pytorch · danielvegamyhre · May 12, 2025 · Feb 22, 2025 · Feb 23, 2025 · Feb 27, 2025
@@ -0,0 +1,130 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import torch
+
+if torch.cuda.is_available():
+    import pandas as pd
+    from tqdm import tqdm
+    from triton.testing import do_bench
+
+    from torchao.float8.float8_utils import compute_error
+    from torchao.prototype.blockwise_fp8.blockwise_quantization import (
+        blockwise_fp8_gemm,
+        fp8_blockwise_act_quant,
+        fp8_blockwise_weight_quant,
+    )
+    from torchao.utils import is_sm_at_least_89
+else:
+    raise RuntimeError("This benchmark is only avaible on CUDA hardware")
+
+
+def benchmark_microseconds(f, *args, warmup=25, rep=100):
+    return (
+        do_bench(lambda: f(*args), warmup=warmup, rep=rep, return_mode="median") * 1e3
+    )
+
+
+def get_blockwise_problem(
+    m: int, n: int, k: int, block_size: int, dtype: torch.dtype, device
+):
+    assert n % block_size == 0 and k % block_size == 0, (
+        "N and K dims must be divisible by block_size"
+    )
+    assert dtype in [
+        torch.float8_e4m3fn,
+        torch.float8_e5m2,
+    ], "dtype must be torch.float8_e4m3fn or torch.float8_e5m2"
+    dtype_max = torch.finfo(dtype).max
+    A = (dtype_max * (2 * torch.rand(m, k, device=device) - 1)).to(dtype)
+    A_scale = torch.randn((m, k // block_size), dtype=torch.half, device=device)
+    B = (dtype_max * (2 * torch.rand(n, k, device=device) - 1)).to(dtype)
+    B_scale = torch.randn(
+        (n // block_size, k // block_size), dtype=torch.half, device=device
+    )
+
+    return A, A_scale, B, B_scale
+
+
+def benchmark_latency(
+    m: int, k: int, n: int, block_size: int, dtype: torch.dtype, device
+):
+    A_ref = torch.randn((m, k), dtype=torch.half, device=device)
+    B_ref = torch.randn((n, k), dtype=torch.half, device=device)
+    fp16_time = benchmark_microseconds(torch.nn.functional.linear, A_ref, B_ref)
+
+    A, A_scale, B, B_scale = get_blockwise_problem(m, n, k, block_size, dtype, device)
+    blockwise_time = benchmark_microseconds(
+        blockwise_fp8_gemm, A, A_scale, B, B_scale, block_size
+    )
+
+    return {
+        "m": m,
+        "k": k,
+        "n": n,
+        "block_size": block_size,
+        "dtype": dtype,
+        "fp16_latency (ms)": fp16_time,
+        "blockwise_latency (ms)": blockwise_time,
+        "blockwise_speedup": fp16_time / blockwise_time,
+    }
+
+
+def benchmark_precision(
+    m: int, k: int, n: int, block_size: int, dtype: torch.dtype, device
+):
+    lin = torch.nn.Linear(k, n, False, device, torch.half)
+    A = torch.randn((m, k), dtype=torch.half, device=device)
+    W = lin.weight
+    output = A @ W.T
+
+    A_q, A_s = fp8_blockwise_act_quant(A, block_size, dtype)
+    W_q, W_s = fp8_blockwise_weight_quant(W, block_size, dtype)
+    output_blockwise = blockwise_fp8_gemm(A_q, A_s, W_q, W_s, block_size)
+
+    return {
+        "m": m,
+        "k": k,
+        "n": n,
+        "block_size": block_size,
+        "dtype": dtype,
+        "error_blockwise (dB)": compute_error(output, output_blockwise),
+    }
+
+
+if __name__ == "__main__" and torch.cuda.is_available():
+    device = torch.device("cuda")
+    k_vals = (8192, 8192, 8192, 28672)
+    n_vals = (8192, 10240, 57344, 8192)
+    block_size_vals = (128, 128, 128, 128)
+
+    latency_results = []
+    precision_results = []
+
+    available_dtypes = (
+        [torch.float8_e4m3fn, torch.float8_e5m2]
+        if is_sm_at_least_89()
+        else [torch.float8_e5m2]
+    )
+    for m in tqdm([1 << i for i in range(14)]):
+        for dtype in available_dtypes:
+            for n, k, block_size in zip(n_vals, k_vals, block_size_vals):
+                latency_results.append(
+                    benchmark_latency(m, k, n, block_size, dtype, device)
+                )
+                precision_results.append(
+                    benchmark_precision(m, k, n, block_size, dtype, device)
+                )
+
+    df_latency = pd.DataFrame(latency_results)
+    df_precision = pd.DataFrame(precision_results)
+
+    df_latency.to_csv("blockwise_triton_latency_results.csv", index=False)
+    df_precision.to_csv("blockwise_triton_precision_results.csv", index=False)
+
+    print(df_latency.to_markdown(index=False))
+    print(df_precision.to_markdown(index=False))
@@ -0,0 +1,72 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+
+import pytest
+import torch
+
+from packaging import version
+
+triton = pytest.importorskip("triton", reason="Triton required to run this test")
+
+from torchao.prototype.blockwise_fp8.blockwise_quantization import (
+    blockwise_fp8_gemm,
+    fp8_blockwise_act_quant,
+    fp8_blockwise_weight_dequant,
+    fp8_blockwise_weight_quant,
+)
+from torchao.utils import is_sm_at_least_89
+
+BLOCKWISE_SIZE_MNK = [
+    (2, 512, 128),
+    (3, 2048, 2048),
+    (4, 3584, 640),
+    (13, 8704, 8576),
+    (26, 18944, 1664),
+    (67, 6656, 1408),
+]
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.parametrize("_, N, K", BLOCKWISE_SIZE_MNK)
+@pytest.mark.parametrize(
+    "dtype",
+    [torch.float8_e4m3fn, torch.float8_e5m2]
+    if is_sm_at_least_89()
+    else [torch.float8_e5m2],
+)
+def test_blockwise_quant_dequant(_, N, K, dtype):
+    x = torch.randn(N, K).cuda()
+    qx, s = fp8_blockwise_weight_quant(x, dtype=dtype)
+    x_reconstructed = fp8_blockwise_weight_dequant(qx, s)
+    error = torch.norm(x - x_reconstructed) / torch.norm(x)
+    print(f"Relative Error: {error.item():.6f}")
+
+    assert error < 0.1, "Quant-Dequant error is too high"
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(
+    version.parse(triton.__version__) < version.parse("3.3.0"),
+    reason="Triton version < 3.3.0, test skipped",
+)
+@pytest.mark.parametrize("M, N, K", BLOCKWISE_SIZE_MNK)
+@pytest.mark.parametrize(
+    "dtype",
+    [torch.float8_e4m3fn, torch.float8_e5m2]
+    if is_sm_at_least_89()
+    else [torch.float8_e5m2],
+)
+def test_blockwise_fp8_gemm(M, N, K, dtype):
+    A = torch.randn(M, K).cuda()
+    B = torch.randn(N, K).cuda()
+    C = A @ B.T
+    A_q, A_s = fp8_blockwise_act_quant(A, dtype=dtype)
+    B_q, B_s = fp8_blockwise_weight_quant(B, dtype=dtype)
+    C_q = blockwise_fp8_gemm(A_q, A_s, B_q, B_s)
+    error = torch.norm(C - C_q) / torch.norm(C)
+    print(f"Relative Error: {error.item():.6f}")
+
+    assert error < 0.1, "Quantize gemm error is too high"
@@ -0,0 +1,29 @@
+# Blockwise Quantization Implementation
+
+## Overview
+
+This directory contains the implementation of blockwise quantization introduced by DeepSeek. The method involves quantizing activations and weight matrices in blocks of 128x1 and 128x128, respectively.
+
+## Quantization Process
+
+### Activation Quantization
+- Activations are quantized in blocks of size 128x1 using the FP8 format
+
+### Weight Matrix Quantization
+- Weights are quantized in blocks of size 128x128 using the FP8 format
+
+## Kernel Implementation in Triton
+
+- The kernel for blockwise quantization is implemented using Triton
+- For now, the only supported types are: torch.float8_e4m3fn and torch.float8_e5m2
+
+## Illustration
+
+![Blockwise Quantization Illustration](https://arxiv.org/html/2412.19437v1/x7.png)
+
+*Illustration of the blockwise quantization process.*
+
+## Original Paper
+
+For detailed motivations and technical specifications, please refer to the original paper:
+- [DeepSeek Blockwise Quantization Paper](https://arxiv.org/html/2412.19437v1)
@@ -0,0 +1,15 @@
+from .blockwise_linear import BlockwiseQuantLinear
+from .blockwise_quantization import (
+    blockwise_fp8_gemm,
+    fp8_blockwise_act_quant,
+    fp8_blockwise_weight_dequant,
+    fp8_blockwise_weight_quant,
+)
+
+__all__ = [
+    "blockwise_fp8_gemm",
+    "BlockwiseQuantLinear",
+    "fp8_blockwise_act_quant",
+    "fp8_blockwise_weight_quant",
+    "fp8_blockwise_weight_dequant",
+]
@@ -0,0 +1,77 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import nn
+
+from torchao.prototype.blockwise_fp8.blockwise_quantization import (
+    blockwise_fp8_gemm,
+    fp8_blockwise_act_quant,
+)
+
+
+class BlockwiseQuantLinear(nn.Module):
+    """
+    Custom linear layer with support for quantized weights and optional bias.
+
+    Args:
+        in_features (int): Number of input features.
+        out_features (int): Number of output features.
+        bias (bool): Whether to include a bias term. Defaults to False.
+        block_size (int): Block size for quantization. Defaults to 128.
+        dtype (torch.dtype): Data type for the weights. Defaults to torch.float8_e4m3fn.
+    """
+
+    dtype = torch.bfloat16
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = False,
+        block_size: int = 128,
+        dtype: torch.dtype = torch.float8_e4m3fn,
+    ):
+        super().__init__()
+        supported_dtypes = [
+            torch.float8_e4m3fn,
+            torch.float8_e5m2,
+        ]
+        assert dtype in supported_dtypes, (
+            f"Unsupported dtype: {dtype}. Supported dtypes: {supported_dtypes}"
+        )
+        scale_in_features = (in_features + block_size - 1) // block_size
+        scale_out_features = (out_features + block_size - 1) // block_size
+        self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype))
+        self.weight.scale = self.scale = nn.Parameter(
+            torch.empty(scale_out_features, scale_in_features, dtype=torch.float32)
+        )
+        self.block_size = block_size
+        self.dtype
+
+        if bias:
+            self.bias = nn.Parameter(torch.empty(out_features))
+        else:
+            self.register_parameter("bias", None)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the custom linear layer.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Transformed tensor after linear computation.
+        """
+        x, scale = fp8_blockwise_act_quant(x, self.block_size, self.dtype)
+        y = blockwise_fp8_gemm(
+            x, scale, self.weight, self.weight.scale, self.block_size
+        )
+
+        if self.bias is not None:
+            y += self.bias
+        return y