158 training=self.training,
1159 key_padding_mask=key_padding_mask, need_weights=need_weights,
1160 attn_mask=attn_mask, average_attn_weights=average_attn_weights)
1161 if self.batch_first and is_batched:
1162 return attn_output.transpose(1, 0), attn_output_weights
File ~/miniconda3/envs/jupyter/lib/python3.8/site-packages/torch/nn/functional.py:5128, in multi_head_attention_forward(query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, bias_k, bias_v, add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training, key_padding_mask, need_weights, attn_mask, use_separate_proj_weight, q_proj_weight, k_proj_weight, v_proj_weight, static_k, static_v, average_attn_weights)
5123 dropout_p = 0.0
5125 #
5126 # (deep breath) calculate attention and out projection
5127 #
-> 5128 attn_output, attn_output_weights = _scaled_dot_product_attention(q, k, v, attn_mask, dropout_p)
5129 attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
5130 attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
File ~/miniconda3/envs/jupyter/lib/python3.8/site-packages/torch/nn/functional.py:4801, in _scaled_dot_product_attention(q, k, v, attn_mask, dropout_p)
4799 # (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns)
4800 if attn_mask is not None:
-> 4801 attn = torch.baddbmm(attn_mask, q, k.transpose(-2, -1))
4802 else:
4803 attn = torch.bmm(q, k.transpose(-2, -1))
RuntimeError: input tensor does not match matmul output shape
1 Like
Hey!
I do not necessarily recommend to use these as they are a little bit work in progress.
But what is mentioned there can be found on this page (make sure you’re logged in to github): macos-arm64-binary-wheel · pytorch/pytorch@bf961d5 · GitHub
At the very bottom, you will find zip files that contain a .whl file that you can pip install torch-XXX.whl.
1 Like
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
Input In [6], in <cell line: 4>()
2 prompt = "A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation."
3 clip_model = clip.load('ViT-B/32', jit=False)[0].eval().requires_grad_(False).to(device)
----> 4 txt = clip_model.encode_text(clip.tokenize(prompt).to(device)).float()
File ~/notebook/CLIP/clip/model.py:355, in CLIP.encode_text(self, text)
351 x = self.ln_final(x).type(self.dtype)
353 # x.shape = [batch_size, n_ctx, transformer.width]
354 # take features from the eot embedding (eot_token is the highest number in each sequence)
--> 355 x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
357 return x
NotImplementedError: Could not run 'aten::index.Tensor' with arguments from the 'MPS' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::index.Tensor' is only available for these backends: [Dense, Negative, UNKNOWN_TENSOR_TYPE_ID, QuantizedXPU, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, SparseCPU, SparseCUDA, SparseHIP, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, SparseXPU, UNKNOWN_TENSOR_TYPE_ID, SparseVE, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, NestedTensorCUDA, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID, UNKNOWN_TENSOR_TYPE_ID].
CPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/build/aten/src/ATen/RegisterCPU.cpp:37399 [kernel]
QuantizedCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/build/aten/src/ATen/RegisterQuantizedCPU.cpp:1294 [kernel]
BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:133 [backend fallback]
Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:18 [backend fallback]
Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:64 [backend fallback]
AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
UNKNOWN_TENSOR_TYPE_ID: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
UNKNOWN_TENSOR_TYPE_ID: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:11242 [autograd kernel]
Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:11951 [kernel]
AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:481 [backend fallback]
Autocast: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:324 [backend fallback]
Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/BatchingRegistrations.cpp:1064 [backend fallback]
VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:89 [backend fallback]
PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:137 [backend fallback]
I do not necessarily recommend to use these as they are a little bit work in progress.
I’ve been waiting GPU acceleration for too long, so I’m eager to test if it’s now ready to run disco diffusion locally.
If you’re running from that binary, you should be able to set PYTORCH_ENABLE_MPS_FALLBACK=1 to get the fallback and avoid this error.