Fix Top K CPU

examples/python/phi3-qa.py

@@ -30,6 +30,10 @@ def main(args):
 
     chat_template = '<|user|>\n{input} <|end|>\n<|assistant|>'
 
+    params = og.GeneratorParams(model)
+    params.set_search_options(**search_options)
+    generator = og.Generator(model, params)
+
     # Keep asking for input prompts in a loop
     while True:
         text = input("Input: ")
@@ -44,9 +48,6 @@ def main(args):
 
         input_tokens = tokenizer.encode(prompt)
 
-        params = og.GeneratorParams(model)
-        params.set_search_options(**search_options)
-        generator = og.Generator(model, params)
         generator.append_tokens(input_tokens)
         if args.verbose: print("Generator created")
 
@@ -74,9 +75,6 @@ def main(args):
         print()
         print()
 
-        # Delete the generator to free the captured graph for the next generator, if graph capture is enabled
-        del generator
-
         if args.timings:
             prompt_time = first_token_timestamp - started_timestamp
             run_time = time.time() - first_token_timestamp

src/generators.h

@@ -161,6 +161,5 @@ std::shared_ptr<GeneratorParams> CreateGeneratorParams(const Config& config);  /
 std::unique_ptr<Generator> CreateGenerator(const Model& model, const GeneratorParams& params);
 
 float Float16ToFloat32(uint16_t v);  // v is a IEEE 752-2008 binary16 format, 1 sign bit, 5 bit exponent, 10 bit fraction
-void top_k_indices(std::span<int32_t> top_k, std::span<const float> inputs);
 
 }  // namespace Generators

src/search.cpp

@@ -161,8 +161,11 @@ void GreedySearch_Cpu::SampleTopK(int k, float temperature) {
     std::vector<int> indices(scores.size());
     std::iota(indices.begin(), indices.end(), 0);
     std::partial_sort(indices.begin(), indices.begin() + k, indices.end(), [scores = scores.data()](int i, int j) { return scores[i] > scores[j]; });
+    std::vector<float> top_k_scores(k);
+    for (int i = 0; i < k; i++)
+      top_k_scores[i] = scores[indices[i]];
     // Sample a token from the top K
-    std::discrete_distribution<> dis(scores.begin(), scores.begin() + k);
+    std::discrete_distribution<> dis(top_k_scores.begin(), top_k_scores.end());
     SetNextToken(batch_id, indices[dis(gen_)]);
   }
   AppendNextTokensToSequences();

test/sampling_tests.cpp

@@ -176,31 +176,57 @@ TEST(SamplingTests, RandomizedSamplingTopPCpu) {
   }
 }
 
+void SoftMax(std::span<float> scores, float temperature) {
+  float const max_score = *std::max_element(scores.begin(), scores.end());
+
+  // Subtract max score and scale by temperature
+  std::transform(scores.begin(), scores.end(), scores.begin(), [max_score, temperature](float score) { return std::exp((score - max_score) / temperature); });
+
+  // Compute sum of exponentials
+  float const exp_sum = std::accumulate(scores.begin(), scores.end(), 0.0f);
+
+  // Divide each score by the sum of exponentials
+  std::transform(scores.begin(), scores.end(), scores.begin(), [exp_sum](float score) { return score / exp_sum; });
+}
+
 TEST(SamplingTests, RandomizedSamplingTopKCpu) {
   auto model = Generators::CreateModel(Generators::GetOrtEnv(), MODEL_PATH "hf-internal-testing/tiny-random-gpt2-fp32");
-  int batch_size = 5;
-  int k = 5;
-  std::vector<int32_t> input_ids{0, 1, 2, 3, 4};
+  const int batch_size = 5;
+  const int k = 5;
 
   Generators::Config config;
-  config.model.vocab_size = 32000;  // vocab size of llama
+  const int vocab_size = 13;  // vocab size of llama
+  config.model.vocab_size = vocab_size;  // vocab size of llama
 
+  // Create a generator
   auto params = Generators::CreateGeneratorParams(config);
   params->search.max_length = 10;
   params->search.do_sample = true;
   params->search.top_k = k;
   params->search.batch_size = batch_size;
   params->p_device = Generators::GetDeviceInterface(Generators::DeviceType::CPU);
   params->device_type = Generators::DeviceType::CPU;
-  std::vector<float> logits_cpu(config.model.vocab_size * batch_size);
+
+  // Create data structures for testing
   std::random_device rd;
   std::mt19937 engine(rd());
-  std::uniform_int_distribution<> dist(5, 25);
-  int num_iter = 100;
+  std::vector<int> indices(vocab_size);
+  std::vector<float> logits_cpu(vocab_size * batch_size);
+  const int num_iter = 100;
+  std::map<float, int> logit_to_count;
+
+  // Run test
   for (int i = 0; i < num_iter; i++) {
-    int num_large = dist(engine);
     auto generator = Generators::CreateGenerator(*model, *params);
-    CreateRandomLogits(logits_cpu.data(), num_large, config.model.vocab_size, batch_size, engine);
+    logits_cpu = std::vector<float>(vocab_size * batch_size, 0.0f);
+    // Shuffle integers 1 to k randomly into logits_cpu
+    for (int b = 0; b < batch_size; b++) {
+      std::iota(indices.begin(), indices.end(), 0);
+      std::shuffle(indices.begin(), indices.end(), engine);
+      for (int j = 0; j < k; j++)
+        logits_cpu[indices[j] + vocab_size * b] = float(k - j);
+    }
+    // Set logits and get generated token
     auto logits_copy = logits_cpu;
     auto logits = params->p_device->WrapMemory<float>(logits_copy);
     generator->SetLogits(logits);
@@ -209,10 +235,22 @@ TEST(SamplingTests, RandomizedSamplingTopKCpu) {
     // Verify outputs match expected outputs
     for (int b = 0; b < batch_size; b++) {
       auto next_token = next_tokens[b];
-      auto next_token_score = logits_cpu[next_token + config.model.vocab_size * b];
-      EXPECT_GT(next_token_score, 10.0f);
+      auto next_token_score = logits_cpu[next_token + vocab_size * b];
+      logit_to_count[next_token_score]++;
+      EXPECT_GT(next_token_score, 0.0f);
     }
   }
+  // Calculate expected distribution of tokens by softmaxing given logits (integers 1 through k)
+  std::vector<float> expected_distributions(k);
+  for (int i = 0; i < k; i++)
+    expected_distributions[i] = float(i + 1);
+  SoftMax(expected_distributions, 1.0f);
+  // Check that the distribution of tokens generated by the model is close to the expected distribution
+  const int total_count = batch_size * num_iter;
+  for (auto& [logit, count] : logit_to_count) {
+    const float expected_distribution = expected_distributions[int(logit) - 1];
+    EXPECT_NEAR(count / float(total_count), expected_distribution, 0.1);
+  }
 }
 
 TEST(SamplingTests, RandomizedSamplingTopPAndKCpu) {
@@ -396,43 +434,65 @@ TEST(SamplingTests, RandomizedSamplingTopPCuda) {
 
 TEST(SamplingTests, RandomizedSamplingTopKCuda) {
   auto model = Generators::CreateModel(Generators::GetOrtEnv(), MODEL_PATH "hf-internal-testing/tiny-random-gpt2-fp32");
-  int batch_size = 5;
-  int k = 5;
-  std::vector<int32_t> input_ids{0, 1, 2, 3, 4};
+  const int batch_size = 5;
+  const int k = 5;
 
   Generators::Config config;
-  config.model.vocab_size = 32000;  // vocab size of llama
+  const int vocab_size = 17;  // vocab size of llama
+  config.model.vocab_size = vocab_size;  // vocab size of llama
 
+  // Create a generator
   auto params = Generators::CreateGeneratorParams(config);
   params->search.max_length = 10;
   params->search.do_sample = true;
   params->search.top_k = k;
   params->search.batch_size = batch_size;
   params->p_device = Generators::GetDeviceInterface(Generators::DeviceType::CUDA);
   params->device_type = Generators::DeviceType::CUDA;
-  auto logits_gpu = params->p_device->Allocate<float>(config.model.vocab_size * batch_size);
-  auto indices_buffer = params->p_device->Allocate<int>(config.model.vocab_size * batch_size);
 
+  // Create data structures for testing
   std::random_device rd;
   std::mt19937 engine(rd());
-  std::uniform_int_distribution<> dist(1, 25);
-  int num_iter = 100;
+  std::vector<int> indices(vocab_size);
+  const int num_iter = 100;
+  std::map<float, int> logit_to_count;
+
+  // Run test
   for (int i = 0; i < num_iter; i++) {
-    int num_large = dist(engine);
-    LaunchGeometricDecayKernel(logits_gpu.Span().data(), config.model.vocab_size, batch_size, num_large, 20.0f, params->cuda_stream);
-    LaunchFisherYatesKernel(logits_gpu.Span().data(), indices_buffer.Span().data(), config.model.vocab_size, batch_size, params->cuda_stream);
     auto generator = Generators::CreateGenerator(*model, *params);
+    Generators::DeviceSpan<float> logits_gpu = params->p_device->Allocate<float>(vocab_size * batch_size);
+    auto cpu_span = logits_gpu.CpuSpan();
+    // Shuffle integers 1 to k randomly into cpu_span
+    for (int b = 0; b < batch_size; b++) {
+      std::iota(indices.begin(), indices.end(), 0);
+      std::shuffle(indices.begin(), indices.end(), engine);
+      for (int j = 0; j < k; j++)
+        cpu_span[indices[j] + vocab_size * b] = float(k - j);
+    }
+    // Copy logits onto device, set logits, and get generated token
+    logits_gpu.CopyCpuToDevice();
     generator->SetLogits(logits_gpu);
     generator->GenerateNextToken();
     auto next_tokens = generator->search_->GetNextTokens().CopyDeviceToCpu();
-    auto logits_cpu = logits_gpu.CopyDeviceToCpu();
     // Verify outputs match expected outputs
     for (int b = 0; b < batch_size; b++) {
       auto next_token = next_tokens[b];
-      auto next_token_score = logits_cpu[next_token + config.model.vocab_size * b];
-      EXPECT_GT(next_token_score, 10.0f);
+      auto next_token_score = cpu_span[next_token + vocab_size * b];
+      logit_to_count[next_token_score]++;
+      EXPECT_GT(next_token_score, 0.0f);
     }
   }
+  // Calculate expected distribution of tokens by softmaxing given logits (integers 1 through k)
+  std::vector<float> expected_distributions(k);
+  for (int i = 0; i < k; i++)
+    expected_distributions[i] = float(i + 1);
+  SoftMax(expected_distributions, 1.0f);
+  const int total_count = batch_size * num_iter;
+  // Check that the distribution of tokens generated by the model is close to the expected distribution
+  for (auto& [logit, count] : logit_to_count) {
+    const float expected_distribution = expected_distributions[int(logit) - 1];
+    EXPECT_NEAR(count / float(total_count), expected_distribution, 0.1);
+  }
 }
 
 TEST(SamplingTests, RandomizedSamplingTopPAndKCuda) {