PP API and nD Distributed Timeline Profiling (#41)

examples/open_llama_4D_benchmark/download_open_llama_ckpt.py

@@ -1,6 +1,6 @@
 ################################################################################
 #
-# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Copyright 2024 ByteDance Ltd. and/or its affiliates. All rights reserved.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

examples/open_llama_4D_benchmark/llama_mfu_calculator.py

@@ -1,6 +1,6 @@
 ################################################################################
 #
-# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Copyright 2024 ByteDance Ltd. and/or its affiliates. All rights reserved.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

examples/open_llama_4D_benchmark/run_open_llama_w_vescale.py

@@ -1,6 +1,6 @@
 ################################################################################
 #
-# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Copyright 2024 ByteDance Ltd. and/or its affiliates. All rights reserved.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

examples/open_llama_4D_benchmark/sharding_plan.py

@@ -1,6 +1,6 @@
 ################################################################################
 #
-# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Copyright 2024 ByteDance Ltd. and/or its affiliates. All rights reserved.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

requirements.txt

@@ -6,6 +6,7 @@ pytest
 tqdm
 optree
 accelerate
-transformers==4.37.2
+transformers==4.40.2
 flash_attn
+matplotlib
 mmh3

test/checkpoint/nano_gpt/test_nano_gpt_load_save.py

@@ -101,9 +101,7 @@ def init_method(self):
     @skip_unless_torch_gpu
     @with_comms
     def test_load(self):
-        ddp_gpt, dist_optimizer, _ = build_gpt_model_optimizer_and_dataset(
-            self.init_method, dp_size=2, tp_size=2
-        )
+        ddp_gpt, dist_optimizer, _ = build_gpt_model_optimizer_and_dataset(self.init_method, dp_size=2, tp_size=2)
 
         # Load the model and optimizer after first data
 

test/checkpoint/open_llama/test_open_llama_dp_reshard.py

@@ -1,6 +1,6 @@
 ################################################################################
 #
-# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Copyright 2024 ByteDance Ltd. and/or its affiliates. All rights reserved.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

test/checkpoint/open_llama/test_open_llama_load_save.py

@@ -1,6 +1,6 @@
 ################################################################################
 #
-# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Copyright 2024 ByteDance Ltd. and/or its affiliates. All rights reserved.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

test/checkpoint/open_llama/test_open_llama_tp_reshard.py

@@ -1,6 +1,6 @@
 ################################################################################
 #
-# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Copyright 2024 ByteDance Ltd. and/or its affiliates. All rights reserved.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at

test/model/open_llama/test_attention.py

@@ -56,7 +56,8 @@ def test_attention(self):
         input.retain_grad()
         non_parallel_attention, _ = get_model()
         non_parallel_attention = non_parallel_attention.cuda()
-        golden_outputs = non_parallel_attention(input)
+        dummy_position_ids = torch.randint(low=0, high=s, size=(bsz, s)).cuda()
+        golden_outputs = non_parallel_attention(input, position_ids=dummy_position_ids)
         golden_loss = golden_outputs[0].mean()
         golden_loss.backward()
 
@@ -84,8 +85,9 @@ def test_attention(self):
         d_input = distribute_tensor(input.detach(), device_mesh, [Shard(1)])
         d_input.requires_grad_()
         d_input.retain_grad()
+        d_position_id = distribute_tensor(dummy_position_ids.detach(), device_mesh, [Replicate()])
 
-        vescale_outputs = vescale_attention(d_input)
+        vescale_outputs = vescale_attention(d_input, position_ids=d_position_id)
         vescale_outputs[0] = vescale_outputs[0].redistribute(placements=[Replicate()] * device_mesh.ndim)
         vescale_loss = vescale_outputs[0].mean()
 

test/model/open_llama/test_decoder_layer.py

@@ -56,7 +56,8 @@ def test_decoder(self):
         input.retain_grad()
         non_parallel_decoder, _ = get_model()
         non_parallel_decoder = non_parallel_decoder.cuda()
-        golden_outputs = non_parallel_decoder(input)
+        dummy_position_id = torch.randint(low=0, high=s, size=(bsz, s)).cuda()
+        golden_outputs = non_parallel_decoder(input, position_ids=dummy_position_id)
         golden_loss = golden_outputs[0].mean()
         golden_loss.backward()
 
@@ -95,8 +96,9 @@ def test_decoder(self):
         d_input = distribute_tensor(input.detach(), device_mesh, [Shard(1)])
         d_input.requires_grad_()
         d_input.retain_grad()
+        d_position_id = distribute_tensor(dummy_position_id.detach(), device_mesh, [Replicate()])
 
-        vescale_outputs = vescale_decoder(d_input)
+        vescale_outputs = vescale_decoder(d_input, position_ids=d_position_id)
         vescale_outputs[0] = vescale_outputs[0].redistribute(placements=[Replicate()] * device_mesh.ndim)
         vescale_loss = vescale_outputs[0].mean()
 

test/ndtimeline/__init__.py

@@ -0,0 +1 @@
+# make pylint happy

test/ndtimeline/test_local_raw_handler.py

@@ -0,0 +1,37 @@
+################################################################################
+#
+# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+################################################################################
+
+import os
+from vescale.ndtimeline.world_info import WorldInfo
+from vescale.ndtimeline.handlers import LocalRawNDHandler
+from vescale.ndtimeline.variables import LOCAL_LOGGING_PATH
+
+
+def test_basic_usage():
+    h = LocalRawNDHandler(run_id=0, chunk_sz=10, backup_cnt=3)
+    file_name = "timeline_run0_raw.log"
+    h("test_metric", 1.0, [1.0], [1.0], [{}], range(0, 1), WorldInfo(0, 0), {})
+    assert os.path.exists(os.path.join(LOCAL_LOGGING_PATH, file_name))
+    for _ in range(4):
+        h("test_metric", 1.0, [1.0], [1.0], [{}], range(0, 1), WorldInfo(0, 0), {})
+    h("test_metric2", 2.0, [1.0], [1.0], [{}], range(0, 1), WorldInfo(0, 0), {})
+    assert os.path.exists(os.path.join(LOCAL_LOGGING_PATH, file_name + ".2"))
+    assert not os.path.exists(os.path.join(LOCAL_LOGGING_PATH, file_name + ".4"))
+    os.remove(os.path.join(LOCAL_LOGGING_PATH, file_name))
+    for i in range(1, 4):
+        os.remove(os.path.join(LOCAL_LOGGING_PATH, file_name + "." + str(i)))
+    assert not os.path.exists(os.path.join(LOCAL_LOGGING_PATH, file_name + ".2"))

test/ndtimeline/test_metric_level.py

@@ -0,0 +1,30 @@
+################################################################################
+#
+# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+################################################################################
+
+from vescale.ndtimeline import NDMetricLevel
+
+
+def test_cmp_level():
+    assert NDMetricLevel.FRAMEWORK_DEBUG >= NDMetricLevel.INFO
+    assert NDMetricLevel.USER_DEBUG >= NDMetricLevel.INFO
+    assert NDMetricLevel.USER_DEBUG > NDMetricLevel.INFO
+    assert NDMetricLevel.USER_INFO < NDMetricLevel.INFO
+    assert NDMetricLevel.USER_INFO <= NDMetricLevel.INFO
+    assert NDMetricLevel.INFO < NDMetricLevel.DEBUG
+    assert NDMetricLevel.TRACE <= NDMetricLevel.TRACE
+    assert NDMetricLevel.TRACE >= NDMetricLevel.TRACE
+    assert NDMetricLevel.TRACE == NDMetricLevel.TRACE

test/ndtimeline/test_parser_handler.py

@@ -0,0 +1,61 @@
+################################################################################
+#
+# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+################################################################################
+
+import pytest
+from vescale.ndtimeline.world_info import WorldInfo
+from vescale.ndtimeline.handlers import ParserNDHandler
+from vescale.ndtimeline.exceptions import NDHandlerError
+
+
+def test_normal_input_with_tags():
+    metric_name = "test_metric"
+    recent_elapsed_raw_parts = [1.0, 3.2, 1.4]
+    elapsed = sum(recent_elapsed_raw_parts)
+    recent_since_start_raw_parts = [1710332816.6118143, 1710332833.2222, 1710332846.1313]
+    single_tag = {"is_test": True}
+    tags = [single_tag] * (len(recent_elapsed_raw_parts) - 1) + [{"is_test": False}]
+    step_range = range(0, 1)
+    world_info = WorldInfo(0, 0)
+    callback = ParserNDHandler()
+    records = callback(
+        metric_name, elapsed, recent_elapsed_raw_parts, recent_since_start_raw_parts, tags, step_range, world_info, {}
+    )
+    assert len(records) == 1
+    assert records[0].step == 0
+
+
+def test_normal_invalid_input():
+    metric_name = "test_metric"
+    recent_elapsed_raw_parts = [1.0, 3.2, 1.4]
+    elapsed = sum(recent_elapsed_raw_parts)
+    recent_since_start_raw_parts = [1710332816.6118143, 1710332846.1313]
+    single_tag = {"is_test": True}
+    tags = [single_tag] * (len(recent_elapsed_raw_parts) - 1) + [{"is_test": False}]
+    step_range = range(0, 1)
+    world_info = WorldInfo(0, 0)
+    callback = ParserNDHandler()
+    with pytest.raises(NDHandlerError):
+        callback(
+            metric_name,
+            elapsed,
+            recent_elapsed_raw_parts,
+            recent_since_start_raw_parts,
+            tags,
+            step_range,
+            world_info,
+            {},
+        )

test/parallel/pipeline/api/four_mlp.py

@@ -0,0 +1,53 @@
+################################################################################
+#
+# Copyright 2023 ByteDance Ltd. and/or its affiliates. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+################################################################################
+
+import torch
+import torch.nn as nn
+import os
+
+
+class MLP(nn.Module):
+    def __init__(self, features_in, feature_middle, features_out, value):
+        super().__init__()
+        self.value = value
+        self.counter = 0
+        self.fc1 = nn.Linear(1024, 1024, bias=False)
+        self.fc1.weight.data.fill_(value)
+        self.fc2 = nn.Linear(1024, 1024, bias=False)
+        self.fc2.weight.data.fill_(value * 2)
+        self.gelu = nn.GELU()
+
+    def forward(self, x):
+        t = self.fc1(x)
+        t = self.gelu(t)
+        t = self.fc2(t)
+        torch.save(t, f"{os.environ['model_name']}_mlp{self.value}_fwd{self.counter}_out_tensor.pt")
+        self.counter += 1
+        return t
+
+
+class FourMLP(nn.Module):
+    def __init__(self, hidden):
+        super().__init__()
+        self.mlp1 = MLP(hidden * 1, hidden * 2, hidden * 3, 0)
+        self.mlp2 = MLP(hidden * 3, hidden * 4, hidden * 5, 1)
+        self.mlp3 = MLP(hidden * 5, hidden * 6, hidden * 7, 2)
+        self.mlp4 = MLP(hidden * 7, hidden * 8, hidden * 9, 3)
+        self.sequence = nn.Sequential(self.mlp1, self.mlp2, self.mlp3, self.mlp4)
+
+    def forward(self, x):
+        return self.sequence(x)