[example] add palm pytorch version (#2172)

examples/language/palm/palm_pytorch/__init__.py

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+from palm_pytorch.palm_pytorch import PaLM

examples/language/palm/palm_pytorch/autoregressive_wrapper.py

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+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from torch import nn
+
+# helper function
+
+
+def exists(val):
+    return val is not None
+
+
+def eval_decorator(fn):
+
+    def inner(model, *args, **kwargs):
+        was_training = model.training
+        model.eval()
+        out = fn(model, *args, **kwargs)
+        model.train(was_training)
+        return out
+
+    return inner
+
+
+# top k filtering
+
+
+def top_k(logits, thres=0.9):
+    k = int((1 - thres) * logits.shape[-1])
+    val, ind = torch.topk(logits, k)
+    probs = torch.full_like(logits, float("-inf"))
+    probs.scatter_(1, ind, val)
+    return probs
+
+
+class AutoregressiveWrapper(nn.Module):
+
+    def __init__(self, net, max_seq_len=2048, pad_value=0):
+        super().__init__()
+        self.max_seq_len = max_seq_len
+        self.pad_value = pad_value
+        self.net = net
+
+    @torch.no_grad()
+    @eval_decorator
+    def generate(self, start_tokens, seq_len, eos_token=None, temperature=1.0, filter_thres=0.9, **kwargs):
+        b, t, device = *start_tokens.shape, start_tokens.device
+
+        out = start_tokens
+
+        for _ in range(seq_len):
+            logits = self.net(out, **kwargs)[:, -1, :]
+
+            filtered_logits = top_k(logits, thres=filter_thres)
+            probs = F.softmax(filtered_logits / temperature, dim=-1)
+
+            sample = torch.multinomial(probs, 1)
+
+            out = torch.cat((out, sample), dim=-1)
+
+            if exists(eos_token):
+                is_eos_token = out == eos_token
+
+                if is_eos_token.any(dim=-1).all():
+                    # mask out everything after the eos tokens
+                    shifted_is_eos_tokens = F.pad(is_eos_tokens, (1, -1))
+                    mask = shifted_is_eos_tokens.float().cumsum(dim=-1) >= 1
+                    out = out.masked_fill(mask, self.pad_value)
+                    break
+
+        out = out[:, t:]
+        return out
+
+    def forward(self, x, **kwargs):
+        x_inp, x_labels = x[:, :-1], x[:, 1:]
+        logits = self.net(x_inp, **kwargs)
+        return F.cross_entropy(rearrange(logits, "b c n -> b n c"), x_labels)

examples/language/palm/palm_pytorch/palm_pytorch.py

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+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from torch import einsum, nn
+
+# normalization
+# they use layernorm without bias, something that pytorch does not offer
+
+
+class LayerNorm(nn.Module):
+
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.eps = eps
+        self.gamma = nn.Parameter(torch.ones(dim))
+        self.register_buffer("beta", torch.zeros(dim))
+
+    def forward(self, x):
+        return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta)
+
+
+# parallel with residual
+# discovered by Wang et al + EleutherAI from GPT-J fame
+
+
+class ParallelResidual(nn.Module):
+
+    def __init__(self, *fns):
+        super().__init__()
+        self.fns = nn.ModuleList(fns)
+
+    def forward(self, x):
+        return x + sum([fn(x) for fn in self.fns])
+
+
+# rotary positional embedding
+# https://arxiv.org/abs/2104.09864
+
+
+class RotaryEmbedding(nn.Module):
+
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1.0 / (10000**(torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+    def forward(self, max_seq_len, *, device):
+        seq = torch.arange(max_seq_len, device=device)
+        freqs = einsum("i , j -> i j", seq.type_as(self.inv_freq), self.inv_freq)
+        return torch.cat((freqs, freqs), dim=-1)
+
+
+def rotate_half(x):
+    x = rearrange(x, "... (j d) -> ... j d", j=2)
+    x1, x2 = x.unbind(dim=-2)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(pos, t):
+    return (t * pos.cos()) + (rotate_half(t) * pos.sin())
+
+
+# feedforward
+# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU
+# https://arxiv.org/abs/2002.05202
+
+
+class SwiGLU(nn.Module):
+
+    def forward(self, x):
+        x, gate = x.chunk(2, dim=-1)
+        return F.silu(gate) * x
+
+
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        LayerNorm(dim),
+        nn.Linear(dim, inner_dim * 2, bias=False),
+        SwiGLU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+# attention
+
+
+class Attention(nn.Module):
+
+    def __init__(self, dim, dim_head=64, heads=8):
+        super().__init__()
+        inner_dim = dim_head * heads
+        self.norm = LayerNorm(dim)
+        self.heads = heads
+        self.scale = dim_head**-0.5
+        self.rotary_emb = RotaryEmbedding(dim_head)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, dim_head * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+        # for caching causal mask and rotary embeddings
+
+        self.register_buffer("mask", None, persistent=False)
+        self.register_buffer("pos_emb", None, persistent=False)
+
+    def get_mask(self, n, device):
+        if self.mask is not None and self.mask.shape[-1] >= n:
+            return self.mask[:n, :n]
+
+        mask = torch.ones((n, n), device=device, dtype=torch.bool).triu(1)
+        self.register_buffer("mask", mask, persistent=False)
+        return mask
+
+    def get_rotary_embedding(self, n, device):
+        if self.pos_emb is not None and self.pos_emb.shape[-2] >= n:
+            return self.pos_emb[:n]
+
+        pos_emb = self.rotary_emb(n, device=device)
+        self.register_buffer("position", pos_emb, persistent=False)
+        return pos_emb
+
+    def forward(self, x):
+        """
+        einstein notation
+        b - batch
+        h - heads
+        n, i, j - sequence length (base sequence length, source, target)
+        d - feature dimension
+        """
+
+        n, device, h = x.shape[1], x.device, self.heads
+
+        # pre layernorm
+
+        x = self.norm(x)
+
+        # queries, keys, values
+
+        q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim=-1))
+
+        # split heads
+        # they use multi-query single-key-value attention, yet another Noam Shazeer paper
+        # they found no performance loss past a certain scale, and more efficient decoding obviously
+        # https://arxiv.org/abs/1911.02150
+
+        q = rearrange(q, "b n (h d) -> b h n d", h=h)
+
+        # rotary embeddings
+
+        positions = self.get_rotary_embedding(n, device)
+        q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k))
+
+        # scale
+
+        q = q * self.scale
+
+        # similarity
+
+        sim = einsum("b h i d, b j d -> b h i j", q, k)
+
+        # causal mask
+
+        causal_mask = self.get_mask(n, device)
+        sim = sim.masked_fill(causal_mask, -torch.finfo(sim.dtype).max)
+
+        # attention
+
+        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
+        attn = sim.softmax(dim=-1)
+
+        # aggregate values
+
+        out = einsum("b h i j, b j d -> b h i d", attn, v)
+
+        # merge heads
+
+        out = rearrange(out, "b h n d -> b n (h d)")
+        return self.to_out(out)
+
+
+# transformer
+
+
+def PaLM(*, dim, num_tokens, depth, dim_head=64, heads=8, ff_mult=4):
+    net = nn.Sequential(
+        nn.Embedding(num_tokens, dim), *[
+            ParallelResidual(
+                Attention(dim=dim, dim_head=dim_head, heads=heads),
+                FeedForward(dim=dim, mult=ff_mult),
+            ) for _ in range(depth)
+        ], LayerNorm(dim), nn.Linear(dim, num_tokens, bias=False))
+
+    # they used embedding weight tied projection out to logits, not common, but works
+    net[-1].weight = net[0].weight
+
+    nn.init.normal_(net[0].weight, std=0.02)
+    return net