add cuda kernels

model.py

@@ -6,28 +6,12 @@ import torch.nn.init as init
 from flash_attn.flash_attn_interface import flash_attn_func
 from flash_attn.layers.rotary import apply_rotary_emb
 import src.distributed.process_group_manager as pgm
-from src.parallel.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear, VocabParallelEmbedding
+from src.nn.layer_norm import LlamaRMSNorm, TritonRMSNorm
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 dtype = torch.bfloat16 if os.getenv('DATA_TYPE', 'bfloat16') == 'bfloat16' else torch.float32
 init_method = init.xavier_normal_
 
-class LlamaRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-5):
-        """
-        LlamaRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
 def apply_rotary_pos_emb(x, cos, sin):
     #TODO: Maybe do class RotaryEmbedding(nn.Module) later
     batch_size, num_head, seq_length, head_dim = x.size()
@@ -66,14 +50,7 @@ class Attention(nn.Module):
         self.q_proj = nn.Linear(config.hidden_size, self.num_heads*self.head_dim, bias=False)
         self.k_proj = nn.Linear(config.hidden_size, self.num_key_values*self.head_dim, bias=False)
         self.v_proj = nn.Linear(config.hidden_size, self.num_key_values*self.head_dim, bias=False)
-        # self.q_proj = ColumnParallelLinear(config.hidden_size, self.num_heads*self.head_dim, bias=False, gather_output=False, init_method=init_method) # why the init method is x? Xavier is better?
-        # self.k_proj = ColumnParallelLinear(config.hidden_size, self.num_key_values*self.head_dim, bias=False, gather_output=False, init_method=init_method)
-        # self.v_proj = ColumnParallelLinear(config.hidden_size, self.num_key_values*self.head_dim, bias=False, gather_output=False, init_method=init_method)
-        # if os.getenv('FLASH_ROPE', '1') == '1':
-        #     self.flash_rope = FlashRotaryEmbedding(dim=self.head_dim, interleaved=False, base=500000.0)
-        
         self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
-        # self.out_proj = RowParallelLinear(self.num_heads * self.head_dim, config.hidden_size, bias=False, input_is_parallel=True, init_method=init_method)
         self.layer_idx = layer_idx
         
         ## TODO support mask
@@ -83,7 +60,7 @@ class Attention(nn.Module):
         q = self.q_proj(x) # [batch_size, seq_length, num_heads*head_dim]
         k = self.k_proj(x) # [batch_size, seq_length, num_key_values*head_dim]
         v = self.v_proj(x) # [batch_size, seq_length, num_key_values*head_dim]
-        if os.getenv('FLASH_ROPE', '0') != '1':
+        if os.getenv('FLASH_ATTEN', '1') != '1':
             q = q.view(batch_size, seq_length, self.num_local_heads, self.head_dim).transpose(1, 2)       # [batch_size, num_heads, seq_length, head_dim]
             k = k.view(batch_size, seq_length, self.num_local_kv_heads, self.head_dim).transpose(1, 2)  # [batch_size, num_key_values, seq_length, head_dim]
             v = v.view(batch_size, seq_length, self.num_local_kv_heads, self.head_dim).transpose(1, 2)  # [batch_size, num_key_values, seq_length, head_dim]
@@ -101,8 +78,9 @@ class Attention(nn.Module):
         k = k.repeat_interleave(self.num_local_heads // self.num_local_kv_heads, dim=1) # [batch_size, num_heads, seq_length, head_dim]
         v = v.repeat_interleave(self.num_local_heads // self.num_local_kv_heads, dim=1) # [batch_size, num_heads, seq_length, head_dim]
         
-        if os.getenv('ATTENTION', 'SDPA') == 'SDPA':
+        if os.getenv('FLASH_ATTEN', '1') != '1':
             causal = True if q.size(2) == k.size(2) else False # During decoding phase. The lenghth of q is usually 1. 
+            # Pytorch scaled dot product attention
             out = F.scaled_dot_product_attention(q, k, v, is_causal=causal) # [batch_size, num_heads, seq_length, head_dim]
             out = out.transpose(1, 2) # [batch_size, seq_length, num_heads, head_dim]
         else:
@@ -119,10 +97,7 @@ class MLP(nn.Module):
         self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
         self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
         self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
-        # self.up_proj = ColumnParallelLinear(config.hidden_size, config.intermediate_size, bias=False, gather_output=False, init_method=init_method)
+
-        # self.gate_proj = ColumnParallelLinear(config.hidden_size, config.intermediate_size, bias=False, gather_output=False, init_method=init_method)
-        # self.down_proj = RowParallelLinear(config.intermediate_size, config.hidden_size, bias=False, input_is_parallel=True, init_method=init_method)
-        
     def forward(self, x):
         #TODO: dont do single line operations as it is harder to debug
         return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
@@ -131,7 +106,7 @@ class DecoderLayer(nn.Module):
     # RMSNorm -> Attention -> Residual -> RMSNorm -> MLP -> Residual
     def __init__(self, config, layer_idx):
         super().__init__()
-        RMSNorm = LlamaRMSNorm
+        RMSNorm = LlamaRMSNorm if os.getenv('FLASH_ATTEN', '1') != '1' else TritonRMSNorm
         self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.attention = Attention(config, layer_idx = layer_idx)
@@ -167,11 +142,10 @@ class Llama(nn.Module):
         
         # modules
         self.embedding = nn.Embedding(self.vocab_size, self.hidden_size)
-        # self.embedding = VocabParallelEmbedding(self.vocab_size, self.hidden_size, init_method=init_method)
         self.decoder_layers = nn.ModuleList([DecoderLayer(config,layer_idx = i) for i in range(self.num_layers)])
         self.final_proj = nn.Linear(self.hidden_size, self.vocab_size, bias=False)
-        # self.final_proj = ColumnParallelLinear(self.hidden_size, self.vocab_size, bias=False, gather_output=True, init_method=init_method) # we can also not gather the output. TODO: add vocab_parallel_cross_entropy
+        RMSNorm = LlamaRMSNorm if os.getenv('FLASH_ATTEN', '1') != '1' else TritonRMSNorm
-        self.final_norm = LlamaRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
+        self.final_norm = RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
         
     def forward(self, input_ids, attention_mask=None, position_ids: torch.Tensor = None):
         batch_size, seq_length = input_ids.size()

src/nn/layer_norm.py

@@ -0,0 +1,43 @@
+import torch
+from torch import nn
+from flash_attn.ops.triton.layer_norm import layer_norm_fn
+
+class TritonRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.register_parameter("bias", None)
+
+    def forward(
+        self, hidden_states, residual=None, dropout_p=0.0, prenorm=False, residual_in_fp32=False, return_dropout_mask=False
+    ):
+        return layer_norm_fn(
+            hidden_states,
+            self.weight,
+            None,
+            residual=residual,
+            eps=self.eps,
+            dropout_p=dropout_p,
+            prenorm=prenorm,
+            residual_in_fp32=residual_in_fp32,
+            is_rms_norm=True,
+            return_dropout_mask=return_dropout_mask,
+        )
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-5):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)

train.py

@@ -177,6 +177,7 @@ if __name__ == "__main__":
     
     os.environ["OMP_NUM_THREADS"] = "1"
     os.environ["TOKENIZERS_PARALLELISM"] = "false"
+    os.environ["FLASH_ATTEN"] = "1" # Use operations from  flash attention repo to accelerate the training. Model dtpe should be torch.float16!
  
     local_rank = int(os.environ["LOCAL_RANK"])
     world_size = int(os.environ["WORLD_SIZE"])
@@ -185,8 +186,9 @@ if __name__ == "__main__":
     
     # SEQ_LEN, GLOBAL_BATCH_SIZE, MICRO_BATCH_SIZE, LEARNING_RATE, NUM_SAMPLES, MAX_TOKENS, SEED = 10, 6, 2, 1e-4, 20, 1800, 42
     ## hyperparameters
-    SEQ_LEN, GLOBAL_BATCH_SIZE, MICRO_BATCH_SIZE, LEARNING_RATE, NUM_SAMPLES, MAX_TOKENS, SEED = 1024, 16, 4, 3e-4, 100000, int(10e8), 42
+    SEQ_LEN, GLOBAL_BATCH_SIZE, MICRO_BATCH_SIZE, LEARNING_RATE, NUM_SAMPLES, MAX_TOKENS, SEED = 1024, 32, 4, 3e-4, 100000, int(10e8), 42
     grad_acc = 16
+    dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32
 
     assert SEQ_LEN % args.cp_size == 0, "SEQ_LEN must be divisible by cp_size for Context Parallelism"
 
@@ -246,7 +248,7 @@ if __name__ == "__main__":
     if pgm.process_group_manager.dp_world_size > 1:
         model = DataParallel(model)
 
-    model.to(device)
+    model.to(dtype).to(device)
     model.train()
     
     data_loader = MicroBatchDataLoader(global_batch_size=GLOBAL_BATCH_SIZE, micro_batch_size=MICRO_BATCH_SIZE, seq_length=SEQ_LEN, dataset_name=dataset_name, tokenizer_name=model_name, grad_acc = grad_acc,num_workers=4, num_proc=4, num_samples=NUM_SAMPLES)