ceos/unsloth
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Update llama.py

Browse source
This commit is contained in:
Daniel Han-Chen 2024-02-03 18:07:55 +11:00
commit 522f6dbfd2
1 changed files with 43 additions and 43 deletions
Download patch file Download diff file Expand all files Collapse all files

86
unsloth/models/llama.py
View file

@ -124,52 +124,52 @@ def LlamaAttention_fast_forward_inference(
# Prefill phase
# if not hasattr(self, "paged_attention"):
if do_prefill:
self.paged_attention = torch.empty((KV_CACHE_INCREMENT+1, 2, bsz, n_kv_heads, head_dim), dtype = dtype, device = "cuda")
self.paged_attention_K = self.paged_attention[:,0]
self.paged_attention_V = self.paged_attention[:,1]
self.paged_attention_K[:seq_len] = K1.permute(2, 0, 1, 3)
self.paged_attention_V[:seq_len] = V1.permute(2, 0, 1, 3)
self.temp_QA = torch.empty((2, bsz, 1, hd), dtype = dtype, device = "cuda")
self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = "cuda")
self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = "cuda")
self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT), dtype = dtype, device = "cuda")
self.scalar = 1.0 / math_sqrt(self.head_dim)
elif kv_seq_len >= self.paged_attention.shape[0]:
self.paged_attention.resize_((self.paged_attention.shape[0]+KV_CACHE_INCREMENT, 2, bsz, n_kv_heads, head_dim))
self.paged_attention_K = self.paged_attention[:,0]
self.paged_attention_V = self.paged_attention[:,1]
self.attention.resize_((bsz, n_heads, 1, self.attention.shape[-1]+KV_CACHE_INCREMENT))
pass
Qn = fast_linear_forward(self.q_proj, Xn, out = self.temp_QA[0])
Kn = fast_linear_forward(self.k_proj, Xn, out = self.temp_KV[0])
Vn = fast_linear_forward(self.v_proj, Xn, out = self.temp_KV[1])
# if do_prefill:
# self.paged_attention = torch.empty((KV_CACHE_INCREMENT+1, 2, bsz, n_kv_heads, head_dim), dtype = dtype, device = "cuda")
# self.paged_attention_K = self.paged_attention[:,0]
# self.paged_attention_V = self.paged_attention[:,1]
# self.paged_attention_K[:seq_len] = K1.permute(2, 0, 1, 3)
# self.paged_attention_V[:seq_len] = V1.permute(2, 0, 1, 3)
# self.temp_QA = torch.empty((2, bsz, 1, hd), dtype = dtype, device = "cuda")
# self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = "cuda")
# self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = "cuda")
# self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT), dtype = dtype, device = "cuda")
# self.scalar = 1.0 / math_sqrt(self.head_dim)
# elif kv_seq_len >= self.paged_attention.shape[0]:
# self.paged_attention.resize_((self.paged_attention.shape[0]+KV_CACHE_INCREMENT, 2, bsz, n_kv_heads, head_dim))
# self.paged_attention_K = self.paged_attention[:,0]
# self.paged_attention_V = self.paged_attention[:,1]
# self.attention.resize_((bsz, n_heads, 1, self.attention.shape[-1]+KV_CACHE_INCREMENT))
# pass
Qn = fast_linear_forward(self.q_proj, Xn)#, out = self.temp_QA[0])
Kn = fast_linear_forward(self.k_proj, Xn)#, out = self.temp_KV[0])
Vn = fast_linear_forward(self.v_proj, Xn)#, out = self.temp_KV[1])
Qn = Qn.view(bsz, 1, n_heads, head_dim).transpose(1, 2)
Kn = Kn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)
Vn = Vn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)
# cos, sin = self.rotary_emb(Vn, seq_len = kv_seq_len)
# Qn, Kn = inplace_rope_embedding(Qn, Kn, cos, sin, position_ids)
cos = self.rotary_emb.cos_cached[seq_len]
sin = self.rotary_emb.sin_cached[seq_len]
h = head_dim // 2
cos, sin = self.rotary_emb(Vn, seq_len = kv_seq_len)
Qn, Kn = inplace_rope_embedding(Qn, Kn, cos, sin, position_ids)
# cos = self.rotary_emb.cos_cached[seq_len]
# sin = self.rotary_emb.sin_cached[seq_len]
# h = head_dim // 2
RH_Q = self.RH_Q
RH_Q[:,:,:,:h] = Qn[:,:,:,h:]; RH_Q[:,:,:,h:] = Qn[:,:,:,:h]; torch.neg(RH_Q[:,:,:,:h], out = RH_Q[:,:,:,:h]);
Qn *= cos; Qn.addcmul_(RH_Q, sin);
# RH_Q = self.RH_Q
# RH_Q[:,:,:,:h] = Qn[:,:,:,h:]; RH_Q[:,:,:,h:] = Qn[:,:,:,:h]; torch.neg(RH_Q[:,:,:,:h], out = RH_Q[:,:,:,:h]);
# Qn *= cos; Qn.addcmul_(RH_Q, sin);
RH_K = RH_Q[:,:n_kv_heads,:,:] # torch.empty((n_kv_heads, 1, head_dim), dtype = dtype, device = "cuda")
RH_K[:,:,:,:h] = Kn[:,:,:,h:]; RH_K[:,:,:,h:] = Kn[:,:,:,:h]; torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h]);
Kn *= cos; Kn.addcmul_(RH_K, sin);
# RH_K = RH_Q[:,:n_kv_heads,:,:] # torch.empty((n_kv_heads, 1, head_dim), dtype = dtype, device = "cuda")
# RH_K[:,:,:,:h] = Kn[:,:,:,h:]; RH_K[:,:,:,h:] = Kn[:,:,:,:h]; torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h]);
# Kn *= cos; Kn.addcmul_(RH_K, sin);
# New KV cache
# Kn = torch.cat([K1, Kn], dim = 2)
# Vn = torch.cat([V1, Vn], dim = 2)
self.paged_attention_K[seq_len] = Kn.permute(2, 0, 1, 3)
self.paged_attention_V[seq_len] = Vn.permute(2, 0, 1, 3)
Kn = self.paged_attention_K[:kv_seq_len].permute(1, 2, 0, 3)
Vn = self.paged_attention_V[:kv_seq_len].permute(1, 2, 0, 3)
Kn = torch.cat([K1, Kn], dim = 2)
Vn = torch.cat([V1, Vn], dim = 2)
# self.paged_attention_K[seq_len] = Kn.permute(2, 0, 1, 3)
# self.paged_attention_V[seq_len] = Vn.permute(2, 0, 1, 3)
# Kn = self.paged_attention_K[:kv_seq_len].permute(1, 2, 0, 3)
# Vn = self.paged_attention_V[:kv_seq_len].permute(1, 2, 0, 3)
# Grouped query attention
if n_groups != 1:
@ -182,13 +182,13 @@ def LlamaAttention_fast_forward_inference(
Knn, Vnn = Kn, Vn
# Attention
A = torch.matmul(Qn, Knn.transpose(2, 3), out = self.attention[:,:,:,:kv_seq_len])
A = torch.matmul(Qn, Knn.transpose(2, 3))#, out = self.attention[:,:,:,:kv_seq_len])
A *= self.scalar
A[:] = torch.nn.functional.softmax(A, dim = -1, dtype = torch.float32)#.to(A.dtype)
A = torch.matmul(A, Vnn, out = Qn)
A = A.transpose(1, 2)
A = A.reshape(bsz, 1, self.hidden_size)
A = fast_linear_forward(self.o_proj, A, out = self.temp_QA[1])
A = fast_linear_forward(self.o_proj, A)#, out = self.temp_QA[1])
return A, (Kn, Vn)
pass
@ -200,13 +200,13 @@ def fast_mlp_inference(self, X):
mlp_size = self.config.intermediate_size
temp = torch.empty((2, bsz, 1, mlp_size), dtype = X.dtype, device = "cuda")
gate = fast_linear_forward(self.gate_proj, X, out = temp[0])
up = fast_linear_forward(self. up_proj, X, out = temp[1])
gate = fast_linear_forward(self.gate_proj)#, X, out = temp[0])
up = fast_linear_forward(self. up_proj)#, X, out = temp[1])
gate = torch.nn.functional.silu(gate, inplace = True)
gate *= up
# X = self.down_proj(gate)
down = fast_linear_forward(self.down_proj, gate, out = up[:,:,:hd])
down = fast_linear_forward(self.down_proj, gate)#, out = up[:,:,:hd])
return down
pass