Mistral Nemo 12b (#777)
* Update gemma2.py * Update llama.py * Update llama.py * Update gemma2.py * init * Update gemma2.py * Update gemma2.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update gemma2.py * Update gemma2.py * Update gemma2.py * All RoPE Scaling support * cleanup * Update llama.py * Update llama.py * Update _utils.py * Update _utils.py * exec * exec * Attention_Module * attention_module * imports * exec * Update llama.py * Update llama.py * boolean mask * revert masking * Update llama.py * Update save.py * Update llama.py * Update gemma2.py * Update gemma2.py * Update gemma2.py * Update utils.py * retry * Update gemma2.py * Update gemma2.py * Update gemma2.py * Update _utils.py * Update _utils.py * Update gemma2.py * Update chat_templates.py * Gemma 2 Ollama support * Update llama.py * Update llama.py * error handling * Update _utils.py * Update _utils.py * Stats for debugging * Update _utils.py * Update _utils.py * Debugging * Update tokenizer_utils.py * Update _utils.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update rms_layernorm.py * Update rms_layernorm.py * Update rms_layernorm.py * Update rms_layernorm.py * Update rms_layernorm.py * Update rms_layernorm.py * Update rms_layernorm.py * Check exec, eval * Update _utils.py * Update _utils.py * Images * Bug fixes * Update pyproject.toml * Bug fixes * Update _utils.py * Update _utils.py * Deprecation fix * Update chat_templates.py * Now permitting use of pre-installed llama.cpp (#763) * Now permitting use of pre-installed llama.cpp * Update save.py --------- Co-authored-by: Giuseppe Strafforello <giuseppe.strafforello@titantechnologies.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> * Update save.py * Deprecation & compile * typo * Update chat_templates.py * Update chat_templates.py * train_on_responses_only * Update llama.py * Update llama.py * Update save.py * Update gemma2.py * Flex Attention * typos * Update _utils.py * Update llama.py * Update __init__.py * Update flex_attention.py * Update llama.py * Update llama.py * emulation * Update __init__.py * Update rope_embedding.py * Update flex_attention.py * Update flex_attention.py * Update rope_embedding.py * libdevice * triton_tanh * Update flex_attention.py * Update flex_attention.py * Update flex_attention.py * Update flex_attention.py * Update flex_attention.py * score * Update llama.py * Update flex_attention.py * Update flex_attention.py * Update flex_attention.py * Update flex_attention.py * Update flex_attention.py * Update llama.py * Update flex_attention.py * Update flex_attention.py * Update flex_attention.py * Update flex_attention.py * Flex Attention removal * upload tensorboard training stats to hub if available (#773) * causal_mask * Update llama.py * Update llama.py * Update flex_attention.py * Update _utils.py * Update mapper.py * Update _utils.py --------- Co-authored-by: pepistrafforello <pepi.strafforello@gmail.com> Co-authored-by: Giuseppe Strafforello <giuseppe.strafforello@titantechnologies.com> Co-authored-by: Sébastien De Greef <sebdg@binarycompany.com>
This commit is contained in:
Daniel Han
GitHub
parent
d5d427a7aa
commit
f3cd5e1925
12 changed files with 181 additions and 79 deletions
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@ -60,12 +60,6 @@ except:
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"We have some installation instructions on our Github page.")
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pass
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# Fix up is_bf16_supported https://github.com/unslothai/unsloth/issues/504
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major_version, minor_version = torch.cuda.get_device_capability()
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SUPPORTS_BFLOAT16 = (major_version >= 8)
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def is_bf16_supported(): return SUPPORTS_BFLOAT16
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torch.cuda.is_bf16_supported = is_bf16_supported
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# We support Pytorch 2
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# Fixes https://github.com/unslothai/unsloth/issues/38
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torch_version = torch.__version__.split(".")
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@ -79,6 +73,19 @@ elif (major_torch == 2) and (minor_torch < 2):
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del os.environ["PYTORCH_CUDA_ALLOC_CONF"]
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pass
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# Torch 2.5 has including_emulation
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major_version, minor_version = torch.cuda.get_device_capability()
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SUPPORTS_BFLOAT16 = (major_version >= 8)
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if (major_torch == 2) and (minor_torch >= 5):
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old_is_bf16_supported = torch.cuda.is_bf16_supported
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def is_bf16_supported(including_emulation = False):
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return old_is_bf16_supported(including_emulation)
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torch.cuda.is_bf16_supported = is_bf16_supported
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else:
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def is_bf16_supported(): SUPPORTS_BFLOAT16
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torch.cuda.is_bf16_supported = is_bf16_supported
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pass
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# Try loading bitsandbytes and triton
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import bitsandbytes as bnb
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@ -17,24 +17,32 @@ from .rms_layernorm import fast_rms_layernorm
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from .rope_embedding import fast_rope_embedding, inplace_rope_embedding
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from .swiglu import swiglu_fg_kernel, swiglu_DWf_DW_dfg_kernel
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from .geglu import (
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geglu_exact_forward_kernel,
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geglu_exact_backward_kernel,
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geglu_approx_forward_kernel,
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geglu_approx_backward_kernel,
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geglu_exact_forward_kernel,
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geglu_exact_backward_kernel,
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geglu_approx_forward_kernel,
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geglu_approx_backward_kernel,
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)
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from .fast_lora import (
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get_lora_parameters,
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get_lora_parameters_bias,
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apply_lora_mlp_swiglu,
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apply_lora_mlp_geglu_exact,
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apply_lora_mlp_geglu_approx,
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apply_lora_qkv,
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apply_lora_o,
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get_lora_parameters,
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get_lora_parameters_bias,
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apply_lora_mlp_swiglu,
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apply_lora_mlp_geglu_exact,
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apply_lora_mlp_geglu_approx,
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apply_lora_qkv,
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apply_lora_o,
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)
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from .utils import fast_dequantize, fast_gemv, QUANT_STATE, fast_linear_forward, matmul_lora
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try:
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print("🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.")
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except:
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print("Unsloth: Will patch your computer to enable 2x faster free finetuning.")
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from .flex_attention import HAS_FLEX_ATTENTION, slow_attention_softcapping
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if HAS_FLEX_ATTENTION:
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from .flex_attention import (
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FLEX_ATTENTION_PADDING,
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)
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pass
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try:
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print("🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.")
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except:
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print("Unsloth: Will patch your computer to enable 2x faster free finetuning.")
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pass
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@ -15,7 +15,7 @@
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import triton
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import triton.language as tl
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import torch
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from .utils import calculate_settings, MAX_FUSED_SIZE
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from .utils import calculate_settings, MAX_FUSED_SIZE, triton_tanh
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from transformers.models.llama.modeling_llama import logger
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@ -63,7 +63,7 @@ def _cross_entropy_forward(
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label_idx = tl.load(labels_ptr).to(tl.int32)
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logits = tl.load(logits_ptr + col_offsets, mask = mask, other = -float("inf"))
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# Do logit softcapping for Gemma 2: t * tanh(1/t * x)
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if DO_SOFTCAPPING: logits = SOFTCAP * tl.math.tanh(logits / SOFTCAP)
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if DO_SOFTCAPPING: logits = SOFTCAP * triton_tanh(logits / SOFTCAP)
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logits = logits.to(tl.float32)
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c = tl.max(logits, 0)
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@ -72,7 +72,7 @@ def _cross_entropy_forward(
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if label_idx != -100:
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x = tl.load(logits_ptr + label_idx)
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# Do logit softcapping for Gemma 2: t * tanh(1/t * x)
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if DO_SOFTCAPPING: x = SOFTCAP * tl.math.tanh(x / SOFTCAP)
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if DO_SOFTCAPPING: x = SOFTCAP * triton_tanh(x / SOFTCAP)
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loss = logsumexp - x.to(tl.float32)
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else:
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loss = 0.0
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@ -131,7 +131,7 @@ def _chunked_cross_entropy_forward(
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label_idx = tl.load(labels_ptr).to(tl.int32)
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logits = tl.load(logits_ptr + col_offsets, mask = mask, other = -float("inf"))
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# Do logit softcapping for Gemma 2: t * tanh(1/t * x)
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if DO_SOFTCAPPING: logits = SOFTCAP * tl.math.tanh(logits / SOFTCAP)
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if DO_SOFTCAPPING: logits = SOFTCAP * triton_tanh(logits / SOFTCAP)
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logits = logits.to(tl.float32)
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c = tl.max(logits, 0)
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@ -143,7 +143,7 @@ def _chunked_cross_entropy_forward(
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if label_idx != -100:
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x = tl.load(logits_ptr + label_idx).to(tl.float32)
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# Do logit softcapping for Gemma 2: t * tanh(1/t * x)
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if DO_SOFTCAPPING: x = SOFTCAP * tl.math.tanh(x / SOFTCAP)
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if DO_SOFTCAPPING: x = SOFTCAP * triton_tanh(x / SOFTCAP)
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loss = -1.0 * x.to(tl.float32)
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else:
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loss = 0.0
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@ -198,7 +198,7 @@ def _cross_entropy_backward(
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# Do logit softcapping for Gemma 2: t * tanh(1/t * x)
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if DO_SOFTCAPPING:
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# d/dx [t * tanh(1/t * x)] = 1 - tanh^2(1/t * x)
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partial = tl.math.tanh(x / SOFTCAP)
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partial = triton_tanh(x / SOFTCAP)
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x = SOFTCAP * partial
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pass
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77
unsloth/kernels/flex_attention.py
Normal file
77
unsloth/kernels/flex_attention.py
Normal file
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@ -0,0 +1,77 @@
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# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from functools import lru_cache
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from transformers.models.llama.modeling_llama import logger
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torch_compile_options = {
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"epilogue_fusion" : True,
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"max_autotune" : True,
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"shape_padding" : True,
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"trace.enabled" : False, # Output Triton kernel outputs!
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"triton.cudagraphs" : False,
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}
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# Flex Attention supported from torch 2.5 onwards only
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import torch.nn.attention
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if hasattr(torch.nn.attention, "flex_attention"):
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import torch.nn.attention.flex_attention
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from torch.nn.attention.flex_attention import flex_attention
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from torch.nn.attention.flex_attention import create_block_mask
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FLEX_ATTENTION_PADDING = getattr(
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torch.nn.attention.flex_attention,
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"_DEFAULT_SPARSE_BLOCK_SIZE",
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1,
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)
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flex_attention = torch.compile(flex_attention, dynamic = False)
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HAS_FLEX_ATTENTION = True
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else:
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HAS_FLEX_ATTENTION = False
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pass
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# Logit softcapping
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@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
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def slow_attention_softcapping(Q, K, V, causal_mask, self, bsz, q_len):
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n_heads = self.num_heads
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head_dim = self.head_dim
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n_kv_heads = self.num_key_value_heads
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n_groups = self.num_key_value_groups
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# Grouped query attention
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K = K[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, q_len, head_dim)
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V = V[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, q_len, head_dim)
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K = K.reshape(bsz, n_heads, q_len, head_dim)
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V = V.reshape(bsz, n_heads, q_len, head_dim)
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# See https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
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# Gemma 9b should use 256 and not 224 (hs / nah). 27b uses the below
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# We default to using the config file itself
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# s = self.config.hidden_size // self.config.num_attention_heads
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s = self.config.query_pre_attn_scalar
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t = self.config.attn_logit_softcapping
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Q = Q * torch.tensor(s**-0.5, dtype = Q.dtype) # Follow Keras exactly
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A = torch.matmul(Q, K.transpose(2, 3))
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A = t * torch.tanh(A / t) # Logit softcapping
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A += causal_mask[:q_len, :q_len]
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# Much slower in torch compile!
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# A.masked_fill_(causal_mask[:q_len, :q_len], -float("inf"))
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A = torch.nn.functional.softmax(A, dim = -1, dtype = torch.float32).to(Q.dtype)
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A = torch.matmul(A, V)
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A = A.transpose(1, 2).contiguous()
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A = A.reshape(bsz, q_len, n_heads*head_dim)
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return A
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pass
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@ -15,7 +15,7 @@
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import triton
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import triton.language as tl
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import torch
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from .utils import calculate_settings
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from .utils import calculate_settings, triton_tanh
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@triton.jit
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@ -119,7 +119,7 @@ def _approx_forward_kernel(e, g, h, n_elements, BLOCK_SIZE : tl.constexpr,):
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g_row = tl.load(g + offsets, mask = mask, other = 0)#.to(tl.float32)
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f_row = 0.5 * e_row * (
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tl.math.tanh(s * e_row * (1.0 + 0.044715 * e_row * e_row)) \
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triton_tanh(s * e_row * (1.0 + 0.044715 * e_row * e_row)) \
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+ 1.0
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)
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f_row = f_row.to(g_row.dtype) # Exact copy from HF
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@ -168,7 +168,7 @@ def _approx_backward_kernel(DW, e, g, n_elements, BLOCK_SIZE : tl.constexpr,):
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s = 0.7978845608028654 # math.sqrt(2 / math.pi)
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a = s * e_row # a = sqrt(2 / pi) * x
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b = a * 0.044715 * e_row * e_row # b = a * 0.044715 * x^2
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T = 1.0 + tl.math.tanh(a + b)
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T = 1.0 + triton_tanh(a + b)
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T2 = 0.5 * T
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# Q = 0.5 * -T * (T - 2.0) * (a + 3.0 * b)
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Q2 = -T2 * (T - 2.0) * (a + 3.0 * b)
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@ -16,7 +16,6 @@ import triton
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import triton.language as tl
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import torch
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from .utils import calculate_settings
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ROPE_GROUP_SIZE = 4
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@triton.heuristics({"BACKWARD_PASS": lambda args: args["BACKWARD_PASS"],})
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@ -36,6 +35,7 @@ def _rope_embedding(
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RoPE is Q * cos + rotate_half(Q) * sin
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See our blog post for more info
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"""
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ROPE_GROUP_SIZE = 4
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row_position = tl.program_id(0)
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group_head_position = tl.program_id(1)
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col_offsets = tl.arange(0, BLOCK_SIZE)
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@ -28,6 +28,18 @@ else:
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pass
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# tl.math.tanh now is libdevice.tanh
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from packaging.version import Version
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import triton
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if Version(triton.__version__) >= Version("3.0.0"):
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from triton.language.extra import libdevice
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triton_tanh = libdevice.tanh
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else:
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import triton.language as tl
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triton_tanh = tl.math.tanh
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pass
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def calculate_settings(n):
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BLOCK_SIZE = next_power_of_2(n)
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if BLOCK_SIZE > MAX_FUSED_SIZE:
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@ -145,7 +145,15 @@ from xformers import __version__ as xformers_version
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# Temporarily disable 0.0.27 and higher - inference issues
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if Version(xformers_version) >= Version("0.0.27"):
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raise ImportError(
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f"Unsloth: Your xformers version of {xformers_version} is too new.\n"\
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"Unsloth: If you are in Colab, we updated the top cell install instructions - please change it to below "\
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"then press Disconnect Runtime and then Restart it.\n"\
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"\n"\
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"%%capture\n"
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"# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n"
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'!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"\n'
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'!pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes\n'\
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'\n'\
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f"Otherwise in local machines, your xformers version of {xformers_version} is too new.\n"\
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'Please downgrade xformers via `pip install --force-reinstall "xformers<0.0.27"'
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)
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pass
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@ -154,7 +162,15 @@ pass
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from trl import __version__ as trl_version
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if Version(xformers_version) >= Version("0.9.0"):
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raise ImportError(
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f"Unsloth: Your TRL version of {trl_version} is too new.\n"\
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"Unsloth: If you are in Colab, we updated the top cell install instructions - please change it to below "\
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"then press Disconnect Runtime and then Restart it.\n"\
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"\n"\
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"%%capture\n"
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"# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n"
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'!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"\n'
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'!pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes\n'\
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'\n'\
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f"Otherwise in local machines, your TRL version of {trl_version} is too new.\n"\
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'Please downgrade TRL via `pip install --force-reinstall "trl<0.9.0"'
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)
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pass
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@ -70,52 +70,6 @@ def fast_rms_layernorm_gemma2_compiled(layernorm, X, gemma = True):
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pass
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# Flex Attention in torch 2.5 and higher
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# try:
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# from torch.nn.attention._flex_attention import _flex_attention
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# from functools import lru_cache
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# @lru_cache
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# def create_block_mask_from_score_mod(score_mod, B, H, M, N):
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# SPARSE_BLOCK = 128
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# block_mask = _create_block_mask(score_mod, B, H, M, N, device = "cuda:0")
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# return block_mask
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||||
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# Logit softcapping
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@torch.compile(fullgraph = True, dynamic = True, options = torch_compile_options)
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def gemma2_attention(Q, K, V, causal_mask, self, bsz, q_len):
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n_heads = self.num_heads
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head_dim = self.head_dim
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n_kv_heads = self.num_key_value_heads
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n_groups = self.num_key_value_groups
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# Grouped query attention
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K = K[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, q_len, head_dim)
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V = V[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, q_len, head_dim)
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K = K.reshape(bsz, n_heads, q_len, head_dim)
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V = V.reshape(bsz, n_heads, q_len, head_dim)
|
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|
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# See https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
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# Gemma 9b should use 256 and not 224 (hs / nah). 27b uses the below
|
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# We default to using the config file itself
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# s = self.config.hidden_size // self.config.num_attention_heads
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s = self.config.query_pre_attn_scalar
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t = self.config.attn_logit_softcapping
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||||
|
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Q = Q * torch.tensor(s**-0.5, dtype = Q.dtype) # Follow Keras exactly
|
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A = torch.matmul(Q, K.transpose(2, 3))
|
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A = t * torch.tanh(A / t) # Logit softcapping
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A += causal_mask[:q_len, :q_len]
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# Much slower in torch compile!
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# A.masked_fill_(causal_mask[:q_len, :q_len], -float("inf"))
|
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A = torch.nn.functional.softmax(A, dim = -1, dtype = torch.float32).to(Q.dtype)
|
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A = torch.matmul(A, V)
|
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A = A.transpose(1, 2).contiguous()
|
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A = A.reshape(bsz, q_len, n_heads*head_dim)
|
||||
return A
|
||||
pass
|
||||
|
||||
|
||||
# Logit softcapping
|
||||
def Gemma2Attention_fast_forward(
|
||||
self,
|
||||
|
|
@ -172,8 +126,8 @@ def Gemma2Attention_fast_forward(
|
|||
V = torch.cat([past_key_value[1], V], dim = 2)
|
||||
pass
|
||||
past_key_value = (K, V) if use_cache else None
|
||||
|
||||
A = gemma2_attention(Q, K, V, causal_mask, self, bsz, kv_seq_len)
|
||||
|
||||
A = slow_attention_softcapping(Q, K, V, causal_mask, self, bsz, kv_seq_len)
|
||||
A = self.apply_o(self, A)
|
||||
return A, None, past_key_value
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -662,6 +662,12 @@ def LlamaModel_fast_forward(
|
|||
offloaded_gradient_checkpointing = True
|
||||
pass
|
||||
|
||||
# Check for Flex Attention
|
||||
# if IS_GEMMA2 and HAS_FLEX_ATTENTION:
|
||||
# if not (seq_length % FLEX_ATTENTION_PADDING == 0):
|
||||
# USE_FLEX_ATTENTION = True
|
||||
|
||||
|
||||
# Gemma2 has alternating SWA and global attn
|
||||
if IS_GEMMA2 and not hasattr(self, "SWA_mask"):
|
||||
n = self.config.max_position_embeddings
|
||||
|
|
|
|||
|
|
@ -210,6 +210,14 @@ __INT_TO_FLOAT_MAPPER = \
|
|||
"unsloth/Phi-3-mini-4k-instruct-v0-bnb-4bit" : ( # Old Phi pre July
|
||||
"unsloth/Phi-3-mini-4k-instruct-v0",
|
||||
),
|
||||
"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit" : ( # New 12b Mistral models
|
||||
"unsloth/Mistral-Nemo-Instruct-2407",
|
||||
"mistralai/Mistral-Nemo-Instruct-2407",
|
||||
),
|
||||
"unsloth/Mistral-Nemo-Base-2407-bnb-4bit" : ( # New 12b Mistral models
|
||||
"unsloth/Mistral-Nemo-Base-2407",
|
||||
"mistralai/Mistral-Nemo-Base-2407",
|
||||
),
|
||||
}
|
||||
|
||||
INT_TO_FLOAT_MAPPER = {}
|
||||
|
|
|
|||
|
|
@ -1359,6 +1359,20 @@ def upload_to_huggingface(
|
|||
uploaded_location = file_location
|
||||
pass
|
||||
|
||||
# find ftevent file from tensorboard and upload it
|
||||
import glob
|
||||
ftevent_files = glob.glob("*out.tfevents*", recursive = True)
|
||||
if len(ftevent_files) > 0:
|
||||
print("Unsloth: Uploading tensorboard files... Please wait...", file_location + "*out.tfevents*")
|
||||
for ftevent_file in ftevent_files:
|
||||
hf_api.upload_file(
|
||||
path_or_fileobj = ftevent_file,
|
||||
path_in_repo = ftevent_file.replace(file_location, ""),
|
||||
repo_id = save_directory,
|
||||
repo_type = "model",
|
||||
commit_message = "(Trained with Unsloth)",
|
||||
)
|
||||
|
||||
hf_api.upload_file(
|
||||
path_or_fileobj = file_location,
|
||||
path_in_repo = uploaded_location,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue