Upload model.py

gpt2/model.py

@@ -0,0 +1,148 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import math
+class GPT2Config:
+    def __init__(self):
+        self.vocab_size = 50257
+        self.n_positions = 1024
+        self.n_ctx = 1024
+        self.n_embd = 768
+        self.n_layer = 12
+        self.n_head = 12
+        self.resid_pdrop = 0.1
+        self.embd_pdrop = 0.1
+        self.attn_pdrop = 0.1
+        self.layer_norm_epsilon = 1e-5
+        self.initializer_range = 0.
+        self.output_attentions = False          
+class GPT2Model(nn.Module):
+    def __init__(self, config):
+        super(GPT2Model, self).__init__()
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+        self.wpe = nn.Embedding(config.n_positions, config.n_embd)
+        self.drop = nn.Dropout(config.embd_pdrop)
+        self.h = nn.ModuleList([GPT2Block(config) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+    def forward(self, input_ids, position_ids=None, past=None):
+        if past is None:
+            past_length = 0
+            past = [None] * len(self.h)
+        else:
+            past_length = past[0][0].size(-2)
+        if position_ids is None:
+            position_ids = torch.arange(past_length, input_ids.size(-1) + past_length, dtype=torch.long, device=input_ids.device)
+            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+        position_ids = position_ids.view(-1, input_shape[-1])
+        inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        if inputs_embeds.size() != position_embeds.size():
+            raise ValueError("the embeddings of inputs and position are not the same size")
+        hidden_states = inputs_embeds + position_embeds
+        hidden_states = self.drop(hidden_states)
+        presents = ()
+        for block, past in zip(self.h, past):
+            hidden_states, present = block(hidden_states, past=past)
+            presents = presents + (present,)
+        hidden_states = self.ln_f(hidden_states)
+        output_shape = input_shape + (hidden_states.size(-1),)
+        return hidden_states.view(*output_shape), presents
+class GPT2Block(nn.Module):
+    def __init__(self, config):
+        super(GPT2Block, self).__init__()
+        nx = config.n_embd
+        self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
+        self.attn = GPT2Attention(config)
+        self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
+        self.mlp = GPT2MLP(config)
+    def forward(self, x, past):
+        a, present = self.attn(self.ln_1(x), past=past)
+        x = x + a
+        m = self.mlp(self.ln_2(x))
+        x = x + m
+        return x, present
+class GPT2Attention(nn.Module):
+    def __init__(self, config):
+        super(GPT2Attention, self).__init__()
+        self.output_attentions = config.output_attentions
+        self.n_head = config.n_head
+        self.split_size = config.n_embd
+        self.scale = self.split_size ** -0.5
+        self.c_attn = Conv1D(3 * self.split_size, self.split_size)
+        self.c_proj = Conv1D(self.split_size, self.split_size)
+        self.attn_dropout = nn.Dropout(config.attn_pdrop)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+        self.bias = nn.Parameter(torch.zeros(1, 1, 1024, 1024))
+    def _attn(self, q, k, v): 
+        w = torch.matmul(q, k) 
+        if self.scale: 
+            w = w / math.sqrt(v.size(-1)) 
+        w = w.softmax(dim=-1) 
+        w = self.attn_dropout(w) 
+        return torch.matmul(w, v) 
+    def merge_heads(self, x): 
+        x = x.permute(0, 2, 1, 3).contiguous() 
+        new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),) 
+        return x.view(*new_x_shape) 
+    def split_heads(self, x, k=False): 
+        new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head) 
+        x = x.view(*new_x_shape) 
+        if k: 
+            return x.permute(0, 2, 3, 1) 
+        else: 
+            return x.permute(0, 2, 1, 3)
+    def forward(self, x, past):
+        x = self.c_attn(x)
+        query, key, value = x.split(self.split_size, dim=2)
+        query = self.split_heads(query)
+        key = self.split_heads(key, k=True)
+        value = self.split_heads(value)
+        if past is not None:
+            past_key, past_value = past
+            key = torch.cat((past_key, key), dim=-1)
+            value = torch.cat((past_value, value), dim=-1)
+        present = (key, value)
+        attn_output = self._attn(query, key, value)
+        attn_output = self.merge_heads(attn_output)
+        attn_output = self.c_proj(attn_output)
+        # Apply bias
+        attn_output += self.bias
+        attn_output = self.resid_dropout(attn_output)
+        return attn_output, present
+class GPT2MLP(nn.Module):
+    def __init__(self, config):
+        super(GPT2MLP, self).__init__()
+        self.c_fc = Conv1D(4 * config.n_embd, config.n_embd)
+        self.c_proj = Conv1D(config.n_embd, 4 * config.n_embd)
+        self.act = F.gelu
+        self.dropout = nn.Dropout(config.resid_pdrop)
+    def forward(self, x):
+        h = self.act(self.c_fc(x))
+        h2 = self.c_proj(h)
+        return self.dropout(h2)
+class Conv1D(nn.Module):
+    def __init__(self, nf, nx):
+        super(Conv1D, self).__init__()
+        self.nf = nf
+        w = torch.empty(nx, nf)
+        nn.init.normal_(w, std=0.02)
+        self.weight = nn.Parameter(w)
+        self.bias = nn.Parameter(torch.zeros(nf))
+
+    def forward(self, x):
+        size_out = x.size()[:-1] + (self.nf,)
+        x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
+        x = x.view(*size_out)
+        return x
+
+config = GPT2Config()
+
+model = GPT2Model(config)
+
+state_dict = torch.load(r'c:\tmp\SI\gpt2\pytorch_model.bin', map_location=torch.device('cpu'))
+
+model.load_state_dict(state_dict)
+
+print(model)