Add GLM-4 and Later GLM Model (Draft)#31977
Add GLM-4 and Later GLM Model (Draft)#31977zRzRzRzRzRzRzR wants to merge 86 commits intohuggingface:mainfrom zRzRzRzRzRzRzR:glm-4
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Support Cache class
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Hi @zRzRzRzRzRzRzR ! Thanks for drafting the PR. The workflow has been failing due to the usage of TikToken. Once the converter scripts converts tiktoken configuration to HF tokenizer configuration, you won't need to import tiktoken during inference at tokenization_glm.py |
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YEP! Will review today 🤗 |
Fix this issue now~ Tks |
Fix attention mask for right padding
ArthurZucker
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ArthurZucker
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I am stopping the review as a LOT of the comments are still not adressed
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this file should be removed as we can map the GPT2Tokenizer direct and use it
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same comment here, we can use GPT2TokenizerFast!
| logger = logging.get_logger(__name__) | ||
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| class GLMConfig(PretrainedConfig): |
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There is still the issue with the camel casing!
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GLM is the name of our model, not Glm. Do we need to stick to camel case in this context as well?
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Yes, it's the same for LLaMa which we set to Llama!
| This is the configuration class to store the configuration of a [`GLMModel`]. It is used to instantiate a Phi-3 | ||
| model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
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| This is the configuration class to store the configuration of a [`GLMModel`]. It is used to instantiate a Phi-3 | |
| model according to the specified arguments, defining the model architecture. Instantiating a configuration with the | |
| This is the configuration class to store the configuration of a [`GLMModel`]. It is used to instantiate a GLM | |
| model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
| if is_flash_attn_2_available(): | ||
| from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa | ||
| from flash_attn import flash_attn_func, flash_attn_varlen_func | ||
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| _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) |
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again this was refactored
| def _get_unpad_data(attention_mask): | ||
| seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) | ||
| indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() | ||
| max_seqlen_in_batch = seqlens_in_batch.max().item() | ||
| cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) | ||
| return ( | ||
| indices, | ||
| cu_seqlens, | ||
| max_seqlen_in_batch, | ||
| ) |
| class GLMRotaryEmbedding(nn.Module): | ||
| def __init__(self, dim, rope_theta=1, original_impl=False, device=None, dtype=None): | ||
| super().__init__() | ||
| inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) | ||
| self.register_buffer("inv_freq", inv_freq) | ||
| self.dim = dim | ||
| self.original_impl = original_impl | ||
| self.rope_theta = rope_theta | ||
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| def forward_impl( | ||
| self, | ||
| seq_len: int, | ||
| n_elem: int, | ||
| dtype: torch.dtype, | ||
| device: torch.device, | ||
| base: int = 10000, | ||
| ): | ||
| """Enhanced Transformer with Rotary Position Embedding. | ||
| Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ | ||
| transformers/rope/__init__.py. MIT License: | ||
| https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. | ||
| """ | ||
| # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ | ||
| base = base * self.rope_theta | ||
| theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem)) | ||
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| # Create position indexes `[0, 1, ..., seq_len - 1]` | ||
| seq_idx = torch.arange(seq_len, dtype=torch.float, device=device) | ||
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| # Calculate the product of position index and $\theta_i$ | ||
| idx_theta = torch.outer(seq_idx, theta).float() | ||
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| cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1).to(dtype=dtype) | ||
| return cache | ||
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| def forward(self, max_seq_len, offset=0): | ||
| return self.forward_impl( | ||
| max_seq_len, | ||
| self.dim, | ||
| dtype=self.inv_freq.dtype, | ||
| device=self.inv_freq.device, | ||
| ) |
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Again same comment here, this is equivalent to LlamaRotaryEmbedidng
| return tensor_list | ||
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| class SelfAttention(torch.nn.Module): |
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This comment is still waiting!
| if self.multi_query_attention: | ||
| self.num_multi_query_groups_per_partition = self.multi_query_group_num | ||
| self.qkv_hidden_size = ( | ||
| self.projection_size + 2 * self.hidden_size_per_attention_head * self.multi_query_group_num | ||
| ) |
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again same comment about GQA and MQA
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
| logger = logging.get_logger(__name__) | ||
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| class GLMConfig(PretrainedConfig): |
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Yes, it's the same for LLaMa which we set to Llama!
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Feel free to ping me again for a review! |
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BTW @zRzRzRzRzRzRzR, I took over and am currently adding the model. You can find the new PR here #33823, should be ready pretty soon |
6 participants
This is a draft and we will continue work
Pull Request section?
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here are tips on formatting docstrings.
Who can review?
@ArthurZucker