global_buffer add hidden_states and attention_mask

src/llamafactory/easy_context/dist_flash_attn/monkey_patch.py

@@ -48,8 +48,15 @@ def init(self, num_layers, offload_percent, shape, dtype, device):
         self.layer_num = num_layers
         self.gpu_layer_num = int(num_layers * offload_percent)
         self.cpu_layer_num = num_layers - self.gpu_layer_num
-        self.gpu_buffer = [torch.empty(shape, dtype=dtype, device=device) for _ in range(self.gpu_layer_num)]
+        self.gpu_buffer = [None for _ in range(self.gpu_layer_num)]
         self.cpu_buffer = drv.pagelocked_empty([self.cpu_layer_num] + shape, dtype=np.float16)
+        bs, num_heads, seq_len, emb_size = shape
+        shape_h = [bs, seq_len, num_heads * emb_size]
+        shape_a = [bs, seq_len]
+        self.hidden_state_gpu_buffer = [None for _ in range(self.gpu_layer_num)]
+        self.hidden_state_cpu_buffer = drv.pagelocked_empty([self.cpu_layer_num] + shape_h, dtype=np.float16)
+        self.position_id_gpu_buffer = [None for _ in range(self.gpu_layer_num)]
+        self.position_id_cpu_buffer = drv.pagelocked_empty([self.cpu_layer_num] + shape_a, dtype=np.float16)
         self.d2h_stream = drv.Stream()
         self.h2d_streams = [drv.Stream() for _ in range(self.gpu_layer_num)]
         self.initialized = True
@@ -61,26 +68,60 @@ def save_flash_attn_out(self, layer_idx, out):
             drv.memcpy_dtoh_async(self.cpu_buffer[layer_idx], out.data_ptr(), self.d2h_stream)
         else:
             idx = layer_idx - self.cpu_layer_num
-            drv.memcpy_dtod(self.gpu_buffer[idx].data_ptr(), out.data_ptr(), out.element_size() * out.nelement())
+            self.gpu_buffer[idx] = out
+
+    def save_hidden_states(self, layer_idx, *hs):
+        if layer_idx < 0:
+            layer_idx = self.layer_num + layer_idx
+        hidden_state = hs[0]
+        position_id = hs[1]
+        if layer_idx < self.cpu_layer_num:
+            drv.memcpy_dtoh_async(self.hidden_state_cpu_buffer[layer_idx], hidden_state.data_ptr(), self.d2h_stream)
+            drv.memcpy_dtoh_async(self.position_id_cpu_buffer[layer_idx], position_id.data_ptr(), self.d2h_stream)
+        else:
+            idx = layer_idx - self.cpu_layer_num
+            self.hidden_state_gpu_buffer[idx] = hidden_state
+            self.position_id_gpu_buffer[idx] = position_id
 
     def get_flash_attn_out(self, layer_idx):
         if layer_idx < 0:
             layer_idx = self.layer_num + layer_idx
-        if layer_idx > self.cpu_layer_num:
+        if layer_idx >= self.cpu_layer_num:
             return self.gpu_buffer[layer_idx - self.cpu_layer_num]
         idx = self.gpu_layer_num -1 - (self.cpu_layer_num - layer_idx) % self.gpu_layer_num
         self.h2d_streams[idx].synchronize()
         return self.gpu_buffer[idx]
+
+    def get_hidden_states(self, layer_idx):
+        if layer_idx < 0:
+            layer_idx = self.layer_num + layer_idx
+        if layer_idx >= self.cpu_layer_num:
+            return self.hidden_state_gpu_buffer[layer_idx - self.cpu_layer_num], self.position_id_gpu_buffer[layer_idx - self.cpu_layer_num]
+        idx = self.gpu_layer_num -1 - (self.cpu_layer_num - layer_idx) % self.gpu_layer_num
+        self.h2d_streams[idx].synchronize()
+        return self.hidden_state_gpu_buffer[idx], self.position_id_gpu_buffer[idx]
 
-    def free_flash_attn_out(self, layer_idx):
+    def free_layer_gpu_buffer(self, layer_idx):
         if layer_idx < 0:
             layer_idx = self.layer_num + layer_idx
+        if layer_idx == self.layer_num - 1:
+            self.d2h_stream.synchronize()
         cpu_layer_idx = layer_idx - self.gpu_layer_num
+        if layer_idx >= self.cpu_layer_num:
+            idx = layer_idx - self.cpu_layer_num
+        else:
+            idx = self.gpu_layer_num -1 - (self.cpu_layer_num - layer_idx) % self.gpu_layer_num
+        self.gpu_buffer[idx].grad = None
         if cpu_layer_idx < 0:
+            self.gpu_buffer[idx] = None
+            self.hidden_state_gpu_buffer[idx] = None
+            self.position_id_gpu_buffer[idx] = None
             return
-        idx = self.gpu_layer_num -1 - (self.cpu_layer_num - layer_idx) % self.gpu_layer_num
-        self.gpu_buffer[idx].grad = None
         drv.memcpy_htod_async(self.gpu_buffer[idx].data_ptr(), self.cpu_buffer[cpu_layer_idx], self.h2d_streams[idx])
+        drv.memcpy_htod_async(self.hidden_state_gpu_buffer[idx].data_ptr(), self.hidden_state_cpu_buffer[cpu_layer_idx], self.h2d_streams[idx])
+        drv.memcpy_htod_async(self.position_id_gpu_buffer[idx].data_ptr(), self.position_id_cpu_buffer[cpu_layer_idx], self.h2d_streams[idx])
+
+global_buffer = GlobalBufferManager()
 
 def init_flash_attn_buffers(num_layers):
     # update the global buffer according to number of layers
@@ -161,16 +202,24 @@ def forward(ctx, run_function, layer_idx, preserve_rng_state, *args):
         # Save non-tensor inputs in ctx, keep a placeholder None for tensors
         # to be filled out during the backward.
         ctx.inputs = []
-        ctx.tensor_indices = []
+        ctx.tensor_indices = {}
         tensor_inputs = []
+        global global_buffer
+        hidden_state = None
+        position_ids = None
         for i, arg in enumerate(args):
             if i == 0 and ctx.layer_idx != 0:
                 # flash attention output is saved to the global buffer during forward
                 ctx.inputs.append(None)
             else:
                 if torch.is_tensor(arg):
-                    tensor_inputs.append(arg)
-                    ctx.tensor_indices.append(i)
+                    # tensor_inputs.append(arg)
+                    if len(arg.shape) == 3:
+                        hidden_state = arg
+                        ctx.tensor_indices[i] = 'hidden_state'
+                    elif len(arg.shape) == 2:
+                        position_ids = arg
+                        ctx.tensor_indices[i] = 'position_ids'
                     ctx.inputs.append(None)
                 else:
                     ctx.inputs.append(arg)
@@ -185,16 +234,13 @@ def forward(ctx, run_function, layer_idx, preserve_rng_state, *args):
 
             # save flash attention output to global buffer
             # save_flash_attn_out_to_global_buffer(ctx.layer_idx, out)
-            GlobalBufferManager().save_flash_attn_out(ctx.layer_idx, out)
-            tensor_inputs += [softmax_lse]
+
+            global_buffer.save_flash_attn_out(ctx.layer_idx, out)
+            global_buffer.save_hidden_states(ctx.layer_idx, hidden_state, position_ids)
+            # tensor_inputs += [softmax_lse]
             ctx.softmax_scale = softmax_scale
 
-        ctx.save_for_backward(*tensor_inputs)
-        tensor_inputs_ma = 0
-        for ti in tensor_inputs:
-            tensor_inputs_ma += ti.element_size() * ti.nelement()
-        if int(os.environ['RANK']) == 0:
-            print(f"layer: {ctx.layer_idx}, MA: {torch.cuda.memory_allocated()/(1<<30):.2f}, MR: {torch.cuda.memory_reserved()/(1<<30):.2f}, tensor_inputs_ma: {tensor_inputs_ma/(1<<30):2f}")
+        ctx.save_for_backward(softmax_lse)
         return out, residual
 
     @staticmethod
@@ -207,18 +253,24 @@ def backward(ctx, *args):
         # Copy the list to avoid modifying original list.
         inputs = list(ctx.inputs)
         tensor_indices = ctx.tensor_indices
-        tensors = ctx.saved_tensors
-        tensors, softmax_lse = tensors[:-1], tensors[-1]
-
+        # tensors = ctx.saved_tensors
+        softmax_lse = ctx.saved_tensors[0]
+        # tensors, softmax_lse = tensors[:-1], tensors[-1]
+        global global_buffer
+        hidden_state, position_ids = global_buffer.get_hidden_states(ctx.layer_idx)
         # Fill in inputs with appropriate saved tensors.
         # Fill the flash attention output first
         if ctx.layer_idx > 0:
             # inputs[0] should be flash attention output
             # inputs[0] = get_flash_attn_out_from_global_buffer(ctx.layer_idx-1)
-            inputs[0] = GlobalBufferManager().get_flash_attn_out(ctx.layer_idx-1)
-        for i, idx in enumerate(tensor_indices):
-            inputs[idx] = tensors[i]
-
+            inputs[0] = global_buffer.get_flash_attn_out(ctx.layer_idx-1)
+        # for i, idx in enumerate(tensor_indices):
+        #     inputs[idx] = tensors[i]
+        for k, v in tensor_indices.items():
+            if v == 'hidden_state':
+                inputs[k] = hidden_state
+            if v == 'position_ids':
+                inputs[k] = position_ids
         # Stash the surrounding rng state, and mimic the state that was
         # present at this time during forward.  Restore the surrounding state
         # when we're done.
@@ -247,9 +299,8 @@ def backward(ctx, *args):
         #dk = torch.empty(k.shape, dtype=q.dtype, device=q.device)
         #dv = torch.empty(v.shape, dtype=q.dtype, device=q.device)
         # out = get_flash_attn_out_from_global_buffer(ctx.layer_idx)
-        out = GlobalBufferManager().get_flash_attn_out(ctx.layer_idx)
-        if int(os.environ['RANK']) == 0:
-            print(f"layer: {ctx.layer_idx}, MA: {torch.cuda.memory_allocated()/(1<<30):.2f}, MR: {torch.cuda.memory_reserved()/(1<<30):.2f}")
+        out = global_buffer.get_flash_attn_out(ctx.layer_idx)
+
         # todo get dout
         dout = args[0]
 
@@ -263,13 +314,12 @@ def backward(ctx, *args):
 
         grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else None
                       for inp in detached_inputs)
-
         # write flash attention output gradients to buffer
         if ctx.layer_idx > 0:
             # write_gradient_to_flash_attn_out(ctx.layer_idx-1, detached_inputs[0].grad)
-            GlobalBufferManager().get_flash_attn_out(ctx.layer_idx-1).grad = detached_inputs[0].grad
+            global_buffer.get_flash_attn_out(ctx.layer_idx-1).grad = detached_inputs[0].grad
         # free_flash_attn_out_buffer(ctx.layer_idx)
-        GlobalBufferManager().free_flash_attn_out(ctx.layer_idx)
+        global_buffer.free_layer_gpu_buffer(ctx.layer_idx)
         return (None, None, None) + grads
 
 
@@ -341,12 +391,12 @@ def backward(ctx, *args):
         inputs = list(ctx.inputs)
         tensor_indices = ctx.tensor_indices
         tensors = ctx.saved_tensors
-
+        global global_buffer
         # Fill in inputs with appropriate saved tensors.
         # Fill the flash attention output first
         # inputs[0] should be flash attention output
         # inputs[0] = get_flash_attn_out_from_global_buffer(-1)
-        inputs[0] = GlobalBufferManager().get_flash_attn_out(-1)
+        inputs[0] = global_buffer.get_flash_attn_out(-1)
         for i, idx in enumerate(tensor_indices):
             inputs[idx] = tensors[i]
 
@@ -384,10 +434,9 @@ def backward(ctx, *args):
         torch.autograd.backward(outputs_with_grad, args_with_grad)
         grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else None
                       for inp in detached_inputs)
-
         # write flash attention output gradients to buffer
         # write_gradient_to_flash_attn_out(-1, detached_inputs[0].grad)
-        GlobalBufferManager().get_flash_attn_out(-1).grad = detached_inputs[0].grad
+        global_buffer.get_flash_attn_out(-1).grad = detached_inputs[0].grad
 
         return (None, None) + grads
 
@@ -549,7 +598,8 @@ def forward(
             pass
         # initialize the global buffer
         # init_flash_attn_buffers(len(self.layers))
-        GlobalBufferManager().init(
+        global global_buffer
+        global_buffer.init(
             self.config.num_hidden_layers,
             offload_percent=0.25,
             shape=[batch_size, self.config.num_attention_heads, seq_length, self.config.hidden_size // self.config.num_attention_heads],