diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6702297 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +model.pt \ No newline at end of file diff --git a/README.md b/README.md index 0bd558a..a184112 100644 --- a/README.md +++ b/README.md @@ -11,9 +11,9 @@
Developed using Databricks with ❤️ +## Overview - -#### Sparse mixture of experts language model from scratch inspired by (and largely based on) Andrej Karpathy's makemore (https://github.com/karpathy/makemore) :) +**Sparse mixture of experts language model from scratch inspired by (and largely based on) Andrej Karpathy's makemore (https://github.com/karpathy/makemore) :)** HuggingFace Community Blog that walks through this: https://huggingface.co/blog/AviSoori1x/makemoe-from-scratch @@ -55,3 +55,16 @@ makeMoE_Concise.ipynb is the consolidated hackable implementation that I encoura **Please note that the implementation emphasizes readability and hackability vs. performance, so there are many ways in which you could improve this. Please try and let me know!** Hope you find this useful. Happy hacking!! + + +## Usage + +* train + +```bash +python makeMoE.py +``` + +* visualize: [visualize-router-output.ipynb](./visualize-router-output.ipynb) + +![](./router-output.log.svg) diff --git a/makeMoE.py b/makeMoE.py index 524f930..ee30a30 100644 --- a/makeMoE.py +++ b/makeMoE.py @@ -1,59 +1,67 @@ +import numpy as np import torch import torch.nn as nn from torch.nn import functional as F from torch.nn import init # hyperparameters -batch_size = 16 # how many independent sequences will we process in parallel? -block_size = 32 # what is the maximum context length for predictions? -max_iters = 5000 +batch_size = 16 # how many independent sequences will we process in parallel? +block_size = 32 # what is the maximum context length for predictions? +max_iters = 2000 eval_interval = 100 learning_rate = 1e-3 -device = 'cuda' if torch.cuda.is_available() else 'cpu' +device = "cuda" if torch.cuda.is_available() else "cpu" eval_iters = 400 head_size = 16 n_embed = 128 n_head = 8 n_layer = 8 dropout = 0.1 -num_experts = 8 # This can be adjusted depending on the overall number of parameters -top_k = 2 # This controls the number of active parameters +num_experts = 8 # This can be adjusted depending on the overall number of parameters +top_k = 2 # This controls the number of active parameters +hook_router_output = True torch.manual_seed(1337) -with open('input.txt', 'r', encoding='utf-8') as f: +with open("input.txt", "r", encoding="utf-8") as f: text = f.read() # here are all the unique characters that occur in this text chars = sorted(list(set(text))) vocab_size = len(chars) # create a mapping from characters to integers -stoi = { ch:i for i,ch in enumerate(chars) } -itos = { i:ch for i,ch in enumerate(chars) } -encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers -decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string +stoi = {ch: i for i, ch in enumerate(chars)} +itos = {i: ch for i, ch in enumerate(chars)} +encode = lambda s: [ + stoi[c] for c in s +] # encoder: take a string, output a list of integers +decode = lambda l: "".join( + [itos[i] for i in l] +) # decoder: take a list of integers, output a string # Train and test splits data = torch.tensor(encode(text), dtype=torch.long) -n = int(0.9*len(data)) # first 90% will be train, rest val +n = int(0.9 * len(data)) # first 90% will be train, rest val train_data = data[:n] val_data = data[n:] + # data loading def get_batch(split): # generate a small batch of data of inputs x and targets y - data = train_data if split == 'train' else val_data + data = train_data if split == "train" else val_data ix = torch.randint(len(data) - block_size, (batch_size,)) - x = torch.stack([data[i:i+block_size] for i in ix]) - y = torch.stack([data[i+1:i+block_size+1] for i in ix]) + x = torch.stack([data[i : i + block_size] for i in ix]) + y = torch.stack([data[i + 1 : i + block_size + 1] for i in ix]) x, y = x.to(device), y.to(device) return x, y + @torch.no_grad() def estimate_loss(model): out = {} model.eval() - for split in ['train', 'val']: + for split in ["train", "val"]: losses = torch.zeros(eval_iters) for k in range(eval_iters): X, Y = get_batch(split) @@ -63,34 +71,36 @@ def estimate_loss(model): model.train() return out + class Head(nn.Module): - """ one head of self-attention """ + """one head of self-attention""" def __init__(self, head_size): super().__init__() self.key = nn.Linear(n_embed, head_size, bias=False) self.query = nn.Linear(n_embed, head_size, bias=False) self.value = nn.Linear(n_embed, head_size, bias=False) - self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size))) + self.register_buffer("tril", torch.tril(torch.ones(block_size, block_size))) self.dropout = nn.Dropout(dropout) def forward(self, x): - B,T,C = x.shape - k = self.key(x) # (B,T,C) - q = self.query(x) # (B,T,C) + B, T, C = x.shape + k = self.key(x) # (B,T,C) + q = self.query(x) # (B,T,C) # compute attention scores ("affinities") - wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T) - wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T) - wei = F.softmax(wei, dim=-1) # (B, T, T) + wei = q @ k.transpose(-2, -1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T) + wei = wei.masked_fill(self.tril[:T, :T] == 0, float("-inf")) # (B, T, T) + wei = F.softmax(wei, dim=-1) # (B, T, T) wei = self.dropout(wei) # perform the weighted aggregation of the values - v = self.value(x) # (B,T,C) - out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C) + v = self.value(x) # (B,T,C) + out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C) return out -#Multi-Headed Self Attention + +# Multi-Headed Self Attention class MultiHeadAttention(nn.Module): - """ multiple heads of self-attention in parallel """ + """multiple heads of self-attention in parallel""" def __init__(self, num_heads, head_size): super().__init__() @@ -102,9 +112,11 @@ def forward(self, x): out = torch.cat([h(x) for h in self.heads], dim=-1) out = self.dropout(self.proj(out)) return out -#Expert module + + +# Expert module class Expert(nn.Module): - """ An MLP is a simple linear layer followed by a non-linearity i.e. each Expert """ + """An MLP is a simple linear layer followed by a non-linearity i.e. each Expert""" def __init__(self, n_embed): super().__init__() @@ -118,33 +130,35 @@ def __init__(self, n_embed): def forward(self, x): return self.net(x) -#noisy top-k gating + +# noisy top-k gating class NoisyTopkRouter(nn.Module): def __init__(self, n_embed, num_experts, top_k): super(NoisyTopkRouter, self).__init__() self.top_k = top_k - #layer for router logits + # layer for router logits self.topkroute_linear = nn.Linear(n_embed, num_experts) - self.noise_linear =nn.Linear(n_embed, num_experts) + self.noise_linear = nn.Linear(n_embed, num_experts) def forward(self, mh_output): # mh_ouput is the output tensor from multihead self attention block logits = self.topkroute_linear(mh_output) - #Noise logits + # Noise logits noise_logits = self.noise_linear(mh_output) - #Adding scaled unit gaussian noise to the logits - noise = torch.randn_like(logits)*F.softplus(noise_logits) + # Adding scaled unit gaussian noise to the logits + noise = torch.randn_like(logits) * F.softplus(noise_logits) noisy_logits = logits + noise top_k_logits, indices = noisy_logits.topk(self.top_k, dim=-1) - zeros = torch.full_like(noisy_logits, float('-inf')) + zeros = torch.full_like(noisy_logits, float("-inf")) sparse_logits = zeros.scatter(-1, indices, top_k_logits) router_output = F.softmax(sparse_logits, dim=-1) return router_output, indices -#Now create the sparse mixture of experts module + +# Now create the sparse mixture of experts module class SparseMoE(nn.Module): @@ -155,19 +169,21 @@ def __init__(self, n_embed, num_experts, top_k, capacity_factor=1.0): self.top_k = top_k self.capacity_factor = capacity_factor self.num_experts = num_experts - + def forward(self, x): - # Assuming x has shape [batch_size, seq_len, n_embd] + # Assuming x has shape [batch_size, seq_len, n_embd] batch_size, seq_len, _ = x.shape gating_output, indices = self.router(x) final_output = torch.zeros_like(x) # Flatten the batch and sequence dimensions to treat each token independently - flat_x = x.view(-1, x.size(-1)) + flat_x = x.view(-1, x.size(-1)) flat_gating_output = gating_output.view(-1, gating_output.size(-1)) tokens_per_batch = batch_size * seq_len * self.top_k - expert_capacity = int((tokens_per_batch / self.num_experts) * self.capacity_factor) + expert_capacity = int( + (tokens_per_batch / self.num_experts) * self.capacity_factor + ) updates = torch.zeros_like(flat_x) @@ -175,7 +191,11 @@ def forward(self, x): expert_mask = (indices == i).any(dim=-1) flat_mask = expert_mask.view(-1) selected_indices = torch.nonzero(flat_mask).squeeze(-1) - limited_indices = selected_indices[:expert_capacity] if selected_indices.numel() > expert_capacity else selected_indices + limited_indices = ( + selected_indices[:expert_capacity] + if selected_indices.numel() > expert_capacity + else selected_indices + ) if limited_indices.numel() > 0: expert_input = flat_x[limited_indices] expert_output = expert(expert_input) @@ -187,12 +207,14 @@ def forward(self, x): final_output += updates.view(batch_size, seq_len, -1) return final_output - -#First create a self attention + mixture of experts block, that may be repeated several number of times -#Copy pasting key architecture variables for clarity + + +# First create a self attention + mixture of experts block, that may be repeated several number of times +# Copy pasting key architecture variables for clarity + class Block(nn.Module): - """ Mixture of Experts Transformer block: communication followed by computation (multi-head self attention + SparseMoE) """ + """Mixture of Experts Transformer block: communication followed by computation (multi-head self attention + SparseMoE)""" def __init__(self, n_embed, n_head, num_experts, top_k): # n_embed: embedding dimension, n_head: the number of heads we'd like @@ -207,8 +229,9 @@ def forward(self, x): x = x + self.sa(self.ln1(x)) x = x + self.smoe(self.ln2(x)) return x - -#Finally putting it all together to crease a sparse mixture of experts language model + + +# Finally putting it all together to crease a sparse mixture of experts language model class SparseMoELanguageModel(nn.Module): def __init__(self): @@ -216,27 +239,32 @@ def __init__(self): # each token directly reads off the logits for the next token from a lookup table self.token_embedding_table = nn.Embedding(vocab_size, n_embed) self.position_embedding_table = nn.Embedding(block_size, n_embed) - self.blocks = nn.Sequential(*[Block(n_embed, n_head=n_head, num_experts=num_experts,top_k=top_k) for _ in range(n_layer)]) - self.ln_f = nn.LayerNorm(n_embed) # final layer norm + self.blocks = nn.Sequential( + *[ + Block(n_embed, n_head=n_head, num_experts=num_experts, top_k=top_k) + for _ in range(n_layer) + ] + ) + self.ln_f = nn.LayerNorm(n_embed) # final layer norm self.lm_head = nn.Linear(n_embed, vocab_size) def forward(self, idx, targets=None): B, T = idx.shape # idx and targets are both (B,T) tensor of integers - tok_emb = self.token_embedding_table(idx) # (B,T,C) - pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C) - x = tok_emb + pos_emb # (B,T,C) - x = self.blocks(x) # (B,T,C) - x = self.ln_f(x) # (B,T,C) - logits = self.lm_head(x) # (B,T,vocab_size) + tok_emb = self.token_embedding_table(idx) # (B,T,C) + pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C) + x = tok_emb + pos_emb # (B,T,C) + x = self.blocks(x) # (B,T,C) + x = self.ln_f(x) # (B,T,C) + logits = self.lm_head(x) # (B,T,vocab_size) if targets is None: loss = None else: B, T, C = logits.shape - logits = logits.view(B*T, C) - targets = targets.view(B*T) + logits = logits.view(B * T, C) + targets = targets.view(B * T) loss = F.cross_entropy(logits, targets) return logits, loss @@ -249,31 +277,64 @@ def generate(self, idx, max_new_tokens): # get the predictions logits, loss = self(idx_cond) # focus only on the last time step - logits = logits[:, -1, :] # becomes (B, C) + logits = logits[:, -1, :] # becomes (B, C) # apply softmax to get probabilities - probs = F.softmax(logits, dim=-1) # (B, C) + probs = F.softmax(logits, dim=-1) # (B, C) # sample from the distribution - idx_next = torch.multinomial(probs, num_samples=1) # (B, 1) + idx_next = torch.multinomial(probs, num_samples=1) # (B, 1) # append sampled index to the running sequence - idx = torch.cat((idx, idx_next), dim=1) # (B, T+1) + idx = torch.cat((idx, idx_next), dim=1) # (B, T+1) return idx - + def kaiming_init_weights(m): - if isinstance (m, (nn.Linear)): + if isinstance(m, (nn.Linear)): init.kaiming_normal_(m.weight) + +class RouterOutputHook: + def __init__(self, block_index): + self.block_index = block_index + + def __call__(self, module, inputs, output): + routed_indices = output[1].detach().cpu().flatten() + expert_indices, token_counts = np.unique(routed_indices, return_counts=True) + # set token count default to 0 + row = ["0"] * 8 + for e, cnt in zip(expert_indices, token_counts): + row[e] = str(cnt) + # row formated: layer_index, token_counts for per expert. + row = [str(self.block_index)] + row + + line = ",".join(row) + "\n" + with open("router-output.log.csv", "a") as f: + f.write(line) + + def main(): model = SparseMoELanguageModel() - model.apply(kaiming_init_weights) + # Register hooks with block indices + if hook_router_output: + hooks = [ + b.smoe.router.register_forward_hook(RouterOutputHook(i)) + for i, b in enumerate(model.blocks) + ] + + try: + model.load_state_dict(torch.load("model.pt").state_dict()) + print("Successfully load checkpoint from model.pt file.") + except (FileNotFoundError, RuntimeError): + # Kaiming uniform initialization, also known as He initialization,work well with layers that use the ReLU activation function. + print("Initialized model parameters using Kaiming uniform initialization.") + model.apply(kaiming_init_weights) model = model.to(device) - print(sum(p.numel() for p in model.parameters()) / 1e6, 'M parameters') + print(sum(p.numel() for p in model.parameters()) / 1e6, "M parameters") optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) m = model.to(device) # print the number of parameters in the model - print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters') + print(sum(p.numel() for p in m.parameters()) / 1e6, "M parameters") # create a PyTorch optimizer optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) @@ -283,16 +344,23 @@ def main(): # every once in a while evaluate the loss on train and val sets if iter % eval_interval == 0 or iter == max_iters - 1: losses = estimate_loss(model) - print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}") + print( + f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}" + ) + torch.save(model, "model.pt") # sample a batch of data - xb, yb = get_batch('train') + xb, yb = get_batch("train") # evaluate the loss logits, loss = model(xb, yb) optimizer.zero_grad(set_to_none=True) loss.backward() optimizer.step() + # remove hooks + if hook_router_output: + [h.remove() for h in hooks] + if __name__ == "__main__": main() diff --git a/router-output.log.csv b/router-output.log.csv new file mode 100644 index 0000000..7885792 --- /dev/null +++ b/router-output.log.csv @@ -0,0 +1,2800 @@ +0,166,114,122,131,162,110,104,115 +1,92,175,205,150,169,72,110,51 +2,203,117,75,2,148,183,131,165 +3,104,22,36,186,158,175,118,225 +4,22,51,323,0,105,252,5,266 +5,6,1,194,141,1,23,153,505 +6,0,493,378,107,9,19,0,18 +7,0,511,9,156,194,142,0,12 +0,192,117,109,136,161,108,77,124 +1,95,170,191,163,165,68,114,58 +2,196,133,68,2,160,168,130,167 +3,90,56,37,202,133,185,120,201 +4,16,70,328,3,125,226,3,253 +5,4,1,184,126,3,37,164,505 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"source": [ + "tokens_by_expert_by_layer = df.groupby('layer').mean().reset_index().drop([\"step\", \"layer\"], axis=1)\n", + "tokens_by_expert_by_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "tokens_by_expert_by_layer.plot(kind='bar')\n", + "plt.title(\"Token counts for each expert per layer\")\n", + "plt.ylabel(\"Average Tokens\")\n", + "plt.xlabel(\"Layer\")\n", + "plt.savefig(\"router-output.log.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.savefig(\"router-output.log.svg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Average token by step (FuncAnimation)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexsteplayere0e1e2e3e4e5e6e7iter
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.......................................
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307557 rows × 12 columns

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" + ], + "text/plain": [ + " index step layer e0 e1 e2 e3 e4 e5 e6 e7 iter\n", + "0 0 0 0 121 128 126 101 131 110 190 117 0\n", + "1 1 8 0 115 140 126 111 121 109 174 128 1\n", + "2 2 16 0 143 117 110 99 134 123 170 128 2\n", + "3 3 24 0 107 164 123 106 140 89 179 116 3\n", + "4 4 32 0 123 129 120 99 129 96 192 136 4\n", + "... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "38439 38439 307524 7 7 512 15 143 175 142 1 29 38439\n", + "38440 38440 307532 7 9 510 14 164 177 107 1 42 38440\n", + "38441 38441 307540 7 7 511 19 154 178 129 0 26 38441\n", + "38442 38442 307548 7 4 512 16 126 208 139 0 19 38442\n", + "38443 38443 307556 7 3 512 15 161 188 129 1 15 38443\n", + "\n", + "[307557 rows x 12 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfs = []\n", + "for l in df.layer.unique():\n", + " msk = df[\"layer\"] == l\n", + " df_ = df[msk]\n", + " df_ = df_.reset_index(drop=True).reset_index(drop=False)\n", + " df_[\"iter\"] = df_[\"index\"]\n", + " df_.drop([\"index\", \"step\"], axis=1)\n", + " dfs.append(df_)\n", + "df2 = pd.concat(dfs)\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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nnNH6+utf/3rq6uoyYsSI9OzZc4e1vXr1ytatW1NdXb3DvT9c351LL700F1xwQevrxsZGgRsAAAAA2Os9etajXT3Cbg0YMCBLlizJeeedl2nTpmXkyJFZsmRJbr311jz33HOZOXNmZs6c2bq+paWlovN0etz+Y+94xzvS3Nyc/fffP2vWrNnh3pYtW1JTU5NBgwalvr5+p+u707Nnz51COQAAAAAAb8zo0aOzYsWKHa4dffTROf/88zt9lk4/luSiiy7Kt771rdbXy5cvT7du3XL44Ydn+fLlrdfXrVuXpqamDBo0KKNHj97h3qpVq3LggQd26twAAAAAAOw9Oj1uv/e9780XvvCF/OhHP8pdd92V6dOn58wzz8yHP/zhNDY25qabbkqSzJ07N8cff3y6d++eyZMn5/7778/dd9+d7du3Z968eRk/fnxnjw4AAAAAwF6i048l+cQnPpHHHnssH/nIR9K9e/d84hOfyNy5c1NdXZ0bbrghU6dOzUUXXZRu3brlnnvuSZIMHjw4CxcuzAknnJC+fftmwIABWbRoUWePDgAAAADAXqJLzty+4oorcsUVV+x0ffLkyXn66aezcuXKHHPMMdl3331b702fPj3jx4/PE088kbFjx6Zv376dOTIAAAAAAHuRLv9AyT+2//77Z9KkSbu8V1dXl7q6uk6eCAAAAKBAc/q3cV1DZed4DfM/dmKb1s247c4KTwKUqNPP3AYAAAAAgDdK3AYAAAAAoDh73bEkAAAAAABvVY+/e0SnPm/EE4932Hv96le/yi9+8YuMHDkyb3vb2zrsfXfHzm0AAAAAAN6Qq666Koceemj++q//OrW1tbnvvvsq/kxxGwAAAACA1+2pp57KlVdemcceeyxr167N+eefn5kzZ1b8ueI2AAAAAACvW1NTU6677roceOCBSZI//dM/zfPPP1/x54rbAAAAAAC0yYMPPpgxY8akf//+mTJlShoaGjJy5MhMnjw5SfLSSy/l6quvzimnnFLxWcRtAAAAAAD26IUXXsjEiRMzceLEPPLII2lsbMyMGTNa7//7v/97DjjggGzatMmxJAAAAAAA7B2WLl2aHj16ZPbs2Rk2bFguvPDCLFmypPX+hz/84fzbv/1bkuTSSy+t+DzVFX8CAAAAAADF27hxY+rr6zNw4MAkSXNzc7Zs2ZKXX345vXr1SnV1dY499th87Wtfy5QpU/KVr3ylovOI2wAAAAAA7FFtbW1GjRqV2267LUnS0tKShoaGfP/738/GjRtbjyipqalJ9+7dKz6PY0kAAAAAANijSZMmZf369XnggQfSu3fv3HHHHZkwYUIOPvjgzJkzJ9/73vfyzDPP5PLLL89pp51W8Xns3AYAAAAA2EuMeOLxrh5htwYMGJAlS5bkvPPOy7Rp0zJy5MgsWbIkRx11VK699tpccMEFeeGFF3Lqqadm/vz5FZ9H3AYAAAAAoE1Gjx6dFStW7HT94x//eD7+8Y936iyOJQEAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4lR39QAAAAAAAPze1dN/3KnPO/ea4zr1eR3Jzm0AAAAAAN6Qe++9NyNGjMjgwYOzYMGCTnmmuA0AAAAAwOtWX1+fyZMnZ+rUqVm+fHkWL16cZcuWVfy54jYAAAAAAK/b4sWLM2TIkMycOTOHHHJIZs2alRtvvLHizxW3AQAAAABokwcffDBjxoxJ//79M2XKlDQ0NGT16tUZN25cqqqqkiRHH310Vq5cWfFZxG0AAAAAAPbohRdeyMSJEzNx4sQ88sgjaWxszIwZM9LY2Ji6urrWdf369cumTZsqPo+4DQAAAADAHi1dujQ9evTI7NmzM2zYsFx44YVZsmRJqqur07Nnz9Z1vXr1ytatWys+T3XFnwAAAAAAQPE2btyY+vr6DBw4MEnS3NycLVu2pE+fPqmvr29dt2XLltTU1FR8HnEbAAAAAIA9qq2tzahRo3LbbbclSVpaWtLQ0JCVK1fm1ltvbV23atWqHHjggRWfx7EkAAAAAADs0aRJk7J+/fo88MAD6d27d+64445MmDAhJ598cu6///7cfffd2b59e+bNm5fx48dXfB47twEAAAAA9hLnXnNcV4+wWwMGDMiSJUty3nnnZdq0aRk5cmSWLFmSwYMHZ+HChTnhhBPSt2/fDBgwIIsWLar4POI2AAAAAABtMnr06KxYsWKn69OnT8/48ePzxBNPZOzYsenbt2/FZxG3AQAAAAB4w+rq6lJXV9dpz3PmNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4lR39QAAAAAAAPze/I+d2KnPm3HbnZ36vI5k5zYAAAAAAG/YU089lUGDBnXa88RtAAAAAADekJ///Oc54YQTsnnz5k57prgNAAAAAMAbctJJJ+VTn/pUpz5T3AYAAAAAoE0efPDBjBkzJv3798+UKVPS0NCQJLnzzjtz6qmnduos4jYAAAAAAHv0wgsvZOLEiZk4cWIeeeSRNDY2ZsaMGUmSurq6Tp+nutOfCAAAAABAcZYuXZoePXpk9uzZqaqqyoUXXpgzzzyzy+YRtwEAAAAA2KONGzemvr4+AwcOTJI0Nzdny5Ytefnll9OrV69On0fcBgAAAABgj2prazNq1KjcdtttSZKWlpY0NDSkR48eXTKPM7cBAAAAANijSZMmZf369XnggQfSu3fv3HHHHZkwYUJaWlq6ZB47twEAAAAA9hIzbruzq0fYrQEDBmTJkiU577zzMm3atIwcOTJLlixJdXXXZGZxGwAAAACANhk9enRWrFixy3vDhw/v1F3cjiUBAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOJUd/UAAAAAAAD83sZL7uvU59VeObZD3ue6667L7Nmz85vf/Cbvf//78+1vfzsHHHBAh7z37ti5DQAAAADA6/bTn/40M2fOzC233JJ169alpaUlF154YcWfK24DAAAAAPC6/exnP8u1116b448/PrW1tZk2bVpWrVpV8eeK2wAAAAAAtMmDDz6YMWPGpH///pkyZUoaGhoybdq0/NVf/VXrmieffDKHHHJIxWcRtwEAAAAA2KMXXnghEydOzMSJE/PII4+ksbExM2bM2GHNb3/721x77bWZPn16xefxgZIAAAAAAOzR0qVL06NHj8yePTtVVVW58MILc+aZZ+6w5txzz8373//+TJw4seLziNsAAAAAAOzRxo0bU19fn4EDByZJmpubs2XLlrz88svp1atXbr755ixbtiyrV6/ulHnEbQAAAAAA9qi2tjajRo3KbbfdliRpaWlJQ0NDevTokYceeiif+cxnsmTJkuy3336dMo8ztwEAAAAA2KNJkyZl/fr1eeCBB9K7d+/ccccdmTBhQp5//vmcdNJJ+Yd/+IccddRRefHFF/Piiy9WfB5xGwAAAACAPRowYECWLFmS+fPn553vfGduv/32LFmyJLfeemuee+65zJw5M/vss0/rV6U5lgQAAAAAYC9Re+XYrh7hNY0ePTorVqzY4drRRx+d888/v9NnsXMbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAA8Ib96le/ygMPPJCXXnqpU55X3SlPAQAAAABgj+bMmVPk86666qrMmTMnQ4YMybPPPpslS5Zk7NixHfLeu2PnNgAAAAAAr9tTTz2VK6+8Mo899ljWrl2b888/PzNnzqz4c8VtAAAAAABet6amplx33XU58MADkyR/+qd/mueff77izxW3AQAAAABokwcffDBjxoxJ//79M2XKlDQ0NGTkyJGZPHlykuSll17K1VdfnVNOOaXis4jbAAAAAADs0QsvvJCJEydm4sSJeeSRR9LY2JgZM2a03v/3f//3HHDAAdm0aZNjSQAAAAAA2DssXbo0PXr0yOzZszNs2LBceOGFWbJkSev9D3/4w/m3f/u3JMmll15a8XmqK/4EAAAAAACKt3HjxtTX12fgwIFJkubm5mzZsiUvv/xyevXqlerq6hx77LH52te+lilTpuQrX/lKRecRtwEAAAAA2KPa2tqMGjUqt912W5KkpaUlDQ0N+f73v5+NGze2HlFSU1OT7t27V3wex5IAAAAAALBHkyZNyvr16/PAAw+kd+/eueOOOzJhwoQcfPDBmTNnTr73ve/lmWeeyeWXX57TTjut4vOI2wAAAAAA7NGAAQOyZMmSzJ8/P+985ztz++23Z8mSJTnqqKNy7bXX5oILLsj73ve+DBs2LPPnz6/4PI4lAQAAAADYS8yZM6erR3hNo0ePzooVK3a6/vGPfzwf//jHO3UWO7cBAAAAACiOuA0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAKALNDc3d/UIXaKjfu7qDnkXAAAAAADapKamJt26dcumTZvy9re/PTU1NamqqurqsSqupaUl27ZtS319fbp165aampo39H7iNgAAAABAJ+rWrVvq6ury7LPPZtOmTV09Tqfr06dPDjrooHTr9sYOFhG3AQAAAAA6WU1NTQ466KC88sorefXVV7t6nE7TvXv3VFdXd8hOdXEbAAAAAKALVFVVpUePHunRo0dXj1IkHygJAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAUR9wGAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADFEbcBAAAAACiOuA0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAUR9wGAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADFEbcBAAAAACiOuA0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAUR9wGAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADF6fK4PWHChCxatChJcu+992bEiBEZPHhwFixYsMO6O+64I8OGDcuQIUNy6623dsGkAAAAAADsLbo0bi9evDj/+Z//mSSpr6/P5MmTM3Xq1CxfvjyLFy/OsmXLkiRr1qzJGWeckZkzZ+Y///M/M2vWrDz55JNdOToAAAAAAF2oy+L2b3/728yYMSOHHnpokt+H7iFDhmTmzJk55JBDMmvWrNx4441JkhtuuCHjxo3L2WefncMPPzznnXdebrnllq4aHQAAAACALtZlcXvGjBk55ZRTcswxxyRJVq9enXHjxqWqqipJcvTRR2flypWt94477rjW7/2f93alqakpjY2NO3wBAAAAAPDm0SVxe9myZfnRj36UefPmtV5rbGxMXV1d6+t+/fpl06ZNe7y3K1dccUX69+/f+jV06NAK/BQAAAAAAHSVTo/bL7/8cv7u7/4u3/jGN7LPPvu0Xq+urk7Pnj1bX/fq1Stbt27d471dufTSS9PQ0ND6tWHDhgr8JAAAAAAAdJXqzn7gl770pYwePTqTJk3a4fqgQYNSX1/f+nrLli2pqanZ471d6dmz5w4xHAAAAACAN5dOj9vf+ta3Ul9fnwEDBiRJtm7dmu985ztJkve///2t61atWpUDDzwwSTJ69OgsX748f/u3f7vTPQAAAAAA3no6/ViS++67L2vWrMnDDz+chx9+OJMnT84Xv/jFrF+/Pvfff3/uvvvubN++PfPmzcv48eOTJB/5yEfy7W9/O48++mhefPHFfO1rX2u9BwAAAADAW0+n79yura3d4XXfvn0zePDgDB48OAsXLswJJ5yQvn37ZsCAAVm0aFGS5L3vfW/OP//8HHXUUenVq1cOOeSQnHPOOZ09OgAAAAAAe4lOj9t/7A8BO0mmT5+e8ePH54knnsjYsWPTt2/f1nv/9E//lDPOOCO//OUvc+yxx77mmdsAAAAAALy5dXnc/mN1dXWpq6vb5b3DDjsshx12WCdPBAAAAADA3qbTz9wGAAAAAIA3StwGAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADFEbcBAAAAACiOuA0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAUR9wGAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADFEbcBAAAAACiOuA0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIpT3dUDAAAAvF6H33x4m9Y9etajFZ5k9zZecl+b1tVeObbCkwAAvLnYuQ0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAUR9wGAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADFEbcBAAAAACiOuA0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAUR9wGAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADFEbcBAAAAACiOuA0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAUR9wGAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADFEbcBAAAAACiOuA0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAUR9wGAAAAAKA44jYAAAAAAMURtwEAAAAAKI64DQAAAABAccRtAAAAAACKI24DAAAAAFAccRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADFEbcBAAAAACiOuA0AAAAAQHHEbQAAAAAAiiNuAwAAAABQHHEbAAAAAIDiiNsAAAAAABRH3AYAAAAAoDjiNgAAAAAAxRG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAUR9wGAAAAAKA4HRK3N2zYkJUrV3bEWwEAAAAAwB61O24fdthheemll3a49vzzz2fSpEkdNhQAAAAAALyWdsftJ554Is3NzTtce/vb354XX3yxw4YCAAAAAIDXUt3WhX/zN3/T+tef/vSnU1NTkyRpaWnJihUrcvLJJ7frwS+88EKefPLJvOtd78rAgQPb9b0AAAAAALy1tXnn9rBhwzJs2LBUVVVl6NChra/f+c535gtf+EJuvvnmNj/09ttvz/Dhw3P22WentrY2t99+e5JkzZo1GT16dAYOHJiLLrooLS0trd9z7733ZsSIERk8eHAWLFjQjh8RAAAAAIA3mzbv3J49e3aS5PLLL8+ll16afv36va4HNjQ05JxzzslPfvKTHHHEEVm0aFEuuuiiTJ48OSeddFLGjx+fb3/72/nsZz+bRYsWZdq0aamvr8/kyZMzY8aMTJ06Naeffnre9773Zdy4ca9rBgAAAAAAytbuM7dvvfXWvO1tb3vdD2xsbMxVV12VI444Iknyp3/6p3n++efzgx/8IA0NDVmwYEEOPvjgzJ07NzfeeGOSZPHixRkyZEhmzpyZQw45JLNmzWq9BwAAAADAW0+bd27/wcc+9rHcc8892bBhww7HhiTJmWeeucfvHzp0aM4444wkyfbt27Nw4cKccsopWb16dY455pj06dMnSXLEEUdk7dq1SZLVq1dn3LhxqaqqSpIcffTRueSSS3b7jKampjQ1NbW+bmxsbN8PCQAAAADAXq3dcfuv//qv853vfCfve9/7Wj9UMkmqqqraFLf/YPXq1TnuuONSU1OTxx9/PF/60pdSV1e3w/t17949mzdvTmNjYw477LDWe/369cumTZt2+95XXHFFLr/88nb+ZAAAAAAAlKLdcfu73/1uli1bljFjxryhBx9xxBG566678rnPfS5nn312Dj744PTs2XOHNb169crWrVtTXV29w70/XN+dSy+9NBdccEHr68bGxgwdOvQNzQsAAAAAwN6j3Wduv/vd786LL774hh9cVVWVUaNG5eabb86//uu/ZtCgQamvr99hzZYtW1JTU7PTvT9c352ePXumX79+O3wBAAAAAPDm0e64PXfu3HzmM5/J1VdfnTVr1mT9+vWtX21x77335qKLLmp9XVNTk6qqqowYMSLLly9vvb5u3bo0NTVl0KBBGT169A73Vq1alQMPPLC9owMAAAAA8CbR7rg9fvz4PPHEE/nMZz6TI444IsOHD8/w4cN3OC/7tbzrXe/Kddddl+uuuy4bNmzI5z//+Xz4wx/OCSeckMbGxtx0001Jfh/Rjz/++HTv3j2TJ0/O/fffn7vvvjvbt2/PvHnzMn78+PaODgAAAADAm0S743Zzc/Muv1599dU2ff8BBxyQO+64I1/96lczcuTIbN26Nd/85jdTXV2dG264Ieedd14GDx6c73//+/nyl7+cJBk8eHAWLlyYE044Ifvtt1+efPLJfOELX2jv6AAAAAAAvEm0+wMlv/nNb+723plnntmm9/jLv/zLPPbYYztdnzx5cp5++umsXLkyxxxzTPbdd9/We9OnT2/dNT527Nj07du3vaMDAAAAAPAm0e64/YdjQ5Jk69ateeKJJ7Jt27b82Z/9WZvj9mvZf//9M2nSpF3eq6ura/PxJwAAAAAAvHm1O24vW7Zsh9cvvfRSvvCFL2TQoEEdNhQAAAAAALyWdp+5/cfe9ra3ZeHChbn99ts7Yh4AAAAAANijdu/cXr9+/Q6vm5ubs3z58mzevLnDhgIAAAAAgNfS7rg9fPjwVFVVpaWlpfVabW1t5s+f36GDAQAAAADA7rQ7bjc3N1diDgAAAAAAaLN2x+0kefbZZ3PLLbdk/fr1GT58eD7xiU9k//337+jZAAAAAABgl9r9gZI//elPc+ihh+buu+/Oq6++mh/+8Ic59NBD83//7/+txHwAAAAAALCTdu/cPv/887NgwYKcffbZrdeuv/76nHfeefl//+//dehwAAAAAACwK+3eub1u3bocf/zxO1z7y7/8yzzzzDMdNRMAAAAAALymdsft8ePH5+///u/zq1/9Kkny3HPP5XOf+1zGjx/f4cMBAAAAAMCutDtuf+Mb38irr76aAw44IH369MmBBx6Y5ubmfOMb36jEfAAAAAAAsJN2n7k9YMCA/Nu//Vuee+65bNiwIQcddFD222+/SswGAAAAAAC71Oa4fc899+TWW2/Ne9/73pxzzjnZf//987d/+7c58MADc9ppp+Uv//IvKzknAAAAAAC0atOxJF/96lfz0Y9+NL17984hhxzSev1DH/pQqqur89GPfjRf//rXKzYkAAAAAAD8T23auf2Vr3wlP/jBDzJq1Kgdrl9wwQVJklNPPTVnnXVWzjnnnI6fEAAAAAAA/kibdm5v27YtW7du3e39vn375ne/+12HDQUAAAAAAK+lTTu3L7744kyZMiWXXnpp/uzP/izveMc70r1797zwwgt56KGH8qUvfSnnnntupWcFAAAAAIAkbYzbF1xwQQ466KB89atfzec///ls27at9d7BBx+c888/v/WIEgAAAAAAqLQ2xe3k9+dqn3rqqWlubs5vfvObbN++Pf3790/fvn0rOR8AAAAAAOykzXH7D7p165Z3vOMdlZgFAAAAAADapE0fKAkAAAAAAHsTcRsAAAAAgOKI2wAAAAAAFEfcBgAAAACgOOI2AAAAAADFEbcBAAAAAChOdXu/oampKbfeems2bNiQlpaWHe7NmjWrwwYDAAAAAIDdaXfc/shHPpKVK1fmuOOOS01NTev1qqqqDh0MAAAAAAB2p91xe9myZXnwwQdz2GGHVWIeAAAAAADYo3afuf1nf/ZnWbNmTSVmAQAAAACANmn3zu2/+7u/y0UXXZQHH3wwxx57bPr169d67wMf+ECHDgcAAAAAALvS7rj9D//wD6mqqsodd9yRO+64o/V6VVVVfv7zn3focAAAAAAAsCvtjtvr1q2rxBwAAAAAANBm7T5zO0l+8Ytf5Jprrslll12WZ599Nl//+tfT0tLS0bMBAAAAAMAutTtu33777Tn00ENz8803Z/78+WlsbMw///M/55JLLqnEfAAAAAAAsJN2x+2LL744t912W5YvX57evXund+/e+e53v5tFixZVYDwAAAAAANhZu+P2yy+/nEMOOWSHaz169Ei3bq/rhBMAAAAAAGi3dhfpT3ziE5k8eXKuvfbavPrqq1m+fHk+85nP5JOf/GQl5gMAAAAAgJ1Ut/cb5s6dm759+2b+/Pl55ZVXMmvWrHzyk5905jYAAAAAAJ2m3XG7uro6s2bNyqxZsyoxDwAAAAAA7FG7jyV57LHHdrr2s5/9LKeeemqHDAQAAAAAAHvS7rj9oQ99KD/5yU+SJL/61a8yffr0jBo1KsOHD+/o2QAAAAAAYJfaHbe/853v5PTTT8/ZZ5+dkSNHpkePHnnyySfzla98pRLzAQAAAADATtp95vYHPvCBLFu2LCeccEL++q//WtQGAAAAAKDTtSluf/GLX9zp2oQJE3LVVVfl17/+df7kT/4kSXzIJAAAAAAAnaJNcXvdunW7vP7JT36y9X5VVVXHTQUAAAAAAK+hTXH7pptuqvQcAAAAAADQZu3+QMkk+drXvpbDDjssffv2zciRI/PP//zPHT0XAAAAAADsVrs/UPLyyy/P4sWLM2fOnBx88MF5+umnM2fOnGzevDkzZ86sxIwAAAAAALCDdsft6667LkuXLs2RRx6ZJBkzZkxGjBiRE088UdwGAAAAAKBTtPtYkpqammzZsmWHa1u2bEmPHj06bCgAAAAAAHgt7d65feGFF+a0007L3//93+ed73xnfv7zn+eqq67KrFmzKjEfAAAAAADspN1x+9xzz83++++f66+/PrfccksOOuigfP3rX8+UKVMqMR8AAAAAAOyk3XE7ST7ykY/kIx/5SEfPAgAAAAAAbdKmM7ff+c535sUXX6z0LAAAAAAA0CZtitvPPPNMWlpaKj0LAAAAAAC0SZuPJfn0pz+dmpqa11zzL//yL294IAAAAAAA2JM2x+1+/fqlZ8+elZwFAAAAAADapM1x+8orr0y/fv0qOQsAAAAAALRJm87cPvbYY1Nd3eYODgAAAAAAFdWmYr1s2bJKzwEAAAAAAG3Wpp3bAAAAAACwNxG3AQAAAAAojrgNAAAAAEBxxG0AAAAAAIojbgMAAAAAUBxxGwAAAACA4ojbAAAAAAAU53XF7W9961s5/fTT8+d//uf52c9+lo9+9KP5zW9+09GzAQAAAADALrU7bl922WW5+OKLU1dXl9WrV6dbt9+/xd/93d91+HAAAAAAALAr1e39huuvvz4/+tGPcvjhh+eaa65Jjx49smDBgowcObIS8wEAAAAAwE7avXN7wIAB2bBhww7XfvnLX2a//fbrsKEAAAAAAOC1tHvn9he+8IWccsopmTJlSpqamrJw4cJ8//vfz+WXX16J+QAAAAAAYCft3rl95pln5oc//GH22WeffPCDH8xLL72Ub37zm/nkJz9ZifkAAAAAAGAn7d65feedd2bcuHH5wAc+UIl5AAAAAABgj9q9c/vcc8/Nc889lxUrVuT5559vvf6P//iPefvb357bb7+9QwcEAAAAAIA/1u64/bGPfSzvfe97c8opp2TYsGH52te+liT5yle+kquvvjr/+I//2OFDAgAAAADA/9TuuL148eLceeed2bRpU1avXp2LL744v/71r/Piiy/mQx/6UJ588slKzAkAAAAAAK3aHbd79uyZX/7yl3n11Vfzy1/+MtXV1dm8eXMGDhyY7du3p0ePHpWYEwAAAAAAWrX7AyVvuOGGnHnmmTnzzDMzcODAjBkzJmeccUZ69+6dyy67LO95z3sqMScAAAAAALRqd9w+7rjjsnHjxtTX12fffffNK6+8kmXLluXP//zPM2/evJxzzjmVmBMAAAAAAFq1O27/wdvf/vYkSU1NTcaPH59t27bli1/8YocNBgAAAAAAu9PuuP3cc8/lH//xH/Pkk0/m1VdfTZK0tLTk8ccfz3PPPdfhAwIAAAAAwB9r9wdKfvKTn8xvf/vb9OnTJ717985pp52WJ5980nEkAAAAAAB0mnbH7f/6r//KwoULc9FFF2XLli359Kc/nRtvvDH/8R//UYn5AAAAAABgJ+2O2wceeGDuuuuujB49OmvXrs3vfve7vOc978mjjz5aifkAAAAAAGAn7T5z+8tf/nKmTp2aCRMm5KSTTspRRx2VlpaW/MVf/EUl5gMAAAAAgJ20O26ffPLJ2bRpU/bZZ59cd911+da3vpUtW7bkrLPOqsR8AAAAAACwk3bH7SQZMGBAkqR79+6iNgAAAAAAna7dZ27/x3/8RzZv3lyJWQAAAAAAoE3aHbenT5+ehx56qBKzAAAAAABAm7Q7bn/2s5/NVVddlVdeeaUS8wAAAAAAwB61+8ztfffdN5s3b86oUaMyffr0vO1tb2u9d+aZZ3bocACwNxl+ydI2rXvmykkVnmTXrp7+4zatO/ea4yo8CQAAAFReu+P2okWL0rNnz/Ts2TPf+c53Wq9XVVWJ2wAAAAAAdIp2x+1ly5ZVYg4AAAB40zn85sPbvPbRsx6t4CS7t/GS+9q0rvbKsRWeBADap91nbv9P27ZtS0tLS5qbmztqHgAAAAAA2KN2x+0tW7bkU5/6VPbbb7/06dMnjzzySGpra7Ny5cpKzAcAAAAAADtpd9yeNm1annnmmdx8881529velgEDBuRzn/tczj333ErMBwAAAAAAO2n3mdt33313Hn300QwdOjTdunVLVVVVzjrrrHzxi1+sxHwAAAAAALCTdu/cfve7351FixYlSaqqqlJVVZWf/OQnGTlyZEfPBgAAAAAAu9Tundv/+3//75xwwgm55pprsmXLlnz0ox/N+vXrs2TJkkrMBwAAAAAAO2l33B49enSeeuqp3HnnnfnlL3+Z2traTJo0Kf3796/EfAAAAAAAsJN2x+3nn38+++67b84444xKzAMAAAAAAHvU7jO3a2trc/zxx+cb3/hGfvWrX1ViJgAAAAAAeE3tjtvPPfdcpk+fnpUrV+boo4/OBz7wgXzta1/Lxo0bKzEfAAAAAADspN1xu3///jn11FNzww035Be/+EW+/vWvZ8WKFRk2bFgl5gMAAAAAgJ20+8ztJGlsbMwPf/jD/OAHP8iPfvSjHHroobnmmms6ejYAAAAAANildsftsWPH5uGHH864cePykY98JP/rf/2vDBw4sBKzAQAAAADALrU7bp9zzjk58cQTs88++1RiHgAAAAAA2KN2n7k9derUHcL29u3bs2zZslx66aUdOhgAAAAAAOzO6zpz+7HHHssPf/jD3HXXXfnJT36Sbdu2ZcyYMR09GwAAAAAA7FKb4nZ9fX1++MMftgbt559/Pu973/uyatWqXH/99TnllFPSt2/fSs8KAAAAAABJ2hi3999//yTJBz/4wVx//fU5/vjjU1NTk4EDB+bYY48VtgEAAAAA6FRtitt33XVX687tk08+Oe9617tyzDHHpKmpKb/+9a9z0EEHVXpOAAAAACrg8JsPb9O6R896tMKT7N7GS+5r07raK8dWeBJgb9KmD5T80Ic+lCuvvDIrV67Ms88+m5kzZ6alpSX77rtvxowZk0MPPTTnnHNOpWcFAAAAAIAkr+MDJQcPHpzTTz89p59+epLk8ccfz3/+53/m7rvv7vDhAAAAAABgV9odt//YiBEjMmL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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib.animation import FuncAnimation\n", + "import numpy as np\n", + "from IPython.display import HTML\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(18, 12))\n", + "\n", + "STEPS = len(df2[\"iter\"].unique())\n", + "STEPS = 2000\n", + "bar_width = 0.06 # Width of each bar\n", + "\n", + "def update_hist(step):\n", + " df_ = df2[df2[\"iter\"] == step]\n", + " \n", + " ax.clear()\n", + " x = np.arange(len(df_['layer'])) # The label locations\n", + " \n", + " for i in range(4):\n", + " ax.bar(x - bar_width * (4-i), df_[f'e{i}'], width=bar_width, label=f'e{i}')\n", + " \n", + " for i in range(4):\n", + " ax.bar(x + bar_width * i, df_[f'e{i}'], width=bar_width, label=f'e{i}')\n", + "\n", + " ax.set_xlabel(\"Layer\")\n", + " ax.set_ylabel(\"Average Token Count\")\n", + " ax.set_title(f\"Step {step + 1}\")\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(df_['layer'])\n", + " ax.legend()\n", + "\n", + "ani = FuncAnimation(fig, update_hist, frames=np.arange(0, STEPS, 10), repeat=False)\n", + "\n", + "HTML(ani.to_jshtml())\n", + "plt.savefig(\"router-output.log.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}