[torch.compile] rework compile control with piecewise cudagraph (#9715)
Signed-off-by: youkaichao <youkaichao@gmail.com>
This commit is contained in:
youkaichao
0
tests/compile/piecewise/__init__.py
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0
tests/compile/piecewise/__init__.py
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@@ -0,0 +1,4 @@
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{
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"use_cudagraph": true,
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"non_cudagraph_ops": ["silly.attention"]
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}
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96
tests/compile/piecewise/test_simple.py
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96
tests/compile/piecewise/test_simple.py
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@@ -0,0 +1,96 @@
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"""
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Test the piecewise compilation with a simple model so that we
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can exactly calculate the expected output and side effects.
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"""
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import os
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import torch
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from torch import nn
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from vllm.compilation.compile_context import set_compile_context
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.decorators import support_torch_compile
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from vllm.compilation.levels import CompilationLevel
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os.environ["VLLM_TORCH_COMPILE_LEVEL"] = str(CompilationLevel.PIECEWISE)
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global_counter = 0
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@torch.library.custom_op("silly::attention", mutates_args=["out"])
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def silly_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
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out: torch.Tensor) -> None:
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global global_counter
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global_counter += 1
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print(f"{global_counter=}")
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out.copy_(q)
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out[0] += 1
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@silly_attention.register_fake
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def _(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
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out: torch.Tensor) -> None:
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return
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@support_torch_compile
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class SillyModel(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Overall effect:
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x += 1
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x[0] += 2
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global_counter += 2
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"""
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x = x + 1
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x = x + 2
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out = torch.empty_like(x)
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torch.ops.silly.attention(x, x, x, out)
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x = out
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x = x - 2
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x = x - 1
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out = torch.empty_like(x)
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torch.ops.silly.attention(x, x, x, out)
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x = out
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x = x + 1
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return x
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def test_simple_piecewise_compile():
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model = SillyModel()
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directory = os.path.dirname(__file__)
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config = os.path.join(directory, "piecewise_compilation_config.json")
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os.environ["VLLM_TORCH_COMPILE_CONFIG"] = config
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input_buffer = torch.randn(100).cuda()
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with compilation_counter.expect(
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num_graphs_seen=1, # one graph for the model
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num_piecewise_graphs_seen=5, # 2 * num_layers + 1
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num_piecewise_capturable_graphs_seen=3, # 1 + num_layers
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num_inductor_compilations=3, # num_piecewise_capturable_graphs_seen
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num_cudagraph_caputured=
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6, # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
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):
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with set_compile_context([1, 2]):
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model(input_buffer)
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model(input_buffer[:2])
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model(input_buffer[:1])
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input_buffer[:2].zero_()
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global global_counter
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global_counter = 0
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output = model(input_buffer[:2])
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assert global_counter == 2
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assert torch.allclose(output.cpu(), torch.tensor([3., 1.]))
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# clean up to avoid side effects for other tests
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del os.environ["VLLM_TORCH_COMPILE_CONFIG"]
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334
tests/compile/piecewise/test_toy_llama.py
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334
tests/compile/piecewise/test_toy_llama.py
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@@ -0,0 +1,334 @@
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"""
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Test the piecewise compilation with a simple model, comparing the output
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with and without the piecewise compilation.
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"""
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import os
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from dataclasses import dataclass
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from typing import Optional, Tuple
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import torch
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from torch import nn
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from vllm.compilation.compile_context import set_compile_context
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from vllm.compilation.config import CompilationConfig
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.decorators import support_torch_compile
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from vllm.compilation.levels import CompilationLevel
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from vllm.plugins import set_compilation_config
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@torch.library.custom_op("silly::attention", mutates_args=["out"])
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def silly_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
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out: torch.Tensor) -> None:
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out.copy_(q)
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out += k
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out += v
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@silly_attention.register_fake
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def _(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
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out: torch.Tensor) -> None:
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return
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@dataclass
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class LlamaConfig:
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hidden_size: int = 128
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mlp_size: int = 256
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vocab_size: int = 128
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num_layers: int = 2
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class LlamaMLP(nn.Module):
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def __init__(self, config: LlamaConfig) -> None:
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super().__init__()
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self.gate_up_projection = nn.Linear(
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in_features=config.hidden_size,
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out_features=config.mlp_size * 2,
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bias=False,
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)
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self.down_projection = nn.Linear(
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in_features=config.mlp_size,
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out_features=config.hidden_size,
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bias=False,
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)
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self.gate_up_projection.weight.data.fill_(0.0)
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self.down_projection.weight.data.fill_(0.0)
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def forward(self, x):
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x = self.gate_up_projection(x)
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x = x[:, :x.size(1) // 2] * torch.nn.functional.relu(
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x[:, x.size(1) // 2:])
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x = self.down_projection(x)
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return x
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class LlamaAttention(nn.Module):
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def __init__(self, config: LlamaConfig) -> None:
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super().__init__()
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self.qkv_projection = nn.Linear(
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in_features=config.hidden_size,
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out_features=config.hidden_size * 3,
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)
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self.output_projection = nn.Linear(
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in_features=config.hidden_size,
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out_features=config.hidden_size,
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)
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self.qkv_projection.weight.data.fill_(0.0)
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self.output_projection.weight.data.fill_(0.0)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv = self.qkv_projection(hidden_states)
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hidden_size = qkv.size(-1) // 3
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q, k, v = qkv.split([hidden_size, hidden_size, hidden_size], dim=-1)
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q = q + positions.unsqueeze(1)
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k = k + positions.unsqueeze(1)
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attn_output = torch.empty_like(q)
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torch.ops.silly.attention(q, k, v, attn_output)
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output = self.output_projection(attn_output)
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return output
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class LlamaDecoderLayer(nn.Module):
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def __init__(self, config: LlamaConfig) -> None:
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super().__init__()
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self.self_attention = LlamaAttention(config)
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self.mlp = LlamaMLP(config)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = hidden_states / 2
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else:
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hidden_states = hidden_states + residual
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residual = hidden_states
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hidden_states = hidden_states / 2
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hidden_states = self.self_attention(positions=positions,
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hidden_states=hidden_states)
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hidden_states = hidden_states + residual
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residual = hidden_states
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hidden_states = hidden_states / 2
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class LlamaModel(nn.Module):
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def __init__(self, config: LlamaConfig) -> None:
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super().__init__()
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self.embedding_tokens = nn.Embedding(
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num_embeddings=config.vocab_size,
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embedding_dim=config.hidden_size,
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)
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self.layers = nn.ModuleList(
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[LlamaDecoderLayer(config) for _ in range(config.num_layers)])
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self.embedding_tokens.weight.data.fill_(0.0)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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positions: torch.Tensor,
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) -> torch.Tensor:
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hidden_states = self.embedding_tokens(input_ids)
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residual = None
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for layer in self.layers:
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hidden_states, residual = layer(positions, hidden_states, residual)
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return hidden_states
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@torch.inference_mode
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def run_model(llama_config,
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use_compile: bool,
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split_attn: bool = False) -> torch.Tensor:
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if use_compile:
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os.environ["VLLM_TORCH_COMPILE_LEVEL"] = str(
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CompilationLevel.PIECEWISE)
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if split_attn:
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set_compilation_config(
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CompilationConfig(
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use_cudagraph=True,
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non_cudagraph_ops=["silly.attention"],
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))
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else:
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set_compilation_config(CompilationConfig(use_cudagraph=True, ))
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else:
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os.environ["VLLM_TORCH_COMPILE_LEVEL"] = str(
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CompilationLevel.NO_COMPILATION)
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set_compilation_config(None)
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cls = LlamaModel
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if use_compile:
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cls = support_torch_compile(LlamaModel)
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model = cls(llama_config).eval().cuda()
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B = 16 # max batch size
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input_ids = torch.randint(0, llama_config.vocab_size, (B, )).cuda()
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positions = torch.arange(B).cuda()
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with set_compile_context([1, 2]):
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model(input_ids, positions)
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model(input_ids[:2], positions[:2])
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model(input_ids[:1], positions[:1])
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input_ids[:2].zero_()
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output = model(input_ids[:2], positions[:2])
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# manual cleanup
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del os.environ["VLLM_TORCH_COMPILE_LEVEL"]
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set_compilation_config(None)
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return output.cpu()
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def test_toy_llama():
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# compare output with and without piecewise compilation
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llama_config = LlamaConfig(hidden_size=128,
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mlp_size=256,
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vocab_size=128,
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num_layers=2)
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outputs = []
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with compilation_counter.expect(
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num_graphs_seen=0,
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num_piecewise_graphs_seen=0,
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num_piecewise_capturable_graphs_seen=0,
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num_inductor_compilations=0,
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num_cudagraph_caputured=0,
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):
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outputs.append(run_model(llama_config, use_compile=False))
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with compilation_counter.expect(
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num_graphs_seen=1, # one graph for the model
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num_piecewise_graphs_seen=1,
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num_piecewise_capturable_graphs_seen=1,
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num_inductor_compilations=1, # num_piecewise_capturable_graphs_seen
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num_cudagraph_caputured=
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2, # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
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):
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outputs.append(run_model(llama_config, use_compile=True))
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with compilation_counter.expect(
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num_graphs_seen=1, # one graph for the model
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num_piecewise_graphs_seen=2 * llama_config.num_layers +
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1, # 2 * num_layers + 1
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num_piecewise_capturable_graphs_seen=1 +
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llama_config.num_layers, # 1 + num_layers
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num_inductor_compilations=1 +
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llama_config.num_layers, # num_piecewise_capturable_graphs_seen
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num_cudagraph_caputured=2 *
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(1 + llama_config.num_layers
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), # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
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):
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outputs.append(
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run_model(llama_config, use_compile=True, split_attn=True))
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for i in range(1, len(outputs)):
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assert torch.allclose(outputs[0], outputs[i])
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@torch.inference_mode
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def benchmark():
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os.environ["VLLM_TORCH_COMPILE_LEVEL"] = str(CompilationLevel.PIECEWISE)
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from triton.testing import do_bench
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cls = support_torch_compile(LlamaModel)
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# similar to llama 3.1-8B
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llama_config = LlamaConfig(hidden_size=4096,
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mlp_size=14336,
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vocab_size=128 * 1024,
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num_layers=32)
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# a tiny model to measure the overhead
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# of piecewise cudagraph
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llama_config = LlamaConfig(hidden_size=40,
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mlp_size=80,
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vocab_size=128,
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num_layers=2)
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cudagraph_sizes = [1, 2, 4] + [i * 8 for i in range(1, 33)]
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eager_time = {}
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full_cudagraph_time = {}
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piecewise_cudagraph_time = {}
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pool = torch.cuda.graph_pool_handle()
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for piecewise in [False, True]:
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if piecewise:
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set_compilation_config(
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CompilationConfig(
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use_cudagraph=True,
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non_cudagraph_ops=["silly.attention"],
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))
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else:
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set_compilation_config(None)
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model = cls(llama_config).eval().cuda().to(torch.bfloat16)
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B = 256 # max batch size
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input_ids = torch.randint(0, llama_config.vocab_size, (B, )).cuda()
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positions = torch.arange(B).cuda().to(torch.bfloat16)
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graphs = {}
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with set_compile_context(cudagraph_sizes):
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model(input_ids, positions)
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for b in cudagraph_sizes[::-1]:
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if not piecewise:
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graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(graph, pool=pool):
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output = model(input_ids[:b], positions[:b])
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graphs[b] = (graph, output)
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else:
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output = model(input_ids[:b], positions[:b])
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graphs[b] = (model, output)
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for b in cudagraph_sizes:
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if piecewise:
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# noqa is for `Function definition does not bind loop variable`
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# it will be problematic if we save the created lambda function
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# and use it later, because it will look up the name `b` in the
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# enclosing scope, and the value of `b` will always be 256.
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# it is fine here, because we only use the lambda function once.
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runtime = do_bench(lambda: graphs[b][0] # noqa
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(input_ids[:b], positions[:b])) # noqa
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piecewise_cudagraph_time[b] = runtime
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else:
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runtime = do_bench(lambda: graphs[b][0].replay()) # noqa
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eager_runtime = do_bench(
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lambda: model(input_ids[:b], positions[:b])) # noqa
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full_cudagraph_time[b] = runtime
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eager_time[b] = eager_runtime
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# print in tabular format
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print("batch size\teager mode\tfull cudagraph\tpiecewise cudagraph")
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for b in cudagraph_sizes:
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print((f"{b}\t{eager_time[b]:.3f}\t{full_cudagraph_time[b]:.3f}"
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f"\t{piecewise_cudagraph_time[b]:.3f}"))
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if __name__ == "__main__":
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benchmark()
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@@ -9,7 +9,7 @@ from .utils import TEST_MODELS, check_full_graph_support
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@pytest.mark.parametrize("model_info", TEST_MODELS)
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@pytest.mark.parametrize(
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"optimization_level",
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[CompilationLevel.DYNAMO_ONCE, CompilationLevel.INDUCTOR])
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[CompilationLevel.DYNAMO_ONCE, CompilationLevel.PIECEWISE])
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@fork_new_process_for_each_test
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def test_full_graph(model_info, optimization_level):
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model = model_info[0]
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@@ -9,17 +9,19 @@ from vllm.platforms import current_platform
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TEST_MODELS = [
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("facebook/opt-125m", {}),
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("nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change", {
|
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"dtype": torch.float16,
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"quantization": "compressed-tensors"
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}),
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# TODO: add fake implementation for compressed-tensors
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# ("nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change", {
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# "dtype": torch.float16,
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# "quantization": "compressed-tensors"
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# }),
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("neuralmagic/Meta-Llama-3-8B-Instruct-FP8", {
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"dtype": torch.float16,
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"quantization": "fp8"
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}),
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("nm-testing/Meta-Llama-3-8B-Instruct-W8A8-Dyn-Per-Token-2048-Samples", {
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"quantization": "compressed-tensors"
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}),
|
||||
# TODO: add fake implementation for compressed-tensors
|
||||
# ("nm-testing/Meta-Llama-3-8B-Instruct-W8A8-Dyn-Per-Token-2048-Samples", {
|
||||
# "quantization": "compressed-tensors"
|
||||
# }),
|
||||
("meta-llama/Meta-Llama-3-8B", {}),
|
||||
]
|
||||
|
||||
@@ -73,7 +75,7 @@ def check_full_graph_support(model,
|
||||
# much memory.
|
||||
quantization = model_kwargs.get("quantization")
|
||||
if ((quantization == "fp8" or model == "meta-llama/Meta-Llama-3-8B")
|
||||
and optimization_level >= CompilationLevel.INDUCTOR):
|
||||
and optimization_level >= CompilationLevel.PIECEWISE):
|
||||
return
|
||||
|
||||
prompts = [
|
||||
|
||||
Reference in New Issue
Block a user