# need a new token dimension (row) for each prompt in the batch new_token=torch.zeros(total_prompts,1,device=device,dtype=torch.int64) res=[[]for_inrange(total_prompts)] num_tokens=40 # Decoding Expand All@@ -41
Tensors and Dynamic neural networks in Python with strong GPU acceleration - add batch_size check for max_pool2d_backward (#141657) · pytorch/pytorch@fa1a4a9
# Convert old format to new format if needed from a PyTorch state_dict old_keys = [] new_keys = [] renamed_keys = {} renamed_gamma = {} renamed_beta = {} warning_msg = f"A pretrained model of type `{model_to_load.__class__.__name__}` " for key in state_dict...
467 + # add a batch dimension if there isn't already one 468 + if mask.ndim == 2: 469 + mask = mask.unsqueeze(0) 470 + # add a heads dimension if there isn't already one 471 + if mask.ndim == 3: 472 + mask = mask.unsqueeze(1) 473 + 474 + out = sageattn...
set(dimension); 120 + } 121 + 122 + @Override 123 + public void afterPropertiesSet() throws Exception { 124 + this.cache = StringUtils.hasText(this.resourceCacheDirectory) 125 + ? new ResourceCacheService(this.resourceCacheDirectory) : new ResourceCacheService(); 126 + this....
Tensors and Dynamic neural networks in Python with strong GPU acceleration - Add view_as_real, view_as_complex for complex tensors (#39099) · pytorch/pytorch@8ec2ae9
#@tab mxnet, pytorch def corr2d_multi_in(X, K): # First, iterate through the 0th dimension (channel dimension) of `X` and # `K`. Then, add them together return sum(d2l.corr2d(x, k) for x, k in zip(X, K)) ``` ```{.python .input} #@tab tensorflow from d2l import ten...
2069 - help='When batch communication is needed across multiple buckets, ' 2070 - 'this environment variable guides the size of communication unit size.') 2068 + group.add_argument('--suggested-communication-unit-size', type=int, default=None, 2069 + help='Specifies the number of elemen...
pytorch#85691) Browse files ### this effectively means that we only allow reshaping/viewing of nt with ONE ragged dimension Behavior before this PR: 1. `-1` allowed for implicit batch dimension 2. multiple `-1`s allowed for pre-existing dimensions 3. for new dimensions, `-1` is not ...
dimension equal to the length of timesteps. :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims. """ res = torch.from_numpy(arr).to(device=timesteps.device)[timesteps].float() while len(res.shape) < len(broadcast_shape): res = res[..., None] return...