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["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +cover = ["pytest-cov"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.8,<4.0" -content-hash = "bf948abb46e5282633d5e369c78b5fb21e1a75e2221bcb1088630360893efca3" +content-hash = "04a5e214e8bd98887fcb615f6b5447fd94a20589041b85287c565041a059d533" diff --git a/pyproject.toml b/pyproject.toml index 5abcfc481..76c56f730 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ {platform="linux", version=">=1.10"}, # We can use any torch version on Linux (e.g colab) ] tqdm=">=4.64.1" - transformers=">=4.37.2" + transformers={ git = "https://github.com/huggingface/transformers.git" } typing-extensions="*" wandb=">=0.13.5" diff --git a/transformer_lens/HookedTransformer.py b/transformer_lens/HookedTransformer.py index 8e32a9cd6..65945e133 100644 --- a/transformer_lens/HookedTransformer.py +++ b/transformer_lens/HookedTransformer.py @@ -145,7 +145,7 @@ def __init__( self.set_tokenizer( AutoTokenizer.from_pretrained( self.cfg.tokenizer_name, - add_bos_token=True, + # add_bos_token=True, trust_remote_code=self.cfg.trust_remote_code, use_fast=use_fast, token=huggingface_token, diff --git a/transformer_lens/components/mlps/moe.py b/transformer_lens/components/mlps/moe.py index e01f25ee9..988b9dd5d 100644 --- a/transformer_lens/components/mlps/moe.py +++ b/transformer_lens/components/mlps/moe.py @@ -63,6 +63,7 @@ def __init__(self, cfg: Union[Dict, HookedTransformerConfig]): self.num_experts: int = self.cfg.num_experts self.experts_per_token: int = self.cfg.experts_per_token + # self.norm_topk_prob: bool = self.cfg.norm_topk_prob assert ( self.cfg.experts_per_token <= self.cfg.num_experts @@ -88,6 +89,8 @@ def forward( # both are [batch, pos, experts_per_token] weights = self.hook_expert_weights(F.softmax(gate_logits, dim=1, dtype=torch.float)) weights, expert_indices = torch.topk(weights, self.experts_per_token, dim=-1) + # if self.norm_topk_prob: + # weights /= weights.sum(dim=-1, keepdim=True) weights /= weights.sum(dim=-1, keepdim=True) expert_indices = self.hook_expert_indices(expert_indices) weights = weights.to(x.dtype) diff --git a/transformer_lens/loading_from_pretrained.py b/transformer_lens/loading_from_pretrained.py index 7c36efdd7..1090e65cc 100644 --- a/transformer_lens/loading_from_pretrained.py +++ b/transformer_lens/loading_from_pretrained.py @@ -35,6 +35,7 @@ convert_neel_solu_old_weights, convert_neo_weights, convert_neox_weights, + convert_olmoe_weights, convert_opt_weights, convert_phi3_weights, convert_phi_weights, @@ -225,6 +226,9 @@ "google-t5/t5-base", "google-t5/t5-large", "ai-forever/mGPT", + "allenai/OLMoE-1B-7B-0924", + "allenai/OLMoE-1B-7B-0924-SFT", + "allenai/OLMoE-1B-7B-0924-Instruct", ] """Official model names for models on HuggingFace.""" @@ -1329,6 +1333,34 @@ def convert_hf_model_config(model_name: str, **kwargs): "use_attn_scale": False, "tie_word_embeddings": hf_config.tie_word_embeddings, } + elif architecture == "OlmoeForCausalLM": + cfg_dict = { + "d_model": hf_config.hidden_size, + "d_head": hf_config.hidden_size // hf_config.num_attention_heads, + "n_heads": hf_config.num_attention_heads, + "d_mlp": hf_config.intermediate_size, + "n_layers": hf_config.num_hidden_layers, + "n_ctx": hf_config.max_position_embeddings, + "eps": hf_config.rms_norm_eps, + "d_vocab": hf_config.vocab_size, + "act_fn": hf_config.hidden_act, + "num_experts": hf_config.num_experts, + "experts_per_token": hf_config.num_experts_per_tok, + # TODO: implement! + # "router_aux_loss_coef": hf_config.router_aux_loss_coef, + # "router_z_loss_coef": hf_config.router_z_loss_coef, + # "norm_topk_prob": hf_config.norm_topk_prob, + # end + "n_key_value_heads": hf_config.num_key_value_heads, + "rotary_base": hf_config.rope_theta, + "tie_word_embeddings": hf_config.tie_word_embeddings, + "initializer_range": hf_config.initializer_range, + "positional_embedding_type": "rotary", + "rotary_dim": hf_config.hidden_size // hf_config.num_attention_heads, + "final_rms": True, + "gated_mlp": True, + "normalization_type": "RMS", + } else: raise NotImplementedError(f"{architecture} is not currently supported.") # All of these models use LayerNorm @@ -1714,6 +1746,8 @@ def get_pretrained_state_dict( state_dict = convert_gemma_weights(hf_model, cfg) elif cfg.original_architecture == "Gemma2ForCausalLM": state_dict = convert_gemma_weights(hf_model, cfg) + elif cfg.original_architecture == "OlmoeForCausalLM": + state_dict = convert_olmoe_weights(hf_model, cfg) else: raise ValueError( f"Loading weights from the architecture is not currently supported: {cfg.original_architecture}, generated from model name {cfg.model_name}. Feel free to open an issue on GitHub to request this feature." diff --git a/transformer_lens/pretrained/weight_conversions/__init__.py b/transformer_lens/pretrained/weight_conversions/__init__.py index b13850ee0..c5806029d 100644 --- a/transformer_lens/pretrained/weight_conversions/__init__.py +++ b/transformer_lens/pretrained/weight_conversions/__init__.py @@ -18,3 +18,4 @@ from .nanogpt import convert_nanogpt_weights from .t5 import convert_t5_weights from .neel_solu_old import convert_neel_solu_old_weights +from .olmoe import convert_olmoe_weights diff --git a/transformer_lens/pretrained/weight_conversions/olmoe.py b/transformer_lens/pretrained/weight_conversions/olmoe.py new file mode 100644 index 000000000..02dc1972e --- /dev/null +++ b/transformer_lens/pretrained/weight_conversions/olmoe.py @@ -0,0 +1,64 @@ +import einops +import torch + +from transformer_lens.HookedTransformerConfig import HookedTransformerConfig + + +def convert_olmoe_weights(olmoe, cfg: HookedTransformerConfig): + state_dict = {} + + assert cfg.n_key_value_heads is not None + assert cfg.d_mlp is not None + assert cfg.num_experts is not None + + state_dict["embed.W_E"] = olmoe.model.embed_tokens.weight + + for l in range(cfg.n_layers): + olmoe_layer = olmoe.model.layers[l] + state_dict[f"blocks.{l}.ln1.w"] = olmoe_layer.input_layernorm.weight + + W_Q = olmoe.model.layers[l].self_attn.q_proj.weight + W_K = olmoe.model.layers[l].self_attn.k_proj.weight + W_V = olmoe.model.layers[l].self_attn.v_proj.weight + W_Q = einops.rearrange(W_Q, "(n h) m->n m h", n=cfg.n_heads) + W_K = einops.rearrange(W_K, "(n h) m->n m h", n=cfg.n_key_value_heads) + W_V = einops.rearrange(W_V, "(n h) m->n m h", n=cfg.n_key_value_heads) + state_dict[f"blocks.{l}.attn.W_Q"] = W_Q + state_dict[f"blocks.{l}.attn._W_K"] = W_K + state_dict[f"blocks.{l}.attn._W_V"] = W_V + + state_dict[f"blocks.{l}.attn.b_Q"] = torch.zeros(cfg.n_heads, cfg.d_head, dtype=cfg.dtype) + state_dict[f"blocks.{l}.attn._b_K"] = torch.zeros( + cfg.n_key_value_heads, cfg.d_head, dtype=cfg.dtype + ) + state_dict[f"blocks.{l}.attn._b_V"] = torch.zeros( + cfg.n_key_value_heads, cfg.d_head, dtype=cfg.dtype + ) + + W_O = olmoe_layer.self_attn.o_proj.weight + W_O = einops.rearrange(W_O, "m (n h)->n h m", n=cfg.n_heads) + state_dict[f"blocks.{l}.attn.W_O"] = W_O + + state_dict[f"blocks.{l}.attn.b_O"] = torch.zeros(cfg.d_model, dtype=cfg.dtype) + + state_dict[f"blocks.{l}.ln2.w"] = olmoe_layer.post_attention_layernorm.weight + + state_dict[f"blocks.{l}.mlp.W_gate.weight"] = olmoe_layer.mlp.gate.weight + + for e in range(cfg.num_experts): + state_dict[f"blocks.{l}.mlp.experts.{e}.W_in.weight"] = olmoe_layer.mlp.experts[ + e + ].up_proj.weight + state_dict[f"blocks.{l}.mlp.experts.{e}.W_gate.weight"] = olmoe_layer.mlp.experts[ + e + ].gate_proj.weight + state_dict[f"blocks.{l}.mlp.experts.{e}.W_out.weight"] = olmoe_layer.mlp.experts[ + e + ].down_proj.weight + + state_dict["ln_final.w"] = olmoe.model.norm.weight + + state_dict["unembed.W_U"] = olmoe.lm_head.weight.T + state_dict["unembed.b_U"] = torch.zeros(cfg.d_vocab, dtype=cfg.dtype) + + return state_dict diff --git a/transformer_lens/utils.py b/transformer_lens/utils.py index 7e5828e14..430d8dc43 100644 --- a/transformer_lens/utils.py +++ b/transformer_lens/utils.py @@ -1171,7 +1171,7 @@ def get_tokenizer_with_bos(tokenizer): huggingface_token = os.environ.get("HF_TOKEN", None) tokenizer_with_bos = AutoTokenizer.from_pretrained( pretrained_model_name_or_path, - add_bos_token=True, + # add_bos_token=True, token=huggingface_token, **init_kwargs, )