From 3169439e4d0e9525d815714dd6e56018ac9b7fb9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 18 Nov 2024 09:47:43 +0000 Subject: [PATCH 1/4] update liger kernels --- poetry.lock | 3950 +++++++++-------- pyproject.toml | 3 +- .../common/tf/loaders/model/model.py | 26 +- turbo_alignment/metrics/distinctness.py | 1 + turbo_alignment/metrics/registry.py | 1 + .../modeling/liger_kernels/__init__.py | 3 - .../modeling/liger_kernels/cross_entropy.py | 199 - .../modeling/liger_kernels/geglu.py | 150 - .../liger_kernels/monkey_patch_liger.py | 42 - .../modeling/liger_kernels/rope.py | 172 - .../modeling/liger_kernels/utils.py | 51 - turbo_alignment/settings/model.py | 18 +- turbo_alignment/trainers/dpo.py | 2 +- 13 files changed, 2173 insertions(+), 2445 deletions(-) delete mode 100644 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b/turbo_alignment/common/tf/loaders/model/model.py @@ -1,4 +1,9 @@ import torch +from liger_kernel.transformers import ( + apply_liger_kernel_to_gemma2, + apply_liger_kernel_to_llama, + apply_liger_kernel_to_qwen2, +) from peft import PeftModel, get_peft_model, prepare_model_for_int8_training from transformers import PreTrainedModel, PreTrainedTokenizerBase from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled @@ -7,7 +12,6 @@ PeftConfigRegistry, TransformersAutoModelRegistry, ) -from turbo_alignment.modeling.liger_kernels import apply_liger_kernel_to_gemma2 from turbo_alignment.settings.model import ( ModelForPeftSettings, ModelType, @@ -47,10 +51,28 @@ def load_model( tokenizer: PreTrainedTokenizerBase, ) -> PreTrainedModel: if model_settings.liger_kernels_settings is not None: + apply_liger_kernel_to_llama( + rope=model_settings.liger_kernels_settings.use_rope, + cross_entropy=model_settings.liger_kernels_settings.use_cross_entropy, + swiglu=model_settings.liger_kernels_settings.use_mlp, + rms_norm=model_settings.liger_kernels_settings.use_rms_norm, + fused_linear_cross_entropy=model_settings.liger_kernels_settings.use_fused_liner_cross_entropy, + ) + apply_liger_kernel_to_gemma2( rope=model_settings.liger_kernels_settings.use_rope, cross_entropy=model_settings.liger_kernels_settings.use_cross_entropy, - geglu=model_settings.liger_kernels_settings.use_geglu, + geglu=model_settings.liger_kernels_settings.use_mlp, + rms_norm=model_settings.liger_kernels_settings.use_rms_norm, + fused_linear_cross_entropy=model_settings.liger_kernels_settings.use_fused_liner_cross_entropy, + ) + + apply_liger_kernel_to_qwen2( + rope=model_settings.liger_kernels_settings.use_rope, + cross_entropy=model_settings.liger_kernels_settings.use_cross_entropy, + swiglu=model_settings.liger_kernels_settings.use_mlp, + rms_norm=model_settings.liger_kernels_settings.use_rms_norm, + fused_linear_cross_entropy=model_settings.liger_kernels_settings.use_fused_liner_cross_entropy, ) model = TransformersAutoModelRegistry.by_name(model_settings.model_type).from_pretrained( diff --git a/turbo_alignment/metrics/distinctness.py b/turbo_alignment/metrics/distinctness.py index a1ff2bd..4f10de2 100755 --- a/turbo_alignment/metrics/distinctness.py +++ b/turbo_alignment/metrics/distinctness.py @@ -1,4 +1,5 @@ from collections import defaultdict + from transformers.tokenization_utils_base import PreTrainedTokenizerBase from turbo_alignment.metrics.metric import Metric diff --git a/turbo_alignment/metrics/registry.py b/turbo_alignment/metrics/registry.py index d9f5abd..c99195c 100755 --- a/turbo_alignment/metrics/registry.py +++ b/turbo_alignment/metrics/registry.py @@ -1,4 +1,5 @@ from enum import Enum + from pydantic import field_validator from turbo_alignment.common.registry import Registrable diff --git a/turbo_alignment/modeling/liger_kernels/__init__.py b/turbo_alignment/modeling/liger_kernels/__init__.py deleted file mode 100644 index b36774c..0000000 --- a/turbo_alignment/modeling/liger_kernels/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from turbo_alignment.modeling.liger_kernels.monkey_patch_liger import ( - apply_liger_kernel_to_gemma2, -) diff --git a/turbo_alignment/modeling/liger_kernels/cross_entropy.py b/turbo_alignment/modeling/liger_kernels/cross_entropy.py deleted file mode 100644 index e679f1e..0000000 --- a/turbo_alignment/modeling/liger_kernels/cross_entropy.py +++ /dev/null @@ -1,199 +0,0 @@ -# mypy: ignore-errors -# pylint: skip-file -import torch -import triton -import triton.language as tl -from torch.nn import CrossEntropyLoss - - -@triton.jit -def liger_cross_entropy_kernel( - X_ptr, - X_stride, - Y_ptr, - Y_stride, - loss_ptr, - loss_stride, - n_cols, - n_non_ignore, - ignore_index, - label_smoothing: tl.constexpr, - reduction: tl.constexpr, - BLOCK_SIZE: tl.constexpr, -): - program_id = tl.program_id(0).to(tl.int64) - - Y_ptr += program_id * Y_stride - y = tl.load(Y_ptr) - - X_ptr += program_id * X_stride - - if y == ignore_index: - for i in range(0, n_cols, BLOCK_SIZE): - X_offsets = i + tl.arange(0, BLOCK_SIZE) - tl.store(X_ptr + X_offsets, 0.0, mask=X_offsets < n_cols) - return - - loss_ptr += program_id * loss_stride - - m = float('-inf') - d = 0.0 - ori_X_y = tl.load(X_ptr + y) - - scaled_x_sum = 0.0 - eps = label_smoothing / n_cols - - for i in range(0, n_cols, BLOCK_SIZE): - X_offsets = i + tl.arange(0, BLOCK_SIZE) - X_block = tl.load(X_ptr + X_offsets, mask=X_offsets < n_cols, other=float('-inf')) - block_max = tl.max(X_block) - if label_smoothing > 0: - scaled_x_sum += tl.sum(tl.where(X_offsets < n_cols, -eps * X_block, 0.0)) - m_new = tl.maximum(m, block_max) - d = d * tl.exp(m - m_new) + tl.sum(tl.exp(X_block - m_new)) - m = m_new - - for i in range(0, n_cols, BLOCK_SIZE): - X_offsets = i + tl.arange(0, BLOCK_SIZE) - X_block = tl.load(X_ptr + X_offsets, mask=X_offsets < n_cols, other=float('-inf')) - if reduction == 'mean': - X_block = (tl.exp(X_block - m) / d - eps) / (n_non_ignore) - else: - X_block = tl.exp(X_block - m) / d - eps - - tl.store(X_ptr + X_offsets, X_block, mask=X_offsets < n_cols) - - tl.debug_barrier() - - loss = -(ori_X_y - m - tl.log(d)) - - if label_smoothing > 0: - smooth_loss = scaled_x_sum + label_smoothing * (m + tl.log(d)) - loss = loss * (1 - label_smoothing) + smooth_loss - - if reduction == 'mean': - loss = loss / n_non_ignore - - X_y = tl.load(X_ptr + y) - if reduction == 'mean': - X_y += -(1 - label_smoothing) / (n_non_ignore) - else: - X_y += -(1 - label_smoothing) - - tl.store(loss_ptr, loss) - tl.store(X_ptr + y, X_y) - - -MAX_FUSED_SIZE = 65536 // 2 - - -@triton.jit -def element_mul_kernel( - X_ptr, - X_stride, - grad_output_ptr, - n_cols, - BLOCK_SIZE: tl.constexpr, -): - program_id = tl.program_id(0).to(tl.int64) - - X_ptr += program_id * X_stride - - grad_output = tl.load(grad_output_ptr) - - for i in range(0, n_cols, BLOCK_SIZE): - X_offsets = i + tl.arange(0, BLOCK_SIZE) - X_block = tl.load(X_ptr + X_offsets, mask=X_offsets < n_cols) - tl.store(X_ptr + X_offsets, X_block * grad_output, mask=X_offsets < n_cols) - - -def cross_entropy_forward(_input, target, ignore_index, label_smoothing, reduction): - BT, V = _input.shape - n_rows = BT - - BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(V)) - - loss_1d = torch.zeros(n_rows, dtype=_input.dtype, device=_input.device) - - n_non_ignore = (target != ignore_index).sum().item() - - if _input.stride(-1) != 1: - _input = _input.contiguous() - if target.stride(-1) != 1: - target = target.contiguous() - - liger_cross_entropy_kernel[(n_rows,)]( - X_ptr=_input, - X_stride=_input.stride(-2), - Y_ptr=target, - Y_stride=target.stride(-1), - loss_ptr=loss_1d, - loss_stride=loss_1d.stride(-1), - n_cols=V, - n_non_ignore=n_non_ignore, - ignore_index=ignore_index, - label_smoothing=label_smoothing, - reduction=reduction, - BLOCK_SIZE=BLOCK_SIZE, - num_warps=32, - ) - - loss = torch.sum(loss_1d) - return loss, _input - - -def cross_entropy_backward(_input, grad_output): - if torch.equal(grad_output, torch.tensor(1.0, device=grad_output.device)): - pass - - else: - BT, V = _input.shape - n_rows = BT - BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(V)) - - element_mul_kernel[(n_rows,)]( - _input, - _input.stride(-2), - grad_output, - V, - BLOCK_SIZE=BLOCK_SIZE, - num_warps=32, - ) - - return _input - - -class LigerCrossEntropyFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, _input, target, ignore_index=-100, label_smoothing=0.0, reduction='mean'): - loss, _input = cross_entropy_forward(_input, target, ignore_index, label_smoothing, reduction) - ctx.save_for_backward(_input.detach()) - return loss - - @staticmethod - def backward(ctx, grad_output): - (_input,) = ctx.saved_tensors - _input = cross_entropy_backward(_input, grad_output) - return ( - _input, - None, - None, - None, - None, - ) - - -class LigerCrossEntropyLoss(CrossEntropyLoss): - def __init__(self, *args, **kwargs): - super(LigerCrossEntropyLoss, self).__init__(*args, **kwargs) - assert (self.label_smoothing >= 0) and ( - self.label_smoothing <= 1 - ), f'label_smoothing must be between 0.0 and 1.0. Got: {self.label_smoothing}' - assert self.reduction in { - 'mean', - 'sum', - 'none', - }, f"reduction must be one of 'mean', 'sum', or 'none'. Got: {self.reduction}" - - def forward(self, _input, target): - return LigerCrossEntropyFunction.apply(_input, target, self.ignore_index, self.label_smoothing, self.reduction) diff --git a/turbo_alignment/modeling/liger_kernels/geglu.py b/turbo_alignment/modeling/liger_kernels/geglu.py deleted file mode 100644 index 6647a36..0000000 --- a/turbo_alignment/modeling/liger_kernels/geglu.py +++ /dev/null @@ -1,150 +0,0 @@ -# mypy: ignore-errors -# pylint: skip-file -import operator - -import torch -import torch.nn as nn -import triton -import triton.language as tl - -from turbo_alignment.modeling.liger_kernels.utils import ( - calculate_settings, - compare_version, - ensure_contiguous, -) - -if compare_version('triton', operator.ge, '3.0.0'): - try: - from triton.language.extra.libdevice import tanh - except ModuleNotFoundError: - from triton.language.extra.cuda.libdevice import tanh -else: - from triton.language.math import tanh - - -@triton.jit -def _geglu_tanh_forward_kernel(a, b, c, stride, n_cols: tl.constexpr, BLOCK_SIZE: tl.constexpr): - program_id = tl.program_id(0) - - a += program_id * stride - b += program_id * stride - c += program_id * stride - - col_offsets = tl.arange(0, BLOCK_SIZE) - mask = col_offsets < n_cols - a_row = tl.load(a + col_offsets, mask=mask, other=0).to(tl.float32) - b_row = tl.load(b + col_offsets, mask=mask, other=0) - - sqrt_2_over_pi = 0.7978845608028654 - a_cubed = a_row * a_row * a_row - tanh_arg = sqrt_2_over_pi * (a_row + 0.044715 * a_cubed) - tanh_result = tanh(tanh_arg) - geglu_a = 0.5 * a_row * (1 + tanh_result) - c_row = geglu_a * b_row - tl.store(c + col_offsets, c_row, mask=mask) - - -@triton.jit -def _geglu_tanh_backward_kernel(dc, a, b, stride, n_cols: tl.constexpr, BLOCK_SIZE: tl.constexpr): - program_id = tl.program_id(0) - - dc += program_id * stride - a += program_id * stride - b += program_id * stride - - col_offsets = tl.arange(0, BLOCK_SIZE) - mask = col_offsets < n_cols - - dc_row = tl.load(dc + col_offsets, mask=mask, other=0) - a_row = tl.load(a + col_offsets, mask=mask, other=0).to(tl.float32) - b_row = tl.load(b + col_offsets, mask=mask, other=0) - - sqrt_2_over_pi = 0.7978845608028654 - a_cubed = a_row * a_row * a_row - tanh_arg = sqrt_2_over_pi * (a_row + 0.044715 * a_cubed) - tanh_result = tanh(tanh_arg) - geglu_a = 0.5 * a_row * (1 + tanh_result) - - db_row = dc_row * geglu_a - - term1 = 0.5 * (1 + tanh_result) - tanh_sq = tanh_result * tanh_result - term2 = 0.5 * a_row * (1 - tanh_sq) * (sqrt_2_over_pi * (1 + 3 * 0.044715 * a_row * a_row)) - da_row = dc_row * b_row * (term1 + term2) - - tl.store(a + col_offsets, da_row, mask=mask) - tl.store(b + col_offsets, db_row, mask=mask) - - -def geglu_forward(a, b): - ori_shape = a.shape - - n_cols = ori_shape[-1] - a = a.view(-1, n_cols) - b = b.view(-1, n_cols) - c = torch.empty_like(a) - n_rows = a.shape[0] - - BLOCK_SIZE, num_warps = calculate_settings(n_cols) - - _geglu_tanh_forward_kernel[(n_rows,)]( - a, - b, - c, - c.stride(-2), - n_cols=n_cols, - BLOCK_SIZE=BLOCK_SIZE, - num_warps=num_warps, - ) - return a, b, c.view(*ori_shape) - - -def geglu_backward(a, b, dc): - ori_shape = dc.shape - n_cols = ori_shape[-1] - dc = dc.view(-1, n_cols) - n_rows = dc.shape[0] - - BLOCK_SIZE, num_warps = calculate_settings(n_cols) - - _geglu_tanh_backward_kernel[(n_rows,)]( - dc, - a, - b, - dc.stride(-2), - n_cols=n_cols, - BLOCK_SIZE=BLOCK_SIZE, - num_warps=num_warps, - ) - - return a.view(*ori_shape), b.view(*ori_shape) - - -class LigerGELUMulFunction(torch.autograd.Function): - @staticmethod - @ensure_contiguous - def forward(ctx, a, b): - a, b, c = geglu_forward(a, b) - ctx.save_for_backward(a, b) - return c - - @staticmethod - @ensure_contiguous - def backward(ctx, dc): - a, b = ctx.saved_tensors - a, b = geglu_backward(a, b, dc) - return a, b - - -class LigerGEGLUMLP(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.hidden_size = config.hidden_size - self.intermediate_size = config.intermediate_size - self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) - self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) - self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) - - def forward(self, x): - return self.down_proj(LigerGELUMulFunction.apply(self.gate_proj(x), self.up_proj(x))) diff --git a/turbo_alignment/modeling/liger_kernels/monkey_patch_liger.py b/turbo_alignment/modeling/liger_kernels/monkey_patch_liger.py deleted file mode 100644 index bfce35d..0000000 --- a/turbo_alignment/modeling/liger_kernels/monkey_patch_liger.py +++ /dev/null @@ -1,42 +0,0 @@ -from transformers import PretrainedConfig, PreTrainedModel - -from turbo_alignment.common.logging import get_project_logger -from turbo_alignment.modeling.liger_kernels.cross_entropy import LigerCrossEntropyLoss -from turbo_alignment.modeling.liger_kernels.geglu import LigerGEGLUMLP -from turbo_alignment.modeling.liger_kernels.rope import liger_rotary_pos_emb - -logger = get_project_logger() - - -def apply_liger_kernel_to_gemma2( - rope: bool = True, - cross_entropy: bool = True, - geglu: bool = True, - model: PreTrainedModel = None, -) -> None: - logger.info('Loading Liger-Kernels for Gemma2...') - - from transformers.models.gemma2 import modeling_gemma2 - - if rope: - modeling_gemma2.apply_rotary_pos_emb = liger_rotary_pos_emb - if cross_entropy: - modeling_gemma2.CrossEntropyLoss = LigerCrossEntropyLoss - if geglu: - modeling_gemma2.Gemma2MLP = LigerGEGLUMLP - - if model is not None: - config: PretrainedConfig = model.config - - if hasattr(model, 'model'): - base_model = model.model - else: - base_model = model - - torch_dtype = config.torch_dtype - - for decoder_layer in base_model.layers: - if geglu: - decoder_layer.mlp = LigerGEGLUMLP(config).to(torch_dtype) - - logger.info('Liger-Kernels have been successfully applied!') diff --git a/turbo_alignment/modeling/liger_kernels/rope.py b/turbo_alignment/modeling/liger_kernels/rope.py deleted file mode 100644 index 21f8909..0000000 --- a/turbo_alignment/modeling/liger_kernels/rope.py +++ /dev/null @@ -1,172 +0,0 @@ -# mypy: ignore-errors -# pylint: skip-file -import torch -import triton -import triton.language as tl - - -@triton.jit -def _triton_rope( - q_ptr, - q_row_stride, - k_ptr, - k_row_stride, - cos, - cos_row_stride, - sin, - sin_row_stride, - sl, - bs: tl.constexpr, - n_qh: tl.constexpr, - n_kh: tl.constexpr, - hd: tl.constexpr, - pad_n_qh: tl.constexpr, - pad_n_kh: tl.constexpr, - pad_hd: tl.constexpr, - BLOCK_SIZE: tl.constexpr, - BACKWARD_PASS: tl.constexpr = False, -): - pid = tl.program_id(0) - - q_ptr = q_ptr + pid * q_row_stride - k_ptr = k_ptr + pid * k_row_stride - - cos_row_idx = pid % (sl) - cos = cos + cos_row_idx * cos_row_stride - sin = sin + cos_row_idx * sin_row_stride - cos_offsets = tl.arange(0, pad_hd // 2) - cos_mask = cos_offsets < hd // 2 - cos_row = tl.load(cos + cos_offsets, mask=cos_mask, other=0) - sin_row = tl.load(sin + cos_offsets, mask=cos_mask, other=0) - - first_half_q_offsets = tl.arange(0, pad_n_qh)[:, None] * hd + tl.arange(0, pad_hd // 2)[None, :] - first_half_k_offsets = tl.arange(0, pad_n_kh)[:, None] * hd + tl.arange(0, pad_hd // 2)[None, :] - first_q_mask = (tl.arange(0, pad_n_qh)[:, None] < n_qh) & (tl.arange(0, pad_hd // 2)[None, :] < hd // 2) - first_k_mask = (tl.arange(0, pad_n_kh)[:, None] < n_kh) & (tl.arange(0, pad_hd // 2)[None, :] < hd // 2) - q_tile_1 = tl.load(q_ptr + first_half_q_offsets, mask=first_q_mask, other=0).to(sin_row.dtype) - k_tile_1 = tl.load(k_ptr + first_half_k_offsets, mask=first_k_mask, other=0).to(sin_row.dtype) - - second_half_q_offsets = first_half_q_offsets + (hd // 2) - second_half_k_offsets = first_half_k_offsets + (hd // 2) - second_q_mask = first_q_mask - second_k_mask = first_k_mask - q_tile_2 = tl.load(q_ptr + second_half_q_offsets, mask=second_q_mask, other=0).to(sin_row.dtype) - k_tile_2 = tl.load(k_ptr + second_half_k_offsets, mask=second_k_mask, other=0).to(sin_row.dtype) - - if not BACKWARD_PASS: - new_q_tile_1 = q_tile_1 * cos_row - q_tile_2 * sin_row - tl.store(q_ptr + first_half_q_offsets, new_q_tile_1, mask=first_q_mask) - new_q_tile_2 = q_tile_2 * cos_row + q_tile_1 * sin_row - tl.store(q_ptr + second_half_q_offsets, new_q_tile_2, mask=second_q_mask) - - new_k_tile_1 = k_tile_1 * cos_row - k_tile_2 * sin_row - tl.store(k_ptr + first_half_k_offsets, new_k_tile_1, mask=first_k_mask) - new_k_tile_2 = k_tile_2 * cos_row + k_tile_1 * sin_row - tl.store(k_ptr + second_half_k_offsets, new_k_tile_2, mask=second_k_mask) - else: - new_q_tile_1 = q_tile_1 * cos_row + q_tile_2 * sin_row - tl.store(q_ptr + first_half_q_offsets, new_q_tile_1, mask=first_q_mask) - new_q_tile_2 = q_tile_2 * cos_row - q_tile_1 * sin_row - tl.store(q_ptr + second_half_q_offsets, new_q_tile_2, mask=second_q_mask) - - new_k_tile_1 = k_tile_1 * cos_row + k_tile_2 * sin_row - tl.store(k_ptr + first_half_k_offsets, new_k_tile_1, mask=first_k_mask) - new_k_tile_2 = k_tile_2 * cos_row - k_tile_1 * sin_row - tl.store(k_ptr + second_half_k_offsets, new_k_tile_2, mask=second_k_mask) - - -def rope_forward(q, k, cos, sin): - q = q.transpose(1, 2) - k = k.transpose(1, 2) - - batch_size, seq_len, n_q_head, head_dim = q.shape - n_kv_head = k.shape[2] - pad_hd = triton.next_power_of_2(head_dim) - pad_n_q_head = triton.next_power_of_2(n_q_head) - pad_n_kv_head = triton.next_power_of_2(n_kv_head) - BLOCK_SIZE = max(pad_n_q_head, pad_n_kv_head) - - n_row = batch_size * seq_len - - q = q.contiguous() - k = k.contiguous() - cos = cos.contiguous() - sin = sin.contiguous() - - _triton_rope[(n_row,)]( - q, - q.stride(1), - k, - k.stride(1), - cos, - cos.stride(-2), - sin, - sin.stride(-2), - seq_len, - batch_size, - n_q_head, - n_kv_head, - head_dim, - pad_n_q_head, - pad_n_kv_head, - pad_hd, - BLOCK_SIZE=BLOCK_SIZE, - BACKWARD_PASS=False, - ) - return q.transpose(1, 2), k.transpose(1, 2), cos, sin - - -def rope_backward(dq, dk, cos, sin): - dq = dq.transpose(1, 2) - dk = dk.transpose(1, 2) - - batch_size, seq_len, n_q_head, head_dim = dq.shape - n_kv_head = dk.shape[2] - pad_hd = triton.next_power_of_2(head_dim) - pad_n_q_head = triton.next_power_of_2(n_q_head) - pad_n_kv_head = triton.next_power_of_2(n_kv_head) - BLOCK_SIZE = max(pad_n_q_head, pad_n_kv_head) - - n_row = batch_size * seq_len - - dq = dq.contiguous() - dk = dk.contiguous() - - _triton_rope[(n_row,)]( - dq, - dq.stride(1), - dk, - dk.stride(1), - cos, - cos.stride(-2), - sin, - sin.stride(-2), - seq_len, - batch_size, - n_q_head, - n_kv_head, - head_dim, - pad_n_q_head, - pad_n_kv_head, - pad_hd, - BLOCK_SIZE=BLOCK_SIZE, - BACKWARD_PASS=True, - ) - return dq.transpose(1, 2), dk.transpose(1, 2) - - -class LigerRopeFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, q, k, cos, sin, position_ids=None, unsqueeze_dim=1): - q, k, cos, sin = rope_forward(q, k, cos, sin) - ctx.save_for_backward(cos, sin) - return q, k - - def backward(ctx, dq, dk): - cos, sin = ctx.saved_tensors - dq, dk = rope_backward(dq, dk, cos, sin) - return dq, dk, None, None, None, None - - -def liger_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): - return LigerRopeFunction.apply(q, k, cos, sin, position_ids, unsqueeze_dim) diff --git a/turbo_alignment/modeling/liger_kernels/utils.py b/turbo_alignment/modeling/liger_kernels/utils.py deleted file mode 100644 index ea1f1f1..0000000 --- a/turbo_alignment/modeling/liger_kernels/utils.py +++ /dev/null @@ -1,51 +0,0 @@ -# mypy: ignore-errors -# pylint: skip-file - -import functools -import importlib -from typing import Callable - -import torch -import triton -from packaging.version import Version - - -def ensure_contiguous(fn): - @functools.wraps(fn) - def wrapper(ctx, *args, **kwargs): - def maybe_to_contiguous(x): - return x.contiguous() if isinstance(x, torch.Tensor) else x - - args = [maybe_to_contiguous(arg) for arg in args] - kwargs = {k: maybe_to_contiguous(v) for k, v in kwargs.items()} - return fn(ctx, *args, **kwargs) - - return wrapper - - -def calculate_settings(n): - MAX_FUSED_SIZE = 65536 - BLOCK_SIZE = triton.next_power_of_2(n) - if BLOCK_SIZE > MAX_FUSED_SIZE: - raise RuntimeError( - f'Cannot launch Triton kernel since n = {n} exceeds ' - f'the recommended Triton blocksize = {MAX_FUSED_SIZE}.' - ) - - num_warps = 4 - if BLOCK_SIZE >= 32768: - num_warps = 32 - elif BLOCK_SIZE >= 8192: - num_warps = 16 - elif BLOCK_SIZE >= 2048: - num_warps = 8 - return BLOCK_SIZE, num_warps - - -def compare_version(package: str, operator: Callable, target: str): - try: - pkg = importlib.import_module(package) - except ImportError: - return False - pkg_version = Version(pkg.__version__) - return operator(pkg_version, Version(target)) diff --git a/turbo_alignment/settings/model.py b/turbo_alignment/settings/model.py index f544bd9..99c87a6 100755 --- a/turbo_alignment/settings/model.py +++ b/turbo_alignment/settings/model.py @@ -1,6 +1,8 @@ from enum import Enum from pathlib import Path +from pydantic import model_validator + from turbo_alignment.settings.base import ExtraFieldsNotAllowedBaseModel from turbo_alignment.settings.tf.model import ModelTransformersSettings from turbo_alignment.settings.tf.peft import PEFT_TYPE @@ -15,8 +17,20 @@ class ModelType(str, Enum): class LigerKernelSettings(ExtraFieldsNotAllowedBaseModel): use_rope: bool = True - use_cross_entropy: bool = True - use_geglu: bool = True + use_cross_entropy: bool = False + use_fused_liner_cross_entropy: bool = False + use_mlp: bool = True + use_rms_norm: bool = False + + @model_validator(mode='after') + def correct_cross_entopy_kernels(self) -> 'LigerKernelSettings': + if self.use_fused_liner_cross_entropy and self.use_cross_entropy: + raise ValueError( + 'You cannot use both FusedLinearCrossEntropy and CrossEntropy kernels. ' + 'FusedLinearCrossEntropy is preferred if possible.' + ) + + return self class PreTrainedModelSettings(ExtraFieldsNotAllowedBaseModel): diff --git a/turbo_alignment/trainers/dpo.py b/turbo_alignment/trainers/dpo.py index 490e6d1..e49231b 100755 --- a/turbo_alignment/trainers/dpo.py +++ b/turbo_alignment/trainers/dpo.py @@ -26,9 +26,9 @@ from turbo_alignment.settings.pipelines.train.dpo import ( APODownLossSettings, APOZeroLossSettings, + ASFTLossSettings, CPOLossSettings, DPOLossesType, - ASFTLossSettings, HingeLossSettings, IPOLossSettings, KTOLossSettings, From ef4425e78c265bd01cd73700472529ce971a8f59 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 18 Nov 2024 09:50:12 +0000 Subject: [PATCH 2/4] fix --- turbo_alignment/common/tf/loaders/model/model.py | 7 ++++--- turbo_alignment/settings/model.py | 4 ++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/turbo_alignment/common/tf/loaders/model/model.py b/turbo_alignment/common/tf/loaders/model/model.py index ce942a8..94fa96f 100755 --- a/turbo_alignment/common/tf/loaders/model/model.py +++ b/turbo_alignment/common/tf/loaders/model/model.py @@ -56,7 +56,7 @@ def load_model( cross_entropy=model_settings.liger_kernels_settings.use_cross_entropy, swiglu=model_settings.liger_kernels_settings.use_mlp, rms_norm=model_settings.liger_kernels_settings.use_rms_norm, - fused_linear_cross_entropy=model_settings.liger_kernels_settings.use_fused_liner_cross_entropy, + fused_linear_cross_entropy=model_settings.liger_kernels_settings.use_fused_linear_cross_entropy, ) apply_liger_kernel_to_gemma2( @@ -64,7 +64,7 @@ def load_model( cross_entropy=model_settings.liger_kernels_settings.use_cross_entropy, geglu=model_settings.liger_kernels_settings.use_mlp, rms_norm=model_settings.liger_kernels_settings.use_rms_norm, - fused_linear_cross_entropy=model_settings.liger_kernels_settings.use_fused_liner_cross_entropy, + fused_linear_cross_entropy=model_settings.liger_kernels_settings.use_fused_linear_cross_entropy, ) apply_liger_kernel_to_qwen2( @@ -72,7 +72,7 @@ def load_model( cross_entropy=model_settings.liger_kernels_settings.use_cross_entropy, swiglu=model_settings.liger_kernels_settings.use_mlp, rms_norm=model_settings.liger_kernels_settings.use_rms_norm, - fused_linear_cross_entropy=model_settings.liger_kernels_settings.use_fused_liner_cross_entropy, + fused_linear_cross_entropy=model_settings.liger_kernels_settings.use_fused_linear_cross_entropy, ) model = TransformersAutoModelRegistry.by_name(model_settings.model_type).from_pretrained( @@ -119,3 +119,4 @@ def load_model( model.base_model.model.score.weight.requires_grad = True return model + diff --git a/turbo_alignment/settings/model.py b/turbo_alignment/settings/model.py index 99c87a6..c57a157 100755 --- a/turbo_alignment/settings/model.py +++ b/turbo_alignment/settings/model.py @@ -18,13 +18,13 @@ class ModelType(str, Enum): class LigerKernelSettings(ExtraFieldsNotAllowedBaseModel): use_rope: bool = True use_cross_entropy: bool = False - use_fused_liner_cross_entropy: bool = False + use_fused_linear_cross_entropy: bool = False use_mlp: bool = True use_rms_norm: bool = False @model_validator(mode='after') def correct_cross_entopy_kernels(self) -> 'LigerKernelSettings': - if self.use_fused_liner_cross_entropy and self.use_cross_entropy: + if self.use_fused_linear_cross_entropy and self.use_cross_entropy: raise ValueError( 'You cannot use both FusedLinearCrossEntropy and CrossEntropy kernels. ' 'FusedLinearCrossEntropy is preferred if possible.' From 68469d70d2298e3ff52e72e0e4130b2e91c6052b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Mon, 18 Nov 2024 09:51:24 +0000 Subject: [PATCH 3/4] fix --- turbo_alignment/common/tf/loaders/model/model.py | 1 - turbo_alignment/settings/model.py | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/turbo_alignment/common/tf/loaders/model/model.py b/turbo_alignment/common/tf/loaders/model/model.py index 94fa96f..9dc407e 100755 --- a/turbo_alignment/common/tf/loaders/model/model.py +++ b/turbo_alignment/common/tf/loaders/model/model.py @@ -119,4 +119,3 @@ def load_model( model.base_model.model.score.weight.requires_grad = True return model - diff --git a/turbo_alignment/settings/model.py b/turbo_alignment/settings/model.py index c57a157..72cd887 100755 --- a/turbo_alignment/settings/model.py +++ b/turbo_alignment/settings/model.py @@ -23,7 +23,7 @@ class LigerKernelSettings(ExtraFieldsNotAllowedBaseModel): use_rms_norm: bool = False @model_validator(mode='after') - def correct_cross_entopy_kernels(self) -> 'LigerKernelSettings': + def check_cross_entopy_kernels(self) -> 'LigerKernelSettings': if self.use_fused_linear_cross_entropy and self.use_cross_entropy: raise ValueError( 'You cannot use both FusedLinearCrossEntropy and CrossEntropy kernels. ' From b7186e8f27aac19bd838fdb3b185f0f24b778814 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BB=D0=B0=D1=85=D0=BE=D0=B2=20=D0=90=D0=BB?= =?UTF-8?q?=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9F=D0=B0=D0=B2=D0=BB=D0=BE?= =?UTF-8?q?=D0=B2=D0=B8=D1=87?= Date: Tue, 26 Nov 2024 13:09:21 +0000 Subject: [PATCH 4/4] fix default value --- turbo_alignment/settings/model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/turbo_alignment/settings/model.py b/turbo_alignment/settings/model.py index 72cd887..8058296 100755 --- a/turbo_alignment/settings/model.py +++ b/turbo_alignment/settings/model.py @@ -16,10 +16,10 @@ class ModelType(str, Enum): class LigerKernelSettings(ExtraFieldsNotAllowedBaseModel): - use_rope: bool = True + use_rope: bool = False use_cross_entropy: bool = False use_fused_linear_cross_entropy: bool = False - use_mlp: bool = True + use_mlp: bool = False use_rms_norm: bool = False @model_validator(mode='after')