Add fallback kernel and interface for rhs only quantized matmul

Summary: as the title

Reviewed By: metascroy

Differential Revision: D71370602

Author: kimishpatel

torchao/experimental/kernels/cpu/fallback/matmul/fp32_a_channelwise_8bit_b_fp32_c.h

@@ -0,0 +1,50 @@
+// Copyright (c) Meta Platforms, Inc. and affiliates.
+// All rights reserved.
+//
+// This source code is licensed under the license found in the
+// LICENSE file in the root directory of this source tree.
+
+#pragma once
+
+#include <cassert>
+
+// TODO: Remove all ::kernels. No need for extra namespace.
+namespace torchao::kernels::cpu::fallback::quantized_matmul {
+namespace fp32_a_input_channelwise_8bit_b_fp32 {
+template <bool b_has_zeros, bool a_transposed, bool b_transposed>
+void kernel(
+    int m,
+    int n,
+    int k,
+    const float* lhs,
+    int lhs_stride_m,
+    const int8_t* rhs,
+    int rhs_stride_n,
+    float* output,
+    int out_stride_m,
+    const int8_t* rhs_zero_points,
+    const float* rhs_scales,
+    const float beta,
+    const int rhs_qparams_stride) {
+  assert(a_transposed == false);
+  for (int m_idx = 0; m_idx < m; m_idx++) {
+    for (int n_idx = 0; n_idx < n; n_idx++) {
+      float res = 0.0;
+      for (int k_idx = 0; k_idx < k; k_idx++) {
+        int lhs_idx = m_idx * lhs_stride_m + k_idx;
+        int rhs_idx = k_idx * rhs_stride_n + n_idx;
+        if (b_transposed) {
+          rhs_idx = n_idx * rhs_stride_n + k_idx;
+        }
+        float rhs_dequant = rhs_scales[k_idx * rhs_qparams_stride] *
+            (static_cast<int16_t>(rhs[rhs_idx]) -
+             static_cast<int16_t>(rhs_zero_points[k_idx * rhs_qparams_stride]));
+
+        res += lhs[lhs_idx] * rhs_dequant;
+      }
+      output[m_idx * n + n_idx] = output[m_idx * n + n_idx] * beta + res;
+    }
+  }
+}
+} // namespace fp32_a_input_channelwise_8bit_b_fp32
+} // namespace torchao::kernels::cpu::fallback::quantized_matmul

torchao/experimental/kernels/cpu/interface/quantized_matmul.h

@@ -9,6 +9,8 @@
 #include <cassert>
 
 #include <torchao/experimental/kernels/cpu/fallback/matmul/channelwise_8bit_a_channelwise_8bit_b.h>
+#include <torchao/experimental/kernels/cpu/fallback/matmul/fp32_a_channelwise_8bit_b_fp32_c.h>
+
 #if defined(__aarch64__) || defined(__ARM_NEON)
 #include <torchao/experimental/kernels/cpu/aarch64/matmul/channelwise_8bit_a_channelwise_8bit_b_1x16x16_f32_smlal-impl.h>
 #include <torchao/experimental/kernels/cpu/aarch64/matmul/channelwise_8bit_a_channelwise_8bit_b_1x8x16_f32_neondot-impl.h>
@@ -85,4 +87,72 @@ get_int8_a_int8_b_channelwise_qmatmul(
         channelwise_8bit_a_channelwise_8bit_b::kernel<true, true, false, false>;
   }
 }
+
+/*
+a_stride_m: stride of a in memory to indiciate how far apart each row is.
+b_stride_n: stride of b in memory to indiciate how far apart each row is.
+If b is transposed (n x k), then this is how many bytes to skip to get to the
+next row. If b is not transposed (k x n), then this is how many bytes to skip to
+get to the next row.
+
+It also returns the stride of a and b, that should be used in the kernel.
+
+Will need to think of a better way to find the right
+ukernel. Perhaps via ukernelconfig + registry?.
+*/
+using fp32_a_input_channelwise_8bit_b_f32_c_matmul_type = void (*)(
+    int,
+    int,
+    int,
+    const float*,
+    int,
+    const int8_t*,
+    int,
+    float*,
+    int,
+    const int8_t*,
+    const float*,
+    const float,
+    const int);
+
+fp32_a_input_channelwise_8bit_b_f32_c_matmul_type
+get_fp32_a_input_channelwise_8bit_b_f32_c_matmul(
+    int m,
+    int n,
+    int k,
+    bool a_transposed,
+    bool b_transposed,
+    int& a_stride_m,
+    int& b_stride_n);
+
+fp32_a_input_channelwise_8bit_b_f32_c_matmul_type
+get_fp32_a_input_channelwise_8bit_b_f32_c_matmul(
+    int m,
+    int n,
+    int k,
+    bool a_transposed,
+    bool b_transposed,
+    int& a_stride_m,
+    int& b_stride_n) {
+#if defined(__aarch64__) || defined(__ARM_NEON)
+  if (!a_transposed && !b_transposed && n >= 16) {
+    a_stride_m = k;
+    b_stride_n = n;
+    return aarch64::quantized_matmul::
+        fp32_a_input_channelwise_8bit_b_1x16x4_f32::kernel<true, false, false>;
+  }
+#endif // defined(__aarch64__) || defined(__ARM_NEON)
+  assert(!a_transposed);
+  if (b_transposed) {
+    a_stride_m = k;
+    b_stride_n = k;
+    return torchao::kernels::cpu::fallback::quantized_matmul::
+        fp32_a_input_channelwise_8bit_b_fp32::kernel<true, false, true>;
+  } else {
+    a_stride_m = k;
+    b_stride_n = n;
+    return torchao::kernels::cpu::fallback::quantized_matmul::
+        fp32_a_input_channelwise_8bit_b_fp32::kernel<true, false, false>;
+  }
+}
 } // namespace torchao::kernels::cpu::quantized_matmul

torchao/experimental/kernels/cpu/interface/test_qmatmul_interface.cpp

@@ -446,3 +446,185 @@ TEST(
       Run(
           /*m=*/4, /*k=*/2, /*n=*/1, 32);
 }
+
+class FP32A_QuantizedB_FP32C_Interface_Test
+    : public ::testing::TestWithParam<float> {
+ public:
+  int m;
+  int k;
+  int n;
+  int stride;
+
+  bool rhs_has_zeros;
+  bool lhs_is_transposed;
+  bool rhs_is_transposed;
+
+  std::vector<float> init_output;
+  std::vector<float> expected_output;
+
+  std::vector<float> lhs;
+
+  std::vector<float> rhs;
+  std::vector<int8_t> rhs_qvals;
+  std::vector<float> rhs_scales;
+  std::vector<int8_t> rhs_zeros;
+
+  void generate(
+      int m_,
+      int k_,
+      int n_,
+      bool rhs_has_zeros_,
+      bool lhs_is_transposed_,
+      bool rhs_is_transposed_,
+      int stride_ = 1) {
+    assert(!lhs_is_transposed_);
+    assert(rhs_has_zeros_);
+    m = m_;
+    k = k_;
+    n = n_;
+    stride = stride_;
+    rhs_has_zeros = rhs_has_zeros_;
+    lhs_is_transposed = lhs_is_transposed_;
+    rhs_is_transposed = rhs_is_transposed_;
+
+    assert(!rhs_is_transposed || stride == 1);
+
+    // Generate activations
+    lhs = get_random_vector(m * k, -1.0, 1.0);
+
+    // The strange thing this is doing is that instead of quantizing
+    // each output channel separately, we are quantizing each input channel
+    // Reason why we do !rhs_is_transposed is because
+    // we actually want k x n matrix not n x k matrix
+    // because each input channel is quantized separately
+    std::tie(rhs, rhs_qvals, rhs_scales, rhs_zeros) =
+        generate_per_token_quantized_tensor(k * stride, n, rhs_is_transposed);
+
+    // Compute expected output
+    init_output = get_random_vector(m * n, -1.0, 1.0);
+
+    assert(init_output.size() == m * n);
+    assert(lhs.size() == m * k);
+    assert(rhs.size() == n * stride * k);
+    assert(rhs_qvals.size() == n * stride * k);
+    assert(rhs_scales.size() == k * stride);
+    assert(rhs_zeros.size() == k * stride);
+  }
+
+  void execute(float beta) {
+    // Compute expected output
+    expected_output = init_output;
+
+    for (int m_idx = 0; m_idx < m; m_idx++) {
+      for (int n_idx = 0; n_idx < n; n_idx++) {
+        float res = 0.0;
+        for (int k_idx = 0; k_idx < k; k_idx++) {
+          int lhs_idx = m_idx * k + k_idx;
+          int rhs_idx = k_idx * stride * n + n_idx;
+          if (rhs_is_transposed) {
+            rhs_idx = n_idx * k * stride + k_idx * stride;
+          }
+          float rhs_dequant = rhs_scales[k_idx * stride] *
+              (static_cast<int16_t>(rhs_qvals[rhs_idx]) -
+               static_cast<int16_t>(rhs_zeros[k_idx * stride]));
+
+          res += lhs[lhs_idx] * rhs_dequant;
+        }
+        expected_output[m_idx * n + n_idx] =
+            expected_output[m_idx * n + n_idx] * beta + res;
+      }
+    }
+  }
+
+  float beta() const {
+    return GetParam();
+  }
+};
+
+static void test_fp32_a_input_channelwise_8bit_b(
+    int m,
+    int k,
+    int n,
+    float beta,
+    FP32A_QuantizedB_FP32C_Interface_Test& test_case,
+    int stride = 1) {
+  test_case.execute(beta);
+
+  int a_stride_m, b_stride_n;
+  auto kernel = torchao::kernels::cpu::quantized_matmul::
+      get_fp32_a_input_channelwise_8bit_b_f32_c_matmul(
+          m, n, k, false, false, a_stride_m, b_stride_n);
+  b_stride_n = b_stride_n * stride;
+
+  std::vector<float> output(test_case.init_output);
+  kernel(
+      m,
+      n,
+      k,
+      test_case.lhs.data(),
+      a_stride_m /*lhs_stride_m*/,
+      test_case.rhs_qvals.data(),
+      b_stride_n /*rhs_stride_n*/,
+      output.data(),
+      n /*out_stride_n*/,
+      test_case.rhs_zeros.data(),
+      test_case.rhs_scales.data(),
+      beta,
+      stride /*rhs qparams stride*/);
+
+  for (int i = 0; i < m * n; i++) {
+    EXPECT_NEAR(output[i], test_case.expected_output[i], kTol);
+  }
+}
+
+TEST_P(FP32A_QuantizedB_FP32C_Interface_Test, BTranposedWithZeroPoints) {
+  generate(3, 128, 16, true, false, false);
+  test_fp32_a_input_channelwise_8bit_b(
+      /*m=*/3, /*k=*/128, /*n=*/16, beta(), *this);
+}
+
+TEST_P(
+    FP32A_QuantizedB_FP32C_Interface_Test,
+    BTranposedWithZeroPointsOddSizes) {
+  generate(4, 37, 19, true, false, false);
+  test_fp32_a_input_channelwise_8bit_b(
+      /*m=*/4, /*k=*/37, /*n=*/19, beta(), *this);
+}
+
+// Test shapes for which we have to use fallback kernel
+TEST_P(
+    FP32A_QuantizedB_FP32C_Interface_Test,
+    BTranposedWithZeroPointsOddSizesFallback) {
+  generate(4, 37, 3, true, false, false);
+  test_fp32_a_input_channelwise_8bit_b(
+      /*m=*/4, /*k=*/37, /*n=*/3, beta(), *this);
+}
+
+TEST_P(
+    FP32A_QuantizedB_FP32C_Interface_Test,
+    BTranposedWithZeroPointsOddSizes2Fallback) {
+  generate(4, 1, 3, true, false, false);
+  test_fp32_a_input_channelwise_8bit_b(
+      /*m=*/4, /*k=*/1, /*n=*/3, beta(), *this);
+}
+
+TEST_P(
+    FP32A_QuantizedB_FP32C_Interface_Test,
+    BTranposedWithZeroPointsOddSizesStrided) {
+  generate(4, 37, 19, true, false, false, 32);
+  test_fp32_a_input_channelwise_8bit_b(
+      /*m=*/4, /*k=*/37, /*n=*/19, beta(), *this, 32);
+}
+
+TEST_P(
+    FP32A_QuantizedB_FP32C_Interface_Test,
+    BTranposedWithZeroPointsOddSizes2FallbackStrided) {
+  generate(4, 5, 3, true, false, false, 32);
+  test_fp32_a_input_channelwise_8bit_b(
+      /*m=*/4, /*k=*/5, /*n=*/3, beta(), *this, 32);
+}
+
+INSTANTIATE_TEST_SUITE_P(
+    F32AInt8BFP32CTest,
+    FP32A_QuantizedB_FP32C_Interface_Test,
+    ::testing::Values(0.0, 1.0, 3.1));