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• blog.md
  • @@ -4,6 +4,8 @@ mkzip: zip project.zip index.html img
    title: "Off-Distribution Generalization of Transformers for Shortest Paths" --- + <!-- Guidelines: https://www.dropbox.com/scl/fi/bet8enscln8ue36kd8t17/final_project_guidelines.pdf?rlkey=knd19cnumk51ho1y9crno56ib&e=2&dl=0 --> + <style> body { /* Shrink margins */
  • @@ -19,12 +21,6 @@ margin: auto;
    } </style> - <!-- - BTW we should run a spell checker on this at some point before submitting - note to self - --> - <!-- Guidelines: https://www.dropbox.com/scl/fi/bet8enscln8ue36kd8t17/final_project_guidelines.pdf?rlkey=knd19cnumk51ho1y9crno56ib&e=2&dl=0 --> - <div style="text-align:center"> Anthony Wang, Alek Westover, Kevin Zhao
  • @@ -35,9 +31,10 @@ ## Introduction
    ### Motivation - Neural networks are capable of impressive feats of off-distribution generalization. For instance, a program trained to convert sketches of cats into realistic looking pictures of cats can draw a cat with three eyes, if the sketch has three eyes, even if such an image never occured in its training data. However, neural networks also often learn non-robust features that cause it to perform very poorly off-distribution (e.g., adversarial examples for an image classifier). In this project, we will investigate the question of when transformers generalize off-distribution through via a case study on a simple synthetic task. More specifically, the goal of our project is to make progress towards answering the following question: + Neural networks are capable of impressive feats of off-distribution generalization. For instance, a program trained to convert sketches of cats into realistic looking pictures of cats can draw a cat with three eyes, if the sketch has three eyes, even if such an image never occurred in its training data. However, neural networks also often learn non-robust features that cause it to perform very poorly off-distribution (e.g., adversarial examples for an image classifier). In this project, we will investigate the question of when transformers generalize off-distribution through via a case study on a simple synthetic task. More specifically, the goal of our project is to make progress towards answering the following question: - > Suppose you train a model to be truthful on task A, and to produce coherent answers on a related task B.\ + > Suppose you train a model to be truthful on task A, and to produce coherent answers on a related task B. + > > Will the model learn to be truthful on task B? Here's a more specific version of this question,
  • @@ -70,32 +67,12 @@ <div style="text-align:center">
     </div> - <!--  --> + Obviously, if you actually want to compute shortest paths in a graph, you should use the standard graph algorithm BFS (breadth-first search). Our motivation for using this problem to study generalization was two-fold: - Obviously, if you actually want to compute shortest paths in a graph, you should use the standard graph algorithm BFS (breadth-first search). Our motivation for using this problem to study generalization was two-fold: + 1. It's very easy to generate data and ground truth labels for this task. - 1. It's very simple to generate data and ground truth labels for this task. 2. There is a simple circuit which computes shortest paths (exactly), by doing something akin to BFS. In fact, we implemented this simple circuit by hand in the section [Handcrafted BFS Transformer](#handcrafted-bfs-transformer). Our hope was that our model can also find a simple circuit that generalized well. - - <!-- - ANT: - RE t<7 - The model couuld've decided to generalize for t > 7 too, but it didn't - so id say - we tested generalization for all t in 3, 15 - it succeeded for t in 3,7 - failed for t in 8 to 15 - reason is because it could do "if statements" instead of realizing that it should just be looking at the dor product - mmm - i feel like the fact that it works for 3,7 and not 8,15 is kind of compelling - anyways this is just a comment explaining how im going to / think you should pitch the generalization section - (it couldve generalized all the way, but didnt, so what we learened is generalization sometimes happens but sometimes doesnt - - yeah that's the intuition - can't really do interpretability to figure out if that's actually what's going on - --> - ### Findings Our main findings were as follows:
  • @@ -118,7 +95,7 @@ The remainder of our post is structured as follows:
    1. [Handcrafted BFS Transformer](#handcrafted-bfs-transformer): To start, we explicitly construct by hand weights for a transformer to solve the shortest paths problem. We also investigate the "robustness" of our construction by showing that, starting from our constructed solution it's possible to recover low loss after perturbing the weights via gradient descent. This gives a proof-of-concept that a transformer could encode a simple generalizable solution to the problem, and gives some reason to believe that a transformer could learn such a solution. - 2. [Training](#training): Next, we discuss the training. First we describe the exact data generation process and model architecture. Then we discuss hyperparameter settings and optimization choices that were important to get the model to train efficiently. Finally, we investigate some interesting phenomena that occured during training, such as phase transitions in loss for different path lengths. + 2. [Training](#training): Next, we discuss the training. First we describe the exact data generation process and model architecture. Then we discuss hyperparameter settings and optimization choices that were important to get the model to train efficiently. Finally, we investigate some interesting phenomena that occurred during training, such as phase transitions in loss for different path lengths. 3. [Generalization](#generalization): Finally we discuss the phenomena that we wanted to study -- generalization! We show that some generalization does occur, but other generalization doesn't. We explain why we think this happens.
  • @@ -187,7 +164,7 @@ ### Data
    We'll represent an $n$ vertex, $m$ edge unweighted, undirected graph as sequence of the endpoints of the $m$ edges, so $[a_1,b_1,a_2,b_2,\ldots,a_m,b_m]$ represents a graph with the edges $\{(a_i,b_i)\}$ where $a_i < b_i$ for $1 \leq i \leq m$. All sequences are padded to the same length using the padding token $0$. - The full input to our model additionally includes the target vertex $t$ after the padding tokens. The label to an input is the length of the shortest path from $1$ to $t$. If no such path exists, we define the length to be $n+1$ which represents infinity. For example, the input $[1, 3, 3, 4, 2, 4, 2, 3, 0, 0, 0, 0, 3]$ has the label $2$. + The full input to our model additionally includes the target vertex $t$ after the padding tokens. The label to an input is the length of the shortest path from $1$ to $t$. If no such path exists, we define the length to be $n$ which represents infinity. For example, the input $[1, 3, 3, 4, 2, 4, 2, 3, 0, 0, 0, 0, 3]$ has the label $2$. <div style="text-align:center"> 
  • @@ -196,8 +173,6 @@
    We have a dataset for each step. 1. **Pre-train data**: For each $n \in [3,15]$, we generated an equal number of graphs on $n$ vertices, with $t = 2$. Each graph was created by choosing $n$ random edges. To balance the dataset, we removed some graphs with shorter paths to obtain the distribution shown below. - - TODO: Add axes titles, switch to bar plot $include`raw="html"` plots/train-dist.html
  • @@ -209,7 +184,6 @@ We wrote some Python code to generate the data during the training loop, but Python is excruciatingly slow and data generation wasted a lot of training time. Our solution was to pre-generate the data before training using a multithreaded version of our Python code.
    ### Architecture - We used a standard transformer architecture. To ensure that it can in theory learn BFS, we ensured that the number of layers in our transformer exceeds the diameter of the input graphs. Since the order of the edges in the input doesn't matter, we did not use positional encodings. Each edge $(a,b)$ is embedded to dimension $d$ where the first $\frac{d}{2}$ elements are the learned embedding of $a$ and the last $\frac{d}{2}$ elements are the learned embedding of $b$. For the target vertex $t$, we pair it with the special token $TARGET$ and embed $(t,TARGET)$ in the same way.
  • @@ -221,25 +195,25 @@ ### Specifications
    For our training run, we used the following specifications: - | Hyperparameter | Value | - | ------------------------ | --------------- | - | Model dimension | $64$ | - | Layers | $11$ | - | Heads | $2$ | - | Epochs | $100$ | - | Batch size | $2^{15}$ | - | Dropout | $0$ | - | Training examples | $10^8$ | - | Parameters | $550433$ | - | Learning rate | $5\cdot10^{-4}$ | - | Loss | MSE | - | Optimizer | Adam | + | Hyperparameter | Value | + | ----------------------- | --------------- | + | Model dimension | $64$ | + | Layers | $11$ | + | Heads | $2$ | + | Epochs | $200$ | + | Batch size | $2^{15}$ | + | Dropout | $0$ | + | Total training examples | $2\cdot10^8$ | + | Parameters | $550433$ | + | Learning rate | $5\cdot10^{-4}$ | + | Loss | MSE | + | Optimizer | Adam | The number of bits required to store the model parameters in float32 is around $1.76\cdot10^6$. The number of possible graphs on 15 vertices generated using our procedure is approximately $$\frac{\binom{15}{2}^{15}}{15!} \approx 1.59\cdot10^{18}.$$ This is because there are $\binom{15}{2}$ choices for each of the 15 edges and we don't care about the order of the edges. This is only an approximation because some edges might be duplicated. Each graph has an answer between 1 and 15 which requires around 4 bits, so memorizing all the answers requires requires $4\cdot1.59\cdot10^{18} = 6.36\cdot10^{18}$ bits, which is $3.61\cdot10^{12}$ times larger than our model size. This implies that in order to get really low loss, our model needs to do something other than brute memorization. - A single training run takes roughly eight hours to run on a Radeon 7900 XTX graphics card. + A single training run takes roughly two hours to run on a Radeon 7900 XTX graphics card. One pitfall we encountered during training is that we initially used bfloat16 to save VRAM, but our huge batch size caused loss-of-precision problems and made training very difficult. It took us two weeks to debug this until we found that switching to float32 improved training significantly.
  • @@ -249,19 +223,19 @@ Here is our training loss curve:
    $include`raw="html"` plots/train-loss.html - Here is a "confusion matrix"-like plot, showing for each true label, the probability distribution over classifications that the model gives. + Here is a "confusion matrix"-like plot, showing for each true label, the probability distribution over classifications that the model gives: $include`raw="html"` plots/train-hist.html - One pattern we consistently noticed during training is that the model often gets stuck and plateaus for many epochs before rapidly decreasing. For instance, this happened between epochs 100 and 300 in the graph above: + One pattern we consistently noticed during training is that the model often gets stuck and plateaus for many epochs before rapidly decreasing. For instance, this happened between epochs 100 and 300 in the graph below: - $include`raw="html"` plots/grokking.html + $include`raw="html"` plots/train-lens.html Such **phase change** phenomena are ubiquitous in deep learning and for simple algorithmic tasks we can sometimes interpret them [^2]. **TODO ANT: training curves for 1, 2, 3 length paths. and add some text about why this is an interesting finding.** - $include`raw="html"` plots/len-loss.html + ## Generalization
  • @@ -273,14 +247,33 @@ $include`raw="html"` plots/tune-loss.html
    $include`raw="html"` plots/tune-hist.html - $include`raw="html"` plots/test-onpath-largetarget.html - $include`raw="html"` plots/test-onpath-smalltarget.html + $include`raw="html"` plots/test-onpath-largetarget.html + $include`raw="html"` plots/test-small-any.html - $include`raw="html"` plots/test-large-any.html + $include`raw="html"` plots/test-large-any-smalltarget.html + + $include`raw="html"` plots/test-large-any-largetarget.html + <!-- + ANT: + RE t<7 + The model couuld've decided to generalize for t > 7 too, but it didn't + so id say + we tested generalization for all t in 3, 15 + it succeeded for t in 3,7 + failed for t in 8 to 15 + reason is because it could do "if statements" instead of realizing that it should just be looking at the dor product + mmm + i feel like the fact that it works for 3,7 and not 8,15 is kind of compelling + anyways this is just a comment explaining how im going to / think you should pitch the generalization section + (it couldve generalized all the way, but didnt, so what we learened is generalization sometimes happens but sometimes doesnt + + yeah that's the intuition + can't really do interpretability to figure out if that's actually what's going on + --> ## Conclusion
• index.html
  • @@ -254,6 +254,7 @@ <header id="title-block-header">
    <h1 class="title">Off-Distribution Generalization of Transformers for Shortest Paths</h1> </header> + <!-- Guidelines: https://www.dropbox.com/scl/fi/bet8enscln8ue36kd8t17/final_project_guidelines.pdf?rlkey=knd19cnumk51ho1y9crno56ib&e=2&dl=0 --> <style> body { /* Shrink margins */
  • @@ -268,11 +269,6 @@ display: block;
    margin: auto; } </style> - <!-- - BTW we should run a spell checker on this at some point before submitting - note to self - --> - <!-- Guidelines: https://www.dropbox.com/scl/fi/bet8enscln8ue36kd8t17/final_project_guidelines.pdf?rlkey=knd19cnumk51ho1y9crno56ib&e=2&dl=0 --> <div style="text-align:center"> <p>Anthony Wang, Alek Westover, Kevin Zhao</p> <p>{xy,alekw,kevinmz}@mit.edu</p>
  • @@ -282,7 +278,7 @@ <h3 id="motivation">Motivation</h3>
    <p>Neural networks are capable of impressive feats of off-distribution generalization. For instance, a program trained to convert sketches of cats into realistic looking pictures of cats can draw a cat with three - eyes, if the sketch has three eyes, even if such an image never occured + eyes, if the sketch has three eyes, even if such an image never occurred in its training data. However, neural networks also often learn non-robust features that cause it to perform very poorly off-distribution (e.g., adversarial examples for an image classifier).
  • @@ -292,8 +288,8 @@ synthetic task. More specifically, the goal of our project is to make
    progress towards answering the following question:</p> <blockquote> <p>Suppose you train a model to be truthful on task A, and to produce - coherent answers on a related task B.<br /> - Will the model learn to be truthful on task B?</p> + coherent answers on a related task B.</p> + <p>Will the model learn to be truthful on task B?</p> </blockquote> <p>Here’s a more specific version of this question, inspired by a <a href="https://www.alignmentforum.org/posts/BxersHYN2qcFoonwg/experimentally-evaluating-whether-honesty-generalizes?commentId=dsDA2BWpHPdgLvaXX">blog
  • @@ -360,38 +356,20 @@ </ol>
    <div style="text-align:center"> <p><img src="img/test.svg" /></p> </div> - <!--  --> <p>Obviously, if you actually want to compute shortest paths in a graph, you should use the standard graph algorithm BFS (breadth-first search). Our motivation for using this problem to study generalization was two-fold:</p> <ol type="1"> - <li>It’s very simple to generate data and ground truth labels for this - task.</li> - <li>There is a simple circuit which computes shortest paths (exactly), - by doing something akin to BFS. In fact, we implemented this simple - circuit by hand in the section <a + <li><p>It’s very easy to generate data and ground truth labels for this + task.</p></li> + <li><p>There is a simple circuit which computes shortest paths + (exactly), by doing something akin to BFS. In fact, we implemented this + simple circuit by hand in the section <a href="#handcrafted-bfs-transformer">Handcrafted BFS Transformer</a>. Our hope was that our model can also find a simple circuit that generalized - well.</li> + well.</p></li> </ol> - <!-- - ANT: - RE t<7 - The model couuld've decided to generalize for t > 7 too, but it didn't - so id say - we tested generalization for all t in 3, 15 - it succeeded for t in 3,7 - failed for t in 8 to 15 - reason is because it could do "if statements" instead of realizing that it should just be looking at the dor product - mmm - i feel like the fact that it works for 3,7 and not 8,15 is kind of compelling - anyways this is just a comment explaining how im going to / think you should pitch the generalization section - (it couldve generalized all the way, but didnt, so what we learened is generalization sometimes happens but sometimes doesnt - - yeah that's the intuition - can't really do interpretability to figure out if that's actually what's going on - --> <h3 id="findings">Findings</h3> <p>Our main findings were as follows:</p> <ol type="1">
  • @@ -449,7 +427,7 @@ <li><p><a href="#training">Training</a>: Next, we discuss the training.
    First we describe the exact data generation process and model architecture. Then we discuss hyperparameter settings and optimization choices that were important to get the model to train efficiently. - Finally, we investigate some interesting phenomena that occured during + Finally, we investigate some interesting phenomena that occurred during training, such as phase transitions in loss for different path lengths.</p></li> <li><p><a href="#generalization">Generalization</a>: Finally we discuss
  • @@ -636,8 +614,8 @@ <span class="math inline">t</span> after the padding tokens. The label
    to an input is the length of the shortest path from <span class="math inline">1</span> to <span class="math inline">t</span>. If no such path exists, we define the length to be <span - class="math inline">n+1</span> which represents infinity. For example, - the input <span class="math inline">[1, 3, 3, 4, 2, 4, 2, 3, 0, 0, 0, 0, + class="math inline">n</span> which represents infinity. For example, the + input <span class="math inline">[1, 3, 3, 4, 2, 4, 2, 3, 0, 0, 0, 0, 3]</span> has the label <span class="math inline">2</span>.</p> <div style="text-align:center"> <p><img src="img/finetune.svg" /></p>
  • @@ -652,14 +630,13 @@ <span class="math inline">n</span> random edges. To balance the dataset,
    we removed some graphs with shorter paths to obtain the distribution shown below.</li> </ol> - <p>TODO: Add axes titles, switch to bar plot</p> <style> </style> - <div id="fig_el1200021399424053838726786165297"></div> + <div id="fig_el3734471397923808578566893788369"></div> <script> function mpld3_load_lib(url, callback){ var s = document.createElement('script');
  • @@ -674,7 +651,7 @@ if(typeof(mpld3) !== "undefined" && mpld3._mpld3IsLoaded){
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"xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139797053797824", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data01", "xindex": 2, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381533200", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data01", "xindex": 3, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381532240", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data01", "xindex": 4, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139797054021056", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data01", "xindex": 5, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381532576", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data12", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381527536", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}], "markers": [], "texts": [{"text": "Label", "position": [0.5, -0.07921476671476674], "coordinates": "axes", "h_anchor": "middle", "v_baseline": "hanging", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 3, "id": "el373447139792380852176"}, {"text": "Count", "position": [-0.10161850358422941, 0.5], "coordinates": "axes", "h_anchor": "middle", "v_baseline": "auto", "rotation": -90.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 3, "id": "el373447139792380853616"}], "collections": [], "images": [], "sharex": [], "sharey": []}], "data": {"data01": [[-0.4, 0.0, 10.6, 11.6, 12.6, 13.6], [0.4, 0.0, 11.4, 12.4, 13.4, 14.4], [0.4, 0.0, 11.4, 12.4, 13.4, 14.4], [-0.4, 0.0, 10.6, 11.6, 12.6, 13.6]], "data02": [[0.6, 0.0], [1.4, 0.0], [1.4, 7279.0], [0.6, 7279.0]], "data03": [[1.6, 0.0], [2.4000000000000004, 0.0], [2.4000000000000004, 6614.0], [1.6, 6614.0]], "data04": [[2.6, 0.0], [3.4000000000000004, 0.0], [3.4000000000000004, 4434.0], [2.6, 4434.0]], "data05": [[3.6, 0.0], [4.4, 0.0], [4.4, 2907.0], [3.6, 2907.0]], "data06": [[4.6, 0.0], [5.3999999999999995, 0.0], [5.3999999999999995, 2324.0], [4.6, 2324.0]], "data07": [[5.6, 0.0], [6.3999999999999995, 0.0], [6.3999999999999995, 1578.0], [5.6, 1578.0]], "data08": [[6.6, 0.0], [7.3999999999999995, 0.0], [7.3999999999999995, 469.0], [6.6, 469.0]], "data09": [[7.6, 0.0], [8.4, 0.0], [8.4, 133.0], [7.6, 133.0]], "data10": [[8.6, 0.0], [9.4, 0.0], [9.4, 27.0], [8.6, 27.0]], "data11": [[9.6, 0.0], [10.4, 0.0], [10.4, 4.0], [9.6, 4.0]], "data12": [[14.6, 0.0], [15.4, 0.0], [15.4, 6999.0], [14.6, 6999.0]]}, "id": "el373447139792380857856", "plugins": [{"type": "reset"}, {"type": "zoom", "button": true, "enabled": false}, {"type": "boxzoom", "button": true, "enabled": false}]}); }); }); }else{
  • @@ -691,7 +668,7 @@ // require.js not available: dynamically load d3 & mpld3
    mpld3_load_lib("https://d3js.org/d3.v5.js", function(){ mpld3_load_lib("https://mpld3.github.io/js/mpld3.v0.5.10.js", function(){ - mpld3.draw_figure("fig_el1200021399424053838726786165297", {"width": 640.0, "height": 480.0, "axes": [{"bbox": [0.125, 0.10999999999999999, 0.775, 0.77], "xlim": [0.29999999999999993, 15.7], "ylim": [0.0, 7533.75], "xdomain": [0.29999999999999993, 15.7], "ydomain": [0.0, 7533.75], "xscale": "linear", "yscale": "linear", "axes": [{"position": "bottom", "nticks": 9, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}, {"position": "left", "nticks": 9, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}], "axesbg": "#FFFFFF", "axesbgalpha": null, "zoomable": true, "id": "el120002139942361034016", "lines": [], "paths": [{"data": "data01", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361126032", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data02", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361031376", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data03", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942360791184", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data04", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361121472", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data05", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361120896", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data06", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361123824", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data07", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361123392", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data08", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361127904", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data09", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361129296", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data10", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361129056", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data11", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361128624", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data12", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361127376", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data12", "xindex": 2, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361126752", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data12", "xindex": 3, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361126320", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data13", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361129872", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}], "markers": [], "texts": [], "collections": [], "images": [], "sharex": [], "sharey": []}], "data": {"data01": [[1.0, 0.0], [1.933333396911621, 0.0], [1.933333396911621, 7175.0], [1.0, 7175.0]], "data02": [[1.933333396911621, 0.0], [2.866666793823242, 0.0], [2.866666793823242, 6576.0], [1.933333396911621, 6576.0]], "data03": [[2.866666793823242, 0.0], [3.799999952316284, 0.0], [3.799999952316284, 4624.0], [2.866666793823242, 4624.0]], "data04": [[3.799999952316284, 0.0], [4.733333587646484, 0.0], [4.733333587646484, 3021.0], [3.799999952316284, 3021.0]], "data05": [[4.733333587646484, 0.0], [5.666666507720947, 0.0], [5.666666507720947, 2275.0], [4.733333587646484, 2275.0]], "data06": [[5.666666507720947, 0.0], [6.599999904632568, 0.0], [6.599999904632568, 1653.0], [5.666666507720947, 1653.0]], "data07": [[6.599999904632568, 0.0], [7.5333333015441895, 0.0], [7.5333333015441895, 492.0], [6.599999904632568, 492.0]], "data08": [[7.5333333015441895, 0.0], [8.466667175292969, 0.0], [8.466667175292969, 119.0], [7.5333333015441895, 119.0]], "data09": [[8.466667175292969, 0.0], [9.399999618530273, 0.0], [9.399999618530273, 28.0], [8.466667175292969, 28.0]], "data10": [[9.399999618530273, 0.0], [10.333333015441895, 0.0], [10.333333015441895, 5.0], [9.399999618530273, 5.0]], "data11": [[10.333333015441895, 0.0], [11.266666412353516, 0.0], [11.266666412353516, 1.0], [10.333333015441895, 1.0]], "data12": [[11.266666412353516, 0.0, 12.199999809265137, 13.133333206176758], [12.199999809265137, 0.0, 13.133333206176758, 14.066666603088379], [12.199999809265137, 0.0, 13.133333206176758, 14.066666603088379], [11.266666412353516, 0.0, 12.199999809265137, 13.133333206176758]], "data13": [[14.066666603088379, 0.0], [15.0, 0.0], [15.0, 6799.0], [14.066666603088379, 6799.0]]}, "id": "el120002139942405383872", "plugins": [{"type": "reset"}, {"type": "zoom", "button": true, "enabled": false}, {"type": "boxzoom", "button": true, "enabled": false}]}); + mpld3.draw_figure("fig_el3734471397923808578566893788369", {"width": 640.0, "height": 480.0, "axes": [{"bbox": [0.125, 0.10999999999999999, 0.775, 0.77], "xlim": [-1.19, 16.19], "ylim": [0.0, 7642.95], "xdomain": [-1.19, 16.19], "ydomain": [0.0, 7642.95], "xscale": "linear", "yscale": "linear", "axes": [{"position": "bottom", "nticks": 11, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}, {"position": "left", "nticks": 9, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}], "axesbg": "#FFFFFF", "axesbgalpha": null, "zoomable": true, "id": "el373447139797053788464", "lines": [], "paths": [{"data": "data01", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381535360", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data02", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792380856416", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data03", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139797054022832", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data04", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381530944", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data05", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381539632", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data06", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139797053780624", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 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  • @@ -753,7 +730,7 @@ <td><span class="math inline">2</span></td>
    </tr> <tr class="even"> <td>Epochs</td> - <td><span class="math inline">100</span></td> + <td><span class="math inline">200</span></td> </tr> <tr class="odd"> <td>Batch size</td>
  • @@ -764,8 +741,8 @@ <td>Dropout</td>
    <td><span class="math inline">0</span></td> </tr> <tr class="odd"> - <td>Training examples</td> - <td><span class="math inline">10^8</span></td> + <td>Total training examples</td> + <td><span class="math inline">2\cdot10^8</span></td> </tr> <tr class="even"> <td>Parameters</td>
  • @@ -799,8 +776,8 @@ class="math inline">4\cdot1.59\cdot10^{18} = 6.36\cdot10^{18}</span>
    bits, which is <span class="math inline">3.61\cdot10^{12}</span> times larger than our model size. This implies that in order to get really low loss, our model needs to do something other than brute memorization.</p> - <p>A single training run takes roughly eight hours to run on a Radeon - 7900 XTX graphics card.</p> + <p>A single training run takes roughly two hours to run on a Radeon 7900 + XTX graphics card.</p> <p>One pitfall we encountered during training is that we initially used bfloat16 to save VRAM, but our huge batch size caused loss-of-precision problems and made training very difficult. It took us two weeks to debug
  • @@ -811,20 +788,19 @@ <p>Here is our training loss curve:</p>
    <p>$include<code>raw="html"</code> plots/train-loss.html</p> <p>Here is a “confusion matrix”-like plot, showing for each true label, the probability distribution over classifications that the model - gives.</p> + gives:</p> <p>$include<code>raw="html"</code> plots/train-hist.html</p> <p>One pattern we consistently noticed during training is that the model often gets stuck and plateaus for many epochs before rapidly decreasing. For instance, this happened between epochs 100 and 300 in the graph - above:</p> - <p>$include<code>raw="html"</code> plots/grokking.html</p> + below:</p> + <p>$include<code>raw="html"</code> plots/train-lens.html</p> <p>Such <strong>phase change</strong> phenomena are ubiquitous in deep learning and for simple algorithmic tasks we can sometimes interpret them <a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref">²</a>.</p> <p><strong>TODO ANT: training curves for 1, 2, 3 length paths. and add some text about why this is an interesting finding.</strong></p> - <p>$include<code>raw="html"</code> plots/len-loss.html</p> <h2 id="generalization">Generalization</h2> <h3 id="fine-tuning-results">Fine Tuning Results</h3> <p>After receiving our initial results, we fine-tuned with a learning
  • @@ -833,11 +809,31 @@ are shown in the images below.</p>
    <p>$include<code>raw="html"</code> plots/tune-loss.html</p> <p>$include<code>raw="html"</code> plots/tune-hist.html</p> <p>$include<code>raw="html"</code> - plots/test-onpath-largetarget.html</p> - <p>$include<code>raw="html"</code> plots/test-onpath-smalltarget.html</p> + <p>$include<code>raw="html"</code> + plots/test-onpath-largetarget.html</p> <p>$include<code>raw="html"</code> plots/test-small-any.html</p> - <p>$include<code>raw="html"</code> plots/test-large-any.html</p> + <p>$include<code>raw="html"</code> + plots/test-large-any-smalltarget.html</p> + <p>$include<code>raw="html"</code> + plots/test-large-any-largetarget.html</p> + <!-- + ANT: + RE t<7 + The model couuld've decided to generalize for t > 7 too, but it didn't + so id say + we tested generalization for all t in 3, 15 + it succeeded for t in 3,7 + failed for t in 8 to 15 + reason is because it could do "if statements" instead of realizing that it should just be looking at the dor product + mmm + i feel like the fact that it works for 3,7 and not 8,15 is kind of compelling + anyways this is just a comment explaining how im going to / think you should pitch the generalization section + (it couldve generalized all the way, but didnt, so what we learened is generalization sometimes happens but sometimes doesnt + + yeah that's the intuition + can't really do interpretability to figure out if that's actually what's going on + --> <h2 id="conclusion">Conclusion</h2> <p>In this post we’ve investigated solving the shortest paths problem with a transformer. We’ve shown that this problem has a simple solution
• plots/train-dist.html
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["M", "L", "L", "L", "Z"], "id": "el120002139942361120896", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data06", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361123824", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data07", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361123392", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data08", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361127904", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data09", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361129296", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data10", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361129056", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data11", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361128624", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data12", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361127376", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data12", "xindex": 2, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361126752", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data12", "xindex": 3, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361126320", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data13", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361129872", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}], "markers": [], "texts": [], "collections": [], "images": [], "sharex": [], "sharey": []}], "data": {"data01": [[1.0, 0.0], [1.933333396911621, 0.0], [1.933333396911621, 7175.0], [1.0, 7175.0]], "data02": [[1.933333396911621, 0.0], [2.866666793823242, 0.0], [2.866666793823242, 6576.0], [1.933333396911621, 6576.0]], "data03": [[2.866666793823242, 0.0], [3.799999952316284, 0.0], [3.799999952316284, 4624.0], [2.866666793823242, 4624.0]], "data04": [[3.799999952316284, 0.0], [4.733333587646484, 0.0], [4.733333587646484, 3021.0], [3.799999952316284, 3021.0]], "data05": [[4.733333587646484, 0.0], [5.666666507720947, 0.0], [5.666666507720947, 2275.0], [4.733333587646484, 2275.0]], "data06": [[5.666666507720947, 0.0], [6.599999904632568, 0.0], [6.599999904632568, 1653.0], [5.666666507720947, 1653.0]], "data07": [[6.599999904632568, 0.0], [7.5333333015441895, 0.0], [7.5333333015441895, 492.0], [6.599999904632568, 492.0]], "data08": [[7.5333333015441895, 0.0], [8.466667175292969, 0.0], [8.466667175292969, 119.0], [7.5333333015441895, 119.0]], "data09": [[8.466667175292969, 0.0], [9.399999618530273, 0.0], [9.399999618530273, 28.0], [8.466667175292969, 28.0]], "data10": [[9.399999618530273, 0.0], [10.333333015441895, 0.0], [10.333333015441895, 5.0], [9.399999618530273, 5.0]], "data11": [[10.333333015441895, 0.0], [11.266666412353516, 0.0], [11.266666412353516, 1.0], [10.333333015441895, 1.0]], "data12": [[11.266666412353516, 0.0, 12.199999809265137, 13.133333206176758], [12.199999809265137, 0.0, 13.133333206176758, 14.066666603088379], [12.199999809265137, 0.0, 13.133333206176758, 14.066666603088379], [11.266666412353516, 0.0, 12.199999809265137, 13.133333206176758]], "data13": [[14.066666603088379, 0.0], [15.0, 0.0], [15.0, 6799.0], [14.066666603088379, 6799.0]]}, "id": "el120002139942405383872", "plugins": [{"type": "reset"}, {"type": "zoom", "button": true, "enabled": false}, {"type": "boxzoom", "button": true, "enabled": false}]}); + mpld3.draw_figure("fig_el3734471397923808578566893788369", {"width": 640.0, "height": 480.0, "axes": [{"bbox": [0.125, 0.10999999999999999, 0.775, 0.77], "xlim": [-1.19, 16.19], "ylim": [0.0, 7642.95], "xdomain": [-1.19, 16.19], "ydomain": [0.0, 7642.95], "xscale": "linear", "yscale": "linear", "axes": [{"position": "bottom", "nticks": 11, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}, {"position": "left", "nticks": 9, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}], "axesbg": "#FFFFFF", "axesbgalpha": null, "zoomable": true, "id": "el373447139797053788464", "lines": [], "paths": [{"data": "data01", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381535360", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data02", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792380856416", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data03", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139797054022832", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data04", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381530944", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data05", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381539632", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data06", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139797053780624", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data07", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381532192", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data08", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381536896", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data09", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381539872", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data10", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381532480", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data11", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139797053797824", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data01", "xindex": 2, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381533200", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data01", "xindex": 3, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381532240", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data01", "xindex": 4, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139797054021056", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data01", "xindex": 5, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381532576", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data12", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el373447139792381527536", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}], "markers": [], "texts": [{"text": "Label", "position": [0.5, -0.07921476671476674], "coordinates": "axes", "h_anchor": "middle", "v_baseline": "hanging", "rotation": -0.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 3, "id": "el373447139792380852176"}, {"text": "Count", "position": [-0.10161850358422941, 0.5], "coordinates": "axes", "h_anchor": "middle", "v_baseline": "auto", "rotation": -90.0, "fontsize": 10.0, "color": "#000000", "alpha": 1, "zorder": 3, "id": "el373447139792380853616"}], "collections": [], "images": [], "sharex": [], "sharey": []}], "data": {"data01": [[-0.4, 0.0, 10.6, 11.6, 12.6, 13.6], [0.4, 0.0, 11.4, 12.4, 13.4, 14.4], [0.4, 0.0, 11.4, 12.4, 13.4, 14.4], [-0.4, 0.0, 10.6, 11.6, 12.6, 13.6]], "data02": [[0.6, 0.0], [1.4, 0.0], [1.4, 7279.0], [0.6, 7279.0]], "data03": [[1.6, 0.0], [2.4000000000000004, 0.0], [2.4000000000000004, 6614.0], [1.6, 6614.0]], "data04": [[2.6, 0.0], [3.4000000000000004, 0.0], [3.4000000000000004, 4434.0], [2.6, 4434.0]], "data05": [[3.6, 0.0], [4.4, 0.0], [4.4, 2907.0], [3.6, 2907.0]], "data06": [[4.6, 0.0], [5.3999999999999995, 0.0], [5.3999999999999995, 2324.0], [4.6, 2324.0]], "data07": [[5.6, 0.0], [6.3999999999999995, 0.0], [6.3999999999999995, 1578.0], [5.6, 1578.0]], "data08": [[6.6, 0.0], [7.3999999999999995, 0.0], [7.3999999999999995, 469.0], [6.6, 469.0]], "data09": [[7.6, 0.0], [8.4, 0.0], [8.4, 133.0], [7.6, 133.0]], "data10": [[8.6, 0.0], [9.4, 0.0], [9.4, 27.0], [8.6, 27.0]], "data11": [[9.6, 0.0], [10.4, 0.0], [10.4, 4.0], [9.6, 4.0]], "data12": [[14.6, 0.0], [15.4, 0.0], [15.4, 6999.0], [14.6, 6999.0]]}, "id": "el373447139792380857856", "plugins": [{"type": "reset"}, {"type": "zoom", "button": true, "enabled": false}, {"type": "boxzoom", "button": true, "enabled": false}]}); }); }); }else{
  • @@ -36,7 +36,7 @@ // require.js not available: dynamically load d3 & mpld3
    mpld3_load_lib("https://d3js.org/d3.v5.js", function(){ mpld3_load_lib("https://mpld3.github.io/js/mpld3.v0.5.10.js", function(){ - mpld3.draw_figure("fig_el1200021399424053838726786165297", {"width": 640.0, "height": 480.0, "axes": [{"bbox": [0.125, 0.10999999999999999, 0.775, 0.77], "xlim": [0.29999999999999993, 15.7], "ylim": [0.0, 7533.75], "xdomain": [0.29999999999999993, 15.7], "ydomain": [0.0, 7533.75], "xscale": "linear", "yscale": "linear", "axes": [{"position": "bottom", "nticks": 9, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}, {"position": "left", "nticks": 9, "tickvalues": null, "tickformat_formatter": "", "tickformat": null, "scale": "linear", "fontsize": 10.0, "grid": {"gridOn": false}, "visible": true}], "axesbg": "#FFFFFF", "axesbgalpha": null, "zoomable": true, "id": "el120002139942361034016", "lines": [], "paths": [{"data": "data01", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361126032", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data02", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361031376", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data03", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942360791184", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data04", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361121472", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data05", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361120896", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data06", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361123824", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data07", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361123392", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data08", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361127904", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data09", "xindex": 0, 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2, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361126752", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data12", "xindex": 3, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361126320", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}, {"data": "data13", "xindex": 0, "yindex": 1, "coordinates": "data", "pathcodes": ["M", "L", "L", "L", "Z"], "id": "el120002139942361129872", "dasharray": "none", "alpha": 1, "facecolor": "#1F77B4", "edgecolor": "none", "edgewidth": 1.0, "zorder": 1}], "markers": [], "texts": [], "collections": [], "images": [], "sharex": [], "sharey": []}], "data": {"data01": [[1.0, 0.0], [1.933333396911621, 0.0], [1.933333396911621, 7175.0], [1.0, 7175.0]], "data02": [[1.933333396911621, 0.0], [2.866666793823242, 0.0], 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• transformer_shortest_paths.ipynb
  • @@ -183,7 +183,7 @@ ]
    }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "execution_state": "idle", "metadata": {}, "outputs": [],
  • @@ -200,35 +200,35 @@ ]
    }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 4, "execution_state": "idle", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(tensor([[1, 3, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 2],\n", - " [1, 5, 3, 4, 1, 3, 4, 5, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 2],\n", - " [2, 9, 1, 3, 4, 8, 8, 9, 3, 9, 5, 7, 6, 7, 1, 6, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 2]]),\n", - " tensor([ 1., 15., 3.]),\n", - " tensor([[False, False, False, False, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True, True,\n", + "(tensor([[ 5, 11, 9, 10, 3, 4, 6, 13, 3, 9, 9, 13, 1, 10, 6, 8, 1, 2,\n", + " 6, 13, 4, 5, 1, 4, 2, 10, 0, 0, 0, 0, 2],\n", + " [ 4, 10, 1, 10, 2, 7, 6, 9, 4, 5, 4, 6, 5, 7, 3, 5, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],\n", + " [ 9, 10, 3, 7, 1, 3, 5, 6, 13, 14, 1, 15, 4, 6, 4, 11, 2, 11,\n", + " 6, 13, 9, 15, 7, 14, 10, 12, 3, 15, 0, 0, 2]]),\n", + " tensor([1., 5., 8.]),\n", + " tensor([[False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, True, True, True, True,\n", " False],\n", " [False, False, False, False, False, False, False, False, False, False,\n", - " True, True, True, True, True, True, True, True, True, True,\n", + " False, False, False, False, False, False, True, True, True, True,\n", " True, True, True, True, True, True, True, True, True, True,\n", " False],\n", " [False, False, False, False, False, False, False, False, False, False,\n", - " False, False, False, False, False, False, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True, True,\n", + " False, False, False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False, False, True, True,\n", " False]]))" ] }, - "execution_count": 95, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }
  • @@ -239,13 +239,13 @@ ]
    }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "execution_state": "idle", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ]
  • @@ -255,32 +255,33 @@ "output_type": "display_data"
    } ], "source": [ - "plt.hist(mkbatch(2**15)[1].cpu(), bins=MAX_VTXS)\n", - "with open(\"train-dist.html\", \"w\") as f:\n", + "bins = torch.bincount(mkbatch(2**15)[1].to(torch.uint8).cpu())\n", + "plt.bar(range(len(bins)), bins)\n", + "plt.xlabel(\"Label\")\n", + "plt.ylabel(\"Count\")\n", + "with open(\"plots/train-dist.html\", \"w\") as f:\n", " mpld3.save_html(plt.gcf(), f)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "execution_state": "idle", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([2.4747e+04, 6.5340e+03, 1.2840e+03, 1.8300e+02, 2.0000e+01]),\n", - " array([1., 2., 3., 4., 5., 6.]),\n", - " <BarContainer object of 5 artists>)" + "Text(0, 0.5, 'Count')" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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BA7R8+fKfNL/H45HD4VB1dbXsdvulnoYmdZ+d36LHQ9MOz0/y9wgAgFb2U39+X5HP5GzevFkRERHq1auXHnvsMX399dfWvtLSUoWHh1uBI0mJiYkKDAzU9u3brTXDhg2zAkeSXC6XDhw4oG+++cZak5iY6PO8LpdLpaWlF52rrq5OHo/HZwMAAGZq8cgZOXKk/va3v6moqEgvvviiiouLNWrUKNXX10uS3G63IiIifB4THBysDh06yO12W2siIyN91jTe/rE1jfubkp2dLYfDYW0xMTEt9KoBAMDPTXBLH3D8+PHWP/ft21f9+vXTTTfdpM2bN2v48OEt/XTNkpmZqYyMDOu2x+MhdAAAMNQV/wr5jTfeqE6dOungwYOSpKioKB0/ftxnzblz53Ty5ElFRUVZa6qqqnzWNN7+sTWN+5sSEhIiu93uswEAADNd8cj58ssv9fXXX6tLly6SJKfTqVOnTqmsrMxas2nTJjU0NCghIcFaU1JSorNnz1prCgsL1atXL11//fXWmqKiIp/nKiwslNPpvNIvCQAAtAHNjpyamhqVl5ervLxcknTo0CGVl5ersrJSNTU1mjFjhrZt26bDhw+rqKhI9913n3r27CmXyyVJ6t27t0aOHKmHH35YO3bs0EcffaT09HSNHz9e0dHRkqQHHnhANptNqamp2rdvn1atWqXFixf7/Krp8ccfV0FBgRYuXKj9+/dr3rx52rVrl9LT01vu7AAAgDar2ZGza9cuDRw4UAMHDpQkZWRkaODAgcrKylJQUJD27NmjX//617r55puVmpqq+Ph4/fOf/1RISIh1jDfffFOxsbEaPny4Ro8erTvuuMPnGjgOh0MbN27UoUOHFB8fryeeeEJZWVk+19K5/fbblZubqxUrVqh///56++23tW7dOvXp0+fyzwoAAGjzLus6OW0d18lp+7hODgBcffx6nRwAAAB/I3IAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYqdmRU1JSojFjxig6OloBAQFat26dz36v16usrCx16dJF7dq1U2Jioj777DOfNSdPntTEiRNlt9sVHh6u1NRU1dTU+KzZs2eP7rzzToWGhiomJkYLFiy4YJY1a9YoNjZWoaGh6tu3r95///3mvhwAAGCoZkdObW2t+vfvr6VLlza5f8GCBXrllVe0fPlybd++Xe3bt5fL5dJ3331nrZk4caL27dunwsJC5eXlqaSkRI888oi13+PxaMSIEerWrZvKysr00ksvad68eVqxYoW1ZuvWrZowYYJSU1P18ccfKzk5WcnJyaqoqGj+WQAAAMYJ8Hq93kt+cECA1q5dq+TkZOn7d3Gio6P1xBNP6Mknn5QkVVdXKzIyUjk5ORo/frw+/fRTxcXFaefOnRo0aJAkqaCgQKNHj9aXX36p6OhoLVu2TE8//bTcbrdsNpskafbs2Vq3bp32798vSRo3bpxqa2uVl5dnzTNkyBANGDBAy5cv/0nzezweORwOVVdXy263X+ppaFL32fktejw07fD8JH+PAABoZT/153eLfibn0KFDcrvdSkxMtO5zOBxKSEhQaWmpJKm0tFTh4eFW4EhSYmKiAgMDtX37dmvNsGHDrMCRJJfLpQMHDuibb76x1pz/PI1rGp+nKXV1dfJ4PD4bAAAwU4tGjtvtliRFRkb63B8ZGWntc7vdioiI8NkfHBysDh06+Kxp6hjnP8fF1jTub0p2drYcDoe1xcTEXMarBQAAP2dX1berMjMzVV1dbW1Hjhzx90gAAOAKadHIiYqKkiRVVVX53F9VVWXti4qK0vHjx332nzt3TidPnvRZ09Qxzn+Oi61p3N+UkJAQ2e12nw0AAJipRSOnR48eioqKUlFRkXWfx+PR9u3b5XQ6JUlOp1OnTp1SWVmZtWbTpk1qaGhQQkKCtaakpERnz5611hQWFqpXr166/vrrrTXnP0/jmsbnAQAAV7dmR05NTY3Ky8tVXl4uff9h4/LyclVWViogIEDTpk3Tc889p3fffVd79+7VH//4R0VHR1vfwOrdu7dGjhyphx9+WDt27NBHH32k9PR0jR8/XtHR0ZKkBx54QDabTampqdq3b59WrVqlxYsXKyMjw5rj8ccfV0FBgRYuXKj9+/dr3rx52rVrl9LT01vu7AAAgDYruLkP2LVrl+6++27rdmN4pKSkKCcnRzNnzlRtba0eeeQRnTp1SnfccYcKCgoUGhpqPebNN99Uenq6hg8frsDAQI0dO1avvPKKtd/hcGjjxo1KS0tTfHy8OnXqpKysLJ9r6dx+++3Kzc3VnDlz9NRTT+mXv/yl1q1bpz59+lzO+QAAAIa4rOvktHVcJ6ft4zo5AHD18ct1cgAAAH4uiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEYicgAAgJGIHAAAYKRgfw8AXI7us/P9PcJV4/D8JH+PAADNwjs5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBILR458+bNU0BAgM8WGxtr7f/uu++Ulpamjh076rrrrtPYsWNVVVXlc4zKykolJSXp2muvVUREhGbMmKFz5875rNm8ebNuvfVWhYSEqGfPnsrJyWnplwIAANqwK/JOzi233KJjx45Z25YtW6x906dP13vvvac1a9aouLhYR48e1f3332/tr6+vV1JSks6cOaOtW7fqjTfeUE5OjrKysqw1hw4dUlJSku6++26Vl5dr2rRpeuihh7Rhw4Yr8XIAAEAbFHxFDhocrKioqAvur66u1l//+lfl5ubqnnvukSStXLlSvXv31rZt2zRkyBBt3LhRn3zyiT744ANFRkZqwIAB+vOf/6xZs2Zp3rx5stlsWr58uXr06KGFCxdKknr37q0tW7bo5ZdflsvluhIvCQAAtDFX5J2czz77TNHR0brxxhs1ceJEVVZWSpLKysp09uxZJSYmWmtjY2N1ww03qLS0VJJUWlqqvn37KjIy0lrjcrnk8Xi0b98+a835x2hc03iMi6mrq5PH4/HZAACAmVo8chISEpSTk6OCggItW7ZMhw4d0p133qnTp0/L7XbLZrMpPDzc5zGRkZFyu92SJLfb7RM4jfsb9/3QGo/Ho2+//fais2VnZ8vhcFhbTExMi71uAADw89Liv64aNWqU9c/9+vVTQkKCunXrptWrV6tdu3Yt/XTNkpmZqYyMDOu2x+MhdAAAMNQV/wp5eHi4br75Zh08eFBRUVE6c+aMTp065bOmqqrK+gxPVFTUBd+2arz9Y2vsdvsPhlRISIjsdrvPBgAAzHTFI6empkaff/65unTpovj4eF1zzTUqKiqy9h84cECVlZVyOp2SJKfTqb179+r48ePWmsLCQtntdsXFxVlrzj9G45rGYwAAALR45Dz55JMqLi7W4cOHtXXrVv3mN79RUFCQJkyYIIfDodTUVGVkZOjDDz9UWVmZpkyZIqfTqSFDhkiSRowYobi4OE2aNEn/+te/tGHDBs2ZM0dpaWkKCQmRJD366KP64osvNHPmTO3fv1+vvvqqVq9erenTp7f0ywEAAG1Ui38m58svv9SECRP09ddfq3Pnzrrjjju0bds2de7cWZL08ssvKzAwUGPHjlVdXZ1cLpdeffVV6/FBQUHKy8vTY489JqfTqfbt2yslJUXPPvustaZHjx7Kz8/X9OnTtXjxYnXt2lWvv/46Xx8HAACWAK/X6/X3EP7i8XjkcDhUXV3d4p/P6T47v0WPB/jb4flJ/h4BAKRm/Pzmb1cBAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASMH+HgBA29B9dr6/R7gqHJ6f5O8RAGPwTg4AADASkQMAAIxE5AAAACMROQAAwEhEDgAAMBKRAwAAjETkAAAAIxE5AADASEQOAAAwEpEDAACMROQAAAAjETkAAMBIRA4AADBSm4+cpUuXqnv37goNDVVCQoJ27Njh75EAAMDPQJuOnFWrVikjI0Nz587V7t271b9/f7lcLh0/ftzfowEAAD8L8Hq9Xn8PcakSEhI0ePBgLVmyRJLU0NCgmJgYTZ06VbNnz/7Rx3s8HjkcDlVXV8tut7fobN1n57fo8QAALefw/CR/j4DL8FN/fge36lQt6MyZMyorK1NmZqZ1X2BgoBITE1VaWtrkY+rq6lRXV2fdrq6ulr4/WS2toe6/LX5MAEDLuBL/v4/W0/jf34+9T9NmI+err75SfX29IiMjfe6PjIzU/v37m3xMdna2nnnmmQvuj4mJuWJzAgB+fhyL/D0BWsLp06flcDguur/NRs6lyMzMVEZGhnW7oaFBJ0+eVMeOHRUQENBiz+PxeBQTE6MjR460+K/B8D+c59bDuW4dnOfWwXluHVfyPHu9Xp0+fVrR0dE/uK7NRk6nTp0UFBSkqqoqn/urqqoUFRXV5GNCQkIUEhLic194ePgVm9Fut/M/oFbAeW49nOvWwXluHZzn1nGlzvMPvYPTqM1+u8pmsyk+Pl5FRUXWfQ0NDSoqKpLT6fTrbAAAwP/a7Ds5kpSRkaGUlBQNGjRIt912mxYtWqTa2lpNmTLF36MBAAA/a9ORM27cOJ04cUJZWVlyu90aMGCACgoKLvgwcmsLCQnR3LlzL/jVGFoW57n1cK5bB+e5dXCeW8fP4Ty36evkAAAAXEyb/UwOAADADyFyAACAkYgcAABgJCIHAAAYichpQSUlJRozZoyio6MVEBCgdevW+XskI2VnZ2vw4MEKCwtTRESEkpOTdeDAAX+PZZxly5apX79+1oW8nE6n1q9f7++xjDd//nwFBARo2rRp/h7FOPPmzVNAQIDPFhsb6++xjPSf//xHf/jDH9SxY0e1a9dOffv21a5du1p9DiKnBdXW1qp///5aunSpv0cxWnFxsdLS0rRt2zYVFhbq7NmzGjFihGpra/09mlG6du2q+fPnq6ysTLt27dI999yj++67T/v27fP3aMbauXOn/vKXv6hfv37+HsVYt9xyi44dO2ZtW7Zs8fdIxvnmm280dOhQXXPNNVq/fr0++eQTLVy4UNdff32rz9Kmr5PzczNq1CiNGjXK32MYr6CgwOd2Tk6OIiIiVFZWpmHDhvltLtOMGTPG5/bzzz+vZcuWadu2bbrlllv8NpepampqNHHiRL322mt67rnn/D2OsYKDgy/6p3/QMl588UXFxMRo5cqV1n09evTwyyy8k4M2r7q6WpLUoUMHf49irPr6er311luqra3lz6ZcIWlpaUpKSlJiYqK/RzHaZ599pujoaN14442aOHGiKisr/T2Scd59910NGjRIv/vd7xQREaGBAwfqtdde88ssvJODNq2hoUHTpk3T0KFD1adPH3+PY5y9e/fK6XTqu+++03XXXae1a9cqLi7O32MZ56233tLu3bu1c+dOf49itISEBOXk5KhXr146duyYnnnmGd15552qqKhQWFiYv8czxhdffKFly5YpIyNDTz31lHbu3Kk//elPstlsSklJadVZiBy0aWlpaaqoqOD36ldIr169VF5erurqar399ttKSUlRcXExodOCjhw5oscff1yFhYUKDQ319zhGO//jBP369VNCQoK6deum1atXKzU11a+zmaShoUGDBg3SCy+8IEkaOHCgKioqtHz58laPHH5dhTYrPT1deXl5+vDDD9W1a1d/j2Mkm82mnj17Kj4+XtnZ2erfv78WL17s77GMUlZWpuPHj+vWW29VcHCwgoODVVxcrFdeeUXBwcGqr6/394jGCg8P180336yDBw/6exSjdOnS5YJ/Eerdu7dffjXIOzloc7xer6ZOnaq1a9dq8+bNfvtA29WooaFBdXV1/h7DKMOHD9fevXt97psyZYpiY2M1a9YsBQUF+W0209XU1Ojzzz/XpEmT/D2KUYYOHXrBZT3+/e9/q1u3bq0+C5HTgmpqanz+jeDQoUMqLy9Xhw4ddMMNN/h1NpOkpaUpNzdX77zzjsLCwuR2uyVJDodD7dq18/d4xsjMzNSoUaN0ww036PTp08rNzdXmzZu1YcMGf49mlLCwsAs+T9a+fXt17NiRz5m1sCeffFJjxoxRt27ddPToUc2dO1dBQUGaMGGCv0czyvTp03X77bfrhRde0O9//3vt2LFDK1as0IoVK1p/GC9azIcffuiVdMGWkpLi79GM0tQ5luRduXKlv0czyoMPPujt1q2b12azeTt37uwdPny4d+PGjf4e66rwq1/9yvv444/7ewzjjBs3ztulSxevzWbz/uIXv/COGzfOe/DgQX+PZaT33nvP26dPH29ISIg3NjbWu2LFCr/MEeD9/z80AAAAjMIHjwEAgJGIHAAAYCQiBwAAGInIAQAARiJyAACAkYgcAABgJCIHAAAYicgBAABGInIAAICRiBwAAGAkIgcAABiJyAEAAEb6f8M11LhmDDKeAAAAAElFTkSuQmCC", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ]
  • @@ -290,31 +291,31 @@ "output_type": "display_data"
    } ], "source": [ - "plt.hist(mkbatch(2**15, large=False, target=\"onpath\")[1].cpu(), bins=5)" + "bins = torch.bincount(mkbatch(2**15, large=False, target=\"onpath\")[1].to(torch.uint8).cpu())\n", + "plt.bar(range(len(bins)), bins)\n", + "plt.xlabel(\"Label\")\n", + "plt.ylabel(\"Count\")" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "execution_state": "idle", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([2.0379e+04, 7.8380e+03, 3.0080e+03, 1.0690e+03, 3.4600e+02,\n", - " 9.8000e+01, 2.5000e+01, 5.0000e+00]),\n", - " array([1., 2., 3., 4., 5., 6., 7., 8., 9.]),\n", - " <BarContainer object of 8 artists>)" + "Text(0, 0.5, 'Count')" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ]
  • @@ -324,7 +325,10 @@ "output_type": "display_data"
    } ], "source": [ - "plt.hist(mkbatch(2**15, large=True, target=\"onpath\", largetarget=False)[1].cpu(), bins=8)" + "bins = torch.bincount(mkbatch(2**15, large=True, target=\"onpath\", largetarget=False)[1].to(torch.uint8).cpu())\n", + "plt.bar(range(len(bins)), bins)\n", + "plt.xlabel(\"Label\")\n", + "plt.ylabel(\"Count\")" ] }, {
  • @@ -338,7 +342,7 @@ ]
    }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "execution_state": "idle", "metadata": { "id": "tLOWhg_CeWzH"
  • @@ -375,7 +379,7 @@ ]
    }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "execution_state": "idle", "metadata": { "colab": {
  • @@ -389,7 +393,7 @@ {
    "name": "stdout", "output_type": "stream", "text": [ - "Training data: 104857K\n", + "Training data: 209715K\n", "Trainable parameters in the model: 550433\n" ] }
  • @@ -398,7 +402,7 @@ "source": [
    "# PARAMS\n", "VOCAB_SIZE = 1 + MAX_VTXS + 1 # pad plus max number of vertices plus target token\n", "MODEL_DIM = 64 # Dimension of model (embedding and transformer)\n", - "NEPOCHS = 100\n", + "NEPOCHS = 200\n", "BSZ = 2**15 # Batch size\n", "NHEADS = 2\n", "NLAYERS = 11\n",
  • @@ -426,7 +430,7 @@ ]
    }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "execution_state": "idle", "metadata": {}, "outputs": [],
  • @@ -470,17 +474,244 @@ {
    "name": "stderr", "output_type": "stream", "text": [ + "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/functional.py:6278: UserWarning: Memory Efficient attention on Navi31 GPU is still experimental. Enable it with TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1. (Triggered internally at ../aten/src/ATen/native/transformers/hip/sdp_utils.cpp:269.)\n", + " attn_output = scaled_dot_product_attention(\n", "/tmp/torchinductor_sipb/lc/clcqc3ufbzrethiy77dmsu54kurxdmh4eji2f3msm347rhmfpf4j.py:1078: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at ../aten/src/ATen/Context.cpp:296.)\n", " extern_kernels.mm(reinterpret_tensor(buf1, (524288, 64), (64, 1), 0), reinterpret_tensor(primals_5, (64, 192), (1, 64), 0), out=buf2)\n", "\n", - "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:42<00:00, 1.33s/it]" + "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:41<00:00, 1.31s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0/200 \t Train Err: 29.90892779827118 11.204990872880444 6.002899471088313 2.7772275000461377 119.98257398605347\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", 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0.0002674301056231343 0.0018643831190274796 0.009087016163903172\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00, 1.04it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 185/200 \t Train Err: 0.011409497121348977 0.00029326116043648653 0.0003596148741280558 0.001658103732552263 0.007742758548488382\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00, 1.05it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 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+ "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 188/200 \t Train Err: 0.012054045568220317 0.000388129293298789 0.0004733345151635149 0.001878096192740486 0.00747043985700202\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "00%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:30<00:00, 1.05it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 189/200 \t Train Err: 0.012783893820596859 0.00031030693571665324 0.00032841233428371197 0.0017014134382407065 0.006328187721919676\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + 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pickle.load(f)\n", " data = pickled_stuff[\"data\"].to(device)\n", " label = pickled_stuff[\"labels\"].to(device).to(torch.float32)\n",
  • @@ -1186,31 +3412,60 @@ ]
    }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, + "execution_state": "idle", "metadata": {}, "outputs": [], "source": [ - "# \"\"\"\n", - "# Now let's figure out what it's doing. \n", + "def evaluate(data):\n", + " model.eval()\n", + " test_loss = 0\n", + " with torch.no_grad():\n", + " batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), data)\n", + " output = model(batch_src, batch_padding_mask)\n", + " loss = criterion(output.squeeze(1), batch_labels)\n", + " return output.detach(), batch_labels.detach().to(torch.uint8), loss.item()\n", "\n", - "# step 1: figure out what people are attending to \n", - "# \"\"\"\n", - "\n", - "# example_graph, answer, padding = mkbatch(1)\n", - "# sentance_embeddings = model.full_embedding(example_graph)[0,:,:][example_graph.flatten() != 0]\n", - "# WQ,WK,WV = torch.split(model.transformer_encoder.layers[0].self_attn.in_proj_weight, (MODEL_DIM, MODEL_DIM, MODEL_DIM))\n", - "\n", - "# Q = sentance_embeddings@WQ\n", - "# K = sentance_embeddings@WK\n", - "\n", - "# raw_scores = Q @ K.T / sqrt(MODEL_DIM)\n", - "# soft = torch.softmax(raw_scores, dim=-1).detach().cpu().to(float).numpy()\n", - "# plt.imshow(soft)\n", - "# plt.show()\n", - "\n", - "# print(example_graph)\n", - "\n", - "# print(Q)" + "def mkhist(data):\n", + " y, x, loss = evaluate(data)\n", + " print(loss)\n", + " cnts = torch.bincount(x)\n", + " weights = [1/cnts[i.item()].item() for i in x] # normalize by label count\n", + " fig, ax = plt.subplots()\n", + " h = ax.hist2d(x.cpu().numpy().flatten(), y.cpu().numpy().flatten(), weights=weights, bins=[15,50], norm=mpl.colors.LogNorm())\n", + " ax.set_xlabel(\"Label\")\n", + " ax.set_ylabel(\"Model Output\")\n", + " fig.colorbar(h[3], ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/modules/transformer.py:871: UserWarning: Memory Efficient attention on Navi31 GPU is still experimental. Enable it with TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1. (Triggered internally at ../aten/src/ATen/native/transformers/hip/sdp_utils.cpp:269.)\n", + " return torch._transformer_encoder_layer_fwd(\n", + "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/modules/transformer.py:871: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at ../aten/src/ATen/Context.cpp:296.)\n", + " return torch._transformer_encoder_layer_fwd(\n" + ] + }, + { + "data": { + "text/plain": [ + "0.031184175983071327" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate(mkbatch(BSZ))[2]" ] }, {
  • @@ -1219,99 +3474,57 @@ "execution_count": null,
    "metadata": {}, "outputs": [], "source": [ - "with open('training-loss') as f:\n", - " train_err = list(map(float, f.read().split()))\n", - " plt.suptitle('Log MSE vs Epochs')\n", - " plt.plot(torch.log(torch.tensor(train_err)[:500]), label='Train', color='blue')\n", - " plt.xlabel('Epochs')\n", - " plt.ylabel('Log MSE')\n", - " plt.show()\n", - "\n", - "plt.suptitle('Log MSE vs Epochs')\n", - "plt.plot(torch.log(torch.tensor(train_err)), label='Train', color='blue')\n", - "plt.plot(torch.log(torch.tensor(len1)).to(torch.float16), label='Len 1', color='red')\n", - "plt.plot(torch.log(torch.tensor(len2)).to(torch.float16), label='Len 2', color='green')\n", - "plt.plot(torch.log(torch.tensor(len3)).to(torch.float16), label='Len 3', color='yellow')\n", - "plt.plot(torch.log(torch.tensor(len15)).to(torch.float16), label='Len 15', color='magenta')\n", + "with open('loss') as f:\n", + " losses = torch.tensor([list(map(float, l.split())) for l in f.readlines()])\n", + "plt.plot(torch.log(losses[:, 0]), label='Train', color='blue')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Log MSE')\n", - "plt.legend()\n", - "plt.show()" + "with open(\"plots/train-loss.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 27, "metadata": { "id": "LoGEmM5lH7_A" }, "outputs": [ { - "ename": "NameError", - "evalue": "name 'mkbatch' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m batch_src, batch_labels, batch_padding_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39mto(device), \u001b[43mmkbatch\u001b[49m(BSZ))\n\u001b[1;32m 2\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n", - "\u001b[0;31mNameError\u001b[0m: name 'mkbatch' is not defined" - ] + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mkbatch(BSZ))\n", - "model.eval()\n", - "with torch.no_grad():\n", - " output = model(batch_src, batch_padding_mask)\n", - "x = batch_labels.detach().to(torch.uint8)\n", - "y = output.detach()\n", - "cnts = torch.bincount(x)\n", - "weights = [1/cnts[i.item()].item() for i in x] # normalize by label count\n", - "fig, ax = plt.subplots()\n", - "h = ax.hist2d(x.cpu().numpy().flatten(), y.to(torch.float16).cpu().numpy().flatten(), weights=weights, bins=[15,50], norm=mpl.colors.LogNorm())\n", - "fig.colorbar(h[3], ax=ax)\n", - "plt.show()" + "mkhist(mkbatch(BSZ))\n", + "plt.show()\n", + "with open(\"plots/train-hist.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/modules/transformer.py:871: UserWarning: Memory Efficient attention on Navi31 GPU is still experimental. Enable it with TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1. (Triggered internally at ../aten/src/ATen/native/transformers/hip/sdp_utils.cpp:269.)\n", - " return torch._transformer_encoder_layer_fwd(\n", - "/home/sipb/.venv/lib64/python3.12/site-packages/torch/nn/modules/transformer.py:871: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at ../aten/src/ATen/Context.cpp:296.)\n", - " return torch._transformer_encoder_layer_fwd(\n", - "/home/sipb/.venv/lib64/python3.12/site-packages/torch/_inductor/compile_fx.py:167: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "0.0005554668023250997" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "def evaluate():\n", - " model.eval()\n", - " test_loss = 0\n", - " with torch.no_grad():\n", - " batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mkbatch(BSZ))\n", - " output = model(batch_src, batch_padding_mask)\n", - " loss = criterion(output.squeeze(1), batch_labels)\n", - " return loss.item()\n", - "\n", - "evaluate()" + "plt.suptitle('Log MSE vs Epochs')\n", + "plt.plot(torch.log(torch.tensor(losses[:, 0])), label='Train', color='blue')\n", + "plt.plot(torch.log(torch.tensor(losses[:, 1])), label='Len 1', color='red')\n", + "plt.plot(torch.log(torch.tensor(losses[:, 2])), label='Len 2', color='green')\n", + "plt.plot(torch.log(torch.tensor(losses[:, 3])), label='Len 3', color='yellow')\n", + "plt.plot(torch.log(torch.tensor(losses[:, 4])), label='Len 15', color='magenta')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Log MSE')\n", + "plt.legend()\n", + "with open(\"plots/train-lens.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" ] }, {
  • @@ -1331,10 +3544,9 @@ "outputs": [],
    "source": [ "N_TUNE_EPOCHS = 100\n", "TUNE_LR = 1e-5\n", - "TUNE_WD = 0 # 1e-5\n", "\n", "tune_criterion = nn.MSELoss()\n", - "tune_optimizer = torch.optim.Adam(model.parameters(), lr=TUNE_LR, weight_decay=TUNE_WD)\n", + "tune_optimizer = torch.optim.Adam(model.parameters(), lr=TUNE_LR)\n", "\n", "tune_train_err = []\n", "\n",
  • @@ -1458,7 +3670,7 @@ "source": [
    "for epoch in range(N_TUNE_EPOCHS):\n", " model.train()\n", " train_loss = 0\n", - " batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mktunebatch(BSZ))\n", + " batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mkbatch(BSZ, large=False, target=\"onpath\"))\n", " optimizer.zero_grad()\n", " output = model(batch_src, batch_padding_mask)\n", " loss = criterion(output.squeeze(1), batch_labels)\n",
  • @@ -1477,55 +3689,48 @@ ]
    }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "<Figure size 640x480 with 1 Axes>" + "0.001733972690999508" ] }, + "execution_count": 19, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "plt.suptitle('MSE vs Epochs')\n", - "plt.plot(tune_train_err, label='Train', color='blue')\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('MSE')\n", - "plt.show()" + "evaluate(mkbatch(BSZ, large=False, target=\"onpath\"))[2]" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "0.001733972690999508" + "<Figure size 640x480 with 1 Axes>" ] }, - "execution_count": 19, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "def tune_evaluate():\n", - " model.eval()\n", - " test_loss = 0\n", - " with torch.no_grad():\n", - " batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mktunebatch(BSZ))\n", - " output = model(batch_src, batch_padding_mask)\n", - " loss = criterion(output.squeeze(1), batch_labels)\n", - " return loss.item()\n", - "\n", - "tune_evaluate()" + "plt.suptitle('Log MSE vs Epochs')\n", + "plt.plot(torch.log(tune_train_err), label='Train', color='blue')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Log MSE')\n", + "plt.show()\n", + "with open(\"plots/tune-loss.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" ] }, {
  • @@ -1581,13 +3786,9 @@ "output_type": "display_data"
    } ], "source": [ - "batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mktunebatch(BSZ))\n", - "model.eval()\n", - "with torch.no_grad():\n", - " output = model(batch_src, batch_padding_mask)\n", - "x = batch_labels.detach().to(torch.float16).cpu().numpy().flatten()\n", - "y = output.detach().to(torch.float16).cpu().numpy().flatten()\n", - "plt.hist2d(x, y, bins=50, norm=mpl.colors.LogNorm())" + "mkhist(mkbatch(BSZ, large=False, target=\"onpath\"))\n", + "with open(\"plots/tune-hist.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" ] }, {
  • @@ -1597,6 +3798,17 @@ "id": "JtTLXn4zC1z_"
    }, "source": [ "# Step 6: Test generalization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mkhist(mkbatch(BSZ, large=True, target=\"onpath\", largetarget=False))\n", + "with open(\"plots/test-onpath-smalltarget.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" ] }, {
  • @@ -1660,14 +3872,42 @@ "output_type": "display_data"
    } ], "source": [ - "batch_src, batch_labels, batch_padding_mask = map(lambda x: x.to(device), mktunebatch(BSZ, test=True))\n", - "model.eval()\n", - "with torch.no_grad():\n", - " output = model(batch_src, batch_padding_mask)\n", - "print(criterion(output.squeeze(1), batch_labels).item())\n", - "x = batch_labels.detach().to(torch.float16).cpu().numpy().flatten()\n", - "y = output.detach().to(torch.float16).cpu().numpy().flatten()\n", - "plt.hist2d(x, y, bins=50, norm=mpl.colors.LogNorm())" + "mkhist(mkbatch(BSZ, large=True, target=\"onpath\", largetarget=True))\n", + "with open(\"plots/test-onpath-largetarget.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mkhist(mkbatch(BSZ, large=False, target=\"any\", largetarget=False))\n", + "with open(\"plots/test-small-any.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mkhist(mkbatch(BSZ, large=True, target=\"any\", largetarget=False))\n", + "with open(\"plots/test-large-any-smalltarget.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mkhist(mkbatch(BSZ, large=True, target=\"any\", largetarget=True))\n", + "with open(\"plots/test-large-any-largetarget.html\", \"w\") as f:\n", + " mpld3.save_html(plt.gcf(), f)" ] } ],