torch.compile failed to legalize operation 'torch.aten.view'
Opened this issue · 0 comments
ziereis commented
import math
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)
# regularization
self.n_head = config.n_head
self.n_embd = config.n_embd
# not really a 'bias', more of a mask, but following the OpenAI/HF naming though
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)
# attention (materializes the large (T,T) matrix for all the queries and keys)
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 = 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)
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 = 50257 # 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)
def forward(self, idx):
# 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)
return logits
@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
# -----------------------------------------------------------------------------
num_return_sequences = 5
max_length = 30
model = GPT.from_pretrained('gpt2')
model.eval()
# model.to('cuda')
print(torch._dynamo.list_backends())
model = torch.compile(model, backend="turbine_cpu")
# prefix tokens
import tiktoken
enc = tiktoken.get_encoding('gpt2')
tokens = enc.encode("Hello, I'm a language model,")
tokens = torch.tensor(tokens, dtype=torch.long) # (8,)
tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1) # (5, 8)
x = tokens
# x = tokens.to('cuda')
# generate! right now x is (B, T) where B = 5, T = 8
# set the seed to 42
torch.manual_seed(42)
torch.cuda.manual_seed(42)
while x.size(1) < max_length:
# forward the model to get the logits
with torch.no_grad():
logits = model(x) # (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)
print(">", decoded)
running this code results in the following error: