Nightly (#646)
* Update llama.py * offload * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * continued pretraining trainer * Update trainer.py * Update trainer.py * Update trainer.py * Update trainer.py * is_bfloat16_supported * Update __init__.py * Update README.md * Update llama.py * is_bfloat16_supported * Update __init__.py * Mistral v3 * Phi 3 medium * Update chat_templates.py * Update chat_templates.py * Phi-3 * Update save.py * Update README.md Mistral v3 to Mistral v0.3 * Untrained tokens * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update llama.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update save.py * Update save.py * Update save.py * checkpoint * Update _utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update llama.py * accelerate * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update tokenizer_utils.py * train_dataloader * Update llama.py * Update llama.py * Update llama.py * use_fast_convert * Update save.py * Update save.py * Update save.py * Update save.py * remove_special_tokens * Ollama * Update chat_templates.py * Update chat_templates.py * Update chat_templates.py * Update llama.py * Update chat_templates.py * Support bfloat16 GGUF * Update save.py * Update llama.py * fast_forward_inference * Update mapper.py * Update loader.py * Update llama.py * Update tokenizer_utils.py * info * edits * Create chat template * Fix tokenizer * Update tokenizer_utils.py * fix case where gguf saving fails due to first_conversion dtype (#630) * Support revision parameter in FastLanguageModel.from_pretrained (#629) * support `revision` parameter * match unsloth formatting of named parameters * clears any selected_adapters before calling internal_model.save_pretrained (#609) * Update __init__.py (#602) Check for incompatible modules before importing unsloth * Fixed unsloth/tokenizer_utils.py for chat training (#604) * Add GGML saving option to Unsloth for easier Ollama model creation and testing. (#345) * Add save to llama.cpp GGML to save.py. * Fix conversion command and path of convert to GGML function. * Add autosaving lora to the GGML function * Create lora save function for conversion to GGML * Test fix #2 for saving lora * Test fix #3 to save the lora adapters to convert to GGML * Remove unwated tokenizer saving for conversion to ggml and added a few print statements. * Needed tokenizer for saving, added it back, also made it more unslothy style by having positional arguments, and added a few messages. * Positional arguments didn't work out, so reverted to older version of the code, and added a few comments. * Test fix 1 for arch * Test fix 2 new Mistral error. * Test fix 3 * Revert to old version for testing. * Upload issue test fix 1 * Fix 2 uploading ggml * Positional ags added. * Temporray remove positional args * Fix upload again!!! * Add print statements and fix link * Make the calling name better * Create local saving for GGML * Add choosing directory to save local GGML. * Fix lil variable error in the save_to_custom_dir func * docs: Add LoraConfig parameters documentation (#619) * llama.cpp failing (#371) llama.cpp is failing to generate quantize versions for the trained models. Error: ```bash You might have to compile llama.cpp yourself, then run this again. You do not need to close this Python program. Run the following commands in a new terminal: You must run this in the same folder as you're saving your model. git clone https://github.com/ggerganov/llama.cpp cd llama.cpp && make clean && LLAMA_CUDA=1 make all -j Once that's done, redo the quantization. ``` But when i do clone this with recursive it works. Co-authored-by: Daniel Han <danielhanchen@gmail.com> * fix libcuda_dirs import for triton 3.0 (#227) * fix libcuda_dirs import for triton 3.0 * Update __init__.py * Update __init__.py --------- Co-authored-by: Daniel Han <danielhanchen@gmail.com> * Update save.py * Update __init__.py * Update fast_lora.py * Update save.py * Update save.py * Update save.py * Update loader.py * Update save.py * Update save.py * quantize now llama-quantize * Update chat_templates.py * Update loader.py * Update mapper.py * Update __init__.py * embedding size * Update qwen2.py * docs * Update README.md * Update qwen2.py * README: Fix minor typo. (#559) * README: Fix minor typo. One-character typo fix while reading. * Update README.md --------- Co-authored-by: Daniel Han <danielhanchen@gmail.com> * Update mistral.py * Update qwen2.py * Update qwen2.py * Update qwen2.py * Update llama.py * Update llama.py * Update llama.py * Update README.md * FastMistralModel * Update mistral.py * Update mistral.py * Update mistral.py * Update mistral.py * Update mistral.py * Auto check rope scaling * Update llama.py * Update llama.py * Update llama.py * GPU support * Typo --------- Co-authored-by: Michael Han <107991372+shimmyshimmer@users.noreply.github.com> Co-authored-by: Eliot Hall <60240707+chrehall68@users.noreply.github.com> Co-authored-by: Rickard Edén <rickardeden@gmail.com> Co-authored-by: XiaoYang <xyangk@gmail.com> Co-authored-by: Oseltamivir <58582368+Oseltamivir@users.noreply.github.com> Co-authored-by: mahiatlinux <110882203+mahiatlinux@users.noreply.github.com> Co-authored-by: Sébastien De Greef <sebdg@binarycompany.com> Co-authored-by: Alberto Ferrer <albertof@barrahome.org> Co-authored-by: Thomas Viehmann <tv.github-private@beamnet.de> Co-authored-by: Walter Korman <lemurware@gmail.com>
This commit is contained in:
Daniel Han
GitHub
parent
96e2fa423e
commit
b1cc523bda
4 changed files with 33 additions and 16 deletions
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@ -372,6 +372,10 @@ def prepare_n_gradient_checkpoints(
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pass
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# Unsloth only works on NVIDIA GPUs for now
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device_ids = os.environ.get("CUDA_VISIBLE_DEVICES", "0") + ","
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device = f"cuda:{device_ids[:device_ids.find(',')]}"
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class Unsloth_Offloaded_Gradient_Checkpointer(torch.autograd.Function):
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"""
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Saves VRAM by smartly offloading to RAM.
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@ -393,7 +397,7 @@ class Unsloth_Offloaded_Gradient_Checkpointer(torch.autograd.Function):
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@torch.cuda.amp.custom_bwd
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def backward(ctx, dY):
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(hidden_states,) = ctx.saved_tensors
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hidden_states = hidden_states.to("cuda", non_blocking = True).detach()
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hidden_states = hidden_states.to(device, non_blocking = True).detach()
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hidden_states.requires_grad = True
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with torch.enable_grad():
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(output,) = ctx.forward_function(hidden_states, *ctx.args)
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@ -457,7 +461,6 @@ transformers.utils.quantization_config.BitsAndBytesConfig.__init__ = _BitsAndByt
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# Offloading to disk for modules (lm_head, embed_tokens)
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import os
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import pickle
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def offload_to_disk(W, model, name, temporary_location : str = "_unsloth_temporary_saved_buffers"):
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@ -38,6 +38,11 @@ except:
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GemmaFlashAttention2 = GemmaAttention
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pass
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# Unsloth currently only works on one GPU
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import os
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device_ids = os.environ.get("CUDA_VISIBLE_DEVICES", "0") + ","
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device = f"cuda:{device_ids[:device_ids.find(',')]}"
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# Please obtain a commercial license
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torch_nn_functional_gelu = torch.nn.functional.gelu
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def fast_geglu_inference(self, X):
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@ -45,7 +50,7 @@ def fast_geglu_inference(self, X):
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# up = self.up_proj(X)
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bsz, _, hd = X.shape
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# mlp_size = self.config.intermediate_size
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# temp = torch.empty((2, bsz, 1, mlp_size), dtype = X.dtype, device = "cuda")
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# temp = torch.empty((2, bsz, 1, mlp_size), dtype = X.dtype, device = device)
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gate = fast_linear_forward(self.gate_proj, X)#, out = temp[0])
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up = fast_linear_forward(self. up_proj, X)#, out = temp[1])
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@ -72,7 +77,7 @@ def GemmaDecoderLayer_fast_forward(
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*args, **kwargs,
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):
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if use_cache and hasattr(self, "_flag_for_generation"): #past_key_value is not None:
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out_weight = torch.empty(self.input_layernorm.weight.shape, dtype = torch.float32, device = "cuda")
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out_weight = torch.empty(self.input_layernorm.weight.shape, dtype = torch.float32, device = device)
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# Self Attention
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residual = hidden_states
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@ -134,7 +139,7 @@ def GemmaModel_fast_forward_inference(
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position_ids,
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attention_mask = None,
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):
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out_weight = torch.empty_like(self.model.layers[0].input_layernorm.weight, dtype = torch.float32, device = "cuda")
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out_weight = torch.empty_like(self.model.layers[0].input_layernorm.weight, dtype = torch.float32, device = device)
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input_ids = input_ids[:,:self.max_seq_length]
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hidden_states = self.model.embed_tokens(input_ids)
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hidden_states = hidden_states.to(self.config.torch_dtype)
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@ -74,6 +74,9 @@ def original_apply_o(self, X):
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return O
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pass
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import os # Unsloth only works on NVIDIA GPUs for now
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device_ids = os.environ.get("CUDA_VISIBLE_DEVICES", "0") + ","
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device = f"cuda:{device_ids[:device_ids.find(',')]}"
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from math import sqrt as math_sqrt
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KV_CACHE_INCREMENT = 256 # KV Cache update size
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@ -132,15 +135,15 @@ def LlamaAttention_fast_forward_inference(
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# Prefill phase
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# if not hasattr(self, "paged_attention"):
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if do_prefill:
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self.paged_attention = torch.empty((KV_CACHE_INCREMENT+seq_len+1, 2, bsz, n_kv_heads, head_dim), dtype = dtype, device = "cuda")
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self.paged_attention = torch.empty((KV_CACHE_INCREMENT+seq_len+1, 2, bsz, n_kv_heads, head_dim), dtype = dtype, device = device)
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self.paged_attention_K = self.paged_attention[:,0]
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self.paged_attention_V = self.paged_attention[:,1]
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self.paged_attention_K[:seq_len] = K1.permute(2, 0, 1, 3)
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self.paged_attention_V[:seq_len] = V1.permute(2, 0, 1, 3)
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self.temp_QA = torch.empty((2, bsz, 1, attention_size), dtype = dtype, device = "cuda")
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self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = "cuda")
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self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = "cuda")
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self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT+seq_len), dtype = dtype, device = "cuda")
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self.temp_QA = torch.empty((2, bsz, 1, attention_size), dtype = dtype, device = device)
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self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = device)
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self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = device)
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self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT+seq_len), dtype = dtype, device = device)
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self.scalar = 1.0 / math_sqrt(self.head_dim)
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self.half_head_dim = head_dim // 2
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elif kv_seq_len >= self.paged_attention.shape[0]:
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@ -170,7 +173,7 @@ def LlamaAttention_fast_forward_inference(
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Qn *= cos
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Qn.addcmul_(RH_Q, sin)
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RH_K = RH_Q[:,:n_kv_heads,:,:] # torch.empty((n_kv_heads, 1, head_dim), dtype = dtype, device = "cuda")
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RH_K = RH_Q[:,:n_kv_heads,:,:] # torch.empty((n_kv_heads, 1, head_dim), dtype = dtype, device = device)
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RH_K[:,:,:,:h] = Kn[:,:,:,h:]
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RH_K[:,:,:,h:] = Kn[:,:,:,:h]
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torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h])
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@ -232,7 +235,7 @@ def fast_swiglu_inference(self, X):
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# up = self.up_proj(X)
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bsz, _, hd = X.shape
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# mlp_size = self.config.intermediate_size
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# temp = torch.empty((2, bsz, 1, mlp_size), dtype = X.dtype, device = "cuda")
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# temp = torch.empty((2, bsz, 1, mlp_size), dtype = X.dtype, device = device)
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gate = fast_linear_forward(self.gate_proj, X)#, out = temp[0])
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up = fast_linear_forward(self. up_proj, X)#, out = temp[1])
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@ -522,7 +525,7 @@ def LlamaModel_fast_forward(
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length,
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dtype = torch.int32,
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device = "cuda",
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device = device,
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)
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position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
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elif position_ids is not None:
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@ -842,8 +845,10 @@ def CausalLM_fast_forward(fast_forward_inference):
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if labels is not None:
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shift_logits = logits
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if not hasattr(self, "extra_ignored_labels"):
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device_ids = os.environ.get("CUDA_VISIBLE_DEVICES", "0") + ","
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device = f"cuda:{device_ids[:device_ids.find(',')]}" # Unsloth only works on NVIDIA GPUs for now
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# Fixes https://github.com/unslothai/unsloth/issues/10
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self.extra_ignored_labels = torch.full((self.max_seq_length, 1), -100, device = "cuda")
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self.extra_ignored_labels = torch.full((self.max_seq_length, 1), -100, device = device)
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pass
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shift_labels = torch.hstack((labels[..., 1:], self.extra_ignored_labels[:labels.shape[0]]))
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@ -1822,7 +1827,9 @@ class FastLlamaModel:
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# Patch cross entropy loss labels
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# Fixes https://github.com/unslothai/unsloth/issues/10
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max_seq_length = model.max_seq_length
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extra_ignored_labels = torch.full((max_seq_length, 1), -100, device = "cuda")
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device_ids = os.environ.get("CUDA_VISIBLE_DEVICES", "0") + ","
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device = f"cuda:{device_ids[:device_ids.find(',')]}" # Unsloth only works on NVIDIA GPUs for now
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extra_ignored_labels = torch.full((max_seq_length, 1), -100, device = device)
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model.model.extra_ignored_labels = extra_ignored_labels
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internal_model = model
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while hasattr(internal_model, "model"):
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@ -239,8 +239,10 @@ def MistralForCausalLM_fast_forward(
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if labels is not None:
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shift_logits = logits
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if not hasattr(self, "extra_ignored_labels"):
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device_ids = os.environ.get("CUDA_VISIBLE_DEVICES", "0") + ","
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device = f"cuda:{device_ids[:device_ids.find(',')]}" # Unsloth only works on NVIDIA GPUs for now
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# Fixes https://github.com/unslothai/unsloth/issues/10
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self.extra_ignored_labels = torch.full((self.max_seq_length, 1), -100, device = "cuda")
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self.extra_ignored_labels = torch.full((self.max_seq_length, 1), -100, device = device)
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pass
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shift_labels = torch.hstack((labels[..., 1:], self.extra_ignored_labels[:labels.shape[0]]))
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