Update llama.py
This commit is contained in:
Daniel Han-Chen
parent
ea9b4eea0c
commit
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1 changed files with 21 additions and 101 deletions
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@ -72,11 +72,12 @@ pass
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from math import sqrt as math_sqrt
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def LlamaAttention_fast_forward_inference_prefill(
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def LlamaAttention_fast_forward_inference(
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self,
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hidden_states: torch.Tensor,
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past_key_value: Optional[Tuple[torch.Tensor]],
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position_ids,
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do_prefill = False,
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):
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"""
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https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L406
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@ -121,16 +122,18 @@ def LlamaAttention_fast_forward_inference_prefill(
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# Prefill phase
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# if not hasattr(self, "paged_attention"):
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self.paged_attention = torch.empty((self.config.max_position_embeddings+1, 2, bsz, n_kv_heads, head_dim), dtype = dtype, device = "cuda")
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self.paged_attention_K = self.paged_attention[:,0]
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self.paged_attention_V = self.paged_attention[:,1]
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self.paged_attention_K[:seq_len] = K1.permute(2, 0, 1, 3)
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self.paged_attention_V[:seq_len] = V1.permute(2, 0, 1, 3)
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self.temp_QA = torch.empty((2, bsz, 1, hd), dtype = dtype, device = "cuda")
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self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = "cuda")
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self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = "cuda")
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self.attention = torch.empty((bsz, n_heads, 1, self.config.max_position_embeddings), dtype = dtype, device = "cuda")
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self.scalar = 1.0 / math_sqrt(self.head_dim)
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if do_prefill:
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self.paged_attention = torch.empty((self.config.max_position_embeddings+1, 2, bsz, n_kv_heads, head_dim), dtype = dtype, device = "cuda")
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self.paged_attention_K = self.paged_attention[:,0]
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self.paged_attention_V = self.paged_attention[:,1]
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self.paged_attention_K[:seq_len] = K1.permute(2, 0, 1, 3)
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self.paged_attention_V[:seq_len] = V1.permute(2, 0, 1, 3)
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self.temp_QA = torch.empty((2, bsz, 1, hd), dtype = dtype, device = "cuda")
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self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = "cuda")
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self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = "cuda")
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self.attention = torch.empty((bsz, n_heads, 1, self.config.max_position_embeddings), dtype = dtype, device = "cuda")
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self.scalar = 1.0 / math_sqrt(self.head_dim)
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pass
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# pass
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Qn = fast_linear_forward(self.q_proj, Xn, out = self.temp_QA[0])
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@ -184,71 +187,6 @@ def LlamaAttention_fast_forward_inference_prefill(
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pass
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def LlamaAttention_fast_forward_inference(
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self,
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hidden_states: torch.Tensor,
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past_key_value: Optional[Tuple[torch.Tensor]],
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position_ids,
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):
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Xn = hidden_states
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bsz, _, hd = hidden_states.size()
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K1, V1 = past_key_value
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n_heads = self.num_heads
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n_groups = self.num_key_value_groups
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n_kv_heads = self.num_key_value_heads
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head_dim = self.head_dim
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seq_len = K1.shape[-2]
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kv_seq_len = seq_len + 1
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Qn = fast_linear_forward(self.q_proj, Xn, out = self.temp_QA[0])
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Kn = fast_linear_forward(self.k_proj, Xn, out = self.temp_KV[0])
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Vn = fast_linear_forward(self.v_proj, Xn, out = self.temp_KV[1])
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Qn = Qn.view(bsz, 1, n_heads, head_dim).transpose(1, 2)
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Kn = Kn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)
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Vn = Vn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)
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# RoPE
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cos = self.rotary_emb.cos_cached[seq_len]
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sin = self.rotary_emb.sin_cached[seq_len]
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h = head_dim // 2
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RH_Q = self.RH_Q
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RH_Q[:,:,:,:h] = Qn[:,:,:,h:]; RH_Q[:,:,:,h:] = Qn[:,:,:,:h]; torch.neg(RH_Q[:,:,:,:h], out = RH_Q[:,:,:,:h]);
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Qn *= cos; Qn.addcmul_(RH_Q, sin);
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RH_K = RH_Q[:,:n_kv_heads,:,:] # torch.empty((n_kv_heads, 1, head_dim), dtype = dtype, device = "cuda")
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RH_K[:,:,:,:h] = Kn[:,:,:,h:]; RH_K[:,:,:,h:] = Kn[:,:,:,:h]; torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h]);
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Kn *= cos; Kn.addcmul_(RH_K, sin);
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# New KV cache
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self.paged_attention_K[seq_len] = Kn.permute(2, 0, 1, 3)
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self.paged_attention_V[seq_len] = Vn.permute(2, 0, 1, 3)
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Kn = self.paged_attention_K[:kv_seq_len].permute(1, 2, 0, 3)
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Vn = self.paged_attention_V[:kv_seq_len].permute(1, 2, 0, 3)
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# Grouped query attention
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if n_groups != 1:
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_, _, cached_len, _ = Kn.shape
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Knn = Kn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)
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Vnn = Vn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)
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Knn = Knn.reshape(bsz, n_heads, cached_len, head_dim)
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Vnn = Vnn.reshape(bsz, n_heads, cached_len, head_dim)
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else:
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Knn, Vnn = Kn, Vn
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# Attention
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A = torch.matmul(Qn, Knn.transpose(2, 3), out = self.attention[:,:,:,:kv_seq_len])
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A *= self.scalar
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A[:] = torch.nn.functional.softmax(A, dim = -1, dtype = torch.float32)#.to(A.dtype)
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A = torch.matmul(A, Vnn, out = Qn)
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A = A.transpose(1, 2)
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A = A.reshape(bsz, 1, self.hidden_size)
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A = fast_linear_forward(self.o_proj, A, out = self.temp_QA[1])
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return A, (Kn, Vn)
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pass
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def fast_mlp_inference(self, X):
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# gate = self.gate_proj(X)
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# up = self.up_proj(X)
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@ -415,7 +353,9 @@ def LlamaDecoderLayer_fast_forward(
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(see `past_key_values`).
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past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
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"""
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if past_key_value is not None and hasattr(self.self_attn, "paged_attention"):
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if past_key_value is not None:
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do_prefill = not hasattr(self.self_attn, "paged_attention")
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# Self Attention
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residual = hidden_states
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hidden_states = fast_rms_layernorm_inference(self.input_layernorm, hidden_states)
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@ -424,23 +364,7 @@ def LlamaDecoderLayer_fast_forward(
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hidden_states,
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past_key_value,
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position_ids,
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)
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hidden_states += residual
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# Fully Connected
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residual = hidden_states
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hidden_states = fast_rms_layernorm_inference(self.post_attention_layernorm, hidden_states)
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hidden_states = fast_mlp_inference(self.mlp, hidden_states)
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hidden_states += residual
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elif past_key_value is not None:
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# Self Attention
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residual = hidden_states
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hidden_states = fast_rms_layernorm_inference(self.input_layernorm, hidden_states)
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hidden_states, present_key_value = LlamaAttention_fast_forward_inference_prefill(
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self.self_attn,
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hidden_states,
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past_key_value,
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position_ids,
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do_prefill = do_prefill,
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)
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hidden_states += residual
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@ -655,12 +579,8 @@ def LlamaModel_fast_forward(
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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pass
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if past_key_values is not None:
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hidden_states = fast_rms_layernorm_inference(self.norm, hidden_states)
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else:
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hidden_states = fast_rms_layernorm(self.norm, hidden_states)
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pass
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hidden_states = fast_rms_layernorm(self.norm, hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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@ -679,7 +599,7 @@ pass
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L825
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@torch.compile
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@torch.inference_mode
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def LlamaModel_fast_forward_inference(
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self,
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input_ids,
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