diff --git a/litgpt/config.py b/litgpt/config.py index acbea699b3..e2c1152063 100644 --- a/litgpt/config.py +++ b/litgpt/config.py @@ -2449,6 +2449,145 @@ def norm_class(self) -> Type: configs.extend(qwq) +############# +# Qwen3 +############# +qwen3 = [ + dict( + name="Qwen3-0.6B", + hf_config=dict(org="Qwen", name="Qwen3-0.6B"), + block_size=40960, + vocab_size=151643, + padded_vocab_size=151936, + n_layer=28, + n_head=16, + n_embd=1024, + n_query_groups=8, + head_size=128, + parallel_residual=False, + rotary_percentage=1.0, + bias=False, + attn_bias=False, + norm_class_name="RMSNorm", + mlp_class_name="LLaMAMLP", + intermediate_size=3072, + norm_eps=1e-6, + rope_base=1000000, + norm_qk=True, + ), + dict( + name="Qwen3-1.7B", + hf_config=dict(org="Qwen", name="Qwen3-1.7B"), + block_size=40960, + vocab_size=151643, + padded_vocab_size=151936, + n_layer=28, + n_head=16, + n_embd=2048, + n_query_groups=8, + head_size=128, + parallel_residual=False, + rotary_percentage=1.0, + bias=False, + attn_bias=False, + norm_class_name="RMSNorm", + mlp_class_name="LLaMAMLP", + intermediate_size=6144, + norm_eps=1e-6, + rope_base=1000000, + norm_qk=True, + ), + dict( + name="Qwen3-4B", + hf_config=dict(org="Qwen", name="Qwen3-4B"), + block_size=40960, + vocab_size=151643, + padded_vocab_size=151936, + n_layer=36, + n_head=32, + n_embd=2560, + n_query_groups=8, + head_size=128, + parallel_residual=False, + rotary_percentage=1.0, + bias=False, + attn_bias=False, + norm_class_name="RMSNorm", + mlp_class_name="LLaMAMLP", + intermediate_size=9728, + norm_eps=1e-6, + rope_base=1000000, + norm_qk=True, + ), + dict( + name="Qwen3-8B", + hf_config=dict(org="Qwen", name="Qwen3-8B"), + block_size=40960, + vocab_size=151643, + padded_vocab_size=151936, + n_layer=36, + n_head=32, + n_embd=4096, + n_query_groups=8, + head_size=128, + parallel_residual=False, + rotary_percentage=1.0, + bias=False, + attn_bias=False, + norm_class_name="RMSNorm", + mlp_class_name="LLaMAMLP", + intermediate_size=12288, + norm_eps=1e-6, + rope_base=1000000, + norm_qk=True, + ), + dict( + name="Qwen3-14B", + hf_config=dict(org="Qwen", name="Qwen3-14B"), + block_size=40960, + vocab_size=151643, + padded_vocab_size=151936, + n_layer=40, + n_head=40, + n_embd=5120, + n_query_groups=8, + head_size=128, + parallel_residual=False, + rotary_percentage=1.0, + bias=False, + attn_bias=False, + norm_class_name="RMSNorm", + mlp_class_name="LLaMAMLP", + intermediate_size=17408, + norm_eps=1e-6, + rope_base=1000000, + norm_qk=True, + ), + dict( + name="Qwen3-32B", + hf_config=dict(org="Qwen", name="Qwen3-8B"), + block_size=40960, + vocab_size=151643, + padded_vocab_size=151936, + n_layer=64, + n_head=64, + n_embd=5120, + n_query_groups=8, + head_size=128, + parallel_residual=False, + rotary_percentage=1.0, + bias=False, + attn_bias=False, + norm_class_name="RMSNorm", + mlp_class_name="LLaMAMLP", + intermediate_size=25600, + norm_eps=1e-6, + rope_base=1000000, + norm_qk=True, + ), +] + +configs.extend(qwen3) ############# # Salamandra diff --git a/litgpt/scripts/convert_hf_checkpoint.py b/litgpt/scripts/convert_hf_checkpoint.py index 341e1a757d..754b6bf704 100644 --- a/litgpt/scripts/convert_hf_checkpoint.py +++ b/litgpt/scripts/convert_hf_checkpoint.py @@ -533,6 +533,75 @@ def copy_weights_qwen_2_5( pbar.update(progress_per_file) +def copy_weights_qwen_3( + config: Config, + qkv_weights: Dict[int, List[Optional[NotYetLoadedTensor]]], + state_dict: Dict[str, torch.Tensor], + hf_weights: Dict[str, Union[torch.Tensor, NotYetLoadedTensor]], + saver: Optional[incremental_save] = None, + dtype: Optional[torch.dtype] = None, + pbar: Optional[tqdm] = None, + progress_per_file: Optional[float] = None, + debug_mode: Optional[bool] = False, +) -> None: + weight_map = { + "model.embed_tokens.weight": "transformer.wte.weight", + "model.layers.{}.input_layernorm.weight": "transformer.h.{}.norm_1.weight", + "model.layers.{}.self_attn.q_proj.weight": None, + "model.layers.{}.self_attn.k_proj.weight": None, + "model.layers.{}.self_attn.v_proj.weight": None, + "model.layers.{}.self_attn.q_norm.weight": "transformer.h.{}.attn.norm_q.weight", + "model.layers.{}.self_attn.k_norm.weight": "transformer.h.{}.attn.norm_k.weight", + "model.layers.{}.self_attn.o_proj.weight": "transformer.h.{}.attn.proj.weight", + "model.layers.{}.post_attention_layernorm.weight": "transformer.h.{}.norm_2.weight", + "model.layers.{}.mlp.gate_proj.weight": "transformer.h.{}.mlp.fc_1.weight", + "model.layers.{}.mlp.up_proj.weight": "transformer.h.{}.mlp.fc_2.weight", + "model.layers.{}.mlp.down_proj.weight": "transformer.h.{}.mlp.proj.weight", + "model.norm.weight": "transformer.ln_f.weight", + "lm_head.weight": "lm_head.weight", + } + + if progress_per_file is not None: + progress_per_file = progress_per_file / max(1, len(hf_weights) + len(qkv_weights)) + + for from_name, param in hf_weights.items(): + name_template, *ids = layer_template(from_name, num_matches=2) + to_name = weight_map[name_template] + param = load_param(param, from_name, dtype, verbose=debug_mode) + if any(w in from_name for w in ("q_proj", "k_proj", "v_proj")): + qkv = qkv_weights.setdefault(ids[0], defaultdict(dict)) + weight_name, weight_type = from_name.split(".")[-2:] + qkv[weight_type][weight_name] = param + if to_name is None: + continue + to_name = to_name.format(*ids) + if saver is not None: + param = saver.store_early(param) + state_dict[to_name] = param + + if progress_per_file is not None: + pbar.update(progress_per_file) + + if "lm_head.weight" not in state_dict: + state_dict["lm_head.weight"] = state_dict["transformer.wte.weight"] + + for i in list(qkv_weights): + for weight_type in list(qkv_weights[i]): + qkv = qkv_weights[i][weight_type] + if len(qkv) != 3: + # qkv is split across different .bin files + continue + q = load_param(qkv["q_proj"], f"layer {i} q {weight_type}", dtype, verbose=debug_mode) + k = load_param(qkv["k_proj"], f"layer {i} k {weight_type}", dtype, verbose=debug_mode) + v = load_param(qkv["v_proj"], f"layer {i} v {weight_type}", dtype, verbose=debug_mode) + qkv = torch.cat((q, k, v)) + state_dict[f"transformer.h.{i}.attn.qkv.{weight_type}"] = qkv + del qkv_weights[i][weight_type] + + if progress_per_file is not None: + pbar.update(progress_per_file) + + def qkv_reassemble( param: Union[torch.Tensor, NotYetLoadedTensor], config: Config ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: diff --git a/tests/test_model.py b/tests/test_model.py index 39d946fb2d..e470df5bcf 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -30,6 +30,7 @@ from transformers.models.mixtral import MixtralConfig, MixtralForCausalLM from transformers.models.olmo import OlmoConfig, OlmoForCausalLM from transformers.models.qwen2 import Qwen2Config, Qwen2ForCausalLM +from transformers.models.qwen3 import Qwen3Config, Qwen3ForCausalLM import litgpt.config as config_module from litgpt import GPT, Config @@ -42,6 +43,7 @@ copy_weights_hf_llama, copy_weights_phi, copy_weights_qwen_2_5, + copy_weights_qwen_3, ) from litgpt.scripts.convert_lit_checkpoint import qkv_reassemble as make_qkv_interleaved from litgpt.utils import _RunIf @@ -1008,6 +1010,66 @@ def test_against_original_qwen_2_5(model_name, device, dtype): torch.testing.assert_close(ours_y, theirs_y) +@torch.inference_mode() +@pytest.mark.parametrize("model_name", ["Qwen3-0.6B", "Qwen3-1.7B", "Qwen3-4B", "Qwen3-8B", "Qwen3-14B", "Qwen3-32B"]) +@pytest.mark.parametrize( + ("device", "dtype"), + [ + (torch.device("cpu"), torch.float32), + pytest.param( + torch.device("cuda"), + torch.float16, + marks=[ + # the reference does softmax upscaled to fp32 during attention. additionally, the final layernorm input + # is slightly different + pytest.mark.xfail(raises=AssertionError, strict=False), + _RunIf(min_cuda_gpus=1), + ], + ), + ], +) +def test_against_original_qwen_3(model_name, device, dtype): + torch.set_default_dtype(dtype) + + T = 20 + ours_config = Config.from_name( + model_name, + block_size=T, + n_layer=2, + n_head=16, + n_embd=32, + intermediate_size=86, + ) + theirs_config = Qwen3Config( + vocab_size=ours_config.padded_vocab_size, + hidden_size=ours_config.n_embd, + head_dim=ours_config.head_size, + num_attention_heads=ours_config.n_head, + num_hidden_layers=ours_config.n_layer, + intermediate_size=ours_config.intermediate_size, + max_position_embeddings=ours_config.block_size, + rms_norm_eps=ours_config.norm_eps, + num_key_value_heads=ours_config.n_query_groups, + rope_theta=ours_config.rope_base, + attention_bias=ours_config.attn_bias, + ) + + theirs_model = Qwen3ForCausalLM(theirs_config).to(device) + theirs_state_dict = theirs_model.state_dict() + + state_dict = {} + copy_weights_qwen_3(ours_config, {}, state_dict, theirs_state_dict) + ours_model = GPT(ours_config).to(device) + ours_model.load_state_dict(state_dict) + + # test end to end + x = torch.randint(low=0, high=ours_config.padded_vocab_size, size=(T,), device=device).unsqueeze(0) + assert x.size(1) == T + ours_y = ours_model(x) + theirs_y = theirs_model(x)["logits"].to(dtype) # HF converts logits to float + torch.testing.assert_close(ours_y, theirs_y) + + @torch.inference_mode() @pytest.mark.parametrize("model_name", ("salamandra-2b", "salamandra-7b")) @pytest.mark.parametrize(