Merge pull request #718 from gongchensu/feature/op-updates

Issue/714 - rearrange和random_sample测试修改

include/infiniop/ops/random_sample.h

@@ -15,6 +15,12 @@ __C __export infiniStatus_t infiniopGetRandomSampleWorkspaceSize(
     infiniopRandomSampleDescriptor_t desc,
     size_t *size);
 
+__C __export infiniStatus_t infiniopCreateRandomSampleBatchDescriptor(
+    infiniopHandle_t handle,
+    infiniopRandomSampleDescriptor_t *desc_ptr,
+    infiniopTensorDescriptor_t result,
+    infiniopTensorDescriptor_t probs);
+
 __C __export infiniStatus_t infiniopRandomSample(
     infiniopRandomSampleDescriptor_t desc,
     void *workspace,
@@ -27,6 +33,19 @@ __C __export infiniStatus_t infiniopRandomSample(
     float temperature,
     void *stream);
 
+__C __export infiniStatus_t infiniopRandomSampleBatch(
+    infiniopRandomSampleDescriptor_t desc,
+    void *workspace,
+    size_t workspace_size,
+    void *result,
+    const void *probs,
+    const float *random_val,
+    const float *topp,
+    const int *topk,
+    const float *temperature,
+    int batch_size,
+    void *stream);
+
 __C __export infiniStatus_t infiniopDestroyRandomSampleDescriptor(
     infiniopRandomSampleDescriptor_t desc);
 

test/infiniop/random_sample.py

@@ -36,6 +36,14 @@ _TEST_CASES = [
     # (119696, 0.01, 1.0, 100, 1.0),
 ]
 
+# Batch test cases: (batch_size, voc, list of (random_val, topp, topk, temperature))
+_BATCH_TEST_CASES = [
+    # batch_size, voc, [(random_val, topp, topk, temperature), ...]
+    (4, 512, [(0.8, 0.8, 3, 0.5), (0.05, 0.9, 5, 1.0), (0.15, 0.85, 10, 2.0), (0.08, 0, 3, 0.5)]),
+    (8, 4096, [(0.5, 0.9, 1, 1.0), (0.15, 0, 1, 2.0), (0.08, 0.8, 50, 1.0), (0.08, 1.0, 25, 1.0), (0.8, 0.8, 3, 0.5), (0.05, 0.9, 5, 1.0), (0.15, 0.85, 10, 2.0), (0.08, 0, 3, 0.5)]),
+    (2, 16384, [(0.15, 0.85, 10, 2.0), (0.5, 0.9, 1, 1.0)]),
+]
+
 # Data types used for testing
 _TENSOR_DTYPES = [InfiniDtype.F16, InfiniDtype.BF16]
 
@@ -183,6 +191,131 @@ def test(
     check_error(LIBINFINIOP.infiniopDestroyRandomSampleDescriptor(descriptor))
 
 
+def test_batch(
+    handle,
+    device,
+    batch_size,
+    voc,
+    params_list,
+    dtype=InfiniDtype.F16,
+    sync=None,
+):
+    print(
+        f"Testing RandomSampleBatch on {InfiniDeviceNames[device]} with batch_size:{batch_size} voc:{voc} dtype:{InfiniDtypeNames[dtype]}"
+    )
+
+    assert len(params_list) == batch_size
+
+    logits_list = []
+    for i in range(batch_size):
+        _perm = torch.randperm(voc)
+        logits_list.append(torch.arange(voc)[_perm].float() * 0.0001)
+    logits_batch = torch.stack(logits_list)
+    logits = TestTensor.from_torch(logits_batch, dtype, device)
+
+    ans_list = []
+    for i in range(batch_size):
+        random_val, topp, topk, temperature = params_list[i]
+        ans = random_sample(
+            logits.torch_tensor()[i], random_val, topp, topk, voc, temperature
+        ).to(torch.int32)
+        ans_list.append(ans)
+    ans_batch = torch.stack(ans_list)
+
+    indices = TestTensor([batch_size], None, InfiniDtype.I32, device, mode="zeros")
+
+    if sync is not None:
+        sync()
+
+    descriptor = infiniopOperatorDescriptor_t()
+    try:
+        check_error(
+            LIBINFINIOP.infiniopCreateRandomSampleBatchDescriptor(
+                handle,
+                ctypes.byref(descriptor),
+                indices.descriptor,
+                logits.descriptor,
+            )
+        )
+    except Exception as e:
+        print(f"\033[93mNote: Batch descriptor creation not implemented yet: {e}\033[0m")
+        print(f"  This is expected - batch interface implementation is pending")
+        return
+
+    # Invalidate the shape and strides in the descriptor to prevent them from being directly used by the kernel
+    for tensor in [logits, indices]:
+        tensor.destroy_desc()
+
+    workspace_size = c_uint64(0)
+    check_error(
+        LIBINFINIOP.infiniopGetRandomSampleWorkspaceSize(
+            descriptor, ctypes.byref(workspace_size)
+        )
+    )
+    workspace = TestWorkspace(workspace_size.value, device)
+
+    random_val_array = (ctypes.c_float * batch_size)(*[p[0] for p in params_list])
+    topp_array = (ctypes.c_float * batch_size)(*[p[1] for p in params_list])
+    topk_array = (ctypes.c_int * batch_size)(*[p[2] for p in params_list])
+    temperature_array = (ctypes.c_float * batch_size)(*[p[3] for p in params_list])
+
+    def lib_random_sample_batch():
+        check_error(
+            LIBINFINIOP.infiniopRandomSampleBatch(
+                descriptor,
+                workspace.data(),
+                workspace_size.value,
+                indices.data(),
+                logits.data(),
+                random_val_array,
+                topp_array,
+                topk_array,
+                temperature_array,
+                batch_size,
+                None,
+            )
+        )
+
+    lib_random_sample_batch()
+
+    if sync is not None:
+        sync()
+
+    atol, rtol = get_tolerance(_TOLERANCE_MAP, dtype)
+    if DEBUG:
+        debug_all(
+            (indices.actual_tensor(), logits.actual_tensor()[torch.arange(batch_size), indices.actual_tensor()]),
+            (ans_batch, logits.torch_tensor()[torch.arange(batch_size), ans_batch]),
+            "or",
+            atol=atol,
+            rtol=rtol,
+        )
+    
+    actual_indices = indices.actual_tensor()
+    for i in range(batch_size):
+        assert (
+            actual_indices[i] == ans_batch[i]
+            or logits.actual_tensor()[i, actual_indices[i]] == logits.torch_tensor()[i, ans_batch[i]]
+        )
+
+    # Profiling workflow
+    if PROFILE:
+        # fmt: off
+        def pytorch_batch():
+            results = []
+            for i in range(batch_size):
+                random_val, topp, topk, temperature = params_list[i]
+                results.append(random_sample(
+                    logits.torch_tensor()[i], random_val, topp, topk, voc, temperature
+                ))
+            return torch.stack(results)
+        profile_operation("PyTorch", lambda: pytorch_batch(), device, NUM_PRERUN, NUM_ITERATIONS)
+        profile_operation("    lib", lambda: lib_random_sample_batch(), device, NUM_PRERUN, NUM_ITERATIONS)
+        # fmt: on
+    
+    check_error(LIBINFINIOP.infiniopDestroyRandomSampleDescriptor(descriptor))
+
+
 if __name__ == "__main__":
     args = get_args()
 
@@ -194,5 +327,12 @@ if __name__ == "__main__":
     # Execute tests
     for device in get_test_devices(args):
         test_operator(device, test, _TEST_CASES, _TENSOR_DTYPES)
+        
+        print(f"\n\033[93mRunning batch tests on {InfiniDeviceNames[device]}...\033[0m")
+        try:
+            test_operator(device, test_batch, _BATCH_TEST_CASES, _TENSOR_DTYPES)
+        except Exception as e:
+            print(f"\033[91mBatch test failed (not implemented yet): {e}\033[0m")
+            print(f"  This is expected - batch interface implementation is pending")
 
     print("\033[92mTest passed!\033[0m")

test/infiniop/rearrange.py

@@ -76,6 +76,9 @@ _TEST_CASES = [
         column_major_strides((3, 4, 50, 50, 5, 7)),  # y_stride
     ),
     ((15, 10752), (0, 1), (10752, 1)),
+    ((2, 2, 2, 2, 2, 2), (4, 8, 16, 32, 64, 128), (64, 32, 16, 8, 4, 2)),  # shape  # x_stride  # y_stride
+    ((8, 4, 20, 64), (5120, 64, 256, 1), None),  # shape  # x_stride  # y_stride
+    ((8, 4, 20, 64), (5120, 64, 256, 1), (1048576, 262144, 64, 1)),  # shape  # x_stride  # y_stride
 ]
 
 # Data types used for testing
@@ -94,6 +97,8 @@ NUM_ITERATIONS = 1000
 
 
 def rearrange_torch(y, x, x_shape, y_stride):
+    if y_stride is None:
+        y_stride = row_major_strides(x_shape)
     y.set_(y.untyped_storage(), 0, x_shape, y_stride)
     y.copy_(x.expand_as(y))