FP6 quantization end-to-end. (microsoft#5234)

The user interface: microsoft/DeepSpeed-MII#433
nv-a6000 ci running against the MII branch linked above is
[here](https://github.com/microsoft/DeepSpeed/actions/runs/8192124606)

Co-authored-by: Zhen Zheng
[[email protected]](mailto:[email protected])
Co-authored-by: Shiyang Chen [[email protected]](mailto:[email protected])
Co-authored-by: Arash Bakhtiari
[[email protected]](mailto:[email protected])
Co-authored-by: Haojun Xia
[[email protected]](mailto:[email protected])

---------

Co-authored-by: ZHENG, Zhen <[email protected]>
Co-authored-by: Shiyang Chen <[email protected]>
Co-authored-by: Haojun Xia <[email protected]>
Co-authored-by: Arash Bakhtiari <[email protected]>
Co-authored-by: Michael Wyatt <[email protected]>
Co-authored-by: Michael Wyatt <[email protected]>

.github/workflows/nv-a6000.yml

@@ -47,7 +47,8 @@ jobs:
       - name: Install deepspeed
         run: |
           python -m pip install docutils==0.18.1 jinja2==3.0 urllib3==1.26.11 ninja
-          python -m pip install .[dev,1bit,autotuning]
+          python -m pip install pydantic==1.10.11
+          python -m pip install .[dev,1bit,autotuning,inf]
           ds_report
       - name: Python environment
         run: |

deepspeed/inference/v2/config_v2.py

@@ -3,8 +3,8 @@
 
 # DeepSpeed Team
 
+from typing import Optional
 from deepspeed.pydantic_v1 import Field
-
 from deepspeed.runtime.config_utils import DeepSpeedConfigModel
 from .ragged import DSStateManagerConfig
 
@@ -16,6 +16,16 @@ class DeepSpeedTPConfig(DeepSpeedConfigModel):
     """ Number of devices to split the model across using tensor parallelism. """
 
 
+class QuantizationConfig(DeepSpeedConfigModel):
+    """ Configure tensor parallelism settings """
+
+    quantization_mode: Optional[str] = None
+    """ The quantization mode in string format. The supported modes are as follows:
+        - 'wf6af16', weight-only quantization with FP6 weight and FP16 activation.
+    """
+    # TODO: may reuse the constants in deepspeed/compression/constants.py
+
+
 class RaggedInferenceEngineConfig(DeepSpeedConfigModel):
     """ Sets parameters for DeepSpeed Inference Engine. """
 
@@ -29,3 +39,5 @@ class RaggedInferenceEngineConfig(DeepSpeedConfigModel):
     """
     Configuration for managing persistent state
     """
+
+    quantization: QuantizationConfig = {}

deepspeed/inference/v2/kernels/core_ops/__init__.py

@@ -8,3 +8,4 @@
 from .cuda_layer_norm import *
 from .cuda_rms_norm import *
 from .gated_activations import *
+from .cuda_linear import *

deepspeed/inference/v2/kernels/core_ops/core_ops.cpp

@@ -8,6 +8,7 @@
 
 #include "bias_activation.h"
 #include "blas.h"
+#include "cuda_linear_kernels.h"
 #include "gated_activation_kernels.h"
 #include "layer_norm.h"
 #include "rms_norm.h"
@@ -33,4 +34,9 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
     // rms_norm.h
     m.def("rms_norm", &rms_norm, "DeepSpeed rms norm in CUDA");
     m.def("rms_pre_norm", &rms_pre_norm, "DeepSpeed rms pre norm in CUDA");
+
+    // cuda_linear_kernels.h
+    m.def("cuda_wf6af16_linear", &cuda_wf6af16_linear, "DeepSpeed Wf6Af16 linear in CUDA");
+    m.def(
+        "preprocess_weight", &preprocess_weight, "preprocess the FP16 weight to be 2bit and 4 bit");
 }

deepspeed/inference/v2/kernels/core_ops/cuda_linear/__init__.py

@@ -0,0 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .cuda_linear import *

deepspeed/inference/v2/kernels/core_ops/cuda_linear/cuda_linear.py

@@ -0,0 +1,207 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import torch
+
+from ....inference_utils import DtypeEnum
+from ....logging import inference_logger
+from deepspeed.ops.op_builder import InferenceCoreBuilder
+from ... import DSKernelBase
+
+
+class CUDAWf6Af16Linear(DSKernelBase):
+    """
+    Wrapper around the CUDA kernel of Wf6Af16 quantized linear.
+
+    Performs z = x @ y
+    """
+    supported_dtypes = [DtypeEnum.fp16]
+
+    def __init__(self):
+        self.inf_module = InferenceCoreBuilder().load()
+        self.inf_module.create_handle()
+        self.kernel = self.inf_module.cuda_wf6af16_linear
+        # The split_k_map is profiled on A100-80G GPU for some common shapes.
+        # It is an array of dictionaries, where the array index is the tokens chunk id.
+        # The dictionary is the mapping from the output channel to the split-K size.
+        self.split_k_map = [
+            {  # tokens: [1, 64]
+                3072: 18,
+                4096: 13,
+                5120: 10,
+                6144: 9,
+                8192: 6,
+                10240: 5,
+                14336: 7,
+                28672: 7,
+                57344: 7
+            },
+            {  # tokens: [65:128]
+                3072: 9,
+                4096: 6,
+                5120: 5,
+                6144: 9,
+                8192: 3,
+                10240: 5,
+                14336: 7,
+                28672: 7,
+                57344: 6
+            },
+            {  # tokens: [129:192]
+                3072: 6,
+                4096: 4,
+                5120: 7,
+                6144: 3,
+                8192: 2,
+                10240: 5,
+                14336: 5,
+                28672: 5,
+                57344: 4
+            },
+            {  # tokens: [193:256]
+                3072: 9,
+                4096: 3,
+                5120: 5,
+                6144: 2,
+                8192: 5,
+                10240: 4,
+                14336: 8,
+                28672: 6,
+                57344: 4
+            },
+            {  # tokens: [257:320]
+                3072: 7,
+                4096: 5,
+                5120: 2,
+                6144: 5,
+                8192: 4,
+                10240: 1,
+                14336: 3,
+                28672: 3,
+                57344: 4
+            },
+            {  # tokens: [321:384]
+                3072: 3,
+                4096: 2,
+                5120: 5,
+                6144: 3,
+                8192: 1,
+                10240: 8,
+                14336: 3,
+                28672: 4,
+                57344: 3
+            },
+            {  # tokens: [385:448]
+                3072: 5,
+                4096: 7,
+                5120: 3,
+                6144: 5,
+                8192: 7,
+                10240: 3,
+                14336: 1,
+                28672: 1,
+                57344: 3
+            },
+            {  # tokens: [449:512]
+                3072: 2,
+                4096: 5,
+                5120: 4,
+                6144: 1,
+                8192: 5,
+                10240: 2,
+                14336: 6,
+                28672: 4,
+                57344: 1
+            },
+            {  # tokens: [513:576]
+                3072: 2,
+                4096: 3,
+                5120: 1,
+                6144: 1,
+                8192: 3,
+                10240: 3,
+                14336: 3,
+                28672: 1,
+                57344: 1
+            },
+            {  # tokens: [577:640]
+                3072: 5,
+                4096: 4,
+                5120: 1,
+                6144: 4,
+                8192: 2,
+                10240: 1,
+                14336: 1,
+                28672: 1,
+                57344: 1
+            },
+            {  # tokens: [641:704]
+                3072: 3,
+                4096: 1,
+                5120: 2,
+                6144: 2,
+                8192: 1,
+                10240: 2,
+                14336: 1,
+                28672: 1,
+                57344: 1
+            },
+            {  # tokens: [705:768]
+                3072: 3,
+                4096: 1,
+                5120: 3,
+                6144: 2,
+                8192: 1,
+                10240: 1,
+                14336: 1,
+                28672: 1,
+                57344: 1
+            }
+        ]
+
+    def __call__(self, output: torch.Tensor, hidden_states: torch.Tensor, weights_2bit: torch.Tensor,
+                 weights_4bit: torch.Tensor, scale: torch.Tensor, out_channels, tokens, in_channels) -> torch.Tensor:
+        """
+        Matmul kernel of FP6 weight-only quantized linear. All inputs should be contiguous.
+        It does not support batched-matmul.
+
+        Parameters:
+            output (torch.Tensor): Output tensor. Shape is of [token_number, out_features]
+            hidden_states (torch.Tensor): Input tensor. Shape is of [token_number, in_features]
+            weights_2bit (torch.Tensor): Input tensor of the 2-bit slice. Shape is of [out_features*2/8, in_features]
+            weights_4bit (torch.Tensor): Input tensor of the 4-bit slice. Shape is of [out_features*4/8, in_features]
+            scale (torch.Tensor): Input tensor. Shape is of [out_features], since the scale is per output channel
+            out_channels (int): The number of output channels
+            tokens (int): The number of tokens
+            in_channels (int): The number of input channels
+        """
+
+        if out_channels % 256 != 0 or in_channels % 64 != 0:
+            raise ValueError("The out and in channel should be multiple of 256 and 64 respectively.")
+
+        # TODO: add a more general heuristic to determine the split-K.
+        split_k = -1  # not initialized
+        if tokens <= 768:
+            # Try to find the split-K from the pre-profiled map.
+            tokens_chunk_id = (tokens - 1) // 64
+            split_k = self.split_k_map[tokens_chunk_id].get(out_channels, -1)
+        if split_k == -1:
+            split_k = 1
+            inference_logger().warning(
+                f"The split-K setting may be suboptimal for shape {tokens}x{in_channels}x{out_channels}...")
+
+        workspace = self.get_workspace(out_channels, tokens, in_channels, split_k, torch.float, hidden_states.device)
+        self.kernel(output, hidden_states, weights_2bit, weights_4bit, scale, workspace, out_channels, tokens,
+                    in_channels, split_k)
+
+    def get_workspace(self, out_channels: int, tokens: int, in_channels: int, split_k: int, dtype,
+                      device) -> torch.Tensor:
+        """
+        Allocate workspace for the kernel. The workspace is used to store the intermediate results of the matmul before
+        split-K. The split-K size is determined by the size of the matmul.
+        """
+        workspace = torch.empty((split_k, out_channels, tokens), dtype=dtype, device=device)
+
+        return workspace