[TPU][Quantization] TPU W8A8 (#11785)

Co-authored-by: Woosuk Kwon <[email protected]>

Author: robertgshaw2-redhat

.buildkite/run-tpu-test.sh

@@ -14,4 +14,13 @@ remove_docker_container
 # For HF_TOKEN.
 source /etc/environment
 # Run a simple end-to-end example.
-docker run --privileged --net host --shm-size=16G -it -e "HF_TOKEN=$HF_TOKEN" --name tpu-test vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git && python3 -m pip install pytest && python3 -m pip install lm_eval[api]==0.4.4 && pytest -v -s /workspace/vllm/tests/entrypoints/openai/test_accuracy.py && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py && python3 /workspace/vllm/tests/tpu/test_compilation.py && python3 /workspace/vllm/examples/offline_inference/offline_inference_tpu.py"
+docker run --privileged --net host --shm-size=16G -it \
+    -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
+    vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
+    && python3 -m pip install pytest \
+    && python3 -m pip install lm_eval[api]==0.4.4 \
+    && pytest -v -s /workspace/vllm/tests/entrypoints/openai/test_accuracy.py \
+    && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
+    && python3 /workspace/vllm/tests/tpu/test_compilation.py \
+    && python3 /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
+    && python3 /workspace/vllm/examples/offline_inference/offline_inference_tpu.py"

tests/tpu/test_quantization_accuracy.py

@@ -0,0 +1,49 @@
+from dataclasses import dataclass
+
+import lm_eval
+import pytest
+
+TASK = "gsm8k"
+FILTER = "exact_match,strict-match"
+RTOL = 0.03
+
+
+@dataclass
+class GSM8KAccuracyTestConfig:
+    model_name: str
+    excepted_value: float
+
+    def get_model_args(self) -> str:
+        return (f"pretrained={self.model_name},"
+                "max_model_len=4096,max_num_seqs=32")
+
+
+# NOTE: Accuracy scores measured on GPUs.
+ACCURACY_CONFIGS = [
+    GSM8KAccuracyTestConfig(
+        model_name="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        excepted_value=0.76),  # no bias
+    # NOTE(rob): We cannot re-initialize VLLM in the same process for TPU,
+    # so only one of these tests can run in a single call to pytest. As
+    # a follow up, move this into the LM-EVAL section of the CI.
+    # GSM8KAccuracyTestConfig(
+    #     model_name="neuralmagic/Qwen2-7B-Instruct-quantized.w8a8",
+    #     excepted_value=0.66),  # bias in QKV layers
+]
+
+
+@pytest.mark.parametrize("config", ACCURACY_CONFIGS)
+def test_gsm8k_correctness(config: GSM8KAccuracyTestConfig):
+
+    results = lm_eval.simple_evaluate(
+        model="vllm",
+        model_args=config.get_model_args(),
+        tasks="gsm8k",
+        batch_size="auto",
+    )
+
+    EXPECTED_VALUE = config.excepted_value
+    measured_value = results["results"][TASK][FILTER]
+    assert (measured_value - RTOL < EXPECTED_VALUE
+            and measured_value + RTOL > EXPECTED_VALUE
+            ), f"Expected: {EXPECTED_VALUE} |  Measured: {measured_value}"

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -1,14 +1,13 @@
-from typing import Callable, List, Optional
+from typing import Callable, List, Optional, Set
 
 import torch
 from compressed_tensors.quantization import QuantizationStrategy
-from torch.nn import Parameter
 
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
-from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
-    apply_int8_linear, convert_to_channelwise)
+from vllm.model_executor.layers.quantization.kernels.scaled_mm import (
+    ScaledMMLinearLayerConfig, choose_scaled_mm_linear_kernel)
 from vllm.model_executor.parameter import (BasevLLMParameter,
                                            ChannelQuantScaleParameter,
                                            ModelWeightParameter,
@@ -18,6 +17,7 @@
 
 
 class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
+    _kernel_backends_being_used: Set[str] = set()
 
     def __init__(self, strategy: str, is_static_input_scheme: bool,
                  input_symmetric: bool):
@@ -30,74 +30,25 @@ def get_min_capability(cls) -> int:
         # turing and up
         return 75
 
-    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
-        # WEIGHT
-        # Cutlass kernels need transposed weight.
-        weight = layer.weight
-        layer.weight = Parameter(weight.t(), requires_grad=False)
-
-        # WEIGHT SCALE
-        # Cutlass kernels support only per-tensor and per-channel.
-        # If we have a fused module (QKV, MLP) with per tensor scales (thus N
-        # scales being passed to the kernel), convert to the per-channel case.
-        is_fused_module = len(self.logical_widths) > 1
-        if is_fused_module and self.strategy == QuantizationStrategy.TENSOR:
-            ws_channelwise = convert_to_channelwise(layer.weight_scale,
-                                                    self.logical_widths)
-            layer.weight_scale = Parameter(ws_channelwise, requires_grad=False)
-        else:
-            layer.weight_scale = Parameter(layer.weight_scale.data,
-                                           requires_grad=False)
-        # INPUT SCALE
-        if self.is_static_input_scheme:
-            if self.input_symmetric:
-                layer.input_scale = Parameter(layer.input_scale.max(),
-                                              requires_grad=False)
-                layer.input_zero_point = None
-            else:
-                # reconstruct the ranges
-                int8_traits = torch.iinfo(torch.int8)
-                azps = layer.input_zero_point.to(dtype=torch.int32)
-                range_max = (layer.input_scale *
-                             (int8_traits.max - azps)).max()
-                range_min = (layer.input_scale *
-                             (int8_traits.min - azps)).min()
-
-                scale = (range_max - range_min) / (int8_traits.max -
-                                                   int8_traits.min)
-                layer.input_scale = Parameter(scale, requires_grad=False)
-
-                # AZP loaded as int8 but used as int32
-                azp = (int8_traits.min -
-                       range_min / scale).to(dtype=torch.int32)
-                layer.input_zero_point = Parameter(azp, requires_grad=False)
-
-        else:
-            layer.input_scale = None
-            layer.input_zero_point = None
-
-        # azp_adj is the AZP adjustment term, used to account for weights.
-        # It does not depend on scales or azp, so it is the same for
-        # static and dynamic quantization.
-        # For more details, see csrc/quantization/cutlass_w8a8/Epilogues.md
-        # https://github.com/vllm-project/vllm/blob/8d59dbb00044a588cab96bcdc028006ed922eb06/csrc/quantization/cutlass_w8a8/Epilogues.md
-        if not self.input_symmetric:
-            azp_adj = layer.weight.sum(dim=0, keepdim=True, dtype=torch.int32)
-            if self.is_static_input_scheme:
-                # cutlass_w8a8 requires azp to be folded into azp_adj
-                #  in the per-tensor case
-                azp_adj = layer.input_zero_point * azp_adj
-
-            layer.azp_adj = azp_adj
-        else:
-            layer.azp_adj = None
-
     def create_weights(self, layer: torch.nn.Module,
                        output_partition_sizes: List[int],
                        input_size_per_partition: int,
                        params_dtype: torch.dtype, weight_loader: Callable,
                        **kwargs):
-        self.logical_widths = output_partition_sizes
+        layer.logical_widths = output_partition_sizes
+
+        scaled_mm_linear_kernel_config = ScaledMMLinearLayerConfig(
+            is_channelwise=(self.strategy == QuantizationStrategy.CHANNEL),
+            is_static_input_scheme=self.is_static_input_scheme,
+            input_symmetric=self.input_symmetric)
+
+        kernel_type = choose_scaled_mm_linear_kernel(
+            scaled_mm_linear_kernel_config)
+
+        if kernel_type.__name__ not in self._kernel_backends_being_used:
+            logger.info("Using %s for CompressedTensorsW8A8Int8",
+                        kernel_type.__name__)
+            self._kernel_backends_being_used.add(kernel_type.__name__)
 
         # WEIGHT
         weight = ModelWeightParameter(data=torch.empty(
@@ -140,12 +91,18 @@ def create_weights(self, layer: torch.nn.Module,
                     weight_loader=weight_loader)
                 layer.register_parameter("input_zero_point", input_zero_point)
 
+        self.kernel = kernel_type(c=scaled_mm_linear_kernel_config,
+                                  w_q_param_name="weight",
+                                  w_s_param_name="weight_scale",
+                                  i_s_param_name="input_scale",
+                                  i_zp_param_name="input_zero_point",
+                                  azp_adj_param_name="azp_adj")
+
+    # Checkpoints are serialized in compressed-tensors format, which is
+    # different from the format the kernel may want. Handle repacking here.
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        self.kernel.process_weights_after_loading(layer)
+
     def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
                       bias: Optional[torch.Tensor]) -> torch.Tensor:
-        return apply_int8_linear(input=x,
-                                 weight=layer.weight,
-                                 weight_scale=layer.weight_scale,
-                                 input_scale=layer.input_scale,
-                                 input_zero_point=layer.input_zero_point,
-                                 azp_adj=layer.azp_adj,
-                                 bias=bias)
+        return self.kernel.apply_weights(layer, x, bias)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py

@@ -6,7 +6,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
-from vllm.model_executor.layers.quantization.kernels import (
+from vllm.model_executor.layers.quantization.kernels.mixed_precision import (
     MPLinearLayerConfig, choose_mp_linear_kernel)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     marlin_repeat_scales_on_all_ranks)

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -11,7 +11,7 @@
                                                set_weight_attrs)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
-from vllm.model_executor.layers.quantization.kernels import (
+from vllm.model_executor.layers.quantization.kernels.mixed_precision import (
     MPLinearLayerConfig, choose_mp_linear_kernel)
 from vllm.model_executor.layers.quantization.utils import replace_parameter
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (

vllm/model_executor/layers/quantization/kernels/__init__.py

@@ -1,74 +0,0 @@
-from typing import List, Optional, Type
-
-import vllm.envs as envs
-from vllm.model_executor.layers.quantization.kernels.exllama import (
-    ExllamaLinearKernel)
-from vllm.model_executor.layers.quantization.kernels.machete import (
-    MacheteLinearKernel)
-from vllm.model_executor.layers.quantization.kernels.marlin import (
-    MarlinLinearKernel)
-from vllm.model_executor.layers.quantization.kernels.MPLinearKernel import (
-    MPLinearKernel, MPLinearLayerConfig)
-from vllm.platforms import current_platform
-
-# in priority/performance order (when available)
-_POSSIBLE_KERNELS: List[Type[MPLinearKernel]] = [
-    MacheteLinearKernel,
-    MarlinLinearKernel,
-    ExllamaLinearKernel,
-]
-
-
-def choose_mp_linear_kernel(
-        config: MPLinearLayerConfig,
-        compute_capability: Optional[int] = None) -> Type[MPLinearKernel]:
-    """
-    Choose an MPLinearKernel that can implement the given config for the given
-     compute capability. Attempts to choose the best kernel in terms of 
-     performance.
-
-    Args:
-        config (MPLinearLayerConfig): Description of the linear layer to be 
-          implemented.
-        compute_capability (Optional[int], optional): The compute capability of
-          the target device, if None uses `current_platform` to get the compute 
-          capability. Defaults to None.
-
-    Raises:
-        ValueError: If no kernel can implement the given config.
-
-    Returns:
-        Type[MPLinearKernel]: Chosen kernel.
-    """
-    if compute_capability is None:
-        if current_platform is None:
-            raise ValueError("Cannot determine compute capability")
-        _cc = current_platform.get_device_capability()
-        compute_capability = _cc[0] * 10 + _cc[1]
-
-    failure_reasons = []
-    for kernel in _POSSIBLE_KERNELS:
-        if kernel.__name__ in envs.VLLM_DISABLED_KERNELS:
-            failure_reasons.append(
-                f' {kernel.__name__} disabled by environment variable')
-            continue
-
-        if kernel.get_min_capability() > compute_capability:
-            failure_reasons.append(
-                f"{kernel.__name__} requires capability "
-                f"{kernel.get_min_capability()}, current compute capability "
-                f"is {compute_capability}")
-            continue
-
-        can_implement, failure_reason = kernel.can_implement(config)
-        if can_implement:
-            return kernel
-        else:
-            failure_reasons.append(
-                f' {kernel.__name__} cannot implement due to: {failure_reason}'
-            )
-
-    raise ValueError(
-        "Failed to find a kernel that can implement the "\
-        "WNA16 linear layer. Reasons: \n"
-        + '\n'.join(failure_reasons))

vllm/model_executor/layers/quantization/kernels/MPLinearKernel.py renamed to vllm/model_executor/layers/quantization/kernels/mixed_precision/MPLinearKernel.py

@@ -0,0 +1,74 @@
+from typing import List, Optional, Type
+
+import vllm.envs as envs
+from vllm.model_executor.layers.quantization.kernels.mixed_precision.exllama import (  # noqa: E501
+    ExllamaLinearKernel)
+from vllm.model_executor.layers.quantization.kernels.mixed_precision.machete import (  # noqa: E501
+    MacheteLinearKernel)
+from vllm.model_executor.layers.quantization.kernels.mixed_precision.marlin import (  # noqa: E501
+    MarlinLinearKernel)
+from vllm.model_executor.layers.quantization.kernels.mixed_precision.MPLinearKernel import (  # noqa: E501
+    MPLinearKernel, MPLinearLayerConfig)
+from vllm.platforms import current_platform
+
+# in priority/performance order (when available)
+_POSSIBLE_KERNELS: List[Type[MPLinearKernel]] = [
+    MacheteLinearKernel,
+    MarlinLinearKernel,
+    ExllamaLinearKernel,
+]
+
+
+def choose_mp_linear_kernel(
+        config: MPLinearLayerConfig,
+        compute_capability: Optional[int] = None) -> Type[MPLinearKernel]:
+    """
+    Choose an MPLinearKernel that can implement the given config for the given
+     compute capability. Attempts to choose the best kernel in terms of 
+     performance.
+
+    Args:
+        config (MPLinearLayerConfig): Description of the linear layer to be 
+          implemented.
+        compute_capability (Optional[int], optional): The compute capability of
+          the target device, if None uses `current_platform` to get the compute 
+          capability. Defaults to None.
+
+    Raises:
+        ValueError: If no kernel can implement the given config.
+
+    Returns:
+        Type[MPLinearKernel]: Chosen kernel.
+    """
+    if compute_capability is None:
+        if current_platform is None:
+            raise ValueError("Cannot determine compute capability")
+        _cc = current_platform.get_device_capability()
+        compute_capability = _cc[0] * 10 + _cc[1]
+
+    failure_reasons = []
+    for kernel in _POSSIBLE_KERNELS:
+        if kernel.__name__ in envs.VLLM_DISABLED_KERNELS:
+            failure_reasons.append(
+                f' {kernel.__name__} disabled by environment variable')
+            continue
+
+        if kernel.get_min_capability() > compute_capability:
+            failure_reasons.append(
+                f"{kernel.__name__} requires capability "
+                f"{kernel.get_min_capability()}, current compute capability "
+                f"is {compute_capability}")
+            continue
+
+        can_implement, failure_reason = kernel.can_implement(config)
+        if can_implement:
+            return kernel
+        else:
+            failure_reasons.append(
+                f' {kernel.__name__} cannot implement due to: {failure_reason}'
+            )
+
+    raise ValueError(
+        "Failed to find a kernel that can implement the "\
+        "WNA16 linear layer. Reasons: \n"
+        + '\n'.join(failure_reasons))