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test_gpt.py
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test_gpt.py
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import torch
import torch.fx as fx
import torch.nn as nn
import os
import math
import logging
from torch.nn import functional as F
logger = logging.getLogger(__name__)
class GPTConfig:
""" base GPT config, params common to all GPT versions """
embd_pdrop = 0.1
resid_pdrop = 0.1
attn_pdrop = 0.1
def __init__(self, vocab_size, block_size, **kwargs):
self.vocab_size = vocab_size
self.block_size = block_size
for k,v in kwargs.items():
setattr(self, k, v)
class GPTSmallConfig(GPTConfig):
""" GPT3-small like network roughly 125M params """
n_layer = 12
n_head = 12
n_embd = 768
class GPTMediumConfig(GPTConfig):
""" GPT3-large like network roughly 350M params """
n_layer = 24
n_head = 16
n_embd = 1024
class GPTLargeConfig(GPTConfig):
""" GPT3-large like network roughly 760M params """
n_layer = 24
n_head = 16
n_embd = 1536
class GPTXLConfig(GPTConfig):
""" GPT3-XL like network roughly 1.3B params """
n_layer = 24
n_head = 24
n_embd = 2064
class GPTXXLConfig(GPTConfig):
""" GPT3-XL like network roughly 2.7B params """
n_layer = 32
n_head = 32
n_embd = 2560
class GPTXXXLConfig(GPTConfig):
""" GPT3-XL like network roughly 6.7B params """
n_layer = 32
n_head = 32
n_embd = 4096
class GPT13BConfig(GPTConfig):
""" GPT3-XL like network roughly 13B params """
n_layer = 48
n_head = 48
n_embd = 5184
class GPT175BConfig(GPTConfig):
""" GPT3-XL like network roughly 175B params """
n_layer = 96
n_head = 96
n_embd = 12288
class GPT1TConfig(GPTConfig):
""" GPT3-XL like network roughly 1T params """
n_layer = 128
n_head = 128
n_embd = 25600
class CausalSelfAttention(nn.Module):
"""
A vanilla multi-head masked self-attention layer with a projection at the end.
It is possible to use torch.nn.MultiheadAttention here but I am including an
explicit implementation here to show that there is nothing too scary here.
"""
def __init__(self, config, device="cpu", dtype=torch.float32):
super().__init__()
assert config.n_embd % config.n_head == 0, f"n_embd={config.n_embd}, n_head={config.n_head}"
# key, query, value projections for all heads
self.key = nn.Linear(config.n_embd, config.n_embd, device=device, dtype=dtype)
self.query = nn.Linear(config.n_embd, config.n_embd, device=device, dtype=dtype)
self.value = nn.Linear(config.n_embd, config.n_embd, device=device, dtype=dtype)
# regularization
self.attn_drop = nn.Dropout(config.attn_pdrop)
self.resid_drop = nn.Dropout(config.resid_pdrop)
# output projection
self.proj = nn.Linear(config.n_embd, config.n_embd, device=device, dtype=dtype)
# causal mask to ensure that attention is only applied to the left in the input sequence
# TODO: leave buffer on CPU for now, until we can do meta_tensor.to_empty()
d = device if torch.device(device).type == "cuda" else "cpu"
self.register_buffer(
"mask",
torch.tril(torch.ones(config.block_size, config.block_size, device=d, dtype=dtype))
.view(1, 1, config.block_size, config.block_size)
)
self.n_head = config.n_head
def reset_parameters(self):
for _, m in self.named_modules():
if isinstance(m, nn.Linear):
m.reset_parameters()
def forward(self, x, layer_past=None):
B, T, C = x.size()
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
v = self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf'))
att = F.softmax(att, dim=-1)
att = self.attn_drop(att)
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.resid_drop(self.proj(y))
return y
class EmbeddingStem(nn.Module):
def __init__(self, config, device="cpu", dtype=torch.float32):
super().__init__()
# input embedding stem
self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd, device=device, dtype=dtype)
self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd, device=device, dtype=dtype))
self.drop = nn.Dropout(config.embd_pdrop)
self.block_size = config.block_size
def reset_parameters(self):
self.tok_emb.reset_parameters()
def forward(self, idx):
b, t = idx.size()
#assert t <= self.block_size, "Cannot forward, model block size is exhausted."
token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector
return self.drop(token_embeddings + position_embeddings)
class Block(nn.Module):
""" an unassuming Transformer block """
def __init__(
self,
config,
device=None,
dtype=torch.float32,
wrapper=lambda m : m,
version="pytorch",
cpu_offload=False,
):
super().__init__()
if version == "pytorch" or not cpu_offload:
self.ln1 = wrapper(nn.LayerNorm(config.n_embd, device=device, dtype=dtype))
self.ln2 = wrapper(nn.LayerNorm(config.n_embd, device=device, dtype=dtype))
self.attn = wrapper(CausalSelfAttention(config, device=device, dtype=dtype))
self.mlp = nn.Sequential(
wrapper(nn.Linear(config.n_embd, 4 * config.n_embd, device=device, dtype=dtype)),
nn.GELU(),
wrapper(nn.Linear(4 * config.n_embd, config.n_embd, device=device, dtype=dtype)),
nn.Dropout(config.resid_pdrop),
)
else:
print("fairscale fsdp for block")
self.ln1 = wrapper(nn.LayerNorm(config.n_embd, device=device, dtype=dtype).cpu())
self.ln2 = wrapper(nn.LayerNorm(config.n_embd, device=device, dtype=dtype).cpu())
self.attn = wrapper(CausalSelfAttention(config, device=device, dtype=dtype).cpu())
self.mlp = nn.Sequential(
wrapper(nn.Linear(config.n_embd, 4 * config.n_embd, device=device, dtype=dtype).cpu()),
nn.GELU(),
wrapper(nn.Linear(4 * config.n_embd, config.n_embd, device=device, dtype=dtype).cpu()),
nn.Dropout(config.resid_pdrop),
)
def reset_parameters(self):
self.attn.reset_parameters()
for _, m in self.named_modules():
if isinstance(m, nn.LayerNorm) or isinstance(m, nn.Linear):
m.reset_parameters()
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class GPT(nn.Module):
""" the full GPT language model, with a context size of block_size """
def __init__(self, config, device="cpu", dtype=torch.float32):
super().__init__()
# input embedding stem
self.emb_stem = EmbeddingStem(config, device=device, dtype=dtype)
# transformer
self.blocks = nn.Sequential(
*[Block(config, device=device, dtype=dtype) for _ in range(config.n_layer)]
)
# decoder head
self.ln_f = nn.LayerNorm(config.n_embd, device=device, dtype=dtype)
self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False, device=device, dtype=dtype)
logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
def forward(self, idx):
x = self.emb_stem(idx)
x = self.blocks(x)
x = self.ln_f(x)
return self.head(x)
vocab_size = 500
block_size = 256
batch_size = 2
model = GPT(GPTSmallConfig(500, 256))
gm = fx.symbolic_trace(model)
#gm = fx.symbolic_trace(model, concrete_args={"idx": torch.randint(0, vocab_size, (batch_size, block_size))})
#gm.graph.print_tabular()
"""
Traceback (most recent call last):
File "test_gpt.py", line 234, in <module>
gm = fx.symbolic_trace(model)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 857, in symbolic_trace
graph = tracer.trace(root, concrete_args)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 566, in trace
self.create_node('output', 'output', (self.create_arg(fn(*args)),), {},
File "test_gpt.py", line 229, in forward
x = self.blocks(x)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 556, in module_call_wrapper
return self.call_module(mod, forward, args, kwargs)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 372, in call_module
return forward(*args, **kwargs)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 552, in forward
return _orig_module_call(mod, *args, **kwargs)
File "/raid/shenli/pytorch/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/raid/shenli/pytorch/torch/nn/modules/container.py", line 141, in forward
input = module(input)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 556, in module_call_wrapper
return self.call_module(mod, forward, args, kwargs)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 372, in call_module
return forward(*args, **kwargs)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 552, in forward
return _orig_module_call(mod, *args, **kwargs)
File "/raid/shenli/pytorch/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "test_gpt.py", line 204, in forward
x = x + self.attn(self.ln1(x))
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 556, in module_call_wrapper
return self.call_module(mod, forward, args, kwargs)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 372, in call_module
return forward(*args, **kwargs)
File "/raid/shenli/pytorch/torch/fx/_symbolic_trace.py", line 552, in forward
return _orig_module_call(mod, *args, **kwargs)
File "/raid/shenli/pytorch/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "test_gpt.py", line 129, in forward
att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf'))
TypeError: slice indices must be integers or None or have an __index__ method
"""