From ffd2d2f1a7b062a068a8a6467088eeb359e6dfde Mon Sep 17 00:00:00 2001 From: martin Date: Sat, 5 Aug 2023 11:50:07 +0200 Subject: [PATCH] hyena first try, trains --- .../generate_sine-checkpoint.ipynb | 6 + data_generation/generate_sine.ipynb | 176 ++++++ .../simple_hyena_model-checkpoint.ipynb | 537 ++++++++++++++++++ .../standalone_hyena-checkpoint.py | 268 +++++++++ hyena_test/simple_hyena_model.ipynb | 537 ++++++++++++++++++ hyena_test/standalone_hyena.py | 268 +++++++++ 6 files changed, 1792 insertions(+) create mode 100644 data_generation/.ipynb_checkpoints/generate_sine-checkpoint.ipynb create mode 100644 data_generation/generate_sine.ipynb create mode 100644 hyena_test/.ipynb_checkpoints/simple_hyena_model-checkpoint.ipynb create mode 100644 hyena_test/.ipynb_checkpoints/standalone_hyena-checkpoint.py create mode 100644 hyena_test/simple_hyena_model.ipynb create mode 100644 hyena_test/standalone_hyena.py diff --git a/data_generation/.ipynb_checkpoints/generate_sine-checkpoint.ipynb b/data_generation/.ipynb_checkpoints/generate_sine-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/data_generation/.ipynb_checkpoints/generate_sine-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/data_generation/generate_sine.ipynb b/data_generation/generate_sine.ipynb new file mode 100644 index 0000000..bbd4ef2 --- /dev/null +++ b/data_generation/generate_sine.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5739f3c3-d71e-41f4-9408-3983e1fe7be2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0.05205668],\n", + " [ 0.01257862, -0.05982817],\n", + " [ 0.02515525, 0.17728985],\n", + " [ 0.03772789, 0.08132162],\n", + " [ 0.05029457, -0.20707477]])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "def generate_sine_with_noise(n_points, frequency, phase, amplitude, noise_sd):\n", + " # Generate an array of points from 0 to 2*pi\n", + " x = np.linspace(0, 2*np.pi, n_points)\n", + " \n", + " # Generate the sine wave\n", + " sine_wave = amplitude * np.sin(frequency * x + phase)\n", + " \n", + " # Generate Gaussian noise\n", + " noise = np.random.normal(scale=noise_sd, size=n_points)\n", + " \n", + " # Add the noise to the sine wave\n", + " sine_wave_noise = sine_wave + noise\n", + " \n", + " # Stack the sine wave and the noisy sine wave into a 2D array\n", + " output = np.column_stack((sine_wave, sine_wave_noise))\n", + " \n", + " return output\n", + "\n", + "# Test the function\n", + "output = generate_sine_with_noise(1000, 2, 0, 1, 0.1)\n", + "output[:5] # display the first 5 rows\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a5da0eae-330e-4391-b8a9-310bf9cb76be", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Unpack the sine wave and the noisy sine wave\n", + "sine_wave, sine_wave_noise = output.T\n", + "\n", + "# Generate the x values\n", + "x = np.linspace(0, 2*np.pi, len(sine_wave))\n", + "\n", + "# Create a figure and axis\n", + "fig, ax = plt.subplots()\n", + "\n", + "# Plot the sine wave\n", + "ax.plot(x, sine_wave, label='Sine wave')\n", + "\n", + "# Plot the noisy sine wave\n", + "ax.plot(x, sine_wave_noise, label='Noisy sine wave', alpha=0.7)\n", + "\n", + "# Add a legend\n", + "ax.legend()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b847578d-a39c-4f3a-b44c-5ad14a00629b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define a list of parameters for generating different sine waves\n", + "parameters = [\n", + " {'n_points': 1000, 'frequency': 2, 'phase': 0, 'amplitude': 1, 'noise_sd': 0.1},\n", + " {'n_points': 1000, 'frequency': 4, 'phase': 0, 'amplitude': 2, 'noise_sd': 0.2},\n", + " {'n_points': 1000, 'frequency': 1, 'phase': np.pi / 4, 'amplitude': 1, 'noise_sd': 0.3},\n", + " {'n_points': 1000, 'frequency': 3, 'phase': np.pi / 2, 'amplitude': 3, 'noise_sd': 0.4}\n", + "]\n", + "\n", + "# Create a figure and axes\n", + "fig, axes = plt.subplots(nrows=len(parameters), figsize=(10, 20))\n", + "\n", + "# Generate and plot sine waves for each set of parameters\n", + "for ax, params in zip(axes, parameters):\n", + " # Generate the sine wave and the noisy sine wave\n", + " output = generate_sine_with_noise(**params)\n", + " \n", + " # Unpack the sine wave and the noisy sine wave\n", + " sine_wave, sine_wave_noise = output.T\n", + " \n", + " # Generate the x values\n", + " x = np.linspace(0, 2*np.pi, len(sine_wave))\n", + "\n", + " # Plot the sine wave\n", + " ax.plot(x, sine_wave, label='Sine wave')\n", + "\n", + " # Plot the noisy sine wave\n", + " ax.plot(x, sine_wave_noise, label='Noisy sine wave', alpha=0.7)\n", + "\n", + " # Add a legend\n", + " ax.legend()\n", + "\n", + "# Show the plots\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5db8166f-6cb0-43f8-abe0-22f71400c715", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hyena_test/.ipynb_checkpoints/simple_hyena_model-checkpoint.ipynb b/hyena_test/.ipynb_checkpoints/simple_hyena_model-checkpoint.ipynb new file mode 100644 index 0000000..0b8e949 --- /dev/null +++ b/hyena_test/.ipynb_checkpoints/simple_hyena_model-checkpoint.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6c6e33cb-72f9-42fa-936a-33b5fe338d15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 1024, 128]) torch.Size([1, 1024, 128])\n", + "Causality check: gradients should not flow \"from future to past\"\n", + "tensor(3.2471e-09) tensor(0.4080)\n" + ] + } + ], + "source": [ + "# %load standalone_hyena.py\n", + "\"\"\"\n", + "Simplified standalone version of Hyena: https://arxiv.org/abs/2302.10866, designed for quick experimentation.\n", + "A complete version is available under `src.models.sequence.hyena`.\n", + "\"\"\"\n", + "\n", + "import math\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from einops import rearrange\n", + "\n", + "\n", + "def fftconv(u, k, D):\n", + " seqlen = u.shape[-1]\n", + " fft_size = 2 * seqlen\n", + " \n", + " k_f = torch.fft.rfft(k, n=fft_size) / fft_size\n", + " u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_size)\n", + " \n", + " if len(u.shape) > 3: k_f = k_f.unsqueeze(1)\n", + " y = torch.fft.irfft(u_f * k_f, n=fft_size, norm='forward')[..., :seqlen]\n", + "\n", + " out = y + u * D.unsqueeze(-1)\n", + " return out.to(dtype=u.dtype)\n", + "\n", + "\n", + "@torch.jit.script \n", + "def mul_sum(q, y):\n", + " return (q * y).sum(dim=1)\n", + "\n", + "class OptimModule(nn.Module):\n", + " \"\"\" Interface for Module that allows registering buffers/parameters with configurable optimizer hyperparameters \"\"\"\n", + "\n", + " def register(self, name, tensor, lr=None, wd=0.0):\n", + " \"\"\"Register a tensor with a configurable learning rate and 0 weight decay\"\"\"\n", + "\n", + " if lr == 0.0:\n", + " self.register_buffer(name, tensor)\n", + " else:\n", + " self.register_parameter(name, nn.Parameter(tensor))\n", + "\n", + " optim = {}\n", + " if lr is not None: optim[\"lr\"] = lr\n", + " if wd is not None: optim[\"weight_decay\"] = wd\n", + " setattr(getattr(self, name), \"_optim\", optim)\n", + " \n", + "\n", + "class Sin(nn.Module):\n", + " def __init__(self, dim, w=10, train_freq=True):\n", + " super().__init__()\n", + " self.freq = nn.Parameter(w * torch.ones(1, dim)) if train_freq else w * torch.ones(1, dim)\n", + "\n", + " def forward(self, x):\n", + " return torch.sin(self.freq * x)\n", + " \n", + " \n", + "class PositionalEmbedding(OptimModule):\n", + " def __init__(self, emb_dim: int, seq_len: int, lr_pos_emb: float=1e-5, **kwargs): \n", + " \"\"\"Complex exponential positional embeddings for Hyena filters.\"\"\" \n", + " super().__init__()\n", + " \n", + " self.seq_len = seq_len\n", + " # The time embedding fed to the filteres is normalized so that t_f = 1\n", + " t = torch.linspace(0, 1, self.seq_len)[None, :, None] # 1, L, 1\n", + " \n", + " if emb_dim > 1:\n", + " bands = (emb_dim - 1) // 2 \n", + " # To compute the right embeddings we use the \"proper\" linspace \n", + " t_rescaled = torch.linspace(0, seq_len - 1, seq_len)[None, :, None]\n", + " w = 2 * math.pi * t_rescaled / seq_len # 1, L, 1 \n", + " \n", + " f = torch.linspace(1e-4, bands - 1, bands)[None, None] \n", + " z = torch.exp(-1j * f * w)\n", + " z = torch.cat([t, z.real, z.imag], dim=-1)\n", + " self.register(\"z\", z, lr=lr_pos_emb) \n", + " self.register(\"t\", t, lr=0.0)\n", + " \n", + " def forward(self, L):\n", + " return self.z[:, :L], self.t[:, :L]\n", + " \n", + "\n", + "class ExponentialModulation(OptimModule):\n", + " def __init__(\n", + " self,\n", + " d_model,\n", + " fast_decay_pct=0.3,\n", + " slow_decay_pct=1.5,\n", + " target=1e-2,\n", + " modulation_lr=0.0,\n", + " modulate: bool=True,\n", + " shift: float = 0.0,\n", + " **kwargs\n", + " ):\n", + " super().__init__()\n", + " self.modulate = modulate\n", + " self.shift = shift\n", + " max_decay = math.log(target) / fast_decay_pct\n", + " min_decay = math.log(target) / slow_decay_pct\n", + " deltas = torch.linspace(min_decay, max_decay, d_model)[None, None]\n", + " self.register(\"deltas\", deltas, lr=modulation_lr)\n", + " \n", + " def forward(self, t, x):\n", + " if self.modulate:\n", + " decay = torch.exp(-t * self.deltas.abs()) \n", + " x = x * (decay + self.shift)\n", + " return x \n", + "\n", + "\n", + "class HyenaFilter(OptimModule):\n", + " def __init__(\n", + " self, \n", + " d_model,\n", + " emb_dim=3, # dim of input to MLP, augments with positional encoding\n", + " order=16, # width of the implicit MLP \n", + " fused_fft_conv=False,\n", + " seq_len=1024, \n", + " lr=1e-3, \n", + " lr_pos_emb=1e-5,\n", + " dropout=0.0, \n", + " w=1, # frequency of periodic activations \n", + " wd=0, # weight decay of kernel parameters \n", + " bias=True,\n", + " num_inner_mlps=2,\n", + " normalized=False,\n", + " **kwargs\n", + " ):\n", + " \"\"\"\n", + " Implicit long filter with modulation.\n", + " \n", + " Args:\n", + " d_model: number of channels in the input\n", + " emb_dim: dimension of the positional encoding (`emb_dim` - 1) // 2 is the number of bands\n", + " order: width of the FFN\n", + " num_inner_mlps: number of inner linear layers inside filter MLP\n", + " \"\"\"\n", + " super().__init__()\n", + " self.d_model = d_model\n", + " self.use_bias = bias\n", + " self.fused_fft_conv = fused_fft_conv\n", + " self.bias = nn.Parameter(torch.randn(self.d_model))\n", + " self.dropout = nn.Dropout(dropout)\n", + " \n", + " act = Sin(dim=order, w=w)\n", + " self.emb_dim = emb_dim\n", + " assert emb_dim % 2 != 0 and emb_dim >= 3, \"emb_dim must be odd and greater or equal to 3 (time, sine and cosine)\"\n", + " self.seq_len = seq_len\n", + " \n", + " self.pos_emb = PositionalEmbedding(emb_dim, seq_len, lr_pos_emb)\n", + " \n", + " self.implicit_filter = nn.Sequential(\n", + " nn.Linear(emb_dim, order),\n", + " act,\n", + " )\n", + " for i in range(num_inner_mlps):\n", + " self.implicit_filter.append(nn.Linear(order, order))\n", + " self.implicit_filter.append(act)\n", + "\n", + " self.implicit_filter.append(nn.Linear(order, d_model, bias=False))\n", + " \n", + " self.modulation = ExponentialModulation(d_model, **kwargs)\n", + " \n", + " self.normalized = normalized\n", + " for c in self.implicit_filter.children():\n", + " for name, v in c.state_dict().items(): \n", + " optim = {\"weight_decay\": wd, \"lr\": lr}\n", + " setattr(getattr(c, name), \"_optim\", optim)\n", + "\n", + " def filter(self, L, *args, **kwargs):\n", + " z, t = self.pos_emb(L)\n", + " h = self.implicit_filter(z)\n", + " h = self.modulation(t, h)\n", + " return h\n", + "\n", + " def forward(self, x, L, k=None, bias=None, *args, **kwargs):\n", + " if k is None: k = self.filter(L)\n", + " \n", + " # Ensure compatibility with filters that return a tuple \n", + " k = k[0] if type(k) is tuple else k \n", + "\n", + " y = fftconv(x, k, bias)\n", + " return y\n", + " \n", + " \n", + "class HyenaOperator(nn.Module):\n", + " def __init__(\n", + " self,\n", + " d_model,\n", + " l_max,\n", + " order=2, \n", + " filter_order=64,\n", + " dropout=0.0, \n", + " filter_dropout=0.0, \n", + " **filter_args,\n", + " ):\n", + " r\"\"\"\n", + " Hyena operator described in the paper https://arxiv.org/pdf/2302.10866.pdf\n", + " \n", + " Args:\n", + " d_model (int): Dimension of the input and output embeddings (width of the layer)\n", + " l_max: (int): Maximum input sequence length. Defaults to None\n", + " order: (int): Depth of the Hyena recurrence. Defaults to 2\n", + " dropout: (float): Dropout probability. Defaults to 0.0\n", + " filter_dropout: (float): Dropout probability for the filter. Defaults to 0.0\n", + " \"\"\"\n", + " super().__init__()\n", + " self.d_model = d_model\n", + " self.l_max = l_max\n", + " self.order = order\n", + " inner_width = d_model * (order + 1)\n", + " self.dropout = nn.Dropout(dropout)\n", + " self.in_proj = nn.Linear(d_model, inner_width)\n", + " self.out_proj = nn.Linear(d_model, d_model)\n", + " \n", + " self.short_filter = nn.Conv1d(\n", + " inner_width, \n", + " inner_width, \n", + " 3,\n", + " padding=2,\n", + " groups=inner_width\n", + " )\n", + " self.filter_fn = HyenaFilter(\n", + " d_model * (order - 1), \n", + " order=filter_order, \n", + " seq_len=l_max,\n", + " channels=1, \n", + " dropout=filter_dropout, \n", + " **filter_args\n", + " ) \n", + "\n", + " def forward(self, u, *args, **kwargs):\n", + " l = u.size(-2)\n", + " l_filter = min(l, self.l_max)\n", + " u = self.in_proj(u)\n", + " u = rearrange(u, 'b l d -> b d l')\n", + " \n", + " uc = self.short_filter(u)[...,:l_filter] \n", + " *x, v = uc.split(self.d_model, dim=1)\n", + " \n", + " k = self.filter_fn.filter(l_filter)[0]\n", + " k = rearrange(k, 'l (o d) -> o d l', o=self.order - 1)\n", + " bias = rearrange(self.filter_fn.bias, '(o d) -> o d', o=self.order - 1)\n", + " \n", + " for o, x_i in enumerate(reversed(x[1:])):\n", + " v = self.dropout(v * x_i)\n", + " v = self.filter_fn(v, l_filter, k=k[o], bias=bias[o])\n", + "\n", + " y = rearrange(v * x[0], 'b d l -> b l d')\n", + "\n", + " y = self.out_proj(y)\n", + " return y\n", + "\n", + " \n", + " \n", + "if __name__ == \"__main__\":\n", + " layer = HyenaOperator(\n", + " \n", + " d_model=128, \n", + " l_max=1024, \n", + " order=2, \n", + " filter_order=64\n", + " )\n", + " x = torch.randn(1, 1024, 128, requires_grad=True)\n", + " y = layer(x)\n", + " \n", + " print(x.shape, y.shape)\n", + " \n", + " grad = torch.autograd.grad(y[:, 10, :].sum(), x)[0]\n", + " print('Causality check: gradients should not flow \"from future to past\"')\n", + " print(grad[0, 11, :].sum(), grad[0, 9, :].sum())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "032ef08a-8cc6-491a-9eb8-4a6b3f2d165e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 1023, 1]) torch.Size([1, 1])\n" + ] + } + ], + "source": [ + "class HyenaOperatorAutoregressive1D(nn.Module):\n", + " def __init__(\n", + " self,\n", + " d_model,\n", + " l_max,\n", + " order=2, \n", + " filter_order=64,\n", + " dropout=0.0, \n", + " filter_dropout=0.0, \n", + " **filter_args,\n", + " ):\n", + " super().__init__()\n", + "\n", + " self.l_max = l_max\n", + " self.d_model = d_model\n", + " self.l_max = l_max\n", + " self.order = order\n", + " inner_width = d_model * (order + 1)\n", + "\n", + " self.dropout = nn.Dropout(dropout)\n", + " self.in_proj = nn.Linear(d_model, inner_width)\n", + " self.out_proj = nn.Linear(d_model, d_model)\n", + " self.fc_before = nn.Linear(1, d_model) # Fully connected layer before the main layer\n", + " self.fc_after = nn.Linear(d_model, 1) # Fully connected layer after the main layer\n", + "\n", + " self.operator = HyenaOperator(\n", + " d_model=d_model,\n", + " l_max=l_max,\n", + " order=order, \n", + " filter_order=filter_order,\n", + " dropout=dropout, \n", + " filter_dropout=filter_dropout, \n", + " **filter_args,\n", + " )\n", + "\n", + " def forward(self, u, *args, **kwargs):\n", + " # Increase the channel dimension from 1 to d_model\n", + " u = self.fc_before(u) \n", + " # Pass through the operator\n", + " u = self.operator(u)\n", + " last_state = u[:,-1,:]\n", + " # Decrease the channel dimension back to 1\n", + " y = self.fc_after(last_state)\n", + " return y\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " layer = HyenaOperatorAutoregressive1D(\n", + " d_model=128, \n", + " l_max=1024, \n", + " order=2, \n", + " filter_order=64\n", + " )\n", + "\n", + " x = torch.randn(1, 1023, 1, requires_grad=True) # 1D time series input\n", + " y = layer(x)\n", + "\n", + " #import pdb;pdb.set_trace()\n", + " print(x.shape, y.shape) # should now be [1, 1024, 1]\n", + "\n", + " #grad = torch.autograd.grad(y[:, 10, 0].sum(), x)[0]\n", + " #print('Causality check: gradients should not flow \"from future to past\"')\n", + " #print(grad[0, 11, 0].sum(), grad[0, 9, 0].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "80cde67b-992f-4cb0-8824-4a6b7e4984ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Epoch: 1 [0/640 (0%)]\tLoss: 0.433575\n", + "Train Epoch: 2 [0/640 (0%)]\tLoss: 0.054185\n", + "Train Epoch: 3 [0/640 (0%)]\tLoss: 0.007312\n", + "Train Epoch: 4 [0/640 (0%)]\tLoss: 0.004312\n", + "Train Epoch: 5 [0/640 (0%)]\tLoss: 0.003393\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[32], line 87\u001b[0m\n\u001b[1;32m 84\u001b[0m train_loader \u001b[38;5;241m=\u001b[39m DataLoader(dataset, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m64\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m11\u001b[39m): \u001b[38;5;66;03m# Train for 10 epochs\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[32], line 59\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(model, device, train_loader, optimizer, epoch)\u001b[0m\n\u001b[1;32m 57\u001b[0m data, target \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mto(device), target\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 58\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 59\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m#import pdb;pdb.set_trace()\u001b[39;00m\n\u001b[1;32m 62\u001b[0m loss \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mmse_loss(output, target)\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn[2], line 40\u001b[0m, in \u001b[0;36mHyenaOperatorAutoregressive1D.forward\u001b[0;34m(self, u, *args, **kwargs)\u001b[0m\n\u001b[1;32m 38\u001b[0m u \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc_before(u) \n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# Pass through the operator\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m u \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moperator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 41\u001b[0m last_state \u001b[38;5;241m=\u001b[39m u[:,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,:]\n\u001b[1;32m 42\u001b[0m \u001b[38;5;66;03m# Decrease the channel dimension back to 1\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn[1], line 237\u001b[0m, in \u001b[0;36mHyenaOperator.forward\u001b[0;34m(self, u, *args, **kwargs)\u001b[0m\n\u001b[1;32m 234\u001b[0m u \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39min_proj(u)\n\u001b[1;32m 235\u001b[0m u \u001b[38;5;241m=\u001b[39m rearrange(u, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb l d -> b d l\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 237\u001b[0m uc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshort_filter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m,:l_filter] \n\u001b[1;32m 238\u001b[0m \u001b[38;5;241m*\u001b[39mx, v \u001b[38;5;241m=\u001b[39m uc\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39md_model, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 240\u001b[0m k \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_fn\u001b[38;5;241m.\u001b[39mfilter(l_filter)[\u001b[38;5;241m0\u001b[39m]\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/conv.py:313\u001b[0m, in \u001b[0;36mConv1d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 313\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/conv.py:309\u001b[0m, in \u001b[0;36mConv1d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv1d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m 307\u001b[0m weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m 308\u001b[0m _single(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 309\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 310\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import torch\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import DataLoader, Dataset\n", + "import numpy as np\n", + "\n", + "def generate_sine_with_noise(n_points, frequency, phase, amplitude, noise_sd):\n", + " # Generate an array of points from 0 to 2*pi\n", + " x = np.linspace(0, 2*np.pi, n_points)\n", + " \n", + " # Generate the sine wave\n", + " sine_wave = amplitude * np.sin(frequency * x + phase)\n", + " \n", + " # Generate Gaussian noise\n", + " noise = np.random.normal(scale=noise_sd, size=n_points)\n", + " \n", + " # Add the noise to the sine wave\n", + " sine_wave_noise = sine_wave + noise\n", + " \n", + " # Stack the sine wave and the noisy sine wave into a 2D array\n", + " output = np.column_stack((sine_wave, sine_wave_noise))\n", + " \n", + " return output\n", + " \n", + " \n", + "class SineDataset(Dataset):\n", + " def __init__(self, n_samples, n_points, frequency_range, phase_range, amplitude_range, noise_sd_range):\n", + " self.n_samples = n_samples\n", + " self.n_points = n_points\n", + " self.frequency_range = frequency_range\n", + " self.phase_range = phase_range\n", + " self.amplitude_range = amplitude_range\n", + " self.noise_sd_range = noise_sd_range\n", + "\n", + " def __len__(self):\n", + " return self.n_samples\n", + "\n", + " def __getitem__(self, idx):\n", + " # Generate random attributes\n", + " frequency = np.random.uniform(*self.frequency_range)\n", + " phase = np.random.uniform(*self.phase_range)\n", + " amplitude = np.random.uniform(*self.amplitude_range)\n", + " noise_sd = np.random.uniform(*self.noise_sd_range)\n", + "\n", + " # Generate sine wave with the random attributes\n", + " sine_wave = generate_sine_with_noise(self.n_points, frequency, phase, amplitude, noise_sd)\n", + "\n", + " return torch.Tensor(sine_wave[:-1, 1, None]), torch.Tensor(sine_wave[-1:, 0])\n", + "\n", + "# Usage:\n", + "dataset = SineDataset(640, 1025, (1, 3), (0, 2*np.pi), (0.5, 1.5), (0.05, 0.15))\n", + "\n", + "def train(model, device, train_loader, optimizer, epoch):\n", + " model.train()\n", + " for batch_idx, (data, target) in enumerate(train_loader):\n", + " #data = data[...,None]\n", + " data, target = data.to(device), target.to(device)\n", + " optimizer.zero_grad()\n", + " output = model(data)\n", + " #import pdb;pdb.set_trace()\n", + "\n", + " loss = F.mse_loss(output, target)\n", + " loss.backward()\n", + " optimizer.step()\n", + " if batch_idx % 10 == 0:\n", + " print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n", + " epoch, batch_idx * len(data), len(train_loader.dataset),\n", + " 100. * batch_idx / len(train_loader), loss.item()))\n", + "\n", + "if __name__ == \"__main__\":\n", + " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + " model = HyenaOperatorAutoregressive1D(\n", + " d_model=128, \n", + " l_max=1024, \n", + " order=2, \n", + " filter_order=64\n", + " ).to(device)\n", + "\n", + " optimizer = optim.Adam(model.parameters())\n", + "\n", + " # Assume 10000 samples in the dataset\n", + " #dataset = SineDataset(10000, 1025, 2, 0, 1, 0.1)\n", + " train_loader = DataLoader(dataset, batch_size=64, shuffle=True)\n", + "\n", + " for epoch in range(1, 11): # Train for 10 epochs\n", + " train(model, device, train_loader, optimizer, epoch)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc9f9031-5ee1-49f8-a70f-ad85ca015596", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b763e03-baab-4b02-bae0-5747461bca7f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hyena_test/.ipynb_checkpoints/standalone_hyena-checkpoint.py b/hyena_test/.ipynb_checkpoints/standalone_hyena-checkpoint.py new file mode 100644 index 0000000..b2b7fa3 --- /dev/null +++ b/hyena_test/.ipynb_checkpoints/standalone_hyena-checkpoint.py @@ -0,0 +1,268 @@ +""" +Simplified standalone version of Hyena: https://arxiv.org/abs/2302.10866, designed for quick experimentation. +A complete version is available under `src.models.sequence.hyena`. +""" + +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + + +def fftconv(u, k, D): + seqlen = u.shape[-1] + fft_size = 2 * seqlen + + k_f = torch.fft.rfft(k, n=fft_size) / fft_size + u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_size) + + if len(u.shape) > 3: k_f = k_f.unsqueeze(1) + y = torch.fft.irfft(u_f * k_f, n=fft_size, norm='forward')[..., :seqlen] + + out = y + u * D.unsqueeze(-1) + return out.to(dtype=u.dtype) + + +@torch.jit.script +def mul_sum(q, y): + return (q * y).sum(dim=1) + +class OptimModule(nn.Module): + """ Interface for Module that allows registering buffers/parameters with configurable optimizer hyperparameters """ + + def register(self, name, tensor, lr=None, wd=0.0): + """Register a tensor with a configurable learning rate and 0 weight decay""" + + if lr == 0.0: + self.register_buffer(name, tensor) + else: + self.register_parameter(name, nn.Parameter(tensor)) + + optim = {} + if lr is not None: optim["lr"] = lr + if wd is not None: optim["weight_decay"] = wd + setattr(getattr(self, name), "_optim", optim) + + +class Sin(nn.Module): + def __init__(self, dim, w=10, train_freq=True): + super().__init__() + self.freq = nn.Parameter(w * torch.ones(1, dim)) if train_freq else w * torch.ones(1, dim) + + def forward(self, x): + return torch.sin(self.freq * x) + + +class PositionalEmbedding(OptimModule): + def __init__(self, emb_dim: int, seq_len: int, lr_pos_emb: float=1e-5, **kwargs): + """Complex exponential positional embeddings for Hyena filters.""" + super().__init__() + + self.seq_len = seq_len + # The time embedding fed to the filteres is normalized so that t_f = 1 + t = torch.linspace(0, 1, self.seq_len)[None, :, None] # 1, L, 1 + + if emb_dim > 1: + bands = (emb_dim - 1) // 2 + # To compute the right embeddings we use the "proper" linspace + t_rescaled = torch.linspace(0, seq_len - 1, seq_len)[None, :, None] + w = 2 * math.pi * t_rescaled / seq_len # 1, L, 1 + + f = torch.linspace(1e-4, bands - 1, bands)[None, None] + z = torch.exp(-1j * f * w) + z = torch.cat([t, z.real, z.imag], dim=-1) + self.register("z", z, lr=lr_pos_emb) + self.register("t", t, lr=0.0) + + def forward(self, L): + return self.z[:, :L], self.t[:, :L] + + +class ExponentialModulation(OptimModule): + def __init__( + self, + d_model, + fast_decay_pct=0.3, + slow_decay_pct=1.5, + target=1e-2, + modulation_lr=0.0, + modulate: bool=True, + shift: float = 0.0, + **kwargs + ): + super().__init__() + self.modulate = modulate + self.shift = shift + max_decay = math.log(target) / fast_decay_pct + min_decay = math.log(target) / slow_decay_pct + deltas = torch.linspace(min_decay, max_decay, d_model)[None, None] + self.register("deltas", deltas, lr=modulation_lr) + + def forward(self, t, x): + if self.modulate: + decay = torch.exp(-t * self.deltas.abs()) + x = x * (decay + self.shift) + return x + + +class HyenaFilter(OptimModule): + def __init__( + self, + d_model, + emb_dim=3, # dim of input to MLP, augments with positional encoding + order=16, # width of the implicit MLP + fused_fft_conv=False, + seq_len=1024, + lr=1e-3, + lr_pos_emb=1e-5, + dropout=0.0, + w=1, # frequency of periodic activations + wd=0, # weight decay of kernel parameters + bias=True, + num_inner_mlps=2, + normalized=False, + **kwargs + ): + """ + Implicit long filter with modulation. + + Args: + d_model: number of channels in the input + emb_dim: dimension of the positional encoding (`emb_dim` - 1) // 2 is the number of bands + order: width of the FFN + num_inner_mlps: number of inner linear layers inside filter MLP + """ + super().__init__() + self.d_model = d_model + self.use_bias = bias + self.fused_fft_conv = fused_fft_conv + self.bias = nn.Parameter(torch.randn(self.d_model)) + self.dropout = nn.Dropout(dropout) + + act = Sin(dim=order, w=w) + self.emb_dim = emb_dim + assert emb_dim % 2 != 0 and emb_dim >= 3, "emb_dim must be odd and greater or equal to 3 (time, sine and cosine)" + self.seq_len = seq_len + + self.pos_emb = PositionalEmbedding(emb_dim, seq_len, lr_pos_emb) + + self.implicit_filter = nn.Sequential( + nn.Linear(emb_dim, order), + act, + ) + for i in range(num_inner_mlps): + self.implicit_filter.append(nn.Linear(order, order)) + self.implicit_filter.append(act) + + self.implicit_filter.append(nn.Linear(order, d_model, bias=False)) + + self.modulation = ExponentialModulation(d_model, **kwargs) + + self.normalized = normalized + for c in self.implicit_filter.children(): + for name, v in c.state_dict().items(): + optim = {"weight_decay": wd, "lr": lr} + setattr(getattr(c, name), "_optim", optim) + + def filter(self, L, *args, **kwargs): + z, t = self.pos_emb(L) + h = self.implicit_filter(z) + h = self.modulation(t, h) + return h + + def forward(self, x, L, k=None, bias=None, *args, **kwargs): + if k is None: k = self.filter(L) + + # Ensure compatibility with filters that return a tuple + k = k[0] if type(k) is tuple else k + + y = fftconv(x, k, bias) + return y + + +class HyenaOperator(nn.Module): + def __init__( + self, + d_model, + l_max, + order=2, + filter_order=64, + dropout=0.0, + filter_dropout=0.0, + **filter_args, + ): + r""" + Hyena operator described in the paper https://arxiv.org/pdf/2302.10866.pdf + + Args: + d_model (int): Dimension of the input and output embeddings (width of the layer) + l_max: (int): Maximum input sequence length. Defaults to None + order: (int): Depth of the Hyena recurrence. Defaults to 2 + dropout: (float): Dropout probability. Defaults to 0.0 + filter_dropout: (float): Dropout probability for the filter. Defaults to 0.0 + """ + super().__init__() + self.d_model = d_model + self.l_max = l_max + self.order = order + inner_width = d_model * (order + 1) + self.dropout = nn.Dropout(dropout) + self.in_proj = nn.Linear(d_model, inner_width) + self.out_proj = nn.Linear(d_model, d_model) + + self.short_filter = nn.Conv1d( + inner_width, + inner_width, + 3, + padding=2, + groups=inner_width + ) + self.filter_fn = HyenaFilter( + d_model * (order - 1), + order=filter_order, + seq_len=l_max, + channels=1, + dropout=filter_dropout, + **filter_args + ) + + def forward(self, u, *args, **kwargs): + l = u.size(-2) + l_filter = min(l, self.l_max) + u = self.in_proj(u) + u = rearrange(u, 'b l d -> b d l') + + uc = self.short_filter(u)[...,:l_filter] + *x, v = uc.split(self.d_model, dim=1) + + k = self.filter_fn.filter(l_filter)[0] + k = rearrange(k, 'l (o d) -> o d l', o=self.order - 1) + bias = rearrange(self.filter_fn.bias, '(o d) -> o d', o=self.order - 1) + + for o, x_i in enumerate(reversed(x[1:])): + v = self.dropout(v * x_i) + v = self.filter_fn(v, l_filter, k=k[o], bias=bias[o]) + + y = rearrange(v * x[0], 'b d l -> b l d') + + y = self.out_proj(y) + return y + + + +if __name__ == "__main__": + layer = HyenaOperator( + d_model=512, + l_max=1024, + order=2, + filter_order=64 + ) + x = torch.randn(1, 1024, 512, requires_grad=True) + y = layer(x) + + print(x.shape, y.shape) + + grad = torch.autograd.grad(y[:, 10, :].sum(), x)[0] + print('Causality check: gradients should not flow "from future to past"') + print(grad[0, 11, :].sum(), grad[0, 9, :].sum()) diff --git a/hyena_test/simple_hyena_model.ipynb b/hyena_test/simple_hyena_model.ipynb new file mode 100644 index 0000000..0b8e949 --- /dev/null +++ b/hyena_test/simple_hyena_model.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6c6e33cb-72f9-42fa-936a-33b5fe338d15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 1024, 128]) torch.Size([1, 1024, 128])\n", + "Causality check: gradients should not flow \"from future to past\"\n", + "tensor(3.2471e-09) tensor(0.4080)\n" + ] + } + ], + "source": [ + "# %load standalone_hyena.py\n", + "\"\"\"\n", + "Simplified standalone version of Hyena: https://arxiv.org/abs/2302.10866, designed for quick experimentation.\n", + "A complete version is available under `src.models.sequence.hyena`.\n", + "\"\"\"\n", + "\n", + "import math\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from einops import rearrange\n", + "\n", + "\n", + "def fftconv(u, k, D):\n", + " seqlen = u.shape[-1]\n", + " fft_size = 2 * seqlen\n", + " \n", + " k_f = torch.fft.rfft(k, n=fft_size) / fft_size\n", + " u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_size)\n", + " \n", + " if len(u.shape) > 3: k_f = k_f.unsqueeze(1)\n", + " y = torch.fft.irfft(u_f * k_f, n=fft_size, norm='forward')[..., :seqlen]\n", + "\n", + " out = y + u * D.unsqueeze(-1)\n", + " return out.to(dtype=u.dtype)\n", + "\n", + "\n", + "@torch.jit.script \n", + "def mul_sum(q, y):\n", + " return (q * y).sum(dim=1)\n", + "\n", + "class OptimModule(nn.Module):\n", + " \"\"\" Interface for Module that allows registering buffers/parameters with configurable optimizer hyperparameters \"\"\"\n", + "\n", + " def register(self, name, tensor, lr=None, wd=0.0):\n", + " \"\"\"Register a tensor with a configurable learning rate and 0 weight decay\"\"\"\n", + "\n", + " if lr == 0.0:\n", + " self.register_buffer(name, tensor)\n", + " else:\n", + " self.register_parameter(name, nn.Parameter(tensor))\n", + "\n", + " optim = {}\n", + " if lr is not None: optim[\"lr\"] = lr\n", + " if wd is not None: optim[\"weight_decay\"] = wd\n", + " setattr(getattr(self, name), \"_optim\", optim)\n", + " \n", + "\n", + "class Sin(nn.Module):\n", + " def __init__(self, dim, w=10, train_freq=True):\n", + " super().__init__()\n", + " self.freq = nn.Parameter(w * torch.ones(1, dim)) if train_freq else w * torch.ones(1, dim)\n", + "\n", + " def forward(self, x):\n", + " return torch.sin(self.freq * x)\n", + " \n", + " \n", + "class PositionalEmbedding(OptimModule):\n", + " def __init__(self, emb_dim: int, seq_len: int, lr_pos_emb: float=1e-5, **kwargs): \n", + " \"\"\"Complex exponential positional embeddings for Hyena filters.\"\"\" \n", + " super().__init__()\n", + " \n", + " self.seq_len = seq_len\n", + " # The time embedding fed to the filteres is normalized so that t_f = 1\n", + " t = torch.linspace(0, 1, self.seq_len)[None, :, None] # 1, L, 1\n", + " \n", + " if emb_dim > 1:\n", + " bands = (emb_dim - 1) // 2 \n", + " # To compute the right embeddings we use the \"proper\" linspace \n", + " t_rescaled = torch.linspace(0, seq_len - 1, seq_len)[None, :, None]\n", + " w = 2 * math.pi * t_rescaled / seq_len # 1, L, 1 \n", + " \n", + " f = torch.linspace(1e-4, bands - 1, bands)[None, None] \n", + " z = torch.exp(-1j * f * w)\n", + " z = torch.cat([t, z.real, z.imag], dim=-1)\n", + " self.register(\"z\", z, lr=lr_pos_emb) \n", + " self.register(\"t\", t, lr=0.0)\n", + " \n", + " def forward(self, L):\n", + " return self.z[:, :L], self.t[:, :L]\n", + " \n", + "\n", + "class ExponentialModulation(OptimModule):\n", + " def __init__(\n", + " self,\n", + " d_model,\n", + " fast_decay_pct=0.3,\n", + " slow_decay_pct=1.5,\n", + " target=1e-2,\n", + " modulation_lr=0.0,\n", + " modulate: bool=True,\n", + " shift: float = 0.0,\n", + " **kwargs\n", + " ):\n", + " super().__init__()\n", + " self.modulate = modulate\n", + " self.shift = shift\n", + " max_decay = math.log(target) / fast_decay_pct\n", + " min_decay = math.log(target) / slow_decay_pct\n", + " deltas = torch.linspace(min_decay, max_decay, d_model)[None, None]\n", + " self.register(\"deltas\", deltas, lr=modulation_lr)\n", + " \n", + " def forward(self, t, x):\n", + " if self.modulate:\n", + " decay = torch.exp(-t * self.deltas.abs()) \n", + " x = x * (decay + self.shift)\n", + " return x \n", + "\n", + "\n", + "class HyenaFilter(OptimModule):\n", + " def __init__(\n", + " self, \n", + " d_model,\n", + " emb_dim=3, # dim of input to MLP, augments with positional encoding\n", + " order=16, # width of the implicit MLP \n", + " fused_fft_conv=False,\n", + " seq_len=1024, \n", + " lr=1e-3, \n", + " lr_pos_emb=1e-5,\n", + " dropout=0.0, \n", + " w=1, # frequency of periodic activations \n", + " wd=0, # weight decay of kernel parameters \n", + " bias=True,\n", + " num_inner_mlps=2,\n", + " normalized=False,\n", + " **kwargs\n", + " ):\n", + " \"\"\"\n", + " Implicit long filter with modulation.\n", + " \n", + " Args:\n", + " d_model: number of channels in the input\n", + " emb_dim: dimension of the positional encoding (`emb_dim` - 1) // 2 is the number of bands\n", + " order: width of the FFN\n", + " num_inner_mlps: number of inner linear layers inside filter MLP\n", + " \"\"\"\n", + " super().__init__()\n", + " self.d_model = d_model\n", + " self.use_bias = bias\n", + " self.fused_fft_conv = fused_fft_conv\n", + " self.bias = nn.Parameter(torch.randn(self.d_model))\n", + " self.dropout = nn.Dropout(dropout)\n", + " \n", + " act = Sin(dim=order, w=w)\n", + " self.emb_dim = emb_dim\n", + " assert emb_dim % 2 != 0 and emb_dim >= 3, \"emb_dim must be odd and greater or equal to 3 (time, sine and cosine)\"\n", + " self.seq_len = seq_len\n", + " \n", + " self.pos_emb = PositionalEmbedding(emb_dim, seq_len, lr_pos_emb)\n", + " \n", + " self.implicit_filter = nn.Sequential(\n", + " nn.Linear(emb_dim, order),\n", + " act,\n", + " )\n", + " for i in range(num_inner_mlps):\n", + " self.implicit_filter.append(nn.Linear(order, order))\n", + " self.implicit_filter.append(act)\n", + "\n", + " self.implicit_filter.append(nn.Linear(order, d_model, bias=False))\n", + " \n", + " self.modulation = ExponentialModulation(d_model, **kwargs)\n", + " \n", + " self.normalized = normalized\n", + " for c in self.implicit_filter.children():\n", + " for name, v in c.state_dict().items(): \n", + " optim = {\"weight_decay\": wd, \"lr\": lr}\n", + " setattr(getattr(c, name), \"_optim\", optim)\n", + "\n", + " def filter(self, L, *args, **kwargs):\n", + " z, t = self.pos_emb(L)\n", + " h = self.implicit_filter(z)\n", + " h = self.modulation(t, h)\n", + " return h\n", + "\n", + " def forward(self, x, L, k=None, bias=None, *args, **kwargs):\n", + " if k is None: k = self.filter(L)\n", + " \n", + " # Ensure compatibility with filters that return a tuple \n", + " k = k[0] if type(k) is tuple else k \n", + "\n", + " y = fftconv(x, k, bias)\n", + " return y\n", + " \n", + " \n", + "class HyenaOperator(nn.Module):\n", + " def __init__(\n", + " self,\n", + " d_model,\n", + " l_max,\n", + " order=2, \n", + " filter_order=64,\n", + " dropout=0.0, \n", + " filter_dropout=0.0, \n", + " **filter_args,\n", + " ):\n", + " r\"\"\"\n", + " Hyena operator described in the paper https://arxiv.org/pdf/2302.10866.pdf\n", + " \n", + " Args:\n", + " d_model (int): Dimension of the input and output embeddings (width of the layer)\n", + " l_max: (int): Maximum input sequence length. Defaults to None\n", + " order: (int): Depth of the Hyena recurrence. Defaults to 2\n", + " dropout: (float): Dropout probability. Defaults to 0.0\n", + " filter_dropout: (float): Dropout probability for the filter. Defaults to 0.0\n", + " \"\"\"\n", + " super().__init__()\n", + " self.d_model = d_model\n", + " self.l_max = l_max\n", + " self.order = order\n", + " inner_width = d_model * (order + 1)\n", + " self.dropout = nn.Dropout(dropout)\n", + " self.in_proj = nn.Linear(d_model, inner_width)\n", + " self.out_proj = nn.Linear(d_model, d_model)\n", + " \n", + " self.short_filter = nn.Conv1d(\n", + " inner_width, \n", + " inner_width, \n", + " 3,\n", + " padding=2,\n", + " groups=inner_width\n", + " )\n", + " self.filter_fn = HyenaFilter(\n", + " d_model * (order - 1), \n", + " order=filter_order, \n", + " seq_len=l_max,\n", + " channels=1, \n", + " dropout=filter_dropout, \n", + " **filter_args\n", + " ) \n", + "\n", + " def forward(self, u, *args, **kwargs):\n", + " l = u.size(-2)\n", + " l_filter = min(l, self.l_max)\n", + " u = self.in_proj(u)\n", + " u = rearrange(u, 'b l d -> b d l')\n", + " \n", + " uc = self.short_filter(u)[...,:l_filter] \n", + " *x, v = uc.split(self.d_model, dim=1)\n", + " \n", + " k = self.filter_fn.filter(l_filter)[0]\n", + " k = rearrange(k, 'l (o d) -> o d l', o=self.order - 1)\n", + " bias = rearrange(self.filter_fn.bias, '(o d) -> o d', o=self.order - 1)\n", + " \n", + " for o, x_i in enumerate(reversed(x[1:])):\n", + " v = self.dropout(v * x_i)\n", + " v = self.filter_fn(v, l_filter, k=k[o], bias=bias[o])\n", + "\n", + " y = rearrange(v * x[0], 'b d l -> b l d')\n", + "\n", + " y = self.out_proj(y)\n", + " return y\n", + "\n", + " \n", + " \n", + "if __name__ == \"__main__\":\n", + " layer = HyenaOperator(\n", + " \n", + " d_model=128, \n", + " l_max=1024, \n", + " order=2, \n", + " filter_order=64\n", + " )\n", + " x = torch.randn(1, 1024, 128, requires_grad=True)\n", + " y = layer(x)\n", + " \n", + " print(x.shape, y.shape)\n", + " \n", + " grad = torch.autograd.grad(y[:, 10, :].sum(), x)[0]\n", + " print('Causality check: gradients should not flow \"from future to past\"')\n", + " print(grad[0, 11, :].sum(), grad[0, 9, :].sum())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "032ef08a-8cc6-491a-9eb8-4a6b3f2d165e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 1023, 1]) torch.Size([1, 1])\n" + ] + } + ], + "source": [ + "class HyenaOperatorAutoregressive1D(nn.Module):\n", + " def __init__(\n", + " self,\n", + " d_model,\n", + " l_max,\n", + " order=2, \n", + " filter_order=64,\n", + " dropout=0.0, \n", + " filter_dropout=0.0, \n", + " **filter_args,\n", + " ):\n", + " super().__init__()\n", + "\n", + " self.l_max = l_max\n", + " self.d_model = d_model\n", + " self.l_max = l_max\n", + " self.order = order\n", + " inner_width = d_model * (order + 1)\n", + "\n", + " self.dropout = nn.Dropout(dropout)\n", + " self.in_proj = nn.Linear(d_model, inner_width)\n", + " self.out_proj = nn.Linear(d_model, d_model)\n", + " self.fc_before = nn.Linear(1, d_model) # Fully connected layer before the main layer\n", + " self.fc_after = nn.Linear(d_model, 1) # Fully connected layer after the main layer\n", + "\n", + " self.operator = HyenaOperator(\n", + " d_model=d_model,\n", + " l_max=l_max,\n", + " order=order, \n", + " filter_order=filter_order,\n", + " dropout=dropout, \n", + " filter_dropout=filter_dropout, \n", + " **filter_args,\n", + " )\n", + "\n", + " def forward(self, u, *args, **kwargs):\n", + " # Increase the channel dimension from 1 to d_model\n", + " u = self.fc_before(u) \n", + " # Pass through the operator\n", + " u = self.operator(u)\n", + " last_state = u[:,-1,:]\n", + " # Decrease the channel dimension back to 1\n", + " y = self.fc_after(last_state)\n", + " return y\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " layer = HyenaOperatorAutoregressive1D(\n", + " d_model=128, \n", + " l_max=1024, \n", + " order=2, \n", + " filter_order=64\n", + " )\n", + "\n", + " x = torch.randn(1, 1023, 1, requires_grad=True) # 1D time series input\n", + " y = layer(x)\n", + "\n", + " #import pdb;pdb.set_trace()\n", + " print(x.shape, y.shape) # should now be [1, 1024, 1]\n", + "\n", + " #grad = torch.autograd.grad(y[:, 10, 0].sum(), x)[0]\n", + " #print('Causality check: gradients should not flow \"from future to past\"')\n", + " #print(grad[0, 11, 0].sum(), grad[0, 9, 0].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "80cde67b-992f-4cb0-8824-4a6b7e4984ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Epoch: 1 [0/640 (0%)]\tLoss: 0.433575\n", + "Train Epoch: 2 [0/640 (0%)]\tLoss: 0.054185\n", + "Train Epoch: 3 [0/640 (0%)]\tLoss: 0.007312\n", + "Train Epoch: 4 [0/640 (0%)]\tLoss: 0.004312\n", + "Train Epoch: 5 [0/640 (0%)]\tLoss: 0.003393\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[32], line 87\u001b[0m\n\u001b[1;32m 84\u001b[0m train_loader \u001b[38;5;241m=\u001b[39m DataLoader(dataset, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m64\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m11\u001b[39m): \u001b[38;5;66;03m# Train for 10 epochs\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[32], line 59\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(model, device, train_loader, optimizer, epoch)\u001b[0m\n\u001b[1;32m 57\u001b[0m data, target \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mto(device), target\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 58\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 59\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m#import pdb;pdb.set_trace()\u001b[39;00m\n\u001b[1;32m 62\u001b[0m loss \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mmse_loss(output, target)\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn[2], line 40\u001b[0m, in \u001b[0;36mHyenaOperatorAutoregressive1D.forward\u001b[0;34m(self, u, *args, **kwargs)\u001b[0m\n\u001b[1;32m 38\u001b[0m u \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc_before(u) \n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# Pass through the operator\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m u \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moperator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 41\u001b[0m last_state \u001b[38;5;241m=\u001b[39m u[:,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,:]\n\u001b[1;32m 42\u001b[0m \u001b[38;5;66;03m# Decrease the channel dimension back to 1\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn[1], line 237\u001b[0m, in \u001b[0;36mHyenaOperator.forward\u001b[0;34m(self, u, *args, **kwargs)\u001b[0m\n\u001b[1;32m 234\u001b[0m u \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39min_proj(u)\n\u001b[1;32m 235\u001b[0m u \u001b[38;5;241m=\u001b[39m rearrange(u, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb l d -> b d l\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 237\u001b[0m uc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshort_filter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m,:l_filter] \n\u001b[1;32m 238\u001b[0m \u001b[38;5;241m*\u001b[39mx, v \u001b[38;5;241m=\u001b[39m uc\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39md_model, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 240\u001b[0m k \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_fn\u001b[38;5;241m.\u001b[39mfilter(l_filter)[\u001b[38;5;241m0\u001b[39m]\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/conv.py:313\u001b[0m, in \u001b[0;36mConv1d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 313\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/conv.py:309\u001b[0m, in \u001b[0;36mConv1d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv1d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m 307\u001b[0m weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m 308\u001b[0m _single(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 309\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 310\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import torch\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import DataLoader, Dataset\n", + "import numpy as np\n", + "\n", + "def generate_sine_with_noise(n_points, frequency, phase, amplitude, noise_sd):\n", + " # Generate an array of points from 0 to 2*pi\n", + " x = np.linspace(0, 2*np.pi, n_points)\n", + " \n", + " # Generate the sine wave\n", + " sine_wave = amplitude * np.sin(frequency * x + phase)\n", + " \n", + " # Generate Gaussian noise\n", + " noise = np.random.normal(scale=noise_sd, size=n_points)\n", + " \n", + " # Add the noise to the sine wave\n", + " sine_wave_noise = sine_wave + noise\n", + " \n", + " # Stack the sine wave and the noisy sine wave into a 2D array\n", + " output = np.column_stack((sine_wave, sine_wave_noise))\n", + " \n", + " return output\n", + " \n", + " \n", + "class SineDataset(Dataset):\n", + " def __init__(self, n_samples, n_points, frequency_range, phase_range, amplitude_range, noise_sd_range):\n", + " self.n_samples = n_samples\n", + " self.n_points = n_points\n", + " self.frequency_range = frequency_range\n", + " self.phase_range = phase_range\n", + " self.amplitude_range = amplitude_range\n", + " self.noise_sd_range = noise_sd_range\n", + "\n", + " def __len__(self):\n", + " return self.n_samples\n", + "\n", + " def __getitem__(self, idx):\n", + " # Generate random attributes\n", + " frequency = np.random.uniform(*self.frequency_range)\n", + " phase = np.random.uniform(*self.phase_range)\n", + " amplitude = np.random.uniform(*self.amplitude_range)\n", + " noise_sd = np.random.uniform(*self.noise_sd_range)\n", + "\n", + " # Generate sine wave with the random attributes\n", + " sine_wave = generate_sine_with_noise(self.n_points, frequency, phase, amplitude, noise_sd)\n", + "\n", + " return torch.Tensor(sine_wave[:-1, 1, None]), torch.Tensor(sine_wave[-1:, 0])\n", + "\n", + "# Usage:\n", + "dataset = SineDataset(640, 1025, (1, 3), (0, 2*np.pi), (0.5, 1.5), (0.05, 0.15))\n", + "\n", + "def train(model, device, train_loader, optimizer, epoch):\n", + " model.train()\n", + " for batch_idx, (data, target) in enumerate(train_loader):\n", + " #data = data[...,None]\n", + " data, target = data.to(device), target.to(device)\n", + " optimizer.zero_grad()\n", + " output = model(data)\n", + " #import pdb;pdb.set_trace()\n", + "\n", + " loss = F.mse_loss(output, target)\n", + " loss.backward()\n", + " optimizer.step()\n", + " if batch_idx % 10 == 0:\n", + " print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n", + " epoch, batch_idx * len(data), len(train_loader.dataset),\n", + " 100. * batch_idx / len(train_loader), loss.item()))\n", + "\n", + "if __name__ == \"__main__\":\n", + " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + " model = HyenaOperatorAutoregressive1D(\n", + " d_model=128, \n", + " l_max=1024, \n", + " order=2, \n", + " filter_order=64\n", + " ).to(device)\n", + "\n", + " optimizer = optim.Adam(model.parameters())\n", + "\n", + " # Assume 10000 samples in the dataset\n", + " #dataset = SineDataset(10000, 1025, 2, 0, 1, 0.1)\n", + " train_loader = DataLoader(dataset, batch_size=64, shuffle=True)\n", + "\n", + " for epoch in range(1, 11): # Train for 10 epochs\n", + " train(model, device, train_loader, optimizer, epoch)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc9f9031-5ee1-49f8-a70f-ad85ca015596", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b763e03-baab-4b02-bae0-5747461bca7f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hyena_test/standalone_hyena.py b/hyena_test/standalone_hyena.py new file mode 100644 index 0000000..b2b7fa3 --- /dev/null +++ b/hyena_test/standalone_hyena.py @@ -0,0 +1,268 @@ +""" +Simplified standalone version of Hyena: https://arxiv.org/abs/2302.10866, designed for quick experimentation. +A complete version is available under `src.models.sequence.hyena`. +""" + +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + + +def fftconv(u, k, D): + seqlen = u.shape[-1] + fft_size = 2 * seqlen + + k_f = torch.fft.rfft(k, n=fft_size) / fft_size + u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_size) + + if len(u.shape) > 3: k_f = k_f.unsqueeze(1) + y = torch.fft.irfft(u_f * k_f, n=fft_size, norm='forward')[..., :seqlen] + + out = y + u * D.unsqueeze(-1) + return out.to(dtype=u.dtype) + + +@torch.jit.script +def mul_sum(q, y): + return (q * y).sum(dim=1) + +class OptimModule(nn.Module): + """ Interface for Module that allows registering buffers/parameters with configurable optimizer hyperparameters """ + + def register(self, name, tensor, lr=None, wd=0.0): + """Register a tensor with a configurable learning rate and 0 weight decay""" + + if lr == 0.0: + self.register_buffer(name, tensor) + else: + self.register_parameter(name, nn.Parameter(tensor)) + + optim = {} + if lr is not None: optim["lr"] = lr + if wd is not None: optim["weight_decay"] = wd + setattr(getattr(self, name), "_optim", optim) + + +class Sin(nn.Module): + def __init__(self, dim, w=10, train_freq=True): + super().__init__() + self.freq = nn.Parameter(w * torch.ones(1, dim)) if train_freq else w * torch.ones(1, dim) + + def forward(self, x): + return torch.sin(self.freq * x) + + +class PositionalEmbedding(OptimModule): + def __init__(self, emb_dim: int, seq_len: int, lr_pos_emb: float=1e-5, **kwargs): + """Complex exponential positional embeddings for Hyena filters.""" + super().__init__() + + self.seq_len = seq_len + # The time embedding fed to the filteres is normalized so that t_f = 1 + t = torch.linspace(0, 1, self.seq_len)[None, :, None] # 1, L, 1 + + if emb_dim > 1: + bands = (emb_dim - 1) // 2 + # To compute the right embeddings we use the "proper" linspace + t_rescaled = torch.linspace(0, seq_len - 1, seq_len)[None, :, None] + w = 2 * math.pi * t_rescaled / seq_len # 1, L, 1 + + f = torch.linspace(1e-4, bands - 1, bands)[None, None] + z = torch.exp(-1j * f * w) + z = torch.cat([t, z.real, z.imag], dim=-1) + self.register("z", z, lr=lr_pos_emb) + self.register("t", t, lr=0.0) + + def forward(self, L): + return self.z[:, :L], self.t[:, :L] + + +class ExponentialModulation(OptimModule): + def __init__( + self, + d_model, + fast_decay_pct=0.3, + slow_decay_pct=1.5, + target=1e-2, + modulation_lr=0.0, + modulate: bool=True, + shift: float = 0.0, + **kwargs + ): + super().__init__() + self.modulate = modulate + self.shift = shift + max_decay = math.log(target) / fast_decay_pct + min_decay = math.log(target) / slow_decay_pct + deltas = torch.linspace(min_decay, max_decay, d_model)[None, None] + self.register("deltas", deltas, lr=modulation_lr) + + def forward(self, t, x): + if self.modulate: + decay = torch.exp(-t * self.deltas.abs()) + x = x * (decay + self.shift) + return x + + +class HyenaFilter(OptimModule): + def __init__( + self, + d_model, + emb_dim=3, # dim of input to MLP, augments with positional encoding + order=16, # width of the implicit MLP + fused_fft_conv=False, + seq_len=1024, + lr=1e-3, + lr_pos_emb=1e-5, + dropout=0.0, + w=1, # frequency of periodic activations + wd=0, # weight decay of kernel parameters + bias=True, + num_inner_mlps=2, + normalized=False, + **kwargs + ): + """ + Implicit long filter with modulation. + + Args: + d_model: number of channels in the input + emb_dim: dimension of the positional encoding (`emb_dim` - 1) // 2 is the number of bands + order: width of the FFN + num_inner_mlps: number of inner linear layers inside filter MLP + """ + super().__init__() + self.d_model = d_model + self.use_bias = bias + self.fused_fft_conv = fused_fft_conv + self.bias = nn.Parameter(torch.randn(self.d_model)) + self.dropout = nn.Dropout(dropout) + + act = Sin(dim=order, w=w) + self.emb_dim = emb_dim + assert emb_dim % 2 != 0 and emb_dim >= 3, "emb_dim must be odd and greater or equal to 3 (time, sine and cosine)" + self.seq_len = seq_len + + self.pos_emb = PositionalEmbedding(emb_dim, seq_len, lr_pos_emb) + + self.implicit_filter = nn.Sequential( + nn.Linear(emb_dim, order), + act, + ) + for i in range(num_inner_mlps): + self.implicit_filter.append(nn.Linear(order, order)) + self.implicit_filter.append(act) + + self.implicit_filter.append(nn.Linear(order, d_model, bias=False)) + + self.modulation = ExponentialModulation(d_model, **kwargs) + + self.normalized = normalized + for c in self.implicit_filter.children(): + for name, v in c.state_dict().items(): + optim = {"weight_decay": wd, "lr": lr} + setattr(getattr(c, name), "_optim", optim) + + def filter(self, L, *args, **kwargs): + z, t = self.pos_emb(L) + h = self.implicit_filter(z) + h = self.modulation(t, h) + return h + + def forward(self, x, L, k=None, bias=None, *args, **kwargs): + if k is None: k = self.filter(L) + + # Ensure compatibility with filters that return a tuple + k = k[0] if type(k) is tuple else k + + y = fftconv(x, k, bias) + return y + + +class HyenaOperator(nn.Module): + def __init__( + self, + d_model, + l_max, + order=2, + filter_order=64, + dropout=0.0, + filter_dropout=0.0, + **filter_args, + ): + r""" + Hyena operator described in the paper https://arxiv.org/pdf/2302.10866.pdf + + Args: + d_model (int): Dimension of the input and output embeddings (width of the layer) + l_max: (int): Maximum input sequence length. Defaults to None + order: (int): Depth of the Hyena recurrence. Defaults to 2 + dropout: (float): Dropout probability. Defaults to 0.0 + filter_dropout: (float): Dropout probability for the filter. Defaults to 0.0 + """ + super().__init__() + self.d_model = d_model + self.l_max = l_max + self.order = order + inner_width = d_model * (order + 1) + self.dropout = nn.Dropout(dropout) + self.in_proj = nn.Linear(d_model, inner_width) + self.out_proj = nn.Linear(d_model, d_model) + + self.short_filter = nn.Conv1d( + inner_width, + inner_width, + 3, + padding=2, + groups=inner_width + ) + self.filter_fn = HyenaFilter( + d_model * (order - 1), + order=filter_order, + seq_len=l_max, + channels=1, + dropout=filter_dropout, + **filter_args + ) + + def forward(self, u, *args, **kwargs): + l = u.size(-2) + l_filter = min(l, self.l_max) + u = self.in_proj(u) + u = rearrange(u, 'b l d -> b d l') + + uc = self.short_filter(u)[...,:l_filter] + *x, v = uc.split(self.d_model, dim=1) + + k = self.filter_fn.filter(l_filter)[0] + k = rearrange(k, 'l (o d) -> o d l', o=self.order - 1) + bias = rearrange(self.filter_fn.bias, '(o d) -> o d', o=self.order - 1) + + for o, x_i in enumerate(reversed(x[1:])): + v = self.dropout(v * x_i) + v = self.filter_fn(v, l_filter, k=k[o], bias=bias[o]) + + y = rearrange(v * x[0], 'b d l -> b l d') + + y = self.out_proj(y) + return y + + + +if __name__ == "__main__": + layer = HyenaOperator( + d_model=512, + l_max=1024, + order=2, + filter_order=64 + ) + x = torch.randn(1, 1024, 512, requires_grad=True) + y = layer(x) + + print(x.shape, y.shape) + + grad = torch.autograd.grad(y[:, 10, :].sum(), x)[0] + print('Causality check: gradients should not flow "from future to past"') + print(grad[0, 11, :].sum(), grad[0, 9, :].sum())