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README.md

@@ -1,13 +1,13 @@
----
-title: ERAV2 S21 124Model
-emoji: 🌖
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 4.37.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: ERAV2 S21 124Model
+emoji: 🌖
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 4.37.2
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import torch
+import tiktoken
+from model import GPT, GPTConfig
+import gradio as gr
+from torch.nn import functional as F
+
+device = "cpu"
+if torch.cuda.is_available():
+    device = "cuda"
+elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+    device = "mps"
+
+# STOP
+num_return_sequences = 1
+# max_length = 100
+model = GPT(GPTConfig())
+model.to(device)
+model.load_state_dict(torch.load('./checkpoints/final_model.pth', map_location=device))
+
+# Set the model to evaluation mode
+model.eval()
+
+
+def generate(text, max_length):
+    enc = tiktoken.get_encoding("gpt2")
+    tokens = enc.encode(text)
+    tokens = torch.tensor(tokens, dtype= torch.long) # (len,) #check tiktoken app
+    tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1) # (1, len)
+    x = tokens.to(device)
+
+    while x.size(1) < max_length:
+        # forward the model to get the logits
+        with torch.no_grad():
+            logits = model(x)[0] # (B, T, vocab_size)
+            # take the logits at the last position
+            logits = logits[:, -1, :] # (B, vocab_size)
+            # get the probabilities
+            probs = F.softmax(logits, dim=-1)
+            # do top-k sampling of 50 (huggingface pipeline default)
+            # topk_probs here becomes (5, 50), topk_indices is (5, 50)
+            topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
+            # select a token from the top-k probabilities
+            # note: multinomial does not demand the input to sum to 1
+            ix = torch.multinomial(topk_probs, 1) # (B, 1)
+            # gather the corresponding indices
+            xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
+            # append to the sequence
+            x = torch.cat((x, xcol), dim=1)
+
+    # print the generated text
+    for i in range(num_return_sequences):
+        tokens = x[i, :max_length].tolist()
+        decoded = enc.decode(tokens)
+    return decoded
+
+title = "Shakespeare Poem generation using GPT - 121M Model."
+description = "A simple Gradio interface to demo genaration of shakespeare poem."
+examples = [["Let us kill him, and we'll have corn at our own price."],
+            ["Would you proceed especially against Caius Marcius?"],
+            ["Nay, but speak not maliciously."]],
+demo = gr.Interface(
+    generate,
+    inputs=[
+        gr.TextArea(label="Enter text"),
+        gr.Slider(10, 100, value = 10, step=1, label="Token Length"),
+    ],
+    outputs=[
+        gr.TextArea(label="Generated Text")
+    ],
+    title=title,
+    description=description,
+    examples=examples,
+    cache_examples=False,
+    live=True
+)
+demo.launch()

model.py

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+# GPT-3 Paper
+# add cosing delay
+import os
+import math
+import time
+import inspect
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        self.c_proj.NANGPT_SCALE_INIT = 1
+        # regularization
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.register_buffer(
+            "bias",
+            torch.tril(torch.ones(config.block_size, config.block_size)).view(
+                1, 1, config.block_size, config.block_size
+            ),
+        )
+
+    def forward(self, x):
+        B, T, C = (
+            x.size()
+        )  # batch size, sequence length, embedding dimensionality (n_embd)
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        # nh is "number of heads", hs is "head size", and C (number of channels) = nh * hs
+        # e.g. in GPT-2 (124M), n_head=12, hs=64, so nh*hs=C=768 channels in the Transformer
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(
+            1, 2
+        )  # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(
+            1, 2
+        )  # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(
+            1, 2
+        )  # (B, nh, T, hs)
+
+        # att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+        # att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
+        # att = F.softmax(att, dim=-1)
+        # y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+
+        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)  # Flash attention
+
+        y = (
+            y.transpose(1, 2).contiguous().view(B, T, C)
+        )  # re-assemble all head outputs side by side
+        # output projection
+        y = self.c_proj(y)
+        return y
+
+
+class MLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
+        self.gelu = nn.GELU(approximate="tanh")
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
+        self.c_proj.NANOGPT_SCALE_INIT = 1
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+@dataclass
+class GPTConfig:
+    block_size: int = 1024  # max sequence length
+    vocab_size: int = (
+        50304  # number of tokens: 50,000 BPE merges + 256 bytes tokens + 1 <|endoftext|> token
+    )
+    n_layer: int = 12  # number of layers
+    n_head: int = 12  # number of heads
+    n_embd: int = 768  # embedding dimension
+
+
+class GPT(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        self.transformer = nn.ModuleDict(
+            dict(
+                wte=nn.Embedding(config.vocab_size, config.n_embd),
+                wpe=nn.Embedding(config.block_size, config.n_embd),
+                h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+                ln_f=nn.LayerNorm(config.n_embd),
+            )
+        )
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # weight sharing
+        self.transformer.wte.weight = self.lm_head.weight
+
+        # weight initialization
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            std = 0.02
+            if hasattr(module, "NANGPT_SCALE_INIT"):
+                std *= (2 * self.config.n_layer) ** -0.5
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        # idx is of shape (B, T)
+        B, T = idx.size()
+        assert (
+            T <= self.config.block_size
+        ), f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
+        # forward the token and posisition embeddings
+        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)  # shape (T)
+        pos_emb = self.transformer.wpe(pos)  # position embeddings of shape (T, n_embd)
+        tok_emb = self.transformer.wte(idx)  # token embeddings of shape (B, T, n_embd)
+        x = tok_emb + pos_emb
+        # forward the blocks of the transformer
+        for block in self.transformer.h:
+            x = block(x)
+        # forward the final layernorm and the classifier
+        x = self.transformer.ln_f(x)
+        logits = self.lm_head(x)  # (B, T, vocab_size)
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+        return logits, loss
+
+    @classmethod
+    def from_pretrained(cls, model_type):
+        """Loads pretrained GPT-2 model weights from huggingface"""
+        assert model_type in {"gpt2", "gpt2-medium", "gpt2-large", "gpt2-xl"}
+        from transformers import GPT2LMHeadModel
+
+        print("loading weights from pretrained gpt: %s" % model_type)
+
+        # n_layer, n_head and n_embd are determined from model_type
+        config_args = {
+            "gpt2": dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
+            "gpt2-medium": dict(n_layer=24, n_head=16, n_embd=1024),  # 350M params
+            "gpt2-large": dict(n_layer=36, n_head=20, n_embd=1280),  # 774M params
+            "gpt2-xl": dict(n_layer=48, n_head=25, n_embd=1600),  # 1558M params
+        }[model_type]
+        config_args["vocab_size"] = 50257  # always 50257 for GPT model checkpoints
+        config_args["block_size"] = 1024  # always 1024 for GPT model checkpoints
+        # create a from-scratch initialized minGPT model
+        config = GPTConfig(**config_args)
+        model = GPT(config)
+        sd = model.state_dict()
+        sd_keys = sd.keys()
+        sd_keys = [
+            k for k in sd_keys if not k.endswith(".attn.bias")
+        ]  # discard this mask / buffer, not a param
+
+        # init a huggingface/transformers model
+        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
+        sd_hf = model_hf.state_dict()
+
+        # copy while ensuring all of the parameters are aligned and match in names and shapes
+        sd_keys_hf = sd_hf.keys()
+        sd_keys_hf = [
+            k for k in sd_keys_hf if not k.endswith(".attn.masked_bias")
+        ]  # ignore these, just a buffer
+        sd_keys_hf = [
+            k for k in sd_keys_hf if not k.endswith(".attn.bias")
+        ]  # same, just the mask (buffer)
+        transposed = [
+            "attn.c_attn.weight",
+            "attn.c_proj.weight",
+            "mlp.c_fc.weight",
+            "mlp.c_proj.weight",
+        ]
+        # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
+        # this means that we have to transpose these weights when we import them
+        assert len(sd_keys_hf) == len(
+            sd_keys
+        ), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
+        for k in sd_keys_hf:
+            if any(k.endswith(w) for w in transposed):
+                # special treatment for the Conv1D weights we need to transpose
+                assert sd_hf[k].shape[::-1] == sd[k].shape
+                with torch.no_grad():
+                    sd[k].copy_(sd_hf[k].t())
+            else:
+                # vanilla copy over the other parameters
+                assert sd_hf[k].shape == sd[k].shape
+                with torch.no_grad():
+                    sd[k].copy_(sd_hf[k])
+
+        return model
+
+    def configure_optimizers(self, weight_decay, learning_rate, device_type):
+        # start with all of the candidate parameters (that require grad)
+        param_dict = {pn: p for pn, p in self.named_parameters()}
+        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
+        # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
+        # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
+        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
+        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
+        optim_groups = [
+            {"params": decay_params, "weight_decay": weight_decay},
+            {"params": nodecay_params, "weight_decay": 0.0},
+        ]
+        num_decay_params = sum(p.numel() for p in decay_params)
+        num_nodecay_params = sum(p.numel() for p in nodecay_params)
+
+        print(
+            f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters"
+        )
+        print(
+            f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters"
+        )
+        # Create AdamW optimizer and use the fused version if it is available
+        fused_available = "fused" in inspect.signature(torch.optim.AdamW).parameters
+        use_fused = fused_available and device_type == "cuda"
+
+        print(f"using fused AdamW: {use_fused}")
+        optimizer = torch.optim.AdamW(
+            optim_groups, lr=learning_rate, betas=(0.9, 0.95), eps=1e-8, fused=use_fused
+        )
+        return optimizer

requirements.txt

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+torch
+numpy
+dataclasses
+tiktoken